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30 Detailed industry affiliation should be determined by for the study contains the name of the firm, its address, and a assigning a four-, five-, or six-digit North American Indus- brief description of the project, then the study can assign a try Classification System (NAICS) code, as appropriate, to detailed SIC or NAICS code. Even without a project descrip- each contractor and subcontractor in the project database. tion, lookups in Dun & Bradstreet, SelectPhone, InfoUSA, or For firms whose work qualifies under more than one NAICS even Google, will almost always turn up enough information code, the assignment should be made based on the firm's pri- to properly assign a detailed industry code to a firm. mary code unless there is enough information available to allo- Without a dollar-weighted overall availability estimate, avail- cate the firm's work by dollars across multiple industries.108 ability in an industry with only $1,000 of contract and subcon- However, if multiple NAICS codes are assigned to firms, the tract spending has as much impact on the overall availability study must be careful to avoid double-counting. estimate as availability in an industry with $1,000,000,000 of We recommend using NAICS codes even if agencies use contract and subcontract spending.111 Without availability esti- systems other than NAICS (such as Construction Specifica- mates by detailed industry, the state DOT is deprived of an tion Institute codes or other internal work code systems) to extremely useful tool for conducting contract-level goal setting. classify contract and subcontract work.109 This is because the Without utilization and disparity analyses by detailed industry, data necessary to implement high-quality availability estimates a study can be criticized for ignoring aggregation issues.112 are classified according to NAICS (i.e., Dun & Bradstreet data). Moreover, the courts are familiar with NAICS and Standard Estimation of DBE Availability Industrial Classification (SIC, NAICS predecessor), but will be less so with other types of work classification systems. Recommended Approach. The recommended approach While all the studies examined provided results according to to estimating availability is a "custom census" designed to aggregate industry sectors (e.g., construction and construction- provide an accurate calculation of the current availability of related professional services), only a few: DBEs in the relevant market.113 This is the only method under Part 26 that has received favorable judicial analysis.114 The Calculated detailed industry weights to be used for produc- custom census approach employs a seven-step analysis that ing dollar-weighted overall availability estimates. (1) creates a database of representative state DOT projects, Provided detailed industry availability estimates and/or (2) identifies the appropriate geographic market for the state carried out utilization and disparity analyses by detailed DOT's contracting activity, (3) identifies the appropriate prod- industry. uct market for the state DOT's contracting activity, (4) counts all businesses in those relevant markets, (5) identifies listed One study asserted that the lack of disaggregated data by minority-owned and women-owned businesses in the relevant industry was due to limitations of the state DOT's contract markets, (6) verifies the ownership status of listed minority- and subcontract data: owned and women-owned businesses, and (7) verifies the ownership status of all other firms. This method results in Some studies have provided for data at the . . . SIC . . . level and an overall DBE availability number that is a dollar-weighted others have aggregated the data into business type categories such average of all the underlying industry availability numbers, as architect & engineering; construction; professional services; with larger weights applied to industries with relatively more and goods and non-professional services. The amount of data spending and lower weights applied to industries with rela- [dis]aggregation is generally limited by a governmental agency's record-keeping format. The business type or procurement cate- tively less spending. The availability figure can also be sub- gories identified above [architecture/engineering, construction, divided by race, ethnicity, and gender group, as well as by professional services, and goods and non-professional services] highway district, where appropriate.115 form the level of aggregation for this study.110 111U.S.DOT "Tips for Goal-Setting in the Disadvantaged Business Enterprise This is not a problem with the state DOT's data but rather (DBE) Program" ("Whenever possible, use weighting . . . to insure that your Step with the study's methods. If the project database assembled One Base Figure is as accurate as possible."). 112Simpson's Paradox states that outcomes observed when data are aggregated may not be observed when data are disaggregated, and vice versa. Some courts 108If the state DOT's contract data contain enough descriptive information about have criticized public agencies for ignoring this issue. See Appendix A, Non-DBE the nature of the work being performed, then this may be possible. Otherwise, Subcontract Data is Just as Important as DBE Subcontract Data, for further dis- the allocation becomes arbitrary. For example, should a firm working in two cussion of this phenomenon. NAICS codes be classified as split between them 50/50, or 70/30, or 90/10? 113See Northern Contracting III, 473 F.3d at 723; Sherbrooke, 345 F.3d at 973. 109Commodities are sometimes classified according to the National Institute of 114Sherbrooke, 345 F.3d at 973; see Concrete Works IV, 321 F.3d at 966 (custom Government Purchasing (NIGP) system. This system is commodity based rather census was "more sophisticated" than earlier studies using Census data and than industry based. For studies that include commodities as a contracting cat- bidders lists). egory, we would recommend classifying according to NAICS. 115Recommended Approach to Measuring DBE Availability, supra, provides an 110 OH (2001, Ch. 3, n.p.). extended discussion of how to implement the "custom census" method.

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31 Review of Studies: Different Approaches gories may not yield an accurate picture. This may be espe- to Measuring Availability cially true for state DOTs that are implementing aggressive race-neutral measures to reduce barriers for DBEs seeking A variety of approaches to measuring availability appear in to perform prime contracts (e.g., setting aside smaller con- the 25 studies we reviewed. The recommended approach, dis- tracts for bidding by small firms, providing bonding and cussed in detail in Recommended Approach to Measuring DBE financing support, reducing prequalification burdens). Availability, was used in seven of 25 studies. Another ten Further, if DBEs suffer discrimination in the relevant mar- studies relied primarily on internal state DOT lists of contrac- kets, then their ability to progress from subcontractors to tors and subcontractors, such as certified DBE directories, prime contracts is likely to have been affected.118 Creating bidders lists, prequalified contractor lists, registered subcon- separate availability measures for primes and subs serves to tractor lists, licensed contractor lists, plan-holder lists, or exacerbate rather than remedy this problem.119 lists of winning contractors or subcontractors. Internal lists The bidders list approach rarely includes attempts to validate were sometimes supplemented with lists gathered from whether firms are correctly classified by race and gender. other sources. However, the internal lists remained primary. We will refer to this as the "bidders list approach." A variation on the bidders list approach was used in four The bidders list approach has appeal since these types of studies. Three studies used one such variation and a fourth lists tend to be readily available and since it seems natural to used another. We will refer to these collectively as the "bid- base an availability measure on firms that are already work- ders list approach with capacity adjustments." ing or trying to work with the state DOT. Some have also This method seeks to test whether firms with lower rev- posited that inclusion on the lists is a measure of whether enues won fewer contracts and subcontracts while firms with DBEs are "ready, willing and able."116 However, there are sev- higher revenues won more. The method was implemented by eral drawbacks to this approach: surveying state DOT bidders to collect data on revenues and then comparing that data to existing data on contract and The remedial aspect of Congress' intent in passing the DBE subcontract awards using regression analysis. Program is easily lost because it limits the availability pool However, the three studies that employed this "capacity" test to only those firms with which the state DOT is already could not find a statistically significant relationship between familiar or already does business. A broader measure is revenues and contract awards.120 In part, the data did not fit preferred.117 because firms of given revenue sizes won a variety of contracts To the extent that there is still discrimination against and subcontracts both large and small.121 DBEs, they are likely to be underrepresented on such lists. Despite finding no empirical support for the hypothesized This approach is therefore likely to lead to lower estimates relationship between revenue size and propensity to win con- of availability than are actually present in the relevant tract and subcontract awards, all three studies proceeded markets. without explanation to eliminate firms from their availability To the extent that economy-wide discrimination in the rel- calculations if they had revenue levels lower than those of sur- evant markets channels DBEs into state DOT work because vey respondents that had actually won state DOT contracts or of the remedy of the DBE Program, DBEs may be overrep- subcontracts. This resulted in an unjustified downward resented on such lists. This approach is therefore likely to adjustment to most of the initial availability estimates. lead to higher estimates of availability than are actually present in the relevant markets. To the extent that such lists are produced using different 118See Northern Contracting II, 2005 U.S. Dist. Lexis at *74 ("The ability of DBEs to methods and criteria (e.g., criteria for DBE certification is compete successfully for prime contracts may be indirectly affected by discrimina- tion in the subcontracting market, or in the bonding and financing markets. Such different from criteria for prime or subcontractor prequal- discrimination is particularly burdensome in the construction industry. . . ."). ification), such lists mix "apples and oranges." 119See Appendix B for further discussion. The bidders list approach often calculates availability sepa- 120Moreover, none of these studies describes why a firm's revenue can be appro- rately for prime contractors and subcontractors. First, firms priately construed as a race-neutral explanatory variable. Additional discussion of the difficulties involved in using variables, such as revenue, that can be may perform as subcontractors on some jobs and prime impacted by discrimination to explain success or failure in the award of contracts contractors on others, so assigning firms to separate cate- or subcontracts appears in Appendix B. 121See Concrete Works IV, 321 F.2d at 981 ("At trial, Denver introduced evidence that the median number of employees of all construction firms in the Denver MSA 116See, e.g., Concrete Works IV, 321 F.3d at 984 (discussing plaintiff's argument is three and presented testimony that even firms with few permanent employees that bidding data are the only measure of availability). can perform large, public contracts by hiring additional employees or subcontrac- 117Northern Contracting III, 473 F.3d at 723 (The "remedial nature of the federal tors and renting equipment. Additionally, the district court found that `most firms scheme militates in favor of a method of DBE availability calculation that casts a have few full-time permanent employees and must grow or shrink their perfor- broader net."). mance capacity according to the volume of business they are doing.' ").

