Abstract
Digitalization generates new opportunities for employment and earnings, but also entails a plethora of uncertainties and challenges. This article highlights the implications of algorithmic capitalism and how it relates to the digital divide in sub-Saharan Africa by discussing specific examples (Ghana and Kenya), considering the existing structure of social inequality. Both case studies refute the World Bank’s argument that economic liberalization and deregulation are sufficient approaches to improve material access to Internet services in the Global South. The article concludes that the digital divide is an extension of the global phenomenon of inequality. Although algorithmic capitalism has increased the number of Internet users in the region, it has failed to bridge the digital divide, particularly the urban–rural division. This article also suggests that privately owned mobile phone service providers can contribute to Internet usage and to bridging the digital divide in sub-Saharan Africa.
Introduction
Algorithmic capitalism 1 and social inequality are highly correlated. Unequal access to information and communications technologies (ICTs) and the rise of digital labor, the new working class, have engendered profound concerns in the Global South, particularly in Africa. Employment insecurity, discrimination, opacity, and intermediation are all products of digitalization. 2 In Africa, the use of ICTs continues to remain very low with an average rate of Internet penetration at around 34 percent, which is far below the average rate of the developing world that hovers at 56 percent, and Africa’s ICT Development Index (IDI), 2.0 on a 7.0 scale, is the lowest compared to the rest of the world (Pick & Sarkar, 2015, p. 278). Also, in 2012, Africa had the second highest coefficient of variation in its IDI behind Asia and the Pacific, providing an indication that Internet service is unevenly distributed, particularly in sub-Saharan Africa. It is worth noting that ICT laggards worldwide are predominantly from the region of sub-Saharan Africa that lingered at the bottom 19 spots in IDI rankings in the same year (International Telecommunication Union [ITU], 2013).
Although many survey studies indicate that the digital divide exists across a variety of demographic and geographic dimensions, income and education have been the most compelling factors to distinguish between ICT users and nonusers in the developing countries (Sarikakis & Thussu, 2006, p. 248). Policymakers often assume an implicit assumption that people will respond the same way to the same technology regardless of their socioeconomic status (Hoffman & Novak, 1999). However, this assumption is greatly questionable. Although the accessibility of technology is an essential step toward ICT usage, it does not guarantee continued use among the less privileged (Bhattacherjee, 2001). The purpose of this research is to gain a better understanding of the relationship between algorithmic capitalism and the digital divide in sub-Saharan Africa. Toward this end, the study questions the following: what are the social implications of algorithmic capitalism and how do they relate to the digital divide in sub-Saharan Africa?
The hypothesis of this study is that algorithmic capitalism exacerbates the digital divide in the region, and, thus, produces more social inequality. This article considers the accessibility and the usage of Internet services as an indicator of the proliferation of ICTs. Ghana and Kenya serve as the case studies to either support, reject, or adjust the hypothesis of this research. The criteria for selection rest on the availability of the information, similar socioeconomics, and other commonalities like the liberalization of the telecom sector.
Theory of Algorithmic Capitalism
At its core, an algorithm is a set of instructions to be carried out mechanically to achieve an intended result (Steiner, 2012, p. 54). When information goes into a given algorithm, answers automatically come out. Algorithms have the capacity to sort, order, and predict, and, thus, they can maintain norms and create notions of abnormality. Algorithms have become an essential feature of a global network of capitalism. Speed, consequently, is a determinant component of the future society and economy. Therefore, algorithmic capitalism has gained an increasing dominance in the global network of the capitalist market.
Algorithmic capitalism is an aspect of informational capitalism or cybernetic capitalism, a term that recognizes the genealogy of postmodern capitalism. Speed, mobility, and wealth have become remarkably connected in the current globalized world (Armitage, 1999, p. 35). Based on that, the change in the nature of wealth is a mere manifestation of the change in the speed of wealth economy (Virilio, 2006, p. 69). Informational capitalism is the third generation of capitalism after mercantilism and the Industrial Revolution. It refers to the Net re-centralization and the creation of large-scale monopolies insofar as it influences and shapes the future of knowledge economy. Informational capitalism ushered new circuits of global capital and a new mode of capital accumulation that depends upon the emergence of immaterial economy: an economy that is based on digitization, formalization, and mathematizing of the knowledge system. 3 Cognitive capitalism captures the gain from innovation which is a central aspect of the knowledge economy. 4
In the earliest stages of capitalist development, commodity production, such as linen and cotton, was the physical aspect of labor power. However, the advancement in technology production triggered the physical labor power as it began to gradually transform the worker’s operations into more mechanical ones. The inevitable and gradual rise of educated laborers created by cognitive economy entailed the notion that industrial laborers are no longer a primary feature in the production process in a more heavily financialized and immaterial system of global exchange (Graham, 2000).
