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On the spatially explicit Gini coefficient: the case study of Chile—a high-income developing country

Author

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  • Ricardo Crespo

    (Universidad de Santiago de Chile)

  • Ignacio Hernandez

    (Universidad de Santiago de Chile)

Abstract

This study suggests the use of a geographically-varying Gini coefficient to better understand local dynamics between income inequality and socioeconomic attributes. We estimate local Gini coefficients by disaggregating the traditional Lorenz curve approach at small census areas using spatial microsimulation techniques. As our case study, we chose the city of Santiago, Chile. Despite being ranked as a high-income country by the World Bank, Chile is the most unequal OECD country and the seventh worst in the world. Results reveal interesting spatial patterns that relate income inequality to income and educational level. For instance, the most unequal areas of the city correspond, perhaps against all odds, to the most well-off neighbourhoods having, in addition, the highest educational level. This result suggests that from a certain level of high income in areas with high-income inequality, the spatial cohesion of neighbourhoods may be mainly caused by the educational level of households. Results also reveal that the least unequal areas are mostly observed across neighbourhoods with low educational levels and low per capita incomes.

Suggested Citation

  • Ricardo Crespo & Ignacio Hernandez, 2020. "On the spatially explicit Gini coefficient: the case study of Chile—a high-income developing country," Letters in Spatial and Resource Sciences, Springer, vol. 13(1), pages 37-47, April.
    • Handle: RePEc:spr:lsprsc:v:13:y:2020:i:1:d:10.1007_s12076-020-00243-4
    DOI: 10.1007/s12076-020-00243-4
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    References listed on IDEAS

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    1. Sergio Rey & Richard Smith, 2013. "A spatial decomposition of the Gini coefficient," Letters in Spatial and Resource Sciences, Springer, vol. 6(2), pages 55-70, July.
    2. Robert Tanton & Yogi Vidyattama & Justine McNamara & Quoc Ngu Vu & Ann Harding, 2009. "Old, Single and Poor: Using Microsimulation and Microdata to Analyse Poverty and the Impact of Policy Change among Older Australians," Economic Papers, The Economic Society of Australia, vol. 28(2), pages 102-120, June.
    3. Malcolm Campbell & Dimitris Ballas, 2013. "A spatial microsimulation approach to economic policy analysis in Scotland," Regional Science Policy & Practice, Wiley Blackwell, vol. 5(3), pages 263-288, August.
    4. Cathal O'Donoghue & Karyn Morrissey & John Lennon, 2014. "Spatial Microsimulation Modelling: a Review of Applications and Methodological Choices," International Journal of Microsimulation, International Microsimulation Association, vol. 7(1), pages 26-75.
    5. Riyana Miranti & Rebecca Cassells & Yogi Vidyattama & Justine Mc Namara, 2015. "Measuring Small Area Inequality Using Spatial Microsimulation: Lessons Learned from Australia," International Journal of Microsimulation, International Microsimulation Association, vol. 8(2), pages 152-175.
    6. Fabrizi, Enrico & Trivisano, Carlo, 2016. "Small area estimation of the Gini concentration coefficient," Computational Statistics & Data Analysis, Elsevier, vol. 99(C), pages 223-234.
    7. Stanislav Kolenikov, 2014. "Calibrating survey data using iterative proportional fitting (raking)," Stata Journal, StataCorp LP, vol. 14(1), pages 22-59, March.
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    Cited by:

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    More about this item

    Keywords

    Income inequality; Gini index; Spatial microsimulation; Small area;
    All these keywords.

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C83 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Survey Methods; Sampling Methods
    • J30 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs - - - General

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