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32 A fourth study also implemented a "capacity" adjustment construction-related professional services contracts were also with its bidders list approach to measuring availability. This discarded. In one of these studies, for example, the original adjustment, however, was implemented in a way that was not pool of 39,911 establishments was reduced by over 90%-- obviously biased by past discrimination against DBEs. The leaving only 3,398 firms in the availability denominator. The study performed a regression analysis of total dollars awarded process used in the other two studies was very similar. to non-DBE contractors and subcontracts against the largest As part of the telephone survey, each firm was asked successful bid from each non-DBE contractor or subcontrac- whether it was 51% or more minority or female owned. If it tor. It then applied the statistical relationship implied by the reported being minority owned, then the survey asked non-DBE regression to DBEs and used the results to estimate which race or ethnicity comprised the bulk of the minority "expected utilization" for DBEs. The expected utilization fig- ownership. In this way, 32% of the 3,398 firms identified in ure was then compared to the actual utilization figure to form the survey were identified as minority owned and/or woman a disparity ratio. This approach is roughly analogous to the owned. approach used to estimate potential business formation among The study examined five years of DOT contract and sub- minorities and women.122 As a potential measure of disparity, contract data. From this, dollar-based weights were derived to it is quite useful. As a measure of availability, however, it suf- account for the amount of contract and subcontract spending fers from the same infirmities as other bidders list approaches in each SIC or NAICS code. Although the study does not to availability measurement.123 describe the method, additional weights were also derived to Moreover, from a practical standpoint, the necessary account for prime contractor versus subcontractor status, data on successful and unsuccessful bid values, particularly contract dollar size categories, and regions within the state. at the subcontractor level, are rarely available. This study did Based on this weighting procedure, the overall estimate of not describe how complete the data were for this exercise, availability fell from 32% to under 18%. although elsewhere there are indications that a significant Finally, firms that were considered too large to meet amount of non-DBE subcontractor data were in fact missing. U.S.DOT DBE certification guidelines were excluded from The study's regression results do not report the number of the availability measure. Any DBE construction firm that DBE and non-DBE observations included. These omissions responded to the telephone survey and reported prior year made it difficult to evaluate this approach's litigation poten- gross revenues exceeding $10 million,124 or any DBE engineer- tial, as an adverse party's expert might be unable to duplicate ing firm reporting more than $5 million, was dropped from the the results from the study. calculation. The final availability figure arrived at through this Another method used to account for capacity appears in process was 13.5%. three other studies we reviewed. We will refer to this as the As with the "bidders list with capacity adjustments" "custom census approach with capacity adjustments." approach, the "custom census with capacity adjustments" The three studies using this method started with Dun & approach is biased downward. It reduces the availability per- Bradstreet data as their source for the availability denomina- centage by controlling for factors that are likely to be directly tor. The Dun & Bradstreet data were then restricted to the rel- affected by the presence of discrimination in the relevant mar- evant geographic market based on the location of contractors kets. Whether firms have worked or attempted to work on state and subcontractors on federally assisted state DOT contracts. DOT projects have been awarded prime contracts or the size The data were further restricted to the relevant product mar- of those contracts should not be used to limit the DBE avail- ket by matching the names and addresses of firms that had ability measure.125 Not only is this a problem in its own right, bid on state DOT contracts as prime contractors or first-tier but also it may hide the existence of discrimination because a subcontractors with the Dun & Bradstreet data to determine downward bias in availability can lead to a conclusion of no the relevant SIC or NAICS codes. significant disparity when, in fact, a disparity exists.126 All the firms identified in this manner were then surveyed by telephone. Firms that could not be reached were dis- carded, including firms that may actually have worked for the 124Cf. 49 C.F.R. 26.65, establishing the size limits for DBE eligibility as those agency as prime contractors or subcontractors. Any firms imposed by the SBA under 13 C.F.R. Part 121. SBA limits at the time of the study that had not worked or attempted to work on construction or ranged from $13 million for specialty trade contractors to $31 million for gen- eral and heavy construction firms. The current overall DBE Program cap is $20.41 million. No explanation is provided in the study of how the lower ceiling was determined or why it was imposed. 122A business formation regression is calculated for nonminority males and the 125See also the discussion of capacity in Appendix B. resulting model is applied to minorities and women to derive their expected rate 126Based on the results of one of these studies, the state DOT has sought a waiver of business formation. See Chapter Three, Economy-Wide Disparity Analysis for to exclude DBEs owned by Hispanic males and Subcontinent Asian males from the Relevant Markets, infra. its race-conscious measures, i.e., denying their eligibility for credit toward meet- 123 See Chapter Three, Estimation of DBE Availability, supra. ing subcontracting goals based on a finding of no significant disparity.

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33 Disparity study authors, along with a number of courts, not be used to limit the examination of how the market have wrestled with the concept of DBE or M/WBE "capacity." would look if it became discrimination free. Concerns regarding capacity arise not only in the context of Although 49 C.F.R. 26.45(c)(1) permits the use of DBE availability measurement but also in the context of assessing directories and Census Bureau data to estimate availability, disparities. Appendix B provides an extended discussion of this is a less than optimal approach. It necessitates a compar- "capacity" issues in the context of disparity and availability ison of "apples to oranges" because the methods used to studies and related litigation. In summary, where "capacity," identify and certify firms as DBEs are entirely different from whether measured by firm revenues, employment size, or the methods used by the Census Bureau to count businesses some other metric, is influenced by the presence of discrimi- or business establishments for inclusion in CBP Survey of nation in the relevant markets, it is inappropriate to use such Business Owners (SBO) or other statistical databases. As a measures to "correct" or "adjust" Step 1 availability or Step 2 result, no defensible comparisons are likely to result from disparity statistics. dividing figures from one of the latter sources by figures Finally, one study calculated DBE availability six different from the former. ways yielding six different estimates. Such an approach can be What has been termed the "custom census" approach to a drawback in litigation as it provides a plaintiff an opportu- measuring DBE availability, when properly executed, is supe- nity to cast doubt on the entire study by pointing out that the rior to the other methods allowable under 49 C.F.R. 26.45 multiple estimates cannot all be correct.127 In fairness, how- for at least four reasons. First, it provides an internally con- ever, this particular study is one of the earliest that we sistent and rigorous "apples to apples" comparison between reviewed, from a period when recipients were still striving to firms in the availability numerator and those in the denomi- find an approach to availability measurement that the courts nator. Second, by "casts[ing] a broader net" it comports with would find acceptable.128 the remedial nature of the DBE Program. Third, a custom census is less likely to be tainted by the effects of past and present discrimination than the other methods. Recommended Approach to Measuring Finally, it has been upheld by every court that has reviewed DBE Availability it. The Tenth Circuit found the custom census approach to Introduction. The determination of DBE availability is be "a more sophisticated method to calculate availability the cornerstone of an availability or disparity study. Accurate, than the earlier studies."131 Likewise, this method was suc- comprehensive estimates of availability are critical. An expan- cessful in the defense of the DBE Programs for Mn/DOT132 sive concept of availability is important because, as the courts and IDOT,133 as well as the M/WBE construction program for have held, looking beyond the recipient's contracting results the City of Chicago.134 helps to further Congress' remedial intent. The following are the seven steps to the custom census approach: [T]he purpose of the overall goal--and, in fact, the DBE pro- gram, as a whole--is to achieve a "level playing field" for DBEs (A) Create a database of representative, recent, and complete seeking to participate in federal-aid transportation contracting. To reach a level playing field, recipients need to examine their state DOT projects; programs and their markets and determine the amount of par- (B) Identify the contracting activity's relevant geographic ticipation they would expect DBEs to achieve in the absence of market; discrimination and the effects of past discrimination.129 (C) Identify the contracting activity's relevant product mar- ket for the contracting activity in question; Limiting the inquiry to agencies' internal lists of firms can- (D) Count all businesses in the relevant markets; not fully result in an annual goal that "reflect[s] . . . the level (E) Identify listed minority-owned and women-owned busi- of DBE participation you would expect absent the effects of nesses in the relevant markets; discrimination."130 Such an approach reflects in part the cur- (F) Verify the ownership status of listed minority-owned rent effects of past or current discrimination and so should and women-owned businesses; and 131 Concrete Works of Colorado, Inc. v. City and County of Denver, 321 F.3d 950, 127 Cf. Associated General Contractors of America v. City of Columbus, 936 F. Supp. 966 (10th Cir. 2003) (Concrete Works IV), cert. denied, 540 U.S. 1027 (2003). 132 1363, (S.D. Ohio 1996) (discussing various measures of availability). Sherbrooke Turf, Inc. v. Minnesota Department of Transportation, 345 F.3d. 964 128Another of these early studies presented seven different availability measures. (8th Cir. 2003), cert. denied, 541 U.S. 1041 (2004). 133 However, the agency ultimately chose just one (a bidders list approach) to use Northern Contracting, Inc. v. Illinois Department of Transportation, 473 F.3d for disparity testing purposes. 715 (7th Cir. 2007). 12964 Fed. Reg. 5108. 134 Builders Association of Greater Chicago v. City of Chicago, 298 F. Supp.2d 725 130 Ibid. (N.D. Ill. 2003).