Since the mid-1980s, the diffusion of technology in financial markets has been remarkable. The technological revolution has immensely changed the ecosphere of finance and banking. The trading process has become increasingly automated. The advent of high-frequency trading (HFT), an algorithmic trading which operates through sophisticated platforms to transact large amounts of trades in a very short time span, has heralded the beginning of a new structure of production that capitalizes on speed. Algorithms increasingly influence labor rationalization, social relations, and recently, the financial sector. The process of financialization refers to the mechanisms of interaction between individuals in an intricate and deregulated global market (Casey, 2012). Financialization has come to the foreground of debates questioning its causal relationship with digital inequality. It links to inequality with two contentious arguments—that financialization causes inequality or inequality causes financialization. The argument is whether algorithmic capitalism is socially beneficial or it is a detrimental investment that will engender unintended socioeconomic implications in the Global South, in sub-Saharan Africa.
Conceptualization of the Digital Divide
There are three main social systems that constitute the core of any society. The first one is the cultural system in which skills and competencies are acquired and enacted as ways of life. Second is the economic system that produces the values and property needed to satisfy human needs. Finally, there is the political system through which power is dispersed and collective decisions are made (Fuchs & Horal, 2008). Therefore, the digital divide is a multifaceted phenomenon which comprises social, political, and economic dimensions that influence any society. In its infancy, the concept of the digital divide is generally defined as uneven access to ICTs (Van Dijk, 2006, p. 178). The argument is that access to the Internet, in a society in which dominant functions and social groups are organized around the Internet, is a prerequisite for tackling social inequality (Castells, 2000, p. 248). Also, the digital divide refers to any form of disparity within the Internet community (Norris, 2001, p. 4). Consequently, there was an implicit consensus that the problem of digital divide could be solved by simply increasing the accessibility of ICTs (Hsieh, Rai, & Keil, 2008). In the same vein, Wilson (2004, p. 300) indicates different aspects of accessibility to ICTs such as the physical access, the financial access, the cognitive access, the design access, the content access, and the political access.
The Organisation for Economic Co-operation and Development (OECD) defines the digital divide as the gap in terms of accessibility and the use of the ICTs among individuals, businesses, and geopolitical areas at different socioeconomic levels, and it reflects the variation in the general level of socioeconomic development between and within countries (OECD, 2001, p. 5). Another definition of digital inequality distinguishes between the social digital divide, the democratic digital divide, and the global digital divide (Norris, 2001, p. 4).
The social dimension of the digital divide indicates the income gap is an obvious determining factor affecting those who have access to ICTs and those who do not. Finally, the global digital divide refers to the unequal distribution and access to ICT between the developed and developing societies. More gaps have been identified in the context of divergent digital access such as the education gap, ethnic and racial divisions, and the ability versus disability gap (Fuchs & Horak, 2008). According to Van Dijk and Hacker (2003), the barriers to ICT access are the following:
lack of mental access which signifies the inadequacy of basic digital knowledge; lack of material access which refers to the lack of physical access to ICT tools such as computers and the Internet; lack of skill access which indicates a shortage in the skills needed to deal with ICTs; and lack of usage access that refers to meaningful usage opportunities.
Physical access to ICTs is still growing in the developing societies, and only about a third of the population now has access to the Internet in Africa (see Table 1), and in terms of skills and usage access, the digital divide is actually widening. The set of skills needed to search and process information and the capacity to use this information for specific purposes are unequally divided among the more developed and the developing societies (Van Dijk, 2006, p. 181). People with a high level of education in the more developed societies tend to use ICTs more than people with low levels of education and income in these societies (Van Dijk, 2006, p. 182). Thus, to put this into proper perspective, accessibility to ICTs does not guarantee high usage and providing greater access in the developing societies alone is not sufficient to remove the digital divide in these countries (Brandtzæg, Heim, & Karahasanović, 2011).
