Abstract
This paper analyses the wage effects of educational mismatch by workers’ origin using a sizeable, detailed matched employer–employee dataset for Belgium. Relying on a fine-grained approach to measuring educational mismatch, the results show that over-educated workers, regardless of their origin, suffer a wage penalty compared to their well-matched former classmates. However, the magnitude of this wage penalty is found to vary considerably depending on workers’ origin. In addition, the estimates show that origin-based differences in over-education wage penalties significantly depend on both demographics (workers’ region of birth, education, gender and tenure) and employer characteristics (firm size and collective bargaining). To our knowledge, the role played by these different moderating variables has been either little or not explored in this context before.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Notes
If the comparison group consists of well-matched former classmates rather than well-matched colleagues.
By developing countries, we refer to both transition and developing countries, as listed in the UNCTAD (2020) classification.
A detailed description of these country categories is provided in “Appendix 1”.
A further, non-negligible, explanation—besides human capital theory—is that collective labour agreements generally provide for normative pay scales in terms of wage levels and progression (e.g. promotion, seniority), especially for white-collar workers in the Belgian context (Garnero et al., 2020).
Note that when faced with multi-modal values for education level, we use the lowest value. However, as this may slightly overestimate the number of over-educated workers, we performed a robustness check by taking the highest modal value (which potentially tends to underestimate the number of over-educated workers). The results, available on request, show that the conclusions remain unchanged when we rely on the highest modal value.
Information on workers’ educational attainments, available in 7 categories in our dataset, has been reported by firms’ HR departments (based on their registers). We converted that information into years of education, applying the following rule: (i) primary education: 6 years of education; (ii) lower secondary education: 9 years; (iii-iv) general, technical, and artistic upper secondary education: 12 years; (v) higher university and non-university education, short type: 15 years; (vi) university and non-university education, long type: 17 years; and (vii) postgraduate education: 18 years. Given that information on workers’ levels of education was provided by firms’ HR departments, these levels might be somewhat under-estimated for immigrants. The findings reported in this paper should therefore be considered as a lower bound.
We have classified the age groups as follows: 15–29; 30–34; 35–39; 40–44; 45–49; and 50 + . The thresholds were chosen to ensure a sufficient percentage of observations in each category and to have a fairly even distribution of observations.
It covers the following sectors: (i) mining and quarrying (B); (ii) manufacturing (C); (iii) electricity, gas, steam and air conditioning supply (D); (iv) water supply, sewerage, waste management and remediation activities (E); (v) construction (F); (vi) wholesale and retail trade, repair of motor vehicles and motorcycles (G); (vii) transportation and storage (H); (viii) accommodation and food service activities (I); (ix) information and communication (J); (x) financial and insurance activities (K); (xi) real estate activities (L); (xii) professional, scientific and technical activities (M); and (xiii) administrative and support service activities (N).
Specifically, the minimum number of observations per cell is 10, while the maximum is 16,522. This results in a mean of 279.7 observations for each cell.
Gross hourly wages have been deflated to constant prices of 2004 by the consumer price index taken from Statistics Belgium.
As in many Western European countries, collective bargaining in Belgium occurs at three levels: the national (interprofessional) level, the sectoral level, and the company level. It generally occurs every two years on a pyramidal basis. In principle, it starts with a national collective agreement defining minimum wages and a margin for wage increases that may be bargained at lower levels. Next, this national agreement is improved within every sector of activity. Sector-level agreements are concluded within Joint Committees that bring together employer and union representatives. They set industry-wide standards, including very detailed pay scales, for all workers covered by the Joint Committee. Finally, firm-level agreements can complement sector-level agreements, and set wages and working time, as well as work organization and other aspects of the working life when a union delegation is present. However, in case of diverging standards between different agreements covering the same workers, the conditions that are the most favourable to employees apply (i.e. the so-called ‘favourability principle’), and firms do not have the possibility to derogate from sector-level agreement as it is the case in Germany, for instance, through so-called ‘opening clauses’. Therefore, firm-level bargaining in Belgium can only improve (or confirm) the conditions set in the sectoral agreement. For more details on the collective bargaining system in Belgium, see Garnero et al. (2020).
Under-education may notably result from labour shortages (i.e. bottleneck vacancies) and technologically-induced changes in job content and complexity.
