RN21_10: Measuring social inequality in Europe
Child Deprivation In Europe. Study On Family And Context Factors
University of Milano Bicocca Department of Sociology and Social Research Italy
The study of child deprivation in contemporary Europe is a relevant topic for social policies. The paper focuses on material deprivation conditions of children, using data from EUSILC. The analysis has been carried out across 32 European countries and two waves (2009 and 2014). A particular attention has been given to the age of children, splitting up the sample in two groups - pre-school age children (1 to 5 years) and school age children (6 to 15 years) – thus measuring the specific deprivation for each group.
The aim of this paper is to evaluate the degree of consistency of different methodologies that allow to capture the multidimensionality involved in the concept of child deprivation. Both synthetic indices (e.g Alkire Foster index) and clustering procedures (e.g. Self Organizing Map) have been implemented.
Our analysis highlights the importance of some heterogeneity factors, such as number of children, family structure, tenure status, living with adults affected of chronic illness, parent’s education level and low work intensity, in accounting for patterns of multiple deprivation in both the samples.
The Effects Of Occupational Mismatches On The Employment Biographies And Income
1Friedrich-Alexander Universität Erlangen-Nürnberg, Germany; 2Institute for Employment Research, Germany
The negative outcomes of overeducation are well studied in the literature. Nevertheless, long-term effects of occupational mismatches on the employment biography are less clear. If mismatches are more than a temporary part of the allocation process on the labour market, they become a major driver of social inequality and economic outcomes.
In the case of Germany, the results of the literature regarding mobility and mismatches are contradictive. Several studies confirm the negative impact of scarring effects, while others found supporting evidence for the career mobility theory. In this study, we will focus on the long-term effects of the different types of mismatch regarding wage development over the employment biography and the persistence of precarious employment conditions.
Adding to the existing literature, we will not only observe formal mismatches, but a broad set of occupational mismatch states. Furthermore, we argue that due to the varying degree of standardisation of academic and vocational education training, the impact of occupational mobility on long-term wages vary between the training groups, depending on the mismatch type. We expect the negative effects of field-of-study mismatches to be higher for occupations with a higher standardisation, while the wage effects of a formal mismatch will be more severe for those with a lower standardisation.
To answer our research question, we are using the NEPS-ADIAB. This unique data set links the National Education Panel Survey biographies to the administrative data of the federal Institute for Employment Research (IAB).
Does Automation Technology Increase Social Inequality? – Robot Installations and the Individual Unemployment Risk in Germany
Friedrich-Alexander University Erlangen-Nürnberg, Germany
Several scholars argue that the current technological development will change future work. Nevertheless, it is highly controversial how and to which extend employment is about to change. A crucial question in a sociological context is whether employment outcomes vary between different social groups at the labour market. In our study, we focus on the individual risk of unemployment and present two alternative explanations how industrial robot installations may affect social inequality. We argue that automation technology can either create new social inequality due to occupational differences in the vulnerability to automation or reproduce existing inequality to the disadvantage of social groups that already have had a higher degree of insecurity on the labour market before. We use the task-biased approach by Autor et al. (2003) to explain why not only low qualified workers but particularly workers with a high share of routine-task in their occupation have an increased risk of being replaced by industrial robots. Further, in our empirical analysis for the years 2011 - 2013, we introduce an innovative measure for the risk of automation on the individual level by considering two important types of measurements. We include an interaction term between actual investments in robot technology at the industry level and the estimated automation potential of an occupation on the individual level to ensure that an employee indeed experiences automation. Therefore, we merge data from the World Robot Survey to administrative data (SIAB) provided by German Institute of Labour Market Research (IAB). Our findings suggest that we indeed can observe social inequality in the unemployment risk between different occupational groups that we cannot explain by skills and education but only by occupational task.