MOBILE MONEY ECOSYSTEM SURVEY IN SOUTH SUDAN Exploring current and future potential of using mobile money for effective humanitarian and development cash- programming Annexes Prepared by Altai Consulting for the World Bank | South Sudan – May 2019 Juba, South Sudan Unless specified otherwise, all pictures in this report are credited to Altai Consulting. Annexes| Mobile Money Research in South Sudan Altai Consulting to the World Bank CONTENT 1. RESEARCH METHODOLOGY .............................................................................................6 1.1. Research Questions ...................................................................................................................................................... 6 1.2. Overall Approach .......................................................................................................................................................... 8 1.3. Component 1: Background Research and Desk Review ............................................................................................. 10 1.4. Component 2: Qualitative Supply-Side Research ....................................................................................................... 10 1.5. Component 3: Quantitative Demand-Side Research .................................................................................................. 11 1.6. Component 4: Qualitative Demand-Side Research .................................................................................................... 18 2. VULNERABLE INDEX ....................................................................................................... 21 3. LIST OF STAKEHOLDERS INTERVIEWED ....................................................................... 23 4. REFERENCES .................................................................................................................... 24 Page 3 of 29 Annexes| Mobile Money Research in South Sudan Altai Consulting to the World Bank TABLES Table 1: Research questions ................................................................................................................... 6 Table 2: Preliminary counties selected for the household survey ........................................................ 12 Table 3: Sample size calculation for urban residents, rural residents, and IDPs .................................. 13 Table 4: Number of EAs per stratum .................................................................................................... 16 Table 5: Characteristics of the FGDs ..................................................................................................... 19 Table 6: Methodology used for computing the vulnerability level of respondents ............................. 21 Table 7: List of stakeholders interviewed for the supply side research ............................................... 23 FIGURES Figure 1: Sampling methodology summary for urban areas, rural areas and IDP camps .................... 12 Figure 2: Dummy - Verifying suitability of an enumeration area in Wau ............................................. 14 Figure 3: Dummy – Map with primary and replacement EAs in Juba................................................... 14 FOCUS BOXES Focus Box 1: Reasons for household replacement ............................................................................... 15 Page 4 of 29 Annexes| Mobile Money Research in South Sudan Altai Consulting to the World Bank ABBREVIATIONS CSRF Camp Coordination and Camp Management EA Enumeration Area FGD Focus Group Discussions GDI Gender Development Index GRSS Government of the Republic of South Sudan ID Identification Document IDI In-Depth Interview IDP Internally Displaced Persons KII Key Informant Interview MNO Mobile Network Operator NBS National Bureau of Statistics POC Protection of Civilians PPS Probability Proportional to Size SNSDP Safety Net and Skills Development Project WB World Bank Page 5 of 29 Annexes| Mobile Money Research in South Sudan Altai Consulting to the World Bank 1. RESEARCH METHODOLOGY 1.1. RESEARCH QUESTIONS The research undertaken has been focused around the following research questions: Table 1: Research questions Research Questions 1. What does the mobile money landscape in South Sudan look like? ▪ Are there formally recognized mobile money services on offer in the South Sudanese market? And, if so, how do they function? Are services interoperable? ▪ Are there informal proxy services for mobile money in South Sudan? And, if so, how do they work and how commonly are they used? ▪ Who are the key actors within South Sudan’s mobile money ecosystem? What is their market coverage, size, shareholder structure, and business model? ▪ What is the relationship between telecommunications and financial sector actors (i.e. formal actors such as banks, as well as informal actors such as informal value transfer systems) in South Sudan? ▪ How are distribution networks structured in both urban and rural areas of South Sudan, and what commission schemes are in place? ▪ Which sectoral and political characteristics currently constrain the development or scale-up of the mobile money ecosystem in South Sudan? 2. What is the current state of regulations governing the mobile money sector and the financial sector in South Sudan? ▪ What are the current licencing arrangements for Mobile Network Operators (MNOs) and mobile money operators? ▪ What are the regulations governing financial transactions, including related to exchange rates and the existence of a parallel exchange rate market? ▪ What regulations are in place around “Know Your Customer� processes and identification for registration of SIM cards and mobile money services? ▪ What regulations are in place to protect customers? For instance, are there mechanisms in place to ensure online/offline parity and to ensure the safety of mobile floats? ▪ What regulations are in place around fraud, money laundering, and illicit financial flows? ▪ What are the primary regulatory barriers to the supply of mobile money services? 3. To what extent is there demand for formal mobile money transfers within South Sudan? ▪ What are the primary unmet needs for financial services in South Sudan? ▪ If there are formally recognized mobile money services on offer in the South Sudanese market, what are current penetration levels and usage patterns? ▪ Considering local-market functionality, exchange-rate and commodity-price volatility, and the broader local economy: is mobile money relevant in the South Sudanese context? Page 6 of 29 Annexes| Mobile Money Research in South Sudan Altai Consulting to the World Bank ▪ Is mobile money adequate for meeting the needs of low-income and vulnerable beneficiary groups (e.g. when considering the level of banking-system development and displacement)? ▪ What is the demand for formal mobile money services among the general public (including the most vulnerable households) in South Sudan? ▪ What is the demand for formal mobile money services among humanitarian and development partners with respect to cash-based assistance and social-protection initiatives? 