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

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  • Astrid Mathiassen

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  • 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
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    References listed on IDEAS

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    1. 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.
    2. Ravallion, M., 1998. "Poverty Lines in Theory and Practice," Papers 133, World Bank - Living Standards Measurement.
    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. 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, vol. 14(1), pages 147-165, January.
    5. Ravallion, M., 1992. "Poverty Comparisons - A Guide to Concepts and Methods," Papers 88, World Bank - Living Standards Measurement.
    6. 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, vol. 17(2), pages 239-276, March.
    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, vol. 14(1), pages 195-219, January.
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    Cited by:

    1. Theresa Beltramo & Hai-Anh Dang & Ibrahima Sarr & Paolo Verme, 2024. "Estimating poverty among refugee populations: a cross-survey imputation exercise for Chad," Oxford Development Studies, Taylor & Francis Journals, vol. 52(1), pages 94-113, January.
    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. Sarr, Ibrahima & Dang, Hai-Anh H & Gutierrez, Carlos Santiago Guzman & Beltramo, Theresa & Verme, Paolo, 2024. "Using Cross-Survey Imputation to Estimate Poverty for Venezuelan Refugees in Colombia," IZA Discussion Papers 17036, Institute of Labor Economics (IZA).
    5. Dang, Hai-Anh H & Kilic, Talip & Hlasny, Vladimir & Abanokova, Kseniya & Carletto, Calogero, 2024. "Using Survey-to-Survey Imputation to Fill Poverty Data Gaps at a Low Cost: Evidence from a Randomized Survey Experiment," IZA Discussion Papers 16792, Institute of Labor Economics (IZA).
    6. 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.
    7. 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.
    8. Dang,Hai-Anh H. & Kilic,Talip & Carletto,Calogero & Abanokova,Kseniya, 2021. "Poverty Imputation in Contexts without Consumption Data : A Revisit with Further Refinements," Policy Research Working Paper Series 9838, The World Bank.
    9. 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.
    10. 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.
    11. Dang, Hai-Anh H & Lanjouw, Peter F., 2021. "Data Scarcity and Poverty Measurement," IZA Discussion Papers 14631, Institute of Labor Economics (IZA).
    12. 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.
    13. World Bank, 2016. "Tunisia Poverty Assessment 2015," World Bank Publications - Reports 24410, The World Bank Group.
    14. Hai-Anh H. Dang & Talip Kilic & Ksenia Abanokova & Gero Carletto, 2024. "Imputing Poverty Indicators without Consumption Data : An Exploratory Analysis," Policy Research Working Paper Series 10867, The World Bank.
    15. 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.
    16. 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.
    17. 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.

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    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

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