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Organic Preference Model in the United States

Permanent Link: http://ufdc.ufl.edu/UFE0042123/00001

Material Information

Title: Organic Preference Model in the United States An Ordered Probit Model Application
Physical Description: 1 online resource (170 p.)
Language: english
Creator: Zhou, Yang
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2010

Subjects

Subjects / Keywords: consumer, demand, ordered, organic, preference, probability, 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: The organic food industry has been growing at a remarkable rate. Tending to be a lifestyle choice in previous years, organic food purchases has evolved into the fact that at least two-thirds of American consumers buy organic products. Given the bottom line that consumption is based on not what the product is, but what consumers perceive and are aware of about the product, it is important to understand consumer behaviors as well as identify the underlying determinants of choosing organic foods. Household data were collected from an internet survey conducted by the private company 'Market Tools' from February 2008 through March 2010. The survey was nationally demographically balanced with a total of 37,582 household entries. The essential response to the statement 'I seek out organic foods' was scored by using a five-point Likert scale. Explanatory variables included in the models were established from demographic questions (including age, gender, ethnicity, income, education, employment, marital status, household size, region etc), expenditures and grocery shopping locations, behavior/attitudes, health concerns, and seasonality. Since household responses to the statement 'I seek out organic foods' were discrete values, ordered probit models were appropriate to estimate the probability of seeking out organic foods. To illustrate how the probabilities of seeking out organic foods differs across socio-demographics, behaviors and attitudes, health concerns, etc., we simulated probabilities for five outcomes with each given a particular set of conditions of explanatory variables by using the coefficients from the results of the ordered probit models. By ranking the relative effects to the average likelihood in descending order, variables could be identified whether they contributed the major effect on the probability of seeking out organic. Overall, behavioral factors were more important than demographic characteristics on the probability of seeking out organic foods. A limitation of the study is that the preference for organics was measured through self-reports of seeking out organics but not reporting the actual consumption level(s). However, an underlying premise is that seeking out organic foods and actual organic consumption are highly correlated.
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 Yang Zhou.
Thesis: Thesis (Ph.D.)--University of Florida, 2010.
Local: Adviser: Ward, Ronald W.

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2010
System ID: UFE0042123:00001

Permanent Link: http://ufdc.ufl.edu/UFE0042123/00001

Material Information

Title: Organic Preference Model in the United States An Ordered Probit Model Application
Physical Description: 1 online resource (170 p.)
Language: english
Creator: Zhou, Yang
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2010

Subjects

Subjects / Keywords: consumer, demand, ordered, organic, preference, probability, 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: The organic food industry has been growing at a remarkable rate. Tending to be a lifestyle choice in previous years, organic food purchases has evolved into the fact that at least two-thirds of American consumers buy organic products. Given the bottom line that consumption is based on not what the product is, but what consumers perceive and are aware of about the product, it is important to understand consumer behaviors as well as identify the underlying determinants of choosing organic foods. Household data were collected from an internet survey conducted by the private company 'Market Tools' from February 2008 through March 2010. The survey was nationally demographically balanced with a total of 37,582 household entries. The essential response to the statement 'I seek out organic foods' was scored by using a five-point Likert scale. Explanatory variables included in the models were established from demographic questions (including age, gender, ethnicity, income, education, employment, marital status, household size, region etc), expenditures and grocery shopping locations, behavior/attitudes, health concerns, and seasonality. Since household responses to the statement 'I seek out organic foods' were discrete values, ordered probit models were appropriate to estimate the probability of seeking out organic foods. To illustrate how the probabilities of seeking out organic foods differs across socio-demographics, behaviors and attitudes, health concerns, etc., we simulated probabilities for five outcomes with each given a particular set of conditions of explanatory variables by using the coefficients from the results of the ordered probit models. By ranking the relative effects to the average likelihood in descending order, variables could be identified whether they contributed the major effect on the probability of seeking out organic. Overall, behavioral factors were more important than demographic characteristics on the probability of seeking out organic foods. A limitation of the study is that the preference for organics was measured through self-reports of seeking out organics but not reporting the actual consumption level(s). However, an underlying premise is that seeking out organic foods and actual organic consumption are highly correlated.
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 Yang Zhou.
Thesis: Thesis (Ph.D.)--University of Florida, 2010.
Local: Adviser: Ward, Ronald W.

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2010
System ID: UFE0042123:00001


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ORGANIC PREFERENCE MODEL IN THE UNITED STATES:
AN ORDERED PROFIT MODEL APPLICATION





















By

YANG ZHOU


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

2010




































2010 Yang Zhou
































To the most important loved ones in my life: my Mom and Dad









ACKNOWLEDGMENTS

First of all, I express my deepest appreciation to all my committee members, Dr. Ronald

W. Ward, Dr. R. Jeffrey Burkhardt, Dr. Stephen Holland, Dr. Jonq-Ying Lee, and Dr. Allen F.

Wysocki.

I would like to gratefully express my sincere gratitude and overall admiration to my

committee chair, Dr. Ronald W. Ward, for his generous guidance and encouragement. He has

been the best mentor and it is my incredible fortune to have the opportunity to work with him

and learned much invaluable knowledge from him. I would like to express my deepest

appreciation to him for his patience, help and thorough review of the manuscript to complete my

study successfully.

I am very appreciative of the support I received from Dr. R. Jeffrey Burkhardt, Dr. Stephen

Holland, Dr. Jonq-Ying Lee, and Dr. Allen F. Wysocki. Thanks for providing me the support,

guidance, comments and suggestions.

I would also like to thank my fellow graduate students in the Food and Resource

Economics Department and all other fellow students for all the unforgettable memories.

I thank my families, especially my Mom and Dad, for their unconditioned love,

encouragement and endless support to me.









TABLE OF CONTENTS

page

A CK N O W LED G M EN T S ................................................................. ........... ............. .....

L IST O F T A B L E S .............. ..... ............... .......................................................... 7

LIST OF FIGURES .................................. .. ..... ..... ................. .8

A B S T R A C T ................................ ............................................................ 10

CHAPTER

1 INTRODUCTION ............... .......................................................... 12

U .S. O organic M arket............. ...... .. ....................... ........... 12
Credence Attributes ................................................................. .. ...... ... 14
O organic A agriculture ......................................... .................. .. ........ .... 15
P problem Statem ent ................................................................................................. .... 17
R research A im s and O objectives .............................................................................. ........ 19
M methodology and D ata ................. .................................... .. ........ .. .............20
Overview of the Study .................................................... ................. ...... .... 20

2 L ITE R A TU R E R E V IE W ........................................................................ .. .......................23

A S h o rt R ev iew ...................................... ................................... ................ 2 3
O organic C onsum er B ehaviors............................................................................... ..... .... 24
D iscrete C choice M odel A pplications......................................................................... ...... 27
Other M ethodology A pplication.................................................. ............................... 29

3 O R G A N IC D A T A B A SE ............................................................................. .....................30

4 ORGANIC PREFERENCE MODEL......................................................... ............... 43

Organic Preference M odel Specifications ........................................ ......................... 43
Predicted Probabilities ................ .......... ..... ............................. ...................... .. 46
Partial Change and Discrete Change in Predicted Probabilities...........................................47
R estricted D um m y V ariables......................................................................... ...................50

5 ANALYSIS OF RESULTS AND SIMULATIONS ................................... .................52

O ordered Probit E stim ates ......... ...... ..................................... .. .........52
O ordered Probit M odel Sim ulations......... ................. .................................... ..................53
Seeking Out Organic Foods across Demographics ............................... ................ 55
Store Choice and Expenditures ............................................... ............................ 58
B ehavior/A attitudes A ttributes............................................................... .....................60
H health C concerns ........................................... ............................ 62









Seasonality........................... ... .... ................ .... ............ 62
Ranking the Effects on Probabilities of Seeking Out Organic Foods ..................................63

6 SUMMARY, CONCLUSION AND IMPLICATIONS......................................................119

APPENDIX

A ORGANIC SURVEY VARIABLES ........................................................ ............. 125

B CORRELATION COEFFICIENTS ............................................. ............................ 126

C T SP C O D E ............... .. ............................................................13 0

L IST O F R E F E R E N C E S ..................................................................................... ..................167

B IO G R A PH IC A L SK E T C H ......................................................................... .. ...................... 170







































6









LIST OF TABLES


Table page

1-1 Organic food sales and penetration of total organic food sales ...................................22

3-1 D descriptions of explanatory variables........................................ ............................ 35

5-1 Results from Organic Preference ordered probit model ......................................... 66

5-2 Organic Preference ordered probit model coefficient estimates............... .......... 69

A -1 Organic survey variables ........................................................... .. ............... 125

B-1 Correlation coefficients of explanatory variables......... ... ......................................126









LIST OF FIGURES


Figure page

3-1 Frequency distribution of the response to "I seek out organic foods".............................40

3-2 Frequency distribution of agreement/disagreement about seeking out organic foods.......40

3-3 Comparison of frequency distribution of agreement about seeking out organic foods
in 2008 and 2009 ............... ................................... ..........................4 1

3-4 Distributions of agreement about seeking out organic foods during the reporting
p period s .................. ........... .......................... ...........................4 1

3-5 Frequency distribution of agreement about seeking out organic foods detailed in
"Completely agree (5)" and "Mostly agree (4)" during the reporting periods ..................42

3-6 Percentage of frequency distribution of complete and partial agreement about
seeking out organic foods during the reporting periods.........................................42

5-1 Probability of seeking out organic foods for the average respondent.............................. 71

5-2 Impact of age of household head on seeking out organic foods...................................71

5-3 Impact of gender of household head on seeking out organic foods..............................73

5-4 Impact of marital status of household head on seeking out organic foods ........................74

5-5 Impact of race of household head on seeking out organic foods.................................76

5-6 Impact of income of household head on seeking out organic foods..............................77

5-7 Impact of education level of household head on seeking out organic foods ...................79

5-8 Impact of household size on seeking out organic foods ..................................................80

5-9 Impact of presence of children under 18 in household on seeking out organic foods.......82

5-10 Impact of employment status of household head seeking out organic foods ....................83

5-11 Impact of census region of household head seeking out organic foods...........................85

5-12 Impact of grocery shopping places seeking out organic foods..........................................86

5-13 Impact of expenditures on grocery shopping on seeking out organic foods....................89

5-14 Impact of servings of fruit on seeking out organic foods ...............................................91

5-15 Impact of servings of vegetables on seeking out organic foods ................ ............. 92









5-16 Impact of seasonality on seeking out organic foods .................................. ............... 94

5-17 Impact of "count calories" on seeking out organic foods ........................................95

5-18 Impact of "eat fresh foods" on seeking out organic foods..............................................97

5-19 Impact of "read label" on seeking out organic foods ............ ... .................98

5-20 Impact of "buy from certain stores" on seeking out organic foods .............. ...............100

5-21 Impact of "go out of way to get certain types of produce" on seeking out organic
fo o d s......... ....... ... .......... ................. ................................................10 2

5-22 Impact of "eat fresh fruit and vegetables" on seeking out organic foods ........................104

5-23 Impact of "feel healthier" on seeking out organic foods .............................................. 105

5-24 Impact of "exercise at least 3 times a week" on seeking out organic foods ....................107

5-25 Impact of "experiment with new foods" on seeking out organic foods ...........................108

5-26 Impact of health concerns of household on head seeking out organic foods.................10

5-27 Ranking of factors impacting the likelihood of seeking out organic foods.....................114









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

ORGANIC PREFERENCE MODEL IN THE UNITED STATES:
AN ORDERED PROFIT MODEL APPLICATION

By

Yang Zhou

August 2010

Chair: Ronald W. Ward
Major: Food and Resource Economics

The organic food industry has been growing at a remarkable rate. In 2008, the retail sales

for organic food products in the United States reached $22.9 billion, with a growth rate of about

15.8% in 2008 over 2007 (OTA, 2009). Tending to be a lifestyle choice in previous years,

organic food purchases have evolved into the fact that at least two-thirds of American consumers

buy organic products at some point in time. Given the bottom line that consumption is based on

not what the product is, but what consumers perceive and are aware of about the product, it is

important to understand consumer behaviors as well as identify the underlying determinants of

choosing organic foods.

Household data were collected from an internet survey conducted by the private company

from February 2008 through March 2010. The survey was nationally demographically balanced

with a total of nearly 38,000 household entries. The essential response to the statement "I seek

out organic foods" was scored by using a five-point Likert scale. Explanatory variables included

in the models were established from demographic questions (including age, gender, ethnicity,

income, education, employment, marital status, household size, region etc), expenditures and

grocery shopping locations, behavior/attitudes statements, health concerns and seasonality.









Since household responses to the statement "I seek out organic foods" were discrete

values, ordered probit models were appropriate to estimate the probability of seeking out organic

foods.

To illustrate how the probabilities of seeking out organic foods differs across socio-

demographics, behaviors and attitudes, health concerns, etc., we simulated probabilities for five

outcomes with each given a particular set of conditions for the explanatory variables by using the

coefficients from the results of the ordered probit models.

By ranking the relative effects to the average likelihood in descending order, variables

identified as contributing the major effect on the probability of seeking out organic foods include

"numbers of daily servings of fruit", "eat fresh foods", "read label", "go out of way to obtain

certain types of produce", and "age"; alternatively, "gender", "limited physical mobility

concerns", "shopping for food in warehouse stores", "cholesterol concerns", and "obesity

concerns" were the five least important factors. Overall, behavioral factors were more important

than demographic characteristics, except age, on the probability of seeking out organic foods.

A limitation of the study is that the preference for organic was measured through self-

reports of seeking out organic but not reporting the actual consumption levelss. However, an

underlying premise is that seeking out organic foods and actual organic consumption are highly

correlated.









CHAPTER 1
INTRODUCTION

U.S. Organic Market

The organic food industry has experienced unprecedented growth with an average annual

growth rate of 20-24% in recent years (Dimitri and Richman, 2000) and a prediction that high

double-digit growth rates would continue into the next decades (NMI, 2007). In 2008, the U.S.

organic food industry gained $24.6 billion in consumer sales, among which the retail sales for

organic food products reached $22.9 billion (93% of all organic product sales). Although organic

food sales only represents 3.47% of the overall food retail share, the level of organic market

penetration (organic food as a percent of total U.S. food sales) has doubled or even tripled in the

past a few years. Organic food sales rose much faster than total food sales; specifically, the

growth rate of organic food sales was about 15.8% in 2008 over 2007, compared to a rise in total

U.S. food sales of 4.9% during the same period (OTA, 2009). However, the organic food sales

have slowed since 2007 (see Table 1-1). Mintel report (2008) provides the same results about the

year-over-year sales trend, and predicts the organic food sales will slow further in the future

partially due to the competition between organic and natural foods. The top three categories -

produce, dairy products, and beverages represent 37%, 16% and 13% of total organic food

sales in 2008 respectively (Nutrition Business Journal, 2008).

A study that interviewed market managers in more than 20 states, referring to the 2002

market season, reported that within the markets that include organic farmers, demand for organic

products was strong in nearly 40%, medium demand in 47%, and low demand in only 13% of

these markets (Kremen, Greene, and Hanson, 2004). The demand for organic products is

growing rapidly. After the publication of the "USDA organic" label and standards in 2000,









consumer demand for organic products significantly expanded. But what else contributed to this

rapid growth?

Formerly organic food purchases were usually lifestyle choices of a smaller number of

consumers who were expected to differ by race, be affluent, well-educated, and to differ by

households size (Dimitri and Oberholtzer, 2006). In recent years, it appears that at least two-

thirds (69%) of American consumers sometime brought organic products with about 28%

purchasing weekly according to the Hartman Group (The Hartman Group, 2008). According to

the Organic Trade Association's (OTA) 2009 U.S. Families' Organic Attitudes & Beliefs Study,

almost three-quarters (73%) of U.S. families purchased organic products at least occasionally

even though the economic slow down induced U.S. families to reduce their spending. The

characteristics of organic consumers have become much more diverse and cannot easily be

profiled by previous significant predictors (such as income, education, etc.). This increase in

popularity may be due to increasing availability and affordability for consumers. According to

USDA Economic Research Service (USDA/ERS), organic products have been available in

nearly 20,000 natural foods stores and in 73% of all conventional grocery stores since 2002. This

accessibility likely facilitated the purchasing of organic food into becoming a mainstream

activity, as evidenced by where consumers purchased their organic foods. Instead of being

limited to just conventional supermarkets or mass merchandisers, organic buyers sometimes

chose to do grocery shopping in a variety of retail outlets. Based on the OTA's 2009 U.S.

Families' Organic Attitudes & Beliefs Study, among the parents who chose to buy organic

products, 19% reported weekly visits to natural food chain stores, 16% reported weekly visits to

local health food/natural food stores, 16% went to farmers' markets, and 12% shopped in

neighborhood co-ops. New organic products continue to be introduced to the U.S. retail market,









rising from 378 products in 2001 to 1,042 products in 2008. The claim "organic" has ranked

among the top 5 advertising claims every year since 2005 (USDA/ERS Briefing Rooms, 2009).

Beverage, packaged and prepared foods, and bread/grains made up of the majority of new

organic product introductions in 2008 (USDA/ERS Briefing Rooms, 2008). The availability of a

wider variety of organic foods appears to be responding to increased consumer demand.

Credence Attributes

Consumers' decision-making about buying organic products is not only determined by

economic factors or by product appearances and tastes, but also by non-material values such as

food safety as well as perceived social and environmental benefits. Those are unique aspects of

the organic market in product differentiation. Three qualities of a product search quality,

experience quality, and credence quality have been distinguished by Nelson (1970) and Darby

and Karni (1973). With search or experience goods, consumers are often incapable of judging the

credence quality of goods even after consumption (McCluskey, 2000). Organic products are a

popular example of credence attributes (McCluskey, 2000) since the information referring to the

nature of the product is asymmetric and additional information is required. That is, consumers do

not know whether the products they are purchasing are organic or not unless this kind of

information is revealed by the product producer or experts. Moreover, consumers must be

confident about the sources that inform them of the underlying production practices. Otherwise,

organically produced foods and conventionally produced foods may not be successfully

differentiated. To avoid this supply-side market issue, it is critically important to establish a

universally accepted definition of the term of "organic", implement a national standard for

organically produced products, as well as utilize labeling based on third party certifications for

organic food. In fact, the USDA organic logo has been considered as a feasible way for

consumers to recognize organic products and feel confident about the attributes of organic









products they buy since the performance of the National Organic Standards in 2002 (Dimitri and

Oberholtzer, 2009).

Organic Agriculture

A current definition of the term "organic agriculture" by the USDA's National Organic

Program in condensed form is as follows:

Organic crops are raised without using most conventional pesticides, petroleum-
based fertilizers, or sewage-based fertilizers. Animals raised on an organic
operation must be fed organic feed and given access to the outdoors. They are
given no antibiotics or growth hormones. (USDA National Organic Program 2005)

Simply speaking, organic food (also referred to as organiccs", is produced relying on

ecologically based practices which virtually exclude the use of synthetic chemical inputs,

antibiotics and hormones, and in addition promotes soil health, biodiversity, animal fair

treatment, and environmental sustainability. Organic agriculture is explicitly defined as an

ecological production system. Environmental benefits connected with organic production include

"reduced pesticide residues in water and food; reduced nutrient pollution; improved soil tilth, soil

organic matter, and productivity; and lower energy use; carbon sequestration; and enhanced

biodiversity" (ERS/USDA, 2009). Previous studies reveal that consumers have a basic

understanding of organic (Smith et al., 2009). Briz and Ward (2009) showed that a minority of

39% of consumer respondents correctly claimed that organic products are "those cultivated

without synthetic pesticides" and only 34% give a tolerable answer. However, consumers are

still confused between "organic" and "natural"; for instance, if a food product is made with

organic ingredients but also contains artificial flavorings, then it would be "organic" but not

"natural". Less than half of respondents could make a distinction between organic and natural

food, and most respondents who are able to distinguish these two concepts are younger

population between age 18 and 34 (Mintel, 2008).









As we know, organic products are differentiated not solely by economic factors but also

perceived social and environmental benefits. Organic price premiums have been considered as an

important factor in making organic farming earn a comparable or even higher profit than

conventional farming (Dimitri and Greene, 2002). The differential production costs and the

relative short supply of organic products can be the basis of a price premium. But a price

premium (reflects the high production cost and the relative supply level) cannot fully reflect the

true social values or environmental benefits of organically produced products. Hence, it may be

necessary to establish public investment in organic agriculture to facilitate accessibility of

organic products to consumers, promote profitability to organic farmers, and protect the

environment as well (ERS/USDA, 2009).

Organic consumers consider a wide variety of reasons when making purchasing decisions,

with health and nutrition (66%), taste (38%), and food safety (30%) as the primary reasons

offered for organic purchases (Hartman Group, 2002; Dimitri and Oberholtzer, 2006).

Specifically, based on the Hartman Group survey Organic 2006: Consumer Attitudes &

Behavior, Five Years Later & Into the Future, the top five reasons given for organic purchases

are: 1) to avoid products that rely on pesticides or other chemicals; 2) to avoid products that rely

on antibiotics or growth hormones; 3) for nutritional needs; 4) to support the environment; 5) to

avoid genetically modified products. However, the debate over organic continues. A UK's Food

Standards Agency (FSA, 2009) provides a review stating that there is little difference between

the nutrient content of organically versus conventionally grown food.

Given that the bottom line of consumption is not what the concept of the product is, but

what consumers perceive and are aware of, it would be insightful to better understand









consumers' attitudes as well as identify the underlying motivations and other factors linked to

organic purchases.

Problem Statement

Numerous industry and academic studies have been dealing with consumer behavior and

trying to identify socio-demographic factors that motivate consumer's choice of organic

products. Consumers of organic foods have in some studies been characterized as Caucasian,

with better education, affluent, and caring about health and food quality (Dimitri and

Oberholtzer, 2006). With growing availability, organic products are no longer just a lifestyle

choice of a select group of consumers but an established practice of two-thirds of American

consumers who purchase organic products at least occasionally (Hartman Group, 2004). Asian

and African-Americans are inclined to purchase organically grown produce more frequently than

Caucasians and Hispanics (Steven-Garmon et al., 2007) and income is not significantly relevant

to organic purchases (Steven-Garmon et al., 2007; Thompson, 1998). Thus, it appears that

organic consumer profiles have likely become more diverse in the last decade, extending over a

wider range of demographics and other consumer distinguishing categories.

"Food is an emotional issue" (The Wall Street Journal, 10.25.2002). While food is

essential, food selection is an emotional issue. Although socio-demographic characteristics are

expected to affect consumption preferences, those consumer characteristics are not easily

changed at least in a relatively short period of time. Nevertheless, consumers have an increasing

desire to take ever-greater control of their lives, including their own and family members' health,

lifestyle and behavior issues. They may pursue organic food products if they believe organic

products are safer, environmental friendly, from local farms and can trace the source, all of

which could reassure consumers that they have some feeling of control. That is, they may buy

organic for purposes other than just the physical attributes of the product. Moreover, fifty-five









percent of consumers express their willingness to explore new products. This desire may

translate to organic opportunities since organically grown products are potentially associated

with "fresh and innovative" concepts (Molyneaux, 2007).

One would expect some broader implications based on current consumer data on a national

level. For example, can the likelihood of seeking organic be adjusted? How could the likelihood

of seeking organic be changed? And it is important to understand what influences the

consumption decision for organic foods. Then, the analytical issue is discussed in the study: what

is the probability of seeking organic? An interesting aspect in this study is to investigate the

effects of consumers' behaviors or attitudes contributing to the levels of seeking out organic

foods besides socio-demographic characteristics.

Drawing from the organic consumer behavior literature, there are a number of studies that

employed discrete choice models to measure consumer preferences for organic. While the

ultimate research goal would be to measure the amount of organic consumed, that level of

consumption detail is often difficult to acquire and often not available on a national basis. Most

of the demand studies rely on some level of consumer recall about organic with the simplest

measure being "did you buy (or not buy) organic foods within some defined consumption

period". While this type question implicitly documents basic buying behavior, it does not

provide the level of intensity. An alternative approach is to ask the consumer if they seek out

organic products. This approach gives greater insight into intensity behaviors about organic but

still does not empirically link the effort to a specific quantity. Yet, an underlying assumption is

that there is a link between intensity (seeking out) and the level of consumption. The specific

tenor of this research lies in the essential statement that "I seek out organic foods" measured on a









five-point Likert scale (where a 5 means you "completely agree" with the statement and 1 means

you "completely disagree").

Often households can more easily respond to inquiries about the level of intensity whereas

it is more difficult to give a precise quantity level, especially when the question is not directed to

a specific product. Since this study focuses on a broad preference for organic and the

information is available, the preference intensity approach will be used as a proxy for the

demand for organic foods. Since preference intensity is an ordinal but ranked scale (i.e., the

intensity of 5 exceeds the intensity of 4 or lower scores), the approach to measuring the proxy

demand for organic is through determining the probability of each intensity score. Given that

intensities are ordered binary values, determining the probabilities is a classical ordered probit

problem.

Research Aims and Objectives

The aim of this study is to investigate the demand for organic foods quantitatively using

the preference intensity approach and expanding into the following questions:

How would one measure the probabilities of each score for seeking for organic foods?

What are the major demand drivers for organic foods? Which factors are significant
determinants in explaining preference intensities or probabilities of moving across the
scaling value of "seeking out organic foods"? Among the drivers, the expectation is that
consumer socio-demographic characteristics, behaviors/attitudes towards organic foods,
and health conditions status will be particularly important.

Several explicit hypotheses drive the empirical analyses:

Consumption levels towards organic foods differ across consumer demographics.

Some behaviors/attitudes factors are important for consumers' decision-making towards
purchasing organic foods.

It is thought that health conditions significantly affect consumers' choices on organic
foods, such as people with diabetes, high blood pressure or other diseases being more
likely to purchase organic foods.









The proportion of expenditures on food compared to the total household disposable
income is assumed to be a negative determinant of organic foods purchases.

Information factors (product differentiation, fancied/fad) would be expected to have
positive effects on consumers' choice of organic foods.

Methodology and Data

Data were collected from an internet survey conducted by the private company during

February, 2008 through March, 2010. It is a national demographically balanced survey with a

total of 37,582 household entries. Every two weeks, at least 1,200 households report through an

internet diary survey. In the data set, the focus is on consumption behavior in each two-week

period (total 24 periods) referring to organic food products; and respondents know they are

submitting a two-week period report. The survey contains questions about demographics

(including age, gender, ethnicity, income, education, employment, marital status, household size,

region etc); store choice and expenditures for grocery shopping; attitudes; use of food labels;

eating habits; and health conditions. Particularly, respondents were asked to score the following

question with a five-point Likert scale: "I seek out organic foods".

In addition, during the survey period while some households stay with the survey from the

beginning, other households drop out after a short period. However, more than half of the

respondents participated longer than a year.

Since the response to "I seek out organic foods" is discrete with five-point scaled values,

the likelihood of seeking out organic foods can be estimated by ordered probit models.

Overview of the Study

The remaining five chapters provide a detailed discussion of methodology, results analysis

and findings of the study. Chapter 2 provides insight into a literature review of the organic

products industry, consumer awareness, consumer preferences, and associated applications of

discrete choice models on organic foods. Chapter 3 focuses on the descriptions of the data used









in the study. The preference intensity approach is developed and organic preference model is

specified in Chapter 4, setting forth the ordered probit model for the response to the statement of

"I seek out organic foods". Regression results including estimated coefficients and supporting

statistics plus sensitivity analysis are presented in Chapter 5, followed by a discussion of findings

and implications in Chapter 6.









Table 1-1. Organic food sales and penetration of total organic food sales
Organic Food Sales Change from Prior Organic Penetration
1. Organic Penetration
($ Million) Year
1997 3,594 Na 0.81%
1998 4,286 19.2% 0.94%
1999 5,039 17.6% 1.06%
2000 6,100 21.0% 1.22%
2001 7,360 20.7% 1.41%
2002 8,635 17.3% 1.63%
2003 10,381 20.2% 1.94%
2004 11,902 14.6% 2.19%
2005 13,831 16.2% 2.48%
2006 16,718 20.9% 2.80%
2007 19,807 18.5% 3.15%
2008 22,929 15.8% 3.47%
Source: OTA's Manufacturer/Organic Industry Surveys, 2006-2009









CHAPTER 2
LITERATURE REVIEW

A Short Review

A large number of studies on several issues of organic consumer behavior have been

conducted by both industry and academic researchers. Industry reports usually focus on how

often consumers purchase organic products, where to buy and the reasons to buy organic

products, as well as demographic data of respondents. Survey reports established by the Hartman

Group, the Organic Trade Association (OTA), and the Natural Marketing Institute (NMI) are

widely cited in many studies. In NMI's 2007 Organic Consumer Trends Report, organic

consumers have been categorized into four distinct segments represented by percentage of the

U.S. primary grocery shoppers: Devoteds (16%), Temperates (22%), Dabblers (44%), and

Reluctants (18%). Devoteds are those who exhibit the highest usage of organic products and the

most knowledge of "organic"; Temperates differ from Devoteds in the belief that organic

products are necessity, so they shop for organic with less frequency and spend less on organic

purchase. About 75% of total organic spending is attributed to Devoteds and Temperates

together; furthermore, they are likely to consume more and more as new organic product

introductions keep raising. NMI also suggests that although Reluctants are educable, the size of

the Devoteds group would remain relatively stable. The 2009 U.S. Families' Organic Attitudes

and Beliefs Study conducted by OTA identifies "organic buyer groups" into four groups by the

length of time in the organic market: Newly Organic parents (32%), Experienced Organic

parents (20%), Seasoned Organic parents (21%), and Non-buyers (27%). The report reveals that

three quarters of U.S. families purchase organic foods no matter how often they shop for

organic and how much they spend on organic purchases. Among the organic buyers, Newly

Organic parents began to buy organic foods partly because organic foods became available in









conventional grocer stores. Seasoned Organic parents are typical organic consumers who were

white, well-educated and wealthy. The Hartman Group (2008) defines "core" organic consumers

as those who are the most integrated in the purchase and use of organic across a wide variety of

categories and would likely continue to increasingly be involved in the organic market.

Unlike industry studies, academic researches attempt to understand the organic consumers'

choices as well as underlying motivations through several different approaches. Thompson

(1998) provided a review of emerging studies of consumer demand for organic products, and

summarized that attitudes, motives, and willingness to pay have been measured except elasticity

estimates due to lack of retail data. After comparing different studies, he concluded that

demographic variables were important in explaining differences in organic purchase behavior.

Organic Consumer Behaviors

Generally, demographic factors (such as age, gender, education, income, employment, etc.)

were expected to have important impacts on explaining consumer behaviors. A stereo-typical

organic consumer was described to be Caucasian, affluent, and well-educated just a few years

ago. According to recent studies focusing on organic consumers, the picture of the typical

organic shopper was no longer easily identified based on a few traditional significant predictors.

Some studies suggested that organic consumers were clustered into two groups of age 18-

29 and of age 40-49 (Lohr and Semali, 2000; Thompson, 1998). Household heads younger than

30 years old or aged 50 and older were more often represented as heavy organic users than

lighter users (Steven-Garmon et al., 2007). Younger population with age 18-34 was more likely

to purchase organic foods, while respondents older than 65 years showed the lowest organic

usage rate (Mintel, 2008). Consumer's organic purchase decisions showed little difference

between genders (Thompson and Kidwell, 1998; Briz and Ward, 2009). A few national studies

(such as the Hartman Group and the Food Marketing Institute) and academic researches









(Loureiro and Hine, 2001; Briz and Ward, 2009) suggested that education had a positive impact

on organic purchasing behaviors. However, Thompson and Kidwell (1998) also provided

evidence that shoppers with graduate or professional degrees were less likely to purchase organic

products. It had been noticed that parents of young children or infants were more likely constant

organic product buyers, which was consistent with the finding that households with children

under eighteen were inclined to buy organic food products (Thompson and Kidwell, 1998;

Loureiro et al., 2001). But Thompson (1998) also summarized that the presence of children was

not the significant indicator in the Delaware studies. Mintel studies (2008) implied a positive

correlation between income and organic purchases since the higher price of organic was a

barrier for lower-income households. But several studies also provided evidence of exceptions

that higher household incomes did not necessarily suggest higher likelihood of organic

purchases (Huang, 1996; Thompson and Kidwell, 1998; Hartman Organic Research Review,

2002); moreover, there might be a declining tendency in higher-income groups, while lower-

income consumers seemed to be more "entrenched" organic buyers (Thompson, 1998). Many

studies focused on geographic factors and suggested that households residing in the U.S. western

region spent more on organic products (Thompson, 1998; Steven-Garmon et al., 2007).

As the organic industry grows, the number of consumers purchasing organic products

continues to increase and they are likely not limited to a single ethnic group. In fact, organic

consumers today represent a quite diverse ethnic picture. Steven-Garmon et al. (2007) concluded

that Asian and African-Americans were more likely to purchase organically grown produce

frequently compared to Caucasians and Hispanics. This was generally consistent with the

Hartman Group Organic 2006 Survey, which reported Asian Americans and Latino Americans

were relatively more likely to purchase organic foods or beverages than Caucasian Americans









based on their representation in the population. And more surprisingly, the ethnic group that was

more likely to be "core" organic consumers was Latino Americans, and to a lesser extent,

African Americans, compared to Caucasian Americans and Asian Americans. The Hartman

group (2006) suggested that this was probably due to the Latino's "historical connection with

organic" and their strong concern for family.

Store effect is another critical variable based on several studies which suggest that

differences in consumer behavior across stores are significant as long as organic products retain

their exclusive availability in a few particular market outlets. "Accounting for where foods are

purchased is likely to be important in understanding where potential growth in organic foods

might occur" (Thompson, 1998). Households with higher disposal income were inclined to shop

in specialty grocer. Furthermore, households who shopped in specialty grocer were sensitive to

the price differences between organic and conventional products and they were less likely to

purchase organic produce (Thompson and Kidwell, 1998). On the contrary, Batte et al. (2007)

concluded that the magnitudes of the willingness to pay for organic by specialty grocery

shoppers were substantially more than traditional grocery shoppers as long as the amount of

organic content level was higher than 70% organic ingredients.

While the effects of a product's appearance (e.g., cosmetic defects) on food choice were

relatively small (Thompson and Kidwell, 1998) or non-significant (Huang, 1996), concerns

about nutrition and price were critical to consumers decision-making with respect to organic

foods (Huang, 1996; Magnusson et al., 2001). It had been shown that organic foods were valued

and experienced not only for their appearances, tastes, prices, but also for their social and

environmental benefits (for example, food safety, animal welfare, supporting local farmers,









healthier choices, environmentally-friendly) (Huang, 1996; Williams and Hammit, 2000;

Lourerio et al, 2001; Torjusen et al., 2001; Dimitri and Richman, 2000).

In addition, subjective norms (like social pressure) affected consumers' attitude and

purchase intentions, but explained little about purchase behaviors (Smith and Paladino, 2009),

which was opposite to the finding of Ajzen (1991). Familiarity was an important factor that gave

a partial explanation of why so few consumers purchased organic products despite having

positive attitudes about organic (Magnusson et al., 2001; Smith and Paladino, 2009; Briz and

Ward, 2009).

Discrete Choice Model Applications

Most studies on the factors affecting consumers' choice for organic products applied a

discrete choice model: Huang (1996)'s study on consumers' preference for organically grown

produce (OGP), in which a bivariate probit model was formulated, suggested that nutritional

consciousness, concern about pesticides use, and verifying that produce was free of pesticides

were three significant factors for consumers who preferred organic fresh produce. He also

examined the probabilities of willingness to buy OGP even if they had sensory defects in trade

for food safety and environmental benefits. The results suggested a negative correlation between

income level and tolerance of sensory defects on OGP, but consumers who were Caucasian, with

better education and large families were more likely to accept it.

In Thompson and Kidwell's study of choice between organic and conventional fresh

produce in 1998, they measured actual choices based on data collected in retail stores, rather than

drawing out willingness to pay for organic produce. They estimated the choice by using a two-

equation probit model, which indicated the possibility that consumer's choice of store and their

choice of products may impact each other simultaneously. The results implied an interesting

connection between the choice of store and the consumers' choice of fresh organic produce:









despite relatively higher income and education level on average, shoppers who shopped at

specialty grocer were less preferred to organic produce, and were sensitive to price differences

between organic and conventional produce; shoppers who were less likely to choose organic

produce preferred specialty grocery stores.

Briz and Ward (2009) studied consumers awareness of organic products in Spain, and

specified a multinomial logit model to predict probabilities of awareness, as well as a probit

model to link awareness and purchase of organic products. Specifically, they built three levels of

awareness of organic foods, and linked only the probability of correctly being aware of what's

organic to consumption of organic products. They indicated that due to credence attributes of

organic products and consumer emotions, the learning curve about organic was probably

nonlinear and its slope might not be always positive. That is, at the estimated average awareness

level of 46%, the likelihood of organic food consumption actually declined as the state of

awareness continued to grow. They also provided a ranking of all determinants in the model to

indicate that the education had the most profound impacts on the awareness of organic products,

followed by age, knowledge about enriched foods, income, region, market size, and finally,

gender having the least effect.

Loureiro et al. (2001) collected survey data directly from consumers in two grocery stores

in Portland, Oregon, to be able to obtain estimates of preferences for organic, eco-labeled, and

regular apples from the actual decision makers. Their analyses were based on a random utility

model and were modeled by using a multinomial logit framework. Results illustrated that

concerns for food safety and environmental benefits had a positive correlation with the

preference for choosing organic apples compared to eco-labeled and regular options.









Other Methodology Application

In the study of consumer reactions to changes in labeling regulations under the National

Organic Program (NOP), Kiesel and Villas-Boas (2010) employed the hedonic price function

approach and a discrete choice model. They concluded that the implementation of the USDA

organic seal on milk labels significantly acted as a positive shifter of the likelihood of purchases;

and the welfare outweighed the costs incurred by labeling regulation based on their cost-benefit

analysis. However, while consumers who were aware of the NOP seal were more likely willing

to pay a premium for organic foods, awareness of the NOP seal was not a significant indicator of

the magnitude of premium (Batte et al., 2007).

A recent study of consumer behavioral intentions towards purchase of food products

(including conventional food, quality low-input food, and organic food) across six European

countries conducted by Ness et al. (2010) developed country-based structural equation models

building on the quality-value-satisfaction-loyalty framework. This study elucidated that

perceived quality, value, and satisfaction were determinants of food consumers' behavioral

intentions. Specifically, satisfaction was the key to developing consumers' intentions since

growing satisfaction had a positive impact on consumers' attitudes; moreover, satisfaction could

be increased by increasing perceived value, which was enhanced by greater perceived quality.









CHAPTER 3
ORGANIC DATABASE

Data used in this study were collected from an internet survey conducted by the private

company "Market Tools" during February, 2008 through March, 2010. The actual source of the

data for research purposes only is through the National Mango Board. The data are private and

only the supplemental questions relating to organic preferences were used out of a much larger

database. A total of 37,582 household entries were retrieved from this national demographically

balanced survey. Each household documented and reported their organic products consumption

behavior during a two-week period. The number of times a household reported varies during the

survey period: while some households kept filing from the beginning to the end of survey, some

households quit submitting after a short period. In fact, more than half of the total respondents

remained with the survey longer than one year.

While the data are preparatory to the National Mango Board, the Board commissioned the

private company "Market Tools" to collect the data for many commodities along with many

questions about the head of the household reporting. Every two-week a selected group of

households (panel) report their buying activities along with their demographics, attitudes, and

preferences, including that of seeking out organic. Households included in the panel are

continually adjusted to maintain a demographically balance panel with the total number usually

around 1200 households reporting at any one period. Some households report for several periods

while the norm is for households to drop out after participating over a few reporting periods.

While not specifically addressed in this study, other results with these data suggest that the

length of participating in the panel has little to no effect on the broader conclusions. For this

analysis, the final database extended over the periods from February 2008 through March 2010,

thus giving as current database as feasible. Details about the company are available on their









website and since the data are privately owned we have been very careful to maintain the

confidentiality of the information. That is, the private information cannot be distributed in the

public domain but the research results can be.

Respondents were asked to score the segmenting question whether she or he sought out

organic foods on a five-point Likert scale. The survey also contained questions on demographic

information such as age, gender, ethnicity, race, income, education, employment level, marital

status, household size, presence of children, census region, etc., as well as where they went

grocery shopping, expenditures for grocery shopping, behavior attributes, eating habits, and

health conditions. The dependent variable in the regression was the score for seeking out organic

foods (Y), and it was posited to be explained with

Y = f (socio-demographics, attitudes/behaviors attributes, store choices, health conditions).

Among the 37,582 household heads responding to the survey, about 19.2% of the respondents

agreed with the statement "I seek out organic food" (using scores 4 and 5 in the five-point Likert

scale as indicators of agreement with organic preference), while about 56.2% of respondents

choose not to seek out organic food and 24.6% of respondents reported a neutral score (Figure 3-

1). Figure 3-2 shows the distributions of five levels of agreement about seeking organic foods:

only 7.0% completely agree with the statement, 12.2% mostly agree, 24.6% neither agree nor

disagree, 21.7% mostly disagree, and 34.5% completely disagree with the statement about

seeking out organic foods during the survey period. Figure 3-3 compares the distribution of

agreement in 2009 to that in 2008 and it indicates that the distribution of completely

disagreement and agreement both declined by 2.2% and 0.8% respectively, while the distribution

of "somewhat disagree" and "somewhat agree" both increased by 2.8% and 0.5% respectively

from 2008 to 2009. The distribution of those with neutral agreement does not change much from









2008 to 2009. Figures 3-4 and 3-5 provide an overview of the distribution of agreement with

seeking out organic foods in each reporting period during the survey for a total 38 periods.

According to the overall distribution, we can observe that the reporting periods with agreement

distributions rising above 20% are concentrated in the mid periods of the survey, while during

the beginning and ending periods of the survey, the distribution was much lower. These slight

shifts during the entire survey period may point to some underlying seasonality effects. The

proportion of agreement with seeking out organic foods is illustrated in Figure 3-6, which shows

most respondents who claim agreement mostly agree with the statement "I seek out organic

foods".

Table 3-1 shows the responses to "I seek out organic food" on a Likert scale and a full

description of each explanatory variable with their corresponding discrete classification and their

frequency in percent based on 37,582 observations. The demographics, behavior and attitudes

attributes, and other important factors expected to influence the decision of buying organic are

recorded (see Appendix A). Only household heads with ages older than 18 years are included in

this survey, otherwise they are screened out the survey. Female heads of household represent 46

percent of the sample. White and non-Hispanic household heads account for the largest

proportion in the sample (nearly 63%), while White/Hispanic, Asian, and African American

household heads represent 9.5%, 3.6%, and 13.2% of the sample respectively. Household size is

measured by adding each number of people currently living in respondents' household in five

age ranges (including the respondent himself or herself); in the sample: 33.7% of households

have two members and only 11.6% of households have more than four members. Members)

with ages less than 18 years were considered as children; in the sample, about 33% of families

reported the presence of children. Regarding income levels among the respondents, about 36% of









households have income between $35,000 and $ 75,000, followed by households with income

below $35,000 with 34%. Among all respondents, nearly half are full-time employed (including

self-employed), and nearly two thirds have some college or a college degree. The geographical

attributes of the respondents also varied among areas based on the United States census. These

nine areas were aggregated into four regions (Northeast, Midwest, South, and West) to reduce

the analyses needed. Approximately 21% of respondents lived in Northeast region, 28% of

respondents lived in Midwest region, 32% of respondents lived in South region, and 19% of

respondents lived in Western region.

The distribution of expenditures on grocery shopping within one week was 38% of

households spent between $100 and $200, 18% spent under $50, and only one percent spent

more than $400. Most households reported that they shopped for food in grocery stores (almost

90%) and the fewest reported shopping for food through internet grocery stores (less than 4%).

Mass merchandisers (56%) and warehouses (30%) were also popular places for grocery

shopping, with lower percentages of households reporting shopping in convenience stores

(21.5%) and farmers' markets (11.5%). Approximately 80% of respondents reported that they

consume about 1-3 servings of fruits and vegetables in a typical day.

Given that there are a large number of dummy variables in the model, it is necessary to

check the correlation between explanatory variables before running the model. Except for a

relatively high correlation between household size (XHWD) and households with children under

18 years old (XCHL), there was no significant correlation among all other dummy variables (see

in Table B-l).

As suggested earlier, a limitation of this study was that the preference for organic food was

measured through reports of seeking out organic rather than actual consumption behaviors.









Again an underlying assumption in this study was that there was a correlation between seeking

out organic food and the actual consumption of organic food.









Table 3-1. Descriptions of explanatory variables
Description Variable Name/Range Frequency in
percent


Demographics
AGE:
Age of household
head (18+)


GENDER:
Gender of household
head
RACE:
Ethnicity



CHL:
With children under
18 years
EDUC:
Highest education
level

EMPLY:
Employment


INCOME:
Household income
(dollars)


MARITAL:
Marital status


HWD:
House size (number of
members)


XAGE1
XAGE2
XAGE3
XAGE4
XGEN=I
XGEN=0

RACE
RACE2
RACE
RACE4
RACE
XCHL=I
XCHL=0

XEDU1
XEDU2
XEDU3
XEDU4
XEMPLY1
XEMPLY2
XEMPLY3
XEMPLY4
XINC1
XINC2
XINC3
XINC4
XINC5
XMAR1
XMAR2
XMAR3
XMAR4
XHWD1
XHWD2
XHWD3
XHWD4
XHWD5


18 to 24
25 to 44
45 to 64
65 and older
Female
Male

White/NONHISPANIC
White/HISPANIC
Black/African American
Asian
Other
Yes
No

High school or less
Some college or college degree
Graduate or professional degree
Other
Employed, full time
Employed, part time
Not employed
Other
Under $35,000
$35,000- $74,999
$75,000 $99,999
More than $100,000
Prefer not to answer
Single, never married
Married
Living with parents
All others
1 member (single)
2 members
3 members
4 members
>4 members


15.81%
39.61%
30.64%
13.94%
46.39%
53.61%

62.93%
9.53%
13.16%
3.63%
10.74%
33.14%
66.86%

21.29%
64.36%
12.31%
2.04%
47.43%
7.88%
7.97%
36.72%
34.05%
35.57%
9.95%
10.07%
10.36%
28.05%
48.30%
7.93%
15.72%
21.83%
33.70%
17.86%
15.04%
11.57%









Table 3-1. Continued
Description


STATE:
Census region


SERV FRU:
Servings of fruit do
you consume in
typical day (#0-10)
SERV VEG:
Servings of vegetable
do you consume in
typical day (#0-10)


Variable


REGION 1

REGION

REGION


REGION

XSERFRU1
XSERFRU2
XSERFRU3
XSERFRU4
XSERVEG1
XSERVEG2
XSERVEG3
XSERVEG4


Name/Range


NORTHEAST: New England
NORTHEAST: Middle Atlantic
MIDWEST: East North Central
MIDWEST: West North Central
SOUTH: South Atlantic
SOUTH: East South Central
SOUTH: West South Central
WEST: Mountain
WEST: Pacific
0 serving
1-3 servings
4-6 servings
>7 servings
0 serving
1-3 servings
4-6 servings
>7 servings


Frequency in
percent
20.73%

28.19%

32.16%


18.92%


6.45%
82.34%
10.58%
0.63%
3.16%
83.71%
11.95%
1.18%


EXPEND: XEXPD1 under $50 17.85%
expenditures on XEXPD2 $50 to $100 33.45%
grocery shopping XEXPD3 $100 to $200 37.70%
within a week XEXPD4 $200 to $400 9.67%
(dollars) XEXPD5 more than $400 1.33%
SHOP GRO: XSHOP GRO=I YES 89.74%
Shopping for food in XSHOPGRO=0 NO 10.26%
grocery store
SHOP WARE: XSHOP WARE=1 YES 29.60%
Shopping for food in XSHOP_WARE=0 NO 70.40%
warehouse
SHOP INTE: XSHOP INTE=1 YES 3.74%
Shopping for food in XSHOP_INTE=0 NO 96.26%
internet grocery store


SHOP MASS:
Shopping for food in
mass merchandiser
SHOP CONV:
Shopping for food in
convenience store
SHOP FARM:
Shopping for food in
farmers' market


XSHOP
XSHOP


MASS=1
MASS=0


XSHOP CONV=1
XSHOP CONV=0

XSHOP FARM=1
XSHOP FARM=0


YES
NO

YES
NO

YES
NO


56.18%
43.82%

21.51%
78.49%

11.51%
88.49%









Table 3-1. Continued
Description


Variable


Name/Range


Frequency in
percent


Behavior/attitude attributes
BHV EXERCISE: B EXEl
I exercise at least 3 B EXE2
times a week B EXE3
B EXE4
B EXE5
CALORIES: CAL
I count calories CAL2
CAL3
CAL4
CAL5
BHV LABEL: B LAB1
Read ingredients on BLAB2
labels of the foods I B LAB3
buy BLAB4
B LAB5
BHV HLTH: B HLT1
I feel healthier than BHLT2
peers B_HLT3
B HLT4
B HLT5
BHV NEWFOOD: B NEW1
I frequently B NEW2
experiment with new B NEW3
foods B NEW4
B NEWS
BHV FRE: B FRE1
I eat fresh foods much B FRE2
more frequently than BFRE3
packaged food BFRE4
B FRE5
BHV FRUVEG: B FV1
I eat fruits and B FV2
vegetable more than BFV3
other people my age BFV4
B FV5
BHV WAY: B WAY1
I go out of my way to B_WAY2
get certain types of B_WAY3
produce B_WAY4
B WAYS


Completely disagree
Mostly disagree
Neither agree nor disagree
Mostly agree
Completely agree
Completely disagree
Mostly disagree
Neither agree nor disagree
Mostly agree
Completely agree
Completely disagree
Mostly disagree
Neither agree nor disagree
Mostly agree
Completely agree
Completely disagree
Mostly disagree
Neither agree nor disagree
Mostly agree
Completely agree
Completely disagree
Mostly disagree
Neither agree nor disagree
Mostly agree
Completely agree
Completely disagree
Mostly disagree
Neither agree nor disagree
Mostly agree
Completely agree
Completely disagree
Mostly disagree
Neither agree nor disagree
Mostly agree
Completely agree
Completely disagree
Mostly disagree
Neither agree nor disagree
Mostly agree
Completely agree


18.68%
18.04%
21.48%
16.35%
25.45%
30.62%
21.56%
24.98%
13.98%
8.86%
8.29%
10.17%
25.44%
26.14%
29.97%
10.07%
14.54%
37.03%
25.21%
13.15%
9.79%
16.76%
33.68%
25.09%
14.68%
6.45%
13.71%
31.71%
27.22%
20.91%
9.51%
15.55%
35.32%
23.16%
16.46%
12.81%
15.32%
30.90%
24.51%
16.45%









Table 3-1. Continued
Description


Variable


Behavior/attitude attributes
BHV STORE: B ST1
I prefer to buy produce B_ST2
from certain stores B ST3
B ST4
B ST5


Health concerns
HLT BLOOD:
No one has high blood
pressure in household
HLT DIABE4:
No one has diabetes in
household
HLT CHOLE4:
No one has high
cholesterol in
household
HLT ALLEG4:
No one has food
allergies in household
HLT OBEST4:
No one has obesity in
household
HLT MOBIL4:
No one has limited
physical mobility in
household
HLT HEAR4:
No one has significant
sight or hearing
impairment in
household


Name/Range


Frequency in
percent


Completely disagree
Mostly disagree
Neither agree nor disagree
Mostly agree
Completely agree


HLT BP=1
HLTBP=0

HLT DB=1
HLT DB=0

HLT CL=I
HLTCL=0


HLT AG=I
HLT AG=0

HLT OB=1
HLTOB=0

HLT MB=1
HLT MB=0


HLT HR=I
HLT HR=0


No one
Otherwise

No one
Otherwise

No one
Otherwise


No one
Otherwise

No one
Otherwise

No one
Otherwise


No one
Otherwise


6.90%
9.25%
28.33%
30.65%
24.88%

58.51%
41.49%

80.16%
19.84%

62.23%
37.77%


83.59%
16.41%

69.88%
30.12%


80%
20%


82.87%
17.13%










Table 3-1. Continued
Description

Seasonality
MTH S:
Months from 1-12


Variable


MTH1
MTH2
MTH3
MTH4
MTH5
MTH6
MTH7
MTH8
MTH9
MTH10
MTH11
MTH12


Name/Range


Frequency in
percent


Jan.
Feb.
Mar.
Apr.
May
Jun.
Jul.
Aug.
Sep.
Oct.
Nov.
Dec.


9.12%
7.02%
8.96%
8.32%
8.22%
8.13%
8.22%
8.00%
8.18%
8.67%
8.57%
8.58%















Disagree
56.2%


Agree
19.2%


Neutral
24.6%


Figure 3-1. Frequency distribution of the responses to "I seek out organic foods" (combining
score 5 and score 4 for responses of agreeing with "I seek out organic foods";
combining score 1 and score 2 for responses of disagreeing with "I seek out organic
foods")







Distributions of agreement about seeking out organic foods


Completely Somewhat Neutral Somewhat Completely
disagree disagree agree agree


Figure 3-2. Frequency distribution of responses to "I seek out organic foods"










Distribution of agreement about seeking out organic foods


Completely Somewhat
Disagree Disagree


Neutral


Somewhat Agree Completely Agree


Figure 3-3. Comparison of frequency distribution of responses of agreeing to "I seek out organic
foods" in 2008 and 2009


Distributions of agreement about seeking out organic foods


2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36


Figure 3-4. Distributions of responses of agreeing to "I seek out organic foods" during the
reporting periods (from 2 = Feb. 2008 to 37 = Feb. 2010)











Distributions of agreement about seeking out organic foods


2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36


Figure 3-5. Frequency distribution of responses of agreeing to "I seek out organic foods"
detailed in "Completely agree (5)" and "Mostly agree (4)" during the reporting
periods (from 2 = Feb. 2008 to 37 = Feb. 2010)



Statistics of agreement about seeking out organic foods


Mean Score


Standard Deviation of Coefficient of Variation
Score of Score


Figure 3-6. Percentage of frequency distribution of responses of agreeing to "I seek out organic
foods" during the reporting periods (from 2 = Feb. 2008 to 37 = Feb. 2010)


51 1.26



0.53

5,^B-------1------ ------









CHAPTER 4
ORGANIC PREFERENCE MODEL

In the case of estimating the determinants of responses to questions using a Likert scale,

the fundamental interest is to determine the probability of each level of outcome and how the

probabilities differ across the respondents' characteristics. To estimate the likelihood of "I seek

out organic foods", we used an ordered probit model which is appropriate and commonly used

when the dependent variable associated with more than two outcomes is both discrete and

ordinal.

Let "0" represent "I seek out organic foods", and define O, as the ith observation in the

survey. The outcomes of "I seek out organic foods" (y =0,) is discrete with scaled values form 1

to 5 increasing in magnitude of agreement (1 = completely disagree; 2 = mostly disagree; 3 =

neither agree nor disagree; 4 = mostly agree; 5 = completely agree). These scores reflect an

ordinal scaling that is exhaustive and mutually exclusive; yet, they are only rankings and have no

cardinal significance. A critical assumption of the ordered probit model is that the model fits the

parallel slopes requirement, which means the slope coefficients of variables do not vary between

different outcomes. Quantitatively, the problem is a classic situation for ordered probit modeling.

Organic Preference Model Specifications

Suppose motivations are captured with a set of variables in matrix X and effects of X are

reflected with f. Then XP/ represents the impacts of each motivating variable once/ 's are

known.

In Ordered Probit models, we build a latent regression

y* = Xp8+e (4-1)









where y is the unobserved latent index ranging from -co to +cc and is determined by observed

factors X's along with unobserved factors' 's. Matrix of / is composed of the intercept and all

parameters associated with the matrix X. E is assumed to be normally distributed with a mean

of zero and variance of one.

Then the measurement equations for y =O, could be illustrated as following specifications:

y =1 if -o < y* < 0

y =2 if 01
y =3 if 02
y=4 if 0,
y = 5 if 64< y* < -o
y=5 if 04
0's are called thresholds, which are unknown values to be estimated with/ 's, and satisfy

the relationship of 01 <02 <03 < 0 (Ok e (-o, +o0), k=l, 2, 3, 4).

The probability for each Likert score of seeking out organic foods can be derived as

follows (letting ( represents the cumulative normal function). Take the probability of score=1

for example:

Prob(y=l) = Prob(O,O, =1) =Prob(- o < y*< 01)
=Prob(- o < XP+<01) (4-3)
(4-3)
=Prob(- oc Xp < e < 0 XP)
= 0(o x8) 0(-oo Xf8)

Since ((-oo Xf) = 0, the scores are exhaustive and mutually exclusive

(i.e., ((oo X,) = 1), and by using ORDPROB procedure in TSP software (TSP econometric

software was used in this study), the lowest effective boundary value of the threshold is










normalized to zero (i.e., 0, = 0), therefore the total number of the thresholds (0's) to be

estimated is the number of values which y takes on less 2. Yields:

Prob(O011,=l)= X(-XP)
Prob(O,O, =2) = ((02 X)p) X(-XP)
Prob(OO,=3) = D(03 Xp) ((02 Xlp) (4-4)
Prob(OO, =4) = D(04 XP) ((03 X p)
Prob(O,lO,=5) = 1- (04 XPl)

For illustration purposes, we suppose that X is made up of Xk (k=l,...,6) being binary and

there is no continuous variable. There are a total of 36 discrete variables (Xk) expected to explain

the motivations moving across the scale of agreement about seeking out organic foods. Each X

could be expressed as: (j denotes each discrete level, while i represents each of the actual

observations)

S 4 2 5
X,1 = Z ajXAGE, + aj+4XGENj, + aj+6RACE,
j=1 j=1 j=1
4 4 5
+ ajXE11XBlDU, + a X15MAAR, + aj+ 19XHWDJ
J=1 J=1 J=1 (4-5)
2 5 5(4-5)
+ a j+24XCHLj, + aj+26XINCj, + aj+31XEXPDJ,
J=1 J=1 J=1
4 4
+I aj+36XEMPLY +Y aj+40REGION,
J=1 j=1

S 5 5 5
X2 62 j= JCAL, + 38 5B _FRE, + J ~3, lB LAB,
J=1 J=1 J=1
5 5 5
+ J+15B _ST, + ~3+20BWAY, + 3~+25B _F, (4-6)
j=1 j =1 J=1
5 5 5
+Jj+30B _HLTJ, + 3j+35 BEXEJ, + +25B _NEWJ,
J=1 J=1 J=1










S 2 2
X3 A3= / YXSHOP ROj + 1 2XSHOP WARE,
J=1 J=1
2 2
+ y+4XSHOP INTE, + y+6XSHOP _MASSj, (4-7)
J=1 J=1
2 2
+ Y1,JXSHOP _CONV, + ~ j+lXSHOP FARMM,
J=1 J=1

S 4 4
X4 4 = Y+12XSERFRUj + Z Y,16XSERVEJG, (4-8)
J=1 ]=1

2 2 2
X35 = co HLT BP, + o /+2HLT DB + oj+4HLT CL,,
J=1 ]=1 ]=1
2 2 2
+ j+6HLT_AGJ, +lw ojHLTOBJ, + ,j+loHLT MB, (4-9)
J=1 J=1 J=1
2
+ COj+12HLT HRJ,
J=1

S 12
X,6 = AJMTHJ (4-10)
J=1


Then, X = 0 + X1 i1 + X2 82 + X3 3 +3X4 a4 + X5 ,+ X6 6 (4-11)


In sum, the fundamental challenges are to specify Xk first and then estimate the impacts of

each motivating variable. In the next chapter, these measures will be comprehensively explained

(see Table 3-1 for the definition of the X variables).

Predicted Probabilities

According to the expressions described in last section, the predicted probability for each

Likert score can be derived in a general way as follows: (m=l, 2, 3, 4, 5)


Pr(y =m) = (,(Om,-X8)- 0(O-i- X-fi) (4-12)


which indicates the relationship between the dependent categories and explanatory

variables. In practice, information about estimated/ 8's and O's can be obtained from the









regression result, and then calculate in equation (4-12) to obtain predicted probabilities. In the

case of more than one independent variable included in a model, the effect of each single

variable can be examined while holding other variables as their actual values. If the independent

variable is discrete, we make a X matrix containing a 1 in the first column for intercept, a 1 in the

column representing the controlled category of dummies, and other columns take their actual

values across the panel. Take gender dummies, for instance; the gender variable contains two

dummies XGEN1 and XGEN2 (a restricted gender dummy DGEN (1 is female and -1 is male) is

used for regression). To illustrate the effect of gender on the probabilities of ordinal outcomes,

we first make the X matrix contain a 1 in the first column for the intercept, a 1 in the column

representing gender (DGEN) to select female respondents (or a -1 to select male respondents),

and other columns again are the actual variable values. The process is similar when discrete

variables contain more than two categories of dummies. Take age dummies for example, age

dummies contains four categories (XAGE1, XAGE2, XAGE3, and XAGE4) and restricted age

dummies (DAGE1, DAGE2, and DAGE3) are included in regressions. The X matrix could

contain a 1 in the first column for intercept, a 1 in the column representing XAGE1 to select the

respondents with ages between 18 and 24, and other variables remain in their corresponding

columns the same values that they actually are. The processes for other categories of age are

similar to XAGE1 except the category of XAGE4, for which the matrix contain a 1 in the first

column for the intercept, a -1 in columns representing age except XAGE4 (-1 in columns

representing XAGE1, XAGE2, and XAGE3 ), with other columns remaining at their actual

values .

