1

Final Report

Logistic Regression Models of the Broadcast

License Award Process for Licenses Awarded by

the FCC

Prepared for the FCC as a deliverable under the contract “Estimation of Utilization Rates/Probabilities

of Obtaining Broadcast Licenses from the Federal Communications Commission or of Obtaining

Broadcast and Wireless Licenses through Secondary Market Transactions”

prepared by

KPMG LLP

Economic Consulting Services

For

Federal Communications Commission

November 2000
2

I. INTRODUCTION........................................................................................................................................................ 3

MAJOR FINDINGS................................................................................................................................................................. 4

FCC LICENSE AWARD PROCESS.......................................................................................................................................... 5

II. DATA COLLECTION ................................................................................................................................................ 6

III. DATA CHARACTERISTICS AND PRELIMINARY THOUGHTS BASED ON BASIC DATA ANALYSIS... 11

IV. LOGISTIC REGRESSION MODEL RESULTS ............................................................................................... 16

A. CONDITIONAL LOGIT MODEL....................................................................................................................................... 18

B. MODEL OF SINGLETON VERSUS COMPARATIVE HEARING LICENSE DISPOSITION......................................................... 24

Setup of the data for Singleton Model:......................................................................................................................... 25

C. UNCONDITIONAL LOGIT MODEL RESULTS ................................................................................................................... 27

V. CONCLUSIONS......................................................................................................................................................... 32

APPENDIX I........................................................................................................................................................................ 35

INFORMATION COLLECTED FROM FORM 301 FOR THE LOGIT MODELS.............................................................................. 35

APPENDIX II....................................................................................................................................................................... 51

DATA DEFINITIONS AND SUMMARY STATISTICS FOR COMPARATIVE HEARINGS AND SINGLETONS................................... 51

APPENDIX III..................................................................................................................................................................... 57

DESCRIPTION OF WEIGHTING METHOD FOR UNCONDITIONAL LOGIT AND SINGLETON MODELS....................................... 57
3

I. Introduction

KPMG LLP has prepared this report as a deliverable under the contract “Estimation of Utilization

Rates/Probabilities of Obtaining Broadcast Licenses from the Federal Communications Commission

or of Obtaining Broadcast and Wireless Licenses through Secondary Market Transactions”.1 Part of

that contract requires KPMG to develop a model of the license award process for participants who

were awarded licenses by the FCC. During the periods of time that we examined, the FCC’s stated

policy was to provide preferences to minorities and women. During this period, the FCC awarded

licenses under two regimes.2 First, the FCC would award a license to individual applicants

(singletons) who were judged as qualified when only a single application was received. Secondly, if

more than one applicant applied for the same license, then the FCC used Comparative Hearings, an

administrative hearing process, to allocate broadcast licenses during the period from the 1940s until

1993.

The overall study will assist the FCC determine if there has been previous discrimination by the

agency or passive participation by the FCC in discrimination by the private sector.

Herein we provide our model results developed from data collected on the participation and success of

applicants in the FCC’s license award process. This includes a model of the comparative hearing

award process for radio and television licenses and a model that examines factors that are deterministic

of whether a license was awarded through a comparative hearing or directly to a singleton applicant.

In addition, we present results for a model of the award process for all licenses regardless of whether

they were awarded through comparative hearings or to singleton applicants. Some of the questions that

this study will address are:

(1) How is the probability of license award affected by minority status when minority status is defined

based on participation and when minority status is defined as minority ownership greater than 50%

of equity?

(2) Did the comparative hearing process result in an allocation of licenses according to the stated rules

of the FCC?

(3) Did the participation and success of minorities and women for the award of licenses differ

depending upon whether they participated through the comparative hearing process or as singleton

applicants?

(4) How did factors such as minority status and gender affect the probability of being a singleton

application that encountered no competition and which did not end up in a comparative hearing?

 

1

 While the RFP for this work also requested a study of secondary market transactions for wireless and broadcast licenses,

the secondary market study was cancelled due to the lack of accessible data sources. KPMG examined data in FCC

archives and found insufficient information on secondary market transactions. Therefore, KPMG petitioned and received

approval from OMB to carry out this data collection effort. However, KPMG first conducted a pilot study to determine

what the response rates would be for a survey of secondary market participants. Because the response rate to the pilot

study was extremely low (less than 5%), it became obvious that the cost of acquiring the necessary data would be

prohibitive and the FCC cancelled this portion of the project.

2

 After 1993, the FCC began to use lotteries to award licenses.
4

Major Findings

Before we present the details, we will briefly describe our major findings. Then we will present the

supporting analysis.

ß Some statistical evidence suggests that applications with high minority participation were more

likely to face competition and to enter the comparative hearing process rather than receive an

award as a singleton applicant. The reasons for this are not clear but the effect was that

participation of minorities in uncontested singleton applications was low.

ß Based on the models that we estimated, we can conclude that there was a lower overall probability

for an application with minority ownership winning a license than a non-minority application after

controlling for a variety of important variables. This is because there was a lower probability of

winning a license as a singleton and no greater chance for an application with minority ownership

to win a license in a comparative hearing.

ß Minority participation in comparative hearings was very low relative to minority representation in

the U.S. population.

ß The minority participation rate for singleton licenses, which appear to be less valuable than those

allocated through comparative hearings, was even lower than the low rate of minority participation

in comparative hearings.

ß The results are consistent with a view that minority and female preferences encouraged applicants

to recruit minorities and females in order to compete more effectively in comparative hearings.

ß The process for awarding licenses through the comparative hearing process provided credit to

applications that contained minorities and females, as was the stated policy of the FCC.

ß However, while minority participation -- as defined by minority percentage of body counts --

appears to have positively influenced the win rates in comparative hearings, minority participation

when defined by percentage ownership or majority percentage ownership, does not significantly

influence the probability of acquiring a license.

ß This finding is consistent with another finding that minority participation is greatest when there is

little minority equity ownership – a finding consistent with the existence of non meaningful (sham)

participation.

ß We found a statistically significant relationship between win rates and minority body count but not

between win rates and minority equity ownership. This finding is consistent with the hypothesis of

sham participation.

ß The mechanism of providing credit to minorities seems to be significantly related to the amount of

assets stated in applications with minority participation.

ß Applicants with minority participation seem to have received extra credit for assets relative to

applicants with lesser or no minority participation.

ß Applications with minority participation seem to have been treated less favorably with respect to

liabilities than those applicants with lesser or no minority participation.

ß The net effect of the credit provided for assets and liabilities was positive for minorities since

assets on applications generally substantially exceed liabilities.

ß We may also interpret the results as suggesting that while financial strength was judged more

favorably when minorities were present, financial weakness was judged more harshly when

minorities were present.
5

ß These data generally support the theory that minority and female participation occurs most when

the stations are most valuable and where the presence of minorities and females can bolster the

probability of winning a license. Height of the station antennas, population, and household income

are higher when minorities and females participate in applications. These are all indicators of the

value of the station.

ß Payments and receipts are higher when there is nominal minority and female participation in

applications; this is another indication that nominal minority and female participation occurs most

in competitive situations.

ß The number of parties in applications is substantially higher when minorities participate; however

this phenomenon is much less obvious when minorities control equity.

ß Because minorities tend to participate when valuable licenses are at stake, and because the number

of participants in these applications is greater by far, it is possible that minorities were added to

these applications in order to improve the likelihood of winning, but may not add much in the way

of meaningful minority ownership to these applications.

FCC License Award Process

From the late 1940s until 1993, the FCC conducted comparative hearings when more than one

applicant applied for the same broadcast license. A comparative hearing was a legal proceeding that

was presided over by an Administrative Law Judge (ALJ). The purpose of the comparative hearing

was to determine which applicant for a broadcast television or radio license was best qualified to hold

the license.3 KPMG submitted a report, History of the Broadcast License Application Process, which

identifies in significant detail the criteria that were pertinent to the award of licenses. In this report,

we develop models that determine whether the FCC applied these criteria as stated in its regulations

and in such a way that the resulting license awards favored minorities as was the stated objective of

the FCC during the period when minority preferences were in place.

A record of the comparative hearing proceedings is maintained in paper files at the National Records

Center in Suitland, MD. These files contain data on the declared minority status of the parties to

applications for broadcast licenses that were considered in the comparative hearing process. The files

also contain the dispositions or outcomes of the comparative hearings, i.e. a record of which

applications have been awarded the licenses.

For this study, KPMG collected data on approximately 60 comparative hearings, which included 203

applications, and 66 singleton license awards4 Data was collected from applicant files, which

included the information that the applicants provided to the FCC during the comparative hearing

process. We collected these data for the period 1978 to 1981 and 1989 to 1993. During these periods,

the FCC had a stated policy of providing preference for minority applicants.5

 

3

 Note that participants could also settle or buy out other participants prior to receiving a decision from the ALJ.

4 These 60 hearings were drawn from a larger sample of 230 comparative hearings that KPMG collected data for for the

purpose of calculating “Utilization Rates, Win Rates, and Disparity Ratios for Broadcast Licenses Awarded by the FCC.”

The larger sample of 230 hearings did not require the level of detailed information that was required for this study.

5

 The FCC also requested an analysis of the period before minority preferences were in place. However, the cost of

acquiring the necessary data prior to the minority and female preference period would have been prohibitive. KPMG

examined the records in the FCC archives and determined that there was insufficient data on race of applicants. This
6

The remainder of this report is organized as follows.

• Section II, Data Collection, outlines the efforts taken to collect these data.

• Section III, Data Characteristics, examines the characteristics of the data collected for the

applications considered in comparative hearings and singleton applications.

• Section IV, Logistic Regression Model Results summarizes the results that were obtained for the

model of the licensing process. Part A of this section describes the results for Comparative

Hearing awards. Part B presents a model that examines what factors were deterministic of whether

a license was allocated through a comparative hearing or was awarded as a singleton license. Part

C describes the findings for a model of license awards in general that includes awards to singleton

applicants as well as awards made through comparative hearings.

ß Appendix I provides an example of the data collection forms used to acquire these data.

ß Appendix II contains formal definitions for all of the measures shown in the various tables of this

report and summary statistics for the data.

ß Appendix III provides details of the weighting schemes used on the data to adjust for the issue of

oversampling of minorities for comparative hearings.

II. Data Collection

KPMG collected data from FCC archives in Suitland Maryland during the period October, 1999

through March, 2000 in order to develop statistics about the success of women and minorities and nonminorities in the comparative hearing process. Data collection involved extracting information for a

sample of 230 comparative hearings that occurred over the periods 1978 to 1981, and 1989-1993.

These two periods were selected to satisfy a number of conditions. First, these were both periods

when financial information was collected on the license application.6 Secondly, during these periods,

the FCC’s stated policy was to provide credit for minority applicants.

 

would have require KPMG to locate and survey license applicants using contact (name and address information) that was

20 or more years old. Based on a pilot survey of secondary market participants who sold a broadcast station between 1993

and 1999, KPMG estimated that a lower than 5% response rate would be achieved from a survey of pre preference period

applicants. Because these contact information were approximately 20 years old, it was highly unlikely that KPMG would

have been able to collect sufficient data for the pre-preference period; therefore this part of the study was terminated.

