IDEAS home Printed from https://ideas.repec.org/a/bla/presci/v93y2014i3p685-701.html
   My bibliography  Save this article

Mapping average equivalized income using robust small area methods

Author

Listed:
  • Enrico Fabrizi
  • Caterina Giusti
  • Nicola Salvati
  • Nikos Tzavidis

Abstract

type="main" xml:lang="es"> A menudo son necesarias medidas de bienestar económico para áreas geográficas pequeñas, ya que los indicadores económicos pueden distribuirse de manera desigual entre subconjuntos de regiones relativamente pequeñas. Se considera una estimación de áreas pequeñas de ingresos equivalentes promedio. A menudo, los datos de ingresos familiares disponibles sólo se logran encontrar para una muestra de hogares por lo general demasiado pequeña como para ofrecer estimaciones confiables para regiones pequeñas. Se considera una técnica de estimación de área pequeña que es robusta frente a valores atípicos, produce resultados consistentes con estimaciones ponderadas del diseño obtenidas para áreas más grandes y capaz de generar mapas sin apenas contracción. La metodología propuesta se aplica a los Sistemas Laborales Locales en la Toscana (Italia).

Suggested Citation

  • 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.
  • Handle: RePEc:bla:presci:v:93:y:2014:i:3:p:685-701
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1111/pirs.12015
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Yogi Vidyattama & Rebecca Cassells & Ann Harding & Justine Mcnamara, 2013. "Rich or Poor in Retirement? A Small Area Analysis of Australian Private Superannuation Savings in 2006 Using Spatial Microsimulation," Regional Studies, Taylor & Francis Journals, vol. 47(5), pages 722-739, May.
    2. Chris Elbers & Jean O. Lanjouw & Peter Lanjouw, 2003. "Micro--Level Estimation of Poverty and Inequality," Econometrica, Econometric Society, vol. 71(1), pages 355-364, January.
    3. Ray Chambers & Nikos Tzavidis, 2006. "M-quantile models for small area estimation," Biometrika, Biometrika Trust, vol. 93(2), pages 255-268, June.
    4. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    5. Frey, Jesse & Cressie, Noel, 2003. "Some results on constrained Bayes estimators," Statistics & Probability Letters, Elsevier, vol. 65(4), pages 389-399, December.
    6. Antal, Erika & Tillé, Yves, 2011. "A Direct Bootstrap Method for Complex Sampling Designs From a Finite Population," Journal of the American Statistical Association, American Statistical Association, vol. 106(494), pages 534-543.
    7. Romina Boarini & Åsa Johansson & Marco Mira d'Ercole, 2006. "Alternative Measures of Well-Being," OECD Economics Department Working Papers 476, OECD Publishing.
    8. Romina Boarini & Åsa Johansson & Marco Mira d'Ercole, 2006. "Alternative Measures of Well-Being," OECD Social, Employment and Migration Working Papers 33, OECD Publishing.
    9. 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.
    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. Marchetti Stefano & Giusti Caterina & Pratesi Monica & Salvati Nicola & Giannotti Fosca & Pedreschi Dino & Rinzivillo Salvatore & Pappalardo Luca & Gabrielli Lorenzo, 2015. "Small Area Model-Based Estimators Using Big Data Sources," Journal of Official Statistics, Sciendo, vol. 31(2), pages 263-281, June.
    2. Zhang Junni L. & Bryant John, 2020. "Fully Bayesian Benchmarking of Small Area Estimation Models," Journal of Official Statistics, Sciendo, vol. 36(1), pages 197-223, March.
    3. 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.
    4. 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.
    5. Francesco Schirripa Spagnolo & Antonella D’Agostino & Nicola Salvati, 2018. "Measuring differences in economic standard of living between immigrant communities in Italy," Quality & Quantity: International Journal of Methodology, Springer, vol. 52(4), pages 1643-1667, July.
    6. 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.

