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Probit and Ordered Probit Analysis of the Demand for Fresh Sweet Corn


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PROBIT AND ORDERED PROBIT ANALYSIS OF THE DEMAND FOR FRESH SWEET CORN By AMANDA C. BRIGGS A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE UNIVERSITY OF FLORIDA 2003

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ACKNOWLEDGMENTS There are several people I would like to thank for helping me complete this thesis and contributing to my graduate experience at the University of Florida. I extend my gratitude to my committee chair, Dr. Robert L. Degner; and committee member, Dr. Ronald W. Ward, for generously sharing their time and knowledge with me. I thank Dr. Chris Andrew for his advisement and guidance. I also thank my fellow graduate students in the Food and Resource Economics Department. Above all, I thank my mother, Barbara Briggs; my sister, Maria Cristina Briggs; my uncle, David Browning; and Giancarlo Espinosa for their encouragement and support. ii

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TABLE OF CONTENTS page ACKNOWLEDGMENTS...ii LIST OF TABLES..v LIST OF FIGURESvi ABSTRACT.viii CHAPTER 1 INTRODUCTION......1 2 OBJECTIVES.....6 3 METHODOLOGY.....8 Probit Model Ordered Probit Model....11 Specification of the Probit Model..12 Ordered Probit Model Specification..13 4 PROBIT RESULTS..17 Probit Estimates.18 Probit Model Simulations.. 5 ORDERED PROBIT RESULTS..29 Ordered Probit Parameter Estimates..29 Ordered Probit Simulations........32 6 SUMMARY AND CONCLUSIONS...46 iii

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APPENDIX A CONSUMER SURVEY INSTRUMENT. B TIME SERIES PROCESSOR PROGRAMS....63 REFERENCES.88 BIOGRAPHICAL SKETCH....90 iv

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LIST OF TABLES Table page 3-1 Number of completed interviews, by city.. 3-2 Probit model variables and descriptions...13 3-3 Ordered probit model variables and descriptions. 4-1 Probit model parameter estimates.19 5-1 Parameter estimates by season..30 v

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LIST OF FIGURES Figure page 1-1 Production of fresh market sweet corn, by state.. 1-2 Sweet corn production areas in Florida...2 1-3 Food expenditures.... 4-1 Households purchase of sweet corn, by city.....17 4-2 Percent buying sweet corn by season, all respondents..18 4-3 Probability of consuming fresh sweet corn, by city of residence..22 4-4 Probability of consuming fresh sweet corn, by educational level. 4-5 Probability of consuming fresh sweet corn, by income level.... 4-6 Probability of consuming fresh sweet corn, by race..23 4-7 Probability of consuming fresh sweet corn, by gender..24 4-8 Probability of consuming fresh sweet corn, by household size. 4-9 Probability of consuming fresh sweet corn, by presence of children 4-10 Probability of consuming fresh sweet corn, by age...25 4-11 Probability of consuming fresh sweet corn, by satisfaction with produce availability.. 4-12 Ranking of factors impacting the probability of consuming fresh sweet corn 5-1 Ordered probit models base probabilities by season......33 5-2 Probabilities for base and magazines (mgz) in winter...35 vi

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5-3 Probabilities for base and good taste, freshness, or tenderness (rsn1) in winter. 5-4 Probabilities for base and habit (rsn3) in winter....36 5-5 Satisfaction level for fresh sweet corn purchased in winter..36 5-6 Probabilities for base and sat1 in spring....38 5-7 Probabilities for base and sat2 in spring....38 5-8 Satisfaction level for fresh sweet corn purchased in spring...39 5-9 Probabilities for base and television (tv) in spring....40 5-10 Probabilities for base and over 55 years of age (age3) in spring...40 5-11 Probabilities for base and household size in spring... 5-12 Probabilities for base and presence of children in household (chd) in summer...42 5-13 Probabilities for base and over 55 years of age (age3) in summer....43 5-14 Probabilities for base and newspapers (nwp) in summer.......43 5-15 Probabilities for base and white race (rac2) in fall 5-16 Satisfaction level for fresh sweet corn purchased in fall...45 vii

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Abstract of Thesis Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Master of Science PROBIT AND ORDERED PROBIT ANALYSIS OF THE DEMAND FOR FRESH SWEET CORN By Amanda C. Briggs August 2003 Chair: Robert L. Degner Major Department: Food and Resource Economics The Fresh Supersweet Corn Council (an organization of sweet corn growers and shippers from Florida, Georgia, and Alabama whose members collectively promote their product) is seeking ways to better utilize marketing resources to build consumer demand. In 2001, the Council contracted the Florida Agricultural Market Research Center of the Institute of Food and Agricultural Sciences at the University of Florida to design a consumer survey. The survey sampled approximately 200 households in each of five cities. Trained, professional interviewers conducted telephone interviews of the primary food shopper in the household. Further analyses of the data collected in the survey provide greater insight into factors contributing to the decision to purchase fresh sweet corn or not; and the frequency of purchase in each season. Using cross-sectional household data from this survey, probit estimates reveal important factors influencing consumers decisions to buy fresh sweet corn. viii

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ix Additionally, ordered probit models are used to predict how a number of factors affect the probability of increasing consumption of fresh sweet corn in each season. These analyses serve to further the understanding of forces driving consumer demand during Fresh Supersweet growers time of production; and help the sweet corn industry design market strategies to increase consumer demand for its product.

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CHAPTER 1 INTRODUCTION There are three distinct markets for sweet corn in the United States canned, frozen, and fresh. For the most part, these markets operate independently of each other. The fresh market represents two-thirds of the total crop value for sweet corn. According to the Economic Research Service of the U.S. Department of Agriculture, 246,900 acres of fresh market sweet corn were harvested in the U.S. in 2000 (Lucier and Lin 2001). Florida leads the nation in the production of fresh sweet corn. Figures from the Florida Agricultural Statistics Service reveal that in 2000, Floridas sweet corn receipts totaled over $121 Million (FASS 2002). Florida accounted for 22% of U.S. production of fresh sweet corn during 1998-2000. The value of sweet corn produced in Georgia in 1999 reached almost $53 Million. Georgias production represented 13% of U.S. fresh sweet corn produced from 1998-2000 (Lucier and Lin 2001). 22%17%13%11%37%0%5%10%15%20%25%30%35%40%FloridaCaliforniaGeorgiaNew YorkOthersPercent of Fresh Market Sweet Corn Figure 1-1. Production of fresh market sweet corn, by state Average fresh-market sweet corn production during 1998-2000. Based on data from National Agricultural Statistics Srevice, USDA. 1

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2 Members of the Fresh Supersweet Corn Council (FSCC), an organization of sweet corn growers and shippers from Florida, Georgia, and Alabama, are the primary suppliers of fresh sweet corn in the United States from late fall through winter until early July. Fresh Supersweet corn growers are virtually the sole suppliers of fresh sweet corn shipped east of the Mississippi River during the fall, winter, and spring seasons. Most of Floridas sweet corn production (over 30,000 acres) takes place in South Florida (IFAS 1999). Some is produced in Miami-Dade County, but the largest production occurs in the Belle Glade area. These areas supply fresh sweet corn from fall, through spring (until Memorial Day, in late May). Production then moves to areas of northern Florida and into South Georgia and Alabama to supply fresh market sweet corn from late May until early July. Figure 1-2. Sweet corn production areas in Florida Based on data released by Florida Agricultural Statistics Service, June 1999 From: Summary of Florida Corn Production, University of Florida Cooperative Extension Service, Institute of Food and Agricultural Sciences.

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3 Sixty percent of fresh market sweet corn in the U.S. is marketed from May to August with the highest volume in July. Only about 10% of volume is marketed during the winter months (January to March) (Lucier and Lin 2001). Peak shipments take place to meet demand for the Memorial Day and the 4 th of July holiday periods. During these holiday times, sweet corn is in high demand and retailers promote the industrys product. However, supersweet corn growers face a challenge in increasing year-round purchases of their product. Although the sweet corn industry has increased consumption of its product through innovations like the introduction of supersweet varieties with a higher sugar content and longer shelf life; and convenient tray-packed corn, several factors still limit potential growth of the industry. According to a 1994-1996 USDA survey, 87% of fresh sweet corn purchases are made at the retail level for home consumption (Lucier and Lin 2001). However, as of 1998, 38% of the consumers food dollar was spent away from home (ERS 2001). Further, between 1990 and 1998, real spending on food away from home increased 24.8% whereas real spending on food at home increased just 4.7% (Clausen 2000). The continuing trend of increased spending on food away from home may have a significant adverse effect on future purchases of fresh sweet corn. Other factors such as product proliferation and convenient ready-to-eat items in supermarket produce sections and the sweet corn industrys inability to gain a substantial share in the foodservice market means sweet corn producers may realize fewer purchase opportunities and a shrinking share of the consumers food dollar. In addition to those concerns faced by the sweet corn industry as a whole, Fresh Supersweet corn growers face a unique concern: significant seasonality in the demand

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4 for fresh sweet corn during the time of year they are marketing their product. Fresh Supersweet corn growers are seeking ways to better use marketing resources to build consumer demand for their product. Understanding the forces influencing consumer demand during their time of production will aid them in designing an effective marketing strategy to expand sales of Fresh Supersweet corn. Figure 1-3. Food expenditures Source: Clauson, Annette. Spotlight on National Food Spending. Food Review, Volume 23, Issue 3. Economic Research Service, USDA. 2000. In an effort to more effectively use its resources to promote fresh sweet corn, the Fresh Supersweet Corn Council needed information from sweet corn retailers and consumers. In response to this need for information, the Florida Agricultural Market Research Center (FAMRC) of the Food and Resource Economics Department at the Institute of Food and Agricultural Sciences of the University of Florida designed comprehensive consumer and retailer surveys. The consumer survey was designed to investigate consumer preferences, attitudes, and behavior regarding the purchase and consumption of fresh sweet corn.

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5 Interviews with executives of 39 of the top 55 supermarket chains operating in the central and eastern regions of the U.S. were conducted for the retailer survey. Senior executives in charge of buying and merchandising produce were interviewed. The survey concentrated on retailers evaluations of: The basic product Shipping containers Retailers in-store merchandising and promotion practices Factors affecting sweet corn advertising Effectiveness of the Southern Supersweet identity (Degner et al. 2001). The retailer survey produced many significant findings; however, the focus of this research is the consumer survey.

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CHAPTER 2 OBJECTIVES The basic goal of the consumer survey was to gain a better understanding of how consumer characteristics, buying habits, usage patterns, and perceptions of quality and availability of sweet corn translate into consumer demand behavior. Using cross-sectional household data, probit estimates are used to reveal important factors influencing consumers decisions to buy fresh sweet corn. The probit model analyzes purchasing decisions for fresh sweet corn based upon consumer satisfaction with produce availability and selected demographics. The demographics include city of residence, number of years respondent has resided in the city, household size, the presence of children in the household, education, age, gender, income, and race. The model allows for comparison and ranking of factors positively or negatively affecting the purchase of fresh sweet corn. The results identify marketing strategies to increase consumer demand for fresh sweet corn. To provide information about the existence and causes of seasonality in consumption of fresh sweet corn, an ordered probit model is used to predict the probability of increasing purchases of fresh sweet corn in each season. For each season an ordered probit model models the frequency of purchase or the number of purchases per month within the season. Variables included in this model are demographics, consumer satisfaction with produce availability, overall satisfaction with sweet corn purchased during the season, the most important reason the consumer buys fresh sweet 6

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7 corn in that season, whether or not the consumer has received information about fresh sweet corn, and sources of information. This research provides information about factors influencing the probability of consuming fresh sweet corn and the frequency of purchasing fresh sweet corn. These results will help the sweet corn industry design market strategies to increase consumer demand during the fall, winter, and spring seasons.

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CHAPTER 3 METHODOLOGY After meeting with several major sweet corn growers and shippers in Florida, a consumer questionnaire was designed by the FAMRC in conjunction with the Florida Survey Research Center (FSRC) and a representative of the Fresh Supersweet Corn Council. The questionnaire was pre-tested by FSRC and was reviewed and approved by the University of Floridas Institutional Review Boards Committee for the Protection of Human Subjects. This survey sampled approximately 200 households in each of five major market areas where FSCC members corn is shipped: Dallas, Atlanta, Chicago, Boston, and Philadelphia. These cities provided for geographical dispersion as well as racial and ethnic diversity in the sample. Additionally, samples contained diversity in terms of education, age, income, and household size. Table 3-1. Number of completed interviews, by city City Number Dallas 204 Atlanta 200 Chicago 201 Boston 224 Philadelphia 202 Telephone interviews of primary food shoppers were conducted by trained, professional interviewers. A random digit dialing technique was used to generate residential telephone numbers while avoiding difficulties associated with unlisted numbers. 8

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9 Consumer interviews took place between September 7 and November 3, 2001. Interviewers attempted to contact each household at various times of the day for a minimum of six times prior to selecting an alternative telephone number. Attempts were made seven days a week at various times of the day (including early evenings) to avoid over representation of non-working consumers. The average interview lasted approximately ten minutes. Computer-assisted telephone interviewing was used to ensure the immediate, computerized recording of responses. In addition, quality control was exercised in the form of random monitoring of real-time interviews and call back verification of ten percent of completed interviews (Degner et al. 2001). Probit Model Linear regression analysis is a statistical method commonly used by social science researchers. This method, however, assumes a continuous dependent variable. Thus the model proves inappropriate for the analysis of many behaviors or decisions measured in a non-continuous manner (Liao 1994). The nature of many social phenomena is discrete rather than continuous (Pampel 2000). For example, consumers decide whether or not to purchase fresh sweet corn. In cases such as these, the adoption of a different model specification is required. One such alternative is probit analysis. The probit model is a probability model with two categories in the dependent variable (Liao 1994). Probit analysis is based on the cumulative normal probability distribution. The binary dependent variable, y, takes on the values of zero and one. The outcomes of y are mutually exclusive and exhaustive. The dependent variable, y, depends on K observable variables x k where k=1, . ,K (Aldrich and Nelson 1984).

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10 While the values of zero and one are observed for the dependent variable in the probit model, there is a latent, unobserved continuous variable, y*. y* = Kk1 k x k + (3-1) is IN (0, 2 ) The dummy variable, y, is observed and is determined by y* as follows: (3-2) y = 1 if y* > 0, 0 otherwise The point of interest relates to the probability that y equals one. From the above equations, we see that: Prob (y=1) = Prob ( Kk1 k x k + > 0) (3-3) = Prob ( > Kk1 k x k ) = 1 (Kk1 k x k ) Where is the cumulative distribution function of (Liao 1994). The probit model assumes that the data are generated from a random sample of size N with a sample observation denoted by i, i = 1, . ,N. Thus the observations of y must be statistically independent of each other. Additionally, the model assumes that the independent variables (the responses to the consumer survey questions) are random variables. There is no exact linear dependence among the x ik s. This implies that N > K, that each x k has some variation across observations (aside from the constant term), and that no two or more x k s are perfectly correlated. The Maximum Likelihood Estimation (MLE) technique is used to estimate probit parameters. Maximum Likelihood Estimation focuses on choosing parameter estimates that give the highest probability or likelihood of obtaining the observed sample y. The

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11 main principle of MLE is to choose as an estimate of the set of K numbers that would maximize the likelihood of having observed this particular y (Aldrich and Nelson 1984). Ordered Probit Model In some instances response categories are inherently ordered. The dependent variable is discrete as well as ordinal. Under these circumstances, conventional regression analysis is not appropriate. Instead, the ordered probit model may be used to estimate such models where the dependent variable associated with more than two outcomes is discrete and ordered (Borooah 2002). The ordered probit model is a latent regression where y* = Kk1 k x k + (3-4) Where y* is the unobserved latent index determined by observed factors (xs) and unobserved factors () and is normally distributed. y = 1 if y* 1 (= 0), (3-5) y = 2 if 1 < y* 2 y = 3 if 2 < y* 3 y = J if j-1 < y*, Where y is observed in J ordered categories. The unknown threshold levels (s) are to be estimated with the s. The probability that the observed y is in category j is shown as follows: Prob(y=J) = 1 [ j-1 Kk1 k x k ] (3-6)

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12 The Prob(y = J) is obtained by taking the difference between two adjacent cumulative probabilities (Liao 1994) with the exception of the first and last categories where: Prob(y1) = Prob(y=1) and Prob(yJ)=1 (3-7) Specification of the Probit Model Several demographic variables are included in the probit model: the respondents city of residence, level of education, income, race, gender, the number of years the respondent had resided in the city, household size, the presence of children in the household, and age. Additionally, the respondents level of satisfaction with the availability of fresh fruits and vegetables in the store where he or she shops most frequently is included as an explanatory variable in the model. The specification of the probit model is as follows. y* ki = k0 + k1 cit1 + k2 cit2 + k3 cit3 + k4 cit4 + k5 edu1 + (3-8) k6 edu2 + k7 inc1 + k8 rac1 + k9 rac2 + k10 gen1 + k11 q24 + k12 hwz + k13 chd + k14 age1 + k15 age3 + k16 sat1 + k17 sat2 y = 1 if respondents household buys fresh sweet corn (3-9) 0 if respondents household does not buy fresh sweet corn The probit model estimates the impact the independent variables have on consumer behavior regarding the purchase of fresh sweet corn. The model also predicts probabilities of change in consumer purchasing behavior under several simulated variable levels.

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13 Table 3-2. Probit model variables and descriptions Variable Description a cit1 Dallas cit2 Atlanta cit3 Chicago cit4 Boston cit5 Philadelphia edu1 Education level of high school graduate or less edu2 Technical/vocational school, some college, or college graduate edu3 Graduate or professional school inc1 Income under $35,000 per year inc2 Income over $35,000 per year rac1 Black rac2 White rac3 Other race gen1 Male gen2 Female q24 Number of years respondent has lived in city of residence hwz Household size chd Presence of children in household age1 Less than 30 years of age age2 30 to 55 years of age age3 Over 55 years of age sat1 Not at all satisfied with produce availability sat2 Somewhat satisfied with produce availability sat3 Very satisfied with produce availability a All variables except q24 and hwz are equal to one if respondent exhibits the characteristic or are equal to zero otherwise. Ordered Probit Model Specification Ordered probit models are used to analyze purchasing behavior in the winter, spring, summer, and fall seasons. For respondents buying fresh sweet corn in the season, the model examines the effects of explanatory variables on the dependent variable, the number of times per month the respondent purchases fresh sweet corn during the season.

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14 There are four ordered categories for the dependent variable: one, two, three, or four or more purchases per month within the season. A number of demographic factors are included as explanatory variables in the ordered probit models. These factors are the respondents level of education, race, gender, the number of years the respondent has resided in the city, household size, the presence of children in the household, and age. The respondents income level was omitted in order to save degrees of freedom as numerous observations of this variable were missing. Additionally, the respondents level of satisfaction with the availability of fresh produce at in the store where he or she shops most frequently is included as an explanatory variable in the models. Whether or not the respondent has ever received any information about the availability, nutritional qualities, or cooking methods for fresh sweet corn is also included as an explanatory variable in the ordered probit models. In addition, survey respondents were asked whether or not they could recall seeing or hearing television commercials or other television spots, radio commercials, magazine ads or magazine feature stories, newspaper food-page stories, recipes, or newspaper ads about fresh sweet corn, and posters in stores or sweet corn recipe cards, leaflets, or booklets in the past year. The respondents satisfaction with fresh sweet corn purchased within the season and the most important reason why the consumer purchased fresh sweet corn in the season were included as explanatory variables in the ordered probit models for the fall, winter, and spring seasons. These variables, however, were not included in the ordered probit model for the summer as they were not included as questions on the survey instrument for the summer season.

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15 Table 3-3. Ordered probit model variables and descriptions Variable Description a edu1 Education level of high school graduate or less edu2 Technical/vocational school, some college, or college graduate edu3 Graduate or professional school rac1 Black rac2 White rac3 Other race gen1 Male gen2 Female q24 Number of years respondent has lived in city of residence hwz Household size chd Presence of children in household age1 Less than 30 years of age age2 30 to 55 years of age age3 Over 55 years of age sat1 Not at all satisfied with produce availability sat2 Somewhat satisfied with produce availability sat3 Very satisfied with produce availability satf Satisfaction with fresh sweet corn purchased in the season tv Respondent has seen/heard television commercials or other television spots about fresh sweet corn in the past year rd Respondent has heard radio commercials about fresh sweet corn in the past year mgz Respondent has seen magazine ads or magazine feature stories about fresh sweet corn in the past year nwp Respondent has seen newspaper food-page stories, recipes, or ads about fresh sweet corn in the past year psr Respondent has seen posters in stores or sweet corn recipe cards, leaflets, or booklets in the past year rsn1 Good taste, freshness, or tenderness is the most important reason why respondent has purchased fresh sweet corn in the season rsn2 Health reasons are the most important reasons why respondent has purchased fresh sweet corn in the season rsn3 Habit is the most important reason why respondent has purchased fresh sweet corn in the season rsn4 All other reasons why respondent has purchased fresh sweet corn in the season inf Respondent has received information about the availability, nutritional qualities, or cooking methods for fresh sweet corn a All variables except q24, hwz, and satf are equal to one if respondent exhibits the characteristic or are equal to zero otherwise.

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16 The ordered probit models for the fall, winter, and spring seasons are specified as follows: y* ki = k0 + k1 edu1 + k2 edu2 + k3 rac1 + k4 rac2 + (3-10) k5 gen1 + k6 q24 + k7 hwz + k8 chd + k9 age3 + k10 sat1 + k11 sat2 + k12 satf + k13 tv + k14 rd + k15 mgz + k16 nwp + k17 psr + k18 rsn1+ k19 rsn2+ k20 rsn3 + k21 inf The ordered probit model for the summer season is specified below. y* ki = k0 + k1 edu1 + k2 edu2 + k3 rac1 + k4 rac2 + (3-11) k5 gen1 + k6 q24 + k7 hwz + k8 chd + k9 age3 + k10 sat1 + k11 sat2 + k12 tv + k13 rd + k14 mgz + k15 nwp + k16 psr + k17 inf

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CHAPTER 4 PROBIT RESULTS The consumer survey revealed several important findings. About two-thirds of all households were found to purchase fresh sweet corn at least one time per year. 62.266.873.663.872.30102030405060708090100DallasAtlantaChicagoBostonPhiladelphiaPercent Percent Buying Corn Percent of Total Figure 4-1. Households purchase of sweet corn, by city Survey results also revealed significant seasonality in the consumption of fresh sweet corn. Virtually all (97.5 %) sweet corn consuming households purchased the product during the summer while only 36.5 % of sweet corn consuming households purchased during the winter months. In the spring 71 % purchased fresh sweet corn and 49.3 % of households purchased during the fall season. Further analyses of data from the FAMRCs consumer survey provides greater insight into factors contributing to the decision to purchase fresh sweet corn or not and the intensity of purchase in each season. 17

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18 36.57149.397.5020406080100winterspringsummerfallPercent Figure 4-2. Percent buying sweet corn by season, all respondents Probit Estimates Using the consumer survey data and maximum likelihood procedures, the probit model was estimated. The parameter estimates, reported in Table 4-1, correspond to k coefficients in Equation 3-8 and represent factors affecting consumers decisions to purchase fresh sweet corn. The R 2 reveals that just over 11 % of consumers decisions to purchase fresh sweet corn are explained by the model. The estimates show that several demographic factors have a statistically significant impact on the consumption of fresh sweet corn. An income level of less than $35,000 per year has a negative impact on the consumption of fresh sweet corn with a coefficient of .2210. This relationship between income and the demand for fresh sweet corn is consistent with economic theory and the demand for a normal good. Inc1 was found to be significant at the 99% confidence level (t-value equal to 3.5745).

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19 Being less than thirty years of age also has a significantly negative effect on the purchase of fresh sweet corn at the 99% confidence level. Age1 has a coefficient of -0.4959 with a t-value of .7653. Table 4-1. Probit model parameter estimates Variable Parameter Estimate T-Value intercept 0.1593 0.8985 cit1 -0.0113 -0.1069 cit2 0.0039 0.0345 cit3 0.1917 1.6468 cit4 -0.1150 -1.0961 edu1 -0.0294 -0.2937 edu2 -0.0743 -1.0095 inc1 -0.2210** -3.5745 rac1 0.2661* 2.0978 rac2 0.1929 1.7419 gen1 -0.0351 -0.6177 q24 0.0040 1.0316 hwz 0.0812 1.9576 chd 0.2871 1.7825 age1 -0.4959** -3.7653 age3 -0.0479 -0.2691 sat1 0.0278 0.1535 sat2 -0.0606 -0.5774 Statistical significance levels are indicated as follows: 10 percent 5 percent ** 1 percent Survey respondents race also appears to play a significant role in the purchase of fresh sweet corn. Both black and white consumers are more likely to purchase fresh sweet corn than the average consumer. Parameter estimates for black and white races are 0.2661 and 0.1929 respectively with t-values of 2.0978 and 1.7419. Household size has a positive statistically significant impact on the decision to buy fresh sweet corn at the 90% level with a coefficient of 0.0812 and t-value of 1.9576. The presence of children in the household also has a statistically significant positive

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20 effect on fresh sweet corn consumption, as is expected. The coefficient for presence of children in the household is 0.2871 with a t-value of 1.7825. Among the demographic factors that do not have a statistically significant impact on the purchase of fresh sweet corn is the respondents city of residence. The consumer survey sample is comprised of respondents from Dallas, Atlanta, Chicago, Boston, and Philadelphia. It is important to note that geographic region is not statistically significant in terms of its impact on buying fresh sweet corn. Probit Model Simulations Probit models provide a means to examine the probability of certain events occurring given a particular set of conditions or range of explanatory variables. The estimated probit model is used to predict probabilities of change in consumer behavior over a range of independent variable values (Verbeke, Ward, and Viaene 2000). The impact individual explanatory variables have on the decision to purchase fresh sweet corn is seen through probit model simulations. First, a base with a clearly defined set of explanatory variables is established and applied to the estimated model. Changes in the probability of consuming fresh sweet corn reveal factors affecting the demand for the product. Defining the Base In order to examine changes in the probability of consuming fresh sweet corn being equal to one, a base is set. The base fixes almost all the explanatory variables at their average value. City of residence, level of education, income, race, gender, satisfaction with produce availability, the number of years the respondent has lived in the city, and household size, and presence of children are set at their average. The base value

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21 for the age variable is age2 or 30 to 55 years of age. This allows for comparison of those under 30 and those over 55 with the base value of 30 to 55 years old. Using this base, the impact from changing each discrete variable value from zero to one and adjusting each continuous variable (q24 and hwz), while holding all other variables constant at their base value, is seen. Results Figures 4-3 through 4-11 illustrate the impact of the explanatory variables on the probability of being a consumer of fresh sweet corn. Each figure compares the base probability of 0.6878 with probabilities resulting from various simulations. Although the respondents city of residence is not a statistically significant factor in the purchase of fresh sweet corn, Figure 4-3 reveals the probability of buying fresh sweet corn for residents of each city. Respondents residing in Dallas and Atlanta have a probability of consumption which is very close to the base. The probability of consumption increases by about nine percent for respondents from Chicago, while residents of Boston and Philadelphia have slightly lower probabilities of purchasing fresh sweet corn. Although education level is not a statistically significant variable, the simulation results reveal the specific probabilities for each level of education. Figure 4-4 shows that those with an education level of high school graduate or less (edu1) or technical/vocational school, some college, or college graduate (edu2) have a slightly lower likelihood of buying fresh sweet corn. Respondents who have attended graduate or professional school have a 5% higher probability of buying when compared to the base.

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22 City of Residence0.690.680.690.750.650.6600.10.20.30.40.50.60.70.80.91BaseDallasAtlantaChicagoBostonPhiladelphi a Probabilit y Figure 4-3. Probability of consuming fresh sweet corn, by city of residence Education0.690.680.660.7200.10.20.30.40.50.60.70.80.91basehigh schoolgrad or lesstech school,some college,or college gradgraduate orprofessionalschoolProbabilit y Figure 4-4. Probability of consuming fresh sweet corn, by education level Figure 4-5 illustrates that income level does have a substantial impact on the consumption of fresh sweet corn. Survey respondents with a total annual household income before taxes of less than $35,000 have an almost 12% lower probability of purchasing fresh sweet corn. Those with income levels greater than $35,000 per year increase their probability of consuming by over 10%.

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23 Income0.690.610.7600.10.20.30.40.50.60.70.80.91baseincome under$35,000 per yearincome over $35,000per yearProbabilit y Figure 4-5. Probability of consuming fresh sweet corn, by income level Race0.690.780.750.5100.10.20.30.40.50.60.70.80.91baseblackwhiteother racesProbabilit y Figure 4-6. Probability of consuming fresh sweet corn, by race Black respondents (rac1) as well as white respondents (rac2) have an increased probability of consuming fresh sweet corn, as is revealed in Figure 4-6. Also of note is that respondents of other races (rac3) have a much lower probability of purchasing fresh sweet corn, over 25% below the base probability of consumption.

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24 Gender0.690.680.7000.10.20.30.40.50.60.70.80.91basemalefemaleProbabilit y Figure 4-7. Probability of consuming fresh sweet corn, by gender Gender is not an important factor in the decision the purchase fresh sweet corn. The probabilities of consuming fresh sweet corn of consuming fresh sweet corn for males (gen1) and females (gen2) are 0.68 and 0.70 respectively. Household Size00.10.20.30.40.50.60.70.80.91base123456789101112131415Probabilit y Figure 4-8. Probability of consuming fresh sweet corn, by household size As household size increases, so does the probability of purchasing fresh sweet corn. This increase, however, tends to lessen as households get very large.

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25 Presence of Children in Household0.690.780.5800.10.20.30.40.50.60.70.80.91basechildrenno childrenProbabilit y Figure 4-9. Probability of consuming fresh sweet corn, by presence of children Figure 4-9 reveals that whether or not children are present in the household is an important component of the decision to purchase fresh sweet corn. The probability of buying is 0.7813 for households with children. This probability is almost 14% higher than the base. Households without children present have a probability of 0.5803. This is over 15% lower than the base probability. Age0.690.500.6700.10.20.30.40.50.60.70.80.91baseunder 30over 55Probabilit y Figure 4-10. Probability of consuming fresh sweet corn, by age

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26 As seen in figure 4-10, respondents over 55 years of age (age3) exhibit a probability of consumption that is very close to the base value in which the age level is set at 30 to 55 years of age (age2). However, those respondents 18 to 30 years of age (age1) have a probability of purchasing of 0.4975. This probability is almost 28% below the base value. Satisfaction with Produce Availability0.690.700.670.7000.10.20.30.40.50.60.70.80.91basenot at allsatisfiedsomewhatsatisfiedvery satisfiedProbabilit y Figure 4-11. Probability of consuming fresh sweet corn, by satisfaction with produce availability Satisfaction with produce availability does not appear to be an important aspect in the purchase of fresh sweet corn. Respondents not at all satisfied with produce availability have a 1.4% increase in the probability of buying fresh sweet corn when compared to the base. Those who are somewhat satisfied with produce availability are about 3% less likely to buy fresh sweet corn when compared to the base probability. And respondents who are very satisfied with produce availability have a 1.7%higher probability of consuming fresh sweet corn. Figure 4-12 shows the ranking of factors impacting the probability of consuming fresh sweet corn. The chart illustrates the effect of each individual discrete explanatory

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27 variable assuming a value of one holding all other variables at their base value. The changes in the probability of being a consumer of fresh sweet corn are ranked from the most negative to the most positive effect. -0.2-0.15-0.1-0.0500.050.1Changes in Probability chdrac1inc2rac2cit3edu3gen2sat3sat1cit2cit1edu1gen1age3sat2cit5edu2cit4inc1no chdrac3age1 Figure 4-12. Ranking of factors impacting the probability of consuming fresh sweet corn Age1, respondents being less than 30 years of age, has the largest negative effect. An increase in marketing efforts focused on young consumers is advised. Rac3, or respondents of races other than black or white, represents the second largest negative effect. Additionally, the absence of children in the household and an income level of less

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28 than $35,000 per year have substantial negative effects on the purchase of fresh sweet corn. The presence of children in the household is the demographic factor with the greatest positive effect on buying fresh sweet corn. An income level of over $35,000 per year as well as black and white race have strong positive effects on consumption as well.

