Crunch the can or throw the bottle? Effect of “bottle deposit laws” and municipal recycling programs

Resources, Conservation and Recycling 106 (2016) 98–109

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Crunch the can or throw the bottle? Effect of “bottle deposit laws” and municipal recycling programs Benjamin Campbell a,∗ , Hayk Khachatryan b,1 , Bridget Behe c,2 , Charles Hall d,3 , Jennifer Dennis e,4 a Assistant Professor and Extension Economist in the Department of Agricultural and Resource Economics, University of Connecticut; Department of Agricultural and Resource Economics, University of Connecticut, Storrs, CT 06269, USA b Assistant Professor in the Food and Resource Economics Department and Mid-Florida Research and Education Center at the University of Florida; Food and Resource Economics Department and Mid-Florida Research and Education Center, University of Florida, Apopka, FL 32703, USA c Professor in the Department of Horticulture at Michigan State University; Department of Horticulture, Michigan State University, East Lansing, MI 48824, USA d Professor and Ellison Chair in International Floriculture in the Department of Horticultural Sciences at Texas A&M University; Department of Horticultural Sciences, Texas A&M University, College Station, TX 77843, USA e Associate Professor in the Department of Horticulture and Landscape Architecture and Department of Agricultural Economics at Purdue University; Departments of Horticulture and Landscape Architecture and Agricultural Economics, Purdue University, West Lafayette, IN 47907, USA

a r t i c l e

i n f o

Article history: Received 20 August 2015 Received in revised form 5 November 2015 Accepted 6 November 2015 Keywords: Recycling behavior Bottle deposit laws Municipal recycling Propensity score matching

a b s t r a c t Although there is growing public awareness about environmental issues, incentive mechanisms leading to individual pro-environmental behaviors remain less investigated. This article examines the impact of bottle deposit laws (BDL), municipal recycling programs (MRP), and the ease of municipal recycling on recycling frequency for numerous products. An online survey of U.S. and Canadian households was conducted to collect data about individuals’ recycling practices and perceptions. We contracted an online survey company, Global Market Insite (GMI), to recruit panelists within their database to take the survey. GMI invited 2700 consumer panelists within their database representing all contiguous U.S. states and all Canadian provinces with 2511 surveys completed. Utilizing specific treatment groups and propensity score matching to control for unobserved heterogeneity, we find that MRP has a greater impact on recycling behaviors than BDL, and that the perceived ease of MRP positively influences individual recycling behaviors. For BDL without a municipal program being present, their effect is only for those products requiring the deposit. This research is useful for agencies interested in encouraging recycling behavior and researchers interested in the determinants of recycling behavior. Both programs should be measured for effectiveness and municipal recycling programs should be implemented where the goal is to promote recycling behavior as it was shown here to have a greater impact on recycling programs and may be perceived as an easier thing to do. © 2015 Elsevier B.V. All rights reserved.

1. Introduction Over the last few decades, public and governmental policy makers have become increasingly aware of human impacts on the

∗ Corresponding author. Tel.: +1 8604861925. E-mail addresses: [email protected] (B. Campbell), hayk@ufl.edu (H. Khachatryan), [email protected] (B. Behe), [email protected] (C. Hall), [email protected] (J. Dennis). 1 +1 407 410 6951. 2 +1 517 355 5191x1346. 3 +1 979 458 3277. 4 +1 765 494 1352. http://dx.doi.org/10.1016/j.resconrec.2015.11.006 0921-3449/© 2015 Elsevier B.V. All rights reserved.

environment. According to a recent report by the U.S. Environmental Protection Agency, American households generated over 250 million tons of trash in 2011, equating to 1606 lb per capita (U.S. EPA, 2013). Canada, on the other hand, generated 25 million tons of non-hazardous waste in 2010 or 1607 lb per capita (Statistics Canada, 2010). To offset increasing amounts of roadside litter or waste headed to landfills, numerous programs have been enacted over the years to solve these issues. As bottles became a prominent form of packaging beverages, retailers (without governmental action) utilized voluntary deposit-and-return systems in order to incentivize consumers to turn in their bottles so they could be reused. However, with the introduction of disposable metal cans, the voluntary retailer

B. Campbell et al. / Resources, Conservation and Recycling 106 (2016) 98–109

deposit-and-return systems fell out of favor. Moreover, with the increase in bottle and cans in the public domain, roadside litter began appearing along roadways. In order to combat this issue, bottle deposit laws (BDL) in their current nature began being adopted by states throughout the U.S. (Jorgensen, 2013). BDLs in their current form mandate that businesses charge a deposit, usually five or ten cents when purchasing certain types of aluminum or glass beverage containers. The deposit is returned once the container is returned to the store creating a direct monetary incentive to recycle (Viscusi et al., 2012). Even though the original nature of the program was not to increase recycling of bottles and cans, BDLs have become a proven, sustainable method of capturing beverage bottles and cans for recycling (Bottlebill.org, 2015). As noted by Dunbar (2014), recycling rates for bottles and cans in BDL states are twice as high as Minnesota which is currently considering enacting a BDL. In order to combat increasing landfill waste, curbside recycling programs have emerged in many municipalities throughout the U.S. For instance, over 9800 curbside recycling programs exist within the U.S., a 10% increase since 2002 (U.S. EPA, 2013), while 93% of Canadian households have access to a recycling program (Statistics Canada, 2013). Municipal recycling programs (MRP) are rather straightforward whereby a person places predefined recyclables on the curbside and they are collected by the municipality. In order to increase recycling rates, curbside recycling programs rely on ease of use to get people to recycle more. As noted by Saphores and Nixon (2014), although overall recycling rates are trending upward, rates for specific recyclables vary by the recyclable category. As these programs have evolved to become major conduits for recycling (BDL for glass and aluminum cans; MRP for glass, aluminum, cardboard, paper, etc.) it is essential to examine their effectiveness given the costs associated with running each. For example, if BDLs are working, then it would provide evidence that they should be expanded to other states. However, if BDLs are not working, then questions should be asked about their place in statelevel policy debates. As with BDLs, MRPs should be evaluated to examine whether changes should be made to increase their effectiveness. In order to provide further insights about the effectiveness of recycling programs, this article examines the impact of BDLs, MRPs, and the ease of MRP usage on increasing recycling behaviors (measured as the frequency of recycling). This study is not the first to examine these issues; however, it differs from other studies in several important ways. First, we utilize propensity score matching (PSM) to control for unobserved heterogeneity caused by comparing groups with underlying differences that are not related to a MRP or BDL. Second, this article examines the impact of BDLs and MRPs on “residual” recyclable materials that are not included in any of the state mandated BDLs, such as cardboard, newspapers, and plant potting containers. In other words, we examine whether BDLs impact recycling of items outside of the BDL system, such as cardboard, newspaper, and potting containers. Third, we utilize a dataset collected through a national survey of the U.S. and Canada as opposed to city- or municipality-level data analyses (e.g., Laidley, 2013). The use of a national survey is similar to that of Saphores and Nixon (2014); however, we also survey Canadian consumers. Finally, consistent with Saphores and Nixon (2014), we explicitly control for MRP (BDL) when examining the impact of BDL (MRP). Specifically, we establish groups of consumers that can definitively be placed in treatment groups based on whether their municipality has a recycling program and whether they live in an area with a BDL. Table 1 provides a breakdown of study participants according to the presence of MRP or BDL program by treatment groups and type of recyclable materials for the U.S. and Canadian sample. We hypothesize that

