ict, governance and inequality in africa

Telecommunications Policy 45 (2021) 102198

Available online 2 June 20210308-5961/© 2021 Elsevier Ltd. All rights reserved.

ICT, governance and inequality in Africa

Samuel Adams a, Eric Akobeng b,*

a Ghana Institute of Management and Public Administration, Accra, Ghana b Department of Economics, University of Ghana, Accra, Ghana

A R T I C L E I N F O

JEL classification: C13 I32 O33 R38

Keywords: ICT Governance Inequality Africa

A B S T R A C T

We examine the direct effect of Information and Communication Technologies (ICTs) on inequality and investigate whether the ICT and inequality relationships can be reinforced by governance indicators of democracy, regulatory quality, rule of law and political stability. Using the panel dataset of 46 African countries over the period 1984–2018 and dynamic two-step system generalized method of moment estimator, we found that ICT measures of internet, fixed broadband and mobile cellular subscription directly reduce inequality, and good governance indicators can reinforce the ICT and inequality links. We execute a threshold analysis that il-lustrates the crucial role of democracy, regulatory quality, rule of law and political stability in facilitating the ICT and inequality relationships in Africa.

1. Introduction

Innovations of information and communication technologies (ICTs) have influenced human life through time-saving, diffusion of knowledge, easy communication, and networks, access to information and automation with artificial intelligence. They not only in-crease productivity, but also improve transparency and governance, build social capital and empower individuals (Maiti & Awasthi, 2020). Also, ICT penetration facilitates information flows and the analysis of credit worthiness in efficient way by maintaining the comprehensive database of clients (Mushtaq & Bruneau, 2019). Kocsis (2020) asserts that a robust information infrastructure may create access to online services for everyone, bridge the digital divide, and create jobs.

Some of the policy objectives on ICT in Africa are to develop an e-Government system and process, protect citizen rights relating to cybercrime, child protection and the right to information and improve internal Government ICT Infrastructure and Support (ICA, 2018). Over the past two decades, the liberalization of the information and communication technology (ICT) sector in Africa has been accompanied by a multitude of positive economic development consequences (Noh & Yoo, 2008).

Though the SSA region lags behind the rest of the world in the digital technologies, in the last few years it has seen a tremendous growth in the access and use of ICT. Evans (2019) notes that digital revolution has unfolded at an unprecedented speed in SSA and gradually the discussion must shift from the digital divide to digital dividend in terms of economic growth, reduction in poverty and income inequality.

The World Bank (2016) report on digital dividends indicates that the world is in the midst of greatest ICT revolution in human history with the poorest households more likely to have access to mobile phones than to toilets or clean water. The question with the tremendous growth in ICT infrastructure, is how is it impacting on the development outcomes in the region, particularly income inequality? It is worthy of mention that many studies have considered the effect on economic growth with a dearth of literature on its

* Corresponding author. E-mail addresses: [email protected] (S. Adams), [email protected] (E. Akobeng).

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https://doi.org/10.1016/j.telpol.2021.102198 Received 2 January 2021; Received in revised form 18 May 2021; Accepted 21 May 2021

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effect on the distribution of income. This study contributes to the extant literature in this light. This study differs from the few studies on ICT and inequality in three main ways. First, our study controls for the regulatory

environment as recent studies do suggest that the ICT – income inequality nexus is influenced by institutional infrastructure. This is consistent with the World Bank (2016) assertion that for digital technologies to benefit everyone everywhere requires, countries to work on the “analogue complements” — by strengthening regulations that ensure competition among businesses and by ensuring that institutions are accountable. Second, we find the differential effects of the various types of ICT infrastructure in terms of internet connectivity, broadband, and mobile cellular phone access and usage. Third, we identify the interactive effects of ICT and governance on inequality in Africa.

The study is relevant mainly because of the negative effects of income inequality, including threats to civic harmony and demo-cratic legitimacy.1 Dabla-Norris et al. (2015) have also noted the health of a society is measured not at its apex but at its base. The World Bank (2016) notes that the greatest rise of ICTs in history will not be truly revolutionary until it benefits everyone in every part of the world. Obviously, understanding the causes of inequality is fundamental to devising policy measures that can allow the rising prosperity of recent decades to be shared (Jaumotte et al., 2013). These issues give credence to this study. Our focus on SSA is also driven by the ambiguity in the various studies which suggest that the drivers of inequality and their impact differ across countries and regions with different socioeconomic and political characteristics.

The objective of this paper is to test the hypothesis that there is a negative relationship between ICT and inequality. A further analysis is conducted on whether governance reinforce the inequality-reducing effect of ICT. This paper contributes to the existing literature by providing the first-hand comprehensive macro-level evidence of the direct impact of ICT on inequality and how ICT and the inequality link can be strengthened by governance measures. We rely on a dynamic panel-data modelling by two-step system generalized method of moment (GMM) estimator developed by Blundell and Bond (1998). The estimator addresses reverse causality, purges the country fixed effects and provides the flexibility to instrument ICT, governance and their interaction term in a single model.

The paper is structured as follows. The next section, 2 reviews the existing literature. Section 3 presents the dataset. Section 4 utilises econometric methodology to estimate the link between ICT, governance and poverty. Section 5 discusses the estimated results and Section 6 provides policy recommendations and concludes.

2. Literature review

Many reasons have been given to explain why ICT expansion and adoption should promote economic development. However, the main argument is the fact that advancement in ICT could reduce transaction costs, and in the process raise the productivity of the factors of production and efficiency improvements in businesses (Evans, 2019; Adeola et al., 2018). Generally, most infrastructure investments have a positive effect on the economy in three ways, namely reducing production costs, increasing income, and increasing employment (Untari et al., 2019; Loh & Chib, 2019). From this perspective, the World Bank (2016) explains that through ICT, businesses are able to offer customers more choice and convenience. The report suggests that digital technologies have dramatically expanded the information base, lowered information costs, and created information goods. In particular, the report indicates that through inclusion, efficiency, and innovation, access provides opportunities that were previously out of reach to the poor and disadvantaged.

The use of ICT is expected to transmit information faster, more easily and less costly which makes it an important tool for economic activity (Noh & Yoo, 2008). The diffusion of ICT therefore could increase overall worker productivity and consequently reduce income inequality (Lloyd-Ellis, 1999). Mushtaq and Bruneau (2019) have also argued that adoption of ICT could improve wellbeing of the rural poor as it helps to provide market information to farmers to improve their bargaining power and income generating capabilities and consequently reducing poverty and income inequality. This supports the claim by Kocsis (2020) that robust information infra-structure may create access to online services for everyone, bridge the digital divide, and create jobs.

