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Planning and Adapting Delivery Systems for Crisis Response
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day according to quality standards? How many payments were processed during the last payment cycle? Assessments are to be conducted regularly using mixed methods and multiple data sources. The subjects may be the target population, applicants, beneficiaries, or the staff who work in the delivery system. • Qualitative methods provide a descriptive approach based mostly on nonnumerical data to understand perceptions, observations, and social interactions. The main data sources are in-depth interviews, focus groups, and case studies. • Quantitative methods rely on numerical data for monitoring and identifying trends. The main data sources are program-specific surveys of potential or actual clients and program administrative data, often triangulated with wider population data from household surveys. The program-specific inquiries may elicit both clients’ and administrators’ perceptions of certain aspects of the program that affect its implementation and outcomes, such as compliance with quality standards, client understanding and satisfaction, and access for vulnerable groups to services, among others. • Program administrative data can be a rich resource as part of regular monitoring and evaluation for measuring program outcomes, including targeting. For example, dashboards can be created to generate information on applicants, time for processing applications, grievance and redress performance, and information management and control mechanisms.
Similarly, administrative data can help in understanding program staff caseloads and program staff work quality, with indicators such as staff turnover rates, budget execution, and compliance with operational procedures and program rules.
The social protection sector is increasingly called on to ensure that its delivery systems are well prepared to handle disaster responses. Because there is a premium on the speed of response in crisis, there is a premium on preparedness. Bowen et al. (2020), OPM (2017), and UNICEF (2019) highlight factors that enable social protection systems to be responsive to shocks and deliver effective shock response. Many parts of delivery systems for programs focused on equity and opportunity for normal times can be the foundation of delivery systems for crisis response, but there are some specificities to crisis response and some extra considerations. This section examines those.
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Covariate shocks can be classified into two broad groups according to their degree of predictability: • Greater predictability. This category includes most of weather- and climaterelated disasters (for example, droughts, storms, wildfires, and floods) that occur periodically and may be increased in frequency or severity by climate change and degradation (depletion) of natural resources. They are often referred to as “hydro-meteorological” shocks, and some can lead to mass casualties and/or major damages to property and disruption of means of livelihoods, roads, and the normal way of life of the people in the affected areas. • Low predictability, infrequent, or unpredicted. This category includes all geophysical disasters like earthquakes, tsunamis, volcanic eruptions, and dry mass-land movements, which are not caused by climate change; economic crises, such as the 1997 Asian Financial Crisis and the 2008
Global Food, Fuel and Financial crisis; epidemic crises (for example,
Ebola, H1N1/swine flu, H5N1/avian flu, and COVID-19); and insect infestations (for example, the locust swarms of 2020 centered in the
Horn of Africa but extending far beyond).
Shocks can be serially correlated. For example, floods can be followed by an increase in illness, malnutrition, and deaths due to the transmission of water-borne diseases.
Preparing to Handle Eligibility Determination for Social Protection Responses to Natural Disasters
A first step in planning for agile responses to natural disasters is assessing hazards ex ante. Hazard assessments should consider the risks related to the most likely or most severe hazards that might affect people or their assets, the expected impacts on consumption or income, and the distribution of impacts across regions or the welfare distribution (see, for example, World Bank [2021] for a stress-testing toolkit and Hill and Porter [2017] and Porter and White [2017] for applications). Past experiences are an important source of information to inform adaptation and changes that could make the social protection system more responsive and adaptive to shocks. For example, Mori et al. (2020) show that the following occurred during 2010–15. Ethiopia experienced seven droughts, affecting a total of 43 million Ethiopians (almost 40 percent of the population), placing them in food insecurity and deepening poverty levels. In the Philippines, during the same period, 131 storms hit the country, killing 19,000 and making 321,000 homeless; Typhoon Haiyan alone increased the number of people in poverty by a million people (Bowen 2016). Pakistan was hit by 14 major earthquakes, resulting in 74,000 deaths, 132,000 injuries, and more than
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5 million people made homeless. Based on such past experiences, countries can develop plans and frameworks to reduce the impacts of shocks through preventive measures such as on improving program delivery chains, designs, data systems, knowledge management activities, intersectoral coordination, and so forth, and consequently be ready for a faster and better response (Bowen et al. 2020; UNICEF 2019).
