**FCC Econometric Analysis of Potential Discrimination**

**Utilization Ratios for Minority- and Women-Owned Companies in FCC
Wireless Spectrum Auctions**

**December 5, 2000**

**A Study Prepared by**

**Ernst & Young LLP**

**for the**

**Federal Communications Commission**

**FCC Econometric Analysis of Potential Discrimination**

**Utilization Ratios for Minority- and Women-Owned Companies in FCC
Wireless Spectrum Auctions**

**1. Introduction and Executive Summary**

Utilization ratios are a means of measuring the participation and
success of minority- and women-owned businesses in the Federal
Communications Commission (FCC) wireless spectrum auctions. In this
context, a utilization ratio indicates the share of, say, minority-owned
companies that were successful in obtaining a spectrum license through
an auction out of all minority-owned companies that participated in the
auction. Comparison of these ratios across groups highlights any
potential systematic differences in auction outcomes.

There are different ways of calculating utilization ratios, depending on
how success and participation are measured. Perhaps the simplest way of
calculating utilization ratios for spectrum auctions is to determine the
number of auction participants who won at least one license as a
percentage of all participants. While conceptually straight-forward,
this measure does not capture, for example, potential differences in the
rates at which participants qualified to bid[^1], or any differences in
the value of the licenses won[^2]. In order to develop a more thorough
understanding of auction outcomes for minority- and women-owned
companies, this report presents a number of different utilization ratio
measures:

-   *General Utilization Ratio.* Percentage of auction winners (those
    who won at least one license) among all auction applicants.

-   *Qualifying Ratio.* Percentage of applicants who qualify to bid
    among all auction applicants.

-   *Success Ratio.* Percentage of auction winners (those who won at
    least one license) among all qualified auction applicants.

-   *Economic Value Ratio.*[^3] An alternative way of assessing the
    extent to which minority- and women-owned companies are able to
    secure wireless spectrum licenses is to evaluate their share of the
    total economic value of the licenses auctioned. The Economic Value
    Ratio is defined as the economic value of licenses secured by
    applicants in a particular group, for example minority-owned
    companies, expressed as a percentage of the total economic value of
    licenses auctioned.

-   *Average Revenue per Winner.* If the number of winners in a group is
    small, then the Economic Value Ratio for that group would naturally
    be low, even if there were no systematic differences in the values
    of licenses obtained across groups. Average revenue per winner
    provides another measure of auction outcomes for different groups,
    while controlling for the number of winners.

-   *Return on Payment Ratio.* Another approach to scaling the revenues
    generated by various groups is to use the upfront payments as a
    scaling factor. The upfront payments determine bidding eligibility
    and hence affect auction outcomes. The return on payment ratio is
    calculated as the percentage of net FCC revenues relative to the
    percentage of upfront payments. For example, suppose that
    minority-owned companies generated 15% of net FCC revenues and paid
    10% of the upfront payments in a particular auction. Then the Return
    on Payment Ratio for minority-owned companies is 1.5, indicating
    that the minority share of revenues is 1.5 times the minority share
    of upfront payments.

Each of these ratios presents a different view on how minority- and
women-owned companies obtain licenses through the spectrum auction
process. In general, these ratios can be classified into two categories:
measures of auction outcomes, and measures of financial implications of
those outcomes.

The general utilization ratio provides an overall view of the auction
outcomes, based on the number of applicants and winners. The qualifying
and success ratios refine this concept further, by breaking the auction
process into two parts: qualifying to bid, and winning after qualifying.
Contrasting these two measures will identify whether any differences
found in the general utilization ratios are attributable to differences
in qualifying or in succeeding after qualifying, thus providing insights
into where in the auction process there may be differences across groups
of applicants.

The other measures are more financial measures of auction outcomes.
Simply counting winners and losers may obscure the fact that some
licenses are more valuable than others, and that there may be
differences in the value of licenses acquired across groups of
applicants. The financial measures of auction outcomes address these
issues by examining revenue shares, and scaling them by numbers of
applicants or by upfront payments.

Both types of measures are useful in evaluating the ability of minority-
and women-owned companies to acquire spectrum licenses through auctions,
as they provide different points of view. Taken together, they will
provide a more comprehensive picture of auction outcomes across
different groups of auction applicants.

Our main findings, by ratio type, from this analysis are:

[Aggregate Utilization Ratios:]{.underline}

-   When participation and success is measured by counting the
    percentages of winners from all auction participants (general
    utilization ratio), minority and women applicants appear to be
    somewhat less likely to win at least one license relative to other
    applicants. These differences are statistically significant.[^4]

-   Examination of the qualifying ratio indicates that minority and
    women applicants tend to qualify at lower rates than other
    applicants and that these differences are statistically significant.
    On the other hand, analysis of success ratios reveals that among
    qualified applicants, there are no statistically significant
    differences between women and other applicants in their likelihood
    of winning licenses. Success ratios also indicate that on average,
    qualified minority applicants are more likely to win than qualified
    non-minority applicants. This difference is statistically
    significant. These findings would suggest that the difference in
    general utilization ratios may be largely attributable to the
    differences in qualifying ratios where minority applicants face a
    lower likelihood of qualifying. However, once qualified, minorities
    appear to have higher odds of success in auctions.

-   Figure 1 presents the differences in average[^5] general
    utilization, qualifying, and success ratios between minority and
    non-minority
    applicants.![](media/image1.wmf){width="3.8965277777777776in"
    height="2.4194444444444443in"}

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```
-   Indicates that differences in the ratios across non minority and
    minority

    groups were statistically significant at the 95% level

    [Impact of Installment Plans]{.underline}

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```
-   When the various utilization ratios are analyzed separately for
    auctions with and without installment plans, it appears that
    installment plans generally increase the rate at which minority and
    women applicants win licenses. Figure 2 presents the same ratios as
    Figure 1 but calculated separately for auctions with installment
    plans and without installment plans. Although the utilization and
    qualifying ratios are still lower for minorities than for
    non-minorities for both auctions with and without installment plans,
    in auctions with installment plans the success ratio is higher for
    minorities than for non-minorities and the difference is
    statistically significant.