The opportunities offered by ICTs could however, lead to digital divide such that some people benefit more than others because of their ability to not only access but to use it more to generate higher level of productivity due to their initial higher level of human and financial capital (Zhang et al., 2020). So that in an environment where inequality of opportunity exists in terms of class, gender, education and income, improvements in the ICT architecture might rather worsen the distribution in income (Lindsay, 2005). This is the so called Matthew Effect, where those who “have” expand their opportunity set while those who “have-not” become progressively disempowered and excluded from the mainstream (Tewathia et al., 2020, p. 2). Acemoglu (2002) argues that the recent increase in income inequality in most developed countries has been attributed to the diffusion of information technology raising the wage pre-mium for ICT related people. The focus is more on use and not just the access to ICT services because people from poor backgrounds do not have the necessary skill or financial capital to make use of ICT services. Jaumotte et al. (2013) also claim that ICT advancement or technological progress both appear to be working by increasing the premium on higher skills and possibly higher returns to capital, rather than limiting opportunities for economic advancement.

Richmond and Triplett (2018) assert that ICT could help in reducing income inequality as it improve access to resources, infor-mation and markets, and thereby, reduce income inequality. However, in many cases ICT represents a skill-biased technological change, and it may also exacerbate wage disparities and deepen income inequality.

The theoretical arguments therefore do not give unambiguous results. Accordingly, many empirical studies have been conducted to

1 See Ahmed et al. (2020), Asongu and Odhiambo (2019) and Bicaba et al. (2017).

S. Adams and E. Akobeng


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give evidence of the ICT – income inequality relationship, but like the theoretical studies the results have been inconsistent. For example, Noh and Yoo (2008) examine a panel data set for 60 countries for the period 1995–2002 and report that internet adoption has a negative effect for countries with high income inequality because the digital divide hinders growth by the internet. Accordingly, the authors conclude that the redistribution of income enhances the positive growth effect of internet. Carabregu Vokshi et al. (2019) studied the Western Balkan countries and find that mobile phone penetration has a positive effect on the distribution of income.

Ali et al. (2019) use Australian household data from 2011 to 2017 based on generalized linear mixed model to show that ICT affordability is positively correlated with the distribution of income. Asongu (2015) examined the effect of impact of mobile phone penetration on the distribution of income of 52 African countries using the robust OLS and 2SLS techniques and reports that mobile phone has a negative effect on income inequality. In a related stud of 48 SSA countries based on GMM technique, Asongu and Odhiambo (2019) find that both mobile phone and internet penetration reduce income inequality. Using the OLS and Tobit estimations to examine the social media and inclusive development relationship of 49 African countries, Asongu and Odhiambo (2020b) demonstrate that Facebook penetration is positively associated with inclusive human development.

Zolfaghari et al. (2020) used the panel corrected standard errors to examine the case of Iran over the period 2007–2016 and show that investment in ICTs, energy and water have a positive effect on the distribution of income. Likewise, Kocsis (2020) find that enhancing IT infrastructure has a negative effect on income inequality in San Francisco. Cioaca et al. (2020) examined the case for the EU and demonstrate that there is a significant positive impact of internet access on the distribution of income. Maiti and Awasthi (2020) investigated a panel of 67 countries during the period 2000–2014 and find that ICT exposure has positive effect on well-being and progress though the impact is lower in the less developed countries. Ahmed et al. (2020) investigate 108 countries and find that internet access increases the likelihood of civic participation and note that government intervention through the use of ICT can help to reduce income inequality. Similarly, D’Onofrio and Giordani (2019) report of a positive effect of technological infrastructure on inequality across Italian provinces over the period 2001–2005.

On the contrary, Tewathia et al. (2020) use the case of India based on data of 40,000 households to show that advances in ICTs deepen inequality as the poor do not have the assets or skills to use them. Employing the Matthew Effect, the authors suggest that investment and adoption of ICTs amplify the social inequalities in India. In another study of India, Kapoor (2020) finds that the growth in capital intensity has led to an increase in income inequality because of the reduction in the role of labor. The results of the study show that the share of total emoluments paid to labor fell from 28.6% to 17.4% of gross value added (GVA) between 2000-2001 and 2011–2012, while the share of wages to workers in GVA declined from 22.2% to 14.3%. Zhang et al. (2020) also studied 155 counties in China from 2010 to 2016 using the fixed effects and 2SLS regressions and report that internet penetration increases consumption inequality especially in regions with higher education levels.

If ICT epitomizes a type of skill-biased technical change, then the benefits may accrue greatly to those sections of the labor force that are situated to take advantage of such opportunities (Acemoglu, 1998; Katz & Goldin, 2008).

In a study of China, Leng et al. (2020) demonstrate that ICT adoption has been pro-poor as it has helped to promote income diversification. Additionally, they report that improving rural education and infrastructure such as roads and broadband facilities can help enhance ICT adoption among rural households, which subsequently increases income diversification.

Jaumotte et al. (2013) examine the relationship between globalization, technological progress and income inequality for 51 countries over the period 1981–2003 and report that technological progress has a more pronounced effect on increasing income inequality than globalization. Some other studies do show that the inequality – ICT relationship is mediated by specific country characteristics. For example, in a cross national study of 109 countries over the period 2001–2014, Richmond and Triplett (2018) demonstrate that the ICT – inequality relationship is conditional on other economic and political characteristics. Dell’Anno and Solomon (2014) consider the case of the Transition economies using Fixed and Random effect estimation techniques and demonstrate that the relationship is dependent on the quality of institutions. They note, for example, that when institutions are weak, agents invest less human capital and ICT in the formal sector (FS), thereby reducing income inequality. Hope and Martelli (2019) investigate the transition to knowledge economy and its effect on income inequality for 18 OECD countries for the period 1970–2007. The results show that expansion of knowledge economy increases income inequality, however, these effects are alleviated by the presence strong labor market institutions.

Hawthorne and Grzybowski (2019) investigate the case of South Africa based on data of 134,000 individuals during the period 2009–2014 and find that regulation moderates the effect of mobile penetration than competition in enhancing the distribution of income. The results show that mobile penetration would have been eight percentage points lower among low-income consumers compared to four percentage points among high-income consumers.

Asteriou et al. (2014) investigate the determinants of income inequality for EU countries during the period 1995–2009 and find that Research and Development significantly reduce inequalities, but technological progress through ICT share, education, and employ-ment were found to be relatively insignificant in reducing inequality.

There are several underlying drivers through which ICT influences inequality. The rapid technological effects of ICT promote, networking, competition, controls rents and disrupts existing concentrations of wealth (Antonelli & Gehringer, 2017; Downes, 2009). ICT is useful for enhancing access to resources, information, and markets. It enables firms to increase productivity and profits and stimulates an increase in income among poor households (Aker & Mbiti, 2010; Qureshi, 2011; Richmond & Triplett, 2018).

Using data on Filipino farmers, Labonne and Chase (2009) find that mobile phones increase the growth rate of per capita con-sumption by 11–17%. The study demonstrates that purchase of a mobile phone allowed poor farmers to access more and improved knowledge and price their goods more competitively. More so, Carte et al. (2011) show that ICT has learning by doing effects that reduces inequality in Sri Lanka. Mobile phone usage leads to improved access to credit and insurance, thereby reducing inequality in risk burdens (Conley & Udry, 2010; De Weerdt & Dercon, 2006).