4. What are the primary barriers to adoption of mobile services and mobile money in South Sudan? ▪ What are the structural barriers to adoption of mobile services and mobile money in South Sudan? ▪ What are the infrastuctural barriers to adoption of mobile services and mobile money in South Sudan? ▪ What are the socioeconomic barriers to adoption of mobile services and mobile money in South Sudan? ▪ What are the cultural barriers to adoption of mobile services and mobile money in South Sudan? ▪ Which population groups are typically more prone to being excluded from the mobile money system? And, what are the specific barriers that these groups face, or could face, more acutely when accessing or using mobile money? 5. What are the potential benefits of mobile money for the general population, including low-income and vulnerable groups in South Sudan? ▪ What benefits could mobile money provide in facilitating payments to the population and in easing transfers? ▪ What role could mobile money transfers play as a form of informal social safety net? ▪ What additional benefits could the population derive from enhanced usage of mobile money (e.g. expanded financial inclusion for the unbanked, increased access to services, greater resilience to shocks)? ▪ What benefits could mobile money provide to the development of local economies? 6. What are the risks associated with the adoption of mobile money? ▪ Given the current political climate, regulatory environment, and operational structure, what are the risks associated with customer protection? ▪ What are the risks connected to the injection of large sums of cash into the local economy? ▪ What are the risks affiliated with fraud, money laundering, and illicit financial flows? 7. What role could mobile money play in the delivery of humanitarian assistance and social protection schemes in South Sudan? ▪ Can mobile money be leveraged to improve delivery of cash-based programming and social- protection schemes by both humanitarian and development actors? ▪ To what extent is there compatibility between existing approaches to, and modalities for, cash- transfer programming and the introduction of mobile money services? ▪ To what extent can mobile money optimize efficiency and cost-effectiveness, while alleviating operational challenges associated with cash-transfer programs in the South Sudanese context? Page 7 of 29 Annexes| Mobile Money Research in South Sudan Altai Consulting to the World Bank ▪ To what extent can mobile money decrease some of the operational risk of cash-based programming (e.g. risk of leakage, security incidents, and the push-and-pull effect associated with aid distribution points)? ▪ What additional operational or system risks could be associated with the introduction of mobile money into the humanitarian response or development initatives? 8. How can the potential of mobile money in South Sudan be unlocked? ▪ What opportunities could be leveraged to foster uptake of mobile money services? ▪ What actions can be taken to tackle current barriers and support future readiness for mobile money adoption? ▪ What would be needed to engender an enabling and conducive environment for the development and rollout of mobile money services? ▪ What policy and program interventions would support mobile money uptake and leverage mobile money to support resilience and financial inclusion? ▪ What measures might be necessary to both optimize aid delivery through mobile money and to mitigate associated risks? 1.2. OVERALL APPROACH The rest of this section details the methodological approaches that were applied to qualitative and quantitative data collection, including sampling strategy and tool development, which were used to respond to the research questions identified (and detailed above). The approach adopted leveraged a combination of descriptive and advanced statistical techniques, as well as qualitative analysis. 1.2.1. RESEARCH COMPONENTS AND SPECIFIC ACTIVITIES The research team leveraged a mixed-method research methodology and organized the research around four research components (detailed in the next section) to provide a complete picture of the landscape, looking at both the supply- and demand-side of the mobile money ecosystem. The research provided demand-side data by consulting households representing potential or existing consumers of mobile money services, and supply-side data by consulting a comprehensive range of stakeholders and market players, including regulators, mobile money service providers, agents, and so on. Research activities included:  Conducting background research and a desk review of South Sudan’s telecommunications and financial services sector, benchmarking both the regulation and use of mobile money in South Sudan against other emerging economies;  Conducting qualitative supply-side research through: ▪ Interviews with the government, the regulator(s), and key actors from the financial services (e.g. banks) and telecommunications sectors (e.g. MNOs) to collect information on the current status of mobile money in South Sudan within the broader political and regulatory environment, and to explore barriers to and opportunities for scaling up; ▪ Interviews with humanitarian and development actors engaged in cash transfers in the social protection sector to inquire into the role that mobile money services and mobile money infrastructure play, or might play, in the provision of cash-based assistance; Page 8 of 29 Annexes| Mobile Money Research in South Sudan Altai Consulting to the World Bank  Conducting a demand-side quantitative household survey to collect baseline data on the current demand for—and potential penetration level of—mobile money services, as well as on the perceived benefits, barriers, and risks associated with usage;  Collecting qualitative demand-side data to complement household-survey findings and to further understand reasons behind the demand for mobile money (or lack thereof), and the drivers and barriers to adoption across different vulnerable groups. Qualitative supply-side research took place early on and was thus able to inform the analytical framework and research-tool designed to undertaken remaining research components. 1.2.2. FOCUS ON THE MOST VULNERABLE HOUSEHOLDS The effects of continued conflict coupled with sustained economic decline in South Sudan have increased the vulnerability of local communities and diminished households’ capacity to face shocks. Vulnerable groups continue to suffer the brunt of conflict and economic pressures. The research team thus adopted a gender-sensitive research approach and focused on issues affecting vulnerable groups by exploring specific access or usage constraints. The following groups— referred to as vulnerable groups in the report—were targeted: ▪ Internally Displaced Persons (IDPs) are defined as persons who have been forced or obliged to flee or to leave their homes or places of habitual residence, particularly as a result of or in order to avoid the effects of armed conflict, situations of generalized violence, violations of human rights, or natural or human-made disasters, and who have not crossed an internationally recognized state border;1 ▪ Urban poor are particularly vulnerable given their heavy market dependence and limited access to alternative coping strategies. Urban poor can be defined as individuals living in urban areas whose housing structures are of visibly poor quality and who lack one or more of the following: i) durable housing of a permanent nature that protects against extreme climatic conditions; ii) sufficient living space, with no more than three people sharing