Cole and Mehran (2010) Gender and Credit Gender and the Availability of Credit to Privately Held...

Cole and Mehran (2010)Cole and Mehran (2010)Gender and CreditGender and Credit

Gender Gender and and

the Availability of Credit the Availability of Credit to Privately Held Firmsto Privately Held Firms

Rebel A. ColeRebel A. Cole

DePaul UniversityDePaul University

Hamid MehranHamid Mehran

Federal Reserve Bank of New YorkFederal Reserve Bank of New York

Academy of Entrepreneurial Finance

September 16 – 17, 2010


BackgroundBackground

There is a growing portion of the literature on There is a growing portion of the literature on small-firm finance that notes significant small-firm finance that notes significant differences in their findings for firms controlled by differences in their findings for firms controlled by male vs. female owners. male vs. female owners.

For example:For example:• Robb (2002) finds that the survival rates are Robb (2002) finds that the survival rates are

lower for firms controlled by women than for lower for firms controlled by women than for firms controlled by men.firms controlled by men.

• Cole and Mehran (2008) find that female CEOs Cole and Mehran (2008) find that female CEOs pay themselves significantly less than do male pay themselves significantly less than do male CEOs.CEOs.

• Cole (2008) finds that firms with female Cole (2008) finds that firms with female controlling owners use less leverage in their controlling owners use less leverage in their capital structure.capital structure.


BackgroundBackground

These studies (and many, many more) point to These studies (and many, many more) point to the need for a better understanding of how the need for a better understanding of how female-owned firms systematically differ over female-owned firms systematically differ over time from male-owned firms.time from male-owned firms.

It is the goal of this research to provide such an It is the goal of this research to provide such an understanding by developing a comprehensive understanding by developing a comprehensive set of “stylized facts” regarding the differences in set of “stylized facts” regarding the differences in firms controlled by women and firms controlled firms controlled by women and firms controlled by men.by men.

We accomplish this by analyzing data from four We accomplish this by analyzing data from four independent and nationally representative independent and nationally representative surveys of small U.S. firms that span 15 years, surveys of small U.S. firms that span 15 years, from 1987 to 2003—the FRB’s SSBFs.from 1987 to 2003—the FRB’s SSBFs.


Contributions to the LiteratureContributions to the Literature

Most importantly, we establish a set of “stylized Most importantly, we establish a set of “stylized facts” about differences in small U.S. firms facts” about differences in small U.S. firms controlled by men and women.controlled by men and women.• These are differences that have been These are differences that have been

consistently observed across the 15 years consistently observed across the 15 years spanned by the SSBFs.spanned by the SSBFs.

We also document how credit-market outcomes We also document how credit-market outcomes do (or do not) differ across U.S. firms controlled do (or do not) differ across U.S. firms controlled by men and women.by men and women.

Finally, we document how firm-creditor Finally, we document how firm-creditor relationships differ across U.S. firms controlled by relationships differ across U.S. firms controlled by men and women.men and women.


Implications for PolicymakersImplications for Policymakers

Our research has important implications for Our research has important implications for policymakers who contemplate (or enact) policymakers who contemplate (or enact) regulations promoting employment and the regulations promoting employment and the availability of credit. For example:availability of credit. For example:• Our research offers insights into why so few Our research offers insights into why so few

CEOs of public firms are female.CEOs of public firms are female.• Our research suggests that disparate credit-Our research suggests that disparate credit-

market outcomes are attributable to other market outcomes are attributable to other differences in male- and female-controlled differences in male- and female-controlled firms, such as firm size, owner experience and firms, such as firm size, owner experience and owner education.owner education.


MethodologyMethodology

Weighted Descriptive Statistics by GenderWeighted Descriptive Statistics by Gender

Univariate tests for differences in weighted Univariate tests for differences in weighted means.means.

Multivariate tests using weighted probit Multivariate tests using weighted probit regression:regression:• where the dependent variable is equal to one if where the dependent variable is equal to one if

the firm is controlled by a female owner and is the firm is controlled by a female owner and is equal to zero otherwise.equal to zero otherwise.

• where the dependent variable is one of three where the dependent variable is one of three credit-market outcomes.credit-market outcomes.


