Are the determinants of per capita incomes spatially homogeneous?
Tibor Bareith and
Adrián Csizmadia
Post-Communist Economies, 2025, vol. 37, issue 6, 517-532
Abstract:
This study examines whether the determinants of per capita incomes in Hungary are spatially homogeneous or exhibit significant local variations. Using 2019 municipal-level data, we apply Geographically Weighted Regression (GWR) to model relationships between total domestic income per working-age population and variables like educational, employment indicators, enterprise activity, and proximity to economic centres. Comparing the global OLS model with the local GWR model, we test if these determinants’ effects vary regionally.Our findings reveal substantial spatial non-stationarity: the GWR model outperforms the OLS model, indicating that factors such as education levels and job seeker proportions impact incomes differently across regions. Specifically, educational attainment and the proportion of job seekers significantly influence incomes in most municipalities, while individual enterprises and municipal investments are significant only in certain areas. These results suggest that policies to improve incomes in Hungary should be tailored to local conditions rather than adopting a uniform national approach.
Date: 2025
References: Add references at CitEc
Citations:
Downloads: (external link)
http://hdl.handle.net/10.1080/14631377.2025.2487237 (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: https://EconPapers.repec.org/RePEc:taf:pocoec:v:37:y:2025:i:6:p:517-532
Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/CPCE20
DOI: 10.1080/14631377.2025.2487237
Access Statistics for this article
Post-Communist Economies is currently edited by Roger Clarke
More articles in Post-Communist Economies from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().