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CHAPTER 5 ORDERED PROBIT RESULTS Ordered Probit Parameter Estimates Parameter estimates for each seasons ordered probit model are shown in Table 5-1. This table reveals that numerous explanatory variables have a statistically significant impact on the frequency of consumption of fresh sweet corn. The table also reveals that the impact of several of these factors varies by season. Winter During the winter months of January to March demographic factors do not have a major impact on frequency of consumption. However, other explanatory variables have a significant impact. Rsn3, or habit being the most important reason consumers purchase fresh sweet corn in the season has a coefficient of 0.6699 and is statistically significant at the 95% confidence level. Respondents citing good taste, freshness, or tenderness as the most important reason why they purchase fresh sweet corn during the winter (rsn1) is also significant at the 95% level with a coefficient of 0.5328. Magazines are an important source of information about fresh sweet corn for consumers during the winter months. This variable has a coefficient of 0.4918 and is significant at the 95% level. Also of note is that respondents satisfaction with fresh sweet corn purchased during the winter is statistically significant at the 90% confidence level. 29

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30 Table 5-1. Parameter estimates by season Variable Parameter Estimates by Season Winter Spring Summer Fall edu1 0.0904 0.1404 0.1070 -0.0835 edu2 -0.0574 -0.0205 -0.0645 0.0030 rac1 -0.0306 -0.0464 -0.0447 -0.1718 rac2 -0.1413 -0.1834 0.0854 -0.4246* gen1 0.1495 0.0714 -0.0036 0.0342 Q24 -0.0033 -0.0044 -0.0005 -0.0067 hwz 0.0783 0.0636 -0.0064 0.0355 chd 0.2416 0.0847 0.3674** 0.0948 age3 -0.3103 0.3532 0.3698* 0.2944 sat1 0.3949 0.5011* -0.0838 0.0783 sat2 -0.2905 -.3981** -0.0055 -0.0836 satf 0.0884 0.1581** N.A. 0.1659** tv -0.1057 -0.3436* -0.1699 -0.0896 rd -0.4373 -0.1485 0.1290 -0.1310 mgz 0.4918* 0.2039 0.0920 0.2557 nwp -0.2455 0.1689 0.2274* -0.0814 psr 0.0006 0.0882 -0.0648 0.0332 rsn1 0.5328* 0.0586 N.A. 0.1132 rsn2 0.1815 -0.2656 N.A. -0.3373 rsn3 0.6699* 0.2856 N.A. 0.2486 inf -0.0091 0.0700 0.0461 -0.3849 Statistical significance levels are indicated as follows: 10 percent 5 percent ** 1 percent Spring Several factors have a statistically significant impact on the frequency of purchase during the spring. Significant demographic factors include household size and an age of over 55 years. Both of these variables are significant at the 90% level. Consumers being somewhat satisfied with overall produce availability has a negative effect on the number of times per month consumers buy fresh sweet corn. This effect is significant at the 99% confidence level. A significant positive effect results from respondents being not at all satisfied with produce availability. The parameter estimate

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31 for sat1 is 0.5011 and the variable is significant at the 95% level. These results reveal the presence of the substitution effect. When consumers are not satisfied with produce availability, they consume fresh sweet corn more frequently. When consumers are somewhat satisfied with produce availability, they appear to substitute other forms of produce for fresh sweet corn. Consumers satisfaction with fresh sweet corn purchased during the spring is an important factor in the frequency of purchase and is significant at the 99% confidence level with a t-value of 4.6664. Summer The presence of children in the household is a highly significant explanatory variable in the summer season with an estimate of 0.3674 at the 99% confidence level. Being above 55 years of age also has a positive effect on the frequency of consumption during the summer. The parameter estimate for age3 is 0.3698 and is significant at the 95% level. Respondents seeing newspaper food-page stories, recipes, or ads about fresh sweet corn (nwp) is a statistically significant variable at the 95% confidence level with a coefficient of 0.2274. Newspaper advertisements promoting the sale of fresh sweet corn are more common during the summer months. Newspapers appear to be successful in increasing consumers frequency of purchasing fresh sweet corn during the summer. Fall The ordered probit model for the fall season reveals that rac2, or the white race, has a negative effect on the frequency of purchase. Rac2 has a coefficient of .4246 and is statistically significant at the 95% confidence level.

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32 Sources of information about fresh sweet corn as well as reasons why consumers purchase fresh sweet corn during the fall are not statistically significant. However, satisfaction with fresh sweet corn purchased during the fall is significant at the 99% level with a parameter estimate of 0.1659. Satisfaction with fresh sweet corn purchased during the season is significant in all seasons in which the question was asked of respondents. Ordered Probit Simulations The ordered probit estimates are incorporated into several simulation analyses to illustrate the effects of the explanatory variables on the frequency of purchase. (Medina and Ward 1999) In order to observe the effects of the independent variables, a base is set for each seasons model. Defining the Base In the ordered probit models for winter, fall, and spring, the demographic variables of education, race, gender, age, the number of years the respondent has lived in the city, and household size are set at their average value. The base assumes there are no children present in the household (chd=0). Satisfaction with produce availability, satisfaction with fresh sweet corn purchased during the season, and respondents main reasons for purchasing fresh sweet corn in the season are each set at their average value. The base presumes that respondents have not received information about the availability, nutritional qualities, or cooking methods for fresh sweet corn (inf=0). In addition, the values for each information source variable (tv, rd, mgz, nwp, and psr) are set at zero. For the most part, the base values for simulations from the summer model are the same as those for the other seasons. The demographic variables as well as satisfaction with produce availability, whether or not the respondent has received information about

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33 fresh sweet corn, and information sources are all set at the same values. However, satisfaction with fresh sweet corn purchased in the season, and respondents main reasons for purchasing fresh sweet corn in the season were not included as variables in the summer model. Results Figures 5-1 through 5-16 show the impact of the explanatory variables on the probability of increasing fresh sweet corn purchases. The base probabilities for each season are illustrated in Figure 5-1. The vertical axis reflects the probability of consuming while the horizontal axis shows the number of times per month consumers purchase fresh sweet corn (one, two, three, and four or more). The figure reveals that the probabilities for the spring, summer, and fall seasons follow each other fairly closely. In contrast, the pattern of probabilities during the winter months takes on a different shape. Figure 5-1. Ordered probit models base probabilities by season

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34 The probability of increasing consumption from one to two times a month increases in the spring, summer, and fall. The probability of purchasing three times per month decreases for these three seasons. However, the probability of increasing purchases to four times per month rises. During the winter, the probability of buying fresh sweet corn just one time per month (0.5509) is higher than it is during the other seasons. The probabilities of purchasing sweet corn two, three, or four or more times per month are lower during the winter than than they are during the spring, summer, and fall. The probability of buying fresh sweet corn two times per month during the winter is 0.2542. The probability of purchasing three times per month decreases further to 0.0962. The probability of buying four or more times per month then increases slightly to 0.0988. Winter Figure 5-2 illustrates the base probabilities for the winter season and the probabilities resulting from a simulation where respondents have seen magazine ads or magazine feature stories about fresh sweet corn in the past year (mgz), all other variables being held at their base value. Figure 5-3 shows the base probabilities for winter and the probabilities from the simulation with good taste, freshness, or tenderness being the most important reason consumers have purchased fresh sweet corn in the winter (rsn1). Figure 5-4 reveals probabilities from a simulation in which habit is the most important reason respondents have purchased fresh sweet corn in the winter (rsn3), with all other variables at their base. These three figures show that the impact of each of these explanatory variables is similar. When each of these variables is present, the probability of consuming just one

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35 time per month decreases while the probabilities of purchasing sweet corn two, three, or four or more times per month increase. Figure 5-2. Probabilities for base and magazines (mgz) in winter Figure 5-3. Probabilities for base and good taste, freshness, or tenderness (rsn1) in winter

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36 Figure 5-4. Probabilities for base and habit (rsn3) in winter Figure 5-5. Satisfaction level for fresh sweet corn purchased in winter Figure 5-5 shows the probability of consuming fresh sweet corn one, two, three, or four or more times per month during the winter given various levels of satisfaction

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37 with fresh sweet corn purchased in the season, holding all other variables at their base value. As the satisfaction level increases, there is a corresponding shift in the probabilities. As the level of satisfaction goes from zero (extremely dissatisfied) to ten (extremely satisfied), the probability of buying fresh sweet corn once decreases while the probabilities of buying two, three or four or more times per month increase. Figure 5-5 illustrates the impact of factors positively affecting the probabilities of increasing purchases of fresh sweet corn. The probability of sweet corn consumers purchasing sweet corn only one time per month tends to decrease, while the probability of increasing the frequency of consumption rises. Spring In Figure 5-6, the base probabilities for the spring season are compared to the probabilities resulting from a simulation in which consumers are not at all satisfied with overall produce availability. The effects of this variable (sat1) are a decrease in the probability of buying fresh sweet corn one or two times per month, a small increase in the probability of purchasing three times per month, and a large increase in the probability of purchasing four times per month or more during the spring. When consumers are not at all satisfied with overall produce availability, they tend to buy fresh sweet corn as a substitute for those goods that are not available. Thus the probability of purchasing fresh sweet corn more frequently during the spring rises. Figure 5-7 shows that when consumers are somewhat satisfied with the overall produce availability (sat2) in the spring, there is a shift in probabilities. Consumers are more likely to purchase fresh sweet corn just one time per month, when compared to the base probability. The probability of buying two times a month remains about the same

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38 while consumers are less likely to purchase fresh sweet corn three or four or more times per month. As consumers become more satisfied with the availability of other types of produce, the probability of purchasing fresh sweet corn more frequently decreases and sweet corn consumers have a higher probability of buying sweet corn one time per month. Figure 5-6. Probabilities for base and sat1 in spring Figure 5-7. Probabilities for base and sat2 in spring

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39 Figure 5-8. Satisfaction level for fresh sweet corn purchased in spring The probabilities of consuming fresh sweet corn one, two, three, or four or more times per month during the spring given increasing levels of satisfaction with fresh sweet corn purchased in the season, while holding all other variables at their base value, are shown in Figure 5-8. As the satisfaction level rises, the probability of consuming fresh sweet corn only once per month decreases, while consumers have a higher probability of purchasing more frequently during the spring. The effects of respondents seeing television commercials about fresh sweet corn in the past year are shown in Figure 5-9. Having been exposed to television as an information source about fresh sweet corn, the probability of consuming once per month increases while the probability of buying two times per month remains almost the same. The probabilities of increasing consumption to three or four or more times per month decrease with exposure to television commercials. A negative effect on the probability of increasing sweet corn consumption to three or four or more times per month with

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40 exposure to television as an information source is not the expected result. Rather an increase in the frequency of purchase is expected. Figure 5-9. Probabilities for base and television (tv) in spring Figure 5-10. Probabilities for base and over 55 years of age (age3) in spring

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41 Figure 5-10 shows the probability levels for respondents over 55 years of age. When compared to the base, the simulated values for the probabilities of consuming one or two times per month are lower while the probabilities of buying fresh sweet corn three or four or more times per month are higher. Figure 5-11. Probabilities for base and household size in spring Figure 5-11 shows the probabilities of buying fresh sweet corn one, two, three, or four or more times per month during the spring given different household sizes. As household size increases from one to its mean (2.8035) and then to five, consumers are less likely to purchase sweet corn just once per month. As household size increases, consumers become more likely to buy sweet corn four or more times per month during the spring. Summer In Figure 5-12, the simulated probabilities for households where children are present are compared to the base probabilities where there are no children present in the household. The simulated probabilities for respondents over 55 years of age are shown in

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42 Figure 5-13. Both of these variables (chd and age3) have the same effect on the probabilities of consumption. The probabilities of buying fresh sweet corn one or two times per month are lower than the corresponding base probabilities, while the probability of buying three times per month remains about the same. However, the probability of increasing the frequency of purchase to four or more times per month during the summer increases sharply for both simulations. Figure 5-12. Probabilities for base and presence of children in household (chd) in summer Figure 5-14 shows the change in probabilities when respondents have seen newspaper food-page stories, recipes, or ads about fresh sweet corn in the past year (nwp). This variable has the effect of lowering the probabilities of purchasing fresh sweet corn one or two times per month while increasing the probability of buying four or more times per month during the summer. Thus as consumers are exposed to newspaper information about fresh sweet corn, they tend to increase the frequency at which they buy sweet corn to four or more times per month.

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43 Figure 5-13. Probabilities for base and over 55 years of age (age3) in summer Figure 5-14. Probabilities for base and newspapers (nwp) in summer

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44 Fall Figure 5-15 illustrates the simulated probabilities for rac2, or the white race, a statistically significant variable from the fall ordered probit model. This simulation reveals that white consumers have a higher probability of consuming one time per month when compared to the base. The probability of purchasing fresh sweet corn twice a month remains about the same and the probabilities of buying three or four or more times per month during the fall decrease. Figure 5-15. Probabilities for base and white race (rac2) in fall Figure 5-16 shows that the effect of increased satisfaction with fresh sweet corn purchased in the fall is similar to the result of an increased satisfaction level in the winter and spring seasons. The probability of purchasing one time time per month decreases sharply while the probabilities of buying two or three times per month increase. As satisfaction level increases from zero to ten, the probability of buying fresh sweet corn four or more times per month in the fall increases substantially.

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45 Figure 5-16. Satisfaction level for fresh sweet corn purchased in fall

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CHAPTER 6 SUMMARY AND CONCLUSIONS The purpose of this study is to provide information about factors influencing the probability of being a consumer of fresh sweet corn and factors positively or negatively affecting consumers frequency of purchase in each season. Results are intended to assist the sweet corn industry in developing market strategies to increase consumer demand for its product. In order to achieve these objectives, a probit model and an ordered probit model for each of the four seasons were estimated. Subsequently, simulations were used to predict probabilities of change in consumer behavior over a range of explanatory variable values. Using maximum likelihood procedures, probit model parameter estimates revealed several variables significantly affecting consumers decisions to purchase fresh sweet corn. An income level of below $35,000 per year and an age of less than thirty have highly significant negative effects on purchasing fresh sweet corn. Increased marketing efforts targeting young consumers have the potential to attract many new consumers under 30 years of age. Increasing the proportion of young shoppers buying sweet corn is an essential component of building demand for fresh sweet corn and sustaining future sales. Probit model simulations revealed that, in addition to an income level under $35,000 per year and an age of less than thirty years, races other than black and white and 46

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47 the absence of children in the household had substantial negative effects on the probability of buying fresh sweet corn. The simulations also showed that households with children present, the black and white races, and household with an income level above $35,000 per year exhibited the highest probabilities of being consumers of fresh sweet corn. Increased efforts to build demand for fresh sweet corn among shoppers of races other than black or white could yield positive results. However, these results may be limited. The United States Census 2000 reported that almost 90 % of respondents were of the white or black races (Grieco and Cassidy 2001). For this reason, marketing dollars may be more effectively allocated elsewhere. Parameter estimates from the ordered probit model for each season revealed significant reasons for purchase as well as demographic, satisfaction, and information variables. Consumers satisfaction level with fresh sweet corn purchased in the season proved to be a significant factor across seasons. Magazines were shown to have a significant impact on increasing consumption during the winter while newspapers had a significant positive effect on the frequency of consumption in the summer. An increased use of these forms of print media would prove to be an effective market strategy to increase consumers frequency of purchasing fresh sweet corn. Ordered probit estimates were incorporated into simulation analyses in order to illustrate the effects of explanatory variables on the frequency of purchase in each season. Comparison of the base probabilities for each season exposed the difference in frequency of buying fresh sweet corn in the winter when compared to the spring, summer, and fall

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48 seasons. Simulations then showed how individual variables positively or negatively impacted consumers intensity of consumption. Consumer survey results revealed significant seasonality in the purchase of fresh sweet corn. Sweet corn consumers were more likely to purchase the product in the summer than in other seasons and had a higher probability of purchasing more frequently during the summer months. Respondents who purchased fresh sweet corn sometime during the year but did not buy during the winter, spring, or fall were asked for the main reason why they did not purchase in the season. Almost 70 % of winter non-buyers, 57 % of spring non-buyers, and 63 % of fall non-buyers believed fresh sweet corn was not available during these times (Degner et al. 2001). The potential exists to greatly increase the demand for fresh sweet corn in the winter, spring, and fall seasons. In order to take advantage of this sizable potential, promotional efforts must focus on making consumers aware of the availability of Fresh Supersweet corn during the winter, spring, and fall. In order to build demand for their product, Fresh Supersweet corn growers must have a better understanding of factors impacting consumption during their production season. The findings of this research provide a means for the sweet corn industry to better target its resources to expand sales of Fresh Supersweet corn.

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APPENDIX A CONSUMER SURVEY INSTRUMENT Florida Agricultural Market Research Center Institute of Food and Agricultural Sciences University of Florida Consumer Questionnaire a Hello, my name is %name and I am calling you from the Florida Survey Research Center at the University of Florida. In cooperation with vegetable farmers, we are conducting a survey about fresh fruits and vegetables. This is not a sales call. Your opinions are important to our farmers, and your identity and comments will remain confidential. This should only take about 8 minutes. May I please speak to the person in your household who is 18 years of age or older who buys most of the fresh fruits and vegetables for your household? %start 1. City (Code from call sheet, DO NOT ASK) [single Dallas=1 Atlanta=2 Chicago=3 Boston=4 Philadelphia=5] 2. How satisfied are you with the availability of fresh fruits and vegetables in the store where you shop most frequently? Would you say that you are very satisfied, somewhat satisfied, or not at all satisfied? [single Very satisfied=3 Somewhat satisfied=2 Not at all satisfied=1 Don't know=8 Refused=9] %line 49

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50 Next, we'd like to ask you some questions about fresh sweet corn on the cob. For the remainder of the survey questions, please think only about fresh sweet corn on the cob, NOT canned or frozen corn. %line 3. Does your household ever buy fresh sweet corn on the cob? [YNDR1289] %if Q3=2 3A. What are the most important reasons why you never buy fresh sweet corn on the cob (DO NOT READ LIST -Probe for three responses, if possible -Ask, "are there any other reasons")? [multipleyndr1289 Do not like taste Price too high Not fresh enough Texture, starchy, tough Short life (goes bad before using) Health (allergies, indigestion, etc.) Health/Diet (too many calories) Size of package too large Damaged or wormy Takes too much time to prepare Too messy Other %comment30] %endif %if Q3=1 4. Most people prefer certain varieties of fruits and vegetables. For example, "red delicious" or "granny smith" apples. Is there any particular variety of fresh sweet corn on the cob that you prefer to buy? [YNDR1289] %if Q4=1 4A. What variety of fresh sweet corn is that? [single Silver Queen=1 Southern Supersweet=2 Kandy Korn=3 Florida Staysweet=4 Sugar Buns=5 Honey Sweet=6 Snogold=7 Other=8] %if Q4A=8

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51 4AOth. Other: [text,25] %endif %endif 4B. If you had a choice of yellow, white, or bicolor (mixed white and yellow kernels) fresh sweet corn, which would you be most likely to buy? [single Yellow=1 White=2 Bicolor=3 No Preference=4 Don't know=8 Refused=9] %if Q4B=1 or Q4B=2 or Q4B=3 4C. Why do you prefer that type of fresh sweet corn? (MARK ALL THAT APPLY) [multipleyndr1289 Tastes Better Color is more appealing Fresher Lower price Habit Lower in calories More nutritious More tender Better in recipes Ads are more appealing Only type available Smells better Sweeter Don't know Other %comment25] %endif %line Next, we'd like to ask you some questions about when you purchase fresh sweet corn on the cob. %line 5. Do you ever buy fresh sweet corn on the cob in the winter (January through March)? [YNDR1289] %if Q5=1 5A. About how many times per month would you say that you buy fresh sweet corn on the cob during the winter?

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52 [numdr89,2,1-31] 5B. In general, using a rating scale where 10 = extremely satisfied and 0 = extremely dissatisfied, how would you rate your overall satisfaction with the fresh sweet corn on the cob you have purchased in the winter months? [numdr89,2,0-10] 5C. What is the single most important reason why you buy fresh sweet corn on the cob during the winter months (DO NOT READ LIST)? [single Good taste=1 Appealing color=2 Freshness=3 Low price=4 Habit=5 Health reasons (low in calories, nutritious)=6 Tender, not dry or starchy=7 Essential in recipes, menus=8 Advertisements=9 Only type of vegetable available=10 Good smell=11 Adds variety=12 Other=13 Don't know=14 Refused=15] %if Q5C=13 5COth. Other: [text,30] %endif %endif %if Q5=2 5D. What is the main reason why you don't buy fresh sweet corn on the cob in the winter? [single Not available=1 Not local=2 Do not like taste=3 Price too high=4 Not fresh enough=5 Texture, starchy, tough=6 Short life (goes bad before using)=7 Health, diet related=8 Size of package too large=9 Damaged or wormy=10 Too much time to prepare=11

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53 Too messy=12 Don't know=13 Refused=14] %endif 6. Do you ever buy fresh sweet corn on the cob in the Spring (April through June)? [YNDR1289] %if Q6=1 6A. About how many times per month would you say that you buy fresh sweet corn on the cob during the spring? [numdr89,2,1-31] 6B. In general, using a rating scale where 10 = extremely satisfied and 0 = extremely dissatisfied, how would you rate your overall satisfaction with the fresh sweet corn on the cob you have purchased in the spring months? [numdr89,2,0-10] 6C. What is the single most important reason why you buy fresh sweet corn on the cob during the spring months (DO NOT READ LIST)? [single Good taste=1 Appealing color=2 Freshness=3 Low price=4 Habit=5 Health reasons (low in calories, nutritious)=6 Tender, not dry or starchy=7 Essential in recipes, menus=8 Advertisements=9 Only type of vegetable available=10 Good smell=11 Adds variety=12 Other=13 Don't know=14 Refused=15] %if Q6C=13 6COth. Other: [text,30] %endif %endif %if Q6=2 6D. What is the main reason why you don't buy fresh sweet corn on the cob in the spring? [single

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54 Not available=1 Not local=2 Do not like taste=3 Price too high=4 Not fresh enough=5 Texture, starchy, tough=6 Short life (goes bad before using)=7 Health, diet related=8 Size of package too large=9 Damaged or wormy=10 Too much time to prepare=11 Too messy=12 Don't know=13 Refused=14] %endif 7. Do you ever buy fresh sweet corn on the cob in the Summer (July through September)? [YNDR1289] %if Q7=1 7A. About how many times per month would you say that you buy fresh sweet corn on the cob during the summer? [numdr89,2,1-31] %endif %if Q7=2 7B. What is the main reason why you don't buy fresh sweet corn on the cob in the summer? [single Not available=1 Not local=2 Do not like taste=3 Price too high=4 Not fresh enough=5 Texture, starchy, tough=6 Short life (goes bad before using)=7 Health, diet related=8 Size of package too large=9 Damaged or wormy=10 Too much time to prepare=11 Too messy=12 Don't know=13 Refused=14] %endif 8. Do you ever buy fresh sweet corn on the cob in the Fall (October through December)?

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55 [YNDR1289] %if Q8=1 8A. About how many times per month would you say that you buy fresh sweet corn on the cob during the fall? [numdr89,2,1-31] 8B. In general, using a rating scale where 10 = extremely satisfied and 0 = extremely dissatisfied, how would you rate your overall satisfaction with the fresh sweet corn on the cob you have purchased in the fall months? [numdr89,2,0-10] 8C. What is the single most important reason why you buy fresh sweet corn on the cob during the fall months (DO NOT READ LIST)? [single Good taste=1 Appealing color=2 Freshness=3 Low price=4 Habit=5 Health reasons (low in calories, nutritious)=6 Tender, not dry or starchy=7 Essential in recipes, menus=8 Advertisements=9 Only type of vegetable available=10 Good smell=11 Adds variety=12 Other=13 Don't know=14 Refused=15] %if Q8C=13 8COth. Other: [text,30] %endif %endif %if Q8=2 8D. What is the main reason why you don't buy fresh sweet corn on the cob in the fall? [single Not available=1 Not local=2 Do not like taste=3 Price too high=4 Not fresh enough=5 Texture, starchy, tough=6 Short life (goes bad before using)=7

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56 Health, diet related=8 Size of package too large=9 Damaged or wormy=10 Too much time to prepare=11 Too messy=12 Don't know=13 Refused=14] %endif %line Now, we would like to ask you about your experiences with purchasing fresh sweet corn on the cob. %line 9. In what type of retail outlet do you usually buy fresh sweet corn on the cob? [single Superstore (very large supermarket, lots of nonfood items)=1 Discount Club=2 Supermarket=3 Small Grocery Store=4 Produce Specialty Store=5 Roadside Stand=6 Other=7 Don't know=8 Refused=9] %if Q9=7 9A. Other: [text,25] %endif 10. If you had a choice of only one type of packaging, which of the following would you select when shopping for fresh sweet corn on the cob? [single Unpackaged, in the husk, loose=1 Prepackaged, partially shucked=2 Prepackaged, completely shucked=3 Don't know=8 Refused=9] %if Q10=1 10A. Would you prefer to shuck fresh sweet corn in the store or at home? [single In the store=1 At Home=2 Don't know=8

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57 Refused=9] %endif 11. Imagine you are shopping for fresh sweet corn on the cob in your usual retail outlet and you have a choice of corn displayed on a refrigerated produce rack or an unrefrigerated table or display. Would you be more likely to select corn from the refrigerated or unrefrigerated display? [single Refrigerated=1 Unrefrigerated=2 Don't Know=8 Refused=9] 12. In you opinion, what is a fair price per ear of fresh sweet corn on the cob? [numdr89,3] 13. On average, about how many individual ears of fresh sweet corn do you buy each time you purchase corn on the cob? [numdr89,2] %line Next, we have a few questions about your use of fresh sweet corn on the cob at home. %line 14. Do you usually use fresh sweet corn on the same day that you purchase it? [YNDR1289] %if Q14=2 14A. On average, how many days do you usually keep fresh sweet corn before you use it? [numdr89,1] 14B. Where do you usually store fresh sweet corn at home? Do you store it in the refrigerator, in the freezer, or outside the refrigerator? [single In Refrigerator=1 In Freezer=2 Outside the Refrigerator=3 Don't know=8 Refused=9] 14C. And, do you usually store fresh sweet corn shucked or unshucked? [single Shucked=1 Unshucked=2 Don't know=8

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58 Refused=9] %endif %line Now, I have a few questions about how you prepare and serve fresh sweet corn in your household. %line 15. When the weather is nice, say 50 degrees or warmer and not raining, how do you usually prepare fresh sweet corn on the cob? (CHECK ALL THAT APPLY) [multipleyndr1289 Outdoor Grill Indoor Grill Raw Microwave Boiled (If yes, how many minutes?) %comment2 Baked Fried Do not prepare in Good Weather Other %comment20] 16. When the weather is "bad," say colder than 50 degrees or raining or snowing, how do you usually prepare fresh sweet corn on the cob? (CHECK ALL THAT APPLY) [multipleyndr1289 Outdoor Grill Indoor Grill Raw Microwave Boiled (If yes, how many minutes?) %comment2 Baked Fried Don't Prepare in Bad Weather Other %comment20] 17. Do you typically serve fresh sweet corn ON the cob, or do you remove it from the cob before serving it? [single On the cob=1 Off the cob=2 Don't know=8 Refused=9] %if Q17=2 17A. Why do you remove the corn from the cob before serving it? (MARK ALL THAT APPLY) [multipleyndr1289

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59 Less messy Easier to eat (teeth) Necessary for recipe Other %comment20 Don't know] %endif 18. How do you use fresh sweet corn in a meal? (READ LIST, MARK ALL THAT APPLY) [multipleyndr1289 In a main dish As a side dish In a salad In salsa Other %comment30] 19. Do you ever serve fresh sweet corn with meat? [YNDR1289] %if Q19=1 19A. What types of meat do you usually serve with fresh sweet corn? [textdr89,30] %endif 20. Do you ever serve fresh sweet corn with other vegetables? [YNDR1289] %if Q20=1 20A. What other types of vegetables do you usually serve with fresh sweet corn? [textdr89,30] %endif 21. Are there any other foods that you typically serve with fresh sweet corn? [YNDR1289] %if Q21=1 21A. What foods are those? [textdr89,30] %endif %line Next, we would like to ask you about where you get information about corn. %line 22. Have you ever received any information about the availability, nutritional qualities, or cooking methods for fresh sweet corn on the cob? [YNDR1289]

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60 %if Q22=1 22A. I will read you a list of information sources. For each, please tell me whether or not you have received any information about fresh sweet corn from them. [multipleyndr1289 Family Member Friend Newspaper Article Magazine Article TV Food Shows Extension Service Grocer Farmer Cookbook Trade Association Internet Home Economics Class Other %comment25] %endif 23. Now, I will read you a list of TYPES of information. For each, please tell me if you can recall seeing or hearing information of this type about fresh sweet corn in the past year. [multipleyndr1289 TV Commercials Other TV spots, like Cooking shows or news stories Magazine Advertisements Magazine Feature Stories Newspaper Food-Page Stories or Recipes Newspaper Food-Page Advertisements Radio Commercials Posters in Stores Sweet Corn Recipe Cards, Leaflets, or Booklets Internet Web Site] %endif %continue %line Finally, I just have a few demographic questions for statistical purposes. %line 24. How many years have you lived in the greater (INSERT CITY NAME) area? [numdr89,3] 25. Including yourself, how many adults age 18 or older live in your household?

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61 [numdr89,2,1-10] %if Q25<>1 25A. How many children under age 18 live in your household? [numdr89,2,0-15] %endif 26. What is the highest level of education that you have completed? [single 8th grade or less=1 Some high school=2 High school graduate=3 Technical / Vocational School=4 Some College=5 College graduate=6 Graduate or Professional School=7 Refused=9] 27. In what year were you born? [numdr89,4,1880-1983] 28. Just for statistical purposes, can you tell me if your family's total yearly income before taxes is less than $35,000 or more than $35,000? [single Less than $35,000=1 More than $35,000=2 Don't know=8 Refused=9] %if Q28=1 28A. And, is that: [single Under $20,000=3 $20,000 to $34,999=4 Don't know=8 Refused=9] %endif %if Q28=2 28B. And, is that: [single $35,000 to $49,999=5 $50,000 to $69,999=6 $70,000 or more=7 Don't know=8 Refused=9] %endif

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62 29. And, just to make sure that we have a representative sample, would you please tell me your race? [single Black / African American=1 White, non-Hispanic=2 Asian=3 American Indian / Aleut=4 Other=5 Refused=9] %if Q29=1 or q29=2 or q29=5 29A. And, would you say that you are of Hispanic ancestry or not? [YNDR1289] %endif 30. Do you have access to the Internet at home? [YNDR1289] 31. Do you have access to the Internet at work? [YNDR1289] 32. Gender (DON'T ASK JUST RECORD -IF UNKNOWN, "I know this sounds silly, but I have to ask, are you male or female?) [single Male=1 Female=2] %line That completes our survey. Thank you very much for your time. Have a nice evening (day). a This Questionnaire format was designed to facilitate Computer Assisted Telephone Interviewing (CATI) and statistical analysis.

