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DFSs, COVID-19 and future financial services landscape in Uganda

Authors:
1
Bank of Uganda
Working Paper Series
Working Paper No. 29/2020
Digital Financial Services, COVID-19, and Future
Financial Services Landscape in Uganda
George Wilson Ssonko and Duncan Roy Kawooya
December 2020
Working Papers describe on-going research by the author(s) and are published to elicit comments
and to further debate. The views expressed in the working paper series are those of the author(s)
and do not in any way represent the official position of the Bank of Uganda. This paper should not
therefore be reported as representing the views of the Bank of Uganda or its management.
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Bank of Uganda WP No. 29/2020
Digital Financial Services, COVID-19, and Future Financial Services Landscape in Uganda
Prepared by
George Wilson Ssonko* and Duncan Roy Kawooya**
Bank of Uganda
December 2020
Abstract
Digital Financial Services (DFS) which is a broad term encompassing the delivery of financial
services (deposits, savings, payments, credit, insurance, and wealth management etc.) through
Information Communication Technology (ICT) based means was popularised through Person to
Person (P2P) payments in Uganda largely driven by the advent of mobile telephony in March
2009. P2P payments dominated the DFS space. The uptake of other value-added services like
payment for utilities, Bank to Wallet (B2W), Wallet to Bank (W2B), insurance, and credit
extension was low. However, the advent of Coronavirus disease 2019 (COVID-19) caused by the
SARS-CoV-2 virus led to various measures aimed at stemming the spread of the pathogen such
as complete national lockdowns and in-land & cross border travel restrictions. Consequently,
traditional approaches to banking in Brick & Mortar Financial Institutions were constrained. As
a result of the constraints and incentives by Mobile Money Service Providers (MMSPs) such as
reduced costs of services, there was an increase in the uptake of other value-added services.
Nevertheless, it is known with certainty whether these uptake trends will be maintained post-
COVID. The paper contributes to the research that explores the effect of COVID-19 on DFS and
provides guidance to stakeholders in the DFS framework. The paper points out that
sustainability of DFS uptake will be influenced by among others cost factors such as MMSPs
surcharges, Over-The-Counter (OTC) taxes, reliability of DFS infrastructure, as well as cost-
benefit comparison of DFS and Traditional Brick & Mortar Financial Services (TBMFS). In
addition, policies such as use of Digital Financial Literacy (DFL) to enhance knowledge, skills,
and confidence of using DFS are vital in the sustainability question. Although the
recommendations therein emanate from the current COVID-19 Pandemic, the rising frequency
of economic shocks and disruptions on account of environmental, health, and financial crises
point to cross-applicability in other similar circumstances.
JEL Classification: O32; O33
Key Words: COVID-19, Uganda, DFS, DFL, and MMSPs
Correspondence Address: *Communications Department and **Economic Research Department,
Bank of Uganda, P.O. BOX 7120, Kampala, Uganda, Tel. +256414230791, Fax.
+256414230791. E-mails: gssonko@bou.or.ug, drkawooya@bou.or.ug
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I. Introduction
On December 31, 2019, the Chinese authorities notified the World Health Organisation (WHO)
about a mysterious respiratory infection which was spreading in one of its provinces (WHO,
2020). By January 12, 2020 the World Health Organization (WHO) had confirmed that a novel
coronavirus (SARS-CoV-2) was the cause of the respiratory illness with pneumonia symptoms
that would later be named COVID-19 in a cluster of people in Wuhan City, Hubei Province,
China. On January 30, 2020, the COVID-19 disease was declared a “public health emergency of
international concern” and on March 12, 2020 it was categorised as a pandemic (an outbreak of a
new infectious pathogen that spreads easily from person to person across the globe).
As at December 03, 2020 (23:26 hours East African Time), the disease had ravaged 190
countries / regions with 64,964,775 confirmed global cases and 1,501,076 deaths (mortality rate
of 2.311 percent) (John Hopkins University Coronavirus Resource Center, 2020). Africa has not
been spared the effects of the COVID-19 disease. As at December 03, 2020 (10:00 hours East
African Time), the total confirmed cases stood at 1,507,349 individuals with 24,464 fatalities, a
mortality rate of 1.623 percent (World Health Organisation Regional Office for Africa, 2020).
During the same period, Uganda had registered 21,409 cases with 206 fatalities translating into a
mortality rate of 0.962 percent (John Hopkins University Coronavirus Resource Center, 2020).
Even though the COVID-19 disease has not yet ravaged the weak public health systems of the
developing countries of Africa to the levels of Advanced Economies, it has not spared their
economies on account of the almost complete national lockdowns and restrictions of in-land and
cross border / international travel. According to the ODI (2020), the estimated impact of
COVID-19 on the African continent is expected to be a contraction in economic growth from 2.4
percent in 2019 to about minus 2.1 percent to minus 5.1 percent in 2020 which translates into
output losses of US$37 billion up to US$79 billion.
According to the African Union (AU) (2020), African economies will be affected through two
ways:- (1) directly through decline in workers’ remittances from African Diaspora; Foreign
Direct Investment (FDI); and Official Development Assistance (ODA) etc.; and (2) indirectly
through morbidity and mortality; disruption of supply chains; contraction of domestic demand
due to loss or decline of income; and rising public expenditure to support economic activities and
safeguard human health against COVID-19. Similar impacts are projected for the Ugandan
economy (MoFPED, 2020; BoU, 2020).
Mugume, Opolot, Kasekende and Namanya (2020) observe that economic activity in Uganda is
expected to contract from 6.8 percent in FY2018/2019 to 3.1 percent in FY2019/2020 before
recovering to 4.0-5.0 percent and 6.0-6.5 percent in FY2020/2021 and FY2021/2022,
respectively. The projected economic contraction is to be expected because Uganda is a small
open economy with various interlinkages in the global trade and financial system such that a
sneeze by the major players can lead to a cold in this East African nation of 43million people.
As a consequence of the negative projections and the recent memories from the Great Recession,
countries across the globe have proposed several measures to mitigate the impact of COVID-19
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on national economies. “In order to cushion the effect of the crisis on households and firms,
Governments are designing a wide range of policy responses, including direct income-support,
tax breaks extension of guarantees, and deferred payments on debt” etc. (AU, 2020). The
responses adopted in various developing countries like Uganda have been collaborative in nature
involving the Central Banks, Central Government, and private sector economic agents.
In Uganda, like the rest of the world, the mechanisms deployed by the Government of Uganda to
combat the SARS-CoV-2 microbes included quarantine, hygiene, and social distancing.
Specifically, measures
1
in Uganda included mandatory use of face masks in public spaces; and
restriction of people movements by closing borders and the airspace as well as banning of
transportation means. In addition, banning of mass gatherings like public prayers; congregating
in educational institutions; clustering in business places; and entertainment related gatherings
coupled with sensitisation of the masses to enhance nutrition were undertaken.
The Government of Uganda restrictions introduced to curb the spread of COVID-19 have
affected businesses across the country. The Financial Service Providers (FSPs) have not been
spared as well. For example at the height of the national lockdown (March to April 2020), FSPs
had closed at least 50 percent of their branch networks and for those branches that remained
open, the enforcement of curfew from 19:00hours to 06:30hours led to the reduction in the
working hours from 08:30hours - 20:00hours to 09:00hours - 15:00hours. In addition, the
Supervised Financial Institutions (SFIs), ceased operating over the weekend. As a consequence,
financial consumers were encouraged to use Information Communication Technology (ICT)
based delivery models such as mobile money / banking, internet banking, and Automated Teller
Machines (ATMs) banking etc. The spatial (movement was not permissible outside one’s
administrative jurisdiction e.g. Greater Kampala Metropolitan Area or District) and temporal
(curfew limitations) restrictions on human movement introduced by the State implied that access
to financial services was largely moved onto digital finance channels.
As a consequence, digital delivery channels that were perceived as complementary to traditional
face to face delivery channels (Ssonko, 2016) were now the defacto means of accessing financial
services. The World Bank observes that ‘the current COVID-19 pandemic has amplified the
urgency of utilizing fintech to keep financial systems functioning and keep people safe during
this time of social distancing, falling demand, reduced input supply, tightening of credit
conditions and rising uncertainty (Pazarbasioglu, Mora, Uttamchandani, Natarajan, Feyen &
Saal, 2020). It is not known with certainty whether the financial services access traffic pushed
towards digital channels on account of COVID-19 will continue in the post COVID-19 era. To
address the issue, the paper explores DFS with specific focus on Mobile Money before and
during COVID-19. In addition, it draws inferences from the data to shed light on the likely
direction of the sub-sector and what ought to be done to ensure the continuance of the digital
financial services uptake. The rest of the paper is organised as follows:- (i) DFS in Uganda; (ii)
Confluence of factors determine uptake and continuance of MMSPs usage; (iii) Drivers of DFS
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Appendix 8.1 provides a list of 35 measures Government of Uganda (GoU) deployed at the height of the lockdown
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before COVID-19; (iv) Drivers of DFS during COVID-19; and (v) Future of DFS landscape in
Uganda.
II. Digital Financial Services in Uganda
Digital Financial Services (DFS) encompass a broad range of financial services accessed and
delivered through digital channels, including payments, credit, savings, remittances, and
insurance’ (Kambale, 2018). The digital channels include the internet, cellular / mobile phone,
automated teller machines (ATMs), and points of sale (PoS) terminals etc. (Kambale, 2018).
DFS includes traditional brick & mortar financial institutions based digitally delivered financial
services e.g. internet banking, cheque truncation, telegraphic transfer, and agency banking etc. as
well as mobile financial services (MFS) such as mobile money, mobile payments, and mobile
banking etc. In Uganda, DFS is dominated by MFS with seven (7) mobile money service
providers (MMSPs)
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in the market, namely Afrimoney, Airtel money, Ezee money, MCash,
Micropay, MTN mobile money, and UTL MSente. Nonetheless, other fintechs exist in the
market.
DFS have numerous benefits which can expand the delivery of financial services to the Bottom
of the Pyramid (BoP) clients through innovative technologies like mobile phone enabled
solutions, electronic money models, and digital payment platforms (AFI, 2020; Pazarbasioglu et
al., 2020; Kambale, 2018). Some of the benefits include cost reduction and lower surcharges on
account of maximising economies of scale; enhanced accessibility / outreach; increased
efficiency; better quality service; increased speed, security, and transparency of transactions;
potential for product customisation; as well as convenience (Masocha & Dzomonda, 2018; AFI,
2020; Pazarbasioglu et al., 2020; Kambale, 2018).
Ssonko (2011) explored the salient characteristics of Uganda’s MMSPs and noted the following:-
(i) The prevalent business model was [mobile network] operator-centric deployed by the three
MMSPs then (MTN mobile money, Airtel ZAP, and UTL MSente). As at February 2011, MTN
mobile money had about 89.40 percent market share measured as a proportion of total registered
customers while Airtel ZAP and UTL MSente had 7.55 percent and 3.05 percent, respectively.
(ii) The regulatory framework had a duality of regulators - between March 2009 and July 2020
the regulatory framework provided for a dual oversight of MMSPs by Bank of Uganda and
Uganda Communications Commission till the Presidential assent to the National Payments Act
2009. At the time, there was no specialised legislation for the regulation of MMSPs.
(iii) The relationship between mobile network operators (MNOs) and commercial banks was
such the former did the bulk of the work (marketing, customer care, mobile account opening and
clientele safety) while the latter provided custody of the ‘physical cash’ through maintenance of
an escrow account. in matters of licensing and supervising MMSPs.
2
Eight, over the time MMSPs have existed in Uganda. Warid Pesa was integrated into Airtel Money with the Airtel -Warid
merger that was finalized on April 23, 2013
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(iv) The MMSPs relied on short messaging services (sms) and USSD (Unstructured
Supplementary Service Data) to deliver services to their clientele.
(v) The services offered at the time were categorised as payment and remittance services which
largely were basic movements of monetary value from one economic agent to another. These
included cash-in, cash-out, purchase of airtime, person to person (P2P) money transfers, person
to business (P2B), mobile accounts enquiry, and bills payment.
(vi) A tiered transaction charges cost structure was in place in which movement of a single
Uganda Shilling became cheaper as one transferred higher amounts.
(vii) Uptake of the mobile money service assessed as the number of registered customers had
increased by over 10,000 percent from 10,011 users in March 2009 to 1,737,904 users in
February 2011. Nevertheless, the paper did not indicate usage activity (number of registered
users active in the last 90days).
(viii) Other metrics such as number of transactions, value of transactions, and balances on
clients’ accounts were on an upward trajectory between March 2009 and February 2011
indicating increased uptake of mobile money services in the country.
Over the ensuing period since February 2011, a number of changes have occurred in the digital
financial services space including among others financial deepening (increased use of existing
mobile money services and products); financial widening (creation of new product offerings);
policy disruptions such as switching off mobile money during 2016 national elections; as well as
introduction of a plethora of taxes on mobile network operators. Twenty one months after the
introduction of the social media tax and taxes on the value of mobile money payments in July
2018 which led to the contraction of MMS business volume, the COVID-19 disruption struck in
March 2020.
