Do Plastic Warning Labels Reduce Consumers' Willingness to Pay For

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Selected Presentation at the 2020 Agricultural & Applied Economics Association Annual Meeting, Kansas City, Missouri, July 26-28

Copyright 2020 by authors. All rights reserved. Readers may make verbatim copies of this document for non-commercial purposes by any means, provided that this copyright notice appears on all such copies. DO PLASTIC WARNING LABELS REDUCE CONSUMERS’ WILLINGNESS TO PAY FOR PLASTIC PACKAGING?

Joanna van Asselt, Yefan Nian, Moonwon Soh, Zhifer Gao, Stephen N. Morgan

Abstract1 Plastic, unlike other packaging materials has been found to pose threats to human, animal and ecosystem health. At the same time, however, plastic generation in the United States has increased by more than 700 percent since the 1990s. Further, plastic recycling rates in the United States are low. Plastic that is not recycled is mainly landfilled or escapes directly into environment, negatively impacting the health of our ecosystems. While some states and municipalities in the United States (U.S.) have begun to address the proliferation of plastic waste, either through implementing fees or deposit refund systems, other states have made it illegal to put in place policies that reduce plastic consumption. Given this environment, one idea that could help reduce plastic use in the U.S. would be to place warning labels on plastic packaging. In this paper, we use a choice experiment to test whether putting warning labels on plastic packaging reduces consumers’ willingness to pay (WTP) for plastic packaging. Further, we test three labels that warn against the negative impacts of plastic on health, environment, and safety. Finally, we check whether knowledge of recycling as well as attitude to the environment and own health impacts willingness to pay for plastic. A mixed logit estimation reveals that warning labels do reduce consumers’ willingness to pay for plastic, but label type has a huge impact on reduction. While all three warning labels reduced willingness to pay for plastic, the environment label had a smaller effect.

Introduction Plastic generation in the United States has increased by 700 percent since 1990 and continues to rise steadily (EPA 2019). The EPA estimates that in 2015, the United States generated 14.7 million tons of plastic containers and packaging and only recycled 2.2 million tons, or 14.6 percent (EPA 2019). Most of the remaining plastic, 68.6 percent, was landfilled or escaped into ecosystems (EPA 2019). When it is not recycled, plastic, unlike other packaging materials, has been found to pose threats to human, animal and ecosystem health.2 While some policy makers in the United States (U.S.) have begun to recognize the need to decrease plastic proliferation, others have opposed any action that attempts to reduce plastic consumption. There are now bans or fines imposed on plastic bags, carryout containers, polystyrene (Styrofoam) and straws in hundreds of municipalities across the U.S. as well as state-wide bans on single-use plastic bags in California and New York (Gibbens 2019). At the

