International Council for Small Business 47th World Conference San Juan, Puerto Rico June 16-19, 2002

icsb 2002-032

Small Business: Financing and Profitability Yolanda Ruiz-Vargas, PhD Abstract This study attempts to explain if differences in small business financing sources exist among ethnic minority and non-minority groups in the United States. In addition, the profitability levels of ethnic minority and non-minority owned businesses are analyzed to determine if differences between these groups are influenced by the type of debt financing source used. It is hypothesized that non-minorities are relatively successful in obtaining funds from financial institutions because they might be perceived as good creditors as compared to other groups. The data for this study were drawn from the 1993 National Survey of Small Business Finances (NSSBF) prepared by Board of Governors of the Federal Reserve Board and U.S. Small Business Administration. The survey provides basic economic information on a sample of small and medium-sized business in the United States. To investigate whether funding differentials exist among the outlined groups, a multinomial logit model is employed to contrast different debt-financing strategies among small business owners. The model assumes that individuals select among different financing sources according to their socio-economic status, the economic characteristics of the business, financing opportunities in the region of residence, and individual tastes and preferences. The possible financing sources evaluated in the study are credit cards, capital leases, mortgage loans, other loans, and lines of credit. The empirical analysis employed to determine the possible sources of differences in profitability between minority and non-minority owned businesses employs an adapted version of the Oaxaca-Blinder method of decomposition. Oaxaca (1973) proposed a method of individual earnings decomposition that can be adapted to analyzed the sources of profit differential between the outlined groups. The results from the multinomial logit model of financing sources support the contention that non-minority owned businesses have a higher access to credit markets owing perhaps, to their

wealth status and economic power. It seems that non-minority owned businesses are more profitable than their counterparts. For ethnic minority-owned businesses, the type of financing source used is not as significant as for the non-minority owned businesses.

I. Introduction

Small businesses play a crucial role in the U.S. economy employing about fifty-three percent of the private work force and contributing forty-seven percent of sales. Since 1982, while small businesses in the United States have increased by about forty-nine percent, small firms owned by Asian American, American Indians and other minorities increased by 87.2 percent (U.S. Small Business Administration, 1996). The relative growth of minority-owned businesses is encouraging. To maintain this sustained growth, however, credit access must continue to be forthcoming for this type of small business survival. The efficient and effective provision of finance to small and medium-sized businesses has long been recognized as a key factor in ensuring that firms with genuine growth potential can expand and compete (Binks & Ennew, 1996). This study attempts to explain if differences in small business debt financing sources exist among ethnic minorities and non-minorities in the United States1. The data used for the study were drawn from the 1993 National Survey of Small Business Finances (NSSBF)2, conducted during 1994-95 for the Board of Governors of the Federal Reserve System and the U.S. Small Business Administration. The results from a multinomial logit model of financing sources show that ethnic minorities tend to have different access to credit markets when compared to non-minority small

1

Ethnic minorities are those individuals that are classified in the NSSBF as African- Americans, Hispanics, and Asians.

2

The 1993 National Survey of Small Business Finances provides economic and financial data on a nationally representative sample of small businesses in the United States. The reference period for the survey data was 1992. The survey collected demographic information on the owners and characteristics of the firm, information related to their financial service suppliers, and information on the recent credit history of the firm and its owners.

1

businesses, even after controlling for the business and/or business owner characteristics. In spite of this, relative to non-minority owned businesses, minority businesses have a higher debt ratio, which could potentially explain their limited access to credit markets. Furthermore, minority owned businesses resort mainly to credit cards as their main debt-financing source. We hypothesized here that non-minority owners are relatively successful in obtaining funds from financial institutions because they are good borrowers. Perhaps non-minority owned business face fewer liquidity constraints and might represent a group with greater return potential. This hypothesis is supported by the data: minority-owned businesses seemingly represent a higher risk for financial institutions. In addition, we analyze the profitability levels of minority and nonminority owned businesses to determine differences between these groups, and if the type of debt financing source used influences those differences. II. Background

Small business financing research has been limited because either the data available is too general, or the statistical techniques used have not been appropriate. Several researchers have used characteristics of business owners and the firms to explain the use or access of small businesses to credit/capital markets (Feldman et al, 1991; Van Auken & Carter 1989). Storey (1994) found that many of the personal characteristics of business owners were unrelated to bank lending: lending appears to be more related to the business owner’s use of personal savings and to the legal status of the business. Little research has been conducted on the financing sources between different types of small businesses. Notable exceptions include Bates’ (1991) descriptive study of commercial bank financing of small businesses owned by black and white males and Ruiz-Vargas’ analysis of the differences in financing sources between immigrants and natives in Puerto Rico (Ruiz-Vargas, 2000). As a result, the purpose of this study is to extend previous work by analyzing whether there are differences in small business financing sources between specific groups in the United States.

