UNITED STATES DISTRICT COURT EASTERN DISTRICT OF TENNESSEE GREENEVILLE DIVISION
ULTIMA SERVS. CORP., ) )
) 2:20-CV-00041-DCLC-CRW Plaintiff, )
) v. )
) U.S. DEP’T OF AGRIC., et al., ) ) Defendants.
MEMORANDUM OPINION AND ORDER
This matter is before the Court on Plaintiff Ultima Services Corporation’s (“Ultima”) and Defendants’ Motions to Exclude [Docs. 58, 59]. The parties have responded and replied to each motion [Docs. 67, 68, 69, 71]. Accordingly, this matter is now ripe for resolution. For the reasons that follow, the parties’ Motions to Exclude [Docs. 58, 59] are DENIED. I. BACKGROUND Ultima is a small business that provides administrative and technical support services [Doc. 73, ¶¶ 1, 3]. Defendant the United States Department of Agriculture (“USDA”) is a cabinet-level agency of the federal government, led by the Secretary of Agriculture [Doc. 1, ¶ 4]. Similarly, Defendant the Small Business Administration (“SBA”) is a cabinet-level agency, led by the Small Business Administrator [Id., ¶ 5]. As relevant here, Ultima competed for federal services contracts with the USDA, earning approximately $37 million since 2015 [Docs. 70-1, ¶ 4; 73, ¶ 3]. Ultima began providing its services to the Natural Resources Conservation Service (“NRCS”), a unit within the USDA, in 2004 [Doc. 73, ¶ 3]. In 2017, Ultima won four regional Indefinite Delivery Indefinite Quantity (“IDIQ”) contracts to provide its services to different NRCS offices in four regions of the country [Id., ¶ 5]. Each contract included one base year, with the option to renew annually over the next four years following that base year [Id.]. Defendants obligated $10 million for each of those IDIQ contracts Ultima won [Id.]. In 2018, Defendant USDA decided not exercise any further options for any of the four regional IDIQ contracts [Id., ¶ 10]. Defendant USDA’s decision prevented it from exercising any
previously unexercised options on task orders or issuing new task orders under the IDIQ contracts [Id., ¶ 11]. To continue providing services to NRCS offices, Defendant USDA, in some instances, awarded sole source contracts with companies participating in the 8(a) Business Development Program (“the 8(a) program”) outlined in 13 C.F.R. § 124.1 [Id., ¶ 13]. Ultima was not a participant in the 8(a) program and, thus, Defendant USDA could not consider it in awarding those sole source contracts [Docs. 70-1, ¶ 5; 73, ¶ 14]. Section 8(a) of the Small Business Act grants Defendant SBA the authority to acquire procurement contracts from other government agencies and to award or otherwise arrange for performance of those contracts by small businesses “whenever [Defendant SBA] determines such
action is necessary[.]” 15 U.S.C. § 637(a)(1). Congress directed Defendant SBA “to arrange for the performance of such procurement contracts by negotiating or otherwise letting subcontracts to socially and economically disadvantaged small business concerns[.]” Id. § 637(a)(1)(B). Congress defined a “socially and economically disadvantaged small business concern” as a business at least 51% owned by a socially and economically disadvantaged individual. Id. § 637(a)(4)(A). Congress further defined “socially disadvantaged individuals” as “those who have been subjected to racial or ethnic prejudice or cultural bias because of their identity as a member of a group without regard to their individual qualities.” Id. § 637(a)(5). Importantly, Congress provided that “[a]ll determinations . . . with respect to whether a group has been subjected to prejudice or bias shall be made by [Defendant SBA.]” Id. § 637(a)(8). Congress also explained that “economically disadvantaged individuals” were “those socially disadvantaged individuals whose ability to compete in the free enterprise system has been impaired due to diminished capital and credit opportunities as compared to others in the same business area who are not socially disadvantaged.” Id. § 637(a)(6)(A).
Following Congress’s direction, Defendant SBA developed the current 8(a) program “to assist eligible small, disadvantaged business [(“SDBs”)] concerns compete in the American economy through business development.” 13 C.F.R. § 124.1; [Doc. 70-1, ¶ 22]. To qualify for the program, an SDB must be 51% owned by an individual who is socially and economically disadvantaged—as mandated in the Small Business Act [Doc. 73, ¶ 26]. Federal regulations match the definition of socially disadvantaged individuals to the statutory definition. See id. § 124.103(a). Individuals can establish social disadvantage by presenting evidence of one objective distinguishing feature, such as race or ethnic origin, that has contributed to social disadvantage. Id. § 124.103(c)(2)(i).
