Behavioral spillovers from food-waste collection in

Behavioral spillovers from food-waste collection in Swedish municipalities

2018-05-18, 15)33

Journal of Environmental Economics and Management 89 (2018) 168–186

Contents lists available at ScienceDirect

Journal of Environmental Economics and Management j o u r n a l h o m e p a g e : w w w . e l s e v i er . c o m / l oc a t e / j e e m

Behavioral spillovers from food-waste collection in Swedish municipalities Claes Ek a ,* , Jurate Miliute-Plepiene b a b

Department of Economics, University of Gothenburg, P.O. Box 640, 40530, Gothenburg, Sweden Lund University Centre for Sustainability Studies (LUCSUS), P.O. Box 170, 22100, Lund, Sweden

article info

abstract

Article history: Received 12 June 2017 Revised 27 November 2017 Accepted 12 January 2018 Available online 27 March 2018

We estimate behavioral spillovers from environmental policy within the context of a natural experiment on food waste in Sweden. Exploiting the staggered implementation of food-waste collection across Swedish municipalities, we use a difference-in-difference design to measure the causal effect of introducing such collection on another pro-environmental behavior, namely the sorting of packaging waste. Results suggest a positive spillover effect on packaging waste which corresponds to 5–10% of the population average and rises gradually over time, possibly due to slow implementation of food-waste collection in many municipalities. These estimates are unconfounded with a number of shifts in the waste-related incentives facing households, e.g. introduction of curbside collection of packaging waste from single-family homes. Although we are unable to directly account for all such factors, indirect robustness tests provide no compelling evidence that estimated spillovers are spurious. © 2018 Elsevier Inc. All rights reserved.

JEL classification: D04 D12 Q53 Keywords: Environmental behavior Recycling Behavioral spillovers Food waste Packaging waste

1. Introduction Ideally, environmental policy evaluation should include all relevant impacts. Foremost among these are immediate and future costs and benefits directly associated with the policy: does it have the desired effect on the environmental variable of interest? What are associated direct costs? Other relevant effects can be viewed as spillover effects. Some of these are mediated by financial incentives and can be readily analyzed using the toolset of economic theory: for example, a subsidy on the recycling of aluminum waste is likely to have indirect effects through its impact on the price of scrap metal (Palmer et al., 1997; Kaffine, 2014). However, it is plausible that spillovers also operate through non-monetary incentives such as social norms or household warm-glow motivation. For example, as this paper will argue, a policy encouraging households to recycle a specific waste fraction may affect their readiness to recycle other waste fractions as well. Such behavioral spillovers across proenvironmental activities have received significant attention only recently, and are rarely considered in cost-benefit and other policy analyses; yet omitting substantial spillovers may produce misleading results. We contribute to filling these gaps by estimating policy-driven spillovers across household environmental (waste) activities. To our knowledge, our paper is the first to

* Corresponding author. E-mail addresses: [email protected] (C. Ek), [email protected] (J. Miliute-Plepiene).

https://reader.elsevier.com/reader/sd/1A0015A5C8BE04B2939F…603CF645E30DEF2E34B562BF79C697070BFDFEDAA3C20C5532860E1132

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E-mail addresses: [email protected] (C. Ek), [email protected] (J. Miliute-Plepiene).

https://doi.org/10.1016/j.jeem.2018.01.004 0095-0696/ © 2018 Elsevier Inc. All rights reserved. C. Ek and J. Miliute-Plepiene / Journal of Environmental Economics and Management 89 (2018) 168–186

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do so by analyzing a real-world policy using robust econometric methods. The intervention that we examine is food-waste collection from Swedish households. Collection has currently been implemented in roughly two-thirds of the 290 Swedish municipalities, with introduction occurring gradually from 1990 onwards. We examine whether these staggered policy changes have affected household efforts to sort a different waste fraction, namely packaging waste. Our data set merges policy-introduction dates with a municipality-by-year panel on collected packaging amounts across 2006–2015, and allows estimation of causal spillover effects. We consider food and packaging waste particularly suitable for spillover estimation, for two reasons. First, both entail wasterelated pro-environmental activities, and so are likely to be perceived as similar in scope and purpose, which may be important in light of evidence that spillovers are larger in magnitude for similar activities than for dissimilar ones (Ek, 2017b; Reinstein, 2012). Second, Swedish recycling rates for packaging are high but not extremely high: in 2012, it stood at 69% (this figure was calculated from Swedish Environmental Protection Agency (2014) as a weighted average of recycling rates for glass, paper packaging, plastic packaging, and metal). Thus, there is room for positive-sign as well as negative-sign spillovers. The present paper adds to a small set of studies using credible empirical analysis to identify the effect of waste-policy interventions; for a review of this literature, see Kinnaman (2016). More broadly, our study contributes to the literature on behavioral environmental economics (Shogren et al., 2010; OECD, 2017). The main focus of this research agenda is on applying psychological insights to suggest novel intervention strategies (e.g. ‘nudges’) and alternative theoretical predictions with respect to direct effects, i.e. effects on the behavior targeted by an intervention. However, behavioral economists are also increasingly using such insights to highlight possible spillovers where standard economic theory is silent (Dolan and Galizzi, 2015). Importantly, the mechanisms that potentially drive behavioral spillovers tend to run in different directions: spillovers may in principle have either sign.1 For example, there is experimental evidence of a ‘moral-licensing’ effect (Merritt et al., 2010), where people who have just behaved prosocially subsequently relax their moral standard constraints (Blanken et al., 2015). In our setting, it is possible that households that begin to recycle food waste will feel licensed to reduce recycling of other waste fractions, suggesting that the overall time budget for recycling is approximately fixed. If so, we should observe a negative-sign spillover effect from the introduction of food-waste collection. On the other hand, if for instance households value being consistent, as in the psychological theories of Festinger (1957) or Bem (1967), increased effort on food-waste recycling might trigger greater engagement with other recycling behaviors, leading to positive spillovers. There are other mechanisms that potentially drive positive spillovers as well. For instance, the introduction of food-waste collection may signal or make salient the general desirability of pro-environmental behavior; or there could be technical complementarities between the two recycling behaviors, e.g. if households find it convenient to install indoor waste bins for food and packaging at the same time.2 In general, while more than one of the above mechanisms may be present, only the policy-relevant ‘net’ (or summed) behavioral spillover can be observed in the field. Our study is no expection. Thus, we do not set out to test a particular hypothesis on the sign of behavioral spillovers. This also reflects the fact that prior evidence regarding the sign of (net) behavioral spillovers is mixed. Most published lab studies point toward negative spillovers in prosocial and environmental behavior. Gneezy et al. (2012) argue that spillovers are more likely to be positive when the initial prosocial act is costly, and stress that in most morallicensing lab studies the ‘initial action’ (to be followed by some possibly prosocial action) is essentially hypothetical, e.g. with subjects writing essays about themselves using positive words (Sachdeva et al., 2009; Mazar and Zhong, 2010). For this reason, external validity may be low. However, even in the field, where the behaviors in question are generally costly, both positive and negative spillovers have been demonstrated. Jacobsen et al. (2012) study a green-electricity program in Memphis, Tennessee, and find that although the program did promote the consumption of clean electricity, it also increased the overall use of electricity, though only among a subset of participants. In the same vein, Tiefenbeck et al. (2013) study an information campaign to conserve water at a housing complex in Massachusetts, and find that while participants did decrease water use, they increased electricity consumption at the same time. By contrast, Carlsson et al. (2016) find positive spillovers on electricity use from a similar Colombian water-use information campaign. Finally, Miliute-Plepiene and Plepys (2015) study the same behavioral spillover effect as we do here — from food-waste collection to packaging waste — noting that waste-management practitioners often claim to observe a positive-sign effect. The authors conduct a survey within a single Swedish municipality, where 47% of households state that they sort more packaging waste due to the introduction of food-waste collection. The paper also presents a simple before-and-after comparison within the same area, confirming that greater household efforts to recycle and prevent waste are observed subsequent to adoption; the authors argue that a set of demographic and socio-economic variables are insufficient to explain the increase. However, the particular municipality studied is a socioeconomic outlier, limiting external validity; also, the method used implies that results

1 Following Dolan and Galizzi (2015), our working definition of a behavioral spillover is any path dependency in behavior — where agents are more/less likely to perform action y after having performed a different action x — that is not mediated through financial incentives. Note that this definition does not exclude mechanisms operating through non-monetary material incentives such as inconvenience costs. 2 We consider several of the above mechanisms compatible with models of prosocial preferences emphasized by behavioral economists (Shogren et al., 2010). For example, moral licensing is sometimes argued to arise through a process of ‘self-concept maintenance’ (Sachdeva et al., 2009). This can be represented as a warm-glow utility component for which the cross-partial derivative with respect to different prosocial activities is negative. For an example of such a model, with a particular emphasis on the degree of technical substitutability, see Ek (2017a).


