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 Summaryiv
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
References29
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 Bradfords 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 applicants 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.   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 broadcasters 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 firms 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 nonminorityowned 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 applicants 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 nonminorityowned 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 Beckers 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 Beckers 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 firms 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.
	Andos 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
	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 firms or an individuals business plan consists of the acquisition 
of one or more broadcast spectrum licenses or other spectrum licenses and associated 
facilities. Assume that the firms 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 firms 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 firms 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 firms 
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 lenders discounted expected cash flow from the potential borrower is positive. That 
is, if 
(1)             .

The amount of the loan is  . The function gt( ) maps the firms cash flow into the 
lender's cash flow. The discount rate is r, and E is the expectation operator. The variable 
 , t = 1, , T, is the random variable for the firms cash flow which includes the 
incremental cash flow of the wireless project.  The variable  , t= 1, , T, is the 
random variable for creditworthiness. Time is indexed by t, and   is composed of 
other random variables presented by the borrowers application, e.g., random variables on 
the equity of the firm over time and debt- to-asset ratios of the firm over time. We specify 
the equation for the probability of debt approval for most recent license acquisitions of 
borrowers that are current license holders. 
 B. Description of the Data
   	For broadcasting, the data are from a survey of current broadcast license holders. 
The FCC contracted the survey for the fall of 1999. The survey instrument was mailed to 
a random sample of non-minority license holders.  In addition, a census was taken of 
minority license holders.   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 firms 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 broadcasters 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.   It is noted that the data only covers broadcasters who obtained licenses 
through the comparative hearings process or on the secondary marketno 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 firms 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

Successful Broadcast
Applicants1
(N=235)
Auction
Participants
(N=251)
Minority-Owned2

Minority
Non-Minority
Missing

37
121
77

15.7%
51.5%
32.8%

36
235

14.3%
85.7%

Woman-Owned

Woman
Not Woman
Missing

20
138
77

8.5%
58.7%
32.8%

37
234

14.7%
85.3%

Sought Financing Other 
than Own Equity3

Sought Financing
Did not seek financing
Missing

136
88
11

57.9%
37.4%
4.7%

108
143

43.0%
57.0%

Obtained Debt Funding4

Obtained
Did not obtain
Missing

125
10
100

53.2%
4.3%
42.6%

86
165

34.3%
65.7%

Notes to Exhibit 1:
1. Successful applicants are those that acquired at least one license in their latest attempt to acquire a 
license.
2. If the respondent did not explicitly indicate minority-owned business, the business was coded as 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.    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.  
	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 respondents 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.   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 lenders analysis of various components of the 
application and associated business plan affects the lenders 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 lenders 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 firms equity and debtto-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
	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 
applicants 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 respondents 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 respondents 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 applicants  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 firms 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 
	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 lenders expectation of a minority-owned firms 
positive cash flow.  Gender was not found to be statistically significant in the models.
	In summary, race and gender matter in a lenders approval of a loan application. 
Moreover, race matters in a lenders 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
	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,Ci), is a function of the 
valuation placed on a license, denoted V, and the amount of funds available to bid, Ci.  
The amount that a participant can bid is the minimum of the value placed on the license 
or the amount of funds available to bid.  This analysis considers that Ci is a function of 
the capital markets.  That is, if there is no capital market discrimination, Ci = f(Zi) where 
f(Zi) is the amount of financing made available to a non-minority firm given the firm and 
owner traits  .  The function G(   ) is defined such that   when 
capital markets exhibit bias against minority- and/or women-owned firms.  If 
  but   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   but V is distributed so that   
for minority- and/or women-owned applicants two outcomes will happen.  First, the 
auctions won by minorities and women will be skewed toward low price auctions.  
Second, the overall proportion of auctions won by minorities and women will be less than 
if no capital market constraints existed, or alternatively, less than if auctions were 
distributed among the applicants by Zi.  The statistical models below consider that when 
minorities or women are constrained in obtaining financing from capital markets, 
  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 ), along with race and 
gender of the firms 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 participants ability to bid; but the capital 
constraints associated with minority status or female status (i.e.,  )  
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 applicants 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
Table 5 contains the result of the econometric analysis of the probability of an 
applicant winning a spectrum license. The results are derived from a logistic/probit 
regression using SAS.   The data supports the hypothesis that minority status results in a 
lower probability of winning spectrum.  That is, for the respondents of this sample, the 
probability of a minority winning a spectrum license in his/her most recent auction is 
lower than the probability of a nonminority firm/entity winning a spectrum license in its 
most recent auction. For each of the seven models, which control for time, auction group, 
startup status, size of company, financial strength, and competition, minority status 
results in a lower probability of winning.  In five of the seven models tested, female 
gender was statistically significant in predicting a lower probability of success in 
spectrum auctions.  In the other two models female gender was found to reduce the 
probability of success, but the results were not statistically significant.  Finally, the results 
in Table 5 show that each of the seven models is statistically significant in specifying 
success of the participants. 
Part 6.  Discussion of the Findings and Recommendations
In Adarand Constructors, Inc. v. Pena, 515 U.S. 200 (1995), the United States 
Supreme Court determined that federal programs that use race or ethnicity as a basis for 
decision-making are subject to strict judicial scrutiny. Thus, any such programs must 
serve a compelling governmental interest, and must be narrowly tailored to serve that 
interest.  The Supreme Court has recognized that remedying past discrimination 
constitutes a compelling government interest.  See Adarand, 515 U.S. at 237; City of 
Richmond v. J.A. Croson Co., 488 U.S. 469, 509 (1989) (plurality opininon); id. At 511 
(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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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
              ---------------------------------------------------

  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 marketno broadcast auctions were included.
2 The response rate for the survey was 30 percent.
  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 Both questions provide financial information about a company based on the most recent application for 
or secondary market acquisition of a license(s). 
5The modified log odds can be found in Wonnacott and Wonnacott (1979), Chapter 4.
6The default year is 1972.

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