Discrimination in Capital Markets, Broadcast/Wireless Spectrum

Service Providers and Auction Outcomes

By

William D. Bradford, Ph.D.

Endowed Professor of Business and Economic Development

and

Professor of Finance

School of Business Administration

University of Washington

December 5, 2000
ii

CONTENTS

 Page

Background of the Author............................................iii

Executive Summary...................................................iv

Part 1. Introduction.....................................................1

Part 2. Background and Literature Review..........................3

Part 3. Theory, Data and Statistical Approach......................7

Part 4. Obtaining Debt Financing in Capital Markets.............11

Part 5. The Probability of Winning in Auctions....................22

Part 6. Discussion of the Findings and Recommendations........27

References...............................................................29

Tables.....................................................................30
iii

William D. Bradford, Ph.D.

William Bradford is Endowed Professor of Business and Economic Development, and

Professor of Finance at the School of Business, University of Washington. He earned a

B.A. in Economics from Howard University, an MBA in Finance and a Ph.D. in Finance

and Economics from Ohio State University. He was Assistant Professor and Associate

Professor of Finance at Stanford University 1972-1980. From 1980-94, he was Professor

of Finance at the University of Maryland, College Park, where he also served as Chair of

the Finance Department and Associate Dean at the Maryland Business School. He has

held visiting professorships at New York University, UCLA and Yale University. From

1994 to 1999 he was Dean and Kirby Cramer Professor of Business at the University of

Washington, and was awarded the Endowed Professorship in Business and Economic

Development upon joining the faculty as a professor.

Professor Bradford’s teaching areas are business finance, money and capital markets, and

management of financial institutions. His areas of research include small and minority

business issues, entrepreneurship and corporate finance. He has written three books and

more than forty articles on finance during his career. He has been a consultant to

numerous firms and has had international lectureships in corporate finance and

entrepreneurship in Indonesia, Egypt and the Ivory Coast.
iv

Executive Summary

The objective of this study is to assist the Federal Communications Commission

(“FCC”) in implementing Sections 257 and 309(j) of the Telecommunications Act of

1996, as amended. Section 257 requires the FCC to identify and eliminate market entry

barriers for small telecommunications businesses. Section 309(j) requires the FCC to

further opportunities in the auctioning of spectrum-based services for small businesses

and businesses owned by women and minorities.

This study is comprised of two parts. The first part examines whether capital

market discrimination may be a market entry barrier for firms seeking to acquire FCC

licenses. Specifically, it explores whether and to what extent applicants for FCC licenses

may have suffered discrimination in capital markets. The second part of this study

compares the success rates of firms owned by minorities, women and non-minorities in

auctions for FCC wireless licenses. Specifically, it examines whether, when controlling

for relevant variables, race or gender is a statistically significant variable in predicting

success in FCC auctions.

The first part of this study stems from a recognition that a lack of adequate capital

is a critical barrier to entering business, business growth, and success in auctions for

broadcast/wireless spectrum service providers. This study investigates the extent to

which the capital markets discriminate against women and minority-owned

broadcast/wireless firms in providing debt capital. Discrimination in capital markets is

indicated when controlling for relevant variables, race or gender is a statistically

significant variable in predicting an applicant’s success in capital markets. Prior studies
v

(reviewed in this report) have not been industry specific, but they lead to the hypothesis

that minority broadcast license holders or minorities attempting to acquire broadcast

licenses experience capital market discrimination.

 The second part of this study analyzes the differences in the ability to acquire a

wireless license between businesses owned by minorities and women, and other

businesses. It tests the hypothesis that minority/women applicants in FCC Spectrum

Auctions encounter discrimination in capital markets. If capital market impediments are

pervasive, then they may lead minorities and women to be less successful in FCC license

auctions and in purchasing FCC licenses in the secondary markets. The objective of this

research is to test each of these hypotheses.

These hypotheses were tested on data from a survey of current broadcast license

holders and a survey of participants in auctions for wireless licenses. For broadcasters,

the survey data covers only broadcast licensees and not applicants who may have been

unsuccessful.1 The FCC contracted for the survey for the fall of 1999. For broadcasters,

the survey instrument was mailed to a random sample of non-minority license holders. In

addition, a census was taken of minority license holders. The survey data provide a

financial portrait of the firm prior to the acquisition of its most recent license, and the

year of the most recent acquisition. For the most recent acquisition, information on the

number of attempts to obtain debt financing from financial institutions is contained in the

data. The survey instrument also provided data on the interest paid on the debt that

financed a broadcaster’s most recent license acquisition. There are ninety-nine current

 

1

 The FCC wanted to include unsuccessful applicants, but the data on the latter were old and had many

incorrect addresses. Thus it was felt that the survey of unsuccessful applicants was too incomplete to be

useful. The data only covers broadcasters who obtained licenses through the comparative hearings process

or on the secondary market—no broadcast auctions were included.
vi

license holders in the data, but the statistical tests use from thirty-eight to sixty-one firms

because of nonresponse for some items.

For wireless firms, financial data and other data on auction applicants are from the

FCC Auction Application Survey. Auction applicants were asked to discuss their latest

license acquisition or attempted acquisition. In addition, information was gathered on

how the license applicants had financed the acquisition and the financial measures

describing their financial condition at that time. The Auction Survey data are

supplemented by data from the FCC Website. Data on both the number of qualified

bidders and the number of applicants are taken from the FCC Website. The statistical

tests on capital market experience of auction participants included twenty to forty-one

firms because of nonresponse on some items. The auction outcome analyses contained as

few as ninety-six and as many as 170 firms because some data were not available on all

of the 251 respondent firms.

 The findings of the study can be divided into two parts. The first part is the capital

market experience of minorities and women compared to non-minorities and the second

part is the auction experience of minorities and women compared to non-minorities.

Minority- and Women-Owned Firms in the Capital

Markets Compared to Non-Minority Owned Firms

Broadcast Firms: The capital market experiences of current broadcast license

holders were examined in regression analyses that considered, in addition to race and

gender, the year of application or acquisition, business cash flow, equity, and size of firm

(full-time employees). It was found that minority broadcast license holders were less

likely to be accepted in their applications for debt financing, after controlling for the

effect of the other variables on the lending decision. This finding was statistically
vii

significant in the each of the three models examined in the report. It was also found that

the applications for debt financing from female applicants were less likely to be

approved, but this finding was not statistically significant. The impact of race on the rate

of interest paid on loans was also examined in regression analyses that included the

variables above, plus collateral and personal guarantees on the loan as variables. It was

found that the minority borrowers paid higher interest rates on their loans, after

controlling for the impact of the other variables. This finding was statistically significant.

Gender was not found to be statistically significant in predicting differences in interest

rates paid by borrowers.

Wireless Firms: The study also analyzes the experience of wireless applicants’

access to capital, and regression analysis was used to examine the data. The models used

considered variables that included the number of licenses won before the latest auction,

whether or not the business was a startup, the type of auction in which the respondent

participated, in addition to the financial variables listed above for broadcast applicants. It

was found that loan applications of minority wireless firms were less likely to be

accepted than those of nonminority firms, after controlling for the effect of the other

variables on the lending decision. This finding was statistically significant in each of the

three models tested. It was also found that the applications for debt financing from

female applicants were less likely to be approved, and this result was also statistically

significant. The impact of race on the rate of interest paid on loans was also examined in

a regression analysis that considered the variables above, plus whether the applicant

provided collateral and a personal guarantee on the loan. It was found that the minority

borrowers paid higher interest rates on their loans, after controlling for the impact of the
viii

other variables. Gender was not found to be statistically significant in predicting

differences in interest rates paid by borrowers.

The Auction Experiences of Minority- and Women-Owned

Firms Compared to Those of Non-Minority Owned Firms

With regard to the auction experience of minority- and women-owned businesses,

seven regression models were developed to examine differences in success between

minority-owned and women-owned auction participants and other participants. The

seven models that were utilized controlled for time, auction group, startup status, size of

company, financial strength, and competition. The data support the hypothesis that

minority status results in a lower probability of winning in spectrum auctions. That is, for

each of the seven models, the probability of a minority-owned firm winning a license in

the firm’s most recent auction is lower than the probability of a non-minority owned firm

winning a license in its most recent auction, and the differences are statistically

significant. The data also support the hypothesis that gender has a negative impact on

winning spectrum auctions when we control for the variables above, but less strongly. In

five of the seven models tested, female gender was statistically significant in predicting a

lower probability of success in spectrum auctions. In the other two models female gender

was found to reduce the probability of success, but the results were not statistically

significant.

Discussion of Results and Recommendations

 It is found that minority-owned firms and women-owned firms are less likely to

receive debt financing in the capital markets than nonminority–owned firms. In addition,

those minority firms that received loans from financial institutions pay higher interest

rates than nonminority firms when we control for relevant variables. These result hold
ix

for both wireless license applicants and broadcast licensees. Second, both minority and

women-owned businesses have lower probabilities of winning wireless spectrum licenses

in auctions after controlling for relevant variables.

It must be noted that the results of both the capital markets and auctions analyses

are not fully conclusive due to incomplete data and in some cases small sample sizes. All

of the analyses reported here explained a statistically significant part of the variation in

loan acceptance, interest rates on loans, and auction outcomes. But none of the models

explained all of the variation, meaning that all of the relevant variables may not be in the

models that are utilized. Nevertheless, the consistent direction of the results suggest that,

without a remedy for capital market discrimination, minority- and women-owned

businesses are inappropriately disadvantaged in obtaining FCC broadcast and wireless

licenses.

The recommendations that result from this study are as follows. First, it is

recommended that the FCC develop and maintain programs that seek and encourage the

participation of minorities and women in the ownership of broadcast and spectrum

licenses. Such efforts may offset the disadvantages that minority- and women-owned

firms experience through the capital markets. Second, it is recommended that the FCC

continue to examine the effect of capital markets on the participation of minority- and

women-owned firms in the ownership and operation of broadcast and spectrum licenses.

Although this study indicates that minority- and women-owned firms are inappropriately

disadvantaged in this line of business due to capital market forces, more research is

needed to confirm and track these effects over time.
Discrimination in Capital Markets, Broadcast/Wireless Spectrum

Service Providers and Auction Outcomes

Part 1. Introduction

The objective of this study is to assist the FCC in implementing Sections 257 and

309(j) of the Telecommunications Act of 1996, as amended. Section 257 requires the

FCC to identify and eliminate market entry barriers for small telecommunications

businesses. Section 309(j) requires the FCC to further opportunities in the auctioning of

spectrum-based services for small businesses and businesses owned by women and

minorities.

This study consists of two parts. The first part examines whether and to what

extent applicants for FCC licenses have experienced discrimination in capital markets.

The history of capital market discrimination is well documented in regulatory, legal and

economic literature. Although this literature is quite comprehensive, the literature does

not specifically investigate whether and to what extent, if any, capital market

discrimination affects minority- and women-owned broadcast/wireless spectrum service

providers. In addition, this literature does not specifically examine whether and to what

extent capital market discrimination adversely affects the opportunities for minorities and

women to obtain FCC spectrum licenses and to enter the broadcast and wireless markets

for the first time. However, this literature does suggest the hypothesis that the inability to

obtain adequate capitalization may be a critical market entry barrier for those seeking to

become broadcast and wireless spectrum service providers and a critical barrier to

business growth in these fields.
2

This study investigates the extent to which the capital markets discriminate

against women and minority-owned broadcast/wireless firms in providing debt capital.

Discrimination in capital markets is indicated when controlling for relevant variables,

race or gender is a statistically significant variable in predicting an applicant’s success in

capital markets. A review of prior research in Part 2 suggests the hypothesis that minority

broadcast license holders or minorities attempting to acquire broadcast licenses

experience capital market discrimination. Another hypothesis is that minority/women

applicants in FCC Wireless Spectrum Auctions encounter discrimination in capital

markets. The objective of this research is to test these hypotheses.

The second part of this study explores the relative success in FCC auctions of

firms owned by minorities, women, and non-minorities. Through logistic regression

analyses, it examines whether, when controlling for relevant variables, race or gender is a

statistically significant variable in predicting success in FCC wireless auctions.

Thus the findings of this study may be divided into two parts. First, with regard

to capital market discrimination, the data suggest that race and gender matter in the

approval/denial of loans from financial institutions and that race affects interest rates on

approved loans. Specifically, after controlling for relevant variables, minority-owned

firms and women-owned firms were less likely to receive debt financing in the capital

markets than nonminority–owned firms. In addition, those minority firms that received

loans from financial institutions paid higher interest rates than non-minority firms when

we controlled for the relevant variables. These results held for both wireless license

applicants and broadcast licensees. Second, we examined the probability of

minorities/women-owned business winning spectrum licenses in FCC Spectrum
3

Auctions. The data support the hypotheses that both minority and women-owned

businesses have lower probabilities of winning spectrum licenses after controlling for

relevant variables.

It must be noted that the results of both the capital markets and auctions analyses

are not fully conclusive due to incomplete data and in some cases small sample sizes. All

of the analyses reported here explained a statistically significant part of the variation in

the loan acceptance, interest rates on loans, and auction outcomes. But none of the

models explained all of the variation, meaning that all of the relevant variables may not

be in the models that are utilized. Nevertheless, the consistent direction of the results

suggest that, without a remedy for capital market discrimination, minority and womenowned businesses are inappropriately disadvantaged in obtaining FCC broadcast and

wireless licenses.

