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Consumers' Use of Food Labels

Permanent Link: http://ufdc.ufl.edu/UFE0021630/00001

Material Information

Title: Consumers' Use of Food Labels An Application of Ordered Probit Models
Physical Description: 1 online resource (171 p.)
Language: english
Creator: Jauregui, Carlos E
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2007

Subjects

Subjects / Keywords: food, labels, ordered, probit
Food and Resource Economics -- Dissertations, Academic -- UF
Genre: Food and Resource Economics thesis, Ph.D.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: While food labels can and do provide a range of potentially useful information to aspiring buyers, consumers must be aware of the information and pay attention to the messages. It is not enough to have the product labeled; the information must be of value to the decision-making process. Obviously, consumers differ and, as such, any importance placed on labels will differ across these consumers. Household data, from a demographically balanced diary survey, gave monthly observations over the years from 1984-2003 for a total of 30,414 household entries. In addition, another more recent attitudinal survey from 1993 to 2003 includes 13,150 households. For each household many attributes are known including demographics, attitudes, eating habits and health concerns. Using a six point Likert scale, each household was asked to score the following statements: (1) I check the labels for harmful ingredients, and (2) I read the labels for my food purchase. While both questions zero in on the consumers importance attached to labels, the first question is more negative where the label is expected to be used to eliminate buying particular products; and the second is more positive to assist with the purchase. Since the household response is discrete with scaled values, the likelihood of reading food labels can be estimated using Ordered Probit models where the probability of each Likert score can be determined. Results of the Ordered Probit models show that consumers WHO are worried about potential harmful ingredients in the packaged food read the labels more often than consumers WHO read the labels looking for general information. The twelve most important variables, ranked according to their impact on the likelihood of reading food labels, in decreasing order, are as follows: conscious of calories, know more than most, doctor gives advice on diet, eating fried chicken, cautious about additives, cautious about cholesterol, eating hotdog, cautious about fat, best known brands are highest quality, age of female head of household, adult female on diet, and avoid foreign food.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility: by Carlos E Jauregui.
Thesis: Thesis (Ph.D.)--University of Florida, 2007.
Local: Adviser: Ward, Ronald W.

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2007
System ID: UFE0021630:00001

Permanent Link: http://ufdc.ufl.edu/UFE0021630/00001

Material Information

Title: Consumers' Use of Food Labels An Application of Ordered Probit Models
Physical Description: 1 online resource (171 p.)
Language: english
Creator: Jauregui, Carlos E
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2007

Subjects

Subjects / Keywords: food, labels, ordered, probit
Food and Resource Economics -- Dissertations, Academic -- UF
Genre: Food and Resource Economics thesis, Ph.D.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: While food labels can and do provide a range of potentially useful information to aspiring buyers, consumers must be aware of the information and pay attention to the messages. It is not enough to have the product labeled; the information must be of value to the decision-making process. Obviously, consumers differ and, as such, any importance placed on labels will differ across these consumers. Household data, from a demographically balanced diary survey, gave monthly observations over the years from 1984-2003 for a total of 30,414 household entries. In addition, another more recent attitudinal survey from 1993 to 2003 includes 13,150 households. For each household many attributes are known including demographics, attitudes, eating habits and health concerns. Using a six point Likert scale, each household was asked to score the following statements: (1) I check the labels for harmful ingredients, and (2) I read the labels for my food purchase. While both questions zero in on the consumers importance attached to labels, the first question is more negative where the label is expected to be used to eliminate buying particular products; and the second is more positive to assist with the purchase. Since the household response is discrete with scaled values, the likelihood of reading food labels can be estimated using Ordered Probit models where the probability of each Likert score can be determined. Results of the Ordered Probit models show that consumers WHO are worried about potential harmful ingredients in the packaged food read the labels more often than consumers WHO read the labels looking for general information. The twelve most important variables, ranked according to their impact on the likelihood of reading food labels, in decreasing order, are as follows: conscious of calories, know more than most, doctor gives advice on diet, eating fried chicken, cautious about additives, cautious about cholesterol, eating hotdog, cautious about fat, best known brands are highest quality, age of female head of household, adult female on diet, and avoid foreign food.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility: by Carlos E Jauregui.
Thesis: Thesis (Ph.D.)--University of Florida, 2007.
Local: Adviser: Ward, Ronald W.

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2007
System ID: UFE0021630:00001


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CONSUMERS' USE OF FOOD LABELS:
AN APPLICATION OF ORDERED PROBIT MODELS

















By

CARLOS E. JAUREGUI


A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF
DOCTOR OF PHILOSOPHY

UNIVERSITY OF FLORIDA

2007




































O 2007 Carlos E. Jauregui





































To my parents, who taught me the value of hard work and honesty.









ACKNOWLEDGMENTS

I am grateful to the Chairman of my committee Dr. Ronald W. Ward for hi s kind mentorship.

My thanks go to Dr. Tom Spreen, Chairman of the Food and Resource Economics Department, for

allowing me the flexibility to pursue my Ph.D. while working full time. My thanks go also to the

members of my committee for their helpful comments.











TABLE OF CONTENTS


ACKNOWLEDGMENTS ......... .......... . .. .. .. .. 4


LIST OF TABLES ............. ............. ......... 6


LIST OF FIGURES ............. ........... ......... ... 7


ABSTRACT ............. ......... ......... ........9


CHAPTER


1 INTRODUCTION ............. .......... ...........11


U. S.PublicPolicy onLabels ............. ......... ....... 13
Food Labeling Problem Statement .. . .___ .... .. ... 15
Labeling Research Goals ....___ . .__ ... .. 16
Methodology and Data ......__ .. . .. .. .. 16


2 LITERATURE REVIEW ....___ ..__ . .. 18


Historical Development of Food Labels .. . .__ . ... 18
Economic and Legal Implications of Food Labeling . .... .. .. 22
Consumer Use of Food Labels .......... . .... .. .. 28


3 DESCRIPTION OF DATA ............. ...................36


4 MODEL SPECIFICATION .......... . ..... .. .. 52


Ordered Probit Models for the 1993-2003 Period . ...... ... .. 52
Ordered Probit Model for the 1984-2003 Period ........ .. ... .. .60


5 ANALYSIS OF RESULTS ............. ...................62


Ordered Probit Estimates and Probabilities for the Period 1993-2003 .. .. .. .. .. .. 63
Sequential Ordered Probit Estimates and Probabilities for the Period 1984-2003 .. .. 71

6 SUMMARY AND CONCLUSIONS ......... ... . .. 159


APPENDIX


A CORRELATION TABLES OF RESTRICTED DUMMY VARIABLES .. .. .. .. 162


REFERENCES ............. ..........................168


BIOGRAPHICAL SKETCH ......... .......... .. . .. 171










LIST OF TABLES


Table Pn

3-1. Variables used in the Ordered Probit models ......... .. .. . .. 38

3-2. Frequency, in percent, of explanatory variables for the 1993 2003 period .. .. .. 40

3-3. Frequency, in percent, of explanatory variables for the 1984 2003 period .. .. .. 41

3-4. Frequency, in percent, of explanatory variables for the 1984 1993 period .. .. .. 42

3-5. Frequency, in percent, of explanatory variables for the 1994 2003 period .. .. .. 43

3-6. Five highest correlation coefficients and their probability under Ho: Rho = 0 of
variables of the 1993-2003 period (13,150 obs) ......... .. .. . .. 44

3-7. Five highest correlation coefficients and their probability under Ho: Rho = 0 of
variables of the 1984-2003 period (30,414 obs) ......... ... . .47

5-1. Results from the ATLAB model for the period 1993-2003 .... .. .. .. .. 77

5-2. Results from the FPLAB model for the period 1993-2003 .... .. .... .. .. 83

5-3. Principal components for NTADD, NTCHL, NTFAT, NTPRE, NTSAL and
NTSUG for the period 1993-2003 ........_ . ... .. .. 89

5-4. Results for ATLAB model with principal components for health variables for
the period 1993-2003 ......_ .... ._ ... .. .. 90

5-5. Results for FPLAB model with principal components for health variables for
the period 1993-2003 ......_ .... ._ ... .. .. 95

5-6. Results from the ATLAB model for the period 1984-1993 .... .. .. . .. 100

5-7. Results from the ATLAB model for the period 1994-2003 .... .. .. . .. 106

5-8. Wald test for the coefficients of the sequential Ordered Probit model for the
periods 1984-1993 and 1994-2003 ......... . .. .. .. 112

A-1. Five highest correlation coefficients and their probability under Ho: Rho = 0
of health concerns restricted dummy variables of the 1993-2003 period (13,150 obs) 162

A-2. Five highest correlation coefficients and their probability under Ho: Rho = 0
of health concerns restricted dummy variables of the 1984-2003 period (30,414 obs) 165










LIST OF FIGURES


figure Page

2-1. Typical U.S. food label ......... .......... ... . .34

2-2. Belgium beef label ............. ............. ........ 35

3-1. Frequency distribution of ATLAB and FPLAB 1993-2003 . .. .. .. ... 50

3-2. Frequency distribution of ATLAB 1984-2003 ......... .. .. . .. 50

3-3. Frequency distribution for two periods of ATLAB . ... ... .. 51

5-1. Probability of reading food labels by the average consumer . . .. ... 113

5-2. Demographics impact on reading food labels ... . ._ .. .. .. 114

5-3. Attitudes impact on reading food labels ......... .. . .. 118

5-4. Eating habits impact on reading food labels ......... .. .. . .. 122

5-5. Health Concern impacts on reading food labels ........ .... . .. 127

5-6. Impact of seasonality on reading food labels ......... .. .. . .. 131

5-7. Ranking of factors impacting the likelihood of reading food labels for
harmful ingredients ......... .......... ... .. .132

5-8. Ranking of factors impacting the likelihood of reading food labels for food purchase 133

5-9. Range of change in probabilities for ATLAB and FPLAB .... .. .. .. .. 134

5-10. Change over time in the likelihood of reading food labels to check for harmful
ingredients for the average consumer ........_ . .. . 135

5-11. Change over time in the impact of demographics on reading food labels to check
for harmful ingredients ........_ . ..... ... .. 137

5-12. Change over time in the impact of attitudes on reading food labels to check
for harmful ingredients ........_ . ..... .. .. .142

5-13. Change over time in the impact of eating habits on reading food labels to check
for harmful ingredients .......... .......... ..... .. .. 146

5-14. Change over time in the impact of health concerns on reading food labels to check
for harmful ingredients ....._ .._ ....... ... .. 150










5-15. Change over time in the impact of seasonality on reading food labels to check
for harmful ingredients ......... . ...... ... .. 155

5-16. Ranking of factors impacting the likelihood of reading food labels for harmful
ingredients in the period 1984-1993 ......... . . 156

5-17. Ranking of factors impacting the likelihood of reading food labels for harmful
ingredients in the period 1994-2003 ......... . . 157

5-18. Range of changes in probabilities of reading the labels for harmful ingredients
in the periods 1984-1993 and 1994-2003 ......... .. .. . .. 158









Abstract of Dissertation Presented to the Graduate School
of the University of Florida in Partial Fulfillment of the
Requirements for the Degree of Doctor of Philosophy

CONSUMERS' USE OF FOOD LABELS:
AN APPLICATION OF ORDERED PROBIT MODELS

By

Carlos E. Jauregui

December 2007

Chair: Ronald W. Ward
Maj or: Food and Resource Economics

While food labels can and do provide a range of potentially useful information to aspiring

buyers, consumers must be aware of the information and pay attention to the messages. It is not

enough to have the product lab eled, the information must be of value to the deci sion making process.

Obviously, consumers differ and, as such, any importance placed on labels will differ across these

consumers .

Household data, from a demographically balanced diary survey, gave monthly observations

over the years from 1984-2003 for a total of 30,414 household entries. In addition, another more

recent attitudinal survey from 1993 to 2003 includes 13,150 households. For each household many

attributes are known including demographics, attitudes, eating habits and health concerns.

Using a six point Likert scale, each household was asked to score the following statements:

(1) I check the labels for harmjfid ingredients, and (2) I read the labels for my food purchase While

both questions zero in on the consumers importance attached to labels, the first question is more

negative where the label is expected to be used to eliminate buying particular products and the

second is more positive to assist with the purchase.










Since the household response is discrete with scaled values, the likelihood of reading food

labels can be estimated using Ordered Probit models where the probability of each Likert score can

be determined.

The results of the Ordered Probit models show that consumers that are worried about

potential harmful ingredients in the packaged food read the labels more often than consumers that

read the labels looking for general information. The twelve most important variables, ranked

according to their impact on the likelihood of reading food labels, in decreasing order are: conscious

of calories, know more than most, doctor gives advice on diet, eating fried chicken, cautious about

additives, cautious about cholesterol, eating hotdog, cautious about fat, best known brands are

highest quality, age of female head of household, adult female on diet and avoid foreign food.









CHAPTER 1
INTRODUCTION

The Fair Packaging and Labeling Act of 1966 was passed to ensure that consumers have

information, in a standardized fashion, about the quantity and ingredients of the products. The Pure

Food and Drug Act passed by the Congress in 1906 was the first law dealing with labeling issues.

Since then, the evolution in food technology, packaging, trade, environmental issues, health

consciousness, and the political environment forced changes in the food industry. New laws were

enacted to regulate the ever changing food industry and related food safety issues (Golan, et al.

2000).

In 1990 the FDA proposed the Nutrition Labeling and Educational Act (NLEA) and in 1994

the NLEA regulations pertaining to nutrition labeling were implemented (Golan et al. 2000,

Kurtzweil 1993, Hadden 1986). Most food products now provide labels with information about fats,

cholesterol and other nutritional information (Kim et al. 2000). Regulatory changes on labeling were

expected to have maj or consequences on food demand and marketing strategies because consumers

understood the linkage between diet and health and because of the proposed nutrition education that

was planned to accompany the introduction of new labels (Caswell, 1 992). Economic efficiency al so

may be enhanced and a public service provided when firms make information about their products

available to consumers (Golan et al. 2000). Changes in consumer behavior can also be expected as

a result of the information on labels. Clark and Russell (2004) mention studies that support this

claim.

Nelson (1970), page 3 11, contends that "limitations of consumer information about quality

have profound effects upon the market structure of consumer goods. In particular, monopoly power

for a consumer good will be greater if consumers know about the quality of only a few brands of that

good." He also classifies the qualities or attributes of goods according to the way information about










them is acquired. If the attributes or qualities of goods are ascertainable through search before

buying them, then the goods have "search attributes". On the other hand, if the attributes are

ascertainable only after the goods are bought and used or consumed, then the attributes are said to

be "experience attributes". Darby and Karnil (1973) distinguish a third class of attributes, "credence

attributes." They point out that credence qualities cannot be evaluated in normal use and that the

assessment of their value needs additional and costly information. Some examples of goods with

credence attributes are organically produced vegetables, genetically modified products, and fish

caught without harming dolphins (Han and Harrison, 2004; Golan et al. 2000).

While food labels can and do provide a range of potentially useful information to aspiring

buyers, consumers must be aware of the information and pay attention to the messages. Further, they

must understand the messages as presented for the information to be useful. Even with the

requirement of labeling, any benefits occur only when the consumer perceives and uses the label

information. It is not enough to have the product labeled, the information must be of value to the

decision making process. Obviously, consumers differ and, as such, any importance placed on labels

will differ across these consumers.

Ultimately there are three maj or issues association with labels: (1) What is the information

content of labels? (2) Should labeling be voluntary or mandatory and who should pay the cost? and

(3) Are consumers interested in the labels? The first issue is driven by the food safety concerns and

the ability to sort out the product content. This is particularly important when such food attributes

cannot be readily and efficiently determined through search and/or experience. Secondly, the means

for achieving the labeling is driven by both economics and political power. Postponement and

revi si ons within the USDA mandatory lab eling requirements are a product of politi cal force s brought

by industries who have a vested interest and who often may carry the cost of stricter labeling

requirements. Finally, consumer interest is a product of demographics attributes, health concerns,









eating habits, and timing. Consumer's interest in labels is the focus of this research since and

without that interest, labeling has little economic and/or social value. Hence, in the following

discussion, we turn to consumers and their expressed attention to food labels.

Labeling may have economic value beyond that of providing immediate assistance when

making buying decisions. Labeling that include traceability dimensions may, at first, seem of little

value to consumers as shown by Verbeke and Ward (2005). Yet when food scares or an event

leading to legal issues relating to product sources and content arise, the labeling content may be

invaluable to litigations and to tracing products back to the source. The value then is if and when

a problem arises and not for the immediate purchasing decision. Most consumers are probably not

even aware of the traceability issues.

U. S. Public Policy on Labels

According to Hadden (1986), labeling laws in the United States have evolved over time to

serve three maj or purposes: ensure fair competition in the market, provide information to buyers and

reduce health risks. Hadden (1986) also mentions that the first law dealing with labeling issues was

the Pure Food and Drug Act passed by the Congress in 1906 and that it required labels to be accurate

regarding ingredients, proportions and quantities.

Over time new laws were enacted to extend and improve the regulation of the food industry

and to inform and protect the consumer. Some of those laws or milestones, as called by Golan et al.

(2000) and Kurtzweil (1994) are:

1938. The Federal Food, Drug, and Cosmetic Act replaces the 1906 Food and Drugs Act.

Among other things, it requires the label of every processed and packaged food to contain

the name and weight of the food. It also prohibits statements in food labeling that are false

or misleading.









1966. The Fair Packaging and Labeling Act requires all consumer products in interstate

commerce to contain accurate information and to facilitate value comparison.

1973. FDA issues regulations regarding nutrition labeling on food containing one or more

added ingredients. Nutrition labeling is voluntary for all other foods.

1994. Regulations of the Nutrition Labeling and Education Act of 1990 pertaining to

nutrition labeling and nutrition content claims are implemented.

A more detailed listing and description of the laws related to labeling, before the year 200 1, can be

found in Golan et al. (2000), Kurtzweil (1994) and Hadden (1986).

One of the current public debates is on mandatory country of origin labeling (COOL). In

2002, President George Bush signed into law The Farm Security and Rural Investment Act of 2002

or Farm Bill. Thi s law requires COOL for beef, lamb, pork, fish, peri shable agricultural commodities

and peanuts. In 2004 and 2006 President Bush signed laws delaying the implementation of some

parts of mandatory COOL until 2008. According to the Farm Bill, the implementation of the

program is the responsibility of the USDA's Agricultural Marketing Service.

In 2004 the Food Allergen Labeling and Consumer Protection Act of 2004 (FALCPA) was

passed by Congress. This Act improves food labeling information and makes it easier for people to

identify and avoid foods that contain allergens. It became effective in January of 2006.

Another current food labeling topic is the Guidelines for Voluntary Nutrition Labeling of

Raw Fruits, Vegetables and Fish. In 2006 the Food and Drug Administration (FDA) updated the

names and the nutrition labeling values for the 20 most frequently consumed raw fruits, vegetables,

and fish in the U.S. These amendments will be effective on January 1, 2008. Under the Federal

Food, Drug and Cosmetic Act the FDA addresses the food labeling requirements.










Food Labeling Problem Statement

Many studies indicate that consumers are not utilizing all the nutritional information on the

labels. Based on a FDA survey, Hadden (1986), page 148, concluded that "Rather than obtaining

positive nutrition information, consumers read the label to find information about ingredients they

wish to avoid, such as sugars, fats and oils, preservatives, artificial flavors and sweeteners, and

cholesterol". Other surveys also provide evidence that consumers in many cases are unable to

understand product information and quantify their nutritional needs. "Consumers questioned about

nutrition labels were unable to compute how much of a product they would need to eat to obtain a

day's allowance of a nutrient if one cup provided 25 percent of the Recommended Daily Allowance"

(Hadden 1986, page 215). It is also reported that many consumers "believed that the very presence

of the label indicates that the manufacturer has tried to make a nutritious product" (Hadden 1986,

page 148).

Since 1991 obesity rates in the adult population have increased from 12% to 20.9% in 2001.

The annual total cost of obesity is estimated at $117 billion. USDA statistics show that the total

amount of calories consumed have increased since 1980. Refined carbohydrates are a nutrient

associated with obesity. The consumption of these carbohydrates have increased from 147 pounds

per capital in 1980 to 200 pounds in 2000 (Richards et al., 2004). To correct these problems

consumers have to be educated.

Assuming the consumer (or potential consumer) pays attention to the label, then the labels)

may provide additional health information; confirm credence attributes; confirm generic claims;

provide a potential source of differentiation; increase confidence and security; provide legal

protection; reduce search cost; facilitates comparisons among products; enhance substitutability

among goods; provide nutritional education; and enhance consistency. Again, the key statement is

if the inquiring buyer pays attention to the label. Given the importance of the consumer to the










economic and social value of labels, the primary goal of this research is to estimate the likelihood

or probability of using the food labels and to determine what factors are likely to influence the

probability.

Labeling Research Goals

The present research centers on the consumer to quantitatively address the following:

*How to measure the levels) of attention consumers indicate about reading labels.

*What factors about consumers impact the attention level?

*What are the probabilities of reading the labels and have those probabilities changed over
time?

Several explicit hypotheses drive much of the empirical analyses:

*Attention to labels differs across consumer demographics.

*Health consciousness is a major factor influencing a person's attentiveness to label
information.

*General attitudes and eating habits are important contributing factors influencing the
attention to food labels.

*The importance of labels differ depending if the consumer is looking for negative versus
positive attributes expressed with the label.

Methodology and Data

To create the database of consumers, household heads were asked to provide a scaled

indication of their interest in labels (NPD). With a six point Likert scale, each household was asked

to score the following questions: (1) I check the labels for harmful ingredients, and (2) I read the

labels for my food purchase. While both statements address the importance that consumers attach

to labels, the first statement is more negative where the label is expected to be used to eliminate

buying particular products and the second is more positive to assist with the purchase.

The household data, from a demographically balanced diary survey, gave monthly

observations over the years from 1984-2003 for a total of 30,414 household entries. In addition,










another more recent attitudinal survey from 1993 to 2003 includes 13,150 households. For each

household many attributes are known including demographics, attitudes, eating habits and health

concerns.

Since the household response is discrete with scaled values, the likelihood of reading food

labels can be estimated using Ordered Probit models where the probability of each Likert score can

be determined.

The complete research will be divided into 6 chapters. Chapter 2 provides a discussion of

the historical development and public policy about food labels and also reviews the literature related

to the use of food labels. A detailed description of the data is covered in Chapter 3. Model

specification and the econometric theory, with emphasis on the use of discrete choice models, is

discussed in Chapter 4. Interpretation of the results and model simulations are shown in Chapter 5.

Finally, Chapter 6 gives the summary and conclusions of the research.









CHAPTER 2
LITERATURE REVIEW

The purpose of this chapter is to briefly describe the evolution of food labeling and review

the literature pertinent to the use of food labeling. The literature review is organized in three

sections:

1. Historical development of food labeling.

2. Economic and legal implications of food labeling.

3. Consumer use of food labels.

Historical Development of Food Labels

The circumstances under which food labeling started are nicely related by Hadden (1986),

pages 4 and 5:

The publication of Upton Sinclair's novel, The jungle, which detailed unsanitary

conditions in Chicago' s meat packing industry, created such a furor that the Congress

finally passed a Meat Inspection Act, which carried on its coattails the first labeling

law of national scope, the Pure Food and Drug Act of 1906.



The Act did not require that all food and drugs be labeled. It did, however, require

that manufacturers who provided labels should be accurate about ingredients,

proportions, and quantity. It also defined the new crimes of adulteration and

misbranding, of which a manufacturer would be guilty if the label statements were

"false or misleading in any particular, or if they did not bear the required

information."









Over the years, the Pure Food and Drug Act was followed by other laws and regulations. The

time line and bri ef descripti on of the following laws relating food l ab eling, for the peri od 1 906-2000,

were taken from Golan et al. (2000), pages 2-5.

1906 The Federal Pure Food and Drugs Act and the Federal Meat Inspection Act authorize the
Federal Government to regulate the safety and quality of food. These acts also defined
adulteration and prohibited selling misbranded or adulterated foods.

1913 The Gould Amendment requires food packages to state the quantity of contents.

1924 In U.S. v. 95 Barrels Alleged Apple Cider Vinegar, the Supreme Court rules that the Food
and Drugs Act condemns every statement, design, or device which may mislead, misdirect,
or deceive, even if technically true.

1930 The McNary-Napes Amendment requires labeling on products that do not meet
common-usage standards.

1938 The Federal Food, Drug, and Cosmetic Act replaces the 1906 Food and Drugs Act. Among
other things, it requires the label of every processed, packaged food to contain the name of
the food, its net weight, and the name and address of the manufacturer or distributor. A list
of ingredients also is required on certain products. The law also prohibits statements in food
labeling that are false or misleading.

1950 The Oleomargarine Act requires prominent labeling of colored oleomargarine to distinguish
it from butter.

1951 Nutrilite Consent Decree allows the FDA to establish industry guidelines for vitamin and
mineral labeling.

1957 The Poultry Products Inspection Act authorizes USDA to regulate, among other things, the
labeling of poultry products.

1958 The Food Additives Amendment (which contains the Delaney Clause) expands the FDA' s
authority to monitor dietary and health claims and food ingredients (including restricting or
banning any additive or food ingredient deemed unsafe). Processors are required to prove
that additives are safe. Creates the he Fair Packaging and Labeling Act requires all consumer
products in interstate commerce to contain accurate information and to facilitate value
comparisons.

1966 The Fair Packaging and Labeling Act requires all consumer products in interstate commerce
to contain accurate information and to facilitate value comparisons.

1966 FDA publishes proposed dietary supplement regulations. Proposal triggers legal challenges
from industry.









1969 The White House Conference on Food, Nutrition, and Health addresses deficiencies in the
U.S. diet. It recommends that the Federal Government consider developing a system for
identifying the nutritional qualities of food.

1973 FDA issues final dietary supplements regulation. Industry continues legal challenges.

1973 FDA issues regulations requiring nutrition labeling on food containing one or more added
nutri ents or whose l ab el or adverti sing include s claim s ab out the food' s nutritional property es
or its usefulness in the daily diet. Nutrition labeling is voluntary for almost all other foods.

1975 Voluntary nutrition labeling, postponed from its originally planned 1974 date, goes into
effect.

1976 Vitamin-Mineral amendments limit FDA' s authority and enforcement power in relation to
vitamin and dietary supplements.

1983 In face of legal setbacks and Federal budget cuts, FDA repeals dietary supplement
regulation.

1988 Surgeon General C. Everett Koop releases The Surgeon General's Report on Nutrition and
Health, the Federal Government's first formal recognition of the role of diet in certain
chronic diseases.

1989 The National Research Council of the National Academy of Sciences issues "Diet and
Health: Implications for Reducing Chronic Disease Risk," which presents additional
evidence of the growing acceptance of diet as a factor in the development of chronic
diseases, such as coronary heart disease and cancer.
Under contract with FDA and USDA' s Food Safety and Inspection Service (FSIS), the Food
and Nutrition Board of the National Academy of Sciences convenes a committee to consider
how food labels could be improved to help consumers adopt or adhere to healthful diets. Its
recommendations are presented in Nutrition Labeling: Issues and Directions for the 1990s.

1990 Dolphin Protection Consumer Information Act regulates labeling of dolphin-safe tuna.

1990 FDA proposes extensive food labeling changes, which include mandatory nutrition labeling
for most foods, standardized serving sizes, and uniform use of health claims. The proposed
Nutrition Labeling and Education Act reaffirms the legal basis for FDA' s labeling initiative
and establishes an explicit timetable.

1991 FDA issues more than 20 proposals to implement NLEA. In addition, the agency issues a
final rule that sets up a voluntary point-of-purchase nutrition information program for raw
produce and fish. FSIS unveils its proposals for mandatory nutrition labeling of processed
meat and poultry and voluntary point-of-purchase nutrition information for raw meat and
poultry .









1992 Dietary Supplement Act delays implementation of new dietary supplement regulation until
the end of 1993. Authorizes the FDA to grant permission to producers to make specific
health claims for products.

1992 FDA' s voluntary point-of-purchase nutrition information program for fresh produce and raw
fish goes into effect.

1993 FDA issues the final regulations implementing NLEA. Regulations covering health claims
become effective.

1994 NLEA regulations pertaining to nutrition labeling and nutrient content claims become
effective (including those for meat and poultry).

1 994 The Dietary Supplement Health and Education Act (D SHEA) defines a "dietary supplement"
as a food, not as a drug, thereby subjecting supplements to less restrictive regulatory and
labeling requirements.

1997 USDA releases the first proposed rule for a national organic foods standard (in compliance
with the Organic Foods Production Act). The proposal drew over 275,000 comments, largely
negative.

1997 FDA issues final rules implementing the maj or provisions of the DSHEA of 1994.

1999 Mandatory labeling of foods containing biotech ingredients is proposed in the House (HR
3377).

2000 USDA releases the second proposed rule for a national organic foods standard (in
compliance with the Organic Foods Production Act). The most controversial aspects of the
first proposal--the potential to allow the use of genetic engineering, irradiation, and sewage
sludge in organic production--were dropped from the second proposal.

2000 White House announces Food and Agricultural Biotechnology Initiatives: Strengthening
Science-Based Regulation and Consumer Access to Information authorizing (1) FDA to
develop guidelines for voluntary efforts to label food products under their authority as
containing or not containing bioengineered ingredients in a truthful and straightforward
manner, consistent with the requirements of the Federal Food, Drug, and Cosmetic Act; (2)
USDA to work with farmers and industry to facilitate the creation of reliable testing
procedures and quality assurance programs for differentiating non-bioengineered
commodities to better meet the needs of the market.

2000 Mandatory labeling of foods containing biotech ingredients is proposed in the Senate (S
2080).

Continuing with the description of food labeling regulations, in 2002 President George Bush

signed into law The Farm Security and Rural Investment Act of 2002 or Farm Bill. This law requires









country of origin labeling (COOL) for beef, lamb, pork, fish, perishable agricultural commodities

and peanuts. In 2004 and 2006 President Bush signed laws delaying the implementation of some

parts of mandatory COOL until 2008. According to the Farm Bill, the implementation of the

program is the responsibility of the USDA's Agricultural Marketing Service.

In 2004 the Food Allergen Labeling and Consumer Protection Act of 2004 (FALCPA) was

passed by Congress. This Act improves food labeling information and makes easier for people to

identify and avoid foods that contain allergens. It became effective in January of 2006.

Another current food labeling topic is the Guidelines for Voluntary Nutrition Labeling of

Raw Fruits, Vegetables and Fish. In 2006 the Food and Drug Administration (FDA) updated the

names and the nutrition labeling values for the 20 most frequently consumed raw fruits, vegetables,

and fish in the U.S. These amendments will be effective on January 1, 2008. Under the Federal

Food, Drug and Cosmetic Act the FDA addresses the food labeling requirements.

Economic and Legal Implications of Food Labeling

According to Hadden (1986), pages 5 and 6

The 1906 act embodied three different but related regulatory purposes. First, it was
intended to ensure fair trade among sellers of food and drugs by requiring accurate
label information. A manufacturer who made false claims could sell his product for
less than a manufacturer who made the same claims accurately. Enforcing the label
as the standard of accuracy deterred unfair competition through false claims. .
Second, the law was intended to help consumers appalled at the high prices and
inflated claims of packaged products that preyed on ignorance and illness.
Finally, the 1906 act reduced risks to health. One of its most important provisions
was the requirement that proportions of addictive substances be shown on the label.

The Fair Packaging and Labeling Act of 1966 defines label as follows:

The term "label" means any written, printed, or graphic matter affixed to any
consumer commodity or affixed to or appearing upon a package containing any
consumer commodity (Miller 1978, page 3).

For Stanton et al. (1991), page 224, "A label is the part of a product that carries information

about the product or the seller. A label may be part of a package, or it may be a tag attached directly










to the product." He also classifies the labels in three groups: (a) Brand, which is just the brand

applied to the product or package; (b) Grade, which identifies the quality with a letter, number or

word; and (c) Descriptive, which gives objective information concerning the product.

Teis1 and Rao (1998), page 140, define "product labeling as any policy instrument of a

government or other third party that somehow regulates the presentation of product-specific

information to consumers. This information might describe use characteristics of the product, such

as price, taste, and nutrition, or nonuse characteristics, such as the environmental impact or

moral/ethical elements surrounding the product's manufacturing process." They also point out that

labeling policy has three maj or components: compulsoriness, explicitness, and standardization. The

degree of compulsoriness can vary from mandatory labeling restrictions, requiring to di splay certain

information on the product, to voluntary labeling restrictions, where the firms choose the type of

information to display. Explicitness has to do with how much detailed has the information presented

to the consumer. The last component, standardization, "is the degree to which the regulation requires

the information to be provided in a presentation format that is standardized and uniform across

products."

To illustrate the labeling content two examples are included, Figures 2-1 and 2-2. Figure 2-1

shows the typical U. S. on package label with most of the information providing nutritional content

and contributions to the daily nutrient intake. In terms of the label format there is considerable

continuity across products in terms of nutritional information. More differences are seen in the

presentation of additional things such as country-of-origin, reduced claims about less cholesterol,

less fat, and unique ingredients. The second example is from a label in Belgium where considerably

more information about traceability is included on the label. Unlike the U.S. labels, this Belgium

example shows an almost overwhelming amount of information with emphasis s on traceability more

than nutritional signals.









Economic Justification for Labeling

As the economic justification for labeling Teis1 and Roe (1998) mention the removal of

information asymmetry or "subsidization of search costs" that benefits the consumer by providing

information about the attributes of the product they want to buy. The size of the subsidies or the

benefits to consumer would depend on the attributes of the goods. Nelson (1970) classifies the

attributes of goods according to the way information about them is acquired. If the attributes or

qualities of goods are ascertainable through search before buying them, then the goods have "search

attributes". On the other hand, if the attributes are ascertainable only after the goods are bought and

used or consumed, then the attributes are said to be "experience attributes". Darby and Karnil (1973)

distinguish a third class of attributes, "credence attributes". They point out that credence qualities

cannot be evaluated in normal use and that the assessment of their value needs additional and costly

information. Some examples of goods with credence attributes are organically produced vegetables,

genetically modified products, and fish caught without harming dolphins (Han and Harrison, 2004;

Golan et al. 2000).

According to Caswell and Moj duszca (1996) the market functions relatively well for goods

with search attributes and informational programs are less likely to be instituted because information

for these kinds of goods is plentiful and easy to obtain. For goods with experience attributes, they

continue, information about the quality of the product is the most important issue and the

government may be able to improve efficiency by facilitating communication between the informed

and uninformed consumers through some form of consumer rating on product labels. In the case of

goods with credence attributes, they point out, that the markets for quality do not function well

because the information is so imperfect. They remark that the consumers cannot learn from their

experience consuming the product and cannot measure its quality.










Benefits and Costs of Mandatory Labeling

Golan et al. (2000) points out that informed consumption and socially desirable changes in

consumption behavior are the main benefits of a government labeling program. The benefits cannot

be known precisely before the information is introduced into the market and according to Beales

1980, page 247:

the greater the risk, the greater the value of information about that risk is likely to be.
Availability of sub stitutes will influence the value of information. If another product
does not have the hazard, and is a very good substitute at prevailing prices for the
product with the risk, greater changes in consumer behavior are likely. If, however,
there are no substitutes available, the costs of changing behavior are much greater,
and less change will result. Thus, information is more likely to produce benefits if
it applies to only a few brands of a product (since other brands are likely to be good
substitutes), than if it applies to the entire product class (since other products are not
likely to be as substitutable as different brands of the same product).

Beales (1980) also points out that even when the number of informed consumers is small,

firms competing for thi s informed minority may make changes that also are going to benefit the rest

of the consumers. "For example, nutritional information is not used by any large fraction of

consumers; most do not read the labels. However, in competing for consumers who do read labels,

many companies fortified their products, thus improving their nutritional quality, as measured by

the information on the label. All consumers who use these products benefit from the information,

even though only a small number actually read the labels" (Beales 1980, page 248).

Teis1 and Roe (1998), page 143, mention that labels allow consumers to verify claims made

by advertisers and that this "may hinder some firms from overstating product qualifications". They

also suggest that standardizing the display across products facilitates the consumers use of product

labels and makes easy to extract information.

Another advantage of labels is realized when legal issues are raised. The label's importance

for legal protection and for traceability is there and the benefits are only realized when a legal issue

is raised.









Beales (1980) recognizes three types of labeling costs: direct costs, indirect or opportunity

costs, and side effects or un-intended costs. The direct costs are the result of generating the

information, testing for the veracity of the claims, printing the labels and enforcing the labeling

rules. The indirect costs results from the loss of flexibility in the production of the good, specially

if there is a short term change in the availability or price of inputs. It is not worthwhile to change

the labels to reflect the new ingredients in the short term. The un-intended costs result when

manufacturers respond in unexpected ways to information disclosure. For example, once a product

qualifies for the highest class in a grading system, "the manufacturer may have no incentive to make

further improvements" (Beales 1980, page 252).

Golan et al. (2000) points out that labeling programs could be costlier, in a per-unit basis,

for small firms than for large firms, putting them in a competitive disadvantage and possible,

changing the industry structure and imposing disproportionate costs on rural economies.

Mazis (1980) mentions that in some cases, the industry may incur in the costs of increasing

packaging size to encompass mandated information. He also mentions that from a legal perspective

the cost of information could be viewed as a restriction on speech. Another aspect of labeling

mentioned by Mazis is that consumerists, a very small proportion of people, often very

well-educated, really find detailed information useful. On the other hand, poor and less-educated

people can't use this information, yet they have to pay for it.

Effectiveness of labeling

Golan et al. (2000) points out that the government may use different policy tools like taxes,

bans, education programs, and regulation of production and marketing, to correct for externalities

and asymmetric information. The following is a list of situations that Golan et al. (2000), pages

1 7- 18, believe may be appropriate for using labeling as a policy tool, after consideration of the costs

and benefits of its application.










Consumer preferences differ. Labeling may be preferable to other policy tools if
consumer preferences differ widely with respect to product characteristics (Magat
and Viscusi, 1992)...

Information is clear and concise. The information on the label must be clear,
concise, and informative. Information that is unread or misunderstood will not lead
to better informed consumption decisions nor to a better matching of preferences
with purchases. Too much information diminishes the value of all the information
on the label...

Information on product use enhances safety. For some products, the manner in which
consumers use or consume the product influences the quality attributes of the
product. In the se cases, informati on ab out how to enhance the positive character stics
of the product or reduce the negative ones could benefit consumers...

Costs and benefits of consumption are borne by the consumer. If the consumption
or production of a food creates externalities (that is, affects someone else's welfare
in a way not reflected in the market), then information-based policies will usually be
insufficient to align private consumption choices with socially optimal choices...

Each of the steps along the labeling tree can be established. Mandatory labeling will
result in confusion and actually increase transaction costs unless it is supported by
clear, achievable quality standards, testing services to measure the validity of
labeling claims, certification services sub stantiating the validity of the quality claim,
and mechanisms for enforcing labeling rules, including mechanisms to punish
producers who make fraudulent claims. The government must either perform these
services or accredit third-party agents to perform them (as described by branch 4 of
the labeling tree).

No political consensus on regulation exists. In many regulatory policy debates, there
is little consensus on the appropriate regulatory response. Some groups may advocate
complete product bans while others advocate no government intervention at all.
These debates could be national or international and could lead to difficult problems
in harmonizing standards for a wide range of goods (biotech labeling is a case in
point). In these cases, labeling may represent not just the best compromise solution
but also the path of least resistance, both domestically and internationally...

Teis1 and Roe (1998), page 143-144, mention that there is research indicating that labeling

can change producer and consumer behavior and that "what is needed is research that develops

understanding of what the conditions need to be for a labeling policy to be effective. That is, what

characteristics of the interaction between the label, the consumer, and the product affect the impact

of information? "









Consumer Use of Food Labels

The following papers describe the regulatory environment, as well as the use of food labels

by consumers, before and after the Nutrition Labeling and Education Act of 1990 (NLEA).

Bender and Derby (1992) used hierarchical discriminant function analyses on FDA national

data for 1982, 1984, 1986 and 1988 to determine both the trends and the relationships between the

use of the ingredient list and the nutrition label and selected other variables. The number of

households in the surveys, which included questions about food labels, were 4000 in the first three

years and 3200 in the last year. Their results, page 293, shows that: "The percentage of consumers

who reported using the ingredient list to avoid or limit particular food ingredients remained constant

between 1986 and 1988 for sodium, sugar, and preservatives, but there were significant increases

reported in the avoidance of fats/oils and cholesterol." They also report that: "The gap between the

least-and most-educated consumers narrowed even more for nutrition labels than for ingredient lists.

Only consumers with advanced degrees remained significantly greater users of nutrition labels than

did those with less than a high school education" (page 294).

Guthrie et al. (1995) used the 1989 USDA's Continuing Survey of Food Intakes by

Individuals (CSFII) and Diet and Health Knowledge Survey (DHKS) surveys, with 2214

households, to study the effect of some consumer characteristics on the use of labels. Among the

explanatory variables that they used in their models are: sex, age, US regions, education,

employment, weight concerns, diet, and nutrition knowledge.

Guthrie et al. (1995), page 168, conclude that:

the most likely person to use the nutrition label is an educated woman who lives with
others, is knowledgeable about nutrition, places more importance on nutrition and
product safety and less on taste when shopping for food, and believes that following
the principles of the Dietary Guidelines for Americans is important. It could also be
said that male meal planners/preparers who live alone, are less educated, less
knowledgeable about nutrition, less concerned with nutrition and product safety and









more concerned with the taste of the food they purchase, and believe following the
Guidelines principles to be less important are the least likely to use food labels.

Moorman (1996) used a longitudinal quasi experimental design, with evaluations at two

points in time, (eight months before label introduction-Octob er 1993 and five months following

label introduction-October 1994) to assess the consumer informational determinants of nutrition

information processing activities.

Moorman used three geographically dispersed sites in two states and selected the consumers

" from 20 different product categories that could be classified by nutrition level: orange juice, cake

mix, peanut butter, ready-to-eat cereal, margarine, salad dressing, cheese, oils, crackers, cookies,

potato chips, pasta, frozen dinners, ice cream, yogurt, hot dogs, bread, soup, frozen pizza, and corn"

(Moorman 1996, page 32). Moorman collected data from 554 participants in the pre-NLEA and 558

in the post-NLEA condition.

Moorman finding suggest that the new labels have increased the level of comprehension of

nutrition information that consumers use at the point of sale, but she also points out that the NLEA

was only partially successful because in the post-NLEA only more motivated and less skeptical

consumers acquired more information and that it is not clear that nutrition labels are the appropriate

tool to motivate less interested or highly skeptical consumers.

Derby and Levy (2001) analyzed the 1994 and 1995 FDA's Food Label Use and Nutrition

Education Survey (FLUNES). The first survey involved 1,653 households and most of the

interviews were conducted in March-April, prior to the NLEA implementation date. In the second

survey, with 1,001 households, the interviews were conducted in November and December of 1995,

about 18 months after the implementation of NLEA. These surveys provided information about the

credibility of label information and consumer use of food labels. According to their conclusions the

NLEA had positive impact in the use of food labels, that because of the new food labels consumers










stop buying or tried new products and that when consumers pay attention to claims and nutrition

facts information they draw the appropriate conclusions about the products healthfulness. Their

research also found out that the food label is not an ideal way of disseminating nutrition education

messages. Derby and Levy (2001), page 395, point out that "With respect to nutrient content and

health claims, research showed little consumer awareness that claims are regulated, and a maj ority

of consumers remain skeptical of health claims on food labels. This suggests that, to date, consumer

education has not adequately addressed the credibility issue".

Kri stal et al. (1 998) used two cross-sectional surveys from the Washington State Cancer Ri sk

Behavior Survey (random-digit-dial survey of adults) to characterize food label use before and after

the introduction of the new label format. The first survey was completed between August 1992 and

August 1993 (n = 1001) and the second between September 1995 and September 1996 (n = 1450).

Both surveys included questions on demographic characteristics and attitudes and behavior related

to cancer risk (diet, smoking, screening), as well as questions relating to the use or non-use of labels

when purchasing packaged foods. Based on the results of their research Kristal et al. (1998), page

1215, conclude that they ...

found evidence of modest, positive impacts of new food labels on use, barriers to
use, and satisfaction. It is important to note that the percentages of residents who
never used labels did not change, and that more than 70% of the respondents wanted
new labels to be easier to understand. The impact of the Nutrition Labeling and
Educati on Act could b e enhanced by further l ab el modifi cati ons to make l ab els easi er
to understand and by programs to help consumers, especially older and less well
educated consumers, interpret label information.

Kim et al. (2000) used the USDA's 1994-96 Continuing Survey of Food Intakes by

Individuals (C SFII) and Diet and Health Knowledge Survey (DHK S) to determine the character sti cs

of consumers who use food labels. Their results show that males use food labels less than females,

more educated consumers read nutrient content information more than less educated consumers.

Also, individuals on special diet, informed consumers about the linkage between diet and health










problems, nonsmokers, and people that exercise regularly are more likely to use nutrient content

information than individuals who are not on diet, or are less informed, smokers, or don't exercise

regul arly.

Lin et al. (2004) studied the association of total fat, saturated fat and cholesterol intakes and

the probabilities of looking for their information on food labels. To estimate the relationships, they

use a generalized logistic model. The data they used came from the 1994-1996 Continuing Survey

of Food Intakes by Individuals (C SFII) and the accompanying Diet and Health Knowledge Survey

(DHKS) conducted by the US Department of Agriculture, with sample sizes of 3995, 3992 and 4024

observations, for each year, respectively. Among the explanatory variables they used are

demographics and knowledge about nutrition.

Almost the entire results of their study are quoted because of the relevance to the present

study. Lin et al. (2004), pages 1962-1964, report the following results:

Respondents who had higher intakes of total fat, saturated fat, or cholesterol
were less likely to report looking for label information on these nutrients. How they
felt about food labels are strongly related to whether they looked for the label
information. Specifically, the probability of information search is higher for those
who agreed that (1) they know how to use food labels to choose a healthy diet, (2)
they would be better off using food labels to choose foods than relying on their own
knowledge about foods, or (3) reading food labels does not take more time than they
could spare.

Respondents who agreed that nutrition is important in their food shopping
decisions were more likely to look for the information. The probability of searching
for information on food labels is also higher among respondents who were on a
special diet, with higher household income, and with better nutrition knowledge. ..
Also, the more important respondents felt it was to choose a diet low in saturated fat,
maintain a healthy weight, choose a diet low in fat, and choose a diet low in
cholesterol, the more likely they looked for the information. Knowledge of problems
caused by eating too much fat or cholesterol is also associated with a higher
probability of information search. The probability of information search is lower
among respondents who resided in the West.

A few variables have differentiated effects on food label information search
across the three nutrients. For instance, respondents who felt that nutrition
information on food labels was hard to interpret were more likely to search for total









fat information on food labels than other respondents, while no similar relationship
is found for saturated fat or cholesterol information. Similar to the Einding by Kim
et al. (2000), respondents who resided in larger households were less likely to search
for fat and saturated fat information; but, contrary to the Einding by Kimet al. (2000),
they were neither more or less likely to look for cholesterol information than those
in smaller households. ... Hispanic respondents were more likely to search for total
fat information on food labels but not saturated fat or cholesterol information.
Respondents who resided in the South and Midwest were less likely to search for
saturated fat or cholesterol information, but not total fat information on food labels
than others. Finally, male respondents were less likely to look for total fat and
saturated fat information, similar to the findings by Kim et al. (2000), but not
cholesterol information. Employment status and shopping status (i.e., major food
shopper or not) were not related to information search.

The probability of search for cholesterol information is higher among
respondents who were older, who believed their diet can make a difference in the
chance of getting a disease (such as heart disease or cancer), who had been
diagnosed of a disease (heart disease, stroke, high blood pressure or diabetes), or
who resided in a suburban area. Also, the probability of search for cholesterol
information is lower among respondents who were White or more educated.

He et al. (2004) used Ordered Probit models to explore factors affecting the intakes of fat,

cholesterol, sodium, vitamins, protein, and dietary fiber. The explanatory variables are age, gender,

education level, ethnic status, marriage status, household income, having a non-adult family

member, and level of engagement in physical activities. The data they used was a nationwide

telephone survey of 2880 U.S. households conducted in 1997. This study is not about using food

labels but their results show that many factors affect in similar way the search for information on

food labels and the food intake. For example, older people tend to pay more attention to nutrient

intake than do younger people. Consumers with college education are more careful about

consumption of cholesterol, sodium, vitamins, protein, and fiber than consumers with lower

education. By the same token, household income positively affect fat and cholesterol consideration.

McLean-Meyinsse (2001), page 114, using a chi-squared contingency test on data from a

random telephone survey of 1,421 primary grocery shoppers and/or meal preparers in Alabama,










Arkansas, Florida, Georgia, Kentucky, Louisiana, Mississippi, North Carolina, Oklahoma, South

Carolina, Tennessee, Texas, and Virginia during August 1998, found out that:

Older consumers are more likely to examine the sodium content of food
products when making purchasing decisions; those between 18 and 35 years of age
are more likely not to pay much attention to the nutritional attributes on food labels.
College-educated consumers show more concerns about the fat content of food
products than non-college graduates. The results also suggest that women,
households with children 18 years old and under, households with incomes in excess
of $35,000, married consumers, and Caucasians are more likely to use labels to
determine the fat content of foods than their corresponding counterparts. Younger
respondents, those without a college diploma, men, those without children and living
in households with income levels below $35,000, unmarried consumers, and
nonwhites are more likely to use attributes besides calories, fat, list of ingredients,
and sodium when making their food purchasing decisions. Overall, when purchasing
food products, consumers read the information on fat content more frequently than
any other single attribute.

Gracia et al. (2007) estimated a multivariate Probit model in order to simultaneously model

three decisions: knowledge about nutritional labels, consumers label use, and perceived benefit from

a mandatory nutritional labeling program. Among the explanatory variables in the models were

gender, age, education, income, household size, health habits, and importance of some label

attributes. The data used in the estimations came from a survey of 400 food shoppers during the

Spring of 2004 in Zaragoza, Spain. Their results in page 172 show that:

individuals who state to be more knowledgeable about nutritional labels are more
likely to use food labels while shopping, and nutritional label users are more likely
to consider a mandatory nutritional labeling program as beneficial. In addition, we
have found that older and more educated consumers are more likely to perceive
benefits from the mandatory implementation of the nutritional label program.
Moreover, consumers who consider that the information provided by nutritional
labels is useful are more likely to think that the mandatory implementation of such
labels will be beneficial. On the contrary, and as expected, consumers who consider
that the provided information is too dense (too much) are less likely to perceive
benefits from the mandatory implementation of nutritional labels.

The above literature provides the foundation for a more thorough analysis of consumers use

of food labels. This study builds on the literature drawing from a much more extensive database

spanning a large cross-section of consumers over a considerable time span.












Nutrition Facts
Sewrving Size cupf (114g)~i
Servigs Per Container 4

&...... Per servine
Clri es 90 Caloris from Fa~t 30

Total Fat 3g 5%
Sa~luded Fa:~t Og 0%,'
leolester ol~ 0r g

'Ita~~sl Casiohystate 13g 4%
Distary Fiber 3@ I 2%
Sugus~s 3
Prolein 30

Vi~tamirel A 80% Vitamin C 60%
Calcium $4% I IraBn 4%~
" Pewrcet Da~ly Vaks ar based On a 2,DDO
cable dict. Your ally vraise niny be hgher
ore kmar deperxhi om you cakxh needs:
calMade: 2,000 2,510~b

set FBr use than SQg 5
encesiame~ Lessman ano~ne soone
rsodknr bmnes to 2,400rug 2,4Dang
Dietary Fiber 29 3Dg

F"al 9I ~rbehythlae 4 P Prdam~ 4


Figure 2-1. Typical U.S. food label. [Download from
http ://www.fda.gov/opacom/backgrounders/foodlae/ewae.html, November 2006].





008294
1 rLo


Figure 2-2. Belgium beef label


6 CHATERUBRIAND

Heel warn braden of reasteren
8 ~Femise Gournlet
bconsonwneshisqu'au Penhrer ,

1(,03,03 0,475
ma-REPIP~ 01005k

20 00 1
une r Dehl


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GCllWEEKT IN: GESLAENT INl:
BtthIt PFI~l IEE 97!
TR ACEERBRARHE l SllI1nER *

11III 11 1l II llllli I l 1#111
50 56 0060 050 550 0 6865P 659









CHAPTER 3
DESCRIPTION OF DATA

The present study uses commercially collected household data from two national

demographically balanced diary surveys (NPD). The first survey is from 1984 to 2003 and includes

a total of 30,414 observations. The second survey is from 1993 to 2003 with a total of 13,150

observations. This second, or new survey, adds questions to the original survey. Households report

their consumption during a two week period with a period commonly referred as a wave. Each wave

can be associated with a certain month (e.g., the waves are not reported since the time dimension

to the data were expressed in month equivalence.) The surveys were collected across waves and

contain questions about demographics (census region, income, age, education, employment), use

of food labels, snacking, dieting, exercises, nutrition, food preparation, brand awareness and

attitudes about health and eating habits. The surveys do not provide information about race or health

status of the consumers.

Among the statements that households were asked to score with a six point Likert scale are:

(1) I check the labels for harmful ingredients, and (2) I read the labels for my food purchase While

both statements emphasize the consumers importance attached to labels, the first statement is more

negative where the label is expected to be used to eliminate buying particular products and the

second is more positive to assist with the purchase. Table 3-1 shows these two statements and their

corresponding Likert scales. Figures 3-1 to 3-3 provide an overview of the consumers scoring for

labels during the different periods of the study. Considering levels 1 and 2 in Likert scale as a

measure in the use of labels, then, in the 1993-2003 period (Figure 3-1), 56.2 percent checked the

labels from harmful ingredients while 59.4 percent used the labels for broader purchasing decisions.

For the rest of the periods in study, Figures 3-2 and 3-3, the use of labels looking for harmful

ingredients varies from 56.4 to 55.2 percent. By the same token, taking the Likert scales 5 and 6 as










indication of lack of interest on labels in general, 10.5 to 14.4 percent of consumers showed little

interest on labels.

For each household surveyed many attributes are known including demographics, health

concerns, eating habits, employment, dieting, brand awareness and concerns about foreign food.

Table 3 -1 provides the description of these attributes and their corresponding discrete classification.

Tables 3-2 to 3-5 show the discrete classification and the frequency distribution, in percent, of the

attributes that are used as explanatory variables in the Ordered Probit models. Table 3-2 shows this

information for the period 1993 -2003, Table 3-3 for the period 1984-2003, Table 3-4 for the period

1984-1993 and Table 3 -5 for the period 1994-2003. As tables show, the di stributions are very similar

across all these periods.

Tables 3-6 and 3-7 show the five highest correlation coefficients between explanatory

variables for the periods 1993 -2003 and 1984-2003 respectively. Health concern variables (cautious

about additives, cholesterol, fat, preservatives, salt and sugar) show the highest correlation

coefficients and therefore they could potentially cause multicollinearity problems. The variables,

because they are discrete in form and with a range of values, must be expressed in binary form (i.e.

dummy variables). Ultimately, before being used in a regression model, the dummy variables have

to be restricted, as explained in Chapter 4. At the end, is the degree of linear combination of the

restricted dummy variables that will have an effect on the degree of multicollinearity. Tables A-1

and A-2 show the restricted dummy variables with the highest correlation coefficients for the periods

1993 -2003 and 1984-2003, respectively. The restricted dummy variables with the highest correlation

coefficients are in the health concerns group.









Table 3-1. Variables used in the Ordered Probit models

Variable Description Likert scale

Household scaled attention to labels (Y)
I check the labels for harmful
ATLAB ingredients 1 = Completely agree 2 = Mostly agree
3 = Somewhat agree 4 = Neither
I read the labels for my food oeh dsge st iare
FPLAB oehtdsge otydsge
purchase
Demographics (X1)
1 =< 35 2 =35 -44 3 =45 -54
DMAGE IAge of female head
4 = 55 64 5 = 65 +

DMCHL Children under 18 years 1 = yes 2 = no
Education of female head of 1 = No high school 2 = High school,
DMEDU'
household 3 = Some college 4 = College graduate
Employment of female head of
DMFEM1=Emlyd2=Ntepod
household mly N epod
1 = 1 Member 2 = 2 Members
DMHSZ Household size
3 = 3-4 Members 4 = 5 + Members
1 = under $30,000 2 = $30 49,999
DMIN2 Household income
3 = $50 69,999 4 = $70 100,000 +
1 = New England 2 = Middle Atlantic
3 = East North Central 4 = West North Central
DMREG ICensus region 5 = South Atlantic 6 = East South Central
7 = West South Central 8 = Mountain
9 = Pacific
Attitudes (X2)
ATCAL Conscious of calories

ATLB S Like to lose 20 pounds
ATSWM Love toswim 1 = Completely agree
2 = Mostly agree
ATWGT Overweight isn't attractive 3 = Somewhat agree
Best known brands are highest 4 = Neither
NTBRN quality 5 = Somewhat disagree
.6 = Mostly disagree
Food should have body building
NTING
ingredients
Know more than most about
NTKNO
nutrition









Table 3-1. Continued
Variable Description Discrete Classification
Eating Habits (X3)
DTFE2 Adult female on diet 1 = Yes 2 = No

FDFCH Eating fried chicken
1 = Always encourage
FDHOT Eating hot dog sandwich 2 = Almost always encourage
3 = Sometimes encourage
FDLUN Eating lunchmeat
4 = Neither
FDPIZ Eating pizza 5 = Sometimes discourage
FDTA Eatng tcos6 = Almost always discourage

ATFOR Avoid foreign food
1 = Completely agree 2 = Mostly agree
FPFAS ITry fast food places 3 = Somewhat agree 4 = Neither
FPRE isi retauant moe tan ost5 = Somewhat disagree 6 = Mostly disagree

Health Concerns (X4)

ATDOC Doctor gives advice on diet
A person should be cautious
NTADD
about additives
A person should be cautious
NTCHL
about cholesterol
1 = Completely agree
A person should be cautious
NTFAT 2 = Mostly agree
about fat
3 = Somewhat agree
NTPREA person should be cautious 4 = Neither
about preservatives 5 = Somewhat disagree
A person should be cautious 6 = Mostly disagree
NTSAL
about salt
A person should be cautious
NTSUG
about sugar
Vitamins recommended by
NTVIT
physician
Seasonality (X5)
1 = First quarter 2 = Second quarter
ZQTR Quarters.
3 = Third quarter 4 = Fourth quarter
* New survey variables










n i
pe
tnecr of ex
p
al n d


.~~~~I~ ,~~ ,
Discrete Classif cation
Variable 1 2 3 4 5 6 7 8 9
Percent of 13,150 observations
DALAGE 21.13 24.03 21.66 15.98 17.20
DMCOL 36.32 63.68
DMEDU 5.32 30123 27.70 36.75
DMFEM 54.97 45.03
ERAHSZ 22.70 34.31 32.72 10.27
DMIN2 41.58 27.48 15.55 15.38
DMREG 4.65 15.95 17.51 8.06 16.81 6.84 10.65 6.14 13.38
ATCAL 8.73 17.81 29.49 19.17 14.52 10.28
ATLB S 36.58 11.09 11.59 8.11 8.64 24.00
AT SWM 16.91 13.13 17.08 18.18 8.95 25.75
ATTVGT 26.36 25.05 20.94 17.86 6.03 3.76
NTB31N 3.45 8.89 18.57 21.03 32.07 15.99
NTING 10.62 16.34 23.13 32.50 11.48 5.93
PERCRO 6.71 14.13 27.37 32127 12.48 7.04
DTFE2 29.76 70.24
FDFCB[ 3.67 4.84 14.55 36.12 21.86 18.97
FDHCIT 2.17 3.19 15.60 46.08 18.24 14.71
FDLUN 4.74 7.67 18.78 41.07 16.68 11.06
FDPIZ 8.08 11.24 23.63 45128 8.46 3.32
FDTAC 5.32 9.01 19.78 48.21 9.93 7.75
ATFOR 17.19 19.40 24.30 15.42 11.46 12.24
FPFAS 1.75 4.68 19.38 17.75 16.10 40.33
FPRES 3.04 5.12 10.25 17.04 16.95 47.60
ATDOC 9.64 10.33 19.13 23.50 9.95 27.46
NTAD)D 28.37 23.33 25.99 17.53 3.03 1.75
NTCBL 32.92 26.39 25.75 11.0:2 2.51 1.41
NTFAT 38.62 26.24 22.59 8.65 2.38 1.51
NTPRE 25.57 22.81 26.17 19.65 3.85 1.95
NT SAL 29.39 26.57 26.97 11.78 3.34 1.95
NT SUG 18.94 18.67 34.00 19.82 5.88 2.70
NTVIT 7.41 7.64 14.43 25.44 26.05 19.03
ZQTR 22.09 23.66 27.30 26.95


Table 3-2 Fr s
que
nc
y










f o ex
p
al nator
y va
ir d


.~~~~I~ ,~-- ,
Discrete Classitication
Variable 1 2 3 4 5 6 7 8 9
Pe cents of 30,414 o aservatio ns
DMALGE 25.80 22.93 18.23 16. 12 16.92
ERACEL 37.14 62.86
DMEDU 6.56 32.49 26.46 34.48
ERAFEM 53.35 46.65
DMHSZ 23.35 32.73 33.24 10.67
DMIN2 49.72 26.59 13.13 10.56
DMREG 4.78 16.42 17.32 8.21 16.76 6.83 10.57 6.61 12.51
ATCAL 10.39 19.72 29.43 17.50 13.28 9.68
ATLB S 35.90 10.05 11.13 7.40 8.52 27.00
AT SHR 19.51 13.19 16.66 17.76 8.11 24.76
ATTVGT 33.40 25128 19.05 14.02 5.06 3.19
NTBRN 3.38 8.50 17.86 18.38 33.64 18.24
NTING 14.69 17.82 23.13 29.47 10.19 4.70
NIDCRO 7.27 14.57 27.86 30.94 12.35 7.01
DTFE2 29.28 70.72
FDFCH 4.60 5.95 16.60 36.12 20.61 16.11
FDHOT 2.58 3.90 17.34 45.61 16.85 13.72
FDLUN 4.75 7.68 18.81 39.82 17.07 11.86
FDPIZ 9.09 12.07 23.50 43.91 7.98 3.45
FDTAC 5.87 8.93 19.47 48.17 9.22 8.34
ATFOR 19.81 19.64 24.60 14.09 10.39 11.46
ATDOC 11.46 10.61 17.85 23.69 8.98 27.41
NTAD)D 33.03 23.38 24.37 14.90 2.75 1.57
NTCBL 37.77 25.68 23.77 9.44 2.14 1.20
NTFAT 41.45 25.97 21.53 7.69 2.08 1129
NTPRE 29.63 23.78 24.54 16.99 3.44 1.61
NT SAL 34.82 26.79 24.39 9.68 2.82 1.49
NT SUG 23.30 20.32 32.53 16.68 5.04 2.12
NTVIT 10.73 9.15 15.13 23.67 24.40 16.93
ZQTR 23.68 23.75 26.62 25.95


e lbaT 3-3 Fr squency i t










f o ex
p
al nator
y va
ir d


.~~- ,--`-~-)~ ----)~- -p---~- ----- I-- ---- -~
Discrete Classification
Variable 1 2 3 4 5 6 7 8 9
Percent of 15,331 observations
DMAGE 28.87 21.97 15.05 16.98 17.14
DMCHL 37.85 62.15
DMEDU 7.95 35.81 25.08 31.16
DMFEM 50.54 49.46
DMHSZ 23.32 31.93 33.68 11.07
DMIN2 58.84 25.66 10.57 4.93
DMREG 4.78 16.95 17.61 8.49 16.25 6.95 10.47 6.80 11.70
ATCAL 12.24 22.03 29.95 15.62 11.54 8.62
ATLB S 34.82 8.85 10.85 6.85 8.54 30.08
ATSWM 21.74 12.85 16.11 17.61 7.21 24.47
ATWGT 40.81 25.62 17.19 9.87 4.15 2.36
NTBRN 3.12 8.02 17.19 15.71 35.41 20.54
NTING 19.02 20.02 23.10 25.99 8.76 3.10
NTKNO 7.66 15.03 28.50 29.93 12.01 6.87
DTFE2 29.33 70.67
FDFCH 5.46 6.81 18.08 35.85 19.80 14.00
FDHOT 2.97 4.46 19.03 45.12 15.64 12.77
FDLUN 4.66 7.08 18.47 38.82 17.99 12.99
FDPIZ 10.23 12.42 23.12 43.11 7.43 3.69
FDTAC 6.46 8.66 18.79 48.35 8.68 9.07
ATFOR 21.90 20.14 24.51 13.23 9.56 10.65
ATDOC 13.32 10.88 16.72 24.02 8.05 27.01
NTADD 38.31 23.70 22.49 11.88 2.36 1.27
NTCHL 43.59 24.98 21.49 7.39 1.62 0.94
NTFAT 45.65 25.55 20.04 6.17 1.59 1.00
NTPRE 34.45 24.81 22.79 13.87 2.82 1.26
NTSAL 40.84 27.23 21.67 7.14 2.12 1.00
NTSUG 28.05 22.03 30.87 13.41 4.18 1.46
NTVIT 13.91 10.81 16.25 21.79 22.91 14.32
ZQTR 22.45 24.53 27.70 25.33


e lbaT 3-4 Fr squency i t










f o ex
p
al nator
y va
ir d


.~~-~ ,-1x-~I ,- ---) --- -- --- --- --
Discrete Classification
Variable 1 2 3 4 5 6 7 8 9
Percent of 15,083 observations
DMAGE 22.68 23.91 21.47 15.24 16.69
DMCHL 36.42 63.58
DMEDU 5.14 29.13 27.87 37.86
DMFEM 56.21 43.79
DMHSZ 23.39 33.55 32.79 10.27
DMIN2 40.46 27.53 15.73 16.29
DMREG 4.79 15.89 17.02 7.93 17.27 6.70 10.67 6.40 13.33
ATCAL 8.51 17.37 28.91 19.41 15.05 10.75
ATLB S 37.00 1 1.26 1 1.42 7.96 8.50 23.87
ATSWM 17.24 13.55 17.22 17.91 9.03 25.05
ATWGT 25.87 24.93 20.94 18.24 5.99 4.03
NTBRN 3.63 8.98 18.54 21.10 31.84 15.91
NTING 10.30 15.57 23.16 33.01 11.65 6.31
NTKNO 6.88 14.11 27.21 31.96 12.70 7.14
DTFE2 29.22 70.78
FDFCH 3.73 5.09 15.10 36.40 21.43 18.25
FDHOT 2.18 3.33 15.62 46.11 18.07 14.68
FDLUN 4.85 8.30 19.15 40.85 16.14 10.71
FDPIZ 7.93 11.71 23.89 44.73 8.53 3.21
FDTAC 5.26 9.21 20.16 47.99 9.78 7.60
ATFOR 17.68 19.13 24.70 14.96 11.24 12.29
ATDOC 9.56 10.34 19.00 23.36 9.92 27.82
NTADD 27.67 23.07 26.27 17.97 3.14 1.88
NTCHL 31.85 26.40 26.09 11.53 2.67 1.47
NTFAT 37.19 26.39 23.03 9.23 2.58 1.57
NTPRE 24.74 22.73 26.32 20.16 4.08 1.97
NTSAL 28.71 26.34 27.16 12.27 3.54 1.99
NTSUG 18.48 18.59 34.22 20.00 5.92 2.80
NTVIT 7.50 7.46 13.98 25.57 25.92 19.57
ZQTR 24.94 22.96 25.53 26.58


e lbaT 3-5 Fr squency i t











of the 1993-2003 period (13,150 obs)
DMCHL DMHSZ DMFEM FDTAC FDLUN
DMAGE 0.5269 -0.3839 0.3216 0.2747 0.2449
<.0001 <.0001 <.0001 <.0001 <.0001