With some notion of disaster profiling in hand, countries can plan what sort of programming they might rely on for recurrent emergencies and how delivery systems would support that. Responses may be multilayered calling on different programs and options depending on the severity of the shocks. • Continuously operating programs with built-in flex can carry part of the load even with no specific disaster-related triggers. Entitlement-based social assistance programs are built to expand when needs are higher. To make these programs work, they need open registration systems, so anyone suffering a loss in income can qualify, and budget provision that guarantees that all who qualify get access. Unemployment insurance programs similarly work automatically, expanding benefits in downturns. Although they are not usually much discussed among disaster responses, such programs can be important. For example, Deryugina (2016) looks at US counties that were hit by hurricanes. He estimates that about 80 percent of the fiscal support in the 10 years following flows through the US regular safety net (unemployment insurance, means-tested social assistance, and public health programs), and the minority of funds flows through the disaster-specific provisions of the
Federal Emergency Management Agency. During economic crises, such programs are an even more natural fit. In the 2009 financial crisis, in
Eastern Europe and Central Asia, the first wave of response came through increased unemployment claims and the second through the region’s last resort social assistance programs. However, many of these programs had eroded in eligibility threshold prior to the crisis, which limited some their responsiveness, and some countries took action during the crisis to reform their programs (Isik-Dikmelik 2012). • Continuously operating programs can have preestablished disaster-related triggers. Ethiopia’s rural Productive Safety Net Program and Kenya’s Hunger
Safety Net are iconic examples of having well-established systems that trigger expanded caseloads based on climatic data. In Ethiopia, the number of people covered under the rural Productive Safety Net Program and the number of months of benefits per year has been adjusted regularly in response to drought. The trigger has been the twice-annual
Humanitarian Requirements Document. To improve the automaticity, speed, and geographic differentiation of response, the government is planning to switch to triggers based on an early warning system and
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incorporate elements such as agrometeorology, crop data, livestock data, market prices, poverty, and population (World Bank 2020e). In Kenya, the Hunger Safety Net program already has a census of households in the covered districts and the information to issue payments directly to their accounts when the predefined triggers are hit. These are based on the vegetative condition index, and depending on the level of the index, expand the number of households, level of benefit, and/or frequency of payment (UNICEF 2019). • Even when not planned so systematically, emergency top-up benefits for social assistance beneficiaries in affected areas are a common policy response, and sometimes eligibility is expanded. For example, in response to Typhoon
Haiyan, top-ups to the Philippines’ cash transfer program (Pantawid
Pamilyang Pilipinoa Program, or 4Ps) reached beneficiaries within a month of the storm (Bowen 2016; Pelly, de Wild, and Inarra 2015).16 In response to the 2020 locust plague in the Horn of Africa, Djibouti worked through the existing Family Solidarity Program with a scale-up of about 15 percent over the base caseload, and the expansion focused on the areas that were most affected (World Bank 2020d).
A relatively new frontier is collecting data at the household level ex ante to predict vulnerability to natural disasters. Geotagging households means that many spatial data on risks of shock exposures can be crossreferenced. (Geotagging is becoming more standard for allied purposes of mapping service provision and identifying households unambiguously.) In the Dominican Republic, the information collected for its social registry (SIUBEN) also includes vulnerability to climate shocks within its database, with information on three dimensions: (1) housing characteristics (walls and ceiling), (2) estimated income, and (3) proximity to a hazardous natural element (river, stream, or ravine). Pakistan is including data on climatic vulnerability in its new PMT, while also making efforts to provide geographic coordinates for all registered households (UNICEF 2019). Colombia’s System for the Selection of Beneficiaries for Social Programs (SISBEN IV) moved in this direction as well. For the first time, it undertook the geolocalization of all households and added to the household questionnaire a module assessing household exposure to natural disasters (World Bank 2020c). Chapter 6 discusses how to incorporate risks into a PMT in more detail.
When assets are destroyed or welfare rankings much changed, there may need to be a postdisaster eligibility assessment geared to the areas affected. These ex-post assessments inherently add a step and expense to response times, but if prior systems are set up well, the assessments can be done relatively expeditiously. From a social protection angle, knowing the characteristics and profile of the population and its vulnerabilities
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ex ante by expansion of registries and interoperability of data systems can help in preparing the ground for emergency response. In Chile, a country with high vulnerability to natural disasters, the integration of social protection and disaster risk management was prompted by citizens’ complaints that in preceding disasters there were too many mistakes in the process of gathering data, extensively long compiling and processing time, and the aid was not designated accordingly. Thus, in 2015, the overall disaster risk management system was revised. The role of the Ministry of Social Development is to coordinate the application of ex-post, household-specific needs assessments. Accordingly, it developed the Emergency Basic Fact Sheet (Ficha Básica de Emergencia, or FIBE) with attendant mobile apps and tools. FIBE is linked to the Chilean National Social Registry, reducing the data collection time substantially according to the ministry, as most of the basic information is prepopulated in the FIBE information system. As a metric of efficiency, data collection in the response to the Coquimbo earthquake in 2015 took 27 days using the new FIBE, in contrast to the response to the Tarapacá earthquake in 2014, which took 115 days (Beazley, Solorzano, and Barca 2019).
