Do ICTs drive wealth inequality? Evidence from a dynamic panel analysis
Introduction
While there is a vast literature on the relationship between information and communication technology (ICT) and economic growth (see, for example, Stanley et al. (2018) and Vu et al. (2020) for a literature review), less is known about the cross-country effect of ICT on wealth inequality. To the best of the author's knowledge, we are not aware of any studies on the relationship between ICT and wealth inequality. The aim of this paper is therefore to investigate, as a first attempt, the empirical effect of ICT on wealth inequality using a global panel of 45 developed and developing countries.
Wealth inequality refers to the unequal distribution of wealth, assets, or income among the countries of the world and within countries. Wealth is defined as the current market value of all assets held by households, net of all their debts (Zucman, 2016). The persistence of wealth inequalities between the rich and poor undermines the achievement of the Sustainable Development Goals (SDGs) on the one hand, and calls into question any possibility of global, sustainable, and inclusive economic growth on the other (Tadadjeu et al., 2021). The figures put forward by Oxfam (2016) are evocative. While the world's richest 1% own more than twice as much wealth as 6.9 billion people, half of humanity lives with less than $5.50 a day and 10,000 people die every day due to lack of access to affordable health care. According to Piketty (2014), wealth inequality is returning to levels not seen since the First World War. He notes with regret that the top decile in the US controls over 70% of the wealth. This increase in inequality is not unique to the United States, but concerns all countries, especially the developed ones. Moreover, Piketty and Zucman (2014) point out that over the last four decades, the ratio of wealth inequality in the eight largest developed countries to total wealth has risen from 200 to 300% in 1970 to 400–600% in 2010.
This accentuation of inequalities goes hand in hand with the ever-increasing number of billionaires. According to Forbes Magazine,1 during the so-called Billionaire Decade (2010–2019), the number of billionaires rose from 1,001 in 2010 to 2,153 in 2019 (an increase of more than 115%) for a total wealth that went from 3.6 trillion dollars in 2010 to more than 8.7 trillion dollars in 2019. Even the coronavirus pandemic has not slowed this progression. While the IMF expected economic growth to contract by 4.4 percent this year, pushing millions of people into poverty, billionaires are growing in number and wealth. According to a report by Swiss Bank UBS,2 billionaires increased their wealth by more than a quarter (exactly 27.5%) at the height of the COVID-19 crisis from April to July 2020. In view of this ever-increasing rise in wealth inequality, it is more than urgent to examine its determinants.
This increasing level of wealth inequality has led policymakers, particularly researchers, to examine the factors that may explain that situation. Several studies have therefore highlighted a number of important determinants of wealth inequality, including: income growth, interest rates, monetary inflation, expansionary monetary policy, financial development, financial knowledge, wars, trade openness, education, transmission of bequests, human capital, entrepreneurship, medical expense risk, labor earnings, precautionary savings, stochastic returns to wealth, inheritance, and genetic endowments (Bagchi et al., 2019; Balac, 2008; Barth et al., 2020; Benhabib et al., 2017; Berisha & Meszaros, 2019; Campanale, 2007; De Nardi & Fella, 2017; Elinder et al., 2018; Hasan et al., 2020; Lusardi et al., 2017). Despite ongoing efforts to understand the factors that may influence wealth inequality, the role of ICT has been overlooked by these earlier studies.
The rate of technological progress has been and continues to be impressive, with ICT growing at an exponential rate (Kurzweil, 1999; Stanley et al., 2018). In 2019, no less than 4.1 billion people have access to the internet, with a penetration rate that has risen from 16.8% in 2005 to over 53.3% in 2019 (ITU, 2019). However, this progression is not homogeneous in all regions. For example, the rate is 87% in developed countries compared to 47% in developing countries (see Fig. 1). As far as mobile phones are concerned, the penetration rate is close to saturation in all regions. The mobile penetration rate is 129% in developed countries, 104% in developing countries and even nearly 75% in the least developed countries (see Fig. 2).
