ICT, Financial Development And Economic Growth in ASEAN Countries


This paper examines the impact of and relationship between ICT diffusion, financial development and economic growth in selected ASEAN countries. In order to investigate this query, panel data estimations are used by utilizing data from 2003 to 2017. The finding indicates that ICT diffusion and financial development have an impact on economic growth of ASEAN countries. Consequently, the findings of the study could be an aid for the future researcher to study deeper in this research. The policymakers need to take a further initiative to improve the information technology and its applications in these countries. To gain benefit from the ICT drivers of economic growth, policymakers should enact several important policies that permit the development of financial sectors, provide a more convenient regulatory and institutional environment, increase economic openness, prioritize the allocation of resources to the development of ICT infrastructure, and contain the negative effects of inflation and government consumption.

Keywords: ASEANICT diffusionfinancial developmenteconomic growth


Over the last two decades, Information and Communication Technologies (ICT) has played a dominant role in economies and societies by broadening the access to information and various types of knowledge, which empowers people and intensifies participation in socio-economic life (Lechman & Marszk, 2019). Through digital technologies, new platforms can be developed to improve economic function especially in rural areas such as by introducing online banking and digital investment in financial sector, and online entrepreneur in retail sector. Hence, digital technologies have a good potential to improve the market functions by improving the limited infrastructure and economic constraints (Hernandez et al, 2016). Moreover, with the exponential growth of digital technologies in financial sector, the global financial system can now become virtual (Lechman & Marszk, 2019). ICT can also improve human participation in the financial system, especially with the reduction of transaction cost and easy accessibility to digital financial services (Pradhan et al., 2017). This shows that ICT can create a more efficient market and increase the productivity of financial market.

The organization of this paper can be divided into a few sections. The first section is the introduction, followed by section two on literature review. Chapter three discusses research methodology, chapter four on findings of the result, and finally chapter five concludes.

Problem Statement

Albeit the world’s extensive focus on the connection between financial development and the environment, previously observed studies on the link Several recent studies have revealed the positive effect of financial development on economic growth, and these include Hassan et al. (2011), Hsueh et al. (2013) and Khoutem et al. (2014) especially in Asian countries including China. However, in contrast, Adu et al. (2013) argued that the positive or negative impact of financial development on economic growth depends on which proxy measure being used for estimation.

The examination by previous studies on the relationship between ICT and economic growth leads to the discovery of positive correlation for both developed countries (Evangelista et al., 2014; Toader et al., 2018) and developing countries, but at different magnitude (Sepehrdoust, 2018; Yousefi, 2011). Financial development especially from financial liberalization can benefit from the diffusion of ICT sector as well as economic growth. Previous studies on the relationship between ICT, financial development and economic growth can be seen in the table 1 below:

Table 1 -
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Research Questions

ICT diffusion, financial development and economic growth in 8 ASEAN countries have a significant impact?

Purpose of the Study

To analyze the impact of ICT diffusion and financial development on the economic growth of 8 ASEAN countries.

Research Methods

Sample and Data Collection

The data were collected from World Development Indicators database from year 2003 to 2017. This study will utilize panel regression analysis to determine how financial development and ICT diffusion (represented by ICT Index) will affect the economic growth of 8 ASEAN Countries.

The regression models for this study can be specified as follows:

GDPit = α0 + α1FDit + α2ICTit + εit(1)

Where GDP is a natural log of GDP per capita (constant 2010 US), FD is a natural log of financial development and ICT index consists of multiple indexes of five sub-indicators; i.e. a landline telephone, mobile phones, internet services, internet users, and fixed broadband. We compiled five sub-indicators of ICT infrastructure by employing Principal component analysis (PCA). This technique had been implemented by Pradhan et al. (2017) and Latif et al. (2018). PCA subsequently transforms the data into variables (Principal components) and generates a factor analysis.


The data will be analysed using panel estimation, namely Panel unit root test and General Least Square (GLS) regression analysis to determine the relationship between the dependant variables and independent variables.