![](media/image2.wmf){width="5.995833333333334in"
height="3.285416666666667in"}

-   Indicates that differences in the ratios across non minority and
    minority

    groups were statistically significant at the 95% level

```{=html}
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```
-   The difference in outcomes for minorities between auctions with and
    without installment plans may reflect various factors. Installment
    plans may relax potential capital constraints, or, alternatively,
    they may lead to inflation of the price of the license and
    aggressive bidding. Further study is required to definitively
    evaluate the effects of installment plans.

[Economic Value Analysis]{.underline}

-   The analysis of economic value shares (i.e., shares of net FCC
    revenue generated from the auctions) revealed that in the aggregate,
    the value of licenses acquired by minority winners is approximately
    12% of the total value of licenses. However, in auctions with
    installment plans, the minority share of total value increases to
    approximately 19% (see Figure 3). The value shares for women winners
    exhibit a similar pattern.

    **Figure 3**![](media/image3.wmf){width="5.995138888888889in"
    height="2.372916666666667in"}

    **Economic Value Shares**

-   When the economic value of licenses acquired is examined on a per
    winner basis (average economic value per winner), there are no
    statistically significant differences between minority and women
    applicants, and other applicants at the aggregate level. In other
    words, while the number of minority and women winners is relatively
    small, compared to the number of other winners, the value of their
    licenses won is comparable to that of other winners, on average.

-   When economic value of licenses relative to the upfront payments is
    compared across different applicant groups (return-on-payment
    ratio), minority and women applicants tend to obtain a larger share
    of the economic value of the licenses than their share of upfront
    payments. If upfront payments are taken as a measure of the value
    and number of licenses applicants are interested in winning, this
    finding would seem to indicate that minority and women applicants
    tend to win at least as many and/or as valuable licenses as they are
    interested in winning. However, upfront payments may be an imperfect
    indicator of interest, if applicants are unable to make upfront
    payments in the amounts they desire. Figure 4 illustrates the
    average return-on-payment ratios for minorities across all auctions.

![](media/image4.wmf){width="3.4569444444444444in"
height="2.5506944444444444in"}

-   When return-on-payment ratios are analyzed separately for auctions
    with and without installment plans (Figure 5), the ratios for
    minorities are much higher in auctions with installment plans.
    Ratios for women have a similar pattern.

[Other findings]{.underline}

-   The analysis by industry groups revealed few statistically
    significant patterns across industries[^6] in the comparison of
    utilization ratios between minority and women applicants, and other
    applicants. In advanced paging/data auctions all three measures of
    utilization (general utilization, qualifying, and success ratios)
    are significantly lower for minorities than for non-minorities.

-   The differences in utilization ratios between minority and women
    applicants, and other applicants are typically less pronounced among
    small companies than among large companies. Among small companies,
    auction outcomes are generally more comparable across applicant
    groups than among large companies.

-   In the first three auctions, minority and women applicants were
    eligible for bidding credits. In these auctions, the economic value
    shares tended to be generally larger for minority and women
    applicants than in other auctions.

When interpreting the utilization ratio calculations, it is important to
keep in mind that they are based on a high-level analysis that does not
control for many important applicant characteristics that may affect
auction outcomes and may provide explanations for the observed
differences across applicant groups. For example, the findings suggest
that installment plans increase the likelihood of winning for minority
applicants, which may reflect the easing of capital constraints, if any,
or inflation in the value of licenses. These results may also be the
artifacts of differing auction strategies employed by different
participants. For example, large companies may strategically place
upfront payments across a wider array of spectrum than their business
needs, and focus their interest as the auction proceeds, leading to a
lower return-on-payments. We are currently developing further more
detailed analysis into the determinants of auction outcomes.

The remaining sections of this report provide a more detailed discussion
of our analysis.

**2. Data**

The FCC provided Ernst & Young LLP (E&Y) with data on 19 auctions[^7]
that E&Y used to calculate preliminary utilization ratios.[^8] For each
auction applicant, the data include the following information:

  -----------------------------------------------------------------------
  • Indicator for Qualifying Applicant

  • Name

  • Indicator for Small Business

  • Indicator for Rural Business

  • Indicator for Woman-owned Business

  • Indicator for Minority-owned Business

  • Number of High Bids

  • Total Population in Areas for each winning bid

  • Sum of High Bids \$ (Net)

  • Sum of High Bids \$ (Gross)

  • Upfront Payment

  • Bidding Eligibility
  -----------------------------------------------------------------------

The indicators for minority-owned, women-owned, and small businesses are
based on self-reported data. It is our understanding that the FCC has
not asked applicants to verify their minority- or women-owned status,
but when an auction applicant identified itself as a small business, it
also had to provide some verification to the FCC of its small business
status.[^9] [^10]

**3. Utilization Ratio Calculations**

Each of the utilization ratios is calculated separately for each
auction, as well as for groups of auctions. Aggregating data across
auctions allows us to detect general high-level patterns, if any, that
might not emerge in an auction-by-auction view. Two natural groupings of
auctions are by rule structure and by industry type. Auction rules have
varied considerably, and in particular, some auctions have provided
special installment plans for small businesses while others have not. An
interesting question is whether these special programs had an impact on
participation of minority/women-owned businesses. Similarly, the
licenses have been sold in a number of distinct industries, and there
