The Impact of ICT on East Asian Economic Growth: Panel Estimation Approach

Transcription

1 PROSIDING PERKEM VII, JILID 1 (2012) ISSN: X The Impact of ICT on East Asian Economic Growth: Panel Estimation Approach Elsadig Musa Ahmed elsadigmusa@yahoo.com, asadiq29@hotmail.com Economics Unit Faculty of Business and Law Multimedia University Rahim Ridzuan aimaz84@yahoo.com Faculty of Business Management Universiti Teknologi Mara, Malacca Campus,Malaysia. ABSTRACT This paper tests the impact of information and communications technology (ICT) on economic growth for ASEAN5+3 countries consisting of Malaysia, Thailand, Singapore, Indonesia, Philippines, Japan, Korea and China using panel data set from In this paper, we investigate the impact of ICT on economic growth (GDP) based on standard production function consisting of capital (CAP) and labour (LAB), (number of employment as proxy of human capital), as independent variables. Telecommunications investment (TELINT) is used as a proxy to gauge the contribution of ICT as new independent variable to output growth in ASEAN5+3 countries. The study divided the ASEAN5+3 countries into two groups; ASEAN5 and ASEAN5+3 countries. We employ panel data set using various test such as panel unit root test, panel cointegration, Hausman test and Generalized Least Squares method in order to detect the relationship between the dependent variable (GDP) and independent variables (CAP,LAB and TELINT) for ASEAN5+3 countries. The existence of long run relationship between GDP and factor of production (capital, labour and telecommunications investment) is proven from the panel cointegration test for ASEAN5. Based on fixed effect for model 1 (ASEAN5) and random effect for model 2 (ASEAN5+3), the paper found out that labour, capital and telecommunications investment have positive relationship towards GDP. Thus, the study concludes that ICT has played an important role as engine of growth for sustainable development in ASEAN5 and ASEAN5+3 countries. In this context, ASEAN leader should take this opportunity from the formation of ASEAN5+3 to widen their co-operation with China, Japan and Korea throughout knowledge sharing especially in the area of ICT. Keywords: ICT, economic growth, panel data, ASEAN5+3 INTRODUCTION The importance of technology to economic development has long been recognized. This may be especially true in Information and Communications Technology (ICT), in which they offer the potential for increasing availability of information, new means of communications, re-organization of productive processes and improving efficiency in many different economic activities (I-Ways, Digest of Electronic Commerce Policy and Regulation, 2005). The importance of ICT use is becoming amplified by the process of economic globalization, which puts a premium on information and communications systems as the means to achieve linkages to international markets and global production networks (Kraemer and Dedrick, 1999). ICT has been an important drive of growth and productivity since 1990s. Recent improvements in growth and productivity performance in developed countries such as Japan, South Korea and Singapore has been linked to the expansion of the production of ICT goods. Of course, ASEAN countries which mostly consist of developing countries do not want to miss this golden opportunity in producing more related ICT product for exports. This rapid development in ICT industries has lead to a big bubble in the world economy especially in the US economy. In 2001, the bubble burst and the world economy turned its face back to brick-and-mortar economy. The ICT revolution has indeed created huge changes on the real economy. According to Hornik (1990), telecommunications can be considered as a complement to development. Using telecommunications, Persidangan Kebangsaan Ekonomi Malaysia ke VII (PERKEM VII), Transformasi Ekonomi dan Sosial Ke Arah Negara Maju, Ipoh, Perak 4 6 Jun 2012