Since panel data would relate to individual country, heterogeneity is unavoidable in these units. The GLS pooled time-series cross sectional specification assumes that all countries have the same behaviours. In other words, it is assumed that the slope and intercept of all countries are constant across individuals and time. These assumptions of uniform behaviour deny any form of heterogeneity, which in practice is very likely to materialize. The pooled regression may result in heterogeneity bias as there are often reasons for the intercept or the slope for each country maybe different across units and time. Therefore, in order to control for any country-specific effect, this study will examine the GLS estimator for random effect (RE) and GLS estimator for fixed effect (FE) in our model.


This section provides the empirical evidence on the role of ICT diffusion and financial development towards ASEAN’s economic performance. A broad description of the characteristics of the variables used in the study is given in Table 2 , which reports the statistical means and standard deviations of the variables.).

Table 2 -
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The results of unit root tests for all variables are tested using Levin et al. (2002) and Im et al. (2003) as reported in Table 3 . For all variables, the null hypothesis of a unit root cannot be rejected at their levels. However, upon taking first differences, the null of unit roots is rejected at 5% and 1% levels. Therefore, it is concluded that all the variables are stationary and integrated of order one.

Table 3 -
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In this study, we implement three regression methods. First, the Pooled OLS is used with the assumption that both the intercept and the slope of every country are the same across time. From the result in Table 4 , it shows that all variables based on Pooled GLS are significant and have positive relationship except for financial development. The R2 shows the goodness of fit of each model, i.e. between 0.13 (Pooled OLS) 0.74 (Random Effect) to 0.73 (Fixed Effect). Therefore, 13%, 74% and 73% variations in the growth are explained by the independent variables for the regression analysis, respectively. The F-test is the test for overall fitness of the model and the results have rejected the null hypothesis for all models. This suggests that at least one or more of the independent variables had influenced the dependent variable, namely, economic growth. The t test on each variable suggests that financial development (FD) is significant in influencing GDP at 1% significance level. Meanwhile, ICT index is significant in affecting economic growth as shown under random effect and fixed effect at 1% significance level, respectively. The coefficients for the ICT index are consistently positive and significant at 1% level. These findings indicate that ICT diffusion has a significant positive impact on the economic growth of ASEAN countries. The result is supported by Das et al. (2016) and Sepehrdoust (2018) whereby financial inclusion can stimulate economic growth with better ICT penetration.

Table 4 -
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The selection of best estimation model is based on the value of Breusch-Pagan and Hausman test. The Breusch-Pagan result in Table 4 shows that the result from GLS with random effect is better than GLS with pooled least square at 1% level for all the models. This result indicates that there are group-specific effects in the data.

In order to treat the group-specific effects, this study will perform GLS with fixed effect and GLS with random effects regressions. To test the appropriate model with the hypothesis that the individual effects are uncorrelated with other regressors in the model, the Hausman test will be utilized. The results of Hausman test, as shown in Table 4 for the estimation of LGDP indicate that the results of GLS with random effect explain better relative to the fixed effects for all the three models. The results from Hausman test also imply that the null hypothesis of absence of correlation between individual effects and the explanatory variables failed to be rejected, and the GLS estimator of the fixed effect is inconsistent.

At the same time, the interaction between financial development and ICT index is also positive and significant, implying that financial sector in ASEAN countries can grow faster when ICT penetration is increased. This means that better connectivity and higher internet usage can cultivate economic growth when the level of credit to private sector increases. These results are supported by Andrianaivo and Kpodar (2011), Das et al. (2016), and Sassi and Goaied (2013).


This main objective of this research is to investigate the relationships among ICT diffusion, financial development and economic growth. FD and ICT diffusion have a positive impact on economic growth. From the above findings, it can be concluded that FDI and ICT diffusion play a very important role to the economic development of ASEAN countries. The policymakers need to take a further initiative to improve the information technology and its applications in these countries. To gain benefit from the ICT drivers of economic growth, policymakers should enact several important policies that permit the development of financial sectors, provide a more convenient regulatory and institutional environment, increase economic openness, prioritize the allocation of resources to the development of ICT infrastructure, and contain the negative effects of inflation and government consumption.


The authors thank University Tenaga National (UNITEN) for the funding from BOLD Grant 2019 (10436494/B/2019053). Finally, gratitude to the respective scholars for their expressive contributions relating to the issues of current study.


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