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Educational Mismatch and Firm Productivity: Do Skills, Technology and Uncertainty Matter?

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Abstract

The authors provide first evidence on whether the direct relationship between educational mismatch and firm productivity varies across working environments. Using detailed Belgian linked employer–employee panel data for 1999–2010, they find the existence of a significant, positive (negative) impact of over- (under-)education on firm productivity. Moreover, their results show that the effect of over-education on productivity is stronger among firms: (i) with a higher share of high-skilled jobs, (ii) belonging to high-tech/knowledge-intensive industries, and (iii) evolving in a more uncertain economic environment. Interaction effects between under-education and working environments are less clear-cut. However, economic uncertainty is systematically found to accentuate the detrimental effect of under-education on productivity.

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Notes

  1. The workers’ educational attainment is available in 7 categories in our dataset. This information, reported by firms’ human capital departments (on the basis of their registers), has been transformed in years of education. To this end, we applied the following rule: (i) primary education: 6 years of education; (ii) lower secondary education: 9 years of education; (iii-iv) general, technical and artistic upper secondary education: 12 years of education; (v) higher non-university education, short: 14 years of education; (vi) university and non-university education, long: 16 years of education; (vii) post-graduate education: 17 years of education.

  2. Note that: \(\frac{1}{m_{j,t}}\left( {\sum _{i=1}^{m_{j,t}} {O_{i,j,t}} +\sum _{i=1}^{m_{j,t}} {R_{i,j,t}} +\sum _{i=1}^{m_{j,t}} {U_{i,j,t}}}\right) =\frac{1}{m_{j,t}}\sum _{i=1}^{m_{j,t}} {Attained_{i,j,t}}\), i.e., the sum of the average years of over-, required, and under-education in firm \(j\) at time \(t\) is equal to the average years of education attained by the workers employed in firm \(j\) at time \(t\).

  3. Expected biases associated with OLS and the relatively poor performance and shortcomings of the FE estimator in the context of firm-level productivity regressions are reviewed in Van Beveren (2012).

  4. By ‘ORU variables’, we mean ORU variables and other endogenous input factors.

  5. The SES is a stratified sample. The stratification criteria refer respectively to the region (NUTS-groups), the principal economic activity (NACE-groups) and the size of the firm. The sample size in each stratum depends on the size of the firm. Sampling percentages of firms are respectively equal to 10, 50 and 100 % when the number of workers is below 50, between 50 and 99, and above 100. Within a firm, sampling percentages of employees also depend on size. Sampling percentages of employees reach respectively 100, 50, 25, 14.3 and 10 % when the number of workers is below 20, between 20 and 49, between 50 and 99, between 100 and 199, and between 200 and 299. Firms employing 300 workers or more have to report information for an absolute number of employees. This number ranges between 30 (for firms with 300 to 349 workers) and 200 (for firms with 12,000 workers or more). To guarantee that firms report information on a representative sample of their workers, they are asked to follow a specific procedure. First, they have to rank their employees in alphabetical order. Next, Statistics Belgium gives them a random letter (e.g., the letter O) from which they have to start when reporting information on their employees (following the alphabetical order of workers’ names in their list). If they reach the letter Z and still have to provide information on some of their employees, they have to continue from the letter A in their list. Moreover, firms that employ different categories of workers, namely managers, blue- and/or white-collar workers, have to set up a separate alphabetical list for each of these categories and to report information on a number of workers in these different groups that is proportional to their share in total firm employment. For example, a firm with 300 employees (namely, 60 managers, 180 white-collar workers and 60 blue-collar workers) will have to report information on 30 workers (namely, 6 managers, 18 white-collar workers and 6 blue-collar workers). For more details, see Demunter (2000).

  6. For instance, we eliminate a (very small) number of firms for which the recorded value added was negative.

  7. We did some robustness tests by fixing the threshold at 50 observations. However, given that the number of data points per occupation at the ISCO 3-digit level is quite large, this alternative threshold has little effect on sample size and leaves results (available on request) unaffected.

