Does e-government promote trade and economic growth? This paper attempts to answer this question by employing simultaneous equation estimation approach and using a cross-section data of 147 countries. This is first study which has empirically estimated the bilateral relationships between economic growth and e-government, trade and e-government and trade and economic growth. The findings indicate that e-government is a stimulant of both economic growth and trade. The results predict the presence of a bilateral relationship between e-government and economic growth, trade and e-government, and unilateral causality exists from trade to growth.

JEL Classification: F14; H10; 040

Keywords: E-government; Economic Growth; Trade

1. INTRODUCTION

The study on economic growth is dated back from Adam Smith (1776) discussed in his famous book "wealth of nation". There are many theories of economic growth presented by different economists, according to the situation that have been prevailed during that time [Ricardo (1817); Harrod (1939); Domer (1946); Solow (1956)]. The pioneer of theoretical framework of economic growth is Solow (1956) and his model was employed by Barro (1991); Mankiw, Romer, and Weil (1992); and Quah (1993, 1997).

Trade is an important topic that has been captured the attention of policy makers since the start of previous century. The debate on trade has been dated back from many decades but yet there is no consensus about the positive consequences of trade on economic growth. The positive influence of trade on economic growth is empirically supported by [Edwards (1998); Wacziarg (2001); Greenaway, et al. (2002)] whereas [Rodriguez and Rodrik (1999)] doubted the robustness of positive relationship between trade and economic growth. In this study we empirically check the association of trade with economic growth by incorporating e-government. The trade and e-government have bilateral relationship. Trade promotes e-government by diffusion of technologies and on the other hand e-government promotes trade by overcoming non- tariff barriers and asymmetric information.

E-government is referred to online availability of government to provide quick and efficient services to masses of people. Von Haldenwang (2004) defined e-government as a practice of information and communication technology (1CT) in public administration. E-government facilitates government in efficient provision of services to citizen by employing ICT infrastructure [Tandon (2005); Chen, et al. (2009); and Krishnan and Teo (2012)].

The theoretical studies on e-government emphasise its role in enhancing the efficiency of public sector and public administration [Al Kibsi, et al. (2001); Von Haldenwang (2004); and West (2004)] and increasing the marginal productivity of labour by mitigating the disguised unemployment [Grimes, Ren, and Steven (2012)]. In spite of its importance the empirical research on e-government is on embryonic stages. After reading vast literature we got insights about empirically investigating its role in promoting trade and economic.

E-government contributes in economic growth through trade openness by providing online availability of government and web connections. Trade also significantly contributes in output growth by tapping full potential of world resources that will help to mitigate poverty, malnourishment, infant mortality rate, illiteracy, unemployment, and inequality. The consensus about the positive relationship between trade and economic growth is yet not achieved. The advocates of positive relationship between trade and economic growth are [Rivera-Batiz and Romer (1990); Grossman and Helpman (1991); Edwards (1998); Wacziarg (2001); and Greenaway, et al. (2002)] whereas [Rodriguez and Rodrik (1999)] questioned the robustness of positive association between trade liberalisation and economic growth. Most of the studies empirically confirm the positive association between trade and economic growth. Thus, to reap full potential of trade we have to mitigate tariff and non-tariff barriers. The hindrance in the way of trade openness is not only tariffs but also non-tariff barriers such as asymmetric information, transportation cost and low interaction between traders.

The non-tariff barriers in the way of liberalise economy can be overwhelmed by e-government through cheap access to refine information and interaction between traders. E-government can upsurge economic development of country by facilitating trade. There are few studies which have taken into account of internet role in facilitating trade liberalisation [Choi and Hoon Yi (2009); Clarke and Wallsten (2006); Freund and Weinhold (2004); Vemuri and Saddiqi (2009)]. These studies predicted internet as a trade stimulant.

The impact of e-government on trade in empirical literature is missing. We fill this gap by taking into account of bilateral relationships between trade, e-government and economic growth by employing simultaneous equation model. Our study empirically explores e-government-trade, trade-economic growth and e-government-trade nexus. By advocating the bilateral relationship between trade and economic growth we are able to find the direct and indirect impact of trade on economic growth through e-government.

The study is arranged as follows: Section 2 presents the literature on economic growth, e-government and trade. Section 3 discusses the empirical framework of our study. Section 4 describes data and its statistical analysis. Section 5 presents and interprets empirical findings of simultaneous equation model. Finally Section 6 concludes and suggests policies.

2. LITERATURE REVIEW

In this section we explore the literature on the relationships between economic growth, e-government and trade.

2.1. E-government and Economic Growth

E-government provides efficient services to masses of people that stimulate economic growth. The efficient provision of responsibilities towards nation facilitates trade by providing cheap access to information and efficient allocation of resources to those projects that reap high returns, and facilitate interaction among investors. The electronic government through information and communication infrastructure enhances productive potential of economy. E-government stimulates output growth of the country by disseminating information and spilling over the knowledge and cutting down transaction and transportation cost. Salvatore (1996) points out that East Asia "miracle" was based on strong government support for domestic industry while stimulating competition and efficiency among domestic firms.

Summer (1999) illustrated the importance of software development in setting up new information based modern economy. He demonstrated that information technology is contributing significantly in output growth of a country. Shamim (2007) empirically analysed the effect of telecommunication technologies on output growth by taking data of 61 countries over the year of 1990 to 2001. She proposed that telecommunication technologies provide refine information, mitigate the data processing cost and asymmetric information, and facilitate interaction between buyer and seller. The results indicated that positive impact of financial development is mediated by e-government or telecommunication technology.