6 While financial information was not necessary for the construction of success ratios for groups in attaining broadcast

licenses (utilization ratios), it was necessary for developing a regression model of the award process based on the factors

and policies identified by the FCC as important to the award of a broadcast license.
7

KPMG collected data from a random sample of the hearings that occurred during these two time

periods. The universe of available hearings was made available to KPMG in two formats. For the

period prior to 1983, the Administrative Law Judge Listing was used. This is a paper database. For

the period after 1983 we relied upon the BAPS database, which is an electronic database containing

information on each comparative hearing that took place from the early 1980’s up to the present. Both

data sources provide the following important information about each hearing:

• unique hearing identifiers (docket number)

• service

• call sign (BAPS only)

• start date for hearing

• end date for hearing

KPMG also utilized a paper database of comparative hearing history cards, which contains a record of

motions filed by applicants and orders from Administrative Law Judges in comparative hearings. This

database was used to collect information on the number and types of motions made during the

comparative hearings. 7For the purposes of our analysis, the number of motions was summed and used

as an indicator of attorney effort in models of the license award outcome.

To identify the location of hearing dockets, KPMG used the ‘314 record listing’ at the FCC. The ‘314

record listing’ contains the date retired and accession number of each comparative hearing docket that

is archived.8

Once the selected hearing docket was located and obtained, researchers collected information on the

following categories: general, legal, financial, attorney and trial, settlement, technical, ownership and

integration, race, and gender.9

ß General Information includes the docket number, application reference number, type of service,

name of applying organization, date applied, date designated, date terminated, and hearing fee.

 

Although the selection of these time periods was guided by the requirements of the regression model, these periods are also

useful for the construction of utilization ratios. Both of these time periods encompass the period when minority preference

policies were used by the FCC in the award of broadcast licenses.

7

Among the types of motions are motion for leave to amend, motion for summary decision, and motion for protective

order.

8

 Date retired and accession number are defined as:

Date Retired: After a comparative hearing is completed, all documents associated with the hearing are bound, boxed, and

sent to the Nation Records Center (NRC) for archival purposes. The date that the documents are sent to archives is the date

retired: in other words, this is the date the hearing is retired to the NRC.

Accession number: When the NRC receives boxes from the FCC, each box receives a number for location purposes. This

number is called the "accession number" and this number is used to locate a box when requesting records from the NRC.

9

 The data collection form show in Appendix I was used to collect these data.
8

ß Legal information includes type of organization, citizenship, alien status and interests, criminal

activity, character, other broadcast interests, and organization stock structure.

ß Financial information includes an itemization of construction costs, itemization of funds

available for construction, assets and liabilities (in some cases), source of funds listing, total debt

relied on.

ß Attorney and trial information includes name of attorney and law firm, hearing fee paid, final

disposition of applicant, number of applicants, presiding judge, and motions filed.

ß Settlement information includes amount paid by an applicant in settlements, amount received,

lawyer’s fees, and number of applications paid to settle.

ß Technical information includes principle community to be served, class of license, elevation of

antenna above average terrain, geographical size of proposed area, population coverage, and power

of signal.

ß Ownership information includes (for each party to the application) number of shares, percent of

shares owned, number of voting shares, percent of voting shares, position at the station,

integration, and full-time/part-time indicator.

ß Race and gender information includes (for each party to the application) gender and race broken

out by Black, Hispanic, American Indian/Eskimo, Asian, and Caucasian.

Tables 1 and 2 show the population counts and sample sizes for hearings and applications for radio

and television licenses for each of the two time periods for which preferences were the stated policy

of the FCC and for which race and financial data were available.

Table 1:

Number of Hearings in Population and Sample Size

Years 1978-

1981

1989 –

1993

Total

Population

Total

Sample

All Licenses 421 142 563 230

 Radio 286 134 420 155

 AM 85 0 85 25

 FM 201 134 335 130

 TV 135 8 143 75
9

Table 2:

Number of Applications in Population and Sample

Years 1978-

1981

1989 –

1993

Total

Population

Total

Sample

All Licenses 1,064 595 1,659 740

 Radio 716 583 1,299 494

 AM 177 0 177 64

 FM 539 583 1,122 430

 TV 348 12 360 246

A sample of 230 comparative hearings (which included 740 applications) was drawn from the

population of hearings using stratified random sampling. The sample was stratified by service (AM,

FM, and TV). This sample size was selected in an effort to balance the cost of data collection with the

need to obtain a reasonable level of precision at various levels of disaggregation.

Once the sample was drawn, data collection personnel, who were primarily made up of individuals

with legal or paralegal background, retrieved the files from the National Records Center in Suitland,

MD. and collected the necessary data.10 There were two objectives for the data collection effort. The

first was to collect data on all 230 comparative hearings for a limited set of information that would

allow us to calculate utilization and disparity ratios.11 The results of this effort were discussed in the

report “Win Rates and Disparity Ratios for Broadcast Licenses Awarded by the FCC”. The second

objective was to collect more detailed data for the purpose of modeling the award process.

For this purpose, we developed a sub-sample of the 230 hearings for which to collect this much more

detailed data about the applicants. Because we were interested in examining the process by which

decisions to award licenses were made, and in particular whether any active or passive discrimination

was apparent in the process, we oversampled hearings with minority participation for the model.

Thus, our sample of hearings contains more minority participation than does the larger set of 230

comparative hearings. For the purposes of modeling the impact of minority status on the license

award process, hearings with applications reflecting a variety of degrees of minority participation are

most informative. The statistical analysis properly accounts for the oversampling of such hearings.

Table 3 in Appendix III, which describes a method for weighting that was applied to correct for

oversampling of minorities, contains the sample sizes that were acquired for the detailed data

necessary for the logistic regression models.

Our period of analysis was chosen to capture the time when preferences were in

place. This period included both 1978-1981 and 1989-1993. Some complexities were introduced as a

result of choosing these 2 periods. They were chosen based on the availability of financial data in the

applications submitted to the FCC and the need to cover the entire period during which comparative

hearings were used. During the early period, applications contained full asset and liability data for

applications while in the latter period only total funds necessary to construct and operate the station

was available. This makes the analysis a bit more complicated because there are discontinuities in

some of these data.

 

10 A combination of KPMG staff and a subcontractor were used to collect these data.

11 The data collected for the development of the utilization ratios was limited to a few key items.
10

Our analysis starts at the point where an application has been made. We do not model the decision

process by which some people decide to submit applications and some do not. It is possible that the

number of female or minority applicants is not optimal because minorities and females may have had

lesser chance to submit an application due to impediments such as inability to secure financing. That

issue is beyond the scope of this analysis which only considers the license award decision after an

application has been made.12 We do note, as the following data will suggest, that minority

participation in broadcasting is very low relative to minority representation in the general population.

Table 3 shows the percentages of minority participation in comparative hearings, singleton

applications, and minority shares of the U.S. population.

 

12 Other studies, which we understand are ongoing, will address the issue of whether there are such impediments.
11

Table 3.

Participation by Race in Comparative Hearings, Singleton Applications and Percent of

U.S. Population in 1990*

Percent of Parties in

Hearings (1)

Percent of

parties in

singleton

applications

(2)

Percent of U.S. Population

 in 1990 (3)

Total Minority 8.9% 3.8% 23.8%

 Black 3.4% n.a. 12.2%

 Asian .4% n.a. 2.7%

 Hispanic 3.9% n.a. 8.7%

 American Indian, Eskimo, Aleut .4% n.a. .7%

White 91.1% 96.2% 76.2%

Male 79.3% 71.7% 48.7%

Female 20.7% 28.3% 51.3%

*Notes: (1) Detailed race and ethnic categories do not sum to total minority for the Percent of parties in hearings, due to

nonreporting of this level of detail for a small portion of the minority applicants (.8% fail to report the level of detail about

their minority status). These percentages are based on the data collected from 230 randomly selected comparative

hearings. (2) Percent of parties in singleton applications is based on data from 66 randomly selected singleton applications.

Data are available for minority only because singleton applications do not designate race of minority. (3) For the percent

of U.S. population (which comes from 1990 Census data), there is slight overlap in the figure for black and Hispanic

because black includes those blacks of Hispanic origin (about .5% of the 12.2% of blacks are of Hispanic origin).

III. Data Characteristics and Preliminary Thoughts Based on Basic Data Analysis

Appendix II presents means, standard deviations and counts for the data used in the logit models. Also

shown are similar data for singleton applications. We present these statistics for raw data before any

imputations were performed to fill in missing values. Imputation techniques were used for some of the

variables where lack of data was problematic for the purpose of estimating models of the license

allocation process.13

 

13 The following variables were imputed for missing values for the logistic regression model. Motions (4 values imputed);

apassets (109 values imputed), totfunds (10 values imputed), and apliabil (138 values imputed). Stata’s imputation

procedures are based on the following primary references.

Goldstein, R. 1996. Sed10: Patterns of Missing Data. Stata Technical Bulletin 32: 12-13.

Little, R. J. A. and D. Rubin. 1987 Statistical Analysis with Missing Data. New York: John Wiley and Sons.
12

As evident in Table 3, there is certainly a large difference between the minority share of participants in

comparative hearings and singleton applications and the minority share of the U.S. population. There

is also a large difference in the minority share of participants in comparative hearings and the minority

share of participants in singleton applications. Part of our analysis will be to try and understand this

latter difference. One hypothesis is that because preferences were part of the comparative hearing

process, this encouraged minority participation in hearings. However, even when we compare

minority participation in the first application filed for a license and compare participation rates for

those applications that were unopposed (singletons) with those that faced competition (first

applications in hearings), a significant difference in minority participation persists. It is puzzling why

we would see more minority participation in comparative hearings than in singleton applications.

One hypothesis is that the preferences incorporated into the comparative hearing process encouraged

recruitment of minorities to participate in applications going to comparative hearings. However,

minority participation in the first application filed for a license for which additional applications were

subsequently filed is substantially higher than minority participation in applications that were

unopposed (singletons).

This difference in minority participation in first applications between those subsequently opposed and

unopposed is consistent with the recruitment hypothesis only if applicants had advance knowledge of

the probability of opposition.

An alternative hypothesis is that first applicants with minority participation were more likely to be

challenged, leading to their higher representation in hearings and lower representation in singleton

applications. This could happen if their applications were perceived as weak relative to singleton

white applicants, and if this encouraged challengers to vie for the license. Other explanations involve

differences in the characteristics of licenses, such that minorities may not have been aware of or

interested in licenses that were not as highly valued, and thus obtained by unopposed applications.

Some of these explanations suggest that there were issues with the comparative hearing system such

that it either encouraged applicants to challenge a perceived weak application or that it encouraged

minority participation in body only and not as meaningful equity ownership and control roles.

One fact that is supportive of the idea of nominal minority participation is the substantially higher

number of parties per application for applications with minority participation. Applications with

minority participation in comparative hearings average 6.1 parties per application, while applications

without minority participation average only 3.0 parties per application. Applications with greater than

50% minority ownership average 4.5 parties per application. It is interesting to note that when

minority participation is substantive, there are fewer parties than when minority participation is

combined with non-minorities and where minorities have no equity control.

