Rural Resilience Impact of ICTs-in-Agriculture

28 January 2020 Leave a comment

What impact do ICT-in-agriculture projects have on rural resilience?

To cope with short-term shocks (e.g. conflict, economic crisis) and long-term trends (e.g. climate change), rural areas in developing countries must become more resilient.  Yet we currently know very little about the impact that information and communication technologies (ICTs) can have on resilience-building.

To address this knowledge gap, we undertook a systematic literature review of 45 ICT4Ag cases from Africa and Asia.  We sought to understand both what the resilience impact of ICTs is, and why.

Measuring resilience using the RABIT (Resilience Assessment Benchmarking and Impact Toolkit) framework, current reported evidence suggests ICTs are strengthening rural resilience far more than weakening it.  But the impact is highly uneven.  Household resilience is built far more than community resilience, and there is a strong differential impact across different resilience attributes: equality in particular is reported as being undermined almost as much as enhanced.

In order to explain these outcomes, we developed a new conceptual model (as shown below) of the relationship between ICTs and resilience.  It highlights the importance of individual user motivations, complementary resources required to make ICT4Ag systems support resilience, and the role of wider systemic factors such as institutions and structural relations.

We make a series of recommendations for resilience policy and practice:

  • More equal focus on both household- and community-level resilience.
  • More attention to the resilience-weakening potential of ICTs.
  • Ensuring perceived utility of digital applications among rural users.
  • Encouraging use of more complex ICT4Ag systems.
  • Looking beyond the technology to make parallel, complementary changes in resource provision and development of rural institutions and social structures.

We also draw conclusions about the conceptualisation of resilience: the need for better incorporation of agency and power, and greater clarity on resilience system boundaries and indicators.

Overall, for those seeking to strengthen rural resilience through use of ICTs, the paper – “Impact of ICTs-in-Agriculture on Rural Resilience in Developing Countries” – offers new frameworks, new evidence, new practical guidance and a research agenda.

Ride-hailing Platform Asymmetries between Riders and Driver-partners in Lagos, Nigeria.

Platform companies, with the help of data, have the potential to formalise the processes of transport systems in global South cities, especially in African cities which are characterised with informal processes. For ride-hailing platforms, this will help improve safety and security for both drivers and riders and reduce the likelihood of workers who attempt to evade formal processes such as tax payments because information is accurately recorded and managed by algorithms. In our contemporary world today, data is important for several tech platforms including Uber, Airbnb and social media platforms. This process is known as datafication, which is defined as a significant feature of modern social life and society through “the drive to turn vast amounts of activity and human behaviour into data points that can be tracked, collected and analysed”1. However, there have been growing concerns about its impacts on society and user-groups at large – in this case, on platform workers/drivers in Lagos.

Registration on platforms like Uber and Bolt (Taxify) is asymmetrical for both workers and riders in Lagos. For workers, it is a stringent process which involves them providing critical details such as home address (which can be verified), vehicle details (plate numbers and vehicle numbers), licenses and at least two guarantors. For riders, registration on the platform is as simple as inputting a contact number, card details and home addresses (which cannot be verified). This has an implication on drivers’ safety, work ethic and motivation because they feel unequally treated. When data is not being equally represented on the platforms, it puts riders and platform companies in a greater position of power. Talking about drivers’ challenges, platform driver and Union president stated that:

Box 1 – Data misrepresentation
“To this present time we have 30 -35 of drivers been killed by riders because they don’t profile them well, many riders don’t use their correct names and don’t put correct information and they are collecting cars and killing people and that’s why we need government to regulate things, to open an account. Either you use BVN or use national ID or migrate their information with the road safety (use the driver’s license), so they are can know the perpetrator of these evil acts”2.

From observation, a rider can register with many accounts in search of trip discounts/bonuses or in rare instances when they may have been blocked by the platform. This makes it difficult to track riders who may have defaulted. It takes a 4.6/5 and 4.5/5-star rating for drivers to be blocked on Uber and Bolt respectively in Lagos, but until summer of 2019, there were no clear rating thresholds to discourage bad rider behaviours3. This has however not been observed in Lagos, especially on the Bolt platform, indicating a low barrier of entry and further perpetuating bad rider behaviours which demotivates drivers from working. Mr AY’s suggestion could be helpful in ensuring that riders are more accountable on platforms such that they are equally aware that bad behaviours cannot be tolerated.

