Abstract
We calculate aggregate and comparable measures of mismatch in the labour market for 30 European countries. These indicators measure vertical mismatch (related to the level of education, e.g. overeducation, and undereducation) and horizontal mismatch (related to the field of education) and are comparable across countries and through time. In European countries, between 15 % to nearly 35 % of workers have a job for which they have more (or less) qualifications than the usual level. Approximately 20 % to nearly 50 % work in a job for which they do not have the usual field qualification. There is a great variability on mismatch across European labour markets. Undereducation affects more workers than overeducation in most European countries. Low correlations between mismatch and unemployment indicate that mismatch should be regarded as an additional informative variable, thus useful to characterize labour markets.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Notes
We have found articles that individually evaluate the effects of mismatch measures in USA, Canada, UK, Netherlands, France, Germany, Spain, Portugal and Hong Kong. Those measures are not comparable due to different sources, year coverage, and methods. Sloane (2002) presents a detailed review of some of these papers.
The alternative methods are discussed below.
A matched employer-employee dataset with information for all firms with at least one wage earner.
Specifically, Job Analysis detects that, of all workers, 33.1 % are overeducated, 37.5 % are undereducated and 29.4 % are correct matches. The methodology of Realized Matches methodology, using the mean as the reference, detects 85.6 % of correct matches (9.4 % overeducation and 5 % of undereducation), but using the mode as the reference, correct matches are no more than 57.5 % (25.5 % overeducation and 17 % undereducation). These results are in line with those we obtain for Portugal (in 1995, near 12 % overeducation and 4 % undereducation, compares with the mean results of 9.4 and 5 %, respectively), although we analyse a more recent period. This comparative study was performed using Quadros de Pessoal for the period 1985–1991. Comparison with Worker Self-Assessment method is not feasible.
This procedure has now become common since large country surveys use ISCED levels and not years of education as a measure of schooling attainment [see e.g. Biagetti and Scicchitano (2011) and Glocker and Steiner (2011)]. The definition of the source HATLEVEL variable and the correspondence scale between the HATLEVEL and years of education are in the “Appendix”.
The use of one and two standard deviation from the mean is based on the 95 % confidence intervals (2 standard-deviations from the mean)—see e.g. Kiker et al. (1997).
An alternative approach here would be to consider any deviation from the mode. However, given that the definitions of the source variable HATFIELD have a certain notion of ‘ proximity’ between the needed skills to attain different fields of study [e.g. Humanities, languages and arts (200) is closer to Foreign languages (222) than to Computer science (481)] we choose the approach that measures distance from the average, which we think better captures this notion of ‘ proximity’ between fields of study.
Undereducation is always higher than overeducation in Austria, Bulgaria, Denmark, Estonia, Finland, France, Hungary, Latvia, Netherlands, Norway, Poland, Sweeden, and Switzerland. Undereducation is higher than overeducation in a majority of the years in the sample in Belgium, Czech Republic, Ireland. Lithuania, Luxembourg, Slovenia and United Kingdom. In Iceland, Cyprus, Germany, Malta, Slovak Republic, and Spain there are mixed results (undereducation and overeducation levels are very close and there are switches of the one that prevails along the time series). In Italy, Greece and Romania overeducation is higher than undereducation in a majority of the years in the sample and Portugal is the only country that presents higher levels of over- than of undereducation in the whole country time-series.
We do not perform this analysis for horizontal mismatch due to the small number of time-series observations per country.
References
Biagetti, M., & Scicchitano, S. (2011). Education and wage inequality in Europe. Economics Bulletin, 31(3), 2620–2628.
Budría, S., & Moro-Egido, A. (2008). Education, educational mismatch, and wage inequality: Evidence for Spain. Economics of Education Review, 27, 332–341.
Coelho, H. M., Soares, L. M., & Feliz, M. I. B. (1982). Os N íveis de Qualificação na Contratação Colectiva, Sua Aplicação a Algumas Empresas Públicas do Sector dos Transportes e Comunicações. Ministério do Trabalho.
den Haan, W., Ramey, G., & Watson, J. (2000). Job destruction and propagation of shocks. American Economic Review, 90(3), 482–498.
