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Consumer Preferences for Value Added Products: A Case Study for South Carolina
English Lane Ratliff Clemson University, [email protected]
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Recommended Citation Ratliff, English Lane, "Consumer Preferences for Value Added Products: A Case Study for South Carolina" (2021). All Dissertations. 2770. https://tigerprints.clemson.edu/all_dissertations/2770
This Dissertation is brought to you for free and open access by the Dissertations at TigerPrints. It has been accepted for inclusion in All Dissertations by an authorized administrator of TigerPrints. For more information, please contact [email protected]. CONSUMER PREFERENCES FOR VALUE ADDED PRODUCTS: A CASE STUDY FOR SOUTH CAROLINA
A Dissertation Presented to the Graduate School of Clemson University
In Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy Plant and Environmental Sciences
by English Lane Ratliff May 2021
Accepted by: Michael Vassalos, Committee Chair Marzieh Motallebi, Committee Co-Chair David Willis Juan Carlos Melgar
i ABSTRACT
This study utilized an online survey administered to South Carolina (SC) consumers in conjunction with a discrete choice modeling approach to examine: i) the knowledge and degree of familiarity with both the USDA Certified Organic and the
Certified Naturally Grown labels, ii) the impact of several factors including demographic characteristics, purchasing behaviors, and frequency of organic consumption on consumer’s preferences and Willingness to Pay (WTP) for organic products in South
Carolina. This study also includes a Meta-Analysis of WTP for organic products.
The first chapter analyzes familiarity of two different labels; USDA Certified
Organic and the Certified Naturally Grown. This is achieved by utilizing a Bivariate
Ordered Probit model in combination with a Tukey Kramer test. The results indicate that demographic characteristics, and lifestyle preferences have a statistically significant effect on consumer’s familiarity of the USDA Certified Organic and the Certified Naturally
Grown labels. However, divergences remain. For example, findings indicate that older consumers are less likely to be familiar with the USDA Certified Organic label as well as the Certified Naturally Grown label but those who spend more time cooking are more likely to be familiar with the USDA Certified Organic label. Similarly, respondents who live in the Lowcountry region of SC are less likely to be familiar with the USDA Certified Organic
Label. Respondents who have graduate degrees or higher are less likely to be familiar with the Certified Naturally Grown Label but those respondents who shop for produce at health food stores or farmers markets are more likely to be familiar with the Certified Naturally
Grown label.
ii The second chapter investigates the impact of different attributes of tomatoes on consumer WTP. This is attained using a choice experiment coupled with the analysis tool of the mixed logit model. The findings indicate consumers have a significant and positive
WTP for tomatoes purchased at a grocery store and at farmers markets compared to purchasing tomatoes online. Respondents also have a significant and positive WTP for tomatoes grown in South Carolina vs. Canada. The price premium for organic tomatoes grown in South Carolina is $0.25 compared to the WTP for organic tomatoes grown in the
U.S. is only $0.15. These findings have important marketing implications as competition in the organic produce industry increases. It can assist the industry to better target their marketing endeavors and better understand the factors influencing consumer preferences.
The third chapter is a meta-analysis which looks at different international WTP studies focusing on different products from 2005 to 2020 and compares the different WTP estimates to derive a common WTP estimate. The results indicate that the WTP estimate that was derived is $3.75/lb. to $3.80/lb. across the different products and countries.
Keywords: Organic, Produce, Labeling, USDA Certified Organic, Certified Naturally Grown, Bivariate Ordered Probit, Random Parameter Logit, Meta-Analysis
iii ACKNOWLEDGMENTS
I would like to extend my sincerest thanks to those who have supported me as I have strived to fulfil my dreams of completing my Ph.D. at Clemson University. Without their unwavering support, helpful guidance, and personal wisdom, this dissertation would not be complete.
First and foremost, I would like to thank my graduate advisors and committee chairs, Dr. Michael Vassalos and Dr. Marzieh Motallebi, for supporting and believing in me at every step of the way through my Ph.D. and through this dissertation. If it were not for them, I would not have the knowledge and drive to continue my focus on consumer demand, especially in the organic market or the passion to do research on South Carolina consumers. I would also like to thank Dr. David Willis and Dr. Juan Carlos Melgar, my committee members, for their continual help and guidance. As the other members of my thesis committee, they offered me continual patient support throughout this process.
Lastly, I want to say thanks my parents and my family for all their support, patience, and unconditional love. Without their encouraging words and wisdom through this time, I could not have accomplished any of this.
iv TABLE OF CONTENTS
Page
TITLE PAGE ...... i
ABSTRACT ...... ii
ACKNOWLEDGMENTS ...... iv
LIST OF TABLES ...... vi
LIST OF FIGURES ...... vii
INTRODUCTION ...... 1
CHAPTER
I. FACTORS INFLUENCING CONSUMERS FAMILIARITY WITH USDA CERTIFIED ORGANIC AND CERTIFIED NATURALLY GROWN LABELS: A CASE STUDY FOR SOUTH CAROLINA ...... 4
Methods...... 8 Results and Discussion ...... 14
II. COMPARING CONSUMERS’ WILLINGNESS TO PAY FOR ORGANIC, LOCAL, FAIR TRADE, LOW CARBON FOOTPRINT TOMATOES ACROSS THREE DIFFERENT MARKETING OUTLETS ...... 20
Methods...... 24 Results and Discussion ...... 29
III. WTP FOR ORGANIC: A META-ANALYSIS ...... 35
Methods...... 38 Results and Discussion ...... 41
CONCLUSIONS...... 45
REFERENCES ...... 65
v LIST OF TABLES
Table Page
1 Table 1. Barriers to Purchasing Organic 48
2 Table 2. WTP Per County 48
1.1 Table 1.1. Summary Statistics 49
1.2 Table 1.2. USDA Certified Organic Descriptive 50
Statistics
1.3 Table 3. Certified Naturally Grown Descriptive 51
Statistics
1.4 Table 4. Bivariate Ordered Probit Model 52
2.1 Table 2.1 Summary Statistics 53
2.2 Table 2.2 Attribute Options for Choice Experiment 54
2.3 Table 2.3 Random Parameter Logit Results 55
2.4 Table 2.4 Willingness to Pay Results 55
3.1 Table 3.1 Journal Articles 56
3.1.1 Table 3.1.1 Journal Articles Narrowed Down 61
3.2 Table 3.2 Summary Statistics 63
3.3 Table 3.3 Weighted Least Squares Results 63
3.4 Table 3.4 Meta Regression Analysis Results 64
3.5 Table 3.5 Correlation Matrix Results 64
vi LIST OF FIGURES
Figure Page
1.1 Figure 1.1 Map of South Carolina Regions 10
1.2 Figure 1.2 Familiarity with USDA Organic Label 18
1.3 Figure 1.3 Familiarity of Certified Naturally Grown Label 18
2.1 Figure 2.1. Growing Method 33
2.2 Figure 2.2. Example of Choice Experiment 34
vii INTRODUCTION
This dissertation consists of three chapters that examine the economic value of value-added attributes for agricultural products. This includes labeling for both USDA
Certified Organic and Certified Naturally Grown products, as well as, different marketing outlets (online, farmer’s market, and grocery stores) and attributes such as organic, local, low carbon footprint, and fair-trade certified for tomatoes. The familiarity of labels, is examined in the first chapter, because studies showed that consumers have preferences for certain labels such as USDA Certified Organic but may not actually know what the label is telling them (Sangkumchaliang & Huang, 2012; Samant & Seo, 2016).
There is limited background on organic farming in South Carolina (SC) because it is one of the states with the lowest adoption of organic practices. For example, Greene,
2003 found that South Carolina is one of the states where adoption of organic farming declined from 1997-2001. Hanson et al. (2004) found that a reason for this could be because the few certifiers in South Carolina had switched from certifying farms to education about organic. According to the United States Department of Agriculture
(2016), out of the 14,093 certified and exempt org anic farms in the United States, only 47 operate in SC. Despite this low number, there was a 200% increase in SC certified organic farms since 2011 (Pew Research, 2020). An online survey was constructed to determine knowledge of labels and preferences for marketing outlets for tomatoes among consumers in SC.
The survey was conducted in January of 2018 and distributed to SC consumers and yielded 520 responses. This dissertation consists of three chapters that explore
1 knowledge of labels, preferences for value added products and willingness to pay for organic around the world. The first two chapters explore label familiarity and organic consumption in SC while the third chapter using Meta-Analysis, explores organic willingness to pay across the world.
A plethora of studies have examined the barriers to consumption of organically grown products (i.e. Lea &Worsley, 2005; Botonaki et al., 2006; Hughner et al., 2007;
Sierra and Strochlic, 2007; Thogersen, 2010; Soltani et al., 2014; Doorn & Verhoef,
2015; Nandi et al., 2015; Bryla, 2016). For example, Bryla, 2016 found that the important barriers for organic in Poland is high price and also insufficient knowledge about organic. The reason for exploring SC consumption of organic is because it is one of the states with the lowest per capita consumption.. Reasons that may prohibit SC consumers from purchasing organic products are listed Table 1. The first and foremost reason was that “the price is too high”. Consumers in SC are concerned with the price of organic products compared to conventional. The second top reason is that SC consumers are satisfied with their current food purchases which is consistent with Roddy et al.
(1994) and Bryla (2016)’s findings. The third top barrier according to the respondents was that there is no difference in taste between conventional and organic products.
After pondering into the barriers to purchasing organic, I examined the areas where
SC consumers would be more likely to purchase organic. In chapter two, I examine the individual consumer willingness to pay (WTP) for organic tomatoes. Each consumer was asked to indicate how much of a price premium they are willing to pay for a Certified
Organic product. The percentage increment is a premium that producers could charge for
2 their organic produce. This is important because switching from conventional to organic is costly and time-consuming due to a required certification process for producers. Measuring consumers’ WTP provide valuable information to producers about the highest premium that they can charge consumers for their organic product. Each survey participant was separated by county to explore which counties in SC are more likely to pay a premium for organic indicated in Table 2. Table 2 shows each county highlighted in blue in South
Carolina in which respondents indicated that they would pay higher premiums for organic such as a 10% premium or a 15% premium for organic products. The cells in the table that are highlighted in pale orange indicates that those counties would not be willing to pay a premium for the organic products. These counties in pale orange would be willing to pay lower premiums or no premium than 10%. Results from Table 2 shows that the consumers who live in counties closer to a major city are willing to pay a higher premium for organic.
This result may suggest that farmers would be better off with selling their organic produce at farmer’s markets in these areas.
Chapter 3 includes a Meta-Analysis for WTP for organic products that looks at different studies from 2005 to 2020. These studies include different products and examines
WTP premiums from around the world. This will be helpful in regards to looking at a unified WTP premium range for organic products. These three papers together provide important information to producers and marketers in SC planning to expand the organic market.
3 CHAPTER ONE
FACTORS INFLUENCING CONSUMERS FAMILIARITY WITH USDA CERTIFIED ORGANIC AND CERTIFIED NATURALLY GROWN LABELS: A CASE STUDY FOR SOUTH CAROLINA
Food labeling began in the 1850s as a safeguard against illness after President
Zachary Taylor died eating contaminated fruit at a picnic (Factual Food Labels, 2018).
