Digital financial inclusion in ten African countries: foregone digital dividends?

The story of mobile money in Africa, such as Mpesa, is one of the success stories to have arisen from the continent in recent years. A World Bank report in 2016, featured mobile money services, together with innovations from other places, to illustrate how digital technologies are yielding digital dividends in development. Specifically, the report highlighted the prevalence of smartphones. With their spread, digital technologies are making a huge impact by enhancing inclusion, increasing efficiency and enabling innovation.

Not just in Africa are digital technologies enabling innovation. In Indonesia for instance, our team has successfully put together a complex social-technical-medical intervention which has at its heart a mobile app in the hands of village health volunteers, that enables effective control of cardiovascular risk outside hospitals and within communities. In an editorial commentary on the intervention, cardiologists noted that empirically such complex interventions have often failed to yield the expected digital dividends in health. A previous Global Development Institute blog explains how, through mobile technology, we are tackling cardiovascular disease risk in rural Indonesia.

In Africa, however, mobile money services are spreading dividends on the back of the diffusion of mobile phones. Financial services are liable to be disrupted by digital technologies; not least by transforming the supply side of banks with machine learning. So a new landscape of financial services is being rewired by digital technologies, enhanced by financial inclusion and particularly digital financial inclusion. Using a survey from last year, we are able to examine the latest picture of the use of digital technologies in ten African countries (Survey of household and individual ICT access and use 2017–2018). The empirical analysis suggests that considerable digital dividends are foregone in these countries.

Financial inclusion and exclusion

I distinguish between financial inclusion and digital financial inclusion as they emphasize different mechanisms in the economy. On this basis I then propose a new index of foregone digital dividends. The first indicator or financial inclusion, as defined by the World Bank, is measured by the rates of bank (financial institution) account ownership in the adult population. The definition in terms of banks or financial institution recognises the fundamental role of banks in pooling funds (savings) and allocating them (investments) in macroeconomic management.

In the successful story of East Asian countries, for example, saving rates or financial inclusion is a distinguishing factor responsible for their remarkable economic growth. High levels of financial inclusion indicate effective pooling of financial resources which were then allocated to entrepreneurs and governments and used as investments. Conversely, low levels of financial inclusion translate to real constraints in macroeconomic management. It is therefore  a priority to explore the survey and find determinants of financial inclusion in African countries.

Equally, high levels of financial inclusion enable better economic management by the government since formal regulations of the banks furnish information invaluable for making policy (‘the financial pulse of the economy’). Low levels of financial inclusion deny policy makers key information for effective economic management. The determinants of financial inclusion are necessary information.

Pooling of funds (or savings) and allocation of funds (or investments) are not the only important banking functions. Banks also support daily commercial transactions or transfer of funds, reducing transaction costs and enhancing economic performance. But daily transactions are increasingly carried out outside traditional banks as the story of mobile money highlights. The technologies enable anyone with a mobile phone to conduct financial transactions in a timely and efficient manner.

So irrespective of whether the pooling and allocating of funds are effectively provided for a client (much depends on the supply side), a mobile money service for peer-to-peer transfer can make a lasting difference for a person. It follows that financial inclusion can be usefully distinguished from digital financial inclusion. Access to mobile money services through a smartphone is thus an indicator of digital financial inclusion, the second key indicator examined here.

The third key indicator is digital financial exclusion as a measure of foregone digital dividends. Digital financial exclusion is defined only among mobile money users, picking those without a bank account (bottom of the penultimate column below). This last indicator is created because among mobile money users, the demand for financial services is real as revealed by their use of the service. These users are therefore primed to demand broader financial services such as longer term saving and timely investments. The fact that they are still financially excluded indicates foregone digital dividends.

		Mobile	money
		Yes	No
Bank	Yes
account	No	Foregone digital div.

Put differently, the deeper the gap between financial inclusion rates and mobile money user rates, the larger the digital dividends foregone by the financial system. This is increasingly recognized by both bankers and mobile money service providers. In Indonesia, for instance, this recognition is becoming clearer among bankers from large state owned banks (Mandiri, BNI) and officials from GoJek which provides mobile money services.

In short, there are three questions to ask of the new data: what are the determinants of financial inclusion in Africa? Similarly, of digital financial inclusion? And what are the determinants of foregone digital dividends?

Analysis of Survey of ICT use in ten African countries

I obtained answers to these questions using a new survey from Ghana, Nigeria, South Africa, Mozambique, Rwanda, Kenya, Tanzania, Lesotho, Uganda and Senegal. The survey collected a rich set of information from adults 15 years and older including: age, gender, location (rural or peri/urban), education, incomes, marital status, household size, access to the internet and personal computers, literacy, number of household members with mobile phones.

