WHY THE BEEF? A PUBLIC CHOICE EXPERIMENT ON MEAT ALTERNATIVES By Benjamin DeMuth A THESIS Submitted to Michigan State University i n partial fulfillment of the requirements f or the degree of Agriculture, Food and Resource Economics Master of Science 201 9 ABSTRACT WHY THE BEEF? A PUBLIC CHOICE EXPERIMENT ON MEAT ALTERNATIVES By Benjamin DeMuth Government - imposed labeling restrictions have become increasingly common, with the stated intention of preventing consumer confusion. One such restriction is proposed U.S. regulation that collected from a repr esentative sample of 1,502 U.S. consumers to empirically examine whether consumers were confused about the ingredients and nutritional content associated with meat and alternatives reduced any consumer confusion as well as substitution between meat and meat alternatives. Results suggested that over 30% of consumers cannot accurately distinguish between meat and meat alternatives and that the labeling restrictions actuall y induced a higher level of consumer confusion. Consumer perceptions of trans - fat and cholesterol decreased by 2.78 and 3.78 percentage points. Perceptions of calories per serving decreased by 10.17 for meat alternatives, but perceptions of protein conte nt in meat alternatives increased by 4.04 percentage points after the regulation was imposed. Furthermore, results suggested that labeling restrictions are likely to have an ambiguous effect on substitution between meat and meat alternatives. iii ACKNOWLEDGMENTS I would first like to thank my advisor Trey Malone for taking me on as an advisee at the beginning of my second year. He pushed me farther than I thought I could go, and my thesis and career a t MSU would not have been the same without him. I would secondly like to thank Brent Ross my first year advisor that unfortunately went on sabbatical. He taught me how to be more strategical in my thinking and I could not have made it through my first year without him. I would also like to thank my committee members Chris Wolf and Jason Rowntree for their insights on my paper. I also cannot forget Bob Myers, Ashleigh Booth, Nancy Creed, Larry Zink, my AFRE classmates, and others at MSU that helped me along the way. Lastly, but not least, I must thank my wife Sandrine and son Emmanuel for helping g et through the long hours . I could not have made it to the end w ithout you constantly supporting me . iv TABLE OF CONTENTS LIST OF TABLES ................................ ................................ ................................ .......................... v LIST OF FIGURES ................................ ................................ ................................ ....................... vi I. INTRODUCTION ................................ ................................ ................................ .................. 7 II. BACKGROUND ................................ ................................ ................................ .................... 9 III. LITERATURE REVIEW ................................ ................................ ................................ ..... 10 A. Public Choice ................................ ................................ ................................ .................... 10 IV. HYPOTHESES ................................ ................................ ................................ ..................... 12 A. Hypothesis 1 and 2 ................................ ................................ ................................ ............ 12 B. Hypothesis 3 ................................ ................................ ................................ ...................... 13 V. METHODS AND DATA ................................ ................................ ................................ ..... 16 A. Methods ................................ ................................ ................................ ............................. 16 B. Data ................................ ................................ ................................ ................................ ... 23 VI. RESULTS ................................ ................................ ................................ ............................. 25 A. Hypotheses 1 and 2: Consumer Confusion ................................ ................................ ....... 25 B. Hypothesis 3: Substitution Effects ................................ ................................ .................... 29 VII. POLICY IMPLICATIONS ................................ ................................ ................................ ... 34 VIII. CONCLUSION ................................ ................................ ................................ ................... 35 A PPENDIX ................................ ................................ ................................ ................................ .. 37 R EFERENCES ................................ ................................ ................................ ............................. 42 v LIST OF TABLES Table 1: Descriptive Statistics for the Survey Experiment B efore and A fter R egulation Table 2 : Difference Between Mean Perceived and Actual Nutrition Values Before Label ing Regulation Table 3: Descriptive Statis tics of Perceived Nutrition Values Before and After Labeling Regulation 27 Table 4: Change of Perceived Nutritional Content Due to Labeling Regulatio n Table 5: Model Estimates for DCE Before and After the Labeling Regulation 0 Table 6: Cross Price Elasticities for Before and After the Labeling Regulation Table 7 : Factors and Factor Levels for the Discrete Choice Experiment 3 8 Table 8 : Main Effects Orthogonal Fractional Factorial Design 3 8 Table 9 : OLS of Incorrect Nutrition of Cholesterol Before Regulation .. . 3 9 Table 1 0 : OLS of Incorrect Nutrition of Sodium Before Regulation ... 3 9 Table 1 1 : OLS of Incorrect Variable of Sodium Before Regulation . 