Choi and Yi (2009) investigated empirically the effect of internet on output growth using the panel data for 217 countries from 1991 to 2000. The findings indicate that 1 percent increase in internet subscribers up surges growth about 0.05 percent. Krishnan, Teo, and Lim (2013) argued that impact of e-government on growth is mediated by control of corruption and environmental degradation. They use averaged data over the period 2004-2008. Their finding inferred that direct impact of e-government on economic prosperity is insignificant and its impact on growth is intermediated by control of corruption and environmental degradation.

According to Czemich, et al. (2011) broadband "fast speed internet" has positive effect on output growth. They have empirically investigated the impact of broadband on economic growth by taking data of OECD countries over the years of 1996 to 2007. Their results indicate that broadband upsurges growth up to 3.9 percent. Mahyideen, et al. (2012) proposed that ICT upsurge economic development by enhancing productivity and cutting down production cost. They empirically explored the relationship between economic prosperity and information and communication technologies (ICT) for ASEAN countries from 1976 to 2010. The results of granger causality supported long run relationship between ICT and economic growth.

2.2. Trade and Economic Growth

There is vast theoretical as well as empirical literature on the impacts of trade liberalisation on economic growth but debate is yet not settled. The contradiction in consequences of trade on growth in not only lies between theoretical but also lies within empirical literature. The positive impacts of trade liberalisation on output growth are advocated by theoretical models of many economists [Grossman and Helpman (1991); Rivera-Batiz and Romer (1990); and Devereux and Lapham (1994)] and theoretical framework on negative implication of trade on economic growth is proposed by Redding (2002). Likewise, empirical studies have also polarised into positive and negative consequences of trade liberalisation on economic growth. The positive impact of trade on economic growth in empirical studies is advocated by [Edwards (1998); Wacziarg (2001); and Greenaway, et al. (2002)] whereas Rodriguez and Rodrik (1999) doubted the robustness of positive effect of trade on output growth. The negative relationship between trade-growth nexus is demonstrated by Clemens and Williamson (2000); and Vamvakidis (2002).

Winter (2004) doubted on robustness of positive impact of trade on economic growth. He demonstrated that relationship between economic growth and trade depends on omitted variables in regression. He proposed that consequences of trade on economic growth can vary in the case of inclusion of education, corruption, institutional strength, political stability, and level of development of a country. Using a panel data for 42 countries, Parikh (2006) estimates the effect of trade liberalisation on growth and growth on trade balance. The study finds out that trade liberalisation promotes growth in most countries, but the growth itself has a negative impact on trade balance.

Kneller, et al. (2008) empirically founded relationship between trade liberalisation and economic growth is heterogonous in different countries. He has taken panel data of 37 countries and introduced dummy variable that is one for the time period when it starts to liberalise. Their findings inferred that trade liberalisation has increased overall growth rate of post-liberalisation period about 2.4 percent per annum but out of 37, the growth rate of 20 countries has decease in after liberalisation. Shachmurove and Spiegel (2010) analysed the welfare of nations in a globalised economy. They point out less welfare effects in a more globalised world.

2.3. E-government and Trade

The impediments of trade are not only tariffs but also non-tariff barriers such as transaction cost and lack of information. Collier and Gunning (1999) alleged that particular obstacle in the way of economic growth in Africa is transaction cost. E-government through online availability and web connection can fill this gap in the way of open economy. E-government also accelerates trade by decreasing transaction cost, facilitating interaction between traders, providing refine and clear information on quality, demand and supply, markets, and prices of different products.

Mattoo, Rathindran, and Subrama (2001) empirically investigated the impact of liberalisation in service on economic growth of the country. He argued that consequences of service liberalisation are different from trade liberalisation. The empirically results of the study inferred that telecommunication services and financial services have positive and significant impact on output growth whereas impact of financial services is stronger than telecommunication services. They conclude that the economy having open telecom and financial services tends to grow 1.5 percent higher.

We cannot blame merely tariff as a resistance of trade liberalisation but various transactions, communication, and fixed entry costs also responsible for restricting smooth trade in the country. Majeed and Ahmad (2006) in their study of determinants of exports in developing countries enunciated the importance of communication technologies in encouraging exports. They proposed that communication technologies such as internet and mobile phones have significant impact on exports of developing countries.

Clarke and Wallsten (2006) scrutinised the impact of internet on trade for both developing and developed countries. The findings of their study suggested that internet has positive impact on trade only in developing countries. Meijers (2014) investigated growth-internet, internet-trade, and trade-growth nexus by taking archive data of 162 countries over the time period 1990-2008. The result of simultaneous equation model confirmed that the growth impact of internet is mediated by trade whereas direct effect of internet on growth is insignificant.

Kurihara and Fukushima (2013) examined how internet facilitates trade in 34 developed and 24 Asian countries for year 2005 and 2010 by employing gravity trade model. Their findings indicate that internet has stronger positive effect on trade in developing countries than developed countries in 2005. Yadav (2014) studied the impact of internet on exports and imports of 52 Asian and sub-Saharan African countries from 2006 to 2010. He proposed that internet has significant effect on export and import of firms in extensive and intensive margin in Asian and Sub-Saharan African countries. He mentioned that firm has to face fixed information cost to enter into international market but internet save firms from entry costs. Freund and Weinhold (2004) also supported that internet is a trade stimulant in a sample of 56 developing countries.