Financial inclusion is an indicator of owning a bank/financial institution account. Digital financial inclusion is an affirmative response to the survey question: have you ever used mobile money services (Mpesa or e-Wallet)? Foregone digital dividend is defined in the scheme above. Probabilities of financial inclusion, digital financial inclusion and foregone digital dividends for various covariates are estimated using probit models.

The data show in Table 1, key variables in 2018 sorted according to average national income or GDP per person (South Africa has the highest average income). Financial inclusion (second column) shows a trend which accords with the average national income: as income increases financial inclusion also increases, with exceptions such as Mozambique which shows higher than expected level of financial inclusion given its low average income.

Country, n	Financial inclusion	Digital fin. inclusion	Digital fin. exclusion
South Africa, 1809	.61	.08	.02
Ghana, 1198	.33	.56	.29
Nigeria, 1802	.45	.04	.001
Kenya, 1208	.43	.88	.45
Senegal, 1230	.12	.35	.21
Lesotho, 2158	.28	.37	.21
Tanzania, 1197	.21	.56	.37
Rwanda, 1203	.35	.34	.13
Uganda, 1858	.09	.47	.39
Mozambique, 1160	.26	.24	.11
Total, 14823	.31	.39	.22

The same data are presented in a series of plots which reveal more than the above trend (Figure 1).  The top left plot shows that financial inclusion positively correlates with average national incomes. But there is no strong correlation between financial inclusion and digital financial inclusion.

Finally, at the bottom right plot, it shows a strong correlation between digital financial inclusion and digital financial exclusion. This inclusion-exclusion correlation pattern might at first appear puzzling. Digital financial exclusion is defined among those users of mobile money, specifically if they do not own a bank account. This plot shows that the more people use mobile money, proportionately more of them are excluded from the conventional banking system. If we assume that use of mobile money encourages users to access broader kinds of financial services, evidently there are considerable foregone digital dividends in these countries.

Figure 1. GDP per person, financial inclusion, digital financial inclusion and digital financial exclusion in ten African countries.

If these dividends were to be reaped, we need to understand the determinants of financial inclusion, digital financial inclusion and exclusion. The results of a series of probit models explaining probabilities of inclusion and exclusion are seen below (Figure 2). They show that education and personal incomes are significant for financial inclusion and its digital form. Having a formal job matters for financial inclusion and digital financial inclusion, more so for financial inclusion (top left pane) than for digital financial inclusion (top right pane). This difference accords with the fact that mobile money services are widely used in the informal sector. The picture of foregone digital dividend or digital financial exclusion is a converse of this story. For the same rates of mobile money users across two locations, education, formal jobs and personal incomes are the key factors that correlate with whether someone uses a bank service.

Figure 2. Determinants of financial inclusion, digital financial inclusion and digital financial exclusion in ten African countries [n = 14823]. Other covariates included are country fixed effects, rural versus urban location, number of household members with a mobile phone, household access to the internet, literacy, own a personal computer and marital status.

To dig deeper I plot marginal probabilities for various ages, separating those of men and  women (Figure 3). Following the above coefficient plots where gender coefficient is not significant, gender difference is also negligible in the marginal plots. Now the shapes of financial inclusion and digital financial inclusion plots are telling on three counts. First, neither are linear: inclusion does not correlate with age in a straightforward manner. As shown in my blog on financial inclusion using the Global Findex 2017, age and cohort effects combine to create this non-linear effect.

Second, digital financial inclusion and financial inclusion peaked far apart in the life course, 35 versus 55 years. This is consistent with a generation game of financial inclusion which arises from the life cycle of financial needs. The younger generation needs more transaction services (conveniently offered by the digital financial services) while the older generation needs more investment services (offered by the conventional financial services for housing, portfolio and pension investments).

Lastly, among adults with demand for financial services (mobile money users), exclusion from formal financial services largely reflects the converse of financial inclusion. Thus the experience of digital financial inclusion has so far failed to induce demand for financial inclusion. If the increasing trend in digital financial inclusion (World Bank report 2016) is not caught by the trend in financial inclusion, then more digital dividends are foregone and the constraints in economic management remain. So transforming the supply side is important, for instance through adopting machine learning technology to efficiently broaden services offered.

Figure 3. Marginal probabilities of financial inclusion, digital financial inclusion and digital financial exclusion in ten African countries.

In sum, although the figures uncovered raise more questions, some answers to the initial questions can be suggested. Financial inclusion, digital financial inclusion and foregone digital dividends are shaped by age and cohort effects, making financial inclusion peak later. Education and formal jobs are important especially for financial inclusion. Efforts to broaden financial inclusion so that younger people get on board earlier should be considered in efforts to include more citizens into an efficient financial system.