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APPENDIX B TIME SERIES PROCESSOR PROGRAMS OPTIONS MEMORY=50; OPTIONS LIMWARN=1; Title 'Probit Analysis for Sweet Corn Amanda Briggs'; ? scorn#1.tsp; in 'd:\abriggs\corndat'; ? in 'd:\zstudent\abriggs\corndat'; ? read(format=excel,file='d:\zstudent\abriggs\amanbas.xls'); ? out 'd:\zstudent\abriggs\corndat'; ? doc id 'household identification'; ? doc interv 'interview'; ? doc q1 'city 1 to 5'; ? doc q2 'satisfaction w/ produce availability 1 to 3'; ? doc q3 'buyer 1=yes 2=no'; ? ? doc q5 'buy in winter 1=yes 2=no'; ? doc q5a 'times/month winter 1 to 31'; ? doc q5b 'satisfaction winter 0 to 10'; ? doc q5c 'reason buy winter 1 to 15'; ? doc q5d 'reason dont buy winter 1 to 14'; ? doc q6 'buy in spring 1=yes 2=no'; ? doc q6a 'times/month spring 1 to 31'; ? doc q6b 'satisfaction spring 0 to 10'; ? doc q6c 'reason buy spring 1 to 15'; ? doc q6d 'reason dont buy spring 1 to 14'; ? doc q7 'buy in summer 1=yes 2=no'; ? doc q7a 'times/month summer 1 to 31'; ? doc q7b 'reason dont buy summer 1 to 14'; ? doc q8 'buy in fall 1=yes 2=no'; ? doc q8a 'times/month fall 1 to 31'; ? doc q8b 'satisfaction fall 0 to 10'; ? doc q8c reason buy fall 1 to 15'; ? doc q8d 'reason dont buy fall 1 to 14'; ? doc q13 '# ears per purchase 1 to 30'; ? ? doc q22 'information 1=yes 2=no'; ? ? doc q22a 'info source 1 to 13'; ? doc q22a1 'family 1=yes 2=no'; ? doc q22a2 'friend 1=yes 2=no'; ? doc q22a3 'newspaper 1=yes 2=no'; ? doc q22a4 'magazine 1=yes 2=no'; ? doc q22a5 'tV 1=yes 2=no'; ? doc q22a6 'extension 1=yes 2=no'; ? doc q22a7 'grocer 1=yes 2=no'; ? doc q22a8 'farmer 1=yes 2=no'; ? doc q22a9 'cookbook 1=yes 2=no'; ? doc q22a10 'trade assoc 1=yes 2=no'; ? doc q22a11 'internet 1=yes 2=no'; ? doc q22a12 'home ec 1=yes 2=no'; ? doc q22a13 'other 1=yes 2=no'; ? doc q23 'info type 1 to 10'; ? doc q23a 'tV commercials 1=yes 2=no'; 63

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64 ? doc q23b 'other TV 1=yes 2=no'; ? doc q23c 'magazine ad 1=yes 2=no'; ? doc q23d 'mag story 1=yes 2=no'; ? doc q23e 'newspaper story 1=yes 2=no'; ? doc q23f 'newspaper ads 1=yes 2=no'; ? doc q23g 'radio 1=yes 2=no'; ? doc q23h 'posters 1=yes 2=no'; ? doc q23i 'recipe cards 1=yes 2=no'; ? doc q23j 'web site 1=yes 2=no'; ? ? doc q24 'years in city 1 to 100'; ? doc q25 '# adults in household 1 to 10'; ? doc q25a '#children in household 0 to 10'; ? doc q26 'education 1 to 7'; ? doc q27 'year of birth 1900 to 1983'; ? doc q28 'income >or< $35,000 1 to 2'; ? doc q28a 'income 3 to 4'; ? doc q28b 'income 5 to 7'; ? doc q29 'race 1 to 5'; ? doc q29a 'hispanic or not 1=yes 2=no'; ? doc q30 'internet access at home 1=yes 2=no'; ? doc q31 'internet access at work 1=yes 2=no'; ? doc q32 'gender 1=male 2=female'; ? doc idu 'identication'; ? out; ? dblist(date,doc) 'd:\zstudent\abriggs\corndat'; dummy(prefix=buy) q3; ? buyer of sweet corn buy1=yes buy2=no; ? Creating total household size; hwz= q25 + q25a; hist(discrete) hwz; ? Presence of children; chd = (q25a>0); hist(discrete) chd; hist(discrete) q3; dot 5-8; hist(discrete) q.; enddot; hist(discrete) q13; ? number of ears per puchase; ? number of times per month during each season with 5=winter, 6=spring, 7=summer, 8=fall; dot 5-8; select 1; dd = (q.a>=20); select dd^=1; qq.a=q.a; select 1; hist(discrete) qq.a; tq.a=(qq.a=1) + (qq.a=2)*2 + (qq.a=3)*3 + (qq.a>=4)*4; dummy(prefix=tmq.) tq.a; select 1; enddot; dot 5-8; hist(discrete) tmq.1-tmq.4; enddot;

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65 ?=====================================================================; ? making dummy variables for the demographics ; ?=====================================================================; ? city=1 Dallas, city=2 Atlanta, city=3 Chicago, city=4 Boston, city=5 Philadelphia; dummy(prefix=cit) q1; ? edu=1 8th grade, edu=2 some hs, edu=3 hs grad, edu=4 tech schl, edu=5 some college, ? edu=6 college grad, edu=7 grad/prof; xedu=q26; xedu= (xedu<=3)*1 + (xedu>=4 & xedu<=6)*2 + (xedu>=7)*3; hist(discrete) xedu; dummy(prefix=edu) xedu; ? inc=1 under $35,000, inc=2 over $35,000; dummy(prefix=inc) q28; ? race=1 black, race=2 white, race=>3 all other; dummy(prefix=rac) q29; ? hispanic=1 yes, hispanic=2 no; dummy(prefix=his) q29a; ? nethome=1 yes, nethome=2 no; dummy(prefix=neth) q30; ? network=1 yes, network=2 no; dummy(prefix=netw) q31; ? gender=1 male, gender=2 female; dummy(prefix=gen) q32; ?=====================================================================; ? making dummy variables ; ?=====================================================================; ? satisfaction w/ produce availability; ? sat=1 not at all satisfied, sat=2 somewhat, sat=3 very; dummy(prefix=sat) q2; ? buyer in winter; ? buyw=1 yes, buyw=2 no; dummy(prefix=buyw) q5; ? most imp reason buy in winter; ? rsnw=1 taste, rsnw=2 color, rsnw=3 fresh, rsnw=4 price, rsnw=5 habit, rsnw=6 health, rsnw=7 tender; ? rsnw=8 recipes, rsnw=9 ads, rsnw=10 avail, rsnw=11 smell, rsnw=12 variety, rsnw=13 other, rsnw=14 dk, rsnw=15 refused; dummy(prefix=rsnw) q5c; ? main reason do not buy in winter; ? nowint=1 not avail, nowint=2 not local, nowint=3 taste, nowint=4 price, nowint=5 not fresh, nowint=6 texture, nowint=7 short life; ? nowint=8 health, nowint=9 size, nowint=10 damaged, nowint=11 time, nowint=12 messy, nowint=13 dk, nowint=14 refused; dummy(prefix=now) q5d; ? buyer in spring; ? buys=1 yes, buys=2 no; dummy(prefix=buys) q6; ? most imp reason buy in spring;

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66 ? rsns=1 taste, rsns=2 color, rsns=3 fresh, rsns=4 price, rsns=5 habit, rsns=6 health, rsns=7 tender; ? rsns=8 recipes, rsns=9 ads, rsns=10 avail, rsns=11 smell, rsns=12 variety, rsns=13 other, rsns=14 dk, rsns=15 refused; dummy(prefix=rsns) q6c; ? main reason do not buy in spring; ? nospr=1 not avail, nospr=2 not local, nospr=3 taste, nospr=4 price, nospr=5 not fresh, nospr=6 texture, nospr=7 short life; ? nospr=8 health, nospr=9 size, nospr=10 damaged, nospr=11 time, nospr=12 messy, nospr=13 dk, nospr=14 refused; dummy(prefix=nos) q6d; ? buyer in summer; ? bsu=1 yes, bsu=2 no; dummy(prefix=bsu) q7; ? main reason do not buy in summer; ? nosum=1 not avail, nosum=2 not local, nosum=3 taste, nosum=4 price, nosum=5 not fresh, nosum=6 texture, nosum=7 short life; ? nosum=8 health, nosum=9 size, nosum=10 damaged, nosum=11 time, nosum=12 messy, nosum=13 dk, nosum=14 refused; dummy(prefix=nosu) q7b; ? buyer in fall; ? buyf=1 yes, buyf=2 no; dummy(prefix=buyf) q8; ? most imp reason buy in fall; ? rsnf=1 taste, rsnf=2 color, rsnf=3 fresh, rsnf=4 price, rsnf=5 habit, rsnf=6 health, rsnf=7 tender; ? rsnf=8 recipes, rsnf=9 ads, rsnf=10 avail, rsnf=11 smell, rsnf=12 variety, rsnf=13 other, rsnf=14 dk, rsnf=15 refused; dummy(prefix=rsnf) q8c; ? main reason do not buy in fall; ? nof=1 not avail, nof=2 not local, nof=3 taste, nof=4 price, nof=5 not fresh, nof=6 texture, nof=7 short life; ? nof=8 health, nof=9 size, nof=10 damaged, nof=11 time, nof=12 messy, nof=13 dk, nof=14 refused; dummy(prefix=nof) q8d; ? have you received info; ? info=1 yes, info=2 no; dummy(prefix=inf) q22; ? received info from family member; ? fam=1 yes, fam=2 no; dummy(prefix=fam) q22a1; ? received info from friend; ? friend=1 yes, friend=2 no; dummy(prefix=frn) q22a2; ? received info from newspaper article; ? nws=1 yes, nws=2 no; dummy(prefix=nws) q22a3; ? received info from magazine article; ? mag=1 yes, mag=2 no; dummy(prefix=mag) q22a4; ? received info from TV food shows; ? tvfood=1 yes, tvfood=2 no;

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67 dummy(prefix=tvf) q22a5; ? received info from extension service; ? ext=1 yes, ext=2 no; dummy(prefix=ext) q22a6; ? received info from grocer; ? groc=1 yes, groc=2 no; dummy(prefix=grc) q22a7; ? received info from farmer; ? fmr=1 yes, fmr=2 no; dummy(prefix=fmr) q22a8; ? received info from cookbook; ? cbk=1 yes, cbk=2 no; dummy(prefix=cbk) q22a9; ? rceived info from trade association; ? tra=1 yes, tra=2 no; dummy(prefix=tra) q22a10; ? received info from internet; ? infonet=1 yes, infonet=2 no; dummy(prefix=net) q22a11; ? received info from home economics class; ? hmec=1 yes, hmec=2 no; dummy(prefix=hmec) q22a12; ? received info from other; ? infoth=1 yes, infoth=2 no; dummy(prefix=ino) q22a13; ? TV commercials about fsc; ? tvcomm=1 yes, tvcomm=2 no; dummy(prefix=tvc) q23a; ? other TV spots about fsc; ? tvother=1 yes, tvother=2 no; dummy(prefix=tvo) q23b; ? magazine ads about fsc; ? magads=1 yes, magads=2 no; dummy(prefix=mads) q23c; ? magazine feature stories about fsc; ? magfeat=1 yes, magfeat=2 no; dummy(prefix=mft) q23d; ? newspaper food-page stories or recipes about fsc; ? fdpg=1 yes, fdpg=2 no; dummy(prefix=fpg) q23e; ? newspaper food-page advertisements about fsc; ? fpgads=1 yes, fpgads=2 no; dummy(prefix=fpa) q23f; ? radio commercials about fsc; ? rco=1 yes, rco=2 no; dummy(prefix=rco) q23g; ? posters in stores about fsc; ? post=1 yes, post=2 no;

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68 dummy(prefix=pst) q23h; ? sweet corn recipe cards; ? rcd=1 yes, rcd=2 no; dummy(prefix=rcd) q23i; ? internet web site about fsc; ? web=1 yes, web=2 no; dummy(prefix=web) q23j; SELECT 1; ?=====================================================================; ? first stage probit BUY1=1 YES TO BUYING ; ?=====================================================================; SELECT BUY1=0; SELECT BUY1=1; SELECT 1; AGE=2001 Q27; HIST(DISCRETE) BUY1; DOT 1-4; ZCIT.=CIT.-CIT5; ENDDOT; DOT 1-2; ZEDU.=EDU.-EDU3; ENDDOT; ZINC1=INC1 INC2; DOT 1-2; ZRAC.=RAC. RAC3; ENDDOT; ZGEN1 = GEN1 GEN2; DOT 1-2; ZSAT.=SAT.-SAT3; ENDDOT; XAGE=AGE; age= (xage<30)*1 + (xage>=30 & xage<=55)*2 + (xage>55)*3; hist(discrete) age; dummy (prefix=dage) age; corr Zcit1 Zcit2 Zcit3 Zcit4 Zedu1 Zedu2 Zinc1 Zrac1 Zrac2 Zgen1 q24 hwz chd dage1 dage3 Zsat1 Zsat2 ; probit BUY1 c Zcit1 Zcit2 Zcit3 Zcit4 Zedu1 Zedu2 Zinc1 Zrac1 Zrac2 Zgen1 q24 hwz chd dage1 dage3 Zsat1 Zsat2 ; dot(value=j) 0-17; set jj=j+1; set b.= @coef(jj); enddot; DOT cit1 cit2 cit3 cit4 edu1 edu2 inc1 rac1 rac2 gen1 q24 hwz chd dage1 dage3 sat1 sat2 ; SET SIM.=0; ENDDOT; SET I=0; MFORM(TYPE=GEN,NROW=150,NCOL=35) ZSIMSCZ=0; ? <<<<<<<<<<<<<<<<<<<>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>; PROC ZSIMZ; ? <<<<<<<<<<<<<<<<<<<>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>; SET XB= B0 + B1*SIMcit1 + B2*SIMcit2 + B3*SIMcit3 + B4*SIMcit4 + B5*SIMedu1 + B6*SIMedu2 + B7*SIMinc1 + B8*SIMrac1 + B9*SIMrac2 + B10*SIMgen1+ B11*SIMq24 + B12*SIMhwz + B13*SIMchd + B14*SIMage1 + B15*SIMage3 + B16*SIMsat1 + B17*SIMsat2 ; SET PROB = CNORM(XB); SET I=I+1; SET J=1; SET ZSIMSCZ(I,J)=SIM; SET J=2; SET ZSIMSCZ(I,J)=PROB; DOT cit1 cit2 cit3 cit4 edu1 edu2 inc1 rac1 rac2

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69 gen1 q24 hwz chd age1 age3 sat1 sat2 ; SET J=J+1; SET ZSIMSCZ(I,J)=SIM.; ENDDOT; ENDPROC; ? <<<<<<<<<<<<<<<<<<<>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>; MSD(NOPRINT) q24 hwz ; SET K=0; DOT q24 hwz ; SET K=K+1; SET SIM.=@MEAN(K); ENDDOT; DOT cit1 cit2 cit3 cit4 edu1 edu2 inc1 rac1 rac2 gen1 chd age1 age3 sat1 sat2 ; SET SIM.=0; ENDDOT; ? <<<<<<<<<<<<<<<<<<<>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>; PROC INIT; ? <<<<<<<<<<<<<<<<<<<>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>; MSD(NOPRINT) q24 hwz ; SET K=0; DOT q24 hwz; SET K=K+1; SET SIM.=@MEAN(K); ENDDOT; DOT cit1 cit2 cit3 cit4 edu1 edu2 inc1 rac1 rac2 gen1 chd age1 age3 sat1 sat2 ; SET SIM.=0; ENDDOT; ENDPROC INIT; ? <<<<<<<<<<<<<<<<<<<>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>; SET SIM=1; INIT; ZSIMZ; SET SIM=2; ? CITY; INIT; SET SIMCIT1=1; ? DALLAS; ZSIMZ; INIT; SET SIMCIT2=1; ? ATLANTA; ZSIMZ; INIT; SET SIMCIT3=1; ? CHICAGO; ZSIMZ; INIT; SET SIMCIT4=1; ? BOSTON; ZSIMZ; INIT; SET SIMCIT1=-1; SET SIMCIT2=-1; SET SIMCIT3=-1; SET SIMCIT4=-1; ? PHILADELPHIA; ZSIMZ; SET SIM=3; ? EDUCATION; INIT; SET SIMEDU1=1; ? HS GRAD OR LESS; ZSIMZ; INIT; SET SIMEDU2=1; ? TECH SCHOOL, SOME COLLEGE, OR COLLEGE GRAD; ZSIMZ; INIT; SET SIMEDU1=-1; SET SIMEDU2=-1; ? GRAD/PROFESSIONAL; ZSIMZ; SET SIM=4; ? AGE; INIT; SET SIMAGE1=1; ? UNDER 30; ZSIMZ; INIT; SET SIMAGE3=1; ? OVER 55; ZSIMZ;

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70 SET SIM=5; ? INCOME; INIT; SET SIMINC1=1; ? INCOME UNDER $35,000; ZSIMZ; SET SIMINC1=-1; ? INCOME OVER $35,000; ZSIMZ; SET SIM=6; ? RACE; INIT; SET SIMRAC1=1; ? WHITE; ZSIMZ; INIT; SET SIMRAC2=1; ? BLACK; ZSIMZ; INIT; SET SIMRAC1=-1; SET SIMRAC2=-1; ? ALL OTHER; ZSIMZ; SET SIM=7; ? GENDER; INIT; SET SIMGEN1=1; ? MALE; ZSIMZ; SET SIMGEN1=-1; ? FEMALE; ZSIMZ; SET SIM=8; ? YEARS IN CITY; INIT; DO ADJ=0 TO 90 BY 5; SET SIMQ24=ADJ; ZSIMZ; ENDDO; SET SIM=9; ? HOUSEHOLD SIZE; INIT; DO ADJ=1 TO 15 BY 1; SET SIMHWZ=ADJ; ZSIMZ; ENDDO; SET SIM=10; ? PRESENCE OF CHILDREN; INIT; SET SIMCHD=1; ? CHILDREN; ZSIMZ; SET SIMCHD=-1; ? NO CHILDREN ZSIMZ; SET SIM=11; ? SATISFACTION W/ PRODUCE AVAILABILITY; INIT; SET SIMSAT1=1; ? NOT AT ALL SATISFIED; ZSIMZ; INIT; SET SIMSAT2=1; ? SOMEWHAT SATISFIED; ZSIMZ; INIT; SET SIMSAT1=-1; SET SIMSAT2=-1; ? VERY SATISFIED; ZSIMZ; WRITE(FORMAT=EXCEL,FILE='H:\SIMSCORN.XLS') ZSIMSCZ; end;

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71 OPTIONS MEMORY=50; OPTIONS LIMWARN=1; Title 'Probit Analysis for Sweet Corn Amanda Briggs'; ? ORDERPROBITSW#1.tsp; in 'd:\abriggs\corndat'; ? in 'c:\zstudent\abriggs\corndat'; ? in 'd:\zstudent\abriggs\corndat'; ? read(format=excel,file='d:\zstudent\abriggs\amanbas.xls'); ? out 'd:\zstudent\abriggs\corndat'; ? doc id 'household identification'; ? doc interv 'interview'; ? doc q1 'city 1 to 5'; ? doc q2 'satisfaction w/ produce availability 1 to 3'; ? doc q3 'buyer 1=yes 2=no'; ? ? doc q5 'buy in winter 1=yes 2=no'; ? doc q5a 'times/month winter 1 to 31'; ? doc q5b 'satisfaction winter 0 to 10'; ? doc q5c 'reason buy winter 1 to 15'; ? doc q5d 'reason dont buy winter 1 to 14'; ? doc q6 'buy in spring 1=yes 2=no'; ? doc q6a 'times/month spring 1 to 31'; ? doc q6b 'satisfaction spring 0 to 10'; ? doc q6c 'reason buy spring 1 to 15'; ? doc q6d 'reason dont buy spring 1 to 14'; ? doc q7 'buy in summer 1=yes 2=no'; ? doc q7a 'times/month summer 1 to 31'; ? doc q7b 'reason dont buy summer 1 to 14'; ? doc q8 'buy in fall 1=yes 2=no'; ? doc q8a 'times/month fall 1 to 31'; ? doc q8b 'satisfaction fall 0 to 10'; ? doc q8c reason buy fall 1 to 15'; ? doc q8d 'reason dont buy fall 1 to 14'; ? doc q12 'price/ear .10 to 1.0'; ? doc q13 '# ears per purchase 1 to 30'; ? ? doc q22a 'info source 1 to 13'; ? doc q22a1 'family 1=yes 2=no'; ? doc q22a2 'friend 1=yes 2=no'; ? doc q22a3 'newspaper 1=yes 2=no'; ? doc q22a4 'magazine 1=yes 2=no'; ? doc q22a5 'tV 1=yes 2=no'; ? doc q22a6 'extension 1=yes 2=no'; ? doc q22a7 'grocer 1=yes 2=no'; ? doc q22a8 'farmer 1=yes 2=no'; ? doc q22a9 'cookbook 1=yes 2=no'; ? doc q22a10 'trade assoc 1=yes 2=no'; ? doc q22a11 'internet 1=yes 2=no'; ? doc q22a12 'home ec 1=yes 2=no'; ? doc q22a13 'other 1=yes 2=no'; ? doc q23 'info type 1 to 10'; ? doc q23a 'tV commercials 1=yes 2=no'; ? doc q23b 'other TV 1=yes 2=no'; ? doc q23c 'magazine ad 1=yes 2=no'; ? doc q23d 'mag story 1=yes 2=no'; ? doc q23e 'newspaper story 1=yes 2=no'; ? doc q23f 'newspaper ads 1=yes 2=no'; ? doc q23g 'radio 1=yes 2=no'; ? doc q23h 'posters 1=yes 2=no'; ? doc q23i 'recipe cards 1=yes 2=no'; ? doc q23j 'web site 1=yes 2=no';

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72 ? ? doc q24 'years in city 1 to 100'; ? doc q25 '# adults in household 1 to 10'; ? doc q25a '#children in household 0 to 10'; ? doc q26 'education 1 to 7'; ? doc q27 'year of birth 1900 to 1983'; ? doc q28 'income >or< $35,000 1 to 2'; ? doc q28a 'income 3 to 4'; ? doc q28b 'income 5 to 7'; ? doc q29 'race 1 to 5'; ? doc q29a 'hispanic or not 1=yes 2=no'; ? doc q30 'internet access at home 1=yes 2=no'; ? doc q31 'internet access at work 1=yes 2=no'; ? doc q32 'gender 1=male 2=female'; ? doc idu 'identication'; ? out; ? dblist(date,doc) 'd:\zstudent\abriggs\corndat'; ? winter, spring and fall; LIST VAR1 Zedu1 Zedu2 Zrac1 Zrac2 Zgen1 q24 HWZ CHD DAGE3 Zsat1 Zsat2 SATF TV RD MGZ NWP PSR DRSN1 DRSN2 DRSN3 DINF; ? summer; LIST VAR4 Zedu1 Zedu2 Zrac1 Zrac2 Zgen1 q24 HWZ CHD DAGE3 Zsat1 Zsat2 TV RD MGZ NWP PSR DINF ; dummy(prefix=buy) q3; ? buyer of sweet corn buy1=yes buy2=no; ? Creating total household size; hwz = q25 + q25a; hist(discrete) hwz; ? Presence of children; chd = (q25a>0); hist(discrete) chd; ? Electronic media; elc = (q23a=1 | q23b=1 |q23g=1 |q23j=1 ); hist(discrete) elc; ? Print media; prn = (q23c=1 | q23d=1 |q23e=1 |q23f=1 |q23h=1 |q23i=1 ); hist(discrete) prn; ? Total media; med = (elc=1) | (prn=1); hist(discrete) med; ? Television; TV = (Q23A=1 | Q23B=1) 1; hist(discrete) tv; ? Radio; RD = (Q23G=1) 1; hist(discrete) rd; ? Magazines; MGZ = (Q23C=1 | Q23D=1) 1; hist(discrete) mgz; ? Newspapers; NWP = (Q23E=1 | Q23F=1) 1;

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73 hist(discrete) nwp; ? Posters, recipe cards; PSR = (Q23H=1 | Q23I=1) 1; hist(discrete) psr; hist(discrete) q3; dot 5-8; hist(discrete) q.; enddot; hist(discrete) q13; ? number of ears per puchase; ? number of times per month during each season with 5=winter, 6=spring, 7=summer, 8=fall; dot 5-8; select 1; dd = (q.a>=20); select dd^=1; qq.a=q.a; select 1; hist(discrete) qq.a; tq.a=(qq.a=1) + (qq.a=2)*2 + (qq.a=3)*3 + (qq.a>=4)*4; dummy(prefix=tmq.) tq.a; select 1; enddot; dot 5-8; hist(discrete) tmq.1-tmq.4; enddot; ?======================================================================; ? making dummy variables for the demographics ?======================================================================; ? city=1 Dallas, city=2 Atlanta, city=3 Chicago, city=4 Boston, city=5 Philadelphia; dummy(prefix=cit) q1; ? edu=1 8th grade, edu=2 some hs, edu=3 hs grad, edu=4 tech schl, edu=5 some college, edu=6 college grad, edu=7 grad/prof; xedu=q26; xedu=(xedu<=3)*1 + (xedu>=4 & xedu<=6)*2 + (xedu>=7)*3; hist(discrete) xedu; dummy(prefix=edu) xedu; ? inc=1 under $35,000, inc=2 over $35,000; dummy(prefix=inc) q28; ? race=1 black, race=2 white, race=>3 all other; dummy(prefix=rac) q29; ? hispanic=1 yes, hispanic=2 no; dummy(prefix=his) q29a; ? nethome=1 yes, nethome=2 no; dummy(prefix=neth) q30; ? network=1 yes, network=2 no; dummy(prefix=netw) q31; ? gender=1 male, gender=2 female; dummy(prefix=gen) q32;

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74 ?======================================================================; ? making dummy variables ; ?======================================================================; ? satisfaction w/ produce availability; ? sat=1 not at all satisfied, sat=2 somewhat, sat=3 very; dummy(prefix=sat) q2; ? buyer in winter; ? buyw=1 yes, buyw=2 no; dummy(prefix=buyw) q5; ? most imp reason buy in winter; ? rsnw=1 taste, rsnw=2 color, rsnw=3 fresh, rsnw=4 price, rsnw=5 habit, rsnw=6 health, rsnw=7 tender; ? rsnw=8 recipes, rsnw=9 ads, rsnw=10 avail, rsnw=11 smell, rsnw=12 variety, rsnw=13 other, rsnw=14 dk, rsnw=15 refused; dummy(prefix=rsnw) q5c; ? main reason do not buy in winter; ? nowint=1 not avail, nowint=2 not local, nowint=3 taste, nowint=4 price, nowint=5 not fresh, nowint=6 texture, nowint=7 short life; ? nowint=8 health, nowint=9 size, nowint=10 damaged, nowint=11 time, nowint=12 messy, nowint=13 dk, nowint=14 refused; dummy(prefix=now) q5d; ? buyer in spring; ? buys=1 yes, buys=2 no; dummy(prefix=buys) q6; ? most imp reason buy in spring; ? rsns=1 taste, rsns=2 color, rsns=3 fresh, rsns=4 price, rsns=5 habit, rsns=6 health, rsns=7 tender; ? rsns=8 recipes, rsns=9 ads, rsns=10 avail, rsns=11 smell, rsns=12 variety, rsns=13 other, rsns=14 dk, rsns=15 refused; dummy(prefix=rsns) q6c; ? main reason do not buy in spring; ? nospr=1 not avail, nospr=2 not local, nospr=3 taste, nospr=4 price, nospr=5 not fresh, nospr=6 texture, nospr=7 short life; ? nospr=8 health, nospr=9 size, nospr=10 damaged, nospr=11 time, nospr=12 messy, nospr=13 dk, nospr=14 refused; dummy(prefix=nos) q6d; ? buyer in summer; ? bsu=1 yes, bsu=2 no; dummy(prefix=bsu) q7; ? main reason do not buy in summer; ? nosum=1 not avail, nosum=2 not local, nosum=3 taste, nosum=4 price, nosum=5 not fresh, nosum=6 texture, nosum=7 short life; ? nosum=8 health, nosum=9 size, nosum=10 damaged, nosum=11 time, nosum=12 messy, nosum=13 dk, nosum=14 refused; dummy(prefix=nosu) q7b; ? buyer in fall; ? buyf=1 yes, buyf=2 no; dummy(prefix=buyf) q8; ? most imp reason buy in fall; ? rsnf=1 taste, rsnf=2 color, rsnf=3 fresh, rsnf=4 price, rsnf=5 habit, rsnf=6 health, rsnf=7 tender; ? rsnf=8 recipes, rsnf=9 ads, rsnf=10 avail, rsnf=11 smell, rsnf=12 variety, rsnf=13 other, rsnf=14 dk, rsnf=15 refused;

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75 dummy(prefix=rsnf) q8c; ? main reason do not buy in fall; ? nof=1 not avail, nof=2 not local, nof=3 taste, nof=4 price, nof=5 not fresh, nof=6 texture, ? nof=7 short life; ? nof=8 health, nof=9 size, nof=10 damaged, nof=11 time, nof=12 messy, nof=13 dk, nof=14 refused; dummy(prefix=nof) q8d; ? have you received info; ? info=1 yes, info=2 no; dummy(prefix=inf) q22; ? received info from family member; ? fam=1 yes, fam=2 no; dummy(prefix=fam) q22a1; ? received info from friend; ? friend=1 yes, friend=2 no; dummy(prefix=frn) q22a2; ? received info from newspaper article; ? nws=1 yes, nws=2 no; dummy(prefix=nws) q22a3; ? received info from magazine article; ? mag=1 yes, mag=2 no; dummy(prefix=mag) q22a4; ? received info from TV food shows; ? tvfood=1 yes, tvfood=2 no; dummy(prefix=tvf) q22a5; ? received info from extension service; ? ext=1 yes, ext=2 no; dummy(prefix=ext) q22a6; ? received info from grocer; ? groc=1 yes, groc=2 no; dummy(prefix=grc) q22a7; ? received info from farmer; ? fmr=1 yes, fmr=2 no; dummy(prefix=fmr) q22a8; ? received info from cookbook; ? cbk=1 yes, cbk=2 no; dummy(prefix=cbk) q22a9; ? rceived info from trade association; ? tra=1 yes, tra=2 no; dummy(prefix=tra) q22a10; ? received info from internet; ? infonet=1 yes, infonet=2 no; dummy(prefix=net) q22a11; ? received info from home economics class; ? hmec=1 yes, hmec=2 no; dummy(prefix=hmec) q22a12; ? received info from other; ? infoth=1 yes, infoth=2 no;

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76 dummy(prefix=ino) q22a13; ? TV commercials about fsc; ? tvcomm=1 yes, tvcomm=2 no; dummy(prefix=tvc) q23a; ? other TV spots about fsc; ? tvother=1 yes, tvother=2 no; dummy(prefix=tvo) q23b; ? magazine ads about fsc; ? magads=1 yes, magads=2 no; dummy(prefix=mads) q23c; ? magazine feature stories about fsc; ? magfeat=1 yes, magfeat=2 no; dummy(prefix=mft) q23d; ? newspaper food-page stories or recipes about fsc; ? fdpg=1 yes, fdpg=2 no; dummy(prefix=fpg) q23e; ? newspaper food-page advertisements about fsc; ? fpgads=1 yes, fpgads=2 no; dummy(prefix=fpa) q23f; ? radio commercials about fsc; ? rco=1 yes, rco=2 no; dummy(prefix=rco) q23g; ? posters in stores about fsc; ? post=1 yes, post=2 no; dummy(prefix=pst) q23h; ? sweet corn recipe cards; ? rcd=1 yes, rcd=2 no; dummy(prefix=rcd) q23i; ? internet web site about fsc; ? web=1 yes, web=2 no; dummy(prefix=web) q23j; SELECT 1; AGE=2001 Q27; HIST(DISCRETE) BUY1; DOT 1-4; ZCIT.=CIT.-CIT5; ENDDOT; DOT 1-2; ZEDU.=EDU.-EDU3; ENDDOT; ZINC1=INC1 INC2; DOT 1-2; ZRAC.=RAC. RAC3; ENDDOT; ZNETH1=NETH1 NETH2; ZNETW1=NETW1 NETW2; ZGEN1 = GEN1 GEN2; DOT 1-2; ZSAT.=SAT.-SAT3; ENDDOT; XAGE=AGE; HIST XAGE; AGE= (XAGE<30) + (XAGE>=30 & XAGE<=55)*2 + (XAGE>55)*3; HIST(DISCRETE) AGE; DUMMY(PREFIX=DAGE) AGE; mform(type=gen,nrow=30,ncol=4) mxcorn=0; SELECT 1;