Internet Usage in Africa 2018
Sub-Saharan Africa and the Global Divide
Africa is the second largest continent in terms of its population, which was estimated to be 1.2 billion in 2016 (United Nations, 2017). Most of the 50 plus countries in sub-Saharan Africa are categorized as low income or lower-middle income (The World Bank, 2012). The African countries in general are ranked low on most social, health, and economic indicators and most of the sub-Saharan countries linger at the bottom of the United Nations (UN) Human Development Index (HDI).
Sub-Saharan Africa is of particular importance in this research because it is the most marginalized and excluded region of the world. The continent of Africa is the one that is most struck by poverty and many other global challenges. As Table 2 indicates, the UN HDI shows that Africa is the least developed major region of the world (UNDP, 2016, p. 27).
Global Human Development Index Comparisons by Regions
This reality interacts with the discourse on the digital divide in terms of unequal access to new ICTs and their usage. In fact, the digital divide is an extension of the global phenomenon of inequality and the distribution of wealth production can serve as a proxy for access to technology and information, given the fact that they are important aspects of relative material wealth. In 2019, only 39.6 percent (523 million) of the total population of Africa (1.3 billion) were Internet users, while 62.7 percent (4 billion) of the rest of the world’s population (6.3 billion) were Internet users (Table 3).
Internet Users Statistics for Africa in 2019
Only eight countries in sub-Saharan Africa have Internet penetration rates that exceed 50 percent of the population (Internet Word Stats, 2019). These countries are South Africa, Kenya, Liberia, Namibia, Mali, Gabon, Senegal, and Nigeria. It is noteworthy that half of the population in Africa is excluded from the global information society. This illustrates the extent to which the digital divide is a pressing problem for the whole continent.
Across six sub-Saharan African countries recently surveyed by the Pew Research Center (2018), a median of 41 percent of the respondents reflects that they use the Internet occasionally or own an Internet-capable smartphone. Sub-Saharan Africa has a lower level of Internet use than any other geographic region in their global survey, ranging from a high of 59 percent in South Africa to a low of 25 percent in Tanzania (see Figure 1). In comparison, 89 percent of the Americans who responded to the same survey said they use the Internet.
Social Implications of Algorithmic Capitalism
In a network society that is characterized by structural inequality, information and knowledge tend to be centralized and unevenly distributed. Thus, a significant proportion of the population is excluded from some fields of information and knowledge. In a digital economy that predominantly relies on accessibility to Internet services, the social structure tends to be fragmented into three classes based on the ownership of the means of production and the ownership of the necessary skills and qualifications (Fuchs & Horak, 2008). These classes are the following:
people with high levels of education and income and nearly full access to ICTs; people with average access to ICTs, particularly the middle-income class, with relatively fewer digital and strategic skills than the first class; and a significant segment of the population with no access to the Internet, who consequently are excluded from participating fully in their societies;
The phenomenon of digital divide concerns not only physical access to ICTs but also the necessary skills required for effective and sustainable usage of this technology and information. Physical access does not guarantee continuous usage. In a network society that is shaped by unequal access to knowledge and algorithm-based ICTs, where structural inequality prevails, the disconnected or less connected members of the population have lower prospects in the labor market, fewer chances to attain higher education, and limited opportunities to participate in politics. In the region of sub-Saharan Africa, where most people are deprived of basic social, educational, and technological resources, the unequal digital divide cannot be ignored.
Digitalization generates new opportunities for employment, investments, and earnings, but also entails a plethora of uncertainties and challenges. The speed of changes in the labor market, increasing forms of temporary employment, and the destruction of traditional jobs are the most imminent risks (The World Bank, 2016, p. 118). Capital tends to increase the production of surplus value in two ways: first, by extending laborers’ time of work, and second, by increasing their productivity through the introduction of new means of production. In algorithmic capitalism, the productivity of labor increases as long as the relative technological advantage is maintained. As competition increases, newer technologies are introduced, and the cost of labor decreases via intensifying production (Wilkie, 2011, p. 72). But the introduction of ICTs has not changed the logic of the labor–wage nexus; instead, it has changed labor productivity and the labor market. In the era of algorithmic capitalism, the labor market has experienced a structural change, from traditional laborers that produce material commodities to digital laborers whose primary production is immaterial commodities.
