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Inter-Regional Wage Differentials in Portugal: An Analysis Across the Wage Distribution

Author

Listed:
  • Maria Aurora Murcho Galego

    (CEFAGE)

  • João Manuel Rodrigues Pereira

    (CEFAGE)

Abstract

Typically, studies on regional wage differentials are based on OLS estimates and use Blinder (1973) and Oaxaca (1973) decomposition. Quantile regression is an alternative approach which allows for studying these differences across the whole wage distribution. In this study, the quantile regression framework is considered for the analysis of regional wage differences in Portugal. Our findings reveal significant differences in wage equations coefficients between regions for the various quantiles. Furthermore, we conclude that the regional wage differentials and the components explained by differences in endowments and differences in returns increase across the whole wage distribution.

Suggested Citation

  • Maria Aurora Murcho Galego & João Manuel Rodrigues Pereira, 2011. "Inter-Regional Wage Differentials in Portugal: An Analysis Across the Wage Distribution," CEFAGE-UE Working Papers 2011_25, University of Evora, CEFAGE-UE (Portugal).
    • Handle: RePEc:cfe:wpcefa:2011_25
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    Cited by:

    1. Rycx, François & Saks, Yves & Tojerow, Ilan, 2016. "Misalignment of Productivity and Wages across Regions? Evidence from Belgian Matched Panel Data," IZA Discussion Papers 10336, Institute of Labor Economics (IZA).
    2. Eva Lajtkepová, 2020. "Distribution of Wages in the Regions of the Czech Republic," ACTA VSFS, University of Finance and Administration, vol. 14(2), pages 123-136.
    3. Joao Pereira & Aurora Galego, 2013. "Intra-Regional Regional Wage Inequality In Portugal: A Quantile Based Decomposition Analisys," ERSA conference papers ersa13p158, European Regional Science Association.
    4. Paula Herrera-Idárraga & Enrique López-Bazo & Elisabet Motellón, 2016. "Regional Wage Gaps, Education and Informality in an Emerging Country: The Case of Colombia," Spatial Economic Analysis, Taylor & Francis Journals, vol. 11(4), pages 432-456, October.
    5. Aurora Galego & João Pereira, 2014. "Decomposition of Regional Wage Differences Along the Wage Distribution in Portugal: The Importance of Covariates," Environment and Planning A, , vol. 46(10), pages 2514-2532, October.
    6. Antonio Garofalo & Rosalia Castellano & Gennaro Punzo & Gaetano Musella, 2018. "Skills and labour incomes: how unequal is Italy as part of the Southern European countries?," Quality & Quantity: International Journal of Methodology, Springer, vol. 52(4), pages 1471-1500, July.
    7. Jean-Baptiste Combes & Eric Delattre & Bob Elliott & Diane Skåtun, 2015. "Hospital staffing and local pay: an investigation into the impact of local variations in the competitiveness of nurses’ pay on the staffing of hospitals in France," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 16(7), pages 763-780, September.
    8. Rokicki, Bartłomiej, . "Ewolucja regionalnego zróżnicowania płac realnych w Polsce," Gospodarka Narodowa-The Polish Journal of Economics, Szkoła Główna Handlowa w Warszawie / SGH Warsaw School of Economics, vol. 2013(9).
    9. Rodrigo Oliveira & Raul da Mota Silveira Neto, 2021. "Re-examining the Brazilian South-Northeast labour income gap: A decomposition approach," WIDER Working Paper Series wp-2021-117, World Institute for Development Economic Research (UNU-WIDER).
    10. Inés P. Murillo Huertas & Raúl Ramos & Hipólito Simón, 2020. "Revisiting interregional wage differentials: New evidence from Spain with matched employer‐employee data," Journal of Regional Science, Wiley Blackwell, vol. 60(2), pages 296-347, March.

    More about this item

    JEL classification:

    • J31 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs - - - Wage Level and Structure; Wage Differentials
    • J38 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs - - - Public Policy
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models

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