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Lee Kuan Yew School of Public Policy Accepted Paper Series
ICT as a Source of Economic Growth in the Information Age: Empirical Evidence from the 1996-2005 Period
Vu Minh Khuong Lee Kuan Yew School of Public Policy National University of Singapore Singapore Email:
[email protected]
May 06, 2014 Accepted Paper No.: LKYSPP11-02
Electronic copy available at: http://ssrn.com/abstract=1787873
Abstract This paper examines the hypothesis that ICT penetration has positive effects on economic growth. On theoretical grounds, this paper discusses three channels through which ICT penetration can affect growth: (i) fostering technology diffusion and innovation; (ii) enhancing the quality of decision-making by firms and households; and (iii) increasing demand and reducing production costs, which together raises the output level. This paper conducts three empirical exercises to provide a comprehensive documentation of the role of ICT as a source of growth in the 19962005 period. The first exercise shows that growth in 1996-2005 improved relative to the previous two decades and experienced a very significant structural change. The second exercise uses the traditional cross-country regression method to identify a strong association between ICT penetration and growth during 1996-2005, controlling for other potential growth drivers and country-fixed effects. The third exercise uses the system Generalized Method of Moment (GMM) for dynamic panel data analysis to tease out the causal link between ICT penetration and growth. This analysis also shows that, for the average country, the marginal effect of the penetration of internet users was larger than that of mobile phones, which in turn is larger than that of personal computers. The marginal effect of ICT penetration, however, lessens as the penetration increases. This paper points out several policy implications drawn from its analyses and findings.
Keywords: ICT penetration; economic growth; technology diffusion; Internet; mobile phones; personal computers. JEL Classification: O40; O47; O50
1. Introduction The development of endogenous growth models in the late 1980s, initiated by the work of Romer (1986; 1990) and Lucas (1988), has sparked a number of empirical studies to investigate the endogenous factors that determine economic growth. A consistent theme across these studies is the notion that the accumulation of human capital plays the role of “the main engine of growth” (Lucas, 1993, p. 270). However, far from being static, human capital is a dynamic and productive stock. Each individual is not only a passive worker, but also a decision maker, a life-long
2 Electronic copy available at: http://ssrn.com/abstract=1787873
learner and a team-member. Any improvement in working conditions that allows people to have better access to information and facilitates their learning productivity and communication abilities, effectively enlarges the existing stock of human capital and enhances its use. Hence, these types of improvements should have positive impacts on economic growth. Quah (2002), emphasizing demand over supply, argues that the Information and Communication Technology (ICT) revolution is fostering improvement in labor skills, consumer sophistication and an increased level of broad-based education. This encourages the improved use of technology and raises labor productivity and as a result, “drives economic growth, one way or another” (Quah, 2002, p. 22). Levine (1997) argues that relaxing barriers to information access, of which ICT is believed to be an important driver, promotes faster growth by encouraging increased investment. Even before the emergence of ICT, the impacts of improved access to information and effective communication on economic growth were observed in several economies, such as Japan, Korea, Hong Kong and Taiwan. Their outstanding economic growth during the second half of the 20th century has been attributed, in part, to the fact that their firms and people had better access to market information. In addition, they benefited from more effective1 communication with foreign partners and each other. The link between investment in information technology and growth has been investigated by a wealth of studies.2 At the firm and industry levels, Brynjolfsson and Hitt (2003) found a positive relationship between computer investment and firm productivity levels: firms that invested more in computers produced more output per unit of input. Lehr and Lichtenberg (1999) inspected firm-level data among service industries in Canada and provided evidence indicating that personal computers made a positive and significant contribution to productivity growth. Stiroh (2002) investigated 57 major US industries, confirming a strong link between ICT accumulation and productivity growth. O’Mahony and Vecchi (2005), using data pooled at the industry level for the US and UK, found a positive and significant effect of ICT on output growth and excess returns to ICT compared with non-ICT assets. Jensen (2007), using micro-level survey data, showed that 1
For example, see Ranis (1995), Ozawa and Sato (1989), and Yu and Choi (1995). Van Reenen, Bloom, Draca, Kretschmer, and Sadun (2010) provided an excellent survey of ICT impact on competitiveness and growth. 2
3 Electronic copy available at: http://ssrn.com/abstract=1787873
the adoption of mobile phones by fishermen and wholesalers in India was associated with a dramatic reduction in price dispersion, elimination of waste and substantial improvements in both consumer and producer welfare. At the national level, the literature on the contribution of investment in ICT to growth can be divided into two streams. In one stream, the studies employ the growth accounting technique to estimate the contribution (in percentage points) of ICT investments to GDP growth. These studies include Jorgenson and Stiroh (2000), Jorgenson (2001), and Oliner and Sichel (2000) for the US; Oulton (2002) for the UK; Jalava and Pohjola (2002) for Finland; Jorgenson and Motohashi (2005) for Japan; Colecchia and Schreyer (2001), Van Ark, Melka, Mulder, Timmer, and Ypma (2002), Daveri (2002), and Timmer, Ypma, and Van Ark (2003) for EU economies; Jorgenson (2003) for the G7 economies; and Jorgenson and Vu (2007) for 110 countries. In the other stream, the studies use crosscountry regression techniques to assess the effects of ICT on economic growth. The seminal paper by Hardy (1980), which analyzed the data for 60 nations over the 19681976 period, found strong evidence for the contribution of telephones to economic development. Roller and Waverman (2001), using data on 21 OECD countries over a 20year period (1970-1990), found a significant positive causal relationship between investment in telecommunication infrastructure and subsequent economic performance. Madden and Savage (1998), examining a sample of 27 Central and Eastern European countries during the period 1990-1995, revealed a strong positive relationship between telecommunication infrastructure investment and economic growth. Jacobsen (2003) and Waverman, Meschi, and Fuss (2005) both found a significant positive effect of mobile phones on growth. Thompson and Garbacz (2007), investigating panel data of 93 countries for the period 1995-2003, found that penetration rates of telecommunications services significantly improved the productive efficiency of the world as a whole and particularly in some subsets of low income countries. Seo, Lee and Oh (2009), analyzing panel data of 29 countries in the 1990s, showed that ICT investment has positive impacts on GDP growth and not vice versa. Gruber and Koutroumpis (2010a), using the data from 192 countries for the 1990-2007 period, found significant effects of mobile telecommunications diffusion on GDP and productivity growth. Koutroumpis (2009), employing the model introduced by Roller and Waverman (2001) for 22 OECD countries 4
over the period 2002-2007, found that broadband penetration, which is a driving factor of ICT penetration, has a significant positive causal link with economic growth when a critical mass of infrastructure is present.3 Other studies, however, have found inconclusive evidence on the growth effect of investments in ICT, especially in computers. Dewan and Kraemer (2000), analyzing panel data of 36 countries over the 1985-1993 period, revealed that returns from IT capital investments are positive and significant for the developed countries in the sample but not statistically significant for the developing ones. Pohjola (2002), examining data on a sample of 43 countries over the period of 1985-1999, found no significant correlation between ICT investment and economic growth. Jacobsen (2003), using data from 84 countries over 10 years between 1990-1999, found no significant growth effect from computer penetration, although it confirmed a significant positive link between mobile phones and growth. This calls for further inquiry into the effect of ICT penetration on growth, especially in developing countries, using more recent data. This paper investigates the causal link of ICT on growth, using panel data of 102 countries for the 10-year period 1996-2005, during which ICT rapidly penetrated across countries. The data is compiled from three main sources: the World Development Indicator dataset (for economic indicators), the International Telecommunication Union (ITU) World Telecommunication Indicators database (for ICT penetration indicators) and the World Bank Governance Indicators (for governance indices). The paper proceeds as follows. Section 2 presents the theoretical grounds that support the hypothesis that ICT penetration has a positive impact on economic growth. Section 3 conducts empirical examinations to reveal comprehensive evidence that ICT was an important source of growth over the 1996-2005 period. Section 4 concludes with policy implications drawn from the findings of the study.
