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The drivers of local income inequality: a spatial Bayesian model-averaging approach

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  • Miriam Hortas-Rico
  • Vicente Rios

Abstract

This study analyzes the drivers of local income inequality in Spain. It derives a novel data set of inequality metrics for a sample of municipalities over the period 2000–06. Spatial Bayesian model selection and model-averaging techniques are used in order to examine the empirical relevance of (1) spatial functional forms, (2) spatial weight matrices and (3) a large set of factors that could affect inequality. The findings suggest that local inequality is mainly explained by human capital, economic factors and local politics. In addition, the use of Bayesian geographically weighted regressions provides evidence in favour of spatially heterogeneous effects.

Suggested Citation

  • Miriam Hortas-Rico & Vicente Rios, 2019. "The drivers of local income inequality: a spatial Bayesian model-averaging approach," Regional Studies, Taylor & Francis Journals, vol. 53(8), pages 1207-1220, August.
    • Handle: RePEc:taf:regstd:v:53:y:2019:i:8:p:1207-1220
    DOI: 10.1080/00343404.2019.1566698
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    Cited by:

    1. David Castells‐Quintana & Vicente Royuela & Paolo Veneri, 2020. "Inequality and city size: An analysis for OECD functional urban areas," Papers in Regional Science, Wiley Blackwell, vol. 99(4), pages 1045-1064, August.
    2. Luis Ayala & Javier Mart n-Rom n & Juan Vicente, 2023. "What Contributes to Rising Inequality in Large Cities?," LIS Working papers 850, LIS Cross-National Data Center in Luxembourg.
    3. Maria Teresa Balaguer‐Coll & Isabel Narbón‐Perpiñá & Jesús Peiró‐Palomino & Emili Tortosa‐Ausina, 2022. "Quality of government and economic growth at the municipal level: Evidence from Spain," Journal of Regional Science, Wiley Blackwell, vol. 62(1), pages 96-124, January.

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