J Knowl Econ (2010) 1:191–201 DOI 10.1007/s13132-010-0013-8
Information and Communications Technology Effects on East Asian Productivity Elsadig Musa Ahmed
Received: 8 April 2010 / Accepted: 23 June 2010 / Published online: 21 July 2010 # Springer Science+Business Media, LLC 2010
Abstract This study empirically assesses the effect of information and communications technology (ICT) on productivity of ASEAN5 (Malaysia, Indonesia, Philippines, Singapore and Thailand) plus 3 (China, Japan and South Korea). Analytical interpretations in this article have successfully corrected the defects of the predecessor study through a statistical estimation by way of arriving at the coefficients of the explanatory variables being used by econometric approach. A second step in a routine procedure has effectively plugged the parameters of the variables into a modified model in order to calculate the growth rates of productivity indicators being used by growth accounting. The examination envisages a key finding that on the productivity growth of the aforesaid ASEAN5, China has been ‘input-driven’ while, on the other hand, Japan and Republic of Korea productivity growths have been shown as ‘productivity-driven’, as reflected from the comparison among the results of total factor productivity growth. The study also exposes a fact that the impact of ICT has been positive in the countries under considerations. Keywords ASEAN5 plus 3 . ICT . TFP growth . Productivity-driven . Knowledge economy JEL classification O11 . O12 . O47 . E23 . C22
Introduction Information and communications technology (ICT) includes an array of hardware, software, telephones, businesses, services and networks that enable to access to internet. ICT is usually supported by equipments such as computers, the internet, CD-ROMS and other software, radio, video, television and digital cameras that can be used in the works. E. M. Ahmed (*) Economics Unit, Faculty of Business and Law, Multimedia University, 75450 Melaka, Malaysia e-mail:
[email protected] e-mail:
[email protected]
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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. Moreover, 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 [6]. Gordon [13], Loveman [25], Roach [32, 33] and Strassman [41] showed that productivity gains from ICT in the aggregate economy have been limited, despite the rapid improvement in price–performance ratio of computers and heavy investment in ICT. This argument was based in part on the fact that the USA invested heavily in ICT during the 1970s and 1980s, yet productivity growth slowed during that period compared to the earlier post-war years. This has been referred to as the ‘productivity paradox’. Among development agencies such as the International Monetary Fund and the World Bank, there is likewise disagreement as to what role ICT should play in various development projects. Some believe that there are strong payoffs from ICT projects, while others are sceptical (Asian Productivity Organisation (APO) [2, 26, 30]). However, there is a growing consensus among economic growth theorists and development specialist that technology innovation and diffusion can play a critical role in stimulating economic growth and productivity (Kraemer and Dedrick [21]). Early proponents of this view included Schumpeter [36], Abramovitz [1], Kendrick [17] and Solow [39]. Economists such as Arthur [4] and Romer [34] have emphasised technological innovation in explaining economic growth and productivity gains. Romer [34] argues that economic growth and technological change are inextricably linked. Thus, widespread technology diffusion creates the possibility for increasing returns to investment [5]. Many studies have been conducted to analyse the contribution of ICT sector to the total factor productivity (TFP) growth of the US and OECD economies. Among others is the study by Pilat and Lee [29], which examined the contributions of ICT-using industries to economy-wide labour productivity growth in Australia, Canada, Denmark, New Zealand, Ireland and Norway. The authors reported that ICT penetration is strongly related to growth in TFP in those countries. Ark [3] finds that productivity growth differentials between the USA and several European countries are at least partly explained by a larger and more productive ICT-producing sector in the USA. Jorgenson and Stiroh [16] and Oliner and Sichel [28] found that one of the most important sources of TFP growth in the USA in the 1990s was from ICTproducing industries. Oliner and Sichel [28] and, to a lesser extent, Jorgenson and Stiroh [16] lean toward the view that ICT has played a significant role in generating a fundamental change in the US economy's growth. Despite some methodological differences, these papers derive similar estimates, attributing around a quarter percentage point of the acceleration in labour productivity since 1995 to ICT (TFP
