Academic journal article Economic Inquiry

Discrimination, Bayesian Updating of Employer Beliefs, and Human Capital Accumulation

Academic journal article Economic Inquiry

Discrimination, Bayesian Updating of Employer Beliefs, and Human Capital Accumulation

Article excerpt

I. INTRODUCTION

Can labor market discrimination persist over time? How do employer beliefs influence minority groups and their choices? Previous theoretical models of discrimination can be grouped into two distinct categories based upon the source of the discrimination. The most common method of modelling discrimination follows the work of Becker [1972] in which the employer, manager, or other employees receive disutility from associating with members of a particular group. In a second class of models referred to as statistical discrimination, initially proposed by Phelps [1972], discrimination results from differences in the groups' ability to signal output or capabilities; this result can be reproduced as a special case of the model presented here. This paper proposes an alternative model in which discrimination results initially from differences in the employer's prior opinion of average group ability. Using a Bayesian updating model, we analyze the dynamic effects of this prior belief on human capital acquisition and the potential for continued discrimination.

Previous research provides an abundance of evidence that society's initial perceptions about numerous characteristics, including those affecting productivity, differ by group. For example, Smith [1990] summarizes the results of the General Social Survey, which asked individuals to rank ethnic groups on a scale of one to seven in various categories.(1) The survey found a significant portion of those surveyed believed that African Americans, Hispanics, and white Southerners lagged behind the rest of society in intelligence and work effort. In fact, 53.2 percent of those surveyed ranked African Americans below whites in intelligence. Unless employers differ systematically from the rest of society, this evidence implies that the initial or prior employer assessment of ability will be lower for members of these groups.

In addition to strong evidence that such general priors exist, numerous studies find that prior beliefs influence the evaluation of employee ability. The Urban Institute recently conducted a study comparing the evaluation of black and Hispanic workers to that of equally qualified white workers.(2) Given equal resumes, the black and Hispanic job candidates trailed white candidates at every stage in the job seeking process, from receiving fewer initial interviews to a lower number of job offers. Similar biases have been found in experiments in which identical resumes were evaluated differently simply due to the race or gender of the applicant.(3) Furthermore, even if worker output is unambiguously observed and evaluated in an unbiased setting, studies find that success of males is attributed to ability while that of females is more likely to be attributed to luck.(4) This adherence to initial perceptions suggests that agents believe strongly in their initial beliefs, and hence would require substantial evidence to alter their perceptions. Our model incorporates the strength of the prior beliefs through a variance parameter surrounding the initial belief of mean ability level; a lower variance implies a decreased willingness to admit error in one's prior beliefs and update accordingly.

Discrimination, in our model, results from the entrepreneur's sincere, although perhaps incorrect, beliefs concerning the distribution of ability levels of a particular group. Although empirical results provide strong evidence of labor market discrimination persisting over many years [Cain 1986], most existing theoretical models of discrimination are static designs used to explain only within-period discrimination. We propose a dynamic model in which a Bayesian employer learns about an employee's ability over time based on output observed up to a stochastic error term, thus updating prior beliefs concerning both the individual and the group in each period. Viewing output as a signal of ability, it is clear that traditional statistical discrimination models are embedded within ours; the special case in which one group's signal is less reliable than another's can produce the statistical discrimination results. …

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