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(while suggestive) cannot readily be given a causal interpretation. The present paper, by contrast, attempts to produce causal estimates using a municipality and year fixed-effects framework. One potential pitfall with this approach is that food-waste collection has sometimes been introduced as part of curbsidecollection systems where packaging waste is also collected directly from households. Because it is very difficult to isolate the spillover effect from food-waste collection per se in these cases, we restrict attention to ‘food-only’ waste systems that do not couple food-waste collection with curbside collection of packaging. We regress a packaging-waste variable on (i) the share of households participating in ‘food-only’ systems, or (ii) an institutional dummy indicating whether a given municipality has introduced such systems. Results in the former case are quantitatively comparable to the difference-in-difference estimates of the latter. This suggests that post-adoption changes in packaging waste are mediated by households’ engagement with ‘foodonly’ systems, and is consistent with the idea that behavioral spillovers are at work. Results indicate that spillovers are positive: ‘food-only’ systems cause more packaging waste to be collected. We also find a large positive direct effect of the adoption of food-only systems on the amount of food waste collected. Spillovers on packaging 3 are on the order of 5–10% and emerge gradually, likely because of slow implementation in many areas. Our regressions cannot directly or fully control for some potential drivers of packaging recycling, including (i) waste bin size, (ii) the frequency of residual-waste collection, (iii) curbside collection of packaging waste in multi-family housing, and (iv) information campaigns directed toward packaging waste. If these variables were subject to shocks coinciding with food-waste collection, estimates may be biased. In some cases there are institutional reasons to doubt that this was the case, however, and we perform various robustness tests that provide no compelling evidence that estimated spillovers are spurious. The remainder of this paper is organized as follows. Section 2 provides institutional and technical background on the collection of food and packaging waste. Section 3 describes our empirical strategy and the data sets that we use. Section 4 then presents results, while section 5 describes robustness tests. Section 6 asks whether or not the effect that we find represents an environmental benefit. Section 7 concludes. 2. Background 2.1. Household food waste4 In 2003, the Swedish government introduced a national target stating that no later than 2010, at least 35% of the food waste 5 The purpose generated by households, restaurants, catering facilities, and grocery stores should undergo biological treatment. of the target, which was the first of its kind in Sweden, was to reduce waste incineration. While it was not met, an updated and somewhat refined target was adopted in 2012. This mandates a 50% biological treatment share by 2018, with 40% treated with energy recovery (i.e. by digestion). Implementation of policies for biological treatment occurs at the local level, where municipalities run waste management either directly or through a (public or private) contractor. As of 2015, approximately two-thirds of Swedish municipalities have introduced systems for the source separation of food waste. A few collect food waste only from e.g. restaurants and schools. According to the latest available data (from 2014), 27% of the total (household and non-household) food waste produced in Sweden is digested, and 11% is composted. These shares have been rising for a number of years. Two different methods for separating household food waste from residual waste are in use. In one system, food and residual waste are deposited in different containers. In the other, only one garbage container is used but food waste is nevertheless separated from residual waste, either into different compartments (multi-compartment bins) or into different-colored bags (e.g. green for food waste) which subsequently undergo automatic optical sorting at a specialized plant. Regardless of system, all food waste is collected curbside by the municipality. Any (food) waste that is deposited as residual waste is incinerated. In around half of the municipalities that now collect food waste, participation is mandatory for households. Where sorting is voluntary, economic incentives, such as lower waste rates for participating homes, are commonly used to induce households to participate. In addition, municipalities often monitor sorting efforts to some extent, though household-level monitoring is typically not possible in multi-family housing, where waste is deposited anonymously. Various means are used to increase compliance: for instance, single-family households may be informed by telephone or mail that sorting efforts are unsatisfactory, and (under voluntary sorting) may be offered the higher rate as a last resort (Britta Moutakis, Swedish Waste Management, personal communication). Fig. 1 shows that municipalities where food waste is collected are spread across most of Sweden. Areas where food is not (yet) source separated tend to be rural, low population density areas, especially in the northern half of Sweden. This is because of higher per-unit collection costs as well as the fact that treatment facilities involve large fixed costs and, to break even, require larger waste inputs than those generated in small rural towns. The same pattern of expansion often holds within municipalities as well, with collection of food waste expanding gradually outward from initial trials within population centers. It is not

3 With respect to the previous discussion of multiple mechanisms, note that this estimate of net spillovers necessarily forms a (weak) lower bound on the summed impact of all mechanisms that specifically drive positive-sign spillovers (e.g. technical complementarities). 4

Much of this section is based on a Swedish-language report by Swedish Waste Management (2016). There are two main types of biological treatment. First, food waste can undergo anaerobic digestion to produce biogas and digestate, with the latter used as soil conditioner. Second, it can be composted, with no biogas production; the resulting compost can be also be used as soil conditioner or fertilizer. Hence, unlike composting, digestion reclaims both the matter and the energy content of food waste. 5


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Fig. 1. Introduction of food-waste collection across Swedish municipalities. Note: This figure was constructed based on information in the Swedish Waste Management table on municipal food-waste collection, described in Section 3. ’Introduction date’ refers to the beginning of implementation. ’Not included in table’ is best interpreted as nonadoption of food-waste collection.

uncommon for this process to take several years. 2.2. Household packaging waste In 1994, the Swedish government issued an ordinance establishing the principle of extended producer responsibility (EPR) with respect to packaging and newsprint waste. Under EPR, producers are required to organize collection and management


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C. Ek and J. Miliute-Plepiene / Journal of Environmental Economics and Management 89 (2018) 168–186 Table 1 Adoption dates for food-waste collection and curbside collection of packaging.

Year

Food-waste collection

Curbside collection of packaging (single-family) 4-compartment bins

Other curbside

1990–1995 1996–2000 2001–2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

11 40 40 7 6 3 7 13 5 5 10 9 7

0 0 5 1 2 0 1 5 1 1 4 2 1

1 4 8 1 2 0 0 1 0 0 1 0 0

Sum

163

23

18

Note: Values refer to the 244 municipalities included in our data set. Exact introduction dates for curbside collection of packaging are not always known, and participation rates vary widely. Three municipalities included in the table have since discontinued their use of curbside-collection systems. Two municipalities have switched from ‘Other curbside’ to 4-compartment bins and are counted twice in the table. For details, see Table A.3 in the Online Appendix.

of waste products and are required to meet government-set targets for packaging-waste collection as well as for materials and energy recovery for particular recyclables. Producers and retailers of packaging have responded by organizing themselves into the Packaging and Newspaper Collection Service, FTI (in Swedish, “Förpacknings- och tidningsinsamlingen”), which runs nationwide collection and recycling of packaging and newsprint waste. Although membership is voluntary, more than 90% of Swedish packaging producers and retailers have joined (Miliute and Plepys, 2009). FTI maintains roughly 6000 local recycling stations where households can drop off their waste. It is also possible to deposit waste at municipal recycling centers, though these are typically fewer in number and further from residential areas than the FTI sites, being also designed for the collection of bulky waste. While household participation in the EPR system is mandatory in principle, it is rarely enforced. Nevertheless, the majority of Swedish households actively sort and drop off packaging waste, so that most targets for separate collection and recycling (set in a 2005 addition to the EPR ordinance) have been fulfilled. In 2015, 35% of all household waste was recycled (Swedish Waste Management, 2016). In addition to FTI and municipal recycling sites, systems for the curbside collection of packaging waste have been introduced in many municipalities. In most areas, packaging is collected curbside only in multi-family housing, where individual property owners (municipal housing companies, housing cooperatives, etc.) typically decide independently whether or not to order curbside collection from municipal waste companies or private contractors. In the municipalities where curbside collection from apartments is common, such systems usually became widespread several years before the introduction, if applicable, of food-waste collection (Jon Nilsson-Djerf, Swedish Waste Management, personal communication). Thus, the typical system for collection of packaging waste in Sweden combines FTI recycling stations with curbside collection from multi-family dwellings. However, roughly 50 municipalities also offer curbside collection of packaging waste from singlefamily homes.6 14 municipalities are known to have introduced such systems concurrently with food-waste collection. The most common curbside-collection system for single-family homes is a pair of four-compartment bins placed on each household’s property. The eight fractions, typically, are: colored glass, uncolored glass, metal containers, plastic packaging, paper packaging, waste paper, food waste, and residual waste. Other systems (e.g. different-colored bags) are also in use, and some municipalities collect only a subset of packaging materials. As with food-waste collection, introduction has often been gradual, with initial trials involving a small number of households but lasting several years. Participation rates vary widely, from less than 1% to all single-family households. In the latter case, redundant FTI recycling stations are often dismantled. As of 2015, 32 municipalities levy weight-based fees for residual waste (‘pay-as-you-throw’) such that households face nonzero marginal cost from failing to recycle. The limited uptake of weight-based fees may be due to concerns that such fees incentivize opportunistic behavior such as illegal waste dumping (Fullerton and Kinnaman, 1995; Bucciol et al., 2015). Indeed, two municipalities have recently discontinued their use of weight-based fees. Table 1 presents a summary of introduction dates for food-waste collection and two types of curbside collection of packaging from single-family homes. More detailed information, including on weight-based waste fees, is given in Online Appendix A.

6 This reflects some dissatisfaction with the perceived inconvenience facing households under the EPR system. Demands to increase the number of recycling stations have been resisted by FTI due to the costs involved. Producers have agreed, however, to increase recycling rates by 2017 and 2020, as well as to improve collection standards.


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Fig. 2. Food waste collection by timing of introduction. Note: This figure depicts yearly-average collected amounts of packaging waste (panels a and c) and food waste (panels b and d). Panels (a) and (b) describe waste amounts for municipalities treated in 2010 (solid lines) and municipalities that did not collect food waste up until and including 2015 (dashed lines), respectively. Panels (c) and (d) include municipalities that introduced food-waste collection in 2006-2015 and describe waste amounts as a function of the distance to the time of treatment, which is normalized as t 0. In all cases, a vertical dotted line is placed at the year of adoption. Average waste amounts are calculated for each line and year by summing total waste amounts across all relevant nonempty observations, and then dividing by total population summed across the same cells.

