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EPUNet Conference Barcelona, 8-9 May 2006

This study examines the impact of social classes on unemployment risks and job stability in four European countries with varying institutional arrangements. Data from the European Community Households Panel across eight waves is used to estimate unemployment risks using a random-coefficient Poisson regression model. The study focuses on the association between social class, job security, and risk of unemployment, testing two hypotheses. The ESeC classification is employed to represent social classes, which include various occupational and employment statuses. The model considers covariates such as education level, civil status, gender, age, health condition, and public/private sector activity to analyze the relationships. The study aims to determine the stability of these associations across different societies and institutional frameworks.

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EPUNet Conference Barcelona, 8-9 May 2006

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  1. EPUNet Conference Barcelona, 8-9 May 2006

  2. Unemployment risks in four European countries Antonio Schizzerotto & Mario Lucchini University of Milano Bicocca Italy

  3. Aims of the study • To analyse the effects of social classes on risks of unemployment and job stability. • To check if the main features of the association between social classes, risks of unemployment and job security are stable across societies with different institutional arrangements.

  4. Four EU countries • Denmark, as representative of countries where the State plays an important role in the functioning of the whole society. • Austria and Italy, as representatives of countries where family has a crucial position in the institutional arrangements of the society. • United Kingdom, as representative of countries that attribute great importance to the market in the workings of the society.

  5. Two hypotheses to be tested • There is a strong relation between social class and both job security and risk of unemployment. In the case of dependent workers, this association depends on: • employment relation • level of skill • Despite differences in institutional arrangements, the above relation should hold across the four selected countries.

  6. Data European Community Households Panel, eight waves (Jan 1994-Dec 2001): information regarding employment and unemployment episodes.

  7. Methods used for the estimation of unemployment risks. Model: to estimate unemployment risks, we specified a random-coefficient Poisson regression model. The dependent variable in this model is the incidence rate ratio of being unemployed. Covariates as class and level of education were considered as causal variables. Class structure was represented using a recently developed nine-fold class scheme known as ESeC (European Socio-economic Classification) Education was used as coded in ECHP, according to ISCED scheme. We also controlled forcivil status, gender, period effects, age, health condition, public/private sector of activity.

  8. The ESeC class schema • The ESeC (European Socio-economic Classification) is based on a widely-used social class schema devised by John Goldthorpe and Robert Erikson, known as the EGP schema. • It enucleates nine socio-economic classes, resulting from the combination of the following factors: • Occupation, coded according to Isco88(com) classification; • Employment status, used to distinguish between employers, the self-employed, managers, supervisors and employees; • Size of organization, used to distinguish between large and small employers.

  9. The ESeC classes • Class 1: Large employers, higher grade professional, administrative and managerial occupations: ‘higher professionals and managers’; • Class 2: Lower grade professional, administrative and managerial occupations: higher grade supervisory and technician occupations: ‘lower professionals and managers’; • Class 3: Intermediate occupations: ‘higher clerical, services and sales workers’; • Classes 4 and 5: Small employers and self-employed in non-professional occupations: ‘small employers and self-employed’ (4) and ‘farmers’ (5); • Class 6: Lower supervisory and lower technician occupations: ‘lower supervisors and technicians’; • Class 7: Lower clerical, services and sales occupations: ‘lower clerical, services and sales workers’; • Class 8: Lower technical occupations: ‘skilled workers’; • Class 9: Routine occupations: ‘semi- and unskilled workers’.

  10. The random-coefficient Poisson regression model • We model the number of unemployment episodes experienced by an individual year after year. • More specifically our dependent variable is the number of months of unemployment episodes during a year (or wave), standardized by the length in months of the overall participation in the labour market (i.e. number of months in unemployment + number of months in employment). • Since the total duration of the ECHP is 8 years (running from 1994-2001), each individual can be repeated until 8 times (from the wave 1 to wave 8). • We implemented a random intercept Poisson regression to model dependence and unobserved heterogeneity. • In our model, the normally distributed random intercept for subject accommodates dependence among the repeated counts of unemployment episodes collected year after year.

  11. Methods to estimate unemployment risks. • Model: to estimate unemployment risks, we specified a random-coefficient Poisson regression model. The dependent variable in this model is the incidence rate ratio of being unemployed. • Class and education were considered as independent, causal variables. • Class structure was represented using a recently developed nine-fold class scheme known as ESeC (European Socio-economic Classification) • Education was used as coded in ECHP, according to ISCED scheme. • civil status, gender, ECHP waves (assumed as expressing period effects), age, health condition, public/private sector of activity are treated as control variables

  12. Methods used for the estimation of job stability. To estimate job stability we carried out an event history analysis in order to compute the survival function of each class in each country.

  13. Results of the estimation of unemployment risks through a random-coefficient Poisson regression model. Denmark, Italy, Austria and United Kingdom-ECHP, 1994-2001.

  14. (a) reference category; *** p <0.01;** p <0.05; * p <0.1

  15. ESeC classes and the duration of employment episodes

  16. Conclusive remarks • Education displays a protective effect against the risk of unemployment also in the UK. UK is a partial ecception but even there tertiary education shows this protective effect. • Class exerts a quite strong effect on the risk of unemployment; generally speaking, members of classes based on service employment relations are less likely to experience an unemployment spell than people belonging to classes based on labour contract.

  17. Yet between classes based on labour contract some differences can be observed. • Namely, the risk of unemployment varies according to the level of technical skills: the higher the technical expertise, the lower the risk of unemployment. • Sometimes, the protective effect of technical skills overcomes the effect of the employment relations.

  18. Moving from incidence rate ratios of unemployment to job stability it can be seen that, by and large, occupational classes based on a service employment relationship display longer duration of employment episodes.

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