40 Table 1 2 : OLS of Incorrect Variable of Calories Before Regulation 40 Table 1 3 : OLS of Incorrect Variable of Trans Fats Before Regulation 1 vi LIST OF FIGURES Figure 1a: Change in Demand Due to Price Decrease of Good Y Figure 1b: Demand Shift for Good X Due to a Change in the Price of Substitute Good Y Figure 2: Beyond Meat and JUST Meat Before and After Regulation Figure 3: Ball Park Beef Patty and Homestyle Beef Patty Figure 4: Consumer Selection of Ingredients in Beyond Protein Figure 5: Consumer Selection of Perceived Cholesterol in Meat and Meat Alternatives 19 Figure 6a: Example of a Choice Set Before Regulation Figure 6b: Example of a Choice Set After Regulation Figure 7: Percent of Respondents Selecting Ground Beef as an Ingredient Before and After Regulation Figure 8: Choice Selection by Product Category Before and After Regulation Figure 9a: Cross - Price Elasticities for JUST Meat Before and After Regulation Figure 9b: Cross - Price Elasticities for Beyond Meat Before and After Regulation 7 I. INTRODUCTION News outlets, politicians, and many on social media have been puzzling for the last year over what relevant after the Missouri Senate passed a law stating the use of used if a product was of animal origin (Missouri State Senate 2018) . Proponents of the bill argued that there was a need to prevent consumer confusion ation 2018) . While the argument for the Missouri regulation might be tenable, empirical analysis regarding the likely consequences of such legislation has been lacking. Without this analysis, it is unclear if consumer confusion existed, leading some to think the true intentions of the regulation were to provide government - granted privilege to one commodity group over another (Weissmueller 2018; Ball 2018) . This research explor ed the likely consequences of state legislation that was passed under the influence of a special interest group. Specifically, we considered Missouri Senate bills No. 627 and 925 passed and signed into law in 2018. The statutes mandated that advertisers n ot engage (Missouri State Senate 2018) . Shortly after passage, the Good Food Institute and the Tofurky Company filed a civil rights action challenging the constitu tionality of the statute ( Turtle Island Foods and The Good Food Instutute v. M.P.A . 2018) . The Good Food Institute made their - from an animal any more than people a re buying almond milk thinking it was squeezed from a (Ball 2018) . On the contrary, the Executive Vice President of the Missouri confusing to consumers and weakens the value of products derived from actual livestock 8 . This legislation raised many questions about the economics of labeling regulat ion. First, was there a need to protect consumers from uncertainty? If so, will this law actually prevent confusion? Finally, might this legislation actually be an attempt to insulate special interests in the meat industry from changing consumer prefere nces? We answered these questions with a unique dataset of consumer decisions. Specifically, we asked 1,502 U.S. consumers about their perceptions of nutrition and ingredients in meat and meat alternatives before and after the regulatory change. We then c onducted a discrete choice experiment (DCE) to test for likely changes in the substitution effects between meat and meat alternatives. Our results are largely consistent with findings in public choice theory, which explains why interest groups often suppo rt increases in regulation even when the regulatory change does not accomplish the stated objective. As such, results suggest that the actual reason for this regulation might have actually been supporting one commodity group over the other. The remainder of this article is organized as follows. The next section provides a background to the regulation. We then reviews the literature on meat labeling and public choice theory. The fourth section outlines the testable hypotheses, while the fifth describes o ur experimental design, which tests for changes in consumer confusion and substitution effects. The sixth section describes the results of our analysis, which suggest that the proposed legislative changes actually induce more consumer confusion, and the seventh section provides a discussion of the policy implications. The article concludes with a discussion of next steps and future research. 9 II. BACKGROUND The U.S. Department of Agriculture ( USDA) Food Safety and Inspection Serv ice (FSIS) does not and meat recovery systems ( U.S. Department of Agriculture, Food Safety and Inspection Service 2011) . As such, the origin 2018) . Missouri Senate bills No. 627 and 92 5 mirror a similar 2018 law in France that states products comprised mostly of vegetables could not use names associated with animal origin, such (France, Assemblée Nationale 2018 ). Proponents of the Missouri Senate bills explained their stance to the Missour i Standing Committee on Agricultural Policy: products. This bill would keep manufacturers of plant - based proteins from calling their products meat and benefitin (Standing Committee of Agriculture Policy 201 8) . After bill No. 627 and 925 passed, the sponsor of the original bill (HB2607) Jeff Knight (Associated Press 2018) . The Missouri bill was contested in the U.S. District Court, and the implementation is still pending the results of the case (Erickson 2019; Turtle Island v. M.P.A. 2018) . One news report has stated that the parties came to a tentative agreement on the meat labeling lawsuit (Erickson 2019) . The potential results of the lawsuit may establish legal precedent for state and local governments to regulate food labeling and advertising, and may invog orate special interest groups to follow similar legislative strategies (U.S. Congress, Senate, Committee on Healh, Education, Labor, and Pensions 2017; U.S. Cattlemen's Association 2018) . 10 III. LITERATURE REVIEW The academic research on food labeling legislation is exhaustive. 1 Often, studies discussed the mechanisms by which labeling might increase consumer surplus (Greaker 2006; Eggert and Greaker 2011; Roe, Teisl and Deans 2014) . Some authors argued that food labels represent a mechanism for fixing asymm etric information (Kolodinsky 2012; Drichoutis et al. 2017) . Other authors argued that mandatory labeling has the potential to lower competition (Roe, Teisl, and Deans 2014) . Understanding the consequences of labeling regulation is of critical importance if the objective of the policy is to create more efficient economic outcomes (Messer, Costanigro and Kaiser 2017) . As such, the next section reviews some of th e research on how government regulation may alter economic outcomes. A. Public Choice Public choice theory focuses on how economic motivations influence the democratic process (Mitchell and Munger 1991) . One key aspect of public choice theory is the role of special interest groups in political decision - making (Buchanan and Tullock 1962) . Specifically, public choice theory argues special interest groups ext ract rents through the political process (Buchanan and Tullock 1962; Lowery and Gray 1995) . (Yandle 1983; Yandle et al. 2008) , which often utilizes historical narratives to describe how two seemingly unrelated interest groups can support the same regulation such as the Depression - era opinions of bootleggers and Baptists re garding alcohol prohibition. The Baptists truly believed in the societal and religious implications of prohibition while the 1 For a review of the food labeling literature, see (Messer, Costanigro, and Kaiser. 