Partial Change and Discrete Change in Predicted Probabilities

A question often asked is how the probabilities of the various outcomes would change

when the value of one variable changes. The signs of the coefficients obtained from regression









do not directly reveal the direction of impact for each restricted dummy variables. In order to

evaluate the marginal effects of explanatory variables, we can estimate the marginal responses,

calculate the odds ratios, or simulate the probabilities across different levels of one particular

variable category.

For continuous variables X, the marginal effect on the probabilities of a small change in

X,k (value of the kth determining variable for person i) for person i, under a normal distribution is:

K
(Z, =flkXAk)
k=l

aPr(Y = 1) d 1 aZ
ax,,k dZ, OXk
0Pr(Y = 2) d [Z
Oaxk dZ, axk
(4-13)


aPr(Y = J) d aZ
ax,, dZ X k,,

Then the marginal effects of the regressor X on probabilities can be obtained by evaluating

the probability density function (1'(X)) multiplied by the relative coefficient. It is clear to state

that when the value of the kth independent variable increases and k > 0, the probability of

outcome Y = will decline as a result of the opposite sign between the derivative

ofPr(Y = 1) and 3,k, while the probability of outcome Y = J will rise since the derivative of

Pr(Y = J) has the same sign as j,. When interpreting the rest of the marginal effects, the

direction of changing the value of a regressor on the probability of outcomes can be ambiguously

determined due to the sign of the derivative being different from the sign of beta in some cases.

Greene (2008) argued: "What happens to the middle cell is ambiguous. It depends on the two









densities. In the general case, relative to the signs of the coefficients, only the signs of the

changes in Prob(y = 0 I X) and Prob(y = J I X) are unambiguous! The upshot is that we must be

very careful in interpreting the coefficients in this model. Indeed, without a fair amount of extra

calculation, it is quite unclear how the coefficients in the ordered probit model should be

interpreted."

In addition, when the independent variable is a dummy variable, interpretation using the

probability density function multiplied by the associated coefficient can also be misleading

(Long, 1997; Borooah, 2001). In fact, we interpret the discrete change instead of the marginal

change in the case of dummy independent variables. Long (1997) implies the discrete change is a

more informative measure for ordered regression models. Discrete change can be expressed as

follows:

A Pr(Y = J| x)
r( Jx= Pr( = J I x,Xk=1)- Pr( = J I x, X k=0) (4-14)


where the notation Pr(Y = J I Xk) indicates the probability of given.

It suggests that when the value of XAk changes from 0 to 1, the predicated probability of

APr(Y = J| x)
outcome J changes by Pr( J Ix) while holding other variables at x. That is, we compare
AXk

the probability when the dummy variable takes one value (i.e., 1) with the probability when it

takes another value (i.e., 0) while holding other variables fixed. The difference between the two

sets of probabilities is the effect from moving one condition to another on the probability of

being at different outcomes.

In the next chapter, the estimated probabilities across demographics, behavior/attitudes

factors, as well as concerns on health problems will be illustrated respectively while letting all

other factors take their actual values, from which we can analyze how a household's probability










of seeking out organic foods at five levels would be affected if he or she moves between

different demographic conditions (age, income, education etc.), behavioral conditions, as well as

health conditions.

Restricted Dummy Variables

Another thing that must be pointed out is that given so many binary variables in this

model, we have to deal with the "dummy-variable trap". If we include all the dummy variables

for one of the categories when running the model, perfect collinearity would be introduced into

the model. To avoid the dummy-variable trap, we can simply choose a dummy variable of a

group to omit from the model. Then the coefficients on the included variables measure how those

4
groups differ from the omitted group. Take the AGE dummies (, a XAGE, ) for example, we
j=1

could simply drop acAGE1, and run regression. The intercept represents the base, and the t-test is

to test against the omitted category. In the case of large number of dummies included in the

model, it is inefficient to interpret coefficients from every combination of benchmarks. Or we

can adopt a method of restricting an unweighted sum of the coefficients to zero for each dummy

category, which is convenient since each coefficient estimated is expressed relative to the

average respondent rather than to each set base. Using this method, we add D notation to each

4
dummy variable included in the regression. Take XAGE dummies (c ajXAGE ) for example:
J=1

4 3
a =0 or a4 =- a
l =1j (4-15)
4 3 34-15)
ZcajXAGEj, =C (JXAGE, XA-GE4) = ADAGE,
j=1 j=1 J=1

In this way, the intercept represents the unweighted average household and all coefficients

and t-values are expressed relative to the average. That is, a statistically significant t-value









implies that the coefficient is statistically different from the unweighted average / For age

dummies, the effect of XAGE1 is /P + a, the effect of XAGE2 is PS + a2, the effect of XAGE3

is ,/ + a3, and the effect of XAGE4 is /7 a, a2 a3; and the t-values of three age dummies in

the regressions are testing if each DAGE is different from the unweighted average respondent.

This method used for the dummy variable is just for convenience when discussing the

statistical test since all t-values are relative to an average instead of just the variables dropped out

in the traditional method for dealing with dummy variables. With either dummy method, the

conclusions about the probability for each Likert score will be identical in the end results.









CHAPTER 5
ANALYSIS OF RESULTS AND SIMULATIONS

In this chapter, we first discussed the ordered probit estimates based on the ordered probit

methodology (see equations 4-5 through 4-10) and econometric models developed in the

previous chapter, and then we focused on reporting how the changes in the explanatory variables

(demographic factors, behavior/attitudes, health conditions etc.) affected the probabilities of

seeking out organic foods. After that, the importance of each variable was ranked to compare the

relatively different effects on the marginal change in the probabilities of seeking out organic

foods.

Ordered Probit Estimates

The models specified in chapter 4 estimated responses to the statement "I seek out organic

foods" with 36 variables by using ordered probit procedures. The results were shown in Table 5-

1, including scaled R-squared, estimated coefficients for the restricted dummies used in

regressions, t-statistics and corresponding p-values, as well as the thresholds for moving across

the Likert scales. Since the total number of thresholds to be estimated was the number of values

which y takes on less 2; for the outcome with five Likert scales, only three thresholds

(02, 03, 04) were estimated in the regression since the others were then predetermined. And

since we applied the method of restricting an unweighted sum of coefficients to zero, all the

coefficients and t-statistics obtained from regression were expressed relative to the unweighted

average household. Hence, the estimated coefficients associated with dummies created originally

were recalculated and shown in Table 5-2 to show the coefficients for every level in each dummy

class.

According to the estimate results, generally the ordered probit model explained many

reasons for seeking out organic, as indicated by the scaled R-squared of 0.457. It suggested that









over 45 percent of variation in the consumer preferences for seeking out organic foods was

explained by the model, recognizing the limited interpretation of the R2 in discrete choice

models. Among the 99 dummy variables included in the regression, only 23 dummy variables

were not statistically differ from the average level of seeking out organic foods at a 95%

confidence level, including the marital status being single, Black/African American, having

incomes between $35,000 and $74,999, household sizes with either two or four members, South

census region, zero daily servings of fruit, 0-3 daily servings of vegetables, mostly disagreeing

and completely agreeing with the statement "buying produce from certain stores", completely

agreeing with the statement "eating fruits and vegetables", "no one in household has obesity

problem", and all the "months" variables. It was important to recall that the t-statistics values

were expressed relative to the average level rather than to the null hypothesis of the true slope

coefficient equaling zero. While not included in the analysis, one could have easily tested

differences between any of the levels within a dummy class using the covariance matrix

associated with the results in Table 5-1. Since the number of possibilities were very large, we

instead would concentrate on showing the estimated probabilities for each level and then one

could easily see the numerical differences across the levels with the class (e.g., compare the

probabilities across all ages).

Ordered Probit Model Simulations

To illustrate how the probabilities of different levels of seeking organic foods differ across

socio-demographics, behaviors and attitudes, as well as health conditions, probabilities for each

of five outcomes given a particular set of conditions of the other explanatory variables were

simulated. One set of conditions is using the actual variables of the variables not being simulated

and then compare the probabilities after averaging across the panel observations.









As a starting point, the probabilities for each level of seeking out organic foods for the

average respondent were predicted. Specifically, using the coefficients from Table 5-1,

probabilities for each respondent were estimated and averaged over the total 37,582

observations. The average probability of each level of seeking out organic provided a reference

base in order to compare probabilities as each variable's impact was considered. As shown in

Figure 5-1, the probability of people who would like to seek out organic foods (combining scores

4 and 5) was estimated to be around 19 percent for the average respondent, while the average

respondent was estimated to be 56 percent unlikely to seek out organic foods (combining scores

1 and 2), and 25 percent that are neutral or indifferent.

Then the probabilities were averaged over the households with only the controlled variable

being changed. The fact that each conditional probability was simulated relative to the overall

average probability made the simulations comparable. The difference between the conditional

probability and the overall mean probability was focused on the impact of the variable being

controlled. By comparing the conditional probabilities, we were able to observe both the

direction and magnitude of the effects of the variables being controlled.

In each of the subsequent figures, the probabilities predicted for each level of seeking out

organic foods and the reference probability were shown on the vertical axis and values of

controlled variable(s) were depicted on the horizontal axis. For the response to the question of

seeking out organic foods with a five-option Likert scales, a set of three figures is shown for each

controlled variable with combining intensities of both "completely agree (disagree)" and "mostly

agree (disagree)" together. The percentages of agreeing and disagreeing with the statement "I

seek out organic foods" as well as the percentages of neutral responses were all presented in

adjacent figures. For each controlled variable, the people with neutral attitude on seeking out









organic foods seemed to have the same likelihood moving pattern as those who agreed with "I

seek out organic foods", exhibiting around the likelihood of 24.8% as the average respondent

with neutral response. People with responses of "complete disagree" or "mostly disagree"

showed an opposite pattern compared to those who agreed with the statement "I seek out organic

foods". The response intensities of "completely agree (disagree)" and "mostly agree (disagree)"

generally kept the similar proportions for each controlling variable. Combining the two levels of

agree provided a much more visual way to see the tendency to favor organic or not instead of

reporting separately the five probabilities. Also it gave a clear indication if the intensity of

seeking out (or not) with the idea that if the probability of completely agreeing was rising

relative to mostly agreeing, then the intensity of seeking out organic was increasing (or not).

Thus in all of the subsequent figures, two aspects were of particular importance. What were the

probabilities under each controlled condition and did the intensities change within the "agree"

(or "disagree") scores.

Seeking Out Organic Foods across Demographics

In Figure 5-2 through Figure 5-11, the predicted probabilities across the demographic

factors averaged over the actual values of the other variables were illustrated. These

demographic factors included age, gender, marital status, race, income, education, employment,

household size, presence of children under 18 years old, and aggregated census region. Again,

the probabilities for each demographic was based on averaging over the probabilities for each

household with only the controlled variable being changed.

A consistently decreasing probability of agreeing with seeking out organic foods was

shown over different ranges of age, from the highest 26 percent for young populations of 18-24

years to the lowest 12 percent for populations of 65 years and older. The older population's

likelihood of seeking out organic was about 7% points below the average (19.3%), comparing to









the younger population with the highest likelihood that was almost 7% points above average.

Correspondingly, the probability of disagreement was shown increasing from younger to older

populations. The result was partially consistent with findings from Lohr and Semali (2000) and

the summary from Thompson (1998) that age brackets of 18-29 and 40-49 were the consumers

with highest percentage of buying organic produce while consumers over 60 purchased the least

amounts. If the goal was to continue to enhance the demand for organic, the age probabilities

suggested targeting the older population was needed since that was where the major weakness

appears to be occurring. Alternatively, if sectors of the organic industry were concerned about

locations where the initial greatest gains could be realized, then locations in areas with less

concentrated say in retirement areas would be suggested since those areas generally had a much

higher numbers in the older age group. Clearly, the age figure provided a number of directions

for marketing and policy, depending on the overall goal of variables sectors of the organic

industry.

Gender and marital status contributed little to explaining the differences in the responses to

seeking out organic foods, while Thompson (1998) implied gender and marital status together

might be important predictors of organic consumer's profiling. Similarly, Thompson and

Kidwell (1998) and Briz and Ward (2009) reported little difference in the organic searching

behavior likelihood was shown between female and male household heads, no matter how much

she or he agreed with seeking out organic foods statement. In Figure 5-3, the differences between

different marital status and the average level was quite small with the single shopper exhibiting a

slightly higher likelihood of seeking organic than the average shopper.

Figure 5-5 showed likelihood of seeking out organic differing across ethnic groups.

Asians Americans (with a substantial probability of 27%) and Black/African Americans (with a









probability of 20%) were relatively more likely to seek out organic foods than Whites (with a

probability of 19%) and Hispanics (with a probability of 18%). Compared to other ethnic groups,

Asians Americans presented the highest level of propensity to seek organic at about 7% above

the average likelihood level. Hispanic shoppers were least inclined to seek organic, showing

with the likelihood below average. Steven-Garmon et al. (2007)'s study pointed to similar results

that Asian and African-Americans were more likely to purchase organically grown produce

frequently compared to Caucasians and Hispanics. But our result did not confirm the finding

from the Hartman Group Organic 2006 Survey, which reported that Hispanic households were

more likely to be "core" organic consumers based on their representation in the population.

A popular opinion that households with higher incomes are more likely to purchase

organic foods makes sense based on the relationship between consumers' affordability and the

generally higher prices of most organic produce. However, our finding did not confirm any

consistent positive connection between household income levels and the likelihood of seeking

organic. Households with incomes between $75,000 and $99,000 had the highest probability of

seeking out organic foods (22%). Households with incomes under $35,000 showed a likelihood

of 20% and those who with income more than $35,000 but less than $75,000 showed a slightly

lower likelihood of seeking organic than the average level. Conversely, households with

incomes more than $100,000 are less likely to choose organic at about 2% below the average

likelihood. In addition, households who are employed part-time or unemployed have greater

probabilities of seeking out organic foods, while those who are not employed show the highest

likelihood (nearly 23%).

A clear positive association between education and awareness of organic foods was

revealed by Briz and Ward (2009). However, education level did not display a profound impact









on the likelihood of seeking out organic foods in this study. According to Figure 5-7, the effects

of education were mixed: having some college or a college degree showed no significant

detectable effect on seeking organic; yet people with less than high school or a high school

education and people with graduate or professional degrees were slightly more likely to seek out

organic products. This result was consistent with the finding of Thompson and Kidwell (1998)

that having a college education had little impact on shoppers' decisions to purchase organic

produce, but contradictory to their conclusion that having advanced degrees lower the probability

of purchasing organic produces.

Household sizes with two or more than two members did not contribute substantial

differences in the likelihood from the average level except for household sizes with a single

person being 2% below average. This implied that single people were less likely to choose

organic foods, whereas the presence of more household members did not indicate a greater

likelihood of seeking out organic foods. Households with children under age eighteen would be

expected more likely to seek out organic foods since some studies had concluded this. However,

surprisingly, this study found that this segment reported being nearly 4% less likely to choose

organic foods than those households without children under age eighteen.

The literature review indicated that households residing in the West region were more

likely to consume organic products. In this study, the geographical differences in seeking out

organic foods were quite small, given that only the West region respondents displayed a slightly

higher probability (20%) than the average level (19.3%), while respondents in all other census

regions presented lower propensities to seek out organic foods relative to the average level.

Store Choice and Expenditures

The six graphs in Figure 5-12 illustrated the impact of store choice on the likelihood of

seeking out organic foods. See Table 3-1 for the shopping categories. The category of food









shopping in "none of the places" mentioned in the survey was not represented in the simulation

because of zero observations in this category. The most substantial differences in the likelihood

of seeking organic were exhibited between those who went food shopping in farmer's markets

or produce stands and those who did not, showing with the probability of 25% and 18%

respectively. The probability of seeking out organic foods was almost 4% greater for households

who chose to shop for food through internet grocery stores (such as Peapod, Fresh Direct, etc.)

than for those who did not or the average respondent. Households who shopped for food in

convenience stores (such as gas station, 7-11, Quik Check, etc.) were slightly more likely to

choose organic foods, while those who shopped in grocery stores and mass merchandisers (such

as Wal-Mart, Target, etc.) were slightly less likely to seek out organic foods. People who chose

to grocery shop in warehouse club stores (such as Costco, Sam's Club, etc.) did not significantly

differ from the average respondent. People who chose not to shop in grocery stores displayed a

higher propensity towards organic products. Overall, since about 90% indicated using traditional

grocery stores for food shopping, the probabilities for those type stores were the more relevant

for most organic food marketing strategies.

In Figure 5-13, expenditures on grocery shopping showed a reasonably positive impact on

the level of seeking out organic foods. The probability of seeking out organic foods rose

consistently as expenditures on grocery shopping increased, showing that shoppers with weekly

grocery spending more than $100 were more likely to consume organic foods. That is, as the

overall average expenditure levels (per two-week food shopping) grew, the likelihood of

including organic purchases in the food basket (product mix) increased. While not directly

shown from the data, one could surmise that larger food stores likely included more consumers









in the higher expenditure levels and those focused on those type stores. Clearly, the correlation

between store size and expenditures per shopper needed to be someway verified.

Behavior/Attitudes Attributes

Graphs in Figures 5-14 and 5-15 included the relationships between the number of servings

of fruit/vegetables per day and the likelihood of seeking out organic foods. The graphs

interestingly implied that the organic propensity changes among different ranges of servings

were opposite between that of fruits and vegetables. People who consumed more than 7 servings

of fruit per day and those who consumed 4-6 servings of vegetables per day displayed the higher

propensity to choose organic foods, showing with the likelihood levels at 33% and 23%

respectively. Alternatively, those who consumed 7 servings of vegetables or more per day and

those who consumed 4-6 servings of vegetables per day had the lowest probability which was

below the average consumer. In addition, the probability of seeking organic consistently

increased as the number of daily servings of vegetables rose until 6 servings per day then

dropped to the lowest probability. On the contrary, the probability of seeking organic

consistently declined as the number of daily servings of fruit increased until 6 servings per day

then began to boost to the highest level of probability of 33%. This high range generally had a

very low level of occurrence.

The graphs in Figures 5-17 through 5-25 illustrated the likelihood differences across five

levels of responses to several behavioral statements (including "count calories", "eat fresh foods

rather than package foods", "read ingredients on labels of the food when buying", "go out of way

to get certain types of produce", "eat fresh fruit and vegetables more than other people with the

same age", "feel healthier than peers", "exercise at least three times per week", and "explore new

foods"). Most graphs illustrated a logically consistent increasing pattern of probability of seeking

out organic foods from disagreeing to agreeing with these behavioral statements. Specifically,









"concerns about calories", "eat fresh food rather than packaged foods", "read ingredients on

labels of the food when buying", "go out of way for certain types of produce", "feel healthier

than his or her peers", and "frequently experiment with new foods" displayed considerable

impacts on the propensity to seek out organic foods. People who did not practice the behaviors

mentioned at all were least likely to seek out organic foods.

Households who were seriously concerned about calories were more likely to seek out

organic foods than those who did not count calories they eat each day, thus implying that people

on a diet might be more interested in organic foods. The profound differences in the propensity

towards organic were exhibited between households who eat fresh foods much more frequently

than packaged foods, showing that the probability increased consistently from the lowest 10%

(who ate packaged food more frequently) to the highest 25% (who ate fresh food more

frequently). Similar to the moving pattern of "eat fresh foods rather than packaged foods" effect,

"read ingredients on labels when buying foods", showing a seeking out level of 25% (those who

did not read labels showing only 10%), concentrated a significant impact on the propensity

towards organic. This was reasonable. Due to the credence attribute of organic foods that

organic foods were difficult to be differentiated from conventional produced foods unless with

clear identification or labels, consumers cannot be aware of the intrinsic qualities unless notified

(through labels). The difference in the likelihood of seeking out organic was also substantial

between those going out of the way to get certain types of produce being at 24% and those who

did not constituting only 8%. People who thought they were healthier than their peers were more

likely to seek out organic foods than those who did perceive themselves were less healthy.

Similarly, people who frequently explored new foods had a greater probability to seeking out

organic compared to those who did not. Among those household considering "eating fresh fruit









(vegetables) more than others", "prefer to buy produce from certain stores", and "exercise at

least three times per week", there were little consistent trends and considerable variation across

the simulated probabilities.

Health Concerns

Health conditions obtained in the survey included concerns about high blood pressure,

diabetes, high cholesterol, food allergies, obesity, limited physical mobility, and significant sight

or hearing impairment on four dimensions (do you, does your spouse/significant other, does

other household member, or no one in household have any of those problems). Only the situation

of no household member having those health concerns was considered in simulation for

simplification. One thing that should be pointed out was that the horizontal axis in Figure 5-26

had the statement "do not have any health concerns".

According to Figure 5-26, only the household heads who had and/or his (her) family

members) had food allergies concerns were more likely to seek out organic foods. On the

contrary, household heads where no one in his (her) household had blood pressure concerns had

greater probabilities of seeking out organic foods. Concerns about diabetes, high cholesterol, and

significant sight or hearing impairment presented the similar moving pattern on the likelihood of

seeking organic. Other health concerns did not indicate enough differences between

probabilities and the average level. Overall and contrary to much of the discussion about

organic and healthiness, the impact of health problems generally had little influence on the

probabilities of seeking out organic foods..

Seasonality

In Figure 5-16, little variation was shown among probabilities of seeking out organic foods

across twelve months of the survey period. And compared to the average respondent, there were

no appreciable differences among the simulated probabilities and the average level for each score









level. Shoppers were very slightly more likely to consume organic foods in September and

October during the whole year, comparing to the average likelihood level. Overall it implied that

seasonality was simply not a factor when seeking out organic foods.

Ranking the Effects on Probabilities of Seeking Out Organic Foods

The effects of explanatory variables being controlled usually contributes different

outcomes on the marginal change in the probabilities of seeking out organic foods. Hence, in

addition to discussing the directional effects of all explanatory variables, it is also insightful to

illustrate the relative effects of variables being controlled on the likelihood of seeking out

organic foods in a perspective way. A ranking of the conditional simulated probabilities was

depicted in Figure 5-27, with horizontal bars showing the minimum and maximum effects

relative to the average on the left side according to the conditional explanatory variables'

correspondingly absolute ranges.

To illustrate the rankings of importance, the difference between the maximum and

minimum values of simulated probabilities based on each controlled variable was calculated and

then these absolute ranges were sorted in descending order. In Figure 5-27, the changes were

expressed relative to the average respondent with a likelihood of 0.07 for score 5 (completely

agree), likelihood of 0.12 for score 4 (mostly agree), likelihood of 0.24 for score (neutral),

likelihood of 0.22 for score 2 (mostly disagree), and likelihood of 0.34 for score 1 (completely

disagree). For all probabilities of response with scaled values from 1 to 5, "number of daily

servings of fruit", "eat fresh foods more frequently than packaged foods", "read labels", "go out

of the way to get certain types of produce", and "age" generally contributed relatively further

impact on the probability of seeking out organic foods than other factors. To the contrary,

moving down the charts, "gender", "limited physical mobility concern", "food shopping in









warehouse stores", "cholesterol concern", and "obesity problem" were the five least important

factors regardless of order.

With respect to the top 10 variables in the ranking, based on probabilities of seeking out

organic foods with "completely agree" (score 5) relative to the average level of 7%, "number of

servings of fruit" had the greatest impact by far, followed by "eat fresh foods", "read labels", "go

out of the way to get certain type of produce", "age", "frequently experiment with new foods",

"expenditures on foods", "ethnicity", "feel healthier than peers", and "number of daily servings

of vegetables". "Daily servings of fruit" was the most important factor impacting the likelihood

of seeking out organic foods, ranging from 3.7% to 10.1%. Reports of "go out of the way for

certain types of produce" had the minimum likelihood (only 2%) among all variables. Only

"age" and "ethnicity" were impactful demographic attributes, ranging from 3.7% to 10.1% and

from 6.4% to 10.7%, respectively. Education level could be an important determinant since it

showed a low level of minimum likelihood at 4.3%, but its absolute range was adequate.

In the second chart in Figure 5-27, the rankings based on probability of seeking out organic

foods with "mostly agree" (score 4) relative to the average of 12.4%, suggested that "go out of

the way for certain type produce", "read labels", and "eat fresh foods rather than packaged

foods" contributed a substantially greater impact than other factors. Reports of "go out of the

way for certain types of produce" still displayed the minimum likelihood of 6.2% among all

variables. "Age", "ethnicity" and "education level" ranked higher than other demographic

factors, ranging from 8.8%, 12.1%, 11% to 15.9%, 16%, and 13.6%, respectively.

The third chart in Figure 5-27 revealed the ranking based on a probability of seeking out

organic foods with "neither agree nor disagree" (score 3) relative to the average level of 24%.

The variable with the largest difference between the minimum and maximum likelihood was "go









out of the way to get certain types of produce", followed by "read labels", "eat fresh foods rather

than packaged foods", "frequently experiment with new foods", as well as "age". Finally, in the

forth chart in Figure 5-27, the rankings based on probability of seeking out organic foods with

"mostly disagree" (score 2) relative to the average of 21.5% showed that most variables did not

present an adequate difference except "number of daily servings of fruits".

The rankings based on probability of seeking out organic foods with "completely disagree"

(score 1) relative to the average of 34.3% was presented in the last chart in Figure 5-27. A

profound difference was seen with "go out of the way for certain types of produce", with an

absolute range of 25.2%. Both "read labels" and "eat fresh foods rather than packaged foods"

had similar effects on the probability, and "age" still showed considerable effects. Similarly,

"number of daily servings of fruit", "experiment with new foods", "feel healthier than my peers",

"expenditures on food", "number of daily servings of vegetables", "education level", "ethnicity",

"count calories", and "food shopping in farmer's markets" can also be important determinants,

but with less substantial effects.

Overall, the Figure 5-27 charts provided a direct way to compare the relative effects of all

determinant variables on the probability of seeking out organic foods. According to the rankings,

behavior and attitude variables were major impacting factors while demographics except age

played a less important role when seeking out organic foods.









Table 5-1. Results from Organic Preference ordered probit model
Ordered
Variables Description Probit t-statistics p-value
Parameters
C Intercept 0.7225 20.9578 [.000]
DAGE1 Age 18-24 0.3046 19.9795 [.000]
DAGE2 Age 25-44 0.0963 9.0858 [.000]
DAGE3 Age 45-64 -0.1520 -12.0182 [.000]
DGEN Female -0.0231 -3.6187 [.000]
DMAR1 Single 0.0224 1.6946 [.090]
DMAR2 Married -0.0345 -2.9880 [.003]
DMAR3 Living with parents -0.0483 -2.7676 [.006]
DRACE1 White/Non-Hispanic -0.1059 -9.5242 [.000]
DRACE2 White/Hispanic -0.1118 -6.4698 [.000]
DRACE3 Black/African American -0.0212 -1.3121 [.189]
DRACE4 Asian 0.2699 10.6685 [.000]
DINC1 Income under $35,000 0.0597 4.8776 [.000]
DINC2 Income $35,000-$74,999 0.0104 0.9918 [.321]
DINC3 Income $75,000-$99,000 0.1343 8.2871 [.000]
DINC4 Income >$100,000 -0.1246 -7.4626 [.000]
DEDU1 High school or less 0.1018 6.4516 [.000]
DEDU2 College 0.0337 2.5351 [.011]
DEDU3 Advanced degree 0.1281 7.2255 [.000]
DHWD1 Household size: 1 -0.1118 -6.3863 [.000]
DHWD2 Household size: 2 0.0173 1.2693 [.204]
DHWD3 Household size: 3 0.0310 2.4501 [.014]
DHWD4 Household size: 4 -0.0035 -0.2330 [.816]
DCHL With children under 18 -0.0843 -8.1726 [.000]
DEMPLY1 Employed full time -0.0522 -4.6580 [.000]
DEMPLY2 Employed part time 0.0592 3.4861 [.000]
DEMPLY3 Not employment 0.1242 7.2048 [.000]
DREG2 Region: Midwest -0.0410 -3.9685 [.000]
DREG3 Region: South -0.0020 -0.2081 [.835]
DREG4 Region: West 0.0438 3.7234 [.000]
DSHOP_GRO Shop in grocery stores -0.0486 -4.7238 [.000]
DSHOPWARE Shop in warehouse stores 0.0230 3.3157 [.001]
DSHOPINTE Shop in internet stores 0.0935 5.5372 [.000]
DSHOPMASS Shop in mass merchandisers -0.0506 -7.8411 [.000]
DSHOP_CONV Shop in convenience stores 0.0454 5.9319 [.000]
DSHOPFARM Shop in farmer's markets 0.1561 15.6844 [.000]









Table 5-1. Continued


Variables

DEXPD1
DEXPD2
DEXPD3
DEXPD4
DSERVF1
DSERVF2
DSERVF3
DSERVV1
DSERVV2
DSERVV3
DCAL1
DCAL2
DCAL4
DCAL5
DB FRE1
DB FRE2
DB FRE4
DB FRE5
DB LAB1
DB LAB2
DB LAB4
DB LAB5
DB ST1
DB ST2
DB ST4
DB ST5
DB WAY1
DB WAY2
DB WAY4
DB WAY5
DB FV1
DB FV2
DB FV4
DB FV5
DB HLT1
DB HLT2
DB HLT4
DB HLT5


Description

Expenditure under $50
Expenditure $50-$100
Expenditure $100-$200
Expenditure >$400
0 servings of fruit
1-3 servings of fruit
4-6 servings of fruit
0 servings of vegetable
1-3 servings of vegetable
4-6 servings of vegetable
Count calories (1)
Count calories (2)
Count calories (4)
Count calories (5)
Eat fresh foods (1)
Eat fresh foods (2)
Eat fresh foods (4)
Eat fresh foods (5)
Read labels (1)
Read labels (2)
Read labels (4)
Read labels (5)
Certain store (1)
Certain store (2)
Certain store (4)
Certain store (5)
Certain type (1)
Certain type (2)
Certain type (4)
Certain type (5)
Eat fruit & vegetable (1)
Eat fruit & vegetable (2)
Eat fruit & vegetable (4)
Eat fruit & vegetable (5)
Feel healthier (1)
Feel healthier (2)
Feel healthier (4)
Feel healthier (5)


Ordered
Probit
Parameters
-0.1908
-0.1133
-0.0466
0.1029
-0.0364
-0.1313
-0.2741
-0.0432
0.0099
0.2029
-0.2192
-0.0377
0.1021
0.1130
-0.3744
-0.1414
0.1297
0.4069
-0.3503
-0.1810
0.0386
0.4436
-0.1421
0.0280
0.0324
0.0066
-0.4983
-0.2411
0.2545
0.3662
-0.1679
0.0628
0.0324
0.0245
-0.2053
-0.1272
0.1731
0.2147


t-statistics

-10.8673
-7.5831
-3.2888
5.4341
-1.1669
-5.6687
-10.7484
-1.2224
0.5073
9.1451
-17.4283
-3.0572
7.2887
6.2442
-12.8631
-8.4793
9.5450
24.8498
-14.6740
-10.2195
3.0463
33.0480
-5.5342
1.5540
2.5884
0.4598
-24.9168
-16.2240
20.4537
22.3867
-6.8491
3.9838
2.4237
1.3893
-9.6842
-8.3431
13.8625
12.1973


p-value

[.000]
[.000]
[.001]
[.000]
[.243]
[.000]
[.000]
[.222]
[.612]
[.000]
[.000]
[.002]
[.000]
[.000]
[.000]
[.000]
[.000]
[.000]
[.000]
[.000]
[.002]
[.000]
[.000]
[.120]
[.010]
[.646]
[.000]
[.000]
[.000]
[.000]
[.000]
[.000]
[.015]
[.165]
[.000]
[.000]
[.000]
[.000]










Table 5-1. Continued


Variables

DB EXE1
DB EXE2
DB EXE4
DB EXE5
DB NEW1
DB NEW2
DB NEW4
DB NEW5
DHLT BP
DHLT DB
DHLT CL
DHLT AG
DHLT OB
DHLT MB
DHLT HR

DMTH2
DMTH3
DMTH4
DMTH5
DMTH6
DMTH7
DMTH8
DMTH9
DMTH10
DMTH11
DMTH12
MU3
MU4
MU5


Description

Exercise (1)
Exercise (2)
Exercise (4)
Exercise (5)
Explore new foods (1)
Explore new foods (2)
Explore new foods (4)
Explore new foods (5)
Do not have blood pressure
Do not have diabetes
Do not have high cholesterol
Do not have food allergies
Do not have obesity
Do not have limited mobility
Do not have sight / hearing
impairment
Feb.
Mar.
Apr.
May
Jun.
Jul.
Aug.
Sep.
Oct.
Nov.
Dec.
Thresholds
Thresholds
Thresholds


Ordered
Probit
Parameters
-0.1288
-0.0549
0.0959
-0.0300
-0.3503
-0.0939
0.0860
0.2641
0.0842
0.0689
0.0235
-0.0873
0.0038
0.0292

0.0377

-0.0255
-0.0347
-0.0152
-0.0025
0.0079
0.0047
0.0095
0.0133
0.0328
0.0013
0.0021
0.7711
1.7561
2.5944


Number of observations = 37582
Mean of dep. var. = 2.35607
Std. dev. of dep. var. = 1.25866
Scaled R-squared = 0.456931


LR (zero slopes)
Schwarz B.I.C =
Log likelihood =


= 20742.8 [0.000]
46058.1
-45515.6


t-statistics

-8.2912
-4.2859
7.2800
-2.4792
-17.0487
-6.5996
7.1489
16.7418
11.1721
8.1704
3.2276
-10.7729
0.5257
3.2861

4.3787

-1.2282
-1.8568
-0.7860
-0.1300
0.4083
0.2411
0.4868
0.6884
1.7478
0.0696
0.1101
100.6900
159.5480
176.5350


p-value

[.000]
[.000]
[.000]
[.013]
[.000]
[.000]
[.000]
[.000]
[.000]
[.000]
[.001]
[.000]
[.599]
[.001]
[.000]

[.219]
[.063]
[.432]
[.897]
[.683]
[.809]
[.626]
[.491]
[.080]
[.944]
[.912]
[.000]
[.000]
[.000]









Table 5-2. Organic Preference ordered probit model coefficient estimates
Variables Variables
Coefficient Coefficient
(see Appendix) (see Appendix)
bO 0.7225 XEMPLY1 -0.0522
XAGE1 0.3046 XEMPLY2 0.0592
XAGE2 0.0963 XEMPLY3 0.1242
XAGE3 -0.1520 XEMPLY4 -0.1312
XAGE4 -0.2489 REGION1 -0.0007
XGEN1 -0.0231 REGION2 -0.0410
XGEN2 0.0231 REGION3 -0.0020
XMAR1 0.0224 REGION4 0.0438
XMAR2 -0.0345 SHOP GRO1 -0.0486
XMAR3 -0.0483 SHOP GRO2 0.0486
XMAR4 0.0604 SHOP WARE1 0.0230
RACE1 -0.1059 SHOP WARE2 -0.0230
RACE2 -0.1118 SHOP INTEl 0.0935
RACE3 -0.0212 SHOP INTE2 -0.0935
RACE4 0.2699 SHOP MASS1 -0.0506
RACES -0.0310 SHOP MASS2 0.0506
XINC1 0.0597 SHOP CONV1 0.0454
XINC2 0.0104 SHOP CONV2 -0.0454
XINC3 0.1343 SHOP FARM1 0.1561
XINC4 -0.1246 SHOP FARM2 -0.1561
XINC5 -0.0800 XEXPD1 -0.1908
XEDU1 0.1018 XEXPD2 -0.1133
XEDU2 0.0337 XEXPD3 -0.0466
XEDU3 0.1281 XEXPD4 0.1029
XEDU4 -0.2635 XEXPD5 0.2478
XHWD1 -0.1118 XSERFRU1 -0.0364
XHWD2 0.0173 XSERFRU2 -0.1313
XHWD3 0.0310 XSERFRU3 -0.2741
XHWD4 -0.0035 XSERFRU4 0.4418
XHWD5 0.0670 XSERVEG1 -0.0432
XCHL1 -0.0843 XSERVEG2 0.0099
XCHL2 0.0843 XSERVEG3 0.2029
XSERVEG4 -0.1696










Table 5-2. Continued
Variables
(see Appendix)
CALl
CAL2
CAL3
CAL4
CAL5
B FRE1
B FRE2
B FRE3
B FRE4
B FRE5
B LAB1
B LAB2
B LAB3
B LAB4
B LAB5
B ST1
B ST2
B ST3
B ST4
B ST5
B WAY1
B WAY2
B WAY3
B WAY4
B WAYS
B FV1
B FV2
B FV3
B FV4
B FV5
B HLT1
B HLT2
B HLT3
B HLT4
B HLT5


Coefficient


-0.2192
-0.0377
0.0417
0.1021
0.1130
-0.3744
-0.1414
-0.0208
0.1297
0.4069
-0.3503
-0.1810
0.0492
0.0386
0.4436
-0.1421
0.0280
0.0752
0.0324
0.0066
-0.4983
-0.2411
0.6824
0.0324
0.0245
-0.1679
0.0628
0.0482
0.0324
0.0245
-0.2053
-0.1272
-0.0553
0.1731
0.2147


Variables
(see Appendix)
B EXE1
B EXE2
B EXE3
B EXE4
B EXE5
B NEW1
B NEW2
B NEW3
B NEW4
B NEWS
HLT BP1
HLT BP2
HLT DB1
HLT DB2
HLT CL1
HLT CL2
HLT AG1
HLT AG2
HLT OB1
HLT OB2
HLT MB1
HLT MB2
HLT HR1
HLT HR2
MTH1
MTH2
MTH3
MTH4
MTH5
MTH6
MTH7
MTH8
MTH9
MTH10
MTH11
MTH12


Coefficient


-0.1288
-0.0549
0.1177
0.0959
-0.0300
-0.3503
-0.0939
0.0941
0.0860
0.2641
0.0842
-0.0842
0.0689
-0.0689
0.0235
-0.0235
-0.0873
0.0873
0.0038
-0.0038
0.0292
-0.0292
0.0377
-0.0377
0.0063
-0.0255
-0.0347
-0.0152
-0.0025
0.0079
0.0047
0.0095
0.0133
0.0328
0.0013
0.0021













Probability of seeking out organic foods

Completely agree (5) m Mostly agree (4)
Neutral (3) U Completely disagree (1)
E Mostly disagree (2)

0.56




0 2 5 1
0.19


Agree Neutral Disagree

Average household

Probability of seeking out organic foods for the average respondent


Probability of seeking out organic foods


* Completely agree (5)


Ei Mostly agree (4)


0. 26
0.21
i- -- --- *- *- *- *- -. .

.. .


18-24


Average (19.3%)


25-44 45-64

Age of household head


Figure 5-2. Impact of age of household head on seeking out organic foods. A) completely agree
and mostly agree with the statement of"I seek out organic foods". B) neither agree
nor disagree with the statement of "I seek out organic foods". C) completely disagree
and mostly disagree with the statement of"I seek out organic foods".


Figure 5-1.


0.5
0.45
0.4
0.35
0.3
0.25
0.2
0.15
0.1
0.05
0












Probability of seeking out organic foods

r Completely disagree (1) O Mostly disagree (2) -----------


----------------------------------------------.6.-.-.
- - -- U .06 -6 -
0.60
......... ........ ........ ............. .....
.--.. .. ..


18-24


Average (55.9%)


25-44 45-64

Age of household head


Probability of seeking out organic foods
<*------------------------------------
U Neutral(3) .....




------. ------ 0.26 ------------------------------
0.24
L -. .24


Average (24.8%)


25-44 45-64

Age of household head


Figure 5-2. Continued


0.5

0.45
0.4

0.35
0.3

0.25
0.2

0.15
0.1

0.05
0


18-24












Probability of seeking out organic foods

Completely agree (5) El Mostly agree (4) --------

----------------------------------------------



yC~ ~ --------- -------- -------- -------- --------
10. 19



--- ---- --- -
52
)


Female


..Average (19.3%)


Male


Gender of household head A

Figure 5-3. Impact of gender of household head on seeking out organic foods. A) completely
agree and mostly agree with the statement of"I seek out organic foods". B) neither
agree nor disagree with the statement of "I seek out organic foods". C) completely
disagree and mostly disagree with the statement of"I seek out organic foods".


Probability of seeking out organic foods


* U Completely disagree (1) O Mostly disagree (2) ---



0. 55
...-.-.--.-.-....-....-......... .--........


m_ ^ ^ _


Female


."Average (55.9%)


Male


Gender of household head


Figure 5-3. Continued











Probability of seeking out organic foods

S0- Neutral(3) ..


-----------------------------------------------

' 0.25 0.25
.n.- ..-.....-.-.----..- .-.....


Female Male


Gender of household head


Average (24.8%)









C


Figure 5-3. Continued




Probability of seeking out organic foods


Completely agree (5) El Mostly agree (4) -



*- *- -- -- *----*-* *-* *--*- --* -* *- *-- Q; -- -- --


0.21
0..20......



lillillill--- a V illll


Single Married Living with parents Others
Marital status of household head


*Average (19.3%)


A
Figure 5-4. Impact of marital status of household head on seeking out organic foods. A)
completely agree and mostly agree with the statement of"I seek out organic foods".
B) neither agree nor disagree with the statement of"I seek out organic foods". C)
completely disagree and mostly disagree with the statement of "I seek out organic
foods".


0.5
0.45
0.4
0.35
0.3
0.25
0.2
0.15
0.1
0.05
0










Probability of seeking out organic foods

e- 0 Completely disagree (1) O Mostly disagree (2) -- -----
, ---------------------------------------
-----------------------------------------------

, ......... ......... ........... .......... ....









Single Married Living with parents Others
Marital status of household head


Average (55.9%)


Probability of seeking out organic foods


SNeutral(3) ..........





0.25 .._ L2..__.... 24 .... 0.25


FEI-_IY:I-_in77E


Average (24.8%)


Single Married Living with parents Others
Marital status of household head


Figure 5-4. Continued


0.5
0.45
0.4
0.35
0.3
0.25
0.2
0.15
0.1
0.05'
0'










Probability of seeking out organic foods
0.5
0.45 .-.-.-.- U Completely agree (5) E Mostly agree (4)
0.4 ----------------------
0.35-----------------------
0.3.._ _. -7 ..

0.25
0.520 0.20
0.2 ..... .- 1.- Average (19.3%)
0.29; ._. 9 ... ............... 0.203
0.15
0.1
0.05

White / Black/African Others
Nonhispanic American

Race of household head
A
Figure 5-5. Impact of race of household head on seeking out organic foods. A) completely agree
and mostly agree with the statement of "I seek out organic foods". B) neither agree
nor disagree with the statement of "I seek out organic foods". C) completely disagree
and mostly disagree with the statement of"I seek out organic foods".


Probability of seeking out organic foods
1
0.9 ...-.- U Completely disagree (1) E Mostly disagree (2) .
0.8
0.7
._.0651 .0.57
0 5 _" _" .
0.4 .5 -
0.3'
0.2
0.1


White/Nonhispanic Black/African Others
American


**Average (55.9%)


Race of household head


Figure 5-5. Continued










Probability of seeking out organic foods
0.5-1
0.45- Neutral (3)
0.4 ------------------------
0.35
0.3 0'' .25 '' 0.25 0.2
0.25
0.2
0.15
0.1
0.05


White /
Nonhispanic


-------0.27----- -- -
5 0.25


EII


Black/African
American


Average (24.8%)


Others


Race of household head


Figure 5-5. Continued


Probability of seeking out organic foods
0.5
0.45 Completely agree (5) El Mostly agree (4) -
0.4
0.35
0.3
0.25 0.20 0.19- -
0.2 ..... ... ...
0.15
0.1
0.05






Income of household head


,Average (19.3%)











A


Figure 5-6. Impact of income of household head on seeking out organic foods. A) completely
agree and mostly agree with the statement of"I seek out organic foods". B) neither
agree nor disagree with the statement of"I seek out organic foods". C) completely
disagree and mostly disagree with the statement of "I seek out organic foods".











Probability of seeking out organic foods
1
0.9 A Completely disagree (1) O Mostly disagree (2) ----
0.8
0.6

0.6g -0-.055 -------0 U.6. ------- -..58.-.-
.. .. ........ ................... ..... ...
0.59 "
0.4
0.3
0.2
0.1


u '5 1, ,Cev


Average (55.9%)


Income of household head


Figure 5-6. Continued


Probability of seeking out organic foods
0.5
0.45- U Neutral (3) -.-.
0.4--------------------
0.35 ----------------------
------- 0~'- -' a:~5 -- -- -0-6 -- -- -- -- -
0.3" 025 0.25 06 0.23 0.24
0.25 .
0.2
0.15
0.1
0.05




Income of household head

Income of household head


Average (24.8%)


Figure 5-6. Continued











Probability of seeking out organic foods
0.51
0.45 Completely agree (5) E Mostly agree (4) -.-
0.4
0.35
0.3
0.25 0 .....0 0. 19 .----- ------ ---
0.1914
0. 15L
0.1

0.05

Less than or high College Graduate or Others
school professional
degree

Education of household head


Average (19.3%)










A


Figure 5-7. Impact of education level of household head on seeking out organic foods. A)
completely agree and mostly agree with the statement of"I seek out organic foods".
B) neither agree nor disagree with the statement of"I seek out organic foods". C)
completely disagree and mostly disagree with the statement of "I seek out organic
foods".


Probability of seeking out organic foods


11
9 Completely disagree (1) E Mostly disagree (2) -.....
8
7 0647-
0.5.6 -- -
6 .. .. ..
5
4
3
2
1

Less than or high College Graduate or Others
school professional
degree

Education of household head


..Average (55.9%)


Figure 5-7. Continued











Probability of seeking out organic foods
0.5-


0.35' ----------------------------------------------
0.45 *. Neutral (3)
0.4

0.3--------*-^-*--*---*--0--2-----------*-*--**
0.35
0.3
0.25 0.25 0.25
0.25 L*r ** .. Ln**
0.2
0.15 L
0.1
0.05

Less than or high College Graduate or Others
school professional
degree

Education of household head


*Average (24.8%)












C


Figure 5-7. Continued


Probability of seeking out organic food
0.5'

0.45 Completely agree (5)

0.4 ------------------------

0.35, -------------------------
0.3

0.25'
0.20
0.2 2- V*-*F7-;-i

0.15'

0.1
0.05


El Mostly agree (4) ......


0.20 0 1 0.21
"-2" nIQ __


Average (19.3%)


4 4+


Household size (members) A

Figure 5-8. Impact of household size on seeking out organic foods. A) completely agree and
mostly agree with the statement of"I seek out organic foods". B) neither agree nor
disagree with the statement of"I seek out organic foods". C) completely disagree and
mostly disagree with the statement of"I seek out organic foods".











Probability of seeking out organic foods
1
0V, -------------------------------------------
0.9 Completely disagree (1) O Mostly disagree (2) .-. -. -.

0.8

0.7
0.70 ----------- -- --.5------- 0-5-- --- --- .0-------------.-.54
0.59
0 ....-.-..-55 .-.--- .-.-5- .--- .-.-.-.5.-.- .--
0.6--,------ 0-^- .54^.
........ .................. .. ...... .. .......... .. ....
0.6
0.5

0.4

0.3

0.2

0.


1 2 3 4 4+

Household size (members)


average (55.9%)


Figure 5-8. Continued


Probability of seeking out organic foods
0.5

0.450 Neutral (3) -....
0.49 -----------------------------------------------------
0.4

0.35

0.3
0.24 0.25 0.25 0.25 0.25
0.25 0.24

0.2

0.15

0.1

0.05
0jI I
1 2 3 4 4+

Household size (members)


Average (24.8%)


Figure 5-8. Continued











Probability of seeking out organic foods
0.5
0.45 -. Completely agree (5) 0 Mostly agree (4) -.-. -.-
0.4------------------------------------------------------
0.35
0.3 ---.-..
0.25 ------ ---0----------------.21- -- -
0.2 t -- -;.--;"-; -" ............ -"- ;.; ;.******** *********7 .'-;Average (19.3%)
0.15
0.1. .... .... --.-.-.-.
0.1
0.05

With children under 18 No children under 18

Presence of children under 18
A
Figure 5-9. Impact of presence of children under 18 in household on seeking out organic foods.
A) completely agree and mostly agree with the statement of "I seek out organic
foods". B) neither agree nor disagree with the statement of"I seek out organic foods".
C) completely disagree and mostly disagree with the statement of "I seek out organic
foods".


Probability of seeking out organic foods

Completely disagree (1) O Mostly disagree (2) -----------



0.59
---------------------------------------0:54------------










With children under 18 No children under 18

Presence of children under 18


*Average (55.9%)


Figure 5-9. Continued











Probability of seeking out organic foods


0.5
0.45
0.4
0.35
0.3
0.25
0.2
0.15
0.1
0.05
0


.......- Neutral (3) -----





0..........0.2.........


*A


With children under 18 No children under 18

Presence of children under 18


Figure 5-9. Continued


Probability of seeking out organic foods
0.5
0.45 Completely agree (5) El Mostly agree (4) -. -.
0.4 LO.-- ------------------------------ ---.- --.- --.--
0.35 L*--------------------------------------------------
0.3L*--------------------------------------------------
0.4
0.35
003
0.25 0.-22- ----


0.15
0.1
0.05
0
Employed Employed part Not employed Others
time


Average (24.8%)











C















average (19.3%)


Employment status of household head
A
Figure 5-10. Impact of employment status of household head seeking out organic foods. A)
completely agree and mostly agree with the statement of"I seek out organic foods".
B) neither agree nor disagree with the statement of"I seek out organic foods". C)
completely disagree and mostly disagree with the statement of "I seek out organic
foods".











Probability of seeking out organic foods
1 1
)- Completely disagree (1) O Mostly disagree (2) ----------


.. 56. ._. ._... 0.57
.... --........ ------------------------------
6 .. ,........ -..'53................. ---.... .. .... .....
5


Employed Employed part Not employed
time

Employment status of household head


-Average (55.9%)


Others


Figure 5-10. Continued


Probability of seeking out organic foods


Neutral (3) ----




0.25 0.26 0.26 0.24








Employed Employed part Not employed Others
time

Employment status of household head


*Average (24.8%)


Figure 5-10. Continued


0.5
0.45
0.4
0.35
0.3
0.25
0.2
0.15
0.1
0.05
0











Probability of seeking out organic foods
0.5

0.45 Completely agree (5) E Mostly agree (4) -- --
0.4f ----------------------------------------------------
0.4

0.35 ---------------------------------------------------

0.3
0 ---------------------------------------------------

0.2..........................

0.15 ". ..

0.1

0.05


Northeast


Midwest


South


.Average (19.3%)


West


Census region A

Figure 5-11. Impact of census region of household head seeking out organic foods. A)
completely agree and mostly agree with the statement of"I seek out organic foods".
B) neither agree nor disagree with the statement of"I seek out organic foods". C)
completely disagree and mostly disagree with the statement of "I seek out organic
foods".

Probability of seeking out organic foods
1
0.- Completely disagree (1) E Mostly disagree (2) ....----......
0 ,-----------------------------------------------------
0.8

0.7
0.6 -- --------- -----
06, ........ ........ ........ .... Average (55.9%)
0.5
0.4
0.3

0.2
0.1'


Northeast


Midwest


South West


Census region


Figure 5-11. Continued










Probability of seeking out organic foods
0.5'
0.45' Neutral(3) ...........
0.4
0.35
0.35'' ----------------------------------------------------
S 0.25 0.24 0.25 0.25
0.25 *
0.2"
0.15
0.1
0.05
0,


Northeast


Midwest South

Census region


Average (24.8%)


West


Figure 5-11. Continued


Probability of seeking out organic foods
8
Completely agree (5) El Mostly agree (4)
7 U Neutral (3) U Completely disagree (1) -
O Mostly disagree (2)
6 ------------------ Q-5 ----
... .... 0,.5 ............. .... ...
6 ------------- -- ---------------------

4 --------------------


0.19 0.21. ...
0.19 0.21


---------0-25---- .-26--
-m- a


Yes No Yes No Yes No

SHOP GRO
A
Figure 5-12. Impact of grocery shopping places seeking out organic foods. A) shopping food in
grocery store. B) shopping food in warehouse. C) shopping food through internet
store. D) shopping food in mass merchandiser. E) shopping food in convenience
store. F) shopping food in farmer's market.











Probability of seeking out organic foods
8
Completely agree (5) E] Mostly agree (4)
7 Neutral (3) U Completely disagree (1) --------
E Mostly disagree (2)
6 .-0 5-6 -
. ..._. _. ._. ._._ ._ ._ ._. .............. .
5*----------------- --- -. -

4 ----------------- ------ -- -- ------
---------------------
0.25
0.20 0.19
2- -

1 -
0mm


Yes No


Yes No

SHOP WARE


Yes No


Figure 5-12. Continued


robability of seeking out organic foods

Completely agree (5) E Mostly agree (4)
Neutral (3) U Completely disagree (1)
E Mostly disagree (2) 0.56



............ ......... ----------

------------------ ---------0-.2

_ _0.19_. _. _.. ._._-------
i------- -------


Yes No


Yes No

SHOP INTE


-
S025




II


Yes No


Figure 5-12. Continued


Pr
0.8

0.7

0.6

0.5

0.4

0.3

0.2

0.1

0











Probability of seeking out organic foods
0.8
Completely agree (5) E Mostly agree (4)
0.7-- Neutral (3) U Completely disagree (1) ----....
E Mostly disagree (2)
0.6 ..-----------..-------....... --.-. .
0.50.54
0.5 -------------------- ------ --------------

0.4 ----------------- ------------------

0.3 ------------------- --------- 0(24- -0125 -
0.21
0.2
0.2 0.18. 21 "..n "

0.1 ........ ..
0.
Yes No Yes No Yes No

SHOP MASS
D
Figure 5-12. Continued



Probability of seeking out organic foods
0.8
Completely agree (5) ] Mostly agree (4)
0.7- Neutral (3) U Completely disagree (1) -
E Mostly disagree (2)
0.6 .------------ ....






0.2f -- ---
................ 54.......
0.6--------------------- ------------



0. -------- .......

0.3
0.21 0.19 .

0.1 -

0
Yes No Yes No Yes No

SHOP CONV

E
Figure 5-12. Continued



88










Probability of seeking out organic foods
8
Completely agree (5) 0 Mostly agree (4)
7 Neutral (3) U Completely disagree (1) --------
E Mostly disagree (2)
6..-.-.-.-.-.-.-.-.-.-.-.-.-.-..-.-.-.-.-.-.-.-..
0.48
5----------------------- --------------------

4----------------- --------------------


0.18
....- --- -5
^'|''*''i''"r /s sss ''' '~ ^ ^ J ^ ^ *'^ ^ ~ ^ ^


Yes No


Yes No

SHOP FARM


Yes No


Figure 5-12. Continued


Probability of seeking out organic foods
0.5
0.450 Completely agree (5) E Mostly agree (4) -.
0.49 -----------------------------------------------------
0.4
0.35 -----------------
0 .3 --..__.-.-.-.-.-.-.-.-.-.-... .._ --- -1 "
0.3 T.-6-.-..--.-..-. -.-- .-. .-23 -----
0.20
0.2 020------
0.15
0.1
0.05


<$50


$50-$100 $100-$200 $200-$400

Expenditures on grocery shopping


Average (19.3%)


$400+


Figure 5-13. Impact of expenditures on grocery shopping on seeking out organic foods. A)
completely agree and mostly agree with the statement of"I seek out organic foods".
B) neither agree nor disagree with the statement of"I seek out organic foods". C)
completely disagree and mostly disagree with the statement of "I seek out organic
foods".











Probability of seeking out organic foods


Completely disagree (1) O Mostly agree (2) ............




0.59
..0-.-.------.-55 -------------------- -
...............- ....... .. ........ ...5........................


<$50


$50-$100 $100-$200 $200-$400 $400+

Expenditures on grocery shopping


Figure 5-13. Continued


Probability of seeking out organic foods
0.5
0.45 ------------------------------------------------
0.45 Neutral (3)
0.4
0.35 -------------------------

0.3 ------------------0.26 -. -- 7 -7-
0.24 0.25 0.25
0.25 -----------.........

0.2

0.15

0.1

0.05

<$50 $50-$100 $100-$200 $200-$400 $400+

Expenditures on grocery shopping


Average (24.8%)


Figure 5-13. Continued










Probability of seeking out organic foods
0.55


0.35' ------------------------------------------1 0-33_ .....
0.35--- -


0.3 ....... .
0.20

0.15 .
0.1
0.05
0


'\raEC (19.3%)


0 1-3 4-6 7+

Servings of fruit per day

Figure 5-14. Impact of servings of fruit on seeking out organic foods. A) completely agree and
mostly agree with the statement of"I seek out organic foods". B) neither agree nor
disagree with the statement of"I seek out organic foods". C) completely disagree and
mostly disagree with the statement of"I seek out organic foods".


Probability of seeking out organic foods
1 --


Completely disagree (1) O Mostly disagree (2) -----------



0.60
0 "53 -----------0.56-------------- ---------------
....................... .................... .........................
---- --..
0.39

--- 1---


U..


0 1-3 4-6

Servings of fruit per day


*Average (55.9%)


Figure 5-14. Continued


0.9
0.8
0.7
0.6
0.5
0.4
0.3











Pro
0.5

0.45

0.4

0.35

0.3

0.25

0.2

0.15

0.1

0.05
0


abilityy of seeking out organic foods


.--- Neutral(3) ...........




------------------------------------------- 0-2-7- ---
0.25 0.25
S_.. _..f23._._......









0 1-3 4-6 7+

Servings of fruit per day


Average (24.8%)


Figure 5-14. Continued


Probability of seeking out organic foods


S* Completely agree (5) El Mostly agree (4) --


L ------------------------------------------------

LO----------------------------------------------

Lo ------------------------------ --w 0.2.3 ---- ---------------

0.19
L ----0-1
L0.15


Average (19.3%)


0 1-3 4-6 7+

Servings of vegetable per day
A
Figure 5-15. Impact of servings of vegetables on seeking out organic foods. A) completely agree
and mostly agree with the statement of"I seek out organic foods". B) neither agree
nor disagree with the statement of "I seek out organic foods". C) completely disagree
and mostly disagree with the statement of"I seek out organic foods".


0.5'

0.45'

0.4

0.35

0.3

0.25

0.2

0.15

0.1

0.05
0











Probability of seeking out organic foods
1

0.9- Completely disagree (1) O Mostly disagree (2) ---- --

0.8

0.7' 0.62
0 ,-------------------56------------------------
00.6.5 ...
.... ..................... ............. .. ...... ....
0.5

0.4

0.3

0.2
0.1

0 1-3 4-6 7+

Servings of vegetable per day


.Average (55.9%)


Figure 5-15. Continued


Probability of seeking out organic foods
0.5E
0.45 -- Neutral (

0.45 L
0.35 ----------------------
-.5 -'~-------------------------


3) ..........


0.24 0.25
... .... ....--... .. ...- ..-.. .- .









0 1-3 4-6 7+

Servings of vegetable per day


Average (24.8%)


Figure 5-15. Continued


0.3

0.25'

0.2

0.15

0.1

0.05
0










Probability of seeking out organic foods
0.5
0.45. Completely agree (5) E Mostly agree (4) .-. .
0.4 -
0 .4 .- .-.-.-.. . .... .... ... .. .. .

0.35 ---------------------------
0.3
0.31 ------------------------------------------------------.

0.25 -...-.-------.---.-.........-
0.19 019 _019. 18 019 019 0.19 0.19 0.20 0.20 019 019 Average (19.3%)
0.2 1. -'- : .... -- .. .. -. ... .
.1 .. .......... .. Average(19.3



0.05

1 2 3 4 5 6 7 8 9 10 11 12

Months
A
Figure 5-16. Impact of seasonality on seeking out organic foods. A) completely agree and
mostly agree with the statement of"I seek out organic foods". B) neither agree nor
disagree with the statement of"I seek out organic foods". C) completely disagree and
mostly disagree with the statement of"I seek out organic foods".


Probability of seeking out organic foods
1
0.9. Completely disagree (1) E Mostly disagree (2) ._._._._._ .
0.8 ---------------------------------------------------..
0.7 -----------------------------------------------------

0.6 0-56- -6557 -57 0.58 0-5 .5 56- -56- -0.56 0-56-- --5 0-56 -
Average (55.9%)
0.5 -
0.4-
0.3
0.2 -
0.1

1 2 3 4 5 6 7 8 9 10 11 12

Months
B
Figure 5-16. Continued










Probability of seeking out organic foods


* Neutral (3)


).25 0.25 0.25 0.24 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25









1 2 3 4 5 6 7 8 9 10 11 12


Months


Figure 5-16. Continued


Probability of seeking out organic foods
0.5


0.4---
0. .-._._._.. Completely agree


0.35 ------------
0.3

0.19
0.2 ..........