DMHSZ DAMAGE FDTAC FDLUN AT SWM
DMCHL -0.7401 0.5269 0.2301 0.2087 0.1880
<.0001 <.0001 <.0001 <.0001 <.0001

DMIN2 NTKNO DMFEM ATFOR ATLB S
DMEDU 0.3419 -0.2636 -0.2451 -0.2025 0.1265
<.0001 <.0001 <.0001 <.0001 <.0001

DAMAGE DMEDU DMIN2 NTVIT ATDOC
DMFEM 0.3216 -0.2451 -0.2084 -0.1385 -0.1017
<.0001 <.0001 <.0001 <.0001 <.0001

DMCHL DAMAGE FDTAC FDLUN FDHOT
DMHSZ -0.7401 -0.3839 -0.2029 -0.1612 -0.1572
<.0001 <.0001 <.0001 <.0001 <.0001

DMEDU DMFEM FDFCH DMHSZ NTKNO
DMIN2 0.3419 -0.2084 0.1726 0.1489 -0.1465
<.0001 <.0001 <.0001 <.0001 <.0001

FDTAC ATFOR FDPIZ DMIN2 DAMAGE
DMREG -0.0704 -0.0682 0.0632 -0.0547 0.0495
<.0001 <.0001 <.0001 <.0001 <.0001

NTFAT NTCHL NTKNO NTSUG NTSAL
ATCAL 0.3607 0.3391 0.3245 0.3075 0.3002
<.0001 <.0001 <.0001 <.0001 <.0001

ATDOC DTFE2 NTSUG DMEDU FPFAS
ATLB S 0.2033 0.1385 0.1286 0.1265 0.1056
<.0001 <.0001 <.0001 <.0001 <.0001

DAMAGE DMCHL DMHSZ FDTAC ATFOR
AT SWM 0.2246 0.1880 -0.1528 0.1309 0.1216
<.0001 <.0001 <.0001 <.0001 <.0001

ATCAL NTING NTFAT NTKNO DAMAGE
ATWGT 0.1725 0.1701 0.1473 0.1448 -0.1366
<.0001 <.0001 <.0001 <.0001 <.0001


Table 3 -6. Five highest correlation coefficients and their probability under Ho: Rho


0 of variables










Table 3-6. Continued
FDFCH FDLUN FDHOT FPRES FDPIZ
NTBRN 0.1457 0.1450 0.1230 0.1217 0.1149
<.0001 <.0001 <.0001 <.0001 <.0001

NTKNO NTSUG NTCHL ATCAL NTFAT
NTING 0.2163 0.2145 0.2141 0.2048 0.1998
<.0001 <.0001 <.0001 <.0001 <.0001

ATCAL DMEDU NTING ATFOR FDFCH
NTKNO 0.3245 -0.2636 0.2163 0.2105 -0.1838
<.0001 <.0001 <.0001 <.0001 <.0001

ATDOC ATCAL NTFAT NTCHL NTSUG
DTFE2 0.2601 0.2453 0.1874 0.1810 0.1713
<.0001 <.0001 <.0001 <.0001 <.0001

FDLUN FDTAC FDHOT FDPIZ ATCAL
FDFCH 0.4764 0.4726 0.4598 0.4096 -0.2668
<.0001 <.0001 <.0001 <.0001 <.0001

FDLUN FDFCH FDPIZ FDTAC FPFAS
FDHOT 0.5011 0.4598 0.3531 0.3291 0.2008
<.0001 <.0001 <.0001 <.0001 <.0001

FDHOT FDFCH FDPIZ FDTAC DAMAGE
FDLUN 0.5011 0.4764 0.4611 0.4251 0.2449
<.0001 <.0001 <.0001 <.0001 <.0001

FDTAC FDLUN FDFCH FDHOT DAMAGE
FDPIZ 0.5113 0.4611 0.4096 0.3531 0.2219
<.0001 <.0001 <.0001 <.0001 <.0001

FDPIZ FDFCH FDLUN FDHOT DAMAGE
FDTAC 0.5113 0.4726 0.4251 0.3291 0.2747
<.0001 <.0001 <.0001 <.0001 <.0001

NTKNO DMEDU FPFAS DMIN2 AT SWM
ATFOR 0.2105 -0.2025 0. 1694 -0.1227 0.1216
<.0001 <.0001 <.0001 <.0001 <.0001

FPRES FDFCH FDHOT FDTAC DAMAGE
FPFAS 0.3521 0.2055 0.2008 0.1981 0.1911
<.0001 <.0001 <.0001 <.0001 <.0001










Table 3-6. Continued
FPFAS DMIN2 NTBRN DMHSZ DMCHL
FPRES 0.3521 -0. 1451 0.1217 0.0996 -0.0935
<.0001 <.0001 <.0001 <.0001 <.0001

ATCAL DTFE2 NTSUG NTVIT ATLB S
ATDOC 0.2785 0.2601 0.2222 0.2132 0.2033
<.0001 <.0001 <.0001 <.0001 <.0001

NTPRE NTFAT NTCHL NTSAL NTSUG
NTADD 0.8424 0.6402 0.6288 0.6153 0.5752
<.0001 <.0001 <.0001 <.0001 <.0001

NTFAT NTSAL NTSUG NTADD NTPRE
NTCHL 0.7872 0.6647 0.6317 0.6288 0.5642
<.0001 <.0001 <.0001 <.0001 <.0001

NTCHL NTSAL NTADD NTPRE NTSUG
NTFAT 0.7872 0.7277 0.6402 0.6006 0.5629
<.0001 <.0001 <.0001 <.0001 <.0001

NTADD NTSAL NTFAT NTCHL NTSUG
NTPRE 0.8424 0.6624 0.6006 0.5642 0.5479
<.0001 <.0001 <.0001 <.0001 <.0001

NTFAT NTCHL NTPRE NTADD NTSUG
NTSAL 0.7277 0.6647 0.6624 0.6153 0.6019
<.0001 <.0001 <.0001 <.0001 <.0001

NTCHL NTSAL NTADD NTFAT NTPRE
NTSUG 0.6317 0.6019 0.5752 0.5629 0.5479
<.0001 <.0001 <.0001 <.0001 <.0001

ATDOC NTING NTSUG DAMAGE NTPRE
NTVIT 0.2132 0. 1967 0. 1690 -0.1516 0.1507
<.0001 <.0001 <.0001 <.0001 <.0001

DMEDU DTFE2 FDTAC DAMAGE FDFCH
ZQTR 0.0323 0.0266 0.0254 0.0215 0.0206
0.0002 0.0023 0.0036 0.0135 0.0182











of the 1984-2003 period (30,414 obs)
DMCHL DMHSZ DMFEM FDTAC FDPIZ
DMAGE 0.5297 -0.3753 0.3165 0.2853 0.2382
<.0001 <.0001 <.0001 <.0001 <.0001

DMHSZ DAMAGE FDTAC FDHOT FDLUN
DMCHL -0.7424 0.5297 0.2335 0.2030 0.2007
<.0001 <.0001 <.0001 <.0001 <.0001

DMIN2 DMFEM NTKNO ATFOR DAMAGE
DMEDU 0.3314 -0.2638 -0.2620 -0. 1920 -0.1527
<.0001 <.0001 <.0001 <.0001 <.0001

DAMAGE DMEDU DMIN2 NTVIT ATDOC
DMFEM 0.3165 -0.2638 -0.2067 -0.1512 -0.1143
<.0001 <.0001 <.0001 <.0001 <.0001

DMCHL DAMAGE FDTAC FDHOT FDLUN
DMHSZ -0.7424 -0.3753 -0.2042 -0.1832 -0.1665
<.0001 <.0001 <.0001 <.0001 <.0001

DMEDU DMFEM FDFCH DMHSZ NTKNO
DMIN2 0.3314 -0.2067 0.1789 0.1503 -0.1312
<.0001 <.0001 <.0001 <.0001 <.0001

FDPIZ FDTAC ATFOR DAMAGE DMCHL
DMREG 0.0800 -0.0770 -0.0714 0.0535 0.0492
<.0001 <.0001 <.0001 <.0001 <.0001

NTFAT NTCHL NTSUG NTKNO NTSAL
ATCAL 0.3517 0.3429 0.3130 0.3061 0.2960
<.0001 <.0001 <.0001 <.0001 <.0001

ATDOC DMEDU DTFE2 NTSUG DMHSZ
ATLB S 0.2030 0.1346 0.1295 0.1078 -0.0998
<.0001 <.0001 <.0001 <.0001 <.0001

DAMAGE DMCHL ATFOR DMHSZ ATCAL
AT SWM 0.2224 0.1698 0.1398 -0.1323 0.1132
<.0001 <.0001 <.0001 <.0001 <.0001

NTING ATCAL NTCHL NTFAT NTKNO
ATWGT 0.2040 0. 1960 0.1573 0.1548 0.1419
<.0001 <.0001 <.0001 <.0001 <.0001


Table 3 -7. Five highest correlation coefficients and their probability under Ho: Rho


0 of variables










Table 3-7. Continued
FDLUN FDFCH FDHOT ATWGT FDPIZ
NTBRN 0.1267 0.1155 0.0997 0.0780 0.0763
<.0001 <.0001 <.0001 <.0001 <.0001

NTSUG NTVIT NTADD NTCHL NTPRE
NTING 0.2437 0.2295 0.2294 0.2274 0.2227
<.0001 <.0001 <.0001 <.0001 <.0001

ATCAL DMEDU NTING ATFOR FDHOT
NTKNO 0.3061 -0.2620 0.2129 0.2112 -0. 1731
<.0001 <.0001 <.0001 <.0001 <.0001

ATCAL ATDOC NTFAT NTCHL DAMAGE
DTFE2 0.2492 0.2443 0.1752 0. 1709 -0.1577
<.0001 <.0001 <.0001 <.0001 <.0001

FDLUN FDHOT FDTAC FDPIZ ATCAL
FDFCH 0.4750 0.4363 0.4265 0.3868 -0.2305
<.0001 <.0001 <.0001 <.0001 <.0001

FDLUN FDFCH FDPIZ FDTAC DMCHL
FDHOT 0.5162 0.4363 0.3458 0.3041 0.2030
<.0001 <.0001 <.0001 <.0001 <.0001

FDHOT FDFCH FDPIZ FDTAC DAMAGE
FDLUN 0.5162 0.4750 0.4334 0.3875 0.2182
<.0001 <.0001 <.0001 <.0001 <.0001

FDTAC FDLUN FDFCH FDHOT DAMAGE
FDPIZ 0.4991 0.4334 0.3868 0.3458 0.2382
<.0001 <.0001 <.0001 <.0001 <.0001

FDPIZ FDFCH FDLUN FDHOT DAMAGE
FDTAC 0.4991 0.4265 0.3875 0.3041 0.2853
<.0001 <.0001 <.0001 <.0001 <.0001

NTKNO DMEDU FDTAC AT SWM ATCAL
ATFOR 0.2112 -0. 1920 0.1402 0.1398 0.1032
<.0001 <.0001 <.0001 <.0001 <.0001

ATCAL DTFE2 NTVIT NTSUG DAMAGE
ATDOC 0.2892 0.2443 0.2310 0.2240 -0.2190
<.0001 <.0001 <.0001 <.0001 <.0001










Table 3-7. Continued
NTPRE NTFAT NTCHL NTSAL NTSUG
NTADD 0.8371 0.6670 0.6392 0.6229 0.5868
<.0001 <.0001 <.0001 <.0001 <.0001

NTFAT NTSAL NTADD NTSUG NTPRE
NTCHL 0.7805 0.6625 0.6392 0.6306 0.5720
<.0001 <.0001 <.0001 <.0001 <.0001

NTCHL NTSAL NTADD NTPRE NTSUG
NTFAT 0.7805 0.7145 0.6670 0.6243 0.5780
<.0001 <.0001 <.0001 <.0001 <.0001

NTADD NTSAL NTFAT NTCHL NTSUG
NTPRE 0.8371 0.6701 0.6243 0.5720 0.5584
<.0001 <.0001 <.0001 <.0001 <.0001

NTFAT NTPRE NTCHL NTADD NTSUG
NTSAL 0.7145 0.6701 0.6625 0.6229 0.6152
<.0001 <.0001 <.0001 <.0001 <.0001

NTCHL NTSAL NTADD NTFAT NTPRE
NTSUG 0.6306 0.6152 0.5868 0.5780 0.5584
<.0001 <.0001 <.0001 <.0001 <.0001

ATDOC NTING DAMAGE NTSUG NTADD
NTVIT 0.2310 0.2295 -0.1985 0.1908 0.1732
<.0001 <.0001 <.0001 <.0001 <.0001

DTFE2 FDFCH DMEDU FDHOT DMREG
ZQTR 0.0284 0.0172 0.0149 0.0134 -0.0120
<.0001 0.0028 0.0095 0.0194 0.0365













"My food purchase is based on
using the labels" (FPLAB)


Somewhat issa re 7.51o

Neither 9.900


Somewhat agree 20.40,




Mostly agree 23.5





Completely agree 32.7 .


Somewhat diss gre 4.3
Neither 9.200


Somewhat agree 20.90h




Afostly agree 25 4






Completely agree 34 =


1993-2003 data with 13,150 observations


Figure 3-1. Frequency distribution of ATLAB and FPLAB 1993-2003


"I check labels for harmful ingredients"
(ATLAB)



Disagree 7.100
Somewhat disagree 6.900

Neither 9.700


Somewhat agree 19.900




Mostly agree 22.7





Completely agree 33.7


1984-2003 data with 30,414 observations


Figure 3-2. Frequency distribution of ATLAB 1984-2003


AB)


"I check labels
or harmful ingredients (ATL,















1984-1993 period


1994-2003 period


Disagree 7 0% -
Somewhat disagree 6 4% -

Neither 9 4% -


Somewhat agree 19 5% -




Mostly agree 22 4%


Disagree 7 1%
Somewhat disagree 7 3%

Neither 10 1%


Somewhat agree 20 4%




Mostly agree 23





Completely agree 32 1


Completely agree 35 3%


I check labels for harmful ingredients (ATLAB)


Figure 3-3. Frequency distribution for two periods of ATLAB









CHAPTER 4
MODEL SPECIFICATION

Since the household response is discrete with scaled values, the likelihood of reading food

labels can be estimated using Ordered Probit models where the probability of each Likert score can

be determined. The present research specifies three Ordered Probit models to analyze the use of food

labels. The first two models use survey data from 1993 to 2003 using the variables described in

Table 3-1. A third model uses the survey data from 1984 to 2003 with 30 explanatory variables

described in Table 3-1. Variables with an asterisk are excluded because they belong to the survey

of 1993-2003.

Ordered Probit Models for the 1993-2003 Period

For notational convenience the five groups of variables in Table 3-1 were noted with the

matrices X1 through X5 with the corresponding variables within the X group. Let X1 through X5

be partitioned matrices of X where X captures all factors expected to have some impact on the

households attention to food labels. As defined in Table 3-1, ATLAB entails the scores for reading

labels for harmful ingredients and FPLAB reflects the scores for using labels when buying foods.

Explicitly,

ATLAB = f(X1...X5) (4-la)

FPLAB = f(X1...X5) (4-1b)

Since ATLAB and FPLAB both are scaled variables taking on discrete and limited values,

standard estimation procedures are no longer appropriate. Specifically, ATLAB and FPLAB are

ordered in that the scores increase when moving from the total disagree response (6) to completely

agree score (1). Note, however, that the scoring while ordered is ordinal in that a score of say 4 does

not mean it is twice the score of 2. This problem is the classic Ordered Probit modeling where one

estimates the probability of each score with the scoring being exhaustive and mutual exclusive.










Ordered Probit models are built around a latent variable y* ranging from co to + co that

is mapped to an observed variable y (attention to labels in this study) which provides incomplete

information about an underlying y* according to the measurement equation (4-2):

y, =m if r,,_, a y* < r,, for m = 1 to J (4-2)

The z's are called thresholds or cutpoints and the extreme categories 1 and Jare defined by the open-

ended intervals with ro= and r,= = (Long 1997, page 116-117).

For illustration purposes let

y* = Xp + E (4-3)

and the intercept and all parameters associated with the partition matrix X [X1...X5] are included

in p. Furthermore, it is assumed that e is normally distributed with mean 0 and variance 1. The

measurement equation can be illustrated using one of the dependent variables of the present

research, "I check the labels for harmfud ingredients" (y = ATLAB), which has six levels in the

Likert scale shown in Table 3-1.

y=1 if = y =2 if z, < y* s t
y =3 if z, < y* s z3
y =4 if z3 < y* s t (4-4)
y = 5 if z, < y* s t
y= 6 if z,

Clearly, the z' s are unknown values along with P and must be estimated. Once these unknowns are

estimated, the probabilities for each Likert score can be derived as indicated below:

Prob[y =1] =Prob(- =< XP + e cr)
= Prob(- = Xp < Es T i Xp ) (4-5)
= Prob( Es T i Xp ) Prob( E < oo Xp )









Since 0(- = Xp ) = 0 letting Q represent the cumulative normal distribution, then:

Prob[y =1]= Q(z, XP) (4-6)

The probabilities for the rest of the values immediately follow where:

Prob[y = 2] = Q( z2 XP ) @(z, XP )
Prob[y = 3] = Q( z3 XP )- @(z2 XP ) (4-7)
Prob[y = 4] = Q( z4 XP ) @(z3 XP )
Prob [y = 5] = Q( z, XP )- @(z4 XP )


Since the scores are exhaustive and mutually exclusive (i.e., Q(= XP ) = 1), the last value is

predetermined:

Prob[y = 6] = Q(= XP )- Q(z, XP ) (4-8a)
Prob[y = 6] = 1 Q(z, XP ) (4-8b)


While values for the z's and P can be estimated with most econometric packages, in this

study TSP econometric software is used because of its very powerful procedures for gaining direct

access to the estimated coefficients. In TSP, page 304, the thresholds are called MU and "the lowest

effective boundary value (MUl) is normalized to 0, ...", thus giving the slight variation from the

general form in (4-7) above with XP including an intercept term:

Prob[y = 1] = Q(- XP)
Prob[y = 2] = Q( z2 XP ) Q(- XP )
Prob[y = 3] = Q( z3 XP )- @(z2 XP ) (4-9)
Prob[y = 4] = Q( z4 XP ) @(z3 XP )
Prob [y = 5] = Q( z, XP )- @(z4 XP )
Prob[y =6] = 1 O(z- XP)

Since X includes a constant term (C), which can be thought of as a replacement for z2; in this

case, the other z can be interpreted as being measured relative to the value of C. Therefore, the TSP

output gives MU3, MU4, MU5 and MU6 as thresholds (Tables 5-1 to 5-7).










Defining XP in the Label Models

From Table 3-1 there are a total of 32 variables expected to impact the scoring of attention

to labels for both the negative and positive uses of labels (i.e., ATLAB and FPLAB). All of these

variables are discrete in form with the range of values depending on the measurement for each as



X, 5 E ZDM4GEs~,2+ Y+5ZDMCHL,ii Y+7ZDMEDU,2+1ii jJ+11ZD M~FE M,
J=1 J=1 J=1 J=1

+ +13,,ZDMHSZ,~t Y+1 2+ZDMIN2~~~3, +21ZDM~REG,,
J=1 J=1 J=1

X 6p +3ZATCnAL,+ YJ+36ZATLBS,2+1 Y+42ZA3TSW,2+1 i;+48 ZA GTJ,
J=1 J=1 J=1 J=1

Sj+54Z~NTBRN,i J+60ZNTINGi,+1 j+66ZNTKNO,,
J=1 J=1 J=1
2 6 6

J=1 J=1 J=1 J=1

J+92ZDPIZ2+1 +98ZFDTAC,,+1 y,+104ZATFOR,, (-0
J=1 J=1 J=1


J=1 J=1

X~ap4 i,+122ZATDOC( ,21 j,+18ZNT~ADD~,t J+134ZNTC HL.,* yJ+140ZNTFAT7,
J=1 J=1 J=1 J=1


J=1 J=1 J=1 J=1

X5P;=C (+170ZZQTRK l
J=1

Xp = o ,+ X, f + X2 p2 + X3 p + X4 p4 + X, f



illustrated in the right column of Table 3-1. Hence, each variable must be expressed in binary form

as set forth in the equation (4-10) where a Z notation is added to each variable in Table 3-1 to

indicate its binary form. For example, concern over calories in Table 3-1 takes six possible levels

and hence there are six categories for ATCAL shown in XP, in equation (4-10) with the "j"

55









subscript denoting the binary level of the variable and the "i" giving the actual observation. This

procedure is followed for each variable using the corresponding levels with "j being defined for

each variable. For each variable, one needs to reference back to Table 3-1 to see the exact meaning

of the "j ". With the complete binary definitions, Xj3 is a concise representation of the right-hand-side

variables of the Ordered Probit models. To fully represent the right hand side, four thresholds would

need to be added to equation (4-10) as initially set forth in (4-9).

The regressors in equation (4-10) are polytomous variables (i.e.,variables that take on more

than two values) and each value represents a category. Each category j in the polytomous variable

has to be converted into a dummy variable. Given there are so many binary variables in the model,

a convenient approach for dealing with the "dummy variable trap" is to restrict a weighted sum of

the coefficients to zero for each di screte variable. With thi s straight forward procedure, the intercept

represents the average household and all coefficients are expressed relative to this average. To

illustrate, let a discrete variable, like age of female head (DMAGE), take 5 values which are

represented by 5 dummy variables (Z). Then restrict the weighted sum of the coefficients to zero as

shown in (4-11) below.

When all variables are at their means the restricted variables (DZ) are zero and hence the



Y0 jZ fyi, andimpose i7 Zii =
j= 1 j= 1

TsZ, = 1 Z, then (4-11)
j= 1


j=1 1

intercept is for the mean set of characteristics and all coefficients represents deviations from the

average household in this study (e.g., To + TI, To + y2, To + 73, To 74y, and 70 C73 3). Now in

(4-1 1) instead of the intercept representing the base when one of the variables is restricted to zero










(i.e., the traditional approach), the intercept represents the average household. Just as with the

traditional approach one can calculate any of the effects but now relative to the average household

instead of a base. This approach is very convenient in that one never has to remember all of the

categories embedded in the base with the traditional approach. Applying this approach to equation

(4-10) and adding the letter D as a first letter to the name of the variables, the new equation with

restricted dummy variables would be created. In this new equation, because of the restriction, each

set of dummy variables would smaller, by a unit, than each set in (4-10).

Since there are 32 sets of dummy variables, created from 32 original variables, for a total of

174 dummy variables, then the new equation, after applying the restriction shown in (4-11), would

have 174-32 = 142 restricted dummy variables. The last coefficient of each set of dummies, lost to

the restriction, is recovered later using also equation (4-11). Their standard errors are calculated

easily because the variance covariance matrix of the rest of the coefficients in the set is known.

Again, one gets the same results either way. It is just a more convenient way to deal with a large

number of discrete variables. With (4-10) and the dummy adjustment, XP is specified and ready to

be used in the Ordered Probit estimation. Two models, one with ATLAB as dependent variable and

the other with FPLAB as dependent variable, use the explanatory variables set forth in equation

4-10.

The high correlation coefficients between some of the explanatory variables (Tables 3-6 and

3-7), specifically between variables "Cautious about additives" (NTADD), "Cautious about

cholesterol" (NT CHL), Cautious about fat" (NTF AT), Cautious about preservativess' (NT PRE),

" Cautious about salt" (NT SAL), and Cautious about sugar (NT SUG), point to a pos sible probl em

of multicollinearity. "While a high correlation coefficient between two explanatory variates can

indeed point to a possible collinearity problem, the absence of high correlations cannot be viewed

as evidence of no problem. It is clearly possible for three or more variates to be collinear while no










two of the variates taken alone are highly correlated. The correlation matrix is wholly incapable of

diagnosing such a situation" (Belsley et al. 1980, page 92). The following section addresses this

issue.

Multicollinearity

According to Guj arati (1988), pages 283-284,

The term multicollinearity is due to Ragnar Frisch. Originally it meant the existence
of a "perfect," or exact, linear relationship among some or all explanatory variables
of a regression model. For the k-variable regression involving explanatory variable
X,, X,, ..., Xk (Where X, = 1 for all observations to allow for the intercept term), an
exact linear relationship is said to exist if the following condition is satisfied:

h, X, + hX, + ... + hk Xk = 0 (10.1.1)

where h,, 3, ..., Ak are constants such that not all of them are zero simultaneously.
Today, however, the term multicollinearity is used in a broader sense to
include the case of perfect multicollinerity, as shown by (10.1.1) as well as the case
where the X variables are intercorrelated but not perfectly so as follows.

h, X, + hX, + ... + hk Xk+ i = 0 1.12

where vi is a stochastic error term.

On the effects of multicollinearity, Guj arati (1988), page 289, cites Achen (1982):

The only effect of multicollinearity is to make hard to get coefficient estimates with
small standard error. But having a small number of observations also has that effect,
as does having independent variables with small variances. (In fact, at a theoretical
level, multicollinearity, few observations and small variances on the independent
variables are essentially all the same problem).

The difficulty caused by high correlation "is not one of identification but of precision. The

higher the correlation between the regressors becomes, the less precise our estimates will be"

(Greene 1990, page 278).

The symptoms or consequences of multicollinearity are: (a) small changes in the number of

observations can produce big changes in the estimated parameters; (b) high R-squared despite high









standard errors (few signifi cant t- stati sti cs); (c) coeffi ci ents are huge in magnitude or have the wrong

sign; (d) high condition number (Greene 1990, Gujarati 1988).

The condition number or condition index of a square matrix is the square root of the ratio

of its maximum characteristic root to its minimum characteristic root. For nonsquare matrices, like

the matrices with the dependent variables X, the matrix X'X is used. "Because the characteristic

roots are affected by the scaling of the columns of X, we scale the columns to have length 1 by

dividing each column by its norm" (Greene 1990, page 3 5). The norm of column i is th square root

of x, 'x, or the square root of the sum of squares of column x,. According to Belsley et al. (1980),

page 105, "... weak dependencies are associated with condition indexes around 5 or 10, whereas

moderate to strong relations are associated with condition indexes of 30 to 100". On the other hand,

Greene (1990), points out that a condition number greater than 20 is large and that a matrix is nearly

singular if the smallest character stic root i s close to zero compared to the largest character stic root.

Greene (1990), page 3 5, also indicates that "Matrices with large condition numbers are difficult to

invert accurately."

Among the remedial measures suggested in the literature are combining cross-sectional and

time series data, dropping a variable and specification bias, transformation of variables, additional

new data, and principal components (Gujarati 1988, Kennedy 1998). In the present research, the

condition number will be used in the diagnosis of multicollinearity and, if a remedial measure is

need it, principal components will be employed.

Principal components

"A principal component is a linear combination of variables that captures as much of the

variation in those variables as it is possible to capture via a linear combination of those variables"

(Kennedy 1998, page 172). "When faced with ill conditioned data, investigators frequently chose

to reduce information demands on the sample by considering only sub spaces of the k-dimensional










parameter space. These sub spaces may be suggested by economic theory, previous stati stical results,

or ad hoc dimensionality procedures" (Judge et al. 1985, page 909). In the present research, principal

components will be applied to a subgroup of the health concern variables showing high correlation

coefficients, "Cautious about additives" (NTADD), "Cautious about cholesterol" (NTCHL),

"Cautious about fat" (NTFAT), "Cautious about preservativess' (NTPRE), "Cautious about salt"

(NTSAL), and "Cautious about sugar" (NTSUG). There are six variables, therefore six principal

components can be constructed, each orthogonal to the others. From the six components, the four

with the highest characteristic roots will be used in the models. These four components will replace

the 30 restricted dummy variables (5 restricted dummy variables from each of the following

variables: NTADD, NTCHL, NTFAT, NTPRE, NTSAL and NTSUG).

Ordered Probit Model for the 1984-2003 Period

The first two models, one with ATLAB and the other with FPLAB as dependent variables

use equation (4-10) and data from 1993-2003 as explanatory variables. A third model is

implemented using ATLAB as dependent variable, and data from 1984-2003, with 30 explanatory

variables. Two explanatory variables shown in Equation 4-10, that were part of models 1 and 2 are

excluded: Try fa;st food places" (FPFAS) and Visit restaurants more than most" (FPRES). They

are shown in Table 3-1 with an asterisk because they are part of the survey that starts in 1993.

This third model is estimated sequentially, with each sequence including 10 years of data or

approximately 50% of the total number of observations (i.e. 1 984- 1993, 1 985-1994, ..., 1 994-2003).

The data in each block should have enough variability and the results would give an insight on how

the likelihood of reading food labels has changed over time. In this model the total number of

unrestricted dummy variables is 162, resulting in 132 restricted dummy variables. After the

coefficients are calculated, a simulation is carried out to find the probabilities for the average

consumer for each block of data. The simulated probabilities for the complete set of explanatory









variables is carried out only for the first (1984-1993) and last block (1994-2003). The estimated

coefficients and simulated probabilities are compared to see the changes over time.









CHAPTER 5
ANALYSIS OF RESULTS

Three models were specified in Chapter 4. Models 1 and 2 use the 1993-2003 survey data

with 32 variables. Model 1 estimates the label responses to "I check the labels for harmful

ingredients "(ATLAB) while Model 2 estimates the label responses to "Iread the labels for my food

purchase (FPLAB). Model 3 estimates the label responses to "I check the labels for harmful

ingredients" (ATLAB) and uses the 1984-2003 data, with 30 variables, but does it recursively, in

blocks of ten years (i.e. 1984-1993, 1985-1994, ..., 1994-2003). The results of the Ordered Probit

models are in Tables 5-1 to 5-7. They show the condition number, scaled R-squared, estimated

coefficients, coefficients for the dummy variables that were left out because of the restriction,

t-statistics and Wald-statistics. At the end of the tables the thresholds for moving across the Likert

scales are reported.

Since the models have an intercept it is not necessary to have the threshold for the lowest

Likert score. The last threshold for the last level is set once the other values are know. Hence for the

six Likert scales, only four thresholds must be estimated. It is important to recall that each t-value

is expressed relative to the average household. A statistically significant t-value means that

coefficient is statistically different from the mean household or Po. Regarding the interpretation of

information as the one provided by Tables 5-1 to 5-7, Green (1990), page 705, points out "In the

general case, relative to the signs of the coefficients, only the signs of the changes in Prob [y = 0] and

Prob[y = J] are unambiguous! The upshot is that we must be very careful in interpreting the

coefficients in this model. This is the least obvious of all the models we have considered. Indeed,

without a fair amount of extra calculation, it is quite unclear how the coefficients in the Ordered

Probit should be interpreted". This is precisely why it is so important to show the estimated

probabilities as derived later in this chapter.









Ordered Probit Estimates and Probabilities for the Period 1993-2003

Ordered Probit Estimates

Table 5 -1 and 5-2 provide the Ordered Probit estimates for both label responses (i.e., "Icheck

the labels for harmful~hhh~~~~hhh~~~hhh ingredients (ATLAB) and "Iread the labels for my food purchase (FPLAB).

Across the variables there are many significant effects, as the t-statistics and the Wald-statistics

show. The Wald-statistics facilitates the comparisons because it is easier to compare 32 variables

than 142 dummy variables.

The adjusted R-squared in Table 5-2 shows that the 32 variables explain better the likelihood

of Checking the labels for harmful ingredients" (ATLAB), than the likelihood of "I read the labels

for my foodpurcha~se "(FPLAB), 49% versus 42%. According to the Wald test, as Tables 5-1 shows

that, of the 32 variables, 13 are not significant at the 1% level and 5 (like to lose 20 pounds, pizza,

visit restaurants more than most, cautious about preservatives and cautious about salt) are not

significant at the 5% level when explaining ATLAB. On the other hand, Table 5-2 shows that 11

variables are not significant at the 1% level and 6 (Education of female, like to loose 20 pounds, love

to swim, tacos, cautious about salt and cautious about sugar) are not significant at the 5% level when

explaining FPLAB. It i s unusual for education not to have a significant effect. The level of education

has been found to be an important factor in the use of labels in other studies (Bender and Derby,

1992; Guthrie et al., 1995; He et al., 2004).

The condition number in both models (Tables 5-1 and 5-2) is 41. According to Greene

(1990), a condition number greater than 20 is large, and, according to Belsley et al. (1980), moderate

to strong relations are associated with condition indexes of 30O to 100. It i s not of extreme importance

in the present research to have a low condition number, because the potential problems that

multicollinearity could cause would not affect the likelihood of using the labels. Nevertheless, to see

how the size of the condition number i s related to the effects of multicollinearity in the models used,










principal components will be applied to the variables that provide the highly correlated restricted

dummy variables (Table 3 -8). These variables are: Cautious about addaitives" (NT ADD), Cautious

about cholesterol" (NTCHL), "Cautious about fat" (NTFAT), "Cautious about preservatives"

(NTPRE), Cautious about salt" (NTSAL), and Cautious about sugar" (NTSUG). Table 5-3 show

that the first four principal components of these 6 variables account for more than 94% of the

cumulative R-squared. There is not a rule that specifies how many components to leave out. Using

all of them is like using the original variables. It was decide to use the first four components. Tables

5-4 and 5-5 show the results of the ATLAB and FPLAB models with principal components. When

compared to the models with no principal components (Tables 5-1 and 5-2) the only noticeable

difference is the smaller condition number, 10. Other than that, the R-squared is the same and the

t-statistics and Wald test do not show a noticeable difference, rejecting or failing to reject the

significance of the coefficients at the same probability level. This would indicate that in the case of

both models the high correlation coefficients shown in Table 3-8 cause no ill-effects on the Ordered

Probit models. Given this conclusion, the models with no principal components will be used to

simulate the likelihood of using the labels.

Probabilities

The most useful aspect of the Ordered Probit label models is showing the probabilities of

using the labels while considering the sensitivity to each variable included in the models (Table 5-1

and 5-2). Since there are some many variables in the specification shown in equation 4-10, an easy

way to discuss the probabilities is to express the them relative to the average household likelihood

of reading the food labels as depicted in Figure 5-1. Around 59 percent of the households checked

food labels for harmful ingredients (combining scores 1 and 2) and 63 percent used the labels for

broader purchasing decisions. Using these average probabilities as our reference base, the effects









from changing household demographics; lifestyle activities and attitudes; health concerns, and

household eating habits are estimated from the Ordered Probit coefficients.

Importance of food labels across demographics

Figure 5-2 shows the estimated probabilities across several demographics while holding all

other factors for the average household with the left bars being the probabilities for the "I read the

labels for harmful ingredients" and the right bars for the "I read the labels for my food purchasee"

Consistent throughout the probabilities are higher levels for using labels in general than just for the

harmful ingredients, thus clearly pointing to the role of labels beyond the preventive dimension.

Note in Figure 5-2 that each bar is the total of "completely" and "mostly agree" with both intensities

being shown. In all remaining figures just the agreement percentages are presented.

Among the demographics shown in Figure 5-2, age of the female household head show the

largest range of change with the probability of reading the labels increasing consistently over the

age range from a low of 53 percent to 66 percent for the "I read labels for harmful ingredients"

statement. Similarly, but to a smaller degree, the probability of reading the labels increases with

education with the big drop being among those in the lowest education level. The presence of

children under 1 8 years and employment of female head of household follow a similar pattern. They

show a bigger difference in the case of "I readla~bels for harmful ingredients" than in the case of "I

read the labels for my food purchase ." Figure 5 -2D shows that the prob ability of reading food labels

is greater for the unemployed than for the employed. For comparison, Lin et al. (2004) results

showed that employment status was not related to information search on labels. The number of

members in the household has a small impact in both cases. Probably the most unexpected result is

with the consistent drop in using labels across income levels which is the opposite to the results

obtained by Lin et al. (2004). For each demographic, the response intensity between mostly and










completely agree remained reasonably proportional. The effects of the demographic variables on

reading food labels are similar to those reported by McLean-Meyinsse (2001).

Figure 5-2 also shows that there are regional differences in using labels with the south and

southeast states showing the highest probabilities of using the labels and the households in the north

and northeastern states showing the least use of the labels. Except for knowing the regional

differences in label importance, one cannot gain much insight into the underlying regional

characteristics. The region with the lowest probability is West North Central.

Attitudes and use of food labels

Concern about calories and the belief of knowing about nutrition more than others have

maj or effects on the use of food labels. Expectations would be that those concerned about calories

are more likely to read food labels. As shown in Figure 5-3A, this is precisely true where there is

almost a linear relationship between the intensity of concern or not and the likelihood of using food

labels. In fact, concern about calories is the single most important factor influencing the use of food

labels for both harmful ingredients and food labels in general. Among those households showing no

interest in calories, the probability of reading food labels drops to 31 percent of harmful ingredients

and 41 percent for labels more broadly used. There is nearly an 87 percent probability of reading

food labels when the household member is strongly concerned about calories.

Figures 5-3B to 5-3D, "Like to lose 20 lbs", "Love to swim" and "Overweight isn 't attractive "

show some inconsistencies. It would be expected that consumers that agree with these statements

would read the labels more often than the consumers that disagree with the statements. The fact that

consumers that disagree with "Overweight isn't attractive" read the food labels more often than

consumers that agree with the statement shows that although the former consumers think overweight

is attractive they still care about what they eat ant that is why they read the labels. In Figure 5-3,

with exception of A and G, the difference in probabilities are small.









Another attitude addresses the issue if brands are substitutes for reading the food label.

Households were given the statement that .. "Best known brands are the highest quality" and were

asked to scale their response, again with the six point scale. One basic argument is that brands

already have some level of consumer support and confidence and, as such, those households

supporting this statement would be less likely to read the food labels since the brand identification

is enough. While the responses are not profound, there does appear to be some sub stitution between

the brand information and food labels. Households indicating total agreement with the best-known

brand statement are less likely to read the food labels. There is a consistent drop in the use of food

labels with the more reliance on brands. A major drop is seen between the scales of completely

versus mostly agree. This tradeoff between brand information versus the food package label is

equally true for both reading labels for harmful ingredients and reading labels more broadly. This

is interesting in that brands incur both the cost of branding and food labels but may have fewer

relative benefits from the labeling compared with less branded food.

Figure 5-3 also shows that the believe that foods should have body building ingredients has

a smaller impact when checking the labels for harmful ingredients than when reading the labels

looking for general information. In the latter case the probabilities change from 68 to 59 percent,

very much in a linear fashion, when going from households that agree completely to households that

mostly disagree with the statement "Foods should have body building ingredients." The last graph

in Figure 5-3 shows the strong impact that the believe "I kmow more than most about nutrition" has

on reading food labels, specially when checking the labels for harmful ingredients. The probability

changes form 76 to 43 percent.

Eating habits and use of food labels

Eating habit variables in the Ordered Probit model included being on a diet, five types of

foods (fried chicken, hot dog, lunchmeat, pizza and tacos), country-of-origin, eating in fast food










places and eating in restaurants. Again, using the average household as the reference base, the

probabilities of reading food labels over each of these eating habits are shown in Figure 5-4. Dieting

and the type of food consumed have major impacts on the use of food labels when making

purchasing decisions. When dieting, the female head of the household is about 12 percentage points

more likely to rely on food labels compared to those not on diets. Lin et al. (2004) results also show

that the probability of searching for information on food labels is higher among consumers on a

special diet. Similarly, household that discourage the consumption of foods like fried chicken, hot

dogs, lunchmeat, pizza and tacos are considerably more likely to read the food labels. For example,

households that discourage the consumption of fried chicken show a probability of 70 percent for

harmful ingredients versus 49 percent when not concerned about consuming this product.

Consumption of hot dogs and fried chicken are indicative of the more general type of eating

habits closely associated with fast foods. That is why it is surprising that the results for "Try fast

food places" don't follow the same pattern. Households completely concerned about foreign foods

show a 65 percent level of probability of reading the label for harmful ingredients; while the

probability drops consistently among households expressing less concern about foreign foods.

Dieting and the type of food consumed have maj or impacts on the use of food labels when making

purchasing decisions.

Health concerns and use of food labels

Health concerns have many dimensions and information about additives, cholesterol and fats

are often the most visible messages on many food labels. The households reflect the level of

concerns of several health measures using the statement .. "A person should be concerned about

cholesterol" (or similar issues). Figure 5-5 shows the probabilities linked to these concerns. As a

general rule, when doctors give advice to consumers they are much more likely to use food labels

and, in fact, the probabilities for harmful ingredients increase for 52 to 76 percent when the doctor









give dieting advice. Importance of food labels increases almost linearly with the level of concern

about additives, from 47 to 66 percent when considering harmful ingredients. Cholesterol shows a

similar pattern also when considering harmful ingredients, changing from 48 to 64. The trend is

more mixed when using food labels in general. According to Lin et al. (2004), page 1962,

"respondents who had higher intakes of total fat, saturated fat, or cholesterol were less likely to

report looking for label information on these nutrients".

Patterns associated with concerns over fats, preservatives, salt and sugar are mixed in both

cases, when considering harmful ingredients and food labels in general. The same mixed patterns

appear when the vitamins are recommended by physician.

Seasonality and use of food labels

Figure 5-6 shows how the probabilities of reading the labels change during the year. In the

last quarter, Thanksgiving and Christmas holidays, the likelihood of reading the labels is the

smallest. The difference between the third and fourth quarter is slightly bigger when "I read' the

labels for my food purchase than when "I read' the labels for harmful ingredients". This would

show that during the holidays consumers worry less about what they consider harmful ingredients

in the food they purchase.

Ranking the food label probabilities

Importance of labels obviously differs across all the variables that capture household food

shopping behavior as initially seen with the Ordered Probit estimates in Tables 5-1 and 5-2. In

addition to measuring the directional effects of each variable, it is equally insightful to put these

variables in perspective by ranking their impacts on the likelihood of using labels. Taking the

difference between the min and max estimated probability for each variable provides a useful way

for ranking the impacts. In the right horizontal bars in both Figures 5-7 and 5-8 theses differences









are shown starting with the largest value down to the least range. See Ward, Briz and de Felipe

(2003) for a detailed application of this ranking method.

By far, "Conscious about calories" is the single most important factor impacting the

likelihood of reading food labels for both label questions. The calories likelihood ranges from 31

to 87 percent when using the labels to discern harmful ingredients and from 41 to 86 percent for

using labels as an aid to making purchasing decisions. The "Conscious about calories" ranges are

substantially greater than any other factors for both label uses as most evident with the rank of

impacts seen with the right horizontal bars in both Figures 5-7 and 5-8.

Consumer knowledge about nutrition is important and contributes to greater use of food

labels. Except for the age demographic, the next several factors relate to health concerns and eating

habits. Moving down both charts, after about the 10th entry the remaining variables have impacts

that are quite small in terms of causing deviations from the average household probabilities. One

becomes particularly impressed with the limited role of many of the demographics except for age.

Note also that the impact of branding on the use of food labels ranks quite high relative to most of

the variables in Figures 5-7 and 5-8. Clearly, there is some underlying tradeoff between

identification with a brand and using labels to gain information. Importance of labels declines when

consumers place greater reliance on brands.

There are also a few strange inconsistencies such as concern about calories versus being on

a diet or would like to lose 20 pounds. Possibly if one is concern about calories, the food selection

process is underway while the selection may have already been made when actually on a diet, hence

placing less importance on the label. Another interesting difference is seen with the issue of foreign

foods when considering harmful ingredients compared with food attributes in general. The range of

change in using labels for determining harmful ingredients over the concerns about foreign foods










is twice that for using labels in general. Consumers turn more to labels for determining harmful

aspects of foreign food than for simply helping making foreign food purchases.

Clearly, Figures 5-7 and 5-8 point to potential focus points if the goal is to influence

consumers' use of food labels. One can quickly determine from these figures where little gain in the

use food labels would be expected. For example, policies to education consumers about labels based

on income groups or families with younger children would have little impact. Whereas, focusing on

educating the younger population could have greater benefits if the policies goal were to improve

the use of food labels.

Figure 5-9 shows the range of change for 15 variables with the highest impact on both, "I

check the labels for harmful ingredients and "Iread the labels for my food purchase ". It can be seen

that, in general, the ranges are larger when looking for harmful ingredients is the reason for reading

the labels than when reading the labels for general information.

Sequential Ordered Probit Estimates and Probabilities for the Period 1984-2003

Ordered Probit Estimates

The sequential Ordered Probit model for the period 1984-2003 gives a total of 11 results.

Tables 5-6 and 5-7 show the results from the first (1984-1993) and last (1994-2003) recursion

respectively, allowing the comparison of periods before and after the implementation of the NLEA.

Thirty explanatory variables were used in the model, from whichl1 did not have a statistically

signifi cant impact on "I check the labels for harnsful ingredients" (ATLAB) in the peri od 1 984- 1993

(Table 5-8). Of the 11 variables 4 are in the demographics group; "Children under 18", "Education

offentale head ofhousehold", "Household income and Census region"; one in the attitudes group,

"Love to swim"; two in the eating habits group, "Eating pizza" and "Eating tacos"; three in the health

concerns group, "A person should be cautious about cholesterol", "A person should be cautious

about salt" and "A person should be cautious about sugar" T he el event variable is Quarters" Of









all these variables only four are not statistically significant in the period 1994-2003; "household

income", "Eating pizza", "Eating tacos" and "Quarters". The only variable that was statistically

significant in the period 1984-1993 but it is not statistically significant in the period 1994-2003 is

"A person should be cautious about preservatives" Looking at these changes one can conclude that

demographics and health issues have become important drivers during the last years. The variables

with the highest Wald statistics are, like in the other models, "Conscious of calories" and "Know

more than most about nutrition". Again, the effect of the numerous variables and changes over time

can be appreciated better graphing the probabilities calculated using the estimated coefficients than

showing them in tables. The next section shows these probabilities.

Probabilities

Starting with the average consumer in general, Figures 5-10 to 5-18 show, in blocks of ten

years, from 1984 to 2003, how the probabilities for "completely agree" plus the probability of

" mostly agree" in the statement "I check the labels for harnsfid ingredients" are affected by the

different explanatory variables over time. Each Eigure, from 5-1 1 to 5-18 contrasts the probabilities

of the first and the last Likert scale in each variable over the 1984-2003 period.

Figure 5-10A shows that the probability of reading the food labels, for the average

consumer, in the categories (1) and (2) of the Likert scale, increased from 1984 to 1998 decreasing

later. In 2003, the probabilities for the average consumer that chose the statement "completely

agree" decreased below the 1984 level, to 26% while the probabilities for the average consumer that

"mostly agree" with the statement increased by 2, to 32%. Figure 5-10B shows the changes of

categories (3), (4), (5) and (6) of Likert scale. The gains (loses) of categories (1) and (2) were the

loses (gains) of the last four categories. As apparent with both Eigures, the importance attached to

food labels has slightly declined since the late 80's.










Importance of food labels across demographics

Figure 5-1 1 shows that the relationship between the categories in each demographic variable

has been maintained over time in most cases. For example, in 1984-1993, the likelihood of reading

food labels label by consumers 65 years or older was higher, by 13, than the likelihood of reading

food labels by 35 years old or younger consumers. The difference is the same in 1994-2003.

Throughout all periods, younger consumers read the labels less often than older consumers. Mueller

(1991), mentions that according to the Food Marketing Institute's 1990 Trends survey older

consumers read the labels more often than their younger counterparts and non-working women are

the most likely of any group to read food labels. Figure 5 11D shows that not employed women read

food labels more often than women that are employed. In the case of education, the likelihood not

only has declined in 1994-2003 relative to 1984-1994, but the difference between consumers with

no high school and consumers that are college graduates has increased. Bender and Derby (1992)

report that betweenl986 and 1988 "A growing proportion ofless-educated consumers (less than high

school) reported using ingredient lists, narrowing the gap between the least-educated consumers and

others" (Page 293). Figure 5-1 1C shows the pattern described by Bender and Derby but then, at the

end of the 1990's, the gap between the most educated and least educated increases. In the case of the

census regions, the ones that show a bigger decline from 1984-1993 to 1994-2003 are New England

(1), East North Central (3), West North Central (4) and Mountain (8). The rest of variables in the

demographics group show similar probabilities in the first and last periods.

Attitudes and use of food labels

In the attitudes group, Figure 5 -12, Conscious ofcalories" and "Know more thannzost about

nutrition" are the two variables that show the widest range between category (1), "completely agree "

and category (6), "mostly disagreee. This difference is not only big but it has been increasing over

time. The probability of reading the labels for consumers that believe they are conscious about









calories increased, during the period of the study, from 78% to 86%. On the other hand, the

probability of reading the labels for consumers that are not conscious about calories decreased from

38% to 31%.

For consumers that believe they know more than most about nutrition, the probability of

reading food labels for harmful ingredients didn't change much over time. It increased from 76 to

77%, but the probability for consumers that di sagree with the statement of being knowledgeable the

probability decreased from 43 to 39% during the same period.

In a smaller scale, the variable "Best Imown brand's are highest quality" follows a similar

pattern over time as "Know more than most about nutrition". The probability of reading food labels

for consumers that disagree with the brands statement decreases from 64 to 48% while the

probability for the consumers that agree with the statement goes from 62 to 61% during the same

period. The change over time in the rest of the variables in this group is really small.

Eating habits and use of food labels

There is no much change over time in this group of variables, Figure 5-13. They follow the

same pattern as the average consumer (Figure 5-10A) with the difference between the first and last

category of the Likert scale in each variable staying more or less constant over time too. The figures

show also what it is expected, that the consumers that discourage the consumption of fast foods have

a greater probability of reading the food labels than consumers that encourage the consumption of

food like fried chicken and pizza.

Health concerns effects on reading food labels

Information about health concern variables are very visible on food labels. The effects of

most of these variables on the likelihood of reading food labels have changed over time, as Figure

5-14 shows. The exception is "Doctor gives advice on dcil~". In this variable the difference between

the probability for "completely agree" (1), 75%, and the probability for "mostly d'isagree"(6), 50%,









is the widest of all variables but it has not changed that much over time. The effect of other variables

that have not changed very much over time are "A person should be cautious about additives" and

"A person should be cautious about salt". This last variable though shows some inconsistencies in

the middle periods: consumers that disagree with the statement show a higher likelihood of reading

the labels than the consumers that agree with it.

Many food labels emphasize the lack of fat and cholesterol in the packaged product because

high levels of cholesterol and fats have been linked to heart disease. Figure 5-14C shows that at the

beginning of the period consumers that mostly disagreed with the statement "A person should be

cautious about cholesterol" read the food labels more often than the consumers that completely

agreed. This situation reversed over time, with the difference increasing every period, because the

probability of reading the label by consumers that disagreed, decreased from 67% to 47%. The

probability of reading the label by consumers that agreed with the statement is 62% at the end of the

period. In the case of fat, the pattern is similar to that of cholesterol but the difference, at the end of

the period, between consumers that agree and disagree with the statement "A person should be

cautious about fat" is only 5%. Bender and Derby (1992) also report that from 1982 to 1988 the

percentage of consumers using the ingredient list to avoid fats and cholesterol increased

signifi cantly.

The likelihood of reading food labels in the case of preservatives and sugar (Figures 5-14E

and 5-14G) follow an opposite pattern to that of fat and cholesterol. For preservatives, the

probability of reading food labels by consumers that agree with the statement has decreased over

time from 65% to 59% while the probability of reading food labels by consumers that disagree with

the statement increased from 52% to 57%. Changes over time in the case of sugar are very similar.









In the case of Vitamins~~~~tttt~~~~ttt reconanended by physician" the likelihood of reading labels

decreased over time for both more for the consumers that agree (1) than for the consumers that

disagree (6)

Changes in the effect of seasonality

Quarters are used in the model to determine if there is a seasonality in the likelihood of

reading food labels during the year. Figure 5-15 shows that the probability of reading food labels

has changed the most over time in the fourth quarter, decreasing from 62% in 1984-1993 to 57% in

1995-2003.

Ranking of the probabilities

Figures 5-16 to 5-18 put the all variables in a format easy to compare, taking the difference

between the min and max estimated probability, and see the changes in the impact of the variables

on reading food labels between the first period, 1984-1993 and the last one, 1994-2003. It is clear

that, although the intensity of the impact of some variables has increased in the last period, the

rankings have not changed much (Figure 5-18). "Conscious of calories", "Know more than most

about nutrition" and "Cautious about cholesterol" have a greater impact on reading food labels in

1994-2003 than in 1984-1993. In general, attitudes, eating habits and health concerns are the most

important factors behind the need to look for information in food labels.

In this chapter the results of three estimated models were discussed, their coefficients and

Wald statistics tabulated and their probabilities compared and ranked The first two models allowed

to compare the probabilities of "I check the labels for harnsfid ingredients" versus "Iread' the labels

for my food purchase". The results of the third model, a sequential model, gives an insight into

changes over time in the probabilities of reading food labels looking for harmful ingredients.









Table 5-1. Results from the ATLAB model for the period 1993-2003
Dependent variable: ATLAB 1993-2003 (13,150 obs) Condition Number = 41
Explanatory Dummy Scaled R-sq = 0 .49
Description
variables variables Coeff t-stats Wald [P-value
Intercept (C) 0.6320 47.13
DZDMAGE1 1=< 35 years 0.1590 7.47
Age of female DZDMAGE2 135-44 years 0.0755 3.91
head DZDMAGE3 145-54 years -0.0174 -0.90 79.20 0.0000
(DMAGE) DZDMAGE4 155-64 years -0. 1006 -4.01
ZDMAGE5 65+ years -0.1853 -6.65
Children under DZDMCHL1 Yes 0.0559 2.35
18 years 5.54 0.0186
(MCHL) ZDMCHL2 No -0.0319 -2.35
DZDMEDU1 INo high school 0.1552 3.65
Eduatonof DZDMEDU2 IHigh school 0.0191 1.19
female head 15.13 0.0017
DZDMEDU3 ISome college -0.0121 -0.76
(DMEDU)
ZDMEDU4 College graduate -0.0291 -1.96
Employment of DZDMFEM I Employed 0.0342 3.33
female head 11.11 0.0009
(DMFEM) IZDMFEM2 INot employed -0.0417 -3.33
DZDMHSZl1 Imember 0.0710 2.88
Household size DZDMHSZ2 2 members 0.0051 0.29
9.69 0.0214
(DMHSZ) DZDMHSZ3 3-4 members -0.0487 -2.41
ZDMHSZ4 5+ members -0.0188 -0.53
DZDMINT21 Under $30,000 -0.0373 -2.71
HousholdDZDMINT22 $30-49,999 0.0028 0.18
income 9.86 0.0198
DZDMINT23 $50-69,999 0.0280 1.19
(DMINT2)
ZDMINT24 $70-100,000+ 0.0675 2.64
DZDMREG1 INew England 0. 1029 2.28
DZDMREG2 IMiddle Atlantic -0.0136 -0.59
DZDMREG3 IEast North Central 0.0748 3.56
DZDMREG4 IWest North Central 0.0670 2.05
Census region
DZDMREG5 ISouth Atlantic -0.0771 -3.50 32.39 0.0001
(DMREG)
DZDMREG6 IEast South Central -0.0527 -1.43
DZDMREG7 IWest South Central -0.0196 -0.68
DZDMREG8 IMountain -0.0210 -0.55
ZDMREG9 Pacific -0.0089 -0.35









Table 5-1. Continued
Dependent var able: ATLAB 1993-2003 (13,150 obs) Condition number = 41
Explanatory Dummy Scaled R-sq = 0 .45
Description
variables variables Coeff t-stats Wald [P-value
DZATCAL1 Agree completely -0.9084 -20.87
DZATCAL2 IAgree mostly -0.3672 -15.63
Conciusof DZATCAL3 Agree somewhat -0.0623 -4.04
calories 1097.62 0.0000
DZATCAL4 Neither 0.1945 9.53
(ATCAL)
DZATCAL5 Disagree somewhat 0.3525 14.68
ZATCAL6 Disagree mostly 0.7256 23.03
DZATLB S1 Agree completely 0.0240 1.75
DZATLBS2 Agree mostly 0.0317 1.16
Lik t lse20 DZATLBS3 Agree somewhat 0.0331 1.23
pounds 10.85 0.0544
DZATLBS4 Neither -0.0126 -0.38
(ATLB S)
DZATLBS5 Disagree somewhat -0.0397 -1.26
ZATLBS6 Disagree mostly -0.0486 -2.62
DZATSWM I Agree completely 0.0440 1.92
DZATSWM2 IAgree mostly 0.0281 1.12
Love to swim DZATSWM3 IAgree somewhat 0.0413 1.92
14.57 0.0124
(ATSWM) DZATSWM4 INeither -0.0496 -2.35
DZATSWM5 IDisagree somewhat 0.0094 0.30
ZATSWM6 Disagree mostly -0.0389 -2.20
DZATWGT1 IAgree completely 0.1087 6.05
DZATWGT2 IAgree mostly 0.0322 1.88
Overweight isn't DAWTAgeso wht-0.0337 -1.77
attractive 59.69 0.0000
DZATWGT4 Neither -0.1018 -4.71
(ATWGT)
DZATWGT5 IDisagree somewhat -0. 1043 -2.71
ZATWGT6 Disagree mostly -0.1384 -2.73
DZNTBRN1 Agree completely 0.2572 4.72
Best known DZNTBRN2 IAgree mostly 0.0330 1.04
brands are DZNTBRN3 Agree somewhat 0.0354 1.72
35.39 0.0000
highest quality DZNTBRN4 Neither -0.0047 -0.24
(NTBRN) DZNTBRN5 Disagree somewhat -0.0126 -0.87
ZNTBRN6 Disagree mostly -0.0835 -3.46









Table 5-1. Continued
Dependent var able: ATLAB 1993-2003 (13,150 obs) Condition number = 41
Explanatory Dummy Scaled R-sq = 0 .45
Description
variables variables Coeff t-stats Wald [P-value
DZNTGING Agree completely -0.1050 -3.19
Food should have DZNTING2 IAgree mostly -0.03011 -1.29
body building DZNTINTG3 Agree somewhat -0.0316 -1.76
27.03 0.0001
ingredients DZNTINTG4 INeither 0.0255 1.74
(NTINTG) DZNTING5 Disagree somewhat 0.0996 3.67
ZNTINTG6 Disagree mostly 0.0617 1.54
DZNTKNO1 IAgree completely -0.4717 -10.63
Know more than IDZNTKNO2 IAgree mostly 1-0.30151 -11.41
most about DZNTKNO3 IAgree somewhat -0.0869 -5.30
415.63 0.0000
nutrition DZNTKNO4 INeither 0.1013 6.89
(NTKNO) DZNTKNO5 IDisagree somewhat 0.2841 11.02
ZNTKNO6 Disagree mostly 0.4246 11.54
Adult female on DZDTFE21 Yes -0.2360 -14.12
diet 199.37 0.0000
(DTFE2) IZDTFE22 INo 0. 1000 14.12
DZFDFCH1 Always encourage 0.2586 4.10
DZFDFCH2 Almost always encourage 0.2004 4.19
Eating fried DZFDFCH3 Sometimes encourage 0.1564 5.94
chicken 147.46 0.0000
DZFDFCH4 Neither 0.0651 4.37
(FDFCH)
DZFDFCH5 Sometimes discourage -0.0608 -3.05
ZFDFCH6 Almost always discourage -0.2750 -10.83
DZFDHOT1 Always encourage 0.1866 2.51
DZFDHOT2 Almost always encourage 0.1226 2.22
Eating hot dog DZFDHOT3 Sometimes encourage 0.0728 2.96
sandwich 69.96 0.0000
DZFDHOT4 Neither 0.0571 4.73
(FDHOT)
DZFDHOT5 Sometimes discourage -0.0910 -4.07
ZFDHOT6 Almost always discourage -0. 1976 -6.69
DZFDLUN 1 Always encourage 0.0793 1.42
DZFDLUN2 Almost always encourage -0.0490 -1.28
Eating lunchmeat IDZFDLUN3 Sometimes encourage -0.0232 -1.02
12.35 0.0303
(FDLUN) DZFDLUN4 INeither 0.0279 1.98
DZFDLUN5 Sometimes discourage 0.0120 0.50
ZFDLUN6 Almost always discourage -0.0824 -2.35









Table 5-1. Continued
Dependent var able: ATLAB 1993-2003 (13,150 obs) Condition number = 41
Explanatory Dummy Scaled R-sq = 0 .45
Description
variables variables Coeff t-stats Wald [P-value
DZFDPIZ l Always encourage 0.0067 0.16
DZFDPIZ2 Almost always encourage 0.0014 0.05
Eating pizza DZFDPIZ3 Sometimes encourage 0.0033 0.17
1.23 0.9424
(FDPIZ) DZFDPIZ4 INeither 0.0022 0.16
DZFDPIZ 5 Sometimes discourage 0.0005 0.01
ZFDPIZ6 Almost always discourage -0.0752 -1.10
DZFD TAC 1 Always encourage 0.1154 2.17
DZFDTAC2 Almost always encourage 0.0304 0.87
Eating tacos DZFDTAC3 Sometimes encourage -0.0116 -0.53
8.05 0.1532
(FDTAC) DZFDTAC4 INeither 0.0039 0.31
DZFDTAC5 Sometimes discourage -0.0337 -1.02
ZFDTAC6 Almost always discourage -0.0659 -1.56
DZATFOR1 Agree completely -0.1536 -6.56
DZATFOR2 Agree mostly -0.0581 -2.89
Avoid foreign DZATFOR3 Agree somewhat 0.0056 0.32
food 75.10 0.0000
DZATFOR4 Neither 0.0616 2.66
(ATFOR)
DZATFOR5 Disagree somewhat 0.0703 2.61
ZATFOR6 Disagree mostly 0.1532 5.60
DZFPFAS 1 Agree completely -0.0243 -0.31
DZFPFAS2 Agree mostly 0.0191 0.43
Try fast food DZFPFAS3 Agree somewhat -0.0202 -0.98
places 6.65 0.2480
DZFPFAS4 Neither -0.0442 -2.06
(FPFAS)
DZFPFAS5 Disagree somewhat 0.0264 1.18
ZFPFAS6 Disagree mostly 0.0174 1.27
DZFPRES 1 Agree completely 0.0470 0.81
DZFPRES2 Agree mostly -0.0223 -0.52
ViitretaratsDZFPRES3 Agree somewhat -0.0619 -2.09
more than most 8.16 0.1478
DZFPRES4 Neither -0.0241 -1.09
(FPRE S)
DZFPRES5 Disagree somewhat 0.0339 1.55
ZFPRES6 Disagree mostly 0.0093 0.81









Table 5-1. Continued
Dependent var able: ATLAB 1993-2003 (13,150 obs) Condition number = 41
Explanatory Dummy Scaled R-sq = 0 .45
Description
variables variables Coeff t-stats Wald [P-value
DZATDOC1 Agree completely -0.4598 -12.68
DZATDOC2 IAgree mostly -0.1652 -5.45
Doctor gives DADC Ageso wht-0.0172 -0.85
advice on diet 262.25 0.0000
DZATDOC4 Neither -0.0096 -0.53
(ATDOC)
DZATDOC5 IDisagree somewhat 0.1475 5.10
ZATDOC6 Disagree mostly 0. 1903 11.12
DZNTADD I Agree completely -0.1752 -5.83

A person should DZNTADD2 IAgree mostly -0.0496 -2. 11
be cautious about IDZNTADD3 IAgree somewhat 0.0870 3.74
48.04 0.0000
additives DZNTADD4 INeither 0.1400 4.25
(NTADD) DZNTADD5 IDisagree somewhat 0.2798 3.65
ZNTADD6 Disagree mostly 0.3221 2.65
DZNTCHL 1 Agree completely -0.1141 -4.86

A person should DZNTCHL2 IAgree mostly 0.0269 1.33
be cautious about IDZNTCHL3 Agree somewhat 0.0442 1.97
27.44 0.0001
cholesterol DZNTCHL4 INeither 0.1133 2.91
(NTCHL) DZNTCHL5 Disagree somewhat 0.1017 1.27
ZNTCHL6 Disagree mostly 0.2867 2.29
DZNTFAT 1 Agree completely -0.1268 -6.02

A person should DZNTFAT2 Agree mostly 0.0038 0. 19
be cautious about IDZNTFAT3 Agree somewhat 0.1168 4.64
44.76 0.0000
fat DZNTFAT4 INeither 0.1889 4.18
(NTFAT) DZNTFAT5 Disagree somewhat 0.2230 2.58
ZNTFAT6 Disagree mostly -0.0046 -0.04
DZNTPRE 1 Agree completely -0.0394 -1.27

A person should DZNTPRE2 Agree mostly -0.004 1 -0. 17
be cautious about IDZNTPRE3 Agree somewhat 0.0126 0.56
2.76 0.7370
preservatives DZNTPRE4 INeither 0.0270 0.88
(NTPRE) DZNTPRE5 Disagree somewhat 0.0771 1.14
ZNTPRE6 Disagree mostly -0.0292 -0.26