might be some industry-specific factors influencing the extent of
minority/women-owned business participation in auctions. The groups of
auctions are[^11]:

-   **Auction Rule Structures Group**

Group 1: Installment Plans (auctions \# 2,3,5,6,7,10,11)

Group 2: No Installment Plans (auctions \#
1,4,8,9,12,14,15,16,17,18,20,21)

-   **Industry Groups**

Group 1: Advanced Paging/Data (auctions \# 1,3,18)

Group 2: Mobile Voice and Data (auctions \# 4,5,7,10,11,12,14,15,20,21)

Group 3: Interactive Data (auction \# 2)

Group 4: Wireless Cable (auction \# 6,17)

Group 5: Multichannel Video (auction \# 8,9)

In another grouping, applicants are grouped on the basis of their
self-reported size into small and large companies[^12], and utilization
ratios are calculated separately for each group. Company size may be one
of the determinants of auction outcomes, and hence it is instructive to
conduct utilization ratio comparisons for groups of companies that are
similarly sized.[^13]

In addition to presenting the utilization ratios for the various groups,
the report lists aggregate (or average) utilization ratios. These are
calculated in two ways: simple averages, and revenue-weighted averages.
First, we calculate the aggregate utilization ratios by using the total
numbers of auction applicants and winners across all auctions. Second,
we calculate average utilization ratios from the per-auction ratios that
are weighted by auctions' revenue shares. For example, suppose that
Auction 1 generated 10% of revenues from all auctions combined, and that
Auction 2 generated 15% of all revenues. Then, in the calculation of the
average revenue-weighted utilization ratio for, say, minority
applicants, the minority utilization ratio from Auction 1 would receive
a weight of 10%, and the ratio from Auction 2 would receive a weight of
15%.[^14] Both average utilization ratio measures are reported at the
bottom of each table for reference.

For each ratio, E&Y conducted tests to determine whether the differences
in the ratios across groups were statistically significant. In these
tests, minority applicants are compared to non-minority applicants
(i.e., all other applicants who did not identify themselves as minority
applicants), and women applicants are compared to all other applicants
(who did not identify themselves as women-owned companies). The concept
of statistical significance is used to differentiate between systematic
patterns and chance occurrences. Even in the absence of any systematic
differences auction outcomes between, say, minority and non-minority
auction applicants, one would not expect the utilization ratios for the
two groups to be exactly the same. The question is then whether a
difference in the ratios indicates a systematic pattern. Statistical
significance tests are a common approach to determining this. For
example, when the statistical significance test indicates a 95% level of
confidence, there is a 95% chance that a difference in utilization
ratios is due to a systematic pattern and only a 5% chance that there is
no true difference. The 95% level is typically used as the threshold
level for deeming a result statistically significant, i.e. if the
confidence level is at least 95%, the result is considered statistically
significant; otherwise it is not.[^15] The confidence level of a test is
affected by not only the absolute magnitude of the difference in
utilization ratios, but also by other factors, such as the number of
applicants on which the utilization ratios are calculated. Hence, a
small difference may at times be statistically significant, while a
large one might not.

Although the full population of auction applicants is used in the
calculations presented in the body of this report, tests of statistical
significance are a good benchmark for comparison across groups, given
the interest in examining utilization ratios in the auction process in
general, rather than in a specific auction at a specific point in time.
Since the auctions are an on-going process, the populations that
participated in each of the auctions can be viewed as draws from a
"superpopulation," and statistical testing of hypotheses is appropriate.

*A. General Utilization Ratio*

The general utilization ratio is an overall measure of the extent to
which minority/women-owned businesses participate in auctions. It is
calculated as the percentage of winners among all auction applicants.
Table 1 shows the general utilization ratios by auction as well as for
all auctions as a whole. For all auctions as a whole, there are some
differences across general utilization ratios across demographic groups:
37.40% of non-minority applicants and 31.97% of minority applicants win
licenses, while the shares are 32.01% for women-owned applicants and
37.35% for other applicants. Both of these differences are statistically
significant, indicating that on average, minority- and women-owned firms
win licenses at slightly lower rates than other firms. The
revenue-weighted average general utilization ratios provide similar
information: 28.89% for non-minorities vs. 22.02% for minorities, and
18.81% for women applicants vs. 29.18% for other applicants.

In the auction-by-auction comparison, the percentage of winning
minorities is sometimes larger (e.g., auctions 2 and 7) and sometimes
smaller (e.g., auctions 6 and 17) than the corresponding percentage for
non-minority applicants. Similarly, women applicants sometimes win
licenses more frequently than other applicants (e.g., auction 17) and
sometimes less frequently (e.g., auction 11). However, the number of
minority and women applicants and/or winners is too small in most
auctions (13 out of 19) to allow the use of statistical tests, so for
most auctions, it is unclear what, if any, underlying patterns these
differences represent on a per-auction basis. In the auctions where the
number of minority and women applicants and/or winners permits the
calculation of valid tests, none of the differences are statistically
significant.

When auctions are analyzed by auction groups based on auction rules
(Table 2), minorities appear to be just as likely to win licenses in
auctions with installment plans, whereas there is a statistically
significant difference between minorities and non-minorities in auctions
without installment plans, with minorities winning licenses less
frequently than non-minorities. In other words, it appears that the
presence or absence of installment plans does affect the outcomes for
minorities, as measured by the general utilization ratio. On the other
hand, installment plans appear to make less of a difference for the
outcomes for women auction applicants. Women are less likely than other
applicants to win licenses in auctions with installment plans. This
difference is statistically significant.

The analysis by industry group (Table 3) shows that minority applicants
win licenses at significantly lower rates than non-minority applicants
