# **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**

###### 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

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.

###### 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 (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 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 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 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 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.

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 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
women-owned 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 women-owned
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 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-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 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 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]{.underline}

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, 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

\(1\) ![](media/image1.wmf){width="2.486111111111111in"
height="0.4861111111111111in"}.

The amount of the loan is
![](media/image2.wmf){width="0.20833333333333334in" height="0.25in"}.
The function *g~t~*( ) 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

![](media/image3.wmf){width="0.5965277777777778in" height="0.25in"}, 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
![](media/image4.wmf){width="0.3194444444444444in" height="0.25in"}, t=
1, ..., T, is the random variable for creditworthiness. Time is indexed
by t, and ![](media/image5.wmf){width="0.3055555555555556in"
height="0.25in"} 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]{.underline}

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.

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

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 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

+------------------+----------------+-------+-------+-------+-------+
|                  |                | Succe |       | Au    |       |
|                  |                | ssful |       | ction |       |
|                  |                | Broa  |       |       |       |
|                  |                | dcast |       | Pa    |       |
|                  |                |       |       | rtici |       |
|                  |                | App   |       | pants |       |
|                  |                | lican |       |       |       |
|                  |                | ts^1^ |       | (N    |       |
|                  |                |       |       | =251) |       |
|                  |                | (N    |       |       |       |
|                  |                | =235) |       |       |       |
+------------------+----------------+-------+-------+-------+-------+
| M                | Minority       | 37    | 15.7% | 36    | 14.3% |
| inority-Owned^2^ |                |       |       |       |       |
|                  | Non-Minority   | 121   | 51.5% | 235   | 85.7% |
|                  |                |       |       |       |       |
|                  | Missing        | 77    | 32.8% |       |       |
+------------------+----------------+-------+-------+-------+-------+
| Woman-Owned      | Woman          | 20    | 8.5%  | 37    | 14.7% |
|                  |                |       |       |       |       |
|                  | Not Woman      | 138   | 58.7% | 234   | 85.3% |
|                  |                |       |       |       |       |
|                  | Missing        | 77    | 32.8% |       |       |
+------------------+----------------+-------+-------+-------+-------+
| Sought Financing | Sought         | 136   | 57.9% | 108   | 43.0% |
| Other than Own   | Financing      |       |       |       |       |
| Equity^3^        |                | 88    | 37.4% | 143   | 57.0% |
|                  | Did not seek   |       |       |       |       |
|                  | financing      | 11    | 4.7%  |       |       |
|                  |                |       |       |       |       |
|                  | Missing        |       |       |       |       |
+------------------+----------------+-------+-------+-------+-------+
| Obtained Debt    | Obtained       | 125   | 53.2% | 86    | 34.3% |
| Funding^4^       |                |       |       |       |       |
|                  | Did not obtain | 10    | 4.3%  | 165   | 65.7% |
|                  |                |       |       |       |       |
|                  | Missing        | 100   | 42.6% |       |       |
+------------------+----------------+-------+-------+-------+-------+

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 non-minority.

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 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, 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, 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 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

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 non-linear 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 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]{.underline}

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.

**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
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
woman-owned 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]{.underline}

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.

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.

**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]{.underline}

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 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,C~i~)*, is a
function of the valuation placed on a license, denoted V, and the amount
of funds available to bid, *C~i~*. 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 *C~i~* is a function of
the capital markets. That is, if there is no capital market
discrimination, *C~i~ = f(Z~i~)* where *f(Z~i~)* is the amount of
financing made available to a non-minority firm given the firm and owner
traits ![](media/image6.wmf){width="0.18055555555555555in"
height="0.25in"}. The function *G*( ) is defined such that
![](media/image7.wmf){width="1.44375in" height="0.25in"} when capital
markets exhibit bias against minority- and/or women-owned firms. If
![](media/image8.wmf){width="1.19375in" height="0.25in"} but
![](media/image9.wmf){width="0.8888888888888888in"
height="0.2222222222222222in"} 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
![](media/image8.wmf){width="1.19375in" height="0.25in"} but V is
distributed so that ![](media/image10.wmf){width="0.9027777777777778in"
height="0.2222222222222222in"} 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 *Z~i~*.
The statistical models below consider that when minorities or women are
constrained in obtaining financing from capital markets,
![](media/image11.wmf){width="1.19375in" height="0.25in"} 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![](media/image12.wmf){width="0.18055555555555555in"
height="0.25in"}), 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., ![](media/image11.wmf){width="1.19375in" height="0.25in"})
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.

**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.

[C. Estimation Results: Probability of Winning]{.underline}

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]{.underline}, 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]{.underline}, 515 U.S. at 237; [City of Richmond v. J.A. Croson
Co.]{.underline}, 488 U.S. 469, 509 (1989) (plurality opininon);
[id]{.underline}. At 511 (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 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.

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Bates, Timothy and William D. Bradford (1992) "Factors Affecting New
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Cavalluzzo, Ken. E., Linda C. Cavalluzzo and John Wolken (2000)
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Cull, Robert and Raymond Suarez (1995) *Capital Formation and Investment
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**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

**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

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

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

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

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

\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\--

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

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

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

\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\--

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

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

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

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

\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\--

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

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

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

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

\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\--

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)

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)

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)

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)

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)

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)

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)

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

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[^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.

[^2]: ^2^ The response rate for the survey was 30 percent.

[^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.

[^4]: ^4^ Both questions provide financial information about a company
    based on the most recent application for or secondary market
    acquisition of a license(s).

[^5]: ^5^The modified log odds can be found in Wonnacott and Wonnacott
    (1979), Chapter 4.

[^6]: ^6^The default year is 1972.