Abstract
In this paper, we analyse the main determinants of the propensity to NEET status in a selection of European countries. The NEET rate is the share of young people, aged between 19 and 30 years, not in Employment, Education or Training. In treating the 19–24 and 25–30 cohorts separately, our hypothesis is that for the younger cohort NEET status is mainly influenced by the school-to-work transition while for the older cohort this status is primarily due to labour market functioning and institutional factors. We apply different specifications of multilevel models with binary outcomes accounting for both personal and macro-economic factors which afford advantages over simple logit models. Estimates refer to 2007 and 2016 in order to verify how the economic crisis changed NEET status. The results confirm our hypothesis, highlighting the crucial role on the NEET propensity of the school-to-work transition in the first approach to the labour market, but also the strong influence of long-term unemployment, confirming the structural nature of the NEET phenomenon especially in countries where NEET levels remain high even during times of economic recovery.
Similar content being viewed by others
Notes
The term NEET was formally introduced in the late 1990 s in the UK government report “Bridging the Gap” in order to identify unemployed 16–18-year-olds who were neither studying nor receiving job training, especially following changes made to unemployment benefits policies (Eurofound 2012; Drakaki et al. 2014). Although the term was initially associated almost exclusively with early school leavers, it currently refers to a heterogeneous category of older people not in education, training or employment for different reasons. We decided to exclude from the analysis 15–18-year-olds—simply termed “young”—because the reasons for the NEET condition in this age class are different and more closely linked to early school leaver status, which requires different types of interventions.
According to personal characteristics affecting the status of NEET, many of them are very difficult to observe, such as the propensities to make sacrifices to improve human capital and the unobservable skills. As the NEET condition is the outcome of two distinct processes, that are being out of education and being out of employment, it would be very interesting to analyse the different mechanisms acting on the condition of NEETs. Unfortunately, the data used for the analysis do not allow for this.
However, to adequately explain the variability in random coefficients, the number of groups has to be sufficiently high (Heck and Thomas 2000; Rabe-Hesketh and Skrondal 2008). Even if the literature suggests that 20 is a sufficiently large number of groups, we accounted for variability in the random intercept alone—different levels in propensity to NEET status due to country of residence—without investigating each covariate’s different impact across countries. Bryan and Jenkins (2016), who analysed the robustness of estimates in multilevel models, estimated country effects with a sufficient number of countries (i.e., 10–50). They found that though estimates of the parameters associated with fixed predictors resulted in unbiased estimates, the estimates of group-level variances underestimated their true values. The smaller the number of groups, the larger the magnitude of such underestimation.
Another caveat in using multilevel models concerns the number of units, since smaller groups exert a smaller impact (Snijders and Berkhof 2008). However, in our study, even if the sample dimension differed considerably across countries, the number of units in each group was always sufficient to guarantee sound estimates.
However, ICC should not be grounds for justifying decisions on multilevel models, because its values can be misleading and, particularly in the social sciences, only rarely exceed 10% (e.g. Nezlek 2008). Lastly, the likelihood ratio test compares estimates fitted by maximum likelihood for a simple and a hierarchical model. When the test result is significant, the model with the hierarchical structure is preferred over the simple one.
The reason why we have estimated four different multilevel models is linked first of all to the wide range of macro-economic covariates considered. Indeed, as in a stepwise perspective, we preferred to introduce the first time separately homogeneous sets of variables. At a second step, we then estimated the joint effect of all covariates. In this way, we also tested the robustness of results.
References
Aiello, F., & Bonanno, G. (2017). Multilevel empirics for small banks in local markets. Papers in Regional Sciences,97(4), 1017–1037.
Alfieri, S., Rosina, A., Sironi, E., Marta, E., & Marzana, D. (2015). Who are Italian ‘NEETs’? Trust in institutions, political engagement, willingness to be activated and attitudes toward the future in a group at risk for social exclusion. Rivista Internazionale di Scienze Sociali,123(3), 285–306.
Arulampalam, W. (2001). Is unemployment really scarring? Effects of unemployment experiences on wages. Economic Journal,111(475), F585–606.
Bagues, M. F., & Sylos Labini, M. (2008). Do on-line labour market intermediaries matter? The impact of AlmaLaurea on the University-to-Work Transition. In D. H. Autor (Ed.), NBER book studies of labor market intermediation (pp. 127–154). Chicago: University of Chicago Press.