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growth in the ICT sector) and a half percentage point to capital deepening (all of which is attributable to the accumulation of ICT capital). In total, they estimate ICT has contributed three fourth of the recent labour productivity acceleration. In contrast, Gordon [13] and Bosworth and Triplett [7] adopt the more agnostic view that the ICT ‘revolution’ has not had the same impact as the general-purpose technologies introduced in the past century (such as electricity or transportation). Gordon [13] focuses on the cyclical component of the US labour productivity surge, suggesting that half of the acceleration after 1995 has been a cyclical phenomenon. Another stream of research has examined economy-level time series data to quantify the contribution of ICT toward output growth of a single country, such as the USA or Singapore [23, 27, 42] (Sichel [37]), with mixed findings on the contributions of ICT. A problem with this approach is that the long-time series of data required for statistical significance is not easily and consistently available for a relatively new technology such as computers [10]. Furthermore, capital and labour inputs for a single country tend to move together, with each other and with the scale of the economy, making it difficult to obtain robust results [10]. In order to minimise this problem and to draw meaningful conclusions about the impact of ICT investment at the country level, it is necessary to conduct empirical studies of multiple countries over time. There is some debate in the literature as to the extent of the impact of ICT on the growth of the developing countries. Dewan and Kraemer [10] estimated an inter-country production function relating ICT and non-ICT inputs to gross domestic product (GDP) output on panel data from 36 countries over the 1985–1993 period and found significant differences between developed and developing countries with respect to their structure of returns from capital investments. The authors concluded that returns on ICT investment are positive and significant for developed countries, but not statistically significant for developing economies. Much of the recent debate on the sources of growth in Asia has been strongly focused at the macro-level [24, 45]. Krugman [22] and Young [44] indicated that most of the growth in Asia was driven by increases in capital intensity rather than by TFP growth. Kraemer and Dedrick [20] conducted an analysis of ICT investment across countries using data from 1984 to 1990 for 12 Asia-Pacific countries that represented different levels of economic development. ICT investment was defined as total spending for computer hardware, software and services within a country. The study found a significant relationship between growth rates in ICT investment and both productivity and economic growth at the national level. Due to the limited data involved, it was not possible to employ more sophisticated models that would control the impacts of other variables. The study did, however, identify several factors that were strongly correlated with levels of ICT investment, which include GDP per capita, education levels, share of employment in the service sector and level of ICT infrastructure. This study was able to identify that earlier studies were based on econometric estimation, which has gap of not being able to calculate the contributions of productivity indicators used in these studies. It was also noticed that the growth-accounting approach was not based on statistical theory, and hence, statistical models cannot be applied to evaluate its reliability, thus casting doubts on its results. Thus, the present suggests closing these gaps by providing a statistical analysis in the first step of the estimation to get the coefficients of the explanatory variables that are used by econometric approach. In addition, a second step plugs the parameters of the variables into the model of the above-
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mentioned divisia translog index approach to calculate the growth rates of productivity indicators, including the calculation of the residual of the model (TFP growth) and output growth that were used by the growth-accounting approach. This paper aims to study the impact of ICT on productivity of ASEAN5 plus 3. The details of this paper are as follows: ‘Methodology and estimation procedures’ section contains descriptions on the estimation methods employed in this paper. ‘Source of data’ section demonstrates details of the data. Results of the empirical analysis are explained in the ‘Results and discussion’ section. Finally, the last section presents the ‘Conclusion and policy implications’.
Methodology and estimation procedures In this study, the Cobb–Douglas production function estimation model and the Solow's residual model have been used as modified models to fill the gaps of both models, which had previously cast doubts on the results generated. The modified Cobb–Douglas production function in this research has followed Dewan and Kraemer [10]. This provides empirical evidence on the contributions of aggregate physical capital, employment and ICT to GDP growth for a group of developed and developing countries, including the ASEAN5 plus 3 countries. Production function is given in Eq. 1: GDPit ¼ FðKit; Lit; ICTit; TitÞ
ð1Þ
where for country i=1, 2,…, 8 in year t=1965–2006, the output GDPit is real annual GDP, and the inputs are real aggregate physical capital Kit, number of persons employed Lit, number of telephone lines per 1,000 ICTit and time Tit that proxies for TFP as a technological progress of these countries. The Cobb–Douglas production function for country i (i=1, 2,…, 8) in year t (t= 1965–2006) is given in Eq. 2: $lnGDPit ¼ a þ a $lnKit þ b ΔlnLit þ l $lnICTit þ "it
ð2Þ
where α β 1 a εit ln Δ
Is Is Is Is Is Is Is
the the the the the the the
output elasticity with respect to aggregate physical capital output elasticity with respect to aggregate labour output elasticity with respect to ICT intercept or constant of the model1 residual term2 log to transform the variables difference operator denoting proportionate change rate
εit is the random error term in the model, representing the net influence of all unmeasured factors. This is explained as the combination of the quality of the inputs involved those proxies for the TFP growth. 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 accounts for the technological progress of the economy through the quality of input terms.
