Kunofiwa Tsaurai¹, Bester Chimbo²

Abstract: The study investigated the impact of internet usage on income inequality in transitional economies using panel data ranging from 2008 to 2019. The second objective was to find out the influence of the complementarity between internet usage and human capital development on income inequality in transitional economies using the same data set. The available literature on the subject matter is divergent, conflicting, and far from reaching a consensus, hence the reason why the author attempted to empirically investigate the impact of internet usage on income inequality. The lag of income inequality was found to have had a significant positive influence on income inequality under the dynamic generalized methods of moments (GMM). Fixed effects produced results which show that internet usage had a significant negative effect on income inequality, consistent with the available literature. Transitional economies are therefore urged to develop and implement policies that promotes a wide coverage of internet usage to reduce income inequality. The interaction between internet usage and human capital development was found to have had a significant positive impact on income inequality under the fixed effects whilst pooled ordinary least squares (OLS) shows that the impact of the complementarity between internet usage and human capital development on income inequality was negative but significant. Transitional economies are therefore urged to implement enhanced human capital development policies to enable internet usage to play a meaningful role in reducing income inequality in the society. Future research needs to establish minimum threshold levels that internet usage in transitional economies must reach to have a significant influence on income inequality. Such a study enriches the income inequality policy making and implementation programmes.

Keywords: Internet; Income Inequality; Transitional Economies; Panel Data Analysis

JEL Classification: L86; E25; P2

1. Introduction

Background of the study, contribution and organization of the study are three sections covered under the introduction.

Background of the study, research gap and problem statement: Income inequality, unemployment and poverty reduction is one of the United Nations Millennium Development Goals, consistent with Rewilak (2017). Consistent with quite a number of empirical studies such as Ahamad and Pandery (2014), Urama and Oduh (2012), Garcia-Mora and Mora-Rivera (2021), Isife et al (2013), Diga et al (2013), Okello et al (2014), Postula et al (2021), Bello and Aderbigbe (2014) and Mogotlhwane et al (2011), internet usage is key to income inequality reduction.

Previous empirical research on the internet usage-income inequality showed results which are mixed. Some noted that internet usage reduces income inequality whilst the other group produced results or argue that internet usage increases income inequality. The other group of empirical researchers noted that income inequality and internet usage influence each other. Majority of the empirical researchers on the internet usage-income inequality nexus wrongly assumed that the relationship between internet usage and income inequality is non-linear. This study assumed a non-linear relationship between internet usage and income inequality hence other factors which influence income inequality are considered in the study. There are five other methodological weaknesses observed from majority of prior research on the nexus between internet usage and income inequality. Firstly, they ignored the non-linear aspect of the relationship between internet usage and income inequality. Secondly, they used outdated data sets which are no longer useful for policy making purposes in the current period. Thirdly, they ignored the endogeneity problem aspect in the relationship between internet usage and income inequality. Fouthly, the argument put forward by Azher (1995) that income inequality is affected by its own lag was totally ignored. Fifthly, transitional economies have largely been ignored by prior empirical research on the subject matter. These gaps and methodological weaknesses of prior empirical research work on the impact of internet usage on income inequality shows that this topic is not yet resolved, still is an unsettled matter and not yet conclusive. The results of the study assist the transitional economies to craft and implement policies that helps to reduce income inequality.

Contribution of the paper: This study contributes to literature in five different ways. Firstly, it is the first study to the author’s best knowledge to investigate the impact of internet usage on income inequality in transitional economies. Previous empirical research on the subject matter did not focus on the transitional economies bloc of countries to the best of the author’s knowledge. Secondly, majority of the prior empirical studies on the internet usage-income inequality nexus wrongly assumed that the relationship between the two variables is linear in nature. This study corrected that wrong assumption by considering the non-linearity nature of the relationship between internet usage and income inequality. Thirdly, this study considered the endogeneous nature of the relationship between income inequality and internet usage. Fourthly, this study considered the fact that income inequality is affected by its own lag. Fifthly, according to the author’s best knowledge, this study is the first of its kind to explore the influence of the complimentarity between internet usage and human capital development on income inequality. Sixthly, unlike available previous research on the subject matter, this study used the most recent data set (2008-2019).

Organization of the paper: The rest of the paper is structured as follows: Section 2 explains the theoretical influence of internet usage on income inequality. Section 3 summarizes the impact of internet usage on income inequality from an empirical point of view. Section 4 discusses the influence of the explanatory variables of income inequality. Section 5 covers the research methodological framework (data description and its character, general and econometric model specification). Section 6 discusses the pre-estimation diagnostics of the study. Section 7 is the main data analysis. Section 8 summarises the study. Section 9 is the bibliography.

