全球智库信息集成服务系统(G2TT): DP16771 OPTIMAL ASSIGNMENT OF BUREAUCRATS: EVIDENCE FROM RANDOMLY ASSIGNED TAX COLLECTORS IN THE DRC

G2TT

来源类型	Discussion paper
规范类型	论文
来源ID	DP16771
	DP16771 OPTIMAL ASSIGNMENT OF BUREAUCRATS: EVIDENCE FROM RANDOMLY ASSIGNED TAX COLLECTORS IN THE DRC
	Augustin Bergeron; Pedro Bessone Tepedino; John Kabeya Kabeya; Gabriel Tourek; Jonathan Weigel
发表日期	2021-12-04
出版年	2021
语种	英语
摘要	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 (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. 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 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%.
主题	Development Economics ; Public Economics
关键词	Bureaucracy Taxation Assortative matching
URL	https://cepr.org/publications/dp16771
来源智库	Centre for Economic Policy Research (United Kingdom)
资源类型	智库出版物
条目标识符	http://119.78.100.153/handle/2XGU8XDN/545706
推荐引用方式 GB/T 7714	Augustin Bergeron,Pedro Bessone Tepedino,John Kabeya Kabeya,et al. DP16771 OPTIMAL ASSIGNMENT OF BUREAUCRATS: EVIDENCE FROM RANDOMLY ASSIGNED TAX COLLECTORS IN THE DRC. 2021.