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Spatial Disaggregation of Social Indicators: An Info-Metrics Approach

Author

Listed:
  • Esteban Fernandez-Vazquez

    (REGIOlab: Regional Economics Laboratory at the University of Oviedo
    University of Oviedo)

  • Alberto Diaz Dapena

    (REGIOlab: Regional Economics Laboratory at the University of Oviedo
    University of Leon)

  • Fernando Rubiera-Morollon

    (REGIOlab: Regional Economics Laboratory at the University of Oviedo
    University of Oviedo)

  • Ana Viñuela

    (REGIOlab: Regional Economics Laboratory at the University of Oviedo
    University of Oviedo)

Abstract

In this paper we propose a methodology to obtain social indicators at a detailed spatial scale by combining the information contained in census and sample surveys. Similarly to previous proposals, the method proposed here estimates a model at the sample level to later project it to the census scale. The main novelties of the technique presented are that (i) the small-scale mapping produced is perfectly consistent with the aggregates -regional or national- observed in the sample, and (ii) it does not require imposing strong distributional assumptions. The methodology suggested here follows the basics presented on Golan (2018) by adapting a cross-moment constrained Generalized Maximum Entropy (GME) estimator to the spatial disaggregation problem. This procedure is compared with the equivalent methodology of Tarozzi and Deaton (2009) by means of numerical experiments, providing a comparatively better performance. Additionally, the practical implementation of the methodology proposed is illustrated by estimating poverty rates for small areas for the region of Andalusia (Spain).

Suggested Citation

  • Esteban Fernandez-Vazquez & Alberto Diaz Dapena & Fernando Rubiera-Morollon & Ana Viñuela, 2020. "Spatial Disaggregation of Social Indicators: An Info-Metrics Approach," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 152(2), pages 809-821, November.
    • Handle: RePEc:spr:soinre:v:152:y:2020:i:2:d:10.1007_s11205-020-02455-z
    DOI: 10.1007/s11205-020-02455-z
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    References listed on IDEAS

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    1. 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.
    2. Rosa Bernardini Papalia & Esteban Fernandez-Vazquez, 2018. "Information theoretic methods in small domain estimation," Econometric Reviews, Taylor & Francis Journals, vol. 37(4), pages 347-359, April.
    3. Modrego, Félix & Berdegué, Julio A., 2015. "A Large-Scale Mapping of Territorial Development Dynamics in Latin America," World Development, Elsevier, vol. 73(C), pages 11-31.
    4. Carmen Sánchez-Cantalejo & Ricardo Ocana-Riola & Alberto Fernández-Ajuria, 2008. "Deprivation index for small areas in Spain," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 89(2), pages 259-273, November.
    5. Stefano Marchetti & Luca Secondi, 2017. "Estimates of Household Consumption Expenditure at Provincial Level in Italy by Using Small Area Estimation Methods: “Real” Comparisons Using Purchasing Power Parities," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 131(1), pages 215-234, March.
    6. Domingo Morales & María del Mar Rueda & Dolores Esteban, 2018. "Model-Assisted Estimation of Small Area Poverty Measures: An Application within the Valencia Region in Spain," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 138(3), pages 873-900, August.
    7. Golan, Amos & Judge, George G. & Miller, Douglas, 1996. "Maximum Entropy Econometrics," Staff General Research Papers Archive 1488, Iowa State University, Department of Economics.
    8. 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.
    9. María Guadarrama & Isabel Molina & J. N. K. Rao, 2016. "A Comparison Of Small Area Estimation Methods For Poverty Mapping," Statistics in Transition New Series, Polish Statistical Association, vol. 17(1), pages 41-66, March.
    10. Dorota Weziak-Bialowolska, 2016. "Spatial Variation in EU Poverty with Respect to Health, Education and Living Standards," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 125(2), pages 451-479, January.
    11. repec:csb:stintr:v:17:y:2016:i:1:p:41-66 is not listed on IDEAS
    12. Mark D. Partridge & Dan S. Rickman, 2008. "Distance From Urban Agglomeration Economies And Rural Poverty," Journal of Regional Science, Wiley Blackwell, vol. 48(2), pages 285-310, May.
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    Cited by:

    1. Paweł Churski & Robert Perdał, 2022. "Geographical Differences in the Quality of Life in Poland: Challenges of Regional Policy," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 164(1), pages 31-54, November.

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