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Matching Household Surveys with DHS Data to Create Nationally Representative Time Series of Poverty: An Application to Bolivia

Author

Listed:
  • Melanie Grosse

    (University of Göttingen)

  • Stephan Klasen

    (University of Göttingen)

  • Julius Spatz

    (University of Göttingen)

Abstract

In many developing countries, there does not exist a time series of nationally repre- sentative household budget or income surveys, while there often are urban household surveys as well as nationally representative Demographic and Health Surveys (DHS) which lack information on incomes. This makes an analysis of trends and determinants of poverty and inequality over a longer time period impossible. This is also the situation in Bolivia where there exist urban household surveys and nationally representative DHS since 1989, while nationally representative household income surveys only exist since 1997. In this paper, we adjust a technique developed for poverty mapping exercises to link urban household income surveys with DHS data to generate a nationally representative time series of household income data from 1989 to 1999. Our technique performs well on validation tests, is superior to proxying welfare with asset ownership in the DHS, and is able to generate new information on poverty and inequality in Bolivia.

Suggested Citation

  • Melanie Grosse & Stephan Klasen & Julius Spatz, 2009. "Matching Household Surveys with DHS Data to Create Nationally Representative Time Series of Poverty: An Application to Bolivia," Courant Research Centre: Poverty, Equity and Growth - Discussion Papers 21, Courant Research Centre PEG.
    • Handle: RePEc:got:gotcrc:021
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    File URL: http://www2.vwl.wiso.uni-goettingen.de/courant-papers/CRC-PEG_DP_21.pdf
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    More about this item

    Keywords

    Microsimulation; survey matching; poverty; inequality; pro-poor growth; poverty profile; growth incidence curve; Bolivia;
    All these keywords.

    JEL classification:

    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • D31 - Microeconomics - - Distribution - - - Personal Income and Wealth Distribution
    • I31 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - General Welfare, Well-Being
    • I32 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - Measurement and Analysis of Poverty
    • O54 - Economic Development, Innovation, Technological Change, and Growth - - Economywide Country Studies - - - Latin America; Caribbean

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