2 672 Elsadig Musa Ahmed, Rahim Ridzuan the benefits from the development policies can be rapidly facilitated and distributed throughout economy. Many economists consider ICT as a general-purpose technology due to its all-encompassing character in which it has already become an indispensable part of production of goods and services in various industries. According to Erdil et al (2009), there are two important channels by which ICT can have real effect on real economy; production of ICT and the use of ICT from various industries. Firstly, the ICT sector itself has quickly become an important industry at global level that coincided with the growth of the service industries. Since our scope of studies is base on ASEAN-5+3 countries, the best example that can explain the above scenario is that ICT industries in Singapore, Malaysia, Japan, China and Korea that has shown significant benefits towards their economy. These countries have been producing and exports various ICT related products such as processors, RAMs, hard disks, motherboards, desktops, notebook and super computer for other ASEAN countries. Secondly, the ICT revolution has contributed significantly to the whole economy by rising productivity. First, ICT increases labour productivity in ICT-using industries by simply making labour to produce more or work more efficiently (c.f., van Ark et al. (2003) and Matteucci (2005)). ICT makes physical capital to be more productive (c.f., Roller and Waverman (2001)). There is a great deal of debate among leaders in developing countries as well as in international development community about the potential of ICT in improving productivity growth. Countries worldwide are investing their resources into their ICT infrastructure despite the widely acknowledged productivity paradox, which states that there is no correlation between ICT spending and productivity growth. Evaluating the contribution of ICT investments has become an elusive but important goal of researchers and economists. While the literature on ICT and economic growth is abundant, much of the work is based on the experience of the developed countries. There have not been many studies conducted on countries in ASEAN. In our studies, we have included Japan, Korea, and China to the group of original ASEAN-5 in order to see and compare any linkages between ICT and economic growth. Besides, previous studies that used dynamic panel data estimation is also known as Generalised Method of Moments (GMM) since they were running the test for a large group of countries. Only few journals have adopted generalized least square method that has been taken into account to none effect, fixed effect and random effect model for this studies. Therefore, another contribution of this paper is to fill this gap by applying generalized least squares method. The organization of the paper is as followed. Section 2 is filling in with literature review of the studies. Section 3, introducing the model used for this paper and proceed with empirical methodology. The sources of data are presented in section 4. Section 5 will be describing the results and analysis. Section 6 will be providing the conclusion of this paper. LITERATURE REVIEW ICT has long been held to contribute to economic growth through productivity improvement, but earlier studies of ICT investments and economic growth has found no significant relationship. Baily (1986), Baily and Gordon (1988), Loveman (1988), Roach (1987 and 1988), and Strassman (1997) showed that productivity gains from IT in the aggregate economy have been limited, despite the rapid improvement in price-performance ratio of computers and heavy investment in IT. This argument was based in part of the fact that the United States invested heavily in IT 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 IT should play in various development projects. Some believe that there are strong payoffs from IT projects while others remained skeptical (Rahim and Pennings, 1987; APO, 1990; Mody and Dahlman, 1992). However, there is a growing consensus among economic growth theorists and development specialists that technology innovation and diffusion can play a critical role in stimulating economic growth and productivity (Kraemer and Dedrick, 1999). Early proponents of this view included Schumpeter (1939), Abramovitz (1956), Kendrick (1956) and Solow (1957). Economists such as Arthur (1994) and Romer (1990) have emphasized technological innovation in explaining economic growth and productivity gains. Romer (1990) argued that economic growth and technological change are inextricably linked. Thus, widespread technology diffusion creates the possibility for increasing returns to investment (Arthur, 1996). The literature concerning the linkages between ICT investment and economic growth is vast and diverse. The focus of the initial studies such as Oliner and Sichel (2000) was focusing at the United