3. Empirical strategy and data 3.1. Empirical strategy The aim of our analysis is to estimate the causal spillover effect of (household participation in) food-waste collection on the amount of packaging waste collected for recycling. In principle, we would like to randomly assign adoption of food-waste 7 However, collection across municipalities and then compare averages in packaging waste between treated and untreated units. Fig. 1 shows that the introduction of food-waste collection has expanded in clusters of municipalities and thus has not been random. In Online Appendix B, we confirm this by comparing a number of municipal characteristics across treatment-status groups, typically finding significant differences between treated and untreated units. If that non-randomness is correlated with packaging amounts, simple differences in means across treated and untreated areas will be misleading. A first step toward addressing this issue is to control for unobserved but time-invariant differences across municipalities, as well as for year-specific shocks that are common to all municipalities. In the simple case where treatment is binary (an area is either treated or not), this is a difference-in-differences strategy which exploits the fact that adoption decisions are staggered across municipalities. Panel (a) of Fig. 2 illustrates the general approach. It plots yearly average collected amounts of packaging waste for two groups: municipalities where food-waste collection was introduced in 2010 (the solid line), and a control group including all municipalities that did not adopt food-waste collection at any point up until and including 2015 (the dashed line). For reference, the year 2010 is marked with a vertical line. Similar graphs can be constructed for all other introduction dates in the data.

7 Note that throughout the rest of this paper, the term ‘treatment’ will refer to the adoption of food-waste collection systems, and should not be confused with literal waste treatment (e.g. incineration, digestion).


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If food-waste introduction has significant spillovers, we expect adopters to experience a shift in packaging-waste collection, relative to nonadopters, starting in 2010. Thus, to identify treatment effects, we examine the treated/control group difference in differences (shifts) in packaging waste over time. In panel (a), there is clear indication of positive spillovers: after 2010, packaging amounts are higher among adopters. This pattern may be benchmarked against panel (b) of Fig. 2, which focuses on direct effects. Here, using the average food-waste amounts collected as outcome variable, we also find a clear pattern that food-waste amounts increase in 2010 for adopters, while nonadopters remain by the horizontal axis. Panels (a) and (b) examine only a single arbitrary introduction year, and most of the graphs corresponding to other introduction years display less clear-cut evidence. In panel (c) we therefore provide additional suggestive evidence of a spillover effect. This figure tracks packaging waste amounts within the set of municipalities that have introduced food-waste collection at some point in 2006–2015 and normalizes all years relative to (eventual) time of treatment. As the bulk of adopter observations lie relatively close to treatment, we restrict attention to the five years before and after treatment. A clear trend break in collected packaging waste appears at t 0, consistent with a spillover effect from treatment. The observed shift is gradual, which is plausible given that the adoption process itself is known to have often been slow, as noted in section 2. Similar evidence of a direct effect on food waste is given in panel (d). Of course, Fig. 2 should not in itself be taken as evidence of causality. Two crucial identifying assumptions underlie our difference-in-difference strategy: (i) the treatment status of one municipality should not determine outcomes in other municipalities, and (ii) at each time of treatment, there should be no year-specific shocks that shift packaging recycling in the treated group relative to the non-treated group. While testing these assumptions directly is not possible, we consider (i) unlikely to pose problems, as there are typically multiple drop-off sites for packaging waste within any given municipality and no obvious incentive to deposit waste elsewhere; though admittedly we cannot rule out more subtle cross-municipality effects due to e.g. diffusion of social norms.8 Assumption (ii) implies that there must be no omitted-variable bias due to time-varying factors. Here there are several obvious candidates. One is a set of shifts in drivers of packaging recycling that may have coincided with the adoption of foodwaste collection. A particular concern is the fact that, in several places, food-waste collection has occurred within waste systems involving curbside collection of both food and packaging from single-family homes, e.g. by four-compartment bins. Such systems can be expected to directly increase efforts to sort packaging at source compared to the typical status quo involving FTI recycling stations (Best and Kneip, 2011; Dahlén et al., 2007). Thus, we are not convinced that it is meaningful to attempt to isolate the ‘pure’ spillover effect from food-waste collection in these situations. Instead, our analysis focuses on spillover effects from waste systems where food-waste collection does not coincide with curbside collection of packaging from single-family dwellings. Such ‘food-only’ systems, which form our treatment category, will thus be mutually exclusive with four-compartment bins and other curbside methods of collecting packaging waste from single-family houses. Our regressions have the following structure: ymt

m

t

CC mt

Tmt

mt

mt

(1)

where m indexes municipalities and t indexes time. The dependent variable ymt is the amount of packaging collected per capita in a municipality and year. m and t are municipality and year fixed effects, respectively. Even with fixed effects, unobserved factors probably produce error terms that are autocorrelated within municipalities (Bertrand et al., 2004), so standard errors are clustered at the municipal level.9 CCmt gives participation rates among single-family dwellings in waste systems involving curbside collection of packaging. Due to lack of data on such rates, participation is typically treated as constant after introduction; for more information, see Online Appendix A. Tmt is our treatment variable, which isolates municipalities that collect food waste and have CCmt 0. Finally, mt is a vector of municipality-specific controls. We alternate between two different treatment variables capturing the effect of ‘food-only’ systems. First, we use the share of households formally participating in ‘food-only’ systems. Second, we also use an indicator which is equal to one if a given municipality has introduced a ‘food-only’ system, and zero otherwise. This treatment dummy is equal to one if such systems were in place at any point during the relevant year. Since the dummy does not capture household participation, results in these difference-in-difference regressions should be interpreted as intent-to-treat estimates at the municipal level. Our data also allows us to support identifying assumption (ii) by controlling for the presence of weight-based fees and the number of FTI stations and municipal recycling centers per capita. In section 5, we attempt to address other drivers of household recycling that we are unable to directly or fully control for, namely bin size, the frequency of waste collection, adoption or expansion of curbside collection of packaging waste from multi-family homes, and information campaigns directed at packaging waste.10 Assumption (ii) can also be supported to some extent by demonstrating that pretreatment trends are parallel across municipalities that are eventually treated and control municipalities that are never treated. Our identification strategy relies on the

8 Bucciol et al. (2015) present evidence that adoption of weight-based waste fees may cause ‘waste tourism’, i.e. households traveling to adjacent municipalities, where no monetary incentive for source separation exists, to drop off unsorted waste. Given that packaging waste is never subject to weight-based fees, we do not think that waste tourism is an issue here. Nevertheless, in separate analyses (results available on request) we control for whether a given municipality is adjacent to one or more other municipality where weight-based fees are used. Our main treatment effects change very little. 9 In separate analyses, we also explored clustering at the county level, with very similar results. 10 Due to lack of reliable data, we are unable to check whether spillover effects differ depending on whether food-waste collection is voluntary or not.


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idea that treated municipalities would have evolved in parallel with control areas in the absence of treatment, so unless this holds before treatment, our approach is unlikely to work. In particular, it may be possible to rule out some potential confounders if pretreatment trends are found to be parallel. For instance, consider increasing environmental awareness within a municipality, which is likely to drive both the introduction of food-waste collection and increased recycling overall. Our regressions include proxies for environmental awareness, namely the share of university graduates as well as municipal (nationalelection) vote shares summed across the Green Party and the Centre Party, which occupy different positions on the political spectrum and are known for being pro-environment. A different approach, however, is to argue that given the inertia likely present in local political processes, we should observe increases in packaging recycling before the start of food-waste collection. While no data on packaging waste is available prior to 2006, we are able to check for the aforementioned pattern in two ways. First, we include leads (and lags) of binary treatment status in regressions; see section 4. Second, we perform a regression-based 11 test using all non-treated observations across 2006–2015, including pretreatment observations for ‘food-only’ municipalities. For this test, we regress packaging-waste amounts on municipality fixed effects and two separate sets of year dummies for control municipalities and eventually treated municipalities, respectively. We then perform an F-test of equality within each of the eight pairs of well-defined time dummies. We reject the null hypothesis that these year dummies are pairwise equal across the two groups, both when the control group includes municipalities with curbside collection of packaging from single-family houses (p 0.030) and when it does not (p 0.041). However, the significance of the test appears to be driven by a single year, 2008. Regardless of how the control group is defined, the set of seven pairwise restrictions which does not include the 2008 restriction yields a test result of p 0.6. 12 Although the test does cast some doubt on assumption (ii), this fact suggests that pretreatment trends are typically close to being parallel.

