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The Impact Of Education On Income Distribution


  • Jan Tinbergen


In this paper the author adds some further empirical tests of his theory of income distribution. This theory (cf. this Review, Series 16, Number 3, September 1970, p. 221 ff) sees income distribution as the distribution of prices of production factors, especially labour, of different quality and prices as the effect of demand and supply factors. The quality of labour is represented only by the number of years of schooling. Its supply is described by the actual numbers of people having each of the possible years of schooling; this frequency distribution can be characterized by its average and by some measure of its dispersion or by one of its deciles (in particular the highest) expressed in terms of its median. The demand for the various qualities of labour can be supposed to be reflected by (i) total demand for commodities, but (ii) more accurately by the percentage of third‐level educated people used in and weighted by the size of the four main sectors of production: agriculture, manufacturing, trade and transport, and other services. Extensive material collected and reworked by Professors B. R. Chiswick for the U.S.A. and Canada and T. P. Schultz and L. S. Burns with H. E. Frech III for the Netherlands is used in cross‐section tests to explain variations in income distribution in the states of the U.S.A. and the provinces of Canada and the Netherlands. The results can be found in the tables. While further increase and smaller dispersion in years of schooling, according to some of the findings presented, would only moderately reduce the degree of inequality in the U.S.A. and Canada, more result seems to be possible according to other findings, including those for the Netherlands. In the latter category the second demand index mentioned above has been used. This paper is one of several devoted in various ways to the testing of the same theory.

Suggested Citation

  • Jan Tinbergen, 1972. "The Impact Of Education On Income Distribution," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 18(3), pages 255-265, September.
  • Handle: RePEc:bla:revinw:v:18:y:1972:i:3:p:255-265
    DOI: 10.1111/j.1475-4991.1972.tb00865.x

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    Cited by:

    1. Alessio Emanuele Biondo & Roberto Cellini & Tiziana Cuccia, 2020. "Choices on museum attendance: An agent‐based approach," Metroeconomica, Wiley Blackwell, vol. 71(4), pages 882-897, November.
    2. Moser, Mathias & Schnetzer, Matthias, 2014. "The Geography of Average Income and Inequality: Spatial Evidence from Austria," Department of Economics Working Paper Series 191, WU Vienna University of Economics and Business.
    3. Patrinos,Harry Anthony, 2020. "The Learning Challenge in the 21st Century," Policy Research Working Paper Series 9214, The World Bank.
    4. Andersen, Torben M, 2015. "Social background, education and inequality," CEPR Discussion Papers 10433, C.E.P.R. Discussion Papers.
    5. Ali R. Cannoni & James J. Jozefowicz, 2008. "Income Inequality and Educational Attainment Rates: The New York Story," New York Economic Review, New York State Economics Association (NYSEA), vol. 39(1), pages 28-43.
    6. Ghosh, sudeshna, 2017. "Education Attainment Forecasting and Economic Inequality United States," MPRA Paper 89712, University Library of Munich, Germany.

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