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Formation Continue du Supérieur
29 janvier 2011

L'éducation dans le Rapport du Conseil d’analyse économique et du Conseil allemand des experts en économie

http://www.cae.gouv.fr/IMG/bandeau/bandeau.jpgLe Rapport "Evaluer la performance économique, le bien-être et la soutenabilité" du Conseil d’analyse économique et du Conseil allemand des experts en économie.
Ce rapport est un travail commun du Conseil d’analyse économique et du Conseil allemand des experts en économie. Il répond à une commande de la Chancelière fédérale d’Allemagne et du Président de la République française lors du Conseil des ministres franco-allemand du 4 février 2010... La principale contribution de ce travail est donc de proposer un tableau de bord de vingt-cinq indicateurs couvrant les trois domaines de la performance économique, de la qualité de la vie et de la soutenabilité (économique, financière et environnementale) du bien-être. L’examen de la situation française et allemande au regard de ces indicateurs permet d’illustrer l’intérêt, mais aussi la difficulté de l’emploi de tels indicateurs, notamment pour des comparaisons.
Parmi les indicateurs "Qualité de la vie" se trouve l'Education: Étudiants âgés de 15 à 24 ans en pourcentage de la population du même groupe d’âge. En voici le contenu:
Education

135. Apart from its immediate contribution to a high quality of life, education exerts indirect effects, since it enables people to intensify the positive experiences of other dimensions. For example, a higher level of education broadens the scope of personal activities that a person can potentially carry out, is usually associated with higher levels of health and reduces economic insecurities by increasing job stability. Therefore, it is important to capture the skills and knowledge of a society’s members with appropriate individual indicators. As (Giovannini et al., 2009) forcefully point out, the focus should thereby be on output measures instead of input measures like education expenditures. Among output indicators, years of schooling or the percentage of people participating in education and training are problematic candidates, as the quality of the respective forms of education is not known and hence international comparability is not ensured.
136. The best output indicators that capture skills and knowledge are probably obtained through testing of achievements in literacy and numeracy. While these output measures do exist in quite some detail for younger age groups, coverage of the whole population is more limited. But since we are interested in an indicator for education as a source of current quality of life, the education level of all age groups is relevant. Among the available (composite) indicators that capture a broader sample of the population, those based on the International Adult Literacy Survey (IALS) and its successors appear to be the most promising starting point.
At the heart of this endeavour lies the understanding that literacy is not a zero-one distinction between those who can read and write and those who cannot, but rather a continuous, multifaceted phenomenon. Specifically, literacy is defined as the ability to use “printed and written information to function in society, to achieve one’s goals, and to develop one’s knowledge and potential” (Kirsch, 2001). The IALS asks a representative sample of people between the age of 16 and 65 to read, understand and interpret various texts, covering prose literacy (continuous texts like medicine labels, descriptions, manuals), document literacy (noncontinuous texts as in figures or tables), and quantitative literacy (calculations based on information from continuous or non-continuous texts). The results are ranked on a scale from zero to 500, and five proficiency levels are derived. IALS was conducted in 20 countries in the years 1994, 1996, and 1998.
The IALS was replaced by the Adult Literacy and Life Skills (ALL) survey conducted in 2003 and 2006 in a subset of these countries. ALL differs from IALS in its third field. Instead of quantitative literacy, ALL features a numeracy scale that covers proficiency in estimation and statistics.
Furthermore, it includes a fourth field problem solving. The OECD picks up these developments in its Programme for the International Assessment of Adult Competencies (PIAAC) survey. It is projected that the first results of this survey will not be available before the end of 2013 and will include the domains literacy, numeracy, problem solving, and information and communication technologies.
137. Studies based on panel data using skill assessments similar to IALS and its follow-ups document the fact that a lack of skills in the respective domains indeed exerts an adverse effect on many features that are associated with a high quality of life (for example, Bynner and Parsons, 1997). In particular, the positive correlation between low levels of literacy and numeracy and the risk of being unemployed, separated or divorced, physically ill, and less engaged in public activities appears to be robust and rather high. Subject to the condition that the OECD uses an appropriate data collection methodology to ensure reliable information, we propose to present the average scores of the PIAAC survey as the composite indicator of the education dimension. Moreover, it would be desirable to increase the survey’s continuity by carrying it out at least every two years and basing it on a survey design that ensures comparability over time. Reference to associated costs was already made in the first chapter.
