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Growth of Real GDP and Total Factor Productivity in Asia with an Emphasis on Malaysian Growth

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Abstract

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.

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Notes

  1. See Figure 1 for real GDP growth for two sub-periods.

  2. The faster growth in Singapore was partly due to larger growth in population in the second period resulting from a higher immigration rate. In per capita terms, real growth in Singapore fell marginally from 5.8% to 5.7%.

  3. Park (2012) used India’s labor share for Pakistan and Thailand’s share for Vietnam. His sample did not include Bangladesh or Nepal.

  4. The PWT labor share is almost identical with 0.56 for 10 Asian countries estimated by Das (2014).

  5. An alternative, the Im-Pesaran-Smith test of panel stationarity, cannot be carried out for fewer than 10 observations per country.

  6. Indeed, the level variables are found to be nonstationary with one unit root in them.

  7. Data availability was also a problem. Despite accessing data from a number of sources including PWT9 (Feenstra et al. 2015; Penn World Tables 9.0, 2015), World Development Indicators (World Bank 2016), World Economic Outlook (International Monetary Fund 2016, 2018b), and (Organization for Economic Cooperation and Development 2016), even a relatively non-comprehensive model could only be estimated with a third of total observations in the dataset. For instance, our preferred model with only six explanatory variables (human capital, the catchup term, human capitalcatchup interaction, openness, M2-GDP ratio (Broad Money as percent of GDP), and R&D expenditure-GDP ratio, with a lag in some cases) retained only 45 out of 135 observations in the sample. This would be just three observations per country, mainly due to scarce R&D data, and hence would make the estimated results inefficient and suspect.

  8. Of course, life expectancy and under-five mortality both indicate overall health situation in a country and cannot both be included in the same regression. The correlation between these two variables is 0.90.

  9. Domestic credit is only one of the important indicators of financial development together with several other variables, such as broad money, and stock market capitalization. Growth should be positively correlated with overall financial development for a poor but growing economy, yet literature has not firmly established such a correlation for countries with a high degree of financial development. Is the relationship nonlinear, particularly because there is a threshold below which economic growth and financial development are correlated and not otherwise? Can all the indicators of financial development be combined in an orthogonal fashion in terms of principal components that can then be used to explore the relationship between finance and GDP growth? These questions are left for future research.

  10. Increases in human capital have remained below 0.04 in all years except for 13 years from 1983 through 1995.

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Authors and Affiliations

  1. Strategy, Policy, and Review Department, International Monetary Fund, Washington, DC, USA

    Sibabrata Das

  2. Department of Economics, Eastern Illinois University, Charleston, IL, USA

    Mukti Upadhyay

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  1. Sibabrata Das
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  2. Mukti Upadhyay
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Correspondence to Sibabrata Das.

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Das, S., Upadhyay, M. Growth of Real GDP and Total Factor Productivity in Asia with an Emphasis on Malaysian Growth. Atl Econ J 47, 391–413 (2019). https://doi.org/10.1007/s11293-019-09642-5

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