The impact of ICT and human capital on achieving ...

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World Review of Science, Technology and Sustainable Development, Vol. 3, No. 3, 2006

The impact of ICT and human capital on achieving knowledge-based economy: applications in Malaysia’s economy Elsadig Musa Ahmed Faculty of Business and Law, Economics Unit, Multimedia University, Jalan Ayer Keroh Lama, 75450 Melaka, Malaysia E-mail: [email protected] E-mail: [email protected] Abstract: The knowledge-based economy (K-economy) is not confined to information and communication technology (ICT) alone. Before the evolution of the ICT, it was knowledge that was embodied in human beings, namely ‘human capital’ and technology that was embodied in the capital investment undertaken by the Asian economies that brought about the so-called Asian miracle. Using ICT in the activities of Malaysia’s economy contributes significantly to its productivity growth, in general, and total factor productivity (TFP) growth of the economy, in particular. The results of this study showed that the contribution of the ICT and human capital used in the economy were the highest among the input terms. The impact of ICT and human capital in TFP contributions is significant. But the growth rate of TFP is lower compared with the growth rate of the ICT and human capital. As a result, the achievement of the K-economy is not like that of the ICT and human capital development. Keywords: ICT; human capital; K-economy; Malaysia’s economy; TFP. Reference to this paper should be made as follows: Ahmed, E.M. (2006) ‘The impact of ICT and human capital on achieving knowledge-based economy: applications in Malaysia’s economy’, World Review of Science, Technology and Sustainable Development, Vol. 3, No. 3, pp.270–283. Biographical notes: Dr. Elsadig Musa Ahmed is a Lecturer at the Faculty of Business and Law, Economics Unit, Multimedia University, Melaka, Malaysia. He is currently teaching International Economic History, International Political Economy, East Asian Economics, Knowledge Economy and Macroeconomics. He is a reviewer and an expert of the second Millennium Ecosystem Assessment report ‘Biodiversity and Human Well-being: A Synthesis Report for the Convention on Biological Diversity’. He has also been a reviewer for many international, regional and local conferences. His research interests include productivity analysis, productivity and environment, development economics, economic growth and environment and knowledge based economy.

1

Introduction

According to Bank Negara Malaysia’s statistics (http://www.bnm.gov.my/index.php? ch=12), the productivity growth of 6.1% registered in 2000 was due to higher capacity utilisation by industries, as demand improved. To ensure the sustainability of this growth, Copyright © 2006 Inderscience Enterprises Ltd.

The impact of ICT and human capital

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the economy should further enhance Total Factor Productivity through efficient utilisation and management of the productive resources necessary for the production of goods and services. In 2000, productivity contributed 69.0% while employment contributed 30.0% to the GDP growth of 8.5%. All economic sectors registered positive output growth. The order was: Manufacturing sector (16.9%) > Transport sector (6.7%) > Trade (5.9%) > Electricity sector (6.3%). For the period 1990–2000, TFP grew by 1.7%, resulting in a corresponding productivity growth of 4.3%. The main sources of TFP growth for the period were Demand Intensity and Education (34.5%) and Training (33.9%). Malaysia’s productivity growth of 6.1% in 2000 surpassed that of several selected major OECD countries such as the USA (3.9%), Japan (2.3%), Canada (2.1%), the UK (1.8%), Italy (1.5%), Germany (1.4%), and France (1.2%). Compared to selected Asian countries in 1999, Malaysia’s productivity growth of 3.9% was better than that of Indonesia (–1.1%), the Philippines (0.7%), and Hong Kong (1.1%). However, Malaysia’s productivity growth was lower than that of South Korea (9.1%), Taiwan (4.6%), Singapore (4.5%), and Thailand (4.3%). Malaysia’s initial National Development Policy and National Vision Policy (1991) emphasised growth and modernisation as the main focus of its economic plan. To achieve this objective, Malaysia has invested in the development of information and telecommunication technology (ICT) as the driving force for productivity and output growth. The economic development of Malaysia passed from agricultural-based, prior to 1970, to the era of industrialisation i.e., from the import-substituting to the export-oriented manufacturing sector (Khalafalla and Webb, 2001; Jomo, 1997). In the 1980s, a period of heavy industrialisation was pursued to further upgrade the country’s technological capacity in order to increase economic growth. This was also seen as the beginning of the development of ICT in Malaysia based on the investment in telecommunication infrastructure and computer hardware, software and related peripherals. A report by ASEAN (2000) acknowledged the benefits and challenges of measuring and monitoring the digital economy driven by ICT. This was further emphasised by a report of OECD (2003) that ICT has been an important catalyst for growth and productivity. Recent research on the positive effects of ICT on productivity and output growth in Asian countries have been reported in the works of Wong (2001), Teong and Shin (2001), Lal (2001), for Singapore, Korea and India, respectively. Barker, (2001a, 2001b) examined the current status of the use of ICT in the context of International Education programmes in Canada’s public Post-Secondary Education system. Four sources were reviewed: international education (IE), distance education (DE), ICT and education (e-learning), and post-secondary education (PSE), largely with a focus on Canada. He concluded that the use of ICT was relatively new in PSE; therefore, there was limited knowledge about the specific use in IE. James (1999) notes that more effective use of learning technologies helps to address the changing characteristics of PSE learners, i.e., more multi-generational blending, growing multi-cultural diversity, extended participation by learners, rising number of part-time learners. He also notes that it may help to reduce the financial burden on learners, improve employment outcomes, and meet learner expectations for just-in-time service, programme flexibility, individualised support, non-traditional formats and programme accountability. To accomplish this, James recommended a reconsideration of