The above literature shows that trade-growth nexus is empirically investigated by economist, but the association of this nexus with e-government is ignored. Furthermore, the existing literature focuses different components of e-government such as internet to explain growth but does not incorporate a comprehensive measure of e-government. The present study fills these gaps in the literature. 3

3. SIMULTANEOUS EQUATION MODEL

E-government stimulates economic growth directly and also indirectly by stimulating trade. Trade regulates e-government by integrating different economies which facilitate diffusion and spillover of knowledge whereas e-government enhances trade by facilitating interaction between traders and foreign investors. The prevailing case of interrelationship among three endogenous variables calls for a need of simultaneous equations model. The empirical framework of our study is based on three simultaneous equations to estimate direct and indirect impacts of e-government on economic growth.

3.1. Equation of Economic Growth

The model of economic growth employed in our study is stemmed from Solow (1956) which has CRS (constant returns to scale) and two inputs

y = f (A, K, L)

Mankiw, et al. (1992) extended the theoretical models of Solow (1956) and Koopmans (1969) by relaxing the convergence condition. According to absolute convergence theory the poor countries will catch up the per-capita of rich countries due to high marginal productivity of capital. In order to fulfil the convergence condition we have introduced initial per-capita in our empirical growth model.

[y.sub.i] = [[beta].sub.0] + [[beta].sub.1][y.sub.initial,i] + [[beta].sub.2][A.sub.i] + [[beta].sub.3][K.sub.i] + [[beta].sub.4][L.sub.i] + [e.sub.i] ... ... ... (1)

The advocates of endogenous growth model see divergence in per-capita income due to divergence on technological potential of country. The steady state growth rate of country varies due to differences in technological progress and innovations [Barro (1991) and Barro and Sala-i-Martin (1991)]. Technological progress has proxied by information and communication technology in different studies and intuition behind using ICT "as a proxy for technology" is high labour marginal productivity due to information and communication technology [Jorgenson, et al. (2007); van Ark, et al. (2008); Oliner, et al. (2008)]. Few studies emphasised on information technology (internet) in order to measure the divergence in per-capita due to gaps in technological potential [Clarke and Wallsten (2006); Meijers (2014)]. We measure technology by e-government. The equation 1 is modified as

[y.sub.i] = [[beta].sub.0] + [[beta].sub.1][y.sub.initial,i] + [[beta].sub.2]E[G.sub.i] + [[beta].sub.3][K.sub.i] + [[beta].sub.4][L.sub.i] + [e.sub.i] ... ... ... (2)

The technological diffusion across the world can be driven by economic integration of world. The economic integration will help in diffusion and spill over knowledge and information and excite innovation in the country. Acemoglu and Ventura (2002) proposed the model that describes convergence in per-capita in terms of international trade. We also address the impact of trade on economic growth following same rationale. The left side variable is economic growth, K. is capital stock, and L is labour force. The error term of equation is shown by [e.sub.i].

[y.sub.i] - [[beta].sub.0] + [[beta].sub.1][y.sub.initial,i] + [[beta].sub.2]E[G.sub.i] + [[beta].sub.3][K.sub.i] + [[beta].sub.4][L.sub.i] + [[beta].sub.5][Trade.sub.i] + [e.sub.i] ... (3)

3.2. Equation of Trade

The economic integration of world can be stimulated by e-government through its information and telecommunication infrastructures, skilled labour, and web connectivity. Different studies have explored merely internet as a stimulant of trade liberalisation [Kurihara and Fukushima (2013); Meijers (2014); and Yadav (2014)].

We are interested to explore reverse relationship between trade liberalisation and economic growth. The equation three explains the impact of trade on economic growth but in equation 4 we have incorporated economic growth as a determinant of trade. Egovernment stimulates trade by delivering cheap information and facilitating interaction among traders.

Trade = [[alpha].sub.0] + [[alpha].sub.1][Trade.sub.initial,i] + [[alpha].sub.2][y.sub.i] + [[alpha].sub.3]E[G.sub.i] + [[alpha].sub.4]Tarif[f.sub.i] + [[alpha].sub.5]Exchange[rate.sub.i]+[u.sub.i] ... ... ... ... ... ... (4)

In order to determine the indirect impact of e-government on trade we have introduced interactive term of economic growth and e-government. The exchange rate is an important determinant of trade. Trade liberalisation depends on protection level in a country. The tariff rate is a measure of trade protection in a country and exerts a negative influence on trade liberalisation.

[Trade.sub.i] = [[alpha].sub.0] + [[alpha].sub.1][Trade.sub.initial,i] + [[alpha].sub.2][y.sub.i] + [[alpha].sub.3]E[G.sub.i] + [[alpha].sub.4]Y x E[G.sub.i] + [[alpha].sub.5]Exchange [rate.sub.i] + [[alpha].sub.6]Tarif [f.sub.i] + [u.sub.i] ... ... ... (5)

3.3. Equation of E-government

The adoption of ICT tools by government for efficient provision of its services depends on economic growth of the country. The installation of latest technology in public sectors is regulated by economic performance of the country. The latest technology is usually innovated and adopted by developed countries because they have sufficient budgets to shift public sector from primitive to modern public administration. According to Comin and Hobjin (2004) famous technologies are first embraced by most of the developed countries. Czemich, et al. (2011) proposed that there is reverse causal relationship between e-government and economic growth. The other possibility of impact of output growth on e-government is state intervention. The installation and penetration of ICT infrastructure in public sector is regulated by state intervention in economic decisions of country and state intervention is regulated by economic growth of the country [OECD (2009)].

