Covid-19EconomyTechnology

Making the case for robust digital financial infrastructure

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The pandemic has been a tough, real-life stress test for government disbursement schemes, highlighting opportunities but also gaps and vulnerabilities.

Governments worldwide reacted to the COVID-19 crisis with an outpouring of financial aid to businesses and individuals that was exceptional for both its size and the speed with which the disbursements were made. According to the IMF, fiscal measures announced globally amounted to $11.7 trillion, or close to 12 percent of global GDP, as of September 2020. As a result, the pandemic has served as a high-stakes, real-life stress test for the financial infrastructure in many countries, bringing into sharp relief critical gaps and opportunities as well as providing valuable lessons about how to improve efficiency and resilience for the future.

What determines how effectively the money is disbursed, for achieving the design ambitions of the program and delivering the funds rapidly to the intended recipients? This question has been drawing the attention of economic researchers, policy makers, fintech innovators and civil society watchdogs alike. Building on our pre-pandemic research on the economic impact of digital identification and digital financial inclusion, we studied a sample of 12 government economic disbursement programs for both individuals and small and medium-size enterprises (SMEs) in seven countries—Brazil, India, Nigeria, Singapore, Togo, the United Kingdom, and the United States.

Second, we estimate that the potential economic gain from building robust digital financial infrastructure is about 20 percent greater now than it was before the pandemic. Before the COVID-19 crisis, we estimated the potential economic gain by 2030 from applying basic and advanced digital ID to a wide range of interactions between individuals and government and nongovernmental institutions to be in the range of 3 to 13 percent of GDP. Finally, the crisis has underscored the resilience and flexibility that a robust financial infrastructure for SMEs and individuals provides, and thus its importance as a critical tool for responding to unexpected and potentially catastrophic events.

We assessed the relationship between financial infrastructure and the performance of disbursement programs in their design ambition and how well they delivered

Government economic disbursements have assumed huge scale and complexity during the COVID-19 crisis, with emerging and advanced economies facing the challenge of supporting as many as 3.7 billion vulnerable individuals and 500 million micro-, small, and medium-size enterprises (MSMEs) worldwide both efficiently and urgently. In the unprecedented circumstances, program rollout has not always gone smoothly. Some programs providing grants or loans to individuals and small businesses have faced criticism because they were not sufficiently broad, not sufficiently inclusive, too slow, or hampered by fraud, among other reasons.

Government disbursements to individuals and businesses during the pandemic assumed huge scale and complexity. The crisis helped identify key elements to ensure successful and speedy delivery.

In our analysis of 12 COVID-19 support programs, we found meaningful patterns when we compared their effectiveness (on a limited set of criteria) with the structural elements of financial infrastructure they were able to use. Exhibit 1 provides a brief description of each program and an assessment of the country-level financial infrastructure available in our sample countries, measured on parameters of digital payment channels, digital ID, and data linked to digital ID. We also estimated specific program-level financial infrastructure for the 12 programs, using the same parameters. Finally, we compared country- and program-level infrastructure strength to the effectiveness of programs, considering both design ambition and delivery success, and using three indicators apiece to measure them.

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It is important to note that our analysis is based on measurement of select parameters of disbursement programs in order to stimulate further thinking and action on how to make them more effective. It is not our intent to provide a formal evaluation of the programs, and we have not undertaken a comprehensive assessment of them. Rather, we relied on government reports on the amount and speed of disbursements, public audits where available, some studies conducted by NGOs, and, in the case of fraud, the level of public interest in fraud related to each program. Each of these is a proxy indicative measure of program effectiveness rather than a definitive evaluation (see sidebar, “Our measurement methodology”).

Three structural features were critical success factors

The effectiveness of the 12 COVID-19 programs we analyzed depended greatly on the presence of the three structural features of financial infrastructure: digital payment channels, the presence of a basic digital identification system with broad population coverage, and simple data on individuals and businesses that are tethered to the ID. Where these features were in place to a large degree, interventions were more effective. Their design tended to be more ambitious and their delivery more successful. Without them, however, programs made trade-offs—sometimes large ones—between the design ambition of the program and its delivery success.