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service provision through holistic services, adjusted staff attitudes, self-service formats, expanded range of support and collaboration with other service providers. Frayer and West (1997) identified the following ways in which instructional technology can support learning: enabling active engagement in construction of knowledge; making available real-world situations; providing representations in multiple modalities (e.g., 3-D, auditory, graphic, text); drilling students on basic concepts to reach mastery; facilitating collaborative activity among students; seeing interconnections among concepts through hypertext; learning to use the tools of scholarship; simulating laboratory work. According to Pennycook (2001), Farrell (2000) and others, the integration of ICT into PSE requires substantial changes within the institution; e.g., enhancing the digital collection, redesigning faculty compensation plans, addressing credit transfer and competency standard issues. In their complexity, these issues sometimes serve as obstacles to the use of ICT. On a theoretical front, new growth theory predicts that physical investments should have a greater impact on productivity growth than traditional growth accounting would suggest, due to the positive externalities associated with such activities. In the contributions of Romer (1986), and Grossman and Helpman (1991), these externalities arise because of ‘knowledge spillovers’ – increases in physical investments of profit-seeking firms contribute to the general stock of knowledge upon which subsequent firms can build. In de Long and Summers (1991), investment externalities arise as a result of the ‘learning by doing effect’ – workers and managers learn new skills and more efficient methods of production by using newly installed equipment. These models suggest that the IT sector, which has been one of the most technologically dynamic sectors of the economy over the last 20 years, is likely to have a greater impact on productivity growth than other sectors. A number of recent empirical studies based on firm-level data have also confirmed a positive and statistically significant relationship between IT and productivity. Brynjolfsson and Hitt (1995) used a data set of the 300 largest firms in the USA economy over the time period 1988–1992 and introduced three novel features in their estimation methodology. These features were as follows: •

control for individual firms’ differences in productivity by employing a ‘firm effects’ specification

•

less restrictive translog production function instead of only the Cobb-Douglas specification

•

flexibility in parameters’ variation between various sub-sectors of the economy.

The study found that the elasticity of IT remained positive and statistically significant. Furthermore, ‘firm effects’ were found to be highly significant. The authors suggested that these effects may account for as much as half of the productivity benefits credited to IT in the earlier studies. Lichtenberg (1993) obtained similar results using the same data, as well as some additional data sets. In fact, the study found that the marginal product of IT was at least six times greater than the marginal product of other types of capital. In contrast, Loveman (1994) estimated a Cobb-Douglas production function using a data set which covered 60 business units of large firms from 1978–1984 and found no evidence of strong productivity gains from IT investments.


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Similar to measures of Research and Development (R&D) spillovers across firms based on their technological closeness, a commonly used measure of disembodied technology across industries is based on inter-industry patent flows (Scherer, 1982; Evenson and Putman, 1993; van Meijl, 1995). The inter industry patent flows show the proportion of patented inventions originating in one industry but used in other industries. The larger the proportion of patented inventions originating in other industries that an industry uses, the greater the spillover benefits that the industry receives from these other industries. To summarise, the findings of empirical studies on IT and productivity carried out on the industry-level data appear to be somewhat mixed. However, improved firm-level studies such as Brynjolfsson and Hitt (1995) suggest a positive relationship between IT and productivity growth in the USA.