The COVID-19 disruption led to the introduction of a national lockdown which saw the closer of
businesses
3
including Micro, Small and Medium Enterprises (MSMEs) that employ over 80
percent of the 20 million strong labour force and contraction of household incomes. Furthermore,
the social distancing measures, banning of public and private transportation, and imposition of
curfew that reduced working hours of Traditional Brick & Mortar Financial Institutions pushed
financial consumers of DFS towards digital channels especially mobile money. The magnitude
and direction of the effect of COVID-19 disruption on MMSPs is still in flux. However, in this
paper, we explore preliminary evidence of the likely effects of the COVID-19 disruption and
how it might shape the digital financial services landscape going forward.
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Sectors such as human health & medical services; agricultural & veterinary services; security; banking;
telecommunications; construction; factories; utilities; groceries; supermarkets; food markets; and segments of
transportation (cargo haulage and delivery services of boda bodas / motorbikes) were exempt from certain forms of
extreme lockdown measures
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III. Confluence of Factors determine Uptake and Continuance of MMSPs Usage
3.1 Determinants based on Theoretical Models of Technology Adoption
The successful deployment of mobile money services (MMS) in a country as well as the
continued patronisation of the MMSP by financial consumers is determined by a confluence of
factors. Table 1 summarizes some studies that investigated determinants of mobile money
adoption.
Table 1: Determinants of Mobile Money adoption from Literature
Author
Econometric
Methodology &
Theoretical Framework
Additional Explanatory
Variables beyond PEOU and
PU
Country & Data
Used
Amoroso and Magnier-
Watnabe (2012:99)
Structural Equation
Modeling (SEM) &
Technology Acceptance
Model (TAM)
Facilitating Conditions;
Perceived Value; Perceived
Security & Privacy; Social
Influence; Trust; Perceived Risk;
Attrativeness of Alternatives
Japan; Primary
Luarn and Lin
(2005:873)
Structural Equation
Modeling (SEM) &
Technology Acceptance
Model (TAM)
Perceived Credibility; Perceived
Self-Efficacy; Perceived
Financial Cost
Taiwan; Primary
Kuo and Yen (2009:103)
Structural Equation
Modeling (SEM) &
Technology Acceptance
Model (TAM)
Perceived Cost; Perceived
Innovatiness
Taiwan; Primary
Tang and Chiang
(2009:1605)
Structural Equation
Modeling (SEM) &
Technology Acceptance
Model (TAM)
Perceived Convenience;
Perceived Self-Efficacy
Taiwan; Primary
Cheney (2008:2)
No clear model; Study
underpinned by two
concepts, that is,
“experience goods” and
“learning by doing”
Consumer experience &
familiarity; Nature of supporting
platform technology; Financial
Inclusion opportunities; Data
Security Problems
USA; Secondary
Chidembo (2009:40-43)
Diffusion Innovation
Theory
Trust & Security; Complexity;
Relative Advantage; Cost;
Compatibility
South Africa; Primary
& Secondary
Tossy (2014:4-5)
Theory of User Acceptance
and Use of Technology
(UTAUT)
Facilitating Conditions;
Performance Expectancy; Effort
Expectancy; Social Influence;
Trust; Perceived Risk
Tanzania; Primary
Padashetty and Kishore
(2013:85-86)
Structural Equation
Modeling (SEM) &
Technology Acceptance
Trust; Expressiveness
India; Primary
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Author
Econometric
Methodology &
Theoretical Framework
Additional Explanatory
Variables beyond PEOU and
PU
Country & Data
Used
Model (TAM)
Larkotey et al.,
(2013:369-371)
Structural Equation
Modeling (SEM) &
Technology Acceptance
Model (TAM)
Perceived positive perception;
Perceived benefits of the service;
Pre-knowledge of the user
Ghana; Primary
Lule, Omwansa and
Waema (2012:32)
Structural Equation
Modeling (SEM) &
Technology Acceptance
Model (TAM)
Perceived Self-Efficacy;
Perceived Credibility; Subjective
Norms; Transaction Costs
Kenya; Primary
Kazi and Mannan (2013:
54-56)
Structural Equation
Modeling (SEM) &
Technology Acceptance
Model (TAM)
Perceived Risk; Social Influence
Pakistan; Primary
Zhao and Kurnia (2014:
1&6)
Qualitative Study
System Quality; Service Quality;
Social Influence; Trust; Users’
characteristics
China; Primary
Kasyoki (2012: online)
Structural Equation
Modeling (SEM) &
Technology Acceptance
Model (TAM)
Relative advantage; Personal
Innovativeness; Perceived Risk;
Social Norms
Kenya; Primary
Oluoch (2012: 29-35)
Multivariate regression
analysis (Probit model); &
Technology Acceptance
Model (TAM)
Perceived Risk
Kenya; Primary
Govender and Sihlali
(2014: 453-454)
Structural Equation
Modeling (SEM) &
Technology Acceptance
Model (TAM)
Perceived Ease of Adoption;
Perceived Value; Trust; Social
Influence
South Africa; Primary
CGAP (2013:2)
Innovative Analytics and
Data Mining Techniques
Social network and social
interactions of the mobile users;
User’s telecom usage profile
(quantity and variety of telecom
products used such as sms, data,
electronic top-ups, and voice
etc.)
Three African
Countries; Primary &
Secondary
Masocha & Dzomonda
(2018:1)
Structural Equation
Modeling (SEM) &
Technology Acceptance
Model (TAM)
Benefits of mobile money;
Challenges of traditional
financial services
Zimbabwe; Primary
Malinga, Maiga, Jehopio
and Kareyo (2017:189)
Descriptive Statistics and
Unified Theory of
Acceptance and Use of
Technology (UTAUT)
Level of exposure, Legal Issues,
Sensitisation, Security,
Performance Expectancy, Effort
Expectancy, Social Influence,
Uganda; Primary
9
Author
Econometric
Methodology &
Theoretical Framework
Additional Explanatory
Variables beyond PEOU and
PU
Country & Data
Used
model
and Facilitating Conditions
Meena (2014:1)
Descriptive Statistics and
Technology Acceptance
Model (TAM)
Perceived Usefulness; Perceived
Ease of Use; Intention to Use
Tanzania; Primary
Maradung (2013:33)
Analysis of Variance
(ANOVA) and Technology
Acceptance Model (TAM)
Age; Gross Income; Educational
Level; Ownership of bank
account
Botswana; Primary
Muir (2015:xi)
Correlation Analysis and
Diffusion of Innovation
Relative advantage; Perceived
risk; Complexity; Compatibility;
Observability
Kenya; Primary
Rolfe (2015: online)
Descriptive Statistics
Social media & messaging apps;
Screening; Trust; Privacy &
Security; Bandwidth; Network
Speed
15 countries world
wide; Secondary
Musiime and Alinda
(2016:1)
Descriptive Statistics and
Linear Regression Analysis
Courtesy of MM agents;
Efficiency of MM agents;
Cleanliness of MM business
premises
Uganda; Primary
Tangirala and Nlondiwa
(2019:1)
Descriptive Statistics
Transactional costs; Connectivity
issues
Botswana; Primary
Muzurura and Chigora
(2019:316)
Structural Equation
Modeling (SEM) &
Technology Acceptance
Model (TAM)
Perceived Usefulness; Perceived
Ease of Use; Compatibility;
Demographic factors; Relative
Advantage; Social Influence;
Perceived risk
Zimbabwe; Primary
Source: Adapted & Modified from Ssonko (2016)
As indicated in Table 1, most factors which have been studied are theoretical constructs from
technology adoption theories. CGAP (2013) states that more than 180 variables were identified
and created as factors which influence mobile money adoption. The categorisations of the factors
were (i) macro-level factors such as national wealth, the vitality of the banking sector, and the
level of investment from mobile network operators (MNOs); and (ii) individual drivers like the
strength of the agent network in close proximity to a subscriber, and the subscriber’s usage of
different types of MNO products (CGAP, 2013). Heyer and Mas (2009) observe that the
adoption and continued usage of MMSPs in developing countries are shaped by environmental
dynamics such as extent of market penetration and political environment over and above
MMSP’s strong strategy and good business models. Pal, De, Herath and Rao (2019) note that
contextual variables both environmental and cultural aspects can be enhancers and / or barriers to
uptake and continued usage of MMS. In this paper, macroeconomic data about various drivers of
adoption and continued usage of MMS namely, products and services offered; transactional
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costs; taxation regime; reliability of mobile network infrastructure; activity of accounts; as well
as number and volumes of transactions are examined. Descriptive statistics are used to analyse
how these factors are likely to shape uptake and continued usage in the post COVID-19 period.
3.2 Disruptions may be either enhancers or barriers to DFS adoption and usage
The factors influencing uptake and usage of mobile money services have been categorised into
either enhancers (positive drivers) and barriers (inhibitors). The enhancers are usually derived
from technology adoption theoretical frameworks. The enhancers include constructs such as
perceived usefulness, perceived ease of use, and system quality. In a bid to examine contextual
environmental variables, scholars have investigated the barriers or hinderances to adoption of
Information Systems such as mobile money.
As a result of including contextual factors in technology uptake models, variants of the most
popular technology adoption theoretical framework (Technology Adoption Model TAM) such
as TAM 2 and the Unified Theory of Acceptance and Use of Technology (UTAUT) (Zhou, Lu,
& Wang, 2010; Venkatesh & Davis, 2000) have been devised. Musa, Meso, and Mbarika, (2005)
state that most of the research work done about technology adoption theoretical frameworks has
occurred in developed countries. In these Advanced Economies researches, there is an inherent
assumption in the technology uptake model which is; technology is readily available and the
choice of whether to use or reject use is individual focused which is not the reality in Sub-
Saharan Africa (Musa, Meso, & Mbarika, 2005). Meso and Musa (2008) as well as Meso, Musa,
Straub and Mbarika (2009) observe that individual choice is limited by socio-economic realities
such as telecommunications infrastructure that are vital to the day-to-day use of modern
technologies. Table 2 provides a synopsis of some of the barriers to mobile money uptake and
usage in developing countries’ contexts.
Table 2: Barriers of Mobile Money adoption and usage from Literature
Author
Methodology
Barriers
Country & Data
Used
Davidovic, Prady, and
Tourpe (2020)
Qualitative (Blog)
Lack of mobile coverage; Lack
of easy access to money agents;
Inadequate access to electricity;
Exchanging mobile money for
cash can be expensive; Digital
and Financial illiteracy
Not indicated
Xiao and Chorzempa
(2020)
Qualitative (Blog)
Infrastructure (Identity, Internet
access, and Legacy Payment
Systems); Financial and
Economic Costs of DFS e.g. data
privacy costs; Market Structure
(Monopoly / Duopoly /
Oligopoly)
China; Secondary
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Author
Methodology
Barriers
Country & Data
Used
Dzokoto and Mensah
(2012)
Qualitative & Technology
Acceptance Model (TAM)
Distance from banks or agents
(accessibility); Geographical
location of members of
household; Price strustructures;
Trust; Historical relationship of
people with money;
Technological glitches /
infrastructural bottlenecks e.g.
power cuts; Knowledge gaps
about mobile money;
ambivalence attitude towards
mobile money; failure of other
cashless payment systems in the
past; inadequate income; security
(fraud & theft, network issues,
and losing gadget); few agents;
competition from TBMFIs; fear
of overspending on account of
electronic nature of money in
DFS
Ghana; Primary
Otieno, Liyala, Odongo
and Abeka (2016)
Qualitative
Lack of national identity cards by
potential users; few mobile
money agents; Inadequate cash
and floats at mobile money
agents; language barrier;
Inadequate awareness and lack of
information on how to access and
operate certain features in mobile
money platforms; preference for
cash transactions over cashless
transactions
Kenya; Primary &
Secondary
Mulwa and Ngigi (2018)
Survey
Inefficiency of shops due to lack
of enough electronic money to
facilitate loading of mobile
money wallets; Insecurity of
funds; Cost of transactions;
Access to outlets; Inability to
transact; Do not trust assistants
Kenya; Primary
Iliasov (2014)
Qualitative
Awareness of MM; Limited
knowledge about MM; Low level
of trust
Nigeria; Primary
Murray (2016)
Qualitative and Survey
Digital illiteracy; Risk of account
deactivation discouraged
experimentation; network &
electricity problems; Limited
knowledge of mobile money
products; Queuing at agent;
Highly vulnerable participants
prioritise immediate
Ethiopia; Primary
12
Author
Methodology
Barriers
Country & Data
Used
consumption over MM usage
Khan and Goldstein
(2014)
Qualitative
Lack of awareness; Lack of trust
Pakistan; Primary
Disruptions may be categorised as climate change induced, technological, financial / economic,
as well as geopolitical and their effects may be systemic or localised (Ssonko & Kawooya,
2020). Disruptions may be barriers or enhancers to adoption and usage of digital financial
services. According to von Allmen, Khera, Ogawa and Sahay (2020), the COVID-19 disruption
will likely increase DFS usage. However on the downside, it is likely to stiffle the growth of the
fintech industry’s smaller players through constraining funding opportunities and exacerbating
unequal infrastructure access (von Allmen et al., 2020). Von Allmen et al. (2020) note that
fintechs will face challenges such as tightening of funding, rising non performing loans, and
decline in volume and value of transactions including credit demand.
