Economics at your fingertips  

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

Miriam Hortas-Rico () and Vicente Rios ()

Regional Studies, 2019, vol. 53, issue 8, 1207-1220

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.

Date: 2019
References: Add references at CitEc
Citations: View citations in EconPapers (3) Track citations by RSS feed

Downloads: (external link) (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link:

Ordering information: This journal article can be ordered from

DOI: 10.1080/00343404.2019.1566698

Access Statistics for this article

Regional Studies is currently edited by Ivan Turok

More articles in Regional Studies from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().

Page updated 2022-12-02
Handle: RePEc:taf:regstd:v:53:y:2019:i:8:p:1207-1220