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Purpose: The purpose of this paper is to investigate which sectors are more vulnerable to human capital depreciation, with an emphasis on potential differences in skills and in ICT intensities. Design/methodology/approach: The authors estimate an extended Mincerian earnings equation based on Neuman and Weiss's (1995) model using the EU-KLEMS international database for 15 sectors for the period from 1980 to 2005. The authors also test structural ruptures in earnings and human capital depreciation in the labor market per decade controlling by technological intensity. Findings: Human capital depreciation ranges from 1 to 6 percent. It is mainly significant in skill-intensive sectors regardless of the sector's technological intensity. The analysis of structural breaks shows that human capital value indeed changed from decade to decade. It even appreciated in low skill-intensive sectors in the 1980s and in the high skill-intensive during the 1990s. Appreciation though, was mainly skill-biased. Research limitations/implications: Information about on-the-job-training and non-cognitive skills that can also affect human capital depreciation are not included due to lack of data. Practical implications: To prevent human capital from depreciating in particular sectors and periods educational systems should provide the tools for ongoing lifelong learning at all skills levels. Education is subject to dynamic effects that should be addressed to increase the potential benefits of technological change. Originality/value: First, instead of using cross-section analysis which is considered to be a pitfall in studying the depreciation of knowledge, the authors observe its dynamic on a longitudinal basis. Second, the international macro-sectoral approach goes beyond limited micro-sectoral analysis in certain countries. [ABSTRACT FROM AUTHOR]

This article applies Oaxaca-Blinder and Shorrocks-Shapley decomposition techniques to a logistic diffusion model in order to explain the differences in Total Factor Productivity Growth (TFPG) in European Union (EU) countries for the period 1950-2011. Human capital has a dual positive effect on TFPG by boosting innovation and increasing the catch-up capacity of countries to absorb and imitate foreign technologies. Our results show that there are statistically significant differences in the intensity of these effects between high and low average income EU countries, while there are not between euro and non-euro countries. The mean difference in technical change between high and low-income EU countries is largely the result of three factors. The first is the higher average foreign technology assimilation capacity of low income countries. This is particularly true because they are further from the technological frontier and are able to benefit from the advantage of backwardness. The second is the higher direct effect of human capital on technical change in these countries, while the third factor is the higher slowdown role of proximity in them. [ABSTRACT FROM AUTHOR]

Previous empirical studies have found a weak nexus between the use of information and communication technologies (ICT) for learning and students' outcomes. However, this literature has not considered the role that the countries' stock of human capital can have in the successful use of ICT for learning. In this paper, we test empirically the existence of complementarities between human capital and technology adoption for learning. We carry out an empirical analysis with PISA data from a large-scale sample of 363,412 students enrolled in 13,215 schools in 48 countries. We estimate a hierarchical linear model (HLM) of three levels: students, schools, and countries. Our results strongly support the evidence of a positive externality of the stock of human capital on ICT use for learning. When we consider the moderator-effect of the stock of human capital, we find that the negative outcome of ICT use on students' outcomes in mathematics, reading and science turns positive (greater and more positive the higher the stocks of human capital are). The greater the stock of human capital an economy has, the more benefits it can get from investments in ICT for learning. [ABSTRACT FROM AUTHOR]

Understanding how growth factors contribute to explaining the large differences in growth rates across countries remains an important research agenda. The common approach to exploring this issue is based on the use of multiple linear regression analyses. This work contributes to growth literature by applying a new perspective based on the use of variance decomposition procedures: Shapley–Owen–Shorrocks and Oaxaca–Blinder. These methodologies have four main advantages with respect to traditional methodologies: they make possible the quantification of the relative contribution of each factor to economic growth, they allow us to estimate the efficiency in the use of the endowments of each factor, they can be used with any functional form and they can be used with estimation methods that are robust regarding endogeneity issues. We illustrate these advantages by analyzing the causes of the economic growth gap between Latin America and East Asia over the period 1980–2014. We find that the economic growth divergence between the two regions can be primarily explained by the differences in institutions and physical capital. In addition, the results indicate that the higher East Asian performance is not only due to its higher levels of endowments in these factors, but also to the higher efficiency in its use. We connect our results with the 2030 Agenda for Sustainable Development. [ABSTRACT FROM AUTHOR]