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Matching espacial para georreferenciar datos de encuestas de hogar

Author

Listed:
  • Mónica Navarrete
  • Patricio Aroca
  • Jorge Bernal

Abstract

This paper develops a methodology using business intelligence (data warehouse) and OLAP tools (Online Analytical Processing) to match individuals from a household survey data to a census one. In order to geo-reference the household data, the method takes advantage of the geographical information of the census. Using the 2003 Household Survey and the 2002 Chilean Census of Population and Housing, the average household income is projected to intra-municipality levels and the results are compared to Elbers et al. (2003) methodology. The results show that the proposed methodology allows for taking into account and display the impact of intra-municipality heterogeneity and the spatial interaction among the spatial units.

Suggested Citation

  • Mónica Navarrete & Patricio Aroca & Jorge Bernal, 2017. "Matching espacial para georreferenciar datos de encuestas de hogar," Estudios de Economia, University of Chile, Department of Economics, vol. 44(1 Year 20), pages 53-80, June.
    • Handle: RePEc:udc:esteco:v:44:y:2017:i:1:p:53-80
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    File URL: http://estudiosdeeconomia.uchile.cl/index.php/EDE/article/viewFile/45214/47276
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    References listed on IDEAS

    as
    1. Claudio Agostini & Philip H. Brown & Diana Paola Góngora, 2008. "Nota Técnica Distribución espacial de la pobreza en Chile," Estudios de Economia, University of Chile, Department of Economics, vol. 35(1 Year 20), pages 79-110, June.
    2. 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.
    3. Claudio Agostini & Philip Brown & Andrei Roman, 2010. "Estimando Indigencia y Pobreza Indígena Regional con Datos Censales y Encuestas de Hogares," Latin American Journal of Economics-formerly Cuadernos de Economía, Instituto de Economía. Pontificia Universidad Católica de Chile., vol. 47(135), pages 125-150.
    4. 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.
    5. Minot, Nicholas & Baulch, Bob, 2005. "Spatial patterns of poverty in Vietnam and their implications for policy," Food Policy, Elsevier, vol. 30(5-6), pages 461-475.
    6. Rajeev H. Dehejia & Sadek Wahba, 2002. "Propensity Score-Matching Methods For Nonexperimental Causal Studies," The Review of Economics and Statistics, MIT Press, vol. 84(1), pages 151-161, February.
    7. 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.
    8. Dusan Paredes & Patricio Aroca, 2008. "Metodología para Estimar un Indice Regional de Costo de Vivienda en Chile," Latin American Journal of Economics-formerly Cuadernos de Economía, Instituto de Economía. Pontificia Universidad Católica de Chile., vol. 45(131), pages 129-143.
    9. Mohamed Ayadi & Mohamed Amara, 2009. "Spatial Patterns and Geographic Determinants of Welfare and Poverty in Tunisia," Working Papers 478, Economic Research Forum, revised Mar 2009.
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    More about this item

    Keywords

    Spatial Matching; Small Areas Estimation; Household Surveys; Household Income; Business Intelligence;
    All these keywords.

    JEL classification:

    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • 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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