After this incident, President Abraham Lincoln created the United States Department of
Agriculture (USDA) in 1862 although a formal food nutrition label was not required on food packaging until the 1960s (Factual Food Labels, 2018). Today, numerous labels exist for food products. This chapter examines two of them: USDA Certified Organic and the
Certified Naturally Grown label.
The Fair Packaging and Labeling Act that was passed in 1967 to ensure that consumers were not deceived by the food they purchase (Federal Trade Commission,
2020). In 1973, the Supreme Court ruled that food companies not include false health claims on their labeling. In 1990, the Food and Drug Administration (FDA) mandated that there needs to be homogeneous information on every label such as nutrition, ingredients as well as vitamins and minerals included.
Although, the “father of modern organic farming” J.I. Rodale began providing information about non-chemical farming in the 1940’s in his magazine Organic Farming and Gardening, there was not a consistent definition of what organic meant. Today, demand for organic goods (produce, dairy products packaged foods etc.) is increasing rapidly for many reasons (USDA ERS, 2019).
4 The Pew Research Center (2020) found that health concerns are the main reason that American consumers purchase organically grown products. Environmental reasons
(i.e. air pollution, clean drinking water) are also often mentioned as reasons why consumers purchase organic products (Nugmanova, 2017), Consumers are increasingly demanding that companies and producers be explicit about the practices and the impacts these have on the planet and their health (Bemporad & Baranowski, 2007). Another reason that consumers purchase organic is quality of organic foods (Ozguven, 2012).
In 1990, congress passed the Organic Foods Production Act to develop a national standard for organic production (Sustainable Agriculture Research & Education, 2020).
The National Organic Program (NOP) was established by Congress in 2001 (USDA, 2020).
The NOP is the group that accredits certain companies that inspect and certify medium and large-scale operations (USDA, 2020). Organic certification is a process that producers can go through to be able to use the label “Certified Organic” which means that the products are produced by following strict rules such as what chemicals can be used and how much time in between spraying and planting for example.
The second label examined is the “Certified Naturally Grown”. Starting in 2002 in the mid-Hudson Valley, Certified Naturally Grown (CNG) is a private non-profit program that consists of farmers who do not use any synthetic fertilizers, pesticides, herbicides or
Genetically Modified Organisms (GMOs). Contrary to the NOP certification process,
CNG’s certification process is based on peer reviews where the inspections are done by other farmers. Less paperwork is required for CNG compared to NOP and CNG is better for small-scale, direct to market farmers. There are also options to be CNG for aquaponics
5 or mushroom operations as well as produce and livestock. To my understanding, the knowledge of the Certified Naturally Grown label has not been addressed in literature.
In order to become a CNG operation, there are membership dues that start at a minimum of $150, the farmer or owner needs to sign an agreement stating the terms of the
CNG program, and last but not least there has to be an annual on-site inspection when the application has been accepted. The farmer is also required to participate in the peer-review process by completing one peer review annually. The certification process is based on the type of certification that is necessary for that individual farm. The farm needs certification in produce, livestock, apiary (beekeeping), aquaponics, and mushroom. There are over 750
CNG farmers and beekeepers throughout Canada and the United States. However, there are only four CNG operations in South Carolina.
Much research has been done on labels and what leads consumers to purchase certain items. Bialkova & Van Trijp (2011) and Van Loo et al. (2015) found that more usable labels that get more visible labels get more attention are more likely to lead to a purchase. Labels that indicate organic, origin, environmental sustainability, and nutrition labels are common, but consumers do not always understand what each label means. For instance, Sireix et al. (2013) found that consumers did not think that labels have enough information. According to Baker (2015) and Annunziata et al. (2018), consumers do not know what most labels mean. Eden (2011) noted that when it comes to labels that claim nutritional benefits, consumers have a harder time understanding what those labels mean.
Bialkova & Van Trijp (2010) found that their participants knowledge of nutritional labels
6 was an important factor in whether consumers pay attention to the label. Valor et al. (2013) stated that there is a significant relationship between label knowledge and label use.
Some consumers are not familiar with certain terms featured on labels. For example, Campbell et al. (2015) observed some consumers do not know the terms eco- friendly or sustainable. Annunziata et al. (2018) found that only a limited number of consumers understand what the Fair-Trade label means. Many studies found that familiarity reduced label uncertainty (i.e. Lähteenmäki et al. 2010; Pieniak et al. 2010;
Thøgersen et al. 2010; Fenko et al. 2016; Taufique et al. 2017). Grzelak & Maciejczak
(2013) concluded that labels are more important for consumers in a less developed organic market.
Some studies found that demographic characteristics influence label knowledge and perception. For instance, Nayga (1999) found that higher educated people are less likely to read labels to choose food. In contrast, Falola (2014) observed that consumers with higher education were more likely to read the food label. Falola (2014) also noticed older consumers are more likely to read food labels. Verbeke et al. (2012) found that men were more aware of certain European Union Quality labels. However, Falola (2014) found that women were more likely to be willing to read food labels. Concurrently, Aban et al.
(2009) found that women are more likely to respond if a seller puts food safety product information on their label. Verbeke et al. (2012) also found that older consumers were more aware of quality labels than younger consumers. Aban et al. (2009) observed that lower income consumers were less familiar with food safety labels than middle- and higher- income consumers.
7 Certain labels, such as organic and health labels, are well known to most consumers. Piedra et al. (1996) found that consumers read nutritional labels on packaged meats. On the other hand, Schupp et al. (1998) found that higher income consumers were less likely to read nutrition labels on ground beef. According to Brčić-Stipčević & Petljak
(2012), while consumers are familiar with the organic label in Croatia, the largest group that recognizes the organic label is the group that rarely purchases organic foods.
Sangkumchaliang & Huang (2012) found that even though some Thai consumers are familiar with the organic label, they might not have correct information for that specific label. Magistris et al. (2012) found that women and consumers with higher incomes utilize organic labels. Samant & Seo (2016) indicates that consumers are still confused by the organic claims.
The objective of the present study is to investigate the role of demographic variables, consumers’ food shopping preferences, and consumers’ lifestyle choices on the probability that SC residents are familiar with both the USDA Certified Organic label and the CNG label.
Methods
The data source for the study was an online survey distributed by Qualtrics to 520 primary grocery shoppers in SC. Qualtrics is an online survey tool that allows the researcher to create a survey and then define the parameters of consumers and then
Qualtrics distributes the survey to their panel of consumers based on certain guidelines from the researcher (i.e. area, consumers demographic). The final sample depends on the
8 funds available to the researcher. The response rate is not known to us. The survey was distributed the second week of January 2018.
The survey instrument was pretested with help of consumers and Clemson
University extension agents. The questionnaire consisted of five sections. The first section includes a set of screening questions. The second section includes general questions about consumers’ lifestyle characteristics and shopping preferences. The third section contains a choice experiment.
The fourth section asked questions about willingness to pay (WTP) for organic, and organic knowledge. This section includes the question which features six labels and asks the question “how familiar are you with this label” in which they were given a picture of each label as a visual aid. The survey concluded with the traditional demographic characteristics. The data set is analyzed using a Bivariate Ordered Probit (BOP) model.
Table 1.1 reports summary statistics for the demographic variables of the sample and of SC. Overall, the females were over sampled relative to the state of SC. However, this is not unexpected since the primary grocery shopper1 in the household completed the survey, which most often is a female member. Furthermore, less educated consumers are underrepresented in the survey (Table 1.1). This is also not unexpected, considering that consumers with lower education levels may use less frequently or not utilize internet. The income distribution of the survey respondents closely represented the income distribution of SC residents.
1 The survey featured a question that asked if the person responding was the primary grocery shopper for the household and if the respondent chose “no”, then the survey ended.
9 The survey respondents were evenly distributed across the four regions of SC
Figure 1.1 is a map of SC that includes the Upstate, Midlands, Lowcountry and Pee Dee regions. Thirty-one percent (31%) of the respondents represented the Upstate region.
Twenty eight percent (28%) of the respondents represented the Midlands region. Twenty three percent (23%) of respondents indicated they reside in the Lowcountry region, while
18% said they live in the Pee Dee region. The actual population of SC categorized by region is as follows: Twenty nine percent (29%) of the respondents represented the Upstate region.
Twenty nine percent (29%) of the respondents represented the Midlands region. Twenty three percent (23%) of respondents indicated they reside in the Lowcountry region, while
18% said they live in the Pee Dee region.
Figure 1.1. Map of South Carolina Regions
10 Model
To evaluate consumers’ familiarity with the USDA Certified Organic and CNG labels, survey participants were asked to rate each of the labels on the following scale:
“This label is completely unknown to me”, “I am somewhat familiar with this label”, or “I am very familiar with this label”.
A Tukey–Kramer test is used to conduct comparisons across the three groups
(completely unknown, somewhat familiar, and very familiar with the USDA Certified
Organic and CNG labels). Based on the Tukey–Kramer test, a statistically significant difference between two means exists when
(1) |푌푖 − 푌푗| ≥ 푞(훼; 푘, 푣), 1 1 ( + ) √ 푛푖 푛푗 푆 2 where, 푌푖 and 푌푗 are the means for groups i and j, respectively, S is the root mean square error (pooled standard deviation), 푛푖 and 푛푗 are the number of observations and q(α;k,v) is the critical value for the studentized distribution of k normally distributed variables with degrees of freedom equal to v and a significance level of α (Katchova, 2006).
These familiarity options are ordered and categorical which means that the
Bivariate Ordered Probit (BOP) model is appropriate for this paper. A BOP model is very similar to a Univariate Ordered Probit model except instead of one dependent variable, there are two dependent variables. In addition, BOP model accounts for the correlation between error terms of two univariate ordered probit models (Wali et al. 2017).
11 Following Cameron & Trivedi (2005), the general specification of the Ordered
Probit model was calculated first since the BOP is an extension of this model and follows a broad ordering system using:
∗ ′ (2) 푦푖 = 훽 푥푖 + 휀푖
where y* is an unobserved latent measuring the preferences of the ith respondent,
denotes the vector of the coefficients to be estimated, 푥푖 is the vector the observed explanatory variables, and is the error term, assumed to be normally distributed. The observed variable yi was given by:
(3)
∗ 1, 푦푖 < 휇1 ∗ 2 , 휇1 ≤ 푦푖 < 휇2 푦 = . 푖 . ∗ 퐹, 푦푖 > 휇퐽 {
where, 휇1-휇2 are unknown cutoff values to be estimated with and F is the number of discrete categories. The categories that the respondents had the option of choosing are
“This label is completely unknown to me”, “I am somewhat familiar with this label”, and
“I am very familiar with this label” to evaluate consumers’ familiarity with the USDA
rd Certified Organic and CNG labels. Therefore, F=3. The probability that 푦푖 falls into the 3 category was given by:
(4)
−훽푥푖 ( ) ( ) Prob(푦푖=1)= ∫−∞ φ 휀푖 푑휀푖=Φ −훽푥푖 ,
휇1−훽푥푖 Prob(푦푖=2)= ∫ φ(휀푖)푑휀푖=Φ(휇1 − 훽푥푖) − Φ(−훽푥푖), −훽푥푖
12 휇2−훽푥푖 Prob(푦푖=3)= ∫ φ(휀푖)푑휀푖=Φ(휇2 − 훽푥푖)Φ(휇1 − 훽푥푖), 휇1−훽푥푖 where φ and Φ signify the standard normal probability density and cumulative distribution functions, respectively (Lewis & Grebitus, 2016).