Concerns About Digital Labor
The expansion of digital connectivity has evolved in tandem with job outsourcing. In response to the need for jobs where they do not currently exist, millions of jobs are now outsourced and digitally mediated to avoid constrains of labor markets (ITU, 2016). Three decades ago, the initial wave of outsourcing moved work opportunities to lower-wage areas within the boundaries of domestic economies. With the international expansion of the digital economy in the 1990s, certain areas in the Global South have managed to capture larger amounts of outsourced work (Graham et al., 2017).
Digital work platforms tend to squeeze the formal relationship between employer and employees. For example, laborers are more likely to be classified as independent contractors even when the nature of their work resembles that of the formal employees. Moreover, digital workers are barely bound by national regulations. These working conditions are exacerbated when transactions cross national borders and regulations become more obscure and conflated (Graham et al., 2017).
In geographically bounded labor markets, certain segments of society may be excluded from the labor market due to discrimination or societal segregation based on religion, gender, ethnicity, or disability (Reskin, 2000). Digitalization, however, can transform these patterns in two ways. First, since digitalization often permeates physical boundaries, it allows certain workers to access new labor markets where there is less discrimination or segregation. Second, it allows workers to access their local market through digital mediums in a way that may eliminate the risk of societal discrimination.
Intermediation
The value chain structures of international trade have garnered increasing attention in many development studies. Intermediaries have gained a significant part of the value of trade, using geographic location, networks, and other positional advantages to mediate between buyers and sellers. This situation potentially contributes to, and reinforces, global inequalities (Pietrobelli & Saliola, 2008). ICTs have also contributed to strengthening mediation in some commodity chains of material products.
Disintermediation (reduction in the use of intermediaries between producers and consumers) involves increasing the functionality of producers in value chain making and adds value to the products and/or service(s) they produce. Being positioned close to customers can give producers more opportunities to learn more about their needs and to develop corresponding skills and capabilities (Graham et al., 2017). Inasmuch as digital labor platforms are associated with disintermediation and directly connect producers with their potential customers, it can result in functional upgrading and movement toward higher value-added work in the value chains (Beerepoot & Hendriks, 2013). ICT-enabled sourcing makes it easier for digital laborers to be close to the core business processes, yet it can hinder the knowledge flow from the core to the periphery and, thus, perpetuate rather than reduce skill inequality (Pietrobelli & Rabellotti, 2011). Digital laborers are usually incapable of accessing information about the wider value chain they are a part of. Furthermore, digital workers may struggle to articulate or make qualified guesses as to how their clients derive value from the labor they perform (Graham et al., 2017).
Digital Divide in Ghana
Ghana was one of the first countries in the Global South that introduced privatization and competition in all areas of service. The liberalization of the telecommunications sector began in 1996 when 30 percent of Ghana’s telecom sector was privatized (The World Bank, 1999, p. 68). Internet services were first introduced in 1995 by the private sector. In 1996, two private operators got access to the telecom market, and by 2001, nine internet service providers (ISPs) were fully operating in Ghana (Ahiabenu, 2001). Many prominent national development strategies, like Ghana’s Poverty Reduction Strategy in 2001 that pushed the county toward the middle-income level, did not emphasize the role of ICTs in the country’s development. While successive governments throughout the 1990s made several attempts to develop the communication sector, ICTs were never taken into consideration. As a result, Wilson (2004, p. 174) noted the following:
It is hardly surprising but still telling that ICT issues or transformation toward a knowledge society were nowhere near the top of the agenda during the historic Ghanaian election that replaced Jerry Rawlings with John Kufour, although the new government claims to want to do more to take up the challenges of ICT.
The first comprehensive ICT policy titled The Ghana ICT for Accelerated Development (ICT4D) was introduced in 2003 (Alhassan, 2004). The policy sought to transform Ghana into a knowledge-based society. However, this noble objective has been undermined by the prevalent inequalities between Ghana’s urban and rural areas. Between 1990 and 2017, Ghana’s improvement in the areas of human development was outstanding. As indicated in Table 4, life expectancy at birth increased by 6.2 years, mean years of schooling increased by 2.2 years, and expected years of schooling increased by 4.0 years (UNDP, 2018). Consequently, Ghana’s HDI score remarkably increased by 30.1 percent from 0.455 to 0.592. The Ghana Statistical Service (GSS) indicates that extreme poverty was substantially reduced from 18 percent to 8.4 percent between 2005 and 2013, although absolute poverty fell at a glacial pace from 28 percent to 24.2 percent of the population over the same period (GSS, 2014).