This rapid growth in ICT adoption is due to its ability to sublimate virtually all sectors of activity. To date, several studies have highlighted the beneficial effects of ICT along several dimensions of economic life, including the productive system (Oulton, 2002; Cardona et al., 2013), trade openness (Freund & Weinhold, 2002; Choi, 2010; Rodríguez-Crespo & Martínez-Zarzoso, 2019), environment (Higón et al., 2017; Asongu et al., 2018; Avom et al., 2020), corruption (Kanyam et al., 2017; Sassi & Ali, 2017; Adam, 2020), institutional quality (Asongu and Nwachukwu, 2016, Asongu and Nwachukwu, 2016; Ali, 2020), economic sophistication (Lapatinas, 2019), industrialization (Njangang & Nounamo, 2020; Müller, 2021), financial development (Edo et al., 2019; Chien et al., 2020; Owusu-Agyei, 2020), health (Dutta et al., 2019; Kouton et al., 2020), education (Hernandez, 2017), inclusive human development (Asongu & Le Roux, 2017; Asongu et al., 2017), employment (Hjort & Poulsen, 2019; Ndubuisi et al., 2021), and most importantly economic growth (Albiman & Sulong, 2017; Appiah-Otoo & Song, 2021; Hong, 2017; Niebel, 2018; Sawng et al., 2021; Vu, 2011). However, whether and how ICT affects wealth inequality is less explored. Owing to the absence of data on the distribution of wealth for enough countries, the existing literature has analyzed the effect of ICT on income inequality (Asongu, 2015; Asongu & Le Roux, 2017; Asongu & Odhiambo, 2019; Bauer, 2018; Canh et al., 2020; Flores, 2003; Jaumotte et al., 2013; Mushtaq & Bruneau, 2019; Richmond and Triplett, 2018a, Richmond and Triplett, 2018b; Shahabadi et al., 2017; Tchamyou et al., 2019). Although a growing number of studies have examined the socio-economic effects of ICT, some research gaps remain. First, although some researchers have looked at the impact of ICT on income inequality, little is known about the impact of ICT on wealth inequality. Second, besides the direct impact, we assume that democracy could mitigate the effects of ICT.
This study, while drawing its theoretical foundations from the literature on the ICT - income inequality nexus, departs from the attendant literature and contributes to filling the gaps in the emerging literature on the determinants of wealth inequality on several fronts. First, to the best of the authors' knowledge, we are not aware of any studies that investigate the link between ICT and wealth inequality, and therefore, we provide one of the first empirical papers using the largest dataset available on wealth inequality. Second, due to the lack of reliable data on wealth inequality, almost all previous studies have focused on income inequality using the Gini index as a dependent variable. This study takes a fresh look at using billionaires’ wealth as a percentage of GDP from Bagchi and Svejnar (2015) as the primary measure of wealth inequality. For robustness purposes, this study uses the top one percent as well as the top ten percent of wealth shares from Credit Suisse (2014) as alternative measures of wealth inequality. Third, in addition to the traditional measures used to measure ICT (Internet and Mobile), we use several other indicators, namely ICT service exports, and a new dataset on the quality and quantity of ICT (see Hilbert, 2019). This paper is therefore the first in the empirical literature to use the new dataset on ICT quality and quantity to investigate the effect of ICT on wealth inequality. Four, this study is the first to investigate the mitigating role of democracy in the ICT-wealth inequality nexus. Five, to obtain more robust results, we use the Generalised Method of Moments that accounts for potential endogeneity issues. To sum up, using a large panel of 45 developed and developing countries over the period 2000–2017, we find robust evidence that ICT increases wealth inequality and that democracy is a mitigating factor.
The remainder of this paper is structured as follows. Section 2 discusses the theoretical mechanism through which ICT impacts wealth inequality. Section 3 presents the data and methodology. Section 4 reports the estimation results, and Section 5 concludes.
Section snippets
Theoretical underpinnings
The economic literature has often explained the accumulation effect and the rise in inequality based on personal capabilities, such as entrepreneurial talent (Guiso & Rustichini, 2018). Entrepreneurship has been seen as a source of economic wealth, and is important in explaining wealth accumulation and distribution (Meh, 2005). The development of ICT has made it easy to learn about business opportunities. Several studies support this idea that ICT promotes entrepreneurship for the creation of
Data
Our sample covers 45 developed and developing countries over the period 2000–2017 with data from various sources: World Bank: World Development Indicators (WDI); Polity IV project; Credit Suisse (2014), Bagchi and Svejnar (2015), Database of Political Institutions (2017), Hilbert (2019), and V-DEM, Version 11.1.3 The periodicity under investigation is chosen according to data availability constraints. Table 1 presents the descriptive statistics, while Appendix Tables
Baseline results
Table 2 reports the estimation results of Equation (1) with Billionaires’ wealth to GDP as a proxy for wealth inequality. In these estimations, we include a subset of the contemporaneous determinants of wealth inequality. In columns (1), (3) and (5), we test the bivariate relationship between ICT indicators and wealth inequality without control variables. The results provide evidence of a positive effect of ICT on wealth inequality, and these effects are significant at the 1% level.
Conclusion
A large body of studies has examined the effects of information and communication technology (ICT) on various macroeconomic variables. Surprisingly, little is known about the effects of ICT on wealth inequality. Due to the lack of reliable data on wealth inequality, previous studies have used the Gini index to measure wealth inequality. Moreover, previous studies have analyzed the link between ICT and income inequality without focusing on the effect of ICT on wealth inequality. The aim of this
Acknowledgements
The authors would like to thank the four anonymous referees of the journal, Erik Bohlin (the editor), Edmond Noubissi and Tii Nchofoung for their helpful comments and suggestions on the earlier version of this article. However, any remaining errors is solely attributable to the authors.
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