The Long-Term Impact of Human Capital Investment on GDP: A Panel Cointegrated Regression Analysis

Abstract

This study aims to determine the long-run impact of physical and human capital on GDP by using the panel data set of 13 developed and 11 developing countries over the period 1970-2010. Gross fixed capital formation is used as physical capital indicator while education expenditures and life expectancy at birth are used as human capital indicators. Panel DOLS and FMOLS panel cointegrated regression models are exploited to detect the magnitude and sign of the cointegration relationship and compare the effect of these physical and human capital variables according to these two different country groups. As a consequence of panels DOLS and FMOLS models, the impact of physical capital and education expenditures on GDP in the developed countries is determined as higher than the impact in the developing countries. On the other hand, the impact of life expectancy at birth on GDP is determined as higher in the developing countries.

Examining the “Natural Resource Curse” and the Impact of Various Forms of Capital in Small Tourism and Natural Resource Dependent Economies

“…Perhaps more significantly, it is clear that in the least developed economies of those observed -Gambia, Lesotho, and Malawi, only in the case of Malawi do we find an initially positive impulse caused by human capital. This conforms to the general hypothesis of several researchers that human capital has a more significant effect in more developed economies (Neagu, 2012 [41]; Akpolat, 2014 [42]). In all of the observed economies we have failed to establish that NC has a long-term significant impact on HC.…”

The battle of health with environmental evils of Asian countries: promises to keep

“…Subsequently, the study estimated single equation panel cointegration estimators including the fully modified OLS (FMOLS) panel estimators outlined by Pedroni (2000), panel dynamic ordinary least squares (DOLS) estimators described by Kao and Chiang (2000) and Mark and Sul (2003), and canonical cointegrating regression (CCR) proposed by Park (1992). These three panel cointegrated regression estimators were employed due to the following reasons: (i) panel FMOLS estimators adjust the endogeneity and serial correlation from the given model and provide optimal estimates of cointegrating regressions; (ii) dynamic ordinary least squares (DOLS) constructs an asymptotically efficient estimator that reduces the feedback in the cointegrating system by using the leads and lag in the regression estimators, and finally (iii) the CCR estimator is based on decomposing the variables in the cointegrating regression that removes the second-order bias of the OLS estimator in the general case (Akpolat, 2014;Mehmood et al 2014). Finally, the study employed robust least squares regression methods that are less sensitive to the outliers.…”

The Impact of Public Health Expenditure on Health Component of Human Capital

“…Literature analysis shows that human capital measurement is problematic and different researchers use different proxies to express human capital. In economic growth studies human capital is expressed by quantitative variables such as primary, secondary school, and higher education enrolment rate (Wolff, 2000), a ratio of employees with tertiary education on total number of employees, (Čadil, Petkovova, & Blatna, 2014), share of labour force with secondary / tertiary education (Odoardi, Muratore, 2019), population (25-64 years) participating in education and training (Odoardi, Muratore, 2019), life expectancy at birth (Akpolat, 2014;Kokotovic, 2016), expenditure on education (Akpolat, 2014;Kokotovic, 2016) and others. For this study the life expectancy variable is selected as a dependent variable.…”