Thinking about Economic Shocks
Widespread economic shocks present their own challenges to social protection systems. Shocks such as the Asian Financial Crisis in 1997/98, the global financial crisis in 2007–09, food price surges in 2007/08 and again in 2011, or the economic consequences of COVID-19 manifest in worsening income of those already poor, loss of earnings and/or jobs in the informal sectors, and some formal sector job loss. In general, physical assets are not destroyed by economic shocks, although savings may be wiped out by inflation, devaluation, or declines in financial markets. Moreover, households and businesses may have to divest themselves of assets, especially more liquid assets, to survive in the short term, thus lowering their future earnings. The effects are usually national in scope rather than geographically delimited, and often the duration is initially uncertain. This means that the one-off, geographically focused benefit often used for natural disaster response will be less appropriate for economic shocks, and it puts a higher demand on delivery systems that can adjust over time, possibly those that can follow changes in welfare dynamically.
The COVID-19-precipitated crisis had a more sudden and precipitous onset than usual for economic shocks, one that called for lightning speed response and thus put an even heavier burden on delivery systems than usual. The desire to move extraordinarily fast and with little social contact implied favoring the use of existing data or virtual application processes
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rather than traditional face-to-face interviews or field work, which was accomplished in different ways: • Sixty-eight countries made use of top-up benefits for those in existing programs. • To achieve the increases in coverage desired, some countries mounted enormous new digital registration efforts. Several middle-income countries (including Brazil, Jordan, Morocco, Namibia, Pakistan, South
Africa, Thailand, and Turkey) used their social registries or other existing government databases to expand coverage hugely from base levels, essentially inviting many to apply and ruling out people with formal sector incomes or recorded assets above certain levels, or those in receipt of various government benefits. Brazil initiated a new Emergency Aid program with online and mobile application technology, which eventually reached about 68 million people, one-third of the population (Gentilini et al. 2020, v14). The government information and communications technology firm DataPrev cross-checked claims against Cadastro Único and some 20 constantly updated databases, including tax, social security, public employment, and Brazilians resident abroad (World Bank and
FCDO 2021). Pakistan’s Ehsaas Emergency Cash program reached about 45 percent of the country’s population, relying on top-up benefits to its ongoing PMT targeted program and giving benefits to those in the social registry who were above the usual threshold PMT and, because the registry was old, via new applications screened for a list of exclusions. • Some countries managed to innovate using untraditional databases.
Togo happened to have a very recent biometric voter registration database from elections in February 2020, which (unusually) contained information on occupation as well as location. The government managed a fully digital registration and payment process to issue payments to residents in areas affected by lockdowns and with occupations in the informal sector because they were presumed to have or be at risk of significant income loss (Boko et al. 2020). In Guatemala, the government introduced an emergency cash transfer, Bono Familia, during three months (1,000 quetzals, or US$130, per month/beneficiary). The program gave benefits to 2.6 million households (80 percent of the population) consuming less than 200 kilowatt hours for areas with electricity and made provision for 0.2 million more lacking connections. • Some countries used more traditional methods. For example, the
Philippines both topped up benefits to those in the 4Ps program and undertook substantial new registrations with face-to-face processes and manual payments, which was a rather slower process. Almost all 4Ps beneficiaries were able to receive the Social Amelioration Program first tranche top-up benefits through the already existing digital channel (for
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example, cash cards) by April 5, 2020. In contrast, about one-quarter of the target group to be served by the new registration had received their first tranche of benefits by April 25 (Cho, Avalos, et al. 2021). The difficulties in manual processes led the government to digitize the processes for a second wave of responses (Cho, Kawasoe, et al. 2021).
Countries with higher base rates of foundational IDs, financial inclusion, mobile penetration, and interoperable government databases or social registries were better able to provide response more quickly. Although most countries made policy announcements quickly after the World Health Organization officially declared COVID-19 to be a pandemic in March 2020, getting cash into the hands of the population was sometimes slower. Palacios (2020) calculates that among 66 programs that announced responses involving new beneficiary intakes (rather than those with payouts of top-up benefits), about half had managed to make payments by the end of June 2020 (see figure 4.2). The ability to roll out quickly was much greater where the building blocks of foundational IDs and bank accounts were widespread. High-coverage social registries were partly helpful, but
Figure 4.2 Mapping the Roll-Out of Coverage of Identification (IDs) and Financial Inclusion
100
Adults with a bank account (%)
90
80
70
60
50
40
30
20
10
Philippines
Myanmar Iran, Islamic Rep.
India Thailand
Bangladesh Zimbabwe
Togo
Pakistan Vietnam Mauritius
Chile Chile
0
0 10 20 30 30 40 50
Identification index (%)
60
Countries that made their first round of payments for emergency programs
70 80
Countries that made or were close to completing their first round of payments for emergency programs by June 30, 2020
Source: Palacios 2020.
