Key Elements for Community-Based Targeting

How to Harness the Power of Data and Inference | 431

systematic prediction errors for a large number of different subgroups, such as gender, ethnicity, and religion, but without careful management, oversights, and audits, machine learning algorithms can encode bias.

It will also be important to learn whether or how much using big data to determine eligibility for benefits may change behavior. If financial transactions are used to model household welfare, will some transactions move back to cash to hide them? Will households avoid purchases of important goods just to remain eligible? Will households change or mask their calling behavior or even their movement patterns? Will they self-censor on social media or post misleading information? For example, when it was realized in Kenya that GiveDirectly was determining household eligibility based on satellite assessments of roofing, households would not upgrade their roofs. As credit based on mobile phone use systems has scaled, manipulation has become commonplace as borrowers learn what behaviors will increase their credit limits (see the references in Björkegren, Blumenstock, and Knight [2020]). Potential incentive issues are not unique to this type of big data,82 but they take on new forms that are not yet well explored or quantified. Efforts are being made to make machine learning approaches more robust to strategic behavior (Björkegren, Blumenstock, and Knight 2020; Hardt et al. 2015).

In summary, private big data offer exciting possibilities for improving eligibility determination, but work on various fronts—technical, social, and legal—will be needed for fully dimensioning and grasping these. This is a field that has been developing quickly with big advances even during the time the rest of this book was being conceived and written. It seems likely that data science will advance as quickly as ground truth data can support, and this will induce further needed attention on the regulatory and policy fronts.

Key Elements for Community-Based Targeting

Traditional CBT takes a far different tack on discerning who is poorer. It forgoes interoperability among government databases, household-byhousehold quantitative surveys, and fancy algorithms. Rather it uses a group of community members or leaders, en masse or in committees, as the main agents in the selection of beneficiaries for social assistance programs. The community members are expected to have enough knowledge about their neighbors from their day-to-day lives—who buys how much of what in the market, how people work, what clothes or shoes they wear, and how they participate in community social interchange—so that they could do some sort of needs assessment without carrying out special purpose data collection. In the sense that the data used are already generated for other purposes (community life), it is like big data but maybe the term

432 | Revisiting Targeting in Social Assistance

traditional data strikes the right chord. Common techniques used in community targeting exercises are community or participatory wealth ranking83 (Kebede 2009; Zeller, Feulefack, and Neef 2006), participatory rural assessment (Chambers 1994), and HEA (Holzmann et al. 2008). Traditional CBT methods not only rely on local information, but may incorporate local notions of deprivation into the selection criteria of social programs.

There is an extensive literature on CBT, for example, Conning and Kevane (2002), Himmelstine and McCord’s (2012) annotated bibliography of more than 100 studies, and McCord’s (2013) distillation of many CBT experiences. In this literature, there is significant accord on the potential importance of the information base of communities and the process of involving them. Handa et al. (2012), for example, show that CBT in East Africa had on average better results than Coady, Grosh, and Hoddinott (2004) found in their benchmarking across many methods and programs. The literature also shows that when it is effectively implemented, CBT can generate widespread program support even if only a portion of the population benefits. There are also frequent citations of a set of challenges to be managed with respect to community dynamics—how to reduce risks of errors of inclusion stemming from elite capture and general data manipulation, how to minimize errors of exclusion from any systemic patterns of exclusion in community life, how to minimize any friction caused by drawing distinctions, and asking community members to help in that process. Alatas et al. (2019) distinguish between formal and informal elites. While they find evidence of formal elite capture (although relatively modest), they find no evidence of informal elite capture. Another theme of the literature is that while in general the method produces progressive outcomes, there is a lot of variation in both outcomes and how CBT methods have been implemented from place to place. For example, Premand and Schnitzer (2018) show that in Niger, CBT was done with three committees, and the results were then triangulated. The authors show that doing CBT with just one committee would lead to manipulation and suffer from information asymmetries. However, using three committees and triangulating across them reduced these risks (although not fully). Hence, on variations of outcomes and implementation, CBT is like other targeting methods.

Without trying to repeat the classic references, this section mentions some of the practical issues that have arisen in recent implementations of social assistance where communities have an important role, although sometimes not an exclusive one, in determining eligibility. These are illustrated with short details from various programs and a more detailed story on the use of CBT for Ethiopia’s rural Productive Safety Net Program in box 6.11.