Defendant SBA also applies a rebuttable presumption to individuals of certain minority groups applying to the 8(a) program that qualifies them as presumptively socially disadvantaged [Id., ¶ 27]. Id. § 124.103(b)(1). The rebuttable presumption tracks Congress’s finding that certain minority groups suffered the effects of discriminatory practices, and it applies to Black Americans, Hispanic Americans, Native Americans, Asian Pacific Americans, Subcontinent Asian Americans, “and members of other groups designated from time to time by [Defendant] SBA.” Compare 15 U.S.C. § 631(f)(1)(C) with 13 C.F.R. § 124.103(b)(1). To qualify for the presumption, members of those groups must hold themselves out as a members of their group. 13 C.F.R. § 124.103(b)(2). Individuals who qualify for the rebuttable presumption do not have to submit evidence of social disadvantage [Id., ¶ 32]. The rebuttable presumption “may be overcome with credible evidence to the contrary,” and individuals with such evidence “should submit the information in writing to the Associate Administrator for Business Development (AA/BD) for consideration.” [Id., ¶ 28]; Id. § 124.103(b)(3). But Defendant SBA does not have a formal process for submitting evidence that could overcome the rebuttable presumption [Id., ¶ 29].
On March 4, 2020, Ultima filed the instant Complaint, alleging that Defendants engaged in race discrimination in violation of the Fifth Amendment of the United States’ Constitution and 42 U.S.C. § 1981 [Doc. 1]. Specifically, Ultima alleged that Defendants’ use of the rebuttable presumption for certain groups in the 8(a) program discriminated on the basis of race [Id., ¶¶ 41-47]. Ultima sought declaratory, injunctive, and monetary relief under several federal laws [Id., pgs. 9-11]. Defendants then moved to dismiss Ultima’s Complaint [Doc. 20]. The Court granted in part and denied in part Defendants’ motion, dismissing only Ultima’s claims under 42 U.S.C. § 1981 [Doc. 32]. The parties then began the discovery phase of litigation. During discovery, the parties each produced expert reports in support of their motions for summary
judgment. A. Expert Reports 1. Mr. Daniel Chow, Senior Economist, U.S. Department of Commerce Defendants produced an expert report by Daniel Chow, senior economist at the U.S. Department of Commerce’s Minority Business Development Agency [Doc. 58-3]. In his report, Mr. Chow noted that he prepared the report for the United States’ Department of Justice in connection with its representation of Defendants in this lawsuit [Id., pg. 4]. Mr. Chow reviewed data on government contracting “to assess the relationship between contracting outcomes for small businesses and the type of ownership of the business.” [Id.]. He focused on federal contracting “and the probability of certain classifiable businesses’ attainment of federal contracts in a specific period . . . (including businesses that participate in [Defendant SBA’s 8(a) program]).” [Id.]. Mr. Chow modeled his study after a 2012 study conducted by the former Deputy Chief Economist for the Department of Commerce’s Economics and Statistics Administration, Robert N. Rubinovitz, Ph.D. [Id.]. He noted that Rubinovitz’s study found that the odds for SDBs not participating in
the 8(a) program to win a federal contract were “roughly 11 percent lower” compared to the odds of non-SDB firms [Id.]. Mr. Chow studied data on government contracts for small businesses and factors that might influence the award of a contract to determine whether SDBs were more or less likely to win federal contracts relative to other small businesses [Id., pg. 5]. He relied on data from April 2019 to August 2020 and considered the impact on the “odds ratio” of small firms winning contracts, while holding other factors constant [Id.]. To conduct his study, Mr. Chow used the logit model of regression, which was the same method used in the Rubinovitz study [Id., pgs. 5-6]. Before discussing the remainder of Mr. Chow’s report, the Court finds it necessary to
explain his methodology. At the most general level, Mr. Chow used regression analysis to arrive at his results [See id., pg. 5]. A regression analysis is a method for modeling the relationships between variables, and it allows a researcher to predict the likelihood that a specific variable will occur based on the presence or absence of other variables [Id.]. The variable that a researcher hopes to predict is called the “dependent” variable, and the variables that a researcher uses to arrive at his predictions are called the “independent” variables [Id.]. In his report, Mr. Chow applied a specific form of regression analysis known as the logit model of regression, or logistic regression [Id.]. Often, a logistic regression predicts the likelihood of a dependent variable called a dichotomous variable. As the name suggests, dichotomous variables are variables with only two possible values. A dichotomous variable can be either true or false, yes or no, 0 or 1, and so on. Thus, a logistic regression helps determine what factors (independent variables) influence or produce a certain outcome (the dependent variable) [See id.]. Mr. Chow used the ownership of the firm, the type of organization, other firm characteristics, and whether the firm identified as an SDB and was part of the 8(a) program as his
independent variables [Id., pgs. 5-6]. Because the outcome is a probability, the dependent variable—the outcome itself—is bounded between 0 and 1. To easier interpret the results of a logistic regression, researches will calculate the results of the regression into an odds ratio [Id., pg. 8]. The odds ratio represents the odds that a dependent variable will occur given a particular independent variable, compared to the odds of the dependent variable occurring in the absence of that independent variable [See id., pg. 10]. If the odds ratio is greater than 1, then the independent variable is associated with a higher probability of generating the dependent variable [See Doc. 58-4, pg. 4]. Conversely, if the odds ratio is less than 1, then the independent variable is associated with a lower probability of that dependent variable occurring [See id.].