3 Prosiding Persidangan Kebangsaan Ekonomi Malaysia Ke VII States, in which the impact of information and communications technology (ICT) on growth seemed most manifested and for which the required data were available. Subsequently, Goldman (2000), Daveri (2000), Bassanini et al (2000), and Cardarelli (2001) have extended the investigation of the impact of ICT on growth to Europe, Japan, and Australia. Previously, most researchers have adopted two different measurement methods namely, the production function approach and the growth accounting framework. Solow (1957) employed the aggregate production function to measure the contribution of technological change on economic growth in US firms over the period and concluded that economic growth in the US during the sample period was largely attributed to the technological change factor rather that the labour or capital factor. Solow s construction sparked wide debate among economists regarding the impact of technological change on output growth, largely because of the assumption that technology is exogenous to output growth. Datta and Agarwal (2004) investigated the long run relationship between telecommunications infrastructure and economic growth, using data from 22 OECD countries. These authors employed a dynamic panel data method for the estimation, which corrected for the omitted variables bias of single equation cross-section regression. The study concluded a significant and positive correlation between telecommunications infrastructure and growth, after controlling for a number of other factors. Roller and Waverman (2001) examined the data on telephone penetration for 36 countries over a 20 years period from They showed that it significantly enhanced economy-wide output, allowing for the fact that the demand for telecoms itself is positively related to GDP. Roller and Waverman (2001) also demonstrated that the scale of impact of the increased penetration of telecoms networks on growth depended on the initial level of penetration. Poh (2001) investigated the impact of ICT investment on overall productivity in Singapore from by estimating the Cobb-Douglas production function that decomposed capital stock into an ICT component and non-ict component. The estimated result showed that ICT capital has generated a rate of return that significantly exceeded the return on non-ict capital. Overall, the study concluded that ICT investment in Singapore has provided significant payoff in terms of productivity gain. Niininen (1998) used the neoclassical growth accounting framework to examine how investment in computer contributes to economic growth in Finland and concluded that compared to other inputs, ICT exerts a strong influence on real growth in output. In a related study, Lau and Tokutsu (1992) estimated a translog unit-cost function with three inputs, namely, computer capital, noncomputer capital, and labour to investigate the contribution of ICT investment to economic growth in the United States for the period The study concluded that nearly half of the growth in aggregate national output in the United State was attributed to computer capital rather than noncomputer capital and labour. Studies conducted in the late 1980s and early 1990s have reported negative or mixed results on the contribution of ICT investment to economic growth in developed countries (Roach, 1987; Strassman, 1990; Morrison and Berndt, 1991; Berndt et al., 1992; Loveman, 1994). However, recent studies by Lichtenberg (1995), Brynjolffson and Hitt (1995), Lehr and Lichtenberg (1999), and Oliner and Sichel (2000) have established that ICT investment yielded higher returns than non-ict investment in the developed countries. Studies of developing countries have found an insignificant relationship between ICT investment and economic growth (Dewan and Kraemer, 2000; Plice and Kraemer, 2001; Ng and Chang, 2003). Overall, the literature shows that most developed countries experienced significant returns from ICT investment. Many studies have been conducted to analyze the contribution of ICT sector to the total factor productivity growth of the US and OECD economies. Among others is the study by Pilat and Lee (2001), 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 (2001) found that productivity growth differentials between the United States and several European countries are at least partly explained by a larger and more productive ICT producing sector in the United States. Jorgenson and Stiroh (2000) and Oliner and Sichel (2000) found that one of the most important sources of TFP growth in US in the 1990s was from ICT producing industries. Oliner and Sichel (2000) and to a lesser extent, Jorgenson and Stiroh (2000) 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 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