2. The Impact of Internet Usage on Income Inequality – Theoretical Literature Review

There are three theoretical rationales explaining how internet usage reduces income inequality (Litan & Rivlin, 2001). Firstly, by building a global information medium of exchange, internet assist to more efficiently allocates materials on a global scale. This decreases production costs and the prices of goods and services. Secondly, the internet marketplaces serve as an alternative to the traditional retail markets, hence providing convenience to the final consumers. Thirdly, internet reduces the consumers ‘search costs and market power, hence improving the welfare of the populace (Brown & Goolsbee. 2002). Fourthly, internet usage creates vast number of employment opportunities, thus taking people out of poverty and leading to income inequality reduction (Bauer, 2015).

On the contrary, internet usage increase income inequality especially when access to online resources is distributed unequally among the people (Hargittai, 1999). People who don’t have access to internet won’t be able to meaningfully participate in the online national economy hence further promoting income inequalities. Poor internet usage leads to cultural and cultural disparities, argued Howard et al (2010). It is against this background of competing predictions that a further empirical investigation on the impact of internet usage on income inequality is well justified.

3. The Impact of Internet Usage on Poverty – Empirical Literature Review Summary

Table 1. Summary of the Empirical Literature on the Impact of Internet Usage on Poverty Reduction

Source: Author compilation

4. The Impact of Internet Usage on Income Inequality – Empirical Literature Review

Table 2. Summary of the Empirical Literature on the Impact of Internet Usage on Poverty Reduction

Source: Author compilation

5. The impact of Explanatory Variables of Income Inequality

Table 3. Theory Intuition and Expected Sign(s)

Source: Author compilation

6. Research Methodological Framework

This section covers data description and its character and general and econometric model specification.

Data description and character: This paper used panel data ranging from 2008 to 2019 to study the impact of internet usage on poverty alleviation in BRICS countries. The latter include Argentina, Colombia, Greece, Peru, Portugal, Thailand, Brazil, Czech Republic, Mexico, Poland, Russia and Turkey. African Development Bank, United Nations Development Programme, World Development Indicators, International Financial Statistics and International Monetary Fund are the internationally recognised and reputable databases from which the panel data was extracted.

General and econometric model specification: The general model specification of the income inequality function is represented by equation 1 below.

INEQ =f(INTERNET, HCD, FDI, SAV, INFL, OPEN, FIN) (1)

As already been described earlier on, INEQ, INTERNET, HCD, FDI, SAV, INFL, OPEN and FIN respectively stands for income inequality, internet usage, human capital development, foreign direct investment, savings, inflation, trade openness and financial development. The proxies which were used to measure these main variables of the study are summarised in Table 4.

Table 4. The Proxies of Variables Used

Source: Author’s compilation

Income inequality is the dependent variable in equation 1 whilst independent (explanatory) variables include internet usage, human capital development, financial development, trade openness, inflation, savings and foreign direct investment. Empirical studies which influenced the choice of the explanatory variables of the income inequality function include but are not limited to Garcia-Mora and Mora-Rivera (2021), Ahamad and Pandery (2014), Urama and Oduh (2012), Diga et al (2013), Isife et al (2013), Postula et al (2021), Okello et al (2014), Mogotlhwane et al (2011) and Bello and Aderbigbe (2014).

Equation 2 is the income inequality’s econometric equation. It is equation 1 transformed into econometric format.

INEQ_it = ₀ +₁INTERNET_it + ₂HCD_it + ₃FDI_it + ₄SAV_it + ₅INFL_it + ₆OPEN_it + ₇FIN_it + Ɛit (2)

Table 5. Variables’ Interpretation

Source: Author compilation

INEQ_it = ₀ +₁INTERNET_it + ₂HCD_it + ₃ (INTERNET_{it .} HCD_it) + ₄FDI_it + ₅SAV_it + ₆INFL_it + ₇OPEN_it + ₈FIN_it + Ɛit (3)

Equation 3 introduced the impact of the complementary variable (internet usage x human capital development) on income inequality, consistent with Canh et al (2020), whose study argued that the usage of internet by educated and skilled people enhances not only innovation but also promotes entrepreneurship and self-employment. Pooled ordinary least squares (OLS), random effects and fixed effects are the panel data analysis methods used to econometrically estimate equation 3.

Equation 4 introduced the lag of the dependable (lag of income inequality), consistent with Azher (1995) whose study argued that poverty and income inequality perpetuates another poverty and income inequality (promotes vicious cycle). According to Liu (2017), the inclusion of the lag of income inequality captures structural and a series of time-invariant institutional features that persistently influence income inequality. The omitted variable bias is to a greater extent reduced by the inclusion of the lag of income inequality in equation 4.

