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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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    References listed on IDEAS

    as
    1. Minot, Nicholas, 2000. "Generating Disaggregated Poverty Maps: An Application to Vietnam," World Development, Elsevier, vol. 28(2), pages 319-331, February.
    2. Baulch, Bob & Minot, Nicholas, 2002. "Poverty mapping with aggregate census data," MSSD discussion papers 49, International Food Policy Research Institute (IFPRI).
    3. Bigman, David, et al, 2000. "Community Targeting for Poverty Reduction in Burkina Faso," The World Bank Economic Review, World Bank Group, vol. 14(1), pages 167-193, January.
    4. Filmer, Deon*Pritchett, Lant, 1998. "Estimating wealth effects without expenditure data - or tears : with an application to educational enrollments in states of India," Policy Research Working Paper Series 1994, The World Bank.
    5. Skoufias, Emmanuel & Davis, Benjamin & de la Vega, Sergio, 2001. "Targeting the Poor in Mexico: An Evaluation of the Selection of Households into PROGRESA," World Development, Elsevier, vol. 29(10), pages 1769-1784, October.
    Full references (including those not matched with items on IDEAS)

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    Cited by:

    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. 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.
    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. Guie Li & Zhongliang Cai & Ji Liu & Xiaojian Liu & Shiliang Su & Xinran Huang & Bozhao Li, 2019. "Multidimensional Poverty in Rural China: Indicators, Spatiotemporal Patterns and Applications," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 144(3), pages 1099-1134, August.
    5. 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.
    6. 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.
    7. Ayala Wineman, 2016. "Multidimensional Household Food Security Measurement in Rural Zambia," Agrekon, Taylor & Francis Journals, vol. 55(3), pages 278-301, July.
    8. Tsehay, Abrham Seyoum & Bauer, Siegfried, 2012. "Poverty and Vulnerability Dynamics: Empirical Evidence from Smallholders in Northern Highlands of Ethiopia," Quarterly Journal of International Agriculture, Humboldt-Universitaat zu Berlin, vol. 51(4), pages 1-32, November.
    9. Rie Muraoka & Hsiaoping Chien & Minjuan Zhao, 2023. "Production and Market Participation of Buckwheat Farmers: Micro-Evidence from Shaanxi Province, China," Sustainability, MDPI, vol. 15(6), pages 1-17, March.
    10. 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.
    11. Sandeep Kumar & Snehashish Chakraverty & Narayan Sethi, 2023. "Multidimensional Poverty: CMPI Development, Spatial Analysis and Clustering," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 169(1), pages 647-670, September.

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    More about this item

    Keywords

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

    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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