1 Joanna van Asselt: Department of Food and Resource Economics (FRED) University of Florida ([email protected], Yefan Nian: FRED University of Florida ([email protected]) , Moonwon Soh: FRED University of Florida ([email protected]), Zhifeng Gao: FRED University of Florida [email protected], Stephen Morgan: FRED University of Florida [email protected]. 2 See Sajiki and Yonekubo, 2003; Frias et al., 2010; Ashton et al., 2010; Engler, 2012; Velzeboer et al., 2014 to understand how plastics leach toxic and endocrine substances into the environment as well as act as sorbents for other toxic pollutants. See Gregory, 2009, Wright et al., 2013 and Li et al., 2016 for an overview of the negative effects of plastic on marine life. See Bläsing and Amelung, 2018; Huerta Lwanga et al., 2016, Cao et al., 2017, Rodriguez-Seijo et al., 2017 for a discussion of the negative effects of plastic on terrestrial ecosystems. See Teuten et al., 2007; and Engler, 2012 for an overview of how plastic is introduced into the food chain. same time, however, 17 states have made it illegal to ban plastics (Gibbens 2019). Many municipalities within these states have passed plastic regulations only to have them overturned by the state-level preemptions. Given this regulatory environment, policy makers will have to find new ways to stem the flow of plastic waste. Labeling has been shown to impact consumer choices (Kozup et al., 2003; Grunert and Wills 2007; Grunert et al., 2010). Labels educate consumers and give them a chance to make informed purchasing decisions. While most product labels focus on nutrition, eco-labeling is a new technique, introduced to help consumers make more sustainable purchase decisions, by ensuring that consumers have information on goods’ environmental consequences (Taufique et al., 2019; Thøgersen et al., 2010). Studies have shown that eco-labeling is effective (Baumeister and Onkila 2017; Testa et al., 2015; Taufique et al., 2017). Further, studies demonstrate that negative labeling leads to more pro-environmental purchases (Grankvist et al., 2004; Biel and Grankvist 2010; Thøgersen and Nielsen 2016). Based on these findings, we attempt to close the information gap for plastic users, by providing negative labels on plastic packaging. Because labeling has been shown to change consumption decisions, we believe it will be a cost-effective strategy for States or municipalities to pursue to reduce plastic consumption. We test our policy proposal using an online survey of principal shoppers in U.S. households. Specifically, we examine whether putting warning labels on plastic packaging reduce consumers’ willingness to pay (WTP) for plastic packaging. We use a hypothetical choice experiment, using eggs as a focal product to test our hypotheses. Using a within subject treatment design, we test whether labels warning against the health, environment, and safety impacts of plastic have different effects on WTP. Finally, we investigate whether WTP for plastic is driven by recycling behavior and knowledge as well as attitude about the environment and own health. By testing the effects of warning labels on WTP for plastic packaging we contribute to three strands of literature: understanding the drivers of plastic consumption, finding solutions to the plastic proliferation problem, as well as more broadly to the discussion on labeling and eco- labeling. While most studies that explore the drivers of plastic consumption either focus only on product attributes (e.g., Draskovic et al., 2009; Gelici-Zeko et al., 2013; Isa and Yao, 2013; Koutsimanis et al., 2012; Scherer et al., 2017; Young, 2008) or explore the demographics of plastic consumers (Afroz et al., 2017; Hohmann et al. 2016; Jeżewska-Zychowicz and Jeznach, 2015; Madigele et al., 2017; Ryan and Jewitt, 1996; Sharp et al., 2010), we explore both. Additionally, we take a step further and measure how both peoples subjective and objective knowledge of plastic, through asking a series of recycling behavior and knowledge as well as lifestyle questions, impact WTP for plastic. Further, we also examine the perceived impact of plastic on the environment and own health and test whether this impacts WTP for plastic. While there have been studies that reveal that consumers do find plastic packaging to be harmful to the environment ( Adane and Muleta, 2011; Fernqvist et al., 2015; Gelcich et al., 2014; Lotze et al., 2018; Otsyina et al., 2018; van Dam and van Trijp, 1994) , none of them test whether this impacts their consumption behavior. And, while there have also been studies that examine whether consumers worry about the health impacts of plastic, none of them test whether this holds for a product that has only an indirect negative impact on health (Aday and Yener, 2014; Fernqvist et al., 2015; Joseph et al., 2016; Omari and Frempong, 2016; Omari et al., 2011). We also contribute to the literature focused on solutions to the plastic problem. Although there are several studies that analyze the current plastic mitigating programs in place, plastic bag taxes and bans, few studies look to new solutions (Convery et al., 2007; Dikgang et al., 2012; Dikgang and Visser, 2012; He, 2012; Poortinga et al., 2013; Thomas et al., 2016). Among the studies that do, they focus on information campaigns for recycling (Cheung et al., 2018; Ofstad et al., 2017; Pearson et al., 2014). Two notable other studies are Longoni et al. (2014) who look at the effect of receiving feedback on each purchase in a fictitious shopping experience and Santos and Van Der Linden (2016) who look at the effect of providing alternatives to plastic on consumption. These studies, however, may have limited policy implications. To the best of our knowledge, we will be the first study to look at the effectiveness of plastic warning labels on mitigating the plastic problem. While our first task is to understand whether plastic warning labels reduce consumption of plastic packaging, we also contribute to the debate on how effective the labels are for different consumers and whether eco-labeling is effective for plastic (e.g., Grankvist et al., 2004; Thøgersen 2000; Larceneux et al., 2011; Koos 2011; Jensen and Webster 2008; Taufique et al., 2019; Thøgersen et al., 2010; Sønderskov and Daugbjerg 2011). Finally, our different treatments , health, safety and environment allow us to test which type of label most reduces willingness to pay for plastics.

Data and Experimental Design The data we use this study is being collected through an online survey using Qualtrics. We hope to have 800 respondents, 200 in the control group, the environment treatment, health treatment and safety treatment respectively. We use screening questions to ensure that our sample will contain only adult primary grocery shoppers who purchased eggs in the past month. We chose eggs as the focal product because eggs are packaged in three separate types of material, pulp, plastic and foam. This allows us to test consumers’ preference for different type of packaging materials and specifically for two plastic products with different levels of recyclability; plastic can be recycled while foam cannot. The packaging for the eggs does not have an impact on the quality or lifespan of the eggs. Further, most consumers find that the three packaging materials have the same level of functionality and protect the eggs equally from breaking. There are some issues associated with using eggs as our focal product, however. In the U.S., commonly white eggs are sold in foam packaging, while brown eggs are sold in pulp and plastic packages. Further, most organic eggs are sold in plastic. We tried to eliminate this bias by labeling all egg packages, “12 Grade A large brown eggs” and by showing two brown eggs outside of the package. We examined participants’ preferences for egg packaging materials through a discreet choice experiment (DCE). DCE is a stated preference method used to elicit people’s preferences for market and non-market goods (Hensher 2006). Using a stated choice experiment, allowed us to create a decision-making environment that is similar to a real food purchase situation, anticipating that participants will make the same choice as they do in the grocery store (Lusk and Schroeder 2004). However, since the survey is hypothetical, some participants might overstate their willingness to pay for certain bundles. Although the differences tend to be minor and have little impact on the outcome of the experiment, we employ several methods to reduce hypothetical bias and check the robustness of our results. To reduce hypothetical bias, we use cheap talk before our experiment and have participants agree to an oath statement (Harrison 2006; Van Loo et al. 2011). Further, we use contingent valuation method (CVM), where individuals state their WTP for the different egg crates, to check the robustness of our results. The egg attributes considered in each shopping experience include packaging material, cage free, organic, and price. The attributes and their levels are summarized in Table 1. Their selection was based on market information and focus groups.