The results of Mr. Chow’s study showed that “woman-owned, minority owned, and other veteran-owned firms have lower odds than other firms to win a contract, all else being equal.” [Doc. 58-3, pg. 10]. Mr. Chow found that the odds of winning contracts for SDBs not participating in the 8(a) program were approximately 37% lower compared to the odds of winning contracts by firms that were not identified as SDBs and that this result was statistically significant [Id., pgs. 5, 10]. According to Mr. Chow, “[f]irms in the 8(a) program . . . have statistically significant and larger odds of winning a contract.” [Id., pg. 10]. Further, “[i]n about 90% of industries, accounting for over 99% of contracts, non-8(a) SDB firms’ odds of winning contracts are lower, all else equal, than other firms,” and in “50% of industries, representing over 93% of contracts, the odds of winning are statistically significantly lower.” [Id.]. Mr. Chow also found that “[m]inoirty[]owned firms’ odds of winning contracts are lower in about 67% of industries, representing over 50% of contracts.” [Id.]. During his deposition, Mr. Chow testified that the lower odds identified in his analysis were consistent with the presence of discrimination [Docs. 61-15, pg. 4; 70-1, ¶ 35]. He explained that he believed the results were consistent with the presence of discrimination because
he controlled for certain nondiscriminatory factors [Doc. 61-15, pg. 4]. Additionally, an economist from the United States’ Census Bureau’s Center for Economic Studies reviewed Mr. Chow’s report and approved his methodology [Doc. 68-4, ¶ 11]. 2. Dr. Jon Wainwright, Ph.D., Consulting Economist Defendants produced a second expert report from Dr. Jon Wainwright, Ph.D., a consulting economist [Doc. 59-1, pg. 10]. From 2010 to 2013, Dr. Wainwright served as an economic and statistical expert for the Department of Justice and testified in four cases raising similar issues as the instant lawsuit [Id., pg. 11]. Dr. Wainwright has “repeatedly qualified as an expert economic and statistical witness in both federal and state courts” and has “testified before the United States
Congress on these matters[.]” [Id., pg. 12]. Dr. Wainwright analyzed “whether minority business owners continue to face discrimination and the lingering effects thereof in the public contracting sector.” [Id., pg. 8]. Dr. Wainwright analyzed evidence from disparity studies commissioned by state and local governments and public contracting entities since 2010, the U.S. Census Bureau’s past and present data collection efforts dedicated to minority owned businesses, and the U.S. Census Bureau’s “American Community Survey.” [Id.] (emphasis omitted). He also reviewed the findings from 205 different disparity studies collected between 2010 and 2021 that spanned 32 states and the District of Columbia [Id.]. From his review of the 205 different disparity studies, Dr. Wainwright found that “disparities continue to exist in the utilization of minority[]owned businesses in public contracting relative to their availability in U.S. markets[.]” [Id., pg. 30]. According to Dr. Wainwright, “4 out of 5 disparities for minority[]owned businesses across all procurement categories are adverse, 3 out of 4 are large and adverse, and minority[]owned businesses facing large adverse disparities
tended to be utilized at less than one-fifth to just one-quarter of their availability.” [Id.]. He stated that his “general findings of widespread large and adverse disparities are documented in all procurement categories and for all minority groups.” [Id., pg. 32]. Dr. Wainwright observed from the 205 studies conducted since 2010 “large, adverse, and often statistically significant disparities facing minority[]owned business enterprises throughout the United States and across all government contracting and procurement categories and among all types of minority[]owned businesses.” [Id., pg. 39]. Additionally, Dr. Wainwright found large, adverse disparities in the utilization of minority owned business enterprises (“MBEs”) even when his analysis was restricted to studies published since 2017 [Doc. 70-1, ¶ 32].
