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Does the way scholars measure inequality of opportunity correspond to how people perceive it? To answer this question we must first clarify how scholars define and measure inequality of opportunity, we will then discuss the possible mechanisms linking objective measures and subjective perception of the phenomenon, and finally we test our hypothesis by merging data coming from two sources: the European Union Statistics on Income and Living Conditions (2011) and the International Social Survey Programme data (2009). We show that individual perception of unequal opportunity is heterogeneous across countries and among individuals. Moreover, the prevailing perception of the degree of unequal opportunity in a large sample of respondents is only weakly correlated with its objective measure. We estimate a multilevel model considering both individual and country level controls to explain individual perception of unequal opportunity. Our estimates suggest that one of the most adopted measure of inequality of opportunity has no significant role in explaining its perception. Conversely, other country level variables and personal experiences of intergenerational social mobility are important determinants of how inequality of opportunity is perceived.

This paper addresses the problem of the normative evaluation of income tax systems and income tax reforms. While most of the existing criteria, framed in the utilitarian tradition, are uniquely based on information about individual incomes, this paper, building upon the opportunity egalitarian theory, proposes new equity criteria which take into account also the socio-economic characteristics of individuals. Suitable dominance conditions that can be used to rank alternative tax systems are derived by means of an axiomatic approach. Moreover, the theoretical results are used to assess the redistributive effects of an hypothetical tax reform in Romania through a microsimulation analysis.

This paper proposes a set of new methods to estimate inequality of opportunity based on conditional inference regression trees. It illustrates how these methods represent a substantial improvement over existing empirical approaches to measure inequality of opportunity. First, the new methods minimize the risk of arbitrary and ad hoc model selection. Second, they provide a standardized way to trade off upward and downward biases in inequality of opportunity estimations. Finally, regression trees can be graphically represented; their structure is immediate to read and easy to understand. This will make the measurement of inequality of opportunity more easily comprehensible to a large audience. These advantages are illustrated by an empirical application based on the 2011 wave of the European Union Statistics on Income and Living Conditions.

We propose a set of new methods to estimate inequality of opportunity based on conditional inference regression trees. In particular, we illustrate how these methods represent a substantial improvement over existing empirical approaches to measure in equality of opportunity. First, they minimize the risk of arbitrary and ad-hoc model selection. Second, they provide a standardized way of trading off upward and downward biases in inequality of opportunity estimations. Finally, regression trees can be graphically represented; their structure is immediate to read and easy to understand. This will make the measurement of inequality of opportunity more easily comprehensible to a large audience. These advantages are illustrated by an empirical application based on the 2011 wave of the European Union Statistics on Income and Living Conditions.