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H1A: BDLs will increase recycling rates for aluminum cans and bottle glass when the MRP are not present, H1B: MRPs will increase recycling rates for aluminum cans and bottle glass when the BDL are not present, H2A: BDLs will increase recycling rates for aluminum cans and bottle glass when the MRP are present, H2B: MRPs will increase recycling rates for aluminum cans and bottle glass when the BDL are present, H3A: BDLs will increase recycling rates for residual recyclables when the MRP is not present, H3B: MRP will increase recycling rates for residual recyclables when the BDL is not present, H4A: BDLs will increase recycling rates for residual recyclables when the MRP is present, H4B: MRP will increase recycling rates for residual recyclables when the BDL is present, H5: Increasing ease of MRP will increase recycling rates for recyclables whether the BDL is present or not. Our findings indicate that MRPs have a greater impact on recycling than BDLs across all recyclables. With respect to the residual products, BDLs alone do little to generate recycling of residual products. Also, as expected, as the perceived ease of a MRP increases, the frequency of recycling increases across almost all materials regardless of whether a BDL is in place. 2. Literature review Over the last several decades, the determinants of proenvironmental behaviors and relevant policy implications have been investigated in a number of disciplines, including social psychology, marketing, and consumer economics (Elgaaied, 2012). Despite the growing public awareness and concerns about modern environmental issues, understanding of incentive mechanisms that lead individuals to engage in pro-environmental behaviors such as recycling remains less investigated (Ebreo and Vining, 2001; Elgaaied, 2012). Recycling behaviors can be considered as a form of social dilemma, which is characterized as a conflict between private and collective interests (Dawes, 1980). As noted by Reschovsky and Stone (1994), the decision to recycle or not has implicit net costs associated with them, such as household’s time valuation, monetary disposal costs, and inconvenience of waste disposal or recycling. Offering services that ease the inconvenience (e.g., curbside recycling) of waste disposal or recycling can lower the cost associated with recycling; however, given the extra costs associated with sorting recyclables from non-recyclables, recycling is most likely always more costly than not recycling (Reschovsky and Stone, 1994). Existing research investigating individual recycling behavior can be characterized into two broad categories. The first category investigates the influence of individual-specific determinants, such as demographics, personality, or attitudes of environmental concerns on recycling behavior (Lindsay and Strathman, 1997; Schultz et al., 1995). The second category focuses on the impact of situational variables such as providing rewards for recycling or convenience of recycling (Ebreo and Vining, 2001; Schultz et al., 1995). In a similar fashion, Hornik et al. (1995) classified variables influencing recycling behavior into internal/external incentives and facilitators. The authors reviewed 67 published and unpublished materials and ranked the strongest predictors of propensity to recycle the following way: (1) consumer knowledge and commitment to recycling (internal facilitators), (2) monetary awards, social influence (external incentives), and (3) frequency of collection (external incentives). In this vein, Elgaaied (2012) showed a positive relationship between anticipated guilt and intentions to

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Table 1 Definitions and frequencies associated with the treatments within the analyses. United Statesa

Recyclable material Brown Green Clear

Plant

Plant

Plant

Food

Treatment 1

Bottle law No Yes

Municipal No No

Glass 272 94

Glass 284 104

Glass 288 105

Newspapers 238 90

Magazines 251 87

Boxes 262 98

Aluminum 225 83

Container 306 117

Tags 301 118

Waste 736 309

Waste 926 432

2

No Yes

Yes Yes

633 353

561 322

562 320

706 381

669 380

677 370

731 390

403 231

362 201

353 239

163 116

3

No No

No Yes

272 633

284 561

288 562

238 706

251 669

262 677

225 731

306 403

301 362

736 353

926 163

4

Yes Yes

No Yes

94 353

104 322

105 320

90 381

87 380

98 370

83 390

117 231

118 201

309 239

432 116

5

No No

Yes; hard Yes; easy

98 502

76 451

76 451

76 585

76 556

91 553

80 616

56 323

52 282

166 135

80 67

6

Yes Yes

Yes; hard Yes; easy

37 303

34 273

36 271

40 325

42 324

41 314

35 337

34 183

30 154

122 85

60 42

Yes Yes

No Yes

75 627

87 553

91 555

56 682

64 674

62 677

67 670

97 449

100 422

323 442

452 313

Yes Yes

Yes; hard Yes; easy

46 560

41 495

38 498

31 631

30 625

37 623

46 606

48 381

46 354

134 243

99 175

Canadaa 4A

6A

Card-board

a Only one Canadian province, Nunavit, has not implemented a bottle deposit law. Therefore, we separated the analyses into U.S. and Canadian consumers. This will also help to control for potential recycling differences between countries.

recycle. Further, Ebreo and Vining (2001) found consideration of future consequences was positively correlated with self-reported recycling intentions. Empirical research on community recycling is multidisciplinary in nature and focuses on estimating household participation in municipal recycling programs. Previous literature has found numerous demographic variables impact household participation rates in recycling programs including income (Feiock and West, 1996; Saltzman et al., 1993), education (DeYoung, 1990; Tilikidou and Delistavrou, 2008), the convenience of the recycling program (Feiock and West, 1996; Jenkins et al., 2003; Judge and Becker, 1993), citizen involvement in program design (Folz, 1991), attitudes, social and personal standards (Hopper and Neilson, 1991; Oom Do Valle et al., 2005; Saphores and Nixon, 2014; Schultz et al., 1995), and a number of other socio-demographic variables (Meneses and Palacio, 2005; Saphores and Nixon, 2014). Furthermore, Saphores and Nixon (2014) examined the impact of several programs (i.e., BDL, drop-off, and curbside recycling) on recycling of select recyclables (aluminum, plastics, glass, and other metals). Their results indicated that MRP’s provided larger odds ratios compared to BDL’s. More recently, Nixon and Saphores (2009) explored how different sources of information (e.g., print, television, radio, family or friends, and others) influence the decision to start recycling. Although print media is influential, face-to-face communication (through family/friends at work or school) appears to be the most effective medium to get people to start recycling. However, it is better to provide households with recycling information from multiple sources. Concerns about storage space, time and the safety of recycling were identified as the main obstacles to start recycling. Saphores and Nixon (2014) examined the impact of several programs (i.e., bottle deposit laws, drop-off, and curbside recycling) on recycling of select recyclables (aluminum, plastics, glass, and other metals). Their results indicated that MRP’s provided larger odds ratios compared to BDL’s, implying that people are more likely to recycle with a MRP than a BDL, ceteris paribus. Previous studies have also linked household participation rates to bag/tag unit pricing for waste disposal (Folz and Giles, 2002;