INEQ_it = ₀ +₁ INEQ_it-1 + ₂INTERNET_it + ₃HCD_it + ₄ (INTERNET_{it .} HCD_it) + ₅FDI_it + ₆SAV_it + ₇NFL_it + ₈OPEN_it + ₉FIN_it + Ɛit (4)

The dynamic GMM econometric methodology was used to estimate equation 4.

7. Pre-Estimation Diagnostics

Correlation analysis, descriptive statistics and mean trend analysis of the major variables constitutes this section.

Table 6. Correlation Results

Note: ***/**/* denotes statistical significance at the 1%/5%/10% level respectively.

Source: Author compilation from E-Views

Internet usage, human capital development, savings, trade opennesss and financial development were separately found to have had a significant negative relationship with income inequality. The results may mean that these variables significantly reduced income inequality in transitional economies during the period under study. However, the weakness of correlation analysis is that it does not show the direction of causality between the variables. A significant positive relationship between (1) FDI and income inequality and (2) inflation and income inequality were also observed, results of which also resonates with available literature. The highest correlation (77%) was found between human capital development and internet usage, evidence of the existence of multicollinearity between and among the variables studied in line with Stead (1996).

Table 7. Results of Descriptive Statistics

Source: Author compilation from E-Views

Across all the variables studied, the difference between the minimum and the maximum is large, which shows that extreme values exist (see Table 7). Standard deviation of trade openness and financial development is greater than 30, a further indication that there exist extreme values. Except internet usage data, all the other variables used in the study are skewed to the right, an indication that the data is not normally distributed. The probability of the Jarque-Bera criteria (not zero) for income inequality, internet, human capital development and savings also provides further evidence that the data for these variables is not normally distributed.

Table 8. Mean Internet Usage and Income Inequality Trends in Transitional Economies (2008-2019)

Source: Author’s compilation

Argentina, Czech Republic, Greece, Poland, Portugal and Russia are the six transitional economies whose mean internet usage exceeded the overall mean internet usage of 55.09% of the population. The remaining countries had their mean internet usage lower than the overall mean internet usage of 55.09% of the population. It is evident that Czech Republic, Peru, Poland and Thailand are outliers because their mean internet usage deviated from the overall mean internet usage of 55.09% of the population by a very wide margin (more than 10 percentage points).

Regarding income inequality, Argentina, Brazil, Colombia, Mexico and Peru had their mean income inequality (GINI ratio) greater than the overall mean income inequality of 0.41. Apart from Turkey (0.41), the remaining transitional economies had their mean income inequality below the overall mean income inequality of 0.41. Brazil, Colombia and Czech Republic are regarded as outliers in this case because their mean income inequality ratios deviated from the overall mean income inequality by more than 10 percentage points.

In summary, (1) multi-collinearity between internet usage and human capital development data was observed, (2) outliers and (3) data not following a normal distribution were noted using both descriptive statistics and mean trend analysis approaches. Consistent with Aye and Edoja (2017), all the data sets were first transformed into natural logarithms in order to effectively deal with the negative effects of these data characteristics (multi-collinearity, outliers, abnormally distributed) on the quality of the final results.

8. Main Data Analysis

From Table 9, it is evident that not all variables were stationary at level. However, all the variables were stationary at first difference. Consistent with Owusu and Odhiambo (2014), all the variables were integrated of order 1, hence paving way for panel co-integration tests.

Table 9. Panel Root Tests – Individual Intercept

Note: LLC, IPS, ADF and PP stands for Levin, Lin and Chu; Im, Pesaran and Shin; ADF Fisher Chi Square and PP Fisher Chi Square tests respectively. *, ** and *** denote 1%, 5% and 10% levels of significance, respectively.

Source: Author’s compilation from E-Views

The Kao (1999) panel co-integration tests were done and produced the results presented in Table 10.

Table 10. Kao Residual Co-integration Test - Individual intercept

Source: Author’s compilation from E-Views

From Table 10, it is evident that the null hypothesis which says that the variables are not co-integrated is rejected. In other words, the Kao (1999) co-integration tests show that there is a long run relationship among the variables studied (income inequality, internet usage, human capital development, foreign direct investment, savings, inflation, trade openness and financial development.