3.2. The data Our main dependent variable — collected amounts of packaging waste per capita and year — is constructed from a panel data set provided by FTI. This panel is balanced and covers 2006–2015 and all 290 Swedish municipalities. It omits some packaging materials that are not collected in collaboration with an FTI member company; including, in some cases, packaging amounts collected through curbside collection from single-family housing (recall, however, that our treatment variable is estimated separately from such systems). Variables for glass, paper packaging, plastic packaging, and metal are summed to obtain a single measure of the packaging waste collected per capita and year in each municipality-year pair. We obtain information on collected amounts of food waste for each municipality and year from Avfall Web, which is a database maintained by Swedish Waste Management (Avfall Sverige), a stakeholder organization representing mostly municipal waste companies. This data set is an unbalanced panel with a large proportion (54%) of missing values across 2006–2015. It includes separate variables for waste that is composted, digested, and treated at sewage plants, respectively; we sum these into a single measure. As our independent variable, we use either a binary measure or a continuous one. The binary variable is equal to one if a municipality has introduced ‘food-only’ systems, and zero otherwise; we construct it by combining 212 food-waste introduction dates provided by Swedish Waste Management with information on curbside collection of packaging from single-family houses. The continuous treatment variable is equal to zero unless the binary treatment variable is equal to one, in which case we set it equal to an Avfall Web variable on the percentage share of households formally participating in ‘food-only’ systems. This raw Avfall Web data again has a large share (39%) of missing values.13 The resulting data set covers 2006–2015 and includes 244 municipalities. We retain all municipalities not included in the original list, as they are very likely to have never implemented food-waste collection; of the 78 municipalities not included, we directly contacted 69, none of which stated that they had such systems in place during 2006–2015. For additional information on the data used in this paper, see Online Appendix A. Table 2 displays summary statistics. Waste amounts are right-skewed, with mean amounts above the median. This is confirmed in the left panel of Fig. 3, which highlights the cross-section dimension of the FTI panel, showing that for most municipality-year pairs, less than 50 kg of packaging was collected per capita and year. The right panel of Fig. 3 displays the time-series dimension of the data by plotting pooled yearly averages of collected packaging amounts against time. The figure

11 Also note that in Fig. 2, adopters and nonadopters means track each other very closely before 2010 in panel (a) and (b). Again however, the figure clearly provides very limited information, and does not separate ‘food-only’ systems from ones involving curbside collection of packaging from single-family housing. 12 Conversely, only one of the 14 sets of seven restrictions which do include the one for 2008 fails to reject the null. We also checked that any set of six restrictions that does not include the one for 2008 similarly yields p 0.5; finally, 2008 is the only year for which a single-restriction t test rejects the null of equality. The discrepancy present in this year does not appear to be an artifact of errors in the data. In Online Appendix C (Table A.5), we rerun our main regressions while excluding all municipalities that introduced food-waste collection after 2008. While this comes at some cost to statistical power, we consider the results broadly consistent with those presented in Section 4. 13 Defining the continuous treatment variable in this way does ‘tidy up’ the raw data somewhat, as it recodes a number of missing values as zero. Moreover, the raw Avfall Web data contains 64 observations with nonzero participation rates despite there being no indication that food-waste collection (whether food-only or otherwise) is occurring. In the majority of these cases we have directly contacted the municipality in question to confirm that the data is in error. Results are robust to dropping the remaining municipalities from the analysis.


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C. Ek and J. Miliute-Plepiene / Journal of Environmental Economics and Management 89 (2018) 168–186 Table 2 Summary statistics: waste data.

Variable, by data source

Mean

Median

Standard deviation

FTI data Packaging waste Glass Paper packaging Plastic packaging Metal

38.18 18.37 12.88 5.00 1.93

36.01 17.32 12.15 4.21 1.75

12.62 7.15 5.70 3.41 0.94

Avfall Web data Food waste Share of households recycling food waste (%) Share participating in ‘food-only’ systems (%)

26.99 40.33 22.99

19.93 8 0

27.76 44.35 39.29

Note: Values refer to the 244 municipalities included in the merged data set. All figures in kg/capita/year except ‘share of households recycling food waste’, which is given in percent.

Fig. 3. Cross-section and time series for packaging waste across all municipality-year pairs.

reflects a clear upward trend in collected packaging over time (p 0.000), of about 1 kg per year.14 Table 2 also summarizes our continuous treatment variable, which is the share of households participating in ‘food-only’ waste systems. Because there are many municipality-year pairs in the data where food-waste collection has not (yet) been introduced, this variable has a mean of 25% but a median of zero. Among the 640 observations where participation is nonzero, 42% are equal to 100% and the rest lie between zero and 100%; most of these are in the upper half. 4. Main results In this section, we estimate the effect of introducing ‘food-only’ waste collection systems on household sorting of packaging waste. Our main regression results are presented in Table 3. First, we perform a preliminary analysis to check the feasibility of separating ‘food-only’ systems and curbside-collection systems into separate variables. In columns 1 and 2, we drop from the data all municipalities that had curbside collection of packaging from single-family houses at some point in 2006–2015, while regressing packaging waste on the continuous and binary treatment variables, respectively. Then, in columns 3 and 4 we include a curbside-collection control variable and run regressions on the entire sample. Results are quite similar, especially for the binary treatment variable. Point estimates are positive and at least marginally significant in all these regressions, indicating a positive spillover of about 2 kg/capita/year, which is about 5% of the data average. Coefficients on the continuous treatment variable represent the effect (in kg/capita/year) of increasing ‘food-only’ participation rates by one percentage point. Conditional-on-positive participation rates in ‘food-only’ systems average 77%, so estimates are broadly comparable across the two treatment variables. This is notable, as it suggests that the intention-to-treat estimates obtained in columns 2 and 4 of Table 3 are being mediated by household participation. In columns 5 and 6, we check the robustness of these results by adding time-varying municipal controls that potentially affect both packaging waste and the decision to introduce ‘food-only’ systems. We include demographic (population, population density, mean age, share of non-native citizens, share of multi-family housing) and socioeconomic variables (mean earned income,

14 For food waste, 36% of nonempty observations are equal to zero. The cross-section pooled distribution of nonzero, nonempty observations is double-peaked, with one maximum close to zero and another at roughly 50 kg/capita/year. Yearly averages are roughly in the range of 30–40 kg/capita/year, apart from 2007 (12 kg/capita/year), when only 5 observations are available. A significant positive time trend (p 0.048) disappears if the observations from 2007 are dropped.


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C. Ek and J. Miliute-Plepiene / Journal of Environmental Economics and Management 89 (2018) 168–186 Table 3 Spillover effects of food-waste collection on household sorting of packaging waste.

(1) Food only, continuous

(2)

0.030 (0.018)

Food only, binary

(3)

(4)

0.038 (0.016) 1.939 (0.799)

Curbside collection

(5)

(6)

(7)

6.468 (3.777) 6.391 (3.774) 2799 (2438)

1.776 (0.822) 6.187 (3.836) 6.561 (4.378) 3072 (2476)

2.015 (0.712) 5.424 (3.613) 10.25 (4.341)

0.033 (0.012)

3.615 (3.815)

2.019 (0.756) 3.668 (3.700)

Weight-based fee Recycling stations/capita

Includes areas with curbside coll. (single-family) Additional controls

NO

NO

YES

YES

YES

YES

YES

NO

NO

NO

NO

YES

YES

YES

Observations Municipalities included R-squared (within)

1648 191 0.183

2050 205 0.208

2009 230 0.138

2440 244 0.169

1452 213 0.219

1507 217 0.206

2440 244 0.239

Robust standard errors clustered at the municipal level. p 0.01, p 0.05, p 0.1. All regressions include municipality and year fixed effects. Additional controls are: population, population density, share of non-native citizens, mean age, share of multi-family housing, mean earned income, share of university graduates, unemployment rate, municipal government majority (left/right/broad coalition), and summed vote share for Green/Centre party. The variable ‘Food only, continuous’ gives the share of households that are (formally) participating in collection of food waste, excluding four-compartment bin systems. ‘Food only, binary’ is equal to one for all municipality-year pairs where the same type of system is in place. Decimals are not reported for estimates and standard errors exceeding 1000.

share of university graduates, unemployment rate) that correspond closely to the set of factors typically analyzed in studies on the determinants of household recycling (Hage et al., 2009; Miafodzyeva and Brandt, 2013). To capture potential drivers of the municipal decision to adopt ‘food-only’ systems, we also add political variables (vote shares in national elections, dummies for left-wing, right-wing, or broad coalition majorities in local government). Finally, we include two variables related to household incentives for recycling: (i) weight-based fees (‘pay-as-you-throw’), and (ii) the number of FTI and municipal recycling stations per capita. Estimates remain positive and are highly significant. Note that the variable ‘Recycling stations/capita’ is taken from Avfall Web and thus introduces a large number of empty cells, implying that samples are not comparable across columns. Because of this, in column 7 we also report results for the binary treatment variable when all controls except the number of recycling stations per capita is included. We find no statistically significant difference between the treatment estimate in column 6 and a variant of the regression in column 7 that uses the restricted sample of column 6 (in the latter, the treatment coefficient is 1.802). This suggests that any difference between columns 6 and 7 can be explained by sample composition alone. The same conclusion applies when ‘Recycling stations/capita’ is dropped from column 5 (in which case the treatment coefficient is 0.042 and has p 0.002). In Table 3, we have made the tacit assumption that the magnitude of the treatment effect is independent of the covariates. If this is not the case, it is desirable to impose common support, i.e. to ensure that each treated municipality is compared with untreated areas that are similar in the covariates. OLS estimators do not do so, but extrapolate linearly across the covariate space. As an alternative, we also explore coarsened exact matching methods (Iacus et al., 2011) using pretreatment (2005) values of all demographic and socioeconomic controls to match all municipalities except those that introduced ‘food-only’ systems prior to 2006. Our preferred specification matches 27 municipalities (13 treated, 14 control), thus improving univariate and multivariate balance statistics. We then run our difference-in-difference regressions either on common support (matched municipalities only) or using matching weights. Because of the low number of observations, only controls that we have not already matched upon are included in these regressions. The results, presented in Table A.6 in the online appendix, are similar to OLS estimates, though larger in magnitude.15 Another point is that results in Table 3 may mask substantial treatment dynamics due to the often slow rate of implementation. In Table 4, we subdivide (binary) treatment into distinct periods by adding lags and leads, with these differential effects presented in chronological order. Each coefficient then captures the effect, if any, at that distance to adoption of ‘food-only’ waste systems. As discussed in section 3.1, this specification also checks for certain types of endogeneity: if significant effects are found prior to introduction, treatment estimates may be confounded e.g. with environmental awareness, for which we include only a proxy. There may likewise be reverse causality, with increased household efforts to sort packaging causing municipalities to adopt collection of food waste, rather than vice versa. Starting from the baseline specification with no controls included (column 1), we add indicator variables corresponding to two years before treatment, through the year of adoption, to four years after treatment, and finally a dummy for all observa-

15 Another concern is that since our data is sorted by municipality, results may disproportionately reflect impacts in low-population municipalities. We therefore also tried weighting the regressions in columns 1–6 by the 2006–2015 mean population in each municipality. Apart from column 6, where estimates are smaller and insignificant (though still positive), estimates in these regressions are again larger than in Table 3.