The communication between platform companies and drivers in comparison to riders is also perceived as unfair to drivers, particularly during a conflict with a rider; algorithmic misinterpretation or network issue which can lead to an undercharged fare or overcharged fare especially on the Bolt platform (see figure 1); or drivers who are blocked without any clear explanation (see box 2). However, drivers highlighted that Uber is more organised and, in many cases, are more likely to reimburse drivers that may have been undercharged. This is hardly the case on the Bolt platform.

Figure 1: Undercharged fare for a Bolt trip
Source: Nigerian Platform drivers forum.
Box 2 – Poor driver relations and algorithmic misinterpretation
“…When I do fare review on Bolt, it takes them years to get back to me. Some riders will pay you that N500 (£1.1); some will tell you that it is promo and there is nothing you can do (for example see figure 1) …They have bad customer relations in our case. They would not check properly when there is a complaint from a rider but be so quick to block drivers. But in a reverse scenario, platforms respond quickly to riders” 4.
 
“The way they block drivers is something else, although some of our drivers are also funny too. But they should look at the history of the driver. Possibly, the driver might not be the issue, but you can call the driver and talk to the person…”. To summarise, While Uber does a good job sometimes in communicating and resolving issues, Mr Raz, went ahead to state how a rider reported him for being cheated for a trip which was originally N400 (£0.8), but the rider went to multiple destinations which was not accounted for. When he reported to Uber, only a fraction of his complaints was resolved 5.

These asymmetries can be resolved if algorithmic processes are closely supervised and communicated to drivers to ensure clarity. As Mr Raz advocates “… sometimes we need to interface more apart from texting on the app. The human angle should come in”. While it is a business that benefits massively from riders, drivers are equally important and should be treated fairly. The future of work hinges on the efficiency and transparency of platforms that might ensure that its processes are clear enough to create a fair working environment for its workers while meeting the needs of its customers.

References.

  1. Dencik, L., Jansen, F., Meltcafe, P. (2019). A conceptual framework for approaching social justice in an age of datafication.
  2. Fieldwork Interview with Platform Union President in Lagos Mr Ay, 2018
  3. Paul, K. (2019). Uber to ban riders with low ratings: will you pass the test? The Guardian. 1 June
  4. Fieldwork Interview with Platform Worker Mr Ed, 2019
  5. Fieldwork Interview with Platform Worker Mr Raz, 2018

Addressing Institutional Voids in Nigeria’s Agricultural Finance Markets through Agri-finance Platforms

5 December 2019 Leave a comment

In my previous blog “Crowdfarming: Platform-enabled Investment in Nigerian Agriculture”, I talked about how digital platforms are being mainstreamed into agricultural finance markets in Nigeria. This blog describes how digital platforms are addressing some of the underlying problems which have constrained rural farmer’s access to agricultural finance thereby creating gaps which manifest as institutional voids in agricultural finance markets. Historically, agricultural finance markets in Nigeria have been characterised by problems in accessing, disbursing and repaying agricultural credit (Akinola, 2013). Specifically, problems relating to accessing agricultural finance by rural farmers stem from three key issues:

  1. Low budgetary allocation to the agricultural sector: Given that agriculture contributes and average of 32% to the Nigeria’s GDP, the budgetary allocation to the sector continues to fall short of the recommended allocation stipulated in the Maputo agreement (PwC, 2018). The Maputo agreement signed in 2003 recommends that the Nigerian government should dedicate 10% of its yearly budgetary allocation to agriculture (NEPAD, 2003). This has however not been the case as Nigeria’s highest percentage budgetary allocation to agriculture since 2003 was 2.23% in 2018 and has dropped to 1.56% in 2019 (Adanikin, 2018). This is still far from the recommended 10% which is deemed necessary for the growth and development of the sector.
  2. Low level of credit extension from commercial banks: Aside the government allocation of finance to agriculture, financial institutions such as banks also tend to allocate less of their lending to agriculture when compared to other productive sectors such as oil and gas, manufacturing and real estate (PwC, 2018 ) (Figure 1). An underlying reason for this low commercial bank extension of credit is because over time banks have become averse to lending to farmers due to high rates of defaults (Akinola, 2013).

Figure 1 - Credit Extension by Sector in Nigeria

Figure 1: Commercial bank credit extension to economic sectors in Nigeria (PwC, 2018)

  1. Unstructured (rural) agricultural investment environment: Asides government funding schemes in partnership with financial institutions, agriculture, unlike other sectors, has not been packaged in a form that investors – both institutional and individual – can engage with easily. While investors might be able to find some agricultural investment opportunities in the Nigerian stock market, these are usually investments in large scale agricultural corporations and not rural agricultural enterprises – which still account for the larger share of agricultural businesses in Nigeria. Mechanisms which could enable individual investors to directly engage with rural agriculture have been largely unstructured and not opened to the general public.

