Several African governments on Sunday closed borders, canceled flights and imposed strict entry and quarantine requirements to contain the spread of the new coronavirus, which has a foothold in at least 26 countries on the continent as cases keep rising.
South African President Cyril Ramaphosa declared a national state of disaster and warned the outbreak could have a “potentially lasting” impact on the continent’s most-developed economy, which is already in recession.
Measures to be taken there include barring travel to and from countries such as Italy, Germany, China and the United States.
“Any foreign national who has visited high-risk countries in the past 20 days will be denied a visa,” he said, adding that South Africans who visited targeted countries would be subjected to testing and quarantine when returning home.
South Africa, which has recorded 61 cases, will also prohibit gatherings of more than 100 people, Ramaphosa said.
Kenyan President Uhuru Kenyatta said his government was suspending travel from any country with reported COVID-19 cases.
“Only Kenyan citizens, and any foreigners with valid residence permits will be allowed to come in, provided they proceed on self-quarantine,” he told the nation in a televised address.
The ban would take effect within 48 hours and remain in place for at least 30 days, he said.
Schools should close immediately and universities by the end of the week, he added. Citizens would be encouraged to make cashless transactions to cut the risk of handling contaminated money.
Kenya and Ethiopia have now recorded three and four cases respectively, authorities in each nation said on Sunday, two days after they both reported their first cases.
In West Africa, Ghana will ban entry from Tuesday to anyone who has been to a country with more than 200 coronavirus cases in the past 14 days, unless they are an official resident or Ghanaian national. Ghana has recorded six cases.
President Nana Akufo-Addo said in a televised Sunday evening address that universities and schools will be closed from Monday until further notice. Public gatherings will be banned for four weeks, he said, though private burials are allowed for groups of less than 25 people.
In southern Africa, Namibia ordered schools to close for a month after recording its first two cases on Saturday.
Djibouti, which has no confirmed case of COVID-19, said on Sunday it was suspending all international flights. Tanzania, which also has no cases yet, canceled flights to India and suspended school games.
Other nations have also shuttered schools, canceled religious festivals and sporting events to minimize the risk of transmission. Some 156,500 people worldwide have been infected and almost 6,000 have died.
As travellers cancel flights, businesses ask workers to stay home, and stocks fall, a global health crisis becomes a global economic crisis. In any health crisis, our first concern is (and should be) with the health of those affected. More than 6,400 people have died worldwide and more than 164,000 cases have been confirmed in 146 countries or territories.
The economic impacts have dramatic effects on the well-being of families and communities. For vulnerable families, lost income due to an outbreak can translate to spikes in poverty, missed meals for children, and reduced access to healthcare far beyond COVID-19. With cases confirmed in many low- and middle-income countries, these impacts may affect the world’s most vulnerable populations.
What are the channels of economic impact we can expect from COVID-19? Beyond the human tragedy, there is a direct economic impact from lives lost in an outbreak. Families and loved ones lose that income and their in-kind contributions to household income such as childcare.
Though less likely to pass away from COVID-19, many working age adults still fall ill and their families will feel the financial burden as they miss work for days or weeks.
Most of the economic impact of the virus will be from “aversion behaviour.” That is, actions people take to avoid catching the virus. Aversion behaviour comes from three sources:
Firms and institutions (including private schools and private companies) take proactive measures to avoid infection. Business closures – whether through government bans or business decisions – result in lost wages for workers, especially in the informal economy, where there is no paid leave.
Individuals reduce travel – to the market, for tourism, on business, and going out for social and other activities.
These actions affect all sectors of the economy. These in turn translate into reduced income both through the supply side (reduced production drives up prices for consumers) and the demand side (reduced demand from consumers hurts business owners and their employees).
These short-term economic impacts can translate into reductions in long-term growth. As the health sector soaks up more resources and as people reduce social activities, countries invest less in physical infrastructure. As schools close, students lose opportunities to learn (hopefully only briefly) but more vulnerable students may not return to the education system, translating to lower long-term earning trajectories for them and their families, and reduced overall human capital for their economies.
For example, unplanned pregnancies rose sharply in Sierra Leone during the Ebola epidemic, likely in part a result of school closures. Adolescent mothers are less likely to return to school, and their children will likely have fewer health and educational investments.
Further, the infection and death of health workers in the front lines of epidemics can lead to worsening health conditions in the long-term, such as maternal and infant mortality. These all have poverty implications well beyond their humanitarian implications.
