Policy evaluation of waste pricing programs using heterogeneous causal effect estimation

TL;DR

This paper uses machine learning to evaluate how waste pricing policies affect household waste disposal, costs, and pollution, revealing nonlinear demand responses and long-term benefits in cost reduction and pollution control.

Contribution

It introduces a novel application of heterogeneous causal effect estimation to assess waste pricing impacts using a rich panel dataset of Italian municipalities.

Findings

Waste demand is nonlinear with respect to prices.

Elasticities are constant at low prices and increase at high prices.

Waste policies reduce costs and pollution after three years.

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, municipal costs and pollution. Using a unique panel of Italian municipalities with large variation in prices and observables, I show that waste demands are nonlinear. I find evidence of constant elasticities at low prices, and increasing elasticities at high prices driven by income effects and waste habits before policy. The policy reduces waste management costs and pollution in all municipalities after three years of adoption, when prices cause significant waste avoidance.