IDEAS home Printed from https://ideas.repec.org/a/spr/soinre/v131y2017i1d10.1007_s11205-015-1193-1.html
   My bibliography  Save this article

Local Comparisons of Small Area Estimates of Poverty: An Application Within the Tuscany Region in Italy

Author

Listed:
  • Caterina Giusti

    (University of Pisa)

  • Lucio Masserini

    (University of Pisa)

  • Monica Pratesi

    (University of Pisa)

Abstract

The aim of this paper is to highlight some key issues and challenges in the analysis of poverty at the local level using survey data. In the last years there was a worldwide increase in the demand for poverty and living conditions estimates at the local level, since these quantities can help in planning local policies aimed at decreasing poverty and social exclusion. In many countries various sample surveys on income and living conditions are currently conducted, but their sample size is not enough to obtain reliable estimates at local level. When this happens, small area estimation (SAE) methods can be used. In this paper, a SAE model is used to compute the mean household equivalised income and the head count ratio for the 57 Labor Local Systems of the Tuscany region in Italy for the year 2011. The caveats of the analysis of poverty at the local level using small area methods are many, and some are still not so well explored in the literature, starting from the definition of the target indicators to the relevant dimensions of their measurement. We suggest in this paper that together with the universally recognized multidimensional, longitudinal and local dimensions of poverty, a new dimension must be considered: the price dimension, which should take into account local purchasing power parities to correctly compare the poverty indicators based on income measures.

