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Small Business Economics

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01.10.2011

Lending technologies, lending specialization, and minority access to small-business loans

verfasst von: Karlyn Mitchell, Douglas K. Pearce

Erschienen in: Small Business Economics | Ausgabe 3/2011

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Abstract

We investigate minority access to small-business loans using a probit model of loan application denial that recognizes two loan types (line-of-credit loans and non-line-of-credit loans) made by two lender types (commercial banks and nonbank financial institutions). We estimate our model on data from the 1998 Survey of Small Business Finances. We find evidence consistent with minority equal access to bank credit lines and nonbank non-line-of-credit loans in highly competitive loan markets; in less competitive markets we find evidence consistent with unequal access to these loans. We also find evidence consistent with unequal minority access to bank non-line-of-credit loans, regardless of loan market competitiveness. Our findings differ from previous research which treats small-business loans as a homogenous product and finds evidence consistent with unequal minority access to small-business loans generally. We argue that the existence of multiple small-business lending technologies and loan specialization by lenders account for our findings and demonstrate the need to treat small-business loans as a heterogeneous product when investigating equal access to small-business credit.

Vorheriger Artikel Candidate gene studies and the quest for the entrepreneurial gene

Nächster Artikel Workforce age and technology adoption in small and medium-sized service firms

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We use the terms “equal access” and “fair access” to credit as concise terms to connote lending decisions that are impartial with respect to demographic identity. We recognize that a lender’s impartiality cannot be directly observed or measured. We also recognize that we cannot distinguish between situations in which lenders deny loans on the basis of racial profiling versus information observable by the lender but not in our data set that happens to be correlated with demographic identity. While we acknowledge these problems, we also note that the 1998 SSBF is the most comprehensive data set with respect to borrower characteristics and coverage of minority-owned small businesses available to academic researchers.

The HHI index measures market power by shares of deposits. HHI is computed by squaring percentage shares and summing. Thus a market with a single lender has an HHI of (100² =) 10,000 and a market with 100 equal-sized lenders has an HHI of (100 × 1² =) 100.

Blanchflower et al. (2003) and Blanchard et al. (2008) are each subject to a criticism that is addressed in this paper. Blanchflower et al. estimate a loan denial model without a loan application model, opening their results to possible sample selection bias. Blanchard et al. estimate their loan denial model jointly with a loan application model but use unweighted survey data, which prevents the drawing of population inferences.

CC (1998) estimate a difference in denial rates for White and minority owners of about 33 percentage points; CCW (2002) estimate a difference of about 28 percentage points in high-concentration markets. Blanchflower et al. (2003) report differences of 20 to 30 percentage points, while Blanchard et al. (2008) report differences of about 15 percentage points.

CCW (2002) and Blanchard et al. (2008) include in their models indicator variables which permit the probability of loan denial to differ by loan type and lender type but neither permits denial probabilities for Whites and minorities to differ by loan type or lender type as does Model (2), presented below. Neither paper makes a connection to the literatures on lending technologies and lending specialization.

We hypothesize that banks are not observed to discriminate statistically against minority firm owners when considering applications for credit-line renewals because the soft information that banks obtain from monitoring original credit lines supersedes the noisy information in minority status about unobservable applicant risk characteristics. As discussed in Sect. 3, the credit-line data in the 1998 SSBF is exclusively on new credit lines, unlike the 1993 NSSBF or the 2003 SSBF.

Footnote 16 presents statistics on the average terms to maturity of credit lines and non-line-of-credit loans in our sample.

The use of credit scoring does not preclude the use of soft information. Berger et al. (2005) classify banks that adopt credit scoring as either “rules banks” or “discretion banks,” with the latter leaving discretion to loan officers to accept loans and set loan terms. They note that all rules banks permit some judgmental overrides.