Methodology: Methodology: Who Needs Credit and Who Gets Credit? Who Needs Credit and Who Gets Credit?

(Cole 2009)(Cole 2009)Figure 1:

Who needs and who gets credit?A sequential model

(1)Need

Credit?

(2)Apply for Credit?

(3)Get

Credit?

No

Yes

Yes

Yes

No

No

Non-Borrower

Discouraged Borrower

Denied Borrower

Approved Borrower


Methodology:Methodology:Correction for Sample-Selection BiasCorrection for Sample-Selection Bias

At steps 2 and 3, we correct for potential At steps 2 and 3, we correct for potential sample-selection bias using a bivariate-sample-selection bias using a bivariate-probit selection model developed by Van probit selection model developed by Van de Ven and Van Praag (1981) and refined de Ven and Van Praag (1981) and refined by Green (1992, 1996).by Green (1992, 1996).• At step 2, our selection equation is At step 2, our selection equation is Need Need

CreditCredit (1=Yes, 0=No) and our equation (1=Yes, 0=No) and our equation of interest is of interest is DiscouragedDiscouraged..

• At step 3, our selection equation is At step 3, our selection equation is Apply for CreditApply for Credit (1=Yes, 0=No) and our (1=Yes, 0=No) and our equation of interest is equation of interest is DeniedDenied..


DataData

We extract data from each of the FRB’s SSBFs:We extract data from each of the FRB’s SSBFs:• Four surveys: Four surveys:

Cross sections as of 1988, 1993, 1998, 2003Cross sections as of 1988, 1993, 1998, 2003 Broadly representative of 5 million privately held Broadly representative of 5 million privately held

firms with fewer than 500 employees.firms with fewer than 500 employees.

• Stratified random samplesStratified random samples Oversample large and minority-owned firms.Oversample large and minority-owned firms. Also stratify by census regionAlso stratify by census region

• Cannot use results from unweighted Cannot use results from unweighted descriptive statistics or from OLS to make descriptive statistics or from OLS to make inferences about the populationinferences about the population


Explanatory Variables:Explanatory Variables:

To select our explanatory variables, we rely To select our explanatory variables, we rely primarily upon the existing literature on the primarily upon the existing literature on the availability of credit, as this may be the most availability of credit, as this may be the most vexing issue facing small firms. These variables vexing issue facing small firms. These variables are motivated by and used in Cole (1998), Cole, are motivated by and used in Cole (1998), Cole, Goldberg and White (2004), and/or Cole (2009).Goldberg and White (2004), and/or Cole (2009).

We group these variables into four vectors:We group these variables into four vectors:• Firm CharacteristicsFirm Characteristics• Market CharacteristicsMarket Characteristics• Owner CharacteristicsOwner Characteristics• Firm-Creditor Relationship CharacteristicsFirm-Creditor Relationship Characteristics


Explanatory Variables:Explanatory Variables:Firm CharacteristicsFirm Characteristics

Size (Sales, Assets, Employment)Size (Sales, Assets, Employment) AgeAge Organizational form (C-Corp, S-Corp, Organizational form (C-Corp, S-Corp,

Partnership, Proprietorship)Partnership, Proprietorship) Creditworthiness (Firm Delinquent Creditworthiness (Firm Delinquent

Obligations, Firm Bankruptcy, Firm Obligations, Firm Bankruptcy, Firm Judgments, D&B Credit Score, Paid Late on Judgments, D&B Credit Score, Paid Late on Trade Credit)Trade Credit)

Financial performance and condition Financial performance and condition (profitability, leverage, liquidity)(profitability, leverage, liquidity)


Explanatory Variables:Explanatory Variables:Market CharacteristicsMarket Characteristics

Very limited information on firm location Very limited information on firm location because of confidentiality concerns. Can because of confidentiality concerns. Can only use what is available from the SSBFs.only use what is available from the SSBFs.• Banking concentrationBanking concentration

Categorical representation with three levels, Categorical representation with three levels, which we convert into dummy variables for which we convert into dummy variables for low, medium and high concentrationlow, medium and high concentration

• Urban/Rural Location of the FirmUrban/Rural Location of the Firm Binary indicator variableBinary indicator variable


