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Using Poverty Maps to Improve the Design of Household Surveys : The Evidence from Tunisia

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  • Betti,Gianni
  • Molini,Vasco
  • Pavelesku,Dan

Abstract

This paper proposes a new method for improving the design effect of household surveys based on a two-stage design in which the first stage clusters, or primary selection units, are stratified along administrative boundaries. Improvement of the design effect can result in more precise survey estimates (smaller standard errors and confidence intervals) or reduction of the necessary sample size, that is, a reduction in the budget needed for a survey. The proposed method is based on the availability of a previously conducted poverty mapping, that is, spatial descriptions of the distribution of poverty, which are finely disaggregated in small geographic units, such as cities, municipalities, districts, or other administrative partitions of a country that are linked to primary selection units. Such information is then used to select primary selection units with systematic sampling by introducing further implicit stratification in the survey design, to maximize the improvement of the design effect. The proposed methodology has been implemented for the new 2021 Household Budget Survey in Tunisia, conducted under a cooperation project funded by the World Bank. The underlying poverty mapping is based on the 2015 Household Budget Survey and the 2014 Population and Housing Census.

Suggested Citation

  • Betti,Gianni & Molini,Vasco & Pavelesku,Dan, 2021. "Using Poverty Maps to Improve the Design of Household Surveys : The Evidence from Tunisia," Policy Research Working Paper Series 9648, The World Bank.
    • Handle: RePEc:wbk:wbrwps:9648
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    References listed on IDEAS

    as
    1. 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.
    2. Gianni Betti & Ruzhdie Bici & Laura Neri & Thomas Pave Sohnesen & Ledia Thomo, 2018. "Local Poverty and Inequality in Albania," Eastern European Economics, Taylor & Francis Journals, vol. 56(3), pages 223-245, May.
    3. 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.
    4. Vijay Verma & Gianni Betti, 2011. "Taylor linearization sampling errors and design effects for poverty measures and other complex statistics," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(8), pages 1549-1576, August.
    5. Elbers, Chris & Lanjouw, Jean O. & Lanjouw, Peter, 2002. "Micro-level estimation of welfare," Policy Research Working Paper Series 2911, The World Bank.
    6. 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.
    7. 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.
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    Cited by:

    1. Nita Handastya & Gianni Betti, 2023. "The ‘Double Fuzzy Set’ Approach to Multidimensional Poverty Measurement: With a Focus on the Health Dimension," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 166(1), pages 201-217, February.

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    More about this item

    Keywords

    Inequality; Public Sector Administrative&Civil Service Reform; Economics and Finance of PublicInstitution Development; Democratic Government; State Owned Enterprise Reform; Public Sector Administrative and Civil Service Reform; De Facto Governments; Labor&Employment Law; Small Area Estimation Poverty Mapping; Poverty Lines; Poverty Assessment; Poverty Monitoring&Analysis; Poverty Diagnostics; Poverty Impact Evaluation; Crime and Society;
    All these keywords.

    JEL classification:

    • C83 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Survey Methods; Sampling Methods
    • D63 - Microeconomics - - Welfare Economics - - - Equity, Justice, Inequality, and Other Normative Criteria and Measurement
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access

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