TelecommunicationsPolicy45(2021)102198

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Table 1 ICT-Inequality relationship - Dynamic two-step SGMM approach.

Dep. Var.: Gini Index

Internet Fixed Broadband Mobile Cellular

Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8 Model 9

ginii,t− 1 0.7553*** (0.0547)

0.7510*** (0.0514)

0.7502*** (0.0544)

0.8572*** (0.0496)

0.8601*** (0.0449)

0.8446*** (0.0519)

0.7735*** (0.0526)

0.7755*** (0.0495)

0.7715*** (0.0523)

lfin − 0.2039 (0.2111)

− 0.2076 (0.2110)

− 0.1941 (0.2315)

− 0.3234 (0.2649)

− 0.3424 (0.3293)

− 0.3335 (0.2717)

− 0.1497 (0.2034)

− 0.1613 (0.1917)

− 0.1290 (0.2260)

ltrade − 0.3642 (0.2378)

− 0.3588* (0.2105)

− 0.3362 (0.2997)

− 0.1723 (0.2665)

− 0.3400 (0.2340)

− 0.1674 (0.2576)

− 0.3221 (0.2361)

− 0.3200 (0.1952)

− 0.3878 (0.3016)

lgdpcap − 0.3180* (0.1797)

− 0.3173* (0.1887)

− 0.3188* (0.1914)

− 0.1061* (0.0596)

− 0.1336* (0.0738)

− 0.0915* (0.0510)

− 0.2319* (0.1295)

− 0.2273* (0.1261)

− 0.2296* (0.1361)

lainf 0.0006*** (0.0002)

0.0007*** (0.0003)

0.0006** (0.0002)

0.0050 (0.0171)

0.0101 (0.0158)

0.0093 (0.0187)

0.0008*** (0.0003)

0.0007*** (0.0002)

0.0007** (0.0003)

dem − 0.0181* (0.0104)

− 0.0322* (0.0179)

− 0.0413* (0.0227)

− 0.0341* (0.0180)

− 0.0125* (0.0069)

− 0.0153* (0.0081)

lgxp − 0.2839* (0.1550)

− 0.3034* (0.1674)

− 0.3113* (0.1729)

− 0.3189* (0.1828)

− 0.3212* (0.1830)

− 0.3308 * (0.1835)

linv − 0.0389 (0.2555)

− 0.0224 (0.1843)

− 0.0131 (0.1671)

internet_pop − 0.0255* (0.0141)

− 0.0255* (0.0140)

− 0.0254* (0.0142)

fixedbrb_100 − 0.0244* (0.0142)

− 0.0264* (0.0153)

− 0.0252* (0.0144)

mob_100 − 0.0041* (0.0023)

− 0.0039* (0.0022)

− 0.0041* (0.0023)

Intercept 15.6972*** (3.6083)

14.2360*** (3.9732)

14.4768*** (3.6674)

10.0970* (5.7144)

7.3779 (5.4025)

7.6909 (5.6766)

14.6190*** (3.7957)

12.5610*** (3.4565)

13.8181*** (4.1298)

Observations 201 201 201 131 131 131 209 209 209 Instruments/Groups 30/44 30/44 30/44 24/43 24/43 24/43 31/44 31/44 31/44 Hansen J test p - value 0.4148 0.4152 0.4076 0.3137 0.3111 0.3410 0.4448 0.3518 0.4060 Resid. AR(2) test p - value 0.5156 0.5349 0.4750 0.6574 0.6018 0.5420 0.8468 0.8583 0.8327

*p < 0.10, **p < 0.05, ***p < 0.01. Windmeijer (2005) finite-sample corrected standard errors are reported in parentheses. Time and country dummies are included in all regressions.

S. Adam

s and E. Akobeng


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3. Data

This paper investigates the relationship between ICT, institutions and inequality in Africa with a data set of 46 African countries. A 5-year non-overlapping average panel data is used essentially because the primary source, the World Bank, does not provide infor-mation on inequality annually. The empirical panel models have a dimension of N*T such that N = 46 and t = 1, 2, 3, …T so that T = 7 (5-year average from 1984 to 2018). The other controlled variables are: real GDP per capita, domestic credit to the private sector as percentage of GDP (finance), inflation, trade openness and government consumption expenditure. The governance variables are de-mocracy, rule of law, regulatory quality and political stability.2 The descriptive statistics for the variables are presented in Table 8. We can infer from Table 8 or Appendix B that Fixed broadband penetration is so low in most African countries. This is likely to limit its overall impact on inequality. Tables 9 and 10 are the construction of variables and sample countries respectively.

Table 2 ICT-institutions-inequality relationship - Dynamic two-step SGMM approach.


Democracy Regulatory Quality

Model 1 Model 2 Model 3 Model 4 Model 5 Model 6

ginii,t− 1 0.7143*** (0.0661)

0.8237*** (0.0568)

0.6615*** (0.0704)

0.7199*** (0.0554)

0.8199*** (0.0627)

0.6909*** (0.0585)

lfin − 0.5458 (0.3887)

− 0.3857 (0.3475)

− 0.0042 (0.1485)

− 0.0549 (0.2084)

− 0.3371 (0.3083)

− 0.0386 (0.2320)

ltrade − 0.5190 (0.4094)

− 0.4367 (0.3299)

− 0.0680 (0.1910)

− 0.4554 (0.3703)

− 0.2604 (0.3421)

− 0.4514 (0.3637)

lgdpcap − 0.3820** (0.1835)

− 0.0972* (0.0525)

− 0.1358* (0.0754)

− 0.3086* (0.1716)

− 0.0991* (0.0534)

− 0.2503* (0.1356)

linv − 0.0208 (0.1932)

− 0.0328 (0.1989)

− 0.0184 (0.1899)

− 0.0257 (0.2443)

− 0.0187 (0.1855)

− 0.0357 (0.2091)

lainf 0.0007** (0.0003)

0.0134 (0.0202)

0.0007** (0.0003)

0.0008*** (0.0003)

0.0195 (0.0210)

0.0009*** (0.0003)

lgxp − 0.2811* (0.1480)

− 0.2903* (0.1548)

− 0.3002* (0.1618)

− 0.3201* (0.1721)

− 0.3344* (0.1788)

− 0.3295* (0.1762)

dem − 0.0267* (0.0143)

− 0.0370* (0.0210)

− 0.0112* (0.0020)

reg_qual − 0.3134* (0.1681)

− 0.1892* (0.1009)

− 0.2177* (0.1127)

internet_pop − 0.0466* (0.0260)

− 0.0241* (0.0130)

intpop*dem 0.0147** (0.0068)

fixedbrb_100 − 0.0499* (0.0268)

− 0.0137 (0.0409)

fixedbrb_100*dem − 0.0206** (0.0083)

mob_100 − 0.0063** (0.0032)