4 674 Elsadig Musa Ahmed, Rahim Ridzuan contrast, Gordon (2000) and Bosworth and Triplett (2000) adopted 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 (2000) focused on the cyclical component of the US labour productivity surge, suggesting that half of the acceleration after 1995 has been a cyclical phenomenon. Studies by Brynjolfsson and Hitt (1996), Lichtenberg (1995), and Dewan and Min (1997) analyzed firm-level US data and found evidence of positive and significant returns from IT capital investment. Kraemer and Dedrick (1999) dictated that the empirical research at the firm level is encouraging, but not sufficient to answer the productivity paradox. The firm level research only captures gains and losses of individual firms and not net gains to the economy. In addition, the firm level research has focused exclusively on large corporations, and therefore might not represent the entire economy (Kraemer and Dedrick, 1999). The fact that a certain set of companies has shown high returns to investment in IT does not mean that these gains are translated into productivity improvement at the national level (Kraemer and Dedrick, 1999). As argued by Sichel (1997), the impacts are mostly redistributional with the gains of some firms coming at the expense of their competitors. Hence, it is important to conduct country-level analyses and inter-country analyses. Another stream of research has examined economy-level time series data to quantify the contribution of IT toward output growth of a single country, such as the US or Singapore (Lau and Tokutsu, 1992; Oliner and Sichel, 1994; Wong, 1994; Sichel, 1997), with mixed findings on the contributions of IT. The 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 (Dewan and Kraemer, 2000). Furthermore, capital and labour inputs for a single country tend to move together and with the scale of the economy, making it difficult to obtain robust results (Dewan and Kraemer, 2000). In order to minimize this problem and to draw meaningful conclusions about the impact of IT investment at the country level, it is necessary to conduct empirical studies of multiple countries over time. THE STANDARD PRODUCTION FUNCTION MODEL Based on standard production function, we introduce new variable known as telecommunications investment (TELINT) as one of the determining variables for output growth (GDP) for ASEAN5+3 countries. In this paper, gross domestic product is represented by the production function as below:- GDP it = f ( CAP it, LAB it, TELINT it ) (1) where at period t and country I, GDP refer to gross domestic product, LAB is labour, CAP is capital and TELINT is telecommunications investment. To test the stationarity of each variable, we used the log form of the variables. Log transformation can reduce the problem of heteroscedasticity because it compresses the scale in which the variables are measured, thereby reducing a tenfold difference between two values to twofold difference (Gujarati, 1995). The new model will be as followed:- LnGDP = α LnCAP it + 2 LnLAB it + 3LnTELINT it + v it + u it (2) where LnGDP is log form for gross domestic product, LnCAP is log form for capital, LnLAB is log form for labour and LnTELINT is log form for telecommunications investment. We allowed for the time and country effect as our model utilized the panel estimation technique. Incorporating this technique also allow for fixed and random effects. The fixed effect assumed constant error of each ASEAN5+3 countries. The panel data estimation for v and u assumed that each country has its own behavior and influenced by different factors represented by the slope and intercepts that is constant across countries and time. In this paper, we divided the ASEAN5+3 countries into two groups. The first group is for the original member of ASEAN countries also known as ASEAN5 while the second group is for ASEAN5+3 countries. Both group applied the same model introduced above and running through all the panel estimation analysis.