Fullerton and Kinnaman, 1996; Hong et al., 1993; Kinnaman and Fullerton, 2000; Miranda et al., 1994; Podolsky and Spiegel, 1999; Reschovsky and Stone, 1994) and subscription-based volume pricing programs (Callan and Thomas, 1997; Jenkins et al., 2003; Podolsky and Spiegel, 1999; Repetto et al., 1992; Reschovsky and Stone, 1994). Jakus et al. (1996) and Tiller et al. (1997) estimated the costs and benefits of drop-off recycling among rural households. The role of social norms in household recycling behavior was explored by Bruvoll and Nyborg (2004), Berglund (2006), and most recently by Halvorsen (2008). Several studies have reported the costs of collecting recyclable materials at the curb. Carroll (1995) initiated the literature by estimating recycling costs with 1992 data from 57 municipalities in Wisconsin, but found no economies of scale in the data. Callan and Thomas (2001) used 1996–1997 data on 101 municipalities in Massachusetts and are the first to find economies of scale in curbside recycling. Bohm et al. (2010) estimated the cost functions for both municipal solid waste collection and disposal services and curbside recycling programs. Results suggest that economies of scale for recycling disappear at high levels of recycling; i.e., marginal and average cost curves for recycling are U-shaped. In a related body of literature, Criner et al. (1995), Renkow and Rubin (1998), and Steuteville (1996) estimated the costs of municipal composting programs. Criner et al. (1995) results indicated that composting is worthwhile for landfill disposal fees between $75 and $115 per ton. Renkow and Rubin (1998) examined 19 cases to find composting is preferred to landfilling when disposal costs are high. Further, the frequency of collection was also found to be a significant factor in predicting propensity to recycle in a study that compared household recycling activities across 10 OECD countries (Halvorsen, 2012). The study found that increasing the presence of recycling programs positively influences household recycling behaviors. Specifically, the authors reported that door-to-door collection and drop-off centers are the two most effective methods in engaging consumers to recycle. Notably, the study discussed the importance of non-economic motivations, such as moral commitment, high expectations about the effectiveness of recycling practices in terms of contribution to environmental conservation.

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3. Materials and methods In May 2011 an online survey was initiated in order to better understand perceptions of various sustainable terminology, as well as to understand the recycling behavior of U.S. and Canadian consumers. Questions within the survey included demographic (i.e., household income, education, marital status, age, gender, household characteristics, and ethnicity), purchasing behavior (i.e., primary shopper in the household, the types of stores generally shopped in, and their purchasing of local and organic produce), their perceptions of local, organic, eco-friendly and sustainable labels, and their recycling habits. An online survey was used due to several advantages to other survey techniques such as: faster to conduct, allows for more accurate information with less human error, and less expensive (Cobanoglu et al., 2001; Dillman et al., 2009; McCullough, 1998). The use of online surveys can have disadvantages, especially if the sampling database is filled with the same panelist under different accounts. To alleviate this concern, the researchers contracted with a company, Global Market Insite, Inc. (GMI), which has mechanisms in place to eliminate duplicate panelists. Online surveys have also been tagged with the negative connotation of a homogenous composition of “professional survey takers.” However, unless the sample is not representative of the population or has views that are inconsistent with the population as a whole, then the sample should maintain its representative. Given that there are millions of panelists in the GMI sample with invites being sent out randomly, the sample for this survey should be random and representative, especially compared to other collection techniques commonly used for surveys encompassing a wide geographical area. Another potential issue with the GMI database, as well as with many other panel databases, is that they do not provide internet access to those without it. However, U.S. and Canadian internet usage in 2011 was estimated around 80% and 83% of the population, respectively (U.S. Department of Commerce, Economics & Statistics Administration, 2011; Canadian Internet Registration Authority, 2013; World Bank, 2013). Based on these numbers, most of the population has access to the internet. The lack of non-internet users in the sample potentially becomes a source of bias in one of two ways. First, if non-internet users recycle more (less) than internet users independent of demographics, which we control for in the model. We cannot test this assumption; however, we believe it highly unlikely that internet use alone, independent of demographics and other environmental attitudes, would dictate recycling frequency. Second, if recycling varies across socio-economic groups of nonusers, then biases can occur. There is no way to measure this bias other than to acknowledge that it may be present. The only prerequisite for qualifying to take the survey was that respondents had to be 18 years or older. The final dataset was made up of 68% U.S. and 32% Canadian consumers. Based on these percentages, we oversampled Canadian consumers compared to the population estimates, but this was done to obtain a large enough sample of Canadian consumers to better understand their behaviors. In recruiting respondents, GMI randomly invited consumers within their database to follow a link to the survey. Invitations were sent to 2700 panelists and 2,525 surveys were completed. Each contiguous state and all provinces and two territories were represented in the survey with larger population states and provinces receiving more responses. The representativeness of the sample with respect to commonly defined demographics indicated a fairly representative sample. The U.S. sample had an average age of 35.8 which was slightly less than the census average of 37.2. Within our sample, 78.1% of the U.S. sample was Caucasian which is in line with the average census percentage for Caucasian, 78.1%. In regard to household income, our U.S. sample had a higher average

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household income ($65,273) compared to the average U.S. population ($52,762). For the Canadian sample, average age (sample-42.7 vs. census-39.7) and average household income (sample-$66,747 vs. census-$69,860) were in line with Statistics Canada estimates. Given the dissimilarity between U.S. and Canadian census questions based on ethnicity, direct comparisons are infeasible. For this survey, we utilized the U.S. census terminology around ethnicity. However, using rough calculations indicates that the percent “Caucasian” in Canada is about 80% which is slightly less than our sample average of 86%; however, this is a rough estimate as no exact “Caucasian” category exists in the Canadian census. Questions of interest to this study revolved around recycling behaviors specifically focusing on the following areas: (1) how often the respondent recycled several common recyclables (see Table 1 for list of recyclables), (2) availability of municipal recycling collection for those recyclables, and (3) perceived ease of recycling via the MRP for each recyclable? To address the focus areas above, respondents were asked a series of questions. First, respondents were asked if their municipality offered recycling for each recyclable listed in Table 1 (e.g., “does your municipality offer recycling for aluminum?”), whereby, the respondent could answer yes, no, or not sure. Second, respondents were asked the ease of using the recycling program for each recyclable with options being not sure, possible but not easy, or possible and easy. Finally, respondents were asked to indicate the frequency of recycling each of the recyclable in Table 1 (i.e., “how often do you recycle the following products?”). The responses answered on a 1–4 Likert scale, where 1 = “never,” 2 = “sometimes,” 3 = “usually,” and 4 = “always” recycle. Respondents could also answer “I do not purchase this product.” By offering the do not purchase option, we were able to focus only on those respondents that were able to use the recycling programs. Further, asking frequency instead of actual amount recycled is a slight limitation within the study, however, it has been noted that people are fairly inaccurate in their recall of quantities (Wagner, 2003; Eisenhower et al., 2011). Thereby, a general frequency question offers a reliable approximation of recycling behavior that can be used to address our questions of interest. For the analysis, respondents indicating they had not purchased the recyclable products in question were dropped from the analyses associated with that recyclable. 3.1. Empirical model In order to understand the impact of BDLs and MRPs on recycling rates, we need to compare respondents with and without exposure to each treatment. However, as noted by Rosenbaum and Rubin (1984), differences between treatments may come from differences in underlying characteristics of the treatment groups, thereby, making comparisons invalid. According to Foster (2003), this nonrandom nature associated with the treatment groups can lead to biased estimates (small samples) or inconsistent estimates (large samples). Propensity score matching has been proposed as a mechanism to adjust for any non-randomization between treatment groups (Rosenbaum and Rubin, 1983, 1984). Using a set of covariates to construct conditional probabilities for those receiving the treatment, the propensity scores are unbiased and consistent with the random nature of the data (Imbens, 2000). The unbiasedness and consistency of PSM is assumed if the conditional mean independence assumption (CMIA) is satisfied. CMIA is satisfied if given a set of covariates, the pre-treatment (mean) outcomes for the matched and treated groups are equal, or P(D = 1|X) (Heckman et al., 1997). As noted by Smith and Todd (2005), there is no single test that guarantees the CMIA is satisfied. However, by satisfying the balancing condition, we can feel more confident that the CMIA condition is satisfied (Dehejia and Wahba, 1999). To check the balancing