Table 5-1. Continued
Dependent var able: ATLAB 1993-2003 (13,150 obs) Condition number = 41
Explanatory Dummy Scaled R-sq = 0 .49
Description
variables variables Coeff t-stats Wald [P-value
DZNTSAL 1 Agree completely -0.0301 -1.29

A person should DZNTSAL2 Agree mostly 0.0113 0.57
be cautious about IDZNTSAL3 Agree somewhat 0.0062 0.30
7.74 0.1714
salt DZNTSAL4 INeither 0.0718 2.05
(NTSAL) DZNTSAL5 Disagree somewhat -0.0449 -0.67
ZNTSAL6 Disagree mostly -0.1422 -1.40
DZNTSUG1 Agree completely 0.0704 2.38

A person should DZNTSUG2 IAgree mostly 0.0235 1.01
be cautious about IDZNTSUG3 Agree somewhat -0.0368 -2.38
11.97 0.0353
sugar DZNTSUG4 Neither -0.0342 -1.44
(NTSUG) DZNTSUG5 Disagree somewhat -0.0245 -0.57
ZNTSUG6 Disagree mostly 0.1117 1.53
DZNTVIT 1 Agree completely 0.0494 1.28
Vitamins DZNTVIT2 Agree mostly 0.1170 3.32
recommended by IDZNTVIT3 Agree somewhat 0.0473 1.97
28.19 0.0000
physician DZNTVIT4 INeither 0.0131 0.75
(NTVIT) DZNTVIT5 Disagree somewhat -0.0267 -1.59
ZNTVIT6 Disagree mostly -0.0831 -3.88
DZZQTR1 First quarter 0.0223 1.22
Quarters DZZQTR2 Second quarter -0.0218 -1.24
12.89 0.0049
(ZQTR) DZZQTR3 Third quarter -0.0425 -2.66
ZZQTR34 Fourth quarter 0.0438 2.73
MU3 0.8705 62.60
MU4 Thehls1.6876 91.94
MU5 2.2174 103.91
MU6 2.7919 107.95









Table 5-2. Results from the FPLAB model for the period 1993-2003
Dependent variable: FPLAB 1993-2003 (13,150 obs) Condition number 41
Explanatory Dummy Scaled R-sq 0 .42
Description
variables variables Coeff t-stats Wald [P-value
Intercept (C) 0.5508 42.77
DZDMAGE1 1=< 35 years 0.1689 7.94
Age of female DZDMAGE2 135-44 years 0.0965 5.02
head DZDMAGE3 145-54 years -0.0370 -1.91 94.65 0.0000
(DMAGE) DZDMAGE4 155-64 years -0. 1070 -4.29
ZDMAGE5 65+ years -0. 1962 -7.07
Children under DZDMCHL1 Yes 0.0261 1.10
18 years 1.21 0.2714
(DMCHL) IZDMCHL2 INo -0.0149 -1.10
DZDMEDU1 INo high school 0.0742 1.74
Eduatonof DZDMEDU2 IHigh school 0.0213 1.32
female head 5.63 0.1310
DZDMEDU3 ISome college -0.0236 -1.47
(DMEDU)
ZDMEDU4 College graduate -0.0105 -0.71
Employment of DZDMFEM I Employed 0.0273 2.68
female head 7.17 0.0074
(DMFEM)ZDMFEM2 INot employed -0.0334 -2.68
DZDMHSZ l 1 member 0.0537 2.19
Household size DZDMHSZ2 12 members -0.0438 -2.50
15.46 0.0015
(DMHSZ) DZDMHSZ3 3-4 members -0.0098 -0.49
ZDMHSZ4 5+ members 0.0588 1.65
DZDMINT21 Under $30,000 -0.0209 -1.52
HousholdDZDMINT22 $30-49,999 -0.0185 -1.16
income 8.62 0.0348
DZDMINT23 $50-69,999 0.0196 0.83
(DMINT2)
ZDMINT24 $70-100,000+ 0.0698 2.74
DZDMREG1 INew England 0.0773 1.73
DZDMREG2 IMiddle Atlantic -0.0644 -2.82
DZDMREG3 IEast North Central 0.0730 3.48
DZDMREG4 IWest North Central 0.1334 4.09
Census region
DZDMREG5 ISouth Atlantic -0.0968 -4.40 64.93 0.0000
(DMREG)
DZDMREG6 IEast South Central -0.0995 -2.71
DZDMREG7 IWest South Central -0.0120 -0.42
DZDMREG8 IMountain 0.1071 2.83
ZDMREG9 Pacific 0.0068 0.27









Table 5-2. Continued
Dependent variable: FPLAB 1993-2003 (13,150 obs) Condition number = 41
Explanatory Dummy Scaled R-sq = 0 .42
Description
variables variables Coeff t-stats Wald [P-value
DZATCAL1 Agree completely -0.7304 -17.51
DZATCAL2 IAgree mostly -0.2773 -11.93
Conciusof DZATCAL3 Agree somewhat -0.0477 -3.10
calories 691.89 0.0000
DZATCAL4 Neither 0.1489 7.28
(ATCAL)
DZATCAL5 Disagree somewhat 0.2903 12.08
ZATCAL6 Disagree mostly 0.5499 17.59
DZATLB S1 Agree completely 0.0281 2.07
DZATLBS2 Agree mostly 0.0184 0.67
Lik t lse20 DZATLBS3 Agree somewhat 0.0351 1.31
pounds 10.81 0.0554
DZATLBS4 Neither -0.0424 -1.28
(ATLB S)
DZATLBS5 Disagree somewhat -0.0431 -1.36
ZATLBS6 Disagree mostly -0.0385 -2.08
DZATSWM I Agree completely 0.0412 1.81
DZATSWM2 IAgree mostly 0.0399 1.59
Love to swim DZATSWM3 IAgree somewhat 0.0147 0.68
8.80 0.1171
(ATSWM) DZATSWM4 INeither -0.0304 -1.44
DZATSWM5 IDisagree somewhat -0.0158 -0.51
ZATSWM6 Disagree mostly -0.0302 -1.71
DZATWGT1 IAgree completely 0.0802 4.49
DZATWGT2 IAgree mostly 0.0310 1.81
Overweight isn't DAWTAgeso wht-0.0193 -1.02
attractive 38.06 0.0000
DZATWGT4 Neither -0.0876 -4.06
(ATWGT)
DZATWGT5 IDisagree somewhat -0.0819 -2.14
ZATWGT6 Disagree mostly -0.1138 -2.27
DZNTBRN1 Agree completely 0.2225 4.13
Best known DZNTBRN2 IAgree mostly 0.0512 1.62
brands are DZNTBRN3 Agree somewhat 0.0373 1.82
33.97 0.0000
highest quality DZNTBRN4 Neither -0.0038 -0.20
(NTBRN) DZNTBRN5 Disagree somewhat -0.0113 -0.78
ZNTBRN6 Disagree mostly -0.0920 -3.83









Table 5-2. Continued
Dependent variable: FPLAB 1993-2003 (13,150 obs) Condition number = 41
Explanatory Dummy Scaled R-sq = 0 .42
Description
variables variables Coeff t-stats Wald [P-value
DZNTGING Agree completely -0.1456 -4.45
Food should have DZNTING2 IAgree mostly -0.04711 -2.03
body building DZNTINTG3 Agree somewhat -0.0127 -0.71
35.53 0.0000
ingredients DZNTINTG4 INeither 0.0336 2.30
(NTINTG) DZNTING5 Disagree somewhat 0.0812 3.00
ZNTINTG6 Disagree mostly 0.0986 2.48
DZNTKNO1 IAgree completely -0.4081 -9.37
Know more than IDZNTKNO2 IAgree mostly -0.2208 -8.46
most about DZNTKNO3 IAgree somewhat -0. 1003 -6.12
311.69 0.0000
nutrition DZNTKNO4 INeither 0.0843 5.74
(NTKNO) DZNTKNO5 IDisagree somewhat 0.2922 11.33
ZNTKNO6 Disagree mostly 0.3174 8.67
Adult female on DZDTFE21 Yes -0.2226 -13.35
diet 178.27 0.0000
(DTFE2) IZDTFE22 INo 0.0943 13.35
DZFDFCH1 Always encourage 0.1294 2.06
DZFDFCH2 Almost always encourage 0.1775 3.72
Eating fried DZFDFCH3 Sometimes encourage 0.1033 3.92
chicken 86.84 0.0000
DZFDFCH4 Neither 0.0558 3.75
(FDFCH)
DZFDFCH5 Sometimes discourage -0.0328 -1.65
ZFDFCH6 Almost always discourage -0.2180 -8.65
DZFDHOT1 Always encourage 0.1566 2.13
DZFDHOT2 Almost always encourage 0.0638 1.16
Eating hot dog DZFDHOT3 Sometimes encourage 0.0834 3.40
sandwich 60.09 0.0000
DZFDHOT4 Neither 0.0479 3.97
(FDHOT)
DZFDHOT5 Sometimes discourage -0.0617 -2.77
ZFDHOT6 Almost always discourage -0. 1990 -6.78
DZFDLUN 1 Always encourage 0.1110 2.01
DZFDLUN2 Almost always encourage 0.0468 1.24
Eating lunchmeat IDZFDLUN3 Sometimes encourage -0.0079 -0.35
6.55 0.2565
(FDLUN) DZFDLUN4 INeither 0.0004 0.03
DZFDLUN5 Sometimes discourage -0.0143 -0.59
ZFDLUN6 Almost always discourage -0.0464 -1.34









Table 5-2 Continued
Dependent variable: FPLAB 1993-2003 (13,150 obs) Condition number = 41
Explanatory Dummy Scaled R-sq = 0 .42
Description
variables variables Coeff t-stats Wald [P-value
DZFDPIZ l Always encourage 0.0412 0.99
DZFDPIZ2 Almost always encourage 0.0197 0.65
Eating pizza DZFDPIZ3 Sometimes encourage 0.0033 0.17
6.84 0.2329
(FDPIZ) DZFDPIZ4 INeither -0.0019 -0.14
DZFDPIZ 5 Sometimes discourage 0.0006 0.02
ZFDPIZ6 Almost always discourage -0.1657 -2.44
DZFD TAC 1 Always encourage 0.0694 1.31
DZFDTAC2 Almost always encourage 0.0340 0.97
Eating tacos DZFDTAC3 Sometimes encourage -0.0203 -0.93
4.33 0.5023
(FDTAC) DZFDTAC4 INeither 0.0036 0.30
DZFDTAC5 Sometimes discourage -0.0112 -0.34
ZFDTAC6 Almost always discourage -0.0437 -1.05
DZATFOR1 Agree completely -0.0902 -3.89
DZATFOR2 Agree mostly -0.0536 -2.67
Avoid foreign DZATFOR3 Agree somewhat 0.0118 0.68
food 30.08 0.0000
DZATFOR4 Neither 0.0405 1.75
(ATFOR)
DZATFOR5 Disagree somewhat 0.0675 2.50
ZATFOR6 Disagree mostly 0.0740 2.72
DZFPFAS 1 Agree completely -0.2649 -3.26
DZFPFAS2 Agree mostly -0.0257 -0.57
Try fast food DZFPFAS3 Agree somewhat -0.0006 -0.03
places 19.79 0.0014
DZFPFAS4 Neither -0.0462 -2.15
(FPFAS)
DZFPFAS5 Disagree somewhat 0.0541 2.42
ZFPFAS6 Disagree mostly 0.0135 0.99
DZFPRES 1 Agree completely -0.2132 -3.57
DZFPRES2 Agree mostly -0.1139 -2.64
ViitretaratsDZFPRES3 Agree somewhat -0.0444 -1.50
more than most 33.51 0.0000
DZFPRES4 Neither -0.0584 -2.64
(FPRE S)
DZFPRES5 Disagree somewhat 0.0287 1.32
ZFPRES6 Disagree mostly 0.0461 4.03









Table 5-2. Continued
Dependent variable: FPLAB 1993-2003 (13,150 obs) Condition number = 41
Explanatory Dummy Scaled R-sq = 0 .42
Description
variables variables Coeff t-stats Wald [P-value
DZATDOC1 Agree completely -0.2873 -8.18
DZATDOC2 IAgree mostly -0.1243 -4.12
Doctor gives DADC Ageso wht-0.0239 -1.18
advice on diet 119.50 0.0000
DZATDOC4 Neither -0.0043 -0.24
(ATDOC)
DZATDOC5 IDisagree somewhat 0.1178 4.07
ZATDOC6 Disagree mostly 0.1253 7.34
DZNTADD I Agree completely -0.1230 -4.12

A person should DZNTADD2 IAgree mostly -0.0368 -1.56
be cautious about IDZNTADD3 IAgree somewhat 0.0586 2.52
27.67 0.0000
additives DZNTADD4 INeither 0.0877 2.66
(NTADD) DZNTADD5 IDisagree somewhat 0.2741 3.58
ZNTADD6 Disagree mostly 0.2591 2.14
DZNTCHL 1 Agree completely -0.0944 -4.04

A person should DZNTCHL2 IAgree mostly 0.0223 1.10
be cautious about IDZNTCHL3 Agree somewhat 0.0328 1.47
23.11 0.0003
cholesterol DZNTCHL4 INeither 0.0931 2.39
(NTCHL) DZNTCHL5 Disagree somewhat 0.2482 3.13
ZNTCHL6 Disagree mostly 0.0185 0.15
DZNTFAT 1 Agree completely -0.1248 -5.93

A person should DZNTFAT2 Agree mostly 0.0045 0.22
be cautious about IDZNTFAT3 Agree somewhat 0.1185 4.71
41.12 0.0000
fat DZNTFAT4 INeither 0.1869 4.14
(NTFAT) DZNTFAT5 Disagree somewhat 0.1126 1.31
ZNTFAT6 Disagree mostly 0.0948 0.76
DZNTPRE 1 Agree completely -0.1083 -3.49

A person should DZNTPRE2 Agree mostly 0.0128 0.53
be cautious about IDZNTPRE3 Agree somewhat 0.0322 1.44
12.91 0.0243
preservatives DZNTPRE4 INeither 0.0702 2.30
(NTPRE) DZNTPRE5 Disagree somewhat 0.0639 0.95
ZNTPRE6 Disagree mostly 0.0038 0.03









Table 5-2. Continued
Dependent variable: FPLAB 1993-2003 (13,150 obs) Condition number = 41
Explanatory Dummy Scaled R-sq = 0 .42
Description
variables variables Coeff t-stats Wald [P-value
DZNTSAL 1 Agree completely -0.0413 -1.77

A person should DZNTSAL2 Agree mostly 0.0290 1.47
be cautious about IDZNTSAL3 Agree somewhat 0.0067 0.33
7.80 0.1679
salt DZNTSAL4 INeither 0.0452 1.29
(NTSAL) DZNTSAL5 Disagree somewhat -0.0886 -1.32
ZNTSAL6 Disagree mostly 0.0125 0.12
DZNTSUG1 Agree completely 0.0575 1.96

A person should DZNTSUG2 IAgree mostly 0.0087 0.37
be cautious about IDZNTSUG3 Agree somewhat -0.0286 -1.85
9.44 0.0928
sugar DZNTSUG4 Neither -0.0283 -1.19
(NTSUG) DZNTSUG5 Disagree somewhat -0.0145 -0.33
ZNTSUG6 Disagree mostly 0.1361 1.87
DZNTVIT 1 Agree completely 0.0121 0.32
Vitamins DZNTVIT2 Agree mostly 0.0646 1.84
recommended by IDZNTVIT3 Agree somewhat 0.0288 1.20
13.92 0.0162
physician DZNTVIT4 INeither -0.0029 -0.17
(NTVIT) DZNTVIT5 Disagree somewhat 0.0163 0.97
ZNTVIT6 Disagree mostly -0.0709 -3.31
DZZQTR1 First quarter 0.0300 1.64
Quarters DZZQTR2 Second quarter -0.0527 -3.01
30.67 0.0000
(ZQTR) DZZQTR3 Third quarter -0.0495 -3.11
ZZQTR34 Fourth quarter 0.0718 4.49
MU3 0.8790 65.21
MU4 Thehls1.7139 94.51
MU5 2.2442 104.52
MU6 2.6143 106.42










Table 5 -3. Principal components for NT ADD, NTCHL, NTFAT, NTPRE, NT SAL and NTSUG for
the period 1993-2003
Component Name Eigenvalue Cumulative R-Squared
1 PCHC 1 4.22265730 0.703 77621
2 PCHC2 0.62483299 0.80791504
3 PCHC3 0.47339228 0.88681376
4 PCHC4 0.34901796 0.94498342
5 PCHC5 0. 19010627 0.97666780
6 PCHC6 0.13999323 1.00000000









Table 5-4. Results for ATLAB model with principal components for health variables for the period
1993-2003
Dependent variable: ATLAB 1993-2003 (13,150 obs) Condition number 10
Explanatory Dummy Scaled R-sq 0 .49
variables variables Dsrpi Coeff t-stats Wald [P-value
Intercept (C) 0.6292 47.07
DZDMAGE1 1=< 35 years 0. 1673 7.89
Age of female DZDMAGE2 135-44 years 0.0784 4.06
head DZDMAGE3 145-54 years -0.0215 -1.11 87.76 0.0000
(DMAGE) DZDMAGE4 155-64 years -0.0994 -3.98
ZDMAGE5 65+ years -0.1955 -7.04
Children under DZDMCHL1 Yes 0.0527 2.23
18 years 4.95 0.0261
(DMCHL) ZDMCHL2 No -0.0301 -2.23
DZDMEDU1 INo high school 0.1440 3.39
Eduatonof DZDMEDU2 IHigh school 0.0156 0.97
female head 12.75 0.0052
(DMEDU) IDZDMEDU3 ISome college 1-0.01461 -0.91
ZDMEDU4 College graduate -0.0226 -1.53
Employment of DZDMFEM I Employed 0.0330 3.23
female head 10.44 0.0012
(DMFEM)ZDMFEM2 INot employed -0.0403 -3.23
DZDMHSZl1 Imember 0.0666 2.71
Household size DZDMHSZ2 2 members 0.0053 0.30
9.00 0.0292
(DMHSZ) DZDMHSZ3 3-4 members -0.0475 -2.35
ZDMHSZ4 5+ members -0.0133 -0.37
DZDMINT21 Under $30,000 -0.0402 -2.92
HousholdDZDMINT22 $30-49,999 0.0042 0.26
income 11.12 0.0111
DZDMINT23 $50-69,999 0.0304 1.29
(DMINT2)
ZDMINT24 $70-100,000+ 0.0704 2.76
DZDMREG1 INew England 0. 1023 2.28
DZDMREG2 IMiddle Atlantic -0.0158 -0.69
DZDMREG3 IEast North Central 0.0763 3.63
DZDMREG4 IWest North Central 0.0715 2.19
Census region
DZDMREG5 ISouth Atlantic -0.0784 -3.57 33.89 0.0000
(DMREG)
DZDMREG6 IEast South Central -0.0516 -1.41
DZDMREG7 IWest South Central -0.0213 -0.74
DZDMREG8 IMountain -0.0233 -0.61
ZDMREG9 Pacific -0.0070 -0.28









Table 5-4. Continued
Depende at variable: AT LAB (13,150 obs) Condition number = 10
Explanatory Dummy Scaled R-sq = 0 .45
Description
variables variables Coeff t-stats Wald [P-value
DZATCAL 1 Agree completely -0.9241 -21.32
DZATCAL2 IAgree mostly -0.3709 -15.85
Conciusof DZATCAL3 Agree somewhat -0.0610 -3.98
calories 1127.55 0.0000
DZATCAL4 Neither 0. 1999 9.84
(ATCAL)
DZATCAL5 Disagree somewhat 0.3589 14.99
ZATCAL6 Disagree mostly 0.7226 23.05
DZATLB S1 Agree completely 0.0212 1.55
DZATLBS2 Agree mostly 0.0344 1.26
Lik t lse20 DZATLBS3 Agree somewhat 0.0358 1.34
pounds 10.60 0.0600
DZATLBS4 Neither -0.0122 -0.37
(ATLB S)
DZATLBS5 Disagree somewhat -0.0405 -1.28
ZATLBS6 Disagree mostly -0.0468 -2.52
DZATSWM I Agree completely 0.0377 1.64
DZATSWM2 IAgree mostly 0.0264 1.05
Love to swim DZATSWM3 IAgree somewhat 0.0425 1.98
13.35 0.0204
(ATSWM) DZATSWM4 INeither -0.0478 -2.27
DZATSWM5 IDisagree somewhat 0.0127 0.41
ZATSWM6 Disagree mostly -0.0371 -2.10
DZATWGT1 IAgree completely 0.1002 5.61
DZATWGT2 IAgree mostly 0.0357 2.09
Overweight isn't DAWTAgeso wht-0.0306 -1.61
attractive 55.00 0.0000
DZATWGT4 Neither -0.0991 -4.60
(ATWGT)
DZATWGT5 IDisagree somewhat -0.1017 -2.65
ZATWGT6 Disagree mostly -0.1358 -2.68
DZNTBRN1 Agree completely 0.2476 4.56
Best known DZNTBRN2 IAgree mostly 0.0322 1.02
brands are DZNTBRN3 Agree somewhat 0.0411 2.01
36.73 0.0000
highest quality DZNTBRN4 Neither -0.0023 -0.12
(NTBRN) DZNTBRN5 Disagree somewhat -0.0122 -0.84
ZNTBRN6 Disagree mostly -0.0917 -3.81









Table 5-4. Continued
Dependent var able: ATLAB 1993-2003 (13,150 obs) Condition number = 10
Explanatory Dummy Scaled R-sq = 0 .45
Description
variables variables Coeff t-stats Wald [P-value
DZNTGING Agree completely -0.1156 -3.53
Food should have DZNTING2 IAgree mostly -0.03191 -1.38
body building DZNTINTG3 Agree somewhat -0.0280 -1.56
28.67 0.0000
ingredients DZNTINTG4 INeither 0.0276 1.89
(NTINTG) DZNTING5 Disagree somewhat 0.0990 3.66
ZNTINTG6 Disagree mostly 0.0611 1.53
DZNTKNO1 IAgree completely -0.4856 -10.99
Know more than IDZNTKNO2 IAgree mostly -0.2970 -11.27
most about DZNTKNO3 IAgree somewhat -0.0869 -5.31
420.39 0.0000
nutrition DZNTKNO4 INeither 0.1021 6.97
(NTKNO) DZNTKNO5 IDisagree somewhat 0.2872 11.17
ZNTKNO6 Disagree mostly 0.4189 11.40
Adult female on DZDTFE21 Yes -0.2379 -14.26
diet 203.40 0.0000
(DTFE2) IZDTFE22 INo 0.1008 14.26
DZFDFCH1 Always encourage 0.2605 4.14
DZFDFCH2 Almost always encourage 0.2060 4.31
Eating fried DZFDFCH3 Sometimes encourage 0.1631 6.21
chicken 163.52 0.0000
DZFDFCH4 Neither 0.0686 4.62
(FDFCH)
DZFDFCH5 Sometimes discourage -0.0592 -2.99
ZFDFCH6 Almost always discourage -0.2902 -11.50
DZFDHOT1 Always encourage 0.1757 2.36
DZFDHOT2 Almost always encourage 0.1269 2.30
Eating hot dog DZFDHOT3 Sometimes encourage 0.0752 3.07
sandwich 74.87 0.0000
DZFDHOT4 Neither 0.0601 5.00
(FDHOT)
DZFDHOT5 Sometimes discourage -0.0956 -4.28
ZFDHOT6 Almost always discourage -0.2032 -6.90
DZFDLUN 1 Always encourage 0.0734 1.32
DZFDLUN2 Almost always encourage -0.0470 -1.23
Eating lunchmeat IDZFDLUN3 Sometimes encourage -0.0241 -1.06
13.47 0.0193
(FDLUN) DZFDLUN4 INeither 0.0296 2.10
DZFDLUN5 Sometimes discourage 0.0150 0.62
ZFDLUN6 Almost always discourage -0.0905 -2.59









Table 5-4. Continued
Dependent var able: ATLAB 1993-2003 (13,150 obs) Condition number = 10
Explanatory Dummy Scaled R-sq = 0 .45
Description
variables variables Coeff t-stats Wald [P-value
DZFDPIZ l Always encourage 0.0036 0.09
DZFDPIZ2 Almost always encourage 0.0029 0.09
Eating pizza DZFDPIZ3 Sometimes encourage 0.0010 0.05
1.64 0.8966
(FDPIZ) DZFDPIZ4 INeither 0.0040 0.30
DZFDPIZ 5 Sometimes discourage 0.0025 0.07
ZFDPIZ6 Almost always discourage -0.0867 -1.27
DZFD TAC 1 Always encourage 0.1080 2.03
DZFDTAC2 Almost always encourage 0.0286 0.82
Eating tacos DZFDTAC3 Sometimes encourage -0.0120 -0.55
7.86 0.1638
(FDTAC) DZFDTAC4 INeither 0.0057 0.46
DZFDTAC5 Sometimes discourage -0.0326 -0.99
ZFDTAC6 Almost always discourage -0.0703 -1.66
DZATFOR1 Agree completely -0.1581 -6.78
DZATFOR2 Agree mostly -0.0563 -2.80
Avoid foreign DZATFOR3 Agree somewhat 0.0071 0.41
food 76.26 0.0000
DZATFOR4 Neither 0.0648 2.80
(ATFOR)
DZATFOR5 Disagree somewhat 0.0727 2.70
ZATFOR6 Disagree mostly 0.1476 5.40
DZFPFAS 1 Agree completely -0.0232 -0.29
DZFPFAS2 Agree mostly 0.0236 0.53
Try fast food DZFPFAS3 Agree somewhat -0.0190 -0.93
places 6.10 0.2962
DZFPFAS4 Neither -0.0423 -1.97
(FPFAS)
DZFPFAS5 Disagree somewhat 0.0245 1.09
ZFPFAS6 Disagree mostly 0.0162 1.19
DZFPRES 1 Agree completely 0.0450 0.78
DZFPRES2 Agree mostly -0.0198 -0.46
ViitretaratsDZFPRES3 Agree somewhat -0.0549 -1.85
more than most 7.03 0.2187
DZFPRES4 Neither -0.0220 -0.99
(FPRE S)
DZFPRES5 Disagree somewhat 0.0345 1.58
ZFPRES6 Disagree mostly 0.0066 0.58









Table 5-4. Continued
Dependent var able: ATLAB 1993-2003 (13,150 obs) Condition number = 10
Explanatory Dummy Scaled R-sq = 0 .49
Description
variables variables Coeff t-stats Wald [P-value
DZATDOC1 Agree completely -0.4700 -13.01
DZATDOC2 IAgree mostly -0. 1643 -5.44
Doctor gives DADC Ageso wht-0.0135 -0.67
advice on diet 269.45 0.0000
DZATDOC4 Neither -0.0088 -0.49
(ATDOC)
DZATDOC5 IDisagree somewhat 0.1470 5.09
ZATDOC6 Disagree mostly 0.1905 11.15
APCH 1 I Principal components for 0.27701 24.41
APCH2 I NTADD, NTCHL, 0.0159 1.60
654.60 0.0000
APCH3 I NTFAT, NTPRE, 0.0500 5.02
APCH4 I NTSAL and NTSUG 0.04871 5.00
DZNTVIT 1 Agree completely 0.0344 0.89
Vitamins DZNTVIT2 Agree mostly 0.1182 3.36
recommended by IDZNTVIT3 Agree somewhat 0.0489 2.04
29.65 0.0000
physician DZNTVIT4 INeither 0.0175 1.01
(NTVIT) DZNTVIT5 Disagree somewhat -0.0238 -1.42
ZNTVIT6 Disagree mostly -0.0888 -4.15
DZZQTR1 First quarter 0.0245 1.34
Quarters DZZQTR2 Second quarter -0.0225 -1.29
12.87 0.0049
(ZQTR) DZZQTR3 Third quarter -0.0422 -2.65
ZZQTR34 Fourth quarter 0.0425 2.65
MU3 0.8653 62.63
MU4 Thehls1.6794 91.98
MU5 2.2085 103.93
MU6 2.7833 107.90









Table 5-5. Results for FPLAB model with principal components for health variables for the period
1993-2003
Dependent variable: FPLAB 1993-2003 (13,150 obs) Condition number 10
Explanatory Dummy Scaled R-sq 0 .42
variables variables Dsrpi Coeff t-stats Wald [P-value
Intercept (C) 0.5489 42.75
DZDMAGE1 1=< 35 years 0. 1767 8.34
Age of female IDZDMAGE2 135-44 years 0.09891 5.15
head DZDMAGE3 145-54 years -0.0412 -2.13 103.32 0.00
(DMAGE) IDZDMAGE4 155-64 years -0.1061 -4.26
ZDMAGE5 65+ years -0.2047 -7.40
Children under DZDMCHL1 Yes 0.0234 0.99
18 years 0.98 0.3214
(DMCHL) ZMCHL2 No -0.0134 -0.99
DZDMEDU1 INo high school 0.0617 1.45
Eduaton f ZDMEDU2 Hgh school 0.0178 1.11
female head 4.68 0.196
(DMEDU) IDZDMEDU3 ISome college 1-0.02591 -1.62
ZMEDU4 College graduate -0.0041 -0.28
Employment of IDZDMFEMI Emloyed 0.0262 2.57
female head 6.62 0.0101
(DMFM) DMFEM2 Not employed -0.0320 -2.57
DZDMHSZl1 Imember 0.0511 2.09
Household size DZDMHSZ2 2 members -0.0432 -2.47
14.97 0.0019
(DMHSZ) IDZDMHSZ3 3-4 members -0.0091 -0.45
ZDMHSZ4 5+ members 0.0605 1.71
DZDMINT21 IUnder $30,000 -0.0230 -1.68
HousholdDZDMINT22 $30-49,999 -0.0180 -1.12
income 9.28 0.0258
DZDMINT23 $50-69,999 0.0220 0.94
(DMINT2)
ZDMINT24 $70-100,000+ 0.0719 2.82
DZDMREG1 INew England 0.0763 1.71
DZDMREG2 IMiddle Atlantic -0.0665 -2.91
DZDMREG3 IEast North Central 0.0744 3.55
DZDMREG4 IWest North Central 0.1359 4.17
Census region
DZDMREG5 ISouth Atlantic -0.0959 -4.37 65.67 0.00
(DMREG)
DZDMREG6 IEast South Central -0.0974 -2.66
DZDMREG7 IWest South Central -0.0162 -0.57
DZDMREG8 IMountain 0. 1044 2.76
ZMREG9 IPacific 0.0088 0.35









Table 5-5. Continued
Dependent variable: FPLAB 1993-2003 (13,150 obs) Condition number = 10
Explanatory Dummy Scaled R-sq = 0 .42
Description
variables variables Coeff t-stats Wald [P-value
DZATCAL 1 Agree completely -0.7501 -18.07
DZATCAL2 IAgree mostly -0.2790 -12.06
Conciusof DZATCAL3 Agree somewhat -0.0452 -2.95
calories 715.31 0.0000
DZATCAL4 Neither 0.1518 7.46
(ATCAL)
DZATCAL5 Disagree somewhat 0.2985 12.47
ZATCAL6 Disagree mostly 0.5454 17.55
DZATLB S1 Agree completely 0.0255 1.87
DZATLBS2 Agree mostly 0.0199 0.73
Lik t lse20 DZATLBS3 Agree somewhat 0.0386 1.44
pounds 10.50 0.0623
DZATLBS4 Neither -0.0422 -1.27
(ATLB S)
DZATLBS5 Disagree somewhat -0.0435 -1.37
ZATLBS6 Disagree mostly -0.0368 -1.99
DZATSWM I Agree completely 0.0347 1.53
DZATSWM2 IAgree mostly 0.0408 1.62
Love to swim DZATSWM3 IAgree somewhat 0.0153 0.72
7.84 0.1650
(ATSWM) DZATSWM4 INeither -0.0299 -1.42
DZATSWM5 IDisagree somewhat -0.0123 -0.40
ZATSWM6 Disagree mostly -0.0284 -1.61
DZATWGT1 IAgree completely 0.0713 4.01
DZATWGT2 IAgree mostly 0.0345 2.02
Overweight isn't DAWTAgeso wht-0.0141 -0.75
attractive 35.02 0.0000
DZATWGT4 Neither -0.0864 -4.03
(ATWGT)
DZATWGT5 IDisagree somewhat -0.0790 -2.07
ZATWGT6 Disagree mostly -0.1136 -2.26
DZNTBRN1 Agree completely 0.2029 3.78
Best known DZNTBRN2 IAgree mostly 0.0523 1.66
brands are DZNTBRN3 Agree somewhat 0.0431 2.11
34.54 0.0000
highest quality DZNTBRN4 Neither -0.0015 -0.08
(NTBRN) DZNTBRN5 Disagree somewhat -0.0102 -0.71
ZNTBRN6 Disagree mostly -0. 1004 -4.19









Table 5-5. Continued
Dependent variable: FPLAB 1993-2003 (13,150 obs) Condition number = 10
Explanatory Dummy Scaled R-sq = 0 .42
Description
variables variables Coeff t-stats Wald [P-value
DZNTGING Agree completely -0.1566 -4.81
Food should have DZNTING2 IAgree mostly -0.0490 -2.12
body building DZNTINTG3 Agree somewhat -0.0080 -0.45
38.39 0.0000
ingredients DZNTINTG4 INeither 0.0351 2.41
(NTINTG) DZNTING5 Disagree somewhat 0.0824 3.05
ZNTINTG6 Disagree mostly 0.0948 2.39
DZNTKNO1 IAgree completely -0.4234 -9.76
Know more than IDZNTKNO2 IAgree mostly -0.2195 -8.43
most about DZNTKNO3 IAgree somewhat -0.0981 -6.01
317.53 0.0000
nutrition DZNTKNO4 INeither 0.0855 5.84
(NTKNO) DZNTKNO5 IDisagree somewhat 0.2949 11.46
ZNTKNO6 Disagree mostly 0.3105 8.50
Adult female on DZDTFE21 Yes -0.2247 -13.51
diet 182.55 0.0000
(DTFE2) IZDTFE22 INo 0.0952 13.51
DZFDFCH1 Always encourage 0.1372 2.19
DZFDFCH2 Almost always encourage 0.1814 3.81
Eating fried DZFDFCH3 Sometimes encourage 0.1103 4.20
chicken 100.43 0.0000
DZFDFCH4 Neither 0.0597 4.03
(FDFCH)
DZFDFCH5 Sometimes discourage -0.0317 -1.60
ZFDFCH6 Almost always discourage -0.2345 -9.37
DZFDHOT1 Always encourage 0.1445 1.97
DZFDHOT2 Almost always encourage 0.0697 1.27
Eating hot dog DZFDHOT3 Sometimes encourage 0.0840 3.43
sandwich 63.57 0.0000
DZFDHOT4 Neither 0.0503 4.19
(FDHOT)
DZFDHOT5 Sometimes discourage -0.0627 -2.82
ZFDHOT6 Almost always discourage -0.2056 -7.03
DZFDLUN 1 Always encourage 0. 1051 1.90
DZFDLUN2 Almost always encourage 0.0473 1.25
Eating lunchmeat IDZFDLUN3 Sometimes encourage -0.0086 -0.38
6.91 0.2276
(FDLUN) DZFDLUN4 INeither 0.0021 0.15
DZFDLUN5 Sometimes discourage -0.0102 -0.42
ZFDLUN6 Almost always discourage -0.0558 -1.61









Table 5-5. Continued
Dependent variable: FPLAB 1993-2003 (13,150 obs) Condition number = 10
Explanatory Dummy Scaled R-sq = 0 .42
Description
variables variables Coeff t-stats Wald [P-value
DZFDPIZ l Always encourage 0.0374 0.90
DZFDPIZ2 Almost always encourage 0.0234 0.77
Eating pizza DZFDPIZ3 Sometimes encourage 0.0016 0.08
7.75 0.1705
(FDPIZ) DZFDPIZ4 INeither -0.0001 -0.01
DZFDPIZ 5 Sometimes discourage -0.0005 -0.01
ZFDPIZ6 Almost always discourage -0.1784 -2.64
DZFD TAC 1 Always encourage 0.0617 1.17
DZFDTAC2 Almost always encourage 0.0359 1.03
Eating tacos DZFDTAC3 Sometimes encourage -0.0184 -0.84
4.50 0.4795
(FDTAC) DZFDTAC4 INeither 0.0045 0.36
DZFDTAC5 Sometimes discourage -0.0084 -0.26
ZFDTAC6 Almost always discourage -0.0540 -1.29
DZATFOR1 Agree completely -0.0965 -4.17
DZATFOR2 Agree mostly -0.0508 -2.53
Avoid foreign DZATFOR3 Agree somewhat 0.0126 0.73
food 31.70 0.0000
DZATFOR4 Neither 0.0435 1.88
(ATFOR)
DZATFOR5 Disagree somewhat 0.0717 2.66
ZATFOR6 Disagree mostly 0.0690 2.53
DZFPFAS 1 Agree completely -0.2653 -3.27
DZFPFAS2 Agree mostly -0.0240 -0.54
Try fast food DZFPFAS3 Agree somewhat 0.0001 0.01
places 19.09 0.0019
DZFPFAS4 Neither -0.0436 -2.03
(FPFAS)
DZFPFAS5 Disagree somewhat 0.0524 2.35
ZFPFAS6 Disagree mostly 0.0125 0.91
DZFPRES 1 Agree completely -0.2168 -3.64
DZFPRES2 Agree mostly -0.1078 -2.50
ViitretaratsDZFPRES3 Agree somewhat -0.0367 -1.25
more than most 31.89 0.0000
DZFPRES4 Neither -0.0571 -2.58
(FPRE S)
DZFPRES5 Disagree somewhat 0.0293 1.35
ZFPRES6 Disagree mostly 0.0433 3.80









Table 5-5. Continued
Dependent variable: FPLAB 1993-2003 (13,150 obs) Condition number = 10
Explanatory Dummy Scaled R-sq = 0 .42
Description
variables variables Coeff t-stats Wald [P-value
DZATDOC1 Agree completely -0.2995 -8.56
DZATDOC2 IAgree mostly -0.1229 -4.09
Doctor gives DADC Ageso wht-0.0199 -0.99
advice on diet 124.83 0.0000
DZATDOC4 Neither -0.0037 -0.21
(ATDOC)
DZATDOC5 IDisagree somewhat 0.1194 4.13
ZATDOC6 Disagree mostly 0.1252 7.34
APCH 1 I Principal components for 0.26671 23.61
APCH2 I NTADD, NTCHL, 0.0181 1.83
599.11 0.0000
APCH3 I NTFAT, NTPRE, 0.0421 4.23
APCH4 I NTSAL and NTSUG 0.03671 3.76
DZNTVIT 1 Agree completely -0.0070 -0.18
Vitamins DZNTVIT2 Agree mostly 0.0667 1.90
recommended by IDZNTVIT3 Agree somewhat 0.0299 1.25
15.28 0.0092
physician DZNTVIT4 INeither 0.0010 0.06
(NTVIT) DZNTVIT5 Disagree somewhat 0.0190 1.14
ZNTVIT6 Disagree mostly -0.0741 -3.48
DZZQTR1 First quarter 0.0322 1.76
Quarters DZZQTR2 Second quarter -0.0529 -3.03
31.25 0.0000
(ZQTR) DZZQTR3 Third quarter -0.0505 -3.18
ZZQTR34 Fourth quarter 0.0713 4.46
MU3 0.8741 65.23
MU4 Thehls1.7063 94.53
MU5 2.2358 104.52
MU6 2.6058 106.37









Table 5-6. Results from the ATLAB model for the period 192:4-1993
Dependent variable: ATLAB 1984-1993 (15,331 obs) Condition number 48
Explanatory Dummy Scaled R-sq 0 .44
Description
variables variables Coeff t-stats Wald [P-value
Intercept (C) 0.5170 43.41
DZDMAGE1 1=< 35 years 0. 1666 9.74
Age of female DZDMAGE2 135-44 years 0.0246 1.30
head DZDMAGE3 145-54 years -0.0146 -0.64 104.42 0.0000
(DMAGE) DZDMAGE4 155-64 years -0.1364 -5.94
ZDMAGE5 65+ years -0. 1642 -6.22
Children under DZDMCHL1 Yes -0.0067 -0.30
18 years 0.09 0.7617
(DMCHL) IZDMCHL2 INo 0.0041 0.30
DZDMEDU1 INo high school 0.0456 1.39
Eduatonof DZDMEDU2 IHigh school 0.0118 0.90
female head 6.32 0.0968
DZDMEDU3 ISome college -0.0366 -2.30
(DMEDU)
ZDMEDU4 College graduate 0.0042 0.27
Employment of DZDMFEM I Employed 0.0412 4.02
female head 16.18 0.0001
(DMFEM)ZDMFEM2 INot employed -0.0421 -4.02
DZDMHSZ l 1 member 0.0788 3.40
Household size DZDMHSZ2 12 members 0.0045 0.26
14.26 0.0026
(DMHSZ) DZDMHSZ3 3-4 members -0.0540 -2.89
ZDMHSZ4 5+ members -0.0145 -0.46
DZDMINT21 Under $30,000 -0.0107 -1.23
Household DZDMINT22 $30-49,999 0.0035 0.22
2.47 0.4802
income (DMIN2) IDZDMIN23 $50-69,999 0.0293 1.06
ZDMINT24 $70-100,000+ 0.0463 1.11
DZDMREG1 INew England 0.0304 0.74
DZDMREG2 IMiddle Atlantic -0.0085 -0.41
DZDMREG3 IEast North Central 0.0426 2.19
DZDMREG4 IWest North Central 0.0509 1.72
Census region
DZDMREG5 ISouth Atlantic -0.0157 -0.76 13.42 0.0981
(DMREG)
DZDMREG6 IEast South Central -0.0120 -0.36
DZDMREG7 IWest South Central -0.0303 -1.14
DZDMREG8 IMountain -0.0731 -2.15
ZDMREG9 Pacific -0.0026 -0.10









Table 5-6. Continued
Dependent var able: ATLAB 1984-1993 (15,331 obs) Condition number = 48
Explanatory Dummy Scaled R-sq = 0 .44
Description
variables variables Coeff t-stats Wald [P-value
DZATCAL1 Agree completely -0.4880 -16.33
DZATCAL2 IAgree mostly -0.2540 -13.73
Conciusof DZATCAL3 Agree somewhat 0.0025 0.18
calories 723.38 0.0000
DZATCAL4 Neither 0.2047 9.59
(ATCAL)
DZATCAL5 Disagree somewhat 0.2820 11.25
ZATCAL6 Disagree mostly 0.5851 18.53
DZATLB S1 Agree completely 0.0422 3.24
DZATLBS2 Agree mostly -0.0188 -0.65
Lik t lse20 DZATLBS3 Agree somewhat 0.0886 3.45
pounds 31.56 0.0000
DZATLBS4 Neither -0.0858 -2.53
(ATLB S)
DZATLBS5 Disagree somewhat -0.0354 -1.21
ZATLBS6 Disagree mostly -0.0457 -3.12
DZATSWM I Agree completely 0.0234 1.29
DZATSWM2 IAgree mostly -0.0227 -0.96
Love to swim DZATSWM3 IAgree somewhat 0.0266 1.30
5.76 0.3300
(ATSWM) DZATSWM4 INeither -0.0297 -1.49
DZATSWM5 IDisagree somewhat -0.0199 -0.62
ZATSWM6 Disagree mostly 0.0008 0.05
DZATWGT1 IAgree completely 0.0820 6.95
DZATWGT2 IAgree mostly -0.0018 -0.11
Overweight isn't DAWTAgeso wht-0.0733 -3.67
attractive 65.69 0.0000
DZATWGT4 Neither -0. 1244 -4.49
(ATWGT)
DZATWGT5 IDisagree somewhat -0. 1079 -2.51
ZATWGT6 Disagree mostly -0.1550 -2.62
DZNTBRN1 Agree completely -0.0822 -1.53
Best known DZNTBRN2 IAgree mostly 0.0434 1.41
brands are DZNTBRN3 Agree somewhat 0.0727 3.67
18.81 0.0021
highest quality DZNTBRN4 Neither -0.0203 -0.95
(NTBRN) DZNTBRN5 Disagree somewhat -0.0097 -0.78
ZNTBRN6 Disagree mostly -0.0329 -1.75









Table 5-6. Continued
Dependent var able: ATLAB 1984-1993 (15,331 obs) Condition number = 48
Explanatory Dummy Scaled R-sq = 0 .44
Description
variables variables Coeff t-stats Wald [P-value
DZNTGING Agree completely -0.0370 -1.72
Food should have DZNTING2 IAgree mostly -0.0202 -1.09
body building DZNTINTG3 Agree somewhat -0.0131 -0.79
11.09 0.0495
ingredients DZNTINTG4 INeither 0.0485 3.06
(NTINTG) DZNTING5 Disagree somewhat 0.0003 0.01
ZNTINTG6 Disagree mostly 0.0480 0.93
DZNTKNO1 IAgree completely -0.4422 -11.91
Know more than IDZNTKNO2 IAgree mostly -0.2422 -10.38
most about DZNTKNO3 IAgree somewhat -0.0333 -2.29
430.53 0.0000
nutrition DZNTKNO4 INeither 0.0553 3.88
(NTKNO) DZNTKNO5 IDisagree somewhat 0.2675 10.97
ZNTKNO6 Disagree mostly 0.4522 13.18
Adult female on DZDTFE21 Yes -0.2009 -12.92
diet 166.83 0.0000
(DTFE2) IZDTFE22 INo 0.0834 12.92
DZFDFCH1 Always encourage 0.2301 5.18
DZFDFCH2 Almost always encourage 0.0845 2.35
Eating fried DZFDFCH3 Sometimes encourage 0.0610 2.91
chicken 90.90 0.0000
DZFDFCH4 Neither 0.0369 2.68
(FDFCH)
DZFDFCH5 Sometimes discourage -0.0566 -2.87
ZFDFCH6 Almost always discourage -0.2242 -8.04
DZFDHOT1 Always encourage 0. 1251 2.21
DZFDHOT2 Almost always encourage 0.1188 2.74
Eating hot dog DZFDHOT3 Sometimes encourage 0.0804 3.97
sandwich 102.16 0.0000
DZFDHOT4 Neither 0.0594 5.23
(FDHOT)
DZFDHOT5 Sometimes discourage -0.1465 -6.39
ZFDHOT6 Almost always discourage -0.2209 -7.34
DZFDLUN 1 Always encourage 0.1065 2.15
DZFDLUN2 Almost always encourage 0.0766 2.14
Eating lunchmeat IDZFDLUN3 Sometimes encourage 0.0257 1.22
59.92 0.0000
(FDLUN) DZFDLUN4 INeither 0.0574 4.32
DZFDLUN5 Sometimes discourage -0.0586 -2.73
ZFDLUN6 Almost always discourage -0.2070 -6.80









Table 5-6. Continued
Dependent var able: ATLAB 1984-1993 (15,331 obs) Condition number = 48
Explanatory Dummy Scaled R-sq = 0 .44
Description
variables variables Coeff t-stats Wald [P-value
DZFDPIZ l Always encourage 0.0338 1.03
DZFDPIZ2 Almost always encourage -0.0020 -0.08
Eating pizza DZFDPIZ3 Sometimes encourage -0.0284 -1.58
6.04 0.3019
(FDPIZ) DZFDPIZ4 INeither 0.0099 0.78
DZFDPIZ 5 Sometimes discourage -0.0429 -1.19
ZFDPIZ6 Almost always discourage 0.0617 1.10
DZFD TAC 1 Always encourage -0.0583 -1.39
DZFDTAC2 Almost always encourage 0.0415 1.30
Eating tacos DZFDTAC3 Sometimes encourage 0.0219 1.06
4.85 0.4348
(FDTAC) DZFDTAC4 Neither -0.0033 -0.30
DZFDTAC5 Sometimes discourage -0.0161 -0.50
ZFDTAC6 Almost always discourage -0.0108 -0.30
DZATFOR1 Agree completely -0.0916 -5.03
DZATFOR2 Agree mostly -0.0299 -1.65
Avoid foreign DZATFOR3 Agree somewhat -0.0243 -1.52
food 65.94 0.0000
DZATFOR4 Neither 0.0372 1.59
(ATFOR)
DZATFOR5 Disagree somewhat 0. 1063 3.88
ZATFOR6 Disagree mostly 0.1591 5.85
DZATDOC1 Agree completely -0.4180 -15.12
DZATDOC2 IAgree mostly -0.1401 -5.23
Doctor gives DADC Ageso wht-0.0554 -2.72
advice on diet 428.89 0.0000
DZATDOC4 Neither -0.0218 -1.33
(ATDOC)
DZATDOC5 IDisagree somewhat 0.1478 4.94
ZATDOC6 Disagree mostly 0.2721 17.03
DZNTADD I Agree completely -0.1446 -6.57

A person should DZNTADD2 IAgree mostly -0.0138 -0.65
be cautious about IDZNTADD3 IAgree somewhat 0. 1046 4.29
58.18 0.0000
additives DZNTADD4 INeither 0.2368 6.36
(NTADD) DZNTADD5 IDisagree somewhat 0.1867 2.46
ZNTADD6 Disagree mostly 0.2053 1.62









Table 5-6. Continued
Dependent var able: ATLAB 1984-1993 (15,331 obs) Condition number = 48
Explanatory Dummy Scaled R-sq = 0 .44
Description
variables variables Coeff t-stats Wald [P-value
DZNTCHL 1 Agree completely -0.0254 -1.48

A person should DZNTCHL2 IAgree mostly 0.02 13 1.12
be cautious about IDZNTCHL3 Agree somewhat 0.0293 1.24
4.81 0.4389
cholesterol DZNTCHL4 INeither 0.0136 0.32
(NTCHL) DZNTCHL5 Disagree somewhat 0.0023 0.03
ZNTCHL6 Disagree mostly -0.1665 -1.35
DZNTFAT 1 Agree completely -0.0975 -5.83

A person should DZNTFAT2 Agree mostly 0.0120 0.62
be cautious about IDZNTFAT3 Agree somewhat 0.1348 5.37
47.71 0.0000
fat DZNTFAT4 INeither 0.2258 4.73
(NTFAT) DZNTFAT5 Disagree somewhat 0.0889 0.94
ZNTFAT6 Disagree mostly -0.0924 -0.67
DZNTPRE 1 Agree completely -0.1269 -5.51

A person should DZNTPRE2 Agree mostly 0.0406 1.99
be cautious about IDZNTPRE3 Agree somewhat 0.0556 2.37
33.18 0.0000
preservatives DZNTPRE4 INeither 0.0982 2.89
(NTPRE) DZNTPRE5 Disagree somewhat 0.1618 2.31
ZNTPRE6 Disagree mostly 0.2204 1.89
DZNTSAL 1 Agree completely 0.0026 0.16

A person should DZNTSAL2 Agree mostly -0.0233 -1.35
be cautious about IDZNTSAL3 Agree somewhat 0.0070 0.32
2.54 0.7708
salt DZNTSAL4 INeither 0.0404 1.02
(NTSAL) DZNTSAL5 Disagree somewhat 0.0400 0.51
ZNTSAL6 Disagree mostly 0.0062 0.05
DZNTSUG1 Agree completely -0.0289 -1.37

A person should DZNTSUG2 IAgree mostly 0.0188 0.99
be cautious about IDZNTSUG3 Agree somewhat -0.0082 -0.52
6.96 0.2237
sugar DZNTSUG4 INeither 0.0239 0.87
(NTSUG) DZNTSUG5 Disagree somewhat 0.0208 0.44
ZNTSUG6 Disagree mostly 0. 1663 1.97









Table 5-6. Continued
Dependent variable: ATLAB 1984-1993 (15,331 obs) Condition number = 48
Explanatory Dummy Scaled R-sq = 0 .44
Description
variables variables Coeff t-stats Wald [P-value
DZNTVIT 1 Agree completely -0.0308 -1.21
Vitamins DZNTVIT2 Agree mostly 0.0612 2.30
recommended by IDZNTVIT3 Agree somewhat 0.0347 1.69
15.32 0.0091
physician DZNTVIT4 INeither -0.0301 -1.71
(NTVIT) DZNTVIT5 Disagree somewhat 0.0220 1.30
ZNTVIT6 Disagree mostly -0.0451 -1.95
DZZQTR1 First quarter 0.0086 0.51
Quarters DZZQTR2 Second quarter -0.0008 -0.05
3.43 0.3306
(ZQTR) DZZQTR3 Third quarter 0.0178 1.22
ZZQTR34 Fourth quarter -0.0263 -1.69
MU3 0.7869 65.48
MU4 Thehls1.5568 96.31
MU5 2.0518 108.79
MU6 2.5331 113.84









Table 5-7. Results from the ATLAB model for the period 1994-2003
Dependent variable: ATLAB 1994-2003 (15,083 obs) Condition number = 39
Explanatory Dummy Scaled R-sq = 0 .49
variables variables Dsrpi Coeff t-stats Wald [P-value
Intercept (C) 0.6540 52.14
DZDMAGE1 1=< 35 years 0.1564 8.29
Age of female DZDMAGE2 135-44 years 0.0583 3.25
head DZDMAGE3 145-54 years -0.0117 -0.64 92.33 0.0000
(DMAGE) DZDMAGE4 155-64 years -0.1005 -4.22
ZDMAGE5 65+ years -0.1891 -7.19
Children under DZDMCHL1 Yes 0.0634 2.89
18 years 8.34 0.0039
(DMCHL) ZDMCHL2 No -0.0363 -2.89
DZDMEDU1 INo high school 0.1340 3.32
Eduatonof DZDMEDU2 IHigh school 0.0205 1.33
female head 13.25 0.0041
(DMEDU) IDZDMEDU3 ISome college 1-0.00811 -0.55
ZDMEDU4 College graduate -0.0280 -2.08
Employment of DZDMFEM I Employed 0.0428 4.58
female head 21.02 0.0000
(DMFEM)ZDMFEM2 INot employed -0.0549 -4.58
DZDMHSZl1 Imember 0.0599 2.64
Household size DZDMHSZ2 2 members 0.0053 0.32
9.26 0.0261
(DMHSZ) DZDMHSZ3 3-4 members -0.0459 -2.44
ZDMHSZ4 5+ members -0.0071 -0.21
DZDMINT21 Under $30,000 -0.0172 -1.32
Household DZDMINT22 $30-49,999 -0.0050 -0.34
3.34 0.3419
income (DMIN2) IDZDMIN23 $50-69,999 0.0137 0.63
ZDMINT24 $70-100,000+ 0.0380 1.66
DZDMREG1 INew England 0.0749 1.81
DZDMREG2 IMiddle Atlantic -0.0105 -0.49
DZDMREG3 IEast North Central 0.0710 3.56
DZDMREG4 IWest North Central 0.0776 2.54
Census region
DZDMREG5 ISouth Atlantic -0.0648 -3.22 32.67 0.0001
(DMREG)
DZDMREG6 IEast South Central -0.0601 -1.74
DZDMREG7 IWest South Central -0.0165 -0.62
DZDMREG8 IMountain -0.0390 -1.12
ZDMREG9 Pacific -0.0052 -0.22









Table 5-7. Continued
Dependent var able: ATLAB 1994-2003 (15,083 obs) Condition number = 39
Explanatory Dummy Scaled R-sq = 0 .45
variables variables Dsrpi Coeff t-stats Wald [P-value
DZATCAL1 Agree completely -0.8936 -21.85
DZATCAL2 Agree mostly -0.3714 -16.77
Conciusof DZATCAL3 Agree somewhat -0.0760 -5.21
calories 1224.26 0.0000
DZATCAL4 INeither 0. 1844 9.80
(ATCAL)
DZATCAL5 Disagree somewhat 0.3371 15.40
ZATCAL6 Disagree mostly 0.7069 24.61
DZATLB S1 Agree completely 0.0286 2.27
DZATLBS2 Agree mostly 0.0265 1.05
Lik t lse20 DZATLBS3 Agree somewhat 0.0199 0.79
pounds 13.46 0.0194
DZATLBS4 Neither -0.0016 -0.05
(ATLB S)
DZATLBS5 Disagree somewhat -0.0203 -0.68
ZATLBS6 Disagree mostly -0.0585 -3.39
DZATSWM I Agree completely 0.0600 2.84
DZATSWM2 IAgree mostly -0.0010 -0.04
Love to swim DZATSWM3 IAgree somewhat 0.0437 2.20
21.06 0.0008
(ATSWM) DZATSWM4 INeither -0.0621 -3.14
DZATSWM5 IDisagree somewhat 0.0151 0.53
ZATSWM6 Disagree mostly -0.0318 -1.90
DZATWGT1 IAgree completely 0.0975 5.76
DZATWGT2 IAgree mostly 0.0267 1.67
Overweight isn't DAWTAgeso wht-0.0253 -1.43
attractive 53.16 0.0000
DZATWGT4 INeither -0.0965 -4.86
(ATWGT)
DZATWGT5 IDisagree somewhat -0.0842 -2.35
ZATWGT6 Disagree mostly -0.0972 -2.14
DZNTBRN1 Agree completely 0.2432 4.94
Best known DZNTBRN2 IAgree mostly 0.0304 1.04
brands are DZNTBRN3 IAgree somewhat 0.0156 0.81
34.71 0.0000
highest quality DZNTBRN4 Neither 0.0080 0.45
(NTBRN) DZNTBRN5 IDisagree somewhat -0.0149 -1.09
ZNTBRN6 Disagree mostly -0.0718 -3.20









Table 5-7. Continued
Dependent var able: ATLAB 1994-2003 (15,083 obs) Condition number = 39
Explanatory Dummy Scaled R-sq = 0 .45
variables variables Dsrpi Coeff t-stats Wald [P-value
DZNTING1 Agree completely -0.0960 -3.08
Food should have DZNTING2 IAgree mostly -0.03361 -1.52
body building DZNTINTG3 Agree somewhat -0.0078 -0.47
18.15 0.0028
ingredients DZNTINTG4 INeither 0.0264 1.96
(NTINTG) DZNTING5 Disagree somewhat 0.0690 2.75
ZNTINTG6 Disagree mostly 0.0028 0.08
DZNTKNO1 IAgree completely -0.5346 -13.09
Know more than IDZNTKNO2 IAgree mostly -0.2893 -11.81
most about DZNTKNO3 IAgree somewhat -0.0947 -6.19
552.36 0.0000
nutrition DZNTKNO4 INeither 0.0991 7.21
(NTKNO) DZNTKNO5 IDisagree somewhat 0.2986 12.55
ZNTKNO6 Disagree mostly 0.4733 13.94
Adult female on DZDTFE21 Yes -0.2347 -14.96
diet 223.88 0.0000
(DTFE2) IZDTFE22 INo 0.0969 14.96
DZFDFCH1 Always encourage 0.1955 3.34
DZFDFCH2 Almost always encourage 0.1454 3.36
Eating fried DZFDFCH3 Sometimes encourage 0.1349 5.63
chicken 142.38 0.0000
DZFDFCH4 Neither 0.0687 4.97
(FDFCH)
DZFDFCH5 Sometimes discourage -0.0597 -3.18
ZFDFCH6 Almost always discourage -0.2589 -10.78
DZFDHOT1 Always encourage 0.1452 2.11
DZFDHOT2 IAlmost always encourage 0.1486 2.97
Eating hot dog DZFDHOT3 Sometimes encourage 0.0814 3.57
sandwich 64.76 0.0000
DZFDHOT4 Neither 0.0409 3.66
(FDHOT)
DZFDHOT5 Sometimes discourage -0.0787 -3.78
ZFDHOT6 Almost always discourage -0.1735 -6.35
DZFDLUN 1 Always encourage 0.1115 2.16
DZFDLUN2 IAlmost always encourage -0.0410 -1.20
Eating lunchmeat IDZFDLUN3 Sometimes encourage -0.0053 -0.25
15.50 0.0084
(FDLUN) DZFDLUN4 INeither 0.0237 1.79
DZFDLUN5 Sometimes discourage -0.0047 -0.21
ZFDLUN6 Almost always discourage -0.0925 -2.78









Table 5-7. Continued
Dependent var able: ATLAB 1994-2003 (15,083 obs) Condition number = 39
Explanatory Dummy Scaled R-sq = 0 .45
variables variables Dsrpi Coeff t-stats Wald [P-value
DZFDPIZ l Always encourage 0.0238 0.60
DZFDPIZ2 Almost always encourage 0.0016 0.06
Eating pizza DZFDPIZ3 Sometimes encourage 0.0005 0.03
0.92 0.9685
(FDPIZ) DZFDPIZ4 INeither 0.0014 0.11
DZFDPIZ 5 Sometimes discourage -0.0255 -0.76
ZFDPIZ6 Almost always discourage -0.0209 -0.33
DZFD TAC 1 Always encourage 0.1132 2.25
DZFDTAC2 Almost always encourage 0.0222 0.69
Eating tacos DZFDTAC3 Sometimes encourage -0.0132 -0.65
8.04 0.1540
(FDTAC) DZFDTAC4 INeither 0.0011 0.09
DZFDTAC5 Sometimes discourage -0.0090 -0.29
ZFDTAC6 Almost always discourage -0.0655 -1.67
DZATFOR1 Agree completely -0. 1647 -7.77
DZATFOR2 Agree mostly -0.0668 -3.55
Avoid foreign DZATFOR3 Agree somewhat 0.0179 1.13
food 101.58 0.0000
DZATFOR4 INeither 0.0583 2.67
(ATFOR)
DZATFOR5 Disagree somewhat 0.0949 3.76
ZATFOR6 Disagree mostly 0.1472 5.84
DZATDOC1 IAgree completely -0.4517 -13.45
DZATDOC2 IAgree mostly -0.1701 -6.05
Doctor gives DADC Ageso wht-0.0276 -1.46
advice on diet 313.98 0.0000
DZATDOC4 Neither -0.0153 -0.92
(ATDOC)
DZATDOC5 IDisagree somewhat 0.1346 5.01
ZATDOC6 Disagree mostly 0.2022 12.83
DZNTADD I Agree completely -0.1800 -6.41

A person should DZNTADD2 IAgree mostly -0.0480 -2.18
be cautious about IDZNTADD3 IAgree somewhat 0.0904 4.27
55.07 0.0000
additives DZNTADD4 Neither 0.1366 4.55
(NTADD) DZNTADD5 IDisagree somewhat 0.2689 3.88
ZNTADD6 Disagree mostly 0.2192 2.01









Table 5-7. Continued
Dependent var able: ATLAB 1994-2003 (15,083 obs) Condition number = 39
Explanatory Dummy Scaled R-sq = 0 .45
variables variables Dsrpi Coeff t-stats Wald [P-value
DZNTCHL 1 Agree completely -0.0969 -4.34

A person should DZNTCHL2 Agree mostly 0.0140 0.75
be cautious about IDZNTCHL3 Agree somewhat 0.0383 1.87
23.45 0.0003
cholesterol DZNTCHL4 INeither 0.0960 2.71
(NTCHL) DZNTCHL5 Disagree somewhat 0.0746 1.03
ZNTCHL6 Disagree mostly 0.2815 2.47
DZNTFAT 1 Agree completely -0.1323 -6.55

A person should DZNTFAT2 Agree mostly 0.0009 0.04
be cautious about IDZNTFAT3 Agree somewhat 0.1142 4.97
52.69 0.0000
fat DZNTFAT4 INeither 0.1886 4.62
(NTFAT) DZNTFAT5 Disagree somewhat 0.2043 2.64
ZNTFAT6 Disagree mostly 0.0001 0.00
DZNTPRE 1 Agree completely -0.0237 -0.81

A person should DZNTPRE2 Agree mostly -0.0146 -0.65
be cautious about IDZNTPRE3 Agree somewhat 0.0111 0.54
1.25 0.9404
preservatives DZNTPRE4 INeither 0.0216 0.77
(NTPRE) DZNTPRE5 Disagree somewhat 0.0303 0.50
ZNTPRE6 Disagree mostly 0.0353 0.34
DZNTSAL 1 Agree completely -0.0463 -2.10

A person should DZNTSAL2 Agree mostly 0.0066 0.36
be cautious about IDZNTSAL3 Agree somewhat 0.0204 1.08
6.59 0.2530
salt DZNTSAL4 INeither 0.0539 1.67
(NTSAL) DZNTSAL5 Disagree somewhat 0.0271 0.44
ZNTSAL6 Disagree mostly -0.0774 -0.84
DZNTSUG1 Agree completely 0.0506 1.82

A person should DZNTSUG2 Agree mostly 0.0325 1.50
be cautious about IDZNTSUG3 Agree somewhat -0.0422 -2.93
11.81 0.0374
sugar DZNTSUG4 Neither -0.0164 -0.74
(NTSUG) DZNTSUG5 Disagree somewhat -0.0052 -0.13
ZNTSUG6 Disagree mostly 0.0940 1.40









Table 5-7. Continued
Dependent variable: ATLAB 1994-2003 (15,083 obs) Condition number = 39
Explanatory Dummy Scaled R-sq = 0 .49
.aibe .aibe Description
varibles variblesCoeff t-stats Wald [P-value
DZNTVIT 1 Agree completely 0.0603 1.70
Vitamins DZNTVIT2 Agree mostly 0. 1027 3.08
recommended by IDZNTVIT3 Agree somewhat 0.0518 2.28
32.85 0.0000
physician DZNTVIT4 INeither 0.0214 1.33
(NTVIT) DZNTVIT5 Disagree somewhat -0.0357 -2.27
ZNTVIT6 Disagree mostly -0.0799 -4.08
DZZQTR1 First quarter 0.0182 1.16
Quarters DZZQTR2 Second quarter -0.0238 -1.43
6.89 0.0756
(ZQTR) DZZQTR3 Third quarter -0.0248 -1.60
ZZQTR34 Fourth quarter 0.0273 1.81
MU3 0.8546 66.38
MU4 Thehls1.6615 97.91
MU5 2.1816 111.15
MU6 2.7422 116.54









Table 5-8. Wald test for the coefficients of the sequential Ordered Probit model for the periods
1984-1993 and 1994-2003
Dependent variable: ATLAB 1984 -1993 1994. 2003
Explanatory variables Wald P-value Wald P-value
Age of female head (DMAGE) 104.42 0.0000 92.33 0.00
Children under 18 years (DMCHL) 0.091 *0.7617 8.34 0.0039
Education of female head (DMEDU) 6.321 *0.0968 13.25 0.0041
Employment of female head (DMFEM) 16.18 0.0001 21.02 0.00
Household size (DMHSZ) 14.26 0.0026 9.26 0.0261
Household income (DMIN2) 2.471 *0.4802 3.341 *0.3419
Census region (DMREG) 13.421 *0.0981 32.67 0.0001
Conscious of calories (ATCAL) 723.38 0.0000 1224.26 0.00
Like to lose 20 pounds (ATLB S) 31.56 0.0000 13.46 0.0194
Love to swim (ATSWM) 5.761 *0.3300 21.06 0.0008
Overweight isn't attractive (ATWGT) 65.69 0.0000 53.16 0.00
Betknown brands are highest quality (NTBRN) 18.81 0.0021 34.71 0.00
Food should have body building ingredients (NTING) 11.09 0.0495 18.15 0.0028
Know more than most about nutrition (NTKNO) 430.53 0.0000 552.36 0.00
Adult female on diet (DTFE2) 166.83 0.0000 223.88 0.00
Eaig fried chicken (FDFCH) 90.90 0.0000 142.38 0.00
Eating hot dog sandwich (FDHOT) 102.16 0.0000 64.76 0.00
Eating lunchmeat (FDLUN) 59.92 0.0000 15.50 0.008
Eaigpizza (FDPIZ) 6.041 *0.3019 0.921 *0.9685
Eaing tacos (FDTAC) 4.851 *0.4348 8.041 *0.1540
Avoid foreign food (ATFOR) 65.94 0.0000 101.58 0.00
Doctor gives advice on diet (ATDOC) 428.89 0.0000 313.98 0.00
Person should be cautious about additives (NTADD) 58.18 0.0000 55.07 0.00
Person should be cautious about cholesterol (NTCHL) 4.811 *0.4389 23.45 0.0003
A person should be cautious about fat (NTFAT) 47.71 0.0000 52.69 0.00
A person should be cautious about preservatives (NTPRE) 33.18 0.0000 1.25 0.940
Person should be cautious about salt (NTSAL) 2.541 *0.7708 6.59 0.2530
Person should be cautious about sugar (NTSUG) 6.961 *0.2237 11.81 0.0374
Vitamins recommended by physician (NTVIT) 15.32 0.0091 32.85 0.00
Quarters (ZQTR) 3.43 0.3306 6.891 *0.0756
* Not statistically significant at least at the 5% level












Probability of reading food label
1.00
OFPLAB Completely agree (1) I FPLAB Mostly agree (2)
O3ATLAB Completely agree (1) I ATLAB Mostly agree (2)

0.80


0.63
0.59
0.60




0.40




0.20


I check the labels
for harmful ingredients (ATLAB)


I read the labels for
my food purchase (FPLAB)


Average consumer


Figure 5-1. Probability of reading food labels by the average consumer




































"


Probability of reading food label


1.00 -


OFPLAB Completely agree (1) OFPLAB Mostly agree (2)
BATLAB Completely agree (1)OATLAB Mostly agree (2)


0.80




0.60 -




0.40 -




0.20




0.00 '


0.70
0.67
I 0.64


0.66


0.63
II II


<35 35-44 45-54 55-64 65 +


<35 35-44 45-54 55-64 65 +


Age of female head of household



Figure 5-2. Demographics impact on reading food labels. A)Age of female head of household.
B) Children under 18. C) Education of female head of household. D) Employment of
female head of household. E) Household size (members). F) Household income. G)
Census region.





