2 017 ) 11 bootleggers sought to eliminate competition and boost profits through their thinly veiled support of prohibition for the common goo d (Yandle et al. 2008 ) . The BAB paradigm has generated insights for recent public policy discussions. For posed a significant health risk. Despite their arguments, research suggested that the cigarette industry advocated for similar regulations to create higher barriers to entry and allow the industry to artificially inflate prices (Yandle et al. 2008) . Few studies have empirically examined the BAB paradigm in agricultural value chains. One exception is Gohmann (2016), who found that the difference between the number of brewery startups in the Northern and Southern United States was consistent with the BAB paradigm. Despite limited quantitative evidence, the BAB paradigm can provide insight into historical regulatory decisions in agricultural production. In fact, one of the first U.S. fo od labeling regulations was consistent with the BAB paradigm (Dupre 1999) . Margarine was invented in France in 1869 as a low - cost substitute for butter (Rupp 2014) , and almost immediately after businesses brought the product to the United States, they were met with fierce opposition ( Dupre 1999) . By 1886, 27 states either banned or hea vily regulated the labeling and manufacturing of margarine. By 1902, the federal government passed legislation requiring that margarine be colored differently from butter (Dupre 1999) . While these regulations were intended to support consumer health and stop margarine producers from falsely selling butter, dairy interest groups were the primary proponents of the regulations (Dupre 1999) . Regulation severity towards margarine was bodies are quite reluctant to tax or restrict the production of a food product, especially if the product 12 ows that considerable political clout can History has shown how special interest groups may run contrary to the interests of specific industry actors, and this may be evident in the modern meat in dustry. While the policy directives retailers, their motivation toward restricting demand growth for meat alternatives has their special interests competing with m any large producers in the meat value chain. Tyson Foods Inc., one of - grown and plant - based proteins (Little 2018) . Similarly, Carg ill, one of the largest agri - food businesses in the world, recently invested in the development of plant - based proteins (Starostinetskaya 2018a) . Meat consumers already have easier access to meat alternatives; TGI Frid ays now sells a Beyond Meat Cheese Burger and Burger King sells the Impossible Whopper (Beyond Meat 2018; Cassetty 2019) . IV. HYPOTHESES A. Hypothesis 1 and 2 Supporters of the Missouri law argued that consumers are unwittingly purchasing meat alternatives with the intention of buying meat . Consumer acceptance of meat and meat alternatives has been studied significantly, and the literature suggests that consumers widely prefer meat to meat alternatives (Malone and Lusk 2017; Lusk and Tonsor 2016; Tonsor, Mintert and Schroeder 2010; Elzerman et al. 2015; Hartmann and Siegrist 2017; Graça, Calheiros and Oliveira 2015a) . As such, we hypothesized: 13 (H1) There is no significant difference between the true nutritional content of meat and alternatives, nor do consumers believe that meat is an ingredient in availab le meat alternatives. Even with labeling regulation, it is possible consumer confusion will persist. Labeling regulations have the potential to reduce confusion, although the effects can often be unclear (Roe, Teisl, and Deans 2014; Kolodinsky 2012; Drichoutis et al. 2017; Balcombe, Fraser, and Hussein 2016; Tonsor, Schroeder and Lusk 2013) . Prior research suggests that government intervention in labeling regulation does not always achieve its intended outcomes (Ellison, Lusk, and Davis 2014; Balcombe, Fraser, and Hussein 2016; Tonsor, Mintert, and Schroeder 2013) . Thus, labeling regulation may cause changes in perceptions and ingredients, but might actually increase consumer conf usion. We hypothesized that: (H2) - not decrease consumer confusion between meat and meat alternatives. B. Hypothesis 3 If the proposed legislation does not effectively reduce uncertainty, the proposed labeling change may still change consumer demand for meat alternatives. There has been extensive research demonstrating how labeling affects willingness to pay (WTP) for food products (Drichoutis et al. 2017; McFadden and Malone 2018; Balcombe, Fraser, and Hussein 2016; Tonsor , Mintert, and Schroeder 2013) . Consumers are more likely to substitute meat for meat 14 alternatives if they are similar to meat (Elzerman et al. 2015; Hartmann and Siegrist 2017) . As such, the BAB paradigm suggests that the actu al objective of the labeling legislation might be limiting competition between meat and meat alternatives. Given this, ceteris paribus , the labeling change may decrease substitution effects between meat and meat alternatives. As such, we hypothesized: (H3) will reduce the likelihood that consumers substitute meat for meat alternatives. Figures 1a and 1b display these substitution effects (Nicholson and Snyder 2012) . In a hypothetical two - good world where good X is a meat product and good Y is a meat alternative product, and these products are gross substitutes and normal goods, a decrease in the price in good Y will shift the budget constraint for the two goods outward from I 0 to I 1. All else equal, utility wo uld shift to U 1 from U 0 and demand would shift to X 1 and Y 1 (figure 1a). This would be an increase in demand for good Y and a decrease in demand for good X . This demand change in X would result in an inward shift of the demand curve to from Demand , thereby decreasing producer surplus (figure 1b). 