4.2 How Big Should a Social Registry Be?

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BOX 4.2

How Big Should a Social Registry Be?

Social registries or interoperable social information systems range from covering just a few percent of the population to nearly all (figure B4.2.1). A social registry that is built to support just one or a few very narrowly targeted programs may cover just a few percent of the population. But the increasing trend is for social registries to be used to support a wide range of social programs, and some of these may be more broadly targeted, with some even reaching high up the welfare distribution. Cash transfers may be targeted at the poor—covering only 10 percent or so of the population in countries with very low poverty or very low budgets. But child allowances or social pensions may strive to include all members of the age group. Subsidies for health

Figure B4.2.1 Coverage of Social Registries

100

90

80

70

Coverage (%)

60

50

40

30

20

Median = 21%

10

0

Argentina Uruguay Pakistan Dominican Republic Peru Saudi Arabia Chile Phillippines Colombia Jordan Turkey Zambia Ecuador Brazil Indonesia Ukraine Honduras Georgia Djibouti Cabo Verde Senegal Tunisia Yemen, Rep. West Bank and Gaza Gabon North Macedonia Mauritania Togo Haiti South Africa Iraq Benin Costa Rica Mauritius Tanzania Azerbaijan Ethiopia Jamaica Lebanon El Salvador Mali Grenada Bolivia Ghana China Sierra Leone Burkina Faso Eswatini Comoros Côte d’Ivoire Congo, Rep. Madagascar São Tomé and Príncipe Chad Zimbabwe Belize

Source: Delivery Systems Global Solutions Group, Social Protection and Jobs Global Practice, World Bank.

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BOX 4.2 (continued)

insurance may reach half or more of the population. Lifeline or “social” tariffs for utilities may be narrow or broadly targeted. Compensation in cash or in social tariffs instituted as part of the reform of food or energy prices can start as broadly as universal, with some sort of self- or affluence-targeting reducing participation at the top.

Social registries or program-specific information systems that include large shares of the population also offer flexibility for shockresponsive programming. With information already gathered on base welfare, contact points, and possibly accounts for payments, it is easier for the government to issue payments in response to shocks, with or without any additional needs assessment based on the household’s welfare following the shock. Countries with large social registries have used this capacity in times of natural disaster: the Philippines is a prime example, and Kenya and Mauritania are countries where the dimension of the social registry in areas prone to drought was planned around the ability to respond when needed (see Bowen et al. 2020). With the COVID-19 crisis, quite a significant number of countries issued emergency payments to people contained in the social registry; where the registry was more inclusive, so too could be the response, covering not just the already poor but the many more affected by the various degrees of stay-at-home orders, closures, and quarantines imposed. Chile’s temporary family emergency income support, for example, was targeted principally to families with only informal sector incomes, below the 90th centile of the welfare distribution and the 80th centile of the emergency index, and with reduced payments to those with some formal sector income. Payouts were automatic for those enrolled in several of the ongoing programs and required online applications for others (https://www.ingresodeemergencia.cl/faq).

Of course, there are costs to bear in having large social registries. The most obvious cost is that it takes resources to collect and update data. Thus, it is not natural for social registries to be much larger than the size of the largest ongoing or common emergency response program the registry is meant to support, although the registry may grow as the social protection system grows. A very large social registry may also be problematic politically, or it may require very good communications, to register many households who may be disappointed not to receive any support immediately. When Sierra Leone initiated its Social Protection Registry for Integrated National Targeting in 2014, it covered only a slightly larger number of households (approximately 20 percent) than could be covered by the main social assistance programs, to avoid raising expectations of a more expansive registry.

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BOX 4.2 (continued)

In Mauritania, the provision for the emergency response program involved preregistering 50,000 households in drought-prone communes. In Kenya, the saturated coverage for the Hunger Safety Net registry is in only the four most drought-prone counties, and periodic payouts have been made using the information.

differentiating eligibility and benefits. Considerations around the selection of a targeting method and the details of its design are discussed in chapters 5 and 6. The information gathered in prior steps and any complementary data brought in from other government databases is compared with the eligibility criteria for one or several programs. The main output from this phase of the delivery chain is the preparation of a list that informs program administrators about the potential eligibility for specific programs or the mix of benefits and services that may be awarded. Another important output of this phase is the preparation of profiling reports on applicants, which allow measurement of the potential demand for social protection programs and determine the characteristics of the applicant population. As such, assessment of needs helps in planning, budgeting, and coordinating programs. Moreover, statistical tools, such as predictive analytics and data integration and analytics, can be used to predict the risks faced by different households and be helpful in disaster risk management.