Table 1. attributes and levels used in the egg’s choice experiment Attribute Number of attribute levels Attribute levels Container 3 Plastic, Cardboard, Styrofoam Cage Free 2 Yes, No Organic 2 Yes, No Price 4 $1.50/dozen, $2.50/dozen, $3.50/dozen, $4.50/dozen,

The DCE was designed based on the SAS label design method which assumes ... We generated 16 choice sets using SAS software. Each choice set includes three eggs options one with pulp, one with plastic, and one with foam, each with different attribute levels and a no- purchase (opt-out) option. The no-purchase (opt-out) option gives consumers an option not to purchase, which better models real consumers purchase behavior. A sample choice task is provided in Figure 1.

Figure 1. An example of choice set

To understand the effect of different warning labels we included three treatments, environment, health and safety. The wording of the three labels are found in Table 2. The specific wording was agreed on after three focus groups with consumers. We employ a within subject treatment design, where every respondent completes eight choice tasks in the control and then respondents are randomly assigned to either the control or one of the three treatments groups for the remaining eight choice tasks. By using a within subject treatment design we reduce errors associated with individual differences.

Table 2. List of warning label treatment Group Warning labels

Warning: chemicals found in plastics can be absorbed by human bodies; some of these 1 Health Treatment chemicals may have adverse effects on human health

Warning: plastic that is not recycled may enter 2 Environment Treatment ecosystems and harm wildlife

Warning: plastic buried in landfills can leach harmful chemicals that may harm humans and 3 Safety Treatment animals

4 Control -

After the DCE, we collected respondents’ trust level of the warning labels, their recycling behavior and knowledge, their attitude towards the environment and own health and their socio- demographic information. This information helped us to account for heterogeneity of participants in our econometric model and analyze differences in their WTP.

Theoretical Model We base our modelling approach on Lancaster’s Utility theory, which asserts that consumers make food consumption choices based on the attributes of the food products. A consumer’s utility resulting from his or her purchase decision depends upon attributes associated with the purchase alternatives. Following Ben-Akiva and Lerman (1994), we define a random utility function which consists of deterministic components or 푃푖푗푡 and is 푋푖푗푡 , the price and non- price attributes and a stochastic component 휀푖푗푡 . The estimated parameters α푖 and 훿푖 capture the effects of different attributes on respondent 푖’s utility, while 휃푖 captures the effects of not purchasing on respondent 푖’s utility. The shopper i, chooses the alternative j, at scenario t, that gives him or her the highest utility specified as: 푈푖푗푡 = 훼푖′푃푖푗푡 + 훽푖′푋푖푗푡 + 휀푖푗푡 (1) In our experiment, at each shopping scenario, respondents are presented with three unique egg alternatives (푘 = 1, 2,3). They also have the option of not purchasing the eggs (optout) (푘 = 4). The optout option is specified as a dummy variable taking the value of one if the respondent choose it and zero otherwise. The observed part of the utility function can be expressed by: ′ ′ 훼푖푃푖푗푡 + 훿푖 푋푖푗푡 if 푘 = 1,2,3 푉푖푗푡 = { ′ (2) 휃푖 ⋅ 표푝푡표푢푡 if 푘 = 4 where 푃푖푗 and 푋푖푗 are the price and non-price attribute consumer 푖 faces for an alternative 푗 at the scenario 푡, respectively and 훼푖 and 훽푖 are measures of the respondent 푖’s preferences for different attributes. The unobservable stochastic error term, 휀푖푗, is assumed to follow an independently and identically distributed (i.i.d) Gumbel distribution with the scale parameter normalized to one (the 2 variance of 휀푖푗is assumed 휋 /6).

Empirical Model This specification can be estimated by the standard Multinomial Logit Model (MNL)(McFadden 1974). However, the model assumes homogeneity in consumers’ preferences. To relax this assumption, we estimate the model using a random parameters logits (RPL) model.

Specifically, we define 훼푖 = 훼 + 휂 ⋅ 휇푖 and 훽푖 = 훽 + 휂 ⋅ 휇푖, where 훼 and 훽 are the mean effects of attributes, 휂 is the lower Cholesky triangular matrix used to calculate the covariance matrix of random parameters such that 훴 = 휂 ⋅ 휂′, and 휇i is interpedently distributed with a certain distribution (Train 2009; Greene 2012). Following Train (2009), the probability that respondent 푖 chooses alternative 푗 is conditional on respondent’s parameters α푖 and β푖. Assuming the parameters 훼푖 and 훽푖 only vary between respondents, the unconditional probability that respondent 푖 chooses alternative 푗 is calculated using the following function: 푒푥푝(푉 ) 푃푟 = ∫ ∏푇 푖푗푡 푓(훽, 훺)푑훽 (3) 푖푗 푡=1 ∑퐽 푖 푗=1 푒푥푝(푉푖푗푡) 푒푥푝[(훼 )′푃 +(훽 )′푋 ] = ∫ ∏푇 푖 푖푗푡 푖 푖푗푡 푓(훽, 훺)푑훽 (4) 푡=1 ∑퐽 ( )′ ( )′ 푖 푗=1 푒푥푝[ 훼푖 푃푖푗푡+ 훽푖 푋푖푗푡] where 푉푖푗푡 represents the observed utility of choosing alternative 푗 at scenario 푡, 푓(β, Ω) is the density distribution with mean β and variance-covariance matrix parameters Ω to be estimated using our data. We use a difference in different approach (DID) to estimate four separate models, one for the control and one for each treatment, environment, safety, and health. Using the means covariance of random estimates, we then simulate willingness to pay (WTP) for different attributes of eggs as the negative ratio between the effects of price and attributes such that;