Ghana’s Human Development Index Trends Between 1990 and 2017
Urban poverty has recently decreased much faster than rural poverty. Consequently, the gap between urban and rural areas has doubled and income inequality has remained staggering with the Gini coefficient for Ghana rising from 37 to 42.3 between 1992 and 2013 (GSS, 2014). Ghana’s Gini coefficient per capita increased by about 115.9 percent between 1990 and 2017 using a consistent series of data (UNDP, 2018). The percentage of the rural population in poverty was 37.9 percent in 2013 compared to only 10.6 percent in the urban areas.
Ghana’s major indicators according to the World Economic Forum (2016) are as follows:
GDP (2016): $36 billion GDP per capita: USD $1340.4 Global Competitiveness Index ranking: 114/138 Health and Primary Education Index ranking: 115/134 Macroeconomic Index ranking: 132/134 Higher Education Training ranking: 99/134 Technological Readiness ranking: 95/113
According to the IDI, Ghana ranks at 116 and only 32.5 percent of households have Internet access (ITU, 2017, p. 31). In terms of accessibility to ICTs, the use of the Internet, and technology skills, Ghana ranks fairly low at 120, 103, and 125 respectively (ITU, 2017, pp. 32–34).
It is worth noting that income inequality is quite salient insofar as rural poverty is almost four times higher than urban poverty, and rural areas disproportionately lag behind in terms of economic and livelihood opportunities compared to the urban areas. Nationally, most of the poor are unemployed or work in low-productivity activities in the informal sector (Awanzam & Okudzeto, 2018). About 34 percent of the working-age population between 15 and 54 years old, that is concentrated in the urban area, scores below the minimum level of literacy proficiency (The World Bank, 2018, p. 119), and nearly 80 percent of Ghana’s working-age population has just level 1 literacy or below (The World Bank, 2018, p. 76). This reality indicates that education level for this age group is limited to only understanding basic texts while they are unable to interpret information from a variety of text materials. Furthermore, the unemployment rate is 5.2 percent according to the GSS (2014) report, which is disaggregated as follows:
Male unemployment: 4.8% Female unemployment: 5.5% Urban unemployment: 6.5% Rural unemployment: 3.9%
General unemployment is attributed to many factors such as weak linkages between the job market and the educational system and inadequate support for entrepreneurs and small-scale businesses.
The rural population in Ghana are disconnected from the international network society as they lack basic telecom infrastructure, electricity, and appropriate buildings. The shortage of skilled labor combined with meager employment opportunities make these locations unattractive for investment (Awowi, 2010). Therefore, Ghana’s community information centers (CiCs) model was promoted as a viable way to offset the low phone and Internet penetration by improving the provision of ICT services to unserved and underserved populations in urban as well as rural communities (Ohemeng & Ofosu-Adarkwa, 2014).
However, the CiCs in rural areas were generally less advanced. In 2001, 90 percent of the telecenters with Internet access were concentrated in the capital Accra. Since they were primarily run as private enterprises, their owners were unable to gain enough profit to compensate for their business expenses and pay their bills (Fuchs & Horak, 2008). The density of telecenters in the business areas of Accra pales in comparison with the density in the outskirts of the city. In the business area, the middle-income population are better equipped with cell phones and have their own direct access to the Internet. Lag in technology, much like the lag in basic needs, stems from poverty, and only poverty reduction can mitigate the technology gap (Zachary, 2002). It is quite evident that low incomes impede the large-scale use of telecenters and telecommunications services (Falch, 2004).
It is self-evident that poverty eradication, in itself, is insufficient to bridge the digital divide. Rather, it is a necessary precondition for overcoming this situation. Societies that are stuck in poverty barely have the time, income, and resources needed to develop their capacities and evolve into informational societies. Also, other factors such as the technology infrastructure and digital literacy are critical components in bridging the digital divide. Table 5 reflects the increase in Internet users and Internet penetration relative to the increase in the population between 2000 and 2016. While the percentage of the population using the Internet has increased since 2000, only 28.4 percent of the population were Internet users in 2016. Lack of accessibility to the Internet and limited use of the Internet by the population are indicative of the digital divide and the larger global phenomenon of social inequality.