Children No children Children No children

Presence of children under 18
B






Probability of reading food label

OFPLAB Completely agree (1) OFPLAB Mostly agree (2)
OATLAB Completely agree (1) DATLAB Mostly agree (2)



n~n 00 06 00 0.62 0.64 0.63


Probability of reading food label


1.00 1



0.80



0.60



0.40 -



0.20 -


OFPLAB Completely agree (1) MFPLAB Mostly agree (2)
MATLAB Completely agree (1) MATLAB Mostly agree (2)


0.63


0.62


0.61


n ~7


No high
school


High
school


Some
college


College
graduate


No high
school


High
school


Some
college


College
graduate


Education of female head of household
C


Figure 5-2. Continued



































I~ I~


Probability of reading food label


1.00



0.80



0.60



0.40



0.20


OFPLAB Completely agree (1) ~FPLAB Mostly agree (2)
MATLAB Completely agree (1) _ATLAB Mostly agree (2)


0.64


0.62


0.61


ii E~


Employed


Not employed


Not employed


Employed


Employment of female head of household
D


Probability of reading food label


OFPLAB Completely agree (1) IFPLAB Mostly agree (2)
JATLAB Completely agree (1) JATLAB Mostly agree (2)


1 2 3-4 5 + 1 2 3-4 5


Household size (members)
E


Figure 5-2. Continued




































Under $30,000 $50,000 $70,000 Under 30,000 50,000 70,000
$30,000 49,999 69,999 100,000 + 30,000 49,999 69,999 100,000 +

Household income








Probability of reading food label

OFPLAB Completely agree (1) ~FPLAB Mostly agree (2)
E~TLAB Completely agree (1) ~ATLAB Mostly agree (2)



0.65 0.66 0.67


Probability of reading food label

OFPLAB Completely agree (1)
EMATLAB Completely agree (1)


1.00


MFPLAB Mostly agree (2)
liATLAB Mostly agree (2)


0.80-



0.60



0.40



0.20-


0.64


0.64 0.62
0.60


0.61


n so


n 49


NE MA ECN WNC SA ESC WSC MTN PAC


NE MA ECN WNC SA ESC WSC MTN PAC


Census region
G


Figure 5-2. Continued











O7FPLAB Completely agree (I FPLAB Mostly agree (2)
M~ATLAB Completely agree (I ATLAB Mostly agree (2)
Probability of reading food label


1.00


0.80


0.60


0.40


0.20


0.00


Conscious of calories



Figure 5-3. Attitudes impact on reading food labels. A) Conscious of calories. B) Like to lose
20 pounds. C) Love to swim. D) Overweight isn't attractive. E) Best known brands are
highest quality. F) Food should have body building ingredients. G) Know more than
most about nutrition






























I~ I~ I~ I~ I~ I~ I~ I~ I~ I~ I~ I~


I II I I I I I I I I I


Probability of reading food label


1.())


().8()


().6() -


().4()


OFPLAB Completely agree (1) ~FPLAB Mostly agree (2)
L~TLAB Completely agree (1) ~ATLAB Mostly agree (2)


().2 (.62 ().2 (.64 ().64 ().64


0 ~ 0 9 0 (9 6) ().61 ().61


~c~i" iC~~
~e I
,0~~\~ N~ \- ,ge~ `~ ~
c C$3 i ~o` o`


~e ,u~ I
~CC;
cO C$3 i ~oi ~o`


Like to lose 2() lbs
B


Probability of reading food label


1.())


().8()


().6() -


().4()


OFPLAB Completely agree (1) FPLAB Mostly agree (2)
lilATLAB Completely agree (1) ~ATLAB Mostly agree (2)


().61 ().61 ().62 ().64 ().63 ().64
-I


().61 ().61
().58 ().58 ().58 ().59
I11


Si j
~ ;
c~"'i ;~-~ ;~"; ; 33`~;, o`


~s ~oa2~r "
~i~' ~S~~ .~-
... C3j~jS Pc
r~C~ ..


Love to swim
C


Figure 5-3. Continued













OFPLAB Completely agree (1) IFPLAB Mostly agree (2)
~ATLAB Completely agree (1) DATLAB Mostly agree (2)


Probability of reading food label
' 7PA opeeyare(1 F LB -Msl ge 2
OFPLAB Completely agree (1) OFPLAB Mostly agree (2)


r _ _ __ _ _ _


Probability of reading food label


1.00 -


0.80


0.60


0.40


0.20


0.00 1


a 0.640.66 0.66 0.67


0.63 0.63 0.65


~
~i

CO q q64~~ 64~~


si u
~- ~~~: ~
c0 "s d~ d~


Overweight isn't attractive
D


1.00


0.80


0.60


0.40


0.20


0.66


0.63
0 ~4 0 ~q 0.60 0.60


a4


~ -
Q ,.,:-he
c` c; ~0`


si n~~ ~- ~i
icc~ n'- .~:-.he
oc~: r 4". ;
C O


Best known brands are highest quality
E


Figure 5-3. Continued












Probability of reading food label


1.00



0.80







0.40



0.20



0.00


OFPLAB Completely agree (1) ~FPLAB Mostly agree (2)
MATLAB Completely agree (1) ~ATLAB Mostly agree (2)


0.68
0.65 n no


0 64


(JII (JII
I II I


~~
o~Y,~ icc~~o~o~'6


~i _i
\u-
i i
C` '.c~J~C; i


Food should have body building ingredients
F


Probability of reading food label


081.00







0.60



0.40



0.20



0.00


OFPLAB Completely agree (1) ~FPLAB Mostly agree (2)
BATLAB Completely agree (1) ~ATLAB Mostly agree (2)
-4 -76 - - -- ( I

S0.67


0.50

- l I I


Zh~~ ~-
icc~

ocC; y
C O


t~ ~. ,2~
c~JO;cc~ \1o`
c` c; ~o`


Know more than most about nutrition

G


Figure 5-3. Continued


































Y '


Probability of reading food label

O7FPLAB Completely agree (1 FPLAB Mostly agree (2)
OATLAB Completely agree (I ;;;;ATLAB Mostly agree (2)


1.00


0.80



0.60



0.40



0.20



0.00


0.71


0.68


0.59


"I "'


Diet


No diet


No diet


Diet


Adult female on diet



Figure 5-4. Eating habits impact on reading food labels. A) Adult female on diet. B) Eating
fried chicken. C) Eating hot dog. D) Eating lunchmeat. E) Eating pizza. F) Eating tacos.
G) Avoid foreign food. H) Try fast food places. I) Visit restaurants more than most.




























rrrrrr rrrrr


Probability of reading food label
' A LB -Cmltl ge 1 A LB -Msl ge 2
IMATLAB Completely agree (1) DATPLAB Mostly agree (2)

0.70 0.71


1.00


0.80


0.60


0.40


0.20


0.00


r, it _C~ ~"~;5"
2-~ ~-
P~ ~iS

P~


";"- ~~".~- ~ :-
p~:L~l
~- ~\
~ccP~:Is~sSc~,~Z;d~S`


Eating fried chicken
B


Probability of reading food label


1.00-


0.80





0.40


0.20


VVV


OFPLAB Completely agree (1) ~FPLAB Mostly agree (2)
CATLAB Completely agree (1) ~ATLAB Mostly agree (2)


0.70
n a


0.67


i i I ( i i i I ( i (JII
I I I


II' II
I I
I II I


2'"
p~t;;p*'~~PT~t~J~;~'c~C: P~`d~~c~C:


.~ ~` it
; ~ ~
\
~J: ~,t"\3 5"~"` J~ c~o~ c~
C'p o" T; ~'\~


Eating hot dog
C


Figure 5-4. Continued












OFPLAB Completely agree (1) IFPLAB Mostly agree (2)
~ATLAB Completely agree (1) MATLAB Mostly agree (2)


I I I I II I II II I
I I~ I~ I~ I~ I~ I~


Probability of reading food label


1.00 -

0.80


0.60


0.63 0.63 0.63 0.65
0.9 0.61


0.61 0.63
0.60 0.58 0.59


n ~h


~ ~pS" ~" .~- ~ 3Q~i~5~"
p:o3C"i:k'$ :,'$ .s
5~
P


~ p ~ d~~31~p~.o
~pS"~,e .~- ;c~7,s~,~
P~ ~ia~
(o~t~ ~ Y~:oO a


Eating lunchmeat
D


Probability of reading food label


1.00


0.80


0.60


0.40


0.20


0.00 -


OFPLAB Completely agree (1) IFPLAB Mostly agree (2)
~ATLAB Completely agree (1) IATLAB Mostly agree (2)


0.69


n ~~


() ~I () ~I () ~I () ~I


~ 4 iC 4
2".

c~lo~;tCC~` ~~EO y ;~C-O"i'-l, B *iui~u~i


~ ^~e~ -i ,e-
, s-"
jo"~:;t~"~` c~'~,J~:o"~:` s-' o"~';~"O
"~`"`~p~ i,
i,


Eating pizza
E


Figure 5-4. Continued



































Eating tacos





Probability of reading food label

OFPLAB Completely agree (1) ~FPLAB Mostly agree (2)
MATLAB Completely agree (1) ~ATLAB Mostly agree (2)

0.65 0.66 0.65
n 6 0.62 n 6l n F~n n c~n


Probability of reading food label


1.00


0.80


O3FPLAB Completely agree (1) ~FPLAB Mostly agree (2)
~ATLAB Completely agree (1) ~ATLAB Mostly agree (2)


0.60 062 0.64 0.63 063 0.64
II II II


0.58 0.60 0.59 061 0.61
I II


n


1~
3C~~
\~~N' \~ \~
~ ~ ~ ~CC~ \-
~ ,, "O o` ~o`


1~
,- ~
~,,,,


Avoid foreign food
G


Figure 5-4. Continued













































OFPLAB Completely agree (1) IFPLAB Mostly agree (2)
UATLAB Completely agree (1) ~ATLAB Mostly agree (2)


Probability of reading food label


1.00


0.80 -


0.60 -


0.40


0.20


0.00


O3FPLAB Completely agree (1) IFPLAB Mostly agree (2)
~ATLAB Completely agree (1) JATLAB Mostly agree (2)

0.72


"'- .63 .65 0.61 0.62
I I I


()ld,,3 ()..1 (J


I I"


..it si
~
-~,:~-
1~' 2~? ~?c~'Oli ~? T
C 64~~


..it s

~
C` d'"


Try fast food places
H


Probability of reading food label


1.00


0.80


0.60


0.40


0.20


0.00


0.71
0.67.6 0.650.6


i i I
i i I ( i i i I ( i
I I


'"


~i ~ ~ ~i.;
~ ~- .~
~tC;
cO~ t ; '


~i ~i ~ ~i .;
~CC; r'
,,I ~3 i c~JOo'- ~o`


Visit restaurant more than most
I


Figure 5-4. Continued































-r -r -r


Probability of reading food label
SOFPLAB Completely agree (1) OFPLAB Mostly agree (2)
OATLAB Completely agree (1) OATLAB Mostly agree (2)

0 73


1.00


0.80


0.60


0.40


0.20


I


n ~7


i i I II


0.64 0.63


i i I It i i i I It i


0.58 0.58


n on


ILe ~LI~9~~3";~~"" ,~
~ o~
.,s~ o~1 P"
c p p :~~a~
o o


IL~i ~ `\ 2~
o~i j~-~ -t- o~sel:~,lal~ee~~~e~e~
~~-~e~~-~"e~
cO CP "j ~04*~1 64*~1


Doctor gives advice on diet



Figure 5-5. Health Concern impacts on reading food labels. A) Doctor give advice on diet.
B) A person should be cautious about additives. C) A person should be cautious
about cholesterol. D) A person should be cautious about fat. E) A person should be
cautious about preservatives. F) A person should be cautious about salt. G) A person
should be cautious about sugar. H) Vitamins recommended by physician.






























I'~ '~ '~ '~ '~ '~


'
'~ '~ '~ '~ '~ '~


Probability of reading food label


1.())


().8()


().6()


().4()


O7FPLAB Completely agree (1) IFPLAB Mostly agree (2)
~ATLAB Completely agree (1) IATLAB Mostly agree (2)


().66 ().6706


c~i C~- iC Zh~~
~5-~" ~- L
-
oc~!
q i qd4~~ 64~~


xSi~
~2~
~s~ - L
~t~ ,-
, 'q i qd4~~ 64~~


A person should be cautious about additives
B


Probability of reading food label


1.())


().8()


().6()


().4()


OFPLAB Completely agree (1) IFPLAB Mostly agree (2)
MATLAB Completely agree (1) JATLAB Mostly agree (2)


().64


(): 62(.62
().59


().62


a


( I" II


~~ "
,a~, :' ~: ~u
CO 'Y ~O\


io~ 5~P~3'
CO 'Y ~O\


A person should be cautious about cholesterol
C


Figure 5-5. Continued












Probability of reading food label


1.00



0.80



0.60



0.40



0.20



0.00


O3FPLAB Completely agree (1) MFPLAB Mostly agree (2)
JATLAB Completely agree (1) MATLAB Mostly agree (2)


0.67
0.64 -


n h?


~1~ ~_ ~ ~_
~o ~o C. .\-
~ ~CS31j~' 0~7
\~N' \~ \~
,c~
~31C; ~
i i i
c`i i i ~o` c` ~o`

A person should be cautious about fat
D


Probability of reading food label


1.00



0.80



0.60 -



0.40



0.20


OFPLAB Completely agree (1) ~FPLAB Mostly agree (2)
LATLAB Completely agree (1) ~ATLAB Mostly agree (2)


0.67


n 62. n 1;? e ,, 0.63


n .1 ,, -,, n .1
II I


~

~-:1 '~-:~'5-1:
cO ~b;'` a


cO c; ~o`


A person should be cautious about preservatives
E


Figure 5-5. Continued











































OFPLAB Completely agree (1) IFPLAB Mostly agree (2)
LATLAB Completely agree (1) DATLAB Mostly agree (2)


Probability of reading food label


1.())


().8()






().4()


O3FPLAB Completely agree (1) MFPLAB Mostly agree (2)
JATLAB Completely agree (1) MATLAB Mostly agree (2)


n l (.65


()64 ( 62 ().63 n 6l ()66 (.62


n l


~c~i"lC~~
~i
~CC;
~ 3 i ~Ooi ~o`


I
W c~ c~- \-
c0 "J3 i ~O oi ~o`


A person should be cautious about salt
F


Probability of reading food label


1.())


().8()


().6()


().4()


().61 ().63 ().64 ().64 ().63


().61 ().61 ().6()


n E~


1C
~:,-~ ,- ~
~ i
c\ So' ~o`


~"
,- ~:,j~e~`
,t~ "'
C` i ~o` o`


A person should be cautious about sugar
G


Figure 5-5. Continued




























1 I I I I I I I I I I


Probability of reading food label


1.00


0.80-


0.60 -


0.40


0.20-


O3FPLAB Completely agree (1) ~FPLAB Mostly agree (2)
~ATLAB Completely agree (1) ~ATLAB Mostly agree (2)


0.66
0.2 0.60 0.62 0.63 0.62


0 ~ 0~s0.59 0.600.3
n- I


1~
.. ~- .. L
o~ -- ~SO:\- -- c~ -
,, ; ~,,,~,,
C` i d'" d'"


1~
~I ,-
"` -~ ,::-
~((j~iC- ~( T- L>OIC~i d~


Vitamins recommended by physician
H


Figure 5-5. Continued


Probability of reading food label

OFPLAB Completely agree (1)
DATLAB Completely agree (1)


Quarter Quarter Quarter Quarter Quarter Quarter Quarter Quarter
1 2 3 4 1 2 3 4


Seasonality


Figure 5-6. Impact of seasonality on reading food labels
















- --- .1-1 1-087

- - - .5 2 I1 I 6 -
- -- --- -- -- .49 C I 8 070 ---
- - .47 I 1-0.6 6 - -
- ~ ~ ~ 0 8 ---0 4 .64 ---------
- - - .5 2 0~ .6 7 - -
- - - 0 5 08.64 -
---- ---- ---- .49 C 7 0;63 -- -
- - - 53- -0 1 .66 - -
- - - - 5 6 0 6 -
--- -- --- --- 0;53f 1 0;65 --

----- --- ---- 0;57C~ tO 65 -
- - - 0 56 1 0 .6 3 - - -

------ ~8.5 -- -- 5 0;61 -- -

-- -- -- -0 5 0 2 -- --
-- -- -- -- 0 5 5 0.6 --- --- --
- - 0;56 0;63 - -
- - .5 5 -0 .6 1 - - -
- - 0 5 7 0~ .6 1 - -



- - - .5 8 0 ~ .61 - -



- - .5 8 e. 6 1 - - -
------ -------- 58 0 61 --- -- --
- - - 0;59 47 62 - -

- - - 5 8 -01.6 1 - -


ATLAB 1993-03
range
I I 0.56
I I 0.34
-I I 0.24
-I 10.20
CI 1 0.19
C I 0.16
C 1 0.15
CI I 0.14
CI I 0.13
CI 1 0.13
C I 0.13
C I 0.12
C 1 0.10
CI 10.08
-~ 0.08
C 1 0.08
1 0.07
-1 0.07
II 0.07
C"I 0.06
1 0.06
1o .os
Cl 0.05
Cl 0.04
-10.04
O 0.04
O 0.03
O~ 0.03
-O 0.03
O( 0.03
O 0.03
O7 0.03


Conscious of calories
Know more than most
Doctor gives advice on diet
Eating fried chicken
Cautious about additives
Cautious about cholesterol
Eating hot dog
Cautious about fat
Best known brands
Age of female head
Adult female on diet
Avoid foreign food
Overweight isn't attractive
Cautious about salt
Body building ingredients
Vitamins by physician
Education of female head
Census region
Eating tacos
Eatinglunchmeat
Cautious about sugar
Household size
Cautious about preservatives
Visit restaurants
Household income
Love to swim
Children under l8 years
Quarters (seasonality)
Like to lose 20 pounds
Eating pizza
Employment of female head
Try fast food places


0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00

Percent adjustment to Check Labels for Harmful Ingredients (ATLAB) of the average consumer
(Average ATLAB = .59)



Figure 5-7. Ranking of factors impacting the likelihood of reading food labels for harmful

ingredients.




























































_____ ____


FPLAB 1993-2003
range
-I I 0.44
-I I 0.26
I I0.15
0.15
- 0 0.15
- 0 0.14
-00.13
- 1 0.13
- 0 0.12
-00.12
-00.12
- 0 0.12
- 0 0.09
D0.09
- ED 0.09
- 00 0.08
D0.07
0.07
- 1 0.06
- 1 0.06
-[ 0.06
-O 0.05
-O 0.05
10.05
-O0.04
-O 0.04
-O 0.04
-O0.03
-O0.03
-O 0.03
-O0.02
-O0.02

.00


- 0 41 I t0.86 --
- - .50 1 1-0. 77 - -
- - 052 1 1 4;67 - - -
- - 0 58 I 0 ~.73 - -
- - - 0;56 I I 0. 71 - -
-- -- -- -- -056 1 I01 7 0 -
--- -- --- -- .57 0~77 0 -
- - 4 5 3 0 .6 6 - - -
- - - 0.54 1 0.66 - -

- - - -0 5 9 0~ .7 1 - -
--- -- --- ---.6f1 0 7 2 -- -
- - .6 1 -1 I 0 7 1 - -
- - .5 9 .6 - -
- - 0 5 8 1 0.6 7 - -
- - - 0.4 1 01 I .69 - -
-- --- -- --0 601 0.~6 7 -

--- --- --- -- 0 60 1 t0.6 6 - -

- - - .59 1 l 0.6 5 - -
- - 0 .6 0 0 6 6 - - -
- - .6 1 -@.6 6 - -

- - 0.6 f 1 0 65 - -

--- --- --- -- 0 60 07] 64 - -

- - - 0 ;60 0.6 4 - -
- - - 60 [ 0 6 4 - -
- - 0 62 0.8 64 - -
- - - 0 1 0 + - -


1


Conscious of calories
Know more than most
Cautious about additives
Doctor gives advice on diet
Eating fried chicken
Age offemale head
Eating hot dog
Cautious about cholesterol
Best known brands
Cautious about fat
Adult female on diet
Try fast food places
Visit restaurants
Body building ingredients
Census region
Eating pizza
Overweight isn't attractive
Cautious about preservatives
Cautious about sugar
Avoid foreign food
Eating lunchmeat
Vitamins by physician
Cautious about salt
Quarters (seasonality)
Household size
Education of female head
Eating tacos
Household income
Like to lose 20 pounds
Love to swim
Employment of female head
Children under 18 years


0.20 0.30 0.40 0.50


0.60 0.70 0.80 0.90


Percent adj ustment to Food Purchase for Labels (FPLAB) of the average consumer
(Average FPLAB = .63)



Figure 5-8. Ranking of factors impacting the likelihood of reading food labels for food

purchase.














Range of changes in probabilities of scores 1 & 2

0.60
OFPLAB range IATLAB range


0.50



0.40



0.30



0.20



0.10



0.00


Figure 5-9. Range of change in probabilities for ATLAB and FPLAB.











Probability for "I check the label for harmful ingredients" (ATLAB)
1 Average Consumer
L ~ Completely agree (1) EMostly agree (2)


1.00

0.90

0.80

0.70

0.60

0.50

0.40

0.30

0.20

0.10

0.00



Mostly agree (2)
Completely agree (1)


1990
1999
0.33


1991
2000
0.33


1992 1993 1994
2001 2002 2003
0.33 0.32 0.32
0.27 0.26 0.26


1984 1985 1986 1987 1988 1989
1993 1994 1995 1996 1997 1998
0.30 0.32 0.32 0.33 0.34 0.34
0.30 0.31 0.31 0.31 0.31 0.30


0.30 0.29


Figure 5-10. Change over time in the likelihood of reading food labels to check for harmful
ingredients for the average consumer. A) Completely agree (1) and Mostly agree (2).
B) Somewhat agree (3), Neither (4), Somewhat disagree (5), and Mostly disagree (6).











Probability for "I check the label for harmful ingredients" (ATLAB)

Average Consumer
E Somewhat agree (3) MINeither (4)
Somewhat disagree (5) M Mostly disagree (6)


_ _ _
r


1.00

0.90

0.80

0.70

0.60

0.50

0.40

0.30

0.20

0.10

0.00


1988 1989
1997 1998
0.01 0.01
0.03 0.03
0.07 0.08
0.24 0.24


1990 1991
1999 2000


1992
2001


1993
2002


1994
2003
0.02
0.04
0.09
0.26


Mostly disagree (6)
Somewhat disagree (5)
Neither (4)
Somewhat agree (3)


Figure 5-10. Continued


0.02 0.02 0.02 0.01
0.04 0.04 0.03 0.03
0.09 0.08 0.08 0.08
0.24 0.24 0.24 0.23


0.01 0.01 0.01 0.02
0.03 0.04 0.04 0.04
0.08 0.08 0.09 0.09
0.25 0.25 0.26 0.26


U--
1984
1993


--11


--
1985
1994





































Y


Probability for "I Check the Label for Harmful Ingredients" (ATLAB)
Completely agree (1) & Mostly agree (2) combined

Age of female head of household
---- --- --- --- I=<3 yers () M65+ years (5)


1.00

0.90

0.80

0.70

0.60

0.50

0.40

0.30

0.20

0.10





:<35 years (1)
65+ years (5)


1986
1995
0.57
0.69


1987
1996
0.58
0.71


1988
1997
0.59
0.70


1991
2000
0.56
0.68


1992
2001
0.54
0.67


1993
2002
0.52
0.66


1984
1993
0.54
0.67


1985
1994
0.56
0.69


1989
1998
0.59
0.70


1990
1999
0.57
0.69


1994
2003
0.52
0.65


Figure 5-11. Change over time in the impact of demographics on reading food labels to check
for harmful ingredients. A) Age of female head of household. B) Children under 18.
C) Education of female head of household. D) Employment of female head of
household. E) Household size (members). F) Household income. G) Census regions:
New England (1), Middle Atlantic (2) and East North Central (3). H) Census regions:
West North Central (4), South Atlantic (5) and East South Central (6). I) Census
regions: West South Central (7), Mountain (8) and Pacific (9).







































U.UU
1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994
1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003
Yes (1) 0.61 0.63 0.64 0.64 0.63 0.63 0.61 0.59 0.58 0.56 0.55
No (2) 0.60 0.62 0.63 0.65 0.65 0.65 0.64 0.63 0.61 0.60 0.59

B





Probability for "I Check the Label for Harmful Ingredients" (ATLAB)
Completely agree (1)& Mostly agree (2) combined
1.00 -
Education of female head of household
0.90 -1 BiNo high school (1) I College graduate (4)

0.80-

0.70-

0.60-

0.50-

0.40-

0.30-

0.20-

0.10-

0.00 *
1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994
1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003
No high school (1) 0.59 0.61 0.63 0.63 0.64 0.63 0.61 0.58 0.55 0.53 0.53

|College graduate (4) 0.60 0.62 0.63 0.64 0.65 0.65 0.64 0.62 0.61 0.60 0.59


Probability for "I Check the Label for Harmful Ingredients" (ATLAB)
Completely agree (1) & liostly agree (2) combined

Presence of children under 18
-- -- ~ e () No (2)


1.00-

0.90 -

0.80

0.70

0.60-

0.50

0.40

0.30

0.20

0.10


Figure 5-11. Continued





































Y


1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994
1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003
Employed (1) 0.59 0.61 0.62 0.63 0.63 0.63 0.62 0.60 0.59 0.57 0.56
Not employed (2) 0.62 0.64 0.65 0.66 0.66 0.66 0.65 0.63 0.62 0.60 0.60

D





Probability for "I Check the Label for Harmful Ingredients" (ATLAB)
Completely agree (1) & Mostly agree (2) combined
1.00 -
Household size (members)
0.90 -1 01member (1) 05+members (4)

0.80-

0.70-

0.60

0.50-

0.40-

0.30-

0.20-

0.10-

0.00 '
1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994
1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003


Probability for "I Check the Label for Harmful Ingredients" (ATLAB)
Completely agree (1) & Mostly agree (2) combined


1.00

0.90

0.80

0.70

0.60

0.50

0.40

0.30

0.20

0.10

0.00


Employment of female head of household
D~Employed (1) ONot employed (2)


1 member (1)
5+ members (4)


0.58 0.59 0.61 0.62 0.61 0.61
0.61 0.62 0.63 0.65 0.66 0.65


0.60 0.58 0.57 0.56 0.56
0.64 0.62 0.61 0.59 0.58


Figure 5-11. Continued




































IILL


under $30,000 (1) 0.61 0.63 0.64 0.65 0.65 0.65 0.64 0.62 0.60 0.59 0.59
$70-100,000+ (4) 0.59 0.60 0.61 0.63 0.63 0.63 0.62 0.60 0.58 0.57 0.56






Probability for "I Check the Label for Harmful Ingredients" (ATLAB)
Completely agree (1) & Mostly agree (2) combined
1.00-
Census region
0.90 -1 ON.E.(1) EM.A.(2) OF.N.C.(3)
0.80-

0.70-

0.60- -

0.50-

0.40-

0.30-

0.20-

0.10-


1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994
1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003


Probability for "I Check the Label for Harmful Ingredients" (ATLAB)
Completely agree (1) & Mostly agree (2) combined


1.00

0.90 -

0.80

0.70

0.60

0.50

0.40

0.30

0.20

0.10

0.00


Household income
O under $30,000 (1) 0$70-100,000+ (4)


1984
1993


1989
1998


1991
2000


1993
2002


1985 1986 1987 1988
1994 1995 1996 1997


1990
1999


1992
2001


1994
2003


N. E. (1)
M. A. (2)
E. N. C. (3)


0.62 0.63
0.62 0.64
0.60 0.61


0.63 0.64 0.64
0.65 0.65 0.64
0.62 0.63 0.62


0.62 0.59 0.57 0.55 0.55
0.63 0.62 0.60 0.59 0.58
0.61 ,0.59 0.58 0.56 I0.55


Figure 5-11. Continued





W. N. C. (4) 0.59 0.61 0.61 0.62 0.62 0.61 0.60 0.59 0.57 0.55 0.55
S. A. (5) 0.61 0.63 0.64 0.65 0.66 0.65 0.65 0.64 0.63 0.61 0.60
E. S. C. (6) 0.61 0.64 0.65 0.66 0.67 0.68 0.67 0.65 0.63 0.61 0.60

H




Probability for "I Check the Label for Harmful Ingredients" (ATLAB)
Completely agree (1)& Mostly agree (2) combined
1.00 -
Census region
0.90
0 W. S. C. (7) OM. (8) O P. (9)
0.80

0.70

0.60 -

0.50

0.40

0.30

0.20

0.10


1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994
1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003
W. S. C. (7) 0.62 0.64 0.65 0.66 0.65 0.64 0.63 0.61 0.61 0.59 0.59
M. (8) 0.63 0.64 0.66 0.67 0.68 0.66 0.65 0.64 0.62 0.60 0.59
P. (9) 0.61 0.62 0.63 0.64 0.64 0.64 0.63 0.62 0.60 0.58 0.58


Probability for "I Check the Label for Harmful Ingredients" (ATLAB)
Completely agree (1) & Mostly agree (2) combined

Census region
OW. N. C. (4) lilS. A. (5) OE. S. C. (6)


1.00-

0.90

0.80-

0.70-

0.60-

0.50-

0.40-

0.30-

0.20-

0.10-

0.00


1984
1993


1985
1994


1986
1995


1987
1996


1988
1997


1989
1998


1990
1999


1991
2000


1992
2001


1993
2002


1994
2003


Figure 5-11. Continued











Probability for "I Check the Label for Harmful Ingredients" (ATLAB)
Completely agree (1) & Mostly agree (2) combined
Conscious of calories
OCompletely agree (1) OMostly disagree (6)


Y


1.00

0.90

0.80

0.70

0.60

0.50

0.40

0.30

0.20

0.10


1986
1995
0.82
0.38


1987
1996
0.84
0.38


1988
1997
0.85
0.37


1989
1998
0.85
0.37


1990
1999


1984
1993
0.78
0.38


1985
1994
0.80
0.38


1991
2000
0.86
0.35


1992
2001
0.86
0.33


1993
2002
0.86
0.32


1994
2003
0.86
0.31


Completely agree (1)
Mostly disagree (6)


Figure 5-12. Change over time in the impact of attitudes on reading food labels to check for
harmful ingredients. A) Conscious of calories. B) Like to lose 20 pounds. C) Love to
swim. D) Overweight isn't attractive. E) Best known brands are highest quality. F) Food
should have body building ingredients. G) Know more than most about nutrition






































1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994
1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003


Completely agree (1) 0.59 0.61 0.62 0.64 0.64 0.63 0.62 0.61 0.59 0.58 0.57
Mostly disagree (6) 0.62 0.64 0.65 0.66 0.67 0.66 0.66 0.64 0.62 0.60 0.60

B





Probability for "I Check the Label for Harmful Ingredients" (ATLAB)
Completely agree (1) & Mostly agree (2) combined
1.00 -
Love to swim
0.90 -1 I Completely agree (1) li lMostly disagree (6)

0.80-

0.70-



0.50-

0.40-

0.30-

0.20-

0.10-

0.00 *
1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994
1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003


Probability for "I Check the Label for Harmful Ingredients" (ATLAB)
Completely agree (1) & Mostly agree (2) combined

Like to lose 20 lbs
li!!Completely agree (1) 0 1ustly disagree (6)


1.00

0.90

0.80

0.70

0.60

0.50

0.40

0.30

0.20

0.10


0.00


Completely agree (1)
|Mostly disagree (6)


0.60 0.61 0.61 0.63 0.63 0.61 0.60 0.59 0.58 0.56 0.56
0.61 0.63 0.64 0.65 0.66 0.65 0.65 0.63 0.62 0.60 0.59


Figure 5-12. Continued




































-r


1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994
1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003
Completely agree (1) 0.57 0.59 0.60 0.61 0.61 0.60 0.59 0.58 0.56 0.54 0.54
Mostly disagree (6) 0.66 0.68 0.68 0.69 0.69 0.68 0.67 0.66 0.65 0.63 0.62

D


Probability for "I Check the Label for Harmful Ingredients" (ATLAB)
Completely agree (1)& Mostly agree (2) combined
1.00 -
Best known brands noe highest quality
0.90 -1 liiCompletely agree (1) O Mostly disagree (6)

0.80-

0.70-

0.60 --

0.50 -

0.40-

0.30-

0.20-

0.10-

0.00 -- --
1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994
1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003
Completely agree (1) 0.64 0.65 0.65 0.64 0.62 0.59 0.57 0.54 0.53 0.50 0.48
Mostly disagree (6) 0.62 0.63 0.65 0.66 0.66 0.65 0.64 0.64 0.62 0.61 0.61


Probability for "I Check the Label for Harmful Ingredients" (ATLAB)
Completely agree (1)& Mostly agree (2) combined


1.00

0.90

0.80

0.70

0.60

0.50

0.40

0.30

0.20

0.10

0.00


Overweight isn't attractive
O~Completely agree (1) liMostly disagree (6)


Figure 5-12. Continued



































1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994
1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003


Completely agree (1) 0.62 0.63 0.65 0.66 0.67 0.66 0.66 0.65 0.64 0.63 0.62
Mostly disagree (6) 0.59 0.61 0.62 0.63 0.63 0.63 0.63 0.61 0.59 0.58 0.58


Probability for "I Check the Label for Harmful Ingredients" (ATLAB)
Completely agree (1)& Mostly agree (2) combined

Food should have body building ingredients
O~Completely agree (1) lilMostly disagree (6)


1.00

0.90

0.80

0.70

0.60

0.50

0.40

0.30

0.20

0.10


I I -r


LII ~I CITC


0.00


Probability for "I Check the Label for Harmful Ingredients" (ATLAB)
Completely agree (1) & Mostly agree (2) combined


Figure 5-12. Continued











Probability for "I Check the Label for Harmful Ingredients" (ATLAB)
Completely agree (1) & Mostly agree (2) combined

Adult female on diet
OYes (1) ONo(2)


"


1.00

0.90

0.80

0.70

0.60

0.50

0.40

0.30

0.20

0.10

0.00


1984
1993
0.68
0.57


1986
1995
0.71
0.60


1987
1996


1988
1997
0.72
0.61


1989
1998
0.72
0.60


1992
2001
0.69
0.56


1985
1994
0.70
0.59


1990
1999


1991
2000
0.70
0.58


1993
2002
0.67
0.55


1994
2003
0.67
0.54


Yes (1)
No (2)


Figure 5-13. Change over time in the impact of eating habits on reading food labels to check
for harmful ingredients. A) Adult female on diet. B) Eating fried chicken. C) Eating hot
dog. D) Eating lunchmeat. E) Eating pizza. F) Eating tacos. G) Avoid foreign food.
H) Try fast food places. I) Visit restaurants more than most.





































IhY LY LY LY LY LY LY LY LY LY LL


1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994
1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003
Always encourage (1) 0.52 0.54 0.56 0.58 0.57 0.56 0.54 0.54 0.53 0.50 0.50
Almost always discourage (6) 0.69 0.71 0.72 0.73 0.74 0.72 0.71 0.70 0.69 0.68 0.68

B





Probability for "I Check the Label for Harmful Ingredients" (ATLAB)
Completely agree (1) & Mostly agree (2) combined
1.00
Eating hot dog
0.90 -r O~Always encourage (1) OAlmost always discourage (6)

0.80

0.70

0.60

0.50

0.40

0.30

0.20

0.10

0.00 -
1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994
1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003
Always encourage (1) 0.56 0.55 0.55 0.58 0.60 0.57 0.55 0.53 0.52 0.53 0.52
|Almost always discourage (6) 0.69 0.70 0.71 0.71 0.72 0.72 0.71 0.69 0.67 0.66 0.65


Probability for "I Check the Label for Harmful Ingredients" (ATLAB)
Completely agree (1) & Mostly agree (2) combined

Eating fried chicken
OAlways encourage (1) DAlmost always discourage (6)


1.00

0.90

0.80

0.70

0.60

0.50

0.40

0.30

0.20

0.10

0.00


I


Figure 5-13. Continued


































IhY LY LY LY LY LY LY LY LY LY LL


Always encourage (1) 0.56 0.58 0.60 0.59 0.59 0.60 0.61 0.58 0.56 0.54 0.54
Almost always discourage (6) 0.68 0.69 0.69 0.71 0.71 0.70 0.68 0.67 0.66 0.63 0.62

D



Probability for "I Check the Label for Harmful Ingredients" (ATLAB)
Completely agree (1) & Mostly agree (2) combined
1.00 -
Eating pizza
0.90 -1 liiAlivays encourage (1) I Almost always discourage (6)

0.80-

0.70-



0.50-

0.40-

0.30-

0.20-

0.10-

0.00 -- -
1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994
1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003
Alivays encourage (1) 0.59 0.61 0.62 0.63 0.63 0.62 0.61 0.60 0.59 0.58 0.57
Almost always discourage (6) 0.58 0.61 0.63 0.63 0.65 0.65 0.66 0.65 0.63 0.60 0.59


Probability for "I Check the Label for Harmful Ingredients" (ATLAB)
Completely agree (1) & Mostly agree (2) combined

Eating lunchmeat
IMIAlways encourage (1) I Almost always discourage (6)


1.00

0.90

0.80

0.70

0.60

0.50

0.40

0.30

0.20

0.10


~ -~


1985
1994


1988
1997


1990
1999


1993
2002


1984
1993


1986 1987
1995 1996


1989
1998


1991 1992
2000 2001


1994
2003


Figure 5-13. Continued












Probability for "I Check the Label for Harmful Ingredients" (ATLAB)
Completely agree (1) & Mostly agree (2) combined

Eating tacos
COAlways encourage (1) liiAlmost always discourage (6)


1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994
1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003


Always encourage (1) 0.63 0.64 0.65 0.64 0.64 0.63 0.62 0.59 0.56 0.54 0.53
Almost always discourage (6) 0.61 0.63 0.65 0.66 0.67 0.66 0.64 0.63 0.61 0.60 0.60


1.00

0.90

0.80

0.70

0.60

0.50

0.40

0.30

0.20

0.10


0.00


Probability for "I Check the Label for Harmful Ingredients" (ATLAB)
Completely agree (1)& Mostly agree (2) combined


Completely agree (1) 0.64 0.66 0.68 0.69 0.70 0.69 0.68 0.67 0.66 0.65 0.64
|Mostly disagree (6) |0.54 |0.56 |0.57 |0.58 |0.58 |0.58 |0.57 |0.56 |0.54 |0.52 |0.52


Figure 5-13. Continued





Probability for "I Check the Label for Harmful Ingredients" (ATLAB)
Completely agree (1) & Mostly agree (2) combined
Doctor gives advice on diet
tICompletely agree (1) EIMostly disagree (6)




nn -~


1.00

0.90

0.80

0.70

0.60



0.40

0.30

0.20

0.10


1984
1993
0.75
0.50


1987
1996


1988
1997
0.79
0.56


1989
1998
0.78
0.56


1992
2001


1993
2002
0.74
0.50


1994
2003
0.74
0.50


1985
1994
0.77
0.52


1986
1995
0.78
0.53


1990
1999
0.77
0.56


1991
2000
0.76
0.54


Completely agree (1)
Mostly disagree (6)


Figure 5-14. Change over time in the impact of health concerns on reading food labels to
check for harmful ingredients. A) Doctor gives advice on diet. B) A person should be
cautious about additives. C) A person should be cautious about cholesterol. D) A person
should be cautious about fat. E) A person should be cautious about preservatives. F) A
person should be cautious about salt. G) A person should be cautious about sugar. H)
Vitamins recommended by physician.



































I _I _I I I _I I 1 _I I
IILL LL LL LL LL LL LL LLI LL LL LL


Completely agree (1) 0.66 0.68 0.69 0.69 0.69 0.68 0.68 0.66 0.66 0.65 0.65
Mostly disagree (6) 0.53 0.59 0.56 0.58 0.55 0.57 0.58 0.57 0.54 0.51 0.49

B




Probability for "I Check the Label for Harmful Ingredients" (ATLAB)
Completely agree (1)& Mostly agree (2) combined
1.00
A person should be cautious about cholesterol
0.90
CJCompletely agree (1) EMMostly disagree (6)
0.80

0.70

0.60

0.50

0.40-

0.30-

0.20

0.10


1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994
1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003
Completely agree (1) 0.62 0.63 0.65 0.66 0.67 0.67 0.67 0.66 0.64 0.62 0.62
|Mostly disagree (6) 0.67 0.64 0.61 0.59 0.59 0.56 0.56 0.53 0.50 0.48 0.47


Probability for "I Check the Label for Harmful Ingredients" (ATLAB)
Completely agree (1)& Mostly agree (2) combined

A person should be cautious about additives
O~Completely agree (1) Mo lstly disagree (6)


1.00

0.90

0.80





0.50

0.40

0.30

0.20

0.10

0.00


1984
1993


1985
1994


1990
1999


1991
2000


1992
2001


1986 1987 1988 1989
1995 1996 1997 1998


1993 1994
2002 2003


Figure 5-14. Continued





Completely agree (1) 0.64 0.66 0.68 0.69 0.69 0.69 0.68 0.67 0.65 0.64 0.63
Mostly disagree (6) 0.64 0.62 0.66 0.64 0.63 0.67 0.63 0.59 0.58 0.56 0.58

D


Probability for "I Check the Label for Harmful Ingredients" (ATLAB)
Completely agree (1) & Mostly agree (2) combined
1.00 -
A person should be cautious about preservatives
0.90 -1 liiCompletely agree (1) lilMostly disagree (6)

0.80-

0.70 -- -

0.60--

0.50-

0.40-

0.30-

0.20-

0.10-

0.00 -- -
1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994
1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003
Completely agree (1) 0.65 0.67 0.68 0.69 0.69 0.68 0.67 0.65 0.62 0.60 0.59
Mostly disagree (6) 0.52 0.56 0.59 0.62 0.63 0.60 0.57 0.56 0.57 0.57 0.57


Probability for "I Check the Label for Harmful Ingredients" (ATLAB)
Completely agree (1)& Mostly agree (2) combined

A person should be cautious about fat
-- -- -- -- -- -DECompletely agree (1) O Jostly disagree (6)


1.00

0.90 -

0.80

0.70-

0.60

0.50

0.40

0.30

0.20

0.10


1985
1994


1989
1998


1990
1999


1993
2002


1994
2003


1984
1993


1986 1987 1988
1995 1996 1997


1991 1992
2000 2001


Figure 5-14. Continued


L, I I I I II I I


L~rCI~LI c





Completely agree (1) 0.61 0.62 0.64 0.64 0.65 0.65 0.64 0.62 0.61 0.60 0.60
Mostly disagree (6) 0.60 0.66 0.64 0.67 0.70 0.66 0.67 0.66 0.63 0.62 0.61





Probability for "I Check the Label for Harmful Ingredients" (ATLAB)
Completely agree (1)& Mostly agree (2) combined
1.00-
A person should be cautious about sugar
0.90 O1 Completely agree (1) I Mostly disagree (6)

0.80-

0.70-

0.60 --

0.50-

0.40-

0.30-

0.20-

0.10-

0.00 *
1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994
1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003
Completely agree (1) 0.62 0.63 0.63 0.64 0.63 0.62 0.61 0.59 0.58 0.56 0.56
Mostly disagree (6) 0.54 0.57 0.62 0.64 0.63 0.63 0.60 0.59 0.59 0.56 0.54


Probability for "I Check the Label for Harmful Ingredients" (ATLAB)
Completely agree (1)& Mostly agree (2) combined


1.00

0.90

0.80

0.70

0.60

0.50

0.40

0.30

0.20

0.10

0.00


A person should be cautious about salt
Ot completely agree (1) OMostly disagree (6)


1985
1994


1986
1995


1990
1999


1991
2000


1992
2001


1984
1993


1987 1988 1989
1996 1997 1998


1993 1994
2002 2003


Figure 5-14. Continued











Probability for "I Check the Label for Harmful Ingredients" (ATLAB)
Completely agree (1) & Mostly agree (2) combined


Figure 5-14. Continued












Probability for "I Check the Label for Harmful Ingredients" (ATLAB)
Completely agree (1) & Mostly agree (2) combined

Seasonality
IEQuarter (1) OQuarter (2)
-- -- I Quarter (3) OQuarter (4)


YLLLY


1.00

0.90 -

0.80 _

0.70

0.60

0.50-

0.40

0.30

0.20

0.10-


0.00


1985
1994


1987
1996


1989
1998


1992
2001


1994
2003


1984
1993


1986
1995


1988
1997


1990 1991
1999 2000


1993
2002


Quarter (1) 0.60 0.62 0.62 0.63 0.63 0.63 0.62 0.61 0.60 0.58 0.57
Quarter (2) 0.61 0.62 0.64 0.65 0.66 0.65 0.64 0.62 0.61 0.59 0.59
Quarter (3) 0.60 0.62 0.64 0.65 0.65 0.65 0.64 0.62 0.61 0.59 0.59
Quarter (4) 0.62 0.63 0.64 0.65 0.64 0.64 0.63 0.61 0.59 0.58 0.57


Figure 5-15. Change over time in the impact of seasonality on reading food labels to check for
harmful ingredients.





ATLAB 1984-1993

range
-1 I0.40
-1 I0.33
-1 I0.26
--I I0.17
-- -I0.15
C """I0.13
-- -]0.13
-- ---] 0.13
-- ---] 0.13
[ ---]0.12
-- ---] 0.11
-C 1"" 0.10
-[ -- 0.09
-[ -- 0.08
-[ -- 0.07
C 0.07
C- 0.06
"" 0.05
-- "" 0.05
[" 0.04
--[" 0.04
CI0.04
00.03
00.03
00.03
--I0.02
--I0.02
-00.02
00.02
-10.00

1.)0


Conscious of calories --
Know more than most --
Doctor gives advice on diet --
Eating fried chicken --
Cautious about additives --
Cautious about preservatives --
Eating hot dog --
Age of female head --
Cautious about fat --

Eating lunchmeat --
Adult female on diet --

Avoid foreign food --
Overweight isn't attractive --
Cautious about sugar --
Cautious about cholesterol --

Like to lose 20 pounds --
Best known brands --
Household size --
Census region --

Vitamins by physician --
Eating pizza --
Eating tacos --

Body building ingredients --
Employment of female head --
Education of female head --
Cautious about salt --
Household income --
Love to swim --

Quarters (seasonality) --
Children under 18 years -

0.30


- .38 1



---.52 1
0 51 C
.52 C


.54 1
.52 1
--------0.56 C
--------0 57 C
.54 C
---057 C
-------0.54 C


---457 C
--- 058 [
--- 058 [
0.59 [
0 58 [
-- -- 0 58 [
.59 {
.59 [
.59 {
.59 E
.59 0
.59 E
- -60 [
- - 0 6 0
--- 0 60


08.78
0 376 -




0 .66 - -
00.65 ----
4 ;69 ----
0 ;67 -----
0 64 -- --
08.68 ---
08.68 ---
4 0 64 ------
4 066 -----
~0 62 - -
0 ~67 -----
3 8.64 ---
3 0.64-- --
] -0 63 - -
10e.63-- -
] 062 - -
] 062 - -
] 0 63 - -
] 0 2 - -
] 0 2 - -
1 0 2 - -
0. 1 - -
0.61 - -
40 62 -
~0 .62 - -
0.61 -


0.10 0.20 0.30 0.40


0.50 0.60 0.70


0.80 0.90


Percent adjustment to Check Labels for Harmful Ingredients (ATLAB) of the average consumer
(Average ATLAB 1984-1993 = 0.61)



Figure 5-16. Ranking of factors impacting the likelihood of reading food labels for harmful

ingredients in the period 1984-1993.





0.86 -


ATLAB 1994-2003

rag 0.56

I 0.38
1- I 0.24
1 0.18
0.18
I- 0 0.15
1 0.13
0.13
I- 0 0.13
0.13
1- 0.12
1 0.12
0.08
0 0.08
0.07
0.07
0 0.06
0.06
0.06
0.05
0.05
I-0 0.05
-O 0.04
0.04
I- 0.04
0.03
0.02
I- 0.02
0.02
S0.02

1.)0


Conscious of calories
Know more than most

Doctor gives advice on diet
Cautious about additives

Eating fried chicken
Cautious about cholesterol

Age of female head
Cautious about fat
Adult female on diet
Eating hot dog
Best known brands

Avoid foreign food
Eating lunchmeat
Overweight isn't attractive
Vitamins by physician
Eating tacos

Body building ingredients
Education of female head

Census region
Cautious~ ~ abu u
Cautious about suar

Love to swim
Household size

Children under 18 years

Employment of female head
Like to lose 20 pounds
Cautious about preservatives
Household income

Quarters (seasonality)
Eating pizza


- 0.31
- - -
- - -
- - -
- - -


0 39-1
--.50 -C
----0 47 C
-- -050 C
-O47- C
0.52 -
---.50 -C
---- .4 C
.52 -
--048 0
.52 -
054 C
---- .4 C
--------0.54 C
0.53 C
-- -- 0 55 C
-------0 53-C
---- 5 C
--------0.54 C
-- -- -0.56 [
---- 0.5& [
-- -- 0.5& C
-- -- 0.55 C
-- -- -4 56 -[
- 0 57- [
- 5 -[
-- -- 56-[
- 0 57 [

0 57 [


08.65 ------
0 ~68 ----
0 ; 62 - -
0.;65 - -
08.63 -------
0 .67 ---
08.65 ------
08.61 ----


4 f)62 -------
0 ; 62 - -
0 .61 ----


0 ; 62 - -
] 0.59 - -
3 -0.60 --------




7 0;60 -

1 0.59 - -


] 060 --------

8 .59 ----
| 0.59 --------

] 0.59 - -


0.30 0.40 0.50 0.60


0.70 0.80 0.90


Percent adjustment to Check Labels for Harmful Ingredients (ATLAB) of the average consumer
(Average ATLAB 1994-2003 = 0.58)



Figure 5-17. Ranking of factors impacting the likelihood of reading food labels for harmful

ingredients in the period 1994-2003.



































157












Range of changes in probabilities of scores 1 & 2

0.60
ORANGE 1984-1993
MRANGE 1994-2003
0.50


0.40


0.30


0.20


0.10


0.00


S
c4~0
cO


Figure 5-18. Range of changes in probabilities of reading the labels for harmful ingredients in
the periods 1984-1993 and 1994-2003.










CHAPTER 6
SUNIMARY AND CONCLUSIONS

While food labels can and do provide a range of potentially useful information to aspiring

buyers, consumers must be aware of the information and pay attention to the messages. Further, they

must understand the messages as presented for the information to be useful. Even with the

requirement of labeling, any benefits occur only when the consumer perceives and uses the label

information. It is not enough to have the product labeled, the information must be of value to the

decision making process. Obviously, consumers differ and, as such, any importance placed on labels

will differ across these consumers.

To create the database of consumers, household heads were asked to provide a scaled

indication of their interest in labels (NPD). With a six point Likert scale, each household was asked

to score the following questions: (1) I check the labels for harmful ingredients, and (2) I read the

labels for my food purchase. While both questions zero in on the consumers importance attached

to labels, the first question is more negative where the label is expected to be used to eliminate

buying particular products and the second is more positive to assist with the purchase.

The household data, from a demographically balanced diary survey, gave monthly

observations over the years from 1984-2003 for a total of 30,414 household entries. In addition,

another more recent attitudinal survey from 1993 to 2003 includes 13,150 households. For each

household many attributes are known including demographics, attitudes, eating habits and health

concerns.

Since the household response is discrete with scaled values, the likelihood of reading food

labels can be estimated using Ordered Probit models where the probability of each Likert score can

be determined.










This study using a rigorous application of Ordered Probit models and a large database both

across households and time is broad enough to have national implications.

The results of the Ordered Probit models show that consumers that are worried about

potential harmful ingredients in the packaged food read the labels more often than consumers that

read the labels looking for general information. The twelve most important variables, ranked

according to their impact on the likelihood of reading food labels, in decreasing order are: conscious

of calories, know more than most about nutrition, doctor gives advice on diet, eating fried chicken,

cautious about additives, cautious about cholesterol, eating hotdog, cautious about fat, best known

brands are highest quality, age of female head of household, adult female on diet and avoid foreign

food.

As most apparent from the rankings and the probabilities for the average household, not

everyone values food labels at least in terms for helping make buying decisions. The fact that a

reasonable share of the buying population places little overt value to labels during the buying

process should be of concern since most of the label content is mandatory and closely monitored.

The content needs to be carefully designed to maximize the usefulness while not overwhelming

consumers with too much information. It is apparent that there is little to no role for targeted labeling

based on demographics except for the case of age. To be relevant, the label content must deal with

health related concerns and particularly dieting issues and nutrition. Much of the current federal

label guidelines require a preci se focus on these dimensions. Finally the limited role of foreign foods

and labeling parallel those discussed by Verbeke and Ward where they showed the limited

importance of country-of-origin labeling in Europe.

The results also show that, over time, the likelihood of reading food labels has changed,

peaking during the early 90's, when the NLEA was implemented and declining later. The reason

could be the one mentioned by Moorman (1996), that the NLEA was only partially successful

160









because there was "a group of highly skeptical consumers who remain pessimistic about the

truthfulness of nutrition information and the healthfulness of food products despite the NLEA"

(Moorman 1996, page 42). Depending on the reasons for this skepticism, ignorance or social

structure as Moorman points out, national programs could be implemented to make consumers

change their behavior related to healthy food consumption. Another suggestion to take into

consideration would be that of Kristal et al. (1998). Based on their research results they suggest

making modifications to the new food labels to make them easier to understand and creating

programs to help less educated consumers interpret food label information.

















DZNTADD3 DZNTADD2 DZNTADD4 DZNTADD5 DZNTPRE1
DZNTADDI1 0.9394 0.9361 0.9255 0.7730 0.7501
<.0001 <.0001 <.0001 <.0001 <.0001

DZNTADDI DZNTADD3 DZNTADD4 DZNTADD5 DZNTPRE2
DZNTADD2 0.9361 0.9336 0.9197 0.7682 0.7424
<.0001 <.0001 <.0001 <.0001 <.0001

DZNTADDI DZNTADD2 DZNTADD4 DZNTADD5 DZNTPRE3
DZNTADD3 0.9394 0.9336 0.9230 0.7709 0.7423
<.0001 <.0001 <.0001 <.0001 <.0001

DZNTADDI DZNTADD3 DZNTADD2 DZNTADD5 DZNTPRE4
DZNTADD4 0.9255 0.9230 0.9197 0.7595 0.7379
<.0001 <.0001 <.0001 <.0001 <.0001

DZNTADDI DZNTADD3 DZNTADD2 DZNTADD4 DZNTPRE5
DZNTADD5 0.7730 0.7709 0.7682 0.7595 0.6291
<.0001 <.0001 <.0001 <.0001 <.0001

DZNTCHL2 DZNTCHL3 DZNTCHL4 DZNTCHL5 DZNTFAT1
DZNTCHL 1 0.9540 0.9534 0.9218 0.7831 0.7036
<.0001 <.0001 <.0001 <.0001 <.0001

DZNTCHL1 DZNTCHL3 DZNTCHL4 DZNTCHL5 DZNTFAT2
DZNTCHL2 0.9540 0.9485 0.9171 0.7791 0.6937
<.0001 <.0001 <.0001 <.0001 <.0001

DZNTCHL1 DZNTCHL2 DZNTCHL4 DZNTCHL5 DZNTFAT3
DZNTCHL3 0.9534 0.9485 0.9166 0.7786 0.6922
<.0001 <.0001 <.0001 <.0001 <.0001

DZNTCHL1 DZNTCHL2 DZNTCHL3 DZNTCHL5 DZNTFAT4
DZNTCHL4 0.9218 0.9171 0.9166 0.7529 0.6742
<.0001 <.0001 <.0001 <.0001 <.0001

DZNTCHL1 DZNTCHL2 DZNTCHL3 DZNTCHL4 DZNTFAT5
DZNTCHL5 0.7831 0.7791 0.7786 0.7529 0.5967
<.0001 <.0001 <.0001 <.0001 <.0001

DZNTFAT2 DZNTFAT3 DZNTFAT4 DZNTFAT5 DZNTCHL1
DZNTFAT1 0.9538 0.9497 0.9050 0.7670 0.7036
<.0001 <.0001 <.0001 <.0001 <.0001


APPENDIX A
CORRELATION TABLES OF RESTRICTED DUMMY VARIABLES


Table A-1. Five highest correlat on coefficients and their probability under Ho: Rho
concerns restricted dumny variables of the 1993-2003 period (13,150 obs)


0 of health










Table A-1. Continued
DZNTFAT1 DZNTFAT3 DZNTFAT4 DZNTFAT5 DZNTCHL2
DZNTFAT2 0.9538 0.9413 0.8971 0.7603 0.6937
<.0001 <.0001 <.0001 <.0001 <.0001

DZNTFAT1 DZNTFAT2 DZNTFAT4 DZNTFAT5 DZNTCHL3
DZNTFAT3 0.9497 0.9413 0.8932 0.7569 0.6922
<.0001 <.0001 <.0001 <.0001 <.0001

DZNTFAT1 DZNTFAT2 DZNTFAT3 DZNTFAT5 DZNTCHL4
DZNTFAT4 0.9050 0.8971 0.8932 0.7213 0.6742
<.0001 <.0001 <.0001 <.0001 <.0001

DZNTFAT1 DZNTFAT2 DZNTFAT3 DZNTFAT4 DZNTCHL5
DZNTFAT5 0.7670 0.7603 0.7569 0.7213 0.5967
<.0001 <.0001 <.0001 <.0001 <.0001

DZNTPRE3 DZNTPRE2 DZNTPRE4 DZNTPRE5 DZNTADDI
DZNTPRE 1 0.9298 0.9250 0.9192 0.7849 0.7501
<.0001 <.0001 <.0001 <.0001 <.0001

DZNTPRE3 DZNTPRE1 DZNTPRE4 DZNTPRE5 DZNTADD2
DZNTPRE2 0.9258 0.9250 0.9153 0.7816 0.7424
<.0001 <.0001 <.0001 <.0001 <.0001

DZNTPRE1 DZNTPRE2 DZNTPRE4 DZNTPRE5 DZNTADD3
DZNTPRE3 0.9298 0.9258 0.9200 0.7856 0.7423
<.0001 <.0001 <.0001 <.0001 <.0001

DZNTPRE3 DZNTPRE1 DZNTPRE2 DZNTPRE5 DZNTADD4
DZNTPRE4 0.9200 0.9192 0.9153 0.7767 0.7379
<.0001 <.0001 <.0001 <.0001 <.0001

DZNTPRE3 DZNTPRE1 DZNTPRE2 DZNTPRE4 DZNTADD5
DZNTPRE5 0.7856 0.7849 0.7816 0.7767 0.6291
<.0001 <.0001 <.0001 <.0001 <.0001

DZNTSAL3 DZNTSAL2 DZNTSAL4 DZNTSAL5 DZNTFAT1
DZNTSAL 1 0.9353 0.9348 0.8971 0.7697 0.6426
<.0001 <.0001 <.0001 <.0001 <.0001

DZNTSAL1 DZNTSAL3 DZNTSAL4 DZNTSAL5 DZNTFAT2
DZNTSAL2 0.9348 0.9322 0.8942 0.7672 0.6360
<.0001 <.0001 <.0001 <.0001 <.0001










Table A-1. Continued
DZNTSAL1 DZNTSAL2 DZNTSAL4 DZNTSAL5 DZNTFAT3
DZNTSAL3 0.9353 0.9322 0.8947 0.7676 0.6285
<.0001 <.0001 <.0001 <.0001 <.0001

DZNTSAL1 DZNTSAL3 DZNTSAL2 DZNTSAL5 DZNTFAT4
DZNTSAL4 0.8971 0.8947 0.8942 0.7363 0.6109
<.0001 <.0001 <.0001 <.0001 <.0001

DZNTSAL1 DZNTSAL3 DZNTSAL2 DZNTSAL4 DZNTFAT5
DZNTSAL5 0.7697 0.7676 0.7672 0.7363 0.5348
<.0001 <.0001 <.0001 <.0001 <.0001

DZNTSUG3 DZNTSUG4 DZNTSUG2 DZNTSUG5 DZNTCHL1
DZNTSUG1 0.9005 0.8777 0.8744 0.7745 0.5059
<.0001 <.0001 <.0001 <.0001 <.0001

DZNTSUG3 DZNTSUG4 DZNTSUG1 DZNTSUG5 DZNTSAL2
DZNTSUG2 0.8997 0.8769 0.8744 0.7738 0.4956
<.0001 <.0001 <.0001 <.0001 <.0001

DZNTSUG4 DZNTSUG1 DZNTSUG2 DZNTSUG5 DZNTCHL3
DZNTSUG3 0.9030 0.9005 0.8997 0.7968 0.4947
<.0001 <.0001 <.0001 <.0001 <.0001

DZNTSUG3 DZNTSUG1 DZNTSUG2 DZNTSUG5 DZNTCHL4
DZNTSUG4 0.9030 0.8777 0.8769 0.7766 0.4905
<.0001 <.0001 <.0001 <.0001 <.0001

DZNTSUG3 DZNTSUG4 DZNTSUG1 DZNTSUG2 DZNTCHL3
DZNTSUG5 0.7968 0.7766 0.7745 0.7738 0.3927
<.0001 <.0001 <.0001 <.0001 <.0001










Table A-2. Five highest correlation coefficients and their probability under Ho: Rho = 0 of health
concerns restricted dumny variables of the 1984-2003 period (30,414 ob:)
DZNTADD3 DZNTADD2 DZNTADD4 DZNTADD5 DZNTPRE1
DZNTADDI1 0.9471 0.9459 0.9294 0.7797 0.7223
<.0001 <.0001 <.0001 <.0001 <.0001

DZNTADDI DZNTADD3 DZNTADD4 DZNTADD5 DZNTPRE2
DZNTADD2 0.9459 0.9383 0.9208 0.7725 0.7154
<.0001 <.0001 <.0001 <.0001 <.0001

DZNTADDI DZNTADD2 DZNTADD4 DZNTADD5 DZNTPRE3
DZNTADD3 0.9471 0.9383 0.9220 0.7734 0.7140
<.0001 <.0001 <.0001 <.0001 <.0001

DZNTADDI DZNTADD3 DZNTADD2 DZNTADD5 DZNTPRE4
DZNTADD4 0.9294 0.9220 0.9208 0.7590 0.7102
<.0001 <.0001 <.0001 <.0001 <.0001

DZNTADDI DZNTADD3 DZNTADD2 DZNTADD4 DZNTPRE5
DZNTADD5 0.7797 0.7734 0.7725 0.7590 0.6031
<.0001 <.0001 <.0001 <.0001 <.0001

DZNTCHL2 DZNTCHL3 DZNTCHL4 DZNTCHL5 DZNTFAT1
DZNTCHL 1 0.9623 0.9605 0.9273 0.7878 0.6460
<.0001 <.0001 <.0001 <.0001 <.0001

DZNTCHL1 DZNTCHL3 DZNTCHL4 DZNTCHL5 DZNTFAT2
DZNTCHL2 0.9623 0.9536 0.9207 0.7822 0.6384
<.0001 <.0001 <.0001 <.0001 <.0001

DZNTCHL1 DZNTCHL2 DZNTCHL4 DZNTCHL5 DZNTFAT3
DZNTCHL3 0.9605 0.9536 0.9190 0.7808 0.6369
<.0001 <.0001 <.0001 <.0001 <.0001

DZNTCHL1 DZNTCHL2 DZNTCHL3 DZNTCHL5 DZNTFAT4
DZNTCHL4 0.9273 0.9207 0.9190 0.7538 0.6231
<.0001 <.0001 <.0001 <.0001 <.0001

DZNTCHL1 DZNTCHL2 DZNTCHL3 DZNTCHL4 DZNTFAT5
DZNTCHL5 0.7878 0.7822 0.7808 0.7538 0.5243
<.0001 <.0001 <.0001 <.0001 <.0001

DZNTFAT2 DZNTFAT3 DZNTFAT4 DZNTFAT5 DZNTCHL1
DZNTFAT1 0.9613 0.9567 0.9116 0.7741 0.6460
<.0001 <.0001 <.0001 <.0001 <.0001










Table A-2. Continued
DZNTFAT1 DZNTFAT3 DZNTFAT4 DZNTFAT5 DZNTCHL2
DZNTFAT2 0.9613 0.9482 0.9035 0.7672 0.6384
<.0001 <.0001 <.0001 <.0001 <.0001

DZNTFAT1 DZNTFAT2 DZNTFAT4 DZNTFAT5 DZNTCHL3
DZNTFAT3 0.9567 0.9482 0.8991 0.7635 0.6369
<.0001 <.0001 <.0001 <.0001 <.0001

DZNTFAT1 DZNTFAT2 DZNTFAT3 DZNTFAT5 DZNTCHL4
DZNTFAT4 0.9116 0.9035 0.8991 0.7275 0.6231
<.0001 <.0001 <.0001 <.0001 <.0001

DZNTFAT1 DZNTFAT2 DZNTFAT3 DZNTFAT4 DZNTCHL5
DZNTFAT5 0.7741 0.7672 0.7635 0.7275 0.5243
<.0001 <.0001 <.0001 <.0001 <.0001

DZNTPRE3 DZNTPRE2 DZNTPRE4 DZNTPRE5 DZNTADDI
DZNTPRE 1 0.9434 0.9425 0.9307 0.8038 0.7223
<.0001 <.0001 <.0001 <.0001 <.0001

DZNTPRE1 DZNTPRE3 DZNTPRE4 DZNTPRE5 DZNTADD2
DZNTPRE2 0.9425 0.9375 0.9249 0.7987 0.7154
<.0001 <.0001 <.0001 <.0001 <.0001

DZNTPRE1 DZNTPRE2 DZNTPRE4 DZNTPRE5 DZNTADD3
DZNTPRE3 0.9434 0.9375 0.9258 0.7995 0.7140
<.0001 <.0001 <.0001 <.0001 <.0001

DZNTPRE1 DZNTPRE3 DZNTPRE2 DZNTPRE5 DZNTADD4
DZNTPRE4 0.9307 0.9258 0.9249 0.7888 0.7102
<.0001 <.0001 <.0001 <.0001 <.0001

DZNTPRE1 DZNTPRE3 DZNTPRE2 DZNTPRE4 DZNTADD5
DZNTPRE5 0.8038 0.7995 0.7987 0.7888 0.6031
<.0001 <.0001 <.0001 <.0001 <.0001

DZNTSAL2 DZNTSAL3 DZNTSAL4 DZNTSAL5 DZNTFAT1
DZNTSAL 1 0.9530 0.9506 0.9115 0.7920 0.5995
<.0001 <.0001 <.0001 <.0001 <.0001

DZNTSAL1 DZNTSAL3 DZNTSAL4 DZNTSAL5 DZNTFAT2
DZNTSAL2 0.9530 0.9448 0.9059 0.7872 0.5934
<.0001 <.0001 <.0001 <.0001 <.0001










Table A-2. Continued
DZNTSAL1 DZNTSAL2 DZNTSAL4 DZNTSAL5 DZNTFAT3
DZNTSAL3 0.9506 0.9448 0.9036 0.7852 0.5887
<.0001 <.0001 <.0001 <.0001 <.0001

DZNTSAL1 DZNTSAL2 DZNTSAL3 DZNTSAL5 DZNTFAT4
DZNTSAL4 0.9115 0.9059 0.9036 0.7529 0.5712
<.0001 <.0001 <.0001 <.0001 <.0001

DZNTSAL1 DZNTSAL2 DZNTSAL3 DZNTSAL4 DZNTFAT5
DZNTSAL5 0.7920 0.7872 0.7852 0.7529 0.4918
<.0001 <.0001 <.0001 <.0001 <.0001

DZNTSUG3 DZNTSUG2 DZNTSUG4 DZNTSUG5 DZNTCHL1
DZNTSUG1 0.9275 0.9109 0.9016 0.8031 0.4707
<.0001 <.0001 <.0001 <.0001 <.0001

DZNTSUG3 DZNTSUG1 DZNTSUG4 DZNTSUG5 DZNTCHL2
DZNTSUG2 0.9219 0.9109 0.8962 0.7982 0.4610
<.0001 <.0001 <.0001 <.0001 <.0001

DZNTSUG1 DZNTSUG2 DZNTSUG4 DZNTSUG5 DZNTCHL3
DZNTSUG3 0.9275 0.9219 0.9125 0.8127 0.4603
<.0001 <.0001 <.0001 <.0001 <.0001

DZNTSUG3 DZNTSUG1 DZNTSUG2 DZNTSUG5 DZNTCHL4
DZNTSUG4 0.9125 0.9016 0.8962 0.7901 0.4563
<.0001 <.0001 <.0001 <.0001 <.0001

DZNTSUG3 DZNTSUG1 DZNTSUG2 DZNTSUG4 DZNTCHL3
DZNTSUG5 0.8127 0.8031 0.7982 0.7901 0.3721
<.0001 <.0001 <.0001 <.0001 <.0001









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Consumption: A Rational Addiction?" Faculty Working Paper MSABR 04-7. Morrison
School of Agribusiness and Resource Management, Arizona State University.