− 0.0035* (0.0019)

mob_100*dem − 0.0159** (0.0075)

intpop*regq − 0.0131** (0.0055)

fixedbrb_100*regq − 0.0580*** (0.0219)

mob_100*regq − 0.0013 (0.0059)

Intercept 17.5680*** (4.3276)

6.4972 (5.2641)

20.3298** (7.9228)

16.7022*** (3.8967)

5.0904 (6.7482)

14.6717** (5.9694)

Observations 181 130 141 198 131 189 Instruments/Groups 40/44 33/44 41/44 41/44 40/43 41/44 Hansen J test p - value 0.7737 0.6848 0.9283 0.4500 0.6353 0.6983 Resid. AR(2) test p - value 0.5085 0.2922 0.7652 0.5783 0.6578 0.6309


2 Other studies that use subjective institutional measures to look at the role of institutions in development are Abdih et al. (2012), Ali et al. (2010), Catrinescu et al. (2009), Busse and Hefeker (2007), Easterly et al. (2006), Kahn (2005), Akobeng (2017), Akobeng (2016) and Knack and Keefer (1995).



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4. Empirical model and methodology

We use a dynamic panel data technique developed by Blundell and Bond (1998) to estimate the effect of ICT on inequality and the reinforcing influence of governance. The past level of inequality may be an important determinant of current level of inequality. The dynamic modelling allows us to include lag of inequality as an explanatory variable to cater for the persistent nature of inequality.3 The baseline specification is:

giniit = δ1ginii,t− 1 + δ2ICTit + δ3gdpcapit + δ4govit + αX′

it + μit (1)

Where. μit = υi + λt + εit i and t are subscripts representing country and year index. giniit is the Gini coefficient for country i at time t. The respective estimates

of δ2 represent the direct effect of the ICT indicators on inequality. The ICT indicators are individuals using the internet as percentage of the population, mobile cellular subscriptions per hundred people and fixed broadband subscriptions per hundred people. govit rep-resents the subjective governance variables (democracy, requlatory quality, rule of law and political stability). X′

it is a vector of other factors that affect inequality. The fixed-effects are contained in the composite error term (μit) which is made up of the unobserved country-specific effects υi, the idiosyncratic errors εit and λt, which is the time-varying parameter. The estimate of α represents the direct impact of the other variables on poverty.

In order to capture the effect of ICT, institutions and inequality, we formulate equation (1) to capture the interaction term of ICT and the governance indicators.

giniit = δ1ginii,t− 1 + δ2ICTit + δ3gdpcapit + δ4govit + δ5(ICTit × govit) + αX ′

it + μit (2)

where ICTit × govit is an interaction term of ICT and governance indicators of country i at time t. Equation (2) provides a pathway for examining the role of governance in the relationship between ICT and inequality. The marginal effects of ICT on inequality at specified levels of the governance indicators is derived from equation (2) as:

∂(giniit)

∂(ICTit)= δ2 + δ5govit (3)

δ5 reflects the extent to which the governance variables moderate or enhance the effect of ICT on inequality. The effect of ICT on inequality is given by the right hand side of (3). There is the possibility of the cross-country findings being biased due to the simul-taneity in the ICT and inequality relationship and reverse causation that may arise because inequality may be affected by ICT and also institution, as well as ICT and institution may be driven by inequality, in a direction that is difficult to establish.

Also, measuring errors in the ICT or the proxies for governance could invalidate the orthogonality condition. The estimates will be biased if there is an omitted variable which appears to be relevant and correlates with ICT, governance or the other included explanatory variables.

The OLS estimator may fail to treat the potential endogeneity of ICT and the governance variables. It may fail to account for the likely country-specific variations which are unobserved. Inequality may affect ICT penetration or any of the governance variables, a clear case of reverse causality. There may also be the possibility of simultaneity bias, a situation of having ICT or governance variables correlated with other uncontrolled variables or the residual disturbance term. It could have been great to use external instruments for ICT penetration and the governance indicators including democracy to deal with these endogeneity concerns. Similarly, external instruments could be used for inequality or the governance variables in a reverse estimation.

External instruments are not available to instrument the ICT indicators, governance and their interaction terms in a single model. We therefore resort to the system GMM estimation technique to address biases due to reverse causality between ICT and inequality by using internal instruments. The dynamic system Generalized Method of Moments estimation technique allows the use of lagged explanatory variables as instruments. The system GMM estimator provides a pathway of including ICT, institutions and their respective interaction terms in the models. Other papers that use internal instruments of the system GMM approach to instrument for ICT are Tchamyou et al. (2019a,b), Asongu and Odhiambo (2020a) and Cheng et al. (2021).

5. Estimation results

Tables 1–3 present the system GMM estimation results of equations (1) and (2). In 1, we estimate the direct effect of ICT on inequality. We find that all the three ICT indicators are negatively signed and ten percent significant. We can infer from Model 3, 6 and 9 that internet access and fixed broadband subscriptions have more inequality-reducing effects than mobile cellular subscriptions. Real GDP per capita and government expenditure emerge negative and significant in all the estimated models.

In models 1, 2 and 3 of Table 2, we interact the ICT measures with democracy. It is very intriguing that all the ICT measures entered individually with an increased coefficient than their coefficients in the direct effect models in Table 1. The interaction terms of the ICT

3 Some scholars identify poverty and inequality as having persistence series (Bane & Ellwood, 1986; Hoynes, 2006; Wright, 1992).



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Table 4 Effect of ICT on inequality at specified levels of democracy.

Institutional democracy at 25% (0.000) 50% (1.000) 75% (5.600) Based on regression

Internet − 0.024* (0.014)

− 0.049* (0.028)

− 0.161* (0.091)

Table 2, Model 1

Fixed Broadband − 0.021** (0.008)

− 0.041** (0.0165)

− 0.136** (0.055)

Table 2, Model 2

Mobile − 0.015* (0.008)

− 0.030* (0.016)

− 0.098* (0.054)

Table 2, Model 3

The marginal analysis is evaluated at the 25th, 50th and 75th percentiles of institutional democracy. Standard errors are in parentheses. * stands for statistical significance at 10%. ** stands for statistical significance at 5%.

Table 3 ICT-institutions-inequality relationship - Dynamic two-step SGMM approach.