138. Until such time as a sufficiently long time series exists, we have to rely on an interim indicator that best serves our purpose. Given our focus on regular reporting and coverage of a broader group of the populace, we propose to use students aged between 15 and 24 years as a percentage of the population of the same age group as an interim indicator. Indicator values are steadily improving in Germany, while values for France show a slight decrease over time.
139. In addition to discussing our preferred composite indicator, we conduct PCA for the education dimension. Ideally we should use output data that directly measure the increase in skills obtained in the educational system. Yet these data are difficult to collect because the skills of an individual are not directly observable and the available achievement surveys have not been evaluated frequently enough to allow a PCA. Therefore, we have to rely on other data sets. Specifically, we use Eurostat data for the period 1999-2007 for Germany and 1998-2007 for France. The data cover variables of participation rates, graduation rates and the share of early school leavers (share of individuals aged 18-24 years who have a lower secondary education or less). We use two participation variables: students aged between 15 and 24 years and students aged over 30 years, as a percentage of the respective population of the same age group. And we employ two graduation rate variables: the number of graduates who finished the first or second stage of tertiary education (ISCED 5-6) aged between 20 and 29 years per 1,000 people of the population, and the percentage of the population aged between 25 and 64 years who hold at least a higher secondary school qualification.
Variables capturing the quality of the educational system (output variables) should be used as soon as a reliable data collection procedure is discovered and its data quality is ascertained to be high. In future, output variables from the PIAAC study could be added as further variables to a PCA analysis. The first wave of PIAAC will be available at the end of 2013, but it will take a long time until these variables could be used for PCA because a relatively long time series is needed.
For the variables used in our analysis, an increase of the share of students aged between 15 and 24 years, the number of graduates between 20 and 29 years and the percentage of the population with at least a higher secondary school qualification tends to indicate an increase in the educational level of a society. Thus, the weights of these variables should be positive.
For the variable “students aged over 30 years” the direction is unclear, because this group tends to be very heterogeneous. The corresponding weight should be positive when the variable mainly captures mature adults engaging in further education. Conversely, it should be negative if the variable mainly reflects the share of long-term students. Finally, an increase in the share of early school leavers is an indication of a decrease in educational performance and therefore the weight is expected to be negative. According to our descriptive results, except for the share of students aged over 30 years, for Germany the variables indicate an improvement in the educational level. For France the overall tendencies are not that clear-cut because the share of students aged 15 and 24 years decreases.
140. As before, we conduct separate PCA for France and Germany and for various subsamples, achieving sensible and robust results (Table 11). As, for France, the number of graduates aged between 20 and 29 years (ISCED 5-6) per 1,000 people of the population is collected irregularly, the results for France are less reliable than those for Germany. Except for the indicator of the relative share of students aged over 30 years where the direction is unclear, all other signs of the weights turn out to match our expectations. The first principal component yields an explanation of the variance of 70 % for Germany and of 93 % for France. According to the KMO value of above 0.65 for Germany and 0.67 for France, the data set warrants a PCA.
140. As before, we conduct separate PCA for France and Germany and for various subsamples, achieving sensible and robust results (Table 11). As, for France, the number of graduates aged between 20 and 29 years (ISCED 5-6) per 1,000 people of the population is collected irregularly, the results for France are less reliable than those for Germany. Except for the indicator of the relative share of students aged over 30 years where the direction is unclear, all other signs of the weights turn out to match our expectations. The first principal component yields an explanation of the variance of 70 % for Germany and of 93 % for France. According to the KMO value of above 0.65 for Germany and 0.67 for France, the data set warrants a PCA.
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