We provide a test for statistical discrimination or
"rational" stereotyping in environments in which agents learn over
time. Our application is to the labor market. If profit maximizing
firms have limited information about the general productivity of
new workers, they may choose to use easily observable characteristics
such as years of education to "statistically discriminate" among
workers. As firms acquire more information about a worker, pay will
become more dependent on actual productivity and less dependent
on easily observable characteristics or credentials that predict
productivity. Consider a wage equation that contains both the interaction
between experience and a hard-to-observe variable that is positively
related to productivity, and the interaction between experience
and a variable that firms can easily observe, such as years of education.
We show that the wage coefficient on the unobservable productivity
variable should rise with time in the labor market and the wage
coefficient on education should fall. We investigate this proposition
using panel data on education, the AFQT test, father's education,
and wages for young men and their siblings from NLSY. We also examine
the empirical implications of statistical discrimination on the
basis of race. Our results support the hypothesis of statistical
discrimination, although they are inconsistent with the hypothesis
that firms fully utilize the information in race. Our analysis has
wide implications for the analysis of the determinants of wage growth
and productivity and the analysis of statistical discrimination
in the labor market and elsewhere.
Joseph G. Altonji, Department
of Economics, Northwestern University
Charles R. Pierret, Bureau of Labor Statistics
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