Interventions were more effective when three features of financial infrastructure were in place: digital payment channels, the presence of a basic digital identification system with broad population coverage, and simple data on individuals and businesses that are tethered to the ID.

Program-level examples illustrate these findings in Exhibit 2. We calculated each program’s position on design ambition (Y-axis), delivery success (X-axis), program financial infrastructure strength (size of the bubble), and country-level financial infrastructure strength (shade of the bubble) by computing the average of metrics for each of these areas. We gave equal weight to each metric and converted the aggregate score to a scale of 0 to 1, setting 1 as the maximum achievable or observed level in our data set. The result is a set of broad indices that do not fully capture the nuances and challenges that agencies faced in designing and implementing disbursement programs under the pandemic’s unprecedented circumstances. Yet these indices help convey the potential scope for improvement in outcomes as well as the link between disbursement success and the strength of structural foundations in financial infrastructure.

The strength of financial infrastructure allows governments to design programs with more effective outcomes.
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Programs leveraging strong financial infrastructure achieved higher program ambition and delivery success

Two of the programs had all three structural features of financial infrastructure in place, at least at the program level. This enabled them to meet relatively ambitious program goals and deliver funds with success.

Singapore’s Job Support Scheme (JSS) targeted a large portion of national SMEs and channeled a substantial amount of fiscal disbursement to them. The program was rolled out in a rapid and streamlined form with reportedly low fraud levels. Funds were transferred automatically to eligible businesses in amounts calculated based on payroll, with no application process needed. This was made possible by the “CorpPass” digital ID system which assigns a unique ID to businesses linked to data on the SME’s tax payment and employee wages from the government’s myTax portal.

The Job Retention Scheme (JRS) in the United Kingdom used the system that collects income tax and national insurance, Pay-As-You-Earn (PAYE), to identify businesses and control fraud. The program aimed to cover up to 80 percent of furloughed workers’ salaries. It reached all employers registered for PAYE, with some 28 million payroll workers enrolled, representing more than 80 percent of national employment. The PAYE reference number was linked to payroll and bank account information provided by the employer, which enabled streamlined rollout. Few cases of fraud were reported.

Programs using financial infrastructure but missing some structural features sacrificed either design ambition or delivery success

In some programs, delivery success was relatively strong, but at the potential cost of some limitations on program ambition.

India’s Targeted Disbursement (TD) program was able to reach about 207 million women with Jan Dhan bank accounts and an additional 28 million elderly, disabled, and widowed beneficiaries, using data from its existing Aadhaar-linked Direct Benefit Transfer scheme. India was able to roll out the TD program rapidly, with some 40 percent of targeted beneficiaries receiving deposits within a day of the program launch in early April and 100 percent within two weeks. Nonetheless, the delivery experience could have been improved by addressing issues such as account dormancy, low use of online payment systems, and limited cash-out points in rural areas. In design ambition, lack of a universal national social registry and data limited the design ambition and scope of the program, which was focused on current beneficiaries of government transfers; for example, the needs of migrant workers were targeted through a separate initiative that introduced interstate portability of food ration benefits.

India’s Emergency Credit Line Guarantee Scheme (ECLGS) targeted small businesses with government-guaranteed loans; more than three million MSMEs received disbursements within one month of the announcement and some six million as of November 2020. GECL leveraged the MSMEs’ credit relationships with commercial banks and nonbanking finance companies because coverage under the digital ID program for businesses was relatively low, and credit relationships with regulated financial intermediaries were considered a reliable proxy for verification. However, this meant the program’s ambit was restricted to MSMEs with those existing credit relationships. It was not able to target many of the country’s 60 million microbusinesses that did not already have formal credit relationships, although this segment needed urgent credit and liquidity support, too.

In other programs that lacked some structural features of financial infrastructure, the design ambition was evident, but delivery success had some limitations.

In Togo’s Novissi program, fortnightly cash transfers were disbursed to the mobile money wallets of informal workers, amounting to 30 percent of the minimum monthly wage each month (about $18 for men and $20 for women) for the duration of lockdown measures in their local area. While country-level infrastructure remains weak, program infrastructure is robust. Because Togo does not yet have a national digital ID program, the eligibility of enrollees to receive transfers was verified against the national voter ID registry that included data on occupation and place of residence, both targeting criteria for the Novissi program. (Togo has used biometric data for voter registration since 2007.) Residence in affected areas was also confirmed using mobile phone records. All informal workers were urged to register for the program, to ensure rapid payment as and when local lockdown restrictions were imposed. As of January 2021, Togo had registered 1.39 million people and paid benefits of $22 million to 580,000 eligible beneficiaries. The country intends to roll out a biometric digital ID card in 2021 to all eight million-plus citizens. The ID system will also serve as a new platform to support national projects such as a single social registry and universal health insurance.

The Paycheck Protection Program (PPP) in the United States was large in scope of the SMEs targeted, and disbursement was relatively fast for about two-thirds of applicants. However, many small businesses—including some high-need SMEs, such as minority-owned firms—were excluded or not prioritized because they lacked banking relationships. At the same time, the program has received public attention for instances of fraud; since May 2020, the US Justice Department has publicly announced charges in more than 60 fraud-related cases. The amount of fraudulently received loans associated with these charges is small relative to the size of the PPP program ($70 million in loans received by defendants versus total loans of over $525 billion). However, official estimates are not currently available for improper payments and error rates.

In some cases, both program ambition and delivery success showed limitations

Multiple programs showed lower scores for both delivery success and design ambition, although this is a matter of degree. The Bounce Bank Loan Scheme (BBLS) and the Coronavirus Business Interruption Loan Scheme (CBILS) programs were structured as loans, wholly or partially guaranteed by the UK government, and designed to make use of bank-level financial infrastructure. Both underscore the challenges of achieving the troika of widespread coverage, speed of disbursement, and low fraud rates in the absence of strong business identification systems with linked data. Disbursement decisions for the programs were made by banks, which had to authenticate and verify applications within program deadlines without the benefit of government digital records (for example, tax numbers and data). BBLS was designed to have an expedited application and disbursement process and achieved a 76 percent approval rate with about 90 percent of loans made to microbusinesses. Businesses self-certified their application documents, and lenders were not required to perform detailed credit or affordability checks. The program was found to have experienced significant risk of identity fraud, money laundering, and duplicative applications, among other issues. CBILS achieved lower levels of fraud, but through a stringent and lengthy underwriting process leading to a low approval rate of about 44 percent. This left many needy SMEs without support. Thus, the low fraud rates may have been at the expense of higher potential for impact by targeting more beneficiaries.

In the United States, the Economic Impact Program (EIP) was large in scope, aiming to pay more than 50 percent of the population, but more limited in targeting. For example, all individual Social Security recipients and tax filers earning less than $75,000 received the same amount. Delivery also met challenges, in both speed and coverage, as a result of partial reliance on paper checks and an incomplete list of eligible recipients. While more than 160 million individuals ultimately received a payment, only 90 million did so in the three weeks after the program began on March 30, 2020. On September 17, the Internal Revenue Service notified nearly nine million individuals who had still not received an EIP and were potentially eligible, supplementing significant outreach and public awareness campaigning that had been in place since March. Structurally, these people may be more likely to be lower income because they are not required to file federal income taxes.

The Emergency Aid (EA) program in Brazil was targeted at informal, unemployed workers, individual microentrepreneurs, intermittent workers, and women-led households. About 44 percent of the target population was automatically included, being registered in the country’s poverty-alleviation program, Bolsa Familia, or in Cadastro Unico, the Unified Registry that contains data on more one-third of the population and is used for federal social programs. However, about half of all beneficiaries were not in the Unified Registry and did not have a digital ID; they were required to apply and register via an online platform. Those who did not have a digital financial account needed to open mobile savings accounts at a state-owned bank. Ultimately the program’s rural coverage was high at 56 percent, while in the urban areas, the coverage was about 36 percent, but the lack of universal coverage under a digital social registry was a limitation. There have been reports of fraud, cybercrime, and payments made to mistaken beneficiaries.

Source:  McKinsey Insights

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