The public sector and convergence with spatial interdependence: empirical evidence from Spain

Inmaculada Álvarez and Javier Barbero

Applied Economics, 2016, vol. 48, issue 24, 2238-2252

Abstract: We propose a framework to analyse convergence between regions, incorporating the public sector and technological knowledge spillovers in the context of a Neoclassical Growth Model. Second, we apply novel estimation methods pertaining to the spatial econometrics literature introducing a spatial Durbin panel data model based on instrumental variables and maximum-likelihood estimation. Our model makes it possible to analyse, in terms of convergence, the results obtained in Spanish regions with the policies implemented during the period 1980--2011. The results support the idea that education and fiscal policies have a positive effect on regional development and cohesion. Therefore, we can conclude that it is possible to obtain better results for regional convergence with higher rates of public investment in education and tax revenues. We also obtain interesting results that confirm the existence of spillover effects in economic growth and public policies, identifying their magnitude and significance.

Date: 2016
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://hdl.handle.net/10.1080/00036846.2015.1117048 (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:applec:v:48:y:2016:i:24:p:2238-2252

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/RAEC20

DOI: 10.1080/00036846.2015.1117048

Access Statistics for this article

Applied Economics is currently edited by Anita Phillips

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