IDEAS home Printed from https://ideas.repec.org/a/kap/jecinq/v7y2009i2p117-135.html
   My bibliography  Save this article

A model based approach for predicting annual poverty rates without expenditure data

Author

Listed:
  • Astrid Mathiassen

Abstract

No abstract is available for this item.

Suggested Citation

  • Astrid Mathiassen, 2009. "A model based approach for predicting annual poverty rates without expenditure data," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 7(2), pages 117-135, June.
  • Handle: RePEc:kap:jecinq:v:7:y:2009:i:2:p:117-135
    DOI: 10.1007/s10888-007-9059-7
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s10888-007-9059-7
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1007/s10888-007-9059-7?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
    ---><---

    As the access to this document is restricted, you may want to search 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. Tarozzi, Alessandro, 2007. "Calculating Comparable Statistics From Incomparable Surveys, With an Application to Poverty in India," Journal of Business & Economic Statistics, American Statistical Association, vol. 25, pages 314-336, July.
    3. Ravallion, M., 1992. "Poverty Comparisons - A Guide to Concepts and Methods," Papers 88, World Bank - Living Standards Measurement.
    4. Nicholas Minot, 2008. "Are Poor, Remote Areas Left behind in Agricultural Development: The Case of Tanzania," Journal of African Economies, Centre for the Study of African Economies (CSAE), vol. 17(2), pages 239-276, March.
    5. Ravallion, M., 1998. "Poverty Lines in Theory and Practice," Papers 133, World Bank - Living Standards Measurement.
    6. Hentschel, Jesko, et al, 2000. "Combining Census and Survey Data to Trace the Spatial Dimensions of Poverty: A Case Study of Ecuador," The World Bank Economic Review, World Bank Group, vol. 14(1), pages 147-165, January.
    7. Fofack, Hippolyte, 2000. "Combining Light Monitoring Surveys with Integrated Surveys to Improve Targeting for Poverty Reduction: The Case of Ghana," The World Bank Economic Review, World Bank Group, vol. 14(1), pages 195-219, January.
    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. Theresa Beltramo & Hai-Anh H. Dang & Ibrahima Sarr & Paolo Verme, 2020. "Estimating Poverty among Refugee Populations: A Cross-Survey Imputation Exercise for Chad," Working Papers 536, ECINEQ, Society for the Study of Economic Inequality.
    2. 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.
    3. Jose Cuesta & Gabriel Lara Ibarra, 2017. "Comparing Cross-Survey Micro Imputation and Macro Projection Techniques: Poverty in Post Revolution Tunisia," Journal of Income Distribution, Ad libros publications inc., vol. 25(1), pages 1-30, March.
    4. 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.
    5. Dang,Hai-Anh H. & Lanjouw,Peter F. & Serajuddin,Umar & Dang,Hai-Anh H. & Lanjouw,Peter F. & Serajuddin,Umar, 2014. "Updating poverty estimates at frequent intervals in the absence of consumption data : methods and illustration with reference to a middle-income country," Policy Research Working Paper Series 7043, The World Bank.
    6. Hai-Anh H. Dang & Paolo Verme, 2023. "Estimating poverty for refugees in data-scarce contexts: an application of cross-survey imputation," Journal of Population Economics, Springer;European Society for Population Economics, vol. 36(2), pages 653-679, April.
    7. Noor Hidayah Zakaria & Rohayanti Hassan & Muhamad Razib Othman & Zalmiyah Zakaria & Shahreen Kasim, 2017. "A Review on Classification of the Urban Poverty Using the Artificial Intelligence Method," Journal of Asian Scientific Research, Asian Economic and Social Society, vol. 7(11), pages 450-458, November.
    8. Dang, Hai-Anh & Lanjouw, Peter F., 2021. "Data Scarcity and Poverty Measurement," IZA Discussion Papers 14631, Institute of Labor Economics (IZA).
    9. Caroline Krafft & Ragui Assaad & Hanan Nazier & Racha Ramadan & Atiyeh Vahidmanesh & Sami Zouari, 2019. "Estimating poverty and inequality in the absence of consumption data: an application to the Middle East and North Africa," Middle East Development Journal, Taylor & Francis Journals, vol. 11(1), pages 1-29, January.
    10. World Bank, 2016. "Tunisia Poverty Assessment 2015," World Bank Publications - Reports 24410, The World Bank Group.
    11. Hai-Anh H. Dang & Peter F. Lanjouw & Umar Serajuddin, 2017. "Updating poverty estimates in the absence of regular and comparable consumption data: methods and illustration with reference to a middle-income country," Oxford Economic Papers, Oxford University Press, vol. 69(4), pages 939-962.
    12. 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.
