2014
DOI: 10.3386/w20189
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Intra-household Welfare

Abstract: In this paper we develop an approach to measuring inequality and poverty that recognizes the fact that individuals within households may have both different preferences and differential access to resources. We argue that a measure based on estimates of the sharing rule is inadequate as an approach that seeks to understand how welfare is distributed in the population because it ignores public good and the allocation of time to market work, leisure and household production. We develop a money metric measure of w… Show more

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Cited by 21 publications
(24 citation statements)
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References 14 publications
(7 reference statements)
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“…We next show that we can use these bounds on the shadow prices θ i E for each individual i to define informative upper and lower bounds on individuals' money metric welfare indices (M M W Is). As indicated in the Introduction, Chiappori and Meghir (2014) advocated the use of these M M W Is for individual welfare analysis based on the collective model.…”
Section: Money Metric Welfare Indicesmentioning
confidence: 99%
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“…We next show that we can use these bounds on the shadow prices θ i E for each individual i to define informative upper and lower bounds on individuals' money metric welfare indices (M M W Is). As indicated in the Introduction, Chiappori and Meghir (2014) advocated the use of these M M W Is for individual welfare analysis based on the collective model.…”
Section: Money Metric Welfare Indicesmentioning
confidence: 99%
“…At this point, however, we must also emphasize that this kind of conclusions should be taken with sufficient caution. In particular, for different reference prices, one may well obtain a reverse ordering of the individual money metric welfare indices (see Chiappori and Meghir (2014) for more discussion and a graphical example). Finally, we note that, for the given reference prices, the difference between the individuals' M M W Is is less pronounced than between the income shares η 1 E and η 2 E .…”
Section: Simulation Analysismentioning
confidence: 99%
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