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This model is based on the econometric estimation having gap of being based on the coefficients of the estimated explanatory variables (as a homogenous measure of the explanatory variables), and there is no calculation of contributions of productivity indicators of these explanatory variables. Moreover, this study effectively attempts to close the gap of the divisia translog index approach that was developed by Jorgenson et al. [15]. This approach, which involves explicit specification of a production function, has a major drawback such as inability to evaluate its reliability using statistical models, thus casting doubts on its results. Therefore, the current study provides a statistical analysis for estimating the coefficients of the explanatory variables that have been used for econometric approach (Eq. 2). These coefficients were substituted into the model (Eq. 3). The divisia translog index approach was then used to calculate the growth rates and the contributions of productivity indicators, which include the calculation of the residual of the model called TFP growth and the output growth that are used by growth-accounting approach. The paper endeavours to apply the conventional accounting growth framework as utilised by Stigler [40], Abramovitz [1], Kendrick [17] and Solow [38, 39], finally brought to fruition by Kendrick [18] and further refined by Denison [8, 9], Griliches and Jorgenson [14] and Jorgenson et al. [15]. Used by Lee and Khatri [24] and modified by Elsadig [11], this approach provides wider space for decomposition of contributions of factor inputs and technological change to economic growth. This study thus develops Cobb–Douglas production function into a growth-accounting framework. Since the intercept a in Eq. 2 has no position in the calculation of the productivity growth indicators, a second step was proposed, which calculates the growth rates of productivity indicators transforming Eq. 2 as an extension of the basic growthaccounting framework. The Cobb–Douglas production function is specified in the parametric form of the above equation as follows: $lnTFPit ¼ $lnGDPit ½a $lnKit þ b $lnLit þ l $lnICTit
ð3Þ
where the weights are given by the average value shares as follows: ΔlnGDPit α·ΔlnKit β·ΔlnLit 1ΔlnICTit ΔlnTFPit
Is Is Is Is Is
the the the the the
growth rate of output contribution of the aggregate physical capital contribution of the aggregate labour contribution of the ICT TFP growth
The framework decomposes the growth rate of GDP into the contributions of the rates of growth of the aggregate physical capital, labour and ICT plus a residual term typically referred to as the growth rate of TFP.
Sources of data The data for this paper were collected from various sources. Real GDP, real aggregate fixed capital, number of employment and number of telephone lines per 1,000 were collected from Asian Development Bank: Key indicators of developing Asia and Pacific countries, Statistical and Data Systems Division, and international financial statistics of International Monetary Fund, yearbook, as well as from the individual country databases,
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World Development indictors of the World Bank and the International Labour Organisation for the period of 1965–2006. Due to lack of data on man-hours of work, the labour input index is constructed based on the number of persons employed.
