2.5 Novissi’s Leapfrogging Delivery Model for Shock-Responsive Social Assistance

From Static toward Dynamic Delivery Systems That Are Updated Periodically Social registries are most effective when they have high coverage and are updated frequently. However, updating social registries tends to be time-consuming and logistically costly. This is because they are not dynamic by design, while the socioeconomic conditions of the populations they are meant to represent are inherently dynamic. Most welfare indicators show that the conditions of households are a moving target. For instance, 29.7 percent of the population in Sub-Saharan Africa moved in and out of transient poverty over a period of six years.34 This is exacerbated in shock-prone environments where household welfare can change abruptly. Thus, to make social registries more dynamic, digital service windows can be set up for remote intake and registration—as much as local connectivity conditions permit—such as the approach to Short Message Service (SMS)–based self-registration taken by the STEP-KIN program in Kinshasa in the Democratic Republic of Congo (box 2.4). Remote and on-demand intake modalities based on Unstructured Supplementary Service Data (USSD), SMS, or webbased are likely to be more cost-efficient than the in-person and administrator-driven intake modalities that have traditionally been the default for most social registries in the region. Social registries can also explore exploiting data sources that are generated more dynamically, such as call detail records CDRs35 or administrative records (passive data), beyond the common selfreported questionnaires (active data). Togo embraced USSD for intake and registration and CDRs targeting the Novissi cash transfer program (see box 2.5), which shares many similarities with STEP-KIN in Kinshasa. Finally, social registries with monolithic data structures stemming from lengthy socioeconomic questionnaires can instead consider using modular data structures that act as “data Lego blocks.” By defining the core data structures that should be populated for all households and complementary—yet harmonized—data structures that can be populated as needed depending on the requirements of each program, social registries can increase the efficiency of data management and thus facilitate updating.

On April 8, 2020, the Togolese government launched the Novissi program as a 100 percent digital cash transfer to respond to the COVID-19 pandemic. Novissi initially supported informal workers in urban areas whose incomes were affected by the containment measures and later expanded to vulnerable rural areas. Through several rounds, the program has so far registered around 1.6 million individuals and delivered $22.5 million in assistance to nearly 820,000 beneficiaries, 63 percent of them being women. Novissi introduced multiple innovations along the social protection delivery chain by exploiting nontraditional data sources and machine learning, dynamically registering individuals, assessing their needs and conditions, and delivering benefits through mobile money. As in many other countries in the region, Togo did not have interoperable, inclusive, and dynamic delivery systems in place before the pandemic. There was no universally available unique ID, nor a whole-of-government and up-todate social registry to locate vulnerable population groups. However, a key enabling factor was that nearly 85 percent of individuals in Togo were members of households with access to a mobile phone. In this relative delivery systems vacuum, Novissi leapfrogged previous delivery models and reached

BOX 2.5: Novissi’s Leapfrogging Delivery Model for ShockResponsive Social Assistance

34 Dang and Dabalen (2017). 35 CDRs are metadata on phone use, including date, time, and duration of calls; type of call (national, international); call cost; and volume of mobile data usage and mobile money transactions, among others.

BOX 2.5 Continued scale in record time to serve those most in need during the crisis. The Togolese government used different methods to deploy Novissi in urban and rural settings. The expansion to rural areas, supported by key stakeholders, is summarized as follows: 1. Intake and registration. Individuals self-register by dialing *855# from a mobile phone and inputting basic information through an Unstructured Supplementary Service Data (USSD) form. 2. Assessment. A two-step approach is used to assess needs and conditions: i. Geospatial prioritization: high-resolution satellite imagery layered with other data sources such as connectivity, population density, and consumption surveys are processed by machine learning algorithms to generate micro-estimates of relative wealth for 2.4–square kilometer grid cells (Relative Wealth Index). These estimates are used to rank the regions of the country (Admin-3 level) and select the poorest 200 “cantons.” ii. Individual prioritization: a supervised machine learning model estimated the daily consumption of 5.8 million mobile phone subscribers by leveraging call detail records and ground-truth phone surveys as training data. An acceptance shortlist was created that included 750,000 mobile phone subscribers with an estimated daily consumption of $1.25 or less. 3. Eligibility determination, enrollment, and notification. Adults who live in the poorest cantons and have an estimated daily consumption of $1.25 or less are enrolled and notified through a

USSD message upon completing self-registration. 4. Payment. Beneficiaries receive their first payment ($13.8 for women, $11.8 for men) immediately after notification through existing mobile money accounts or newly created ones based on the information received during self-registration. 5. Management. A toll-free number set up to handle complaints, as well as external audits and real-time monitoring facilitates the management of the program and its beneficiaries. The Novissi delivery model brings significant efficiency gains in the delivery of social assistance programs, yet these need to be balanced against new concerns. Because of its heavy dependence on digital technologies, this approach can potentially exacerbate exclusion risks for households without access to a mobile phone and connectivity. Earlier investments in digital infrastructure and literacy were not necessarily distributed uniformly or progressively. Data privacy is another concern that is potentially heightened since mobile subscribers do not necessarily intend for their phone usage to be a proxy of their socioeconomic status. New opt-in and opt-out mechanisms should be considered as these innovations mature and inform other initiatives.

Sources: Aiken et al. (2021); World Bank (forthcoming c).

Growing Importance of Data and Digital Technology as Fundamental Inputs to Social Protection Delivery Systems

Data are becoming more ubiquitous, yet there are trade-offs to consider. The response to the COVID-19 pandemic has served as a catalyst to explore nontraditional data sources and novel methodologies to increase the coverage of social protection programs in response to shocks. Examples include layering satellite imagery with connectivity data and face-to-face surveys,

such as the recently generated Relative Wealth Index.36 Likewise, the use of machine learning algorithms to estimate consumption based on CDR data can provide a rapid assessment of needs and conditions to deploy emergency cash transfers, such as Novissi in Togo.37

However, when determining which data to exploit and how, policy makers and development practitioners should carefully contemplate multiple trade-offs and constraints. Collecting rich socioeconomic data through harmonized questionnaires can potentially lead to more accurate welfare assessments with lower inclusion and exclusion errors. Yet, on the one hand, actively producing such data is costly, slow, and hard to update—particularly when done through door-to-door sweeps—and thus not best suited to respond to a rapid-onset shock such as a flood or a pandemic. On the other hand, passively generated data, such as CDRs, may bring important cost-efficiency gains and can be updated much more dynamically but may lead to less accurate or potentially exclusionary welfare assessments for those without access to a mobile phone. Finally, beyond richness and cost-efficiency, data protection is another critical factor to consider. Safely exchanging, storing, and processing large quantities of data containing personally identifiable information requires putting into place effective and proportionate data protection safeguards to prevent and deter inappropriate uses such as unauthorized disclosures, commercialization, or illegitimate surveillance, among others. This will not only be critical to protect individuals’ privacy but also toward strengthening citizens’ trust in expanding social protection systems, thereby fostering the development of the social registry.

Leapfrogging with technology is possible, but local constraints and new concerns need to be factored into policy choices. Leapfrogging can be understood as the implementation of a new technology in an area in which the previous version of that technology has not been fully deployed yet. As such, the rapid adoption of mobile communications and mobile money are two relevant examples of leapfrogging technologies on the African continent that can be leveraged to increase the reach of social protection delivery systems. However, to reap the benefits of these two digital megatrends, previous investments in mobile network infrastructure, mobile phone penetration, payment ecosystems, and literacy are paramount to enable digitally ready social protection programs. Yet, digital readiness is not uniformly distributed among or within countries, with stark differences between densely populated urban and sparse rural settings. For instance, 38.1 percent of individuals in West Africa Economic and Monetary Union countries living in urban areas are connected to mobile broadband, relative to only 7.1 percent in rural areas (figure 2.10).38 Thus, having a clear understanding of the relevant barriers to mobile internet adoption, such as low household consumption levels or high prices of mobile services, is necessary to incorporate local constraints into the design choices of adaptive social protection delivery systems. Not considering these constraints can exacerbate the digital divide and create new sources of exclusion errors that are particularly regressive and hence counterproductive to the social protection objectives. The provision of mobile phones or SIM cards to poor and vulnerable groups as part of the rollout of safety nets can improve the digital readiness of these populations and may be explored further.

36 https://data.humdata.org/dataset/relative-wealth-index. 37 Aiken et al. (2021). 38 Rodriguez-Castelan et al. (2021).