훽푖/휎푖 훽푖 푊푇푃푖 = − = − (5) 훼푖/휎푖 훼푖 Using this approach to measure WTP may be problematic, since we assume in our RPL that price follows a log-normal distribution while our non-price parameters are normally distributed. This specification may result in large WTP estimated with hard to evaluate distributions (Train 2009). While some studies try and overcome this shortcoming by fixing the price parameter, research suggests that this may lead to positive estimates on the price coefficient which is inconsistent with economic theory (Gao and Schroeder 2009). Alternatively, recent literature has suggested that instead of estimating coefficients of the model in preference space, we can estimate consumer’s utility in WTP space by reparametrizing the model. The key advantage of this model is that it allows researchers to assume the distribution of WTP directly, enabling the estimates to capture both preference heterogeneity (by 휂) and scale heterogeneity (by 휏) (Fiebig et al. 2010; Greene 2012) . We assume WTP follows a normal distribution following (Balcombe, Chalak and Fraser 2009) . To estimate the model in WTP space, we specify the utility function as; 훽푖 푈푖푗푡 = −(훼푖)′ [푃푖푗 + (− ) ′ 푋푖푗푡] + 푒푖푗푡 훼푖 (6) = −(훼푖)′[푃푖푗 + (푊푇푃푖)′ 푋푖푗푡] + 푒푖푗푡 Finally, to explore the differences in WTP among participants we run an OLS regression. We estimate the following equation;

푊푇푃(푐표푛푡푎푖푛푒푟)푖 = δ푆푖 + η푇푖 + λ퐾푖 + μ푖 (7) where the dependent variable, 푊푇푃(푐표푛푡푎푖푛푒푟)푖, is respondent 푖’s WTP for different packaging materials. The independent variables consist of a vector 푆푖 containing the respondents’ socio- demographic characteristics, including participants’ age, gender, education, race, family size, the number of children in household, and income. A vector 푇푖 , which is a vector of dummy variables indicating the types of warning labels that respondents received in the experiment. And a vector 퐾푖, which is a calculated recycling score, based on how many packaging types were correctly recycled.

Results Our current results are based on a pre-test with UF undergraduate students and our friends. We had 72 respondents that passed our screening questions and completed the entire survey: 18 respondents in the control group, the environment treatment, and safety treatment, and 17 respondents in the health treatment group. Our final results will be uploaded before the AAEA conference. Table 3 shows the summary statistics for our sample. Most of our respondents are students, so the average age and income level is low compared to the US census sample. In appendix table 1, we present a comparison of demographics for the treatment and three control groups; each group shares roughly the same characteristics.

Table 3. Summary statistics of all sample Variables Mean Std. Dev Min Max Income Inc below 25k 0.41 0.50 0 1 Inc between 25k and 50k 0.10 0.30 0 1 Inc between 50k and 75k 0.06 0.23 0 1 Inc above 100k 0.06 0.23 0 1 Education Edu - High School 0.13 0.34 0 1 Edu – Associate 0.38 0.49 0 1 Edu – Bachelor 0.25 0.44 0 1 Edu – Master 0.20 0.40 0 1 Edu – Phd 0.04 0.20 0 1 Age Age between 18 and 24 0.61 0.49 0 1 Age between 25 and 34 0.31 0.47 0 1 Age between 45 and 54 0.06 0.23 0 1 Age between 55 and 64 0.01 0.12 0 1 Female 0.48 0.50 0 1 Born in US 0.80 0.40 0 1 Race White 0.72 0.45 0 1 Black or African American 0.04 0.20 0 1 Native American 0.11 0.32 0 1 Asian 0.10 0.30 0 1 Hispanic 0.03 0.17 0 1 Observations 71

Table 4 presents our results from our mixed logit and WTP model and Figure 2 and 3 presents results for our WTP calculations from our mixed logit and our WTP model. For our discussion, we focus on the results from the mixed logit regression and their corresponding WTPs. Our base in our models is pulp(cardboard). All estimates are highly significant. Further, our standard deviations are significant, indicating that there was preference heterogeneity among respondents in our model. The alternative specific constant, no purchase option, is negative and significant for all groups, indicating that participants preferred to buy eggs in each shopping scenario.

