Technical Report:
Methodology for Computing Proposed FTA DBE Goals
Minnesota Department of Transportation (MnDOT)
FY2016-2018
Submitted to:
Minnesota Department of Transportation
395 John Ireland Blvd, St. Paul, MN 55155-1899
Submitted by:
The Roy Wilkins Center for Human Relations and Social Justice
Hubert H. Humphrey School of Public Affairs
University of Minnesota
July 31, 2015
Copyright © 2015 by the Roy Wilkins Center for Human Relations and Social Justice. All rights reserved.
ACKNOWLEDGEMENTS
Dr. Samuel L. Myers, Jr., Roy Wilkins Professor of Human Relations and Social Justice and
director of the Roy Wilkins Center, Hubert H. Humphrey School of Public Affairs,
University of Minnesota, served as the principal investigator for the project, developing the
methodology for the analysis and providing overall leadership to the project. Blanca Monter,
Associate Director at the Roy Wilkins Center, oversaw the project, and acted as a liaison
with the Office of Civil Rights. Dr. Inhyuck Ha, Professor of Economics at Western Carolina
University, performed the statistical analysis including the utilization and discrimination
analysis. Dr. Ha was assisted by several graduate students at the University of Minnesota.
We are grateful to Kim Collins, Director of Civil Rights, Minnesota Department of
Transportation and her entire staff, especially Mary Schmidt, Delores Aguirre and Thomas
Mebrahtu who compiled and provided the data used to prepare this report.
Methodology for Computing MnDOT FTA DBE Goals FY2016-2018
2
TABLE OF CONTENTS
ACKNOWLEDGEMENTS .......................................................................................................................... 2
EXECUTIVE SUMMARY ........................................................................................................................... 4
SECTION 1: INTRODUCTION ............................................................................................................. 11
SECTION 2: DATA .................................................................................................................................. 12
SECTION 3: METHODOLOGY TO ESTABLISH OVERALL GOAL ............................................... 12
3.1 DEFINING THE GEOGRAPHICAL MARKETPLACE ............................................................................................................. 12
3.2 UTILIZATION ANALYSIS ...................................................................................................................................................... 13
3.3 DISTRIBUTION OF ESTIMATED EXPENDITURES BY INDUSTRY CLASSIFICATION FOR FY 2016-2018................. 14
3.4 CALCULATING THE WEIGHTED AVAILABILITY MEASURE ............................................................................................. 15
3.4.1 Certified DBE List Method .................................................................................................................................. 15
3.4.2 Dun & Bradstreet Method ................................................................................................................................... 16
3.4.5 The Base Goal .......................................................................................................................................................... 18
3.5 ADJUSTMENT TO THE BASE GOAL ..................................................................................................................................... 19
3.5.1 Data Used for Adjustment to the Base Goal ................................................................................................. 19
3.5.2 Dummy Variable Method .................................................................................................................................... 20
3.5.3 Residual Difference Method ............................................................................................................................... 20
3.5.4 Adjustment ............................................................................................................................................................... 20
3.6 RACE NEUTRAL ANALYSIS ................................................................................................................................................. 21
SECTION 4: THE MNDOT PROPOSED DBE GOALS ...................................................................... 22
SECTION 5: APPENDICES .................................................................................................................... 23
APPENDIX A AVAILABILITY ANALYSIS ........................................................................................................... 23
Table A-1. Weights used for the Availability Analysis ...................................................................................................... 23
PMJ-1......................................................................................................................................................................................................... 25
PMJ-2......................................................................................................................................................................................................... 27
VJM-1 ........................................................................................................................................................................................................ 29
Table A-3. Availability Analysis - D&Bt Method, All NAICS ............................................................................................ 31
PMJ-1......................................................................................................................................................................................................... 31
PMJ-2......................................................................................................................................................................................................... 33
VJM-1 ........................................................................................................................................................................................................ 35
APPENDIX B DISCRIMINATION ANALYSIS...................................................................................................... 37
Table B-1. Description of Variables in the Regression Analysis................................................................................... 37
Table B-2. Summary Statistics for Subcontracts.................................................................................................................. 38
Table B-3. Discrimination Analysis Dummy Interaction Variable Method for Prime Contracts: By Award
Amount .................................................................................................................................................................................................... 39
Table B-4. Residual Difference Analysis using Oaxaca Decomposition .................................................................... 40
Methodology for Computing MnDOT FTA DBE Goals FY2016-2018
3
EXECUTIVE SUMMARY
This report provides proposed DBE goal and race neutral participation for the Minnesota
Department of Transportation (MnDOT) for fiscal years 2016-2018 on Federal
Transportation Administration funded expenditures. To satisfy the requirements set forth
in the USDOT regulations, availability rates of willing, able and qualified firms must be
computed for well-defined geographic market areas. This report was constructed based
on the best available information received from MnDOT as well as the governmentpublished secondary data.
This report corrects and updates the base availability rate and the adjustment found in the
Executive Summary Report of June 18, 2015.
The analysis undertaken suggests a proposed agency-wide DBE goal of 7.13 percent for
2016-2018 on FTA-funded projects.
This goal was derived in the following manner:



A base goal of 5.54 percent was computed.
An adjustment to the base goal was made to account for disparities in prime and
subcontract awards that cannot be attributed to differences in industry, location,
firm size, credit risk, or other characteristics of DBE versus non-DBE contracts.
The adjustment was to increase the base goal by 28.79 percent, resulting in the
adjusted goal of 7.13 percent (= 5.54 × 1.2879).
The maximum portion of the adjusted goal deemed to be achievable by raceneutral means was found to be equal to 36.07 percent. Therefore, the race neutral
goal was computed to be equal to 2.57 percent (= 7.13 × 0.3607) and the raceconscious goal was computed to be equal to 4.56 percent (= 7.13 × (1 – 0.3607) =
7.13 × 0.6393).
Table 1 provides the detailed breakdowns:
Table 1. Proposed MnDOT FTA DBE Goal FY2016-2018
Goals as
June 18
Base Goal
5.71%
Discrimination Gap for Adjustment
15.02%
6.57%
Adjusted Goal
Race-Neutral Portion
29.65%
1.95%
Race-Neutral Goal
Race-Conscious Portion
70.35%
4.62%
Race-Conscious Goal
Goals as
July 21
5.54%
28.79%
7.13%
36.07%
2.57%
63.93%
4.56%
Source: Author's calculation using the FTA data files.
Methodology for Computing MnDOT FTA DBE Goals FY2016-2018
4
BACKGROUND
As a recipient of federal transportation dollars awarded through the U.S. Department of
Transportation’s Federal Transportation Administration (FTA), MnDOT is required to
establish and submit a three-year goal to the FTA for review. This goal is to be
established in compliance with the federal regulations governing the Participation by
Disadvantaged Business Enterprises in Department of Transportation Financial
Assistance Programs (hereafter referred to as “USDOT regulations”). The USDOT
regulations instruct state and local grant recipients to follow a two-step process to
establish their annual DBE goal [49 C.F.R. §26.45]. The analysis conducted by the Roy
Wilkins Center complies with these guidelines.
METHODOLOGY
In order for the MnDOT FTA DBE goal to satisfy the requirements set forth in the
USDOT regulations, availability rates of willing, able and qualified firms must be
computed for well-defined geographic market areas. We established three different
geographic market areas. Two were based on political jurisdictions and the other one was
based on zip codes where there were substantial numbers of prime and subcontractors for
MnDOT contracts between fiscal year 2012 and 2014. Relevant industries with MnDOT
contracts were identified by examining the distribution of MnDOT contract dollars by
industry classification for contracts awarded between October 1, 2011 and September 30,
2014. We then estimated the distribution of anticipated MnDOT contract dollars by
industry classification for FY 2016-2018 from the information about future projects that
was provided by MnDOT.
Availability rates were computed from multiple data sets and were appropriately
weighted to produce a base goal. The base goal was then adjusted to account for
disparities in prime contract and subcontract award amounts. The result is the proposed
goal. The proposed goal was further partitioned between a race conscious and race
neutral portion using a methodology upheld by the 3rd Circuit Federal Court in GEOD v.
New Jersey Transit and published in the peer reviewed journal, Applied Economics
Letters.
DATA
The research team obtained all prime contract and subcontract files from the Office of
Civil Rights, Minnesota Department of Transportation for the years 2012 to 2014.
Obvious data entry errors, improbable measures, possible duplicates and related
questionable items were flagged and forwarded to MnDOT staff for clarification and/or
correction.
Contract files were supplemented with data obtained from Dun & Bradstreet. Other data
used included: the MnDOT DBE Certification Directory and the U.S. Census ZIP code
Business Patterns (ZBP) data for 2012.