    13. Jose Cuesta & Gabriel Lara Ibarra, 2018. "Comparing Cross-Survey Micro Imputation and Macro Projection Techniques: Poverty in Post Revolution Tunisia," Journal of Income Distribution, Ad libros publications inc., vol. 25(1), pages 1-30, 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. Astrid Mathiassen, 2006. "A Statistical Model for Simple, Fast and Reliable Measurement of Poverty. A revised version of DP 415," Discussion Papers 415, Statistics Norway, Research Department.
    2. Benjamin Davis, 2002. "Is it Possible to Avoid a Lemon? Reflections on Choosing a Poverty Mapping Method," Working Papers in Food Policy and Nutrition 08, Friedman School of Nutrition Science and Policy.
    3. Sebastian Levine & Benjamin Roberts, 2013. "Robust Estimates of Changes in Poverty and Inequality in Post-Independence Namibia," South African Journal of Economics, Economic Society of South Africa, vol. 81(2), pages 167-191, June.
    4. World Bank, 2015. "Tanzania Poverty Assessment," World Bank Publications - Reports 21871, The World Bank Group.
    5. Jehu-Appiah, Caroline & Aryeetey, Genevieve & Spaan, Ernst & Agyepong, Irene & Baltussen, Rob, 2010. "Efficiency, equity and feasibility of strategies to identify the poor: An application to premium exemptions under National Health Insurance in Ghana," Health Policy, Elsevier, vol. 95(2-3), pages 166-173, May.
    6. Healy, Andrew J. & Jitsuchon, Somchai, 2007. "Finding the poor in Thailand," Journal of Asian Economics, Elsevier, vol. 18(5), pages 739-759, October.
    7. Melanie Grosse & Stephan Klasen & Julius Spatz, 2005. "Creating National Poverty Profiles and Growth Incidence Curves with Incomplete Income or Consumption Expenditure Data: An Application to Bolivia," Ibero America Institute for Econ. Research (IAI) Discussion Papers 129, Ibero-America Institute for Economic Research.
    8. John A. Maluccio, 2009. "Household targeting in practice: The Nicaraguan Red de Protección Social," Journal of International Development, John Wiley & Sons, Ltd., vol. 21(1), pages 1-23.
    9. Claudio A. Agostini & Philip H. Brown, 2010. "Local Distributional Effects Of Government Cash Transfers In Chile," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 56(2), pages 366-388, June.
    10. Norbert R. Schady, 2002. "Picking the Poor: Indicators for Geographic Targeting in Peru," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 48(3), pages 417-433, September.
    11. Simler, Kenneth R., 2006. "Nutrition mapping in Tanzania: an exploratory analysis," FCND discussion papers 204, International Food Policy Research Institute (IFPRI).
    12. Nguyen Viet CUONG, 2008. "Is A Governmental Micro‐Credit Program For The Poor Really Pro‐Poor? Evidence From Vietnam," The Developing Economies, Institute of Developing Economies, vol. 46(2), pages 151-187, June.
    13. 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.
    14. Graw, Valerie & Husmann, Christine Ladenburger, 2012. "Mapping Marginality Hotspots – Geographical Targeting for Poverty Reduction," Working Papers 147917, University of Bonn, Center for Development Research (ZEF).
    15. 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.
    16. Jean-Pierre Lachaud, 1998. "Modélisation des déterminants de la pauvreté et marché du travail en Afrique : le cas du Burkina Faso," Documents de travail 32, Groupe d'Economie du Développement de l'Université Montesquieu Bordeaux IV.
    17. Coudouel, Aline & Hentschel, Jesko & Wodon, Quentin, 2002. "Измерение И Анализ Бедности [Poverty Measurement and Analysis]," MPRA Paper 10492, University Library of Munich, Germany.
    18. Hai-Anh H. Dang & Peter F. Lanjouw, 2023. "Regression-based imputation for poverty measurement in data-scarce settings," Chapters, in: Jacques Silber (ed.), Research Handbook on Measuring Poverty and Deprivation, chapter 13, pages 141-150, Edward Elgar Publishing.
    19. Carlo Azzarri & Gero Carletto & Benjamin Davis & Alberto Zezza, 2006. "Monitoring Poverty Without Consumption Data : An Application Using the Albania Panel Survey," Eastern European Economics, Taylor & Francis Journals, vol. 44(1), pages 59-82, February.
    20. Talip Kilic & Thomas Pave Sohnesen, 2019. "Same Question But Different Answer: Experimental Evidence on Questionnaire Design's Impact on Poverty Measured by Proxies," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 65(1), pages 144-165, March.

    More about this item

    Keywords

    Stochastic model; Poverty measurement; Money metric poverty; Survey methods; C31; C42; C81; D12; D31; I32;
    All these keywords.

    JEL classification:

    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • C42 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Survey Methods
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis
    • D31 - Microeconomics - - Distribution - - - Personal Income and Wealth Distribution
    • I32 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - Measurement and Analysis of Poverty

    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:kap:jecinq:v:7:y:2009:i:2:p:117-135. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.