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Income inequalities for recently graduated French workers: a multilevel modeling approach

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Abstract

This paper presents a simultaneous study of the impact of gender, occupational and localization inequalities on the earnings of higher education graduates. The framework draws on 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. To take into account the selection process for employment, our multilevel model uses the Heckman two-step procedure. Occupational Groups (OG) are found to capture around 40 % of the wage heterogeneity, whereas Employment Area (EA) nests capture less than 10 %. Higher wages are offered to young workers in (1) OG dominated by seniors and (2) OG dominated by men. These group characteristics also influence gender inequalities: there is a higher wage penalty for women in (1) OG dominated by men and (2) OG dominated by senior workers. In contrast to gender inequality, immigrant inequalities manifest closer links to EA.

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Notes

  1. See for a survey Fortin et al. (2011).

  2. In a recent paper, Pan (2015) has pointed out nonlinearities in the female share by occupation.

  3. Refers to the classification of occupations, such as manager, engineers, skilled workers.

  4. The French National Statistics Institute (INSEE) lists 348 EA, which are homogeneous labor market zones, i.e., an area within which most of the labor force lives and works, and in which firms can find the main part of the labor force necessary to occupy the jobs on offer.

  5. Centre d’études et de recherches sur les qualifications (Céreq).

  6. For previous uses of this database for research in the social sciences, see Barros et al. (2011), Guironnet and Peypoch (2007).

  7. 1/12th of this administrative database is available to researchers.

  8. All the statistics in this database are averages over the period 2004–2007.

  9. However, other unobservable factors can influence such concerns (Chevalier 2007).

  10. For this approach, see Hamermesh (2012) for an analysis of agents’ perceptions of some subjective characteristics that are subject to discrimination.

  11. The group mean centering increases the model complexity (Kreft et al. 1995) and the comments for the associated results. Level-1 units are then the performance of individuals relative to their pertaining group. The two methods of centering have been tried. The estimated coefficients, however, differ slightly, suggesting a relative homogeneity between grand mean and group mean values for level-1 covariates. Furthermore, in our research topic, no theoretical intuition suggests the use of group mean centering. For comment simplicity, a grand mean centering has, thus, been favored.

  12. The Business School variable has been dropped from estimations since all students from these faculties have found a job.

  13. Similar statistical results have been found elsewhere in addition, ethnic inequalities seem to be decreasing in France (Céreq 2008).

  14. To calculate this term, we take the grand mean of the following operation: \(\widehat{PFT}-\widehat{PFT}_{c}=\widehat{PFT_{g}}\), where \(\widehat{PFT}\) is the probability of being employed as calculated by our probit, and \(\widehat{PFT}_{c}\) is the group mean \(\widehat{PFT}\).

  15. A similar decomposition has been tested for migrants; this decomposition is, however, not significant.

  16. The R-squared of level-2 units cannot be compared between models I, II and III since they are calculated on the basis of different benchmark models (i.e., a hierarchical model with an intercept term only).

  17. The level-1 units have been tested for EA clusters, but the results are not significant.

  18. Ethnicity return is then not significant when EA nests are used.

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Authors and Affiliations

  1. LEDi (UMR 6307 CNRS), Bourgogne University, Pôle d’économie et de gestion, 21066, Dijon, France

    Mathieu Bunel

  2. CREM (UMR 6211 CNRS), Normandy University (Caen), Bâtiment A, Campus 1, Esplanade de la Paix, 14000, Caen, France

    Jean-Pascal Guironnet

Authors
  1. Mathieu Bunel
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  2. Jean-Pascal Guironnet
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Correspondence to Jean-Pascal Guironnet.

Appendix

Appendix

See Tables 6, 7, 8 and 9.

Table 6 Weighted descriptive statistics for OG variables
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Table 7 Weighted descriptive statistics for individual variables
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Table 8 Probability of full-time job
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Table 9 Complete estimations from MCO and multilevel models
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Bunel, M., Guironnet, JP. Income inequalities for recently graduated French workers: a multilevel modeling approach. Empir Econ 53, 755–778 (2017). https://doi.org/10.1007/s00181-016-1130-4

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