Methodology for Computing MnDOT FTA DBE Goals FY2016-2018
5
UTILIZATION
The utilization analysis shows that 100 percent of prime contract dollars were awarded to
non-DBE prime contractors while 1.8 percent of contract dollars were awarded to DBE
subcontractors. The vast majority of prime contract dollars were awarded to Minnesota
firms (95.9%). The bulk of contracts and contract dollars awarded were in the
professional and technical service industry.
Table 2. DBE Share of FTA Contract Amount
N
Contract Amount
Percent
Total
50
$8,613,712.90
DBE
0
$0.00
0.00%
50
$8,613,712.90
100.00%
Total
3
$154,223.34
Sub Contracts DBE
3
$154,223.34
100.00%
0
$0.00
0.00%
3
$154,223.34
1.79%
Prime
Contracts
Non-DBE
Non-DBE
Both Prime and Sub Contracts
DBE
Source: MnDOT FTA contracts: Oct. 1, 2011 - Sep. 30, 2014
GEOGRAPHIC MARKET AREAS
In order for the MnDOT FTA DBE goal to satisfy the requirements set forth in the
USDOT regulations as well as comply with the Supreme Court’s narrowly-tailored
standard, the DBE goal must be based on a narrowly-defined geographic market. In order
to define the geographic market in such a manner that the vast majority of contract
dollars awarded would be incorporated in the definition, the research team considered
two broad methods: a) political jurisdiction method (PJM), based on the counties where
contracts were awarded; and b) virtual jurisdiction method (VJM), based on the zip code
location of contracts and/or contractors in the client’s database. All methods yield
different counts or estimates of the numbers of firms within industry codes, and
accordingly will yield alternative measures of availability. For this report, the following
alternative markets were used.
Methodology for Computing MnDOT FTA DBE Goals FY2016-2018
6
Table 3. MnDOT Geographic Market Areas (GMAs) for FTA DBE Goals
GMA
Definition
Total Amount
Share of
Dollars
Political Jurisdiction Method (PJM)
PJM-1
All Minnesota counties
$8,405,636.07
95.9%
PJM-2
Ranked Counties in USA where the total
contract dollars for the sum of the counties
exceeds 75% and the marginal contribution
to the overall total is at least 1%
$8,596,667.24
98.0%
Ranked zip codes anywhere in the USA
where the total contract dollars awarded
for the sum of the zip codes exceeds 75%
and the marginal contribution to the overall
total is at least 0.25%
$8,690,942.24
99.1%
Total Contract Amount (Primes and Subs)
$8,767,936.24
Virtual Jurisdiction Method (VJM)
VJM-1
Source: FTA contracts between Oct. 1, 2011 and Sept. 30, 2014
The first method, PJM-1 represents the State of Minnesota. The second method, PJM-2,
defines those counties where there are enough contracts to represent the Minnesota
counties where the total contract amount exceeds 75 percent and the marginal
contribution of each county to the overall total contract amount is at least 1 percent. The
other measure, VJM-1, is similar to PJM-2, but focuses on zip codes.
The distribution of dollars per GMA is presented in Table 3 as well. Each geographic
market area has more than 95 percent of contract dollars.
DISCUSSION OF AVAILABILITY METHODS
The research team obtained from MnDOT a list of all firms that were in their various
databases, which include prime contractors and subcontractors, certified DBEs. NAICS
codes for the firms were obtained from the BizTrak database, and Dun & Bradstreet
(D&B). When no NAICS could be found from D&B, observations were not used in the
weighted availability counts.
The research team also obtained from MnDOT the list of projects that MnDOT expects to
undertake and identified 54 separate six-digit NAICS codes associated with comparable
Methodology for Computing MnDOT FTA DBE Goals FY2016-2018
7
projects from the period of 2011-2014. Weights were obtained by using the projected
expenditures provided by MnDOT.1
Dun and Bradstreet Method
The research team obtained access to Hoover’s database, a subsidiary of Dun &
Bradstreet, to compute the number of firms in each relevant NAICS code within a
specified geographic market area. This research product covers some 900 industries,
which include about 65 million corporations and other entities (i.e. government agencies,
partnerships, non-profits, and educational institutions) and 85 million individuals. For the
state of Minnesota, information included information on headquarters, branches, and
single locations.2
The availability rate is computed by finding the weighted share of women- and minorityowned businesses within each NAICS code for a specified geographic market area.
Unlike the other methods, the D&B method uses “self-reported” minority/gender
designations. Thus, the D&B method can include firms that are not MnDOT certified
DBEs. On the other hand, not every certified DBE is included in this database because a
requirement of inclusion is the existence of a DUNS number. According to Hoover’s
customer service, D&B contacts firms directly to verify their gender or minority status
and checks with third party sources and proprietary databases for further verification.
DBE List Method
We obtained the list of certified DBEs from MnDOT. The numerator in the availability
ratio is the number of certified DBE firms for specified NAICS codes within a given
geographic market area. The denominator is the number of firms in the County Business
Patterns (CBP) or ZIP code Business Patterns (ZBP) database, depending on the
definition of the geographic market area, for specified NAICS codes within a geographic
market area. The numerator and denominator come from different sources. The
numerator counts firms and the denominator counts establishments with paid employees.
The Census Bureau explains:
An establishment is a single physical location at which business is conducted or services or industrial
operations are performed. An establishment is not necessarily equivalent to a company or enterprise,
which may consist of one or more establishments. A single-unit company owns or operates only one
establishment. A multi-unit company owns or operates two or more establishments.
http://www.census.gov/econ/cbp/methodology.htm, accessed, June 1, 2015 3 (Census Bureau, County
Business Patters, “How the Data Are Collected (Coverage and Methodology)”).
According to Department of Transportation regulations, the availability rate should be weighted by the “amount of
money to be spent” in each industry. The research team requested a copy of MnDOT’s estimated expenditures for the
next three years, broken down by NAICS code. Projected expenditures for the next three years included in the State
Transportation Improvement Program (STIP) 2016-2018 data files were provided and categorized by type of work. In
order to calculate the weights for the availability analysis, the research team categorized projected expenditures by
NAICS code. The result was 42 NAICS codes.
2 Headquarter: indicates that the company has subsidiaries or branches; branch indicates a company location other than
the headquarters; and single location indicates that the company does not have any subsidiaries or branches.
3 As a result, the denominator includes many establishments that might not normally contract with MnDOT.
1
Methodology for Computing MnDOT FTA DBE Goals FY2016-2018
8
THE BASE GOAL
Depending on the method used to calculate availability rates, each geographic market
area captures a different share of available contract dollars. As a result, each method also
yields a different DBE availability goal for each geographic market.
In order to derive a single base goal for MnDOT that is based on all the goals calculated
for each geographical market, it is necessary to weight each geographical market-specific
goal according to the percentage of contract dollars awarded in that area.
The two different methods use data that report multiple industries for many of the firms
in their databases. Table 4 presents the details of the weighted availability rate using all
NAICS codes level. This base goal is used in subsequent analyses.
Table 4. FTA Availability Rates and Base Goal
Method
DBE List Method
PJM-1
0.83%
PJM-2
1.69%
VJM-1
Base Goal
0.02%
5.54%
D&B Method
9.16% 12.21%
9.31%
Source: Author's calculation using FTA data files, DBE, D&B, and US Census
data
ADJUSTMENTS TO THE BASE GOAL
According to the USDOT regulations, recipients of federal funds are required to adjust
their base goal in light of other evidence regarding the market area [49 C.F.R. §26.45(d)].
One valid reason for adjusting the goal would be if the analysis showed discrimination,
either in the overall market place or in the specific agency or governmental unit
undertaking the procurement and contracting process.
A standard method for estimating discrimination is to compute the difference in contract
award amounts attributable to DBE status. This requires enough observations on DBE
awards to obtain regression estimates. Because of the unique nature of the FTA contracts,
it was not possible to estimate such standard regression models or separate regressions
for DBEs and non-DBEs using FTA data alone. There were no DBE primes in the
dataset. All of the non-DBEs were primes. There were no subcontracts that were nonDBEs. Moreover, many of the observations were individuals on whom we had no
information on industry classification or other relevant factors.
To remedy this problem, we identified the NAICs codes for FTA prime contracts and
subcontracts. We merged the FTA contract files with the FHWA subcontract awards only
for this restricted set of NAICs codes.
Methodology for Computing MnDOT FTA DBE Goals FY2016-2018
9
We identified five plausible measures for the adjustment and averaged across them using
three different statistical methods. The first computes the unadjusted difference in award
amounts between non-DBEs and DBEs arising from the FTA contracts. The second
estimates a log-linear regression model and controls for FTA, DBE and an interaction
term of FTA and DBE contracts. This interaction term permits us to estimate the
difference in contract award amounts due to DBE and FTA awards. The final method
decomposes the gap into explained and unexplained portions and computes the share of
the unexplained portion that is due to FTA awards. The average across these methods
yields an adjustment of 28.79 percent.