Table 4. Regression result using mixed logit model mixed logit Control group Health Environment Safety treatment treatment treatment group group group

Price Coef -2.796*** -0.914*** -1.168*** -0.639*** (0.062) (0.058) (0.058) (0.058) Std. Dev 2.606*** 0.743*** 3.316*** 2.569*** (0.086) (0.031) (0.151) (0.09) Container Styrofoam Coef -2.451*** -2.158*** -1.825*** -3.341*** (0.152) (0.244) (0.179) (0.188) Std. Dev 2.77*** 10.47*** 4.263*** 4.719*** (0.178) (0.607) (0.305) (0.27) Plastic Coef -1.126*** -2.88*** -1.531*** -2.009*** (0.136) (0.172) (0.176) (0.154) Std. Dev 3.088*** 1.265*** 2.967*** 3.525*** (0.192) (0.124) (0.202) (0.212) Other attributes Cage free Coef 2.795*** 3.924*** 2.258*** 0.84*** (0.18) (0.237) (0.209) (0.121) Std. Dev 6.389*** 3.155*** 3.63*** 2.206*** (0.34) (0.227) (0.264) (0.159) Organic Coef 2.697*** 2.603*** 2.598*** 1.117*** (0.169) (0.22) (0.191) (0.115) Std. Dev 2.447*** 4.155*** 2.819*** 3.101*** (0.149) (0.279) (0.193) (0.19) Local Coef 1.521*** 1.411*** 1.421*** 0.921*** (0.179) (0.186) (0.219) (0.125) Std. Dev 1.486*** 4.669*** 2.726*** 2.241*** (0.192) (0.273) (0.216) (0.188) None Coef -15.133*** -14.855*** -21.183*** -14.157*** (0.749) (0.764) (1.244) (0.679) Std. Dev 5.789*** 5.562*** 13.178*** 13.65*** (0.377) (0.304) (0.843) (0.631) Logliklihood -805.139 -637.452 -581.423 -925.151 AIC 1638.278 1302.903 1190.846 1878.303 BIC 1715.215 1379.041 1267.784 1955.24 N respondents 18 17 18 18 N observations 180 170 180 180 Notes: Standard errors are reported in parentheses. *, **, *** indicate levels of statistical significance at 10%, 5%, and 1%, respectively

Figure 2. WTP for Styrofoam container across groups

Figure 3. WTP for plastic container across groups

Our non-package attributes are all significant. Across groups, there was a strong preference for cage free and organic eggs. Consumers were willing to pay $.47 more for cage- free eggs and $.62 cents more for organic eggs. These results indicate that these are indeed attributes shoppers pay attention to when making their egg purchase decisions. Local eggs were on average less desired, WTP for local eggs was less than 10 cents for the control and health treatment groups. On the other hand, for the environmental treatment group, local eggs were strongly desired, these consumers were willing to pay an additional dollar for local eggs. In our control group there was already a strongly negative WTP among our respondents for Styrofoam and plastic. In the control group, participants were willing to pay $.88 cents more for pulp than Styrofoam and $.40 cents more for pulp than plastic. That negative WTP for plastic and Styrofoam increasing among participants in all of the treatment groups. In the health and safety groups, negative WTP for plastic was $3.15 and for Styrofoam was $2.36 and $5.23 respectively. While participants in the environment treatment group also had a larger negative willingness to pay than participants in the control, they had a smaller negative willingness to pay than those in the health and safety groups. This finding is very interesting, but we need a larger sample size to determine if this is really the case. In addition to our choice experiment, we also collected data on lifestyle and recycling behavior and knowledge. We then used these indicators to run a linear regression to try and explain differences in willingness to pay (Table 5). However, because our sample is students, there was very little variance in our lifestyle data, and therefore none of these variables had an explanatory power in our regression. Our only significant variables were age and having a college degree, both of which significantly decreased willingness to pay for Styrofoam.

Table 5. Determinant of WTP for different packaging materials Control Health Environment Safety WTP for WTP for WTP for WTP for WTP for WTP for WTP for WTP for Styrofoam plastic Styrofoam plastic Styrofoam plastic Styrofoam plastic container container container container container container container container Age between 18 -27.27*** 4.335* -26.45*** 4.182* -22.57* 4.348* -27.20*** 4.266* and 24 (9.379) (2.290) (9.451) (2.389) (11.54) (2.237) (9.264) (2.393) Female -13.93** -0.0781 -14.08** -0.0945 -8.992 -0.385 -11.89** -0.287 (5.477) (1.337) (5.486) (1.339) (6.190) (1.200) (5.545) (1.381) White 9.249 -1.989 9.262 -1.991 1.097 -2.266* 8.723 -1.929 (5.754) (1.405) (5.782) (1.408) (6.625) (1.284) (5.671) (1.410) Income less than 7.738 -2.253* 8.031 -2.254* 6.349 -2.362* 5.972 -2.075 25k (5.487) (1.340) (5.562) (1.358) (6.578) (1.275) (5.596) (1.404) Have a college -22.22*** 2.285 -20.81*** 2.321 -22.74** 2.394 -19.48*** 2.259 degree (7.250) (1.770) (7.539) (1.776) (9.147) (1.773) (7.430) (1.776) Household size 0.447 -0.0959 0.0452 -0.0986 1.473 -0.349 0.469 -0.143 (3.014) (0.736) (3.071) (0.736) (3.523) (0.683) (3.018) (0.738) Number of -14.63** 2.363 -14.69** 2.218 -11.61 1.786 -15.94** 2.335 children (6.731) (1.644) (6.744) (1.780) (7.360) (1.427) (6.649) (1.800) Recycling score -0.223 0.0693 -0.400 0.0336 (0.286) (0.0554) (0.253) (0.0633) Styrofoam harms -3.264 0.224 -5.423 0.181 health (6.822) (1.446) (6.825) (1.472) Styrofoam harms 4.880 0.350 5.917 0.443 environment (7.753) (1.989) (7.625) (2.000) Constant 23.83** -4.301 23.07* -4.591 33.37 -6.513 47.99** -6.654 (11.55) (2.821) (13.44) (3.241) (22.00) (4.265) (20.49) (5.100) Observations 71 71 71 71 71 71 71 71 Notes: Standard errors are reported in parentheses. *, **, *** indicate levels of statistical significance at 10%, 5%, and 1%, respectively.