15 y x Demand for goods x and y Figure 1a: Change in Demand Due to Price Decrease of Good Y Price Quantity Supply and Demand Good X Demand '' Demand ' Supply Demand Figure 1b: Demand Shift for Good X Due to a Change in the Price of Substitute Good Y 16 Implementation of the labeling policy might make consumers less likely to substitute meat for a meat alternative. If there was a decrease in the price of meat alternative good Y , the budget constraint for the two goods would shift outwards from I 0 to I 2 an d demand would be X 2 and Y 2 (figure 1a). Of critical importance is the demand curve of the substitute product X (figure 1b). We can see the demand shift for good X going to the demand curve labeled instead of the demand curve labeled . This shift in demand represents a BAB attempt to decrease the substitutability of the demand for good X and increase producer surplus. Hence our hypothesis that the legislation is an attempt to increase produce r surplus by restricting substitution effects. V. METHODS AND DATA A. Methods At the time of survey development, lab - grown products were not available in the marketplace although many companies were in the startup phase. Given these challenges, we utilized a stated preferences approach via online surveys to capture a representative sample of U.S. consumers. Preliminary market research was conducted by visiting eight supermarkets and retailers in the greater Detroit Metropolitan Statistical Area in O ctober 2018. Survey pretesting was conducted with 100 participants recruited on Amazon Mechanical Turk and each participant was paid $0.80 for survey completion. Their responses provided feedback on topics regarding survey flow and question wording to imp rove the survey usability on cellphones and desktop computers. Consumers were identified via Survey Sampling International and paid roughly $1.50 in gift cards for completing the survey. The data were collected through a survey designed in Qualtrics ® . The first section of the survey consisted of a discrete choice experiment (DCE) as well 17 as questions on the ingredients and nutritional content of meat and meat alternatives. The second section included questions on their past and future expenditures of food, and their perceptions of meat and non - meat environment effects. The last section collected demographic information to assure the sample was consistent with the U.S. population and two sample groups. A between subjects survey design was used to test the hypotheses. Participants were randomly placed into a group with labels consistent with the marketplace before or after regulation. The group before regulation received a survey where meat and meat alternative products had pre - law labels and the group after regulation received a survey with product labels consistent with the new legislation (figure 2). Two meat alternatives were selected for the project. The Beyond Beef Burger ® was selected as it was often cited in the Missouri regulation controversy (Starostinetskaya 2018b) . JUST Meat ® was chosen as it was one of the first lab - grown meat startups with a concept at the time of survey. Figure 2: Beyond Meat and JUST Meat Before and After Regulation 18 Figure 3 displays the two animal products presented to both survey groups. The two animal products selected are commonly available at Walmart, Kroger, or other supermarkets. In the first set of ingredient questions, participants identified which ingredients were in each of the meat and meat alternative products (figure 4). Possible choices included ground beef, natural and artificial flavors, onions, soy, sesame oil, corn, whe at, beets, and peas. These ingredients were selected as many could be found in the Beyond Beef, Ball Park, and Homestyle products. Figure 3: Ball Park Beef Patty and Homestyle Beef Patty Figure 4: Consumer Selection of Ingredients in Beyond Protein 19 Even if consumers could accurately identify which products contain animal protein, they may be unsure of the differe nces in nutritional content. Consumers were asked to identify the total calories in a single serving and percent daily value of cholesterol, trans fats, proteins, and sodium in each product (figure 5). To empirically test for confusion in nutritional content (H1), unpaired t - tests were conducted between the mean perceived nutritional values of the five nutritional categories (per - law labeling ) and the actual nutritional values. To test the effectiveness of the labeling change in reducing consumer confusion (H2), we estimated the following econometric model : where treatment is a dummy variable indicating the group who saw the label after the legislative change and represented whether the product was a meat alternative. The variable of Figure 5: Consumer Selection of Perceived Cholesterol in Meat and Meat Alternatives 20 interest was the interaction between treatment and , , which represented a change in the average difference in nutritional values between the meat and meat alternativ e products after the labeling changes. A discrete choice experiment (DCE) was conducted to examine (H3) - changes in substitution due to the new regulation. DCEs effectively generate an instantaneous panel, allowing researchers to exogenously vary price, t hereby generating causal inferences (Hensher, Rose and Greene 2015) . The DCE method was chosen as they are largely consistent with neoclassical microeconomic theory (Maples, Lusk and Peel 2016; Lusk and Tonsor 2016; Malone and Lusk 2017) . While stated preferences DCEs often overestimate consumer WTP because of hypothetical with revealed preference approaches (Lusk and Schroeder 2004) . A branded discrete choice experiment was designed with five choice alternatives: the same products for testing (H1) and (H2) and a no choice option; and three price levels ( Appendix Table 7 ). A DCE can be burdensome to the survey taker due to the many combinations of attributes and levels of attributes (Hensher, Rose and Greene 2015) . To prevent cognitive burden in this survey, a main effects orthogonal fractional factorial design was created in SAS ® . This design reduced the DCE to nine choice sets for the five choice alternatives and three price levels, and assured brands could be paired with prices while being uncorrelated with one another ( Appendix Table 8 ). An example of one question for the groups before and after regulation ar e is figure 6a and 6b. Participants randomly assignment to the two groups for (H1) and (H2) remained consistent for the DCE. To minimize the potential consequences of inattention bias for (H3), the discrete choice experiment was at the beginning of the sur vey (Malone and Lu sk 2018) . 