Rule of Law Political Stability

Model 1 Model 2 Model 3 Model 4 Model 5 Model 6

ginii,t− 1 0.7194*** (0.0470)

0.8186*** (0.0516)

0.7178*** (0.0559)

0.7422*** (0.0524)

0.8324*** (0.0592)

0.7278*** (0.0560)

lfin − 0.2495 (0.2742)

− 0.4305 (0.3924)

− 0.2552 (0.2191)

− 0.2199 (0.2504)

− 0.3721 (0.3647)

− 0.1743 (0.2505)

ltrade − 0.3764 (0.3296)

− 0.2828 (0.2838)

− 0.4403 (0.3381)

− 0.3485 (0.3402)

− 0.3054 (0.3773)

− 0.4409 (0.3658)

lgdpcap − 0.3141* (0.1653)

− 0.0689* (0.0365)

− 0.2737* (0.1450)

− 0.2633* (0.1402)

− 0.0697* (0.0368)

− 0.2392* (0.1217)

linv − 0.0193 (0.1807)

− 0.0243 (0.1928)

− 0.0322 (0.2008)

− 0.0187 (0.1793)

− 0.0186 (0.1745)

− 0.0166 (0.1699)

lainf 0.0007*** (0.0002)

0.0186 (0.0200)

0.0009*** (0.0002)

0.0008*** (0.0003)

0.0125 (0.0210)

0.0008** (0.0004)

lgxp − 0.2813* (0.1477)

− 0.3165* (0.1684)

− 0.3112* (0.1638)

− 0.3231* (0.1797)

− 0.3281* (0.1747)

− 0.3270* (0.1740)

rol − 0.1043* (0.0549)

− 0.0520* (0.0287)

− 0.0499* (0.0277)

pol _st − 0.1240* (0.0670)

− 0.1013* (0.0536)

− 0.0903* (0.0478)

internet_pop − 0.0279* (0.0163)

− 0.0238* (0.0144)

intpop*rol − 0.0050** (0.0024)

fixedbrb_100 − 0.0300** (0.0158)

− 0.0148 (0.0100)

fixedbrb_100*rol − 0.0791* (0.0465)

mob_100 − 0.0069* (0.0036)

− 0.0054* (0.0028)

mob_100*rol − 0.0043* (0.0025)

intpop*pol_st − 0.2436** (0.1154)

fixedbrb_100*pol_st − 0.2387** (0.1131)

mob_100*pol_st − 0.1337** (0.0605)

Intercept 15.7310*** (3.5912)

5.6943 (5.6192)

15.5930*** (4.8869)

16.2924*** (4.6661)

8.1392* (4.5633)

15.7964*** (4.5465)

Observations 198 131 191 196 129 191 Instruments/Groups 40/44 32/43 41/44 41/44 38/43 41/44 Hansen J test p - value 0.5414 0.6872 0.5791 0.7321 0.8479 0.7708 Resid. AR(2) test p - value 0.4664 0.5616 0.6695 0.4819 0.6067 0.6590




8

measures with democracy are all negatively signed and significant at 5% levels. Thus, ICT interacts effectively with democracy in reducing inequality in Africa. In models 4, 5 and 6 of Table 2, we interact the ICT indicators with regulatory quality. The interaction terms of internet access and fixed broadband with regulatory quality are negatively signed and significant at 5% and 1% levels respectively. Fixed broadband as a standalone variable loses its significance. The interaction term of mobile subscription and regu-latory quality is insignificant. However, the marginal effect analysis in Table 5 indicates that the significant level of mobile sub-scription and regulatory quality can be attained at 75th percentile of regulatory quality. Real GDP per capita and government expenditure emerge significant in in all the estimated models.

In Table 3, we interact the ICT indicators with rule of law and political stability. In Models 1, 2, and 3, the ICT measures entered significant and their interaction terms with rule of law are negatively signed and significant. The interaction terms of the ICT indicators and political stability are negatively signed and significant at 5% levels. Fixed broadband entered insignificant but its interaction with political stability is significant. Real GDP per capita and government expenditure entered significant in in all the estimated models.

The results of the study show that trade openness and financial development per se are not key factors in promoting inequality in the African region. Though the coefficients are negative they are not significant. This might indicate that the variables might not have reached the threshold required to have positive effect on the distribution of income.

The results demonstrate that ICT has a significant inequality-reducing effect. The magnitude of the effect depends on the ICT indicator used. We find evidence of a negative and significant interaction between ICT measures and governance indicators of de-mocracy, regulatory quality, rule of law and political stability. These results imply that the inequality-reducing effect of ICT is an increasing function of good governance.

5.1. Threshold analysis

A marginal effect analysis is done with the interaction term between the ICT measures and the governance indicators. This ex-amines the marginal effect of ICT on inequality at the different percentile levels of the governance indicators. Tables 4–7 provide the marginal effect analysis.

The democratic score is evaluated at 25% (0.000), 50% (1.00) and 75% (5.600) in Table 4. The analysis shows that a 1% increase in internet access will decrease inequality by 0.024%, 0.049% and 0.161% at the 25th, 50th and 75th percentiles of democracy at 10% level of statistical significance.

The regulatory quality is evaluated at 25% (− 0.801), 50% (− 0.421) and 75% (− 0.161) in Table 5. The analysis shows that a 1% increase in fixed broadband subscription will decrease inequality by 0.027%, 0.078% and 0.113% at the 25th, 50th and 75th per-centiles of regulatory quality at 1% level of statistical significance.

Rule of law is assessed at 25% (− 0.821), 50% (− 0.484) and 75% (− 0.149) in Table 6. The analysis provides that a 1% increase in internet access will decrease inequality by 0.002%, 0.007% and 0.011% at the 25th, 50th and 75th percentiles of rule of law respectively, at 5% level of statistical significance.

Political Stability is also evaluated at 25% (− 0.924), 50% (− 0.331) and 75% (0.047) in Table 7. The analysis shows that a 1% increase in fixed broadband subscription will decrease inequality by 0.241%, 0.258% and 0.302% at the 25th, 50th and 75th per-centiles of political stability at 5% level of statistical significance.

In general, the marginal effect analysis indicates that the inequality reducing effects of ICT increase at higher percentile levels of the governance indicators. These imply that governance indicators reinforce ICT and inequality link in Africa.

5.2. Dynamic panel two-step GMM estimation diagnoses

We examine the validity of the instruments with the Hansen (1982) J-test for over-identifying restrictions. The null hypothesis of the Hansen test is that the over-identifying instruments are not correlated with the error term. The p-values of the Hansen J-statistics of all the estimated regressions are above 0.1, meaning that the null hypothesis that the instruments are valid cannot be rejected. The maximum lag of the dependent variable is restricted to one and the endogenous variables are instrumented with their levels lagged by two periods. The instruments are applied with the collapse option in order to minimize instruments “proliferation” (Roodman, 2006, p. 1). We test for second-order serial correlation using the Arellano and Bond (1991) AR(2) test. Fairly robust estimates are obtained when the variables are winsorised to remove extreme values in the data, in order to minimize the effect of spurious outliers. The use of the three ICT measures supports the robustness of the results.

6. Conclusion and policy implications

The main objective of this paper is to test the hypothesis that there is a negative relationship between ICT and inequality. A further analysis is conducted on whether governance indicators reinforce the inequality-reducing effects of ICTs. The paper is motivated by the conventional wisdom that the increase in ICT will not be groundbreaking until it benefits everyone in every parts of the world. The paper explores how ICTs can allow the rising prosperity of recent decades to be shared in the presence of good governance in Africa.

