In examining the links between human capital and economic growth, two empirical models were adapted, namely the human capital and technology diffusion model proposed by Benhabib and Spiegel (2005), and the model of economic growth proposed by Aghion, Howitt and Murtin (2010), hereafter known as AHM, as explained in detailed in Chapter 4. These two models will be examined to test their capacity to explain economic growth in regional Malaysia. Following this, the empirical specifications will define (i) links between human capital, technology diffusion and TFP growth, with additional specifications on education level and (ii) links between human capital and economic growth, with additional specifications on education level. Since Malaysia is a federation with a centralised governance structure, the concentration of authority at the national level enables education policies to be standardised throughout the country. Although the centralisation of authority in education raises questions on the efficiency of the delivery of services, the centralisation does contribute in ensuring the standard of education as well as access to education are equal for every student.
Additional estimation related to FDI as controlled variable and education quality were also performed for both baseline models. However, details of the extended interaction models on education level and education quality will be discussed in Chapter 6. The interaction models in improving empirical analyses were adapted from Brambor, Clark and Golder (2006). In all models, the state dummies and year dummies are also estimated. Summary of the empirical specifications as adapted from Benhabib and Spiegel (2005) and Aghion, Howitt and Murtin (2010) models are discussed below.
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5.2.1 Empirical Model of Human Capital, Technology Diffusion and TFP Growth
Following Benhabib and Spiegel (2005), this studyβs baseline empirical specification on the link between education and TFP growth is written as follows: βππ,π‘ = π + (π + π)π π,π‘βπ,π‘β (π)π π,π‘βπ,π‘( π΄π π΄π ) π + ππ,π‘ (5.1)
or can be re-written as:
βππΉππ,π‘ = πΌ0+ π½1πππππ,π‘+ π½2πππΈπΆππΉπ,π‘ + ππ,π‘
(i = 1,β¦,N; t = 1,β¦,T)
(5.2)
where the dependent variable, ΞTFPi,t represents the average annual growth rate of total factor productivity. The independent variables, YSi,t represents the log years of schooling (indicates the education level in state i) and ECTFi,t (in logs) represents the interaction between education and closeness to frontier (EDU x CTF). The coefficient Ξ²1 represents the combined effect of YS (h) on ΞTFP from (a) innovation, and (b) the exponential component of the distance to frontier (Amax β A)/A, with higher level of YS having a positive effect on TFP growth. As in equation (4.7), Ξ²1 = (g+m) and g (pure innovation effect) = (Ξ²1 β Ξ²2). Lastly, the Ξ΅i,t represents the error term. The subscript i and t denote state and time period, respectively and this denotation of i and t also applies to other sets of specification described hereafter. The coefficient Ξ²1 is expected to have a positive sign, while Ξ²2 is expected to have a negative sign. The coefficient Ξ²2 indicates the effect of the gap in frontier technology between leader state and followers. States that are near the frontier will focus more on innovation activities, whereas states that are far from the frontier will adopt technologies from the frontier country, thus represented by the negative sign.
Previous research findings have found a positive link between productivity growth and level of human capital and that a higher initial level of human capital has significantly positive impact on per capita GDP growth (Nelson & Phelps 1966). Kyriacou (1991) suggests that without relatively higher level of initial
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human capital stocks, it would be difficult for laggard countries to converge to more advanced economies. Therefore, the estimates for the empirical model as specified in equation (5.2) will examine the evidence on the contribution of human capital for productivity growth in Malaysia. From the estimation model, TFP growth depends on two factors, that is (i) the innovation capability of that country, which depends on the human capital stock (lnYS); and (ii) the interactive component (lnECTF), that should capture the process of catch-up described by the NelsonβPhelps hypothesis, in which the rate of technology diffusion depends on the existing technology gap and on the stock of human capital.
In addition to estimating the baseline model given by the above equation, estimation was also performed to examine the effect of FDI (as control variable) on TFP growth, using the model specification as in equation (5.3) below.
βππΉππ,π‘ = π½0+ π½1πππ,π‘+ π½2πΈπ·ππΉπ,π‘ + π½3πΉπ·πΌπ,π‘+ ππ,π‘ (i = 1,β¦,N; t = 1,β¦,T)
(5.3)
The FDI per capita is selected as a control variable to explain other related factors that may affect TFP growth. Islam, Ang and Madsen (2014) propose that FDI embodies new technology, know-how and knowledge that contribute to economic growth. FDI could provide new inputs and foreign technologies in the production process of the host country and will also provide knowledge enhancement through skills training and more effective management structures. Borensztein et al. (1998) and Xu (2000) claim that the adoption of new technologies needs higher skilled labour and developed countries with higher level of human capital are more likely to gain from FDIs than developing countries. However, past empirical studies have also found positive relationship between FDI and economic growth in developing countries as well (Wang 2003). The underlying assumption here is that FDI is treated as an exogenous variable, and from the equation model above, the coefficient Ξ²3 is expected to have a positive sign. Similarly, the coefficient Ξ²1 is expected to have a positive sign, while Ξ²2 is expected to have a negative sign.
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5.2.2 Empirical Model of Human Capital and Economic Growth
Next, the link between education and economic growth will be defined. Using the production function and adapting the empirical model by Aghion, Howitt and Murtin (2010), the empirical model of growth equation in this study, which includes both the level and accumulation of human capital, as previously discussed in Chapter 4, is defined as below:
βππππ¦π,π‘ = π + πβππππππ,π‘+ πππππππ,0+ πππππ¦π,0+ π’π,π‘ (5.4)
or can be re-written as:
βπΊπ·ππ,π‘ = π + πβπΈπ·ππ,π‘+ ππΈπ·ππ,0+ ππΊπ·ππ,0+ π’π,π‘ (5.5)
Following the AHM model, the dependent variable ΞGDPi,t represents the change per year in the log of GDP per capita in state i over a given time period, multiplied by 100; ΞEDUi,t represents the change per year in the log of years of schooling in the respective states over the same period, also multiplied by 100; log EDUi,0 represents the log of years of schooling at the beginning of the period; log GDPi,0 represents initial log of GDP per capita and ui,t is a residual term. The subscript i and t denote state and time period, respectively.
The coefficient (b) from the above equation model represents the accumulation of human capital that affects economic growth. Higher growth or accumulation of human capital will have a positive effect on economic growth and thus, the coefficient is expected to have a positive sign. In addition, coefficient (c) on initial years of schooling is also expected to have a positive sign as higher initial level of human capital will contribute towards higher growth. Meanwhile, coefficient (d) for initial level of per capita GDP is predicted to have a negative sign, which suggests that growth of per capita GDP should depend negatively upon its initial level. In earlier estimation FDI was included as explanatory variable, however the results are not significant and the variable was dropped.
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