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34 (G) Verify the ownership status of all other firms in the rele- likely to be inadequate due to the smaller sample sizes yielded. vant markets. For example, a significant share of projects awarded during that period may still not yet be complete. This could cause the Each step is described in more detail below, in the context of resulting database to be biased by the exclusion of larger proj- estimating availability for construction and construction- ects with longer completion times. related professional services--the two most commonly studied The median study period length was 5 years and the aver- (and litigated) sectors in disparity and availability studies. age was 5.3 years. The disparity or availability studies intro- The methods generalize to other industry sectors as well. duced as evidence in the Sherbrooke and Northern Contracting cases covered 5 years. Step A--Create a Database of Representative, Recent, and Because the types of subcontracting that occur early in a Complete State DOT Projects. The first step is to create a project are often different from those that occur later, we rec- database of state DOT projects. This database provides the ommend that only complete or substantially complete proj- empirical basis for many of the statistical analyses in the study. ects be studied; that is, enough of the project has been finished Each project in the database should contain information on all (e.g., it is now open to traffic) so an accurate picture of all sub- relevant prime contracts and all associated first-tier agree- contracting activity emerges. If payment data are being used ments with subcontractors, subconsultants, or suppliers.135 in the analysis (as opposed to or in addition to award data), It is imperative to obtain subcontract information for both then including incomplete projects could lead to inaccurate DBEs and non-DBEs. If all of the necessary subcontract data conclusions about the relative weight of different subcontract- have been maintained, then all of it should typically be included ing activities. in the project database. If the state DOT has not collected and In order to assist in the determination of the level of DBE maintained this data, then it will be necessary to either request participation that would be expected in a race-neutral con- the information directly from each relevant prime contractor tracting environment, it is important that the project database or consultant or reconstruct the required information by include contracts with and without DBE goals.140 In states other means.136 without a M/WBE program for state-funded contracts, this The project database must be representative of the type of can be achieved by including non-federally assisted projects work usually undertaken by the state DOT. It is therefore in the database. advisable to study several years' worth of contract and sub- For states without these data, more creative approaches contract data so that atypical projects do not unduly affect the can be applied. For example, in preparing for the Northern statistical analysis.137 Contracting trial, IDOT initiated a "zero goals" experiment, Most state DOT disparity and availability studies have relied where selected prime contracts that ordinarily would have on a study period of 5 years--typically the most recent five full been subject to a DBE goal based on the scopes of work and fiscal or calendar years.138 Of the 28 state DOTs that have per- DBEs certified in those industries were solicited with a "zero formed, or are currently performing, a disparity or availability goal." The result was that DBEs received approximately 1.5% study, we were able to obtain the number of years of contract of the total value of these contracts. This evidence of the and subcontract data studied for 25. This is shown in Table 5.139 results of totally race-neutral measures was found to be pro- Of these 23 studies, study periods ranged from 2 years to bative by the court.141 Several courts have likewise held that a 14 years. A study period of only two years, in our view, is large contrast between DBE participation on contracts with and without goals can be probative of the continuing need for race-conscious remedies.142 135Hereafter, unless otherwise indicated, we refer to these collectively as "subcon- tractors" or "subcontracts." 136 For additional discussion of the subcontract data collection issue, see Appendix A. 137 140 In some cases, it can be appropriate to ignore or prorate unusual projects. For One objective of the DBE Program is to "assist the development of firms that example, in Denver's study, 1999 projects associated with the construction of the can compete successfully in the marketplace outside the DBE program." 49 C.F.R. Denver International Airport were excluded from the main analysis, because this 26.1(f) (emphasis added). A disparity study can examine how DBEs are faring sort of project was not representative of typical Denver construction projects and on contracts that are not federally assisted, which may provide information that was unlikely to be undertaken again for decades. In that same study, several large is useful to the agency in this regard, as well as the need to continue to use race- bond-funded public library construction projects that were likely to be under- conscious goals to meet the annual goal. taken only once per decade were prorated. Since the study period was 5 years, 141Northern Contracting III, 473 F.3d at 719. half of the value of these projects was included in the database. 142See, e.g., Western States, 407 F.3d at 992 (Congress properly considered evi- 138 Typically, the federal fiscal year (which runs from October through Septem- dence of the "significant drop in racial minorities' participation in the construc- ber) is used, although calendar years are sometimes used depending on the tion industry" after state and local governments removed affirmative action study's scope and on organization of the agency's data. provisions); Adarand VII, 228 F.3d at 1186 (evidence included "studies of local 139 Of the remaining three, one is ongoing (North Carolina), one could not be subcontracting markets after the removal of affirmative action programs"); Con- located (Florida), and one was never released (Tennessee). crete Works IV, 321 F.3d at 98485.

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35 Table 5. Study period length for state DOTs that are currently performing or have recently performed disparity or availability studies. Years of Contract Data State Consultant in Most Recent Study NC EuQuant ongoing FL MGT of America, Inc. not obtained TN Mason Tillman Associates, Ltd. not released LA D.J. Miller & Associates 2 NJ Mason Tillman Associates, Ltd. 2 CO MGT of America, Inc.a 3.5 ID BBC Research & Consulting 4 AK D. Wilson Consulting Group, LLC 5 CA BBC Research & Consulting 5 CO D. Wilson Consulting Group, LLCb 5 NV BBC Research & Consulting 5 NM BBC Research & Consulting 5 OH D.J. Miller & Associates 5 NE MGT of America, Inc. 5 NC MGT of America, Inc.c 5 VA MGT of America, Inc. 5 HI NERA Economic Consulting 5 IL NERA Economic Consulting 5 MD NERA Economic Consulting 5 MN NERA Economic Consulting 5 NY NERA Economic Consulting 5 WA NERA Economic Consulting 5 AZ MGT of America, Inc. 6 GA Boston Research Group 6 MT D. Wilson Consulting Group, LLCb 6 MO NERA Economic Consultingd 6.5 OR MGT of America, Inc. 8 SC MGT of America, Inc. 14 Notes: (a) Colorado's current study is ongoing--this figure is for its 2001 study; (b) study is ongoing, study period based on consultant's proposal; (c) North Carolina's current study is ongoing--this figure is for its 2004 study; (d) figure is for construction contracts--only 3 years of data were available for construction-related professional services and local assistance contracts.

