Internal Rate of Return on Investment in Higher Education in Europe

The study aims to update the well-settled issue in literature of rate of return on investment in higher education in Europe. The proposed approach slightly modifies the existing methodology based on the Mincer equation, using an updated set of data for the period 2014–2016 from the European Central Bank's ‘Household Finance and Consumption Survey’. The results obtained, ranging between 13% and 21%, are higher compared to historical records for the years 1996–2013. As for the microeconometric estimates of the Mincer-type equation, they are sound and comparable, although showing higher margins in relation to the recent figures. Thus, they may suggest an increasing trend in valuation and importance of human capital based on high school degree, especially for the Central–Eastern European Union countries, resulting probably from rapid economic and cultural convergence.

Human capital and job opportunities according to educational level in Perú

Education promotes progress and economic and social growth, improves the quality of life of the population. The first objective of the study was to identify people's income according to the years of schooling, the second was to estimate the income gap according to gender, residence and working conditions, the third was to identify the return of education, work experience towards the income of the Peruvian inhabitants. Parametric tests and the two-stage Heckman model were used to obtain the results. The data come from the National Household Survey. Income differs according to schooling. There are gaps in earned income. For one more year of education, the monetary return amounts to 12,46%, if it is a woman, it is 13,23%, if it is a man, it is 11,51%, if it resides in an urban area it amounts to 10,62%, if it is a resident in rural areas it amounts to 9,83%. Keywords: Labor income, returns to education, Mincer equation, Heckman methodology. References [1]J. Mincer, “Schooling, Experience, and Earnings,” Natl. Bur. Econ. Res., 1974, [Online]. Available: https://www.nber.org/books-and-chapters/schooling-experience-and-earnings. [2]T. W. Schultz, “Investment in Human capital,” Am. Econ. Rev., vol. Vil. (1)2, 1961. [3]J. Freire and M. Teijeiro, “Las ecuaciones de Mincer y las tasas de rendimiento de la educación en Galicia,” Investig. Econ. la Educ. 5 - Univ. A Coruña, 2010. [4]K. Ogundari and A. Abdulai, “Determinants of Household’s Education and Healthcare Spending in Nigeria: Evidence from Survey Data,” African Dev. Rev., vol. Vol. 26, N, pp. 1–14, 2014. [5]C. Montenegro and H. Patrinos, “Comparable estimates of returns to schooling around the world,” Policy Res. Work. Pap. Ser. 7020, World Bank., 2014. [6]G. Fink and E. Peet, “Returns to Education in Low and Middle-Income Countries: Evidence from the Living Standards and Measurement Surveys,” Progr. Glob. Demogr. Aging Harvard Univ., vol. PGDA Worki, 2014, [Online]. Available: https://cdn1.sph.harvard.edu/wp-content/uploads/sites/1288/2015/06/PGDA_WP_120_Fink.pdf. [7]L. Godínez, E. Figueroa, and F. Pérez, “Rentabilidad privada de la educación en el Estado de México,” Papeles Poblac. - Univ. Auton. Mex., vol. Vol. 22 N°, 2016. [8]M. Diaz, “Brecha Salarial por Género en Colombia.,” Econ. y Finanz. Int. - Univ. la Sabana - Colomb., 2014. [9]M. Urroz and M. Salgado, “La relación entre educación e ingresos: estimación de las diferencias salariales por nivel educativo alcanzado,” Fund. Zamora Terán, 2014. [10]E. Tarupi, “El capital humano y los retornos a la educación en Ecuador,” Gest. - Rev. Int. Adm., 2015, [Online]. Available: https://revistas.uasb.edu.ec/index.php/eg/article/view/571. [11]R. Arpi and L. Arpi, “Retornos Heterogeneos a La Educación En el Mercado Laboral Peruano, 2015,” Rev. Investig. Altoandina, vol. Vol. 18, 2016. [12]R. Paz and J. C. Quilla, “Retornos a la Educación de los Jefes de Hogar en la Región de Puno, 2011 – 2015,” Rev. Investig. Altoandina, vol. V. 18, 2016. [13]INEI, “Instituto Nacional de Estadistica e Informatica - Evolucion de la Pobreza Monetaria 2008 - 2019,” 2020. [Online]. Available: https://www.inei.gob.pe/media/cifras_de_pobreza/informe_pobreza2019.pdf. [14]A. Smith, An Inquiry into the Nature and Causes of the Wealth of Nations. Londres: Londres - Reino Unido, 1776. [15]G. Becker, “A Theory of the Allocation of Time,” Econ. J., vol. Vol. 75 N°, p. pp.493-517, 1964. [16]R. Hernández, C. Fernández, and M. del P. Baptista, Metodologia de la Investigación, vol. 6ta Ed. 2014. [17]W. Mendoza, Cómo Investigan los Economistas, 1ra Ed. Lima - Perú, 2014. [18]D. Alfaro and E. Guerrero, “Brechas de genero en el ingreso: Una mirada mas alla de la media en el sector agropecuario,” Consorc. Investig. Econ. y Soc. - CIES, 2013, [Online]. Available: http://cies.org.pe/sites/default/files/investigaciones/1_informe_final_pb19_-_alfaro_y_guerrero_final.pdf. [19]J. Wooldridge, Introduccion a la Econometria. Un enfoque moderno, 4ta Ed. Mexico, D.F., 2009. [20]D. Gujarati and D. Porter, Econometría. 2010.