The tables below show the results of “t tests” testing the statistical difference in means of variables

under various minority, singleton, and gender groupings. As discussed above, number of parties per

application (cntpty) and the number of minority parties per application (mincnt) are included in the

variables below. Table 4 considers minority versus non-minority differences. Table 5 considers

differences between applications that went to comparative hearings and singletons. Table 6 considers

differences between females and white males. Formal definitions for all of these variables are provided

in Appendix II.
13

Table 4

t tests for Differences in Mean Values for Variables in Minority Applications

(1)

Minority

Ownership

>= 50%

(2)

Minority

count >=1

(3)

Minority

Count >=1

& < 50%

ownership

(4)

Minority

count = 0

Apassets 644,620 704,973 820,650 1,354,609

Apequity 551,011 579,083 628,210 449,688

Apliabil 207,969 321,419 605,044 302,788

Applcnt 3.6** 3.7* 3.9*** 2.8

Corp .65** .70* .79* .49

Cntpty 4.5** 6.1* 8.9* 3

ExperM 64.4 68.3 75.4 76.3

Femcnt 1.3 1.8* 2.6* 0.9

Fempctb 0.26 0.29 0.34 0.31

Fempcto 0.31 0.30 0.27 0.27

First Year Revenue 144,955 183,449 252,419 103,355

Height 1,161*** 1,192*** 1,247 757

Income 26,897 27,555** 28,613 24,993

IntegM 18.5* 18.1* 17.5*** 8.6

Mincnt 2.8* 2.6* 2* 0

Minpctb 0.71* 0.56* 0.29* 0

Minpcto 0.82* 0.60* 0.22* 0

Motions 19.9* 21.1* 23.2* 6.6

Otherown 0.8* 1.2 2 1.8

Population stated on

Application

202,774 213,861 232,535 158,248

Census Population 67,608*** 67,300** 66,748 38,461

ResidM 88.8 87.7 85.8 55.6

Settlpay 3,661* 23,858 59,591*** 19,445

Settlrec 26,670 35,035 51,767 26,463

Totcons 677,127*** 868,734* 1,180,096** 363,630

Totfunds 650,611 952,176* 1,476,635** 449,405

* Significant difference at 1% level; ** Significant difference at

5% level; *** Significant difference at 10% level.

Variable definitions appear in Appendix II.

Group 1 includes applications where minority ownership is at least 50%. Group 2 includes

applications where there is any minority participation. Group 3 includes applications where there is
14

minority participation, but there is less than 50% minority ownership. Group 4 includes applications

where there is only non-minority participation. Group 5 includes all applications in comparative

hearings and group 6 includes only singleton applicants. Group 7 includes applications with any

female participation and Group 8 includes applications with female ownership of at least 50%. Group

9 includes applications where there is only white male participation.

Table 5

t tests for Differences in Mean Values for Variables in Singleton and

Comparative Hearing Applications

(5)

All Hearings

(6)

Singleton

Applications

Apassets 1,144,388 505,379

Apequity 503,484 462,015

Apliabil 312,902 179,827

Applcnt 3.7 1*

Corp .58 .42**

Cntpty 4.2 2.4*

ExperM 76.1 68.7

Femcnt 1.2 0.7*

Fempctb 0.31 0.30

Fempcto 0.29 0.25

First Year

Revenue

162,334 3,256*

Height 1,034 348*

Income 25,633 25,474

IntegM 12.7 5.6*

Mincnt 0.8 0.1*

Minpctb 0.17 0.045*

Minpcto 0.18 0.046*

Motions 13.4 0.6*

Otherown 1.8 1.2**

Population stated

on Application

203,571 75,594*

Census Population 54,850 17,328*

ResidM 63 65.4

Settlpay 20,320 -

Settlrec 28,912 -

Totcons 599,449 127,571*

Totfunds 705,234 162,925*

* Significant difference at 1% level; ** Significant difference at

5% level; *** Significant difference at 10% level.

Variable definitions appear in Appendix II.
15

T tests were conducted on each of these groups to determine if there was a difference relative to the

mean for applications with either no minorities or no females. Groups 1, 2, and 3 are tested against

group 4. Group 5 is tested against group 6. Groups 7 and 8 are tested against group 9. Where there is

statistical significance, it is indicated with asterisks. (see note to table).

Table 6

t tests for Differences in Mean Values for Variables

in Female and White Male Applications

(7)

Female count

>= 1

(8)

Female

ownership >=

50%

(9)

White

Males

Apassets 1,312,130*** 558,755 457,519

Apequity 507,695 157,814 387,501

Apliabil 373,083*** 234,957 155,160

Applcnt 3.2 3 2.8

Corp .63* .43 .39

Cntpty 4.7* 2.8** 2.1

ExperM 57.1** 51.8* 97.2

Femcnt 1.8* 1.7* -

Fempctb 0.51* 0.69* -

Fempcto 0.46* 0.77* -

First Year

Revenue

161,859* 106,017 60,008

Height 977** 919 622

Income 26,274 26,895*** 24,501

IntegM 13.1* 10.7* 6.9

Mincnt 0.8* 0.4* -

Minpctb 0.15* 0.11* -

Minpcto 0.17* 0.15* -

Motions 12.5* 9.8*** 5.8

Otherown 1.6 1.3** 2.0

Population stated

on Application

187,417 219,402 143,085

Census

Population

49,563 41,879 40,785

ResidM 70.8 89.1 54.3

Settlpay 28,095* 23,908** 4,136

Settlrec 31,215 20,084 23,733

Totcons 569,680** 392,380 368,930

Totfunds 684,427** 410,671 428,781

* Significant difference at 1% level; ** Significant difference at

5% level; *** Significant difference at 10% level.

Variable definitions appear in Appendix II.
16

These data generally support the theory that greater minority and female participation occurs when the

stations are most valuable and where the presence of minorities and females can bolster the probability

of winning a license. Height of the station, population, and household income are higher when

minorities participate in applications. These are all indicators of the value of the station. Because

minorities and females tend to participate more when these valuable licenses are at stake, and because

the number of participants in these applications is greater by far, it is possible that minorities and

females are added to these applications in order to improve the likelihood of winning, but do not add

much in the way of meaningful minority or female ownership to these applications.

Other support for this theory that the acquisition of more valuable licenses is more likely to include

minority and female presence comes from the fact that total construction costs for the station and

expected first year revenues for the station are higher when there are more minorities and females

present in applications. In addition, more applications were received when minorities and females

were present than when only non-minorities were included in applications. Finally, settlements in the

form of both payments and receipts are higher when minorities are present, which is yet another

indicator of the competitive nature of the hearings in which these applications were involved.

Another interesting observation that we make from these data is that when minorities own greater than

50% of the application, which is suggestive of meaningful participation, the count of parties to

applications is much lower than when they are present in applications without significant minority

ownership. This is consistent with the hypothesis that minorities supplement rather than substitute for

others in applications. We also note that when minorities have substantive ownership, most of the

measures that we noted previously such as income, population, and first year revenues expected, are

all lower than when they are present but not substantial equity participants in applications. This fact

supports the idea that minorities may be participating marginally (in body only) in hearings where

more valuable licenses were awarded.

IV. Logistic Regression Model Results

As we described previously, licenses were awarded by the FCC under two regimes. 1) if a single

applicant applied for a license, then the license was granted provided that basic minimum

qualifications were met. 2) If more than 1 applicant applied for the license, then an administrative

process called a comparative hearing was used to allocate the license. This process encouraged

competition and those with a good understanding of the process were likely motivated to put forth

stronger applications. One criterion that the FCC stated as providing positive credit was whether

applicants included minority or female participation in the application. The FCC claimed that it

provided some credit for minority or female participation.

Other characteristics of applicants that the FCC claimed influenced the decision to award a license

were:

ß Integration in station affairs (FCC stated as positively influencing outcome)

ß Broadcast experience (FCC stated as positively influencing outcome)

ß Local residence (FCC stated as positively influencing outcome)

ß Financial condition of applicants (amount and sources of funds, application assets, liabilities)

(FCC stated financial condition as influencing outcome)
17

ß Ownership of other stations (FCC interest in diversity suggests a negative influence on outcome)

Other variables that we have included to reflect the competitive nature of the hearing process are:

ß Number of motions filed (proxy for attorney effort or quality)

ß Experience of the attorney

ß Form of Organization

ß Number of parties in the application

ß Number of applications in the comparative hearing

ß Order in which the Application is filed

Variables that we have included to reflect the value of the station are:

ß Height of the proposed station above natural terrain

ß Population in the proposed area of service as stated on the application

ß Population in the proposed area of service based on Census Bureau data

ß Household income in the proposed area of service

We have estimated three models as part of the analysis.

The first is a conditional logit model of the comparative hearing process. This model is designed to

examine what factors were important in determining which applicant received a license and

specifically to establish whether the criteria for award stated by the FCC were indeed applied in the

comparative hearing licensing process. The conditional logit model estimates the importance of

various independent variables in determining the outcome of the hearing process.14 In this model, our

data set is based on the licenses awarded in 60 comparative hearings. Singleton applications are not

considered because they did not participate in the comparative hearing process.

One major problem in modeling the outcome of a comparative hearing process is the presence of

varying numbers of applicants. The chance that a particular applicant is successful depends on that

applicant's characteristics, and on the number and type of rival applicants for the license. The

conditional logit model is able to account for the different probability of selection across the hearings

given the varying numbers of applications and different characteristics of applicants for each hearing.

The model groups applications that are in the same hearing together, and recognizes the fact that the

Administrative Law Judge was required to make an award to only a single applicant.

The second model that we estimate is a model of the decision of competitors to contest the awarding

of a license to an initial applicant by submitting competing applications. In the case of the first model

of the comparative hearing process, we were modeling the choice function of the judge. In this case,

we are modeling the choices of potential competitors who must decide whether they should apply for a

license that already has an initial applicant. Obviously, we expect that potential applicants are more

likely to submit competing applications when the license in question is particularly valuable or the

 

14 Stata’s conditional logit model procedure is based on the work of Hosmer and Lsmeshow (1989), Bresnow and Day

(1980, 247-249), and Collett (1991, 262-276). Other references in Stata documentation are: Green (1997, chapter 19),

Chamberlin (1980), and Hamerle and Ronning (1995).
18

initial application seems to be weak. However, other factors may complicate this simple explanation.

Entry may also be likely when the initial applicant has substantial financial resources that could be

used to buy out competing applicants. We are specifically interested in the effects of race and gender

which should indicate, all other things equal, a stronger application and hence deter competing

applications.

The third model that we estimated is an unconditional logit model. This model examines the

probability of attaining a license, regardless of whether the license is awarded by a comparative

hearing or based on the receipt of a single application for the license. A feature of the unconditional

logit model is that it looks across all of the hearings and attempts to classify winners and losers based

on the characteristics of the applications. In this model, our data set is based on licenses awarded in 60

comparative hearings and 66 licenses awarded in cases where only a single application was filed. As

discussed below, statistical issues require the results of the model to be interpreted with caution.

Because the objective of the analysis is primarily to determine if there was differential treatment of

minorities and women in the process of awarding licenses, we focus on the role of race and gender in

the models.

A. Conditional Logit Model

Table 7 presents estimation results for a variety of specifications of the conditional logit model. The

results that are presented below represent the most consistent and reasonable estimated results from a

variety of specifications. Statistical significance is indicated in the table by 1, 2 or 3 asterisks ‘*’

depending upon whether significance is evident at the 1%, 5% or 10% confidence level. We provide a

detailed discussion of the variables in this model but will not repeat this level of detail in our

discussion of the following two models.