in advanced paging/data auctions but not in other industry groups. Women
applicants also win licenses less frequently than other applicants in
advanced paging/data auctions, as well as in mobile voice and data
auctions.

When auction applicants are grouped by size (Table 4), there are no
systematic differences in outcomes between minority/non-minority and
women/other applicants among small companies. In contrast, both
minority- and women-owned applicants have significantly lower general
utilization ratios among large companies.

*B. Qualifying Ratio*

In order to bid in the auctions, applicants must qualify by submitting
Form 175 ("short form") and an upfront payment. The upfront payment
depends on desired bidding eligibility. Bidding eligibility is measured
in bidding units, and each license to be auctioned requires a certain
number of bidding units. For example, an applicant who provides an
upfront payment that qualifies him for 100 bidding units is able to bid
on any combination of licenses such that the total bidding eligibility
requirement does not exceed the 100 units. Failure to make the upfront
payment or to complete the "short form" will result in disqualification.
A potential reason explaining the differences in general utilization
ratio between minority and non-minority applicants and between women and
other applicants, described above, is differences in rates at which
minorities and women qualify to bid, relative to other applicants. To
examine the impact of the qualifying process, we study the qualifying
ratio separately from the general utilization ratio.

The qualifying ratio is defined as the percentage of
minority/non-minority and women/other applicants that qualify to bid.
Table 5 shows qualifying ratios by auction as well as for all auctions
as a whole. The average qualifying ratios across all auctions indicate
significant differences between the outcomes of minority and women
applicants, relative to other applicants. The average qualifying ratio
for minority applicants is 48.3%, while it is 67.5% for non-minority
applicants. Similarly, the average qualifying ratio for women applicants
is 51.9%, and 66.6% for other applicants. Both of these differences are
statistically significant, indicating that there are systematic
differences in qualifying rates between minorities and women applicants
as compared to other applicants.

In eight of the 19 auctions, the number of minority/women applicants
and/or qualified applicants is too small to permit the calculation of
statistical tests regarding the significance of the difference in
qualifying ratios. In other auctions, the tests indicate some
statistically significant differences in qualifying ratios. Minorities
qualify at significantly lower rates than non-minority applicants in
auctions 3, 5, 6, 10, and 11. Women qualify at significantly lower rates
than other applicants in auctions 5, 10, and 11.

Table 6 presents the qualifying ratios when auctions are grouped by
auction rules, i.e., whether auctions had installment plans or not.
Minorities qualify at significantly lower rates regardless of whether
auctions had installment plans or not. Women qualify at significantly
lower rates in auctions with installment plans, but no such difference
appears in auctions without installment plans.

In the analysis by industry group, minorities again qualify at
significantly lower rates in three of the five industry groups (advanced
paging/data, mobile voice and data, and interactive data). The number of
minorities is too small to permit statistical tests in wireless cable
auctions, and there are no statistically significant differences in
qualifying ratios in the fifth industry group, multichannel video. For
women applicants, the only industry group with statistically significant
differences in qualifying ratios is mobile voice and data. As for
minority applicants, the number of women applicants is too small in
wireless cable auctions to permit the calculation of statistical tests.

Company size appears to make no difference in qualifying ratios. When
large minority/women companies are compared to other large companies,
they qualify at statistically lower rates. The same holds in the
comparison of small minority/women companies to other small companies.

These findings from the study of the qualifying ratio indicate that at
least some of the differences evident in general utilization ratios
between minority/women applicants and other applicants can be attributed
to differences in qualifying ratios. In general, minority and women
applicants tend to qualify at significantly lower rates than other
applicants. A possible reason for the lower qualifying rates may be
differential access to capital, which may hinder the ability of
minorities and women to make the necessary upfront payments. However, a
more in-depth analysis is required to arrive at authoritative
conclusions about the reasons behind this apparent disparity.

The next step in the analysis is to evaluate success rates for qualified
applicants to determine whether after qualifying there are differences
in auction outcomes between minority and women applicants relative to
other applicants.

*C. Success Ratio*

The success ratio is a measure of qualified applicants who win bids, and
is calculated as the percentage of winners among qualified applicants.

Table 9 shows the success ratios by auction as well as the average
success ratios. After having qualified to bid, minority applicants in
fact win licenses at a higher rate than non-minority applicants (66.1%
vs. 55.4%), and this difference is statistically significant. Qualified
women applicants also win licenses at a higher rate than other
applicants (61.7% vs. 56.1%), although this difference is not
statistically significant. Again, in 13 of the 19 auctions, the number
of minority and women qualified applicants and winners is too small to
permit the calculation of statistical tests. In auctions where testing
is possible, however, minorities often win at significantly higher rates
(auctions 2, 5, and 11). No such systematic pattern appears in the
comparison of women applicants and other applicants on a per auction
basis.

When success ratios are examined by auction group, minority and women
applicants are more likely to win than other applicants in auctions with
installment plans. However, in auctions without installment plans
minorities win less frequently than non-minorities and the difference is
statistically significant. Women applicants also win less frequently but
the difference is not statistically significant. These results mirror
those from the analysis of general utilization ratio: again, installment
plans appear to enhance the ability of minorities in particular to
secure wireless spectrum licenses, while the difference is less marked
for women applicants.

Analysis of auctions by industry groups reveals some differences across