21 We utilized indirect utility theory to estimate our empirical model (Hensher, Rose, and Greene 2015) : where the observed portion of the indirect utility function is and the unobserved portion is . Subscript refers to the survey participant and subscript refers to the randomized choice set with choice alternatives varying by attri bute levels. Subscript is the choice alternative, and the last subscript represents the group before or after regulation. The observed portion was as follows: Figure 6a: Example of a Choice Set Before the Regulation Figure 6b : Example of a Choice Set After the Regulation Figure 6b: Example of a Choice Set After Regulation 22 where is participant observed indirect utility for choice during choice set in the group before or after regulation group . Equation (3) is a function of price and alternative specific constants (ASC), which were the meat, meat alternative, and no choice options. represents the price for choice , and represents the utility for . represents the utility of the ASCs, and for estimation purposes, our model normalized the no choice option to zero. We estimated the indirect utility model of equation (3) based on random utility maximization (RUM) theory (McFadden 1973) through a Multinomial Logit (MNL), generating the probability individual i selects of choice j , which can be defined as: where represented the attributes that determined the utility of choice for the group before and after regulation conditional on the no choice alternative . For this model we assumed that the error term was i.i.d. with a normal distribution of mean zero and standard deviation of one. One assumption in MNL estimation is the principal of independence of irrel evant alternatives (IIA). The IIA principal states the ranking of alternative choices remain constant for all subsets of choices (Hensher, Rose, and Greene 2015) . Because this principal restricts heterogeneity in cross price elasticities, we opted to estimate a random parameter logit (RPL) models (Lusk and Tonsor 2016) . The RPL model was estimated by changing the generic form of the observed portion of utility equation (4) to equation (5) as shown by Lusk and Tonsor (2016): 23 where the ASCs were randomized with a normal distribution of mean zero and standard deviation one, and was the lower triangular matrix of the Cholesky decomposition. remained fixed to assure negativity and the no choice variable was not rand omized as this variable was normalized to zero for estimation purposes. One thousand iterations were completed for the RPL to be consistent with the literature (Hensher, Rose, and Greene 2015) . Likelihood ratio tests were utilized to determine whether the regulation significantly alters consumer decision - maki ng. With the parameter estimates of equation 5 we calculated the cross price elasticities with equations 6 (Hensher, Rose, and Greene 2015) : B. Data The survey sample included 1,502 consumers in the United States and was collected in January 2019. To participate in the survey, consumers needed to be the primary or joint primary shopper of their household and above 18 years of age. The group before regu lation (pre - law labeling) had 732 consumers and group after regulation (post - law labeling) had 772 consumers. The survey took approximately seventeen minutes to complete. Table 1 summarizes the demographic information of the sample groups as well as infor mation from the 2017 U.S. Census Bureau. Although there is minimal variation in the groups before and after regulation, both groups were consistent with the U.S. Census on gender, region, household size, and ethnicity. Income, age, and education were comp arable to the U.S. Census, but the survey underrepresented participants with incomes greater than $100,000, educations 24 Table 1: Descriptive Statistics for the Survey Experiment B efore a nd A fter R egulation Demographic Before (N = 732) After (N = 772) US Census Gender Male 46.0 47.7 49.2 Age 18 to 24 years old 13.3 12.6 9.7 25 to 34 years old 19.0 19.2 13.7 35 to 44 years old 16.9 17.9 12.7 45 to 54 years old 16.0 14.8 13.4 55 to 64 years old 18.2 18.8 12.7 65 to 74 years old 16.7 16.7 8.6 75 or older 0.0 1.3 6.3 Region Northeast 18.6 17.4 17.2 Midwest 20.8 23.3 21.0 South 39.8 39.0 38.0 West 20.8 20.2 23.8 Income Less than $20,000 17.9 18.9 11.2 $20,000 - $39,000 26.2 29.0 15.9 $40,000 - $59,999 23.4 21.9 15.1 $60,000 - $79,999 16.4 16.8 13.1 $80,000 - $99,999 7.9 5.7 10.6 $100,000+ 8.2 7.7 34.1 Education High School or less 24.3 24.1 40.5 Some College 39.8 38.0 31.2 Bachelors, Grad, Professional Degree 35.9 38.0 28.4 Household Size 2.44 2.36 2.61 Ethnicity White 60.5 60.0 61.5 Black or African American 17.5 17.4 12.3 Hispanic or Latin American 9.4 8.8 17.6 Asian 5.1 6.1 5.3 Other 7.5 7.8 3.3 Diet Vegetarian 8.2 8.4 - Vegan 4.8 4.9 - Neither 87.0 86.7 - Footnotes: U.S. Census Bureau, Current Population Survey, Annual Social and Economic Supplement, 2017 Numbers reported are percentages of the column total except for household size. 25 VI. RESULTS A. Hypotheses 1 and 2: Consumer Confusion The mean perceived values of nutritional content pre - label regulation for the meat and meat alternatives is displayed in table 2 alongside the actual nutritional values. There are no values for the product JUST Meat as this product was not yet commercially available. Unpaired t - tests indicated that participants could not accurately identify the nutritional values of the meat and meat alternatives for all nutritional questions. S pecifically, participants overestimated cholesterol, protein, sodium and trans fats, and underestimated calorie content in all of the products. As such, we concluded that consumers are confused by the nutritional content of th e meat, and meat alternatives. Table 2: Difference Between Mean Perceived and Actual Nutrition Values Before Labeling Regulation Variable Mean Std. Dev Actual Difference t - test statistics Cholesterol % Daily Value Beyond 34 26 0 34 - 35.62*** Just 45 26 - - - Ballpark 52 26 22 30 - 47.02*** Homestyle 47 26 23 24 - 30.34*** Protein % Daily Value Beyond 38 27 32 6 - 12.87*** Just 42 27 - - - Ballpark 53 27 26 27 - 23.33*** Homestyle 44 26 34 10 - 15.35*** Sodium % Daily Value Beyond 45 27 16 29 - 22.57*** Just 46 26 - - - Ballpark 49 26 16 33 - 36.95*** Homestyle 48 25 3 45 - 41.70*** Daily Calories Beyond 133.92 70.05 270 - 136.08 52.56*** Just 162.55 73.68 - - - Ballpark 184.30 73.09 210 - 25.70 9.52*** Homestyle 165.41 70.66 250 - 84.59 32.39*** 26 Trans Fat % Daily Value Beyond 32 27 0 32 32.70*** Just 41 27 - - - Ballpark 49 28 0 49 - 46.64*** Homestyle 43 26 0 