IV. Drivers of Digital Financial Services before COVID-19
4.1 Infrastructure
Infrastructure has been defined broadly to include aspects such as access to electricity; mobile
and internet coverage / connectivity; number of gadgets; possession of national identity
documents for Know Your Customer (KYC) purposes; legacy of payment systems; as well as
status of data privacy & protection (von Allmen et al., 2020; Davidovic et al., 2020; Xiao &
Chorzempa, 2020; Frankfurt School of Finance & Management gGmbH, 2020:18). Infrastructure
was one of the issues identified by the stocktaking survey of the country’s payment system in
1998 by the then Bank of Uganda National Payment Systems Secretariat (Bank of Uganda
Annual Report 1998/1999:61).The survey intended to provide context to the National Payment
Systems Strategy which was designed to support the modernisation of payment systems had the
following major findings:- a narrow payment instrument base; inadequate legal, communication
and energy infrastructure; a preponderance of debit instruments over credit instruments; and
inadequate risk management.”
4.1.1 Access to Electricity
Access to electricity is key on account of the fact that most DFS gadgets such as ATM
deployments, mobile phones, and MNO cell sites etc. rely upon electricity to function (Frankfurt
School of Finance & Management gGmbH, 2020:18). Electricity access is a challenge in both
rural and urban areas in developing countries (Frankfurt School of Finance & Management
gGmbH, 2020:18). Figure 1 shows the evolution of access to electricity (defined as the
percentage of population with access to electricity) in Uganda from 1991 to 2018 (World Bank
Data, 2020). The urban-rural divide is evident in electricity access.
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Figure 1: Evolution of access to electricity in Uganda from 1991 to 2018
Source: World Bank Data (2020)
Access to electricity is key on account of the fact that most DFS gadgets such as ATM
deployments, mobile phones, and MNO cell sites etc. rely upon electricity to function (Frankfurt
School of Finance & Management gGmbH, 2020:18). Electricity access is a challenge in both
rural and urban areas in developing countries (Frankfurt School of Finance & Management
gGmbH, 2020:18). Figure 1 shows the evolution of access to electricity (defined as the
percentage of population with access to electricity) in Uganda from 1991 to 2018 (World Bank
Data, 2020). The urban-rural divide is evident in electricity access.
The proportion of the urban, rural, and entire population with access to electricity increased to
57.50%, 38.02%, and 42.65%, respectively in 2018 from 33.60%, 1.98%, and 5.60% in 1991
(World Bank Data, 2020). These low electricity access figures are further underscored by Ikonjo-
Iwela (2016) who points out that Uganda’s per capita energy consumption of 3.7kWh is one of
the lowest in the world. The inadequate access to electricity is one of the primary causes of huge
discrepancies in urbanrural internet use and mobile phone penetration rates in Uganda
(Gillwald, Mothobi, Ndiwalana & Tusubira, 2019:iv).
4.1.2 Internet Connectivity
Even though mobile money can be delivered without internet connectivity via channels such as
Unstructured Supplementary Service Data (USSD) and Short Messaging Services (SMS)-based
means (Frankfurt School of Finance & Management gGmbH, 2020:26), internet connectivity is
key for the delivery of certain value added services and m-commerce or e-commerce. Table 3
14
summarises key metrics about mobile phone coverage and internet connectivity in Uganda.
Internet connectivity is increasing partly driven by uptake of mobile data subscriptions.
Table 3: Mobile Subscriptions and Internet Penetration as at September 2019
Key Indicators
Q4 2018
(December
2018)
Q2 2019
(June 2019)
Q3 2019
(September
2019)
Mobile Subscriptions
24,472,033
24,456,617
25,603,296
Fixed line Subscriptions
186,780
203,132
145,521
Teledensity (%)
63.0
63.6
63.9
Internet Subscription
(Mobile)
14,360,847
15,155,921
15,261,314
Internet Subscription
(Fixed)
9,485
9,929
11,144
Internet Penetration (%)
36.8
37.6
37.9
Estimated Internet Users
21,636,121
22,833,172
23,003,411
Source: UCC (2020).
4.1.3 Mobile or Cellular Phone Penetration
According to the National IT survey 2017/2018 carried out by CIPESA (The Collaboration on
International ICT Policy for East and Southern Africa) under the ausipices of the National
Information Technology Authority Uganda (NITA-Uganda) which sampled 2,400 individuals /
households, mobile phone ownership is summarised as indicated in Table 4.
Table 4: Mobile Phone Ownership in Uganda
Categorisation
Percentage
Of all individuals, those who own mobile phones
70.9%
Of individuals who own mobile phones, those who own smart phones
15.8%
Of all individuals, those who do not own a mobile phone
29.1%
Of individuals who owned a mobile phone but did not share their phone with
anyone
64.2%
Of individuals who did not own mobile phones, but owned an active SIM card
27.4%
Of individuals who did not own mobile phones, but had used someone’s phone in
the past three months
72.0%
Source: CIPESA (2018:130)
There has been an improvement in mobile phone ownership over the years as shown in Table 5.
However, gaps remain such as digital features phones compared to smartphones; urban/rural, and
female/male. However, the share of smart phones in the total mobile phone ownership is one of
15
the lowest in Africa (Gillwald et al., 2019:iv). In addition, phone sharing is also still practiced
just like in other African countries (See Sey, 2008).
Table 5: Trends in mobile phone ownership
Survey
All
Individuals
Rural
Urban
Female
Male
NITA-Uganda
2017/2018
YES
70.9%
65.7%
78.5%
63.2%
81.6%
NO
29.1%
34.3%
21.5%
36.8%
18.4%
Uganda
Communications
Commission
(UCC) 2014/2015
YES
52.3%
46.6%
77.9%
44.4%
61.6%
NO
47.7%
53.4%
22.1%
55.6%
38.4%
Source: CIPESA (2018:131)
4.1.4 Possession of National Identity Documents for KYC
Identification documentation is key for fostering financial inclusion through support for Know
Your Customer (KYC) / Customer Due Diligence (CDD) (Frankfurt School of Finance &
Management gGmbH, 2020:20). Despite its critical nature, in many developing countries, it
remains a major limiting factor (Frankfurt School of Finance & Management gGmbH, 2020:20).
By 2016, 14.8 million Ugandans had registered for a National Identification Number (NIN) /
National Identification Card (NIC) (Handforth and Wilson, 2019:3). The 14.8 million NICs are
way below the population of Ugandans 15 years and above which stood at 24,692,872 as at 2020
(PopulationPyramid.net, 2019). However, replacement of the NIC one lost requires one to wait
for at least 60 days and travel to the capital city in Kampala (Handforth and Wilson, 2019:3).
In 2017, Government of Uganda (GoU), as part of its national security measures, embarked on
an exercise of ensuring that every Subscriber Identification Module (SIM) card of an individual
gets validated and verified for purposes of ensuring secure and safer communications. The
primary identification document for Ugandan citizens was the NIC. As such, the Uganda
Communications Commission (UCC) sought to enforce this requirement, instrucing all telecom
providers to ensure mandatory sim card registration. A deadline was set, although this was
extended several times. Without a NIC, a Ugandan citizen’s SIM card should ideally not be
registered on any Mobile Network Operator’s platform.
Similarly, in February 2019, Bank of Uganda issued a directive making the NIC the primary
identification document for KYC purposes in all financial institutions under its supervisory
purview. Though intended to ease identification of financial consumers in the event of
liquidating a financial institution and payout of account insurance monies by the Deposit
Protection Fund of Uganda (DPFU), the use of the NIC for KYC purposes whether updating
existing financial products access and / or setting up to access new ones is now mandatory.
In order to ease NIC verification by supervised financial institutions (SFIs), in January 2020,
Bank of Uganda (BoU) together with the UBA, National Identification and Registration
16
Authority (NIRA) and Laboremus launched a shared E-Gateway between NIRA, SFIs, and BoU
that would improve on the process of the verification and authentication of SFIs current and
prospective customer information against records maintained by NIRA (BoU, 2020). A good
proportion of individuals provided mobile money account numbers as the alternate payment
channels which could be used in settling insurance monies in case of liquidation of a supervised
financial institution. As a consequence, a bulk of customer accounts in SFIs had a registered
mobile phone number attached to them, and this made the enrollement onto digital / mobile
banking rather seamless.
4.1.5 Legacy of Payment Systems
At the time of the advent of mobile money services in Uganda in March 2009, automation of the
payment systems had been around for about a decade with the first Automated Teller Machine
(ATM) having arrived in 1997. As indicated in Table 6, the bulk of these automations focused on
wholesale payment systems. Thus, the advent of mobile money in March 2009 which is
primarily a retail payment system did not have serious competition to contend with. In fact, the
issues of public policy discussion focused on mobile money security and interoperability across
networks and with wholesale payment systems.
Table 6: Major Milestones in Uganda’s Payment Systems Automation Drive prior to
advent of mobile money in March 2009
Time Period
Milestone
1997
First ATM introduced in Uganda by Standard Chartered Bank
February 1998
Bank of Uganda resolves to modernise country’s payment systems
Administrative structure and Strategy devised
1998/1999
Survey to stocktake status of payment systems in Uganda cash was
dominant; cheques major non-cash instrument; low usage of electronic
instruments; inadequate regulatory framework etc.
1999
National Cheque Standard adopted to harmonise cheques issuance, quicken
clearing of cheques, and minimise cheque frauds
May 2002
Electronic Clearing System (ECS) - All clearing banks submit cheque data to
the clearinghouse in electronic form and the electronic data is then fed into the
ECS for derivation of net clearing positions, and generation of inward
electronic cheque data for each bank.
February 2005
Uganda National Interbank Settlement (UNIS) system, a Real Time Gross
Settlement (RTGS) system implemented
July 2007
Capping of cheques regulatory upper limit on the amount on cheques
(UGX20million)
February 2007
Implementation of EFT Direct Debit for school fees
Source: Bank of Uganda Annual Reports 2001/02; 2000/01; 1998/99; Ogwang (2009:10-11)
17
4.1.6 Status of Data Privacy and Protection
One of the issues that the G20 High-Level Principles for Digital Financial Inclusion 2016
emphasise is the need for consumer data protection and privacy (GPFI, 2016). In Uganda, data
protection is safeguarded under the Data Protection and Privacy Act 2019 that seeks to “protect
the privacy of the individual and of personal data by regulating the collection and processing of
personal information”. While the data collected by MNOs is safeguarded through Information
Communication Technology (ICT) security measures such as Personal Identification Numbers
(PINs) deployed on the mobile money platforms, there is a loophole at agents’ premises. Most
mobile money agents leaving records of mobile phones and associated transactions visible to all
financial consumers who patronise their kiosks presents a major risk of unauthorised exposure of
personal information.
In addition to agents’ inadvertent release of financial consumer information, there are practices
which are done which further compromise data privacy. These include writing PINs on calendars
and walls alongside cellular phone numbers; sharing PINs with mobile money agents, relatives,
and money lenders; use of PINs which are easy to decipher such as birthdays; accepting help
from strangers at mobile money agents’ kiosks thereby exposing your PIN; and helping strangers
register SIM cards using one’s NIC.
4.1.7 Stakeholders of MMSPs
There mobile money ecosystem exists only through the collaboration between MMSPs and a
diverse arrangement of stakeholders from several sectors including IT, finance,
telecommunication, customers, merchants and the regulatory authorities. All these work together
to deliver DFS in Uganda. The interests of these stakeholder are diverse, however most of them
enjoy a symbiotic relationship and the roles they play are discussed in the subsequent
paragraphs.
i) Agents: The success of MMSPs depends on heavily accessibility and availability. The agent
remains the principal access channel and face of mobile money most especially for agent-centred
transactions such as deposits, withdrawals, and in very limited cases, SIM card registration. The
agent usually earns a commission from the MMSP for the services rendered to their customers.
By February 2020, the total number of mobile money agent points across the country stood at
396,731, which is impressive compared to the approx. 91,000 from 5 years ago. These agents
are at the moment also 250 fold compared to the combined 1,534 SFI access points (695 SFI
and 839 ATMs) reported by the BoU in 2018.
ii) Financial Institutions: Supervised financial institutions have took centre stage when MMSPs
started owing to the gaps in the legislation. The Financial Institutions Act (2004), which
empowers the central bank to intervene in financial sector matters, especially those invlolving
mobilisation of some sort of consumer deposits, was silent on the licencing and regulation
payment service providers. The middle ground was for this to be done via a proxy and SFIs
seemed like a good place to start. SFIs at the start viewed MMSPs as a competitor, but of recent
have embraced them and MMSPs very often are augmenting the traditional SFI banking
18
experience. Customers of the SFIs are able to move funds to and from their accounts to mobile
wallets, a process that was impossible 12 years ago.
iii) Teleommunication & IT: The MMSP system needs a paltfom to host it, and the most
convinient came in the form of telecom. It is no wonder that four of the seven MMSPs in
Uganda are innately providers of telecommunication services (both voice and data), and offer
mobile money as a secondary service. The telecom interface goes hand in hand with the IT
services available, especially as the adoption of smart devices has ecouraged the use of
applications along side the traditional USSD option available for mobile money.
iv) Customers: These form the core of the business and are the reason MMSPs exist. Their
choices, behaviour, attitudes and patterns often influence the uptake of new services on offer.
v) Merchants: There is an increasing appetite for merchants to own a merchant codes (line),
which is separate from their business or personal mobile money account. Airtel Pay and MTN
Momo Pay offer the highest number of merchant codes, although a number of payment solution
providers have developed products that utilize the existing mobile money infrastructure, and
essentially create a separate series of merchant codes. The merchant codes of other providers
other than MTN and Airtel are not as wide spread.
