Experience, Tenure, and the Perceptions of Employers.

• Author: Lewis, Danielle
• Position: Labor relations research - Statistical Data Included

Danielle Lewis [*]

Dek Terrell [+]

This paper examines how group-based assessments concerning employee ability impact employee compensation. The employer learns about worker ability through Bayesian updating, creating an additional channel for wage growth that is not available to those workers with only general labor market experience. Consistent with the model's predictions, results from National Longitudinal Survey of Youth (NLSY) indicate that black workers fare much better relative to white workers in returns to tenure than in returns to experience. Finally, parameter estimates in the structural model suggest that employers initially undervalue black males but that their wages rise with learning by employers over time.

1. Introduction

   On average black males earn lower wages than white males, just as average wages differ across many groups. This paper examines one explanation for this wage gap: the possibility that employers underestimate the ability of workers in one or more groups. In particular, this paper proposes a model that provides explicit predictions about the affect of employer's perceptions on workers' returns to general experience and tenure on the job. We then examine the wage growth of young males to see whether patterns are consistent with the model.

   Economic theory provides many other explanations for differences in wages across groups. One possible explanation is that, on average, the marginal product of labor is lower for one group than other groups. There are many possible explanations for the disparity in the marginal productivity across groups. Card and Krueger (1992) argue that differences in the quality of education between black and white workers have been a key factor in generating the wage gap over time. However, wages may also differ due to some unobservable factors. Herrnstein and Murray's (1994) controversial Bell Curve asserts that wage differences simply reflect differences in average intelligence across groups. Lundberg and Startz (1998) offer a dynamic model where one group possesses lower ethnic capital, perhaps as the result of past discrimination, which leads to persistent wage gaps.

   Discrimination offers another explanation for observed wage gaps. Dainty and Mason (1998) summarize the theories and empirical evidence of discrimination. In Becker's (1971) neoclassical "taste for discrimination model," discrimination occurs because employers, managers, or customers prefer not to associate with the members of a specific group. Statistical discrimination provides another explanation. In these models, the variance of ability is higher for one group than another group. It is interesting to note that the implications of statistical discrimination vary across models. In Phelps's (1972) original model, some individuals in the high-variance group earned higher wages than the other group, while others earn lower wages. However, the average wage of the high-variance group and low-variance group was equal. Lundberg and Startz's (1983) extension of this model generates lower average wages for the high-variance group. However, Berk (1999) offers an alternative model where the high-variance group earns higher wages.

   Still another potential explanation for wage differences is that employers inaccurately perceive some groups as less productive. The literature contains both survey evidence and experiments that suggest that employers perceive blacks negatively in terms of one or more attributes that affect worker productivity. [1] Furthermore, learning may not quickly eliminate misperceptions. Almeida and Kanekar (1989) and Wong, Derlega, and Colson (1988) provide evidence that agents tend to attribute positive outcomes to good luck if they begin with negative opinions about a group. Given these results, Farmer and Terrell (1996) suggest the possibility that employers underestimate the ability of workers in some groups.

   Farmer and Terrell (1996) examine the impact of inaccurate perceptions of ability in a model with two types of learning. In their model, employers learn about individual ability through observing individual output but use group averages to estimate group ability. A key result of that model is that employers' perceptions of average group ability do not always converge to the true average. This result is driven by the fact that perceptions affect the return to investment in human capital.

   This paper proposes a variant of the Farmer and Terrell model that provides explicit predictions about wage growth of undervalued workers as they accumulate tenure on the job and experience. Implicitly, we take employer perceptions of group ability as given and do not allow them to change with observations of individual ability. [2] Clearly, the output of a single worker tells the employer much more about her ability than the average ability of a group of millions. Thus, we focus exclusively on the problem of learning about individual ability. The key prediction of our model is that undervalued employees benefit from employer learning, which boosts the returns to tenure on the job.

   To investigate the empirical content of this model, we analyze the returns to general labor market experience and tenure on the job for black and white workers in the National Longitudinal Survey of Youth (NLSY). Then we estimate the parameters of the employer's prior distribution. The prior mean summarizes the extent to which the employer's prior assessments undervalue black workers and the prior variance measures the strength of these beliefs and ascertains the rate of learning.

   Section 2 of this paper specifies the theoretical model, and section 3 summarizes the data. Section 4 contains estimates of the returns to general labor market experience and tenure on the job for blacks and whites and examines returns across groups stratified on the basis of occupation and AFQT score. Section 5 includes the structural model's prior parameter estimates, and section 6 contains some final remarks.