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77 ?======================================================================; ? first stage probit BU1=1 YES TO BUYING ; ?======================================================================; SELECT BUY1=0; SELECT BUY1=1; DOT 5 6 7 8; HIST(DISCRETE) Q.; ? WINTER BUYING YES OR NO; HIST(DISCRETE,PERCENT) Q.A; ? TIMES; HIST(DISCRETE) Q.C; HIST(DISCRETE) Q.D; ? FREQUENCY OF BUYING SWEET CORN WITHIN A TYPICAL MONTH OF A SEASON; FRQ.=(Q.A=1)*1 + (Q.A=2)*2 + (Q.A=3)*3 + (Q.A>=4)*4; HIST(DISCRETE) FRQ.; ENDDOT; DOT 1-13; HIST(DISCRETE) Q22A.; ENDDOT; DOT A B J D E F G H I J; HIST(DISCRETE) Q23.; ENDDOT; HIST(DISCRETE) Q22; DOT 1 4; DRSN.=0; ENDDOT; DINF=0; Y=FRQ5; SET N1=0; SET N2=0; SET N3=0; SET N4=0; SATF=Q5B; RSN = Q5C; NOT= Q5D; INF= Q22; ? 1=YES 2=NO; LIST ZVARZ Zcit1 Zcit2 Zcit3 Zcit4 Zedu1 Zedu2 Zinc1 Zrac1 Zrac2 Zneth1 Znetw1 Zgen1 q24 q25 q25a DAGE2 DAGE3 Zsat1 Zsat2 DRSN1 DRSN2 DRSN3 DNOT1 DNOT2 DNOT3 DNOT4 DNOT5 DINF ; ?======================================================================; ? ordered probit model procedure winter ; ?======================================================================; proc oprob; dummy y y1-y4; msd (noprint) y1-y4; unmake @sum n1-n4; VRSN=0; VRSN=(RSN=1 | RSN=3 | RSN=7)*1 + (RSN=6)*2 + (RSN=5)*3 + (RSN^=1 & RSN^=3 & RSN^=7 & RSN^=6 & RSN^=5)*4; hist(discrete) vrsn; DUMMY(PREFIX=DRSN) VRSN; DINF=(INF=1); frml xb B5*Zedu1 + B6*Zedu2 + B12*Zrac1 + B13*Zrac2 + B16*Zgen1 + B17*q24 + B18*HWZ + B19*CHD + B21*DAGE3 + B22*Zsat1 + B23*Zsat2 +B24*SATF + B25*TV + B26*RD +B27*MGZ + B28*NWP +B29*PSR + E1*DRSN1 +E2*DRSN2 + E3*DRSN3 + D1*DINF ;

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78 set n=n1+n2+n3+n4; set sumn=0; dot 1-3; set sumn=sumn + n.; set f.=sumn/n; set a. = cnormi(f.); ? Seed values for the a1 to a3 parameters; enddot; print a1-a3; param B5 B6 B12-B13 B16-B19 B21-B29 E1 E2 E3 D1 a1 a2 a3 ; dot 1-3; frml xb. xb-a.; eqsub xb. xb; enddot; frml eq1 log{ y1*cnorm(-xb1) + y2*(cnorm(-xb2) -cnorm(-xb1)) + y3*(cnorm(-xb3) -cnorm(-xb2)) + y4*(1cnorm(-xb3)) }; eqsub(name=ordprob) eq1 xb1-xb3; hist(discrete) y; yy=(y>1); hist(discrete) yy; SELECT YY>=0; HIST(DISCRETE) DAGE1 DAGE2 DAGE3; dot Zedu1 Zedu2 Zrac1 Zrac2 Zgen1 q24 HWZ CHD DAGE3 Zsat1 Zsat2 SATF TV RD MGZ NWP PSR DRSN1 DRSN2 DRSN3 DINF ; HIST(DISCRETE) .; enddot; corr Zedu1 Zedu2 Zrac1 Zrac2 Zgen1 q24 HWZ CHD DAGE3 Zsat1 Zsat2 SATF TV RD MGZ NWP PSR DRSN1 DRSN2 DRSN3 DINF ; probit yy c Zedu1 Zedu2 Zrac1 Zrac2 Zgen1 q24 HWZ CHD DAGE3 Zsat1 Zsat2 SATF TV RD MGZ NWP PSR DRSN1 DRSN2 DRSN3 DINF ; ml(hiter=n,hcov=nbw) ordprob; mat mxcorn=@coef; print mxcorn; ?======================================================================; endproc; ?======================================================================; ?======================================================================; ? ordered probit model summer; ? =======================================================================; proc oprob2; dummy y y1-y4; msd(noprint) y1-y4; unmake @sum n1-n4; DINF=(INF=1);

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79 frml xb B5*Zedu1 + B6*Zedu2 + B12*Zrac1 + B13*Zrac2 + B16*Zgen1 + B17*q24 + B18*HWZ + B19*CHD + B21*DAGE3 + B22*Zsat1 + B23*Zsat2 + B24*TV+ B25*RD +B26*MGZ + B27*NWP + B28*PSR + D1*DINF ; set n=n1+n2+n3+n4; set sumn=0; dot 1-3; set sumn=sumn + n.; set f.=sumn/n; set a. = cnormi(f.); ? Seed values for the a1 to a3 parameters; enddot; print a1-a3; param B5 B6 B12-B13 B16-B19 B21-B28 D1 a1 a2 a3 ; dot 1-3; frml xb. xb-a.; eqsub xb. xb; enddot; frml eq1 log{ y1*cnorm(-xb1) + y2*(cnorm(-xb2) -cnorm(-xb1)) + y3*(cnorm(-xb3) -cnorm(-xb2)) + y4*(1cnorm(-xb3)) }; eqsub(name=ordprob) eq1 xb1-xb3; hist(discrete) y; yy=(y>1); hist(discrete) yy; SELECT YY>=0; HIST(DISCRETE) DAGE1 DAGE2 DAGE3; dot Zedu1 Zedu2 Zrac1 Zrac2 Zgen1 q24 HWZ CHD DAGE3 Zsat1 Zsat2 TV RD MGZ NWP PSR DINF ; HIST(DISCRETE) .; enddot; CORR Zedu1 Zedu2 Zrac1 Zrac2 Zgen1 q24 HWZ CHD DAGE3 Zsat1 Zsat2 TV RD MGZ NWP PSR DINF ; probit yy c Zedu1 Zedu2 Zrac1 Zrac2 Zgen1 q24 HWZ CHD DAGE3 Zsat1 Zsat2 TV RD MGZ NWP PSR DINF ; ml(hiter=n,hcov=nbw) ordprob; mat mxcorn=@coef; print mxcorn; ?======================================================================; endproc oprob2 ; ?======================================================================; ? <<<<<<<<<<<<<<<<<<<<<<<<<<< >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>; ? Initializing all coefficents before going to the procedure; dot aa1 aa2 aa3 bb5 bb6 bb12 bb13 bb16 bb17 bb18 bb19 bb21 bb22 bb23 bb24 bb25 bb26 bb27 bb28 bb29 ee1 ee2 ee3 dd1; set .=0; enddot; set zz=0; ? Base simulation; mat mmx=mxcorn1; ? winter matrix;

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80 proc simul_1; set aa1=mmx(24,1); set aa2=mmx(23,1); set aa3=mmx(22,1); set bb5=mmx(1,1); ? education 1 (1/-1); set bb6=mmx(2,1); ? education 2 (1/-1); set bb12=mmx(3,1); ? race 1 black (1/-1); set bb13=mmx(4,1); ? race 2 white (1/-1); set bb16=mmx(5,1); ? gender 1 male (1/-1); set bb17=mmx(6,1); ? q24 number of years in the city; set bb18=mmx(7,1); ? hwz household size number of adults and children; set bb19=mmx(8,1); ? presence of children yes/no (1/0); set bb21=mmx(9,1); ? over 55 years of age (1/0); set bb22=mmx(10,1); ? satisfaction with produce in general yes/no (1/-1); set bb23=mmx(11,1); ? somewhat satisfaction yes/no (1/-1); set bb24=mmx(12,1); ? coefficient for satifaction 0 to 10; set bb25=mmx(13,1); ? tv yes/no (1/0); set bb26=mmx(14,1); ? radio yes/no (1/0); set bb27=mmx(15,1); ? magazines yes/no (1/0); set bb28=mmx(16,1); ? newspapers yes/no (1/0); set bb29=mmx(17,1); ? posters yes/no (1/0); set ee1=mmx(18,1); ? reason for buying 1 taste yes/no (1/0); set ee2=mmx(19,1); ? reason for buying 2 health yes/no (1/0); set ee3=mmx(20,1); ? reason for buying 3 habit yes/no (1/0); set dd1=mmx(21,1); ? information received yes/no (1/0); set zz= bb5*Zedu1_sm + bb6*Zedu2_sm + bb12*Zrac1_sm + bb13*Zrac2_sm + bb16*Zgen1_sm + bb17*q24_sm + bb18*HWZ_sm + bb19*CHD_sm + bb21*DAGE3_sm + bb22*Zsat1_sm + bb23*Zsat2_sm + bb24*SATF_sm + bb25*TV_sm + bb26*RD_sm + bb27*MGZ_sm + bb28*NWP_sm + bb29*PSR_sm + ee1*DRSN1_sm + ee2*DRSN2_sm + ee3*DRSN3_sm + dd1*DINF_sm; set prob1=cnorm(aa1 zz); set prob2=cnorm(aa2 zz) cnorm(aa1 zz); set prob3=cnorm(aa3 zz) cnorm(aa2 zz); set prob4=1 prob1 prob2 prob3; set zprobz(i,1) = sim; set zprobz(i,2) = prob1; set zprobz(i,3) = prob2; set zprobz(i,4) = prob3; set zprobz(i,5) = prob4; dot var1; set j=j+1; set jj=j+5; set zprobz(i,jj)= ._sm; enddot; endproc simul_1; mform(type=gen,nrow=500,ncol=50) zprobz=0; set i=0; mat mmx=mxcorn1; ?======================================================================; ? Simulation sim=101 Winter base (satf at mean); ?======================================================================; set sim=101; dot var1; set ._sm = 0; enddot; msd(noprint) q24; set q24_sm=@mean(1); msd(noprint) hwz; set hwz_sm=@mean(1); msd(noprint) satf; set satf_sm=@mean(1); set j=0; set i=i+1; simul_1;

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81 ?======================================================================; ? Simulation sim=102 Winter magazines(satf at mean); ?======================================================================; set sim=119; dot var1; set ._sm = 0; enddot; msd(noprint) q24; set q24_sm=@mean(1); msd(noprint) hwz; set hwz_sm=@mean(1); set mgz_sm=1; msd(noprint) satf; set satf_sm=@mean(1); set j=0; set i=i+1; simul_1; ?======================================================================; ? Simulation sim=103 Winter rsn1(satf at mean); ?======================================================================; set sim=122; dot var1; set ._sm = 0; enddot; msd(noprint) q24; set q24_sm=@mean(1); msd(noprint) hwz; set hwz_sm=@mean(1); set drsn1_sm=1; msd(noprint) satf; set satf_sm=@mean(1); set j=0; set i=i+1; simul_1; ?======================================================================; ? Simulation sim=104 Winter rsn3(satf at mean); ?======================================================================; set sim=124; dot var1; set ._sm = 0; enddot; msd(noprint) q24; set q24_sm=@mean(1); msd(noprint) hwz; set hwz_sm=@mean(1); set drsn3_sm=1; msd(noprint) satf; set satf_sm=@mean(1); set j=0; set i=i+1; simul_1; ?======================================================================; ? Simulation sim=105 Winter base; ?======================================================================; set sim=101; dot var1; set ._sm = 0; enddot; msd(noprint) q24; set q24_sm=@mean(1); msd(noprint) hwz; set hwz_sm=@mean(1); do satf_sm= 0 to 10 by 1; set j=0; set i=i+1; simul_1; enddo; write(format=excel, file='d:\abriggs\new_winter.xls') zprobz; ?<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>; mat mmx2=mxcorn2; ? spring matrix; proc simul_2; set aa1=mmx2(24,1); set aa2=mmx2(23,1); set aa3=mmx2(22,1);

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82 set bb5=mmx2(1,1); ? education 1 (1/-1); set bb6=mmx2(2,1); ? education 2 (1/-1); set bb12=mmx2(3,1); ? race 1 black (1/-1); set bb13=mmx2(4,1); ? race 2 white (1/-1); set bb16=mmx2(5,1); ? gender 1 male (1/-1); set bb17=mmx2(6,1); ? q24 number of years in the city; set bb18=mmx2(7,1); ? hwz household size number of adults and children; set bb19=mmx2(8,1); ? presence of children yes/no (1/0); set bb21=mmx2(9,1); ? over 55 years of age (1/0); set bb22=mmx2(10,1); ? satisfaction with produce in general yes/no (1/-1); set bb23=mmx2(11,1); ? somewhat satisfaction yes/no (1/-1); set bb24=mmx2(12,1); ? coefficient for satifaction 0 to 10; set bb25=mmx2(13,1); ? tv yes/no (1/0); set bb26=mmx2(14,1); ? radio yes/no (1/0); set bb27=mmx2(15,1); ? magazines yes/no (1/0); set bb28=mmx2(16,1); ? newspapers yes/no (1/0); set bb29=mmx2(17,1); ? posters yes/no (1/0); set ee1=mmx2(18,1); ? reason for buying 1 taste yes/no (1/0); set ee2=mmx2(19,1); ? reason for buying 2 health yes/no (1/0); set ee3=mmx2(20,1); ? reason for buying 3 habit yes/no (1/0); set dd1=mmx2(21,1); ? information received yes/no (1/0); set zz= bb5*Zedu1_sm + bb6*Zedu2_sm + bb12*Zrac1_sm + bb13*Zrac2_sm + bb16*Zgen1_sm + bb17*q24_sm + bb18*HWZ_sm + bb19*CHD_sm + bb21*DAGE3_sm + bb22*Zsat1_sm + bb23*Zsat2_sm + bb24*SATF_sm + bb25*TV_sm + bb26*RD_sm + bb27*MGZ_sm + bb28*NWP_sm + bb29*PSR_sm + ee1*DRSN1_sm + ee2*DRSN2_sm + ee3*DRSN3_sm + dd1*DINF_sm; set prob1=cnorm(aa1 zz); set prob2=cnorm(aa2 zz) cnorm(aa1 zz); set prob3=cnorm(aa3 zz) cnorm(aa2 zz); set prob4=1 prob1 prob2 prob3; set zprobz(i,1) = sim; set zprobz(i,2) = prob1; set zprobz(i,3) = prob2; set zprobz(i,4) = prob3; set zprobz(i,5) = prob4; dot var1; set j=j+1; set jj=j+5; set zprobz(i,jj)= ._sm; enddot; endproc simul_2; mform(type=gen,nrow=500,ncol=50) zprobz=0; set i=0; mat mmx2=mxcorn2; ? spring matrix; ?======================================================================; ? Simulation sim=201 Spring base (satf at mean); ?======================================================================; set sim=201; dot var1; set ._sm = 0; enddot; msd(noprint) q24; set q24_sm=@mean(1); msd(noprint) hwz; set hwz_sm=@mean(1); msd(noprint) satf; set satf_sm=@mean(1); set j=0; set i=i+1; simul_2; ?======================================================================; ? Simulation sim=202 Spring age 3 (satf at mean); ?======================================================================; set sim=213; dot var1; set ._sm = 0; enddot;

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83 msd(noprint) q24; set q24_sm=@mean(1); msd(noprint) hwz; set hwz_sm=@mean(1); set dage3_sm=1; msd(noprint) satf; set satf_sm=@mean(1); set j=0; set i=i+1; simul_2; ?======================================================================; ? Simulation sim=203 Spring sat1 (satf at mean); ?======================================================================; set sim=214; dot var1; set ._sm = 0; enddot; msd(noprint) q24; set q24_sm=@mean(1); msd(noprint) hwz; set hwz_sm=@mean(1); set zsat1_sm=1; msd(noprint) satf; set satf_sm=@mean(1); set j=0; set i=i+1; simul_2; ?======================================================================; ? Simulation sim=204 Spring sat2 (satf at mean); ?======================================================================; set sim=215; dot var1; set ._sm = 0; enddot; msd(noprint) q24; set q24_sm=@mean(1); msd(noprint) hwz; set hwz_sm=@mean(1); set zsat2_sm=1; msd(noprint) satf; set satf_sm=@mean(1); set j=0; set i=i+1; simul_2; ?======================================================================; ? Simulation sim=205 Spring televison (satf at mean); ?======================================================================; set sim=217; dot var1; set ._sm = 0; enddot; msd(noprint) q24; set q24_sm=@mean(1); msd(noprint) hwz; set hwz_sm=@mean(1); set tv_sm=1; msd(noprint) satf; set satf_sm=@mean(1); set j=0; set i=i+1; simul_2; ?======================================================================; ? Simulation sim=206 Spring base; ?======================================================================; set sim=201; dot var1; set ._sm = 0; enddot; msd(noprint) q24; set q24_sm=@mean(1); msd(noprint) hwz; set hwz_sm=@mean(1); do satf_sm= 0 to 10 by 1; set j=0; set i=i+1; simul_2; enddo; ?======================================================================; ? Simulation sim=207 Spring hwz (satf at mean); ?======================================================================; set sim=202;

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84 dot var1; set ._sm = 0; enddot; msd(noprint) q24; set q24_sm=@mean(1); msd(noprint) satf; set satf_sm=@mean(1); do hwz_sm= 1 to 15 by 1; set j=0; set i=i+1; simul_2; enddo; write(format=excel, file='d:\abriggs\new_spring.xls') zprobz; ?<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>; mat mmx3=mxcorn3; ? fall matrix; proc simul_3; set aa1=mmx3(24,1); set aa2=mmx3(23,1); set aa3=mmx3(22,1); set bb5=mmx3(1,1); ? education 1 (1/-1); set bb6=mmx3(2,1); ? education 2 (1/-1); set bb12=mmx3(3,1); ? race 1 black (1/-1); set bb13=mmx3(4,1); ? race 2 white (1/-1); set bb16=mmx3(5,1); ? gender 1 male (1/-1); set bb17=mmx3(6,1); ? q24 number of years in the city; set bb18=mmx3(7,1); ? hwz household size number of adults and children; set bb19=mmx3(8,1); ? presence of children yes/no (1/0); set bb21=mmx3(9,1); ? over 55 years of age (1/0); set bb22=mmx3(10,1); ? satisfaction with produce in general yes/no (1/-1); set bb23=mmx3(11,1); ? somewhat satisfaction yes/no (1/-1); set bb24=mmx3(12,1); ? coefficient for satifaction 0 to 10; set bb25=mmx3(13,1); ? tv yes/no (1/0); set bb26=mmx3(14,1); ? radio yes/no (1/0); set bb27=mmx3(15,1); ? magazines yes/no (1/0); set bb28=mmx3(16,1); ? newspapers yes/no (1/0); set bb29=mmx3(17,1); ? posters yes/no (1/0); set ee1=mmx3(18,1); ? reason for buying 1 taste yes/no (1/0); set ee2=mmx3(19,1); ? reason for buying 2 health yes/no (1/0); set ee3=mmx3(20,1); ? reason for buying 3 habit yes/no (1/0); set dd1=mmx3(21,1); ? information received yes/no (1/0); set zz= bb5*Zedu1_sm + bb6*Zedu2_sm + bb12*Zrac1_sm + bb13*Zrac2_sm + bb16*Zgen1_sm + bb17*q24_sm + bb18*HWZ_sm + bb19*CHD_sm + bb21*DAGE3_sm + bb22*Zsat1_sm + bb23*Zsat2_sm + bb24*SATF_sm + bb25*TV_sm + bb26*RD_sm + bb27*MGZ_sm + bb28*NWP_sm + bb29*PSR_sm + ee1*DRSN1_sm + ee2*DRSN2_sm + ee3*DRSN3_sm + dd1*DINF_sm; set prob1=cnorm(aa1 zz); set prob2=cnorm(aa2 zz) cnorm(aa1 zz); set prob3=cnorm(aa3 zz) cnorm(aa2 zz); set prob4=1 prob1 prob2 prob3; set zprobz(i,1) = sim; set zprobz(i,2) = prob1; set zprobz(i,3) = prob2; set zprobz(i,4) = prob3; set zprobz(i,5) = prob4; dot var1; set j=j+1; set jj=j+5; set zprobz(i,jj)= ._sm; enddot; endproc simul_3;

PAGE 94

85 mform(type=gen,nrow=500,ncol=50) zprobz=0; set i=0; mat mmx3=mxcorn3; ? fall matrix; ?======================================================================; ? Simulation sim=301 Fall base (satf at mean); ?======================================================================; set sim=301; dot var1; set ._sm = 0; enddot; msd(noprint) q24; set q24_sm=@mean(1); msd(noprint) hwz; set hwz_sm=@mean(1); msd(noprint) satf; set satf_sm=@mean(1); set j=0; set i=i+1; simul_3; ?======================================================================; ? Simulation sim=302 Fall race 2 (satf at mean); ?======================================================================; set sim=306; dot var1; set ._sm = 0; enddot; msd(noprint) q24; set q24_sm=@mean(1); msd(noprint) hwz; set hwz_sm=@mean(1); set zrac2_sm=1; msd(noprint) satf; set satf_sm=@mean(1); set j=0; set i=i+1; simul_3; ?======================================================================; ? Simulation sim=303 Fall base; ?======================================================================; set sim=301; dot var1; set ._sm = 0; enddot; msd(noprint) q24; set q24_sm=@mean(1); msd(noprint) hwz; set hwz_sm=@mean(1); do satf_sm= 0 to 10 by 1; set j=0; set i=i+1; simul_3; enddo; write(format=excel, file='d:\abriggs\new_fall.xls') zprobz; ?<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>; ? Initializing all coefficents before going to the procedure; dot aa1 aa2 aa3 bb5 bb6 bb12 bb13 bb16 bb17 bb18 bb19 bb21 bb22 bb23 bb24 bb25 bb26 bb27 bb28 dd1; set .=0; enddot; set zz=0; ? Base simulation; mat mmx4=mxcorn4; ? summer matrix; proc simul_4; set aa1=mmx4(20,1); set aa2=mmx4(19,1); set aa3=mmx4(18,1); set bb5=mmx4(1,1); ? education 1 (1/-1); set bb6=mmx4(2,1); ? education 2 (1/-1); set bb12=mmx4(3,1); ? race 1 black (1/-1); set bb13=mmx4(4,1); ? race 2 white (1/-1); set bb16=mmx4(5,1); ? gender 1 male (1/-1);

PAGE 95

86 set bb17=mmx4(6,1); ? q24 number of years in the city; set bb18=mmx4(7,1); ? hwz household size number of adults and children; set bb19=mmx4(8,1); ? presence of children yes/no (1/0); set bb21=mmx4(9,1); ? over 55 years of age (1/0); set bb22=mmx4(10,1); ? satisfaction with produce in general yes/no (1/-1); set bb23=mmx4(11,1); ? somewhat satisfaction yes/no (1/-1); set bb24=mmx4(12,1); ? tv yes/no (1/0); set bb25=mmx4(13,1); ? radio yes/no (1/0); set bb26=mmx4(14,1); ? magazines yes/no (1/0); set bb27=mmx4(15,1); ? newspapers yes/no (1/0); set bb28=mmx4(16,1); ? posters yes/no (1/0); set dd1=mmx4(17,1); ? information received yes/no (1/0); set zz= bb5*Zedu1_sm + bb6*Zedu2_sm + bb12*Zrac1_sm + bb13*Zrac2_sm + bb16*Zgen1_sm + bb17*q24_sm + bb18*HWZ_sm + bb19*CHD_sm + bb21*DAGE3_sm + bb22*Zsat1_sm + bb23*Zsat2_sm + bb24*TV_sm + bb25*RD_sm + bb26*MGZ_sm + bb27*NWP_sm + bb28*PSR_sm + dd1*DINF_sm; set prob1=cnorm(aa1 zz); set prob2=cnorm(aa2 zz) cnorm(aa1 zz); set prob3=cnorm(aa3 zz) cnorm(aa2 zz); set prob4=1 prob1 prob2 prob3; set zprobz(i,1) = sim; set zprobz(i,2) = prob1; set zprobz(i,3) = prob2; set zprobz(i,4) = prob3; set zprobz(i,5) = prob4; dot var1; set j=j+1; set jj=j+5; set zprobz(i,jj)= ._sm; enddot; endproc simul_4; mform(type=gen,nrow=500,ncol=50) zprobz=0; set i=0; mat mmx4=mxcorn4; ? summer matrix; ?======================================================================; ? Simulation sim=401 Summer base; ?======================================================================; set sim=401; dot var1; set ._sm = 0; enddot; msd(noprint) q24; set q24_sm=@mean(1); msd(noprint) hwz; set hwz_sm=@mean(1); set j=0; set i=i+1; simul_4; ?======================================================================; ? Simulation sim=402 Summer presence of children; ?======================================================================; set sim=410; dot var1; set ._sm = 0; enddot; msd(noprint) q24; set q24_sm=@mean(1); msd(noprint) hwz; set hwz_sm=@mean(1); set chd_sm=1; set j=0; set i=i+1; simul_4; ?======================================================================; ? Simulation sim=403 Summer age 3; ?======================================================================;

PAGE 96

87 set sim=413; dot var1; set ._sm = 0; enddot; msd(noprint) q24; set q24_sm=@mean(1); msd(noprint) hwz; set hwz_sm=@mean(1); set dage3_sm=1; set j=0; set i=i+1; simul_4; ?======================================================================; ? Simulation sim=404 Summer newspapers; ?======================================================================; set sim=420; dot var1; set ._sm = 0; enddot; msd(noprint) q24; set q24_sm=@mean(1); msd(noprint) hwz; set hwz_sm=@mean(1); set nwp_sm=1; set j=0; set i=i+1; simul_4; write(format=excel, file='d:\abriggs\new_summer.xls') zprobz; end;

PAGE 97

REFERENCES Aldrich, John H. and Forrest D. Nelson. Linear Probability, Logit, and Probit Models. Newbury Park, CA: Sage Publications, Inc., 1984. Borooah, Vani K. Logit and Probit: Ordered and Multinomial Models. Thousand Oaks, CA: Sage Publications, Inc., 2002. Clauson, Annette. Spotlight on National Food Spending. Food Review, Volume 23, Issue 3. Economic Research Service, USDA. 2000. Daganzo, Carlos. Multinomial Probit: The Theory and Its Application to Demand Forecasting. New York: Academic Press, 1979. Degner, Robert L., Kimberly L. Morgan, Chris deBodisco, and Lisa House. Market Development Strategies for Fresh Sweet Corn Based Upon Consumer and Trade Surveys. Industry Report 01-1. University of Florida, 2001. Economic Research Service. Food Consumption: Household Food Expenditures. Economic Research Service, USDA. 2001. Grieco, Elizabeth and Rachel Cassidy. Census 2000 Shows Americas Diversity. U.S. Census Bureau Public Information Office, 2001. Hall, Bronwyn H. and Clint Cummins. TSP International Reference Manual Version 4.4. Palo Alto, CA: TSP International, 1998. Hall, Bronwyn H. and Clint Cummins. TSP International Users Guide Version 4.4. Palo Alto, CA: TSP International, 1997. Liao, Tim Futig. Interpreting Probability Models: Logit, Probit, and Other Generalized Linear Models. Thousand Oaks, CA: Sage Publications, Inc., 1994. Lucier, Gary and Biing-Hwan Lin. How Sweet It Is: Fresh Sweet Corn. Agricultural Outlook. Economic Research Service, USDA. August 2001. Medina, Sara and Ronald W. Ward. A Model of Retail Outlet Selection for Beef. International Food and Agribusiness Management Review, 2 (2): 195-219, 1999. 88

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89 Pampel, Fred C. Logistic Regression: A Primer. Thousand Oaks, CA: Sage Publications, Inc., 2000. Summary of Florida Corn Production. University of Florida Cooperative Extension Service, Institute of Food and Agricultural Sciences. October, 1999. Vegetables-Acreage, Production, and Value. Florida Agricultural Statistics Service. February 4, 2002. Verbeke, Wim, Ronald W. Ward, and Jacques Viaene. Probit Analysis of Fresh Meat Consumption in Belgium: Exploring B.S.E. and Television Communication Impact. Agribusiness: An International Journal, 16 (Spring), 215-234, 2000. Ward, Ronald W., Julian Briz, and Isabel de Felipe. Competing Supplies of Olive Oil in the German Market: An Application of Multinomial Logit Models.

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BIOGRAPHICAL SKETCH Amanda Champion Briggs was born on January 20, 1978 in Sarasota, Florida. She received her Bachelor of Science degree with honors from the University of Florida in May 2001 with a major in Food and Resource Economics and specialization in Applied Economics. She went on to receive her Master of Science degree in Food and Resource Economics from the University of Florida in August 2003. 90


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Title: Probit and Ordered Probit Analysis of the Demand for Fresh Sweet Corn
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Title: Probit and Ordered Probit Analysis of the Demand for Fresh Sweet Corn
Physical Description: Mixed Material
Copyright Date: 2008

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Holding Location: University of Florida
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PROFIT AND ORDERED PROFIT ANALYSIS OF THE DEMAND FOR FRESH
SWEET CORN















By

AMANDA C. BRIGGS


A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF
FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE
DEGREE OF MASTER OF SCIENCE

UNIVERSITY OF FLORIDA


2003















ACKNOWLEDGMENTS

There are several people I would like to thank for helping me complete this thesis

and contributing to my graduate experience at the University of Florida. I extend my

gratitude to my committee chair, Dr. Robert L. Degner; and committee member, Dr.

Ronald W. Ward, for generously sharing their time and knowledge with me. I thank

Dr. Chris Andrew for his advisement and guidance. I also thank my fellow graduate

students in the Food and Resource Economics Department. Above all, I thank my

mother, Barbara Briggs; my sister, Maria Cristina Briggs; my uncle, David Browning;

and Giancarlo Espinosa for their encouragement and support.