2

III. Methodology

Differences in Debt Financing Sources

To investigate whether funding differentials exist among the outlined groups (ethnic minorities and non-minorities), an econometric model that compares the selection among different financing sources by small business owners is employed. The model assumes that individuals select among different financing sources according to their socio-economic status, the economic characteristics of the business, financing opportunities in the region of residence, and individual tastes and preferences. A conditional probability function then identifies the likelihood that a business owner i selects financing source j. That depends on a vector of i’s exogenous variables (Xi):

P( financing source = j ) = exp(X i′β j )

(1)

J

∑ exp(X ′β ). i

j

j =1

P follows a logistic conditional probability function and βj represents K × 1 vector of coefficients for choice j. These parameters are estimated using maximum likelihood estimation (Greene, 1997). Table 1 provides a list of the variables contained in Xi. Table 1 Definitions of Variables

= 1 if business is located in the East region; 0 otherwise

East

MIDWEST

= 1 if business is located in the Midwest region; 0 otherwise

SOUTH

= 1 if business is located in the South region; 0 otherwise

WEST

= 1 if business is located in the West region; 0 otherwise

PARTNERSHIP

= 1 if business is organized as a partnership; 0 otherwise

CORPORATION

= 1 if business is organized as a corporation; 0 otherwise

SOLE PROPRIETORSHIP

= 1 if business is organized as a sole proprietorship; 0 otherwise

BOUGHT

= 1 if the business was bought; 0 otherwise

INHERITED

= 1 if the business was inherited; 0 otherwise

PUBLICLY

TRADED

= 1 if the business was publicly traded; 0 otherwise 3

FOUNDED

= 1 if the business was founded; 0 otherwise

NO HIGH SCHOOL

= 1 if the business owner has not completed high school; 0 otherwise

HIGH SCHOOL

= 1 if the business owner has completed high school; 0 otherwise

SOME COLLEGE

= 1 if the business owner has some college education; 0 otherwise

COLLEGE

= 1 if the business owner has a bachelor degree; 0 otherwise

ADVANCED DEGREE

= 1 if the business owners has a master or a doctoral degree; 0 otherwise

CONSTRUCTION

= 1 if the business is in the construction industry; 0 otherwise

MANUFACTURING

= 1 if the business is in the manufacturing industry; 0 otherwise

TRANSPORTATION

= 1 if the business is in the transportation industry; 0 otherwise

WHOLESALE

= 1 if the business is in the wholesale industry; 0 otherwise

RETAIL

= 1 if the business is in the retail industry; 0 otherwise

SERVICES

= 1 if the business is in the service industry; 0 otherwise

FIRMAGE

= Years of established

EMPLOYEES

= Number of employees

OWNAGE

= Age of business owner

MALE

= 1 if business owner is male; 0 otherwise

EXPERIENCE

= Years of business experience

The causes of national origin differences in financing sources, are estimated using (1) for ∧

a sample of non-minority small business owners, and the estimated coefficients of (1) (β j ) generate the distribution of minority owned businesses. This distribution assumes the financial market structure faced by non-minority owned businesses. The predicted probability that the ith ∧

minority owned business mainly finances its operations by source j (Pij ) given the characteristics of the non-minority owned business ( X jM ), is then given by: (2)

∧ ′ ∧ Pij = exp( X jM β j )

∧ M′ exp( X β ∑ j j ). J

j =1

Adding the jth financing-source predicted probabilities for each minority-owned business across this sample yields a predicted “financing source” distribution for minority businesses. The 4

predicted distribution assumes financial markets treat non-minority and minority owned small businesses equally.

The extent of inequality in financing sources can be then intuitively

quantified by a distribution dissimilarity index (DDI) of the form: J

∧

DDI = 0.5∑ PjM − PjM ,

(3)

j =1

where PjM represents the proportion of minority-owned businesses that use (or have access to) ∧

financing source j, and PjM represents the (multinomial logit) predicted proportion of minority businesses that finance their operations through source j if minority status differences in financing sources did not exist.