Increase in Internet Penetration in Ghana between 2000 and 2016
Digital Divide in Kenya
The Internet became available in Kenya in 1993. It was first introduced through the African Regional Center for Computing, which was the first Internet provider in the country. Soon afterward, the Communications Commission of Kenya (CCK) decided to liberalize the telecommunications market. The subsequent competition in the telecom market has contributed to the increase in Internet users. For example, the Internet subscribers in all modes of connectivity rose by 250,000 over the 3 months between March and June 2009 (Okong’o, 2011). Kenya now ranks among the 10 African economies with the largest improvements in access to ICTs (ITU, 2010, p. 18).
Kenya’s population is about 45 million people (UNDP, 2018) with approximately 70 percent living in rural areas where Internet service is largely unavailable (Kenya National Bureau of Statistics, 2016, p. 19). Based on 2009 estimates, 45.2 percent of the population lives below the national poverty line and the majority—14 million people—live in rural areas (see Table 6). In 2015, half of the people in rural areas and one-third in urban areas lived below the poverty line, a difference of 18 percentage points between rural and urban areas (Kenya National Bureau of Statistics, 2016, p. 20).
Distribution of Population by Broad Age Groups in Kenya (percentages)
Rapid population growth, that exceeds 2.5 percent, poses far-reaching implications for Kenya such as escalating land pressure, high unemployment, and rising demand for social protection services (Odongo & Rono, 2016). Approximately 25 percent of the Kenyan population has no education and one-third of the rural population has no education compared to only 15 percent in urban areas. In the period between 1990 and 2017, Kenya’s life expectancy has risen by 9.8 years and mean years of schooling has improved by 2.8 years (Table 7). Meanwhile, Kenya’s GNI per capita increased by about 28.9 percent, indicating a rising pattern of income inequality (UNDP, 2018). According to the HDI for 2017, Kenya’s value of 0.590—which places the country in the medium human development category—ranks it at 142 out of 189 countries and territories (UNDP, 2018). Kenya’s HDI trends (see Table 7) are based on consistent time series data and new goalposts.
Kenya’s Human Development Index Trends (1990–2017)
Across Africa, Kenya is one of the leading nations in terms of telecommunication and Internet development, but primarily in the mobile phone sector. In 2016, about 27 million of Kenya’s population had a mobile phone compared with 14 million in 2008 (Odongo & Rono, 2016). M-Pesa, a widely used mobile phone-based application for the easy transfer of funds, was launched in 2007. It has changed the landscape of financial transactions in Kenya. About 17 million individuals use M-Pesa through a network of 40,000 agents (Milek, Stork, & Gillwald, 2011).
According to the World Economic Forum (2016), Kenya’s major indicators are as follows:
Population (Millions): 45.5 GDP (US$ Billions): 68.9 GDP per capita (US$): 1516.3 Global Competitive Index ranking: 91/137 countries Health and primary education ranking: 114/137 countries Higher education and training ranking: 97/137 countries Technological readiness ranking: 88/137 countries
Survey data indicate that about 15 percent of households have a computer while 22 percent of households have Internet access. Internet users in Kenya represent 26 percent of the population, while 81.28 out of every 100 inhabitants have a mobile cellular phone subscription (ITU, 2017). Approximately, 6.9 million people use the Internet, and the most common usage of the Internet is via mobile devices. As indicated in Table 8, the usage of ICTs in rural areas pales in comparison with the urban areas (Kenya National Bureau of Statistics, 2015).
Use of Information and Communications Technology Equipment in Kenya (Aged 3 Years and Above)
On the national level, Table 8 indicates there is an uneven distribution of ICTs accessibility and usage between the urban and rural areas in Kenya. In the urban areas, access and use of the Internet is three times higher than in the rural areas. Table 9 also highlights the digital divide between rural and urban areas. As a local household survey has revealed, about 30 percent of the nonusers of the Internet say they do not need Internet service, and 29.5 percent of the nonusers lack knowledge about how to use Internet accounts (Kenya National Bureau of Statistics, 2015).