Dr. Wainwright next examined data from the 2012 “Survey of Business Owners and Self- Employed Persons” (“SBO”) and the 2017 “Annual Survey of Businesses” (“ABS”) both conducted by the United States’ Census Bureau [Id., pgs. 40-58]. He concluded that “regardless of whether 2012 SBO data or the 2017 ABS data is examined, a pattern of large, adverse, and statistically significant disparities is consistently observed.” [Id., pg. 58]. Dr. Wainwright further concluded that the pattern “is evident in the economy as a whole, as well as in each major procurement category and industry sector . . . . [and] is observed for every minority group in the data—Blacks, Hispanics, Asians, Native Hawaiians and other Pacific Islanders, and American Indians and Alaska Natives.” [Id.]. Dr. Wainwright concluded, overall, that the data he reviewed “provide[d] strong evidence of large, adverse, and statistically significant disparities facing minority-owned business enterprises in the United States.” [Id., pg. 82; Doc. 70-1, ¶ 29]. Dr. Wainwright determined that “these disparities cannot be adequately explained by differences between the relevant populations in factors untainted by the effects of discrimination and are therefore consistent with the presence
of discrimination in the business market.” [Docs. 59-1, pg. 82; 70-1, ¶ 29]. He also concluded that these disparities occurred in the industries Ultima identified as relevant to this matter and that the disparities persisted when the results were disaggregated into race and ethnicity categories [Docs. 59-1, pg. 82; 70-1, ¶¶ 30-31]. 3. Dr. Jonathan Guryan, Ph.D., Labor Economist In response to Defendants’ experts, Ultima produced a report from Dr. Jonathan Guryan, Ph.D. [Doc. 59-4]. Dr. Guryan, a labor economist, received his Ph.D. in economics from the Massachusetts Institute of Technology after completing his undergraduate degree at Princeton University [Id., pg. 6]. He teaches at Northwestern University and previously served as the editor
of the Journal of Labor Economics [Id.]. Relevant to the instant lawsuit, Dr. Guryan has significant experience in statistical methods and regression analysis, and Ultima asked him to review the Defendants’ experts’ reports [Id., pgs. 4-7]. Dr. Guryan analyzed Defendants’ experts’ reports and offered his own opinions on their conclusions. Before addressing those reports, Dr. Guryan noted that disparities and discrimination are not the same thing [Id., pg. 10]. He explained that the existence of disparities in a particular market does not mean the disparity was caused by discrimination in the same market because those disparities could have been caused by non-discriminatory factors, discrimination outside the relevant market, and discrimination that affects individuals before their participation in the relevant market [Id., pg. 11]. According to Dr. Guryan, “[i]f [a] statistical analysis cannot rule out with a reasonable degree of certainty the possibility that factors other than discrimination in the market in question caused the disparities, then it cannot reject the hypothesis that the disparities are caused by things other than discrimination in the particular market.” [Id.]. Additionally, Dr. Guryan noted a “consensus among social scientists who study discrimination in markets that a regression
analysis [of the type used by Defendants’ experts] is a significantly flawed method of testing for the presence of discrimination.” [Id.]. Dr. Guryan addressed Dr. Wainwright’s report first, noting that some of his findings were based on studies of markets different from the markets covered by the 8(a) program and different from the markets in which Ultima operates [Id., pg. 17]. He stated that Dr. Wainwright’s review of the 205 disparity studies between 2010 and 2021 could be tainted by flaws in methods used by those studies [Id.]. Dr. Guryan also stated that those 205 studies “may be a selected sample” and that the majority of the studies were not specific to the industry in which Ultima operates [Id., pgs. 18-19]. According to Dr. Guryan, the disparity indexes on which Dr. Wainwright relied did not
account for non-discriminatory factors [Id., pgs. 20-22]. Dr. Guryan contended that many of the studies Dr. Wainwright reviewed did not control for firm capacity, which could explain the disparities Dr. Wainwright observed [Id., pgs. 22-23]. He next opined that Dr. Wainwright’s review of SBO, ABS, and ACS data was flawed because Dr. Wainwright did not distinguish between revenue businesses receive through contracting as opposed through other means, which shows that Dr. Wainwright cannot conclude the disparities are consistent with discrimination [Id., pgs. 23-27]. Dr. Guryan further opined that Dr. Wainwright’s review of that data is not specific to the industry in which Ultima operates [Id.]. As to Mr. Chow’s report, Dr. Guryan made similar comments regarding the lack of focus on the industry in which Ultima operates [Id., pgs. 27-28]. He also noted that Mr. Chow did not account for bidding behavior by firms in his report, which could explain the disparities Mr. Chow found in his report [Id., pgs. 28-29]. Dr. Guryan contended that Mr. Chow’s data and methodology do not support his conclusions because the data file that Mr. Chow produced to support his report
only shows firms that won contracts [Id., pgs. 30-31]. * * * Ultima now moves to exclude Mr. Chow’s report, and Defendants move to exclude Dr. Guryan’s report [Docs. 58, 59]. The parties have responded and replied to each motion [Docs. 67, 68, 69, 71]. This matter is now ripe for resolution. II. LEGAL STANDARDS Ultima moves to exclude the testimony of Mr. Chow under Federal Rule of Civil Procedure 26(a)(2)(B)(ii) and Federal Rule of Evidence 702 [Doc. 58], and Defendants move to exclude Dr. Guryan’s testimony under Fed. R. Evid. 702 only [Doc. 59].
