a Fragile State

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Chapters 3 and 4 cover various considerations for differentiating eligibility around covariate shocks but mostly with respect to planning and delivery systems and thus in a manner relatively neutral to methods. As this chapter examines each method, it adds a few notes that are pertinent to this choice, including each method’s relevance to idiosyncratic shocks to individual people or households.

Finally, there can be a degree of path dependency, although history is not destiny. It is perhaps easiest to build on existing administrative capacities or not to disturb existing consensus or institutional arrangements. In some cases, such factors can lead to a different choice of methods than might occur by starting from scratch. Over time, countries can move from using only simple methods to more complex ones as they build their social protection programming and delivery systems (see box 5.2).

BOX 5.2

Djibouti: The First Steps toward a Targeting System in a Fragile State

At the time of the response to the food, fuel, and financial crisis in 2008, Djibouti had neither a flagship social assistance program nor a social registry. The principal instrument used in response to the food crisis was the elimination of taxes on selected food items, a rather blunt instrument. Over the years, various programs were established in the wake of drought shocks; these were largely time-limited, donordriven initiatives, working outside government systems, and mainly focused on providing food to vulnerable populations.

As Djibouti began to expand its social assistance programming, in 2010, it launched the Employment and Human Capital Safety Net (EHCSN) project, implemented by the Djibouti Social Development Agency with support from community-based organizations, nongovernmental organizations, and small and medium-size enterprises. The EHCSN was a workfare program with a nutrition component focused on nutrition education and provision of micronutrients. The Employment and Human Capital Safety Net project applied multiple simple targeting mechanisms. It used geographic targeting based on poverty maps. Participation in public works implied selftargeting. Demographic criteria focused the program on “nutritionally vulnerable households”—those with a pregnant or lactating woman or

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BOX 5.2 (continued)

a child younger than age two in the household. Communities helped to identify which households met these criteria. An initial assessment indicated that 73 percent of the beneficiaries were poor.

Another milestone in the development of social assistance came in 2016 when Djibouti introduced the National Programme of Family Solidarity (Programme National de Solidarité Famille [PNSF]), a conditional cash transfer. For this program, community-based targeting was used in rural areas. A proxy means test was used in urban areas as it was deemed that community cohesion was not as high in urban areas (communities remained involved in the delivery system and mobilization activities).

In parallel with this new program, the government developed the Djibouti Social Registry, managed by the Ministry of Social Affairs and Solidarity, which now covers about half of the population. After initial en masse registrations, new households can register themselves in one of 12 Ministry of Social Affairs and Solidarity branches across the country. The social registry includes the capacity for including biometric information on individuals benefiting from the programs, which is used to verify that no duplicate benefits are provided by the programs using the registry. Currently, 13 programs share the social registry data for eligibility assessment, most prominently for the universal health care program, the provision of social housing, the provision of microfinance, and PNSF. The Djibouti registry was established by a law (decree 2017-311/PR/SEAS) in September 2017. In 2020, the government began registering refugee households in the social registry, permitting them to be considered for eligibility for the programs that use the registry for eligibility determination.

The value of the social registry became apparent as it permitted a rapid response to the COVID-19 pandemic, facilitating targeting and enabling the rapid deployment of in-kind transfers during the pandemic lockdown. Just weeks after the onset of the pandemic, the government put in place a program to provide food vouchers to 90,000 poor and vulnerable urban households. Using the social registry, the intervention targeted households under the poverty line and those active in day labor or temporary and/or independent work—reaching about half of the country’s population. Vouchers worth DF 30,000 (US$170) entitled beneficiaries to a basket of food staples. At the same time, the PNSF coverage was expanded, bringing the total program coverage to about 9 percent of the population.

Despite fragility and limited capacity, Djibouti developed a longterm strategy to create the foundations of a permanent social

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BOX 5.2 (continued)

assistance system that would strengthen the country’s ability to respond to future shocks as well as build human capital and fight chronic poverty. The initial program was designed to address emergency needs, but it evolved based on assessments, with coverage increasing as programs matured. Djibouti has shifted from only selftargeting (for food subsidies) to both geographic and demographic targeting (for public works) to household assessments (for cash transfers and food vouchers).

Sources: Mendiratta et al. (2020); World Bank (2013). See also Brodmann, Devoto, and Galasso (2015); Devoto, Galasso, and Brodmann (2017); Leite et al. (2017); Machado et al. (2018); UNICEF (2010).