Barbieri, P., & Scherer, S. (2009). Labour market flexibilization and its consequences in Italy. European Sociological Review,25(6), 677–692.
Bassanini, A., & Duval R. (2006). Employment patterns in OECD countries: Reassessing the role of policies and institutions. OECD Social, Employment and Migration Working Papers, 35, Paris.
Becker, G. S. (1964). Human capital: A theoretical and empirical analysis with special reference to education. New York: Columbia University Press.
Becker, G. (1967). Human capital and the personal distribution of income: An analytical approach. Ann Arbor, MI: University of Michigan Institute of Public Administration.
Bell, D. N. F., & Blanchflower, D. G. (2010). Youth unemployment: Déjà Vu? IZA discussion paper, 4705, Bonn.
Bell, D. N. F., & Blanchflower, D. G. (2011). Youth unemployment in Europe and the United States. Nordic Economic Policy Review,1, 11–37.
Bernal-Verdugo, L. E., Furceri, D., & Guillaume, D. M. (2012). Crises, labour market policy, and unemployment. International monetary fund working paper, 65, Washington.
Bickel, R. (2007). Multilevel analysis for applied research: It’s just regression!. New York: Guilford Press.
Blanchard, O. J., & Wolfers, J. (2000). The role of shocks and institutions in the rise of European unemployment: The aggregate evidence. The Economic Journal,110(462), C1–C33.
Brunello, G., & De Paola, M. (2014). The costs of early school leaving in Europe. IZA Journal of Labor Policy,3(22), 1–11.
Brunetti, I., & Corsini, L. (2019). School-to-work transition and vocational education: A comparison across Europe. International Journal of Manpower (forthcoming).
Bruno, G. S. F., Caroleo, F. E., & Dessy, O. (2013). Stepping stones versus dead end jobs: Exits from temporary contracts in Italy after the 2003 reform. Rivista Internazionale di Scienze Sociali, Vita e Pensiero, Pubblicazioni dell’Universita’ Cattolica del Sacro Cuore,121(1), 31–62.
Bruno, G. S. F., Caroleo, F. E., & Dessy, O. (2014). Temporary contracts and young workers’ job satisfaction in Italy. In M. A. Malo & D. Sciulli (Eds.), Disadvantaged workers: empirical evidence and labour policies. AIEL Series in Labour Economics (pp. 95–120). Heidelberg: Springer Verlag.
Bryan, M. L., & Jenkins, S. P. (2016). Multilevel modelling of country effects: A cautionary tale. European Sociological Review,32(1), 3–22.
Bynner, J., & Parsons, S. (2002). Social exclusion and the transition from school to work: The case of young people not in education, employment, or training (NEET). Journal of Vocational Behavior,60(2), 289–309.
Caporale, G. M., & Gil-Alana, L. (2012). Persistence in youth unemployment. CESifo working paper: labour markets, 3961, Munich.
Carcillo, S., Fernández, R., Königs, S. & Minea, A. (2015). NEET Youth in the Aftermath of the Crisis: Challenges and policies. OECD Social, Employment and Migration Working Papers, 164, Paris.
Caroleo, F. E. (2012). The hard access to the labour market of youth leaving school: What policy choices? MPRA - Munich Personal RePEc Archive Paper, 37645, Munich.
Caroleo, F. E., Ciociano, E., & De Stefanis, S. (2017). Youth labour-market performance, institutions and vet systems: A cross-country analysis. Italian Economic Journal,3(1), 39–69.
Caroleo, F. E., De Stefanis, S., & Ciociano, E. (2018). Youth unemployment, labour-market institutions and education. In F. E. Caroleo, O. Demidova, E. Marelli, & M. Signorelli (Eds.), Young people and the labour market. A comparative perspective. London: Routledge.
Caroleo, F. E. & Pastore, F. (2007). The youth experience gap: Explaining differences across EU countries. Quaderni del Dipartimento di Economia, Finanza e Statistica, 41, Università degli Studi di Perugia, Perugia.
Caroleo, F. E., & Pastore, F. (2012). Talking about the Pigou paradox: Socio-educational background and educational outcomes of AlmaLaurea. International Journal of Manpower,33(1), 27–50.
Caroleo, F. E., & Pastore, F. (2019). The Italian low growth conundrum: An assessment and some policy issues. Cesifo Forum,20(1), 33–39.
Cavalca, G. (2015). Young people in transitions: Conditions, indicators and policy implications. To NEET or not to NEET? In G. Coppola & N. O’Higgins (Eds.), Youth and the crisis: Unemployment, education and health in Europe. London: Routledge.