The rest of this report is organized as follows. Part 2 discusses previous studies

on discrimination in capital markets relating to applications for debt financing of

minority- and women-owned firms. Part 3 discusses the hypotheses, data and statistical

approach to the study. Part 4 reports the analyses on experience of minority and womenowned firms in seeking debt financing in the capital markets. Part 5 discusses the results

of the analyses on the experience of minority- and women-owned firms in spectrum

auctions. Part 6 discusses the findings in terms of policies for the FCC.

Part 2. Background and Literature Review

The economic studies of discrimination draw on the analyses in Nobel Laureate

Gary Becker’s The Economics of Discrimination (1957). His main contribution was to

translate the notion of discrimination into economic terms. His model, when translated
4

into lending decisions of financial institutions, holds that discrimination will result in

either a) higher interest rates being charged to undesired groups having otherwise similar

characteristics to the desired group, or b) requiring better characteristics (i.e. a lower

expected default rate) for the undesired group at any given interest rate. In other words,

firms of the disfavored group might either be appraised more rigorously or, be given less

favorable terms on the loan.

Subsequent to Becker’s analysis, there has evolved a small but growing number

of empirical studies on discrimination against minority- and women-owned businesses in

the U.S. capital markets. One problem hindering unambiguous analysis of discrimination

has been the availability of the appropriate data. Blanchflower, Levine and Zimmerman

(1998), Bostic (1999), Cavalluzzo and Cavalluzzo (1998) and Cavalluzzo, Cavalluzzo

and Wolken (2000) used the National Survey on Small Business Finances (NSSBF) to

study discrimination against minorities and women in capital markets. Bates (1991)

(1997) and Bates and Bradford (1994) use the Characteristics of Business Owners Survey

(CBO). Ando (1988) utilizes a special survey. On the whole, these studies indicate that

discrimination exists in markets. But the evidence is not complete because of problems

with sufficient data. Cavalluzzo, Cavalluzzo and Wolken use data from the 1993 NSSBF

to examine the degree to which information on firm and owner characteristics explain

observed differences in the capital market experiences of small businesses, with special

emphasis on minority businesses. They supplement NSSBF data with information

furnished by the Board of Governors of the Federal Reserve on local bank market

structure and Dun and Bradstreet firm credit (risk) scores. Their analyses reveal

substantial unexplained differences in denial rates between black- and non-minority-
5

owned firms: the denial rates of black-owned firms were higher. Blanchflower et al also

used the 1993 NSSBF data. They found that, after controlling for credit worthiness and

other relevant factors, black-owned firms were twice as likely to be denied credit. They

also found that black-owned firms pay higher interest rates than non-minority-owned

firms after controlling for firm characteristics. Bostic used the 1993 NSSBF data

supplemented with detailed demographic and economic data on each firm’s local market.

He found a statistically significant difference in denial rates between non-minority-owned

firms and those owned by blacks, after controlling for firm traits. He also found that the

differences between the treatments of black and non-minority businesses could be related

to business location, which implies an association between discrimination and redlining.

Cavalluzzo and Cavalluzzo used the 1988-89 NSSBF data to examine discrimination

against minority businesses in the capital markets. They found that black and Hispanic

firms had statistically significantly higher probabilities of loan application rejection than

the non-minority firms, after controlling for firm and owner traits. But they concluded

that they could not unambiguously show that there was prejudice leading to their results,

because of the small sample size, and other data issues. Their conclusion here led to the

study above (Cavalluzzo, Cavalluzzo and Wolken).

Bates (1997) used data from the 1987 CBO. The dependent variable in his study

was the loan amount, and the independent variables were firm and owner traits that

would normally be considered in the lending decision of a financial institution. Bates

found that holding other variables constant, financial institutions loaned more per dollar

of equity to non-minority firms than to black-owned firms. Bates (1991) used the 1982

(CBO) data and finds the same result: controlling for the impact of other firm and owner
6

traits, financial institutions loaned more per dollar of equity to non-minority firms than to

black-owned firms. Bates and Bradford used a subsample of firms from the 1982 CBO to

determine (1) the variables that were statistically significant in predicting survival in

1976-82, and (2) the extent to which the variables from the study were used (along with

race) to determine the firms that received venture capital. They found that although race

was not statistically significant in determining the failure of firms after controlling for

other owner and firm traits, minority firms were less likely to have received venture

capital financing than non-minority firms.

Ando’s data consisted of self-reported information from business owners that

responded to a survey of business owners whose firms were at least two years old in

1984. To examine discrimination in bank lending, she used regression analyses with

success in obtaining a loan as the dependent variable, and owner and firm traits as

independent variables. Ando concluded that her study found evidence of discrimination

against black business owners in obtaining financing.

Although there is a consistency of findings that minorities seeking financing have

experienced discrimination in the capital markets, each of the above studies can be

criticized in one way or another with regard to data used, model(s) tested, and variables

analyzed. In this regard none of the studies is perfect in terms of conclusively proving

that discrimination exists. But the preponderance of the findings of discrimination should

lead to the hypothesis that discrimination does exist.

In addition, previous studies do not reveal whether minorities or women in

particular lines of business experience discrimination. But they do provide the hypothesis

that minorities who attempt to acquire broadcast or wireless licenses are hindered in
7

business by discrimination in capital markets. That is, to the extent that they need funds

from the capital markets to obtain licenses or operate their businesses and those funds are

differentially restricted to them, the minority businesses will be less successful than their

non-minority counterparts. NTIA (1995) does, however, present evidence that minorities

in telecommunications encounter discrimination in capital markets, although again, it is

inconclusive because the data do not provide sufficient information on the firms in the

study. The approach in this analysis is to take the best data available referring to

broadcast and wireless firms, and use appropriate statistical tools to determine the

evidence of discrimination that exists for minority firms in this line of business.

Part 3. Theory, Data and Statistical Approach

A. A Basic Theory of Lending

Financial institutions make lending decisions based on the business plans and

other financial information provided by loan applicants. In this analysis the loan applicant

is either a broadcast licensee seeking to acquire a station/license(s) on the secondary

market or a broadcast licensee seeking to acquire a license from the FCC. Alternatively,

the loan applicant is a wireless entreprenuer that requires debt financing in order to

participate in FCC Spectrum Auctions and perhaps purchase associated equipment. In

any case, financial institutions evaluate the business plans, the separate components of

business plans, and other financial information from potential borrowers.

Suppose that a firm’s or an individual’s business plan consists of the acquisition

of one or more broadcast spectrum licenses or other spectrum licenses and associated

facilities. Assume that the firm’s primary source of financing the potential license(s) is

financial institutions such as commercial banks, investment bankers, finance companies,
8

etc. The business calculus of the firm is based on the discounted expected earnings before

interest, depreciation, and taxes. That is the firm’s decision to invest and acquire a

broadcast license(s) or other spectrum license(s) is undertaken if the discounted expected

cash flow of the licensing opportunity is positive.

Financial institutions that consider the firm’s loan application have access to and

evaluate the business plan of the firm. The lender evaluates the information in the

business plan and forms expectations on the likely success or failure of the proposed

license acquisition. Indeed, the expectations of the potential lender may sharply diverge

from the expectations of the entrepreneur, which are embedded in the business plan. In

addition, financial institutions are also likely to consider the experience of the firm’s

management and the human capital of top management. The institutions will examine the

credit worthiness of the firm, the size of the firm, the years that the firm has been in the

broadcast business, the overall cash flow of the firm, and the growth of the firm. The

evaluation of these variables is the basis of the expectations of the lender on the likely

success of the proposed broadcast license. We assume that the lender approves the loan if

the lender’s discounted expected cash flow from the potential borrower is positive. That

is, if

(1) 0

(1 )

( , , )

1

0 >

+

− +∑=

T

t

t

t t t

r

g Cashflo CW C EL .

The amount of the loan is Co. The function gt( ) maps the firm’s cash flow into the

lender's cash flow. The discount rate is r, and E is the expectation operator. The variable

Cashflot, t = 1, ..., T, is the random variable for the firm’s cash flow which includes the

incremental cash flow of the wireless project. The variable CWt, t= 1, ..., T, is the
9

random variable for creditworthiness. Time is indexed by t, and () gt is composed of

other random variables presented by the borrower’s application, e.g., random variables on

the equity of the firm over time and debt- to-asset ratios of the firm over time. We specify

the equation for the probability of debt approval for most recent license acquisitions of

borrowers that are current license holders.

B. Description of the Data

 For broadcasting, the data are from a survey of current broadcast license holders.

The FCC contracted the survey for the fall of 1999. The survey instrument was mailed to

a random sample of non-minority license holders. In addition, a census was taken of

minority license holders.2 The data consist of firm characteristics. Data are also available

on the financial condition of the firm at the time of its most recent license acquisition.

That is, excluding the specific acquisition, the data provide a financial portrait of the firm

before and during the actual acquisition of its most recent license. Data are also available

on the firm’s characteristics during the year of the most recent acquisition. For the most

recent acquisition, information on the number of attempts required to obtain debt

financing from financial institutions is contained in the data. The survey instrument also

provided data on the interest paid on the debt that financed a broadcaster’s most recent

license acquisition. There are 99 current license holders in the data, however there is item

non-response on variables that may have been useful in understanding approval/denial

decisions.3 It is noted that the data only covers broadcasters who obtained licenses

through the comparative hearings process or on the secondary market—no broadcast

auctions were included.

 

2

 The response rate for the survey was 30 percent.
10

For attempts to acquire wireless licenses, financial data and other data on auction

applicants are from the FCC Auction Application Survey. Auction applicants were asked

to discuss their latest license acquisition or attempted acquisition. In addition,

information was gathered on how the license applicants had financed the acquisition and

the financial measures describing their financial condition at that time. In the Auction

survey, the number of eligible respondents was 1,515. There were 251 responses and 38

refusals, for an overall response rate of 19%. The Auction Survey data are supplemented

by data from the FCC Website. Data on both the number of qualified bidders and the

number of applicants are taken from the FCC Website.

C. Statistical Approach

In order to determine if the results of the analyses below are sufficiently robust for

purpose of generalization, the meaning of how the results are described should be

clarified. The statistical tests below compare the effect of race (e.g. minority) on the

dependent variable (e.g. the probability of a loan application acceptance) after

considering the impact of other independent variables on the probability of the

application being accepted. These other independent variables include the firm’s cash

flow, equity, and other business traits that would normally be considered in the lending

decision of a financial institution. The standard format is followed in this study, which is

 

3

 The FCC also attempted to survey broadcast applicants, but the data on addresses were too old and thus

contained many errors. Thus the information provided was not sufficiently useful.
11

to test the hypothesis that the independent variable such as race, has no effect on the

dependent variable, when the effects of the other independent variables are considered.

A conclusion that race or gender is “statistically significant” in a regression means that

the hypothesis of no effect is rejected because the probability of finding those results if

race (or gender) has no effect is 10% or less. Although 10% is the cutoff used in this

study, many of the results hold at less than 1%, and some less than 0.1%.

The regression results may not be conclusive because the results may omit other

confounding variables. In its data collection efforts, the FCC attempted to collect all

relevant data that would explain debt and equity financing in capital markets. The data

collection was characterized by low response rates and item non-response. Additional

efforts to collect market and firm characteristics may be characterized by the same

problems of non-minority non-response and item non-response.

Part 4. Obtaining Debt Financing in Capital Markets

This section focuses on the lending decisions of financial institutions. For the

most recent acquisition or attempted acquisition of both broadcast licensees and wireless

auction applicants, the lending decisions of financial institutions are examined in order to

determine whether minority or female status reduced the likelihood of approval on loans.

This section also examines whether minority- and women-owned firms pay higher

interest rates when they obtain loans from financial institutions.

A. Descriptive Statistics on Broadcast Firms and Auction Participants

Before presenting the statistical analyses, it will be informative to examine

relevant statistics that describe the capital market experience of the firms in the study.

Exhibit 1 shows that 58% of the successful broadcast firms and 43% of the auction
12

participants applied for debt financing in the capital markets. These percentages may be

understated since some firms may have inquired but not applied because they were

discouraged. These data show that there is significant dependence on capital markets by

broadcast firms and by auction participants.

Exhibit 1

Summary Statistics for Broadcast Firms and Auction Participants

Successful Broadcast

Applicants1

(N=235)

Auction

Participants

(N=251)

Minority-Owned2

Minority

Non-Minority

Missing

37

121

77

15.7%

51.5%

32.8%

36

235

14.3%

85.7%

Woman-Owned

Woman

Not Woman

Missing

20

138

77

8.5%

58.7%

32.8%

37

234

14.7%

85.3%

Sought Financing Other

than Own Equity3Sought Financing

Did not seek financing

Missing

136

88

11

57.9%

37.4%

4.7%

108

143

43.0%

57.0%

Obtained Debt Funding4

Obtained

Did not obtain

Missing

125

10

100

53.2%

4.3%

42.6%

86

165

34.3%

65.7%

Notes to Exhibit 1:

1. Successful applicants are those that acquired at least one license in their latest attempt to acquire a

license.