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Shideler, Badasyan, and Taylor (2007), Crandall, Lehr, and Litan (2007), and Greenstein and McDevitt (2009), using the U.S. data, showed significant positive effects of broadband deployment on output and employment. Czernich, Falck, Kretschmer, and Woessmann (2009) found a strong effect of broadband infrastructure on economic growth in the panel of OECD countries in 1996-2007. Katz (2009) quantified the economic impact of broadband technology on employment and productivity in Latin American countries.
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2. Potential positive effects of ICT penetration on growth: Some theoretical grounds With the Internet at its core, the diffusion of ICT has initiated a profound transformation of the world into an information society. It is obvious that, thanks to the Internet, people, businesses, and governments now have much better access to information, knowledge, and wisdom than before in terms of scale, scope, and speed. Furthermore, this wealth of information, knowledge and wisdom is endlessly growing through unprecedented fast and robust communication and exchange. With this premise, this section employs existing theoretical models to highlight three main channels for ICT to have positive effects on economic growth, namely, fostering innovation and technology diffusion, improving efficiency of resources allocation, and reducing production costs and promoting demand and investment. 2.1. Fostering innovation and technology diffusion Romer (1990), Grossman and Helpman (1991) and Aghion and Howitt (1998, p. 5380) provide models that treat R&D activities as the engine for long-term economic growth. Kuznets (1966) emphasizes the importance of increasing the “transnational stock of knowledge” to facilitate economic growth in each nation, asserting that “no matter where these innovations emerge… the economic growth of any given nation depends upon their adoption” (p. 286). Barro and Sala-i-Martin (1995, Chapters 6, 8) present a simple leader-follower model to examine how innovation and technology imitation affect the rate of economic growth. In this model, growth of the leader economy (economy 1) is driven by its innovations, while growth of the follower economy (economy 2) depends on its imitation of the innovations that have been made in the leader economy. This model can be interpreted in a novel way to reveal how ICT penetration can boost economic growth in both leader and follower economies.4 According to the model (p. 268-273), the growth rate 1 of the leader economy is
4
Barro and Sala-i-Martin (1995) do not discuss the role of information, assuming that the information is perfectly shared by the leader and follower economies.
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1
1 (1 / ).( L1 / ).(
).( A1 )1/(1 ) . 2 /(1 )
(1)
where 0 and 0 are related to the rates of time preference of households, and 0 1 is the elasticity of the marginal product of intermediate goods in the production
function (the model assumes that the parameters , , and are similar for the two economies). A1 > 0 is a productivity parameter that represents the quality of governance and the level of technology. L1 is the labor endowment, and is the unit cost of inventing a new variety. The growth rate 2 of the follower economy5 is approximated as:
N 2 / N1 * ( N 2 / N1 )
2 1 . log
(2)
where N1 is the number of varieties of intermediate products that have been discovered in the leader economy, N 2 ( N 2 N1 ) is the number of varieties that have been introduced into the follower economy, 0 is a positive parameter that determines the speed of convergence, and 0< N 2 / N1 0 because x1 y1 and x2 y2 by assumption. That is, as the firm improves its q market assessment capability q , it raises the expected value of its business decisions and, therefore, boosts its business performance. By providing the business sector with more efficient and effective tools for market research, business intelligence, and communication with customers and suppliers, ICT has indeed enhanced the typical firm’s market assessment capability. The simple model presented above suggests that deepened ICT penetration would raise the business performance of the average firm and, consequently, have a positive impact on economic growth. 2.3. Reducing production costs and fostering demand and investment The ICT revolution enables firms to reduce production costs significantly because of much lower costs of communications and better access to suppliers. As a result, the aggregate supply curve is expected to shift to the right. On the other hand, offering unprecedented features and enormous potential benefits, the ICT revolution has spurred substantial spending on ICT products and services, including investment in ICT assets in most countries in the past decade or so. Global ICT spending rose from $1,300 billion in 1993 to $2,100 billion in 2001 and about $3,000 billion in 20066 (WITSA). In 2005, the share of ICT expenditures of GDP ranged
6
From reports “Digital Planet”, various issues for 1998-2006 by the World Information Technology and Services Alliance (WITSA), Washington, DC.
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between 4% and 8% for most countries with the aggregate level of 6.4% for the world economy.7 This surge in demand and investment is expected to shift the demand curve to the right. The aforementioned discussions suggest that ICT penetration may have shifted the supply and demand curves to the right in many countries. Therefore, a positive effect on output level and growth is expected. 3. Empirical evidence on the effect of ICT on growth over the 1996-2005 period Since the mid-1990s, the progress and diffusion of ICT have accelerated substantially, with the rapid penetration of personal computers, mobile phones, and the Internet across nations in the world. The penetration of personal computers, on average, increased six times from 1.0 (per 100 inhabitants) in 1995 to 6.0 in 2005; this figure is 14 times for mobile phones (from 2.0 to 28), and nearly 16 times for the Internet (from 0.4 to 15.7).8 The penetration of these ICT technologies depend on a wide range of factors, especially the level of income, the ICT infrastructure conditions (such as the size of the current fixed telecommunications network, broadband penetration), human capital, the ICT regulatory framework and completion among vendors, openness, and the network effect (Gruber & Verboven, 2001; Beilock & Dimitrova, 2003; Koski & Kretschmer, 2005; Chinn & Fairlie, 2007; Gruber & Koutoumpis, 2010b; Andrés, Cuberes, Diouf, & Serebrisky, 2010). These findings, as such, also suggest that the relationship between ICT penetration and growth is shaped by an endogenous process with significant reverse effects of growth on the adoption of ICT. Therefore, while the positive effect of ICT penetration on growth is expected, it is not easy to tease out its causal effect on growth. This section is set to provide comprehensive evidence that the rapid penetration of ICT in 1996-2005 was an important source of growth over this period. For this purpose, the section conducts the empirical examination in three main exercises. The first exercise is to see if the world economic performance in 1996-2005 improved relative to the previous decades and to test if the growth pattern in 1996-2005 7 8
Source: World Development Indicators, World Bank. ITU World Telecommunication Indicators database.
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experienced a significant structural change. The second exercise investigates the association between ICT penetration and growth in 1996-2005, controlling for the other potential growth drivers and country-fixed effects. Although the first and second exercises are not sufficient to discern the causal link between ICT penetration and growth, their positive results support the hypothesis that ICT was an important source of growth over the 1995-2006 period. The third exercise employs the system Generalized Method of Moment (GMM) for dynamic panel data analysis to tease out the causal link between ICT penetration and growth. 3.1. Economic growth in 1996-2005: improvements and structural change If rapid ICT penetration has emerged as an important driver of economic growth in 1996-2005, one may plausibly expect growth in this period to have experienced a significant structural change with some notable improvements relative to the previous two decades, 1976-1985 and 1986-1995. To make comparisons meaningful, this exercise is conducted for only a subsample of 85 countries (from the full sample of 102 countries) for which data on GDP is available for all three decades of examination (Table 1).9 3.1.1. Improvements The world economy during the period of 1996-2005 was severely affected by three severe shocks: the Asian financial crisis in 1997, the dot-com crash in 2000 and the consequences of the 9/11 terrorist attacks in 2001. These shocks, indeed, caused sharp plunges in global economic growth in 1998-1999 and 2001-2002 as shown in Fig. 1. However, economic growth, averaged for this period, was notably higher than those for previous decades, 1976-1985 and 1986-1995. Table 1: List of the 102 countries North America and Western Europe
Latin America
Eastern Europe
Asia
Sub-Saharan Africa
Middle East and North Africa
9
For the remaining 17 countries, most of which are transition economies, the data on GDP is not available for the first period, 1976-1985. These countries are specified in the list of 102 countries provided in Table 1.
13
Australia Austria Belgium Canada Denmark Finland France Germany Greece Ireland Italy Netherlands New Zealand Norway Portugal Spain Sweden Switzerland United Kingdom United States
Argentina Bolivia Brazil Chile Colombia Costa Rica Ecuador El Salvador Guatemala Honduras Jamaica Mexico Nicaragua Panama Paraguay Peru Trinidad and Tobago Uruguay Venezuela
Albania* Bulgaria* Croatia* Czech Republic* Estonia* Hungary Latvia Poland* Romania* Russia* Ukraine*
Bangladesh China Hong Kong, China India Indonesia Japan Malaysia Nepal Pakistan Philippines Singapore South Korea Sri Lanka Thailand Vietnam*
Benin Botswana Burkina Faso Cameroon Central African Republic Chad Congo, Rep. Cote d'Ivoire Ethiopia* Gabon Ghana Guinea* Kenya Madagascar Malawi Mali Mauritius* Mozambique* Namibia* Niger Nigeria Senegal South Africa Swaziland Tanzania* Togo Uganda* Zambia
Algeria Egypt, Arab Rep. Iran, Islamic Rep. Jordan Mauritania Morocco Syrian Arab Republic Tunisia Turkey
Note: The list consists of 102 economies, of which 85 have complete GDP data for all three decades, 19761985, 1986-1995, and 1996-2005. The countries for which some GDP data are missing in these periods are marked with *.
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Fig. 1: World economic growth, 1996-2005 Mean Annual GDP Growth
Median Annual GDP Growth
.048583
.02275 1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
Note: This figure uses data from the subsample of 85 countries for which GDP data is available for all three decades, 1976-1985, 1986-1995, and 1996-2005. Source: Data from WDI
A summary of descriptive statistics on GDP growth of the 85 economies in Table 2 reveals that there were notable improvements in economic growth over 1996-2005 relative to the previous two decades. The following facts stand out: a) The economic performance of the 85 countries as a whole improved significantly in 1996-2005 relative to 1976-1985 and 1986-1995.
The mean (median) of GDP growth rate of the world sample (85 economies) rose from 3.2% (2.9%) for 1976-1985 and 3.1% (2.9%) for 1986-1995 to 3.5% (3.4%) for 1996-2005 (Table 2).
The distribution of countries by average period growth rate shifted notably to the right for 1996-2005 relative to that for 1976-1985 and 1986-1995 (Fig. 2). This shift means that economic growth in 1996-2005 surged in more than a majority of countries in the subsample.
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b) The standard deviation of growth rates reduced sharply for the 85-economy sample, from 2.5% in 1976-1985 and 2.4% in 1986-1995 to 1.5% in 1996-2005 (Table 2). This implies that countries had a more equal playing field in the 1996-2005 period compared to 19975-1985 and 1986-1995. This improvement, to some extent, has been enabled by the ICT revolution which allows countries to have more effective communications and equal access to information and knowledge on markets, technology, and management. Table 2: Economic Growth: 1996-2005 vs. 1986-1995 and 1976-1985 Indicator
Growth over Period (%) 1976-1985 1986-1995 1996-2005 3.2 3.1 3.5 2.9 2.9 3.4 2.5 2.4 1.5
Mean Median Std Deviation
Note: The descriptive statistics are confined to the subset of 85 economies for which GDP data is available for all the three decades, 1976-1985, 1986-1995, and 1996-2005.
Fig. 2: Distribution of countries by average period growth rate: 1996-2005 vs. 19861995 and 1976-1985 Period 1996-2005 Period 1976-1985
Period 1986-1995
Density (% of Countries)
30
20
10
0 -4
-3
-2
-1 0 1 2 3 4 5 6 7 Average Period Growth Rate by Country (%)
8
9
10
Note: This figure uses data from the subsample of 85 countries for which GDP data is available for all three decades, 1976-1985, 1986-1995, and 1996-2005. Source: Data from WDI.
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3.1.2. Structural change The following simple model is used to conduct the Chow test of structural stability in growth pattern over the two consecutive decades: YGROWi ,t 0 1YGROWi , t 1 2 LnYPCAPi ,t 1 0 DT 1DT * YGROWi , t 1 2 DT * LnYPCAPi ,t 1 i Zi i ,t
(5) where subscript i indicates country i ; and subscripts t and t 1 indicate year t and t 1 (one-time lag). YGROW is annual GDP growth rate. LnYPCAP is logarithm of the initial level of per capita income; DT is the dummy for period T of interest (which is 1986-1995 or 1996-2005); DT * YGROWi , t 1 and DT * LnYPCAPi ,t 1 are the interaction terms between the dummy DT and YGROWi , t 1 and
L n Y P Ci ,t A 1 , Prespectively; Z i are country
dummies that capture unobserved country-specific effects; and i,t is the error term. The Chow test based on Equation (5) allows one to test if the growth pattern in a 10year period T is significantly different from the previous 10-year period (denoted by T-1). The procedure for this test consists of two steps: (i) regressing Equation (5) using the combined data for period T and T-1; (ii) performing an F-test of the joint hypothesis that the coefficients 0 , 1 and 2 , which are associated with the period dummy DT , are all equal to zero. If the F-test fails to reject this null hypothesis at a chosen significance level, one can conclude that the growth pattern in period T is not significantly different from period T-1. However, if the F-test rejects the null hypothesis, it confirms that the growth pattern has experienced a significant change. Results from the Chow tests for the two periods, 1996-2005 and 1986-1995 were reported in Table 3. The Chow test for period T = 1986-1995 produces an F-statistic of 3.25 with pvalue=0.0214, which indicates that this test fails to reject the null hypothesis at the 1% significant level, for which the critical value is 3.79.10 On the other hand, the test for period T = 1996-2005 yields an F-statistic of 17.7 with p-value=0.000, which means that 10
This F-test uses the F(q, n-k-1) distribution, for which q=3 (number of restrictions), n=1700 (number of observations, k=89 (five explanatory variables and 84 country dummies). Note the number of observations for each country in a regression is 20 instead of 21 because one time lag is used; and hence there are1700 observations for the panel of 85 countries.
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the null hypothesis is strongly rejected at any conventional significance level. The evidence from the Chow tests thus shows that growth in 1996-2005 experienced a significant structural change, which was far more profound than the change observed for the previous period, 1986-1995. The notable improvements and significant structural change in economic growth in 1996-2005 presented above suggest that important factors driving growth have emerged in this period. Although one may think of other possible factors such as accelerated globalization, the rapid penetration of ICT in 1996-2005 is plausibly expected to be among these emerging drivers. Table 3: Chow tests for structural change in growth pattern Dependent Variable: YGROWt Explanatory Variable YGROWt-1 LnYPCAPt-1 DT DT * YGROWt-1 DT * LnYPCAPt-1 Country fixed effects Panel data Number of Observations R2 F-test for joint null hypothesis: (i) DT=0 (ii) DT * YGROWt-1=0 (iii) DT * LnYPCAPt-1=0
T=1986-1995 (1) 0.217*** (7.28) -0.069*** (-8.73) -0.016 (-1.43) 0.057 (1.22) 0.003* (1.83) Yes
T=1996-2005 (2) 0.194*** (6.82) -0.077*** (-9.96) 0.010 (1.14) 0.040 (0.80) 0.005* (0.43) Yes
85 countries over 1976-1995 1700 0.25
85 countries over 1986-2005 1700 0.23
F(3, 1610)=3.24 [0.0214]
F(3, 1610)=17.70 [0.000]
Notes: The tests are conducted for the 85 economies for which GDP data is available for all the three decades, 1976-1985, 1986-1995, and 1996-2005. All of the regressions use robust standard errors to adjust for heteroskedasticity. The 10%, 5%, and 1% significance levels are denoted by *, **, and ***, respectively; t-statistics are reported in parentheses under the coefficient estimates. Numbers in brackets refer to the p-values of the tests.
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3.2. Association between ICT penetration and growth in 1996-2005 This section explores the association between ICT penetration and economic growth over 1996-2005, using the static cross-country regression framework. This method used to be a popular approach to gauge the causal effect of various policy and environmental variables on growth, which sparked by the seminal work of Barro (1991). The advantage of this approach is to identify the potential relationship between various policy variables such as institutions, government spending, financial intermediaries, and social inequality - and growth. However, this approach has been criticized for its shortcomings related to its treatments of country fixed effects and the endogeneity of explanatory variables (for example, see Caselli, Esquivel, & Lefort, 1996; Durlauf, Johnson, & Temple, 2005). Recognizing the merits and shortfalls of this approach, Levined and Zervos (1993, p. 426) suggested that “cross-country regressions should be viewed as evaluating the strength of partial correlations, and not behavioral relationships”. According to them, cross-country regressions can be very helpful if this approach is used for demonstrating certain associations between policy variables and growth that hold well across countries, which suggest some potential link between policy and economic performance. In this spirit, this subsection employs the cross-country regression framework to simply investigate the association between ICT penetration and growth in 1996-2005. Model The cross-country regression model for examining the partial correlation between ICT and growth is designed to allow country-fixed effects to be included. For this purpose, the 1996-2005 period is divided into two five-year subperiods, 1996-2000 and 2001-2005 and the association between ICT penetration and growth will be examined using the panel data of 102 countries over these two subperiods. The model takes the following form:
YGROWis X is i is ,
(6)
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where subscripts i and s indicate, respectively, country i and subperiod s ; YGROWis is the average GDP growth rate11 of country i over subperiod s . X is is a vector of variables that are expected to have potential effects on growth. i captures country-fixed effects and is is the error term. The vector X includes the variables described below.
YPCAP 0is is per capita GDP of country i in the initial year of subperiod s (1996 for subperiod 1996-2000; and 2001 for subperiod 2001-2005). This variable is expected to have negative association with growth due to the convergence effect, which means low-income countries tend to grow faster than high-income countries, all else being equal.
YPCAP 0 _ sqis is YPCAP0 squared. This variable is included to capture some non-linear relationship between the initial income level and growth.
The remaining variables in the vector X is take the mean values of their measures for country i over the subperiod s . All these variables are expected to have a positive partial correlation with growth.
INSTITUTION is is the country’s World Bank “Rule of Law” index12. This variable captures the institutional quality.
POPSHARE is is the share of the country in the world population. This variable captures the country’s advantages associated with the size of labor force and market.
11
The average growth rate of variable Y during the period [t1, t2] is defined as [ln(Y t2)-ln(Yt1)]/(t2-t1). The index is constructed from over 33 sources of perception‐based governance data, which capture perceptions of the extent to which agents have confidence in and abide by the rules of society, and in particular the quality of contract enforcement, property rights, the police, and the courts, as well as the likelihood of crime and violence (Kaufmann, Kraay, & Mastruzzi, 2006). The index takes values between 2.5 and 2.5, the higher the better. This index has been used by a number of studies. For example, Rodrik, Subramanian, and Trebbi (2004) examine the effect of institutional quality on economic growth and development. 12
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EDUCATIONis is gross secondary enrollment rate, which is a rough measure of a country’s average quality of human capital.
OPENNESS is is the merchandise trade-to-GDP ratio, which is a rough measure of a country’s openness.
AGRISHARE is is the agricultural sector’s value-added share in GDP. Growth in many developing countries is driven by reallocation of resources from the agricultural sector to higher productivity sectors such as manufacturing and services; and hence this variable is expected to have a positive association with growth.
INVESTMENTis is the ratio of gross fixed capital formation to GDP.
Table 4: Summary of Variables Variable
YGROW ICT_pc ICT_mb ICT_iu YPCAP0 INSTITUTION EDUCATION OPENNESS POPSHARE AGRSHARE INVESTMENT
Definition
Unit
GDP growth rate Penetration of personal computer Penetration of mobile phones Penetration of Internet users Per capita GDP at the begining of the subperiod** Rule of Law index Gross secondary school enrollment*** Trade-to-GDP ratio Share of the world's population Agricultural sector’s value added share in GDP Gross fixed capital formation-to-GDP ratio
% % % % US$’000
Mean Value* 199620012000 2005 3.5 3.8 6.2 15.2
8.6 4.4 6.1
35.6 17.7 6.9
% % % % %
0.219 67.3 51.0 0.69 19.3
0.152 73.1 66.5 0.99 13.4
%
21.3
21.3
Note: *the mean value is for the entire sample of 102 countries; ** the income is measured in the 2000 price level; ***the data for Singapore, Madagascar, and Tanzania is missing for 2001-2005. Sources: World Development Indicators (WDI); World Bank Governance Index.