2. The Model

   We assume a competitive labor market where a worker supplies one unit of labor each period and is paid his expected marginal product. The output of the worker is determined by the Cobb-Douglas production function:

   y = [A.sup.[alpha]][X.sup.[beta]][J.sup.[gamma]][e.sup.[epsilon]], (1)

   where y denotes worker output, A denotes worker ability, X denotes general labor market experience, J denotes job specific experience, and [epsilon] is a stochastic error component. We assume that [epsilon] [sim] IID N(O, [[sigma].sup.2]). Following, Becker's (1975) and Mincer's (1974) human capital models, workers accumulate general human capital with general work experience and specific human capital with tenure on the job. We assume that the parameters [alpha], [beta], and [gamma] are constant over time and add subscripts to y, X, J, and [epsilon] to denote values for specific time periods. Adding subscripts and taking the natural logarithms implies that the employee's output in period t is

   ln [y.sub.t] = [alpha] ln A + [beta] ln [X.sub.t] + [gamma] ln [J.sub.t] + + [[epsilon].sub.t]. (2)

   We assume that worker ability (A) remains constant over time. However, wages are determined by the employer's perception of worker ability, which does change with observations of output. We summarize the employer's initial belief about employee ability with the prior distribution:

   P([alpha] ln A) [sim] N([[micro].sub.prior], [[[sigma].sup.2].sub.prior] (3)

   As in most applications, the mean of a normal prior distribution reflects the best guess about the parameter, and the variance is a measure of the certainty of beliefs. Because prior opinions depend on the employee's group, [[micro].sub.prior] and [[[sigma].sup.2].sub.prior] vary across worker groups. [3] For new employees, the employer has no other information regarding ability; thus, E[[alpha] ln A] = [[micro].sub.prior], and the employee's initial log wage is 1n [w.sub.0] = E[ln [y.sub.0] = [[micro].sub.prior] + [beta] 1n [X.sub.0]. [4] In later periods, the employer updates priors on the basis of observations of output. [5] Thus, after T observations of output, the employer sets the wage in T + 1 based on the conditional expectation:

   ln [w.sub.T+1] = E[ln [y.sub.T+1]\[y.sub.1],[y.sub.2],...,[y.sub.T]] = E[[alpha] ln A]\[y.sub.1],[y.sub.2],...,[y.sub.T]] + [beta] ln [X.sub.T+1] + [gamma] ln [J.sub.T+1]. (4)

   The key problem is to determine E[[alpha] ln A]\ [y.sub.1], [y.sub.2],...,[y.sub.T]], the employer's perception of ability conditional on T observations of output. We approach the problem by assuming that the employer updates priors using Bayesian updating. In this approach, the first step requires finding the distribution of output conditional on ability. Based on Equation 2, we know that

   P(ln [y.sub.t]\[alpha] In A) [sim] N([alpha] ln A + [beta] ln [X.sub.t] + [gamma] ln [J.sub.t], [[sigma].sup.2]) or (5)

   f(ln [y.sub.t]\[alpha] ln A) = 1/[sigma][square root]2[pi] exp [[-1/2[[sigma].sup.2][(ln [y.sub.t] - [alpha] ln A - [beta] ln [X.sub.t] - [gamma] ln [J.sub.t]).sup.2]]. (6)

   Assuming independence of the errors over time, we can summarize the distribution for these T observations of output, conditional on ability, as

   f(ln [y.sub.t], ln [y.sub.2], ..., ln [y.sub.T]\[alpha] ln A)

   = [(2[pi][[sigma].sup.2]).sup.-T/2] exp[-1/2[[sigma].sup.2] [[[sigma].sup.T].sub.t=l] [(ln [y.sub.t] - [alpha] ln A - [beta] ln [X.sub.t] - [gamma] ln [J.sub.t]).sup.2]]. (7)

   We combine the employer's prior information with the observed levels of output to summarize the employer's assessment of worker ability after T periods using Bayes's rule:

   P([alpha] ln A\ln [y.sub.1],..., ln [y.sub.T]) = P(ln [y.sub.1], ..., ln [y.sub.T]\[alpha] ln A)P([alpha] ln A)/P(ln [y.sub.t], ..., ln [y.sub.T]) (8)

   This problem simply combines a normal prior with known variance with a normal likelihood function with known variance and implies the following posterior density function: [6]

   P([alpha] ln A\ln [y.sub.1], ..., ln...