RACE NEUTRAL ANALYSIS
In compliance with federal regulations, state and local transit authorities must identify the
maximum feasible portion of the DBE goal that can be achieved through race-neutral
measures and the percentage of the goal that can only be achieved through race-conscious
measures [49 C.F.R. §26.51(a)]. Myers and Ha have pioneered the use of a detailed
econometric procedure that maximizes the race-neutral component of the DBE goals.4
This method has established a rigorous standard for maximizing the race neutral portion
of the overall DBE goal.5
The logic of the analysis is that some share of DBE dollars awarded would have gone to
DBEs without goals. One can compute the share of dollars that would have gone to DBEs
without goals for contracts and firms that are comparable. This method requires the
estimation of a regression model that controls for a host of relevant variables.
The race neutral analysis uses the best regression model for predicting DBE contract
amounts with and without goals. The data set is the merged data set combining FHWA
and FTA contracts with the same NAIC codes found in the FTA dataset. Following this
specification, our analysis shows that 36.07 percent of the goal can be achieved through
race neutral measures and 63.93 percent can be achieved through race conscious goals.
Myers, Samuel L. and Inhyuck “Steve” Ha. "Estimation of Race Neutral Goals in Public Procurement and
Contracting," Applied Economics Letters, 2009, vol. 16, issue 3, pages 251-256.
5 2010-10-19, Civil Action No. 04-2425, GEOD CORPORATION, et al., Plaintiffs v. NEW JERSEY TRANSIT
CORPORATION, et al., Defendants.
4
Methodology for Computing MnDOT FTA DBE Goals FY2016-2018
10
SECTION 1: INTRODUCTION
Minnesota Department of Transportation (MnDOT) is a recipient of federal
transportation dollars awarded through the U.S. Department of Transportation’s Federal
Transit Administration (FTA). As such, MnDOT is required to establish a three-year goal
and submit it to the FTA for review (Final Rule, 2010; FTA Memorandum HCR-1,
March 16, 2010). This goal must be in compliance with the federal regulations governing
the Participation by Disadvantaged Business Enterprises in Department of
Transportation Financial Assistance Programs (hereafter referred to as “USDOT
regulations”). The USDOT regulations instruct state and local grant recipients to follow a
two-step process to establish their annual DBE goal [49 C.F.R. §26.45].
Step 1:
Determine the base figure for the relative availability of DBEs in
the specific industries and geographical market from which DBE
and non-DBE contractors are drawn.
Step 2:
Adjust the base figure in light of other evidence regarding the
market area.
This two-step process is designed to satisfy several important objectives [49 C.F.R.
§26.1]. According to USDOT regulations, DBE programs are designed to:
(a)
Ensure non-discrimination in the award and administration of
DOT-assisted contracts in the Department’s highway, transit, and
airport financial assistance programs.
(b)
Create a level playing field on which DBEs can compete fairly for
DOT-assisted contracts.
(c)
Ensure that the Department’s DBE program is narrowly tailored in
accordance with applicable law.
(d)
Ensure that only firms that fully meet this part’s eligibility standards
are permitted to participate as DBEs.
(e)
Help remove barriers to the participation of DBEs in DOT-assisted
contracts.
(f)
Assist the development of firms that can compete successfully in
the marketplace outside the DBE program.
(g)
Provide appropriate flexibility to recipients of federal financial
assistance in establishing and providing opportunities for DBEs
(emphasis added).
The MnDOT DBE program and goal-setting process is designed to meet these objectives.
On behalf of MnDOT, the University of Minnesota research team completed the required
Methodology for Computing MnDOT FTA DBE Goals FY2016-2018
11
two-step process outlined in the USDOT regulations. This report details the methodology
used to provide MnDOT with the information it needs to make its decision about the
setting of the DBE goals for 2016-2018.
SECTION 2: DATA
Staff at the Office of Civil Rights, Minnesota Department of Transportation, provided the
research team with electronic copies of all known prime contract and subcontract files for
the years 2011 to 2014. Obvious data entry errors, improbable measures, possible
duplicates and related questionable items were flagged and forwarded to MnDOT staff
for clarification or correction.
Contract files were supplemented with data obtained from Dun & Bradstreet. Other data
used included: MnDOT DBE Certification Directory, MnDOT Vendors List, and the U.S.
Census Zip code Business Patterns (ZBP) data set for 2012.
SECTION 3: METHODOLOGY TO ESTABLISH OVERALL GOAL
The USDOT suggests rather than prescribes how the base figure of relative availability of
DBEs is to be calculated [49 C.F.R. § 26.45(c)]. The base figure for the MnDOT goal
was calculated using several variations of the availability methodology proposed in the
USDOT regulations. The methodologies performed and results produced are summarized
here.
3.1 Defining the Geographical Marketplace
In order for the MnDOT FTA DBE goal to satisfy the requirements set forth in the
USDOT regulations as well as comply with the Supreme Court’s narrowly-tailored
standard, the DBE goal must be based on a narrowly-defined geographic market.
The Wilkins Center analyzed contracts awarded between October 1, 2011 and September
30, 2014. Our analysis of contracts confirmed that the majority of contracts (92.6 percent
for prime contracts) and contract dollars (97.6 percent for prime contracts) were awarded
to Minnesota-based firms.6 A small portion of contract dollars was awarded to firms
located in Canada (1.7%) and Texas (0.7%).
Table 5. Prime Contracts by State
Contracts
Percent
Contract Amount
Canada
1
2.0%
$142,427.83
Minnesota
46
92.0%
$8,405,636.07
North Dakota
2
4.0%
$4,999.00
Texas
1
2.0%
$60,650.00
Total
50
100.0%
$8,613,712.90
Source: MnDOT FTA contracts: Oct. 1, 2011 - Sep. 30, 2014
6
Percent
1.7%
97.6%
0.1%
0.7%
100.0%
Subcontracts are not reported at the state level due to the small number of contracts.
Methodology for Computing MnDOT FTA DBE Goals FY2016-2018
12
In order to define the geographic market in such a manner that the vast majority of
contract dollars awarded would be incorporated in the definition, the research team
considered two broad methods: a) political jurisdictional method (PJM), based on the
counties where contracts were awarded; and b) virtual jurisdictional method (VJM),
based on the zip code location of contracts and/or contractors in the client’s database. All
methods yield different counts or estimates of the numbers of firms within industry
codes, and accordingly will yield alternative measures of availability. For this report, the
following alternative markets were used.
As shown in Table 6 below, the first method, PJM-1, represents the State of Minnesota.
The second method, PJM-2, defines those counties where there are enough contracts to
represent the Minnesota counties where the total contract amount exceeds 75 percent and
the marginal contribution of each county to the overall total contract amount is at least 1
percent. The other measure, VJM-1, is similar to PJM-2, but focuses on zip codes.
Table 6. Geographic Market Areas (GMAs)
GMA
Definition
Political Jurisdiction Method (PJM)
PJM-1
All Minnesota counties
PJM-2
Ranked Counties in USA where the total contract dollars for the
sum of the counties exceeds 75% and the marginal contribution
to the overall total is at least 1%
Virtual Jurisdiction Method (VJM)
VJM-1
Ranked zip codes anywhere in the USA where the total contract
dollars awarded for the sum of the zip codes exceeds 75% and
the marginal contribution to the overall total is at least 0.25%
Source: FTA contracts between Oct. 1, 2011 and Sept. 30, 2014
3.2 Utilization Analysis
As shown in Table 2 above, the utilization analysis shows that 100 percent of prime
contract dollars were awarded to non- DBE prime contractors while 1.8 percent of
contract dollars were awarded to DBE subcontractors. The vast majority of prime
contract dollars were awarded to Minnesota firms (97.6%). The bulk of contracts and
contract dollars awarded were in the professional and technical service industry. Table 7
shows that 60 percent of prime contracts were awarded to private individuals.
Methodology for Computing MnDOT FTA DBE Goals FY2016-2018
13
Table 7. Prime Contracts by Type of Entity
Contracts
Percent
Contract Amount
1
2.0%
$1,182,456.60
13.7%
Private Company
19
38.0%
$7,352,772.30
85.4%
Private Individual
30
60.0%
$78,484.00
0.9%
Total
50 100.0%
$8,613,712.90
Source: MnDOT FTA contracts: Oct. 1, 2011 - Sep. 30, 2014
100.0%
Not-for-Profit Agency
Percent
3.3 Distribution of Estimated Expenditures by Industry Classification for FY 20162018
According to the Department of Transportation regulations, the availability rate should be
weighted by the “amount of money to be spent” in each industry. The research team
requested a copy of MnDOT’s estimated expenditures for the next three years, broken
down by NAICS code. Projected expenditures for the next three years included in the
State Transportation Improvement Program (STIP) 2015-2018 data files were provided.
