Optimal Assignment of Bureaucrats: Evidence from Randomly Assigned Tax Collectors in the DRC

Abstract

The assignment of workers to tasks and teams is a key margin of firm productivity and a potential source of state effectiveness. This paper investigates whether a low-capacity state can increase its tax revenue through the optimal assignment of its tax collectors. We study the two-stage random assignment of property tax collectors into teams and to neighborhoods in a large Congolese city. The optimal assignment involves positive assortative matching on both dimensions: high (low) ability collectors should be paired together, and high (low) ability teams should be paired with high (low) payment propensity households. Positive assortative matching stems from complementarities in collector-to-collector and collector-to-household match types. We provide evidence that these complementarities reflect in part high-ability collectors exerting greater effort when matched with other high-ability collectors. Implementing the optimal assignment would increase tax compliance by an estimated 2.94 percentage points (37%) relative to the status quo (random) assignment. By contrast, to achieve a similar increase under the status quo assignment, the government would have to replace 63% of low-ability collectors with high-ability ones or to increase collectors’ performance wages by 69%.