IDEAS home Printed from https://ideas.repec.org/a/ags/bdbjaf/304090.html
   My bibliography  Save this article

Small Area Estimation Of Poverty In Rural Bangladesh

Author

Listed:
  • Imam, M. F.
  • Islam, Mohammad Amirul
  • Alam, M. A.
  • Hossain, M. Jamal.
  • Das, Sumonkanti

Abstract

Poverty is a complex phenomenon and most of the developing countries are struggling to overcome the problem. Small area estimation offers help to allocate resources efficiently to address poverty at lower administrative level. This study used data from Census 2011 and Household Income and Expenditure Survey (HIES)-2010. Using ELL and M-Quantile methods, this study identified Rangpur division as the poorest one where Kurigram is the poorest district. Finally, considering both upper and lower poverty lines this study identified the poverty estimates at upazila level of Rangpur division using ELL and M-Quantile methods. The analyses found that 32% of the households were absolute poor and 19% were extremely poor in rural Bangladesh. Among the upazilas under Rangpur division Rajarhat, Ulipur, Char Rajibpur, Phulbari, Chilmari, Kurigram Sadar, Nageshwari, and Fulchhari Upazilas have been identified as the poorest upazilas.

Suggested Citation

  • Imam, M. F. & Islam, Mohammad Amirul & Alam, M. A. & Hossain, M. Jamal. & Das, Sumonkanti, 2020. "Small Area Estimation Of Poverty In Rural Bangladesh," Bangladesh Journal of Agricultural Economics, Bangladesh Agricultural University, vol. 40(1&2), February.
    • Handle: RePEc:ags:bdbjaf:304090
    DOI: 10.22004/ag.econ.304090
    as

    Download full text from publisher

    File URL: https://ageconsearch.umn.edu/record/304090/files/Imam.pdf
    Download Restriction: no
    File URL: https://libkey.io/10.22004/ag.econ.304090?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. 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.
    2. 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.
    3. 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.
    4. Nikos Tzavidis & Nicola Salvati & Monica Pratesi & Ray Chambers, 2008. "M-quantile models with application to poverty mapping," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 17(3), pages 393-411, July.
    5. Elbers, Chris & van der Weide, Roy, 2014. "Estimation of normal mixtures in a nested error model with an application to small area estimation of poverty and inequality," Policy Research Working Paper Series 6962, The World Bank.
    Full references (including those not matched with items on IDEAS)

    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. 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.
    2. 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.
    3. Tara Vishwanath & Dhiraj Sharma & Nandini Krishnan & Brian Blankespoor, 2015. "Where are Iraq’s Poor?," World Bank Publications - Reports 22351, The World Bank Group.
    4. Thomas Pave Sohnesen & Alemayehu Azeze Ambel & Peter Fisker & Colin Andrews & Qaiser Khan, 2017. "Small area estimation of child undernutrition in Ethiopian woredas," PLOS ONE, Public Library of Science, vol. 12(4), pages 1-17, April.
    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. 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.
    7. 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.
    8. Domingo Morales & María del Mar Rueda & Dolores Esteban, 2018. "Model-Assisted Estimation of Small Area Poverty Measures: An Application within the Valencia Region in Spain," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 138(3), pages 873-900, August.
    9. 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.
    10. 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.
    11. Ana Flavia Machado & Rafael Perez Ribas, 2010. "Do Changes in the Labour Market Take Families Out of Poverty? Determinants of Exiting Poverty in Brazilian Metropolitan Regions," Journal of Development Studies, Taylor & Francis Journals, vol. 46(9), pages 1503-1522.
    12. 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.
    13. 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(1), July.
    14. 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.
    15. 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.
    16. Marchetti Stefano & Tzavidis Nikos, 2021. "Robust Estimation of the Theil Index and the Gini Coeffient for Small Areas," Journal of Official Statistics, Sciendo, vol. 37(4), pages 955-979, December.
    17. José Lombardía, María & Sperlich, Stefan, 2012. "A new class of semi-mixed effects models and its application in small area estimation," Computational Statistics & Data Analysis, Elsevier, vol. 56(10), pages 2903-2917.
    18. Van Der Weide,Roy & Blankespoor,Brian & Elbers,Chris T.M. & Lanjouw,Peter F., 2022. "How Accurate Is a Poverty Map Based on Remote Sensing Data ? An Application to Malawi," Policy Research Working Paper Series 10171, The World Bank.
    19. World Bank Group, 2016. "Knowledge Externalities from Poverty Mapping in the European Union," World Bank Publications - Reports 23905, The World Bank Group.
    20. Mohamed Amara & Mohamed Ayadi & Hatem Jemmali, 2019. "Rural–urban migration and income disparity in Tunisia: A decomposition analysis," Papers in Regional Science, Wiley Blackwell, vol. 98(2), pages 1053-1083, April.

    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:ags:bdbjaf:304090. 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: AgEcon Search (email available below). General contact details of provider: https://edirc.repec.org/data/febaubd.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.