TABLE OF CONTENTS

page

ACKNOW LEDGM ENTS ........................................................ .......... ii

LIST OF TABLES................... ................................... .......v

LIST OF FIGURE S................... ................... .......................... vi

ABSTRACT.................................. .......................... viii

CHAPTER

1 INTRODUCTION .............................. ...... ................................... 1

2 OBJECTIVES ...................................... .......................... .........6

3 M ETHODOLOGY ......................................... ...........................8

Probit M odel ........... ................ ............ ........................................ 9
O ordered Probit M odel ...................................................... ..... .. .........
Specification of the Probit M odel........... ......... ......... ........... ...... .....12
Ordered Probit M odel Specification...... ............. ... ... ... .............. 13

4 PR OBIT RESULTS.................. .......................................... .... .... 17

Probit E stim ates................... ............................................... 18
Probit Model Simulations ............. ............................ .................20

5 ORDERED PROBIT RESULTS......... ................................... ...............29

Ordered Probit Parameter Estimates................ ..................... .. ...............29
Ordered Probit Simulations................ .............. ... .. ...................... 32

6 SUMMARY AND CONCLUSIONS........... ..................... ................ 46









APPENDIX

A CONSUMER SURVEY INSTRUMENT........... ...........................49

B TIME SERIES PROCESSOR PROGRAMS....................................63

R E FER EN C E S ................................................................ ........... 88

BIOGRAPHICAL SKETCH........... ................................................90














































iv















LIST OF TABLES

Table page

3-1 Number of completed interviews, by city................ ...... ................8

3-2 Probit model variables and descriptions ............................. ................13

3-3 Ordered probit model variables and descriptions ..................................15

4-1 Probit model parameter estimates............. ......................................19

5-1 Parameter estimates by season......................................................... .30















LIST OF FIGURES


Figure p

1-1 Production of fresh market sweet corn, by state................... .................... 1

1-2 Sweet corn production areas in Florida........ ...................... ...... ..........2

1-3 Food expenditures.................. .................................. .......... .4

4-1 Households' purchase of sweet corn, by city........... ............. ............17

4-2 Percent buying sweet corn by season, all respondents............ ...........18

4-3 Probability of consuming fresh sweet corn, by city of residence...................22

4-4 Probability of consuming fresh sweet corn, by educational level.................. 22

4-5 Probability of consuming fresh sweet corn, by income level........................23

4-6 Probability of consuming fresh sweet corn, by race.................................23

4-7 Probability of consuming fresh sweet corn, by gender...........................24

4-8 Probability of consuming fresh sweet corn, by household size.................. 24

4-9 Probability of consuming fresh sweet corn, by presence of children ..............25

4-10 Probability of consuming fresh sweet corn, by age.................................25

4-11 Probability of consuming fresh sweet corn, by satisfaction with produce
availability ...................... .............. ................... .... ........ 26

4-12 Ranking of factors impacting the probability of consuming fresh sweet
corn .................................... ................ ......... 27

5-1 Ordered probit models base probabilities by season ................................33

5-2 Probabilities for base and magazines (mgz) in winter .............................35









5-3 Probabilities for base and good taste, freshness, or tenderness (rsnl) in
w inter................... .................... ...... .................. ... ....... 3 5

5-4 Probabilities for base and habit (rsn3) in winter......................................36

5-5 Satisfaction level for fresh sweet corn purchased in winter.......................... 36

5-6 Probabilities for base and satl in spring................. ................. .... .........38

5-7 Probabilities for base and sat2 in spring............... ... ..................... ...........38

5-8 Satisfaction level for fresh sweet corn purchased in spring ......... ...........39

5-9 Probabilities for base and television (tv) in spring ................................40

5-10 Probabilities for base and over 55 years of age (age3) in spring....................40

5-11 Probabilities for base and household size in spring................................41

5-12 Probabilities for base and presence of children in household (chd)
in sum m er...................................... ................... .... ........ 42

5-13 Probabilities for base and over 55 years of age (age3) in summer..................43

5-14 Probabilities for base and newspapers (nwp) in summer..........................43

5-15 Probabilities for base and white race (rac2) in fall.................................44

5-16 Satisfaction level for fresh sweet corn purchased in fall.........................45















Abstract of Thesis Presented to the Graduate School of the University of Florida in
Partial Fulfillment of the Requirements for the Degree of Master of Science

PROFIT AND ORDERED PROFIT ANALYSIS OF THE DEMAND FOR FRESH
SWEET CORN

By

Amanda C. Briggs

August 2003

Chair: Robert L. Degner
Major Department: Food and Resource Economics

The Fresh Supersweet Corn Council (an organization of sweet corn growers and

shippers from Florida, Georgia, and Alabama whose members collectively promote their

product) is seeking ways to better utilize marketing resources to build consumer demand.

In 2001, the Council contracted the Florida Agricultural Market Research Center

of the Institute of Food and Agricultural Sciences at the University of Florida to design a

consumer survey. The survey sampled approximately 200 households in each of five

cities. Trained, professional interviewers conducted telephone interviews of the primary

food shopper in the household. Further analyses of the data collected in the survey

provide greater insight into factors contributing to the decision to purchase fresh sweet

corn or not; and the frequency of purchase in each season.

Using cross-sectional household data from this survey, probit estimates reveal

important factors influencing consumers' decisions to buy fresh sweet corn.









Additionally, ordered probit models are used to predict how a number of factors affect

the probability of increasing consumption of fresh sweet corn in each season.

These analyses serve to further the understanding of forces driving consumer

demand during Fresh Supersweet growers' time of production; and help the sweet corn

industry design market strategies to increase consumer demand for its product.
















CHAPTER 1
INTRODUCTION

There are three distinct markets for sweet corn in the United States canned,

frozen, and fresh. For the most part, these markets operate independently of each other.

The fresh market represents two-thirds of the total crop value for sweet corn. According

to the Economic Research Service of the U.S. Department of Agriculture, 246,900 acres

of fresh market sweet corn were harvested in the U.S. in 2000 (Lucier and Lin 2001).

Florida leads the nation in the production of fresh sweet corn. Figures from the

Florida Agricultural Statistics Service reveal that in 2000, Florida's sweet corn receipts

totaled over $121 Million (FASS 2002). Florida accounted for 22% of U.S. production

of fresh sweet corn during 1998-2000. The value of sweet corn produced in Georgia in

1999 reached almost $53 Million. Georgia's production represented 13% of U.S. fresh

sweet corn produced from 1998-2000 (Lucier and Lin 2001).



Others 37".

New York 11".:.

Georgia 3'.-.

California 17".

Florida

0% 5% 10% 15% 20% 25% 30% 35% 40%
Percent of Fresh Market Sweet Corn

Figure 1-1. Production of fresh market sweet corn, by state
Average fresh-market sweet corn production during 1998-2000. Based on
data from National Agricultural Statistics Srevice, USDA.











Members of the Fresh Supersweet Corn Council (FSCC), an organization of sweet


corn growers and shippers from Florida, Georgia, and Alabama, are the primary suppliers


of fresh sweet corn in the United States from late fall through winter until early July.


Fresh Supersweet corn growers are virtually the sole suppliers of fresh sweet corn


shipped east of the Mississippi River during the fall, winter, and spring seasons.


Most of Florida's sweet corn production (over 30,000 acres) takes place in South


Florida (IFAS 1999). Some is produced in Miami-Dade County, but the largest


production occurs in the Belle Glade area. These areas supply fresh sweet corn from fall,


through spring (until Memorial Day, in late May). Production then moves to areas of


northern Florida and into South Georgia and Alabama to supply fresh market sweet corn


from late May until early July.

Holme Gadsden Madison
S ta ackn / Jefferson / H n assau
Esca ia
Washington aohn
'aylo J/ \ f-Bradford
Fr n St Johns
Calhoun X achu utnam Flagler
Levy
Plantsd Plantsd | ,a, i
Area F 1a.e Lafayet Mai restt i lus Seminole
Area A e A rsilcrest
1996-97 1997-98 Sumter tr
West& North 4,100 3,100 He ando ran
e 1 S cPasco osceola Brevard
Central (North n, Jun 1
e9,150 9,400 Plnellas Polk Okeechobee
"en t l o lt hr n P d ` t'o Indian River
EveCglades Extesio SdHIebrUh nste
Co) 24 00 23050 Hilsboough Hgh
alm Beach C.).---- --- Manatee -- ae
Southwest 7,250 7,150 Sarasota Sto Martin
Southeart Gades
1uh 0- 1.-Charlotte Lee Hendry Pa
Planted Planted
State Acres Acres Broward
__ 1996-97 1997-98 Collier
ISep-DecE 7,600 I6,00 Dade
Jan ul 35800 35.800 Monroe



Figure 1-2. Sweet corn production areas in Florida
Based on data released by Florida Agricultural Statistics Service, June 1999
From: "Summary of Florida Corn Production," University of Florida
Cooperative Extension Service, Institute of Food and Agricultural Sciences.









Sixty percent of fresh market sweet corn in the U.S. is marketed from May to

August with the highest volume in July. Only about 10% of volume is marketed during

the winter months (January to March) (Lucier and Lin 2001). Peak shipments take place

to meet demand for the Memorial Day and the 4th of July holiday periods. During these

holiday times, sweet corn is in high demand and retailers promote the industry's product.

However, supersweet corn growers face a challenge in increasing year-round purchases

of their product.

Although the sweet corn industry has increased consumption of its product

through innovations like the introduction of supersweet varieties with a higher sugar

content and longer shelf life; and convenient tray-packed corn, several factors still limit

potential growth of the industry. According to a 1994-1996 USDA survey, 87% of fresh

sweet corn purchases are made at the retail level for home consumption (Lucier and Lin

2001). However, as of 1998, 38% of the consumer's food dollar was spent away from

home (ERS 2001). Further, between 1990 and 1998, real spending on food away from

home increased 24.8% whereas real spending on food at home increased just 4.7%

(Clausen 2000). The continuing trend of increased spending on food away from home

may have a significant adverse effect on future purchases of fresh sweet corn.

Other factors such as product proliferation and convenient ready-to-eat items in

supermarket produce sections and the sweet corn industry's inability to gain a substantial

share in the foodservice market means sweet corn producers may realize fewer purchase

opportunities and a shrinking share of the consumer's food dollar.

In addition to those concerns faced by the sweet corn industry as a whole, Fresh

Supersweet corn growers face a unique concern: significant seasonality in the demand









for fresh sweet corn during the time of year they are marketing their product. Fresh

Supersweet corn growers are seeking ways to better use marketing resources to build

consumer demand for their product. Understanding the forces influencing consumer

demand during their time of production will aid them in designing an effective marketing

strategy to expand sales of Fresh Supersweet corn.

Food Expenditures at 1988 Prices

Billion 1988 dollars
300 -
Food at home "

275


250


Food away from home
225 -


200 1 I I I I I I I I
1990 92 94 96 98

Figure 1-3. Food expenditures
Source: Clauson, Annette. "Spotlight on National Food Spending." Food
Review, Volume 23, Issue 3. Economic Research Service, USDA. 2000.

In an effort to more effectively use its resources to promote fresh sweet corn, the

Fresh Supersweet Corn Council needed information from sweet corn retailers and

consumers. In response to this need for information, the Florida Agricultural Market

Research Center (FAMRC) of the Food and Resource Economics Department at the

Institute of Food and Agricultural Sciences of the University of Florida designed

comprehensive consumer and retailer surveys. The consumer survey was designed to

investigate consumer preferences, attitudes, and behavior regarding the purchase and

consumption of fresh sweet corn.









Interviews with executives of 39 of the top 55 supermarket chains operating in the

central and eastern regions of the U.S. were conducted for the retailer survey. Senior

executives in charge of buying and merchandising produce were interviewed. The survey

concentrated on retailers' evaluations of:

The basic product
Shipping containers
Retailers' in-store merchandising and promotion practices
Factors affecting sweet corn advertising
Effectiveness of the Southern Supersweet identity (Degner et al. 2001).

The retailer survey produced many significant findings; however, the focus of this

research is the consumer survey.















CHAPTER 2
OBJECTIVES

The basic goal of the consumer survey was to gain a better understanding of how

consumer characteristics, buying habits, usage patterns, and perceptions of quality and

availability of sweet corn translate into consumer demand behavior. Using cross-

sectional household data, probit estimates are used to reveal important factors influencing

consumers' decisions to buy fresh sweet corn. The probit model analyzes purchasing

decisions for fresh sweet corn based upon consumer satisfaction with produce availability

and selected demographics. The demographics include city of residence, number of years

respondent has resided in the city, household size, the presence of children in the

household, education, age, gender, income, and race. The model allows for comparison

and ranking of factors positively or negatively affecting the purchase of fresh sweet corn.

The results identify marketing strategies to increase consumer demand for fresh sweet

corn.

To provide information about the existence and causes of seasonality in

consumption of fresh sweet corn, an ordered probit model is used to predict the

probability of increasing purchases of fresh sweet corn in each season. For each season

an ordered probit model models the frequency of purchase or the number of purchases

per month within the season. Variables included in this model are demographics,

consumer satisfaction with produce availability, overall satisfaction with sweet corn

purchased during the season, the most important reason the consumer buys fresh sweet






7


corn in that season, whether or not the consumer has received information about fresh

sweet corn, and sources of information.

This research provides information about factors influencing the probability of

consuming fresh sweet corn and the frequency of purchasing fresh sweet corn. These

results will help the sweet corn industry design market strategies to increase consumer

demand during the fall, winter, and spring seasons.















CHAPTER 3
METHODOLOGY

After meeting with several major sweet corn growers and shippers in Florida, a

consumer questionnaire was designed by the FAMRC in conjunction with the Florida

Survey Research Center (FSRC) and a representative of the Fresh Supersweet Corn

Council. The questionnaire was pre-tested by FSRC and was reviewed and approved by

the University of Florida's Institutional Review Board's Committee for the Protection of

Human Subjects.

This survey sampled approximately 200 households in each of five major market

areas where FSCC members' corn is shipped: Dallas, Atlanta, Chicago, Boston, and

Philadelphia. These cities provided for geographical dispersion as well as racial and

ethnic diversity in the sample. Additionally, samples contained diversity in terms of

education, age, income, and household size.

Table 3-1. Number of completed interviews, by city
City Number
Dallas 204
Atlanta 200
Chicago 201
Boston 224
Philadelphia 202

Telephone interviews of primary food shoppers were conducted by trained,

professional interviewers. A random digit dialing technique was used to generate

residential telephone numbers while avoiding difficulties associated with unlisted

numbers.









Consumer interviews took place between September 7 and November 3, 2001.

Interviewers attempted to contact each household at various times of the day for a

minimum of six times prior to selecting an alternative telephone number. Attempts were

made seven days a week at various times of the day (including early evenings) to avoid

over representation of non-working consumers. The average interview lasted

approximately ten minutes. Computer-assisted telephone interviewing was used to

ensure the immediate, computerized recording of responses. In addition, quality control

was exercised in the form of random monitoring of real-time interviews and call back

verification of ten percent of completed interviews (Degner et al. 2001).

Probit Model

Linear regression analysis is a statistical method commonly used by social science

researchers. This method, however, assumes a continuous dependent variable. Thus the

model proves inappropriate for the analysis of many behaviors or decisions measured in a

non-continuous manner (Liao 1994). The nature of many social phenomena is discrete

rather than continuous (Pampel 2000). For example, consumers decide whether or not to

purchase fresh sweet corn.

In cases such as these, the adoption of a different model specification is required.

One such alternative is probit analysis. The probit model is a probability model with two

categories in the dependent variable (Liao 1994). Probit analysis is based on the

cumulative normal probability distribution. The binary dependent variable, y, takes on

the values of zero and one. The outcomes of y are mutually exclusive and exhaustive.

The dependent variable, y, depends on K observable variables Xkwhere k=l, .. ,K

(Aldrich and Nelson 1984).









While the values of zero and one are observed for the dependent variable in the

probit model, there is a latent, unobserved continuous variable, y*.

Y* = f kXk + (3-1)
g is IN (0,02)

The dummy variable, y, is observed and is determined by y* as follows:
(3-2)
y= lify* > 0,
S0 otherwise
The point of interest relates to the probability that y equals one. From the above

equations, we see that:

Prob(y=l)= Prob ( k 3kXk+ > 0) (3-3)
=Prob ( >- k fx k )
=1-- (- k fkXk)

Where D is the cumulative distribution function of s (Liao 1994).

The probit model assumes that the data are generated from a random sample of

size N with a sample observation denoted by i, i = 1, ... ,N. Thus the observations ofy

must be statistically independent of each other. Additionally, the model assumes that the

independent variables (the responses to the consumer survey questions) are random

variables. There is no exact linear dependence among the Xik's. This implies that N > K,

that each Xk has some variation across observations (aside from the constant term), and

that no two or more Xk's are perfectly correlated.

The Maximum Likelihood Estimation (MLE) technique is used to estimate probit

parameters. Maximum Likelihood Estimation focuses on choosing parameter estimates

that give the highest probability or likelihood of obtaining the observed sample y. The









main principle of MLE is to choose as an estimate of 3 the set of K numbers that would

maximize the likelihood of having observed this particular y (Aldrich and Nelson 1984).

Ordered Probit Model

In some instances response categories are inherently ordered. The dependent

variable is discrete as well as ordinal. Under these circumstances, conventional

regression analysis is not appropriate. Instead, the ordered probit model may be used to

estimate such models where the dependent variable associated with more than two

outcomes is discrete and ordered (Borooah 2002).

The ordered probit model is a latent regression where

y*= I PkXk + (3-4)

Where y* is the unobserved latent index determined by observed factors (xs) and

unobserved factors (s) and ; is normally distributed.

y = if y* < t (= 0), (3-5)
y=2 if wi y =3 if 1t2

y=J if tj- i

Where y is observed in J ordered categories. The unknown threshold levels ([ts)

are to be estimated with the 3s. The probability that the observed y is in category j is

shown as follows:

Prob(y=J) = -- [([4j-1 i- kXk] (3-6)









The Prob(y = J) is obtained by taking the difference between two adjacent

cumulative probabilities (Liao 1994) with the exception of the first and last categories

where:

Prob(y<1) = Prob(y=l) and Prob(y
Specification of the Probit Model

Several demographic variables are included in the probit model: the respondents'

city of residence, level of education, income, race, gender, the number of years the

respondent had resided in the city, household size, the presence of children in the

household, and age. Additionally, the respondent's level of satisfaction with the

availability of fresh fruits and vegetables in the store where he or she shops most

frequently is included as an explanatory variable in the model.

The specification of the probit model is as follows.

y*ki = PkO + Pk1 citi + Pk2 cit2 + Pk3 cit3 + Pk4 cit4 + Pk5 edul + (3-8)

Pk6 edu2 + 3k7 incl + 3k8 rac + Pk9 rac2 + Pk10 genl + Pkll q24 +

Pk12 hwz + Pk13 chd + Pk14 agel + Pk15 age3 + 3k16 satl + Pk17 sat2

1 if respondent's household buys fresh sweet corn (3-9)
Y 0 if respondent's household does not buy fresh sweet corn

The probit model estimates the impact the independent variables have on

consumer behavior regarding the purchase of fresh sweet corn. The model also predicts

probabilities of change in consumer purchasing behavior under several simulated variable

levels.









Table 3-2. Probit model variables and descriptions
Variable Description a

citi Dallas
cit2 Atlanta
cit3 Chicago
cit4 Boston
cit5 Philadelphia
edul Education level of high school graduate or less
edu2 Technical/vocational school, some college, or college graduate
edu3 Graduate or professional school
incl Income under $35,000 per year
inc2 Income over $35,000 per year
racl Black
rac2 White
rac3 Other race
geni Male
gen2 Female
q24 Number of years respondent has lived in city of residence
hwz Household size
chd Presence of children in household
agel Less than 30 years of age
age2 30 to 55 years of age
age3 Over 55 years of age
satl Not at all satisfied with produce availability
sat2 Somewhat satisfied with produce availability
sat3 Very satisfied with produce availability
All variables except q24 and hwz are equal to one if respondent exhibits the
characteristic or are equal to zero otherwise.

Ordered Probit Model Specification

Ordered probit models are used to analyze purchasing behavior in the winter,

spring, summer, and fall seasons. For respondents buying fresh sweet corn in the season,

the model examines the effects of explanatory variables on the dependent variable, the

number of times per month the respondent purchases fresh sweet corn during the season.









There are four ordered categories for the dependent variable: one, two, three, or four or

more purchases per month within the season.

A number of demographic factors are included as explanatory variables in the

ordered probit models. These factors are the respondents' level of education, race,

gender, the number of years the respondent has resided in the city, household size, the

presence of children in the household, and age. The respondents' income level was

omitted in order to save degrees of freedom as numerous observations of this variable

were missing. Additionally, the respondent's level of satisfaction with the availability of

fresh produce at in the store where he or she shops most frequently is included as an

explanatory variable in the models.

Whether or not the respondent has ever received any information about the

availability, nutritional qualities, or cooking methods for fresh sweet corn is also included

as an explanatory variable in the ordered probit models. In addition, survey respondents

were asked whether or not they could recall seeing or hearing television commercials or

other television spots, radio commercials, magazine ads or magazine feature stories,

newspaper food-page stories, recipes, or newspaper ads about fresh sweet corn, and

posters in stores or sweet corn recipe cards, leaflets, or booklets in the past year.

The respondents' satisfaction with fresh sweet corn purchased within the season

and the most important reason why the consumer purchased fresh sweet corn in the

season were included as explanatory variables in the ordered probit models for the fall,

winter, and spring seasons. These variables, however, were not included in the ordered

probit model for the summer as they were not included as questions on the survey

instrument for the summer season.









Table 3-3. Ordered probit model variables and descriptions
Variable Description a
edul Education level of high school graduate or less
edu2 Technical/vocational school, some college, or college graduate
edu3 Graduate or professional school
racl Black
rac2 White
rac3 Other race
geni Male
gen2 Female
q24 Number of years respondent has lived in city of residence
hwz Household size
chd Presence of children in household
agel Less than 30 years of age
age2 30 to 55 years of age
age3 Over 55 years of age
satl Not at all satisfied with produce availability
sat2 Somewhat satisfied with produce availability
sat3 Very satisfied with produce availability
satf Satisfaction with fresh sweet corn purchased in the season
tv Respondent has seen/heard television commercials or other television spots
about fresh sweet corn in the past year
rd Respondent has heard radio commercials about fresh sweet corn in the past
year
mgz Respondent has seen magazine ads or magazine feature stories about fresh
sweet corn in the past year
nwp Respondent has seen newspaper food-page stories, recipes, or ads about fresh
sweet corn in the past year
psr Respondent has seen posters in stores or sweet corn recipe cards, leaflets, or
booklets in the past year
rsnl Good taste, freshness, or tenderness is the most important reason why
respondent has purchased fresh sweet corn in the season
rsn2 Health reasons are the most important reasons why respondent has purchased
fresh sweet corn in the season
rsn3 Habit is the most important reason why respondent has purchased fresh sweet
corn in the season
rsn4 All other reasons why respondent has purchased fresh sweet corn in the
season
inf Respondent has received information about the availability, nutritional
qualities, or cooking methods for fresh sweet corn
All variables except q24, hwz, and satf are equal to one if respondent exhibits the
characteristic or are equal to zero otherwise.








The ordered probit models for the fall, winter, and spring seasons are specified as

follows:

y*ki = PkO + Pkl edul + Pk2 edu2 + Pk3 racl + k4rac2 + (3-10)

Pk5 genl + Pk6 q24 + 3k7 hwz + 3k8 chd + Pk9 age3 + PklO sati +

3k 1 sat2 + Pk12 satf + Pk13 tv + Pk14 rd + Pk15 mgz + Pk16 nwp +

Pk17 psr + kl8 rsnl+ Pk19 rsn2+ Pk20 rsn3 + Pk2 inf

The ordered probit model for the summer season is specified below.

y*ki = kO + Pkl edul + Pk2 edu2 + Pk3 rac + Pk4 rac2 + (3-11)

Pk5 genl + Pk6 q24 + Pk7 hwz + Pk8 chd + Pk9 age3 + Pk10 sati +

Pkl1 sat2 + Pk12 tv + Pk13 rd + Pk14 mgz + Pk15 nwp + k16 psr + Pk17 inf
















CHAPTER 4
PROFIT RESULTS

The consumer survey revealed several important findings. About two-thirds of all

households were found to purchase fresh sweet corn at least one time per year.



100
90
80
70 *--
60
S50
40
30 62.2 66.8 73.6 63.8 72.3
20
10
0 -
Dallas Atlanta Chicago Boston Philadelphia

Sl Percent Buying Corn -*- Percent of Total

Figure 4-1. Households' purchase of sweet corn, by city

Survey results also revealed significant seasonality in the consumption of fresh

sweet corn. Virtually all (97.5 %) sweet corn consuming households purchased the

product during the summer while only 36.5 % of sweet corn consuming households

purchased during the winter months. In the spring 71 % purchased fresh sweet corn and

49.3 % of households purchased during the fall season.

Further analyses of data from the FAMRC's consumer survey provides greater

insight into factors contributing to the decision to purchase fresh sweet corn or not and

the intensity of purchase in each season.












100

80

60

~ 40

20

0
winter spring summer fall

Figure 4-2. Percent buying sweet corn by season, all respondents


Probit Estimates

Using the consumer survey data and maximum likelihood procedures, the probit

model was estimated. The parameter estimates, reported in Table 4-1, correspond to Pk

coefficients in Equation 3-8 and represent factors affecting consumers' decisions to

purchase fresh sweet corn. The R2 reveals that just over 11 % of consumers' decisions to

purchase fresh sweet corn are explained by the model.

The estimates show that several demographic factors have a statistically

significant impact on the consumption of fresh sweet corn. An income level of less than

$35,000 per year has a negative impact on the consumption of fresh sweet corn with a

coefficient of-0.2210. This relationship between income and the demand for fresh sweet

corn is consistent with economic theory and the demand for a normal good. Incl was

found to be significant at the 99% confidence level (t-value equal to -3.5745).









Being less than thirty years of age also has a significantly negative effect on the

purchase of fresh sweet corn at the 99% confidence level. Agel has a coefficient of

-0.4959 with a t-value of -3.7653.

Table 4-1. Probit model parameter estimates
Variable Parameter Estimate T-Value
intercept 0.1593 0.8985
citi -0.0113 -0.1069
cit2 0.0039 0.0345
cit3 0.1917 1.6468
cit4 -0.1150 -1.0961
edul -0.0294 -0.2937
edu2 -0.0743 -1.0095
incl -0.2210** -3.5745
raci 0.2661* 2.0978
rac2 0.19291 1.7419
geni -0.0351 -0.6177
q24 0.0040 1.0316
hwz 0.08121 1.9576
chd 0.2871 1.7825
agel -0.4959** -3.7653
age3 -0.0479 -0.2691
satl 0.0278 0.1535
sat2 -0.0606 -0.5774
Statistical significance levels are indicated as follows: f 10 percent
5 percent
** 1 percent

Survey respondents' race also appears to play a significant role in the purchase of

fresh sweet corn. Both black and white consumers are more likely to purchase fresh

sweet corn than the average consumer. Parameter estimates for black and white races are

0.2661 and 0.1929 respectively with t-values of 2.0978 and 1.7419.

Household size has a positive statistically significant impact on the decision to

buy fresh sweet corn at the 90% level with a coefficient of 0.0812 and t-value of 1.9576.

The presence of children in the household also has a statistically significant positive









effect on fresh sweet corn consumption, as is expected. The coefficient for presence of

children in the household is 0.2871 with a t-value of 1.7825.

Among the demographic factors that do not have a statistically significant impact

on the purchase of fresh sweet corn is the respondents' city of residence. The consumer

survey sample is comprised of respondents from Dallas, Atlanta, Chicago, Boston, and

Philadelphia. It is important to note that geographic region is not statistically significant

in terms of its impact on buying fresh sweet corn.

Probit Model Simulations

Probit models provide a means to examine the probability of certain events

occurring given a particular set of conditions or range of explanatory variables. The

estimated probit model is used to predict probabilities of change in consumer behavior

over a range of independent variable values (Verbeke, Ward, and Viaene 2000). The

impact individual explanatory variables have on the decision to purchase fresh sweet corn

is seen through probit model simulations. First, a base with a clearly defined set of

explanatory variables is established and applied to the estimated model. Changes in the

probability of consuming fresh sweet corn reveal factors affecting the demand for the

product.

Defining the Base

In order to examine changes in the probability of consuming fresh sweet corn

being equal to one, a base is set. The base fixes almost all the explanatory variables at

their average value. City of residence, level of education, income, race, gender,

satisfaction with produce availability, the number of years the respondent has lived in the

city, and household size, and presence of children are set at their average. The base value









for the age variable is age2 or 30 to 55 years of age. This allows for comparison of those

under 30 and those over 55 with the base value of 30 to 55 years old.

Using this base, the impact from changing each discrete variable value from zero

to one and adjusting each continuous variable (q24 and hwz), while holding all other

variables constant at their base value, is seen.

Results

Figures 4-3 through 4-11 illustrate the impact of the explanatory variables on the

probability of being a consumer of fresh sweet corn. Each figure compares the base

probability of 0.6878 with probabilities resulting from various simulations.

Although the respondents' city of residence is not a statistically significant factor

in the purchase of fresh sweet corn, Figure 4-3 reveals the probability of buying fresh

sweet corn for residents of each city. Respondents residing in Dallas and Atlanta have a

probability of consumption which is very close to the base. The probability of

consumption increases by about nine percent for respondents from Chicago, while

residents of Boston and Philadelphia have slightly lower probabilities of purchasing fresh

sweet corn.

Although education level is not a statistically significant variable, the simulation

results reveal the specific probabilities for each level of education. Figure 4-4 shows that

those with an education level of high school graduate or less (edul) or

technical/vocational school, some college, or college graduate (edu2) have a slightly

lower likelihood of buying fresh sweet corn. Respondents who have attended graduate or

professional school have a 5% higher probability of buying when compared to the base.











City of Residence


0.69 0.68 0.69


Figure 4-3. Probability


of consuming fresh sweet corn, by city of residence


Education


0.69


0.68


0.72


0.66


U I I I
base high school tech school, graduate or
grad or less some college, professional
or college grad school

Figure 4-4. Probability of consuming fresh sweet corn, by education level

Figure 4-5 illustrates that income level does have a substantial impact on the

consumption of fresh sweet corn. Survey respondents with a total annual household

income before taxes of less than $35,000 have an almost 12% lower probability of

purchasing fresh sweet corn. Those with income levels greater than $35,000 per year

increase their probability of consuming by over 10%.


0.75


0.65


0.66


Bzr











Income
1
0.9
0.8 00.76
0.7 0.61
0.6 -
0.5 -
S0.4
0.3
0.2
0.1
0

base income under income over $35,000
$35,000 per year per year

Figure 4-5. Probability of consuming fresh sweet corn, by income level


Race
1
0.9
0.8 0.78 0.75
0.69
0.7
0.6 0.51
0.5
0.4
0.3
0.2
0.1
0

base black white other races

Figure 4-6. Probability of consuming fresh sweet corn, by race

Black respondents (racl) as well as white respondents (rac2) have an increased

probability of consuming fresh sweet corn, as is revealed in Figure 4-6. Also of note is

that respondents of other races (rac3) have a much lower probability of purchasing fresh

sweet corn, over 25% below the base probability of consumption.











Gender
1
0.9
0.8
0 0.69 0.68 0.70
0.7
0.6
0.5
-,.o
0.4
0.3
0.2
0.1
0

base male female

Figure 4-7. Probability of consuming fresh sweet corn, by gender

Gender is not an important factor in the decision the purchase fresh sweet corn.

The probabilities of consuming fresh sweet corn of consuming fresh sweet corn for males

(genl) and females (gen2) are 0.68 and 0.70 respectively.


Household Size
1
0.9
0.8
0.7
0.6 -
S0.5
2 0.4
0.3
0.2
0.1

0 1 -7- -- -- -


Figure 4-8. Probability of consuming fresh sweet corn, by household size

As household size increases, so does the probability of purchasing fresh sweet

corn. This increase, however, tends to lessen as households get very large.










Presence of Children in Household


0.78


0.69


0.58


base children no children

Figure 4-9. Probability of consuming fresh sweet corn, by presence of children

Figure 4-9 reveals that whether or not children are present in the household is an

important component of the decision to purchase fresh sweet corn. The probability of

buying is 0.7813 for households with children. This probability is almost 14% higher

than the base. Households without children present have a probability of 0.5803. This is

over 15% lower than the base probability.


Age


0.69


0.67


0.50


Figure 4-10.


base under 30

Probability of consuming fresh sweet corn, by age


over 55










As seen in figure 4-10, respondents over 55 years of age (age3) exhibit a

probability of consumption that is very close to the base value in which the age level is

set at 30 to 55 years of age (age2). However, those respondents 18 to 30 years of age

(agel) have a probability of purchasing of 0.4975. This probability is almost 28% below

the base value.


Satisfaction with Produce Availability
1
0.9
0.8 0.69 0.70 0.67 0.70
0.7
0.6 -
0.5
-,.o
o 0.4
0.3
0.2
0.1
0
base not at all somewhat very satisfied
satisfied satisfied

Figure 4-11. Probability of consuming fresh sweet corn, by satisfaction with produce
availability

Satisfaction with produce availability does not appear to be an important aspect in

the purchase of fresh sweet corn. Respondents not at all satisfied with produce

availability have a 1.4% increase in the probability of buying fresh sweet corn when

compared to the base. Those who are somewhat satisfied with produce availability are

about 3% less likely to buy fresh sweet corn when compared to the base probability. And

respondents who are very satisfied with produce availability have a 1.7%higher

probability of consuming fresh sweet corn.

Figure 4-12 shows the ranking of factors impacting the probability of consuming fresh

sweet corn. The chart illustrates the effect of each individual discrete explanatory












variable assuming a value of one holding all other variables at their base value. The


changes in the probability of being a consumer of fresh sweet corn are ranked from the


most negative to the most positive effect.


agel
rac3
no chd
mcl
cit4
edu2
cit5
sat2
age3
geni
edul
citl
cit2
satl
sat3
gen2
edu3
cit3
rac2
mc2
rac
chd


-0.2




Figure 4-12.