The depth of ICT penetration is alternatively proxied by the penetration (per 100 inhabitants) of personal computers ( ICT_ pcis ), mobile phones ( ICT_ mbis ), and internet users ( ICT_ iuis ). To capture the possible non-linear relationship between
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each of these three ICT variables and growth, their quadratic term (ICT_pc_sq = ICT_pc^2; ICT_mb_sq = ICT_mb^2; and ICT_iu_sq=ICT_iu^2) is also incorporated into the model. The three ICT variables capture different aspects of ICT penetration in a country: ICT_pc represents ICT infrastructure conditions; ICT_mb portray the diffusion of the modern telecommunications technology; and ICT_iu depicts the connection of the country to the Internet. These ICT variables are expected to a positive association with growth. Table 4 provides a summary of the variables defined above. 3.1.2. Estimation results and discussions Results from OLS regressions based on equation (6) are reported in Tables 5A, 5B, and 5C, for the three ICT variables ICT_pc, ICT_mb, and ICT_iu, respectively. Results for the regressions with ICT_pc (Table 5A) The inclusion of country-fixed effect into the model increases R-squared substantially, from 0.30 in regression (1) and 0.31 in regression (2) to 0.73 in regression (3) and 0.74 in regression (4). This suggests that country-fixed effects must be included in the model for assessing the association between the explanatory variables and growth. Furthermore, the statistical significance of the coefficients on ICT_pc and ICT_pc_sq in both regressions (2) and (4), in which country fixed effects are included, suggests that a quadratic function is better in capturing the relationship between ICT_pc and growth. As a result, regression (4), which includes both country-fixed effects and ICT_pc_sq, is used for discerning the association between ICT_pc as well as other explanatory variables and growth. The coefficient on ICT_pc is strongly significant in regression (4), which shows that the association between personal computers penetration and growth is robust. However, the coefficient on ICT_pc_sq is negative and statistically significant; which implies that the marginal positive change in the association between the penetration of personal computers and growth lessens as the penetration gets higher.
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The coefficient on YPCAP0 is robustly significant in regression (4) (as well as in other regressions), which implies a strong “convergence effect” as found in the growth literature inspired by the works of Barro (1991) and Barro and Sala-i-Martin (1995). The coefficient on YPCAP0_sq is positive and statistically significant in regression (4), which implies that the link between the initial income level and growth is non-linear. With regard to other explanatory variables, it is interesting to see that most of them are strongly significant in regressions (1) and (2), in which the country-fixed effect is omitted, but not significant in regressions (3) and (4), in which the country fixed effect is included. For example, the coefficient on INSTITUTION is statistically significant at the 1% significance level in regressions (1) and (2) but insignificant in regressions (3) and (4). In addition, the magnitude of this coefficient is higher in regressions (1) and (2) relative to (3) and (4), respectively. This means, omitting the country-fixed effect causes an upward-bias in estimating the association between INSTITUTION and growth. Similar patterns are also observed for POPSHARE, INVESTMENT, and AGRISHARE. The coefficients on these variables become not statistically significant and their magnitudes are reduced once country-fixed effects are included. The coefficients on EDUCATION and OPENNESS are not statistically significant in any of the regressions. However, their estimates in the regressions with the country-fixed effects are more in line with what is expected. They have the expected positive signs and larger magnitudes. In sum, three important points stand out from the discussion above. First, the penetration of personal computers and growth has a strong positive association and this relationship is better captured in a quadratic function. Second, the estimation of the link between the explanatory variable and growth is biased if the country-fixed effect is omitted. Third, once the country-fixed effect is included, only the variable on ICT penetration and the initial income level are robustly statistically significant, probably because they are the most dynamic factors over the period of examination.
23
Table 5A: Partial correlation between ICT and growth over 5-year subperiods – Personal Computers Dependent Variable: YGROW (in percentage points) Explanatory Variable ICT_pc
(1)
(2)
(3)
(4)
0.012 (1.05)
0.087** (2.48) -0.001** (-2.44) -0.218*** (-3.11) 0.003 (1.60) 0.848*** (3.21) -0.011 (-1.34) 0.003 (0.94) 0.154*** (4.85) 0.028* (1.86) 0.119*** (3.07) No 201 0.31
0.042*** (2.65)
0.148*** (2.60) -0.001** (-2.18) -1.932*** (-3.78) 0.026*** (2.82) 0.676 (0.65) 0.011 (0.43) 0.010 (1.03) -0.075 (-0.52) -0.023 (-0.78) 0.105 (1.32) Yes 201 0.74
ICT_pc_sq YPCAP0 YPCAP0_sq INSTITUTION EDUCATION OPENNESS POPSHARE AGRSHARE INVESTMENT Country fixed effect N R2
-0.159** (-2.32) 0.001 (0.81) 0.924*** (3.48) -0.009 (-1.10) 0.004 (1.38) 0.152*** (4.77) 0.026* (1.76) 0.120*** (3.08) No 201 0.30
-1.385*** (-3.64) 0.013* (1.90) 0.563 (0.54) 0.022 (0.95) 0.012 (1.31) -0.075 (-0.53) -0.026 (-0.85) 0.098 (1.23) Yes 201 0.73
Notes: All of the regressions use robust standard errors to adjust for heteroskedasticity. The 10%, 5%, and 1% significance levels are denoted by *, **, and ***, respectively; t-statistics are reported in parentheses next to the coefficient estimates. Panel data is from 102 countries over two 5-year subperiods, 1996-2000 and 2001-2005. The data on education is missing for three observations (Singapore, Madagascar, and Tanzania for the 2001-2005 subperiod).
Results from the regressions with ICT_mb (Table 5B) The results from the regressions with ICT_mb are similar to those with ICT_pc. In particular, the association between ICT_mb and growth is strongly significant and this relationship is better captured with a quadratic function; the problem of omitted variable bias is severe if the country-fixed effect is not included; and only ICT_mb and the initial income level have statistically significant association with growth once country effects are included (as shown in regression (4)).
24
Table 5B: Partial correlation between ICT and growth over 5-year subperiods – Mobile Phones Dependent Variable: YGROW (in percentage points) Explanatory Variable ICT_mb
(1) 0.005 (0.69)
(2) (3) (4) 0.053*** 0.042*** 0.083*** (2.85) (3.43) (3.17) -0.001*** -0.0005** ICT_mb_sq (-2.92) (-2.20) -0.159** -0.160** -2.295*** -2.197*** YPCAP0 (-2.32) (-2.37) (-3.99) (-4.22) 0.002 0.002 0.028*** 0.030*** YPCAP0_sq (0.94) (1.04) (3.26) (3.45) 0.957*** 0.929*** 0.693 0.963 INSTITUTION (3.59) (3.57) (0.72) (1.01) -0.009 -0.012 0.001 -0.017 EDUCATION (-1.10) (-1.41) (0.02) (-0.58) 0.004 0.005 0.006 0.011 OPENNESS (1.24) (1.39) (0.68) (1.12) 0.152*** 0.151*** -0.042 -0.074 POPSHARE (4.75) (4.71) (-0.29) (-0.47) 0.028* 0.041*** -0.008 0.012 AGRSHARE (1.88) (2.60) (-0.29) (0.45) 0.120*** 0.118*** 0.126 0.130 INVESTMENT (3.06) (2.99) (1.61) (1.68) Country fixed effect No No Yes Yes N 201 201 201 201 R2 0.30 0.33 0.76 0.78 Notes: All of the regressions use robust standard errors to adjust for heteroskedasticity. The 10%, 5%, and 1% significance levels are denoted by *, **, and ***, respectively; t-statistics are reported in parentheses next to the coefficient estimates. Panel data is from 102 countries over two 5-year subperiods, 1996-2000 and 2001-2005. The data on education is missing for three observations (Singapore, Madagascar, and Tanzania for the 2001-2005 subperiod).
Results from the regressions with ICT_iu (Table 5C) The results from the regressions with ICT_iu also resemble those with ICT_pc and ICT_mb: three important points stand out (i) the association between ICT_iu and growth is strongly significant in the regressions with the country-fixed effect and this relationship is slightly better captured with a quadratic function; (ii) the omitted variable bias is severe if the country-fixed effect is not included; and (iii) only ICT_iu and the initial income level show statistically significant associations with growth. For the regressions with ICT-iu, it is interesting to note that unlike ICT_pc and ICT_mb, the coefficient on ICT_iu is not statistically significant in regression (2), in which country-fixed effects are omitted. One possible reason is the heterogeneity across
25
country in the method for measuring the penetration of the Internet. As pointed out by Samarajiva and Lucas (2010), the heterogeneity bias of the current method of measuring Internet users is severe. In this method, the total number of Internet users in a given country is estimated by multiplying the total internet subscriptions by a multiplier, which is estimated through surveys or at the discretion of national administrations as permitted by ITU. For example, the multiplier (in 2009) is 2.56 for China, 4.02 for India, 5.50 for Pakistan, 1.99 for the Philippines, and 4.58 for Vietnam (Samarajiva & Lucas, 2010, p. 27-29). This case is an example that shows that the omitted variable bias is severe if country-fixed effects are not included. Table 5C: Partial correlation between ICT and growth over 5-year subperiods – Internet Users Dependent Variable: YGROW5 (in percentage points) Explanatory Variable ICT_iu
(1) 0.003 (0.31)
(2) (3) (4) 0.048 0.045*** 0.111*** (1.28) (2.80) (2.61) -0.001 -0.001* ICT_iu_sq (-1.44) (-1.86) -0.149** -0.173** -1.565*** -1.963*** YPCAP0 (-2.18) (-2.40) (-3.63) (-4.13) 0.001 0.002 0.015** 0.025*** YPCAP0_sq (0.83) (1.16) (2.32) (2.96) 0.938*** 0.908*** 0.521 0.660 INSTITUTION (3.54) (3.43) (0.52) (0.65) -0.009 -0.010 0.020 0.004 EDUCATION (-1.05) (-1.15) (0.85) (0.12) 0.005 0.004 0.009 0.008 OPENNESS (1.43) (1.20) (0.99) (0.79) 0.153*** 0.151*** -0.061 -0.072 POPSHARE (4.74) (4.67) (-0.43) (-0.50) 0.026* 0.030** -0.019 -0.017 AGRSHARE (1.78) (2.03) (-0.64) (-0.59) 0.118*** 0.118*** 0.107 0.111 INVESTMENT (2.96) (2.95) (1.35) (1.38) Country fixed effect No No Yes Yes N 201 201 201 201 R2 0.30 0.30 0.74 0.75 Notes: All of the regressions use robust standard errors to adjust for heteroskedasticity. The 10%, 5%, and 1% significance levels are denoted by *, **, and ***, respectively; t-statistics are reported in parentheses next to the coefficient estimates. Panel data is from 102 countries over two 5-year subperiods, 1996-2000 and 2001-2005. The data on education is missing for three observations (Singapore, Madagascar, and Tanzania for the 2001-2005 subperiod).
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In sum, the cross-country regressions presented above show a strong association between each of the ICT variables with growth. Although these results cannot be used to infer causal links between ICT penetration and growth, they further support the hypothesis that ICT as a dynamic factor was an important source of growth in 1996-2005. 3.3. The causal link between ICT penetration and growth Model and estimation procedure The results from cross-country regressions above also suggest some features of an appropriate model for teasing out the causal effect of ICT penetration on growth. First, the model needs to include country-fixed effects; second the relationship between the ICT variable and growth should be captured in a quadratic function; and third, once countryfixed effects are included, the model may need to contain only the variables on ICT and the initial income level because they are the most dynamic factors, whose changes may have significant effects on growth. In this spirit, the following model for dynamic panel analysis is used:
YGROWi ,t YGROWi ,t 1 X i ,t 1 t Dt i ui ,t
(7)
where YGROWi ,t is GDP growth of country i in year t . YGROWi ,t 1 , the lagged value of the country’s growth, is included to capture the persistence of growth pattern affected by the unobservable factors that are not fully captured by the country-specific and time-fixed effects. X i ,t 1 is a vector of the variables taking the lagged values of the penetration of ICT and the level of income. Again, the ICT penetration is proxied alternatively by the current levels of penetration of personal computers (ICT_pc), mobile phones (ICT_mb), and internet users (ICT_iu). YPCAP is the current level of income. The quadratic terms of the ICT variables (ICT_pc_sq, ICT_mb_sq, and ICT_iu_sq) and the level of income (YPCAP_sq) are included in the model to capture the non-linear relationship between them and growth. Dt are year dummies. i are country-fixed effects and ui ,t is the random error term.