4.1.8 Legislative Framework Policy & Regulations
From the onset, it was clear that the service being proposed by the first MMSP was a financial
service. Even though the provider was a telecom under the jurisdition of the Uganda
Communications Commission (UCC); there was general agreement that this service was better
supervised by a financial regulator, BoU. However, the existing legislation at the time did not
enable the BoU to fully licence and regulate the service. The middle ground was for the issuance
of a “letter of no objection”, following a lengthy survey process by the BoU. The main concern
of the BoU remains the safety of public mobilised funds that are used to create electronic value.
MMSPs are required to partner with a supervised financial institution (under BoU jurisdiction),
usually deposit taking, and must hold an escrow account in the latter which should at all times
match the electronic value that has been extended to all their customers and agents. However,
there are other entities that provide payment solutions, including FinTech companies that offer
DFS and are neither regulated by BoU nor UCC.
As an interim measure, the BoU issued the 2013 Mobile Money Guidelines, in the absence of a
clear legislation on the regulation of mobile money. The National Payment Systems Law which
was passed by Parliament on May 28, 2020 and assented to by the President on July 29, 2020 is
set to be a game changer with regard to MM regulation.
4.1.9 Institutional Framework MMSPs Ecosystem
Given the dual regulatory regime in DFS, and more specifically for MMSPs, the importance of
institutional mapping on the regulatory side is critical for the success of the sector. It is standard
practice for regulators to enter into Memoranda of Understanding (MoU) to mainly exchange
regulatory information and also provide for other regulatory issues in which they can collaborate.
19
The same can be said for BoU and the UCC, who entered into a MoU, on which the oversight
role for mobile money in Uganda was built upon.
On one hand, the main concern of the BoU is deposit taking and financial intermediation which
is a business liable for regulation under the Financial Institutions Act (FIA) 2004 as amended in
2016. However, the law stipulates that the persons being regulated must be one of the categories
of financial institutions that BoU can supervise. Unfortunately, telecoms, which were the main
providers of mobile money did not meet this criteria, as such, a strategy was taken to supervise
these by proxy. MMSPs were required to partner with an SFI. The partnership would be vetted
and approved by the BoU if all requirements were met.
On the other hand, the UCC is positioned to regulate telecom providers to provide mobile money
as a value added service. In addition, it is responsible for ensuring network availability (network
system uptime), which is necessary for mobile money services to run. UCC has also to ensure
that there is no unfair competition in which that telecoms neither lock out, nor unfairly charge
other mobile money service providers who wish to use their networks.
With the assent of the National Payment Systems Law, BoU is poised to become the regulator
for payment systems. BoU already sits in the Financial Stability Surveillance Committee (FSSC)
It is thus an advantage that issues of payment sytem over sight will be brought to the forefront of
discussions amongst financial sector regulators. With the numerous and fast changing advances
in technology, and by extension payment systems, disruptions in one segment the market could
spread quickly throughout the financial sector and put the whole system at risk. As such a
payment system regulator working closely with other established institutions allows smoother
functioning of the sector.
4.1.10 User Interfaces and Security
The successful adoption of mobile money has very much depended on a user interface that was
designed to match the technological capabilities of the region and adaptability of users at the
time. While smartphones have become more prevalent for today’s urban user today than they
were a few years ago, MMSPs still largely deliver their service via Unstructured Supplementary
Service Data (USSD). The USSD is a protocol used by GSM
4
cellphones to communicate with
their service provider's computers via text messages. MMSPs have continued to provide their
servies using this simple user interface. It is required that the customer approves the transaction
using a 4 or 5 digit code Personal Identification Number (PIN/Code), depending on the provider.
Of recent MMSPs provide confirmation details such as name of reciepient, merchant name or
account title in the case of utilities, before a user approves any transaction over their mobile
money account. This is to prevent funds transfer to the wrong beneficiary, for which a reversal is
not instant.
4
Global System for Mobile Communications, a standard that describes protocols for second-generation (2G) digital cellular networks used by
mobile devices such as mobile phones and tablets.
20
4.2 Services provided by MMSPs
According to Ssettimba (2016), the services which were being offered by mobile money service
providers as at March 2016 are indicated in Table 7. Over the last 54 months (4.5years), financial
widening has taken place and newer products / services have been devised by MMSPs. Some of
these are merely automation of financial services / products provided by TBMFIs while others
are innovative solutions to address needs of the public which may not have existed in the
TBMFIs space. In this section, the services examined include person-to-person (P2P); deposits;
withdrawals; bank-to-wallet (B2W); wallet-to-bank (W2B); bill and merchant payments; as well
as cross border remittances.
Table 7: Current Mobile Money Services being offered in Uganda [as at March 2016]
Product / Service
Status
Domestic transfers
Live
Merchant Payments enabling Small Medium Enterprises and Corporates to receive payments (P2B)
Live
Statutory Payments (Taxes) P2G
Live
Bulk Payments: Salaries, Wages, B2P e.g. Sugar, Tea, and Construction Firms
Live
Micro Loans and Savings
Pilot
Group Wallets for SACCOs and VSLA
Pilot
Cross border
Live
Mobile Banking transfers from bank account to M-Wallet
Live
Government Payments (Social Benefits) G2P
Live
Source: Ssettimba (2016)
The section below reflects the services offered by MMSPs as at February 2020, which has been
considered a cut off date for the pre-COVID analysis.
4.2.1 Person-to-Person
Peer to Peer or simply put personal transfers, involve the movement of funds between two parties
using their mobile money accounts. This was the first service on offer when mobile money rolled
out in March 2009. By February 2020, there were 11.573 million P2P transactions worth
UGX822.49 billion up from 6.062 million transactions worth UGX404.80 billion recorded in
August 2018 (date when P2P was first reported to BoU by MNOs).
4.2.2 Deposits
Deposits, also referred to as Cash-In within the mobile money ecosystem, are defined as funds
handed over to a mobile money agent in order to to acquire electronic value (e-value)
colloquially known as float. By February 2020, there were 46.598 million deposit transactions
worth UGX1,984.640 billion up from 27.961 million transactions worth UGX1,355.209 billion
in August 2018.
21
4.2.3 Withdrawals
These are also referred to as cash out and represent the volume and value of funds received
from an agent when a customer decides to convert the e-value held on the Subscriber Identity
Module (SIM) card into physical cash. This action reduces the e-value balances on a given
customers’ mobile money account. Mobile Money Service Providers (MMSPs) prefer that
customers customer use their mobile money balances to meet and settle obligations electronically
other than withdrawals. Consequently, the cost structure is such that the withdrawal charges are
often prohibitive to dissuade customers from drawing their accounts to zero balances. By
February 2020, there were 27.751 million withdrawal transactions worth UGX1,858.051 billion
up from 19.821 million transactions worth UGX1,313.815 billion in August 2018.
4.2.4 Bank to Wallet (B2W)
The B2W is a service that allows bank clients to transfer funds from their bank account to a
mobile wallet (mobile money account). This service was introduced many years later and caters
for fund transfers between the traditional brick and mortar financial institution (TBMFI) and
MMSPs. By February 2020, there were 901,152 B2W transactions worth UGX174.465 billion up
from 616,525 B2W transactions worth UGX102.247 billion in August 2018.
4.2.5 Wallet to Bank (W2B)
Wallet to Bank (W2B) is a service that allows mobile money account holders to transfer funds
from their mobile wallet (mobile money account) to a bank account. In comparison to B2W,
W2B is not very common and remains the preserve of a few. The low uptake of W2B is partly
due to the delayed granting of access by TBMFIs to the first layer of information that provides
the financial consumer with the capacity to verify account details prior to transacting. By
February 2020, there were 188,413 W2B transactions worth UGX76.843 billion up from 57,564
W2B transactions worth UGX14.526 billion in August 2018.
4.2.6 Bill and Merchant Payments (Person to Business P2B)
Mobile Money Service Providers (MMSPs) have made available via their platforms / systems,
options for bill payments, most especially for utilities (water, electricity and internet), as well as
payments due to merchants as a result of purchases. The latter currently may be seen in the form
of Airtel Pay and MTN Momo Pay; which allow the transfer of balances to the merchant
without the subscriber incurring costs.
By December 2011, both the Uganda Revenue Authority (URA) and the National Water and
Sewerage Corporation (NWSC), had phased out cash offices at their respective institutions in a
bid to avoid long lines at their premises and improve efficiency in revenue collection. Similarly,
in 2014, the electricity distributor Umeme phased out cash payments for power bills in a bid to
make it easier for customers to meet their power obligations. At the time Umeme still held 25
cash offices across the country for walk-in customers to use in paying their electricity bills.
By February 2020, there were 11,576,315 P2B transactions worth UGX244.786 billion up from
5,573,707 P2B transactions worth UGX99.846 billion in August 2018. The large volume and
22
values registered in the category reflect the breadth of items included therein such as utilities,
cable / pay TV, merchant payments (school fees, supermarkets), and government revenues (tax
and non-tax) etc.
4.2.7 Cross border remittances
Remittances from outside the country can be received on mobile money. MMSPs have provided
a safe platform for fast and efficient money transfers from foreign sources. In many parts of the
African continent, those wishing to send and receive money across the border still incurr the
highest transaction fees globally. As such the mobile money cross-border payments, especially
within East Africa provide a cheaper alternative.
By February 2020, the volume of inward and outward crossborder flows was 141,366 and 27,661
transactions respectively from 66,456 and 7,821 transactions in August 2018. Similarly, the the
value of inward and outward crossborder flows was UGX37.325 billion and UGX3.048 billion,
respectively in February 2020 from UGX18.840 billion and UGX0.907 billion in August 2018.
4.2.8 Airtime
The purchase and consumption of MNO airtime was done using scratch cards before the advent
of mobile money in March 2009. Even after the advent of mobile money, scratch cards remained
a major mechanism through which airtime was being consumed. However, in August 2018,
Uganda Communications Commission (UCC), a regulatory body of the communications sector
in Uganda issued an order to phase out the use of airtime scratch cards which were to be replaced
by ‘easy load’ (use of mobile money) (Walubiri, 2019; Kisekka, 2018). Efforts to re-introduce
scratch cards by Parliamentarians in 2019 on account of the lack of accessibility to easy load
facilities in rural areas did not yield positive results. By February 2020, the number of airtime
loading transactions stood at 92,452,892 (worth UGX105.613 billion) compared to 105,180,385
transactions (worth UGX117.158 billion) in August 2018.
4.2.9 Data
Data was one of the services which MNOs added onto their business offerings once the average
revenue per user (ARPU) from voice sources started to decline on account of increased
competition (Musazi, 2010). Despite the perception that the cost of data is high in Uganda
relative to other jurisdictions (Daily Monitor, 2019), mobile broad band access and usage is on
the rise on account of increasing 4G and 3G coverage, a drop in smartphones and modem prices,
and a fall in bandwidth prices (CIPESA, 2018:38). As at September 2017, Uganda had an
estimated 18.1 million internet users of which 14.8 million were mobile internet subscribers
(CIPESA, 2018:38). The high number of mobile internet subscribers suggests an upward
trajectory for data usage. By February 2020, the number of data loading transactions stood at
34,089,830 (worth UGX46.671) up from 4,789,376 transactions (worth UGX13.095 billion) in
August 2018.
23
4.2.10 Loans
Digital credit has the potential to enhance financial inclusion (through provision of much needed
loans without the bureaucratic process of paper work in TBMFIs) as well as exacerbate financial
exclusion (through blacklisting of defaulters of digital micro-loans) (Microsave, 2019). For
instance in Kenya, most digital loans are used for consumption purposes increasing the
probability of default. Indeed, more than 10 percent of the adult population are negatively listed
(Microsave, 2019). This 10 percent translates into about 400,000 people blacklisted for
defaulting on loans as low as KES200 (about US$2) and who are now rationed out of the formal
credit market (Robinson & Wright, 2016) because providers of micro-loans are obligated to file
such adverse / negative information with the Credit Reference System (CRS).
In Uganda the Financial Institutions (Credit Reference System) Regulations 2020 which would
allow providers of digital credit access to the credit reference services as Accredited Credit
Providers and file such information with the credit reference system are yet to be finalised (Bank
of Uganda Annual Report FY2018/2019:29 and FY2019/2020). Nonetheless, the repayment rates
of digital loans in Uganda are still relatively high. For example, Jumo / Airtel Uganda registered
repayment rates in the range of 93% to 95% (Microsave, 2019). However, there are isolated
cases of digital credit defaults. For instance a man was arrested for failure to repay
UGX400,000/= (US$106) digital loan (Zawedde, 2019).
In Uganda, digital credit is extended by two major players, that is, Airtel Uganda Limited’s
Wewole (Airtel Uganda Limited, 2020; 2017) and MTN Uganda Limited’s MoKash (MTN
Uganda Limited, 2020). as indicated in Table 8.