Institutional voids arise in the absence or weakness of market institutions which perform intermediating functions that improve the efficiency of market activities at a lower cost (Khanna and Palepu, 2005). The problems discussed above can be understood as manifestations of voids in agricultural markets which have come about due to the absence of intermediating institutions; those should effectively facilitate agricultural investment procedure by matching the demand and supply of agricultural finance.

Although research on the use of platform to address constraints in accessing agricultural finance is still nascent, there is however anecdotal evidence that suggests that digital platforms have been mainstreamed into agricultural finance markets by innovators who are using platforms to crowdsource agricultural finance for rural agricultural enterprises in Nigeria (Akeredolu, 2019). These platform-enabled businesses have been able to package rural agriculture into ‘investable units’ which are made available to the general public to invest through mobile or web applications thereby tapping into a new pool of agricultural finance (the crowd) outside conventional sources of agricultural finance. Specifically, these agri-finance platforms address institutional voids which manifest as poor access to agricultural finance by:

  1. Serving as intermediaries who efficiently match demand and supply of agricultural finance: These platform-enabled agribusinesses addresses constraints to assessing agricultural finance by intermediating between farmers – who need finance, and investors – who have money to invest in agriculture. Using a digital platform enables these businesses to gather investment funds from large numbers of people in order to fund larger numbers of rural enterprises. Therefore, the use of a digital platform is now attracting new sources of finance into the sector which were previously not accessible by rural farmers.
  2. Ensuring loan repayment through complementary offline intermediation: These platform-enabled businesses also perform other non-platform intermediating functions to ensure that crowdsourced funds are efficiently used by rural farmers and repaid. This is achieved through close monitoring of agricultural enterprises that have benefitted from crowdsourced funds. For instance, primary data collected from 21 Thrive Agric’s platform users – showed a 100% repayment rate for funds crowdsourced through the platform. This is also supporting the re-branding of rural agriculture from a venture with low credibility to a legitimate and trustworthy investment opportunity for investors both within and outside Nigeria.
  3. Improving farmer-identity and visibility through data gathering: Through their on-boarding activities, which entail identifying credible farmers who crowdsourced funds will be invested in, they gather farmers’ bio-, geospatial-, socio-economic and farm enterprise- data. This will improve the confidence of financial institutions in extending finance to rural farmers through the platform. Gathering these data also reduces the transaction cost incurred by financial institutions in extending credit to farmers. As a result, these platform-enabled businesses are able to access high volumes of agricultural finance, not only from individual investors, but also from financial institutions such as commercial banks due to improved farmer-identification procedures.

Although agri-finance platforms in Nigeria have the potential to ensure increased access to finance by rural agricultural, there is still the question of the sustainability of this model, especially in light of uncertainties regarding the formalisation of crowdsourcing as a channel for accessing agricultural finance in Nigeria. For instance, the securities and allied matters act 2004; and the investments and securities act 2007 both limit private companies from inviting the public to subscribe to company units or raising capital from the general public (Uwaleke, 2018). Aside this restriction, the Security and Exchange Commission still has no specific policy provision for crowdsourcing activities in Nigeria. As a result, although it has becoming widely accepted as an investment channel, crowdsourcing is still a bit of a grey area to investors.

Therefore, although the use of digital platforms is opening up agricultural finance markets to new participants and attracting new streams of finance into rural agriculture; further research is needed to understand the long term sustainability of platform-enabled agri-business as well as the broader developmental implications of agri-finance platforms in Nigeria’s agricultural finance markets.

References

Adanikin, O. (2018) 2019 Budget: 16 Years after, Nigeria fails to implement Maputo Declaration on Agrc, food security [online], Available https://www.icirnigeria.org/2019-budget-16-years-after-nigeria-fails-to-implement-maputo-declaration-on-agric-food-security/ [Date accessed: 27/11/19]

Akeredolu, D. (2019) Crowdfunding in Nigeria: Investing in Agriculture [online], Available: https://businessinnigeria.com.ng/crowdfunding-in-nigeria-agriculture/ [Date accessed: 27/11/19]

Akinola, F. (2013) The challenges of agricultural finance in Nigeria: Constraints to sustainable agricultural and economic revival. International Journal of Business and Social Research, 3(5): 234-244.

