Back Matter
  • 1, International Monetary Fund
  • | 2, International Monetary Fund
  • | 3, International Monetary Fund
  • | 4, International Monetary Fund


Appendix 1: Key Enablers Maturity Map

This section details key enablers at each maturity stage across the eight building blocks of a Mobile G2P framework. Policymakers can use this maturity map to (1) identify where their country is currently situated, and (2) discuss options for developing the next evolution along each enabler. The framework is descriptive, not prescriptive: it provides guidance for countries to self-assess their current maturity stage but does not explicitly chart out a course of action or decision.

A. Beneficiaries

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B. Government Digital Tools

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C. Mobile Money Operators (MMOs)

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D. Financial Institutions

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E. Cash-out Network

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F. Payment Acceptance Network:

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G. Business Model Elements

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H. Digital Inclusion Foundations

The three enablers below are out of reach and out of scope for a G2P Program. However, close coordination with stakeholders of these goals is important to (i) further define and prioritize the business model (building block 7) and policy and regulation (building block 8), and (ii) coordinate work with other government agencies to maximize the impact of mobile G2P payments, and (iii) boost the impact of mobile platforms beyond G2P (see section II).

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Appendix 2: G2Px’s Key Considerations and Options in Designing at G2P Social Assistance Payment Solution

The framework presented in the paper focuses on the “Mobile Money Account” component of the G2Px framework, illustrated below (G2Px, 2020d).

Appendix 3: Common Technologies Involved

The framework can help understand the role of specific technologies for each building block. It should be noted that most of these technologies already exist and are being used in almost all countries. Understanding where and when to use this expertise, in order to maximize the impacts and to minimize the risks of G2P mobile payment is key to accelerate the maturity of such a mobile environment.

Appendix 4: Variables Used in the Enhanced Digital Access Index (EDAI)

As defined in Alper and Miktus (2019) and further refined in the IMF’s REO Chapter 3 (IMF, 2020), the EDAI is composed of variables selected for their representation of digital connectivity across countries.35

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The authors are grateful for the useful contributions from Digital Disruptions consulting company (Amitabh Saxena, Maria Clara Rezende), and comments from IMF staff, including Boele Bonthuis, Corinne Delechat, Diane Kostroch, Lusine Lusinyan, Tara Lyer, Stephen Maurer, Tao Sun, Hector Carcel Villanova, Arina Viseth. The framework also draws on work from organizations, including the GSM Association (GSMA), UNCDR, Association for Financial Inclusion (AFI), and several WBG groups such as the Consultative Group to Assist the Poor (CGAP) and the WBG and Gates Association’s G2Px.


For a comparison of the magnitude of fiscal support between the CO VID crisis and the Great Financial Crisis in selected countries, see McKinsey (June 2020) “The 10 trillion rescue: how governments can deliver impact”.


In countries with very high prevalence of informality (Ayana Aga, Jolevski, Muzi. 2020), the informal business is very often the sole source of income for the owner’s family, with about 45 percent of businesses making USD 2 or less per day.


Advanced economies also face important challenges in providing timely and adequate support to hard hit workers and households, particularly to gig workers, self-employed and independent contractors. Insufficient administrative capacity and complex enrollment processes have led to important delays in the face of massive simultaneous requests for unemployment and social assistance benefits. For example, in March 2020, the UK Department for Work and Pensions had moved more than 10,000 staff to deal with claims and was recruiting more to reduce delays in ID verification and process the 950,000 applications received in one week compared to a normal flow of about 100,000 applicants in any given two-week period.


The Philippines is in the process of developing its digital national identification system starting with 5 million individuals by end of 2020. Since bank coverage is limited, the implementation of the system will require utilization of low-cost touchpoints such as bank agents.


Globally, there are 228 mobile money agents (the small retailers where customers can deposit or withdraw cash in and out of mobile accounts, buy phone airtime cards, etc.) per 100,000 adults compared to only 11 banks and 33 ATMs.


Mobile money is here defined as digital medium of exchange and store of value using mobile money accounts, which are typically offered by a mobile network operator (MNO) or another entity in partnership with an MNO (Chabra and Das, 2019).


World Bank survey data for nine cities in four African countries (Mozambique, Somalia, Zambia and Zimbabwe) shows that between 20 percent (in Nampula, Mozambique) and 82 percent (in Mogadishu, Somalia) of informal businesses use mobile money in their operations, and that in Mozambique, twice as many informal business owners use mobile money as the average population as measured by Findex.


The Financial Action Task Force (FATF) recently promoted a simplified, risk-based approach to use “trustworthy digital identity [...] to identify people remotely for both onboarding and conducting transactions” (FATF, 2020a).