Teisl, Mario F. and Brian Roe. 1998. "The Economics of Labeling: An Overview of Issues for
Health and Environmental Di closuree" Agricultural anadResource Econontics Review 27(2):
140-150.

TSP International. 2005. Reference Manual. Version 5.0. Palo Alto, California: TSP International.

Verbeke, Wim and Ronald Ward. 2006. "Consumer interest in information cues denoting quality,
traceability and origin: An application of ordered probit models to beef labels." Food Quality
and Preference 17(6): 453-467.









BIOGRAPHICAL SKETCH

Carlos Jauregui was born in Peru. He studied agronomy and specialized in horticulture at the

Universi dad Naci ona Agrari a La Molina." Upon graduating as agri cultural engineer he worked first

for the "Comision Nacional de Fruticultura" in Mexico city and then for the Mexican Agricultural

Extension Service. He came to the University of Florida to pursue a M.S. in food and resource

economics. He has been working as Coordinator for Statistical Research in this Department since

his graduation.





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1CONSUMERS' USE OF FOOD LABELS: AN APPLICATION OF ORDERED PROBIT MODELS By CARLOS E. JAUREGUI A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 2007

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2 2007 Carlos E. Juregui

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3To my parents, who taught me the value of hard work and honesty.

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4ACKNOWLEDGMENTS I am grateful to the Chairman of my committee Dr. Ronald W. Ward for his kind mentorship. My thanks go to Dr. Tom Spreen, Chairman of the Food and Resource Economics Department, for allowing me the flexibility to pursue my Ph.D. while working full time. My thanks go also to the members of my committee for their helpful comments.

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5TABLE OF CONTENTS ACKNOWLEDGMENTS . . . . . . . . . . . . . .4 LIST OF TABLES . . . . . . . . . . . . . . . .6 LIST OF FIGURES . . . . . . . . . . . . . . .7 ABSTRACT . . . . . . . . . . . . . . . . .9 CHAPTER 1INTRODUCTION ......................................................11 U. S. Public Policy on Labels.............................................13 Food Labeling Problem Statement ..........................................15 Labeling Research Goals.................................................16 Methodology and Data ...................................................16 2LITERATURE REVIEW................................................18 Historical Development of Food Labels .....................................18 Economic and Legal Implications of Food Labeling ............................22 Consumer Use of Food Labels .............................................28 3DESCRIPTION OF DATA...............................................36 4MODEL SPECIFICATION...............................................52 Ordered Probit Models for the 1993-2003 Period ..............................52 Ordered Probit Model for the 1984-2003 Period ...............................60 5ANALYSIS OF RESULTS ...............................................62 Ordered Probit Estimates and Probabilities for the Period 1993-2003 ..............63 Sequential Ordered Probit Estimates and Probabilities for the Period 1984-2003 .....71 6SUMMARY AND CONCLUSIONS ......................................159 APPENDIX ACORRELATION TABLES OF RESTRICTED DUMMY VARIABLES..........162 REFERENCES.............................................................168 BIOGRAPHICAL SKETCH...................................................171

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6LIST OF TABLES Table Page 3-1. Variables used in the Ordered Probit models...................................38 3-2. Frequency, in percent, of explanatory variables for the 1993 2003 period ...........40 3-3. Frequency, in percent, of explanatory variables for the 1984 2003 period ...........41 3-4. Frequency, in percent, of explanatory variables for the 1984 1993 period ...........42 3-5. Frequency, in percent, of explanatory variables for the 1994 2003 period ...........43 3-6. Five highest correlation coefficients and their probability under Ho: Rho = 0 of variables of the 1993-2003 period (13,150 obs) .................................44 3-7. Five highest correlation coefficients and their probability under Ho: Rho = 0 of variables of the 1984-2003 period (30,414 obs) ................................47 5-1. Results from the ATLAB model for the period 1993-2003 ........................77 5-2. Results from the FPLAB model for the period 1993-2003 .........................83 5-3. Principal components for NTADD, NTCHL, NTFAT, NTPRE, NTSAL and NTSUG for the period 1993-2003 ............................................89 5-4. Results for ATLAB model with principal components for health variables for the period 1993-2003 .....................................................90 5-5. Results for FPLAB model with principal components for health variables for the period 1993-2003 .....................................................95 5-6. Results from the ATLAB model for the period 1984-1993 .......................100 5-7. Results from the ATLAB model for the period 1994-2003 .......................106 5-8. Wald test for the coefficients of the sequential Ordered Probit model for the periods 1984-1993 and 1994-2003 ..........................................112 A-1. Five highest correlation coefficients and their probability under Ho: Rho = 0 of health concerns restricted dummy variables of the 1993-2003 period (13,150 obs)..162 A-2. Five highest correlation coefficients and their probability under Ho: Rho = 0 of health concerns restricted dummy variables of the 1984-2003 period (30,414 obs)..165

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7LIST OF FIGURES Figure Page 2-1. Typical U.S. food label ....................................................34 2-2. Belgium beef label........................................................35 3-1. Frequency distribution of ATLAB and FPLAB 1993-2003 ........................50 3-2. Frequency distribution of ATLAB 1984-2003 ..................................50 3-3. Frequency distribution for two periods of ATLAB...............................51 5-1. Probability of reading food labels by the average consumer ......................113 5-2. Demographics impact on reading food labels ..................................114 5-3. Attitudes impact on reading food labels ......................................118 5-4. Eating habits impact on reading food labels ...................................122 5-5. Health Concern impacts on reading food labels ................................127 5-6. Impact of seasonality on reading food labels ..................................131 5-7. Ranking of factors impacting the likelihood of reading food labels for harmful ingredients......................................................132 5-8. Ranking of factors impacting the likelihood of reading food labels for food purchase..133 5-9. Range of change in probabilities for ATLAB and FPLAB ........................134 5-10. Change over time in the likelihood of reading food labels to check for harmful ingredients for the average consumer.......................................135 5-11. Change over time in the impact of demographics on reading food labels to check for harmful ingredients..................................................137 5-12. Change over time in the impact of attitudes on reading food labels to check for harmful ingredients..................................................142 5-13. Change over time in the impact of eating habits on reading food labels to check for harmful ingredients..................................................146 5-14. Change over time in the impact of health concerns on reading food labels to check for harmful ingredients..................................................150

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85-15. Change over time in the impact of seasonality on reading food labels to check for harmful ingredients..................................................155 5-16. Ranking of factors impacting the likelihood of reading food labels for harmful ingredients in the period 1984-1993 ........................................156 5-17. Ranking of factors impacting the likelihood of reading food labels for harmful ingredients in the period 1994-2003 ........................................157 5-18. Range of changes in probabilities of reading the labels for harmful ingredients in the periods 1984-1993 and 1994-2003 ....................................158

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9Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy CONSUMERS' USE OF FOOD LABELS: AN APPLICATION OF ORDERED PROBIT MODELS By Carlos E. Juregui December 2007 Chair: Ronald W. Ward Major: Food and Resource Economics While food labels can and do provide a range of potentially useful information to aspiring buyers, consumers must be aware of the informati on and pay attention to the messages. It is not enough to have the product labeled, the information mu st be of value to the decision making process. Obviously, consumers differ and, as such, any im portance placed on labels will differ across these consumers. Household data, from a demographically bala nced diary survey, gave monthly observations over the years from 1984-2003 for a total of 30,414 household entries. In addition, another more recent attitudinal survey from 1993 to 2003 in cludes 13,150 households. For each household many attributes are known including demographics, attitudes, eating habits and health concerns. Using a six point Likert scale, each household was asked to score the following statements: (1) I check the labels for harmful ingredients and (2) I read the labels for my food purchase While both questions zero in on the consumers importance attached to labels, the first question is more negative where the label is expected to be used to eliminate buying particular products and the second is more positive to assist with the purchase.

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10Since the household response is discrete with scaled values, the likelihood of reading food labels can be estimated using Ordered Probit mode ls where the probability of each Likert score can be determined. The results of the Ordered Probit models s how that consumers that are worried about potential harmful ingredients in the packaged food read the labels more often than consumers that read the labels looking for general information. The twelve most important variables, ranked according to their impact on the lik elihood of reading food labels, in decreasing order are: conscious of calories, know more than most, doctor gives advice on diet, eating fried chicken, cautious about additives, cautious about cholesterol, eating hotdog, cautious about fat, best known brands are highest quality, age of female head of househol d, adult female on diet and avoid foreign food.

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11CHAPTER 1 INTRODUCTION The Fair Packaging and Labeling Act of 1966 was passed to ensure that consumers have information, in a standardized fashion, about the quantity and ingredients of the products. The Pure Food and Drug Act passed by the Congress in 1906 was the first law dealing with labeling issues. Since then, the evolution in food technology, p ackaging, trade, environmental issues, health consciousness, and the political environment for ced changes in the food industry. New laws were enacted to regulate the ever changing food industry and related food safety issues (Golan, et al. 2000). In 1990 the FDA proposed the Nutrition Labe ling and Educational Act (NLEA) and in 1994 the NLEA regulations pertaining to nutrition labeling were implemented (Golan et al. 2000, Kurtzweil 1993, Hadden 1986). Most f ood products now provide labels with information about fats, cholesterol and other nutritional information (Kim et al. 2000). Regulatory changes on labeling were expected to have major consequences on food de mand and marketing strategies because consumers understood the linkage between diet and health a nd because of the proposed nutrition education that was planned to accompany the introduction of new labels (Caswell, 1992). Economic efficiency also may be enhanced and a public service provided wh en firms make information about their products available to consumers (Golan et al. 2000). Changes in consumer behavior can also be expected as a result of the information on labels. Clark and Russell (2004) mention studies that support this claim. Nelson (1970), page 311, contends that "limita tions of consumer information about quality have profound effects upon the market structure of consumer goods. In particular, monopoly power for a consumer good will be greater if consumers know about the quality of only a few brands of that good." He also classifies the qualities or attribut es of goods according to the way information about

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12them is acquired. If the attributes or qualities of goods are ascertainable through search before buying them, then the goods have "search attributes". On the other hand, if the attributes are ascertainable only after the goods are bought and used or consumed, then the attributes are said to be "experience attributes". Darby and Karnil (1973) di stinguish a third class of attributes, "credence attributes." They point out that credence qualities cannot be evaluated in normal use and that the assessment of their value needs additional and co stly information. Some examples of goods with credence attributes are organically produced vege tables, genetically modified products, and fish caught without harming dolphins (Han and Harrison, 2004; Golan et al. 2000). While food labels can and do provide a range of potentially useful information to aspiring buyers, consumers must be aware of the information and pay attention to the messages. Further, they must understand the messages as presented for th e information to be useful. Even with the requirement of labeling, any benefits occur only wh en the consumer perceives and uses the label information. It is not enough to have the product la beled, the information must be of value to the decision making process. Obviously, consumers di ffer and, as such, any importance placed on labels will differ across these consumers. Ultimately there are three major issues associa tion with labels: (1) What is the information content of labels? (2) Should labeling be voluntar y or mandatory and who should pay the cost? and (3) Are consumers interested in the labels? The firs t issue is driven by the food safety concerns and the ability to sort out the product c ontent. This is particularly im portant when such food attributes cannot be readily and efficiently determined thr ough search and/or experience. Secondly, the means for achieving the labeling is driven by both economics and political power. Postponement and revisions within the USDA mandatory labeling re quirements are a product of political forces brought by industries who have a vested interest and who often may carry the cost of stricter labeling requirements. Finally, consumer interest is a produc t of demographics attributes, health concerns,

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13eating habits, and timing. Consumer’s interest in labels is the focus of this research since and without that interest, labeling has little economic and/or social value. Hence, in the following discussion, we turn to consumers and their expressed attention to food labels. Labeling may have economic value beyond that of providing immediate assistance when making buying decisions. Labeling that include traceab ility dimensions may, at first, seem of little value to consumers as shown by Verbeke and Ward (2005). Yet when food scares or an event leading to legal issues relating to product sources and content arise, the labeling content may be invaluable to litigati ons and to tracing products back to the source. The value then is if and when a problem arises and not for the immediate pur chasing decision. Most consumers are probably not even aware of the traceability issues. U. S. Public Policy on Labels According to Hadden (1986), labeling laws in the United States have evolved over time to serve three major purposes: ensure fair competition in the market, provide information to buyers and reduce health risks. Hadden (1986) also mentions th at the first law dealing with labeling issues was the Pure Food and Drug Act passed by the Congress in 1906 and that it required labels to be accurate regarding ingredients, proportions and quantities. Over time new laws were enacted to extend a nd improve the regulation of the food industry and to inform and protect the consumer. Some of t hose laws or milestones, as called by Golan et al. (2000) and Kurtzweil (1994) are: -1938. The Federal Food, Drug, and Cosmetic Act replaces the 1906 Food and Drugs Act. Among other things, it requires the label of every processed and packaged food to contain the name and weight of the food. It also prohib its statements in food labeling that are false or misleading.

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14-1966. The Fair Packaging and Labeling Act requires all consumer products in interstate commerce to contain accurate information and to facilitate value comparison. -1973. FDA issues regulations regarding nutr ition labeling on food containing one or more added ingredients. Nutrition labeling is voluntary for all other foods. -1994. Regulations of the Nutrition Labeling and Education Act of 1990 pertaining to nutrition labeling and nutrition content claims are implemented. A more detailed listing and description of the laws related to labeling, before the year 2001, can be found in Golan et al. (2000), Kurtzweil (1994) and Hadden (1986). One of the current public debates is on manda tory country of origin labeling (COOL). In 2002, President George Bush signed into law The Fa rm Security and Rural Investment Act of 2002 or Farm Bill. This law requires COOL for beef, lamb, pork, fish, perishable agricultural commodities and peanuts. In 2004 and 2006 President Bush signed laws delaying the implementation of some parts of mandatory COOL until 2008. According to the Farm Bill, the implementation of the program is the responsibility of the USDA's Agricultural Marketing Service. In 2004 the Food Allergen Labeling and Cons umer Protection Act of 2004 (FALCPA) was passed by Congress. This Act improves food labeli ng information and makes it easier for people to identify and avoid foods that contain allerg ens. It became effective in January of 2006. Another current food labeling topic is the Gu idelines for Voluntary Nutrition Labeling of Raw Fruits, Vegetables and Fish. In 2006 the Food and Drug Administration (FDA) updated the names and the nutrition labeling values for the 20 mo st frequently consumed raw fruits, vegetables, and fish in the U.S. These amendments will be effective on January 1, 2008. Under the Federal Food, Drug and Cosmetic Act the FDA addresses the food labeling requirements.

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15Food Labeling Problem Statement Many studies indicate that consumers are not utilizing all the nutritional information on the labels. Based on a FDA survey, Hadden (1986), pa ge 148, concluded that "Rather than obtaining positive nutrition information, consumers read the la bel to find information about ingredients they wish to avoid, such as sugars, fats and oils, pr eservatives, artificial flavors and sweeteners, and cholesterol". Other surveys also provide evid ence that consumers in many cases are unable to understand product information and quantify their nutritional needs. "Consumers questioned about nutrition labels were unable to compute how much of a product they would need to eat to obtain a day's allowance of a nutrient if one cup provide d 25 percent of the Recommended Daily Allowance" (Hadden 1986, page 215). It is also reported that many consumers "believed that the very presence of the label indicates that the manufacturer ha s tried to make a nutritious product" (Hadden 1986, page 148). Since 1991 obesity rates in the adult population have increased from 12% to 20.9% in 2001. The annual total cost of obesity is estimated at $117 billion. USDA statistics show that the total amount of calories consumed have increased since 1980. Refined carbohydrates are a nutrient associated with obesity. The consumption of these carbohydrates have increased from 147 pounds per capita in 1980 to 200 pounds in 2000 (Richards et al., 2004). To correct these problems consumers have to be educated. Assuming the consumer (or potential consumer) pa ys attention to the label, then the label(s) may provide additional health information; confirm credence attributes; confirm generic claims; provide a potential source of differentiation; in crease confidence and security; provide legal protection; reduce search cost; facilitates comparisons among products; enhance substitutability among goods; provide nutritional education; and e nhance consistency. Again, the key statement is if the inquiring buyer pays attention to the label. Given the importance of the consumer to the

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16economic and social value of labels, the primary goa l of this research is to estimate the likelihood or probability of using the food labels and to determine what factors are likely to influence the probability. Labeling Research Goals The present research centers on the consumer to quantitatively address the following: • How to measure the level(s) of attention consumers indicate about reading labels. •What factors about consumers impact the attention level? •What are the probabilities of reading the labels and have those probabilities changed over time? Several explicit hypotheses drive much of the empirical analyses: •Attention to labels differs across consumer demographics. •Health consciousness is a major factor influencing a person's attentiveness to label information. •General attitudes and eating habits are important contributing factors influencing the attention to food labels. •The importance of labels differ depending if the consumer is looking for negative versus positive attributes expressed with the label. Methodology and Data To create the database of consumers, house hold heads were asked to provide a scaled indication of their interest in labels (NPD). With a six point Likert scale, each household was asked to score the following questions: (1) I check the labels for harmful ingredients and (2) I read the labels for my food purchase While both statements address the importance that consumers attach to labels, the first statement is more negative where the label is e xpected to be used to eliminate buying particular products and the second is more positive to assist with the purchase. The household data, from a demographically balanced diary survey, gave monthly observations over the years from 1984-2003 for a total of 30,414 household entries. In addition,

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17another more recent attitudinal survey from 1993 to 2003 includes 13,150 households. For each household many attributes are known including demographics, attitudes, eating habits and health concerns. Since the household response is discrete with scaled values, the likelihood of reading food labels can be estimated using Ordered Probit mode ls where the probability of each Likert score can be determined. The complete research will be divided into 6 chapters. Chapter 2 provides a discussion of the historical development and public policy about f ood labels and also reviews the literature related to the use of food labels. A detailed descripti on of the data is covered in Chapter 3. Model specification and the econometric theory, with em phasis on the use of discrete choice models, is discussed in Chapter 4. Interpretation of the resu lts and model simulations are shown in Chapter 5. Finally, Chapter 6 gives the summary and conclusions of the research.

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18CHAPTER 2 LITERATURE REVIEW The purpose of this chapter is to briefly desc ribe the evolution of food labeling and review the literature pertinent to the use of food labeli ng. The literature review is organized in three sections: 1.Historical development of food labeling. 2.Economic and legal implications of food labeling. 3.Consumer use of food labels. Historical Development of Food Labels The circumstances under which food labeling started are nicely related by Hadden (1986), pages 4 and 5: The publication of Upton Sinclair's novel, The jungle, which detailed unsanitary conditions in Chicago's meat packing industr y, created such a furor that the Congress finally passed a Meat Inspection Act, which carried on its coattails the first labeling law of national scope, the Pure Food and Drug Act of 1906. The Act did not require that all food and drugs be labeled. It did, however, require that manufacturers who provided labels should be accurate about ingredients, proportions, and quantity. It also define d the new crimes of adulteration and misbranding, of which a manufacturer would be guilty if the label statements were "false or misleading in any particular, or if they did not bear the required information."

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19Over the years, the Pure Food and Drug Act was followed by other laws and regulations. The time line and brief description of the following laws relating food labeling, for the period 1906-2000, were taken from Golan et al. (2000), pages 2-5. 1906The Federal Pure Food and Drugs Act and the Federal Meat Inspection Act authorize the Federal Government to regulate the safety and quality of food. These acts also defined adulteration and prohibited selling misbranded or adulterated foods. 1913The Gould Amendment requires food packages to state the quantity of contents. 1924In U.S. v. 95 Barrels Alleged Apple Cider Vi negar, the Supreme Court rules that the Food and Drugs Act condemns every statement, de sign, or device which may mislead, misdirect, or deceive, even if technically true. 1930The McNary-Napes Amendment requires labeling on products that do not meet common-usage standards. 1938The Federal Food, Drug, and Cosmetic Act replaces the 1906 Food and Drugs Act. Among other things, it requires the label of every pr ocessed, packaged food to contain the name of the food, its net weight, and the name and addr ess of the manufacturer or distributor. A list of ingredients also is required on certain produc ts. The law also prohibits statements in food labeling that are false or misleading. 1950The Oleomargarine Act requires prominent labe ling of colored oleomargarine to distinguish it from butter. 1951Nutrilite Consent Decree allows the FDA to establish industry guidelines for vitamin and mineral labeling. 1957The Poultry Products Inspection Act authori zes USDA to regulate, among other things, the labeling of poultry products. 1958The Food Additives Amendment (which contai ns the Delaney Clause) expands the FDA’s authority to monitor dietary and health claims and food ingredients (including restricting or banning any additive or food ingredient deemed unsafe). Processors are required to prove that additives are safe. Creates the he Fair Packaging and Labeling Act requires all consumer products in interstate commerce to contain accurate information and to facilitate value comparisons. 1966The Fair Packaging and Labeling Act requires all consumer products in interstate commerce to contain accurate information and to facilitate value comparisons. 1966FDA publishes proposed dietary supplement regul ations. Proposal triggers legal challenges from industry.

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201969The White House Conference on Food, Nutrition, a nd Health addresses deficiencies in the U.S. diet. It recommends that the Federal Government consider developing a system for identifying the nutritional qualities of food. 1973FDA issues final dietary supplements regulation. Industry continues legal challenges. 1973FDA issues regulations requiring nutrition la beling on food containing one or more added nutrients or whose label or advertising include s claims about the food’s nutritional properties or its usefulness in the daily diet. Nutrition la beling is voluntary for almost all other foods. 1975Voluntary nutrition labeling, postponed from its originally planned 1974 date, goes into effect. 1976Vitamin-Mineral amendments limit FDA’s aut hority and enforcement power in relation to vitamin and dietary supplements. 1983In face of legal setbacks and Federal budget cuts, FDA repeals dietary supplement regulation. 1988Surgeon General C. Everett Koop releases The Surgeon General’s Report on Nutrition and Health, the Federal Government’s first formal recognition of the role of diet in certain chronic diseases. 1989The National Research Council of the Nationa l Academy of Sciences issues “Diet and Health: Implications for Reducing Chronic Disease Risk,” which presents additional evidence of the growing acceptance of diet as a factor in the development of chronic diseases, such as coronary heart disease and cancer. Under contract with FDA and USDA’s Food Safe ty and Inspection Service (FSIS), the Food and Nutrition Board of the National Academy of Sciences convenes a committee to consider how food labels could be improved to help consum ers adopt or adhere to healthful diets. Its recommendations are presented in Nutrition Labe ling: Issues and Directions for the 1990s. 1990Dolphin Protection Consumer Information Ac t regulates labeling of dolphin-safe tuna. 1990FDA proposes extensive food labeling changes, which include mandatory nutrition labeling for most foods, standardized serving sizes, a nd uniform use of health claims. The proposed Nutrition Labeling and Education Act reaffirms the legal basis for FDA’s labeling initiative and establishes an explicit timetable. 1991FDA issues more than 20 proposals to implem ent NLEA. In addition, the agency issues a final rule that sets up a vol untary point-of-purchase nutrition information program for raw produce and fish. FSIS unveils its proposals for mandatory nutrition labeling of processed meat and poultry and voluntary point-of-purch ase nutrition information for raw meat and poultry.

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211992Dietary Supplement Act delays implementati on of new dietary supplement regulation until the end of 1993. Authorizes the FDA to grant permission to producers to make specific health claims for products. 1992FDA’s voluntary point-of-purchase nutrition in formation program for fresh produce and raw fish goes into effect. 1993FDA issues the final regulations implemen ting NLEA. Regulations covering health claims become effective. 1994 NLEA regulations pertaining to nutrition labeling and nutrient content claims become effective (including those for meat and poultry). 1994The Dietary Supplement Health and Education Act (DSHEA) defines a “dietary supplement” as a food, not as a drug, thereby subjecting supplements to less restrictive regulatory and labeling requirements. 1997USDA releases the first proposed rule for a national organic foods standard (in compliance with the Organic Foods Production Act). Th e proposal drew over 275,000 comments, largely negative. 1997FDA issues final rules implementing the major provisions of the DSHEA of 1994. 1999Mandatory labeling of foods containing biotech ingredients is proposed in the House (HR 3377). 2000USDA releases the second proposed rule for a national organic foods standard (in compliance with the Organic Foods Production Ac t). The most controversial aspects of the first proposal—the potential to allow the use of genetic engineering, irradiation, and sewage sludge in organic production—were dropped from the second proposal. 2000White House announces Food and Agricultural Biotechnology Initiatives: Strengthening Science-Based Regulation and Consumer Access to Information authorizing (1) FDA to develop guidelines for voluntary efforts to label food products under their authority as containing or not containing bioengineered ingr edients in a truthful and straightforward manner, consistent with the requirements of the Federal Food, Drug, and Cosmetic Act; (2) USDA to work with farmers and industry to facilitate the creation of reliable testing procedures and quality assurance programs for differentiating non-bioengineered commodities to better meet the needs of the market. 2000Mandatory labeling of foods containing biotech ingredients is proposed in the Senate (S 2080). Continuing with the description of food labe ling regulations, in 2002 President George Bush signed into law The Farm Security and Rural Invest ment Act of 2002 or Farm Bill. This law requires

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22country of origin labeling (COOL) for beef, lam b, pork, fish, perishable agricultural commodities and peanuts. In 2004 a nd 2006 President Bush signed laws delaying the implementation of some parts of mandatory COOL until 2008. According to the Farm Bill, the implementation of the program is the responsibility of the USDA's Agricultural Marketing Service. In 2004 the Food Allergen Labeling and Cons umer Protection Act of 2004 (FALCPA) was passed by Congress. This Act improves food labeling information and makes easier for people to identify and avoid foods that contain allerg ens. It became effective in January of 2006. Another current food labeling topic is the Gu idelines for Voluntar y Nutrition Labeling of Raw Fruits, Vegetables and Fish. In 2006 the Food and Drug Administration (FDA) updated the names and the nutrition labeling values for the 20 mo st frequently consumed raw fruits, vegetables, and fish in the U.S. These amendments will be effective on January 1, 2008. Under the Federal Food, Drug and Cosmetic Act the FDA addresses the food labeling requirements. Economic and Legal Implications of Food Labeling According to Hadden (1986), pages 5 and 6 The 1906 act embodied three different but re lated regulatory purposes. First, it was intended to ensure fair trade among selle rs of food and drugs by requiring accurate label information. A manufacturer who made false claims could sell his product for less than a manufacturer who made the same claims accurately. Enforcing the label as the standard of accuracy deterred unfair competition through false claims. ... Second, the law was intended to help consumers appalled at the high prices and inflated claims of packaged products that preyed on ignorance and illness. Finally, the 1906 act reduced risks to hea lth. One of its most important provisions was the requirement that proportions of a ddictive substances be shown on the label. The Fair Packaging and Labeling Act of 1966 defines label as follows: The term "label" means any written, printed, or graphic matter affixed to any consumer commodity or affixed to or appearing upon a package containing any consumer commodity (Miller 1978, page 3). For Stanton et al. (1991), page 224, "A label is the part of a product that carries information about the product or the seller. A label may be part of a package, or it may be a tag attached directly

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23to the product." He also classifies the labels in three groups: (a) Brand, which is just the brand applied to the product or package; (b) Grade, wh ich identifies the quality w ith a letter, number or word; and (c) Descriptive, which gives objective information concerning the product. Teisl and Rao (1998), page 140, define product labeling as any policy instrument of a government or other third party that somehow regulates the presentation of product-specific information to consumers. This information might describe use characteristics of the product, such as price, taste, and nutrition, or nonuse characteristics, such as the environmental impact or moral/ethical elements surrounding the product's ma nufacturing process." They also point out that labeling policy has three major components: compulsoriness, explicitness, and standardization. The degree of compulsoriness can vary from mandatory labeling restrictions, requiring to display certain information on the product, to voluntary labeling restrictions, where the firms choose the type of information to display. Explicitness has to do with how much detailed has the information presented to the consumer. The last component, standardiza tion, "is the degree to which the regulation requires the information to be provided in a presentation format that is standardized and uniform across products." To illustrate the labeling content two examples are included, Figures 21 and 2-2. Figure 2-1 shows the typical U.S. on package label with most of the information providing nutritional content and contributions to the daily nutrient intake. In terms of the label format there is considerable continuity across products in terms of nutritional information. More differences are seen in the presentation of additional things such as country -of-origin, reduced claims about less cholesterol, less fat, and unique ingredients. The second exampl e is from a label in Belgium where considerably more information about traceability is included on the label. Unlike the U.S. labels, this Belgium example shows an almost overwhelming amount of information with emphasis on traceability more than nutritional signals.

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24Economic Justification for Labeling As the economic justification for labeling Teisl and Roe (1998) mention the removal of information asymmetry or "subsidization of search costs" that benefits the consumer by providing information about the attributes of the product they want to buy. The size of the subsidies or the benefits to consumer would depend on the attri butes of the goods. Nelson (1970) classifies the attributes of goods according to the way information about them is acquired. If the attributes or qualities of goods are ascertainable through search before buying them, then the goods have "search attributes". On the other hand, if the attributes are ascertainable only after the goods are bought and used or consumed, then the attributes are said to be "experience attributes". Darby and Karnil (1973) distinguish a third class of attributes, "credence attributes". They point out that credence qualities cannot be evaluated in normal use and that the a ssessment of their value needs additional and costly information. Some examples of goods with credence attributes are organically produced vegetables, genetically modified products, and fish caught without harming dolphins (Han and Harrison, 2004; Golan et al. 2000). According to Caswell and Mojduszca (1996) th e market functions relatively well for goods with search attributes and informational programs are less likely to be instituted because information for these kinds of goods is plentiful and easy to obtain. For goods with experience attributes, they continue, information about the quality of the product is the most important issue and the government may be able to improve efficiency by facilitating communication between the informed and uninformed consumers through some form of consumer rating on product labels. In the case of goods with credence attributes, they point out, that the markets for quality do not function well because the information is so imperfect. They re mark that the consumers cannot learn from their experience consuming the product and cannot measure its quality.

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25Benefits and Costs of Mandatory Labeling Golan et al. (2000) points out that informed consumption and socially desirable changes in consumption behavior are the main benefits of a government labeling program. The benefits cannot be known precisely before the information is introduced into the market and acoording to Beales 1980, page 247: the greater the risk, the greater the value of information about that risk is likely to be. Availability of substitutes will influence th e value of information. If another product does not have the hazard, and is a very good substitute at prevailing prices for the product with the risk, greater changes in consumer behavior are likely. If, however, there are no substitutes available, the cost s of changing behavior are much greater, and less change will result. Thus, informati on is more likely to produce benefits if it applies to only a few brands of a product (since other brands are likely to be good substitutes), than if it applies to the entire product class (since other products are not likely to be as substitutable as different brands of the same product). Beales (1980) also points out that even when the number of informed consumers is small, firms competing for this informed minority may make changes that also are going to benefit the rest of the consumers. "For example, nutritional information is not used by any large fraction of consumers; most do not read the labels. However, in competing for consumers who do read labels, many companies fortified their products, thus im proving their nutritional quality, as measured by the information on the label. All consumers who use these products benefit from the information, even though only a small number actually read the labels" (Beales 1980, page 248). Teisl and Roe (1998), page 143, mention that labe ls allow consumers to verify claims made by advertisers and that this "may hinder some firms from overstating product qualifications". They also suggest that standardizing the display acro ss products facilitates the consumers use of product labels and makes easy to extract information. Another advantage of labels is realized when legal issues are raised. The label's importance for legal protection and for traceability is there and th e benefits are only realized when a legal issue is raised.

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26Beales (1980) recognizes three types of labeling costs: direct costs, indirect or opportunity costs, and side effects or un-intended costs. Th e direct costs are the result of generating the information, testing for the veracity of the claims, printing the labels and enforcing the labeling rules. The indirect costs results from the loss of flexibility in the production of the good, specially if there is a short term change in the availability or price of inputs. It is not worthwhile to change the labels to reflect the new ingredients in the short term. The un-intended costs result when manufacturers respond in unexpected ways to info rmation disclosure. For example, once a product qualifies for the highest class in a grading system "the manufacturer may have no incentive to make further improvements" (Beales 1980, page 252). Golan et al. (2000) points out that labeling pr ograms could be costlier, in a per-unit basis, for small firms than for large firms, putting them in a competitive disadvantage and possible, changing the industry structure and imposing disproportionate costs on rural economies. Mazis (1980) mentions that in some cases, the industry may incur in the costs of increasing packaging size to encompass mandated information. He also mentions that from a legal perspective the cost of information could be viewed as a restriction on speech. Another aspect of labeling mentioned by Mazis is that consumerists, a very small proportion of people, often very well-educated, really find detailed information useful. On the other hand, poor and less-educated people can't use this information, yet they have to pay for it. Effectiveness of labeling Golan et al. (2000) points out that the governme nt may use different policy tools like taxes, bans, education programs, and regulation of produc tion and marketing, to correct for externalities and asymmetric information. The following is a list of situations that Golan et al. (2000), pages 17-18, believe may be appropriate for using labeling as a policy tool, after consideration of the costs and benefits of its application.

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27Consumer preferences differ Labeling may be preferable to other policy tools if consumer preferences differ widely with respect to product characteristics (Magat and Viscusi, 1992)... Information is clear and concise. The information on the label must be clear, concise, and informative. Information that is unread or misunderstood will not lead to better informed consumption decisions nor to a better matching of preferences with purchases. Too much information dimi nishes the value of all the information on the label... Information on product use enhances safety. For some products, the manner in which consumers use or consume the product influences the quality attributes of the product. In these cases, information about how to enhance the positive characteristics of the product or reduce the negative ones could benefit consumers... Costs and benefits of consumption are borne by the consumer If the consumption or production of a food creates externalities (that is, affects someone else's welfare in a way not reflected in the market), th en information-based policies will usually be insufficient to align private consumption choices with socially optimal choices... Each of the steps along the l abeling tree can be established. Mandatory labeling will result in confusion and actually increase transaction costs unless it is supported by clear, achievable quality standards, testing services to measure the validity of labeling claims, certification services subs tantiating the validity of the quality claim, and mechanisms for enforcing labeling rules, including mechanisms to punish producers who make fraudulent claims. The government must either perform these services or accredit third-party agents to perform them (as described by branch 4 of the labeling tree). No political consensus on regulation exists. In many regulatory policy debates, there is little consensus on the appropriate regulatory response. Some groups may advocate complete product bans while others advocate no government intervention at all. These debates could be national or interna tional and could lead to difficult problems in harmonizing standards for a wide range of goods (biotech labeling is a case in point). In these cases, labeling may represen t not just the best compromise solution but also the path of least resistance, both domestically and internationally... Teisl and Roe (1998), page 143-144, mention that there is research indicating that labeling can change producer and consumer behavior and that "what is needed is research that develops understanding of what the conditions need to be for a labeling policy to be effective. That is, what characteristics of the interaction between the labe l, the consumer, and the product affect the impact of information?"

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28Consumer Use of Food Labels The following papers describe the regulatory e nvironment, as well as the use of food labels by consumers, before and after the Nutrition Labeling and Education Act of 1990 (NLEA). Bender and Derby (1992) used hierarchical di scriminant function analyses on FDA national data for 1982, 1984, 1986 and 1988 to determine both the trends and the relationships between the use of the ingredient list and the nutrition label and selected other variables. The number of households in the surveys, which included questions about food labels, were 4000 in the first three years and 3200 in the last year. Their results, page 293, shows that: "The percentage of consumers who reported using the ingredient list to avoid or limit particular food ingredients remained constant between 1986 and 1988 for sodium, sugar, and preservatives, but there were significant increases reported in the avoidance of fats/oils and choleste rol." They also report th at: "The gap between the least-and most-educated consumers narrowed even more for nutrition labels than for ingredient lists. Only consumers with advanced degrees remained significantly greater users of nutrition labels than did those with less than a high school education" (page 294). Guthrie et al. (1995) used the 1989 USDA's Continuing Survey of Food Intakes by Individuals (CSFII) and Diet and Health Knowledge Survey (DHKS) surveys, with 2214 households, to study the effect of some consumer characteristics on the use of labels. Among the explanatory variables that they used in their models are: sex, age, US regions, education, employment, weight concerns, diet, and nutrition knowledge. Guthrie et al. (1995), page 168, conclude that: the most likely person to use the nutrition la bel is an educated woman who lives with others, is knowledgeable about nutrition, places more importance on nutrition and product safety and less on taste when s hopping for food, and believes that following the principles of the Dietary Guidelines fo r Americans is important. It could also be said that male meal planners/preparers who live alone, are less educated, less knowledgeable about nutrition, less concerne d with nutrition and product safety and

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29more concerned with the taste of the f ood they purchase, and believe following the Guidelines principles to be less important are the least likely to use food labels. Moorman (1996) used a longitudinal quasi expe rimental design, with evaluations at two points in time, (eight months before label introduction-October 1993 and five months following label introduction-October 1994) to assess the consumer informational determinants of nutrition information processing activities. Moorman used three geographically dispersed site s in two states and selected the consumers from 20 different product categorie s that could be classified by nut rition level: orange juice, cake mix, peanut butter, ready-to-eat cereal, margarine, salad dressing, cheese, oils, crackers, cookies, potato chips, pasta, frozen dinners, ice cream, yogurt, hot dogs, bread, soup, frozen pizza, and corn" (Moorman 1996, page 32). Moorman collected data fr om 554 participants in the pre-NLEA and 558 in the post-NLEA condition. Moorman finding suggest that the new labels ha ve increased the level of comprehension of nutrition information that consumers use at the point of sale, but she also points out that the NLEA was only partially successful because in the pos t-NLEA only more motivated and less skeptical consumers acquired more information and that it is not clear that nutrition labels are the appropriate tool to motivate less interested or highly skeptical consumers. Derby and Levy (2001) analyzed the 1994 a nd 1995 FDA's Food Label Use and Nutrition Education Survey (FLUNES). The first surv ey involved 1,653 households and most of the interviews were conducted in March-April, prior to the NLEA implementation date. In the second survey, with 1,001 households, the interviews were conducted in November and December of 1995, about 18 months after the implementation of NLEA These surveys provided information about the credibility of label information and consumer use of food labels. According to their conclusions the NLEA had positive impact in the use of food labels that because of the new food labels consumers

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30stop buying or tried new products and that when c onsumers pay attention to claims and nutrition facts information they draw the appropriate conc lusions about the products healthfulness. Their research also found out that the food label is not an ideal way of disseminating nutrition education messages. Derby and Levy (2001), pa ge 395, point out that "With re spect to nutrient content and health claims, research showed little consumer aw areness that claims are regulated, and a majority of consumers remain skeptical of health claims on food labels. This suggests that, to date, consumer education has not adequately addressed the credibility issue". Kristal et al. (1998) used two cross-sectional surveys from the Washington State Cancer Risk Behavior Survey (random-digit-dial survey of adults ) to characterize food label use before and after the introduction of the new label format. The fi rst survey was completed between August 1992 and August 1993 (n = 1001) and the second between September 1995 and September 1996 (n = 1450). Both surveys included questions on demographic ch aracteristics and attitudes and behavior related to cancer risk (diet, smoking, screening), as well as questions relating to the use or non-use of labels when purchasing packaged foods. Based on the results of their research Kr istal et al. (1998), page 1215, conclude that they ... found evidence of modest, positive impacts of new food labels on use, barriers to use, and satisfaction. It is important to note that the percentages of residents who never used labels did not change, and that more than 70% of the respondents wanted new labels to be easier to understand. The impact of the Nutrition Labeling and Education Act could be enhanced by further label modifications to make labels easier to understand and by programs to help consumers, especially older and less well educated consumers, interpret label information. Kim et al. (2000) used the USDA's 1994-96 Continuing Survey of Food Intakes by Individuals (CSFII) and Diet and Health Knowledge Survey (DHKS) to determine the characteristics of consumers who use food labels. Their results s how that males use food labels less than females, more educated consumers read nutrient content information more than less educated consumers. Also, individuals on special diet, informed consumers about the linkage between diet and health

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31problems, nonsmokers, and people that exercise regularly are more likely to use nutrient content information than individuals who are not on diet or are less informed, smokers, or don't exercise regularly. Lin et al. (2004) studied the association of tota l fat, saturated fat and cholesterol intakes and the probabilities of looking for their information on food labels. To estimate the relationships, they use a generalized logistic model. The data they used came from the 1994-1996 Continuing Survey of Food Intakes by Individuals (CSFII) and the accompanying Diet and Health Knowledge Survey (DHKS) conducted by the US Department of Agri culture, with sample sizes of 3995, 3992 and 4024 observations, for each year, respectively. Among the explanatory variables they used are demographics and knowledge about nutrition. Almost the entire results of their study are quot ed because of the relevance to the present study. Lin et al. (2004), pages 1962-1964, report the following results: Respondents who had higher intakes of tota l fat, saturated fat, or cholesterol were less likely to report looking for labe l information on these nutrients. How they felt about food labels are strongly related to whether they looked for the label information. Specifically, the probability of information search is higher for those who agreed that (1) they know how to use food labels to choose a healthy diet, (2) they would be better off using food labels to choose foods than relying on their own knowledge about foods, or (3) reading food la bels does not take more time than they could spare. Respondents who agreed that nutrition is important in their food shopping decisions were more likely to look for th e information. The probability of searching for information on food labels is also higher among respondents who were on a special diet, with higher household inco me, and with better nutrition knowledge. ... Also, the more important respondents felt it wa s to choose a diet low in saturated fat, maintain a healthy weight, choose a diet low in fat, and choose a diet low in cholesterol, the more likely they looked for the information. Knowledge of problems caused by eating too much fat or cholesterol is also associated with a higher probability of information search. The probability of information search is lower among respondents who resided in the West. A few variables have differentiated e ffects on food label information search across the three nutrients. For instance, respondents who felt that nutrition information on food labels was hard to interp ret were more likely to search for total

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32fat information on food labels than other respondents, while no similar relationship is found for saturated fat or cholesterol information. Similar to the finding by Kim et al. (2000), respondents who resided in la rger households were less likely to search for fat and saturated fat information; but, contrary to the finding by Kimet al. (2000), they were neither more or less likely to look for cholesterol information than those in smaller households. ... Hispanic respondent s were more likely to search for total fat information on food labels but not sa turated fat or cholesterol information. Respondents who resided in the South and Midwest were less likely to search for saturated fat or cholesterol information, but not total fat information on food labels than others. Finally, male respondents were less likely to look for total fat and saturated fat information, similar to the findings by Kim et al. (2000), but not cholesterol information. Employment stat us and shopping status (i.e., major food shopper or not) were not related to information search. The probability of search for cholesterol information is higher among respondents who were older, who believed th eir diet can make a difference in the chance of getting a disease (such as h eart disease or cancer), who had been diagnosed of a disease (heart disease, st roke, high blood pressure or diabetes), or who resided in a suburban area. Also, th e probability of search for cholesterol information is lower among respondents who were White or more educated. He et al. (2004) used Ordered Probit models to explore factors affecting the intakes of fat, cholesterol, sodium, vitamins, protein, and dietary fiber. The explanatory variables are age, gender, education level, ethnic status, marriage status, household income, having a non-adult family member, and level of engagement in physical activities. The data they used was a nationwide telephone survey of 2880 U.S. households conducted in 1997. This study is not about using food labels but their results show that many factors a ffect in similar way the search for information on food labels and the food intake. For example, olde r people tend to pay more attention to nutrient intake than do younger people. Consumers with college education are more careful about consumption of cholesterol, sodium, vitamins, protein, and fiber than consumers with lower education. By the same token, household income pos itively affect fat and cholesterol consideration. McLean-Meyinsse (2001), page 114, using a chi-squared contingency test on data from a random telephone survey of 1,421 primary grocery shoppers and/or meal preparers in Alabama,

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33Arkansas, Florida, Georgia, Kentucky, Louisian a, Mississippi, North Carolina, Oklahoma, South Carolina, Tennessee, Texas, and Virginia during August 1998, found out that: Older consumers are more likely to examine the sodium content of food products when making purchasing decisions; those between 18 and 35 years of age are more likely not to pay much attention to the nutritional attributes on food labels. College-educated consumers show more concerns about the fat content of food products than non-college graduates. The results also suggest that women, households with children 18 years old and under, households with incomes in excess of $35,000, married consumers, and Caucasians are more likely to use labels to determine the fat content of foods than their corresponding counterparts. Younger respondents, those without a college diplom a, men, those without children and living in households with income levels below $35,000, unmarried consumers, and nonwhites are more likely to use attributes besides calories, fat, list of ingredients, and sodium when making their food purchas ing decisions. Overall, when purchasing food products, consumers read the informa tion on fat content more frequently than any other single attribute. Gracia et al. (2007) estimated a multivariate Probit model in order to simultaneously model three decisions: knowledge about nutritional labels, consumers label use, and perceived benefit from a mandatory nutritional labeling program. Among th e explanatory variables in the models were gender, age, education, income, household size, health habits, and importance of some label attributes. The data used in the estimations came from a survey of 400 food shoppers during the Spring of 2004 in Zaragoza, Spain. Their results in page 172 show that: individuals who state to be more knowle dgeable about nutritio nal labels are more likely to use food labels while shopping, a nd nutritional label users are more likely to consider a mandatory nutritional labeli ng program as beneficial. In addition, we have found that older and more educated consumers are more likely to perceive benefits from the mandatory implementation of the nutritional label program. Moreover, consumers who consider that the information provided by nutritional labels is useful are more likely to thi nk that the mandatory implementation of such labels will be beneficial. On the contrar y, and as expected, consumers who consider that the provided information is too de nse (too much) are less likely to perceive benefits from the mandatory implementation of nutritional labels. The above literature provides the foundation for a more thorough analysis of consumers use of food labels. This study builds on the literature drawing from a much more extensive database spanning a large cross-section of consumers over a considerable time span.

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34Figure 2-1. Typical U.S. food label. [Download from http://www.fda.gov/opacom/backgrounders/foodlabel/newlabel.html, November 2006].

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35 Figure 2-2. Belgium beef label

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36 CHAPTER 3 DESCRIPTION OF DATA The present study uses comme rcially collected household data from two national demographically balanced diary surveys (NPD). The first survey is from 1984 to 2003 and includes a total of 30,414 observations. The second survey is from 1993 to 2003 with a total of 13,150 observations. This second, or new survey, adds ques tions to the original survey. Households report their consumption during a two week period with a period commonly referred as a wave. Each wave can be associated with a certain month (e.g., the waves are not reported since the time dimension to the data were expressed in month equivalence.) The surveys were collected across waves and contain questions about demographics (census region, income, age, education, employment), use of food labels, snacking, dieting, exercises, nut rition, food preparation, brand awareness and attitudes about health and eating habits. The survey s do not provide informati on about race or health status of the consumers. Among the statements that households were asked to score with a six point Likert scale are: (1) I check the labels for harmful ingredients and (2) I read the labels for my food purchase While both statements emphasize the consumers importance attached to labels, the first statement is more negative where the label is expected to be used to eliminate buying particular products and the second is more positive to assist with the purchase. Table 3-1 shows these two statements and their corresponding Likert scales. Figures 3-1 to 3-3 pr ovide an overview of the consumers scoring for labels during the different periods of the study. Considering levels 1 and 2 in Likert scale as a measure in the use of labels, then, in the 1993-2003 period (Figure 3-1), 56.2 percent checked the labels from harmful ingredients while 59.4 percent used the labels for broader purchasing decisions. For the rest of the periods in study, Figures 3-2 and 3-3, the use of labels looking for harmful ingredients varies from 56.4 to 55.2 percent. By th e same token, taking the Likert scales 5 and 6 as

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37 indication of lack of interest on labels in ge neral, 10.5 to 14.4 percent of consumer s showed little interest on labels. For each household surveyed many attributes are known including demographics, health concerns, eating habits, employment, dieting, br and awareness and concerns about foreign food. Table 3-1 provides the description of these attri butes and their corresponding discrete classification. Tables 3-2 to 3-5 show the discrete classification and the frequency distribution, in percent, of the attributes that are used as explanatory variable s in the Ordered Probit models. Table 3-2 shows this information for the period 1993-2003, Table 3-3 fo r the period 1984-2003, Table 3-4 for the period 1984-1993 and Table 3-5 for the period 1994-2003. As tables show, the distributions are very similar across all these periods. Tables 3-6 and 3-7 show the five highest correlation coefficients between explanatory variables for the periods 1993-2003 and 1984-2003 resp ectively. Health concern variables (cautious about additives, cholesterol, fat, preservativ es, salt and sugar) show the highest correlation coefficients and therefore they could potentially cause multicollinearity problems. The variables, because they are discrete in form and with a range of values, must be expressed in binary form (i.e. dummy variables). Ultimately, before being used in a regression model, the dummy variables have to be restricted, as explained in Chapter 4. At the end, is the degree of linear combination of the restricted dummy variables that will have an e ffect on the degree of multicollinearity. Tables A-1 and A-2 show the restricted dummy variables with the highest correlation coe fficients for the periods 1993-2003 and 1984-2003, respectively. The restricted dummy variables with the highest correlation coefficients are in the health concerns group.

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38 Table 3-1. Variables used in the Ordered Probit models VariableDescriptionLikert scale Household scaled attention to labels (Y) ATLAB I check the labels for harmful ingredients 1 = Completely agree 2 = Mostly agree 3 = Somewhat agree 4 = Neither 5 = Somewhat disagree 6 = Mostly disagree FPLAB I read the labels for my food purchase Demographics (X1) DMAGEAge of female head 1 =< 35 2 = 35 44 3 = 45 54 4 = 55 64 5 = 65 + DMCHLChildren under 18 years 1 = yes 2 = no DMEDU Education of female head of household 1 = No high school 2 = High school, 3 = Some college 4 = College graduate DMFEM Employment of female head of household 1 = Employed 2 = Not employed DMHSZHousehold size 1 = 1 Member 2 = 2 Members 3 = 3-4 Members 4 = 5 + Members DMIN2Household income 1 = under $30,000 2 = $30 49,999 3 = $50 69,999 4 = $70 100,000 + DMREGCensus region 1 = New England 2 = Middle Atlantic 3 = East North Central 4 = West North Central 5 = South Atlantic 6 = East South Central 7 = West South Central 8 = Mountain 9 = Pacific Attitudes (X2) ATCALConscious of calories 1 = Completely agree 2 = Mostly agree 3 = Somewhat agree 4 = Neither 5 = Somewhat disagree 6 = Mostly disagree ATLBSLike to lose 20 pounds ATSWMLove to swim ATWGTOverweight isn't attractive NTBRN Best known brands are highest quality NTING Food should have body building ingredients NTKNO Know more than most about nutrition

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39 Table 3-1. Continued VariableDescriptionDiscrete Classification Eating Habits (X3) DTFE2Adult female on diet 1 = Yes 2 = No FDFCHEating fried chicken 1 = Always encourage 2 = Almost always encourage 3 = Sometimes encourage 4 = Neither 5 = Sometimes discourage 6 = Almost always discourage FDHOTEating hot dog sandwich FDLUNEating lunchmeat FDPIZEating pizza FDTACEating tacos ATFORAvoid foreign food 1 = Completely agree 2 = Mostly agree 3 = Somewhat agree 4 = Neither 5 = Somewhat disagree 6 = Mostly disagree FPFAS *Try fast food places FPRES *Visit restaurants more than most Health Concerns (X4) ATDOCDoctor gives advice on diet 1 = Completely agree 2 = Mostly agree 3 = Somewhat agree 4 = Neither 5 = Somewhat disagree 6 = Mostly disagree NTADD A person should be cautious about additives NTCHL A person should be cautious about cholesterol NTFAT A person should be cautious about fat NTPRE A person should be cautious about preservatives NTSAL A person should be cautious about salt NTSUG A person should be cautious about sugar NTVIT Vitamins recommended by physician Seasonality (X5) ZQTRQuarters 1 = First quarter 2 = Second quarter 3 = Third quarter 4 = Fourth quarter New survey variables

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40 Table 3-2. Frequency, in percent, of explanatory variables for the 1993 2003 period Variable Discrete Classification 123456789 Percents of 13,150 observations DMAGE21.1324.0321.6615.9817.20 DMCHL36.3263.68 DMEDU5.3230.2327.7036.75 DMFEM54.9745.03 DMHSZ22.7034.3132.7210.27 DMIN241.5827.4815.5515.38 DMREG4.6515.9517.518.0616.816.8410.656.1413.38 ATCAL8.7317.8129.4919.1714.5210.28 ATLBS36.5811.0911.598.118.6424.00 ATSWM16.9113.1317.0818.188.9525.75 ATWGT26.3625.0520.9417.866.033.76 NTBRN3.458.8918.5721.0332.0715.99 NTING10.6216.3423.1332.5011.485.93 NTKNO6.7114.1327.3732.2712.487.04 DTFE229.7670.24 FDFCH3.674.8414.5536.1221.8618.97 FDHOT2.173.1915.6046.0818.2414.71 FDLUN4.747.6718.7841.0716.6811.06 FDPIZ8.0811.2423.6345.288.463.32 FDTAC5.329.0119.7848.219.937.75 ATFOR17.1919.4024.3015.4211.4612.24 FPFAS1.754.6819.3817.7516.1040.33 FPRES3.045.1210.2517.0416.9547.60 ATDOC9.6410.3319.1323.509.9527.46 NTADD28.3723.3325.9917.533.031.75 NTCHL32.9226.3925.7511.022.511.41 NTFAT38.6226.2422.598.652.381.51 NTPRE25.5722.8126.1719.653.851.95 NTSAL29.3926.5726.9711.783.341.95 NTSUG18.9418.6734.0019.825.882.70 NTVIT7.417.6414.4325.4426.0519.03 ZQTR22.0923.6627.3026.95

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41 Table 3-3. Frequency, in percent, of explanatory variables for the 1984 2003 period Variable Discrete Classification 123456789 Percents of 30,414 observations DMAGE25.8022.9318.2316.1216.92 DMCHL37.1462.86 DMEDU6.5632.4926.4634.48 DMFEM53.3546.65 DMHSZ23.3532.7333.2410.67 DMIN249.7226.5913.1310.56 DMREG4.7816.4217.328.2116.766.8310.576.6112.51 ATCAL10.3919.7229.4317.5013.289.68 ATLBS35.9010.0511.137.408.5227.00 ATSWM19.5113.1916.6617.768.1124.76 ATWGT33.4025.2819.0514.025.063.19 NTBRN3.388.5017.8618.3833.6418.24 NTING14.6917.8223.1329.4710.194.70 NTKNO7.2714.5727.8630.9412.357.01 DTFE229.2870.72 FDFCH4.605.9516.6036.1220.6116.11 FDHOT2.583.9017.3445.6116.8513.72 FDLUN4.757.6818.8139.8217.0711.86 FDPIZ9.0912.0723.5043.917.983.45 FDTAC5.878.9319.4748.179.228.34 ATFOR19.8119.6424.6014.0910.3911.46 ATDOC11.4610.6117.8523.698.9827.41 NTADD33.0323.3824.3714.902.751.57 NTCHL37.7725.6823.779.442.141.20 NTFAT41.4525.9721.537.692.081.29 NTPRE29.6323.7824.5416.993.441.61 NTSAL34.8226.7924.399.682.821.49 NTSUG23.3020.3232.5316.685.042.12 NTVIT10.739.1515.1323.6724.4016.93 ZQTR23.6823.7526.6225.95

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42 Table 3-4. Frequency, in percent, of explanatory variables for the 1984 1993 period Variable Discrete Classification 123456789 Percents of 15,331 observations DMAGE28.8721.9715.0516.9817.14 DMCHL37.8562.15 DMEDU7.9535.8125.0831.16 DMFEM50.5449.46 DMHSZ23.3231.9333.6811.07 DMIN258.8425.6610.574.93 DMREG4.7816.9517.618.4916.256.9510.476.8011.70 ATCAL12.2422.0329.9515.6211.548.62 ATLBS34.828.8510.856.858.5430.08 ATSWM21.7412.8516.1117.617.2124.47 ATWGT40.8125.6217.199.874.152.36 NTBRN3.128.0217.1915.7135.4120.54 NTING19.0220.0223.1025.998.763.10 NTKNO7.6615.0328.5029.9312.016.87 DTFE229.3370.67 FDFCH5.466.8118.0835.8519.8014.00 FDHOT2.974.4619.0345.1215.6412.77 FDLUN4.667.0818.4738.8217.9912.99 FDPIZ10.2312.4223.1243.117.433.69 FDTAC6.468.6618.7948.358.689.07 ATFOR21.9020.1424.5113.239.5610.65 ATDOC13.3210.8816.7224.028.0527.01 NTADD38.3123.7022.4911.882.361.27 NTCHL43.5924.9821.497.391.620.94 NTFAT45.6525.5520.046.171.591.00 NTPRE34.4524.8122.7913.872.821.26 NTSAL40.8427.2321.677.142.121.00 NTSUG28.0522.0330.8713.414.181.46 NTVIT13.9110.8116.2521.7922.9114.32 ZQTR22.4524.5327.7025.33

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43 Table 3-5. Frequency, in percent, of explanatory variables for the 1994 2003 period Variable Discrete Classification 123456789 Percents of 15,083 observations DMAGE22.6823.9121.4715.2416.69 DMCHL36.4263.58 DMEDU5.1429.1327.8737.86 DMFEM56.2143.79 DMHSZ23.3933.5532.7910.27 DMIN240.4627.5315.7316.29 DMREG4.7915.8917.027.9317.276.7010.676.4013.33 ATCAL8.5117.3728.9119.4115.0510.75 ATLBS37.0011.2611.427.968.5023.87 ATSWM17.2413.5517.2217.919.0325.05 ATWGT25.8724.9320.9418.245.994.03 NTBRN3.638.9818.5421.1031.8415.91 NTING10.3015.5723.1633.0111.656.31 NTKNO6.8814.1127.2131.9612.707.14 DTFE229.2270.78 FDFCH3.735.0915.1036.4021.4318.25 FDHOT2.183.3315.6246.1118.0714.68 FDLUN4.858.3019.1540.8516.1410.71 FDPIZ7.9311.7123.8944.738.533.21 FDTAC5.269.2120.1647.999.787.60 ATFOR17.6819.1324.7014.9611.2412.29 ATDOC9.5610.3419.0023.369.9227.82 NTADD27.6723.0726.2717.973.141.88 NTCHL31.8526.4026.0911.532.671.47 NTFAT37.1926.3923.039.232.581.57 NTPRE24.7422.7326.3220.164.081.97 NTSAL28.7126.3427.1612.273.541.99 NTSUG18.4818.5934.2220.005.922.80 NTVIT7.507.4613.9825.5725.9219.57 ZQTR24.9422.9625.5326.58

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44 Table 3-6. Five highest correlation coefficients and their probability under Ho: Rho = 0 of variables of the 1993-2003 period (13,150 obs) DMAGE DMCHL 0.5269 <.0001 DMHSZ -0.3839 <.0001 DMFEM 0.3216 <.0001 FDTAC 0.2747 <.0001 FDLUN 0.2449 <.0001 DMCHL DMHSZ -0.7401 <.0001 DMAGE 0.5269 <.0001 FDTAC 0.2301 <.0001 FDLUN 0.2087 <.0001 ATSWM 0.1880 <.0001 DMEDU DMIN2 0.3419 <.0001 NTKNO -0.2636 <.0001 DMFEM -0.2451 <.0001 ATFOR -0.2025 <.0001 ATLBS 0.1265 <.0001 DMFEM DMAGE 0.3216 <.0001 DMEDU -0.2451 <.0001 DMIN2 -0.2084 <.0001 NTVIT -0.1385 <.0001 ATDOC -0.1017 <.0001 DMHSZ DMCHL -0.7401 <.0001 DMAGE -0.3839 <.0001 FDTAC -0.2029 <.0001 FDLUN -0.1612 <.0001 FDHOT -0.1572 <.0001 DMIN2 DMEDU 0.3419 <.0001 DMFEM -0.2084 <.0001 FDFCH 0.1726 <.0001 DMHSZ 0.1489 <.0001 NTKNO -0.1465 <.0001 DMREG FDTAC -0.0704<.0001 ATFOR -0.0682 <.0001 FDPIZ 0.0632 <.0001 DMIN2 -0.0547 <.0001 DMAGE 0.0495 <.0001 ATCAL NTFAT 0.3607 <.0001 NTCHL 0.3391 <.0001 NTKNO 0.3245 <.0001 NTSUG 0.3075 <.0001 NTSAL 0.3002 <.0001 ATLBS ATDOC 0.2033 <.0001 DTFE2 0.1385 <.0001 NTSUG 0.1286 <.0001 DMEDU 0.1265 <.0001 FPFAS 0.1056 <.0001 ATSWM DMAGE 0.2246 <.0001 DMCHL 0.1880 <.0001 DMHSZ -0.1528 <.0001 FDTAC 0.1309 <.0001 ATFOR 0.1216 <.0001 ATWGT ATCAL 0.1725 <.0001 NTING 0.1701 <.0001 NTFAT 0.1473 <.0001 NTKNO 0.1448 <.0001 DMAGE -0.1366 <.0001