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C. Ek and J. Miliute-Plepiene / Journal of Environmental Economics and Management 89 (2018) 168–186 Table 4 Spillover effects on packaging waste, by distance to time of adoption.

Food only t

2

Food only t

1

Food only t0 Food only t

1

Food only t

2

Food only t

3

Food only t

4

Food only t

5 forward

Curbside collection

(1)

(2)

(3)

(4)

0.451 (0.739) 0.829 (1.300) 0.059 (1.245) 2.478 (1.175) 3.747 (1.190) 5.128 (1.399) 4.415 (1.882) 5.106 (2.194) 2.633 (3.701)

1.368 (0.943) 2.119 (1.065) 2.595 (1.424) 1.645 (1.512) 3.293 (1.541) 3.718 (1.631) 4.430 (1.934) 5.206 (2.623) 5.660 (3.771) 6.442 (3.667) 2844 (2455)

0.538 (0.678) 0.855 (1.188) 0.229 (1.160) 2.453 (1.175) 3.999 (1.129) 5.714 (1.258) 5.219 (1.705) 6.285 (1.964) 4.493 (3.514) 10.029 (3.749)

1.347 (0.947) 2.135 (1.067) 2.577 (1.417) 1.687 (1.507) 3.385 (1.532) 3.783 (1.628) 4.470 (1.942) 5.275 (2.644) 5.634 (3.757) 6.461 (3.672)

NO 2440 244 0.182

YES 1507 217 0.234

YES 2440 244 0.257

Weight-based fee Recycling stations/capita

Additional controls Observations Municipalities included R-squared (within)

YES 1507 217 0.231

Robust standard errors clustered at the municipal level. p 0.01, p 0.05, p 0.1. All regressions include municipality and year fixed effects. Additional controls are: population, population density, share of non-native citizens, mean age, share of multi-family housing, mean earned income, share of university graduates, unemployment rate, municipal government majority (left/right/broad coalition), and summed vote share for Green/Centre party. The variable ‘Food only, continuous’ gives the share of households that are (formally) participating in collection of food waste, excluding four-compartment bin systems. ‘Food only, binary’ is equal to one for all municipality-year pairs where the same type of system is in place. Dummy variables ‘Food only t 2 ’ through ‘Food only t 4 ’ are associated with the binary treatment variable and equal to one in only the corresponding year, while ‘Food only t 5 forward ’ is equal to one for all observations at least five years subsequent to introduction. Decimals are not reported for estimates and standard errors exceeding 1000.

tions five or more years after treatment. Spillovers are indeed seen to emerge gradually, consistent with slow implementation. We observe no significant pretreatment effects, suggesting that shifts in packaging waste are at least driven by factors which systematically coincide with food-waste collection. The effect peaks after the around the third post-adoption year, where it is estimated at about 5 kg/capita/year, or 13% of the data average.16 Post-treatment dynamics change little when we add the full set of control variables (column 2), but a significant negative effect emerges in the year prior to treatment. As in Table 3, it is useful to ask whether this is caused by differences in the regression sample or differences in omitted-variable bias. Thus, in column 3, we drop the “Recycling stations/capita” variable, making the sample identical to that of column 1. Results are very similar to the case of no controls. Moreover, in column 4, we rerun the same specification on the smaller sample used in column 2. This again produces significant pretreatment effects. A joint significance test of pairwise equality of the pretreatment dummies (or pretreatment and time-of-adoption dummies) across columns 2 and 4 fails to reject, suggesting that the pretreatment effect observed in column 2 is due to the restricted sample. Column 3, then, is our preferred regression, making use of the full sample and all but one control variable. Fig. 4 plots the treatment estimates (and associated 95% confidence intervals) given in this regression, depicting the evolution of collected packaging prior to, during, and after treatment, while controlling for municipality and year fixed effects as well as all municipal variables except the number of recycling stations/capita. In Online Appendix D, we repeat the above main regressions for each individual type of packaging waste (glass, paper, plastic, metal). This exercise can be framed as a robustness test: since households face similar conditions in recycling each packaging type, there is little reason to expect large differences across fractions. Treatment-effect point estimates for individual fractions are often insignificant, though overwhelmingly positive for our continuous and binary treatment variables. When we allow for treatment dynamics, estimates grow subsequent to adoption in a way which is broadly in line with Fig. 4. Overall, there is no

16 While post-treatment results are robust to adding at least up to four lead years, we then also find a significant negative pretreatment effect of roughly 2 kg/capita/year in the fourth year before adoption.


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Fig. 4. Estimated treatment effect over time. Note: The figure shows the estimated effect (controlling for municipal variables) of food-waste collection on collected packaging waste, for years before, during, and after introduction. Vertical lines denote 95% confidence intervals, i.e. 1.96 times the standard error of estimates. The raw data for the figure is taken from column 7 of Table 3.

strong indication that food-waste collection has affected different packaging types in qualitatively different ways. Finally, partly as a benchmark, we also consider the direct effect of ‘food-only’ systems on the source separation of food waste itself (Table 5). Columns 1 and 2 report results for the continuous treatment variable, with and without municipal controls, while columns 3 and 4 repeat the analysis using the treatment dummy.17 In all cases, we find a positive and significant treatment effect consistent with Fig. 2, indicating that food-waste collection in a given municipality does cause collected amounts of food waste to increase. Adjusting for participation rates, estimates are again broadly comparable across columns, implying a sizable effect of 10 kg/capita/year or more that is little changed when control variables are added. In column 5, we again subdivide (binary) treatment into distinct periods by adding lags and leads. Major effects appear only after introduction, and rise in a monotonic fashion over time.18 5. Checking for remaining bias As discussed in Section 3.1, the regressions in Table 3 do not control for all potential sources of bias. In particular, we may be concerned that the observed treatment effect is not a spillover, but is caused by concurrent shifts in drivers for (packaging) recycling that remain uncontrolled for. This section considers the impact of several such factors, namely (i) waste-bin size, (ii) frequency of waste collection, (iii) curbside collection of packaging from multi-family dwellings, and (iv) information campaigns directed at packaging waste. In each case we will argue that our results can plausibly be given a causal interpretation. 5.1. Bin size and frequency of collection We saw in Table 5 that ‘food-only’ systems cause major increases in the amount of food waste that is separated at source. If waste companies anticipate or experience an associated drop in the amount of unsorted residual waste, they may respond by lowering the default bin size or the frequency of waste collection, e.g. from once a week to once every two weeks. In turn, this could cause households to recycle more packaging waste (or more of any combination of food and packaging) simply to keep waste bins from overflowing. The estimated behavioral spillover from food-waste collection per se may therefore be spurious. Avfall Web includes municipal data on bin size and the number of waste collection runs per year, separated by singlefamily and multi-family housing. The data set includes 59–66% missing values, depending on the variable. It reports only the modal (most commonly used) bin size and collection frequency, which obviously does not capture the entire distribution of bin sizes or collection frequencies within a municipality. Furthermore, households are commonly free to choose bin size and collection frequency among a few alternatives, so these variables could well reflect voluntary changes in demand rather than some imposed default. Thus, they may represent an effect, rather than a cause, of increased recycling efforts. Above caveats notwithstanding, if treatment effects are in fact caused by systematic shifts in bin size or collection frequency, we would expect to observe this in the data. As a first step, in the Online Appendix (Table A.7) we regress each bin-size and

17 There are only 64 observations for which food-waste amounts are known but the number of recycling stations per capita is not. Results in Table 5 are robust to dropping the latter variable. 18 It may seem surprising that weight-based fees have a strong positive effect on packaging but a negative coefficient for food waste. However, the estimate in Table 5 is driven by variation in only three municipalities. Two of these have not adopted food-waste collection, and the third introduced food-waste collection and weight-based fees in the same year. In general, coefficients other than treatment effects should be interpreted with some caution in our regressions.


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C. Ek and J. Miliute-Plepiene / Journal of Environmental Economics and Management 89 (2018) 168–186 Table 5 The direct effect of ‘food-only’ systems on collected amounts of food waste.