Winner is the dichotomous dependent variable and equals 1 if the application was the winner in a

comparative hearing and 0 for a losing applicant. All of these regression results in table 7 attempt to

explain how the independent variables affect the probability of winning.

One of the criterion that the FCC stated would be used to judge applicants in comparative hearings

was integration of the applicants in station affairs. That is, what positions would the applicants hold?

To measure this for the purpose of the model, we created a variable called IntegM0 which is a dummy

variable that equals 1 where there was no integration at all in station affairs. The negative coefficient

for this variable indicates that no integration significantly reduces the likelihood of award.

We also used a variable called IntegM, which represents a measure of how much integration in station

affairs was planned. The negative sign on the coefficient for IntegM suggests that more integration

into station affairs reduces the probability of license award. This result was unexpected because the

FCC’s stated policy was to provide credit for integration. However the regression results for this

variable are not statistically significant.15

 

15 We separated integration, residence, and experience into bifurcated variables to determine if judges may have treated

zero experience, residence, or integration differently. We found that it made no difference to the overall regression results

if the 3 variables that represented zero experience, residence and integration were removed. We also specified these
19

ExperM represents a measure of experience of the parties in applications. The coefficient on ExperM

is close to zero and insignificant. ExperM0 is a dummy variable that equals 1 if the applicant has no

experience at all. The negative sign is reasonable, i.e. no experience at all reduces the likelihood of

award. However the results for both of these variables suggest that any impact from experience was

minimal and the results are not statistically significant.

ResidM represents a measure of years of local residence of the parties in applications. The sign on the

estimated coefficient for ResidM is negative but close to zero, suggesting that no credit is provided for

local residence.

ResidM0 is a dummy variable, which equals 1 if the applicant has no local residence at all and zero

otherwise. The negative sign is reasonable, i.e. no experience at all reduces the likelihood of award.

However the results for both of these variables are not significantly different from zero, suggesting no

effect on hearing outcome for years of local residence.

Number of motions (Motioni) is the total number of motions filed by the applicant during a hearing.

The results indicate that the number of motions is very significant and increases the likelihood of

license award. The same variable squared is intended to determine if there is any nonlinear influence

of motions. The negative and significant sign on this variable indicates that the importance of the

marginal motion falls as the number of motions increases. If this were not the case, then we might

expect lawyers to file even more motions than they did.

Assets (Apassi) and liabilities (Apliabi) stated on the application have the expected effect.16 Assets

increase the likelihood of award while liabilities diminish the likelihood of award. Both of these

variables are significant. However the size of the coefficients suggests that the influence of a dollar of

liabilities has a greater negative influence on the probability of a win than the influence that a dollar of

assets has on positively influencing the probability of a win. However, because assets are

significantly greater than liabilities, it is unclear which effect dominates. To address this question, we

also included a variable in the model to measure net assets (Netassi).

The effect of an increase in net assets, measured as the difference in assets and liabilities, is small and

positive as indicated by the positive estimated coefficient of the variable Netassi (see specification 3

in table 7). The positive effect of net assets in comparative hearings may reflect the ability of an

applicant to pay other applicants to withdraw. It is important to remember that the outcome of a

 

variables to give more weight to greater experience, integration, and residence – see appendix II for the precise definitions.

However, we also ran this model with a version of these variables that were simple sums of the parties’ stated experience,

planned integration, and stated years of residence. Estimation of the model using these simpler versions produced results

that were no different than the results using the more sophisticated specifications of residence, experience, and integration.

16 Note that we only have data on assets and liabilities during the early period of analysis (93 observations). While we

imputed the rest of the observations for the late period, the imputation is having no substantial impact on results. We have

verified this by running the same regression on only the early period data and the results are similar to those for the entire

period. We have also included a variable (apassm) which is a dummy variable that equals 1 when assets were missing on

an application. This coefficient on this variable is insignificant, suggesting that there is nothing different about the

applications that did not report assets.
20

comparative hearing reflects both judgment of the Administrative Law Judge and prior agreements

among applicants that may cause one or more to withdraw.

Ownership of other media interests (Otherown) significantly diminishes the probability of license

award. This is consistent with the FCC’s stated policy of promoting diversification and diffusion of

ownership in broadcasting.

Corporate form of organization (Corp) has a positive impact on the likelihood of award. We can

speculate that this form of organization may be correlated with sophistication of the applicant or that it

reflects access to lower cost capital that is roughly equivalent to additional net worth. Note that both

have a positive effect on winning.

Interestingly, total funds (Totfundi) available to fund the station for the first year of operation has

almost no impact on the likelihood of award once assets and liabilities and net worth are taken into

account.17

As seen in the first three specifications in table 7, both minority participation (Minpctb) and female

participation (Fempctb) -- as measured by percentage body count in the application -- increase the

likelihood of award. This is unsurprising given that the FCC’s stated policy was to provide credit for

applications with female and minority participation. The effect of female or minority presence on the

likelihood of award is stronger for females than for minorities. The minority squared and female

squared terms are also both significant but they are negative and relatively very small. This indicates

that positive influence of minority and female presence diminishes as the percentage of female and

minority presence rises. There is a positive but diminishing return to minority and female

participation in terms of how they influence the likelihood of license award.

While minority participation as measured by percentage body count seems to have had a positive

impact on the likelihood of award, there is no statistically significant positive relationship between

minority ownership and probability of license award. This is true when we measure ownership based

on the percentage that is minority (see specification 4 in Table 7) or when we define true minority

ownership as occurring only when minorities own greater than 50% of the application (see

specification 5 of Table 7). This finding is consistent with our previous finding that minority

participation is greatest when there is little minority equity ownership – a finding suggestive of the

existence of non- meaningful (sham) participation. If the comparative hearing process encouraged

sham participation, we would find a statistically significant relationship between win rates and

minority body count but not between win rates and minority equity ownership.

The results for female ownership (Fempcto) are different than the results for minority ownership

(Minpcto). There is a positive and significant relationship between female ownership and probability

 

17 This variable was developed by using two different application forms. The 1989 form asked the applicant to enter only 1

data element for the total cost of constructing and operating the station for 3 months. Applications from earlier years

requested detailed construction cost and operation data for the entire year. The variable was developed by splicing the two

periods together and adjusting the pre 1989 data to put operating costs on a quarterly basis. While we believe that the

adjustment is able to concord the data from the 2 time periods, we note that the mean value from the early period is

significantly higher than the mean value from the later period. This is indicative of measurement error across the two

periods and is likely due to the two different means that the FCC used to collect data during these periods.
21

of license award. This is true when we measure ownership based on the percentage that is female or

when we define true female ownership as occurring only when females own greater than 50% of the

application.

Submission of the first application (First) tends to have a negative and sometimes marginally

significant estimated coefficient. This might appear to be a surprising result because first movers

should be well qualified applicants. However, there is an important selection effect at work here.

These estimates are conditional on a comparative hearing which implies that the first applicant faces

competition from subsequent applicants. The presence of subsequent applicants is an indication that

they believe the first applicant’s case can be challenged. Put another way, first applicants who are

challenged and face a comparative hearing may not be strong applicants.18

There are also some curious results for one of the variables in the model (attorney experience).

We know that the lack of an attorney was fatal for those who applied without them. No applicant

without an attorney won a comparative hearing. A logical result from our perspective is that use of an

attorney with zero experience should reduce the likelihood of winning (see the negative and significant

sign on attny0 which is a dummy that equals 1 when the attorney has no experience.)

However, while lack of an attorney eliminates the possibility of winning a license, and the use of an

attorney with no experience lowers the probability of winning, further increases in attorney

experience (Attnyexp) also seem to reduce the likelihood of winning a license. This is a curious

result, which can have a number of explanations. For example, it may be due to measurement error in

the variable used as a measure of attorney experience. This measure was developed by counting the

number of times an attorney showed up as having been involved in any of the hearings in our sample.

So the measure represents experience based only on the sample of 60 hearings that are used in this

analysis. Alternatively, attorneys who are employed by many applicants may simply be less expensive,

rather than better. Finally, attorneys involved in more cases may be more inclined to settle for

payments from competing applicants.

 

18

Winning First Applicants by Minority Status and Type of Award

First Applicants % Singleton

Awards

%Comparative

Hearing Awards

(1) Total 95/133 (71%) 64/95 (67%) 31/69 (45%)

 (2) Non Minority 78/102 (76%) 58/78 (74%) 20/44 (45%)

 (3) Minority Count>0 17/31 (55%) 6/17 (35%) 11/25 (44%)

 (4) Minority % ownership>50% 11/16 (54%) 3/11 (27%) 8/13 (61%)

 (5)Minority % ownership<=50% 6/15 (40%) 3/6 (50%) 3/12 (25%)

(1) = sum of (2),(3) and (3) = sum of (4), (5).

According to these data, 58 of 78 or 74% of non-minority first applicants were awarded licenses without the need to

compete in a comparative hearing. For minorities, 6 of 17 or 35% of first applicants was awarded a license without the

need to compete in a comparative hearing. Once first applicants got into comparative hearings, minorities and nonminorities won the hearings at about the same rate. (non-minorities won in 20 of 44 or 45% while minorities won in 11 of

25 or 44%.
22

As we discussed earlier, we place a great deal of the focus on the minority status of applicants.

Therefore, we have included some interaction terms in the models to determine if there is any sort of

disparate treatment of minorities. For example, we have included two variables that measure the

interaction of race and assets (Aimpctb) and the interaction of race and liabilities (Limpctb). The

interaction of race (where race is defined as minority headcounts as a percentage of total headcounts in

the application) and assets is significant, indicating that the importance of assets is enhanced when

minorities are present in applications. We might also say that as the percentage of minority parties in

applications increases, a dollar of assets contributes more toward increasing the probability of license

award. The interaction of race and liabilities is also significant though not as robust as the results for

the interaction of race and assets. This result works conversely to the results for assets. A dollar of

liabilities contributes more adversely to the probability of license award as the percentage of minority

parties in applications rises.

These results are interesting because it means that a dollar of assets in an application with minority

presence was treated more favorably than a dollar of assets generally. At the same time though, a

dollar of liabilities had a more adverse impact on the probability of a win for an application with

minority presence than for an application with lesser minority involvement. Because the coefficient

on the liabilities variable is generally about twice as large as the coefficient on the asset variable, this

indicates that an extra dollar of liabilities may have hurt minorities more than they were helped by an

extra dollar of assets.

However because average assets exceed average liabilities, this effect is mitigated by the fact that

there is a greater volume of assets than liabilities. This is supported by the fact that when we estimate

the model with a term for net assets (where net assets = assets minus liabilities), we get a positive and

significant sign on the variable indicating that overall, minorities received more credit for their assets

than they lost for their liabilities.

Another way to interpret these results is that financial weakness may have been judged more harshly

when minorities were present in applications and financial strength may have been judged more

favorably when minorities were present in applications.

The fit of the conditional logit model is quite good. The regression explains as much as 76% of the

variation in the dependent variable (win or lose license).
23

 

Table 7.