Projected expenditures were categorized by type of work. However, in order to calculate
the weights for the availability analysis, projected expenditures must be categorized by
NAICS code. In collaboration with construction engineers in MnDOT, NAICS codes
have been assigned to each of the relevant projects listed in the STIP data files.
Due to the important of the expenditure projection in calculation of the weights, NAICS
codes from the past contract file is assigned to each work type based on the description of
the work type and the NAICS definition. There were 93 work types with MnDOT’s
expenditure projection in the STIP report, with 43 distinct 6-digit NAICS, however, in
the FTA contract file, there are 13 available distinct NAICS codes where there were only
one distinct NAICS code in common between the two files, which makes it hard to use
the conventional method of calculating weights. As an alternative, the research team used
NAICS codes from both STIP projection data and FTA contract file and calculated the
weights based on summed dollar amount of past- and projected-expenditures.
Step 1:
Obtained the projected expenditures for the next three years (STIP data)
Step 2:
Identified six-digit NAICS codes for each projected contract based on its
type of work
Step 3:
Combined expenditure amount between STIP data and FTA contract data
by the six-digit NAICs.
The weights by NAICS code are shown in Table A-1 in Appendix A.
Methodology for Computing MnDOT FTA DBE Goals FY2016-2018
14
3.4 Calculating the Weighted Availability Measure
The formula below, which was prescribed in the Final Rule (1999), was used to perform
the availability analysis. This formula computes the weighted share of DBE firms within
the geographic market area. The weights are the share of dollars DBEs can expect to be
awarded within each industrial NAICS code.
𝐴𝑣𝑎𝑖𝑙𝑎𝑏𝑖𝑙𝑖𝑡𝑦 𝑅𝑎𝑡𝑒
𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝐷𝐵𝐸𝑠 𝑖𝑛 𝑁𝐴𝐼𝐶𝑆1
= [(
) × 𝑤𝑒𝑖𝑔ℎ𝑡1
𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑇𝑜𝑡𝑎𝑙 𝐹𝑖𝑟𝑚𝑠 𝑖𝑛 𝑁𝐴𝐼𝐶𝑆1
𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝐷𝐵𝐸𝑠 𝑖𝑛 𝑁𝐴𝐼𝐶𝑆2
+(
) × 𝑤𝑒𝑖𝑔ℎ𝑡2 + ⋯ ]
𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑇𝑜𝑡𝑎𝑙 𝐹𝑖𝑟𝑚𝑠 𝑖𝑛 𝑁𝐴𝐼𝐶𝑆2
To calculate the availability rates, the number of ready, willing, and able minority and
women-owned firms in the defined geographic market area were compared to the total
number of ready, willing, and able firms in the same geographic market area. Although
the U.S. Department of Transportation does not dictate a single method for calculating
the DBE availability rate or the source of the base goal, it does suggest several methods
for calculating the base goal. Moreover, the federal rule mandates that all available data
be used. In order to produce the best estimate of the available number of women and
minority-owned firms in MnDOT’s geographic market areas, the research team used a
combination of relevant methods suggested in the regulations.
The U.S. Department of Transportation recommends that grant recipients use 1) a DBE
directory and census data, 2) a bidders list, 3) data from a disparity study, 4) the goal of
another DOT recipient, or 5) alternative methods that are “rationally related to the
relative availability of DBEs in your market” [49 CFR §26.45(c)]. The availability rates
from the last statewide disparity study were deemed too old. The availability rates from
the most recent local disparity studies (e.g., Minneapolis) were deemed inapplicable.
Accordingly, the research team adopted two different measures of availability, each of
which meets the standards articulated in the federal regulations. These two methods use
alternative data sources, each with different advantages and disadvantages.
1)
MnDOT Certified DBE Directory
2)
Dun & Bradstreet database
3.4.1 Certified DBE List Method
Step 1:
The certified DBE directory includes the firms that are certified as DBEs
by MnDOT. The research team obtained the list of DBEs. The directory
contains detailed information on each DBE firm including NAICS codes.
Step 2:
Ensured the consistency of the NAICS standards used by converting the
original NAICS codes into NAICS 2012 codes if needed.
Methodology for Computing MnDOT FTA DBE Goals FY2016-2018
15
Step 3:
Obtained the 2012 ZIP Code Business Patterns (ZBP) and County
Business Patterns (CBP) from the Census website (most recent year
available to prepare the availability ratios). This dataset provides, for each
NAICS code, the total number of firms headquartered in a given zip code.
This list is used for the denominator in the availability calculation, while
the merged DBE list is used to determine the numerator.7
Step 4:
Narrowly defined the geographic markets as PJM–1, PJM–2, and VJM–1.
Step 5:
Transformed the list into the database where the NAICS code of each firm
is the unit of observation, in accordance with the NAICS code-based
definition of the prescribed availability formula.
Step 6:
Derived, in each geographic market and for each relevant NAICS code,
the total number of DBE firms (using the DBE list) and the total number
of firms (using the ZBP or CBP list).
Step 7:
Calculated the share, or the un-weighted availability, of DBEs, which is
the ratio of DBEs to total firms in a given relevant industry (NAICS code)
and narrowly tailored geographic market.
Step 8:
In some cases the NAICS code for future expenditures produces zero
counts of total available firms. The resulting DBE share is not calculable
and setting the value to zero provides the erroneous conclusion that there
are no available DBEs. Therefore, these cases were treated as missing
values, and the values were replaced by the un-weighted mean of the
values with positive denominators.
Step 9:
Calculated, for each geographical area, the weighted availability rate of
DBEs according to the prescribed availability formula.
Step 10:
Averaged availability results with and without imputing means in order to
obtain the final availability rate for each geographic market area.
Please see Tables A-2 in Appendix A for the availability rates for three different
geographic market areas.
3.4.2 Dun & Bradstreet Method
Step 1:
The research team obtained access to Hoover’s database, a subsidiary of
Dun & Bradstreet (D&B), to compute the number of firms in each relevant
7 The
Census Bureau explains: An establishment is a single physical location at which business is conducted or services
or industrial operations are performed. An establishment is not necessarily equivalent to a company or enterprise,
which may consist of one or more establishments. A single-unit company owns or operates only one establishment. A
multi-unit company owns or operates two or more establishments. (Census Bureau, County Business Patters, “How the
Data Are Collected http://www.census.gov/econ/cbp/methodology.htm, accessed, June 5, 2012). As a result, the
denominator includes many establishments that may not normally contract with MnDOT.
Methodology for Computing MnDOT FTA DBE Goals FY2016-2018
16
NAICS code within a specific geographic market area. This research
product covers some 900 industries, which include about 65 million
corporations and other entities (i.e., government agencies, partnerships,
non-profits, and educational institutions) and 85 million individuals. For
the state of Minnesota, Hoover’s database included information on
headquarters, branches and single locations8.
Step 2:
Queried the Hoover’s database to obtain the list of all the firms, minorityowned firms, and women-owned firms from the relevant NAICS codes in
the relevant market areas9. These lists have NAICS codes for each listed
firm, along with the firm’s zip code, minority ownership status, and
women ownership status. This dataset was used to calculate both the
numerator and denominator of the availability rate.
Step 3:
Narrowly defined the geographic market as PJM-1, PJM-2, and VJM-1.
Step 4:
Transformed the list into the database where the NAICS code of each firm
is the unit of observation, in accordance with the NAICS code-based
definition of the prescribed availability formula.
Step 5:
Calculated the numerator of the D&B availability measure by summing
the number of minority-owned business or women-owned enterprises in
the geographic market area for each NAICS code.
Step 6:
Calculated the denominator of the D&B availability measure by summing
the number of total firms in in the geographic market area for each NAICS
code.
Step 7:
Calculated the share, or the un-weighted availability, of women-owned
business enterprises (WBEs) or minority-owned business enterprises
(MBEs), which is the ratio of WBE or MBE firms to total firms in a given
relevant industry and narrowly tailored geographic market.
Step 8:
In some cases the NAICS code for future expenditures produces zero
counts of total available firms. The resulting women-owned enterprises or
minority-owned business share is not calculable and setting the value to
zero provides the erroneous conclusion that there are no available DBEs.
Therefore, these cases were treated as missing values, and the values were
replaced by the un-weighted mean of the values with positive
denominators.