Food only, continuous

(1)

(2)

0.232 (0.041)

0.214 (0.040)

Food only, binary Curbside collection

20.473 (5.936)

17.934 (5.316) 0.678 (1.505) 530.567 (4794)

NO 1073 214 0.226

YES 1032 209 0.242

Weight-based fee Recycling stations/capita Food only t

2

Food only t

1

(3)

(4)

10.536 (3.767) 17.820 (5.435)

11.767 (3.720) 15.208 (5.233) 2.058 (2.098) 1642 (5048)

NO 1131 218 0.112

YES 1067 213 0.158


1

Food only t

2

Food only t

3

Food only t

4

Food only t

5 forward

Additional controls Observations Municipalities included R-squared (within)

(5)

12.039 (3.451) 1.639 (2.030) 1939 (4972) 0.736 (2.836) 0.720 (3.233) 7.644 (6.793) 18.678 (5.836) 22.545 (5.948) 25.775 (6.456) 28.683 (6.766) 30.573 (7.309) YES 1067 213 0.231

Robust standard errors clustered at the municipal level. p 0.01, p 0.05, p 0.1. All regressions include municipality and year fixed effects. Additional controls are: population, population density, share of non-native citizens, mean age, share of multi-family housing, mean earned income, share of university graduates, unemployment rate, municipal government majority (left/right/broad coalition), and summed vote share for Green/Centre party. The variable ‘Food only, continuous’ gives the share of households that are (formally) participating in collection of food waste, excluding four-compartment bin systems. ‘Food only, binary’ is equal to one for all municipality-year pairs where the same type of system is in place. Dummy variables ‘Food only t 2 ’ through ‘Food only t 4 ’ are associated with the binary treatment variable and equal to one in only the corresponding year, while ‘Food onlyt 5 forward ’ is equal to one for all observations at least five years subsequent to introduction. Decimals are not reported for estimates and standard errors exceeding 1000.

collection-frequency variable on the treatment dummy and the full set of municipal controls. We find no significant indication that treatment causes a reduction in the modal bin size, but there is a marginally significant negative effect on modal collection frequencies, suggesting that waste is collected roughly 3 times fewer per year. This effect is modest, however, given that 96% of the observations in the data collect either 26 or 52 times a year. To check whether the observed treatment effect is mediated through modal collection frequency, Table 6 includes these factors as control variables in our main regression specifications. Our aim is to see whether treatment estimates change, so to make the comparison valid, we adjust for differences in the number of nonempty observations by running our main regressions on only those observations for which the new controls are available. We then perform two-sided Wald tests of the hypothesis that treatment effects are identical across models with and without the additional controls. Columns 1 and 2 compares outcomes for the continuous treatment variable, while columns 3 and 4 examines binary treatment status. Columns 5 and 6 account for the apparent pattern that treatment has little impact already in the year of introduction by considering a lagged treatment dummy which is equal to one whenever ‘food-only’ systems were introduced at least one year previously. The Wald tests indicate that estimates are significantly smaller when modal collection frequency is included, suggesting that the treatment effect arises partly due to endogenous shifts in the number of waste pickups per year. On the other hand, whenever a treatment estimate is positive and significant at the 5% level in the restricted model, it remains so in the unrestricted model.19

19 If the analysis is repeated with both bin-size variables included as well, we find point estimates and significance levels similar to those in Table 6, but none of the Wald tests reject the null of equal treatment coefficients.


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C. Ek and J. Miliute-Plepiene / Journal of Environmental Economics and Management 89 (2018) 168–186 Table 6 Spillover effects, controlling for bin size and frequency of collection.

Food only, continuous

(1)

(2)

0.037 (0.012)

0.034 (0.011)

Food only, binary

(3)

(4)

2.142 (1.195)

1.719 (1.128)

Food only, binary (lagged) Curbside collection Weight-based fee Recycling stations/capita

16.604 (6.358) 4.046 (3.195) 2399 (2661)

17.065 (6.427) 1.420 (3.394) 2488 (2595) 0.055 (0.115) 0.074 (0.027)

15.962 (6.626) 4.359 (3.337) 1967 (2567)

813 189 0.230

813 189 0.242

824 189 0.211

Pickups/year, single-family Pickups/year, multi-family Observations Municipalities included R-squared (within)

Two-sided cross-model Wald tests for equality of treatment-effect coefficients, p-values: Model (1) vs. (2) 0.071 Model (3) vs. (4) Model (5) vs. (6)

16.559 (6.716) 1.449 (3.631) 2111 (2510) 0.091 (0.099) 0.073 (0.025) 824 189 0.225

(5)

(6)

4.778 (1.034) 17.953 (6.675) 0.813 (3.399) 1511 (2514)

4.427 (1.023) 18.600 (6.847) 1.605 (3.570) 1657 (2476) 0.095 (0.103) 0.062 (0.025)

824 189 0.236

824 189 0.248

0.037 0.041

Robust standard errors clustered at the municipal level. p 0.01, p 0.05, p 0.1. All regressions include municipality fixed effects, year dummies, and additional controls (population, population density, share of non-native citizens, mean age, share of multi-family housing, mean earned income, share of university graduates, unemployment rate, municipal government majority, and summed vote share for Green/Centre party). The variable ‘Food only, continuous’ gives the share of households that are (formally) participating in collection of food waste, excluding four-compartment bin systems. ‘Food only, binary’ is equal to one for all municipality-year pairs where the same type of system is in place. ‘Food only, binary (lagged)’ is equal to one whenever the system was introduced at least one year previously. Decimals are not reported for estimates and standard errors exceeding 1000.

Also, because collection frequency is plausibly endogenous to treatment, it may be ‘bad control’ and thus itself a source of positive or negative bias (Angrist and Pischke, 2009). Consider some number Z z of waste pickups per year and suppose we wish to estimate the treatment effect conditional on Z z. Then the bad-control bias is the difference (conditional on fixed effects and exogenous municipal controls) in average untreated potential outcomes Y0i between those observations that would have Z z if treated (so Z 1i z), and those that would have Z z if untreated (Z 0i z). If, as our preliminary analysis suggested, treatment reduces collection frequencies, this bias is probably negative. To see why, note that municipality-year pairs with Z 1i z will then mostly have Z 0i z. If, as seems likely, higher potential collection frequency Z0i is associated with less recycling of packaging waste Y 0i, potential outcomes will be larger for observations with Z0i z than for those with Z 1i z (i.e. Z 0i z), so bad-control bias will be negative. This suggests that a positive and significant treatment effect remains even when conditioning on modal collection frequency. 5.2. Curbside collection in multi-family housing and information campaigns Estimates may also be non-causal if introduction of ‘food-only’ systems coincided with the adoption of curbside collection systems in multi-family housing, or with general-purpose information campaigns that target both food and packaging waste. Although no data is available for these variables, the institutional details of food-waste collection suggest an indirect test. With respect to curbside collection in multi-family dwellings, individual property owners are unable to organize independent collection of food waste. Thus, bias from curbside collection of packaging in multi-family housing will arise only if property owners adopt curbside systems at the same time as food-waste collection is being introduced at the municipal level. In reality, as we have noted, curbside collection is much more widespread in multi-family dwellings than in singlefamily homes, and crucially most major housing companies that operate such systems have done so since several years prior to food-waste collection. Hence, the risk of confounding food-waste collection with curbside collection is probably small. With respect to information campaigns, we expect these to target both food and packaging waste only in areas where both waste fractions are being collected by municipal authorities. This is true mostly in the presence of curbside collection of packag-


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(1) Food only, continuous Food only, continuous

I Large MF share

Food only, binary Food only, binary Food only t

2

Food only t

1

(2)

0.230 (1.055) 3.406 (1.512)

I Large MF share

5.370 (3.758) 10.213 (3.652)

5.690 (3.667) 10.318 (4.209)

0.827 (0.743) 1.166 (1.882) 0.329 (1.785) 2.565 (1.700) 2.806 (1.456) 4.830 (1.597) 2.468 (1.956) 3.609 (2.248) 2.689 (1.238) 3.685 (2.164) 0.004 (2.070) 0.387 (2.187) 1.920 (2.053) 1.376 (2.332) 4.514 (3.173) 4.248 (3.604) 4.328 (3.486) 9.939 (3.503)

2009 230 0.228

2440 244 0.242

2440 244 0.264


1

Food only t

2

Food only t

3

Food only t

4

Food only t

5 forward

Food only t

2

I Large MF share

Food only t

1

I Large MF share

Food only t0

I Large MF share

Food only t

1

I Large MF share

Food only t

2

I Large MF share

Food only t

3

I Large MF share

Food only t

4

I Large MF share

Food only t

5 forward

I Large MF share

Curbside collection Weight-based fee Observations Municipalities included R-squared (within)

(3)

0.021 (0.015) 0.039 (0.030)

Robust standard errors clustered at the municipal level. p 0.01, p 0.05, p 0.1. All regressions include municipality fixed effects, year dummies, and additional controls (population, population density, share of non-native citizens, mean age, share of multi-family housing, mean earned income, share of university graduates, unemployment rate, municipal government majority, and summed vote share for Green/Centre party). The variable ‘Food only, continuous’ gives the share of households that are (formally) participating in collection of food waste, excluding four-compartment bin systems. ‘Food only, binary’ is equal to one for all municipality-year pairs where the same type of system is in place. Dummy variables ‘Food only t 2 ’ through ‘Food only t 4 ’ are associated with the binary treatment variable and equal to one in only the corresponding year, while ‘Food only t 5 forward ’ is equal to one for all observations at least five years subsequent to introduction. I Large MF share is a binary function indicating whether a municipality has multi-family housing share above the between-municipality median.

ing, such as under four-compartment bin systems.20 Since our treatment effect is estimated separately from curbside collection of packaging from single-family houses, this again indicates that curbside collection from multi-family housing is the main potential cause of bias. In this case, however, prior presence of such systems may be sufficient, provided that the information

20 We cannot exclude the possibility that information campaigns specifically directed toward food waste (and coinciding with treatment) had spillover effects on efforts to sort packaging waste. Such effects might arise if, for instance, directed information campaigns also signal the general desirability of protecting the environment.