the same room; iii) easy access to safe water, in sufficient amounts at an affordable price; iv) access to adequate sanitation, in the form of a private or public toilet shared by a reasonable number of people; and/or v) security of tenure that prevents forced evictions;2 ▪ Rural residents are vulnerable as well, given the lack of economic opportunities and low access to social services available in areas outside urban centers. As part of this study, rural residents were defined as individuals living in rural enumeration areas (EAs), according to the sampling frame provided by the National Bureau of Statistics (NBS) (see section 1.5.2). ▪ Women are often also deemed to fall under the same vulnerable umbrella term, even though they are not vulnerable in all contexts (though are more likely to be so than men). South Sudan ranked 169th out of 188 countries in the UN Gender Development Index (GDI) in 2015, which signals important disparities between women and men related to the three basic dimensions of human development: health, knowledge, and living standards.3 1United Nations Guiding Principles on Internal Displacement, accessed at: https://emergency.unhcr.org/entry/250553/idp- definition 2 UN Habitat definition as per the “UN Habitat: State of the World’s Cities 2006/2007�, accessed at: https://unhabitat.org/books/state-of-the-worlds-cities-20062007/. For the analysis, the research team chose to abide by the definition of “extremely urban poor� indicated in this report, i.e. only individuals who were lacking at least three of the five criteria mentioned were defined as urban poor. 3 United Nations Development Programme (2015). Human Development Reports: Gender Inequality Index. http://hdr.undp.org/en/composite/GII Page 9 of 29 Annexes| Mobile Money Research in South Sudan Altai Consulting to the World Bank The research team is cognizant that this list of vulnerable groups is neither exhaustive nor comprehensive. Some groups may, for instance, be marginalized due to their ethnic identity. Other vulnerability factors identified by the humanitarian community include health status and disability, number of dependents, housing and food security status, as well as age (e.g. elderly, child-headed households and unemployed youth). To the extent possible, the research team disaggregated the analysis, highlighting sub-group-specific usage patterns, barriers, risks and benefits. 1.2.3. SYNERGIES WITH THE MOBILE MONEY RESEARCH IN SOMALIA The approach and methodology adopted for this research project drew on Altai’s recent experience exploring mobile money in Somalia for the WB: first, in 2016-2017, when Altai was appointed to collect supply- and demand-side data on the mobile money ecosystem in Somalia to shed light on the use of mobile money and on the wider state of the mobile money industry in Somalia; and, then in 2018, when Altai was commissioned to conduct a thematic extension, aimed at analyzing how mobile money had been used by drought-affected communities, exploring how the mobile money ecosystem functions in crisis mode, and how it could be further strengthened to build resilience. 1.3. COMPONENT 1: BACKGROUND RESEARCH AND DESK REVIEW Objectives of Component 1: Provide insights on South Sudan’s telecommunications and financial ecosystem, benchmarking the regulation and use of mobile money in South Sudan against other emerging economies (e.g. Uganda or Kenya); provide secondary research and detailed context to allow for further refinement of the research tools applied. Formative research was undertaken during the initial stages of the project to add depth to the analytical approach adopted, and to inform research tool design. This formative research drew on a comprehensive literature review and meta-analysis of relevant existing data and reports. This included reviewing previous pertinent work completed by the WB and its partners within the domestic telecommunications sector, and any preliminary research undertaken by humanitarian and development partners on cash-programming in South Sudan, including the one recently undertaken by the Conflict Sensitivity Resource Facility (CSRF). 1.4. COMPONENT 2: QUALITATIVE SUPPLY-SIDE RESEARCH Objectives of Component 2: Document the ecosystem’s supply-side and gain an in-depth understanding of the domestic mobile money market, sectoral constraints, and possible applications of mobile money within humanitarian and development programming. The module was split into the following sub-objectives: ▪ Document the status of mobile money services offered, whether there exist formally recognized and regulated mobile money services (with separate mobile money wallets) – and, if so, what services are in use? Or, whether or not only proxy offerings of airtime trading are available; ▪ Map the ecosystem: key actors, relationships, and practices; ▪ Describe the regulatory framework (e.g. licensing of MNOs providing mobile money services) and regulatory constraints, adopting a political-economy lens and considering the economic- reform agenda within the broader political and regulatory environment; ▪ Pinpoint the risks of fraud and risks to potential customers; ▪ Capture humanitarian and development stakeholders’ perceptions on the current and potential role of mobile money platforms in cash-based programming; Page 10 of 29 Annexes| Mobile Money Research in South Sudan Altai Consulting to the World Bank ▪ If relevant, understand the role played by mobile money agents, and appreciate what would be required for agents to facilitate greater consumer understanding and mobile-payment uptake; ▪ Identify policy and program interventions that would support mobile money uptake, and that would leverage mobile money to support resilience and financial inclusion. Information was captured in a multi-dimensional way through i) Key Informant Interviews (KIIs) with government institutions, MNOs, other key actors from the financial and telecommunications sectors, as well as humanitarian and development actors, and ii) In-Depth Interviews (IDIs) with mobile money agents, airtime resellers, informal money-transfer institutions and local traders. Ultimately, this review found that formal domestic mobile money services were not yet available , which meant that later component would focus on potential demand, un-met financial needs and enabling factors for adoption and uptake. 1.5. COMPONENT 3: QUANTITATIVE DEMAND-SIDE RESEARCH Objectives of Component 3: Implement a demand-side household survey—representative across selected counties—with a sample of around 1,500 households, to collect primary quantitative data on the demand for and/or current use of mobile money services. In the end, the survey collected information from a total of 1,648 households. 