‍

"Optimal Assignment of Bureaucrats: Evidence DRC", with Jonathan Weigel, University of California

‍

The assignment of workers to tasks and teams is a key margin of firm productivity and apotential source of state effectiveness. This paper investigates whether a low-capacity statecan increase its tax revenue through the optimal assignment of its tax collectors. We study thetwo-stage random assignment of property tax collectors (i) into teams and (ii) to neighborhoodsin a large Congolese city. The optimal assignment involves positive assortative matching onboth dimensions: high (low) ability collectors should be paired together, and high (low) abilityteams should be paired with high (low) payment propensity households. Positive assortativematching stems from complementarities in collector-to-collector and collector-to-householdmatch types. We provide evidence that these complementarities reflect high-ability collectorsexerting greater effort when matched with other high-ability collectors. Implementing theoptimal assignment would increase tax compliance by an estimated 36% relative to the statusquo (random) assignment. By contrast, the government would need to replace 62% of lowability collectors with high-ability ones or increase collectors’ performance wages by 69% toachieve a similar increase under the status quo assignment.

‍

“NOVAFRICA Sustainable Development Talks” Podcast

Listen here to the “NOVAFRICA Sustainable Development Talks” Podcast. NOVAFRICA Sustainable Development Talks

These series of audio talks about development in Africa bring together experts from a wide range of institutions. Alex Armand, resident member of NOVAFRICA and Assistant Professor at Nova SBE, is the coordinator of this Podcast.

Join us!

‍