Policy evaluation of waste pricing programs using heterogeneous causal effect estimation - Marica Valente (Deutsches Institut für Wirtschaftsforschung (DIW), 2021)
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: C14; C21; C52; Q53; 330; Waste pricing; Causal effect heterogeneity; Machine learning; Welfare