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36 For each project in the database, the following are key Unique contract number for prime contract; prime contract fields that should be included:143 Brief description of the subcontract; Original dollar amount of subcontract; Unique contract number for prime contract; Total dollar amount of subcontract (inclusive of all change Brief description of the prime contract; orders); Department or subdepartment for which the project was Total dollar amount paid through completion or substan- performed; tial completion; Date of prime contract award; Business name of subcontractor or consultant; Original dollar amount of prime contract; Unique identification code for the subcontractor; Total dollar amount of prime contract (including all Street address, city, state, and zip code of subcontractor; change orders); Telephone number of subcontractor; Date of completion or substantial completion; Contact person name and title for subcontractor; Total dollar amount paid through completion or substan- Certification status of subcontractor; tial completion; Race and gender of subcontractor ownership;147 and Business name of prime contractor or consultant; Brief description of subcontractor work specialties. Unique identification code for the prime contractor or consultant;144 Steps B & C--Identify the Relevant Markets. Markets Street address, city, state, and zip code of prime contractor have both a geographic and an industry dimension.148 Once or consultant; the project database is assembled, the next step in determin- Telephone number of prime contractor or consultant; ing availability is to identify the geographic locations and the Contact person name and title for prime contractor or industries from which the state DOT draws the preponder- consultant; ance of its prime contractors and consultants and from which Certification status of prime contractor or consultant; the state DOT's prime contractors and consultants draw the Race and gender of prime contractor or consultant preponderance of their subcontractors, subconsultants, and ownership;145 suppliers. Brief description of prime contractor work specialties; The unit of analysis to define "preponderance" should be Indication of whether prime contract was federally assisted; the number of contract dollars since subcontracting goals are DBE goal for the prime contract; and set as a percentage of total dollars awarded and since contracts DBE goal for associated change orders.146 and subcontracts vary greatly by dollar size.149 For each project in the database, the following key sub- contract fields should be included: 147 If the subcontractor is publicly owned, this should be recorded in lieu of race and gender. See footnote 145. 148 See, e.g., Spectrum Sports, Inc. v. McQuillan, 506 U.S. 447, 459 (1993). 143 149 For projects procured using the construction manager or construction For example, data from WSDOT for FFY 19992003 [NERA Economic Con- manager at-risk process, similar data should be included for the construction sulting (2005)] included 624 federally funded prime construction contracts val- manager. ued at $1.52 billion. These contracts ranged in value from a minimum of $54,000 144 The use of unique codes to identify vendors in contracting records is preferable to a maximum of $204 million, with a median of $961,000 and an average of to using the vendor's name as an identifier, since vendor names can (and usually $2.44 million. Of the 624 prime contracts, 182 (29%) were valued at $500,000 or are) entered inconsistently. Reconciling this inconsistent information can be a less. However, these 182 contracts accounted for less than 3.5% of total dollars. huge, costly, and time-consuming task for a study consultant, especially in con- Even if we exclude the largest prime contracts (e.g., those in excess of $20 million), tract files containing thousands (or millions) of records. The use of unique ven- which collectively account for 35% of all dollars, the share of total dollars dor IDs eliminates the inconsistency inherent in the use of names as identifiers. It accounted for by contracts of $500,000 or less is still only 5.0% of the total. A sim- also facilitates the integration of contracting and contractor data into larger rela- ilar pattern is observed in subcontracts. In the WSDOT data, there were 4,998 tional database systems, which have many advantages, from an information man- subcontracts totaling almost $560 million. Subcontracts ranged in size from $100 agement perspective, over more traditional, or flat file database management to almost $15 million, with a median of $18,000 and an average of $111,000. Sub- systems. See, e.g. Wiley Publishing, Inc. (2008). contracts of $15,000 or less accounted for 46% of all subcontracts but less than 145 If the contractor is publicly owned, this should be noted. In the absence of 2.5% of all subcontract dollars. Once again, even if we ignore all subcontracts val- information to the contrary on a specific company, however, it is appropriate to ued above $2 million (27% of all subcontract dollars), subcontracts of $15,000 or treat publicly owned companies as non-DBEs. According to the Census Bureau less account for just over 3.0% of all subcontract dollars. Finally, in the WSDOT (2008, Tables 2 and 5), of the total value of stock and mutual fund shares owned data, more than 96% of all DBE participation occurred on contracts valued above by U.S. households in 2002 (the latest data available), black households owned $500,000. Similar patterns are observed in the construction-related professional only 3.5%, Hispanic households 6.9%, Asian households 4.3%. Non-Hispanic services contracts data. Not only does this example demonstrate the wide varia- white households owned 77.3%. tion in prime contract and subcontract sizes, but it also casts in doubt a method 146 We have observed many instances where DBE goals on awarded contracts have that limits most or all statistical analyses to prime contracts of $500,000 or less. not been applied to change orders. Since change orders can often account for a This approach will cause a very large share of its contract spending, including the large share of a construction project's dollar value, this can lead to a significant DBE spending, to go unanalyzed. See, e.g., Texas Building and Procurement Com- dilution of DBE participation. mission (2007, 56).

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37 Table 6. Distribution of contract dollars by contract category. Construction Consulting Subrecipients Location (%) (%) (%) Inside State 82.1 83.8 82.4 Outside State 17.9 16.2 17.6 Inside Metropolitan Statistical Area 86.7 89.2 99.6 Outside Metropolitan Statistical Area 13.3 10.8 0.4 Using the project database, the county in which each con- and construction-related professional services, a four-digit tractor is located can be identified using the firm's zip code. NAICS code is most comparable to a four-digit SIC code for Then, the percent of dollars awarded and/or paid to contrac- general contracting categories.153 For specialty trades and pro- tors and subcontractors in each state/county unit can be cal- fessional services, a five-digit NAICS code is most comparable. culated. Drawing on work in the antitrust field, a geographic Once NAICS codes are assigned to all firms in the project market can be defined as that area in which the state DOT database, the percent of dollars awarded and/or paid to con- operates and as well as the area where it "can predictably turn" tractors and subcontractors in each detailed industry can be to obtain construction services and where "the vast bulk" of calculated to determine which ones are most relevant for a the agency's contract and subcontract dollars are spent.150 state DOT's contracting activity. Some NAICS codes will have Although there is no single numerical percentage that deter- much larger percentage shares than others, as some industries mines "the vast bulk," a figure of 75% is often employed as a account for a larger share of agency contract spending than reasonable approximation.151 Table 6, drawn from an actual others. These percentage shares are referred to as "product availability study, provides an example of how the informa- market weights" because they will be used to create an over- tion from the project database can be arrayed by geography to all availability figure that is a weighted average of all the assist in determining the appropriate geographic market. For individual four-digit and five-digit NAICS level availability that study, the geographic market was defined to be the state estimates, with the weights being the percentage dollar shares as a whole. of spending. Table 7, drawn from an actual availability study In order to identify the product market, each contactor we performed, provides an example of how the information and subcontractor in the project database can be assigned a from the project database can be arrayed by industry (in this primary industry code, using the NAICS system. If enough case using SIC rather than NAICS codes) to assist in deter- detail is provided in the project database, the project name mining the appropriate product market. and description can be used to assign primary industry codes As Table 7 shows, 22 SIC codes were identified in this state to prime contractors and the subcontract task descriptions DOT's subrecipient contracting market. Ninety percent of can be used to assign codes to subcontractors. such contracting and subcontracting activity occurred in just When the description of the task does not clearly indicate eight industries and that one industry, SIC 1611, accounted the appropriate NAICS code for a contract or subcontract, for 24% of all such activity. lookups can be performed using Dun & Bradstreet's Market- As with the geographic market, it is important that the dis- Place, SelectPhone, InfoUSA, or other sources. Most of these parity or availability study captures the "vast bulk" of an sources still use SIC codes, the predecessor to the NAICS sys- agency's spending. Here too, the courts have not established tem, to classify firms. Crosswalk tables allowing conversion any clear numerical boundary.154 Since one of the deliverables between SIC and the several editions of NAICS and vice versa from a high-quality study should be detailed (i.e., four-digit are available online from the Census Bureau.152 In construction or five-digit NAICS) estimates of DBE availability that can be used to assist in establishing DBE contract goals, the agency should have as many relevant NAICS codes included in its 150See, e.g., Tampa Electric Co. v. Nashville Coal Co., 365 U.S. 320, 367 (1963); study as possible. On the other hand, it is not cost-effective to United States v. Philadelphia National Bank, 374 U.S. 321, 359 (1963); also Areeda, Kaplow and Edlin (2004). 151This was the benchmark employed in the availability studies that were upheld 153 Four digits is the most detailed classification available in the SIC system. 154 in the Sherbrooke and Northern Contracting cases. See fn. 149; however, it is not advisable to structure a study so that only a small 152 See NAICS at http://www.census.gov/epcd/www/naics.html. fraction of overall spending is captured.

