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The Interpretation of Multiple Dummy Variable Coefficients: An Application to Industry Effects in Wage Equations



The traditional textbook approach to avoiding the dummy trap problem is to delete a category from each qualitative variable. We illustrate an alternative constraint introduced by Sweeney and Ulveling (1972) which can be used to transform conventional dummy variable coefficients. This constraint serves to simplify their interpretation when the regression equation contains several qualitative variables and allows the computation of a coefficient for the deleted class. Using this constraint the intercept term can be written as the mean and the coefficients for the dummy variables are now interpreted as differences from the mean of the dependent variable rather than the deleted class. Computation of a standard error for the estimated coefficient of the deleted class is also discussed. An application to examine the importance of industry wage affiliation in explaining relative wages is presented.

Suggested Citation

  • Hirschberg, J. & Lye, J., 1999. "The Interpretation of Multiple Dummy Variable Coefficients: An Application to Industry Effects in Wage Equations," Department of Economics - Working Papers Series 716, The University of Melbourne.
  • Handle: RePEc:mlb:wpaper:716

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    References listed on IDEAS

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    Cited by:

    1. Nivedita Mukherji & Jonathan Silberman, 2013. "Absorptive Capacity, Knowledge Flows, And Innovation In U.S. Metropolitan Areas," Journal of Regional Science, Wiley Blackwell, vol. 53(3), pages 392-417, August.
    2. Hipolito Simon & Raul Ramos & Esteban Sanroma, 2006. "Collective bargaining and regional wage differences in Spain: an empirical analysis," Applied Economics, Taylor & Francis Journals, vol. 38(15), pages 1749-1760.
    3. James Bugden, 2014. "Quality-Adjusted Repeat-Sale House Price Indices," Working Papers 2014.01, School of Economics, La Trobe University.
    4. Marco Corsino & Roberto Gabriele & Sandro Trento, 2010. "Job flows in Italian SMEs: a longitudinal analysis of growth, size and age," DISA Working Papers 1008, Department of Computer and Management Sciences, University of Trento, Italy, revised 22 Dec 2010.
    5. Manfred Grotenhuis & Ben Pelzer & Rob Eisinga & Rense Nieuwenhuis & Alexander Schmidt-Catran & Ruben Konig, 2017. "When size matters: advantages of weighted effect coding in observational studies," International Journal of Public Health, Springer;Swiss School of Public Health (SSPH+), vol. 62(1), pages 163-167, January.

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    JEL classification:

    • C60 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - General
    • C00 - Mathematical and Quantitative Methods - - General - - - General


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