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Signaling and the Education Premium

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  • Gregory Kurtzon

Abstract

A large portion of the rise in the education premium can be explained by a signaling theory of education which predicts that in the future, increases in the education level of the workforce will actually cause the education premium to rise, simply because different workers are being labeled as “highly educated†. This prediction is supported by past behavior of the high school education premium. It runs counter to the view that increases in the relative supply of high education workers will always lower education’s relative price. Suppose education does not affect an individual's productivity, but acts only as a signal of it because individuals select education based on their productivity, and wages are determined by productivity. It is shown that this implies additional education in the economy would not change the wage distribution. The education premium, or relative price of highly educated workers, is the ratio of mean high education wages to mean low education wages. If all workers gained more education, it would mean the “bar†(or productivity minimum) for a given level of education was being lowered. For example, suppose “highly educated†referred to a college education. If there were few college grads, lowering the bar (the most productive non-college grads becoming college grads) would reduce mean college wages significantly by adding lower productivity workers. Because there would be many non-college grads vs. college grads, a drop in the bar would cause a smaller fall in the mean non-college graduate wage by removing the most productive workers. It is shown that this implies the education premium would fall. However, if the bar was low enough so that there were many college grads and few non-college grads, the reverse would happen and further declines in the bar would cause education’s relative price to rise. This effect would not be due to real changes, but to changes in labeling. To measure how large this effect could have been, simulations were done to create counterfactual education premiums for three definitions of “highly educated†: (1) those with a college degree; (2) those with some college education; (3) those with a high school education. Premiums were created for the Census years 1950-2000 that hold the wage distribution the same as the previous decade, but allow the distribution of education across wage ranks to be the from the present year. These show what the premiums would have been if wages didn’t change but education levels changed as in the data. The simulations for (1) and (2) perform as expected: the simulated premiums fall when there are more high education individuals, and this can explain some or all of the observed changes in the education premium between the past six decades of census data. However, (3) also acts as the model predicts: because this definition has many more highly educated individuals, further increases in the supply of highly educated individuals lower the counterfactual premium. Thus, this model predicts that as the number of college graduates rises, additional grads will eventually cause the premium to increase.

Suggested Citation

  • Gregory Kurtzon, 2004. "Signaling and the Education Premium," Econometric Society 2004 North American Summer Meetings 397, Econometric Society.
    • Handle: RePEc:ecm:nasm04:397
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    1. Kevin M. Murphy & Finis Welch, 1992. "The Structure of Wages," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 107(1), pages 285-326.
    2. David Card & John E. DiNardo, 2002. "Skill-Biased Technological Change and Rising Wage Inequality: Some Problems and Puzzles," Journal of Labor Economics, University of Chicago Press, vol. 20(4), pages 733-783, October.
    3. Claudia Goldin & Robert A. Margo, 1992. "The Great Compression: The Wage Structure in the United States at Mid-Century," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 107(1), pages 1-34.
    4. Stiglitz, Joseph E, 1975. "The Theory of "Screening," Education, and the Distribution of Income," American Economic Review, American Economic Association, vol. 65(3), pages 283-300, June.
    5. Riley, John G, 1979. "Testing the Educational Screening Hypothesis," Journal of Political Economy, University of Chicago Press, vol. 87(5), pages 227-252, October.
    6. Juhn, Chinhui & Murphy, Kevin M & Pierce, Brooks, 1993. "Wage Inequality and the Rise in Returns to Skill," Journal of Political Economy, University of Chicago Press, vol. 101(3), pages 410-442, June.
    7. Michael Spence, 1973. "Job Market Signaling," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 87(3), pages 355-374.
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    More about this item

    Keywords

    signaling; education premium;

    JEL classification:

    • J31 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs - - - Wage Level and Structure; Wage Differentials
    • J24 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Human Capital; Skills; Occupational Choice; Labor Productivity
    • I20 - Health, Education, and Welfare - - Education - - - General

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