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45 Table 3-6. Continued NTBRN FDFCH 0.1457 <.0001 FDLUN 0.1450 <.0001 FDHOT 0.1230 <.0001 FPRES 0.1217 <.0001 FDPIZ 0.1149 <.0001 NTING NTKNO 0.2163 <.0001 NTSUG 0.2145 <.0001 NTCHL 0.2141 <.0001 ATCAL 0.2048 <.0001 NTFAT 0.1998 <.0001 NTKNO ATCAL 0.3245 <.0001 DMEDU -0.2636 <.0001 NTING 0.2163 <.0001 ATFOR 0.2105 <.0001 FDFCH -0.1838 <.0001 DTFE2 ATDOC 0.2601 <.0001 ATCAL 0.2453 <.0001 NTFAT 0.1874 <.0001 NTCHL 0.1810 <.0001 NTSUG 0.1713 <.0001 FDFCH FDLUN 0.4764 <.0001 FDTAC 0.4726 <.0001 FDHOT 0.4598 <.0001 FDPIZ 0.4096 <.0001 ATCAL -0.2668 <.0001 FDHOT FDLUN 0.5011 <.0001 FDFCH 0.4598 <.0001 FDPIZ 0.3531 <.0001 FDTAC 0.3291 <.0001 FPFAS 0.2008 <.0001 FDLUN FDHOT 0.5011 <.0001FDFCH 0.4764 <.0001 FDPIZ 0.4611 <.0001 FDTAC 0.4251 <.0001 DMAGE 0.2449 <.0001 FDPIZ FDTAC 0.5113 <.0001 FDLUN 0.4611 <.0001 FDFCH 0.4096 <.0001 FDHOT 0.3531 <.0001 DMAGE 0.2219 <.0001 FDTAC FDPIZ 0.5113 <.0001 FDFCH 0.4726 <.0001 FDLUN 0.4251 <.0001 FDHOT 0.3291 <.0001 DMAGE 0.2747 <.0001 ATFOR NTKNO 0.2105 <.0001 DMEDU -0.2025 <.0001 FPFAS 0.1694 <.0001 DMIN2 -0.1227 <.0001 ATSWM 0.1216 <.0001 FPFAS FPRES 0.3521 <.0001 FDFCH 0.2055 <.0001 FDHOT 0.2008 <.0001 FDTAC 0.1981 <.0001 DMAGE 0.1911 <.0001

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46 Table 3-6. Continued FPRES FPFAS 0.3521 <.0001 DMIN2 -0.1451 <.0001 NTBRN 0.1217 <.0001 DMHSZ 0.0996 <.0001 DMCHL -0.0935 <.0001 ATDOC ATCAL 0.2785 <.0001 DTFE2 0.2601 <.0001 NTSUG 0.2222 <.0001 NTVIT 0.2132 <.0001 ATLBS 0.2033 <.0001 NTADD NTPRE 0.8424 <.0001 NTFAT 0.6402 <.0001 NTCHL 0.6288 <.0001 NTSAL 0.6153 <.0001 NTSUG 0.5752 <.0001 NTCHL NTFAT 0.7872 <.0001 NTSAL 0.6647 <.0001 NTSUG 0.6317 <.0001 NTADD 0.6288 <.0001 NTPRE 0.5642 <.0001 NTFAT NTCHL 0.7872 <.0001 NTSAL 0.7277 <.0001 NTADD 0.6402 <.0001 NTPRE 0.6006 <.0001 NTSUG 0.5629 <.0001 NTPRE NTADD 0.8424 <.0001 NTSAL 0.6624 <.0001 NTFAT 0.6006 <.0001 NTCHL 0.5642 <.0001 NTSUG 0.5479 <.0001 NTSAL NTFAT 0.7277 <.0001NTCHL 0.6647 <.0001 NTPRE 0.6624 <.0001 NTADD 0.6153 <.0001 NTSUG 0.6019 <.0001 NTSUG NTCHL 0.6317 <.0001 NTSAL 0.6019 <.0001 NTADD 0.5752 <.0001 NTFAT 0.5629 <.0001 NTPRE 0.5479 <.0001 NTVIT ATDOC 0.2132 <.0001 NTING 0.1967 <.0001 NTSUG 0.1690 <.0001 DMAGE -0.1516 <.0001 NTPRE 0.1507 <.0001 ZQTR DMEDU 0.0323 0.0002 DTFE2 0.0266 0.0023 FDTAC 0.0254 0.0036 DMAGE 0.0215 0.0135 FDFCH 0.0206 0.0182

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47 Table 3-7. Five highest correlation coefficients and their probability under Ho: Rho = 0 of variables of the 1984-2003 period (30,414 obs) DMAGE DMCHL 0.5297 <.0001 DMHSZ -0.3753 <.0001 DMFEM 0.3165 <.0001 FDTAC 0.2853 <.0001 FDPIZ 0.2382 <.0001 DMCHL DMHSZ -0.7424 <.0001 DMAGE 0.5297 <.0001 FDTAC 0.2335 <.0001 FDHOT 0.2030 <.0001 FDLUN 0.2007 <.0001 DMEDU DMIN2 0.3314 <.0001 DMFEM -0.2638 <.0001 NTKNO -0.2620 <.0001 ATFOR -0.1920 <.0001 DMAGE -0.1527 <.0001 DMFEM DMAGE 0.3165 <.0001 DMEDU -0.2638 <.0001 DMIN2 -0.2067 <.0001 NTVIT -0.1512 <.0001 ATDOC -0.1143 <.0001 DMHSZ DMCHL -0.7424 <.0001 DMAGE -0.3753 <.0001 FDTAC -0.2042 <.0001 FDHOT -0.1832 <.0001 FDLUN -0.1665 <.0001 DMIN2 DMEDU 0.3314 <.0001 DMFEM -0.2067 <.0001 FDFCH 0.1789 <.0001 DMHSZ 0.1503 <.0001 NTKNO -0.1312 <.0001 DMREG FDPIZ 0.0800<.0001 FDTAC -0.0770 <.0001 ATFOR -0.0714 <.0001 DMAGE 0.0535 <.0001 DMCHL 0.0492 <.0001 ATCAL NTFAT 0.3517 <.0001 NTCHL 0.3429 <.0001 NTSUG 0.3130 <.0001 NTKNO 0.3061 <.0001 NTSAL 0.2960 <.0001 ATLBS ATDOC 0.2030 <.0001 DMEDU 0.1346 <.0001 DTFE2 0.1295 <.0001 NTSUG 0.1078 <.0001 DMHSZ -0.0998 <.0001 ATSWM DMAGE 0.2224 <.0001 DMCHL 0.1698 <.0001 ATFOR 0.1398 <.0001 DMHSZ -0.1323 <.0001 ATCAL 0.1132 <.0001 ATWGT NTING 0.2040 <.0001 ATCAL 0.1960 <.0001 NTCHL 0.1573 <.0001 NTFAT 0.1548 <.0001 NTKNO 0.1419 <.0001

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48 Table 3-7. Continued NTBRN FDLUN 0.1267 <.0001 FDFCH 0.1155 <.0001 FDHOT 0.0997 <.0001 ATWGT 0.0780 <.0001 FDPIZ 0.0763 <.0001 NTING NTSUG 0.2437 <.0001 NTVIT 0.2295 <.0001 NTADD 0.2294 <.0001 NTCHL 0.2274 <.0001 NTPRE 0.2227 <.0001 NTKNO ATCAL 0.3061 <.0001 DMEDU -0.2620 <.0001 NTING 0.2129 <.0001 ATFOR 0.2112 <.0001 FDHOT -0.1731 <.0001 DTFE2 ATCAL 0.2492 <.0001 ATDOC 0.2443 <.0001 NTFAT 0.1752 <.0001 NTCHL 0.1709 <.0001 DMAGE -0.1577 <.0001 FDFCH FDLUN 0.4750 <.0001 FDHOT 0.4363 <.0001 FDTAC 0.4265 <.0001 FDPIZ 0.3868 <.0001 ATCAL -0.2305 <.0001 FDHOT FDLUN 0.5162 <.0001 FDFCH 0.4363 <.0001 FDPIZ 0.3458 <.0001 FDTAC 0.3041 <.0001 DMCHL 0.2030 <.0001 FDLUN FDHOT 0.5162 <.0001FDFCH 0.4750 <.0001 FDPIZ 0.4334 <.0001 FDTAC 0.3875 <.0001 DMAGE 0.2182 <.0001 FDPIZ FDTAC 0.4991 <.0001 FDLUN 0.4334 <.0001 FDFCH 0.3868 <.0001 FDHOT 0.3458 <.0001 DMAGE 0.2382 <.0001 FDTAC FDPIZ 0.4991 <.0001 FDFCH 0.4265 <.0001 FDLUN 0.3875 <.0001 FDHOT 0.3041 <.0001 DMAGE 0.2853 <.0001 ATFOR NTKNO 0.2112 <.0001 DMEDU -0.1920 <.0001 FDTAC 0.1402 <.0001 ATSWM 0.1398 <.0001 ATCAL 0.1032 <.0001 ATDOC ATCAL 0.2892 <.0001 DTFE2 0.2443 <.0001 NTVIT 0.2310 <.0001 NTSUG 0.2240 <.0001 DMAGE -0.2190 <.0001

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49 Table 3-7. Continued NTADD NTPRE 0.8371 <.0001 NTFAT 0.6670 <.0001 NTCHL 0.6392 <.0001 NTSAL 0.6229 <.0001 NTSUG 0.5868 <.0001 NTCHL NTFAT 0.7805 <.0001 NTSAL 0.6625 <.0001 NTADD 0.6392 <.0001 NTSUG 0.6306 <.0001 NTPRE 0.5720 <.0001 NTFAT NTCHL 0.7805 <.0001 NTSAL 0.7145 <.0001 NTADD 0.6670 <.0001 NTPRE 0.6243 <.0001 NTSUG 0.5780 <.0001 NTPRE NTADD 0.8371 <.0001 NTSAL 0.6701 <.0001 NTFAT 0.6243 <.0001 NTCHL 0.5720 <.0001 NTSUG 0.5584 <.0001 NTSAL NTFAT 0.7145 <.0001 NTPRE 0.6701 <.0001 NTCHL 0.6625 <.0001 NTADD 0.6229 <.0001 NTSUG 0.6152 <.0001 NTSUG NTCHL 0.6306 <.0001 NTSAL 0.6152 <.0001 NTADD 0.5868 <.0001 NTFAT 0.5780 <.0001 NTPRE 0.5584 <.0001 NTVIT ATDOC 0.2310 <.0001NTING 0.2295 <.0001 DMAGE -0.1985 <.0001 NTSUG 0.1908 <.0001 NTADD 0.1732 <.0001 ZQTR DTFE2 0.0284 <.0001 FDFCH 0.0172 0.0028 DMEDU 0.0149 0.0095 FDHOT 0.0134 0.0194 DMREG -0.0120 0.0365

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50 Completely agree 32.7% Mostly agree 23.5% Somewhat agree 20.4% Neither 9.9% Somewhat disagree 7.1% Disagree 6.5% Completely agree 34.0% Mostly agree 25.4% Somewhat agree 20.9% Neither 9.2% Somewhat disagree 4.3% Disagree 6.2%"I check labels for harmful ingredients (ATLAB) "My food purchase is based on using the labels" (FPLAB) 1993-2003 data with 13,150 observations Figure 3-1. Fr equency distribution of ATLAB and FPLAB 1993-2003 Completely agree 33.7% Mostly agree 22.7% Somewhat agree 19.9% Neither 9.7% Somewhat disagree 6.9% Disagree 7.1%"I check labels for harmful ingredients" (ATLAB) 1984-2003 data with 30,414 observationsFigure 3-2. Frequency distribution of ATLAB 1984-2003

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51 Completely agree 35.3% Mostly agree 22.4% Somewhat agree 19.5% Neither 9.4% Somewhat disagree 6.4% Disagree 7.0% Completely agree 32.1% Mostly agree 23.1% Somewhat agree 20.4% Neither 10.1% Somewhat disagree 7.3% Disagree 7.1%1984-1993 period 1994-2003 period I check labels for harmful ingredients (ATLAB) Figur e 3-3. Frequency distribution for two periods of ATLAB

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52CHAPTER 4 MODEL SPECIFICATION Since the household response is discrete with scaled values, the likelihood of reading food labels can be estimated using Ordered Probit mode ls where the probability of each Likert score can be determined. The present research specifies thr ee Ordered Probit models to analyze the use of food labels. The first two models use survey data from 1993 to 2003 using the variables described in Table 3-1. A third model uses the survey data from 1984 to 2003 with 30 explanatory variables described in Table 3-1. Variables with an asterisk are excluded because they belong to the survey of 1993-2003. Ordered Probit Models for the 1993-2003 Period For notational convenience the five groups of variables in Table 3-1 were noted with the matrices X1 through X5 with the corresponding variables within the X group. Let X1 through X5 be partitioned matrices of X where X captures all factors expected to have some impact on the households attention to food labels. As defined in Table 3-1, ATLAB entails the scores for reading labels for harmful ingredients and FPLAB reflect s the scores for using labels when buying foods. Explicitly, ATLAB = f (X1...X5)(4-1a) FPLAB = f (X1...X5)(4-1b) Since ATLAB and FPLAB both are scaled variables taking on discrete and limited values, standard estimation procedures are no longer appropriate. Specifically, ATLAB and FPLAB are ordered in that the scores increase when moving from the total disagree response (6) to completely agree score (1). Note, however, that the scoring while ordered is ordinal in that a score of say 4 does not mean it is twice the score of 2. This problem is the classic Ordered Probit modeling where one estimates the probability of each score with the scoring being exhaustive and mutual exclusive.

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53Ordered Probit models are built around a latent variable y ranging from to that is mapped to an observed variable y (attention to labels in this study) which provides incomplete information about an underlying y* according to the measurement equation (4-2): yi =m if m-1 y* < m for m = 1 to J (4-2) The 's are called thresholds or cutpoints and the extreme categories 1 and J are defined by the openended intervals with 0 = and j = (Long 1997, page 116-117). For illustration purposes let y* = X + (4-3) and the intercept and all parameters associated with the partition matrix X [X1...X5] are included in Furthermore, it is assumed that is normally distributed with mean 0 and variance 1. The measurement equation can be illustrated using one of the dependent vari ables of the present research, I check the labels for harmful ingredients ( y = ATLAB), which has six levels in the Likert scale shown in Table 3-1. y = 1if < y* 1 y = 2if 1 < y* 2y = 3if 2 < y* 3y = 4if 3 < y* 4(4-4) y = 5if 4 < y* 5y = 6if 5 < y* < Clearly, the ’s are unknown values along with and must be estimated. Once these unknowns are estimated, the probabilities for each Likert score can be derived as indicated below: Prob[y = 1] = Prob(< X + 1 ) = Prob(X < 1 X )(4-5) = Prob( 1 X ) Prob( < X ) = ( 1 X ) (X )

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54Since (X ) = 0 letting represent the cumulative normal distribution, then: Prob[y = 1] = ( 1 X )(4-6) The probabilities for the rest of the values immediately follow where: Prob[y = 2] = ( 2 X ) ( 1 X ) Prob[y = 3] = ( 3 X ) ( 2 X )(4-7) Prob[y = 4] = ( 4 X ) ( 3 X ) Prob[y = 5] = ( 5 X ) ( 4 X ) Since the scores are exhaustive and mutually exclusive (i.e., ( X ) = 1), the last value is predetermined: Prob[y = 6] = ( X ) ( 5 X )(4-8a) Prob[y = 6] = 1 ( 5 X )(4-8b) While values for the ’s and can be estimated with most econometric packages, in this study TSP econometric software is used because of its very powerful procedures for gaining direct access to the estimated coefficients. In TSP, page 304, the thresholds are called MU and "the lowest effective boundary value (MU1) is normalized to 0, ...", thus giving the slight variation from the general form in (4-7) above with X including an intercept term: Prob[y = 1] = (X ) Prob[y = 2] = ( 2 X ) (X ) Prob[y = 3] = ( 3 X ) ( 2 X )(4-9) Prob[y = 4] = ( 4 X ) ( 3 X ) Prob[y = 5] = ( 5 X ) ( 4 X ) Prob[y = 6] = 1 ( 5 X ) Since X includes a constant term (C), wh ich can be thought of as a replacement for 2; in this case, the other can be iterpreted as being measured relativ e to the value of C. Therefore, the TSP output gives MU3, MU4, MU5 and MU6 as thresholds (Tables 5-1 to 5-7).

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55Defining X in the Label Models From Table 3-1 there are a total of 32 variables expected to impact the scoring of attention to labels for both the negative and positive uses of labels (i.e., ATLAB and FPLAB). All of these variables are discrete in form with the range of values depending on the measurement for each as illustrated in the right column of Table 3-1. Hence, each variable must be expressed in binary form as set forth in the equation (4-10) where a Z notation is added to each variable in Table 3-1 to indicate its binary form. For example, concern ove r calories in Table 3-1 takes six possible levels and hence there are six categories for ATCAL shown in X22 in equation (4-10) with the "j"XZDMAGEZDMCHLZDMEDUZDMFEM ZDMHSZZDMINZDMREG XZATCALZATLBSZATSWMZATWGTjji j jji j jji j jji j jji j jji j jji j jji j jji j jji j jji j11 1 5 5 1 2 7 1 4 11 1 2 13 1 4 17 1 4 21 1 9 2230 1 6 36 1 6 42 1 6 482 1 6 54 1 6 60 1 6 66 1 6 3372 1 2 74 1 6 80 1 6 86 1 6 92 1 6 98 1 6 104 1 6 1102 jji j jji j jji j jji j jji j jji j jji j jji j jji j jji j jZNTBRNZNTINGZNTKNO XZDTFEZFDFCHZFDHOTZFDLUN ZFDPIZZFDTACZATFOR ZFPFASji j jji j jji j jji j jji j jji j jji j jji j jji j jji j jji jZFPRES XZATDOCZNTADDZNTCHLZNTFAT ZNTPREZNTSALZNTSUGZNTVIT XZZQTR X 1 6 116 1 6 44122 1 6 128 1 6 134 1 6 140 1 6 146 1 6 152 1 6 158 1 6 164 1 6 55170 1 4 01122334455XXXXX(4-10)

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56 0 1 5 1 5 55 1 4 05 5 1 40 jji j jj j jj j jjijijii j jZandimposeZ ZZthen DZwhereDZZZ Z Z()() (4-11) subscript denoting the binary level of the variable and the "i" giving the actual observation. This procedure is followed for each variable using th e corresponding levels with "j" being defined for each variable. For each variable, one needs to refere nce back to Table 3-1 to see the exact meaning of the "j". With the complete binary definitions, X is a concise representation of the right-hand-side variables of the Ordered Probit models. To fully re present the right hand side, four thresholds would need to be added to equation (4-10) as initially set forth in (4-9). The regressors in equation (4-10) are polytom ous variables (i.e.,variables that take on more than two values) and each value represents a category. Each category j in the polytomous variable has to be converted into a dummy variable.Given there are so many binary variables in the model, a convenient approach for dealing with the "dummy variable trap" is to restrict a weighted sum of the coefficients to zero for each discrete variable. W ith this straight forward procedure, the intercept represents the average household and all coefficien ts are expressed relative to this average. To illustrate, let a discrete variable, like age of female head (DMAGE), take 5 values which are represented by 5 dummy variables (Z). Then restrict the weighted sum of th e coefficients to zero as shown in (4-11) below. When all variables are at their means the re stricted variables (DZ) are zero and hence the intercept is for the mean set of characteristics and all coefficients represen ts deviations from the average household in this study (e.g., 0 + 1, 0 + 2, 0 + 3, 0 + 4, and 0 jZ j). Now in (4-11) instead of the intercept representing the base when one of the variables is restricted to zero

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57(i.e., the traditional approach), the intercept represents the average household. Just as with the traditional approach one can calculate any of the effects but now relative to the average household instead of a base. This approach is very convenien t in that one never has to remember all of the categories embedded in the base with the traditiona l approach. Applying this approach to equation (4-10) and adding the letter D as a first letter to the name of the variables, the new equation with restricted dummy variables would be created. In this new equation, because of the restriction, each set of dummy variables would smaller, by a unit, than each set in (4-10). Since there are 32 sets of dummy variables, creat ed from 32 original variables, for a total of 174 dummy variables, then the new equation, after applying the restriction shown in (4-11), would have 174-32 = 142 restricted dummy variables. The la st coefficient of each set of dummies, lost to the restriction, is recovered later using also equation (4-11). Their standard errors are calculated easily because the variance covariance matrix of th e rest of the coefficients in the set is known. Again, one gets the same results either way. It is just a more convenient way to deal with a large number of discrete variables. With (4-10) and the dummy adjustment, X is specified and ready to be used in the Ordered Probit estimation. Two models, one with ATLAB as dependent variable and the other with FPLAB as dependent variable, use the explanatory variables set forth in equation 4-10. The high correlation coefficients between some of the explanatory variables (Tables 3-6 and 3-7), specifically between variables Cautious about additives (NTADD), Cautious about cholesterol (NTCHL), Cautious about fat (NTFAT), Cautious about preservatives (NTPRE), Cautious about salt (NTSAL), and Cautious about sugar (NTSUG), point to a possible problem of multicollinearity. "While a high correlation coefficient between two explanatory variates can indeed point to a possible collinearity problem, th e absence of high correlations cannot be viewed as evidence of no problem. It is clearly possible fo r three or more variates to be collinear while no

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58two of the variates taken alone are highly correla ted. The correlation matrix is wholly incapable of diagnosing such a situation" (Belsley et al. 1980, page 92). The followi ng section addresses this issue. Multicollinearity According to Gujarati (1988), pages 283-284, The term multicollinearity is due to Ragnar Frisch. Originally it meant the existence of a "perfect," or exact, linear relationshi p among some or all explanatory variables of a regression model. For the k -variable regression involving explanatory variable X1, X2, ..., Xk (where X1 = 1 for all observations to allow for the intercept term), an exact linear relationship is said to exist if the following condition is satisfied: 1 X1 + 2 X2 + ... + k Xk = 0(10.1.1) where 1, 2, ..., k are constants such that not all of them are zero simultaneously. Today, however, the term multicollinearity is used in a broader sense to include the case of perfect multicollinerit y, as shown by (10.1.1) as well as the case where the X variables are intercorrelated but not perfectly so as follows. 1 X1 + 2 X2 + ... + k Xk + i = 0(10.1.2) where i is a stochastic error term. On the effects of multicollinearity, Gujarati (1988), page 289, cites Achen (1982): The only effect of multicollinearity is to ma ke hard to get coefficient estimates with small standard error. But having a small number of observations also has that effect, as does having independent variables with small variances. (In fact, at a theoretical level, multicollinearity, few observations and small variances on the independent variables are essentially all the same problem). The difficulty caused by high correlation "is not one of identification but of precision. The higher the correlation between the regressors becomes, the less precise our estimates will be" (Greene 1990, page 278). The symptoms or consequences of multicollinearity are: (a) small changes in the number of observations can produce big changes in the estimat ed parameters; (b) high R-squared despite high

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59standard errors (few significant t-statistics); (c) co efficients are huge in ma gnitude or have the wrong sign; (d) high condition number (Greene 1990, Gujarati 1988). The condition number or condition index of a square matrix is the square root of the ratio of its maximum characteristic root to its minimu m characteristic root. For nonsquare matrices, like the matrices with the dependent variables X, the matrix X'X is used. "Because the characteristic roots are affected by the scaling of the columns of X, we scale the colu mns to have length 1 by dividing each column by its norm" (Gr eene 1990, page 35). The norm of column i is th square root of xi 'xi or the square root of the sum of squares of column xi. According to Belsley et al. (1980), page 105, "... weak dependencies are associated with condition indexes around 5 or 10, whereas moderate to strong relations are associated with condition indexes of 30 to 100". On the other hand, Greene (1990), points out that a condition number greater than 20 is large and that a matrix is nearly singular if the smallest characteristic root is close to zero compared to the largest characteristic root. Greene (1990), page 35, also indicat es that "Matrices with large condition numbers are difficult to invert accurately." Among the remedial measures suggested in the literature are combining cross-sectional and time series data, dropping a variable and specification bias, transformation of variables, additional new data, and principal components (Gujarati 1988, Kennedy 1998). In the present research, the condition number will be used in the diagnosis of multicollinearity and, if a remedial measure is need it, principal components will be employed. Principal components "A principal component is a linear combination of variables that captures as much of the variation in those variables as it is possible to capture via a linear combination of those variables" (Kennedy 1998, page 172). "When faced with ill cond itioned data, investigators frequently chose to reduce information demands on the sample by considering only subspaces of the k-dimensional

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60parameter space. These subspaces ma y be suggested by economic theory, previous statistical results, or ad hoc dimensionality procedures" (Judge et al 1985, page 909). In the present research, principal components will be applied to a subgroup of the h ealth concern variables showing high correlation coefficients, Cautious about additives (NTADD), Cautious about cholesterol (NTCHL), Cautious about fat (NTFAT), Cautious about preservatives (NTPRE), Cautious about salt (NTSAL), and Cautious about sugar (NTSUG). There are six variab les, therefore six principal components can be constructed, each orthogonal to the others. From the six components, the four with the highest characteristic roots will be used in the models. These four components will replace the 30 restricted dummy variable s (5 restricted dummy variables from each of the following variables: NTADD, NTCHL, NTFAT, NTPRE, NTSAL and NTSUG). Ordered Probit Model for the 1984-2003 Period The first two models, one with ATLAB and th e other with FPLAB as dependent variables use equation (4-10) and data from 1993-2003 as explanatory variables. A third model is implemented using ATLAB as dependent variab le, and data from 1984-2003, with 30 explanatory variables. Two explanatory variables shown in Equa tion 4-10, that were part of models 1 and 2 are excluded: Try fast food places (FPFAS) and Visit restaurants more than most (FPRES). They are shown in Table 3-1 with an asterisk because they are part of the survey that starts in 1993. This third model is estimated sequentially, w ith each sequence including 10 years of data or approximately 50% of the total number of observations (i.e 1984-1993, 1985-1994, ..., 1994-2003). The data in each block should have enough variability and the results would give an insight on how the likelihood of reading food labels has change d over time. In this model the total number of unrestricted dummy variables is 162, resulting in 132 restricted dummy variables. After the coefficients are calculated, a simulation is carried out to find the probabilities for the average consumer for each block of data. The simulated probabilities for the complete set of explanatory

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61variables is carried out only for the first (1984-1993) and last block (1994-2003). The estimated coefficients and simulated probabilities are compared to see the changes over time.

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62CHAPTER 5 ANALYSIS OF RESULTS Three models were specified in Chapter 4. Models 1 and 2 use the 1993-2003 survey data with 32 variables. Model 1 estimates the label responses to I check the labels for harmful ingredients" (ATLAB) while Model 2 estimates the label responses to I read the labels for my food purchase" (FPLAB). Model 3 estimates the label responses to I check the labels for harmful ingredients" (ATLAB) and uses the 1984-2003 data, with 30 variables, but does it recursively, in blocks of ten years (i.e. 1984-1993, 1985-1994, ..., 1994-2003). The results of the Ordered Probit models are in Tables 5-1 to 5-7. They show the condition number, scaled R-squared, estimated coefficients, coefficients for the dummy variables that were left out because of the restriction, t -statistics and Wald-statistics. At the end of the tables the thresholds for moving across the Likert scales are reported. Since the models have an intercept it is not necessary to have the threshold for the lowest Likert score. The last threshold for the last level is set once the other values are know. Hence for the six Likert scales, only four thre sholds must be estimated. It is important to recall that each t -value is expressed relative to the average household. A statistically significant t -value means that coefficient is statistically diffe rent from the mean household or 0. Regarding the interpretation of information as the one provided by Tables 5-1 to 5-7, Green (1990), page 705, points out "In the general case, relative to the signs of the coefficien ts, only the signs of the changes in Prob[y = 0] and Prob[y = J] are unambiguous! The upshot is that we must be very careful in interpreting the coefficients in this model. This is the least obvi ous of all the models we have considered. Indeed, without a fair amount of extra calculation, it is qu ite unclear how the coefficients in the Ordered Probit should be interpreted". This is precisely why it is so important to show the estimated probabilities as derived later in this chapter.

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63Ordered Probit Estimates and Probabilities for the Period 1993-2003 Ordered Probit Estimates Table 5-1 and 5-2 provide the Ordered Probit estimates for both label responses (i.e., I check the labels for harmful ingredients" (ATLAB) and I read the labels for my food purchase" (FPLAB). Across the variables there are many significant eff ects, as the t-statistic s and the Wald-statistics show. The Wald-statistics facilitates the comparisons because it is easier to compare 32 variables than 142 dummy variables. The adjusted R-squared in Table 5-2 shows th at the 32 variables explain better the likelihood of Checking the labels for harmful ingredients (ATLAB), than the likelihood of I read the labels for my food purchase" (FPLAB), 49% versus 42%. According to the Wald test, as Tables 5-1 shows that, of the 32 variables, 13 are not significant at the 1% level and 5 (like to lose 20 pounds, pizza, visit restaurants more than most, cautious about preservatives and cautious about salt) are not significant at the 5% level when explaining ATLA B. On the other hand, Table 5-2 shows that 11 variables are not significant at the 1% level and 6 (Education of female, like to loose 20 pounds, love to swim, tacos, cautious about salt and cautious about sugar) are not significant at the 5% level when explaining FPLAB. It is unusual for education not to have a significant effect. The level of education has been found to be an important factor in the use of labels in other studies (Bender and Derby, 1992; Guthrie et al., 1995; He et al., 2004). The condition number in both models (Tables 5-1 and 5-2) is 41. According to Greene (1990), a condition number greater than 20 is large, and, according to Belsley et al. (1980), moderate to strong relations are associated with condition i ndexes of 30 to 100. It is not of extreme importance in the present research to have a low cond ition number, because the potential problems that multicollinearity could cause would not affect the lik elihood of using the labels. Nevertheless, to see how the size of the condition number is related to th e effects of multicollinearity in the models used,

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64principal components will be applied to the variables that provide the highly correlated restricted dummy variables (Table 3-8). These variables are: Cautious about additives (NTADD), Cautious about cholesterol (NTCHL), Cautious about fat (NTFAT), Cautious about preservatives (NTPRE), Cautious about salt (NTSAL), and Cautious about sugar (NTSUG). Table 5-3 show that the first four principal co mponents of these 6 variables account for more than 94% of the cumulative R-squared. There is not a rule that specifies how many components to leave out. Using all of them is like using the origin al variables. It was decide to us e the first four components. Tables 5-4 and 5-5 show the results of the ATLAB and FPLAB models with principal components. When compared to the models with no principal components (Tables 5-1 and 5-2) the only noticeable difference is the smaller condition number, 10. Other than that, the R-squared is the same and the t-statistics and Wald test do not show a noticeable difference, rejecting or failing to reject the significance of the coefficients at the same probability level. This would indicate that in the case of both models the high correlation coefficients show n in Table 3-8 cause no ill-effects on the Ordered Probit models. Given this conclusion, the models with no principal components will be used to simulate the likelihood of using the labels. Probabilities The most useful aspect of the Ordered Probit label models is showing the probabilities of using the labels while considering the sensitivity to each variable included in the models (Table 5-1 and 5-2). Since there are some many variables in the specification shown in equation 4-10, an easy way to discuss the probabilities is to express th e them relative to the average household likelihoods of reading the food labels as depicted in Fi gure 5-1. Around 59 percent of the households checked food labels for harmful ingredients (combining scor es 1 and 2) and 63 percent used the labels for broader purchasing decisions. Using these average probabilities as our reference base, the effects

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65from changing household demographics; lifestyle activities and attitudes; health concerns, and household eating habits are estimated from the Ordered Probit coefficients. Importance of food labels across demographics Figure 5-2 shows the estimated probabilities acr oss several demographics while holding all other factors for the average household with th e left bars being the probabilities for the I read the labels for harmful ingredients and the right bars for the I read the labels for my food purchase ." Consistent throughout the probabilities are higher levels for using labels in general than just for the harmful ingredients, thus clearly pointing to th e role of labels beyond the preventive dimension. Note in Figure 5-2 that each bar is the total of completely and mostly agree with both intensities being shown. In all remaining figures just the agreement percentages are presented. Among the demographics shown in Figure 5-2, ag e of the female household head show the largest range of change with the probability of reading the labels increasing consistently over the age range from a low of 53 percent to 66 percent for the "I read labels for harmful ingredients statement. Similarly, but to a smaller degree, th e probability of reading the labels increases with education with the big drop being among those in the lowest education level. The presence of children under 18 years and employment of female head of household follow a similar pattern. They show a bigger difference in the case of I read labels for harmful ingredients than in the case of I read the labels for my food purchase ." Figure 5-2D shows that the probability of reading food labels is greater for the unemployed than for the em ployed. For comparison, Li n et al. (2004) results showed that employment status was not related to information search on labels. The number of members in the household has a small impact in both cases. Probably the most unexpected result is with the consistent drop in using labels across income levels which is the opposite to the results obtained by Lin et al. (2004). For each demographi c, the response intensity between mostly and

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66completely agree remained reasonably proportional. The effects of the demographic variables on reading food labels are similar to those reported by McLean-Meyinsse (2001). Figure 5-2 also shows that there are regional di fferences in using labels with the south and southeast states showing the highest probabilities of using the labels and the households in the north and northeastern states showing the least use of the labels. Except for knowing the regional differences in label importance, one cannot gain much insight into the underlying regional characteristics. The region with the lowest probability is West North Central. Attitudes and use of food labels Concern about calories and the belief of know ing about nutrition more than others have major effects on the use of food la bels. Expectations would be that those concerned about calories are more likely to read food labels. As shown in Figure 5-3A, this is precisely true where there is almost a linear relationship between the intensity of concern or not and the likelihood of using food labels. In fact, concern about calories is the single most important factor influencing the use of food labels for both harmful ingredients and food labe ls in general. Among those households showing no interest in calories, the probability of reading food labels drops to 31 percent of harmful ingredients and 41 percent for labels more broadly used. There is nearly an 87 percent probability of reading food labels when the household member is strongly concerned about calories. Figures 5-3B to 5-3D, Like to lose 20 lbs ", Love to swim and Overweight isn't attractive show some inconsistencies. It would be expected that consumers that agree with these statements would read the labels more often than the consumers that disagree w ith the statements. The fact that consumers that disagree with Overweight isn't attractive read the food labels more often than consumers that agree with the statement shows that although the former consumers think overweight is attractive they still care about what they eat ant that is why they read the labels. In Figure 5-3, with exception of A and G, the difference in probabilities are small.

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67Another attitude addresses the issue if bra nds are substitutes for reading the food label. Households were given the statement that Best known brands are the highest quality and were asked to scale their response, again with the si x point scale. One basic argument is that brands already have some level of consumer support and confidence and, as such, those households supporting this statement would be less likely to r ead the food labels since the brand identification is enough. While the responses are not profound, ther e does appear to be some substitution between the brand information and food labels. Households indicating total agreement with the best-known brand statement are less likely to read the food labe ls. There is a consistent drop in the use of food labels with the more re liance on brands. A major drop is seen between the scales of completely versus mostly agree. This tradeoff between brand information versus the food package label is equally true for both reading labels for harmful ingredients and reading labels more broadly. This is interesting in that brands incur both the cost of branding and food labels but may have fewer relative benefits from the labeling compared with less branded food. Figure 5-3 also shows that the believe that foods should have body building ingredients has a smaller impact when checking the labels for ha rmful ingredients than when reading the labels looking for general information. In the latter cas e the probabilities change from 68 to 59 percent, very much in a linear fashion, when going from hous eholds that agree completely to households that mostly disagree with the statement Foods should have body building ingredients ." The last graph in Figure 5-3 shows the strong impact that the believe I know more than most about nutrition has on reading food labels, specially when checking th e labels for harmful ingredients. The probability changes form 76 to 43 percent. Eating habits and use of food labels Eating habit variables in the Ordered Probit model included being on a diet, five types of foods (fried chicken, hot dog, lunchmeat, pizza and tacos), country-of-origin, eating in fast food

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68places and eating in restaurants. Again, using the average household as the reference base, the probabilities of reading food labels over each of thes e eating habits are shown in Figure 5-4. Dieting and the type of food consumed have major im pacts on the use of food labels when making purchasing decisions. When dieting, the female head of the household is about 12 percentage points more likely to rely on food labels compared to thos e not on diets. Lin et al. (2004) results also show that the probability of searching for informa tion on food labels is higher among consumers on a special diet. Similarly, household that discourage the consumption of foods like fried chicken, hot dogs, lunchmeat, pizza and tacos are considerably mo re likely to read the food labels. For example, households that discourage the consumption of fr ied chicken show a probability of 70 percent for harmful ingredients versus 49 percent when not concerned about consuming this product. Consumption of hot dogs and fried chicken are indicative of the more general type of eating habits closely associated with fast foods. That is why it is surprising that the results for Try fast food places don't follow the same pattern. Households completely concerned about foreign foods show a 65 percent level of probability of reading the label for harmful ingredients; while the probability drops consistently among households expressing less concern about foreign foods. Dieting and the type of food consumed have majo r impacts on the use of food labels when making purchasing decisions. Health concerns and use of food labels Health concerns have many dimensions and in formation about additives, cholesterol and fats are often the most visible messages on many f ood labels. The households reflect the level of concerns of several health meas ures using the statement A person should be concerned about cholesterol (or similar issues). Figure 5-5 shows the probabilities linked to these concerns. As a general rule, when doctors give advice to consum ers they are much more likely to use food labels and, in fact, the probabilities for harmful ingredie nts increase for 52 to 76 percent when the doctor

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69give dieting advice. Importance of food labels incr eases almost linearly with the level of concern about additives, from 47 to 66 percent when consid ering harmful ingredients. Cholesterol shows a similar pattern also when considering harmful ingredients, changing from 48 to 64. The trend is more mixed when using food labels in genera l. According to Lin et al. (2004), page 1962, "respondents who had higher intakes of total fat, sa turated fat, or cholesterol were less likely to report looking for label information on these nutrients". Patterns associated with concerns over fats, preservatives, salt and sugar are mixed in both cases, when considering harmful ingredients and food labels in general. The same mixed patterns appear when the vitamins are recommended by physician. Seasonality and use of food labels Figure 5-6 shows how the probabilities of reading the labels change during the year. In the last quarter, Thanksgiving and Christmas holidays, the likelihood of reading the labels is the smallest. The difference between the third a nd fourth quarter is slightly bigger when "I read the labels for my food purchase" than when "I read the labels for harmful ingredients" This would show that during the holidays consumers worry less about what they consider harmful ingredients in the food they purchase. Ranking the food label probabilities Importance of labels obviously differs across all the variables that capture household food shopping behavior as initially seen with the Ordered Probit estimates in Tables 5-1 and 5-2. In addition to measuring the directi onal effects of each variable, it is equally insightful to put these variables in perspective by ranking their impacts on the likelihood of using labels. Taking the difference between the min and max estimated proba bility for each variable provides a useful way for ranking the impacts. In the right horizontal ba rs in both Figures 5-7 a nd 5-8 theses differences

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70are shown starting with the largest value down to the least range. See Ward, Briz and de Felipe (2003) for a detailed application of this ranking method. By far, Conscious about calories is the single most important factor impacting the likelihood of reading food labels for both label questions. The calories likelihood ranges from 31 to 87 percent when using the labels to discern harmful ingredients and from 41 to 86 percent for using labels as an aid to ma king purchasing decisions. The Conscious about calories ranges are substantially greater than any other factors for both label uses as most evident with the rank of impacts seen with the right horizontal bars in both Figures 5-7 and 5-8. Consumer knowledge about nutrition is important and contributes to greater use of food labels. Except for the age demographic, the next seve ral factors relate to health concerns and eating habits. Moving down both charts, after about the 10t h entry the remaining variables have impacts that are quite small in terms of causing devia tions from the average household probabilities. One becomes particularly impressed with the limited ro le of many of the demographics except for age. Note also that the impact of branding on the use of food labels ranks quite high relative to most of the variables in Figures 5-7 and 5-8. Clear ly, there is some underlying tradeoff between identification with a brand and using labels to ga in information. Importance of labels declines when consumers place greater reliance on brands. There are also a few strange inconsistencies su ch as concern about calories versus being on a diet or would like to lose 20 pounds. Possibly if one is concern about calories, the food selection process is underway while the selection may have already been made when actually on a diet, hence placing less importance on the label. Another interesti ng difference is seen with the issue of foreign foods when considering harmful ingredients compared with food attributes in general. The range of change in using labels for determining harmful ingredients over the concerns about foreign foods

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71is twice that for using labels in general. Consum ers turn more to labels for determining harmful aspects of foreign food than for simply helping making foreign food purchases. Clearly, Figures 5-7 and 5-8 point to potential focus points if the goal is to influence consumers’ use of food labels. One can quickly dete rmine from these figures where little gain in the use food labels would be expected. For example, po licies to education consumers about labels based on income groups or families with younger childre n would have little impact. Whereas, focusing on educating the younger population could have greater benefits if the policies goal were to improve the use of food labels. Figure 5-9 shows the range of change for 15 variables with the highest impact on both, "I check the labels for harmful ingredients" and "I read the labels for my food purchase" It can be seen that, in general, the ranges are larger when looki ng for harmful ingredients is the reason for reading the labels than when reading the labels for general information. Sequential Ordered Probit Estimates and Probabilities for the Period 1984-2003 Ordered Probit Estimates The sequential Ordered Probit model for the period 1984-2003 gives a total of 11 results. Tables 5-6 and 5-7 show the results from the first (1984-1993) and last (1994-2003) recursion respectively, allowing the comparison of periods be fore and after the implementation of the NLEA. Thirty explanatory variables were used in the model, from which11 did not have a statistically significant impact on I check the labels for harmful ingredients (ATLAB) in the period 1984-1993 (Table 5-8). Of the 11 variables 4 are in the demographics group; Children under 18 ", Education of female head of household ", Household income and Census region "; one in the attitudes group, Love to swim "; two in the eating habits group, Eating pizza and Eating tacos "; three in the health concerns group, A person should be cautious about cholesterol ", A person should be cautious about salt and A person should be cautious about sugar ". The eleventh variable is Quarters ". Of

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72all these variables only four are not statistically significant in the period 1994-2003; household income ", Eating pizza ", Eating tacos and Quarters ". The only variable that was statistically significant in the period 1984-1993 but it is not statistically si gnificant in the period 1994-2003 is A person should be cautious about preservatives ". Looking at these changes one can conclude that demographics and health issues have become impor tant drivers during the last years. The variables with the highest Wald statistics are, like in the other models, Conscious of calories and Know more than most about nutrition ". Again, the effect of the numerous variables and changes over time can be appreciated better graphing the probabilities calculated using the estimated coefficients than showing them in tables. The next section shows these probabilities. Probabilities Starting with the average consumer in general, Figures 5-10 to 5-18 s how, in blocks of ten years, from 1984 to 2003, how the probabilities for completely agree plus the probability of mostly agree in the statement I check the labels for harmful ingredients are affected by the different explanatory variables over time. Each fi gure, from 5-11 to 5-18 contrasts the probabilities of the first and the last Likert scale in each variable over the 1984-2003 period. Figure 5-10A shows that the probability of reading the food labels, for the average consumer, in the categories (1) and (2) of the Li kert scale, increased from 1984 to 1998 decreasing later. In 2003, the probabilities for the averag e consumer that chose the statement completely agree decreased below the 1984 level, to 26% while the probabilities for the average consumer that mostly agree with the statement increased by 2, to 32%. Figure 5-10B shows the changes of categories (3), (4), (5) and (6) of Likert scale. The gains (loses) of categories (1) and (2) were the loses (gains) of the last four categories. As a pparent with both figures, the importance attached to food labels has slightly declined since the late 80's.

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73Importance of food labels across demographics Figure 5-11 shows that the relationship between the categories in each demographic variable has been maintained over time in most cases. For example, in 1984-1993, the likelihood of reading food labels label by consumers 65 years or older was higher, by 13, than the likelihood of reading food labels by 35 years old or younger consumers. The difference is the same in 1994-2003. Throughout all periods, younger consumers read the labe ls less often than older consumers. Mueller (1991), mentions that according to the Food Ma rketing Institute's 1990 Trends survey older consumers read the labels more often than their younger counterparts and non-working women are the most likely of any group to read food labels Figure 5-11D shows that not employed women read food labels more often than women that are em ployed. In the case of education, the likelihood not only has declined in 1994-2003 relative to 1984-1994, but the difference between consumers with no high school and consumers that are college graduates has increased. Bender and Derby (1992) report that between1986 and 1988 "A growing proportion of less-educat ed consumers (less than high school) reported using ingredient lists, narrowing th e gap between the least-educated consumers and others" (Page 293). Figure 5-11C shows the pattern described by Bender and Derby but then, at the end of the 1990's, the gap between th e most educated and least educated increases. In the case of the census regions, the ones that show a bigger decline from 1984-1993 to 1994-2003 are New England (1), East North Central (3), West North Central (4) and Mountain (8). The rest of variables in the demographics group show similar probabilities in the first and last periods. Attitudes and use of food labels In the attitudes group, Figure 5-12, Conscious of calories and Know more than most about nutrition are the two variables that show the widest range between category (1), completely agree and category (6), mostly disagree ". This difference is not only bi g but it has been increasing over time. The probability of reading the labels for consumers that believe they are conscious about

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74calories increased, during the period of the st udy, from 78% to 86%. On the other hand, the probability of reading the labels for consumers th at are not conscious about calories decreased from 38% to 31%. For consumers that believe they know more than most about nutrition, the probability of reading food labels for harmful ingredients didn't change much over time. It increased from 76 to 77%, but the probability for consumers that disagr ee with the statement of being knowledgeable the probability decreased from 43 to 39% during the same period. In a smaller scale, the variable Best known brands are highest quality follows a similar pattern over time as Know more than most about nutrition ". The probability of reading food labels for consumers that disagree with the brands statement decreases from 64 to 48% while the probability for the consumers that agree with the statement goes from 62 to 61% during the same period. The change over time in the rest of the variables in this group is really small. Eating habits and use of food labels There is no much change over time in this gr oup of variables, Figure 5-13. They follow the same pattern as the average consumer (Figure 510A) with the difference be tween the first and last category of the Likert scale in each variable stay ing more or less constant over time too. The figures show also what it is expected, that the consumers that discourage the consumption of fast foods have a greater probability of reading the food labels th an consumers that encourage the consumption of food like fried chicken and pizza. Health concerns effects on reading food labels Information about health concern variables are very visible on food labels. The effects of most of these variables on the likelihood of readi ng food labels have changed over time, as Figure 5-14 shows. The exception is Doctor gives advice on diet ". In this variable the difference between the probability for completely agree (1), 75%, and the probability for mostly disagree "(6), 50%,

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75is the widest of all variables but it has not changed that much over tim e. The effect of other variables that have not changed very much over time are A person should be cautious about additives and A person should be cautious about salt ". This last variable though shows some inconsistencies in the middle periods: consumers that disagree with the statement show a higher likelihood of reading the labels than the consumers that agree with it. Many food labels emphasize the lack of fat a nd cholesterol in the packaged product because high levels of cholesterol and fats have been linke d to heart disease. Figure 5-14C shows that at the beginning of the period consumers that mostly disagreed with the statement A person should be cautious about cholesterol read the food labels more often than the consumers that completely agreed. This situation reversed over time, with the difference increasing every period, because the probability of reading the label by consumers th at disagreed, decreased from 67% to 47%. The probability of reading the label by consumers that agr eed with the statement is 62% at the end of the period. In the case of fat, the pattern is similar to that of cholesterol but the difference, at the end of the period, between consumers that agree and disagree with the statement A person should be cautious about fat is only 5%. Bender and Derby (1992) also report that from 1982 to 1988 the percentage of consumers using the ingredient list to avoid fats and cholesterol increased significantly. The likelihood of reading food labels in the cas e of preservatives and sugar (Figures 5-14E and 5-14G) follow an opposite pattern to that of fat and cholesterol. For preservatives, the probability of reading food labels by consumers th at agree with the statement has decreased over time from 65% to 59% while the probability of r eading food labels by consumers that disagree with the statement increased from 52% to 57%. Changes over time in the case of sugar are very similar.

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76In the case of Vitamins recommended by physician the likelihood of reading labels decreased over time for both more for the consumer s that agree (1) than for the consumers that disagree (6) Changes in the effect of seasonality Quarters are used in the model to determine if there is a seasonality in the likelihood of reading food labels during the year. Figure 5-15 s hows that the probability of reading food labels has changed the most over time in the fourth quarter, decreasing from 62% in 1984-1993 to 57% in 1995-2003. Ranking of the probabilities Figures 5-16 to 5-18 put the all variables in a format easy to compare, taking the difference between the min and max estimated probability, and s ee the changes in the impact of the variables on reading food labels between the first pe riod, 1984-1993 and the last one, 1994-2003. It is clear that, although the intensity of the impact of some variables has increased in the last period, the rankings have not changed much (Figure 5-18). Conscious of calories ", Know more than most about nutrition and Cautious about cholesterol have a greater impact on reading food labels in 1994-2003 than in 1984-1993. In general, attitudes, eati ng habits and health concerns are the most important factors behind the need to look for information in food labels. In this chapter the results of three estimated models were discussed, their coefficients and Wald statistics tabulated and their probabilities comp ared and ranked The first two models allowed to compare the probabilities of I check the labels for harmful ingredients versus I read the labels for my food purchase ". The results of the third model, a sequential model, gives an insight into changes over time in the probabilities of reading food labels looking for harmful ingredients.

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77 Table 5-1. Results from the ATLAB model for the period 1993-2003 Dependent variable: ATLAB 1993-2003 (13,150 obs)Condition Number = 41 Explanatory variables Dummy variables Description Scaled R-sq = 0 .49 Coefft-statsWaldP-value Intercept (C)0.632047.13 Age of female head (DMAGE) DZDMAGE1=< 35 years0.15907.47 79.200.0000 DZDMAGE235-44 years0.07553.91 DZDMAGE345-54 years-0.0174-0.90 DZDMAGE455-64 years-0.1006-4.01 ZDMAGE565+ years-0.1853-6.65 Children under 18 years (DMCHL) DZDMCHL1Yes0.05592.35 5.540.0186 ZDMCHL2No-0.0319-2.35 Education of female head (DMEDU) DZDMEDU1No high school0.15523.65 15.130.0017 DZDMEDU2High school0.01911.19 DZDMEDU3Some college-0.0121-0.76 ZDMEDU4College graduate-0.0291-1.96 Employment of female head (DMFEM) DZDMFEM1Employed0.03423.33 11.110.0009 ZDMFEM2Not employed-0.0417-3.33 Household size (DMHSZ) DZDMHSZ11 member0.07102.88 9.690.0214 DZDMHSZ22 members0.00510.29 DZDMHSZ33-4 members-0.0487-2.41 ZDMHSZ45+ members-0.0188-0.53 Household income (DMIN2) DZDMIN21Under $30,000-0.0373-2.71 9.860.0198 DZDMIN22$30-49,9990.00280.18 DZDMIN23$50-69,9990.02801.19 ZDMIN24$70-100,000+0.06752.64 Census region (DMREG) DZDMREG1New England0.10292.28 32.390.0001 DZDMREG2Middle Atlantic-0.0136-0.59 DZDMREG3East North Central0.07483.56 DZDMREG4West North Central0.06702.05 DZDMREG5South Atlantic-0.0771-3.50 DZDMREG6East South Central-0.0527-1.43 DZDMREG7West South Central-0.0196-0.68 DZDMREG8Mountain-0.0210-0.55 ZDMREG9Pacific-0.0089-0.35

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78 Table 5-1. Continued Dependent variable: ATLAB 1993-2003 (13,150 obs)Condition number = 41 Explanatory variables Dummy variables Description Scaled R-sq = 0 .49 Coefft-statsWaldP-value Conscious of calories (ATCAL) DZATCAL1Agree completely -0.9084-20.87 1097.620.0000 DZATCAL2Agree mostly-0.3672-15.63 DZATCAL3Agree somewhat-0.0623-4.04 DZATCAL4Neither0.19459.53 DZATCAL5Disagree somewhat0.352514.68 ZATCAL6Disagree mostly0.725623.03 Like to lose 20 pounds (ATLBS) DZATLBS1Agree completely0.02401.75 10.850.0544 DZATLBS2Agree mostly 0.03171.16 DZATLBS3Agree somewhat0.03311.23 DZATLBS4Neither-0.0126-0.38 DZATLBS5Disagree somewhat-0.0397-1.26 ZATLBS6Disagree mostly-0.0486-2.62 Love to swim (ATSWM) DZATSWM1Agree completely0.04401.92 14.570.0124 DZATSWM2Agree mostly0.02811.12 DZATSWM3Agree somewhat0.04131.92 DZATSWM4Neither-0.0496-2.35 DZATSWM5Disagree somewhat0.00940.30 ZATSWM6Disagree mostly-0.0389-2.20 Overweight isn't attractive (ATWGT) DZATWGT1Agree completely0.10876.05 59.690.0000 DZATWGT2Agree mostly0.03221.88 DZATWGT3Agree somewhat-0.0337-1.77 DZATWGT4Neither-0.1018-4.71 DZATWGT5Disagree somewhat-0.1043-2.71 ZATWGT6Disagree mostly-0.1384-2.73 Best known brands are highest quality (NTBRN) DZNTBRN1Agree completely 0.25724.72 35.390.0000 DZNTBRN2Agree mostly0.03301.04 DZNTBRN3Agree somewhat0.03541.72 DZNTBRN4Neither-0.0047-0.24 DZNTBRN5Disagree somewhat-0.0126-0.87 ZNTBRN6Disagree mostly-0.0835-3.46

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79 Table 5-1. Continued Dependent variable: ATLAB 1993-2003 (13,150 obs)Condition number = 41 Explanatory variables Dummy variables Description Scaled R-sq = 0 .49 Coefft-statsWaldP-value Food should have body building ingredients (NTING) DZNTING1Agree completely-0.1050-3.19 27.030.0001 DZNTING2Agree mostly-0.0301-1.29 DZNTING3Agree somewhat-0.0316-1.76 DZNTING4Neither0.02551.74 DZNTING5Disagree somewhat0.09963.67 ZNTING6Disagree mostly0.06171.54 Know more than most about nutrition (NTKNO) DZNTKNO1Agree completely-0.4717-10.63 415.630.0000 DZNTKNO2Agree mostly-0.3015-11.41 DZNTKNO3Agree somewhat -0.0869-5.30 DZNTKNO4Neither0.10136.89 DZNTKNO5Disagree somewhat0.284111.02 ZNTKNO6Disagree mostly0.424611.54 Adult female on diet (DTFE2) DZDTFE21Yes-0.2360-14.12 199.370.0000 ZDTFE22No0.100014.12 Eating fried chicken (FDFCH) DZFDFCH1Always encourage0.25864.10 147.460.0000 DZFDFCH2Almost always encourage 0.20044.19 DZFDFCH3Sometimes encourage 0.15645.94 DZFDFCH4Neither 0.06514.37 DZFDFCH5Sometimes discourage-0.0608-3.05 ZFDFCH6Almost always discourage-0.2750-10.83 Eating hot dog sandwich (FDHOT) DZFDHOT1Always encourage0.18662.51 69.960.0000 DZFDHOT2Almost always encourage 0.12262.22 DZFDHOT3Sometimes encourage 0.07282.96 DZFDHOT4Neither 0.05714.73 DZFDHOT5Sometimes discourage-0.0910-4.07 ZFDHOT6Almost always discourage-0.1976-6.69 Eating lunchmeat (FDLUN) DZFDLUN1Always encourage0.07931.42 12.350.0303 DZFDLUN2Almost always encourage -0.0490-1.28 DZFDLUN3Sometimes encourage -0.0232-1.02 DZFDLUN4Neither 0.02791.98 DZFDLUN5Sometimes discourage0.01200.50 ZFDLUN6Almost always discourage-0.0824-2.35

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80 Table 5-1. Continued Dependent variable: ATLAB 1993-2003 (13,150 obs)Condition number = 41 Explanatory variables Dummy variables Description Scaled R-sq = 0 .49 Coefft-statsWaldP-value Eating pizza (FDPIZ) DZFDPIZ1Always encourage0.00670.16 1.230.9424 DZFDPIZ2Almost always encourage 0.00140.05 DZFDPIZ3Sometimes encourage 0.00330.17 DZFDPIZ4Neither 0.00220.16 DZFDPIZ5Sometimes discourage0.00050.01 ZFDPIZ6Almost always discourage-0.0752-1.10 Eating tacos (FDTAC) DZFDTAC1Always encourage0.11542.17 8.050.1532 DZFDTAC2Almost always encourage 0.03040.87 DZFDTAC3Sometimes encourage -0.0116-0.53 DZFDTAC4Neither 0.00390.31 DZFDTAC5Sometimes discourage-0.0337-1.02 ZFDTAC6Almost always discourage-0.0659-1.56 Avoid foreign food (ATFOR) DZATFOR1Agree completely -0.1536-6.56 75.100.0000 DZATFOR2Agree mostly -0.0581-2.89 DZATFOR3Agree somewhat 0.00560.32 DZATFOR4Neither0.06162.66 DZATFOR5Disagree somewhat0.07032.61 ZATFOR6Disagree mostly0.15325.60 Try fast food places (FPFAS) DZFPFAS1Agree completely -0.0243-0.31 6.650.2480 DZFPFAS2Agree mostly 0.01910.43 DZFPFAS3Agree somewhat -0.0202-0.98 DZFPFAS4Neither-0.0442-2.06 DZFPFAS5Disagree somewhat0.02641.18 ZFPFAS6Disagree mostly0.01741.27 Visit restaurants more than most (FPRES) DZFPRES1Agree completely 0.04700.81 8.160.1478 DZFPRES2Agree mostly -0.0223-0.52 DZFPRES3Agree somewhat -0.0619-2.09 DZFPRES4Neither-0.0241-1.09 DZFPRES5Disagree somewhat0.03391.55 ZFPRES6Disagree mostly0.00930.81

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81 Table 5-1. Continued Dependent variable: ATLAB 1993-2003 (13,150 obs)Condition number = 41 Explanatory variables Dummy variables Description Scaled R-sq = 0 .49 Coefft-statsWaldP-value Doctor gives advice on diet (ATDOC) DZATDOC1Agree completely -0.4598-12.68 262.250.0000 DZATDOC2Agree mostly -0.1652-5.45 DZATDOC3Agree somewhat -0.0172-0.85 DZATDOC4Neither-0.0096-0.53 DZATDOC5Disagree somewhat0.14755.10 ZATDOC6Disagree mostly0.190311.12 A person should be cautious about additives (NTADD) DZNTADD1Agree completely -0.1752-5.83 48.040.0000 DZNTADD2Agree mostly -0.0496-2.11 DZNTADD3Agree somewhat 0.08703.74 DZNTADD4Neither0.14004.25 DZNTADD5Disagree somewhat0.27983.65 ZNTADD6Disagree mostly0.32212.65 A person should be cautious about cholesterol (NTCHL) DZNTCHL1Agree completely -0.1141-4.86 27.440.0001 DZNTCHL2Agree mostly 0.02691.33 DZNTCHL3Agree somewhat 0.04421.97 DZNTCHL4Neither0.11332.91 DZNTCHL5Disagree somewhat0.10171.27 ZNTCHL6Disagree mostly0.28672.29 A person should be cautious about fat (NTFAT) DZNTFAT1Agree completely -0.1268-6.02 44.760.0000 DZNTFAT2Agree mostly 0.00380.19 DZNTFAT3Agree somewhat 0.11684.64 DZNTFAT4Neither0.18894.18 DZNTFAT5Disagree somewhat0.22302.58 ZNTFAT6Disagree mostly-0.0046-0.04 A person should be cautious about preservatives (NTPRE) DZNTPRE1Agree completely -0.0394-1.27 2.760.7370 DZNTPRE2Agree mostly -0.0041-0.17 DZNTPRE3Agree somewhat 0.01260.56 DZNTPRE4Neither0.02700.88 DZNTPRE5Disagree somewhat0.07711.14 ZNTPRE6Disagree mostly-0.0292-0.26

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82 Table 5-1. Continued Dependent variable: ATLAB 1993-2003 (13,150 obs)Condition number = 41 Explanatory variables Dummy variables Description Scaled R-sq = 0 .49 Coefft-statsWaldP-value A person should be cautious about salt (NTSAL) DZNTSAL1Agree completely -0.0301-1.29 7.740.1714 DZNTSAL2Agree mostly 0.01130.57 DZNTSAL3Agree somewhat 0.00620.30 DZNTSAL4Neither0.07182.05 DZNTSAL5Disagree somewhat-0.0449-0.67 ZNTSAL6Disagree mostly-0.1422-1.40 A person should be cautious about sugar (NTSUG) DZNTSUG1Agree completely 0.07042.38 11.970.0353 DZNTSUG2Agree mostly 0.02351.01 DZNTSUG3Agree somewhat -0.0368-2.38 DZNTSUG4Neither-0.0342-1.44 DZNTSUG5Disagree somewhat-0.0245-0.57 ZNTSUG6Disagree mostly0.11171.53 Vitamins recommended by physician (NTVIT) DZNTVIT1Agree completely 0.04941.28 28.190.0000 DZNTVIT2Agree mostly 0.11703.32 DZNTVIT3Agree somewhat 0.04731.97 DZNTVIT4Neither0.01310.75 DZNTVIT5Disagree somewhat-0.0267-1.59 ZNTVIT6Disagree mostly-0.0831-3.88 Quarters (ZQTR) DZZQTR1 First quarter 0.02231.22 12.890.0049 DZZQTR2 Second quarter-0.0218-1.24 DZZQTR3 Third quarter -0.0425-2.66 ZZQTR34Fourth quarter0.04382.73 MU3 Thresholds 0.870562.60 MU41.687691.94 MU5 2.2174103.91 MU6 2.7919107.95

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83 Table 5-2. Results from the FPLAB model for the period 1993-2003 Dependent variable: FPLAB 1993-2003 (13,150 obs)Condition number = 41 Explanatory variables Dummy variables Description Scaled R-sq = 0 .42 Coefft-statsWaldP-value Intercept (C)0.550842.77 Age of female head (DMAGE) DZDMAGE1=< 35 years0.16897.94 94.650.0000 DZDMAGE235-44 years0.09655.02 DZDMAGE345-54 years-0.0370-1.91 DZDMAGE455-64 years-0.1070-4.29 ZDMAGE565+ years-0.1962-7.07 Children under 18 years (DMCHL) DZDMCHL1Yes0.02611.10 1.210.2714 ZDMCHL2No-0.0149-1.10 Education of female head (DMEDU) DZDMEDU1No high school0.07421.74 5.630.1310 DZDMEDU2High school0.02131.32 DZDMEDU3Some college-0.0236-1.47 ZDMEDU4College graduate-0.0105-0.71 Employment of female head (DMFEM) DZDMFEM1Employed0.02732.68 7.170.0074 ZDMFEM2Not employed-0.0334-2.68 Household size (DMHSZ) DZDMHSZ11 member0.05372.19 15.460.0015 DZDMHSZ22 members-0.0438-2.50 DZDMHSZ33-4 members-0.0098-0.49 ZDMHSZ45+ members0.05881.65 Household income (DMIN2) DZDMIN21Under $30,000-0.0209-1.52 8.620.0348 DZDMIN22$30-49,999-0.0185-1.16 DZDMIN23$50-69,9990.01960.83 ZDMIN24$70-100,000+0.06982.74 Census region (DMREG) DZDMREG1New England0.07731.73 64.930.0000 DZDMREG2Middle Atlantic-0.0644-2.82 DZDMREG3East North Central0.07303.48 DZDMREG4West North Central0.13344.09 DZDMREG5South Atlantic-0.0968-4.40 DZDMREG6East South Central-0.0995-2.71 DZDMREG7West South Central-0.0120-0.42 DZDMREG8Mountain0.10712.83 ZDMREG9Pacific0.00680.27

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84 Table 5-2. Continued Dependent variable: FPLAB 1993-2003 (13,150 obs)Condition number = 41 Explanatory variables Dummy variables Description Scaled R-sq = 0 .42 Coefft-statsWaldP-value Conscious of calories (ATCAL) DZATCAL1Agree completely -0.7304-17.51 691.890.0000 DZATCAL2Agree mostly-0.2773-11.93 DZATCAL3Agree somewhat-0.0477-3.10 DZATCAL4Neither0.14897.28 DZATCAL5Disagree somewhat0.290312.08 ZATCAL6Disagree mostly0.549917.59 Like to lose 20 pounds (ATLBS) DZATLBS1Agree completely0.02812.07 10.810.0554 DZATLBS2Agree mostly 0.01840.67 DZATLBS3Agree somewhat0.03511.31 DZATLBS4Neither-0.0424-1.28 DZATLBS5Disagree somewhat-0.0431-1.36 ZATLBS6Disagree mostly-0.0385-2.08 Love to swim (ATSWM) DZATSWM1Agree completely0.04121.81 8.800.1171 DZATSWM2Agree mostly0.03991.59 DZATSWM3Agree somewhat0.01470.68 DZATSWM4Neither-0.0304-1.44 DZATSWM5Disagree somewhat-0.0158-0.51 ZATSWM6Disagree mostly-0.0302-1.71 Overweight isn't attractive (ATWGT) DZATWGT1Agree completely0.08024.49 38.060.0000 DZATWGT2Agree mostly0.03101.81 DZATWGT3Agree somewhat-0.0193-1.02 DZATWGT4Neither-0.0876-4.06 DZATWGT5Disagree somewhat-0.0819-2.14 ZATWGT6Disagree mostly-0.1138-2.27 Best known brands are highest quality (NTBRN) DZNTBRN1Agree completely 0.22254.13 33.970.0000 DZNTBRN2Agree mostly0.05121.62 DZNTBRN3Agree somewhat0.03731.82 DZNTBRN4Neither-0.0038-0.20 DZNTBRN5Disagree somewhat-0.0113-0.78 ZNTBRN6Disagree mostly-0.0920-3.83

