Voluntary recycling despite disincentives

Abstract

This paper assesses the determinants of take-up of a voluntary waste separation scheme, in a scenario where residents sorted, stored and paid for collection of recycling waste even though mixed waste was collected at the kerbside more conveniently, free of charge and without any quantity limits. Uptake of the scheme was positive, persistent and diverse across localities, offering an opportunity to assess the factors determining voluntary participation in the presence of disincentives. We employ a unique panel data-set (n = 4,644) from Malta, including data on recyclable waste kilogrammes collected over the first 86 weeks of the scheme's operation. Drawing on insights from environmental economics and psychology, a model is empirically estimated. Results indicate that uptake is suppressed by the initial constraints households may face and stimulated by collection frequency. Political vote is an important determinant of participation and this interacts with scheme promotion to create diverse uptake rates.

Keywords:

• voluntary
• recycling
• vote
• policy
• regional

Acknowledgements

This work was supported by a Scholarship awarded by the University of Malta to Marie Briguglio. The University of Malta had no involvement in the study design, nor in the collection, analysis, and interpretation of data, in the writing of the report, or in the decision to submit the article for publication.

Disclosure statement

No potential conflict of interest was reported by the authors.

Supplemental data

A theoretical appendix which fleshes out the conceptual model in this article can be accessed here (online supplemental data).

Notes

1. In Maltese, the word ‘Tlieta’ means both ‘Tuesday’ and ‘three,’ referring to the number of waste streams that could be mixed in the grey bag for recycling.

2. In weeks 41, 48, 71, and 84, Tuesday coincided with a national holiday. In the few localities which collected on other days, public holidays were likewise accounted for.

3. The random effects estimator makes the assumption that differences across units are orthogonal to the regressors. The Hausman test examines this by comparing the results of the random effects estimator with those of the fixed effects estimator which does not make this assumption.

4. Random effects estimation makes the assumption that errors have a constant variance. Estimating robust standard errors returns the same coefficients but does not rely on the assumption of identical distribution of errors and independence within clusters.

5. Using the overall r² as a measure of goodness-of-fit involved considering both ‘within’ and ‘between’ variation.

6. The fact that VOTE is not statistically significant and that PROMOTION has negative sign may suggest that the variables are correlated, leading to biased estimate on PRXVOTE. However, the variables show very low levels of correlation (Pearson correlation coefficient < 0.054). Indeed regressing the variable PROMOTION on any of the initial conditions in the model produces no significant outcomes. PROMOTION can be partly predicted by week into the scheme.

7. In the case of the model estimated with time dummies, we are constrained to omit the variables that capture price effects (FREEBAG and MSWPRICE) due to multicollinearity.