1.1 ICT productivity in Malaysia The extent of the effects of ICT has been shown to vary between countries. In general, the developed countries have shown a more significant positive effect on productivity growth, compared to the developing countries. The initial study on ICT development in Malaysia, based on the development of telecommunication infrastructure development and GDP growth, had a positive impact on the economy as a whole (Ramlan, 2001). The decision to achieve a developed country status by the year 2020 using ICT as the vehicle is further strengthened by the development of the multimedia super corridor (MSC). Despite the economic plans and policy changes in embracing ICT as a catalyst for economic growth, to date, the empirical effect of ICT in Malaysia has yet to be determined. This research examined the impact of ICT and human capital on Malaysia’s productivity growth.

1.2 Human capital productivity in Malaysia In its effort towards shifting to a knowledge-based economy, Malaysia has the responsibility to make sure that development of human and intellectual capital to produce adequate supply of intellectual assets to support and sustain a flexible, agile and mobile workforce with relevant knowledge and skills. Malaysians need to strive hard to enhance the nation’s human capital through a process of continuous learning. Workers must adopt the attitude of life-long learning to improve and upgrade their level of knowledge and skills, especially through self-learning and self-improvement programmes. At the same time, employers must incorporate investment in human capital into their strategic objectives rather than treating human resources as a factor that is to be manipulated to produce short-term financial gains. Such myopic and tunnel visions should be replaced by a more strategic view of workers as human capital. The most important variable of human capital is education and training which can be measured in several ways including expenditure on education and training. Government expenditure on education and training as a representative of human capital variables is significantly related to economic growth. From past research, the amount of the Federal Government development allocation, and expenditure by the sector during the Seventh Malaysia Plan had expanded during Eighth Malaysia Plan. Consequently, there has been a significant increase in proportion of the population having access to education at all levels over the past decades. The total enrollment at the tertiary level in local public

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educational institutions has doubled over the past few years. The increase in tertiary enrolment was consistent with the overall pattern of employment which registered highest average annual growth rates for the administrative and managerial categories, followed by the professional and technical categories, suggesting a strong demand for manpower with skills and tertiary education. The crucial role of human capital development requires firm commitments, support and direction from the government.

1.3 Malaysia’s experience in knowledge-based economy There is no one standard definition of the knowledge-based economy but an acceptable one must place importance on the generation and exploitation of knowledge to create new value in the economy. Indeed, knowledge is information that is put to productive work. Knowledge includes information in any form, know-how and know-why. Knowledge is not only embodied in goods and services, particularly in high technology based industries, but also in knowledge as a commodity itself, manifested in forms such as intellectual property rights or in the tacit knowledge of highly mobile key employees. And it involves the way people interact as individuals and as a community. Unlike capital and labour, knowledge is a public good and sharing it with others involves zero marginal cost. In addition, technological breakthrough based on knowledge creates technical platforms that support further innovations and drive economic growth (Bank Negara Malaysia, 1999). The knowledge-based economy is not confined to information and ICT. Before the advent and proliferation of ICT, it was knowledge that was embodied in human beings, namely ‘human capital’ and technology that was embodied in the capital investment undertaken by the Asian economies that brought about the so called Asian miracle. These two types of investments had helped to close the ‘knowledge gap’ between the developed and emerging countries on how to transform inputs into desired outputs. With ICT developments, the management of this knowledge gap has become more complex as the globalisation process gains momentum (Bank Negara Malaysia, 1999). Meanwhile, according to Han (2003) and to date, the contributions to the development of the Malaysian Knowledge-based economy have come largely from government policy makers, management consultants and businesspersons. There are some academic researchers working on various aspects of ICT developments in the country, particularly the progress of the MSC and a number of National IT Agendas. A major effort is the book on Malaysia and K-economy (2001) by a number of academicians of the Malaysian Multimedia University. Also, a number of local studies on some aspects of ICT development and related innovative practices are also reported in the Proceedings of the Asia Pacific Economics and Business Conference 2002 Universiti Putra Malaysia (UPM). The studies of Elsadig (2004) and Elsadig et al. (2004) using secondary data have added to the pool of knowledge in the knowledge-based economy development in the Malaysian manufacturing sector. The studies also examined the role of the manufacturing sector’s productivity growth in achieving knowledge-based economy through information technology and human capital. In order to close the gap, the current paper uses knowledge workers and ICT used separately in the pervious papers due to the fact that the Knowledge-based economy is not confined to ICT. As mentioned earlier, these two types of investments had helped to close the ‘knowledge gap’ between the developed and emerging countries on how to transform inputs into desired outputs, and to see how much the difference between using knowledge workers and ICT in