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38 Table 7. Product market for subrecipient contracts. Cumulative SIC Code SIC Description Percentage Percentage 1611 Highway and Street Construction 24.26 24.26 1622 Bridge, Tunnel, and Elevated Highway 22.10 46.35 1794 Excavation Work 14.69 61.04 1771 Concrete Work 11.43 72.48 1623 Water, Sewer, and Utility Lines 6.02 78.49 1542 Nonresidential Construction, n.e.c. 5.30 83.80 1629 Heavy Construction, n.e.c. 3.42 87.22 0782 Lawn and Garden Services 3.27 90.49 1731 Electrical Work 3.12 93.61 3273 Ready-Mixed Concrete 1.44 95.05 1521 Single-Family Housing Construction 1.08 96.13 5051 Metals Service Centers and Offices 1.05 97.18 1791 Structural Steel Erection 0.86 98.05 4212 Local Trucking without Storage 0.73 98.78 1541 Industrial Buildings and Warehouses 0.39 99.17 7359 Equipment Rental and Leasing, n.e.c. 0.30 99.47 8711 Engineering Services 0.22 99.69 3272 Concrete Products, n.e.c. 0.18 99.86 1721 Painting 0.06 99.92 Electrical Apparatus and Equipment, Wiring 5063 0.04 99.97 Supplies, and Construction Materials Petroleum and Petroleum Products Wholesalers, 5172 0.02 99.98 except Bulk Stations and Terminals 3446 Architectural Metal Work 0.02 100.00 TOTAL $48,540,859

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39 create availability estimates for NAICS codes in which the Beyond the information already in MarketPlace, this includes total spending over a 5-year study period is relatively negligi- obtaining the state's Unified Certification Program directory as ble; some balance must be struck. Our experience is that in well as DBE listings from numerous other public and private using a well-constructed project database, it is usually possi- entities in the relevant market. ble to analyze at least 80% of contract spending and in some As an example, the following is a listing of directories cases as much as 95% or more.155 obtained and/or agencies contacted as part of the WSDOT (2006) availability study: Steps D & E--Count All Businesses in the Relevant Mar- kets and Identify Listed Minority-Owned and Women- WSDOT certified DBE directory Owned Firms. Once the geographic and product markets Associated General Contractors of Washington have been defined, it is possible to define the "baseline" pop- Bank of America Supplier Diversity Program ulation of relevant businesses for the study. In the custom Black Chamber of Commerce Pacific Northwest census approach, Dun & Bradstreet's MarketPlace database is Boeing Company Supplier Diversity Program used to define the baseline business population. MarketPlace Boise Cascade Corp Supplier Diversity Program is the most comprehensive and objective available micro- Business Research Services National Directory of Minority- level database of U.S. businesses.156 MarketPlace contains over Owned Businesses 14 million records, is updated continuously, and revised each Business Research Services National Directory of Women- quarter. Using MarketPlace, it is possible to purchase a list of Owned Businesses all businesses within the geographic market area that have an Caltrans NAICS code to which a product market weight has been Central Contractor Registration database assigned using the project database. CH2M Hill While extensive, MarketPlace does not sufficiently identify Chevron/Texaco Supplier Diversity Program businesses owned by minorities or women. That is, although City of Bellevue these firms are included in MarketPlace, they are not always City of Olympia identified as being minority or women owned. Although many City of Portland Sheltered Market Program such businesses are correctly identified in MarketPlace, experi- City of Seattle Boost Program ence has demonstrated that many are not. For this reason, the City of Seattle Vendor & Contractor Registration minority- and women-owned share of the baseline business City of Spokane population cannot be calculated directly from MarketPlace. City of Tacoma Doing so would yield an availability estimate that was lower City of Vancouver than the actual share of minority- and women-owned busi- City of Olympia nesses in the relevant markets. Coca-Cola Supplier Diversity Program To compensate for this limitation, the custom census Community Capital Development SMWBE list method supplements the existing MarketPlace race and gender Conoco/Phillips Supplier Diversity Program identifiers with race and gender identifiers from other directo- Urban League of Metropolitan Seattle ries and business listings gathered within and around the rele- Diversity Information Resources vant geographic market. This requires conducting an "extensive Georgia-Pacific Supplier Diversity Program and intensive" search for information on minority-owned and Howard S. Wright Construction Supplier Diversity Program women-owned businesses in the relevant geographic market.157 Idaho Transportation Department King County 155 This can be done by excluding all prime contracts below a certain dollar Kroger Company threshold, say $25,000, or by cumulating total spending by NAICS code and then Microsoft Supplier Diversity Program excluding contract spending in those NAICS codes above a certain percentage Montana Department of Transportation cutoff, say 90%, or by some combination of these two methods. See also the dis- cussion at fn. 149. National Association of Minority Contractors 156 "Micro-level" means a database where the individual firms within the database National Association of Women Business Owners--Inland can be identified. Many Census databases, by contrast, simply provide counts of Northwest Chapter businesses in different categories. 157 We say "extensive" because, as the list below illustrates, a wide variety and large National Association of Women in Construction (various number of organizations were contacted as potential sources of information on Chapters) minority- and women-owned businesses. We say "intensive" because obtaining National Center for American Indian Economic Development a given list in the necessary timeframe with the necessary information (i.e., race National Minority Business Council and gender identification in addition to standard data such as business name, address, and telephone number) and in a usable format often requires a great Nevada Department of Transportation deal of patience and perseverance. Nike Supplier Diversity Program

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40 Nordstrom Department Stores Supplier Diversity Program ever, in order to maintain the "apples to apples" comparabil- Northwest Minority Business Council ity between M/W/DBEs and non-M/W/DBEs, firms in the Northwest Native American Business Development Center master directory for which there is no corresponding record Oregon Association of Minority Entrepreneurs in MarketPlace are not included in the analysis. Oregon Office of Minority, Women and Emerging Small Business Steps F & G. Verifying the Ownership Status of Firms in Pepsico the Relevant Markets. If the listed DBEs158 identified above Port of Portland are, in fact, all DBEs and are the only DBEs among all the Port of Seattle businesses identified, then the custom census estimate of Port of Tacoma DBE availability reduces simply to the number of listed DBEs Qwest Communications divided by the total number of businesses in the relevant mar- Raytheon ket. However, neither of these two conditions holds true in Safeco Insurance Company Supplier Diversity Program practice, and therefore this is not a complete measure of DBE Seattle Mariners Supplier Diversity Program availability. Seattle Monorail Project In order to derive a more accurate measure of availability, Seattle/Washington State Minority Business Development the possibility that some firms have been incorrectly classified Center as DBEs must be taken into account. This type of misclassifi- Sound Transit Diversity Programs cation, if uncorrected, will lead to estimates of DBE availabil- South Puget Sound Hispanic Chamber of Commerce ity that are upwardly biased. Similarly, the possibility that Starbucks Supplier Diversity Program some firms not initially identified as DBEs are, in fact, DBEs Tabor 100 (Northwest Association of African-American must also be accounted for. This type of misclassification Businesses) (which we refer to as "nonclassification" to avoid confusion), Tacoma Housing Authority if uncorrected, will lead to estimates of DBE availability that Thurston County are downwardly biased. U.S. Army Corps of Engineers In the custom census method, these two types of classifi- University of Washington cation bias are corrected by a supplementary telephone sur- W.W. Grainger Co. Supplier Diversity Program vey administered to a stratified random sample of firms in Washington Mutual the baseline business population. These firms are contacted Washington State Hispanic Chamber of Commerce and asked directly about the race and gender of the firm's Washington State Office of Minority & Women's Business primary owner(s). The results of the survey are then used to Enterprise statistically adjust the estimates of DBE availability for mis- Wells Fargo Supplier Diversity Program classification by race and gender. Women and Emerging Small Business Tables 8 through 11 summarize the results from a misclas- Women Business Owners of Puget Sound sification survey we recently completed for a large municipal- Women's Business Enterprise National Council ity in the southwest. Tables 8 and 9 show the fraction of firms Xerox Corporation. originally classified as DBEs that were verified as such through the survey. Table 8 arrays the results by industry grouping and Not every agency in the list above ultimately provided a Table 9 by race and gender. Tables 10 and 11 show compara- directory or listing to the study team. Some entities use the ble results for the firms originally classified as non-DBE. As lists of other agencies, some do not track race or gender infor- these four tables make clear, misclassification fractions are mation (rendering the list much less useful for disparity or not insubstantial. availability study purposes), and some had lists but were Carrying Out Final Step 1 Availability Calculations. Once unwilling or unable to cooperate with the study effort. steps A through G are completed, final overall and detailed All of the lists and directories obtained in this manner are availability figures can be calculated. Below we define terms and combined in a standardized format. Duplicate entries are elim- provide the specific formula for estimating custom census avail- inated and information from multiple records consolidated ability. We also provide a prose description of how the estimates and reconciled. Obvious out-of-scope and non-M/W/DBE are calculated in practice using the formula. listings should be dropped. The result is a "master directory" of listed (or "known") M/W/DBEs. 158 The master directory is then merged with the correspon- As used here, "DBE" includes not only certified DBEs but also all other minority-owned and/or women-owned firms. See the discussion from the ding Dun & Bradstreet MarketPlace database to enhance the Northern Contracting case in Appendix C, Judicial Review of DBE Goal Setting identification of minority- and women-owned firms. How- Under Part 26.