43 - 43.97*** T - Test Statistics tests null hypothesis perceived and actual nutritional values are equal. Figure 7 displays the percentage of consumers that correctly chose ground beef as one of the ingredients in the meat and meat alternatives. Respondents selected ground beef in the meat products 85% of the time. Selection of ground beef was mixed for meat alternatives. For the Beyond Burger, 31.4% of participants selected ground beef prior to the l abel restrictions. Results were nearly similar at 30.4% when the labels changed. For the cultured - meat meat alternative, 80.6% of respondents selected JUST Meat as having ground beef, but respondents decreased selection to 63.08% for the JUST Protein burge r. Figure 7: Percent of Respondents Selecting Ground Beef as an Ingredient Before and After Regulation 87.43% 87.84% 80.60% 31.42% 88.73% 85.23% 63.08% 30.44% 0.00% 10.00% 20.00% 30.00% 40.00% 50.00% 60.00% 70.00% 80.00% 90.00% 100.00% Ballpark Homestyle JUST Beyond Before After 27 Table 3 displays the descriptive statistics of the nutritional content perceptions for the survey participants in each group. A comparison of the mean values for the meat and meat alternatives suggested that the policy will lower consumer perception of daily valu es of cholesterol, sodium, and trans fats as well as the total number of calories, but perceptions of protein content increased after the legislation was implemented Results on the effects of reducing consumer confusion are in table 4. The varia ble labeling regulation (the interaction variable of treatment and ) suggested that after the labeling change, consumers decreased their perception of trans - fat and cholesterol by 2.78 and 3.78 percentage points. The labeling change also decreased perceptions of calories per serving for the Table 3: Descriptive Statistics of Perceived Nutrition Values Before and After Labeling Regulation Before After Variable Mean Std. Dev Mean Std. Dev Cholesterol % Daily Value Meat 49.23 26.18 48.60 25.50 Meat Sub 39.62 26.48 35.21 26.10 Protein % Daily Value Meat 48.33 25.49 47.43 24.80 Meat Sub 45.42 26.25 48.55 26.18 Sodium % Daily Value Meat 48.44 27.24 46.84 25.98 Meat Sub 40.18 27.04 38.89 25.81 Calories Meat 174.85 72.48 172.00 71.23 Meat Sub 148.23 73.28 135.22 68.09 Trans Fats % Daily Value Meat 45.89 27.56 44.88 27.47 Meat Sub 36.61 27.19 32.81 26.76 Footnote: Results from 732 observations in the group before the regulation and 774 in the group after the regulation 28 non - meat products by 10.17. Consu mers showed an inverse response to perception of protein; daily perception of protein for meat alternatives increased by 4.036 percentage points. Results suggested that we were able to reject the null hypothesis two, suggesting that the proposed legislatio n may actually increase consumer confusion. Table 4: Change of Perceived Nutritional Content Due to Labeling Regulation Sodium % Daily Value Trans Fat % Daily Value Calories Per patty Cholesterol % Daily Value Protein % Daily Value Treatment - 1.598* - 1.014 - 2.845 - 0.628 - 0.904 (0.967) (0.994) (2.599) (0.951) (0.937) Meat Sub - 8.253*** - 9.282*** - 26.621*** - 9.610*** - 2.917*** (0.980) (1.007) (2.634) (0.963) (0.949) Labeling Regulation 0.304 - 2.783** - 10.172*** - 3.782*** 4.036*** (1.368) (1.406) (3.676) (1.344) (1.325) _cons 48.436*** 45.893*** 174.855*** 49.231*** 48.334*** (0.693) (0.712) (1.862) (0.681) (0.671) R 2 0.02 0.04 0.05 0.05 0.00 N 6,016 6,016 6,016 6,016 6,016 Footnotes: Numbers in parentheses are standard errors. Regression analysis was conducted to see who among the subpopulation were most likely to be confused prior to the regulation change by creating a variable that represented confusion. 2 This variable was regressed on a set of demographic and socioeconomic variables, 3 and the results are shown in the Appendix for tables 9 to 13 . From the results, those with low income w ere less likely to falsely perceive the nutritional contents of the Beyond Meat, Ballpark, and Homestyle burgers for all of the nutritional questions. Middle - income participants perceived nutritional values 2 This was done by subtracting the mean average of the perceived nutritional value for the five nutritional questions for the Beyond Burger, Ball Park Beef Patty, and Homestyle Patty, from their actual nutritional values 3 low income incomes of less than $40,000 per year, middle income incomes of $40,000 to $100,000. no college high school degree or less, some college an associate degree or classes as some college, Gender one if female and zero if male, Veg/Vegan one if a vegetarian or vegan and zero if not. 29 to be closer to the actual values than other part icipants. For those without a college degree, the results of correctly perceiving the actual nutritional values were mixed. Those with no college degree overestimated cholesterol for the Beyond Burger, but they correctly chose the level of protein. Perhaps most surprising was that vegetarian and vegan consumers. They were most likely to incorrectly perceive the results of all the nutritional values . B. Hypothesis 3: Substitution Effects Figure 8 displays the aggregate choice selection for the sample groups before and after regulation for meat, meat alternatives, and no - choice. Meat was among 61.9% and 62.0% of the choices selected in both groups respectively. Before and after regulation, meat alternative selection changed from 20.4% to 18.7% and appears to have been substituted for no - choice which increased from 17.7% in the before regulation group to 19.3% in the group after regulation. These results suggested that consumer selection tow ards meat changed minimally before and after regulation, but the labeling effects may have pushed people to no selection. Baseline multinomial logit (MNL) parameters for the pooled and un - pooled data for the groups before and after regulation are d isplayed in table 5. Likelihood ratio tests suggested that Figure 8: Choice Selection by Product Category Before and After Regula tion 61.9% 20.4% 17.7% 62.0% 18.7% 19.3% 0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0% Meat Meat-Sub No Choice Before After 30 the MNL has improved when the pooled group is separated into groups before and after regulation. Consistent with economic theory, the price coefficients were negative for both groups; suggesting th at price increases lead to a decrease in the probability a consumer will choose the product. Similar to other choice experiments with meat and meat alternatives, the meat products