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (6)

The diffusion of technology is stirred by economic and social integration of countries. Here we are going to address only trade as a measure of economic integration, to find out that how it promotes e-government. The equation 6 can be written as

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (7)

According to Czernich, et al. (2011) access to broadband usually comes from fixed telephones and TV-cables lines. The fixed telephone lines regulate online presence of government. Anderson (2008) proposed that according to UDT (urban density theory) internet subscription depends on urban population because cost of internet decreases as share of urban population increases because of knowledge spillover, availability of other substitutes of internet such as broadband. For these reasons we incorporated fixed-telephones line and share of urban population as determinants of e-government

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (8)

4. DATA DESCRIPTION

The secondary data of 147 countries for the years of 2003 to 2012 is employed in this study. We have supported our study through cross sectional data by taking averages of dataset from 2003 to 2012. The main reason behind the choice of cross sectional data is missing data of e-government. The data on e-government is not continuous but have missing values. So the best alternate was to take the multiyear averages of data produce more efficient and robust results than single year data. The cross sectional data of multiyear averages give less sensitive results [Wiggins and Ruefli (2005)].

E-government index is weighted average of online service and web connection, telecommunication infrastructure, and human capital that can use the tools of information and communication technologies. All the components of e-government have equal weight of 0.33. The data lies in the range of 0 to 1: zero indicates worst e-government quality whereas one indicates best e-government quality.

The measure of economic growth is natural log of per capita GDP (at current U.S dollars) whereas 1990 is base year of initial per-capita income, measure of tariff is tariff rate of weighted mean, measure of physical capital is gross fixed capital formation (percentage of GDP), and measure of trade liberalisation is export plus import (percentage of GDP).The data source of these variables is WDI [World Development Indicators (2014)]. The data of fixed telephone lines is derived from ITU (International Communication Union).

Figures 1, 2 and 3 show a positive association of trade and e-government with economic growth and with each other. The bar charts (Figures 4 and 5) indicate that trade is high in countries having good quality e-government (see Appendix).

Table 4.2 presents the summary statistics of data. The minimum value of e-government is 0.087 that is the value for Niger. Niger has poorest e-government quality and Denmark has best e-government quality. United States has minimum trade to GDP ratio that is only 27 percent and Luxembourg has highest trade to GDP ratio and per capita income. Burundi has lowest per capita income. The country having lowest tariff rate is Macao (China) and country having highest tariff rate is Liberia.

In order to avoid biased result it is necessary to check the functional forms of specified equations. Linktest (1) serves to find that whether functional form of the equation is correct or not. Table 4 presents the results of linktest which indicate that coefficients of hat square are not significant in Equations 3 and 8 which is a signal of no concern of specification error in the equations. Hat square is the square of the independent variables in an auxiliary regression to check leverage. The insignificance of hat square illustrates that the variance in independent variables is not causing fluctuation in dependent variables.

5. EMPIRICAL FINDINGS

We have applied Seemingly Unrelated Regressions (SUR), Two Stage Least Squares (2SLS), and Three Stage Least Squares (3SLS). (2) The SUR model takes into account correlation among error terms of all the equations. The empirical finding of SUR model indicates that there is a positive and significant relationship between economic growth and e-government. The coefficient of e-government in 1st column of Table 5.1 implies that 1 percent increase in e-government quality brings 3.67 percent increase in economic growth. The coefficient of trade in 1st column of Table 5.1 is also positive and significant which implies that 1 percent increase in trade causes 0.35 percent increment in economic growth. The coefficient of initial per capita income 1st column of Table 5.1 implies that 1 percent increase in initial income will upsurge growth about 0.52 percent. (3)

The 2nd column of Table 5.1 presents the empirical finding of equation 5. The results confirm that e-government is a trade stimulant. The coefficient of economic growth in 2nd column of Table 5.1 implies that 1 percent increase in economic growth will cause 0.10 percent increment in trade. The findings indicate the reverse causal relationship from economic growth to trade. The interactive impact of "e-government and economic growth" on trade is negative and significant. The net effect of e-government on trade is 2.07 (2.33- 0.26) whereas net effect of economic growth on trade is -0.16 (0.10-0.26). It indicates that high per capita income is not strengthening the positive impact of e-government on trade, the likely reason of this effect may be self-sufficiency of a country after certain threshold level of high per capita income.

The high economic growth can make country self-sufficient that probably has negative impact on trade due to cutting down imports and exports (retaliation of foreign country due to decreasing theirs exports). Our data also indicates that United States has lowest trade to GDP ratio. Tokarick (2008) stated that rich countries use an array of protectionism policies in agriculture sector in order to protect their farm industry. They usually protect their agriculture sector by employing import qoutas, tariff on imports, and subsidies. Stiglitz and Charleton (2005) also stated that the spending on agriculture subsidies in OECD countries is more than 300 billion US$ per annum. Exchange rate has insignificant impact on trade liberalisation whereas initial trade value of trade has positive significant impact on trade liberalisation. The coefficient of tariff indicates that 1 percent increase in tariff rate will decrease trade about 1 percent.

The findings of equation 8 are reported in 3rd column of Table 5.1. The coefficients of trade and economic growth in 3rd column of Table 5.1 infer that 1 percent increase in trade and economic growth will improve quality of e-government about 0.038 percent and 0.032 percent. The initial urban population and fixed telephone lines have also positive influence on e-government. The findings of SUR indicate that there is bilateral relationship between per capita income and e-government, per capita income and trade liberalisation, and trade and e-government.

Columns (4-6) of Table 5.1 present the results of 2SLS model. The results show that there is a bilateral causality between "e-government and trade" and "per capita income and e-government". There is unilateral causality between trade and per capita income from trade openness to per capita income. Initial urban population and fixed telephones lines is positively influencing the e-government quality. High per capita income is offsetting the positive impact of e-government on trade due to certain threshold level of per capita income when country adopts protection policy.

