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

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Table 6.10. Alternative Models of Loan Denials All Specification 1) Proprietorships and Partnerships 2) Corporations 3) 12 Years or Under 4) Over 12 Years 5) Fewer than 10 Employees 6) 10 or More Employees 7) Working Capital 8) Other Use 9) Local 10) Regional, National, or International 11) No Past Problems 12) One Past Problem 13) More Than One Problem Source: See Table 6.1. African American 0.237 (5.14) 0.262 (3.05) 0.210 (3.80) 0.258 (3.91) 0.229 (3.33) 0.239 (3.71) 0.199 (2.82) 0.274 (4.61) 0.149 (2.04) 0.214 (2.81) 0.188 (4.44) 0.244 (4.15) 0.254 (2.34) 0.309 (2.91) African American* ENC -0.003 (0.04) Statistical Disparities in Capital Markets Asian/ Pacific Islander 0.122 (2.14) Organization Type 0.027 (0.15) -0.017 (0.15) 0.069 (0.47) -0.087 (0.83) Age of Firm 1993 Firm Size -0.001 (0.01) 0.147 (0.82) 0.213 (1.86) 0.099 (1.47) 0.210 (2.61) -0.077 (1.03) 0.128 (1.49) 0.099 (1.21) Intended Use of Loan -0.042 (0.41) 0.118 (0.57) 0.049 (0.68) 0.250 (2.66) Scope of Sales Market -0.130 (1.20) 0.205 (1.60) 0.169 (2.13) 0.030 (0.57) Creditworthiness -0.041 (0.41) 0.075 (0.34) -0.033 (0.11) 0.185 (3.06) -0.102 (0.61) 0.251 (1.65) Hispanic 0.067 (1.33) 0.033 (0.38) 0.070 (1.08) 0.029 (0.36) 0.108 (1.55) 0.040 (0.56) 0.110 (1.39) -0.007 (0.12) 0.165 (2.04) 0.011 (0.16) 0.093 (1.74) 0.034 (0.72) 0.190 (1.34) 0.051 (0.29) Nonminority Female 0.050 (1.37) 0.034 (0.47) 0.047 (1.09) 0.043 (0.74) 0.072 (1.36) 0.003 (0.06) 0.108 (2.01) 0.058 (1.13) 0.047 (0.91) 0.052 (0.97) 0.036 (1.12) 0.069 (1.93) 0.007 (0.07) -0.010 (0.06) Sample Size Notes: (1) Reported estimates are derivatives from Probit models, t-statistics are in parentheses. (2) Each line of this table represents a separate regression with the same control variables as Column 3 of Table 6.8. (3) The dependent variable in all specifications represents an indicator for whether or not a loan application was denied. (4) Control for ENC also included. We also estimate models that address a potential weakness in the specific functional form with which we control for differences in credit history across firms. As shown in Tables 6.1 and 6.2, NERA Economic Consulting 204 2,001 533 1,455 1,069 924 864 1,132 1,086 912 872 1,127 1,383 376 231
Statistical Disparities in Capital Markets African American-owned firms are considerably more likely to have had troubles in the past in the form of judgments against them, late payments by the firm or its owner, or past bankruptcies. The model specifications reported in Tables 6.8 and 6.9 implicitly assume that these past problems are additive in their effect on loan denials and one might suspect the marginal impact would rise as past problems rise. Therefore, in the final three rows of Table 6.10, we separated firms by the number of past problems experienced. In Rows 11 through 13, we restricted the sample to those firms that have never had any past credit problems, those firms that reported one problem only, and those firms that reported more than one of these problems, respectively. The results indicate that even African American-owned firms with clean credit histories are at a significant disadvantage in getting their loans approved, holding constant their other characteristics. In fact, the estimated differential in loan approval rates between African American- and nonminority-owned firms is statistically indistinguishable within each of these groups. Asian/Pacific Islander-owned firms and nonminority female-owned firms with clean credit histories are also at a significant disadvantage relative to nonminority-male owned firms. Finally, we considered whether African American-owned firms are treated differently from nonminority-owned firms when requesting credit from other sources. The source of credit we examined is credit cards. Such an analysis provides a unique advantage because credit card applications are more likely to be filled out and mailed in, so it is more likely that the race of the applicant is unknown to the financial institution, at least in the case of African American-owned firms and Native American-owned firms, where surname is unlikely to provide any signal about minority status. On the other hand, for Asian/Pacific Islander and Hispanic applicants, it is possible that surname does provide such a signal, albeit a somewhat noisy one. The 1993 NSSBF asked respondents whether they used either a business or personal credit card for business purposes. Although our analysis of use of credit cards does not condition on application, a finding that African American- and nonminority-owned small businesses are equally likely to use credit cards may still provide evidence supporting discrimination in small-business lending. In fact, if financial institutions discriminate against African Americans in providing small business loans, we may even expect to see African Americans use credit cards more often than nonminorities since they have fewer alternatives. Even though many institutions may offer both types of credit, they may only be aware of the race of the applicant in a small business loan. 283 In Tables 6.11 and 6.12, we examine the probability that a firm uses either a business credit card (Row 1) or a personal credit card (Row 2) to finance business expenses holding constant other differences across firms. 284 There is no evidence, either for the U.S. as a whole or for the ENC, that African American-owned firms are less likely to access either business or personal credit 283 It appears that race may also rarely be known to those institutions that issue credit ratings. As we mentioned above, Cavalluzo, Cavalluzo, and Wolken (1999) show that Dun & Bradstreet Credit Ratings are not helpful in explaining racial disparities in loan denials. Although we are not privy to Dun & Bradstreet’s methodology for establishing its credit ratings, we do know from long experience that the good indicators of ownership by race are lacking in Dun & Bradstreet’s master business identifier file. Indeed, this is the reason why NERA’s availability estimation methodology requires us to create a master directory of disadvantaged, minority, and women-owned businesses for merging with Dun & Bradstreet’s data. 284 On average, 29 percent of all firms use business credit cards and 41 percent use personal credit cards for business use; these levels vary only modestly by race and ethnicity. In the ENC division, the figures are 27 percent and 38 percent, respectively. NERA Economic Consulting 205
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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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M/WBE Utilization and Disparity in
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Anecdotal Evidence of Disparities i
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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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