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The interpretation of multiple dummy variable coefficients: an application to industry effects in wage equations

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Abstract

The traditional textbook approach to avoiding the dummy trap problem is to delete a category from each qualitative variable. This paper illustrates an alternative constraint 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 Y macron 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.

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  • Joe Hirschberg & Jenny Lye, 2001. "The interpretation of multiple dummy variable coefficients: an application to industry effects in wage equations," Applied Economics Letters, Taylor & Francis Journals, vol. 8(11), pages 701-707.
  • Handle: RePEc:taf:apeclt:v:8:y:2001:i:11:p:701-707
    DOI: 10.1080/13504850110042187
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    1. Luisa Zanchi, "undated". "Alternative Parameterizations of Dummy Variable Models," Discussion Papers 95/26, Department of Economics, University of York.
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    Cited by:

    1. James Bugden, 2014. "Quality-Adjusted Repeat-Sale House Price Indices," Working Papers 2014.01, School of Economics, La Trobe University.
    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. 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.
    4. 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.
    5. 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.
    6. 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.
    7. Ostapchuk, Igor & Gagalyuk, Taras & Epshtein, David & Dibirov, Abusupyan, 2021. "What drives the acquisition behavior of agroholdings? Performance analysis of agricultural acquisition targets in Northwest Russia and Ukraine," International Food and Agribusiness Management Review, International Food and Agribusiness Management Association, vol. 24(4), February.
    8. Ostapchuk, Igor & Gagalyuk, Taras & Epshtein, David & Dibirov, Abusupyan, 2021. "What drives the acquisition behavior of agroholdings? Performance analysis of agricultural acquisition targets in Northwest Russia and Ukraine," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 24(4), pages 593-613.

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    More about this item

    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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