-

-
U -


-



-
-I


-0.15 -0.1 -0.05 0 0.05 0.1

Changes in Probability


Ranking of factors impacting the probability of consuming fresh sweet corn


Agel, respondents being less than 30 years of age, has the largest negative effect.


An increase in marketing efforts focused on young consumers is advised. Rac3, or


respondents of races other than black or white, represents the second largest negative


effect. Additionally, the absence of children in the household and an income level of less


~







28


than $35,000 per year have substantial negative effects on the purchase of fresh sweet

corn.

The presence of children in the household is the demographic factor with the

greatest positive effect on buying fresh sweet corn. An income level of over $35,000 per

year as well as black and white race have strong positive effects on consumption as well.
















CHAPTER 5
ORDERED PROFIT RESULTS

Ordered Probit Parameter Estimates

Parameter estimates for each season's ordered probit model are shown in

Table 5-1. This table reveals that numerous explanatory variables have a statistically

significant impact on the frequency of consumption of fresh sweet corn. The table also

reveals that the impact of several of these factors varies by season.

Winter
During the winter months of January to March demographic factors do not have a

major impact on frequency of consumption. However, other explanatory variables have a

significant impact. Rsn3, or habit being the most important reason consumers purchase

fresh sweet corn in the season has a coefficient of 0.6699 and is statistically significant at

the 95% confidence level. Respondents citing good taste, freshness, or tenderness as the

most important reason why they purchase fresh sweet corn during the winter (rsnl) is

also significant at the 95% level with a coefficient of 0.5328.

Magazines are an important source of information about fresh sweet corn for

consumers during the winter months. This variable has a coefficient of 0.4918 and is

significant at the 95% level. Also of note is that respondents' satisfaction with fresh

sweet corn purchased during the winter is statistically significant at the 90% confidence

level.










Table 5-1. Parameter estimates by season
Variable Parameter Estimates by Season


edul
edu2
rac
rac2
gen
Q24
hwz
chd
age3
satl
sat2
satf
tv
rd
mgz
nwp
psr
rsnl
rsn2
rsn3
inf


Winter
0.0904
-0.0574
-0.0306
-0.1413
0.1495
-0.0033
0.0783
0.2416
-0.3103
0.3949
-0.2905
0.0884
-0.1057
-0.4373
0.4918*
-0.2455
0.0006
0.5328*
0.1815
0.6699*
-0.0091


Spring
0.1404
-0.0205
-0.0464
-0.1834
0.0714
-0.0044
0.0636
0.0847
0.3532
0.5011*
-.3981**
0.1581**
-0.3436*
-0.1485
0.2039
0.1689
0.0882
0.0586
-0.2656
0.2856
0.0700


Summer
0.1070
-0.0645
-0.0447
0.0854
-0.0036
-0.0005
-0.0064
0.3674**
0.3698*
-0.0838
-0.0055
N.A.
-0.1699
0.1290
0.0920
0.2274*
-0.0648
N.A.
N.A.
N.A.
0.0461


Fall
-0.0835
0.0030
-0.1718
-0.4246*
0.0342
-0.0067
0.0355
0.0948
0.2944
0.0783
-0.0836
0.1659**
-0.0896
-0.1310
0.2557
-0.0814
0.0332
0.1132
-0.3373
0.2486
-0.3849


Statistical significance levels are indicated as follows: f 10 percent
5 percent
** 1 percent

Spring

Several factors have a statistically significant impact on the frequency of purchase

during the spring. Significant demographic factors include household size and an age of

over 55 years. Both of these variables are significant at the 90% level.

Consumers being somewhat satisfied with overall produce availability has a

negative effect on the number of times per month consumers buy fresh sweet corn. This

effect is significant at the 99% confidence level. A significant positive effect results from

respondents being not at all satisfied with produce availability. The parameter estimate









for sati is 0.5011 and the variable is significant at the 95% level. These results reveal the

presence of the substitution effect. When consumers are not satisfied with produce

availability, they consume fresh sweet corn more frequently. When consumers are

somewhat satisfied with produce availability, they appear to substitute other forms of

produce for fresh sweet corn.

Consumers' satisfaction with fresh sweet corn purchased during the spring is an

important factor in the frequency of purchase and is significant at the 99% confidence

level with a t-value of 4.6664.

Summer

The presence of children in the household is a highly significant explanatory

variable in the summer season with an estimate of 0.3674 at the 99% confidence level.

Being above 55 years of age also has a positive effect on the frequency of consumption

during the summer. The parameter estimate for age3 is 0.3698 and is significant at the

95% level.

Respondents seeing newspaper food-page stories, recipes, or ads about fresh

sweet corn (nwp) is a statistically significant variable at the 95% confidence level with a

coefficient of 0.2274. Newspaper advertisements promoting the sale of fresh sweet corn

are more common during the summer months. Newspapers appear to be successful in

increasing consumers' frequency of purchasing fresh sweet corn during the summer.

Fall

The ordered probit model for the fall season reveals that rac2, or the white race,

has a negative effect on the frequency of purchase. Rac2 has a coefficient of -0.4246 and

is statistically significant at the 95% confidence level.









Sources of information about fresh sweet corn as well as reasons why consumers

purchase fresh sweet corn during the fall are not statistically significant. However,

satisfaction with fresh sweet corn purchased during the fall is significant at the 99% level

with a parameter estimate of 0.1659. Satisfaction with fresh sweet corn purchased during

the season is significant in all seasons in which the question was asked of respondents.

Ordered Probit Simulations

The ordered probit estimates are incorporated into several simulation analyses to

illustrate the effects of the explanatory variables on the frequency of purchase. (Medina

and Ward 1999) In order to observe the effects of the independent variables, a base is set

for each season's model.

Defining the Base

In the ordered probit models for winter, fall, and spring, the demographic

variables of education, race, gender, age, the number of years the respondent has lived in

the city, and household size are set at their average value. The base assumes there are no

children present in the household (chd=0). Satisfaction with produce availability,

satisfaction with fresh sweet corn purchased during the season, and respondents' main

reasons for purchasing fresh sweet corn in the season are each set at their average value.

The base presumes that respondents have not received information about the availability,

nutritional qualities, or cooking methods for fresh sweet corn (inf=0). In addition, the

values for each information source variable (tv, rd, mgz, nwp, and psr) are set at zero.

For the most part, the base values for simulations from the summer model are the

same as those for the other seasons. The demographic variables as well as satisfaction

with produce availability, whether or not the respondent has received information about









fresh sweet corn, and information sources are all set at the same values. However,

satisfaction with fresh sweet corn purchased in the season, and respondents' main reasons

for purchasing fresh sweet corn in the season were not included as variables in the

summer model.

Results

Figures 5-1 through 5-16 show the impact of the explanatory variables on the

probability of increasing fresh sweet corn purchases. The base probabilities for each

season are illustrated in Figure 5-1. The vertical axis reflects the probability of

consuming while the horizontal axis shows the number of times per month consumers

purchase fresh sweet corn (one, two, three, and four or more). The figure reveals that the

probabilities for the spring, summer, and fall seasons follow each other fairly closely. In

contrast, the pattern of probabilities during the winter months takes on a different shape.


Base Probabilities by Season

0.6

0.5 -

0.4 winter

I 0.3 spring
2o summer

0.1


1 2 3 4+
Fresh Sweet Corn Consumption, Times Per
Month

Figure 5-1. Ordered probit models base probabilities by season









The probability of increasing consumption from one to two times a month

increases in the spring, summer, and fall. The probability of purchasing three times per

month decreases for these three seasons. However, the probability of increasing

purchases to four times per month rises.

During the winter, the probability of buying fresh sweet corn just one time per

month (0.5509) is higher than it is during the other seasons. The probabilities of

purchasing sweet corn two, three, or four or more times per month are lower during the

winter than than they are during the spring, summer, and fall. The probability of buying

fresh sweet corn two times per month during the winter is 0.2542. The probability of

purchasing three times per month decreases further to 0.0962. The probability of buying

four or more times per month then increases slightly to 0.0988.

Winter

Figure 5-2 illustrates the base probabilities for the winter season and the

probabilities resulting from a simulation where respondents have seen magazine ads or

magazine feature stories about fresh sweet corn in the past year (mgz), all other variables

being held at their base value. Figure 5-3 shows the base probabilities for winter and the

probabilities from the simulation with good taste, freshness, or tenderness being the most

important reason consumers have purchased fresh sweet corn in the winter (rsnl). Figure

5-4 reveals probabilities from a simulation in which habit is the most important reason

respondents have purchased fresh sweet corn in the winter (rsn3), with all other variables

at their base.

These three figures show that the impact of each of these explanatory variables is

similar. When each of these variables is present, the probability of consuming just one









time per month decreases while the probabilities of purchasing sweet corn two, three, or

four or more times per month increase.


Magazines


- base
-- magazines


1 2 3 4+
Fresh Sweet Corn Consumption, Times Per
Month

Figure 5-2. Probabilities for base and magazines (mgz) in winter


Good Taste, Freshness, or Tenderness


- base
- reason 1


1 2 3 4+
Fresh Sweet Corn Consumption, Times Per Month

Figure 5-3. Probabilities for base and good taste, freshness, or tenderness (rsnl) in
winter


0.6
0.5
S0.4
c 0.3
& 0.2
0.1
0


0.6
0.5
S0.4
c 0.3
.0
0.2
0.1
0


















S-base
-- reason 3


1 2 3 4+
Fresh Sweet Corn Consumption, Times Per
Month


Figure 5-4. Probabilities for base and habit (rsn3) in winter


Satisfaction with Fresh Sweet Corn Purchased in
Winter


i.






1 2 3 4+
Fresh Sweet Corn Consumption, Times Per
Month


# satf at mean
--- satf = 0
satf= 10


Figure 5-5. Satisfaction level for fresh sweet corn purchased in winter

Figure 5-5 shows the probability of consuming fresh sweet corn one, two, three,

or four or more times per month during the winter given various levels of satisfaction


Habit


0.6
0.5
0.4
0.3
.0 3
2 0.2
0.1
0


0.9
0.8
0.7
S0.6
S 0.5
. 0.4
S0.3
0.2
0.1
0









with fresh sweet corn purchased in the season, holding all other variables at their base

value. As the satisfaction level increases, there is a corresponding shift in the

probabilities. As the level of satisfaction goes from zero (extremely dissatisfied) to ten

(extremely satisfied), the probability of buying fresh sweet corn once decreases while the

probabilities of buying two, three or four or more times per month increase. Figure 5-5

illustrates the impact of factors positively affecting the probabilities of increasing

purchases of fresh sweet corn. The probability of sweet corn consumers purchasing

sweet corn only one time per month tends to decrease, while the probability of increasing

the frequency of consumption rises.

Spring

In Figure 5-6, the base probabilities for the spring season are compared to the

probabilities resulting from a simulation in which consumers are not at all satisfied with

overall produce availability. The effects of this variable (satl) are a decrease in the

probability of buying fresh sweet corn one or two times per month, a small increase in the

probability of purchasing three times per month, and a large increase in the probability of

purchasing four times per month or more during the spring. When consumers are not at

all satisfied with overall produce availability, they tend to buy fresh sweet corn as a

substitute for those goods that are not available. Thus the probability of purchasing fresh

sweet corn more frequently during the spring rises.

Figure 5-7 shows that when consumers are somewhat satisfied with the overall

produce availability (sat2) in the spring, there is a shift in probabilities. Consumers are

more likely to purchase fresh sweet corn just one time per month, when compared to the

base probability. The probability of buying two times a month remains about the same









while consumers are less likely to purchase fresh sweet corn three or four or more times

per month. As consumers become more satisfied with the availability of other types of

produce, the probability of purchasing fresh sweet corn more frequently decreases and

sweet corn consumers have a higher probability of buying sweet corn one time per

month.


Not at All Satisfied with Produce Availability

0.4
0.35
0.3
S0.25 base
.0 base
S0.2
015 -u--sat 1
0" 0.15 ----
0.1
0.05
0
1 2 3 4+
Fresh Sweet Corn Consumption, Times Per Month

Figure 5-6. Probabilities for base and sat1 in spring


Somewhat Satisfied with Produce Availability

0.5

0.4

0.3 --- base

2 0.2 s___at2
0.1

0 ----I I
1 2 3 4+
Fresh Sweet Corn Consumption, Times Per Month

Figure 5-7. Probabilities for base and sat2 in spring











Satisfaction with Fresh Sweet Corn Purchased in
Spring
0.8
0.7 -
0.6
0.5
S-- satf at mean
0.4 -
0. satf = 0
0 0.3
a. 0.2 satf= 10
0.1
0
1 2 3 4+
Fresh Sweet Corn Consumption, Times Per
Month

Figure 5-8. Satisfaction level for fresh sweet corn purchased in spring

The probabilities of consuming fresh sweet corn one, two, three, or four or more

times per month during the spring given increasing levels of satisfaction with fresh sweet

corn purchased in the season, while holding all other variables at their base value, are

shown in Figure 5-8. As the satisfaction level rises, the probability of consuming fresh

sweet corn only once per month decreases, while consumers have a higher probability of

purchasing more frequently during the spring.

The effects of respondents seeing television commercials about fresh sweet corn

in the past year are shown in Figure 5-9. Having been exposed to television as an

information source about fresh sweet corn, the probability of consuming once per month

increases while the probability of buying two times per month remains almost the same.

The probabilities of increasing consumption to three or four or more times per month

decrease with exposure to television commercials. A negative effect on the probability of

increasing sweet corn consumption to three or four or more times per month with









exposure to television as an information source is not the expected result. Rather an

increase in the frequency of purchase is expected.


Television


0.5

0.4

0.3 --- base
0.2 --i -television

S0.
a 0 .1 -

0 I I I
1 2 3 4+
Fresh Sweet Corn Consumption, Times Per
Month

Figure 5-9. Probabilities for base and television (tv) in spring


Age over 55


I I


-- base
-- over 55


1 2 3 4+
Fresh Sweet Corn Consumption, Times Per
Month

Figure 5-10. Probabilities for base and over 55 years of age (age3) in spring


0.4

0.3

S0.2
0.
a-
o
a- 0.1

0









Figure 5-10 shows the probability levels for respondents over 55 years of age.

When compared to the base, the simulated values for the probabilities of consuming one

or two times per month are lower while the probabilities of buying fresh sweet corn three

or four or more times per month are higher.


Household Size


0.4

0.3
.- hwz at mean
o 0.2 --m-- hwz = 1
I hwz = 5
a- 0.1



1 2 3 4+
Fresh Sweet Corn Consumption, Times
Per Month
Figure 5-11. Probabilities for base and household size in spring

Figure 5-11 shows the probabilities of buying fresh sweet corn one, two, three, or

four or more times per month during the spring given different household sizes. As

household size increases from one to its mean (2.8035) and then to five, consumers are

less likely to purchase sweet corn just once per month. As household size increases,

consumers become more likely to buy sweet corn four or more times per month during

the spring.

Summer

In Figure 5-12, the simulated probabilities for households where children are

present are compared to the base probabilities where there are no children present in the

household. The simulated probabilities for respondents over 55 years of age are shown in









Figure 5-13. Both of these variables (chd and age3) have the same effect on the

probabilities of consumption. The probabilities of buying fresh sweet corn one or two

times per month are lower than the corresponding base probabilities, while the

probability of buying three times per month remains about the same. However, the

probability of increasing the frequency of purchase to four or more times per month

during the summer increases sharply for both simulations.


Presence of Children in Household


0.5
0.4
0.3 -.-base
0.2 b children
S0.1 -

0 I I I
1 2 3 4+
Fresh Sweet Corn Consumption, Times Per
Month

Figure 5-12. Probabilities for base and presence of children in household (chd) in
summer

Figure 5-14 shows the change in probabilities when respondents have seen

newspaper food-page stories, recipes, or ads about fresh sweet corn in the past year

(nwp). This variable has the effect of lowering the probabilities of purchasing fresh

sweet corn one or two times per month while increasing the probability of buying four or

more times per month during the summer. Thus as consumers are exposed to newspaper

information about fresh sweet corn, they tend to increase the frequency at which they buy

sweet corn to four or more times per month.










Age over 55


- base
- over 55


4+


Fresh Sweet Corn Consumption, Times Per
Month


Figure 5-13. Probabilities for base and over 55 years of age (age3) in summer


Newspapers


-*-base
--newspapers


Fresh Sweet Corn Consumption, Times
Per Month


Figure 5-14. Probabilities for base and newspapers (nwp) in summer


0.5

0.4

0.3

0.2

0.1


'p


0.4

0.3

0.2

0.1


or









Fall

Figure 5-15 illustrates the simulated probabilities for rac2, or the white race, a

statistically significant variable from the fall ordered probit model. This simulation

reveals that white consumers have a higher probability of consuming one time per month

when compared to the base. The probability of purchasing fresh sweet corn twice a

month remains about the same and the probabilities of buying three or four or more times

per month during the fall decrease.


White

0.5
0.4

0 -- base
-0 0.2" -- white

0.1-
0
1 2 3 4+
Fresh Sweet Corn Consumption, Times Per
Month

Figure 5-15. Probabilities for base and white race (rac2) in fall

Figure 5-16 shows that the effect of increased satisfaction with fresh sweet corn

purchased in the fall is similar to the result of an increased satisfaction level in the winter

and spring seasons. The probability of purchasing one time time per month decreases

sharply while the probabilities of buying two or three times per month increase. As

satisfaction level increases from zero to ten, the probability of buying fresh sweet corn

four or more times per month in the fall increases substantially.










Satisfaction with Fresh Sweet Corn Purchased in
Fall
0.8
0.7
0.6 '\
S0.5 satf at mean
0.4 -
.M 0. satf = 0
o 0.3 .--
a 0.2 satf= 10
0.1
0
1 2 3 4+
Fresh Sweet Corn Consumption, Times
Per Month

Figure 5-16. Satisfaction level for fresh sweet corn purchased in fall
















CHAPTER 6
SUMMARY AND CONCLUSIONS

The purpose of this study is to provide information about factors influencing the

probability of being a consumer of fresh sweet corn and factors positively or negatively

affecting consumers' frequency of purchase in each season. Results are intended to assist

the sweet corn industry in developing market strategies to increase consumer demand for

its product.

In order to achieve these objectives, a probit model and an ordered probit model

for each of the four seasons were estimated. Subsequently, simulations were used to

predict probabilities of change in consumer behavior over a range of explanatory variable

values.

Using maximum likelihood procedures, probit model parameter estimates

revealed several variables significantly affecting consumers' decisions to purchase fresh

sweet corn. An income level of below $35,000 per year and an age of less than thirty

have highly significant negative effects on purchasing fresh sweet corn. Increased

marketing efforts targeting young consumers have the potential to attract many new

consumers under 30 years of age. Increasing the proportion of young shoppers buying

sweet corn is an essential component of building demand for fresh sweet corn and

sustaining future sales.

Probit model simulations revealed that, in addition to an income level under

$35,000 per year and an age of less than thirty years, races other than black and white and









the absence of children in the household had substantial negative effects on the

probability of buying fresh sweet corn. The simulations also showed that households

with children present, the black and white races, and household with an income level

above $35,000 per year exhibited the highest probabilities of being consumers of fresh

sweet corn.

Increased efforts to build demand for fresh sweet corn among shoppers of races

other than black or white could yield positive results. However, these results may be

limited. The United States Census 2000 reported that almost 90 % of respondents were

of the white or black races (Grieco and Cassidy 2001). For this reason, marketing dollars

may be more effectively allocated elsewhere.

Parameter estimates from the ordered probit model for each season revealed

significant reasons for purchase as well as demographic, satisfaction, and information

variables. Consumers' satisfaction level with fresh sweet corn purchased in the season

proved to be a significant factor across seasons. Magazines were shown to have a

significant impact on increasing consumption during the winter while newspapers had a

significant positive effect on the frequency of consumption in the summer. An increased

use of these forms of print media would prove to be an effective market strategy to

increase consumers' frequency of purchasing fresh sweet corn.

Ordered probit estimates were incorporated into simulation analyses in order to

illustrate the effects of explanatory variables on the frequency of purchase in each season.

Comparison of the base probabilities for each season exposed the difference in frequency

of buying fresh sweet corn in the winter when compared to the spring, summer, and fall









seasons. Simulations then showed how individual variables positively or negatively

impacted consumers' intensity of consumption.

Consumer survey results revealed significant seasonality in the purchase of fresh

sweet corn. Sweet corn consumers were more likely to purchase the product in the

summer than in other seasons and had a higher probability of purchasing more frequently

during the summer months.

Respondents who purchased fresh sweet corn sometime during the year but did

not buy during the winter, spring, or fall were asked for the main reason why they did not

purchase in the season. Almost 70 % of winter non-buyers, 57 % of spring non-buyers,

and 63 % of fall non-buyers believed fresh sweet corn was not available during these

times (Degner et al. 2001).

The potential exists to greatly increase the demand for fresh sweet corn in the

winter, spring, and fall seasons. In order to take advantage of this sizable potential,

promotional efforts must focus on making consumers aware of the availability of Fresh

Supersweet corn during the winter, spring, and fall.

In order to build demand for their product, Fresh Supersweet corn growers must

have a better understanding of factors impacting consumption during their production

season. The findings of this research provide a means for the sweet corn industry to

better target its resources to expand sales of Fresh Supersweet corn.















APPENDIX A
CONSUMER SURVEY INSTRUMENT

Florida Agricultural Market Research Center
Institute of Food and Agricultural Sciences
University of Florida

Consumer Questionnaire a

Hello, my name is %name and I am calling you from the Florida Survey Research Center
at the University of Florida. In cooperation with vegetable farmers, we are conducting a
survey about fresh fruits and vegetables. This is not a sales call. Your opinions are
important to our farmers, and your identity and comments will remain confidential. This
should only take about 8 minutes.

May I please speak to the person in your household who is 18 years of age or older who
buys most of the fresh fruits and vegetables for your household?

%start

1. City (Code from call sheet, DO NOT ASK)
[single
Dallas=l
Atlanta=2
Chicago=3
Boston=4
Philadelphia=5]

2. How satisfied are you with the availability of fresh fruits and vegetables in the store
where you shop most frequently? Would you say that you are very satisfied, somewhat
satisfied, or not at all satisfied?
[single
Very satisfied=3
Somewhat satisfied=2
Not at all satisfied=1
Don't know=8
Refused=9]

%line









Next, we'd like to ask you some questions about fresh sweet corn on the cob. For the
remainder of the survey questions, please think only about fresh sweet corn on the cob,
NOT canned or frozen corn.
%line

3. Does your household ever buy fresh sweet corn on the cob?
[YNDR1289]

%if Q3=2
3A. What are the most important reasons why you never buy fresh sweet corn on the cob
(DO NOT READ LIST -- Probe for three responses, if possible -- Ask, "are there any
other reasons")?
[multipleyndrl289
Do not like taste
Price too high
Not fresh enough
Texture, starchy, tough
Short life (goes bad before using)
Health (allergies, indigestion, etc.)
Health/Diet (too many calories)
Size of package too large
Damaged or wormy
Takes too much time to prepare
Too messy
Other %comment30]
%endif

%ifQ3=1
4. Most people prefer certain varieties of fruits and vegetables. For example, "red
delicious" or "granny smith" apples. Is there any particular variety of fresh sweet corn on
the cob that you prefer to buy?
[YNDR1289]

%ifQ4=1
4A. What variety of fresh sweet corn is that?
[single
Silver Queen=l
Southern Supersweet=2
Kandy Korn=3
Florida Staysweet=4
Sugar Buns=5
Honey Sweet=6
Snogold=7
Other=8]


%if Q4A=8









4AOth. Other:
[text,25]
%endif
%endif

4B. If you had a choice of yellow, white, or bicolor (mixed white and yellow kernels)
fresh sweet corn, which would you be most likely to buy?
[single
Yellow=1
White=2
Bicolor=3
No Preference=4
Don't know=8
Refused=9]

%ifQ4B=l or Q4B=2 or Q4B=3
4C. Why do you prefer that type of fresh sweet corn? (MARK ALL THAT APPLY)
[multipleyndrl289
Tastes Better
Color is more appealing
Fresher
Lower price
Habit
Lower in calories
More nutritious
More tender
Better in recipes
Ads are more appealing
Only type available
Smells better
Sweeter
Don't know
Other %comment25]
%endif

%line
Next, we'd like to ask you some questions about when you purchase fresh sweet corn on
the cob.
%line

5. Do you ever buy fresh sweet corn on the cob in the winter (January through March)?
[YNDR1289]
%ifQ5=1

5A. About how many times per month would you say that you buy fresh sweet corn on
the cob during the winter?









[numdr89,2,1-31]

5B. In general, using a rating scale where 10 = extremely satisfied and 0 = extremely
dissatisfied, how would you rate your overall satisfaction with the fresh sweet corn on the
cob you have purchased in the winter months?
[numdr89,2,0-10]

5C. What is the single most important reason why you buy fresh sweet corn on the cob
during the winter months (DO NOT READ LIST)?
[single
Good taste=
Appealing color=2
Freshness=3
Low price=4
Habit=5
Health reasons (low in calories, nutritious)=6
Tender, not dry or starchy=7
Essential in recipes, menus=8
Advertisements=9
Only type of vegetable available=10
Good smell= 1
Adds variety=12
Other=13
Don't know= 14
Refused=15]
%ifQ5C=13
5COth. Other:
[text,30]
%endif
%endif

%if Q5=2
5D. What is the main reason why you don't buy fresh sweet corn on the cob in the
winter?
[single
Not available
Not local=2
Do not like taste=3
Price too high=4
Not fresh enough=5
Texture, starchy, tough=6
Short life (goes bad before using)=7
Health, diet related=8
Size of package too large=9
Damaged or wormy=10
Too much time to prepare= 1









Too messy=12
Don't know= 13
Refused=14]
%endif

6. Do you ever buy fresh sweet corn on the cob in the Spring (April through June)?
[YNDR1289]
%ifQ6=1

6A. About how many times per month would you say that you buy fresh sweet corn on
the cob during the spring?
[numdr89,2,1-31]

6B. In general, using a rating scale where 10 = extremely satisfied and 0 = extremely
dissatisfied, how would you rate your overall satisfaction with the fresh sweet corn on the
cob you have purchased in the spring months?
[numdr89,2,0-10]

6C. What is the single most important reason why you buy fresh sweet corn on the cob
during the spring months (DO NOT READ LIST)?
[single
Good taste=
Appealing color=2
Freshness=3
Low price=4
Habit=5
Health reasons (low in calories, nutritious)=6
Tender, not dry or starchy=7
Essential in recipes, menus=8
Advertisements=9
Only type of vegetable available=10
Good smell= 1
Adds variety=12
Other=13
Don't know= 14
Refused=15]

%ifQ6C=13
6COth. Other:
[text,30]
%endif
%endif

%if Q6=2
6D. What is the main reason why you don't buy fresh sweet corn on the cob in the spring?
[single









Not available
Not local=2
Do not like taste=3
Price too high=4
Not fresh enough=5
Texture, starchy, tough=6
Short life (goes bad before using)=7
Health, diet related=8
Size of package too large=9
Damaged or wormy=10
Too much time to prepare= 1
Too messy=12
Don't know= 13
Refused=14]
%endif

7. Do you ever buy fresh sweet corn on the cob in the Summer (July through September)?
[YNDR1289]
%ifQ7=1

7A. About how many times per month would you say that you buy fresh sweet corn on
the cob during the summer?
[numdr89,2,1-31]
%endif

%if Q7=2
7B. What is the main reason why you don't buy fresh sweet corn on the cob in the
summer?
[single
Not available
Not local=2
Do not like taste=3
Price too high=4
Not fresh enough=5
Texture, starchy, tough=6
Short life (goes bad before using)=7
Health, diet related=8
Size of package too large=9
Damaged or wormy=10
Too much time to prepare= 1
Too messy=12
Don't know= 13
Refused=14]
%endif

8. Do you ever buy fresh sweet corn on the cob in the Fall (October through December)?









[YNDR1289]
%ifQ8=1

8A. About how many times per month would you say that you buy fresh sweet corn on
the cob during the fall?
[numdr89,2,1-31]

8B. In general, using a rating scale where 10 = extremely satisfied and 0 = extremely
dissatisfied, how would you rate your overall satisfaction with the fresh sweet corn on the
cob you have purchased in the fall months?
[numdr89,2,0-10]

8C. What is the single most important reason why you buy fresh sweet corn on the cob
during the fall months (DO NOT READ LIST)?
[single
Good taste=
Appealing color=2
Freshness=3
Low price=4
Habit=5
Health reasons (low in calories, nutritious)=6
Tender, not dry or starchy=7
Essential in recipes, menus=8
Advertisements=9
Only type of vegetable available=10
Good smell=l
Adds variety=12
Other=13
Don't know= 14
Refused=15]
%ifQ8C=13
8COth. Other:
[text,30]
%endif
%endif

%if Q8=2
8D. What is the main reason why you don't buy fresh sweet corn on the cob in the fall?
[single
Not available
Not local=2
Do not like taste=3
Price too high=4
Not fresh enough=5
Texture, starchy, tough=6
Short life (goes bad before using)=7









Health, diet related=8
Size of package too large=9
Damaged or wormy=10
Too much time to prepare= 1
Too messy=12
Don't know= 13
Refused=14]
%endif

%line
Now, we would like to ask you about your experiences with purchasing fresh sweet corn
on the cob.
%line

9. In what type of retail outlet do you usually buy fresh sweet corn on the cob?
[single
Superstore (very large supermarket, lots of nonfood items)=l
Discount Club=2
Supermarket=3
Small Grocery Store=4
Produce Specialty Store=5
Roadside Stand=6
Other=7
Don't know=8
Refused=9]

%if Q9=7
9A. Other:
[text,25]
%endif

10. If you had a choice of only one type of packaging, which of the following would you
select when shopping for fresh sweet corn on the cob?
[single
Unpackaged, in the husk, loose=1
Prepackaged, partially shucked=2
Prepackaged, completely shucked=3
Don't know=8
Refused=9]

%if Q10=1
10A. Would you prefer to shuck fresh sweet corn in the store or at home?
[single
In the store=
At Home=2
Don't know=8









Refused=9]
%endif

11. Imagine you are shopping for fresh sweet corn on the cob in your usual retail outlet
and you have a choice of corn displayed on a refrigerated produce rack or an
unrefrigerated table or display. Would you be more likely to select corn from the
refrigerated or unrefrigerated display?
[single
Refrigerated=1
Unrefrigerated=2
Don't Know=8
Refused=9]

12. In you opinion, what is a fair price per ear of fresh sweet corn on the cob?
[numdr89,3]

13. On average, about how many individual ears of fresh sweet corn do you buy each
time you purchase corn on the cob?
[numdr89,2]

%line
Next, we have a few questions about your use of fresh sweet corn on the cob at home.
%line

14. Do you usually use fresh sweet corn on the same day that you purchase it?
[YNDR1289]

%if Q14=2
14A. On average, how many days do you usually keep fresh sweet corn before you use
it?
[numdr89,1]

14B. Where do you usually store fresh sweet corn at home? Do you store it in the
refrigerator, in the freezer, or outside the refrigerator?
[single
In Refrigerator= 1
In Freezer=2
Outside the Refrigerator=3
Don't know=8
Refused=9]

14C. And, do you usually store fresh sweet corn shucked or unshucked?
[single
Shucked=1
Unshucked=2
Don't know=8









Refused=9]
%endif

%line
Now, I have a few questions about how you prepare and serve fresh sweet corn in your
household.
%line

15. When the weather is nice, say 50 degrees or warmer and not raining, how do you
usually prepare fresh sweet corn on the cob? (CHECK ALL THAT APPLY)
[multipleyndrl289
Outdoor Grill
Indoor Grill
Raw
Microwave
Boiled (If yes, how many minutes?) comment2
Baked
Fried
Do not prepare in Good Weather
Other %comment20]

16. When the weather is "bad," say colder than 50 degrees or raining or snowing, how do
you usually prepare fresh sweet corn on the cob? (CHECK ALL THAT APPLY)
[multipleyndrl289
Outdoor Grill
Indoor Grill
Raw
Microwave
Boiled (If yes, how many minutes?) comment2
Baked
Fried
Don't Prepare in Bad Weather
Other %comment20]

17. Do you typically serve fresh sweet corn ON the cob, or do you remove it from the cob
before serving it?
[single
On the cob=l
Off the cob=2
Don't know=8
Refused=9]

%if Q17=2
17A. Why do you remove the corn from the cob before serving it? (MARK ALL THAT
APPLY)
[multipleyndrl289









Less messy
Easier to eat (teeth)
Necessary for recipe
Other %comment20
Don't know]
%endif

18. How do you use fresh sweet corn in a meal? (READ LIST, MARK ALL THAT
APPLY)
[multipleyndrl289
In a main dish
As a side dish
In a salad
In salsa
Other %comment30]

19. Do you ever serve fresh sweet corn with meat?
[YNDR1289]

%ifQ19=1
19A. What types of meat do you usually serve with fresh sweet corn?
[textdr89,30]
%endif

20. Do you ever serve fresh sweet corn with other vegetables?
[YNDR1289]

%if Q20=1
20A. What other types of vegetables do you usually serve with fresh sweet corn?
[textdr89,30]
%endif

21. Are there any other foods that you typically serve with fresh sweet corn?
[YNDR1289]

%if Q21=1
21A. What foods are those?
[textdr89,30]
%endif
%line
Next, we would like to ask you about where you get information about corn.
%line

22. Have you ever received any information about the availability, nutritional qualities, or
cooking methods for fresh sweet corn on the cob?
[YNDR1289]










%ifQ22=1
22A. I will read you a list of information sources. For each, please tell me whether or not
you have received any information about fresh sweet corn from them.
[multipleyndrl289
Family Member
Friend
Newspaper Article
Magazine Article
TV Food Shows
Extension Service
Grocer
Farmer
Cookbook
Trade Association
Internet
Home Economics Class
Other %comment25]
%endif

23. Now, I will read you a list of TYPES of information. For each, please tell me if you
can recall seeing or hearing information of this type about fresh sweet corn in the past
year.
[multipleyndrl289
TV Commercials
Other TV spots, like Cooking shows or news stories
Magazine Advertisements
Magazine Feature Stories
Newspaper Food-Page Stories or Recipes
Newspaper Food-Page Advertisements
Radio Commercials
Posters in Stores
Sweet Corn Recipe Cards, Leaflets, or Booklets
Internet Web Site]
%endif

%continue

%line
Finally, I just have a few demographic questions for statistical purposes.
%line

24. How many years have you lived in the greater (INSERT CITY NAME) area?
[numdr89,3]

25. Including yourself, how many adults age 18 or older live in your household?









[numdr89,2,1-10]
%if Q25<>1
25A. How many children under age 18 live in your household?
[numdr89,2,0-15]
%endif

26. What is the highest level of education that you have completed?
[single
8th grade or less=1
Some high school=2
High school graduate=3
Technical / Vocational School=4
Some College=5
College graduate=6
Graduate or Professional School=7
Refused=9]

27. In what year were you born?
[numdr89,4,1880-1983]

28. Just for statistical purposes, can you tell me if your family's total yearly income
before taxes is less than $35,000 or more than $35,000?
[single
Less than $35,000=1
More than $35,000=2
Don't know=8
Refused=9]

%ifQ28=1
28A. And, is that:
[single
Under $20,000=3
$20,000 to $34,999=4
Don't know=8
Refused=9]
%endif

%if Q28=2
28B. And, is that:
[single
$35,000 to $49,999=5
$50,000 to $69,999=6
$70,000 or more=7
Don't know=8
Refused=9]
%endif









29. And, just to make sure that we have a representative sample, would you please tell me
your race?
[single
Black / African American=1
White, non-Hispanic=2
Asian=3
American Indian / Aleut=4
Other=5
Refused=9]

%ifQ29=1 or q29=2 or q29=5
29A. And, would you say that you are of Hispanic ancestry or not?
[YNDR1289]
%endif

30. Do you have access to the Internet at home?
[YNDR1289]

31. Do you have access to the Internet at work?
[YNDR1289]

32. Gender (DON'T ASK, JUST RECORD -- IF UNKNOWN, "I know this sounds silly,
but I have to ask, are you male or female?)
[single
Male=l
Female=2]

%line
That completes our survey. Thank you very much for your time. Have a nice evening
(day).

a This Questionnaire format was designed to facilitate Computer Assisted Telephone
Interviewing (CATI) and statistical analysis.















