CORRODE: Corroding the social? An empirical evaluation of the relationship between unemployment and social stratification in OECD countries

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P.I.: Markus Gangl
Team: Carlotta Giustozzi, Pilar Goñalons-Pons, Timo Lepper, Kristina Lindemann
Funding: European Research Council (ERC)
Duration: 9/2014-8/2019


The project aims to deliver a comprehensive evaluation of the relationship between unemployment and social stratification in Europe and North America. Our core goal is to provide empirical estimates of the causal impact of unemployment on four critical domains of social life, namely household incomes, demographic behaviour, educational attainment, as well as social integration and civic participation. Our research will examine the persistence of such effects in the medium and longer run, and will evaluate the role of moderating factors like coupled unemployment and unemployment duration. The distinction between the stratification impacts of household experiences of unemployment and those of aggregate macroeconomic conditions will be a particular focus in the analysis, as will be the evaluation of a mediation model including changing household incomes, changing economic expectations and changing norms and preferences as relevant factors. The project will also address heterogeneity in the effects of unemployment e.g. by level of education, household demographics, household income or social class, and will evaluate the extent of cross-country variation in the impacts of unemployment, as well as any mitigating role of labour market and social policies, along the four dimensions of stratification considered. The empirical analysis rests on cross-nationally harmonized multilevel life course datasets constructed from various representative household panel studies, notably the EU Statistics on Income and Living Conditions (EU-SILC), the European Community Household Panel (ECHP) and several national panel studies, merged with time-series data on aggregate unemployment at the regional level. To achieve robust causal inference, the project utilizes multilevel panel data modelling, notably two-way fixed-effects and related estimators that statistically control for unobserved heterogeneity at both the household and contextual level.

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