Table 8: Uganda’s Digital Credit Providers
Digital
Credit
Provider
Product
Descriptio
n and
Date of
Launch
Partners
Accessibili
ty Terms
and
Conditions
Limits
Loan
Repayme
nt
Periods
Interest Rate
Range
Default
Penalty
Uptake
Credit
Scoring
MoKash
(‘More
Cash’)
Micro
savings
and loan
product
launched
on August
09, 2016
MTN
Uganda
Ltd
(MNO)
and
NCBA
Bank
Uganda
Ltd (SFI)
MTN
registered
customer
At least 6
months
using
platform
18 years &
above
Opt in by
dialing
*165*5#
Savings
UGX50/= to
UGX10
million
Loans
UGX3,000/=
to UGX1
million
Interest on
savings is
paid
quarterly
Loan is
repayable
in 30 days
Savings (Tiered
Interest
Strucure)
UGX1 to
UGX300,000/=
2%
UGX300,001/=
to
UGX800,000/=
3%
UGX800,001/=
to
UGX1,600,000/
== 4%
>
UGX1,600,001/
= 5%
Loans
10% on
outstandi
ng
amount;
reduced
credit
amount at
next
attempt
2000 to
4500
loans
disburse
d per day
9000
new
customer
s
registerin
g gdaily
Alternati
ve credit
scoring
(mobile
money
data, and
mobile
phone
data)
24
Digital
Credit
Provider
Product
Descriptio
n and
Date of
Launch
Partners
Accessibili
ty Terms
and
Conditions
Limits
Loan
Repayme
nt
Periods
Interest Rate
Range
Default
Penalty
Uptake
Credit
Scoring
9% per month
Wewole
(‘Borrow
’)
Micro
credit
service
launched
on March
16, 2017
Airtel
Uganda
Ltd
(MNO)
and
JUMO
(Non-
Bank
Institutio
n)
Airtel
registered
customer
At least 6
months
using
platform
18 years &
above
Opt in by
dialing
*185*8#
Persons
UGX8,000/=
to
UGX500,000/
=
Agents
UGX100,000/
= to
UGX1,000,000
/=
7, 14, and
21 days
6.75% to 15%
per month
10% on
outstandi
ng
amount;
reduced
credit
amount at
next
attempt
1 million
users per
month
Alternati
ve credit
scoring
(mobile
money
data, and
mobile
phone
data)
Source: Websites of Airtel Uganda Limited and MTN Uganda Limited; Words in parentheses
indicate the meaning of the digital credit brand
By February 2020, the number of loan disbursements stood 549,111 transactions (worth
UGX58.081 billion) up from 428,031 transactions (worth UGX20.871 billion) in January 2019.
Similarly, the number of loan repayments rose to 1,405,518 transactions (UGX61.511 billion) in
February 2020 from 1,019,436 transactions (worth UGX20.237 billion) in January 2019.
Overall, mobile network operators offer a plethora of other services which have not been
examined in this paper. These include Business-to-Business (B2B); Busines-to-Person (B2P);
Agent-to-Agent (A2A); insurance; as well as money transfer to non-users of mobile money. AFI
(2019) provides a full list of all services offered via mobile money services. Nonetheless, the
quality of data could benefit from further disaggregation into clearly defined groupings namely,
Business-to-Business (B2B); Business-to-Government (B2G); Business-to-Person (B2P);
Government-to-Business (G2B); Government-to-Person (G2P); Person-to-Business (P2B);
Person-to-Government (P2G); and Person-to-Person (P2P) (Frankfurt School of Finance &
Management gGmbH, 2020:8).
4.3 MMSPs Surcharges
4.3.1 Charges of MMSPs
The MMSPs charge for the different services rendered as indicated in Appendix 8.2. The fees
vary across networks. The MMSPs have structured their tarrif plan in tiers. The design of the
charge structure is such that a financial consumer pays more per unit Shilling transacted (sending
or withdrawing) at lower tiers compared to the higher tiers.
In addition, the MMSPs do not charge for deposits but charge for withdrawals with higher fees
for monies being accessed from a competing MMSP. Furthermore, withdrawing money from an
25
agent is cheaper than accessing it from an ATM. Similar patterns are reflected in the sending of
money in P2P, W2B, and cross border transactions.
4.3.2 Cost-Benefit Comparison of DFS and TBMFIs
In a study by Ssonko and Tait (2018), it was observed that
The nature of the data submitted by commercial banks to the Central Bank of
Uganda showed that the wholesale payment systems namely, ATMs, cheques, bank
drafts, telegraphic transfers, RTGS, and EFT were not comparable to retail payment
systems in terms of pricing structure given that the latter has a multi-tiered charge system
while the former has a lumpsome charge system. Nonetheless, both the pricing structure
of retail and wholesale payment systems share one common trend namely, the unit cost of
transacting across the platforms decreases with increases in the amounts transacted. This
observation concurs with earlier studies such as Mohapatra (2011) who found that unit
remittance costs decreased with an increase in the amount transferred across a particular
payment system.
The data used in the above study was administratively collected as at 2016. Nevertheless, the
trend has not changed in the last four years.
However, as shown in the Appendix 8.2, while MMSPs charge for payment for services like pay
TV / cable TV and other utilities, TBMFIs do not charge for such deposits. This is explained by
the fact that MMSPs are merely payment channels who do not intermediate funds which go
through their platforms while TBMFIs are deposit taking and would benefit from deposits made
by financial consumers paying for utilities through the credit creation process. Furthermore,
TBMFIs have adopted the multi-tiered charge structure for ATM services mimicking the
strategies deployed by MMSPs.
4.4 Taxation
Uganda’s taxation of its digital financial services especially the mobile financial services
component is examined in a policy brief by Stork and Esselaar (2018). The implications on the
tax base, Gross Domestic Product (GDP), financial inclusion, and employment are explored in
the policy brief. Uganda’s ICT sector taxes are shown in Table 9.
Table 9: ICT sector taxes in Uganda
Product / Service
April 2002
July 2005
July 2014
July 2018
Airtime
7%
12%
12%
12%
Value Added Service (VAS)
20%
20%
Landlines
5%
12%
International Calls**
US$0.09
per minute
US$0.09
per minute
Mobile money fees
10%
15%
26
Product / Service
April 2002
July 2005
July 2014
July 2018
Value of mobile money payments,
transfers, and withdrawals*
0.5%
Social Media Tax
UGX200/=
per day
Source: Modified from Stork and Esselaar (2018:1); * Originally 1 percent and covering all MM
transactions but was lowered to 0.5% and focused strictly on withdrawals; ** Calls from Kenya,
Rwanda, and South Sudan are exempt in the 2018 amendment
Using an example of the monthly social media tax of UGX6,000/=; Stork and Esselaar (2018:9)
suggest that on average 71 percent of a customer’s communications budget would be spent on
tax before even consuming data or airtime. Similarly, GSMA (2008:11) cited in Musazi
(2010:19) ranks Uganda’s airtime as the most taxed out of 16 countries in Sub-Saharan Africa
with 18 percent VAT (Value Added Tax) and 12 percent in other airtime specific taxes.
Stork and Esselaar (2018:6) note that mobile money is highly price elastic and the July 2018
introduction of mobile money transaction tax of 1.0 percent led to a price hike (unweighted
average) of between 4.7 percent (Airtel) and 71 percent (MTN) with a disproportionate increase
experienced in transacting higher amounts. The mobile money transaction tax led to the
transaction values declining more than the number of transactions suggesting discrimination
against higher value transactions (Stork and Esselaar, 2018:6). Furthermore, the mobile money
transaction tax threatened the trust and simplicity associated with mobile money and the resultant
“increase in transaction costs made mobile money unaffordable to the poor, incentivising cash
use and weakening tax complaince” (Stork and Esselaar, 2018:7).
By February 2020, there were 8,876,012 Over the Top (OTT) tax also known as social media tax
transactions worth UGX4.418 billion from 6,738,782 transactions worth UGX4.192 billion in
August 2018. Nonetheless, in FY2018/2019, OTT raised UGX49.5 billion out of an expected
amount of UGX284 billion (about 17 percent performance) (Daily Monitor, 2019). A similar
pattern was repeated in FY2019/2020 on account of the fact that 7.6 million mobile internet
subscribers do not pay OTT (Lyattu, 2020). Only 11.3 million mobile internet subscribers pay
OTT out of 18.9 million subscribers with the rest evading the tax through use of virtual private
networks (VPNs) and / or WiFi (which is not subject to OTT).
4.5 Number of registered and active customers
Financial inclusion is a multi-dimensional concept which focuses on access, usage, and quality
etc. The number of registered users is a reflection of access while active customers (individual
who has performed at least one transaction using mobile money in the last 90days) is indicative
of usage. The number of registered users has steadily grown from 10,011 individuals in March
2009 (advent of mobile money in Uganda) to 27,529,017 consumers in February 2020. Similarly,
the number of active mobile money accounts stood at 17,322,229 in February 2020 up from
27
14,030,577 in August 2018. Active accounts in a month followed a similar trajectory rising to
13,322,123 in February 2020 from 10,433,580 in August 2018.
4.6 Volume and Value of Transactions
The number and value of transactions rose to 263,417,445 transactions worth UGX6,954.783
billion in February 2020 up from 11,016 transactions worth UGX0.489 billion in March 2009.
4.7 Balances on Customers’ Accounts
The balances on customers’ accounts rose to UGX697.827 billion in February 2020 from
UGX0.601 billion in March 2009. This is on account of the increased uptake of mobile money
services and rise in trust by users.
V. Drivers of Digital Financial Services during COVID-19
During the COVID-19 pandemic, the infrastructure and taxation regime for MMSPs did not
change. However, there were changes to the financial services offered on account of the
responses the industry took to shield the financial consumers against COVID-19. The subsequent
paragraphs examine how COVID-19 shaped the industry. The COVID-19 era for Uganda is
assumed to have commenced in March 2020 with the first case having been reported on March
21, 2020.
5.1 Responses by the Industry to minimise and / or mitigate impacts of COVID-19 on DFS
5.1.1 Business Continuity and Risk Mitigation measures underpinned the Industry
Responses
During the pandemic, it was imperative that DFS be promoted in a bid to prevent the spread of
the virus by limiting person-to-person contact as well as adhering to social distancing norms.
Generally, financial services providers including both Traditional Brick and Mortar Financial
Institutions (TBMFIs) and fintechs implemented measures aimed at safeguarding their staff and
financial consumers against contracting the COVID-19 disease as well as ensuring that the
business continued.
In line with the Uganda National COVID-19 taskforce, the FSPs provided personal protective
equipment (PPE) to staff who stayed working on-site; moved several staff to work off-site; and
shortened work days from 08:00hours-17:00hours to 09:00hours-15:00hours. Furthermore, FSPs
provided handwashing / sanitisation facilities on-site; undertook temperature monitoring using
infrared thermometers for all stakeholders who accessed the premises; and regular tests for
symptomatic staff amongst others.
In order to continue supporting their financial intermediation business, FSPs moved a lot of their
financial services business onto digital channels. Indeed, even the credit relief and loan
restructuring measures introduced by Bank of Uganda effective April 01, 2020 through March
31, 2021 could be negotiated and agreed remotely with physical signing of paperwork being
undertaken much later.
28
5.1.2 Collaboration amongst the Regulators and Industry Stakeholders shaped the COVID-
19 Responses
The efforts to support the financial services sector to continue undertaking the role of financial
intermediation and the broader economy to sustain residents’ livelihoods during the COVID-19
pandemic involved a diverse group of stakeholders including financial services providers (FSPs);
member association of FSPs; Mobile Money Service Providers (MMSPs); Bank of Uganda
(BoU); Ministry of Finance, Planning and Economic Development (MoFPED); and development
practitioners like Financial Sector Deepening Uganda (FSDU) etc.
Uganda Bankers’ Association (UBA) was the first to respond when the national lockdown was
instituted through a Presidential pronouncement. The UBA is an umbrella organisation,
established in 1981 and is currently made up of 35 members (All 25 licensed Commercial Banks,
as well as 5 Tier II and III FSPs, Uganda Development Bank, and East African Development
Bank).
UBA’s proposed mechanism of tackling COVID-19 covered precautions to safeguard people
against the disease; business continuity to minimize disruptions of bank operations; contingency
plans to monitor liquidity and minimize credit risk; as well as support to ensure smooth flow and
running of payment processes and access points including alternative electronic / digital channels
(UBA, 2020). Specifically, the operationalization of the framework entailed UBA members
“reviewing payment related tariffs to cushion customers during [the] difficult [COVID-19]
period and minimize congestion at traditional service points”. The cost reduction entailed the
following measures:-
a) For a period of 30 days, banks would not charge for the Bank to Wallet (B2W) transactions
below UGX30,000/= (approx. US$ 9) per day;
b) Charges were waived off for withdrawals made at agency banking terminals for up to
UGX50,000/= (approx. US$ 15) for a period of 30 days;
c) There would be no withdrawal charges levied for transactions undertaken at the ATM of the
SFI for which a customer holds an account, for amounts up to UGX50,000/= for a period of 30
days
5
; and
d) Transactions undertaken on any other online platform up to UGX30,000/=
per day, would be zero rated (free of charge) for 30 days.
Similarly, Bank of Uganda issued several measures which would enable FSPs navigate the
economic shock occasioned by the COVID-19 pandemic. The measures entailed accommodative
monetary policy stance; readiness to intervene in the foreign exchange market to smoothen
volatility; macro-prudential policy measures (COVID Liquidity Assistance Program CLAP);
micro-prudential policy measures (credit relief and loan restructuring); moratorium on payment
of dividends for 90 days; and moral suasion (encouraging MMSPs to further reduce costs of
5
This clearly excludes transactions carried out by consumers at ATMs other those of their own bank, courtesy of Visa, Master
Card, Union Pay and Interswitch, for which interconnectivity charges may apply
29
services, as well as expand the scope and duration of coverage of the reductions) (BoU, 2020a;
BoU, 2020b; BoU, 2020c; Daily Monitor, 2020). The Ministry of Finance, Planning and
Economic Development (MoFPED) deployed a number of fiscal policy measures to tame the
likely impacts of COVID-19 (MoFPED, 2020; GoU, 2020).