The SES is conducted on the basis of a two-stage random sampling approach of enterprises or local units (first stage) and employees (second stage). The establishments, randomly chosen from the population, report data on a random sample of their workforce. The SES is thus a stratified sample. The stratification criteria refer to the region where the local unit is located (Nomenclature of Territorial Units for Statistics (NUTS) categories), the principal economic activity (NACE groups) and the size of the local unit (this size is determined by data collected from the Social Security Organization). Sampling percentages of local units depend positively on the size of the unit. Within a local unit, the number of workers to be considered also depends on size but negatively. Because of this sampling strategy, weights have to be used to extrapolate employees and local units in the sample to the entire stratum. For more details, see Demunter (2000).
The estimated parameters of our control variables (available upon request) are generally significant and are consistent with the results reported in the literature. In particular, regardless of the origin, we observe that women suffer from a wage penalty and that being employed in the same firm for at least 10 years increases the wage. This latter finding is compatible with the asymmetrical information on workers’ true productivity, as highlighted by Allen and van der Velden (2001) and Tsai (2010). The type of contract also has an impact on wages, which are lower in the case of non-open-end contracts. Finally, wages increase with the size of the firm and in the presence of a firm-level collective agreement, which is consistent with the findings of Lallemand et al. (2007) and Garnero et al. (2020), for instance.
The mean educational attainment is 11.8 years for workers coming from developed countries, compared to 9.8 years for those coming from the Middle and Near East, 10.6 years for those coming from Eastern Europe, 10.7 for those coming from Africa, 11.6 years for those coming from Asia, and 12.1 years for those coming from Latin and Central America.
In “Appendix 2”, we further rely on the Oaxaca-Blinder (1973) method to decompose the developed-developing wage gap into two parts: a part explained by differences in observable productive characteristics and a part that can be attributed to differences in returns to those characteristics (i.e. the so-called ‘unexplained’ part). We run this decomposition on the subsamples of adequately educated workers (column (1) of “Appendix 2”) and of over-educated workers (column (2)), respectively. Regarding the sample of workers with the adequate level of education, the results show that differences in returns to observable characteristics account for slightly less than a third (i.e. 29%) of the overall wage gap. In contrast, when taking the sample of over-educated workers into account, the unexplained part amounts to 53%. Although not all variables reflecting workers’ productivity could be included in our regression (information on knowledge of languages is notably missing), these results suggest that wage discrimination on the basis of origin is more prevalent among over-educated workers than among workers with the required level of education for their job.
As a sensitivity test, we also estimated an ORU specification separating workers born in Belgium from those born in other developed countries. The estimates (available on request) show that the returns to over-education are comparable for these two groups of workers, and significantly larger than those estimated for workers born in developing countries. Therefore, for the sake of brevity and clarity, especially when focusing on the role of moderating variables, we have chosen to group together workers born in Belgium and those born in other advanced economies. This choice is also likely to facilitate the comparison of our results with those of other studies that focus specifically on the situation of over-educated workers born in developing countries.
Because the estimates of the dummy specification are less precise, and for the sake of conciseness, in the remainder of this paper we will directly focus on results obtained with the Mincer and ORU specifications. However, the estimates of the dummy specification are reported in “Appendices 3 and 4”. Overall, they corroborate our main conclusions.
The ‘OE/RE ratio’ variable measures the ratio of the return to over- and required education, respectively. Therefore, the smaller the value of this variable, the larger the over-education wage penalty.
However, these results should be taken with caution as over-educated female workers born in developing countries only represent 5% of the sample of workers originating from these countries.
We also performed a sensitivity analysis to test whether the results remain stable when we fix the threshold at 5 years of tenure, instead of 10 years. Overall, and with respect to over-education, the results, available on request, lead to similar conclusions whether one takes a 5- or a 10-year threshold. That said, while the return to an additional year of over-education increases similarly with the number of years of tenure for both categories of workers when considering a 10-year tenure threshold, a result which we have linked to statistical discrimination theory, this is no longer the case when considering a 5-year threshold, since the return to over-education does not yet increase after 5 years of tenure for workers from developing countries. This seems to suggest that some time is needed for the information asymmetry to decrease for this category of workers.
Overall, results obtained with the dummy specification are quite consistent with those presented in this section.
