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Measuring Income Inequality and Poverty at the Regional Level in OECD Countries

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  • Mario Piacentini

    (OECD)

Abstract

The extent to which income inequality and poverty vary within countries across different regions is very relevant for policy decisions and monitoring. However, sub-national measures are scarce, given the complexity of producing indicators at the regional level from the available data and the methodological issues related to cross-countries comparability. This paper presents a set of indicators of income inequality and poverty across and within regions for 28 OECD countries. These indicators were produced through a new household-level data collection based on internationally harmonized income definitions undertaken as part of the OECD project on “Measuring regional and local well-being for policymaking”. The data were collected at the OECD TL2 territorial level, corresponding to NUTS2 regions in Europe and to large administrative subdivisions (e.g. States in Mexico and Unites States) for non-European countries. These estimates confirm that there are significant variations in levels of income inequality within countries, and that regional breakdowns are useful for understanding sources and patterns of income disparities and poverty. For most of the countries relying on survey data for measuring income distribution, standard cross-sectional indicators of income inequality and relative poverty at this regional level are estimated with low precision in the smallest regions due to small samples. This has two main implications for data producers and analysts. First, systematic reporting of confidence intervals is needed to make meaningful comparisons of inequality levels across regions and with respect to the national averages. Second, averaged measures for multiple years or small area estimation methods should be considered as means for obtaining more robust measures. The issues related to the estimation of standard errors for three-year averages in rotational panel surveys and to the definition of the computational sampling structure for sub-national estimates are discussed in the paper. Il est très utile, pour les décisions des pouvoirs publics et leur suivi, de mesurer les variations entre les régions d’un même pays en termes d’inégalités de revenu et de pauvreté. Or les mesures infranationales dans ce domaine sont rares, compte tenu des difficultés liées à l’élaboration d’indicateurs régionaux à partir des données disponibles et des problèmes méthodologiques inhérents à la comparabilité entre pays. Ce rapport présente une série d’indicateurs régionaux des inégalités de revenu et de la pauvreté couvrant 28 pays de l'OCDE. Ces indicateurs sont issus d’une nouvelle collecte de données réalisée auprès des ménages, fondée sur des définitions du revenu harmonisées à l’échelle internationale dans le cadre du projet de l'OCDE sur la mesure du bien-être au niveau régional et local aux fins de l’élaboration des politiques publiques. Les données ont été recueillies au niveau territorial 2 de l'OCDE, qui correspond aux régions du niveau 2 de la NUTS en Europe et aux grandes subdivisions administratives (comme les États au Mexique ou aux États-Unis) dans les pays non européens. Ces estimations confirment l’existence de fortes variations du niveau des inégalités de revenu dans les pays, et elles montrent que les ventilations régionales sont utiles pour comprendre les causes et l’évolution des disparités de revenu et de la pauvreté. Pour la plupart des pays qui s’appuient sur des données d’enquêtes pour mesurer la distribution des revenus, les indicateurs transversaux standards des inégalités de revenu et de la pauvreté relative au niveau régional sont peu précis en ce qui concerne les régions les plus petites, en raison de la taille restreinte des échantillons. Ce phénomène a deux implications majeures pour les producteurs de données et les analystes : tout d’abord, une notification systématique des intervalles de confiance est nécessaire pour procéder à des comparaisons utiles des inégalités entre les régions et par rapport aux moyennes nationales. Ensuite, il convient d’envisager la possibilité d’utiliser des mesures moyennes sur plusieurs années ou des méthodes d’estimation spécifiques aux petits zones afin d’aboutir à des mesures plus précises. Le rapport examine également les problèmes liés à l’estimation des erreurs types pour les moyennes sur trois ans dans les enquêtes par panel avec échantillonnage par rotation, ainsi qu’à la définition de la structure d’échantillonnage pour les estimations infranationales. Les correspondants nationaux de la Base de données de l’OCDE sur la distribution des revenus et les délégués du Groupe de travail sur les indicateurs territoriaux sont invités à commenter les conclusions de ce rapport et à faire part de leur avis sur la possibilité d’améliorer et de reproduire les statistiques régionales sur le revenu des ménages à l’avenir.

Suggested Citation

  • Mario Piacentini, 2014. "Measuring Income Inequality and Poverty at the Regional Level in OECD Countries," OECD Statistics Working Papers 2014/3, OECD Publishing.
  • Handle: RePEc:oec:stdaaa:2014/3-en
    DOI: 10.1787/5jxzf5khtg9t-en
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    Cited by:

    1. Mauro Mussini, 2017. "Decomposing Changes in Inequality and Welfare Between EU Regions: The Roles of Population Change, Re-Ranking and Income Growth," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 130(2), pages 455-478, January.
    2. Stossberg Sibylle & Blöchliger Hansjörg, 2017. "Fiscal Decentralisation and Income Inequality: Empirical Evidence from OECD Countries," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 237(3), pages 225-273, June.
    3. Daniele, Vittorio, 2021. "Socioeconomic inequality and regional disparities in educational achievement: The role of relative poverty," Intelligence, Elsevier, vol. 84(C).
    4. Agnieszka Sompolska-Rzechuła & Agnieszka Kurdyś-Kujawska, 2022. "Assessment of the Development of Poverty in EU Countries," IJERPH, MDPI, vol. 19(7), pages 1-18, March.
    5. Verma, Vijay & Lemmi, Achille & Betti, Gianni & Gagliardi, Francesca & Piacentini, Mario, 2017. "How precise are poverty measures estimated at the regional level?," Regional Science and Urban Economics, Elsevier, vol. 66(C), pages 175-184.
    6. Natascha Hainbach & Christoph Halbmeier & Timo Schmid & Carsten Schröder, 2019. "A Practical Guide for the Computation of Domain-Level Estimates with the Socio-Economic Panel (and Other Household Surveys)," SOEPpapers on Multidisciplinary Panel Data Research 1055, DIW Berlin, The German Socio-Economic Panel (SOEP).
    7. Ilaria Benedetti & Federico Crescenzi & Tiziana Laureti, 2020. "Measuring Uncertainty for Poverty Indicators at Regional Level: The Case of Mediterranean Countries," Sustainability, MDPI, vol. 12(19), pages 1-19, October.
    8. Zehra Bilgen Susanlı, 2017. "Türkiye’de İşgücüne Katılım, İstihdam ve Beşeri Sermaye Dışsallıkları," Yildiz Social Science Review, Yildiz Technical University, vol. 3(1), pages 47-58.
    9. Vicente Roca-Puig, 2020. "The Symbiotic Bond of Income Equality and Organizational Equilibrium," Sustainability, MDPI, vol. 12(21), pages 1-14, November.
    10. Gianni Betti & Francesca Gagliardi, 2018. "Extension of JRR Method for Variance Estimation of Net Changes in Inequality Measures," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 137(1), pages 45-60, May.
    11. Fathim Rashna Kallingal & Mohammed Firoz C, 2022. "Developing a methodological framework for capturing regional disparities in social development," Regional Science Policy & Practice, Wiley Blackwell, vol. 14(5), pages 1085-1112, October.
    12. Stefano Boscolo, 2022. "The contribution of tax-benefit instruments to income redistribution in Italy," ECONOMIA PUBBLICA, FrancoAngeli Editore, vol. 2022(2), pages 181-231.

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