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 by optimally assigning 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. According to our estimates, implementing the optimal assignment would increase tax compliance by 2.94 percentage points and revenue by 26% relative to the status quo (random) assignment. Alternative policies, such as replacing low-ability collectors with new ones of average ability or increasing collectors’ performance wages, are likely incapable of achieving a similar revenue increase.
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