EUROSTAT. (2012). Labour Force Survey Microdata, supplied by the EUROSTAT under contract LFS/2012/22.
Glocker, D., & Steiner, V. (2011). Returns to education across Europe. CEPR discussion paper series no. 8568.
Hartog, J. (2000). Over-education and earnings: Where are we, where should we go? Economics of Education Review, 19, 131–147.
Kiker, B. F., & Santos, M. C. (1991). Human capital and earnings in Portugal. Economics of Education Review, 10(3), 187–203.
Kiker, B. F., Santos, M. C., & Mendes de Oliveira, M. (1997). Overeducation and undereducation: Evidence for Portugal. Economics of Education Review, 16(2), 111–125.
Leuven, E., & Oosterbeek, H. (2011). Overeducation and mismatch in the labor market. Handbook of the Economics of Education, 4, 283–326.
Mauro, L., & Carmeci, G. (2003). Long run growth and investment in education: Does unemployment matter? Journal of Macroeconomics, 25(1), 123–137.
Mendes de Oliveira, M., Santos, M. C., & Kiker, B. F. (2000). The role of human capital and technological change in overeducation. Economics of Education Review, 19, 199–206.
Ordine, P., & Rose, G. (2011). Educational mismatch and wait unemployment. Alma Laurea working papers 19.
Pesaran, M. H. (2007). A simple panel unit root test in the presence of cross-section dependence. Journal of Applied Econometrics, 22(2), 265–312.
Robst, J. (2007). Education and job match: The relatedness of college major and work. Economics of Education Review, 26(4), 397–407.
Sloane, P. (2002). Much ado about nothing? What does the over-education literature really tell us? Keynote address, International Conference on Over-education in Europe.
Wolbers, M. (2003). Job mismatches and their labour market effects among school leavers in Europe. European Sociological Review, 19(3), 249–266.
Acknowledgments
We kindly acknowledge financial support by Fundação para a Ciê ncia e Tecnologia, under project Education Mismatches and Productivity Differences (PTDC/EGE-ECO/112499/2009). The raw micro-data used in this paper are from the Labour Force Survey (LFS) and were supplied by the Eurostat, under contract LFS/2012/22, which we acknowledge. The responsibility for the conclusions in this paper is the authors’ and not of Eurostat, the European Commission, or any of the national authorities whose data have been used. An earlier version of this work has circulated under the same title as a Working-Paper (CEFAGE-UE Working-Paper 2014/13).
Author information
Authors and Affiliations
Corresponding author
Appendix
Appendix
1.1 Definitions of the Source Variables
HATLEVEL | Definition | Years of schooling |
|---|---|---|
0 | No formal education or below ISCED 1 | 0 |
10 | ISCED 0–1 (pre-primary education) | 1 |
11 | ISCED 1 (primary education or first stage of basic education) | 5 |
21 | ISCED 2 (lower secondary education or first stage of basic education) | 8 |
22 | ISCED 3 (upper secondary education; access to labour market)-shorter than 2 years | 10 |
31 | ISCED 3c (2 years and more) | 13 |
32 | ISCED 3a,b (upper secondary education providing access to level 5) | 13 |
30 | ISCED 3 (without distinction a,b, or c possible, 2 years +) | 13 |
33 | ISCED 3c (3 years or longer) or ISCED 4c | 13 |
34 | ISCED 3b or ISCED 4b | 13 |
35 | ISCED 3a or ISCED 4a | 13 |
36 | ISCED 3 or 4 (without distinction a, b or c possible) | 13 |
41 | ISCED 4a, b (post secondary, non-tertiary education giving access to level 5) | 14 |
42 | ISCED 4c | 14 |
43 | ISCED 4 (without distinction a, b or c possible) | 14 |
51 | ISCED 5b (first stage of tertiary education; provides access to an occupation) | 17 |
52 | ISCED 5b (first stage of tertiary education theoretically based; provides access to research programmes) | 17 |