ranked highest in indirect utility (Lusk and Tonsor 2016; Slade 2018; Apostolidis and McLeay 2016) . For both MNL models , the two meat products had the highest level of indirect u tility followed by Beyond Burger and Beyond Patty, relative to not choosing any product. Two random parameter logit (RPL) models were estimated with 1,000 normally distributed iterations for the groups before and after regulation. The alternative specific constants for the meat and meat alternatives were randomized with a normal distribution of mean zero and standard deviation of one while price was held fixed to assure negativity. Most important to hypothesis 3 is comparing the change in the substitutabili ty of the meat and meat alternatives between the groups before and after regulation. Estimated mean/median cross - price elasticities are in table 6 for the cross - price elasticities at the 95% confidence interval and displayed in figure 9a and 9b. The results of the MNL and RPL are in Table 5 below. Table 5: Model Estimates for DCE Before and After the Labeling Regulation Pooled Before After Before After Variable MNL Standard MNL Random Parameter Logit Means of Random Parameter JUST Meat 0.0340 (0.052) 0.100 (0.075) - 0.028 (0.074) 0.051 (0.215) - 0.232 (0.228) Beyond Beef 0.773*** (0.057) 0.885*** (0.081) 0.665*** (0.080) 0.557** (0.257) 0.437* (0.230) Ball Park Beef Patty 1.274*** (0.042) 1.354*** (0.059) 1.120*** (0.058) 2.093*** (0.176) 1.568*** (0.187) Homestyle Beef Patty 1.132*** (0.034) 1.150*** (0.049) 1.117*** (0.047) 1.862*** (0.157) 1.958*** (0.168) Mean Non - random Parameter Price - 0.145*** (0.006) - 0.145*** (0.008) - 0.145*** (0.008) - 0.302*** (0.0129) - 0.301*** (0.013) 31 Std Dev Random Parameter JUST Meat 3.080*** (0.199) 3.249*** (0.202) Beyond Beef 3.673*** (0.214) 3.669*** (0.230) BallPark Beef Patty 3.688*** (0.201) 3.513*** (0.168) Homestyle Beef Patty 3.345*** (0.168) 3.492*** (0.190) N choices 13,536 6,588 6,948 6,588 6,948 Log Likelihood - 19820.004 - 9,675.465 - 10,135.082 - 6503.481 - 6776.506 Likelihood Test 4 20289.078*** 19369.844*** 6343.968*** 6717.153*** Footnotes: Numbers in parentheses are standard errors. Figure 9a shows the cross - price elasticities for the change in price of the Beyond Meat and JUST Meat in the groups before and after regulation due to a 1% increase in the cross price of the Ballpark Beef Patty. Results revealed that th e cross elasticities remained consistent with the label change suggesting consumers remain rather consistent for the substitution effect. These results suggested that we were not able to reject the null hypothesis that the label change will lower the subst itution effect between products for the average U.S. consumer. 4 Loglikelihood Ratio Test: - 2 (LL base model LL estimated model ) ~ X 2 number of new parameters estimated in the estimated model) (Hensher, Rose, and Greene 2015) 32 0.316% 0.314% 0.000% 0.050% 0.100% 0.150% 0.200% 0.250% 0.300% 0.350% 0.400% Beyond Burger Beyond Patty Figure 9b: Cross - Price Elasticities for Beyond Meat Before and After Regulation Figure 9a: Cross - Price Elasticities for JUST Meat Before and After Regulation 0.204% 0.205% 0.000% 0.040% 0.080% 0.120% 0.160% 0.200% 0.240% 0.280% JUST Meat JUST Protein 33 The results in table 6 suggested that the labeling restrictions might reduce substitution between meat and meat alternatives for some consumers. Prior research showed consumers are consuming less red meat due to their perceived negative health and environm ental effects (Van Loo, Hoef kens and Verbeke 2017; Apostolidis and McLeay 2016; MINTEL 2017; Tonsor, Mintert, and Schroeder 2010) . Concurrently, research has shown that consumers have strong affinity for meat and meat attributes (Graça, Calheiros, and Oliveira 2015a; Graça, Oliveira and Calheiros 2015b; Malone and Lusk 2017; Lusk and Briggeman 2009) . If consumers are to accept meat alternatives, they are more likely to accept those that are similar to meat (Elzerman et al. 2015; Elzerman et al. 20 11; de Boer, Schösler and Boersema 2013) . If there is a subcategory of consumers that are most likely to have their substitution effects decreased, it may be flexitarians, consumers that want a meat alternative similar to meat but with a perceived healt h or environmental benefit. Table 6: Cross Price Elasticities for Before and After the Labeling Regulation Product Before After JUST Meat/Protein (JM/JP) 0.204 % [0.164, 0.249] 0.205 % [0.141, 0.28] Beyond Beef/Protein (BB/BP) 0.316 % [0.261, 0.375] 0.314 % [0.243, 0.402] Ball Park Beef Patty (BPB) 0.551 % [0.481, 0.628] 0.469 % [0.397, 0.542] Homestyle Beef Patties (HP) 0.480 % [0.421, 0.547] 0.532 % [0.455, 0.621] Footnotes: Numbers in brackets are the 95% confidence intervals as calculated using (Krinsky and Robb 1986) . Interpreted as the effect of a 1% increase in price on quantity demanded for the alternative products. 34 VII. POLICY IMPLICATIONS Findings of this article have key implications for public policy. First, our results supported the industry claim that consumers are confused about the nutritional and ingredient content of meat alternativ es. Indeed, more than 30% of our sample believed that ground beef could be found in Beyond Meat ® . At first glance, this finding might justify the need for labeling intervention, but kely to reduce consumer confusion. If anything, the labeling restriction might actually increase consumer confusion. Of course, this article cannot accurately identify what proportion of this confusion is due to actual confusion on the part of the consumer or deception on the part of the meat alternative industry. Regardless, this study suggests that regulators would benefit from considering likely unintended consequences of the labeling restriction. Results from the DCE suggest that substitution e ffects will not change for the average consumer if the labeling restriction were to come to fruition. As such, findings of this article have important implications for both the policy agenda of the meat and meat alternative industries. As noted by the re sults reported in table 4, labeling restrictions decreased consumer perceptions of cholesterol and trans fats in the meat alternatives and increased consumer perceptions of protein content. In other words, labeling restrictions might actually increase dema nd for the meat alternatives for health - interest groups might be better off focusing their policy strategy on promoting the health benefits in meat that cannot be found in meat alterna tives, including ketogenic diets high in fat (Poff, Ari, . 