Revisiting Targeting in Social Assistance

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the logisticians who plan for good delivery systems; the social workers or community development officers who build bridges between ministries, communities, and individuals; and the advocates, organizers, technocrats, and elected officials who build consensus for good social protection policy.

Targeting methods evolve. Although the suite of methods has the same names as were written about two decades ago, the practice and potential of each is changing as new data, new technology, or new capacities and expectations push them to evolve.

Notes 1. https://www.nytimes.com/2021/03/01/technology/china-national-digital​ -currency.html 2. See Waze (2018, 2020), https://www.waze.com/wazeforcities/casestudies.

3. https://www.businessinsider.com/how-google-retains-more-than-90-of​ -market-share-2018-4.

4. The most popular area-level poverty map is that of Fay and Herriot (1979). The Elbers, Lanjouw, and Lanjouw (2003) methodology has since been supplanted in many cases by the empirical best/Bayes approach of Molina and Rao (2010).

This chapter focuses on poverty maps developed with big data and does not review the different econometric models currently used in traditional poverty maps. For a good review of the models, see Molina, Corral, and Nguyen (2021).

For implementation of the models in Stata, see Corral Rodas, Molina, and Nguyen (2021).

5. Head et al. (2017) use the same transfer learning procedure as Jean et al.

(2016) with a different set of countries and an expanded set of human development indicators at the level of household sample survey clusters. Steele et al.

(2017) use satellite imagery and CDRs to predict regional poverty and wealth measures. The authors employ hierarchical Bayesian geostatistical models for the prediction task. The models serve as a way of averaging or smoothing across a spatial area while also accounting for uncertainty.

6. Several papers demonstrate a correlation between phone activity (as measured using CDR) and regional economic activity. Toole et al. (2015) use CDR to predict employment. Schmid et al. (2017) predict literacy using CDR. Deville et al.

(2014) generate population estimates using CDR, and Blumenstock and Eagle (2012), Dong et al. (2014), and Schmid et al. (2017) use CDR to predict demographic characteristics at the subnational level. Eagle, Macy, and Claxton (2010) explain regional economic rankings using social network diversity as measured using CDR. Frias-Martinez and Virseda (2012) also show that regional economic indicators can be predicted by CDR activity. Mao et al.

(2013) and Smith-Clarke, Mashhadi, and Capra (2014) present similar results for Côte d’Ivoire. Pokhriyal and Jacques (2017) predict regional poverty in Senegal using a combination of CDR and satellite imagery. Njuguna and McSharry (2017) also use CDR and satellite imagery to predict the regional Multidimensional Poverty Index in Rwanda using a least absolute shrinkage