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condition (propensity scores that are equal have observable characteristics that have the same distribution that are independent of treatment status) we compared the covariates for each treatment comparison while also utilizing the “hit-or-miss” criterion and pseudo-R2 as a relative measure of satisfying the balancing condition (Heckman et al., 1997) as well as using the pre-matching test specified by Becker and Inchino (2002). Given that we are interested in comparing multiple treatment groups (Table 1), a multinomial logit framework can be used. However, binary logit has been shown to provide similar results compared to the multinomial logit framework (Lechner, 2002). Therefore, we used a binary logit model which was represented as Prob(Y = 1|X) =

eˇX 1 + ˇ X

(1)

where, Y is an indicator dependent variable for either being in a treatment or not (e.g., for treatment 1 in Table 1, a respondent was coded 1 if they had a bottle law and no municipal recycling, or coded as a 0 if they did not have either a bottle deposit law or municipal recycling) and X is a vector of explanatory variables (Greene, 2003). Potential explanatory variables included demographic (e.g., age, income, gender, etc.), environmental attitudes, and purchasing behavior (e.g., local and organic purchasing) (Table 2). The final covariate list was balanced for each model. Results for the “hit-ormiss” and pseudo-R2 mentioned above can be found in Appendix Tables A1–A3. Of note in Appendix Table A1, the BDL binary logit models correctly classified around 60–70% of respondents into the correct treatment group which is similar other propensity score matching studies. The MRP binary logit models for U.S. respondents correctly classified around the same percentage correctly as the BDL model; however, the MRP comparison for Canadian consumers’ average around 80–90% correctly classified (Appendix Table A2). Further, the ease of MRP models correctly classified a large percentage of respondents correctly which further reaffirms that our models are balanced (Appendix Table A3). The results for the pre-matching test are not presented for brevity. In order to further test the balancing condition, we used the method described by Smith and Todd (2005), whereby we conditioned on the covariates and then compared the means of the treated and controls. As can be seen in Table 3, several matching algorithms were used. Our results indicate that for most all matching algorithms used, we saw a bias-reduction which gives more confidence that the balancing condition has been satisfied (Table 3). The Radius with 0.1 caliper was chosen for final estimation given it consistently produced the highest bias reduction. Radius matching works by only matching the treated group with control group members that have a propensity score that falls into the specified radius (or caliper), which in our case was 0.1 (Becker and Inchino, 2002). As a robustness check, we estimated the results with several of the other algorithms but results and implications remained unchanged. The matched vs. unmatched t-tests for the Radius with 0.1 caliper can be found in the online Appendix Tables A1–A8). Understanding the robustness of the results to unobserved heterogeneity/hidden bias is critical. Rosenbaum bounds provide a means to assess how hidden bias might impact the results and can be interpreted as the unobserved bias that must increase the odds of participation in the treatment, given the same covariates, by the value of the bound to change the statistical inference to insignificant at a specified level (Rosenbaum, 2002). Rosenbaum bounds can only be calculated using a 1 × 1 matched pairs, so the bounds are constructed using the Nearest Neighbor with replacement matching algorithm. The R-bounds indicate that most of our results are robust to unobserved heterogeneity. A caveat to the results of the study is that all MRPs and BDLs are not exactly the same. For MRPs, numerous recyclables surveyed

will be a part of all programs (e.g., aluminum cans, clear glass, magazines, cardboard); however, other recyclables may or may not be included in some MRPs (e.g., food/plant waste, potting containers, green glass). BDLs, on the other hand, collect deposits on similar items across the board, notably clear glass and aluminum cans. In some instance, different types of glass are part of the law, but by and large it is clear glass. So implications from our results should keep this caveat in mind. Further, most states charge a $0.05 deposit on items specified in the BDL. Two exceptions are Michigan and California. Michigan charges a $0.10 deposit on all items and California has a split deposit system whereby $0.05 is charged for under 24-ounces and $0.10 for over 24-ounce containers (Bottlebill.org, 2015). In order to test the robustness of our results, we ran the analyses with and without California and Michigan respondents in the analyses and found our results were robust to their inclusion except for aluminum in the Treatment 1 comparison. We therefore present the results with California and Michigan included. Finally, given almost all but one Canadian province has a bottle deposit law in place and since there could be differences in U.S. and Canadian regulations, we analyzed U.S. and Canadian respondents separately. 4. Results and discussion In the results section, we discuss the results of the PSM treatment comparisons. First, we focus on the BDLs impact within the U.S. followed by a discussion about the effects of the MRP in the U.S. Next, we examine how ease of MRP use affects recycling rates in the U.S. Finally, we discuss the MRPs impact on recycling in Canada. 4.1. Bottle deposit law impact: United States In examining the impact of a BDL when holding municipality constant (Table 4), we see some interesting results. First, when there is no MRP the impact of the BDL is limited to glass (clear and brown) and aluminum cans. The average treatment effect for the treated (ATE) for clear glass is 0.36 or an increase of 17.2% in frequency from the treatment (BDL, no MRP) and control (no BDL, no MRP). There is also a significant ATE of 0.27 (11.2% increase) and 0.30 (10.9% increase) for brown glass and aluminum cans, respectively. These results are expected given BDLs specifically target glass and aluminum cans. However, when we remove California and Michigan from the analysis due to their higher deposit requirements, BDLs no longer have a significant impact on aluminum recycling. However, removing responses from California and Michigan does not change the significant impact BDL on recycling of clear glass. With respect to understanding the sensitivity of our results to hidden bias/unobserved heterogeneity is important. The R-bounds of the significant variables are 1.29 (clear glass), 1.34 (brown glass), and 1.10 (aluminum cans), indicating some robustness to our results to unobserved heterogeneity. When examining the BDLs impact in conjunction with a MRP, there are a couple of interesting results. First, when comparing with the earlier scenario where only a BDL was present, the means of the treated and control groups are quite different in the two comparisons. For instance, the treated and control recycling rate means for clear glass in the BDL/no MRP comparison are 2.46 and 2.10, respectively, while the treated and control means for the BDL/MRP comparison were 3.49 and 3.38. Second, we again see that the BDL impacts the recycling rates for only those recyclables specifically addressed in the deposit law. When a BDL and MRP are present, the BDL increases clear glass and aluminum can recycling frequency by 3.2% and 3.0%, respectively. Based on the above results, we fail to reject H1A and H2A that BDLs increase recycling rates for aluminum cans and bottle glass regardless of whether a MRP is present. However, we reject H3A

Table 2 Means of variables used in the propensity score calculations. Total Mean

a b

Min

Max

Mean

Std. Dev.