References
Aleksynska, M., & Tritah, A. (2013). Occupation-education mismatch of immigrant workers in Europe: Context and policies. Economics of Education Review, 36, 229–244. https://doi.org/10.1016/j.econedurev.2013.06.001
Allen, J., & van der Velden, R. (2001). Educational mismatches versus skill mismatches: Effects on wages, job satisfaction, and on-the-job search. Oxford Economic Papers, 53(3), 434–452.
Arrow, K. (1973). The Theory of discrimination. In O. Ashenfelter & A. Rees (Eds.), Discrimination in the labour market (pp. 3–42). Princeton.
Basilio, L., Bauer, T., & Kramer, A. (2017). Transferability of human capital and immigrant assimilation: An analysis for Germany. Labour, 31(3), 245–264. https://doi.org/10.1111/labr.12096
Battu, H., Seaman, P., & Sloane, P. (1999). Overeducation, undereducation and the British labour market. Applied Economics, 31(11), 1437–1453. https://doi.org/10.1080/000368499323319
Battu, H., & Sloane, P. (2004). Overeducation and ethnic minorities in Britain. The Manchester School, 72(4), 535–559.
Becker, G. (1957). The economics of discrimination. University of Chicago Press.
Becker, G. (1964). Human capital. National Bureau of Economic Research.
Blinder, A. (1973). Wage discrimination: Reduced from and structural estimates. Journal of Human Resources, 8(4), 436–455.
Chiswick, B., & Miller, P. (2008). Why is the payoff to schooling smaller for immigrants? Labour Economics, 15(6), 1317–1340. https://doi.org/10.1016/j.labeco.2008.01.001
Chiswick, B., & Miller, P. (2009a). The international transferability of immigrants’ human capital. Economics of Education Review, 28(2), 162–169. https://doi.org/10.1016/j.econedurev.2008.07.002
Chiswick, B., & Miller, P. (2009b). Educational mismatch: Are high-skilled immigrants really working at high-skilled jobs and the price they pay if they aren’t ? IZA Discussion Paper No. 4280, Bonn.
Chiswick, B., & Miller, P. (2010). The Effects of educational-occupational mismatch on immigrant earnings in Australia, with international comparisons. International Migration Review, 44(4), 869–898. https://doi.org/10.1111/j.1747-7379.2010.00829.x
Cohn, E., & Ng, Y. (2000). Incidence and wage effects of overschooling and underschooling in Hong Kong. Economics of Education Review, 19(2), 159–168. https://doi.org/10.1016/S0272-7757(99)00006-0
Cornelissen, T., & Jirahn, U. (2012). September 11th and the earnings of Muslims in Germany: The moderating role of education and firm size. Journal of Economics Behavior and Organization, 81(2), 490–504. https://doi.org/10.1016/j.jebo.2011.07.014
Daly, M., Büchel, F., & Duncan, G. (2000). Premiums and penalties for surplus and deficit education: Evidence from the United States and Germany. Economics of Education Review, 19(2), 169–178.
Dell’Aringa, C., & Lucifora, C. (1994). Wage dispersion and unionism: Do unions protect low pay? International Journal of Manpower, 15(2–3), 150–169. https://doi.org/10.1108/01437729410059413
Demunter, C. (2000). Structure and distribution of earnings survey: analysis 1995. Brussels: Statistics Belgium working paper.
Dolton, P., & Vignoles, A. (2000). The incidence and effects of overeducation in the UK graduate labour market. Economics of Education Review, 19(2), 179–198.
Duncan, G., & Hoffman, S. (1981). The incidence and wage effects of overeducation. Economics of Education Review, 1(1), 75–86. https://doi.org/10.1016/0272-7757(81)90028-5
European Commission. (2012). Education and training: Monitor 2012. Luxembourg: Publications Office of the European Union.
European Commission. (2021). Education and training: Monitor 2021. Luxembourg: Publications Office of the European Union.
Eurostat. (2021). Over-qualification Rate by Economic Activity: Whole Economy. Luxembourg: Eurostat Database.
Fays, V., Mahy, B., Rycx, F., & Volral, M. (2021). Wage discrimination based on the country of birth: Do tenure and product market competition matter? Applied Economics, 53(13), 1551–1571. https://doi.org/10.1080/00036846.2020.1838431
FPS Employment and Unia. (2017). Monitoring socio-économique: Marché du travail et origine. FPS Employment and Unia Publishing.