Selected Conditional Logit Model Results

Variables 1 2 3 4 5

Coef. z Coef. Z Coef. Z Coef. Z Coef. z

Attnyexp -.68** -2.4 -1.10** -2.4 -.33** -2.4 -.40* -2.6 -.318** -2.5

Attny0 -3.2 -1.4 -4.3 -1.6 -.9 -.8 -1.8 -1.2 -1.72 -1.2

Totfundi (1) .00008 .4 -.00025 -.9 .00008 .4 -.00018 -.7 -.0003 -1.2

IntegM -.07 -1.6 -.05 -1.1 -.03 -1.2 -.04 -1.5 -.028 -1.2

IntegM0 -3.3** -1.7 -4.9** -2.0 -1.1 -1.2 -3.2* -2.7 -2.35** -2.4

ExperM .0019 .6 -.0012 -.3 -.0001 .0 -.0033 -1.2 -.0017 -.6

ExperM0 -.48 -.4 -.62 -.6 -.69 -.9 -.22 -.3 -.071 -.1

ResidM -.003 -.7 -.006 -1.1 .003 1.2 -.002 -.7 -.0003 -.1

ResidM0 -1.86 -1.0 -2.8 -1.4 .20 .2 -.77 -.7 -.35 -.4

Motioni .77* 2.8 1.20** 2.5 .41* 3.3 .53* 3.3 .44* 3.3

Motion2 -.0049** -2.5 -.0088*** -1.7 -.0029** -2.4 -.0031** -2.4 -.0026** -2.5

Apassi (1) .0026** 2.4 .0046* 2.7 .0023* 3.2 .002* 3.4

Apassm 1.32 .7

Apliabi (1) -.0111** -2.5 -.0202** -2.5 -.0010* -3.1 -.009* -3.1

Netassi (1) .0011** 2.3

Minpcto 7.2 1.4

Minpcto2 -.0007 -1.3

Minpctb 16.6** 2.6 16.5** 2.1 10.6** 2.4

Minpctb2 -.0022* -2.7 -.0022** -2.1 -.0011** -2.2

Fempcto 10.3** 2.1

Fempcto2 -.0007*** -1.7

Fempctb 20.5** 2.3 33.0** 2.3 8.0** 2.3

Fempctb2 -.0011*** -1.9 -.0022** -2.2 -.0004 -1.4

Minmajo .74 .7

Femmajo 2.5* 2.7

AimPctb .006*** 1.8

LimPctb -.024 -1.5

NetPctb -.00029 -.3

Height .0034*** 1.9 .0009 1.5 .0009*** 1.7

Cntpty -.12 -.7 -.46*** -1.7 -.04 -.40 -.14 -1.0 .01 .1

Corp 3.5** 2.0 4.1*** 1.8 .7 .9 2.1** 2.4 1.99** 2.4

First -2.4*** -1.7 -1.3 -1.2 -.6 -1.2 -.18 -.3 -.16 -.3

Otherown -.84* -2.7 -.52 -1.2 -.42** -2.6 -.27 -1.3 -.26 -1.2

Pseudo R2.69 .76 .55 .62 .58

Number of

observations

201 199 201 199 199

*Significant at 1% level; ** Significant at 5% level; *** Significant at 10% level.

Variable definitions appear in Appendix II.

(1) All these dollar denominated values have been deflated using data from

The BEA National Income and Product Accounts Price Deflator series
24

B. Model of Singleton versus Comparative Hearing License Disposition

In this model, we investigate whether there are any significant effects of characteristics of applications

on whether an application is challenged once the first application for a station is filed. I.e., does the

first application filed remain a singleton application or is it challenged and thus result in a comparative

hearing? Our hypothesis is that weak applications are more likely to be challenged and also that more

valuable licenses will be contested. A third possibility is that applicants with greater resources may

provoke competitive applicants seeking to be bought out as part of the comparative hearing process.

In order to test these hypotheses, we have estimated an equation where the dependent variable is 1 if

the application is a singleton and 0 if the application was the first application filed in a comparative

hearing. We refer to the applicant who files the first application as a first mover.

First we review some of the details of the disposition of an application within a comparative hearing or

as a singleton application. These procedures are important because there may have been a strategic

aspect to the decision to file an application first and also a decision about how to configure that

application with both types of people and financial backing.

The first applicant for a station followed somewhat different procedures based on whether they applied

for an AM or FM/TV license.

A party interested in broadcasting over an AM frequency was required to prove to the FCC, through

an engineering study, that a usable bandwidth is available for a certain geographical area during some

portion of the day. After the interested party completed the study, at his or her own expense, the FCC

reviewed the study, and if the study was valid, created a permit for the available space. The interested

party had to submit an application for this permit and was also required to publish a public notice (in

the federal register) announcing the intent to gain a construction permit. Other interested parties were

then allowed a window of time (one to three months) to file complaints against the initial party or

apply for the permit themselves. If no other applications were received and no substantial reason to

deny the applicant the construction permit existed, the FCC issued the permit to the first and only

applicant. If more than one valid application was received, and none of the applicants were

disqualified for reasons other than those considered in comparative hearings, the FCC assigned the

applicants to a comparative hearing to decide which applicant would receive the permit. While a fixed

deadline existed at the FCC for receiving applications, amendments to applications were accepted

throughout the comparative hearing process where good cause existed.

Unlike AM, the FCC identified bandwidths and geographic areas available for FM and TV

broadcasting and published dates when interested parties could apply. Interested parties, however,

could also petition the FCC to make licenses available for certain unconsidered bandwidth/geographic

spaces. If, after the window of time elapsed, only one application for permit was received and the

application was deemed valid, the application was granted. If multiple applications are received and at

least two remained after dismissing invalid applications, the applicants were assigned to a comparative

hearing.
25

Setup of the data for Singleton Model:

We have a sample of 66 singleton applicants - these are applicants who faced no competition from

alternative bidders. We have also identified the most likely first mover for our sample of comparative

hearings. Recall that a first mover is the first applicant to file an application for a license. We identified

the first movers by using the application file date that was available in the comparative hearing file. Taken

together, these two groups form what can be termed the first applicants for a license.

The object of the "Singleton" test is to determine what factors induce competition - i.e. what

characteristics of an initial applicant prompt competing applications to emerge. Accordingly the

dependent variable of the singleton test = 0 if the first applicant faced competition that forced a

comparative hearing; and the dependent variable = 1 if the first applicant was the only applicant - i.e. a

singleton. This singleton test can be thought of as the first stage, with the conditional logit analysis

describing the second stage. Taken together, these two stages model the ultimate determination of the

license holder.

The independent variables in the singleton test are the characteristics of the license or applicant that were

readily observable when the application was made. These include variables reflecting the financial value

of the station and the resources of the initial applicant. The minority and female status indicators are also

appropriate to the extent that these are determined before the precise nature of any competing applications

can be observed.

The interpretation of the Singleton test is as follows. If the estimated coefficient of minority or female is

negative and significant, it means that higher minority or female percentages were associated with lower

chance of being a singleton. This may mean that competing firms are not deterred by high minority and

female percentages on an initial application; indeed they see these cases as opportunities to win in the

comparative hearing process. This contradicts the notion that an initial applicant with high minority

percentages would deter competing proposals.

This model is specified as follows:

Singleton = f(height, houseinc, totfunds, population, fmDum, TVDum, minpctb, fempctb)

We expect that the most significant factor determining whether an application is awarded to a

singleton or goes to a comparative hearing is the value of the station. Value of the station in our model

is based on three variables, height of the antenna above the natural terrain, population of the area, and

median household income of the area.

We also include a variable for total funds available for constructing and operating the station and

minority and female participation percentages within applications. Recall the hypothesis that greater

resources of an initial applicant may either suggest a stronger application that will deter competition,

or greater ability to buy off competition in the comparative hearing process and promote competition.

In addition, we include dummy variables that indicate whether the station is a TV station (TVDum) or

an FM (FMDum) station.
26

Table 8 reports the most reasonable results from estimating this model.

Results from this specification show that height of the station, minority percentage participation in the

application or minority percentage ownership of the application, and total funds available to construct

and operate the station all significantly enhance the probability that an application will end up in a

comparative hearing, i.e. that the application will be challenged. This is entirely consistent with our

hypothesis that more valuable licenses attract more competing applications.

The negative and significant effects of total funds available suggests that strength of application is less

important in deterring competition than possibility of settlement is at attracting competitors. It appears

that entry into the comparative hearing process, for some applicants, was strategic behavior designed

to achieve a buy out.

Most interesting for our purposes is the finding that applications with higher proportions of minority

first movers are more likely to be challenged, i.e. less likely to be singletons. This is not true for initial

applications with more female participants. Given that the comparative hearing process results

suggest minority preferences were important, higher minority presence on the initial application

should have deterred rival applications and resulted in singleton status. Failure to find this result for

minorities means that the singleton selection process tended to work against the goal of higher

minority participation. It may also reflect an ability of rival applicants to easily increase minority

participation to compete with initial applicants. This is consistent with other results suggesting that

applications subject to comparative hearings that had high minority participation also had greater total

participation than singleton applications with high minority participation.
27

 Table 8.

Singleton Model Results

1 2 3 4 5

Coef. z Coef. Z Coef. z Coef. z Coef. Z

height -.0011 -1.3 -.0006 -.8 -.0014*** -1.9 -.0008 -.99 -.001 -1.4

Minpctb -3.84*** -1.8 -1.16 -1.3

Minpcto -1.53*** -1.6 -1.76*** -1.8

Minmajo -1.44 -1.4

Fempctb .48 .6 .44 .65

Fempcto .066 .08 .0026 .003

Femmajo -.1 -.1

Corp -.56 -.95 -.58 -1.0 -.653 -1.1 -.61 -1.0

Totfundi -.006** -2.4 -.007* -2.8 -.0048** -2.3 -.006** -2.1 -.006** -2.5

fundsM0 5.5* 3.2

Populati -.007 -.6 -.012 -.9

Otherown -.245 -1.05 -.302 -1.4 -.31 -1.3 -.24 -1.0

IntegM .012 .21 .024 .5 .0123 .2 .01 .3

IntegM0 1.69*** 1.83 2.37** 2.4 1.83*** 1.9 1.67*** 1.8

ExperM -.00045 -.2 -.0015 -.6 -.00068 -.3 -.0006 -.3

experM0 .44 .59 -.032 -.04 .53 .7 .42 .6

ResidM .0013 .46 .0016 .5 .0018 .6 .0011 .4

ResidM0 -.98 -1.17 -.51 -.5 -.42 -.86 -.82 -.9 -.98 -1.2

hou_inci -.06 -1.1

hou_inc0 2.9** 2.3

Pseudo R2 .23 .29 .15 .23 .23

Observations 133 132 133 133 133

* Significant difference at 1% level; ** Significant difference

at 5% level; *** Significant difference at 10% level.

 C. Unconditional Logit Model Results

The unconditional logit model attempts to capture the probability of an applicant receiving a license,

regardless of whether the license is awarded as a singleton or through the comparative hearing process.

The dependence of this overall probability on minority status is an important issue in assessing the role

of minority status in the broadcast license award process. It is not captured either by the conditional

logit model, which considers comparative hearings only, or the singleton model, which considers only

whether or not first applications are contested. The unconditional logit model uses data on all

applicants in the comparative hearing sample used for the conditional logit, and data on all singletons

sampled. In this model, the dependent variable is equal to 1 if a license was awarded, regardless of

whether the award was made through a comparative hearing or as a singleton application. The

dependent variable equals zero if the applicant was a loser in a comparative hearing. Thus, this

model treats the hearing process and the singleton awards jointly.
28

The results of the unconditional logit model are suggestive of the characteristics of applications more

and less likely to end up with licenses. However, the results should be interpreted with caution for at

least two reasons. First, unlike either the conditional logit model or the singleton model, this model

does not attempt to reflect the decision making of a single class of actors in the license award process.