Following assessment of needs and conditions, the next steps in the delivery chain are the conclusive steps of eligibility determination and enrollment decisions, notification, and onboarding. As presented in figure 4.1, exclusion errors are still possible if process failures occur at these stages. After the decision is made about eligibility to participate in a program, individuals, families, or households are classified as beneficiaries, wait-listed, or ineligible. This requires clear and proactive procedures for notification of eligibility as people need to be informed about their status, and miscommunication between administrations and people can undermine a program’s credibility and transparency. Any applicant who does not understand what further steps they need to take to complete enrollment (or, if needed, to file a grievance) may yet lapse into errors of exclusion.

For those who are eligible, notification should include the next steps and procedures for official program enrollment. Notifications for this group should indicate the decision; what the beneficiary will receive; when, where, and how they will receive it; their rights and responsibilities; contact points and information; and next steps. At this enrollment point, eligible

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beneficiaries need to have a more detailed and operational understanding of how the program works; who to contact; where and how to get benefits and services (payment points and service providers); payment and service provision schedules; the timing and location of any monitoring meetings; their rights, roles, and responsibilities; where and how to file grievances; and so forth. In this phase, some programs may require presentation of documents such as a photo (if a program-specific ID is created), cell phone number, bank account or e-wallet information, any relevant consent forms signed by the beneficiaries (or designated recipient), school enrollment form, medical certificates, vaccination cards, and so forth. There is a risk that applicants who are notified as being eligible may not be able to provide all the paperwork or that with inadequate notification, eligible people will lose benefits because they have improperly understood their responsibilities and fail to comply with them, thus leading to targeting error.

Take-up issues in the enrollment stage are like those seen during the intake and registration phase and require a strong understanding of the target group. Minimizing exclusion errors during notification requires a careful understanding of the capacity, effective communication channels, context, and constraints faced by the population of interest. For instance, designing literacy-appropriate notifications and enrollment materials is critical in most settings. This includes information on the mechanism to deliver the payments or benefits, which may be beyond what the potential beneficiary is accustomed to and can lead to issues downstream. Distance and associated costs may also be a constraint for potential beneficiaries.14 Whether the notification process is public or private can also be important contextually. In some cases, there may be stigma associated with any type of public notification, while in others, a community meeting or other form of public notification is essential for the program’s credibility or transparency.

Waiting lists bear important consequences for errors of exclusion. Anyone who is wait listed meets the eligibility criteria but is excluded from the program due to lack of budget space, which is a clear and direct error of exclusion, a situation that reveals clearly when there is a disconnect between eligibility criteria and budget that requires a policy solution. Moreover, when waiting lists are substantial and/or waiting times are long, they may have second-round effects. Individuals will come to understand that applications may be unsuccessful, so they may not bother to apply. Governments or individual staff workers may become less assiduous in outreach efforts since the program is already full, so that hidden errors of exclusion may occur. Recurring wait lists indicate a problem that needs fixing at the policy/budget level. Until that is done, not even allowing a wait list for a budget-rationed program can be worse. The wait list pressures the government for appropriations and allows quick response when they are forthcoming. Brazil’s experience with wait lists for Bolsa Familia is a

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case in point. From 2011 to 2014, Bolsa Familia carried out its iconic Busca Ativa program to reduce errors of exclusion among the homeless, riverine, and ethnic minorities. Then the economic downturn began, and gross domestic product growth decelerated by 10 percentage points and turned negative in 2015 and 2016. Extreme poverty doubled. The government stepped up cross-checking data sources and recertifications to ensure that ineligible claimants were not crowding out eligible ones, which, together with an eroding value of the eligibility threshold and the benefit fixed in nominal terms, allowed the program to keep a zero wait list even with a flat number of households enrolled in 2017 and 2018. Eventually a wait list began to build in 2019, reaching 1.2 million families just prior to the COVID-19 emergency declaration. As part of the emergency response to COVID-19, the government inaugurated a temporary emergency program of larger coverage and benefit than the regular Bolsa Familia parameters and increased funding of the basic Bolsa Familia program to take up after the temporary program, enough to cover the additional 1.2 million families on the wait list (World Bank 2020b).

