A data set of comparable estimates of the private rate of return to schooling in the world, 1970–2014

PurposeYoung people experience lower employment, income and participation rates, as well as higher unemployment, compared to adults. Theory predicts that people respond to labor market information. For more than 50 years, researchers have reported on the patterns of estimated returns to schooling across economies, but the estimates are usually based on compilations of studies that may not be strictly comparable. The authors create a dataset of comparable estimates of the returns to education.Design/methodology/approachThe data set on private returns to education includes estimates for 142 economies from 1970 to 2014 using 853 harmonized household surveys. This effort holds the constant definition of the dependent variable, the set of controls, sample definition and the estimation method for all surveys.FindingsThe authors estimate an average private rate of return to schooling of 10%. This provides a reasonable estimate of the returns to education and should be useful for a variety of empirical work, including critical information for youth.Originality/valueThis is the first attempt to bring together surveys from so many countries to create a global data set on the returns to education.

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.

Tourism specialisation and education: Leading the way to better labour conditions?

This article studies the returns to education in the Argentinian tourism sector considering tourism specialisation. With data from the Permanent Household and Hotel Occupancy Surveys, we make use of Mincer equations to understand the relation between wages and education. For that, we take into account three different types of agglomerates: Autonomous City of Buenos Aires (CABA), agglomerates more specialised in tourism (AT) and agglomerates less specialised in tourism (ALT). We find that, for tourism workers, there is a penalty in returns to education, which differs according to the type of agglomerate. These negative returns are partially mitigated by tourism specialisation. In CABA, the most developed and specialised destination, there is almost no penalty. In the case of AT, the mitigation is greater than in ALT. Consequently, this specialisation positions AT in the tourism development path leading to the improvement of labour conditions. There should therefore be an interaction between tourism and education, and it is also imperative to generate incentives and partnerships for the improvement of the sector’s training and labour conditions.

Returns to education in Russia: Nowhere below?

The paper presents a wide set of estimates for returns to education in Russia, introducing a number of new sources of microdata that previosly remained unused by both Russian and foreign researchers. Until now virtually all available estimates for Russia were based on data from a single source — The Russia Longitudinal Monitoring Survey — Higher School of Economics (RLMS-HSE). According to these data, since the mid-2000s returns to education rapidly declined and have dropped to abnormally low levels. The paper tests the thesis of ultra-low economic value of Russian education using data from three alternative representative surveys regularly conducted by Rosstat. The analysis shows that currently returns to education in Russia reach 12—13%, which is much higher than the standard RLMS estimates. University-type tertiary education almost doubles earnings (its premium approaches to 100%), and even short-cycle tertiary education provides a premium of about 20—30%. Alternative sources also indicate that over the past 15 years, returns to education in Russia remained stable and, therefore, no decreasing trend in the economic value of education has been observed. This makes it possible to reject the currently popular thesis about abnormally low returns to education in Russia.