Khanna, T, and Palepu, K. G “Spotting Institutional Voids in Emerging Markets.” Harvard Business School Background Note 106-014, August 2005

NEPAD (2003) AU 2003 Maputo Declaration on Agriculture and Food Security [Online], Available: https://www.nepad.org/caadp/publication/au-2003-maputo-declaration-agriculture-and-food-security [Date accessed: 27/11/19]

PwC (2018) Evaluating Agriculture Finance in Nigeria: Towards the US$1 trillion African food market by 2030 [Online], Available: https://www.pwc.com/ng/en/assets/pdf/evaluating-agric-finance-nigeria.pdf [Date accessed: 27/11/19]

Uwaleke, U. (2018) Equity crowdfunding: An idea whose time has come, Punch Newspaper [Online], Available: https://punchng.com/equity-crowdfunding-an-idea-whose-time-has-come/ [Date accessed: 27/11/19]

ICTs and Precision Development: Towards Personalised Development

5 November 2019 Leave a comment

Are ICTs about to deliver a new type of socio-economic development: personalised development?

ICTs can only have a significant development impact if they work at scale; touching the lives of thousands or better still millions of people.  Traditionally, this meant a uniform approach where everyone gets to use the same application in the same way.

Increasingly, though, ICTs have been enabling “precision development”: increasingly-precise in terms of who or what is targeted, what is known about the target, and the specificity of the associated development intervention.  The ultimate end-point would be “personalised development”: interventions customised to each individual.

Elements of digitally-enabled individualisation have already emerged: farmers navigating through web- or IVR-based systems to find the specific information they need; micro-entrepreneurs selecting the m-money savings and loan scheme and level that suited them.  But there is still rigidity and constraints within these systems.

Though we are far from its realisation, the potential for truly personalised development is now emerging.  For example:

  • Personalised Learning: “a methodology, according to which teaching and learning are focused on the needs and abilities of individual learners”[1]. ICTs are integral to personalised learning and technology-enabled personalisation has had a demonstrable positive impact on educational performance[2].
  • Precision Agriculture: though around as a concept for at least two decades, precision agriculture is only now starting to find implementations – often still at pilot stage – in the global South[3]. Combining data from on-ground sensors and remote sensing, precision agriculture provides targeted guidance in relation to “seeds, fertilizers, water, pesticides, and energy”.  The ultimate intention is that guidance will be customised to the very specific soil, micro-climate, etc. parameters of individual farms; even smallholder farms.
  • Personalised Healthcare: diagnosis and treatment may appear personalised but typically involve identifying which illness group a person belongs to, and then prescribing the generic treatment for that group. This is becoming more accurate with improvements in electronic health records that provide a more person-specific history and context[4].  Precision medicine prescribes even more narrowly for the individual; typically based on genetic analysis that requires strong digital capabilities.  Though at early stages, this is already being implemented in developing countries[5].

ICTs are thus leading us on a precision development track that will lead to personalised development.  The promise of this can be seen in the examples above: individualised information on learning level, farm status, or health status that then enables a much more effective development intervention.

It will be interesting to log other examples of “ICT4PD” as they emerge . . .

[1] Izmestiev, D. (2012). Personalized Learning: A New ICT-Enabled Education Approach, UNESCO Institute for Information Technologies in Education, Moscow.

[2] Kumar, A., & Mehra, A. (2018). Remedying Education with Personalized Learning: Evidence from a Randomized Field Experiment in India, ResearchGate.

[3] Say, S. M., Keskin, M., Sehri, M., & Sekerli, Y. E. (2018). Adoption of precision agriculture technologies in developed and developing countriesThe Online Journal of Science and Technology8(1), 7-15.

[4] Haskew, J., Rø, G., Saito, K., Turner, K., Odhiambo, G., Wamae, A., … & Sugishita, T. (2015). Implementation of a cloud-based electronic medical record for maternal and child health in rural KenyaInternational Journal of Medical Informatics84(5), 349-354.

[5] Mitropoulos, K., Cooper, D. N., Mitropoulou, C., Agathos, S., Reichardt, J. K., Al-Maskari, F., … & Lopez-Correa, C. (2017). Genomic medicine without borders: Which strategies should developing countries employ to invest in precision medicine? Omics: A Journal of Integrative Biology21(11), 647-657.

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.

  Mobilemoney
  YesNo
BankYes  
accountNoForegone
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, nFinancial
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.