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If the right hand side variables in equation (7) were exogenous, the standard OLS that takes into account both country and time fixed effects would be sufficient to yield consistent estimates. However, this is not the case. The right hand side variables such as
YGROWi ,t 1 and ICTi ,t 1 are predetermined but they could be endogenous because they have potential correlations with the error term ui ,t 1 and earlier shocks. To overcome this problem, the system Generalized Method of Moments (GMM) estimator is employed. The system GMM estimator was developed by Arellano and Bover (1995) and Blundell and Bond (1998). This estimator is an important improvement from the firstdifference GMM estimator, which first proposed by Holtz-Eakin, Newey, and Rosen (1988), and Arellano and Bond (1991). As demonstrated by Soto (2009), the system GMM estimator is superior to the other estimators, including the first-difference GMM estimator, because it has a lower bias and higher efficiency. Furthermore, the system GMM works better than the first-difference GMM when the number of entities (such as countries) is small.13 As pointed out by Roodman (2009), the system GMM estimator as well as the firstdifference GMM estimator, which hereafter are referred to as Arrelano-Bond (AB) GMM, is suited for estimating Equation (7) due to their following desirable features.14 First, the AB GMM estimators are designed for panel analysis in situations with “large N” and “small T”, in which the correlation between the lagged dependent variables and the error term may be significant. This feature is good for this exercise (N=102 countries and T=10 years, from 1996 to 2005). Second, as with other fixed-effect panel estimators, the AB GMM estimators consider the presence of country-fixed effects, which is important for avoiding the omitted variable bias as discussed in subsection 3.2. Third, the error term, ui ,t , may have individual-specific patterns of heteroskedasticity and serial correlation. Fourth, some regressors are allowed to be endogenous in these models
13
A discussion of the reason why the system GMM estimator outperforms the difference GMM estimator can also be found in Arellano and Bover (1995), Blundell and Bond (1998), and Bond, Hoeffler, and Temple (2001). 14 These features are also shared by the difference GMM estimator.
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and the techniques allow the use of only “internal” instruments, which are lags of the instrumented variables. The AB GMM techniques, in fact, have become increasingly popular in the empirical economic growth literature for examining causal relationships from panel data. For example, Caselli et al. (1996) estimated the effect of initial income level on growth in its discussion of weaknesses faced the standard cross-country empirical work on growth. Beck, Levine, and Loayza (2000) found that development of financial intermediaries has a positive impact on economic growth and total factor productivity; Forbes (2000) evidenced a causal link between inequality and economic growth. Acemoglu, Johnson, Robinson, and Yared (2008) examined the causal effect of income on democracy and Giuliano and Ruiz-Arranz (2009) found that remittances boost growth in the countries with less developed financial systems. In order for a GMM estimation to yield consistent estimates, it has to pass two tests. One is the Arellano-Bond AR(2) test of the absence of second order autocorrelation in the residuals of the first-difference equation. The other is the Hansen test of the exogeneity of the instruments as a group. As shown in Table 6, the p-values of the Arellano-Bond AR(2) tests in the GMM estimations with ICT_pc, ICT_mb, and ICT_iu are, respectively, 0.844 (column (1a)), 0.536 (column (2a)), and 0.818 (column (3a)), which are far above the 10% threshold. The tests, therefore, fail to reject the null hypothesis that there is no second order autocorrelation in the residuals of the first-difference equation. Similarly, the p-values of the Hansen tests for the GMM estimations with ICT_pc, ICT_mb, and ICT_iu are, correspondingly, 0.330, 0.299, and 0.495, which all also far exceed the 10% threshold. That is, the null hypothesis of the exogeneity of the instruments cannot be rejected. In sum, the test results confirm that the GMM estimations in Table 6 satisfy the requirements for their estimates to be consistent.
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Table 6: Causal link between ICT and growth: results from Arrelano Bond system GMM and FE OLS regressions Dependent Variable: YGROWt Explanatory Variable
YGROWt-1 ICTt-1 (ICTt-1)_sq YPCAP t-1 (YPCAP t-1 )_sq Year 1997 Year 1998 Year 1999 Year 2000 Year 2001 Year 2002 Year 2003 Year 2004 Year 2005
Personal Computers (ICT=ICT_pc) GMM FE OLS (1a) (1b) 0.333*** 0.134*** (2.90) (4.27) 0.016*** 0.016*** (3.02) (2.55) -0.00002*** -0.00001** (-3.16) (-2.12) -0.262*** -2.034*** (-2.98) (-4.37) 0.004** 0.025*** (2.32) (2.68) -0.345 -0.119 (-0.79) (-0.27) -1.480*** -1.029** (-2.80) (-2.39) -1.071** -0.843** (-2.10) (-1.96) -0.072 0.311 (-0.14) (0.71) -1.322*** -0.546 (-2.76) (-1.22) -1.530*** -0.879* (-2.73) (-1.91) -0.224 0.376 (-0.38) (0.80) 0.647 1.540*** (1.29) (3.17) -0.357 0.916* (-1.03) (1.83) [0.844]
Mobile Phones (ICT=ICT_mb) GMM FE OLS (2a) (2b) 0.216* 0.128*** (1.81) (4.09) 0.055** 0.043** (2.37) (2.07) -0.0005*** -0.0003 (-3.02) (-1.34) -0.123* -1.886*** (-1.91) (-4.12) 0.002 0.023*** (1.06) (2.71) -1.221 0.001 (-1.57) (0.00) -3.114*** -0.940** (-3.42) (-2.23) -2.020** -0.735* (-2.42) (-1.71) -1.577* 0.324 (-1.68) (0.73) -2.804*** -0.575 (-2.62) (-1.24) -2.612** -0.943** (-2.51) (-1.96) -1.681 0.302 (-1.61) (0.61) -0.687 1.464*** (-0.64) (2.86) -1.406 0.777 (-1.16) (1.44) [0.536]
Internet Users (ICT=ICT_iu) GMM FE OLS (3a) (3b) 0.282** 0.118*** (2.41) (3.71) 0.101** 0.105*** (2.50) (3.09) -0.001*** -0.001*** (-2.88) (-2.79) -0.164** -1.972*** (-2.53) (-4.48) 0.003* 0.025*** (1.77) (2.87) -1.065 -0.046 (-1.49) (-0.10) -3.187*** -1.103** (-3.51) (-2.43) -2.076** -0.966** (-2.52) (-2.09) -1.715* 0.092 (-1.94) (0.19) -2.954*** -0.760 (-3.14) (-1.55) -2.719*** -1.125** (-2.81) (-2.24) -1.716* 0.127 (-1.84) (0.25) -0.811 1.328** (-0.90) (2.51) -1.657* 0.719 (-1.67) (1.32) [0.818]
Arellano-Bond AR(2) test in first differences [0.330] [0.299] [0.495] Hansen test of overid. restrictions Number of 991 991 1019 1019 993 993 Observationsa R2 0.32 0.31 0.32 Notes: All of the regressions use robust standard errors to adjust for heteroskedasticity. The 10%, 5%, and 1% significance levels are denoted by *, **, and ***, respectively; t-statistics are reported in parentheses under the coefficient estimates. Numbers in brackets refer to the p-values of the tests. a
Due to data missing, there are only 991 observations for the estimation with ICT_pc, 1019 with ICT_mb, and 993 for ICT_iu (if data was not missing, there would be 1020 in the panel data of 102 countries over 10 years, 1996-2005). Most missing data are encountered in the early years.