Results and discussion Autoregressive estimator has been applied to Eq. 2 of the model being generated from Cobb–Douglas production function to measure the shift in the production functions of ASEAN5 plus 3. An annual time series data over the period of 1965–2006 for GDP, aggregate psychical capital, number of employment and number of telephone lines per 1,000 have been employed for the individual countries. In view of the fact that the model used in this study is specified in first differences and the calculated growth rates are used in the discussion of results and findings of the study, the model is found to be stationary. In addition, Table 1 presents the results of the unit root tests conducted. Likewise, Engle and Granger [12] state that if economic relationships are specified in first differences instead of levels, the statistical difficulties due to non-stationary variables can be avoided because the differenced variables are usually stationary even if the original variables are not. Analysis of the data using Eq. 2 has shown that the estimated coefficients of the explanatory variables of the model are mainly significant at 5% and 10% levels. According to Durbin-H values, the model has no problem of autocorrelation (Table 2). Table 1 Results of the Phillips–Perron (PP) unit root test first difference Country
GDP
Capital
Labour
ICT
China
−6.26a
−6.13a
−6.32a
−3.64a
−6.25
−6.15
−6.24
−3.74b
−3.34
−4.00
−7.17
−3.46a
−3.89
−4.59
−7.07
−3.59b
−1.53
−2.42
−4.75
−3.61a
−3.67
−3.72
−6.01
−3.83b
−2.30
−3.65
−6.14
−5.11a
−3.90
−4.81
−6.06
−6.58a
−5.16
−4.08
a
−6.34
−3.54a
−5.11b
−4.13b
−6.26b
−3.89b
−4.91
−4.37
−6.26
−3.66a
−5.50
−4.82
−6.19
−3.52a
−3.46
−2.92
−6.07
−3.51a
−4.31
−3.78
−6.29
−3.45b
−3.51
−3.48
−6.27
−3.31a
−3.67
−3.55
−6.25
−3.08b
b
Indonesia
a b
Japan
a b
Korea
a b
Malaysia Philippines
a
a b
Singapore
a b
Thailand
a b
b a b a b a b a
a b a b a b
Figures are t test values showing significance at 1%, 5% and 10% a
Constant without trend
b
Constant with trend
b a b a b a b
a b a b a b
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Table 2 Estimated coefficients of ASEAN5 plus 3, 1965–2006 Country
Intercept
Capital
1.89 (2.81)b
Labour
ICT
Adjusted
D-W
D-H
0.53 (17.4)b
0.30 (3.9)b
b
0.17 (3.59)b
0.91
1.95
0.65
1.89 (0.89)
0.59 (1.96)
0.21 (1.60)
0.20 (1.65)a
0.94
1.95
0.66
Japan
−0.21 (−0.45)
0.61 (14.8)b
0.19 (3.41)b
0.20 (1.69)a
0.95
1.96
0.56
Korea
−0.41 (−2.29)b
0.56 (18.3)b
0.24 (2.81)b
0.20 (2.33)b
0.92
1.90
0.53
Malaysia
−3.25 (−7.29)b
0.63 (14.4)b
0.10 (3.21)b
0.27 (3.96)b
0.90
1.96
0.47
Philippines
−0.53 (−0.36)
0.36 (4.44)b
0.29 (2.39)b
0.35 (2.35)b
0.95
1.95
0.39
b
a
b
China Indonesia
a
Singapore
2.30 (1.70)
0.46 (3.78)
0.24 (1.94)
0.30 (1.96)
0.93
1.85
0.49
Thailand
5.82 (4..81)b
0.52 (3.12)b
0.28 (1.68)a
0.20 (1.45)
0.94
1.96
0.62
Figures in parentheses are t values Figures in Table 2 were estimated using Eq. 2 AdjustedR2 a Significant at 10% level b Significant at 5% level
Empirical analysis Analysis was carried out to compare the productivity indicators between the ASEAN5 plus 3 economies for the entire period of 1965–2006. In order to study the effect of governments' policies in improving the productivity growth, the study period was divided into two phases. These phases, which corresponded to the major policy changes, were 1965–1987 and 1988–2006. The period of the 1960s and 1970s witnessed the labour-driven policies in these countries and the birth of a new era of export-oriented economies. The decades of 1980s, 1990s and 2000s saw a further diversification of the economies of these countries into more advanced industries through investment-driven policies and trade liberalisation that had attracted foreign direct investment (FDI) which was brought to these countries through Transnational Corporations (TNCs) investment. As a result of these polices, the range of economic activities and sources of growth had become more diversified. During these decades, the economic structural transformation took place in most economies of these countries. The manufacturing sector became the engine of growth in these countries. Finally, it includes the period of 1988–2006, i.e. the period of pre- and post-Asian financial crisis of 1997. The use of TFP 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 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 and economies of scale [31]. However, the contribution of TFP growth to the economies of these countries in terms of average annual productivity growth was very low except Japan, which was 3.62% in the entire period, 3.88% and 4.71% for the sub-periods, respectively (Table 3). Comparing the Japan and Korea models of economic development with other Asian countries, the TFP contribution of this study showed that Japan and somehow Korea had developed
