Costly investment, complementarities and the skill premium
Oscar Afonso and
Maria Thompson
Economic Modelling, 2011, vol. 28, issue 5, 2254-2262
Abstract:
We propose a new framework to analyse the relationship between the relative high-skilled labour endowment, the skill premium and economic growth. Building on Acemoglu and Zilibotti (2001), we introduce physical capital; internal costly investment in both capital and R&D; and complementarities between intermediate goods. We only find a positive relationship between the relative labour endowment and both the skill premium and economic growth within determined intervals of relative labour endowment values, which vary with the absolute productive advantage of high over low-skilled labour. The model thus accommodates theoretically mixed empirical results on the relative labour endowment-skill premium relationship. We further find that the impact on both the relative labour endowment and the skill premium of a rise in investment costs or in the complementarities degree depends on: (i) the absolute productivity advantage of high over low-skilled labour; and (ii) the relative labour endowment.
Keywords: R&D; Technological-knowledge; bias; Skill; premium; Complementarities; Costly; investment (search for similar items in EconPapers)
Date: 2011
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9)
Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0264999311001507
Full text for ScienceDirect subscribers only
Related works:
Working Paper: Costly Investment, Complementarities and the Skill Premium (2009) 
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:eee:ecmode:v:28:y:2011:i:5:p:2254-2262
Access Statistics for this article
Economic Modelling is currently edited by S. Hall and P. Pauly
More articles in Economic Modelling from Elsevier
Bibliographic data for series maintained by Catherine Liu ().