A. Federal Rule of Civil Procedure 26(a)(B)(ii) Federal Rule of Civil Procedure 37(c)(1) states that “[i]f a party fails to provide information or identify a witness as required by Rule 26(a) or (e), the party is not allowed to use that information or witness to supply evidence on a motion, at a hearing, or at a trial unless the failure was substantially justified or harmless.” Fed. R. Civ. P. 37(c)(1). Rule 26(a)(2)(B) requires all expert reports to contain: (1) a complete statement of all opinions the witness will express and the basis and reasons for them; (2) the facts or data considered by the witness in forming them; (3) any exhibits that will be used to summarize or support them; (4) the witness's qualifications, including a list of all publications authored in the previous 10 years; (5) a list of all other cases from the previous 4 years in which the witness testified as an expert at trial or by deposition; and (6) a statement of the compensation to be paid for the study and testimony in the case. Fed. R. Civ. P. 26(a)(2)(B). Rule 37(c)(1) “requires absolute compliance with Rule 26(a).” R.C. Olmstead, Inc., v. CU Interface, LLC, 606 F.3d 262, 271 (6th Cir. 2010) (quoting Roberts v. Galen of Va., Inc., 325 F.3d 776, 782 (6th Cir. 2003).
B. Federal Rule of Evidence 702 Federal Rule of Evidence 702 governs the admissibility of expert evidence. It provides that a witness qualified as an expert by knowledge, skill, experience, training, or education may testify in the form of an opinion or otherwise if: (a) the expert's scientific, technical, or other specialized knowledge will help the trier of fact to understand the evidence or to determine a fact in issue; (b) the testimony is based on sufficient facts or data; (c) the testimony is the product of reliable principles and methods; and (d) the expert has reliably applied the principles and methods to the facts of the case.
Fed. R. Evid. 702. This rule “imposes a special obligation on a trial judge to ‘ensure that any and all scientific testimony . . . is not only relevant, but reliable.’” Kumho Tire Co. v. Carmichael, 526 U.S. 137, 147 (1999) (quoting Daubert v. Merrell Dow Pharmaceuticals, Inc., 509 U.S. 579, 589 (1993)). Federal courts serve a “basic gatekeeping obligation” for expert testimony. Id. The Court must strike a balance between liberal admissibility of relevant evidence and the need to exclude misleading “junk science.” Best v. Lowe’s Home Ctrs., Inc., 563 F.3d 171, 176–77 (6th Cir. 2009). III. ANALYSIS A. Ultima’s Motion to Exclude under Fed. R. Civ. P. 26(a)(B)(ii) [Doc. 58] Ultima argues that Mr. Chow’s report does not contain the basis and reasons for his opinions or the facts and data he considered in forming his opinions [Doc. 58-1, pg. 7]. Defendants respond that Mr. Chow’s report complied with the requirements of Rule 26(a)(2)(B) [Doc. 68, pgs. 6-7]. They contend that Mr. Chow accurately described his methodology and produced the data on which he relied [Id., pg. 9]. Defendants argue that Mr. Chow provided a detailed description of the data and methodology he used, the variables he controlled for, the ways in which he controlled for potential errors, and a discussion of his results [Id., pg. 10]. “[A]n expert opinion must ‘set forth facts’ and, in doing so, outline a line of reasoning
arising from a logical foundation.” Brainard v. Am. Skandia Life Assur. Corp., 432 F.3d 655, 657 (6th Cir. 2005). Specifically, “a report must be complete such that opposing counsel is not forced to depose an expert in order to avoid an ambush at trial; and moreover the report must be sufficiently complete as to shorten or decrease the need for expert depositions and thus to conserve resources.” R.C. Olmstead, Inc., 606 F.3d at 271 (quoting Salgado v. Gen. Motors Corp., 150 F.3d 735, 742 n.6 (7th Cir. 1998)). “Expert reports must include ‘how’ and ‘why’ the expert reached a particular result, not merely the expert’s conclusory opinions.” Id. In R.C. Olmstead, the Sixth Circuit affirmed the district court’s decision to exclude a plaintiff’s expert because the expert’s report did not comply with Rule 26(a)(2)(B). Id. at 270–72.