0.1
0.1
0.05


Completely
disagree


Mostly
disagree


Neutr


(5) 0 Mostly agree (4) -





0 0-22------ -0.22 -








al Mostly agree Completely
agree


Count calories

Figure 5-17. Impact of "count calories" on seeking out organic foods. A) completely agree and
mostly agree with the statement of"I seek out organic foods". B) neither agree nor
disagree with the statement of"I seek out organic foods". C) completely disagree and
mostly disagree with the statement of"I seek out organic foods".


(24.8%)


*Average (19.3%)











Probability of seeking out organic foods
1
0.9 ---------. Completely disagree (1) E Mostly disagree (2) -
0.8

0.7 0.61
0.69' --8~4 ---- --0.47- -- -- -
0.6 ... .........................0.52........... ......
0.5
0.5 .-.-.-- .- -

0.4 .
0.3 -
0.2 .
0.1

Completely Mostly Neutral Mostly agree Completely
disagree disagree agree


.Average (55.9%)


Count calories


Probability of seeking out organic foods
0.5
0.45 --------- Neutral (3) ....
0.4-----------------------
0.35
0.3 ---------------------26--------0-26---- 026-
0.23
0.251 1 1 11U 6 d
0.2 '
0.15
0.1
0.05

Completely Mostly Neutral Mostly agree Completely
disagree disagree agree


*Average (24.8%)


Count calories


Figure 5-17. Continued










Probability of seeking out organic foods
0.5
0.45 .-.-.-.- Completely agree (5) El Mostly agree (4)
0.4
0.35
0.3 .. 0.25
0 .2 5 _..__. .
0.19
0. ........................ "
1114


0.05I


Completely Mostly Neutral Mostly agree Completely
disagree disagree agree

Eating fresh foods


"Average (19.3%)










A


Figure 5-18. Impact of "eat fresh foods" on seeking out organic foods. A) completely agree and
mostly agree with the statement of"I seek out organic foods". B) neither agree nor
disagree with the statement of"I seek out organic foods". C) completely disagree and
mostly disagree with the statement of"I seek out organic foods".

Probability of seeking out organic foods
1


0.8 I-------------------
0.69
0.7 ------ --- --- 0-.63----"-0



0.4
...................................................Average (55( 9


0.3
0.6'""6~' -------- ---,5 ------.- -------







0.2 -
0.1

Completely Mostly Neutral Mostly agree Completely
disagree disagree agree


%)


Eating fresh foods


Figure 5-18. Continued










Probability of seeking out organic foods
0.5
0.45 .-.-.-.- Neutral (3)
0.4 ------------------------------------------------
0.35
0.28
0.3 .----------------- "0-2------ 0r26-----------.
0.23 0.25
0.2.


0.15
0.1
0.05

Completely Mostly Neutral Mostly agree Completely
disagree disagree agree


*Average (24.8%)


Eating fresh foods


Figure 5-18. Continued


Probability of seeking out organic foods
0.5
0.45-1 -.-.--.- Completely agree
0.4-------------------------


(5) E Mostly agree (4) .
.......................


0.25


......03- .......
0.10




Completely Mostly Neutral Mostly agree Completely
disagree disagree agree


Read label A
Figure 5-19. Impact of "read label" on seeking out organic foods. A) completely agree and
mostly agree with the statement of"I seek out organic foods". B) neither agree nor
disagree with the statement of"I seek out organic foods". C) completely disagree and
mostly disagree with the statement of"I seek out organic foods".


0.35
0.3
0.25
0.2
0.15
0.1
0.05
0


'Average (19.3%)











Probability of seeking out organic foods
1
0.9 --------- U Completely disagree (1) E Mostly disagree (2) -
0.8 ----.7--------------------------------------------

0.8
0 .7 .. .. .. .. ...0-65.. .. .
0.58 0.59
0.6 ...............................
0.
0.5 -----0.46-
0.4

0.3 -
0.2 -

0.1
0.1

Completely Mostly Neutral Mostly agree Completely
disagree disagree agree


-Average (55.9%)


Read label


Probability of seeking out organic foods
0.5
0.45------ N
0.4-------------

0.35 ------------
039.___...___ ----------------
0. 0.25
0.325 f. .............. .... ..

0.2
0.15
0.1
0.05


Completely
disagree


Mostly
disagree


Neutr


utral (3)



0.28
...... .. ..............
0.25









al Mostly agree Completely
agree


Average (24.8%)


Read label


Figure 5-19. Continued










Probability of seeking out organic foods
0.5
0.45 .-.-.-.- Completely agree (5) E0 Mostly agree (4)
0.4
0.35 ------------------------------------------------
0.3
0 .2 5 ..
0.19 0.19 019
0.2 ....14 .
0.15
0.1

0.05

Completely Mostly Neutral Mostly agree Completely
disagree disagree agree


.Average (19.3%)


Buy from certain stores


Figure 5-20. Impact of "buy from certain stores" on seeking out organic foods. A) completely
agree and mostly agree with the statement of"I seek out organic foods". B) neither
agree nor disagree with the statement of"I seek out organic foods". C) completely
disagree and mostly disagree with the statement of "I seek out organic foods".


Probability of seeking out organic foods
1
0., -----------------------------------------
0.9 .-.-.-.-. U Completely disagree (1) O Mostly disagree (2) _
0.8
0.7 -----61---------------------
0.6
0.6. ... 56... -5-- -0...... -56...... -- 7-.-
0.5g
0.4 -
0.3 -
0.2 -
0.1

Completely Mostly Neutral Mostly agree Completely
disagree disagree agree

Buy from certain stores


.Average (55.9%)













B


Figure 5-20. Continued










Probability of seeking out organic foods
0.5
0.45,.-.-.-.- Neutral(3)
0.4 -----------------------
0.35
0.3 0.23
0.23 0.25 0.25 0.25 0.25
0.25

0.15
0.1
0.05

Completely Mostly Neutral Mostly agree Completely
disagree disagree agree


*Average (24.8%)


Buy from certain stores


Figure 5-20. Continued










Probability of seeking out organic foods
0.5'
0.45, ---. Completely agree (5) E Mostly agree (4) --
0.4
0.35
0.3
0.25 ---------------------------------------------- 0.21
0.21' 0. ..... .
0.15 -- ).12-
0.08
0.1
0.05
01
Completely Mostly Neutral Mostly agree Completely
disagree disagree agree


-Average (19.3%)


Go out of way to get certain
types of produce

Figure 5-21. Impact of "go out of way to get certain types of produce" on seeking out organic
foods. A) completely agree and mostly agree with the statement of "I seek out organic
foods". B) neither agree nor disagree with the statement of"I seek out organic foods".
C) completely disagree and mostly disagree with the statement of "I seek out organic
foods".










Probability of seeking out organic foods
1
0.9 -------- Completely disagree (1) E Mostly disagree (2)
0.8 ---.-73------------------------------------------
0.7 -----0 -------------------------------
0.6 .. o.- -0:55-...................
...... ................ Q.-5 .......... 0.74 .....
0.5 -. ...- .. ...
0.4
0.3
0.2
0.1
0
Completely Mostly Neutral Mostly Completely
disagree disagree agree agree

Go out of way to get certain
types of produce


Probability of seeking out organic foods
0.5
0.45 -------- Neutral (3)
0.4
0.35
0.35 ---------------------- -------------------------
S0290. ..
0.3 ---------------------- 0-.27 .-_0--- ......S -------.
0.5 3..--.-.-.-.-.-. .-. -o .2 -

0.19

0.15
0.1
0.05

Completely Mostly Neutral Mostly Completely
disagree disagree agree agree

Go out of way to get certain
types of produce


Average (55.9%)














B











Average (24.8%)


Figure 5-21. Continued










Probability of seeking out organic foods
0.5
0.45 (.-.-..- U Completely agree (5) Ei Mostly agree (4) -
0.4
0.35
0.3
0.2,1------------------------------------------------

0.25 ..........
0.20 0.20 0.19 0.19
0.2 -;- ;
0.15
0.1
0.05

Completely Mostly Neutral Mostly agree Completely
disagree disagree agree

Eating fresh fruits and vegetables


.Average (19.3%)










A


Figure 5-22. Impact of "eat fresh fruit and vegetables" on seeking out organic foods. A)
completely agree and mostly agree with the statement of"I seek out organic foods".
B) neither agree nor disagree with the statement of"I seek out organic foods". C)
completely disagree and mostly disagree with the statement of "I seek out organic
foods".

Probability of seeking out organic foods
1
0.9 .-.-.-.- U Completely disagree (1) E Mostly disagree (2) -
0.8
0.7'- 0962
0.6- 0-.55--------55----- .--6-------0.56-.-
.disagre dgAverage (55.

0.ing freh fs ad
0.4 ..
0.3 .
0.2 .... .
0.1

Completely Mostly Neutral Mostly agree Completely
disagree disagree agree

Eating fresh fruits and vegetables


9%)


Figure 5-22. Continued










Pro
0.5
0.45
0.4
0.35
0.3
0.25
0.2
0.15
0.1
0.05
0I


abilityy of seeking out organic foods

.-.-..- Neutral (3)





0.25 0.25 0.25 0.25
0 23









Completely Mostly Neutral Mostly agree Completely
disagree disagree agree

Eating fresh fruits and vegetables


*Average (24.8%)













C


Figure 5-22. Continued


Probability of seeking out organic foods
0.5
0.45,- -------- U Completely agree
0.4-


0.35
0.3
0.25
0.2
0.15
0.1
0.05
V


(5) El Mostly agree (4) -


- - -22- _-0.23 .-
........ ...... ........... ...... ....2 ...... 0..23 ..

0.14

- -- -- -- --


.Average (19.3%)


Completely Mostly Neutral Mostly agree Completely
disagree disagree agree

Feel healthier than my peers A

Figure 5-23. Impact of "feel healthier" on seeking out organic foods. A) completely agree and
mostly agree with the statement of"I seek out organic foods". B) neither agree nor
disagree with the statement of"I seek out organic foods". C) completely disagree and
mostly disagree with the statement of"I seek out organic foods".









Probability of seeking out organic foods


.-.-.-.-. U Completely disagree (1) E Mostly disagree (2) -

" "D63 ~ "--" o 60---------------------------------

---- --------- -- .----- -- ----------------
..-................ .. ...... ,..................

:H :::M::: r: ~"


Completely
disagree


Mostly Neutral Mostly agree Completely
disagree agree
Feel healthier than my peers


.Average (55.9%)


Probability of seeking out organic foods
0.5
0.45 .-----.--- -
0.4 .--------
0.35-------------
0.3 -------------------------
0.23 0.24 0.25

0.25
0.15
0.1
0.05


Completely
disagree


Mostly Neutr
disagree
Feel healthier tha


Jeutral (3)



--- ----.7- -- 0.-- .27_.








al Mostly agree Completely
agree

in my peers


Figure 5-23. Continued


-Average (24.8%)










Probability of seeking out organic foods
0.5
0.45 -. .-.-- Completely agree (5) E Mostly agree (4)
0.4
0.35------------------------------------------------
0.3



0.15
0.25'" ~---~--~-~-~-'-'-'-'-'-0------2r---01 ------



0.11

0.05

Completely Mostly Neutral Mostly agree Completely
disagree disagree agree


. Average (19.3%)


Exercise at least 3 times a week


Figure 5-24. Impact of "exercise at least 3 times a week" on seeking out organic foods. A)
completely agree and mostly agree with the statement of"I seek out organic foods".
B) neither agree nor disagree with the statement of"I seek out organic foods". C)
completely disagree and mostly disagree with the statement of "I seek out organic
foods".


Probability of seeking out organic foods
1
0.9 .-.-.-. U Completely disagree (1) O Mostly disagree (2) -
0.8
0.7 1 "- 0.60 ......... .. .................... .. ........
0.60 0.58 0.57

0.51
0.4 -
0.3 -
0.2 -
0.1- -.


Completely Mostly Neutral Mostly agree Completely
disagree disagree agree

Execise at least 3 times a week


'Average (55.9%)













B


Figure 5-24. Continued










Probability of seeking out organic foods
0.5
0.45-- N
0.4 ------------
0.35
0.3 -- --- --4 0-1 26
0.25 .
0.2
0.15,
0.11

0.05
0 II


Completely
disagree


Mostly
disagree


Neutral


eutral (3)




- 026 ---- 0.25--









l Mostly agree Completely
agree


Execerse at least 3 times a week


Figure 5-24. Continued


Probability of seeking out organic foods
0.5
0.45, Completely agree (5) E Mostly agree (4) -
0.4
0.35 -----------------------
0.3
0.25
0.19 0.19
0. 2, -
0.2 *.* -: rl ........... .

0.1
0.05

Completely Mostly Neutral Mostly agree Completely
disagree disagree agree


Experiment with new foods


Figure 5-25. Impact of "experiment with new foods" on seeking out organic foods. A)
completely agree and mostly agree with the statement of"I seek out organic foods".
B) neither agree nor disagree with the statement of"I seek out organic foods". C)
completely disagree and mostly disagree with the statement of "I seek out organic
foods".


*Average (24.8%)


'Average (19.3%)










Probability of seeking out organic foods
1
0.9 -.-.-.-. Completely disagree(
0 .8 -- -.-.-.. .. .-
0.68
0.7 ---.......
0.60
0.6," ----- 0-5-5
0.5
0.4
0.3
0.2
0.1
0


Completely
disagree


Mostly
disagree


Neutr


S(1) E Mostly disagree (2) .-




------0.55- ------------










al Mostly agree Completely
agree


Experiment with new foods


Figure 5-25. Continued


abilityy of seeking out organic foods

-.-.- Neutral (3)




206. .._0.----- -27- _.
0.24
0"21"...............








Completely Mostly Neutral Mostly agree Completely
disagree disagree agree

Experiment with new foods


Average (24.8%)


Figure 5-25. Continued


.Average (55.9%)


Pro
0.5
0.45
0.4
0.35
0.3
0.25
0.2
0.15
0.1
0.05
0I










Probability of seeking out organic foods
0.8S
Completely agree (5) E Mostly agree (4)
0.7 Neutral (3) U Completely disagree (1) -
E Mostly disagree (2)
0.59
0.6 -. 5..4. .. .. .. .
0.50.54
................"I .. ........
0.5 ----------------------


0.3 ---------- ----- -- --0.2-4


0.2, W -- -

0.1 -

0
Yes No Yes No Yes No

Do not have blood pressure
A
Figure 5-26. Impact of health concerns of household on head seeking out organic foods. A) no
one in household has blood pressure. B) no one in household has diabetes. C) no one
in household has cholesterol problem. D) no one in household has food allergies. E)
no one in household has obesity problem. F) no one in household has limited physical
mobility problem. G) no one in household has sight/hearing problem.

Probability of seeking out organic foods
0.8
Completely agree (5) l Mostly agree (4)
0.7 E Neutral (3) U Completely disagree (1) -
E Mostly disagree (2)
0.59
06---- ---------------- r.5,5------------------ ------- --
0.6 .------.-------- -------------.5


0.4- -----------------

0.3 .---------------- --------0.25--- -04
0.20 "".


0.1 ------ -------


Yes No Yes No Yes No

Do not have diabetes
B
Figure 5-26. Continued










Probability of seeking out organic foods
0.8
Completely agree (5) El Mostly agree (4)
0.7-- Neutral (3) U Completely disagree (1) -- --
E Mostly disagree (2)
0.6 --.-------- ----------. 55 -0.5-7 .. ..-.-. .

0.5------------------- ------

0.4------------------- ------

0.3 -----------------. --. ------ .25
0.20 0.19
0.2

0.1


Yes No Yes No Yes No

Do not have cholesterol problems
C





Probability of seeking out organic foods
0.8
Completely agree (5) E Mostly agree (4)
0.7 U Neutral (3) U Completely disagree (1) -
E Mostly disagree (2)
0.6 ---------------------5 ----------7-.-.-- --- ---

0.5 ---- --- -- -- --- -- --- ---

0.4-------------- ---- -------

0.3 ..------------ -. -- --.. .. 0.25-". .-2. -
0.22
0. 0.19

0.1 --- ---


Yes No Yes No Yes No

Do not have food allergies
D
Figure 5-26. Continued










Probability of seeking out organic foods
0.8
Completely agree (5) E Mostly agree (4)
0.7-- Neutral (3) U Completely disagree (1) -
o Mostly disagree (2)
0.6 -------------------- 0.564--56 ----------------------

0.5 ------------------- ....................

0.4--- -------------- ....................

0.3 ------------------- ------- 0-25- --0.25-
0.19 0.19 "
0. .- --- -

0.1 -j---- .- ---

0.
Yes No Yes No Yes No

Do not have obesity problems
E






Probability of seeking out organic foods
0.8
Completely agree (5) EM Mostly agree (4)
0.7-- Neutral (3) U Completely disagree (1) ---
E Mostly disagree (2)


0. ---6-------------- -------------

0. ..----------------- ------------------- -

0.3 ------------------ ------ 0-.25 -----0.24
0.19 0.18


0 .1 -

0.
Yes No Yes No Yes No

Do not have mobility problems

F
Figure 5-26. Continued



112










Probability of seeking out organic foods
0.8
U Completely agree (5) El Mostly agree (4)
0.7 0 Neutral (3) U Completely disagree (1) ------
E Mostly disagree (2)
0.6 .50,58

0.5 ----------------- -------------------

0.4----- -----3------ -------------------

0.3 ----- --------------- ------ 0-25-- -0.24-
0.20 0.18
0.21 .............

0.1


Yes No Yes No Yes No

Do not have sight/hearing problems

G
Figure 5-26. Continued













Serve Fru---
B F r e -- I- -I- - -
B L a b .. I.. -- - -, - - - -
B Lab ---
B W a y -- I. -- I. -- - -- -- -

B_New ----
Expenditure -
Race
B H It --

ServVeg
Shop_Farm
Education
Cal
Income
B_Exe -
Employment -
Shopn_ nte --_- L -
B_Fv
BSt
Hit_Ag
Hwd
Hit_Bp
Chi
HitDb
Marital
ShopMass
Shop_Gro
M o n t h s- ---
Region
Shop_Conv
Hit_Hr
Gender
Hit Mb
Shop_Ware --
Hit CI
HH i t CB p : - :I:-- : --: - --: - --: : - --:: : - -








Hit Ob

0 0.05 0.1 0.15 0.2 0.25 0.3

Probability of Seeking Out Organic Foods
Completely Agree (5)-Average 0.0688

A

Figure 5-27. Ranking of factors impacting the likelihood of seeking out organic foods. A) Completely agree (5). B) Mostly agree (4).

C)Neither agree nor disagree. D) Mostly disagree. E) Completely disagree.










114













B_Way I
B Lab
B Fre
Serv Fru
age
B F r e - .. . - - -


B_New -
B HIt
Expenditure
E x p e n d it u r ee - --. I - -. --.- - --- - -. I .- - -
Serv_Veg m m
Race ---------------------------- -----------
Education
E d u c a t io n - -- - - - - - --.. . .
Cal -------------------------------------- ------------------------------
Shop_Farm
B_Exe
Income a
B Fv
B F v - --.I- - --.- -.- --.- - - ---- - --.. . .
Employment ------------
B St
Shop_lnte
HIt_ Ag
Hwd
HIt_Bp
Chi
HItDb
marital
ShopMass
Shop_Gro
Months ------------
Region
Shop_Cony
S H it_ o n ---, . .- -.. . .- --N . - - - - -





HIt_Hr
gender
HIt Mb
Shop_Ware
Hit Cl
HIt ob -

0 0.05 0.1 0.15 0.2 0.25 0.3


Probability of Seeking Out Organic Foods
Mostly Agree (4)-Average 0.1242

B

Figure 5-27. Continued












115













B_Way -
B Lab
B Fre
B New
Age
ServFru
B Hit
Education
Serv_Veg ------------ -
Expenditure
Cal
Race
B_Exe
Shop_Farm
Income
B Fv
BSt
Employment
Hwd
Shop_lnte ----------------------------------------
HIt_Bp
Chi

HItDb
Marital
Shop_Mass
Months
Shop_Gro "
Region
Shop_Conv
Hlt_Hr
HItMb
Gender
Hit C -
Shop_Ware
Hit Ob

0 0.05 0.1 0.15 0.2 0.25 0.3


Probability of Seeking Out Organic Foods
Neither Agree or Disagree (3)-Average =.2408

C

Figure 5-27. Continued












116













S e r v F rr u r - - - -. - - --.. ^ ..-- - .- - -
B F e --------------------------- --------------------------- --- --------------------------- -- -- ---- ----------- ----------------------------
B_Fre --------------------------- --------------------------- ---------------------------- ----------------------------.------ .----------- ---------------------------
B --------------------------- I ---------------------------- I ----------------------------- ----------------------------- I ---.-- ------------- ---------------------------

RAge --------------------------- --------------------------- ------------------------------ --------------------------- ^^ ----------------- ---------------------------
Expenditure --------------------------- --------------------------- ---------------------------------------------------------- ...----------------- ----------------------------
R e --------------------------- ---------------------------- --------------------------------------------------------- -- J------ ---------- -------------------------- -
BSWay --------------------------- --------------------------- ---------------------------- ---------------------------- ----- ------- ---------------------------
Shop_arm --------------------------- ---------------------------- ----------------------------- ---------------------------. ---- ------------------ ---------------------------
B H it - - -- -



Bmpoy ent --------------------------- ---------------------------- ---------------------------- ---------------------------- .-----^H------------------r ----------------------------
S e r v V e g - -. ..-- - .-- - .-- - .- - --.. - .
C a l -I-- - - M -
S h o p I n t e -- - ..-- - -- - .- .- - -- .I.-- - -.I .- .- .- - .
Employment --------------------------- --------------------------- ---------------------------- --------------------------------..------------------ ---------------------------

H ltco m ge --------------------------- --------------------------- ------------------------------ ------------------------------ ----- ---------------- ---------------------------
Serv Fru
B Fre
B Lab
Age -











B E x e - ..-- - .I .-.-- - .-- .-- - - -.I .- - -
Expenditure
Race
BWay -
Shoparm
tB p Hit
B_New













Marital --------------------------- --------------------------- ---------------------------- -----------------------------. ------ ------------------- ---------------------------
Serv b _Veg















Shop_I oass I --------------------------- -- ---------... .---------------------------- ---. ------ ----------------- ---------------------------
Fli op Co vp ------------------------ -- -- ------------------------------------------ -------.------ ------------------ ----------------------------
Stio ---------------- ------------ -------------------------- -- ---------------- -- --------------------
Shop_ nte -
















Months ---------------------------- --------------------------- ---------------------------- ---------------------------- ------- ----------------- ---------------------------
Employment
Income
B Exe
HIt_ Ag














G ender --------------------------- --------------------------- -------------------------------- -------------------------- -------- ------------------- ---------------------------

ShopWar e ----------------------- ------------------- ---------------------------- ------
S Hop_ ar Mb --------------------------- I --------------------------- ---------------------------- ---------------------------- -------- ------------------ ---------------------------

H l b ------------------------ -------------------------------------------------------- ------j-------------------
HIt_Bp



















0 0.05 0.1 0.15 0.2 0.25 0.3

Probability of Seeking Out Organic Foods
Mostly Disagree (2)-Average .2155
B_St
Chi
B_Fv
Marital
ShopSro









HItDb
Shopass









ShopFigure 5-27. Continued






















117
Region
Months
HIt_Hr
Gender
Hit Mb
ShopWare -
HIt Cl
HIt Ob

0 0.05 0.1 0.15 0.2 0.25 0.3

Probability of Seeking Out Organic Foods
Mostly Disagree (2)-Average .2155

D

Figure 5-27. Continued











117














BWay
B Lab -
B Fre

Serv Fru -
B_New- M
B Hit
Expenditure L
ServVeg
Education
Race
--a- -'.-- -
Cal -
Shop_Farm
B Exe
S h o p F a r m - - -- - - -..- - -.- - - .

Income
B_Fv
B St- -
Employment
Shop_lnte
HIlt_ Ag
Hwd
HIlt_Bp
Chi
HIt_, -
Marital
ShopMass
S h o pp G r o - -.L. - --. - --.- --.- I .- --.. . .-
Shop_Gro
M onths- -- I
Region
S h o p C o n y - - - - - I.. -.. . .- -.. .-m- -.. .- -.. . .
ShopCony
HIt_Hr
Gender .
Hit Mb
ShopWare
HIt Cl
HItob O b

0 0.1 0.2 0.3 0.4 0.5


Probability of Seeking Out Organic Foods
Completely Disagree (1)-Average 0.3433

E

Figure 5-27. Continued













118









CHAPTER 6
SUMMARY, CONCLUSION AND IMPLICATIONS

With growing availability, organic purchases have been changing from a lifestyle choice of

a small group of consumers into a more popular choice with indications from earlier studies that

two-thirds of American consumers purchase organic products at least occasionally. But the

organic market share at retail food outlets remains still quite small compared to that of

conventionally grown products. To aid in the effective marketing of organic, it would be

insightful to better understand consumer preferences and identify the underlying motivations

behind organic food purchases. This studies shows that among shoppers "seeking out organic

food", the level of purchases within a two-week shopping periods is likely considerable less than

the two-thirds suggested above. Granted, a fundamental difference is in the period specified for

defining the purchasing time span (i.e., two-week say versus a year). This study concentrates all

decisions taking place within the two-week shopping window.

Instead of relying on the simple measure of "did you buy organic foods or not", an

alternative approach was utilized in this study. Consumers were asked if they would like to seek

out organic products with a measure of intensity as a response option. An underlying assumption

was that the intensity of seeking out organic and organic food purchases were highly correlated,

recognizing that within the data set available that assumption could not be tested. Households

responded to the statement "I seek out organic foods" with a five-point Likert scale (where a 5

indicates "completely agree" and 1 indicates "completely disagree") in the questionnaire. Given

that intensities were ordered binary values, determining the probabilities of seeking out organic

was a classical ordered probit problem. Probabilities for five levels of agreement were simulated

based on the probit models given a particular set of conditions of explanatory variables. By









comparing the conditional probabilities, both the direction and magnitude of the effects of the

variables being controlled were estimated.

Estimates suggested that demographic variables, except age, were not as important as

expected in explaining and predicting households' likelihood of seeking out organic foods.

Nevertheless, several other factors were shown to contribute significant effects on seeking out

organic foods, especially households' reported behavior and attitude attributes.

Selected demographic variables such as age and ethnicity partially influenced the

propensity to value organic products. The results found negative associations between age and

the propensity towards organic. The consumer segment with the highest propensity to seek

organic products was among the younger population between 18 and 24 years old, whereas

consumers over 65 years old were the least likely to seek out organic. Consumers above age 45

became much less likely to seek organic compared to the average likelihood (19.2%). While

younger population would be the main marketing target for organic products in general,

marketing to older populations could also be beneficial, despite, their lower likelihood of seeking

out organic because of the substantial proportion (e.g., people who are older than 45 years

represented 44.6% of the total number of respondents, and those over 65 represented nearly

14%) of the population in these age segments.

The Hartman Group (2006) suggested: "more than just advertising products and price, it's

an opportunity to connect with the ethnic groups through unique messaging that resonates at the

cultural level". Based on the results of this study, two ethnic groups were relatively more likely

to seek organic foods: Asian Americans (with a probability of 27%) and, to a lesser extent,

Black/African Americans (with a probability of 20%). However, based on the total

representation in the population (63%), the Caucasian American consumers was a segment that









organic producers and marketers cannot afford to ignore even though this group tended to show

less interest in organic relative to the other ethnicities.

Although specific income and education levels were characteristics that could sometimes

be targeted, the impacts of these factors on seeking out organic were mixed and relative small in

the differences across each controlled variable. The likelihood of seeking organic declined

somewhat in higher-income groups. There was little evidence that education levels were

positively correlated to organic propensity owing to the insignificant effect of having college

degree, but shoppers with graduate or professional degrees were slightly more inclined to choose

organic. Consumers with two or more members in the household were relatively more likely to

seek out organic than single household shoppers. Surprisingly, households with children under

the age of eighteen displayed much less likelihood of seeking organic.

Store choice was an important factor in explaining propensities to consume organic

products. Households who grocery shopped on internet stores and farmer's markets presented the

highest likelihood of seeking out organic foods, while those who shopped in grocery stores, mass

merchandisers, or convenience stores were less likely to search for organic. These results

suggested that supplementary types of retail places with organic availability could promote

organic consumption effectively.

The number of daily servings of fruit indicated the most important single factor this study

identified in the propensity to seek out organic, which suggested the promotion in organic

fruits could increase the demand for organic products. However, since those respondents who did

not consume any fruit per day still had a likelihood higher than average levels, the benefits might

not be as incremental as other significant attributes. Despite little evidence of income's impact

on the probability of seeking out organic foods, there was evidence supporting that the amount of









expenditures on grocery shopping had a positive impact on organic food preference. Households

who spent more money on grocery shopping were more likely to seek organic. Clearly, in the

case of organic food consumption, grocery budget constraints played a more important role on

explaining the propensity to spend on the less staple goods of which organic most likely fitted.

That is, the results suggested that buying organic was a second tier in the consumer preference

after spending on the more stable goods. This was true to the extent that expenditures reflected

more latitude in the shopper choices.

Most consumers' socio-demographic characteristics had limited influence on explaining

and predicting consumer propensity to seek organic foods. However, several reported behavior

and attitude attributes turned out to have significant impacts on organic foods choices, such as

concerns about calories; eating fresh food rather than packaged foods; reading ingredients of the

food on labels when buying; going out of the way for certain types of produce; feeling healthier

than his or her peers; and frequently experimenting with new foods. A positive connection was

presented between the frequency of eating fresh foods rather than packaged food and likelihood

level of seeking out organic. Households who reviewed the ingredients on product labels when

buying foods and those who would like to experience new foods are more inclined to seek out

organic. Thompson (1998) suggested that the decision of purchasing food away from home

could be a potentially important issue. Moreover, our findings confirmed a substantial difference

in the probability of seeking organic between households who went out of the way to get certain

types of produce and those who did not.

The results also implied that people on diets might be more interested in organic foods,

given that households who counted the number of calories they ate each day were more likely to

seek out organic foods. A habit of exercising had mixed effect on organic' inclination, given









that people with flexible (less frequent) exercise schedules were slightly more likely to seek

organic while those who exercised at least three times a week were less likely to seek organic.

In sum, our findings implied that potential gains could result from efforts to target the shoppers

with those behavioral characteristics rather than simple economic and demographic traits.

Finally, this study provided weak evidence that health concerns such as high blood

pressure, diabetes, etc. were factors explaining the potential for organic food consumption.

Health concerns were expected to be significant contributors on the propensity to value organic

foods; however, the results indicated that only a concern about food allergies had a positive

impact on seeking out organic.

This study was probably the most current in terms of the database and extensiveness of the

data since nearly 38,000 observations were included through March 2010. While many factors

seemed to confirm results from other studies, the role of health was surprisingly weak given the

generally reported perception that organic were healthier. While healthy was a scientific

measure and the health aspects of organic might have both factual and perception components,

health generally had little impact on the probabilities of "seeking out organic foods."

The main limitation of the study was the stated propensity on seeking out organic foods

instead of the actual purchases was evaluated. But, since the awareness of organic was expected

to be the significant shifter of the probability of organic consumption, it would have been

insightful if we provided additional information about consumers' organic knowledge and

subsequent actual purchases of organic foods. Secondly, some additional environmental and

social benefits of organically produced products perceived by consumers might also be important

factors in explaining organic food demand, which was not confirmed in this study due to the lack

of such information available in the survey. In conclusion, organic foods belonged to products









with credence attributes and were generic in nature, hence, cooperation from both the organic

industry and government played a critically important role in the promotion of organic food

markets.

Finally, there was still much within the reported models that could be discussed and/or

simulated in more detail. For example, we did not consider the combined effects of changing

several variables. Likewise, a number of additional tests between levels within each category

could have been completed although we still feel that the graphed probabilities were more

revealing. Some of those tests would be reported in subsequent papers.









APPENDIX A
ORGANIC SURVEY VARIABLES


Table A-1. Organic survey variables
Variable Description
AGE age of household head (18+)
GENDER gender of household head
RACE ethnicity
CHL with children under 18 years
EDUC highest education level
EMPLY employment
INCOME household income (dollars)
MARITAL marital status
HWD house size (number of members)
STATEO census region
SERVFRU servings of fruit do you consume in typical day (#0-10)
SERV_VEG servings of vegetable do you consume in typical day (#0-10)
EXPEND expenditures on grocery shopping within a week (dollars)
SHOP_GRO shopping for food in grocery store
SHOP_WARE shopping for food in warehouse
SHOPINTE shopping for food in internet grocery store
SHOPMASS shopping for food in mass merchandiser
SHOP_CONV shopping for food in convenience store
SHOPFARM shopping for food in farmers' market
BHV EXERCISE I exercise at least 3 times a week
CALORIES I count calories
BHV_LABEL Read ingredients on labels of the foods I buy
BHVHLTH I feel healthier than peers
BHVNEWFOOD I frequently experiment with new foods
BHVFRE I eat fresh foods much more frequently than packaged food
BHVFRUVEG I eat fruits and vegetable more than other people my age
BHVWAY I go out of my way to get certain types of produce
BHVSTORE I prefer to buy produce from certain stores
HLTBLOOD4 No one has high blood pressure in household
HLT DIABE4 No one has diabetes in household
HLT_CHOLE4 No one has high cholesterol in household
HLT_ALLEG4 No one has food allergies in household
HLT_OBEST4 No one has obesity in household
HLTMOBIL4 No one has limited physical mobility in household
HLT_HEAR4 No one has significant sight or hearing impairment in
household
MTHS Months from January to December









APPENDIX B
CORRELATION COEFFICIENTS

Table B-1. Correlation coefficients of explanatory variables
XINC XEDU RACE XAGE XGEN XMAR
XINC 1.00000
XEDU 0.22848 1.00000
RACE 0.00423 0.04732 1.00000
XAGE 0.06228 0.03379 -0.13037 1.00000
XGEN -0.08627 -0.10196 -0.06012 -0.06012 1.00000
XMAR -0.06927 -0.04981 -0.02460 0.35995 0.09955 1.00000
XEMPLY -0.05519 -0.09666 -0.05112 0.25204 0.19017 0.07333
XEXPD 0.13403 0.05154 0.00181 0.00434 -0.03337 0.02585
XSERFRU 0.04641 0.01539 0.05075 -0.03501 0.04574 0.02410
XSERVEG 0.05194 0.05544 -0.01964 0.05445 0.01694 0.03009
XHWD 0.07031 -0.09159 0.09219 -0.30972 0.06689 -0.10156
XCHL -0.00220 -0.03445 0.08043 -0.31180 0.06208 0.00791
XSHOP GRO 0.08411 0.02719 -0.03611 0.12073 -0.00522 0.03223
XSHOP WARE 0.14618 0.11862 0.02192 0.00344 -0.07386 -0.04451
XSHOP INTE 0.03760 0.07424 0.04104 -0.09907 -0.07756 -0.04458
XSHOP MASS -0.09275 0.00737 -0.00947 -0.15880 0.05258 -0.05426
XSHOP CONV 0.00850 0.04090 0.05229 -0.16897 -0.12506 -0.07431
XSHOP FARM 0.05784 0.14007 0.02005 -0.06357 -0.00615 -0.02045
CAL 0.05128 0.07257 0.02337 -0.03490 0.02930 -0.03239
B FRE 0.09565 0.07044 0.03684 0.13158 0.03045 0.07043
B LAB 0.07245 0.11570 0.00936 0.10280 0.04185 0.02755
B ST 0.08674 0.11036 0.01230 0.08201 0.03602 0.03854
B WAY 0.11416 0.10761 0.04384 0.06056 0.01122 0.02463
B FV 0.06361 0.09912 0.02654 0.02350 0.01576 0.05408
B HLT 0.10251 0.09252 0.02668 0.02302 -0.06328 0.00666
B EXE 0.05473 0.12242 0.03067 -0.03367 -0.06504 -0.02062
B NEW 0.08838 0.10851 0.00044 -0.13474 0.02020 -0.03294
HLT BP -0.00117 0.04816 0.00724 -0.31453 0.00048 -0.04009
HLT DB 0.04449 0.08294 -0.05826 -0.17205 0.04424 -0.01806
HLT CL -0.01684 0.06707 0.06522 -0.27354 0.01409 -0.03921
HLT AG -0.02978 -0.04871 -0.02903 0.02855 -0.03490 -0.00292
HLT OB 0.05327 0.02271 -0.00924 -0.05744 -0.03620 0.00485
HLT MB 0.13269 0.03602 0.03499 -0.21723 0.00334 -0.08340
HLT HR 0.05278 0.07169 0.01687 -0.15435 -0.00056 -0.02180









Table B-1. Continued


XEMPLY


XEXPD XSERFRU XSERVEG XHWD XCHL


XEMPLY 1.00000
XEXPD -0.07190 1.00000
XSERFRU 0.01782 0.18296 1.00000
XSERVEG 0.03281 0.16842 0.47123 1.00000
XHWD -0.03163 0.34562 0.10262 0.05019 1.00000
XCHL -0.11603 0.27832 0.09183 0.03926 0.72993 1.00000
XSHOP GRO -0.01004 0.04810 0.02498 0.01548 -0.03691 -0.04370
XSHOP WARE -0.05498 0.20343 0.07236 0.08692 0.11481 0.05103
XSHOP INTE -0.04859 0.14234 0.12534 0.08042 0.11215 0.10173
XSHOP MASS -0.01377 0.07421 0.05311 0.04815 0.13760 0.13465
XSHOP CONV -0.11619 0.09594 0.08260 0.04431 0.08840 0.08022
XSHOP FARM -0.06677 0.15046 0.12625 0.13540 0.04550 0.02877
CAL -0.06042 0.11203 0.25103 0.17789 0.02914 0.02847
B FRE 0.03931 0.14461 0.29877 0.20511 0.01813 -0.00564
B LAB 0.05352 0.06485 0.25116 0.21860 -0.05345 -0.05503
B ST 0.03639 0.15162 0.21831 0.16900 0.02953 -0.00076
B WAY 0.01557 0.20443 0.29741 0.21880 0.04540 0.03222
B FV 0.03497 0.13336 0.35927 0.29751 0.02883 0.03026
B HLT -0.07465 0.05442 0.19512 0.14944 -0.02347 -0.01083
B EXE -0.04762 0.02291 0.19448 0.14186 -0.03317 -0.00254
B NEW -0.10412 0.17271 0.20537 0.21862 0.11489 0.09473
HLT BP -0.19015 -0.06456 -0.00487 -0.02396 0.01329 0.12245
HLT DB -0.13511 -0.09351 -0.06150 -0.06419 -0.03257 0.06427
HLT CL -0.16582 -0.05795 -0.00589 -0.00808 -0.00432 0.07874
HLT AG -0.00197 -0.02410 -0.02408 -0.03627 -0.10646 -0.07885
HLT OB -0.04818 -0.11460 -0.02314 -0.03644 -0.08940 -0.04194
HLT MB -0.18572 -0.01451 -0.03838 -0.05153 0.03974 0.14380
HLT HR -0.11669 -0.00688 -0.01958 -0.01717 -0.01651 0.07234









Table B-1. Continued
XSHOP XSHOP XSHOP XSHOP XSHOP XSHOP
GRO WARE INTE MASS CONV FARM
XSHOP GRO 1.00000
XSHOP WARE 0.04802 1.00000
XSHOP INTE 0.00930 0.16045 1.00000
XSHOP MASS -0.20577 0.12074 0.10166 1.00000
XSHOP CONV 0.04124 0.09355 0.21737 0.13223 1.00000
XSHOP FARM 0.06121 0.18769 0.31875 0.09149 0.23120 1.00000
CAL -0.00041 0.09270 0.15385 0.08414 0.13161 0.16510
B FRE 0.04667 0.12702 0.07275 -0.00270 0.01919 0.15776
B LAB 0.05442 0.10125 0.08128 0.00365 0.07884 0.15293
B ST 0.05227 0.10716 0.06299 0.02600 0.04714 0.18133
B WAY 0.04642 0.15197 0.11336 0.08269 0.11890 0.22541
B FV 0.02106 0.11226 0.10376 0.06558 0.08483 0.17531
B HLT 0.00436 0.14226 0.12244 0.01410 0.07115 0.12799
B EXE 0.01528 0.11685 0.09504 0.03423 0.08658 0.13277
B NEW 0.02236 0.14089 0.12760 0.08134 0.15969 0.18042
HLT BP -0.01557 -0.02650 0.04383 0.00814 0.03708 0.01690
HLT DB -0.01056 -0.04424 -0.06091 -0.02391 -0.01945 -0.01307
HLT CL -0.00305 -0.04116 0.02159 0.00057 0.03726 0.02764
HLT AG -0.02055 0.00564 -0.04937 0.02273 0.00646 -0.01573
HLT OB 0.00607 -0.02914 -0.00207 -0.02425 -0.00548 -0.00524
HLT MB 0.01836 0.04654 -0.03713 0.00569 0.01673 0.01428
HLT HR 0.00133 0.00466 -0.02433 0.00748 0.01283 0.01194










Table B-1. Continued
CAL B FRE B LAB B ST B WAY B FV
CAL 1.00000
B FRE 0.31488 1.00000
B LAB 0.45958 0.41693 1.00000
B ST 0.23412 0.43901 0.37174 1.00000
B WAY 0.33545 0.49818 0.40898 0.50734 1.00000
B FV 0.33147 0.62256 0.41820 0.39578 0.51860 1.00000
B HLT 0.27463 0.43302 0.29925 0.30006 0.34644 0.54384
B EXE 0.34440 0.37598 0.32011 0.24835 0.31225 0.39220
B NEW 0.30035 0.37627 0.33083 0.28284 0.44102 0.41422
HLT BP -0.04866 -0.03388 -0.09131 -0.03558 -0.03377 0.00112
HLT DB -0.05680 -0.06336 -0.07545 -0.04512 -0.05158 -0.02426
HLT CL -0.01630 -0.03368 -0.04606 -0.01644 -0.01981 0.04286
HLT AG -0.02425 -0.03264 -0.08487 -0.00927 -0.02733 0.00646
HLT OB 0.00545 0.10203 -0.00228 0.01025 0.01437 0.09637
HLT MB 0.00681 -0.03629 -0.05120 -0.01930 0.01709 0.00379
HLT HR 0.00172 -0.07827 -0.02877 -0.02200 -0.01736 -0.01129


Table B-1. Continued
B HLT B EXE B NEW HLT BP HLT DB HLT CL
B HLT 1.00000
B EXE 0.49226 1.00000
B NEW 0.31983 0.26318 1.00000
HLT BP 0.10866 0.09982 0.07279 1.00000
HLT DB 0.07371 0.03557 0.05083 0.37219 1.00000
HLT CL 0.10578 0.09904 0.06879 0.47702 0.30612 1.00000
HLT AG -0.00272 -0.00436 -0.01791 0.08186 0.05075 0.05783
HLT OB 0.23708 0.16367 0.06624 0.28508 0.23534 0.24220
HLT MB 0.16626 0.15347 0.05438 0.29528 0.24880 0.25442
HLT HR 0.05771 0.05620 0.02861 0.22322 0.20676 0.24184


Table B-1. Continued
HLT AG HLT OB HLT MB HLT HR
HLT AG 1.00000
HLT OB 0.09750 1.00000
HLT MB 0.13032 0.27704 1.00000
HLT HR 0.12553 0.17287 0.32027 1.00000









APPENDIX C
TSP CODE

OPTIONS MEMORY=1000;
TITLE 'Consumers Preferences for Organics';
? Organics#02.tsp

? READING STAT VERSION 7.0

IN'D:\ZZORGANIC\Organics\ORGANICDATA';
? ?V105 A I SEEK OUT ORGANIC FOODS;
? 5= completely agree
? 4= somewhat agree
? 3= neutral
? 2= somewhat disagree
? 1= completely disagree


? NOTES

? FOR EACH HEALTH VARIABLE USED THE LAST CODE WITH 4 MEANING THAT
YOU DO NOT THIS HEALTH PROBLEM IN YOUR FAMILY;
? Q100UTLE IS NOT IN YOUR DATASET THAT IS WHERE YOU PURCHASED A
SPECIFIC FRUIT;
? EXCLUSIVE HAS NO MEANING IN YOUR FILE SO IGNORE IT;
?END;


LIST ZVARZ
RD PERIOD
ORGANIC AGE
SHOP GRO
SHOP WARE SH
SHOP NONE EX


IDD3 YEAR MONTH YRS S MTH S
GENDER ETHNIC HISPANIC DEVICE

OP INTE SHOP MASS SHOP CONV SHOP FARM
PEND


SERV FRU SERV VEG CALORIES BHV ORG BHV FRESH
BHV LABEL BHV STORE BHV WAY BHV FRUVEG BHV HLTH
BHV EXERCISE BHV NEWFOOD


HLT BLOOD
HLT DIABE2


HLT
HLT
HLT
HLT
HLT


DIABE4
ALLEG1
ALLEG3
OBEST4
MOBIL2


HLT BLOOD HLT BLOOD:
HLT DIABE3
HLT CHOLE1 HLT CHOLE2
HLT ALLEG2
HLT ALLEG4 HLT OBEST1
HLT MOBILE
HLT MOBIL3 HLT MOBIL4


HLT HEAR3 HLT HEAR
RACE MARITAL HWD1
STATE STATE Q21STATE
PERIODS PERIOD


HWD2
EDUC


HLT BLOOD HLT DIABE1


HLT CHOLE3


HLT CHOLE4


HLT OBEST2 HLT OBEST3

HLT HEAR HLT HEAR


HWD3 HWD4
EMPLY INCOME


HWD5









'HOUSEHOLD IDENTIFICATION';


? DOC PERIOD 'REPORTING PERIODS';
? DOC IDD3 'IDD3=1 TO REMOVE BAD HOUSEHOLDS';
? DOC YEAR 'REPORTING YEAR';
? DOC MONTH 'REPORTING MONTH';
? DOC YRS S 'REPORTING YEAR';
? DOC MTH S 'REPORTING MONTH';
? DOC ORGANIC 'SEEKING OUT ORGANIC FOODS';
? DOC AGE 'AGE OF HOUSEHOLD HEAD';
? DOC GENDER 'GENDER OF HOUSEHOLD HEAD';
? DOC ETHNIC 'RACE OF HOUSEHOLD HEAD';
? DOC HISPANIC 'HISPANIC';
? DOC DEVICE 'ELECTRONIC DEVICES';
? DOC SHOP_GRO 'Grocery store Where have you personally shopped for food in';
? DOC SHOP_WARE 'Warehouse club store (Costco, Sams Club, etc) Where have you
personally shopped for food in';
? DOC SHOPINTE 'Internet grocery store (Peapod, Fresh Direct, etc) Where have you
personally shopped for food in';
? DOC SHOPMASS 'Mass merchandiser (Wal-Mart, Target, etc) Where have you
personally shopped for food in';
? DOC SHOP_CONV 'Convenience Store (Gas station, 7-11, Quik Check etc.) Where have
you personally shopped for food in';
? DOC SHOPFARM 'Farmer's market / Produce stand (including free-standing carts) Where
have you personally shopped for food in';
? DOC SHOPNONE 'None of the above Where have you personally shopped for food in';
? DOC EXPEND 'Weekly grocery spending $ In a typical week, about how much money do
you spend on groceries';
? DOC SERVFRU '# How many servings of fruit do you consume in typical day?';
? DOC SERVVEG '# How many servings of vegetables do you consume in typical day?';
? DOC CALORIES 'I count calories';
? DOC BHVORG 'I seek out organic foods Please tell us how much you agree on';
? DOC BHVFRESH 'I eat fresh foods much more frequently than packaged foods Please tell
us how much you agree on';
? DOC BHVLABEL 'I read ingredients on labels of the foods I buy Please tell us how much
you agree on';
? DOC BHVSTORE 'I prefer to buy my produce from certain stores/outlets Please tell us
how much you agree on';
? DOC BHVWAY 'I go out of my way to get certain types of produce Please tell us how
much you agree on';
? DOC BHVFRUVEG 'I eat fruits and vegetables more than other people my age Please tell
us how much you agree on';
? DOC BHV_HLTH 'I feel that I am healthier than my peers Please tell us how much you
agree on';
? DOC BHVEXERCISE 'I exercise at least 3 times a week Please tell us how much you agree
on';
? DOC BHV NEWFOOD 'I frequently experiment with new foods Please tell us how much


? DOC RD









you agree on';
? DOC HLT
any of;
? DOC HLT
any of;
? DOC HLT_
any of;
? DOC HLT
any of;
? DOC HLT
? DOC HLT
? DOC HLT
? DOC HLT
? DOC HLT_
of;
? DOC HLT_
of;
? DOC HLT_
of;
? DOC HLT_
of;
? DOC HLT
? DOC HLT
? DOC HLT
? DOC HLT
? DOC HLT_
? DOC HLT_
? DOC HLT_
? DOC HLT_
? DOC HLT
have any of;
? DOC HLT
have any of;
? DOC HLT
have any of;
? DOC HLT
have any of;
? DOC HLT


BLOC

BLOC

BLOC

BLOC

DIAB
DIAB
DIAB
DIAB
CHOL

CHOL

CHOL

CHOL

ALLE
ALLE
ALLE
ALLE
OBES
OBES
OBES
OBES
MOBI

MOBI

MOBI

MOBI

HEAR


household have any
? DOC HLT HEAR
household have any
? DOC HLT HEAR
household have any
? DOC HLT HEAR
household have any
? DOC RACE


)D1 'High blood pressure Do you or does anyone in your household have

)D2 'High blood pressure Do you or does anyone in your household have

)D3 'High blood pressure Do you or does anyone in your household have

)D4 'High blood pressure Do you or does anyone in your household have

El 'Diabetes Do you or does anyone in your household have any of;
E2 'Diabetes Do you or does anyone in your household have any of;
E3 'Diabetes Do you or does anyone in your household have any of;
E4 'Diabetes Do you or does anyone in your household have any of;
.El 'High cholesterol Do you or does anyone in your household have any

.E2 'High cholesterol Do you or does anyone in your household have any

.E3 'High cholesterol Do you or does anyone in your household have any

.E4 'High cholesterol Do you or does anyone in your household have any

G1 'Food allergies Do you or does anyone in your household have any of;
G2 'Food allergies Do you or does anyone in your household have any of;
G3 'Food allergies Do you or does anyone in your household have any of;
G4 'Food allergies Do you or does anyone in your household have any of;
T1 'Obesity Do you or does anyone in your household have any of;
T2 'Obesity Do you or does anyone in your household have any of;
T3 'Obesity Do you or does anyone in your household have any of;
T4 'Obesity Do you or does anyone in your household have any of;
EL 'Limited physical mobility Do you or does anyone in your household

L2 'Limited physical mobility Do you or does anyone in your household

L3 'Limited physical mobility Do you or does anyone in your household

L4 'Limited physical mobility Do you or does anyone in your household

L1 'Significant sight or hearing impairment Do you or does anyone in your
of;
L2 'Significant sight or hearing impairment Do you or does anyone in your
of;
L3 'Significant sight or hearing impairment Do you or does anyone in your
of;
4 'Significant sight or hearing impairment Do you or does anyone in your
of;
'Which of the following most closely describes your family heritage';









? DOC MARITAL 'Marital Status Which one of the following best describes yours';
? DOC HWD1 '5 years of age and younger Household Composition Including yourself,
how many people currently living in your household';
? DOC HWD2 '6-8 years of age Household Composition Including yourself, how many
people currently living in your household';
? DOC HWD3 '9-12 years of age Household Composition Including yourself, how many
people currently living in your household';
? DOC HWD4 '13-17 years of age Household Composition Including yourself, how many
people currently living in your household';
? DOC HWD5 '18 years of age and older Household Composition Including yourself, how
many people currently living in your household';
? DOC STATEO 'State of Residence In which state do you currently live?';
? DOC STATE1 'Do have another state that you consider your primary residence';
? DOC EDUCO 'Education What is the highest level of education you have completed';
? DOC EMPLY 'Employment Status Which one of the following best describes yours';
? DOC INCOME 'Household Income Which one of the following ranges includes your total
yearly household income before tax';
? DOC PERIODS 'Time Period Start Date';
? DOC PERIODE 'Time Period End Date';
SELECT PERIOD>2 & IDD3=1;

? AGE;

? Q1AGE 17 or younger 1[SCREEN OUT];
?Q1AGE 18-24 2
? Q1AGE 25-34 3
? Q1AGE 35-44 4
? Q1AGE 45-54 5
? Q1AGE 55-64 6
? Q1AGE 65-70 7
? Q1AGE Over 70 8
?HIST(DISCRETE,PERCENT) AGE;
XAGE=(AGE<3) + ((AGE=3)|(AGE=4))*2 + ((AGE=5)|(AGE=6))*3 + (AGE>6)*4;
DUMMY XAGE;
?HIST(DISCRETE,PERCENT) XAGE;
DOT 1-3;
DAGE.=XAGE.-XAGE4; ENDDOT;

? GENDER;

?GENDER=Q2GENDER;
? Q2GENDER 1 MALE ;
? Q2GENDER 2 FEMALE;
XGEN =(GENDER=2);
?HIST(DISCRETE,PERCENT) XGEN;
DGEN =(XGEN=1) + (XGEN=0)*-1;










? MARITAL;

? Q19MARIT 1 SINGLE, NEVER MARRIED;
? Q19MARIT 2 MARRIED
? Q19MARIT 3 LIVING WITH PARENTS ;
? Q19MARIT 4 SEPARATED
? Q19MARIT 5 DIVORCED
? Q19MARIT 6 WIDOWED
? Q19MARIT 7 PREFER NOT TO ANSWER;
XMAR =(MARITAL=1) +(MARITAL=2)*2 +(MARITAL=3)*3 +(MARITAL>3)*4;
DUMMY XMAR;
?HIST(DISCRETE,PERCENT) XMAR;
DOT 1-3;
DMAR.=XMAR.-XMAR4;ENDDOT;

? ETHNICITY;

? Q3ETHNIC 1 White/Caucasian
? Q3ETHNIC 2 Black/African American;
? Q3ETHNIC 3 Asian
? Q3ETHNIC 4 Pacific Islander
? Q3ETHNIC 5 Native American
? Q3ETHNIC 6 Other
? Q3ETHNIC 7 Prefer not to answer
?DUMMY ETHNIC;
?HIST(DISCRETE,PERCENT) ETHNIC;
?DOT 1-6;
?ZETHN.= ETHNIC.-ETHNIC7;ENDDOT;
? HISPANIC;
? Q4ETHNIC 1 HISPANIC
? Q4ETHNIC 2 NOT HISPANIC
? Q4ETHNIC 3 Prefer not to answer;
?DUMMY HISPANIC;
?HIST(DISCRETE,PERCENT) HISPANIC;
?DOT 1-2;
?ZHISP.=HISPANIC.-HISPANIC3; ENDDOT;
HISP =(HISPANIC=1);
RACE =((ETHNIC=1)&(HISP=0))*1 +((ETHNIC=1)&(HISP= 1))*2 +(ETHNIC=2)*3
+(ETHNIC=3)*4 +(ETHNIC>=4)*5;
?HIST(DISCRETE,PERCENT) RACE;
DUMMY RACE;
DOT 1-4;
DRACE.=RACE.-RACE5;ENDDOT;










? INCOME CATEGORIES ;


Q26INCOM
Q26INCOM
Q26INCOM
Q26INCOM
Q26INCOM
Q26INCOM
Q26INCOM
Q26INCOM
Q26INCOM


Under $15,000
$15,000-$24,999
$25,000-$34,999
$35,000-$49,999
$50,000-$74,999
$75,000-$99,999
$100,000-$149,999
$150,000 and over
Prefer not to answer;


XINC=(INCOME<4) + ((INCOME>=4)&(INCOME<=5))*2 + (INCOME=6)*3 +
((INCOME>6)&(INCOME<9))*4
+ (INCOME=9)*5;
DUMMY XINC;
?HIST(DISCRETE,PERCENT) XINC;
DOT 1-4;
DINC. =XINC.-XINC5;ENDDOT;

? EDUCATION


?EDUC=Q14GS E
? Q24GS_ED 1
? Q24GS_ED 2
? Q24GS ED 3
? Q24GS_ED 4
? Q24GS_ED 5
? Q24GS_ED 6
? Q24GS_ED 7
? Q24GS_ED 8
? Q24GSED 9
XEDU=(EDUC<=3


D;
Less than 9th grade
9th to 12th grade, no diploma
High school graduate or equivalent;
Some college, no degree
Associate degree
Bachelor's degree
Graduate or professional degree
Other, please specify
Prefer not to answer
) + ((EDUC>=4)&(EDUC<=6))*2 +


(EDUC=7)*3 + (EDUC>=8)*4;


DUMMY XEDU;
?HIST(DISCRETE,PERCENT) XEDU;
DOT 1-3;
DEDU.=XEDU.-XEDU4; ENDDOT;


? HOUSEHOLD SIZE ;

?Q20HHCOM;
?HWD1 5 years of age and younger-Household Composition (Including yourself);
?HWD2 6-8 years of age-Household Composition (Including yourself)
?HWD3 9-12 years of age-Household Composition (Including yourself)
?HWD4 13-17 years of age-Household Composition (Including yourself)
?HWD5 18 years of age and older-Household Composition (Including yourself);









HWD =HWD1 + HWD2 + HWD3 + HWD4 + HWD5;
CHL=((HWD1 + HWD2 + HWD3 + HWD4)>0); ?WITH CHILDREN UNDER 18 YEARS;
XHWD =(HWD=1) +(HWD=2)*2 +(HWD=3)*3 +(HWD=4)*4 +(HWD>4)*5;
DUMMY XHWD;
?HIST(DISCRETE,PERCENT) XHWD;
DOT 1-4;
DHWD.=XHWD.-XHWD5; ENDDOT;
XCHL =(CHL=1);
?HIST(DISCRETE,PERCENT) XCHL;
DCHL =(XCHL=1) +(XCHL=0)*-1;

? EMPLOYMENT

?EMPLY=Q25EMPLO;
? Q25EMPLO 1 EMPLOYED FULL TIME;
? Q25EMPLO 2 EMPLOYED PART TIME;
? Q25EMPLO 3 SELF-EMPLOYED;
? Q25EMPLO 4 NOT EMPLOYED, BUT LOOKING FOR WORK;
? Q25EMPLO 5 NOT EMPLOYED, AND NOT LOOKING FOR WORK;
? Q25EMPLO 6 RETIRED;
? Q25EMPLO 7 STUDENT;
? Q25EMPLO 8 HOMEMAKER;
? Q25EMPLO 9 PREFER NOT TO ANSWER;
XEMPLY=((EMPLY=1)|(EMPLY=3)) + (EMPLY=2)*2 + ((EMPLY=4)|(EMPLY=5))*3 +
(EMPLY>=6)*4;
DUMMY XEMPLY;
? 1=EMPLOYED FULL TIME & SELF-EMPLOYED 2=EMPLOYED PART TIME 3=NOT
EMPLOYED 4=OTHERS;
?HIST(DISCRETE,PERCENT) XEMPLY;
DOT 1-3;
DEMPLY.=XEMPLY.-XEMPLY4; ENDDOT;

? MONTHS;

MTH=MTH S;
DUMMY MTH;
?HIST(DISCRETE,PERCENT) MTH;
DOT 2-12;
DMTH.=MTH.-MTH1 ;ENDDOT;

? GROCERY SHOPPING STORE Q7GROCER ;

? SHOP GRO;
XSHOP_GRO=(SHOP_GRO= 1);
?HIST(DISCRETE,PERCENT) XSHOP_GRO;
DSHOP GRO=(SHOP GRO=1) + (SHOP GRO=0)*-1;









? SHOP WARE;
XSHOPWARE=(SHOP_WARE= 1);
?HIST(DISCRETE,PERCENT) XSHOP_WARE;
DSHOP_WARE=(SHOP_WARE=1) + (SHOP_WARE=0)*-1;
? SHOP INTE;
XSHOPINTE=(SHOP_INTE= 1);
?HIST(DISCRETE,PERCENT) XSHOPINTE;
DSHOPINTE=(SHOPINTE=1) + (SHOPINTE=0)*-1;
? SHOP MASS;
XSHOPMASS=(SHOPMASS=1);
?HIST(DISCRETE,PERCENT) XSHOPMASS;
DSHOPMASS=(SHOPMASS=1) + (SHOPMASS=0)*-1;
? SHOP CONV;
XSHOP_CONV=(SHOP_CONV= 1);
?HIST(DISCRETE,PERCENT) XSHOP_CONV;
DSHOP_CONV=(SHOP_CONV= 1) + (SHOP_CONV=0)*-1;
? SHOP FARM;
XSHOPFARM=(SHOPFARM= 1);
?HIST(DISCRETE,PERCENT) XSHOPFARM;
DSHOPFARM=(SHOPFARM=1) + (SHOPFARM=0)*-1;
? SHOPNONE[EXCLUSIVE] [SCREEN OUT];
XSHOPNONE=(SHOPNONE= 1);
?HIST(DISCRETE,PERCENT) XSHOPNONE;
DSHOPNONE=(SHOPNONE= 1) + (SHOPNONE=0)*-1;

? EXPENDITURES ON GROCERY SHOPPING Q13DOLLA;

? TITLE 'WEEKLY GROCERY SPENDING DOLLARS';
?SELECT PERIOD>2 & IDD3=1;
?HIST(NBINS=30) EXPEND;
?HIST(NBINS=30,PERCENT) EXPEND; MSD EXPEND;
?MAT HISTEXPD=@HIST;
?MAT NR=NROW(@HIST);
?PRINT NR;
XEXPD =(EXPEND<50) +((EXPEND>=50)&(EXPEND<100))*2
+((EXPEND>=100)&(EXPEND<200))*3+((EXPEND>=200)&(EXPEND<400))*4
+((EXPEND>=400))*5;
DUMMY XEXPD;
?HIST(DISCRETE,PERCENT) XEXPD;
DOT 1-4;
DEXPD. =XEXPD.-XEXPD5;ENDDOT;

? SERVING OF FRUIT & VEG #0-10

? TITLE 'HOW MANY SERVINGS OF FRUIT DO YOU CONSUME IN TYPICAL DAY?';
? Q11 SERV FRU #0-10;









XSERFRU =(SERV FRU=0) +((SERVFRU>=1)&(SERV FRU<=3))*2 +
((SERV FRU>=4)&(SERV_FRU<=6))*3+(SERV FRU>=7)*4;
DUMMY XSERFRU;
?HIST(DISCRETE,PERCENT) XSERFRU;
DOT 1-3;
DSERVF. =XSERFRU.-XSERFRU4;ENDDOT;
? TITLE 'HOW MANY SERVINGS OF VEG DO YOU CONSUME IN TYPICAL DAY?';
? Q12 SERV VEG #0-10;
XSERVEG =(SERV VEG=0) +((SERV VEG>=1)&(SERVVEG<=3))*2 +
((SERV VEG>=4)&(SERVVEG<=6))*3+(SERV VEG>=7)*4;
DUMMY XSERVEG;
?HIST(DISCRETE,PERCENT) XSERVEG;
DOT 1-3;
DSERVV. =XSERVEG.-XSERVEG4;ENDDOT;
?<<<<<<<<<<<<<<< BEHAVIOR & ATTITUDE >>>>>>>>>>>>>>>>>>>>>;

? COUNT CALORIES

?CALORIES;
?5=COMPLETE AGREE 1=TOTALLY DISAGREE;
?TITLE 'I TRY TO COUNT THE NUMBER OF CALORIES I EAT EACH DAY AND BUY
MANGOS';
? Q15AGREE 5 Completely agree (5)
? Q15AGREE 4 Somewhat agree
? Q15AGREE 3 Neither
? Q15AGREE 2 Somewhat disagree
? Q15AGREE 1 Completely disagree (1);
CAL =CALORIES; ?VARIABLE CONSISTENT;
DUMMY CAL;
?HIST(DISCRETE,PERCENT) CAL;
DOT 1,2,4,5;
DCAL.=CAL.-CAL3; ENDDOT;

? EATING FRESH FOODS

?BHV FRESH;
?TITLE 'I EAT FRESH FOODS MORE FREQUENTLY THAN PACKAGED FOODS';
? Q15AGREE 5 Completely agree (5)
? Q15AGREE 4 Somewhat agree
? Q15AGREE 3 Neither
? Q15AGREE 2 Somewhat disagree
? Q15AGREE 1 Completely disagree (1);
BFRE =BHV FRESH; ?VARIABLE CONSISTENT;
DUMMY B FRE;
?HIST(DISCRETE,PERCENT) BFRE;
DOT 1,2,4,5;









DB FRE.=B FRE.-B FRE3; ENDDOT;

? READ LABEL

?BHV LABEL;
?TITLE 'I READ INGREDIENTS ON LABELS OF THE FOODS I BUY';
? Q15AGREE 5 Completely agree (5)
? Q15AGREE 4 Somewhat agree
? Q15AGREE 3 Neither
? Q15AGREE 2 Somewhat disagree
? Q15AGREE 1 Completely disagree (1);
BLAB =BHVLABEL; ?VARIABLE CONSISTENT;
DUMMY B LAB;
?HIST(DISCRETE,PERCENT) BLAB;
DOT 1,2,4,5;
DB LAB.=B LAB.-B LAB3; ENDDOT;

? BUY FROM CERTAIN STORES

?BHV STORE;
?TITLE 'I PREFER TO BUY PRODUCE FROM CERTAIN STORES';
? Q15AGREE 5 Completely agree (5)
? Q15AGREE 4 Somewhat agree
? Q15AGREE 3 Neither
? Q15AGREE 2 Somewhat disagree
? Q15AGREE 1 Completely disagree (1);
BST =BHVSTORE; ?VARIABLE CONSISTENT;
DUMMY BST;
?HIST(DISCRETE,PERCENT) B_ST;
DOT 1,2,4,5;
DB ST.=B ST.-BST3; ENDDOT;

? WAY TO GET CERTAIN TAYPES OF PRODUCE
?