  8. This restriction is unlikely to affect our results as it leads to a very small drop in sample size.

  9. Note that, given that the measures of mean years of under-education variable are negative, a positive estimated coefficient means that productivity increases if this variable numerically increases by one unit, in other words when under-education decreases by 1 year.

  10. The FE estimator only controls for the potential bias related to the time-invariant unobserved workplace characteristics. So, only GMM and LP results are further reported. FE results are available on request.

  11. Interestingly, the GMM coefficient on the lagged dependent variable falls between the OLS and FE estimates (available on request). As highlighted by Roodman (2009), this result supports the appropriateness of our dynamic GMM-SYS specification.

  12. \(t\) tests for equality of regression coefficients are statistically significant at the 5 % level and reach respectively \(-\)52 and \(-\)2 for the GMM-SYS and LP estimates.

  13. Corresponding \(t\) statistics are equal to 0 and \(-\)42.

  14. Corresponding \(t\) statistics are equal to 208 and 314.

  15. Note that we ran a test of differences between means in order to know whether a significant difference appears between the estimated parameters for each of the two subsamples, where the two parameters are not significantly different under the null hypothesis, while the two parameters are significantly different under the alternative. The results, showing that all coefficients are statistically different, are available on request.

  16. We also examined the interaction between the degree of technology/knowledge intensity and the uncertainty of the firm’s economic environment. In particular, we compared the return to over-education in high-tech/knowledge and low-tech/knowledge intensive firms operating in a more uncertain environment. Estimates reported in Appendix Table 8 show that the productivity gains from over-education are significantly larger in the former category of firms. Although caution is required as GMM-SYS estimates do not pass the AR(2) test, these results suggest that the wage premium associated to over-education can be interpreted as a risk premium paid by a firm to protect itself against the negative consequences of the uncertainty faced. We thank an anonymous referee for suggesting this interpretation.

References

  • Arellano, M., & Bond, S. (1991). Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. Review of Economic Studies, 58, 277–297.

    Article  Google Scholar 

  • Arellano, M., & Bover, O. (1995). Another look at the instrumental variable estimation of error-components models. Journal of Econometrics, 68, 29–51.

    Article  Google Scholar 

  • Autor, D., Katz, L., & Krueger, A. (1998). Computing inequality: Have computers changed the labor market? Quarterly Journal of Economics, 113, 1169–1213.

    Article  Google Scholar 

  • Bandura, A. (1986). Social Foundations of Thought and Action: A social Cognitive View. Englewood Cliffs: Prentice-Hall.

    Google Scholar 

  • Barrick, M., & Mount, M. (1991). The big five personality dimensions and job performance: A meta-analysis. Personnel Psychology, 44, 1–26.

    Article  Google Scholar 

  • Bartel, A., & Sicherman, N. (1998). Technological change and the skill acquisition of young workers. Journal of Labor Economics, 16, 718–755.

    Article  Google Scholar 

  • Battu, H., Seaman, P., & Sloane, P. (1999). Overeducation, undereducation and the British labour market. Applied Economics, 31, 1437–1453.

    Article  Google Scholar 

  • Becker, G. (1964). Human Capital. New York: NBER.

    Google Scholar 

  • Blanchflower, D., & Bryson, A. (2010). The wage impact of trade unions in the UK public and private sectors. Economica, 77, 92–109.

    Article  Google Scholar 

  • Blundell, R., & Bond, S. (1998). Initial conditions and moment restrictions in dynamic panel data models. Journal of Econometrics, 87, 115–143.

    Article  Google Scholar 

  • Booth, A., & Snower, D. (1996). Acquiring skills: Market failures, their symptoms and policy responses. Cambridge: Cambridge University Press.

    Book  Google Scholar 

  • Brown, C. (1990). Firms’ choice of method of pay. Industrial and Labor Relations Review, 43, 165–182.

    Article  Google Scholar 

  • Büchel, F. (2002). The effects of overeducation on productivity in Germany—The firms’ viewpoint. Economics of Education Review, 21, 263–275.