2. If the respondent did not explicitly indicate minority-owned business, the business was coded as nonminority.

3. If the respondent indicated that the firm had sought financing or that it had received financing, then

coded as having sought financing.

4. If the respondent sought financing and indicated loan amounts, terms or conditions the loan was coded as

approved.

A. Estimation: Approval/Denial for Debt Financing: Broadcast Licensees

The Broadcast Survey Instrument requests information on the number of attempts

made to obtain debt financing from sources other than family/friends. Another question

requests information on the number of denials of attempts to obtain debt financing from
13

sources other than family/friends.4 For any respondent that answered these questions,

the loan application denial rate is the ratio of denials to attempts, and the approval rate is

one minus the rate of denial.

A binary dependent variable may be created by assuming that a loan is approved

whenever attempts exceed denials: Loan Approval = “Yes” if Attempts > Denials (i.e.,

the applicant received a loan) or Loan Approval = “No” if Attempt = Denials (the

applicant did not receive a loan). However, this approach does not consider information

that is critical to understanding discrimination in capital markets. For example, suppose

that a minority firm has 10 attempts and 8 denials, and a majority firm has 2 attempts and

1 denial. The binary dependent variable for both firms would be Loan Approval = “Yes”.

However, the average probability of approval for the minority firm is 20%, and the

average probability of approval for the majority firm is 50%. Use of a binary dependent

variable would ignore this information. To the extent that filing a loan application

requires both direct and indirect costs, the expenses associated with search, out-of pocket,

and managerial time may be a manifestation of capital market discrimination. Therefore,

a modified logistic model is used to estimate the average probability of approval. The

modified log odds is used in order to fully utilize all the information on attempts and

denials in capital markets.5

The probability of approval is an implicit function, g(Independent Variables), of

observed values. Thus here the independent variables are those variables equal to or

highly associated with those in expression (1) that affect the probability of loan

acceptance (cash flow, etc.). Because the exact form of the function g( ) is unknown,

 

4

 Both questions provide financial information about a company based on the most recent application for

or secondary market acquisition of a license(s).
14

seven models are estimated . The models of Table 1 utilize available data and variables

that are informed by our theory of behavior in debt markets. The variables in the models

include the following:

Cash flow: The respondent’s cash flow prior to the most recent auction. Equal to net

income plus depreciation and amortization plus deferred taxes.

Interaction between Race and Cash Flow: The product of Race and Cash Flow.

Cash flow Squared: Cash flow squared.

Equity: Assets minus total debt.

Equity Squared: Equity squared.

Interaction between Race and Equity: The product of Race and Equity.

Full Time Employees (FTE): The number of full-time employees.

FTE Squared: The number of full-time employees squared.

Gender: One if the respondent identified the company as women-owned, and zero

otherwise.

Race: One if the respondent identified the company as minority-owned, and zero

otherwise.

 The period of license applications or attempted acquisition by current broadcast

licensees spans 1970 through 1999. Over the course of these twenty-nine years,

conditions in capital markets have varied significantly. Therefore, the models in Table 1

include controls for the year of the license application or attempted acquisition. In

addition, the models in Table 1 consider interactions between race and other explanatory

variables in order to determine whether race is used as a possible discriminatory filter

when financial institutions evaluate applications and associated business plans. Finally,

 

5The modified log odds can be found in Wonnacott and Wonnacott (1979), Chapter 4.
15

nonlinear relationships are included in the models in Table 1 through the cash flow

squared and equity squared.

 The models are analyzed by the use of ordinary least squares estimation using

SAS software. The results of the analyses support the hypothesis that race matters and

that race is a determining variable in the approval/denial of a loan application of current

broadcast license.

In Model 1, the control variables are cash flow, equity, and size of the firm as

measured by the number of full time employees, gender and race. In Model 1, the

coefficient of the variable that controls for minority ownership is negative and

statistically significant. This result implies that a minority-owned broadcast licensee is

less likely to obtain a loan approval from financial institutions than a nonminority

broadcast licensee. Model 2 reduces the number of time groups, but adds the

nonlinearity in the size of the firm into the specification. The result is that, after

controlling for relevant variables, the coefficient of the variable reflecting minority

ownership is negative and statistically significant. The results of this model also indicate

that the loan application of a minority-owned broadcast licensee is less likely to be

accepted by financial institutions. In both Model 1 and Model 2 the variable that controls

for female ownership is included in the specification. For both models, a female-owned

broadcast licensee has a lower probability of debt approval than a male-owned firm, but

these results are not statistically significant. Model 3 eliminates consideration of gender

and has the same adjustment for different time periods as Model 2. The results of Model

3 also conclude that a minority-owned broadcast licensee has a lower probability of a

loan approval from financial institutions than nonminority firms. This follows from the
16

results that in Model 3, the coefficient reflecting minority ownership is negative and

statistically significant.

B. Interest Rates Paid by Broadcast Licensees

The survey provides data on the interest paid by borrowers on loans from

financial institutions. Table 2 contains two models of interest rates charged by financial

institutions on loans to broadcast licensees on their most recent acquisitions. Both models

in Table 2 include dummy variables for the period that the application/acquisition

occurred.6 Our theory suggests that the decision to approve a loan is a simultaneous

decision on the interest rate and other terms of the loan. Therefore, explanatory variables

of the debt approval models of Table 1 are used to form the Models of Table 2. The

models of Table 2 are estimated with the ordinary least squares procedures of the SAS

Software.

 The results in Table 2 indicate that race affects interest rates that are charged to

minority licensees in their most recent acquisitions. Specifically, minority-owned firms

pay higher interest rates when we control for the other variables in the models. Just as in

the models of loan approval, the lender’s analysis of various components of the

application and associated business plan affects the lender’s decision on the interest rate

that is charged the borrower. This effect is shown in Model 1 of Table 2. The coefficient

for the interaction between race and the firm's debt-to-asset ratio is positive and

significant. This means that when a non-minority firm and a minority firm present the

same debt-to-assets ratio to a financial institution, the minority firm is charged a

statistically significant higher interest rate by the financial institution. The decision to

 

6The default year is 1972.
17

lend is a simultaneous decision to lend an amount, at an interest rate, for a term, with

conditions. Thus we also add the condition of collateral and personal guarantees as

explanatory variables of the interest. These variables are defined as follows:

Indicator variable for personal guarantee: One if the respondent was required to make

a personal guarantee, and zero otherwise.

Indicator variable for collateral required: One if the respondent indicated that

collateral was required on the debt, and zero otherwise.

Because of item non-response on questions addressing collateral and personal guarantees,

10 observations are deleted because of missing data. In addition, we include two nonlinear measures in the specification of Model 2: FTE squared and Cash flow squared. The

results of Model 2 are that collateral and personal guarantees affect the interest rate paid

by the borrower. Model 2 also indicates that race does matter in the lender’s decision on

interest rates. The coefficient of the interaction between race and the equity of the firm is

positive and statistically significant: minorities pay higher interest rates relative to

amount of equity, when we control for the impact of the other variables. This indicates

that a minority firm with the same equity as a nonminority firm is charged a higher

interest rate than the nonminority firm, controlling for the other variables in the model. In

addition, the coefficient of the interaction between race and the debt-to-assets ratio is

positive and statistically significant as in Model 1. This adverse reaction of lenders to the

minority firm’s equity and debt–to-assets ratio is somewhat moderated by the negative

sign of the interaction between race and cash flow. That is, lenders reduce the interest

rates on loans to minorities in reaction to cash flows more than they reduce the interest

rate charged to nonminority firms. The interest rates paid by women-owned businesses

were also examined in regression models. It was found that any differences in the
18

interest rates paid by women-owned businesses were not statistically significant. The

models were hampered by small sample sizes, and will not be reported. The impact of

gender on the interest rates of auction participants is similar to that of broadcast licensees

and those results will be reported below.

C. Approval/Denial of Debt: Auction Applicants

The models of financial institutions’ decisions on loan applications of auction

applicants are analogous to the models of financial institutions’ decisions on loan

applications of broadcast licensees. The dependent variable of this regression is the

average probability of the financial institution approving the loan application. The

dependent variable is one minus the average denial rate, where the average denial rate is

constructed from the reported number of loan applications a respondent submitted to

financial institutions and the number of loan denials the respondent reported in the

Auction Survey. The data include applicants that were unsuccessful in obtaining debt

financing.

Again, a modified logistic model is specified and then estimated with ordinary

least squares using SAS software. The modified log odds is used in order to fully utilize

all the information on attempts and denials in capital markets. Since the Auction Survey

spans the years from 1994 into 1999, dummy variables control for the year of an

applicant’s most recent auction. Other variables in the specifications are listed below:

Number of licenses won before last auction: The reported number of licenses won prior

to the respondent’s most recent auction.

Number of licenses won before last auction squared: The square of the number of

licenses won before the last auction.

Cash flow: The respondent’s cash flow prior to the most recent auction.
19

Cash flow Squared: Cash flow squared.

Equity: Assets minus total debt.

Equity Squared: Equity squared.

Indicator variables for auction groups: The dummy variable Auction Group Other

Wireless Spectrum takes the value of one when the applicant’s participated in Auctions

1, 2, 3, 14, or 21, and zero otherwise. A dummy variable called Mobile Voice and Data

Wireless Spectrum takes the value of one when the applicants most recent auction was

Auction 4, 5, 7, 10, 11, 12, 16, 18, 20, 22, or 24, and zero otherwise. The grouping of

auctions is based upon potential economic spectrum usage and technology as indicated by

the Office of Engineering and Technology.

Indicator variable for a Start-up: One if the company was a startup at auction time, and

zero otherwise.

Gender: One when the respondent identified the company as women-owned, and zero

otherwise.

Race: One whenever a respondent identified the company as minority-owned, and zero

otherwise.

Full Time Employees: The number of full time employees.

Interaction between Race and Cash Flow: The product of Race and Cash Flow.

Interaction between Gender and Cash Flow: The product of Gender and Cash Flow.

Interaction between Startup Status and Cash Flow: The product of Startup Status and

Cash Flow.

Interaction between Race and Equity: The product of Race and Equity.

Interaction between Gender and Equity: The product of Gender and Equity.

Interaction between Startup Status and Equity: The product of Startup and Equity.

Table 3 contains models of the approval decisions of financial institutions on the

loan applications of auction applicants. The results of the three models of Table 3 suggest

that race and gender matter in the approval of debt for auction applicants. This result

follows because the regression coefficients associated with race and gender are negative
20

and statistically significant. After controlling for the time of the most recent auction, the

size of the firm, the equity of the firm, the cash flow of the firm and other relevant

variables, a minority-owned firm’s loan application had a lower probability of acceptance

than that of a non-minority firm. In addition, the coefficient associated with a womanowned firm is negative and statistically significant. This implies that a woman-owned

firm was less likely to be approved for debt by financial institutions. The direction of

some of the interaction variables may, however, moderate these results.

D. Interest Rates on the Debt of Auction Applicants

This section investigates whether minority- or woman-owned auction applicants

are charged higher interest rates by financial institutions after controlling for relevant

variables. Since a loan approval is associated with a specific interest rate, many of the

variables influencing a loan approval should influence the interest rate that the financial

institution selects to charge the spectrum applicant. Additional variables are incorporated

in the analysis of interest rates. The additional variables are listed below:

Personal guarantee: One if the respondent was required to make a personal guarantee,

and zero otherwise.

Collateral required: One if the respondent indicated that collateral was required on the

debt, and zero otherwise.

The responses of auction applicants on interest rates were characterized by high

item nonresponse. Thus in order to include as many cases as possible, the interest rate

model includes firms that obtained loans from family/friends. That is, a dummy variable

is created to control for the effect of family/friends lending activities. The variable

Family is one if the lender is family/friend, and zero otherwise.
21

Table 4 contains three models of the interest rate charged to auction applicants.

The regression coefficient associated with a minority-owned firm is positive and

statistically significant. Thus, after controlling for time, whether collateral was required,

whether a personal guarantee was required, the size of the applicant, equity, cash follow,

and other relevant variables the data support the hypothesis that minority-owned firms

were charged higher interest rates than nonminority firms. This result is, however,

somewhat moderated by the effect of a lender’s expectation of a minority-owned firm’s

positive cash flow. Gender was not found to be statistically significant in the models.

In summary, race and gender matter in a lender’s approval of a loan application.

Moreover, race matters in a lender’s decision on interest rates charged to borrowers. The

data suggest that lenders can and do evaluate components of applications and associated

business plans of minority-owned firms and women-owned firms differently from those

of non-minority firms. That is, when a specific and identical characteristic of a minority

firm and a non-minority firm is presented to a lender, the lender differentially evaluates

the characteristic. The result of this process is that among current broadcast licensees,

minority status has a statistically significant negative impact on the approval of loans in

their most recent application. Also, minority status increases the interest rates that the

borrowers pay. This result is also statistically significant. Female gender also has a

statistically significant negative impact on loan acceptance. However, female gender

was not found to have a differential impact on the interest rate on loans granted by

lenders. These relationships are also true for spectrum applicants.
22

Part 5. The Probability of Winning in Auctions

A preliminary examination of the rate at which minorities and women

successfully obtained licenses in auctions for wireless licenses, compared to the rate at

which minorities and women applied to participate in FCC auctions, suggests that

minorities and women may have been underrepresented in obtaining FCC licenses

through auctions. Such “utilization calculations” suggest the need for further evaluation,

using more sophisticated economic calculations and controlling for other relevant

variables. Are the differences in average utilization due to race or other factors? Given

evidence of capital market discrimination in applicants’ financing of their most recent

auction acquisition, the hypothesis of whether minority or women applicants have lower

probabilities of winning a spectrum license is investigated. The analysis of the probability

of winning a spectrum licenses is informed by auction theory and multiple data sources.