“Headquarters” indicates that the company has subsidiaries or branches; “branch” indicates a company location other
than the headquarters and single Location indicates that the company does not have any subsidiaries or branches.
9 Unlike the other methods, the D&B method uses “self-reported” minority/gender designations. Thus, the D&B
method can include firms that are not MnDOT Certified DBEs. On the other hand, not every certified DBE is included
in this database because a requirement of inclusion is the existence of a DUNS number. According to Hoover’s
customer service, D&B contacts firms directly to verify their gender or minority status and checks with third party
sources and proprietary databases for further verification.
8
Methodology for Computing MnDOT FTA DBE Goals FY2016-2018
17
Step 9:
Calculated, for each geographic market area, the weighted availability rate
of women-owned enterprises or minority-owned business according to the
prescribed availability formula.
Step 10:
Averaged availability results with and without imputing means in order to
obtain the final availability rate for each geographic market area.
Please see Tables A-3 in Appendix A for the availability rates for three different GMAs.
3.4.5 The Base Goal
The base goal is the weighted average across the two availability methods: Certified DBE
List method and Dun & Bradstreet method. Depending on the method used to calculate
availability, each defined market area captures a different share of available contract
dollars. As a result, each method also yields a different DBE availability goal for each
market. In order to derive a single base goal for MnDOT that is based on all the goals
calculated for each geographical market, it is necessary to weight each geographical
market-specific goal according to the percentage of contract dollars awarded in that area.
The two different methods use data that report multiple industries for many of the firms
in their databases. We count the firms at the NAICS level. Table 8 presents the details of
the calculation of the weighted availability rate at the NAICS code level. This base goal
is used in subsequent analysis.
Table 8. Computing Base Goal
Method
PJM-1
PJM-2
VJM1
Unweighted Weighted
Average
average
Certified DBE List
0.83% 1.69% 0.02%
Method
5.5367%
D&B Method
9.16% 12.21% 9.31%
Distribution of Award Amount to compute weights for base
goal
Percent
95.9% 98.1% 99.2%
Distribution of
(a)
(b)
(c)
Award Amount
32.7% 33.5% 33.8%
Proportional
Weight
(d)
(e)
(f)
0.8%
Weighted
Base
Goal
5.5375%
10.2%
(d) = (a)/[(a)+(b)+(c)]
(e) = (b)/[(a)+(b)+(c)]
(f) = (c)/[(a)+(b)+(c)]
Note 1: Weighted average is the sum of the availability rates of each GMA multiplied by its proportional weight (d, e, and f),
respectively.
Note 2: Weighted base goal is the average of weighted averages of all four methods.
Methodology for Computing MnDOT FTA DBE Goals FY2016-2018
18
3.5 Adjustment to the Base Goal
According to the USDOT regulations, recipients of federal funds are required to adjust
their base goals in light of other evidence regarding the market area [49 C.F.R.
§26.45(d)]. One valid reason for adjusting the goal would be if the analysis showed
discrimination, either in the overall market place or in the specific agency or
governmental unit undertaking the procurement and contracting process. The regressions
and computations are based on a combined file of FTA and FHWA contract awards from
2012-2014 to DBEs and non-DBEs in the restricted set of NAICs codes. The logic is that
MnDOT makes awards in both FTA and FHWA in the same industries.
The research team used three methods to measure the degree of discrimination: 1) the
dummy interaction variable method, which estimates a coefficient on the DBE dummy
variable to control for the relevant observable factors that influence award amount. The
purpose of this method is to include the impact FTA funding has on contracts awarded to
DBEs. The method enables the research team to capture that impact by adding an
interaction term between the variables for DBE and FTA; 2) the Blinder-Oaxaca-Duncan
residual difference decomposition estimates separately the contract amounts to DBEs and
non-DBEs and computes the amount that DBEs would have received had they been
treated like equally situated non-DBEs. The difference between the actual contract
amounts and the “equal treatment” amounts defines the discriminatory portion of the gap
between DBEs and non-DBEs. In addition to estimating an unexplained portion as a
measure for discrimination, an alternative measure of discrimination is computed from a
portion of the gap between DBEs and non-DBEs in the FTA variable; and 3) the
unadjusted difference in Award Amount between non-DBEs and DBEs due to FTA is
calculated by comparing the average award amount between Non-DBEs and DBEs which
were weighted by the FTA share of total award amount from the combined FHWA and
FTA dataset.
3.5.1 Data Used for Adjustment to the Base Goal
The original FTA contract data from MnDOT contains 53 observations, which includes
30 individual vendors that are not identifiable through D&B database, a step that is
crucial for acquiring control variables necessary for the regression analysis, thus, the 30
individual vendors were dropped from the data. After dropping the individual vendors,
the FTA contract data contains 23 observations with 3 DBEs, which makes it difficult to
run any statistical analysis. In order to overcome this limitation, the research team
merged the FTA contract data with the FHWA contract data. Then, the team identified
the NAICS codes relevant to FTA prime contracts and subcontracts. In the “interaction”
of the dataset, there are 693 observations. The summary statistics of the dataset used for
the analysis is shown in the summary table.
The additional 37 observations (5.3% of the total sample) have dollar values equal to zero
for the dependent variable, which is the contract award amount. These observations are
dropped from the sample because they are undefined when log-transformed for the final
analysis. There were some missing values in a couple of variables, such as credit risk and
Methodology for Computing MnDOT FTA DBE Goals FY2016-2018
19
age of firm (missing variable percentages range from 5% to 7%). In order to account for
the missing values, extra dummy variables were created for missing observations of the
corresponding variables (coded 1 for the missing values and 0 otherwise) and missing
values were imputed with a constant value of 1 to retain the full sample. The description
of the variables used in the analysis is shown in Table B-1 in Appendix B. The summary
statistics of the dataset used in the analysis is shown in Tables B-2 and B-3 in Appendix
B for the primes and subcontracts, respectively. The description of the variables used in
the analysis is shown in Table B-1 in Appendix B. The summary statistics of the dataset
used in the analysis is shown in Table B-2.
3.5.2 Dummy Variable Method
The research team developed six statistical models in order to conduct thorough analysis
of the FTA and FHWA combined data. The dependent variable is the logarithm of the
contract amount. In moving from Model 1 through Model 3, the number of variables
being controlled for increases. The variable of interest is the interaction term between
DBE and FTA variables (DBE × FTA) where its coefficient indicates incremental
discrimination made by FTA in reference to the discrimination made by non-FTA nonDBEs. The models from 1 – 3 use dataset that encompass both prime contracts and
subprime contracts whereas models from 4 – 6 use subprime data only. One important
thing to notice is that the variable of interest, although the sign is negative, is statistically
insignificant throughout different models. These results suggest that FTA-funded project
itself, when covariates are controlled, does not contribute to additional discrimination
against DBEs. The results are shown in Tables B-3 in Appendix B.
3.5.3 Residual Difference Method
Another standard method of measuring discrimination is the residual difference method.
The research team used Model 3 of the dummy variable method as a base to run the
residual difference method as well as the race neutral portion of DBE goals because they
were considered the best models for both groups in terms of goodness of fit. In addition
to estimating the portion unexplained as a measure for discrimination, the portion of the
gap between DBEs and non-DBEs explained by the FTA is computed as an alternative
measure of discrimination. The estimates from these two methods indicates the portion of
the gap unexplained after controlling for relevant variables is 60.17%, and the portion of
the gap contributed by FTA is 20.13%. The results are detailed in Table B-4 in Appendix
B.
3.5.4 Adjustment
The base goal is adjusted using the average of the five measures produced from the three
above-mentioned methods. The adjustment is 28.79% as shown in Table 9.
Methodology for Computing MnDOT FTA DBE Goals FY2016-2018
20
Table 9. Discrimination Gap for Adjustment
Method
Percent
Coefficients on Interaction terms using subs and primes
0.00%
Coefficients on Interaction terms using subs
0.00%
Oaxaca: Portion of explained gap explained by FTA
20.11%
Oaxaca: total unexplained
60.02%
UNADJUSTED PERCENT OF GAP DUE TO FTA
63.79%
AVERAGE ACROSS METHODS
28.79%
* p<0.1, ** p<0.05, *** p<0.01
Add asterisk whenever the difference is statistically significant at 1%,
5% or 10% level, one tail test.
3.6 Race Neutral Analysis
In compliance with federal regulations, state and local authorities must identify the
maximum feasible portion of the DBE goal that can be achieved through race-neutral
measures and the percentage of the goal that can only be achieved through race-conscious
measures [49 C.F.R. §26.51(a)]. Myers and Ha have pioneered the use of a detailed
econometric procedure that maximizes the race-neutral component of the DBE goals.