industries. While minority applicants are statistically more likely to
win in mobile voice and data auctions than non-minority applicants, they
are less likely to win other industry groups (with the exception of
wireless cable, for which the low number of minority qualified
applicants precludes the use of statistical tests). For women
applicants, the only significant difference emerges in mobile voice and
data auctions, in which they are less likely to win than other
applicants.

When applicants are grouped by size, there are no differences in success
ratios among large companies, either between minority and non-minority
applicants or between women and other applicants. However, among small
companies, minority applicants win licenses more frequently than
non-minorities, and the difference is statistically significant.

In sum, the success ratios indicate that among qualified applicants,
minorities and women tend to win licenses just as frequently as other
applicants, if not more frequently. This is in marked contrast to the
findings from the analysis of qualifying ratios. Those findings showed
that minority applicants in particular tend to qualify less frequently
than other applicants. On the basis of these results, the lower general
utilization ratios for minority and women applicants appear to be
related to hurdles in qualifying to bid, but that among those who
qualified, the ability of minority and women applicants to secure
licenses is comparable to that of other applicants.

D.  *Economic Value Ratio*

The preceding measures of utilization (general utilization ratio,
qualifying ratio, and success ratio) have measured utilization as
qualifying to bid for or winning at least one license, relative to the
number of applicants. These calculations omit any considerations of the
economic value of the licenses obtained.[^16]

An alternative measure of the extent to which minority/women-owned
businesses win FCC licenses is their share of the total economic value
of licenses.

Table 13 presents the Economic Value Ratios by auction as well as for
all auctions as a whole. For all auctions as a whole, the value of the
licenses won by minority applicants was 11.9% of the total value of
licenses. For women applicants, the share was 7.9%. In other words,
minority (women) applicants secured roughly a tenth of the licenses,
when measured by the economic value of the licenses.[^17] In the
auction-by-auction analysis, the value shares for minority applicants
have ranged from a low of 0.0% to a high of 31.6%. The range for women
applicants is from 0.0% to 45.2%.

Economic value ratios by auction group are provided in Table 14. In
auctions with installment plans, the economic value ratios for both
minority and women applicants are higher than on average (19.4% and
12.7%, respectively). Consistent with the evidence from the general
utilization ratios and success ratios, these figures indicate that in
auctions with installment plans, minority and women applicants tend to
acquire larger shares of the spectrum for sale, as measured by the
economic value of the spectrum.

The economic value shares by industry group are shown in Table 15. In
terms of economic value, minority and women applicants obtain their
largest shares of the total value of spectrum auctioned in multichannel
video auctions.

In table 16, economic value shares are calculated by company size. Among
small firms, minority and women applicants obtain higher value shares
than on average (22.1% and 14.6%, respectively). In contrast, their
shares are very low among large firms (less than 1%).

The findings from the analysis of economic value shares indicates that
in general, minority and women applicants acquire approximately a tenth
of the licenses, as measured by their economic value. However, in
auctions with installment plans and among small firms, their shares of
economic value are larger.

*E. Economic Value Per Winner*

The economic value ratio calculations show that the bulk of the revenues
collected from auctions come from non-minority owned companies, or
companies not owned by women. However, this reflects at least partially
the fact that the number of minority and women winners is smaller than
the number of other winners. In order to evaluate the economic value of
licenses acquired by minority and women applicants while controlling for
the number of winners, we calculated the average economic value per
winner.

Table 17 displays the results on a by-auction basis, and also gives the
averages across all auctions. On a per winner basis, the differences in
economic value of licenses obtained between minority and women
applicants on one hand, and other applicants on the other, appear to be
less distinct than in the preceding analysis of overall value shares.
The average value per winner for minority applicants is \$22.5 million,
while it is \$32.0 million for non-minority applicants. When comparing
women applicants to other applicants, the average values are \$15.4
million for women and \$33.2 million for others. Neither of these
differences is statistically significant. In the analysis by auction,
the average value of a license per winner is significantly lower for
minorities in two auctions (Auctions 6 and 11). In 10 out of the 19
auctions, there are not enough minority winners to perform a valid test
of statistical significance for the average value of licenses won. The
findings for women applicants are quite similar. In Auctions 6 and 11,
there are statistically significant differences between women and other
winners in the average economic value of licenses won, with women having
lower values. In 9 of the 19 auctions, there are not enough women
winners for the calculation of statistical tests.

When auctions are grouped by auction rules (Table 18), differences in
the average values of licenses per winner are not statistically
significant in auctions with installment plans, but they are in auctions
without installment plans. This finding echoes those from the previous
calculations in which minority and women applicants had higher general
utilization and success ratios, and higher economic value shares in
auctions with installment plans.

In the analysis of average economic value per winner by industry group
(Table 19), the only significant difference is found in the comparison
between minority and non-minority winners in interactive data auctions.
No significant differences are found in other industry groups, or in the
comparison between women and other winners. However, there are no
minority or women winners in wireless cable auctions so the tests cannot
be calculated for this industry group.

Table 20 presents the average economic values per winner by company
size. Again, the differences are significant among large companies
(i.e., large minority- and women-owned companies pay on average
significantly less for their licenses than other large companies), but