Grievance and redress mechanisms (GRMs) can help to reduce targeting errors as well, especially errors of exclusion. By giving people the capacity to provide feedback to program administrators, a GRM provides beneficiaries and the general public a voice in the program’s administration and performance management. The first role of the GRM is to correct applicant- or claimant-specific mistakes. People can check that the information used in determining their eligibility or payment was correct and get it corrected if there was an error. Of course for this to have teeth, there needs to be budget to allocate space to cases found eligible upon the handling of a grievance. In some programs, the GRM may provide a venue to request a judgment-based exception to the basic eligibility procedures. GRMs may also serve as a mechanism that provides an alert about systemic problems so that actions can be taken to reduce them. If complaints are consistently filed about certain parts of the process, that may signal the need for improved information, streamlined processes, or higher processing capacity. If complaints are filed about the competence of or discrimination or abuse by specific frontline workers, supervisors can intervene with (re-)training, sanctions for misperforming staff, or restaffing to prevent future instances, just as they work to rectify damage done in the specific case.

Similarly, a subsystem for error and fraud15 control can help reduce miscompliance errors that could lead to both inclusion and exclusion errors. Social protection programs channel a large amount of public resources to potentially millions of beneficiaries, with complex eligibility and recertification rules. Amid these myriad transactions, it is impossible to operate a program that it is completely free of error and fraud. In five OECD countries reviewed by the United Kingdom National Audit Office (2006),

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this fraction varied between 2 and 5 percent of the total social protection spending. Information from developing countries is scarcer. Romania carried out benefit inspections of six large and error and fraud risk–prone social assistance programs during 2011–13 and found rates of irregularities between 8 and 20 percent. The rate of error and fraud varied by type of program, being higher in programs with more complex eligibility criteria and/or reassessment requirements, such as those that vary eligibility and benefit levels across the population. For means-tested programs, the rate of error and fraud was about 10 percent.

For the error and fraud risk–prone programs, on top of the existing and likely fragmented measures to reduce error and fraud, the social protection ministry or program administration should develop a comprehensive, end-to-end system. Generally, such a system would comprise two parts: an administrative unit that performs data analytics to detect suspicious cases, and a unit that uses this information to investigate and correct the associated over- or underpayments. The volume of such referrals can be larger than the human capacity to handle them. In such cases, a triage is typically done, with checks or inspections focused on the cases with the higher potential losses times the ability to correct them. To reduce error and fraud, governments and/or program administrators may focus efforts on highbudget, risk-prone programs such as income replacement programs or proxy- or means-tested benefits.

A decade ago, such error and fraud systems were only present in OECD countries. They have recently spread more and more into middle-income countries. A typical example of detection and correction is exemplified by the data-driven fraud detection system in France (OECD 2020). Since 2012, the Family Allowance Administration (Caisse d’allocations familiales) uses data mining and predictive modeling to determine which beneficiaries may be at risk of committing fraud, by identifying cases with similar characteristics to those already identified as fraudulent. In addition, the Family Allowance Administration checks the validity of administrative documents with the issuers (banks, internet and telephone access providers, utility companies, and so forth), mostly by automated exchange of information. For the applications with the highest risk, the Family Allowance Administration deploys additional verifications, which could go up to sending certified inspectors to the homes of claimants to conduct inspections and face-to-face interviews to determine the veracity of their claim. In Romania in February 2021, the Social Inspection Unit of the National Social Assistance Agency carried out a joint review with the Employment Agency to assess the effectiveness of the work conditionality for low-income households (ANPIS/ANPOFM 2021). The review used data cross-checks for the entire caseload of guaranteed minimum income beneficiaries, which were subsequently used to identify high-risk cases for in-person follow-up. In

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Moldova, the Social Protection Ministry has a process for inspection based on a risk-profiling system for detecting errors and fraud, which includes home visits for a sample of households. This risk-profiling system is based on a statistical algorithm that flags cases that have a high likelihood of fraud. Since its introduction in 2009, the program has maintained low levels of errors of inclusion, with 80–90 percent of benefits accruing to households in the poorest quintile (World Bank 2018c). Other examples of error and fraud systems and their use are provided in Lindert et al. (2020) and Van Stolk and Tesliuc (2010).

