The Greek economy under the twin-deficit pressure: a demand orientated growth approach

Elias Soukiazis (), Micaela Antunes and Ioannis Kostakis

International Review of Applied Economics, 2018, vol. 32, issue 2, 215-236

Abstract: This paper uses an alternative growth approach in line with Thirlwall’s model in order to predict economic growth in Greece taking into account internal and external imbalances caused by public deficit/debt and lack of trade competitiveness. It is shown that the simple Thirlwall’s Law (given by the product of the ratio of the income elasticities of demand for exports and imports, and the growth of foreign demand) over-predicts real growth in Greece while the more complete extended model, makes a closer prediction which is consistent with the high deficit/debt and current account deficit experienced in this country. The simulation approach shows that the most efficient policy to attain higher growth is to reduce external imbalances while policies to reduce internal imbalances are low growth enhancing.

Date: 2018
References: View complete reference list from CitEc
Citations: View citations in EconPapers (6)

Downloads: (external link)
http://hdl.handle.net/10.1080/02692171.2017.1338678 (text/html)
Access to full text is restricted to subscribers.

Related works:
Working Paper: The Greek economy under the twin-deficit pressure: a demand orientated growth approach (2016)
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:irapec:v:32:y:2018:i:2:p:215-236

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/CIRA20

DOI: 10.1080/02692171.2017.1338678

Access Statistics for this article

International Review of Applied Economics is currently edited by Professor Malcolm Sawyer

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