Explanatory Variables:Explanatory Variables:Owner CharacteristicsOwner Characteristics

AgeAge ExperienceExperience Education (Grad, College, Some College, Education (Grad, College, Some College,

High School)High School) Personal WealthPersonal Wealth Personal Creditworthiness (Delinquent Personal Creditworthiness (Delinquent

Obligations, Judgments, Bankruptcy)Obligations, Judgments, Bankruptcy) Race and Ethnicity (Asian, Black, Hispanic)Race and Ethnicity (Asian, Black, Hispanic)


Explanatory Variables:Explanatory Variables:Firm-Creditor Relationship CharacteristicsFirm-Creditor Relationship Characteristics

Type of Primary Financial Institution Type of Primary Financial Institution (Commercial Bank, Savings Association, (Commercial Bank, Savings Association, Finance Company, or “Other”)Finance Company, or “Other”)

Distance from firm HQ to Primary FI.Distance from firm HQ to Primary FI. Length of Relationship with Primary FI.Length of Relationship with Primary FI. Total Number of FIs (also split by number Total Number of FIs (also split by number

of Commercial Banks and number of Non-of Commercial Banks and number of Non-Bank FIs.Bank FIs.


Results:Results:Descriptive StatisticsDescriptive Statistics

2003 1998 1993 1987

Weighted Number of Firms 5,969,096 5,069,096 4,884,636 3,222,041

Weighted Number of Female-Owned Firms 1,568,082 1,223,173 1,019,424 451,730

Female-Owned Firms, Percent of Total 0.263 0.241 0.209 0.140

Panel A:Female-Owned Firms in the Surveys of Small Business Finances

The percentage of female-owned firms has steadily increased across time, almost doubling from 14.0% in 1987 to 26.3% in 2003.


Results:Results:Descriptive StatisticsDescriptive Statistics

Sales Quartile 2003 1998 1993 1987

Q1 Percentage of Quartile 0.408 0.369 0.297 0.196Percentage of Total 0.102 0.094 0.076 0.055Weighted Number of Firms 610,247 477,463 370,255 178,179




Female-Owned Firms by Sales Quartile (Q1 is smallest, q4 is largest)

Increase has not been uniform across size quartiles.


Results:Results:Weighted Descriptive StatisticsWeighted Descriptive Statistics

Female-Owned Male-Owned Difference

Obs. 805 3,357Mean 0.540 0.509 0.031 bStd. Error 0.018 0.009 0.01

Obs. 741 2,444Mean 0.613 0.608 0.005Std. Error 0.018 0.010 0.01

Obs. 847 2,776Mean 0.577 0.552 0.025 cStd. Error 0.017 0.009 0.01

"No-Need" Firms

1993

1998

2003




Obs. 425 1,859Mean 0.356 0.294 0.062Std. Error 0.023 0.011 0.02 a



1998

2003

"Discouraged" Firms

1993





Obs. 164 667Mean 0.259 0.235 0.024Std. Error 0.034 0.016 0.024

Obs. 274 1,389Mean 0.148 0.126 0.023Std. Error 0.022 0.011 0.014

1993

"Denied" Firms

2003

1998


Results:Results:Weighted Descriptive Statistics (2003)Weighted Descriptive Statistics (2003)

Variable Mean Std. Err Mean Std. Err Mean Std. Err Difference t-stat

Observations 3,623 2,776 847Firm CharacteristicsSales 624,307 20,030 724,353 24,380 343,458 28,575 380,895 10.14 aAssets 306,646 12,148 350,335 14,964 184,003 16,395 166,333 7.49 aEmployment 7.024 0.218 7.580 0.261 5.461 0.366 2.119 4.71 aln (Assets) 11.042 0.033 11.287 0.036 10.353 0.077 0.935 11.05 aROA 0.607 0.014 0.601 0.016 0.624 0.033 -0.023 -0.63Liabilities to assets 0.845 0.032 0.887 0.037 0.726 0.063 0.160 2.20 bCash to assets 0.257 0.005 0.245 0.006 0.290 0.012 -0.045 -3.35 a

All Firms Male-Owned Female-Owned

Female-owned firms are much smaller as measured by Sales, Assets or Employment.