The Mobile Money Service Providers (MMSPs) initially targeted the lower tiers for reducing
P2P transaction surcharges (zero rated). However, the MMSPs later reduced surcharges across
the entire spectrum of P2P transactions. The reduction in surcharges lasted from March 21, 2020
through to mid-May 2020. Thereafter, the surcharges were revised upwards to 50 percent of pre-
COVID rates.
5.2 Evolution of DFS during COVID-19
5.2.1 Services provided by MMSPs
During the pandemic, no new services were created by the FSPs. The subsequent paragraphs
provide an analysis of the evolution of existing services.
5.2.1.1 Person-to-Person
As shown in Table 10, on average the number and value of transactions were greater in the six
months after the official declaration of a COVID-19 case in Uganda compared to a similar period
before. The higher outturn of P2P volume and value of transactions was expected on account of
the national lockdown and social distancing guidelines which made other channels of remittance
relatively inaccessible.
Table 10: Comparison of P2P Volumes and Values before and after COVID-19
P2P Volumes and Values
12
months
before
(March
2019 to
February
2020)
6 months
before
(September
2019 to
February
2020)
6 months
after
(March
2020 to
August
2020)
CY 2018
(August to
December
2018)
CY 2019
(January
2019 to
December
2019)
CY 2020
{so far}
(January
2020 to
August
2020)
Volume (No.
of
Transactions
in Millions)
Sum
119.960
67.324
103.595
31.647
110.833
126.238
Average
9.997
11.221
17.266
6.329
9.236
15.780
Value of
Transactions
(UGX
Billions)
Sum
8,427.502
4,676.81
7,068.241
2,127.991
7,844.789
8,649.434
Average
702.292
779.468
1,178.040
425.598
653.732
1,081.179
Source: Bank of Uganda Mobile Money Statistics
30
5.2.1.2 Deposits
As shown in Table 11, on average the volume and value of deposits were lower in the six months
after the official declaration of a COVID-19 case in Uganda compared to a similar period before.
The lower outturn of deposits volumes and values suggests that as the national lockdown
progressed from March 2020 through June 2020, the amounts of deposits declined as most
people were no longer earning. The informal sector which employs about 80 percent of the
labour force was not operating. Furthermore, other sectors like tourism were also at a stand still.
Table 11: Comparison of Deposit Volumes and Values before and after COVID-19
Deposit Volumes and
Values
12 months
before
(March
2019 to
February
2020)
6 months
before
(September
2019 to
February
2020)
6 months
after
(March
2020 to
August
2020)
CY 2018
(August
to
December
2018)
CY 2019
(January
2019 to
December
2019)
CY 2020
{so far}
(January
2020 to
August
2020)
Volume
(No. of
Transactions
in Millions)
Sum
509.999
284.100
267.756
149.428
475.465
366.472
Average
42.500
47.350
44.626
29.886
39.622
45.809
Value of
Transactions
(UGX
Billions)
Sum
21,627.783
11,390.133
10,833.881
7,203.008
20,850.396
14,690.092
Average
1,802.315
1,898.356
1,805.647
1,440.602
1,737.533
1,836.261
Source: Bank of Uganda Mobile Money Statistics
5.2.1.3 Withdrawals
As shown in Table 12, on average the volume and value of withdrawals were lower in the six
months after the official declaration of a COVID-19 case in Uganda compared to a similar period
before. The lower outturn of withdrawals volumes and values suggests that individuals might
have decided to maintain the monies received on their mobile money accounts (balances on
customers’ accounts) for a rainy day later. Alternatively, the financial consumers might have
decided to take advantage of the free digital payments via products like MoMo pay. In addition,
the lowered cost of sending money (P2P) might have encouraged movement of monetary value
electronically rather than cashing it out.
31
Table 12: Comparison of Withdrawals Volumes and Values before and after COVID-19
Withdrawals Volumes
and Values
12 months
before
(March
2019 to
February
2020)
6 months
before
(September
2019 to
February
2020)
6 months
after
(March
2020 to
August
2020)
CY 2018
(August
to
December
2018)
CY 2019
(January
2019 to
December
2019)
CY 2020
{so far}
(January
2020 to
August
2020)
Volume
(No. of
Transactions
in Millions)
Sum
317.423
165.108
161.469
105.687
305.654
216.904
Average
26.452
27.518
26.911
21.137
25.471
27.113
Value of
Transactions
(UGX
Billions)
Sum
20,555.799
10,851.984
10,628.250
7,023.012
19,837.297
14,272.742
Average
1,712.983
1,808.664
1,771.375
1,404.602
1,653.108
1,784.093
Source: Bank of Uganda Mobile Money Statistics
5.2.1.4 Bank to Wallet (B2W)
As shown in Table 13, on average the volume and value of B2W were higher in the six months
after the official declaration of a COVID-19 case in Uganda compared to a similar period before.
The higher outturn of B2W volumes and values suggests that individuals moved monies from
their bank accounts to mobile wallets to ease transacting during the COVID-19 pandemic.
During the COVID-19 pandemic, there was a national lockdown which made accessing TBMFIs
physical premises challenging. As a consequence, people took up DFS hence the increased B2W.
Table 13: Comparison of B2W Volumes and Values before and after COVID-19
B2W
12
months
before
(March
2019 to
February
2020)
6 months
before
(September
2019 to
February
2020)
6 months
after
(March
2020 to
August
2020)
CY 2018
(August to
December
2018)
CY 2019
(January
2019 to
December
2019)
CY 2020
{so far}
(January
2020 to
August
2020)
Volume (No.
of
Transactions
in
Thousands)
Sum
9,769.731
4,708.469
7,203.728
3,555.776
9,469.432
8,964.897
Average
814.144
784.745
1,200.621
711.155
789.119
1,120.612
Value of
Sum
1,810.316
914.840
1,453.734
593.234
1,730.058
1,787.546
32
B2W
12
months
before
(March
2019 to
February
2020)
6 months
before
(September
2019 to
February
2020)
6 months
after
(March
2020 to
August
2020)
CY 2018
(August to
December
2018)
CY 2019
(January
2019 to
December
2019)
CY 2020
{so far}
(January
2020 to
August
2020)
Transactions
(UGX
Billions)
Average
150.860
152.473
242.289
118.647
144.171
223.443
Source: Bank of Uganda Mobile Money Statistics
5.2.1.5 Wallet to Bank (W2B)
As shown in Table 14, on average the volume and value of W2B were higher in the six months
after the official declaration of a COVID-19 case in Uganda compared to a similar period before.
The higher outturn of W2B volumes and values suggests that individuals who sought to save
used mobile money platforms to move money into their TBMFIs accounts.
Table 14: Comparison of W2B Volumes and Values before and after COVID-19
W2B
12
months
before
(March
2019 to
February
2020)
6 months
before
(September
2019 to
February
2020)
6 months
after
(March
2020 to
August
2020)
CY 2018
(August to
December
2018)
CY 2019
(January
2019 to
December
2019)
CY 2020
{so far}
(January
2020 to
August
2020)
Volume (No.
of
Transactions
in
Thousands)
Sum
2,068.362
1,061.820
1,297.616
526.449
2,003.655
1,673.216
Average
172.364
176.970
216.269
105.290
166.971
209.152
Value of
Transactions
(UGX
Billions)
Sum
845.400
407.678
846.075
153.225
828.411
996.202
Average
70.450
67.946
141.013
30.645
69.034
124.525
Source: Bank of Uganda Mobile Money Statistics
33
5.2.1.6 Bill and Merchant Payments (Person to Business P2B)
The P2B constitutes payments for goods and services as well as utilities (cable TV, water,
electricity, and solar suppliers). As shown in Table 15, on average the volume and value of P2B
were lower in the six months after the official declaration of a COVID-19 case in Uganda
compared to a similar period before. The lower outturn of P2B was on account of the fact that
during the national lockdown the President had directed the utility companies (Umeme and
National Water & Sewerage Corporation) not to disconnect these services even if the clients do
not pay. In addition, the shops which use MoMo Pay and Airtel Pay as a means of receiving
payment for their goods and services were closed.
Table 15: Comparison of P2B Volumes and Values before and after COVID-19
P2B
12
months
before
(March
2019 to
February
2020)
6 months
before
(September
2019 to
February
2020)
6 months
after
(March
2020 to
August
2020)
CY 2018
(August to
December
2018)
CY 2019
(January
2019 to
December
2019)
CY 2020
{so far}
(January
2020 to
August
2020)
Volume (No.
of
Transactions
in Millions)
Sum
173.888
96.380
78.804
31.336
167.993
101.998
Average
14.491
16.063
13.134
6.267
13.999
12.750
Value of
Transactions
(UGX
Billions)
Sum
2,307.805
1,303.400
1,032.175
498.709
2,146.744
1,472.527
Average
192.317
217.233
172.029
99.742
178.895
184.066
Source: Bank of Uganda Mobile Money Statistics
5.2.1.7 Cross border remittances
Inflows / Inward
As shown in Table 16, on average the volume and value of inward cross border remittances were
higher in the six months after the official declaration of a COVID-19 case in Uganda compared
to a similar period before. The higher outturn of inward cross border remittances volumes and
values suggests that individuals did receive private flows from outside Uganda during the
COVID-19 pandemic to supplement their incomes which had dwindled to zero on account of the
national lockdown which led to the closure of all sectors save those considered to be critical such
as financial services, agriculture, and health etc.
34
Table 16: Comparison of Cross border remittances inflows Volumes and Values before
and after COVID-19
Cross border remittances -
Inflows
12
months
before
(March
2019 to
February
2020)
6 months
before
(September
2019 to
February
2020)
6
months
after
(March
2020 to
August
2020)
CY 2018
(August to
December
2018)
CY 2019
(January
2019 to
December
2019)
CY 2020
{so far}
(January
2020 to
August
2020)
Volume (No.
of
Transactions
in Millions)
Sum
1.380
0.775
1.293
0.348
1.315
1.567
Average
0.115
0.129
0.216
0.070
0.110
0.196
Value of
Transactions
(UGX
Billions)
Sum
383.348
215.000
438.956
97.172
352.827
515.159
Average
31.946
35.833
73.159
19.434
29.402
64.395
Source: Bank of Uganda Mobile Money Statistics
Outflows / Outward
As shown in Table 17, on average the volume and value of outward cross border remittances
were higher in the six months after the official declaration of a COVID-19 case in Uganda
compared to a similar period before. The higher outturn of outward cross border remittances
volumes and values suggests that individuals did send out money to relatives outside Uganda.
This was expected given that Uganda has a high number of foreigners from the East African
region (Rwanda, Kenya, and South Sudan) working in the country. Given that these countries
were also under lockdown, it is not surprising that the outturn for outward cross border
remittances was high.
Table 17: Comparison of Cross border remittances outflows Volumes and Values before
and after COVID-19
Cross border remittances -
Outflows
12
months
before
(March
2019 to
February
2020)
6 months
before
(September
2019 to
February
2020)
6
months
after
(March
2020 to
August
2020)
CY 2018
(August to
December
2018)
CY 2019
(January
2019 to
December
2019)
CY 2020
{so far}
(January
2020 to
August
2020)
Volume (No.
of
Transactions
Sum
0.239
0.153
0.219
0.046
0.252
0.275
35
Cross border remittances -
Outflows
12
months
before
(March
2019 to
February
2020)
6 months
before
(September
2019 to
February
2020)
6
months
after
(March
2020 to
August
2020)
CY 2018
(August to
December
2018)
CY 2019
(January
2019 to
December
2019)
CY 2020
{so far}
(January
2020 to
August
2020)
in Millions)
Average
0.020
0.026
0.037
0.009
0.021
0.034
Value of
Transactions
(UGX
Billions)
Sum
27.855
17.691
26.092
5.021
25.692
32.522
Average
2.321
2.948
4.349
1.004
2.141
4.065
Source: Bank of Uganda Mobile Money Statistics
5.2.1.8 Airtime
Airtime can be used for making voice calls and / or purchase of data to be used to access the
internet thereby being able to use social media platforms like WhatsApp.
Table 18: Comparison of Airtime Volumes and Values before and after COVID-19
Airtime
12
months
before
(March
2019 to
February
2020)
6 months
before
(September
2019 to
February
2020)
6
months
after
(March
2020 to
August
2020)
CY 2018
(August to
December
2018)
CY 2019
(January
2019 to
December
2019)
CY 2020
{so far}
(January
2020 to
August
2020)
Volume (No.
of
Transactions
in Millions)
Sum
1,182.850
588.158
539.626
520.625
1,194.489
731.840
Average
98.571
98.026
89.938
104.125
99.541
91.480
Value of
Transactions
(UGX
Billions)
Sum
1,304.830
647.983
607.372
576.126
1,312.601
826.511
Average
108.736
107.997
101.229
115.225
109.383
103.314
Source: Bank of Uganda Mobile Money Statistics
36
As shown in Table 18, on average the volume and value of airtime were lower in the six months
after the official declaration of a COVID-19 case in Uganda compared to a similar period before.