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achieving knowledge-based economy through total factor productivity growth has contributed to Malaysia’s economy productivity growth. In addition, this study attempts also to close the gap of the divisia translog index approach that was developed by Jorgenson et al. (1987). This approach gives explicit specifications of a production function that had created a major drawback in the pervious studies on the Malaysian manufacturing sector’s productivity growth. Those approaches were not based on statistical theory and hence, statistical models cannot be applied to evaluate their reliability, thus casting doubts on their results. This study suggests closing this gap by providing a statistical analysis in the first step of the estimation and in the second step of plugging the parameters of the variables into the model of the above mentioned divisia translog index approach to calculate the growth rates of productivity indicators, including the calculation of the residual of the model (total factor productivity growth (TFP)) and output growth. The major objective of this paper was to estimate the knowledge based economy achievement through the contribution of human capital and ICT to TFP growth of Malaysian economy.

2

Methodology and estimation procedures

The paper attempts to apply the conventional growth accounting framework as utilised by Stigler (1947), Abramovitz (1956), Kendrick (1956), and Solow (1956, 1957), finally brought to fruition by Kendrick (1961) and further refined by Denison (1962), Denison and Edward (1979), Dollar and Sokoloff (1990), Griliches and Jorgenson (1962) and Jorgenson et al. (1987). The production of the economy is expressed as a function of aggregate fiscal capital, labour, human capital, ICT and time. This approach provides more room for decomposition of contributions of factor inputs and technological change to economic growth. The production function for economy can be represented as follows: GDP = F(K, L, H, ICT, T)

(1)

where aggregate output GDP is a function of aggregate fiscal capital input K, labour input L, human capital (expenditure in education) H, ICT (number of telephone lines) and time T, that proxies for total factor productivity as a technological progress of the Malaysian economy. The divisia index basically decomposes the output growth into the contribution of changes in inputs (such as aggregate fiscal capital, labour, human capital and ICT growth), and TFP growth. In other words, considering the data at any two discrete points of time, say T and T – 1, the growth rate of aggregate output (GDP) for an economy can be expressed as the weighted average of the growth rates of aggregate fiscal capital (K), labour (L), human capital (H) and ICT, plus a residual term typically referred to as the rate of growth of TFP. Hence the TFP growth of the economy is computed as the difference between the rate of growth of aggregate output and weighted average of the growth in the aggregate fiscal capital, labour, human capital and ICT, where the weights are the respective shares of each input in the economy’s aggregate output. According to Mahadevan (2001), the TFP growth studies on the Malaysian manufacturing sector have used the nonparametric translog-divisia index approach developed by Jorgenson et al. (1987). This approach does not require the explicit specification of a production function, but the major drawback is that it was not based on

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statistical theory and, hence, statistical methods cannot be applied to evaluate their reliability, thus casting doubts on their results. The present study attempts to close this gap by developing this model into a parametric model and providing statistical analysis for it in the first step as follows: ln GDPT = a + α .ln KT + β .ln LT + λ.ln H T + θ .ln ICTT + ε T T

= 1960 − 2003

(2)

where

α: β: λ: θ:

a:

εT: ln:

Output elasticity with respect to fiscal capital Output elasticity with respect to labour Output elasticity with respect to human capital Output elasticity with respect to ICT Intercept or constant of the model1 Residual term2 log to reduce the problem of heteroskedasticity.

Since the intercept (a) has no position in the calculation of the productivity growth rate indicators, a second step was proposed, which calculates the growth rates of productivity indicators transforming equation (2) as ∆ ln TFPT = ∆ ln GDPT − [α .∆ ln KT + β .∆ ln LT + λ .∆ ln H T + θ .∆ ln ICTT ]

(3)

where the weights are given by the average value shares as follows: ∆ lnGDPT:

α.∆ lnKT: β.∆ lnLT: λ.∆ lnHT: θ.∆ lnICTT:

∆ lnTFPT: ∆:

Growth rate of output Contribution of the fiscal capital Contribution of the labour Contribution of the human capital Contribution of the ICT Total factor productivity growth Difference operator denoting proportionate change rate.