Observations 3,623 2,776 847Firm CharacteristicsC-Corporation 0.140 0.006 0.152 0.007 0.105 0.011 0.047 3.64 aS-Corporation 0.310 0.008 0.327 0.009 0.263 0.016 0.064 3.50 aPartnership 0.084 0.005 0.081 0.005 0.092 0.011 -0.010 -0.88Proprietorship 0.466 0.005 0.440 0.005 0.541 0.011 -0.101 -13.62 aFirm Age 14.190 0.181 14.827 0.207 12.404 0.363 2.423 5.80 a


Female-owned firms are younger and much more likely to organize as Proprietorships rather than S- or C-corporations.




Observations 3,623 2,776 847Firm CharacteristicsFirm bankruptcy 0.010 0.002 0.007 0.002 0.016 0.005 -0.008 -1.68 cFirm delinquencies 0.157 0.006 0.154 0.007 0.163 0.013 -0.009 -0.57D&B Credit Score 3.610 0.024 3.660 0.027 3.471 0.050 0.189 3.33 aBusiness credit card 0.472 0.008 0.465 0.009 0.489 0.018 -0.024 -1.18Personal credit card 0.482 0.008 0.495 0.009 0.447 0.018 0.048 2.36 bPaid late on trade credit 0.245 0.007 0.255 0.008 0.217 0.015 0.038 2.23 b


Female-owned firms are less creditworthy, except as measured by paying late on trade credit.




Observations 3,623 2,776 847Industrial ClassificationConstruction 0.117 0.005 0.137 0.006 0.061 0.009 0.076 7.05 aPrimary Manufacturing 0.031 0.003 0.033 0.003 0.025 0.006 0.007 1.11Other Manufacturing 0.040 0.003 0.047 0.004 0.018 0.005 0.029 4.64 aTransportation 0.039 0.003 0.045 0.004 0.022 0.005 0.023 3.44 aWholesale Trade 0.057 0.004 0.062 0.005 0.041 0.007 0.021 2.50 bRetail Trade 0.187 0.006 0.175 0.007 0.219 0.015 -0.044 -2.64 aInsurance and Real Estate 0.067 0.004 0.071 0.005 0.057 0.008 0.014 1.41Business Services 0.253 0.007 0.232 0.008 0.310 0.017 -0.078 -4.18 aProfessional Services 0.210 0.007 0.197 0.007 0.246 0.016 -0.049 -2.81 a


Female-owned firms are disproportionately over-represented among service and retail trade firms, under-represented among construction, manufacturing, transportation and wholesale trade firms.




Observations 3,623 2,776 847Market CharacteristicsMSA 0.793 0.007 0.798 0.007 0.780 0.015 0.018 1.06HHI High 0.479 0.008 0.468 0.009 0.509 0.018 -0.041 -2.01 bHHI Medium 0.461 0.008 0.466 0.009 0.447 0.018 0.019 0.91


Female-owned firms are over-represented in concentrated banking markets.




Observations 3,623 2,776 847Owner CharacteristicsOwner Age 51.506 0.190 51.797 0.214 50.691 0.409 1.106 2.39 bOwner Experience 19.610 0.194 20.983 0.221 15.756 0.371 5.227 12.10 aGraduate Degree 0.208 0.007 0.219 0.008 0.178 0.014 0.041 2.57 bCollege Degree 0.291 0.008 0.298 0.009 0.268 0.016 0.030 1.66 cSome College 0.267 0.007 0.250 0.008 0.316 0.017 -0.066 -3.54 a


Female owners are younger, less experienced and less educated than their male counterparts.




Observations 3,623 2,776 847Owner CharacteristicsBlack 0.039 0.003 0.035 0.003 0.052 0.008 -0.017 -1.95 cAsian 0.044 0.003 0.047 0.004 0.037 0.007 0.010 1.31Hispanic 0.044 0.003 0.042 0.004 0.048 0.008 -0.006 -0.73


Female-owners are more likely to also be African American.