APPENDIX B
TIME SERIES PROCESSOR PROGRAMS

OPTIONS MEMORY=50;
OPTIONS LIMWARN=1;
Title 'Probit Analysis for Sweet Corn Amanda Briggs';
? scorn#1.tsp;

in 'd:\abriggs\corndat';
? in 'd:\zstudent\abriggs\corndat';

? read(format=excel,file='d:\zstudent\abriggs\amanbas.xls');
? out 'd:\zstudent\abriggs\corndat';

? doc id 'household identification';
? doc interv 'interview';
? doc ql 'city 1 to 5';
? doc q2 'satisfaction w/ produce availability 1 to 3';
? doc q3 'buyer l=yes 2=no';

? doc q5 'buy in winter l=yes 2=no';
? doc q5a 'times/month winter 1 to 31';
? doc q5b 'satisfaction winter 0 to 10';
? doc q5c 'reason buy winter 1 to 15';
? doc q5d 'reason don't buy winter 1 to 14';
? doc q6 'buy in spring l=yes 2=no';
? doc q6a 'times/month spring 1 to 31';
? doc q6b 'satisfaction spring 0 to 10';
? doc q6c 'reason buy spring 1 to 15';
? doc q6d 'reason don't buy spring 1 to 14';
? doc q7 'buy in summer l=yes 2=no';
? doc q7a 'times/month summer 1 to 31';
? doc q7b 'reason don't buy summer 1 to 14';
? doc q8 'buy in fall l=yes 2=no';
? doc q8a 'times/month fall 1 to 31';
? doc q8b 'satisfaction fall 0 to 10';
? doc q8c reason buy fall 1 to 15';
? doc q8d 'reason don't buy fall -1 to 14';
? doc q13 '# ears per purchase 1 to 30';

? doc q22 'information l=yes 2=no';

? doc q22a 'info source 1 to 13';
? doc q22al 'family l=yes 2=no';
? doc q22a2 'friend l=yes 2=no';
? doc q22a3 'newspaper l=yes 2=no';
? doc q22a4 'magazine l=yes 2=no';
? doc q22a5 'tV l=yes 2=no';
? doc q22a6 'extension l=yes 2=no';
? doc q22a7 'grocer l=yes 2=no';
? doc q22a8 'farmer l=yes 2=no';
? doc q22a9 'cookbook l=yes 2=no';
? doc q22a10 'trade assoc l=yes 2=no';
? doc q22all 'internet l=yes 2=no';
? doc q22a12 'home ec l=yes 2=no';
? doc q22a13 'other l=yes 2=no';

? doc q23 'info type 1 to 10';
? doc q23a 'tV commercials l=yes 2=no';







64


? doc q23b 'other TV l=yes 2=no';
? doc q23c 'magazine ad l=yes 2=no';
? doc q23d 'mag story l=yes 2=no';
? doc q23e 'newspaper story l=yes 2=no';
? doc q23f 'newspaper ads l=yes 2=no';
? doc q23g 'radio l=yes 2=no';
? doc q23h 'posters l=yes 2=no';
? doc q23i 'recipe cards l=yes 2=no';
? doc q23j 'web site l=yes 2=no';

? doc q24 'years in city 1 to 100';
? doc q25 '# adults in household 1 to 10';
? doc q25a '#children in household 0 to 10';
? doc q26 'education 1 to 7';
? doc q27 'year of birth 1900 to 1983';
? doc q28 'income >or< $35,000 1 to 2';
? doc q28a 'income 3 to 4';
? doc q28b 'income 5 to 7';
? doc q29 'race 1 to 5';
? doc q29a 'hispanic or not l=yes 2=no';
? doc q30 'internet access at home l=yes 2=no';
? doc q31 'internet access at work l=yes 2=no';
? doc q32 'gender l=male 2=female';
? doc idu 'identication';
? out;
? dblist(date,doc) 'd:\zstudent\abriggs\corndat';

dummy(prefix=buy) q3; ? buyer of sweet corn buyl=yes buy2=no;

? Creating total household size;
hwz= q25 + q25a;
hist(discrete) hwz;

? Presence of children;
chd = (q25a>0);
hist(discrete) chd;

hist(discrete) q3;
dot 5-8;
hist(discrete) q.;
enddot;

hist(discrete) q13; ? number of ears per purchase;

? number of times per month during each season with 5=winter, 6=spring,
7=summer, 8=fall;

dot 5-8;
select 1;
dd = (q.a>=20);
select dd^=l; qq.a=q.a;
select 1;
hist(discrete) qq.a;
tq.a=(qq.a=l) + (qq.a=2)*2 + (qq.a=3)*3 + (qq.a>=4)*4;
dummy(prefix=tmq.) tq.a;
select 1;
enddot;

dot 5-8;
hist(discrete) tmq.l-tmq.4;
enddot;












? making dummy variables for the demographics
?=======================================-- - - - - - -

? city=l Dallas, city=2 Atlanta, city=3 Chicago, city=4 Boston, city=5
Philadelphia;
dummy(prefix=cit) ql;

? edu=l 8th grade, edu=2 some hs, edu=3 hs grad, edu=4 tech schl, edu=5
some college,
? edu=6 college grad, edu=7 grad/prof;
xedu=q26;
xedu= (xedu<=3)*l + (xedu>=4 & xedu<=6)*2 + (xedu>=7)*3;
hist(discrete) xedu;
dummy(prefix=edu) xedu;

? inc=l under $35,000, inc=2 over $35,000;
dummy(prefix=inc) q28;

? race=l black, race=2 white, race=>3 all other;
dummy(prefix=rac) q29;

? hispanic=l yes, hispanic=2 no;
dummy(prefix=his) q29a;

? nethome=l yes, nethome=2 no;
dummy(prefix=neth) q30;

? network=l yes, network=2 no;
dummy(prefix=netw) q31;

? gender=l male, gender=2 female;
dummy(prefix=gen) q32;
?=======================================-- - - - - - -
? making dummy variables
?=--------------------------------------------------

? satisfaction w/ produce availability;
? sat=l not at all satisfied, sat=2 somewhat, sat=3 very;
dummy(prefix=sat) q2;

? buyer in winter;
? buyw=l yes, buyw=2 no;
dummy(prefix=buyw) q5;

? most imp reason buy in winter;
? rsnw=l taste, rsnw=2 color, rsnw=3 fresh, rsnw=4 price, rsnw=5 habit,
rsnw=6 health, rsnw=7 tender;
? rsnw=8 recipes, rsnw=9 ads, rsnw=10 avail, rsnw=11 smell, rsnw=12
variety, rsnw=13 other, rsnw=14 dk, rsnw=15 refused;
dummy(prefix=rsnw) q5c;

? main reason do not buy in winter;
? nowint=l not avail, nowint=2 not local, nowint=3 taste, nowint=4
price, nowint=5 not fresh, nowint=6 texture, nowint=7 short life;
? nowint=8 health, nowint=9 size, nowint=10 damaged, nowint=11 time,
nowint=12 messy, nowint=13 dk, nowint=14 refused;
dummy(prefix=now) q5d;

? buyer in spring;
? buys=l yes, buys=2 no;
dummy(prefix=buys) q6;


? most imp reason buy in spring;











? rsns=l taste, rsns=2 color, rsns=3 fresh, rsns=4 price, rsns=5 habit,
rsns=6 health, rsns=7 tender;
? rsns=8 recipes, rsns=9 ads, rsns=10 avail, rsns=11 smell, rsns=12
variety, rsns=13 other, rsns=14 dk, rsns=15 refused;
dummy(prefix=rsns) q6c;

? main reason do not buy in spring;
? nospr=l not avail, nospr=2 not local, nospr=3 taste, nospr=4 price,
nospr=5 not fresh, nospr=6 texture, nospr=7 short life;
? nospr=8 health, nospr=9 size, nospr=10 damaged, nospr=ll time,
nospr=12 messy, nospr=13 dk, nospr=14 refused;
dummy(prefix=nos) q6d;

? buyer in summer;
? bsu=l yes, bsu=2 no;
dummy(prefix=bsu) q7;

? main reason do not buy in summer;
? nosum=l not avail, nosum=2 not local, nosum=3 taste, nosum=4 price,
nosum=5 not fresh, nosum=6 texture, nosum=7 short life;
? nosum=8 health, nosum=9 size, nosum=10 damaged, nosum=11 time,
nosum=12 messy, nosum=13 dk, nosum=14 refused;
dummy(prefix=nosu) q7b;

? buyer in fall;
? buyf=l yes, buyf=2 no;
dummy(prefix=buyf) q8;

? most imp reason buy in fall;
? rsnf=l taste, rsnf=2 color, rsnf=3 fresh, rsnf=4 price, rsnf=5 habit,
rsnf=6 health, rsnf=7 tender;
? rsnf=8 recipes, rsnf=9 ads, rsnf=10 avail, rsnf=11 smell, rsnf=12
variety, rsnf=13 other, rsnf=14 dk, rsnf=15 refused;
dummy(prefix=rsnf) q8c;

? main reason do not buy in fall;
? nof=l not avail, nof=2 not local, nof=3 taste, nof=4 price, nof=5 not
fresh, nof=6 texture, nof=7 short life;
? nof=8 health, nof=9 size, nof=10 damaged, nof=11 time, nof=12 messy,
nof=13 dk, nof=14 refused;
dummy(prefix=nof) q8d;

? have you received info;
? info=l yes, info=2 no;
dummy(prefix=inf) q22;

? received info from family member;
? fam=l yes, fam=2 no;
dummy(prefix=fam) q22al;

? received info from friend;
? friendly yes, friend=2 no;
dummy(prefix=frn) q22a2;

? received info from newspaper article;
? nws=l yes, nws=2 no;
dummy(prefix=nws) q22a3;

? received info from magazine article;
? mag=l yes, mag=2 no;
dummy(prefix=mag) q22a4;


? received info from TV food shows;
? tvfood=l yes, tvfood=2 no;











dummy(prefix=tvf) q22a5;

? received info from extension service;
? ext=l yes, ext=2 no;
dummy(prefix=ext) q22a6;

? received info from grocer;
? groc=l yes, groc=2 no;
dummy(prefix=grc) q22a7;

? received info from farmer;
? fmr=l yes, fmr=2 no;
dummy(prefix=fmr) q22a8;

? received info from cookbook;
? cbk=l yes, cbk=2 no;
dummy(prefix=cbk) q22a9;

? received info from trade association;
? tra=l yes, tra=2 no;
dummy(prefix=tra) q22a10;

? received info from internet;
? infonet=l yes, infonet=2 no;
dummy(prefix=net) q22all;

? received info from home economics class;
? hmec=l yes, hmec=2 no;
dummy(prefix=hmec) q22a12;

? received info from other;
? infoth=l yes, infoth=2 no;
dummy(prefix=ino) q22a13;

? TV commercials about fsc;
? tvcomm=l yes, tvcomm=2 no;
dummy(prefix=tvc) q23a;

? other TV spots about fsc;
? tvother=l yes, tvother=2 no;
dummy(prefix=tvo) q23b;

? magazine ads about fsc;
? magads=l yes, magads=2 no;
dummy(prefix=mads) q23c;

? magazine feature stories about fsc;
? magfeat=l yes, magfeat=2 no;
dummy(prefix=mft) q23d;

? newspaper food-page stories or recipes about fsc;
? fdpg=l yes, fdpg=2 no;
dummy(prefix=fpg) q23e;

? newspaper food-page advertisements about fsc;
? fpgads=l yes, fpgads=2 no;
dummy(prefix=fpa) q23f;

? radio commercials about fsc;
? rco=l yes, rco=2 no;
dummy(prefix=rco) q23g;


? posters in stores about fsc;
? post=l yes, post=2 no;








68


dummy(prefix=pst) q23h;

? sweet corn recipe cards;
? rcd=l yes, rcd=2 no;
dummy(prefix=rcd) q23i;

? internet web site about fsc;
? web=l yes, web=2 no;
dummy(prefix=web) q23j;

SELECT 1;
?=--------------------------------------------------------------------
? first stage probit BUY1=1 YES TO BUYING
?SELECT BUY1=0-------------

SELECT BUY1=0;

SELECT BUY1=1;

SELECT 1;
AGE=2001 Q27;
HIST(DISCRETE) BUY1;

DOT 1-4; ZCIT.=CIT.-CIT5; ENDDOT;
DOT 1-2; ZEDU.=EDU.-EDU3; ENDDOT;
ZINC1=INC1 INC2;
DOT 1-2; ZRAC.=RAC. RAC3; ENDDOT;
ZGEN1 = GEN1 GEN2;
DOT 1-2; ZSAT.=SAT.-SAT3; ENDDOT;

XAGE=AGE;
age= (xage<30)*l + (xage>=30 & xage<=55)*2 + (xage>55)*3;
hist(discrete) age;
dummy (prefix=dage) age;

corr Zcitl Zcit2 Zcit3 Zcit4 Zedul Zedu2 Zincl Zracl Zrac2
Zgenl q24 hwz chd dagel dage3 Zsatl Zsat2 ;

probit BUY1 c Zcitl Zcit2 Zcit3 Zcit4 Zedul Zedu2 Zincl Zracl Zrac2
Zgenl q24 hwz chd dagel dage3 Zsatl Zsat2 ;

dot(value=j) 0-17; set jj=j+l; set b.= @coef(jj); enddot;

DOT citl cit2 cit3 cit4 edul edu2 incl raci rac2 genl q24 hwz
chd dagel dage3 satl sat2
SET SIM.=0; ENDDOT;

SET I=0;
MFORM(TYPE=GEN,NROW=150,NCOL=35) ZSIMSCZ=0;


PROC ZSIMZ;


SET XB= BO + B1*SIMcitl + B2*SIMcit2 + B3*SIMcit3 + B4*SIMcit4 +
B5*SIMedul + B6*SIMedu2 + B7*SIMincl +
B8*SIMracl + B9*SIMrac2 + B10*SIMgenl+ Bll*SIMq24 + B12*SIMhwz +
B13*SIMchd +
B14*SIMagel + B15*SIMage3 + B16*SIMsatl + B17*SIMsat2
SET PROB = CNORM(XB);
SET I=I+1;
SET J=l; SET ZSIMSCZ(I,J)=SIM;
SET J=2; SET ZSIMSCZ(I,J)=PROB;
DOT citl cit2 cit3 cit4 edul edu2 incl raci rac2












genl q24 hwz chd agel age3 satl sat2 ;
SET J=J+1; SET ZSIMSCZ(I,J)=SIM.; ENDDOT;
ENDPROC;
? <<<<<<<<<<<<<<<<<<<>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>;


MSD(NOPRINT) q24 hwz ;
SET K=0;
DOT q24 hwz ; SET K=K+1; SET SIM.=@MEAN(K); ENDDOT;
DOT citl cit2 cit3 cit4 edul edu2 incl raci
genl chd agel age3 satl sat2
SET SIM.=0; ENDDOT;


rac2


? <<<<<<<<<<<<<<<<<<<>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>;
PROC INIT;

MSD(NOPRINT) q24 hwz
SET K=0;
DOT q24 hwz; SET K=K+1; SET SIM.=@MEAN(K); ENDDOT;
DOT citl cit2 cit3 cit4 edul edu2 incl raci rac2
genl chd agel age3 satl sat2 ;
SET SIM.=0; ENDDOT;
ENDPROC INIT;


SET SIM=1;
INIT;
ZSIMZ;


SET SIM=2; ? CITY;
INIT;
SET SIMCIT1=1; ? D
ZSIMZ;
INIT;
SET SIMCIT2=1; ? A
ZSIMZ;
INIT;
SET SIMCIT3=1; ? C
ZSIMZ;
INIT;
SET SIMCIT4=1; ? B
ZSIMZ;
INIT;
SET SIMCIT1=-1; SET
PHILADELPHIA;
ZSIMZ;


ALLAS;


TLANTA;


HICAGO;


OSTON;


SIMCIT2


1; SET SIMCIT3


1; SET SIMCIT4


SET SIM=3;
INIT;
SET SIMEDU1=
ZSIMZ;
INIT;
SET SIMEDU2=
ZSIMZ;
INIT;
SET SIMEDU1=
ZSIMZ;


? EDUCATION;

:1; ? HS GRAD OR LESS;


:1; ? TECH SCHOOL, SOME COLLEGE, OR COLLEGE GRAD;


:-1; SET SIMEDU2=-1; ? GRAD/PROFESSIONAL;


SET SIM=4; ? AGE;
INIT;
SET SIMAGE1=1; ?
ZSIMZ;
INIT;
SET SIMAGE3=1; ? 0
ZSIMZ;


1; ?


UNDER 30;


VER 55;













SET SIM=5;
INIT;
SET SIMINC1=
ZSIMZ;
SET SIMINC1=
ZSIMZ;


? INCOME;

:1; ? INCOME UNDER $35,000;

:-1; ? INCOME OVER $35,000;


SET SIM=6; ? RACE;
INIT;
SET SIMRAC1=1; ? W
ZSIMZ;
INIT;
SET SIMRAC2=1; ? B
ZSIMZ;
INIT;
SET SIMRAC1=-1; SET
ZSIMZ;


SET SIM=7;
INIT;
SET SIMGEN1=
ZSIMZ;
SET SIMGEN1=
ZSIMZ;


WHITE;


LACK;


SIMRAC2


1; ? ALL OTHER;


? GENDER;

:1; ? MALE;

:-1; ? FEMALE;


SET SIM=8; ? YEARS IN CITY;
INIT;
DO ADJ=0 TO 90 BY 5;
SET SIMQ24=ADJ;
ZSIMZ;
ENDDO;

SET SIM=9; ? HOUSEHOLD SIZE;
INIT;
DO ADJ=1 TO 15 BY 1;
SET SIMHWZ=ADJ;
ZSIMZ;
ENDDO;


SET SIM=10; ? PRESENCE OF CHILDREN;
INIT;
SET SIMCHD=1; ? CHILDREN;
ZSIMZ;
SET SIMCHD=-1; ? NO CHILDREN
ZSIMZ;

SET SIM=11; ? SATISFACTION W/ PRODUCE AVAILABILITY;
INIT;
SET SIMSAT1=1; ? NOT AT ALL SATISFIED;
ZSIMZ;
INIT;
SET SIMSAT2=1; ? SOMEWHAT SATISFIED;
ZSIMZ;
INIT;
SET SIMSAT1=-1; SET SIMSAT2=-1; ? VERY SATISFIED;
ZSIMZ;

WRITE(FORMAT=EXCEL,FILE='H:\SIMSCORN.XLS') ZSIMSCZ;


end;











OPTIONS MEMORY=50;
OPTIONS LIMWARN=1;
Title 'Probit Analysis for Sweet Corn Amanda Briggs';
? ORDERPROBITSW#1.tsp;

in 'd:\abriggs\corndat';
? in 'c:\zstudent\abriggs\corndat';
? in 'd:\zstudent\abriggs\corndat';
? read(format=excel,file='d:\zstudent\abriggs\amanbas.xls');
? out 'd:\zstudent\abriggs\corndat';

? doc id 'household identification';
? doc interv 'interview';
? doc ql 'city 1 to 5';
? doc q2 'satisfaction w/ produce availability 1 to 3';
? doc q3 'buyer l=yes 2=no';

? doc q5 'buy in winter l=yes 2=no';
? doc q5a 'times/month winter 1 to 31';
? doc q5b 'satisfaction winter 0 to 10';
? doc q5c 'reason buy winter 1 to 15';
? doc q5d 'reason don't buy winter 1 to 14';
? doc q6 'buy in spring l=yes 2=no';
? doc q6a 'times/month spring 1 to 31';
? doc q6b 'satisfaction spring 0 to 10';
? doc q6c 'reason buy spring 1 to 15';
? doc q6d 'reason don't buy spring 1 to 14';
? doc q7 'buy in summer l=yes 2=no';
? doc q7a 'times/month summer 1 to 31';
? doc q7b 'reason don't buy summer 1 to 14';
? doc q8 'buy in fall l=yes 2=no';
? doc q8a 'times/month fall 1 to 31';
? doc q8b 'satisfaction fall 0 to 10';
? doc q8c reason buy fall 1 to 15';
? doc q8d 'reason don't buy fall 1 to 14';
? doc q12 'price/ear .10 to 1.0';
? doc q13 '# ears per purchase 1 to 30';

? doc q22a 'info source 1 to 13';
? doc q22al 'family l=yes 2=no';
? doc q22a2 'friend l=yes 2=no';
? doc q22a3 'newspaper l=yes 2=no';
? doc q22a4 'magazine l=yes 2=no';
? doc q22a5 'tV l=yes 2=no';
? doc q22a6 'extension l=yes 2=no';
? doc q22a7 'grocer l=yes 2=no';
? doc q22a8 'farmer l=yes 2=no';
? doc q22a9 'cookbook l=yes 2=no';
? doc q22a10 'trade assoc l=yes 2=no';
? doc q22all 'internet l=yes 2=no';
? doc q22a12 'home ec l=yes 2=no';
? doc q22a13 'other l=yes 2=no';

? doc q23 'info type 1 to 10';
? doc q23a 'tV commercials l=yes 2=no';
? doc q23b 'other TV l=yes 2=no';
? doc q23c 'magazine ad l=yes 2=no';
? doc q23d 'mag story l=yes 2=no';
? doc q23e 'newspaper story l=yes 2=no';
? doc q23f 'newspaper ads l=yes 2=no';
? doc q23g 'radio l=yes 2=no';
? doc q23h 'posters l=yes 2=no';
? doc q23i 'recipe cards l=yes 2=no';
? doc q23j 'web site l=yes 2=no';












? doc q24 'years in city 1 to 100';
? doc q25 '# adults in household 1 to 10';
? doc q25a '#children in household 0 to 10';
? doc q26 'education 1 to 7';
? doc q27 'year of birth 1900 to 1983';
? doc q28 'income >or< $35,000 1 to 2';
? doc q28a 'income 3 to 4';
? doc q28b 'income 5 to 7';
? doc q29 'race 1 to 5';
? doc q29a 'hispanic or not l=yes 2=no';
? doc q30 'internet access at home l=yes 2=no';
? doc q31 'internet access at work l=yes 2=no';
? doc q32 'gender l=male 2=female';
? doc idu 'identication';
? out;

? dblist(date,doc) 'd:\zstudent\abriggs\corndat';

? winter, spring and fall;
LIST VAR1 Zedul Zedu2 Zracl Zrac2
Zgenl q24 HWZ CHD DAGE3 Zsatl Zsat2 SATF
TV RD MGZ NWP PSR DRSN1 DRSN2 DRSN3 DINF;

? summer;
LIST VAR4 Zedul Zedu2 Zracl Zrac2
Zgenl q24 HWZ CHD DAGE3 Zsatl Zsat2 TV RD MGZ NWP PSR DINF ;

dummy(prefix=buy) q3; ? buyer of sweet corn buyl=yes buy2=no;

? Creating total household size;
hwz = q25 + q25a;
hist(discrete) hwz;

? Presence of children;
chd = (q25a>0);
hist(discrete) chd;

? Electronic media;
elc = (q23a=l q23b=1 lq23g=1 |q23j=l );
hist(discrete) elc;

? Print media;
prn = (q23c=1 q23d=1 lq23e=1 |q23f=1 lq23h=1 |q23i=1 );
hist(discrete) prn;

? Total media;
med = (elc=l) (prn=l);
hist(discrete) med;

? Television;
TV = (Q23A=1 Q23B=1) 1;
hist(discrete) tv;

? Radio;
RD = (Q23G=1) 1;
hist(discrete) rd;

? Magazines;
MGZ = (Q23C=1 Q23D=1) 1;
hist(discrete) mgz;

? Newspapers;
NWP = (Q23E=1 Q23F=1) 1;











hist(discrete) nwp;

? Posters, recipe cards;
PSR = (Q23H=1 Q23I=1) 1;
hist(discrete) psr;

hist(discrete) q3;
dot 5-8;
hist(discrete) q.;
enddot;

hist(discrete) q13; ? number of ears per purchase;

? number of times per month during each season with 5=winter, 6=spring,
7=summer, 8=fall;

dot 5-8;
select 1;
dd = (q.a>=20);
select dd^=l; qq.a=q.a;
select 1;
hist(discrete) qq.a;
tq.a=(qq.a=l) + (qq.a=2)*2 + (qq.a=3)*3 + (qq.a>=4)*4;
dummy(prefix=tmq.) tq.a;
select 1;
enddot;

dot 5-8;
hist(discrete) tmq.l-tmq.4;
enddot;

? making dummy variables for the demographics---------------------------------------
? making dummy variables for the demographics
?=======================================-- - - - -


? city=l Dallas, city=2 Atlanta, city=3 Chicago,
Philadelphia;
dummy(prefix=cit) ql;


city=4 Boston, city=5


? edu=l 8th grade, edu=2 some hs, edu=3 hs grad, edu=4 tech schl, edu=5
some college, edu=6 college grad, edu=7 grad/prof;
xedu=q26;
xedu=(xedu<=3)*1 + (xedu>=4 & xedu<=6)*2 + (xedu>=7)*3;
hist(discrete) xedu;
dummy(prefix=edu) xedu;


? inc=l under $35,000, inc=2 over $35,000;
dummy(prefix=inc) q28;

? race=l black, race=2 white, race=>3 all other;
dummy(prefix=rac) q29;

? hispanic=l yes, hispanic=2 no;
dummy(prefix=his) q29a;

? nethome=l yes, nethome=2 no;
dummy(prefix=neth) q30;

? network=l yes, network=2 no;
dummy(prefix=netw) q31;


? gender=l male, gender=2 female;
dummy(prefix=gen) q32;








74


?=---------------------------------------------------------------------
? making dummy variables ;
?=======================================-- - - - - - - - -

? satisfaction w/ produce availability;
? sat=l not at all satisfied, sat=2 somewhat, sat=3 very;
dummy(prefix=sat) q2;

? buyer in winter;
? buyw=l yes, buyw=2 no;
dummy(prefix=buyw) q5;

? most imp reason buy in winter;
? rsnw=l taste, rsnw=2 color, rsnw=3 fresh, rsnw=4 price, rsnw=5 habit,
rsnw=6 health, rsnw=7 tender;
? rsnw=8 recipes, rsnw=9 ads, rsnw=10 avail, rsnw=ll smell, rsnw=12
variety, rsnw=13 other, rsnw=14 dk, rsnw=15 refused;
dummy(prefix=rsnw) q5c;

? main reason do not buy in winter;
? nowint=l not avail, nowint=2 not local, nowint=3 taste, nowint=4
price, nowint=5 not fresh, nowint=6 texture, nowint=7 short life;
? nowint=8 health, nowint=9 size, nowint=10 damaged, nowint=ll time,
nowint=12 messy, nowint=13 dk, nowint=14 refused;
dummy(prefix=now) q5d;

? buyer in spring;
? buys=l yes, buys=2 no;
dummy(prefix=buys) q6;

? most imp reason buy in spring;
? rsns=l taste, rsns=2 color, rsns=3 fresh, rsns=4 price, rsns=5 habit,
rsns=6 health, rsns=7 tender;
? rsns=8 recipes, rsns=9 ads, rsns=10 avail, rsns=ll smell, rsns=12
variety, rsns=13 other, rsns=14 dk, rsns=15 refused;
dummy(prefix=rsns) q6c;

? main reason do not buy in spring;
? nospr=l not avail, nospr=2 not local, nospr=3 taste, nospr=4 price,
nospr=5 not fresh, nospr=6 texture, nospr=7 short life;
? nospr=8 health, nospr=9 size, nospr=10 damaged, nospr=ll time,
nospr=12 messy, nospr=13 dk, nospr=14 refused;
dummy(prefix=nos) q6d;

? buyer in summer;
? bsu=l yes, bsu=2 no;
dummy(prefix=bsu) q7;

? main reason do not buy in summer;
? nosum=l not avail, nosum=2 not local, nosum=3 taste, nosum=4 price,
nosum=5 not fresh, nosum=6 texture, nosum=7 short life;
? nosum=8 health, nosum=9 size, nosum=10 damaged, nosum=ll time,
nosum=12 messy, nosum=13 dk, nosum=14 refused;
dummy(prefix=nosu) q7b;

? buyer in fall;
? buyf=l yes, buyf=2 no;
dummy(prefix=buyf) q8;

? most imp reason buy in fall;
? rsnf=l taste, rsnf=2 color, rsnf=3 fresh, rsnf=4 price, rsnf=5 habit,
rsnf=6 health, rsnf=7 tender;
? rsnf=8 recipes, rsnf=9 ads, rsnf=10 avail, rsnf=ll smell, rsnf=12
variety, rsnf=13 other, rsnf=14 dk, rsnf=15 refused;











dummy(prefix=rsnf) q8c;

? main reason do not buy in fall;
? nof=l not avail, nof=2 not local, nof=3 taste, nof=4 price, nof=5 not
fresh, nof=6 texture,
? nof=7 short life;
? nof=8 health, nof=9 size, nof=10 damaged, nof=11 time, nof=12 messy,
nof=13 dk, nof=14 refused;
dummy(prefix=nof) q8d;

? have you received info;
? info=l yes, info=2 no;
dummy(prefix=inf) q22;

? received info from family member;
? fam=l yes, fam=2 no;
dummy(prefix=fam) q22al;

? received info from friend;
? friendly yes, friend=2 no;
dummy(prefix=frn) q22a2;

? received info from newspaper article;
? nws=l yes, nws=2 no;
dummy(prefix=nws) q22a3;

? received info from magazine article;
? mag=l yes, mag=2 no;
dummy(prefix=mag) q22a4;

? received info from TV food shows;
? tvfood=l yes, tvfood=2 no;
dummy(prefix=tvf) q22a5;

? received info from extension service;
? ext=l yes, ext=2 no;
dummy(prefix=ext) q22a6;

? received info from grocer;
? groc=l yes, groc=2 no;
dummy(prefix=grc) q22a7;

? received info from farmer;
? fmr=l yes, fmr=2 no;
dummy(prefix=fmr) q22a8;

? received info from cookbook;
? cbk=l yes, cbk=2 no;
dummy(prefix=cbk) q22a9;

? received info from trade association;
? tra=l yes, tra=2 no;
dummy(prefix=tra) q22a10;

? received info from internet;
? infonet=l yes, infonet=2 no;
dummy(prefix=net) q22all;

? received info from home economics class;
? hmec=l yes, hmec=2 no;
dummy(prefix=hmec) q22a12;


? received info from other;
? infoth=l yes, infoth=2 no;











dummy(prefix=ino) q22a13;

? TV commercials about fsc;
? tvcomm=l yes, tvcomm=2 no;
dummy(prefix=tvc) q23a;

? other TV spots about fsc;
? tvother=l yes, tvother=2 no;
dummy(prefix=tvo) q23b;

? magazine ads about fsc;
? magads=l yes, magads=2 no;
dummy(prefix=mads) q23c;

? magazine feature stories about fsc;
? magfeat=l yes, magfeat=2 no;
dummy(prefix=mft) q23d;

? newspaper food-page stories or recipes about fsc;
? fdpg=l yes, fdpg=2 no;
dummy(prefix=fpg) q23e;

? newspaper food-page advertisements about fsc;
? fpgads=l yes, fpgads=2 no;
dummy(prefix=fpa) q23f;

? radio commercials about fsc;
? rco=l yes, rco=2 no;
dummy(prefix=rco) q23g;

? posters in stores about fsc;
? post=l yes, post=2 no;
dummy(prefix=pst) q23h;

? sweet corn recipe cards;
? rcd=l yes, rcd=2 no;
dummy(prefix=rcd) q23i;

? internet web site about fsc;
? web=l yes, web=2 no;
dummy(prefix=web) q23j;

SELECT 1;
AGE=2001 Q27;
HIST(DISCRETE) BUY1;

DOT 1-4; ZCIT.=CIT.-CIT5; ENDDOT;
DOT 1-2; ZEDU.=EDU.-EDU3; ENDDOT;
ZINC1=INC1 INC2;
DOT 1-2; ZRAC.=RAC. RAC3; ENDDOT;
ZNETH1=NETH1 NETH2;
ZNETW1=NETW1 NETW2;
ZGEN1 = GEN1 GEN2;
DOT 1-2; ZSAT.=SAT.-SAT3; ENDDOT;

XAGE=AGE;
HIST XAGE;
AGE= (XAGE<30) + (XAGE>=30 & XAGE<=55)*2 + (XAGE>55)*3;
HIST(DISCRETE) AGE;
DUMMY(PREFIX=DAGE) AGE;

mform(type=gen,nrow=30,ncol=4) mxcorn=0;


SELECT 1;











3-----------------------------------------------------
? first stage probit BU1=1 YES TO BUYING ;
=======================================

SELECT BUY1=0;
SELECT BUY1=1;

DOT 5 6 7 8;
HIST(DISCRETE) Q.; ? WINTER BUYING YES OR NO;
HIST(DISCRETE,PERCENT) Q.A; ? TIMES;
HIST(DISCRETE) Q.C;
HIST(DISCRETE) Q.D;
? FREQUENCY OF BUYING SWEET CORN WITHIN A TYPICAL MONTH OF A SEASON;
FRQ.=(Q.A=1)*1 + (Q.A=2)*2 + (Q.A=3)*3 + (Q.A>=4)*4;
HIST(DISCRETE) FRQ.;
ENDDOT;


DOT 1-13;
HIST(DISCRETE) Q22A.;
ENDDOT;

DOT A B J D E F G H I J;
HIST(DISCRETE) Q23.;
ENDDOT;

HIST(DISCRETE) Q22;


DOT 1 -
DINF=0;


4; DRSN.=0; ENDDOT;


Y=FRQ5;
SET N1=0; SET N2=0; SET N3=0;
SATF=Q5B;
RSN = Q5C;
NOT= Q5D;
INF= Q22; ? 1=YES 2=NO;


SET N4=0;


LIST ZVARZ Zcitl Zcit2 Zcit3 Zcit4 Zedul Zedu2 Zincl Zracl Zrac2 Znethl
Znetwl Zgenl q24 q25 q25a DAGE2 DAGE3 Zsatl Zsat2 DRSN1 DRSN2 DRSN3
DNOT1 DNOT2 DNOT3
DNOT4 DNOT5 DINF ;

?=---------------------------------------------------------------------
? ordered probit model procedure winter ;
?=======================================-- - - - - - - - -
proc oprob;
dummy y yl-y4;
msd (noprint) yl-y4;
unmake @sum nl-n4;

VRSN=0;
VRSN=(RSN=1 RSN=3 RSN=7)*1 + (RSN=6)*2 + (RSN=5)*3
+ (RSN^=1 & RSN^=3 & RSN^=7 & RSN^=6 & RSN^=5)*4;
hist(discrete) vrsn;
DUMMY(PREFIX=DRSN) VRSN;

DINF=(INF=1);

frml xb B5*Zedul + B6*Zedu2 + B12*Zracl + B13*Zrac2
+ B16*Zgenl + B17*q24 + B18*HWZ + B19*CHD
+ B21*DAGE3 + B22*Zsatl + B23*Zsat2 +B24*SATF
+ B25*TV + B26*RD +B27*MGZ + B28*NWP +B29*PSR
+ E1*DRSN1 +E2*DRSN2 + E3*DRSN3 + D1*DINF













set n=nl+n2+n3+n4;
set sumn=0;


dot 1-3;
set sumn=sumn + n.;
set f.=sumn/n;
set a. = cnormi(f.); ? Seed values
enddot;


for the al to a3 parameters;


print al-a3;


param B5 B6 B12-B13 B16-B19 B21-B29
dot 1-3;
frml xb. xb-a.;
eqsub xb. xb; enddot;


El E2 E3 D1 al a2 a3 ;


frml eql log{ yl*cnorm(-xbl)
+ y2*(cnorm(-xb2) -cnorm(-xbl))
+ y3*(cnorm(-xb3) -cnorm(-xb2))
+ y4*(l- cnorm(-xb3)) };

eqsub(name=ordprob) eql xbl-xb3;
hist(discrete) y;
yy=(y>l);
hist(discrete) yy;

SELECT YY>=0;

HIST(DISCRETE) DAGE1 DAGE2 DAGE3;

dot Zedul Zedu2 Zracl Zrac2
Zgenl q24 HWZ CHD DAGE3 Zsatl Zsat2 SATF TV RD MGZ NWP PSR DRSN1
DRSN2 DRSN3 DINF ;
HIST(DISCRETE)
enddot;


corr
Zgenl
DRSN2


Zedul Zedu2 Zracl Zrac2
q24 HWZ CHD DAGE3 Zsatl Zsat2 SATF
DRSN3 DINF ;


probit yy c Zedul Zedu2 Zracl Zrac2
Zgenl q24 HWZ CHD DAGE3 Zsatl Zsat2 SATF
DRSN2 DRSN3 DINF ;


TV RD MGZ NWP PSR DRSN1



TV RD MGZ NWP PSR DRSN1


ml(hiter=n,hcov=nbw) ordprob;
mat mxcorn=@coef;
print mxcorn;

?=------------------------------
endproc;
?===============================

?=------------------------------
? ordered probit model summer;
?


proc oprob2;
dummy y yl-y4;
msd(noprint) yl-y4;
unmake @sum nl-n4;


DINF=(INF=1);












frml xb B5*Zedul + B6*Zedu2 + B12*Zracl + B13*Zrac2
+ B16*Zgenl + B17*q24 + B18*HWZ + B19*CHD
+ B21*DAGE3 + B22*Zsatl + B23*Zsat2
+ B24*TV+ B25*RD +B26*MGZ + B27*NWP + B28*PSR + D1*DINF

set n=nl+n2+n3+n4;
set sumn=0;


dot 1-3;
set sumn=sumn + n.;
set f.=sumn/n;
set a. = cnormi(f.); ? Seed values for the al to
enddot;

print al-a3;

param B5 B6 B12-B13 B16-B19 B21-B28 D1 al a2 a3 ;
dot 1-3;
frml xb. xb-a.;
eqsub xb. xb; enddot;

frml eql log{ yl*cnorm(-xbl)
+ y2*(cnorm(-xb2) -cnorm(-xbl))
+ y3*(cnorm(-xb3) -cnorm(-xb2))
+ y4*(l- cnorm(-xb3)) };

eqsub(name=ordprob) eql xbl-xb3;
hist(discrete) y;
yy=(y>l);
hist(discrete) yy;

SELECT YY>=0;

HIST(DISCRETE) DAGE1 DAGE2 DAGE3;


dot Zedul Zedu2 Zracl Zrac2
Zgenl q24 HWZ CHD DAGE3 Zsatl Zsat2
HIST(DISCRETE)
enddot;

CORR Zedul Zedu2 Zracl Zrac2
Zgenl q24 HWZ CHD DAGE3 Zsatl Zsat2


a3 parameters;


TV RD MGZ NWP PSR DINF ;




TV RD MGZ NWP PSR DINF ;


probit yy c Zedul Zedu2 Zracl Zrac2
Zgenl q24 HWZ CHD DAGE3 Zsatl Zsat2 TV RD MGZ NWP PSR DINF ;

ml(hiter=n,hcov=nbw) ordprob;
mat mxcorn=@coef;
print mxcorn;
?=---- ---- ----- ---- ----- ---- ----- ---- -----====================-
endproc oprob2
?======================================= -- - -- - -- --<<>;
<<<<<<<<<<<<<<<<<<<<<<<<<<< >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>


? Initializing all coefficents before going
dot aal aa2 aa3 bb5 bb6 bbl2 bbl3 bbl6 bbl7
bb22 bb23 bb24 bb25 bb26 bb27 bb28 bb29 eel
set .=0; enddot;
set zz=0;


to the procedure;
bbl8 bbl9 bb21
ee2 ee3 ddl;


? Base simulation;
mat mmx=mxcornl; ? winter matrix;












proc simul 1;
set aal=mmx(24,1);
set aa2=mmx(23,1);
set aa3=mmx(22,1);
set bb5=mmx(1,1);
set bb6=mmx(2,1);
set bbl2=mmx(3,1);
set bbl3=mmx(4,1);
set bbl6=mmx(5,1);
set bbl7=mmx(6,1);
set bbl8=mmx(7,1);
set bbl9=mmx(8,1);
set bb21=mmx(9,1);
set bb22=mmx(10,1);
(1/-1);
set bb23=mmx(11,1);
set bb24=mmx(12,1);
set bb25=mmx(13,1);
set bb26=mmx(14,1);
set bb27=mmx(15,1);
set bb28=mmx(16,1);
set bb29=mmx(17,1);
set eel=mmx(18,1);
set ee2=mmx(19,1);
set ee3=mmx(20,1);
set ddl=mmx(21,1);

set zz= bb5*Zedul sm


education 1 (1/-1);
education 2 (1/-1);
race 1 black (1/-1);
race 2 white (1/-1);
gender 1 male (1/-1);
q24 number of years in the city;
hwz household size number of adults and children;
presence of children yes/no (1/0);
over 55 years of age (1/0);
satisfaction with produce in general yes/no -

somewhat satisfaction yes/no (1/-1);
coefficient for satisfaction 0 to 10;
tv yes/no (1/0);
radio yes/no (1/0);
magazines yes/no (1/0);
newspapers yes/no (1/0);
posters yes/no (1/0);
reason for buying 1 taste yes/no (1/0);
reason for buying 2 health yes/no (1/0);
reason for buying 3 habit yes/no (1/0);
information received yes/no (1/0);

+ bb6*Zedu2 sm + bbl2*Zracl sm + bbl3*Zrac2 sm +


bbl6*Zgenl_sm + bbl7*q24 sm + bbl8*HWZ sm + b
bb21*DAGE3 sm + bb22*Zsatl sm + bb23*Zsat2 sm + b
bb25*TV sm + bb26*RD sm + bb27*MGZ sm + b
bb29*PSR sm + eel*DRSN1 sm + ee2*DRSN2 sm + e
ddl*DINF sm;
set probl=cnorm(aal zz);
set prob2=cnorm(aa2 zz) cnorm(aal zz);
set prob3=cnorm(aa3 zz) cnorm(aa2 zz);
set prob4=l probl prob2 prob3;
set zprobz(i,l) = sim;
set zprobz(i,2) = probl;
set zprobz(i,3) = prob2;
set zprobz(i,4) = prob3;
set zprobz(i,5) = prob4;
dot varl; set j=j+l; set jj=j+5; set zprobz(i,jj)
endproc simul 1;


,bl9*CHD sm +
,b24*SATF sm +
,b28*NWP sm +
e3*DRSN3 sm +


. sm; enddot;


mform(type=gen,nrow=500,ncol=50) zprobz=0;
set i=0;

mat mmx=mxcornl;

? Simulation sim====================101 Winter base (satf at mean)
? Simulation sim=101 Winter base (satf at mean);
?=--------------------------------------------


set sim=101;
dot varl; set ._sm
msd(noprint) q24;
msd(noprint) hwz;
msd(noprint) satf;
set j=0;
set i=i+l;
simul 1;


= 0; enddot;
set q24 sm=@mean(1);
set hwz sm=@mean(1);
set satf sm=@mean(1);















? Simulation sim=102 Winter magazines(satf at mean);
?=======================================-- - -- - -- - -
set sim=119;
dot varl; set ._sm = 0; enddot;
msd(noprint) q24; set q24_sm=@mean(1);
msd(noprint) hwz; set hwz sm=@mean(1);
set mgz sm=l;
msd(noprint) satf; set satf sm=@mean(1);
set j=0;
set i=i+l;
simul 1;

?==- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- ---====================
? Simulation sim=103 Winter rsnl(satf at mean);
?=-----------------------------------------------------------
set sim=122;
dot varl; set ._sm = 0; enddot;
msd(noprint) q24; set q24 sm=@mean(1);
msd(noprint) hwz; set hwz sm=@mean(1);
set drsnl sm=l;
msd(noprint) satf; set satf sm=@mean(1);
set j=0;
set i=i+l;
simul 1;

?==- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- ---====================
? Simulation sim=104 Winter rsn3(satf at mean);
?=-----------------------------------------------------------
set sim=124;
dot varl; set ._sm = 0; enddot;
msd(noprint) q24; set q24 sm=@mean(1);
msd(noprint) hwz; set hwz sm=@mean(1);
set drsn3 sm=l;
msd(noprint) satf; set satf sm=@mean(1);
set j=0;
set i=i+l;
simul 1;

?=-----------------------------------------------------------
? Simulation sim=105 Winter base;
?=-----------------------------------------------------------
set sim=101;
dot varl; set ._sm = 0; enddot;
msd(noprint) q24; set q24 sm=@mean(1);
msd(noprint) hwz; set hwz sm=@mean(1);
do satf sm= 0 to 10 by 1;
set j=0;
set i=i+l;
simul 1;
enddo;

write(format=excel, file='d:\abriggs\new winter.xls') zprobz;

?<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<>>>>>>>>>>>>>>>>>>>>>>>>>

mat mmx2=mxcorn2; ? spring matrix;

proc simul_2;
set aal=mmx2(24,1);
set aa2=mmx2(23,1);
set aa3=mmx2(22,1);











set bb5=mmx2(1,1);
set bb6=mmx2(2,1);
set bbl2=mmx2(3,1);
set bbl3=mmx2(4,1);
set bbl6=mmx2(5,1);
set bbl7=mmx2(6,1);
set bbl8=mmx2(7,1);
children;
set bbl9=mmx2(8,1);
set bb21=mmx2(9,1);
set bb22=mmx2(10,1);
(1/-1);
set bb23=mmx2(11,1);
set bb24=mmx2(12,1);
set bb25=mmx2(13,1);
set bb26=mmx2(14,1);
set bb27=mmx2(15,1);
set bb28=mmx2(16,1);
set bb29=mmx2(17,1);
set eel=mmx2(18,1);
set ee2=mmx2(19,1);
set ee3=mmx2(20,1);
set ddl=mmx2(21,1);

set zz= bb5*Zedul sm


? education 1 (1/-1);
? education 2 (1/-1);
? race 1 black (1/-1);
? race 2 white (1/-1);
? gender 1 male (1/-1);
? q24 number of years in the city;
? hwz household size number of adults and

? presence of children yes/no (1/0);
? over 55 years of age (1/0);
? satisfaction with produce in general yes/no -

? somewhat satisfaction yes/no (1/-1);
? coefficient for satisfaction 0 to 10;
? tv yes/no (1/0);
? radio yes/no (1/0);
? magazines yes/no (1/0);
? newspapers yes/no (1/0);
? posters yes/no (1/0);
? reason for buying 1 taste yes/no (1/0);
? reason for buying 2 health yes/no (1/0);
? reason for buying 3 habit yes/no (1/0);
? information received yes/no (1/0);

+ bb6*Zedu2 sm + bbl2*Zracl sm + bbl3*Zrac2 sm +


bbl6*Zgenl_sm + bbl7*q24 sm + bbl8*HWZ sm + b
bb21*DAGE3 sm + bb22*Zsatl sm + bb23*Zsat2 sm + b
bb25*TV sm + bb26*RD sm + bb27*MGZ sm + b
bb29*PSR sm + eel*DRSN1 sm + ee2*DRSN2 sm + e
ddl*DINF sm;
set probl=cnorm(aal zz);
set prob2=cnorm(aa2 zz) cnorm(aal zz);
set prob3=cnorm(aa3 zz) cnorm(aa2 zz);
set prob4=l probl prob2 prob3;
set zprobz(i,l) = sim;
set zprobz(i,2) = probl;
set zprobz(i,3) = prob2;
set zprobz(i,4) = prob3;
set zprobz(i,5) = prob4;
dot varl; set j=j+l; set jj=j+5; set zprobz(i,jj)
endproc simul 2;


,bl9*CHD sm
,b24*SATF sm
,b28*NWP sm
e3*DRSN3 sm


. sm; enddot;


mform(type=gen,nrow=500,ncol=50) zprobz=0;
set i=0;

mat mmx2=mxcorn2; ? spring matrix;

?=---------------------------------------------------
? Simulation sim=201 Spring base (satf at mean);
?=---------------------------------------------------
set sim=201;
dot varl; set ._sm = 0; enddot;
msd(noprint) q24; set q24 sm=@mean(1);
msd(noprint) hwz; set hwz sm=@mean(1);
msd(noprint) satf; set satf sm=@mean(1);
set j=0;
set i=i+l;
simul 2;

?=---------------------------------------------------
? Simulation sim=202 Spring age 3 (satf at mean);
?=---------------------------------------------------
set sim=213;
dot varl; set sm = 0; enddot;












msd(noprint) q24; set q24_sm=@mean(1);
msd(noprint) hwz; set hwz sm=@mean(1);
set dage3 sm=l;
msd(noprint) satf; set satf sm=@mean(1);
set j=0;
set i=i+l;
simul 2;

?==- -- -- -- -- -- -- -- -- -- -- -- -- -- ---====================
? Simulation sim=203 Spring satl (satf at mean);
?=======================================---- --- ---- --- ---
set sim=214;
dot varl; set ._sm = 0; enddot;
msd(noprint) q24; set q24_sm=@mean(1);
msd(noprint) hwz; set hwz sm=@mean(1);
set zsatl sm=l;
msd(noprint) satf; set satf sm=@mean(1);
set j=0;
set i=i+l;
simul 2;

?==- -- -- -- -- -- -- -- -- -- -- -- -- -- ---====================
? Simulation sim=204 Spring sat2 (satf at mean);
?=-------------------------------------------------------
set sim=215;
dot varl; set ._sm = 0; enddot;
msd(noprint) q24; set q24 sm=@mean(1);
msd(noprint) hwz; set hwz sm=@mean(1);
set zsat2 sm=l;
msd(noprint) satf; set satf sm=@mean(1);
set j=0;
set i=i+l;
simul 2;

?==- -- -- -- -- -- -- -- -- -- -- -- -- -- ---====================
? Simulation sim=205 Spring television (satf at mean);
?=-------------------------------------------------------
set sim=217;
dot varl; set ._sm = 0; enddot;
msd(noprint) q24; set q24 sm=@mean(1);
msd(noprint) hwz; set hwz sm=@mean(1);
set tv sm=l;
msd(noprint) satf; set satf sm=@mean(1);
set j=0;
set i=i+l;
simul 2;

?=-------------------------------------------------------
? Simulation sim=206 Spring base;
?=-------------------------------------------------------
set sim=201;
dot varl; set ._sm = 0; enddot;
msd(noprint) q24; set q24 sm=@mean(1);
msd(noprint) hwz; set hwz sm=@mean(1);
do satf sm= 0 to 10 by 1;
set j=0;
set i=i+l;
simul 2;
enddo;

?=-------------------------------------------------------
? Simulation sim=207 Spring hwz (satf at mean);
=set sim==============202;
set sim=202;







84


dot varl; set ._sm = 0; enddot;
msd(noprint) q24; set q24_sm=@mean(1);
msd(noprint) satf; set satf sm=@mean(1);
do hwz sm= 1 to 15 by 1;
set j=0;
set i=i+l;
simul 2;
enddo;

write(format=excel, file='d:\abriggs\new spring.xls') zprobz;


mat mmx3=mxcorn3; ? fall matrix;


proc simul 3;
set aal=mmx3(24,1);
set aa2=mmx3(23,1);
set aa3=mmx3(22,1);
set bb5=mmx3(1,1);
set bb6=mmx3(2,1);
set bbl2=mmx3(3,1);
set bbl3=mmx3(4,1);
set bbl6=mmx3(5,1);
set bbl7=mmx3(6,1);
set bbl8=mmx3(7,1);
children;
set bbl9=mmx3(8,1);
set bb21=mmx3(9,1);
set bb22=mmx3(10,1);
(1/-1);
set bb23=mmx3(11,1);
set bb24=mmx3(12,1);
set bb25=mmx3(13,1);
set bb26=mmx3(14,1);
set bb27=mmx3(15,1);
set bb28=mmx3(16,1);
set bb29=mmx3(17,1);
set eel=mmx3(18,1);
set ee2=mmx3(19,1);
set ee3=mmx3(20,1);
set ddl=mmx3(21,1);

set zz= bb5*Zedul sm


? education 1 (1/-1);
? education 2 (1/-1);
? race 1 black (1/-1);
? race 2 white (1/-1);
? gender 1 male (1/-1);
? q24 number of years in the city;
? hwz household size number of adults and

? presence of children yes/no (1/0);
? over 55 years of age (1/0);
? satisfaction with produce in general yes/no -

? somewhat satisfaction yes/no (1/-1);
? coefficient for satisfaction 0 to 10;
? tv yes/no (1/0);
? radio yes/no (1/0);
? magazines yes/no (1/0);
? newspapers yes/no (1/0);
? posters yes/no (1/0);
? reason for buying 1 taste yes/no (1/0);
? reason for buying 2 health yes/no (1/0);
? reason for buying 3 habit yes/no (1/0);
? information received yes/no (1/0);

+ bb6*Zedu2 sm + bbl2*Zracl sm + bbl3*Zrac2 sm +


bbl6*Zgenl_sm + bb17*q24 sm + bbl8*HWZ sm + bbl9*CHD sm +
bb21*DAGE3 sm + bb22*Zsatl sm + bb23*Zsat2 sm + bb24*SATF sm +
bb25*TV sm + bb26*RD sm + bb27*MGZ sm + bb28*NWP sm +
bb29*PSR sm + eel*DRSN1 sm + ee2*DRSN2 sm + ee3*DRSN3 sm +
ddl*DINF sm;
set probl=cnorm(aal zz);
set prob2=cnorm(aa2 zz) cnorm(aal zz);
set prob3=cnorm(aa3 zz) cnorm(aa2 zz);
set prob4=1 probl prob2 prob3;
set zprobz(i,l) = sim;
set zprobz(i,2) = probl;
set zprobz(i,3) = prob2;
set zprobz(i,4) = prob3;
set zprobz(i,5) = prob4;
dot varl; set j=j+l; set jj=j+5; set zprobz(i,jj)= sm; enddot;
endproc simul 3;











mform(type=gen,nrow=500,ncol=50) zprobz=0;
set i=0;

mat mmx3=mxcorn3; ? fall matrix;
?=======================================-- - - -
? Simulation sim=301 Fall base (satf at mean);
?=-------------------------------------------------
set sim=301;
dot varl; set ._sm = 0; enddot;
msd(noprint) q24; set q24 sm=@mean(1);
msd(noprint) hwz; set hwz sm=@mean(1);
msd(noprint) satf; set satf sm=@mean(1);
set j=0;
set i=i+l;
simul 3;


? Simulation sim=302
?=--------------------


Fall race 2 (satf at mean);


set sim=306;
dot varl; set ._sm = 0; enddot;
msd(noprint) q24; set q24 sm=@mean(1);
msd(noprint) hwz; set hwz sm=@mean(1);
set zrac2 sm=l;
msd(noprint) satf; set satf sm=@mean(1);
set j=0;
set i=i+l;
simul 3;


? Simulation sim=303


Fall base;


set sim=301;
dot varl; set ._sm = 0; enddot;
msd(noprint) q24; set q24 sm=@mean(1);
msd(noprint) hwz; set hwz sm=@mean(1);
do satf sm= 0 to 10 by 1;
set j=0;
set i=i+l;
simul 3;
enddo;

write(format=excel, file='d:\abriggs\new fall.xls') zprobz;

?<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<>>>>>>>>>>>>>>>>>>>>>>>>>>>>

? Initializing all coefficents before going to the procedure;
dot aal aa2 aa3 bb5 bb6 bbl2 bbl3 bbl6 bbl7 bbl8 bbl9 bb21
bb22 bb23 bb24 bb25 bb26 bb27 bb28 ddl;
set .=0; enddot;
set zz=0;


? Base simulation;
mat mmx4=mxcorn4; ?

proc simul_4;
set aal=mmx4(20,1);
set aa2=mmx4(19,1);
set aa3=mmx4(18,1);
set bb5=mmx4(1,1);
set bb6=mmx4(2,1);
set bbl2=mmx4(3,1);
set bbl3=mmx4(4,1);
set bbl6=mmx4(5,1);


summer matrix;





? education 1 (1/-1)
? education 2 (1/-1)
? race 1 black (1/
? race 2 white (1/
? gender 1 male (1


I;

-1) ;
-1);
/-1);











set bbl7=mmx4(6,1);
set bbl8=mmx4(7,1);
children;
set bbl9=mmx4(8,1);
set bb21=mmx4(9,1);
set bb22=mmx4(10,1);
(1/-1);
set bb23=mmx4(11,1);
set bb24=mmx4(12,1);
set bb25=mmx4(13,1);
set bb26=mmx4(14,1);
set bb27=mmx4(15,1);
set bb28=mmx4(16,1);
set ddl=mmx4(17,1);


? q24 number of years in the city;
? hwz household size number of adults and

? presence of children yes/no (1/0);
? over 55 years of age (1/0);
? satisfaction with produce in general yes/no

? somewhat satisfaction yes/no (1/-1);
? tv yes/no (1/0);
? radio yes/no (1/0);
? magazines yes/no (1/0);
? newspapers yes/no (1/0);
? posters yes/no (1/0);
? information received yes/no (1/0);


set zz= bb5*Zedul sm + bb6*Zedu2 sm + bbl2*Zracl sm + bbl3*Zrac2 sm +
bbl6*Zgenl_sm + bbl7*q24 sm + bbl8*HWZ sm + bbl9*CHD sm +
bb21*DAGE3 sm + bb22*Zsatl sm + bb23*Zsat2 sm +
bb24*TV sm + bb25*RD sm + bb26*MGZ sm + bb27*NWP sm +
bb28*PSR sm + ddl*DINF sm;
set probl=cnorm(aal zz);
set prob2=cnorm(aa2 zz) cnorm(aal zz);
set prob3=cnorm(aa3 zz) cnorm(aa2 zz);
set prob4=l probl prob2 prob3;
set zprobz(i,l) = sim;
set zprobz(i,2) = probl;
set zprobz(i,3) = prob2;
set zprobz(i,4) = prob3;
set zprobz(i,5) = prob4;
dot varl; set j=j+l; set jj=j+5; set zprobz(i,jj)= ._sm; enddot;
endproc simul 4;


mform(type=gen,nrow=500,ncol=50) zprobz=0;
set i=0;

mat mmx4=mxcorn4; ? summer matrix;


? Simulation sim=
?==================
set sim=401;
dot varl; set ._sm
msd(noprint) q24;
msd(noprint) hwz;


Summer base;


= 0; enddot;
set q24_sm=@mean(1);
set hwz sm=@mean(1);


set j=0;
set i=i+l;
simul 4;


? Simulation sim=4
?==================
set sim=410;
dot varl; set ._sm
msd(noprint) q24;
msd(noprint) hwz;
set chd sm=l;


Summer presence of children;


= 0; enddot;
set q24_sm=@mean(1);
set hwz sm=@mean(1);


set j=0;
set i=i+l;
simul 4;


? Simulation sim=403
? = =- - - --= -


Summer age 3;











set sim=413;
dot varl; set sm
msd(noprint) q24;
msd(noprint) hwz;
set dage3 sm=l;


= 0; enddot;
set q24 sm=@mean(1);
set hwz sm=@mean(1);


set j=0;
set i=i+l;
simul 4;


? Simulation sim=4
?==-----------------
set sim=420;
dot varl; set ._sm
msd(noprint) q24;
msd(noprint) hwz;
set nwpsm=l;


Summer newspapers;


= 0; enddot;
set q24 sm=@mean(1);
set hwz sm=@mean(1);


set j=0;
set i=i+l;
simul 4;

write(format=excel, file='d:\abriggs\new summer.xls') zprobz;


end;
















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Pampel, Fred C. Logistic Regression: A Primer. Thousand Oaks, CA: Sage
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BIOGRAPHICAL SKETCH

Amanda Champion Briggs was born on January 20, 1978 in Sarasota, Florida.

She received her Bachelor of Science degree with honors from the University of Florida

in May 2001 with a major in Food and Resource Economics and specialization in

Applied Economics. She went on to receive her Master of Science degree in Food and

Resource Economics from the University of Florida in August 2003.