Higher Education

, Volume 60, Issue 4, pp 357–368 | Cite as

Wage determinants among medical doctors and nurses in Spain

  • Manuel Salas-Velasco


This paper examines the determination of wage rates for health professionals using three well known, and commonly used, econometric techniques: ordinary least squares, instrumental variables, and Heckman’s method. The data come from a graduate survey and the analysis focuses on a regional labor market, due to nationwide information on salaries is absent in Spain. After estimating different wage equations, the results suggest that OLS estimates are preferable. The findings show an important wage premium for medical doctors relative to nurses, but also a wage advantage for workers who are civil servants and a gender wage-gap that favors men. Although the expansion of higher education in Spain has reduced social inequalities in access, the main policy implication from this paper is that social class differences can still persist at the degree level—in the choice of degree—if students of higher socioeconomic status get a place at university in a degree of higher earnings, a fact that is corroborated in this study.


Health professionals Instrumental variables Heckit method Earnings differential Degree choice 



The author would like to thank the anonymous referees of this paper for the useful comments and suggestions on a previous manuscript.


  1. Albert, C. (2000). Higher education demand in Spain: The influence of labour market signals and family background. Higher Education, 40, 147–162.CrossRefGoogle Scholar
  2. Barnow, B. S., Cain, G. G., & Goldberger, A. S. (1980). Issues in the analysis of selection bias. Institute for Research on Poverty Discussion Papers 600.Google Scholar
  3. Becker, G. S. (1964). Human capital. New York: National Bureau of Economic Research.Google Scholar
  4. Cameron, S. V., & Heckman, J. J. (1998). Life cycle schooling and dynamic selection bias: Models and evidence for five cohorts of American males. Journal of Political Economy, 106, 262–333.CrossRefGoogle Scholar
  5. Cameron, S. V., & Heckman, J. J. (2001). The dynamics of educational attainment for Black, Hispanic, and White males. Journal of Political Economy, 109, 455–499.CrossRefGoogle Scholar
  6. Doeringer, P. B., & Piore, M. J. (1971). Internal labor markets and manpower analysis. Lexington, MA: DC Heath.Google Scholar
  7. Duncan, G. M., & Leigh, D. E. (1985). The endogeneity of union status: An empirical test. Journal of Labor Economics, 3, 385–402.CrossRefGoogle Scholar
  8. Elliott, B. (2003). Labour markets in the NHS: An agenda for research. Health Economics, 12, 797–801.CrossRefGoogle Scholar
  9. Galarneau, D. (2004). Health care professionals. Perspectives on Labour and Income, 16, 16–29.Google Scholar
  10. Greene, W. H. (1995). LIMDEP: Version 7.0. User’s manual. Bellport, NY: Econometric Software.Google Scholar
  11. Halvorsen, R., & Palmquist, R. (1980). The interpretation of dummy variables in semilogarithmic equations. American Economic Review, 70, 474–475.Google Scholar
  12. Hausman, J. A. (1978). Specification test in econometrics. Econometrica, 46, 1251–1272.CrossRefGoogle Scholar
  13. Heckman, J. J. (1976). The common structure of statistical models of truncation, sample selection, and limited dependent variables and a simple estimator for such models. Annals of Economic and Social Measurement, 5, 475–492.Google Scholar
  14. Heckman, J. J. (1979). Sample selection bias as a specification error. Econometrica, 47, 153–161.CrossRefGoogle Scholar
  15. Hill, R. C., Adkins, L. C., & Bender, K. A. (2003). Test statistics and critical values in selectivity models. In T. B. Fomby & R. C. Hill (Eds.), Maximum likelihood estimation of misspecified models: Twenty years later, advances in econometrics (Vol. 17, pp. 75–105). New York: Elsevier.CrossRefGoogle Scholar
  16. Lassibille, G., & Navarro, M. L. (1998). The evolution of returns to education in Spain 1980–1991. Education Economics, 6, 3–9.CrossRefGoogle Scholar
  17. Levin, J., & Plug, E. J. S. (1999). Instrumenting education and the returns to schooling in the Netherlands. Labour Economics, 6, 521–534.CrossRefGoogle Scholar
  18. Mincer, J. (1974). Schooling, experience and earnings. New York: National Bureau of Economic Research.Google Scholar
  19. Mora, J. G. (1997). Equity in Spanish higher education. Higher Education, 33, 233–249.CrossRefGoogle Scholar
  20. Puhani, P. A. (2000). The Heckman correction for sample selection and its critique. Journal of Economic Surveys, 14, 53–68.CrossRefGoogle Scholar
  21. Schumacher, E. J. (2009). Does public or not-for-profit status affect the earnings of hospital workers? Journal of Labor Research, 30, 9–34.CrossRefGoogle Scholar
  22. Thurow, L. C. (1975). Generating inequality: Mechanisms of distribution in the U.S. economy. New York: Basic Books.Google Scholar
  23. Vella, F. (1998). Estimating models with sample selection bias: A survey. Journal of Human Resources, 33, 127–172.CrossRefGoogle Scholar
  24. Vella, F., & Verbeek, M. (1999). Estimating and interpreting models with endogenous treatment effects. Journal of Business and Economic Statistics, 17, 473–478.CrossRefGoogle Scholar
  25. Vila, L., & Mora, J. G. (1998). Changing returns to education in Spain during the 1980s. Economics of Education Review, 17, 173–178.CrossRefGoogle Scholar
  26. Willis, R., & Rosen, S. (1979). Education and self-selection. Journal of Political Economy, 87, S1–S36.CrossRefGoogle Scholar
  27. Wilson, R. A. (1987). Returns to entering the medical profession in the U.K. Journal of Health Economics, 6, 339–363.CrossRefGoogle Scholar
  28. Wooldridge, J. M. (2003). Introductory econometrics: A modern approach (2nd ed.). Mason, OH: South-Western.Google Scholar

Copyright information

© Springer Science+Business Media B.V. 2009

Authors and Affiliations

  1. 1.University of GranadaGranadaSpain

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