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41 Table 8. Listed DBE survey--amount of misclassification by NAICS code grouping. Listed DBE By Misclassification Percentage Actually Number of Businesses NAICS Code (Percentage M/WBE Owned Interviewed Grouping White Male) NAICS 236 24.0 76.0 104 NAICS 237 37.8 62.2 37 NAICS 238 20.2 79.8 252 NAICS 327, 332 25.0 75.0 12 NAICS 484 18.0 82.0 39 NAICS 42 31.8 68.2 129 NAICS 5413 19.5 80.5 174 Balance of NAICS 12.2 87.8 557 Codes All NAICS Codes 18.6 81.4 1,304 Note: NAICS 236--Building Construction, NAICS 237--Heavy Construction, NAICS 238--Special Trades Construction, NAICS 327--Nonmetallic Mineral Product Mfg., NAICS 332--Fabricated Metal Product Mfg., NAICS 484--Truck Transportation, NAICS 42--Wholesale Trade, NAICS 5413--Architecture, Engineering & Related Services. Table 9. Listed DBE survey--amount of misclassification by putative DBE type. Misclassi- Misclassification Percentage Number of Putative fication (Percentage Correctly Businesses Race/Gender (Percentage Other DBE Classified Interviewed White Male) Type) African-American 12.3 5.2 82.5 114 (either gender) Hispanic 15.5 4.4 80.1 401 (either gender) Asian (either gender) 17.1 3.1 73.7 76 Native American 47.6 26.2 26.2 42 (either gender) White female 20.0 5.5 74.5 671 All DBE Types 18.6 N/A 81.4 1,304 Table 10. Nonclassified businesses survey-- by NAICS code grouping. Number of Listed DBE by SIC Percentage Actually Percentage DBE Businesses Code Grouping White Male Owned Interviewed NAICS 236 88.4 11.6 335 NAICS 237 88.6 11.4 140 NAICS 238 81.1 18.9 874 NAICS 327, 332 92.1 7.9 63 NAICS 484 67.6 32.4 145 NAICS 42 82.8 17.2 442 NAICS 5413 89.6 10.4 574 Balance of NAICS 80.9 19.1 507 Codes All NAICS Codes 83.6 16.4 3,080

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42 Table 11. Nonclassified businesses survey--by race and gender. Number of Businesses Verified Race/Gender Percentage of Total Interviewed White male 2,575 83.6 White female 254 8.3 African-American 26 0.8 Hispanic 170 5.5 Asian 31 1.0 Native American 24 0.8 Total 3,080 100.0 All terms defined below are restricted to the relevant geo- (F2) Availability percentage in industry i: graphic market. Individual industry categories are denoted by the sub- script i, which runs from 1 through n, where n is the total Ai = Dai( Ni ) 100 number of detailed industries included in the analyses, that is (F3) Overall percentage availability: (i = 1, 2, . . . , n). In this particular example, n is 20. 20 Di -- Total number of "listed DBE" establishments in A = ( Ai w i ) i =1 industry i. Ni -- Total number of establishments in industry i. (F4) The full estimated availability formula is therefore: xi -- Percentage of listed DBE establishments in industry i that aren't actually minority or female owned--the "misclas- A = 20 ( Di (1 - xi ) + ( N i - Di ) ( yi )) ( N i ) 100 w i sification percentage." yi -- Percentage of establishments other than listed DBEs in i =1 industry i that aren't actually majority male owned--the "nonclassification percentage." Below we provide a numerical representation of how this 1-xi -- Percentage of listed DBEs in industry i that are actu- formula was used to derive the Step 1 DBE availability figure ally minority or female owned. of 24.34% in a state DOT study we recently completed, along 1-yi -- Percentage of establishments other than listed DBEs in with a detailed explanation of the derivation. Table 12 pro- industry i that are actually majority male owned. vides the actual numbers used for that study. Dai -- Total number of DBE establishments in industry i, Step 1 availability is calculated as a weighted average of the adjusted for misclassification and nonclassification. 20 individual estimated availability percentages (Ai). There is Ai -- Estimated percentage availability in industry i. one individual availability estimate for each of the 20 SIC wi-- Percentage of total dollars spent in industry i, ("dollar- codes included in the calculation. These 20 individual availabil- based industry weights"). Note: these weights sum to 1.0. ity percentages appear in column (9) of Table 12. For example, A-- Overall estimated percentage availability. Ai for SIC code 1611 is 20.95%, for SIC code 8711 it is 24.51%, and so on. The overall availability percentage, A, is then derived as To derive the overall Step 1 availability estimate, A, of follows:159 24.34%, these 20 individual estimates must be averaged together, using Formula F3 above and the individual weights, (F1) Total adjusted number of DBE establishments in wi, that appear in column (8) of Table 12. To calculate this industry i: weighted average, each individual availability estimate, Ai, from column (9) is multiplied by its corresponding weight, Dai = Di (1 - x i ) + ( N i - Di ) ( yi ) wi, from column (8). The result of this multiplication is shown in column (10). For SIC code 1611, for example, Ai equals 20.95%, wi equals 0.4293, and the product of the two 159 Asterisk (*) indicates multiplication. is equal to 8.99, as shown in column (10). The sum of the 20

