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Earning Inequalities Between and Within Nests: A Multilevel Modeling Approach Applied to the Case of France

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Author Info

  • Jean-Pascal Guironnet

    (University of Caen Basse-Normandie, France - CREM-CNRS)

  • Matthieu Bunel

    (University of Caen Basse-Normandie - CREM-CNRS)

Abstract

This paper presents a simultaneously study of the impact of gender and localization inequalities on the earnings of under-graduates. Using multilevel modeling, the framework draws both individual-level (i.e., pertaining to the individual elements of groups) and aggregate-level (i.e., pertaining to the group as a whole) data under a single specification, in order to study their potential interactions. These inequalities are studied with respect to young workers who left higher education in 2004 and who had a full-time job in the private sector three years after graduation (i.e., in 2007). To take into account the process of selection for employment, our multilevel model uses the Heckman two-step procedure. Following this approach, Occupational Groups (OG) are found to capture 59.4% of the earning heterogeneity whereas Employment Area (EA) nests capture 7.6%. This 59.4% figure is explained by two phenomena: (i) OG are dominated by seniors, and (ii) OG are dominated by males with higher earnings. These group characteristics also influence gender inequalities: there is a higher wage penalty for females in (i) OG dominated by males, and (ii) OG dominated by senior workers. In contrast to the gender gap, immigrant inequalities manifest closer links to EA. Policy implications are derived from our results.

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Bibliographic Info

Paper provided by Center for Research in Economics and Management (CREM), University of Rennes 1, University of Caen and CNRS in its series Economics Working Paper Archive (University of Rennes 1 & University of Caen) with number 201118.

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Date of creation: Oct 2011
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Handle: RePEc:tut:cremwp:201118

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Postal: CREM (UMR CNRS 6211) - Faculty of Economics, 7 place Hoche, 35065 Rennes Cedex - France
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Keywords: Multilevel Models; Earnings; Gender Inequality; Local Labor Market;

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References

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  1. Barros, Carlos P. & Guironnet, Jean-Pascal & Peypoch, Nicolas, 2011. "Productivity growth and biased technical change in French higher education," Economic Modelling, Elsevier, vol. 28(1), pages 641-646.
  2. Aubert Patrick & Caroli Eve & Roger Muriel, 2005. "New Technologies, Workplace Organisation and the Age Structure of the Workforce: Firm-Level Evidence," Research Unit Working Papers, Laboratoire d'Economie Appliquee, INRA 0505, Laboratoire d'Economie Appliquee, INRA.
  3. Roy J. & Lin X., 2002. "Analysis of Multivariate Longitudinal Outcomes With Nonignorable Dropouts and Missing Covariates: Changes in Methadone Treatment Practices," Journal of the American Statistical Association, American Statistical Association, American Statistical Association, vol. 97, pages 40-52, March.
  4. Anthony Recchia, . "R-Squared Measures for Two-Level Hierarchical Linear Models Using SAS," Journal of Statistical Software, American Statistical Association, American Statistical Association, vol. 32(c02).
  5. Erica L. Groshen, 1991. "The Structure of the Female/Male Wage Differential: Is It Who You Are, What You Do, or Where You Work?," Journal of Human Resources, University of Wisconsin Press, vol. 26(3), pages 457-472.
  6. Guironnet, J.-P. & Peypoch, N., 2007. "Human capital allocation and overeducation: A measure of French productivity (1987, 1999)," Economic Modelling, Elsevier, vol. 24(3), pages 398-410, May.
  7. Patrick Sillard & Roland Rathelot, 2008. "Zones Franches Urbaines : quels effets sur l'emploi salarié et les créations d'établissements ?," Économie et Statistique, Programme National Persée, vol. 415(1), pages 81-96.
  8. Carlos Barros & Jean-Pascal Guironnet & Nicolas Peypoch, 2011. "How to quickly get a job? The transition from higher education to French labour market by a survival model," Applied Economics, Taylor & Francis Journals, Taylor & Francis Journals, vol. 43(4), pages 439-448.
  9. Frederiksen, Anders, 2008. "Gender differences in job separation rates and employment stability: New evidence from employer-employee data," Labour Economics, Elsevier, vol. 15(5), pages 915-937, October.
  10. Gubta, Nabanita Datta & Rothstein, Donna S., 2001. "The Impact of Worker and Establishment-level Characteristics on Male-Female Wage Differentials: Evidence from Danish Matched Employee-Employer Data," CLS Working Papers 01-9, University of Aarhus, Aarhus School of Business, Centre for Labour Market and Social Research.
  11. A. R. Cardoso, 2000. "Wage differentials across firms: an application of multilevel modelling," Journal of Applied Econometrics, John Wiley & Sons, Ltd., John Wiley & Sons, Ltd., vol. 15(4), pages 343-354.
  12. Manon Domingues Dos Santos & Yannick L’Horty & Elisabeth TOVAR, 2010. "Une introduction," Revue d'économie régionale et urbaine, Armand Colin, vol. 0(1), pages 4-25.
  13. Joseph G. Altonji & Rebecca M. Blank, . "Race and Gender in the Labor Market," IPR working papers 98-18, Institute for Policy Resarch at Northwestern University.
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