?BHV WAY;
?TITLE 'I GO OUT OF WAY TO GET CERTAIN TYPES
? Q15AGREE 5 Completely agree (5)
? Q15AGREE 4 Somewhat agree
? Q15AGREE 3 Neither
? Q15AGREE 2 Somewhat disagree
? Q15AGREE 1 Completely disagree (1);
B WAY =BHV WAY; ?VARIABLE CONSISTENT;
DUMMY B WAY;
?HIST(DISCRETE,PERCENT) B WAY;
DOT 1,2,4,5;
DB WAY.=B WAY.-B WAY3; ENDDOT;


OF PRODUCE';










? EATING FRESH FRUITS AND VEGETABLES ;

?BHV FRUVEG;
?TITLE 'I EAT FRUITS AND VEGETABLES MORE THAN OTHER PEOPLE MY AGE';
? Q15AGREE 5 Completely agree (5)
? Q15AGREE 4 Somewhat agree
? Q15AGREE 3 Neither
? Q15AGREE 2 Somewhat disagree
? Q15AGREE 1 Completely disagree (1);
B FV =BHV FRUVEG; ?VARIABLE CONSISTENT;
DUMMY B FV;
?HIST(DISCRETE,PERCENT) B FV;
DOT 1,2,4,5;
DB FV.=B FV.-B FV3; ENDDOT;

? FEEL HEATLHIER THAN MY PEERS

?BHV HLTH;
?TITLE 'I FEEL THAT I AM HEALTHIER THAN MY PEERS';
? Q15AGREE 5 Completely agree (5)
? Q15AGREE 4 Somewhat agree
? Q15AGREE 3 Neither
? Q15AGREE 2 Somewhat disagree
? Q15AGREE 1 Completely disagree (1) ;
B HLT =BHV HLTH; ?VARIABLE CONSISTENT;
DUMMY B HLT;
?HIST(DISCRETE,PERCENT) B_HLT;
DOT 1,2,4,5;
DB HLT.=B HLT.-B HLT3; ENDDOT;

? EXERCISE

?BHV EXERCISE;
?TITLE 'I EXERCISE AT LEAST 3 TIMES A WEEK'
? Q15AGREE 5 Completely agree (5)
? Q15AGREE 4 Somewhat agree
? Q15AGREE 3 Neither
? Q15AGREE 2 Somewhat disagree
? Q15AGREE 1 Completely disagree (1);
B EXE =BHV EXERCISE; ?VARIABLE CONSISTENT;
DUMMY B EXE;
?HIST(DISCRETE,PERCENT) BEXE;
DOT 1,2,4,5;
DB EXE.=B EXE.-B EXE3; ENDDOT;










? EXPERIMENT WITH FOODS

?BHV NEWFOOD;
?TITLE 'I FREQUENTLY EXPERIMENT WITH NEW FOODS';
? Q15AGREE 5 Completely agree (5)
? Q15AGREE 4 Somewhat agree
? Q15AGREE 3 Neither
? Q15AGREE 2 Somewhat disagree
? Q15AGREE 1 Completely disagree (1);
B NEW =BHV NEWFOOD; ?VARIABLE CONSISTENT;
DUMMY B NEW;
?HIST(DISCRETE,PERCENT) B NEW;
DOT 1,2,4,5;
DB NEW.=B NEW.-B NEW3; ENDDOT;
?<<<<<<<<<<<<<<< HEALTH CONDITIONS >>>>>>>>>>>>>>>>>>>>>;

? HIGH BLOOD PRESSURE V119 A DO NOT HAVE BLOOD PRESSURE;

?HLT BLOOD YOU
? HLT BLOOD YOU SPONSE
?HLT BLOOD OTHER
? HLTBLOOD4 NONE [EXCLUSIVE]
? HLT BLOOD;
HLTBP=(HLTBLOOD4=1);
?HIST(DISCRETE,PERCENT) HLTBP;
DHLTBP=(HLTBP=1)+ (HLTBP=0)*-1;

? DIABETES V123 A DO NOT HAVE DIABETES

? HLT DIABE1 YOU
? HLT DIABE2 YOU SPONSE
? HLT DIABE3 OTHER
? HLTDIABE4 NONE [EXCLUSIVE]
? HLT DIABE4;
HLT_DB=(HLTDIABE4= 1);
?HIST(DISCRETE,PERCENT) HLT_DB;
DHLT_DB=(HLT_DB=1)+ (HLT_DB=0)*-1;

? HIGH CHOLESTEROL- V127 A DO NOT HAVE CHOLESTEROL PROBLEMS ;

?HLT CHOLE1 YOU
? HLT CHOLE2 YOU SPONSE
? HLT CHOLE3 OTHER
? HLT_CHOLE4 NONE [EXCLUSIVE]
? HLT CHOLE4;









HLT_CL=(HLT_CHOLE4= 1);
?HIST(DISCRETE,PERCENT) HLT_CL;
DHLT_CL=(HLT_CL=1) + (HLT_CL=0)*-1;

? HIGH FOOD ALLERGIES- V131 A DO NOT HAVE FOOD ALLERGIES

? HLT ALLEG1 YOU
? HLT ALLEG2 YOU SPONSE
? HLT ALLEG3 OTHER
? HLTALLEG4 NONE [EXCLUSIVE]
? HLT ALLEG4;
HLTAG=(HLT_ALLEG4=1);
?HIST(DISCRETE,PERCENT) HLTAG;
DHLT_AG=(HLTAG=1) + (HLTAG=0)*-1;

? OBESITY V135 A DO NOT HAVE OBESITY PROBLEMS

?HLT OBEST1 YOU
? HLT OBEST2 YOU SPONSE
?HLT OBEST3 OTHER
? HLT_OBEST4 NONE [EXCLUSIVE]
? HLT OBEST4;
HLT_OB=(HLT_OBEST4= 1);
?HIST(DISCRETE,PERCENT) HLT_OB;
DHLT_OB=(HLT_OB=1) + (HLT_OB=0)*-1;

? MOBILITY V139 A DO NOT HAVE MOBILITHY PROBLEMS

? HLT MOBIL1 YOU
? HLT MOBIL2 YOU SPONSE
? HLT MOBIL3 OTHER
? HLT_MOBIL4 NONE [EXCLUSIVE]
? HLT MOBIL4;
HLT_MB=(HLTMOBIL4= 1);
?HIST(DISCRETE,PERCENT) HLTMB;
DHLTMB=(HLTMB=1) + (HLTMB=0)*-1;

? SIGHT V143 A DO NOT HAVE SIGHT/HEARING PROBLEMS

? HLT HEAR YOU
? HLT HEAR2 YOU SPONSE
? HLT HEAR3 OTHER
? HLTHEAR4 NONE [EXCLUSIVE]
? HLT HEAR;
HLTHR=(HLTHEAR4=1);
?HIST(DISCRETE,PERCENT) HLT HR;









DHLTHR=(HLT HR=1)+ (HLTHR=0)*-1;

? US REGIONS

? STATE =Q21STATE
WSTATE=STATEO;
? HIST(DISCRETE) STATE;
DIVISION=[ STATE= 7 | WSTATE=22 I STATE = 201 STATE =31 1 STATE =40|
WSTATE =47]*1 +
[ STATE= 321 WSTATE=35 I STATE = 39]*2 +
[ STATE= 161 WSTATE=15 I STATE = 231 STATE =36 1 STATE =49]*3 +
[ STATE= 13 STATE= 17 1 STATE = 24 1 STATE = 25 | STATE = 301 STATE
= 29 | WSTATE = 42]*4 +
[ STATE= 9 | STATE= 8 1 STATE =10 | STATE = 11| STATE =21 1 STATE =


28
I STATE = 411 STATE =46 1 STATE = 50]*5 +
STATET= 2 | STATE= 18 STATE = 26 STATE
[ STATE= 3 | STATE= 19 | STATE = 37 | STATE
STATET= 4 STATE= 6 STATE = 14 STATE
45 | WSTATE =34| WSTATE = 51 ]*8 +
[ STATE= 1 I STATE= 5 1 STATE = 121 STATE =


? DIVISION
? DIVISION
? DIVISION
? DIVISION
? DIVISION
? DIVISION
? DIVISION
? DIVISION
? DIVISION


=43 ]*6 +
=44 ]*7 +
= 33 | STATE = 27 | STATE

38 1 WSTATE = 48 ]*9;


=1 NORTHEAST(1):NEW ENGLAND
=2 NORTHEAST(1):MIDDLE ATLANTIC
=3 MIDWEST(2): EAST NORTH CENTRAL
=4 MIDWEST(2): WEST NORTH CENTRAL
=5 SOUTH(3): SOUTH ATLANTIC
=6 SOUTH(3): EAST SOUTH CENTRAL
=7 SOUTH(3): WEST SOUTH CENTRAL
=8 WEST(4): MOUNTAIN
=9 WEST(4): PACIFIC


REGION = [ DIVISION I DIVISION=2 ] + [ DIVISION=3 I DIVISION=4 ]*2
+ [DIVISION=5 I DIVISION=6 I DIVISION=7]*3 +[ DIVISION=8 I DIVISION=9 ]*4;


FL
GA
HI
IA
ID
IL
IN
KS
KY


19
20
21
22
23
24
25
26


LA 1 27
MA 28
MD| 29
ME 30
MI 31
MN| 32
MOI 33
MS 34
135


MT 136
NC 137


ND
NE
NH
NJ
NM
NV


|38
139
140
141
142
143


OH 145
OK 146


OR
PA
RI
SC
SD
TN


147
148
149
150
151
1


UT
VA
VT
WA
WI
WV
WY


NY 144 TX


?DUMMY DIVISION;


AK
AL
AR
AZ
CA
CO
CT
DC
DE









?HIST(DISCRETE) DIVISION;
?DOT 2-9;
?WDIV. = DIVISION. DIVISION;
?ZDIV. = DIVISION.;
?ENDDOT;

DUMMY REGION;
?HIST(DISCRETE,PERCENT) REGION;
DOT 2-4;
DREG.= REGION.-REGION1 ;ENDDOT;

? CORRELATIONS AMONG ALL VARIABLES;
?CORR(COVA,MSD,PRINT) XINC XEDU RACE XAGE XGEN XMAR XEMPLY XEXPD
XSERFRU XSERVEG XHWD XCHL;
?XSHOP GRO XSHOP WARE XSHOP INTE XSHOP MASS XSHOP CONV
XSHOP FARM CAL B FRE B LAB BST;
?B WAY B FVB HLTB EXEB NEW HLT BP HLT DB HLT CL HLT AG HLT OB
HLT MB HLT HR;

? HISTOGRAMS OF ALL DUMMIES INCLUDED IN MODEL;
?LIST HVARH
?XAGE XGEN XMAR RACE XINC XEDU XHWD XCHL XEMPLY REGION MTH
?XSHOP GRO XSHOP WARE XSHOP INTE XSHOP MASS XSHOP CONV
XSHOP FARM
?XEXPD XSERFRU XSERVEG
?CAL1B FREB LABB STB WAY B FVB HLTB EXEB NEW
?HLT BP HLT_DB HLTCL HLTAG HLT_OB HLTMB HLTHR;
?DOT HVARH;
?HIST(DISCRETE).;
?HIST(DISCRETE,PERCENT) .;ENDDOT;


? ORDERED PROBIT MODEL VARIABLES

LIST XMODELX
DAGE1 DAGE2 DAGE3
DGEN DMAR1 DMAR2 DMAR3
DRACE1 DRACE2 DRACE3 DRACE4
DINC1 DINC2 DINC3 DINC4
DEDU1 DEDU2 DEDU3
DHWD1 DHWD2 DHWD3 DHWD4 DCHL
DEMPLY1 DEMPLY2 DEMPLY3
DREG2 DREG3 DREG4
DSHOP GRO DSHOP WARE DSHOP INTE DSHOP MASS DSHOP CONV
DSHOP FARM
DEXPD1 DEXPD2 DEXPD3 DEXPD4









DSERVF1 DSERVF2 DSERVF3 DSERVV1 DSERVV2 DSERVV3
DCAL1 DCAL2 DCAL4 DCAL5
DB FRE1 DB FRE2 DB FRE4 DB FRE5
DB LAB1 DB LAB2DB LAB4DB LAB5
DB ST1 DB ST2 DB ST4 DB ST5
DB WAY1 DB WAY2 DB WAY4 DB WAY5
DB FV1 DB FV2 DB FV4 DB FV5
DB HLT1 DB HLT2 DB HLT4 DB HLT5
DB EXE1 DB EXE2 DB EXE4 DB EXE5
DB NEW1 DB NEW2 DB NEW4 DB NEW5
DHLT BP DHLT DB DHLT CL DHLT AG DHLT OB DHLT MB DHLT HR
DMTH2 DMTH3 DMTH4 DMTH5 DMTH6 DMTH7 DMTH8 DMTH9 DMTH10 DMTH11
DMTH12;

ORDPROB ORGANIC C XMODELX;
WRITE(FORMAT=EXCEL,FILE='D:\ZZORGANIC\Organics\HIST5.XLS') @COEF;

MAKE STATS @COEF @T %T;
print STATS;
SET NU=3; ? NUMBER OF ORDER CATEGORIES LESS 2;

FIT=@FIT;
SELECT PERIOD>2 & IDD3=1;
DFIT=( (FIT**2)>=0);
MAT BB=@COEF;
MAT NR=NROW(BB); SET RR=NR(1)-NU;
MFORM(TYPE=GEN,NROW=RR,NCOL= 1) AA=0;
DO J=1 TO RR; SET AA(J)=BB(J); ENDDO; PRINT AA;
DOT(VALUE=K) 2-4; SET L=RR + K -1;
SET MU.=BB(L); PRINT K L MU.; ENDDOT;


? SETTING FOR THE SIMULATION PORTION OF THE ANALYSIS

LIST ZVAR2Z
DAGE1 DAGE2 DAGE3
DGEN DMAR1 DMAR2 DMAR3
DRACE1 DRACE2 DRACE3 DRACE4
DINC1 DINC2 DINC3 DINC4
DEDU1 DEDU2 DEDU3
DHWD1 DHWD2 DHWD3 DHWD4 DCHL
DEMPLY1 DEMPLY2 DEMPLY3
DREG2 DREG3 DREG4
DSHOP GRO DSHOP WARE DSHOP INTE DSHOP MASS DSHOP CONV
DSHOP FARM
DEXPD1 DEXPD2 DEXPD3 DEXPD4









DSERVF1 DSERVF2 DSERVF3 DSERVV1 DSERVV2 DSERVV3
DCAL1 DCAL2 DCAL4 DCAL5
DB FRE1 DB FRE2 DB FRE4 DB FRE5
DB LAB1 DB LAB2DB LAB4DB LAB5
DB ST1 DB ST2 DB ST4 DB ST5
DB WAY1 DB WAY2 DB WAY4 DB WAY5
DB FV1 DB FV2 DB FV4 DB FV5
DB HLT1 DB HLT2 DB HLT4 DB HLT5
DB EXE1 DB EXE2 DB EXE4 DB EXE5
DB NEW1 DB NEW2 DB NEW4 DB NEW5
DHLT BP DHLT DB DHLT CL DHLT AG DHLT OB DHLT MB DHLT HR
DMTH2 DMTH3 DMTH4 DMTH5 DMTH6 DMTH7 DMTH8 DMTH9 DMTH10 DMTH11
DMTH12;

LIST SVARW
WDAGE1 WDAGE2 WDAGE3
WDGEN WDMAR1 WDMAR2 WDMAR3
WDRACE1 WDRACE2 WDRACE3 WDRACE4
WDINC1 WDINC2 WDINC3 WDINC4
WDEDU1 WDEDU2 WDEDU3
WDHWD1 WDHWD2 WDHWD3 WDHWD4 WDCHL
WDEMPLY1 WDEMPLY2 WDEMPLY3
WDREG2 WDREG3 WDREG4
WDSHOP GRO WDSHOP WARE WDSHOP INTE WDSHOP MASS WDSHOP CONV
WDSHOP FARM
WDEXPD1 WDEXPD2 WDEXPD3 WDEXPD4
WDSERVF1 WDSERVF2 WDSERVF3 WDSERVV1 WDSERVV2 WDSERVV3
WDCAL1 WDCAL2 WDCAL4 WDCAL5
WDB FRE1 WDB FRE2 WDB FRE4 WDB FRE5
WDB LAB1 WDB LAB2 WDB LAB4 WDB LAB5
WDB ST1 WDB ST2 WDB ST4 WDB ST5
WDB WAY1 WDB WAY2 WDB WAY4 WDB WAY5
WDB FV1 WDB FV2 WDB FV4 WDB FV5
WDB HLT1 WDB HLT2 WDB HLT4 WDB HLT5
WDB EXE1 WDB EXE2 WDB EXE4 WDB EXE5
WDB NEW1 WDB NEW2 WDB NEW4 WDB NEW5
WDHLT BP WDHLT DB WDHLT CL WDHLT AG WDHLT OB WDHLT MB
WDHLT HR
WDMTH2 WDMTH3 WDMTH4 WDMTH5 WDMTH6 WDMTH7 WDMTH8 WDMTH9
WDMTH10 WDMTH11 WDMTH12;

LIST SVARS
SDAGE1 SDAGE2 SDAGE3
SDGEN SDMAR1 SDMAR2 SDMAR3
SDRACE1 SDRACE2 SDRACE3 SDRACE4
SDINC1 SDINC2 SDINC3 SDINC4









SDEDU1 SDEDU2 SDEDU3
SDHWD1 SDHWD2 SDHWD3 SDHWD4 SDCHL
SDEMPLY1 SDEMPLY2 SDEMPLY3
SDREG2 SDREG3 SDREG4
SDSHOP GRO SDSHOP WARE SDSHOP INTE SDSHOP MASS SDSHOP CONV
SDSHOP FARM
SDEXPD1 SDEXPD2 SDEXPD3 SDEXPD4
SDSERVF1 SDSERVF2 SDSERVF3 SDSERVV1 SDSERVV2 SDSERVV3
SDCAL1 SDCAL2 SDCAL4 SDCAL5
SDB FRE1 SDB FRE2 SDB FRE4 SDB FRE5
SDB LAB1 SDB LAB2 SDB LAB4 SDB LAB5
SDB ST1 SDB ST2 SDB ST4 SDB ST5
SDB WAY1 SDB WAY2 SDB WAY4 SDB WAY5
SDB FV1 SDB FV2 SDB FV4 SDB FV5
SDB HLT1 SDB HLT2 SDB HLT4 SDB HLT5
SDB EXE1 SDB EXE2 SDB EXE4 SDB EXE5
SDB NEW1 SDB NEW2 SDB NEW4 SDB NEW5
SDHLT BP SDHLT DB SDHLT CL SDHLT AG SDHLT OB SDHLT MB SDHLT HR
SDMTH2 SDMTH3 SDMTH4 SDMTH5 SDMTH6 SDMTH7 SDMTH8 SDMTH9 SDMTH10
SDMTH11 SDMTH12;

DOT ZVAR2Z;
SET S.=0; SET W.=0; SET IHH=1; ENDDOT;
SET I=0;


PROC INIT;

DOT ZVAR2Z; SET S.=0; SET W.=0; SET IHH=1; ENDDOT;
ENDPROC;
MFORM(TYPE=GEN,NROW=150,NCOL=12) MSIM=0;
SET SIMNUM=0;

PROC ZSIMZ;

SELECT PERIOD>2 & IDD3=1;
IZZ=1;
SET IWW=0;
SET I=I+1;
MAKE SX1 IZZ ZVAR2Z ;
MAKE SX2 IWW SVARS;
MAKE SX3 IWW SVARW;
MAT X2= SX1%(IZZ#SX2');
MAT X3= IZZ#SX3';
MAT X1 = SX1 X2 + X3;
MAT NRX1=NROW(X1);









MAT XB=X1*AA;
MAT PROB1= CNORM(-XB);
MAT PROB2= CNORM( MU2 XB) CNORM(-XB);
MAT PROB3= CNORM( MU3 XB) CNORM( MU2 XB);
MAT PROB4= CNORM( MU4 XB) CNORM( MU3 XB);
MAT PROB5= 1- CNORM(MU4 XB);

MAT NN=NROW(PROB 1);
DOT 1-5; UNMAKE PROB. LPROB.; DLPROB. = LPROB.*DFIT; ENDDOT;
MSD(NOPRINT) DLPROB1 DLPROB2 DLPROB3 DLPROB4 DLPROB5;
SET MSIM(I, 1)=I;
SET MSIM(I,2)=SIMNUM;
SET MSIM(I,3)=VARNUM;
SET MSIM(I,4)= @MEAN(1);
SET MSIM(I,5)= @MEAN(2);
SET MSIM(I,6)= @MEAN(3);
SET MSIM(I,7)= @MEAN(4);
SET MSIM(I,8)= @MEAN(5);
ENDPROC;

? STARTING THE SIMULATIONS;


? SIMULATION #1 AVERAGE PERSON RESPONDING

SET SIMNUM=1;
SET VARNUM=1;
INIT; ZSIMZ;

? SIMULATION #2 AGE OF RESPONDENT

SET SIMNUM=2;
SET VARNUM=1; INIT;
SET SDAGE1=1; SET SDAGE2=1; SET SDAGE3=1;
SET WDAGE1=1; SET WDAGE2=0; SET WDAGE3=0; ? XAGE=1;
ZSIMZ;

SET VARNUM=2; INIT;
SET SDAGE1=1; SET SDAGE2=1; SET SDAGE3=1;
SET WDAGE1=0; SET WDAGE2=1; SET WDAGE3=0; ? XAGE=2;
ZSIMZ;

SET VARNUM=3; INIT;
SET SDAGE1=1; SET SDAGE2=1; SET SDAGE3=1;
SET WDAGE1=0; SET WDAGE2=0; SET WDAGE3=1; ? XAGE=3;
ZSIMZ;









SET VARNUM=4; INIT;
SET SDAGE1=1; SET SDAGE2=1; SET SDAGE3=1;
SET WDAGE1=-1; SET WDAGE2=-1; SET WDAGE3=-1; ? XAGE=4;
ZSIMZ;

? SIMULATION #3 GENDER

SET SIMNUM=3;
SET VARNUM=1; INIT;
SET SDGEN=1;
SET WDGEN=1; ? XGEN=1 [GENDER=FEMALE];
ZSIMZ;


SET VARNUM=2; INIT;
SET SDGEN=1;
SET WDGEN=-1; ? XGEN=0 [GENDER=MALE];
ZSIMZ;


? SIMULATION #4 MARITAL STATUS OF RESPONDENT ;
9


SET SIMNUM=4;
SET VARNUM=1; INIT;
SET SDMAR1=1; SET SDMAR2=1; SET SDMAR3=1;
SET WDMAR1=1; SET WDMAR2=0; SET WDMAR3=
ZSIMZ;

SET VARNUM=2; INIT;
SET SDMAR1=1; SET SDMAR2=1; SET SDMAR3=1;
SET WDMAR1=0; SET WDMAR2=1; SET WDMAR3=
ZSIMZ;

SET VARNUM=3; INIT;
SET SDMAR1=1; SET SDMAR2=1; SET SDMAR3=1;
SET WDMAR1=0; SET WDMAR2=0; SET WDMAR3=
ZSIMZ;


0; ? XMAR=1;




0; ? XMAR=2;




1; ? XMAR=3;


SET VARNUM=4; INIT;
SET SDMAR1=1; SET SDMAR2=1; SET SDMAR3=1;
SET WDMAR1=-1; SET WDMAR2=-1; SET WDMAR3=-1; ? XMAR=4;
ZSIMZ;

? SIMULATION #5 RACE OF RESPONDENT

SET SIMNUM=5;
SET VARNUM=1; INIT;
SET SDRACE1=1; SET SDRACE2=1; SET SDRACE3=1; SET SDRACE4=1;









SET WDRACE1=1; SET WDRACE2=0; SET WDRACE3=
ZSIMZ;

SET VARNUM=2; INIT;
SET SDRACE1=1; SET SDRACE2=1; SET SDRACE3=1;
SET WDRACE1=0; SET WDRACE2=1; SET WDRACE3=
ZSIMZ;

SET VARNUM=3; INIT;
SET SDRACE1=1; SET SDRACE2=1; SET SDRACE3=1;
SET WDRACE1=0; SET WDRACE2=0; SET WDRACE3
ZSIMZ;

SET VARNUM=4; INIT;
SET SDRACE1=1; SET SDRACE2=1; SET SDRACE3=1;
SET WDRACE1=0; SET WDRACE2=0; SET WDRACE3=
ZSIMZ;

SET VARNUM=5; INIT;
SET SDRACE1=1; SET SDRACE2=1; SET SDRACE3=1;


SET WDRACE1:
RACE=5;
ZSIMZ;


0; SET WDRACE4=0; ? RACE=1;


SET SDRACE4=1;
0; SET WDRACE4-



SET SDRACE4=1;
1; SET WDRACE4-



SET SDRACE4=1;
0; SET WDRACE4-


=0; ? RACE=2;




=0; ? RACE=3;




=1; ?RACE=4;


SET SDRACE4=1;


=-1; SET WDRACE2=-1; SET WDRACE3=-1; SET WDRACE4=-1; ?


? SIMULATION #6- INCOME


SET SIMNUM=6;
SET VARNUM= 1; INIT;
SET SDINC1=1; SET SDINC2=1; SET SDINC3=1;
SET WDINC1=1; SET WDINC2=0; SET WDINC3=
ZSIMZ;

SET VARNUM=2; INIT;
SET SDINC1=1; SET SDINC2=1; SET SDINC3=1;
SET WDINC1=0; SET WDINC2=1; SET WDINC3=
ZSIMZ;

SET VARNUM=3; INIT;
SET SDINC1=1; SET SDINC2=1; SET SDINC3=1;
SET WDINC 1=0; SET WDINC2=0; SET WDINC3=
ZSIMZ;


SET SDINC4=1;
-0; SET WDINC4-


0; ? XINC=1;


SET SDINC4=1;
0; SET WDINC4=0; ? XINC=2;



SET SDINC4=1;
:1; SET WDINC4=0; ? XINC=3;


SET VARNUM=4; INIT;


SET SDINC1=
SET WDINC 1:
ZSIMZ;


1; SET SDINC2=1; SET SDINC3=1;
=0; SET WDINC2=0; SET WDINC3=


SET SDINC4=1;
0; SET WDINC4-


1; ? XINC=4;









SET VARNUM=5; INIT;
SET SDINC1=1; SET SDINC2=1; SET SDINC3=1; SET SDINC4=1;
SET WDINC1=-1; SET WDINC2=-1; SET WDINC3=-1; SET WDINC4=-1; ? XINC=5;
ZSIMZ;

? SIMULATION #7 EDUCATION
9- - - -


SET SIMNUM=7;
SET VARNUM=1; INIT;
SET SDEDU1=1; SET SDEDU2=1; SET SDEDU3=1;
SET WDEDU1=1; SET WDEDU2=0; SET WDEDU3=0;
ZSIMZ;

SET VARNUM=2; INIT;
SET SDEDU1=1; SET SDEDU2=1; SET SDEDU3=1;
SET WDEDU1=0; SET WDEDU2=1; SET WDEDU3=0;
ZSIMZ;

SET VARNUM=3; INIT;
SET SDEDU1=1; SET SDEDU2=1; SET SDEDU3=1;
SET WDEDU1=0; SET WDEDU2=0; SET WDEDU3=1;
ZSIMZ;


? XEDU=1;





? XEDU=2;





? XEDU=3;


SET VARNUM=4; INIT;
SET SDEDU1=1; SET SDEDU2=1; SET SDEDU3=1;
SET WDEDU1=-1; SET WDEDU2=-1; SET WDEDU3=-1; ? XEDU=4;
ZSIMZ;

? SIMULATION #8 HOUSEHOLD SIZE

SET SIMNUM=8;
SET VARNUM=1; INIT;
SET SDHWD1=1; SET SDHWD2=1; SET SDHWD3=1; SET SDHWD4=1;
SET WDHWD1=1; SET WDHWD2=0; SET WDHWD3=0; SET WDHWD4=0; ? XHWD=1;
ZSIMZ;

SET VARNUM=2; INIT;
SET SDHWD1=1; SET SDHWD2=1; SET SDHWD3=1; SET SDHWD4=1;
SET WDHWD1=0; SET WDHWD2=1; SET WDHWD3=0; SET WDHWD4=0; ? XHWD=2;
ZSIMZ;

SET VARNUM=3; INIT;
SET SDHWD1=1; SET SDHWD2=1; SET SDHWD3=1; SET SDHWD4=1;
SET WDHWD1=0; SET WDHWD2=0; SET WDHWD3=1; SET WDHWD4=0; ? XHWD=3;
ZSIMZ;









SET VARNUM
SET SDHWD1=
SET WDHWD1
ZSIMZ;

SET VARNUM
SET SDHWD1=
SET WDHWD1
ZSIMZ;


=4; INIT;
1; SET SDHWD2=1; SET SDHWD3=1; SET SDHWD4=1;
=0; SET WDHWD2=0; SET WDHWD3=0; SET WDHWD4=1;


=5; INIT;
1; SET SDHWD2=1; SET SDHWD3=1; SET SDHWD4=1;
=-1; SET WDHWD2=-1; SET WDHWD3=-1; SET WDHWD4=


? XHWD=4;




-1; ?XHWD=5;


? SIMULATION #9 WITH CHILDREN UNDER 18

SET SIMNUM=9;
SET VARNUM=1; INIT;
SET SDCHL=1;
SET WDCHL=1; ? XCHL=1;
ZSIMZ;

SET VARNUM=2; INIT;
SET SDCHL=1;
SET WDCHL=-1; ? XCHL=0;
ZSIMZ;

? SIMULATION #10 -EMPLOYMENT

SET SIMNUM=10;
SET VARNUM=1; INIT;
SET SDEMPLY1=1; SET SDEMPLY2=1; SET SDEMPLY3=1;
SET WDEMPLY1=1; SET WDEMPLY2=0; SET WDEMPLY3=
ZSIMZ;

SET VARNUM=2; INIT;
SET SDEMPLY1=1; SET SDEMPLY2=1; SET SDEMPLY3=1;
SET WDEMPLY1=0; SET WDEMPLY2=1; SET WDEMPLY3=
ZSIMZ;

SET VARNUM=3; INIT;
SET SDEMPLY1=1; SET SDEMPLY2=1; SET SDEMPLY3=1;
SET WDEMPLY1=0; SET WDEMPLY2=0; SET WDEMPLY3=
ZSIMZ;


0; ?XEMPLY=1;




0; ?XEMPLY=2;




1; ?XEMPLY=3;


SET VARNUM=4; INIT;
SET SDEMPLY1=1; SET SDEMPLY2=1; SET SDEMPLY3=1;
SET WDEMPLY1=-1; SET WDEMPLY2=-1; SET WDEMPLY3=-1; ? XEMPLY=4;
ZSIMZ;










? SIMULATION #11 REGION

SET SIMNUM=11;
SET VARNUM=1; INIT;
SET SDREG2=1; SET SDREG3=1; SET SDREG4=1;
SET WDREG2=-1; SET WDREG3=-1; SET WDREG4=-1; ? REGION=1;
ZSIMZ;

SET VARNUM=2; INIT;
SET SDREG2=1; SET SDREG3=1; SET SDREG4=1;
SET WDREG2=1; SET WDREG3=0; SET WDREG4=0; ? REGION=2;
ZSIMZ;

SET VARNUM=3; INIT;
SET SDREG2=1; SET SDREG3=1; SET SDREG4=1;
SET WDREG2=0; SET WDREG3=1; SET WDREG4=0; ? REGION=3;
ZSIMZ;

SET VARNUM=4; INIT;
SET SDREG2=1; SET SDREG3=1; SET SDREG4=1;
SET WDREG2=0; SET WDREG3=0; SET WDREG4=1; ? REGION=4;
ZSIMZ;

? SIMULATION #12 STORE CHOICES

SET SIMNUM=12.1;
SET VARNUM=1; INIT;
SET SDSHOP GRO=1;
SET WDSHOP GRO= 1; ? XSHOP GRO=1;
ZSIMZ;
SET VARNUM=2; INIT;
SET SDSHOP GRO=1;
SET WDSHOP GRO=-1; ? XSHOP GRO=0;
ZSIMZ;

SET SIMNUM=12.2;
SET VARNUM=1; INIT;
SET SDSHOP WARE=1;
SET WDSHOPWARE=1; ? XSHOPWARE=1;
ZSIMZ;
SET VARNUM=2; INIT;
SET SDSHOP WARE=1;
SET WDSHOPWARE=-1; ? XSHOPWARE=0;
ZSIMZ;









SET SIMNUM=12.3;
SET VARNUM=1; INIT;
SET SDSHOP INTE=1;
SET WDSHOP INTE=1;
ZSIMZ;
SET VARNUM=2; INIT;
SET SDSHOP INTE=1;
SET WDSHOP INTE=-1;
ZSIMZ;

SET SIMNUM=12.4;
SET VARNUM=1; INIT;
SET SDSHOP MASS=1;
SET WDSHOP MASS=1
ZSIMZ;
SET VARNUM=2; INIT;
SET SDSHOP MASS=1;
SET WDSHOP MASS=-1
ZSIMZ;

SET SIMNUM=12.5;
SET VARNUM=1; INIT;
SET SDSHOP CONV=1;
SET WDSHOP CONV=I
ZSIMZ;
SET VARNUM=2; INIT;
SET SDSHOP CONV=1;
SET WDSHOP CONV=-
ZSIMZ;

SET SIMNUM=12.6;
SET VARNUM=1; INIT;


? XSHOPINTE=1;



? XSHOPINTE=0;






; ?XSHOP MASS=1;



; ? XSHOP MASS=0;






; ?XSHOPCONV=1;



1; ? XSHOPCONV=0;


SET SDSHOP FARM=1;
SET WDSHOP FARM=1; ? XSHOP FARM=1;
ZSIMZ;
SET VARNUM=2; INIT;
SET SDSHOP FARM=1;
SET WDSHOP FARM=-1; ? XSHOP FARM=0;
ZSIMZ;


? SIMULATION #13 EXPENDITURE ON GROCERIES

SET SIMNUM=13;
SET VARNUM=1; INIT;
SET SDEXPD1=1; SET SDEXPD2=1; SET SDEXPD3=1; SET SDEXPD4=1;









SET WDEXPD1=1; SET WDEXPD2=0; SET WDEXPD3=
ZSIMZ;

SET VARNUM=2; INIT;
SET SDEXPD1=1; SET SDEXPD2=1; SET SDEXPD3=1;
SET WDEXPD1=0; SET WDEXPD2=1; SET WDEXPD3=
ZSIMZ;

SET VARNUM=3; INIT;
SET SDEXPD1=1; SET SDEXPD2=1; SET SDEXPD3=1;
SET WDEXPD1=0; SET WDEXPD2=0; SET WDEXPD3=
ZSIMZ;


0; SET WDEXPD4=0; ? XEXPD=1;



SET SDEXPD4=1;
0; SET WDEXPD4=0; ? XEXPD=2;



SET SDEXPD4=1;
1; SET WDEXPD4=0; ? XEXPD=3;


SET VARNUM=
SET SDEXPD1=
SET WDEXPD1
ZSIMZ;


=4; INIT;
=1; SET SDEXPD2=1; SET SDEXPD3=1;
=0; SET WDEXPD2=0; SET WDEXPD3=


SET SDEXPD4=1;
0; SET WDEXPD4=


1; ?XEXPD=4;


SET VARNUM=5; INIT;
SET SDEXPD1=1; SET SDEXPD2=1; SET SDEXPD3=1;


SET SDEXPD4=1;


SET WDEXPD1=-1; SET WDEXPD2=-1; SET WDEXPD3=-1; SET WDEXPD4=-1; ?
XEXPD=5;
ZSIMZ;


? SIMULATION #14 SERVINGS OF FRUIT&VEG PER DAY;


SET SIMNUM=14.1; ? SERVINGS OF FRUIT;
SET VARNUM=1; INIT;
SET SDSERVF1=1; SET SDSERVF2=1; SET SDSERVF3=1;
SET WDSERVF1=1; SET WDSERVF2=0; SET WDSERVF3=
ZSIMZ;
SET VARNUM=2; INIT;
SET SDSERVF1=1; SET SDSERVF2=1; SET SDSERVF3=1;
SET WDSERVF1=0; SET WDSERVF2=1; SET WDSERVF3=
ZSIMZ;
SET VARNUM=3; INIT;
SET SDSERVF1=1; SET SDSERVF2=1; SET SDSERVF3=1;
SET WDSERVF1=0; SET WDSERVF2=0; SET WDSERVF3=
ZSIMZ;
SET VARNUM=4; INIT;
SET SDSERVF1=1; SET SDSERVF2=1; SET SDSERVF3=1;
SET WDSERVF1=-1; SET WDSERVF2=-1; SET WDSERVF:
ZSIMZ;


0; ?XSERFRU=1;



0; ? XSERFRU=2;



1; ?XSERFRU=3;


3=-1; ?XSERFRU=4;


SET SIMNUM=14.2; ? SERVINGS OF VEG;
SET VARNUM=1; INIT;









SET SDSERVV1=1; SET SDSERVV2=1; SET SDSERVV3=1;
SET WDSERVV1=1; SET WDSERVV2=0; SET WDSERVV3=0; ? XSERVEG=1;
ZSIMZ;
SET VARNUM=2; INIT;
SET SDSERVV1=1; SET SDSERVV2=1; SET SDSERVV3=1;
SET WDSERVV1=0; SET WDSERVV2=1; SET WDSERVV3=0; ? XSERVEG=2;
ZSIMZ;
SET VARNUM=3; INIT;
SET SDSERVV1=1; SET SDSERVV2=1; SET SDSERVV3=1;
SET WDSERVV1=0; SET WDSERVV2=0; SET WDSERVV3=1; ? XSERVEG=3;
ZSIMZ;
SET VARNUM=4; INIT;
SET SDSERVV1=1; SET SDSERVV2=1; SET SDSERVV3=1;
SET WDSERVV1=-1; SET WDSERVV2=-1; SET WDSERVV3=-1; ? XSERVEG=4;
ZSIMZ;

? SIMULATION #15 COUNT CALORIES

SET SIMNUM=15;
SET VARNUM=1; INIT;
SET SDCAL1=1; SET SDCAL2=1; SET SDCAL4=1; SET SDCAL5=1;
SET WDCAL1=1; SET WDCAL2=0; SET WDCAL4=0; SET WDCAL5=0; ? CAL=1;
ZSIMZ;

SET VARNUM=2; INIT;
SET SDCAL1=1; SET SDCAL2=1; SET SDCAL4=1; SET SDCAL5=1;
SET WDCAL1=0; SET WDCAL2=1; SET WDCAL4=0; SET WDCAL5=0; ? CAL=2;
ZSIMZ;

SET VARNUM=3; INIT;
SET SDCAL1=1; SET SDCAL2=1; SET SDCAL4=1; SET SDCAL5=1;
SET WDCAL1=-1; SET WDCAL2=-1; SET WDCAL4=-1; SET WDCAL5=-1; ? CAL=3;
ZSIMZ;

SET VARNUM=4; INIT;
SET SDCAL1=1; SET SDCAL2=1; SET SDCAL4=1; SET SDCAL5=1;
SET WDCAL1=0; SET WDCAL2=0; SET WDCAL4=1; SET WDCAL5=0; ? CAL=4;
ZSIMZ;

SET VARNUM=5; INIT;
SET SDCAL1=1; SET SDCAL2=1; SET SDCAL4=1; SET SDCAL5=1;
SET WDCAL1=0; SET WDCAL2=0; SET WDCAL4=0; SET WDCAL5=1; ? CAL=5;
ZSIMZ;

? SIMULATION #16 -EATING FRESH FOODS
9- - - -









SET SIMNUM=16;
SET VARNUM=1; INIT;
SET SDB FRE1=1; SET SDB FRE2=1; SET SDB FRE4=1; SET SDB FRE5=1;
SET WDB FRE1=1; SET WDBFRE2=0; SET WDBFRE4=0; SET WDBFRE5=0; ?
BFRE=1;
ZSIMZ;

SET VARNUM=2; INIT;
SET SDBFRE1=1; SET SDB FRE2=1; SET SDB FRE4=1; SET SDB FRE5=1;
SET WDB FRE1=0; SET WDB FRE2=1; SET WDB FRE4=0; SET WDB FRE5=0; ?
B FRE=2;
ZSIMZ;

SET VARNUM=3; INIT;
SET SDB FRE1=1; SET SDB FRE2=1; SET SDB FRE4=1; SET SDB FRE5=1;
SET WDBFRE1=-1; SET WDBFRE2=-1; SET WDBFRE4=-1; SET WDBFRE5=-1; ?
BFRE=3;
ZSIMZ;

SET VARNUM=4; INIT;
SET SDBFRE1=1; SET SDB FRE2=1; SET SDB FRE4=1; SET SDB FRE5=1;
SET WDB FRE1=0; SET WDB FRE2=0; SET WDB FRE4=1; SET WDB FRE5=0; ?
B FRE=4;
ZSIMZ;

SET VARNUM=5; INIT;
SET SDBFRE1=1; SET SDB FRE2=1; SET SDB FRE4=1; SET SDB FRE5=1;
SET WDB FRE1=0; SET WDBFRE2=0; SET WDBFRE4=0; SET WDBFRE5=1; ?
B FRE=5;
ZSIMZ;

? SIMULATION #17 -READ LABEL

SET SIMNUM=17;
SET VARNUM=1; INIT;
SET SDBLABI=1; SET SDB LAB2=1; SET SDB LAB4=1; SET SDB LAB5=1;
SET WDB LAB1=1; SET WDB LAB2=0; SET WDB LAB4=0; SET WDB LAB5=0; ?
BLAB=1;
ZSIMZ;

SET VARNUM=2; INIT;
SET SDB LABi=1; SET SDB LAB2=1; SET SDB LAB4=1; SET SDB LAB5=1;
SET WDBLAB1=0; SET WDBLAB2=1; SET WDBLAB4=0; SET WDBLAB5=0; ?
BLAB=2;
ZSIMZ;









SET VARNUM=3; INIT;
SET SDB_LABI=1; SET SDB LAB2=1; SET SDB LAB4=1; SET SDB LAB5=1;
SETWDB LABI=-1; SET WDB LAB2=-1; SET WDB LAB4=-1; SET WDB LAB5=-1; ?
BLAB=3;
ZSIMZ;

SET VARNUM=4; INIT;
SET SDB_LABI=1; SET SDB LAB2=1; SET SDB LAB4=1; SET SDB LAB5=1;
SET WDBLAB1=0; SET WDBLAB2=0; SET WDBLAB4=1; SET WDBLAB5=0; ?
BLAB=4;
ZSIMZ;

SET VARNUM=5; INIT;
SET SDB LABI=1; SET SDB LAB2=1; SET SDB LAB4=1; SET SDB LAB5=1;
SET WDB LAB1=0; SET WDB LAB2=0; SET WDB LAB4=0; SET WDB LAB5=1; ?
BLAB=5;
ZSIMZ;


? SIMULATION #18 BUY FROM CERTAIN STORES
9


SET SIMNUM=18;
SET VARNUM=1; INIT;
SET SDB ST1=1; SET SDB ST2=1; SET SDB_ST4=1;
SET WDBST1=1; SET WDBST2=0; SET WDB_ST4=
ZSIMZ;


SET SDB ST5=1;
0; SET WDB ST5


-0; ? B ST=1;


SET VARNUM=2; INIT;


SET SDB ST1=
SET WDB ST1-
ZSIMZ;

SET VARNUM:
SET SDB ST1=
SET WDB ST1:
ZSIMZ;


1; SET SDBST2=1; SET SDB_ST4=1;
=0; SET WDB ST2=1; SET WDB ST4=


SET SDB ST5=1;
0; SET WDB ST5:


=0; ? B ST=2;


=3; INIT;
1; SET SDB ST2=1; SET SDB_ST4=1; SET SDB ST5=1;
=-1; SET WDB ST2=-1; SET WDB ST4=-1; SET WDB ST5=-1;?B ST=3;


SET VARNUM=4; INIT;
SET SDB ST1=1; SET SDB ST2=1; SET SDB_ST4=1;
SET WDBST1=0; SET WDBST2=0; SET WDB_ST4=
ZSIMZ;


SET VARNUM:
SET SDB ST1=
SET WDB ST1-
ZSIMZ;


=5; INIT;
1; SET SDBST2=1; SET SDB_ST4=1;
=0; SET WDBST2=0; SET WDB_ST4=


SET SDB ST5=1;
=1; SET WDBST5



SET SDB ST5=1;
=0; SET WDBST5


=0; ? BST=4;




=1; ?B_ST=5;










? SIMULATION #19 WAY TO GET CERTAIN PRODUCE ;

SET SIMNUM=19;
SET VARNUM=1; INIT;
SET SDB WAY1=1; SET SDB WAY2=1; SET SDB WAY4=1; SET SDB WAY5=1;
SET WDB WAY1=1; SET WDB WAY2=0; SET WDB WAY4=0; SET WDB WAY5=0; ?
BWAY= 1;
ZSIMZ;

SET VARNUM=2; INIT;
SET SDBWAY1=1; SET SDB WAY2=1; SET SDB WAY4=1; SET SDB WAY5=1;
SET WDBWAY1=0; SET WDBWAY2=1; SET WDBWAY4=0; SET WDBWAY5=0; ?
B WAY=2;
ZSIMZ;

SET VARNUM=3; INIT;
SET SDB WAY1=1; SET SDB WAY2=1; SET SDB WAY4=1; SET SDB WAY5=1;
SET WDB WAY1=-1; SET WDB WAY2=-1; SET WDB WAY4=-1; SET WDB WAY5=-1; ?
B WAY=3;
ZSIMZ;

SET VARNUM=4; INIT;
SET SDBWAY1=1; SET SDB WAY2=1; SET SDB WAY4=1; SET SDB WAY5=1;
SET WDBWAY1=0; SET WDBWAY2=0; SET WDBWAY4=1; SET WDBWAY5=0; ?
B WAY=4;
ZSIMZ;

SET VARNUM=5; INIT;
SET SDB WAY1=1; SET SDB WAY2=1; SET SDB WAY4=1; SET SDB WAY5=1;
SET WDBWAY1=0; SET WDBWAY2=0; SET WDBWAY4=0; SET WDBWAY5=1; ?
B WAY=5;
ZSIMZ;

? SIMULATION #20 EATING FRESH FRUITS & VEG

SET SIMNUM=20;
SET VARNUM=1; INIT;
SET SDB FV1=1; SET SDB FV2=1; SET SDB FV4=1; SET SDB FV5=1;
SET WDB FV1=1; SET WDBFV2=0; SET WDB FV4=0; SET WDBFV5=0; ? B FV=1;
ZSIMZ;

SET VARNUM=2; INIT;
SET SDB FV1=1; SET SDB FV2=1; SET SDB FV4=1; SET SDB FV5=1;
SET WDB FV1=0; SET WDB FV2=1; SET WDB FV4=0; SET WDB FV5=0; ? B FV=2;
ZSIMZ;










SET VARNUM:
SET SDB FV1=
SET WDB FV1
ZSIMZ;

SET VARNUM:
SET SDB FV1=
SET WDB FV1
ZSIMZ;

SET VARNUM:
SET SDB FV1=
SET WDB FV1
ZSIMZ;


=3; INIT;
1; SET SDB FV2=1; SET SDB FV4=1; SET SDB FV5=1;
=-1; SET WDB FV2=-1; SET WDB FV4=-1; SET WDB FV5=-1; ? B FV=3;


=4; INIT;
1; SET SDB FV2=1; SET SDB FV4=1;
=0; SET WDBFV2=0; SET WDB FV4=


=5; INIT;
1; SET SDB FV2=1; SET SDB FV4=1;
=0; SET WDB FV2=0; SET WDB FV4=


SET SDB FV5=1;
1; SET WDB FV5=0; ? B FV=4;



SET SDB FV5=1;
0; SET WDB FV5=1; ? B FV=5;


? SIMULATION #21 FEEL HEALTHIER THAN PEERS


SET SIMNUM=21;
SET VARNUM=1; INIT;
SET SDBHLT1=1; SET SDBHLT2=1; SET SDBHLT4=1;
SET WDB HLT1=1; SET WDB HLT2=0; SET WDB HLT4=
B HLT=1;
ZSIMZ;


SET SDBHLT5=1;
-0; SET WDB HLT5=0; ?


SET VARNUM=2; INIT;
SET SDBHLT1=1; SET SDBHLT2=1; SET SDBHLT4=1; SET SDBHLT5=1;
SET WDBHLT1=0; SET WDBHLT2=1; SET WDB HLT4=0; SET WDBHLT5=0; ?
B HLT=2;
ZSIMZ;

SET VARNUM=3; INIT;
SET SDB HLT1=1; SET SDB HLT2=1; SET SDB HLT4=1; SET SDB HLT5=1;
SET WDB HLT1=-1; SET WDB HLT2=-1; SET WDB HLT4=-1; SET WDB HLT5=-1; ?
B HLT=3;
ZSIMZ;

SET VARNUM=4; INIT;
SET SDBHLT1=1; SET SDBHLT2=1; SET SDBHLT4=1; SET SDBHLT5=1;
SET WDBHLT1=0; SET WDBHLT2=0; SET WDB HLT4=1; SET WDBHLT5=0; ?
B HLT=4;
ZSIMZ;

SET VARNUM=5; INIT;
SET SDB HLT1=1; SET SDB HLT2=1; SET SDB HLT4=1; SET SDB HLT5=1;
SETT W WDB HLT SETDB HLT20; SET WDB HLT4=0; SET WDB HLT5=1; ?









BHLT=5;
ZSIMZ;

? SIMULATION #22 EXERCISE

SET SIMNUM=22;
SET VARNUM=1; INIT;
SET SDBEXE1=1; SET SDB EXE2=1; SET SDB EXE4=1; SET SDB EXE5=1;
SET WDBEXE1=1; SET WDBEXE2=0; SET WDBEXE4=0; SET WDBEXE5=0; ?
B EXE=1;
ZSIMZ;

SET VARNUM=2; INIT;
SET SDB EXE1=1; SET SDB EXE2=1; SET SDB EXE4=1; SET SDB EXE5=1;
SET WDB EXE1=0; SET WDB EXE2=1; SET WDB EXE4=0; SET WDB EXE5=0; ?
BEXE=2;
ZSIMZ;

SET VARNUM=3; INIT;
SET SDBEXE1=1; SET SDB EXE2=1; SET SDB EXE4=1; SET SDB EXE5=1;
SET WDBEXE1=-1; SET WDBEXE2=-1; SET WDBEXE4=-1; SET WDBEXE5=-1; ?
B EXE=3;
ZSIMZ;

SET VARNUM=4; INIT;
SET SDB EXE1=1; SET SDB EXE2=1; SET SDB EXE4=1; SET SDB EXE5=1;
SET WDBEXE1=0; SET WDBEXE2=0; SET WDBEXE4=1; SET WDBEXE5=0; ?
BEXE=4;
ZSIMZ;


SET VARNUM=5; INIT;
SET SDBEXE1=1; SET SDB EXE2=1; SET SDB EXE4=1;
SET WDB EXE1=0; SET WDB EXE2=0; SET WDB EXE4=
B EXE=5;
ZSIMZ;
9


SET SDB EXE5=1;
-0; SET WDB EXE5=1; ?