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43 Table 12. Numeric representation of Step 1 DBE availability calculation. (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) SIC i Code Di xi Ni yi Dai wi Ai Ai * wi 1 1611 78 0.238 644 0.13338 134.93 0.4293 20.95 8.99 2 8711 655 0.193 3582 0.11932 877.83 0.1243 24.51 3.05 3 1622 8 0.238 24 0.15458 8.57 0.0862 35.71 3.08 4 1541 33 0.253 307 0.12547 59.03 0.0736 19.23 1.42 5 5051 19 0.159 208 0.26484 66.03 0.0610 31.75 1.94 6 1731 331 0.231 3305 0.17129 763.94 0.0357 23.11 0.82 7 1771 131 0.231 1068 0.16293 253.40 0.0311 23.73 0.74 8 1791 24 0.231 114 0.18101 34.75 0.0239 30.48 0.73 9 1623 29 0.238 291 0.14372 59.75 0.0189 20.53 0.39 10 3273 9 0.159 70 0.26653 23.83 0.0179 34.04 0.61 11 1794 65 0.231 980 0.16959 205.16 0.0145 20.93 0.30 12 1799 278 0.231 3523 0.17113 769.08 0.0140 21.83 0.31 13 1721 330 0.231 2748 0.16933 663.21 0.0129 24.13 0.31 14 3569 2 0.159 29 0.26462 8.83 0.0123 30.44 0.37 15 782 202 0.159 2792 0.26543 857.35 0.0090 30.71 0.28 16 8748 2432 0.159 10449 0.27544 4253.52 0.0087 40.71 0.35 17 3531 2 0.159 45 0.25857 12.80 0.0076 28.45 0.22 18 1629 35 0.238 372 0.13153 70.99 0.0071 19.08 0.13 19 1741 87 0.231 1064 0.16877 231.79 0.0069 21.78 0.15 20 6512 90 0.159 1841 0.26450 538.82 0.0052 29.27 0.15 Overall Step 1 M/W / DBE Availability: 24.34 numbers in column (10) is 24.34%--the overall Step 1 DBE estimates is multiplied by its corresponding dollar-based availability figure for the agency. industry weight, wi, to yield the Ai wi figures in column (10). The individual availability estimates, Ai, were calculated The 20 figures in column (10) are then added together to using formulas F1 and F2 above. Formula F2 says that each obtain the overall Step 1 DBE availability figure, A, of 24.34%. Ai is equal to the total number of listed DBE establishments Below, we repeat formulas F1 through F4 and provide in industry i, adjusted for misclassification and nonclassifi- numerical tables below each formula showing the exact calcu- cation (Dai), divided by the total number of establishments lations undertaken at each step for this particular example. in industry i. The result is then multiplied by 100 to yield a percentage. (F1) Total adjusted number of DBE establishments in Before we can carry out this calculation, however, we industry i: must calculate Dai. For that, we use formula F1 above, where Dai is equal to the number of "listed" DBE establishments in Dai = Di (1 - x i ) + ( N i - Di ) ( yi ) industry i, Di, multiplied by 1 minus the misclassification percentage, xi, plus the total number of remaining establish- ments in industry i, (Ni - Di), multiplied by the nonclassifi- SIC cation percentage yi. That is, Dai = Di (1 - xi) + (Ni - Di) i Code Dai = Di * (1xi) + (Ni Di) * yi (yi). In SIC code 1611, for example, Di equals 78, 1 minus xi 1 1611 134.93 = 78 * 0.762 + 566 * 0.13338 equals (1 - 0.238) = 0.762, Ni - Di equals 644 minus 78, or 2 8711 877.83 = 655 * 0.807 + 2927 * 0.11932 3 1622 8.57 = 8 * 0.762 + 16 * 0.15458 566, and yi equals 0.13338. Therefore, Dai is (78 0.762) + 4 1541 59.03 = 33 * 0.747 + 274 * 0.12547 (566 0.13338) which equals 134.93, as shown in column (7) 5 5051 66.03 = 19 * 0.841 + 189 * 0.26484 of Table 12. 6 1731 763.94 = 331 * 0.769 + 2974 * 0.17129 7 1771 253.40 = 131 * 0.769 + 937 * 0.16293 Now that we know Dai is equal to 134.93 for SIC code 1611, 8 1791 34.75 = 24 * 0.769 + 90 * 0.18101 we can calculate estimated availability for that industry, Ai, 9 1623 59.75 = 29 * 0.762 + 262 * 0.14372 10 3273 23.83 = 9 * 0.841 + 61 * 0.26653 using formula (2) above. That is, we divide 134.93 by 644 and 11 1794 205.16 = 65 * 0.769 + 915 * 0.16959 multiply the result by 100 to yield the estimated availability 12 1799 769.08 = 278 * 0.769 + 3245 * 0.17113 figure for SIC code 1611 of 20.95%, as shown in column (9) 13 1721 663.21 = 330 * 0.769 + 2418 * 0.16933 14 3569 8.83 = 2 * 0.841 + 27 * 0.26462 above. 15 782 857.35 = 202 * 0.841 + 2590 * 0.26543 This process is repeated for each of the 20 SIC codes in the 16 8748 4253.52 = 2432 * 0.841 + 8017 * 0.27544 17 3531 12.80 = 2 * 0.841 + 43 * 0.25857 agency's product market, which yields the 20 individual 18 1629 70.99 = 35 * 0.762 + 337 * 0.13153 industry availability estimates, Ai, that appear in column (9) 19 1741 231.79 = 87 * 0.769 + 977 * 0.16877 of Table 12. Finally, as explained above, each one of these Ai 20 6512 538.82 = 90 * 0.841 + 1751 * 0.26450

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44 (F2) Availability percentage in industry i: Relationship Between Choice of Availability Measure and Resulting Annual DBE Goals ( Ai = Dai Ni ) 100 The difference between the custom census method and other methods is evident when the annual DBE goals set by SIC state DOTs during FFYs 2006, 2007, and 2008 are examined. i Code Ai = (Dai Ni) * 100 Table 13 shows the overall DBE goals set by each state DOT 1 1611 20.95 = 134.93 644 * 100 during this period, as well as their race-conscious portions.160 2 8711 24.51 = 877.83 3582 * 100 Using the goal information in Table 13, we calculated some 3 1622 35.71 = 8.57 24 * 100 4 1541 19.23 = 59.03 307 * 100 summary statistics to compare DBE goals derived from dis- 5 5051 31.75 = 66.03 208 * 100 parity or availability studies to those derived using bidders 6 1731 23.11 = 763.94 3305 * 100 lists, prequalified contractor list, or other types of contractor 7 1771 23.73 = 253.40 1068 * 100 8 1791 30.48 = 34.75 114 * 100 lists or from DBE directories and Census Bureau data. The 9 1623 20.53 = 59.75 291 * 100 results of this analysis are shown in Table 14. 10 3273 34.04 = 23.83 70 * 100 Consistent with the concept of "casting a broader net," it is 11 1794 20.93 = 205.16 980 * 100 12 1799 21.83 = 769.08 3523 * 100 clear from the top six rows of Table 14 that disparity or avail- 13 1721 24.13 = 663.21 2748 * 100 ability studies tend to yield larger estimates of DBE availabil- 14 3569 30.44 = 8.83 29 * 100 15 782 30.71 = 857.35 2792 * 100 ity than the other methods. Compared to the bidders list or 16 8748 40.71 = 4253.52 10449 * 100 prequalified contractors list approach, the disparity or avail- 17 3531 28.45 = 12.80 45 * 100 ability study approach yielded median overall DBE goals that 18 1629 19.08 = 70.99 372 * 100 19 1741 21.78 = 231.79 1064 * 100 were 3.8 percentage points higher on average, and mean over- 20 6512 29.27 = 538.82 1841 * 100 all DBE goals that were 5.6 percentage points higher on aver- age. Compared to the method of DBE directories and Census (F3) Overall percentage availability for state DOT: Bureau data, the figures were 1.6 and 3.2 percentage points higher, respectively. 20 A = ( Ai w i ) The difference is even more pronounced when the race- i =1 conscious portion of the goal is examined. The disparity or availability study approach yielded median race-conscious SIC DBE goals that were 4.5 percentage points higher and mean i Code Ai * wi = Ai * wi goals that were 5.8 percentage points higher, on average, 1 1611 8.99 = 20.95 * 0.4293 compared to using bidders lists, prequalified contractor lists, 2 8711 3.05 = 24.51 * 0.1243 or other contractor lists. Compared to using DBE directories 3 1622 3.08 = 35.71 * 0.0862 4 1541 1.42 = 19.23 * 0.0736 and Census Bureau data, the race-conscious figures were 6.3 5 5051 1.94 = 31.75 * 0.0610 and 6.3 percentage points higher, respectively.161 6 1731 0.82 = 23.11 * 0.0357 It is possible that these differences do not arise because the 7 1771 0.74 = 23.73 * 0.0311 8 1791 0.73 = 30.48 * 0.0239 availability estimation methods of disparity studies generally 9 1623 0.39 = 20.53 * 0.0189 cast a broader net, but are instead due to larger M/WBE shares 10 3273 0.61 = 34.04 * 0.0179 in those states that commissioned (and used) disparity or 11 1794 0.30 = 20.93 * 0.0145 12 1799 0.31 = 21.83 * 0.0140 availability studies for their goal setting. To check for this 13 1721 0.31 = 24.13 * 0.0129 possibility, we standardized the comparisons by dividing all 14 3569 0.37 = 30.44 * 0.0123 15 782 0.28 = 30.71 * 0.0090 the goals by the percentage of the construction business pop- 16 8748 0.35 = 40.71 * 0.0087 ulation that was minority owned or women owned accord- 17 3531 0.22 = 28.45 * 0.0076 ing to the 2002 SBO.162 The results appear in the last six rows 18 1629 0.13 = 19.08 * 0.0071 19 1741 0.15 = 21.78 * 0.0069 20 6512 0.15 = 29.27 * 0.0052 160 The race-neutral portion, of course, is obtained by subtracting the race- (F4) The full estimated availability formula is therefore, once conscious portion from the overall goal. 161 again: Similar differences are observed in both overall and race-conscious goals if state DOTs from the Ninth Circuit are excluded from the comparison. ( Di (1 - xi ) + ( N i - Di ) ( yi )) 162 These percentages of minority-owned and women-owned businesses in each A = 20 i =1 ( N i ) 100 w i state were derived from U.S. Census Bureau (2006). The 2002 data are the most recent available.