The rest of this chapter considers program objectives and practical or contextual factors when choosing targeting methods, beginning with the former. Figure 5.2 shows the decision process for selecting one or more targeting methods. The first question is whether a program’s objectives determine household eligibility by self-targeting, category, or an attempt to rank households on a money-metric welfare basis. Methods for the former are discussed first, including self-targeting, geographic targeting, and categorical targeting. The options for ranking household eligibility by welfare are discussed next, covering means tests, HMTs, PMTs, and CBT. These two choices—categories or rankings—are not mutually exclusive; locations can be selected by geographic targeting and households within those locations ranked to select beneficiaries. Mixed methods are the most common approach in practice. As each method is covered, the second set of practical considerations are also discussed. The text also pays attention to the hard cases: places with low inequality where it is difficult to distinguish between households, places with low capacity to implement different targeting methods and low budgets to build capacity, and places with conflict and displacement that may be particularly sensitive to social discord. In many cases, these difficulties coexist in the same place.

Self-Targeting Methods

Self-targeting programs are open to all, but they are designed in such a way that they are used disproportionately by the poor. The nonpoor choose of their own accord not to use them. The factors that contribute to this choice are preferences about quality, private or transaction costs of participation, and possibly stigma associated with the use of the service or program. The basics have been well known for years (see, for example, the treatment

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Figure 5.2 Factors to Consider in Choosing a Targeting Method

METHODS

Self-targeting (transaction costs, prestige) Self-targeting (design features)

Categorical

Place (geographic) Age Disability Civil status

Lottery

Welfare based

Means test

Hybrid means test Proxy means test Community based

PURPOSE

Principally poverty/inequality? Principally supporting people in other defined categories? Shock response?

FEASIBILITY

Financial data, and technical capacities? Degree of inequality? Path dependency? Political economy considerations?

Choice of method(s)

Source: Original compilation for this publication.

within the targeting literature in Besley and Coates [1992]; Besley and Kanbur [1988]; Coady, Grosh, and Hoddinott [2004]; and Devereux et al. [2017] or the social assistance literature, including Grosh et al. [2008] and Pinstrup-Anderson [1988]) and considerations about self-targeting have changed less than for other methods. The chapter treats the method somewhat lightly for that reason but provides a briefing to round out the treatment of choice of methods.

A classic and still prevalent application of self-targeting is the offer of work on public works jobs for a low wage. According to The State of Social Safety Nets, nearly 100 countries operate such programs (World Bank 2018b). Figure 2.6 in chapter 2 shows that the incidence of public works programs is in the same range as other types of programs, although few rely only on self-targeting. Among the attractions of self-targeting through a work requirement is that the work requirement may increase the political support for the program, overcoming the notion that nonworking adults are not deserving.

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To ensure that the program favors participation by the poor, the work is low skilled and low paid. The work in such temporary employment programs is also commonly, although not necessarily, physically strenuous and performed outdoors in the creation or maintenance of small-scale infrastructure.11 Rwanda has an expanded public works program that includes paying women to provide childcare in their communities (World Bank 2020d). Gaza’s public works program focuses on providing social services (World Bank 2018c). The wage is a key variable; regularity of work and working conditions also matter. Obviously, the lower the wage is set, the lower is the chance that nonpoor persons would sign up. In the conceptual ideal, the program may pay just a little less than the market wage for similar work as a way to balance the goals of self-selection and adequate pay. There are various brakes on setting a very low wage. First, if it is set too low, the benefit to the worker would be little, especially if they have to lose some hours or days of work they would otherwise do. Estimates of such forgone earnings vary widely depending on the setting, ranging from 7 to 50 percent (Subbarao et al. 2013). In general, countries try to time works to seasonal down periods, for example, the slack agricultural season, and sometimes have relatively short workdays to minimize forgone earnings. There may also be legislative barriers to paying less than the minimum wage for the country, although if market wages surpass minimum wages as they do in many countries, this will not be a concern. In Subbarao et al.’s (2013) survey of public works programs, the majority pay less than the market wage.

Self-targeting via low-wage work may be sufficient to ensure that applicants are relatively poor, but quite often the size of the program is too small to accommodate all the applicants and so ancillary mechanisms may be used. Public works schemes typically cap the duration of public works employment to share the benefits more widely and avoid attracting laborers with steady employment. It is also common to see elements of geographic targeting of the public works. Sometimes other methods are used—categorical (as in the case of Djibouti in box 5.2), CBT (for example, in Ethiopia and Rwanda), lottery (for example, in the Democratic Republic of Congo), or occasionally PMT (for example, in Tanzania).