Observations 3,623 2,776 847Owner CharacteristicsOwner Bankruptcy 0.025 0.003 0.021 0.003 0.036 0.007 -0.015 -2.10 bOwner Delinquency 0.121 0.005 0.112 0.006 0.145 0.013 -0.033 -2.34 bOwner Judgement 0.023 0.002 0.023 0.003 0.024 0.006 -0.001 -0.19Owner Personal Wealth 0.700 0.016 0.758 0.018 0.536 0.029 0.223 6.41 a


Female owners are less creditworthy and have less personal wealth.




Relationship CharacteristicsPrimary FI is Comm Bank 0.801 0.007 0.811 0.007 0.772 0.015 0.039 2.30 bPrimary FI is Sav Inst 0.128 0.006 0.121 0.006 0.146 0.013 -0.024 -1.71 cPrimary FI is Finance Co 0.010 0.002 0.012 0.002 0.004 0.002 0.008 2.43 bPrimary FI is other 0.035 0.003 0.038 0.004 0.027 0.006 0.011 1.59Months with Primary FI 123.792 1.863 125.248 2.137 119.705 3.803 5.543 1.27Distance to Primary FI 32.911 3.134 36.256 3.819 23.521 4.795 12.735 2.08 bNumber of Bank Sources 1.229 0.013 1.276 0.015 1.099 0.027 0.176 5.73 aNumber of Nonbank Sources 1.142 0.020 1.185 0.023 1.021 0.042 0.164 3.42 a


Female-owned firms have fewer banking relationships and are less likely to choose a bank as their primary financial institution.


Results:Results:Weighted Descriptive Statistics (All)Weighted Descriptive Statistics (All)

2003 1998 1993 1987

Variable Difference t-stat Difference t-stat Difference t-stat Difference t-stat

ObservationsFirm CharacteristicsSales 380,895 10.14 a 282,406 7.78 a 262,874 7.33 a 278,781 5.93 aAssets 166,333 7.49 a 117,752 5.68 a 147,070 7.29 a 9,826 0.18Employment 2.119 4.71 a 1.602 2.225 b 2.049 4.02 a 2.907 4.32 aln (Assets) 0.935 11.05 a 0.768 8.569 a 0.682 9.23 a 0.470 5.03 aROA -0.023 -0.63 0.003 0.050 0.044 0.81 0.038 0.81Liabilities to assets 0.160 2.20 b -0.025 -0.486 -0.012 -0.52 0.002 0.07Cash to assets -0.045 -3.35 a -0.027 -2.018 b -0.006 -0.60 -0.017 -1.36

Female-owned firms are consistently larger as measured by annual sales, total assets and employment.



2003 1998 1993 1987

Variable Difference t-stat Difference t-stat Difference t-stat Difference t-stat Firm CharacteristicsC-Corporation 0.047 3.64 a 0.042 2.674 a 0.048 2.77 a 0.033 1.23S-Corporation 0.064 3.50 a 0.050 2.841 a 0.010 0.60 -0.011 -0.57Partnership -0.010 -0.88 0.003 0.264 -0.005 -0.42 0.039 3.07 aProprietorship -0.101 -13.62 a -0.093 -4.420 a -0.053 -7.53 a -0.061 -2.19 bFirm Age 2.423 5.80 a 2.688 6.107 a 2.653 6.38 a 3.275 5.44 a

Female-owned firms are consistently younger and more likely to be organized as proprietorships and less likely to be organized as corporations.



2003 1998 1993 1987

Variable Difference t-stat Difference t-stat Difference t-stat Difference t-stat Firm CharacteristicsFirm bankruptcy -0.008 -1.68 c 0.000 -0.018Firm delinquencies -0.009 -0.57 0.005 0.377 -0.037 -2.26 bD&B Credit Score 0.189 3.33 a -0.086 -2.159 bBusiness credit card -0.024 -1.18 -0.020 -0.933 0.010 0.54Personal credit card 0.048 2.36 b 0.060 3.060 a -0.042 -2.11 bPaid late on trade credit 0.038 2.23 b 0.033 1.798 c -0.009 -0.46 -0.011 -0.39

Female-owned firms are non significantly different from male-owned firms in terms of credit quality.