The Sixth Circuit noted that the expert produced only a two-page report that did not include a complete statement of all his opinions or the reasons for his opinions. Id. The Sixth Circuit further concluded that the expert provided only cursory support for his opinions. See id. Here, Mr. Chow provides a 22-page report that details his findings and conclusions [Doc. 58-3]. His report is organized and shows a breakdown of his methods and results [Id.]. In explaining his methodology, Mr. Chow states that he modeled his study on a widely available prior study and used the logit model of regression discussed above [Id., pg. 4]. Mr. Chow explains how the logit model of regression operates and the variables that he used in his regression analyses. [Id., pgs. 4-6]. Additionally, he includes the tables and figures on which he relied in conducting his regression analyses [Id.]. Mr. Chow’s report “outline[s] a line of reasoning arising from a logical foundation,” includes the “‘how’ and ‘why’” Mr. Chow reached his results, and avoids any ambush of Ultima at trial. Brainard, 432 F.3d at 657; R.C. Olmstead, Inc., 606 F.3d at 271. But Ultima contends that Mr. Chow did not mention “a critical 50 variable file which was vital to his conclusions and analysis.” [Doc. 58-1, pg. 8]. It asserts Mr. Chow’s failure to disclose
that file prevented it from evaluating the methods he used to reach his conclusions and questioning him properly during his deposition [Id.]. Ultima argues that when Mr. Chow finally disclosed the 50-variable file, he did not explain how he collapsed the data in it [Id., pg. 9]. Defendants respond that Rule 26(a)(2)(B) does not require them to disclose “every scrap of paper, every intermediate dataset, and every calculation” on which Mr. Chow relied [Doc. 68, pg. 7]. They state that they did not produce the data sets from the intermediate steps Mr. Chow took because those data sets were generated from the original data Defendants initially produced [Id., pg. 9]. Ultima replies that Mr. Chow’s testimony and later declarations are affirmatively misleading [Doc. 69, pgs. 2-4]. In his report, Mr. Chow noted that he used Stata statistical software in his analyses, which
is “a commonly used statistical software package.” [Docs. 68, pg. 3 n.1]. Shortly after disclosing his report, Defendants produced two data files to Ultima that contained all the raw data Mr. Chow used in his report [Doc. 68-2, ¶ 6]. Defendants also later produced the Stata code that Mr. Chow used in conducting his analyses and a third data file that merged the two previous data files Defendants produced [Id., ¶ 7; Doc. 68, pg. 5]. Ultima does not dispute that Defendants produced that information to it. Thus, Ultima possessed all the relevant information to conduct its own tests and analyses of the data in Mr. Chow’s report. As Ultima’s own expert notes, there are different ways to analyze data for disparities, with regression analysis being one of those methods. [See Doc. 59-4, pgs. 12-13; 68-2, ¶ 4]. Defendants produced all the raw data Mr. Chow relied on in late February, nearly a month before Dr. Guryan produced his report [Doc. 59-4]. That raw data encompasses the universe of information Mr. Chow relied on in his report and gave Dr. Guryan all the information he needed to review Mr. Chow’s methodology, which is what Rule 26(a)(2)(B) principally requires. Brainard, 432 F.3d at 657; R.C. Olmstead, Inc., 606 F.3d at 271. Further, Dr. Guryan anticipated that his own opinions about Defendants’ expert reports
might change, “[s]hould additional information become available.” [Id., pg. 32]. But Ultima does not provide a supplemental report from Dr. Guryan addressing the information that Defendants produced. Under such circumstances, the Court does not find that Defendants violated Rule 26(a)(2)(B), and Ultima’s Motion to Exclude is DENIED in this respect. B. Ultima’s Motion to Exclude under Fed. R. Evid. 702 [Doc. 58] Ultima next argues that Mr. Chow’s report does not satisfy the requirements of Rule 702 [Doc. 58-1, pg. 9]. Ultima states that Mr. Chow’s methodology is flawed because he did not consider bidding data and failed to separate out individual minority groups in his analysis [Id., pg. 11]. It explains that Mr. Chow did not include information about which businesses actually bid
on federal contracts [Id., pgs. 11-12]. Defendants respond that Mr. Chow’s report is both reliable and relevant [Doc. 68, pg. 12]. They explain that Mr. Chow used a testable method in his analysis and that regression analysis is well-regarded and accepted within the scientific community [Id., pg. 12]. Defendants note that Mr. Chow’s analysis included standard errors, tested for statistical significance, and was reviewed by an economist for the United States’ Census Bureau’s Center for Economic Studies [Id., pg. 13]. Determining the admissibility of expert testimony under Rule 702 entails a flexible inquiry. Daubert, 509 U.S. at 594. “In short, under Daubert and its progeny, a party proffering expert testimony must show by a preponderance of proof that the expert whose testimony is being offered is qualified and will testify to scientific knowledge that will assist the trier of fact in understanding and disposing of relevant issues.” Sigler v. Am. Honda Motor Co., 532 F.3d 469, 478 (6th Cir. 2008) (citing Pride v. BIC Corp., 218 F.3d 566, 578 (6th Cir. 2000)) (internal quotation marks omitted). Daubert thus requires a two-pronged inquiry for expert testimony. First, the Court must address whether the expert testimony is based on scientific knowledge. See id. Second, the Court