5 Prosiding Persidangan Kebangsaan Ekonomi Malaysia Ke VII SOURCES OF DATA These studies used annual panel data for ASEAN5+3 countries namely Malaysia, Thailand, Singapore, Indonesia, Philippines, China, Japan and Korea from the period 1975 until The variables are real gross domestic product (GDP), labour (LAB) (number of employment as proxy of human capital), real capital (CAP) and real ICT-investment (TELINT). The data 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 in addition to the World Development Indicators, the individual countries databases and the International Labour Organization. RESULTS AND ANALYSIS Recent studies on panel data analysis have become more popular among researchers. Panel data sets provide multiple advantages over cross-section and time series data set. In this paper, we had begun the analysis by testing the stationarity of the available data using panel unit root test. Once the stationarity of all data is detected, we proceeded with panel cointegration test. Here, we were able to see if there exists a long run relationship between the variables. Next, we analyzed the descriptive statistic of the data to establish that the OLS estimation was not fit for running panel data estimation. We proceeded with Generalized Least Squares (GLS) estimation since it can consider three different model known as none effect model, fixed effect model and random effect model. In order to determine the type of our model, we used Hausman Test. The Hausman test is a test of the significance of the difference between the fixed effects estimates and the random effects estimates. Once we know the true form of the model, we made a brief analysis base on the result that we derive from the GLS estimation. The empirical result of panel unit root First of all, panel unit root test were used to examine the stationarity properties of the variables. The use of panel-based tests is necessary because the power of standard or conventional time series unit root tests may be quite low given the sample sizes and time spans typically available in economics. While for panel data, it is found out that the power of the test increases with an increase in the number of cross-sections. The results are separated into two tables. The first table is for model 1 while the second table is for model 2. TABLE 1: Results of panel unit root tests for Model 1 (ASEAN5) Method Test Test Statistic Level Significance level for rejection Test Statistic First Difference Significance level for rejection Null : unit root (assumes common unit root process) Levin, Lin and Chu (2002) t*-statistics 1. LnGDP i t * LnCAP i t * LnLAB i t * LnTELINT i t * Breitung (2000) t*-statistics 1. LnGDP i t * LnCAP i t * LnLAB i t * LnTELINT i t * * Null : unit root (assumes individual unit root process) Im, Pesaran and Shin (2003) W-Statistic 1. LnGDP i t * LnCAP i t * LnLAB i t * LnTELINT i t * Maddala and Wu (1999) and Choi (2001) ADF-Fisher Chi-Square 1. LnGDP i t * 0.000

6 676 Elsadig Musa Ahmed, Rahim Ridzuan 2. LnCAP i t * LnLAB i t * LnTELINT i t * PP-Fisher Chi-Square 1. LnGDP i t * LnCAP i t * LnLAB i t * LnTELINT i t * Null : no unit root (assumes common unit root) Hadri (2000) Z-Statistics 1. LnGDP i t 4.304* LnCAP i t 3.811* LnLAB i t 4.760* LnTELINT i t 2.672* A* indicates the rejection of the null hypothesis of non-stationary (Levin, Lin and Chu (2000), Breitung (2000), Im, Pesaran an Shin (2003), Fisher-Type test using ADF and PP-Test (Maddala and Wu (1999) and Choi (2001)) or stationary (Hadri (1999)) at least at the 5% level of significance. TABLE 2: Results of panel unit root tests for Model 2 (ASEAN5+3) Method Test Level Significance Test Statistic level for rejection First Difference Significance Test Statistic level for rejection Null : unit root (assumes common unit root process) Levin, Lin and Chu (2002) t*-statistics 1. LnGDP i t * LnCAP i t * * LnLAB i t * LnTELINT i t * Breitung (2000) t*-statistics 1. LnGDP i t * LnCAP i t * LnLAB i t LnTELINT i t * Null : unit root (assumes individual unit root process) Im, Pesaran and Shin (2003) W-Statistic 1. LnGDP i t * LnCAP i t * LnLAB i t * LnTELINT i t * Maddala and Wu (1999) and Choi (2001) ADF-Fisher Chi-Square 1. LnGDP i t * LnCAP i t * LnLAB i t * LnTELINT i t * PP-Fisher Chi-Square 1. LnGDP i t * LnCAP i t * LnLAB i t * LnTELINT i t * Null : no unit root (assumes common unit root) Hadri (2000) Z-Statistics 1. LnGDP i t 6.385* * LnCAP i t 5.664* LnLAB i t 6.123* LnTELINT i t 3.539* * 0.001