1.5.1. QUESTIONNAIRE The questionnaire investigated the role that mobile money or airtime-credit transfers play as a form of informal social safety net. The questionnaire also sought to understand which groups that are typically less prone to using mobile money or more prone to being excluded from the mobile money system. For instance, the questionnaire explored the barriers that are or could be faced by different groups (e.g. IDPs, women) when accessing or using mobile money. Additional aspects were explored: ▪ Financial literacy related to the tracking of income and expenses, loans and debt repayments, and savings. This is critical in mobile money deployment and uptake, even if the households do not have prior experience with digital/formal financial services. ▪ Cost/ease of access to operators that serve/could serve as mobile money agents. ▪ Digital/National IDs would be crucial in the context of cash-based programming, in order to prevent misuse of money and funding of warring parties. The unit of analysis was the individual, as individual usage patterns and individual perceptions are being studied. The final analysis based on the quantitative household survey presents disaggregated indicators related to gender, age, education, literacy, income level, geography (county and urban versus rural areas), and whether or not respondents have been internally displaced or otherwise affected by conflict. The questionnaire also drew on the one developed for the Mobile Money Ecosystem Research undertaken in Somalia, yet was fully tailored to the South Sudan context. It featured close-ended questions and comprised several modules, sub-sections of which were administered depending on respondents’ answers to certain questions. Page 11 of 29 Annexes| Mobile Money Research in South Sudan Altai Consulting to the World Bank The questionnaire was pilot-tested (through cognitive and field testing) among at least 30 respondents with diverse profiles to refine the questionnaire before rollout. The objective of the pilot test was to ensure that all topics were adequately covered and, most importantly, that the questionnaire was tailored to the local culture and language of the target audiences, including vulnerable groups with varying levels of literacy. 1.5.2. SAMPLING STRATEGY Sampling Design The design adopted for this survey was a stratified four-stage cluster sample. While counties were purposively selected, the selection was randomized at the second, third, and fourth levels. The resulting sample is representative for each strata of interest (resident households in urban and rural areas, as well as IDPs), and by gender, and ensures ethnic diversity. Figure 1: Sampling methodology summary for urban areas, rural areas and IDP camps Sampling Strategy Selection Stages 1st stage Counties Purposive Type of residence Stratification 2nd stage Enumeration Areas PPS 3rd stage Households Random walk 4th stage Respondents Randomization tool Based on existing access and security constraints, the survey encompassed the seven counties currently targeted by the World Bank’s Safety Net and Skills Development Project (SNSDP), where Enumerators had guaranteed access and benefited from the support of local authorities, based on goodwill created by the project. However, the survey did not exclusively target beneficiaries from the SNSDP, and the sampling frame comprised all households living in these counties. Two more counties (Wau, Malakal) were added to enhance representation of conflict-affected households. Table 5 presents the list of counties selected for the household survey. While the survey is only representative at the county level, it should also provide a comprehensive snapshot of the country as a whole, as it i) covers the three greater regions in South Sudan, including both urban and rural areas, ii) includes IDPs, iii) includes ethnically diverse households, and iv) covers some areas that have been severely impacted by the conflict, including more recent waves of displacement following the uptick in violence throughout the country after July 2016 when clashes re- erupted in Juba. Table 2: Preliminary counties selected for the household survey Counties Kapoeta East Pibor Page 12 of 29 Annexes| Mobile Money Research in South Sudan Altai Consulting to the World Bank Torit Malakal City Juba City Gogrial West Wau Tonj South Bor Sample Size Calculations The household survey originally targeted some 1,440 households, or 480 households in each strata (i.e. for urban and rural populations, as well as IDPs). This target was exceeded, as 1,648 households were ultimately interviewed. While the overall sample size was decided based on budget considerations, this sample size optimizes the margin of error and guarantees that, with a 95% confidence level, the margin of error of estimations is less than 5% both at the strata level and at the gender level. It also guarantees that estimations at the strata x gender level (e.g. female IDPs) yield a margin of error inferior to 7% (6.33%). As such, robust analysis can be derived from comparisons between different groups. Table 3: Sample size calculation for urban residents, rural residents, and IDPs Parameter Value Result Value Confidence Interval 95% (standard) Total Sample Size 1,440 Statistical Power 80% (standard) Number of Counties 9 EA Size (Number of individuals Margin of Error 5% 12 per EA) Expected Proportion 50% (conservative) Total Number of EAs 120 Enumeration Areas Selection The sampling frame was refined with the help of the NBS, which acts as the depositary of the latest ‘Census of Population and Housing’ and thus has access to data that provides a comprehensive sampling frame for South Sudan. The NBS provided the research team with outlines of urban versus rural areas. These outlines were laid over recent satellite imagery to verify whether they appeared to be correct. A list of EAs – or selection lists, were then prepared for both urban and rural areas, in all target locations. From the selection lists, the NBS extracted and ordered a list of EAs, selected at random with a probability proportional to size (PPS). Outlines of these EAs were given to Altai as georeferenced PDF- documents, which were then digitized into GIS-shapefiles and laid over satellite imagery for inspection. EAs which were empty, or had too few households, were discarded and replaced. Building on Altai’s experience in Somalia, where drought-related displacement had also compromised the sampling frame, this project employed remote sensing and satellite imagery to check the EAs ahead of fieldwork. This led to the replacement of several EAs, which were randomly selected to preserve the sample’s statistical robustness. Page 13 of 29 Annexes| Mobile Money Research in South Sudan Altai Consulting to the World Bank Figure 2: Dummy - Verifying suitability of an enumeration area in Wau Figure 3: Dummy – Map with primary and replacement EAs in Juba Household Selection Within a suitable EA, the selection of respondents was randomized using a random walk protocol: ▪ Each EA was covered by two Enumerators, supervised by one Field Coordinator. ▪ Starting points (usually road intersections) were selected at random within the EA. ▪ The Enumerator initiated a walk from the starting point, stoping at every fifth house on one side of the road to conduct interviews. Page 14 of 29 Annexes| Mobile Money Research in South Sudan Altai Consulting to the World Bank ▪ At the end of the road, or when the EA limit was reached, the Enumerator relocated to a new starting point. ▪ The method was repeated with a new starting point until the requisite number of interviews had been collected. IDPs Selection To interview IDPs, Altai got in touch with the Camp Coordination and Camp Management (CCCM) cluster and UNITAR (United Nations for Training and Research) to obtain a list of Protection of Civilians (PoC) sites in the target locations. The Enumerators then followed the same random walk protocol within these sites. Respondent Selection The selection