IDEAS home Printed from https://ideas.repec.org/a/bla/jorssa/v180y2017i4p1137-1161.html
   My bibliography  Save this article

Robust mean‐squared error estimation for poverty estimates based on the method of Elbers, Lanjouw and Lanjouw

Author

Listed:
  • Sumonkanti Das
  • Ray Chambers

Abstract

The method of Elbers, Lanjouw and Lanjouw (ELL) is the small area estimation method developed by the World Bank for poverty mapping and is widely used in developing countries. However, it has been criticized because of its assumption of negligible between‐area variability when used to calculate small area poverty estimates. In particular, the mean‐squared errors (MSEs) of these estimates are significantly underestimated when this between‐area variability cannot be adequately explained by the model covariates. A method of MSE estimation for ELL‐type estimates is proposed which is robust to significant unexplained between‐area variability. Simulation results show that the method proposed performs better than standard ELL MSE estimators when the area homogeneity assumption is violated. An application to a Bangladesh poverty mapping study provides some empirical evidence for this robustness.

Suggested Citation

  • Sumonkanti Das & Ray Chambers, 2017. "Robust mean‐squared error estimation for poverty estimates based on the method of Elbers, Lanjouw and Lanjouw," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 180(4), pages 1137-1161, October.
    • Handle: RePEc:bla:jorssa:v:180:y:2017:i:4:p:1137-1161
    DOI: 10.1111/rssa.12311
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/rssa.12311
    Download Restriction: no
    File URL: https://libkey.io/10.1111/rssa.12311?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
    ---><---

    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. Foster, James & Greer, Joel & Thorbecke, Erik, 1984. "A Class of Decomposable Poverty Measures," Econometrica, Econometric Society, vol. 52(3), pages 761-766, May.
    3. Elbers, Chris & Lanjouw, Peter & Leite, Phillippe George, 2008. "Brazil within Brazil : testing the poverty map methodology in Minas Gerais," Policy Research Working Paper Series 4513, The World Bank.
    4. Gabriel DEMOMBYNES & Chris ELBERS & Jean O. LANJOUW & Peter LANJOUW, 2008. "How Good is a Map? Putting Small Area Estimation to the Test," Rivista Internazionale di Scienze Sociali, Vita e Pensiero, Pubblicazioni dell'Universita' Cattolica del Sacro Cuore, vol. 116(4), pages 465-493.
    5. Ray Chambers & Nikos Tzavidis, 2006. "M-quantile models for small area estimation," Biometrika, Biometrika Trust, vol. 93(2), pages 255-268, June.
    6. Elbers, Chris & Lanjouw, Jean O. & Lanjouw, Peter, 2002. "Micro-level estimation of welfare," Policy Research Working Paper Series 2911, The World Bank.
    7. Alessandro Tarozzi & Angus Deaton, 2009. "Using Census and Survey Data to Estimate Poverty and Inequality for Small Areas," The Review of Economics and Statistics, MIT Press, vol. 91(4), pages 773-792, 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. Corral Rodas,Paul Andres & Kastelic,Kristen Himelein & Mcgee,Kevin Robert & Molina,Isabel, 2021. "A Map of the Poor or a Poor Map ?," Policy Research Working Paper Series 9620, The World Bank.
    2. Corral Rodas,Paul Andres & Molina,Isabel & Nguyen,Minh Cong, 2020. "Pull Your Small Area Estimates up by the Bootstraps," Policy Research Working Paper Series 9256, The World Bank.
    3. Masaki,Takaaki & Newhouse,David Locke & Silwal,Ani Rudra & Bedada,Adane & Engstrom,Ryan, 2020. "Small Area Estimation of Non-Monetary Poverty with Geospatial Data," Policy Research Working Paper Series 9383, The World Bank.
    4. Dang,Hai-Anh H., 2018. "To impute or not to impute ? a review of alternative poverty estimation methods in the context of unavailable consumption data," Policy Research Working Paper Series 8403, The World Bank.
    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. Paul Corral & Kristen Himelein & Kevin McGee & Isabel Molina, 2021. "A Map of the Poor or a Poor Map?," Mathematics, MDPI, vol. 9(21), pages 1-40, November.
    7. Hai‐Anh H. Dang, 2021. "To impute or not to impute, and how? A review of poverty‐estimation methods in the absence of consumption data," Development Policy Review, Overseas Development Institute, vol. 39(6), pages 1008-1030, November.
    8. Md Jamal Hossain & Sumonkanti Das & Hukum Chandra & Mohammad Amirul Islam, 2020. "Disaggregate level estimates and spatial mapping of food insecurity in Bangladesh by linking survey and census data," PLOS ONE, Public Library of Science, vol. 15(4), pages 1-16, April.

