scholarly journals Estimating the return to schooling using the Mincer equation

2016 ◽  
Author(s):  
Harry Patrinos ◽  
Keyword(s):  
Return To Schooling ◽  
Mincer Equation

Education, Earnings, and Returns to Schooling in Tunisia

2018 ◽  
Author(s):  
Imed Limam ◽  
Abdelwahab Ben Hafaiedh
Keyword(s):  
Basic Education ◽  
Returns To Education ◽  
International Standards ◽  
Returns To Schooling ◽  
High Productivity ◽  
Return To Schooling ◽  
Early Life Conditions ◽  
Low Exposure ◽  
Main Determinants

This chapter aims at identifying the main determinants of earnings and at estimating the private returns to education in Tunisia. The private rate of return to schooling is relatively low by international standards, especially for basic education. It is argued that in addition to the limited capacity of the economy to create high-productivity jobs, institutional factors may explain the low and heterogeneous returns to education in Tunisia. The returns to schooling are found to increase with the level of education. Regional disparities in earnings and returns to higher education may be explained by the lack of economic opportunities and low exposure to market forces in many inland regions, and also by differentiated early-life conditions as well as inequality of opportunity in access to quality education. These results are used to suggest directions to strengthen the role of public policies in reducing inequality of opportunities in both schooling and earnings.

ICT and the Returns to Schooling and Job-Specific Experience

2007 ◽  
Vol 201 ◽  
pp. 76-85 ◽  
Author(s):  
Simon Kirby ◽  
Rebecca Riley
Keyword(s):  
Labour Force ◽  
Capital Intensity ◽  
Marginal Effect ◽  
Skill Levels ◽  
Returns To Schooling ◽  
Specific Experience ◽  
The United Kingdom ◽  
Return To Schooling ◽  
The Uk ◽  
Industry Sectors

We use the United Kingdom Labour Force Survey to estimate the returns to schooling and job-specific experience in sixteen different industry sectors over the period 1994-2001. Next, assuming skill levels are fixed, we assess the marginal effect on these returns of the capital intensity of production and the ICT intensity of capital. Our results indicate that in the UK, over the period 1994-2001, the rising ICT intensity of capital was associated with a rise in the return to schooling, and a reduction in the return to job-specific experience.

scholarly journals Human capital and job opportunities according to educational level in Perú

2021 ◽  
Vol 25 (110) ◽  
pp. 48-57
Author(s):  
Freddy Carrasco Choque ◽  
Rudy Francheska Castillo Araujo
Keyword(s):  
Human Capital ◽  
Rural Areas ◽  
Work Experience ◽  
Returns To Education ◽  
Household Survey ◽  
Healthcare Spending ◽  
Middle Income ◽  
Heckman Model ◽  
Schooling Experience ◽  
Mincer Equation

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.

scholarly journals Human capital depreciation and job tasks

2021 ◽  
Author(s):  
Sonja Walter ◽  
Jeong-Dong Lee
Keyword(s):  
Human Capital ◽  
Industrial Revolution ◽  
Job Tasks ◽  
Core Skill ◽  
Technological Advances ◽  
Education Levels ◽  
Capital Depreciation ◽  
Mincer Equation ◽  
Learning Policies ◽  
Using Data

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.

scholarly journals DETERMINANT RETURN TO EDUCATION IN INDONESIA

2018 ◽  
Vol 17 (1) ◽  
pp. 52 ◽  
Author(s):  
Mukhamad Azhar ◽  
S. Suwatno ◽  
Amir Mahmud
Keyword(s):  
Human Capital ◽  
Human Resources ◽  
School Enrollment ◽  
Return To Education ◽  
Learning Session ◽  
Rates Of Return ◽  
Developed Country ◽  
Returns To Schooling ◽  
Return To Schooling ◽  
Less Developed Country