This method has established a rigorous standard for maximizing the race neutral portion
of the overall DBE goal. The logic of the analysis is that some share of DBE dollars
awarded would have gone to DBEs without goals. The following method is utilized for
the race neutral analysis: To compute the ratio of predicted award dollar amount without
goals over predicted award dollar amount with and without goals using the DBE
subsample in contract file. Table 10 shows how the race-neutral portion of the goal was
calculated. The race neutral analysis based on the above-mentioned method indicates
Race-Neutral Percent is 36.07%.
Table 10. Race Neutral Analysis
Predicted Award
Amount without
Goals
Amount
Number of
Observations
Source: Author's computation
Predicted Award
Amount with
Goals
Race Neutral
Percent
$14,755.07
$26,148.62
36.07%
693
693
Methodology for Computing MnDOT FTA DBE Goals FY2016-2018
21
SECTION 4: THE MnDOT PROPOSED DBE GOALS
In summary, the Roy Wilkins Center presented the following recommendation, as shown
in Table 11, to the Office of Civil Rights, MnDOT for the proposed overall DBE Goal
and race neutral participation.
Table 11. Proposed MnDOT FTA DBE Goals FY2016-2018
Base Goal
Discrimination Gap for Adjustment
Adjusted Goal
Race-Neutral Portion
Race-Neutral Goal
Race-Conscious Portion
Race-Conscious Goal
5.54%
28.79%
7.13%
36.07%
2.57%
63.93%
4.56%
Source: Author's calculation using the FTA data files.
Methodology for Computing MnDOT FTA DBE Goals FY2016-2018
22
SECTION 5: APPENDICES
APPENDIX A
AVAILABILITY ANALYSIS
Table A-1. Weights used for the Availability Analysis
NAICS
236220
Description
Commercial and Institutional Building Construction
Weight
0.00352
237130
Power and Communication Line and Related Structures
Construction
0.00022
238110
238120
238130
238140
238150
238160
Poured Concrete Foundation and Structure Contractors
Structural Steel and Precast Concrete Contractors
Framing Contractors
Masonry Contractors
Glass and Glazing Contractors
Roofing Contractors
0.00643
0.01707
0.00011
0.00086
0.00183
0.00094
238190
Other Foundation, Structure, and Building Exterior
Contractors
0.00101
238210
Electrical Contractors and Other Wiring Installation
Contractors
0.00698
238220
238310
238320
238350
238390
238910
238990
323111
423130
424710
441310
442110
444130
447110
453210
485111
485113
485310
485410
485510
488410
Plumbing, Heating, and Air-Conditioning Contractors
Drywall and Insulation Contractors
Painting and Wall Covering Contractors
Finish Carpentry Contractors
Other Building Finishing Contractors
Site Preparation Contractors
All Other Specialty Trade Contractors
Commercial Printing (except Screen and Books)
Tire and Tube Merchant Wholesalers
Petroleum Bulk Stations and Terminals
Automotive Parts and Accessories Stores
Furniture Stores
Hardware Stores
Gasoline Stations with Convenience Stores
Office Supplies and Stationery Stores
Mixed Mode Transit Systems
Bus and Other Motor Vehicle Transit Systems
Taxi Service
School and Employee Bus Transportation
Charter Bus Industry
Motor Vehicle Towing
0.00962
0.00132
0.00043
0.00284
0.00180
0.00396
0.00616
0.01047
0.01196
0.08842
0.03021
0.00008
0.01609
0.08842
0.01331
0.05330
0.09171
0.05437
0.04963
0.02827
0.00460
Methodology for Computing MnDOT FTA DBE Goals FY2016-2018
23
488490
524126
524210
524298
541110
541211
541310
541330
541430
Other Support Activities for Road Transportation
Direct Property and Casualty Insurance Carriers
Insurance Agencies and Brokerages
All Other Insurance Related Activities
Offices of Lawyers
Offices of Certified Public Accountants
Architectural Services
Engineering Services
Graphic Design Services
0.02684
0.01252
0.01252
0.00050
0.00517
0.00517
0.02419
0.02938
0.01111
541611
Administrative Management and General Mgmt
Consulting Services
0.00000
541614
Process, Physical Distribution, and Logistics Consulting
Services
0.01319
541690
541850
541990
561320
561499
561720
561730
611519
621112
811111
Other Scientific and Technical Consulting Services
Outdoor Advertising
All Other Professional, Scientific, and Technical
Services
Temporary Help Services
All Other Business Support Services
Janitorial Services
Landscaping Services
Other Technical and Trade Schools
Offices of Physicians, Mental Health Specialists
General Automotive Repair
0.00130
0.01111
0.09453
0.05506
0.00273
0.00599
0.00007
0.01272
0.00130
0.04810
811121
Automotive Body, Paint, and Interior Repair and
Maintenance
0.01047
812320
Drycleaning and Laundry Services (except CoinOperated)
0.01010
Methodology for Computing MnDOT FTA DBE Goals FY2016-2018
24
PMJ-1
NAICS
Weight
236220
237130
238110
238120
238130
238140
238150
238160
238190
238210
238220
238310
238320
238350
238390
238910
238990
423130
424710
441310
442110
444130
447110
453210
485113
488410
524126
524210
524298
541110
541211
541430
541611
541690
541850
541990
0.00579
0.00037
0.01058
0.02806
0.00017
0.00141
0.00301
0.00154
0.00167
0.01147
0.01582
0.00217
0.00071
0.00467
0.00297
0.00651
0.01013
0.01966
0.14536
0.04967
0.00013
0.02645
0.14536
0.02188
0.15076
0.00756
0.02059
0.02059
0.00082
0.00849
0.00849
0.01826
0.00000
0.00214
0.01826
0.09983
Number of
DBE firms
15
4
12
6
7
11
5
8
9
25
13
15
22
23
8
39
25
2
1
1
3
1
11
27
11
1
6
Total
Number of
Firms
Unweighted
Rate
Weighted
Rate
704
141
540
50
332
509
78
333
96
1570
1725
488
694
592
193
894
1220
42
74
729
378
476
1888
103
23
117
284
3254
61
2428
969
463
1307
366
71
476
0.0213
0.0284
0.0222
0.1200
0.0211
0.0216
0.0641
0.0240
0.0938
0.0159
0.0075
0.0307
0.0317
0.0389
0.0415
0.0436
0.0205
0.0000
0.0000
0.0000
0.0053
0.0021
0.0000
0.0097
0.0000
0.0000
0.0000
0.0000
0.0000
0.0012
0.0010
0.0238
0.0207
0.0301
0.0141
0.0126
0.0001
0.0000
0.0002
0.0034
0.0000
0.0000
0.0002
0.0000
0.0002
0.0002
0.0001
0.0001
0.0000
0.0002
0.0001
0.0003
0.0002
0.0000
0.0000
0.0000
0.0000
0.0001
0.0000
0.0002
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0004
0.0000
0.0001
0.0003
0.0013
Methodology for Computing MnDOT FTA DBE Goals FY2016-2018
25
561720
561730
611519
621112
811111
812320
Total
0.00984
0.00011
0.02091
0.00214
0.07908
0.01661
1.00000
15
31
1
1
359
1084
1955
47
125
1647
212
28738
0.0138
0.0159
0.0213
0.0000
0.0006
0.0000
0.0125
Methodology for Computing MnDOT FTA DBE Goals FY2016-2018
0.0001
0.0000
0.0004
0.0000
0.0000
0.0000
0.0083
26
PMJ-2
NAICS
Weight
236220
237130
238110
238120
238130
238140
238150
238160
238190
238210
238220
238310
238320
238350
238390
238910
238990
423130
424710
441310
442110
444130
447110
453210
485113
488410
524126
524210
524298
541110
541211
541430
541611
541690
541850
541990
0.00579
0.00037
0.01058
0.02806
0.00017
0.00141
0.00301
0.00154
0.00167
0.01147
0.01582
0.00217
0.00071
0.00467
0.00297
0.00651
0.01013
0.01966
0.14536
0.04967
0.00013
0.02645
0.14536
0.02188
0.15076
0.00756
0.02059
0.02059
0.00082
0.00849
0.00849
0.01826
0.00000
0.00214
0.01826
0.09983
Number of
DBE firms
8
1
3
3
6
8
4
8
5
20
10
7
14
14
5
13
15
1
3
1
9
21
10
1
7
Total
Number of
Firms
Unweighted
Rate
Weighted
Rate
264
29
125
10
77
146
35
129
20
473
526
135
310
190
78
217
359
19
17
223
167
146
632
48
8
43
99
1317
36
1518
478
319
820
191
43
273
0.0303
0.0345
0.0240
0.3000
0.0779
0.0548
0.1143
0.0620
0.2500
0.0423
0.0190
0.0519
0.0452
0.0737
0.0641
0.0599
0.0418
0.0000
0.0000
0.0000
0.0060
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0020
0.0021
0.0282
0.0256
0.0524
0.0233
0.0256
0.0002
0.0000
0.0003
0.0084
0.0000
0.0001
0.0003
0.0001
0.0004
0.0005
0.0003
0.0001
0.0000
0.0003
0.0002
0.0004
0.0004
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0005
0.0000
0.0001
0.0004
0.0026
Methodology for Computing MnDOT FTA DBE Goals FY2016-2018
27
561720
561730
611519
621112
811111
812320
Total
0.00984
0.00011
0.02091
0.00214
0.07908
0.01661
1.00000
15
18
1
1
232
516
665
27
92
605
119
11544
0.0291
0.0271
0.0370
0.0000
0.0017
0.0000
0.0201
Methodology for Computing MnDOT FTA DBE Goals FY2016-2018
0.0003
0.0000
0.0008
0.0000
0.0001
0.0000
0.0169
28
VJM-1
NAICS
Weight
236220
237130
238110
238120
238130
238140
238150
238160
238190
238210
238220
238310
238320
238350
238390
238910
238990
423130
424710
441310
442110
444130
447110
453210
485113
488410
524126
524210
524298
541110
541211
541430
541611
541690
541850
541990
0.00579
0.00037
0.01058
0.02806
0.00017
0.00141
0.00301
0.00154
0.00167
0.01147
0.01582
0.00217
0.00071
0.00467
0.00297
0.00651
0.01013
0.01966
0.14536
0.04967
0.00013
0.02645
0.14536
0.02188
0.15076
0.00756
0.02059
0.02059
0.00082
0.00849
0.00849
0.01826
0.00000
0.00214
0.01826
0.09983
Number of
DBE firms
1
1
Total
Number of
Firms
Unweighted
Rate
Weighted
Rate
45
10
27
1
9
24
7
11
4
70
84
20
33
17
8
29
35
1
5
50
23
27
88
9
1
5
27
254
6
414
156
41
202
64
7
41
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0119
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0050
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0002
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
Methodology for Computing MnDOT FTA DBE Goals FY2016-2018
29
561720
561730
611519
621112
811111
812320
Total
0.00984
0.00011
0.02091
0.00214
0.07908
0.01661
1.00000
2
55
60
5
22
69
22
2088
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0010
Methodology for Computing MnDOT FTA DBE Goals FY2016-2018
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
0.0002
30
Table A-3. Availability Analysis - D&B Method, All NAICS
PMJ-1
NAICS
Weight
Number of
DBE firms
Total
Number of
Firms
Unweighted
Rate
Weighted
Rate
236220
237130
238110
238120
238130
238140
238150
238160
238190
238210
238220
238310
238320
238350
238390
238910
238990
423130
424710
441310
442110
444130
447110
453210
485113
488410
524126
524210
524298
541110
541211
541430
541611
541690
541850
0.0058
0.0004
0.0106
0.0281
0.0002
0.0014
0.0030
0.0015
0.0017
0.0115
0.0158
0.0022
0.0007
0.0047
0.0030
0.0065
0.0101
0.0197
0.1454
0.0497
0.0001
0.0264
0.1454
0.0219
0.1508
0.0076
0.0206
0.0206
0.0008
0.0085
0.0085
0.0183
0.0000
0.0021
0.0183
344
13
78
21
54
65
22
71
5
280
261
59
224
18
11
115
239
10
4
40
124
75
0
53
5
24
10
446
18
685
126
525
1,505
404
13
2,272
87
1,173
67
1,338
928
189
1,031
29
3,241
4,572
1,351
2,930
360
255
1,560
3,267
120
199
866
973
929
6
224
29
491
166
5,411
192
9,014
1,376
1,725
7,270
1,669
95
0.1514
0.1494
0.0665
0.3134
0.0404
0.0700
0.1164
0.0689
0.1724
0.0864
0.0571
0.0437
0.0765
0.0500
0.0431
0.0737
0.0732
0.0833
0.0201
0.0462
0.1274
0.0807
0.0000
0.2366
0.1724
0.0489
0.0602
0.0824
0.0938
0.0760
0.0916
0.3043
0.2070
0.2421
0.1368
0.0009
0.0001
0.0007
0.0088
0.0000
0.0001
0.0004
0.0001
0.0003
0.0010
0.0009
0.0001
0.0001
0.0002
0.0001
0.0005
0.0007
0.0016
0.0029
0.0023
0.0000
0.0021
0.0000
0.0052
0.0260
0.0004
0.0012
0.0017
0.0001
0.0006
0.0008
0.0056
0.0000
0.0005
0.0025
Methodology for Computing MnDOT FTA DBE Goals FY2016-2018
31
541990
561720
561730
611519
621112
811111
812320
Total
0.0998
0.0098
0.0001
0.0209
0.0021
0.0791
0.0166
1.0000
870
388
181
50
44
161
170
7,811
10,625
2,089
3,354
215
435
3,307
676
76,106
0.0819
0.1857
0.0540
0.2326
0.1011
0.0487
0.2515
0.0082
0.0018
0.0000
0.0049
0.0002
0.0038
0.0042
0.1026
0.0916
Methodology for Computing MnDOT FTA DBE Goals FY2016-2018
32
PMJ-2
NAICS
Weight
Number of
DBE firms
Total
Number of
Firms
Unweighted
Rate
Weighted
Rate
236220
237130
238110
238120
238130
238140
238150
238160
238190
238210
238220
238310
238320
238350
238390
238910
238990
423130
424710
441310
442110
444130
447110
453210
485113
488410
524126
524210
524298
541110
541211
541430
541611
541690
541850
541990
0.0058
0.0004
0.0106
0.0281
0.0002
0.0014
0.0030
0.0015
0.0017
0.0115
0.0158
0.0022
0.0007
0.0047
0.0030
0.0065
0.0101
0.0197
0.1454
0.0497
0.0001
0.0264
0.1454
0.0219
0.1508
0.0076
0.0206
0.0206
0.0008
0.0085
0.0085
0.0183
0.0000
0.0021
0.0183
0.0998
258
10
47
5
28
41
13
47
1
182
160
34
155
12
9
34
145
6
2
20
77
42
0
41
4
13
5
237
11
595
90
417
1,334
358
10
678
1,272
37
376
21
543
335
103
480
11
1,419
1,967
434
1,507
127
113
380
1,527
63
30
293
536
328
2
144
15
259
102
2,674
131
7,197
1,034
1,274
5,650
1,255
72
6,417
0.2028
0.2703
0.1250
0.2381
0.0516
0.1224
0.1262
0.0979
0.0909
0.1283
0.0813
0.0783
0.1029
0.0945
0.0796
0.0895
0.0950
0.0952
0.0667
0.0683
0.1437
0.1280
0.0000
0.2847
0.2667
0.0502
0.0490
0.0886
0.0840
0.0827
0.0870
0.3273
0.2361
0.2853
0.1389
0.1057
0.0012
0.0001
0.0013
0.0067
0.0000
0.0002
0.0004
0.0002
0.0002
0.0015
0.0013
0.0002
0.0001
0.0004
0.0002
0.0006
0.0010
0.0019
0.0097
0.0034
0.0000
0.0034
0.0000
0.0062
0.0402
0.0004
0.0010
0.0018
0.0001
0.0007
0.0007
0.0060
0.0000
0.0006
0.0025
0.0105
Methodology for Computing MnDOT FTA DBE Goals FY2016-2018
33
561720
561730
611519
621112
811111
812320
Total
0.0098
0.0001
0.0209
0.0021
0.0791
0.0166
1.0000
271
97
38
40
108
151
5,826
1,159
1,282
167
339
1,544
530
43,149
0.2338
0.0757
0.2275
0.1180
0.0699
0.2849
0.0023