Min

Mean

Std. Dev.

Min

Max

38.0 2.6 1.7 $65,745 0.55 0.27 0.53 0.20

14.7 1.2 1.0 $42,481 0.50 0.44 0.50 0.40

18.0 1.0 1.0 $19,999 0.0 0.0 0.0 0.0

88.0 11.0 8.0 $200,000 1.0 1.0 1.0 1.0

35.8 2.6 1.7 $65,273 0.59 0.21 0.59 0.20

14.4 1.2 1.0 $43,373 0.49 0.41 0.49 0.40

18.0 1.0 1.0 $19,999 0.0 0.0 0.0 0.0

88.0 11.0 8.0 $200,000 1.0 1.0 1.0 1.0

42.7 2.5 1.6 $66,747 0.49 0.40 0.40 0.20

14.2 1.2 0.9 $40,533 0.50 0.49 0.49 0.40

18 1 1 $19,999 0.0 0.0 0.0 0.0

85 8 8 $200,000 1.0 1.0 1.0 1.0

0.20 0.42 0.27 0.11 0.79 0.69 0.93 0.74 3.3

0.40 0.49 0.44 0.31 0.41 0.46 0.26 0.44 0.91

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0

1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 5.0

0.20 0.43 0.26 0.11 0.77 0.67 0.92 0.73 3.2

0.40 0.49 0.44 0.31 0.42 0.47 0.27 0.45 0.93

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0

1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 5.0

0.20 0.41 0.28 0.11 0.84 0.73 0.95 0.76 3.5

0.40 0.49 0.45 0.31 0.37 0.45 0.21 0.43 0.84

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0

1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 5.0

3.3 3.7 4.5 4.1 4.4 4.0 3.0 2.8

1.2 1.1 0.8 1.0 0.9 1.0 1.0 1.0

1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0

5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0

3.3 3.7 4.5 4.0 4.4 4.0 3.0 2.8

1.2 1.1 0.8 1.0 0.9 1.0 1.1 1.0

1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0

5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0

3.5 3.8 4.5 4.1 4.4 4.1 3.0 2.7

1.1 1.1 0.8 1.0 0.9 1.0 1.0 1.0

1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0

5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0

3.4 3.8 4.3 4.1 4.3 3.7 3.0 3.5 3.4

1.2 1.1 1.0 1.0 1.0 1.2 1.1 1.2 1.3

1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0

5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0

3.3 3.7 4.2 4.1 4.3 3.7 3.0 3.2 3.3

1.2 1.2 1.0 1.0 1.0 1.2 1.1 1.3 1.3

1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0

5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0

3.4 3.9 4.3 4.2 4.3 3.7 3.0 4.2 3.5

1.2 1.1 1.0 1.0 1.0 1.1 1.0 0.9 1.3

1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0

5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0

2525

Std. Dev.

Canada

1716

Max

B. Campbell et al. / Resources, Conservation and Recycling 106 (2016) 98–109

Age # Adults # Children Income Gender: 1 = male Urban Suburb Rural Education High school or less Between high school and 4-yr 4-yr degree Greater than 4-yr Caucasian: 1 = yes Purchased plants in last year: 1 = yes Heard of term eco-friendly: 1 = yes Heard of term sustainability: 1 = yes How often purchase locala For buying local, importance of:b Carbon footprint Environmentally friendly Freshness Price Safe to eat Support local economy Other How often purchase organic For buying organic, importance of:b Carbon footprint Environmentally friendly Freshness Price Safe to eat Support local economy Other How often use re-usable shopping bagsa How often use low flow water devices in homea Number

U.S.

809

Evaluated on a 5-point scale where 1 = never, 2 = seldom, 3 = sometimes, 4 = most times, and 5 = always. Transformed to a 5- point continuous scale for analyses. Measured on a 5-point scale where 1 = not important, 3 = somewhat important, and 5 = very important.

103

104

B. Campbell et al. / Resources, Conservation and Recycling 106 (2016) 98–109

Table 3 Covariate balancing statistics utilized in matching algorithm selection for bottle deposit law with no municipality recycling treatment. Matching algorithma

Bias reduction (%)

T1: Bottle law comparison with no municipal recycling: United States respondents onlya , b Clear Brown Green Newspapers Magazines glass glass glass (%) (%) (%) (%) (%) −4 −13 −19 −35 −16 Kernel—Epanechnikov −4 −13 −19 −35 −16 Kernel—Gaussian −68 −62 −58 −74 −71 Radius .1 −67 −78 −56 −65 −45 Radius .01 Radius .05 −73 −63 −60 −69 −71 −53 K-nearest w repl.—no −37 −54 −44 6 caliper −56 −50 −30 −49 −51 K-nearest without repl.—no caliper Local linear regression −53 −37 −54 −44 6

−8 −8 −67 −62 −65 −45

Plant waste (%) −9 −9 −77 −81 −81 −34

Food waste (%) −11 −11 −75 −63 −85 −47

−32

−42

12

5

−63

−57

−45%

−34

−47

−7 −7 −64 −70 −77 −4

−7 −7 −71 −84 −84 −60

−9 −9 −58 −57 −49 2

−3 −3 −60 −47 −59 −1

−10 −10 −69 −64 −79 −17

−40 −40 −62 −34 −52 −13

86

39

10

−23

−56

−37

−4

−60

2

−1

−17

−13

0 0 −70 −73 −75 −50

−2 −2 −62 −53 −64 −39

−13 −13 −75 −54 −72 −45

−8 −8 −74 −70 −71 −61

−6 −6 −90 −88 −92 −80

−34 −34 −84 −88 −90 −77

132

139

56

36

−67

−40

−50

−39

−45

−61

−80

−77

−27 −27 −79 −65 −77 −59

−30 −30 −71 −53 −66 −12

−27 −27 −78 −64 −77 −51

−22 −22 −76 −61 −75 −37

−40 −40 −71 −50 −67 −53

−19 −19 −86 −80 −82 −43

−6 −6 −79 −72 −88 −31

100

76

87

96

35

−73%

−13

−59

0

−51

−37

−53

−43

−31

−17 −17 −73 −66 −73 −57

−38 −38 −69 −54 −61 −48

−38 −38 −62 −54 −61 −46

−12 −12 −54 −34 −46 −23

−19 −19 −59 −37 −53 −21

0 0 −77 −68 −77 −52

−6 0 −79 −64 −80 18

55

55

63

110

83

57

−64

−57

−48

−46

−23

−21

−52

18

−21 −21 −58 −34 −58 −8

−13 −13 −21 −25 −8 9

−22 −22 −31 −16 −33 −14

−32 −32 −51 −43 −54 −37

−17 −17 −75 −52 −72 −33

−36 −36 −59 −52 −64 −47

115

208

131

99

−57

−42

−8

9

−14

−37

−33

−47

Cardboard boxes (%) −5 −5 −74 −68 −75 −36

Aluminum cans (%) −27 −27 −83 −73 −90 −63

Plant containers (%) −13 −13 −75 −74 −73 −57

−62

−58

−36

Plant tags (%)

a,b

T2: Bottle law comparison with municipal recycling: United States respondents only Kernel—Epanechnikov −7 −8 −6 −7 −14 −7 −14 Kernel—Gaussian −7 −8 −6 Radius .1 −73 −80 −77 −66 −56 −83 −83 −81 −74 −55 Radius .01 −88 −85 −84 −75 −61 Radius .05 −53 −44 −23 −21 8 K-nearest w repl.—no caliper 4 −8 0 69 94 K-nearest without repl.—no caliper Local linear regression −53 −44 −23 −21 8