? SIMULATION #23 EXPLORE NEW FOODS

SET SIMNUM=23;
SET VARNUM=1; INIT;
SET SDB NEW1=1; SET SDB NEW2=1; SET SDB NEW4=1; SET SDB NEW5=1;
SET WDB NEW1=1; SET WDB NEW2=0; SET WDB NEW4=0; SET WDB NEW5=0; ?
B NEW=1;
ZSIMZ;


SET VARNUM=2; INIT;









SET SDB NEW1=1; SET SDB NEW2=1; SET SDB NEW4=1; SET SDB NEW5=1;
SET WDBNEW1=0; SET WDB NEW2=1; SET WDBNEW4=0; SET WDBNEW5=0; ?
B NEW=2;
ZSIMZ;

SET VARNUM=3; INIT;
SET SDB NEW1=1; SET SDB NEW2=1; SET SDB NEW4=1; SET SDB NEW5=1;
SET WDB NEW1=-1; SET WDB NEW2=-1; SET WDBNEW4=-1; SET WDBNEW5=-1; ?
B NEW=3;
ZSIMZ;

SET VARNUM=4; INIT;
SET SDBNEW1=1; SET SDB NEW2=1; SET SDBNEW4=1; SET SDBNEW5=1;
SET WDB NEW1=0; SET WDB NEW2=0; SET WDB NEW4=1; SET WDB NEW5=0; ?
B NEW=4;
ZSIMZ;

SET VARNUM=5; INIT;
SET SDB NEW1=1; SET SDB NEW2=1; SET SDB NEW4=1; SET SDB NEW5=1;
SET WDBNEW1=0; SET WDBNEW2=0; SET WDBNEW4=0; SET WDBNEW5=1; ?
BNEW=5;
ZSIMZ;

? SIMULATION #24 HEALTH CONDITION

SET SIMNUM=24.1; ? IF ANYONE IN HOUSEHOLD HAS BLOOD PRESSURE;
SET VARNUM=1; INIT;
SET SDHLT BP=1;
SET WDHLT BP=1; ? HLT BP=1 NO ONE IN HOUSEHOLD HAS BLOOD PRESSURE;
ZSIMZ;
SET VARNUM=2; INIT;
SET SDHLT BP=1;
SET WDHLT BP=-1; ? HLT BP=0;
ZSIMZ;

SET SIMNUM=24.2; ? IF ANYONE IN HOUSEHOLD HAS DIABETES;
SET VARNUM=1; INIT;
SET SDHLT DB=1;
SET WDHLT DB=1; ? HLTDB=1 NO ONE IN HOUSEHOLD HAS DIABETES;
ZSIMZ;
SET VARNUM=2; INIT;
SET SDHLT DB=1;
SET WDHLTDB=-1; ? HLT DB=0;
ZSIMZ;

SET SIMNUM=24.3; ? IF ANYONE IN HOUSEHOLD HAS CHOLESTEROL;









SET VARNUM=1; INIT;
SET SDHLTCL=1;
SET WDHLT CL=1; ? HLT CL=1 NO ONE IN HOUSEHOLD HAS CHLOESTEROL;
ZSIMZ;
SET VARNUM=2; INIT;
SET SDHLT CL=1;
SET WDHLT CL=-1; ? HLT CL=0;
ZSIMZ;

SET SIMNUM=24.4; ? IF ANYONE IN HOUSEHOLD HAS FOOD ALLERGIES;
SET VARNUM=1; INIT;
SET SDHLTAG=1;
SET WDHLTAG=1; ? HLT AG=1 NO ONE IN HOUSEHOLD HAS FOOD ALLERGIES;
ZSIMZ;
SET VARNUM=2; INIT;
SET SDHLTAG=1;
SET WDHLTAG=-1; ? HLT AG=0;
ZSIMZ;

SET SIMNUM=24.5; ? IF ANYONE IN HOUSEHOLD HAS OBESITY PROBLEMS;
SET VARNUM=1; INIT;
SET SDHLT OB=1;
SET WDHLT OB=1; ? HLT OB=1 NO ONE IN HOUSEHOLD HAS OBESITY PROBLEMS;
ZSIMZ;
SET VARNUM=2; INIT;
SET SDHLT OB=1;
SET WDHLTOB=-1; ? HLTOB=0;
ZSIMZ;

SET SIMNUM=24.6; ? IF ANYONE IN HOUSEHOLD HAS MOBILITY PROBLEMS;
SET VARNUM=1; INIT;
SET SDHLTMB=1;
SET WDHLT MB=1; ? HLT MB=1 NO ONE IN HOUSEHOLD HAS MOBILITY
PROBLEMS;
ZSIMZ;
SET VARNUM=2; INIT;
SET SDHLT MB=1;
SET WDHLT MB=-1; ? HLT MB=0;
ZSIMZ;

SET SIMNUM=24.7; ? IF ANYONE IN HOUSEHOLD HAS SIGHT/HEARING PROBLEMS;
SET VARNUM=1; INIT;
SET SDHLT HR=1;
SET WDHLT HR=1; ? HLTHR=1 NO ONE IN HOUSEHOLD HAS SIGHT/HEARING
PROBLEMS;
ZSIMZ;









SET VARNUM=2; INIT;
SET SDHLT HR=1;
SET WDHLT HR=-1; ? HLT HR=0;
ZSIMZ;

? SIMULATION #25 MONTHS

SET SIMNUM=25;
SET VARNUM=1; INIT;
SET SDMTH2=1; SET SDMTH3=1; SET SDMTH4=1; SET SDMTH5=1;
SET SDMTH6=1; SET SDMTH7=1; SET SDMTH8=1; SET SDMTH9=1;
SET SDMTH10=1; SET SDMTH11=1; SET SDMTH12=1;

SET WDMTH2=-1; SET WDMTH3=-1; SET WDMTH4=-1; SET WDMTH5=-1;
SET WDMTH6=-1; SET WDMTH7=-1; SET WDMTH8=-1; SET WDMTH9=-1;
SET WDMTH10=-1; SET WDMTH11=-1; SET WDMTH12=-1; ? MTH=1;
ZSIMZ;

SET VARNUM=2; INIT;
SET SDMTH2=1; SET SDMTH3=1; SET SDMTH4=1; SET SDMTH5=1;
SET SDMTH6=1; SET SDMTH7=1; SET SDMTH8=1; SET SDMTH9=1;
SET SDMTH10=1; SET SDMTH11=1; SET SDMTH12=1;

SET WDMTH2=1; SET WDMTH3=0; SET WDMTH4=0; SET WDMTH5=0;
SET WDMTH6=0; SET WDMTH7=0; SET WDMTH8=0; SET WDMTH9=0;
SET WDMTH10=0; SET WDMTH11=0; SET WDMTH12=0; ? MTH=2;
ZSIMZ;

SET VARNUM=3; INIT;
SET SDMTH2=1; SET SDMTH3=1; SET SDMTH4=1; SET SDMTH5=1;
SET SDMTH6=1; SET SDMTH7=1; SET SDMTH8=1; SET SDMTH9=1;
SET SDMTH10=1; SET SDMTH11=1; SET SDMTH12=1;

SET WDMTH2=0; SET WDMTH3=1; SET WDMTH4=0; SET WDMTH5=0;
SET WDMTH6=0; SET WDMTH7=0; SET WDMTH8=0; SET WDMTH9=0;
SET WDMTH10=0; SET WDMTH11=0; SET WDMTH12=0; ? MTH=3;
ZSIMZ;

SET VARNUM=4; INIT;
SET SDMTH2=1; SET SDMTH3=1; SET SDMTH4=1; SET SDMTH5=1;
SET SDMTH6=1; SET SDMTH7=1; SET SDMTH8=1; SET SDMTH9=1;
SET SDMTH10=1; SET SDMTH11=1; SET SDMTH12=1;

SET WDMTH2=0; SET WDMTH3=0; SET WDMTH4=4; SET WDMTH5=0;
SET WDMTH6=0; SET WDMTH7=0; SET WDMTH8=0; SET WDMTH9=0;
SET WDMTH10=0; SET WDMTH11=0; SET WDMTH12=0; ? MTH=4;









ZSIMZ;


SET VARNUM=5; INIT;
SET SDMTH2=1; SET SDMTH3=1; SET SDMTH4=1; SET SDMTH5=1;
SET SDMTH6=1; SET SDMTH7=1; SET SDMTH8=1; SET SDMTH9=1;
SET SDMTH10=1; SET SDMTH11=1; SET SDMTH12=1;

SET WDMTH2=0; SET WDMTH3=0; SET WDMTH4=0; SET WDMTH5=1;
SET WDMTH6=0; SET WDMTH7=0; SET WDMTH8=0; SET WDMTH9=0;
SET WDMTH10=0; SET WDMTH11=0; SET WDMTH12=0; ? MTH=5;
ZSIMZ;

SET VARNUM=6; INIT;
SET SDMTH2=1; SET SDMTH3=1; SET SDMTH4=1; SET SDMTH5=1;
SET SDMTH6=1; SET SDMTH7=1; SET SDMTH8=1; SET SDMTH9=1;
SET SDMTH10=1; SET SDMTH11=1; SET SDMTH12=1;

SET WDMTH2=0; SET WDMTH3=0; SET WDMTH4=0; SET WDMTH5=0;
SET WDMTH6=1; SET WDMTH7=0; SET WDMTH8=0; SET WDMTH9=0;
SET WDMTH10=0; SET WDMTH11=0; SET WDMTH12=0; ? MTH=6;
ZSIMZ;

SET VARNUM=7; INIT;
SET SDMTH2=1; SET SDMTH3=1; SET SDMTH4=1; SET SDMTH5=1;
SET SDMTH6=1; SET SDMTH7=1; SET SDMTH8=1; SET SDMTH9=1;
SET SDMTH10=1; SET SDMTH11=1; SET SDMTH12=1;

SET WDMTH2=0; SET WDMTH3=0; SET WDMTH4=0; SET WDMTH5=0;
SET WDMTH6=0; SET WDMTH7=1; SET WDMTH8=0; SET WDMTH9=0;
SET WDMTH10=0; SET WDMTH11=0; SET WDMTH12=0; ? MTH=7;
ZSIMZ;

SET VARNUM=8; INIT;
SET SDMTH2=1; SET SDMTH3=1; SET SDMTH4=1; SET SDMTH5=1;
SET SDMTH6=1; SET SDMTH7=1; SET SDMTH8=1; SET SDMTH9=1;
SET SDMTH10=1; SET SDMTH11=1; SET SDMTH12=1;

SET WDMTH2=0; SET WDMTH3=0; SET WDMTH4=0; SET WDMTH5=0;
SET WDMTH6=0; SET WDMTH7=0; SET WDMTH8=1; SET WDMTH9=0;
SET WDMTH10=0; SET WDMTH11=0; SET WDMTH12=0; ? MTH=8;
ZSIMZ;

SET VARNUM=9; INIT;
SET SDMTH2=1; SET SDMTH3=1; SET SDMTH4=1; SET SDMTH5=1;
SET SDMTH6=1; SET SDMTH7=1; SET SDMTH8=1; SET SDMTH9=1;
SET SDMTH10=1; SET SDMTH11=1; SET SDMTH12=1;










SET WDMTH2=0; SET WDMTH3=0; SET WDMTH4=0; SET WDMTH5=0;
SET WDMTH6=0; SET WDMTH7=0; SET WDMTH8=0; SET WDMTH9=1;
SET WDMTH10=0; SET WDMTH11=0; SET WDMTH12=0; ? MTH=9;
ZSIMZ;

SET VARNUM=10; INIT;
SET SDMTH2=1; SET SDMTH3=1; SET SDMTH4=1; SET SDMTH5=1;
SET SDMTH6=1; SET SDMTH7=1; SET SDMTH8=1; SET SDMTH9=1;
SET SDMTH10=1; SET SDMTH11=1; SET SDMTH12=1;

SET WDMTH2=0; SET WDMTH3=0; SET WDMTH4=0; SET WDMTH5=0;
SET WDMTH6=0; SET WDMTH7=0; SET WDMTH8=0; SET WDMTH9=0;
SET WDMTH10=1; SET WDMTH11=0; SET WDMTH12=0; ? MTH=10;
ZSIMZ;

SET VARNUM=11; INIT;
SET SDMTH2=1; SET SDMTH3=1; SET SDMTH4=1; SET SDMTH5=1;
SET SDMTH6=1; SET SDMTH7=1; SET SDMTH8=1; SET SDMTH9=1;
SET SDMTH10=1; SET SDMTH11=1; SET SDMTH12=1;

SET WDMTH2=0; SET WDMTH3=0; SET WDMTH4=0; SET WDMTH5=0;
SET WDMTH6=0; SET WDMTH7=0; SET WDMTH8=0; SET WDMTH9=0;
SET WDMTH10=0; SET WDMTH11=1; SET WDMTH12=0; ? MTH=11;
ZSIMZ;

SET VARNUM=12; INIT;
SET SDMTH2=1; SET SDMTH3=1; SET SDMTH4=1; SET SDMTH5=1;
SET SDMTH6=1; SET SDMTH7=1; SET SDMTH8=1; SET SDMTH9=1;
SET SDMTH10=1; SET SDMTH11=1; SET SDMTH12=1;

SET WDMTH2=0; SET WDMTH3=0; SET WDMTH4=0; SET WDMTH5=0;
SET WDMTH6=0; SET WDMTH7=0; SET WDMTH8=0; SET WDMTH9=0;
SET WDMTH10=0; SET WDMTH11=0; SET WDMTH12=1; ? MTH=12;
ZSIMZ;

WRITE(FORMAT=EXCEL,FILE='D:\ZZORGANIC\Organics\HIST6.XLS') MSIM;
END;









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BIOGRAPHICAL SKETCH

Yang Zhou was born in Sichuan Province, P. R. China. After she received her Bachelor of

Art degree in Public Finance from the School of Economics in Sichuan University in 2005, she

enrolled at the University of Florida to pursue graduate study.

In May 2007, Yang Zhou was awarded the Master of Agribusiness degree from the

Department of Food and Resource Economics at the University of Florida. She was then

admitted into the Ph.D. program in the Food and Resource Economics Department specializing

in marketing and econometrics. She received her Ph.D. from the University of Florida in the

summer of 2010.





PAGE 1

1 ORGANIC PREFERENCE MODEL IN THE UNITED STATES: AN ORDERED PROBIT MODEL APPLICATION By YANG ZHOU A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLOR IDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 2010

PAGE 2

2 2010 Yang Zhou

PAGE 3

3 To the most important loved one s in my life: my Mom and Dad

PAGE 4

4 ACKNOWLEDGMENTS First of all, I express m y deepest appreciation to all my committee members, Dr. Ronald W. Ward, Dr. R. Jeffrey Burkhardt, Dr. Stephen Holland, Dr. Jonq-Ying L ee, and Dr. Allen F. Wysocki. I would like to gratef ully express my sincere gratitude and overall admiration to my committee chair, Dr. Ronald W. Ward, for his generous guidance and encouragement. He has been the best mentor and it is my incredible fo rtune to have the opportunity to work with him and learned much invaluable knowledge from him. I would like to express my deepest appreciation to him for his patience, help and thorough review of the manuscript to complete my study successfully. I am very appreciative of the support I received from Dr. R. Je ffrey Burkhardt, Dr. Stephen Holland, Dr. Jonq-Ying Lee, and Dr Allen F. Wysocki. Thanks for providing me the support, guidance, comments and suggestions. I would also like to thank my fellow graduate students in the Food and Resource Economics Department and all other fellow stud ents for all the unforgettable memories. I thank my families, especially my Mo m and Dad, for their unconditioned love, encouragement and endless support to me.

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5 TABLE OF CONTENTS page ACKNOWLEDGMENTS...............................................................................................................4 LIST OF TABLES................................................................................................................. ..........7 LIST OF FIGURES.........................................................................................................................8 ABSTRACT...................................................................................................................................10 CHAP TER 1 INTRODUCTION..................................................................................................................12 U.S. Organic Market...............................................................................................................12 Credence Attributes......................................................................................................... 14 Organic Agriculture.........................................................................................................15 Problem Statement.............................................................................................................. ....17 Research Aims and Objectives............................................................................................... 19 Methodology and Data...........................................................................................................20 Overview of the Study............................................................................................................20 2 LITERATURE REVIEW.......................................................................................................23 A Short Review.......................................................................................................................23 Organic Consumer Behaviors................................................................................................. 24 Discrete Choice Model Applications...................................................................................... 27 Other Methodology Application............................................................................................. 29 3 ORGANIC DATABASE........................................................................................................ 30 4 ORGANIC PREFERENCE MODEL..................................................................................... 43 Organic Preference Model Specifications.............................................................................. 43 Predicted Probabilities............................................................................................................46 Partial Change and Discrete Cha nge in Predicted Probabilities ............................................. 47 Restricted Dummy Variables.................................................................................................. 50 5 ANALYSIS OF RESULTS AND SIMULATIONS.............................................................. 52 Ordered Probit Estimates....................................................................................................... .52 Ordered Probit Model Simulations......................................................................................... 53 Seeking Out Organic Foods across Demographics......................................................... 55 Store Choice and Expenditures.......................................................................................58 Behavior/Attitudes Attributes.......................................................................................... 60 Health Concerns..............................................................................................................62

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6 Seasonality.................................................................................................................... ...62 Ranking the Effects on Probabilities of Seeking Out Organic Foods ....................................63 6 SUMMARY, CONCLUSION AND IMPLICATIONS....................................................... 119 APPENDIX A ORGANIC SURVEY VARIABLES.................................................................................... 125 B CORRELATION COEFFICIENTS..................................................................................... 126 C TSP CODE....................................................................................................................... .....130 LIST OF REFERENCES.............................................................................................................167 BIOGRAPHICAL SKETCH.......................................................................................................170

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7 LIST OF TABLES Table page 1-1 Organic food sales and penetra tion of total organic food sales ......................................... 22 3-1 Descriptions of explanatory variables................................................................................35 5-1 Results from Organic Pref erence ordered probit model.................................................... 66 5-2 Organic Preference ordered probit model coefficient estimates........................................ 69 A-1 Organic survey variables..................................................................................................125 B-1 Correlation coefficients of explanatory variables............................................................126

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8 LIST OF FIGURES Figure page 3-1 Frequency distribution of the res ponse to I seek out organic foods ............................... 40 3-2 Frequency distribution of agreement/disagreement about seeking out organic foods....... 40 3-3 Comparison of frequency distribution of agreement about seeking out organic foods in 2008 and 2009............................................................................................................... .41 3-4 Distributions of agreement about seeking out organic foods during the reporting periods................................................................................................................................41 3-5 Frequency distribution of agreement about seeking out organic foods detailed in Completely agree (5) and Mostly agr ee (4) during the reporting periods.................. 42 3-6 Percentage of frequenc y distribution of complete and partial agreement about seeking out organic foods during the reporting periods..................................................... 42 5-1 Probability of seeking out organi c foods for the average respondent................................ 71 5-2 Impact of age of household head on seeking out organic foods........................................ 71 5-3 Impact of gender of household head on seeking out organic foods................................... 73 5-4 Impact of marital status of house hold head on seeking out organic foods........................74 5-5 Impact of race of household h ead on seeking out organic foods.......................................76 5-6 Impact of income of household head on seeking out organic foods.................................. 77 5-7 Impact of education level of house hold head on seeking out organic foods..................... 79 5-8 Impact of household size on seeking out organic foods.................................................... 80 5-9 Impact of presence of children under 18 in household on seeking out organic foods.......82 5-10 Impact of employment status of hou sehold head seeking out organic foods.................... 83 5-11 Impact of census region of house hold head seeking out organic foods.............................85 5-12 Impact of grocery shopping places seeking out organic foods..........................................86 5-13 Impact of expenditures on grocery shopping on seeking out organic foods...................... 89 5-14 Impact of servings of fr uit on seeking out organic foods.................................................. 91 5-15 Impact of servings of vegeta bles on seeking ou t organic foods........................................ 92

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9 5-16 Impact of seasonality on seeking out organic foods .......................................................... 94 5-17 Impact of count calories on seeking out organic foods.................................................. 95 5-18 Impact of eat fresh foods on seeking out organic foods................................................. 97 5-19 Impact of read label on seeking out organic foods......................................................... 98 5-20 Impact of buy from certain stores on seeking out organic foods.................................100 5-21 Impact of go out of way to get cert ain types of produce on seeking out organic foods.................................................................................................................................102 5-22 Impact of eat fresh fruit and vegetables on seeking out organic foods........................ 104 5-23 Impact of feel healthier on seeking out organic foods.................................................105 5-24 Impact of exercise at least 3 ti mes a week on seeking out organic foods.................... 107 5-25 Impact of experiment with new foods on seeking out organic foods...........................108 5-26 Impact of health concerns of household on head seeking out organic foods................... 110 5-27 Ranking of factors impacting the lik elihood of seeking out organic foods..................... 114

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10 Abstract of Dissertation Pres ented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy ORGANIC PREFERENCE MODEL IN THE UNITED STATES: AN ORDERED PROBIT MODEL APPLICATION By Yang Zhou August 2010 Chair: Ronald W. Ward Major: Food and Resource Economics The organic food industry has been growing at a remarkable rate. In 2008, the retail sales for organic food products in the United States re ached $22.9 billion, with a growth rate of about 15.8% in 2008 over 2007 (OTA, 2009). Tending to be a lifestyle choice in previous years, organic food purchases have evolved into the fact that at least tw o-thirds of American consumers buy organic products at some point in time. Given the bottom line that consumption is based on not what the product is, but what consumers perceive and are aware of about the product, it is important to understand consumer behaviors as well as identify the underlying determinants of choosing organic foods. Household data were collected from an inte rnet survey conducted by the private company from February 2008 through March 2010. The survey was nationally demographically balanced with a total of nearly 38,000 household entries. The essential response to the statement I seek out organic foods was scored by using a five-point Likert scale. Explanatory variables included in the models were established from demogra phic questions (including age, gender, ethnicity, income, education, employment, marital status, household size, region etc), expenditures and grocery shopping locations, behavior/attitudes statements, health concerns and seasonality.

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11 Since household responses to the statement I seek out organic foods were discrete values, ordered probit models were appropriate to estimate the proba bility of seeking out organic foods. To illustrate how the probabilities of seek ing out organic foods differs across sociodemographics, behaviors and attitudes, health con cerns, etc., we simulated probabilities for five outcomes with each given a particular set of cond itions for the explanatory variables by using the coefficients from the results of the ordered probit models. By ranking the relative effects to the averag e likelihood in descending order, variables identified as contributing the major effect on th e probability of seeking out organic foods include numbers of daily servings of fruit, eat fres h foods, read label, go out of way to obtain certain types of produce, and age; alternat ively, gender, lim ited physical mobility concerns, shopping for food in warehouse stores, cholesterol concer ns, and obesity concerns were the five least impor tant factors. Overall, behavioral factors were more important than demographic characteristics, except age, on the probability of s eeking out organic foods. A limitation of the study is that the preferen ce for organics was measured through selfreports of seeking out organics but not reporting the actual consumption level(s). However, an underlying premise is that seeking out organic foods and actual organic consumption are highly correlated.

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12 CHAPTER 1 INTRODUCTION U.S. Organic Market The organic food industry has experienced unpr ecedented growth with an average annual growth rate of 20-24% in recen t years (Dimitr i and Richman, 2000) and a prediction that high double-digit growth rates would continue into the next decades (NMI, 2007). In 2008, the U.S. organic food industry gained $24.6 billion in cons umer sales, among which the retail sales for organic food products reached $22.9 billion (93% of all organic product sales). Although organic food sales only represents 3.47% of the overall f ood retail share, the leve l of organic market penetration (organic food as a pe rcent of total U.S. food sales) ha s doubled or even tripled in the past a few years. Organic food sales rose much faster than total food sa les; specifically, the growth rate of organic food sales was about 15.8 % in 2008 over 2007, compared to a rise in total U.S. food sales of 4.9% during the same peri od (OTA, 2009). However, the organic food sales have slowed since 2007 (see Table 1-1). Mintel report ( 2008) provides the same results about the year-over-year sales trend, and predicts the organic food sales will slow further in the future partially due to the competition between organi c and natural foods. The top three categories produce, dairy products, and beverages repres ent 37%, 16% and 13% of total organic food sales in 2008 respectively (Nutrition Business Journal, 2008). A study that interviewed market managers in more than 20 states, referring to the 2002 market season, reported that within the markets that include organic farmers, demand for organic products was strong in nearly 40 %, medium demand in 47%, and low demand in only 13% of these markets (Kremen, Greene, and Hanson, 2004). The demand for organic products is growing rapidly. After the publication of th e USDA organic label and standards in 2000,

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13 consumer demand for organic products significantly expanded. But what else contributed to this rapid growth? Formerly organic food purchases were usually lifestyle choices of a smaller number of consumers who were expected to differ by race, be affluent, well-educated, and to differ by households size (Dimitri and Oberholtzer, 2006). In re cent years, it appear s that at least twothirds (69%) of American consumers someti me brought organic products with about 28% purchasing weekly according to the Hartman Group (The Hartman Group, 2008). According to the Organic Trade Association s (OTA) 2009 U.S. Families Or ganic Attitudes & Beliefs Study, almost three-quarters (73%) of U.S. families pur chased organic products at least occasionally even though the economic slow down induced U.S. families to reduce their spending. The characteristics of organic consumers have beco me much more diverse and cannot easily be profiled by previous significant pr edictors (such as income, education, etc.). This increase in popularity may be due to increasing availability and affordability for consumers. According to USDA Economic Research Service (USDA/ERS), organic products have been available in nearly 20,000 natural foods stores and in 73% of all conventional grocer y stores since 2002. This accessibility likely facilitated the purchasing of organic food into becoming a mainstream activity, as evidenced by where consumers purch ased their organic foods. Instead of being limited to just conventional supermarkets or mass merchandisers, organic buyers sometimes chose to do grocery shopping in a variety of retail outlets. Based on the OTA's 2009 U.S. Families Organic Attitudes & Beliefs Study, among the parents who chose to buy organic products, 19% reported weekly visits to natural food chain stores, 16% reported weekly visits to local health food/natura l food stores, 16% went to farmer s markets, and 12% shopped in neighborhood co-ops. New organic products continue to be introduced to the U.S. retail market,

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14 rising from 378 products in 2001 to 1,042 products in 2008. The claim organic has ranked among the top 5 advertizing claims every ye ar since 2005 (USDA/ERS Briefing Rooms, 2009). Beverage, packaged and prepared foods, and br ead/grains made up of the majority of new organic product introductions in 2008 (USDA/ERS Briefing Rooms, 2008). The availability of a wider variety of organic foods appears to be responding to increas ed consumer demand. Credence Attributes Consum ers decision-making about buying or ganic products is not only determined by economic factors or by product appearances and tast es, but also by non-material values such as food safety as well as perceived social and environmental benefits. Those are unique aspects of the organic market in product differentiation. Three qualities of a pr oduct search quality, experience quality, and credence quality have been distinguished by Nelson (1970) and Darby and Karni (1973). With search or experience goods consumers are often incapable of judging the credence quality of goods even after consum ption (McCluskey, 2000). Organic products are a popular example of credence attri butes (McCluskey, 2000) since th e information referring to the nature of the product is asymmetric and additiona l information is required. That is, consumers do not know whether the products th ey are purchasing are organic or not unless this kind of information is revealed by the product producer or experts. Moreover, consumers must be confident about the sources that inform them of the underlying production practices. Otherwise, organically produced foods and conventionally produced foods may not be successfully differentiated. To avoid this supply-side market is sue, it is critically important to establish a universally accepted definition of the term of organic, implement a national standard for organically produced products, as well as utilize labeling based on third party certifications for organic food. In fact, the USDA organic logo has been consid ered as a feasible way for consumers to recognize organic products and feel confident about the attributes of organic

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15 products they buy since the performance of the National Organic Standards in 2002 (Dimitri and Oberholtzer, 2009). Organic Agriculture A current definition of the term organic agriculture by the USDAs National Organic Program in condensed form is as follows: Organic crops are raised without using most conventional pe sticides, petroleumbased fertilizers, or sewage-based fert ilizers. Animals raised on an organic operation must be fed organic feed and given access to the outdoors. They are given no antibiotics or growth hormones. (USDA National Organic Program 2005) Simply speaking, organic food (also referred to as organics), is produced relying on ecologically based practices whic h virtually exclude the use of synthetic chemical inputs, antibiotics and hormones, and in addition prom otes soil health, biodiversity, animal fair treatment, and environmental sustainability. Or ganic agriculture is explicitly defined as an ecological production system. Environmental benefits connected with organic production include reduced pesticide residues in water and food; reduced nutrient pollution; improved soil tilth, soil organic matter, and productivity; and lower ener gy use; carbon sequestration; and enhanced biodiversity (ERS/USDA, 2009). Previous studies reveal th at consumers have a basic understanding of organics (Smith et al., 2009). Briz and Ward (2009) showed that a minority of 39% of consumer respondents correctly claime d that organic products are those cultivated without synthetic pesticides and only 34% give a tolerable answer. However, consumers are still confused between organic and natural; for instance, if a food product is made with organic ingredients but also cont ains artificial fla vorings, then it would be organic but not natural. Less than half of respondents could make a distinction betw een organic and natural food, and most respondents who are able to distinguish these two concepts are younger population between age 18 and 34 (Mintel, 2008).

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16 As we know, organic products are differentia ted not solely by economic factors but also perceived social and environmenta l benefits. Organic price premiums have been considered as an important factor in making organic farming ear n a comparable or even higher profit than conventional farming (Dimitri and Greene, 2002 ). The differential production costs and the relative short supply of organic products can be the basis of a price premium. But a price premium (reflects the high production cost and the relativ e supply level) cannot fully reflect the true social values or environmental benefits of organically produced prod ucts. Hence, it may be necessary to establish public investment in or ganic agriculture to faci litate accessibility of organic products to consumers, promote profit ability to organic farmers, and protect the environment as well (ERS/USDA, 2009). Organic consumers consider a wide variety of reasons when making purchasing decisions, with health and nutrition (66%), taste (38%), and food safety (30%) as the primary reasons offered for organic purchases (Hartman Group, 2002; Dimitri and Oberholtzer, 2006). Specifically, based on the Hartman Group survey Organic 2006: Consumer Attitudes & Behavior, Five Years Later & Into the Future, the top five reasons given for organics purchases are: 1) to avoid products that rely on pesticides or other chemicals; 2) to avoid products that rely on antibiotics or growth hormones; 3) for nutritio nal needs; 4) to support the environment; 5) to avoid genetically modified products. However, th e debate over organics continues. A UKs Food Standards Agency (FSA, 2009) provides a review st ating that there is li ttle difference between the nutrient content of organically versus conventionally grown food. Given that the bottom line of consumption is not what the concept of the product is, but what consumers perceive and are aware of, it would be insightful to better understand

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17 consumers attitudes as well as identify the underlying motivations and other factors linked to organic purchases. Problem Statement Num erous industry and academic studies have been dealing with consumer behavior and trying to identify socio-demographic factors that motivate consume rs choice of organic products. Consumers of organic f oods have in some studies been characterized as Caucasian, with better education, afflue nt, and caring about health and food quality (Dimitri and Oberholtzer, 2006). With growing availability, or ganic products are no longer just a lifestyle choice of a select group of consumers but an es tablished practice of tw o-thirds of American consumers who purchase organic products at least occasionally (Hartman Group, 2004). Asian and African-Americans are inclined to purchase or ganically grown produce more frequently than Caucasians and Hispanics (Steven-Garmon et al., 2007) and income is not significantly relevant to organic purchases (Steven-Garmon et al., 2007; Thompson, 1998). Thus, it appears that organic consumer profiles have li kely become more di verse in the last decade, extending over a wider range of demographics and other consumer distinguishing categories. Food is an emotional issue (The Wall Street Journal, 10.25.2002). While food is essential, food selection is an emotional issu e. Although socio-demogra phic characteristics are expected to affect consumpti on preferences, those consumer characteristics are not easily changed at least in a relatively short period of time. Nevertheless, consumers have an increasing desire to take ever-greater control of their liv es, including their own and family members health, lifestyle and behavior issues. They may pursue organic food products if they believe organic products are safer, environmental friendly, from local farms and can trace the source, all of which could reassure consumers that they have some feeling of control. That is, they may buy organics for purposes other than just the physical attributes of the product. Moreover, fifty-five

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18 percent of consumers express their willingne ss to explore new products. This desire may translate to organic opportunitie s since organically grown products are potentially associated with fresh and innovative c oncepts (Molyneaux, 2007). One would expect some broader implications based on current consumer data on a national level. For example, can the like lihood of seeking organics be adjusted? How could the likelihood of seeking organics be changed? And it is important to understand what influences the consumption decision for organic foods. Then, the an alytical issue is discu ssed in the study: what is the probability of seeking organics? An interes ting aspect in this study is to investigate the effects of consumers behaviors or attitudes cont ributing to the levels of seeking out organic foods besides socio-demographic characteristics. Drawing from the organic consumer behavior li terature, there are a number of studies that employed discrete choice models to measure consumer preferences for organics. While the ultimate research goal would be to measure the amount of organics consumed, that level of consumption detail is often difficult to acquire and often not available on a national basis. Most of the demand studies rely on some level of cons umer recall about organi cs with the simplest measure being did you buy (or not buy) organi c foods within some defined consumption period. While this type question implicitly documents basic buying behavior, it does not provide the level of intensity. An alternative appr oach is to ask the consumer if they seek out organic products. This approach gives greater insight in to intensity behaviors about organics but still does not empirically link the effort to a specific quantity. Yet, an underlying assumption is that there is a link between intensity (seeking out) and the level of consumption. The specific tenor of this research lies in the essential statement that I seek out organic foods measured on a

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19 five-point Likert scale (where a 5 means you comp letely agree with th e statement and 1 means you completely disagree). Often households can more easily respond to inqu iries about the level of intensity whereas it is more difficult to give a precise quantity level, especially when the question is not directed to a specific product. Since this study focuse s on a broad preference for organics and the information is available, the preference intensity approach wi ll be used as a proxy for the demand for organic foods. Since preference intensity is an ordinal but ranked scale (i.e., the intensity of 5 exceeds the intensity of 4 or lower scores), the approach to measuring the proxy demand for organics is through dete rmining the probability of each intensity score. Given that intensities are ordered binary va lues, determining the probabilitie s is a classica l ordered probit problem. Research Aims and Objectives The aim of this study is to investigate the demand for organic foods quantitatively using the preference intensity approach and e xpanding into the following questions: How would one measure the probabilities of each score for seeking for organic foods? What are the major demand drivers for orga nic foods? Which factors are significant determinants in explaining preference intens ities or probabilities of moving across the scaling value of seeking out organic foods? Among the driver s, the expectation is that consumer socio-demographic characteristics, behaviors/attitudes to wards organic foods, and health conditions status w ill be particularly important. Several explicit hypotheses dr ive the empirical analyses: Consumption levels towards organic foods differ across consumer demographics. Some behaviors/attitudes factors are importa nt for consumers decision-making towards purchasing organic foods. It is thought that health conditions signifi cantly affect consumers choices on organic foods, such as people with diabetes, high bl ood pressure or other diseases being more likely to purchase organic foods.

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20 The proportion of expenditures on food compar ed to the total household disposable income is assumed to be a negative determinant of organic foods purchases. Information factors (product di fferentiation, fancied/fad) wo uld be expected to have positive effects on consumers choice of organic foods. Methodology and Data Data were c ollected from an internet survey conducted by the private company during February, 2008 through March, 2010. It is a national demographically balanced survey with a total of 37,582 household entries. Every two weeks, at least 1,200 households report through an internet diary survey. In the data set, the focu s is on consumption behavior in each two-week period (total 24 periods) referr ing to organic food products; and respondents know they are submitting a two-week period report. The survey contains questions about demographics (including age, gender, ethnicity, income, educat ion, employment, marital status, household size, region etc); store choice and expenditures for gr ocery shopping; attitudes; use of food labels; eating habits; and health conditi ons. Particularly, respondents were asked to score the following question with a five-point Likert sc ale: I seek out organic foods. In addition, during the survey period while some households stay with the survey from the beginning, other households drop out after a shor t period. However, more than half of the respondents participated longer than a year. Since the response to I seek out organic foods is discrete w ith five-point scaled values, the likelihood of seeking out organic foods can be estimated by ordered probit models. Overview of the Study The rem aining five chapters provide a deta iled discussion of methodology, results analysis and findings of the study. Chapte r 2 provides insight into a liter ature review of the organic products industry, consumer awaren ess, consumer preferences, a nd associated applications of discrete choice models on organic foods. Chapter 3 focuses on the descriptions of the data used

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21 in the study. The preference intensity approach is developed and organic preference model is specified in Chapter 4, setting fo rth the ordered probit model for the response to the statement of I seek out organic foods. Regression result s including estimated coefficients and supporting statistics plus sensitivity analysis are presente d in Chapter 5, followed by a discussion of findings and implications in Chapter 6.

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22 Table 1-1. Organic food sales and pene tration of total organic food sales Organic Food Sales ($ Million) Change from Prior Year Organic Penetration 1997 3,594 Na 0.81% 1998 4,286 19.2% 0.94% 1999 5,039 17.6% 1.06% 2000 6,100 21.0% 1.22% 2001 7,360 20.7% 1.41% 2002 8,635 17.3% 1.63% 2003 10,381 20.2% 1.94% 2004 11,902 14.6% 2.19% 2005 13,831 16.2% 2.48% 2006 16,718 20.9% 2.80% 2007 19,807 18.5% 3.15% 2008 22,929 15.8% 3.47% Source: OTAs Manufacturer/Organ ic Industry Surveys, 2006-2009

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23 CHAPTER 2 LITERATURE REVIEW A Short Review A large number of studies on several issues of organic consum er behavior have been conducted by both industry and academic resear chers. Industry reports usually focus on how often consumers purchase organic products, where to buy and the reasons to buy organic products, as well as demographic data of responde nts. Survey reports established by the Hartman Group, the Organic Trade Association (OTA), a nd the Natural Marketin g Institute (NMI) are widely cited in many studies. In NMIs 2007 Organic Consumer Trends Report, organic consumers have been categorized into four dis tinct segments represente d by percentage of the U.S. primary grocery shoppers: Devoteds (16%), Temperates (22%), Dabblers (44%), and Reluctants (18%). Devoteds are those who exhibit the highest us age of organic products and the most knowledge of organic; Temperates diffe r from Devoteds in the belief that organic products are necessity, so they shop for organics with less frequency and spend less on organic purchase. About 75% of total organic spending is attributed to Devoteds and Temperates together; furthermore, they are likely to c onsume more and more as new organic product introductions keep raising. NMI al so suggests that although Reluctan ts are educable, the size of the Devoteds group would remain relatively stab le. The 2009 U.S. Families Organic Attitudes and Beliefs Study conducted by OTA identifies org anic buyer groups into four groups by the length of time in the organic market: Newly Organic parents (32%), Experienced Organic parents (20%), Seasoned Organic parents (21%), and Non-buyers (27%). The report reveals that three quarters of U.S. families purchase orga nic foods no matter how often they shop for organics and how much they spend on orga nic purchases. Among the organic buyers, Newly Organic parents began to buy organic foods part ly because organic foods became available in

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24 conventional grocer stores. Seasoned Organic pa rents are typical organic consumers who were white, well-educated and wealthy. The Hartman Group (2008) defines core organic consumers as those who are the most integrated in the purchase and use of organics across a wide variety of categories and would likely conti nue to increasingly be involved in the organic market. Unlike industry studies, academic researches attempt to understand the organic consumers choices as well as underlying motivations thr ough several different approaches. Thompson (1998) provided a review of emerging studies of consumer demand for organic products, and summarized that attitudes, motives, and willingness to pay have been measured except elasticity estimates due to lack of retail data. After comparing different studi es, he concluded that demographic variables were important in explai ning differences in orga nic purchase behavior. Organic Consumer Behaviors Generally, dem ographic factors (such as age, gender, education, income, employment, etc.) were expected to have important impacts on ex plaining consumer behaviors. A stereo-typical organic consumer was described to be Caucasia n, affluent, and well-edu cated just a few years ago. According to recent studies focusing on orga nic consumers, the picture of the typical organic shopper was no longer easily identified based on a few trad itional signific ant predictors. Some studies suggested that organic consumers were cluste red into two groups of age 1829 and of age 40-49 (Lohr and Semali, 2000; Thompson, 1998). Household heads younger than 30 years old or aged 50 and older were more of ten represented as heavy organic users than lighter users (Steven-Garmon et al., 2007). Younger population with age 18-34 was more likely to purchase organic foods, while respondents older than 65 year s showed the lowest organic usage rate (Mintel, 2008). Consumers organic purchase decisions showed little difference between genders (Thompson and Kidwell, 1998; Briz and Ward, 2009). A few national studies (such as the Hartman Group and the Food Mark eting Institute) and academic researches

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25 (Loureiro and Hine, 2001; Briz and Ward, 2009) suggested that education had a positive impact on organic purchasing behaviors. However, T hompson and Kidwell (1998) also provided evidence that shoppers with gradua te or professional degrees were less likely to purchase organic products. It had been noticed that parents of young children or infants were more likely constant organic product buyers, which was consistent wi th the finding that hous eholds with children under eighteen were inclined to buy organic food products (Thomps on and Kidwell, 1998; Loureiro et al., 2001). But Thompson (1998) also summarized that the presence of children was not the significant indicator in the Delaware studies. Mintel studies (2008) implied a positive correlation between income and organic purchases since the higher price of organics was a barrier for lower-income households But several studies also provi ded evidence of exceptions that higher household incomes did not necessar ily suggest higher likelihoods of organic purchases (Huang, 1996; Thompson and Kidwell, 1998; Hartman Organi c Research Review, 2002); moreover, there might be a declining te ndency in higher-income groups, while lowerincome consumers seemed to be more entr enched organic buyers (Thompson, 1998). Many studies focused on geographic factors and suggested that households residing in the U.S. western region spent more on organic products (Thom pson, 1998; Steven-Garmon et al., 2007). As the organic industry grows, the number of consumers purchasing organic products continues to increase and they are likely not limited to a single ethnic group. In fact, organic consumers today represent a quite diverse ethnic picture. Steven-Garmon et al. (2007) concluded that Asian and African-Americans were more likely to purchase organically grown produce frequently compared to Caucas ians and Hispanics. This was generally consistent with the Hartman Group Organic 2006 Survey, which report ed Asian Americans and Latino Americans were relatively more likely to purchase organi c foods or beverages than Caucasian Americans

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26 based on their representation in the population. And more surprisi ngly, the ethnic group that was more likely to be core organic consumers wa s Latino Americans, and to a lesser extent, African Americans, compared to Caucasian Americans and Asian Americans. The Hartman group (2006) suggested that this was probably du e to the Latinos histo rical connection with organics and their strong concern for family. Store effect is another criti cal variable based on several studies which suggest that differences in consumer behavior across stores ar e significant as long as organic products retain their exclusive availability in a few particular market outlets. Accounting for where foods are purchased is likely to be important in understa nding where potential gr owth in organic foods might occur (Thompson, 1998). Households with hi gher disposal income were inclined to shop in specialty grocer. Furthermore, households wh o shopped in specialty grocer were sensitive to the price differences between organic and conven tional products and they were less likely to purchase organic produce (Thompson and Kidwell, 1998). On the contrary, Batte et al. (2007) concluded that the magnitudes of the willingness to pay for organics by specialty grocery shoppers were substantially more than traditiona l grocery shoppers as long as the amount of organic content level was higher th an 70% organic ingredients. While the effects of a product s appearance (e.g., cosmetic defects) on food choice were relatively small (Thompson and Kidwell, 1998) or non-significant (H uang, 1996), concerns about nutrition and price were critical to cons umers decision-making with respect to organic foods (Huang, 1996; Magnusson et al., 2001). It had b een shown that organic foods were valued and experienced not only for their appearances, tastes, prices, but also for their social and environmental benefits (for example, food safety, animal welfare, supporting local farmers,

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27 healthier choices, en vironmentally-friendl y) (Huang, 1996; Williams and Hammit, 2000; Lourerio et al, 2001; To rjusen et al., 2001; Di mitri and Richman, 2000). In addition, subjective norms (like social pr essure) affected consumers attitude and purchase intentions, but explained little abou t purchase behaviors (Smith and Paladino, 2009), which was opposite to the finding of Ajzen (1991). Fa miliarity was an important factor that gave a partial explanation of why so few consum ers purchased organic products despite having positive attitudes about organics (Magnusson et al., 2001; Smith and Paladino, 2009; Briz and Ward, 2009). Discrete Choice Model Applications Most studies on the factors affecting consum ers choice for organic products applied a discrete choice m odel: Huang (1996)s study on c onsumers preference for organically grown produce (OGP), in which a bivariate probit model was formulated, suggested that nutritional consciousness, concern about pesticides use, and verifying that produce was free of pesticides were three significant factors for consumers w ho preferred organic fresh produce. He also examined the probabilities of w illingness to buy OGP even if they had sensory defects in trade for food safety and environmental benefits. The re sults suggested a negati ve correlation between income level and tolerance of sensory defects on OGP, but consum ers who were Caucasian, with better education and large families were more likely to accept it. In Thompson and Kidwells study of choice between organic and conventional fresh produce in 1998, they measured actual choices based on data collected in reta il stores, rather than drawing out willingness to pay for organic produ ce. They estimated the choice by using a twoequation probit model, which indicated the possibili ty that consumers choice of store and their choice of products may impact each other simulta neously. The results implied an interesting connection between the choice of store and the consumers c hoice of fresh organic produce:

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28 despite relatively higher income and educat ion level on average, shoppers who shopped at specialty grocer were less preferred to organic produce, and were sensitive to price differences between organic and conventional produce; shoppers who were less likely to choose organic produce preferred specialty grocery stores. Briz and Ward (2009) studied consumer s awareness of organic products in Spain, and specified a multinomial logit model to predict pr obabilities of awareness, as well as a probit model to link awareness and purchas e of organic products. Specificall y, they built three levels of awareness of organic foods, and linked only the pr obability of correctly be ing aware of whats organic to consumptions of orga nic products. They indicated that due to credence attributes of organic products and consumer emotions, th e learning curve about organics was probably nonlinear and its slope might not be always positiv e. That is, at the estimated average awareness level of 46%, the likelihood of organic food consumption actually declined as the state of awareness continued to grow. They also provided a ranking of all determinants in the model to indicate that the education had the most profound impacts on the awareness of organic products, followed by age, knowledge about enriched foods income, region, market size, and finally, gender having the least effect. Loureiro et al. (2001) collected survey data directly from consumers in two grocery stores in Portland, Oregon, to be able to obtain estimates of preferences for organic, eco-labeled, and regular apples from the actual decision makers. Their analyses were based on a random utility model and were modeled by using a multinomial logit framework. Results illustrated that concerns for food safety and environmental benefits had a positive correlation with the preference for choosing organic apples compar ed to eco-labeled and regular options.

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29 Other Methodology Application In the study of consum er reac tions to changes in labeling regulations under the National Organic Program (NOP), Kiesel and Villas-Bo as (2010) employed the hedonic price function approach and a discrete choice model. They c oncluded that the implem entation of the USDA organic seal on milk labels sign ificantly acted as a pos itive shifter of the likelihood of purchases; and the welfare outweighed the costs incurred by labeling regulation based on their cost-benefit analysis. However, while consumers who were aw are of the NOP seal were more likely willing to pay a premium for organic foods, awareness of the NOP seal was not a si gnificant indicator of the magnitude of premium (Batte et al., 2007). A recent study of consumer behavioral inte ntions towards purchase of food products (including conventional food, qua lity low-input food, and organic food) across six European countries conducted by Ness et al. (2010) developed country-based structural equation models building on the quality-value-sat isfaction-loyalty framework. This study elucidated that perceived quality, value, and sa tisfaction were determinants of food consumers behavioral intentions. Specifically, satisfaction was the key to developing consumers intentions since growing satisfaction had a positive impact on consum ers attitudes; moreover, satisfaction could be increased by increasing perceived value, which was enhanced by grea ter perceived quality.

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30 CHAPTER 3 ORGANIC DATABASE Data used in this study were collected from an internet survey conducted by the private company Market Tools during February, 2008 through March, 2010. The actual source of the data for research purposes only is through the National Mango Boar d. The data are private and only the supplemental questions re lating to organic preferences were used out of a much larger database. A total of 37,582 household entries were retrieved from this national demographically balanced survey. Each household documented and reported their organic products consumption behavior during a two-week period. The number of times a household reported varies during the survey period: while some households kept filing from the beginning to the end of survey, some households quit submitting after a short period. In f act, more than half of the total respondents remained with the survey longer than one year. While the data are preparatory to the Na tional Mango Board, the Board commissioned the private company Market Tools to collect the data for many commodities along with many questions about the head of the household reporting. Ever y two-week a selected group of households (panel) report their buying activities along with their demogra phics, attitudes, and preferences, including that of seeking out organics. House holds included in the panel are continually adjusted to maintain a demographica lly balance panel with the total number usually around 1200 households reporting at any one period. Some households report for several periods while the norm is for households to drop out after participating over a few reporting periods. While not specifically addressed in this study, other results with these data suggest that the length of participating in the panel has little to no effect on the broader conclusions. For this analysis, the final database extended over the periods from February 2008 through March 2010, thus giving as current database as feasible. Details about th e company are available on their

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31 website and since the data are privately owned we have been very careful to maintain the confidentiality of the informati on. That is, the private informa tion cannot be distributed in the public domain but the res earch results can be. Respondents were asked to score the segmen ting question whether sh e or he sought out organic foods on a five-point Like rt scale. The survey also co ntained questions on demographic information such as age, gender, ethnicity, race income, education, employment level, marital status, household size, presence of children, census region, etc., as well as where they went grocery shopping, expenditures for grocery shoppi ng, behavior attributes eating habits, and health conditions. The dependent variable in the regression was the score for seeking out organic foods (Y), and it was posite d to be explained with Y = f (socio-demographics, attitudes/behaviors attributes, store choices, health conditions). Among the 37,582 household heads responding to th e survey, about 19.2% of the respondents agreed with the statement I seek out organic food (using scores 4 and 5 in the five-point Likert scale as indicators of agreemen t with organic preference), wh ile about 56.2% of respondents choose not to seek out organic food and 24.6% of respondents repor ted a neutral score (Figure 31). Figure 3-2 shows the distributi ons of five levels of agreem ent about seeking organic foods: only 7.0% completely agree with the statement, 12.2% mostly agree, 24.6% neither agree nor disagree, 21.7% mostly disagree, and 34.5% completely disagree with the statement about seeking out organic foods during the survey period. Figure 3-3 compares the distribution of agreement in 2009 to that in 2008 and it indicates that the distribution of completely disagreement and agreement both declined by 2.2% and 0.8% respectively, while the distribution of somewhat disagree and somewhat agree both increased by 2.8% and 0.5% respectively from 2008 to 2009. The distribution of those with neutral agreement does not change much from

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32 2008 to 2009. Figures 3-4 and 3-5 provide an overv iew of the distribution of agreement with seeking out organic foods in each reporting pe riod during the survey for a total 38 periods. According to the overall distribution, we can obs erve that the reporting periods with agreement distributions rising above 20% ar e concentrated in the mid periods of the survey, while during the beginning and ending periods of the survey, the distribution was much lower. These slight shifts during the entire survey period may poi nt to some underlying seasonality effects. The proportion of agreement with seekin g out organic foods is illustra ted in Figure 3-6, which shows most respondents who claim agreement mostly ag ree with the statement I seek out organic foods. Table 3-1 shows the responses to I seek out organic food on a Likert scale and a full description of each explanatory va riable with their corresponding di screte classification and their frequency in percent based on 37,582 observations. The demographics, be havior and attitudes attributes, and other important f actors expected to influence th e decision of buying organics are recorded (see Appendix A). Only household heads w ith ages older than 18 years are included in this survey, otherwise th ey are screened out the survey. Female heads of household represent 46 percent of the sample. White and non-Hispanic household heads account for the largest proportion in the sample (nearly 63%), while White/Hispanic, Asian, and African American household heads represent 9.5%, 3.6%, and 13.2% of the sample respectively. Household size is measured by adding each number of people curren tly living in respondents household in five age ranges (including the responde nt himself or herself); in the sample: 33.7% of households have two members and only 11.6% of households have more than four members. Member(s) with ages less than 18 years were considered as children; in th e sample, about 33% of families reported the presence of childre n. Regarding income levels among the respondents, about 36% of

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33 households have income between $35,000 and $ 75,000, followed by households with income below $35,000 with 34%. Among all respondents, near ly half are full-time employed (including self-employed), and nearly two thirds have some college or a college degree. The geographical attributes of the respondents also varied among areas based on the United States census. These nine areas were aggregated into four regions (Northeast, Midwest, Sout h, and West) to reduce the analyses needed. Approximately 21% of respondents lived in Northeast region, 28% of respondents lived in Midwest re gion, 32% of respondents lived in South region, and 19% of respondents lived in Western region. The distribution of expenditu res on grocery shopping with in one week was 38% of households spent between $100 and $200, 18% sp ent under $50, and only one percent spent more than $400. Most households reported that they shopped for food in grocery stores (almost 90%) and the fewest reported shopping for food th rough internet grocery stores (less than 4%). Mass merchandisers (56%) and warehouses (30%) were also popular places for grocery shopping, with lower percentages of households reporting shopping in convenience stores (21.5%) and farmers markets (11.5%). Approxima tely 80% of respondents reported that they consume about 1-3 servings of fruits and vegetables in a typical day. Given that there are a large number of dummy variables in the model, it is necessary to check the correlation between explanatory vari ables before running the model. Except for a relatively high correlation between household si ze (XHWD) and households with children under 18 years old (XCHL), there was no significant co rrelation among all other dummy variables (see in Table B-1). As suggested earlier, a limitation of this study was that the preference for organic food was measured through reports of seeking out organics rather than actual consumption behaviors.

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34 Again an underlying assumption in this study was that there was a corre lation between seeking out organic food and the actual consumption of organic food.

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35 Table 3-1. Descriptions of explanatory variables Description Variable Name/Range Frequency in percent Demographics AGE: Age of household head (18+) XAGE1 XAGE2 XAGE3 XAGE4 18 to 24 25 to 44 45 to 64 65 and older 15.81% 39.61% 30.64% 13.94% GENDER: Gender of household head XGEN=1 XGEN=0 Female Male 46.39% 53.61% RACE: Ethnicity RACE1 RACE2 RACE3 RACE4 RACE5 White/NONHISPANIC White/HISPANIC Black/African American Asian Other 62.93% 9.53% 13.16% 3.63% 10.74% CHL: With children under 18 years XCHL=1 XCHL=0 Yes No 33.14% 66.86% EDUC: Highest education level XEDU1 XEDU2 XEDU3 XEDU4 High school or less Some college or college degree Graduate or professional degree Other 21.29% 64.36% 12.31% 2.04% EMPLY: Employment XEMPLY1 XEMPLY2 XEMPLY3 XEMPLY4 Employed, full time Employed, part time Not employed Other 47.43% 7.88% 7.97% 36.72% INCOME: Household income (dollars) XINC1 XINC2 XINC3 XINC4 XINC5 Under $35,000 $35,000 $74,999 $75,000 $99,999 More than $100,000 Prefer not to answer 34.05% 35.57% 9.95% 10.07% 10.36% MARITAL: Marital status XMAR1 XMAR2 XMAR3 XMAR4 Single, never married Married Living with parents All others 28.05% 48.30% 7.93% 15.72% HWD: House size (number of members) XHWD1 XHWD2 XHWD3 XHWD4 XHWD5 1 member (single) 2 members 3 members 4 members >4 members 21.83% 33.70% 17.86% 15.04% 11.57%

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36 Table 3-1. Continued Description Variable Name/Range Frequency in percent STATE0: Census region REGION1 REGION2 REGION3 REGION4 NORTHEAST: New England NORTHEAST: Middle Atlantic MIDWEST: East North Central MIDWEST: West North Central SOUTH: South Atlantic SOUTH: East South Central SOUTH: West South Central WEST: Mountain WEST: Pacific 20.73% 28.19% 32.16% 18.92% SERV_FRU: Servings of fruit do you consume in typical day (#0-10) XSERFRU1 XSERFRU2 XSERFRU3 XSERFRU4 0 serving 1-3 servings 4-6 servings >7 servings 6.45% 82.34% 10.58% 0.63% SERV_VEG: Servings of vegetable do you consume in typical day (#0-10) XSERVEG1 XSERVEG2 XSERVEG3 XSERVEG4 0 serving 1-3 servings 4-6 servings >7 servings 3.16% 83.71% 11.95% 1.18% EXPEND: expenditures on grocery shopping within a week (dollars) XEXPD1 XEXPD2 XEXPD3 XEXPD4 XEXPD5 under $50 $50 to $100 $100 to $200 $200 to $400 more than $400 17.85% 33.45% 37.70% 9.67% 1.33% SHOP_GRO: Shopping for food in grocery store XSHOP_GRO=1 XSHOP_GRO=0 YES NO 89.74% 10.26% SHOP_WARE: Shopping for food in warehouse XSHOP_WARE=1 XSHOP_WARE=0 YES NO 29.60% 70.40% SHOP_INTE: Shopping for food in internet grocery store XSHOP_INTE=1 XSHOP_INTE=0 YES NO 3.74% 96.26% SHOP_MASS: Shopping for food in mass merchandiser XSHOP_MASS=1 XSHOP_MASS=0 YES NO 56.18% 43.82% SHOP_CONV: Shopping for food in convenience store XSHOP_CONV=1 XSHOP_CONV=0 YES NO 21.51% 78.49% SHOP_FARM: Shopping for food in farmers market XSHOP_FARM=1 XSHOP_FARM=0 YES NO 11.51% 88.49%

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37 Table 3-1. Continued Description Variable Name/Range Frequency in percent Behavior/attitude attributes BHV_EXERCISE: I exercise at least 3 times a week B_EXE1 B_EXE2 B_EXE3 B_EXE4 B_EXE5 Completely disagree Mostly disagree Neither agree nor disagree Mostly agree Completely agree 18.68% 18.04% 21.48% 16.35% 25.45% CALORIES: I count calories CAL1 CAL2 CAL3 CAL4 CAL5 Completely disagree Mostly disagree Neither agree nor disagree Mostly agree Completely agree 30.62% 21.56% 24.98% 13.98% 8.86% BHV_LABEL: Read ingredients on labels of the foods I buy B_LAB1 B_LAB2 B_LAB3 B_LAB4 B_LAB5 Completely disagree Mostly disagree Neither agree nor disagree Mostly agree Completely agree 8.29% 10.17% 25.44% 26.14% 29.97% BHV_HLTH: I feel healthier than peers B_HLT1 B_HLT2 B_HLT3 B_HLT4 B_HLT5 Completely disagree Mostly disagree Neither agree nor disagree Mostly agree Completely agree 10.07% 14.54% 37.03% 25.21% 13.15% BHV_NEWFOOD: I frequently experiment with new foods B_NEW1 B_NEW2 B_NEW3 B_NEW4 B_NEW5 Completely disagree Mostly disagree Neither agree nor disagree Mostly agree Completely agree 9.79% 16.76% 33.68% 25.09% 14.68% BHV_FRE: I eat fresh foods much more frequently than packaged food B_FRE1 B_FRE2 B_FRE3 B_FRE4 B_FRE5 Completely disagree Mostly disagree Neither agree nor disagree Mostly agree Completely agree 6.45% 13.71% 31.71% 27.22% 20.91% BHV_FRUVEG: I eat fruits and vegetable more than other people my age B_FV1 B_FV2 B_FV3 B_FV4 B_FV5 Completely disagree Mostly disagree Neither agree nor disagree Mostly agree Completely agree 9.51% 15.55% 35.32% 23.16% 16.46% BHV_WAY: I go out of my way to get certain types of produce B_WAY1 B_WAY2 B_WAY3 B_WAY4 B_WAY5 Completely disagree Mostly disagree Neither agree nor disagree Mostly agree Completely agree 12.81% 15.32% 30.90% 24.51% 16.45%

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38 Table 3-1. Continued Description Variable Name/Range Frequency in percent Behavior/attitude attributes BHV_STORE: I prefer to buy produce from certain stores B_ST1 B_ST2 B_ST3 B_ST4 B_ST5 Completely disagree Mostly disagree Neither agree nor disagree Mostly agree Completely agree 6.90% 9.25% 28.33% 30.65% 24.88% Health concerns HLT_BLOOD4: No one has high blood pressure in household HLT_BP=1 HLT_BP=0 No one Otherwise 58.51% 41.49% HLT_DIABE4: No one has diabetes in household HLT_DB=1 HLT_DB=0 No one Otherwise 80.16% 19.84% HLT_CHOLE4: No one has high cholesterol in household HLT_CL=1 HLT_CL=0 No one Otherwise 62.23% 37.77% HLT_ALLEG4: No one has food allergies in household HLT_AG=1 HLT_AG=0 No one Otherwise 83.59% 16.41% HLT_OBEST4: No one has obesity in household HLT_OB=1 HLT_OB=0 No one Otherwise 69.88% 30.12% HLT_MOBIL4: No one has limited physical mobility in household HLT_MB=1 HLT_MB=0 No one Otherwise 80% 20% HLT_HEAR4: No one has significant sight or hearing impairment in household HLT_HR=1 HLT_HR=0 No one Otherwise 82.87% 17.13%

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39 Table 3-1. Continued Description Variable Name/Range Frequency in percent Seasonality MTH_S: Months from 1-12 MTH1 MTH2 MTH3 MTH4 MTH5 MTH6 MTH7 MTH8 MTH9 MTH10 MTH11 MTH12 Jan. Feb. Mar. Apr. May Jun. Jul. Aug. Sep. Oct. Nov. Dec. 9.12% 7.02% 8.96% 8.32% 8.22% 8.13% 8.22% 8.00% 8.18% 8.67% 8.57% 8.58%

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40 Figure 3-1. Frequency distributi on of the responses to I seek out organic foods (combining score 5 and score 4 for responses of agre eing with I seek out organic foods; combining score 1 and score 2 for responses of disagreeing with I seek out organic foods) Figure 3-2. Frequency distribut ion of responses to I seek out organic foods Agree 19.2% Disagree 56.2% Neutral 24.6% 0.34 0.22 0.25 0.12 0.07 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 Completely disagree Somewhat disagree NeutralSomewhat agree Completely agree Distributions of agreement about seeking out organic foods

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41 Figure 3-3. Comparison of frequenc y distribution of responses of ag reeing to I seek out organic foods in 2008 and 2009 Figure 3-4. Distributions of re sponses of agreeing to I se ek out organic foods during the reporting periods (from 2 = Feb. 2008 to 37 = Feb. 2010) 0.0% 5.0% 10.0% 15.0% 20.0% 25.0% 30.0% 24681012141618202224262830323436 Distributions of agreement about seeking out organic foods Mostly agree Completely agree 35.5% 33.3% 20.3% 23.1% 24.8% 24.4% 12.0% 12.5% 7.4% 6.6% 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 Completely Disagree Somewhat Disagree NeutralSomewhat AgreeCompletely AgreeDistribution of agreement about seeking out organic foods 2008 2009

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42 Figure 3-5. Frequency distributi on of responses of agreeing to I seek out organic foods detailed in Completely agree (5) and Mostly agree (4) during the reporting periods (from 2 = Feb. 2008 to 37 = Feb. 2010) Figure 3-6. Percentage of frequency distribution of responses of agreeing to I seek out organic foods during the reporting periods (from 2 = Feb. 2008 to 37 = Feb. 2010) 0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0% 80.0% 90.0% 100.0% 24681012141618202224262830323436 Distributions of agreement about seeking out organic foods Mostly agree Completely agree 2.36 1.26 0.53 0 0.5 1 1.5 2 2.5 3 Mean ScoreStandard Deviation of Score Coefficient of Variation of Score Statistics of agreement about seeking out organic foods

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43 CHAPTER 4 ORGANIC PREFERENCE MODEL In the c ase of estimating the determinants of responses to questions using a Likert scale, the fundamental interest is to determine the probability of each level of outcome and how the probabilities differ across the respon dents characteristics. To es timate the likelihood of I seek out organic foods, we used an ordered probit model which is appropria te and commonly used when the dependent variable a ssociated with more than two outcomes is both discrete and ordinal. Let O represent I seek out or ganic foods, and define iO as the thi observation in the survey. The outcomes of I s eek out organic foods (y =iO ) is discrete with scaled values form 1 to 5 increasing in magnitude of agreement (1 = completely disagree; 2 = mostly disagree; 3 = neither agree nor disagree; 4 = mostly agree; 5 = completely agree). These scores reflect an ordinal scaling that is exhaustiv e and mutually exclusive; yet, they are only rankings and have no cardinal significance. A critical assumption of the ordered probit model is that the model fits the parallel slopes requirement, which means the slope coefficients of variables do not vary between different outcomes. Quantitatively, the problem is a classic situation for ordered probit modeling. Organic Preference Model Specifications Suppose m otivations are captured with a set of variables in matrix X and effects of X are reflected with Then X represents the impacts of each motivating variable once's are known. In Ordered Probit models, we build a latent regression *yX (4-1)

