Detecting Discrimination: Analyzing Racial Disparities in Public Contracting

Abstract

InCity of Richmond v. J. A. Croson Co.(1989), the Supreme Court established strict scrutiny as the standard applicable to affirmative action programs which set aside quotas of public contracts for minority-owned businesses, andAderand v. Pena(1995) extended the strict scrutiny standard to federal programs. Although the requirements of these decisions clearly require multivariate statistical analysis, most “disparity studies” have used a univariate comparison between the expected and the observed shares of contracts going to minority-owned firms. We examine four statistical methods—ordinary least square multiple regression, logit and tobit models, and a multivariate procedure for comparing expected and observed outcomes. Because no data are presently available at the level of specificity required byCroson,we constructed synthetic data sets to represent typical variations among large U.S. cities. Applying the statistical methods to each data set allows evaluation of the extent to which each method is able to both remove spurious and detect valid estimates of racial disparity when relevant control variables are added. Findings: (a) All four models removed apparent disparities which, although significant in univariate analysis, were known to be spurious. (b) Tobit and logit models, whose underlying assumptions better fit the nature of public contracting data, provided more accurate and more sensitive estimates than OLS regression. (c) Comparison of expected and observed outcomes within categories of control variables yielded results very similar to logit and tobit models and, because of the nature of the comparison specified inCroson,produced slightly more sensitive probability estimates.