Table 11. Panel Data Analysis Results

***, ** and * denote 1%, 5% and 10% levels of significance, respectively.

Source: Author’s compilation from E-Views

In Table 11, income inequality was positively and significantly influenced by its own lag, consistent with Azher (1995) whose study argued that income inequality perpetuates income inequality. Internet usage had a non-significant negative impact on income inequality under the dynamic GMM, random effects and pooled OLS. Fixed effects produced results which show that internet usage had a significant negative influence on income inequality. The results mean that internet usage reduced income inequality, consistent with Bauer (2015) whose study argued that

internet usage creates vast number of employment opportunities, thus taking people out of poverty and leading to income inequality reduction.

Human capital development’s influence on income inequality was found to be positive but non-significant under the dynamic GMM and pooled OLS, in line with Afzal et al (2010) whose study noted that inequalities in the provision of education, training and skills exacerbate income inequality and poverty trap. Fixed and random effects show that human capital development influenced income inequality in a negative but non-significant way, results which resonates with Chaudhry and Rehman (2009) whose study observed that the chances of securing not just a job but a better paying one are increased as one becomes more and more skilled.

The complementarity between internet usage and human capital development produced results which show that it had a non-significant negative influence on income inequality under the dynamic GMM and random effects. The pooled OLS noted that the interaction between internet usage and human capital development had a significant deleterious effect on income inequality. These results are in line with Canh et al (2020) whose study argued that the usage of internet by educated and skilled people enhances not only innovation but also promotes entrepreneurship and self-employment. Contrary to the available literature, the interaction between internet usage and human capital development had a significant positive effect on income inequality under the fixed effects.

FDI had a non-significant negative effect on income inequality under the dynamic GMM approach, in line with Amin (1974) whose study noted that the economy and income distribution is negatively influenced by over-reliance on foreign direct investment. However, the influence of FDI on income inequality was found to be positive but non-significant under the random effects, pooled OLS and fixed effects. These results support Nguyen (2003)’s argument that foreign direct investment flows with it alongside capital, skills, jobs creation and managerial skills, all of which contribute towards income inequality and poverty reduction.

Under the pooled OLS and the dynamic GMM, the impact of savings on income inequality was found to be negative and significant. This means that savings reduced income inequality, in line with Azher (1995) whose study argued that increased amount of savings can enable one to commence self-help projects, self-employment, and the creation of more community-based jobs, hence contributing to income inequality and poverty reduction. However, fixed and random effects show that savings’ impact on income inequality was positive and significant, in contradiction to the available literature on the subject matter.

Under the dynamic GMM, inflation was found to have had a significant positive influence on income inequality. On the other hand, a non-significant positive relationship running from inflation towards income inequality was observed under the fixed effects, pooled OLS and random effects. The results are consistent with an argument by Shahidur (2012) whose study noted that the purchasing power of people’s savings is eroded by high levels of inflation hence further subjecting the people to misery and vicious cycle of poverty and income inequality.

Trade openness was found to have had a significant deleterious effect on income inequality under the dynamic GMM, random effects and pooled OLS. The results show that trade openness reduced income inequality in transitional economies, in line with Pradhan and Mahesh (2014). The latter argued that high levels of trade openness enable industry and consumers to have easy and cheaper access to variety of international goods and services hence enhancing job creation, poverty and income inequality reduction among the people. The impact of trade openness on income inequality under the fixed effects was observed to be positive and significant, in contradiction to most of the available literature on the subject matter.

Financial development’s deleterious influence on income inequality was confirmed under all the four econometric approaches, consistent with an argument by Stiglitz (1998) whose study observed that higher levels of financial development enable the people to easily access cheaper loans, capital which they can use to kick start their own income generating projects, hence contributing to income inequality and poverty reduction.

9. Summary of the Study

The study investigated the impact of internet usage on income inequality in transitional economies using panel data ranging from 2008 to 2019. The second objective was to find out the influence of the complementarity between internet usage and human capital development on income inequality in transitional economies using the same data set. The available literature on the subject matter is divergent, conflicting, and far from reaching a consensus, hence the reason why the author attempted to empirically investigate the impact of internet usage on income inequality. The lag of income inequality was found to have had a significant positive influence on income inequality under the dynamic GMM. Fixed effects produced results which show that internet usage had a significant negative effect on income inequality, consistent with the available literature. Transitional economies are therefore urged to develop and implement policies that promotes a wide coverage of internet usage to reduce income inequality. The interaction between internet usage and human capital development was found to have had a significant positive impact on income inequality under the fixed effects whilst pooled OLS shows that the impact of the complementarity between internet usage and human capital development on income inequality was negative but significant. Transitional economies are therefore urged to implement enhanced human capital development policies to enable internet usage to play a meaningful role in reducing income inequality in the society. Future research needs to establish minimum threshold levels that internet usage in transitional economies must reach to have a significant influence on income inequality. Such a study enriches the income inequality policy making and implementation programmes.

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