Abstract
This paper aims at identifying the causal effect of reducing behavioral costs of participation in household waste recycling through curbside collection. Using propensity score matching and differences-in-differences estimation with individual-level panel data we estimate the effect of curbside collection, its variation between types of recyclables and sociodemographic background variables, and its elasticity with regard to the distance to collection containers in the bring scheme condition. We argue that in a quasi-experimental setting DD may be systematically upward biased due to the outcome variable being self-reported while DDD may be systematically downward biased in the presence of spillover effects. Accordingly, both estimators can be combined to derive upper and lower bounds of the true effect. We find that a curbside scheme has no effect on paper recycling but increases recycling participation by between 10 and 25% points for plastic and packaging. Moreover, we find systematic treatment effect heterogeneity with regard to pre-treatment distance to collection sites and individual environmental attitudes, but not by socio-demography. The results of our analysis therefore have important implications for effective and cost-efficient implementation of environmental protection policies in urban areas.
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Notes
Strictly speaking, self-selection or policy endogeneity are only an issue for the control group if one is interested in estimating an average treatment effect on the treated (ATT). In empirical applications, however, the analytical distinction between heterogeneity—and particularly selection on the pre-treatment trend—and endogeneity may be of little relevance. This is because available control variables (like, e.g., socioeconomic status) can be seen as (proxy-)measures for antecedent conditions as well as expected costs or benefits of treatment (Gangl 2010). In effect, by successfully incorporating all relevant variables the ATT will also converge to the average treatment effect (ATE).
While the notion of misreported recycling behavior perceived as "socially desired" is a well-known argument against survey data, particularly among proponents of objective aggregate level data (e.g. Kuo and Perrings 2010), we are not aware of any study in the context of recycling, which has explicitly addressed this issue. Note that a general social desirability bias in the answering behavior of respondents does not create a problem when using differences-in-differences. If some people tend to always over-rate recycling participation it would only affect ξ [c.f. Eq. (3c) below], which is allowed to correlate with treatment status.
The curbside scheme was not used for the collection of glass. Rather, recyclable glass continued to have to be brought to drop-off containers by participants.
Official aggregate-level administrative data back the validity of our self-reported measure: Between 2005 (i.e. the pre-treatment year) and 2007 (by which curbside collection was implemented city-wide) recycling output in Cologne increased substantially while at the same time residual output decreased, resulting in a 3% drop in total waste output (for details, see Table 5 in the “Appendix”).
Presented results are based on the Epanechnikov Kernel and a bandwidth of 0.06. We tested different varieties of propensity score matching (different bandwidth with the Epanechnikov Kernel (0.06 ± 0.04), different kernel types (gaussian, uniform, biweight), nearest neighbor matching, logit selection model instead of probit), and the results are robust against changes of these specifications.
Note that standard errors will usually be larger when relying on reported instead of actual behavior due to random measurement error, leading to reduced statistical power. However, this qualification applies to survey research in general and we see no reason why there should be particular noise in the case of an everyday behavior like waste disposal.
For glass, distance to container does not capture a reduction but an increase in behavioral cost. In the presence of substantial spillover effects, we would thus expect the treatment effect to be greater in the low distance condition. For over-reporting, distance to container should be irrelevant.
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Acknowledgements
This work was supported by the Fritz-Thyssen-Foundation, Cologne, Germany. We thank Tobias Rüttenauer, Susumu Shikano, Martin Spindler, several anonymous reviewers and the editor for helpful comments.
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Appendix
Appendix
See Fig. 2 and Tables 4, 5, 6, 7 and 8.
Common support and distribution of propensity scores
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Best, H., Kneip, T. Assessing the Causal Effect of Curbside Collection on Recycling Behavior in a Non-randomized Experiment with Self-reported Outcome. Environ Resource Econ 72, 1203–1223 (2019). https://doi.org/10.1007/s10640-018-0244-x
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DOI: https://doi.org/10.1007/s10640-018-0244-x