Financial Inclusion and Institutional Voids: Are Digital Platforms a Solution?

2 September 2019 1 comment

“What drives development” has long been a fundamental question for many scholars and policymakers. One consensus reached is that financial inclusion – access to and use of formal financial services by all members of an economy – is a central tenet of development [1]. It facilitates efficient allocation of productive resources by reducing the volume of money outside the banking sector, i.e. by shrinking the informal sector [2]. Conversely, exclusion from the formal financial system reinforces social inequalities and deepens poverty.

However, the World Bank’s (2017) Global Findex Database indicates that about 1.7 billion adults across the world are still unbanked and therefore they are excluded from the formal financial system (see figure below). Evidence further indicates that almost all the unbanked population lives in developing countries and nearly half of these live in just seven countries: Bangladesh, China, India, Indonesia, Mexico, Nigeria and Pakistan.

But why are developing countries the home for the unbanked? In order to answer this question, we first need to address the cause of market failures leading to exclusion in emerging markets; namely institutional voids.

Globally, 1.7 billion adults lack an account [Source: Global Findex database [1]. Note: Data are not displayed for economies where the share of adults without an account is 5 percent or less.

Institutional theory suggests that institutions shape the conditions that drive socio-economic development and the particular rules that actors follow within a given context [3]. As Scott (1995) points out, institutions are socially constructed and embedded structures that provide the rules and frameworks upon which societies operate. If these institutions are absent or weak, it can create institutional voids that impact economic growth and development [4,5].

Therefore, while every economy should have a range of institutions (e.g. payment processing systems, contract enforcement, etc.) to support financial inclusion, many developing countries fall short. Thus, it is lack of, or weak institutional frameworks, i.e. institutional voids, in developing countries that differentiate them to a great degree from developed countries. The presence of institutional voids is apparent in the inefficiency of the formal financial systems to provide appropriate services, quality of institutions and legal origins [6]. Such institutional voids cause buyers and sellers to experience information asymmetries and uncertainty, thus creating serious operating challenges and higher transaction costs (i.e. the cost of capital) in emerging markets. All these act to constrain financial inclusion, and in effect hamper prosperity and wealth creation.

Digital platforms, which have rapidly grown in number and reach in developing countries, have the potential to generate social and economic value by filling the voids and thus building well-functioning markets. For instance, digital platforms for financial services can enable individuals and businesses to more easily reach and find previously hard-to-locate financial services. They also have the potential to facilitate the accumulation of capital by lowering search and transaction costs, reducing information asymmetries and thus fostering trust between parties [7].

Despite this recognition, however, the linkage between digital platforms and institutional voids in these contexts remains significantly underexplored. Apart from a few studies (e.g. [7]) that explored the importance of digital platforms in institutional development and capacity building in developing countries, the focus of the literature on digital platforms in emerging and transitioning economies has been mainly on the general functioning of digital platforms (e.g. business strategy, platform governance and consumer behaviour) [8]. Therefore, we need more research to better understand how digital platforms could help alleviate the negative impact of institutional voids in developing countries, such as in the banking and financial system.

References

[1] World Bank Group (2017). The Global Findex Database, Measuring Financial Inclusion and the Fintech Revolution. World Bank, Washington, DC.

[2] Sarma, M. & Pais, J. (2011). Financial inclusion and development. Journal of International Development, 23(5), 613-628.

[3] Acemoglu, D. & Robinson, J.A. (2012). Why Nations Fail: The Origins of Power, Prosperity, and Poverty. Crown Books.

[4] Scott, W. R. (1995). Institutions and Organizations. Thousand Oaks, CA: Sage.

[5] Khanna, T., & Palepu, K. (2010). Winning in Emerging Markets. Harvard: Harvard Business Press.

[6] Zins, A. & Weill, L. (2016). The determinants of financial inclusion in Africa. Review of Development Finance, 6(1), 46-57.

[7] Drouillard, M. (2016). Addressing voids: How digital start-ups in Kenya create market infrastructure. In B. Ndemo & T. Weiss (Eds.), Digital Kenya: An Entrepreneurial Revolution in the Making(pp. 97–131). London: Palgrave Macmillan UK.

[8] Koskinen, K., Bonina, C., & Eaton, B. (2018).Digital Platforms in the Global South: Foundations and Research Agenda, Working Paper no.8Manchester, UK: Centre for Development Informatics, University of Manchester.

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
%d bloggers like this: