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ResumenEn este artículo se busca identificar qué causa las diferencias en resultados académicos entre colegios costarricenses públicos y privados empleando la técnica de descomposición de Oaxaca-Blinder, aplicada a la base pisa 2012. La conclusión es que las diferencias, por orden de importancia, se deben a: a) las características de los hogares, b) los recursos de los centros, c) las características de los estudiantes, y d) el ambiente de trabajo en los centros. Pero también porque hay diferencias en características y factores que se aprovechan de modo distinto. Así, si los estudiantes de escuelas públicas poseen en conjunto peores características, saben sacarles mejor partido, aunque los estudiantes de los colegios privados sabrían aprovechar mejor el ambiente de trabajo en este tipo de centros educativos. AbstractIn this paper we use the Oaxaca-Blinder decomposition methodology, applied to the pisa 2012 database, in order to identify the causes of the differences in academic results between public and private Costa Rican schools. In order of importance, these are caused by differences in: a) family characteristics, b) school resources, c) student characteristics, and d) working environment in the schools. The differences in the results are not only explained by the differences in characteristics and factors, but also by the differences in the way they are used. So, while students in public schools have collectively worst characteristics, they make better use of them. However, students in private schools obtain more output from the working environment in their schools.Palabras clave: resultados escolares, educación pública, educación privada, Costa Rica, pisa, descomposición Oaxaca-Blinder, funciones de producción educativa.

Aunque los trabajos teóricos y empíricos que emplean el concepto de capital humano son muy numerosos, no hay una definición generalmente aceptada de él, y en muchos casos se le identifica con educación formal. En estas páginas se precisará el concepto de capital humano, atendiendo a sus vías de adquisición. Además, en ellas se elabora un indicador internacional que recoge todos los matices que contempla la definición planteada y que, habitualmente, son dejados de lado por los indicadores tradicionales. Así, el indicador propuesto tendrá en cuenta la salud, la educación de tipo formal e informal y la experiencia. El análisis de las dotaciones de capital humano de los países de América Latina y el Caribe pone de manifiesto una situación de atraso con respecto a otras regiones. Sin embargo, cabe precisar que existen grandes diferencias entre países, que se han reducido en las últimas décadas gracias a un proceso de convergencia regional.

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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.

This paper uses a multi-equation model to achieve an overall study of two key factors which explain growth, technology and institutions. The paper focuses on the process of the accumulation of these factors and the interrelationship arising among them. A theoretical model is given, together with empirical evidence for the joint impact of these factors on economic growth in a wide-ranging sample of countries between 1985 and 1997. This paper also contributes certain novel aspects in the variables employed. Thus, an indicator of human capital and an index reflecting institutional infrastructure have been used. The human capital indicator considers health, formal education, informal education and accumulated experience. It embraces a wider range of factors than the variables conventionally used in empirical studies. As to the institutional infrastructure index, it has been constructed on the basis of six institutional sub-indices, comprising voice and accountability, political stability, government effectiveness, regulatory quality, rule of law and control of corruption. Thus, the index constructed captures a greater wealth of the items commonly covered by the concept of institutions.

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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.