As self-targeting via low wages for public works may exclude some needy households or individuals, care is needed to minimize this. Some of the poorest people typically live in households with few or no working-age adults or adults whose work is limited by caregiving responsibilities, social norms, or disability, although there are some approaches to lower such barriers. Burkina Faso provides childcare for female workers on traditional public works.12 India’s Mahatma Gandhi National Rural Employment Guarantee Act program requires that work be located within 5 kilometers of each claimant’s home, which bounds commute time and the cultural challenge of women being far from home; moreover, the program pays

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equal wages to women and men, despite prevalent wage gaps in the private sector. Various countries have minimum quotas for women’s participation (see, for example, Curry 2019). Malawi has been piloting ways to improve the access of persons with disabilities to jobs on public works projects (Vikan and Diekmann 2017). South Africa sets aside 2 percent of assignments for persons with disabilities (Letswalo 2020).

Another classic self-targeting method is subsidization of the prices of basic food stuffs, ideally of foods that are more consumed by the poor than the nonpoor. The idea is to find different staples or variations on them that are nutritionally equivalent or closely so but differ in terms of prestige— sorghum versus corn, broken rice versus whole, coarse flour versus fine, and yellow versus white corn are examples in which the former is usually less prestigious but (at least) as nutritionally equivalent as the latter. If the price of the less desired commodity is subsidized enough, the poor who are still trying to meet their caloric needs will buy it, while the nonpoor will purchase the more prestigious variant. Of course, the sorting will be inexact and dependent on the relative strengths of people’s preferences and the differences in prices. Moreover, there may not be a commodity that is consumed more by the poor than the nonpoor (especially if this is judged in absolute terms rather than in relative ones). Even if there is one such commodity, it needs to have a production and trade chain that makes it easy to attach the subsidy. For example, grain grown by smallholders and sold in a thriving private market to dispersed outlets will be harder to subsidize than a product that is largely imported by a monopoly state trading agency. Consumption patterns are important as well. Sorghum or millet, for example, may be consumed not only by poor humans, but also used as animal feed. Thus, subsidies on these grains may result in a costly indirect subsidy to the livestock industry. There may also be regional variations. The urban poor may purchase tortillas daily, but the rural poor may make them at home.

Whether the benefits of food subsidies are close to neutral in their distribution or somewhat regressive depends on the commodity that is subsidized and patterns of consumption. The logic of why subsidies on food staples can be reasonably self-targeting is intuitive—even an overfed nonpoor person will only eat a modest amount more rice, bread, or porridge than a poor and hungry person. The richer man will diversify his food basket to more nutrient dense, tasty, convenient, or luxury foods and his consumption basket to a smaller share of food with more nonfood, while a poorer person’s food basket will remain more concentrated in the staple grain. Thus, a subsidy for rice may result in rather flat incidence. A subsidy for sugar or meat would have a more regressive incidence because the rich person can more feasibly eat more sugar or meat than the scant amount of these that the poor eat. In the Arab Republic of Egypt, for example, in the 2000s, about 96 percent of the poor were benefiting from the food subsidy

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system, but those in the richest quintile were receiving about 12.6 percent more from food subsidies than those in the poorest quintile. The baladi bread and cooking oil subsidies were the most regressive of all the food subsidies. The only subsidized food that was progressive was baladi wheat flour, which provided the poorest quintile as much as six times the benefits as the richest (World Bank 2010a). Because food price subsidies are a relatively blunt tool, some programs also use some other rationing or eligibility determination tool. For example, Indonesia’s variously named rice price subsidy first used CBT and then PMT (Holmemo et al. 2020), and India allows the poor larger purchases at discount prices in its Public Distribution System (Dreze et al. 2018).

As practiced, most food subsidy programs leave the government defending a set price with high upward risk for fiscal costs and political risks for price changes. Sometimes there are significant leakages into the black market. Thus, there is a long history and rich literature on attempts to reform food price subsidies (Pinstrup-Anderson [1988] is an authoritative source). For example, Tunisia engaged in a multiyear reform of food subsidies, which saved 2 percent of gross domestic product (GDP) by reducing the range of foods subsidized and shifting the degree of subsidy among nutritionally similar items (for example, eliminating subsidies on white flour baguette but maintaining them on coarser flour gros pain; liberalizing the market for fine olive oil but subsidizing generic grain-based cooking oils sold in small qualities with bring-your-own bottle packaging, and so forth (Tuck and Lindert 1996). Alderman, Gentilini, and Yemtsov (2018) provide multidecade treatments of the stories of reforms in Egypt, India, Indonesia, Mexico, Sri Lanka, and the United States. Eventually, Mexico moved to cash transfers in lieu of the food subsidy programs; Indonesia reformed its program many times and, in a subsequent move, converted to a food voucher (Holmemo et al. 2020); the United States maintained the food stamp system, eventually using quite sophisticated targeting and payment mechanisms; and Egypt and India reformed, reduced market distortions, and improved governance of their subsidized rations systems but still maintain programs with very high rates of coverage and significant budget.