30
Empirical results The results from estimating Equation (7) using the system GMM estimator are reported in Table 6 (Columns (1a), (2a), and (3a)). These results will be used for assessing the causal links between the explanatory variables and growth. The results from the fixed effect (FE) OLS estimator are provided (in columns (1b), (2b), and (3b)) only for reference. The results show that ICT penetration had a robust causal link with growth. The coefficient on ICT is positive and statistically significant in the GMM estimation for all three ICT variables, ICT_pc, ICT_mb, and ICT_iu. On the other hand, the coefficient on the quadratic term of the ICT variables is negative and statistically significant in the GMM estimation. These results, therefore, suggest that the marginal effect of ICT penetration on growth is positive but it lessens as the level of ICT penetration increases. The marginal effect15 of ICT penetration on growth (measured in percentage points) depends on the level of penetration and can be computed as 0.016-2*ICT_pc*(0.00002) = 0.016-0.00004*ICT_pc for personal computers; 0.055-2*ICT_mb*(0.0005) = 0.0550.001*ICT_mb for mobile phones; and 0.101- 2*ICT_iu*(0.001) = 0.101 - 0.002*ICT_iu for internet users. For example, for the average country in 2000 (for which, ICT_pc≈11.0; ICT_mb≈20; and ICT_iu≈10), the marginal effect of ICT penetration on growth was 0.0156 percentage points for personal computers, 0.025 percentage points for mobile phones, and 0.08 percentage points for internet users. That is, for an average country, the marginal effect of ICT penetration on growth is larger for the Internet than for mobile phones, and larger for the latter than for personal computers. The coefficient on the lagged growth variable is positive and significant in all the regressions. This implies that lagged growth is an important predictor of a country’s economic performance. The coefficient on YPCAP is negative and significant at the 1% significance level in all the GMM estimations, which means that the conditional convergence effect is strongly significant. On the other hand, the coefficient on YPCAP_sq is positive and significant in 15
For y 0 1 x 2 x , the marginal effect of 2
x on y is 1 22 x 31
all regressions except for (2a). This result suggests that, to some extent, the conditional convergence effect lessens as the income increases. The estimates for the year dummies capture global effects on growth across countries. The base category is 1996, for which the dummy is not included. The coefficient on the dummy for a year represents the difference in growth between this year and 1996. The coefficients on the year dummies for 1998, 1999, 2001, and 2002 were negative and highly significant in all regressions. This means the year dummies well capture the severe adverse effects of the Asian financial crisis (1998 and 1999) and the consequences of the dotcom burst and the 9/11 terrorist attack (2001 and 2002) on growth across countries. 4. Conclusions Since the mid-1990s the ICT revolution has rapidly penetrated across nations and transformed the way people communicate, work, and live. At the core of the driving force of this transformation is the quantum progress across countries in the speed, scope, intensity,
and
quality
of
access
to
information,
knowledge
diffusion,
and
communications. These powerful impacts are expected to have been translated into economic performance. This paper examines the hypothesis that ICT penetration has a positive effect on economic growth through both theoretical and empirical examinations. On theoretical front, the paper presents three theoretical grounds supporting this hypothesis. First, ICT penetration affects growth through fostering knowledge diffusion (especially from developed to developing countries) and innovation. Second, ICT penetration enhances the quality of decision-making of firms and households, which improves the efficiency and effectiveness of resource allocations. Third, ICT penetration reduces production costs and fosters demand and investment; and hence raises the level of output and growth. The paper then conducts a comprehensive empirical examination to investigate the effect of ICT on growth for a sample of 102 countries in the 1996-2005 period, during which ICT has rapidly penetrated across nations.
This empirical examination is
conducted in three exercises. The first exercise inspects if the growth in 1996-2005 improved and experienced a significant structural change relative to the previous two
32
decades. The results from descriptive statistics and the Chow tests for structural stability show that growth in 1996-2005 substantially improved relative to the 1976-1985 and 1986-1995 periods and it experienced a very significant structural change. This implies that some new strong growth drivers have emerged and materialized in 1996-2005. These findings as such strengthen the hypothesis that ICT was an important source of growth in 1996-2005. The second exercise, examines whether the association between ICT penetration and growth over 1996-2005 was significant, controlling for the other potential growth determinants and country-fixed effects. If ICT penetration was a new strong driver of growth in this period, its strong association with growth is expected. This examination employs cross-country regressions for the panel data of 102 countries over two subperiods, 1996-2000 and 2001-2005, examining the link between growth and a number of potential growth drivers, while controlling for country fixed effects. Three important findings come out from this exercise: (i) the association between growth and ICT penetration (which is alternatively measured as the subperiod average level of penetration of personal computers, mobile phones, and internet users), is highly significant; (ii) only the associations of ICT penetration and the initial income level with growth are significant, once the country-fixed effects are included in the model; and (iii) the quadratic function better captures the relationship between ICT penetration as well as the initial income level and growth. The results from the second exercise do not confirm but further support the hypothesis that ICT has a significant effect on growth in 1996-2005. The third exercise is devoted to tease out the causal effect of ICT penetration on growth. The exercise uses the system GMM estimator for dynamic panel data analysis, developed by Arellano and Bover (1995) and Blundell and Bond (1998). This method has been proved to be an effective tool for discerning causal links. Results from this estimation show that the penetration of personal computers, mobiles phones and internet users had a significant causal effect on growth. Furthermore, for the average case, the marginal effect of the penetration of internet users is larger than that of mobile phones, which in turn is larger than that of personal computers. However, the marginal effect of the ICT penetration lessens as the penetration increases.
33
The evidence of the role of ICT as an important source of growth suggests several policy implications. First, all countries, need a more strategic focus on promoting ICT penetration as an important source of growth. This promotion should not be confined only to upgrading the ICT infrastructure and reducing the costs of ICT use; but also needs to focus on increasing the eventual effects of ICT penetration on growth. Among the top priorities in this direction, is improving the availability of information on market and technology, fostering innovation and exchange and enhancing the quality of decision making. Furthermore, promoting ICT penetration requires careful analyses16 and wellthought out strategy to ensure the most productive gains from fostering ICT diffusion. For example, enhancing the benefits of ICT use may be more effective than overemphasis on reducing its use costs. It is worth noting that the benefits from ICT use is unlimited and are far higher than the costs of ICT use. Second, the larger effect of the penetration of the Internet on growth suggests that promoting the diffusion of this technology is both urgent and strategic. For this effort, investing in broadband infrastructure, reforming education system to better prepare people for the information age, and fostering Internet-enabled services and Internet presence, including e-government and e-commerce should be of top priorities. Third, the countries with lower level of ICT penetration should be more aggressive in promoting the diffusion of ICT, especially the Internet. The results from this study show that the marginal effect of the penetration of ICT is larger when at its lower level, especially for the Internet and mobile phone. Furthermore, as pointed out by Wong (2002), the disparity in the intensity of ICT adoption among countries is wider than the inequality in their income level. Therefore, catching up on ICT penetration should be a top priority in any country’s endeavor to further economic development.
16
Wallsten (2008) pointed out that policy maker should not rely only on simple rankings for policy decisions because they may not well reflect the true nature of ICT penetration as in the case of broadband.
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