    Article  Google Scholar 

  • Bulmahn, G., & Kräkel, M. (2002). Overeducated workers as an insurance device. Labour, 16, 383–402.

    Article  Google Scholar 

  • Caplan, B. (2003). Stigler–Becker versus Myers–Briggs: Why preference-based explanations are scientifically meaningful and empirically important. Journal of Economic Behaviour & Organization, 50, 391–405.

    Article  Google Scholar 

  • Demunter, C. (2000). Structure and distribution of earnings survey. DGSIE Working Paper. Brussels: Belgium.

  • Dolton, P., & Silles, M. (2008). The effects of over-education on earnings in the graduate labour market. Economics of Education Review, 27, 125–139.

    Article  Google Scholar 

  • Duncan, G., & Hoffman, S. (1981). The incidence and wage effects of overeducation. Economics of Education Review, 1, 75–86.

    Article  Google Scholar 

  • European Commission. (2012). Education and training—Monitor 2012. Luxembourg: Publications Office of the European Union.

  • European Union. (2009). Council Conclusions of 12 May 2009 on a strategic framework for European cooperation in education and training. Luxembourg: Official Journal C119 of 28.5.2009.

  • European Union. (2012). Employment and Social Developments in Europe 2012. Luxembourg: Publications Office of the European Union.

  • Eurostat, (2012). High-tech statistics—Statistics explained. Luxembourg: Eurostat.

    Google Scholar 

  • Freeman, R. (1976). The Overeducated American. New York: Academic Press.

    Google Scholar 

  • Green, F., & Zhu, Y. (2010). Overqualification, job dissatisfaction, and increasing dispersion in the returns to graduate education. Oxford Economic Papers, 62, 740–763.

  • Grunau, P. (2014). The impact of overeducated and undereducated workers on firm-level productivity—First evidence for Germany, mimeo, Institute for Employment Research (IAB), Nuremberg, Germany. Paper presented at the workshop firm-level analysis of labour market issues, Université Catholique de Louvain, Belgium, May 28, 2014.

  • Hansen, L. (1982). Large sample properties of generalized method of moments estimators. Econometrica, 50, 1029–1054.

    Article  Google Scholar 

  • Hartog, J. (2000). Over-education and earnings: Where are we, where should we go? Economics of Education Review, 19, 131–147.

    Article  Google Scholar 

  • Hersch, J. (1991). Education match and job match. Review of Economics and Statistics, 73, 140–144.

    Article  Google Scholar 

  • Hogan, J., & Ones, D. (1997). Conscientiousness and integrity at work. In R. Hogan, J. Johnson, & S. Briggs (Eds.), Handbook of Personality Psychology. San Diego, NY: Academic Press.

    Google Scholar 

  • Ilmakunnas, P., & Ilmakunnas, S. (2011). Diversity at the workplace: Whom does it benefit? De Economist, 159, 223–255.

    Article  Google Scholar 

  • Judge, T., Thoresen, C., Bono, J., & Patton, G. (2001). The job satisfaction-job performance relationship: A qualitative and quantitative review. Psychological Bulletin, 127, 376–407.

    Article  Google Scholar 

  • Kampelmann, S., & Rycx, F. (2012). The impact of educational mismatch on firm productivity: Evidence from linked panel data. Economics of Education Review, 31, 918–931.

    Article  Google Scholar 

  • Krueger, D., & Kumar, K. (2004). Skill-specific rather than general education: A reason for US-Europe growth differences? Journal of Economic Growth, 9, 167–207.

    Article  Google Scholar 

  • Lazear, E. (1989). Pay equality and industrial politics. Journal of Political Economy, 97, 561–580.

    Article  Google Scholar 

  • Lazear, E., & Rosen, S. (1981). Rank-order tournaments as optimum labor contracts. Journal of Political Economy, 89, 841–864.

    Article  Google Scholar 

  • Levinsohn, J., & Petrin, A. (2003). Estimating production functions using inputs to control for unobservables. Review of Economic Studies, 70, 317–341.