    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. 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. Modrego, Félix & Berdegué, Julio A., 2015. "A Large-Scale Mapping of Territorial Development Dynamics in Latin America," World Development, Elsevier, vol. 73(C), pages 11-31.
    3. 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.
    4. Marchetti, Stefano & Tzavidis, Nikos & Pratesi, Monica, 2012. "Non-parametric bootstrap mean squared error estimation for M-quantile estimators of small area averages, quantiles and poverty indicators," Computational Statistics & Data Analysis, Elsevier, vol. 56(10), pages 2889-2902.
    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. Francesco Schirripa Spagnolo & Antonella D’Agostino & Nicola Salvati, 2018. "Measuring differences in economic standard of living between immigrant communities in Italy," Quality & Quantity: International Journal of Methodology, Springer, vol. 52(4), pages 1643-1667, July.
    7. Otto-Sobotka, Fabian & Salvati, Nicola & Ranalli, Maria Giovanna & Kneib, Thomas, 2019. "Adaptive semiparametric M-quantile regression," Econometrics and Statistics, Elsevier, vol. 11(C), pages 116-129.
    8. Johannes Gräb & Michael Grimm, 2008. "Spatial inequalities explained - Evidence from Burkina Faso," Ibero America Institute for Econ. Research (IAI) Discussion Papers 173, Ibero-America Institute for Economic Research.
    9. Paul Allin, 2015. "Official Statistics On Personal Well-Being: Some Reflections On The Development And Some Reflections On The Development And In The Uk," Statistics in Transition New Series, Polish Statistical Association, vol. 16(3), pages 397-408, September.
    10. Florence Jany-Catrice & Stephan Kampelmann, 2007. "L'indicateur de bien-être économique : une application à la France," Revue Française d'Économie, Programme National Persée, vol. 22(1), pages 107-148.
    11. Lee, Kamwoo & Braithwaite, Jeanine, 2022. "High-resolution poverty maps in Sub-Saharan Africa," World Development, Elsevier, vol. 159(C).
    12. Francesca Giambona & Mariano Porcu & Isabella Sulis, 2023. "Does education protect families' well-being in times of crisis? Measurement issues and empirical findings from IT-SILC data," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 32(1), pages 299-328, March.
    13. Donadelli, M. & Jüppner, M. & Paradiso, A. & Ghisletti, M., 2020. "Tornado activity, house prices, and stock returns," The North American Journal of Economics and Finance, Elsevier, vol. 52(C).
    14. Lars Osberg & Andrew Sharpe, 2011. "Moving from a GDP-Based to a Well-Being Based Metric of Economic Performance and Social Progress: Results from the Index of Economic Well-Being for OECD Countries, 1980-2009," CSLS Research Reports 2011-12, Centre for the Study of Living Standards.
    15. Paul Dolan & Tessa Peasgood, 2008. "Measuring Well-Being for Public Policy: Preferences or Experiences?," The Journal of Legal Studies, University of Chicago Press, vol. 37(S2), pages 5-31, June.
    16. Delbosc, Alexa & Currie, Graham, 2011. "Exploring the relative influences of transport disadvantage and social exclusion on well-being," Transport Policy, Elsevier, vol. 18(4), pages 555-562, August.
    17. Bhuiyan, M. Kamruj Jaman & Hossain, M. Jamal & Islam, Mohammad Amirul & Imam, M. Farouq & Quddus, Md. Abdul, 2020. "Small Area Estimation Of Nutritional Status Of Under-Five Children In Sylhet Division: An M-Quantile Approach," Bangladesh Journal of Agricultural Economics, Bangladesh Agricultural University, vol. 41(01), July.
    18. Merlo, Luca & Petrella, Lea & Salvati, Nicola & Tzavidis, Nikos, 2022. "Marginal M-quantile regression for multivariate dependent data," Computational Statistics & Data Analysis, Elsevier, vol. 173(C).
    19. Walter, Paul & Groß, Markus & Schmid, Timo & Tzavidis, Nikos, 2017. "Estimation of linear and non-linear indicators using interval censored income data," Discussion Papers 2017/22, Free University Berlin, School of Business & Economics.
    20. Allin Paul, 2015. "Official Statistics on Personal Well-Being: Some Reflections on the Development and use of Subjective Well-Being Measures in the UK," Statistics in Transition New Series, Statistics Poland, vol. 16(3), pages 397-408, September.

    More about this item

    Statistics

    Access and download statistics

    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:bla:presci:v:93:y:2014:i:3:p:685-701. 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: Wiley Content Delivery (email available below). General contact details of provider: http://www.blackwellpublishing.com/journal.asp?ref=1056-8190 .

    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.