Badan Pusat Statistik. (2016). Penduduk Berumur 15 Tahun ke Atas yang Bekerja Selama Seminggu yang Lalu Menurut Lapangan Pekerjaan Utama dan Pendidikan Tertinggi yang Ditamatkan. Jakarta: Badan Pusat Statistik.Badan Pusat Statistik.(2016). Keadan Angkatan Kerja Provinsi Banten Agustus 2016. BPS Banten.Becker, Gary S. (1975). Human Capital, A Theoretical and Empirical Analysis with Special Reference to Education, 2nd Edition. Diakses dari http://www.nber.org/Deolalikar, Anil. (1993). Gender Differences in the Returns to Schooling and in School Enrollment Rates in Indonesia. Journal of Human Resources. 28 (4), 899-932[Friedman, Howard S., Schustack, Miriam W. (2008). Kepzribadian Teori Klasik dan Riset Modern. Jakarta: Penerbit Erlangga.Heckman, James J., Lochner, Lance J., dan Todd, Petra E. (2003) Fifty Years of Mincer Earnings aKrueger, Alan B., and Lindahl, Mikael. (2000). Education for Growth: Why and For Whom?. Working Paper No. 7591.Megasari,  Diah Nurulia, (2014). Analisis Tingkat Pengembalian Investasi Pendidikan Antara Laki-Laki Dan Perempuan Di Provinsi Jawa Barat Tahun 2014. Universitas Negeri YogyakartaOECD Stat. Extract. Dzaiakses dari: http://stats.oecd.org, pada 1 April 2015.OECD. (2000). Estimating Economic and Social Returns to Learning: Session 3 Issues for Discussion.Perkins, D.H, Radelet, S, Snograss, R.R, Gillis, M, and Roemer, M. 2001. Economics of Development.WW. Norton & Company, Inc. United States of America.Psacharopoulos, G. 1985. “Returns to education: A further international update andimplication”. The Journal of Human Resources, 20 (4), 583-597.Psacharopoulos, George 1994 “Returns to Investment in Education: A Global Update”.World development vol. 22 no. 9 pp 1325-43.Psacharopoulos, George. (1993). Return to Investment in Education: A Global    Update.               Diaksesdari:             http://www- wds.worldbank.org/servlet, pada 10 Agustus 2015.Psacharopoulos, George. (2006). The Value of Investment in Education: Theory, Evidence, and Policy. Journal of Education Finance. 32(2), 113-136.Purnastuti, L., dkk. (2011). Economic Return to Schooling in a Less Developed Country: Evidence for Indonesia. Diakses dari: http://kastoria.teikoz.gr/icoae2/, pada 20 Desember 2014.Purnastuti, L., dkk. (2015). Analisis Tingkat Pengembalian Investasi Pendidikan di Daerah Istimewa Yogyakarta. Prosiding Seminar Nasional 9 Mei 2015. Hlm. 797-806Purnastuti, L., Miller, P., dan Salim, R. (2013). Decilining Rates of Return to evidence for Indonesia. Bulletin of Indonesia Economic Studies.49(2), 213-236.Purnastuti, Losina., Miller, Paul., and Salim, Ruhul (2012). Economic Returns to Schooling in A Less Developed Country: Evidence for Indonesia. Journal of European Economy. Vol. 11. Sepecial Issue.Purnastuti, Losina., Miller, Paul., and Salim, Ruhul (2013). Declining rates of return to education: evidence for Indonesia, Bulletin of Indonesian Economic Studies.Schultz, Theodore, W (1961). Investment in Human Capital. Diakses dari: www.ssc.wisc.edu, pada 23 Februari 2015.

scholarly journals Does Education and Experience Matter in the Distribution of Wages in Nepal? A Quantile Regression Approach

2019 ◽  
pp. 1-14
Author(s):  
Pujan Adhikari ◽  
Kishor KC ◽  
Siddha Raj Bhatta
Keyword(s):  
Quantile Regression ◽  
Informal Sector ◽  
Wage Structure ◽  
Maximum Effect ◽  
Formal Sector ◽  
Market Returns ◽  
Agriculture Sector ◽  
Wage Returns ◽  
Return To Schooling ◽  
Experience Effect

 Labor market returns depend on the level of education as well as experience of the labors. Though education is argued to be the key determinant of wage rate, other factors such as the sector of employment, gender of the employee, marital status and work industry also matter. This paper investigates the returns from years of schooling and experience by examining the wage structure in formal, informal and agriculture sectors of Nepal. The Mincerion wage equation and quantile regression technique has been used to analyze such impact by utilizing the recent labor force survey data of Nepal. Our results show that wage returns are positively associated with schooling in all the three sectors. However, return to experience has negative association in case of agriculture sector. Furthermore, return to schooling has higher impact at higher quantile along with the distribution of wages in formal sector and informal sector. The maximum effect of education is 4 percent at 0.90 quantile in formal sector. An additional year of experience has high impact at lower-wage group in case of informal and formal sector. The effect varies from 9.2 percent at 0.1 quantile and 4.9 percent at 0.9 quantile in formal sector. The experience effect is higher at median (4.06 percent) in case of informal sector.

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