of the respondent within the household was randomized at the beginning of the interview, through a randomization formula within the encoded questionnaire. The Enumerator started by introducing the survey to the household. He/she then recorded the number of adults present above the age of 16 and their names. Adults present at the time of the interview (and within the eligible age range) were then randomized to select a respondent. Altai decided to do the randomization based on household members present at the time of the interview (rather than among all household members) because selecting a respondent based on all household members would have sharply increased the number of needed follow-up visits and time spent in a specific EA, which would have significantly increased the required fieldwork budget. Non-response and Replacements Measures were put in place to ensure that non-responses were properly referenced and that follow- up visits were respected in cases of ‘no one’ or ‘no eligible adult’ present. Any replacements were recorded: ▪ To account for non-responses, a form was finalized and completed for the households/respondents that did not give their consent and/or refused to answer the survey. All non-responses were thus properly referenced. ▪ Each replacement at the EA-level (for example, due to security concerns for the Enumerators or logistical reasons (leading to the inability to operate) were decided by the Project Manager, and the specific reason for their replacement were recorded. ▪ At the household level, Enumerators were asked to reschedule two follow-up visits to all households where eligible respondents were not present, prior to replacing the household. Focus Box 1: Reasons for household replacement A household was replaced if: ▪ The respondent refused to give his/her consent to complete the survey. ▪ The household was found to be empty after three visits. ▪ An adult above 16 was not available - even after three visits to the household. ▪ The interview that was conducted with that household was incomplete (e.g. the respondent stopped the interview in the middle). Sampling Weights Calculation Sampling weights were calculated at the household-level and at the respondent-level. The weights are equal to the sampling fraction employed to select the number of sampled respondents in each Page 15 of 29 Annexes| Mobile Money Research in South Sudan Altai Consulting to the World Bank stratum. The selection probability was calculated based on the most precise data available regarding the population distribution and number of households in each stratum4. Sampling weights were calculated using the following formula: 1 𝑊𝑟ℎ𝑖𝑗 = 𝑃𝑟ℎ𝑖𝑗 𝑃𝑟ℎ𝑖𝑗 = 𝑃𝑖𝑗 𝑥 𝑃ℎ𝑖 𝑥 𝑃𝑟ℎ = 𝑃𝑖 𝐻𝑆𝑖 1 = (𝐸𝐴𝑗 𝑥 ) 𝑥 𝑥 𝑃𝑗 𝐻𝑖 𝑅ℎ Where: 1. 𝑊𝑟ℎ𝑖𝑗 is the sampling weight 2. 𝑃𝑟ℎ𝑖𝑗 is the probability of selecting respondent 𝑟 in household ℎ in EA 𝑖 in strata 𝑗 3. 𝑃𝑖𝑗 is the probability of selecting EA 𝑖 in strata 𝑗 (Probability-Poportional-to-Size methodology) 4. 𝑃ℎ𝑖 is the probability of selecting household ℎ in EA 𝑖 (Equiprobability-of-Selection methodology) 5. 𝑃𝑟ℎ is the probability of selecting respondent 𝑟 in household ℎ (Equiprobability-of-Selection methodology) 6. 𝐸𝐴𝑗 is the number of EAs selected in stratum 𝑗 7. 𝑃𝑖 is the population of EA 𝑖 8. 𝑃𝑗 is the population of stratum 𝑗 9. 𝐻𝑆𝑖 is the number of households selected in EA 𝑖 10. 𝐻𝑖 is the total number of households in EA 𝑖 11. 𝑅ℎ is the total number of eligible members (>16 years old) in household ℎ Computation of EAj: The number of EAs selected in each stratum5 was driven by the sampling design chosen. A breakdown of EAs selected is provided in the table below. Table 4: Number of EAs per stratum Rural Urban PoC site Total Juba City 5 7 7 19 Bor 5 6 6 17 Pibor 5 6 0 11 Torit 5 6 0 11 Kapoeta East 4 6 0 10 4 Source were Population projections for South Sudan by County: 2015-2020 (NBS, March 2015) and Southern Sudan Counts: Tables from the 5th Sudan Population and Housing Census (SSCCSE, November 2010). 5 A stratum is the level of county x zone of residence (i.e. urban/rural/PoC). An example is Kapoeta East rural. There are in total 22 strata. Page 16 of 29 Annexes| Mobile Money Research in South Sudan Altai Consulting to the World Bank Gogrial West 4 6 0 10 Tonj South 4 6 0 10 Wau 4 6 6 16 Malakal 4 6 6 16 Total 40 55 25 120 Calculation of Pj: PoC strata For PoC strata in Bor, Wau, Malakal and Juba, population estimates were extracted from the following sources: ▪ Bor PoC site: Bor Poc Site (Jonglei) Biometric Registration (IOM DTM, Oct 2018)6. ▪ Wau PoC site: Wau PoC site profile (reliefweb, May 2018)7; ▪ Malakal PoC site: Malakal Combined Assessment (IOM DTM, Feb 2018)8; ▪ Juba PoC site: Protection of Civilian sites, Juba Biometric Registration (IOM DTM, Jan 2019)9. Urban and rural strata Population projections (2015 – 2020) are available at the level county x gender, but not at the county x urban/rural level. Even from the 2008 census data, urban/rural population distribution is only available at the state level10. As such, the urban/rural population distribution at the state level from 2008 Census data11 is used to interpolate the urban/rural population distribution at the county level, and the derived percentages are applied to 2019 county-level population projections12. Calculation of Pi and Hi: Urban/rural strata For enumeration areas in urban and rural strata, population and number of households’ estimates were provided by the NBS. PoC strata From the shapefiles of PoC sites in Malakal, Juba and Bor, the team was provided with information related to the number of structures (i.e. buildings, shelters, tents) contained in each EA. From that: 6https://reliefweb.int/sites/reliefweb.int/files/resources/20181003%20IOM%20DTM%20SSD%20Bor%20PoC%20Registratio n_.pdf 7 https://reliefweb.int/sites/reliefweb.int/files/resources/20180517_wau_pocaa_site_profile_may_2018.pdf 8 https://www.iom.int/sites/default/files/dtm/south_sudan_dtm_201802.pdf 9https://reliefweb.int/sites/reliefweb.int/files/resources/20190119%20IOM%20DTM%20Juba%20PoC%20Sites%20BMR%2 0SSD.pdf 10 See note 1 for data sources. 11 Tables from the 5th Sudan Population and Housing Census (SSCCSE, November 2010). 12 Population projections for South Sudan by County: 2015-2020 (NBS, March 2015). Page 17 of 29 Annexes| Mobile Money Research in South Sudan Altai Consulting to the World Bank ▪ The total number of households per EA (Hi): The team estimated the average number of households per structure and multiplied it by the number of structures in the EA;13 14 ▪ The total population in the EA (Pi): The team estimated the average number of household members per household and multiplied it by the number of households in the EA (Hi). Calculation of HSi: HSi is equal to the number of interviews conducted per stratum. Calculation of Rh: Rh is equal to the variable nhhm_eligible (number of household members eligible to be interviews, i.e. older than 16 years old). 