Choudry Tanveer, M., Marelli, E., & Signorelli, M. (2012). Youth unemployment rate and impact of financial crises. International Journal of Manpower,33(1), 76–95.
Clarck, K. B., & Summers, L. H. (1982). The dynamics of youth unemployment. In R. B. Freeman & D. A. Wise (Eds.), The youth labour market problem: Its nature, causes, and consequences. Chicago: University of Chicago Press.
Crawford, C., Gregg P., Macmillan, L., Vignoles, A., & Wyness, G. (2016). Higher education, career opportunities, and intergenerational inequality. Oxford Review of Economic Policy, 32(4), 553–575. ISSN 0266-903X.
De Luca, G., Mazzocchi, P., Quintano, C., & Rocca, A. (2019). Italian NEETs in 2005–2016: Have the recent labour market reforms produced any effect? CESifo Economic Studies,65(2), 154–176.
Drakaki, M., Papadakis, N., Kyridis, A., & Papargyris, A. (2014). Who’s the greek neet? Neets’ profile in Greece: Parameters, trends and common characteristics of a heterogeneous group. International Journal of Humanities and Social Science,4(6), 240–254.
Eichhorst, W., & Neder, F. (2014). Youth unemployment in mediterranean countries. IZA Policy Paper no. 80, published in IEMed Mediterranean Yearbook 2014, pp. 265–271.
Elder, S., & Matsumoto, M. (2010). Characterizing the school-to-work transitions of young men and women: Evidence from the ILO school-to-work transition surveys. ILO Employment Working Paper 51, Geneva.
Eurofound. (2012). NEETs: Young people not in employment, education and training: Characteristics, costs and policy responses in Europe. Publications Office of the European Union. Luxembourg.
Eurofound. (2014). Mapping youth transitions in Europe. Publications Office of the European Union, Luxembourg.
Eurofound. (2016). Exploring the diversities of NEET. Publications Office of the European Union, Luxembourg.
Eurofound. (2017). Reactivate: Employment opportunities for economically inactive people. Publications Office of the European Union, Luxembourg.
Eurostat. (2010). Combating poverty and social exclusion, 2010 edition: A statistical portrait of the European Union. Eurostat Statistical Books, Luxembourg.
Finegan, T. A. (1978). Should discouraged workers be counted as unemployed? Challenge,21(5), 20–25.
Flisi, S., Goglio, V., Meroni, E. C., & Toscano, E. V. (2015). School-to-work transition of young individuals: What can the ELET and NEET indicators tell us? Joint Research Centre Technical Report 27219, European Commission, Luxembourg.
Furlong, A. (2006). Not a very NEET solution: Representing problematic labour market transitions among early school leavers, work. Employment and Society,20(3), 553–569.
Gregg, P., & Tominey, E. (2004). The wage scar from youth unemployment. Labour Economics,12(4), 487–509.
Hadjivassiliou, K. P., Tassinari, A., Eichhorst, W., & Wozny, F. (2018). How does the performance of school-to-work transition regimes in the European union vary? In J. O’Reilly, J. Leschke, R. Ortlieb, M. Seeleib-Kaiser, & P. Villa (Eds.), Youth labor in transition. New York: Oxford University Press.
Heck, R. H., & Thomas, S. L. (2000). Quantitative methodology series: An introduction to multilevel modeling techniques. Mahwah: Lawrence Erlbaum Associates Publishers.
ISTAT. (2018). Rapporto annuale, La situazione del Paese. Rome: StreetLib.
Jimeno, J. F., & Rodríguez–Palenzuela, D. (2002), Youth unemployment in the OECD: Demographic shifts, Labour Market Institutions, and Macroeconomic Shocks. European Central Bank Working Paper, 155.
Longford, N. (1993). Random coefficient models. Oxford: Clarendon Press.
Luijkx, R., & Wolbers, M. H. J. (2009). The effects of non-employment in early work life of subsequent chances of individuals in the Netherlands. European Sociological Review,25(6), 647–660.
MacMillan, L., Gregg P., & Britton J. (2012). Youth unemployment: The crisis we cannot afford. Technical report, The ACEVO Commission on Youth Development.
Manfredi, T., Scarpetta, S., & Sonnet, A. (2010). Rising youth unemployment during the crisis: How to prevent negative long-term consequences on a generation? OECD social, employment and migration working papers 106, OECD publications, Paris.
