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Small Business vulnerability to floods and the effects of disaster loans

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Abstract

In this paper, we examine the impacts of floods on businesses and the efficacy of small business administration (SBA) disaster loans on mitigating disaster aftereffects. We find lack of business adaptation to extreme events in the short term, indicating their extreme vulnerability to flood disasters. Our results further indicate that subsidized disaster loans are important for businesses, with statistically significant effects estimated for businesses employing fewer than 50 people. At the margin, for every additional dollar spent on disaster loans per establishment in a county, four small businesses survive. Gloomy projections about increasing frequency and severity of disasters imply there will be significant loss in local economic activities because of increased vulnerability of small businesses to these incidents. Moreover, these effects will have implications nationwide, given the vital role small businesses play in creating jobs.

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Notes

  1. Cavallo and Noy (2011) and Kousky (2014) provide a comprehensive review of literature related to economics of disasters.

  2. The SBA is generally known for providing financial support to small businesses. Craig et al. (2009) reviewed the literature examining economic impacts of SBA-guaranteed loans on various economic indicators. A recent study by Brown and Earle (2013), to the best of our knowledge, is the only study that uses firm-level data to examine the effects of SBA guaranteed loans on employment. The authors find positive growth effects of SBA guaranteed loans on employment, but disregard firms receiving disaster loans.

  3. Barro (1981) shows that the “temporary” effect of defense spending, which is associated with wartime, has twice as much expansionary impact on output as the one generated by an equal-sized permanent shift in defense spending. In another study, Barro and Redlick (2011), using new defense variables, show that multiplier of the shift in permanent defense expense is higher than that of a temporary one.

  4. Unlike small businesses, global enterprises (that produce tradable goods) have a high chance of survival even if local residents do not return because they produce goods that are not traded locally.

  5. SBA disaster loan documents are used as a source for our description. Details can be found in the SBA’s Standard Operating Procedure document at https://www.sba.gov/sites/default/files/sops/SOP_50_30_8_Final.pdf and https://www.sba.gov/loans-grants/see-what-sba-offers/sba-loan-programs/disaster-loans.

  6. Bartelsman and Doms (2000) show a clear evidence of a large degree of heterogeneity in the productivity and growth patterns of businesses in various industries in the USA.

  7. If this is the case, there may be a selection bias problem. However, given low overall insurance penetration for non-residential sector (i.e., as of October 30, 2016, only 5.3% of policies in force are designated for non-residential type), the selection bias is unlikely in our sample.

  8. The employment size that qualifies businesses as small is debated among researchers. SBA reports size standards matched to industries in North American Industry Classification System (US SBA Table of Small Business Standards). Employment size of 50 or less is commonly matched with published size standards based on NAICS industry classifications.

  9. All financial variables are inflation adjusted (2005 = 195.3) using Urban CPI.

  10. FDIC SUMD reports number of institutions, amount of deposits and market share of institutions in each county. The market share of institutions in each county = the amount of deposit for each institution/the total amount of deposits. The HHI for each county equals the sum of squared market share. If HHI approaches zero, this means the market contains a relatively equal number of large-sized firms—approaching a pure competition. As the HHI approaches high levels, this is a sign of a decrease in the number of firms in a market and an increase in the size differences of those firms—approaching a pure monopoly.

  11. Precipitation data are reported daily by weather stations, identified in the dataset by their latitude and longitude. To compute county annual precipitation, weather stations are mapped relative to county locations. We then compute annual total rainfall for a given county. In counties with multiple weather stations, we average precipitations for all stations to create total rainfall in a county. This approach has been undertaken in other climate economics research (e.g., Deryugina and Hsiang, 2014).

  12. We used ln(damage + 1) log transformation to keep counties and years not experiencing damages.

  13. In the sample with a large number of time periods using all available lags as instruments may compromise model efficiency and overfit the model indicated by probabilities close to one associated with Hansen test statistic (Roodman, 2009a). Although overfitting is not a concern for our models, the models in which instruments are limited to lags 5–7, 5–8, and 5–10 comparable results. These results are reported in Appendix Table A.14.

  14. As part of diagnostics test, we performed the unit root tests for all establishment sizes used as dependent variables in our estimable models, employing the Levin–Lin–Chu (LLC) unit root test for panel data. The null hypothesis of the LLC test is that panels contain unit root or that series are nonstationary, while the alternative hypothesis suggests stationarity series (Levin, Lin and Chu 2002). Bias adjusted LLC t-statistics with and without trend are reported in Appendix Table A.1 and suggest no evidence of unit root.

  15. Details can be found at http://www.ers.usda.gov/data-products/rural-urban-continuum-codes/documentation.aspx.

  16. Similarly, Blundell and Bond (2000) investigate the estimation of a production function using panel data with large cross-sectional units for a small number of time-periods and conclude that the system GMM generates preferable estimates.

  17. Summary statistics for these three samples are provided in Appendix Tables A.2–A.4, respectively. Furthermore, results for smaller establishment sizes are also reported in Appendix Tables A.5–A.7.

  18. Appendix Tables A.8 and A.9 report summary statistics and the results from this sample, respectively, for relatively smaller establishment sizes.

  19. Roodman (2009b) shows similar findings across models.

  20. National Flood Insurance Policy Program (NFIP) is the only federally backed program available to insure against flood losses. As of October 30, 2016 only 5.3% of policies in force were issued for non-residential type establishments.

  21. See Van Praag and Versloot (2007) for an extensive literature review on entrepreneurs and their contribution to productivity and growth.

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Acknowledgements

This research is being supported by the NSF PIRE-Coastal Flood Risk Reduction Program: Integrated, multi-scale approaches for understanding how to reduce vulnerability to damaging events (award #: 1545837).

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  1. Department of Marine Sciences, Texas A&M University, Galveston Campus, 1001 Texas Clipper Rd, Bld# 3029, Office 362, Galveston, TX, 77554, USA

    Meri Davlasheridze

  2. Palumbo-Donahue School of Business, Duquesne University, Pittsburgh, PA, USA

    Pinar C. Geylani

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  1. Meri Davlasheridze
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  2. Pinar C. Geylani
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Correspondence to Meri Davlasheridze.

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Davlasheridze, M., Geylani, P.C. Small Business vulnerability to floods and the effects of disaster loans. Small Bus Econ 49, 865–888 (2017). https://doi.org/10.1007/s11187-017-9859-5

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