Human capital depreciation and job tasks

This research aims to investigate the link between human capital depreciation and job tasks, with an emphasis on potential differences between education levels. We estimate an extended Mincer equation based on Neumann and Weiss’s (1995) model using data from the German Socio-Economic Panel. The results show that human capital gained from higher education levels depreciates at a faster rate than other human capital. Moreover, the productivity-enhancing value of education diminishes faster in jobs with a high share of non-routine analytical, non-routine manual, and routine cognitive tasks. These jobs are characterized by more frequent changes in core-skill or technology-skill requirements. The key implication of this research is that education should focus on equipping workers with more general skills in all education levels. With ongoing technological advances, work environments, and with it, skill demands will change, increasing the importance to provide educational and lifelong learning policies to counteract the depreciation of skills. The study contributes by incorporating a task perspective based on the classification used in works on job polarization. This allows a comparison with studies on job obsolescence due to labor-replacing technologies and enables combined education and labor market policies to address the challenges imposed by the Fourth Industrial Revolution.

Entrepreneurship in Brazil: A Worthy Endeavor?

This study analyzes if there are differences between the income of entrepreneurs and wage earners in Brazil. Using data from the 2015 National Household Sample Survey database, we estimate a Mincer equation, correcting for self-selection, which explains the choice of entrepreneurship in the function of earnings related to salaried work. Subsequently, the wage differential per category is decomposed using the Oaxaca-Blinder procedure. To complete the analysis, a detailed decomposition is used to identify the explained and unexplained components of the wage gap. The results indicate that personal, cultural, and demographic characteristics affect the entrepreneurial occupational choice as well the differential in the income of entrepreneurs and employees. On average, entrepreneurs earn approximately 19.68% more than salaried workers.

An examination of the relationship between competences and wages of higher education graduates: Evidence from Morocco

To provide research insights in line with the Tuning project approach, we estimate the effects of competences on wages of higher education graduates with work experience. Using the conventional earnings regressions methods (Mincer equation) on data from a survey of graduates, we investigate the way in which the labour market reacts and rewards competences. The results show small significant evidence for an effect of competences on wages in our dataset; however, methodological and social skills display positive payoff returns. Our empirical findings also suggest that the labour market rewards less specialised competences, and unlikely methodological and social competences are deemed more necessary compared to cognitive skills (theoretical knowledge). Finally, wages tend to decrease for those who are female and working in the private sector. Overall, the findings of the study are highly related to the specification and structure of the Moroccan labour markets.Published online: 30 November 2017

The Trend of The Returns to Educations in Indonesia

This paper describes the rate of return to education in Indonesia. The purpose of this paper was to determine how the trend of return to education from 1993 to 2007. By using Mincer equation, we analyzed return to education in Indonesia with using Indonesia Family Life Survey (IFLS) data collected in 1993, 1997, 2000, and 2007. Mincer specification linked between income and education. Income used in this paper was real income of a person who works. The estimation of the rate of return to education started by separating each year data. Then, it used pool data by adding year variable and multiplication variable between year and education. Estimation was also carried out by comparing between men and women. Further, estimation was divided into two age cohorts, young cohort and old cohort. All the results of estimation indicated a decreasing rate of return, the greatest decrease occurred on men with old cohort.Keywords: education, return to education, Mincer equation, trendJEL codes: I26, J30