The dynamic and persistent effects of tax increment financing as an example of place-based policy: evidence from Cook County, Illinois

ABSTRACT

Using a staggered difference-in-differences design, this paper analyses the effects of tax increment financing (TIF) in Cook County, Illinois. TIF is negatively correlated with the number of establishments in Chicago, likely due to unintended tax burdens and competition between TIF districts. In non-Chicago areas, TIF shows positive effects on employment for 6–20 years. Lastly, this study provides evidence of the persistent effects of TIF and its potential to move localities from low- to high-density economic activity, calling for a re-evaluation of the argument against subsidising economically lagging areas.

KEYWORDS:

• place-based policy
• tax increment financing
• economic development
• employment
• difference-in-differences

JEL:

• H25
• H71
• R58

ACKNOWLEDGEMENTS

A part of the analysis uses data created with the support of the University of Illinois Institute of Government and Public Affairs. The author thanks Joshua Drucker, Rachel Weber, Kazuya Kawamura, Andrew Hanson, Stacey Sutton and Dayoon Kim for valuable feedback and suggestions.

DISCLOSURE STATEMENT

No potential conflict of interest was reported by the author.

Notes

1. Chicago, which makes extensive use of TIF, reviews TIF districts annually and publishes the rules and conditions under which they can be terminated. However, in many other cases, termination decisions are made on a case-by-case basis, although the criteria outlined above apply similarly.

2. Lester (2014) and Yadavalli and Landers (2017) use a 50% threshold when they estimate the effects of TIF at the block group level. Given the relatively smaller size of the census blocks and the effects, I use the smaller thresholds of 30%.

3. When applying the 30% threshold to define the treated group, years 1990–93 exhibit no associated blocks, and so are excluded from the analysis. Additionally, the associated blocks for 1994 and 1995 in Chicago are smaller than 30 (26 and 8, respectively) and, hence, they are also excluded from the analysis. I also estimate the effects TIF with all available years and the results are similar and are not changed substantially. These results are available from the author upon request.

4. Standard errors clustered at the census block group and place level are also tested. The findings are largely unaffected with identical coefficients and slight decreases in significance.

5. I evaluate the spillover effects on the contiguous blocks around the TIF districts versus other blocks that are not treated (see Figures B1 and B2 in Appendix B in the supplemental data online). I find weak evidence of the spillover effects on contiguous areas to TIF districts (see panels A and C in Figure B1 in Appendix B online).

6. This is perhaps due to the offsetting positive effects from the non-Chicago area, as shown in section 6.3.

7. I recognise that possible selection bias where places with lower tax rates could have been deliberately designated as TIF districts to attract firms. Also, due to the unavailability of census data at the tax codes level, the regression only measures the association between variables, rather than establishing a causal relationship.

8. The local taxing bodies include, for instance, the Chicago Park District, the Metropolitan Water Reclamation District of Greater Chicago, the Board of Education and the Chicago of Chicago. For a more detailed description on tax codes in Illinois, see Drucker et al. (2020).

9. The termination years included are 2006, 2009, 2010, 2011 and 2014.

10. A concern that expired TIF districts and active TIF districts may be significantly different can be arisen. In particular, cases where TIF have been terminated early due to inactivity may be considered different from active TIF districts. However, municipalities typically decide at city council meetings whether to continue a TIF if there is no redevelopment activity a few or several years after its creation, as discussed in section 2. Therefore, this study, which considers only TIF that lasted 15 years or more as expired TIF, is unlikely to include cases that were repealed early due to a lack of activity. Moreover, the results measured with TIF that lasted 23 years or more (see Figures 13 and 14 in Appendix C in the supplemental data online) and the finding that the pre-trend is not different between active and expired TIF (Figures 8 and 9) provide corroborative evidence.

11. For instance, to determine the coefficients for year 0, I compute the change in the outcome variable between 2006 and 2005 for TIF districts at work in 2006, the change in the outcome variable between 2007 and 2006 for TIF districts at work in 2007, the change in the outcome variable of the then-active TIF districts between 2008 and 2007, and so forth, as the control group. The same computation method is applied to the treatment group to calculate differences between the control group and treatment group. I then compute the average effect of treatment across treatment time groups.

12. Some results in Figures 8 and 9 have relatively larger confidence intervals in the later years and thus they are less informative. This is partly due to the rarity of observations five years after TIF termination compared with the earlier period.

Additional information

Funding

This research was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea [grant number NRF-2022S1A3A2A01089625].