    Article  Google Scholar 

  • Mahy, B., Rycx, F., & Volral, M. (2011). Wage dispersion and firm productivity in different working environments. British Journal of Industrial Relations, 49, 460–485.

    Article  Google Scholar 

  • Manning, A. (2003). Monopsony in motion. Imperfect competition in labor markets. Princeton: Princeton University Press.

    Google Scholar 

  • Marshall, A. (1890). Principles of Economics. New York: Macmillan.

    Google Scholar 

  • Mavromaras, K., McGuinness, S., O’Leary, N., Sloane, P., & Fok, Y. (2010). The problem of overskilling in Australian and Britain. The Manchester School, 78, 219–241.

    Article  Google Scholar 

  • Mavromaras, K., & McGuinness, S. (2012). Overskilling dynamics and education pathways. Economics of Education Review, 31, 619–628.

    Article  Google Scholar 

  • McGuinness, S. (2006). Overeducation in the labour market. Journal of Economic Surveys, 20, 387–418.

    Article  Google Scholar 

  • McGuinness, S., & Sloane, P. (2011). Labour market mismatch among UK graduates: An analysis using REFLEX data. Economics of Education Review, 30, 130–145.

    Article  Google Scholar 

  • McQuaid, R., Raeside, R., Canduela, J., Egdell, V., & Colin, L. (2012). Engaging low skilled employees in workplace learning. UK Commission for Employment and Skills (UKCES), Evidence Report 43.

  • Mortensen, D. (2003). Wage dispersion: Why are similar workers paid differently. Cambridge, MA: MIT Press.

    Google Scholar 

  • Nelson, R., & Phelps, E. (1966). Investment in humans, technological diffusion and economic growth. American Economic Review, 56, 69–75.

    Google Scholar 

  • Olley, S., & Pakes, A. (1996). The dynamics of productivity in the telecommunications equipment industry. Econometrica, 64, 1263–1297.

    Article  Google Scholar 

  • Petrin, A., Poi, B., & Levinsohn, J. (2004). Production function estimation in Stata using inputs to control for unobservables. Stata Journal, 4, 113–123.

    Google Scholar 

  • Roodman, D. (2009). How to do xtabond2: An introduction to difference and system GMM in Stata. Stata Journal, 9, 86–136.

    Google Scholar 

  • Rumberger, R. (1987). The impact of surplus schooling on productivity and earnings. Journal of Human Resources, 22, 24–50.

    Article  Google Scholar 

  • Sattinger, M. (2012). Qualitative mismatches. Hanover, MA: Now Publishers.

    Google Scholar 

  • Sattinger, M., & Hartog, J. (2013). Nash bargaining and the wage consequences of educational mismatches. Labour Economics, 23, 50–56.

    Article  Google Scholar 

  • Sicherman, N. (1991). Overeducation in the labor market. Journal of Labor Economics, 9, 101–122.

    Article  Google Scholar 

  • Spence, M. (1973). Job market signalling. Quarterly Journal of Economics, 87, 355–374.

    Article  Google Scholar 

  • Stankiewics, F. (2004). Travail, progrès technique et valorité différentielle : le problème de la sélection des travailleurs. Revue d’Economie Politique, 114, 111–131.

    Article  Google Scholar 

  • Thurow, L. (1975). Generating inequality. Mechanisms of distribution in the U.S. Economy. New York: Basic Books.

    Google Scholar 

  • Thurow, L. (1979). A job-competition model. In M. Piore (Ed.), Unemployment and inflation: Institutionalist and structuralist views. New York: Sharpe.

    Google Scholar 

  • Tsang, M. (1987). The impact of underutilization of education on productivity: A case study of the U.S. bell companies. Economics of Education Review, 6, 239–254.

    Article  Google Scholar 

  • Tsang, M., Rumberger, R., & Levin, H. (1991). The impact of surplus schooling on worker productivity. Industrial Relations, 30, 209–228.

