Combining rules and discretion in economic development policy: Evidence on the impacts of the California Competes Tax Credit q

• 成果类型: Article
• 署名作者: Freedman, Matthew; Khanna, Shantanu; Neumark, David
• 署名单位: University of California System; University of California Irvine; Northeastern University; National Bureau of Economic Research; University of California System; University of California Irvine; IZA Institute Labor Economics
• 刊物名称: JOURNAL OF PUBLIC ECONOMICS
• ISSN/ISSBN: 0047-2727
• DOI: 10.1016/j.jpubeco.2022.104777
• 发表日期: 2023
• 关键词: Economic development; Business incentives; tax credits; Hiring incentives; Place -based policies

摘要

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 rig-orous 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.(c) 2022 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http:// creativecommons.org/licenses/by/4.0/).