The State of Minority- and Women- Owned ... - Cleveland.com

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Table 6.31. Determinants of Interest Rates—Nine Jurisdictions African American Asian/Pacific Islander Hispanic Native American Other race Nonminority female Judgments Firm delinquent Personally delinquent Bankrupt past 3 years Statistical Disparities in Capital Markets (1) (2) 1.683 (3.44) 1.221 (2.16) 0.820 (1.48) 1.241 (1.52) -1.115 (0.63) 0.046 (0.16) 1.491 (2.98) 0.789 (1.34) 0.895 (1.56) 1.008 (1.24) -1.072 (0.61) 0.018 (0.06) 0.537 (0.85) -0.041 (0.36) 0.644 (3.65) 1.184 (1.13) Creditworthiness, Firm, and Owner Characteristics No Yes Loan Characteristics Yes Yes N 1,490 1,463 Adjusted R 2 .0831 .1046 F 11.4 11.05 Source: See Table 6.30. Notes: (1) Reported estimates are OLS regression models, t-statistics are in parentheses. (2) Five indicators for primary owner’s education level, four indicators for legal form of organization, loan amount applied for, loan amount granted, and month and year of loan application were included. (3) Seven additional indicators for jurisdiction were also included. K. Conclusions The results presented in this chapter indicate that African American-owned firms face serious obstacles in obtaining credit that are unrelated to their creditworthiness, industry, or geographic location. In a number of cases this is true as well for Hispanic-owned firms, Asian/Pacific Islander-owned firms, Native American-owned firms, and nonminority female-owned firms. NERA Economic Consulting 234
Statistical Disparities in Capital Markets As in any regression-based study, our analysis hinges upon the proposition that all of the factors that are related to loan denial rates have been included in our statistical model. If, for example, African American business owners possess some unobservable characteristic that makes them less creditworthy, then our statistical finding would overstate the difference in loan denial rates. To check on this possibility, the models we have estimated include an extensive array of factors that could conceivably affect loan decisions. Additionally, we have also estimated several alternative specifications that could potentially identify the impact of such a bias. Moreover, we have conducted our own surveys on numerous occasions and in numerous places across the U.S. Throughout, we have consistently found that African Americans are disadvantaged in the small business credit market and that our specification tests support the interpretation of discrimination. Another potential criticism is that this study has examined loan denial rates rather than loan default rates; some have claimed that the latter provides a more appropriate strategy for identifying discrimination. For example, if banks only approve loans for relatively good African American firms then African American firms should exhibit relatively low default rates. Such an approach has several significant shortcomings that are detailed in Browne and Tootell (1995) and Ladd (1998). For instance, one problem is that it relies on the distribution of default probabilities being similar for African American and nonminority applicants meeting the acceptance standard used for nonminority firms. A further problem is that it assumes that the loan originators know with a high degree of precision what determines defaults; however, little hard information exists on what causes default. Additionally, it would be hard to disentangle the factors associated with differences in default rates between nonminority- and African American-owned firms given the fact that the African American-owned firms that obtain credit are typically charged higher interest rates, as we have demonstrated. Finally, such an analysis would require longitudinal data, tracking firms for several years following loan origination. Such data do not exist. While we have highlighted the potential limitations of such an analysis, we believe that it would be fruitful for this sort of longitudinal data collection to take place and for future research to investigate this question more fully. In addition, many of the criticisms levied against the home mortgage loan discrimination study of Munnell, et al. (1996) could perhaps be used here as well. Yet these criticisms appear to have been effectively countered by, for example, Browne and Tootell (1995) and Tootell (1996). What is important to keep in mind in reference to this work compared with Munnell, et al. (1996), is the magnitude of the estimated racial disparity. The absolute size of the raw racial differences found in the mortgage study are considerably smaller than those observed in this study regarding business credit. 301 The magnitude of the racial difference in small business loan approval rates is substantial, even after controlling for observed differences in creditworthiness, and considerably larger than that found in the analysis of discrimination in mortgage markets. Why do the results for small 301 In the Boston Fed study, 10 percent of White mortgage applications were rejected compared with 28 percent for African Americans. Loan denial rates (weighted) for business credit in this study ranged from 8.3 to 26.2 percent for White males and between 50.0 and 65.9 percent for African American-owned firms (depending on which NSSBF or SSBF survey is used). NERA Economic Consulting 235
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The State of Minority- and Women- O
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About the Project Team NERA Economi
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Notice This report sets forth the i
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Contents C. Race and Gender Dispari
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List of Tables Table 4.5. Services
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List of Tables Table 5.14. Disparit
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List of Tables Table 7.10. M/WBE Ut
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List of Tables Table 8.10. Percent
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List of Tables Table AC.32. Industr
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C. Defining the Relevant Markets Ex
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Table A. Overall Current Availabili
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Executive Summary disparity analysi
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Executive Summary • A greater sha
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Executive Summary Table C. Utilizat
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Contracting Category/ M/WBE Type Ex
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Executive Summary contract performa
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I. Introduction Introduction The Ci
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Legal Standards for Government Affi
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B. Preparing the Master Contract/Su
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Defining the Relevant Markets Table
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• Franklin County, OH (Columbus)
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NAICS Industry Group NAICS Descript
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Defining the Relevant Markets In AE
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NAICS Industry Group 8112 5412 5324
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NAICS Industry Group Source: See Ta
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NAICS Industry Group 4233 NAICS Des
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M/WBE Availability in the City of C
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NAICS Industry Group M/WBE Availabi
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Detailed Industry Group Navigationa
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Table 4.24. Detailed M/WBE Availabi
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Detailed Industry Group M/WBE Avail
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Detailed Industry Group General Med
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Detailed Industry Group Machine Sho
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Detailed Industry Group Products Me
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Table 4.28. Detailed DBE Availabili
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Table 4.30. Detailed DBE Availabili
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Market-Based Disparities in Busines
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VI. Statistical Disparities in Capi
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Page 204 and 205: Statistical Disparities in Capital
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Anecdotal Evidence of Disparities i
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The City of Cleveland’s Cleveland
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Blanchflower, D. G., P. Levine and
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References Fairlie, R. W. (1999),
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References Ladd, H. F. (1998), “E
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References U.S. Census Bureau (2004
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Appendix A. Glossary Demand-side: R
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Appendix A. Glossary NSSBF or SSBF.
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Appendix B. Master M/WBE Directory
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Appendix B. Master M/WBE Directory
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Appendix C. Detailed Utilization, A
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NERA Economic Consulting Barton Cre
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