1.5.3. TECHNOLOGY Quantitative data was collected on smartphones equipped with the SurveyCTO application, allowing it to be directly transferred to a central database directly managed by Altai. Using electronic data collection methods introduced higher levels of efficiency, eliminated the need for data transcription from paper to electronic form, and allowed for more sophisticated data collection monitoring to be used that collectively increased data reliability.15 Each interviewer’s smartphone was pre-loaded with the final version of the questionnaire, as well as GeoPDF maps of his/her enumeration areas. The GeoPDF map allowed the interviewer to see his/her GPS location on a map, displaying the boundaries of the target enumeration area (for geo- fencing) as well as the starting points and directions to be used in the random walk protocol. As GPS coordinates were recorded for each interview, the implementation of the household selection protocol was monitored, in detail. 1.5.4. COMMUNITY ENGAGEMENT When engaging with communities, the Field Coordinators and Enumerators employed a clear and consistent communications strategy, and explained that the survey would not provide individual benefits (no money, no food or water supplies etc.) to avoid raising expectations of monetary gain or additional assistance among the surveyed communities, which could both skew the survey results and increase grievances or spur mistrust in development actors. Instead, they clarified the objectives of the survey - i.e. that the collected information would be used to inform humanitarian and development policies aimed at improving the lives of people in South Sudan. Similarly, to avoid raising suspicion among respondents, the Field Coordinators and Enumerators explained that households had been randomly selected to take part in this study, that the participation was voluntary, and that all shared information would be confidential. 1.6. COMPONENT 4: QUALITATIVE DEMAND-SIDE RESEARCH Objective of Component 4: Incorporate qualitative insights into the demand-side research to fully understand and contextualize the data, allowing researchers to gain a deeper understanding of the target populations’ needs and behaviours. The qualitative analysis also provided valuable insight into the local socioeconomic and cultural context, and as to whether or not local markets were functioning and could feasibly absorb an injection of cash via mobile money. 13 We took the average number of HH/structure across the three PoC sites for which this information was available (Malakal, Juba and Bor) to limit the fact that households and structures might be counted differently across different PoC sites. As for Wau we do not have the information of the number of structures per EA, we take the average number of HH/structure across the three PoC sites as the number of HH/structure for Wau PoC site. 14Using the population estimates for the four PoC sites, see foonotes,6,7,8 and 9. 15 Note that the real-time monitoring functionality is dependent on a presence of 3G data connection in order to upload interview data directly from the phone to an online server. Page 18 of 29 Annexes| Mobile Money Research in South Sudan Altai Consulting to the World Bank 1.6.1. FOCUS GROUP DISCUSSIONS (FGDS) AND FIELD VISITS FGDs and field visits were included to allow the research team to collect primary qualitative and observational data on the demand side. This module sought to aid the research team to gain a more in-depth understanding of mobile money ecosystem and unmet financial service needs, as well as drivers of behavioural change (e.g. what would be required to move from cash to digital, elements that would attract or dissuade consumers). Levels of financial literacy were also investigated with a view to identifying areas where additional knowledge and awareness would be needed to support increased uptake of mobile money services. In addition, the FGDs also interrogated the impacts of fluctuating exchange rates and the parallel exchange market on people’s financial behaviors, while exploring the context-specific decisions people make around financial services in rural versus urban areas. FGDs were implemented after the household survey, allowing the research team to probe issues highlighted by the survey in greater detail. FGDs took place across the different selected counties and targeted different segments of interest. By convening thematic FGDs, researchers were able to distil gender and group-specific benefits and barriers. In particular, FGDs took place with IDPs, urban poor, and rural residents, as these populations were more likely to face additional barriers when adopting/using mobile money services, and might have had different needs. Similarly, FGDs were conducted solely with women and elderly respondents in order to better understand any specific challenges they were facing, or may face, in accessing and using mobile money services, including possible protection risks. 1.6.2. CHARACTERISTICS OF THE FGDS The purpose of the FGDs was to allow for deeper exploration of key themes, thus complementing the quantitative survey results. The groups were limited to 8 participants, to ensure that productive discussion took place. The FGDs were implemented by dedicated National Consultants with the support of note-takers and lasted around two hours. Standardized tools and guidelines were developed for each group of interest to ensure completeness and comparability, and the National Consultants received specific training on how to conduct the interviews. Table 5: Characteristics of the FGDs Description Number of FGDs ~10 Thematic FGDs – 2 for each group of interest: ▪ 2 FGDs with IDPs; ▪ 2 FGDs with urban poor; Quotas ▪ 2 FGDs with rural residents; ▪ 2 FGDs with women; ▪ 2 FGDs with elderly people. Participants’ Purposive selection Data collection Moderation among groups of 6 to 8 participants method Note taking, full recording and picture Page 19 of 29 Annexes| Mobile Money Research in South Sudan Altai Consulting to the World Bank Length of FGD ~2 hours Page 20 of 29 Annexes| Mobile Money Research in South Sudan Altai Consulting to the World Bank 2. VULNERABLE INDEX A vulnerability index was developed, based on criteria identified in existing literature and aligned with international organizations’ best practices. It rates respondents’ vulnerability from 0 to 1, with 1 being minimum vulnerability and 0 maximum vulnerability. The rating captures respondents’ scores based on four key indexes, which have been equally weighted, that take into account current food intake (in terms of both quantity and diversity), application of extreme coping strategies and livelihood conditions. Table 6 below describes how each index has been calculated. Table 6: Methodology used for computing the vulnerability level of respondents Vulnerability Index How often over the past 7 days the household had to: • Rely on less preferred and less expensive foods? (limit_preferred) • Borrow food, or rely on help from a friend or relative? (help) Reduced Coping Strategy Index (rCSI) • Limit portion size at mealtimes? Food insecurity in terms of quantity of food (limit_portions) • Restrict consumption by adults in order for small children to eat? (limit_adults) • Reduce the number of meals eaten in a day? (limit_meals) rCSI = limit_preferred + (help*2)+ limit_portions + (limit_adults*3) + limit_meals How many days in the last 7 days the household ate: starches / pulses / vegetables / fruits / Food Consumption Score (FCS) meat / dairy / fats / sugar Food insecurity in terms of diversity of food FCS = (starch*2)+ (pulse*3)+ vegetable + fruit + (meat*4)+ (dairy*4)+ (fat*.5)+ (sugar*.5) During the past 30 days, if because of lack of food or money the household had to: • Sell household assets or goods (stress) • Spend savings (stress) • Sell more animals (non-productive) than usual (stress) • Send household members to eat elsewhere (stress) Livelihood coping strategy index • Reduce expenses on health and education (lCSI) (crisis) Extreme coping strategies that were adopted • Consume seed stocks that were to be saved for the next season (crisis) • Decrease expenditures on agricultural inputs (crisis) • Sell the last female animals (emergency) • Sell house or land (emergency) lCSI is equal to the most extreme strategy that was adopted, 1 corresponding to the absence of Page 21 of 29 Annexes| Mobile Money Research in South Sudan Altai Consulting to the World Bank a coping strategy, 2 to a stress strategy, 3 to a crisis strategy and 4 to an emergency strategy • Type of house • Main source of drinking water • Type of toilet • Type of material the roof of the house is Livelihood condition index (LCI) made of For each item, categories are assigned points. The livelihood condition index is the sum of points Page 22 of 29 Annexes| Mobile Money Research in South Sudan Altai Consulting to the World Bank 3. LIST OF STAKEHOLDERS INTERVIEWED In total, some 34 stakeholder interviews were conducted. Table 7: List of stakeholders interviewed for the supply-side research Supply-side Research - Stakeholders Government Bodies 1. Project Implementation Unit (PIU) for the Safety Net and Skills Development Project (SNSDP) 2. National Communication Authority 3. Ministry of Finance and Economic Planning 4. Bank of South Sudan 5. Ministry of Gender, Child and Social Welfare 6. National Bureau of Statistics 7. Ministry of Humanitarian Affairs 8. Ministry of Agriculture and Food Security 9. Ministry of Information and Broadcasting Telecommunications Sector 1. Zain 2. MTN 3. Trinity Technologies 4. Nilepay Financial Sector 1. Equity Bank 2. KCB Bank 3. Alpha Bank 4. Cooperative Bank 5. Eco Bank Page 23 of 29 Annexes| Mobile Money Research in South Sudan Altai Consulting to the World Bank 6. L.E.M International Humanitarian and Development Agencies 1. European Commission’s Humanitarian Aid Office (ECHO) 2. United States Agency for International Development (USAID) 3. Department for International Development (DFID) 4. United Nations Children’s Fund (UNICEF) 5. Office for the Coordination of Humanitarian Affairs (OCHA) 6. Danish Church Aid (DCA) 7. Danish Refugee Council (DRC) 8. Norwegian Refugee Council (NRC) 9. Agency for Technical Cooperation and Development (ACTED) 10. Cooperative for Assistance and Relief Everywhere (CARE) 11. Mercy Corps 12. Oxford Committee for Famine Relief (Oxfam) 13. Charlie Goldsmith Associates 14. World Food Programme (WFP) 15. World Vision 4. REFERENCES Ministry of Telecommunication and Postal Services. 2011. "Strategic Objectives Report. Development of Strategic Plan for Ministry of Telecommunication and Postal Services." Strategic Objectives Report. African Development Bank. 2013. "Chapter 10. Creation of a Communications Network In South Sudan: An Infrastructure Action Plan: A Program for Sustained Strong Economic Growth." Page 24 of 29 Annexes| Mobile Money Research in South Sudan Altai Consulting to the World Bank Africanews. 2010. Sudan to print 100-pound banknotes to ease liquidity crunch. January. https://www.africanews.com/2018/10/01/sudan-to-print-100-pound-banknotes-to-ease- liquidity-crunch//. Alushula, P. 2018. "Safaricom Mulls Finger Biometrics to Stem Fraud." Business Daily. Arol Arang, N. 2010. "Sudan Tribune." South Sudan admints sale of Gemtel to Libyan Company. http://www.sudantribune.com/spip.php?article34155. n.d. Bank of South Sudan. Accessed March 13, 2019. https://bankofsouthsudan.org/. Bank of South Sudan. 2017. "Electronic Money Regulation." —. 2013. "South Sudan’s Financial Sector." South Sudan Investment Conference. BuddeComm. 2017. "South Sudan’s Mobile Infrastructure Struggles with VivaCell Closure." August. https://www.budde.com.au/Research/South-Sudan-Telecoms-Mobile-and-Broadband- Statistics-and-Analyses. Buys, P., S. Dasgupta, T. Thomas, and D. Wheeler. 2009. "Determinants of a Digital Divide in Sub- Saharan Africa: A Spatial Econometric Analysis of Cell Phone Coverage." World Development, Policy Research Working Paper 4516. CARE Interantional. 2016. "Care in South Sudan." Cash Learning Partnership. February 2018. "The State of the World's Cash Report." Cash Transfers and Markets Working Group. 2015. "Guidance for cash transfer programming within South Sudan ." CGAP. 2014. "Price Sensitivity and New M-PESA tariffs." n.d. Cooperative Bank South Sudan. Accessed March 13, 2019. https://co-opbankss.com/. CSRF. 2018. "Cash-based programmes and conflict: Key areas of interaction and options for conflict- sensitive programming in South Sudan." Dan Church Aid. 2016. "Cash in conflict: cash programming in South Sudan ." Daniels, M., and G. Anderson. 2018. Evaluation of the 2017 Somalia Humanitarian Cash-Based Response. The Cash Learning Parnership. de Waal, A. 2014. "When kleptocracy becomes insolvent: Brute causes of the civil war in South Sudan." African Affairs 113.452. Deng, L. 2015. The Impact of Exchange Rate Adjustment on The Economy of South Sudan. Ebony Policy Review, Ebony Center for Strategic Studies . DFID. 2016. "Girls’ Education in South Sudan. Quarterly progress report." Ding, S., K. Wyett, and E. Werker. 2012. "South Sudan: The Birth of an Economy." Innovations Vol. 7, No. 1. Donkin, C. 2018. "South Sudan approves two mobile money services." Mobile World Live. https://www.mobileworldlive.com/featured-content/money-home-banner/south-sudan- approves-two-mobile-money-services/. Dutch Relief Alliance. 2017. "South Sudan Joint Response." Page 25 of 29 Annexes| Mobile Money Research in South Sudan Altai Consulting to the World Bank n.d. Eco Bank South Sudan. Accessed March 13, 2019. https://www.ecobank.com/sd/personal- banking/countries. Emergency Telecommunications Cluster. 2019. "South Sudan." Erickson, J., and C. Faria. 2011. We want empowerment for our women: Transnational Feminism, Neoliberal Citizenship, and the Gendering of Women’s Political Subjectivity in Postconflict South Sudan. The University of Chicago Press. Ferullo, M. 2018. "Banking on war: Ending the abuse of South Sudan’s banking sector by political elites and pushing for peace." The Sentry. Forcier Consulting. 2016. Effects of Inflationary Environment on Isolated Market Systems for DFID- Funded ‘Emergency Cash Transfer Programme’ in Unity State, South Sudan. Mercy Corps. Garang Atem, G. 2018. "Bank of South Sudan’s Governance, Nature, Growth and Impact of Banking Sector on the Economy." IOSR Journal of Economics and Finance Vol. 9, No. 1. Garang, J. A. 2014. "Development in Africa: Essays on Access to Finance for Small and Medium Sized Enterprises in South Sudan." Doctoral Dissertations 83. Gertler, P., S. Higgings, A. Scott, and E. Seira. March 2018. "The Long-Term Effects of Temporary Incentives to Save: Evidence from a Prize-Linked Savings Field Experiment." Working Paper. Global Partnership for Effective Development and Co-operation. 2016. "South Sudan: Monitoring Profile." GSMA. 2019. "Essential considerations for humanitarian practitioners handbook." GSMA. 2017. "Humanitarian Payment Digitisation: Focus on Uganda’s Bidi Bidi Refugee Settlement." GSMA. 2018. "Mobile Money Policy and Regulatory Handbook." 2016. Gurtong Trust Website. Accessed March 15, 2019. http://www.gurtong.net/Business/Finance/tabid/68/Default.aspx. Hammond, L., J. Bush, K. Savage, and P. Harvey. 