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44 where y is the unobserved late nt index ranging from to and is determined by observed factors X s along with unobserved factors' s Matrix of is composed of the intercept and all parameters associated with the matrix X is assumed to be normally distributed with a mean of zero and variance of one. Then the measurement equations for y =iO could be illustrated as following specifications: 11 if *yy 122 if *yy 233 if *yy (4-2) 344 if *yy 45 if *yy s are called thresholds, which are unknown values to be estimated with s, and satisfy the relationship of 1234 ((,)k k=1, 2, 3, 4). The probability for each Likert score of seeking out organi c foods can be derived as follows (letting represents the cumulative normal func tion). Take the probability of score=1 for example: ii 1 1 1Prob(=1) = Prob(O|O=1) = Prob(*) = Prob() = Prob() yy X X X 1 = ()()XX (4-3) Since()0X the scores are exhaustive and mutually exclusive (i.e.,()1X ), and by using ORDPROB procedure in TSP software (TSP econometric software was used in this study), the lowest effective boundary value of the threshold is

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45 normalized to zero (i.e.,10 ), therefore the total number of the thresholds ( s) to be estimated is the number of values which y takes on less 2. Yields: ii ii2 ii32 ii43 ii4Prob(O|O=1) = () Prob(O|O=2) = ()() Prob(O|O=3) = ()() Prob(O|O=4) = ()() Prob(O|O=5) = 1()X XX X X X X X (4-4) For illustration purposes, we suppose that X is made up of Xk (k=1,,6) being binary and there is no continuous variable. There ar e a total of 36 disc rete variables (Xk) expected to explain the motivations moving across the scale of agreement about seeking out organic foods. Each X could be expressed as: (j denotes each discrete level, while i represents each of the actual observations) 425 1146 111 445 111519 111 255 242631 111 36 jjijjijji jjj jjijjijji jjj jjijjijji jjj jji jXXAGEXGENRACE X EDUXMARXHWD X CHLXINCXEXPD XEMPLY 44 40 11jj i jREGION (4-5) 555 22510 111 555 152025 111 555 303525 111__ ___ ___jjijjijji jjj jjijjijji jjj jjijjijji jjjXCALBFREBLAB BSTBWAYBFV B HLTBEXEBNEW (4-6)

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46 22 332 11 22 46 11 22 810 11__ __ __jj i jj i jj jj i jj i jj jj i jj i jjXXSHOPGROXSHOPWARE XSHOPINTEXSHOPMASS X SHOPCONVXSHOPFARM (4-7) 44 441216 11 jjijji jj X XSERFRUXSERVEG (4-8) 222 5524 111 222 6810 111 2 12 1___ ___ _jj ijj ijj i jjj jj ijj ijj i jjj jj i jXHLTBPHLTDBHLTCL HLTAGHLTOBHLTMB HLTHR (4-9) 12 66 1 jji j X MTH (4-10) Then,0112233445566XXXXXXX (4-11) In sum, the fundamental challenges are to specify Xk first and then estimate the impacts of each motivating variable. In the next chapter, these measures will be comprehensively explained (see Table 3-1 for the defin ition of the X variables). Predicted Probabilities According to the expressions described in last section, the predicte d probability for each Likert score can be derived in a genera l way as follows: (m=1, 2, 3, 4, 5) 1Pr(=m) = ()()mm iyXX (4-12) which indicates the relationship between the dependent categor ies and explanatory variables. In practice, in formation about estimated s and s can be obtained from the

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47 regression result, and then calcula te in equation (4-12) to obtain predicted probabilities. In the case of more than one independent variable in cluded in a model, the effect of each single variable can be examined while holding other vari ables as their actual values. If the independent variable is discrete, we make a X matrix containing a 1 in the first column for intercept, a 1 in the column representing the controlled category of dummies, and other columns take their actual values across the panel. Take gender dummies, for instance; the gender variable contains two dummies XGEN1 and XGEN2 (a rest ricted gender dummy DGEN (1 is female and -1 is male) is used for regression). To illustra te the effect of gender on the pr obabilities of ordinal outcomes, we first make the X matrix contain a 1 in the fi rst column for the intercept, a 1 in the column representing gender (DGEN) to select female re spondents (or a -1 to select male respondents), and other columns again are the actual variable values. The process is similar when discrete variables contain more than two categories of dummies. Take age dummies for example, age dummies contains four categories (XAGE1, XAG E2, XAGE3, and XAGE4) and restricted age dummies (DAGE1, DAGE2, and DAGE3) are incl uded in regressions. The X matrix could contain a 1 in the first column for intercept, a 1 in the column representing XAGE1 to select the respondents with ages between 18 and 24, and other variables remain in their corresponding columns the same values that they actually ar e. The processes for other categories of age are similar to XAGE1 except the category of XAGE4, for which the matrix contain a 1 in the first column for the intercept, a -1 in columns representing ag e except XAGE4 (-1 in columns representing XAGE1, XAGE2, a nd XAGE3 ), with other column s remaining at their actual values Partial Change and Discrete Change in Predicted Probabilities A question often asked is how the probabiliti es of the various outcomes would change when the value of one variable changes. The signs of the coefficients obtained from regression

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48 do not directly reveal the direction of impact fo r each restricted dummy variables. In order to evaluate the marginal effects of explanatory va riables, we can estimate the marginal responses, calculate the odds ratios, or simu late the probabilities across diffe rent levels of one particular variable category. For continuous variables X, the marginal effect on the probabilities of a small change in Xik (value of the kth determining variable for person i) for person i, under a normal distribution is: (1 K ikik k Z X) 11 2121 11Pr(1) ()() Pr(2) ()()()() Pr() 1()()ii iik ikiik ii iiiik ikiik ii JiJik ikiikYZ d ZZ XdZX YZ d ZZZZ XdZX YJZ d ZZ XdZX (4-13) Then the marginal effects of the regressor X on probabilities can be obtained by evaluating the probability density function (() X ) multiplied by the relative coefficient. It is clear to state that when the value of the kth independent variable increases and0k the probability of outcome 1iY will decline as a result of th e opposite sign between the derivative ofPr(1)iY andk while the probability of outcome iYJ will rise since the derivative of Pr()iYJ has the same sign ask When interpreting the rest of the marginal effects, the direction of changing the value of a regressor on the probability of outcomes can be ambiguously determined due to the sign of the derivative bein g different from the sign of beta in some cases. Greene (2008) argued: What happens to the middle cell is ambiguous. It depends on the two

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49 densities. In the general case, relative to the signs of the coefficients only the signs of the changes in Prob(y = 0 | X) and Prob(y = J | X) ar e unambiguous! The upshot is that we must be very careful in interpreting the coefficients in th is model. Indeed, without a fair amount of extra calculation, it is quite unclear how the coefficients in the ordered probit model should be interpreted. In addition, when the independent variable is a dummy variable, interpretation using the probability density function multiplied by the asso ciated coefficient can also be misleading (Long, 1997; Borooah, 2001). In fact we interpret the discrete ch ange instead of the marginal change in the case of dummy inde pendent variables. Long (1997) imp lies the discrete change is a more informative measure for ordered regression models. Discrete change can be expressed as follows: Pr(|x) Pr(|x,=1)Pr(|x, =0)i iikiik ikYJ YJXYJX X (4-14) where the notation Pr(|)ii kYJX indicates the probability of given It suggests that when the value of ik X changes from 0 to 1, the predicated probability of outcome J changes by Pr(|x)i ikYJ X while holding other variables at x. That is, we compare the probability when the dummy variable takes on e value (i.e., 1) with the probability when it takes another value (i.e., 0) while holding other variables fixed. The difference between the two sets of probabilities is the e ffect from moving one condition to another on the probability of being at different outcomes. In the next chapter, the estimated probabil ities across demographics, behavior/attitudes factors, as well as concerns on health problems will be illustrated respectively while letting all other factors take their actual values, from which we can analyze how a households probability

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50 of seeking out organic foods at five levels w ould be affected if he or she moves between different demographic conditions (age, income, education etc.), behavioral conditions, as well as health conditions. Restricted Dummy Variables Another thing that must be pointed out is th at given so many binary variables in this model, we have to deal with the dummy-variabl e trap. If we include all the dummy variables for one of the categories when running the model, perfect collinearity w ould be introduced into the model. To avoid the dummy-variable trap, we can simply choose a dummy variable of a group to omit from the model. Then the coeffici ents on the included variables measure how those groups differ from the omitted group. Take the AGE dummies (4 1 jji j X AGE) for example, we could simply drop 11 iAGE and run regression. The intercept represents the base, and the t-test is to test against the omitted category. In the case of large number of dummies included in the model, it is inefficient to interpret coefficients from every combination of benchmarks. Or we can adopt a method of restricting an unweighted sum of the coeffi cients to zero for each dummy category, which is convenient since each coeffi cient estimated is expr essed relative to the average respondent rather than to each set base. Using this method, we add D notation to each dummy variable included in the regression. Take XAGE dummies (4 1 jji j X AGE) for example: 43 4 11 433 4 1110 or ()jj jj jjijjiijji jjj X AGEXAGEXAGEDAGE (4-15) In this way, the intercept represents the unw eighted average household and all coefficients and t-values are expressed relativ e to the average. That is, a st atistically significant t-value

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51 implies that the coefficient is statistically different from the unweighted average0 For age dummies, the effect of XAGE1 is01 the effect of XAGE2 is02 the effect of XAGE3 is03 and the effect of XAGE4 is0123 ; and the t-values of three age dummies in the regressions are testing if each DAGE is di fferent from the unweighted average respondent. This method used for the dummy variable is just for convenience when discussing the statistical test since all t-values are relative to an average in stead of just the variables dropped out in the traditional method for dea ling with dummy variables. W ith either dummy method, the conclusions about the probability for each Likert score will be identical in the end results.

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52 CHAPTER 5 ANALYSIS OF RESULTS AND SIMULATIONS In this chapter, we f irst discussed the orde red probit estimates based on the ordered probit methodology (see equations 4-5 through 4-10) an d econometric models developed in the previous chapter, and then we focused on reporti ng how the changes in the explanatory variables (demographic factors, behavior/at titudes, health conditions etc.) affected the probabilities of seeking out organic foods. After that, the importa nce of each variable was ranked to compare the relatively different effects on the marginal change in the probabilities of seeking out organic foods. Ordered Probit Estimates The models specified in chapter 4 estimated re sponses to the statement I seek out organic foods with 36 variables by using ordered probit procedures. The results were shown in Table 51, including scaled R-squared, estimated coefficients for the restricted dummies used in regressions, t-statistics and co rresponding p-values, as well as the thresholds for moving across the Likert scales. Since the tota l number of thresholds to be es timated was the number of values which y takes on less 2; for the outcome with five Likert scales, only three thresholds (234, ) were estimated in the regression since the others were then predetermined. And since we applied the method of restricting an un weighted sum of coeffici ents to zero, all the coefficients and t-statistics obtai ned from regression were expresse d relative to the unweighted average household. Hence, the estimated coefficients associated with dummies created originally were recalculated and shown in Table 5-2 to show the coefficients for every level in each dummy class. According to the estimate results, generally the ordered probit model explained many reasons for seeking out organics, as indicated by the scaled R-squared of 0.457. It suggested that

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53 over 45 percent of variation in the consumer preferences for seeking out organic foods was explained by the model, recognizing the limited interpretation of the R2 in discrete choice models. Among the 99 dummy variables included in the regression, only 23 dummy variables were not statistically differ from the average level of seeking out organic foods at a 95% confidence level, including the marital status being single, Black/African American, having incomes between $35,000 and $74,999, household sizes with either two or four members, South census region, zero daily servings of fruit, 0-3 daily servings of vegeta bles, mostly disagreeing and completely agreeing with the statement buying produce from certain stores, completely agreeing with the statement eating fruits and ve getables, no one in household has obesity problem, and all the months variables. It was im portant to recall that the t-statistics values were expressed relative to the average level rath er than to the null hypothesis of the true slope coefficient equaling zero. While not included in the analysis, one coul d have easily tested differences between any of the levels within a dummy class using the covariance matrix associated with the results in Table 5-1. Since the number of possibilitie s were very large, we instead would concentrate on showing the estimat ed probabilities for each level and then one could easily see the numerical differences across the levels with the class (e.g., compare the probabilities across all ages). Ordered Probit Model Simulations To illustrate how the probabilities of different levels of seeking organic foods differ across socio-demographics, behaviors and attitudes, as well as health conditions, probabilities for each of five outcomes given a particular set of conditions of the other explanatory variables were simulated. One set of conditions is using the actu al variables of the variables not being simulated and then compare the probabilities afte r averaging across the panel observations.

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54 As a starting point, the probabilities for each level of seeking out organic foods for the average respondent were predicted. Specifica lly, using the coeffici ents from Table 5-1, probabilities for each respondent were estimated and averaged over the total 37,582 observations. The average probability of each leve l of seeking out organics provided a reference base in order to compare probabilities as each va riables impact was considered. As shown in Figure 5-1, the probability of pe ople who would like to seek out organic foods (combining scores 4 and 5) was estimated to be around 19 percen t for the average respondent, while the average respondent was estimated to be 56 percent unlikel y to seek out organic foods (combining scores 1 and 2), and 25 percent that are neutral or indifferent. Then the probabilities were averaged over the households with only the controlled variable being changed. The fact that each conditional pr obability was simulated relative to the overall average probability made the simulations comparable. The difference between the conditional probability and the overall mean probability was focused on the impact of the variable being controlled. By comparing the conditional probabilities, we were able to observe both the direction and magnitude of the effects of the vari ables being controlled. In each of the subsequent figures, the probabi lities predicted for each level of seeking out organic foods and the reference probability were shown on the vertical axis and values of controlled variable(s) were depict ed on the horizontal axis. For th e response to the question of seeking out organic foods with a five-option Likert scales, a set of three figures is shown for each controlled variable with combining intensities of both completely agree (disagree) and mostly agree (disagree) together. The percentages of agreeing and disagreeing with the statement I seek out organic foods as well as the percentages of neutral responses were all presented in adjacent figures. For each controlled variable, the people with neutral attitude on seeking out

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55 organic foods seemed to have the same likelihoo d moving pattern as those who agreed with I seek out organic foods, exhibiting around the likelihood of 24.8% as the average respondent with neutral response. People with responses of complete disagree or mostly disagree showed an opposite pattern compared to those who agreed with the statement I seek out organic foods. The response intensities of completely agree (disagree) and m ostly agree (disagree) generally kept the similar proportions for each cont rolling variable. Combining the two levels of agree provided a much more visual way to see th e tendency to favor organics or not instead of reporting separately the five proba bilities. Also it gave a clea r indication if the intensity of seeking out (or not) with the idea that if th e probability of completely agreeing was rising relative to mostly agreeing, then the intensity of seeking out organics was increasing (or not). Thus in all of the subsequent figures, two aspects were of particular importance. What were the probabilities under each controlled condition and did the intensities change within the agree (or disagree) scores. Seeking Out Organic Foods across Demographics In Figure 5-2 through Figure 5-11, the pred icted probabilities across the demographic factors averaged over the actua l values of the other variables were illustrated. These demographic factors included age, gender, marital status, race, income, education, employment, household size, presence of child ren under 18 years old, and aggreg ated census region. Again, the probabilities for each demographic was base d on averaging over the probabilities for each household with only the controlled variable being changed. A consistently decreasing probability of agr eeing with seeking out organic foods was shown over different ranges of age, from th e highest 26 percent for young populations of 18-24 years to the lowest 12 percent for populations of 65 years and older. The older populations likelihood of seeking out organics was about 7% points below the average (19.3%), comparing to

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56 the younger population with the highest likelihood that was almost 7% points above average. Correspondingly, the probability of disagreeme nt was shown increasing from younger to older populations. The result was partia lly consistent with findings fr om Lohr and Semali (2000) and the summary from Thompson (1998) that age br ackets of 18-29 and 40-49 were the consumers with highest percentage of buying organic produce while consumers over 60 purchased the least amounts. If the goal was to continue to enhan ce the demand for organics the age probabilities suggested targeting the older population was need ed since that was where the major weakness appears to be occurring. Altern atively, if sectors of the orga nic industry were concerned about locations where the initial greatest gains could be realized, then locati ons in areas with less concentrated say in retirement areas would be suggested since those areas generally had a much higher numbers in the older age group. Clearly, the age figure provided a number of directions for marketing and policy, depending on the overa ll goal of variables se ctors of the organic industry. Gender and marital status contributed little to explaining the differences in the responses to seeking out organic foods, while Thompson (1998) implied gender and marital status together might be important predictors of organic consumers profiling. Similarly, Thompson and Kidwell (1998) and Briz and Ward (2009) repor ted little difference in the organic searching behavior likelihoods was shown between female and male household heads, no matter how much she or he agreed with seeking out organic foods statement. In Figure 5-3, the differences between different marital status and the average level was quite small with the single shopper exhibiting a slightly higher likelihood of seeki ng organics than the average shopper. Figure 5-5 showed likelihoods of seeking out organics differi ng across ethnic groups. Asians Americans (with a substantial probability of 27%) and Black/African Americans (with a

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57 probability of 20%) were relatively more likely to seek out organic foods than Whites (with a probability of 19%) and Hispanics (with a probability of 18%). Compared to other ethnic groups, Asians Americans presented the highest level of propensity to seek organi cs at about 7% above the average likelihood level. Hispanic shoppers we re least inclined to seek organics, showing with the likelihood below average. Steven-Garmon et al. (2007)s st udy pointed to similar results that Asian and African-Americans were more likely to purchase organically grown produce frequently compared to Caucasians and Hispanics. But our result did not confirm the finding from the Hartman Group Organic 2006 Survey, wh ich reported that Hispanic households were more likely to be core organic consumers ba sed on their representation in the population. A popular opinion that households with high er incomes are more likely to purchase organic foods makes sense based on the relations hip between consumers affordability and the generally higher prices of most organic produc e. However, our finding did not confirm any consistent positive connection between household income levels and the likelihood of seeking organics. Households with incomes between $75,000 and $99,000 had the highest probability of seeking out organic foods (22% ). Households with incomes under $35,000 showed a likelihood of 20% and those who with income more than $35,000 but less than $75,000 showed a slightly lower likelihood of seeking organics than the average level. Conversely, households with incomes more than $100,000 are less likely to ch oose organics at about 2% below the average likelihood. In addition, households who are em ployed part-time or une mployed have greater probabilities of seeking out organic foods, while those who are not employed show the highest likelihood (nearly 23%). A clear positive association between education and awareness of organic foods was revealed by Briz and Ward (2009). However, education level did not di splay a profound impact

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58 on the likelihood of seeking out organic foods in th is study. According to Figure 5-7, the effects of education were mixed: having some college or a college degree showed no significant detectable effect on seeking organics; yet pe ople with less than high school or a high school education and people with graduate or professional degrees were sl ightly more likely to seek out organic products. This result was consistent with the finding of Thompson and Kidwell (1998) that having a college educati on had little impact on shoppers decisions to purchase organic produce, but contradictory to their conclusion th at having advanced degrees lower the probability of purchasing organic produces. Household sizes with two or more than two members did not c ontribute substantial differences in the likelihood from the average level except for househol d sizes with a single person being 2% below average. This implied th at single people were less likely to choose organic foods, whereas the presence of more household members did no t indicate a greater likelihood of seeking out organic foods. Househol ds with children under age eighteen would be expected more likely to seek out organic foods since some studies had concluded this. However, surprisingly, this study found that this segment reported being nearly 4% less likely to choose organic foods than those households without children under age eighteen. The literature review indicated that househol ds residing in the West region were more likely to consume organic products. In this study, the geographical differences in seeking out organic foods were quite small, given that only the West region respondent s displayed a slightly higher probability (20%) than the average level (19.3%), while respondents in all other census regions presented lower propensi ties to seek out orga nic foods relative to the average level. Store Choice and Expenditures The six graphs in Figure 5-12 illustrated the impact of st ore choice on the likelihood of seeking out organic foods. S ee Table 3-1 for the shopping cate gories. The category of food

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59 shopping in none of the places mentioned in the survey was not represented in the simulation because of zero observations in this category. The most substantial differences in the likelihood of seeking organics were exhibited between t hose who went food shopping in farmers markets or produce stands and those who did not, show ing with the probabil ity of 25% and 18% respectively. The probability of seeking out organi c foods was almost 4% greater for households who chose to shop for food through internet grocer y stores (such as Peap od, Fresh Direct, etc.) than for those who did not or the average re spondent. Households who shopped for food in convenience stores (such as gas st ation, 7-11, Quik Check, etc.) were slightly more likely to choose organic foods, while those who shopped in grocery stores and mass merchandisers (such as Wal-Mart, Target, etc.) were slightly less lik ely to seek out organic foods. People who chose to grocery shop in warehouse club stores (such as Costco, Sams Club, etc.) did not significantly differ from the average respondent. People who chos e not to shop in grocery stores displayed a higher propensity towards organic products. Overa ll, since about 90% indicated using traditional grocery stores for food shopping, the probabilities for those type stores were the more relevant for most organic food marketing strategies. In Figure 5-13, expenditures on grocery shopping showed a reasonably positive impact on the level of seeking out organi c foods. The probability of seeking out organic foods rose consistently as expenditures on grocery shopping increased, showing that shoppers with weekly grocery spending more than $100 were more likel y to consume organic foods. That is, as the overall average expenditure levels (per tw o-week food shopping) gr ew, the likelihood of including organic purchases in the food basket (product mix) increased. While not directly shown from the data, one could surmise that larg er food stores likely included more consumers

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60 in the higher expenditure levels and those focu sed on those type stores Clearly, the correlation between store size and expe nditures per shopper needed to be someway verified. Behavior/Attitudes Attributes Graphs in Figures 5-14 and 5-15 included the re lationships between the number of servings of fruit/vegetables per day and the likelihood of seeking out organic foods. The graphs interestingly implied that the organic propensit y changes among different ranges of servings were opposite between that of fru its and vegetables. People who c onsumed more than 7 servings of fruit per day and those who consumed 4-6 servings of vegetables per day displayed the higher propensity to choose organic foods, showing w ith the likelihood levels at 33% and 23% respectively. Alternatively, those who consumed 7 servings of vegetables or more per day and those who consumed 4-6 servings of vegetables per day had the lowest probability which was below the average consumer. In addition, the probability of seeking organics consistently increased as the number of daily servings of vegetables rose until 6 servings per day then dropped to the lowest probability. On the contrary, the probability of seeking organics consistently declined as the number of daily serv ings of fruit increased until 6 servings per day then began to boost to the highest level of pr obability of 33%. This hi gh range generally had a very low level of occurrence. The graphs in Figures 5-17 through 5-25 illust rated the likelihood differences across five levels of responses to several behavioral statem ents (including count ca lories, eat fresh foods rather than package foods, read ingredients on labels of the food when buying, go out of way to get certain types of produce, eat fresh fruit a nd vegetables more than other people with the same age, feel healthier than peers, exercise at least three times per week, and explore new foods). Most graphs illustrated a logically consistent in creasing pattern of probability of seeking out organic foods from disagree ing to agreeing with these beha vioral statements. Specifically,

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61 concerns about calories, eat fresh food rather than packaged foods, read ingredients on labels of the food when buying, go out of way for certain types of produce, feel healthier than his or her peers, and frequently experi ment with new foods displayed considerable impacts on the propensity to seek out organic fo ods. People who did not practice the behaviors mentioned at all were least like ly to seek out organic foods. Households who were seriously concerned about calories were more likely to seek out organic foods than those who did not count calories they eat each day, thus implying that people on a diet might be more interested in organic foods. The profound differences in the propensity towards organics were exhibited between households who eat fresh foods much more frequently than packaged foods, showing that the probability increased consistently from the lowest 10% (who ate packaged food more frequently) to the highest 25% (who ate fresh food more frequently). Similar to the moving pattern of eat fresh foods rather than packaged foods effect, read ingredients on labels when buying foods, showing a seeki ng out level of 25% (those who did not read labels showing only 10%), concen trated a significant impact on the propensity towards organics. This was reasonable. Due to the credence attribute of organic foods that organic foods were difficult to be differentiate d from conventional produced foods unless with clear identification or labels, consumers cannot be aware of the intrinsic qualities unless notified (through labels). The difference in the likelihood of seeking out organics was also substantial between those going out of the way to get certai n types of produce being at 24% and those who did not constituting only 8%. People who thought they were healthier than their peers were more likely to seek out organic foods than those w ho did perceive themselves were less healthy. Similarly, people who frequently explored new foods had a great er probability to seeking out organics compared to those who did not. Among those household consideri ng eating fresh fruit

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62 (vegetables) more than others, prefer to buy produce from certain stor es, and exercise at least three times per week, there were little c onsistent trends and cons iderable variation across the simulated probabilities. Health Concerns Health conditions obta ined in the survey included c oncerns about high blood pressure, diabetes, high cholesterol, food allergies, obes ity, limited physical mobility, and significant sight or hearing impairment on four dimensions ( do you, does your spouse/significant other, does other household member, or no one in household have any of those problems). Only the situation of no household member having those health co ncerns was considered in simulation for simplification. One thing that should be pointed out was that the horizontal axis in Figure 5-26 had the statement do not have any health concerns. According to Figure 5-26, only the household heads who had and/or his (her) family member(s) had food allergies concerns were mo re likely to seek out organic foods. On the contrary, household heads where no one in his (her) household had blood pressure concerns had greater probabilities of seeking out organic foods. Concerns about diabetes, high cholesterol, and significant sight or hearing impa irment presented the similar m oving pattern on the likelihood of seeking organics. Other health concerns did not indicate enough differences between probabilities and the average level. Overall a nd contrary to much of the discussion about organics and healthiness, the impact of hea lth problems generally had little influence on the probabilities of seeking out organic foods.. Seasonality In Figure 5-16, little variation was shown among probabilities of seeking out organic foods across twelve months of the survey period. And compared to the average respondent, there were no appreciable differences among the simulated pr obabilities and the average level for each score

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63 level. Shoppers were very slightly more like ly to consume organic foods in September and October during the whole year, comparing to the average likelihood level. Overall it implied that seasonality was simply not a factor when seeking out organic foods. Ranking the Effects on Probabilities of Seeking Out Organic Foods The effects of explanatory variables bei ng controlled usually c ontributes different outcomes on the marginal change in the probabil ities of seeking out organic foods. Hence, in addition to discussing the directiona l effects of all explanatory variables, it is also insightful to illustrate the relative effects of variables bei ng controlled on the likelihood of seeking out organic foods in a perspective way. A ranking of the conditional simu lated probabilities was depicted in Figure 5-27, with horizontal ba rs showing the minimum and maximum effects relative to the average on the left side accord ing to the conditional explanatory variables correspondingly absolute ranges. To illustrate the rankings of importance, the difference between the maximum and minimum values of simulated probabilities based on each controlled variable was calculated and then these absolute ranges were sorted in descending order. In Figure 5-27, the changes were expressed relative to the average respondent w ith a likelihood of 0.07 for score 5 (completely agree), likelihood of 0.12 for score 4 (mostly ag ree), likelihood of 0.24 for score3 (neutral), likelihood of 0.22 for score 2 (mostly disagree), and likelihood of 0.34 fo r score 1 (completely disagree). For all probabilities of response with scaled values fr om 1 to 5, number of daily servings of fruit, eat fresh foods more frequently than packaged foods, read labels, go out of the way to get certain types of produce, a nd age generally contributed relatively further impact on the probability of seeking out organi c foods than other factors. To the contrary, moving down the charts, gender, limited phys ical mobility concern, food shopping in

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64 warehouse stores, cholesterol concern, and obesity problem we re the five least important factors regardless of order. With respect to the top 10 variables in the ranking, based on probabi lities of seeking out organic foods with completely agree (score 5) relative to the average level of 7%, number of servings of fruit had the greatest impact by far, followed by eat fresh foods read labels, go out of the way to get certain t ype of produce, age, frequently experiment with new foods, expenditures on foods, ethnicity, feel healthie r than peers, and num ber of daily servings of vegetables. Daily servings of fruit was the most important factor impacting the likelihood of seeking out organic foods, ranging from 3.7% to 10.1%. Reports of go out of the way for certain types of produce had the minimum lik elihood (only 2%) among all variables. Only age and ethnicity were impactful demographic attributes, ranging from 3.7% to 10.1% and from 6.4% to 10.7%, respectively. Education le vel could be an important determinant since it showed a low level of minimum likelihood at 4. 3%, but its absolute range was adequate. In the second chart in Figure 5-27, the rankings based on probability of seeking out organic foods with mostly agree (score 4) relative to the av erage of 12.4%, suggested that go out of the way for certain type produce, read labels, and eat fresh foods rather than packaged foods contributed a substantially greater impact than other factor s. Reports of go out of the way for certain types of produce still displa yed the minimum likelihood of 6.2% among all variables. Age, ethnicity and education level ranked higher than other demographic factors, ranging from 8.8%, 12.1%, 11% to 15.9%, 16%, and 13.6%, respectively. The third chart in Figure 5-27 revealed the ranking based on a probability of seeking out organic foods with neither agr ee nor disagree (score 3) relative to the average level of 24%. The variable with the largest difference betw een the minimum and maximum likelihood was go

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65 out of the way to get certain t ypes of produce, followed by read la bels, eat fresh foods rather than packaged foods, frequently experiment w ith new foods, as well as age. Finally, in the forth chart in Figure 5-27, the rankings based on probability of seeking out organic foods with mostly disagree (score 2) relative to the averag e of 21.5% showed that most variables did not present an adequate difference except number of daily servings of fruits. The rankings based on probability of seeking out organic foods with completely disagree (score 1) relative to the averag e of 34.3% was presented in the last chart in Figure 5-27. A profound difference was seen with go out of the way for certain types of produce, with an absolute range of 25.2%. Both read labels a nd eat fresh foods rather than packaged foods had similar effects on the probabi lity, and age still showed cons iderable effects. Similarly, number of daily servings of fruit, experiment with new foods, feel healthier than my peers, expenditures on food, number of daily servings of vegetables, educa tion level, ethnicity, count calories, and food shopping in farmers markets can also be important determinants, but with less substantial effects. Overall, the Figure 5-27 charts provided a direct way to compar e the relative e ffects of all determinant variables on the probability of seekin g out organic foods. According to the rankings, behavior and attitude variables were major impacting fact ors while demographics except age played a less important role when seeking out organic foods.

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66 Table 5-1. Results from Organi c Preference ordered probit model Variables Description Ordered Probit Parameters t-statistics p-value C Intercept 0.7225 20.9578 [.000] DAGE1 Age 18-24 0.3046 19.9795 [.000] DAGE2 Age 25-44 0.0963 9.0858 [.000] DAGE3 Age 45-64 -0.1520 -12.0182 [.000] DGEN Female -0.0231 -3.6187 [.000] DMAR1 Single 0.0224 1.6946 [.090] DMAR2 Married -0.0345 -2.9880 [.003] DMAR3 Living with parents -0.0483 -2.7676 [.006] DRACE1 White/Non-Hispanic -0.1059 -9.5242 [.000] DRACE2 White/Hispanic -0.1118 -6.4698 [.000] DRACE3 Black/African American -0.0212 -1.3121 [.189] DRACE4 Asian 0.2699 10.6685 [.000] DINC1 Income under $35,000 0.0597 4.8776 [.000] DINC2 Income $35,000-$74,999 0.0104 0.9918 [.321] DINC3 Income $75,000-$99,000 0.1343 8.2871 [.000] DINC4 Income >$100,000 -0.1246 -7.4626 [.000] DEDU1 High school or less 0.1018 6.4516 [.000] DEDU2 College 0.0337 2.5351 [.011] DEDU3 Advanced degree 0.1281 7.2255 [.000] DHWD1 Household size: 1 -0.1118 -6.3863 [.000] DHWD2 Household size: 2 0.0173 1.2693 [.204] DHWD3 Household size: 3 0.0310 2.4501 [.014] DHWD4 Household size: 4 -0.0035 -0.2330 [.816] DCHL With children under 18 -0.0843 -8.1726 [.000] DEMPLY1 Employed full time -0.0522 -4.6580 [.000] DEMPLY2 Employed part time 0.0592 3.4861 [.000] DEMPLY3 Not employment 0.1242 7.2048 [.000] DREG2 Region: Midwest -0.0410 -3.9685 [.000] DREG3 Region: South -0.0020 -0.2081 [.835] DREG4 Region: West 0.0438 3.7234 [.000] DSHOP_GRO Shop in grocery stores -0.0486 -4.7238 [.000] DSHOP_WARE Shop in warehouse stores 0.0230 3.3157 [.001] DSHOP_INTE Shop in internet stores 0.0935 5.5372 [.000] DSHOP_MASS Shop in mass merchandisers -0.0506 -7.8411 [.000] DSHOP_CONV Shop in convenience stores 0.0454 5.9319 [.000] DSHOP_FARM Shop in farmers markets 0.1561 15.6844 [.000]

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67 Table 5-1. Continued Variables Description Ordered Probit Parameters t-statistics p-value DEXPD1 Expenditure under $50 -0.1908 -10.8673 [.000] DEXPD2 Expenditure $50-$100 -0.1133 -7.5831 [.000] DEXPD3 Expenditure $100-$200 -0.0466 -3.2888 [.001] DEXPD4 Expenditure >$400 0.1029 5.4341 [.000] DSERVF1 0 servings of fruit -0.0364 -1.1669 [.243] DSERVF2 1-3 servings of fruit -0.1313 -5.6687 [.000] DSERVF3 4-6 servings of fruit -0.2741 -10.7484 [.000] DSERVV1 0 servings of vegetable -0.0432 -1.2224 [.222] DSERVV2 1-3 servings of vegetable 0.0099 0.5073 [.612] DSERVV3 4-6 servings of vegetable 0.2029 9.1451 [.000] DCAL1 Count calories (1) -0.2192 -17.4283 [.000] DCAL2 Count calories (2) -0.0377 -3.0572 [.002] DCAL4 Count calories (4) 0.1021 7.2887 [.000] DCAL5 Count calories (5) 0.1130 6.2442 [.000] DB_FRE1 Eat fresh foods (1) -0.3744 -12.8631 [.000] DB_FRE2 Eat fresh foods (2) -0.1414 -8.4793 [.000] DB_FRE4 Eat fresh foods (4) 0.1297 9.5450 [.000] DB_FRE5 Eat fresh foods (5) 0.4069 24.8498 [.000] DB_LAB1 Read labels (1) -0.3503 -14.6740 [.000] DB_LAB2 Read labels (2) -0.1810 -10.2195 [.000] DB_LAB4 Read labels (4) 0.0386 3.0463 [.002] DB_LAB5 Read labels (5) 0.4436 33.0480 [.000] DB_ST1 Certain store (1) -0.1421 -5.5342 [.000] DB_ST2 Certain store (2) 0.0280 1.5540 [.120] DB_ST4 Certain store (4) 0.0324 2.5884 [.010] DB_ST5 Certain store (5) 0.0066 0.4598 [.646] DB_WAY1 Certain type (1) -0.4983 -24.9168 [.000] DB_WAY2 Certain type (2) -0.2411 -16.2240 [.000] DB_WAY4 Certain type (4) 0.2545 20.4537 [.000] DB_WAY5 Certain type (5) 0.3662 22.3867 [.000] DB_FV1 Eat fruit & vegetable (1) -0.1679 -6.8491 [.000] DB_FV2 Eat fruit & vegetable (2) 0.0628 3.9838 [.000] DB_FV4 Eat fruit & vegetable (4) 0.0324 2.4237 [.015] DB_FV5 Eat fruit & vegetable (5) 0.0245 1.3893 [.165] DB_HLT1 Feel healthier (1) -0.2053 -9.6842 [.000] DB_HLT2 Feel healthier (2) -0.1272 -8.3431 [.000] DB_HLT4 Feel healthier (4) 0.1731 13.8625 [.000] DB_HLT5 Feel healthier (5) 0.2147 12.1973 [.000]

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68 Table 5-1. Continued Variables Description Ordered Probit Parameters t-statistics p-value DB_EXE1 Exercise (1) -0.1288 -8.2912 [.000] DB_EXE2 Exercise (2) -0.0549 -4.2859 [.000] DB_EXE4 Exercise (4) 0.0959 7.2800 [.000] DB_EXE5 Exercise (5) -0.0300 -2.4792 [.013] DB_NEW1 Explore new foods (1) -0.3503 -17.0487 [.000] DB_NEW2 Explore new foods (2) -0.0939 -6.5996 [.000] DB_NEW4 Explore new foods (4) 0.0860 7.1489 [.000] DB_NEW5 Explore new foods (5) 0.2641 16.7418 [.000] DHLT_BP Do not have blood pressure 0.0842 11.1721 [.000] DHLT_DB Do not have diabetes 0.0689 8.1704 [.000] DHLT_CL Do not have high cholesterol 0.0235 3.2276 [.001] DHLT_AG Do not have food allergies -0.0873 -10.7729 [.000] DHLT_OB Do not have obesity 0.0038 0.5257 [.599] DHLT_MB Do not have limited mobility 0.0292 3.2861 [.001] DHLT_HR Do not have sight / hearing impairment 0.0377 4.3787 [.000] DMTH2 Feb. -0.0255 -1.2282 [.219] DMTH3 Mar. -0.0347 -1.8568 [.063] DMTH4 Apr. -0.0152 -0.7860 [.432] DMTH5 May -0.0025 -0.1300 [.897] DMTH6 Jun. 0.0079 0.4083 [.683] DMTH7 Jul. 0.0047 0.2411 [.809] DMTH8 Aug. 0.0095 0.4868 [.626] DMTH9 Sep. 0.0133 0.6884 [.491] DMTH10 Oct. 0.0328 1.7478 [.080] DMTH11 Nov. 0.0013 0.0696 [.944] DMTH12 Dec. 0.0021 0.1101 [.912] MU3 Thresholds 0.7711 100.6900 [.000] MU4 Thresholds 1.7561 159.5480 [.000] MU5 Thresholds 2.5944 176.5350 [.000] Number of observations = 37582 Mean of dep. var. = 2.35607 Std. dev. of dep. var. = 1.25866 Scaled R-squared = 0.456931 LR (zero slopes) = 20742.8 [0.000] Schwarz B.I.C = 46058.1 Log likelihood = -45515.6

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69 Table 5-2. Organic Preference ordere d probit model coefficient estimates Variables (see Appendix) Coefficient Variables (see Appendix) Coefficient b0 0.7225 XEMPLY1 -0.0522 XAGE1 0.3046 XEMPLY2 0.0592 XAGE2 0.0963 XEMPLY3 0.1242 XAGE3 -0.1520 XEMPLY4 -0.1312 XAGE4 -0.2489 REGION1 -0.0007 XGEN1 -0.0231 REGION2 -0.0410 XGEN2 0.0231 REGION3 -0.0020 XMAR1 0.0224 REGION4 0.0438 XMAR2 -0.0345 SHOP_GRO1 -0.0486 XMAR3 -0.0483 SHOP_GRO2 0.0486 XMAR4 0.0604 SHOP_WARE1 0.0230 RACE1 -0.1059 SHOP_WARE2 -0.0230 RACE2 -0.1118 SHOP_INTE1 0.0935 RACE3 -0.0212 SHOP_INTE2 -0.0935 RACE4 0.2699 SHOP_MASS1 -0.0506 RACE5 -0.0310 SHOP_MASS2 0.0506 XINC1 0.0597 SHOP_CONV1 0.0454 XINC2 0.0104 SHOP_CONV2 -0.0454 XINC3 0.1343 SHOP_FARM1 0.1561 XINC4 -0.1246 SHOP_FARM2 -0.1561 XINC5 -0.0800 XEXPD1 -0.1908 XEDU1 0.1018 XEXPD2 -0.1133 XEDU2 0.0337 XEXPD3 -0.0466 XEDU3 0.1281 XEXPD4 0.1029 XEDU4 -0.2635 XEXPD5 0.2478 XHWD1 -0.1118 XSERFRU1 -0.0364 XHWD2 0.0173 XSERFRU2 -0.1313 XHWD3 0.0310 XSERFRU3 -0.2741 XHWD4 -0.0035 XSERFRU4 0.4418 XHWD5 0.0670 XSERVEG1 -0.0432 XCHL1 -0.0843 XSERVEG2 0.0099 XCHL2 0.0843 XSERVEG3 0.2029 XSERVEG4 -0.1696

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70 Table 5-2. Continued Variables (see Appendix) Coefficient Variables (see Appendix) Coefficient CAL1 -0.2192 B_EXE1 -0.1288 CAL2 -0.0377 B_EXE2 -0.0549 CAL3 0.0417 B_EXE3 0.1177 CAL4 0.1021 B_EXE4 0.0959 CAL5 0.1130 B_EXE5 -0.0300 B_FRE1 -0.3744 B_NEW1 -0.3503 B_FRE2 -0.1414 B_NEW2 -0.0939 B_FRE3 -0.0208 B_NEW3 0.0941 B_FRE4 0.1297 B_NEW4 0.0860 B_FRE5 0.4069 B_NEW5 0.2641 B_LAB1 -0.3503 HLT_BP1 0.0842 B_LAB2 -0.1810 HLT_BP2 -0.0842 B_LAB3 0.0492 HLT_DB1 0.0689 B_LAB4 0.0386 HLT_DB2 -0.0689 B_LAB5 0.4436 HLT_CL1 0.0235 B_ST1 -0.1421 HLT_CL2 -0.0235 B_ST2 0.0280 HLT_AG1 -0.0873 B_ST3 0.0752 HLT_AG2 0.0873 B_ST4 0.0324 HLT_OB1 0.0038 B_ST5 0.0066 HLT_OB2 -0.0038 B_WAY1 -0.4983 HLT_MB1 0.0292 B_WAY2 -0.2411 HLT_MB2 -0.0292 B_WAY3 0.6824 HLT_HR1 0.0377 B_WAY4 0.0324 HLT_HR2 -0.0377 B_WAY5 0.0245 MTH1 0.0063 B_FV1 -0.1679 MTH2 -0.0255 B_FV2 0.0628 MTH3 -0.0347 B_FV3 0.0482 MTH4 -0.0152 B_FV4 0.0324 MTH5 -0.0025 B_FV5 0.0245 MTH6 0.0079 B_HLT1 -0.2053 MTH7 0.0047 B_HLT2 -0.1272 MTH8 0.0095 B_HLT3 -0.0553 MTH9 0.0133 B_HLT4 0.1731 MTH10 0.0328 B_HLT5 0.2147 MTH11 0.0013 MTH12 0.0021

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71 Figure 5-1. Probability of seeking out organic foods for the average respondent A Figure 5-2. Impact of age of household head on seeking out organic foods. A) completely agree and mostly agree with the statement of I seek out organic foods. B) neither agree nor disagree with the statement of I seek out organic foods. C) completely disagree and mostly disagree with the statement of I seek out organic foods. 0.26 0.21 0.16 0.12 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 18-24 25-44 45-64 65+ Age of household head Probability of seeking out organic foods Completely agree (5) Mostly agree (4) 0.19 0.25 0.56 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Agree Neutral Disagree Average household Probability of seeking out organic foods Completely agree (5) Mostly agree (4) Neutral (3) Completely disagree (1) Mostly disagree (2) Average (19.3%)

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72 B C Figure 5-2. Continued 0.47 0.53 0.60 0.66 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 18-24 25-44 45-64 65+ Age of household head Probability of seeking out organic foods Completely disagree (1) Mostly disagree (2) 0.27 0.26 0.24 0.21 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 18-24 25-44 45-64 65+ Age of household head Probability of seeking out organic foods Neutral(3) Avera g e ( 55.9% ) Average (24.8%)

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73 A Figure 5-3. Impact of gender of household head on seeking out organic foods. A) completely agree and mostly agree with the statement of I seek out organic foods. B) neither agree nor disagree with the statement of I seek out organic foods. C) completely disagree and mostly disagree with the st atement of I seek out organic foods. B Figure 5-3. Continued 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 Female Male Gender of household head Probability of seeking out organic foods Completely agree (5) Mostly agree (4) 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Female Male Gender of household head Probability of seeking out organic foods Completely disagree (1) Mostly disagree (2) Average (19.3%) Avera g e ( 55.9% )

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74 C Figure 5-3. Continued A Figure 5-4. Impact of marital status of household head on seeking out organic foods. A) completely agree and mostly agree with the statement of I seek out organic foods. B) neither agree nor disagree with the stat ement of I seek out organic foods. C) completely disagree and mostly disagree w ith the statement of I seek out organic foods. 0.25 0.25 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 Female Male Gender of household head Probability of seeking out organic foods Neutral(3) 0.20 0.19 0.19 0.21 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 Single MarriedLiving with parentsOthers Marital status of household head Probability of seeking out organic foods Completely agree (5) Mostly agree (4) Average (24.8%) Average (19.3%)

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75 B C Figure 5-4. Continued 0.55 0.57 0.57 0.54 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Single MarriedLiving with parentsOthers Marital status of household head Probability of seeking out organic foods Completely disagree (1) Mostly disagree (2) 0.25 0.24 0.24 0.25 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 Single MarriedLiving with parentsOthers Marital status of household head Probability of seeking out organic foods Neutral(3) Average (55.9%) Average (24.8%)

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76 A Figure 5-5. Impact of race of household head on seeking out organic foods. A) completely agree and mostly agree with the statement of I seek out organic foods. B) neither agree nor disagree with the statement of I seek out organic foods. C) completely disagree and mostly disagree with the statement of I seek out organic foods. B Figure 5-5. Continued 0.19 0.18 0.20 0.27 0.20 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 White / Nonhispanic Black/African American OthersRace of household head Probability of seeking out organic foods Completely agree (5) Mostly agree (4) 0.57 0.57 0.54 0.46 0.55 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 White/NonhispanicBlack/African American Others Race of household head Probability of seeking out organic foods Completely disagree (1) Mostly disagree (2) Average (19.3%) Average (55.9%)

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77 C Figure 5-5. Continued A Figure 5-6. Impact of income of household h ead on seeking out organic foods. A) completely agree and mostly agree with the statement of I seek out organic foods. B) neither agree nor disagree with the statement of I seek out organic foods. C) completely disagree and mostly disagree with the st atement of I seek out organic foods. 0.25 0.25 0.25 0.27 0.25 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 White / Nonhispanic Black/African American Others Race of household head Probability of seeking out organic foods Neutral (3) 0.20 0.19 0.22 0.17 0.18 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 < $ 3 5 ,0 0 0 $ 3 5 0 0 0 $ 7 4 ,9 9 9 $ 7 5 0 0 0 $ 9 9 ,0 0 0 $ 1 0 0 ,0 0 0 + P r e f e r n o t t o a n s w e rIncome of household head Probability of seeking out organic foods Completely agree (5) Mostly agree (4) Average (24.8%) Average (19.3%)

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78 B Figure 5-6. Continued C Figure 5-6. Continued 0.55 0.56 0.53 0.60 0.58 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 < $ 3 5 ,0 0 0 $ 3 5 0 0 0 $ 7 4 9 9 9 $ 7 5 0 0 0 $ 9 9 ,0 0 0 $ 1 0 0 ,0 0 0 + P r e f e r n o t t o a n s w e rIncome of household head Probability of seeking out organic foods Completely disagree (1) Mostly disagree (2) 0.25 0.25 0.26 0.23 0.24 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 < $ 3 5 0 0 0 $ 3 5 0 0 0 $ 7 4 ,9 9 9 $ 7 5 0 0 0 $ 9 9 ,0 0 0 $ 1 0 0 0 0 0 + P r e f e r n o t t o a n s w e rIncome of household head Probability of seeking out organic foods Neutral (3) Average (55.9%) Average (24.8%)

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79 A Figure 5-7. Impact of educa tion level of household head on seeking out organic foods. A) completely agree and mostly agree with the statement of I seek out organic foods. B) neither agree nor disagree with the stat ement of I seek out organic foods. C) completely disagree and mostly disagree w ith the statement of I seek out organic foods. B Figure 5-7. Continued 0.20 0.19 0.21 0.14 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 Less than or high school CollegeGraduate or professional degree Others Education of household head Probability of seeking out organic foods Completely agree (5) Mostly agree (4) 0.54 0.56 0.54 0.64 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Less than or high school CollegeGraduate or professional degree Others Education of household head Probability of seeking out organic foods Completely disagree (1) Mostly disagree (2) Average (19.3%) Average (55.9%)

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80 C Figure 5-7. Continued A Figure 5-8. Impact of household size on seek ing out organic foods. A) completely agree and mostly agree with the statem ent of I seek out organic foods. B) neither agree nor disagree with the statement of I seek out organic foods. C) completely disagree and mostly disagree with the statement of I seek out organic foods. 0.17 0.20 0.20 0.19 0.21 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 12344 + Household size (members) Probability of seeking out organic foods Completely agree (5) Mostly agree (4) 0.25 0.25 0.25 0.22 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 Less than or high school CollegeGraduate or professional degree Others Education of household head Probability of seeking out organic foods Neutral (3) Average (24.8%) Average (19.3%)

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81 B Figure 5-8. Continued C Figure 5-8. Continued 0.59 0.55 0.55 0.56 0.54 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 12344 + Household size (members) Probability of seeking out organic foods Completely disagree (1) Mostly disagree (2) 0.24 0.25 0.25 0.25 0.25 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 12344 + Household size (members) Probability of seeking out organic foods Neutral (3) Average (55.9%) Average (24.8%)

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82 A Figure 5-9. Impact of presence of children under 18 in household on seek ing out organic foods. A) completely agree and mos tly agree with the statemen t of I seek out organic foods. B) neither agree nor disagree with th e statement of I seek out organic foods. C) completely disagree and mostly disagree wi th the statement of I seek out organic foods. B Figure 5-9. Continued 0.17 0.21 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 With children under 18 No children under 18 Presence of children under 18 Probability of seeking out organic foods Completely agree (5) Mostly agree (4) 0.59 0.54 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 With children under 18 No children under 18 Presence of children under 18 Probability of seeking out organic foods Completely disagree (1) Mostly disagree (2) Average (19.3%) Average (55.9%)

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83 C Figure 5-9. Continued A Figure 5-10. Impact of employment status of household head seeking out organic foods. A) completely agree and mostly agree with the statement of I seek out organic foods. B) neither agree nor disagree with the stat ement of I seek out organic foods. C) completely disagree and mostly disagree w ith the statement of I seek out organic foods. 0.24 0.25 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 With children under 18 No children under 18 Presence of children under 18 Probability of seeking out organic foods Neutral (3) 0.19 0.22 0.23 0.18 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 EmployedEmployed part time Not employedOthers Employment status of household head Probability of seeking out organic foods Completely agree (5) Mostly agree (4) Average (24.8%) Average (19.3%)

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84 B Figure 5-10. Continued C Figure 5-10. Continued 0.25 0.26 0.26 0.24 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 EmployedEmployed part time Not employedOthers Employment status of household head Probability of seeking out organic foods Neutral (3) 0.56 0.53 0.51 0.57 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 EmployedEmployed part time Not employedOthers Employment status of household head Probability of seeking out organic foods Completely disagree (1) Mostly disagree (2) Average (55.9%) Average (24.8%)

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85 A Figure 5-11. Impact of census region of household head seeking out organic foods. A) completely agree and mostly agree with the statement of I seek out organic foods. B) neither agree nor disagree with the stat ement of I seek out organic foods. C) completely disagree and mostly disagree w ith the statement of I seek out organic foods. B Figure 5-11. Continued 0.19 0.18 0.19 0.20 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 NortheastMidwestSouth West Census region Probability of seeking out organic foods Completely agree (5) Mostly agree (4) 0.55 0.57 0.56 0.54 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 NortheastMidwestSouth West Census region Probability of seeking out organic foods Completely disagree (1) Mostly disagree (2) Average (19.3%) Average (55.9%)

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86 C Figure 5-11. Continued A Figure 5-12. Impact of grocery shopping places seeking out organic food s. A) shopping food in grocery store. B) shopping food in warehouse. C) shopping food through internet store. D) shopping food in mass merchandi ser. E) shopping food in convenience store. F) shopping food in farmers market. 0.25 0.24 0.25 0.25 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 NortheastMidwestSouth West Census region Probability of seeking out organic foods Neutral (3) 0.19 0.21 0.56 0.53 0.25 0.26 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 YesNo YesNo YesNo SHOP_GRO Probability of seeking out organic foods Completely agree (5) Mostly agree (4) Neutral (3) Completely disagree (1) Mostly disagree (2) Average (24.8%)

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87 B Figure 5-12. Continued C Figure 5-12. Continued 0.20 0.19 0.55 0.56 0.25 0.25 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 YesNo YesNo YesNo SHOP_WARE Probability of seeking out organic foods Completely agree (5) Mostly agree (4) Neutral (3) Completely disagree (1) Mostly disagree (2) 0.23 0.19 0.51 0.56 0.26 0.25 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 YesNo YesNo YesNo SHOP_INTE Probability of seeking out organic foods Completely agree (5) Mostly agree (4) Neutral (3) Completely disagree (1) Mostly disagree (2)

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88 D Figure 5-12. Continued E Figure 5-12. Continued 0.18 0.21 0.57 0.54 0.24 0.25 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 YesNo YesNo YesNo SHOP_MASS Probability of seeking out organic foods Completely agree (5) Mostly agree (4) Neutral (3) Completely disagree (1) Mostly disagree (2) 0.21 0.19 0.54 0.56 0.25 0.25 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 YesNo YesNo YesNo SHOP_CONV Probability of seeking out organic foods Completely agree (5) Mostly agree (4) Neutral (3) Completely disagree (1) Mostly disagree (2)

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89 F Figure 5-12. Continued A Figure 5-13. Impact of expenditures on gro cery shopping on seeking ou t organic foods. A) completely agree and mostly agree with the statement of I seek out organic foods. B) neither agree nor disagree with the stat ement of I seek out organic foods. C) completely disagree and mostly disagree w ith the statement of I seek out organic foods. 0.25 0.18 0.48 0.57 0.27 0.25 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 YesNo YesNo YesNo SHOP_FARM Probability of seeking out organic foods Completely agree (5) Mostly agree (4) Neutral (3) Completely disagree (1) Mostl y disa g ree ( 2 ) 0.17 0.18 0.20 0.23 0.26 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 <$50$50-$100$100-$200$200-$400$400+ Expenditures on grocery shopping Probability of seeking out organic foods Completely agree (5) Mostly agree (4) Average (19.3%)

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90 B Figure 5-13. Continued C Figure 5-13. Continued 0.59 0.57 0.55 0.51 0.46 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 <$50$50-$100$100-$200$200-$400$400+ Expenditures on grocery shopping Probability of seeking out organic foods Completely disagree (1) Mostly agree (2) 0.24 0.25 0.25 0.26 0.27 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 <$50$50-$100$100-$200$200-$400$400+ Expenditures on grocery shopping Probability of seeking out organic foods Neutral (3) Average (24.8%) Average (55.9%)

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91 A Figure 5-14. Impact of servings of fruit on se eking out organic foods. A) completely agree and mostly agree with the statem ent of I seek out organic foods. B) neither agree nor disagree with the statement of I seek out organic foods. C) completely disagree and mostly disagree with the statement of I seek out organic foods. B Figure 5-14. Continued 0.22 0.20 0.17 0.33 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 01 34 67 + Servings of fruit per day Probability of seeking out organic foods Completely agree (5) Mostly agree (4) 0.53 0.56 0.60 0.39 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 01 34 67 + Servings of fruit per day Probability of seeking out organic foods Completely disagree (1) Mostly disagree (2) Average (19.3%) Average (55.9%)

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92 C Figure 5-14. Continued A Figure 5-15. Impact of servings of vegetables on seeking out organic food s. A) completely agree and mostly agree with the statement of I seek out organic foods. B) neither agree nor disagree with the statement of I seek out organic foods. C) completely disagree and mostly disagree with the statement of I seek out organic foods. 0.25 0.25 0.23 0.27 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 01 34 67 + Servings of fruit per day Probability of seeking out organic foods Neutral (3) 0.18 0.19 0.23 0.15 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 01 34 67 + Servings of vegetable per day Probability of seeking out organic foods Completely agree (5) Mostly agree (4) Average (19.3%) Average (24.8%)

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93 B Figure 5-15. Continued C Figure 5-15. Continued 0.58 0.56 0.51 0.62 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 01 34 67 + Servings of vegetable per day Probability of seeking out organic foods Completely disagree (1) Mostly disagree (2) 0.24 0.25 0.26 0.23 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 01 34 67 + Servings of vegetable per day Probability of seeking out organic foods Neutral (3) Average (55.9%) Average (24.8%)

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94 A Figure 5-16. Impact of seasonality on seeki ng out organic foods. A) completely agree and mostly agree with the statem ent of I seek out organic foods. B) neither agree nor disagree with the statement of I seek out organic foods. C) completely disagree and mostly disagree with the statement of I seek out organic foods. B Figure 5-16. Continued 0.19 0.19 0.19 0.18 0.19 0.19 0.19 0.19 0.20 0.20 0.19 0.19 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 123456789101112 Months Probability of seeking out organic foods Completely agree (5) Mostly agree (4) 0.56 0.57 0.57 0.58 0.56 0.56 0.56 0.56 0.56 0.55 0.56 0.56 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 123456789101112 Months Probability of seeking out organic foods Completely disagree (1) Mostly disagree (2) Average (19.3%) Average (55.9%)

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95 C Figure 5-16. Continued A Figure 5-17. Impact of count calories on seeking out organic foods. A) completely agree and mostly agree with the statem ent of I seek out organic foods. B) neither agree nor disagree with the statement of I seek out organic foods. C) completely disagree and mostly disagree with the statement of I seek out organic foods. 0.25 0.25 0.25 0.24 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 123456789101112 Months Probability of seeking out organic foods Neutral (3) 0.15 0.19 0.20 0.22 0.22 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 Completely disagree Mostly disagree NeutralMostly agreeCompletely agree Count calories Probability of seeking out organic foods Completely agree (5) Mostly agree (4) Average (19.3%) Average (24.8%)

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96 B C Figure 5-17. Continued 0.61 0.56 0.54 0.52 0.52 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Completely disagree Mostly disagree NeutralMostly agreeCompletely agree Count calories Probability of seeking out organic foods Completely disagree (1) Mostly disagree (2) 0.23 0.25 0.26 0.26 0.26 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 Completely disagree Mostly disagree NeutralMostly agreeCompletely agree Count calories Probability of seeking out organic foods Neutral (3) Average (55.9%) Average (24.8%)

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97 A Figure 5-18. Impact of eat fresh foods on s eeking out organic foods. A) completely agree and mostly agree with the statem ent of I seek out organic foods. B) neither agree nor disagree with the statement of I seek out organic foods. C) completely disagree and mostly disagree with the statement of I seek out organic foods. B Figure 5-18. Continued 0.10 0.14 0.16 0.19 0.25 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 Completely disagree Mostly disagree NeutralMostly agreeCompletely agree Eating fresh foods Probability of seeking out organic foods Completely agree (5) Mostly agree (4) 0.69 0.63 0.59 0.55 0.46 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Completely disagree Mostly disagree NeutralMostly agreeCompletely agree Eating fresh foods Probability of seeking out organic foods Completely disagree (1) Mostly disagree (2) Average (19.3%) Average (55.9%)

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98 C Figure 5-18. Continued A Figure 5-19. Impact of read label on seek ing out organic foods. A) completely agree and mostly agree with the statem ent of I seek out organic foods. B) neither agree nor disagree with the statement of I seek out organic foods. C) completely disagree and mostly disagree with the statement of I seek out organic foods. 0.21 0.23 0.25 0.26 0.28 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 Completely disagree Mostly disagree NeutralMostly agreeCompletely agree Eating fresh foods Probability of seeking out organic foods Neutral (3) 0.10 0.13 0.17 0.16 0.25 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 Completely disagree Mostly disagree NeutralMostly agreeCompletely agree Read label Probability of seeking out organic foods Completely agree (5) Mostly agree (4) Average (19.3%) Average (24.8%)

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99 B C Figure 5-19. Continued 0.70 0.65 0.58 0.59 0.46 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Completely disagree Mostly disagree NeutralMostly agreeCompletely agree Read label Probability of seeking out organic foods Completely disagree (1) Mostly disagree (2) 0.20 0.22 0.25 0.25 0.28 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 Completely disagree Mostly disagree NeutralMostly agreeCompletely agree Read label Probability of seeking out organic foods Neutral (3) Average (55.9%) Average (24.8%)

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100 A Figure 5-20. Impact of buy from certain stor es on seeking out organic foods. A) completely agree and mostly agree with the statement of I seek out organic foods. B) neither agree nor disagree with the statement of I seek out organic foods. C) completely disagree and mostly disagree with the st atement of I seek out organic foods. B Figure 5-20. Continued 0.16 0.19 0.20 0.19 0.19 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 Completely disagree Mostly disagree NeutralMostly agreeCompletely agree Buy from certain stores Probability of seeking out organic foods Completely agree (5) Mostly agree (4) 0.61 0.56 0.55 0.56 0.57 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Completely disagree Mostly disagree NeutralMostly agreeCompletely agree Buy from certain stores Probability of seeking out organic foods Completely disagree (1) Mostly disagree (2) Average (19.3%) Average (55.9%)

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101 C Figure 5-20. Continued 0.23 0.25 0.25 0.25 0.25 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 Completely disagree Mostly disagree NeutralMostly agreeCompletely agree Buy from certain stores Probability of seeking out organic foods Neutral (3) Average (24.8%)

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102 A Figure 5-21. Impact of go out of way to get certain types of produce on seeking out organic foods. A) completely agree and mostly agree with the statement of I seek out organic foods. B) neither agree nor disagree with th e statement of I seek out organic foods. C) completely disagree and mostly disagree wi th the statement of I seek out organic foods. 0.08 0.12 0.19 0.21 0.24 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 Completely disagree Mostly disagree NeutralMostly agreeCompletely agree Go out of way to get certain types of produce Probability of seeking out organic foods Completely agree (5) Mostly agree (4) Average (19.3%)

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103 B C Figure 5-21. Continued 0.19 0.22 0.27 0.28 0.29 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 Completely disagree Mostly disagree NeutralMostly agree Completely agree Go out of way to get certain types of produce Probability of seeking out organic foods Neutral (3) 0.73 0.66 0.55 0.51 0.47 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Completely disagree Mostly disagree NeutralMostly agree Completely agree Go out of way to get certain types of produce Probability of seeking out organic foods Completely disagree (1) Mostly disagree (2) Average (55.9%) Average (24.8%)

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104 A Figure 5-22. Impact of eat fresh fruit a nd vegetables on seeking out organic foods. A) completely agree and mostly agree with the statement of I seek out organic foods. B) neither agree nor disagree with the stat ement of I seek out organic foods. C) completely disagree and mostly disagree w ith the statement of I seek out organic foods. B Figure 5-22. Continued 0.15 0.20 0.20 0.19 0.19 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 Completely disagree Mostly disagree NeutralMostly agreeCompletely agree Eating fresh fruits and vegetables Probability of seeking out organic foods Completely agree (5) Mostly agree (4) 0.62 0.55 0.55 0.56 0.56 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Completely disagree Mostly disagree NeutralMostly agreeCompletely agree Eating fresh fruits and vegetables Probability of seeking out organic foods Completely disagree (1) Mostly disagree (2) Average (19.3%) Average (55.9%)

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105 C Figure 5-22. Continued A Figure 5-23. Impact of feel healthier on seek ing out organic foods. A) completely agree and mostly agree with the statem ent of I seek out organic foods. B) neither agree nor disagree with the statement of I seek out organic foods. C) completely disagree and mostly disagree with the statement of I seek out organic foods. 0.23 0.25 0.25 0.25 0.25 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 Completely disagree Mostly disagree NeutralMostly agreeCompletely agree Eating fresh fruits and vegetables Probability of seeking out organic foods Neutral (3) 0.14 0.16 0.17 0.22 0.23 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 Completely disagree Mostly disagree NeutralMostly agreeCompletely agree Feel healthier than my peers Probability of seeking out organic foods Completely agree (5) Mostly agree (4) Average (19.3%) Average (24.8%)

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106 B C Figure 5-23. Continued 0.63 0.60 0.58 0.51 0.50 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Completely disagree Mostly disagree NeutralMostly agreeCompletely agree Feel healthier than my peers Probability of seeking out organic foods Completely disagree (1) Mostly disagree (2) 0.23 0.24 0.25 0.27 0.27 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 Completely disagree Mostly disagree NeutralMostly agreeCompletely agree Feel healthier than my peers Probability of seeking out organic foods Neutral (3) Average (55.9%) Average (24.8%)

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107 A Figure 5-24. Impact of exercise at least 3 times a week on seeking out organic foods. A) completely agree and mostly agree with the statement of I seek out organic foods. B) neither agree nor disagree with the stat ement of I seek out organic foods. C) completely disagree and mostly disagree w ith the statement of I seek out organic foods. B Figure 5-24. Continued 0.16 0.18 0.21 0.21 0.18 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 Completely disagree Mostly disagree NeutralMostly agreeCompletely agree Exercise at least 3 times a week Probability of seeking out organic foods Completely agree (5) Mostly agree (4) 0.60 0.58 0.53 0.53 0.57 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Completely disagree Mostly disagree NeutralMostly agreeCompletely agree Execise at least 3 times a week Probability of seeking out organic foods Completely disagree (1) Mostly disagree (2) Average (19.3%) Average (55.9%)

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108 C Figure 5-24. Continued A Figure 5-25. Impact of experiment with new foods on seeking out organic foods. A) completely agree and mostly agree with the statement of I seek out organic foods. B) neither agree nor disagree with the stat ement of I seek out organic foods. C) completely disagree and mostly disagree w ith the statement of I seek out organic foods. 0.24 0.24 0.26 0.26 0.25 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 Completely disagree Mostly disagree NeutralMostly agreeCompletely agree Execerse at least 3 times a week Probability of seeking out organic foods Neutral (3) 0.11 0.16 0.19 0.19 0.23 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 Completely disagree Mostly disagree NeutralMostly agreeCompletely agree Experiment with new foods Probability of seeking out organic foods Completely agree (5) Mostly agree (4) Average (19.3%) Average (24.8%)

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109 B Figure 5-25. Continued C Figure 5-25. Continued 0.68 0.60 0.55 0.55 0.50 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Completely disagree Mostly disagree NeutralMostly agreeCompletely agree Experiment with new foods Probability of seeking out organic foods Completely disagree (1) Mostly disagree (2) 0.21 0.24 0.26 0.26 0.27 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 Completely disagree Mostly disagree NeutralMostly agreeCompletely agree Experiment with new foods Probability of seeking out organic foods Neutral (3) Average (55.9%) Average (24.8%)

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110 A Figure 5-26. Impact of health concerns of hous ehold on head seeking out organic foods. A) no one in household has blood pressure. B) no one in household has diabetes. C) no one in household has cholesterol problem. D) no one in household has food allergies. E) no one in household has obesity problem. F) no one in household has limited physical mobility problem. G) no one in household has sight/hearing problem. B Figure 5-26. Continued 0.17 0.54 0.59 0.25 0.24 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 YesNo YesNo YesNo Do not have blood pressure Probability of seeking out organic foods Completely agree (5) Mostly agree (4) Neutral (3) Completely disagree (1) Mostl y disa g ree ( 2 ) 0.21 0.20 0.17 0.55 0.59 0.25 0.24 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 YesNo YesNo YesNo Do not have diabetes Probability of seeking out organic foods Completely agree (5) Mostly agree (4) Neutral (3) Completely disagree (1) Mostl y disa g ree ( 2 )

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111 C D Figure 5-26. Continued 0.20 0.19 0.55 0.57 0.25 0.25 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 YesNo YesNo YesNo Do not have cholesterol problems Probability of seeking out organic foods Completely agree (5) Mostly agree (4) Neutral (3) Completely disagree (1) Mostl y disa g ree ( 2 ) 0.19 0.22 0.57 0.52 0.25 0.26 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 YesNo YesNo YesNo Do not have food allergies Probability of seeking out organic foods Completely agree (5) Mostly agree (4) Neutral (3) Completely disagree (1) Mostly disagree (2)

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112 E F Figure 5-26. Continued 0.19 0.19 0.56 0.56 0.25 0.25 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 YesNo YesNo YesNo Do not have obesity problems Probability of seeking out organic foods Completely agree (5) Mostly agree (4) Neutral (3) Completely disagree (1) Mostl y disa g ree ( 2 ) 0.19 0.18 0.56 0.57 0.25 0.24 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 YesNo YesNo YesNo Do not have mobility problems Probability of seeking out organic foods Completely agree (5) Mostly agree (4) Neutral (3) Completely disagree (1) Mostly disagree (2)

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113 G Figure 5-26. Continued 0.20 0.18 0.56 0.58 0.25 0.24 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 YesNo YesNo YesNo Do not have sight/hearing problems Probability of seeking out organic foods Completely agree (5) Mostly agree (4) Neutral (3) Completely disagree (1) Mostl y disa g ree ( 2 )

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114 A Figure 5-27. Ranking of factors impacting th e likelihood of seeking out organic foods. A) Completely agree (5). B) Mostly agre e (4). C)Neither agree nor disagree. D) Mostly disagree. E) Completely disagree.