Participants were asked whether they thought pulp, plastic, or Styrofoam were bad for their health or the environment. While almost no participants felt that pulp was bad for their health or the environment, 40.5 percent and 54.1 percent of participants felt that plastic and foam were bad for their health and 43 percent felt that plastic and Styrofoam were bad for the environment. We also tried putting this into our OLS regression as an explanatory variable, but it was not significant. One issue is that we non-option provide participants with a non-option in this question, and although they could move on without answering it, every respondent circled at least one option. We have fixed this in the current version of our survey. We also constructed a recycling score for each respondent, based on their knowledge of what items can be recycled in the blue and red boxes. Concerningly, 72 percent of our sample thought that they could recycle plastic egg cartons and 38 percent of the sample thought they could recycle foam egg cartons. Further, 64 percent of participants said they did recycle their plastic egg cartons weekly and 33 percent said they recycled their foam cartons. While house owners, in Alachua county, would be fined for this behavior, because our respondents lived almost exclusively in apartment buildings or residential halls, there was no direct communication between them and Alachua county, resulting in incorrect recycling behavior. There was also not much variance in the recycling score, most individuals had low scores, and therefore it was not significant in our regression. Conclusion In our paper, we test whether an alternative policy instrument to reduce plastic waste, putting warning labels on plastic and foam egg cartons, reduces willingness to pay for plastic packaging. Labeling has been shown to impact consumer choices in a variety of settings by educating consumers and giving them a chance to make informed purchase decisions (Grunert and Wills 2007). Our pilot study demonstrates that putting warning labels on plastic packages does impact consumer choice by reducing consumers’ WTP for plastic packaging. While our preliminary results suggest putting warning labels on plastic packages does reduce consumers’ WTP for eggs, contrary to our hypothesis, health and safety warning labels generated larger reductions in WTP than the environment label. In our control group there was already a strongly negative WTP for products packaged in foam and plastic. Participants were willing to pay $.88 cents more for eggs packaged with pulp than foam and $.40 cents more for pulp than plastic. For respondents in the treatment groups, the difference between WTP for pulp and the two plastic packages was much greater. In the health and safety groups, the WTP for eggs packaged in pulp was $3.15 and $3.12 more than those in plastic and $2.36 and $5.23 more than the eggs in foam, respectively. Although participants in the environment treatment group had a higher WTP for eggs in pulp than participants in the control, they had a smaller WTP than those in the health and safety groups. Because we used a between subject treatment design in our pilot study, it is hard to tease out whether this is a significant result or simply a result of fundamental differences between the treatment groups. We will be able to eliminate this sense of uncertainty with our new survey data. Reduction of plastic use is already an important issue facing U.S. policy makers and therefore it is important to discuss the most effective, cost-efficient policy options. Our results indicate that warning labels should be considered as a strategy to reduce plastic use in the U.S. Further, labels that focus on the impacts of plastic on health or safety will be most effective at reducing plastic demand. Our results also shed light on a disconnect between attitudes towards the environment and health and consumption decisions. Warning labels, by reminding consumers of the negative attributes of plastic, can bring consumption habits closer towards attitudes. Finally, our pilot study revealed that U.S. consumers have limited knowledge of what products are recyclable. Perhaps closing this information gap will also be an important tool for reducing plastic consumption.

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Appendix Appendix Table 1. Summary statistics of demographic characteristic among different treatment groups Control Health Environment Safety treatment

group treatment group treatment group group Age 2518 - 3424 0.3330.556 0.3750.625 0.2220.611 0.3330.667 (0.485) (0.500) (0.428) (0.485) Age 45 - 54 0.0556 0 0.111 0 (0.236) (0) (0.323) (0) Age 55 - 64 0.0556 0 0 0 (0.236) (0) (0) (0) Female 0.500 0.438 0.278 0.667 (0.514) (0.512) (0.461) (0.485) Household size 2.667 2.875 2.889 2.944 (1.029) (1.025) (1.183) (1.110) Number of children 0.111 0.125 0.222 0.167 (0.323) (0.500) (0.548) (0.383) Black or African 0.0556 0 0.0556 0.0556 American (0.236) (0) (0.236) (0.236) Native American 0.111 0.0625 0.167 0.111 (0.323) (0.250) (0.383) (0.323) Asian 0.167 0.0625 0.0556 0.111 (0.383) (0.250) (0.236) (0.323) Hispanic 0 0 0.111 0 (0) (0) (0.323) (0) Inc less than 25k 0.333 0.313 0.500 0.500 (0.485) (0.479) (0.514) (0.514) College degree 0.389 0.438 0.611 0.500 (0.502) (0.512) (0.502) (0.514) Observations 18 17 18 18

