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APPENDIX A
Importance of Comprehensive
Subcontract Data Collection
Introduction DBE subcontract awards is imperative when building a proj-
ect database for use in a disparity or availability study.
Twenty years after Croson, many public agencies with race- An example may illustrate the point. Consider the con-
conscious subcontracting programs (including state DOTs) struction contract shown in Table A1, below. The overall
still do not systematically collect and maintain adequate sub- value of the contract is $12 million. The total value of the DBE
contracting data. Although state DOTs maintain records on
subcontracts is $1 million and the total DBE share is therefore
their DBE subcontracting, many do not maintain compara-
8.3%. For a disparity study, however, the consultant would
ble records regarding non-DBE subcontracting. Although
also want to know the dollar share accounted for by each
non-DBE subcontract data are not necessary to produce the
NAICS code and the DBE share for each code. The former is
standard types of DBE utilization reports requested by
needed in order to calculate properly weighted availability
U.S.DOT or the DOT's executive leadership, which include,
estimates. The latter is needed in order to calculate utilization
for example, only that share of construction dollars awarded
and disparity statistics. These NAICS shares are shown in the
during a given time to DBEs, such data are critical to produc-
last six rows of Table A1. This exercise should be repeated for
ing a high-quality and legally defensible availability or dispar-
each contract in the project database.
ity study.
Some studies have ignored the problem of missing subcon-
tract data. For example, one study states bluntly:
Non-DBE Subcontract Data Is Just as
Important as DBE Subcontract Data The utilization analysis of [M/WBEs] . . . does not include their
utilization as sub-contractors or sub-consultants. . . . [We were]
In contrast to such limited reporting, a high-quality dispar- unable to secure sub-contracting/sub-consulting dollars from
ity or availability study, among other things, should document the actual contract files during the data collection effort, simply
the share of all construction dollars awarded by detailed indus- because the contract files do not contain the data relative to sub-
try during a given period. In addition, a disparity study should contracting or subconsulting activities. . . . This . . . is striking in
that . . . sub-contracting opportunities are often where much of
detail the share of dollars in each detailed industry awarded to
the utilization of M/WBEs is found.
DBEs. Several courts have warned defendants about the use of
overly aggregated statistical comparisons in disparity studies
Another study states:
because such aggregation can provide misleading results.191
This phenomenon is known as Simpson's Paradox. Since The data made available to [us] . . . contain primarily reports of
many industries are only represented in state DOT projects subcontracts involving DBE participation, as the overall percent-
through subcontracting, knowledge of non-DBE as well as age of subcontracting (less than 8 percent) is significantly lower
191 See Engineering Contractors Association v. Metropolitan Dade County, 943 F. Supp. 1546, 1560 (S.D. Fla. 1996) ("The MRD study presents . . . data aggregated for all cap-
ital construction contracts (i.e., SIC 15, 16 and 17 together) and disaggregated by SIC category. . . . The Court will focus primarily on statistical analyses of the disaggre-
gated data because these data are more likely to reflect the realities of competition in the construction industry. Firms that build hospitals (SIC 15) do not compete for
County contracts with firms that lay asphalt (SIC 16) or firms that install plumbing (SIC 17), therefore comparisons between these disparate entities would not produce
a reliable portrait of County contracting trends."). Simpson's Paradox has also been used to criticize combining highway districts together into statewide statistics. See
Phillips & Jordan, Inc. v. Watts, 13 F. Supp. 2d 1308, 1315 (N. D. Fla. 1998); Thompson Building and Wrecking Co. v. Augusta, 2007 U.S. Dist. LEXIS 27127, at * 8 (S.D. Geo.
March 14, 2007) (criticizing combining MBE statistics with WBE statistics).
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Table A1. Subcontract data collection example.
Bid Package
Contractor Type NAICS DBE
Dollar Amount
Prime $9,500,000 23621
Subcontractor $750,000 23621 Yes
Subcontractor $300,000 23621
Subcontractor $50,000 23731
Subcontractor $50,000 23731
Subcontractor $200,000 23731
Subcontractor $100,000 23731 Yes
Subcontractor $30,000 23811
Subcontractor $700,000 23821
Subcontractor $140,000 23821 Yes
Subcontractor $10,000 23899 Yes
Subcontractor $60,000 23899
Subcontractor $160,000 33599
TOTAL $12,000,000
NAICS Dollar Share DBE Share
23621 87.6% 7.1%
23731 3.3% 25.0%
23811 0.2% 0.0%
23821 7.0% 16.7%
23899 0.6% 14.3%
33599 1.3% 0.0%
than subcontracting for most state Departments of Transporta- In the first study, no subcontract data were available from
tion (between 40 and 45 percent). the client. In the second and third studies, the only data avail-
able from the client were for prime contracts that happened
And, another states: to have DBE subcontractors. While it is apparent that all three
studies recognized the importance of capturing complete
[D]ata were missing [pertaining] to subcontract awards to Non-
DBEs. This study disaggregated all contracting activity by prime subcontracting activity data, the problem remained unad-
and subcontracting categories. As such, it would have been helpful dressed in the final reports. Any statistical conclusions result-
to have information on attainments by Non-DBE subcontractors. ing from such methods may be rejected by the courts since
