Abstract
This paper assesses the impact of the recent crisis on the NEET (neither in employment or education or training) rate and the youth unemployment rate in EU regions. We use Eurostat data for the 2000–2010 period and focus on changes in both indices from 2000–2008 to 2009–2010. Employing Generalized Method of Moments (GMM) and bias-corrected Least Squares Dummy Variables (LSDV) dynamic panel data estimators, implemented by pooling both all regions and different groups of countries, we find that NEET rates are persistent and that persistence increases over the crisis period but that results vary depending on which of five regional groups is considered.
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Notes
Notice that NEET is not computed for the labor force. Thus, the different denominator (compared to UR) explains the lower values compared with the unemployment rate. However, the numerator includes not only the unemployed but also young people who are not in education or training.
International institutions have also recognized the importance of the NEET indicator, which was initially adopted to study problems of young workers in the United Kingdom. The initiative, ‘Youth on the Move’, part of the Europe 2020 program (European Commission, 2010), emphasizes the importance of focusing on the NEET rate.
Bulgaria, Ireland, Italy and Spain have very high NEET rates (above 17%); high rates are also found in the United Kingdom; average rates are found in France, Portugal and some Eastern European countries; low rates are found in Germany, Sweden and Finland; the lowest rates (less than 7%) are found in the Netherlands and Luxembourg.
See Arpaia and Curci (2010), who produced a broad analysis of labor market adjustments in the EU-27 after the 2008–2009 recession in terms of employment, unemployment, hours worked and wages.
According to the ILO (2012), if the unemployment rate is adjusted for drop-outs induced by the economic crisis, the global YUR in 2011 would rise from 12.6% to 13.6%.
For a recent review of the main determinants (macroeconomic, demographic, structural, institutional, etc), see Marelli et al. (2013).
To provide a flavor: taxes on labor, unemployment benefits (in terms of amount, duration and replacement ratio), degree of unionization (union density and union coverage), collective bargaining (degree of coordination and/or centralization), minimum wages, employment protection legislation (EPL), incidence of temporary or part-time contracts, active labor market policies and, in the case of young people, educational systems and school-to-work transitions.
For a recent exception, see Marelli et al. (2012).
In this way, we have GDP (computed) data through 2010, while the regional data for gross value added in real terms are available only through 2009.
In this estimation framework, the time dimension spans from 2001 through 2010, as only the first time observation, 2000, is sacrificed to the dynamics.
A further complication is that the bias approximations of xtlsdvc do not support interactions involving the lagged dependent variable. Entirely new bias approximations would be necessary in this case.
We are grateful to a referee of this journal for suggesting such extension.
Observe that some Eastern European countries with many regions, such as Poland, were only mildly affected by the Great Recession.
Time lags, more remote than the first, of the dependent variable spatial lag yield valid instruments in the absence of serial correlation and are used in Baltagi et al. (2014). We decided not to include them to minimize problems associated with instrument proliferation.
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Bruno, G., Marelli, E. & Signorelli, M. The Rise of NEET and Youth Unemployment in EU Regions after the Crisis. Comp Econ Stud 56, 592–615 (2014). https://doi.org/10.1057/ces.2014.27
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DOI: https://doi.org/10.1057/ces.2014.27