FPS Employment and Unia. (2019). Monitoring socioéconomique: Marché du travail et origine. FPS Employment and Unia Publishing.
Freeman, R. (1976). The overeducated American. Academic Press.
Friedberg, R. (2000). You can’t take it with you? Immigrant assimilation and the portability of human capital. Journal of Labor Economics, 18(2), 221–251. https://doi.org/10.1086/209957
Gagliardi, N., Mahy, B., & Rycx, F. (2021). Upstreamness, wages and gender: Equal benefits for all? British Journal of Industrial Relations, 59(1), 52–83. https://doi.org/10.1111/bjir.12486
Garnero, A., Kampelmann, S., & Rycx, F. (2014). Part-time work, wages and productivity: Evidence from Belgian matched panel data. Industrial and Labour Relations Review, 67(3), 926–954.
Garnero, A., Rycx, F., & Terraz, I. (2020). Productivity and wage effects of firm-level collective agreements: Evidence from Belgian linked panel data. British Journal of Industrial Relations, 58(4), 936–972. https://doi.org/10.1111/bjir.12525
Green, C., Kler, P., & Leeves, G. (2007). Immigrant overeducation: Evidence from recent arrivals to Australia. Economics of Education Review, 26(4), 420–432. https://doi.org/10.1016/j.econedurev.2006.02.005
Grinza, E., Kampelmann, S., & Rycx, F. (2020). L’union fait la force? Evidence for wage discrimination in firms with high diversity. Journal of Economic Inequality, 18(2), 181–211. https://doi.org/10.1007/s10888-019-09433-7
Harcourt, M., Lam, H., Harcourt, S., & Flynn, M. (2008). Discrimination in hiring against immigrants and ethnic minorities: The effect of unionization. International Journal of Human Resource Management, 19(1), 98–115. https://doi.org/10.1080/09585190701763958
Hartog, J. (2000). Over-education and earnings: Where are we, where should we go? Economics of Education Review, 19(2), 131–147. https://doi.org/10.1016/S0272-7757(99)00050-3
Heckman, J. (1998). Detecting discrimination. Journal of Economic Perspective, 12(2), 101–116.
Hirsch, B., & Jahn, E. (2015). Is there monopsonistic discrimination against immigrants? Industrial and Labor Relations Reviews, 68(3), 131–147. https://doi.org/10.1177/0019793915572124
Jacobs, V., Mahy, B., Rycx, F., & Volral, M. (2021). Over-education among immigrants: The role of demographics, time and firm characteristics. Applied Economics, 53(1), 61–78. https://doi.org/10.1080/00036846.2020.1795070
Joona, P., Gupta, N., & Wadensjö, E. (2014). Overeducation among immigrants in Sweden: Incidence, wage effects and state dependence. IZA Journal of Migration, 3(1), 9. https://doi.org/10.1186/2193-9039-3-9
Kalfa, E., & Piracha, M. (2017). Immigrants’ Educational Mismatch and the Penalty of Over-education. Education Economics, 25(5), 462–481. https://doi.org/10.1080/09645292.2017.1298728
Kampelmann, S. (2011). The Socio-economics of Pay Rules. Doctoral dissertation in economics, Université Lille 1 and Université libre de Bruxelles, May.
Kampelmann, S., Mahy, B., Rycx, F., & Vermeylen, G. (2020). Over-, required and under-education: Consequences on the bottom lines of firms. Labour, 34(1), 80–112.
Kampelmann, S., & Rycx, F. (2012). The impact of educational mismatch on firm productivity: Evidence from linked panel data. Economics of Education Review, 31(6), 918–931.
Kampelmann, S., & Rycx, F. (2016). Wage discrimination against immigrants: Measurement with firm-level productivity data. IZA Journal of Migration, 5(15), 1–24. https://doi.org/10.1186/s40176-016-0063-1
Kampelmann, S., Rycx, F., Saks, Y., & Tojerow, I. (2018). Does education raise productivity and wages equally? The moderating role of age and gender. IZA Journal of Labor Economics, 7(1), 1–37.