For a discussion on the importance of digital solutions for public finance management see IMF Special Series Notes on COVID-19 “Enhancing Digital Solutions to Implement Emergency Responses” and “Digital Solutions for Direct Cash Transfers in Emergencies”.


G2Px provides further insights on the market aspects to consider when choosing emergency social assistance payment options (G2Px, 2020d).


E.g., built-in triggers that can adapt a program to an emergency context to ensure delivery continuity such as transforming free school-meal programs into cash transfers for the family or in-house food distribution.


For instance, in Namibia, the government put in place a new monetary transfer for all adult informal workers and unemployed, explicitly excluding out formal workers and recipients of existing social protection programs. In one week, 579,000 SMS applications were received out of 739,000 adults expected to be eligible.


In 2015–2016, during the Ebola crisis, only 7 percent of all unconditional cash transfers implemented to provide lifeline supports in Sierra-Leone and Liberia were mobile transfers, despite the overwhelming incentive to use digital rather than cash distribution to contain the virus (Dumas et al, 2017). A largely inadequate mobile ecosystem—weak infrastructure, lack of awareness among beneficiaries and operational challenges—prevented the use of mobile transfers at scale.


The framework builds on the work done by many organizations, including the GSM Association (GSMA), Association for Financial Inclusion (AFI), and several WBG’s groups such as the Consultative Group to Assist the Poor (CGAP) and the WBG and Gates Association’s G2Px. It also incorporates the authors’ original research in association with Digital Disruptions consulting company. Appendix 2 presents the G2Px’s work on the different options for the payment of social assistance benefits. The framework in this paper focuses on the mobile payment option.


For instance, in Nigeria, the authorities are collaborating with mobile network operators to identify vulnerable informal workers in urban areas through their purchase pattern of airtime. Beyond MMO’s data, other “proxy registries” can be leveraged to identify workers in the informal economy, such as: i) company/individuals registries held by informal business unions or associations, ii) utility bills, iii) invoices of sales by wholesalers, iv) local governments’ registries of poor households and local informal businesses.


A mobile wallet is either a mobile app, or a code to access a remote application via SMS or Unstructured Supplementary Service Data (USSD) – see Box 2


Recourse mechanisms should be in place to limit errors.


See Scheme Exclusion under


See also IMF Spring 2018 Fiscal Monitor, Chapter 2 “Digital Government”.


Roessler et al. (2019) studied in Tanzania how providing free phones to women mattered for financial access, but other than for remittances, physical cash was preferred to mobile money. It is worth noting that acceptance of mobile money was higher for literate participants, highlighting the need for clear and simple communication (Building Block 7).


While the fee structure of the indirect channels is not treated in this building block, stakeholders should remain aware of its importance. More information on fee structure is provided in building block 6.


The reduction and elimination of charges has taken many forms, from central banks eliminating transaction taxes for person-to-person mobile transfers, or taxes paid by merchants on mobile money transactions, to MMOs agreeing to temporarily reduce their charges.


In 2018, a new tax on mobile transactions in Uganda led to street protests. The tax was seen as overwhelmingly impacting the poorer in the country who don’t have access to banks. In a few months, P2P values fell by over 50 percent, in favor of cash.


The GSMA Mobile Money API and Gates Foundation’s Mojaloop, described in Box 1, are often seen as foundational projects for the future of interoperable payment in developing countries (Martins, 2020)


The government frequently updates all data on its website:


On average, women are 10 percent less likely to own a mobile phone (Iskenderian, 2020) with affordability being the most significant barrier to womens’ mobile phone ownership (Lindsey and Wilson, 2019).


Notably, in March 2020 South Africa allowed the use of a new spectrum called TV Whitespace for the roll-out of affordable or free data services, particularly in rural and remote areas (ICASA, 2020).


This enabler can benefit from the work of the Zimmerman, May, and Kellison (2020) and other actors who promote the D3 Framework (Digitize, Direct, Design) to enhance women’s economic empowerment through cash transfers.


Machine learning bias occurs when an algorithm produces results that are unequal with differences in gender, geolocation, race or other distinctions due to erroneous assumptions in the underlying algorithm or flawed training data sets


In February 2020, Pakistan started to distribute free smartphone and biometrically-protected bank account to seven million poor women.


Note (IMF, 2020): Variables were selected based on the following criterion: at least one observation for each variable is available during one of the previous three years leading up to the year for which the index is being calculated. When a given economy has more than one observation for a given variable, the latest data point is selected. The variable “Percentage of the population covered by at least an LTE/WiMAX mobile network” was dropped for 2010 as LTE/WiMAX was still an emerging technology. The indicators are aggregated using the Adjusted Mazziotta-Pareto Index (AMPI) methodology.