2003 1998 1993 1987

Variable Difference t-stat Difference t-stat Difference t-stat Difference t-stat Industrial ClassificationConstruction 0.076 7.05 a 0.082 7.429 a 0.092 19.97 a 0.087 6.24 aPrimary Manufacturing 0.007 1.11 -0.008 -0.907 -0.001 -0.12 -0.006 -0.50Other Manufacturing 0.029 4.64 a 0.024 3.157 a 0.017 2.38 b 0.011 0.98Transportation 0.023 3.44 a 0.002 0.210 -0.003 -0.49 0.005 0.56Wholesale Trade 0.021 2.50 b 0.040 4.393 a 0.014 1.37 0.032 2.22 bRetail Trade -0.044 -2.64 a -0.060 -3.399 a -0.066 -3.74 a -0.130 -4.82 aInsurance and Real Estate 0.014 1.41 0.011 1.094 0.025 2.74 a -0.012 -0.78Business Services -0.078 -4.18 a -0.074 -3.840 a -0.085 -4.75 a -0.081 -3.37 aProfessional Services -0.049 -2.81 a -0.010 -0.595 0.008 0.51 0.094 6.87 a

Female-owned firms are consistently more likely to be in retail trade and business services, and less likely to be in construction, manufacturing and wholesales trade.



2003 1998 1993 1987

Variable Difference t-stat Difference t-stat Difference t-stat Difference t-stat Market CharacteristicsMSA 0.018 1.06 -0.008 -0.483 -0.001 -0.03 -0.001 -0.03HHI High -0.041 -2.01 b 0.008 1.052 -0.042 -2.07 b -0.027 -1.25HHI Medium 0.019 0.91 0.003 0.307 0.013 0.51

There are no consistent differences by urban/rural location or banking market concentration.



2003 1998 1993 1987

Variable Difference t-stat Difference t-stat Difference t-stat Difference t-stat Owner CharacteristicsOwner Age 1.106 2.39 b 1.371 2.920 a 1.947 4.37 aOwner Experience 5.227 12.10 a 4.447 9.887 a 4.609 11.69 aGraduate Degree 0.041 2.57 b 0.055 3.604 a 0.061 4.07 aCollege Degree 0.030 1.66 c 0.023 1.186 -0.025 -1.40Some College -0.066 -3.54 a -0.046 -2.358 b -0.059 -3.21 a

Female-owned firms have owners who are consistently younger, less experienced and less educated.



2003 1998 1993 1987

Variable Difference t-stat Difference t-stat Difference t-stat Difference t-stat Owner CharacteristicsBlack -0.017 -1.95 c -0.013 -1.454 -0.003 -0.37 -0.007 -0.75Asian 0.010 1.31 -0.004 -0.447 -0.007 -0.86 -0.019 -1.64Hispanic -0.006 -0.73 0.007 0.775 -0.009 -1.01 -0.011 -1.19

There are no consistent differences by race or ethnicity.



2003 1998 1993 1987

Variable Difference t-stat Difference t-stat Difference t-stat Difference t-stat Owner CharacteristicsOwner Bankruptcy -0.015 -2.10 b 0.001 0.440 0.000 0.06Owner Delinquency -0.033 -2.34 b -0.008 -0.588 -0.026 -1.80 cOwner Judgement -0.001 -0.19 0.006 0.794 0.000 0.01Owner Personal Wealth 0.223 6.41 a 0.057 1.228

There are no consistent differences by the credit quality of the primary owner.



2003 1998 1993 1987

Variable Difference t-stat Difference t-stat Difference t-stat Difference t-stat

Relationship CharacteristicsPrimary FI is Comm Bank 0.039 2.30 b 0.061 3.514 a 0.023 1.44 0.038 2.01 bPrimary FI is Sav Inst -0.024 -1.71 c -0.019 -1.473 -0.025 -1.93 c -0.034 -2.15 bPrimary FI is Finance Co 0.008 2.43 b 0.010 2.034 b 0.005 1.28 -0.001 -0.27Primary FI is other 0.011 1.59 -0.013 -1.491 -0.002 -0.26 0.002 0.25Months with Primary FI 5.543 1.27 12.110 2.939 a 20.318 5.52 a 20.55 2.82 aDistance to Primary FI 12.735 2.08 b -20.025 -1.809 c -5.626 -1.14 5.166 2.54 bNumber of Bank Sources 0.176 5.73 a 0.141 4.466 a 0.070 2.20 b 0.059 1.55Number of Nonbank Sources 0.164 3.42 a 0.042 0.898 0.041 0.93 -0.065 -1.14

Female-owned firms are consistently less likely to rely upon a commercial bank as their primary source of financial services, to have shorter relationships with their primary source, and to have fewer sources of financial services.


