Incentive Mechanisms and the Allocation of Local Government Attention: A Fuzzy-Set Qualitative Comparative Analysis of 36 Townships in China

In governance systems that face multiple tasks and limited administrative capacity, the allocation of governmental attention is central to policy implementation. Existing studies emphasize institutional constraints and resource scarcity. However, the influence of incentive mechanisms on attention allocation remains insufficiently explored. This study examines how different configurations of incentive mechanisms shape governmental attention. It uses data from veterans affairs in 36 townships in District X between 2020 and 2025. Drawing on official documents, performance rankings and in-depth interviews, the study applies fuzzy set qualitative comparative analysis (fsQCA) to identify patterns of attention formation. The analysis identifies three configurations of incentive mechanisms that generate high levels of attention concentration. A competition-driven configuration combines value incentives, ranking incentives and performance incentives. An honor compensatory configuration connects honorary recognition with value incentives. This configuration continues to function effectively even when promotion incentives or performance incentives are not available. A configuration that combines honorary recognition, promotion opportunities, value incentives and performance incentives forms the most stable pattern of attention concentration. This combination strengthens officials’ motivation in several dimensions and ensures consistent behavioral responses across different contexts. These results show that governmental attention arises from combinations of incentive mechanisms instead of single instruments. This study examines incentive mechanisms that influence governmental attention allocation. It develops a configurational explanation that integrates attention at the organizational level and at the individual level. The study further proposes a design approach for incentive structures that can improve the efficiency of attention incentives and support effective policy implementation.