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campaigns themselves are timed to coincide with the introduction of food-waste collection. In checking for signs that either of the above concerns is valid, we start from the observation that any bias due to curbside collection in multi-family housing is likely to be heterogenous across municipalities. Specifically, if bias is an issue, treatment estimates should be smaller when derived from a sample consisting mostly of single-family homes than when estimated from a sample with mostly multi-family dwellings. Moreover, if no causal spillover exists and there are no other omitted variables of note, treatment effects in a sample of mostly single-family homes should be small and insignificant. We can thus perform an indirect test for both potential causes of bias by comparing treatment estimates between a ‘higheffect’ (biased) and a ‘low-effect’ (less biased) group. The former includes only areas with high multi-family housing shares, while the other includes only areas with low multi-family housing shares. As curbside collection of packaging from singlefamily houses is orthogonal to treatment, we do not exclude areas with such systems from either group. To identify which areas have a large (small) share of multi-family housing, we first calculate a set of within-municipality medians, and then interact our treatment variables with a dummy for whether a given area lies above the between-municipality median of these quantities (roughly 34.1%). Our main hypothesis is that this dummy is positive. Table 7 presents the analysis. To maximize sample size, we do not include the “Recycling stations/capita” variable. Interaction point estimates are positive in columns 1 and 2, suggesting that treatment effects are indeed larger in areas with a large share of multi-family dwellings. Also, in column 2 the uninteracted food-only variable is very small and insignificant. On the other hand, when lags and leads of binary treatment are included (and interacted) in column 3, the uninteracted variables imply treatment dynamics similar to those found in our baseline analysis. Moreover, in Online Appendix Table A.7, we isolate groups of municipalities with an even smaller average share of multifamily housing, i.e. we change the group threshold to the 40th and 20th percentiles rather than the median. If multi-family housing is driving bias, these regressions should yield relatively larger interacted coefficients, and smaller uninteracted ones, compared to Table 7. However, the opposite is true. Thus, we conclude that there is no consistent evidence that our estimated

Table 8 The effect of food-waste collection on paper and residual waste.

(1) Paper waste Food only, continuous

Weight-based fee Recycling stations/capita Food only t

2

Food only t

1

1

Food only t

2

Food only t

3

Food only t

4

Food only t

5 forward

Observations Municipalities included R-squared (within)

(4) Residual waste

(5) Residual waste

(6) Residual waste

0.374 (0.075)

11.715 (4.830) 1.331 (1.650) 285.216 (3182)

2.299 (1.771) 11.385 (4.927) 0.960 (1.714) 413.141 (3154)

1452 213 0.467

1507 217 0.468


(3) Paper waste

0.015 (0.017)

Food only, binary Curbside collection

(2) Paper waste

11.848 (4.447) 0.643 (1.734) 625.304 (3218) 0.164 (1.413) 1.190 (1.810) 4.104 (2.137) 6.318 (2.288) 6.417 (2.419) 5.483 (2.310) 7.634 (3.422) 8.965 (3.253) 1507 217 0.477

22.707 (10.702) 39.121 (4.731) 4026 (8974)

1030 209 0.193

25.626 (6.989) 21.683 (11.819) 35.124 (5.461) 5444 (9618)

1065 213 0.148

14.434 (8.117) 34.708 (6.393) 6083 (9301) 0.492 (6.401) 1.423 (8.072) 17.305 (13.460) 41.677 (13.055) 41.905 (12.577) 48.679 (13.531) 54.536 (14.426) 52.068 (15.938) 1065 213 0.200

Robust standard errors clustered at the municipal level. p 0.01, p 0.05, p 0.1. All regressions include municipality fixed effects, year dummies, and additional controls (population, population density, share of non-native citizens, mean age, share of multi-family housing, mean earned income, share of university graduates, unemployment rate, municipal government majority, and summed vote share for Green/Centre party). The variable ‘Food only, continuous’ gives the share of households that are (formally) participating in collection of food waste, excluding four-compartment bin systems. ‘Food only, binary’ is equal to one for all municipality-year pairs where the same type of system is in place. Dummy variables ‘Food only t 2 ’ through ‘Food only t 4 ’ are associated with the binary treatment variable and equal to one in only the corresponding year, while ‘Food only t 5 forward ’ is equal to one for all observations at least five years subsequent to introduction. Decimals are not reported for estimates and standard errors exceeding 1000.


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21 spillover effect is wholly driven by mechanisms related to curbside collection of packaging in multi-family dwellings.

6. Environmental benefits from spillovers A final concern is that the spillovers we find need not represent an environmental benefit, because increases in collected packaging waste may be driven by consumption of packaging as well as household recycling efforts. Suppose that, all else being equal, we observe that packaging waste shifts by P 0 and that residual waste changes by some R in response. If P R, some proportion of the additional recycled packaging waste must derive from an increase in packaging consumption. This may or may not imply an environmental gain, depending on the relative costs and benefits of waste recycling and incineration. On the other hand, if P R, we are assured that the change in packaging amounts collected is entirely driven by increased recycling efforts and can be counted as an environmental benefit. In the real world, of course, all else is not equal: for instance, we saw in section 4 that treatment has a substantial direct effect. While Avfall Web includes data on residual waste, this information does not allow us isolate a shift in residual waste caused specifically by the spillover effect. Nevertheless, given the set of recyclable waste fractions ix, if treatment causes both R, it seems relatively safe to assume that each effect x i involves an environmental benefit.22 x i 0 for all i and i xi In any case, we may conclude that ‘food-only’ systems as a whole produce environmental gains. For the test to be reliable, we will want to include all recyclable fractions. Besides food and packaging waste, we use data on paper waste from FTI. In columns 1–3 of Table 8, we regress this paper-waste variable on our usual treatment variables and controls. Although results are less than conclusive (we find no significant effects in columns 1 and 2), there is nothing to suggest that treatment causes a reduction in collected paper waste. Column 3 does indicate that, like food and packaging waste, collected newsprint waste increases gradually subsequent to treatment; this is suggestive of a second positive spillover effect from ‘food-only’ waste systems, on the order of as much as 15–20% of the data average. Columns 4–6 run the same regressions using data on residual waste from Avfall Web. Here we find a strong and highly significant negative effect which also grows in magnitude over time. The hypothesis is checked by performing a one-sided cross-model Wald test of the null hypothesis that the sum of treatment 23 coefficients in column 6 of Table 3, column 4 of Table 5, and columns 2 and 5 of Table 8 are greater than or equal to zero. The test reports p 0.008, so we reject the null hypothesis that i xi R and conclude that the spillover effect we identify is unlikely to be driven by an increase in household consumption of packaging.24 7. Concluding remarks This paper has attempted to identify behavioral spillover effects from an environmental policy intervention, namely the introduction of systems for collecting food waste in Swedish municipalities. We have done so by regressing, within a fixedeffects framework, collected amounts of packaging waste on variables representing food-waste collection. The regressions display a positive treatment effect which is generally not present prior to introduction of food-waste collection and which increases gradually up to roughly 10% of the population-average amount. Besides the spillover, we also find a large positive direct impact of treatment on the amount of food waste collected, indicating that the policy is working as intended. Given the assumption that these results can be accepted as prima facie evidence of a causal effect on packaging, they indicate that environmental policy can have substantial co-benefits in the form of positive behavioral spillovers. In principle, this has at least two implications for policy. The first is that analysts should be more open to estimating and including such effects in cost-benefit analyses, even if this makes the task of assessing impacts and efficiency more difficult. For example, the EU has adopted separate directives for different waste fractions (packaging, batteries, electronic waste, etc.); if behavioral spillovers exist across all of these fractions, it may be difficult for cost-benefit analyses to isolate the causal effect of a single directive, but ignoring spillovers would not make results more accurate. The second implication is that spillovers should be taken into account in policy design, including the choice of which behavior to target. In particular, it may be cost-efficient to target only a subset

21 We have run a number of variants of this test. First, we have used a split sample, i.e. run separate regression for each group; this yields point estimates very similar to those implied by Table 7. Second, results are also broadly similar if municipalities with curbside collection are dropped. Third, the same is true when including “Recycling stations/capita” in Table 7 (though evidence of bias is then slightly less clear); as in Section 4, any differences appear to be due to the different sample rather than inclusion of the variable per se. Finally, we have run a combined test for bias by adding the collection-frequency variables used in Section 5.1. Given that these variables are missing for most observations, uninteracted treatment coefficients are then estimated using at most a sixth of the full sample, depending on whether the high/low effect groups are defined based on the median or some lower percentile. Thus, results should be interpreted with a great deal of caution; however, the test does not provide compelling evidence against the conclusions of this paper. 22 In principle, these patterns could be compatible with a partly consumption-driven increase in packaging if the non-recyclable fraction of residual waste (on which we have no reliable data) decreases sufficiently to mask the increase in unrecycled packaging. We consider this unlikely. For example, an analysis of residual waste from Swedish households (Swedish Waste Management, 2011) showed that in the absence of food-waste collection, unsorted packaging and paper waste constitute 31–34% of residual waste, while non-recyclables make up 23–24%. When food waste is collected, these proportions are 36% and 32–39%, respectively, so the non-recyclable share of residual waste clearly increases in relation to packaging and newsprint waste. 23 When “Recycling stations/capita” is dropped from Table 8, a significant positive effect for paper waste (of almost 4 kg/capita/year) appears in column 2, but significant positive pretreatment trends also emerge in column 3. As discussed in Section 3.1, this suggests some degree of upward bias. Such bias would work in the direction of nonrejection for our test. 24 As an alternative to this test, one may simply sum all fraction variables (including residual waste) and regress this total waste amount on binary or continuous treatment. With fixed effects and all controls added, this yields significant negative treatment coefficients in both cases.