We find a negative and significant effects of ICT on inequality in Africa. The negative and significant effects of the ICT variables (internet, broadband, mobile phones) indicate that ICT infrastructure help to reduce income inequality in the African region. This means that the massive improvement in ICTs is good for the region and therefore governments in the region are encouraged to invest more in ICT infrastructure.

The results show that the ICT indicators interact effectively with democracy, rule of law and political stability in reducing



9

inequality in Africa. Additionally, Internet and fixed broadband interact significantly with regulatory quality in reducing inequality. The marginal effect analysis reveals that the interaction term of mobile subscription and regulatory quality can reduce inequality at 75th percentile of regulatory quality.

In the end, this paper argues that as the debate shifts from digital divide to digital dividends, reducing inequality could be enhanced through institutional development. Thus, ICT and quality governance indicators may serve as an important mix towards the achievement of the Sustainable Development Goal 10 (reducing inequality).

Future studies could look at the adoption of newer technologies (for example, distributed ledgers and platforms) as to their possible impact on the African continent. In the era of serious environmental problems associated with climate change, it will be important for future studies to look at how ICT infrastructure could help mitigate the problems of climate change using a country specific study.

Acknowledgements

We are thankful to the Ghana Institute of Management and Public Administration, Lancaster University Ghana, and Economics Department of the University of Ghana, for their helpful suggestions. We are indebted to the four anonymous referees and the Editor in Chief, Professor Erik Bohlin, at the Journal of Telecommunications Policy for their valuable suggestions. The standard disclaimer applies.

Table 5 Effect of ICT on inequality at specified levels of regulatory quality.

Regulatory Quality at 25% (− 0.801) 50% (− 0.421) 75% (− 0.161) Based on regression

Internet − 0.003** (0.001)

− 0.008** (0.003)

− 0.011** (0.005)

Table 2, Model 4

Fixed Broadband − 0.027*** (0.010)

− 0.078*** (0.029)

− 0.113*** (0.042)

Table 2, Model 5

Mobile − 0.001 (0.002)

− 0.002 (0.003)

− 0.005* (0.003)

Table 2, Model 6

The marginal analysis is evaluated at the 25th, 50th and 75th percentiles of regulatory quality. Standard errors are in parentheses. * stands for statistical significance at 10%. ** stands for statistical significance at 5%. *** stands for statistical significance at 1%.

Table 6 Effect of ICT on inequality at specified levels of rule of law.

Rule of Law at 25% (− 0.821) 50% (− 0.484) 75% (− 0.149) Based on regression

Internet 0.002** (0.001)

− 0.007** (0.003)

− 0.011** (0.005)

Table 3, Model 1

Fixed Broadband − 0.030* (0.016)

− 0.087* (0.045)

− 0.143* (0.074)

Table 3, Model 2

Mobile − 0.003* (0.002)

− 0.009* (0.005)

− 0.015* (0.009)

Table 3, Model 3

The marginal analysis is evaluated at the 25th, 50th and 75th percentiles of rule of law. Standard errors are in parentheses. * stands for statistical significance at 10%. ** stands for statistical significance at 5%.

Table 7 Effect of ICT on inequality at specified levels of political stability.

Political stability at 25% (− 0.924) 50% (− 0.331) 75% (0.046) Based on regression

Internet − 0.230** (0.109)

− 0.264** (0.126)

− 0.303** (0.146)

Table 3, Model 4

Fixed Broadband − 0.241** (0.106)

− 0.258** (0.114)

− 0.302** (0.134)

Table 3, Model 5

Mobile − 0.121** (0.067)

− 0.143** (0.081)

− 0.168** (0.096)

Table 3, Model 6

The marginal analysis is evaluated at the 25th, 50th and 75th percentiles of political stability. Standard errors are in parentheses. * stands for statistical significance at 10%. ** stands for statistical significance at 5%.



10

Appendix. A

Definition of variables

• Gini index

Gini index measures the extent to which the distribution of income (or consumption expenditure) among individuals or households within an economy deviates from a perfectly equal distribution. A Gini index of zero represents perfect equality, while an index of 100 implies perfect inequality.

• Individuals Using The Internet (% Of Population) Internet users are individuals who have used the Internet (from any location) in the last 3 months. The Internet can be used via a computer, mobile phone, personal digital assistant, games machine, digital TV etc.

• Mobile Cellular Subscriptions (Per 100 People)

Mobile cellular telephone subscriptions are subscriptions to a public mobile telephone service that provide access to the PSTN using cellular technology. The indicator includes (and is split into) the number of postpaid subscriptions, and the number of active prepaid accounts (i.e. that have been used during the last three months). The indicator applies to all mobile cellular subscriptions that offer voice communications. It excludes subscriptions via data cards or USB modems, subscriptions to public mobile data services, private trunked mobile radio, telepoint, radio paging and telemetry services.

• Fixed broadband subscriptions (per 100 people)

Fixed broadband subscriptions refers to fixed subscriptions to high-speed access to the public Internet (a TCP/IP connection), at downstream speeds equal to, or greater than, 256 kbit/s. This includes cable modem, DSL, fiber-to-the-home/building, other fixed (wired)-broadband subscriptions, satellite broadband and terrestrial fixed wireless broadband. This total is measured irrespective of the method of payment. It excludes subscriptions that have access to data communications (including the Internet) via mobile-cellular networks. It should include fixed WiMAX and any other fixed wireless technologies. It includes both residential subscriptions and subscriptions for organizations.

• Real GDP Per capita (constant 2010 international dollar)

GDP per capita is gross domestic product divided by midyear population. GDP is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products. It is calculated without making deductions for depreciation of fabricated assets or for depletion and degradation of natural resources. Data are in constant U.S. dollars.

• Gross fixed capital formation

Gross fixed capital formation (expressed as percentage of GDP) includes land improvements (fences, ditches and drains); plant, machinery, and equipment purchases; and the construction of roads, railways, and the like, including schools, offices, hospitals, private residential dwellings, and commercial and industrial buildings. The net acquisitions of valuables are also considered capital formation.

• Inflation

Inflation as measured by the consumer price index reflects the annual percentage change in the cost to the average consumer of acquiring a fixed basket of goods and services that may be fixed or changed at specified intervals, such as yearly. The Laspeyres formula is used.

• Trade Openness

The sum of exports and imports of goods and services as share of GDP.

• Finance

Domestic credit to the private sector as share of GDP.

• General Government Final Consumption Expenditure (% Of GDP)

General government final consumption expenditure (formerly general government consumption) includes all government current



11

expenditures for purchases of goods and services (including compensation of employees). It also includes most expenditures on na-tional defense and security, but excludes government military expenditures that are part of government capital formation.

• Institutional democracy

Democracy is conceived as three essential, interdependent elements. One is the presence of institutions and procedures through which citizens can express effective preferences about alternative policies and leaders. Second is the existence of institutionalised constraints on the exercise of power by the executive. Third is the guarantee of civil liberties to all citizens in their daily lives and in acts of political participation. The Democracy indicator is an additive 11-point scale with a maximum of 10.