Results:Results:Multivariate Logit (Female=1, Male=0)Multivariate Logit (Female=1, Male=0)

These results largely confirm univariate These results largely confirm univariate differences in means.differences in means.

However, a number of these differences lose However, a number of these differences lose statistical significance because they are collinear statistical significance because they are collinear with other differences.with other differences.

Among firm characteristics, only Among firm characteristics, only sizesize is is consistently significant.consistently significant.

Among owner characteristics, Among owner characteristics, experienceexperience and and education remain significant, but age flips sign education remain significant, but age flips sign from positive to negativefrom positive to negative..


Results:Results:Multivariate Probit (No Need=1, Need=0)Multivariate Probit (No Need=1, Need=0)

Explanatory Marginal StandardVariable Effect Error t-statistic

Female-controlled 1993 0.029 0.090 1.71 c

Female-controlled 1998 0.007 0.102 0.37

Female-controlled 2003 -0.033 0.095 -1.86 c

No consistent and significant differences in male-owned and female-owned firms that need credit.


Results:Results:Multivariate Probit (Discouraged=1, Applied=0)Multivariate Probit (Discouraged=1, Applied=0)

Explanatory Marginal StandardVariable Effect Error t-statistic Female-controlled 1993 0.006 0.135 0.32



Highly significant univariate differences disappear when we control for other factors in a multivariate setting.


Results:Results:Bivariate Probit with SelectionBivariate Probit with Selection

(Denied=1, Approved=0)(Denied=1, Approved=0)

Explanatory Marginal StandardVariable Effect Error t-statistic Female-controlled 1993 0.016 0.187 0.76


Female-controlled 2003 -0.034 0.267 -2.13 b

Only significant difference shows female-controlled firms were less likely to be denied credit in 2003.


Summary and ConclusionsSummary and Conclusions

In this study, we have analyzed data from four In this study, we have analyzed data from four nationally representative surveys of privately nationally representative surveys of privately held U.S. firms for evidence on how firms held U.S. firms for evidence on how firms controlled by males and females differ.controlled by males and females differ.

We have established a We have established a baseline set of stylized baseline set of stylized factsfacts about female-controlled firms. about female-controlled firms.



Female-controlled firms Female-controlled firms are much are much smallersmaller, no , no matter whether we measure size by sales, assets matter whether we measure size by sales, assets or employment.or employment.

Female-controlled firms are Female-controlled firms are youngeryounger, more likely , more likely to be organized as to be organized as proprietorshipsproprietorships and less and less likely to be organized as corporations.likely to be organized as corporations.

By industry, female-owned firms are more likely By industry, female-owned firms are more likely to be in to be in retail traderetail trade and and business servicesbusiness services, , but less likely to be in construction, secondary but less likely to be in construction, secondary manufacturing and wholesale trade.manufacturing and wholesale trade.

Female ownersFemale owners are are youngeryounger, , less less experienced experienced and and less educatedless educated..

Female-controlled firms have Female-controlled firms have fewer sources of fewer sources of financial servicesfinancial services and and shorter relationshipsshorter relationships..



With respect to With respect to credit-market outcomescredit-market outcomes, , female-controlled firmsfemale-controlled firms are significantly more are significantly more likely to report that they are “likely to report that they are “discourageddiscouraged” ” borrowers.borrowers.

However, this difference disappears once we However, this difference disappears once we control for firm and owner characteristics in a control for firm and owner characteristics in a multivariate framework.multivariate framework.

Our results suggest that observed gender Our results suggest that observed gender differences in credit-market outcomes are differences in credit-market outcomes are attributable to differences in other firm and firm-attributable to differences in other firm and firm-owner characteristics.owner characteristics.

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