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85 Table 5-2. Continued Dependent variable: FPLAB 1993-2003 (13,150 obs)Condition number = 41 Explanatory variables Dummy variables Description Scaled R-sq = 0 .42 Coefft-statsWaldP-value Food should have body building ingredients (NTING) DZNTING1Agree completely-0.1456-4.45 35.530.0000 DZNTING2Agree mostly-0.0471-2.03 DZNTING3Agree somewhat-0.0127-0.71 DZNTING4Neither0.03362.30 DZNTING5Disagree somewhat0.08123.00 ZNTING6Disagree mostly0.09862.48 Know more than most about nutrition (NTKNO) DZNTKNO1Agree completely-0.4081-9.37 311.690.0000 DZNTKNO2Agree mostly-0.2208-8.46 DZNTKNO3Agree somewhat -0.1003-6.12 DZNTKNO4Neither0.08435.74 DZNTKNO5Disagree somewhat0.292211.33 ZNTKNO6Disagree mostly0.31748.67 Adult female on diet (DTFE2) DZDTFE21Yes-0.2226-13.35 178.270.0000 ZDTFE22No0.094313.35 Eating fried chicken (FDFCH) DZFDFCH1Always encourage0.12942.06 86.840.0000 DZFDFCH2Almost always encourage 0.17753.72 DZFDFCH3Sometimes encourage 0.10333.92 DZFDFCH4Neither 0.05583.75 DZFDFCH5Sometimes discourage-0.0328-1.65 ZFDFCH6Almost always discourage-0.2180-8.65 Eating hot dog sandwich (FDHOT) DZFDHOT1Always encourage0.15662.13 60.090.0000 DZFDHOT2Almost always encourage 0.06381.16 DZFDHOT3Sometimes encourage 0.08343.40 DZFDHOT4Neither 0.04793.97 DZFDHOT5Sometimes discourage-0.0617-2.77 ZFDHOT6Almost always discourage-0.1990-6.78 Eating lunchmeat (FDLUN) DZFDLUN1Always encourage0.11102.01 6.550.2565 DZFDLUN2Almost always encourage 0.04681.24 DZFDLUN3Sometimes encourage -0.0079-0.35 DZFDLUN4Neither 0.00040.03 DZFDLUN5Sometimes discourage-0.0143-0.59 ZFDLUN6Almost always discourage-0.0464-1.34

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86 Table 5-2 Continued Dependent variable: FPLAB 1993-2003 (13,150 obs)Condition number = 41 Explanatory variables Dummy variables Description Scaled R-sq = 0 .42 Coefft-statsWaldP-value Eating pizza (FDPIZ) DZFDPIZ1Always encourage0.04120.99 6.840.2329 DZFDPIZ2Almost always encourage 0.01970.65 DZFDPIZ3Sometimes encourage 0.00330.17 DZFDPIZ4Neither -0.0019-0.14 DZFDPIZ5Sometimes discourage0.00060.02 ZFDPIZ6Almost always discourage-0.1657-2.44 Eating tacos (FDTAC) DZFDTAC1Always encourage0.06941.31 4.330.5023 DZFDTAC2Almost always encourage 0.03400.97 DZFDTAC3Sometimes encourage -0.0203-0.93 DZFDTAC4Neither 0.00360.30 DZFDTAC5Sometimes discourage-0.0112-0.34 ZFDTAC6Almost always discourage-0.0437-1.05 Avoid foreign food (ATFOR) DZATFOR1Agree completely -0.0902-3.89 30.080.0000 DZATFOR2Agree mostly -0.0536-2.67 DZATFOR3Agree somewhat 0.01180.68 DZATFOR4Neither0.04051.75 DZATFOR5Disagree somewhat0.06752.50 ZATFOR6Disagree mostly0.07402.72 Try fast food places (FPFAS) DZFPFAS1Agree completely -0.2649-3.26 19.790.0014 DZFPFAS2Agree mostly -0.0257-0.57 DZFPFAS3Agree somewhat -0.0006-0.03 DZFPFAS4Neither-0.0462-2.15 DZFPFAS5Disagree somewhat0.05412.42 ZFPFAS6Disagree mostly0.01350.99 Visit restaurants more than most (FPRES) DZFPRES1Agree completely -0.2132-3.57 33.510.0000 DZFPRES2Agree mostly -0.1139-2.64 DZFPRES3Agree somewhat -0.0444-1.50 DZFPRES4Neither-0.0584-2.64 DZFPRES5Disagree somewhat0.02871.32 ZFPRES6Disagree mostly0.04614.03

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87 Table 5-2. Continued Dependent variable: FPLAB 1993-2003 (13,150 obs)Condition number = 41 Explanatory variables Dummy variables Description Scaled R-sq = 0 .42 Coefft-statsWaldP-value Doctor gives advice on diet (ATDOC) DZATDOC1Agree completely -0.2873-8.18 119.500.0000 DZATDOC2Agree mostly -0.1243-4.12 DZATDOC3Agree somewhat -0.0239-1.18 DZATDOC4Neither-0.0043-0.24 DZATDOC5Disagree somewhat0.11784.07 ZATDOC6Disagree mostly0.12537.34 A person should be cautious about additives (NTADD) DZNTADD1Agree completely -0.1230-4.12 27.670.0000 DZNTADD2Agree mostly -0.0368-1.56 DZNTADD3Agree somewhat 0.05862.52 DZNTADD4Neither0.08772.66 DZNTADD5Disagree somewhat0.27413.58 ZNTADD6Disagree mostly0.25912.14 A person should be cautious about cholesterol (NTCHL) DZNTCHL1Agree completely -0.0944-4.04 23.110.0003 DZNTCHL2Agree mostly 0.02231.10 DZNTCHL3Agree somewhat 0.03281.47 DZNTCHL4Neither0.09312.39 DZNTCHL5Disagree somewhat0.24823.13 ZNTCHL6Disagree mostly0.01850.15 A person should be cautious about fat (NTFAT) DZNTFAT1Agree completely -0.1248-5.93 41.120.0000 DZNTFAT2Agree mostly 0.00450.22 DZNTFAT3Agree somewhat 0.11854.71 DZNTFAT4Neither0.18694.14 DZNTFAT5Disagree somewhat0.11261.31 ZNTFAT6Disagree mostly0.09480.76 A person should be cautious about preservatives (NTPRE) DZNTPRE1Agree completely -0.1083-3.49 12.910.0243 DZNTPRE2Agree mostly 0.01280.53 DZNTPRE3Agree somewhat 0.03221.44 DZNTPRE4Neither0.07022.30 DZNTPRE5Disagree somewhat0.06390.95 ZNTPRE6Disagree mostly0.00380.03

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88 Table 5-2. Continued Dependent variable: FPLAB 1993-2003 (13,150 obs)Condition number = 41 Explanatory variables Dummy variables Description Scaled R-sq = 0 .42 Coefft-statsWaldP-value A person should be cautious about salt (NTSAL) DZNTSAL1Agree completely -0.0413-1.77 7.800.1679 DZNTSAL2Agree mostly 0.02901.47 DZNTSAL3Agree somewhat 0.00670.33 DZNTSAL4Neither0.04521.29 DZNTSAL5Disagree somewhat-0.0886-1.32 ZNTSAL6Disagree mostly0.01250.12 A person should be cautious about sugar (NTSUG) DZNTSUG1Agree completely 0.05751.96 9.440.0928 DZNTSUG2Agree mostly 0.00870.37 DZNTSUG3Agree somewhat -0.0286-1.85 DZNTSUG4Neither-0.0283-1.19 DZNTSUG5Disagree somewhat-0.0145-0.33 ZNTSUG6Disagree mostly0.13611.87 Vitamins recommended by physician (NTVIT) DZNTVIT1Agree completely 0.01210.32 13.920.0162 DZNTVIT2Agree mostly 0.06461.84 DZNTVIT3Agree somewhat 0.02881.20 DZNTVIT4Neither-0.0029-0.17 DZNTVIT5Disagree somewhat0.01630.97 ZNTVIT6Disagree mostly-0.0709-3.31 Quarters (ZQTR) DZZQTR1 First quarter 0.03001.64 30.670.0000 DZZQTR2 Second quarter-0.0527-3.01 DZZQTR3 Third quarter -0.0495-3.11 ZZQTR34Fourth quarter0.07184.49 MU3 Thresholds 0.879065.21 MU41.713994.51 MU5 2.2442104.52 MU6 2.6143106.42

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89 Table 5-3. Principal components for NTADD, NT CHL, NTFAT, NTPRE, NTSAL and NTSUG for the period 1993-2003 ComponentName EigenvalueCumulative R-Squared 1PCHC14.222657300.70377621 2PCHC20.624832990.80791504 3PCHC30.473392280.88681376 4PCHC40.349017960.94498342 5PCHC50.190106270.97666780 6PCHC60.139993231.00000000

PAGE 90

90 Table 5-4. Results for ATLAB model with principa l components for health variables for the period 1993-2003 Dependent variable: ATLAB 1993-2003 (13,150 obs)Condition number = 10 Explanatory variables Dummy variables Description Scaled R-sq = 0 .49 Coefft-statsWaldP-value Intercept (C) 0.629247.07 Age of female head (DMAGE) DZDMAGE1=< 35 years0.16737.89 87.760.0000 DZDMAGE235-44 years0.07844.06 DZDMAGE345-54 years-0.0215-1.11 DZDMAGE455-64 years-0.0994-3.98 ZDMAGE565+ years-0.1955-7.04 Children under 18 years (DMCHL) DZDMCHL1Yes0.05272.23 4.950.0261 ZDMCHL2No-0.0301-2.23 Education of female head (DMEDU) DZDMEDU1No high school 0.14403.39 12.750.0052 DZDMEDU2High school 0.01560.97 DZDMEDU3Some college -0.0146-0.91 ZDMEDU4College graduate-0.0226-1.53 Employment of female head (DMFEM) DZDMFEM1Employed 0.03303.23 10.440.0012 ZDMFEM2Not employed-0.0403-3.23 Household size (DMHSZ) DZDMHSZ11 member 0.06662.71 9.000.0292 DZDMHSZ22 members 0.00530.30 DZDMHSZ33-4 members-0.0475-2.35 ZDMHSZ45+ members -0.0133-0.37 Household income (DMIN2) DZDMIN21Under $30,000-0.0402-2.92 11.120.0111 DZDMIN22$30-49,999 0.00420.26 DZDMIN23$50-69,999 0.03041.29 ZDMIN24$70-100,000+ 0.07042.76 Census region (DMREG) DZDMREG1New England 0.10232.28 33.890.0000 DZDMREG2Middle Atlantic -0.0158-0.69 DZDMREG3East North Central0.07633.63 DZDMREG4West North Central0.07152.19 DZDMREG5South Atlantic -0.0784-3.57 DZDMREG6East South Central-0.0516-1.41 DZDMREG7West South Central-0.0213-0.74 DZDMREG8Mountain -0.0233-0.61 ZDMREG9Pacific -0.0070-0.28

PAGE 91

91 Table 5-4. Continued Dependent variable: ATLAB (13,150 obs)Condition number = 10 Explanatory variables Dummy variables Description Scaled R-sq = 0 .49 Coefft-statsWaldP-value Conscious of calories (ATCAL) DZATCAL1Agree completely -0.9241-21.32 1127.550.0000 DZATCAL2Agree mostly -0.3709-15.85 DZATCAL3Agree somewhat -0.0610-3.98 DZATCAL4Neither 0.19999.84 DZATCAL5Disagree somewhat0.358914.99 ZATCAL6Disagree mostly 0.722623.05 Like to lose 20 pounds (ATLBS) DZATLBS1Agree completely 0.02121.55 10.600.0600 DZATLBS2Agree mostly 0.03441.26 DZATLBS3Agree somewhat 0.03581.34 DZATLBS4Neither -0.0122-0.37 DZATLBS5Disagree somewhat-0.0405-1.28 ZATLBS6Disagree mostly -0.0468-2.52 Love to swim (ATSWM) DZATSWM1Agree completely 0.03771.64 13.350.0204 DZATSWM2Agree mostly 0.02641.05 DZATSWM3Agree somewhat 0.04251.98 DZATSWM4Neither -0.0478-2.27 DZATSWM5Disagree somewhat0.01270.41 ZATSWM6Disagree mostly -0.0371-2.10 Overweight isn't attractive (ATWGT) DZATWGT1Agree completely 0.10025.61 55.000.0000 DZATWGT2Agree mostly 0.03572.09 DZATWGT3Agree somewhat -0.0306-1.61 DZATWGT4Neither -0.0991-4.60 DZATWGT5Disagree somewhat-0.1017-2.65 ZATWGT6Disagree mostly -0.1358-2.68 Best known brands are highest quality (NTBRN) DZNTBRN1Agree completely 0.24764.56 36.730.0000 DZNTBRN2Agree mostly 0.03221.02 DZNTBRN3Agree somewhat 0.04112.01 DZNTBRN4Neither -0.0023-0.12 DZNTBRN5Disagree somewhat-0.0122-0.84 ZNTBRN6Disagree mostly -0.0917-3.81

PAGE 92

92 Table 5-4. Continued Dependent variable: ATLAB 1993-2003 (13,150 obs)Condition number = 10 Explanatory variables Dummy variables Description Scaled R-sq = 0 .49 Coefft-statsWaldP-value Food should have body building ingredients (NTING) DZNTING1Agree completely -0.1156-3.53 28.670.0000 DZNTING2Agree mostly -0.0319-1.38 DZNTING3Agree somewhat -0.0280-1.56 DZNTING4Neither 0.02761.89 DZNTING5Disagree somewhat0.09903.66 ZNTING6Disagree mostly 0.06111.53 Know more than most about nutrition (NTKNO) DZNTKNO1Agree completely -0.4856-10.99 420.390.0000 DZNTKNO2Agree mostly -0.2970-11.27 DZNTKNO3Agree somewhat -0.0869-5.31 DZNTKNO4Neither 0.10216.97 DZNTKNO5Disagree somewhat0.287211.17 ZNTKNO6Disagree mostly 0.418911.40 Adult female on diet (DTFE2) DZDTFE21Yes-0.2379-14.26 203.400.0000 ZDTFE22No0.100814.26 Eating fried chicken (FDFCH) DZFDFCH1Always encourage 0.26054.14 163.520.0000 DZFDFCH2Almost always encourage 0.20604.31 DZFDFCH3Sometimes encourage 0.16316.21 DZFDFCH4Neither 0.06864.62 DZFDFCH5Sometimes discourage -0.0592-2.99 ZFDFCH6Almost always discourage-0.2902-11.50 Eating hot dog sandwich (FDHOT) DZFDHOT1Always encourage 0.17572.36 74.870.0000 DZFDHOT2Almost always encourage 0.12692.30 DZFDHOT3Sometimes encourage 0.07523.07 DZFDHOT4Neither 0.06015.00 DZFDHOT5Sometimes discourage -0.0956-4.28 ZFDHOT6Almost always discourage-0.2032-6.90 Eating lunchmeat (FDLUN) DZFDLUN1Always encourage 0.07341.32 13.470.0193 DZFDLUN2Almost always encourage -0.0470-1.23 DZFDLUN3Sometimes encourage -0.0241-1.06 DZFDLUN4Neither 0.02962.10 DZFDLUN5Sometimes discourage 0.01500.62 ZFDLUN6Almost always discourage-0.0905-2.59

PAGE 93

93 Table 5-4. Continued Dependent variable: ATLAB 1993-2003 (13,150 obs)Condition number = 10 Explanatory variables Dummy variables Description Scaled R-sq = 0 .49 Coefft-statsWaldP-value Eating pizza (FDPIZ) DZFDPIZ1Always encourage 0.00360.09 1.640.8966 DZFDPIZ2Almost always encourage 0.00290.09 DZFDPIZ3Sometimes encourage 0.00100.05 DZFDPIZ4Neither 0.00400.30 DZFDPIZ5Sometimes discourage 0.00250.07 ZFDPIZ6Almost always discourage-0.0867-1.27 Eating tacos (FDTAC) DZFDTAC1Always encourage 0.10802.03 7.860.1638 DZFDTAC2Almost always encourage 0.02860.82 DZFDTAC3Sometimes encourage -0.0120-0.55 DZFDTAC4Neither 0.00570.46 DZFDTAC5Sometimes discourage -0.0326-0.99 ZFDTAC6Almost always discourage-0.0703-1.66 Avoid foreign food (ATFOR) DZATFOR1Agree completely -0.1581-6.78 76.260.0000 DZATFOR2Agree mostly -0.0563-2.80 DZATFOR3Agree somewhat 0.00710.41 DZATFOR4Neither 0.06482.80 DZATFOR5Disagree somewhat0.07272.70 ZATFOR6Disagree mostly 0.14765.40 Try fast food places (FPFAS) DZFPFAS1Agree completely -0.0232-0.29 6.100.2962 DZFPFAS2Agree mostly 0.02360.53 DZFPFAS3Agree somewhat -0.0190-0.93 DZFPFAS4Neither -0.0423-1.97 DZFPFAS5Disagree somewhat0.02451.09 ZFPFAS6Disagree mostly 0.01621.19 Visit restaurants more than most (FPRES) DZFPRES1Agree completely 0.04500.78 7.030.2187 DZFPRES2Agree mostly -0.0198-0.46 DZFPRES3Agree somewhat -0.0549-1.85 DZFPRES4Neither -0.0220-0.99 DZFPRES5Disagree somewhat0.03451.58 ZFPRES6Disagree mostly 0.00660.58

PAGE 94

94 Table 5-4. Continued Dependent variable: ATLAB 1993-2003 (13,150 obs)Condition number = 10 Explanatory variables Dummy variables Description Scaled R-sq = 0 .49 Coefft-statsWaldP-value Doctor gives advice on diet (ATDOC) DZATDOC1Agree completely -0.4700-13.01 269.450.0000 DZATDOC2Agree mostly -0.1643-5.44 DZATDOC3Agree somewhat -0.0135-0.67 DZATDOC4Neither -0.0088-0.49 DZATDOC5Disagree somewhat0.14705.09 ZATDOC6Disagree mostly 0.190511.15 APCH1 Principal components for NTADD, NTCHL, NTFAT, NTPRE, NTSAL and NTSUG 0.277024.41 654.600.0000 APCH20.01591.60 APCH30.05005.02 APCH40.04875.00 Vitamins recommended by physician (NTVIT) DZNTVIT1Agree completely 0.03440.89 29.650.0000 DZNTVIT2Agree mostly 0.11823.36 DZNTVIT3Agree somewhat 0.04892.04 DZNTVIT4Neither 0.01751.01 DZNTVIT5Disagree somewhat-0.0238-1.42 ZNTVIT6Disagree mostly -0.0888-4.15 Quarters (ZQTR) DZZQTR1 First quarter 0.02451.34 12.870.0049 DZZQTR2 Second quarter-0.0225-1.29 DZZQTR3 Third quarter -0.0422-2.65 ZZQTR34Fourth quarter0.04252.65 MU3 Thresholds 0.865362.63 MU4 1.679491.98 MU5 2.2085103.93 MU6 2.7833107.90

PAGE 95

95 Table 5-5. Results for FPLAB model with principa l components for health variables for the period 1993-2003 Dependent variable: FPLAB 1993-2003 (13,150 obs)Condition number = 10 Explanatory variables Dummy variables Description Scaled R-sq = 0 .42 Coefft-statsWaldP-value Intercept (C) 0.548942.75 Age of female head (DMAGE) DZDMAGE1=< 35 years0.17678.34 103.320.0000 DZDMAGE235-44 years0.09895.15 DZDMAGE345-54 years-0.0412-2.13 DZDMAGE455-64 years-0.1061-4.26 ZDMAGE565+ years-0.2047-7.40 Children under 18 years (DMCHL) DZDMCHL1Yes0.02340.99 0.980.3214 ZDMCHL2No-0.0134-0.99 Education of female head (DMEDU) DZDMEDU1No high school 0.06171.45 4.680.1966 DZDMEDU2High school 0.01781.11 DZDMEDU3Some college -0.0259-1.62 ZDMEDU4College graduate-0.0041-0.28 Employment of female head (DMFEM) DZDMFEM1Employed 0.02622.57 6.620.0101 ZDMFEM2Not employed-0.0320-2.57 Household size (DMHSZ) DZDMHSZ11 member 0.05112.09 14.970.0019 DZDMHSZ22 members -0.0432-2.47 DZDMHSZ33-4 members-0.0091-0.45 ZDMHSZ45+ members 0.06051.71 Household income (DMIN2) DZDMIN21Under $30,000-0.0230-1.68 9.280.0258 DZDMIN22$30-49,999 -0.0180-1.12 DZDMIN23$50-69,999 0.02200.94 ZDMIN24$70-100,000+ 0.07192.82 Census region (DMREG) DZDMREG1New England 0.07631.71 65.670.0000 DZDMREG2Middle Atlantic -0.0665-2.91 DZDMREG3East North Central0.07443.55 DZDMREG4West North Central0.13594.17 DZDMREG5South Atlantic -0.0959-4.37 DZDMREG6East South Central-0.0974-2.66 DZDMREG7West South Central-0.0162-0.57 DZDMREG8Mountain 0.10442.76 ZDMREG9Pacific 0.00880.35

PAGE 96

96 Table 5-5. Continued Dependent variable: FPLAB 1993-2003 (13,150 obs)Condition number = 10 Explanatory variables Dummy variables Description Scaled R-sq = 0 .42 Coefft-statsWaldP-value Conscious of calories (ATCAL) DZATCAL1Agree completely -0.7501-18.07 715.310.0000 DZATCAL2Agree mostly -0.2790-12.06 DZATCAL3Agree somewhat -0.0452-2.95 DZATCAL4Neither 0.15187.46 DZATCAL5Disagree somewhat0.298512.47 ZATCAL6Disagree mostly 0.545417.55 Like to lose 20 pounds (ATLBS) DZATLBS1Agree completely 0.02551.87 10.500.0623 DZATLBS2Agree mostly 0.01990.73 DZATLBS3Agree somewhat 0.03861.44 DZATLBS4Neither -0.0422-1.27 DZATLBS5Disagree somewhat-0.0435-1.37 ZATLBS6Disagree mostly -0.0368-1.99 Love to swim (ATSWM) DZATSWM1Agree completely 0.03471.53 7.840.1650 DZATSWM2Agree mostly 0.04081.62 DZATSWM3Agree somewhat 0.01530.72 DZATSWM4Neither -0.0299-1.42 DZATSWM5Disagree somewhat-0.0123-0.40 ZATSWM6Disagree mostly -0.0284-1.61 Overweight isn't attractive (ATWGT) DZATWGT1Agree completely 0.07134.01 35.020.0000 DZATWGT2Agree mostly 0.03452.02 DZATWGT3Agree somewhat -0.0141-0.75 DZATWGT4Neither -0.0864-4.03 DZATWGT5Disagree somewhat-0.0790-2.07 ZATWGT6Disagree mostly -0.1136-2.26 Best known brands are highest quality (NTBRN) DZNTBRN1Agree completely 0.20293.78 34.540.0000 DZNTBRN2Agree mostly 0.05231.66 DZNTBRN3Agree somewhat 0.04312.11 DZNTBRN4Neither -0.0015-0.08 DZNTBRN5Disagree somewhat-0.0102-0.71 ZNTBRN6Disagree mostly -0.1004-4.19

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97 Table 5-5. Continued Dependent variable: FPLAB 1993-2003 (13,150 obs)Condition number = 10 Explanatory variables Dummy variables Description Scaled R-sq = 0 .42 Coefft-statsWaldP-value Food should have body building ingredients (NTING) DZNTING1Agree completely -0.1566-4.81 38.390.0000 DZNTING2Agree mostly -0.0490-2.12 DZNTING3Agree somewhat -0.0080-0.45 DZNTING4Neither 0.03512.41 DZNTING5Disagree somewhat0.08243.05 ZNTING6Disagree mostly 0.09482.39 Know more than most about nutrition (NTKNO) DZNTKNO1Agree completely -0.4234-9.76 317.530.0000 DZNTKNO2Agree mostly -0.2195-8.43 DZNTKNO3Agree somewhat -0.0981-6.01 DZNTKNO4Neither 0.08555.84 DZNTKNO5Disagree somewhat0.294911.46 ZNTKNO6Disagree mostly 0.31058.50 Adult female on diet (DTFE2) DZDTFE21Yes-0.2247-13.51 182.550.0000 ZDTFE22No0.095213.51 Eating fried chicken (FDFCH) DZFDFCH1Always encourage 0.13722.19 100.430.0000 DZFDFCH2Almost always encourage 0.18143.81 DZFDFCH3Sometimes encourage 0.11034.20 DZFDFCH4Neither 0.05974.03 DZFDFCH5Sometimes discourage -0.0317-1.60 ZFDFCH6Almost always discourage-0.2345-9.37 Eating hot dog sandwich (FDHOT) DZFDHOT1Always encourage 0.14451.97 63.570.0000 DZFDHOT2Almost always encourage 0.06971.27 DZFDHOT3Sometimes encourage 0.08403.43 DZFDHOT4Neither 0.05034.19 DZFDHOT5Sometimes discourage -0.0627-2.82 ZFDHOT6Almost always discourage-0.2056-7.03 Eating lunchmeat (FDLUN) DZFDLUN1Always encourage 0.10511.90 6.910.2276 DZFDLUN2Almost always encourage 0.04731.25 DZFDLUN3Sometimes encourage -0.0086-0.38 DZFDLUN4Neither 0.00210.15 DZFDLUN5Sometimes discourage -0.0102-0.42 ZFDLUN6Almost always discourage-0.0558-1.61

PAGE 98

98 Table 5-5. Continued Dependent variable: FPLAB 1993-2003 (13,150 obs)Condition number = 10 Explanatory variables Dummy variables Description Scaled R-sq = 0 .42 Coefft-statsWaldP-value Eating pizza (FDPIZ) DZFDPIZ1Always encourage 0.03740.90 7.750.1705 DZFDPIZ2Almost always encourage 0.02340.77 DZFDPIZ3Sometimes encourage 0.00160.08 DZFDPIZ4Neither -0.0001-0.01 DZFDPIZ5Sometimes discourage -0.0005-0.01 ZFDPIZ6Almost always discourage-0.1784-2.64 Eating tacos (FDTAC) DZFDTAC1Always encourage 0.06171.17 4.500.4795 DZFDTAC2Almost always encourage 0.03591.03 DZFDTAC3Sometimes encourage -0.0184-0.84 DZFDTAC4Neither 0.00450.36 DZFDTAC5Sometimes discourage -0.0084-0.26 ZFDTAC6Almost always discourage-0.0540-1.29 Avoid foreign food (ATFOR) DZATFOR1Agree completely -0.0965-4.17 31.700.0000 DZATFOR2Agree mostly -0.0508-2.53 DZATFOR3Agree somewhat 0.01260.73 DZATFOR4Neither 0.04351.88 DZATFOR5Disagree somewhat0.07172.66 ZATFOR6Disagree mostly 0.06902.53 Try fast food places (FPFAS) DZFPFAS1Agree completely -0.2653-3.27 19.090.0019 DZFPFAS2Agree mostly -0.0240-0.54 DZFPFAS3Agree somewhat 0.00010.01 DZFPFAS4Neither -0.0436-2.03 DZFPFAS5Disagree somewhat0.05242.35 ZFPFAS6Disagree mostly 0.01250.91 Visit restaurants more than most (FPRES) DZFPRES1Agree completely -0.2168-3.64 31.890.0000 DZFPRES2Agree mostly -0.1078-2.50 DZFPRES3Agree somewhat -0.0367-1.25 DZFPRES4Neither -0.0571-2.58 DZFPRES5Disagree somewhat0.02931.35 ZFPRES6Disagree mostly 0.04333.80

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99 Table 5-5. Continued Dependent variable: FPLAB 1993-2003 (13,150 obs)Condition number = 10 Explanatory variables Dummy variables Description Scaled R-sq = 0 .42 Coefft-statsWaldP-value Doctor gives advice on diet (ATDOC) DZATDOC1Agree completely -0.2995-8.56 124.830.0000 DZATDOC2Agree mostly -0.1229-4.09 DZATDOC3Agree somewhat -0.0199-0.99 DZATDOC4Neither -0.0037-0.21 DZATDOC5Disagree somewhat0.11944.13 ZATDOC6Disagree mostly 0.12527.34 APCH1 Principal components for NTADD, NTCHL, NTFAT, NTPRE, NTSAL and NTSUG 0.266723.61 599.110.0000 APCH20.01811.83 APCH30.04214.23 APCH40.03673.76 Vitamins recommended by physician (NTVIT) DZNTVIT1Agree completely -0.0070-0.18 15.280.0092 DZNTVIT2Agree mostly 0.06671.90 DZNTVIT3Agree somewhat 0.02991.25 DZNTVIT4Neither 0.00100.06 DZNTVIT5Disagree somewhat0.01901.14 ZNTVIT6Disagree mostly -0.0741-3.48 Quarters (ZQTR) DZZQTR1 First quarter 0.03221.76 31.250.0000 DZZQTR2 Second quarter-0.0529-3.03 DZZQTR3 Third quarter -0.0505-3.18 ZZQTR34Fourth quarter0.07134.46 MU3 Thresholds 0.874165.23 MU4 1.706394.53 MU5 2.2358104.52 MU6 2.6058106.37

PAGE 100

100 Table 5-6. Results from the ATLAB model for the period 1984-1993 Dependent variable: ATLAB 1984-1993 (15,331 obs)Condition number = 48 Explanatory variables Dummy variables Description Scaled R-sq = 0 .44 Coefft-statsWaldP-value Intercept (C)0.517043.41 Age of female head (DMAGE) DZDMAGE1=< 35 years0.16669.74 104.420.0000 DZDMAGE235-44 years0.02461.30 DZDMAGE345-54 years-0.0146-0.64 DZDMAGE455-64 years-0.1364-5.94 ZDMAGE565+ years-0.1642-6.22 Children under 18 years (DMCHL) DZDMCHL1Yes-0.0067-0.30 0.090.7617 ZDMCHL2No0.00410.30 Education of female head (DMEDU) DZDMEDU1No high school 0.04561.39 6.320.0968 DZDMEDU2High school0.01180.90 DZDMEDU3Some college-0.0366-2.30 ZDMEDU4College graduate0.00420.27 Employment of female head (DMFEM) DZDMFEM1Employed0.04124.02 16.180.0001 ZDMFEM2Not employed-0.0421-4.02 Household size (DMHSZ) DZDMHSZ11 member0.07883.40 14.260.0026 DZDMHSZ22 members 0.00450.26 DZDMHSZ33-4 members-0.0540-2.89 ZDMHSZ45+ members -0.0145-0.46 Household income (DMIN2) DZDMIN21Under $30,000-0.0107-1.23 2.470.4802 DZDMIN22$30-49,9990.00350.22 DZDMIN23$50-69,9990.02931.06 ZDMIN24$70-100,000+ 0.04631.11 Census region (DMREG) DZDMREG1New England0.03040.74 13.420.0981 DZDMREG2Middle Atlantic-0.0085-0.41 DZDMREG3East North Central0.04262.19 DZDMREG4West North Central0.05091.72 DZDMREG5South Atlantic-0.0157-0.76 DZDMREG6East South Central-0.0120-0.36 DZDMREG7West South Central-0.0303-1.14 DZDMREG8Mountain-0.0731-2.15 ZDMREG9Pacific-0.0026-0.10

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101 Table 5-6. Continued Dependent variable: ATLAB 1984-1993 (15,331 obs)Condition number = 48 Explanatory variables Dummy variables Description Scaled R-sq = 0 .44 Coefft-statsWaldP-value Conscious of calories (ATCAL) DZATCAL1Agree completely -0.4880-16.33 723.380.0000 DZATCAL2Agree mostly-0.2540-13.73 DZATCAL3Agree somewhat0.00250.18 DZATCAL4Neither0.20479.59 DZATCAL5Disagree somewhat0.282011.25 ZATCAL6Disagree mostly 0.585118.53 Like to lose 20 pounds (ATLBS) DZATLBS1Agree completely 0.04223.24 31.560.0000 DZATLBS2Agree mostly-0.0188-0.65 DZATLBS3Agree somewhat0.08863.45 DZATLBS4Neither-0.0858-2.53 DZATLBS5Disagree somewhat-0.0354-1.21 ZATLBS6Disagree mostly -0.0457-3.12 Love to swim (ATSWM) DZATSWM1Agree completely 0.02341.29 5.760.3300 DZATSWM2Agree mostly-0.0227-0.96 DZATSWM3Agree somewhat0.02661.30 DZATSWM4Neither-0.0297-1.49 DZATSWM5Disagree somewhat-0.0199-0.62 ZATSWM6Disagree mostly 0.00080.05 Overweight isn't attractive (ATWGT) DZATWGT1Agree completely 0.08206.95 65.690.0000 DZATWGT2Agree mostly-0.0018-0.11 DZATWGT3Agree somewhat-0.0733-3.67 DZATWGT4Neither-0.1244-4.49 DZATWGT5Disagree somewhat-0.1079-2.51 ZATWGT6Disagree mostly -0.1550-2.62 Best known brands are highest quality (NTBRN) DZNTBRN1Agree completely -0.0822-1.53 18.810.0021 DZNTBRN2Agree mostly0.04341.41 DZNTBRN3Agree somewhat0.07273.67 DZNTBRN4Neither-0.0203-0.95 DZNTBRN5Disagree somewhat-0.0097-0.78 ZNTBRN6Disagree mostly -0.0329-1.75

PAGE 102

102 Table 5-6. Continued Dependent variable: ATLAB 1984-1993 (15,331 obs)Condition number = 48 Explanatory variables Dummy variables Description Scaled R-sq = 0 .44 Coefft-statsWaldP-value Food should have body building ingredients (NTING) DZNTING1Agree completely -0.0370-1.72 11.090.0495 DZNTING2Agree mostly-0.0202-1.09 DZNTING3Agree somewhat-0.0131-0.79 DZNTING4Neither0.04853.06 DZNTING5Disagree somewhat0.00030.01 ZNTING6Disagree mostly 0.04800.93 Know more than most about nutrition (NTKNO) DZNTKNO1Agree completely -0.4422-11.91 430.530.0000 DZNTKNO2Agree mostly-0.2422-10.38 DZNTKNO3Agree somewhat-0.0333-2.29 DZNTKNO4Neither0.05533.88 DZNTKNO5Disagree somewhat0.267510.97 ZNTKNO6Disagree mostly 0.452213.18 Adult female on diet (DTFE2) DZDTFE21Yes-0.2009-12.92 166.830.0000 ZDTFE22No0.083412.92 Eating fried chicken (FDFCH) DZFDFCH1Always encourage 0.23015.18 90.900.0000 DZFDFCH2Almost always encourage 0.08452.35 DZFDFCH3Sometimes encourage0.06102.91 DZFDFCH4Neither0.03692.68 DZFDFCH5Sometimes discourage-0.0566-2.87 ZFDFCH6Almost always discourage-0.2242-8.04 Eating hot dog sandwich (FDHOT) DZFDHOT1Always encourage 0.12512.21 102.160.0000 DZFDHOT2Almost always encourage 0.11882.74 DZFDHOT3Sometimes encourage0.08043.97 DZFDHOT4Neither0.05945.23 DZFDHOT5Sometimes discourage-0.1465-6.39 ZFDHOT6Almost always discourage-0.2209-7.34 Eating lunchmeat (FDLUN) DZFDLUN1Always encourage 0.10652.15 59.920.0000 DZFDLUN2Almost always encourage 0.07662.14 DZFDLUN3Sometimes encourage0.02571.22 DZFDLUN4Neither0.05744.32 DZFDLUN5Sometimes discourage-0.0586-2.73 ZFDLUN6Almost always discourage-0.2070-6.80

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103 Table 5-6. Continued Dependent variable: ATLAB 1984-1993 (15,331 obs)Condition number = 48 Explanatory variables Dummy variables Description Scaled R-sq = 0 .44 Coefft-statsWaldP-value Eating pizza (FDPIZ) DZFDPIZ1Always encourage 0.03381.03 6.040.3019 DZFDPIZ2Almost always encourage -0.0020-0.08 DZFDPIZ3Sometimes encourage-0.0284-1.58 DZFDPIZ4Neither0.00990.78 DZFDPIZ5Sometimes discourage-0.0429-1.19 ZFDPIZ6Almost always discourage0.06171.10 Eating tacos (FDTAC) DZFDTAC1Always encourage -0.0583-1.39 4.850.4348 DZFDTAC2Almost always encourage 0.04151.30 DZFDTAC3Sometimes encourage0.02191.06 DZFDTAC4Neither-0.0033-0.30 DZFDTAC5Sometimes discourage-0.0161-0.50 ZFDTAC6Almost always discourage-0.0108-0.30 Avoid foreign food (ATFOR) DZATFOR1Agree completely -0.0916-5.03 65.940.0000 DZATFOR2Agree mostly-0.0299-1.65 DZATFOR3Agree somewhat-0.0243-1.52 DZATFOR4Neither0.03721.59 DZATFOR5Disagree somewhat0.10633.88 ZATFOR6Disagree mostly 0.15915.85 Doctor gives advice on diet (ATDOC) DZATDOC1Agree completely -0.4180-15.12 428.890.0000 DZATDOC2Agree mostly-0.1401-5.23 DZATDOC3Agree somewhat-0.0554-2.72 DZATDOC4Neither-0.0218-1.33 DZATDOC5Disagree somewhat0.14784.94 ZATDOC6Disagree mostly 0.272117.03 A person should be cautious about additives (NTADD) DZNTADD1Agree completely -0.1446-6.57 58.180.0000 DZNTADD2Agree mostly-0.0138-0.65 DZNTADD3Agree somewhat0.10464.29 DZNTADD4Neither0.23686.36 DZNTADD5Disagree somewhat0.18672.46 ZNTADD6Disagree mostly0.20531.62

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104 Table 5-6. Continued Dependent variable: ATLAB 1984-1993 (15,331 obs)Condition number = 48 Explanatory variables Dummy variables Description Scaled R-sq = 0 .44 Coefft-statsWaldP-value A person should be cautious about cholesterol (NTCHL) DZNTCHL1Agree completely-0.0254-1.48 4.810.4389 DZNTCHL2Agree mostly0.02131.12 DZNTCHL3Agree somewhat0.02931.24 DZNTCHL4Neither0.01360.32 DZNTCHL5Disagree somewhat0.00230.03 ZNTCHL6Disagree mostly-0.1665-1.35 A person should be cautious about fat (NTFAT) DZNTFAT1Agree completely-0.0975-5.83 47.710.0000 DZNTFAT2Agree mostly0.01200.62 DZNTFAT3Agree somewhat0.13485.37 DZNTFAT4Neither0.22584.73 DZNTFAT5Disagree somewhat0.08890.94 ZNTFAT6Disagree mostly -0.0924-0.67 A person should be cautious about preservatives (NTPRE) DZNTPRE1Agree completely -0.1269-5.51 33.180.0000 DZNTPRE2Agree mostly0.04061.99 DZNTPRE3Agree somewhat0.05562.37 DZNTPRE4Neither0.09822.89 DZNTPRE5Disagree somewhat0.16182.31 ZNTPRE6Disagree mostly 0.22041.89 A person should be cautious about salt (NTSAL) DZNTSAL1Agree completely 0.00260.16 2.540.7708 DZNTSAL2Agree mostly-0.0233-1.35 DZNTSAL3Agree somewhat0.00700.32 DZNTSAL4Neither0.04041.02 DZNTSAL5Disagree somewhat0.04000.51 ZNTSAL6Disagree mostly 0.00620.05 A person should be cautious about sugar (NTSUG) DZNTSUG1Agree completely -0.0289-1.37 6.960.2237 DZNTSUG2Agree mostly0.01880.99 DZNTSUG3Agree somewhat-0.0082-0.52 DZNTSUG4Neither0.02390.87 DZNTSUG5Disagree somewhat0.02080.44 ZNTSUG6Disagree mostly 0.16631.97

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105 Table 5-6. Continued Dependent variable: ATLAB 1984-1993 (15,331 obs)Condition number = 48 Explanatory variables Dummy variables Description Scaled R-sq = 0 .44 Coefft-statsWaldP-value Vitamins recommended by physician (NTVIT) DZNTVIT1Agree completely -0.0308-1.21 15.320.0091 DZNTVIT2Agree mostly0.06122.30 DZNTVIT3Agree somewhat0.03471.69 DZNTVIT4Neither-0.0301-1.71 DZNTVIT5Disagree somewhat0.02201.30 ZNTVIT6Disagree mostly -0.0451-1.95 Quarters (ZQTR) DZZQTR1 First quarter 0.00860.51 3.430.3306 DZZQTR2 Second quarter-0.0008-0.05 DZZQTR3 Third quarter 0.01781.22 ZZQTR34Fourth quarter-0.0263-1.69 MU3 Thresholds 0.786965.48 MU41.556896.31 MU52.0518108.79 MU62.5331113.84

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106 Table 5-7. Results from the ATLAB model for the period 1994-2003 Dependent variable: ATLAB 1994-2003 (15,083 obs)Condition number = 39 Explanatory variables Dummy variables Description Scaled R-sq = 0 .49 Coefft-statsWaldP-value Intercept (C)0.654052.14 Age of female head (DMAGE) DZDMAGE1=< 35 years0.15648.29 92.330.0000 DZDMAGE235-44 years0.05833.25 DZDMAGE345-54 years-0.0117-0.64 DZDMAGE455-64 years-0.1005-4.22 ZDMAGE565+ years-0.1891-7.19 Children under 18 years (DMCHL) DZDMCHL1Yes0.06342.89 8.340.0039 ZDMCHL2No-0.0363-2.89 Education of female head (DMEDU) DZDMEDU1No high school 0.13403.32 13.250.0041 DZDMEDU2High school0.02051.33 DZDMEDU3Some college-0.0081-0.55 ZDMEDU4College graduate-0.0280-2.08 Employment of female head (DMFEM) DZDMFEM1Employed0.04284.58 21.020.0000 ZDMFEM2Not employed-0.0549-4.58 Household size (DMHSZ) DZDMHSZ11 member0.05992.64 9.260.0261 DZDMHSZ22 members 0.00530.32 DZDMHSZ33-4 members-0.0459-2.44 ZDMHSZ45+ members -0.0071-0.21 Household income (DMIN2) DZDMIN21Under $30,000-0.0172-1.32 3.340.3419 DZDMIN22$30-49,999-0.0050-0.34 DZDMIN23$50-69,9990.01370.63 ZDMIN24$70-100,000+ 0.03801.66 Census region (DMREG) DZDMREG1New England0.07491.81 32.670.0001 DZDMREG2Middle Atlantic-0.0105-0.49 DZDMREG3East North Central0.07103.56 DZDMREG4West North Central0.07762.54 DZDMREG5South Atlantic-0.0648-3.22 DZDMREG6East South Central-0.0601-1.74 DZDMREG7West South Central-0.0165-0.62 DZDMREG8Mountain-0.0390-1.12 ZDMREG9Pacific-0.0052-0.22

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107 Table 5-7. Continued Dependent variable: ATLAB 1994-2003 (15,083 obs)Condition number = 39 Explanatory variables Dummy variables Description Scaled R-sq = 0 .49 Coefft-statsWaldP-value Conscious of calories (ATCAL) DZATCAL1Agree completely -0.8936-21.85 1224.260.0000 DZATCAL2Agree mostly-0.3714-16.77 DZATCAL3Agree somewhat-0.0760-5.21 DZATCAL4Neither0.18449.80 DZATCAL5Disagree somewhat0.337115.40 ZATCAL6Disagree mostly 0.706924.61 Like to lose 20 pounds (ATLBS) DZATLBS1Agree completely 0.02862.27 13.460.0194 DZATLBS2Agree mostly0.02651.05 DZATLBS3Agree somewhat0.01990.79 DZATLBS4Neither-0.0016-0.05 DZATLBS5Disagree somewhat-0.0203-0.68 ZATLBS6Disagree mostly -0.0585-3.39 Love to swim (ATSWM) DZATSWM1Agree completely 0.06002.84 21.060.0008 DZATSWM2Agree mostly-0.0010-0.04 DZATSWM3Agree somewhat0.04372.20 DZATSWM4Neither-0.0621-3.14 DZATSWM5Disagree somewhat0.01510.53 ZATSWM6Disagree mostly -0.0318-1.90 Overweight isn't attractive (ATWGT) DZATWGT1Agree completely 0.09755.76 53.160.0000 DZATWGT2Agree mostly0.02671.67 DZATWGT3Agree somewhat-0.0253-1.43 DZATWGT4Neither-0.0965-4.86 DZATWGT5Disagree somewhat-0.0842-2.35 ZATWGT6Disagree mostly -0.0972-2.14 Best known brands are highest quality (NTBRN) DZNTBRN1Agree completely 0.24324.94 34.710.0000 DZNTBRN2Agree mostly0.03041.04 DZNTBRN3Agree somewhat0.01560.81 DZNTBRN4Neither0.00800.45 DZNTBRN5Disagree somewhat-0.0149-1.09 ZNTBRN6Disagree mostly -0.0718-3.20

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108 Table 5-7. Continued Dependent variable: ATLAB 1994-2003 (15,083 obs)Condition number = 39 Explanatory variables Dummy variables Description Scaled R-sq = 0 .49 Coefft-statsWaldP-value Food should have body building ingredients (NTING) DZNTING1Agree completely -0.0960-3.08 18.150.0028 DZNTING2Agree mostly-0.0336-1.52 DZNTING3Agree somewhat-0.0078-0.47 DZNTING4Neither0.02641.96 DZNTING5Disagree somewhat0.06902.75 ZNTING6Disagree mostly 0.00280.08 Know more than most about nutrition (NTKNO) DZNTKNO1Agree completely -0.5346-13.09 552.360.0000 DZNTKNO2Agree mostly-0.2893-11.81 DZNTKNO3Agree somewhat-0.0947-6.19 DZNTKNO4Neither0.09917.21 DZNTKNO5Disagree somewhat0.298612.55 ZNTKNO6Disagree mostly 0.473313.94 Adult female on diet (DTFE2) DZDTFE21Yes-0.2347-14.96 223.880.0000 ZDTFE22No0.096914.96 Eating fried chicken (FDFCH) DZFDFCH1Always encourage 0.19553.34 142.380.0000 DZFDFCH2Almost always encourage 0.14543.36 DZFDFCH3Sometimes encourage0.13495.63 DZFDFCH4Neither0.06874.97 DZFDFCH5Sometimes discourage-0.0597-3.18 ZFDFCH6Almost always discourage-0.2589-10.78 Eating hot dog sandwich (FDHOT) DZFDHOT1Always encourage 0.14522.11 64.760.0000 DZFDHOT2Almost always encourage 0.14862.97 DZFDHOT3Sometimes encourage0.08143.57 DZFDHOT4Neither0.04093.66 DZFDHOT5Sometimes discourage-0.0787-3.78 ZFDHOT6Almost always discourage-0.1735-6.35 Eating lunchmeat (FDLUN) DZFDLUN1Always encourage 0.11152.16 15.500.0084 DZFDLUN2Almost always encourage -0.0410-1.20 DZFDLUN3Sometimes encourage-0.0053-0.25 DZFDLUN4Neither0.02371.79 DZFDLUN5Sometimes discourage-0.0047-0.21 ZFDLUN6Almost always discourage-0.0925-2.78

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109 Table 5-7. Continued Dependent variable: ATLAB 1994-2003 (15,083 obs)Condition number = 39 Explanatory variables Dummy variables Description Scaled R-sq = 0 .49 Coefft-statsWaldP-value Eating pizza (FDPIZ) DZFDPIZ1Always encourage 0.02380.60 0.920.9685 DZFDPIZ2Almost always encourage 0.00160.06 DZFDPIZ3Sometimes encourage0.00050.03 DZFDPIZ4Neither0.00140.11 DZFDPIZ5Sometimes discourage-0.0255-0.76 ZFDPIZ6Almost always discourage-0.0209-0.33 Eating tacos (FDTAC) DZFDTAC1Always encourage 0.11322.25 8.040.1540 DZFDTAC2Almost always encourage 0.02220.69 DZFDTAC3Sometimes encourage-0.0132-0.65 DZFDTAC4Neither0.00110.09 DZFDTAC5Sometimes discourage-0.0090-0.29 ZFDTAC6Almost always discourage-0.0655-1.67 Avoid foreign food (ATFOR) DZATFOR1Agree completely -0.1647-7.77 101.580.0000 DZATFOR2Agree mostly-0.0668-3.55 DZATFOR3Agree somewhat0.01791.13 DZATFOR4Neither0.05832.67 DZATFOR5Disagree somewhat0.09493.76 ZATFOR6Disagree mostly 0.14725.84 Doctor gives advice on diet (ATDOC) DZATDOC1Agree completely -0.4517-13.45 313.980.0000 DZATDOC2Agree mostly-0.1701-6.05 DZATDOC3Agree somewhat-0.0276-1.46 DZATDOC4Neither-0.0153-0.92 DZATDOC5Disagree somewhat0.13465.01 ZATDOC6Disagree mostly 0.202212.83 A person should be cautious about additives (NTADD) DZNTADD1Agree completely -0.1800-6.41 55.070.0000 DZNTADD2Agree mostly-0.0480-2.18 DZNTADD3Agree somewhat0.09044.27 DZNTADD4Neither0.13664.55 DZNTADD5Disagree somewhat0.26893.88 ZNTADD6Disagree mostly0.21922.01

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110 Table 5-7. Continued Dependent variable: ATLAB 1994-2003 (15,083 obs)Condition number = 39 Explanatory variables Dummy variables Description Scaled R-sq = 0 .49 Coefft-statsWaldP-value A person should be cautious about cholesterol (NTCHL) DZNTCHL1Agree completely-0.0969-4.34 23.450.0003 DZNTCHL2Agree mostly0.01400.75 DZNTCHL3Agree somewhat0.03831.87 DZNTCHL4Neither0.09602.71 DZNTCHL5Disagree somewhat0.07461.03 ZNTCHL6Disagree mostly0.28152.47 A person should be cautious about fat (NTFAT) DZNTFAT1Agree completely-0.1323-6.55 52.690.0000 DZNTFAT2Agree mostly0.00090.04 DZNTFAT3Agree somewhat0.11424.97 DZNTFAT4Neither0.18864.62 DZNTFAT5Disagree somewhat0.20432.64 ZNTFAT6Disagree mostly 0.00010.00 A person should be cautious about preservatives (NTPRE) DZNTPRE1Agree completely -0.0237-0.81 1.250.9404 DZNTPRE2Agree mostly-0.0146-0.65 DZNTPRE3Agree somewhat0.01110.54 DZNTPRE4Neither0.02160.77 DZNTPRE5Disagree somewhat0.03030.50 ZNTPRE6Disagree mostly 0.03530.34 A person should be cautious about salt (NTSAL) DZNTSAL1Agree completely -0.0463-2.10 6.590.2530 DZNTSAL2Agree mostly0.00660.36 DZNTSAL3Agree somewhat0.02041.08 DZNTSAL4Neither0.05391.67 DZNTSAL5Disagree somewhat0.02710.44 ZNTSAL6Disagree mostly -0.0774-0.84 A person should be cautious about sugar (NTSUG) DZNTSUG1Agree completely 0.05061.82 11.810.0374 DZNTSUG2Agree mostly0.03251.50 DZNTSUG3Agree somewhat-0.0422-2.93 DZNTSUG4Neither-0.0164-0.74 DZNTSUG5Disagree somewhat-0.0052-0.13 ZNTSUG6Disagree mostly 0.09401.40

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111 Table 5-7. Continued Dependent variable: ATLAB 1994-2003 (15,083 obs)Condition number = 39 Explanatory variables Dummy variables Description Scaled R-sq = 0 .49 Coefft-statsWaldP-value Vitamins recommended by physician (NTVIT) DZNTVIT1Agree completely 0.06031.70 32.850.0000 DZNTVIT2Agree mostly0.10273.08 DZNTVIT3Agree somewhat0.05182.28 DZNTVIT4Neither0.02141.33 DZNTVIT5Disagree somewhat-0.0357-2.27 ZNTVIT6Disagree mostly -0.0799-4.08 Quarters (ZQTR) DZZQTR1 First quarter 0.01821.16 6.890.0756 DZZQTR2 Second quarter-0.0238-1.43 DZZQTR3 Third quarter -0.0248-1.60 ZZQTR34Fourth quarter0.02731.81 MU3 Thresholds 0.854666.38 MU41.661597.91 MU52.1816111.15 MU62.7422116.54

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112 Table 5-8. Wald test for the coefficients of the sequential Ordered Probit model for the periods 1984-1993 and 1994-2003 Dependent variable: ATLAB1984-19931994-2003 Explanatory variablesWaldP-valueWaldP-value Age of female head (DMAGE)104.420.000092.330.0000 Children under 18 years (DMCHL)0.09 0.76178.340.0039 Education of female head (DMEDU)6.32 0.096813.250.0041 Employment of female head (DMFEM)16.180.000121.020.0000 Household size (DMHSZ)14.260.00269.260.0261 Household income (DMIN2)2.47 0.48023.34 0.3419 Census region (DMREG)13.42 0.098132.670.0001 Conscious of calories (ATCAL)723.380.00001224.260.0000 Like to lose 20 pounds (ATLBS)31.560.000013.460.0194 Love to swim (ATSWM)5.76 0.330021.060.0008 Overweight isn't attractive (ATWGT)65.690.000053.160.0000 Best known brands are highest quality (NTBRN)18.810.002134.710.0000 Food should have body building ingredients (NTING)11.090.049518.150.0028 Know more than most about nutrition (NTKNO)430.530.0000552.360.0000 Adult female on diet (DTFE2)166.830.0000223.880.0000 Eating fried chicken (FDFCH)90.900.0000142.380.0000 Eating hot dog sandwich (FDHOT)102.160.000064.760.0000 Eating lunchmeat (FDLUN)59.920.000015.500.0084 Eating pizza (FDPIZ)6.04 0.30190.92 0.9685 Eating tacos (FDTAC)4.85 0.43488.04 0.1540 Avoid foreign food (ATFOR)65.940.0000101.580.0000 Doctor gives advice on diet (ATDOC)428.890.0000313.980.0000 A person should be cautious about additives (NTADD)58.180.000055.070.0000 A person should be cautious about cholesterol (NTCHL)4.81 0.438923.450.0003 A person should be cautious about fat (NTFAT)47.710.000052.690.0000 A person should be cautious about preservatives (NTPRE)33.180.00001.25 0.9404 A person should be cautious about salt (NTSAL)2.54 0.77086.590.2530 A person should be cautious about sugar (NTSUG)6.96 0.223711.810.0374 Vitamins recommended by physician (NTVIT)15.320.009132.850.0000 Quarters (ZQTR)3.43 0.33066.89 0.0756 Not statistically significant at least at the 5% level

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113 0.59 0.63 I check the labels for harmful ingredients (ATLAB) I read the labels for my food purchase (FPLAB) Average consumer 0.00 0.20 0.40 0.60 0.80 1.00 Probability of reading food label ATLAB Completely agree (1)ATLAB Mostly agree (2) FPLAB Completely agree (1)FPLAB Mostly agree (2) Figure 5-1. Probability of reading food labels by the average consumer

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114 0.53 0.56 0.60 0.63 0.66 0.56 0.59 0.64 0.67 0.70 <3535 4445 5455 6465 +<3535 4445 5455 6465 + Age of female head of household 0.00 0.20 0.40 0.60 0.80 1.00 Probability of reading food label ATLAB Completely agree (1)ATLAB Mostly agree (2) FPLAB Completely agree (1)FPLAB Mostly agree (2) A Figure 5-2. Demographics impact on reading f ood labels. A)Age of female head of household. B) Children under 18. C) Education of female head of household. D) Employment of female head of household. E) Household size (members). F) Household income. G) Census region.

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115 0.57 0.61 0.62 0.63 ChildrenNo childrenChildrenNo children Presence of children under 18 0.00 0.20 0.40 0.60 0.80 1.00 Probability of reading food label ATLAB Completely agree (1)ATLAB Mostly agree (2) FPLAB Completely agree (1)FPLAB Mostly agree (2) B 0.53 0.59 0.60 0.61 0.60 0.62 0.64 0.63 No high school High school Some college College graduate No high school High school Some college College graduate Education of female head of household 0.00 0.20 0.40 0.60 0.80 1.00 Probability of reading food label ATLAB Completely agree (1)ATLAB Mostly agree (2) FPLAB Completely agree (1)FPLAB Mostly agree (2) C Figure 5-2. Continued

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116 0.58 0.61 0.62 0.64 EmployedNot employedEmployedNot employed Employment of female head of household 0.00 0.20 0.40 0.60 0.80 1.00 Probability of reading food label ATLAB Completely agree (1)ATLAB Mostly agree (2) FPLAB Completely agree (1)FPLAB Mostly agree (2) D 0.57 0.59 0.61 0.60 0.61 0.65 0.63 0.61 123-45 +123-45 + Household size (members) 0.00 0.20 0.40 0.60 0.80 1.00 Probability of reading food label ATLAB Completely agree (1)ATLAB Mostly agree (2) FPLAB Completely agree (1)FPLAB Mostly agree (2) E Figure 5-2. Continued

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117 0.61 0.59 0.58 0.57 0.64 0.64 0.62 0.60 Under $30,000 $30,000 49,999 $50,000 69,999 $70,000 100,000 + Under 30,000 30,000 49,999 50,000 69,999 70,000 100,000 + Household income 0.00 0.20 0.40 0.60 0.80 1.00 Probability of reading food label ATLAB Completely agree (1)ATLAB Mostly agree (2) FPLAB Completely agree (1)FPLAB Mostly agree (2) F 0.55 0.60 0.57 0.57 0.62 0.61 0.60 0.60 0.60 0.60 0.65 0.60 0.58 0.66 0.67 0.63 0.59 0.63 NEMAECNWNCSAESCWSCMTNPACNEMAECNWNCSAESCWSCMTNPAC Census region 0.00 0.20 0.40 0.60 0.80 1.00 Probability of reading food label ATLAB Completely agree (1)ATLAB Mostly agree (2) FPLAB Completely agree (1)FPLAB Mostly agree (2) G Figure 5-2. Continued

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118 0.87 0.73 0.62 0.52 0.45 0.31 0.86 0.73 0.65 0.57 0.52 0.41 C o m p l e t e l y A g r e e M o s t l y A g r e e S o m e w h a t A g r e e N e i t h e r S o m e w h a t D i s a g r e e M o s t l y D i s a g r e e C o m p l e t e l y A g r e e M o s t l y A g re e S o m e w h a t A g r e e N e i t h e r S o m e w h a t D i s a g r e e M o s t l y D i s a g r e eConscious of calories 0.00 0.20 0.40 0.60 0.80 1.00 Probability of reading food label ATLAB Completely agree (1)ATLAB Mostly agree (2) FPLAB Completely agree (1)FPLAB Mostly agree (2) A Figure 5-3. Attitudes impact on reading food labels A) Conscious of calories. B) Like to lose 20 pounds. C) Love to swim. D) Overweight is n't attractive. E) Best known brands are highest quality. F) Food should have body build ing ingredients. G) Know more than most about nutrition

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119 0.58 0.58 0.58 0.61 0.59 0.61 0.61 0.61 0.62 0.64 0.63 0.64 C o m p l e t e l y A g r e e M o s t l y A g r e e S o m e w h a t A g r e e N e i t h e r S o m e w h a t D i s a g r e e M o s t l y D i s a g r e e C o m p l e t e l y A g r e e M o s t l y A g re e S o m e w h a t A g r e e N e i t h e r S o m e w h a t D i s a g r e e M o s t l y D i s a g r e eLove to swim 0.00 0.20 0.40 0.60 0.80 1.00 Probability of reading food label ATLAB Completely agree (1)ATLAB Mostly agree (2) FPLAB Completely agree (1)FPLAB Mostly agree (2) C Figure 5-3. Continued 0.58 0.58 0.58 0.60 0.61 0.61 0.62 0.62 0.62 0.64 0.64 0.64 C o m p l e t e l y A g r e e M o s t l y A g r e e S o m e w h a t A g r e e N e i t h e r S o m e w h a t D i s a g r e e M o s t l y D i s a g r e e C o m p l e t e l y A g r e e M o s t l y A g re e S o m e w h a t A g r e e N e i t h e r S o m e w h a t D i s a g r e e M o s t l y D i s a g r e eLike to lose 20 lbs 0.00 0.20 0.40 0.60 0.80 1.00 Probability of reading food label ATLAB Completely agree (1)ATLAB Mostly agree (2) FPLAB Completely agree (1)FPLAB Mostly agree (2) B

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120 0.49 0.58 0.58 0.60 0.60 0.63 0.54 0.61 0.61 0.63 0.63 0.66 C o m p l e t e l y A g r e e M o s t l y A g r e e S o m e w h a t A g r e e N e i t h e r S o m e w h a t D i s a g r e e M o s t l y D i s a g r e e C o m p l e t e l y A g r e e M o s t l y A g re e S o m e w h a t A g r e e N e i t h e r S o m e w h a t D i s a g r e e M o s t l y D i s a g r e eBest known brands are highest quality 0.00 0.20 0.40 0.60 0.80 1.00 Probability of reading food label ATLAB Completely agree (1)ATLAB Mostly agree (2) FPLAB Completely agree (1)FPLAB Mostly agree (2) E Figure 5-3. Continued 0.55 0.58 0.61 0.63 0.63 0.65 0.60 0.62 0.64 0.66 0.66 0.67 C o m p l e t e l y A g r e e M o s t l y A g r e e S o m e w h a t A g r e e N e i t h e r S o m e w h a t D i s a g r e e M o s t l y D i s a g r e e C o m p l e t e l y A g r e e M o s t l y A g re e S o m e w h a t A g r e e N e i t h e r S o m e w h a t D i s a g r e e M o s t l y D i s a g r e eOverweight isn't attractive 0.00 0.20 0.40 0.60 0.80 1.00 Probability of reading food label ATLAB Completely agree (1)ATLAB Mostly agree (2) FPLAB Completely agree (1)FPLAB Mostly agree (2) D

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121 0.63 0.61 0.61 0.58 0.56 0.57 0.68 0.65 0.63 0.62 0.60 0.59 C o m p l e t e l y A g r e e M o s t l y A g r e e S o m e w h a t A g r e e N e i t h e r S o m e w h a t D i s a g r e e M o s t l y D i s a g r e e C o m p l e t e l y A g r e e M o s t l y A g re e S o m e w h a t A g r e e N e i t h e r S o m e w h a t D i s a g r e e M o s t l y D i s a g r e eFood should have body building ingredients 0.00 0.20 0.40 0.60 0.80 1.00 Probability of reading food label ATLAB Completely agree (1)ATLAB Mostly agree (2) FPLAB Completely agree (1)FPLAB Mostly agree (2) F 0.76 0.71 0.63 0.55 0.48 0.43 0.77 0.71 0.67 0.60 0.51 0.50 C o m p l e t e l y A g r e e M o s t l y A g r e e S o m e w h a t A g r e e N e i t h e r S o m e w h a t D i s a g r e e M o s t l y D i s a g r e e C o m p l e t e l y A g r e e M o s t l y A g re e S o m e w h a t A g r e e N e i t h e r S o m e w h a t D i s a g r e e M o s t l y D i s a g r e eKnow more than most about nutrition 0.00 0.20 0.40 0.60 0.80 1.00 Probability of reading food label ATLAB Completely agree (1)ATLAB Mostly agree (2) FPLAB Completely agree (1)FPLAB Mostly agree (2) G Figure 5-3. Continued

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122 0.68 0.56 0.71 0.59 DietNo dietDietNo diet Adult female on diet 0.00 0.20 0.40 0.60 0.80 1.00 Probability of reading food label ATLAB Completely agree (1)ATLAB Mostly agree (2) FPLAB Completely agree (1)FPLAB Mostly agree (2) A Figure 5-4. Eating habits impact on reading f ood labels. A) Adult female on diet. B) Eating fried chicken. C) Eating hot dog. D) Eating l unchmeat. E) Eating pizza. F) Eating tacos. G) Avoid foreign food. H) Try fast food pl aces. I) Visit restaurants more than most.