Martin, J. P., Martin, S., & Quintini, G. (2007). The changing nature of the school-to-work transition process in OECD countries. IZA Discussion Paper 2582. Bonn.
Mroz, T. A., & Savage, T. H. (2006). The long-term effects of youth unemployment. Journal of Human Resources,41(2), 259–293.
Nezlek, J. B. (2008). An introduction to multilevel modeling for social and personality psychology. Social and Personality Psychology Compass,2(2), 842–860.
O’Higgins, N. (2011). Italy: Limited policy responses and industrial relations in flux, leading to aggravated inequalities. In D. Vaughan-Whitehead (Ed.), Inequalities in the world of work: The effects of the crisis. Cheltenham: Edward Elgar.
O’Higgins, N. (2012). This time it’s different? Youth labor markets during the great recession. Comparative Economic Studies,54(2), 395–412.
OECD. (2006). Employment outlook. Paris: OECD publications.
OECD. (2008). Employment outlook. Paris: OECD Publishing.
OECD. (2012). Education at a glance. Paris: OECD Publications.
OECD. (2018). Active labour market policies: Connecting people with jobs. Paris: OECD Publishing.
Pastore, F. (2014). The youth experience gap. Berlin: Springer Book Series.
Pastore, F. (2018). Why so slow? The school-to-work transition in Italy. IZA working paper. Forthcoming in Studies in Higher Education.
Pastore, F., & Zimmermann, K. F. (2019). Understanding school-to-work transitions. International Journal of Manpower, Special Issue on Advances on School-to-Work Transitions: Part I,40(3), 374–378.
Piopiunik, M., & Ryan, P. (2012). Improving the transition between education/training and the labour market: What can we learn from various national approaches? Report for the European Commission. EENEE. Analytical Report 13.
Pohl, A., & Walther, A. (2007). Activating the disadvantaged: Variations in addressing youth transitions across Europe. International Journal of Lifelong Education,25(6), 533–553.
Quintano, C., Mazzocchi, P., & Rocca, A. (2018). The determinants of Italian NEETs and the effects of the economic crisis. GENUS: Journal of Population Sciences,74(5), 2–24.
Quintano, C., & Rocca, A. (2017). Migration flows in the European Labour Markets, Rivista RIEDS, Società Italiana di Economia, Demografia e Statistica, Vol. LXXI, No. 3, luglio-settembre, pp. 41–52.
Quintini, G., Martin, J. P., & Martin, S. (2007). The changing nature of the school-to-work transition process in OECD countries. IZA discussion papers 2582, Bonn.
Rabe-Hesketh, S., & Skrondal, A. (2008). Multilevel and longitudinal modeling using stata. College Station: Stata Press.
Robson, K. (2008). Becoming NEET in Europe: A comparison of predictors and later-life outcomes. In: Global network on inequality mini-conference, New York City.
Rosina, A. (2015). NEET. Giovani che non studiano e non lavorano. Milan: Vita e Pensiero.
Ryan, P. (2001). The school-to-work transition: A cross-national perspective. Journal of Economic Literature,39(1), 34–92.
SALTO-YOUTH Inclusion Research Centre. (2011). On track: Different youth work approaches for different NEET situations. European Commission. Retrieved 27 January 2019 from http://www.salto-youth.net/inclusion.
Snijders, T. A. B., & Berkhof, J. (2008). Diagnostic checks for multilevel models. In J. de Leeuw & E. Meijer (Eds.), Handbook of multilevel analysis. New York: Springer.
Thompson, R. (2011). Individualisation and social exclusion: The case of young people not in education. Employment or Training, Oxford Review of Education,37(6), 785–802.
Zijl, M., van den Berg, G. J., & Heyma, A. (2011). Stepping stones for the unemployed: The effect of temporary jobs on the duration until (regular) work. Journal of Population Economics,24, 107–139.
Zimmermann, K., Biavaschi, C., Eichhorst, W., Giulietti, C., Kendzia, M. J., Pieters, M. A., et al. (2013). Youth unemployment and vocational training. Foundations and Trends in Microeconomics,9(1–2), 1–157.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Caroleo, F.E., Rocca, A., Mazzocchi, P. et al. Being NEET in Europe Before and After the Economic Crisis: An Analysis of the Micro and Macro Determinants. Soc Indic Res 149, 991–1024 (2020). https://doi.org/10.1007/s11205-020-02270-6
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11205-020-02270-6