Suggested Citation

  • Caterina Giusti & Lucio Masserini & Monica Pratesi, 2017. "Local Comparisons of Small Area Estimates of Poverty: An Application Within the Tuscany Region in Italy," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 131(1), pages 235-254, March.
  • Handle: RePEc:spr:soinre:v:131:y:2017:i:1:d:10.1007_s11205-015-1193-1
    DOI: 10.1007/s11205-015-1193-1
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11205-015-1193-1
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s11205-015-1193-1?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. N. Salvati & N. Tzavidis & M. Pratesi & R. Chambers, 2012. "Small area estimation via M-quantile geographically weighted regression," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 21(1), pages 1-28, March.
    2. repec:pri:rpdevs:deaton_price_indexes_inequality_and_the_measurement_of_world_poverty_aer. is not listed on IDEAS
    3. Angus Deaton, 2010. "Price Indexes, Inequality, and the Measurement of World Poverty," American Economic Review, American Economic Association, vol. 100(1), pages 5-34, March.
    4. Jiming Jiang & P. Lahiri, 2006. "Mixed model prediction and small area estimation," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 15(1), pages 1-96, June.
    5. Besharov, Douglas J. & Couch, Kenneth A. (ed.), 2012. "Counting the Poor: New Thinking About European Poverty Measures and Lessons for the United States," OUP Catalogue, Oxford University Press, number 9780199860586.
    6. Enrico Fabrizi & Caterina Giusti & Nicola Salvati & Nikos Tzavidis, 2014. "Mapping average equivalized income using robust small area methods," Papers in Regional Science, Wiley Blackwell, vol. 93(3), pages 685-701, August.
    7. Lee Rainwater & Anthony Atkinson & Timothy Smeeding, 1994. "Income Distribution in Advanced Economies: The Evidence from the Luxembourg Income Study (LIS)," LIS Working papers 120, LIS Cross-National Data Center in Luxembourg.
    8. Ray Chambers & Nikos Tzavidis, 2006. "M-quantile models for small area estimation," Biometrika, Biometrika Trust, vol. 93(2), pages 255-268, June.
    9. Isabel Molina & Nicola Salvati & Monica Pratesi, 2009. "Bootstrap for estimating the MSE of the Spatial EBLUP," Computational Statistics, Springer, vol. 24(3), pages 441-458, August.
    10. Monica Pratesi & Nicola Salvati, 2008. "Small area estimation: the EBLUP estimator based on spatially correlated random area effects," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 17(1), pages 113-141, February.
    11. repec:pri:rpdevs:presidential%20address%2017january%202010%20all.pdf is not listed on IDEAS
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Simona Ballabio & Marianna Filandri & Lia Pacelli & Flavio Verrecchia, 2022. "Poverty of Young People: Context and Household Effects in North-Western Italy," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 161(2), pages 819-842, June.
    2. Roberto Benavent & Domingo Morales, 2021. "Small area estimation under a temporal bivariate area-level linear mixed model with independent time effects," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 30(1), pages 195-222, March.
    3. Petr Jansky & Marek Sedivy, 2018. "How Do Regional Price Levels Affect Income Inequality? Household-Level Evidence from 21 Countries," Working Papers IES 2018/24, Charles University Prague, Faculty of Social Sciences, Institute of Economic Studies, revised Sep 2018.
    4. Monica Pratesi & Luciana Quattrociocchi & Gaia Bertarelli & Alessandro Gemignani & Caterina Giusti, 2021. "Spatial Distribution of Multidimensional Educational Poverty in Italy using Small Area Estimation," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 156(2), pages 563-586, August.
    5. Isabel Molina & Paul Corral & Minh Nguyen, 2022. "Estimation of poverty and inequality in small areas: review and discussion," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 31(4), pages 1143-1166, December.
    6. Luca Secondi, 2021. "Estimating Household Consumption Expenditure at Local Level In Italy: The Potential of the Cokriging Spatial Predictor," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 153(2), pages 651-674, January.
    7. Benedetti, Ilaria & Crescenzi, Federico, 2023. "The role of income poverty and inequality indicators at regional level: An evaluation for Italy and Germany," Socio-Economic Planning Sciences, Elsevier, vol. 87(PA).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Baldermann, Claudia & Salvati, Nicola & Schmid, Timo, 2016. "Robust small area estimation under spatial non-stationarity," Discussion Papers 2016/5, Free University Berlin, School of Business & Economics.
    2. Timo Schmid & Ralf Münnich, 2014. "Spatial robust small area estimation," Statistical Papers, Springer, vol. 55(3), pages 653-670, August.
    3. Timo Schmid & Nikos Tzavidis & Ralf Münnich & Ray Chambers, 2016. "Outlier Robust Small-Area Estimation Under Spatial Correlation," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 43(3), pages 806-826, September.
    4. N. Salvati & N. Tzavidis & M. Pratesi & R. Chambers, 2012. "Small area estimation via M-quantile geographically weighted regression," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 21(1), pages 1-28, March.
    5. Ralf Münnich & Jan Burgard & Martin Vogt, 2013. "Small Area-Statistik: Methoden und Anwendungen," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 6(3), pages 149-191, March.
    6. Paolo Frumento & Nicola Salvati, 2020. "Parametric modelling of M‐quantile regression coefficient functions with application to small area estimation," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(1), pages 229-250, January.
    7. Schmid, Timo & Tzavidis, Nikos & Münnich, Ralf & Chambers, Ray, 2015. "Outlier robust small area estimation under spatial correlation," Discussion Papers 2015/8, Free University Berlin, School of Business & Economics.
    8. Kordos Jan, 2016. "Development of Small Area Estimation in Official Statistics," Statistics in Transition New Series, Polish Statistical Association, vol. 17(1), pages 105-132, March.
    9. Jan Kordos, 2016. "Development Of Smallarea Estimation In Official Statistics," Statistics in Transition New Series, Polish Statistical Association, vol. 17(1), pages 105-132, March.
    10. repec:csb:stintr:v:17:y:2016:i:1:p:105-132 is not listed on IDEAS
    11. Luis Pereira & Pedro Coelho, 2013. "Estimation of house prices in regions with small sample sizes," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 50(2), pages 603-621, April.
    12. J. N. K. Rao, 2015. "Inferential issues in model-based small area estimation: some new developments," Statistics in Transition new series, Główny Urząd Statystyczny (Polska), vol. 16(4), pages 491-510, December.
    13. Yogi Vidyattama & Robert Tanton & Nicholas Biddle, 2015. "Estimating small-area Indigenous cultural participation from synthetic survey data," Environment and Planning A, , vol. 47(5), pages 1211-1228, May.
    14. Dian Handayani & Henk Folmer & Anang Kurnia & Khairil Anwar Notodiputro, 2018. "The spatial empirical Bayes predictor of the small area mean for a lognormal variable of interest and spatially correlated random effects," Empirical Economics, Springer, vol. 55(1), pages 147-167, August.
    15. G. Bertarelli & R. Chambers & N. Salvati, 2021. "Outlier robust small domain estimation via bias correction and robust bootstrapping," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 30(1), pages 331-357, March.
    16. Giulia Romano & Nicola Salvati & Andrea Guerrini, 2014. "Factors Affecting Water Utility Companies’ Decision to Promote the Reduction of Household Water Consumption," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(15), pages 5491-5505, December.
    17. Tomasz Ża̧dło, 2015. "On longitudinal moving average model for prediction of subpopulation total," Statistical Papers, Springer, vol. 56(3), pages 749-771, August.
    18. Enrico Fabrizi & Caterina Giusti & Nicola Salvati & Nikos Tzavidis, 2014. "Mapping average equivalized income using robust small area methods," Papers in Regional Science, Wiley Blackwell, vol. 93(3), pages 685-701, August.
    19. Chandra, Hukum & Salvati, Nicola & Chambers, Ray & Tzavidis, Nikos, 2012. "Small area estimation under spatial nonstationarity," Computational Statistics & Data Analysis, Elsevier, vol. 56(10), pages 2875-2888.
    20. Angelo Moretti, 2023. "Estimation of small area proportions under a bivariate logistic mixed model," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(4), pages 3663-3684, August.
    21. Ranjbar, Setareh & Salvati, Nicola & Pacini, Barbara, 2023. "Estimating heterogeneous causal effects in observational studies using small area predictors," Computational Statistics & Data Analysis, Elsevier, vol. 184(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:soinre:v:131:y:2017:i:1:d:10.1007_s11205-015-1193-1. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.