In 7th to 9th column of Table 5.1 the empirical findings of 3SLS are discussed. The results of 3SLS indicate that there is a bilateral relationship between e-government and per capita income and trade and per capita income. However, there is one way causality between e-government and trade that is from e-government to trade. The results of SUR model, 2SLS and 3SLS are almost consistent. All the simultaneous equation techniques confirm that e-government is a stimulant of trade but high per capita income offset its positive impact on trade because of protection in the form of subsidies to its sectors [Stiglitz and Charleton (2005)].

Simultaneous equation econometric techniques is ideal to estimate the simultaneous equation if all the equation are correctly specified. If one of the equations is miss specified then estimation with simultaneous equation approach will spread biasness in all the equation. In that case OLS is recommended. In order to evade from biasness and for sensitivity analysis we have applied Ordinary Least Square model. Table 5.2 presents the results of OLS which also infer that there is a two way causality between e-government and trade liberalisation and e-government and per capita income but unilateral causality between trade liberalisation and per capita income that is from trade to per capita income. The coefficient of e-government in 1st and 2nd column of table 5.2 indicates that 1 percent increase in e-government quality will enhance per capita growth about 3.22 percent and trade about 2.188 percent, respectively. The coefficient of trade in 1st and 3rd column of Table 5.2 denotes that 1 percent increase in trade increase per capita growth about 0.247 percent and e-government quality about 0.034 percent, respectively.

The positive sign of the coefficients of initial urban population and fixed telephones lines is consistent with urban density theory (4) and the study of Anderson (2008). Anderson proposed that online service and broadband are usually delivered from fixed telephone lines and cable TV lines. We have also controlled our result by introducing the control of corruption (as a proxy of institution) in growth equation and our results remain consistent (see Table A3 in Appendix).

6. CONCLUSION

E-government is an important tool that enhances trade. The online service of e-government promotes frequent interactions among traders and improves the quality of information regarding price, quality, and demands of goods. It can serve as an efficient tool to increases marginal productivity of labour and alleviate disguise unemployment in country by increasing trade. E-government is an important tool to mitigate non-tariff barriers lies in the way of liberalise and open economy. It facilitates a country in tapping the full potential of world resources.

We determine the relationships between economic growth and e-government, trade and e-government and trade and economic growth employing simultaneous equation estimation approach and a cross-section data of 147 countries. The bilateral relationships between trade and e-government are supported by SUR, OLS, and 2SLS whereas 3SLS model support one way causality between trade and e-government from e-government to trade openness.

Kim (2001) argued that main reason behind resistance in e-government is insufficient allocation of budget in area of e-government that will result into inappropriate usage of IT infrastructure. The lack of modern education and training on usage of information technology keep public servant unaware about usage of IT tools and impede development of e-government. In order to tap full potential of resources and trade liberalisation, the investment on e-government may make mandatory.

APPENDIX

Table A 1

Summary of Variables of Interest and Their Data Sources

Variables             Description                              Sources

Economic growth       Natural log GDP per capita at              [1]
                      current US $.

Initial per-capita    Natural log of per-capita GDP in 1990      [1]
                      (measured in current U.S dollars.

E-govemment           The extent of the online availability      [2]
                      of the government, telecom
                      infrastructure, and human capital.

Initial level         The year 2003 is taken as a initial        [2]
of E-govememnt        value of e-governemnt index.

Physical capital      Gross fixed capital formation in           [1]
                      percentage of GDP.

Labour supply         Share of labour force participation        [1]
                      total % of population.

Exchage rate          Official Exchage rate measured as          [1]
                      the average value of local currency
                      in terms of U.S dollars.

Trade                 Export plus import share of GDP.           [1]

Inflation             GDP deflator.                              [1]

Urban population      Initial level of urban population          [1]
                      (year 1990).

Fix_Telephone         Fixed telephone lines per 100              [3]
                      inhabitants.

Tariff                Weighted mean applied tariff is the        [1]
                      average of effectively applied rates
                      weighted by the product import
                      shares corresponding to each partner
                      country.

[1] World development indicator (2014); [2] Global E-govemment
reports (2003-2012); [3] International telecommunication Unions (2014)

Table A 2

Correlation Matrix

Variables                1         2         3         4

1.    Per capita       1.000
2.    Trade            0.01      1.000
3.    EG               0.733     0.075     1.000
4.    Tariffs         -0.4078   -0.1421   -0.6021   1.0000
5.    Capital         -0.062     0.226    -0.021    0.1421
6.    Labour          -0.015    -0.157    -0.267    0.2628
7.    Urban_pop        0.568    0.0484    0.6814    -0.4035
8.    Fix_Tele         0.788    0.0635     0.848    -0.4837
9.    Exchange rate   -0.1055   -0.1297   -0.0693   0.0629

Variables                5        6         7         8        9

1.    Per capita
2.    Trade
3.    EG
4.    Tariffs
5.    Capital          1.000
6.    Labour          0.0237
7.    Urban_pop       -0.0043   0.657     1.000
8.    Fix_Tele        -0.0344   0.7576   0.6447     1.000
9.    Exchange rate   0.0665    0.0776   -0.0068   -0.0665   1.000

Table A3

SEM with Control Variables

                                  SUR

                      (1)         (2)           (3)
Models               (Eq3)       (Eq5)         (Eq8)
Variables           Growth       Trade         E-gov

[Y.sub.initial]    0.542 ***
                   (0.0639)

Labour              -0.253
                    (0.289)

Capital            0.324 **
                    (0.145)

E-govemment        3.716 ***   2.314 ***
                    (0.439)     (0.816)

Trade              0.348 ***                0.0380 ***
                    (0.109)                  (0.00932)

Corruption          -0.0394
Control            (0.0852)

[Trade.sub.                    0.550 ***
initial]                        (0.0456)

ln(per capita)                  0.101 **    0.0316 ***
                                (0.0479)     (0.00479)

EG*Y                           -0.259 ***
                                (0.0825)

Exchange Rate                   0.00705
                               (0.00988)

Tariff                         -0.0101 *
                               (0.00601)

[E-gov.sub.                                  0.520 ***
initial]                                     (0.0349)

Urban                                       0.00992 ***
Population                                   (0.00203)

Fix_Tele                                    0.00194 ***
                                            (0.000404)

Constant             1.098     1.310 ***    -0.373 ***
                    (1.540)     (0.346)      (0.0618)

Observations          127         127           127

R-squared            0.880       0.622         0.956

                                 2SLS

                      (4)         (5)          (6)
Models               (Eq3)       (Eq5)        (Eq8)
Variables           Growth       Trade        E-gov

[Y.sub.initial]    0.554 ***
                   (0.0694)

Labour              -0.223
                    (0.315)

Capital              0.237
                    (0.159)

E-govemment        3.850 ***    3.023 **
                    (0.481)     (1.357)

Trade              0.497 ***                0.0295 **
                    (0.148)                  (0.0149)

Corruption          -0.0826
Control            (0.0922)

[Trade.sub.                    0.515 ***
initial]                        (0.0541)

ln(per capita)                  0.193 **    0.0453 ***
                                (0.0839)    (0.00686)

EG*Y                           -0.376 ***
                                (0.141)

Exchange Rate                    0.0100
                                (0.0109)

Tariff                          -0.0106
                               (0.00686)

[E-gov.sub.                                 0.489 ***
initial]                                     (0.0405)

Urban                                       0.0105 ***
Population                                  (0.00245)

Fix_Tele                                    0.00126 **
                                            (0.000489)

Constant             0.442       0.829      -0.433 ***
                    (1.722)     (0.539)      (0.0834)

Observations          127         127          127

R-squared            0.876       0.604        0.951

                                 3SLS

                      (7)         (8)          (9)
Models               (Eq3)       (Eq5)        (Eq8)
Variables           Growth       Trade        E-gov

[Y.sub.initial]    0.316 ***
                   (0.0605)

Labour              0.0717
                    (0.257)

Capital              0.210
                    (0.131)

E-govemment        5.088 ***     2.027
                    (0.434)     (1.288)

Trade              0.611 ***                 0.0129
                    (0.142)                 (0.0142)

Corruption          0.0213
Control            (0 0781)

[Trade.sub.                    0.474 ***
initial]                       (0.0517)

ln(per capita)                 0.268 ***   0.0605 ***
                               (0.0805)     (0.00618)

EG*Y                           -0.337 **
                                (0.135)

Exchange Rate                   0.0106
                               (0.00977)

Tariff                         -0.0112 *
                               (0.00608)

[E-gov.sub.                                 0.401 ***
initial]                                     (00362)

Urban                                      0.00981 ***
Population                                  (0.00224)

Fix_Tele                                   0.00107 **
                                           (0.000426)

Constant            0.0299       0.672     -0.437 ***
                    (1.420)     (0.518)     (0.0779)

Observations          127         127          127

R-squared            0.850       0.560        0.939

Standard errors in parentheses.

*** p<0.01, ** p<0.05, * p<0.1.

Table A3

List of Countries

List of Under Developed, Developing, and Developed Countries Under
Develop Countries

Armenia, Bangladesh, Benin, Burkina Faso, Burundi, Central African
Republic, Chad, Comoros, Congo. Dem, Ethiopia, Gambia, Guiana,
Kenya, Kyrgyz Republic, Liberia, Malawi, Mali, Mozambique, Nepal,
Nigeria, Rwanda, Tajikistan, Togo, Uganda, Zimbabwe.

Developing Countries

Albania, Algeria, Angola, Argentina, Azerbaijan, Belarus, Belize,
Bhutan, Bolivia, Botswana, Bulgaria, Cape Verde, China, Cameroon ,
Columbia, Congo. Rep, Costa Rica, Cuba,

Djibouti, Dominican Republic, Ecuador, Egypt, El Salvador, Fiji,
Georgia, Ghana, Guatemala, Guyana, Honduras, Hungary, India,
Indonesia, Iran, Jamaica, Jordan, Kazakhstan. Lao PDR, Lebanon,
Lesotho, Macedonia. FYR, Malaysia, Mauritania, Mauritius, Mexico,
Moldova, Mongolia, Montenegro, Morocco, Namibia, Nicaragua, Niger,
Pakistan, Panama, Papua new Guinea, Paraguay, Peru, Philippine,
Romania, Senegal, Serbia, Solomon island, South Africa, Serbia, Sri
Lanka, St Lucia, St. Vincent and the Grenadines, Suriname,
Swaziland, Syria, Thailand , Tonga, Tunisia, Turkey , Ukraine,
Uzbekistan , Vanuatu, Venezuela, Yemen.

