Using Divide-and-Conquer to Improve Tax Collection: Evidence from the Field (joint with Sylvain Chassang and Sam Kapon)
In the context of collecting property taxes from 13,432 households in a district of Lima (Peru), we investigate whether prioritized enforcement can improve the effective use of limited enforcement capacity. We randomly assign households to two treatment arms: one replicating the city’s usual collection policy, and one implementing a prioritized enforcement rule in which households are ordered according to a suitable rank and sequentially issued clear short-term promises of collection should they fail to make minimum tax payments. Prioritized enforcement improves the efficiency of tax collection: over 5 months, it increases tax revenue by 9.4% while also decreasing the number of collection actions taken. We also identify an important friction ignored by existing theory: tax-payers’ response to incentives is slow, which changes the effective management of collection promises. We estimate a model of tax-payer behavior and use it to produce counterfactual treatment estimates for other collection policies of interest. Combining prioritized enforcement and a greater use of cheap legal writs yields an estimated 17% increase in tax revenue.