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Table 3 ASEAN5 plus 3 productivity indicators (in percentage) Country
GDP
Capital
Labour
ICT
TFP
China 1965–2006
8.33
11.2
10.1
1.56
1.92
1965–1987
9.81
10.6
11.3
1.65
1.91
11.8
10.3
2.80
1.94
10.2
10.9
1.56
0.76
1988–2006
10.1
Indonesia 1965–2006
5.92
1965–1987
7.29
1988–2006
5.34
11.7
1.65
0.78
11.9
8.88
11.2
2.80
0.96
Japan 1965–2006
11.2
10.8
3.97
3.62
1965–1987
10.4
9.43
10.6
11.8
5.40
3.88
1988–2006
10.1
11.81
12.0
5.61
4.71
Korea 1965–2006
9.28
9.60
1965–1987
9.37
8.03
1988–2006
10.8
11.7
3.11
2.40
10.6
9.19
3.12
2.46
11.2
3.18
3.2 0.93
Malaysia 1965–2006
6.58
9.21
5.93
2.45
1965–1987
6.72
8.11
6.67
2.46
0.85
1988–2006
7.34
10.7
7.28
2.80
0.94
1965–2006
7.93
10.18
6.78
1.45
0.91
1965–1987
8.29
8.09
7.65
1.98
0.85
1988–2006
6.36
11.9
7.92
2.08
0.95
1965–2006
8.19
10.1
7.29
2.41
1.54
1965–1987
9.29
7.97
3.00
1.69
1988–2006
8.34
11.8
8.83
4.01
1.85
1965–2006
7.98
10.1
7.50
2.07
0.92
1965–1987
8.29
7.85
2.56
0.89
1988–2006
7.23
8.13
3.23
0.91
Philippines
Singapore 8.89
Thailand 8.89 11.9
Figures in Table 3 were calculated using Eq. 3
real productivity with technological progress, while other Asians countries gained the chance to develop their economies through input-driven process without making significant technological progress. Korea has developed significant knowledge stoke that enabled the development of such companies as Daewoo, Samsung and LG that compete globally. The highest contribution of GDP by including the proxy of ICT in the model to the productivity growth of the ASEAN5 plus 3 was observed during the sub-periods of
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1987–2006 and 1988–2006 (Table 3). The sub-period of 1965–1987 was found to be a combined period of labour- and investment-driven policies. On the other hand, the sub-period of 1988–2006 was the perceived period of investment driven with particular focus on ICT and human capital development. As a result, the performance of the economies of these countries was rapid compared with the period before the transformation of these economies into investment driven, which was supported by FDI. The TFP growth contribution was low and not the highest one to contribute to the economy's productivity growth. The reasons behind that were the economic recession of 1973, 1985, the financial crisis of 1997, the quality of human capital and the technology involved in the production of these economies except Japan and Korea. The highest contribution of aggregate physical capital to GDP in terms of average annual productivity growth of the ASEAN5 plus 3 was during the sub-period of 1987– 2006. Likewise, the contribution of aggregate labour to GDP in terms of average annual productivity growth of these countries was fair during all the periods of the study (Table 2). This reflects the comparative advantage in unskilled labour-intensive goods that eventually helped to attract FDI in the latter half of the 1980s. These countries accelerated trade liberalisation policies and drastically eased restrictions with respect to capital ownership of foreign companies. That fostered the significant increase of global capital. Unlike the case of the US ‘productivity paradox’, by examining the role of ICT to achieve knowledge-based economy through the contribution of TFP growth, it was found from the results that there was a positive contribution of ICT to TFP growth of the economies of these countries during all the periods of study (Table 3). Meanwhile, FDI is considered to be the source of technology transfer to these countries through TNCs investment.