7 Prosiding Persidangan Kebangsaan Ekonomi Malaysia Ke VII A* indicates the rejection of the null hypothesis of non-stationary (Levin, Lin and Chu (2000), Breitung (2000), Im, Pesaran an Shin (2003), Fisher-Type test using ADF and PP-Test (Maddala and Wu (1999) and Choi (2001)) or stationary (Hadri (1999)) at least at the 5% level of significance. The empirical results of Panel Unit Root test Five standard method tests for panel data are tested namely Levin et al (2002), Breitung (2000), Im et al (2003), Fisher-Type test using ADF and PP-Test (Maddala and Wu (1999) and Choi (2001)) and Hadri (2000) for testing the panel unit root for all variables. All variables that have unit root is categorized as I(0). In order to proceed with panel cointegration test, all variables must take first differencing in order to get I(1) data which mean that the variable accept the alternate hypothesis of no unit root. Table 1 presents the results of panel unit root at level and after first difference for model 1 which consists of the original member of ASEAN countries which is also known as ASEAN5. At level, the Levin et al (2002) method test indicates that LnGDP it, LnLAB it, LnCAP it, and LnTELINT it have a unit root at I (0). The Breitung (2000) method indicates that all variables fail to reject the null of unit root except for LnTELINT it where the evidence of stationary was high for this variable even at level. The Im et al (2003) method as well as Maddalla and Wu (1999) and Choi (2001) also indicated that all variable have a unit root. Hadri (2000) method indicated that LnGDP it, LnCAP it, LnLAB it, and LnTELINT it fail to reject the null hypothesis of no unit root and therefore we can classify it as I (1). After first difference, the first five standard methods show that the data are stationary at I (1) while as for Hadri (2000) method, all variables are not stationary or I (0). These results confirmed that the model meet the closest requirement to proceed with panel cointegration test. Table 2 illustrates the results of panel unit root for model 2 which consists of ASEAN5+3 countries at level and after first difference. At level, the Levin et al (2002) method test indicated that LnGDP it, LnLAB it and LnTELINT it have a unit root while LnCAP it has a significant level for the rejection of a unit root. The Breitung (2000) method indicated that all variables fail to reject the null of unit root. The Im et al (2003) method as well as Maddalla and Wu (1999) and Choi (2001) also indicated that all variable have a unit root. Hadri (2000) method test indicated that LnGDP it, LnCAP it, LnLAB it, and LnTELINT it stationary at 1 (1) as their significance level fail to reject the null hypothesis of no unit root. After first difference, the first four methods show that all variables are able to reject the null hypothesis of unit root and therefore it is categorized as I (1). Hadri (2000) method indicates that LnGDP it and LnTELINT it still stationary at I (1) even after first difference. Therefore, model 2 fails to meet the optimum requirement to proceed with the panel cointegration test due to the evidence of stationarity on certain variables at level and after first difference for Levin, Lin and Chu (2002) as well as Hadri (2000) method. The empirical result of Panel Cointegration test for Model 1 (ASEAN5) The next step is to investigate whether some linear combination of these variables can be described as stationary. The test for cointegration developed by Pedroni (1999) is the panel analogue of Engle and Granger (1987) test for the pure time series case. As such, they are based on the residual of the static (cointegrating) regression and they test the null of no cointegration. As with unit roots tests, the application of cointegration test to panel data increases the power of the test especially when the series are of moderate length. Panel cointegration techniques are intended to pool information regarding common long-run relationship across the panel while allowing the short-run dynamics and fixed effect to be heterogeneous across units. In our specific case, we wanted to test for the presence of the following cointegrating relationship: LnGDP it = α 0i + β 1i LnCAPit + β 2i LnLABit + β 3i LnTELINT it + ε it where Ln denotes logarithms. The hypothesis of cointegration implies that there exists a long run relationship between gross domestic product (GDP) and capital (CAP), labour (LAB) and telecommunications investment (TELINT). Pedroni (1999, 2004) developed seven cointegration test for panel data, four for the within model and three for the between model. The former, also called panel cointegration statistics, are based on pooling the autoregressive coefficient across different members for the unit root tests on the estimated residuals, while the latter, also called group mean panel cointegration statistics, are based on estimators that simply average the individually estimated coefficients for each member i.