T3: Municipal recycling comparison with no bottle law: United States respondents onlyb Kernel—Epanechnikov 1 0 2 −3 −6 0 2 −3 −6 Kernel—Gaussian 1 Radius .1 −70 −77 −71 −63 −71 Radius .01 −59 −64 −63 −57 −69 Radius .05 −70 −79 −70 −66 −76 −34 −58 −44 −40 −57 K-nearest w repl.—no caliper 144 K-nearest without 111 121 134 116 repl.—no caliper −34 −58 −44 −40 −57 Local linear regression T4: Municipal recycling comparison with bottle law: U.S. respondents onlya , b Kernel—Epanechnikov −11 −35 −20 −31 Kernel—Gaussian −11 −35 −20 −31 Radius .1 −75 −75 −80 −80 Radius .01 −64 −65 −66 −67 Radius .05 −74 −77 −76 −78 −57 −57 −54 −67 K-nearest w repl.—no caliper 94 72 120 80 K-nearest without repl.—no caliper −57 −57 −54 −67 Local linear regression

a,b

T4A: Municipal recycling comparison with bottle law: Canadian respondents only Kernel—Epanechnikov −2 −13 −6 −20 −2 −13 −6 −20 Kernel—Gaussian −51 −64 −68 −68 Radius .1 −42 −27 −55 −55 Radius .01 Radius .05 −48 −60 −69 −67 8 −11 −47 −51 K-nearest w repl.—no caliper 134 K-nearest without 66 85 67 repl.—no caliper Local linear regression 8 −11 −47 −51

T5: Ease of municipal recycling with no bottle law: United States respondents onlyb Kernel—Epanechnikov −4 −17 −7 −11 −22 −4 −17 −7 −11 −22 Kernel—Gaussian −50 −43 −52 −43 −46 Radius .1 −51 −44 −32 −37 −33 Radius .01 −57 −36 −51 −43 −42 Radius .05 −23 −21 −20 −21 −35 K-nearest w repl.—no caliper 159 103 105 154 133 K-nearest without repl.—no caliper −23 −21 −20 −21 −35 Local linear regression

B. Campbell et al. / Resources, Conservation and Recycling 106 (2016) 98–109

105

Table 3 (Continued) Matching algorithma

Bias reduction (%)

T6: Ease of municipal recycling with bottle law: United States respondents onlya , b −57 −57 −29 −35 Kernel—Epanechnikov −35 Kernel—Gaussian −57 −57 −29 Radius .1 −52 −67 −52 −45 Radius .01 17 −36 −7 30 −52 −65 −50 −49 Radius .05 K-nearest w repl.—no 4 −25 4 15 caliper 13 38 48 55 K-nearest without repl.—no caliper Local linear regression 4 −25 4 15 T6A: Ease of municipal recycling with bottle law: Canadian respondents onlyb Kernel—Epanechnikov −16 −27 −26 −15 −16 −27 −26 −15 Kernel—Gaussian −55 −47 −43 −56 Radius .1 −27 −32 −5 −42 Radius .01 −55 −47 −33 −57 Radius .05 −2 −25 −12 −32 K-nearest w repl.—no caliper 57 60 33 63 K-nearest without repl.—no caliper −2 −25 −12 −32 Local linear regression

−49 −49 −54 −6 −55 −10

−46 −46 −68 −51 −67 −42

−37 −37 −49 −40 −48 −43

−44 −44 −56 −29 −38 −26

−50 −50 −27 −9 −22 −14

−21 −21 −70 −55 −67 −50

−56 −56 −77 −40 −81 −61

24

26

0

35

21

−54

−64

−10

0

−43

−26

−14

−50

−61

−37 −35 −13 10 8 28

−41 −41 −77 −47 −63 −23

−28 −28 −51 −60 −63 −49

−25 −25 −52 −45 −52 −43

−39 −39 −65 −44 −56 −22

−25 −25 −79 −68 −78 −48

−10 −10 −65 −34 −58 −15

33

41

62

66

53

62

81

28

−23

−49

−43

−22

−48

−15

a

Michigan and California have a different deposit amount compared to the other states with bottle laws, so we estimated the ATT with both Michigan included and excluded and the signs, magnitudes and significance did not change. b Analyses for U.S. and Canadian respondents was separate given all but all but one Canadian province has bottle laws.

and H4A that BDLs increase recycling rates for residual recyclables. From these results, it is clear that BDLs have more of an impact when a MRP is not present as recycling rates are higher overall when MRPs are present. However, there is no spillover effect on recyclables outside those clearly set forth in the BDL. These results do not imply that BDLs (or MRPs) do not work, as such a study would need to delve into the costs/benefits of these programs (such as Morris and Morawski, 2011). Rather, this study gives an indication how BDLs impact recycling rates with and without MRPs so that policy strategies can be developed in order to maximize the benefits of programs already in place.

Examining the ATEs when BDLs are not in effect shows recycling rates increase for all recyclables surveyed. ATE increases ranged from 16.1% for food waste to 57% for plant tags. With respect to the scale used, plant waste recycling frequency moved from the never/sometimes range of the scale to sometimes/usually. While the difference in frequency may not seem substantial, small increases in recycling could have a considerable benefit to the environment. With respect to the MRP with BDL comparison, we see a similar pattern to the MRP with no BDL results. Notably, all recyclables had significantly increased recycling frequency. The largest increases were for plant tags (39.5%), paper (38.5%), and magazines (38.3%). As noted by Saphores and Nixon (2014), there are disagreements about whether BDLs negatively impact MRPs. Based on the our results, we fail to reject H1B and H2B that MRPs increase recycling rates for aluminum cans and bottle glass regardless of whether a BDL is present. Further, we fail to reject H3B and H4B

4.2. Municipality recycling program: United States Results from the MRP comparisons while holding BDL constant indicates that respondents with access to MRPs have higher recycling rates than respondents with no access (Table 5).