Crucially, putting too much stock in mobile money services maybe misguided. The service experience on the demand side is ineffective to bring the users into the fold of the banking system. Supply side transformation is needed. An inclusive financial system where citizens participate early and actively is a strong determinant of inclusive development.

First appeared in Global Development Institute blog 15 Oct 2019.

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Trust Issues and Ride-Hailing Platforms in Lagos, Nigeria.

The idea of building trust is often central to the adoption and use of technology platforms in general such that the processes and governance of these platforms ought to align with the realities of user-groups which are essential for a seamless service. Since 2013, the entry of ride-hailing platforms in Nigeria has increased because of an overall technology awareness in Lagos and continuous successes of existing ride-hailing companies such as Uber and Taxify (see Table 1). Ease of access, trip predictability and ease of fare calculations and payment, amongst other things have improved.

Despite its growing impact on urban transport in Nigeria, the industry has suffered several challenges such as insecurity and lack of safety for user-groups. Prior to ride-hailing platforms, the notion of trust has been integral for taxi businesses or technologies to thrive. For instance, a passenger who builds a bond with a local taxi driver such that the driver runs personal errands such as dropping off school kids.

Trust in simple terms is the belief in the ability of someone or something. There has been increasing interest in the concept of trust in online transactions since the development of the internet and e-commerce in the early 1990s ⁽¹⁾. The concept of ‘trust’ encapsulates both offline environments and online environments such that the difference lies in the varying characteristics of these environments as well as the context in which trust is formed and maintained. In technology, “it is a belief that a specific technology has the attributes necessary to perform as expected in a given situation in which negative consequences are possible” ⁽²⁾.Risks and uncertainties are exacerbated because users lack total control of the processes governing ride-hailing apps.

Table 1: Ride-hailing companies in Lagos
Source: Author’s fieldwork

In the ride-hailing industry in Lagos, both drivers and passengers are aware of the risk in engaging with a complete stranger via an app which is monitored by platform companies through data analytics and algorithms. Unlike the conventional taxi industry, user-groups often build trust in platform companies based on the efficiency and reliability of their apps over time. For example, Mr Ayo, the Taxify driver has just accepted his first trip for the day, but later declines because the rider would only pay via an ‘online bank transfer’ and from experience, the driver does not trust this process because it is often a fraudulent tactic used by riders without money. Using a third-party banking app to make a transfer to the driver’s account gives the rider more power in this situation because the payment could be reversed in 24 hours if reported by the rider. If it were a card-paid trip, the driver would feel safer because the ride-hailing app acts as an intermediary between both parties such that if a conflict occurs, it can be resolved amicably.

One of the many instances where the rider loses trust is through trip manipulations by drivers.  Since Uber slashed the base fare of trips by 40% in Lagos, drivers have reacted with strategies for increasing the fare of trips through manipulative techniques ⁽³⁾. In 2017, Lockito, designed for testing geofencing-based apps, was being used in inflating fares by manipulating the distance of a trip.  For example, a trip that should be about 5.9km would be double the distance when the Lockito app is being used (see Figure 1).

Although drivers are responsible for altering the GPS function in the Uber app, riders become aware that the app is also vulnerable to fraudulent activities. Riders frequently monitor the Uber app, drivers’ behaviour and prefer cash payments to card payments to avoid being defrauded during trips. Although there are other factors involved such as low smartphone and card penetration overall ⁽⁴⁾, the psychological construct of trust remains central to the reliability and predictability of drivers, riders, and the algorithms behind ride-hailing apps.

Figure 1: Incorrect GPS reading vs correct GPS reading
Source: BrandSpurNG (2017)

Regardless of ride-hailing platforms’ success in Nigeria, trust issues surrounding usability and culture remain a stumbling block especially for indigenous start-ups like Oga-Taxi. More research would be needed to understand the implications on user behaviour and what coping strategies are needed to thrive in an increasingly ‘networked’ environment as well as how these strategies may create new realities in the global South.

References.

1. Li, F., Pieńkowski, D., van Moorsel, A. & Smith, C. (2012). A Holistic Framework for Trust in Online Transactions. International Journal of Management Reviews, 14(1), pp. 85-103
2. McKnight, D. G., Carter, M., Thatcher, J.B., & Clay, P.F. (2011). Trust in a specific technology: An investigation of its components and measures. ACM Transactions on Management Information Systems, 2(2), pp. 12 – 32.
3. Adegoke, Y. (2017). Uber drivers in Lagos are using a fake GPS app to inflate rider fares, Quartz Africa, 13 Nov
4. appsafrica (2015). Can Uber really work in Lagos, Nigeria? appsafrica, 2 Jun
5. BrandSpurNG (2017). Uber Drivers In Lagos Using Fake GPS App To Inflate Fares – Report, Nairaland Forum, 14 Nov

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