Second, the model does not fully account for the statistical implications of varying numbers of

applicants for each license. The results, and particularly the statistical significance of various factors,

are fully statistically valid only under an assumption that every applicant made a decision to apply for

a license independently of any knowledge regarding the number of applicants for that license, and that

the characteristics of the application (including minority equity participation, assets included in the

application, etc.) were determined independently of any knowledge of the number of applications for

the license. With these cautions, however, the unconditional logit model is useful as an overall

summary of the characteristics of applications more and less likely to ultimately receive a broadcast

license.

Because we collected data for a sample of comparative hearings that oversampled for minorities, it

was necessary to construct sample weights to adjust the data for the purpose of estimating the logistic

regression models. The results below are provided for an unweighted version of the regression and

for the weighted results using weights that are based on minority participation in the sample relative

to minority participation in the population. Details of the weighting scheme are provided in appendix

III.

The results for the unconditional logit model are somewhat different than for the conditional logit

model. The unconditional model does not fit nearly as well as the conditional logit model. This is

because it reflects the effects of two different decision processes, the choice of competitors to enter, and

the choice of Administrative Law Judges in comparative hearings. While most of the signs on the

variables are reasonable, there are not very many statistically significant19 relationships between

license awards and the characteristics of the applicants. Table 9 shows the model results before we

weight the data to account for the issue of oversampling of minorities in comparative hearings. Table 10

shows the model results after weighting the data to account for the oversampling.

The sign on the variable for total funds available for constructing and operating the station (Totfundi) is

negative. While this makes little sense from the perspective of the license award process, it does make

sense given that singleton applicants generally have much lower funds available than do comparative

hearing applicants. This is naturally true since the licenses that were decided by comparative hearing

are generally much more valuable than the singleton licenses. Therefore, total funds available is highly

correlated with the comparative hearing observations, and because, unlike in the singleton case, the

chance of winning is always less than certain in a comparative hearing, the total funds regression

coefficient is negative and statistically significant.

What this really suggests is that the model may suffer from misspecification. While we place less faith

in what the unconditional logit model is telling us due to possible misspecification, there are some

 

19 In this section, statements of statistical significance are made under special assumption required for statistical validity in

this model. As discussed above, such validity requires strong assumptions on the process determining the characteristics of

the applications.
29

results from this model that corroborate what the conditional logit model revealed. For example, the

importance of assets and liabilities is also evident in the unconditional logit model.

We have used three separate specifications for the measure of minority participation in this model. The

three are minmajo (a dummy variable which equals 1 when minorities own more than 50% of the

equity of and application, and zero otherwise), minpcto (percentage of application that is minority

based on equity ownership), and minpctb (percentage of application that is minority based on body

count). While the sign is negative for all of these specifications, indicating minority presence reduces

the likelihood of a win, these results are not statistically significant. Female percentage of body count

or ownership is positive but is also not statistically significant.

Also important in this specification is that ownership of other media reduced the chances of wining a

license while corporate form of organization increased the chance of winning a license.
30

Table 9.

Unweighted Unconditional Logit Model Results

Variables 1 2 3

Coef. z Coef. z Coef. z

Totfundi (1) -.00035** -2.1 -.0003** -20 -.0003** -2.0

IntegM -.001 -.1 -.003 -.31 -.003 -.30

ExperM -.001 -.9 -.001 -.88 -.001 -.74

ResidM .0015 1.5 .0016 1.5 .0015 1.5

Apassi (1) .0003** 2.1 .00036** 2.3 .00037** 2.3

Apliabi (1) -.002** -2.0 -.002** -2.4 -.002** -2.4

Aimb .0002 .3

Limb -.003 -.8

Minmajo -.31 -.6

Femmajo .3 .9

Minpcto -.66 -1.5

Fempcto .42 1.1

Minpctb -.71 -1.5

Fempctb .54 1.3

Height -.0001 -.95 -.0001 -.84 -.0001 -.86

Corp .15 .53 .167 .58 .15 .53

Cntpty .076 1.5

4

.074 1.5 .07 1.5

First .096 .34 .107 .38 .12 .43

Otherown -.115** -2.0 -.119** -2.1 -.12** -2.1

Pseudo R2.074 .074 .076

Number of Obs 264 264 264

 * Significant difference at 1% level; ** Significant difference at

 5% level; *** Significant difference at 10% level.

Variable definitions appear in Appendix II.

(1) All these dollar denominated values have been deflated using data from

The BEA National Income and Product Accounts Price Deflator series
31

Table 10.

Weighted Unconditional Logit Model Results

Variables 4 5 6

Coef. z Coef. z Coef. z

Totfundi (1) -.0002 -1.25 -.0002 -1.21 -.0002 -1.2

IntegM .012 1.20 .010 1.0 .010 1.0

ExperM -.0007 -.59 -.0007 -.58 -.0005 -.37

ResidM .0017 1.43 .0015 1.3 .0014 1.3

Apassi (1) .0004** 2.2 .0004** 2.2 .0004** 2.2

Apliabi (1) -.0012 -1.3 -.0014 -1.6 -.0015 -1.6

Aimb .0002 .3

Limb -.003 -.96

Minmajo -.023 -.04

Femmajo .67*** 1.8

Minpcto -.192 -.46

Fempcto .75*** 1.8

Minpctb -.15 -0.3

Fempctb .98** 2.0

Height -.00008 -.58 -.00007 -0.54 -.00007 -0.6

Corp .432 1.3 .393 1.2 .362 1.1

Cntpty .016 .3 .013 0.24 .015 0.3

First -.47** 1.5 .47 1.5 .491 1.6

Otherown -.13* -2.3 -.13** -2.3 -.13** -2.3

Pseudo R2.084 .081 .085

Number of Obs 264 264 264

* Significant difference at 1% level; ** Significant difference

 at 5% level; *** Significant difference at 10% level.

Variable definitions appear in Appendix II.

(1) All these dollar denominated values have been deflated using data from

The BEA National Income and Product Accounts Price Deflator series
32

 

V. Conclusions

Based on the comparative hearing model and the singleton model, we can reach the following

conclusions.

The comparative hearing process during the period of minority preferences that we examined seems to

have awarded credit for minority participation that was the stated objective of the FCC. However, the

process seems to have encouraged figurative minority participation that supplemented rather than

substituted for non-minority participation.

This conclusion is supported by the fact that there is no statistically significant influence of minority

ownership on the probability of winning a license in a comparative hearing after controlling for the

factors that the FCC announced were used to award licenses in comparative hearings. This is true

whether minority ownership is defined as a continuous variable (i.e on a zero to 100 percent interval)

or when ownership is defined as minority only if minorities own more than 50% of the application.

The probability of winning a license is lower for a minority (where minority is defined either by

ownership or percentage body count) who files as a singleton than for a non-minority who files as a

singleton because minority singleton applications are challenged more often than non-minority

singleton applicants.

Based on the models that we estimated, we can conclude that there was a lower overall probability for

an application with minority ownership winning a license than a non-minority application after

controlling for a variety of important variables. This is because there was a lower probability of

winning a license as a singleton and no greater chance for an application with minority ownership to

win a license in a comparative hearing.

We have provided some evidence to support the hypothesis that some minority participation was sham

in the sense that minorities were probably recruited by non-minorities in order to enhance the prospect

of non-minority firms to win a license. The evidence of this phenomenon comes from the fact that

there are fewer minority parties participating when licenses are not as valuable. In addition, the total

number of parties in applications is greater when minorities participate in applications for more

valuable licenses which suggests that minority participation supplements rather than substitutes for

other parties in applications. Also supporting this idea of sham participation is the fact that the

phenomenon of increased overall participation only applies to applications that do not have significant

minority ownership. If there is greater than 50% minority equity in an application, the number of

parties is not nearly as high, suggestive that minorities supplement rather than substitute in nonminority applications.

Minority participation in comparative hearings was very low relative to minority representation in the

U.S. population.

The minority participation rate for singleton licenses, which appear to be less valuable than those

allocated through comparative hearings, was even lower than the low rate of minority participation in

comparative hearings. A reason for this may be due to the fact that minority and female preferences
33

encouraged applicants to recruit minorities and females in order to compete more effectively in

comparative hearings.

The process for awarding licenses through the comparative hearing process provided credit to

applications that contained minorities and females, as was the stated policy of the FCC.

However, while minority participation -- as defined by minority percentage of body counts -- appears

to have positively influenced the win rates in comparative hearings, minority participation when

defined by percentage ownership or majority percentage ownership, does not significantly influence

the probability of acquiring a license.

This finding is consistent with another finding that minority participation is greatest when there is little

minority equity ownership – a finding suggestive of the existence of non meaningful (sham)

participation.

We found a statistically significant relationship between win rates and minority body count but not

between win rates and minority equity ownership. This finding supports a hypothesis of sham

participation.

The mechanism of providing credit to minorities seems to be significantly related to the amount of

assets stated in applications with minority participation. Applicants with minority participation seem

to have received extra credit for assets relative to applicants with lesser or no minority participation.

Applications with minority participation seem to have been treated less favorably with respect to

liabilities than those applicants with lesser or no minority participation.

The net effect of the credit provided for assets and liabilities was positive for minorities since assets on

applications generally substantially exceed liabilities. We may also interpret the results as suggesting

that while financial strength was judged more favorably when minorities were present, financial

weakness was judged more harshly when minorities were present.

These data generally support the theory that minority and female participation occurs most when the

stations are most valuable and where the presence of minorities and females can bolster the probability

of winning a license. Height of the station antennas, population, and household income are higher

when minorities and females participate in applications. These are all indicators of the value of the

station.

Payments and receipts are higher when there is nominal minority and female participation in

applications; this is another indication that nominal minority and female participation occurs most in

competitive situations.

The number of parties in applications is substantially higher when minorities participate; however this

phenomenon is much less obvious when minorities control equity.

Because minorities tend to participate when valuable licenses are at stake, and because the number of

participants in these applications is greater by far, it is possible that minorities were added to these
34

applications in order to improve the likelihood of winning, but may not add much in the way of

meaningful minority ownership to these applications.