The empirical results are based on both survey data and FCC auction data. The data

supports the hypotheses that both minorities and women have lower probabilities of

winning spectrum licenses in auctions after controlling for relevant variables. These

results, however, are not conclusive due to the lack of comprehensive data. The

information from the survey is characterized by a high non-response rate and high rates

of item nonresponse.

A. Auction Behavior

In order to model auction outcomes we outline the economic behavior of auction

applicants. It is assumed that individuals/firms participate in auctions because they

ultimately expect to make a profit from the control of a spectrum license(s). An auction

participant calculates its private valuation of spectrum licenses in a given auction. In
23

doing so the participant calculates the discounted expected value of its wireless project,

where the project may or may not include the building of facilities. In any event, the

bidder is likely to consider expected demand and costs for any planned wireless services

in targeted markets. The demand and cost data would ultimately depend on population

and income of the targeted market areas. In addition, the business plan of the spectrum

applicant would consider the number of competitors for the particular spectrum and

associated services.

The probability of applicant i winning a license, P(V,Ci), is a function of the

valuation placed on a license, denoted V, and the amount of funds available to bid, Ci.

The amount that a participant can bid is the minimum of the value placed on the license

or the amount of funds available to bid. This analysis considers that Ci is a function of

the capital markets. That is, if there is no capital market discrimination, Ci = f(Zi) where

f(Zi) is the amount of financing made available to a non-minority firm given the firm and

owner traits Zi. The function G( ) is defined such that 0 [ ( )] ( ) G i i ≤ < f Z f Z when

capital markets exhibit bias against minority- and/or women-owned firms. If

[ ( )] ( ) G i i f Z < f Z but V < G[ ( f Z)] for minority- and/or women-owned applicants, then

minority and/or women applicants are not limited by capital market constraints, and they

fully participate. However, if [ ( )] ( ) G i i f Z < f Z but V is distributed so that V > G[ ( f Z)]

for minority- and/or women-owned applicants two outcomes will happen. First, the

auctions won by minorities and women will be skewed toward low price auctions.

Second, the overall proportion of auctions won by minorities and women will be less than

if no capital market constraints existed, or alternatively, less than if auctions were

distributed among the applicants by Zi. The statistical models below consider that when
24

minorities or women are constrained in obtaining financing from capital markets,

[ ( )] ( ) G i i f Z < f Z for minorities and/or women, and they are constrained in their ability

to bid.

The unique features of an auction along with the array of maximum bidders

determine the actual outcome of the auction. The statistical models used below consider

the characteristics of the auctions and firm traits (elements of Zi), along with race and

gender of the firm’s owner. We test to determine if race and gender are statistically

significant in predicting success, controlling for the other variables in the models. We do

not have information on the amount of financing available for each participant. But when

controlling for auction features and firm traits, if minority status or female-owned status

results in a lower probability of winning an auction, our interpretation is that minority

status or female status, per se, does not affect a participant’s ability to bid; but the capital

constraints associated with minority status or female status (i.e., [ ( )] ( ) G i i f Z < f Z )

reduces the ability of the minority- or woman-owned firm to win in an auction.

B. Description of the Data

The data for the analysis are from the FCC Auction Survey (see above). On the

Auction Survey the respondent is asked to provide the number of licenses won in its most

recent auction. The most recent acquisition occurred in 1999. The response to the

number of licenses won in its most recent auction is the dependent variable. The

dependent variable is binary, equal to one if the applicant won spectrum licenses in its

most recent auction, and zero otherwise. The explanatory variables are listed and

described below.

Indicator variables for time: Dummy variables are used to control for the year of the

most recent auction that applicants participated.
25

Auction groups: Auction Group Other Wireless Spectrum takes the value of one when

the applicant’s participated in Auctions 1, 2, 3, 14, or 21, and zero otherwise. Mobile

Voice and Data Wireless Spectrum takes the vale of one when the applicants most recent

auction was Auction 4, 5, 7, 10, 11, 12, 16, 18, 20, 22, or 24, and zero otherwise.

Grouping of auctions is based upon potential economic spectrum usage and technology as

indicated by the Office of Engineering and Technology.

Start-up: One if the company was a startup at auction time, and zero otherwise.

Bidding credit: One if the respondent indicated having participated in a bidding credit

program, and zero otherwise.

Installment Plan: One if the respondent indicated having participated in an installment

program, and zero otherwise.

Upfront Payment: The amount of upfront payment in the most recent auction of a

respondent.

Gender: One if the respondent identified the company as women-owned, and zero

otherwise.

Race: One if the respondent identified the company as minority-owned, and zero

otherwise.

Full Time Employees: The number of full time employees.

Number of Competing Applicants: The total number of applicants in the most recent

auction of the respondent.

Number of Qualified Bidders: The total number of qualified bidders in the most recent

auction of the respondent

Availability of Minority Bidding Credit: One if the minority bidding credit was

available in the most recent auction of the respondent, and zero otherwise.

Debt-to-Asset Ratio (“D/A”): Total debt divided by total assets.

Interaction between Race and D/A: The product of Race and D/A.

Interaction between Gender and D/A: The product of Gender and D/A.
26

C. Estimation Results: Probability of Winning

Table 5 contains the result of the econometric analysis of the probability of an

applicant winning a spectrum license. The results are derived from a logistic/probit

regression using SAS. The data supports the hypothesis that minority status results in a

lower probability of winning spectrum. That is, for the respondents of this sample, the

probability of a minority winning a spectrum license in his/her most recent auction is

lower than the probability of a nonminority firm/entity winning a spectrum license in its

most recent auction. For each of the seven models, which control for time, auction group,

startup status, size of company, financial strength, and competition, minority status

results in a lower probability of winning. In five of the seven models tested, female

gender was statistically significant in predicting a lower probability of success in

spectrum auctions. In the other two models female gender was found to reduce the

probability of success, but the results were not statistically significant. Finally, the results

in Table 5 show that each of the seven models is statistically significant in specifying

success of the participants.

Part 6. Discussion of the Findings and Recommendations

In Adarand Constructors, Inc. v. Pena, 515 U.S. 200 (1995), the United States

Supreme Court determined that federal programs that use race or ethnicity as a basis for

decision-making are subject to strict judicial scrutiny. Thus, any such programs must

serve a compelling governmental interest, and must be narrowly tailored to serve that

interest. The Supreme Court has recognized that remedying past discrimination

constitutes a compelling government interest. See Adarand, 515 U.S. at 237; City of

Richmond v. J.A. Croson Co., 488 U.S. 469, 509 (1989) (plurality opininon); id. At 511
27

(Stevens, J., concurring in part and concurring in the judgment). To prove such a

remedial government interest, evidence of discrimination, which may include statistical

analyses, is required. This analysis of broadcast license holders and wireless applicants

suggests that minorities and women experience discrimination in capital markets.

Moreover, that discrimination adversely affected their most recent acquisitions of

broadcast licenses and wireless licenses. It is suggested that a national policy of

auctioning spectrum, without remedying discrimination in capital markets, is a national

policy of discrimination against minorities and women in the allocation of spectrum

licenses. This is because the auctions of the FCC require up-front payments and because

spectrum licenses go to the highest bidder. When there is capital market discrimination,

minorities will be capital constrained and less likely to qualify for any auction and less

likely to win auctions. The data presented suggest that minorities are less like to win

wireless licenses after controlling for relevant variables.

The regression results may not be conclusive because the results may omit other

confounding variables. In its data collection efforts, the FCC attempted to collect all

relevant data that would explain debt and equity financing in capital markets. The data

collection was characterized by low response rates and item non-response. Nevertheless,

the results of this study are consistent with and reflect the long history and contemporary

problems of race in the culture, economy, politics, and jurisprudence of the United States.

The recommendations that result from this study are as follows. First, it is

recommended that the FCC develop and maintain programs that seek and encourage the

participation of minorities and women in the ownership of broadcast and spectrum

licenses. Such efforts may offset the disadvantages that minority- and women-owned
28

firms experience through the capital markets. Second, it is recommended that the FCC

continue to examine the effect of capital markets on the participation of minority- and

women-owned firms in the ownership and operation of broadcast and spectrum licenses.

Although this study indicates that minority- and women-owned firms are inappropriately

disadvantaged in this line of business due to capital market forces, more research is

needed to confirm and track these effects over time.
29

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Bates, Timothy. (1997) “Unequal Access: Financial Institution Lending to Black-andWhite-Owned Small Business Start-ups.” Journal of Urban Affairs 19 November, 487-

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Bates, Timothy and William D. Bradford (1992) “Factors Affecting New Firm Success

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Becker, Gary S. (1957) The Economics of Discrimination. Chicago: University of

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Blanchflower, David G., Phillip B. Levine and David J. Zimmerman (1998)

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Bostic, R.W. and K. P. Lampani (1999) “Racial Differences in Patterns of Small Business

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Cavalluzzo, Ken. E. and Linda C. Cavalluzzo (1998) “Market Structure and

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Cavalluzzo, Ken. E., Linda C. Cavalluzzo and John Wolken (2000) “Competition, Small

Business Financing, and Discrimination: Evidence From a New Survey”, Working Paper,

Board of Governors of the Federal Reserve System.

Cull, Robert and Raymond Suarez (1995) Capital Formation and Investment in Minority

Business Enterprises in the Telecommunications Industries. National

Telecommunications and Information Administration.

Ross, Stephen A., Randolph W. Westerfield, and Jeffrey Jaffe (1996) Corporate Finance.

Chicago: Irwin.

Wonnacott, Ronald J. and Thomas H. Wonnacott (1979) Econometrics. New York: John

Wiley & Sons.
30

Glossary of Explanatory Variables in the Tables

BIDCRED: Respondent participated in the bidding credit program

CASHFLO: Cash Flow

CASHFLO2: Cash Flow Squared

COLLAT: Collateral

COMAPPS: Number of competing applicants

DA: Debt divided by Assets

EQUITY: Business Equity

EQUITY2: Business Equity Squared

REQUITY: Race Times Equity

FAMILY: Lender is a family/friend

FTE: Full-Time Employees

FTE2: Full-Time Employees Squared

FTECASHF: Full-Time Employees Times Cash Flow

INSTALL: Respondent participated in the Installment Plan

MINBIDCR: Minority bidding credit was available in the most recent auction of the

respondent

MOBILEW: Mobile Voice and Wireless Spectrum Auction

OTHERW: Not a Mobile Voice and Wireless Spectrum Auction

PGUAR/PERGTY: Personal Guarantee

PREVLIC: Number of Licenses Won Before the Last Auction

PREVLIC2: Number of Licenses Won Before the Last Auction Squared

QBIDDERS: Number of qualified bidders in the most recent auction of the respondent

RACE: Race

RCF: Race Times Cash Flow

RDA/RACEDA: Race Times Debt/Assets

STARTUP: Respondent was a startup company at auction time

STARTCF: Start-up (1 or 0) times cash flow

SEQUITY: Start-up (1 or 0) times equity

SEX: Gender

SXCF: Gender Times Cash Flow

SXEQUITY: Gender Times Equity

SXDA: Gender Times Debt/Assets

UPFRONT: The Upfront Payment in the most recent auction of the respondent
31

Glossary of Explanatory Variables in the Tables (Con.)