• Regulatory quality

Regulatory Quality captures perceptions of the ability of the government to formulate and implement sound policies and regu-lations that permit and promote private sector development. Estimate gives the country’s score on the aggregate indicator, in units of a standard normal distribution, i.e. ranging from approximately − 2.5 to 2.5.

• Rule of law

Rule of Law captures perceptions of the extent to which agents have confidence in and abide by the rules of society, and in particular the quality of contract enforcement, property rights, the police, and the courts, as well as the likelihood of crime and violence. Estimate gives the country’s score on the aggregate indicator, in units of a standard normal distribution, i.e. ranging from approximately − 2.5 to 2.5.

• Political stability

Political Stability and Absence of Violence/Terrorism measures perceptions of the likelihood of political instability and/or politically-motivated violence, including terrorism. Estimate gives the country’s score on the aggregate indicator, in units of a standard normal distribution, i.e. ranging from approximately − 2.5 to 2.5.

Appendix B

Variable construction and source

Table 8 Summary Statistics

Variable Mean Std. Dev. Min. Max. N

Gini 46.2 10.2 27 79.3 322 Internet 7.2 11.6 0 59.7 235 Mobile subscription 34.3 41.4 0 166.6 245 Fixed broadband 0.8 2.4 0.0 17.6 148 Government expenditure 14.4 6.2 0 40.5 301 Real GDP Per Capita 2596.3 3713.6 258.9 21644.2 322 Gross fixed capital formation 19.8 8.3 0 50.83 301 Inflation 49.4 419.3 − 2.9 5618.0 290 Trade openness 66.4 31.1 13.1 206.7 304 Finance 19.8 22.4 1.0 146.0 319 Political Stability − 0.4 0.8 − 2.5 1.0 230 Rule of Law − 0.5 0.6 − 2.5 1.0 230 Regulatory Quality − 0.5 0.5 − 2.5 1.1 230 Democracy 2.6 3.2 0.0 10.0 322

This table provides the descriptive statistics for the data of 41 Sub-Saharan African countries over the period 1984 to 2018. The summary statistics are based on the raw data.



12

Appendix C

Variable construction and source

Table 9 Variables

Variable Abbreviation Construction Source

Gini Index lgini log of Gini index WDI-World Bank Internet Access internet_pop WDI-World Bank Mobile Subscription mob_100 WDI-World Bank Fixed Broadband fixedbrb_100 WDI-World Bank Government Expenditure lgxp log of government exp. WDI-World Bank Democracy Score dem WDI-World Bank Regulatory Quality reg_qual WDI-World Bank Rule of Law rol WDI-World Bank Political Stability pol_st WDI-World Bank Real GDP per capita lgdpcap log of real GDP per capita WDI-World Bank Inflation lainf Log of 1 plus inflation rate WDI-World Bank Finance lfin Log domestic credit as % of GDP WDI-World Bank Gross fixed capital formation linv Log gross fixed capital as % GDP WDI-World Bank Trade Openness ltrade Log of trade openness WDI-World Bank

Appendix D

Sample countries

Table 10 Sample Countries

Angola Benin Botswana Burkina Faso Burundi Cameroon Cape Verde C.A. Republic Chad Comoros Congo Cote d’Ivoire Democratic Republic of Congo Ethiopia Gabon The Gambia Ghana Guinea Guinea-Bissau Kenya Lesotho Madagascar Malawi Mali Mauritania Mauritius Mozambique Namibia Niger Nigeria Rwanda Senegal Seychelles Sierra Leone South Africa Sudan Swaziland Tanzania Togo Uganda Zambia Zimbabwe Algeria Egypt Morocco Tunisia

References

Abdih, Y., Chami, R., Dagher, J., & Montiel, P. (2012). Remittances and institutions: Are remittances a curse? World Development, 40, 657–666. Acemoglu, D. (1998). Why do new technologies complement skills? Directed technical change and wage inequality. Quarterly Journal of Economics, 113, 1055–1089. Acemoglu, D. (2002). Technical change, inequality, and the labor market. Journal of Economic Literature, 40, 7–72. Adeola, O., Evans, O., & Hilson, E. (2018). Tourism and economic wellbeing in Africa. Ahmed, S., Cho, J., Jaidka, K., Eichstaedt, J. C., & Ungar, L. H. (2020). The internet and participation inequality: A multilevel examination of 108 countries.

International Journal of Communication, 14, 22. Aker, J. C., & Mbiti, I. M. (2010). Mobile phones and economic development in africa. The Journal of Economic Perspectives, 24, 207–232. Akobeng, E. (2016). Growth and institutions: A potential medicine for the poor in sub-saharan africa. African Development Review, 28, 1–17. Akobeng, E. (2017). Gross capital formation, institutions and poverty in sub-saharan africa. Journal of Economic Policy Reform, 20, 136–164. Ali, M. A., Alam, K., Taylor, B., & Rafiq, S. (2019). Do income distribution and socio-economic inequality affect ict affordability? Evidence from australian household

panel data. Economic Analysis and Policy, 64, 317–328. Ali, F. A., Fiess, N., & MacDonald, R. (2010). Do institutions matter for foreign direct investment? Open Economies Review, 21, 201–219.


http://refhub.elsevier.com/S0308-5961(21)00102-6/sref1













13

Antonelli, C., & Gehringer, A. (2017). Technological change, rent and income inequalities: A schumpeterian approach. Technological Forecasting and Social Change, 115, 85–98.

Arellano, M., & Bond, S. (1991). Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. The Review of Economic Studies, 58, 277–297.

Asongu, S. (2015). The impact of mobile phone penetration on African inequality. International Journal of Social Economics, 42(8), 706–716. Asongu, S. A., & Odhiambo, N. M. (2019). How enhancing information and communication technology has affected inequality in Africa for sustainable development:

An empirical investigation. Sustainable Development, 27, 647–656. Asongu, S. A., & Odhiambo, N. M. (2020a). Inequality and gender inclusion: Minimum ict policy thresholds for promoting female employment in sub-saharan africa.

