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经济发展政策中规则与自由裁量的结合:加州竞争税收抵免影响的证据

Combining rules and discretion in economic development policy: Evidence on the impacts of the California Competes Tax Credit
Journal of Public Economics · 2022 · Matthew Freedman、Shantanu Khanna、David Neumark

中文摘要

我们评估了新一代经济发展项目之一——加州竞争税收抵免(CCTC)——对当地就业创造的影响。CCTC吸纳了以往举措中被认为是最佳实践的做法,将明确的资格门槛与项目官员在选择税收抵免获得者时的一定自由裁量权相结合。该项目的结构与实施有助于开展严格评估。我们利用了CCTC获批及被拒申请者的详细数据,包括依据项目目标对申请者进行评分的信息和资助决定信息,并结合了反映当地经济状况的美国社区调查(ACS)受限访问数据。采用双重差分方法,我们发现,人口普查区内每个受CCTC激励的就业岗位都会使在该区工作的人数增加近3人,体现出显著的当地乘数效应。授予非制造业的税收抵免所产生的当地乘数大于授予制造业的税收抵免。CCTC授予的税收抵免增加了各社会经济群体劳动者的就业,也增加了条件较好和条件较差社区中的就业,但对受影响社区居民的影响有限。我们使用另一套数据集以及近期开发的双重差分方法,验证了实证策略并确认了核心结果;这些方法能够校正处理时点差异和处理效应异质性可能产生的偏误。

Abstract

We evaluate the effects of one of a new generation of economic development programs, the California Competes Tax Credit (CCTC), on local job creation. Incorporating perceived best practices from previous initiatives, the CCTC combines explicit eligibility thresholds with some discretion on the part of program officials to select tax credit recipients. The structure and implementation of the program facilitates rigorous evaluation. We exploit detailed data on accepted and rejected applicants to the CCTC, including information on the scoring of applicants with regard to program goals as well as on funding decisions, together with restricted-access American Community Survey (ACS) data on local economic conditions. Using a difference-in-differences approach, we find that each CCTC-incentivized job in a census tract increases the number of individuals working in that tract by close to 3 – a significant local multiplier. Local multipliers are larger for non-manufacturing awards than for manufacturing awards. CCTC awards increase employment among workers across socioeconomic groups and in more- as well as less-advantaged neighborhoods, but have limited impact on residents of affected communities. We validate our empirical strategy and confirm our core results using an alternative dataset and recently developed difference-in-differences methods that correct for potential biases generated by variation in treatment timing and treatment effect heterogeneity.
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