not among small companies. In fact, for minority small companies, the
average economic value per winner is higher than for non-minority small
companies.

Taken together, the findings from the analysis of average economic
values per winner suggest that in general, there appear to be no
systematic differences in the average values between minority and women
winners, and other companies. While in some auctions the differences are
significant, as they are also among large companies, this is offset by
the fact that in other auctions and among small companies, the
differences are small enough to be statistically insignificant.

*F. Return-on-Payment Ratio*

The final utilization ratio analyzed in this report is the
return-on-payment ratio. The idea behind this ratio is to scale the
economic value of licenses won by the upfront payments. Upfront payments
determine the number of licenses the applicant is able to bid on, and
are indicative of the number and value of licenses the applicant is
interested in acquiring.[^18] Therefore we would expect applicants who
made larger upfront payments to win more licenses and/or more valuable
licenses. Differences in the value of licenses won might then be related
to differences in upfront payments, and it is interesting to examine to
what extent the value of licenses won, relative to upfront payments,
differs across groups of applicants.

The return-on-payment ratio is calculated as the percentage of net
revenues from minority/women winners relative to the percentage of
upfront payments from minority/women applicants. For example, if
minority applicants generated 15% of the total FCC net revenue, and paid
10% of the total upfront payments, then this ratio would be 15%/10%=1.5.
The calculations of this ratio are limited to 11 of the 19 auctions
because we did not have data on upfront payments for all auctions.

Table 21 presents the overall return-on-payment ratios as well as the
ratios by auction. Overall, the value share of licenses won by minority
applicants is nearly twice as large as the minority share of upfront
payments, as indicated by the return-on-payment ratio of 1.86. For
non-minority applicants this ratio is close to 1. Similarly, for women
applicants the ratio exceeds 2, whereas for other applicants it is
nearly 1. In other words, minority and women winners tend to generate
relatively more revenue than their share of upfront payments. If upfront
payments are taken as a measure of the number and/or value of licenses
applicants are interested in winning, this finding would seem to
indicate that qualified minority and women applicants win at least as
many and/or as valuable licenses as they are interested in winning. Of
course, if minority and women applicants are not able to make as large
upfront payments as they would like, upfront payments are an imperfect
indicator of the extent of their interest in licenses. Nevertheless,
throughout this analysis of utilization ratios it is important to keep
in mind that differences in the number and value of licenses won reflect
at least partly differences in applicants' interest in licenses.

In the analysis by auction, the ratio has been relatively constant and
close to 1 for non-minorities across all auctions, while for minorities
the ratio has varied from 0 to nearly 4. Similarly, while the ratio
varies from nearly 0 to almost 2 for women winners, it has been close to
1 for other winners.

When auctions are examined by auction group (Table 22), again minorities
and women tend to generate relatively more revenue than their share of
the upfront payments in auctions with installment plans. This result
again shows that the outcomes for minority and women applicants appear
to be influenced by the existence of installment plans.

Table 23 presents the results from the analysis by industry group.
Because of lack of data, we were unable to calculate the
return-on-payment ratio for two of the five industry groups. In the
remaining three groups, the ratios exceed one for minorities in two
groups and are well below one in one group, while they are close to one
for non-minorities in all groups. The ratios are greater than one for
women in all groups, and consistently close to one for other applicants.
Again, minority and women winners appear to generally secure licenses
with a greater share of the total value of licenses than their share of
the upfront payments.

The same pattern is repeated in the analysis of return-on-payment ratios
by company size (Table 24): minorities and women have return-on-payment
ratios in excess of one, while for other applicants the ratios are close
to one.

The analysis of the return-on-payment ratios illustrates that among
qualified applicants, minorities and women win licenses of generally the
same, or higher, value than other applicants, relative to their upfront
payments.

**Table 1**

**General Utilization Ratios of All Applicants by Auction**

> \+ Not enough minority or women winners or applicants for valid
> chi-square test.
>
> \* Significant at the 95% level.

\*\* Auctions where women or minorities did not participate are excluded
from weight calculations.

> \^ See Appendix A for details on chi-square tests.
>
> Auction group 1: With installment plans Auction group 2: Without
> installment plans
>
> Industry group 1: Advanced paging/data Industry group 2: Mobile
> voice&data Industry group 3: Interactive data Industry group 4:
> Wireless cable
>
> Industry group 5: Multichannel video
>
> **Table 2**
>
> **General Utilization Ratios of All Applicants by Auction Group**

\- Auction group 1 = With installment plans (consists of auctions 2, 3,
5, 6, 7, 10, 11).

\- Auction group 2 = No installment plans (consists of auctions 1, 4, 8,
9, 12, 14, 15, 16, 17, 18, 20, 21).

\* Statistically significant at the 95% level.

\*\* Auctions where women or minorities did not participate are excluded
from weight calculations.

\^ See Appendix A for details on chi-square tests.

**Table 3**

**General Utilization Ratios of All Applicants by Industry Group**

\- Industry group 1 = Advanced paging/data (consists of auctions 1, 3,
18).

\- Industry group 2 = Mobile voice and data (consists of auctions 4, 5,
7, 10, 11, 12, 14, 15, 16, 20 , 21).

\- Industry group 3 = Interactive data (consists of auctions 6, 17).

\- Industry group 4 = Wireless cable (consists of auctions 8, 9).

\- Industry group 5 = Multichannel video (consists of auction 2).

**Table 4**

**General Utilization Ratios of All Applicants by Company Size**

> \+ Not enough minority or women winners or applicants for valid
> chi-square test.
>
> \* Significant at the 95% level.

\*\* Auctions where women or minorities did not participate are excluded
from weight calculations.

\^ See Appendix A for details on chi-square tests.

**Table 5**

**Qualifying Ratios By Auction**

\+ Not enough minority or women winners or applicants for valid