Telecommunications Policy, 44, 101900. Asongu, S. A., & Odhiambo, N. M. (2020b). Social media and inclusive human development in Africa. Information Development. Asteriou, D., Dimelis, S., & Moudatsou, A. (2014). Globalization and income inequality: A panel data econometric approach for the eu27 countries. Economic

Modelling, 36, 592–599. Bane, M. J., & Ellwood, D. T. (1986). Slipping into and out of poverty: The dynamics of spells. Journal of Human Resources, 21, 1–23. http://www.jstor.org/stable/

145955. Bicaba, Z., Brixiova, Z., & Ncube, M. (2017). Can extreme poverty in Sub-Saharan Africa be eliminated by 2030? Journal of African Development, 19, 93–110. Blundell, R., & Bond, S. (1998). Initial conditions and moment restrictions in dynamic panel data models. Journal of Econometrics, 87, 115–143. Busse, M., & Hefeker, C. (2007). Political risk, institutions and foreign direct investment. European Journal of Political Economy, 23, 397–415. Carabregu Vokshi, M., Dedaj, B., Youssef, A. B., & Toçi, V. (2019). Mobile phone penetration and its impact on inequality in the western balkan countries. Zagreb

International Review of Economics and Business, 22, 111–133. Carte, T. A., Dharmasiri, A., & Perera, T. (2011). Building it capabilities: Learning by doing. Information Technology for Development, 17, 289–305. Catrinescu, N., Leon-Ledesma, M., Piracha, M., & Quillin, B. (2009). Remittances, institutions, and economic growth. World Development, 37, 81–92. Cheng, C. Y., Chien, M. S., & Lee, C. C. (2021). Ict diffusion, financial development, and economic growth: An international cross-country analysis. Economic Modelling,

94, 662–671. Cioaca, S. I., Cristache, S. E., Vuta, M., Marin, E., & Vuta, M. (2020). Assessing the impact of ict sector on sustainable development in the European Union: An

empirical analysis using panel data. Sustainability, 12, 592. Conley, T. G., & Udry, C. R. (2010). Learning about a new technology: Pineapple in Ghana. The American Economic Review, 100, 35–69. Dabla-Norris, M. E., Kochhar, M. K., Suphaphiphat, M. N., Ricka, M. F., & Tsounta, E. (2015). Causes and consequences of income inequality: A global perspective.

International Monetary Fund. De Weerdt, J., & Dercon, S. (2006). Risk-sharing networks and insurance against illness. Journal of Development Economics, 81, 337–356. Dell’Anno, R., & Solomon, H. O. (2014). Informality, inequality, and ict in transition economies. Eastern European Economics, 52, 3–31. D’Onofrio, A., & Giordani, P. E. (2019). Infrastructures and income inequality: The case of Italian provinces. Politica Economica, 35, 27–54. Downes, L. (2009). The laws of disruption: Harnessing the new forces that govern life and business in the digital age. Basic Books (AZ). Easterly, W., Ritzen, J., & Woolcock, M. (2006). Social cohesion, institutions, and growth. Economics & Politics, 18, 103–120. Evans, O. (2019). Repositioning for increased digital dividends: Internet usage and economic well-being in sub-Saharan Africa. Journal of Global Information

Technology Management, 22, 47–70. Hansen, L. P. (1982). Large sample properties of generalized method of moments estimators. Econometrica, 50, 1029–1054. Hawthorne, R., & Grzybowski, L. (2019). Benefits of regulation vs. competition where inequality is high: The case of mobile telephony in South Africa. Hope, D., & Martelli, A. (2019). The transition to the knowledge economy, labor market institutions, and income inequality in advanced democracies. World Politics,

71, 236–288. Hoynes, H. W., & Page, M. H. S. A. (2006). Poverty in America: Trends and explanations. The Journal of Economic Perspectives, 20, 47–68. ICA. (2018). Infrastructure Consortium for Africa (ICA), ICT financing Needs and Trends. Jaumotte, F., Lall, S., & Papageorgiou, C. (2013). Rising income inequality: Technology, or trade and financial globalization? IMF Economic Review, 61, 271–309. Kahn, M. E. (2005). The death toll from natural disasters: The role of income, geography, and institutions. The Review of Economics and Statistics, 87, 271–284. Kapoor, R. (2020). Technology, jobs and inequality: Evidence from India’s manufacturing sector. In Accelerators of India’s growth—industry, trade and employment (pp.

301–321). Springer. Katz, L. F., & Goldin, C. D. (2008). The race between education and technology. Cambridge: Harvard university press. Knack, S., & Keefer, P. (1995). Institutions and economic performance: Cross-country tests using alternative institutional measures. Economics & Politics, 7, 207–227. Kocsis, D. (2020). Exploring the relationship between it infrastructure and income inequality through diffusion of innovations theory. In Proceedings of the 53rd Hawaii

international conference on system sciences. Labonne, J., & Chase, R. S. (2009). The power of information: The impact of mobile phones on farmers’ welfare in the Philippines. The World Bank. Leng, C., Ma, W., Tang, J., & Zhu, Z. (2020). Ict adoption and income diversification among rural households in China. Applied Economics, 1–15. Lindsay, C. (2005). Employability, services for unemployed job seekers and the digital divide. Urban Studies, 42, 325–339. Lloyd-Ellis, H. (1999). Endogenous technological change and wage inequality. The American Economic Review, 89, 47–77. Loh, Y. A. C., & Chib, A. (2019). Tackling social inequality in development: Beyond access to appropriation of icts for employability. Information Technology for

Development, 25, 532–551. Maiti, D., & Awasthi, A. (2020). Ict exposure and the level of wellbeing and progress: A cross country analysis. Social Indicators Research, 147, 311–343. Mushtaq, R., & Bruneau, C. (2019). Microfinance, financial inclusion and ict: Implications for poverty and inequality. Technology in Society, 59, 101154. Noh, Y. H., & Yoo, K. (2008). Internet, inequality and growth. Journal of Policy Modeling, 30, 1005–1016. Qureshi, S. (2011). Globalization in development: Do information and communication technologies really matter?. Richmond, K., & Triplett, R. E. (2018). Ict and income inequality: A cross-national perspective. International Review of Applied Economics, 32, 195–214. Roodman, D. (2006). How to do xtabond2: An introduction to difference and system GMM in Stata. Center for Global Development working paper. Tchamyou, V. S., Asongu, S. A., & Odhiambo, N. M. (2019a). The role of ict in modulating the effect of education and lifelong learning on income inequality and

economic growth in africa. African Development Review, 31, 261–274. Tchamyou, V. S., Erreygers, G., & Cassimon, D. (2019b). Inequality, ict and financial access in africa. Technological Forecasting and Social Change, 139, 169–184. Tewathia, N., Kamath, A., & Ilavarasan, P. V. (2020). Social inequalities, fundamental inequities, and recurring of the digital divide: Insights from India. Technology in

Society, 101251. Untari, R., Priyarsono, D. S., & Novianti, T. (2019). Impact of information and communication technology (ict) infrastructure on economic growth and income

inequality in Indonesia. Int. J. Sci. Res. Sci. Eng. Technol, 6, 109–116. Windmeijer, F. (2005). A finite sample correction for the variance of linear efficient two-step GMM estimators. Journal of Econometrics, 126, 25–51. World Bank. (2016). World development report 2016: Digital dividends. Technical Report. Wright, R. E. (1992). A feminisation of poverty in Great Britain? Review of Income and Wealth, 38, 17–25. Zhang, S., Li, F., & Xiao, J. J. (2020). Internet penetration and consumption inequality in China. International Journal of Consumer Studies, 44(5), 407–422. Zolfaghari, M., Kabiri, M., & Saadatmanesh, H. (2020). Impact of socio-economic infrastructure investments on income inequality in Iran. Journal of Policy Modeling,

42(5), 1146–1168.














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