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

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States regularly assign agents to teams and postings, yet rarely with the explicit goal of boosting public sector productivity. This paper asks whether a low-capacity state can improve performance through the optimal assignment of bureaucrats. We study the two-stage random assignment of property tax collectors (i) into teams, and (ii) 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. We provide suggestive evidence that the mechanism involves greater skill transmission between high-ability collectors. Implementing the optimal assignment would result in a 36% increase in tax compliance. By contrast, the government would need to replace 62% of lower-ability collectors with high-ability collectors to achieve a similar increase.