The index estimates the proportion of minority-owned

businesses that would have to change to other financing sources to equalize the distributions (the actual and the model-predicted) of the two groups.

Relative profitability between minorities and non-minorities The empirical analysis employed to determine the possible sources of differences in profitability between ethnic minority and non-minority owned businesses utilizes an adapted version of the Oaxaca-Blinder method of decomposition (Oaxaca, 1973). We first estimate a semilogarithmic profit function of the form (4)

Ln Profit = Xδ + V

where X is a vector of individual and firm-specific characteristics, δ represents a vector of parameters to be estimated, and V is a stochastic error term. Following Mincer (1974), these earnings equations consist of human capital variables, personal attributes and other control variables. Equation (4) is estimated for separate non-minority and minority samples. Oaxaca (1973) proposed a method of individual earnings decomposition that can be adapted to analyze the sources of profit differences between minority and non-minority businesses: (5)

Ln ProfitNM - Ln ProfitM = (XNM - XM)′ δNM + (δNM - δM)′ XM

The overall profit differential between ethnic minorities and non-minorities is decomposed into an explained and unexplained portion. The first term of the previous equation (explained portion) measures differences in the value of the regressors of the two groups, while the second term measures differences in the regression coefficients. Part of the second term may arise as a result of discriminatory treatment of minority-owned businesses in the financial markets.

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IV. Empirical Analysis

The data for this study were drawn from the 1993 National Survey of Small Business Finances (NSSBF) [Board of Governors of the Federal Reserve Board and U.S. Small Business Administration]. The survey provides basic economic information on a sample of 4,637 small and medium-sized businesses, such as sources of financing, age of the business, number of employees, business location, industry, and primary information of the business owners in the United States. For analysis purposes and according to the Small Business Administration, only firms with less than 500 employees and with use/access to specific debt financing sources were used, reducing the sample size to 3,401 firms. Table 2 presents basic descriptive statistics of the NSSBF sample partitioned according to the major groups of interest: ethnic minority and non-minority owned businesses. A higher concentration of small businesses can be found in the Southern region of the United States. Moreover, both ethnic minorities and non-minorities tend to organize their businesses as corporations (55.5 percent and 68.9 percent, respectively). The current owners founded more than 70 percent of the small businesses within both groups. Non-minority owned businesses tend to be older as compared to minority businesses and about 80 percent of small business owners have college education. In terms of owner’s experience, the non-minority sample reflects more business experience than their minority counterpart. Table 2 Descriptive Statistics

Non-Minorities Variable

Minorities

Mean

Standard Error

Mean

Standard Error

East

0.197

0.398

0.143

0.350

Midwest

0.262

0.440

0.147

0.355

South

0.323

0.468

0.381

0.486

West

0.218

0.413

0.329

0.470

Partnership

0.074

0.525

0.054

0.452

Corporation

0.686

0.464

0.555

0.497

Sole Proprietorship

0.240

0.408

0.391

0.488

6

Bought

0.210

0.408

0.185

0.389

Inherited

0.080

0.271

0.025

0.156

Publicly Traded

0.009

0.094

0.003

0.054

Founded

0.701

0.458

0.787

0.787

No High School

0.028

0.166

0.047

0.211

High School

0.178

0.382

0.144

0.352

Some College

0.236

0.425

0.273

0.446

College

0.336

0.473

0.305

0.461

Advanced Degree

0.222

0.416

0.232

0.422

Construction

0.000

0.019

0.000

0.000

Manufacturing

0.273

0.445

0.190

0.392

Transportation

0.045

0.208

0.038

0.191

Wholesale

0.194

0.395

0.206

0.404

Retail

0.182

0.386

0.136

0.343

Services

0.306

0.461

0.432

0.496

Firmage

16.193

14.387

11.528

9.345

Employees

43.911

71.529

12.569

30.982

Ownage

50.620

11.032

47.057

10.126

Male

0.831

0.375

0.802

0.399

Experience

20.709

11.091

15.841

9.153

N

2715

686

Tables 3 and 4 report the estimated coefficients of the multinomial logit model for nonminorities and minorities. The possible financing sources used by the sample groups are credit cards, capital leases, mortgage loans, other loans and lines of credit. The reported coefficients reflect the effects of a change in the independent variables on the likelihood of choosing a particular debt financing source over another (e.g. credit cards). The coefficients of the variables 7