Karakaya, G., Plasman, R., & Rycx, F. (2007). Overeducation on the Belgian labour market: Evaluation and analysis of the explanatory factors through two types of approaches. Compare, 37(4), 513–532. https://doi.org/10.1080/03057920701366317
Kiker, B., Santos, M., & Mendes de Oliviera, M. (1997). Overeducation and undereducation: Evidence for Portugal. Economics of Education Review, 16(2), 111–125. https://doi.org/10.1016/S0272-7757(96)00040-4
Kler, P. (2007). A panel data investigation into over-education among tertiary educated Australian immigrants. Journal of Economic Studies, 34(3), 179–193. https://doi.org/10.1108/01443580710772759
Lallemand, T., Plasman, R., & Rycx, F. (2007). The establishment-size wage premium: Evidence from European countries. Empirica, 34(5), 427–451. https://doi.org/10.1007/s10663-007-9042-3
Lallemand, T., & Rycx, F. (2006). Establishment size and the dispersion of wages: Evidence from European countries. Applied Economics Quartely - Konjoncturpolitik, 52(4), 309–336.
Lindley, J. (2009). The over-education of UK immigrants and minority ethnic groups: Evidence from the Labour Force Survey. Economics of Education Review, 28(1), 80–89. https://doi.org/10.1016/j.econedurev.2007.11.003
Mahy, B., Rycx, F., & Vermeylen, G. (2015). Educational mismatch and firm productivity: Do skills, technology and uncertainty matter? De Economist, 163(2), 233–262. https://doi.org/10.1007/s10645-015-9251-2
Matano, A., Nieto, S., & Ramos, R. (2015). Educational mismatches in the EU: Immigrants vs natives. International Journal of Manpower, 36(4), 540–561. https://doi.org/10.1108/IJM-11-2013-0260
McGuinness, S., & Sloane, P. (2011). Labour market mismatch among UK graduates: An analysis using REFLEX data. Economics of Education Review, 30, 130–145. https://doi.org/10.1016/j.econedurev.2010.07.006
Mincer, J. (1974). Schooling, experience, and earnings. Human Behavior & Social Institutions No. 2., Cambridge (Ma.): NBER.
Nielsen, C. P. (2011). Immigrant over-education: Evidence from Denmark. Journal of Population Economics, 24(2), 499–520. https://doi.org/10.1007/s00148-009-0293-0
Nieto, S., & Ramos, R. (2017). Overeducation, skills and wage penalty: Evidence for Spain using PIAAC data. Social Indicators Research, 134(1), 219–236. https://doi.org/10.1007/s11205-016-1423-1
Oaxaca, R. (1973). Male-female wage differential in urban labor market. International Economic Review, 14(3), 693–709. https://doi.org/10.2307/2525981
OECD. (2017). Making integration work: Assessment and recognition of foreign qualifications. OECD.
OECD. (2018). OECD employment outlook. OECD.
OECD. (2019). Foreign-born employment (indicator). OECD.
Phelps, E. (1972). The statistical theory of racism and sexism. American Economic Review, 62(4), 659–661.
Piton, C., & Rycx, F. (2021). A broken social elevator? Employment outcomes of first- and second-generation immigrants in Belgium. De Economist, 169(3), 319–365. https://doi.org/10.1007/s10645-021-09385-2
Plasman, R., Rusinek, M., & Rycx, F. (2007). Wages and the bargaining regime under multi-level bargaining: Belgium, Denmark and Spain. European Journal of Industrial Relations, 13(2), 161–180. https://doi.org/10.1177/0959680107078251
Ramos, R., Sanromá, E., & Simón, H. (2015). Portability of human capital and immigrant overeducation in Spain. Population Research and Policy Review, 34(2), 223–241. https://doi.org/10.1007/s11113-014-9340-y
Ren, W., & Miller, P. W. (2011). Changes over time in the return to education in urban China: Conventional and ORU estimates. China Economic Review, 23(1), 154–169. https://doi.org/10.1016/j.chieco.2011.08.008
Rubb, S. (2003). Post-college schooling, over-education, and hourly earnings in the United States. Education Economics, 11(1), 53–72. https://doi.org/10.1080/09645290210127453
Rumberger, R. (1987). The impact of surplus schooling on productivity and earnings. Journal of Human Resources, 22(1), 24–50.
