IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v55y2011i4p1736-1747.html

Hierarchical Bayes multivariate estimation of poverty rates based on increasing thresholds for small domains

Author

Listed:
  • Fabrizi, Enrico
  • Ferrante, Maria Rosaria
  • Pacei, Silvia
  • Trivisano, Carlo

Abstract

A model-based small area method for calculating estimates of poverty rates based on different thresholds for subsets of the Italian population is proposed. The subsets are obtained by cross-classifying by household type and administrative region. The suggested estimators satisfy the following coherence properties: (i) within a given area, rates associated with increasing thresholds are monotonically increasing; (ii) interval estimators have lower and upper bounds within the interval (0, 1); (iii) when a large domain-specific sample is available the small area estimate is close to the one obtained using standard design-based methods; (iv) estimates of poverty rates should also be produced for domains for which there is no sample or when no poor households are included in the sample. A hierarchical Bayesian approach to estimation is adopted. Posterior distributions are approximated by means of MCMC computation methods. Empirical analysis is based on data from the 2005 wave of the EU-SILC survey.

Suggested Citation

  • Fabrizi, Enrico & Ferrante, Maria Rosaria & Pacei, Silvia & Trivisano, Carlo, 2011. "Hierarchical Bayes multivariate estimation of poverty rates based on increasing thresholds for small domains," Computational Statistics & Data Analysis, Elsevier, vol. 55(4), pages 1736-1747, April.
    • Handle: RePEc:eee:csdana:v:55:y:2011:i:4:p:1736-1747
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167-9473(10)00429-9
    Download Restriction: Full text for ScienceDirect subscribers only.
    ---><---

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

    for a different version of it.

    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. Sharon L. Lohr & J. N. K. Rao, 2009. "Jackknife estimation of mean squared error of small area predictors in nonlinear mixed models," Biometrika, Biometrika Trust, vol. 96(2), pages 457-468.
    3. Isabel Molina & Ayoub Saei & M. José Lombardía, 2007. "Small area estimates of labour force participation under a multinomial logit mixed model," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 170(4), pages 975-1000, October.
    4. Longford, Nicholas T., 2010. "Small area estimation with spatial similarity," Computational Statistics & Data Analysis, Elsevier, vol. 54(4), pages 1151-1166, April.
    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. Bilton, Penny & Jones, Geoff & Ganesh, Siva & Haslett, Steve, 2017. "Classification trees for poverty mapping," Computational Statistics & Data Analysis, Elsevier, vol. 115(C), pages 53-66.
    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. Silvia De Nicol`o & Maria Rosaria Ferrante & Silvia Pacei, 2021. "Mind the Income Gap: Bias Correction of Inequality Estimators in Small-Sized Samples," Papers 2107.08950, arXiv.org, revised May 2023.
    4. Souza Debora F. & Moura Fernando A. S., 2016. "Multivariate Beta Regression with Application in Small Area Estimation," Journal of Official Statistics, Sciendo, vol. 32(3), pages 747-768, September.
    5. Tsvetana Spasova, 2019. "Regional Income Distribution in the European Union: A Parametric Approach," Research on Economic Inequality, in: What Drives Inequality?, volume 27, pages 1-18, Emerald Group Publishing Limited.
    6. Maria Rosaria Ferrante & Silvia Pacei, 2017. "Small domain estimation of business statistics by using multivariate skew normal models," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 180(4), pages 1057-1088, October.
    7. Fabrizi, Enrico & Trivisano, Carlo, 2016. "Small area estimation of the Gini concentration coefficient," Computational Statistics & Data Analysis, Elsevier, vol. 99(C), pages 223-234.
    8. Enrico Fabrizi & Maria Rosaria Ferrante & Carlo Trivisano, 2020. "A functional approach to small area estimation of the relative median poverty gap," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(3), pages 1273-1291, June.
    9. Enrico Fabrizi & Maria Ferrante & Carlo Trivisano, 2013. "Small area estimation of labor productivity for the Italian manufacturing SME cross-classified by region, industry and size," ERSA conference papers ersa13p894, European Regional Science Association.

