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Targeted business incentives and the debt behavior of households

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Abstract

The empirical effects of place-based tax incentive schemes designed to aid low-income communities are unclear. While a growing number of studies find beneficial effects on employment, there is little investigation into other behaviors of households affected by such programs. We analyze the impact of the Texas Enterprise Zone Program on household debt and delinquency. Specifically, we utilize detailed information on all household liabilities, delinquencies, and credit scores from the Federal Reserve Bank of New York Consumer Credit Panel/Equifax, a quarterly longitudinal 5% random sample of all individuals in the USA with a social security number and a credit report. We identify the causal effect of the program by using a sharp regression discontinuity approach that exploits the known institutional rules of the program. We find a modest positive impact on the repayment of retail loans, but also evidence of an increase in the delinquency rates of auto loans and in Chapter 13 bankruptcy filings.

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Notes

  1. See Unemployment Rates for Metropolitan Areas, Bureau of Labor Statistics, http://www.bls.gov/web/metro/laummtrk.htm.

  2. See Labor Force Statistics for Texas Counties , Texas Workforce Commission, http://www.txcip.org/tac/census/morecountyinfo.php?MORE=1042.

  3. Kline and Moretti (2014) note that examples go back much further in time as the tax system was used to redistribute resources from the remainder of the Empire to citizens of Rome.

  4. See https://texaswideopenforbusiness.com/services/tax-incentives.

  5. Refer to https://texaswideopenforbusiness.com/services/tax-incentives.

  6. Texas contained two Empowerment Zones (San Antonio and El Paso) and two Renewal Communities (Corpus Christi and El Paso).

  7. See https://texaswideopenforbusiness.com/services/tax-incentives.

  8. See https://texaswideopenforbusiness.com/services/tax-incentives. The program charges a fee of 3% of the amount of refund.

  9. See https://texaswideopenforbusiness.com/sites/default/files/frequently_asked_questions.doc.

  10. Estimation is performed using OLS weighted by the size of the Equifax sample in each census block group in the initial period.

  11. A similar strategy of excluding locations covered by multiple programs is pursued in Ham et al. (2011) and Freedman (2013). Comparison of the excluded block groups with the EZs included in the sample reveals little difference in standardized means between the two groups in terms of attributes. Full summary statistics are available upon request.

  12. The value of the test statistic (standard error) is −0.08 (0.05) when using the full sample and −0.08 (0.19) when restricting the sample to block groups with a poverty rate between 18 and 22%.

  13. A similar strategy is pursued in Kolko and Neumark (2010), Freedman (2012, 2013), and Gobillon et al. (2012).

  14. The Supplemental Appendix is available at http://faculty.smu.edu/millimet/pdf/EZSupplementalAppendix.pdf.

  15. A similar strategy is pursued in Ham et al. (2011), Givord et al. (2013), and Hanson and Rohlin (2013).

  16. See http://egis.hud.gov/ezrclocator/.

  17. Lee and van der Klaauw (2010) provide an extensive overview of the data.

  18. While the appeal of the RD design is that potentially confounding macroeconomic factors that equally affect all block groups near the cutoff are removed, we nonetheless utilize the two terminal dates to explore differential effects of the treatment over the business cycle.

  19. Auto loans include “loans taken out to purchase a car, including Auto Bank loans provided by banking institutions (banks, credit unions, savings and loan associations), and Auto Finance loans, provided by automobile dealers and automobile financing companies.” Bank cards (or credit cards) are “revolving accounts for banks, bankcard companies, national credit card companies, credit unions and savings & loan associations.” Retail loans include loans from specific retail outlets such as clothing stores, grocery stores, department stores, home furnishing stores, gas stations. See https://www.newyorkfed.org/medialibrary/interactives/householdcredit/data/pdf/data_dictionary_HHDC.pdf.

  20. The mean (median) number of individual credit reports observed per census block group is roughly 57 (45). We explore the impact of discarding block groups with 10 and 28 individual credit reports and below, which correspond to the 10th and 25th percentiles of the distribution of number of credit reports in either the beginning or the end of the study period. Moreover, it should be made explicit that aggregate block level outcomes are based on all credit reports available at a given point in time. Thus, changes in outcomes over time reflect, in part, changes in sample composition within a block group. Of the sample of credit reports observed in Texas in 2002:Q4, 40.4% are observed in the same block group in 2009:Q4, 25.4% are observed in a different block group but with the same EZ or non-EZ status, 7.8% are observed in a different block group with a different EZ status, and 26.3% exit the sample (due to death or moving outside of Texas).

  21. Note the sample sizes are smaller for the three delinquency outcomes as the percentage of delinquent accounts or balance for a certain type of loan is computed only using individuals with that type of account. As a result, some block groups do not have any individuals in the sample in certain years with a particular type of account.

  22. Note retail loans account for a small fraction of consumer debt. In Texas in 2014:Q4, per capita total debt was nearly $40,000. Of this, consumer finance loans (including sales financing and personal loans) and retail loans make up about 5% (Federal Reserve Bank of New York 2015).

  23. See http://www.uscourts.gov/services-forms/bankruptcy/bankruptcy-basics/chapter-13-bankruptcy-basics.

  24. In addition, we performed the placebo tests for all cutoffs from 0.10, 0.11, ..., 0.17, 0.175, 0.225, 0.23, ..., 0.30. The results are available upon request. However, Figure A3 in the Supplemental Appendix plots the results for one outcome, median risk score, for illustration.

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Authors and Affiliations

  1. Research Department, Federal Reserve Bank of Dallas, 2200 N. Pearl Street, Dallas, TX, 75201, USA

    Wenhua Di & Daniel L. Millimet

  2. Department of Economics, Southern Methodist University, Box 0496, Dallas, TX, 75275-0496, USA

    Daniel L. Millimet

  3. IZA, Bonn, Germany

    Daniel L. Millimet

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  1. Wenhua Di
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Correspondence to Daniel L. Millimet.

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The information, analyses, and conclusions set forth are those of the presenters and do not necessarily indicate concurrence by the Board of Governors of the Federal Reserve System, the Federal Reserve Banks, or members of their staffs. The authors are grateful to two anonymous referees, Matthew Freedman for sharing his data, and Sarah Greer for excellent research assistance.

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Di, W., Millimet, D.L. Targeted business incentives and the debt behavior of households. Empir Econ 52, 1115–1142 (2017). https://doi.org/10.1007/s00181-016-1188-z

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