Under Developed Countries

Australia, Austria, Bahamas, Bahrain, Barbados, Belgium , Brunei
Darussalam, Canada, Chile, Cyprus, Czech Republic, Denmark,
Equatorial Guiana, Finland, France,

Germany, Greece, Iceland, Ireland, Israel, Italy, Japan, Korea.
Rep, Latvia, Lithuania, Luxembourg, Macao SAR, China, Malta,
Netherland, New Zealand, Norway, Oman, Poland, Portugal, Russia,
Saudi Arabia, Slovak Republic, Slovenia, Spain, Sweden ,
Switzerland, Trinidad and Tobago, United Kingdom, United States,
and Uruguay.

Muhammad Tariq Majeed <[email protected], [email protected]> is Assistant Professor, School of Economics, Quaid-i-Azam University, Islamabad. Amna Malik <[email protected]> is Officer Grade 1, State Bank of Pakistan, Karachi.

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(1) Specification error in model occurs when one or more irrelevant variables are incorporated or one or more relevant variables are omitted in the specified equation. When relevant variables are excluded from the model or irrelevant variables are included in the model, the common variance they share with excluded/included variables may be wrongly attributed to those variables. In the case of such specification errors, the error term tends to inflate and creates biased results. Specification error in any equation leads to biasness in all the results. Prior to estimate the model we have checked the specification of our model by employing the linktest. The test generates two variables, predicted independent variable (_hat), square of predicted variable (_hatsq). The model is then re-estimated using these two variables as predictors. If model is free from specification error then hatsq should not have much prediction power. Table 4.3 indicates that hatsq has not explanatory power and our all equations are free from specification error.

(2) There is significant systematic difference between OLS and SUR model, OLS and 2SLS, OLS and 3SLS according to Hausman Test. The variance-covariance matrix of error terms indicates correlation between the error terms of Equation 3, equation5, and equation8. The correlation between error terms of equation3 and equation5 is 0.13, equation3 and equation8 is 0.172. and equation 5 and equation 8 is 0.105 that is significant and greater than 10 percent. 3SLS technique takes into account of both endogeniety and correlation among error terms. 2SLS only takes into account of endogeniety, and SUR model takes merely into account of correlation between error terms. In order to check the robustness of results we have employed all the techniques of Simultaneous equation model. All equations in simultaneous equation model are identified according to order condition because the number of endogenous variables included in equation less one (M-l) is less than the number of exogenous variables excluded in equation [Gujrati (2003)]. The internal instruments are used to tackle endogeniety. The instruments are initial quality of e-government, physical capital, labour force, fixed telephone lines, initial urban population, initial per capita income, exchange rate, and tariffs.

(3) The sign of initial per-capita income can be positive in the case of poor or developing economies because they will likely to grow rapidly The hypothesis of "catching per-capita of rich economies by poor economies is consistent with convergence theory that is supported by Solow (1956).

Caption: Fig. 1. Relationship between Growth and E-government

Caption: Fig. 2. Relationship between Trade and Growth

Caption: Fig. 3. Relationship between E-governemnt (EG) and Growth

Caption: Fig. 4. E-government (EG) and Share of Trade in GDP in Different Geographical Regions

Table 4.1

Dimensions of E-government

Description of E-government Dimensions

Online Service and   It measures the extant of online and web content
Web Connections      availability and focused on growing web
                     connection and online presence which improves
                     the accessibility of information by utilisation
                     of multimedia which facilitates online delivery
                     of transactional services and communication
                     between government and citizens.

Telecommunication    The telecommunication infrastructure is founded
Infrastructure       on five indicators: internet users, personal
                     computers, fix-telephones lines, mobile phone
                     subscription, and broadband subscription (all
                     the indicators are in per 100 inhabitants).

Human Capital        Human capital is based on four indicators that
                     are adult literacy rate, gross secondary
                     enrolment, gross secondary enrolment, and gross
                     tertiary enrolment.

Table 4.2

Summary Statistics of Data

Variable               Observation     Mean       Min        Max

Y                          147       11692.31   179.401    94654.22
[Y.sub.initial]            145       5342.72    182.797    36337.09
Capital                    147       23.4887    9.85652    68.78322
Labour                     147        63.29      41.42      86.63
E-govemment                147       0.44781    0.08738     0 .87
Exchange Rate              133        675.99     0.344      25000
[EG.sub.initial]           145       0.40274     0.0000    0 .92706
Trade                      147       89.4306    27.0795    303.446
[Trade.sub.initiai]        142       73.24068   14.56329   210.161
Urban population           147       1.39e+07    21606     3.00e+08
Fix_Telephone              147       19.6930    0 .04629   65.9294
Tariff                     146       6.616705   4.839402    21.42

Table 4.3

Linktest Results

Dependent Variable: Natural log of per-capita GDP

Variables       Coef      Std. Err     T     P>[absolute
                                             value of t]

_hat          1.374505    0.316188   4.35       0.000
_hatsq       -0.022062    0.018553   -1.19      0.236
_cons        -1.542248    1.318649   -1.17      0.244

Dependent Variable: Trade liberalisation
_hat          1.318894    1.333089   0.99       0.324
_hatsq       -0.0367583   0.153453   -0.24      0.811
_cons        -0.6875192   2.886441   -0.24      0.812

Dependent Variable: E-government
_hat          0.96947     0.091681   10.57      0.000
_hatsq        0.031816    0.093483   0.34       0.734
_cons         .0062143    0.020458   0.30       0.762

Table 5.1

Empirical Results of Simultaneous Equations Model

Empirical Findings of Simultaneous Equation Model

                           SUR Model           2SLS

                        (1)         (2)         (3)
Independent            (Eq3)       (Eq5)       (Eq8)
Variables              Growth      Trade       E-gov

[Trade.sub.iniiiai]              0.55 ***
                                  (0.045)

