Inequality of Opportunity in Europe: Is There a Role for Institutions?

Checchi, Daniele; Peragine, Vito; Serlenga, Laura

doi:10.1108/s0147-912120160000043008

Cited by 38 publications

(44 citation statements)

References 25 publications

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“…To minimize the frequency of sparsely populated types we divert from the occupational list given in Table 1 by re-coding occupations into three categories: highly skilled non-manual (I-01-I-03), lower-skilled non-manual (I-04-I-05 and I-10), skilled manual and elementary occupation (I-06-I-09 and father/mother unknown or dead). This partition is similar but more parsimonious than the one used in Checchi et al (2016), who base their analysis on a total of 96 types.…”

Section: Benchmark Methodsmentioning

confidence: 99%

“…The set of observed circumstancesΩ and their respective expressions x p are listed in Table 1 whereas descriptive statistics concerning circumstances are reported in Appendix A.2. As an additional advantage, EU-SILC has been extensively studied by the empirical literature on inequality of opportunity and thus provides appropriate benchmarks against which we can compare our method (Checchi et al, 2016;Marrero and Rodríguez, 2012;Palomino et al, 2016).…”

Section: Datamentioning

confidence: 99%

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The Roots of Inequality: Estimating Inequality of Opportunity from Regression Trees

Brunori¹,

Hufe²,

Mahler³

2018

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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 inequality 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.

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Section: Benchmark Methodsmentioning

confidence: 99%

Section: Datamentioning

confidence: 99%

The Roots of Inequality: Estimating Inequality of Opportunity from Regression Trees

Brunori¹,

Hufe²,

Mahler³

2018

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“…The wave collected in 2010 contains a module about intergenerational transmission of disadvantages which includes information about socioeconomic background. We follow other contributions by limiting our analysis to a subsample of respondents: working age, adult individuals aged between 25 and 65 (Marrero and Rodríguez, ; Checchi et al ., ). We implement a non‐parametric approach to estimate IOp , identifying groups of individuals sharing the same circumstances and then partitioning each group into three income tranches.…”

Section: Inequality Of Opportunity and Its Perception In 23 European mentioning

confidence: 97%

“…In order to control whether different measures of inequality of opportunity would better correlate with IOpP , we consider inequality of opportunity as measured by Checchi et al . () and Brzeziński (). The two studies are based on the same data but follow different measurement approaches.…”

Section: Inequality Of Opportunity and Its Perception In 23 European mentioning

confidence: 99%

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The Perception of Inequality of Opportunity in Europe

Brunori

2016

Review of Income and Wealth

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Does the way in which scholars measure inequality of opportunity correspond to how people perceive it? What other factors influence individual perception of this phenomenon? To answer these questions, we must first clarify how scholars define and measure inequality of opportunity. We discuss the possible mechanisms linking objective measures to subjective perception of the phenomenon, then propose a measure of perceived inequality of opportunity, and finally test our hypothesis by merging data from two sources: the European Union Statistics on Income and Living Conditions (2011) and the International Social Survey Programme (2009). We suggest that 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 the two most adopted measures of inequality of opportunity have no clear 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. JEL Codes: D63, A14, D31Note: I am grateful to Pasquale Recchia, Tina Rampino, and Raymond Schmelzer for useful comments. Significant improvements were made possible thanks to comments by Peter Blossfeld and three anonymous referees. All errors remain my own.

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A lost land of opportunity? The geography of intergenerational educational mobility in China

Hong,

Gruijters

2024

Population Space and Place

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Despite the significant political, economic and geographical diversity in China, there is limited research on spatial differences in intergenerational mobility in China. This research aims to fill this gap by exploring the spatial and temporal dimensions of intergenerational educational mobility in China. The data used for the analysis is the 2010–2018 China Family Panel Studies (CFPS), a nationally representative longitudinal general social survey. The analysis incorporates both relative and absolute mobility measures to provide a comprehensive description of intergenerational educational mobility. The results reveal substantial regional differences in intergenerational educational mobility across various economic zones in China, with a rising geographic inequality over time. The southwest and northeast regions stand out as the areas where the educational prospects of the young generation have become not only bleaker but dependent more on their parents. Additionally, this study presents the first education Great Gatsby Curve for China, highlighting the strong relationship between intergenerational mobility and education inequality at the regional level, particularly after China's market reform. The findings highlight the need for regionally targeted policies and levelling up agendas to promote educational opportunities in low‐mobility regions.

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Inequality of Opportunity in Europe: Is There a Role for Institutions?

Cited by 38 publications

References 25 publications

The Roots of Inequality: Estimating Inequality of Opportunity from Regression Trees

The Roots of Inequality: Estimating Inequality of Opportunity from Regression Trees

The Perception of Inequality of Opportunity in Europe

A lost land of opportunity? The geography of intergenerational educational mobility in China

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