Optimal Resource Allocation on Physical and Human Capital: Theoretical Modelling and Empirical Case Study of the United States
- 1 Department of Economics, National Taipei University, New Taipei City
Abstract
This study expands the theoretical framework proposed by Mankiw et al. (1992) to explore optimal resource allocation between physical capital and human capital accumulation. Using United States data spanning from 1950 to 2019, it empirically examines the long-term cointegration relationships among per-capita final output, physical capital, and human capital. The empirical estimates, derived from a theory-based model, indicate that sustaining economic optimality in the US necessitates allocating approximately 25.13% to developing human capital and 26.23% to accumulating physical capital from real GDP. These allocations underscore the crucial roles of both physical and human capital in production, highlighting the equal importance of human capital in shaping economic output. Furthermore, the analysis reveals short-term interdependencies between these capitals and their immediate responses to output fluctuations. These insights into short-term dynamics provide essential implications for policymaking, enabling informed decisions on resource allocation and strategic economic planning.
- Barro, R. J., & Lee, J.-W. (2013). A New Data Set of Educational Attainment in the World, 1950-2010. Journal of Development Economics, 104, 184-198. https://doi.org/10.1016/j.jdeveco.2012.10.001
- Bellman, R. E. (1957). Dynamic Programming. Princeton University Press.
- Carnevale, A. P., & Rose, S. J. (2011). The Undereducated American. Center on Education and the Workforce.
- Feenstra, R. C., Inklaar, R., & Timmer, M. (2015). The Next Generation of the Penn World Table. American Economic Review, 105, 3150-3182. https://doi.org/10.1257/aer.20130954
- Frankel, M. (1962). The Production Function in Allocation and Growth: A Synthesis. American Econimics Review, 52, 996-1022.
- Glomm, G., & Ravikumar, B. (1994). Public Investment in Infrastructure in a Simple Growth Model. Journal of Economic Dynamics and Control, 18, 1173-1187. https://doi.org/10.1016/0165-1889(94)90052-3
- Griliches, Z. (1979). Issues in Assessing the Contribution of Research and Development to Productivity Growth. Bell Journal of Economics, 10, 92-116. https://doi.org/10.2307/3003321
- Johansen, S. (1991). Estimation and Hypothesis Testing of Cointegration Vectors in Gaussian Vector Autoregressive Models. Econometrica, 59, 1551-1580. https://doi.org/10.2307/2938278
- Jones, C. I., & Romer, P. M. (2010). The New Kaldor Facts: Ideas, Institutions, Population, and Human Capital. American Economic Journal: Macroeconomics, 2, 224-245. https://doi.org/10.1257/mac.2.1.224
- Koop, G., Pesaran, M. H., & Potter, S. M. (1996). Impulse Response Analysis in Nonlinear Multivariate Models. Journal of Econometrics, 74, 119-147. https://doi.org/10.1016/0304-4076(95)01753-4
- Lucas, R. E. (1988). On the Mechanics of Economic Development. Journal of Monetary Economics, 22, 3-42. https://doi.org/10.1016/0304-3932(88)90168-7
- Mankiw, N. G., Romer, D., & Weil, D. N. (1992). A Contribution to the Empirics of Economic Growth. Quarterly Journal of Economics, 107, 407-437. https://doi.org/10.2307/2118477
- Pesaran, M. H., & Shin, Y. (1998). Generalized Impulse Response Analysis in Linear Multivariate Models. Economics letters, 58, 17-29. https://doi.org/10.1016/S0165-1765(97)00214-0
- Psacharopoulos, G. (1994). Returns to Investment in Education: A Global Update. World Development, 22, 1325-1343. https://doi.org/10.1016/0305-750X(94)90007-8