    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. Tarozzi, Alessandro, 2011. "Can census data alone signal heterogeneity in the estimation of poverty maps?," Journal of Development Economics, Elsevier, vol. 95(2), pages 170-185, July.
    2. Lanjouw, P. & Marra, M.R., 2018. "Urban poverty across the spectrum of Vietnam’s towns and cities," World Development, Elsevier, vol. 110(C), pages 295-306.
    3. Luc Christiaensen & Peter Lanjouw & Jill Luoto & David Stifel, 2012. "Small area estimation-based prediction methods to track poverty: validation and applications," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 10(2), pages 267-297, June.
    4. Channing Arndt & Azhar M. Hussain & Vincenzo Salvucci & Finn Tarp & Lars Peter Østerdal, 2016. "Poverty Mapping Based on First‐Order Dominance with an Example from Mozambique," Journal of International Development, John Wiley & Sons, Ltd., vol. 28(1), pages 3-21, January.
    5. Jesse Naidoo, 2009. "Finite-Sample Bias and Inconsistency in the Estimation of Poverty Maps," SALDRU Working Papers 36, Southern Africa Labour and Development Research Unit, University of Cape Town.
    6. Nguyen, Cuong & Tran Ngoc, Truong & Van der Weide, Roy, 2009. "Rural Poverty and Inequality Maps in Vietnam: Estimation using Vietnam Household Living Standard Survey 2006 and Rural Agriculture and Fishery Census 2006," MPRA Paper 36378, University Library of Munich, Germany.
    7. Lang, Corey & Barrett, Christopher B. & Naschold, Felix, 2013. "Targeting Maps: An Asset-Based Approach to Geographic Targeting," World Development, Elsevier, vol. 41(C), pages 232-244.
    8. Nguyen Viet, Cuong, 2011. "Poverty projection using a small area estimation method: Evidence from Vietnam," Journal of Comparative Economics, Elsevier, vol. 39(3), pages 368-382, September.
    9. 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.
    10. Channing Arndt & Azhar M. Hussain & Vincenzo Salvucci & Finn Tarp & Lars Peter Østerdal, 2016. "Poverty Mapping Based on First‐Order Dominance with an Example from Mozambique," Journal of International Development, John Wiley & Sons, Ltd., vol. 28(1), pages 3-21, January.
    11. Sims, Katharine R.E., 2010. "Conservation and development: Evidence from Thai protected areas," Journal of Environmental Economics and Management, Elsevier, vol. 60(2), pages 94-114, September.
    12. Piyush Kant Rai & Sarla Pareek & Hemlata Joshi, 2017. "Met And Unmet Need For Contraception: Small Area Estimation For Rajasthan State Of India," Statistics in Transition New Series, Polish Statistical Association, vol. 18(2), pages 329-360, June.
    13. Corral Rodas,Paul Andres & Kastelic,Kristen Himelein & Mcgee,Kevin Robert & Molina,Isabel, 2021. "A Map of the Poor or a Poor Map ?," Policy Research Working Paper Series 9620, The World Bank.
    14. 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.
    15. Elbers, Chris & Fujii, Tomoki & Lanjouw, Peter & Ozler, Berk & Yin, Wesley, 2007. "Poverty alleviation through geographic targeting: How much does disaggregation help?," Journal of Development Economics, Elsevier, vol. 83(1), pages 198-213, May.
    16. 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.
    17. Tara Vishwanath & Dhiraj Sharma & Nandini Krishnan & Brian Blankespoor, 2015. "Where are Iraq’s Poor?," World Bank Publications - Reports 22351, The World Bank Group.
    18. Matthieu Clément & Lucie Piaser, 2022. "Geography of Income and Education Inequalities in Mexico: Evidence from Small Area Estimation and Exploratory Spatial Analysis," The European Journal of Development Research, Palgrave Macmillan;European Association of Development Research and Training Institutes (EADI), vol. 34(2), pages 703-732, April.
    19. Cazzuffi, Chiara & Pereira-López, Mariana & Soloaga, Isidro, 2017. "Local poverty reduction in Chile and Mexico: The role of food manufacturing growth," Food Policy, Elsevier, vol. 68(C), pages 160-185.
    20. Lovaton Davila, Rodrigo & McCarthy, Aine Seitz & Gondwe, Dorothy & Kirdruang, Phatta & Sharma, Uttam, 2022. "Water, walls, and bicycles: wealth index composition using census microdata," Journal of Demographic Economics, Cambridge University Press, vol. 88(1), pages 79-120, March.

    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:jorssa:v:180:y:2017:i:4:p:1137-1161. 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: https://edirc.repec.org/data/rssssea.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.