The framework decomposes the growth rate of aggregate output into the contributions of the rates of growth of the aggregate fiscal capital, labour, human capital and ICT, plus a residual term typically referred to as the rate of growth of TFP.

3

Sources of data

The data for this paper were collected from various sources. Gross domestic product was collected from the Asian Development Bank: Key indicators of developing Asia and Pacific countries, Statistical and Data Systems Division and the international financial statistics of the International Monetary Fund, yearbook. Human capital is proxied by educational expenditure and it was collected also from the Asian Development Bank and UNESCO databases. Aggregate fixed capital, number of employments and number of telephone lines were obtained from the Department of Statistics, Malaysia.


4

277

Results and discussion

An autoregressive estimator was applied to equation (2) of the model that was generated from a production function, to measure the shift in the production functions of Malaysia’s economy. An annual time series data over the period 1960–2003 for gross domestic GDP, gross fixed capital, number of employments, expenditure in education and number of telephone lines were employed. Analysis of the data of equation (2) showed that the estimated coefficient of the fiscal capital was significant at the 5% level and the estimated coefficients of the rest of the variables were significant at the 10% level. According to the Durbin–Watson value the model has no problem of autocorrelation (Table 1). Table 1

Output elasticity of Malaysian economy productivity indicators 1960–2003

Intercept

–3.502 (–1.616)*

Fiscal capital

0.249 (1.862)**

Labour

0.723 (1.672)*

Human capital

0.015 (1.845)*

ICT

0.013 (1.684)*

Adjusted R

2

Durbin–Watson

0.896 1.81

Figures in Table 1 were estimated using equation (2); Figures in parentheses are T-values. *Significant at 10% level. **Significant at 5% level.

4.1 Empirical analysis Analysis was carried out to compare the productivity indicators within the Malaysian economy for the entire period, 1960–2003. In order to study the effect of government policies in improving the economy’s productivity growth, as well as the impact of knowledge workers and ICT used in Malaysian economy in achieving the knowledge-based economy status, the study period was divided into three phases. These phases, which corresponded to the major policy changes, were 1971–1979, 1980–1986, and 1987–2003. The period of the 1970s witnessed the birth of Malaysia’s era of export-oriented economy. The policy shifted from import substitution to labour intensive and export oriented industries with electronics and textiles as the main areas of emphasis and growth. The decade of 1980s saw a further diversification of the economy into more advanced industries. The heavy industries corporation of Malaysia (HICOM) was conceived in 1980s. As a result of these polices the range of economic activities and sources of growth had become more diversified. The period 1987–2003 witnessed further diversification of the economy into more advanced industries. During this period, economic structural transformation took place in the Malaysian economy and the manufacturing sector became the engine of growth. The use of total factor productivity overcomes the problems of single productivity indicators such as labour productivity and capital deepening by measuring the relationship between output and its total inputs (a weighted sum of all inputs), thereby giving the residual output changes not accounted for by total factor input changes. Being

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a residual, changes in TFP are not influenced by changes in the various factors which affect technological progress, such as the quality of factors of production, flexibility of resource use, capacity utilisation, quality of management, economies of scale and so on so forth (Rao and Preston, 1984). The improvement and slowdown of TFP contribution to Malaysian economy in terms of average annual growth rates are dependent on the inputs used in the production of the economy, that were reported to be of low quality and insufficient. However, the contribution of TFP growth to the Malaysian economy in terms of average annual productivity growth by using human capital and ICT was 1.348% during the entire period and the contribution of TFP was 3.133%, 1.016% and –1.533% for the sub-periods respectively (Table 2). Table 2

Productivity indicators of Malaysian economy (in percentage)

Productivity indicators

1960–2003

1971–1979

1980–1986

1987–2003

Total factor productivity

1.348

3.133

1.016

–1.533

GDP

6.695

3.134

1.788

3.625

Fiscal capital Labour

10.85 3.081

Human capital

13.55

ICT

16.50

7.633 2.591 15.17 5.246

19.10 3.099 15.86 7.205

9.783 3.641 12.41 22.66

Figures in Table 2 were calculated using equation (3).