Dum82: Year of Application/Acquisition 1982

Dum83: Year of Application/Acquisition 1983

Dum84: Year of Application/Acquisition 1984

Dum85: Year of Application/Acquisition 1985

Dum86: Year of Application/Acquisition 1986

Dum87: Year of Application/Acquisition 1987

Dum88: Year of Application/Acquisition 1988

Dum89: Year of Application/Acquisition 1989

Dum90: Year of Application/Acquisition 1990

Dum91: Year of Application/Acquisition 1991

Dum92: Year of Application/Acquisition 1992

Dum93: Year of Application/Acquisition 1993

Dum94: Year of Application/Acquisition 1994

Dum95: Year of Application/Acquisition 1995

Dum96: Year of Application/Acquisition 1996

Dum97: Year of Application/Acquisition 1997

Dum98: Year of Application/Acquisition 1998

Dum99: Year of Application/Acquisition 1999
32

 Table 1: Broadcast License Holders: Probability of Loan Approval

Model: MODEL1

Dependent Variable: Probability of Loan Approval

 Analysis of Variance

 Sum of Mean

 Source DF Squares Square F Value Prob>F

 Model 20 40.76714 2.03836 1.595 0.1030

 Error 40 51.11559 1.27789

 C Total 60 91.88273

 Root MSE 1.13044 R-square 0.4437

 Dep Mean 0.01345 Adj R-sq 0.1655

 C.V. 8405.19418

 Parameter Estimates

 Parameter Standard T for H0:

 Variable DF Estimate Error Parameter=0 Prob > |T|

 INTERCEP 1 0.486075 1.23661049 0.393 0.6964

 DUM82 1 -1.000880 1.67553961 -0.597 0.5536

 DUM87 1 -2.514406 1.40011149 -1.796 0.0801

 DUM88 1 1.979853 1.79752964 1.101 0.2773

 DUM89 1 0.537430 1.35341533 0.397 0.6934

 DUM92 1 -0.434693 1.40704315 -0.309 0.7590

 DUM94 1 -0.084142 1.30489383 -0.064 0.9489

 DUM95 1 0.362198 1.39902180 0.259 0.7970

 DUM96 1 -0.674898 1.27398598 -0.530 0.5992

 DUM97 1 -0.649381 1.28971990 -0.504 0.6174

 DUM98 1 0.057690 1.27441723 0.045 0.9641

 DUM99 1 -0.019775 1.26004925 -0.016 0.9876

 CASHFLO 1 0.000000213 0.00000008 2.654 0.0114

 RCF 1 0.000000147 0.00000027 0.546 0.5882

 CASHFLO2 1 1.385824E-15 0.00000000 1.519 0.1366

 EQUITY 1 -5.761445E-8 0.00000003 -2.234 0.0311

 EQUITY2 1 3.162939E-17 0.00000000 2.385 0.0219

 REQUITY 1 4.6622972E-8 0.00000004 1.182 0.2441

 FTE 1 -0.007847 0.00332223 -2.362 0.0231

 SEX 1 -0.124183 0.65350455 -0.190 0.8502

 RACE 1 -0.925320 0.50102574 -1.847 0.0722
33

 Table 1: Broadcast License Holders: Probability of Loan Approval

Model: MODEL2

Dependent Variable: Probability of Loan Approval

 Analysis of Variance

 Sum of Mean

 Source DF Squares Square F Value Prob>F

 Model 15 37.31262 2.48751 2.051 0.0322

 Error 45 54.57011 1.21267

 C Total 60 91.88273

 Root MSE 1.10121 R-square 0.4061

 Dep Mean 0.01345 Adj R-sq 0.2081

 C.V. 8187.89405

 Parameter Estimates

 Parameter Standard T for H0:

 Variable DF Estimate Error Parameter=0 Prob > |T|

 INTERCEP 1 0.111081 0.27649290 0.402 0.6898

 DUM87 1 -2.125834 0.69833466 -3.044 0.0039

 DUM88 1 2.401342 1.33022341 1.805 0.0777

 DUM89 1 0.943864 0.68599983 1.376 0.1757

 DUM98 1 0.439985 0.42507597 1.035 0.3062

 DUM99 1 0.363195 0.46859730 0.775 0.4424

 CASHFLO 1 0.000000210 0.00000008 2.596 0.0127

 RCF 1 0.000000171 0.00000026 0.660 0.5126

 CASHFLO2 1 9.56721E-16 0.00000000 0.539 0.5928

 EQUITY 1 -4.764333E-8 0.00000004 -1.131 0.2642

 EQUITY2 1 1.946682E-17 0.00000000 0.484 0.6306

 REQUITY 1 3.848309E-8 0.00000005 0.853 0.3984

 FTE 1 -0.009210 0.00617613 -1.491 0.1429

 FTE2 1 0.000001392 0.00000429 0.324 0.7471

 SEX 1 -0.134139 0.57198989 -0.235 0.8157

 RACE 1 -0.954878 0.45289376 -2.108 0.0406
34

 Table 1: Broadcast License Holders: Probability of Loan Approval

Model: MODEL3

Dependent Variable: Probability of Loan Approval

 Analysis of Variance

 Sum of Mean

 Source DF Squares Square F Value Prob>F

 Model 14 37.24593 2.66042 2.240 0.0203

 Error 46 54.63680 1.18776

 C Total 60 91.88273

 Root MSE 1.08984 R-square 0.4054

 Dep Mean 0.01345 Adj R-sq 0.2244

 C.V. 8103.35337

 Parameter Estimates

 Parameter Standard T for H0:

 Variable DF Estimate Error Parameter=0 Prob > |T|

 INTERCEP 1 0.082274 0.24515018 0.336 0.7387

 DUM87 1 -2.095518 0.67917966 -3.085 0.0034

 DUM88 1 2.418837 1.31441673 1.840 0.0722

 DUM89 1 0.920081 0.67145689 1.370 0.1773

 DUM98 1 0.467916 0.40383515 1.159 0.2526

 DUM99 1 0.385914 0.45374038 0.851 0.3994

 CASHFLO 1 0.000000211 0.00000008 2.649 0.0110

 RCF 1 0.000000166 0.00000026 0.650 0.5188

 CASHFLO2 1 9.073584E-16 0.00000000 0.520 0.6057

 EQUITY 1 -4.656666E-8 0.00000004 -1.123 0.2672

 EQUITY2 1 1.848782E-17 0.00000000 0.467 0.6426

 REQUITY 1 3.7865202E-8 0.00000004 0.849 0.4002

 FTE 1 -0.009338 0.00608848 -1.534 0.1320

 FTE2 1 0.000001488 0.00000423 0.352 0.7265

 RACE 1 -0.926626 0.43206956 -2.145 0.0373
35

 Table 1A: Summary Statistics on Firms in the Tests

-----------------------------------Non-Minority ------------------------------

 Variable N Mean Std Dev

 -------------------------------------------------

 MLOGODDS 50 0.0965189 1.2321886

 CASHFLO 50 7444399.28 24375669.69

 EQUITY 50 48755558.54 235133153

 FTE 50 200.7000000 715.1416086

 SEX 50 0.1000000 0.3030458

 ASSETS 50 117004357 577857546

 TOTDEBT 50 68248798.16 344424528

 DA 50 1.0619019 2.2751280

 -------------------------------------------------

-------------------------------------Minority-----------------------------------

 Variable N Mean Std Dev

 -------------------------------------------------

 MLOGODDS 11 -0.3641400 1.2479281

 CASHFLO 11 1535988.55 3197147.66

 EQUITY 11 9633775.91 28345969.77

 FTE 11 28.8181818 47.8159350

 SEX 11 0 0

 ASSETS 11 15672046.82 44609766.13

 TOTDEBT 11 6038270.91 16337562.35

 DA 11 0.5279662 0.4651315

 -------------------------------------------------
36

 Table 2: Broadcast License Holders: Interest Rate on Debt

Model: MODEL1

Dependent Variable: Interest Rate

 Analysis of Variance

 Sum of Mean

 Source DF Squares Square F Value Prob>F

 Model 16 315.64396 19.72775 4.895 0.0001

 Error 31 124.94840 4.03059

 C Total 47 440.59236

 Root MSE 2.00763 R-square 0.7164

 Dep Mean 9.24656 Adj R-sq 0.5700

 C.V. 21.71222

 Parameter Estimates

 Parameter Standard T for H0:

 Variable DF Estimate Error Parameter=0 Prob > |T|

 INTERCEP 1 20.619465 2.13341524 9.665 0.0001

 DUM82 1 3.247191 3.49951656 0.928 0.3606

 DUM87 1 -11.821321 2.93237954 -4.031 0.0003

 DUM88 1 -12.225452 2.92561814 -4.179 0.0002

 DUM89 1 -12.382787 2.40556032 -5.148 0.0001

 DUM92 1 -6.110023 3.98521176 -1.533 0.1354

 DUM94 1 -10.100362 2.43717253 -4.144 0.0002

 DUM95 1 -9.975752 2.44629931 -4.078 0.0003

 DUM96 1 -10.693508 2.26521048 -4.721 0.0001

 DUM97 1 -10.244491 2.22124099 -4.612 0.0001

 DUM98 1 -11.597604 2.29265185 -5.059 0.0001

 DUM99 1 -11.377870 2.17097819 -5.241 0.0001

 CASHFLO 1 -5.304775E-8 0.00000005 -1.111 0.2752

 EQUITY 1 -2.355342E-9 0.00000001 -0.456 0.6513

 DA 1 -1.530191 0.80486380 -1.901 0.0666

 RDA 1 2.131115 1.25136019 1.703 0.0986

 FTE 1 0.002075 0.00284005 0.731 0.4705
37

 Table 2: Broadcast License Holders: Interest Rate on Debt

Model: MODEL2

Dependent Variable: Interest Rate

 Analysis of Variance

 Sum of Mean

 Source DF Squares Square F Value Prob>F

 Model 21 350.08338 16.67064 4.962 0.0010

 Error 16 53.75905 3.35994

 C Total 37 403.84242

 Root MSE 1.83301 R-square 0.8669

 Dep Mean 9.11987 Adj R-sq 0.6922

 C.V. 20.09913

 Parameter Estimates

 Parameter Standard T for H0:

 Variable DF Estimate Error Parameter=0 Prob > |T|

 INTERCEP 1 20.329470 3.00185762 6.772 0.0001

 DUM82 1 6.753416 3.61825293 1.866 0.0804

 DUM87 1 -14.665399 3.16461436 -4.634 0.0003

 DUM89 1 -13.825057 2.35750165 -5.864 0.0001

 DUM92 1 -1.130778 4.27122639 -0.265 0.7946

 DUM94 1 -6.750752 2.67620737 -2.523 0.0226

 DUM95 1 -9.282876 2.36898533 -3.919 0.0012

 DUM96 1 -8.630330 2.22862676 -3.872 0.0013

 DUM97 1 -10.725231 2.29279698 -4.678 0.0003

 DUM98 1 -11.263926 2.30964456 -4.877 0.0002

 DUM99 1 -11.610284 2.20359343 -5.269 0.0001

 COLLAT 1 4.015156 1.77100701 2.267 0.0376

 PGUAR 1 -3.112184 1.51191895 -2.058 0.0562

 CASHFLO 1 0.000001409 0.00000108 1.301 0.2116

 RCF 1 -0.000011552 0.00000480 -2.405 0.0286

 EQUITY 1 -0.000000731 0.00000043 -1.705 0.1075

 REQUITY 1 0.000001788 0.00000075 2.380 0.0301

 DA 1 -3.118057 0.95960891 -3.249 0.0050

 RDA 1 3.144400 1.78099609 1.766 0.0965

 FTE2 1 0.000867 0.00056826 1.526 0.1466

 CASHFLO2 1 4.32593E-13 0.00000000 1.479 0.1584

 FTECASHF 1 -4.174271E-8 0.00000003 -1.505 0.1518
38

 Table 2A: Summary Statistics on Firms in the Tests

-----------------------------------Non-Minority --------------------------------

 Variable Label N Mean Std Dev

 --------------------------------------------------

 INTEREST 39 8.8583333 2.7513304

 CASHFLO 39 9158823.03 27402943.48

 EQUITY 39 61062512.00 265642261

 FTE 39 244.6153846 805.9124811

 SEX 39 0.1282051 0.3386884

 ASSETS 39 146366644 653034036

 TOTDEBT 39 85304131.77 389318755

 DA 39 0.7443939 0.7187128

 PERSGTY 38 0.7368421 0.4462583

 COLLAT 32 0.8750000 0.3360108

 --------------------------------------------------

-----------------------------------Minority ----------------------------------

 Variable Label N Mean Std Dev

 --------------------------------------------------

 INTEREST 9 10.9288889 3.8985428

 CASHFLO 9 1876687.11 3472603.08

 EQUITY 9 11721618.00 31263122.68

 FTE 9 32.2222222 52.5708517

 SEX 9 0 0

 ASSETS 9 19061784.89 49157308.62

 TOTDEBT 9 7340166.89 17976437.17

 DA 9 0.5303571 0.4694680

 PERSGTY 9 0.6666667 0.5000000

 COLLAT 7 1.0000000 0

 --------------------------------------------------
39

 Table 3: Spectrum Auctions: Probability of Loan Approval

Model: MODEL1

Dependent Variable: Probability of Loan Approval

 Analysis of Variance

 Sum of Mean

 Source DF Squares Square F Value Prob>F

 Model 20 108.72868 5.43643 2.544 0.0213

 Error 20 42.73701 2.13685

 C Total 40 151.46569

 Root MSE 1.46180 R-square 0.7178

 Dep Mean -0.20624 Adj R-sq 0.4357

 C.V. -708.78662

 Parameter Estimates

 Parameter Standard T for H0:

 Variable DF Estimate Error Parameter=0 Prob > |T|

 INTERCEP 1 2.751368 1.42566577 1.930 0.0679

 DUM94 1 -3.581520 1.35156685 -2.650 0.0154

 DUM95 1 -2.736925 1.28061959 -2.137 0.0451

 DUM96 1 -4.106341 1.36440798 -3.010 0.0069

 DUM97 1 -2.075218 1.48034179 -1.402 0.1763

 DUM98 1 -5.254307 1.46157811 -3.595 0.0018

 PREVLIC 1 0.027806 0.02274753 1.222 0.2358

 PREVLIC2 1 -0.000298 0.00020420 -1.459 0.1602

 CASHFLO 1 0.000000242 0.00000020 1.216 0.2382

 EQUITY 1 -0.000000102 0.00000023 -0.441 0.6638

 EQUITY2 1 8.08638E-15 0.00000000 1.223 0.2355

 STARTUP 1 1.531649 0.78938757 1.940 0.0666

 RCF 1 -0.000006860 0.00001130 -0.607 0.5505

 CASHFLO2 1 -1.20426E-14 0.00000000 -1.270 0.2186

 SXCF 1 0.000024430 0.00001207 2.025 0.0564

 REQUITY 1 0.000002375 0.00000113 2.102 0.0485

 SXEQUITY 1 -0.000002881 0.00000151 -1.905 0.0713

 SEX 1 -2.194958 0.94456295 -2.324 0.0308

 RACE 1 -2.301300 0.78304100 -2.939 0.0081

 FTE 1 0.001775 0.01759636 0.101 0.9207

 SEQUITY 1 -0.000000282 0.00000024 -1.192 0.2471
40

 Table 3: Spectrum Auctions: Probability of Loan Approval

Model: MODEL2

Dependent Variable: Probability of Loan Approval

 Analysis of Variance

 Sum of Mean

 Source DF Squares Square F Value Prob>F

 Model 21 116.01549 5.52455 2.961 0.0104

 Error 19 35.45019 1.86580

 C Total 40 151.46569

 Root MSE 1.36594 R-square 0.7660

 Dep Mean -0.20624 Adj R-sq 0.5073

 C.V. -662.30944

 Parameter Estimates

 Parameter Standard T for H0:

 Variable DF Estimate Error Parameter=0 Prob > |T|

 INTERCEP 1 5.821061 1.81496255 3.207 0.0046

 DUM94 1 -0.259113 2.25115559 -0.115 0.9096

 DUM95 1 -0.033850 1.61065483 -0.021 0.9835

 DUM96 1 -1.483911 1.69009206 -0.878 0.3909

 DUM97 1 0.013323 1.71391916 0.008 0.9939

 DUM98 1 -7.322728 1.69782325 -4.313 0.0004

 PREVLIC 1 0.012616 0.01976198 0.638 0.5308

 PREVLIC2 1 -0.000124 0.00012008 -1.031 0.3153

 CASHFLO 1 0.000000276 0.00000018 1.520 0.1449

 EQUITY 1 -0.000000345 0.00000018 -1.967 0.0640

 EQUITY2 1 9.686663E-15 0.00000000 1.616 0.1227

 STARTUP 1 0.509419 0.76154503 0.669 0.5116

 RCF 1 -0.000025246 0.00001344 -1.879 0.0757

 CASHFLO2 1 -1.33378E-14 0.00000000 -1.599 0.1264

 SXCF 1 0.000027311 0.00001155 2.364 0.0289

 REQUITY 1 0.000002315 0.00000106 2.176 0.0423

 SXEQUITY 1 -0.000003250 0.00000144 -2.255 0.0361

 SEX 1 -2.842163 0.97549021 -2.914 0.0089

 RACE 1 -2.151439 0.73502137 -2.927 0.0086

 FTE 1 0.009704 0.01261783 0.769 0.4513

 MOBILEW 1 -4.790942 2.08181455 -2.301 0.0329

 OTHERW 1 -5.793244 2.75684502 -2.101 0.0492
41

 Table 3: Spectrum Auctions: Probability of Loan Approval

Model: MODEL3

Dependent Variable: Probability of Loan Approval

 Analysis of Variance

 Sum of Mean

 Source DF Squares Square F Value Prob>F

 Model 22 113.48683 5.15849 2.445 0.0293

 Error 18 37.97886 2.10994

 C Total 40 151.46569

 Root MSE 1.45256 R-square 0.7493

 Dep Mean -0.20624 Adj R-sq 0.4428

 C.V. -704.30882

 Parameter Estimates

 Parameter Standard T for H0:

 Variable DF Estimate Error Parameter=0 Prob > |T|

 INTERCEP 1 3.720930 1.72521048 2.157 0.0448

 DUM94 1 -4.443256 1.47611462 -3.010 0.0075

 DUM95 1 -1.650273 1.51567259 -1.089 0.2906

 DUM96 1 -3.391173 1.49931057 -2.262 0.0363

 DUM97 1 -0.721770 1.73001089 -0.417 0.6815

 DUM98 1 -6.290328 1.70998263 -3.679 0.0017

 PREVLIC 1 0.018924 0.02337608 0.810 0.4288

 PREVLIC2 1 -0.000233 0.00020944 -1.113 0.2802

 CASHFLO 1 2.6569282E-8 0.00000025 0.105 0.9176

 EQUITY 1 3.6484038E-8 0.00000027 0.136 0.8934

 EQUITY2 1 1.25207E-15 0.00000000 0.152 0.8808

 STARTUP 1 1.588768 0.79984881 1.986 0.0624

 RCF 1 -0.000017080 0.00001347 -1.268 0.2208

 CASHFLO2 1 -2.13348E-15 0.00000000 -0.180 0.8592

 SXCF 1 0.000025571 0.00001232 2.076 0.0525

 STARTCF 1 0.000002434 0.00000180 1.356 0.1919

 REQUITY 1 0.000002572 0.00000113 2.267 0.0359

 SXEQUITY 1 -0.000002953 0.00000154 -1.912 0.0720

 SEX 1 -2.752045 1.04090864 -2.644 0.0165

 RACE 1 -2.128832 0.78878484 -2.699 0.0147

 FTE 1 0.001028 0.01843530 0.056 0.9561

 SEQUITY 1 -0.000000643 0.00000039 -1.639 0.1185

 MOBILEW 1 -1.941421 1.63473891 -1.188 0.2504
42

 Table 3A: Summary Statistics on Firms in the Tests

----------------------------------Non-Minority-------------------------------

 Variable N Mean Std Dev

 -------------------------------------------------

 MLOGODDS 31 0.1947513 1.6992927

 MOBILEW 31 0.8064516 0.4016097

 FIXEDW 31 0.1290323 0.3407771

 SATW 31 0 0

 OTHERW 31 0.0645161 0.2497310

 ASSETS 31 29871173.95 134652245

 CASHFLO 31 6531240.56 28213211.97

 TOTDEBT 31 20019149.46 101810845

 PREVLIC 31 22.8387097 42.2722106

 RECWIN 30 3.6000000 6.4358907

 STARTUP 31 0.5161290 0.5080005

 FTE 31 75.3548387 357.9546106

 SEX 31 0.1290323 0.3407771

 EQUITY 31 9852024.49 33348151.09

 -------------------------------------------------

-------------------------------------Minority-----------------------------------

 Variable N Mean Std Dev

 -------------------------------------------------

 MLOGODDS 10 -1.4493106 2.2211346

 MOBILEW 10 0.5000000 0.5270463

 FIXEDW 10 0.4000000 0.5163978

 SATW 10 0 0

 OTHERW 10 0.1000000 0.3162278

 ASSETS 10 527000.00 1238370.79

 CASHFLO 10 41300.00 67452.12

 TOTDEBT 10 185600.00 396269.04

 PREVLIC 10 0 0

 RECWIN 10 0.9000000 1.5238839

 STARTUP 10 0.7000000 0.4830459

 FTE 10 1.9500000 1.6741499

 SEX 10 0.3000000 0.4830459

 EQUITY 10 341400.00 908375.01

 -------------------------------------------------
43

 Table 4: Spectrum Auction Firms: Interest Rate on Debt

Model: MODEL1

Dependent Variable: Interest Rate

 Analysis of Variance

 Sum of Mean

 Source DF Squares Square F Value Prob>F

 Model 16 0.00330 0.00021 4.911 0.1076

 Error 3 0.00013 0.00004

 C Total 19 0.00342

 Root MSE 0.00648 R-square 0.9632

 Dep Mean 0.09142 Adj R-sq 0.7671

 C.V. 7.08757

 Parameter Estimates

 Parameter Standard T for H0:

 Variable DF Estimate Error Parameter=0 Prob > |T|

 INTERCEP 1 0.048183 0.01288369 3.740 0.0333

 DUM94 1 0.054360 0.01976234 2.751 0.0707

 DUM95 1 0.025672 0.01330331 1.930 0.1492

 DUM96 1 0.027895 0.01312109 2.126 0.1235

 DUM97 1 0.018982 0.01532351 1.239 0.3035

 DUM98 1 0.009554 0.01518741 0.629 0.5739

 PERGTY 1 0.037069 0.00626197 5.920 0.0096

 COLLAT 1 -0.010997 0.00648184 -1.697 0.1883

 EQUITY 1 -1.279349E-9 0.00000000 -1.696 0.1884

 CASHFLO 1 1.5936881E-8 0.00000000 4.331 0.0227

 CASHFLO2 1 -7.65173E-16 0.00000000 -4.210 0.0245

 RCF 1 -0.000000441 0.00000008 -5.692 0.0108

 SXCF 1 -6.209373E-9 0.00000001 -0.562 0.6134

 FTE 1 0.000001474 0.00000049 3.015 0.0570

 FAMILY 1 -0.028414 0.00939553 -3.024 0.0566

 SEX 1 -0.001199 0.00815096 -0.147 0.8924

 RACE 1 0.050145 0.01116139 4.493 0.0206
44

 Table 4: Spectrum Auction Firms: Interest Rate on Debt

Model: MODEL2

Dependent Variable: Interest Rate

 Analysis of Variance

 Sum of Mean

 Source DF Squares Square F Value Prob>F

 Model 17 0.00340 0.00020 18.555 0.0523

 Error 2 0.00002 0.00001

 C Total 19 0.00342

 Root MSE 0.00328 R-square 0.9937

 Dep Mean 0.09142 Adj R-sq 0.9401

 C.V. 3.59284

 Parameter Estimates

 Parameter Standard T for H0:

 Variable DF Estimate Error Parameter=0 Prob > |T|

 INTERCEP 1 0.037027 0.00745104 4.969 0.0382

 DUM94 1 0.066430 0.01074327 6.183 0.0252

 DUM95 1 -0.008981 0.01302314 -0.690 0.5617

 DUM96 1 -0.011244 0.01423302 -0.790 0.5123

 DUM97 1 -0.019110 0.01450242 -1.318 0.3183

 DUM98 1 0.017880 0.00815092 2.194 0.1595

 PERGTY 1 0.039933 0.00330519 12.082 0.0068

 COLLAT 1 -0.013522 0.00338455 -3.995 0.0573

 EQUITY 1 -3.06719E-9 0.00000000 -4.443 0.0471

 CASHFLO 1 2.0144161E-8 0.00000000 8.742 0.0128

 CASHFLO2 1 -9.70576E-16 0.00000000 -8.562 0.0134

 RCF 1 -0.000000262 0.00000007 -3.757 0.0641

 SXCF 1 5.1113798E-9 0.00000001 0.765 0.5241

 FTE 1 0.000001055 0.00000028 3.740 0.0646

 FAMILY 1 -0.033179 0.00500309 -6.632 0.0220

 SEX 1 -0.000212 0.00414405 -0.051 0.9638

 RACE 1 0.058181 0.00621991 9.354 0.0112

 MOBILEW 1 0.049061 0.01577336 3.110 0.0897
45

 Table 4: Spectrum Auction Firms: Interest Rate on Debt

Model: MODEL3

Dependent Variable: Interest Rate

 Analysis of Variance

 Sum of Mean

 Source DF Squares Square F Value Prob>F

 Model 15 0.00339 0.00023 30.543 0.0023

 Error 4 0.0000296386 7.4096481E-6

 C Total 19 0.00342

 Root MSE 0.00272 R-square 0.9913

 Dep Mean 0.09142 Adj R-sq 0.9589

 C.V. 2.97770

 Parameter Estimates

 Parameter Standard T for H0:

