IDEAS home Printed from https://ideas.repec.org/p/ecr/col027/48107.html
   My bibliography  Save this paper

Disaggregating data in household surveys: Using small area estimation methodologies

Author

Listed:
  • Molina, Isabel

Abstract

Household surveys are widely used as a tool for obtaining information on people's socio-economic status and well-being. However, the accuracy of household survey estimates decreases significantly when it comes to making inferences for population groups who represent disaggregations for which the survey was not designed. It is possible, in this context, to use estimation processes that combine information from household surveys with existing auxiliary information at population level, such as censuses or administrative records. This paper offers a methodological guide to the combination of survey statistical techniques with probabilistic models in order to produce disaggregations for interest groups, known as small area estimation (SAE) techniques.

Suggested Citation

  • Molina, Isabel, 2022. "Disaggregating data in household surveys: Using small area estimation methodologies," Estudios Estadísticos 48107, Naciones Unidas Comisión Económica para América Latina y el Caribe (CEPAL).
    • Handle: RePEc:ecr:col027:48107
    as

    Download full text from publisher

    File URL: http://repositorio.cepal.org/handle/11362/48107
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Achille Lemmi & Gianni Betti (ed.), 2006. "Fuzzy Set Approach to Multidimensional Poverty Measurement," Economic Studies in Inequality, Social Exclusion, and Well-Being, Springer, number 978-0-387-34251-1, July.
    3. Foster, James & Greer, Joel & Thorbecke, Erik, 1984. "A Class of Decomposable Poverty Measures," Econometrica, Econometric Society, vol. 52(3), pages 761-766, May.
    4. Marhuenda, Yolanda & Molina, Isabel & Morales, Domingo, 2013. "Small area estimation with spatio-temporal Fay–Herriot models," Computational Statistics & Data Analysis, Elsevier, vol. 58(C), pages 308-325.
    5. Bates, Douglas & Mächler, Martin & Bolker, Ben & Walker, Steve, 2015. "Fitting Linear Mixed-Effects Models Using lme4," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 67(i01).
    6. Gianni Betti & Bruno Cheli & Achille Lemmi & Vijay Verma, 2006. "Multidimensional and Longitudinal Poverty: an Integrated Fuzzy Approach," Economic Studies in Inequality, Social Exclusion, and Well-Being, in: Achille Lemmi & Gianni Betti (ed.), Fuzzy Set Approach to Multidimensional Poverty Measurement, chapter 6, pages 115-137, Springer.
    7. Sen, Amartya K, 1976. "Poverty: An Ordinal Approach to Measurement," Econometrica, Econometric Society, vol. 44(2), pages 219-231, March.
    8. 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.
    9. Molina, Isabel & Rao, J.N.K., 2009. "Small area estimation on poverty indicators," DES - Working Papers. Statistics and Econometrics. WS ws091505, Universidad Carlos III de Madrid. Departamento de Estadística.
    10. Malay Ghosh & Rebecca Steorts, 2013. "Two-stage benchmarking as applied to small area estimation," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 22(4), pages 670-687, November.
    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. Yegnanew A. Shiferaw, 2023. "Mapping Disaggregate-Level Agricultural Households in South Africa Using a Hierarchical Bayes Small Area Estimation Approach," Agriculture, MDPI, vol. 13(3), pages 1-17, March.

    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. Belhadj, Besma & Limam, Mohamed, 2012. "Unidimensional and multidimensional fuzzy poverty measures: New approach," Economic Modelling, Elsevier, vol. 29(4), pages 995-1002.
    2. Burhan Can Karahasan & Fırat Bilgel, 2021. "The Topography and Sources of Multidimensional Poverty in Turkey," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 154(2), pages 413-445, April.
    3. Espinoza-Delgado, José & Klasen, Stephan, 2018. "Gender and multidimensional poverty in Nicaragua: An individual based approach," World Development, Elsevier, vol. 110(C), pages 466-491.
    4. Stefano Marchetti & Maciej Beręsewicz & Nicola Salvati & Marcin Szymkowiak & Łukasz Wawrowski, 2018. "The use of a three‐level M‐quantile model to map poverty at local administrative unit 1 in Poland," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 181(4), pages 1077-1104, October.
    5. Sen, Sugata, 2019. "Decomposition of intra-household disparity sensitive fuzzy multi-dimensional poverty index: A study of vulnerability through Machine Learning," MPRA Paper 93550, University Library of Munich, Germany.
    6. Vito Peragine & Maria G. Pittau & Ernesto Savaglio & Stefano Vannucci, 2021. "On multidimensional poverty rankings of binary attributes," Journal of Public Economic Theory, Association for Public Economic Theory, vol. 23(2), pages 248-274, April.
    7. Guadarrama Sanz, Maria & Rao, J. N. K., 2015. "A Comparison of Small Area Estimation Methods for Poverty Mapping," DES - Working Papers. Statistics and Econometrics. WS ws1505, Universidad Carlos III de Madrid. Departamento de Estadística.
    8. LABAR, Kelly & BRESSON, Florent, 2011. "A multidimensional analysis of poverty in China from 1991 to 2006," China Economic Review, Elsevier, vol. 22(4), pages 646-668.
    9. Dipesh Gangopadhyay & Robert B. Nielsen & Velma Zahirovic-Herbert, 2021. "Methodology and Axiomatic Characterization of a Multidimensional and Fuzzy Measure of Deprivation," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 153(1), pages 1-37, January.
    10. José Roche, 2013. "Monitoring Progress in Child Poverty Reduction: Methodological Insights and Illustration to the Case Study of Bangladesh," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 112(2), pages 363-390, June.
    11. Alkire, Sabina & Foster, James, 2011. "Counting and multidimensional poverty measurement," Journal of Public Economics, Elsevier, vol. 95(7-8), pages 476-487, August.
    12. Sandip Sarkar & Manoranjan Pal, 2018. "On the Estimation of Lower and Upper Bounds of Poverty Line: An Illustration with Indian Data," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 138(3), pages 901-924, August.
    13. Sabina Alkire, 2011. "Multidimensional Poverty and its Discontents," OPHI Working Papers 46, Queen Elizabeth House, University of Oxford.
    14. Belhadj Besma, 2016. "Inequality among the poor in poverty measure case of Tunisia (2005–2010)," OPSEARCH, Springer;Operational Research Society of India, vol. 53(2), pages 409-425, June.
    15. 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.
    16. Yélé Batana, 2013. "Multidimensional Measurement of Poverty Among Women in Sub-Saharan Africa," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 112(2), pages 337-362, June.
    17. Gudeta Alemu Zerihun, 2015. "Working Paper - 220 - Developing a Food (in) Security Map for South Africa," Working Paper Series 2158, African Development Bank.
    18. Yolanda Marhuenda & Isabel Molina & Domingo Morales & J. N. K. Rao, 2017. "Poverty mapping in small areas under a twofold nested error regression model," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 180(4), pages 1111-1136, October.
    19. Chakravarty, Satya R. & Deutsch, Joseph & Silber, Jacques, 2008. "On the Watts Multidimensional Poverty Index and its Decomposition," World Development, Elsevier, vol. 36(6), pages 1067-1077, June.
    20. Oihana Aristondo & Casilda Lasso De La Vega & Ana Urrutia, 2010. "A New Multiplicative Decomposition For The Foster–Greer–Thorbecke Poverty Indices," Bulletin of Economic Research, Wiley Blackwell, vol. 62(3), pages 259-267, July.

    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:ecr:col027:48107. 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: Biblioteca CEPAL (email available below). General contact details of provider: https://edirc.repec.org/data/eclaccl.html .

    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.