The highest contribution of GDP to the productivity growth of the Malaysian economy by using ICT and human capital was the contribution of the entire period of 1960–2003 at 6.695% (Table 2). The lowest contribution of GDP to the productivity growth of the Malaysian economy was the contribution of the sub-period of 1980–1986 at 1.788% (Table 2). This was found to be the period of economic crisis of 1985, and the performance of the Malaysian economy was very low compared with the period after the transformation of Malaysian economy into an exported-oriented one. The TFP contributed negatively and the GDP did not make the highest contribution to the economy’s productivity growth. The reasons were the financial crises of 1997 and the quality of human capital and the technology involved in the production of the economy. The highest contribution of fiscal capital to GDP in terms of the average annual productivity growth of the Malaysian economy was during the sub-period 1980–1986 and was 19.10%. Likewise, the highest contribution of labour to GDP in terms of average annual productivity growth of the Malaysian economy was during the sub-period 1987–2003 and it was 3.641% (Table 2). The contribution of human capital and ICT used in the economy was the highest among the input terms during most of the periods of the study. By examining the role of human capital and ICT to achieve knowledge based-economy through TFP growth, it was found from the results that there was a positive contribution of human capital and ICT to TFP growth of Malaysian economy during all the periods of study except for the sub-period of 1987–2003 which contributed negatively to TFP growth (Table 2). It should be noted that these results have proved that the productivity growth of Malaysia’s economy is input driven but rather, productivity growth driven when the results of TFP were compared. It was also found that there was declining contribution of


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the TFP during the sub-period of 1987–2003, although the contribution of the GDP was one of the highest periods during this sub-period. It was also confirmed by Lall (1995), as in other Asian newly industrialised countries where their productivity was input driven, as stated by Young (1992, 1995) and Kim and Lau (1994). Sarel (1996) also expressed his concerns that some East Asian countries may face the same fate as the Soviet Union. This is because these countries invested primarily in labour and capital rather than in technology over the past few decades and there is no real technological development that can sustain the progress of the industrial development.

5

Conclusions

This study closed the gap in the extensive growth theory model by providing statistical analysis in a parametric form which removed the doubts in the results generated. The factors affecting the GDP growth of the Malaysian economy identified in this study using this model were the individual contributions of aggregate fiscal capital, labour, human capital, ICT and the combined contribution of the quality of these inputs expressed as the TFP growth. The results of the study confirmed that the productivity growth of Malaysia’s economy is input driven rather than total factor productivity driven. In order to reach knowledge-based economy through the productivity of Malaysian economy the contribution of the human capital and ICT should be more than other traditional inputs (such as fiscal capital and labour); this is reflected in TFP growth of the economy. Also to move towards knowledge-based economy the TFP growth of the economy must show a significant increase in performance, and the results of this study showed that there was a very low contribution of TFP. TFP can also be explained as the growth in a K-based economy because it captures endogenous technical change and other characteristics of the K-based economy, including diffusion of knowledge, organisation, restructuring, networking, and new business models that would contribute to market efficiency and productivity. A few limitations have been identified with regard to the data used in this study related to human capital and ICT involved. The results of this study will prove to be useful for ICT and human capital policy formulation. In this context, a comparison of the contributions of ICT and human capital to productivity growth in the Malaysian economy provides a guideline for the policy makers to formulate appropriate national and international ICT and human capital policies. The findings from this study will also help policy formulation in promoting ICT investment and in developing the human resources and infrastructure needed to support effective use of the technology. It is possible that Malaysia can capitalise on its synergy with the other nations in ASEAN and make full use of the competitive advantages in these countries to overcome its insufficiencies. In that case, Malaysia will be able to accelerate the movement towards becoming a technology-savvy nation. In accordance with the initiatives taken by ASEAN in narrowing the ‘digital divide’ among the ASEAN member countries, the results from this study will provide empirical evidence on the extent of the ‘digital divide’ among these countries, which will help the formulation of appropriate policies to bridge the ‘digital divide’, if any, among these countries. In addition, benchmarking the extent of ICT and human capital development

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allows comparisons between countries and indicates how well countries are doing compared to others in terms of adaptation, mastery and development. Comparing with better-performing countries helps to identify policies for further improvement. Furthermore, identifying which country lags behind with respect to ICT adoption provides a benchmark to enhance the cooperation among the ASEAN member countries in developing the ICT sector for the region as a whole.

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Notes 1

The intercept term, as usual, gives the mean or average effect on dependent variable of all the variables excluded from the model. 2 The residual term proxies for the total factor productivity growth that accounting for the technological progress of the manufacturing sector through the quality of input terms.

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