Again, we hypothesize that neither banks nor nonbanks are observed to discriminate statistically when they have a strong prior relationship with a minority applicant because they regard the applicant’s soft information as a more reliable indicator of unobserved risk characteristics than minority status. Chakraborty and Hu (2006) find evidence that banks adjust collateral requirements for non-line-of-credit loans the more bank-provided services a loan applicant uses.

It is also possible that less competitive markets allow lenders to discriminate in the sense of Becker (1957), whereas competitive markets should eventually drive out prejudicially discriminating firms because they would be less profitable.

Model (2) uses HHI_HI_i, an indicator variable for a high-concentration loan market, rather than the value of the HHI index, which is not included in the 1998 SSBF.

We do not test for equal access to nonbank line-of-credit loans due to the small number of minority applicants for such loans in our sample.

Data considerations led us to winnow the sample down slightly. We excluded 76 of the 3,561 firms because they had zero or negative assets; this left 3,485 firms including 701 minority-owned firms, of which 259, 199, and 258 were owned by African-Americans, Asians, and Hispanics, respectively. (We use these observations to estimate our loan application models.) Of these 3,485 firms, 952 firms applied for a loan; however 64 firms lacked data on loan type applied for or lender type applied to, and another 18 reported having their loan applications both denied and approved. We excluded these observations, leaving 870 firms that applied for credit. Firms owned by African-Americans, Asians, Hispanics, “Other” minorities, and White Americans number 68, 43, 70, 7, and 688, respectively. Because the number of “Other” minority loan applicants is so small we dropped them when estimating Model (2), leaving 863 observations on which to estimate our loan application denial models.

The 1998 SSBF actually oversampled minority-owned firms. Oversampling biases summary statistics (e.g., means and medians) with respect to population parameters unless the observations are weighted. All statistical and econometric analyses reported below were weighted using weights provided with the 1998 SBBF. For technical details of the 1998 SSBF, see Haggerty et al. (2001).

The three loan source variables—LN_LENGTH, NO_RELATION, and PRIMARY—are highly collinear, leading us to drop NO_RELATION when estimating the models reported in Tables 6 and 7. Estimated models that include NO_RELATION in place of LN_LENGTH and PRIMARY are nearly identical in all other respects.

In the 1998 SSBF 82% of approved non-line-of-credit loans have fixed rates, compared with 45% of line-of-credit loans. Sixty-seven percent of approved non-line-of-credit loans are collateralized, versus 33% of approved credit lines. In addition, the average term on an approved non-line-of-credit loan is 68 months, versus 27 months on approved credit lines. These differences are significant at the 1% level for a two-tailed test. The 1998 SSBFs also includes a miscellaneous (“other”) loan application category, which we include with non-line-of-credit loans. In the 1998 SSBF the contract terms of approved “other” loans resemble the terms on approved leases, mortgages, auto, and equipment loans.

The results reported in Tables 6 and 7 are qualitatively similar if we drop savings banks and credit unions from the sample.

White- and minority-owned firms that applied for B_NLOC loans differ significantly in the proportion of firms with negative equity, having prior legal judgments against them, and paying business obligations late, with minority-owned firms having the more favorable characteristics.

Because the loan approval equation is estimated with the loan application model, there is a potential identification issue. As discussed in Cavalluzzo and Wolken (2005), the data collected in the SSBF surveys are likely relevant to both application and approval decisions. We follow them in achieving identification through the nonlinearity of the model. As robustness tests, we also estimated models that excluded some variables from the denial equation that were retained in the application equation and obtain qualitatively similar results. We also estimated a denial equation without the application equation and again get similar results for our hypothesis tests.

Equation 6.2 also includes FEMALE × HHI_HI, as do (6.3) and (6.4). Its estimated coefficient is always statistically insignificant at the 10% level for a two-tailed test.

Equation 6.4 also includes FEMALE × LOC and FEMALE × BANK. The estimated coefficients of both variables are statistically insignificant at the 10% level for a two-tailed test.