PARTNERSHIP and CORPORATION (significant at the 1 percent level) suggest that nonminority businesses are more likely to use leases, mortgage loans, other loans and lines of credits as debt financing sources (see Table 3). On the other hand, the coefficients of the variable EAST suggest that non-minority owned businesses located in the Eastern region of the United States are less likely to use leases, mortgages, other loans, and lines of credit as a financing source. In terms of marginal effects, (Table 3 bold), the probability that non-minority owned businesses organized as corporations selected/used credit cards as a debt financing source would decreased by 24.4 percentage points.3 Likewise, non-minority businesses in the manufacturing sector are more likely to use lines of credit and, specifically the probability of selecting this type of debt financing source increases by 16.8 percentage points if firms are in the manufacturing sector. The coefficients of the variable COLLEGE suggest that non-minority business owners with bachelor degrees are less likely to use credit card financing. Moreover, in terms of marginal effects (Table 4 bold), the higher the level of education, the higher the probability that business owners select lines of credit as their debt financing source. Noteworthy is the fact that the marginal impact of education on the probability of selecting this financing source is much larger for ethnic minority than non-minority business owners.

Table 3 Multinomial Logit Estimates and Marginal Effects of Debt Financing Alternatives for Non Minorities

3

The partials were estimated by,

∂Pj ∂ Xi

J

∂Pj

j =1

∂X i

= Pj (β j − ∑ Pj β j ) , where

represents the marginal effect of an

independent variable on the probability of choosing a particular financing source. Partial derivatives are evaluated at the sample mean of each variable (Greene, 1997).

8

Credit Cards

Leases

Mortgages

Other

Lines of Credit

Loans

Constant East 0.037

Midwest -0.014

South 0.007

Partnership -0.090

Corporation -0.244

Bought -0.124

Inherited -0.117

Public Traded -0.346

High School -0.059

-4.144

-3.215

-2.021

-2.438

(0.811)

(0.606)

(0.434)

(0.345)

-0.345

-0.167

-0.062

-0.187

(0.23)

(0.244)

(0.205)

(0.157)

-0.016

-0.003

0.008

-0.026

-0.337

0.273

0.006

0.112

(0.232)

(0.219)

(0.196)

(0.147)

-0.029

0.018

-0.005

0.030

-0.331

-0.071

-0.119

0.049

(0.213)

(0.218)

(0.188)

(0.139)

-0.023

-0.004

-0.012

0.032

0.558

0.412

0.323

0.454

(0.177)

(0.133)

(0.131)

(0.106)

0.019

0.008

-0.001

0.062

1.561

0.270

0.677

1.447

(0.234)

(0.179)

(0.160)

(0.124)

0.054

-0.046

-0.021

0.258

0.383

0.569

0.805

0.594

(0.206)

(0.194)

(0.165)

(0.134)

-0.004

0.011

0.047

0.070

0.374

0.844

0.341

0.618

(0.346)

(0.296)

(0.299)

(0.208)

-0.002

0.035

-0.008

0.092

2.066

2.027

-0.885

1.785

(1.134)

(1.240)

(1.423)

(1.039)

0.065

0.067

-0.039

0.253

1.147

0.795

-0.201

0.180

(0.765)

(0.564)

(0.395)

(0.309)

0.071

0.048

-0.050

-0.010

9

Some College -0.068

College -0.108

Advanced Degree -0.093

Manufacturing -0.127

1.183

0.715

-0.146

0.250

(0.759)

(0.560)

(0.387)

(0.304)

0.071

0.038

-0.048

0.006

1.169

0.852

0.143

0.466

(0.758)

(0.559)

(0.383)

(0.303)

0.060

0.038

-0.029

0.039

1.241

0.817

-0.039

0.391

(0.763)

(0.568)

(0.394)

(0.308)

0.069

0.040

-0.045

0.029

0.236

0.360

0.290

0.832

(0.219)

(0.231)

(0.200)

(0.134)

-0.015

-0.007

-0.019

0.168

10

Table 3(cont) Multinomial Logit Estimates and Marginal Effects of Debt Financing Alternatives for Non Minorities