    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. Tzavidis, Nikos & Zhang, Li-Chun & Luna Hernandez, Angela & Schmid, Timo & Rojas-Perilla, Natalia, 2016. "From start to finish: A framework for the production of small area official statistics," Discussion Papers 2016/13, Free University Berlin, School of Business & Economics.
    2. Nikos Tzavidis & Li‐Chun Zhang & Angela Luna & Timo Schmid & Natalia Rojas‐Perilla, 2018. "From start to finish: a framework for the production of small area official statistics," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 181(4), pages 927-979, October.
    3. Paul Walter & Marcus Groß & Timo Schmid & Nikos Tzavidis, 2021. "Domain prediction with grouped income data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(4), pages 1501-1523, October.
    4. Peralta,Isabel Molina, 2024. "Frontiers in Small Area Estimation Research: Application to Welfare Indicators," Policy Research Working Paper Series 10828, The World Bank.
    5. Tomoki Fujii, 2013. "Geographic decomposition of inequality in health and wealth: evidence from Cambodia," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 11(3), pages 373-392, September.
    6. Coulombe, Harold & Wodon, Quentin, 2008. "Assessing the geographic impact of higher food prices in Guinea," Policy Research Working Paper Series 4743, The World Bank.
    7. Araujo, M. Caridad & Ferreira, Francisco H.G. & Lanjouw, Peter & Özler, Berk, 2008. "Local inequality and project choice: Theory and evidence from Ecuador," Journal of Public Economics, Elsevier, vol. 92(5-6), pages 1022-1046, June.
    8. Dang, Hai-Anh & Carleto, Gero & Gourlay, Sydney & Abanokova, Kseniya, 2023. "Addressing Soil Quality Data Gaps with Imputation: Evidence from Ethiopia and Uganda," 2023 Annual Meeting, July 23-25, Washington D.C. 335648, Agricultural and Applied Economics Association.
    9. Demombynes, Gabriel & Ozler, Berk, 2005. "Crime and local inequality in South Africa," Journal of Development Economics, Elsevier, vol. 76(2), pages 265-292, April.
    10. María Dolores Esteban & María José Lombardía & Esther López-Vizcaíno & Domingo Morales & Agustín Pérez, 2020. "Small area estimation of proportions under area-level compositional mixed models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 29(3), pages 793-818, September.
    11. Ravnborg, Helle Munk & Gómez, Ligia Ivette, 2015. "Poverty Reduction Through Dispossession: The Milk Boom and the Return of the Elite in Santo Tomás, Nicaragua," World Development, Elsevier, vol. 73(C), pages 118-128.
    12. van der Weide, Roy, 2014. "GLS estimation and empirical bayes prediction for linear mixed models with Heteroskedasticity and sampling weights : a background study for the POVMAP project," Policy Research Working Paper Series 7028, The World Bank.
    13. Emilie Perge & Raisa Behal, 2019. "How to Frequently and Accurately Measure Poverty and Forest Dependence?," World Bank Publications - Reports 32802, The World Bank Group.
    14. Alex Rivadeneira, 2024. "Attached once, attached forever: The persistent effects of concertaje in Ecuador," Working Papers 2024-01, Banco de México.
    15. Leonardo Gasparini & Pablo Gluzmann & Leopoldo Tornarolli, 2022. "Caracterización de la población vulnerable: una propuesta con estimaciones para Argentina," Económica, Departamento de Economía, Facultad de Ciencias Económicas, Universidad Nacional de La Plata, vol. 68, pages 135-157, January-D.
    16. Paul J. Gertler & Orie Shelef & Catherine D. Wolfram & Alan Fuchs, 2016. "The Demand for Energy-Using Assets among the World's Rising Middle Classes," American Economic Review, American Economic Association, vol. 106(6), pages 1366-1401, June.
    17. GIBSON, John & ZHANG, Xiaoxuan & PARK, Albert & YI, Jiang & XI, Li, 2024. "Remotely measuring rural economic activity and poverty : Do we just need better sensors?," CEI Working Paper Series 2023-08, Center for Economic Institutions, Institute of Economic Research, Hitotsubashi University.
    18. Arouri, Mohamed & Nguyen, Cuong & Youssef, Adel Ben, 2015. "Natural Disasters, Household Welfare, and Resilience: Evidence from Rural Vietnam," World Development, Elsevier, vol. 70(C), pages 59-77.
    19. Pave Sohnesen,Thomas & Ambel,Alemayehu A. & Fisker,Peter Simonsen & Andrews,Colin & Khan,Qaiser M., 2016. "Small area estimation of child malnutrition in Ethiopian woredas," Policy Research Working Paper Series 7581, The World Bank.
    20. Lee, Kamwoo & Braithwaite, Jeanine, 2022. "High-resolution poverty maps in Sub-Saharan Africa," World Development, Elsevier, vol. 159(C).

    More about this item

    Keywords

    ;

    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:eee:csdana:v:55:y:2011:i:4:p:1736-1747. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/csda .

    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.