Since the BOP model is an extension of the Univariate Ordered Probit model, instead of considering one scenario, it considers two scenarios of consumers’ familiarity of the “USDA Certified Organic” (푦1푖) and “Certified Naturally Grown” labels (푦2푖). The
BOP probability is as follows:
(5)
Prob(푦1푖=j, 푦2푖 = 푘)
휇2푘−훽2푥2푖 휇1푗−1푥1푖 = ∫ ∫ φ(휀1푖, 휀2푖, 𝜌)푑휀1푖푑휀2푖 휇2(푘−1)−훽2푥2푖 휇1(푗−1)−훽1푥1푖
= Φ2(휇1푗 − 훽1푥1푖, 휇2푘 − 훽2푥2푖, 𝜌)−Φ2(휇1(푗−1)),
−훽1푥1푖, 휇2푘 − 훽2푥2푖, 𝜌
= Φ2(휇1푗 − 훽1푥1푖, 휇2(푘−1) − 훽2푥2푖, 𝜌)−Φ2(휇1(푗−1)),
−훽1푥1푖, 휇2(푘−1) − 훽2푥2푖, 𝜌
Where 푦1푖 is the observed preference for the “USDA Certified Organic” label
(j=1,2 ,3) and 푦2푖 is the observed preference for the “Certified Naturally Grown” label
(k=1, 2,3), Φ2 are the standard Bivariate normal probability density and cumulative distribution functions and 𝜌 is an unknown correlation between 휀1푖 and 휀2푖 (Lewis &
Grebitus, 2016).
Results and Discussion
Familiarity with Labels
13 The relative familiarity of the two labels, USDA Organic and Certified Naturally
Grown, is reported in Figures 1.2 and 1.3. More than 50% of the survey respondents indicated that they are either very or somewhat familiar with the USDA organic label and the Certified Naturally Grown label. Only 20% of the respondents indicated that they are
“very familiar” with the Certified Naturally Grown label while 53% said they were “very familiar” with the USDA organic label which makes sense because Magistris & Gracia
(2012) found that most consumers report knowledge of the organic label. Thirty nine percent (39%) of consumers responded that they were “completely unfamiliar” with the
Certified Naturally grown label which is larger than the 10% that indicated that they were
“completely unfamiliar” with the USDA organic label. Magistris & Gracia (2012) indicated that 7.1% of consumers in Italy have low knowledge of the organic label compared to the 44.4% of respondents who said that they have high knowledge of the organic label.
Descriptive Statistics by Familiarity Group
Table 1.2 reports summary statistics by respondents’ level of familiarity with the
USDA Certified Organic label and indicates whether the pairwise comparisons between degree of familiarity are statistically significant based on the Tukey-Kramer procedure.
The findings indicate that consumers who are familiar with the USDA Organic label tend to be middle age and also have at least a high school education with higher average income, which is consistent with Verbeke et al. (2012) who found that consumers with a higher education level are more familiar with specialty production labels. The results also
14 indicated that compared to respondents who are very familiar or somewhat familiar with the logo, survey participants who are unfamiliar with the label tend to shop for produce at grocery stores or box stores. Consumers who are very familiar or someone familiar shop for their produce at health food stores or direct to consumer purchasing outlets like farmers markets or CSA’s.
Table 1.3 reports summary statistics by respondents’ level of familiarity with the
Certified Naturally Grown label along with whether the pairwise comparisons between degree of familiarity are statistically significant based on the Tukey-Kramer procedure.
Multiple significant independent variables had a statistically significant effect on the familiarity with the CNG label. Consistent with the results for the USDA organic label familiarity, the average age of respondents who are very familiar with the label is 20 years old, which still contradicts Verbeke et al. (2012)’s findings that older consumers are more aware of quality labels. Respondents who have an average income of $49,000 are likely to be very familiar with the label. Compared to respondents who are very familiar or somewhat familiar with the logo, survey participants who are unfamiliar with the label tend to shop for produce at grocery stores. The results also indicate that consumers who spend about 4 hours a week cooking are very familiar with the CNG label.
Bivariate Ordered Probit Model Results
The estimated BOP model incorporated both demographic characteristics and lifestyle choices. The dependent variables are consumers familiarity of the two labels,
USDA organic and CNG. Table 1.4 shows the coefficients and standard errors, and the level of significance of the variables. The rho (𝜌) coefficient was significant and positive
15 at 0.299 which shows that the BOP model was the appropriate model and that the familiarity of the two labels were not independent of each other.
Organic Label Familiarity
Older consumers are less likely to be familiar with the organic label which is consistent with Cannoosamy et al. (2014). This contradicts Verbeke et al. (2012)’s findings that older consumers are more aware of quality labels. The organic consumption also has certain significant behavioral and lifestyle variables associated with it. Consumers who responded that they would order produce online if they could get the same day delivery were more likely to be familiar with the organic label. Concurrently, respondents that indicated they spend more time cooking per week are more likely to be familiar with the organic label. This finding is logical because consumers that spend more time cooking are more likely buying ingredients in the grocery store.
On the other hand, respondents that currently reside in the Lowcountry region of
SC are less likely to be familiar with the organic label. This could be explained by the fact that there are counties in the Lowcountry region that are more rural and have less access to organic labeled products.
Purchasing outlet such as grocery store, health food stores, or direct-to-consumer outlets such as online and Farmer’s Markets also had a significant effect on the familiarity of the organic label compared to purchasing at a box store such as Sam’s Club. Consumers who chose to purchase their produce at the grocery store, a health-food store (such as
Whole Foods, Earth Fare, and Trader Joes), and direct-to-consumer outlets (such as
Farmer’s Markets, Community Supported Agriculture farms, and Online) are more likely
16 to be familiar with the organic label compared to those consumers who purchase their produce at a box store. This makes sense because these respondents are already going out of their way to get fresh produce at certain places and having it delivered to them, so they should be more aware of the organic label.
Certified Naturally Grown Label Familiarity
Contrary to Cannoosamy et al. (2014), results indicate consumers with graduate school degrees or higher are less likely familiar with the CNG label. Nayga (1999) explains that consumers with higher education feel that it does not make choosing certain food items easier to read the labels before purchasing so that could be the reason for unfamiliarity with the CNG label. Unlike the USDA organic label, behavioral and lifestyle variables were not significant.
However, consumers who shop at health food stores such as Whole Foods or Earth
Fare for their produce are more likely to be familiar with the CNG label. Consumers who shop at these places are looking for healthy foods which is the main reason consumers who shop there are more familiar with the CNG label. Consistent with consumers who go out of their way to get fresh produce at a farmer’s market or a CSA who are more likely to be familiar with the CNG label as well.
17 USDA ORGANIC LABEL
This label is completely unknown to me I am somewhat familiar with this label I am very familiar with this label This label is completely unknown to me 10%
I am very familiar with I am this label somewhat 53% familiar with this label 37%
Figure 1.2. Familiarity with USDA Organic Label
CERTIFIED NATURALLY GROWN LABEL
This label is completely unknown to me I am somewhat familiar with this label
I am very familiarI am very with this label familiar with this label 20% This label is completely unknown to me 39%
I am somewhat familiar with this label 41% Figure 1.3. Familiarity with Certified Naturally Grown Label
18 The present paper utilized data from 520 SC residents through an online survey to evaluate how different consumer characteristics including demographics and lifestyle choices effect knowledge of certain labels. A BOP was employed to analyze the data. This model was chosen because it considers two scenarios which is what is needed when looking at both Certified Naturally Grown and USDA Certified Organic labels.
In general, the Certified Naturally Grown label is less recognized in SC compared to the USDA Organic Label. Older consumers are less likely to be familiar with both the
USDA organic and the CNG label. More highly educated consumers are less likely to be familiar with the CNG label, which is consistent with previous literature. Consumers who purchase fresh produce by going to a health-food store or a direct-to-consumer outlet such as a farmer’s market are more likely to be familiar with the USDA organic and the CNG label. Producers should be selling their produce at these outlets such as health-food stores and farmer’s markets and should be marketing to younger consumers. They could use social media to market to younger consumers as well as online advertising.
A limitation of this paper is that only consumers from South Carolina are surveyed so inferences to the national population cannot be made. Future research endeavors should expand the geographic the geographic scope of the analysis.
19 CHAPTER TWO
COMPARING CONSUMERS’ WILLINGNESS TO PAY FOR ORGANIC, LOCAL, FAIR TRADE, LOW CARBON FOOTPRINT TOMATOES ACROSS THREE DIFFERENT MARKETING OUTLETS
Traditionally, the primary marketing outlets for food products with value added attributes (such as organic, local, etc.) included direct marketing outlets, specialty and natural stores (Ellison et al., 2016). However, as mainstream consumers increasingly focus on healthy diets, social, and environmental implications of food production/consumption, and are willing to pay price premiums for value added attributes, the number of marketing outlets has expanded (Ellison et al. 2016). A survey by Nielsen (2017) showed that supermarkets and discount grocery channels account for only 25% of the organic food sales.
Concurrently, online grocery sales have shown a continuous growth over the last years (Anesbury et al. 2016). The rise in the popularity of online grocery shopping is evident in a recent survey by the Food Marketing Institute (2017), which reported that 51% of respondents are willing to buy groceries online in the future. This share of untapped potential consumers highlights an opportunity to increase the online grocery market.
Furthermore, motivated by this expansion, conglomerates (e.g. Amazon, Walmart) as well as entrepreneurs (e.g. Instacart.com, FreshDirect.com), are entering the online grocery shopping realm.
Although numerous studies have evaluated consumers’ willingness to pay (WTP) for value-added attributes, limited literature (such as Lim et al., 2018; Printezis & Grebitus,
20 2018) exists that compares consumer preferences and WTP across different marketing outlets. The present study is an effort to cover this gap in the literature.
Specifically, the objective of the present study is to examine and compare consumers’ valuation of organic, local, low carbon footprint, and fair-trade certified tomatoes in South Carolina (SC) across three different marketing outlets: 1) brick and mortar grocery store, 2) farmers market, 3) and online. Tomatoes are selected as a focus product because it is a product most consumers are familiar with and sold in all three marketing outlets. South Carolina was chosen because Instacart2 has become available there to order groceries online. Another factor is that in SC there are three large cities that attract young professionals who are more likely to use online grocery shopping.
A plethora of studies have examined consumers perceptions towards online grocery shopping and different marketing outlets for certain products with specific attributes. Liang
& Lim (2011) indicated that consumers with higher computer skills are more likely to purchase specialty food online. Pozzi (2012) observed that websites that have features such as “favorites lists” that reduce time spent shopping significantly limits online brands.
According to the Food Marketing Institute (2020), online food purchases are becoming more popular faster than any other product purchases from online markets. Lim et al.
(2018) note that historically online grocery store purchases were not common because of quality problems and shipping costs.
2 Instacart is a company that facilitates online delivery of groceries. Consumers can order groceries online of via the app and get them delivered to their door from features stores.
21 Farmers Markets are popular retailers for food products. Toler et al. (2009) and
McEachern et al. (2010) state that consumers who care about purchasing local food are more likely to purchase produce at farmers markets. Zepeda (2009) states that religious consumers, consumers who live with another person, and consumers who enjoy cooking are more likely to shop at farmers markets. McEachern et al. (2010) observed that the most important reason consumers shop at farmers markets is because they are opposed to the environmental footprint left from shipping food long distances (food miles3). In contrast,
Printezis & Grebitus (2018) and Carroll et al. (2013) found that consumers do not have a preference on where to purchase local food, whether it is at a farmer’s market or at a grocery store. Certain food attributes also have an impact on which outlets consumers use to purchase food. Detre et al. (2010) and Ellison et al. (2016) observed that farmers markets and other “fresh” retailers are more likely to attract consumers with preference towards products with organic attribute.