Salinas-Jiménez, M. D. M., Rahona-Lopèz, M., & Murillo-Huertas, I. P. (2013). Gender wage differentials and educational mismatch: An application to the Spanish case. Applied Economics, 45(30), 4226–4235. https://doi.org/10.1080/00036846.2013.781260
Sattinger, M. (1993). Assignment models of the distribution of earnings. Journal of Economic Literature, 31(2), 831–880.
Schwientek, C. (2016). Are immigrants overeducated in Germany? Determinants and wage effects of educational mismatch. FAU Discussion Paper in Economics No. 07/2016, Nürnberg.
Sloane, P. (2003). Much ado about nothing? What does the overeducation literature really tell us. In Buchel, de Grip and Mertens (eds), Overeducation in Europe (pp. 11–49). Cheltenham: Edward Elgar.
Thurow, L. (1975). Generating inequality. Basic Books.
Tsai, Y. (2010). Returns to overeducation: A longitudinal analysis of the U.S. labor market. Economics of Education Review, 29(4), 606–617. https://doi.org/10.1016/j.econedurev.2010.01.001
UNCTAD. (2020). World economic situation and prospects 2020 (pp. 160–200). United Nations.
Verdugo, R., & Verdugo, N. (1989). The impact of surplus schooling on earnings. Some additional findings. JouRnal of Human Resources, 24(4), 629–643. https://doi.org/10.2307/145998
Vroom, V. (1964). Work and motivation. Wiley.
Wald, S., & Fang, T. (2008). Overeducated immigrants in the Canadian labour market: Evidence from the workplace and employee survey. Canadian Public Policy, 34(4), 457–479.
Acknowledgements
We would like to thank the Editor (Arthur van Soest) and two anonymous referees for their useful suggestions on an earlier version of this paper. We are also most grateful to Statistics Belgium for giving access to the data. Financial support from the Belgian Federal Science Policy Office (BELSPO, IMMILAB project) is also kindly acknowledged.
Funding
This work was supported by the Belgian Federal Science Policy Office under Grant [IMMILAB].
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
No potential conflict of interest was supported by the authors.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Appendices
Appendix 1: Description of country categories (based on UNCTAD, 2020)
-
(a)
Developed countries
Western Europe Andorra, Austria, Belgium, Denmark, Finland, France, Germany, Greece, Iceland, Ireland, Italy, Liechtenstein, Luxembourg, Monaco, the Netherlands, Norway, Portugal, San Marino, Spain, Sweden, Switzerland, and the United Kingdom.
Eastern Europe (EU-13) Bulgaria, the Czech Republic, Estonia, Hungary, Latvia, Lithuania, Malta, Poland, and Romania.
North America and South Pacific Australia, Canada, French Polynesia, Hawaii, New Zealand, New Caledonia, Papua New Guinea, Tahiti, the United States of America, and Wallis and Futuna.
Japan Japan
-
(b)
Developing countries
Africa Algeria, Angola, Burundi, Cameroon, Cote d’Ivoire, Democratic Republic of the Congo, Ghana, Libya, Mauritania, Morocco, Nigeria, Rwanda, Senegal, South Africa, Togo, etc.
The Middle and Near East Afghanistan, Brunei Darussalam, Cyprus, Egypt, Iran, Iraq, Israel, Jordan, Kuwait, Palestine, Saudi Arabia, Syria, Turkey, the United Arab Emirates, and Yemen.
Asia Bangladesh, Cambodia, China, India, Indonesia, Japan, Laos, Philippines, South Korea, Taiwan, Thailand, Vietnam, etc.
Eastern Europe (non-EU) Albania, Armenia, Kazakhstan, Kosovo, Russia, and Serbia.
Latin and Central America Argentina, Bolivia, Brazil, Chile, Colombia, Cuba, the Dominican Republic, Ecuador, Guatemala, Mexico, Nicaragua, Peru, Venezuela, etc.
Note: by developing countries, we actually refer to both transition and developing countries listed in the UNCTAD (2020) classification.
Appendix 2
See Table
6.
Appendix 3
See Table
7.
Appendix 4
See Table
8.
Rights and permissions
Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Jacobs, V., Rycx, F. & Volral, M. Wage Effects of Educational Mismatch According to Workers’ Origin: The Role of Demographics and Firm Characteristics. De Economist 170, 459–501 (2022). https://doi.org/10.1007/s10645-022-09413-9
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10645-022-09413-9