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123 0.49 0.52 0.53 0.57 0.62 0.70 0.58 0.56 0.59 0.61 0.64 0.71 A l w a y s E n c o u r a g e A l m o s t A l w a y s E n c o u r a g e S o m e t i m e s E n c o u r a g e N e i t h e r S o m e t i m e s D i s c o u r a g e A l m o s t a l w a y s D i s c o ur a g e A l w a y s E n c o u r a g e A l m o s t A l w a y s E n c o u r a g e S o m e t i m e s E n c o u r a g e N e i t h e r S o m e t i m e s D i s c o u r a g e A l m o s t a l w a y s D is c o u r a g eEating fried chicken 0.00 0.20 0.40 0.60 0.80 1.00 Probability of reading food label ATLAB Completely agree (1)ATLAB Mostly agree (2) FPLAB Completely agree (1)FPLAB Mostly agree (2) B 0.52 0.55 0.57 0.57 0.63 0.67 0.57 0.60 0.60 0.61 0.65 0.70 A l w a y s E n c o u r a g e A l m o s t A l w a y s E n c o u r a g e S o m e t i m e s E n c o u r a g e N e i t h e r S o m e t i m e s D i s c o u r a g e A l m o s t a l w a y s D i s c o ur a g e A l w a y s E n c o u r a g e A l m o s t A l w a y s E n c o u r a g e S o m e t i m e s E n c o u r a g e N e i t h e r S o m e t i m e s D i s c o u r a g e A l m o s t a l w a y s D is c o u r a g eEating hot dog 0.00 0.20 0.40 0.60 0.80 1.00 Probability of reading food label ATLAB Completely agree (1)ATLAB Mostly agree (2) FPLAB Completely agree (1)FPLAB Mostly agree (2) C Figure 5-4. Continued

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124 0.56 0.61 0.60 0.58 0.59 0.63 0.59 0.61 0.63 0.63 0.63 0.65 A l w a y s E n c o u r a g e A l m o s t A l w a y s E n c o u r a g e S o m e t i m e s E n c o u r a g e N e i t h e r S o m e t i m e s D i s c o u r a g e A l m o s t a l w a y s D i s c o ur a g e A l w a y s E n c o u r a g e A l m o s t A l w a y s E n c o u r a g e S o m e t i m e s E n c o u r a g e N e i t h e r S o m e t i m e s D i s c o u r a g e A l m o s t a l w a y s D is c o u r a g eEating lunchmeat 0.00 0.20 0.40 0.60 0.80 1.00 Probability of reading food label ATLAB Completely agree (1)ATLAB Mostly agree (2) FPLAB Completely agree (1)FPLAB Mostly agree (2) D 0.59 0.59 0.59 0.59 0.59 0.62 0.61 0.62 0.63 0.63 0.63 0.69 A l w a y s E n c o u r a g e A l m o s t A l w a y s E n c o u r a g e S o m e t i m e s E n c o u r a g e N e i t h e r S o m e t i m e s D i s c o u r a g e A l m o s t a l w a y s D i s c o ur a g e A l w a y s E n c o u r a g e A l m o s t A l w a y s E n c o u r a g e S o m e t i m e s E n c o u r a g e N e i t h e r S o m e t i m e s D i s c o u r a g e A l m o s t a l w a y s D is c o u r a g eEating pizza 0.00 0.20 0.40 0.60 0.80 1.00 Probability of reading food label ATLAB Completely agree (1)ATLAB Mostly agree (2) FPLAB Completely agree (1)FPLAB Mostly agree (2) E Figure 5-4. Continued

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125 0.55 0.58 0.60 0.59 0.61 0.61 0.60 0.62 0.64 0.63 0.63 0.64 A l w a y s E n c o u r a g e A l m o s t A l w a y s E n c o u r a g e S o m e t i m e s E n c o u r a g e N e i t h e r S o m e t i m e s D i s c o u r a g e A l m o s t a l w a y s D i s c o ur a g e A l w a y s E n c o u r a g e A l m o s t A l w a y s E n c o u r a g e S o m e t i m e s E n c o u r a g e N e i t h e r S o m e t i m e s D i s c o u r a g e A l m o s t a l w a y s D is c o u r a g eEating tacos 0.00 0.20 0.40 0.60 0.80 1.00 Probability of reading food label ATLAB Completely agree (1)ATLAB Mostly agree (2) FPLAB Completely agree (1)FPLAB Mostly agree (2) F 0.65 0.62 0.59 0.57 0.57 0.53 0.66 0.65 0.62 0.61 0.60 0.60 C o m p l e t e l y A g r e e M o s t l y A g r e e S o m e w h a t A g r e e N e i t h e r S o m e w h a t D i s a g r e e M o s t l y D i s a g r e e C o m p l e t e l y A g r e e M o s t l y A g re e S o m e w h a t A g r e e N e i t h e r S o m e w h a t D i s a g r e e M o s t l y D i s a g r e eAvoid foreign food 0.00 0.20 0.40 0.60 0.80 1.00 Probability of reading food label ATLAB Completely agree (1)ATLAB Mostly agree (2) FPLAB Completely agree (1)FPLAB Mostly agree (2) G Figure 5-4. Continued

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126 0.60 0.59 0.60 0.61 0.58 0.59 0.72 0.64 0.63 0.65 0.61 0.62 C o m p l e t e l y A g r e e M o s t l y A g r e e S o m e w h a t A g r e e N e i t h e r S o m e w h a t D i s a g r e e M o s t l y D i s a g r e e C o m p l e t e l y A g r e e M o s t l y A g re e S o m e w h a t A g r e e N e i t h e r S o m e w h a t D i s a g r e e M o s t l y D i s a g r e eTry fast food places 0.00 0.20 0.40 0.60 0.80 1.00 Probability of reading food label ATLAB Completely agree (1)ATLAB Mostly agree (2) FPLAB Completely agree (1)FPLAB Mostly agree (2) H 0.58 0.60 0.62 0.60 0.58 0.59 0.71 0.67 0.65 0.65 0.62 0.61 C o m p l e t e l y A g r e e M o s t l y A g r e e S o m e w h a t A g r e e N e i t h e r S o m e w h a t D i s a g r e e M o s t l y D i s a g r e e C o m p l e t e l y A g r e e M o s t l y A g re e S o m e w h a t A g r e e N e i t h e r S o m e w h a t D i s a g r e e M o s t l y D i s a g r e eVisit restaurant more than most 0.00 0.20 0.40 0.60 0.80 1.00 Probability of reading food label ATLAB Completely agree (1)ATLAB Mostly agree (2) FPLAB Completely agree (1)FPLAB Mostly agree (2) I Figure 5-4. Continued

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127 0.76 0.66 0.60 0.60 0.54 0.52 0.73 0.67 0.64 0.63 0.58 0.58 C o m p l e t e l y A g r e e M o s t l y A g r e e S o m e w h a t A g r e e N e i t h e r S o m e w h a t D i s a g r e e M o s t l y D i s a g r e e C o m p l e t e l y A g r e e M o s t l y A g re e S o m e w h a t A g r e e N e i t h e r S o m e w h a t D i s a g r e e M o s t l y D i s a g r e eDoctor gives advice on diet 0.00 0.20 0.40 0.60 0.80 1.00 Probability of reading food label ATLAB Completely agree (1)ATLAB Mostly agree (2) FPLAB Completely agree (1)FPLAB Mostly agree (2) A Figure 5-5. Health Concern impacts on reading food labels. A) Doctor give advice on diet. B) A person should be cautious about additives. C) A person should be cautious about cholesterol. D) A person should be cautious about fat. E) A person should be cautious about preservatives. F) A person should be cautious about salt. G) A person should be cautious about sugar. H) Vitamins recommended by physician.

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128 0.66 0.61 0.56 0.54 0.48 0.47 0.67 0.64 0.61 0.60 0.52 0.53 C o m p l e t e l y A g r e e M o s t l y A g r e e S o m e w h a t A g r e e N e i t h e r S o m e w h a t D i s a g r e e M o s t l y D i s a g r e e C o m p l e t e l y A g r e e M o s t l y A g re e S o m e w h a t A g r e e N e i t h e r S o m e w h a t D i s a g r e e M o s t l y D i s a g r e eA person should be cautious about additives 0.00 0.20 0.40 0.60 0.80 1.00 Probability of reading food label ATLAB Completely agree (1)ATLAB Mostly agree (2) FPLAB Completely agree (1)FPLAB Mostly agree (2) B 0.64 0.58 0.58 0.55 0.55 0.48 0.66 0.62 0.62 0.59 0.53 0.62 C o m p l e t e l y A g r e e M o s t l y A g r e e S o m e w h a t A g r e e N e i t h e r S o m e w h a t D i s a g r e e M o s t l y D i s a g r e e C o m p l e t e l y A g r e e M o s t l y A g re e S o m e w h a t A g r e e N e i t h e r S o m e w h a t D i s a g r e e M o s t l y D i s a g r e eA person should be cautious about cholesterol 0.00 0.20 0.40 0.60 0.80 1.00 Probability of reading food label ATLAB Completely agree (1)ATLAB Mostly agree (2) FPLAB Completely agree (1)FPLAB Mostly agree (2) C Figure 5-5. Continued

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129 0.64 0.59 0.55 0.52 0.51 0.60 0.67 0.63 0.58 0.56 0.59 0.59 C o m p l e t e l y A g r e e M o s t l y A g r e e S o m e w h a t A g r e e N e i t h e r S o m e w h a t D i s a g r e e M o s t l y D i s a g r e e C o m p l e t e l y A g r e e M o s t l y A g re e S o m e w h a t A g r e e N e i t h e r S o m e w h a t D i s a g r e e M o s t l y D i s a g r e eA person should be cautious about fat 0.00 0.20 0.40 0.60 0.80 1.00 Probability of reading food label ATLAB Completely agree (1)ATLAB Mostly agree (2) FPLAB Completely agree (1)FPLAB Mostly agree (2) D 0.61 0.60 0.59 0.58 0.56 0.61 0.67 0.62 0.62 0.60 0.60 0.63 C o m p l e t e l y A g r e e M o s t l y A g r e e S o m e w h a t A g r e e N e i t h e r S o m e w h a t D i s a g r e e M o s t l y D i s a g r e e C o m p l e t e l y A g r e e M o s t l y A g re e S o m e w h a t A g r e e N e i t h e r S o m e w h a t D i s a g r e e M o s t l y D i s a g r e eA person should be cautious about preservatives 0.00 0.20 0.40 0.60 0.80 1.00 Probability of reading food label ATLAB Completely agree (1)ATLAB Mostly agree (2) FPLAB Completely agree (1)FPLAB Mostly agree (2) E Figure 5-5. Continued

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130 0.61 0.59 0.59 0.57 0.61 0.65 0.64 0.62 0.63 0.61 0.66 0.62 C o m p l e t e l y A g r e e M o s t l y A g r e e S o m e w h a t A g r e e N e i t h e r S o m e w h a t D i s a g r e e M o s t l y D i s a g r e e C o m p l e t e l y A g r e e M o s t l y A g re e S o m e w h a t A g r e e N e i t h e r S o m e w h a t D i s a g r e e M o s t l y D i s a g r e eA person should be cautious about salt 0.00 0.20 0.40 0.60 0.80 1.00 Probability of reading food label ATLAB Completely agree (1)ATLAB Mostly agree (2) FPLAB Completely agree (1)FPLAB Mostly agree (2) F 0.57 0.59 0.61 0.61 0.60 0.55 0.61 0.63 0.64 0.64 0.63 0.58 C o m p l e t e l y A g r e e M o s t l y A g r e e S o m e w h a t A g r e e N e i t h e r S o m e w h a t D i s a g r e e M o s t l y D i s a g r e e C o m p l e t e l y A g r e e M o s t l y A g re e S o m e w h a t A g r e e N e i t h e r S o m e w h a t D i s a g r e e M o s t l y D i s a g r e eA person should be cautious about sugar 0.00 0.20 0.40 0.60 0.80 1.00 Probability of reading food label ATLAB Completely agree (1)ATLAB Mostly agree (2) FPLAB Completely agree (1)FPLAB Mostly agree (2) G Figure 5-5. Continued

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131 0.58 0.55 0.58 0.59 0.60 0.63 0.62 0.60 0.62 0.63 0.62 0.66 C o m p l e t e l y A g r e e M o s t l y A g r e e S o m e w h a t A g r e e N e i t h e r S o m e w h a t D i s a g r e e M o s t l y D i s a g r e e C o m p l e t e l y A g r e e M o s t l y A g re e S o m e w h a t A g r e e N e i t h e r S o m e w h a t D i s a g r e e M o s t l y D i s a g r e eVitamins recommended by physician 0.00 0.20 0.40 0.60 0.80 1.00 Probability of reading food label ATLAB Completely agree (1)ATLAB Mostly agree (2) FPLAB Completely agree (1)FPLAB Mostly agree (2) H Figure 5-5. Continued 0.59 0.60 0.61 0.58 0.62 0.65 0.65 0.60 Quarter 1 Quarter 2 Quarter 3 Quarter 4 Quarter 1 Quarter 2 Quarter 3 Quarter 4 Seasonality 0.00 0.20 0.40 0.60 0.80 1.00 Probability of reading food label ATLAB Completely agree (1)ATLAB Mostly agree (2) FPLAB Completely agree (1)FPLAB Mostly agree (2) Figure 5-6. Impact of seasonality on reading food labels

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132 0.87 0.76 0.76 0.70 0.66 0.64 0.67 0.64 0.63 0.66 0.68 0.65 0.65 0.65 0.63 0.63 0.61 0.62 0.61 0.63 0.61 0.61 0.61 0.62 0.61 0.61 0.61 0.61 0.61 0.62 0.61 0.61 0.31 0.43 0.52 0.49 0.47 0.48 0.52 0.51 0.49 0.53 0.56 0.53 0.55 0.57 0.56 0.55 0.53 0.55 0.55 0.56 0.55 0.57 0.56 0.58 0.57 0.58 0.57 0.58 0.58 0.59 0.58 0.58 Conscious of calories Know more than most Doctor gives advice on diet Eating fried chicken Cautious about additives Cautious about cholesterol Eating hot dog Cautious about fat Best known brands Age of female head Adult female on diet Avoid foreign food Overweight isn't attractive Cautious about salt Body building ingredients Vitamins by physician Education of female head Census region Eating tacos Eating lunchmeat Cautious about sugar Household size Cautious about preservatives Visit restaurants Household income Love to swim Children under 18 years Quarters (seasonality) Like to lose 20 pounds Eating pizza Employment of female head Try fast food places 0.000.100.200.300.400.500.600.700.800.901.00 Percent adjustment to Check Labels for Harmful Ingredients (ATLAB) of the average consumer (Average ATLAB = .59) 0.56 0.34 0.24 0.20 0.19 0.16 0.15 0.14 0.13 0.13 0.13 0.12 0.10 0.08 0.08 0.08 0.07 0.07 0.07 0.06 0.06 0.05 0.05 0.04 0.04 0.04 0.03 0.03 0.03 0.03 0.03 0.03 ATLAB 1993-03 rangeFigure 5-7. Ranking of factors impacting the likelihood of reading food labels for harmful ingredients.

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133 0.86 0.77 0.67 0.73 0.71 0.70 0.70 0.66 0.66 0.67 0.71 0.72 0.71 0.68 0.67 0.69 0.67 0.67 0.64 0.66 0.65 0.66 0.66 0.65 0.65 0.64 0.64 0.64 0.64 0.64 0.64 0.63 0.41 0.50 0.52 0.58 0.56 0.56 0.57 0.53 0.54 0.56 0.59 0.61 0.61 0.59 0.58 0.61 0.60 0.60 0.58 0.60 0.59 0.60 0.61 0.60 0.61 0.60 0.60 0.60 0.62 0.61 0.62 0.62 Conscious of calories Know more than most Cautious about additives Doctor gives advice on diet Eating fried chicken Age of female head Eating hot dog Cautious about cholesterol Best known brands Cautious about fat Adult female on diet Try fast food places Visit restaurants Body building ingredients Census region Eating pizza Overweight isn't attractive Cautious about preservatives Cautious about sugar Avoid foreign food Eating lunchmeat Vitamins by physician Cautious about salt Quarters (seasonality) Household size Education of female head Eating tacos Household income Like to lose 20 pounds Love to swim Employment of female head Children under 18 years 0.200.300.400.500.600.700.800.901.00 Percent adjustment to Food Purchase for Labels (FPLAB) of the average consumer (Average FPLAB = .63) 0.44 0.26 0.15 0.15 0.15 0.14 0.13 0.13 0.12 0.12 0.12 0.12 0.09 0.09 0.09 0.08 0.07 0.07 0.06 0.06 0.06 0.05 0.05 0.05 0.04 0.04 0.04 0.03 0.03 0.03 0.02 0.02 FPLAB 1993-2003 rangeFigure 5-8. Ranking of factors impacting the likelihood of reading food labels for food purchase.

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134 C o n s c i o u s o f c a l o r i e s K n o w m o r e t h a n m o s t D o c t o r g i v e s a d v i c e o n d i e t E a t i n g f r i e d c h i c k e n C a u t i o u s a b o u t a d d i t i v e s C a u t i o u s a b o u t c h o l e s t e r o l E a t i n g h o t d o g C a u t i o u s a b o u t f a t B e s t k n o w n b r a n d s A g e o f f e m a l e h ea d A d u l t f e m a l e o n d i e t A v o i d f o r e i g n f o o d O v e r w e i g h t i s n t a t t r a c t i v e C a u t i o u s a b o u t s a l t B o d y b u i l d i n g in g r e d i e n t s0.00 0.10 0.20 0.30 0.40 0.50 0.60 Range of changes in probabilities of scores 1 & 2 FPLAB rangeATLAB range Figure 5-9. Range of change in probabilities for ATLAB and FPLAB.

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135 1984 1993 1985 1994 1986 1995 1987 1996 1988 1997 1989 1998 1990 1999 1991 2000 1992 2001 1993 2002 1994 2003 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00 Probability for "I check the label for harmful ingredients" (ATLAB) Average Consumer Completely agree (1) Mostly agree (2) Completely agree (1) 0.300.310.310.310.310.300.300.290.270.260.26 Mostly agree (2) 0.300.320.320.330.340.340.330.330.330.320.32A Figure 5-10. Change over time in the likelihood of reading food labels to check for harmful ingredients for the average consumer. A) Completely agree (1) and Mostly agree (2). B) Somewhat agree (3), Neither (4), Somewhat disagree (5), and Mostly disagree (6).

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136 1984 1993 1985 1994 1986 1995 1987 1996 1988 1997 1989 1998 1990 1999 1991 2000 1992 2001 1993 2002 1994 2003 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00 Probability for "I check the label for harmful ingredients" (ATLAB) Average Consumer Somewhat agree (3) Neither (4) Somewhat disagree (5) Mostly disagree (6) Somewhat agree (3) 0.240.240.240.230.240.240.250.250.260.260.26 Neither (4) 0.090.080.080.080.070.080.080.080.090.090.09 Somewhat disagree (5) 0.040.040.030.030.030.030.030.040.040.040.04 Mostly disagree (6) 0.020.020.020.010.010.010.010.010.010.020.02B Figure 5-10. Continued

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137 1984 1993 1985 1994 1986 1995 1987 1996 1988 1997 1989 1998 1990 1999 1991 2000 1992 2001 1993 2002 1994 2003 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00 Probability for "I Check the Label for Harmful Ingredients" (ATLAB) Completely agree (1) & Mostly agree (2) combined Age of female head of household =< 35 years (1) 65+ years (5) =< 35 years (1) 0.540.560.570.580.590.590.570.560.540.520.52 65+ years (5) 0.670.690.690.710.700.700.690.680.670.660.65A Figure 5-11. Change over time in the impact of demographics on reading food labels to check for harmful ingredients. A) Age of female head of household. B) Children under 18. C) Education of female head of household. D) Employment of female head of household. E) Household size (members). F) Household income. G) Census regions: New England (1), Middle Atlantic (2) and East North Central (3). H) Census regions: West North Central (4), South Atlantic (5) and East South Central (6). I) Census regions: West South Central (7), Mountain (8) and Pacific (9).

PAGE 138

138 1984 1993 1985 1994 1986 1995 1987 1996 1988 1997 1989 1998 1990 1999 1991 2000 1992 2001 1993 2002 1994 2003 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00 Probability for "I Check the Label for Harmful Ingredients" (ATLAB) Completely agree (1) & Mostly agree (2) combined Education of female head of household No high school (1) College graduate (4) No high school (1) 0.590.610.630.630.640.630.610.580.550.530.53 College graduate (4) 0.600.620.630.640.650.650.640.620.610.600.59C Figure 5-11. Continued 1984 1993 1985 1994 1986 1995 1987 1996 1988 1997 1989 1998 1990 1999 1991 2000 1992 2001 1993 2002 1994 2003 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00 Probability for "I Check the Label for Harmful Ingredients" (ATLAB) Completely agree (1) & Mostly agree (2) combined Presence of children under 18 Yes (1)No (2) Yes (1)0.610.630.640.640.630.630.610.590.580.560.55 No (2)0.600.620.630.650.650.650.640.630.610.600.59B

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139 1984 1993 1985 1994 1986 1995 1987 1996 1988 1997 1989 1998 1990 1999 1991 2000 1992 2001 1993 2002 1994 2003 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00 Probability for "I Check the Label for Harmful Ingredients" (ATLAB) Completely agree (1) & Mostly agree (2) combined Employment of female head of household Employed (1) Not employed (2) Employed (1) 0.590.610.620.630.630.630.620.600.590.570.56 Not employed (2) 0.620.640.650.660.660.660.650.630.620.600.60D 1984 1993 1985 1994 1986 1995 1987 1996 1988 1997 1989 1998 1990 1999 1991 2000 1992 2001 1993 2002 1994 2003 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00 Probability for "I Check the Label for Harmful Ingredients" (ATLAB) Completely agree (1) & Mostly agree (2) combined Household size (members) 1 member (1) 5+ members (4) 1 member (1) 0.580.590.610.620.610.610.600.580.570.560.56 5+ members (4) 0.610.620.630.650.660.650.640.620.610.590.58E Figure 5-11. Continued

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140 1984 1993 1985 1994 1986 1995 1987 1996 1988 1997 1989 1998 1990 1999 1991 2000 1992 2001 1993 2002 1994 2003 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00 Probability for "I Check the Label for Harmful Ingredients" (ATLAB) Completely agree (1) & Mostly agree (2) combined Household income under $30,000 (1) $70-100,000+ (4) under $30,000 (1) 0.610.630.640.650.650.650.640.620.600.590.59 $70-100,000+ (4) 0.590.600.610.630.630.630.620.600.580.570.56F 1984 1993 1985 1994 1986 1995 1987 1996 1988 1997 1989 1998 1990 1999 1991 2000 1992 2001 1993 2002 1994 2003 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00 Probability for "I Check the Label for Harmful Ingredients" (ATLAB) Completely agree (1) & Mostly agree (2) combined Census region N. E. (1) M. A. (2) E. N. C. (3) N. E. (1) 0.590.620.630.630.640.640.620.590.570.550.55 M. A. (2) 0.610.620.640.650.650.640.630.620.600.590.58 E. N. C. (3) 0.590.600.610.620.630.620.610.590.580.560.55G Figure 5-11. Continued

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141 1984 1993 1985 1994 1986 1995 1987 1996 1988 1997 1989 1998 1990 1999 1991 2000 1992 2001 1993 2002 1994 2003 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00 Probability for "I Check the Label for Harmful Ingredients" (ATLAB) Completely agree (1) & Mostly agree (2) combined Census region W. S. C. (7) M. (8) P. (9) W. S. C. (7) 0.620.640.650.660.650.640.630.610.610.590.59 M. (8) 0.630.640.660.670.680.660.650.640.620.600.59 P. (9) 0.610.620.630.640.640.640.630.620.600.580.58I Figure 5-11. Continued 1984 1993 1985 1994 1986 1995 1987 1996 1988 1997 1989 1998 1990 1999 1991 2000 1992 2001 1993 2002 1994 2003 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00 Probability for "I Check the Label for Harmful Ingredients" (ATLAB) Completely agree (1) & Mostly agree (2) combined Census region W. N. C. (4) S. A. (5) E. S. C. (6) W. N. C. (4) 0.590.610.610.620.620.610.600.590.570.550.55 S. A. (5) 0.610.630.640.650.660.650.650.640.630.610.60 E. S. C. (6) 0.610.640.650.660.670.680.670.650.630.610.60H

PAGE 142

142 1984 1993 1985 1994 1986 1995 1987 1996 1988 1997 1989 1998 1990 1999 1991 2000 1992 2001 1993 2002 1994 2003 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00 Probability for "I Check the Label for Harmful Ingredients" (ATLAB) Completely agree (1) & Mostly agree (2) combined Conscious of calories Completely agree (1) Mostly disagree (6) Completely agree (1) 0.780.800.820.840.850.850.860.860.860.860.86 Mostly disagree (6) 0.380.380.380.380.370.370.360.350.330.320.31A Figure 5-12. Change over time in the impact of attitudes on reading food labels to check for harmful ingredients. A) Conscious of calor ies. B) Like to lose 20 pounds. C) Love to swim. D) Overweight isn't attractive. E) Best known brands are highest quality. F) Food should have body building ingredients. G) Know more than most about nutrition

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143 1984 1993 1985 1994 1986 1995 1987 1996 1988 1997 1989 1998 1990 1999 1991 2000 1992 2001 1993 2002 1994 2003 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00 Probability for "I Check the Label for Harmful Ingredients" (ATLAB) Completely agree (1) & Mostly agree (2) combined Like to lose 20 lbs Completely agree (1) Mostly disagree (6) Completely agree (1) 0.590.610.620.640.640.630.620.610.590.580.57 Mostly disagree (6) 0.620.640.650.660.670.660.660.640.620.600.60B 1984 1993 1985 1994 1986 1995 1987 1996 1988 1997 1989 1998 1990 1999 1991 2000 1992 2001 1993 2002 1994 2003 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00 Probability for "I Check the Label for Harmful Ingredients" (ATLAB) Completely agree (1) & Mostly agree (2) combined Love to swim Completely agree (1) Mostly disagree (6) Completely agree (1) 0.600.610.610.630.630.610.600.590.580.560.56 Mostly disagree (6) 0.610.630.640.650.660.650.650.630.620.600.59C Figure 5-12. Continued

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144 1984 1993 1985 1994 1986 1995 1987 1996 1988 1997 1989 1998 1990 1999 1991 2000 1992 2001 1993 2002 1994 2003 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00 Probability for "I Check the Label for Harmful Ingredients" (ATLAB) Completely agree (1) & Mostly agree (2) combined Overweight isn't attractive Completely agree (1) Mostly disagree (6) Completely agree (1) 0.570.590.600.610.610.600.590.580.560.540.54 Mostly disagree (6) 0.660.680.680.690.690.680.670.660.650.630.62D 1984 1993 1985 1994 1986 1995 1987 1996 1988 1997 1989 1998 1990 1999 1991 2000 1992 2001 1993 2002 1994 2003 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00 Probability for "I Check the Label for Harmful Ingredients" (ATLAB) Completely agree (1) & Mostly agree (2) combined Best known brands are highest quality Completely agree (1) Mostly disagree (6) Completely agree (1) 0.640.650.650.640.620.590.570.540.530.500.48 Mostly disagree (6) 0.620.630.650.660.660.650.640.640.620.610.61E Figure 5-12. Continued

PAGE 145

145 1984 1993 1985 1994 1986 1995 1987 1996 1988 1997 1989 1998 1990 1999 1991 2000 1992 2001 1993 2002 1994 2003 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00 Probability for "I Check the Label for Harmful Ingredients" (ATLAB) Completely agree (1) & Mostly agree (2) combined Food should have body building ingredients Completely agree (1) Mostly disagree (6) Completely agree (1) 0.620.630.650.660.670.660.660.650.640.630.62 Mostly disagree (6) 0.590.610.620.630.630.630.630.610.590.580.58F 1984 1993 1985 1994 1986 1995 1987 1996 1988 1997 1989 1998 1990 1999 1991 2000 1992 2001 1993 2002 1994 2003 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00 Probability for "I Check the Label for Harmful Ingredients" (ATLAB) Completely agree (1) & Mostly agree (2) combined Know more than most about nutrition Completely agree (1)Mostly disagree (6) Completely agree (1)0.760.780.790.800.800.800.790.790.780.770.77 Mostly disagree (6)0.430.450.460.460.460.460.450.430.420.400.39G Figure 5-12. Continued

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146 1984 1993 1985 1994 1986 1995 1987 1996 1988 1997 1989 1998 1990 1999 1991 2000 1992 2001 1993 2002 1994 2003 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00 Probability for "I Check the Label for Harmful Ingredients" (ATLAB) Completely agree (1) & Mostly agree (2) combined Adult female on diet Yes (1) No (2) Yes (1) 0.680.700.710.720.720.720.720.700.690.670.67 No (2) 0.570.590.600.610.610.600.590.580.560.550.54A Figure 5-13. Change over time in the impact of eating habits on reading food labels to check for harmful ingredients. A) Adult female on diet. B) Eating fried chicken. C) Eating hot dog. D) Eating lunchmeat. E) Eating pizza. F) Eating tacos. G) Avoid foreign food. H) Try fast food places. I) Visit restaurants more than most.

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147 1984 1993 1985 1994 1986 1995 1987 1996 1988 1997 1989 1998 1990 1999 1991 2000 1992 2001 1993 2002 1994 2003 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00 Probability for "I Check the Label for Harmful Ingredients" (ATLAB) Completely agree (1) & Mostly agree (2) combined Eating fried chicken Always encourage (1)Almost always discourage (6) Always encourage (1)0.520.540.560.580.570.560.540.540.530.500.50 Almost always discourage (6)0.690.710.720.730.740.720.710.700.690.680.68B 1984 1993 1985 1994 1986 1995 1987 1996 1988 1997 1989 1998 1990 1999 1991 2000 1992 2001 1993 2002 1994 2003 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00 Probability for "I Check the Label for Harmful Ingredients" (ATLAB) Completely agree (1) & Mostly agree (2) combined Eating hot dog Always encourage (1)Almost always discourage (6) Always encourage (1)0.560.550.550.580.600.570.550.530.520.530.52 Almost always discourage (6)0.690.700.710.710.720.720.710.690.670.660.65C Figure 5-13. Continued

PAGE 148

148 1984 1993 1985 1994 1986 1995 1987 1996 1988 1997 1989 1998 1990 1999 1991 2000 1992 2001 1993 2002 1994 2003 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00 Probability for "I Check the Label for Harmful Ingredients" (ATLAB) Completely agree (1) & Mostly agree (2) combined Eating lunchmeat Always encourage (1)Almost always discourage (6) Always encourage (1)0.560.580.600.590.590.600.610.580.560.540.54 Almost always discourage (6)0.680.690.690.710.710.700.680.670.660.630.62D 1984 1993 1985 1994 1986 1995 1987 1996 1988 1997 1989 1998 1990 1999 1991 2000 1992 2001 1993 2002 1994 2003 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00 Probability for "I Check the Label for Harmful Ingredients" (ATLAB) Completely agree (1) & Mostly agree (2) combined Eating pizza Always encourage (1)Almost always discourage (6) Always encourage (1)0.590.610.620.630.630.620.610.600.590.580.57 Almost always discourage (6)0.580.610.630.630.650.650.660.650.630.600.59E Figure 5-13. Continued

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149 1984 1993 1985 1994 1986 1995 1987 1996 1988 1997 1989 1998 1990 1999 1991 2000 1992 2001 1993 2002 1994 2003 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00 Probability for "I Check the Label for Harmful Ingredients" (ATLAB) Completely agree (1) & Mostly agree (2) combined Eating tacos Always encourage (1)Almost always discourage (6) Always encourage (1)0.630.640.650.640.640.630.620.590.560.540.53 Almost always discourage (6)0.610.630.650.660.670.660.640.630.610.600.60F 1984 1993 1985 1994 1986 1995 1987 1996 1988 1997 1989 1998 1990 1999 1991 2000 1992 2001 1993 2002 1994 2003 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00 Probability for "I Check the Label for Harmful Ingredients" (ATLAB) Completely agree (1) & Mostly agree (2) combined Avoid foreign food Completely agree (1)Mostly disagree (6) Completely agree (1)0.640.660.680.690.700.690.680.670.660.650.64 Mostly disagree (6)0.540.560.570.580.580.580.570.560.540.520.52G Figure 5-13. Continued

PAGE 150

150 1984 1993 1985 1994 1986 1995 1987 1996 1988 1997 1989 1998 1990 1999 1991 2000 1992 2001 1993 2002 1994 2003 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00 Probability for "I Check the Label for Harmful Ingredients" (ATLAB) Completely agree (1) & Mostly agree (2) combined Doctor gives advice on diet Completely agree (1)Mostly disagree (6) Completely agree (1)0.750.770.780.790.790.780.770.760.750.740.74 Mostly disagree (6)0.500.520.530.550.560.560.560.540.520.500.50A Figure 5-14. Change over time in the impact of health concerns on reading food labels to check for harmful ingredients. A) Doctor gives advice on diet. B) A person should be cautious about additives. C) A person should be cautious about cholesterol. D) A person should be cautious about fat. E) A person s hould be cautious about preservatives. F) A person should be cautious about salt. G) A person should be cautious about sugar. H) Vitamins recommended by physician.

PAGE 151

151 1984 1993 1985 1994 1986 1995 1987 1996 1988 1997 1989 1998 1990 1999 1991 2000 1992 2001 1993 2002 1994 2003 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00 Probability for "I Check the Label for Harmful Ingredients" (ATLAB) Completely agree (1) & Mostly agree (2) combined A person should be cautious about additives Completely agree (1)Mostly disagree (6) Completely agree (1)0.660.680.690.690.690.680.680.660.660.650.65 Mostly disagree (6)0.530.590.560.580.550.570.580.570.540.510.49B 1984 1993 1985 1994 1986 1995 1987 1996 1988 1997 1989 1998 1990 1999 1991 2000 1992 2001 1993 2002 1994 2003 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00 Probability for "I Check the Label for Harmful Ingredients" (ATLAB) Completely agree (1) & Mostly agree (2) combined A person should be cautious about cholesterol Completely agree (1)Mostly disagree (6) Completely agree (1)0.620.630.650.660.670.670.670.660.640.620.62 Mostly disagree (6)0.670.640.610.590.590.560.560.530.500.480.47C Figure 5-14. Continued

PAGE 152

152 1984 1993 1985 1994 1986 1995 1987 1996 1988 1997 1989 1998 1990 1999 1991 2000 1992 2001 1993 2002 1994 2003 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00 Probability for "I Check the Label for Harmful Ingredients" (ATLAB) Completely agree (1) & Mostly agree (2) combined A person should be cautious about fat Completely agree (1)Mostly disagree (6) Completely agree (1)0.640.660.680.690.690.690.680.670.650.640.63 Mostly disagree (6)0.640.620.660.640.630.670.630.590.580.560.58D 1984 1993 1985 1994 1986 1995 1987 1996 1988 1997 1989 1998 1990 1999 1991 2000 1992 2001 1993 2002 1994 2003 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00 Probability for "I Check the Label for Harmful Ingredients" (ATLAB) Completely agree (1) & Mostly agree (2) combined A person should be cautious about preservatives Completely agree (1)Mostly disagree (6) Completely agree (1)0.650.670.680.690.690.680.670.650.620.600.59 Mostly disagree (6)0.520.560.590.620.630.600.570.560.570.570.57E Figure 5-14. Continued

PAGE 153

153 1984 1993 1985 1994 1986 1995 1987 1996 1988 1997 1989 1998 1990 1999 1991 2000 1992 2001 1993 2002 1994 2003 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00 Probability for "I Check the Label for Harmful Ingredients" (ATLAB) Completely agree (1) & Mostly agree (2) combined A person should be cautious about salt Completely agree (1)Mostly disagree (6) Completely agree (1)0.610.620.640.640.650.650.640.620.610.600.60 Mostly disagree (6)0.600.660.640.670.700.660.670.660.630.620.61F 1984 1993 1985 1994 1986 1995 1987 1996 1988 1997 1989 1998 1990 1999 1991 2000 1992 2001 1993 2002 1994 2003 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00 Probability for "I Check the Label for Harmful Ingredients" (ATLAB) Completely agree (1) & Mostly agree (2) combined A person should be cautious about sugar Completely agree (1)Mostly disagree (6) Completely agree (1)0.620.630.630.640.630.620.610.590.580.560.56 Mostly disagree (6)0.540.570.620.640.630.630.600.590.590.560.54G Figure 5-14. Continued

PAGE 154

154 1984 1993 1985 1994 1986 1995 1987 1996 1988 1997 1989 1998 1990 1999 1991 2000 1992 2001 1993 2002 1994 2003 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00 Probability for "I Check the Label for Harmful Ingredients" (ATLAB) Completely agree (1) & Mostly agree (2) combined Vitamins recommended by physician Completely agree (1)Mostly disagree (6) Completely agree (1)0.620.640.640.650.650.640.630.600.570.560.56 Mostly disagree (6)0.620.650.660.670.670.660.650.640.630.620.61H Figure 5-14. Continued

PAGE 155

155 1984 1993 1985 1994 1986 1995 1987 1996 1988 1997 1989 1998 1990 1999 1991 2000 1992 2001 1993 2002 1994 2003 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00 Probability for "I Check the Label for Harmful Ingredients" (ATLAB) Completely agree (1) & Mostly agree (2) combined Seasonality Quarter (1)Quarter (2) Quarter (3)Quarter (4) Quarter (1)0.600.620.620.630.630.630.620.610.600.580.57 Quarter (2)0.610.620.640.650.660.650.640.620.610.590.59 Quarter (3)0.600.620.640.650.650.650.640.620.610.590.59 Quarter (4)0.620.630.640.650.640.640.630.610.590.580.57Figure 5-15. Change over time in the impact of seasonality on reading food labels to check for harmful ingredients.

PAGE 156

156 0.78 0.76 0.75 0.69 0.66 0.65 0.69 0.67 0.64 0.68 0.68 0.64 0.66 0.62 0.67 0.64 0.64 0.63 0.63 0.62 0.62 0.63 0.62 0.62 0.62 0.62 0.61 0.62 0.62 0.61 0.38 0.43 0.50 0.52 0.51 0.52 0.56 0.54 0.52 0.56 0.57 0.54 0.57 0.54 0.60 0.57 0.58 0.58 0.59 0.58 0.58 0.59 0.59 0.59 0.59 0.59 0.59 0.60 0.60 0.60 Conscious of calories Know more than most Doctor gives advice on diet Eating fried chicken Cautious about additives Cautious about preservatives Eating hot dog Age of female head Cautious about fat Eating lunchmeat Adult female on diet Avoid foreign food Overweight isn't attractive Cautious about sugar Cautious about cholesterol Like to lose 20 pounds Best known brands Household size Census region Vitamins by physician Eating pizza Eating tacos Body building ingredients Employment of female head Education of female head Cautious about salt Household income Love to swim Quarters (seasonality) Children under 18 years 0.000.100.200.300.400.500.600.700.800.901.00 Percent adjustment to Check Labels for Harmful Ingredients (ATLAB) of the average consumer (Average ATLAB 1984-1993 = 0.61) 0.40 0.33 0.26 0.17 0.15 0.13 0.13 0.13 0.13 0.12 0.11 0.10 0.09 0.08 0.07 0.07 0.06 0.05 0.05 0.04 0.04 0.04 0.03 0.03 0.03 0.02 0.02 0.02 0.02 0.00 ATLAB 1984-1993 rangeFigure 5-16. Ranking of factors impacting the likelihood of reading food labels for harmful ingredients in the period 1984-1993.

PAGE 157

157 0.86 0.77 0.74 0.65 0.68 0.62 0.65 0.63 0.67 0.65 0.61 0.64 0.62 0.62 0.61 0.60 0.62 0.59 0.60 0.60 0.61 0.60 0.60 0.59 0.60 0.60 0.59 0.59 0.59 0.59 0.31 0.39 0.50 0.47 0.50 0.47 0.52 0.50 0.54 0.52 0.48 0.52 0.54 0.54 0.54 0.53 0.55 0.53 0.55 0.54 0.56 0.56 0.56 0.55 0.56 0.57 0.57 0.56 0.57 0.57 Conscious of calories Know more than most Doctor gives advice on diet Cautious about additives Eating fried chicken Cautious about cholesterol Age of female head Cautious about fat Adult female on diet Eating hot dog Best known brands Avoid foreign food Eating lunchmeat Overweight isn't attractive Vitamins by physician Eating tacos Body building ingredients Education of female head Census region Cautious about sugar Cautious about salt Love to swim Household size Children under 18 years Employment of female head Like to lose 20 pounds Cautious about preservatives Household income Quarters (seasonality) Eating pizza 0.200.300.400.500.600.700.800.901.00 Percent adjustment to Check Labels for Harmful Ingredients (ATLAB) of the average consumer (Average ATLAB 1994-2003 = 0.58) 0.56 0.38 0.24 0.18 0.18 0.15 0.13 0.13 0.13 0.13 0.12 0.12 0.08 0.08 0.07 0.07 0.06 0.06 0.06 0.05 0.05 0.05 0.04 0.04 0.04 0.03 0.02 0.02 0.02 0.02 ATLAB 1994-2003 rangeFigure 5-17. Ranking of factors impacting the likelihood of reading food labels for harmful ingredients in the period 1994-2003.

PAGE 158

158 C o n s c i o u s o f c a l o r i e s K n o w m o r e t h a n m o s t D o c t o r g i v e s a d v i c e o n d i e t C a u t i o u s a b o u t a d d i t i v e s E a t i n g f r ie d c h i c k e n C a u t i o u s a b o u t c h o l e s t e r o l A g e o f f e m a l e h e a d C a u t i o u s a b o u t f a t A d u l t f e m a l e o n d i e t E a t i n g h ot d o g B e s t k n o w n b r a n d s A v o i d f o r e i g n f o o d E a t i n g l u n c h m e a t O v e r w e i g h t i s n t a t t r a c t i v e V i t a m i n s b y p h y s i ci a n0.00 0.10 0.20 0.30 0.40 0.50 0.60 Range of changes in probabilities of scores 1 & 2 RANGE 1984-1993 RANGE 1994-2003 Figure 5-18. Range of changes in probabilities of reading the labels for harmful ingredients in the periods 1984-1993 and 1994-2003.

PAGE 159

159CHAPTER 6 SUMMARY AND CONCLUSIONS While food labels can and do provide a range of potentially useful information to aspiring buyers, consumers must be aware of the information and pay attention to the messages. Further, they must understand the messages as presented for the information to be useful. Even with the requirement of labeling, any benefits occur only when the consumer perceives and uses the label information. It is not enough to have the product la beled, the information must be of value to the decision making process. Obviously, consumers di ffer and, as such, any importance placed on labels will differ across these consumers. To create the database of consumers, house hold heads were asked to provide a scaled indication of their interest in labels (NPD). With a six point Likert scal e, each household was asked to score the following questions: (1) I check the labels for harmful ingredients, and (2) I read the labels for my food purchase. While both questions zero in on the consumers importance attached to labels, the first question is more negative where the label is expected to be used to eliminate buying particular products and the second is more positive to assist with the purchase. The household data, from a demographically balanced diary survey, gave monthly observations over the years from 1984-2003 for a total of 30,414 household entries. In addition, another more recent attitudinal survey from 1993 to 2003 includes 13,150 households. For each household many attributes are know n including demographics, attitudes, eating habits and health concerns. Since the household response is discrete with scaled values, the likelihood of reading food labels can be estimated using Ordered Probit mode ls where the probability of each Likert score can be determined.

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160This study using a rigorous application of Orde red Probit models and a large database both across households and time is broad enough to have national implications. The results of the Ordered Probit models s how that consumers that are worried about potential harmful ingredients in the packaged food read the labels more often than consumers that read the labels looking for general information. The twelve most important variables, ranked according to their impact on the likelihood of readi ng food labels, in decreasing order are: conscious of calories, know more than most about nutriti on, doctor gives advice on diet, eating fried chicken, cautious about additives, cautious about choleste rol, eating hotdog, cautious about fat, best known brands are highest quality, age of female head of household, adult female on diet and avoid foreign food. As most apparent from the rankings and the probabilities for the average household, not everyone values food labels at least in terms fo r helping make buying decisions. The fact that a reasonable share of the buying population places little overt value to labels during the buying process should be of concern since most of the label content is mandatory and closely monitored. The content needs to be carefully designed to maximize the usefulness while not overwhelming consumers with too much information. It is apparent that there is little to no role for targeted labeling based on demographics except for the case of age. To be relevant, the label content must deal with health related concerns and particularly dieting issues and nutrition. Much of the current federal label guidelines require a precise focus on these di mensions. Finally the limited role of foreign foods and labeling parallel those discussed by Verbeke and Ward where they showed the limited importance of country-of-origin labeling in Europe. The results also show that, over time, the likelihood of reading food labels has changed, peaking during the early 90's, when the NLEA was implemented and declining later. The reason could be the one mentioned by Moorman (1996), that the NLEA was only partially successful

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161because there was "a group of highly skeptical consumers who remain pessimistic about the truthfulness of nutrition information and the h ealthfulness of food products despite the NLEA" (Moorman 1996, page 42). Depending on the reasons for this skepticism, ignorance or social structure as Moorman points out, national program s could be implemented to make consumers change their behavior related to healthy food consumption. Another suggestion to take into consideration would be that of Kristal et al. (1998). Based on their research results they suggest making modifications to the new food labels to make them easier to understand and creating programs to help less educated consumers interpret food label information.

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162 APPENDIX A CORRELATION TABLES OF RESTRICTED DUMMY VARIABLES Table A-1. Five highest correlation coefficients and their probability under Ho: Rho = 0 of health concerns restricted dummy variables of the 1993-2003 period (13,150 obs) DZNTADD1 DZNTADD3 0.9394 <.0001 DZNTADD2 0.9361 <.0001 DZNTADD4 0.9255 <.0001 DZNTADD5 0.7730 <.0001 DZNTPRE1 0.7501 <.0001 DZNTADD2 DZNTADD1 0.9361 <.0001 DZNTADD3 0.9336 <.0001 DZNTADD4 0.9197 <.0001 DZNTADD5 0.7682 <.0001 DZNTPRE2 0.7424 <.0001 DZNTADD3 DZNTADD1 0.9394 <.0001 DZNTADD2 0.9336 <.0001 DZNTADD4 0.9230 <.0001 DZNTADD5 0.7709 <.0001 DZNTPRE3 0.7423 <.0001 DZNTADD4 DZNTADD1 0.9255 <.0001 DZNTADD3 0.9230 <.0001 DZNTADD2 0.9197 <.0001 DZNTADD5 0.7595 <.0001 DZNTPRE4 0.7379 <.0001 DZNTADD5 DZNTADD1 0.7730 <.0001 DZNTADD3 0.7709 <.0001 DZNTADD2 0.7682 <.0001 DZNTADD4 0.7595 <.0001 DZNTPRE5 0.6291 <.0001 DZNTCHL1 DZNTCHL2 0.9540 <.0001 DZNTCHL3 0.9534 <.0001 DZNTCHL4 0.9218 <.0001 DZNTCHL5 0.7831 <.0001 DZNTFAT1 0.7036 <.0001DZNTCHL2 DZNTCHL1 0.9540 <.0001 DZNTCHL3 0.9485 <.0001 DZNTCHL4 0.9171 <.0001 DZNTCHL5 0.7791 <.0001 DZNTFAT2 0.6937 <.0001 DZNTCHL3 DZNTCHL1 0.9534 <.0001 DZNTCHL2 0.9485 <.0001 DZNTCHL4 0.9166 <.0001 DZNTCHL5 0.7786 <.0001 DZNTFAT3 0.6922 <.0001 DZNTCHL4 DZNTCHL1 0.9218 <.0001 DZNTCHL2 0.9171 <.0001 DZNTCHL3 0.9166 <.0001 DZNTCHL5 0.7529 <.0001 DZNTFAT4 0.6742 <.0001 DZNTCHL5 DZNTCHL1 0.7831 <.0001 DZNTCHL2 0.7791 <.0001 DZNTCHL3 0.7786 <.0001 DZNTCHL4 0.7529 <.0001 DZNTFAT5 0.5967 <.0001 DZNTFAT1 DZNTFAT2 0.9538 <.0001 DZNTFAT3 0.9497 <.0001 DZNTFAT4 0.9050 <.0001 DZNTFAT5 0.7670 <.0001 DZNTCHL1 0.7036 <.0001

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163 Table A-1. Continued DZNTFAT2 DZNTFAT1 0.9538 <.0001 DZNTFAT3 0.9413 <.0001 DZNTFAT4 0.8971 <.0001 DZNTFAT5 0.7603 <.0001 DZNTCHL2 0.6937 <.0001 DZNTFAT3 DZNTFAT1 0.9497 <.0001 DZNTFAT2 0.9413 <.0001 DZNTFAT4 0.8932 <.0001 DZNTFAT5 0.7569 <.0001 DZNTCHL3 0.6922 <.0001 DZNTFAT4 DZNTFAT1 0.9050 <.0001 DZNTFAT2 0.8971 <.0001 DZNTFAT3 0.8932 <.0001 DZNTFAT5 0.7213 <.0001 DZNTCHL4 0.6742 <.0001 DZNTFAT5 DZNTFAT1 0.7670 <.0001 DZNTFAT2 0.7603 <.0001 DZNTFAT3 0.7569 <.0001 DZNTFAT4 0.7213 <.0001 DZNTCHL5 0.5967 <.0001 DZNTPRE1 DZNTPRE3 0.9298 <.0001 DZNTPRE2 0.9250 <.0001 DZNTPRE4 0.9192 <.0001 DZNTPRE5 0.7849 <.0001 DZNTADD1 0.7501 <.0001 DZNTPRE2 DZNTPRE3 0.9258 <.0001 DZNTPRE1 0.9250 <.0001 DZNTPRE4 0.9153 <.0001 DZNTPRE5 0.7816 <.0001 DZNTADD2 0.7424 <.0001 DZNTPRE3 DZNTPRE1 0.9298 <.0001DZNTPRE2 0.9258 <.0001 DZNTPRE4 0.9200 <.0001 DZNTPRE5 0.7856 <.0001 DZNTADD3 0.7423 <.0001 DZNTPRE4 DZNTPRE3 0.9200 <.0001 DZNTPRE1 0.9192 <.0001 DZNTPRE2 0.9153 <.0001 DZNTPRE5 0.7767 <.0001 DZNTADD4 0.7379 <.0001 DZNTPRE5 DZNTPRE3 0.7856 <.0001 DZNTPRE1 0.7849 <.0001 DZNTPRE2 0.7816 <.0001 DZNTPRE4 0.7767 <.0001 DZNTADD5 0.6291 <.0001 DZNTSAL1 DZNTSAL3 0.9353 <.0001 DZNTSAL2 0.9348 <.0001 DZNTSAL4 0.8971 <.0001 DZNTSAL5 0.7697 <.0001 DZNTFAT1 0.6426 <.0001 DZNTSAL2 DZNTSAL1 0.9348 <.0001 DZNTSAL3 0.9322 <.0001 DZNTSAL4 0.8942 <.0001 DZNTSAL5 0.7672 <.0001 DZNTFAT2 0.6360 <.0001

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164 Table A-1. Continued DZNTSAL3 DZNTSAL1 0.9353 <.0001 DZNTSAL2 0.9322 <.0001 DZNTSAL4 0.8947 <.0001 DZNTSAL5 0.7676 <.0001 DZNTFAT3 0.6285 <.0001 DZNTSAL4 DZNTSAL1 0.8971 <.0001 DZNTSAL3 0.8947 <.0001 DZNTSAL2 0.8942 <.0001 DZNTSAL5 0.7363 <.0001 DZNTFAT4 0.6109 <.0001 DZNTSAL5 DZNTSAL1 0.7697 <.0001 DZNTSAL3 0.7676 <.0001 DZNTSAL2 0.7672 <.0001 DZNTSAL4 0.7363 <.0001 DZNTFAT5 0.5348 <.0001 DZNTSUG1 DZNTSUG3 0.9005 <.0001 DZNTSUG4 0.8777 <.0001 DZNTSUG2 0.8744 <.0001 DZNTSUG5 0.7745 <.0001 DZNTCHL1 0.5059 <.0001 DZNTSUG2 DZNTSUG3 0.8997 <.0001 DZNTSUG4 0.8769 <.0001 DZNTSUG1 0.8744 <.0001 DZNTSUG5 0.7738 <.0001 DZNTSAL2 0.4956 <.0001 DZNTSUG3 DZNTSUG4 0.9030 <.0001 DZNTSUG1 0.9005 <.0001 DZNTSUG2 0.8997 <.0001 DZNTSUG5 0.7968 <.0001 DZNTCHL3 0.4947 <.0001 DZNTSUG4 DZNTSUG3 0.9030 <.0001DZNTSUG1 0.8777 <.0001 DZNTSUG2 0.8769 <.0001 DZNTSUG5 0.7766 <.0001 DZNTCHL4 0.4905 <.0001 DZNTSUG5 DZNTSUG3 0.7968 <.0001 DZNTSUG4 0.7766 <.0001 DZNTSUG1 0.7745 <.0001 DZNTSUG2 0.7738 <.0001 DZNTCHL3 0.3927 <.0001

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165 Table A-2. Five highest correlation coefficients and their probability under Ho: Rho = 0 of health concerns restricted dummy variables of the 1984-2003 period (30,414 obs) DZNTADD1 DZNTADD3 0.9471 <.0001 DZNTADD2 0.9459 <.0001 DZNTADD4 0.9294 <.0001 DZNTADD5 0.7797 <.0001 DZNTPRE1 0.7223 <.0001 DZNTADD2 DZNTADD1 0.9459 <.0001 DZNTADD3 0.9383 <.0001 DZNTADD4 0.9208 <.0001 DZNTADD5 0.7725 <.0001 DZNTPRE2 0.7154 <.0001 DZNTADD3 DZNTADD1 0.9471 <.0001 DZNTADD2 0.9383 <.0001 DZNTADD4 0.9220 <.0001 DZNTADD5 0.7734 <.0001 DZNTPRE3 0.7140 <.0001 DZNTADD4 DZNTADD1 0.9294 <.0001 DZNTADD3 0.9220 <.0001 DZNTADD2 0.9208 <.0001 DZNTADD5 0.7590 <.0001 DZNTPRE4 0.7102 <.0001 DZNTADD5 DZNTADD1 0.7797 <.0001 DZNTADD3 0.7734 <.0001 DZNTADD2 0.7725 <.0001 DZNTADD4 0.7590 <.0001 DZNTPRE5 0.6031 <.0001 DZNTCHL1 DZNTCHL2 0.9623 <.0001 DZNTCHL3 0.9605 <.0001 DZNTCHL4 0.9273 <.0001 DZNTCHL5 0.7878 <.0001 DZNTFAT1 0.6460 <.0001 DZNTCHL2 DZNTCHL10.9623 <.0001 DZNTCHL3 0.9536 <.0001 DZNTCHL4 0.9207 <.0001 DZNTCHL5 0.7822 <.0001 DZNTFAT2 0.6384 <.0001 DZNTCHL3 DZNTCHL1 0.9605 <.0001 DZNTCHL2 0.9536 <.0001 DZNTCHL4 0.9190 <.0001 DZNTCHL5 0.7808 <.0001 DZNTFAT3 0.6369 <.0001 DZNTCHL4 DZNTCHL1 0.9273 <.0001 DZNTCHL2 0.9207 <.0001 DZNTCHL3 0.9190 <.0001 DZNTCHL5 0.7538 <.0001 DZNTFAT4 0.6231 <.0001 DZNTCHL5 DZNTCHL1 0.7878 <.0001 DZNTCHL2 0.7822 <.0001 DZNTCHL3 0.7808 <.0001 DZNTCHL4 0.7538 <.0001 DZNTFAT5 0.5243 <.0001 DZNTFAT1 DZNTFAT2 0.9613 <.0001 DZNTFAT3 0.9567 <.0001 DZNTFAT4 0.9116 <.0001 DZNTFAT5 0.7741 <.0001 DZNTCHL1 0.6460 <.0001

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166 Table A-2. Continued DZNTFAT2 DZNTFAT1 0.9613 <.0001 DZNTFAT3 0.9482 <.0001 DZNTFAT4 0.9035 <.0001 DZNTFAT5 0.7672 <.0001 DZNTCHL2 0.6384 <.0001 DZNTFAT3 DZNTFAT1 0.9567 <.0001 DZNTFAT2 0.9482 <.0001 DZNTFAT4 0.8991 <.0001 DZNTFAT5 0.7635 <.0001 DZNTCHL3 0.6369 <.0001 DZNTFAT4 DZNTFAT1 0.9116 <.0001 DZNTFAT2 0.9035 <.0001 DZNTFAT3 0.8991 <.0001 DZNTFAT5 0.7275 <.0001 DZNTCHL4 0.6231 <.0001 DZNTFAT5 DZNTFAT1 0.7741 <.0001 DZNTFAT2 0.7672 <.0001 DZNTFAT3 0.7635 <.0001 DZNTFAT4 0.7275 <.0001 DZNTCHL5 0.5243 <.0001 DZNTPRE1 DZNTPRE3 0.9434 <.0001 DZNTPRE2 0.9425 <.0001 DZNTPRE4 0.9307 <.0001 DZNTPRE5 0.8038 <.0001 DZNTADD1 0.7223 <.0001 DZNTPRE2 DZNTPRE1 0.9425 <.0001 DZNTPRE3 0.9375 <.0001 DZNTPRE4 0.9249 <.0001 DZNTPRE5 0.7987 <.0001 DZNTADD2 0.7154 <.0001 DZNTPRE3 DZNTPRE1 0.9434 <.0001DZNTPRE2 0.9375 <.0001 DZNTPRE4 0.9258 <.0001 DZNTPRE5 0.7995 <.0001 DZNTADD3 0.7140 <.0001 DZNTPRE4 DZNTPRE1 0.9307 <.0001 DZNTPRE3 0.9258 <.0001 DZNTPRE2 0.9249 <.0001 DZNTPRE5 0.7888 <.0001 DZNTADD4 0.7102 <.0001 DZNTPRE5 DZNTPRE1 0.8038 <.0001 DZNTPRE3 0.7995 <.0001 DZNTPRE2 0.7987 <.0001 DZNTPRE4 0.7888 <.0001 DZNTADD5 0.6031 <.0001 DZNTSAL1 DZNTSAL2 0.9530 <.0001 DZNTSAL3 0.9506 <.0001 DZNTSAL4 0.9115 <.0001 DZNTSAL5 0.7920 <.0001 DZNTFAT1 0.5995 <.0001 DZNTSAL2 DZNTSAL1 0.9530 <.0001 DZNTSAL3 0.9448 <.0001 DZNTSAL4 0.9059 <.0001 DZNTSAL5 0.7872 <.0001 DZNTFAT2 0.5934 <.0001

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167 Table A-2. Continued DZNTSAL3 DZNTSAL1 0.9506 <.0001 DZNTSAL2 0.9448 <.0001 DZNTSAL4 0.9036 <.0001 DZNTSAL5 0.7852 <.0001 DZNTFAT3 0.5887 <.0001 DZNTSAL4 DZNTSAL1 0.9115 <.0001 DZNTSAL2 0.9059 <.0001 DZNTSAL3 0.9036 <.0001 DZNTSAL5 0.7529 <.0001 DZNTFAT4 0.5712 <.0001 DZNTSAL5 DZNTSAL1 0.7920 <.0001 DZNTSAL2 0.7872 <.0001 DZNTSAL3 0.7852 <.0001 DZNTSAL4 0.7529 <.0001 DZNTFAT5 0.4918 <.0001 DZNTSUG1 DZNTSUG3 0.9275 <.0001 DZNTSUG2 0.9109 <.0001 DZNTSUG4 0.9016 <.0001 DZNTSUG5 0.8031 <.0001 DZNTCHL1 0.4707 <.0001 DZNTSUG2 DZNTSUG3 0.9219 <.0001 DZNTSUG1 0.9109 <.0001 DZNTSUG4 0.8962 <.0001 DZNTSUG5 0.7982 <.0001 DZNTCHL2 0.4610 <.0001 DZNTSUG3 DZNTSUG1 0.9275 <.0001 DZNTSUG2 0.9219 <.0001 DZNTSUG4 0.9125 <.0001 DZNTSUG5 0.8127 <.0001 DZNTCHL3 0.4603 <.0001 DZNTSUG4 DZNTSUG3 0.9125 <.0001DZNTSUG1 0.9016 <.0001 DZNTSUG2 0.8962 <.0001 DZNTSUG5 0.7901 <.0001 DZNTCHL4 0.4563 <.0001 DZNTSUG5 DZNTSUG3 0.8127 <.0001 DZNTSUG1 0.8031 <.0001 DZNTSUG2 0.7982 <.0001 DZNTSUG4 0.7901 <.0001 DZNTCHL3 0.3721 <.0001

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168REFERENCES Beales, Howard. 1980. "Benefits and Costs of Label Information Programs." pages 243-255 in Product Labeling and Health Risks Banbury Report 6, ed. Morris, Louis A., Michael B. Mazis, and Ivan Barofsky. Cold Spring Ha rbor, NY: Cold Spring Harbor Laboratory. Belsley, David A., Edwin Kuh and Roy E. Welsch. (1980). Regression Diagnostics: Identifying influential data and sources of collinearity. New York: John Wiley & Sons. Bender, Mary M. and Brenda M. Derby. 1992. "P revalence of Reading Nutrition and Ingredient Information on Food Labels Among Adult Americans: 1982-1988." Journal of Nutrition Education 24(6):163-72. Caswell, Julie. 1992. "Current Information Levels On Food Labels." American Journal of Agricultural Economics 74(5): 1196-1201. Caswell, Julie and Eliza M. Mojduszka. 1996. "U sing Informational Labeling to Influence the Market for Quality in Food Products" American Journal of Agricultural Economics 78(5): 1248-1253. Clark, Christopher D. and Clifford S. Russell. 2004. "Ecolabels and Economic Efficiency: Some Preliminary Results." Presented at the American Agricultural Association Annual Meeting, Denver, Colorado. Darvy, Michael R. and Edi Karnil. 1973. "Free Competition and the Optimal Amount of Fraud." Journal of Law and Economics, 16(1): 67-88. Derby, Brenda S. and Alan S. Levy (2001), “Do Food Labels Work? Gauging the Effectiveness of Food Labels Preand Post-NLEA.” In Handbook of Mar keting and Society ed. Paul N. Bloom and Greg T. Gundlach. Thousand Oaks, CA: Sage Publications. Golan, Elise, Fred Kuchler, and Lorraine Mitchell 2000. Economics of Food Labeling Agricultural Economic Report No. 793. Washinghton, D.C. : Economic Research Service, U.S. Department of Agriculture. Gracia, Azucena, Maria L. Loureiro and Rodolfo M. Nayga, Jr. 2007. "Do consumers perceive benefits from the implementation of a EU mandatory nutritional labelling program?" Food Policy 32(2): 160-174. Greene, H. William. 1990. Econometric Analysis New York: Macmillan Publishing Company. Gujarati, Damodar. 1988. Basic Econometrics Second Edition. New York: McGraw-Hill Publishing Company. Guthrie, J., J. Fox, L. Cleveland, and S. Welsh. 1995. "Who Uses Nutrition Labeling and What Effects Does Label Use Have on Diet Quality?" Journal of Nutrition Education 27(4):163-72.

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169Hadden, Susan G. 1986. Read the Label. Reducing Risk by Providing Information. Boulder, CO:Westview Press. Han, Jae-Hwan and R. Wes Harrison 2004. "The Effects of Risk Perceptions on Consumer Preferences for Biotech Labeling" Presented at the Southern Agricultural Association Annual Meeting, Tulsa, Oklahoma. He, Senhui, Stanley Fletcher, and Arbindra Rimal. 2004. "Nutrition Consideration in Food Choice." Journal of Food Distribution Research, 35(1): 124-126. Judge, George G., R. Carter Hill, William E. Griffiths, Helmut Ltkepohl and Tsoung-Chao Lee. 1985. The Theory and Practice of Econometrics. Second Edition. New York: John Wiley & Sons. Kennedy, Peter. 1998. A Guide to Econometrics Fourth Edition. Cambridge, Massachusetts: The MIT Press. Kim, Sung-Yong, Rodolfo M. Nayga Jr., and Oral Capps, Jr. 2000. "T he Effect of Food Label Use on Nutrient Intakes: An Endogenous Switching Regression Analysis." Journal of Agricultural and Resource Economics 25(1):215-231. Klopp, P., and M. McDonald. 1981. "Nutrition Labels : An Exploratory Study of Consumer Reasons for Nonuse." Journal of Consumer Affairs 15:301-16. Kristal, Alan R., Lisa Levy, Ruth E. Patterson, Sue S. Li and Emily White. 1998. "Trends in Food Label Use Associated With New Nutritional Labeling Regulations." Amererican Journal of Public Health 88(August):1212-1215. Kurtzweil, Paula. 1994. Good Reading for Good Eating U.S. Food and Drug Administration. http://www.fda.gov/fdac/special/foodlabel/goodread.html. Accessed Oct. 11, 2005 Lin, Chung-Tung Jordan, Jonq-Ying Lee and Steven T. Yen. 2004. "Do dietary intakes affect search for nutrient information on food labels?" Social Science & Medicine 59(9):1955-1967. Long, J. Scott. 1997. Regression Models for Categorical and Limited Dependent Variables Thousand Oaks, California: Sage Publications. Mazis, Michael B. 1980. "An Overview of Produc t Labeling and Health Risks." pages 3-11 in Product Labeling and Health Risks Banbury Report 6, ed. Morris, Louis A., Michael B. Mazis, and Ivan Barofsky. Cold Spring Ha rbor, NY: Cold Spring Harbor Laboratory. McCulloug, James and Roger Best. 1980. "Consumer Preferences for Food Label Information: A Basis for Segmentation." The Journal of Consumer Affairs 14(1):180-192. McLean-Meyinsse, Patricia E. 2001. "An Analysis of Nutritional Label Use in the Southern United States." Journal of Food Distribution Research, 32(1):110-114.

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170Miller, John A. 1978. Labeling Research The State of the Art. Report No. 78-115. Cambridge, Massachusetts: Marketing Science Institute. Moorman, Christine. 1996. "A Quasi Experiment to Assess the Consumer and Informational Determinants of Nutrition Information Pro cessing Activities: The Ca se of the Nutrition Labeling and Education Act," Journal of Public Policy and Marketing 15(1): 28-44. Mueller, William. 1991. "Who reads the label?" American Demographics 13(1): 36-41 Nelson, Phillip. 1970. "Information and Consumer Behavior." Journal of Political Economy 78(2): 311329. NPD. 2004. "Household Servings Survey." Chicago, IL: NPD Group. Richards, Timothy J., Paul M. Patterson and Abe Tegene. 2004. "Obesity and Nutrient Consumption: A Rational Addiction?" Faculty Working Paper MSABR 04-7. Morrison School of Agribusiness and Resource Management, Arizona State University. Teisl, Mario F. and Brian Roe. 1998. "The Economic s of Labeling: An Overview of Issues for Health and Environmental Disclosure." Agricultural and Resource Economics Review 27(2): 140-150. TSP International. 2005. Reference Manual. Version 5.0. Palo Alto, California: TSP International. Verbeke, Wim and Ronald Ward. 2006. "Consumer in terest in information cues denoting quality, traceability and origin: An application of ordered probit models to beef labels." Food Quality and Preference 17(6): 453-467.

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171BIOGRAPHICAL SKETCH Carlos Juregui was born in Per. He studied agronomy and specialized in horticulture at the Universidad Naciona Agraria "La Molina." Upon graduating as agricultural engineer he worked first for the "Comision Nacional de Fruticultura" in Me xico city and then for the Mexican Agricultural Extension Service. He came to the University of Florida to pursue a M.S. in food and resource economics. He has been working as Coordinator for Statistical Research in this Department since his graduation.