chi-square test.

\* Statistically significant at the 95% level.

\*\* Auctions where women or minorities did not participate are excluded
from weight calculations.

\^ See Appendix A for details on chi-square tests.

> Auction group 1: With installment plans Auction group 2: Without
> installment plans
>
> Industry group 1: Advanced paging/data Industry group 2: Mobile
> voice&data Industry group 3: Interactive data Industry group 4:
> Wireless cable
>
> Industry group 5: Multichannel video

**Table 6**

**Qualifying Ratios by Auction Group**

\- Auction group 1 = With installment plans (consists of auctions 2, 3,
5, 6, 7, 10, 11).

\- Auction group 2 = No installment plans (consists of auctions 1, 4, 8,
9, 12, 14, 15, 16, 17, 18, 20, 21).

\* Statistically significant at the 95% level.

\*\* Auctions where women or minorities did not participate are excluded
from weight calculations.

\^ See Appendix A for details on chi-square tests.

**Table 7**

**Qualifying Ratios by Industry Group**

\- Industry group 1 = Advanced paging/data (consists of auctions 1, 3,
18).

\- Industry group 2 = Mobile voice and data (consists of auctions 4, 5,
7, 10, 11, 12, 14, 15, 16, 20 , 21).

\- Industry group 3 = Interactive data (consists of auctions 6, 17).

\- Industry group 4 = Wireless cable (consists of auctions 8, 9).

\- Industry group 5 = Multichannel video (consists of auction 2).

**Table 8**

**Qualifying Ratios by Company Size**

\+ Not enough minority or women winners or applicants for valid
chi-square test.

\* Statistically significant at the 95% level.

\^ See Appendix A for details on chi-square tests.

\*\* Auctions where women or minorities did not participate are excluded
from weight calculations.

**Table 9**

**Success Ratios of Qualified Applicants by Auction**

\+ Not enough minority or women winners or applicants for valid
chi-square test.

\* Statistically significant at the 95% level.

\*\* Auctions where women or minorities did not participate are excluded
from weight calculations.

\^ See Appendix A for details on chi-square tests.

> Auction group 1: With installment plans Auction group 2: Without
> installment plans
>
> Industry group 1: Advanced paging/data Industry group 2: Mobile
> voice&data Industry group 3: Interactive data Industry group 4:
> Wireless cable
>
> Industry group 5: Multichannel video

**Table 10**

**Success Ratios of Qualified Applicants by Auction Group**

\- Auction group 1 = With installment plans (consists of auctions 2, 3,
5, 6, 7, 10, 11).

\- Auction group 2 = No installment plans (consists of auctions 1, 4, 8,
9, 12, 14, 15, 16, 17, 18, 20, 21).

\* Statistically significant at the 95% level.

\*\* Auctions where women or minorities did not participate are excluded
from weight calculations.

\^ See Appendix A for details on chi-square tests.

**Table 11**

**Success Ratios of Qualified Applicants by Industry Group**

\- Industry group 1 = Advanced paging/data (consists of auctions 1, 3,
18).

\- Industry group 2 = Mobile voice and data (consists of auctions 4, 5,
7, 10, 11, 12, 14, 15, 16, 20 , 21).

\- Industry group 3 = Interactive data (consists of auctions 6, 17).

\- Industry group 4 = Wireless cable (consists of auctions 8, 9).

\- Industry group 5 = Multichannel video (consists of auction 2).

**Table 12**

**Success Ratios of Qualified Applicants by Company Size**

\* Statistically significant at the 95% level.

\*\* Auctions where women or minorities did not participate are excluded
from weight calculations.

\^ See Appendix A for details on chi-square tests.

**Table 13**

**Economic Value Ratio by Auction**

> Auction group 1: With installment plans Auction group 2: Without
> installment plans
>
> Industry group 1: Advanced paging/data Industry group 2: Mobile
> voice&data Industry group 3: Interactive data Industry group 4:
> Wireless cable
>
> Industry group 5: Multichannel video

**Table 14**

**Economic Value Ratio by Auction Group**

> \- Auction group 1 = With installment plans (consists of auctions 2,
> 3, 5, 6, 7, 10, 11).
>
> \- Auction group 2 = No installment plans (consists of auctions 1, 4,
> 8, 9, 12, 14, 15, 16, 17, 18, 20, 21).

**Table 15**

**Economic Value Ratio by Industry Group**

> \- Industry group 1 = Advanced paging/data (consists of auctions 1, 3,
> 18).
>
> \- Industry group 2 = Mobile voice and data (consists of auctions 4,
> 5, 7, 10, 11, 12, 14, 15, 16, 20 , 21).
>
> \- Industry group 3 = Interactive data (consists of auctions 6, 17).
>
> \- Industry group 4 = Wireless cable (consists of auctions 8, 9).
>
> \- Industry group 5 = Multichannel video (consists of auction 2).

**Table 16**

**Economic Value Ratio by Company Size**

**Table 17**

**Average Economic Value per Winner by Auction**

\* Statistically significant at 95% level.

\+ Not enough minority or women winners or applicants for valid t-test.

\^ See Appendix B for details on t-tests.

> Auction group 1: With installment plans Auction group 2: Without
> installment plans
>
> Industry group 1: Advanced paging/data Industry group 2: Mobile
> voice&data Industry group 3: Interactive data Industry group 4:
> Wireless cable
>
> Industry group 5: Multichannel video

**Table 18**

**Average Economic Value per Winner by Auction Group**

\- Auction group 1 = With installment plans (consists of auctions 2, 3,
5, 6, 7, 10, 11).

\- Auction group 2 = No installment plans (consists of auctions 1, 4, 8,
9, 12, 14, 15, 16, 17, 18, 20, 21).

\* Statistically significant at the 95% level.

\^ See Appendix B for details on t-tests.

**Table 19**

**Average Economic Value per Winner by Industry Group**

\- Industry group 1 = Advanced paging/data (consists of auctions 1, 3,
18).

\- Industry group 2 = Mobile voice and data (consists of auctions 4, 5,
7, 10, 11, 12, 14, 15, 16, 20 , 21).

\- Industry group 3 = Interactive data (consists of auctions 6, 17).

\- Industry group 4 = Wireless cable (consists of auctions 8, 9).

\- Industry group 5 = Multichannel video (consists of auction 2).

**Table 20**

**Average Economic Value per Winner by Company Size**

\*Statistically significant at the 95% level.

\+ Not enough minority or women winners or applicants for valid t-test.

\^ See Appendix B for details on t-tests.

**Table 21**

**Return on Payment Ratios for Qualified Applicants by Auction**

No data indicates that no data were available for these categories.

Not included indicates that data exist for these categories but have
been omitted so that totals are calculated for auctions with valid
upfront payment data only.

> Auction group 1: With installment plans Auction group 2: Without
> installment plans
>
> Industry group 1: Advanced paging/data Industry group 2: Mobile
> voice&data Industry group 3: Interactive data Industry group 4:
> Wireless cable
>
> Industry group 5: Multichannel video

**Table 22**

> **Return on Payment Ratios for Qualified Applicants by Auction Group**