These are all important conclusions because we examined the differences in minority and nonminority license award during a period when minority preferences were in place. These results

suggest that the impact of minority preferences on license award rates was minimal at best.
35

Appendix I

Information Collected from Form 301 for the Logit Models

General Hearing Information

1. Docket number

2. Number of Applications

3. Community

4. Channel

5. Frequency

6. Service (select one) : ® AM ® FM ® TV

APPLICATION (form 301) INFORMATION

For each Application:

1. Name of Applying Organization:

2. Number of Parties

3. Date Application Filed

4. Winner ® Yes ® No

5. Merger ® Yes ® No

6. Dismiss: ® Yes ® No

7. Appeal: ® Yes ® No

For Each Party to an Application:

1. Party Name

2. Party number

3. Position

4. Ownership (percentage of ownership)

5. Voting (percent of control)

6. Gender (pick one) ® Male ® Female

7. Minority status: ® Yes ® No

8. Race/Ethnicity: ® Black ® Hispanic ® Asian ® American Indian/Eskimo
36

Comparative Hearing Data Collection FormFor Disparity Ratio, Utilization Ratio

Calculations

KPMG LLP: Economic Consulting Services:

2001 M St., NW

Washington, DC 20036-3389

SECTION 1: APPLYING ORGANIZATION

General Information

1. Check all that apply:

® Winner ® Minority ®Female

® Application Found ® No Application found

® Financial Data ® No Financial Data

2. Docket number: ___________________________

3. Location Number: ___________________________

4. Application number: __________________________

5. Number of applicants in this hearing : ____________

6. Service (select one) : ® AM ® FM ® TV (UHF) ® TV (VHF)

7. Community Served ________________________________

8. Channel and Frequency _____________________

9. Is this application for a new construction permit? ® Yes ® No (specify)

10.Name of Applying Organization:

__________________________________________________________

11.Number of parties to this application: _____________

12.Date application filed: __________________

13.FCC 301 Form date: ___________________
37

SECTION 2: PARTIES TO THE APPLICATION

(Use Separate Form for Each Party)

Contact Information

14.Party _______ of _______

15.Name of party: ______________________________________________

16.Position Held: __________________________________

17.Final Ownership Stock: ______/_______ Percent Stock Held: ______________

18.(held) (subsc.) 

19.Voting Stock: ______________ Percent Stock Held: _______________

20.Gender : ® Male ® Female ® Claimed Female Status

21.Race: ® Minority ® Non-minority

Check one: ® Black ® Hispanic ® American Indian / Eskimo

 ® Asian ® Caucasian ® Other (specify)

 ® Claimed Minority

Notes:
38

Comparative Hearing Data Collection Form for Logit Model

This form is to be completed in connection with a data collection effort for a study of broadcast

licensing. Data will be extracted from comparative hearing files. A participant in the comparative

hearing must have filled out an Application for Construction Permit (Form 301) to initiate the process.

This application is filled out by an organization or group, and for purposes of this form will be referred

to here as the applying "group" or the "Applicant." Information that pertains to the applying

organization will be entered in Section 1 of this form. The individuals or constituents who are

collectively the applying group, are referred to here as "Parties" or Parties to the application."

Information specific to each party should be entered in Section 2 of this form. Because there are

generally multiple parties to an application and this number varies, multiple copies of Section 2 should

be used (one for each party).

KPMG LLP: Economic Consulting Services:

2001 M St., NW

Washington, DC 20036-3389

SECTION 1: APPLYING ORGANIZATION

General Information

1. Docket number: ___________________________

2. Application number: __________________________

3. Hearing number: _________________________

4. Service (select one) : ® AM ® FM ® TV

2. Name of Applying Organization:

_________________________________________________

APPLICATION (form 301) INFORMATION

Application fee paid: $_____________________

Date application filed: ______/_______/_______
39

Legal Qualifications

1) Type of Organization: Individual General Partnership

 Corporation Limited Liability Partnership

 Not for Profit For Profit

2) Are all parties to this application citizens of the United States of

America.

® Yes ® No

a) Is any party to this application a representative of an alien or foreign

government?

® Yes ® No

b) Is more than 20% of the capital stock of this application owned by a

foreign government?

® Yes ® No

c) Is any controlling corporation more than 25% owned by a foreign

entity?

® Yes ® No

3)

a) Has applicant or any party to this application had a station license

revoked by order or decree of any Federal Court?

® Yes ® No

b) Has applicant or any party to this application been found guilty by a

federal court of the violation of the laws of the United States relating

to unlawful restraints and monopolies and to combinations,

contracts, or agreements in restraint of trade?

® Yes ® No

c) Has the applicant or any party to this application been finally

ajudged guilty by a federal court of unlawfull monopolizing or

attempting unlawfully to monopolize radio communications, directly

or indirectly, through the control of the manufacture or sale of radio

apparatus, though exclusive traffic arrangements, or by any other

means, or to have been using unfair methods of competition?

® Yes ® No

d) Has the applicant or any party to this application been found guilty by

any court of (1) any felony or any other crime, (2) the violation of

any state, territorial or local law relating to unlawful lotteries,

restraints and monopolies and combinations, contracts or

agreements in the restraint of trade, or (3) using unfair methods of

competition?

® Yes ® No

e) Is ther now pending in any court or administrative body against the

applican or party to this application in any action involving any of the

matters referred to in Paragraphs 8a, b, c, and d above?

® Yes ® No

f) Have voluntary proceedings in bankruptcy been instituted by, or have

involuntary proceedings in bankruptcy ever been brought against

applicant or any party to this application?

® Yes ® No

g) Are ther outstanding any unsatisfied judgements ofr decrees ® Yes ® No
40

against applicant or any party to this application?

4)

Stock of Corporation

a) Class of

stock (a)

b) Par

value

c) Vote per

share

d) Number

of

authorized

shares

e) No of

shares

issued

f) No of

shares

subscribed

g) Total

number of

stockholder

s

5) At the last meting, were any of the shares of stock voted by

proxy?

® Yes ® No

If so, state

6) a) Class

of stock

b) Par

value

c) Vote per

share

d) Number

of

authorized

shares

e) No of

shares

issued

f) No of

shares

subscribed

g) Total

number of

stockholder

s

Other Broadcast Interests

17) Does applicant or any party to this application have now, or has

applicant or any shuch party had, any interest is, connection with, the

following:

a) Any standard, FM, or Television broadcast stations? ® Yes ® No

b) Any application pending before the Commission? ® Yes ® No

c) Any application which was beed denied by the FCC? ® Yes ® No

d) Any broadcast station the license of which has been revoked? ® Yes ® No

If yes to any of the above , please describe below

Name of Party having

such interest

Nature of interest or

connection

Call letters of station

or file number of

application

Location
41

19) Is the applicant or any party to this application controlled, directly

or indirectly, by any person who has interest in or connestion with

any broadcast stations or applications of the type referred to in the

above section.

® Yes ® No

Are any of the parties to this application realted to each other ( as

husband, wife, father, mother, brother, sister, son, daughter)? ® Yes ® No

Does any member of the immediate family (i.e. husband, wife, father,

mother, brother, sister, son, daughter) of any party to this application

have any interest in or connection with any other broadcast station or

pending application?

® Yes ® No

For any Appendicies, List Other Broadcast / Media Interests

1. Number of pending applications:

Party Call Sign

or file

number

Service Ownership Share or

nature of interest

Location Will this party

divest
42

Financial Information

USE TABLES Financial Information Early period I & II only for applications dated before 1982

Estimated Costs

Financial Information Early period I

Antenna system (fq1) $___________

RF Generating Equipment: (fq2) $___________

Monitoring and Test Equipmentv (fq3) $___________

Program Origination Equipment: (fq4) $___________

Acquiring Land (fq5) $___________

Acquiring, Remodeling or constructing buildings:

(fq6)

$___________

Other Items:

Legal Costs(fq7a) $___________

Engineering Costs(fq7b): $___________

Installation Costs(fq7c): $___________

Other Misc(fq7d): $___________

Total Other Items : (fq8) $___________

Total Construction Costs: (fq9) $___________

Add Estimated Cost of Operation for First

Year(fq10_

$___________

Total First year Cost to be met by the Applicant

(fq11)

$___________

Estimated revenues for the first year: (fq12) $___________
43

Financial Information Early period II

Existing Capital: (fq13) $__________

New Capital (fq14) $__________

Loans From Bank or Other: (fq15) $__________

(less repayments of principle and interest @ ______% due

during first year) (fq16)

$__________

Net Total Available from Loans(fq17) $__________

Profits from Existing Operations: (fq18) $__________

Donations: (fq19) $__________

Other Sourses: (fq20) $__________

Deferred Credit from Equipment Supplier: (fq21) $__________

(a) Less: Down Payment _____% (fq22) $__________

(b) Less: 1st Year Payment to Principle(fq23) $__________

(c) Less: 1st year Interest @ _____%(fq24) $__________

Net Deferred Credit Available(fq25) $__________

Net Total Available(fq26) $__________

Amount of fund on deposit in a bank or other depository: (fq27) $____________________

For attached balance sheets, fill out for all included :

Party or Entity

Name

Total Assets Total Liabilities Total Equity Total Liabilities

and Capital
44

Use the following two tables for application dated after 1982

Financial Information for Late Period I

The applicant certifies that sufficient net liquid assets are on hand

or that sufficient funds are available from committed sources to

construct and operate the requested facilities for three months

without revenues. (fq1.1)

® Yes ® No

State the total funds you estimate are necessary to construct and

operate the requested facilities for three month without

revenues.(fq1-2)

$__________________

_

Identify each source or funds and the amount of funds from each

source,

Financial Information for Late Period II

1. Source of funds: amount funded Debt

® Yes ® No

® Yes ® No

® Yes ® No

® Yes ® No

® Yes ® No

® Yes ® No

® Yes ® No

(If a source of funds is also a party to the application, include this information in section II

also)

Integration Statement

Name of Party Minority Residency

years

Female Broadca

st years

Dayti

me

Full

Time

Part

Time

® Yes ® No ® Yes ®

No

® Yes ® No ® Yes ®

No

® Yes ® No ® Yes ®

No
45

® Yes ® No ® Yes ®

No

® Yes ® No ® Yes ®

No

® Yes ® No ® Yes ®

No

Trial Information

1. Hearing fee: amount owed $_______________ amount paid $________________

2. Was this organization eventually granted the construction permit? ® Yes ® No

3. If no to question 2, what was the outcome of the application:

® Dismissed ® Dismissed with prejudice ® Denied ® Settled ® Withdrawal w/o

settlement

4. How many mutually exclusive applicants were assigned to this comparative hearing? -

__________

5. Who was the presiding judge in the trial? ______________________________

6. Application Date: ___________________

7. Designation Date: ___________________

8. Termination Date: ___________________

9. How long did the proceedings last from date assigned to date disposed? ______mos.

________years

ATTORNEY INFORMATION

10.Did an attorney represent this organization in the proceedings? ®6 Yes ®7 No

11.If yes, provide the following information about the attorney:

Name of Attorney__________________________________________

Name of Firm __________________________________________

12.Total number of motions filed by the attorney on behalf of applicant: ______________
46

Motion Number Granted Granted in

Part

Denied

Notice of Appearance

Proof of Service

Proof of Publication

Continuance

Motion in Opposition

Petition for leave to Amend

Motion for Summary Decision

Comments in Support of Motion

Request for Approval of

Agreement

Motion to Enlarge issues

Request for removal of conflict

Motion for leave to file a reply

Finding of Fact and Conclusion of

Law

Other (Specify)

Interrogatories

Amendment

Dismissal of Application

Motion for Production of

Documents

Motion for Suspension of Hearing

Reply

Motion for Protective Order

Other

SETTLEMENT INFORMATION

This information will not be found in the application. Places to look.

• History cards will contain information on when a settlement happened. Look for

documents entitled "(joint) request for approval of settlement agreement" or "(joint)

petition for settlement agreement" or simply settlement agreement. Find the associated

date and look in the docket for that date.

• If no history card is available, look for motions that include less that the initial number of

applicants in the heading. If you find an applicant missing, you know that someone has

dropped out.