The lower outturn of airtime suggests that individuals might have moved voice calls to other
channels like WhatsApp, skype, Zoom, and Microsoft Teams which use internet data. Internet
data may be purchased directly.
5.2.1.9 Data
As shown in Table 19, on average the volume and value of data were higher (almost double) in
the six months after the official declaration of a COVID-19 case in Uganda compared to a
similar period before. The higher outturn of data suggests that individuals used more data during
the COVID-19 pandemic. This was expected given that a good proportion of individuals were
working from home and used platforms like WhatsApp, skype, Zoom, and Microsoft Teams
which rely on internet data. In addition, children of urbanites consumed data as their classes were
moved online after the Government barred physical face to face classes in educational
institutions.
Table 19: Comparison of Data Volumes and Values before and after COVID-19
Data
12
months
before
(March
2019 to
February
2020)
6 months
before
(September
2019 to
February
2020)
6
months
after
(March
2020 to
August
2020)
CY 2018
(August to
December
2018)
CY 2019
(January
2019 to
December
2019)
CY 2020
{so far}
(January
2020 to
August
2020)
Volume (No.
of
Transactions
in Millions)
Sum
215.366
138.147
265.540
50.982
166.556
333.509
Average
17.947
23.024
44.257
10.196
13.879
41.689
Value of
Transactions
(UGX
Billions)
Sum
304.515
192.705
370.962
75.308
247.276
464.469
Average
25.376
32.118
61.827
15.062
20.606
58.059
Source: Bank of Uganda Mobile Money Statistics
5.2.1.10 Loans
Loans Disbursements
As shown in Table 20, on average the volume and value of loans disbursements were lower in
the six months after the official declaration of a COVID-19 case in Uganda compared to a
similar period before. The lower outturn of loans disbursements suggests risk averseness on the
37
part of the digital credit lenders due to the adverse economic status which had been projected to
persist through FY2020/2021.
Table 20: Comparison of the Volumes and Values of Transactions of Loan Disbursements
before and after COVID-19
Loan Disbursements
12 months
before
(March
2019 to
February
2020)
6 months
before
(September
2019 to
February
2020)
6 months
after
(March
2020 to
August
2020)
CY 2018
(August
2018 to
December
2018)
CY 2019
(January
2019 to
December
2019)
CY 2020
{so far}
(January
2020 to
August
2020)
Volume (No
of
transactions
in Millions)
Sum
7.845
3.849
2.496
-
7.763
3.647
Average
0.654
0.641
0.416
-
0.647
0.456
Value of
Transactions
(UGX
Billions)
Sum
520.823
327.362
238.590
-
448.513
361.451
Average
43.401
54.560
39.765
-
37.376
45.181
Source: Bank of Uganda Mobile Money Statistics
Loans Repayments
As shown in Table 21, on average the volume and value of loans repayments were lower in the
six months after the official declaration of a COVID-19 case in Uganda compared to a similar
period before. The lower outturn of loans repayments is on account of the credit relief extended
by MMSPs.
Table 21: Comparison of the Volumes and Values of Transactions of Loan Repayments
before and after COVID-19
Loan Repayments
12 months
before
(March
2019 to
February
2020)
6 months
before
(September
2019 to
February
2020)
6 months
after
(March
2020 to
August
2020)
CY 2018
(August
2018 to
December
2018)
CY 2019
(January
2019 to
December
2019)
CY 2020
{so far}
(January
2020 to
August
2020)
Volume (No
of
transactions
in Millions)
Sum
17.282
8.836
6.784
-
16.811
9.685
Average
1.440
1.473
1.131
-
1.401
1.211
Value of
Transactions
(UGX
Billions)
Sum
501.862
300.647
267.191
-
426.407
393.326
Average
41.822
50.108
44.532
-
35.534
49.166
Source: Bank of Uganda Mobile Money Statistics
38
According to the Background to the Budget FY2020/2021 page 58,
During April 2020, both Airtel and MTN provided some relief to the borrowers
who subscribe to their mobile micro credit solutions in a bid to manage the impact of the
COVID-19 pandemic that was expected to have adverse effects on incomes and
repayment abilities of their customers. Specifically, Airtel removed all penalty fees on
customers who would fail to clear the outstanding Wewole loan on the agreed date,
allowing them to pay at a later date. Likewise, MTN and its partner Commercial Bank for
Africa (CBA), deferred by 30 days, all sanctions for late loan payments and removed
charges on moving value between the mobile money and Mokash wallets. These
deferments would hold over the next three months.
5.3 MMSPs Surcharges
5.3.1 Charges of MMSPs
Though initially planned for low value transactions (below UGX30,000/=) for MTN mobile
money (Gilbert, 2020; Obwot, 2020), MTN mobile money and Airtel money, on March 19,
2020, “implemented a zero charge on all Person to Person (P2P) and Mobile Wallet to Bank
Transactions for a period of 30 days” (MTN and Airtel Joint Press Statement, 2020). The 30 day
period which would have expired around April 19, 2020 was extended to May 25, 2020.
Effective May 26, 2020, “all MTN mobile money and Airtel money Person to Person (P2P)
transactions on the same network and mobile wallet to bank transactions [surcharges were]
gradually re-introduced [initially] at a 50 percent discount to standard tariffs for a period of 30
days” (MTN and Airtel Joint Press Statement, 2020).In addition, all MoMo / Airtel Money Pay
transactions between customers and merchants continued to attract zero transaction charges for
30 days from May 26, 2020. By June 26, 2020, the MMSPs had reinstated their normal tariff
structure (Gilbert, 2020).
Even though P2P, W2B, and B2W transactions had their surcharges reduced, withdrawing
money was charged including the GoU tax of 0.5 percent on each transaction (The Independent,
2020), cross MMSP transactions as well as bill payments amongst others were not subsidized.
Such price reduction measures might not necessarily encourage a movement towards a ‘cashless’
society because they foster monetary value movement without necessarily encouraging
settlement of transactions electronically. Indeed, the essence of zero charge on P2P and W2B
transactions was to reduce the likelihood of contracting COVID-19 from the physical exchange
of currency notes (MTN and Airtel Joint Press Statement, 2020). However, in a move intended to
shore up its transaction volumes, MTN mobile money cut its withdrawal charges effective
November 02, 2020 (Twaha, 2020).
5.3.2 Cost-Benefit Comparison of DFS and TBMFIs
Traditional Brick and Mortar Financial Institutions (TBMFIs) did not alter their pricing structure
during the COVID-19 pandemic except for B2W and the MMSPs only altered surcharges for
39
P2P and W2B. As a consequence, the cost-benefit did not change from that described in section
4.3.2.
5.4 Taxation
The taxation tracked in this section is “Over-The-Top” (OTT). As shown in Table 22, on average
the volume and value of OTT were higher in the six months after the official declaration of a
COVID-19 case in Uganda compared to a similar period before. The higher outturn of OTT
suggests that more individuals paid the tax to access social media platforms like Whatsapp after
the onset of COVID-19.
Table 22: Comparison of the Volumes and Values of “Over-The-Top” (OTT) Tax
Transactions before and after COVID-19
“Over-The-Top” (OTT)
Tax Transactions
12
months
before
(March
2019 to
February
2020)
6 months
before
(September
2019 to
February
2020)
6
months
after
(March
2020 to
August
2020)
CY 2018
(August
2018 to
December
2018)
CY 2019
(January
2019 to
December
2019)
CY 2020
{so far}
(January
2020 to
August
2020)
Volume (No
of
transactions
in Millions)
Sum
103.768
53.442
63.629
35.061
97.459
82.031
Average
8.647
8.907
10.605
7.012
8.122
10.254
Value of
Transactions
(UGX
Billions)
Sum
53.705
27.036
29.705
21.724
53.088
38.854
Average
4.475
4.506
4.951
4.345
4.424
4.857
5.5 Number of registered and active customers
As shown in Table 23, on average the number of registered customers as well as active
customers (monthly and quarterly) were higher in the six months after the official declaration of
a COVID-19 case in Uganda compared to a similar period before. The higher outturn suggests
that accessibility (registration of new mobile money accounts) as well as usage (activity on a
monthly and quarterly basis) were high. It appears that the national lockdown ocassioned by the
COVID-19 pandemic drove people to take up and use MMS.
40
Table 23: Comparison of the Number of Customers before and after COVID-19
Number of Customers
12 months
before
(March
2019 to
February
2020)
6 months
before
(September
2019 to
February
2020)
6 months
after
(March
2020 to
August
2020)
CY 2018
(August
2018 to
December
2018)
CY 2019
(January
2019 to
December
2019)
CY 2020
{so far}
(January
2020 to
August
2020)
Registered
Customers
(Millions)
Sum
313.977
161.478
172.195
120.288
306.633
226.996
Average
26.165
26.913
28.699
24.058
25.553
28.374
Registered
Customers -
Active in
90days
(Millions)
Sum
191.152
98.607
107.890
70.895
187.105
142.184
Average
15.929
16.434
17.982
14.179
15.592
17.773
Registered
Customers -
Active in
30days
(Millions)
Sum
145.972
75.985
83.637
54.006
142.511
109.726
Average
12.164
12.664
13.939
10.801
11.876
13.716
Source: Bank of Uganda Mobile Money Statistics
5.6 Volume and Value of Transactions
Related to increased uptake and usage of MMS, on average the number and value of transactions
increased in the six months after the official declaration of a COVID-19 case in Uganda
compared to a similar period before (Table 24).
Table 24: Comparison of the Number and Value of Transactions before and after COVID-
19
Number and Value of
Transactions
12 months
before
(March
2019 to
February
2020)
6 months
before
(September
2019 to
February
2020)
6 months
after
(March
2020 to
August
2020)
CY 2018
(August
2018 to
December
2018)
CY 2019
(January
2019 to
December
2019)
CY 2020
{so far}
(January
2020 to
August
2020)
No. of
Transactions
(Billions)
Sum
2.898
1.551
1.645
0.941
2.785
2.197
Average
0.241
0.258
0.274
0.188
0.232
0.275
Value of
Transactions
(Trillions)
Sum
74.001
39.703
42.562
26.828
71.774
56.520
Average
6.167
6.617
7.094
5.366
5.981
7.065
Source: Bank of Uganda Mobile Money Statistics
41
5.7 Balances on Customers’ Accounts
As shown in Table 25, on average the balances on customers’ mobile money accounts increased
to UGX0.770 Trillion in the six months after the official declaration of a COVID-19 case in
Uganda compared to UGX0.458 Trillion in a similar period before. The higher outturn suggests
that customers were using mobile money accounts which are primarily transaction accounts for
savings purposes.
Table 25: Comparison of the Balances on Customers’ Mobile Money Accounts before and
after COVID-19
Balances on Customers’
Mobile Money Accounts
12 months
before
(March
2019 to
February
2020)
6 months
before
(September
2019 to
February
2020)
6 months
after
(March
2020 to
August
2020)
CY 2018
(August
2018 to
December
2018)
CY 2019
(January
2019 to
December
2019)
CY 2020
{so far}
(January
2020 to
August
2020)
Value of
Transactions
(Trillions)
Sum
4.708
2.747
4.619
1.800
4.089
5.901
Average
0.392
0.458
0.770
0.360
0.341
0.738
Source: Bank of Uganda Mobile Money Statistics
5.8 Independent Samples t-tests to compare means six months before and after COVID-19
The COVID-19 pandemic provided a confluence of factors that shaped the DFS landscape albeit
of a temporary nature. The measures introduced by DFS players to slow the advance of COVID-
19 such as zero rated P2P transfers, grace period for the repayment of digital credit for 30 days
without penalty fees; free MoMo Pay and Airtel Money Pay (P2B); as well as reduced tariffs for
W2B and B2W happened against a backdrop of reduced economic activity induced by the
Coronavirus pandemic. Nevertheless, the situation provides a natural experiment of how a
reduction in the pricing of mobile money services (P2P, W2B, B2W, and P2B); a grace period
for the repayment of digital credit without penalty feees; and a disruptive force (disease
pandemic which led to cessation of economic activity in most sectors of the macro-economy)
affected the usage of mobile financial services (MFS) in Uganda.
Independent samples t-tests were conducted to compare whether differences in variables six
months before official declaration of COVID-19 in Uganda (September 2019 to February 2020)
and six months after (March 2020 to August 2020) were statistically significant. Table 26
provides a summary of all the results at an alpha level of 0.05.
42
Table 26: Comparison of Means six months before and after COVID-19 for select MFS
variables
MFSProduct /
Service
Unit of
Measures
Six months before
COVID-19
Six months after
COVID-19
df
Computed
t-value
Computed
p-value
Sig. OR
Not Sig.
Mean
S.D.
Mean
S.D.
1.
Transactions
No. of
Trans (Bn)
0.258
0.015
0.274
0.020
10
-1.543
0.154
Not Sig.
Value
(UGX
Trillion)
6.617
0.388
7.094
1.002
10
-1.086
0.303
Not Sig.
2. Balances on
Customers'
Accounts
UGX
Trillion
0.458
0.143
0.770
0.054
10
-5.009
0.0005
Sig.
3. Registered
Customers
Total
Registered
Customers
(Mn)
26.657
0.469
28.304
0.631
10
-5.129
0.0004
Sig.
Active
Customers
on
Quarterly
Basis (Mn)
16.434
0.649
17.982
0.527
10
-4.532
0.001
Sig.