What we know so far and what to expect
Economic estimates of the likely global impact vary dramatically. Tom Orlik and others at Bloomberg hypothesise $2.7 trillion in lost output. The Asian Development Bank projects losses ranging from $77 billion to $347 billion, and an OECD report talks about a halving of global economic growth.
Some recent analysis of the actual and potential economic impacts of the crisis provides a snapshot. Across sectors in African countries, the economic impact stems from the slowing down of the Chinese economy, with reduced Chinese demand for raw materials. This analysis projects reduced investments in energy, mining, and other sectors, and a fall in travel and tourism.
Another analysis reports that Chinese factory closings have adversely affected consumers in Africa. In Zimbabwe and Angola, exports to China have crashed.
About a fourth of Ugandan imports come from China. Supply chains have been interrupted for weeks because many Chinese factories shut down production. Small traders selling textiles, electronics or household goods are in trouble … In Niger, stocks of certain goods, including groceries, from China have already been significantly decimated, leading to higher prices.
Most of the data and observed impacts in the developing world so far stem from production and export stoppages from China, and those estimates pre-date the worsening economic conditions in Europe and the US. But as the economies of other countries slow down with the spread of the disease, these impacts will show up more clearly in economic data and likely grow over time.
What should be done
Beyond three stimulus and liquidity recommendations from the International Monetary Fund, we add three recommendations.
First, contain the pandemic. As our colleague Jeremy Konyndyk puts it,
To assuage market reactions to the outbreak, you have to present a viable plan to defeat the outbreak.
As long as the outbreak is actively spreading, many aversion behaviours are rational and wise. Containing the disease is the first step to mitigating not only the health impacts but also the economic impacts.
Second, strengthen the safety net. The most vulnerable households are those most likely to be affected economically. Low-wage workers are often those most likely to lose their jobs if they miss work due to an extended illness. They are often the least able to work remotely to avoid contracting the virus. And they are the least likely to have savings to survive an economic downturn.
Making sure there is an economic safety net in place – cash transfers, sick leave, subsidised health coverage – helps the most vulnerable survive and provides support to enterprises that serve those populations.
Third, measure the impact. Systematic data on which populations are experiencing the greatest hardships and which industries are failing is essential to providing assistance. During the Ebola epidemic of 2014-2015, researchers used phone surveys in Sierra Leone and Liberia – building on the sample frames from existing surveys – to gather information on the impacts of both ill health and aversion behaviour on households and enterprises across the countries.
Many commentators have raised concerns about the interest rates charged for digital credit. And, given that the entire process is automated and dependent on computer algorithms rather than expensive human intervention and analysis, this seems reasonable.
On the face of it, it is strange that the interest rates charged for digital credit should be closer to those common in the informal sector than those charged for other formal sector loans. So what is going on?
There are three key drivers of the high interest rates: 1. The small size of loans; 2. The cost of data analytics; and 3. The risk premium priced in.
Small Loans: We all know that, broadly-speaking, it costs the same amount of money to make a $10 or a $10,000 loan. Digital credit loans, absent the personal relationship, start by lending small amounts (typically $10-20) to gauge repayment behaviours and base future lending decisions (largely) on the basis of these. The interest on these minimal amounts is often inadequate to cover even the variable costs associated with making a digital loan (SMSs or data charges etc).
Data Analytics: Digital credit providers not only need to invest significant amounts upfront to build their platforms and algorithms, but also on an on-going basis to keep refining them as they learn through the behaviour of their customers. One large provider tells us that they are spending $200-300,000 per month on analysts to maintain and develop their system.
Risk Premium: MicroSave’s recent analysis of a credit reference bureau’s data has highlighted the extraordinarily high default rates amongst digital credit borrowers in Kenya, where the best data is available. We can safely assume that this is a pervasive problem. Inevitably, providers of digital credit have to price these losses into the interest rates charged for loans. This means that all borrowers (whether they repay on a timely basis or not) have to pay the risk premium for those that default.
While providers of digital credit will always struggle with the mathematics and economics of small loans and the cost of data analytics, there is clear opportunity to reduce the level of defaults and thus the risk premium that has to be charge and perhaps that smart algorithms alone will not be enough to do so.
CGAP’s Greg Chen highlights six early errors made by digital credit pilots and deployments. Several of these contribute to the high levels of default.
Addressing 1. – 5. could allow providers of digital credit to improve targeting, increase loyalty and reduce both risk and default … thus increasing the profitability of providers of digital credit.