If this applicant excluded itself from the hearing as part of a settlement,

1. Which party paid the settlement?________________________

2. How much was paid?______________________
47

3. How much of this settlement was Lawyers Fees?________________________

4. List all of the parties that this applicant paid to withdraw.

Applicant that settled How much received Lawyers fees Date settled

If settlement included other considerations describe the agreement below

Technical Information

Information on technical information is usually located in the Engineering Data (section V b)

of the application form 301. The questions below will, in most cases, follow the order of the

information on the application.

1. Principle community to be served: ________________________

2. Class of license: ____________

3. Elevation of the antenna above average terrain: _____________ meters / feet (FM, T.V.)

4. Power of signal: ___________Kw

5. Geographical size of proposed area: _________________sq./mi.

6. Population coverage: _______________________

7. Number of other stations that serve the proposed area:

 Radio: AM: ________ FM: ________

 TV: Total: ________
48

SECTION 2: PARTIES TO THE APPLICATION

(Use Separate Form for Each Party)

Interest in Application

1. Number of ownership shares: __________

2. Percent of total ownership shares owned: ___________%

3. Number of voting shares owned: _____________

4. Percent or voting shares owned: ____________%

5. Position: _____________________________________

Management Integration and Public Service Information (Integration Statement)

1. Personal participation: ® Yes ® No

2. If yes, will this party work ® Full Time ® Part Time ?

3. Proposed position: _____________________________

4. Years of previous broadcast experience: _________ years

5. Years of local residency: _________ years

6. Unresolved negative character issues: ® Yes ® No

Race and Gender Information

1. Did this person claim minority credit? ® Yes ® No

2. Race: ® Black ® Hispanic ® American Indian / Eskimo

 ® Asian ® Caucasian ® Other

3. Did this person claim female credit: ® Yes ® No
49

Financial Information

1. Amount contributed to the construction cost of the station: $_______________

2. Value of the total assets of this party: $_________________

3. Value of liabilities: $_________________

4. Value of equity: $ __________________

5. Value of Assets and liabilities: $_________________
50

Party

 name

Position Ownership Voting Full Time /

Part Time

Years

Broad

cast

Exp.

Years

Resident

Amount

Contributed to

construction

Total

Assets

Total

Liabilities

Total

Equity

Assets

and

Liabilities

Female Minority
kpmg

Page 51

Appendix II

Data Definitions and Summary Statistics for Comparative Hearings and Singletons

Variable Description Data Collection form

Location

aimb Interaction term for assets and minority count

greater than one.

aimpctb Interaction term for assets and minority

percentage

apassets Sum of all assets reported by an applicant

(available only in the 1978-1981 period)

Deflated by CPI in regressions

Financial Information

apeqcap Sum of all equity plus capital reported by an

applicant (available only in the 1978-1981

period)

Financial Information

apequity Sum of all equity reported by an applicant

(available only in the 1978-1981 period)

Deflated by CPI

Financial Information

apliabil Sum of all liabilities reported by an applicant

(available only in the 1978-1981 period)

Deflated by CPI in regressions

Financial Information

attnyexp Sum of the instances that a particular

applicant’s attorney appears in the sample.

cntpty Number of persons who are party to an

application

Section II

Corp

(Dummy)

Indicates incorporated applications.

ExperM Measure of Experience: (Years of broadcast

experience)x(10 if full time or 5 if part time)

(summed across all parties to application)

Section II

Femb

(Dummy)

Indicates applications with one or more

female participants.

Fempctb Percentage female parties in application

Femcnt Number of female parties to an application Section II

Fempcto Percent of applying organization owned by

females.

Section II

Femmajo Application more than 50% female owned Calculated

fq12 Estimated first year revenues Financial Information

height Average height of antenna above average

terrain.

Technical information
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houseinc Median household income for the principle

community served.

Income and population figures used in connection with the

logistic regressions were taken from the US Census Bureau

products CPH-L-125, CPH-L-126, and CPH-L-127, CPH-L128, CPH-L-129 which are "Income Summary Measures" for

places in the United States (1989). These data were merged

with the data collected by KPMG using the "Primary

Community Served" field in the KPMG data and the "Place

Name" field in the Census data.

Deflated by CPI in regressions

1989 census tables

integM Measure of Management / Station Integration:

equals 10 if full time or 5 if part time

(summed across all parties who will work at

station)

Section II

Limb Interaction variable for liabilities and

applications with at least one minority party.

Limpctb Interaction variable for liabilities and

applications with minority percentage.

Minb

(Dummy)

Indicates application with at least one minority

party.

Mincnt* Number of minority parties within an

application.

Section II

Minpcto* Percent of applying organization owned by

minorities.

Section II

Minpctb* Percent of minority parties in an application. Race and Gender

Minmajo

(Dummy)

Application more than 50% minority owned Calculated

Motions Number of motions filed. Attorney Information

Netimb Interaction term for net worth and applications

with at least one minority party.

Otherown Count of number of stations in which any

party has an ownership interest.

Legal Information

pop2 Reported population served by a specified

signal strength.

Technical Information

(page 11)

Populati 1989 census population of community served.

The populations figures KPMG used in connection with the

data for utilization rates were taken from the Census product

SU-98-3 "Population Estimates for Cities with Populations of

10,000 and Greater (Sorted Within State by 1998 Population

Size): July 1, 1998 (includes revised April 1, 1990 census

population counts)." These data were merged with the data

collected by KPMG using the "Primary Community Served"

1989 census tables
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Page 53

field in the KPMG data and the "Place Name" field in the

Census data. Applications proposing service to a "Principle

Community" not included in the above Census file above was

given the population value of 10,000.

ResidM Measure of Local Residency: (Years of local

residency) x (10 if full time or 5 if part time)

(summed across all parties to application)

Section II

Settlpay Total amount paid in settlement agreements

to other applicants in comparative hearing.

Settlement Information

(page 10)

Settlrec Total amount received in settlement

agreements by other applicants in

comparative hearing.

Settlement Information

(page 10)

Squaremi Geographic area covered by specified signal

strength.

Technical Information

(page 11)

Totcons Measure of total construction costs.

Generally reported in variable fq11. When not

available, this number is calculated as a

combination of itemized costs listed on

application (for constituent variables, see next

cell to right). Deflated by CPI in regressions.

fq9, fq1, fq2, fq3, fq4,

fq5, fq6, fq8, timefram,

fq7a, fq7b, fq7c, fq7d,

fq10, year

Totdebt Measure of total debt of applicant. This

variable factors in all reported debt necessary

to fund the projected construction cost.

Deflated by CPI in regressions.

fq17, fq25, year, and

debt89

Totfunds Measure of total funds available. This

variable is generally reported as “net total

available” on the application. When not

available, this number is calculated as a

combination of the itemized list of fund

sources on the application (for a list of

constituent variables, see next cell to right).

Deflated by CPI in regressions.

timefram, fq10, fq11,

fq14, fq15, fq16, fq17,

fq18, fq19, fq20, fq21,

fq22, fq23, fq24, fq25,

fq26, apassets, apliabil,

fq13

 *Note that minorities were oversampled for comparative hearings
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Summary statistics describing applicants involved in comparative hearings.

Variable Observations Mean St. Deviation

Apassets 92 1,144,388 3,249,754

Apeqcap 79 1,078,993 2,162,031

Apequity 52 503,484 1,088,195

Apliabil 63 312,902 550,587

Attnyexp 203 3.5 3.2

Corp 203 .58 .49

Cntpty 203 4.2 4.4

ExperM 203 76 130

Femcnt 203 1.2 1.5

Fempctb 203 0.31 0.31

Fempcto 203 0.29 0.36

Femown 203 29.0 35.8

Fq12 203 162,334 407,885

Height 202 1,034 1,480

Houseinc 196 25,633 7,650

IntegM 203 12.7 17.6

Mincnt 203 0.81 1.78

Minown 203 18.4 34.1

Minpctb 203 0.17 0.32

Minpcto 203 0.18 0.34

Motions 200 13.4 18.2

Otherown 203 1.8 2.9

Owner 203 100 0

Pop2 172 203,571 357,226

Populati 203 54,850 88,130

ResidM 203 63.04 150.07

Settlpay 118 20,320 50,528

Settlrec 105 28,912 51,630

Squaremi 143 1,608 2,266

Totcons 194 599,449 950,586

Totdebt 158 704,266 1,104,457

Totfunds 191 705,234 1,163,197
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Summary statistics describing singleton applications

Variable Observations Mean St. Deviation

corp 66 .42 .49

cntpty 66 2.4 1.7

experM 66 69 111

femcnt 66 0.7 0.8

fempctb 66 0.30 0.37

fempcto 66 0.25 0.37

femown 66 25.2 37.2

fq12 66 3,256 18,504

height 66 348 373

houseinc 59 25,474 11,040

integM 66 5.6 7.0

mincnt 66 0.09 0.28

minown 66 4.7 16.5

minpctb 66 0.046 0.16

minpcto 66 0.047 0.17

motions 65 0.58 3.32

otherown 66 1.2 1.7

owner 66 100 0

pop2 60 75,594 116,264

populati 66 17,328 40,199

residM 66 65.36 134.19

squaremi 60 1,816 1,820

totcons 59 127,571 92,339

totdebt 29 153,177 105,633

totfunds 60 162,925 106,328
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Page 56

Annual CPI Used to Deflate Various Dollar

Denominated Variables for Regression Analyses

Year CPI

1975 .55

1976 .582

1977 .621

1978 .677

1979 .767

1980 .863

1981 .94

1982 .976

1983 1.013

1984 1.053

1985 1.093

1986 1.105

1987 1.154

1988 1.205

1989 1.261

1990 1.338

1991 1.379

1992 1.419

Source: BEA NIPA data
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Page 57

APPENDIX III

Description of Weighting Method for Unconditional Logit and Singleton Models

This is a description of the design and the implementation of the weighting scheme applied in the

unconditional logit and singleton versus comparative hearing regression models.

Since the minority participation in comparative hearings is generally low, stratification sampling by

singleton status and race ensures a minimum number of minority applications in each singleton status

category in the sample. Depending on the context of the analysis, the stratification sampling scheme

could have an influence on the regression estimates. To account for this influence, a weight equal to

the sample-population ratio is assigned to each observation prior to the analyses.

Since we sampled all the singleton applications, no distortion was introduced by stratification

sampling. Therefore, the assigned weights are equal across minority and non-minority applications.

However, not all of the 230 comparative hearing applications were included in the sample.

Stratification sampling by singleton category and minority status introduces a bias to the regression

estimates. We drew the sample by oversampling hearings where we knew that a minority was present

within at least 1 application. In the comparative hearing sub-population, the ratio of non-minority to

minority applications is about 14 to 1. However, given the distribution of the comparative hearing

sample, the same ratio is about 8 to 1, i.e., the sample minority application ratio is 1.75 times larger

than the true population ratio. To account for this distortion due to stratification sampling, we down

weighted the influence of minority applications by a discount factor of 1/1.75, or by 56%. The

weights assigned achieve the desired ratio.

The following tables summarize the computation of the weight.
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Page 58

Table III.1. Minority Application if at least one minority applicant

Singleton

Status

Minority Status

(3)

No. of

Applications

in Population

No. of

Applications

in Sample

Assigned Weights

Singleton =1

Minority

Applications 6 6 1

Non-Minority

Applications 58 58 1

Comparative

Hearing =0

Minority

Applications 122 60 122/60

Non-Minority

Applications 618 141 618/141