must consider whether the testimony will assist the trier of fact to understand or determine a fact in issue. See id. In considering the first prong, courts focus on “whether the reasoning or methodology underlying the testimony is scientifically valid.” Decker v. GE Healthcare Inc., 770 F.3d 378, 391 (6th Cir. 2014). The Supreme Court in Daubert set out the following non-exclusive list of factors for courts to consider whether an expert’s testimony is reliable: “(1) whether the theory or technique can be or has been tested; (2) whether it ‘has been subjected to peer review and publication’; (3) whether there is a ‘known or potential rate of error’; and (4) whether the theory or technique enjoys general acceptance in the relevant scientific community.” Pluck v. BP Oil
Pipeline Co., 640 F.3d 671, 677 (6th Cir. 2011) (quoting Daubert, 509 U.S. at 593–94). These factors “are not dispositive in every case” and should be applied “only where there are reasonable measures of reliability of expert testimony.” Gross v. Comm'r, 272 F.3d 333, 339 (6th Cir. 2001). Mr. Chow’s report meets each of the above factors. First, Mr. Chow relies on regression analysis, a commonplace and testable analytical technique that Ultima can replicate [See Docs. 58-3, pgs. 4-6; 59-4, pgs. 12-13]. Second, an economist from the United States’ Census Bureau’s Center for Economic Studies reviewed Mr. Chow’s report and approved his methodology [Doc. 68-4, ¶ 11]. Third, Mr. Chow accounted for standard errors in his analysis and included those values in his results [Doc. 58-3, pgs. 7, 12]. Fourth, Ultima’s own expert admits that regression analysis enjoys some acceptance among data scientists for use in studying disparities between groups [Doc. 59-4, pgs. 12-13]. Thus, Mr. Chow’s report bears the marks of reliability and uses standard scientific methodology to produce the results that he relies on to reach his conclusions. Decker, 770 F.3d at 391. Ultima next contends that Mr. Chow failed to break down his results by individual race or
national origin, which prevents the parties from determining the extent to which discrimination occurred against a particular race or national origin group [Doc. 58-1, pg. 13]. Defendants respond that Ultima offered no evidence that individuals of different races bid at different rates and that Ultima’s expert did not conduct any testing to determine whether bidding behavior would have changed Mr. Chow’s results [Doc. 68, pgs. 13-14]. Defendants assert that the omission of a variable from a regression analysis goes to the weight of the evidence and not its admissibility [Id., pg. 15]. Defendants contend that Mr. Chow’s decision not to separate out minority groups in his report does not diminish his findings about the disparities for minority-owned businesses overall [Id., pg. 17]. Ultima replies that the burden to show a variable does not differ by race falls on Mr.
Chow and that his failure to consider bidding data in his analysis renders his conclusions suspect [Doc. 69, pgs. 5-6]. Further, Ultima contends that Mr. Chow’s inability to separate out minority groups does not relieve Defendants of the burden to show disparities for individual race groups [Id., pgs. 6-7]. At this stage, the Court’s focus is on reliability rather than credibility, which is a question for the finder of fact. In re Scrap Metal Antitrust Litig., 527 F.3d 517, 529 (6th Cir. 2008). Thus, the Court must focus “solely on the principles and methodology, not on the conclusions they generate.” Daubert, 509 U.S. at 595. If expert testimony is “shaky but admissible,” the party challenging such testimony should do so through “[v]igorous cross-examination, presentation of contrary evidence, and careful instruction on the burden of proof.” Id. 596. Ultimately, “‘rejection of expert testimony is the exception, rather than the rule,’ and [courts] will generally permit testimony based on allegedly erroneous facts when there is some support for those facts in the record.” In re Scrap Metal Antitrust Litig., 527 F.3d at 530. Moreover, challenges to an expert’s factual assumptions go to the weight of the opinion rather than its admissibility. Id. at 529. If
Ultima does not agree with Mr. Chow’s conclusions, it can challenge the findings of his report through “cross-examination, presentation of contrary evidence, and careful instruction on the burden of proof.” Daubert, 509 U.S. at 596. It would be improper at this stage of litigation for the Court to determine whether Mr. Chow’s conclusions are credible. Id. at 595. The second Daubert prong relates to relevance and is straightforward. The Court must determine whether the proffered expert testimony is sufficiently tied to the facts of the case such that it will “assist the trier of fact to understand the evidence or to determine a fact in issue.” Daubert, 509 U.S. at 591. Here, Mr. Chow’s report analyzes disparities in several industries in which the federal government contracts with private businesses [Doc. 58-3]. Mr. Chow collected