Identification systems: A growing number of countries are introducing formal identification systems – many of which are bio-metrically enabled. And, even where no such systems are available, app-based ID systems (including for example Yoti, Taqanu, Trulio) are increasingly common.
These, of course, require smart phones, but the growing penetration of smartphones continues despite some set-backswith low cost smart phones. Digital credit providers will need to leverage these ID systems to have a firm fix on their customers – this will be key to identification, credit assessment, collection and delinquency management. ID will also be key to running effective credit bureaus.
Unfortunately, few countries outside Kenya have credit reference bureaus designed to help with the management of the small loans offered by digital credit … and thus to allow people to develop a credit history. While Kenya’s credit reference bureau is still finding its feet, it is playing an immensely important role in creating transparency and allowing those who do repay on time to create a positive records.
Poor targeting: Getting the right balance between credit scoring models that are too conservative and those that are too liberal is key to building an effective system. But there are other drivers of poor targeting. Digital credit lenders will also need to achieve the right balance between aggressive “push” marketing and ensuring that their product is properly understood in the market. As we have seen, too many people respond to push marketing by borrowing out of curiosity, and without any real need or purpose in mind.
Providers can clarify their marketing to give customers a better understanding of their terms and conditions, as well as the penalties for non-repayment. This approach would also allow them to address challenges with consumer protection.They can also use behavioural nudgesto facilitate appropriate behaviour.
Mobile network operators (MNOs) can also reduce targeting risk by completing initial credit screening through lending airtime credit. Airtime has marginal costs for an MNO, and thus represents a much lower risk than e-value credit. Thus this approach could allow MNOs to test borrower’s credit behaviour at much lower cost before opening a window to borrowing e-value.
Loan application processes: Many SMS-based and USSD-based digital credit systems make it almost too easy to access credit, thus potentially encouraging frivolous applications for credit. This, may need management through behavioural nudges – for example to encourage the potential borrower to view the terms and conditions, or to reaffirm the need for the loan after a nominal “cooling off” period.
In contrast most app-based systems require the user to go through many screens(and, in some cases, what are seen as invasive requests for data and photographs) before they are given their loan. These systems need a thorough review to ensure that each step in the process is optimised, really adds value and does not put off high potential borrowers.
Poor product design: Currently, few of the digital credit products available reward those that consistently repay on a timely basis – except by offering larger loans. As borrowers demonstrate their credit-worthiness it would make sense to reduce the risk premium (and thus the interest rate) that they have to pay for each successive loan.
This approach might be further reinforced and optimally communicated by creating a tiered status system (similar to those for airline miles) so that borrowers can aspire to move up the tiers and thus qualify for lower interest rates, larger loans, variable repayment periods and other benefits. Additional product innovation might include:
1. Loans with a tenure of a day for market traders who are currently having to use loans repayable over weeks or a month to finance their business cycles, which run from early morning to afternoon;
2. Goal-based savings/loan products with an appropriate financial planning tool embedded in the app or USSD interface;
3. Longer-term loans for those with an excellent credit record who want to borrow for their business – once again these need to reflect their business cycles.
Absence of a sound collections strategy: At present most digital credit providers use SMS to encourage repayment, but otherwise have little interaction with their borrowers. Only a few are using call centres to talk to borrowers struggling to repay. The important human touch is missing, and thus digital credit loans are last on the list to repayamongst households with multiple loans outstanding. For larger loans it may also be valuable to involve agents in both loan origination and repayment/delinquency management.
Readers will note that none of the above refers to using “big data” – in a way that has been so successfully done in the developed world (for example by Lending Club in the US). This is because the vast majority of low income people in the developing world do not leave adequately deep “digital footprints” to reliably inform credit decisions. This will change over time, but for now the most effective (and commonly used) indicators of credit worthiness lie in credit history and behaviour, and (to a lesser extent) top-up and call/SMS behaviour.
It maybe that for larger loans app-based providers of digital credit may also want to use psychometric indictors to assess willingness to pay. However, this would be dependent on reducing the typical screening questionnaire from 200-300 down to 40-50 questions without losing predictive capability – quite a challenge.
There is a clear need to reduce the risk premium for borrowers of digital credit. While this may be difficult (but by no means impossible) to do for the first couple of loan cycles, it should be eminently feasible for later loans cycles once the borrower has established credit history and wants to borrow larger amounts. Doing so should incentivise timely repayment and increase borrower loyalty … and thus profitability of the providers of digital credit