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Overeducation at a Glance. Determinants and Wage Effects of the Educational Mismatch Based on AlmaLaurea Data

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Abstract

This essay delivers two main innovations with respect to the existing literature. First, and foremost, by extending the work of Nicaise (2010) relative to the reservation wage to the case of overeducation, we propose a statistical test to discriminate between alternative theoretical interpretations of the determinants of overeducation through the Heckman sample selection procedure. Second, the essay provides the first available economic analysis of the consequences of the educational mismatch in Italy as based on AlmaLaurea data, the largest and richest data bank available in the country. The data includes a large number of university graduates enrolled in a given year before the Bologna reform and asks a large number of questions allowing us measuring among others the quality of education from high school. This wealth of information is a condition to provide the most comprehensive, accurate and reliable assessment of overeducation in the country. The educational mismatch 5 years from graduation is relatively high—at 11.4 and 8% for overeducation and overskilling, respectively—by EU standards. Ceteris paribus the parents of the mismatched have lower educational levels according to school tracking. Most humanities and social sciences degrees but also geology, biology and psychology are associated with both types of mismatch. The quality of education also correlates to the educational mismatch. We find a non-conditional wage penalty associated to overeducation and overskilling of 20 and 16% and a conditional one of about 12 and 7%, respectively. The Heckman sample selection model returns a slightly higher sample selection corrected wage penalty, supporting not only the job competition and job assignment models, but also the human capital model. Other concurrent statistical tests point to the difficulty that the educational system faces in providing work-related skills to graduates.

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Fig. 1
Fig. 2

Source own elaboration of AlmaLaurea data

Fig. 3

Source own elaboration of AlmaLaurea data

Fig. 4

Source own elaboration of AlmaLaurea data

Fig. 5

Source own elaboration of AlmaLaurea data

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Notes

  1. This is a special indicator created by AlmaLaurea by merging answers to the two questions available in the questionnaire on the educational mismatch (A16 and A17).

  2. The AlmaLaurea data do have a longitudinal dimension, but it is not accessible for research purposes.

  3. Unfortunately, the AlmaLaurea data provides no information on what Duncan and Hoffman (1981) call the “surplus education” (and the “deficit education”), namely the number of years in excess (in deficit) with respect to those required for the job, which is to be preferred, according to Leuven and Oosterbeek (2011). Moreover, the data does not allow us to measure under-education.

  4. We exclude job characteristics, partly endogenous.

  5. Heckit: ‘Heck-' from Heckman and ‘-it' as in probit, tobit, and logit.

  6. We thank Peter Sloane for suggesting this extension to us.

  7. As at 2016, 72 Italian universities were members, representing 91% of all Italian graduates. For further details about AlmaLaurea, see its homepage: http://www.almalaurea.it.

  8. We do not include in the sample students enrolled after the implementation of the 3 + 2 reform in 2001 (the so-called Bologna reform), because only few of them, the most skilled, have graduated. In fact, due to the widespread phenomenon of delayed graduation, very few graduates would be included in the sample.

  9. Following the suggestion of one of the anonymous referees, we tried to assess whether this reduction in the share of overeducated and overskilled from 1 to 5 years after graduation was due to attrition and namely a higher than average rate of dropout from the survey of the overeducated. We have found that the share of dropouts from the survey by labor market status between 1 and 3 years, between 3 and 5 years and between 1 and 5 years is distributed quite similarly across labor market statuses, with only a slightly higher probability to dropout for the individuals who are both overeducated and overskilled. This suggests that the reduction in the share of overeducated/overskilled is not due to attrition.

  10. For the sake of brevity, we omit the table which is available on request from the authors.

  11. The estimates are available on request.

  12. OLS and interval regressions are available on request from the authors.

  13. The earnings equations include only 16,591 graduates because 796 workers do not report their wage, either out of apathy or because it is too low to report.

  14. We thank one of the anonymous referees of the Journal for suggesting this further robustness check.

  15. In omitted estimates, available on request, the educational background of parents does not affect wages.

  16. We thank Peter Sloane for suggesting us this extension.

  17. Since more individuals experience at least one form or another of the mismatch, the other components (non-employment and well-matched) are slightly smaller than in Fig. 2.

  18. We thank one of the anonymus referees for suggesting us this robustness check.

Abbreviations

CEDEFOP:

European Centre for the Development of Vocational Training

EU:

European Union

GSOEP:

German Socio-Economic Panel

IALS:

International Adult Literacy Survey

IR:

Interval regression

ISFOL-Plus:

Istituto per lo sviluppo della formazione professionale dei lavoratori (En. Tr.: Institute for the vocational training of workers)

ISTAT:

Italian Institute of Statistics

IV:

Instrumental variables

NUTS:

Nomenclature of Territorial Statistical Units (Nomenclature des unités territoriales statistiques)

LSCU:

Laurea Specialistica a Ciclo Unico

ML:

Maximum likelihood

OECD:

Organization for Economic Cooperation and Development

OLS:

Ordinary least squares

ORU:

Over-, required and undereducation

PIAAC:

Program for the International Assessment of Adult Competencies

REFLEX:

Flexible Professional in the Knowledge Society

ROC curve:

Receiver operator characteristic curve

UK:

United Kingdom

USA:

United States of America

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Acknowledgements

Most part of this research was conceived during several periods of stay of Francesco Pastore at the AlmaLaurea headquarters in Bologna. We are grateful to Andrea Cammelli, Angelo Di Francia and Silvia Ghiselli not only for providing us with AlmaLaurea data, but also for detailed and helpful suggestions on data analysis. Previous versions of this paper have been presented at: AlmaLaurea Conference, Bologna (2011), IAB of Nuremberg (2011), XXVI AIEL Conference, Catholic University of Milan (September 2011), University of Naples “Parthenope” (2011), University of Naples “Federico II” (2011), Second University of Naples (2011; 2012), University of Pescara (2012); University of Salerno (2013). We thank all seminar participants and particularly Lilia Costabile, Giuseppe Croce, Hans Dietrich, Francesco Ferrante, Claudio Lucifora, Nadia Netti, Patrizia Ordine, Marco Pecoraro, Claudia Pigini, Giuseppe Rose, Dario Sciulli, Francesca Sgobbi and Peter Sloane for valuable comments on earlier versions of this paper. This notwithstanding, the authors are solely responsible for the opinions expressed in this paper.

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  1. University of Naples “Parthenope”, Naples, Italy

    Floro Ernesto Caroleo

  2. IZA (Institute for the Study of Labor), Bonn, Germany

    Floro Ernesto Caroleo & Francesco Pastore

  3. Università della Campania Luigi Vanvitelli, Caserta, Italy

    Francesco Pastore

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  1. Floro Ernesto Caroleo
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Correspondence to Francesco Pastore.

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Caroleo, F.E., Pastore, F. Overeducation at a Glance. Determinants and Wage Effects of the Educational Mismatch Based on AlmaLaurea Data. Soc Indic Res 137, 999–1032 (2018). https://doi.org/10.1007/s11205-017-1641-1

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