8 678 Elsadig Musa Ahmed, Rahim Ridzuan Out of the seven tests, two tests were selected for the analyses which are Panel ADF-statistic and the Group ADF statistic. As these name suggest these tests are analogous to the augmented Dickey-Fuller t-statistic. Pedroni (1997) showed that the ADF-based tests perform best in small samples, which justify our choice. The group ADF test is likely to be more accurate in our specific case, given the heterogeneity of our data set. TABLE 3: Pedroni (1999, 2004) panel cointegration test for Model 1 ( ), ASEAN-5+3 Standard Panel ADF-Statistic Group ADF-Statistic b a and b denotes significance at 1% and 5% levels. The results presented in Table 3 showed that we reject the null of no cointegration which it implied that there is an evidence of long-run relationship between output (GDP) and inputs (CAP, LAB and TELINT) for model 1. TABLE 4: Descriptive Analysis Model 1: ASEAN5 Model 2: ASEAN5+3 GDP CAP LAB TELINT GDP CAP LAB TELINT Mean Median Maximum Minimum Std.Dev Skewness Kurtosis Jarque-Bera Probability Sum Sum Sq. Dev Observations The empirical distributions of the data from samples at initial analysis comprises of 31 balance panel observations. Table 4 presents the results of descriptive statistics involving the mean, median, standard deviation, skweness, kurtosis and Jarque-bera value for both model 1 and model 2. Among the criteria need to be fulfilled to produce the good, unbiased and consistent estimation for normally distributed data are the value of skewness should be equal to zero, the kurtosis value should be three and the Jarque-bera should not be significant for all variables. Nevertheless, the estimation of the impact of labour (human capital), and ICT-Investment towards total productivity growth of GDP shows above criteria are mostly not met. Thus, an initial finding in Table 4 indicates that the estimations neither will nor produced better result using OLS estimation techniques. Moreover, the inclusion of lagged independent variable makes the OLS estimator both biased and inconsistent. With reards to the above discussions, our analysis found that the utilization of panel data is well suited for this type of samples. The model estimations results for this panel regression analysis are shown in Table 6. This analysis used generalized least squares (GLS) instead of OLS. Thus, it takes consideration for none effect, fixed effect and random effect. Before proceeding with the analysis in Table 6, we have utilized Hausman test upon the random effect model in order to test the best model between fixed and the random effect for both model 1 and model 2.

9 Prosiding Persidangan Kebangsaan Ekonomi Malaysia Ke VII TABLE 5: Hausman Test for Model 1 (ASEAN5) and Model 2 (ASEAN5+3) Model 1: ASEAN5 Chi-Sq. Statictic Prob Model 2: ASEAN5+3 Chi-Sq. Statictic Prob Test Summary Cross section random Test Summary Cross section random Base on Table 5 above, the analysis for model 1 would be based on fixed effect model. It corresponding p-value of suggests the null hypothesis that no correlation between the explanatory variables and the random effect should be rejected. The analysis for model 2 would be random effect model given that the probability value is lesser than chi-sq statistic. It shows that the GLS with random effect should explain better relative to fixed effect model. In other words, output growth in ASEAN5+3 countries is influenced by only country-specific random effects. Thus, the following discussion focuses on fixed effect result for model 1 and random effects results for model 2. The empirical result of Panel Generalized Least Squares Based on fixed effect or model 1 in table 6 above, we found out that all independent variables; CAP, LAB and TELINT have positive correlation with the dependent variables, GDP. In particular, one unit increase in CAP and TELINT usage lead to about 0.98 and 0.02 percentage rise in GDP growth. Although the amount of ICT contribution towards GDP growth is very small compared to CAP, the detection of positive sign between these two variables have proven the importance of ICT towards achieving sustainable economic growth in developing countries especially for ASEAN5 excluding Singapore. CAP is the only variable that shows a significant result at 1% level while LAB and TELINT are not significant. The goodness of fit of the specification, that is R 2 and adjusted R 2, remains superior which are and respectively. TABLE 6: Model 1 (ASEAN5) Panel Estimation Results None-Effect Fixed Effect Random Effect C *** ** *** ( ) ( ) ( ) CAP *** *** *** ( ) ( ) ( ) LAB ( ) ( ) ( ) TELINT *** *** ( ) ( ) ( ) R Adjusted R F Note: t values are in parentheses. ***,**,* denotes significance level for 1%,5% and 10% level. Following random effect for model 2 on Table 7, we found out that CAP has a positive and significant impact at 1% significant level on total productivity or economic growth (GDP). Over the sample period studied, one unit increase in CAP usage lead to about 0.97 percentage rises in GDP growth for ASEAN5+3. The result also shows that LAB and TELINT are positively correlated with GDP with LAB significant at 10%. With the positive sign for each variable, we can conclude that besides CAP and LAB, TELINT also play critical roles in sustaining economic growth for ASEAN region which is ASEAN5+3 countries. It is important for the leaders of ASEAN countries to take this opportunity to learn and adapt the knowledge of ICT through knowledge sharing such as transfer technology programme from Korea, Japan and China. The goodness of fit of the specification for second model also remains superior which is for R 2 and for adjusted R 2.