Credit Cards

Leases

Mortgages

Other

Lines of Credit

Loans

Transportation -0.119

Wholesale -0.193

Retail -0.045

Firmage 0.000

Male -0.087

Experience -0.003

1.014

0.563

0.518

0.528

(0.334)

(0.408)

(0.359)

(0.263)

0.045

0.012

0.013

0.049

0.240

0.645

0.931

1.121

(0.219)

(0.254)

(0.209)

(0.155)

-0.032

-0.002

0.033

0.195

0.171

0.916

0.739

-0.052

(0.230)

(0.207)

(0.186)

(0.153)

-0.001

0.061

0.073

-0.090

-0.025

-0.020

-0.006

0.006

(0.009)

(0.008)

(0.006)

(0.004)

-0.002

-0.002

-0.001

0.003

0.462

-0.076

0.134

0.592

(0.225)

(0.192)

(0.173)

(0.138)

0.012

-0.030

-0.021

0.126

0.023

0.032

0.005

0.009

(0.009)

(0.008)

(0.007)

(0.005)

0.001

0.002

-0.000

-0.000

χ2

633.069

N

2715

Notes: ( i ) The logit coefficients are followed by the standard errors (in parentheses) and the partial

11

derivatives (in bold) evaluated at the sample means. The partials for dummy variables are interpreted as the difference in predicted probability between zero and one. ( ii ) The reference region is West; the reference business organization is sole proprietorship; the reference “start-up” category is “original founder”; the reference educational category is no high school; construction and services are the reference industry. Table 4 Multinomial Logit Estimates and Marginal Effects of Debt Financing Alternatives for Minorities Credit Cards

Leases

Mortgages

Other

Lines of Credit

Loans

Constant East -0.050

Midwest -0.050

South -0.045

Partnership -0.050

Corporation -0.147

Bought

-3.441

-4.046

-2.313

-3.675

(1.006)

(1.028)

(0.665)

(0.723)

-0.548

-0.357

0.750

0.245

(0.496)

(0.494)

(0.388)

(0.330)

-0.049

-0.027

0.090

0.036

-0.550

-0.350

0.371

0.442

(0.498)

(0.470)

(0.397)

(0.316)

-0.049

0.027

0.037

0.089

-0.299

-0.607

0.258

0.449

(0.345)

(0.360)

(0.302)

(0.244)

-0.029

-0.041

0.022

0.094

0.186

0.323

0.192

0.191

(0.415)

(0.285)

(0.269)

(0.243)

0.006

0.012

0.011

0.021

1.351

0.024

0.047

0.796

(0.364)

(0.328)

(0.265)

(0.222)

0.076

-0.017

-0.039

0.127

0.338

0.721

0.488

-0.047

(0.394)

(0.344)

(0.304)

(0.290)

12

-0.057

Inherited 0.079

Public Traded 2.302

High School -0.131

Some College -0.224

College -0.300

Advanced Degree -0.181

Manufacturing -0.108

0.016

0.035

0.051

-0.046

1.460

-90.503

1.580

0.211

(0.849)

(152.290)

(0.715)

(0.746)

0.121

-0.549

0.246

0.103

-11.628

-11.122

0.020

-13.083

(603.670)

(602.240)

(1.463)

(545.420)

-0.489

-0.359

0.707

-2.161

-0.015

0.968

-1.070

1.420

(0.958)

(0.875)

(0.635)

(0.635)

-0.022

0.040

-0.190

0.304

0.835

1.199

0.135

1.743

(0.849)

(0.841)

(0.525)

(0.625)

0.026

0.042

-0.050

0.207

0.848

1.132

0.405

1.743

(0.855)

(0.861)

(0.531)

(0.625)

0.014

0.028

-0.035

0.292

0.916

0.896

-0.475

1.268

(0.861)

(0.877)

(0.568)

(0.635)

0.039

0.030

-0.122

0.234

-0.891

-0.232

0.058

1.143

(0.582)

(0.486)

(0.380)

(0.272)

-0.083

-0.027

-0.025

0.243

Table 4(cont) Multinomial Logit Estimates and Marginal Effects of Debt Financing Alternatives for Minorities

Credit Cards

Leases

Mortgages

Other

Lines of Credit

Loans

Transportation

1.218 13

0.937

-0.380

0.302

-0.079

Wholesale -0.195

Retail -0.094

Firmage 0.001

Male -0.027

Experience -0.008

(0.600)

