Policy evaluation of waste pricing programs using heterogeneous causal effect estimation

Arbeitspapier

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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: DIW Discussion Papers ; No. 1980

Classification and Topics

• Classification: Wirtschaft ; Semiparametric and Nonparametric Methods: General ; Single Equation Models; Single Variables: Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions ; Model Evaluation, Validation, and Selection ; Air Pollution; Water Pollution; Noise; Hazardous Waste; Solid Waste; Recycling
• Subject: Waste pricing ; Causal effect heterogeneity ; Machine learning ; Welfare

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Valente, Marica
• Event: Veröffentlichung
• (who): Deutsches Institut für Wirtschaftsforschung (DIW)
• (where): Berlin
• (when): 2021

Further information

• Handle: http://hdl.handle.net/10419/248483
• Last update: 10.03.2025, 11:41 AM CET