Table 4 Comparing the impact of bottle laws on recycling of different recyclable materials with varying municipal recycling availability. United States respondentsa Bottle law comparison with no municipal recyclingb , c , d

Clear glass Brown glass Green glass Paper Magazines Boxes Aluminum Potting containers Plant tags Plant waste Food waste a

Bottle law comparison with municipal recyclingb , c , d

TRT

CTL

ATT

p-value

% change

R-boundse

TRT

CTL

ATT

p-value

% change

R-boundse

ATT-no CA/MI

p-value

2.46 2.69 2.36 2.34 2.28 2.51 3.03 2.28 2.01 1.67 1.54

2.10 2.42 2.18 2.51 2.39 2.48 2.73 2.14 1.86 1.61 1.52

0.36 0.27 0.18 −0.17 −0.11 0.03 0.30 0.14 0.14 0.06 0.02

0.021 0.039 0.252 0.289 0.518 0.826 0.055 0.279 0.302 0.407 0.685

17.2 11.2 8.3 −6.8 −4.6 1.4 10.9 6.6 7.8 3.6 1.3

1.29 1.34 1.12 1.04 1.13 1.48 1.1 1.14 1.21 1.63 1.78

3.49 3.36 3.45 3.51 3.47 3.49 3.61 3.24 3.00 2.05 2.18

3.38 3.31 3.36 3.46 3.45 3.46 3.51 3.24 3.00 2.02 2.24

0.11 0.05 0.09 0.06 0.02 0.04 0.11 0.01 0.00 0.03 −0.05

0.067 0.306 0.166 0.274 0.775 0.512 0.039 0.943 0.990 0.767 0.706

3.2 1.6 2.6 1.6 0.4 1.0 3.0 0.2 0.0 1.5 −2.4

1.50 1.32 1.33 1.15 1.51 1.19 1.59 1.46 1.39 1.14 1.24

0.08 0.06 0.08 0.06 0.06 0.06 0.05 0.10 0.13 0.13 0.19

0.350 0.316 0.458 0.254 0.540 0.885 0.266 0.748 0.793 0.103 0.971

Analyses for U.S. and Canadian respondents was separate given all but all but one Canadian province has a bottle deposit law. Sample sizes for each treatment group and recyclable can be found in Table 1. ATT = Average Treatment Effect on Treated, calculated as treated minus control. TRT = Treated. CTL = Control. % change = (TRT − CTL)/CTL. d Michigan and California have a different deposit amount compared to the other states with bottle laws, so we estimated the ATT with both Michigan and California included and excluded and the signs, magnitudes and significance did not change. e Rosenbaum bounds (R-bounds) measures the unobserved bias needed to increase the odds of participation in the treatment so that the bounds becomes insignificant at the 0.05 level, given the same covariates. b c

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B. Campbell et al. / Resources, Conservation and Recycling 106 (2016) 98–109

Table 5 Comparing the impact of municipal recycling programs on recycling of different recyclable materials with varying bottle law availability. United States respondentsa Municipal recycling comparison with no bottle lawb , c , d

Clear glass Brown glass Green glass Paper Magazines Boxes Aluminum Potting containers Plant tags Plant waste Food waste

Municipal recycling comparison with bottle lawb , c , d

TRT

CTL

ATT

p-value

% change

R-boundse

TRT

CTL

ATT

p-value

% change

R-boundse

3.34 3.26 3.34 3.42 3.42 3.42 3.47 3.19 2.94 1.98 2.23

2.14 2.43 2.21 2.45 2.40 2.43 2.76 2.23 1.87 1.70 1.65

1.20 0.83 1.13 0.97 1.01 0.99 0.72 0.96 1.07 0.27 0.58

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

55.9 34.1 51.1 39.6 42.1 40.7 25.9 43.0 57.0 16.1 35.1

7.00 5.80 7.00 7.00 7.00 7.00 3.23 6.00 5.44 1.24 2.21

3.49 3.35 3.44 3.46 3.45 3.48 3.60 3.25 2.98 2.03 2.14

2.66 2.79 2.51 2.50 2.49 2.63 3.19 2.49 2.13 1.82 1.67

0.83 0.56 0.93 0.96 0.96 0.85 0.41 0.77 0.84 0.22 0.47

0.000 0.000 0.000 0.000 0.000 0.000 0.003 0.000 0.000 0.045 0.000

31.0 20.0 36.9 38.5 38.3 32.3 12.8 30.8 39.5 11.9 28.5

5.22 4.44 4.57 4.62 4.63 3.70 1.91 4.03 3.93 1.09 1.47

a

Analyses for U.S. and Canadian respondents was separate given all but all but one Canadian province has bottle laws. Sample sizes for each treatment group and recyclable can be found in Table 1. ATT = Average Treatment Effect on Treated, calculated as treated minus control. TRT = Treated. CTL = Control. % change = (TRT − CTL)/CTL. d Michigan and California have a different deposit amount compared to the other states with bottle laws, so we estimated the ATT with both Michigan and California included and excluded and the signs, magnitudes and significance did not change. e Rosenbaum bounds (R-bounds) measures the unobserved bias needed to increase the odds of participation in the treatment so that the bounds becomes insignificant at the 0.05 level, given the same covariates. b c

that MRPs increase recycling rates for residual recyclables not specified in BDLs. This is not unexpected as MRPs are generally set up to collect varying types of recyclables and is not limited to glass bottles or aluminum cans. From these results it is clear that MRPs are almost as effective without a BDL as with a BDL. This can be observed by examining the treated group means of the treatment groups.

on these results, we fail to reject H5 that MRPs that are perceived to easier to use increase recycling rates across all recyclables. 4.4. Municipality recycling program: Canada Examining only the Canadian respondents, we see that MRP/bottle law show a significant increase in recycling rates (Table 7). For instance, we see a 0.74 ATE gain (24.7%) for clear glass when a MRP is present. Interestingly, we see that plant waste and food waste recycling rates increase by 80.6% and 73.4%, respectively. This is most likely due to MRPs consistently offering plant and food waste recycling options. In contrast, the rates for recycling plant and food waste by U.S. respondents when a MRP was present or not is considerably lower, most likely implying that U.S. MRPs do not offer plant or food waste recycling (composting) options. With respect to perceived ease of use, we see similar results to the U.S. sample (Table 8). Notably as a MRP is perceived to be easy to use, recycling rates for all recyclables surveyed increased. Based

4.3. Ease of municipality recycling: United States Table 6 shows the ATEs for recycling when the perception that recycling was easy in the absence and presence of a BDL. Findings were similar for the MRP/BDL and MRP/no BDL in that increased perception of ease of use provided significantly higher recycling rates than when recycling was perceived to be not easy. For example, recycling magazines in a MRP/no BDL where recycling was perceived to be easy resulted in an ATE of 0.87 (32.5% increase), while there was a 0.83 ATE gain (30.2% increase) for MRP/BDL. Based

Table 6 Comparing the perceived ease of municipal recycling programs on recycling of different recyclable materials with varying bottle law availability. United States respondents a Ease of municipal recycling with no bottle lawb , c , d

Clear glass Brown glass Green glass Paper Magazines Boxes Aluminum Potting containers Plant tags Plant waste Food waste a