data from the industries to determine whether and to what extent there are disparities in federal contracting between MBEs and non-minority owned businesses [Id., pg. 6]. He then controlled for a number of different variables that might explain any observed disparities [Id., pgs. 5-6]. The results of his report plainly are relevant to the issues before the Court in this matter. Thus, Mr. Chow’s expert report as to the disparities MBEs face in federal contracting meets the standards for admissibility. Utlima’s Motion to Exclude [Doc. 58] is DENIED. C. Defendants’ Motion to Exclude under Fed. R. Evid. 702 [Doc. 59] Defendants move to exclude Ultima’s rebuttal expert, Dr. Jonathan Guryan, under Rule 702 because his opinions are not reliable or relevant [Doc. 59, pg. 2]. Defendants assert that Dr. Guryan’s methodology is not scientifically reliable [Id., pg. 10]. They contend that Dr. Guryan did not employ any scientifically reliable methods to reach his conclusions and that his opinions are speculation [Id., pg. 11]. Defendants further contend that Dr. Guryan did not conduct an independent statistical analysis or collect any data in reaching his conclusions [Id.]. Ultima responds that Dr. Guryan is qualified to offer expert testimony on the use of statistics in quantifying
the effects of racial discrimination in labor markets [Doc. 67, pg. 3]. Ultima argues that Dr. Guryan’s report explains the role of statistics in Defendants’ expert reports and that a rebuttal expert is not required to conduct his own independent analysis or study in critiquing an opposing expert’s report [Id., pgs. 5-12]. Defendants reply that Dr. Guryan ignored “half of the data” used in Mr. Chow’s report when making his own conclusions [Doc. 71, pg. 2]. Defendants contend Dr. Guryan’s report and testimony go beyond general opinions about statistical principles, without a sufficient foundation in data or evidence [Id., pgs. 6-9]. Beginning with the first prong under Daubert, Dr. Guryan is imminently qualified to offer his analysis on Defendants’ experts’ reports. He holds a Ph.D. in economics from Massachusetts
Institute of Technology and taught graduate-level courses in quantitative methods and regression analysis [Doc. 59-4, pgs. 6-8]. Dr. Guryan has published research in a number of journals, some of which required him to analyze data sets similar to the sets used by Defendants’ experts [Id.]. Indeed, Dr. Guryan’s time as an editor of the Journal of Labor Economics provides directly relevant experience for him to review the statistics and results of Defendants’ experts’ reports [Id., pg. 6]. His background qualifies him to render opinions on the statistical methods on which Defendants’ experts rely. Further, Dr. Guryan’s review of Defendants’ experts’ reports is reliable because he uses a collection of generalized principles of statistical research gleaned from his background [Id., pgs. 10-16]. Dr. Guryan’s conclusions that statistical reliability and validity depend on the quality of data, that bias can result when variables are omitted from regression analyses, and that linking disparity to discrimination requires accounting for as many variables as possible, appear to be supported by those principles [Id.]. Defendants charge Dr. Guryan with relying on supposition rather than data [Doc. 59, pg. 12]. Defendants argue that Dr. Guryan’s critiques of disparity studies generally are non-expert
opinions based on unfounded assumptions [Id., pgs. 13-15]. Similarly, Defendants assert that Dr. Guryan’s critiques on the use of regression analyses to show discrimination, the evidence of disparities in the relevant industries, and Mr. Chow’s methodology are non-expert opinions [Id., pgs. 15-23]. Defendants lastly argue that Dr. Guryan’s report will not assist the Court in establishing necessary facts or understanding the issues [Id., pg. 24-25]. Ultima responds that Dr. Guryan’s opinions are relevant because they will help the Court evaluate Defendants’ experts’ opinions [Doc. 67, pgs. 12-16]. Ultima asserts that Dr. Guryan did not express any legal opinions in his report and that he only opined on the standards of social science [Id., pgs. 16-17]. Defendants’ arguments about Dr. Guryan’s analysis as speculative go to the probative
value that a fact finder should give his analysis rather than to its admissibility. Daubert, 509 U.S. at 595. The principles on which Dr. Guryan relies are broadly accepted ideas in social science and statistical analysis. The Court understands Dr. Guryan’s conclusions are directed generally at the type of studies that Defendants’ experts offer, but the generality of Dr. Guryan’s conclusions does not render them inadmissible. Indeed, the Court finds Dr. Guryan’s report helpful in framing the context for Defendants’ experts’ findings. The Court has not had much cause to engage in Defendants’ experts’ fields of study, and Dr. Guryan’s report and testimony setting out basic principles to consider while reviewing Defendants’ experts’ reports will foster a broader understanding of the issues in this case and provide much needed context to the tomes of results presented. For those reasons, the Court finds that Dr. Guryan’s report is relevant and would aid a fact finder in addressing the factual issues in this matter. Accordingly, Defendants’ motion [Doc. 59] is DENIED. IV. CONCLUSION For the reasons stated herein, the parties’ motions to exclude [Docs. 58, 59] are DENIED.
SO ORDERED: s/ Clifton L. Corker United States District Judge