Growth of Real GDP and Total Factor Productivity in Asia with an Emphasis on Malaysian Growth
This paper estimates growth models for a panel of 15 Asian countries during 40-plus years from the early 1970s to 2014. The focus then shifts to Malaysia for which an annual time-series model is also estimated for the same time period. Both types of models indicate significant influence of human capital either directly on output growth or on growth through total factor productivity. We find that the lower the development level of a country (i.e., a greater income gap), the greater the total factor productivity growth which therefore permits faster income convergence. There is some evidence of interaction between human capital and the income gap as well which leads to an even bigger impact of human capital on total factor productivity growth. The interaction between human capital and openness produces a greater effect on growth in Malaysia based on the time-series model than its effect based on the panel model. Our results suggest that, barring large external shocks, a continued focus on human capital development should help to prevent growth slowdown in Malaysia over the next 15 years.
KeywordsReal GDP growth Total factor productivity Panel data model Time series model Malaysia
JEL CodesO47 O53
The views expressed in the paper are those of the authors alone and not those of the International Monetary Fund or Eastern Illinois University.
- Anand, R., Cheng, K., Rehman, S., and Zhang, L. (2014). “Potential Growth in Emerging Asia.” IMF Working Paper WP 14/2. https://www.imf.org/en/Publications/WP/Issues/2016/12/31/Potential-Growth-in-Emerging-Asia-41198 .
- Arora, V., & Vamvakidis, A. (2005). How much do trading partners matter for economic growth? IMF Staff Papers, 52(1).Google Scholar
- Bernanke, B., and Gurkaynak, R. (2001). “Is Growth Exogenous? Taking Mankiw, Romer and Weil Seriously,” NBER Working Paper 8365 (Cambridge, MA: National Bureau of Economic Research).Google Scholar
- Bosworth, B. P., & Collins, S. M. (2003). The empirics of growth: An update. Brookings Paper on Economic Activity, 2, 113–206 https://www.brookings.edu/wp-content/uploads/2003/06/2003b_bpea_bosworth.pdf.CrossRefGoogle Scholar
- Brynjolfsson, E., Rock, D., & Syverson, C. (2018). "artificial intelligence and the modern productivity paradox: A clash of expectations and statistics," NBER chapters. In The economics of artificial intelligence: An agenda (pp. 23–57). Inc: National Bureau of Economic Research.Google Scholar
- Ciccone, A., & Jarocinski, M. (2010). Determinants of economic growth: Will data tell? American Economic Journal: Macroeconomics, 2(4), 222–246.Google Scholar
- Crafts, N. (1999). East Asian growth before and after the crisis. IMF Staff Papers, 49(2), 139–166.Google Scholar
- Das, S. (2014). “Total Factor Productivity in Economic Growth: Evidence from a Panel Data Model for Africa and Asia,” Chapter 4 in Government Spending, Migration, Human Capital: Impact on Economic Welfare and Growth – Theory and Evidence. Ph.D. Dissertation, Sussex Univ. 2014. http://sro.sussex.ac.uk/id/eprint/48312/ .
- Delpachitra, S., & van Dai, P. (2012). The determinants of TFP growth in middle income economies in ASEAN: Implications of financial crises. International Journal of Business and Economics, 11(1), 63–88.Google Scholar
- Galor, O. (2005). “From Stagnation to Growth: Unified Growth Theory,” in Handbook of Economic Growth, ed. by P. Aghion and S. Durlauf (Amsterdam: Elsevier).Google Scholar
- International Monetary Fund. (2016). World economic outlook. Washington: D.C. Data available for downloading at https://www.imf.org/external/pubs/ft/weo/2016/01/weodata/index.aspx.Google Scholar
- International Monetary Fund. (2018a). World economic outlook. Washington: D.C. Data available for downloading at https://www.imf.org/external/pubs/ft/weo/2018/02/weodata/index.aspx.Google Scholar
- International Monetary Fund. (2018b). Direction of trade statistics yearbook. Washington: D.C. Data available for downloading at https://data.imf.org/?sk=9D6028D4-F14A-464C-A2F2-59B2CD424B85.Google Scholar
- Levine, R., & Renelt, D. (1992). A sensitivity analysis of cross-country growth regressions. American Economic Review, 82, 942–963.Google Scholar
- Organisation for Economic Co-operation and Development (2016). OECD.Stat, Data downloaded from 701 online from https://stats.oecd.org.
- Penn World Tables.9.0. (2015). Online database. https://cid.econ.ucdavis.edu/pwt.html.
- Sala-i-Martin, X. (1997). I have just run two million regressions. American Economic Review, 87, 178–183.Google Scholar
- Stansbury, A and Summers, L. (2017). “Productivity and pay: Is the link broken?”, NBER Working Paper 24165. https://www.nber.org/papers/w24165 .
- World Bank. (2016). World development indicators. Washington D.C. Data available for downloading at: World Bank https://openknowledge.worldbank.org/handle/10986/23969.Google Scholar