WAGES, EMPLOYMENT, AND STATISTICAL DISCRIMINATION: EVIDENCE FROM THE LABORATORY

I. INTRODUCTION While taste-based discrimination (Becker 1957) is driven by prejudice, research on statistical discrimination attempts to explain differential treatment of individuals unrelated to prejudice. In essence, statistical discrimination results when the actual or assumed statistical properties of a group are applied to anyone belonging to that group. Differential treatment based on lower average outcomes of one's group (e.g., minorities, females) was considered a starting point for modeling of statistical discrimination (see Phelps 1972). However, labor market researchers then considered that statistical discrimination could result from measures other than the average outcomes of one's group (see Aigner and Cain 1977; Lundberg and Startz 1983). Theoretical models have explored various reasons why statistical discrimination might arise in varied contexts. Most notable are the models based on differential screening or communication costs (Cornell and Welch 1996; Lang 1986), noisier productivity signals (see discussion in Aigner and Cain 1977), or incomplete information (Lundberg and Startz 1983). (1) Field studies, some of which involve experimental manipulations, have uncovered evidence of statistical discrimination in mortgage lending (Ladd 1998), auto sales (Ayers and Siegelman 1995; Goldberg 1996; Harless and Hoffer 2002), sports card price negotiations (List 2004), law enforcement decisions (Applebaum 1996), exam grading (Hanna and Linden 2012), taxi fare negotiations (Castillo et al. 2013), and vehicle repair estimates (Gneezy and List 2006). In labor markets, some discrimination may be due to factors other than productivity characteristics (Neumark 1999), but the evidence for statistical discrimination based on race has been elusive (see Altonji and Pierret 2001). Identifying statistical discrimination from field data is complicated by the fact that it may arise as first-moment or second-moment statistical discrimination, and they are difficult to disentangle empirically. More controlled laboratory studies have also examined statistical discrimination (Anderson and Haupert 1999; Castillo and Petrie 2010; Davis 1987; Dickinson and Oaxaca 2009; Fershtman and Gneezy 2001; Masclet, Peterle, and Larribeau 2012). (2) Findings from these laboratory studies indicate that statistical discrimination may result from aversion to risk, mistaken stereotypes, biased probability assessments, or incomplete information. The benefit of the laboratory approach is our ability to control and cleanly identify the source of the discrimination, if it exists. Whether taste-based or statistical, discrimination is most always measured along a single dimension, such as vehicle pricing, labor market wages, group choice, or job assignments. (3) However, in many instances multiple avenues for discrimination exist simultaneously and to focus on only one may produce a systematically biased view of the prevalence of statistically based discrimination. (4) In this paper, we examine statistical discrimination in a controlled experimental environment. Statistical discrimination in our study can only be based on productivity-distribution risk attached to worker groups. We not only cleanly separate taste-based from statistical discrimination, but we also cleanly isolate second-moment statistical discrimination in a way not possible from field data. Building on Dickinson and Oaxaca (2009), our key contribution is to examine an environment in which discrimination may be exercised simultaneously along the dimensions of both wages and employment rates. This more closely approximates the field environments we hope to study, where discrimination may exist in terms of labor market wages and/or hiring practices, auto sales prices and/or sales rates, mortgage rates and/or home sales. As in Dickinson and Oaxaca (2009), subjects negotiate in a simulated labor market where worker subjects are given an induced common-knowledge productivity distribution. …