Appendix 2. Regression result using generalized mixed logit (GMNL-II) model preference space model WTP space model

Health Environment Safety Health Environment Safety Control treatment treatment treatment Control treatment treatment treatment group group group group group group group group Price Coef -2.531*** -0.757*** -2.696*** -4.213*** -30.025 -6.595 -50.22 -40.98 (0.048) (0.012) (0.087) (0.178) (0.017) (0.007) (0.027) (0.016) Std. 0.283*** 0.73*** 1.106*** 7.463*** Dev (0.015) (0.02) (0.09) (0.382) Container - - -7.257*** -8.84*** -5.357*** -1.924*** -0.269*** -0.357*** Styrofoam Coef 0.312*** 2.626*** (0.413) (0.427) (0.382) (0.205) (0.023) (0.102) (0.036) (0.022) Std. 11.88*** 12.214*** 6.827*** 4.267*** 2.204*** 1.696*** 3.324*** 2.053*** Dev (0.658) (0.585) (0.433) (0.348) (0.034) (0.083) (0.35) (0.076) - - -0.883** -3.001*** -4.875*** -1.707*** -1.133*** -0.407*** Plastic Coef 0.942*** 0.705*** (0.415) (0.213) (0.443) (0.152) (0.034) (0.057) (0.067) (0.028) Std. 13.629*** 5.948*** 5.505*** 1.281*** 4.99*** 1.353*** 1.143*** 0.572*** Dev (0.788) (0.34) (0.419) (0.119) (0.099) (0.092) (0.22) (0.043) Other attributes Cage free Coef 0.558 4.946*** 11.961*** 3.628*** 0.622*** 1.183*** 0.711*** 0.444*** (0.341) (0.26) (0.843) (0.26) (0.038) (0.043) (0.151) (0.022) Std. 15.934*** 4.19*** 21.005*** 2.889*** 2.903*** 1.139*** 4.87*** 1.051*** Dev (0.909) (0.237) (1.424) (0.318) (0.069) (0.039) (0.945) (0.069) Organic Coef 7.279*** 1.451*** 0.143 0.784*** 0.9*** 0.371*** 0.703*** 0.574*** (0.48) (0.187) (0.421) (0.149) (0.027) (0.049) (0.045) (0.026) Std. 7.884*** 0.107 12.825*** 2.868*** 0.596*** 0.79*** 1.205*** 0.675*** Dev (0.558) (0.168) (0.877) (0.217) (0.021) (0.049) (0.13) (0.045) Local Coef 1.627** 1.807*** 0.774 3.036*** 0.112*** 0.655*** 0.16** 0.277*** (0.616) (0.196) (0.54) (0.27) (0.025) (0.057) (0.049) (0.024) Std. 6.606*** 4.793*** 12.582*** 2.058*** 0.492*** 2.811*** 0.262*** 0.482*** Dev (0.555) (0.28) (0.991) (0.18) (0.036) (0.118) (0.051) (0.016) ------47.299*** -4.419*** None Coef 66.877*** 21.441*** 25.934*** 5.627*** 3.819*** (3.607) (0.79) (2.722) (1.418) (0.033) (0.098) (NaN) (0.137) Std. 25.066*** 10.209*** 9.749*** 12.188*** 0.06*** 0.938*** 0.88*** 1.51*** Dev (2.055) (0.467) (1.374) (0.767) (0.016) (0.055) (0.132) (0.095) TauScale Coef 1.281*** -2.076*** 1.185*** 1.264*** 2.165*** 1.049*** 2.434*** 2.243*** (0.026) (0.034) (0.037) (0.034) (0.097) (0.095) (0.076) (0.107) Logliklihood -632.492 -622.484 -558.479 -1039.843 -692.742 -674.956 -524.544 -959.691 AIC 1294.983 1274.968 1146.957 2109.687 1413.484 1377.912 1077.087 1947.382 BIC 1377.416 1356.544 1229.39 2192.12 1490.421 1454.049 1154.025 2024.319 N respondents 18 17 18 18 18 17 18 18 N observations 180 170 180 180 180 170 180 180 Notes: Standard errors are reported in parentheses. *, **, *** indicate levels of statistical significance at 10%, 5%, and 1%, respectively. Appendix 3. Determinant of WTP for different packaging materials for student sample Preference space model WTP space model WTP WTP WTP WTP WTP WTP WTP WTP WTP WTP WTP WTP WTP WTP WTP WTP for for for for for for for for for for for for for for for for Styrofo Styrofo Styrofo Styrofo Styrofo Styrofo Styrofo Styrofo plastic plastic plastic plastic plastic plastic plastic plastic am am am am am am am am contain contain contain contain contain contain contain contain contain contain contain contain contain contain contain contain er er er er er er er er er er er er er er er er Age 3.805 - 4.335* - 4.182* - 4.420* - between 18 27.27* 26.45* 28.18* 27.20* 2.935 5.165 3.621 3.897 2.259 5.070 3.080 2.935 and 24 ** ** ** ** (3.663) (9.379) (2.290) (9.451) (2.389) (9.219) (2.289) (9.264) (6.808) (3.599) (6.826) (3.651) (6.684) (3.602) (6.680) (6.808) Female 1.549 - -0.0781 - -0.0945 - -0.270 - 13.93* 14.08* 11.88* 11.89* 0.282 1.437 0.213 1.301 1.815 1.652 1.822 0.282 * * * * (2.115) (5.477) (1.337) (5.486) (1.339) (5.557) (1.380) (5.545) (3.975) (2.102) (3.963) (2.046) (4.029) (2.172) (3.998) (3.975) White -0.173 9.249 -1.989 9.262 -1.991 8.649 -1.933 8.723 -3.860 -0.0706 -3.960 -0.0932 -4.308 -0.133 -4.357 -3.860 (2.159) (5.754) (1.405) (5.782) (1.408) (5.658) (1.405) (5.671) (4.177) (2.208) (4.176) (2.152) (4.102) (2.211) (4.089) (4.177) Income -0.756 7.738 -2.253* 8.031 -2.254* 5.970 -2.088 5.972 -1.471 -0.503 -1.083 -0.525 -2.791 -0.688 -2.592 -1.471 less than (2.148) (5.487) (1.340) (5.562) (1.358) (5.519) (1.370) (5.596) (3.983) (2.106) (4.017) (2.076) (4.001) (2.157) (4.035) (3.983) 25k Have a 0.233 - 2.285 - 2.321 - 2.220 - - - college 22.22* 20.81* 21.52* 19.48* 10.36* -0.133 -9.254* 0.157 -9.834* -0.0598 -8.258 10.36* degree ** ** ** ** * * (2.719) (7.250) (1.770) (7.539) (1.776) (7.126) (1.769) (7.430) (5.262) (2.782) (5.446) (2.714) (5.166) (2.785) (5.357) (5.262) Household -1.407 0.447 -0.0959 0.0452 -0.0986 0.928 -0.141 0.469 -0.0978 -1.441 -0.444 -1.464 0.261 -1.390 -0.139 -0.0978 size (1.131) (3.014) (0.736) (3.071) (0.736) (2.975) (0.739) (3.018) (2.188) (1.157) (2.218) (1.125) (2.157) (1.162) (2.176) (2.188) Number of 3.448 - 2.363 - 2.218 - 2.474 - children 14.63* 14.69* 15.82* 15.94* 3.194 4.790* 3.063 3.585 2.306 4.665* 2.142 3.194 * * * * (2.755) (6.731) (1.644) (6.744) (1.780) (6.652) (1.652) (6.649) (4.886) (2.583) (4.871) (2.720) (4.823) (2.599) (4.795) (4.886) -0.0431 -0.357 0.0333 -0.400 -0.266 -0.0374 -0.295 Choice Experiment: Use of Health and Environmental Warning Labels to Reduce Plastic Consumption

