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degree of community cohesion may not allow CBT. This may be true in urban settings where density and mobility (in both residence and where time is spent during each day) are so high that people do not know their neighbors well, or where geographical communities are socially divided by ethnicity or conflict. The Djibouti case study provides an example of geographic targeting combined with CBT or CBT in rural areas and PMT in urban areas. Still, PMT, CBT, and their combination are methods that are on the table and used in a large share of developing countries. Where these are insufficient or undesired, some rationing, such as by self-targeting, geography, or another observable characteristic, or even by lottery, may still be an option.

Whichever method is selected, it is important to do it as well as possible. One part of that involves all the elements of the delivery chain reviewed in chapter 4. These require careful planning, adequate resources, realistic implementation plans, coordination, and a plan for building capacity, learning by doing, and adjusting. They also involve the details of the targeting method itself—the specific data and methods of inference to be used, which is the topic of chapter 6.

Welfare targeting systems often evolve as constraints change and social policy develops. Constraints can change in response to capacities built by social protection programs and as general secular trends in the economy or governance change. Programs and goals can evolve, usually from simpler or quick and dirty to more elaborate or precise. Implementing, learning from constant monitoring and periodic process evaluations, and then adjusting are necessary. Adjustments may improve the implementation or accuracy of the original targeting method, but they may also involve shifting individual programs or a whole social registry from one method to another.

Notes

1. Coady, Grosh, and Hoddinott (2004); Devereux et al. (2017); Slater and

Farrington (2009). 2. There are limitations to the coverage and quality of these descriptors of program design, which is why ASPIRE has redesigned the formats and processes it will use in a large updating of administrative data in 2022. This is also why so far ASPIRE has not made much use of the data on program design. But in the internal review process for this book, the reviewers voiced an appetite to see some numbers, with all due caveats: (1) there are no targeting data for about a quarter of the programs, although there does not appear to be any marked bias by region or country income level; (2) the coding is done with a different list of categories and some possible unevenness, as described in the text; and (3) the coding of the qualitative variables was mostly done in the first year of the collection of the expenditure data and often not updated, which is a minor

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concern. Moreover, the results are not weighted, so a small program and a large program contribute the same to the analysis. In some ways this is not conceptually problematic—a means tested guaranteed minimum income may have lower coverage than a categorical/demographically targeted child allowance, but both are important parts of the country’s social protection system and worthy of observation. In trying to be complete in an expenditure sense,

ASPIRE tries to capture as much spending as it can and thus captures not only the flagship programs of the main types in each country, but it also often captures many smaller programs. Thus, the results in the database sometimes feel unfamiliar to those used to reading principally about the flagship programs with big name recognition. Finally, the methods cannot be connected to outcomes. As explained in chapter 2, the household data are processed in groupings not at a program-specific level. 3. The coding in ASPIRE does not allow HMT as an option. Programs that might be labeled HMT in the terminology used in this book were largely coded as means testing in the data. 4. Bance and Schnitzer (2021); Blumenstock (2020); Blumenstock, Cadamuro, and On (2015); Jean et al. (2016). 5. Another initiative is the Pula Advisors, a Kenya-based agricultural insurance scheme that uses rainfall data collected by satellites to estimate the amount of precipitation for relatively small areas to which farmers can be matched. Using machine learning algorithms, Pula aims to provide individual farmers insurance rather than geographical-level area insurance, providing tailor-made protection against adverse growing conditions and thus protecting them more effectively from income shocks. However, Ohlenburg (2020) indicates that

Pula still needs detailed data collection at the household level to counteract unrepresentative data, as its modeling was biased toward larger farms that typically have higher and more stable yields due to the limited availability of data for small farmers with variable yields. 6. Center for Effective Global Action at the University of California, Berkeley (https://cega.berkeley.edu/). 7. https://news.berkeley.edu/2020/06/02/satellite-images-phone-data-help -guide-pandemic-aid-in-at-risk-developing-countries. 8. Evidence suggests that CBT seems to be focused on factors other than monetary poverty, such as possession of livestock and land, human and physical capital asset holding, and household earning capacity (Alatas et al. 2012;

Karlan and Thuysbaert 2016; Stoeffler, Mills, and del Ninno 2016). 9. See Holzmann et al. (2008) for a description of the HEA. 10. Lotteries are used as well as in other public policies ranging from school admissions (for example, the Federal Pedro II school for basic education and grades 1 and 2 in Brazil or the Prince George’s County Public School Specialty

Programs in Maryland in the United States) to special visa allocation (for example, the Diversity Visa Program in the United States). 11. The programs can be set up in various ways—giving more or less emphasis to the income support the workers achieve versus the public investment value of the works done, whether there is any attempt to provide training or increased chances of private sector employment, and whether there is a guarantee or

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not. (See, for example, Gentilini et al. [2020, chapter 2] or Subbarao et al. [2013] for more on public works programs generally.) 12. https://www.worldbank.org/en/news/feature/2021/03/08/enabling-women -to-work-and-their-children-to-blossom-the-double-success-story-of-mobile -childcare-units-in-burkina-faso. 13. For more on energy subsidy analysis, see Younger, Osei-Assibey, and Oppong (2017) for Ghana; World Bank (2015c) for Indonesia; Martinez-Aguilar (2019) for Mexico; Arunatilake, Jayawardena, and Abayasekara (2019) for Sri Lanka; and Younger (2019) for Tanzania. 14. Occasionally, an economic shock may affect certain sectors of the economy much more than others and these may be spatially concentrated. For example, a fall in international coffee prices will affect the zones of countries where coffee is a major crop more than other parts of the same countries. 15. In Zambia, a HelpAge (2009) report underscores the problems caused by lack of an open registration process for social pensions in the Katete region, and exclusion of anyone who turned 60 (the cutoff for receiving pensions) after the initial registration was completed. An assessment of the Old Age Allowance in

Bangladesh found that 24 percent of households receiving the grant did not have a member who was age 60 or older (Slater and Farrington 2009). 16. The Global Monitoring Database is the World Bank’s repository of multitopic income and expenditure household surveys, which are used to monitor global poverty and shared prosperity. The household survey data are typically collected by national statistical offices in each country and then compiled, processed, and harmonized. https://www.worldbank.org/en/topic/poverty /brief/global-database-of-shared-prosperity. 17. The focus here is on the income/consumption measures that are most used in poverty targeting, although the methods can be used analogously for other welfare measures, such as an asset index, calorie consumption, and food security/consumption. 18. Pairing means testing and PMT may initially seem a bit paradoxical but is not necessarily. For example, a means test can be done for those with formal income and a PMT for those without observed formal income. 19. The infrequent updating of registry data is often due to the use of timeconsuming and costly survey sweeps. Such a practice is not inherent in PMT and there is no reason the registry data cannot be updated more frequently using other approaches. However, the relatively static nature of the underlying variables used in the PMT model makes PMT less responsive to shocks. 20. Evidence from Indonesia suggests that the degree of welfare loss from elite capture is relatively low (Alatas et al. 2019). 21. The Distribution Characteristic Index is also a useful measure in simulations. It not only assesses greater value for benefits received by the very poor compared with the just poor, but also provides some value (although less) for benefits received by those just above the poverty line (unlike the poverty gap). However, it is more complex to explain to policy makers and the public. 22. From chapter 2, the poverty rate is the percentage of people who are below the poverty line, and the poverty gap is how far below the line they are on average.