60 | ISCED 6 (second stage of tertiary education, leading to advanced research qualification) | 19 |
HATFIELD | Definition |
|---|---|
0 | General programmes |
100 | Teacher training and education science |
200 | Humanities, languages and arts |
222 | Foreign languages |
300 | Social sciences, businesses and law |
400 | Science, mathematics and computing |
420 | Life sciences (including biology and environmental science) |
440 | Physical science (including physics, chemistry and earth science) |
460 | Mathematics and statistics |
481 | Computer science |
482 | Computer use |
500 | Engineering, manufacturing and construction |
600 | Agriculture and veterinary |
700 | Health and welfare |
800 | Services |
Economic activity | Definition |
|---|---|
A | Agriculture, forestry and fishing |
B | Mining and quarrying |
C | Manufacturing |
D | Electricity, gas and steam |
E | Water and waste |
F | Construction |
G | Wholesale and retail trade, repair of vehicles |
H | Transportation and storage |
I | Accommodation and food service |
J | Information and communication |
K | Financial and insurance |
L | Real estate |
M | Professional, scientific and technical |
N | Administrative and support service |
O | Public administration and defense |
P | Education |
Q | Human health and social work |
R | Arts, entertainment and recreation |
S | Other service activities |
T | Households production |
U | Extraterritorial activities |
Occupation | Definition |
|---|---|
100 | Managers |
200 | Professionals |
300 | Technicians |
400 | Clerical |
500 | Service and sales workers |
600 | Agricultural, forestry and fishery workers |
700 | Craft and related trades |
800 | Plant and machine operators |
900 | Elementary occupations |
1.2 Number of Observations by Country for HATLEVEL Variable
Country | Obs. | Country | Obs. | Country | Obs. | Country | Obs. |
|---|---|---|---|---|---|---|---|
Austria | 1,288,556 | France | 3,087,955 | Lithuania | 341,138 | Slovak Rep. | 672,149 |
Belgium | 942,070 | Germany | 1,130,341 | Luxembourg | 296,124 | Slovenia | 464,774 |
Bulgaria | 531,795 | Greece | 2,232,709 | Malta | 52,721 | Spain | 2,433,548 |
Cyprus | 234,948 | Hungary | 1,830,379 | Netherlands | 1,293,398 | Sweden | 1,647,500 |
Czech Rep. | 1,342,063 | Iceland | 94,197 | Norway | 351,623 | Switzerland | 491,162 |
Denmark | 574,749 | Ireland | 1,562,564 | Poland | 1,643,658 | United Kingdom | 1,435,754 |
Estonia | 147,989 | Italy | 3,858,707 | Portugal | 1,143,686 | ||
Finland | 488,471 | Latvia | 216,173 | Romania | 1,437,500 |
1.3 Number of Observations by Country for HATFIEL Variable
Country | Obs. | Country | Obs. | Country | Obs. | Country | Obs. |
|---|---|---|---|---|---|---|---|
Austria | 711,155 | France | 1,251,307 | Lithuania | 237,564 | Slovak Rep. | 460,423 |
Belgium | 322,322 | Germany | 649,987 | Luxembourg | 145,196 | Slovenia | 281,122 |
Bulgaria | 281,195 | Greece | 821,746 | Malta | 15,973 | Spain | 306,184 |
Cyprus | 141,340 | Hungary | 1,043,699 | Netherlands | 614,926 | Sweden | 1,139,786 |
Czech Rep. | 749,145 | Iceland | 43,033 | Norway | 173,987 | Switzerland | 269,252 |
Denmark | 327,599 | Ireland | 431,997 | Poland | 1,002,464 | United Kingdom | 477,807 |
Estonia | 84,242 | Italy | 1,606,183 | Portugal | 221,632 | ||
Finland | 233,986 | Latvia | 117,825 | Romania | 836,328 |
Rights and permissions
About this article
Cite this article
Morgado, A., Sequeira, T.N., Santos, M. et al. Measuring Labour Mismatch in Europe. Soc Indic Res 129, 161–179 (2016). https://doi.org/10.1007/s11205-015-1097-0
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11205-015-1097-0