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Using Geographic Variation in College Proximity to Estimate the Return to Schooling

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  • David Card

    (Princeton University)

Abstract

Although schooling and earnings are highly correlated, social scientists have argued for decades over the causal effect of education. A convincing analysis of the causal link between education and earnings requires an exogenous source of variation in education outcomes. This paper explores the use of college proximity as an exogenous determinant of schooling. An examination of the NLS Young Men Cohort reveals that men who grew up in local labor markets with a nearby college have significantly higher education and significantly higher earnings than other men. The education and earnings gains are concentrated among men with poorly- educated parents -- men who would otherwise stop schooling at relatively low levels. When college proximity is taken as an exogenous determinant of schooling the implied instrumental variables estimates of the return to schooling are 25-60% higher than conventional ordinary least squares estimates. Since the effect of a nearby college on schooling attainment varies by family background it is possible to test whether college proximity is a legitimately exogenous determinant of schooling. The results affirm that marginal returns to education among children of less-educated parents are as high and perhaps much higher than the rates of return estimated by conventional methods.

Suggested Citation

  • David Card, 1993. "Using Geographic Variation in College Proximity to Estimate the Return to Schooling," Working Papers 696, Princeton University, Department of Economics, Industrial Relations Section..
  • Handle: RePEc:pri:indrel:317
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    References listed on IDEAS

    as
    1. George Psacharopoulos, 1985. "Returns to Education: A Further International Update and Implications," Journal of Human Resources, University of Wisconsin Press, vol. 20(4), pages 583-604.
    2. Imbens, G. & Angrist, J.D., 1992. "Average Causal Response with Variable Treatment Intensity," Harvard Institute of Economic Research Working Papers 1611, Harvard - Institute of Economic Research.
    3. David Card, 1994. "Earnings, Schooling, and Ability Revisited," Working Papers 710, Princeton University, Department of Economics, Industrial Relations Section..
    4. Thomas J. Kane & Cecilia E. Rouse, 1993. "Labor Market Returns to Two- And Four-Year College: Is a Credit a Credit And Do Degrees Matter?," Working Papers 690, Princeton University, Department of Economics, Industrial Relations Section..
    5. Thomas J. Kane & Cecilia Rouse, 1993. "Labor Market Returns to Two- And Four-Year College: Is A Credit a Credit And Do Degrees Matter?," Working Papers 690, Princeton University, Department of Economics, Industrial Relations Section..
    6. Orley Ashenfelter & Alan Krueger, 1992. "Estimates of the Economic Return to Schooling from a New Sample of Twins," Working Papers 683, Princeton University, Department of Economics, Industrial Relations Section..
    7. repec:fth:prinin:311 is not listed on IDEAS
    8. Thomas J. Kane & Cecilia E. Rouse, 1993. "Labor Market Returns to Two- and Four-Year Colleges: Is a Credit a Credit and Do Degrees Matter?," NBER Working Papers 4268, National Bureau of Economic Research, Inc.
    9. Lang, Kevin, 1993. "Ability Bias, Discount Rate Bias and the Return to Education," MPRA Paper 24651, University Library of Munich, Germany.
    10. Griliches, Zvi, 1976. "Wages of Very Young Men," Journal of Political Economy, University of Chicago Press, vol. 84(4), pages 69-85, August.
    11. Ashenfelter, Orley & Krueger, Alan B, 1994. "Estimates of the Economic Returns to Schooling from a New Sample of Twins," American Economic Review, American Economic Association, vol. 84(5), pages 1157-1173, December.
    12. Griliches, Zvi, 1977. "Estimating the Returns to Schooling: Some Econometric Problems," Econometrica, Econometric Society, vol. 45(1), pages 1-22, January.
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    More about this item

    Keywords

    return to education; college attendance; family background; NLS; young men;

    JEL classification:

    • C02 - Mathematical and Quantitative Methods - - General - - - Mathematical Economics
    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General

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