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of activities, so long as these produce positive behavioral spillovers with respect to other activities; this is potentially a partial explanation for the patchwork nature of waste-related policies across countries. Crucially however, economists are still a long way off from being able to consistently predict the sign and magnitude of spillovers with any degree of accuracy. The relative strength of various plausible mechanisms across different settings remains largely unknown, and unfortunately our data does not allow us to make major progress in this regard. In future research, carefully designed field experiments using household-level or individual-level data are likely to be useful tools for disentangling different mechanisms. Our data does paint a somewhat rosier picture than much of the literature on behavioral spillovers in moral contexts, where spillovers are most often found to be negative. We suspect that most lab studies omit real-world factors which may be crucial for driving synergies across prosocial activities. One is the signaling effect of policy, revealing to individuals that not only is the targeted activity more important than previously recognized, but (we might assume) so are e.g. all environmental or wasterelated activities. Another is the fact that most lab experiments study individual behaviors in anonymous settings. By contrast, waste behavior tends to be observable by (at least) other members of the household, and there is lab evidence to suggest that public behaviors are not subject to moral licensing (Greene and Low, 2014). Finally, waste sorting may also be special in that targeted and non-targeted activities (source separation of food and packaging waste, respectively) are performed in very close temporal and spatial proximity and also have similar motives. As a result, if and when a household begins source separating food waste, a broad review of recycling behavior may be triggered at the same time. This is in line with the psychological ‘habit discontinuity hypothesis’, in which an initial habitual behavior will be disrupted when the context within which it occurs changes (Wood et al., 2005). For the economist, it may be natural to think of this process in terms of technical complementarities across different recycling activities (Ek, 2017a). One example of such complementarities is finding it convenient to install indoor recycling bins for food and packaging at the same time. Another is that conscientious food-waste recycling requires emptying out food that has passed its expiration date, leaving an empty container and thus reducing the effort of also recycling packaging waste. Importantly, certain concerns do remain that our estimates are a product of upward omitted-variable bias. In particular, the data does not permit us to fully control for all shifts in drivers of household recycling of packaging waste. Tests for some plausible specific causes of endogeneity suggest that these factors may moderate spillovers to some degree, while being insufficient to fully explain the positive estimates found in our main regressions. In conclusion, while not all policies may lend themselves to careful empirical analysis as readily as food-waste collection, we believe that identification of spillovers is in some ways relatively straightforward: the very distance between targeted and non-targeted activities may mitigate some of the endogeneity issues inherent in traditional policy analysis. For example, the idea that policy decisions are more likely to consider direct outcomes than spillovers may strike empirical researchers as quite convenient. Caveats notwithstanding, then, it is hoped that at the very least this study demonstrates the feasibility of using data from natural experiments to analyze the effect of environmental policy on variables other than the one targeted. Appendix A. Supplementary data Supplementary data related to this article can be found at https://doi.org/10.1016/j.jeem.2018.01.004. References Angrist, J.D., Pischke, J., 2009. Mostly Harmless Econometrics: an Empiricist’s Companion. Princeton University Press, Princeton, NJ, USA. Bem, D.J., 1967. Self-perception: an alternative interpretation of cognitive dissonance phenomena. Psychol. Rev. 74 (3). Bertrand, M., Duflo, E., Mullainathan, S., 2004. How much should we trust differences-in-differences estimates? Q. J. Econ. 119 (1). Best, H., Kneip, T., 2011. The impact of attitudes and behavioral costs on environmental behavior: a natural experiment on household waste recycling. Soc. Sci. Res. 40 (3). Blanken, I., van de Ven, N., Zeelenberg, M., 2015. A meta-analytic review of moral licensing. Pers. Soc. Psychol. Bull. 41 (4). Bucciol, A., Montinari, N., Piovesan, M., 2015. Do not trash the incentive! Monetary incentives and waste sorting. Scand. J. Econ. 117 (4). Carlsson, F., Jaime Torres, M.M., Villegas, C.I., 2016. Spillover Effects from a Social Information Campaign. Environment for Development Discussion Paper No. 16–06. Dahlén, L., Vukicevic, S., Meijer, J., Lagerkvist, A., 2007. Comparison of different collection systems for sorted household waste in Sweden. Waste Manag. 27 (10). Dolan, P., Galizzi, M.M., 2015. Like ripples on a pond: behavioral spillovers and their implications for research and policy. J. Econ. Psychol. 47. Ek, C., 2017a. Prosocial Behavior and Policy Spillovers: a Multi-activity Approach. Department of Economics, Lund University. Working Paper 2015:26. Ek, C., 2017b. Some causes are more equal than Others? The effect of similarity on substitution in charitable giving. J. Econ. Behav. Organ. 136. Festinger, L., 1957. A Theory of Cognitive Dissonance. Stanford University Press. Fullerton, D., Kinnaman, T.C., 1995. Garbage, recycling, and illicit burning or dumping. J. Environ. Econ. Manag. 29 (1). Gneezy, A., Imas, A., Brown, A., Nelson, L.D., Norton, M.I., 2012. Paying to Be nice: consistency and costly prosocial behavior. Manag. Sci. 58 (1). Greene, M., Low, K., 2014. Public integrity, private hypocrisy, and the moral licensing effect. Soc. Behav. Pers. 42 (3). Hage, O., Söderholm, P., Berglund, C., 2009. Norms and economic motivation in household recycling: empirical evidence from Sweden. Resour. Conserv. Recycl. 53 (3). Iacus, S.M., King, G., Porro, G., 2011. Causal inference without balance checking: coarsened exact matching. Polit. Anal. 20 (1). Jacobsen, G.D., Kotchen, M.J., Vandenbergh, M.P., 2012. The behavioral response to voluntary provision of an environmental public good: evidence from residential electricity demand. Eur. Econ. Rev. 56 (5). Kaffine, D.T., 2014. Scrap prices, waste, and recycling policy. Land Econ. 90 (1). Kinnaman, T.C., 2016. Understanding the economics of waste: drivers, policies, and external costs. Int. Rev. Environ. Resour. Econ. 8, 3–4.


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186

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Mazar, N., Zhong, C., 2010. Do Green products make us better people? Psychol. Sci. 21 (4). Merritt, A.C., Effron, D.A., Monin, B., 2010. Moral self-licensing: when being good frees us to Be bad. Soc. Personal. Psychol. Compass 4 (5). Miafodzyeva, S., Brandt, N., 2013. Recycling behaviour among householders: synthesizing determinants via a meta-analysis. Waste Biomass Valoriz. 4 (2). Miliute, J., Plepys, A., 2009. Driving forces for high household waste recycling. Lessons from Sweden. Environ. Res. Eng. Manag. 1 (47). Miliute-Plepiene, J., Plepys, A., 2015. Does food sorting prevent and improve sorting of household waste? A case in Sweden. J. Clean. Prod. 101. OECD, 2017. Tackling Environmental Problems with the Help of Behavioural Insights. OECD Publishing, Paris, https://doi.org/10.1787/9789264273887-en. Palmer, K., Sigman, H., Walls, M., 1997. The cost of reducing municipal solid waste. J. Environ. Econ. Manag. 33 (2). Reinstein, D., 2012. Substitution Among Charitable Contributions: an Experimental Study. Unpublished manuscript. Sachdeva, S., Iliev, R., Medin, D.L., 2009. Sinning saints and saintly sinners. Psychol. Sci. 20 (4). Shogren, J.F., Parkhurst, G.M., Banerjee, P., 2010. Two cheers and a Qualm for behavioral environmental economics. Environ. Resour. Econ. 46 (2). Swedish Environmental Protection Agency, 2014. Avfall i Sverige 2012. Report 6619. Swedish Waste Management, 2011. Nationell Kartläggning av Plockanalyser av Hushållens Kärl- Och Säckavfall. Report U2011:04. Swedish Waste Management, 2016. Hushållsavfall i Siffror — Kommun- Och Länsstatistik 2015. Report 2016:33. Tiefenbeck, V., Staake, T., Roth, K., Sachs, O., 2013. For better or for worse? Empirical evidence of moral licensing in a behavioral energy conservation campaign. Energy Pol. 57. Wood, W., Tam, L., Guerrero Witt, M., 2005. Changing circumstances, disrupting habits. J. Pers. Soc. Psychol. 88 (6).


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