nep-ltv New Economics Papers
on Unemployment, Inequality and Poverty
Issue of 2016‒06‒04
three papers chosen by
Maximo Rossi
Universidad de la República

  1. Big Data Measures of Well-Being: Evidence From a Google Well-Being Index in the United States By Yann Algan; Elizabeth Beasley; Florian Guyot; Kazuhito Higa; Fabrice Murtin; Claudia Senik
  2. Dimensional and Distributional Contributions to Multidimensional Poverty By Sabina Alkire and James Foster
  3. Local Neighbors as Positives, Regional Neighbors as Negatives: Competing Channels in the Relationship between Others' Income, Health, and Happiness By Ifcher, John; Zarghamee, Homa; Graham, Carol Lee

  1. By: Yann Algan; Elizabeth Beasley; Florian Guyot; Kazuhito Higa; Fabrice Murtin; Claudia Senik
    Abstract: We build an indicator of individual subjective well-being in the United States based on Google Trends. The indicator is a combination of keyword groups that are endogenously identified to fit with the weekly time-series of subjective well-being measures disseminated by Gallup Analytics. We find that keywords associated with job search, financial security, family life and leisure are the strongest predictors of the variations in subjective well-being. The model successfully predicts the out-of-sample evolution of most subjective well-being measures at a one-year horizon. Un indicateur de bien-être subjectif est construit pour les États-Unis sur la base des données de Google Trends. L’indicateur est une combinaison de mots-clés qui sont identifiés pour reproduire les séries hebdomadaires de bien-être subjectif de Gallup Analytics. Nous trouvons que les mots-clés associés à la recherche d’emploi, à la sécurité financière, à la vie de famille et aux loisirs sont les plus forts prédicteurs des variations du bien-être subjectif. Le modèle prévoit l’évolution hors échantillon de la plupart des mesures de bien-être à l’horizon d’un an.
    Date: 2016–05–13
    URL: http://d.repec.org/n?u=RePEc:oec:stdaaa:2016/3-en&r=ltv
  2. By: Sabina Alkire and James Foster
    Abstract: The adjusted headcount ratio M0 of Alkire and Foster (2011a) is increasingly being adopted by countries and international organizations to measure poverty. Three properties are largely responsible for its growing use: Subgroup Decomposability, by which an assessment of subgroup contributions to overall poverty can be made, facilitating regional analysis and targeting; Dimensional Breakdown, by which an assessment of dimensional contributions to overall poverty can be made after the poor have been identified, facilitating coordination; and Ordinality, which ensures that the method can be used in cases where variables only have ordinal meaning. Following Sen (1976), a natural question to ask is whether sensitivity to inequality among the poor can be incorporated into this multidimensional framework. We propose a Dimensional Transfer axiom that applies to multidimensional poverty measures and specifies conditions under which poverty must fall as inequality among the poor decreases. An intuitive transformation is defined to obtain multidimensional measures with desired properties from unidimensional FGT measures having analogous properties; in particular, Dimensional Transfer follows from the standard Transfer axiom for unidimensional measures. A version of the unidimensional measures yields the M-gamma class Mγ/0 containing the multidimensional headcount ratio for γ=0, the adjusted headcount ratio M0 for γ=1, and a squared count measure for γ=2, satisfying Dimensional Transfer. Other examples show the ease with which measures can be constructed that satisfy Subgroup Decomposability, Ordinality, and Dimensional Transfer. However, none of these examples satisfies Dimensional Breakdown. A general impossibility theorem explains why this is so: Dimensional Breakdown is effectively inconsistent with Dimensional Transfer. Given the importance of Dimensional Breakdown for policy analysis, we suggest maintaining the adjusted headcount ratio as a central measure, augmented by the squared count measure or other indices that capture inequality among the poor. The methods are illustrated with an example from Cameroon.
    Date: 2016–03
    URL: http://d.repec.org/n?u=RePEc:qeh:ophiwp:ophiwp100_2.pdf&r=ltv
  3. By: Ifcher, John (Santa Clara University); Zarghamee, Homa (Barnard College); Graham, Carol Lee (Brookings Institution)
    Abstract: We develop a theoretical framework that considers four distinct explanatory channels through which neighbors' income could affect utility: public goods, cost of living, expectations of future income, and the direct effect (relative income hypothesis (RIH) and altruism). The relationship is theoretically ambiguous. We then empirically estimate the relationship with subjective well-being (SWB) data from the U.S. Gallup-Healthways Well-Being Index and geographically-based median-income data from the American Community Survey for both ZIP codes and MSAs. We find that the sign is proximity-dependent: the relationship is positive (negative) when using ZIP-code (MSA) median income as the reference income. This suggests that positive channels dominate locally while negative channels dominate regionally. These findings are consistent across multiple SWB measures and a wide range of health-related indices, for a variety of specification checks, and for most subgroups. Conditioning on explanatory-channel proxies, we find that the relationship between SWB and neighbors' income is either nullified or rendered positive, suggesting that the RIH is either inoperant or offset by altruism. Of the other channels, the public-goods channel is operant at the ZIP-code- and MSA-levels, and the cost-of-living channel is operant at the MSA-level.
    Keywords: subjective well-being, relative income hypothesis, others' income, reference group, relative utility, income comparison, happiness
    JEL: D6 D31 I31
    Date: 2016–05
    URL: http://d.repec.org/n?u=RePEc:iza:izadps:dp9934&r=ltv

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