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Estimating Poverty Over Time and Space: Construction of a time-variant poverty index for Costa Rica

Author

Listed:
  • Romina Cavatassi

    (Agricultural and Development Economics Division, Food and Agriculture Organization)

  • Benjamin Davis

    (Agricultural and Development Economics Division, Food and Agriculture Organization)

  • Leslie Lipper

    (Agricultural and Development Economics Division, Food and Agriculture Organization)

Abstract

This paper presents the construction of a spatially explicit, nationally disaggregated measure of poverty over time in Costa Rica. The paper first describes the two possible methods considered for the construction of a poverty map: principal component analysis (PCA) versus small area estimation. Next, reasons for choosing PCA and a description of its application both at one point in time (1973) and over time are presented together with the resulting poverty maps. The methodology applied represents a methodological innovation in that the resulting poverty map is time variant rather than concentrated in a single moment in time. A comparison of the results obtainable using various techniques and a discussion on the relative merits of the various options available concludes the paper.

Suggested Citation

  • Romina Cavatassi & Benjamin Davis & Leslie Lipper, 2004. "Estimating Poverty Over Time and Space: Construction of a time-variant poverty index for Costa Rica," Working Papers 04-21, Agricultural and Development Economics Division of the Food and Agriculture Organization of the United Nations (FAO - ESA).
  • Handle: RePEc:fao:wpaper:0421
    as

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    File URL: ftp://ftp.fao.org/docrep/fao/007/ae403e/ae403e00.pdf
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    Citations

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    Cited by:

    1. Andam, Kwaw S. & Ferraro, Paul J. & Holland, Margaret B., 2009. "What are the social impacts of land use restrictions on local communities? Empirical evidence from Costa Rica," 2009 Conference, August 16-22, 2009, Beijing, China 51783, International Association of Agricultural Economists.
    2. Canavire-Bacarreza, Gustavo & Hanauer, Merlin M., 2013. "Estimating the Impacts of Bolivia’s Protected Areas on Poverty," World Development, Elsevier, vol. 41(C), pages 265-285.
    3. Abera Demeke & Alwin Keil & Manfred Zeller, 2011. "Using panel data to estimate the effect of rainfall shocks on smallholders food security and vulnerability in rural Ethiopia," Climatic Change, Springer, vol. 108(1), pages 185-206, September.
    4. Paul Ferraro & Merlin Hanauer, 2011. "Protecting Ecosystems and Alleviating Poverty with Parks and Reserves: ‘Win-Win’ or Tradeoffs?," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 48(2), pages 269-286, February.
    5. Tsehay, Abrham Seyoum & Bauer, Siegfried, 0. "Poverty and Vulnerability Dynamics: Empirical Evidence from Smallholders in Northern Highlands of Ethiopia," Quarterly Journal of International Agriculture, Humboldt-Universität zu Berlin, vol. 51.
    6. Wineman, Ayala, 2014. "Multidimensional Household Food Security Measurement in Rural Zambia," 2014 Annual Meeting, July 27-29, 2014, Minneapolis, Minnesota 169819, Agricultural and Applied Economics Association.
    7. Tsehay, Abrham Seyoum & Bauer, Siegfried, 2012. "Poverty Dynamics and Vulnerability: Empirical Evidence from Smallholders in Northern Highlands of Ethiopia," 2012 Conference, August 18-24, 2012, Foz do Iguacu, Brazil 126873, International Association of Agricultural Economists.

    More about this item

    Keywords

    Poverty Mapping; Principal Component Analysis; Time-variant Poverty Index; Small-area Estimation.;

    JEL classification:

    • C43 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Index Numbers and Aggregation
    • I32 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - Measurement and Analysis of Poverty
    • 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

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