Since the onset of the Great Recession, it could be argued that it is the young who have been hardest hit in their living conditions. This paper offers a comprehensive description of youth living conditions and how they evolved during the recession period. To do so, we develop a synthetic index combining the indicators proposed by experts in the dimensions of Education and Training, Employment and Entrepreneurship, and Social Inclusion, through a multi-criteria approach based on the double reference point method. This technique enriches the debate by shifting the focus to acceptable and desirable thresholds for each indicator and by overcoming limitations inherent in previous youth indexes that allow for total compensation between the indicators, whilst ignoring potential imbalances. Results show that, in a context of convergence in policy instruments across countries during the Great Recession, there was an improvement in education performance, whereas cross-country divergences in terms of youth labour market prospects and social inclusion increased. This evolution has led to a more complex picture which is characterized by greater polarization in the spatial distribution of youth living conditions, with two noticeable poles: north-central Europe as opposed to the south and east of Europe. Differences in institutional configurations in the fields of education and training, active labour market policies, employment protection legislation and welfare provision together with macroeconomic trends, particularly levels of demand for youth labour and fiscal resources, have played an important role in shaping European youth living conditions.
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Youth is defined as a period of transition between childhood and adulthood. The length of this period varies hugely across socio-economic and political contexts. Any attempt to delimit it proves a difficult task, since a young person may be regarded as an adult in one domain but as a minor in others.
We would like to remind that the aim of the group of experts was to provide a dashboard of indicators to monitor young people living conditions. The aim of the dashboard is not therefore to build a composite indicator.
In very few cases, the indicator for a specific country in a particular year was not available. As usual in these circumstances, an imputation method is applied. When there is information for another different year, the gap was filled using the most recent prior value for the indicator (cold deck imputation).
This means that two countries with a difference in the share of young people in education will display different youth unemployment rates if they have equal numbers of unemployed youth. To solve this problem, Hill (2012) suggested the use of ratios, as they provide a more accurate measure because those not looking for full-time work, in other words full-time students, are included in the denominator.
As already mentioned, data from the Eurobarometer are only available for 2011.
The approach relies on the assumptions of optimising behaviour (Luque et al. 2012).
It will be assumed that all indicators are of the ‘the more, the better’ type. Thus, we have transformed indicators of the type ‘the less, the better’ to the ‘the more, the better’ by computing 100 minus the indicator.
It must be borne in mind that the effect of the weights is the opposite for positive and negative values of the achievement functions. For negative achievement values, a greater weight produces a worse strong indicator value, and for positive achievement values, a greater weight produces a better value of the strong indicator. Thus, in order to avoid this bias we need to correct weights and the values of the achievements function.
A weighted geometric mean, as an aggregation method, is a partial solution for compensability. While linear aggregation offers constant compensation, geometric aggregation offers inferior compensability for indicators with lower values (dismissing returns). In both linear and geometric aggregation, weights express trade-offs between indicators, with the idea being that deficits in one indicator or dimension can be offset by surplus in another. However, when different goals are legitimate and important, non-compensatory logic is necessary (Nardo et al. 2005).
We have also carried out all the calculations applying the min–max approach as a standardization criterion. The country’s ranking obtained from both criterions are not very different, thus confirming the robustness of our results (the Spearman rank correlation is 0.96). In the same vein, we have also carried out all the calculations using the geometric mean as an aggregation method. As expected, the Spearman rank correlation between the weak index and that obtained with the geometric mean is only 0.41, whereas the rank correlation with the strong index is close to 0 (−0.04). Table 8 of the Appendix show the country final ranks. The results reinforce the favourability of non-compensatory aggregation techniques derived from the multi-criteria approach (Castellano and Rocca 2014 and 2015). Detailed results are available from the authors upon request.
By definition, it is not possible to have a positive value in the strong index and a negative value in the weak index. Thus, there cannot be countries in the upper-left quadrant.
A simple comparison of the index computed in 2007 and 2016 does not necessarily indicate any real change in the situation of youth, but only reflects changes in the place each country occupies within an international ranking. A country might improve its position in the ranking merely because other countries do worse in terms of youth living condition indicators.
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Corrales-Herrero, H., Rodriguez-Prado, B. Measuring Youth Living Conditions in Europe: A Multidimensional Cross-Country Approach. Soc Indic Res (2021). https://doi.org/10.1007/s11205-021-02608-8
- Living conditions
- Multi-criteria approach
- Double reference point method
- EU countries