Poor Households in Togo

422 | Revisiting Targeting in Social Assistance

Improving PMT with the New Big Data

The use of household- or individual-specific big data for improving targeting is generating excitement.76 The prior section showed the modest results from applying new machine learning methods applied to traditional survey data. New proxies are needed—ideally, proxies that are related to household monetary welfare but not to the traditional proxies used in the models. Two main sources for these new data are public sector big data and private sector big data. The previous section showed how using the former— administrative data—as ancillary data can drive improvements in PMT; for example, in Costa Rica and Colombia. This section reviews how close social protection is to using private data for eligibility determination. The section also asks some bigger questions about whether some of these data should be used even if they are accessible.

A few recent and nascent experiences, Togo prominent among them, show how CDR data can be used for household-specific eligibility determination (beyond poverty mapping or proof of concept in research papers). In Togo, a collaboration between a team of academics, GiveDirectly, mobile phone operators, and the government used big data, including CDR data from private sector mobile operators, to target poor households in poor rural areas of the country (box 6.9). Similar work is being pursued in Bangladesh,77 the Democratic Republic of Congo, Nigeria, and elsewhere.

BOX 6.9

Use of Private Big Data to Select Poor Areas and Poor Households in Togo

Togo is a small country in West Africa with high levels of poverty, even before the COVID-19 crisis. In response to the crisis and the economic pain of COVID-19-related lockdowns, the government immediately launched an emergency cash assistance program (Novissi), which provided electronic (contactless) transfers to nearly 600,000 informal workers in the areas most affected by the lockdowns and curfews, through the beneficiaries’ cell phones. Individuals made their application to Novissi through a new cell phone interface created for Novissi. Eligibility was determined from a recently updated voter database, and transfers were made to informal sector workers (categorical targeting) in areas of cities where quarantines were most stringent (geographic targeting).

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How to Harness the Power of Data and Inference | 423

BOX 6.9 (continued)

Given the success of the fast response, a second phase was designed by the government in collaboration with GiveDirectly, expanding Novissi to rural areas, which, while not as affected by lockdowns, have the highest levels of poverty. To identify beneficiaries, an academic teamb used big data to conduct a mixed geographic and call detail record (CDR)–based proxy means testing (PMT) approach (henceforth, the big data model). The team first used satellite and other geographic information system (GIS) data to develop poverty maps at the canton level, using the big data methods described earlier in this chapter in the section on geographic targeting. The estimated 100 poorest cantons were then selected to receive the Novissi transfers. The second step was to identify the poorest 10 percent of the residents in those cantons to receive the expanded assistance. To do this, the team conducted a phone survey of 8,900 individuals in these cantons to “ground truth” the models, matching the survey estimate of consumption of the surveyed individuals to their mobile phone data to enable the models to predict consumption from phone patterns, that is, allowing PMT modeling to use CDR as proxies to model consumption (CDR-based PMT). In the end, electronic transfers were made to 57,000 individuals.

A recently completed assessment compares the predictive power of the big data model in simulations, with several interesting findings:

• Compared with the sort of occupation-based (categorical) targeting of informal workers used in the first phase of Novissi and the straight geographical targeting being considered by the government as an alternative, the big data model worked better. • The assessment also examined potential sources of exclusion and found that over half of the rural households surveyed were excluded from the program because they did not apply or were not able to register successfully using the cell phone application process, making these much larger potential sources of exclusion error than estimated by the model; 60 percent of those who successfully registered were deemed eligible by the model. Thus, only 19 percent of those surveyed were excluded at the model stage, highlighting the importance of outreach and ease of application. • A pre-COVID-19 more face-to-face survey with more traditional consumption data could also be matched to phone records and allowed the simulation of a hypothetical nationwide program.

◦ The big data model again outperformed straight geographic targeting and categorical targeting to the informal sector.

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424 | Revisiting Targeting in Social Assistance

BOX 6.9 (continued)

◦ The big data model performed similarly to narrow, occupationbased (categorical) targeting to agricultural workers only, which a priori was known as the occupation with a lower income level among workers. ◦ However, the inclusion and exclusion errors in the big data model were estimated to be 50 percent, compared with 37 percent for a traditional PMT using proxies available in traditional household surveys (as presented in the section in this chapter on traditional PMT models).

The assessment concludes that the big data model was the most accurate option available to the government at the time and for the emergency transfer, and that such models may be useful for humanitarian response when traditional data are missing or out of date, but in broader circumstances such models should be seen as a complement to traditional methods and not a substitute.

Sources: Aiken et al. (2021); Boko et al. (2020); https://medium.com /center-for-effective-global-action/using-mobile-phone-and-satellite-data -to-target-emergency-cash-transfers-f0651b2c1f3f; https://www.worldbank .org/en/results/2021/04/13/prioritizing-the-poorest-and-most-vulnerable -in-west-africa-togo-s-novissi-platform-for-social-protection-uses-machine-l. This box references unpublished and ongoing work, which is summarized at https://medium.com/center-for-effective-global-action/using-mobile-phone -and-satellite-data-to-target-emergency-cash-transfers-f0651b2c1f3f.

a. The academic team was based at the University of California, Berkeley, Innovations for Poverty Action, and Northwestern University. It is rare for eligibility determination for a government-led program to be done by unaffiliated institutions. It was done in this case with great care around data security and privacy, prompted by the crisis and more feasible because the financing was basically from GiveDIrectly rather than the government. The Agence Nationale d’Identification was recently created in Togo to collect information for the dynamic assessment of needs and conditions and eligibility determination for multiple social protection programs, under the supervision of the presidency. This will require both significant capacity building as well as establishing a regulatory framework that will (1) ensure that only the targeting institution could handle the data directly, and (2) ensure informed consent when people apply for the transfer program or in the way mobile phone operators enroll customers.