10 680 Elsadig Musa Ahmed, Rahim Ridzuan TABLE 7: Model 2 (ASEAN5+3) Panel Estimation Results None-Effect Fixed Effect Random Effect C *** *** *** ( ) ( ) ( ) CAP *** *** *** ( ) ( ) ( ) LAB * * ( ) ( ) ( ) TELINT *** ( ) ( ) ( ) R Adjusted R F Note: t values are in parentheses. ***,**,* denotes significance level for 1%, 5% and 10% level. CONCLUSION The estimation of the impact of labour (human capital), and ICT-Investment towards total productivity growth of GDP shows above criteria is mostly not met. Thus, an initial finding in Table 4 indicates that the estimation neither will not produce better result using OLS estimation techniques. Moreover, the inclusion of lagged independent variable makes the OLS estimator both biased and inconsistent. The empirical distributions of the data from samples at initial analysis comprises of 31 balance panel observations. Table 4 presents the results of descriptive statistics involving the mean, median, standard deviation, skweness, kurtosis and Jarque-bera value for both model 1 and model 2. Among the criteria need to be fulfilled to produce the good, unbiased and consistent estimation for normally distributed data are the value of skewness should be equal to zero, the kurtosis value should be three and the Jarque-bera should not be significant for all variables. With regards to the above discussions, our analysis found that the utilization of panel data is well suited for this type of samples. The model estimations results for this panel regression analysis are shown in Table 6. This analysis used generalized least squares (GLS) instead of OLS. Further, it takes consideration for none effect, fixed effect and random effect. This study used GLS estimation since it can consider three different models known as none effect model, fixed effect model and random effect model. In order to determine the type of our model, we used Hausman Test. The Hausman test is a test of the significance of the difference between the fixed effects estimates and the random effects estimates. Once we know the true form of the model, we make a brief analysis base on the result that we derive from the GLS estimation. We began our analysis by using panel unit root test which is more powerful test compared to conventional unit root test. The results are separated into two tables. The first table is for model 1 while the second table is for model 2. The positive impact of ICT or telecommunications investment on economic growth has not been sufficiently studied for ASEAN5+3 countries in the literature. In this study, we have run model to test whether ICT has a positive impact on economic growth for ASEAN5 and ASEAN5+3 countries. The answer for this question is important for the policy makers because if the impact of ICT use is significant and carries a positive element towards economic growth, then the East Asian countries should reserve resources for the ICT to achieve sustainable growth. This is important especially for ASEAN5 as the result from table 3 shows that there is a long run relationship between the output (GDP) and the input (capital, labour and telecommunications investment). Unlike, the case of the US productivity paradox, by examining the role of ICT to achieve knowledge-based economy, it was found from the results that there was a positive contribution of ICT to economic growth of the economies of these countries. In this regards, the results of this study show that the ICT has a positive impact on economic growth for ASEAN5 and ASEAN5+3. This shows that ICT play crucial roles in economic development of these countries besides defying the assumption of productivity paradox where it can stand alone without the influence from capital and labour factors.

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