To explore whether different urban–rural distributions of small-business owners across demographic groups might exert a separate influence on loan denial probabilities, we re-estimated (6.2–6.4) after adding an interaction term between minority and MSA. The coefficient estimates of this interaction term were always statistically insignificant at conventional levels, as were the estimated coefficients of MSA. Thus differences in the rural–urban distributions of White- and minority-owned firms do not appear to exert a separate influence. We thank an anonymous referee for pointing out this possible problem.

We do not report results for Asian owners due to the small number of observations. Equations 6.5–6.8 also include FEMALE and interaction terms with FEMALE.

The estimated models for both the loan denial equation and the loan application equation for Eqs. 6.4 and 6.8 are presented in the Appendix. Similar estimates for the other models are available from the authors.

As noted by an anonymous referee, the results in Table 6 could reflect differences in the size distribution of firms owned by White and non-White owners if, for example, firms owned by non-Whites are smaller and, therefore, more prone to failure. To explore this possibly we re-estimated the models in Table 6 on two subsamples of firms: firms having 100 or fewer employees and firms having 50 or fewer employees. (These estimates are available upon request.) The re-estimated models and associated hypothesis tests show qualitatively similar results to those reported in Table 6.

We do not include interaction terms between BANK or BANK × LOC and the following variables due to lack of variation in the data: BANKRUPT, JUDGMENT, OWNR_PAY_LATE, and DENIED_TRADE_CR. In addition, we do not include interaction terms between BANK or BANK × LOC and the indicators for application year, region or industry. Equations 7.1–7.3 also include FEMALE, FEMALE × BANK, FEMALE × LOC, and FEMALE × HHI_HI.

The only μ _k estimates close to statistical significance at conventional levels appear on interactions of BANK with LN_SALES, USE_BUS_CCARD, and MSA. The probability of loan denial is unrelated to sales revenue at a nonbank but weakly negatively related to sales at a bank, a result consistent with the conventional wisdom that banks lend on the basis of cash flow. For firms that use business credit cards and firms located outside of metropolitan areas, loan denial probabilities are weakly lower at banks than at nonbanks.

The only ν _k estimate close to statistical significance at conventional levels appears on the interaction term between BANK × LOC and LN_NET_WRTH, the log of owner net worth. The coefficient estimate implies that greater net worth reduces an applicant’s denial probability more sharply on a bank credit-line application than on other applications.

We do not include interaction terms between MINORITY and BANKRUPT, JUDGMENT, OWNR_PAY_LATE, and DENIED_TRADE_CR due to lack of variation in the data, or between MINORITY and the indicators for application year, region or industry.

To predict denial probabilities for White (minority) owners we set MINORITY equal to 0 (1); we report the predictions in the W (M) column. To predict probabilities for line-of-credit (non-line-of-credit) loans at banks (nonbanks) we set LOC and BANK equal to 1 (0); we set HHI_HI equal to 0 or 1 to predict denial probabilities in low- and high-concentration markets, respectively.

To predict denial probabilities for African-American (Hispanic) owners we set AFROAM = 1 (HISPANIC = 1); we report the predictions in the AA (H) columns. To predict denial probabilities for White owners we set AFROAM and HISPANIC equal to 0; we report the results in the W column.

As an anonymous referee notes, an interesting question is whether minority borrowers learn which loan types are more likely to be approved and subsequently apply for those loans types more often. Current regulations do not allow lenders to collect information about race and gender on loan applications, so the availability of this information is limited; however there is evidence consistent with learning, as we note in the final paragraph of the paper.

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Titel: Lending technologies, lending specialization, and minority access to small-business loans
verfasst von: Karlyn Mitchell
Douglas K. Pearce
Publikationsdatum: 01.10.2011
Verlag: Springer US
Erschienen in: Small Business Economics / Ausgabe 3/2011
Print ISSN: 0921-898X
Elektronische ISSN: 1573-0913
DOI: https://doi.org/10.1007/s11187-009-9243-1

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