0.0000
0.0048
0.0003
0.0055
0.0047
0.1350
0.1221
Methodology for Computing MnDOT FTA DBE Goals FY2016-2018
34
VJM-1
NAICS
Weight
Number of
DBE firms
Total
Number of
Firms
236220
237130
238110
238120
238130
238140
238150
238160
238190
238210
238220
238310
238320
238350
238390
238910
238990
423130
424710
441310
442110
444130
447110
453210
485113
488410
524126
524210
524298
541110
541211
541430
541611
541690
541850
541990
0.0058
0.0004
0.0106
0.0281
0.0002
0.0014
0.0030
0.0015
0.0017
0.0115
0.0158
0.0022
0.0007
0.0047
0.0030
0.0065
0.0101
0.0197
0.1454
0.0497
0.0001
0.0264
0.1454
0.0219
0.1508
0.0076
0.0206
0.0206
0.0008
0.0085
0.0085
0.0183
0.0000
0.0021
0.0183
0.0998
10
0
4
0
3
5
2
2
0
8
8
3
5
1
0
4
10
0
0
3
10
2
0
2
1
0
1
20
1
63
10
31
97
29
1
54
130
2
39
1
58
39
25
47
0
123
180
44
90
10
16
48
137
7
9
45
51
42
0
16
3
15
10
341
12
800
133
109
487
130
7
463
Unweighted
Rate
Weighted
Rate
0.0769
0.0000
0.1026
0.0000
0.0517
0.1282
0.0800
0.0426
0.0004
0.0000
0.0011
0.0000
0.0000
0.0002
0.0002
0.0001
0.0650
0.0444
0.0682
0.0556
0.1000
0.0000
0.0833
0.0730
0.0000
0.0000
0.0667
0.1961
0.0476
0.0007
0.0007
0.0001
0.0000
0.0005
0.0000
0.0005
0.0007
0.0000
0.0000
0.0033
0.0000
0.0013
0.1250
0.3333
0.0000
0.1000
0.0587
0.0833
0.0788
0.0752
0.2844
0.1992
0.2231
0.1429
0.1166
0.0027
0.0503
0.0000
0.0021
0.0012
0.0001
0.0007
0.0006
0.0052
0.0000
0.0005
0.0026
0.0116
Methodology for Computing MnDOT FTA DBE Goals FY2016-2018
35
561720
561730
611519
621112
811111
812320
Total
0.0098
0.0001
0.0209
0.0021
0.0791
0.0166
1.0000
14
5
0
3
0
7
419
68
123
11
22
131
36
4,060
0.2059
0.0407
0.0000
0.1364
0.0000
0.1944
0.0020
0.0000
0.0000
0.0003
0.0000
0.0032
0.1032
0.0931
Methodology for Computing MnDOT FTA DBE Goals FY2016-2018
36
APPENDIX B
DISCRIMINATION ANALYSIS
Table B-1. Description of Variables in the Regression Analysis
Variable
Description
DBE
Dummy variable to indicate DBE status
FTA
Dummy variable to indicate FTA status
FFY2012
Dummy variable for federal fiscal year 2012
FFY2013
Dummy variable for federal fiscal year 2013
FFY2014
Dummy variable for federal fiscal year 2014
Credit2
Dummy variable for low credit (from Dun&Brad)
Credit3
Dummy variable for medium credit (from
Dun&Brad)
Credit4
Dummy variable for high credit (from Dun&Brad)
Missing_credit_risk
Dummy for missing credit risk
Minnesota
Dummy variable for location in Minnesota
Large firm
Dummy variable for firms with more than 100
employees and total sales greater than $1 million
Firm_age
Continuous variable for age of firm
Missing_firm_age
Dummy for missing firm age
ArchiEnginServ
Dummy: NAICS defined industry for architectural
engineering service
ManaScienTechnicServ
Dummy: NAICS defined industry for foundation for
management science technical service
Source: MnDOT FTA contracts: Oct. 1, 2011 - Sep. 30, 2014
Methodology for Computing MnDOT FTA DBE Goals FY2016-2018
37
Table B-2. Summary Statistics for Subcontracts
Variable
Mean Std. Dev. Min. Max.
ln award amount
10.182
1.497
5.293 15.193
DBE
0.229
0.421
0
1
FTA
0.033
0.179
0
1
DBE FTA interaction
0.004
0.066
0
1
FFY2013
0.388
0.488
0
1
FFY2014
0.231
0.422
0
1
minnesota
0.97
0.172
0
1
ArchiEnginServ
0.199
0.4
0
1
ManaScienTechnicServ 0.039
0.194
0
1
credit risk3
0.039
0.194
0
1
Credit Risk 4
0.043
0.204
0
1
missing_credit_risk
0.068
0.252
0
1
large firm2
0.052
0.222
0
1
firm age
20.413 16.436
0
100
missing firm age
0.049
0.216
0
1
N
693
Source: MnDOT FTA contracts: Oct. 1, 2011 - Sep. 30, 2014
Methodology for Computing MnDOT FTA DBE Goals FY2016-2018
38
Table B-3. Discrimination Analysis Dummy Interaction Variable Method for Prime
Contracts: By Natural Log (ln) Award Amount
Both Primes and Sub data
DBE_FTA_interaction
Prime
FFY2013
FFY2014
minnesota
ArchiEnginServ
ManaScienTechnicServ
credit_risk3
credit_risk4
missing_credit_risk
(1)
(2)
(3)
(4)
(5)
(6)
ln_AA
ln_AA
ln_AA
ln_AA
ln_AA
ln_AA
-1.372
-1.294
-0.996
-1.452
-1.388
-1.452
(1.421)
(1.434)
(1.466)
(1.381)
(1.394)
(1.381)
2.440***
2.419***
2.668***
(0.205)
(0.206)
(0.265)
0.237*
0.249*
0.234*
0.236*
0.246*
0.236*
(0.127)
(0.13)
(0.128)
(0.133)
(0.135)
(0.133)
0.151
0.154
0.151
0.125
0.126
0.125
(0.138)
(0.138)
(0.138)
(0.142)
(0.142)
(0.142)
0.14
0.177
0.119
-0.043
-0.00967
-0.043
(0.465)
(0.461)
(0.461)
(0.483)
(0.479)
(0.483)
0.155
0.161
0.151
0.183
0.189
0.183
(0.134)
(0.134)
(0.134)
(0.143)
(0.143)
(0.143)
0.28
0.309
0.277
0.212
0.238
0.212
(0.608)
(0.601)
(0.608)
(0.634)
(0.627)
(0.634)
0.0291
0.00893
0.0285
0.0418
0.0255
0.0418
(0.279)
(0.28)
(0.279)
(0.28)
(0.281)
(0.28)
0.672*
0.661*
0.633*
0.829*
0.818*
0.829*
(0.369)
(0.368)
(0.38)
(0.43)
(0.43)
(0.43)
0.15
0.17
0.159
0.0714
0.0886
0.0714
(0.255)
(0.255)
(0.257)
(0.263)
(0.264)
(0.263)
DBE
-0.0919
-0.0741
(0.129)
(0.129)
FTA
_cons
Subs Only data
-0.392
0
(0.377)
(.)
9.722***
9.701***
9.745***
9.903***
9.882***
9.903***
-0.465
-0.462
-0.462
-0.482
-0.479
-0.482
656
656
656
621
621
621
adj. R2
0.153
0.153
0.153
0.01
0.008
0.01
F
18.16
16.66
19.41
1.077
1.011
1.077
N
Standard errors in parentheses
* p<0.1, ** p<0.05, *** p<0.01
Methodology for Computing MnDOT FTA DBE Goals FY2016-2018
39
Table B-4. Residual Difference Analysis using Oaxaca Decomposition
(1)
ln_award_amount
Differential
Prediction_1
Prediction_2
Difference
Explained
FTA
FHWAprime
FFY2013
FFY2014
minnesota
ArchiEnginServ
ManaScienTechnicServ
credit_risk3
credit_risk4
missing_credit_risk
large_firm2
firm_age
missing_firm_age
Total
10.23***
(0.068)
10.01***
(0.121)
0.226
(0.139)
0.0455
(0.033)
0.0600**
(0.0286)
-0.0152
(0.0242)
0.00742
(0.0112)
0.00062
(0.00777)
0.012
(0.0127)
0.000893
(0.0033)
0.00407
(0.0119)
0.0164
(0.0138)
-0.00232
(0.0292)
0.00235
(0.0168)
0.000377
(0.00202)
-0.0417
(0.0299)
0.0905
(0.0681)
Unexplained
Methodology for Computing MnDOT FTA DBE Goals FY2016-2018
40
FTA
0.110*
(0.0638)
0.00706
(0.00743)
-0.276*
(0.164)
-0.0457
(0.0639)
2.928**
(1.437)
-0.235***
(0.0897)
-0.0194
(0.0496)
0.00994
(0.0102)
-0.00262
(0.0135)
0.0223
(0.089)
0.0248
(0.0209)
0.239
(0.22)
0.116
(0.0752)
-2.743*
(1.487)
0.136
(0.152)
656
FHWAprime
FFY2013
FFY2014
minnesota
ArchiEnginServ
ManaScienTechnicServ
credit_risk3
credit_risk4
missing_credit_risk
large_firm2
firm_age
missing_firm_age
_cons
Total
N
adj. R2
F
Standard errors in parentheses
* p<0.1, ** p<0.05, *** p<0.01
Unexplained Portion is
60.17%
Explained by FTA is
20.13%
Methodology for Computing MnDOT FTA DBE Goals FY2016-2018
41