(0.668)

(0.820)

(0.596)

0.077

0.045

-0.078

0.035

0.324

-0.063

0.640

1.176

(0.403)

(0.455)

(0.333)

(0.270)

-0.007

-0.029

0.030

0.201

0.216

0.643

0.844

0.122

(0.451)

(0.410)

(0.351)

(0.355)

0.001

0.026

0.090

-0.022

0.009

-0.022

0.012

0.002

(0.019)

(0.024)

(0.017)

(0.016)

0.001

-0.001

0.002

0.000

0.070

0.731

0.210

-0.069

(0.374)

(0.468)

(0.321)

(0.253)

0.001

-0.040

0.021

-0.034

0.014

0.063

0.015

0.039

(0.021)

(0.020)

(0.018)

(0.015)

-0.000

0.003

-0.000

0.006

χ2

185.795

N

686

Notes: ( i ) The logit coefficients are followed by the standard errors (in parentheses) and the partial derivatives (in bold) evaluated at the sample means. The partials for dummy variables are interpreted as the difference in predicted probability between zero and one. ( ii ) The reference region is West; the reference business organization is sole proprietorship; the reference “start-up” category is “original founder”; the reference educational category is no high school; construction and services are the reference industry. The econometric model [equations (1) - (3)] used to explain differences in debt financing sources was estimated for the minority and non-minority owned business samples from the NSSBF data. Table 5 presents the actual and predicted share of financing sources resulting from assigning

14

minority-owned businesses to financing alternatives according to the non-minority owned businesses multinomial logit model. Table 5 Actual and (Multinomial Logit) Predicted Shares of Financing Alternatives (Figures are in Percentages)

Non-Minorities

Minorities

Financing Alternatives

Actual

Actual

Predicted

Credit Cards

29.72

41.40

36.71

Lease Loans

7.73

8.60

7.46

Mortgage Loans

8.21

8.45

7.62

Other Loans

11.79

13.99

12.26

Lines of Credit

42.54

27.55

35.95

N

2715

DDI

686 14.99

8.40

At first glance, the actual distribution of financing sources used by minority and nonminority owned businesses reveal some differences in the debt financing sources selected or used by these groups. Non-minority owned businesses resort mainly to lines of credit, as opposed to minority owned businesses who mainly tend to use credit cards as their debt financing source. The distribution dissimilarity index (DDI) was calculated for both actual and predicted distributions of 15

financing sources for minority business owners. The index for the actual distribution of minorityowned businesses shows that 14.99 percent of the businesses in this group would have to change to other financing sources to be treated equally by the credit markets. However, the multinomial logit model predicts that only 8.40 percent of the minority-owned businesses would have to change financing sources to have a similar distribution to that predicted by the model. So far, the empirical analysis done has show that there are some differences in debt financing sources between minority and non-minority owned businesses. However, it is important to determine if also these businesses differ in profitability levels and whether these profitability levels are related to inter-group differences regarding debt financing sources. Tables 6 and 7 presents the estimations of the model used to explain the factors that affect profitability among these businesses. Table 6 Regression Equation of Log Profit