    Google Scholar 

  • Van Beveren, I. (2012). Total factor productivity estimation: A practical review. Journal of Economic Surveys, 26, 98–128.

    Article  Google Scholar 

  • Van der Meer, P. (2006). The validity of two education requirement measures. Economics of Education Review, 25, 211–219.

    Article  Google Scholar 

  • Verhaest, D., & Omey, E. (2006). The impact of overeducation and its measurement. Social Indicators Research, 77, 419–448.

    Article  Google Scholar 

  • Verhaest, D., & Omey, E. (2009). Objective over-education and worker well-being: A shadow price approach. Journal of Economic Psychology, 30, 469–481.

    Article  Google Scholar 

  • Vroom, V. (1964). Work and motivation. New York: Wiley.

    Google Scholar 

  • Weiss, A. (1995). Human capital vs. signalling explanations of wages. Journal of Economic Perspectives, 9, 133–154.

    Article  Google Scholar 

Download references

Acknowledgments

The authors are most grateful to statistics Belgium for giving access to the data. They also would like to thank Rob Alessie (the Editor), two anonymous referees, Mirella Damiani, Muriel Dejemeppe, Seamus McGuinness, Fabrizio Pompei, Bruno Van der Linden, Vincent Vandenberghe, Francesco Venturi, Mélanie Volral and participants at the 10th annual Conference of the CNRS institute TEPP on ‘Research on Health and Labour’ (Le Mans, France, 2013), the ZEW Workshop on ‘Skill Mismatch: Microeconomic Evidence and Macroeconomic Relevance’ (Mannheim, Germany, 2014), the Society of Labor Economists (SOLE) Annual Conference (Arlington, United States, 2014), the BELSPO-IRES Workshop on ‘Firm-Level Analysis of Labour Issues’ (Louvain-la-Neuve, Belgium, 2014), and research seminars at IRES (Louvain-la-Neuve, Belgium, 2014) and the Economics’ Department of the University of Perugia (Perugia, Italy, 2014) for very useful comments on an earlier version of this article. Funding for this research was provided by the Walloon Region (IPRA Research Grant, IWEPS). François Rycx gratefully acknowledges financial support from the Belgian Federal Science Policy Office (BELSPO): SPP Politique Scientifique, programme “Société et Avenir”, Employment, Wage Discrimination, and Poverty , Research contract TA/00/046/EDIPO.

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Authors and Affiliations

  1. HumanOrg and Warocqué School of Business and Economics, University of Mons, Mons, Belgium

    Benoît Mahy & Guillaume Vermeylen

  2. SBS-EM (CEB and DULBEA), Université Libre de Bruxelles, CP 114/02, Avenue F.D. Roosevelt, 50, 1050, Brussels, Belgium

    François Rycx

  3. IZA, Bonn, Germany

    François Rycx

  4. Walloon Institute for Evaluation, Prospective and Statistics (IWEPS), Belgrade, Belgium

    Guillaume Vermeylen

  5. DULBEA, Université Libre de Bruxelles, Brussels, Belgium

    Benoît Mahy

  6. CEB and DULBEA, Université Libre de Bruxelles, Brussels, Belgium

    Guillaume Vermeylen

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  1. Benoît Mahy
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Correspondence to François Rycx.

Appendix

Appendix

See Tables 6, 7, and 8.

Table 6 Attained education and productivity (GMM and LP estimates, 1999–2010)
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Table 7 Educational mismatch and productivity according to workers’ age (GMM and LP estimates, 1999–2010)
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Table 8 Educational Mismatch and Productivity: High-Tech/Knowledge vs. Low-Tech/Knowledge Intensive Firms in a More Uncertain Economic Environment (GMM and LP estimates, 1999–2010)
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Mahy, B., Rycx, F. & Vermeylen, G. Educational Mismatch and Firm Productivity: Do Skills, Technology and Uncertainty Matter?. De Economist 163, 233–262 (2015). https://doi.org/10.1007/s10645-015-9251-2

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