B5.3.2 Woreda Selection Does Not Add Much to the PSNP’s Targeting Performance

286 | Revisiting Targeting in Social Assistance

BOX 5.3 (continued)

contributes little to the PSNP’s targeting performance as poverty is widespread rather than confined to a subset of woredas (figure B5.3.2). The second-level selection of subdistricts (kebeles) within the participating woredas contributes more to poverty targeting, and the selection of households by community-based targeting contributes still more.

To reduce the errors of exclusion in the geographic targeting of the PSNP, the government is working on two fronts. First, in 2016, the Urban PSNP was launched. To update the geographic targeting in rural areas and reduce structural errors of exclusion, in 2020, the government decided to revise the allocation of caseloads among woredas, including additional woredas in the program and reallocating the caseload among participating woredas. The new-to-the-program woredas will use a combination of (1) recent history of receipt of drought-related emergency food assistance, (2) remote-sensing satellite data showing the frequency of drought shocks, and (3) the prevalence of extreme poverty. The redistribution of the existing caseload among new-to-theprogram and already participating woredas will be informed by poverty data, but it may take other factors into account, including high vulnerability rates in certain regions and/or the political risks of

Figure B5.3.2 Woreda Selection Does Not Add Much to the PSNP’s Targeting Performance

Decomposition of the targeting differential

7

Targeting differential (percentage points)

6

5

4

3

2

1

0

4.9 3.4

1.7

Woreda

Geographical selection

3.2

Kebele

Household selection

Source: Calculations using data from the Household Consumption and Expenditure Survey (HCES) and the Welfare Monitoring Survey (WMS) 2016. Note: The targeting differential is the difference between coverage of the poor and that of the nonpoor. PSNP = Productive Safety Net Program.

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BOX 5.3 (continued)

significant caseload reductions. At a minimum, this redistribution will achieve the following: no regional caseload should exceed the projected number of people living below the 15 percent poverty line by more than 10 percent; regional caseloads will be primarily redistributed within the respective region (to new-to-the-program and previously covered woredas) according to the poverty data; and expansion will be planned for a minimum of 70 new woredas.

In the future, such data-driven reallocation of the caseload is planned to take place every four years. Thus, the PSNP continues to use geographic targeting but with the criteria, process, and locations refreshed to match changes in context and the availability of data.

Sources: World Bank 2015a, World Bank 2015b, World Bank 2020c, World Bank 2020e, World Bank 2021a.

be errors of exclusion due to budget, although they will be more subtle and distributed across subnational polities. • In a fourth variant, there is no rationing, but geospatial analysis is used in program planning and monitoring to ensure good outreach, optimal allocation of staff, and so forth to reduce transaction costs for clients. The program per se is not geographically targeted, but administrative resources are geographically targeted in an effort to minimize errors of exclusion.

In each variant of geographic targeting, it is necessary to consider the technical and political process by which the budgets will be allocated and updated over time as patterns of need change, new data are collected, program coverage or impacts evolve, and so forth. As chapter 6 discusses, whether traditional survey-to-survey imputation methods are used or newer approaches using big data are employed, the technical requirements to guide geographic targeting are significant. It is now possible in most cases to provide reasonably up-to-date poverty estimates at low levels of disaggregation, usually the third level of administrative unit (for example, the county, parish, or subdistrict) and sometimes even more finely (village or urban neighborhood). The political factors may be significant as well. If the poorest or most vulnerable areas are concentrated in a few of the secondlevel subnational jurisdictions (for example, states or departments used in defining representation in the national legislature), then there may be tension between allocating the program or program slots to the poorest or most vulnerable third-level units observed in the geospatial analysis over the whole nation (for example, the poorest 100 districts), or allocating the

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program or program slots so that all or most legislative districts have some and the slots are allocated to the poorest areas within each legislative district (for example, the poorest 10 districts in each of 10 departments). Reallocations over time will again have a political element as some areas/ jurisdictions would gain slots and others lose as the geospatial distribution of poverty or vulnerability changes over time.