Non-Minorities Variable

Minorities

Coefficient

Standard Error

Coefficient

Constant

9.045***

0.323

9.106***

0.610

Partnership

0.535***

0.083

-0.091

0.147

Corporation

0.907***

0.091

0.609***

0.151

Bought

0.250***

0.095

0.140

0.184

Inherited

0.291

0.182

0.563

0.602

Publicly Traded

1.947***

0.278

-1.941***

0.375

Firmage

0.012***

0.004

0.035***

0.010

High School

0.233

0.244

0.485

0.547

Some College

0.338

0.245

0.568

0.540

College

0.698***

0.242

0.767

0.551

Advanced Degree

0.815***

0.250

0.885

0.577

Experience

0.014***

0.004

-0.075

0.010

Male

0.377***

0.111

0.205

0.198

16

Standard Error

East

0.126

0.126

-0.368

0.275

Midwest

-0.070

0.116

0.207

0.222

South

0.038

0.113

-0.070

0.158

Manufacturing

0.337***

0.109

0.359*

0.191

Transportation

0.559***

0.216

0.378

0.328

Wholesale

0.197

0.124

0.609***

0.194

Retail

-0.136

0.120

0.418*

0.241

Access1

0.530***

0.147

0.336

0.266

Access2

-0.617***

0.149

-0.242

0.257

Access3

-0.059

0.185

-0.330

0.298

Access4

0.057

0.206

-0.073

0.343

Adjusted R2

0.251

0.115

N

1986

473

Notes: The Access_ variables were used to identify the impact of a particular debt financing source in the small business profits. The use of mortgage loans was used as the base debt financing source. Access1: firms used lines of credit as a debt financing source Access2: firms used credit cards as a debt financing source Access3: firms used other loans as a debt financing source Access4: firms used leases as a debt financing source *** Significant at the 1% level *

Significant at the 10% level

Table 7 Average Log Profits and Decomposition of the Differential

Log Profits

Decomposition

17

(XNM - XM)′ δNM

(δNM - δM)′ XM

Non-

Minorities

Minorities 11.36

10.75

0.61

Percentage explained by each component

0.45

0.16

74.0

26.0

Non-minority owned businesses are more profitable than their counterparts. To explain these inter-group differences, several factors were analyzed including the debt financing sources used. It seems that those non-minority owned businesses who resort to lines of credit as their debt financing source are more profitable.

However, credit card usage by non-minority owned

businesses is correlated with lower profits. For minority-owned businesses, the type of financing source used is not as significant as for the non-minority owned businesses. The adjusted R2 value shows that the model for this group explains about 25 percent of the variation in profits. The analysis suggests that even after controlling for several factors minority-owned businesses are less profitable than non-minority businesses. In Table 7, the mean of log profits is broken down into the portion explained by the specific business or business owners mean characteristics of ethnic minorities and non-minorities, and the unexplained portion. A relatively large explained portion suggests that part of the profit differential between the groups arises because of specific characteristics of the business and/or business owners. Even though the profits for non-minorities are higher, about seventy four percent of the difference in profits can be explained by specific variables. Only twenty-six percent is unexplained, meaning that there is little evidence of discriminatory differentials affecting the profits of minorities against non-minorities. V. Sources of Financing Differentials

Even though the previous empirical analysis does not show substantial unexplained differences in profitability between minority and non-minority owned businesses, financing sources may differ across groups because of credit rationing (Dow, 1996). This study seems to reject the contention that high levels of human capital will ease the access of business to credit 18

markets. In this study, both ethnic minority and non-minority business owners have higher levels of education, albeit, only for non-minorities (whose education variable is significantly higher at the one percent level). Perhaps it can be argued that even when both groups are highly educated, nonminorities seem to be perceived as good borrowers in terms of character. Another reason for this differential is that business owners might face liquidity constraints that affect capital flows owing to imperfect information in capital markets (Evans and Jovanovic, 1989). In addition, banks vary their willingness to extend credit on the basis of their expectations of the viability of investment projects, the value of the collateral, and the type of industry the firm is in (Dow, 1996; Petersen & Rajan, 1995). That is, if banks perceive that businesses are not as good borrowers as they require, the businesses will have a hard time finding their required financing. Attempting to explain the access to credit markets, debt levels for minority and nonminority owned businesses were evaluated. Minority-owned businesses have a higher debt ratio, meaning that for each dollar invested in total assets, minority owned businesses have incurred about seventy six percent of it in total debt, creating high levels of leverage, which make it more difficult for firms to meet their fixed obligations. It could be argued that minority-owned businesses are sending riskier signals to the market because of the amount of debt used with respect to their total assets, and this may partially explain their limited access to credit markets. According to the NSSBF, about 45 percent of the minority-owned businesses applied for a loan. Moreover, the financial institutions for several reasons rejected about 30 percent of those applications. Some of the reasons for rejection were: insufficient collateral (20 percent), poor balance sheet and low sales (20 percent), business credit history (12 percent), personal credit history (7 percent), high leverage (4 percent), and the possible inability to repay the loans (3 percent).

Approximately eight percent of the loans were rejected without further reasons or the

financial institutions did not disclose them. Table 8 Distribution of Loan and Debt Information (Figures are in Percentages)

Groups Loans

Minorities

NonMinorities

19

Total

Applied

45.04

54.07

52.25

Applied/Rejected

30.10

11.98

7.90

Do not apply

54.96

45.93

47.75

686

2715

3401

75.55

64.80

N Debt Ratio

Evidence suggests that many small businesses use credit cards as a form of financing. Firms with little experience or credit history are thought to use credit card loans as substitutes for traditional bank loans. Some banks have actively promoted the use of credit cards as a costeffective method of delivering credit lines to small businesses (Cole & Wolken, 1995).