35 VIII. CONCLUSION Missouri Senate Bills 627 and 925 were allegedly passed in order to protect consumers, but this article suggests that labeling changes are unlikely to reduce consumer harms associated with labeling confusion. Indeed, when respondents were asked to state t he ingredients and nutrition of meat and meat alternatives, respondents incorrectly distinguished the ingredients and nutritional differences for all products, and this remained consistent with the labeling changes. This paper tested whether the economic o utcomes of the proposed meat labeling policy are likely to be consistent with the bootlegger and Baptist paradigm. Results of the branded DCE suggest that the substitutability from the meat to the meat alternatives is unlikely to change due to the new labe ling regulations. Some limitations remain. While this research is one of the first to estimate the substitution effects between meat and meat alternatives, it is possible key variables were omitted including product expiration dates (Tonsor 2011) . Future studies might incl ude brands of meat alternatives such as company Beyond Burger has been the most aggressive in their product placement, and has drawn controversy, if consumers are conf used with this brand it is likely they are confused with others. The selection of the JUST Meat and Beyond Burger are also novelty items that might be receiving choice because consumers are curious about the products (Alemu and Olsen 2018) . Despite these limitations, results suggested the proposed regulation targeted at protecting consumers from food labeling confusion is unlikely to be successful as the regul ation may cause more problems than solutions. If the attempt is to protect consumers from confusion, regulators have proposed regulating product choice at supermarke t checkouts to lower impulse purchases of 36 unhealthy foods (Cohen and Babey 2012) . Finally, given the growing trend in food labeling regulation, future resear ch might consider labeling for other products such as rice or milk. In addition, the literature might also benefit from comparing our results to results of studies on other meats. Despite these limitations, our results suggest that meat labeling restrictio ns are unlikely to effectively mitigate consumer confusion. 37 APPENDIX 38 APPENDIX Table 7 : Factors and Factor Levels for the Discrete Choice Experiment Factor JUST Meat/Protein Beyond Burger/Patty Ball Park Beef Patty Homestyle Beef Patty No Buy Factor Levels $4.61 $5.99 $3.50 $1.99 If these were the only options, I'd buy something else $6.45 $7.98 $5.75 $3.98 $8.99 $11.98 $7.99 $5.99 Discrete Choice Experiment design before and after regulation Five choice factors and three price factor levels Table 8 : Main Effects Orthogonal Fractional Factorial Design Choice Set JUST Meat/Protein Beyond Burger/Patty Ball Park Beef Patty Homestyle Beef Patty No Buy 1 $4.61 $5.99 $3.50 $1.99 1 2 $4.61 $7.98 $7.99 $5.99 1 3 $4.61 $11.98 $5.75 $3.98 1 4 $6.45 $5.99 $7.99 $3.98 1 5 $6.45 $7.98 $5.75 $1.99 1 6 $6.45 $11.98 $3.50 $5.99 1 7 $8.99 $5.99 $5.75 $5.99 1 8 $8.99 $7.98 $3.50 $3.98 1 9 $8.99 $11.98 $7.99 $1.99 1 39 Table 9 : OLS of Incorrect Nutrition of Cholesterol Before Regulation Incorrect Beyond Incorrect Ballpark Incorrect Homestyle Low Income - 7.010*** - 6.815** - 4.964* (2.634) (2.710) (2.610) Mid Income - 5.093** - 5.977** - 3.929 (2.544) (2.617) (2.520) No college 6.212*** 1.688 1.694 (1.796) (1.848) (1.780) Some College 0.233 - 2.978* - 2.267 (1.549) (1.593) (1.534) Gender - 2.967** - 1.541 - 1.835 (1.302) (1.339) (1.290) Veg/Vegan 17.719*** 9.524*** 11.145*** (1.933) (1.989) (1.915) _cons 36.195*** 35.683*** 27.389*** (2.449) (2.519) (2.426) R 2 0.07 0.03 0.03 N 1,504 1,504 1,504 Footnotes: Numbers in parentheses are standard errors. Table 10 : OLS of Incorrect Variable of Sodium Before Regulation Incorrect Beyond Incorrect Ballpark Incorrect Homestyle Low Income - 4.674* - 5.548** - 6.849*** (2.706) (2.648) (2.553) Mid Income - 4.613* - 4.302* - 5.305** (2.612) (2.557) (2.465) No college 2.647 2.199 1.028 (1.845) (1.806) (1.741) Some College - 1.207 - 1.449 - 1.243 (1.590) (1.556) (1.501) Gender - 0.362 - 3.140** - 1.881 (1.337) (1.309) (1.262) Veg/Vegan 12.452*** 12.052*** 8.681*** (1.986) (1.943) (1.873) _cons 16.117*** 26.799*** 19.246*** (2.515) (2.462) (2.373) R 2 0.03 0.04 0.02 N 1,504 1,504 1,504 Footnotes: Numbers in parentheses are standard errors. 40 Table 12: OLS of Incorrect Variable of Calories Before Regulation Incorrect Beyond Incorrect Ballpark Incorrect Homestyle Low Income - 18.329*** - 16.102** - 13.521* (7.063) (7.582) (7.277) Mid Income - 9.739 - 7.080 - 3.376 (6.819) (7.321) (7.026) No college - 3.838 - 10.601** - 12.842*** (4.816) (5.171) (4.962) Some College - 6.556 - 2.350 - 4.341 (4.151) (4.457) (4.277) Gender - 6.972** - 2.818 - 1.676 (3.491) (3.747) (3.596) Veg/Vegan 27.135*** 4.034 14.010*** (5.183) *** (5.565) (5.340) _cons - 122.594 - 12.649)* - 73.846*** (6.566) (7.049 (6.765) R 2 0.03 0.01 0.02 N 1,504 1,504 1,504 Footnotes: Numbers in parentheses are standard errors. Table 11 : OLS of Incorrect Variable of Sodium Before Regulation Incorrect Beyond Incorrect Ballpark Incorrect Homestyle Low Income - 4.805* - 6.368** - 3.992 (2.701) (2.776) (2.641) Mid Income - 2.712 - 5.391** - 4.133 (2.608) (2.680) (2.550) No college 2.365 2.036 2.376 (1.842) (1.893) (1.801) Some College 0.117 0.644 - 0.399 (1.588) (1.632) (1.552) Gender - 4.079*** 0.502 - 2.926** (1.335) (1.372) (1.305) Veg/Vegan 14.494*** 10.098*** 14.140*** (1.982) (2.037) (1.938) _cons 25.260*** 39.398*** 42.909*** (2.511) (2.580) (2.455) R 2 0.05 0.02 0.04 N 1,504 1,504 1, 504 Footnotes: Numbers in parentheses are standard errors. 41 Table 13 : OLS of Incorrect Variable of Trans Fats Before Regulation Incorrect Beyond Incorrect Ballpark Incorrect Homestyle Low Income - 7.714*** - 6.207** - 5.761** (2.687) (2.900) (2.762) Mid Income - 5.200** - 4.652* - 3.087 (2.594) (2.800) (2.666) No college 7.011)*** 5.664*** 3.611* (1.832 (1.977) (1.883) Some College - 0.548 - 0.712 - 0.469 (1.579) (1.705) (1.623) Gender - 3.219** - 0.597 - 1.265 (1.328) (1.433) (1.365) Veg/Vegan 16.308*** 11.569*** 12.268*** (1.972) (2.128) (2.027) _cons 34.968*** 50.813*** 45.062*** (2.498) (2.696) (2.567) R 2 0.07 0.03 0.03 N 1,504 1,504 1,504 42 REFEREN CES 43 REFERENCES - Out Reminder Mitigate Hypothetical Bias in Discrete Choice Experiments? 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