Path Independent Inequality Measures

Foster, James E.; Shneyerov, Artyom

doi:10.1006/jeth.1999.2565

Cited by 149 publications

(125 citation statements)

References 21 publications

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“…To overcome this weakness, an index must carry out this aggregation sequence in reverse and first aggregate over dimensions for each individual i, and then aggregate these individual-level measures over the population. The IHDI circumvents this issue by invoking the Foster and Shneyerov (2000) path independence property. The IHDI consists of a symmetric double geometric mean aggregation, and therefore satisfies the property that either sequencing of aggregations-whether aggregation is first carried out over Measuring Well-being 3 individuals or over dimensions-yields the same result.…”

Section: Hdi-type Multidimensional Indicesmentioning

confidence: 99%

Measuring Well-being: A Multidimensional Index Integrating Subjective Well-being and Preferences

Yang

2018

Journal of Human Development and Capabilities

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Policymakers have begun looking for multidimensional alternatives to incomebased measures for assessing well-being in societies. The Human Development Index (HDI) and related composite indices have been widely criticized in the welfare economic literature, yet are still some of the most influential income-alternatives in the research and policy arena. What are the theoretical links that bridge the gap between these composite indices and the criticisms levelled at them? This paper introduces the "preference index approach," a multidimensional measure bringing together the "equivalence approach" and the "distance function" in welfare economic theory. It retains convenient similarities with HDI-type composite indices, but assesses well-being in a way that reflects interpersonal differences in preferences between dimensions of well-being, whilst retaining comparability of well-being levels between individuals. The approach is applied empirically with data from the British Household Panel Survey (BHPS) to estimate different preference types between well-being dimensions. The empirical application finds that preferences differ by age, education level and unemployment status, and finds a weaker preference for the health and income dimension within older groups. Across all groups, health is strongly prioritized over income. When preference heterogeneities are taken into account, the picture of wellbeing looks quite different than that painted by standard welfare measures.

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Section: Hdi-type Multidimensional Indicesmentioning

confidence: 99%

Measuring Well-being: A Multidimensional Index Integrating Subjective Well-being and Preferences

Yang

2018

Journal of Human Development and Capabilities

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“…The decomposition of inequality according to a partition of the aggregate population into geographical regions (or, more generally, into any set of mutually exclusive and exhaustive subgroups) is most often undertaken with one of the entropy indices popularised by Theil (1967Theil ( , 1972 and later explored in more detail by Bourguignon (1979), Shorrocks (1980Shorrocks ( , 1984Shorrocks ( , 1988, Cowell and Kuga (1981), and Foster and Shneyerov (2000), amongst others. The single parameter entropy family may be written:…”

Section: Theoretical Foundations Of Spatial Decomposition Of Inequalitymentioning

confidence: 99%

Spatial decomposition of inequality

Shorrocks¹,

Wan²

2005

Journal of Economic Geography

212

142

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“…In our case, this corresponds to identical mean wages across types. Measuring IOp thus means capturing the extent to which µ k (w) = µ l (w), for k = l. To compute a measure of IOp, Checchi and Peragine (2010) suggest constructing a hypothetical smoothed distribution (Foster and Shneyerov 2000), µ k (w), which is obtained when each individual outcome, w k i , is replaced by the group-specific mean for each type, µ k (w).…”

Section: Measuring Iop: a Simple Modelmentioning

confidence: 99%

“…In order to respect the axioms of anonymity, Pigou-Dalton transfer principle, normalization, population replication, scale invariance and subgroup decomposability, we choose a member of the Generalized Entropy class (Shorrocks 1980) as an inequality measure. By introducing the further requirement of path-independent decomposability (see Foster and Shneyerov (2000)), the set of eligible indices reduces 5 As is common in the majority of EOp literature, we do not explicitly take into account the role of luck in our estimations. Hence, we (implicitly) assume that luck belongs to the sphere of individual responsibility and in our deterministic model, the individual is held responsible for any random component that may affect his income and that cannot be attributed to the observed circumstances.…”

Section: Measuring Iop: a Simple Modelmentioning

confidence: 99%

Accounting for the spouse when measuring inequality of opportunity

Peichl

Ungerer

2016

Soc Choice Welf

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Die Dis cus si on Pape rs die nen einer mög lichst schnel len Ver brei tung von neue ren For schungs arbei ten des ZEW. Die Bei trä ge lie gen in allei ni ger Ver ant wor tung der Auto ren und stel len nicht not wen di ger wei se die Mei nung des ZEW dar.Dis cus si on Papers are inten ded to make results of ZEW research prompt ly avai la ble to other eco no mists in order to encou ra ge dis cus si on and sug gesti ons for revi si ons. The aut hors are sole ly respon si ble for the con tents which do not neces sa ri ly repre sent the opi ni on of the ZEW. Accounting for the Spouse when Measuring Inequality of OpportunityAndreas Peichl and Martin Ungerer May 27, 2015 * AbstractExisting literature on inequality of opportunity (IOp) has failed to address the question as to how the circumstances and choices of spouses in a couple should be treated. By omitting information relevant to the spouse in IOp estimations, the implicit assumption was full responsibility for the partner's income, effort and circumstance variables. In this paper, we discuss whether or not the partner's characteristics should be treated as responsibility factors. Using German micro data, we analyze empirically, how IOp estimates are affected when a partner's circumstance or effort variables are included as own circumstances in the analysis. Our analysis indicates that including spouse's variables can increase IOp measures by more than 20 (35) percent for gross (net) earnings. The less the responsibility assumed for the partner's variables, the higher the IOp estimate.JEL Codes: D63, H2, J62, J7

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Path Independent Inequality Measures

Cited by 149 publications

References 21 publications

Measuring Well-being: A Multidimensional Index Integrating Subjective Well-being and Preferences

Measuring Well-being: A Multidimensional Index Integrating Subjective Well-being and Preferences

Spatial decomposition of inequality

Accounting for the spouse when measuring inequality of opportunity

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