EG*Y                             -0.26 ***
                                 (0.0824)

Exchange Rate                     0.00715
                                 (0.0099)

Tariff                           -0.0101 *
                                 (0.0060)

ln(per capita)                   0.101 **    0.032 ***
                                 (0.0478)    (0.00479)

E-gov                 3.67 ***   2.33 ***
                      (0.416)     (0.815)

[Y.sub.initial]       0.53 ***
                      (0.055)

Capital               0.319 **
                      (0.145)

Labour                 -0.281
                      (0.282)

Trade                 0.35 ***               0.038 ***
                      (0.109)                (0.00932)

Urban pop                                    0.010 ***
                                             (0.00203)

[E-gov.sub.initial]                          0.519 ***
                                              (0.0349)

Fix Tele                                     0.002 ***
                                             (0.00040)

Constant               1.364     1.30 ***    -0.376 ***
                      (1.427)     (0.345)     (0.0617)

Observations            127         127         127

R-squared

Observation            0.880       0.622       0.956

F-stat                  127         127         127

Chi-Square             959.11     217.22      2846.20

                             2SLS

                        (4)        (5)         (6)
Independent            (Eq3)      (Eq5)       (Eq8)
Variables              Growth     Trade       E-gov

[Trade.sub.iniiiai]              0.53 ***
                                 (0.057)

EG*Y                             -0.321 *
                                 (0.169)

Exchange Rate                    0.00876
                                 (0.0110)

Tariff                           -0.0108
                                 (00068)

ln(per capita)                    0.159     0 046 ***
                                 (0.102)    (0.00701)

E-gov                 3.67 ***   2.601 *
                      (0.451)    (1.526)

[Y.sub.initial]       0.53 ***
                      (0.059)

Capital                0.242
                      (0.158)

Labour                 -0.311
                      (0.304)

Trade                 0.46 ***              0.0309 **
                      (0.148)                (0.0151)

Urban pop                                   0.011 ***
                                            (0.00248)

[E-gov.sub.initial]                         0.484 ***
                                             (0.0411)

Fix Tele                                    0 0012 **
                                            (0.00049)

Constant               1.242      1.040     -0.450 ***
                      (1.591)    (0.645)     (0.0857)

Observations            127        127         127

R-squared

Observation            0.877      0.613       0.951

F-stat                  127        127         127

Chi-Square             177.23     31.73       474.64

                                   3SLS

                        (7)         (8)         (9)
Independent            (Eq3)       (Eq5)       (Eq8)
Variables              Growth      Trade       E-gov

[Trade.sub.iniiiai]              0.51 ***
                                  (0.055)

EG*Y                              -0.209
                                  (0.161)

Exchange Rate                     0.00778
                                 (0.0099)

Tariff                           -0.0119 *
                                 (0.0061)

ln(per capita)                    0.175 *    0.062 ***
                                 (0.0978)    (0.00627)

E-gov                 5.08 ***     1.140
                      (0.411)     (1 449)

[Y.sub.initial]       0.33 ***
                      (0.053)

Capital               0.242 *
                      (0.131)

Labour                 0.0125
                      (0.259)

Trade                 0.55 ***                 0.0138
                      (0.143)                 (0.0143)

Urban pop                                    0.010 ***
                                             (0.00221)

[E-gov.sub.initial]                          0.397 ***
                                              (0.0365)

Fix Tele                                     0.0010 **
                                             (0.00042)

Constant               0.344      1.190 *    -0.447 ***
                      (1.366)     (0.619)     (0.0785)

Observations            127         127         127

R-squared

Observation            0.854       0.592       0.937

F-stat                  127         127         127

Chi-Square             912.74     197.42      2468.93

Standard errors in parentheses.

*** p<0.01, ** p<0.05, * p<0.1.

Table 5.2

Empirical Findings of Ordinary Least Squares

                               Empirical Findings of OLS
                           Ordinary Least Square Technique

                        (Equation 3)     (Equation 5)   (Equation 8)
Variables              Economic Growth      Trade          E-gov

[Trade.sub.initial]                       0.560 ***
                                           (0.0473)

EG*per Capita                             -0.231 ***
                                           (0.0858)

Exchange Rate                              0.00519
                                           (0.0103)

Tariff                                     -0.00952
                                          (0.00628)

In (per capita)                             0.0669       0.0254 ***
                                           (0.0497)      (0.00497)

E-govemment               3.215 ***        2.188 **
                           (0.431)         (0.849)

[Y.sub.initial]           0.581 ***
                          (0.0572)

Capital                   0.314 **
                           (0.152)

Labour                     -0.437
                           (0.295)

Trade                     0.247 **                       0.0344 ***
                           (0.112)                       (0.00961)

Urban Population                                        0.00937 ***
                                                         (0.00212)

[EG.sub.initial]                                         0.554 ***
                                                          (0.0364)

Fix_Tele                                                0.00208 ***
                                                         (0.000423)

Constant                    2.261         1.496 ***      -0.314 ***
                           (1.494)         (0.359)        (0.0641)

Observations                 127             127            127

R-squared                   0.882           0.624          0.957

F-stat                     180.63           33.20          540.65

Observation                  127             127            127

Standard errors in parentheses.

*** p<0.01, ** p<0.05, * p<0.1.

Fig. 5. E-government and Share of Trade in GDP in Developing,
Developed and Under Developed Countries

Average of E-government and share of Trade in GDP

                            Trade    EG

Under developed countries   0.67    0.23
Developing countries        0.9     0.4
Develop countries           0.1     0.64

Note: Table made from bar graph.