Data Systems and Their Role in Supporting Eligibility Determination and Recertification

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Data Systems and Their Role in Supporting Eligibility Determination and Recertification

Data are at the heart of decisions about eligibility and important for managing all the steps in the delivery chain; thus, good data systems can improve the targeting and impacts of social protection programs. Data systems require the support of those with specialized skills, but their basic functions are intuitive: to collect or assemble correct data and store them in a way that makes their use both easy and secure. This section is a brief reminder of the issues, with references to the wider and more technical literature on the topic.

Data Collection or Aggregation

Data may be collected new from applicants or gathered from existing data that are already accessible by the government. For the assessment of needs and conditions or eligibility determination, new data collection comes in the form of the traditional face-to-face interview—in a program office when a person comes to apply or in a survey sweep when a program or social registry goes to the field to collect data in people’s homes. The modern variant is the virtual interview by phone, app, or online form.

Although eligibility determination is particularly heavy on data and inference (an issue covered in chapter 6), data are collected and used throughout the whole program cycle. Good outreach, GRMs, and error and fraud detection subsystems all require the collection and use of large amounts of data. Data may be sent to agencies where recipients of income support programs are referred to other programs for which they may be qualified. Of course, data with respect to the payments to be made will need to be conveyed to payment service providers. Data may be drawn from other agencies not just for eligibility determination, but also to verify that claimants have fulfilled any co-responsibilities, for example, registration for public employment services or attendance at training, school, or health care.

In many instances, data collected from applicants are the only or main source of program data. This is particularly true in low-income countries with large informal sectors and few administrative databases that cover only a minority of the population, with an emerging but incomplete legal and regulatory framework, lacking a secure and trusted architecture for exchanging data among different ministries and agencies, and with an insufficiently resourced public sector (for example, lacking the capacity to develop or contract IT expertise), or with insufficient human resources that can analyze and make use of the data (Lindert et al. 2020). These difficulties notwithstanding, programs can develop efficient data systems by collecting and digitizing the information from all business functions

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(from outreach to payments), developing data quality control routines, and using the different data subsystems from each business function in an integrated way to detect data gaps, incompatibilities, outliers, or changes that trigger changes in other parts of the data system. To improve the completeness, timeliness, and accuracy of data, programs can open multiple channels of communications with beneficiaries, to ease the cost of updating and correcting their data, and integrate with relevant information from other stakeholders (for example, payment providers or health, education, or employment agencies).

The advantages of collecting new data for eligibility determination are two. First, the program itself has agency as it does not need to wait for the surrounding data ecosystem, which leads to the second advantage—the program can work with people or aspects of their welfare that are not recorded in data systems that are accessible to program administrators.

When data are being collected anew in interviews, data quality can be supported with lessons from household surveying and process evaluations. Because there is a sound literature and practice for surveys,17 this section does not dwell on the topic but provides a few examples as reminders of its importance. • The use of computer-assisted personal interviewing, which is commonly conducted on tablets or smartphones, can facilitate more efficient quality control through automated checks (examples include range limits, logic checks, coverage errors triggering supervisor visits, and location flags to prevent duplicates and other errors). This technology also reduces cumbersome processes, like double data entry. For a small example of the power of these kinds of tools—in performance diagnostics for Colombia’s social registry (the System for the Selection of Beneficiaries for Social Programs, or SISBEN III)—DNP (2016) notes substantial errors in the handwritten addresses and ID numbers, problems for which it posed solutions for the implementation of SISBEN IV by instituting a range of checks in the computer-assisted personal interviewing technology. • Video is also becoming a more common tool that can help standardize training, allow new people to be trained as they enter the social assistance workforce in a continuous rather than cohort approach,18 and allow staff to brush up on their skills as needed. A study in the Philippines (Velarde 2018) shows the problems of the former “cascade training” or

“training of trainers” approaches commonly used prior to the advent of affordable video. The study documents that loss of key concepts occurred at each stage of the cascade, resulting in additional time spent on supervision and correction of errors, problems that were reduced by the videos.

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• Digital training and quality control have been successfully tried in India in the Aadhaar (unique digital ID) program.19 To enroll the 1.24 billion people, Aadhaar has used a mix of private and governmental actors, organized hierarchically into registrars, who selected enrollment agents, who in turn organized enrollment centers. The whole process was decentralized to 628 enrollment agencies with more than half a million enrollment operators. To ensure that all enrollment operators are properly trained and produce valid enrollments, Aadhaar has organized a multimode system of training, testing, and certification standards.

Given the size of the country and program, these functions were delivered not only through traditional instruction manuals and face-to-face training, but also digitally: training in e-documents and videos and testing and certification were computer assisted. • Real-time supervision of data collection is important. For example,

Pakistan’s National Socioeconomic Registry update process closely monitors coverage through multiple sources, including directly from its regional control center in real time, using geotagging of questionnaires as well as indirectly through an independent operational review firm.

Based on this information, it regularly contacts enumerator teams to resolve exclusion issues, particularly in harder to reach and more vulnerable areas. It also conducts early monitoring and household surveys to identify constraints to the poor in accessing desk-based centers (for example, placement of centers, costs, and waiting times) and error, fraud, and corruption in the process.

New data collection is costly and time consuming, imposing administrative costs, transaction costs, and sometimes stigma on applicants, which may result in biased information.20 Thus, there is a strong impetus toward using, to the extent possible, data that have already been collected. Instead of asking the applicant about their income or assets, the social assistance program asks for the client’s consent to use other records and then draws on data held by other agencies—for example, those that track social security contributions, registrations for automobiles, and so forth.

The buzz in data collection is about increasing the use of data that are already available. The society-wide secular trends of the falling costs of computers and communication and the exponentially increasing use of mobile technology in communications and commerce are key drivers. Moreover, as countries move from a model of individual, island-type social protection programs toward an archipelago of programs, often through the development of a social registry or an integrated system, the emphasis shifts further toward the use of outside-the-program sources and interoperability between different programs’ data systems. (Box 4.5 provides examples of these.)