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115 B Figure 5-27. Continued

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116 C Figure 5-27. Continued

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117 D Figure 5-27. Continued

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118 E Figure 5-27. Continued

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119 CHAPTER 6 SUMMARY, CONCLUSION AND IMPLICATIONS W ith growing availability, organic purchases ha ve been changing from a lifestyle choice of a small group of consumers into a more popular choice with indications fr om earlier studies that two-thirds of American consumers purchase or ganic products at leas t occasionally. But the organic market share at retail food outlets remains still quite small compared to that of conventionally grown products. To aid in the effective marketing of organics, it would be insightful to better understand consumer prefer ences and identify the underlying motivations behind organic food purchases. This studies sh ows that among shoppers seeking out organic food, the level of purchases with in a two-week shopping periods is likely considerable less than the two-thirds suggested above. Granted, a fundame ntal difference is in the period specified for defining the purchasing time span (i.e., two-week say versus a year). This study concentrates all decisions taking place within the two-week shopping window. Instead of relying on the simple measure of did you buy organic foods or not, an alternative approach was utilized in this study. C onsumers were asked if they would like to seek out organic products with a measure of intensity as a response option. An underlying assumption was that the intensity of seeki ng out organics and orga nic food purchases were highly correlated, recognizing that within the data set available th at assumption could not be tested. Households responded to the statement I seek out organic f oods with a five-point Likert scale (where a 5 indicates completely agree and 1 indicates completely disagree ) in the questionnaire. Given that intensities were ordered bi nary values, determining the proba bilities of seeking out organics was a classical ordered probit probl em. Probabilities for five levels of agreement were simulated based on the probit models given a particular set of conditions of expl anatory variables. By

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120 comparing the conditional probabi lities, both the direction and ma gnitude of the effects of the variables being contro lled were estimated. Estimates suggested that demographic variable s, except age, were not as important as expected in explaining and pr edicting households lik elihood of seeking out organic foods. Nevertheless, several other fact ors were shown to contribute si gnificant effects on seeking out organic foods, especially households repor ted behavior and atti tude attributes. Selected demographic variables such as ag e and ethnicity partia lly influenced the propensity to value organic products. The results found negative associations between age and the propensity towards organics. The consumer segment with the highest propensity to seek organic products was among the younger populat ion between 18 and 24 years old, whereas consumers over 65 years old were the least likely to seek out organics. Consumers above age 45 became much less likely to seek organics comp ared to the average likelihood (19.2%). While younger population would be the main marketing target for organic products in general, marketing to older populations coul d also be beneficial, despite, their lower like lihood of seeking out organics because of the substantial propor tion (e.g., people who are older than 45 years represented 44.6% of the total number of respondents, and th ose over 65 represented nearly 14%) of the population in these age segments. The Hartman Group (2006) suggested: more than just advertising products and price, its an opportunity to connect with the ethnic groups through unique messaging that resonates at the cultural level. Based on the results of this study, two ethnic groups were relatively more likely to seek organic foods: Asian Americans (with a probability of 27%) and, to a lesser extent, Black/African Americans (with a probability of 20%). However, based on the total representation in the population (63%), the Caucasian American consumers was a segment that

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121 organic producers and marketers cannot afford to ignore even though this group tended to show less interest in organics relative to the other ethnicities. Although specific income and education levels were characteristics that could sometimes be targeted, the impacts of thes e factors on seeking out organics were mixed and relative small in the differences across each controlled variable. The likelihood of seeking organics declined somewhat in higher-income groups. There was li ttle evidence that ed ucation levels were positively correlated to organic propensity owing to the insignificant effect of having college degree, but shoppers with graduate or professional degrees were slightly more inclined to choose organics. Consumers with two or more members in the household were relatively more likely to seek out organics than single household shoppers Surprisingly, households with children under the age of eighteen displayed much le ss likelihood of seeking organics. Store choice was an important factor in explaining propensitie s to consume organic products. Households who grocery shopped on internet stores and farmers markets presented the highest likelihood of seeking out organic foods, while those who shopped in grocery stores, mass merchandisers, or convenience stores were less likely to search for organics. These results suggested that supplementary types of retail pl aces with organics av ailability could promote organic consumption effectively. The number of daily servings of fruit indicated the most important single factor this study indentified in the propensity to seek out orga nics, which suggested the promotion in organic fruits could increase the demand for organic prod ucts. However, since those respondents who did not consume any fruit per day still had a likelihoo d higher than average leve ls, the benefits might not be as incremental as other si gnificant attributes. Despite little evidence of incomes impact on the probability of seeking out organic foods, there was evidence suppor ting that the amount of

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122 expenditures on grocery shopping had a positive impact on organic food preference. Households who spent more money on grocery shopping were mo re likely to seek organics. Clearly, in the case of organic food consumption, grocery budget constraints played a more important role on explaining the propensity to spend on the less stap le goods of which organics most likely fitted. That is, the results suggested th at buying organics was a second tier in the consumer preference after spending on the more stable goods. This was true to the extent that expenditures reflected more latitude in the shopper choices. Most consumers socio-demographic charac teristics had limited influence on explaining and predicting consumer propensity to seek orga nic foods. However, several reported behavior and attitude attributes turned out to have significant impacts on organic foods choices, such as concerns about calories; eating fr esh food rather than packaged f oods; reading ingredients of the food on labels when buying; going out of the way fo r certain types of produ ce; feeling healthier than his or her peers; and frequently experimenting with new foods. A positive connection was presented between the frequency of eating fresh foods rather than packaged food and likelihood level of seeking out organics. Households who reviewed the i ngredients on product labels when buying foods and those who would like to experien ce new foods are more inclined to seek out organics. Thompson (1998) suggested that the decision of purchasing food away from home could be a potentially important issue. Moreover, our findings confirmed a substantial difference in the probability of seeking organics between h ouseholds who went out of the way to get certain types of produce and those who did not. The results also implied that people on diets might be more interested in organic foods, given that households who counted the number of cal ories they ate each day were more likely to seek out organic foods. A habit of exercising ha d mixed effect on organics inclination, given

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123 that people with flexible (less frequent) exerci se schedules were slightly more likely to seek organics while those who exercised at least three times a week were less likely to seek organics. In sum, our findings implied that potential gains could result from efforts to target the shoppers with those behavioral characte ristics rather than simple ec onomic and demographic traits. Finally, this study provided weak evidence th at health concerns such as high blood pressure, diabetes, etc. were factors explaining the potential for organic food consumption. Health concerns were expected to be significan t contributors on the prope nsity to value organic foods; however, the results indica ted that only a concern about food allergies had a positive impact on seeking out organics. This study was probably the most current in term s of the database and extensiveness of the data since nearly 38,000 observations were in cluded through March 2010. While many factors seemed to confirm results from other studies, the role of health was surprisingly weak given the generally reported perception that organics were healthier. While healthy was a scientific measure and the health aspects of organics mi ght have both factual a nd perception components, health generally had little impact on the probabilities of seeki ng out organic foods. The main limitation of the study was the stated propensity on seeki ng out organic foods instead of the actual purchases was evaluated. But, since the awareness of organics was expected to be the significant shifter of the probability of organic consumption, it would have been insightful if we provided addi tional information about cons umers organic knowledge and subsequent actual purchases of organic foods. Secondly, some additional environmental and social benefits of organically produced products perceived by consumers might also be important factors in explaining organic food demand, which was not confirmed in this study due to the lack of such information available in the survey. In conclusion, organic foods belonged to products

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124 with credence attributes and we re generic in nature, hence, cooperation from both the organic industry and government played a critically impo rtant role in the promotion of organic food markets. Finally, there was still much within the repor ted models that could be discussed and/or simulated in more detail. For example, we did not consider the combined effects of changing several variables. Likewise, a number of additi onal tests between levels within each category could have been completed although we still feel that the graphed probabilities were more revealing. Some of those tests woul d be reported in subsequent papers.

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125 APPENDIX A ORGANIC SURVEY VARIABLES Table A-1. Organic survey variables Variable Description AGE age of household head (18+) GENDER gender of household head RACE ethnicity CHL with children under 18 years EDUC highest education level EMPLY employment INCOME household income (dollars) MARITAL marital status HWD house size (number of members) STATE0 census region SERV_FRU servings of fruit do you consume in typical day (#0-10) SERV_VEG servings of vegetable do you consume in typical day (#0-10) EXPEND expenditures on grocery s hopping within a week (dollars) SHOP_GRO shopping for food in grocery store SHOP_WARE shopping for food in warehouse SHOP_INTE shopping for food in internet grocery store SHOP_MASS shopping for food in mass merchandiser SHOP_CONV shopping for food in convenience store SHOP_FARM shopping for food in farmers market BHV_EXERCISE I exercise at least 3 times a week CALORIES I count calories BHV_LABEL Read ingredients on labels of the foods I buy BHV_HLTH I feel healthier than peers BHV_NEWFOOD I frequently e xperiment with new foods BHV_FRE I eat fresh foods much more frequently than packaged food BHV_FRUVEG I eat fruits and vegetabl e more than other people my age BHV_WAY I go out of my way to get certain types of produce BHV_STORE I prefer to buy produce from certain stores HLT_BLOOD4 No one has high blood pressure in household HLT_DIABE4 No one has diabetes in household HLT_CHOLE4 No one has high cholesterol in household HLT_ALLEG4 No one has food allergies in household HLT_OBEST4 No one has obesity in household HLT_MOBIL4 No one has limited physical mobility in household HLT_HEAR4 No one has significant sight or hearing impairment in household MTH_S Months from January to December

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126 APPENDIX B CORRELATION COE FFICIENTS Table B-1. Correlation coeffici ents of explanatory variables XINC XEDURACEXAGEXGEN XMAR XINC 1.00000 XEDU 0.22848 1.00000 RACE 0.00423 0.04732 1.00000 XAGE 0.06228 0.03379 -0.13037 1.00000 XGEN -0.08627 -0.10196 -0.06012 -0.06012 1.00000 XMAR -0.06927 -0.04981 -0.02460 0.35995 0.09955 1.00000 XEMPLY -0.05519 -0.09666 -0.05112 0.25204 0.19017 0.07333 XEXPD 0.13403 0.05154 0.00181 0.00434 -0.03337 0.02585 XSERFRU 0.04641 0.01539 0.05075 -0.03501 0.04574 0.02410 XSERVEG 0.05194 0.05544 -0.01964 0.05445 0.01694 0.03009 XHWD 0.07031 -0.09159 0.09219 -0.30972 0.06689 -0.10156 XCHL -0.00220 -0.03445 0.08043 -0.31180 0.06208 0.00791 XSHOP_GRO 0.08411 0.02719 -0.03611 0.12073 -0.00522 0.03223 XSHOP_WARE 0.14618 0.11862 0.02192 0.00344 -0.07386 -0.04451 XSHOP_INTE 0.03760 0.07424 0.04104 -0.09907 -0.07756 -0.04458 XSHOP_MASS -0.09275 0.00737 -0.00947 -0.15880 0.05258 -0.05426 XSHOP_CONV 0.00850 0.04090 0.05229 -0.16897 -0.12506 -0.07431 XSHOP_FARM 0.05784 0.14007 0.02005 -0.06357 -0.00615 -0.02045 CAL 0.05128 0.07257 0.02337 -0.03490 0.02930 -0.03239 B_FRE 0.09565 0.07044 0.03684 0.13158 0.03045 0.07043 B_LAB 0.07245 0.11570 0.00936 0.10280 0.04185 0.02755 B_ST 0.08674 0.11036 0.01230 0.08201 0.03602 0.03854 B_WAY 0.11416 0.10761 0.04384 0.06056 0.01122 0.02463 B_FV 0.06361 0.09912 0.02654 0.02350 0.01576 0.05408 B_HLT 0.10251 0.09252 0.02668 0.02302 -0.06328 0.00666 B_EXE 0.05473 0.12242 0.03067 -0.03367 -0.06504 -0.02062 B_NEW 0.08838 0.10851 0.00044 -0.13474 0.02020 -0.03294 HLT_BP -0.00117 0.04816 0.00724 -0.31453 0.00048 -0.04009 HLT_DB 0.04449 0.08294 -0.05826 -0.17205 0.04424 -0.01806 HLT_CL -0.01684 0.06707 0.06522 -0.27354 0.01409 -0.03921 HLT_AG -0.02978 -0.04871 -0.02903 0.02855 -0.03490 -0.00292 HLT_OB 0.05327 0.02271 -0.00924 -0.05744 -0.03620 0.00485 HLT_MB 0.13269 0.03602 0.03499 -0.21723 0.00334 -0.08340 HLT_HR 0.05278 0.07169 0.01687 -0.15435 -0.00056 -0.02180

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127 Table B-1. Continued XEMPLY XEXPDXSERFRUXSERVEGXHWD XCHL XEMPLY 1.00000 XEXPD -0.07190 1.00000 XSERFRU 0.01782 0.18296 1.00000 XSERVEG 0.03281 0.16842 0.47123 1.00000 XHWD -0.03163 0.34562 0.10262 0.05019 1.00000 XCHL -0.11603 0.27832 0.09183 0.03926 0.72993 1.00000 XSHOP_GRO -0.01004 0.04810 0.02498 0.01548 -0.03691 -0.04370 XSHOP_WARE -0.05498 0.20343 0.07236 0.08692 0.11481 0.05103 XSHOP_INTE -0.04859 0.14234 0.12534 0.08042 0.11215 0.10173 XSHOP_MASS -0.01377 0.07421 0.05311 0.04815 0.13760 0.13465 XSHOP_CONV -0.11619 0.09594 0.08260 0.04431 0.08840 0.08022 XSHOP_FARM -0.06677 0.15046 0.12625 0.13540 0.04550 0.02877 CAL -0.06042 0.11203 0.25103 0.17789 0.02914 0.02847 B_FRE 0.03931 0.14461 0.29877 0.20511 0.01813 -0.00564 B_LAB 0.05352 0.06485 0.25116 0.21860 -0.05345 -0.05503 B_ST 0.03639 0.15162 0.21831 0.16900 0.02953 -0.00076 B_WAY 0.01557 0.20443 0.29741 0.21880 0.04540 0.03222 B_FV 0.03497 0.13336 0.35927 0.29751 0.02883 0.03026 B_HLT -0.07465 0.05442 0.19512 0.14944 -0.02347 -0.01083 B_EXE -0.04762 0.02291 0.19448 0.14186 -0.03317 -0.00254 B_NEW -0.10412 0.17271 0.20537 0.21862 0.11489 0.09473 HLT_BP -0.19015 -0.06456 -0.00487 -0.02396 0.01329 0.12245 HLT_DB -0.13511 -0.09351 -0.06150 -0.06419 -0.03257 0.06427 HLT_CL -0.16582 -0.05795 -0.00589 -0.00808 -0.00432 0.07874 HLT_AG -0.00197 -0.02410 -0.02408 -0.03627 -0.10646 -0.07885 HLT_OB -0.04818 -0.11460 -0.02314 -0.03644 -0.08940 -0.04194 HLT_MB -0.18572 -0.01451 -0.03838 -0.05153 0.03974 0.14380 HLT_HR -0.11669 -0.00688 -0.01958 -0.01717 -0.01651 0.07234

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128 Table B-1. Continued XSHOP _GRO XSHOP _WARE XSHOP _INTE XSHOP _MASS XSHOP _CONV XSHOP _FARM XSHOP_GRO 1.00000 XSHOP_WARE 0.04802 1.00000 XSHOP_INTE 0.00930 0.16045 1.00000 XSHOP_MASS -0.20577 0.12074 0.10166 1.00000 XSHOP_CONV 0.04124 0.09355 0.21737 0.13223 1.00000 XSHOP_FARM 0.06121 0.18769 0.31875 0.09149 0.23120 1.00000 CAL -0.00041 0.09270 0.15385 0.08414 0.13161 0.16510 B_FRE 0.04667 0.12702 0.07275 -0.00270 0.01919 0.15776 B_LAB 0.05442 0.10125 0.08128 0.00365 0.07884 0.15293 B_ST 0.05227 0.10716 0.06299 0.02600 0.04714 0.18133 B_WAY 0.04642 0.15197 0.11336 0.08269 0.11890 0.22541 B_FV 0.02106 0.11226 0.10376 0.06558 0.08483 0.17531 B_HLT 0.00436 0.14226 0.12244 0.01410 0.07115 0.12799 B_EXE 0.01528 0.11685 0.09504 0.03423 0.08658 0.13277 B_NEW 0.02236 0.14089 0.12760 0.08134 0.15969 0.18042 HLT_BP -0.01557 -0.02650 0.04383 0.00814 0.03708 0.01690 HLT_DB -0.01056 -0.04424 -0.06091 -0.02391 -0.01945 -0.01307 HLT_CL -0.00305 -0.04116 0.02159 0.00057 0.03726 0.02764 HLT_AG -0.02055 0.00564 -0.04937 0.02273 0.00646 -0.01573 HLT_OB 0.00607 -0.02914 -0.00207 -0.02425 -0.00548 -0.00524 HLT_MB 0.01836 0.04654 -0.03713 0.00569 0.01673 0.01428 HLT_HR 0.00133 0.00466 -0.02433 0.00748 0.01283 0.01194

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129 Table B-1. Continued CAL B_FREB_LABB_STB_WAY B_FV CAL 1.00000 B_FRE 0.31488 1.00000 B_LAB 0.45958 0.41693 1.00000 B_ST 0.23412 0.43901 0.37174 1.00000 B_WAY 0.33545 0.49818 0.40898 0.50734 1.00000 B_FV 0.33147 0.62256 0.41820 0.39578 0.51860 1.00000 B_HLT 0.27463 0.43302 0.29925 0.30006 0.34644 0.54384 B_EXE 0.34440 0.37598 0.32011 0.24835 0.31225 0.39220 B_NEW 0.30035 0.37627 0.33083 0.28284 0.44102 0.41422 HLT_BP -0.04866 -0.03388 -0.09131 -0.03558 -0.03377 0.00112 HLT_DB -0.05680 -0.06336 -0.07545 -0.04512 -0.05158 -0.02426 HLT_CL -0.01630 -0.03368 -0.04606 -0.01644 -0.01981 0.04286 HLT_AG -0.02425 -0.03264 -0.08487 -0.00927 -0.02733 0.00646 HLT_OB 0.00545 0.10203 -0.00228 0.01025 0.01437 0.09637 HLT_MB 0.00681 -0.03629 -0.05120 -0.01930 0.01709 0.00379 HLT_HR 0.00172 -0.07827 -0.02877 -0.02200 -0.01736 -0.01129 Table B-1. Continued B_HLT B_EXEB_NEWHLT_BPHLT_DB HLT_CL B_HLT 1.00000 B_EXE 0.49226 1.00000 B_NEW 0.31983 0.26318 1.00000 HLT_BP 0.10866 0.09982 0.07279 1.00000 HLT_DB 0.07371 0.03557 0.05083 0.37219 1.00000 HLT_CL 0.10578 0.09904 0.06879 0.47702 0.30612 1.00000 HLT_AG -0.00272 -0.00436 -0.01791 0.08186 0.05075 0.05783 HLT_OB 0.23708 0.16367 0.06624 0.28508 0.23534 0.24220 HLT_MB 0.16626 0.15347 0.05438 0.29528 0.24880 0.25442 HLT_HR 0.05771 0.05620 0.02861 0.22322 0.20676 0.24184 Table B-1. Continued HLT_AG HLT_OB HLT_MBHLT_HR HLT_AG 1.00000 HLT_OB 0.09750 1.00000 HLT_MB 0.13032 0.27704 1.00000 HLT_HR 0.12553 0.17287 0.32027 1.00000

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130 APPENDIX C TSP CODE OPTIONS MEMORY= 1000; TITLE 'Consumers Preferences for Organics'; ? Organics#02.tsp ?================= ========================= =================; ? READING STAT VERSION 7.0 ?================= ========================= =================; IN 'D:\ZZORGANIC\Organics\ORGANICDATA'; ? ?V105_A I SEEK OUT ORGANIC FOODS; ? 5= completely agree ? 4= somewhat agree ? 3= neutral ? 2= somewhat disagree ? 1= completely disagree ?======================== =====================================; ? NOTES ?======================== =====================================; ? FOR EACH HEALTH VARIABLE USED TH E LAST CODE WITH 4 MEANING THAT YOU DO NOT THIS HEALTH PR OBLEM IN YOUR FAMILY; ? Q10OUTLE IS NOT IN YOUR DATASET THAT IS WHERE YOU PURCHASED A SPECIFIC FRUIT; ? EXCLUSIVE HAS NO MEANING IN YOUR FILE SO IGNORE IT; ?END; LIST ZVARZ RD PERIOD IDD3 YEAR MONTH YRS_S MTH_S ORGANIC AGE GENDER ETHNIC HISPANIC DEVICE SHOP_GRO SHOP_WARE SHOP_INTE SHOP_MASS SHOP_CONV SHOP_FARM SHOP_NONE EXPEND SERV_FRU SERV_VEG CALORIES BHV_ORG BHV_FRESH BHV_LABEL BHV_STORE BHV_WAY BHV_FRUVEG BHV_HLTH BHV_EXERCISE BHV_NEWFOOD HLT_BLOOD1 HLT_BLOOD2 HLT_BLOOD3 HLT_BLOOD4 HLT_DIABE1 HLT_DIABE2 HLT_DIABE3 HLT_DIABE4 HLT_CHOLE1 HLT_CHOLE2 HLT_CHOLE3 HLT_CHOLE4 HLT_ALLEG1 HLT_ALLEG2 HLT_ALLEG3 HLT_ALLEG4 HLT_OBEST1 HLT_OBEST2 HLT_OBEST3 HLT_OBEST4 HLT_MOBIL1 HLT_MOBIL2 HLT_MOBIL3 HLT_MOBIL4 HLT_HEAR1 HLT_HEAR2 HLT_HEAR3 HLT_HEAR4 RACE MARITAL HWD1 HWD2 HWD3 HWD4 HWD5 STATE0 STATE1 Q21STATE EDUC EMPLY INCOME PERIODS PERIODE ;

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131 ? DOC RD 'HOUSEHOLD IDENTITI CATION'; ? DOC PERIOD 'REPORTING PERIODS'; ? DOC IDD3 'IDD3=1 TO REMOVE BAD HOUSEHOLDS'; ? DOC YEAR 'REPORTING YEAR'; ? DOC MONTH 'REPORTING MONTH'; ? DOC YRS_S 'REPORTING YEAR'; ? DOC MTH_S 'REPORTING MONTH'; ? DOC ORGANIC 'S EEKING OUT ORGANIC FOODS'; ? DOC AGE 'AGE OF HOUSEHOLD HEAD'; ? DOC GENDER 'GENDER OF HOUSEHOLD HEAD'; ? DOC ETHNIC 'RACE OF HOUSEHOLD HEAD'; ? DOC HISPANIC 'HISPANIC'; ? DOC DEVICE 'ELECTRONIC DEVICES'; ? DOC SHOP_GRO 'Grocery store Where have you personally shopped for food in'; ? DOC SHOP_WARE 'Warehouse club stor e (Costco, Sams Club, etc) Where have you personally shopped for food in'; ? DOC SHOP_INTE 'Internet grocery st ore (Peapod, Fresh Direct etc) Where have you personally shopped for food in'; ? DOC SHOP_MASS 'Mass merchandise r (Wal-Mart, Target, etc) Where have you personally shopped for food in'; ? DOC SHOP_CONV 'Conveni ence Store (Gas station, 7-11, Quik Check etc.) Where have you personally shopped for food in'; ? DOC SHOP_FARM 'Farmer's market / Pr oduce stand (including free -standing carts) Where have you personally shopped for food in'; ? DOC SHOP_NONE 'None of the above Wh ere have you personally shopped for food in'; ? DOC EXPEND 'Weekly grocery spending $ In a typical week, about how much money do you spend on groceries'; ? DOC SERV_FRU '# How many servings of fruit do you consume in typical day?'; ? DOC SERV_VEG '# How many servings of vegetables do you c onsume in typical day?'; ? DOC CALORIES 'I count calories'; ? DOC BHV_ORG 'I seek out organi c foods Please tell us how much you agree on'; ? DOC BHV_FRESH 'I eat fr esh foods much more frequently than packaged foods Please tell us how much you agree on'; ? DOC BHV_LABEL 'I read ingredients on la bels of the foods I buy Please tell us how much you agree on'; ? DOC BHV_STORE 'I prefer to buy my pr oduce from certain stores/outlets Please tell us how much you agree on'; ? DOC BHV_WAY 'I go out of my way to get certain ty pes of produce Please tell us how much you agree on'; ? DOC BHV_FRUVEG 'I eat fruits and vegetables more than other people my age Please tell us how much you agree on'; ? DOC BHV_HLTH 'I feel that I am h ealthier than my peers Please tell us how much you agree on'; ? DOC BHV_EXERCISE 'I exerci se at least 3 times a week Pl ease tell us how much you agree on'; ? DOC BHV_NEWFOOD 'I frequently experiment with new foods Please tell us how much

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132 you agree on'; ? DOC HLT_BLOOD1 'Hi gh blood pressure Do you or doe s anyone in your household have any of'; ? DOC HLT_BLOOD2 'Hi gh blood pressure Do you or doe s anyone in your household have any of'; ? DOC HLT_BLOOD3 'Hi gh blood pressure Do you or doe s anyone in your household have any of'; ? DOC HLT_BLOOD4 'Hi gh blood pressure Do you or doe s anyone in your household have any of'; ? DOC HLT_DIABE1 'Diabetes Do you or does anyone in your household have any of'; ? DOC HLT_DIABE2 'Diabetes Do you or does anyone in your household have any of'; ? DOC HLT_DIABE3 'Diabetes Do you or does anyone in your household have any of'; ? DOC HLT_DIABE4 'Diabetes Do you or does anyone in your household have any of'; ? DOC HLT_CHOLE1 'High cholesterol Do you or does anyone in y our household have any of'; ? DOC HLT_CHOLE2 'High cholesterol Do you or does anyone in y our household have any of'; ? DOC HLT_CHOLE3 'High cholesterol Do you or does anyone in y our household have any of'; ? DOC HLT_CHOLE4 'High cholesterol Do you or does anyone in y our household have any of'; ? DOC HLT_ALLEG1 'Food allergies Do you or does anyone in your household have any of'; ? DOC HLT_ALLEG2 'Food allergies Do you or does anyone in your household have any of'; ? DOC HLT_ALLEG3 'Food allergies Do you or does anyone in your household have any of'; ? DOC HLT_ALLEG4 'Food allergies Do you or does anyone in your household have any of'; ? DOC HLT_OBEST1 'Obesity Do you or does anyone in your hous ehold have any of'; ? DOC HLT_OBEST2 'Obesity Do you or does anyone in your hous ehold have any of'; ? DOC HLT_OBEST3 'Obesity Do you or does anyone in your hous ehold have any of'; ? DOC HLT_OBEST4 'Obesity Do you or does anyone in your hous ehold have any of'; ? DOC HLT_MOBIL1 'Limited physical mobility Do you or does anyone in your household have any of'; ? DOC HLT_MOBIL2 'Limited physical mob ility Do you or does anyone in your household have any of'; ? DOC HLT_MOBIL3 'Limited physical mob ility Do you or does anyone in your household have any of'; ? DOC HLT_MOBIL4 'Limited physical mob ility Do you or does anyone in your household have any of'; ? DOC HLT_HEAR1 'Signifi cant sight or hearing impairment Do you or does anyone in your household have any of'; ? DOC HLT_HEAR2 'Signifi cant sight or hearing impairment Do you or does anyone in your household have any of'; ? DOC HLT_HEAR3 'Signifi cant sight or hearing impairment Do you or does anyone in your household have any of'; ? DOC HLT_HEAR4 'Signifi cant sight or hearing impairment Do you or does anyone in your household have any of'; ? DOC RACE 'Which of the follo wing most closely describe s your family heritage';

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133 ? DOC MARITAL 'Marital Status Whic h one of the following best describes yours'; ? DOC HWD1 '5 years of ag e and younger Household Com position Including youself, how many people currently linving in your household'; ? DOC HWD2 '6-8 years of ag e Household Composition Including youself, how many people currently linving in your household'; ? DOC HWD3 '9-12 years of ag e Household Composition In cluding youself, how many people currently linving in your household'; ? DOC HWD4 '13-17 years of ag e Household Composition In cluding youself, how many people currently linving in your household'; ? DOC HWD5 '18 years of age and older Household Composition Including youself, how many people currently li nving in your household'; ? DOC STATE0 'State of Resi dence In which state do you currently live?'; ? DOC STATE1 'Do have another state that you consider your primary residence'; ? DOC EDUC0 'Education What is th e highest level of educa tion you have completed'; ? DOC EMPLY 'Employment Status Which one of the followi ng best describes yours'; ? DOC INCOME 'Household Income Wh ich one of the following ranges includes your total yearly household income before tax'; ? DOC PERIODS 'Time Period Start Date'; ? DOC PERIODE 'Time Period End Date'; SELECT PERIOD>2 & IDD3=1; ?===============; ? AGE; ?===============; ? Q1AGE 17 or younger 1[SCREEN OUT]; ? Q1AGE 18-24 2 ; ? Q1AGE 25-34 3 ; ? Q1AGE 35-44 4 ; ? Q1AGE 45-54 5 ; ? Q1AGE 55-64 6 ; ? Q1AGE 65-70 7 ; ? Q1AGE Over 70 8 ; ?HIST(DISCRETE,PERCENT) AGE; XAGE=(AGE<3) + ((AGE=3)|(AGE=4))*2 + ((AGE=5)|(AGE=6))*3 + (AGE>6)*4; DUMMY XAGE; ?HIST(DISCRETE,PERCENT) XAGE; DOT 1-3; DAGE.=XAGE.-XAGE4; ENDDOT; ?===============; ? GENDER; ?===============; ?GENDER=Q2GENDER; ? Q2GENDER 1 MALE ; ? Q2GENDER 2 FEMALE; XGEN =(GENDER=2); ?HIST(DISCRETE,PERCENT) XGEN; DGEN =(XGEN=1) + (XGEN=0)*-1;

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134 ?===============; ? MARITAL; ?===============; ? Q19MARIT 1 SINGLE, NEVER MARRIED; ? Q19MARIT 2 MARRIED ; ? Q19MARIT 3 LIVING WITH PARENTS ; ? Q19MARIT 4 SEPARATED ; ? Q19MARIT 5 DIVORCED ; ? Q19MARIT 6 WIDOWED ; ? Q19MARIT 7 PREFER NOT TO ANSWER ; XMAR =(MARITAL=1) +(MARITAL=2) *2 +(MARITAL=3)*3 +(MARITAL>3)*4; DUMMY XMAR; ?HIST(DISCRETE,PERCENT) XMAR; DOT 1-3; DMAR.=XMAR.-XMAR4;ENDDOT; ?===============; ? ETHNICITY; ?===============; ? Q3ETHNIC 1 White/Caucasian ; ? Q3ETHNIC 2 Black/African American; ? Q3ETHNIC 3 Asian ; ? Q3ETHNIC 4 Pacific Islander ; ? Q3ETHNIC 5 Native American ; ? Q3ETHNIC 6 Other ; ? Q3ETHNIC 7 Prefer not to answer ; ?DUMMY ETHNIC; ?HIST(DISCRETE,PERCENT) ETHNIC; ?DOT 1-6; ?ZETHN.= ETHNIC.-ETHNIC7;ENDDOT; ? HISPANIC; ? Q4ETHNIC 1 HISPANIC ; ? Q4ETHNIC 2 NOT HISPANIC ; ? Q4ETHNIC 3 Prefer not to answer; ?DUMMY HISPANIC; ?HIST(DISCRETE,PERCENT) HISPANIC; ?DOT 1-2; ?ZHISP.=HISPANIC.-HISPANIC3; ENDDOT; HISP =(HISPANIC=1); RACE =((ETHNIC=1)&(HISP=0))*1 +(( ETHNIC=1)&(HISP=1))*2 +(ETHNIC=2)*3 +(ETHNIC=3)*4 +(ETHNIC>=4)*5; ?HIST(DISCRETE,PERCENT) RACE; DUMMY RACE; DOT 1-4; DRACE.=RACE.-RACE5;ENDDOT;

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135 ?====================; ? INCOME CATEGORIES ; ?====================; ? Q26INCOM 1 Under $15,000 ; ? Q26INCOM 2 $15,000-$24,999 ; ? Q26INCOM 3 $25,000-$34,999 ; ? Q26INCOM 4 $35,000-$49,999 ; ? Q26INCOM 5 $50,000-$74,999 ; ? Q26INCOM 6 $75,000-$99,999 ; ? Q26INCOM 7 $100,000-$149,999 ; ? Q26INCOM 8 $150,000 and over ; ? Q26INCOM 9 Prefer not to answer; XINC=(INCOME<4) + ((INCOME>=4)& (INCOME<=5))*2 + (INCOME=6)*3 + ((INCOME>6)&(INCOME<9))*4 + (INCOME=9)*5; DUMMY XINC; ?HIST(DISCRETE,PERCENT) XINC; DOT 1-4; DINC. =XINC.-XINC5;ENDDOT; ?==============; ? EDUCATION ; ?==============; ?EDUC=Q14GS_ED; ? Q24GS_ED 1 Less than 9th grade ; ? Q24GS_ED 2 9th to 12th grade, no diploma ; ? Q24GS_ED 3 High school graduate or equivalent; ? Q24GS_ED 4 Some college, no degree ; ? Q24GS_ED 5 Associate degree ; ? Q24GS_ED 6 Bachelor's degree ; ? Q24GS_ED 7 Graduate or professional degree ; ? Q24GS_ED 8 Other, please specify ; ? Q24GS_ED 9 Prefer not to answer ; XEDU=(EDUC<=3) + ((EDUC>=4)&(EDUC<=6))*2 + (EDUC=7)*3 + (EDUC>=8)*4; DUMMY XEDU; ?HIST(DISCRETE,PERCENT) XEDU; DOT 1-3; DEDU.=XEDU.-XEDU4; ENDDOT; ?====================; ? HOUSEHOLD SIZE ; ?====================; ?Q20HHCOM; ?HWD1 5 years of age and younger~Household Co mposition (Including yourself); ?HWD2 6-8 years of age~Household Composition (In cluding yourself) ; ?HWD3 9-12 years of age~Household Composition (In cluding yourself) ; ?HWD4 13-17 years of age~Hous ehold Composition (Including yourself) ; ?HWD5 18 years of age and older~H ousehold Composition (Including yourself) ;

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136 HWD =HWD1 + HWD2 + HWD3 + HWD4 + HWD5; CHL=((HWD1 + HWD2 + HWD3 + HWD4)> 0); ?WITH CHILDREN UNDER 18 YEARS; XHWD =(HWD=1) +(HWD=2)*2 +(HW D=3)*3 +(HWD=4)*4 +(HWD>4)*5; DUMMY XHWD; ?HIST(DISCRETE,PERCENT) XHWD; DOT 1-4; DHWD.=XHWD.-XHWD5; ENDDOT; XCHL =(CHL=1); ?HIST(DISCRETE,PERCENT) XCHL; DCHL =(XCHL=1) +(XCHL=0)*-1; ?==================; ? EMPLOYMENT ; ?==================; ?EMPLY=Q25EMPLO; ? Q25EMPLO 1 EMPLOYED FULL TIME; ? Q25EMPLO 2 EMPLOYED PART TIME; ? Q25EMPLO 3 SELF-EMPLOYED; ? Q25EMPLO 4 NOT EMPLOYED, BUT LOOKING FOR WORK; ? Q25EMPLO 5 NOT EMPLOYED, AND NOT LOOKING FOR WORK; ? Q25EMPLO 6 RETIRED; ? Q25EMPLO 7 STUDENT; ? Q25EMPLO 8 HOMEMAKER; ? Q25EMPLO 9 PREFER NOT TO ANSWER; XEMPLY=((EMPLY=1)|(EMPLY=3)) + (EMPLY=2)*2 + ((EMPLY=4)|(EMPLY=5))*3 + (EMPLY>=6)*4; DUMMY XEMPLY; ? 1=EMPLOYED FULL TIME & SELF-EMPLOYED 2=EMPLOYED PART TIME 3=NOT EMPLOYED 4=OTHERS; ?HIST(DISCRETE,PERCENT) XEMPLY; DOT 1-3; DEMPLY.=XEMPLY.-XEMPLY4; ENDDOT; ?================= =============; ? MONTHS ; ?================= =============; MTH=MTH_S; DUMMY MTH; ?HIST(DISCRETE,PERCENT) MTH; DOT 2-12; DMTH.=MTH.-MTH1;ENDDOT; ?================= =================; ? GROCERY SHOPPING STORE Q7GROCER ; ?================= =================; ? SHOP_GRO; XSHOP_GRO=(SHOP_GRO=1); ?HIST(DISCRETE,PERCENT) XSHOP_GRO; DSHOP_GRO=(SHOP_GRO=1) + (SHOP_GRO=0)*-1;

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137 ? SHOP_WARE; XSHOP_WARE=(SHOP_WARE=1); ?HIST(DISCRETE,PERCENT) XSHOP_WARE; DSHOP_WARE=(SHOP_WARE=1) + (SHOP_WARE=0)*-1; ? SHOP_INTE; XSHOP_INTE=(SHOP_INTE=1); ?HIST(DISCRETE,PERCENT) XSHOP_INTE; DSHOP_INTE=(SHOP_INTE=1) + (SHOP_INTE=0)*-1; ? SHOP_MASS; XSHOP_MASS=(SHOP_MASS=1); ?HIST(DISCRETE,PERCENT) XSHOP_MASS; DSHOP_MASS=(SHOP_MASS=1) + (SHOP_MASS=0)*-1; ? SHOP_CONV; XSHOP_CONV=(SHOP_CONV=1); ?HIST(DISCRETE,PERCENT) XSHOP_CONV; DSHOP_CONV=(SHOP_CONV=1) + (SHOP_CONV=0)*-1; ? SHOP_FARM; XSHOP_FARM=(SHOP_FARM=1); ?HIST(DISCRETE,PERCENT) XSHOP_FARM; DSHOP_FARM=(SHOP_FARM=1) + (SHOP_FARM=0)*-1; ? SHOP_NONE[EXCLUSIVE][SCREEN OUT]; XSHOP_NONE=(SHOP_NONE=1); ?HIST(DISCRETE,PERCENT) XSHOP_NONE; DSHOP_NONE=(SHOP_NONE=1) + (SHOP_NONE=0)*-1; ?======================== ===================; ? EXPENDITURES ON GROCERY SHOPPING Q13DOLLA ; ?======================== ===================; ? TITLE 'WEEKLY GROCER Y SPENDING DOLLARS'; ?SELECT PERIOD>2 & IDD3=1; ?HIST(NBINS=30) EXPEND; ?HIST(NBINS=30,PERCENT) EXPEND; MSD EXPEND; ?MAT HIST_EXPD=@HIST; ?MAT NR=NROW(@HIST); ?PRINT NR; XEXPD =(EXPEND<50) +((EXPEND>=50)&(EXPEND<100))*2 +((EXPEND>=100)&(EXPEND<200))*3+((EXPEND>=200)&(EXPEND<400))*4 +((EXPEND>=400))*5; DUMMY XEXPD; ?HIST(DISCRETE,PERCENT) XEXPD; DOT 1-4; DEXPD. =XEXPD.-XEXPD5;ENDDOT; ?=======================================; ? SERVING OF FRUIT & VEG #0-10 ; ?=======================================; ? TITLE 'HOW MANY SERVINGS OF FRUI T DO YOU CONSUME IN TYPICAL DAY?'; ? Q11 SERV_FRU #0-10;

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138 XSERFRU =(SERV_FRU=0) +((SER V_FRU>=1)&(SERV_FRU<=3))*2 + ((SERV_FRU>=4)&(SERV_FRU<=6))*3+(SERV_FRU>=7)*4; DUMMY XSERFRU; ?HIST(DISCRETE,PERCENT) XSERFRU; DOT 1-3; DSERVF. =XSERFRU.-XSERFRU4;ENDDOT; ? TITLE 'HOW MANY SERVINGS OF VE G DO YOU CONSUME IN TYPICAL DAY?'; ? Q12 SERV_VEG #0-10; XSERVEG =(SERV_VEG=0) +((SER V_VEG>=1)&(SERV_VEG<=3))*2 + ((SERV_VEG>=4)&(SERV_VEG<=6))*3+(SERV_VEG>=7)*4; DUMMY XSERVEG; ?HIST(DISCRETE,PERCENT) XSERVEG; DOT 1-3; DSERVV. =XSERVEG.-XSERVEG4;ENDDOT; ?<<<<<<<<<<<<<<< BEHAVIOR & A TTITUDE >>>>>>>>>>>>>>>>>>>>>; ?======================; ? COUNT CALORIES ; ?================= =====; ?CALORIES; ?5=COMPLETE AGREE 1= TOTALLY DISAGREE; ?TITLE 'I TRY TO COUNT THE NUMBER OF CALORIES I EAT EACH DAY AND BUY MANGOS'; ? Q15AGREE 5 Completely agree (5) ; ? Q15AGREE 4 Somewhat agree ; ? Q15AGREE 3 Neither ; ? Q15AGREE 2 Somewhat disagree ; ? Q15AGREE 1 Completely disagree (1); CAL =CALORIES; ?VARIABLE CONSISTENT; DUMMY CAL; ?HIST(DISCRETE,PERCENT) CAL; DOT 1,2,4,5; DCAL.=CAL.-CAL3; ENDDOT; ?=======================; ? EATING FRESH FOODS ; ?=======================; ?BHV_FRESH; ?TITLE 'I EAT FRESH FOODS MORE FREQUENTLY THAN PACKAGED FOODS'; ? Q15AGREE 5 Completely agree (5) ; ? Q15AGREE 4 Somewhat agree ; ? Q15AGREE 3 Neither ; ? Q15AGREE 2 Somewhat disagree ; ? Q15AGREE 1 Completely disagree (1) ; B_FRE =BHV_FRESH; ? VARIABLE CONSISTENT; DUMMY B_FRE; ?HIST(DISCRETE,PERCENT) B_FRE; DOT 1,2,4,5;

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139 DB_FRE.=B_FRE.-B_FRE3; ENDDOT; ?=======================; ? READ LABEL ; ?=======================; ?BHV_LABEL; ?TITLE 'I READ INGREDIENTS ON LA BELS OF THE FOODS I BUY'; ? Q15AGREE 5 Completely agree (5) ; ? Q15AGREE 4 Somewhat agree ; ? Q15AGREE 3 Neither ; ? Q15AGREE 2 Somewhat disagree ; ? Q15AGREE 1 Completely disagree (1) ; B_LAB =BHV_LABEL; ? VARIABLE CONSISTENT; DUMMY B_LAB; ?HIST(DISCRETE,PERCENT) B_LAB; DOT 1,2,4,5; DB_LAB.=B_LAB.-B_LAB3; ENDDOT; ?======================== ============; ? BUY FROM CERTAIN STORES ; ?======================== ============; ?BHV_STORE; ?TITLE 'I PREFER TO BUY PRODU CE FROM CERTAIN STORES'; ? Q15AGREE 5 Completely agree (5) ; ? Q15AGREE 4 Somewhat agree ; ? Q15AGREE 3 Neither ; ? Q15AGREE 2 Somewhat disagree ; ? Q15AGREE 1 Completely disagree (1) ; B_ST =BHV_STORE; ?VARIABLE CONSISTENT; DUMMY B_ST; ?HIST(DISCRETE,PERCENT) B_ST; DOT 1,2,4,5; DB_ST.=B_ST.-B_ST3; ENDDOT; ?========================== ================ =========; ? WAY TO GET CERTAIN TAYPES OF PR ODUCE ; ?========================== ================ =========; ?BHV_WAY; ?TITLE 'I GO OUT OF WAY TO G ET CERTAIN TYPES OF PRODUCE'; ? Q15AGREE 5 Completely agree (5) ; ? Q15AGREE 4 Somewhat agree ; ? Q15AGREE 3 Neither ; ? Q15AGREE 2 Somewhat disagree ; ? Q15AGREE 1 Completely disagree (1) ; B_WAY =BHV_WAY; ?VARIABLE CONSISTENT; DUMMY B_WAY; ?HIST(DISCRETE,PERCENT) B_WAY; DOT 1,2,4,5; DB_WAY.=B_WAY.-B_WAY3; ENDDOT;

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140 ?======================================; ? EATING FRESH FRUITS AND VEGETABLES ; ?======================================; ?BHV_FRUVEG; ?TITLE 'I EAT FRUITS AND VEGETABLES MORE THAN OTHER PEOPLE MY AGE'; ? Q15AGREE 5 Completely agree (5) ; ? Q15AGREE 4 Somewhat agree ; ? Q15AGREE 3 Neither ; ? Q15AGREE 2 Somewhat disagree ; ? Q15AGREE 1 Completely disagree (1) ; B_FV =BHV_FRUVEG; ?VARIABLE CONSISTENT; DUMMY B_FV; ?HIST(DISCRETE,PERCENT) B_FV; DOT 1,2,4,5; DB_FV.=B_FV.-B_FV3; ENDDOT; ?================= ================; ? FEEL HEATLHIER THAN MY PEERS ; ?================= ================; ?BHV_HLTH; ?TITLE 'I FEEL THAT I AM HEALTHIER THAN MY PEERS'; ? Q15AGREE 5 Completely agree (5) ; ? Q15AGREE 4 Somewhat agree ; ? Q15AGREE 3 Neither ; ? Q15AGREE 2 Somewhat disagree ; ? Q15AGREE 1 Completely disagree (1) ; B_HLT =BHV_HLTH; ?VARIABLE CONSISTENT; DUMMY B_HLT; ?HIST(DISCRETE,PERCENT) B_HLT; DOT 1,2,4,5; DB_HLT.=B_HLT.-B_HLT3; ENDDOT; ?================= ================; ? EXERCISE ; ?================= ================; ?BHV_EXERCISE; ?TITLE 'I EXERCISE AT LEAST 3 TIMES A WEEK' ; ? Q15AGREE 5 Completely agree (5) ; ? Q15AGREE 4 Somewhat agree ; ? Q15AGREE 3 Neither ; ? Q15AGREE 2 Somewhat disagree ; ? Q15AGREE 1 Completely disagree (1) ; B_EXE =BHV_EXERCISE; ? VARIABLE CONSISTENT; DUMMY B_EXE; ?HIST(DISCRETE,PERCENT) B_EXE; DOT 1,2,4,5; DB_EXE.=B_EXE.-B_EXE3; ENDDOT;

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141 ?================= ================; ? EXPERIMENT WITH FOOD S ; ?================= ================; ?BHV_NEWFOOD; ?TITLE 'I FREQUENTLY EXPE RIMENT WITH NEW FOODS' ; ? Q15AGREE 5 Completely agree (5) ; ? Q15AGREE 4 Somewhat agree ; ? Q15AGREE 3 Neither ; ? Q15AGREE 2 Somewhat disagree ; ? Q15AGREE 1 Completely disagree (1) ; B_NEW =BHV_NEWFOOD; ?VARIABLE CONSISTENT; DUMMY B_NEW; ?HIST(DISCRETE,PERCENT) B_NEW; DOT 1,2,4,5; DB_NEW.=B_NEW.-B_NEW3; ENDDOT; ?<<<<<<<<<<<<<<< HEALTH CONDI TIONS >>>>>>>>>>>>>>>>>>>>>; ?================= =========================================; ? HIGH BLOOD PRESSURE V119_A DO NOT HAVE BLOOD PRESSURE ; ?================= =========================================; ? HLT_BLOOD1 YOU ? HLT_BLOOD2 YOU SPONSE ? HLT_BLOOD3 OTHER ? HLT_BLOOD4 NONE [EXCLUSIVE] ? HLT_BLOOD4; HLT_BP=(HLT_BLOOD4=1); ?HIST(DISCRETE,PERCENT) HLT_BP; DHLT_BP=(HLT_BP=1) + (HLT_BP=0)*-1; ?================= ========================= ==================; ? DIABETES V123_A DO NOT HAVE DIABETES ; ?================= ========================= ==================; ? HLT_DIABE1 YOU ? HLT_DIABE2 YOU SPONSE ? HLT_DIABE3 OTHER ? HLT_DIABE4 NONE [EXCLUSIVE] ? HLT_DIABE4; HLT_DB=(HLT_DIABE4=1); ?HIST(DISCRETE,PERCENT) HLT_DB; DHLT_DB=(HLT_DB=1) + (HLT_DB=0)*-1; ?================= ========================= ==================; ? HIGH CHOLESTEROLV127_A DO NO T HAVE CHOLESTER OL PROBLEMS ; ?================= ========================= ==================; ? HLT_CHOLE1 YOU ? HLT_CHOLE2 YOU SPONSE ? HLT_CHOLE3 OTHER ? HLT_CHOLE4 NONE [EXCLUSIVE] ? HLT_CHOLE4;

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142 HLT_CL=(HLT_CHOLE4=1); ?HIST(DISCRETE,PERCENT) HLT_CL; DHLT_CL=(HLT_CL=1) + (HLT_CL=0)*-1; ?================= ========================= ==================; ? HIGH FOOD ALLERGIESV131_A DO NOT HAVE FOOD ALLERGIES ; ?================= ========================= ==================; ? HLT_ALLEG1 YOU ? HLT_ALLEG2 YOU SPONSE ? HLT_ALLEG3 OTHER ? HLT_ALLEG4 NONE [EXCLUSIVE] ? HLT_ALLEG4; HLT_AG=(HLT_ALLEG4=1); ?HIST(DISCRETE,PERCENT) HLT_AG; DHLT_AG=(HLT_AG=1) + (HLT_AG=0)*-1; ?================= ========================= ==================; ? OBESITY V135_A DO NOT HAVE OBESITY PROBLEMS ; ?================= ========================= ==================; ? HLT_OBEST1 YOU ? HLT_OBEST2 YOU SPONSE ? HLT_OBEST3 OTHER ? HLT_OBEST4 NONE [EXCLUSIVE] ? HLT_OBEST4; HLT_OB=(HLT_OBEST4=1); ?HIST(DISCRETE,PERCENT) HLT_OB; DHLT_OB=(HLT_OB=1) + (HLT_OB=0)*-1; ?================= ========================= ==================; ? MOBILITY V139_A DO NOT HAVE MOBI LITHY PROBLEMS ; ?================= ========================= ==================; ? HLT_MOBIL1 YOU ? HLT_MOBIL2 YOU SPONSE ? HLT_MOBIL3 OTHER ? HLT_MOBIL4 NONE [EXCLUSIVE] ? HLT_MOBIL4; HLT_MB=(HLT_MOBIL4=1); ?HIST(DISCRETE,PERCENT) HLT_MB; DHLT_MB=(HLT_MB=1) + (HLT_MB=0)*-1; ?================= ========================= =================; ? SIGHT V143_A DO NOT HAVE SIGHT /HEARING PROBLEMS ; ?================= ========================= =================; ? HLT_HEAR1 YOU ? HLT_HEAR2 YOU SPONSE ? HLT_HEAR3 OTHER ? HLT_HEAR4 NONE [EXCLUSIVE] ? HLT_HEAR4; HLT_HR=(HLT_HEAR4=1); ?HIST(DISCRETE,PERCENT) HLT_HR;

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143 DHLT_HR=(HLT_HR=1) + (HLT_HR=0)*-1; ?======================== ==================== ==================; ? US REGIONS ; ?======================== ==================== ==================; ? STATE0 =Q21STATE WSTATE=STATE0; ? HIST(DISCRETE) WSTATE; DIVISION=[ WSTATE= 7 | WSTATE=22 | WS TATE = 20| WSTATE =31 | WSTATE =40| WSTATE =47]*1 + [ WSTATE= 32| WSTATE=35 | WSTATE = 39]*2 + [ WSTATE= 16| WSTATE=15 | WSTATE = 23| WSTATE =36 | WSTATE =49]*3 + [ WSTATE= 13 | WSTATE= 17 | WSTATE = 24 | WSTATE = 25 | WSTATE = 30| WSTATE = 29 | WSTATE = 42]*4 + [ WSTATE= 9 | WSTATE= 8 | WSTATE =10 | WSTATE = 11| WSTATE =21 | WSTATE = 28 | WSTATE = 41| WSTATE =46 | WSTATE = 50]*5 + [ WSTATE= 2 | WSTATE= 18 | WSTATE = 26 | WSTATE =43 ]*6 + [ WSTATE= 3 | WSTATE= 19 | WSTATE = 37 | WSTATE = 44 ]*7 + [ WSTATE= 4 | WSTATE= 6 | WSTATE = 14 | WSTATE = 33 | WSTATE = 27 | WSTATE = 45 | WSTATE =34| WSTATE = 51 ]*8 + [ WSTATE= 1 | WSTATE= 5 | WSTATE = 12| WSTATE = 38 | WSTATE = 48 ]*9; ? DIVISION =1 NORTHEAST(1):NEW ENGLAND ? DIVISION =2 NORTHEAST(1):MIDDLE A TLANTIC ? DIVISION =3 MIDWEST(2): EAST NO RTH CENTRAL ? DIVISION =4 MIDWEST( 2): WEST NORTH CENTRAL ? DIVISION =5 SOUTH(3): SOUTH ATLANTIC ? DIVISION =6 SOUTH(3): EAST SOUTH CENTRAL ? DIVISION =7 SOUTH(3): WEST SOUTH CENTRAL ? DIVISION =8 WEST(4): MOUNTAIN ? DIVISION =9 WEST(4): PACIFIC REGION = [ DIVISION=1 | DIVISION=2 ] + [ DIVISI ON=3 | DIVISION=4 ]*2 + [ DIVISION=5 | DIVISION= 6 | DIVISION=7]*3 +[ DIVI SION=8 | DIVISION=9 ]*4; ? 1 AK | 10 FL | 19 LA | 27 MT |36 OH | 45 UT ? 2 AL | 11 GA | 20 MA | 28 NC |37 OK | 46 VA ? 3 AR | 12 HI | 21 MD | 29 ND |38 OR | 47 VT ? 4 AZ | 13 IA | 22 ME | 30 NE |39 PA | 48 WA ? 5 CA | 14 ID | 23 MI | 31 NH |40 RI | 49 WI ? 6 CO | 15 IL | 24 MN | 32 NJ |41 SC | 50 WV ? 7 CT | 16 IN | 25 MO | 33 NM |42 SD | 51 WY ? 8 DC | 17 KS | 26 MS | 34 NV |43 TN | ? 9 DE | 18 KY | | 35 NY |44 TX | ?DUMMY DIVISION;