Geographic targeting will clearly be suitable as part of response planning for a large share of natural disasters as these are normally geographically delimited (although for small island states they may encompass the whole country). In contrast, geographic targeting is not particularly useful for widespread economic shocks nor for individual health or employment shocks.14

Demographic Targeting

Demographic targeting is used in half or more of social assistance programs. (As coded in the ASPIRE database, categorical targeting is used in threequarters of the programs, and about three-quarters of these cases are probably using a demographic category.) Around a third of the categorically targeted programs (not all of which are demographic) also assess eligibility with another method. Looking at cases of categorical programs with two or more methods, the most common additional methods are geographic (43 percent), means testing (40 percent), and PMT (25 percent).

Demographic targeting demands careful thought about the unit of assistance. Society has a dual concern for both individuals and families. Discourse about human, social, and economic rights is framed around individuals. Yet, since time immemorial, families have been the way in which resources and risks were shared and most people live in families, so it is hard to divorce the discussion of individuals from the context in which most live.

The following list unpacks the appeal of demographic targeting. Although in some cases it is difficult to quantify, several of the advantages of demographic targeting are generally accepted (Devereux et al. 2017; HelpAge 2006; UNICEF-ODI 2020) and universal child allowances and social pensions are included in the International Labour Organization’s vision and costing of social floors (Durán-Valverde et al. 2020; ILO 2019; Ortiz, Cummins, and Karunanethy 2017). The following are among the advantages that are commonly referenced: • Political consensus for supporting the meritorious or deserving. The groups supported through demographic targeting are generally viewed as deserving–children are to be treasured and protected, and their human capital and future are highly sensitive to any deprivations. The elderly are to be respected and rewarded for their life service. Widows have suffered misfortune in losing their life partners.

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• Stigma, transparency, and human rights. There is a consensus that age-based targeting carries no stigma. Age is a natural state of life, not subject to any lack of initiative by the individual (or even family). Indeed, families are congratulated on the good fortune of a new birth and the elderly on the good fortune of a new birthday. Demographic targeting also has the positive feature of transparency, making it easy for claimants to understand what is due and seek redress if they have not received it.

Demographic targeting and its ability to meet the principles of equality and nondiscrimination are among the most acceptable under human rights critiques. • Demographic targeting is simpler to implement than other forms of targeting.

Programs that use only demographic targeting have lower information requirements than programs that try to measure or estimate welfare, employability, or disability to establish eligibility. However, programs that are only demographically targeted still need the whole delivery chain elaborated in chapter 4—outreach, intake and registration, payment, recertification, grievance redress, and monitoring. These functions must operate continuously so that each new cohort of births and birthdays is accommodated and with low transaction costs. If these functions are not done well, there may be errors, especially of exclusion, and sometimes of inclusion and/or loss of reputation for the program.15 • Less direct concerns about labor disincentives. Sometimes the political consensus to provide social assistance to those in need is a bit frail, and/or concern over labor disincentives is strong so that programs for those not expected to be able to work—children, the elderly, and those living with disability—achieve more significant funding. (Although this phenomenon seems strong, it seems to undervalue the evidence that labor disincentives are usually minor and gives little acknowledgment that most individuals in the favored groups live in families that share resources and most of those families contain working-age adults whose employment and time use decisions may be sensitive to unearned income.) • Empowerment within the family. A strand of the literature clearly recognizes the family and that while there may be some pooling of resources, not all individuals have equal voice or share. This strand of the literature reminds that having an independent income stream can elevate the status of the elderly within the family (see, for example, Kidd 2016; Tran,

Kidd, and Dean 2019).

Sometimes demographic targeting is a good fit for purpose for a program. Vaccination of young children is a good example. Individual children benefit from gaining immunity as early as they can. There is a benefit to others in the community as each child’s vaccine helps lower the potential for the illnesses to spread in the community.