2005. The effects of food aid on household migration patterns and implications for emergency food assessments. Food Economy Group and Overseas Development Institute. IMF. 2014. "Republic of South Sudan: 2014 Article IV Consultation-Staff Report; Staff Statement; and Press Release." IMF Country Report No. 14/245. IMF. 2017. "Republic of South Sudan: Staff Report for 2016 Article IV Consultation – Debt Sustainability Analysis." ITU. 2018. "South Sudan Profile." ITU Country Profile. Jaspars, S., P. Harvey, C. Hudspeth, and L. Rumble . 2007. A Review of UNICEF’s Role in Cash Transfers to Emergency Affected Populations. EMOPS Working Paper, UNICEF. Kayiira, D. December 2017. South Sudan: Landscape of investment. Centre for Affordable Housing Finance in Africa. Ligami, C. 2015. "Mobile money now crosses borders in four East African countries." The East African. Maina, S. 2018. "Techweez." M-PESA loses ground on P2P transactions. July 25. Accessed May 2019, 26. https://techweez.com/2018/07/25/m-pesa-loses-ground-on-p2p-transactions/. Page 26 of 29 Annexes| Mobile Money Research in South Sudan Altai Consulting to the World Bank Manyang Mayar, D. 2018. South Sudan Dispute with Mobile Firm Disrupts Service. Voice of America. Maxwell, D., R. Gordon, L. Moro, M. Santschi, and P. Dau. 2016. Livelihoods and Conflict in South Sudan. Secure Livelihoods Research Consortium. MEDICI team. 2019. "GoMedici." M-PESA: Mobile Phone-Based Money Transfer – Global Presence. February 25. Accessed May 27, 2019. https://gomedici.com/m-pesa-mobile-phone-based- money-transfer-global-presence/. Mercy Corps. 2015. "Emergency Market Mapping and Analysis. Livestock Off-Take and Sorghum Market Systems in Leer County, Unity, South Sudan." Mercy Corps. 2019. "The currency of connections: Why local support systems are integral to helping people recover in South Sudan ." Ministere de la Population de la Protection Sociale et de la Promotion de la Femme - Republique du Madagascar. 2017. "Résumé Exécutif de l'Atelier sur les Modalités des Transferts monétaires En Réponse a des Situations d'Urgence. Leçons Apprises et Perspectives." Morawczynski, O. 2009. "Examining the Usage and Impact of Transformational M-Banking in Kenya." Moss, T. 2011. Oil to cash: Fighting the resource curse through cash transfers. Center for Global Development . MTN South Sudan. 2015. "Welcome to the new world of better money ." Mobile Money Presentation . Munda, C. 2018. "Kenyan banks hope to revive South Sudan branches after new peace deal ." Business Daily. Ngahu, J., and S. Stefanski. 2012. IFC Mobile Money Scoping Country Report. IFC. OCHA. May 2019. "South Sudan Situation Report." Orange and MTN. 2018. "Orange.com." Orange and MTN launch pan-African mobile money interoperability to scale up mobile financial services across Africa. https://www.orange.com/en/Press-Room/press-releases/press-releases-2018/Orange-and- MTN-launch-pan-African-mobile-money-interoperability-to-scale-up-mobile-financial- services-across-Africa. Paulino, M. J., F. Mwambia, and M. M. Mithinji. 2018. "Effect of credit risk management on the financial performance of commercial banks in Juba city, South Sudan." International Academic Journal and Finance Vol. 3, No. 2. REACH Initiative. 2017. "Media and Telecommunications Landscape Guide South Sudan." Republic of South Sudan. 2011. "Bank of South Sudan Act." Republic of South Sudan. 2011. "National Communication Bill." Reuters. 2017. "Reuters." Kenya’s Equity closes bank branches in war-torn South Sudan. May. https://af.reuters.com/article/investingNews/idAFKBN18K1JA-OZABS. RiskScreen. 2017. "KYC360." South Sudan's central bank adopts thougher financial regulations. https://www.riskscreen.com/kyc360/news/south-sudans-central-bank-adopts-tougher- financial-regulations/. Page 27 of 29 Annexes| Mobile Money Research in South Sudan Altai Consulting to the World Bank n.d. Safaricom. Accessed May 27, 2019. https://www.safaricom.co.ke/. 2018. Safaricom Annual Report 2018. Accessed March 14, 2019. https://www.safaricom.co.ke/annualreport_2018/. Shankleman, J. 2011. Oil and state building: New Country, Old Industry. USIP Special Report 282, United States Insitute of Peace. Sloane, E., and S. Pietzsch. 2010. "Cash Grant Supported Income Generating Activities.Twic and Gogrial West Counties, Warrap State, Souther Sudan." ACF International. South Sudan: ICT Sector Background. 2012. "South Sudan: ICT Sector Background." Southern Sudan Centre for Census, Statistics and Evaluation. 2018. "Southern Sudan Counts: Tables from the 5th Sudan Population and Housing Census." Styles, L. 2016. 3.4 South Sudan Telecommunications. Logistics Cluster. Sudan Tribune. February 2018. "South Sudan call rates to increase, says regulatory body." Sulaiman, T. 2012. "MTN, Zain target South Sudan’s unbanked." Business Report. TeleGeography. 2018. "GlobalComms Services Database." March. https://www2.telegeography.com/globalcomms-database-service. —. 2017. "TeleGeography." South Sudan launches new operator Niletel amid economic crisis and civil war. https://www.telegeography.com/products/commsupdate/articles/2017/07/21/south- sudan-launches-new-operator-niletel-amid-economic-crisis-and-civil-war/. —. 2018. "TeleGeography." Authoritative Telecom Data. https://www.telegeography.com/products/commsupdate/lists/country/south-sudan/. The East African. July 2017. "New South Sudan rules to tame illicit fund transfers." The Sentry. September 2016. "War crimes shouldn't pay. Stopping the looting and destruction in South Sudan." Twijnstra, R. 2015. "Recycling oil money: procurement politics and (un)productive entrepreneurship in South Sudan." Journal of Eastern African Studies. UNDP. 2018. "South Sudan-Human Development Indicators, Human Development Reports." UNICEF. 2018. "South Sudan Country Case Study." US Chamber of Commerce. 2011. "Investment Climate Update: South Sudan." Africa Business Update. US Department of State. 2018. South Sudan. Bureau of Economic and Business Affairs. WFP. March 2019. "WFP South Sudan Situation Report #240." World Bank. 2017. "Country Engagement Note for the Republic of South Sudan ." Report No. 120369- SS. World Bank. 2018. "Human Development Reports: South Sudan. Humanitarian Development Indicators." Human Development Reports. World Bank. 2018. "Identification for Development. ID4D Data: Global Identification Challenge by the Numbers." Page 28 of 29 Annexes| Mobile Money Research in South Sudan Altai Consulting to the World Bank World Bank. 2014. "Report on regulatory framework for mobile payments and banking in South Sudan ." World Bank. 2015. Republic of South Sudan. Systematic Country Diagnosis. World Bank, Country Departmnet, Eastern Africa. World Bank. 2018. "South Sudan." MPO. World Bank. April 2019. "South Sudan Economic Brief." World Bank. 2018. "South Sudan Economic Updated." World Bank. 2015. "South Sudan Poverty Profile." World Bank. 2018. "Study of Options for Mutual Recognition of National IDs in the East African Community." World Bank, commissioning Altai Consulting. 2019. "Mobile Money Ecosystem, Thematic Extension." World Vision. n.d. "Cash-based programming in emergency context: the case of PoC voucher project, Juba, South Sudan." World Vision. 2016. "Cash-based programming to address hunger in conflict-affected South Sudan: A case study." Page 29 of 29