Inequality Analysis


To understand the interactions of education and income inequality as “drivers” of overall inequality, within and across generations. The causation link between income and education inequality goes both ways. (Parental) income inequality may prevent access to education, especially from the bottom part of the income distribution. In addition, parental education (cumulating cultural constraints with liquidity constraints) may be a significant obstacle to achieving equality of opportunities, creating an important source of (next-generation) educational inequality. Once this new generation has entered the labour market, thanks to educational credentials achieved, as well as role models from home, a newly generated earnings inequality appears. From the policy point of view we will point to policies aimed at breaking the vicious circle and improving equality in income and in education. Implications for the longer term will be important here.

Background and motivation: Treat inequalities and impacts with care

First, the ‘new’ (or emerging) inequalities highlighted in the Call relate in the first instance to areas - incomes, earnings and wealth – that are the subject of an immense literature, spanning from measurement issues, passing through inequality accounting and decomposition by subgroups, up to causal models of inequality generation. So from the very onset we will be as precise as possible about the nature of inequality in many dimensions across the project:
- to whom it relates: household or individual – and how the two are related;
- what it involves: individual incomes or household income, or wealth – and how these are defined and measured: by source such as wage earnings, transfers, pensions and capital income, before or after tax, including or excluding social transfers and provisions such as health care or child care which in some countries or for some groups or income bands are publicly supported or even provided;
- what it concerns: a static cross-section view or a dynamic approach related to mobility over time and even lifetime outcomes;
- how far back in time it goes: a long-term approach is desirable for the focus on changes but also for methodological reasons more generally – i.e. for generating sufficient variation that can be analysed – but there may be a trade-off between the period that can be adequately covered and the effort required for gathering additional data;
- how inequality is measured: by a single indicator such as Gini or the coefficient of variation (in cross-section descriptive analysis), by decomposing according to income sources and/or recipients (also in cross-section and/or in time series analysis) or by providing measures of intertemporal association (intergenerational elasticities, rank correlations) as measure of the degree of inequality of opportunities. The main advantage of these measures comes with their simplicity (a single number providing information for the entire distribution), but arguably less complex measures, such as a set of decile ratios or grouped positions by e.g. percentiles or broader bands such as low, intermediate and high, or the median-to-mean ratio, may provide important additional information enabling a more refined analysis of causes and impacts depending on relative position in the dispersion. Jenkins and Van Kerm’s (2009) chapter in the Oxford Handbook of Economic Inequality provides an important point of departure here. Recent theoretical developments in the inequality literature have clarified the relationship of ‘traditional’ inequality indices (such as the Gini coefficient) to concepts such as relative deprivation and ‘complaints’ about income distribution that are also relevant for the shaping of policy (Cowell 2008). In recent years concern has also been shown for the possible polarisation of the income distribution which in some cases is related to, but distinct from, `traditional’ inequality (Wolfson 1997).

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