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UNITED STATES DEPARTMENT OF LABOR

Do targeted business subsidies improve income and reduce poverty? A synthetic control approach (Bundrick & Yuan 2019)

Review Guidelines

Causal Evidence Guidelines

Absence of conflict of interest.

Citation

Bundrick, J., & Yuan, W. (2019). Do targeted business subsidies improve income and reduce poverty? A synthetic control approach. Economic Development Quarterly, 33(4), 351–375. https://doi.org/10.1177/0891242419875502

Highlights

  • The study’s objective was to examine the impact of the Quick Action Closing Fund (QACF) on per capita personal income.  

  • The authors used a nonexperimental comparison-group analysis to estimate the effects of the QACF. They obtained per capita personal income data from the Bureau of Economic Analysis’ Local Area Personal Income and Employment database and QACF data from annual QACF reports. The authors used a statistical model to compare the outcomes of treatment and comparison group members. 

  • The study found a statistically significant relationship between the QACF and personal income in the year the funds were awarded. However, the findings were not significant in any of the five years that followed.    

  • This study receives a low evidence rating. This means we are not confident that the estimated effects are attributable to the QACF; other factors are likely to have contributed. 

Features of the Intervention

The QACF was signed into law by the Arkansas State Legislature in 2007. Through the QACF, Arkansas can provide cash grants to quickly bring new businesses to Arkansas from other states or to retain existing businesses in Arkansas. The QACF is similar to targeted economic development incentive programs that are commonly referred to as deal-closing funds. The QACF is financed through excess state revenues from state income taxes and sales and use taxes. At the end of fiscal year 2018, QACF accumulated $186 million. 

As of 2016, the state of Arkansas provided 85 companies in 25 of 27 counties with more than $117 million in QACF funds to support them in creating or retaining jobs. The QACF funds were awarded to businesses, local governments, and public institutions, which used them for different purposes such as to finance the purchase of manufacturing equipment, subsidies to long-standing businesses, and road and rail development.  

Features of the Study

The authors used a nonexperimental comparison-group analysis to estimate the effects of the QACF. Counties within the state of Arkansas with QACF projects (intervention group) were compared to counties from Arkansas and neighboring states (Louisiana, Mississippi, Missouri, Oklahoma, Tennessee, and Texas) without QACF or similar projects (comparison group).  

The authors used a statistical model to compare the outcomes of treatment and comparison group members using data that covered the years 2007 to 2016. The authors included two data sources. Per capita personal income data came from the Bureau of Economic Analysis’ Local Area Personal Income and Employment database. The QACF data were prepared annually by the Arkansas Economic Development Commission, as required by the Arkansas Act 510 of 2007.  

Study Sites

The study sites included 13 of Arkansas’ 27 counties.  

Findings

  • Earnings. The study found a positive, statistically significant relationship between the QACF and personal income. The results suggest that per capita personal income was $898 greater in counties with a QACF compared to counties without one in the year the QACF funds were first awarded.  

  • Earnings. The study found no statistically significant relationships between the QACF and per capita personal income in any of the five years after the year funding was awarded.    

Considerations for Interpreting the Findings

  • The authors did not account for preexisting differences between the groups before program participation. These preexisting differences between the groups—and not the QACF—could explain the observed differences in outcomes.  

  • The authors estimated multiple related impacts on outcomes related to earnings. Performing multiple statistical tests on related outcomes makes it more likely that some impacts will be found statistically significant purely by chance and not because they reflect program effectiveness. The authors did not perform statistical adjustments to account for the multiple tests, so the number of statistically significant findings in this domain are likely to be overstated. 

Causal Evidence Rating

The quality of causal evidence presented in this report is low. This means we are not confident that the estimated effects are attributable to the QACF; other factors are likely to have contributed.  

Reviewed by CLEAR

May 2021