Survey of Households without Internet Connection in Kenya
At the national level, 70 percent of the households are disconnected from the Internet, and the proportion of households with no Internet connection in rural areas represents 83 percent compared to almost 53 percent in urban areas. The most prevalent causes for nonusers were no need to use Internet which was reported by 50.7 percent, followed by lack of knowledge or skills on how to use Internet which was cited by 45.1 percent of the households. One of the major causes of digital divide in Kenya is the limited availability of digital equipment at virtually all levels of education. ICTs accessibility in Kenya is approximately one computer to 150 students, whereas, in the developing world, the average is one computer to 15 students (Odongo & Rono, 2016).
Conclusion
This article focuses on the implications of algorithmic capitalism and how it relates to the digital divide in sub-Saharan Africa. It argues that algorithmic capitalism has contributed to the digital divide and social inequality. This is manifested in most of the countries of Sub-Saharan Africa which are near the bottom end of the UN HDI. Many studies indicate that the digital divide extends across diverse demographics and geopolitical dimensions, including human development, income, and education, which remain comparatively low in these countries. This reality correlates with the phenomenon of unequal ICTs access and usage.
This study disputes the World Bank’s argument that the Global South has been slow in spreading Internet services because of a technological lag that can be mitigated by improving the supply of Internet access. The Bank also contends that reducing barriers to the telecom market in terms of liberalization and deregulation will increase access to Internet services and this will consequently bridge the digital divide (The World Bank, 2016, p. 25). The cases of Ghana and Kenya demonstrate that neither liberalization of the telecom sector nor improved access to the ICTs is sufficient to bridge the digital divide. Although the privatization of the telecom sector has increased the number of Internet users, it has failed to bridge the digital divide, particularly the urban–rural gap.
Van Dijk (2006) and Castells (2000, p. 248) contend that structural, social, and economic inequalities are the root cause behind the digital divide. The digital divide is a correlate to the educational divide, the gender divide, the income divide, and the skills and ability divide. In Ghana, although the CiCs have contributed to increasing Internet penetration, poverty and social inequality have greatly influenced the impact and sustainability of the CiCs. Rural poverty is four times higher than urban poverty in Ghana (GSS, 2014), and this is mirrored by the uneven access to ICTs between the rural and urban areas in the country. Therefore, it is self-evident that poverty reduction is a necessary precondition to close the digital gap between the rural and urban areas in sub-Saharan Africa.
This study, however, does not support the view of Fuchs and Horak (2008) who contend that liberalization policies have a negligible impact on the digital divide in sub-Saharan Africa. In Kenya, the liberalization of the telecom market appears to have played an important role in improving accessibility to the Internet and its usage via the use of smartphones. Kenya is leading the telecommunications sector in the Global South with mobile/cellular telephone subscriptions for 81.2 per 100 inhabitants (ITU, 2017). M-Pesa has changed the landscape of the telecommunications sector in the country and has provided effective and efficient mobile cellular phone services to Kenyans. Also, this has contributed to increasing the number of Internet users. One important finding in Kenya and Ghana is that privately owned mobile phone service providers can contribute to Internet usage and to bridging the digital divide. In Kenya, M-Pesa has counted on the local people to run and use algorithmic applications that provide access to the Internet and access to financial services via mobile cellular phones.
In sum, it is quite evident that access to ICTs is insufficient to bridge the digital divide in sub-Saharan Africa. Although algorithmic capitalism can improve access to ICTs, it also contributes to widening the digital divide, especially between urban and rural areas. The larger economic context of low levels of human development in terms of educational attainment and income inequality plays a major role in determining who gains access to ICTs and acquires the necessary knowledge and skills to use them. The success story of M-Pesa in Kenya suggests that there may be entrepreneurial opportunities waiting to be exploited that can contribute to bridging the gap of the digital divide in sub-Saharan Africa. Therefore, entrepreneurial development in this sector of the economy should be considered as a strategy to help close the digital divide. It may promote accessibility to ICTs and help provide the digital knowledge which is a necessary precondition for bridging the digital divide associated with algorithmic capitalism.
Footnotes
Acknowledgements
I would like to thank Professor James Mittelman for the patient guidance and encouragement he has provided throughout my time as his student.
Declaration of conflicting interests
The author declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding
The author received no financial support for the research, authorship, and/or publication of this article.