Policy Evaluation of Waste Pricing Programs Using Heterogeneous Causal Effect Estimation

Marica Valente

No 1980, Discussion Papers of DIW Berlin from DIW Berlin, German Institute for Economic Research

Abstract: Using machine learning methods in a quasi-experimental setting, I study the heterogeneous effects of introducing waste prices - unit prices on household unsorted waste disposal - on waste demands and social welfare. First, using a unique panel of Italian municipalities with large variation in prices and observables, I show that waste demands are nonlinear. - find evidence of constant elasticities at low prices, and increasing elasticities at high prices driven by income effects and waste habits before policy. Second, I estimate policy impacts on pollution and municipal management costs, and compute the overall social cost savings for each municipality. Social welfare effects are positive for all municipalities after three years of adoption, when waste prices cause significant waste avoidance.

Keywords: Waste pricing; causal effect heterogeneity; machine learning; welfare (search for similar items in EconPapers)
JEL-codes: C14 C21 C52 Q53 (search for similar items in EconPapers)
Pages: 53 p.
Date: 2021
New Economics Papers: this item is included in nep-big, nep-env, nep-eur and nep-his
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Citations: View citations in EconPapers (4)

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Related works:
Journal Article: Policy evaluation of waste pricing programs using heterogeneous causal effect estimation (2023)
Working Paper: Policy evaluation of waste pricing programs using heterogeneous causal effect estimation (2022)
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