Active
Customers
on Monthly
Basis (Mn)
12.664
0.450
13.939
0.590
10
-4.210
0.001
Sig.
4. “Over The
Top” (OTT)
Tax
No. of
Trans.
(Millions)
8.907
0.406
10.605
0.684
10
-5.229
0.0004
Sig.
Value of
Trans.
(UGX
Billions)
4.506
0.188
4.951
0.194
10
-4.037
0.002
Sig.
5. Loans
Repayments
No. of
Trans.
(Millions)
1.473
0.124
1.131
0.110
10
5.051
0.0005
Sig.
Value of
Trans.
(UGX
Billions)
50.108
18.055
44.532
7.589
10
0.697
0.501
Not Sig.
6. Loans
Disbursements
No. of
Trans.
(Millions)
0.641
0.095
0.416
0.057
10
4.980
0.0006
Sig.
Value of
Trans.
(UGX
54.560
22.792
39.765
7.290
10
1.515
0.161
Not Sig.
43
MFSProduct /
Service
Unit of
Measures
Six months before
COVID-19
Six months after
COVID-19
df
Computed
t-value
Computed
p-value
Sig. OR
Not Sig.
Mean
S.D.
Mean
S.D.
Billions)
7. Data
No. of
Trans.
(Millions)
23.024
9.510
44.257
4.990
10
-4.843
0.0007
Sig.
Value of
Trans.
(UGX
Billions)
32.118
12.602
61.827
5.286
10
-5.325
0.0003
Sig.
8. Airtime
No. of
Trans.
(Millions)
98.026
3.523
89.938
6.333
10
2.734
0.021
Sig.
Value of
Trans.
(UGX
Billions)
107.997
5.118
101.229
8.128
10
1.726
0.115
Not Sig.
9. Cross
Border
Remittances
Outflows /
Outward
No. of
Trans.
(Millions)
0.026
0.004
0.037
0.006
10
-3.661
0.004
Sig.
Value of
Trans.
(UGX
Billions)
2.948
0.456
4.349
0.956
10
-3.238
0.004
Sig.
10. Cross
Border
Remittances
Inflows /
Inward
No. of
Trans.
(Millions)
0.129
0.010
0.216
0.030
10
-6.737
5.125X10-5
Sig.
Value of
Trans.
(UGX
Billions)
35.833
2.967
73.159
22.875
10
-3.964
0.003
Sig.
11. P2B
No. of
Trans.
(Millions)
16.063
3.682
13.134
2.705
10
1.570
0.147
Not Sig.
Value of
Trans.
(UGX
Billions)
217.233
26.669
172.029
38.540
10
2.363
0.040
Sig.
12. W2B
No. of
Trans.
(Thousands)
176.970
11.193
216.269
64.287
10
-1.475
0.171
Not Sig.
44
MFSProduct /
Service
Unit of
Measures
Six months before
COVID-19
Six months after
COVID-19
df
Computed
t-value
Computed
p-value
Sig. OR
Not Sig.
Mean
S.D.
Mean
S.D.
Value of
Trans.
(UGX
Billions)
67.946
12.397
141.013
47.220
10
-3.666
0.004
Sig.
13. B2W
No. of
Trans.
(Thousands)
784.745
261.004
1,200.621
108.447
10
-3.604
0.0048
Sig.
Value of
Trans.
(UGX
Billions)
152.473
53.293
242.289
36.915
10
-3.394
0.0034
Sig.
14.
Withdrawals
No. of
Trans.
(Millions)
27.518
0.767
26.911
3.432
10
0.422
0.682
Not Sig.
Value of
Trans.
(UGX
Billions)
1,808.664
107.754
1,771.375
369.007
10
0.238
0.817
Not Sig.
15. Deposits
No. of
Trans.
(Millions)
47.350
3.387
44.626
6.234
10
0.940
0.369
Not Sig.
Value of
Trans.
(UGX
Billions)
1,898.356
92.820
1,805.647
308.370
10
0.705
0.497
Not Sig.
16. P2P
No. of
Trans.
(Millions)
11.221
0.802
17.266
2.160
10
-6.426
7.576X10-5
Sig.
Value of
Trans.
(UGX
Billions)
779.468
59.855
1,178.040
175.076
10
-5.277
0.0004
Sig.
Source: Authors’computation using MS-Excel Data Analysis Functionality
While most products show that there were statistically significant changes in their usage in the
six months before COVID-19 (September 2019 to February 2020) compared to the six months
after official declaration of COVID-19 in Uganda (March 2020 to August 2020), which was to
be expected, there are notable exceptions. For instance, the number and value of transactions
even though increased in the six months after official declaration of COVID-19 in Uganda, there
were not statistically significant.
The confounding nature of results points to the complexity of consumer behaviour in the use of
MFS in particular and DFS in general; the interconnectedness of MFS services and products; the
45
granularity of the data to capture differences in product characteristics and usage is still
inadequate; and the increased integration of MFS into TBMFIs to create a resilient DFS
ecosystem.
The withdrawals and deposits which are the most common transactions in Uganda’s MFS
ecosystem (both in terms of number and value of transactions) were not statistically significant
when compared over the six months period before and after the official declaration of COVID-19
in Uganda. So how were the increased person-to-person (P2P) transactions funded? P2P
transactions were statistically significant in both number and value of transactions. The results
suggest that in addition to the commonly used approach of deposits at agents, financial
consumers used Bank to Wallet (B2W) to fund these P2P transactions. Given that the TBMFIs
were operating about 30 percent branch network capacity, withdrawing funds for later depositing
at MM agents while possible due to the wide reaching ATM network especially in urban areas
would be irrational consumer behaviour. The economic costs of withdrawing money from
TBMFIs and cashing it in at MMSPs would be greater than those of B2W due to restrictions on
public transportation and COVID-19 exposure risks. As a consequence, use of the B2W was the
optimal consumer behaviour for funding MM accounts.
The use of wallets to fund TBMFIs accounts (W2B) was not statistically significant in terms of
number of transactions partly reflecting the slow down in economic activity and thereby the
reduction in the number of Bottom of the Pyramid (BoP) users who would use mobile wallets to
send money to their TBMFIs accounts after earning a wage in the informal sector at the end of
the business day. However, the value of transactions were statistically significant suggesting that
a good proportion of people in the population continued to send money from their mobile wallets
to bank accounts. This may imply that not all P2P receipts were necessarily consumed, but some
might have been saved in TBMFIs accounts for a rainy day.
In the case of P2B, the value of transactions were significant yet number of transactions were not
statistically significant. Person-to-Business (P2B) is a broad category where individuals are
paying money for utilities (electricity, water, post paid solar power systems, and cable TV etc.)
as well as goods and services. Due to the closure of most businesses especially in the hospitality
and travel sector, the payment for goods and services slowed down thereby impacting transaction
numbers. The low uptake of electronic or mobile commerce by Ugandans means that once the
physical shops where MoMo Pay or Airtel Money Pay could be used to pay for goods and
services were closed, the transaction numbers in P2B had to dwindle.
On the other hand, however, the regular payments for utilities must have remained due to the
need for such services when individuals are confined to their homesteads. However, what
appears to have happened is that individuals for fear of being in a blackout might have paid for
say electricity and cable-TV for relatively longer durations than they would ideally have done in
case of daily incomes. Instead of paying for cable-TV on a daily basis, some might have instead
paid on a monthly basis. This does not impact on the monthly value of transactions significantly
but affects the number of transactions. Despite the presidential directive to differ utility bill
payments till after COVID-19, individuals with electricity pre-paid meters popularly known as
46
YAKA had to fork out money before accessing the service. During the lockdown, there was a
concerted effort by Umeme (the power distribution conncession holder in Uganda) to change
post paid customers to pre-paid customers by replacing old post-paid meters with new pre-paid
meters known as YAKA.
Airtime, the number of transactions were significant yet the value of transactions were not
statistically significant. Airtime has two purposes when consumed by customers, that is, make
voice calls and / or purchase data. However, voice calls can now be made via other models such
as Whatsapp, Zoom, Microsoft Teams, and Skype etc. which require internet data. Mobile
internet data may be purchased using USSD prompts without first purchasing airtime.
Alternatively, it might be purchased via the airtime channel / route. The decline in airtime usage
during this period might suggest that individuals reduced their spend on airtime and probably
replaced traditional voice calls with “voice over the internet calls”.
The number of transactions for loans disbursements and the value therein dropped, but the latter
was not statistically significant while the former was statistically significant. Overall, the decline
points to risk averseness to the digital credit lenders due to the slow down in economic activity
ocassioned by COVID-19. It appears that the number of micro digital credit applicants and
thereby approvals (disbursements) dropped immensely reflecting the self censure by borrowers
(self credit rationing) during tough economic times as well as a more stringent evaluation and
therefore rationing by microcredit providers.
The number of transactions for loan repayments and the value therein dropped, but the latter was
not statistically significant while the former was statistically significant. Overall, the decline
reflects the moratorium on charging default penalties for 30 days imposed by the digital credit
providers and the tough economic times. The tough economic times must have been more
straining for borrowers whose income was largely from the informal sector which was shutdown
during the national lockdown. Even though the value of loan repayments dropped, it was not
statistically different across the two periods suggesting that financial consumers who could
afford servicing loans (probably fixed income earners in Government Ministries, Departments,
and Agencies and / or private sector corporate jobs) continued honouring their obligations even
during tough economic times to prevent being disadvantaged in future credit worthiness
assessments by the MFS algorithms.
VI. Future of DFS Landscape in Uganda
6.1 Disruptions in the DFS Landscape in Uganda
In this paper, the effect of changes to the pricing structure of P2P on DFS amongst other anti-
COVID measures amidst the Coronavirus pandemic context were explored. Nonetheless, the
DFS (especially the Mobile Financial Services component) landscape has dealt with various
disruptions since mobile money was introduced in Uganda in March 2009. As shown in Figure 2,
there have been four major disruptions.
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Figure 2: Disruptions in Uganda’s Mobile Money Services from 2009 to 2020
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REGISTERED USERS ( MN)
TOTAL TRANSACTION VALUES (BN SHS)
VALUE OF TRANSACTIONS NUMBER OF REGISTERED USERS
A
B
C
D
Source: Bank of Uganda
A: System upgrade
In September 2014, there was a scheduled 3-day upgrade of mobile money systems that forced
the service to experience down time. The system was taken down for 3 days and a new one
installed. This followed the repeated concerns about the security of users. BoU had in the
previous year issued mobile money guidelines, which although not binding, offered some relief
to customers. The provider did announce via its various platforms that an upgrade would be
taking place between Saturday 20 to Monday 22 September 2014 to make mobile money more
secure, reliable and easier to use. In reality, the down time went on for a total of 5 days.
B: Nation wide shut down
This occurred during the presidential and parliamentary elections of 2016. On on February 18,
2016, the Uganda Communications Commission, citing a threat to "national security," ordered
mobile network operators to shut down key social media sites (WhatsApp, Facebook, Twitter)
and disable mobile money platforms. Restrictions on the latter were lifted after 4 days, while
users found a way round to access social media using virtual private networks (VPN). Bold and
Pillai (2016) state that the abrupt shut down left millions of customers stranded as many had
topped up their accounts on account of bank closures on public holidays and fear for election
violence. This shut down saw volume of transactions falling by 6.94 million and total value of
transactions declining by UGX139.4 billion.
C: OTT and Mobile Money Tax
In July 2018, an amendment on the excise duty came into effect. This would be known as the
Excise Duty (Amendment) Act 2018, which stated that “A tax of 1 percent of the value of the
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transaction will apply on mobile money transactions on receiving money, making payments and
withdrawals of money”
Customers were up in arms over this seemingly unfair and double taxation. Several studies were
done to explain the impact of this policy. The Government of Uganda, later in the month,
clarified that the 1 percent was meant to be charged on the received amounts and not the
deposits, even though customers had been charged on both deposits as well as transfers off their
mobile money accounts. The 1 percent also applied to bill and merchant payments; but did not
apply to the payment of URA taxes. Later on during the parliamentary proceedings of October 3,
2018, the house approved the Excise Duty (Amendment) (No.2) Bill, 2018 effectively reducing
the mobile money transaction tax from 1 to 0.5 percent on withdrawals.
Customers responded by making changes to their mobile money behaviour almost immediately.
Starting June 2018, the value of transactions dropped by UGX743 billion, even before the tax
was to come into force owing to the media campaign by various civil society originations
decrying the tax, which had been passed by the parliament months earlier. The reaction was
intensified the following month when the value of transactions fell by UGX 2.45 trillion in July
2018 alone. The service would take another 19 months to recover transaction values equal to pre-
mobile money tax levels, and just as it did, COVID-19 struck.
D: COVID-19 Lockdown
A detailed description of this particular disruption on the services of MMSPs is the focus of this
paper.
6.2 Implications
The paper set out to examine how the change in pricing of mobile financial services and products
(P2P, W2B, B2W, and P2B); easing of digital credit repayment terms and conditions
(moratorium on application of late repayment penalties for 30 days and enforcement of
collection); and disruptions in the financial services consumption patterns ocassioned by a health
risk (COVID-19 pandemic). The measures taken by Ugandan MNOs are largely in line with
those taken at the international level to contribute to the prevention of the spread of COVID-19
and support economic activity during the pandemic. Muthiora (2020) list