Caputo et al. (2013), McEachern et al. (2010), and Kemp et al. (2010) studied consumers perceptions of the effects of environmental impacts of places of origin for food.
Kemp et al. (2010) indicated that food miles, place of purchase, or origin are not as important to consumers as other attributes such as price. However, Yu et al. (2009) and
McEachern et al. (2010) found that consumers are sensitive to country of origin when purchasing wine and produce. Another attribute consumers are sensitive to is price. Meas et al. (2015) found that for consumers, organic and state proud logos were complements meaning when someone buys organic, they would prefer it also to have a state proud logo.
3 Food miles are the distance that produce has to travel from production to market
22 Small family certification also had a significant effect on consumer preferences (Meas et al. 2015). However, Onken et al. (2011) found that consumers were more willing to pay a premium for products with local and state programs labels when they run their models in sub-state regions.
Certain attributes can affect price, which is important to both the producer and the consumer. Many studies have examined the influence of demographic characteristics on consumers’ organic consumption and their WTP for organic products. However, limited consensus exists. For example, Meas et al. (2015) studied consumers’ WTP for organic blackberry products in Kentucky and Ohio and found consumers are willing to pay a premium for 100% and 70% organic but not for the 95% organic. According to Bernard &
Bernard (2010), households with children are more likely to be willing to pay for organic in Delaware, Pennsylvania, Maryland, and New Jersey. On the other hand, Loureiro &
Hine (2002) found that consumers with children were unlikely to pay a premium for organic in Colorado.
The perceived quality of organic products was among the most important factors for consumers’ willingness to pay a premium. In line with this conclusion, Bernard &
Bernard (2010) and Voon et al. (2011) found that if consumers are willing to accept organic, they are willing to pay a higher premium. Owunsu & Owunsu (2013), Xu et al.
(2014), and Govindasamy et al. (2018) indicated that consumers with a higher education are more willing to pay a higher premium for organic products. Muhammed et al. (2015) found that nationality is an indicator of consumer WTP for organic products. Govindasamy et al. (2018) found that consumers who notice the Certified Organic label are more likely
23 to pay a higher premium for organic. Schott & Bernard (2015) found that consumers who believe conventional farming is an appropriate production technique were willing to pay a premium for both locally grown conventional and organic products. Van Loo et al. (2013) found that some of their organic purchasers were only occasional organic buyers and a smaller number were habitual buyers.
Methods
The main data source for the study is an online survey conducted by Qualtrics to primary grocery shoppers in SC and was distributed the second week of January 2018. The original survey consisted of 520 responses. In this chapter, I only included the consumers who stated that they purchased tomatoes so the number of responses for this chapter is 465.
The questionnaire consisted of five different sections. The first two sections included screening questions and general questions about consumers’ lifestyle characteristics and shopping preferences. The third section included a choice experiment on marketing outlets for tomatoes. Tomatoes was chosen for the choice experiment because they are one of the most commonly grown and purchased products in South Carolina.
In each choice set, respondents select among three options. Specifically, each choice set consists of two non-empty alternatives and an opt-out option, indicating that respondents prefer neither of the first two alternatives. Each of the non-empty alternatives is characterized by different combinations of the tomato attributes. The following six tomato attributes describe the tomato non-empty alternatives: i) whether or not the tomato is fair trade certified, ii) the point of origin of the tomato (product of U.S., product of
24 Canada, SC Certified), iii) if the tomato production has a high, medium, or low carbon footprint4, iv) if the tomato is produced organically or conventionally, v) the marketing outlet (brick and mortar, farmers’ market, online grocery), and vi) price, which is indicated in Table 2.2. The price attribute contains four levels, consistent with retail prices at the time of the survey, however we did not tell consumers what base price to compare the tomatoes price options to. Earlier in the survey, the respondents were given the instructions “assume that the cost of the following conventional products (e.g. non- organic) in your grocery store is the price in the parenthesis. Please indicate how much of a price premium you are willing to pay if the product is certified organic”. The price of tomatoes which was $1.68/lb. for conventional was acquired by looking at actual conventional prices given by USDA (Fruit and Vegetable Prices, 2018).
An example of the choice set is represented in Figure 2.2. The definition of Carbon
Footprint was given to respondents in the survey to assure they were familiar with the topic.
The fourth section asked questions about WTP for organic produces and organic knowledge. The survey concluded with traditional demographic characteristic questions.
Consumer utility for the examined tomato attributes across the marketing channels was elicited through the hypothetical online choice experiment.
4 Carbon footprint represents the amount of greenhouse gases that are released during production. Survey respondents were not given a description to what high, medium and low means but they were told what carbon footprint is.
25 Growing Method
The relative importance of growing method for tomatoes on respondents purchasing decisions is reported in Figure 2.1. More than 50% of respondents indicated they purchase both conventionally and organically grown tomatoes. A quarter of respondents only purchase conventionally grown while 10% of respondents indicated they only purchase organically grown tomatoes.
An important part of the choice experiment is the carbon footprint analysis. Carbon footprint is the amount of greenhouse gases that are produced by actions such as farming.
There is no specific numbers of measurement for Carbon Footprint so I decided to categorize this as low, medium, or high carbon footprint.
The data set is analyzed using a Random Parameter Logit (RPL) model. This model controls for heterogeneity among consumers and is also known as the Conditional Logit and Mixed Logit model. Table 2.1 reports summary statistics for the demographic variables. The sample was first analyzed to see which respondents actually purchase tomatoes and the ones that said that they did not purchase tomatoes were disregarded which left the sample size to equal 465 respondents. Overall, the sample overrepresented females, compared to the general South Carolina population. However, this was expected since the primary grocery shopper in the household completed the survey, which most often is a female member. Furthermore, less educated consumers are underrepresented in the survey
(Table 2.1). This was also expected, considering that consumers with lower education levels may not utilize internet as much. The income distribution of the survey respondents closely represented the income distribution of SC residents.
26 Model Random utility theory was used in this dissertation. Random utility theory is based on a hypothesis that every individual is a rational decision maker who makes decisions solely to maximize his or her utility (Hole, 2013). The utility that the decision maker n receives from choosing alternative j is given by
1) 푈푛푗 = 푉푛푗 + 휀푛푗
Where 푉푛푗 is a function of attributes of the alternatives that can be observed, 푥푛푗, and of the person making the decisions, 푧푛. 휀푛푗 is unknown and is treated as a random term
(Hole, 2013). The probability that the individual n choosing alternative i is
2) 푃푛푖 = Pr (푈푛푖 > 푈푛푗)∀푗 ≠ 푖
= 푃푟 (푉푛푖 + 휀푛푖 > 푉푛푗 + 휀푛푗)∀푗 ≠ 푖
= 푃푟 (휀푛푗 − 휀푛푖 < 푉푛푖 − 푉푛푗)∀푗 ≠ 푖
Assuming that the random terms are IID type 1 extreme value distributed, the
Conditional Logit model is obtained (Hole, 2013):
푒푥푝(휎푛푉푛푖) 3) 푝푛푖 = 퐽 ∑푗=1 푒푥 푝(휎푛푉푛푖)
Usually, the representative utility is specifically a linear-in parameters function such as:
′ ′ 4) 푉푛푖 = 푥푛푖훽 + 푧푛훾푖
𝜎푛 is a scale parameter and is normalized to 1 and 휀푛푗 is homoscedastic implying that the error term is the constant for all values of the independent variables. There are limitations of the Conditional Logit such as the Conditional Logit assumes that the respondents all have the same preferences. Concurrently the equal proportional substitution between alternatives is:
27 ∗ 휕푝푛푖 푥푛푗 ∗ ∗ 5) ∗ = −푥푛푗푃푛푗훽 휕푥푛푗 푃푛푖
This expression is not dependent on i because of the assumption that the error term is independent, but the limitation is the independence of irrelevant alternatives (IIA) property
(Hole, 2013):
퐽 푃 exp(푉푛푖)/ ∑ exp(푉푛푗) exp(푉 ) 푛푖 = 푗=1 = 푛푖 6) 푃 퐽 exp(푉 ) 푛푘 exp(푉푛푘)/ ∑푗=1 exp(푉푛푗) 푛푘
The IIA property states that the likelihood that of choosing choice A or B will not change
if a third option is added (Fry & Harris, 1996). The Mixed Logit solves these limitations
by allowing different decision makers to make different decisions. The Mixed Logit
choice probability is given by:
′ exp(푥푛푖훽) 7) 푃푛푖 = ∫ 퐽 ′ 푓(훽|휃)푑훽 ∑푗=1 exp(푥푛푗훽)
Where 푓(훽|휃) is the density function of 훽. 훽 is the vector of coefficients. Allowing these
coefficients to vary allows for each consumer to have different preferences. It also means
the IIA property restriction does not hold anymore (Hole, 2013). The difference between
the mixed logit model and the Conditional Logit model is that the Mixed Logit presumes
that consumers do not have identical preferences whereas the Conditional Logit assumes
that the consumers will have identical preferences (Hole, 2013). The mixed logit has
multiple advantages including that it is a highly flexible model, it allows for different tastes,
with multiple substitution patterns and unlike the Probit model (another option), it is not
limited to normal distributions (Train, 2002).
Willingness to Pay Estimates
28 Price is looked at to be a fixed parameter. In order to calculate WTP, this expression is utilized:
퐸(훽푘) 8) 퐸(푊푇푃푘) = − 훽푝푟푖푐푒
Results and Discussion
Random Parameter Logit Results
Table 2.4 presents the results of the RPL and has the significance and the base categories that the results are compared to at the bottom of the table. In the context of a mixed logit model, the magnitude of the coefficients cannot be directly compared, but theoretical relevance can be tested on their signs and statistical significance of them. First, the price variable has a significant negative coefficient which shows if the price increases, consumers will decrease purchases. The variable “buyno” represents the third alternative in the choice and indicates that the respondents would prefer neither of the first two options.
This parameter is significant and negative which suggests that if the respondents were not able to choose either option, then their utility would significantly diminish.
The RPL model indicated a statistical significance of the mean coefficients for grocery store, farmers market, conventionally grown, non-fair trade certified, SC origin and U.S. origin. Thus, the respondents showed high preferences for purchasing their tomatoes at a grocery store or at the farmers market as opposed to purchasing their tomatoes online. As Bulsara & Trivedi (2016) indicated, a major problem with purchasing fruits and vegetables online is that there is no way to touch the perishable goods which is how many consumers check the quality of the products.
29 Respondents of our survey also showed high preferences for purchasing tomatoes with no fair-trade5 certification on them. This result is in line with Liu et al. (2019)’s finding where fair trade certification was not a priority for WTP for consumers in Taiwan.
Respondents also showed high preferences for purchasing tomatoes that were grown in SC.
This result supports Moser et al. (2011) who found that consumers like to support local farmers. Respondents also showed high preferences for purchasing conventional tomatoes rather than organic tomatoes which can be explained by Olsen & Wagner (2018)’s study.
They found that the majority of respondents in their study either “couldn’t tell the difference” or “weren’t sure they could tell the difference” between organically and conventionally grown peaches.
WTP results
The WTP estimation results for different choices are reported in Table 2.4 and also indicates the base categories that are compared. Table 2.4 looks at the WTP premiums for grocery store, farmer’s market, carbon footprint, production location, as well as fair trade.
WTP is calculated using STATA 16 and utilizing the command wtp. All attributes but two, high carbon footprint and medium carbon footprint, have positive WTP which indicates respondents willingness to pay is significantly less for tomatoes that have a high or medium carbon footprint compared to tomatoes with low carbon footprint. Other than carbon footprint, respondents are willing to pay more for other attributes such as location of purchase. Results suggests that consumers have a significant and positive WTP for
5 Fair-trade was not introduced to the respondents in my survey so they were expected to be familiar with this concept.
30 tomatoes sold in grocery stores and at farmer’s markets compared to online which is consistent with results of Darby et al. (2006)’s study.
The results suggest that consumers have a significant and positive WTP for tomatoes that were conventionally grown compared to organic but only at a $0.05 premium. Also, consumers’ WTP premium is $0.33 and $0.27 when they purchase tomatoes from grocery store and farmers’ market, respectively. Tomatoes that are not fair trade certified has less WTP at only $0.04 premium. In agreement with Carpio and
Isengildina-Massa (2009), the results suggests that consumers have a significant and positive WTP for tomatoes that were grown in SC versus grown in Canada. Along with
Burnett et al. (2011), there is a WTP for a larger portion of the U.S. such as Midwest, but they found that the WTP increases as the geographic region gets smaller from a whole region to a state level. This is consistent with my results that the WTP for SC grown tomatoes is $0.25 as opposed to the WTP for tomatoes grown in the U.S. which is only
$0.15.
This study utilized data collected from SC residents through an online survey to compare consumers’ valuation of organic, local, low carbon footprint, and fair-trade certified tomatoes across three different marketing outlets: 1) brick and mortar grocery store, 2) farmer’s market, 3) and online. A random parameter logit model was utilized to analyze the data. A limited number of studies have looked at online marketing outlets as a valid option for selling and buying fruits and vegetables.
The results indicate that SC consumers are more likely to purchase their tomatoes from grocery stores and farmer’s markets than online, which is consistent with the
31 literature. The results reveal that SC consumers are more likely to purchase conventionally grown produce rather than organic but for conventional they are willing to pay a $0.05 premium. This could be different for consumers who live in a different country who value organic production more such as Skreli et al. (2017) in Albania. Consumers in SC also do not put a high value on the fair-trade certified label and would only pay a premium of
$0.04. On the other hand, SC consumers significantly value produce that is grown locally in SC and would be willing to pay a premium of $0.25 for products that are local. Similarly,
SC consumers also value produce grown in the U.S. and would be willing to pay a premium of $0.15, which is consistent in what Skreli et al. (2017) found that location is important for WTP for tomatoes.
The findings summarized above can be useful for producers in South Carolina as well as brick and mortar grocery stores and farmer’s markets coordinators. Consumers in
SC prefer purchasing their tomato at grocery stores and farmer’s markets. Thus, making those options available to everyone in SC would increase their purchases. Also utilizing the Certified South Carolina grown label would be helpful for SC producers because our results indicate that they could charge a premium and would still be able to sell large quantities. Future research on online marketing outlets needs to be conducted on a larger scale as opposed to focusing on just one state. A survey limitation was that some of the attributes could have been better explained to the respondents while they were taking the survey. Fair-trade certified is not necessarily a well-known topic as well as carbon footprint and these should have been better explained.
32 Please indicate if you purchase tomatoes. If you buy them, please indicate whether you buy only conventionally grown, organically grown or both.
11%
I do not purchase
25% I only purchase conventionally grown
54% I only purchase organically grown
I purchase both conventionally and organically grown
10%
Figure 2.1. Type of tomatoes purchased by surveyed consumers
33 Figure 2.2 Example of Choice Experiment
34 CHAPTER THREE
WILLINGNESS TO PAY FOR ORGANIC: A META-ANALYSIS
Over the last two decades, consumers do not just purchase food to satisfy their nutritional needs; they buy food that is also environmentally safe and nutritional (Guney &
Giraldo, 2019). Moreover, consumers claim to be "conscious consumers" and increasingly purchase from companies that focus on energy efficiency and health and safety (Bemporad
& Baranowski, 2007). Other common reasons for buying organic are perceived higher quality and taste (Bryla, 2016).
The attraction to healthy choices and environmentally safe foods has been a driving force for consumers to switch from purchasing conventional products to organic products
(Mayrowani, 2012). For example, consumer demand regarding organically grown products has seen continual double-digit growth in the U.S. over the past three decades (Organic
Market Overview, 2020). In Europe, the organic sector has risen over 70% over the past ten years (European Commission, 2019). Consumers in the European Union are the second-largest group to consume organic products in the world (European Commission,
2020). According to Hasselbach & Roosen (2015), Germany is the largest organic food consumer and producer in Europe. The price premiums continue to remain high in multiple markets for these products, such as produce and processed foods (Organic Market
Overview, 2020).
An organic product, according to USDA, is a product that certifies not having used any prohibited chemicals in production (USDA, 2020). There is a list of certified substances that producers of Certified Organic are allowed to use on their crops. According
35 to the European Commission, organic means natural processes must be used when producing the products (European Commission, 2020). Moreover, the products must have as little impact on the environment as possible. Livestock with a USDA Certified Organic label must live in natural living conditions and be fed organic feed and forage. It must not have added hormones or antibiotics (USDA, 2020).
Consumers in different regions purchase organic for different reasons and there is a range of knowledge among consumers. Asia, Khan et al. (2018) found in Pakistan that consumers concerned for their health and the environment were willing to pay for organic.
There is a small market for organic in Thailand with little common knowledge about organic products (Sriwaranun et al., 2015). According to Yin et al. (2019), consumers in
China trust organic products that come from the U.S. before trusting organic products coming from China. Regardless of the location of the studies, organic is steadily becoming an important food sector.
Although there are positive attitudes about organic and markets are rapidly increasing, only a small share of consumers has made this choice to exclusively purchase organic products. Most consumers purchase organic occasionally (Organic Market
Overview, 2020). According to Gil et al. (2000), fresh fruits and vegetables are the highest willingness to pay (WTP) category of organic in Spain. It is important to understand the
WTP because it affects the price that consumers pay in purchasing outlets Producers need to know how much they can charge for organic products. A broad range of research for organic foods in terms of premiums and prices is available worldwide. According to Boys et al. (2014), consumers are willing to pay a premium of $0.35/kg for organic produce.
36 Curtis et al. (2014) found that consumers are willing to pay $1.94, $1.25, and $1.00 for green peppers, cucumbers, and yellow squash, respectively. It is interesting to note that consumers were WTP a higher amount for conventional green peppers, cucumbers, and yellow squash than organic. According to Akgüngör et al. (2010), Mesías Díaz et al.
(2012), and Skreli et al. (2017), consumers were willing to pay a price premium of 0.81
Turkish Lira, 0.81 English pounds, and €2.3 more per kilogram for tomatoes, respectively.
Consumers are also willing to pay a premium for meat raised with organic practices
(Yooyen & Leerattanakorn, 2012; Lacaze et al., 2010; Wang et al. 2018; and Picardy et al.,
2020). Yooyen & Leerattanakorn (2012) found that consumers are willing to pay a price premium of 34.30 Baht for organic pork in Thailand. Consumers who purchase organic orange juice are willing to pay $6.33 per half-gallon of organic orange juice in the U.S. (Bi et al., 2015). According to Naspetti et al. (2019), consumers in Italy are willing to pay €5 for organically produced wine in Italy. Other than food products, according to Hustvedt
(2008) and Ellis et al. (2012), consumers are also willing to pay a premium of $1.86 for socks and $14.20 T-shirts produced with organic cotton in the U.S.
This paper aims to provide a succinct amalgamation of the previous WTP studies for organic from 2005 through 2020. This analysis will take into account methods that the studies employed to elicit WTP estimates.
37 Methods
Meta-analysis methods are useful tools to synthesize results from numerous studies because it limits bias while also changing the results from each paper into a standard metric, so the results are comparative. A meta-analysis can also increase the strength of the results' general statistical power because it is combining sample sizes from multiple studies (Lee,
2019).
Table 3.1 presents an overview of 54 articles reviewed for inclusion in the Meta
Regression Analysis (MRA). For each article , the author, year of publication, country of origin, and type of product analyzed is reported. I identified these 54 articles by conducting a thorough review of the literature using the following electronic databases: AgEcon
Search and Google Scholar. The search includes studies for organic WTP that were written in English between 2005 and 2020. Keywords such as "willingness to pay" and "Organic" wore used as search parameters to make sure all results from the literature are included
(Printezis et al., 2019).
The search comprised titles, journal article keywords, and abstracts (Printezis et al.,
2019). If there were multiple WTP estimates for one product, this could mean that either the sample is split up into groups or different products were evaluated (Printezis et al.,
2019). After reviewing the 54 studies that are initially looked at are not all were acceptable for the MRA. Some papers were not be included because there was not a standard unit to evaluate; for instance, Batte et al. (2007) identified that consumers were willing to pay
$0.45 more for a box of organic cereal while others such as Tagbata et al. (2008) found that consumers’ WTP for organic chocolate was €1.25 per chocolate bar. A chocolate bar
38 and a box of cereal cannot be evaluated because there is no standard unit. Other excluded papers did not have a defined sample size, such as Sabbaghi, et al. (2013), who conducted a survey for the WTP for vegetables in Iran. After the evaluation, 24 papers are included in the analysis and are shown in Table 3.1 extended, which is organized by publication year. Although 24 papers are a small number for a Meta-Analysis, I followed the process of Lusk et al (2005) who included 25 studies and Palupi et al. (2012) who included 13 studies.
. The retained papers were evaluated by the differences such as respondents' location, product type, and valuation elicitation option. There were 30 products evaluated in total which is larger than the number of papers because some papers examined multiple products. Summary statistics for the 24 papers are reported in Table 3.2. The majority
(29.17%) of valuations were received from U.S. respondents. Respondents from European countries, Asian counties and other countries such as Chile and Argentina represented
20.83% of the valuations, respectively. Middle Eastern consumers represented 8.33% of the observations. As reported in Table 3.2, product type was also evaluated. Product type included produce (fruits and vegetables), meat (poultry and pork), and other products such as coffee and wine. Produce included 60% of the products in the articles. Meat comprised
20% or five studies of the products in the 24 papers evaluated. Other products included the remaining 20% of the products in the articles.
The next methodological difference examined was valuation elicitation approach, which included a hypothetical experiment or non-hypothetical experiment. If the papers being evaluated used contingent valuation methods, they were categorized as a
39 hypothetical experiment. If articles used an auction experiment or any other type of experiment where the real money is being used, this was categorized as a non-hypothetical experiment. Hypothetical experiments represented 75% of the articles that were studied.
Non-hypothetical experiments represented the remaining 25%, as shown in Table 3.2.
Meta-regression Models for Estimating WTP for Organic Products
Meta-regression analysis focuses on the differences of the findings as the explanatory variables in a regression. Meta-analysis uses the results from previous papers and expands upon the findings of a single study to more accurately estimate WTP (Printezis et al., 2019). Table 3.1.1 reports the study designs of the papers that are in the analysis including year of the study, country, and product type. Several parameters are estimated in the following MRA model:
퐾 (1) 푊푇푃푖 = 훽0 + 훽1푝푟푒푐푖푠푖표푛푖 + ∑푘=1 훽푘 푋푘푖 + 휀푖
Where 푊푇푃푖 is the dependent variable which captures the i=2, …, 30 identified. WTP estimates are analyzed with a regression on precision measure (which is either sqrt(n) or n), 푋푘 is a vector containing k variables related to the study design used to estimate the
WTP for “organic”, and 휀푖 is a classical i.i.d error term (Printezis et al., 2019).
We start with a simple version of equation (1) that only includes precision measured by sqrt(n) as independent variable:
(2) 푊푇푃푖 = 훽0 + 훽1푠푞푟푡(푛)푖 + 휀푖
̂ According to Printezis et al. (2019), the estimated constant of equation (2) (훽0) provides an estimation for the WTP effect. Exclusive of publication selection bias, the observed
WTP effects should randomly stray around the “true” WTP effect. This occurs if 훽1 = 0.
40 ̂ Hence the t-test for 훽1, also known as the funnel asymmetry test (FAT) is utilized to locate
̂ publication bias (Printezis et al., 2019). Therefore, rejection of the null hypothesis H0: 훽1=0
̂ indicates publication bias. The test of H0: 훽0=0 which is also called the Precision effect test
(PET) estimates WTP for “organic” effect after the correction of publication bias (Printezis et al., 2019). In order to check for robustness, I also estimated (Eq. 2) using the number of participants (n) as a precision measure because the WTP estimates in each study are based on the number of participants per study. My final MRA model extends equation (2) including all variables in the study design Xk which is utilized to estimate the WTP:
퐾 (3) 푊푇푃푖 = 훽0 + 훽1푠푞푟푡(푛)푖 + ∑푘=1 훽푘 푋푙푖 + 휀푖
My econometric approximation of the MRA models specified by equations (2) and (3) includes an obstacle. It is important to check if there is heteroskedasticity in the error terms
(휀푖), which in equations (2) and (3) can cause biased standard errors (Printezis et al. 2019).
To control for heteroskedasticity, I used the square root of the number of respondents
(sqrt(n)) in a weighted regression because the square root of the sample size is positively correlated to the estimation precision (Printezis et al. 2019, Hedges & Olkin, 2002). Thus,
I used weighted least squares (WLS) regression with sqrt(n) as weights is used to generate efficient estimates of equations (2) and (3) (Hedges & Olkin, 2002).
Results and Discussion
The results of the MRA are presented in Tables 3.3 and 3.4. As pointed out above, sqrt(n) was utilized as well as n as precision measures. Stata, version 16, (Stata, College
Station, TX) was used to analyze the data. Table 3.3 indicates the WLS results which looks
41 at the comparison of the WTP for organic across all papers including location of consumers, the type of product, and the year of publication. As mentioned in the methods section, I examined the sqrt(n) and n specifically. The estimated constant which is going to exemplify the main parameter of interest as WTP is positive and significant for organic products, see Table 3.3. This implies that the WTP for organic across the articles that were included ranged between $3.75/lb. and $3.80 /lb.
Table 3.4 presents the results of the MRA model. Based on the F-test, the model is significant. In addition, I ran a correlation matrix and there was not high correlations reported among the independent variables. The results of this matrix are reported in Table
3.5. This indicates that there is not a high degree of multicollinearity. The next step was to evaluate the degree of heteroskedasticity after analyzing the data using WLS. I calculated the Breusch-Pagan (B.P.) test statistic for the model. Heteroskedasticity is rejected because the p-value = 0.0023 and the Chi2 = 1.12 and this included the sqrt(n) and n as precision measures.
I found that in regard to location of respondents, the U.S. is significant and positive, implying that consumers have a higher WTP for organic in the U.S. This could be because in the U.S., the organic market has been growing according to Food Business News (2019).
On the other hand, early years of studies was significant and negative. This could be because organic is becoming more popular and more well-known now than when the earliest of these studies was conducted in 2005. I did not find significant differences in type of products when conducting my analysis. I found that the coefficient of hypothetical experiment was significant and positive indicating that there was a higher WTP elicited
42 from hypothetical experiments versus experiments where money actually changed hands.
Gracia et al. (2012) and Grebitus et al. (2013) did find that WTP measures were different from valuation methods such as choice experiments and auctions.
The amount of research on organic products is increasing steadily. While many researchers are interested in organic products, particular articles focus on the premium consumers are willing to pay for organic products. After investigating this literature, there is a range of willingness to pay estimates which appears to be based on different motivations such as health (Ghorbani & Hamraz 2009; Gianni et al. 2009; Amin et al.
2020), income (Adarsha et al. 2018; Bhavsar et al. 2018), and environmental quality
(Huang & Lee 2014; Güney & Giraldo 2019).
Hence, this paper’s objective was to derive an estimate of the WTP for the organic attribute using the most recent literature. To accomplish this goal, I utilized a MRA, a quantitative method that analyses multiple articles results due to specific characteristics.
The mean WTP for organic ranges from $3.75/lb. to $3.80/lb. which is located in Table
3.3. This research contributes to the already substantial literature regarding WTP for organic by comparing literature that includes articles published from 2005 to 2020.
I evaluate how study characteristics of the studies affect consumer WTP estimates.
For example, my results show that consumers do not favor a specific type of product to be organic. Therefore, those organic farmers should continue to grow their organic produce or raise their organic animal products but should further their profit margin by getting their products transitioned to value-added products . For example, they use organic grapes to make organic wine or use organic ingredients in products such as cereal or baked goods.
43 This addition of processed foods can increase the available products for the farmer, which can increase profitability.
My results indicate that there is a difference between hypothetical experiments and non-hypothetical experiments. There seems to result of a higher WTP as compared to other valuation elicitation styles. I included choice experiments in hypothetical experiments while there could be more future research on the topic of choice experiments as a valuation technique for WTP for organic. There could also be future research on more extensive types of products.
There are limitations to this meta-analysis. First, the small number of studies in this research is a limitation. There could be papers that could have been missed when searching for WTP and organic that might not have included in the title. Hence, future research could be conducted with a more extensive search of the literature. Another limitation was the small number of characteristics that I examined. They were found throughout all papers but extended characteristic research would be beneficial for the future.
44 CONCLUSIONS
Demand for organic products has increased on a national level since 2005
(USDA, 2020). According to Pew Research (2020), the South in general has seen an increase of organic adoption at a slower rate than the rest of the country. Although SC has seen an increase in adoption for organic, it has been at a slower rate compared to other states such as New York and California (Pew Research, 2020). This is a challenge for producers and marketers who are willing to expand into the organic industry in
SC. Moreover, it is important for producers and marketers in SC to know type of consumer, locations of consumers, and methods to reach these consumers.
The present dissertation utilized data collected from SC residents through an online survey to evaluate how different consumer characteristics ranging from demographics to location of their grocery purchase could influence the familiarity and WTP for organic.
Consumer familiarity for USDA Certified Organic and the Certified Naturally Grown labels was measured using a Bivariate Ordered Probit model. In addition, consumer WTP for organic tomatoes was measured using a random parameter logit model.
In regard to label familiarity, there are a number of characteristics that were significant when measuring familiarity to labels. Age was a significant and negative factor for both the USDA Certified Organic and the Certified Naturally Grown labels which indicates that producers should be reaching out to younger generations when marketing organic products. Consumers who shop for their produce at direct-to-consumer outlets such as Farmer’s Markets and CSA’s are more likely to be familiar with both the USDA
Certified Organic and the Certified Naturally Grown labels which indicates that farmers
45 should be going to these locations to sell their products. Moreover, consumers who shop at health food stores such as whole foods or earth fare for their produce are more likely to be familiar with both the USDA Certified Organic and the Certified Naturally Grown labels which indicates producers and marketers should be trying to expand to these stores as well.
Future research on consumers familiarity with labels in different states can help us understand how consumers knowledge might differ from one region to another.
In Chapter 2, I found that SC consumers prefer conventional tomatoes over organic tomatoes and also, they prefer to purchase tomatoes that are grown in South Carolina. The findings indicate that consumers have a significant and positive WTP for tomatoes that are retailed in grocery stores and Farmer’s Markets. My results show that consumers in SC are willing to pay a $0.33 premium for tomatoes sold at grocery stores. South Carolina consumers do not prefer purchasing tomatoes that are sold online but prefer that tomatoes that are fair-trade certified. These results are important for SC producers because sales at grocery stores are preferred as well as Farmer’s Markets. Producers should market their products at these locations rather than online since grocery stores and Farmer’s Markets are consumers’ preference for retailers.
The third chapter of the dissertation included a Meta-analysis to determine the
WTP for organic products, I found that there was a mean WTP for organic that ranges from $0.16/lb. and $6.29/lb. I found that there seems to be a higher WTP when the researcher used a hypothetical experiment vs. a non-hypothetical experiment. I also found that consumers do not favor a specific type of organic product. I recommend that farmers try to expand into value added foods as well to gain even more profit. In regard to the
46 meta-analysis, there needs to be future research that specifies more types of organic products other than produce, meat products and cotton products such as cereal, milk, orange juice, chocolate bar for example.
47 Tables
Table 1. Barriers to Purchasing Organic Barriers to Purchasing Organic (Select all that apply) Percent of Respondents The price is too high 83% I am satisfied with my current food purchases 31% I believe there is no difference in taste between conventional and organic products 20% Organic products are not easily available for me 16% I am not familiar with organic production 10% I do not trust the labels 8% I do not trust the organic certifying agencies 8% Organic products are not available close to my residence 6%
Table 2. WTP per County for Organic. (Blue indicates Counties of Highest WTP)
Products Counties Peaches Dorchester Oconee Beaufort Blueberries Dorchester Beaufort Anderson Watermelons Dorchester Berkeley Oconee Apples Dorchester Horry Greenville Tomatoes Dorchester Oconee York Squash Dorchester Horry Greenville Peppers Dorchester Pickens Charleston Cucumbers Dorchester Charleston Richland
Eggs Dorchester Charleston Greenville
Milk Greenville Charleston Dorchester Ground Beef Oconee Dorchester Greenville Whole Chicken Oconee Dorchester Charleston This table indicates that survey respondents in the counties in blue indicated that they were WTP a 10% or higher premium for certain organic products while the respondents in counties in the pale orange indicated that they would be WTP less than a 10% premium.
48 Table 1.1. Summary Statistics Variable Sample N=520 Population Female 84.13% 51.40% Married 59.00% 43.00% Education Bachelor's Degree or Higher 38.89% 25.40% Some College/Tech 39.46% 21.00% School/Associates Degree High School Graduate 19.73% 30.00% Age 18-25 11.66% 7.30% 26-34 23.14% 12.80% 35-54 33.84% 26.40% 55-64 13.96% 13.00% Above 65 yrs. Old 14.70% Annual Income Less than $14,999 10.15% 15.50% $15,000-$24,999 12.64% 12.70% $25,000-$49,999 28.16% 26.40% $50,000-$74,999 22.80% 18.00% $75,000-$99,999 12.84% 11.20% $100,000-$149,000 9.58% 10.40% Above $150,000 3.83% 6.00%
49 Table 1.2. USDA Certified Organic Descriptive Statistics (Tukey-Kramer) Completely Somewhat Familiar Very Familiar Variables Unknown Demographic Information Mean SD Mean SD Mean SD Female 0.100 0.301 0.364 0.482 0.535 0.499 Average Income 42.501 25.147 41.377cb 31.645 59.663 49.265 Average Age 44.500 24.210 44.704cb 15.498 51.977 50.103 Yes, Children Live with Me 0.083 0.277 0.333 0.472 0.583 0.494 Higher Education 0.127 0.335 0.324 0.471 0.549 0.501 Bachelor's Degree 0.061 0.240 0.374 0.486 0.565 0.498 Associate's Degree 0.107 0.310 0.325 0.470 0.568 0.497 High School Education 0.126 0.334 0.456 0.501 0.417cb 0.496 Cooking and Purchase Tendency Average Hours Spent Cooking 6.506 2.553 6.472 2.411 6.492 2.436 Per Week Order Online Yes if It Was 0.070 0.256 0.287 0.454 0.643 0.481 Able to Get Produce the Day Of Order Online Maybe if It Was 0.097ca 0.297 0.388 0.488 0.515cb 0.501 Able to Get Produce the Day of Order Online No If It Was Able 0.150 0.358 0.425 0.496 0.425 0.496 to Get Produce the Day Of SC Residency (Regarding Map in Chapter 1) More than 10 Years in SC 0.096 0.296 0.394 0.489 0.510 0.501 Upstate Region 0.069 0.254 0.350 0.478 0.581 0.495 Midlands Region 0.089 0.286 0.390 0.490 0.521 0.501 Lowcountry Region 0.169 ca 0.377 0.347ba 0.478 0.483 0.502 Pee Dee Region 0.093 0.292 0.381 0.488 0.526 0.502 Purchasing Outlet Grocery Store 0.076ba 0.266 0.376 0.485 0.547 0.499 Health Food Store 0.130 0.344 0.130 0.344 0.739cb 0.449 Box Stores 0.189ba 0.393 0.451cb 0.500 0.361ca 0.482 Direct to Consumers 0.028ca 0.167 0.139cb 0.351 0.833 0.378 Note: a indicates statistically significant difference between the completely unfamiliar and somewhat familiar groups. b indicates statistically significant difference between the completely unfamiliar and very familiar groups. c indicates statistically significant difference between the somewhat familiar and very familiar groups, at the 5% level. More than 10 years in SC was determined to see if the respondent was a native of SC. The different regions are the different regions in SC the respondent resides in.
50 Table 1.3. Certified Naturally Grown Descriptive Statistics (Tukey-Kramer)
Variables Completely Somewhat Very Familiar Unknown Familiar Demographic Information Mean SD Mean SD Mean SD Female 0.399 0.490 0.417 0.494 0.208 0.407 Average Income 32.773 47.137 46.218 50.067 49.205 54.732 Average Age 36.158ba 48.182 44.067 49.787 19.774 39.949 Yes, Children Live with Me 0.352 0.479 0.440 0.498 0.208 0.407 Higher Education 0.493 0.504 0.380 0.489 0.127 0.335 Bachelor's Degree 0.443 0.499 0.382 0.488 0.176 0.382 Associate's Degree 0.379 0.486 0.413 0.494 0.209 0.407 High School Education 0.311 0.465 0.466 0.501 0.223 0.418 Cooking and Purchase Tendency Average hours spent cooking 3.354 4.736 4.113 4.936 4.002 4.645 per week Order Online Yes if it was able 0.350 0.479 0.439 0.498 0.210 0.409 to get produce the day of Order Online Maybe if it was 0.363 0.482 0.447 0.498 0.190 0.393 able to get produce the day of Order Online No if it was able 0.504 0.502 0.315 0.466 0.181 0.387 to get produce the day of SC Residency (Regarding Map in Chapter 1) More than 10 years in SC 0.388 0.488 0.421 0.494 0.190 0.393 Upstate region 0.369 0.484 0.400 0.491 0.231 0.423 Midlands region 0.425 0.496 0.397 0.491 0.178 0.384 Lowcountry region 0.432 0.497 0.398 0.492 0.169 0.377 Pee Dee region 0.340 0.476 0.474 0.502 0.186 0.391 Purchasing Outlet Grocery Store 0.426ca 0.495 0.412 0.493 0.162 0.369 Health Food Store 0.174 0.388 0.522 0.511 0.304 0.470 Box Stores 0.393 0.491 0.418 0.495 0.189 0.393 Direct to Consumers 0.222 0.422 0.333cb 0.478 0.444ca 0.504 Note: a indicates statistically significant difference between the completely unfamiliar and somewhat familiar groups. b indicates statistically significant difference between the completely unfamiliar and very familiar groups. c indicates statistically significant difference between the somewhat familiar and very familiar groups, at the 5% level. More than 10 years in SC was determined to see if the respondent was a native of SC. The different regions are the different regions in SC the respondent resides in.
51 Table 1.4. Bivariate Ordered Probit Model (Results from Chapter 1)
USDA Organic Label Certified Naturally Grown Label Variables Coefficient Standard Coefficient Standard Error Error Demographics Age -0.007* 0.004 -0.007** 0.004 Female -0.094 0.149 -0.226 0.141 Consumer Income 0.001 0.002 0 0.002 Yes, Children Live with Me 0.038 0.12 0.01 0.113 Education Highschool Graduatea 0.078 0.387 -0.249 0.374 Associates or Two Year Degreea 0.294 0.382 -0.386 0.368 Bachelors or Four Year Degreea 0.288 0.395 -0.563 0.381 Graduate School or Highera 0.217 0.404 -0.732* 0.391 Behavioral and Lifestyle Yes, I Would Order Produce Online If I 0.334** 0.155 0.136 0.149 Could Get Same Day Deliveryb Maybe I Would Order Produce Online if 0.072 0.135 0.212 0.133 I Could Get Same Day Deliveryb Lived in SC More than 10 Yrs. -0.117 0.116 0.033 0.11 Upstate Regionc 0.15 0.14 0.085 0.132 Lowcountry Regionc -0.253* 0.147 0.01 0.143 Pee Dee Regionc 0.038 0.16 0.103 0.152 Hours Cooking 0.088*** 0.029 0.008 0.028 Purchasing Outlet Grocery Storesd 0.523*** 0.129 -0.018 -0.126 Health Food Storesd 0.664** 0.302 0.638** -0.264 Direct-To-Consumersd 1.225*** 0.272 0.692*** -0.222 Coefficient Standard Error Athrho 0.296*** 0.061 Cut11 -1.781*** 0.56 Cut12 -0.474 0.557 Cut21 -1.646*** 0.521 Cut22 -0.448 0.519 *** p<0.01, ** p<0.05, * p<0.1 Note: Athrho is the transformed version of rho that shows the correlation between the error terms. Base Variables: a Did not graduate High School; b No, I would not order produce online if I could get same day delivery; c Midlands Region; d Big Box Stores
52 Table 2.1 Summary Statistics for Chapter 2 Variable Sample Population N=465 South Carolina Female 84.30% 51.40% Married 61.51% 43.00% Education Bachelor's Degree or Higher 38.71% 25.40% Some College/Tech School/Associates Degree 39.57% 21.00% High School Graduate 19.78% 30.00% Age 18-25 10.11% 7.30% 26-34 22.15% 12.80% 35-54 35.70% 26.40% 55-64 17.42% 13.00% Above 65 Yrs. Old 14.62% 14.70% Annual Income Less Than $14,999 9.46% 15.50% $15,000-$24,999 12.90% 12.70% $25,000-$49,999 27.31% 26.40% $50,000-$74,999 23.23% 18.00% $75,000-$99,999 13.76% 11.20% $100,000-$149,000 9.46% 10.40% Above $150,000 3.87% 6.00% Note: Respondents who stated they did not purchase tomatoes were dropped. Originally the N=520
53 Table 2.2. Attribute Options for Choice Experiment Attribute Options
Location of purchase Grocery Store Farmers Market Online Store Method of Production Conventional Fairtrade Certified Origin of Production South Carolina United States Canada Carbon Footprint High Carbon Footprint Medium Carbon Footprint Low Carbon Footprint Price and No Choice Price Levels ($/lb) $2.29 $2.40 $2.51 Buy No Option
54 Table 2.3. Random Parameter Logit Results for Tomatoes (Results from Chapter 2) Variables Coefficient Standard Error Mean Estimates Location Grocery Storea 0.953*** 0.078 Location Farmers Marketa 0.787*** 0.084 Method-Conventionala 0.158*** 0.057 No Fair Trade Certifiedb 0.122** 0.056 Origin: South Carolinac 0.734*** 0.081 Origin: United Statesc 0.442*** 0.071 High Carbon Footprintd -0.301*** 0.076 Medium Carbon Footprintd -0.080 0.069 Price -2.905*** 0.385 Buyno -8.423*** 0.933 Standard Deviation Estimates Location Grocery Storea 0.689*** 0.113 Location Farmers Marketa 0.517*** 0.134 Method-Conventionala 0.526*** 0.086 No Fair Trade Certifiedb 0.395*** 0.097 Origin: South Carolinac 0.949*** 0.093 Origin: United Statesc 0.104 0.152 High Carbon Footprintd 0.366** 0.155 Medium Carbon Footprintd 0.001 0.141 Buyno 2.536 0.223 McFaddens’ R2 0.156 Log likelihood = -3033.8185 Base Variables: a Online; b Organic; c Canada; d Low Carbon Footprint *** p<0.01, ** p<0.05, * p<0.1
Table 2.4. Willingness to Pay Results for Tomatoes (Results from Chapter 2) Variables Willingness to Pay ($/lb.) Mean Estimates Location Grocery Storea $0.33 Location Farmers Marketa $0.27 Method-Conventionala $0.05 No Fair Trade Certifiedb $0.04 Origin: South Carolinac $0.25 Origin: United Statesc $0.15 High Carbon Footprintd $-0.10 Medium Carbon Footprintd $-0.03 Price Buyno Base Variables: a Online; b Organic; c Canada; d Low Carbon Footprint
55 Table 3.1 List of Articles for Meta-Analysis
Year Author Journal Country of Total Type Of WTP $/lb research Number of Product participants 1 2014 Curtis et al. Journal of USA 819 Green Peppers 1.94$/lb Food Cucumbers 1.25$/lb Distribution Yellow Squash 1.00$/lb Research 2 2012 Mesías Díaz British Food Spain 361 Tomatoes .81 et al. Journal pounds/kg 3 2015 Skuza, N et International US 147 Apples 1.44 $/lb al. Journal of Food and Agricultural Economics 4 2020 Amin, Z. et Journal of India 30 Rice 3,067 Rp per al. Critical kg Reviews 5 2019 Güney, O. British Food Turkey 552 Eggs 0.76 per egg and Giraldo, Journal L. 6 2018 Maaya, L. et Sustainability Belgium 262 Coffee 2.2 euros for al. a 250 g package 7 2019 Waldrop, Agribusiness USA 292 Beer $9.37 per 6 ME et al. pack 8 2019 Khan, J. et Sarhad Pakistan 600 Fruit 26 rupees al. Journal of Agriculture 9 2015 Sriwaranun, International Thailand 502 Organic Kale, 90 baht/kg, Y. Journal of Organic Rice 400 Social Organic Pork baht/5 kg Economics 210 baht/kg 10 2008 Krishna, V. Review of Spain 361 Vegetables $1.55 per kg V. and Qai Agricultural m, M. Economics 11 2018 Gangnat, I. Poultry Turkey 552 Chicken Breast 37.4 D.M. et al. Science 6-Pack Eggs 4.39
56 12 2016 Wageli, S.et International Germany 597 Eggs 0.78 euros al. Journal of Consumer Studies 13 2014 Boys K. et Environment, Dominican 188 Produce $2.35/kg al. Development Republic and Sustainability: A Multidisciplin ary Approach to the Theory and Practice of Sustainable Development 14 2017 Mcfadden Food Policy U.S. 102 Apples $3.05/ 3lbs and Eggs $2.24/dozen Huffman Broccoli $2.26/1 and1/4 lbs 15 2014 Huang, China Taiwan 582 Milk $21.95 C. and Lee, Agricultural annually C. Economics review 16 2014 Osadebamw Journal of Australia 2,099 Wine $2.25 en A. et al. Food Products Marketing 17 2015 Hasselbach Journal of Germany 720 Bread 0.27 & Roosen Food Products Beer 0.11 Marketing Milk 0.83 18 2010 Akgüngör S. Journal of Turkey 202 Tomatoes 0.81 et al. International YTL/kg Food & Agribusiness Marketing 19 2019 Wang et al. Int. J. Environ. China 407 Fruit 0.69 Res. Public yuan/kg Health 20 2011 Van Loo et Food Quality U.S. 976 Chicken Breast 1.193 $/lb al. and Preference
57 21 2016 de magistris, Journal of Spain 171 Almond EURO Ti & Cleaner 0.27/100 g Azucena G. Production 22 2019 Yin et al. Journal of china 907 Label $ 0.693 Food Quality 23 2016 Bruno C. Journal of U.S. 288 Meal Plan $41.74 and B. Food Campbell Distribution Research 24 2017 Skreli, E. et Spanish Albania 220 Tomatoes (~€2.3) al. Journal of more per Agricultural kilogram Research 25 2012 Akaichi, F. Canadian Spain 80 Milk 1.04€/unit et al. Journal of Agricultural Economics 26 2015 Urrutia, A et ciencia e Chile 504 Pears 0.50/kg al. investigación agraria 27 2013 Sabbaghi, M Advances in Iran ? Vegetables 2200 et al. Environmenta Rials/kg l Biology 28 2009 K., D., ICFAI Journal India 90 Vegetables 4.6 Rs. per Thomas et of Fruits kg al. Environmenta Spices 7.4 Rs. per l Economics Milk kg 11.9 Rs. per kg 2.5 Rs. per kg 29 2012 Yooyen, A Chinese Thailand 400 Pork 34.30 Baht & Business per kg Leerattanak Review orn, N 30 2010 M.V. Journal of Argentina 227 Chicken $0.42 per kg Lacaze et al Agricultural Science and Technology
58 31 2009 Gianni C et Journal of Italy 203 Tomatoes €0.86\kg al. Food Products Marketing 32 2007 Batte, M et Food Policy USA 102 Cereal $0.45 al. 33 2016 Cagalj M. et British Food Croatia 258 Apples €1.28 per kg al. Journal Tomatoes €1.31 per kg 34 2019 Ha, T M et Appetite Vietnam 498 Vegetables 22 thousand al. VND (rural) 30 thousand VND (urban) 35 2020 Lai, Y et al. Journal of USA 12,301,734 Eggs $0.034 per Agricultural observations egg and Resource for eggs, and Milk ($0.41/doze Economics 103,235,716 n) observations $0.015/ounc for milk e ($1.92/gallo n) 36 2008 Tagbata, International France 102 Chocolate 1.25 euros Didier & Journal of per Sirieix, Consumer chocolate Lucie. Studies 37 2012 Ellis J. L. et Journal of USA 128 Cotton T-Shirts US$14.21 al. Fashion Marketing and Management 38 2015 Bi, X. et al. International USA 98 Orange Juice $6.33 per Journal of half gallon Consumer Studies 39 2016 Vecchio, R International Italy 100 Yogurt 38 Euro cent et al. Journal of Consumer Studies 40 2012 Cerda A et ciencia e Chile 378 Apples 61 pesos al. investigación more for a agraria kilogram of
59 organic apples. 41 2019 Naspetti, S British Food Italy 240 Wine 5 euros per et al. Journal bottle 42 2018 Wang, J. et Sustainability China 844 Pork 26.78 yuan al. (Jiangsu province) 18.94 yuan (Anhui Provence) 43 2016 Gallenti, G Rivista di Italy 420 Coffee 2.8 euro/250 et al Economia g package Agraria - REA 44 2006 Krystallis A Journal of Greece 130 Olive Oil 4.6$ 1lt et al International Raisens 0.75$/0.25 Consumer Bread kg Marketing Oranges 0.86$/ 0.5 Wine kg 0.92$/ kg 5.9$ / 0.75 kg 45 2005 Canavari, M Journal of Italy 1308 Apples WTP values et al Food Products associated Marketing with this model are 99 cents for Web surveys and 72 cents for in-person surveys. 46 2008 Hustvedt, G International US 69 Organic Sock $1.86 Journal of Consumer Studies 47 2012 Kalashami International Iran 200 Chicken 37279 M. Journal of Rials/kg Agricultural Management & Development
60 48 2020 Picardy J. A. Journal of US 388 Pork $14/lb et al. Food Distribution Research 49 2020 Drugova T British Food US 1009 Bread $8.33 Journal 50 2014 Costanigro Food Quality US 109 Apple .68 per et al. and pound Preference 51 2017 Faysseet al. New Medit Morocco 427 Mint 0.45 52 2005 Bernard and Consumer US 79 Potato Chips 0.49 Gifford Interest Tortilla Chips 0.56 Annual Milk Chocolate 0.39 53 2019 Dung Tien Economic Vietnam 210 Agriculture 2.29 Luu Journal of Products Emerging Markets 54 2015 Chen et al. British Food China 878 Tomatoes 0.33 Journal.
Table 3.1.1 Extended Articles List from Meta-Analysis
Author Year Country of Total Type of WTP Hypothetical Non- research Number of Product ($/lb. Experiment Hypothetical participants in 2020) 1 Curtis et al. 2014 USA 819 Green 2.13 Yes No Peppers 1.37 Cucumbers 1.10 Yellow Squash 2 Mesías Díaz et al. 2012 Spain 361 Tomatoes 0.48 Yes No 3 Skuza, N et al. 2015 US 147 Apples 1.56 No Yes 4 Sriwaranun, Y. 2015 Thailand 502 Kale 1.30 Yes No Rice 5.8 Pork 3.04 5 Krishna, V.V. and 2008 Spain 361 Vegetables 1.91 Yes No Qaim, M.
61 6 Boys K. et al. 2014 Dominican 188 Produce 2.58 Yes No Republic 7 Mcfadden and 2017 U.S. 102 Apples 3.25 No Yes Huffman Eggs 2.39 Broccoli 2.41 8 Akgüngör S. et al. 2010 Turkey 202 Tomatoes 0.10 Yes No 9 Wang et al. 2019 China 407 Fruit 0.05 Yes No 10 Van Loo et al. 2011 U.S. 976 Chicken 1.40 Yes No Breast 11 Urrutia, A et al. 2015 Chile 504 Pears 0.23 Yes No 12 Yooyen, A & 2012 Thailand 400 Pork 0.63 Yes No Leerattanakorn, N 13 M. V. Lacaze et al 2010 Argentina 227 Chicken 0.50 No Yes 14 Gianni C et al. 2009 Italy 203 Tomatoes 0.48 Yes No 15 Cagalj M. et al. 2016 Croatia 258 Tomatoes 1.55 Yes No 16 Cerda A et al. 2012 Chile 378 Apples 0.05 Yes No 17 Gallenti, G et al 2016 Italy 420 Coffee 6.02 Yes No 18 Kalashami M. 2012 Iran 200 Chicken 3.39 Yes No 19 Picardy J. A. et al. 2020 US 388 Pork Yes No 20 Costanigro et al. (2014) US 109 Apple 0.75 No Yes 21 Faysseet al. 2017 Morocco 427 Mint 0.45 Yes No 22 Bernard and 2005 US 79 Potato 0.49 No Yes Gifford Chips 0.56 Tortilla 0.39 Chips Milk Chocolate 23 Dung Tien Luu 2019 Vietnam 210 Agriculture 2.29 Yes No Products 24 Chen et al. 2015 China 878 Tomatoes 0.33 No Yes
62 Table 3.2. Summary Statistics for the 24 papers used in the Meta-Analysis Variable Description Mean Standard Deviation WTP Premium for Organic Product 1.97 2.94 Country US 1 if data is from the US, 0 otherwise 0.29 0.46 Europe 1 if data is from Europe, 0 otherwise 0.21 0.42 Middle East 1 if data is from the Middle East, 0 otherwise 0.08 0.28 Asia 1 if data is from Asia, 0 otherwise 0.21 0.42 Other Country 1 if data is from another location other than US, 0.21 0.42 Europe, Middle East Asia, 0 otherwise Product Produce 1 if product valued was organic fruit or .60 .50 vegetable, or unspecified, 0 if otherwise Meat 1 if product valued was organic meat, 0 if 0.20 .41 otherwise Other Product 1 if product valued was neither produce or meat, 0.20 .41 0 if otherwise Survey Method Hypothetical 1 if valuation task did not involve actual 0.75 0.44 Experiment purchase; 0 if otherwise Non-Hypothetical 1 if valuation task involved actual purchase; 0 if 0.25 0.44 Experiment hypothetical
Table 3.3 Sample Weighted Least Squares Model for WTP for Organic
Coefficient Confidence Interval Constant (Sqrt(n)) 3.810** (0.028; 7.591) (1.863) Constant (n) 3.756*** (1.203; 6.309) (1.240) Observations 30 30 F 6.93 6.93 Prob >F 0.0004 0.0004 푅2 0.5908 0.5908 Adjusted 푅2 0.5055 0.5055 Note: Dependent variable is WTP for organic; Standard Errors are in parentheses *** p<0.1, **p<0.05, *p<0.1
63 Table 3.4. Meta-Regression Analysis for WTP for Organic
Variables Constant Standard Error USa 2.576** -1.172 Produceb -1.321 -1.16 Meatb 1.707 -1.425 Earlyc -2.707*** -0.948 Hypothetical Experimentd 2.110* -1.227 Constant 1.731 -1.48 a base category: countries of respondents that are not the US b base category: processed products c base category: studies published after 2015 d base category: valuation techniques that were non-hypothetical experiments
Table 3.5. Correlation Matrix for the Meta-Regression Analysis US Produce Other Early Late Other Hypothetical Non- Products Country Experiment Hypothetical cal Experiment t US 1 Produce 0.0275 1 Other -0.0275 -1 1 Products Early -0.0053 -0.193 -0.193 1 Late 0.0053 0.193 0.193 -1 1 Other -1 -0.0275 -0.028 0.005 -0.0053 1 Country Hypothetical -0.5232 0.1443 0.1443 -0.279 0.2786 0.5232 1 Experiment Non- 0.5232 -0.1443 -0.144 0.279 -0.2786 -0.5232 -1 1 hypothetical Experiment *** p<0.1, **p<0.05, *p<0.1
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