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Does tax increment financing pass the “but-for” test in Missouri? (El-Khattabi & Lester, 2019)

Review Guidelines

Absence of conflict of interest.  

Citation

El-Khattabi, A. R., & Lester, T. W. (2019). Does tax increment financing pass the “but-for” test in Missouri? Economic Development Quarterly, 33(3), 187-202. [St. Louis]

Highlights

  • The study’s objective was to examine the impact of tax increment financing (TIF) on employment in St. Louis, Missouri. The authors investigated similar research questions for Kansas City, Missouri, the profile of which can be found here.

  • The study used a difference-in-difference design to compare total employment over time in areas that received a TIF versus those that did not. The study used data from the U.S. Census and the National Establishment Time Series, a national census of businesses. 

  • The study suggested that there was a negative relationship between TIF designation and total employment in St. Louis. 

  • The quality of causal evidence presented in this report is low because the authors did not ensure that the groups being compared were similar before the intervention. This means we cannot be confident that any estimated effects are attributable to the TIF designation; other factors are likely to have contributed. 

Intervention Examined

Tax increment financing (TIF)

Features of the Intervention

Tax Increment Financing (TIF) is a public incentive tool meant to encourage private investment in a specific locale. Local governments freeze assessed property values in each area at their current level, subsidizing redevelopment to occur in that area. TIFs are designed to promote job creation and business growth, but they also tie up property tax revenue for long periods of time, diverting resources from other public services. 

This study examined the use of Missouri’s TIF statute which allows up to half of economic activity taxes to be diverted to a TIF. This study profile focuses on TIFs implemented in St. Louis, where TIFs are assigned at the property level. 

Features of the Study

The study uses a difference-in-difference design to compare employment over time in census block groups that received a TIF versus those that did not. Block groups containing one or more TIF properties were included in the treatment group. Out of 1,054 block groups in the city and county of St. Louis, 92 had a TIF property at some point during the study period (1990 – 2012). Block groups were considered to be treated starting in the first full calendar year after TIF designation. The comparison group is 962 non-TIF block groups. Propensity score weighting was used to ensure block groups were similar based on observable pretreatment characteristics from the 1990 U.S. Census. After weighting, block groups in the two conditions were statistically similar, having a poverty rate of about 28 percent, a four percent unemployment rate, and an average of 32 percent of residents identifying as African American.  

The study uses data from the U.S. Census and the National Establishment Time Series (NETS), a national census of businesses. NETS contains detailed geographic information on each U.S. business’s location and number of employees. The analysis focuses on Census block groups because these are the smallest geographic unit for which the sociodemographic data needed to select the comparison areas are available and because TIF projects are intended to incentivize development very locally, not at a broader geographic scale. Addresses for TIF properties came from a report by Better Together.  

The authors used a difference-in-difference model to compare before-and-after total employment data for block groups that did and did not receive a TIF. The statistical model included fixed effects for year, block group, and establishment/business so that the main estimates were identified solely by changes within a given block group over time. 

Findings

Employment

  • The study suggested that, in St. Louis, there was a negative relationship between TIF designation and total employment. 

Considerations for Interpreting the Findings

The authors accounted for several potential preexisting differences between the two groups, including unemployment rates in each area prior to the intervention and some demographic characteristics of the block groups. However, the authors did not control for potential differences in the average age or gender of individuals in each block group. Therefore, preexisting differences between the groups—and not the TIF designation— could explain any observed differences in outcomes. 

Causal Evidence Rating

The quality of causal evidence presented in this report is low because the authors did not ensure that the groups being compared were similar before the intervention. This means we cannot be confident that any estimated effects are attributable to the TIF designation; other factors are likely to have contributed. 

Reviewed by CLEAR

January 2023