 Variable DF Estimate Error Parameter=0 Prob > |T|

 INTERCEP 1 0.037959 0.00601438 6.311 0.0032

 DUM94 1 0.065201 0.00861647 7.567 0.0016

 DUM97 1 -0.008753 0.00222061 -3.942 0.0169

 DUM98 1 0.017537 0.00653172 2.685 0.0549

 PERGTY 1 0.039368 0.00261788 15.038 0.0001

 COLLAT 1 -0.013264 0.00278506 -4.762 0.0089

 EQUITY 1 -2.625422E-9 0.00000000 -10.524 0.0005

 CASHFLO 1 1.9320901E-8 0.00000000 13.030 0.0002

 CASHFLO2 1 -9.30314E-16 0.00000000 -12.695 0.0002

 RCF 1 -0.000000306 0.00000002 -12.920 0.0002

 SXCF 1 1.8411497E-9 0.00000000 0.424 0.6933

 FTE 1 0.000001202 0.00000015 7.906 0.0014

 FAMILY 1 -0.032784 0.00411798 -7.961 0.0013

 SEX 1 0.000255 0.00340448 0.075 0.9438

 RACE 1 0.056784 0.00487436 11.650 0.0003

 MOBILEW 1 0.037910 0.00610088 6.214 0.0034
46

 Table 4A: Summary Statistics on Firms in the Tests

---------------------------------Non-Minority-----------------------------------

 Variable N Mean Std Dev

 --------------------------------------------------

 INTEREST 16 0.0917688 0.0138186

 MOBILEW 16 0.8125000 0.4031129

 FIXEDW 16 0.1250000 0.3415650

 SATW 16 0 0

 OTHERW 16 0.0625000 0.2500000

 FTE 16 17.8750000 30.4212097

 ASSETS 16 6984261.50 11007258.85

 CASHFLO 16 2401151.69 5065808.56

 TOTDEBT 16 2759363.56 4459505.19

 NOWOR 16 0.0625000 0.2500000

 STARTUP 16 0.6250000 0.5000000

 PERGTY 16 0.6250000 0.5000000

 COLLAT 16 0.6250000 0.5000000

 SEX 16 0.1875000 0.4031129

 EQUITY 16 4224897.94 7989259.80

 FAMILY 16 0.1250000 0.3415650

 --------------------------------------------------

-------------------------------------Minority-----------------------------------

 Variable N Mean Std Dev

 --------------------------------------------------

 INTEREST 4 0.0900000 0.0135401

 MOBILEW 4 0.2500000 0.5000000

 FIXEDW 4 0.7500000 0.5000000

 SATW 4 0 0

 OTHERW 4 0 0

 FTE 4 9132.00 18263.33

 ASSETS 4 1027500.00 1982344.99

 CASHFLO 4 77500.00 96738.48

 TOTDEBT 4 452500.00 558890.27

 NOWOR 4 0.5000000 0.5773503

 STARTUP 4 0.2500000 0.5000000

 PERGTY 4 0.2500000 0.5000000

 COLLAT 4 0.2500000 0.5000000

 SEX 4 0.5000000 0.5773503

 EQUITY 4 575000.00 1496941.33

 FAMILY 4 0.2500000 0.5000000

 --------------------------------------------------
47

 Table 5: Spectrum Auctions: The Probability of Winning, Model 1

 The LOGISTIC Procedure

 Response Variable: WINNER

 Response Levels: 2

 Number of Observations: 170

 Link Function: Normit

 Response Profile

 Ordered

 Value WINNER Count

 1 1 93

 2 0 77

 Model Fitting Information and Testing Global Null Hypothesis BETA=0

 Intercept

 Intercept and

 Criterion Only Covariates Chi-Square for Covariates

 AIC 236.162 204.524 .

 SC 239.298 245.290 .

 -2 LOG L 234.162 178.524 55.638 with 12 DF (p=0.0001)

 Score . . 48.592 with 12 DF (p=0.0001)

 Analysis of Maximum Likelihood Estimates

 Parameter Standard Wald Pr > Standardized Variable

 Variable DF Estimate Error Chi-Square Chi-Square Estimate Label

 INTERCPT 1 -0.4008 0.3141 1.6283 0.2019 . Intercept

 DUM94 1 -0.4023 0.7443 0.2922 0.5888 -0.124149 DUM94

 DUM95 1 -0.4578 0.3668 1.5573 0.2121 -0.202552 DUM95

 DUM96 1 -0.0917 0.4472 0.0420 0.8376 -0.027588 DUM96

 DUM97 1 -0.9294 0.4069 5.2164 0.0224 -0.312871 DUM97

 DUM98 1 -0.3569 0.3326 1.1518 0.2832 -0.146257 DUM98

 OTHERW 1 0.8325 0.7251 1.3182 0.2509 0.250490 OTHERW

 STARTUP 1 0.2000 0.2318 0.7446 0.3882 0.100206 STARTUP

 BIDCRED 1 1.0046 0.2696 13.8872 0.0002 0.477675 BIDCRED

 INSTALL 1 0.8679 0.3209 7.3122 0.0068 0.405235 INSTALL

 UPFRONT 1 2.918E-8 1.557E-8 3.5112 0.0610 0.228722 UPFRONT

 SEX 1 -0.7110 0.3195 4.9529 0.0260 -0.271838 SEX

 RACE 1 -0.8856 0.3346 7.0048 0.0081 -0.319703 RACE

 Association of Predicted Probabilities and Observed Responses

 Concordant = 80.0% Somers' D = 0.609

 Discordant = 19.1% Gamma = 0.615

 Tied = 0.9% Tau-a = 0.303

 (7161 pairs) c = 0.804

 Hosmer and Lemeshow Goodness-of-Fit Test

 WINNER = 1 WINNER = 0

 -------------------- --------------------

 Group Total Observed Expected Observed Expected

 1 18 1 1.61 17 16.39

 2 18 2 3.86 16 14.14

 3 17 6 5.36 11 11.64

 4 17 11 7.35 6 9.65

 5 17 8 9.91 9 7.09

 6 18 15 11.58 3 6.42

 7 17 12 12.01 5 4.99

 8 17 10 13.13 7 3.87

 9 17 15 14.77 2 2.23

 10 14 13 13.04 1 0.96

 Goodness-of-fit Statistic = 11.704 with 8 DF (p=0.1649)
48

 Table 5: Spectrum Auctions: The Probability of Winning, Model 2

 The LOGISTIC Procedure

 Response Variable: WINNER

 Response Levels: 2

 Number of Observations: 169

 Link Function: Normit

 Response Profile

 Ordered

 Value WINNER Count

 1 1 93

 2 0 76

 Model Fitting Information and Testing Global Null Hypothesis BETA=0

 Intercept

 Intercept and

 Criterion Only Covariates Chi-Square for Covariates

 AIC 234.571 207.914 .

 SC 237.701 254.863 .

 -2 LOG L 232.571 177.914 54.657 with 14 DF (p=0.0001)

 Score . . 47.715 with 14 DF (p=0.0001)

 Analysis of Maximum Likelihood Estimates

 Parameter Standard Wald Pr > Standardized Variable

 Variable DF Estimate Error Chi-Square Chi-Square Estimate Label

 INTERCPT 1 -0.3491 0.3269 1.1406 0.2855 . Intercept

 DUM94 1 -0.3494 0.7604 0.2111 0.6459 -0.108109 DUM94

 DUM95 1 -0.3776 0.4265 0.7840 0.3759 -0.167395 DUM95

 DUM96 1 -0.0484 0.4594 0.0111 0.9161 -0.014592 DUM96

 DUM97 1 -0.9120 0.4128 4.8815 0.0271 -0.301751 DUM97

 DUM98 1 -0.3099 0.3533 0.7692 0.3805 -0.127255 DUM98

 OTHERW 1 0.9137 0.7555 1.4625 0.2265 0.275647 OTHERW

 STARTUP 1 0.2157 0.2345 0.8460 0.3577 0.108030 STARTUP

 BIDCRED 1 0.9932 0.2718 13.3526 0.0003 0.470960 BIDCRED

 INSTALL 1 0.8954 0.3258 7.5515 0.0060 0.418740 INSTALL

 UPFRONT 1 2.914E-8 1.614E-8 3.2578 0.0711 0.229073 UPFRONT

 SEX 1 -0.7111 0.3194 4.9564 0.0260 -0.272519 SEX

 RACE 1 -0.8660 0.3363 6.6303 0.0100 -0.313380 RACE

 FTE 1 -4.63E-6 0.000022 0.0429 0.8360 -0.021853 FTE

 COMAPPS 1 -0.00058 0.00124 0.2198 0.6392 -0.078800 COMAPPS

 Association of Predicted Probabilities and Observed Responses

 Concordant = 79.7% Somers' D = 0.599

 Discordant = 19.7% Gamma = 0.603

 Tied = 0.6% Tau-a = 0.298

 (7068 pairs) c = 0.800

 Hosmer and Lemeshow Goodness-of-Fit Test

 WINNER = 1 WINNER = 0

 -------------------- --------------------

 Group Total Observed Expected Observed Expected

 1 17 1 1.53 16 15.47

 2 17 2 3.58 15 13.42

 3 18 6 5.60 12 12.40

 4 17 11 7.41 6 9.59

 5 18 10 10.48 8 7.52

 6 17 13 11.02 4 5.98

 7 18 13 12.78 5 5.22

 8 17 11 13.25 6 3.75

 9 17 14 14.85 3 2.15

 10 13 12 12.12 1 0.88

 Goodness-of-fit Statistic = 7.4157 with 8 DF (p=0.4925)
49

 Table 5: Spectrum Auctions: The Probability of Winning, Model 3

 The LOGISTIC Procedure

 Response Variable: WINNER

 Response Levels: 2

 Number of Observations: 169

 Link Function: Normit

 Response Profile

 Ordered

 Value WINNER Count

 1 1 93

 2 0 76

 Model Fitting Information and Testing Global Null Hypothesis BETA=0

 Intercept

 Intercept and

 Criterion Only Covariates Chi-Square for Covariates

 AIC 234.571 207.454 .

 SC 237.701 254.403 .

 -2 LOG L 232.571 177.454 55.116 with 14 DF (p=0.0001)

 Score . . 48.008 with 14 DF (p=0.0001)

 Analysis of Maximum Likelihood Estimates

 Parameter Standard Wald Pr > Standardized Variable

 Variable DF Estimate Error Chi-Square Chi-Square Estimate Label

 INTERCPT 1 -0.2941 0.3363 0.7650 0.3818 . Intercept

 DUM94 1 -0.3550 0.7559 0.2205 0.6386 -0.109826 DUM94

 DUM95 1 -0.2793 0.4428 0.3980 0.5281 -0.123840 DUM95

 DUM96 1 0.00339 0.4658 0.0001 0.9942 0.001024 DUM96

 DUM97 1 -0.9316 0.4141 5.0625 0.0244 -0.308244 DUM97

 DUM98 1 -0.2749 0.3514 0.6121 0.4340 -0.112906 DUM98

 OTHERW 1 0.9838 0.7605 1.6734 0.1958 0.296793 OTHERW

 STARTUP 1 0.2212 0.2353 0.8836 0.3472 0.110780 STARTUP

 BIDCRED 1 0.9996 0.2723 13.4709 0.0002 0.473989 BIDCRED

 INSTALL 1 0.9113 0.3272 7.7572 0.0053 0.426173 INSTALL

 UPFRONT 1 2.846E-8 1.608E-8 3.1316 0.0768 0.223769 UPFRONT

 SEX 1 -0.7042 0.3195 4.8581 0.0275 -0.269890 SEX

 RACE 1 -0.8566 0.3365 6.4815 0.0109 -0.309984 RACE

 FTE 1 -3.73E-6 0.000022 0.0275 0.8682 -0.017589 FTE

 QBIDDERS 1 -0.00174 0.00209 0.6886 0.4067 -0.140092 QBIDDERS

 Association of Predicted Probabilities and Observed Responses

 Concordant = 79.9% Somers' D = 0.604

 Discordant = 19.5% Gamma = 0.608

 Tied = 0.7% Tau-a = 0.301

 (7068 pairs) c = 0.802

 Hosmer and Lemeshow Goodness-of-Fit Test

 WINNER = 1 WINNER = 0

 -------------------- --------------------

 Group Total Observed Expected Observed Expected

 1 17 1 1.53 16 15.47

 2 17 1 3.48 16 13.52

 3 18 7 5.54 11 12.46

 4 17 11 7.53 6 9.47

 5 17 10 9.77 7 7.23

 6 17 13 11.03 4 5.97

 7 18 11 12.73 7 5.27

 8 17 12 13.15 5 3.85

 9 17 14 14.82 3 2.18

 10 14 13 13.04 1 0.96

 Goodness-of-fit Statistic = 8.4925 with 8 DF (p=0.3869)
50

 Table 5: Spectrum Auctions: The Probability of Winning, Model 4

 The LOGISTIC Procedure

 Response Variable: WINNER

 Response Levels: 2

 Number of Observations: 169

 Link Function: Normit

 Response Profile

 Ordered

 Value WINNER Count

 1 1 93

 2 0 76

 Model Fitting Information and Testing Global Null Hypothesis BETA=0

 Intercept

 Intercept and

 Criterion Only Covariates Chi-Square for Covariates

 AIC 234.571 208.709 .

 SC 237.701 258.787 .