For programs without fixed time limits for the duration of the social assistance unit’s benefits, reassessment of eligibility (often called recertification) is logically required from time to time and important to maintain targeting performance over time. As illustrated in chapter 3, both the passage of time—with possible changes in households’ income, demographics, and surrounding services—and positive effects of the programs themselves may change the household’s welfare and thus eligibility status. To neglect this would certainly lead to errors of inclusion as some families prosper. If there are no new intakes, there will be errors of exclusion as other families fall into hardship, are formed, or move into the location. Even if there are some provisions for new intake but budgets are rationed, the continuing enrollment of families that were initially needy but no longer are will crowd out other now needier families.

As with the original registration processes, recertification may be done continuously or via periodic administrator-led processes, with powerful implications for logistics and targeting outcomes. Continuous recertification processes are more commonly used for on-demand programs and those that rely on interoperability with other government data systems rather than field work or home visits to gather information on welfare. Periodic survey sweeps or community-based targeting exercises are used in many countries as they establish large-scale programs and build social registries. This modality may endure for long periods, although a few of the early pioneers of survey sweep–based registries are finally moving toward more continuous processes.

Having a continuous process for recertification allows administrators to ensure that there are no periods of special friction when large groups of people are exited or a large wait list of eligible but unserved potential clients are excluded. With continuous modalities for recertification, it is logistically easy to recertify clients at different periodicities—for example, recertifying those whose employment and earnings might change more often than others, thus recertifying eligibility for young urban workers more often than for the elderly or people living with severe permanent disabilities. Frequent or differentiated periods for recertification are easiest when eligibility determination can draw heavily on administrative records that are updated

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fairly often and automatically by other agencies and commonly seen means-tested guaranteed minimum income programs. Because people entered at different times, they come due for their periodic recertification in a continuous way. Handling their recertification requires a consistent level of staffing and should result in a fairly smooth flow of households in and out of the program each period, varying principally with periods of prosperity or recession.

Recertification by census sweep has the advantage of some economies of scale (for outreach and travel times) as the teams sweep through an area. However, this approach carries disadvantages. First, the big wave produces a spike in administrative costs, along with a need to mount, recruit, and train for a large field operation almost from scratch each time. Second, such big waves are done infrequently, contributing to exclusion and inclusion errors between the waves. It is not possible to customize for the different clienteles, and in the now nearly ubiquitous case that the social registry supports more than one social program, recertification is on the same schedule for all programs, irrespective of how that fits with the logic of the program. Third, the sweeps produce a large number of people to be moved off the roster at once, which may generate more political waves than a more routine process would. The greater is the number of people who might lose eligibility through a periodic recertification process, the greater will be the need for excellent communication and grievance redress mechanisms. It will also be more important that the cycle is regularly implemented and conducted during a low stress part of the political cycle, perhaps at the midpoint between elections, to minimize accusations of punishing or bribing voters. (Medellin et al. [2015] provide a summary of such issues for Latin American conditional cash transfer programs.)

Cost and capacity are obviously important in deciding how often to recertify and need to be balanced with the potential changes in targeting outcomes that would ensue in various cases. As chapter 2 discusses, the administrative costs of targeting have been kept manageable, in part by using fairly lengthy periods for recertification when it is done by field sweeps. However, chapter 3 notes that there are also significant losses in accuracy that come from this tactic. It is important to seek a balance. Each country (or program) should make some estimates for its own context and parameters, but as a rule of thumb, recertification periods should be no longer than every two or three years. More frequent recertification would make sense depending on the program and purpose, especially for programs that rely largely on data matching and interoperability. For programs or social registries that depend on new client contact for recertification, periods of more than five years would likely result in significant errors of inclusion and exclusion; therefore, investment in recertification every two or three years would likely pay off.