Ease of municipal recycling with bottle lawb , c , d

TRT

CTL

ATT

p-value

% change

R-boundse

TRT

CTL

ATT

p-value

% change

R-boundse

3.59 3.44 3.55 3.59 3.54 3.59 3.63 3.36 3.07 2.81 2.89

2.49 2.47 2.26 2.39 2.67 2.59 2.64 2.49 2.39 1.65 1.83

1.09 0.97 1.30 1.20 0.87 1.00 0.99 0.86 0.68 1.17 1.06

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

43.8 39.4 57.4 50.1 32.5 38.5 37.5 34.6 28.3 70.7 57.8

7.00 7.00 7.00 6.83 4.39 7.00 5.99 3.30 2.25 7.00 4.29

3.67 3.48 3.61 3.64 3.59 3.64 3.70 3.40 3.17 3.09 3.06

2.56 2.61 2.47 2.77 2.76 2.73 3.08 2.74 2.36 1.48 1.56

1.11 0.88 1.13 0.87 0.83 0.91 0.61 0.66 0.82 1.60 1.50

0.000 0.000 0.000 0.000 0.000 0.000 0.001 0.017 0.006 0.000 0.000

43.4 33.6 45.8 31.4 30.2 33.2 19.9 24.2 34.6 107.9 96.4

3.54 5.18 3.70 5.18 3.86 4.35 2.49 2.39 3.24 1.26 1.22

Analyses for U.S. and Canadian respondents was separate given all but all but one Canadian province has bottle laws. Sample sizes for each treatment group and recyclable can be found in Table 1. ATT = Average Treatment Effect on Treated, calculated as treated minus control. TRT = Treated. CTL = Control. % change = (TRT − CTL)/CTL. d Michigan and California have a different deposit amount compared to the other states with bottle laws, so we estimated the ATT with both Michigan and California included and excluded and the signs, magnitudes and significance did not change. e Rosenbaum bounds (R-bounds) measures the unobserved bias needed to increase the odds of participation in the treatment so that the bounds becomes insignificant at the 0.05 level, given the same covariates. b c

B. Campbell et al. / Resources, Conservation and Recycling 106 (2016) 98–109

107

Table 7 Comparing the impact of municipal recycling programs and bottle law availability for canadian residents. Canadian respondentsa Ease of municipal recycling with no bottle lawb , c , d

Clear glass Brown glass Green glass Paper Magazines Boxes Aluminum Potting containers Plant tags Plant waste Food waste

Ease of municipal recycling with bottle lawb , c , d

Treated

Control

ATT

p-value

% change

R-bounds e

Treated

Control

ATT

p-value

% change

R-bounds e

3.68 3.66 3.69 3.75 3.74 3.72 3.75 3.54 3.38 2.52 2.68

2.88 2.89 2.92 2.68 2.73 2.82 3.44 2.53 1.99 2.00 1.86

0.79 0.77 0.77 1.07 1.01 0.91 0.31 1.01 1.39 0.53 0.82

0.000 0.000 0.000 0.000 0.000 0.000 0.021 0.000 0.000 0.000 0.000

27.5 26.6 26.2 39.8 37.0 32.1 9.1 39.9 69.9 26.3 43.8

3.72 5.47 5.96 7.00 5.68 6.31 2.80 7.00 7.00 1.99 3.14

3.75 3.73 3.75 3.81 3.78 3.77 3.81 3.66 3.55 3.26 3.38

3.01 2.94 2.99 2.73 2.94 3.05 3.03 2.84 2.48 1.80 1.95

0.74 0.80 0.76 1.08 0.84 0.72 0.78 0.82 1.06 1.45 1.43

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

24.7 27.1 25.4 39.5 28.6 23.6 25.9 29.0 42.9 80.6 73.4

3.77 2.40 2.30 3.80 2.37 2.32 3.82 4.12 2.05 1.42 1.20

a

Analyses for U.S. and Canadian respondents was separate given all but all but one Canadian province has bottle laws. Sample sizes for each treatment group and recyclable can be found in Table 1. ATT = Average Treatment Effect on Treated, calculated as treated minus control. TRT = Treated. CTL = Control. % change = (TRT − CTL)/CTL. d Michigan and California have a different deposit amount compared to the other states with bottle laws, so we estimated the ATT with both Michigan and California included and excluded and the signs, magnitudes and significance did not change. e Rosenbaum bounds (R-bounds) measures the unobserved bias needed to increase the odds of participation in the treatment so that the bounds becomes insignificant at the 0.05 level, given the same covariates. b c

Table 8 Comparing the perceived ease of municipal recycling programs on recycling of different recyclable materials with varying bottle law availability. Canadian respondentsa Municipal recycling comparison with bottle lawb , c

Clear glass Brown glass Green glass Paper Magazines Boxes Aluminum Potting containers Plant tags Plant waste Food waste

Treated

Control

ATT

p-value

% change

R-boundsd

3.75 3.73 3.75 3.81 3.78 3.77 3.81 3.66 3.55 3.26 3.38

3.01 2.94 2.99 2.73 2.94 3.05 3.03 2.84 2.48 1.80 1.95

0.74 0.80 0.76 1.08 0.84 0.72 0.78 0.82 1.06 1.45 1.43

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

24.7 27.1 25.4 39.5 28.6 23.6 25.9 29.0 42.9 80.6 73.4

3.77 2.40 2.30 3.80 2.37 2.32 3.82 4.12 2.05 1.42 1.20

a

Analyses for U.S. and Canadian respondents was separate given all but all but one Canadian province has bottle laws. Sample sizes for each treatment group and recyclable can be found in Table 1. ATT = Average Treatment Effect on Treated, calculated as treated minus control. TRT = Treated. CTL = Control. % change = (TRT − CTL)/CTL. d Rosenbaum bounds (R-bounds) measures the unobserved bias needed to increase the odds of participation in the treatment so that the bounds becomes insignificant at the 0.05 level, given the same covariates. b c

on these results, we fail to reject H1-H5 when applied only to the Canadian sample. 5. Conclusion Findings in this study provide practical implications for state and local governments that are interested in encouraging household recycling and for researchers who are investigating the determinants of individual recycling behavior. We find evidence that BDLs are working for recyclables specified in the laws (bottle glass and aluminum cans). However, we do not find any spillover effect to residual recyclables not specified in BDLs. Further, we find that MRPs increase recycling rates for all recyclables surveyed. When MRPs are used in conjunction with BDLs we only see small gains, implying MRPs impact recycling rates more than BDLs. However, BDLs do provide significant increases for bottle glass and aluminum cans, especially when MRPs are not present. Our results indicate that MRPs impact recycling rates more than BDLs. This result matches the findings of Saphores and Nixon (2014) in that they find MRP’s in the U.S., notably curbside recycling, have a higher odds ratio for recycling than BDL’s, implying MRP’s have a bigger potential impact on recycling rates. However, BDLs do

increase recycling rates for regulated recyclables and do not negatively impact non-regulated recyclables. Given BDLs impact states which encompass respondents that do and do not have access to MRPs, BDLs can provide an incentive for respondents to increase their recycling rates of regulated recyclables. In this vein, BDLs can be improved by trying to not only increase recycling rates of prescribed recyclables, but also incorporate initiatives to increase recycling of residual recyclables. With respect to MRPs, recycling rates can be improved by increasing their ease of use. As our results demonstrate, as MRPs are perceived easier to use, recycling rates increase. There were several limitations to the study as discussed throughout. Notably, only internet users were sampled and any recycling differences associated with socio-demographic differences on recycling could create biases in our results. However, by using propensity score matching we can match respondents with like demographics which should mitigate some of the bias, if any exist. Further, we did not ask respondents quantity of their recycling, but rather their frequency. Frequency does limit the policy implications somewhat, but it does still allow us to make viable policy comparisons and recommendations. With respect to future research, efforts could be devoted to examining

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