Recycling (0.0969 (0.248) (0.0616 (0.253) (0.0969 (0.180) (0.182) score ) ) ) Styrofoam 1.948 -3.264 0.224 -5.423 -1.773 1.908 -3.407 harms (2.250) (6.822) (1.446) (6.825) (4.925) (2.209) (4.914) health Styrofoam 2.602 4.880 0.350 5.917 4.514 2.772 5.359 harms (3.057) (7.753) (1.989) (7.625) environme (5.597) (3.038) (5.489) nt Health -1.267 -5.246 2.098 -5.000 2.095 -2.230 1.817 -1.768 - - - treatment 12.45* -1.549 12.05* -1.600 -10.20* -1.233 -9.669* 12.45* group * * * (3.080) (7.899) (1.929) (7.977) (1.968) (8.024) (1.992) (8.072) (5.733) (3.031) (5.762) (3.008) (5.817) (3.135) (5.821) (5.733) Environme 0.0938 4.122 -0.682 3.056 -0.620 6.109 -0.867 4.767 1.450 -0.630 0.710 -0.131 2.933 -0.422 1.954 1.450 nt (2.954) (7.718) (1.884) (7.901) (1.902) (7.693) (1.910) (7.808) treatment (5.602) (2.962) (5.707) (2.907) (5.577) (3.006) (5.630) (5.602) group Safety 1.870 -4.747 2.957* -6.148 2.876 -1.831 2.685 -3.508 1.128 2.182 0.113 1.510 3.304 2.487 2.035 1.128 treatment (2.823) (7.158) (1.748) (7.476) (1.785) (7.306) (1.814) (7.503) (5.196) (2.747) (5.400) (2.728) (5.297) (2.855) (5.410) (5.196) group Constant -4.812 23.83* -4.301 23.07* -4.591 44.99* -6.274 47.99* -1.623 -5.197 -3.476 -7.501 14.17 -2.982 14.84 -1.623 * * * (7.805) (11.55) (2.821) (13.44) (3.241) (18.56) (4.607) (20.49) (8.385) (4.433) (9.704) (4.953) (13.45) (7.251) (14.77) (8.385) Observatio 52 52 52 52 52 52 52 52 52 52 52 52 52 52 52 52 ns Notes: Standard errors are reported in parentheses. *, **, *** indicate levels of statistical significance at 10%, 5%, and 1%, respectively.