 -2 LOG L 232.571 176.709 55.862 with 15 DF (p=0.0001)

 Score . . 48.524 with 15 DF (p=0.0001)

 Analysis of Maximum Likelihood Estimates

 Parameter Standard Wald Pr > Standardized Variable

 Variable DF Estimate Error Chi-Square Chi-Square Estimate Label

 INTERCPT 1 -0.4454 0.3862 1.3301 0.2488 . Intercept

 DUM94 1 -0.4741 0.7708 0.3783 0.5385 -0.146686 DUM94

 DUM95 1 -0.3763 0.4570 0.6781 0.4102 -0.166828 DUM95

 DUM96 1 -0.1443 0.4927 0.0858 0.7696 -0.043538 DUM96

 DUM97 1 -1.0371 0.4296 5.8274 0.0158 -0.343137 DUM97

 DUM98 1 -0.2340 0.3569 0.4299 0.5120 -0.096104 DUM98

 OTHERW 1 1.1779 0.7980 2.1790 0.1399 0.355353 OTHERW

 STARTUP 1 0.2072 0.2365 0.7674 0.3810 0.103742 STARTUP

 BIDCRED 1 1.0057 0.2734 13.5293 0.0002 0.476866 BIDCRED

 INSTALL 1 0.9416 0.3303 8.1255 0.0044 0.440362 INSTALL

 UPFRONT 1 2.943E-8 1.546E-8 3.6246 0.0569 0.231343 UPFRONT

 SEX 1 -0.6945 0.3203 4.7033 0.0301 -0.266178 SEX

 RACE 1 -0.8628 0.3376 6.5318 0.0106 -0.312218 RACE

 FTE 1 -2.33E-6 0.000022 0.0109 0.9169 -0.010999 FTE

 QBIDDERS 1 -0.00142 0.00213 0.4460 0.5043 -0.114664 QBIDDERS

 MOBILEW 1 0.2443 0.2850 0.7350 0.3913 0.120446 MOBILEW

 Association of Predicted Probabilities and Observed Responses

 Concordant = 80.3% Somers' D = 0.611

 Discordant = 19.2% Gamma = 0.614

 Tied = 0.5% Tau-a = 0.304

 (7068 pairs) c = 0.806

 Hosmer and Lemeshow Goodness-of-Fit Test

 WINNER = 1 WINNER = 0

 -------------------- --------------------

 Group Total Observed Expected Observed Expected

 1 16 1 1.36 15 14.64

 2 17 4 3.35 13 13.65

 3 17 3 5.04 14 11.96

 4 17 10 7.43 7 9.57

 5 18 10 10.04 8 7.96

 6 17 13 10.86 4 6.14

 7 17 9 11.93 8 5.07

 8 17 13 13.16 4 3.84

 9 17 15 14.74 2 2.26

 10 16 15 14.86 1 1.14

 Goodness-of-fit Statistic = 6.6641 with 8 DF (p=0.5733)
51

 Table 5: Spectrum Auctions: The Probability of Winning, Model 5

 The LOGISTIC Procedure

 Response Variable: WINNER

 Response Levels: 2

 Number of Observations: 169

 Link Function: Normit

 Response Profile

 Ordered

 Value WINNER Count

 1 1 93

 2 0 76

 Model Fitting Information and Testing Global Null Hypothesis BETA=0

 Intercept

 Intercept and

 Criterion Only Covariates Chi-Square for Covariates

 AIC 234.571 207.941 .

 SC 237.701 261.149 .

 -2 LOG L 232.571 173.941 58.630 with 16 DF (p=0.0001)

 Score . . 49.564 with 16 DF (p=0.0001)

 Analysis of Maximum Likelihood Estimates

 Parameter Standard Wald Pr > Standardized Variable

 Variable DF Estimate Error Chi-Square Chi-Square Estimate Label

 INTERCPT 1 -0.5632 0.3955 2.0275 0.1545 . Intercept

 DUM94 1 0.2216 0.9188 0.0582 0.8094 0.068570 DUM94

 DUM95 1 -0.5063 0.4669 1.1761 0.2782 -0.224454 DUM95

 DUM96 1 -0.2162 0.4953 0.1905 0.6625 -0.065216 DUM96

 DUM97 1 -1.1288 0.4430 6.4945 0.0108 -0.373484 DUM97

 DUM98 1 -0.2735 0.3585 0.5823 0.4454 -0.112332 DUM98

 OTHERW 1 6.4301 309.3 0.0004 0.9834 1.939845 OTHERW

 STARTUP 1 0.2226 0.2378 0.8765 0.3492 0.111474 STARTUP

 BIDCRED 1 1.0405 0.2761 14.2015 0.0002 0.493408 BIDCRED

 INSTALL 1 0.9414 0.3310 8.0897 0.0045 0.440264 INSTALL

 UPFRONT 1 2.985E-8 1.499E-8 3.9667 0.0464 0.234644 UPFRONT

 SEX 1 -0.6506 0.3222 4.0757 0.0435 -0.249321 SEX

 RACE 1 -0.8730 0.3400 6.5940 0.0102 -0.315899 RACE

 FTE 1 -2.43E-6 0.000022 0.0118 0.9134 -0.011467 FTE

 QBIDDERS 1 -0.00042 0.00225 0.0344 0.8529 -0.033640 QBIDDERS

 MOBILEW 1 0.2695 0.2855 0.8912 0.3451 0.132871 MOBILEW

 MINBIDCR 1 -6.2590 309.3 0.0004 0.9839 -1.785316 MINBIDCR

 Association of Predicted Probabilities and Observed Responses

 Concordant = 80.7% Somers' D = 0.620

 Discordant = 18.7% Gamma = 0.624

 Tied = 0.6% Tau-a = 0.309

 (7068 pairs) c = 0.810

 Hosmer and Lemeshow Goodness-of-Fit Test

 WINNER = 1 WINNER = 0

 -------------------- --------------------

 Group Total Observed Expected Observed Expected

 1 17 1 1.24 16 15.76

 2 17 3 3.29 14 13.71

 3 17 4 5.14 13 11.86

 4 17 10 7.81 7 9.19

 5 17 11 9.60 6 7.40

 6 17 12 10.93 5 6.07

 7 17 10 12.03 7 4.97

 8 17 12 13.11 5 3.89

 9 17 15 14.62 2 2.38

 10 16 15 15.06 1 0.94

 Goodness-of-fit Statistic = 4.0018 with 8 DF (p=0.8570)
52

 Table 5: Spectrum Auctions: The Probability of Winning, Model 6

 The LOGISTIC Procedure

 Response Variable: WINNER

 Response Levels: 2

 Number of Observations: 96

 Link Function: Normit

 Response Profile

 Ordered

 Value WINNER Count

 1 1 52

 2 0 44

 Model Fitting Information and Testing Global Null Hypothesis BETA=0

 Intercept

 Intercept and

 Criterion Only Covariates Chi-Square for Covariates

 AIC 134.417 121.617 .

 SC 136.981 157.518 .

 -2 LOG L 132.417 93.617 38.800 with 13 DF (p=0.0002)

 Score . . 32.609 with 13 DF (p=0.0019)

 Analysis of Maximum Likelihood Estimates

 Parameter Standard Wald Pr > Standardized Variable

 Variable DF Estimate Error Chi-Square Chi-Square Estimate Label

 INTERCPT 1 0.0834 0.4790 0.0303 0.8618 . Intercept

 DUM94 1 -1.4980 1.1086 1.8261 0.1766 -0.416208 DUM94

 DUM95 1 -1.0265 0.6818 2.2668 0.1322 -0.463964 DUM95

 DUM96 1 -0.4013 0.6118 0.4302 0.5119 -0.128492 DUM96

 DUM97 1 -1.0248 0.5288 3.7558 0.0526 -0.374065 DUM97

 DUM98 1 -0.4319 0.4998 0.7467 0.3875 -0.169458 DUM98

 OTHERW 1 1.3413 1.1659 1.3236 0.2499 0.350578 OTHERW

 BIDCRED 1 0.6250 0.3807 2.6947 0.1007 0.300484 BIDCRED

 INSTALL 1 1.6260 0.5389 9.1034 0.0026 0.734890 INSTALL

 UPFRONT 1 1.153E-7 8.498E-8 1.8419 0.1747 1.007329 UPFRONT

 COMAPPS 1 -0.00125 0.00212 0.3442 0.5574 -0.160731 COMAPPS

 DA 1 -0.0657 0.0838 0.6150 0.4329 -0.174298

 SEXDA 1 -0.5875 1.2938 0.2062 0.6498 -0.073075

 RACEDA 1 -0.6203 0.3515 3.1148 0.0776 -0.329350

 Association of Predicted Probabilities and Observed Responses

 Concordant = 84.0% Somers' D = 0.684

 Discordant = 15.6% Gamma = 0.686

 Tied = 0.3% Tau-a = 0.343

 (2288 pairs) c = 0.842

 Hosmer and Lemeshow Goodness-of-Fit Test

 WINNER = 1 WINNER = 0

 -------------------- --------------------

 Group Total Observed Expected Observed Expected

 1 10 0 0.82 10 9.18

 2 10 2 1.42 8 8.58

 3 10 2 2.83 8 7.17

 4 10 4 4.30 6 5.70

 5 10 7 5.17 3 4.83

 6 10 8 6.42 2 3.58

 7 10 6 7.42 4 2.58

 8 10 9 8.40 1 1.60

 9 10 8 8.96 2 1.04

 10 6 6 5.82 0 0.18

 Goodness-of-fit Statistic = 6.471 with 8 DF (p=0.5946)
53

 Table 5: Spectrum Auctions: The Probability of Winning, Model 7

 The LOGISTIC Procedure

 Response Variable: WINNER

 Response Levels: 2

 Number of Observations: 96

 Link Function: Normit

 Response Profile

 Ordered

 Value WINNER Count

 1 1 52

 2 0 44

 Model Fitting Information and Testing Global Null Hypothesis BETA=0

 Intercept

 Intercept and

 Criterion Only Covariates Chi-Square for Covariates

 AIC 134.417 119.939 .

 SC 136.981 153.276 .

 -2 LOG L 132.417 93.939 38.478 with 12 DF (p=0.0001)

 Score . . 32.066 with 12 DF (p=0.0014)

 Analysis of Maximum Likelihood Estimates

 Parameter Standard Wald Pr > Standardized Variable

 Variable DF Estimate Error Chi-Square Chi-Square Estimate Label

 INTERCPT 1 -0.0254 0.4464 0.0032 0.9546 . Intercept

 DUM94 1 -1.6747 1.0636 2.4791 0.1154 -0.465280 DUM94

 DUM95 1 -1.2306 0.5661 4.7247 0.0297 -0.556175 DUM95

 DUM96 1 -0.4639 0.6011 0.5955 0.4403 -0.148527 DUM96

 DUM97 1 -0.9900 0.5228 3.5866 0.0582 -0.361359 DUM97

 DUM98 1 -0.5451 0.4645 1.3772 0.2406 -0.213880 DUM98

 OTHERW 1 1.0275 0.9987 1.0584 0.3036 0.268545 OTHERW

 BIDCRED 1 0.6142 0.3809 2.6006 0.1068 0.295303 BIDCRED

 INSTALL 1 1.6624 0.5386 9.5277 0.0020 0.751368 INSTALL

 UPFRONT 1 1.205E-7 8.856E-8 1.8506 0.1737 1.052286 UPFRONT

 DA 1 -0.0673 0.0843 0.6363 0.4250 -0.178399

 SEXDA 1 -0.6656 1.2881 0.2670 0.6053 -0.082787

 RACEDA 1 -0.6267 0.3514 3.1811 0.0745 -0.332719

 Association of Predicted Probabilities and Observed Responses

 Concordant = 83.8% Somers' D = 0.681

 Discordant = 15.7% Gamma = 0.685

 Tied = 0.5% Tau-a = 0.342

 (2288 pairs) c = 0.840

 Hosmer and Lemeshow Goodness-of-Fit Test

 WINNER = 1 WINNER = 0

 -------------------- --------------------

 Group Total Observed Expected Observed Expected

 1 10 0 0.89 10 9.11

 2 10 2 1.39 8 8.61

 3 10 2 2.87 8 7.13

 4 10 4 4.42 6 5.58

 5 10 8 5.20 2 4.80

 6 10 8 6.28 2 3.72

 7 10 5 7.32 5 2.68

 8 10 9 8.42 1 1.58

 9 10 8 8.92 2 1.08

 10 6 6 5.86 0 0.14

 Goodness-of-fit Statistic = 10.15 with 8 DF (p=0.2547)
54

 Table 5A: Summary Statistics on Firms in the Tests

--------------------------------Non-Minority-----------------------------------

 Variable N Mean Std Dev

 ---------------------------------------------------

 WINNER 143 0.5734266 0.4963176

 MOBILEW 143 0.6013986 0.4913313

 FIXEDW 143 0.3006993 0.4601740

 SATW 143 0.0069930 0.0836242

 OTHERW 143 0.0909091 0.2884903

 STARTUP 143 0.4405594 0.4981993

 FTE 143 509.9860140 4149.07

 UPFRONT 143 1584931.46 8533567.23

 QUALIFY 143 0.9230769 0.2674060

 MINBIDCR 143 0.0769231 0.2674060

 INSTALL 143 0.2797203 0.4504394

 BIDCRED 143 0.6573427 0.4762662

 QBIDDERS 143 120.7972028 78.4772829

 COMAPPS 143 177.0209790 131.3718231

 PREVLIC 136 9.8970588 22.6474688

 SEX 143 0.1468531 0.3552036

 ---------------------------------------------------

-------------------------------------Minority-----------------------------------

 Variable N Mean Std Dev

 ---------------------------------------------------

 WINNER 26 0.4230769 0.5038315

 MOBILEW 26 0.5384615 0.5083911

 FIXEDW 26 0.3076923 0.4706787

 SATW 26 0 0

 OTHERW 26 0.1538462 0.3679465

 STARTUP 26 0.6538462 0.4851645

 FTE 26 1410.46 7162.41

 UPFRONT 26 283751.27 442575.38

 QUALIFY 26 0.8076923 0.4019185

 MINBIDCR 26 0.1538462 0.3679465

 INSTALL 26 0.5384615 0.5083911

 BIDCRED 26 0.6923077 0.4706787

 QBIDDERS 26 166.8846154 83.4373187

 COMAPPS 26 248.0769231 146.3390373

 PREVLIC 26 0.9230769 2.7989009

 SEX 26 0.3461538 0.4851645

 ---------------------------------------------------