Evaluating the Impact of Education on Earnings in Nepal

Vol 18 , Issue 1 , January - June 2017 | Pages: 1-14 | Research Paper

https://doi.org/10.51768/dbr.v18i1.181201711

Published Online: January 07, 2017

Author Details ( * ) denotes Corresponding author

1. * Hari Gopal Risal, Assistant Professor, Finance and Account, Kathmandu University School of Management, Lalitpur, Nepal (harigopal@kusom.edu.np)

2. C.P. Gupta, Professor, Department of Financial Studies, South Campus, University of Delhi, Delhi, India

Purpose: This study contributes to the literature of return on investment in education and help policymakers while allocating the budget to education sector since education level causes significant increment in earnings in the context of Nepal.

Design/Methodology/Approach: This paper has applied a Semi-log multivariate regression model using data from Nepal Labor Force Survey to provide empirical evidence in the entire analysis on this issue. Not just academic qualifications, but also there are several other factors which cause increase in income. We explore this fact to identify the impact of experience, age, training, gender, and other variables in their earnings.

Findings: The empirical evidence of this study has shown a direct relationship between the education and earnings and we estimated that an increase in education of people by 1 level causes 16.64% increment in their wages on an average. Apart from the academic qualification, their experiences in the similar field have statistically significant relationship (i.e., one level increase in experience causes 11.57% increase in monthly earnings). The training dummy is found to be statistically insignificant to cause increment in earnings which is also an interesting finding of the study.

Research Limitations/Implications: Due to having the low R-squared value after introducing dummies, we can suggest that there may be other quantitative and qualitative factors like ability of the individual and the contextual fact that may cause the increment in earnings, which can be the future scope of this study.

Practical Implications: Two major implications of this empirical study are: firstly, this will help policymakers while allocating the budget in education sector and secondly, encourage people towards higher education.

Originality/Value: This is an original research and also examines the discrepancies in earning caused by gender, marital status, and area of living and work.

Keywords

Secondary Data, Multivariate Regression, Inferential Analysis, Monthly Earnings, and Education.

Ashenfelter, O., Harmon, C., & Oosterbeek, H. (1999). A review of estimates of the schooling/earnings relationship, with tests for publication bias. Labour Economics, 6(3), 453-470.
Becker, G.S. (1962). Investment in human capital: A theoretical analysis. The Journal of Political Economy, 70(5), 9-49.
Blundell, R., Dearden, L., & Sianesi B. (2004). Evaluating the impact of education on earnings in the UK: Models, methods and results from the NCDS; The Institute for Fiscal Studies, WP03/20.
Card, D. (1999). The causal effect of education on earnings. Handbook of Labor Economics, 3A, (1801-1863). NJ, USA: Elsevier.
Central Bureau of Statistics, National Planning Commission Secretariat. (2008). National Labour Force Survey 2008. Statistical Report. Retrieved from http:://www.ilo.org/wcmsp5/groups/public/@asia/@ro-bangkok/@ilo-kathmandu/documents/publication/wcms_118294.pdf, Accessed on September 4, 2016.
Cook, R. D., & Weisberg, S. (1983). Diagnostics for heteroscedasticity in regression. Biometrika, 70(1), 1-10.
Dickson, M., & Harmon, C. (2011). Economic returns to education: what we know, what we don’t know, and where we are going—some brief pointers. Economics of education review, 30(6), 1118-1122.
Dee, T.S. (2004). Are there civic returns to education? Journal of Public Economics. 88(9-10), 1697-1720.
Education is the most powerful weapon to change the world. (2009). Retrieved from http://www.ngopulse.org/article/education-most-powerful-weapon-change-world, Accessed on September 23, 2016
Gujarati, D. (2014). Econometrics by example. London, UK: Palgrave Macmillan. Retrieved from http://charitythinking.weebly.com/uploads/4/5/5/4/45542031/[damodar_gujarati]_econometrics_by_example_manzoor_ ahmad_.pdf, Accessed on September 5, 2016.
Lamichhane, K., & Sawada, Y. (2009). Disability and returns to education in a developing country. Tokyo: University of Tokyo Research Center for Advanced Science and Technology. Retrieved from https://pdfs.semanticscholar.org/8607/5b2505728119490e72477f9dab92ace2c30f.pdf, Accessed on September 6, 2016.
Mincer, J., & Polachek, S. (1974). Family investments in human capital: Earnings of women. Journal of political Economy, 82(2, Part 2), S76-S108.
Ministry of Finance (2015-16). Estimates of Expenditure: Red Book FY 2015-16. Nepal. Retrieved from http://www.mof.gov.np/uploads/document/file/RedBook_20150714124721.pdf, Accessed on September 5, 2016.
Parajuli, D. (1999). External efficiency and equity in Nepalese education, Mimeo, World Bank. Retrieved from https://www.coursehero.com/file/p6m20jq/Parajuli-D-1999-External-efficiency-and-equity-in-Nepalese-education-mimeo/, Accessed on September 3, 2016.
Pillania, R.K. (2004). Is Privatization Improving the Competitiveness of Indian Higher Education. Delhi Business Review, 5(2), 1-11.
Prakash, O. (2006). Business, Government and Education. Delhi Business Review, 7 (1), 39-45.
Punia, B.K., & Kavitri. (2004). Career Orientation Preferences of New Age Managers: A Study Across Personal, Familial, Institutional, and Educational Domain. Delhi Business Review, 15(2), 57-69.
White, H. (1980). A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity. Econometrica, 48(4), 817-838.