\- Auction group 1 = With installment plans (consists of auctions 2, 3,
5, 6, 7, 10, 11).

\- Auction group 2 = No installment plans (consists of auctions 1, 4, 8,
9, 12, 14, 15, 16, 17, 18, 20, 21).

**Table 23**

> **Return on Payment Ratios for Qualified Applicants by Industry
> Group**

\- Industry group 1 = Advanced paging/data (consists of auctions 1, 3,
18).

\- Industry group 2 = Mobile voice and data (consists of auctions 4, 5,
7, 10, 11, 12, 14, 15, 16, 20 , 21).

\- Industry group 3 = Interactive data (consists of auctions 6, 17).

\- Industry group 4 = Wireless cable (consists of auctions 8, 9).

\- Industry group 5 = Multichannel video (consists of auction 2).

**Table 24**

**Return on Payment Ratios for Qualified Applicants by Company Size**

No data indicates that no data were available for these categories.

Not included indicates that data exist for these categories but have
been omitted so that totals are calculated for auctions with valid
upfront payment data only.

**[Appendix A: Chi-Square Test of Statistical
Significance]{.underline}**

A chi-square test is used to determine whether there are systematic
differences in utilization ratios across demographic groups for each
auction. The null hypothesis of the chi-square test is that there are no
such differences in ratios.

The chi-square test statistic is based on a frequency table. In the case
of utilization ratios, the frequency tables are 2x2 tables with
demographic group (e.g. minority/non-minority) along one dimension and
auction outcome (e.g., winner/non-winner) along the other. The chi
square test statistic is calculated as:

χ~df~^2^ =

> where f~ij~ is the observed frequency in the ij cell of the frequency
> table
>
> e~ij~ is the expected frequency in the ij cell if the null hypothesis
> is true
>
> R is the number of rows in the frequency table

C is the number of columns in the frequency table

The expected frequency under the null hypothesis is calculated as:

> e~ij~ =

where f~i~ is the total in the ith row marginal

f~j~ is the total in the jth row marginal

N is the total number of observations in the frequency table

The degrees of freedom for this test statistic are given by:

> df = (R-1)(C-1)

*Reference:*

Knoke, David, and George W. Bohrnstedt, *Statistics for Social Data
Analysis*, F.E. Peacock Publishers Inc., Itasca IL.,1994.

**[Appendix B: t test: Testing for the Difference Between Two
Means]{.underline}**

A t-test is used to determine whether two means are equal. The null
hypothesis under the t-test is that there are no differences in the
means. To determine if the difference of the average net revenues
between the minority and non-minority license winners and the male and
female winners were significantly different, we used the following form
of the t-test:

t~df~ =

where is the mean of the first group

is the mean of the second group

S~1~^2^ is the variance from the first group

S~2~^2^ is the variance of the second group

n~1~ is the total number of observations in the first group

n~2~ is the total number of observations in the second group

The degrees of freedom can be approximated as follows:

df =

The null hypothesis that the mean difference is zero is rejected if the
test statistic t is greater than the critical value at a .05 level of
significance. In other words, if the calculated t is greater than the
critical value, we would conclude that there is a statistically
significant difference between the two means.

*Reference:*

Dixon, Wilfrid J., and Frank J. Massey Jr., *Introduction to Statistical
Analysis*, McGraw-Hill Company, New York, 1969.

[^1]: Throughout this report, an auction applicant is defined as a
    company (or an individual) who submitted Form 175 ("Short Form")
    indicating an interest to participate in an auction. In order to bid
    in an auction, applicants had to qualify by paying an upfront
    payment. Not all applicants qualified to bid.

[^2]: As measured by the FCC's net revenue. Net revenue is the revenue
    collected from the winning bidder, after deducting bidding credits,
    if any.

[^3]: For the purposes of this report, "economic value of a license" is
    defined as the net revenue the FCC received for the license. Net
    revenue equals the winning bid, minus bidding credits, if any.

[^4]: See pages 9-10 for a discussion of the concept of statistical
    significance.

[^5]: Calculated across all auctions.

[^6]: Advanced paging/data; mobile voice and data; interactive data;
    wireless cable; multichannel video.

[^7]: Auctions 1-21, with the exception of auctions 13 and 19, which
    were never held.

[^8]: E&Y has not conducted an independent audit of the data and makes
    no representations of the accuracy of the data.

[^9]: The type of verification has varied over time.

[^10]: In the very first auctions (Auctions 1 through 3), minority and
    women owned businesses were granted bidding credits, but
    subsequently, only small businesses have qualified for bidding
    credits. Hence, in most auctions applicants have not had a direct
    reason to identify themselves as minority- or women-owned
    businesses. On the other hand, bidding credits for small businesses
    have been in place in many auctions (Auctions 3, 4, 5, 6, 7, 10, 11,
    14, 16, 17, 18, 20, 21).

[^11]: The auction rule structures groups are based on the information
    provided to us by the FCC. The industry groups are based on our
    understanding of the information in the bidder packages provided to
    us by the FCC.

[^12]: For the purposes of this report, a company is considered large,
    if it has not identified itself as a small company on the auction
    application.

[^13]: It should be noted, though, that the category 'large' may
    encompass very wide differences in company size, given the way it
    has been defined.

[^14]: Auctions or auction groups without any minority/women applicants
    are omitted from these averages.

[^15]: A Chi-square test was applied when the ratios were based on
    counts, such as number of auction winners. A t-test was applied when
    the ratios were based on continuous variables, such as revenue
    amounts. Appendices A and B describe the calculation of these
    statistics in detail.

[^16]: Recall that for the purposes of this report, economic value of a
    license is defined as the winning bid for that license, minus any
    bidding credits, i.e., the FCC's net revenue for the license.

[^17]: No statistical tests of significance are reported for these
    shares. If the minority share of economic value is X% then the
    non-minority share is by definition (100-X)%, and testing for
    statistical significance in the shares is not meaningful.

[^18]: Other factors, such as ability to raise capital for upfront
    payments, may also play a role in the amount of the upfront payment.