Conclusion and policy implications This study claims to fill in the gaps of previous studies by providing a statistical analysis in the first step of the estimation to attain the coefficients of the explanatory variables that have been used by econometric approach. It can be reiterated here that, in addition, a second step plugs the parameters of the variables into the model in order to compute the growth rates of productivity indicators including the calculation of the residual of the model (TFP) and output growth being used by growth-accounting approach. The results show that the productivity growth of ASEAN5 plus China is input driven, but rather, productivity growth-driven along with Japan, and somehow, Korea achieve productivity-driven when the results of TFP growth were compared. The study also finds that the impact of ICT is positive with little contribution of TFP growth. These findings are in line with the findings of the studies undertaken by Young [43, 44] and Kim and Lau [19], in which the authors state that other Asian newly industrialised countries' productivity was input driven. Sarel [35] also expressed concerns that some East Asian countries may face the same fate as the Soviet Union. His perception bears reasonable assumptions as these countries invested primarily in labour and capital rather than in technology over the past few decades, and there was no real technological drive that can sustain the progress of the industrial development. According to Krugman [22], the high growth rates in East Asian are, however, not
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sustainable because Asian growth has come primarily from increases in the amount of labour and capital rather than in TFP (i.e. knowledge and technical change). At some point, according to his argument, it will no longer be possible to continue raising levels of capital and labour. Consequently, East Asian growth rates must eventually fall in the absence of improvements in TFP. Meanwhile, the results of this study are expected to be useful for ICT policy formulation. In this context, a comparison of the contributions of ICT to productivity growth in the ASEAN5 plus 3 economies provides a guideline for the policy makers to formulate appropriate national, regional and international ICT 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 ASEAN5 plus 3 can capitalise on its synergy with the other nations and make full use of the competitive advantages in these countries to overcome its insufficiencies. In that case, ASEAN5 plus 3 will be able to accelerate the movement towards becoming technology-savvy nations.
References 1. Abramovitz M (1956) Resource and output trends in the United States since 1870'. Am Econ Rev 46 (2):5–23 2. Asian Productivity Organisation (APO) (1990) Information Technology-Led Development, Tokyo 3. Ark B (2001) The renewal of the old economy: an international comparative perspective. OECD Science, Technology and Industry: Working Papers, 2001/5, OECD Publishing, Paris 4. Arthur WB (1994) Increasing returns and path dependence in the economy. University of Michigan Press, Ann Arbor 5. Arthur WB (1996) Increasing returns and the new world of business. Harvard Business Review, July– Aug, pp 100–109 6. Bank Negara Malaysia (1999) Annual report. Government of Malaysia Printers, Kula Lumpur 7. Bosworth B, Triplett J (2000) What's new about the new economy? IT, economic growth and productivity. Brooking Institution, Washington. Available at: http://www.brook.edu/views/papers/ bosworth/20001020.htm 8. Denison EF (1962) The sources of economic growth in the United States and the alternative before use. A supplementary paper. Committee for Economic Development, New York, pp 229–255 9. Denison EF, Edward P (1979) Accounting of slower economic growth; the United States in the 1970s. The Brooking Institution, Washington 10. Dewan S, Kraemer KL (2000) Information technology and productivity: evidence from country-level data. Manage Sci 46(4):548–562 11. Elsadig MA (2006) ICT and human capital role in achieving knowledge-based economy: applications on Malaysia's manufacturing. J Inf Knowl Manage 5:117–128 12. Engle RF, Granger, CWJ (2003) Time-series econometrics: cointegration and autoregressive conditional heteroskedasticity. Advanced Information ON THE Bank of Sweden Prize in Economic Sciences in Memory of Alfred Nobel 13. Gordon RJ (2000) Does the ‘new economy’ measure up to the great inventions of the past? J Econ Perspect 14:49–74 14. Griliches Z, Jorgenson DW (1962) Capital theory: technical progress and capital structure, sources of measured productivity change capital input. Am Econ Rev 52(1):50–61 15. Jorgenson DW, Gollop FM, Fraumenri B (1987) Productivity and US economic growth. North Holland Publishing, Amsterdam 16. Jorgenson DW, Stiroh KJ (2000) Raising the speed limit: US economic growth in the information age. Brookings Papers on Economic Activity: 1. Brookings Institution, Washington, pp 161–167 17. Kendrick JW (1956) Producative trends capital and labour. Rev Econ Statistics 38(3):248–257 18. Kendrick JW (1961) Productivity trends in the United States. Princeton, Princeton