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144 ?HIST(DISCRETE) DIVISION; ?DOT 2-9; ?WDIV. = DIVISI ON. DIVISION1; ?ZDIV. = DIVISION.; ?ENDDOT; DUMMY REGION; ?HIST(DISCRETE,PERCENT) REGION; DOT 2-4; DREG.= REGION.-REGION1;ENDDOT; ? CORRELATIONS AMONG ALL VARIABLES; ?CORR(COVA,MSD,PRINT) XINC XEDU RACE XAGE XGEN XMAR XEMPLY XEXPD XSERFRU XSERVEG XHWD XCHL; ?XSHOP_GRO XSHOP_WARE XSHOP_INTE XSHOP_MASS XSHOP_CONV XSHOP_FARM CAL B_FRE B_LAB B_ST; ?B_WAY B_FV B_HLT B_EX E B_NEW HLT_BP HLT_DB HLT_CL HLT_AG HLT_OB HLT_MB HLT_HR; ? HISTOGRAMS OF ALL DUMMIES INCLUDED IN MODEL; ?LIST HVARH ?XAGE XGEN XMAR RACE XINC XEDU XHWD XCHL XEMPLY REGION MTH ?XSHOP_GRO XSHOP_WARE XSHOP_INTE XSHOP_MASS XSHOP_CONV XSHOP_FARM ?XEXPD XSERFRU XSERVEG ?CAL1 B_FRE B_LAB B_ST B_ WAY B_FV B_HLT B_EXE B_NEW ?HLT_BP HLT_DB HLT_CL HLT_AG HLT_OB HLT_MB HLT_HR; ?DOT HVARH; ?HIST(DISCRETE) .; ?HIST(DISCRETE,PERCENT) .;ENDDOT; ?======================== ==================================== ========; ? ORDERED PROBIT MODEL VARIABLES ?======================== ==================================== ========; LIST XMODELX DAGE1 DAGE2 DAGE3 DGEN DMAR1 DMAR2 DMAR3 DRACE1 DRACE2 DRACE3 DRACE4 DINC1 DINC2 DINC3 DINC4 DEDU1 DEDU2 DEDU3 DHWD1 DHWD2 DHWD3 DHWD4 DCHL DEMPLY1 DEMPLY2 DEMPLY3 DREG2 DREG3 DREG4 DSHOP_GRO DSHOP_WARE DSHOP_INTE DSHOP_MASS DSHOP_CONV DSHOP_FARM DEXPD1 DEXPD2 DEXPD3 DEXPD4

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145 DSERVF1 DSERVF2 DSERVF3 DSERVV1 DSERVV2 DSERVV3 DCAL1 DCAL2 DCAL4 DCAL5 DB_FRE1 DB_FRE2 DB_FRE4 DB_FRE5 DB_LAB1 DB_LAB2 DB_LAB4 DB_LAB5 DB_ST1 DB_ST2 DB_ST4 DB_ST5 DB_WAY1 DB_WAY2 DB_WAY4 DB_WAY5 DB_FV1 DB_FV2 DB_FV4 DB_FV5 DB_HLT1 DB_HLT2 DB_HLT4 DB_HLT5 DB_EXE1 DB_EXE2 DB_EXE4 DB_EXE5 DB_NEW1 DB_NEW2 DB_NEW4 DB_NEW5 DHLT_BP DHLT_DB DHLT_CL DHLT_AG DHLT_OB DHLT_MB DHLT_HR DMTH2 DMTH3 DMTH4 DMTH5 DMTH6 DM TH7 DMTH8 DMTH9 DMTH10 DMTH11 DMTH12; ORDPROB ORGANIC C XMODELX; WRITE(FORMAT=EXCEL,FILE='D:\ZZORGANI C\Organics\HIST5.XLS') @COEF; ?======================== ==================================== ========; MMAKE STATS @COEF @T %T; print STATS; SET NU=3; ? NUMBER OF ORDER CATEGORIES LESS 2; FIT=@FIT; SELECT PERIOD>2 & IDD3=1; DFIT=( (FIT**2)>=0); MAT BB=@COEF; MAT NR=NROW(BB); SET RR=NR(1)-NU; MFORM(TYPE=GEN,NROW=RR,NCOL=1) AA=0; DO J=1 TO RR; SET AA(J)= BB(J); ENDDO; PRINT AA; DOT(VALUE=K) 2-4; SET L=RR + K -1; SET MU.=BB(L); PRINT K L MU.; ENDDOT; ?======================== ==================================== ========; ? SETTING FOR THE SIMULATION PORTION OF THE ANALYSIS ; ?======================== ==================================== ========; LIST ZVAR2Z DAGE1 DAGE2 DAGE3 DGEN DMAR1 DMAR2 DMAR3 DRACE1 DRACE2 DRACE3 DRACE4 DINC1 DINC2 DINC3 DINC4 DEDU1 DEDU2 DEDU3 DHWD1 DHWD2 DHWD3 DHWD4 DCHL DEMPLY1 DEMPLY2 DEMPLY3 DREG2 DREG3 DREG4 DSHOP_GRO DSHOP_WARE DSHOP_INTE DSHOP_MASS DSHOP_CONV DSHOP_FARM DEXPD1 DEXPD2 DEXPD3 DEXPD4

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146 DSERVF1 DSERVF2 DSERVF3 DSERVV1 DSERVV2 DSERVV3 DCAL1 DCAL2 DCAL4 DCAL5 DB_FRE1 DB_FRE2 DB_FRE4 DB_FRE5 DB_LAB1 DB_LAB2 DB_LAB4 DB_LAB5 DB_ST1 DB_ST2 DB_ST4 DB_ST5 DB_WAY1 DB_WAY2 DB_WAY4 DB_WAY5 DB_FV1 DB_FV2 DB_FV4 DB_FV5 DB_HLT1 DB_HLT2 DB_HLT4 DB_HLT5 DB_EXE1 DB_EXE2 DB_EXE4 DB_EXE5 DB_NEW1 DB_NEW2 DB_NEW4 DB_NEW5 DHLT_BP DHLT_DB DHLT_CL DHLT_AG DHLT_OB DHLT_MB DHLT_HR DMTH2 DMTH3 DMTH4 DMTH5 DMTH6 DM TH7 DMTH8 DMTH9 DMTH10 DMTH11 DMTH12; LIST SVARW WDAGE1 WDAGE2 WDAGE3 WDGEN WDMAR1 WDMAR2 WDMAR3 WDRACE1 WDRACE2 WDRACE3 WDRACE4 WDINC1 WDINC2 WDINC3 WDINC4 WDEDU1 WDEDU2 WDEDU3 WDHWD1 WDHWD2 WDHWD3 WDHWD4 WDCHL WDEMPLY1 WDEMPLY2 WDEMPLY3 WDREG2 WDREG3 WDREG4 WDSHOP_GRO WDSHOP_WARE WDSHOP_INTE WDSHOP_MASS WDSHOP_CONV WDSHOP_FARM WDEXPD1 WDEXPD2 WDEXPD3 WD EXPD4 WDSERVF1 WDSERVF2 WDSERVF3 WDSERVV1 WDSERVV2 WDSERVV3 WDCAL1 WDCAL2 WDCAL4 WDCAL5 WDB_FRE1 WDB_FRE2 WDB_FRE4 WDB_FRE5 WDB_LAB1 WDB_LAB2 WDB_LAB4 WDB_LAB5 WDB_ST1 WDB_ST2 WDB_ST4 WDB_ST5 WDB_WAY1 WDB_WAY2 WDB_WAY4 WDB_WAY5 WDB_FV1 WDB_FV2 WDB_FV4 WDB_FV5 WDB_HLT1 WDB_HLT2 WDB_HLT4 WDB_HLT5 WDB_EXE1 WDB_EXE2 WDB_EXE4 WDB_EXE5 WDB_NEW1 WDB_NEW2 WDB_NEW4 WDB_NEW5 WDHLT_BP WDHLT_DB WDHLT_CL WDHLT_AG WDHLT_OB WDHLT_MB WDHLT_HR WDMTH2 WDMTH3 WDMTH4 WDMTH5 WDMTH6 WDMTH7 WDMTH8 WDMTH9 WDMTH10 WDMTH11 WDMTH12; LIST SVARS SDAGE1 SDAGE2 SDAGE3 SDGEN SDMAR1 SDMAR2 SDMAR3 SDRACE1 SDRACE2 SDRACE3 SDRACE4 SDINC1 SDINC2 SDINC3 SDINC4

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147 SDEDU1 SDEDU2 SDEDU3 SDHWD1 SDHWD2 SDHWD3 SDHWD4 SDCHL SDEMPLY1 SDEMPLY2 SDEMPLY3 SDREG2 SDREG3 SDREG4 SDSHOP_GRO SDSHOP_WARE SDSHOP _INTE SDSHOP_MASS SDSHOP_CONV SDSHOP_FARM SDEXPD1 SDEXPD2 SDEXPD3 SDEXPD4 SDSERVF1 SDSERVF2 SDSERVF3 SDSERVV1 SDSERVV2 SDSERVV3 SDCAL1 SDCAL2 SDCAL4 SDCAL5 SDB_FRE1 SDB_FRE2 SDB_FRE4 SDB_FRE5 SDB_LAB1 SDB_LAB2 SDB_LAB4 SDB_LAB5 SDB_ST1 SDB_ST2 SDB_ST4 SDB_ST5 SDB_WAY1 SDB_WAY2 SDB_WAY4 SDB_WAY5 SDB_FV1 SDB_FV2 SDB_FV4 SDB_FV5 SDB_HLT1 SDB_HLT2 SDB_HLT4 SDB_HLT5 SDB_EXE1 SDB_EXE2 SDB_EXE4 SDB_EXE5 SDB_NEW1 SDB_NEW2 SDB_NEW4 SDB_NEW5 SDHLT_BP SDHLT_DB SDHLT_CL SDHLT_AG SDHLT_OB SDHLT_MB SDHLT_HR SDMTH2 SDMTH3 SDMTH4 SDMTH5 SDMTH6 SDMTH7 SDMTH8 SDMTH9 SDMTH10 SDMTH11 SDMTH12; DOT ZVAR2Z; SET S.=0; SET W.=0; SET IHH=1; ENDDOT; SET I=0; ?======================; PROC INIT; ?======================; DOT ZVAR2Z; SET S.=0; SET W.=0; SET IHH=1; ENDDOT; ENDPROC; MFORM(TYPE=GEN,NROW=150,NCOL=12) MSIM=0; SET SIMNUM=0; ?======================; PROC ZSIMZ; ?======================; SELECT PERIOD>2 & IDD3=1; IZZ=1; SET IWW=0; SET I=I+1; MMAKE SX1 IZZ ZVAR2Z ; MMAKE SX2 IWW SVARS ; MMAKE SX3 IWW SVARW ; MAT X2= SX1%(IZZ#SX2'); MAT X3= IZZ#SX3'; MAT X1 = SX1 X2 + X3; MAT NRX1=NROW(X1);

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148 MAT XB=X1*AA; MAT PROB1= CNORM(-XB); MAT PROB2= CNORM( MU 2 XB) CNORM(-XB); MAT PROB3= CNORM( MU3 XB) CNORM( MU2 XB); MAT PROB4= CNORM( MU4 XB) CNORM( MU3 XB); MAT PROB5= 1CNORM(MU4 XB); MAT NN=NROW(PROB1); DOT 1-5; UNMAKE PROB. LPROB.; DLPROB. = LPROB.*DFIT; ENDDOT; MSD(NOPRINT) DLPROB1 DLPROB2 DLPROB3 DLPROB4 DLPROB5; SET MSIM(I,1)=I; SET MSIM(I,2)=SIMNUM; SET MSIM(I,3)=VARNUM; SET MSIM(I,4)= @MEAN(1); SET MSIM(I,5)= @MEAN(2); SET MSIM(I,6)= @MEAN(3); SET MSIM(I,7)= @MEAN(4); SET MSIM(I,8)= @MEAN(5); ENDPROC; ?========================; ? STARTING THE SIMULATIONS; ?========================; ?========================== ================ ========; ? SIMULATION #1 AVERAGE PERSON RESPONDING ; ?========================== ================ ========; SET SIMNUM=1; SET VARNUM=1; INIT; ZSIMZ; ?========================== ================ ========; ? SIMULATION #2 AGE OF RESPONDENT ; ?========================== ================ ========; SET SIMNUM=2; SET VARNUM=1; INIT; SET SDAGE1=1; SET SDAGE2=1; SET SDAGE3=1; SET WDAGE1=1; SET WDAGE2=0; SET WDAGE3=0; ? XAGE=1; ZSIMZ; SET VARNUM=2; INIT; SET SDAGE1=1; SET SDAGE2=1; SET SDAGE3=1; SET WDAGE1=0; SET WDAGE2=1; SET WDAGE3=0; ? XAGE=2; ZSIMZ; SET VARNUM=3; INIT; SET SDAGE1=1; SET SDAGE2=1; SET SDAGE3=1; SET WDAGE1=0; SET WDAGE2=0; SET WDAGE3=1; ? XAGE=3; ZSIMZ;

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149 SET VARNUM=4; INIT; SET SDAGE1=1; SET SDAGE2=1; SET SDAGE3=1; SET WDAGE1=-1; SET WDAGE2=-1; SET WDAGE3=-1; ? XAGE=4; ZSIMZ; ?========================== ================ ========; ? SIMULATION #3 GENDER ; ?========================== ================ ========; SET SIMNUM=3; SET VARNUM=1; INIT; SET SDGEN=1; SET WDGEN=1; ? XGEN =1 [GENDER=FEMALE]; ZSIMZ; SET VARNUM=2; INIT; SET SDGEN=1; SET WDGEN=-1; ? XGE N=0 [GENDER=MALE]; ZSIMZ; ?========================== ================ ========; ? SIMULATION #4 MARITAL STATUS OF RESPONDENT ; ?========================== ================ ========; SET SIMNUM=4; SET VARNUM=1; INIT; SET SDMAR1=1; SET SDMAR2=1; SET SDMAR3=1; SET WDMAR1=1; SET WDMAR2=0; SET WDMAR3=0; ? XMAR=1; ZSIMZ; SET VARNUM=2; INIT; SET SDMAR1=1; SET SDMAR2=1; SET SDMAR3=1; SET WDMAR1=0; SET WDMAR2=1; SET WDMAR3=0; ? XMAR=2; ZSIMZ; SET VARNUM=3; INIT; SET SDMAR1=1; SET SDMAR2=1; SET SDMAR3=1; SET WDMAR1=0; SET WDMAR2=0; SET WDMAR3=1; ? XMAR=3; ZSIMZ; SET VARNUM=4; INIT; SET SDMAR1=1; SET SDMAR2=1; SET SDMAR3=1; SET WDMAR1=-1; SET WDMAR2=-1; SET WDMAR3=-1; ? XMAR=4; ZSIMZ; ?========================== ================ ========; ? SIMULATION #5 RACE OF RESPO NDENT ; ?========================== ================ ========; SET SIMNUM=5; SET VARNUM=1; INIT; SET SDRACE1=1; SET SDRACE2=1; SET SDRACE3=1; SET SDRACE4=1;

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150 SET WDRACE1=1; SET WDRACE 2=0; SET WDRACE3=0; S ET WDRACE4=0; ? RACE=1; ZSIMZ; SET VARNUM=2; INIT; SET SDRACE1=1; SET SDRACE2=1; SET SDRACE3=1; SET SDRACE4=1; SET WDRACE1=0; SET WDRACE 2=1; SET WDRACE3=0; S ET WDRACE4=0; ? RACE=2; ZSIMZ; SET VARNUM=3; INIT; SET SDRACE1=1; SET SDRACE2=1; SET SDRACE3=1; SET SDRACE4=1; SET WDRACE1=0; SET WDRACE 2=0; SET WDRACE3=1; S ET WDRACE4=0; ? RACE=3; ZSIMZ; SET VARNUM=4; INIT; SET SDRACE1=1; SET SDRACE2=1; SET SDRACE3=1; SET SDRACE4=1; SET WDRACE1=0; SET WDRACE 2=0; SET WDRACE3=0; S ET WDRACE4=1; ? RACE=4; ZSIMZ; SET VARNUM=5; INIT; SET SDRACE1=1; SET SDRACE2=1; SET SDRACE3=1; SET SDRACE4=1; SET WDRACE1=-1; SET WDRACE2=-1; S ET WDRACE3=-1; SET WDRACE4=-1; ? RACE=5; ZSIMZ; ?========================== ================ ========; ? SIMULATION #6 INCOME ; ?========================== ================ ========; SET SIMNUM=6; SET VARNUM=1; INIT; SET SDINC1=1; SET SDINC2=1; SET SDINC3=1; SET SDINC4=1; SET WDINC1=1; SET WDINC2=0; SET WD INC3=0; SET WDINC4=0; ? XINC=1; ZSIMZ; SET VARNUM=2; INIT; SET SDINC1=1; SET SDINC2=1; SET SDINC3=1; SET SDINC4=1; SET WDINC1=0; SET WDINC2=1; SET WD INC3=0; SET WDINC4=0; ? XINC=2; ZSIMZ; SET VARNUM=3; INIT; SET SDINC1=1; SET SDINC2=1; SET SDINC3=1; SET SDINC4=1; SET WDINC1=0; SET WDINC2=0; SET WD INC3=1; SET WDINC4=0; ? XINC=3; ZSIMZ; SET VARNUM=4; INIT; SET SDINC1=1; SET SDINC2=1; SET SDINC3=1; SET SDINC4=1; SET WDINC1=0; SET WDINC2=0; SET WD INC3=0; SET WDINC4=1; ? XINC=4; ZSIMZ;

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151 SET VARNUM=5; INIT; SET SDINC1=1; SET SDINC2=1; SET SDINC3=1; SET SDINC4=1; SET WDINC1=-1; SET WDINC2 =-1; SET WDINC3=-1; S ET WDINC4=-1; ? XINC=5; ZSIMZ; ?========================== ================ ========; ? SIMULATION #7 EDUCATION ; ?========================== ================ ========; SET SIMNUM=7; SET VARNUM=1; INIT; SET SDEDU1=1; SET SDEDU2=1; SET SDEDU3=1; SET WDEDU1=1; SET WDEDU2=0; SET WDEDU3=0; ? XEDU=1; ZSIMZ; SET VARNUM=2; INIT; SET SDEDU1=1; SET SDEDU2=1; SET SDEDU3=1; SET WDEDU1=0; SET WDEDU2=1; SET WDEDU3=0; ? XEDU=2; ZSIMZ; SET VARNUM=3; INIT; SET SDEDU1=1; SET SDEDU2=1; SET SDEDU3=1; SET WDEDU1=0; SET WDEDU2=0; SET WDEDU3=1; ? XEDU=3; ZSIMZ; SET VARNUM=4; INIT; SET SDEDU1=1; SET SDEDU2=1; SET SDEDU3=1; SET WDEDU1=-1; SET WDEDU2=-1; SET WDEDU3=-1; ? XEDU=4; ZSIMZ; ?========================== ================ ========; ? SIMULATION #8 HOUSEHOLD SIZE ; ?========================== ================ ========; SET SIMNUM=8; SET VARNUM=1; INIT; SET SDHWD1=1; SET SDHWD2=1; SET SDHWD3=1; SET SDHWD4=1; SET WDHWD1=1; SET WDHWD2=0; SET WD HWD3=0; SET WDHWD4=0; ? XHWD=1; ZSIMZ; SET VARNUM=2; INIT; SET SDHWD1=1; SET SDHWD2=1; SET SDHWD3=1; SET SDHWD4=1; SET WDHWD1=0; SET WDHWD2=1; SET WD HWD3=0; SET WDHWD4=0; ? XHWD=2; ZSIMZ; SET VARNUM=3; INIT; SET SDHWD1=1; SET SDHWD2=1; SET SDHWD3=1; SET SDHWD4=1; SET WDHWD1=0; SET WDHWD2=0; SET WD HWD3=1; SET WDHWD4=0; ? XHWD=3; ZSIMZ;

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152 SET VARNUM=4; INIT; SET SDHWD1=1; SET SDHWD2=1; SET SDHWD3=1; SET SDHWD4=1; SET WDHWD1=0; SET WDHWD2=0; SET WD HWD3=0; SET WDHWD4=1; ? XHWD=4; ZSIMZ; SET VARNUM=5; INIT; SET SDHWD1=1; SET SDHWD2=1; SET SDHWD3=1; SET SDHWD4=1; SET WDHWD1=-1; SET WDHWD2 =-1; SET WDHWD3=-1; SET WDHWD4=-1; ? XHWD=5; ZSIMZ; ?========================== ================ ========; ? SIMULATION #9 WITH CHILDREN UNDER 18 ; ?========================== ================ ========; SET SIMNUM=9; SET VARNUM=1; INIT; SET SDCHL=1; SET WDCHL=1; ? XCHL=1; ZSIMZ; SET VARNUM=2; INIT; SET SDCHL=1; SET WDCHL=-1; ? XCHL=0; ZSIMZ; ?========================== ================ ========; ? SIMULATION #10 EMPLOYMENT ; ?========================== ================ ========; SET SIMNUM=10; SET VARNUM=1; INIT; SET SDEMPLY1=1; SET SDEMPLY2=1; SET SDEMPLY3=1; SET WDEMPLY1=1; SET WDEMPLY2=0; SET WDEMPLY3=0; ? XEMPLY=1; ZSIMZ; SET VARNUM=2; INIT; SET SDEMPLY1=1; SET SDEMPLY2=1; SET SDEMPLY3=1; SET WDEMPLY1=0; SET WDEMPLY2=1; SET WDEMPLY3=0; ? XEMPLY=2; ZSIMZ; SET VARNUM=3; INIT; SET SDEMPLY1=1; SET SDEMPLY2=1; SET SDEMPLY3=1; SET WDEMPLY1=0; SET WDEMPLY2=0; SET WDEMPLY3=1; ? XEMPLY=3; ZSIMZ; SET VARNUM=4; INIT; SET SDEMPLY1=1; SET SDEMPLY2=1; SET SDEMPLY3=1; SET WDEMPLY1=-1; SET WDEMPLY2=-1; SET WDEMPLY3=-1; ? XEMPLY=4; ZSIMZ;

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153 ?========================== ================ ========; ? SIMULATION #11 REGION ; ?========================== ================ ========; SET SIMNUM=11; SET VARNUM=1; INIT; SET SDREG2=1; SET SDREG3=1; SET SDREG4=1; SET WDREG2=-1; SET WDREG3=-1; SET WDREG4=-1; ? REGION=1; ZSIMZ; SET VARNUM=2; INIT; SET SDREG2=1; SET SDREG3=1; SET SDREG4=1; SET WDREG2=1; SET WDREG3=0; SET WDREG4=0; ? REGION=2; ZSIMZ; SET VARNUM=3; INIT; SET SDREG2=1; SET SDREG3=1; SET SDREG4=1; SET WDREG2=0; SET WDREG3=1; SET WDREG4=0; ? REGION=3; ZSIMZ; SET VARNUM=4; INIT; SET SDREG2=1; SET SDREG3=1; SET SDREG4=1; SET WDREG2=0; SET WDREG3=0; SET WDREG4=1; ? REGION=4; ZSIMZ; ?========================== ================ ========; ? SIMULATION #12 STORE CHOICES ; ?========================== ================ ========; SET SIMNUM=12.1; SET VARNUM=1; INIT; SET SDSHOP_GRO=1; SET WDSHOP_GRO=1; ? XSHOP_GRO=1; ZSIMZ; SET VARNUM=2; INIT; SET SDSHOP_GRO=1; SET WDSHOP_GRO=-1; ? XSHOP_GRO=0; ZSIMZ; SET SIMNUM=12.2; SET VARNUM=1; INIT; SET SDSHOP_WARE=1; SET WDSHOP_WARE=1; ? XSHOP_WARE=1; ZSIMZ; SET VARNUM=2; INIT; SET SDSHOP_WARE=1; SET WDSHOP_WARE=-1; ? XSHOP_WARE=0; ZSIMZ;

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154 SET SIMNUM=12.3; SET VARNUM=1; INIT; SET SDSHOP_INTE=1; SET WDSHOP_INTE=1; ? XSHOP_INTE=1; ZSIMZ; SET VARNUM=2; INIT; SET SDSHOP_INTE=1; SET WDSHOP_INTE=-1; ? XSHOP_INTE=0; ZSIMZ; SET SIMNUM=12.4; SET VARNUM=1; INIT; SET SDSHOP_MASS=1; SET WDSHOP_MASS=1; ? XSHOP_MASS=1; ZSIMZ; SET VARNUM=2; INIT; SET SDSHOP_MASS=1; SET WDSHOP_MASS=-1; ? XSHOP_MASS=0; ZSIMZ; SET SIMNUM=12.5; SET VARNUM=1; INIT; SET SDSHOP_CONV=1; SET WDSHOP_CONV=1; ? XSHOP_CONV=1; ZSIMZ; SET VARNUM=2; INIT; SET SDSHOP_CONV=1; SET WDSHOP_CONV=-1; ? XSHOP_CONV=0; ZSIMZ; SET SIMNUM=12.6; SET VARNUM=1; INIT; SET SDSHOP_FARM=1; SET WDSHOP_FARM=1; ? XSHOP_FARM=1; ZSIMZ; SET VARNUM=2; INIT; SET SDSHOP_FARM=1; SET WDSHOP_FARM=-1; ? XSHOP_FARM=0; ZSIMZ; ?========================== ================ ========; ? SIMULATION #13 EXPENDITUR E ON GROCERIES ; ?========================== ================ ========; SET SIMNUM=13; SET VARNUM=1; INIT; SET SDEXPD1=1; SET SDEXPD2=1; SET SDEXPD3=1; SET SDEXPD4=1;

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155 SET WDEXPD1=1; SET WDEXPD2=0; SET WD EXPD3=0; SET WDEXPD4=0; ? XEXPD=1; ZSIMZ; SET VARNUM=2; INIT; SET SDEXPD1=1; SET SDEXPD2=1; SET SDEXPD3=1; SET SDEXPD4=1; SET WDEXPD1=0; SET WDEXPD2=1; SET WD EXPD3=0; SET WDEXPD4=0; ? XEXPD=2; ZSIMZ; SET VARNUM=3; INIT; SET SDEXPD1=1; SET SDEXPD2=1; SET SDEXPD3=1; SET SDEXPD4=1; SET WDEXPD1=0; SET WDEXPD2=0; SET WD EXPD3=1; SET WDEXPD4=0; ? XEXPD=3; ZSIMZ; SET VARNUM=4; INIT; SET SDEXPD1=1; SET SDEXPD2=1; SET SDEXPD3=1; SET SDEXPD4=1; SET WDEXPD1=0; SET WDEXPD2=0; SET WD EXPD3=0; SET WDEXPD4=1; ? XEXPD=4; ZSIMZ; SET VARNUM=5; INIT; SET SDEXPD1=1; SET SDEXPD2=1; SET SDEXPD3=1; SET SDEXPD4=1; SET WDEXPD1=-1; SET WDEXPD2=-1; S ET WDEXPD3=-1; SET WDEXPD4=-1; ? XEXPD=5; ZSIMZ; ?========================== ================ ========; ? SIMULATION #14 SERVINGS OF FRUIT&VEG PER DAY ; ?========================== ================ ========; SET SIMNUM=14.1; ? SE RVINGS OF FRUIT; SET VARNUM=1; INIT; SET SDSERVF1=1; SET SDSERVF2=1; SET SDSERVF3=1; SET WDSERVF1=1; SET WDSERVF2=0; SET WDSERVF3=0; ? XSERFRU=1; ZSIMZ; SET VARNUM=2; INIT; SET SDSERVF1=1; SET SDSERVF2=1; SET SDSERVF3=1; SET WDSERVF1=0; SET WDSERVF2=1; SET WDSERVF3=0; ? XSERFRU=2; ZSIMZ; SET VARNUM=3; INIT; SET SDSERVF1=1; SET SDSERVF2=1; SET SDSERVF3=1; SET WDSERVF1=0; SET WDSERVF2=0; SET WDSERVF3=1; ? XSERFRU=3; ZSIMZ; SET VARNUM=4; INIT; SET SDSERVF1=1; SET SDSERVF2=1; SET SDSERVF3=1; SET WDSERVF1=-1; SET WDSERVF2=-1; SET WDSERVF3=-1; ? XSERFRU=4; ZSIMZ; SET SIMNUM=14.2; ? SERVINGS OF VEG; SET VARNUM=1; INIT;

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156 SET SDSERVV1=1; SET SDSERVV2=1; SET SDSERVV3=1; SET WDSERVV1=1; SET WDSERVV2=0; SET WDSERVV3=0; ? XSERVEG=1; ZSIMZ; SET VARNUM=2; INIT; SET SDSERVV1=1; SET SDSERVV2=1; SET SDSERVV3=1; SET WDSERVV1=0; SET WDSERVV2=1; SET WDSERVV3=0; ? XSERVEG=2; ZSIMZ; SET VARNUM=3; INIT; SET SDSERVV1=1; SET SDSERVV2=1; SET SDSERVV3=1; SET WDSERVV1=0; SET WDSERVV2=0; SET WDSERVV3=1; ? XSERVEG=3; ZSIMZ; SET VARNUM=4; INIT; SET SDSERVV1=1; SET SDSERVV2=1; SET SDSERVV3=1; SET WDSERVV1=-1; SET WD SERVV2=-1; SET WDSERVV3=-1; ? XSERVEG=4; ZSIMZ; ?========================== ================ ========; ? SIMULATION #15 COUNT CALORIES ; ?========================== ================ ========; SET SIMNUM=15; SET VARNUM=1; INIT; SET SDCAL1=1; SET SDCAL2=1; SET SDCAL4=1; SET SDCAL5=1; SET WDCAL1=1; SET WDCAL2=0; SET WD CAL4=0; SET WDCAL5=0; ? CAL=1; ZSIMZ; SET VARNUM=2; INIT; SET SDCAL1=1; SET SDCAL2=1; SET SDCAL4=1; SET SDCAL5=1; SET WDCAL1=0; SET WDCAL2=1; SET WD CAL4=0; SET WDCAL5=0; ? CAL=2; ZSIMZ; SET VARNUM=3; INIT; SET SDCAL1=1; SET SDCAL2=1; SET SDCAL4=1; SET SDCAL5=1; SET WDCAL1=-1; SET WDCAL2 =-1; SET WDCAL4=-1; S ET WDCAL5=-1; ? CAL=3; ZSIMZ; SET VARNUM=4; INIT; SET SDCAL1=1; SET SDCAL2=1; SET SDCAL4=1; SET SDCAL5=1; SET WDCAL1=0; SET WDCAL2=0; SET WD CAL4=1; SET WDCAL5=0; ? CAL=4; ZSIMZ; SET VARNUM=5; INIT; SET SDCAL1=1; SET SDCAL2=1; SET SDCAL4=1; SET SDCAL5=1; SET WDCAL1=0; SET WDCAL2=0; SET WD CAL4=0; SET WDCAL5=1; ? CAL=5; ZSIMZ; ?========================== ================ ========; ? SIMULATION #16 EATING FRESH FOODS ; ?========================== ================ ========;

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157 SET SIMNUM=16; SET VARNUM=1; INIT; SET SDB_FRE1=1; SET SDB_FRE2=1; SET SDB_FRE4=1; SET SDB_FRE5=1; SET WDB_FRE1=1; SET WDB_FRE2=0; S ET WDB_FRE4=0; SET WDB_FRE5=0; ? B_FRE=1; ZSIMZ; SET VARNUM=2; INIT; SET SDB_FRE1=1; SET SDB_FRE2=1; SET SDB_FRE4=1; SET SDB_FRE5=1; SET WDB_FRE1=0; SET WDB_FRE2=1; S ET WDB_FRE4=0; SET WDB_FRE5=0; ? B_FRE=2; ZSIMZ; SET VARNUM=3; INIT; SET SDB_FRE1=1; SET SDB_FRE2=1; SET SDB_FRE4=1; SET SDB_FRE5=1; SET WDB_FRE1=-1; SET WDB_FRE2=-1; S ET WDB_FRE4=-1; S ET WDB_FRE5=-1; ? B_FRE=3; ZSIMZ; SET VARNUM=4; INIT; SET SDB_FRE1=1; SET SDB_FRE2=1; SET SDB_FRE4=1; SET SDB_FRE5=1; SET WDB_FRE1=0; SET WDB_FRE2=0; S ET WDB_FRE4=1; SET WDB_FRE5=0; ? B_FRE=4; ZSIMZ; SET VARNUM=5; INIT; SET SDB_FRE1=1; SET SDB_FRE2=1; SET SDB_FRE4=1; SET SDB_FRE5=1; SET WDB_FRE1=0; SET WDB_FRE2=0; S ET WDB_FRE4=0; SET WDB_FRE5=1; ? B_FRE=5; ZSIMZ; ?========================== ================ ========; ? SIMULATION #17 READ LABEL ; ?========================== ================ ========; SET SIMNUM=17; SET VARNUM=1; INIT; SET SDB_LAB1=1; SET SDB_LAB2=1; SET SDB_LAB4=1; SET SDB_LAB5=1; SET WDB_LAB1=1; SET WDB_LAB2=0; SE T WDB_LAB4=0; SET WDB_LAB5=0; ? B_LAB=1; ZSIMZ; SET VARNUM=2; INIT; SET SDB_LAB1=1; SET SDB_LAB2=1; SET SDB_LAB4=1; SET SDB_LAB5=1; SET WDB_LAB1=0; SET WDB_LAB2=1; SE T WDB_LAB4=0; SET WDB_LAB5=0; ? B_LAB=2; ZSIMZ;

PAGE 158

158 SET VARNUM=3; INIT; SET SDB_LAB1=1; SET SDB_LAB2=1; SET SDB_LAB4=1; SET SDB_LAB5=1; SET WDB_LAB1=-1; SET WDB_LAB2=-1; S ET WDB_LAB4=-1; SET WDB_LAB5=-1; ? B_LAB=3; ZSIMZ; SET VARNUM=4; INIT; SET SDB_LAB1=1; SET SDB_LAB2=1; SET SDB_LAB4=1; SET SDB_LAB5=1; SET WDB_LAB1=0; SET WDB_LAB2=0; SE T WDB_LAB4=1; SET WDB_LAB5=0; ? B_LAB=4; ZSIMZ; SET VARNUM=5; INIT; SET SDB_LAB1=1; SET SDB_LAB2=1; SET SDB_LAB4=1; SET SDB_LAB5=1; SET WDB_LAB1=0; SET WDB_LAB2=0; SE T WDB_LAB4=0; SET WDB_LAB5=1; ? B_LAB=5; ZSIMZ; ?========================== ================ ========; ? SIMULATION #18 BUY FROM CERTAIN STORES ; ?========================== ================ ========; SET SIMNUM=18; SET VARNUM=1; INIT; SET SDB_ST1=1; SET SDB_ST2=1; SET SDB_ST4=1; SET SDB_ST5=1; SET WDB_ST1=1; SET WDB_ST2=0; SET WDB_ST4=0; SET WDB_ST5=0; ? B_ST=1; ZSIMZ; SET VARNUM=2; INIT; SET SDB_ST1=1; SET SDB_ST2=1; SET SDB_ST4=1; SET SDB_ST5=1; SET WDB_ST1=0; SET WDB_ST2=1; SET WDB_ST4=0; SET WDB_ST5=0; ? B_ST=2; ZSIMZ; SET VARNUM=3; INIT; SET SDB_ST1=1; SET SDB_ST2=1; SET SDB_ST4=1; SET SDB_ST5=1; SET WDB_ST1=-1; SET WDB_ST2=-1; SET WD B_ST4=-1; SET WDB_ST5=-1; ? B_ST=3; ZSIMZ; SET VARNUM=4; INIT; SET SDB_ST1=1; SET SDB_ST2=1; SET SDB_ST4=1; SET SDB_ST5=1; SET WDB_ST1=0; SET WDB_ST2=0; SET WDB_ST4=1; SET WDB_ST5=0; ? B_ST=4; ZSIMZ; SET VARNUM=5; INIT; SET SDB_ST1=1; SET SDB_ST2=1; SET SDB_ST4=1; SET SDB_ST5=1; SET WDB_ST1=0; SET WDB_ST2=0; SET WDB_ST4=0; SET WDB_ST5=1; ? B_ST=5; ZSIMZ;

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159 ?========================== ================ ========; ? SIMULATION #19 WAY TO GET CERTAIN PRODUCE ; ?========================== ================ ========; SET SIMNUM=19; SET VARNUM=1; INIT; SET SDB_WAY1=1; SET SDB_WAY2=1; SET SDB_WAY4=1; SET SDB_WAY5=1; SET WDB_WAY1=1; SET WDB_WAY2=0; S ET WDB_WAY4=0; SET WDB_WAY5=0; ? B_WAY=1; ZSIMZ; SET VARNUM=2; INIT; SET SDB_WAY1=1; SET SDB_WAY2=1; SET SDB_WAY4=1; SET SDB_WAY5=1; SET WDB_WAY1=0; SET WDB_WAY2=1; S ET WDB_WAY4=0; SET WDB_WAY5=0; ? B_WAY=2; ZSIMZ; SET VARNUM=3; INIT; SET SDB_WAY1=1; SET SDB_WAY2=1; SET SDB_WAY4=1; SET SDB_WAY5=1; SET WDB_WAY1=-1; SET WDB_WAY2=-1; S ET WDB_WAY4=-1; SET WDB_WAY5=-1; ? B_WAY=3; ZSIMZ; SET VARNUM=4; INIT; SET SDB_WAY1=1; SET SDB_WAY2=1; SET SDB_WAY4=1; SET SDB_WAY5=1; SET WDB_WAY1=0; SET WDB_WAY2=0; S ET WDB_WAY4=1; SET WDB_WAY5=0; ? B_WAY=4; ZSIMZ; SET VARNUM=5; INIT; SET SDB_WAY1=1; SET SDB_WAY2=1; SET SDB_WAY4=1; SET SDB_WAY5=1; SET WDB_WAY1=0; SET WDB_WAY2=0; S ET WDB_WAY4=0; SET WDB_WAY5=1; ? B_WAY=5; ZSIMZ; ?========================== ================ ========; ? SIMULATION #20 EATING FRESH FRUITS & VEG ; ?========================== ================ ========; SET SIMNUM=20; SET VARNUM=1; INIT; SET SDB_FV1=1; SET SDB_FV2=1; SET SDB_FV4=1; SET SDB_FV5=1; SET WDB_FV1=1; SET WDB_FV 2=0; SET WDB_FV4=0; S ET WDB_FV5=0; ? B_FV=1; ZSIMZ; SET VARNUM=2; INIT; SET SDB_FV1=1; SET SDB_FV2=1; SET SDB_FV4=1; SET SDB_FV5=1; SET WDB_FV1=0; SET WDB_FV 2=1; SET WDB_FV4=0; S ET WDB_FV5=0; ? B_FV=2; ZSIMZ;

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160 SET VARNUM=3; INIT; SET SDB_FV1=1; SET SDB_FV2=1; SET SDB_FV4=1; SET SDB_FV5=1; SET WDB_FV1=-1; SET WDB_FV2=-1; SET WD B_FV4=-1; SET WDB_FV5=-1; ? B_FV=3; ZSIMZ; SET VARNUM=4; INIT; SET SDB_FV1=1; SET SDB_FV2=1; SET SDB_FV4=1; SET SDB_FV5=1; SET WDB_FV1=0; SET WDB_FV 2=0; SET WDB_FV4=1; S ET WDB_FV5=0; ? B_FV=4; ZSIMZ; SET VARNUM=5; INIT; SET SDB_FV1=1; SET SDB_FV2=1; SET SDB_FV4=1; SET SDB_FV5=1; SET WDB_FV1=0; SET WDB_FV 2=0; SET WDB_FV4=0; S ET WDB_FV5=1; ? B_FV=5; ZSIMZ; ?========================== ================ ========; ? SIMULATION #21 FEEL HEALTHIER THAN PEERS ; ?========================== ================ ========; SET SIMNUM=21; SET VARNUM=1; INIT; SET SDB_HLT1=1; SET SDB_HLT2=1; SET SDB_HLT4=1; SET SDB_HLT5=1; SET WDB_HLT1=1; SET WDB_HLT2=0; S ET WDB_HLT4=0; SET WDB_HLT5=0; ? B_HLT=1; ZSIMZ; SET VARNUM=2; INIT; SET SDB_HLT1=1; SET SDB_HLT2=1; SET SDB_HLT4=1; SET SDB_HLT5=1; SET WDB_HLT1=0; SET WDB_HLT2=1; S ET WDB_HLT4=0; SET WDB_HLT5=0; ? B_HLT=2; ZSIMZ; SET VARNUM=3; INIT; SET SDB_HLT1=1; SET SDB_HLT2=1; SET SDB_HLT4=1; SET SDB_HLT5=1; SET WDB_HLT1=-1; SET WDB_ HLT2=-1; SET WDB_HLT4=1; SET WDB_HLT5=-1; ? B_HLT=3; ZSIMZ; SET VARNUM=4; INIT; SET SDB_HLT1=1; SET SDB_HLT2=1; SET SDB_HLT4=1; SET SDB_HLT5=1; SET WDB_HLT1=0; SET WDB_HLT2=0; S ET WDB_HLT4=1; SET WDB_HLT5=0; ? B_HLT=4; ZSIMZ; SET VARNUM=5; INIT; SET SDB_HLT1=1; SET SDB_HLT2=1; SET SDB_HLT4=1; SET SDB_HLT5=1; SET WDB_HLT1=0; SET WDB_HLT2=0; S ET WDB_HLT4=0; SET WDB_HLT5=1; ?

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161 B_HLT=5; ZSIMZ; ?========================== ================ ========; ? SIMULATION #22 EXERCISE ; ?========================== ================ ========; SET SIMNUM=22; SET VARNUM=1; INIT; SET SDB_EXE1=1; SET SDB_EXE2=1; SET SDB_EXE4=1; SET SDB_EXE5=1; SET WDB_EXE1=1; SET WDB_EXE2=0; S ET WDB_EXE4=0; SET WDB_EXE5=0; ? B_EXE=1; ZSIMZ; SET VARNUM=2; INIT; SET SDB_EXE1=1; SET SDB_EXE2=1; SET SDB_EXE4=1; SET SDB_EXE5=1; SET WDB_EXE1=0; SET WDB_EXE2=1; S ET WDB_EXE4=0; SET WDB_EXE5=0; ? B_EXE=2; ZSIMZ; SET VARNUM=3; INIT; SET SDB_EXE1=1; SET SDB_EXE2=1; SET SDB_EXE4=1; SET SDB_EXE5=1; SET WDB_EXE1=-1; SET WDB_EXE2=-1; S ET WDB_EXE4=-1; S ET WDB_EXE5=-1; ? B_EXE=3; ZSIMZ; SET VARNUM=4; INIT; SET SDB_EXE1=1; SET SDB_EXE2=1; SET SDB_EXE4=1; SET SDB_EXE5=1; SET WDB_EXE1=0; SET WDB_EXE2=0; S ET WDB_EXE4=1; SET WDB_EXE5=0; ? B_EXE=4; ZSIMZ; SET VARNUM=5; INIT; SET SDB_EXE1=1; SET SDB_EXE2=1; SET SDB_EXE4=1; SET SDB_EXE5=1; SET WDB_EXE1=0; SET WDB_EXE2=0; S ET WDB_EXE4=0; SET WDB_EXE5=1; ? B_EXE=5; ZSIMZ; ?========================== ================ ========; ? SIMULATION #23 EXPLORE NEW FOODS ; ?========================== ================ ========; SET SIMNUM=23; SET VARNUM=1; INIT; SET SDB_NEW1=1; SET SDB_NEW2=1; SET SDB_NEW4=1; SET SDB_NEW5=1; SET WDB_NEW1=1; SET WDB_NEW2=0; SE T WDB_NEW4=0; SET WDB_NEW5=0; ? B_NEW=1; ZSIMZ; SET VARNUM=2; INIT;

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162 SET SDB_NEW1=1; SET SDB_NEW2=1; SET SDB_NEW4=1; SET SDB_NEW5=1; SET WDB_NEW1=0; SET WDB_NEW2=1; SE T WDB_NEW4=0; SET WDB_NEW5=0; ? B_NEW=2; ZSIMZ; SET VARNUM=3; INIT; SET SDB_NEW1=1; SET SDB_NEW2=1; SET SDB_NEW4=1; SET SDB_NEW5=1; SET WDB_NEW1=-1; SET WDB_ NEW2=-1; SET WDB_NEW4=1; SET WDB_NEW5=-1; ? B_NEW=3; ZSIMZ; SET VARNUM=4; INIT; SET SDB_NEW1=1; SET SDB_NEW2=1; SET SDB_NEW4=1; SET SDB_NEW5=1; SET WDB_NEW1=0; SET WDB_NEW2=0; SE T WDB_NEW4=1; SET WDB_NEW5=0; ? B_NEW=4; ZSIMZ; SET VARNUM=5; INIT; SET SDB_NEW1=1; SET SDB_NEW2=1; SET SDB_NEW4=1; SET SDB_NEW5=1; SET WDB_NEW1=0; SET WDB_NEW2=0; SE T WDB_NEW4=0; SET WDB_NEW5=1; ? B_NEW=5; ZSIMZ; ?========================== ================ ========; ? SIMULATION #24 HEALTH CONDITION ; ?========================== ================ ========; SET SIMNUM=24.1; ? IF ANYONE IN HOUSEHOLD HAS BLOOD PRESSURE; SET VARNUM=1; INIT; SET SDHLT_BP=1; SET WDHLT_BP=1; ? HLT_BP=1 NO ONE IN HOUSEHOLD HAS BLOOD PRESSURE; ZSIMZ; SET VARNUM=2; INIT; SET SDHLT_BP=1; SET WDHLT_BP=-1; ? HLT_BP=0; ZSIMZ; SET SIMNUM=24.2; ? IF ANYON E IN HOUSEHOLD HAS DIABETES; SET VARNUM=1; INIT; SET SDHLT_DB=1; SET WDHLT_DB=1; ? HLT_DB=1 NO ONE IN HOUSEHOLD HAS DIABETES; ZSIMZ; SET VARNUM=2; INIT; SET SDHLT_DB=1; SET WDHLT_DB=-1; ? HLT_DB=0; ZSIMZ; SET SIMNUM=24.3; ? IF ANYONE IN HOUSEHOLD HAS CHOLESTEROL;

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163 SET VARNUM=1; INIT; SET SDHLT_CL=1; SET WDHLT_CL=1; ? HLT_CL=1 NO ON E IN HOUSEHOLD HA S CHLOESTEROL; ZSIMZ; SET VARNUM=2; INIT; SET SDHLT_CL=1; SET WDHLT_CL=-1; ? HLT_CL=0; ZSIMZ; SET SIMNUM=24.4; ? IF ANYONE IN HOUSEHOLD HAS FOOD ALLERGIES; SET VARNUM=1; INIT; SET SDHLT_AG=1; SET WDHLT_AG=1; ? HLT_AG=1 NO ONE IN HOUSEHOLD HAS FOOD ALLERGIES; ZSIMZ; SET VARNUM=2; INIT; SET SDHLT_AG=1; SET WDHLT_AG=-1; ? HLT_AG=0; ZSIMZ; SET SIMNUM=24.5; ? IF ANYONE IN HOUSEHOLD HAS OBESITY PROBLEMS; SET VARNUM=1; INIT; SET SDHLT_OB=1; SET WDHLT_OB=1; ? HLT_OB=1 NO ONE IN HOUSEHOLD HAS OBESITY PROBLEMS; ZSIMZ; SET VARNUM=2; INIT; SET SDHLT_OB=1; SET WDHLT_OB=-1; ? HLT_OB=0; ZSIMZ; SET SIMNUM=24.6; ? IF ANYONE IN HOUSEHOLD HAS MOBILITY PROBLEMS; SET VARNUM=1; INIT; SET SDHLT_MB=1; SET WDHLT_MB=1; ? HLT_MB=1 NO ONE IN HOUSEHOLD HAS MOBILITY PROBLEMS; ZSIMZ; SET VARNUM=2; INIT; SET SDHLT_MB=1; SET WDHLT_MB=-1; ? HLT_MB=0; ZSIMZ; SET SIMNUM=24.7; ? IF ANYONE IN HOUSEHOLD HAS SIGHT/HEARING PROBLEMS; SET VARNUM=1; INIT; SET SDHLT_HR=1; SET WDHLT_HR=1; ? HLT_HR=1 NO ONE IN HOUSEHOLD HAS SIGHT/HEARING PROBLEMS; ZSIMZ;

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164 SET VARNUM=2; INIT; SET SDHLT_HR=1; SET WDHLT_HR=-1; ? HLT_HR=0; ZSIMZ; ?========================== ================ ========; ? SIMULATION #25 MONTHS ; ?========================== ================ ========; SET SIMNUM=25; SET VARNUM=1; INIT; SET SDMTH2=1; SET SDMTH3=1; SET SDMTH4=1; SET SDMTH5=1; SET SDMTH6=1; SET SDMTH7=1; SET SDMTH8=1; SET SDMTH9=1; SET SDMTH10=1; SET SDMTH11=1; SET SDMTH12=1; SET WDMTH2=-1; SET WDMTH3=-1; S ET WDMTH4=-1; SET WDMTH5=-1; SET WDMTH6=-1; SET WDMTH7=-1; S ET WDMTH8=-1; SET WDMTH9=-1; SET WDMTH10=-1; SET WDMTH11=-1; SET WDMTH12=-1; ? MTH=1; ZSIMZ; SET VARNUM=2; INIT; SET SDMTH2=1; SET SDMTH3=1; SET SDMTH4=1; SET SDMTH5=1; SET SDMTH6=1; SET SDMTH7=1; SET SDMTH8=1; SET SDMTH9=1; SET SDMTH10=1; SET SDMTH11=1; SET SDMTH12=1; SET WDMTH2=1; SET WDMTH3=0; S ET WDMTH4=0; SET WDMTH5=0; SET WDMTH6=0; SET WDMTH7=0; S ET WDMTH8=0; SET WDMTH9=0; SET WDMTH10=0; SET WDMTH11= 0; SET WDMTH12=0; ? MTH=2; ZSIMZ; SET VARNUM=3; INIT; SET SDMTH2=1; SET SDMTH3=1; SET SDMTH4=1; SET SDMTH5=1; SET SDMTH6=1; SET SDMTH7=1; SET SDMTH8=1; SET SDMTH9=1; SET SDMTH10=1; SET SDMTH11=1; SET SDMTH12=1; SET WDMTH2=0; SET WDMTH3=1; S ET WDMTH4=0; SET WDMTH5=0; SET WDMTH6=0; SET WDMTH7=0; S ET WDMTH8=0; SET WDMTH9=0; SET WDMTH10=0; SET WDMTH11= 0; SET WDMTH12=0; ? MTH=3; ZSIMZ; SET VARNUM=4; INIT; SET SDMTH2=1; SET SDMTH3=1; SET SDMTH4=1; SET SDMTH5=1; SET SDMTH6=1; SET SDMTH7=1; SET SDMTH8=1; SET SDMTH9=1; SET SDMTH10=1; SET SDMTH11=1; SET SDMTH12=1; SET WDMTH2=0; SET WDMTH3=0; S ET WDMTH4=4; SET WDMTH5=0; SET WDMTH6=0; SET WDMTH7=0; S ET WDMTH8=0; SET WDMTH9=0; SET WDMTH10=0; SET WDMTH11= 0; SET WDMTH12=0; ? MTH=4;

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165 ZSIMZ; SET VARNUM=5; INIT; SET SDMTH2=1; SET SDMTH3=1; SET SDMTH4=1; SET SDMTH5=1; SET SDMTH6=1; SET SDMTH7=1; SET SDMTH8=1; SET SDMTH9=1; SET SDMTH10=1; SET SDMTH11=1; SET SDMTH12=1; SET WDMTH2=0; SET WDMTH3=0; S ET WDMTH4=0; SET WDMTH5=1; SET WDMTH6=0; SET WDMTH7=0; S ET WDMTH8=0; SET WDMTH9=0; SET WDMTH10=0; SET WDMTH11= 0; SET WDMTH12=0; ? MTH=5; ZSIMZ; SET VARNUM=6; INIT; SET SDMTH2=1; SET SDMTH3=1; SET SDMTH4=1; SET SDMTH5=1; SET SDMTH6=1; SET SDMTH7=1; SET SDMTH8=1; SET SDMTH9=1; SET SDMTH10=1; SET SDMTH11=1; SET SDMTH12=1; SET WDMTH2=0; SET WDMTH3=0; S ET WDMTH4=0; SET WDMTH5=0; SET WDMTH6=1; SET WDMTH7=0; S ET WDMTH8=0; SET WDMTH9=0; SET WDMTH10=0; SET WDMTH11= 0; SET WDMTH12=0; ? MTH=6; ZSIMZ; SET VARNUM=7; INIT; SET SDMTH2=1; SET SDMTH3=1; SET SDMTH4=1; SET SDMTH5=1; SET SDMTH6=1; SET SDMTH7=1; SET SDMTH8=1; SET SDMTH9=1; SET SDMTH10=1; SET SDMTH11=1; SET SDMTH12=1; SET WDMTH2=0; SET WDMTH3=0; S ET WDMTH4=0; SET WDMTH5=0; SET WDMTH6=0; SET WDMTH7=1; S ET WDMTH8=0; SET WDMTH9=0; SET WDMTH10=0; SET WDMTH11= 0; SET WDMTH12=0; ? MTH=7; ZSIMZ; SET VARNUM=8; INIT; SET SDMTH2=1; SET SDMTH3=1; SET SDMTH4=1; SET SDMTH5=1; SET SDMTH6=1; SET SDMTH7=1; SET SDMTH8=1; SET SDMTH9=1; SET SDMTH10=1; SET SDMTH11=1; SET SDMTH12=1; SET WDMTH2=0; SET WDMTH3=0; S ET WDMTH4=0; SET WDMTH5=0; SET WDMTH6=0; SET WDMTH7=0; S ET WDMTH8=1; SET WDMTH9=0; SET WDMTH10=0; SET WDMTH11= 0; SET WDMTH12=0; ? MTH=8; ZSIMZ; SET VARNUM=9; INIT; SET SDMTH2=1; SET SDMTH3=1; SET SDMTH4=1; SET SDMTH5=1; SET SDMTH6=1; SET SDMTH7=1; SET SDMTH8=1; SET SDMTH9=1; SET SDMTH10=1; SET SDMTH11=1; SET SDMTH12=1;

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166 SET WDMTH2=0; SET WDMTH3=0; S ET WDMTH4=0; SET WDMTH5=0; SET WDMTH6=0; SET WDMTH7=0; S ET WDMTH8=0; SET WDMTH9=1; SET WDMTH10=0; SET WDMTH11= 0; SET WDMTH12=0; ? MTH=9; ZSIMZ; SET VARNUM=10; INIT; SET SDMTH2=1; SET SDMTH3=1; SET SDMTH4=1; SET SDMTH5=1; SET SDMTH6=1; SET SDMTH7=1; SET SDMTH8=1; SET SDMTH9=1; SET SDMTH10=1; SET SDMTH11=1; SET SDMTH12=1; SET WDMTH2=0; SET WDMTH3=0; S ET WDMTH4=0; SET WDMTH5=0; SET WDMTH6=0; SET WDMTH7=0; S ET WDMTH8=0; SET WDMTH9=0; SET WDMTH10=1; SET WDMTH11= 0; SET WDMTH12=0; ? MTH=10; ZSIMZ; SET VARNUM=11; INIT; SET SDMTH2=1; SET SDMTH3=1; SET SDMTH4=1; SET SDMTH5=1; SET SDMTH6=1; SET SDMTH7=1; SET SDMTH8=1; SET SDMTH9=1; SET SDMTH10=1; SET SDMTH11=1; SET SDMTH12=1; SET WDMTH2=0; SET WDMTH3=0; S ET WDMTH4=0; SET WDMTH5=0; SET WDMTH6=0; SET WDMTH7=0; S ET WDMTH8=0; SET WDMTH9=0; SET WDMTH10=0; SET WDMTH11= 1; SET WDMTH12=0; ? MTH=11; ZSIMZ; SET VARNUM=12; INIT; SET SDMTH2=1; SET SDMTH3=1; SET SDMTH4=1; SET SDMTH5=1; SET SDMTH6=1; SET SDMTH7=1; SET SDMTH8=1; SET SDMTH9=1; SET SDMTH10=1; SET SDMTH11=1; SET SDMTH12=1; SET WDMTH2=0; SET WDMTH3=0; S ET WDMTH4=0; SET WDMTH5=0; SET WDMTH6=0; SET WDMTH7=0; S ET WDMTH8=0; SET WDMTH9=0; SET WDMTH10=0; SET WDMTH11= 0; SET WDMTH12=1; ? MTH=12; ZSIMZ; WRITE(FORMAT=EXCEL,FILE='D:\ZZORGANI C\Organics\HIST6.XLS') MSIM; END;

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167 LIST OF REFERENCES Ajzen, I., 1991. The theory of planned behavi or. Organizational Behavi or and Hum an Decision Processes, 50, 179-211. Batte, M.T., Hooker, N.H., Haab, T.C., Beaverson, J., 2007. Putting their money where their mouths are: consumer willingness to pay for multi-ingredient, processed organic food producs. Food Policy 32, 145-159. Briz, T., Ward, R.W., 2003. Competing supplies of olive oil in the German market: an application of multinomial logit m odels. Agribusiness 19 (3), 393-406. Briz, T., Ward, R.W., 2009. Consumer awareness of organic products in Spai n: an application of multinomial logit models. Food Policy 34, 295-304. Darby, M.R., Karni, E., 1973. Free competition a nd the optimal amount of fraud. J. Law Econ. 16 (1), 67-88. Dimitri, C., Richman, N., 2000. Organic food mark ets in transition. Henry A. Wallace Inst. For Altern. Agric. Public Policy, Rep. No. 14. Greenbelt, MD. Dimitri, C., Richman, N., 2000. Organic foods: niche marketers venture into the mainstream. Economic Research Service, U.S. Department of Agriculture, Agricu ltural Outlook, June 2000-July 2000. Available: http://www.ers.usda.gov/publications/agoutlook/jun2000/ao272f.pdf Di mitri, C., Greene, C., 2002. Recent growth patterns in the U.S. organic foods market. U.S. Department of Agriculture, Economic Resear ch Service, Market and Trade Economics Division and Resource Economics Division. Agri culture Information Bulletin Number 777. Dimitri, C., Oberholtzer, L., 2006. A brief retr ospective on the U.S. organic sector: 1997 and 2003. Crop Management doi:10.1094/CM-2006-0921-07-PS. Dimitri, C., Oberholtzer, L., 2009. Marketing U.S. organic foods: Recent trends from farms to consumers. U.S. Department of Agricultur e, Economic Research Service, Economic information Bulletin Number 58. Food Standard Agency, 2009. Organic review published. Available: http://www.food.gov.uk/news/n ewsarchive/2009/jul/organic Hartm an Group, 2006. Whos buying organi c?: Demographics 2006. Available: http://www.hartman-group.com/hartbeat/who-buying-organic-dem ographics-2006 Hartman Group, 2008. Whos buying and whats next. Available: http://www.hartmangroup.com /hartbeat/organics-t oday-who-buying-and-what-next

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170 BIOGRAPHICAL SKETCH Yang Zhou was born in Sichuan Province, P. R. China. After she received her Bachelor of Art degree in Public Finance from the School of Economics in Sichuan University in 2005, she enrolled at the University of Florida to pursue graduate study. In May 2007, Yang Zhou was awarded the Master of Agribusiness degree from the Department of Food and Resour ce Economics at the University of Florida. She was then admitted into the Ph.D. program in the Food and Resource Economics Department specializing in marketing and econometrics. She received her Ph.D. from the University of Florida in the summer of 2010.