Life cycle inequality dynamics – LINDY

Concerns about inequality and questions of social justice and cohesion have re-entered the public arena and animate public debate, provoked by the well-documented recent rapid increases in cross-sectional income inequality. While much has been learnt from the recent literature on inequality,Deaton's (2015) Nobel lecture outlines several imperatives that are key to understanding inequalities and formulating welfare-enhancing policies, i.e.:(i) differences in resources across individuals should be measured not only at specific points in time but also across the life course; (ii) direct economic measures of well-being should be developed in order to assess better socio-economic outcomes; and (iii) data should be reconciled with lifecycle models to investigate the causal mechanisms behind socio-economic outcomes.
Our research proposal is built on these imperatives, and brings together several coherently linked work packages (WPs) that focus,from a life-course perspective,on the causes of inequalities (income and wealth) and income fluctuations (income risk and mobility),their welfare and policy implications.Empirically, coherence is achieved by working with a common unique data source, the SOEP-RV (housed at team member DIW) which matches to the German Socio-Economic Panel (SOEP) exactly linked administrative records from the statutory German Pension system. These data are thus unique in their accurate recording of labour market events and income histories over the life-course during the active and retirement phase, while adding to administrative side the rich contextual data contained in SOEP available at individual and household-level. We also extend the traditional life-cycle perspective beyond the usual retirement data (labelled below the “extended life-cycle”).
Our central research questions are:
1. How can the life-course dynamics of individual incomes be characterised?
2. How can we systematically evaluate the effects of negative events (unemployment or health shocks), or positive ones (job changes) on individual trajectories and well-being?
3. How can we construct comprehensive measures of economic inequality and mobility taking into account wealth, income and the uncertainty of future incomes?
4. Taking a comprehensive life course view, how can policy intervene effectively to combat inequality? What is the equity-efficiency trade-off?
LINDY is organised as a set of four integrated WPs that address these questions, moving from descriptive and statistical work in WP1 to explanations using dynamic models in WP2 that seek to distinguish between events (e.g. bad health shock) and individual decisions given constraints. Having characterised the life-course dynamics, in WP3 we develop integrated measures of economic capacity that allow to measure inequality in a comprehensive way, going beyond the common focus on current-period incomes. WP4 synthesizes the results and puts forward policy measures to efficiently combat inequality.