This

evidence may support the high use of credit cards by minority-owned businesses as their debt financing source. Several other reasons might be used to explain the differential in debt financing sources. The longer the relationship firms had with a financial institution, the higher the probability of higher access to credit markets. In addition, a firm’s access to capital (debt financing) may depend upon the tangibility or the liquidity of its assets. A good proxy can be the industry the firm belongs to. Considering this, then non-minority owned businesses may have higher access to capital because about 64 percent of the firms are either in the manufacturing, wholesale or retail trade industry, as opposed to 52 percent of the minority-owned businesses.

VI. Summary and Conclusions

According to the data used in this study, minority-owned businesses have less access to credit markets, in terms of the debt financing sources used. Some theoretical foundations were used to explain these findings; 1) non-minorities face fewer liquidity constraints than minorities (perhaps because the type of industry the business is at), 2) a higher level of human capital 20

(formal education) reduces the possibility of credit rationing, especially for non-minorities, and 3) the higher level of debt incurred by minorities makes them a riskier lending group for financial institutions, reducing the access of this group to credit markets. This may partially explain why minorities resort to credit cards as their main debt financing source. This study yields insights into the specific factors that may affect the selection of financing sources by small businesses in the United States. Understanding the factors that affect small business financing could help explain differences in sectoral participation and firm performance across ethnic minority status in the small business sector. Because the small business sector represents a growing and potentially profitable market for banks, these findings could help policy makers to formulate better policies to foster small business formation among minorities in the United States. In addition, this study could be used as a foundation to evaluate the use and access that small firms have to credit markets in other parts of the world. For example, it would be policy relevant to evaluate the use of equity financing to stimulate small business growth in the economy, and see how this differs, if that is the case, among different minority groups.

References:

Bates, Timothy. (1991). Commercial Bank Financing of White-and-Black-Owned Small Business StartUps. Quarterly Review of Economics and Business. 31(1):64-80. Binks, Martin R. and Christine T. Enew.(1996). Growing Firms and the Credit Constraint. Small Business Economics. 8(1): 17-25. Cole, Rebel A. and John D. Wolken. (1995). Financial Services used by Small Businesses: Evidence from the 1993 National Survey of Small Business Finances. Federal Reserve Bulletin. 81(7): 629667. Dow, Sheila C. (1996). Horizontalism: a critique. Cambridge Journal of Economics. 20: 497508. Evans, David S. and Borjan Jovanovic.(1989). An Estimated model of Entrepreneurial Choice Under Liquidity Constraints. Journal of Political Economy. 97(4): 808-827.

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Feldman, Howard D., Christine S. Koberg and Thomas J. Dean. (1991). Minority Small Business Owners and their Paths to Ownership. Journal of Small Business Management. 29(4): 12-27. Greene, William H. (1997) Econometric Analysis. New Jersey: Prentice Hall. Mincer, J. (1974). Schooling, experience and earnings. National Bureau of Economic Research. New York Oaxaca, Ronald.(1973). Male-Female Wage Differentials in Urban Labor Markets. International Economic Review. 14:693-709. Petersen, Mitchell A. and Raghuram G. Rajan. (1995). The effect of Credit Market Competition on Lending Relationships. The Quarterly Journal of Economics. May: 407-443. Ruiz-Vargas, Yolanda.(2000). Small Business Financing Sources between Immigrants and Natives in Puerto Rico”. The Quarterly Review of Economics and Finance. 40(3): 387-399. Storey, D.J. (1994). New firm Growth and Banking Financing. Small Business Economics. 6(2): 139-150. U.S. Small Business Administration. Office of Advocacy. Handbook of Small Business Data 1996 ed. Van Auken, Howard E. and Richard B. Carter. “Acquisition of Capital by Small Business”. Journal of Small Business Management. 27(2). (Apr. 1989): 1-9.

About the Author

Author: Yolanda Ruiz-Vargas, PhD Company or Institution: University of Puerto Rico – Mayagüez Campus Country: Puerto Rico E-mail: [email protected]

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