J Knowl Econ (2010) 1:191–201
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19. Kim, J.L. and Lau, L.J. (1994) The Sources of Economic Growth of the East Asian Newly Industrialised Countries. Journal of the Japanese and International Economics 8:235–271 20. Kraemer KL, Dedrick J (1994) Payoffs from investment in information technology: lessons from Asia-Pacific region. World Dev 22(12):1921–1931 21. Kraemer KL, Dedrick J (1999) Information technology and productivity: results and policy implications of cross-country studies. Working Paper: #PAC-144. Center for Research on Information Technology and Organizations, University of California 22. Krugman P (1994) The myth of Asia's miracle. Foreign Affairs, Nov/Dec, pp 62–78 23. Lau LJ, Tokutsu I (1992) The impact of computer technology on the aggregate productivity of the United States: an indirect approach. Working Paper. Department of Economics, Stanford University, Stanford 24. Lee IH, Khatri Y (2003) Information technology and productivity growth in Asia. IMF Working Paper WP/03/15. International Monetary Fund, Washington 25. Loveman GW (1988) An assessment of the productivity impact of information technologies. Working Paper No. 88-054, Management in the 1990s Project. MIT Sloan School, Cambridge 26. Mody A, Dahlman C (1992) Performance and potential of information technology: an international perspective. World Dev 20(12):1703–1719 27. Oliner SD, Sichel DE (1994) Computers and output growth revisited: how big is the puzzle? Brookings Pap Econ Activ 2:273–333 28. Oliner SD, Sichel DE (2000) The resurgence of growth in the late 1990s: is information technology the story. J Econ Perspect 14:3–22 29. Pilat D, Lee F (2001) Productivity growth in ICT-producing and ICT-using industries: a source of growth differentials in the OECD? STI Working Paper, OECD, Paris 30. Rahim SA, Pennings AJ (1987) Computerization and development in Southeast Asia. Asian Mass Communications Research and Information Center, Singapore 31. Rao PS, Preston RS (1984) Inter-factor substitution, economic of scale, and technical change: evidence from Canadian Industries. Empirical Econ 9(4):247–262 32. Roach S (1987) America's technology dilemma: a profile of the information economy. Morgan Stanley, New York 33. Roach S (1988) White-collar productivity: a glimmer of hope? Morgan Stanley, New York 34. Romer PM (1990) Endogenous technological change. Journal of Political Economy, 98, 5, Part 2, Reprinted. In: Buchanan JM, Yong JY (eds) The return to increasing returns. University of Michigan Press, Ann Arbor, pp 287–318 35. Sarel M (1996) Growth in East Asia: what we can and cannot infer. IMF Working Paper 95/98. International Monetary Fund, Washington 36. Schumpeter JS (1939) Business cycles: a theoretical, historical and statistical analysis of the capitalist process. McGraw Hill, New York 37. Sichel D (1997) The computer revolution: an economic perspective. Brookings Institution, January 38. Solow RM (1956) The production function and the theory of capital. Rev Econ Stud 21:101–108 39. Solow RM (1957) Technical change and the aggregate production function. Rev Econ Stat 39:312– 320 40. Stigler, George J (1947) Trends in output and employment. National Bureau of Economic Research, New York 41. Strassman P (1997) Computers are yet to make companies more productive. Computerworld, 15 Sep 42. Wong P (1994) Productivity impact of IT investment in Singapore. Proceedings International Conference on Information Systems, Vancouver, 14–17 Dec 1994 43. Young A (1992) A tale of two cities: factor accumulation and technological change in Hong-Kong and Singapore. National Bureau of Economic Research Macroeconomics Annual, pp 13–54 44. Young A (1995) The tyranny of numbers: confronting the statistical realities of the East Asian growth experience. Q J Econ 110:641–680 45. Zukime M (2004) Predicting GDP growth in Malaysia using knowledge-based economy indicators: a comparison between neural network and econometric approaches. Sunway Coll J 1:39–50