Energy price subsidies are still common and tend to be much more markedly regressive than food subsidies. The intuition is again simple— everyone consumes food and there are limits to how much more staple food a wealthy person can eat than a poor one, but there is no such analogy for fossil fuels. The poor may consume no or very little fossil fuel, and the rich may consume a great deal because they have many appliances, possibly air conditioning or an automobile. In the Islamic Republic of Iran, prior to subsidy reform, the government spent about 20 percent of GDP on food and energy subsides. The prices of bread and flour were subsidized, with the value of the subsidies essentially flat across the income distribution, whereas the value of energy subsidies was more than five times higher in

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Figure 5.3 Percentage of Energy Subsidies, by Household Decile, Selected Countries

60

50

Percentage

40

30

20

10

0

Ghana (2012, electricity)

Mexico

(2014, electricity)

Mexico

(2014, fuel)

Sources: Based on CEQ Institute database.

Panama

(2016, electricity)

Sri Lanka

(2009, electricity)

Tanzania

(2011, electricity)

Indonesia

(2012, fuel)

the highest decile than the lowest. Silva, Levin, and Morgandi (2012) provide other examples of flattish food subsidy incidence and markedly regressive energy subsidy incidence in the Middle East and North Africa. Figure 5.3 shows highly regressive electricity and fuel subsidies for selected countries in the year of analysis (several of the countries have since implemented various reforms and changes in pricing regimes; for example, Indonesia has transitioned to an expanded targeted direct transfer system).13

There have been some attempts to subsidize some fuels more than others, to favor those more used by the poor, yet with little success. For example, kerosene is often subsidized to help the poor use it rather than biomass for lighting or cooking. But this commonly results in commercial malpractice, such as kerosene being used to dilute diesel or diverted to the aviation sector, or various black market, smuggling, or “commercial tourism” schemes (see Kojima 2013).

Moreover, energy subsidies can promote overconsumption of energy, usually that derived from fossil fuels, and are thus inefficient and harmful to the environment and a target for policy reform for multiple reasons (see, for example, Coady et al. 2015; Flochel and Gooptu 2017). Kojima (2013) documents how 65 countries struggled with energy subsidies in the years around and following the 2009 fuel price crisis. Kojima (2021) provides a recent overview and nine case studies around the reform of liquefied petroleum gas (LPG) subsidies, including several well-known cases that moved to household-specific alternative targeting mechanisms for compensatory cash transfers.

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Overall, the price subsidy experience underscores the value of being able to go directly to households, although often with fairly high thresholds. India’s LPG reforms and the Islamic Republic of Iran’s comprehensive reforms started with universal cash compensation (Gelb and Mukherjee 2019; Salehi-Isfahani and Mostafavi-Dehzooei 2018). Jordan’s fuel subsidy reform gave compensation to two-thirds of households (Atamanov, Jellema, and Serajuddin 2015), as did El Salvador’s LPG reform, the Dominican Republic’s somewhat lower at about a third of the population, and Brazil’s closer to a fifth (Kojima 2021).

Categorical Methods

Several methods work by assigning people to a category based on easy-toobserve characteristics and assuming that needs are relatively homogeneous among the group. The most commonly used methods are geographic targeting and demographic targeting. Geographic targeting is quite simple to understand conceptually, although the data and inference issues are more complex. Thus, it gets short treatment in this chapter; more details are provided in chapter 6.

Geographic Targeting Some variant of geographic targeting is applicable in many settings, although two of the options are designed to cope with rationing of programs in ways that it is hoped will diminish as social protection systems continue to develop. • First, the most extreme variant of geographic targeting selects areas where the program will operate and gives benefits to all in those areas.

This may be highly pertinent in situations such as natural disasters that affect only some areas with widespread losses. • Second, in a more common variant, the neediest areas are selected as places where programs will operate, with the decision based on some spatial analysis indicator(s) related to need—such as poverty, drought, or malnutrition that is pertinent to the purpose of the program—and then additional eligibility criteria are used within the areas of operation to select the households that will benefit. The exclusion of whole parts of the country leads to clear and politically visible errors of exclusion since poverty and most other vulnerabilities occur everywhere, just at different rates (see box 5.3 for an example). This variant is often chosen where need is highly geographically differentiated and/or for programs that are in a roll-out phase. • In the third variant, the program operates in all geographic areas, but geospatial analysis is used to define benefit quotas for each area, with specific households selected via some other method. When used to allocate the caseload or places in a budget rationed program, there will still