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45 Table 13. FHWA annual DBE goals submitted by state DOTs, FFYs 20062008. DBE DBE DBE DBE Race- DBE Race- DBE Race- Overall Overall Overall State Method Conscious Conscious Conscious Goal Goal Goal Goal 2006 Goal 2007 Goal 2008 2006 2007 2008 CA 3 10.50% 0.00% 10.50% 0.00% 13.50% 6.75% CO 3 12.19% 10.89% 13.76% 10.45% 12.80% 10.50% GA 3 12.00% 6.00% 12.00% 6.00% 12.00% 6.00% IL 3 22.77% 20.74% 22.77% 20.74% 22.77% 18.85% MD 3 16.10% 8.90% 24.34% 19.79% 25.30% 21.90% MN 3 9.57% 8.75% 6.27% 4.27% 15.30% 13.60% MO 3 13.50% 9.45% 13.37% 9.36% 13.34% 10.00% NC 3 10.40% 7.66% 9.90% 7.40% 10.10% 7.60% NV 3 3.30% 0.00% 3.00% 0.00% 5.70% 0.00% WA 3 12.70% 9.34% 18.77% 14.70% 18.77% 14.70% HI 1 11.00% 0.00% 9.00% 0.00% 7.50% 0.00% NJ 1 15.10% 14.10% 15.10% 14.50% 15.60% 0.38% NY 1 12.00% 9.20% 12.00% 9.50% 12.00% 5.00% AK 2 or 5 7.50% 3.50% 4.00% 0.00% 5.00% 0.00% AL 2 or 5 9.14% 4.32% 9.54% 5.34% * * AR 2 or 5 8.40% 6.40% 8.00% 6.10% 7.80% 5.00% AZ 2 or 5 10.50% 0.00% 9.67% 0.00% 9.91% 0.00% CT 2 or 5 15.50% 9.50% 13.40% 8.40% 13.60% 8.40% DC 2 or 5 32.84% 7.16% 32.70% 8.50% 26.89% 19.37% DE 2 or 5 11.02% 9.40% 10.01% 8.52% 11.73% 10.66% FL 2 or 5 7.87% 0.00% 8.12% 0.00% 8.07% 0.00% IA 2 or 5 4.80% 4.32% 4.50% 4.10% 4.60% 4.10% ID 2 or 5 11.00% 10.39% 11.00% 7.98% 11.00% 7.98% IN 2 or 5 9.85% 4.97% 8.80% 3.73% 9.90% 4.18% KS 2 or 5 9.19% 7.49% 10.02% 7.07% 10.19% 6.85% KY 2 or 5 7.00% 5.00% 7.00% 5.00% 7.00% 5.00% LA 2 or 5 10.00% 9.00% 10.00% 9.00% 10.50% 9.50% (continued on next page)

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46 Table 13. (Continued). DBE DBE DBE DBE Race- DBE Race- DBE Race- Overall Overall Overall State Method Conscious Conscious Conscious Goal Goal Goal Goal 2006 Goal 2007 Goal 2008 2006 2007 2008 MA 2 or 5 13.80% 8.00% 13.80% 9.00% 13.80% 10.00% ME 2 or 5 6.60% 1.60% 6.00% 1.00% 4.50% 1.00% MI 2 or 5 11.00% 8.50% 11.00% 8.50% 11.00% 8.50% MS 2 or 5 10.00% 4.00% 10.00% 5.00% 10.00% 4.00% MT 2 or 5 0.00% 0.00% 10.50% 0.00% 9.89% 0.00% ND 2 or 5 8.12% 3.25% 7.68% 3.88% 7.38% 3.76% NE 2 or 5 7.08% 4.32% 6.91% 4.44% 6.48% 4.53% NH 2 or 5 5.00% 1.00% 4.00% 1.00% 5.00% 1.00% NM 2 or 5 9.69% 0.00% 8.79% 0.00% 9.32% 0.00% OH 2 or 5 6.70% 5.60% 6.70% 5.90% 7.10% 5.80% OK 2 or 5 8.50% 6.50% 8.10% 5.10% 8.10% 4.52% OR 2 or 5 10.26% 5.89% 11.32% 0.00% 11.58% 0.00% PA 2 or 5 9.52% 6.88% 9.49% 5.37% 7.70% 4.52% RI 2 or 5 10.00% 7.00% 11.00% 6.20% 10.00% 7.04% SC 2 or 5 10.50% 7.50% 10.50% 7.50% 10.50% 7.50% SD 2 or 5 8.30% 3.59% 8.33% 3.64% 8.30% 3.94% TN 2 or 5 8.48% 6.96% 9.87% 6.63% 8.38% 5.04% TX 2 or 5 12.54% 6.00% 12.12% 6.00% 12.12% 6.00% UT 2 or 5 8.30% 4.40% 8.20% 4.00% 8.00% 3.80% VI 2 or 5 10.02% 10.02% 10.20% 10.20% 9.80% 9.50% VT 2 or 5 6.90% 0.00% 5.00% 0.00% 5.20% 0.00% WI 2 or 5 14.44% 11.20% 10.65% 7.15% 11.00% 8.80% WV 2 or 5 5.20% 4.00% 6.75% 5.46% 7.89% 5.15% WY 2 or 5 5.00% 0.00% 6.00% 0.00% 4.05% 0.00% * We were unable to obtain a copy of ALDOT's FFY 2008 DBE goals. Notes: (1) Method 1 corresponds to 49 C. F. R. 26.45(c)(1) (DBE directories and Census Bureau data), method 2 or 5 corresponds to 49 C. F. R. 26.45(c)(2) or (c)(5) (bidders lists or other types of contractor lists), method 3 corresponds to 49 C. F. R. 26.45(c)(3) (disparity or availability study); (2) the race-neutral portion of the goal for each FFY is not presented due to space limitations. To calculate it, simply subtract the race-conscious goal from the corresponding overall goal; (3) goals were not necessarily approved as submitted to U.S.DOT; (4) Caltrans' goals were set using method 1 in FFYs 2006 and 2007 and method 3 in FFY 2008; (5) Mn/DOT's goals were set using method 3 in FFYs 2006 and 2008 and method 2 in FFY 2007; (6) NDOT's goals were set using method 2 in FFYs 2006 and 2007 and method 3 in FFY 2008.

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47 Table 14. Summary statistics for DBE goals set by different methods. Goal- Summary Setting Overall FFY DBE Goal Race-Conscious FFY DBE Goal Statistic Method 2006 2007 2008 2006 2007 2008 1 Median 11.5% 11.3% 12.0% 4.6% 4.8% 0.4% 2 or 5 Median 9.2% 9.5% 9.6% 5.3% 5.1% 4.8% 3 Median 12.4% 13.8% 13.4% 9.1% 10.5% 10.3% 1 Mean 12.2% 11.7% 11.7% 5.8% 6.0% 1.8% 2 or 5 Mean 9.6% 9.4% 9.4% 5.2% 4.7% 5.2% 3 Mean 13.7% 16.4% 15.0% 10.2% 12.6% 11.0% 1 Minimum 10.5% 9.0% 7.5% 0.0% 0.0% 0.0% 2 or 5 Minimum 3.3% 3.0% 4.1% 0.0% 0.0% 0.0% 3 Minimum 9.6% 9.9% 5.7% 6.0% 6.0% 0.0% 1 Maximum 15.1% 15.1% 15.6% 14.1% 14.5% 5.0% 2 or 5 Maximum 32.8% 32.7% 26.9% 11.2% 10.2% 19.4% 3 Maximum 22.8% 24.3% 25.3% 20.7% 20.7% 21.9% 1 No. Obs. 4 4 3 4 4 4 2 or 5 No. Obs. 38 39 36 38 39 36 3 No. Obs. 8 7 10 8 7 10 1 Std. Median 0.420 0.420 0.485 0.186 0.192 0.021 2 or 5 Std. Median 0.726 0.752 0.758 0.482 0.460 0.436 3 Std. Median 0.874 0.825 0.805 0.704 0.671 0.680 1 Std. Mean 0.465 0.457 0.492 0.289 0.298 0.074 2 or 5 Std. Mean 0.791 0.781 0.802 0.461 0.415 0.442 3 Std. Mean 0.881 0.978 0.922 0.680 0.751 0.698 Note: Method 1 corresponds to 49 C. F. R. 26.45(c)(1) (used DBE directories and Census Bureau data); method 2 or 5 corresponds to 49 C. F. R. 26.45(c)(2) or (c)(5) (used bidders lists or other types of contractor lists); method 3 corresponds to 49 C. F. R. 26.45(c)(3) (used disparity or availability study).