Macro Analysis /

Covid-19: The Emerging Markets best-placed to withstand the pandemic

  • We look at the ability to withstand Covid-19 domestically and the ability to weather the external economic storm

  • The BRIC economies appear relatively well positioned, as do Kazakhstan and Saudi ‒ together, the ‘BRICKS’

  • South America is positioned well, but African countries seem exposed, and emerging Europe is particularly poorly placed

Covid-19: The Emerging Markets best-placed to withstand the pandemic
Paul Domjan
Paul Domjan

Senior Contributing Analyst

Tellimer Research
17 April 2020
Published byTellimer Research

Having argued that Africa is better placed to cope with Covid-19 than many realise and that the impact of Covid-19 on EMs will be as much through the withdrawal of external demand (as developed countries hibernate) as through the domestic impact of the virus, a number of readers have asked me to break this down with data to show which developing markets are best placed to cope ‒ both with the domestic impact of the virus itself and with the withdrawal of external demand.

To avoid preconceptions about the results and allow the data to speak for itself, I have identified the relevant data, assembled it using percentile ranks to transform the data to a common scale,[1] and then built two indices:[2] ability to withstand Covid-19 domestically and ability to weather the external economic storm.

So, without further ado, this is the EM and FM investable universe. I was surprised by some of the results, and I’m sure that you will be, too:

Figure 1: Global vulnerability to Covid-19 (interactive chart; hover over countries for more detail)

Source: Tellimer Research, Paul Domjan

At a high level, we can draw at least four conclusions from this initial data:

  • The largest EMs appear relatively well positioned to cope with the crisis domestically and to withstand the withdrawal of external demand as developed markets hibernate: This includes all four BRIC (but not the S of South Africa), along with Kazakhstan and Saudi Arabia, albeit for very different reasons. From the perspective of portfolio strategy (and spelling pedantry), this gives us new ‘BRICKS’ that can form the core of a Covid-19-resilient approach to EM investing.
  • African countries appear more exposed: Despite the various advantages that I have previously argued that Africa has in dealing with Covid-19, some of which, like previous experience of Ebola and lack of indoor spaces are not reflected in this data, in this model African countries tend to score worse than the rest of the developing world because of a high level of co-morbidity and poor systems for both health care and public health.
  • Emerging Europe appears to be particularly badly positioned: These countries have elderly, densely packed populations; high levels of travel; high levels of co-morbidity; and only mediocre public health capabilities at points of entry. They are also exposed externally, as highly trade-dependent economies with low levels of savings and reserves. EU assistance, and particularly EU coronabonds, would be a key lifeline for these countries.
  • South America scores quite well as a region: It benefits from lower population density and travel and, in many countries, strong public health administration at points of entry. South American economies also benefit from lower reliance on trade and a greater share of food, which developed countries will still import during hibernation, in their exports.

Going deeper, let us look at three particularly strong performers, and three particularly weak ones.

Strong performers

  • Brazil: Although no quantitative data can capture President Bolsonaro’s negative impact on Brazil’s Covid-19 response, our model suggests that, fortunately, Brazil begins the crisis in a relatively strong position to cope. Although it is a highly urbanised country with high-profile slums, overall population density and the percentage of the urban population living in the slums are relatively low. Travel within Brazil is limited, reducing the risk of the disease spreading. Brazil is also positioned well to survive economically while developed markets hibernate, as trade is a relatively small share of GDP, and food, which developed countries will still import during hibernation, represents a large share of its exports. The country suffers from low domestic savings, but has little reliance on remittances from abroad.
  • Russia benefits from a well-developed hospital network, limited domestic travel and strong administration at ports, but its vulnerability is increased by low temperatures, an elderly age structure and relatively high levels of co-morbidity. It also benefits from large reserves and a high savings rate, while the Russia-OPEC agreement to cut production will help maintain its oil and gas export earnings.
  • Sri Lanka: Although Sri Lanka’s reliance on remittances and tourism, combined with limited reserves and a low savings rate, leave it exposed to a withdrawal of external demand during hibernation, its low levels of urbanisation, a relatively small share of the urban population living in slums, limited travel, high temperatures and surprisingly extensive hospital network leave it relatively well-prepared domestically.

Vulnerable countries

  • Hungary: Although Hungary has a large hospital network, it suffers from a large elderly population, high levels of co-morbidity, low temperatures, high levels of travel and mediocre health administration at ports of entry. Externally, low reserves, a very large share of trade in GDP and reliance on remittances leave it exposed to a withdrawal of developed market demand during hibernation.
  • Pakistan: High density, significant slums, a very underdeveloped hospital network and high levels of both travel and co-morbidity leave Pakistan very exposed domestically, while limited reserves, poor quality public administration, low domestic savings and high reliance on remittances leave it exposed externally.
  • Botswana: Despite a strong external position, supported by large reserves and domestic savings, exceptionally high levels of co-morbidity drive significant domestic risk.

In this report, I will explore domestic vulnerability in detail, and what this data looks like globally. The forthcoming second part will go into detail about countries’ ability to weather the storm externally.

Domestically, most developing markets lack the ability to easily hibernate, so we look at how able they are to manage infections and how likely the virus is to spread. It is important to remember that developing markets are hugely heterogenous, and, even within countries, some regions, such as Kerala in India, are responding more effectively than their peers ‒ so not all of these factors will have the same relevance for all economies. Moreover, data is not available for some important factors, such as on past experience of epidemics, the availability of quarantine facilities and the availability of ventilators and intensive care beds.

Accepting these caveats, I have assembled 10 factors to approximate domestic vulnerability. Not all readers will agreed with the factors I have chosen or my decision to weight them equally, so I have provided access to the complete scaled dataset, as well as interactive visualisations of each factor (green on the maps equates to resilience against the economic impact of Covid-19 on that factor; purple indicates weakness), to allow you to focus on the factors you consider most relevant or to construct your own index.

To successfully withstand Covid-19 domestically, a developing country needs:

1. Low population density: Tellimer’s Hasnain Malik has argued that extreme population density is a significant challenge to the ability of developing markets to respond to Covid-19. Low population density reduces the likelihood of the disease spreading and makes it easier to implement social distancing measures.

Figure 2: Low population density reduces Covid-19 vulnerability - interactive map

2. Small percentage of the urban population in slums: Similarly, the fewer people who live in slums, the easier it is to implement social distancing. Moreover, people in slums are more likely to be migrants from the countryside, without social and family support networks in the cities upon which to fall back.

Figure 3: Urban slums are an ideal breeding ground for Covid-19 (% of urban population living in slums) - interactive map

3. Low level of urbanisation: The more rural the country is, the less likely people are to come together and transmit the virus. Moreover, rural residents are more likely to have local support structures, and a lower level of urbanisation makes it less likely that urban residents will seek to return to families in rural areas during lockdown.

Figure 4: Low levels of urbanisation may slow spread of Covid-19 - interactive map

4. Large percentage of homes with access to soap and water handwashing: Basic hygiene is the first line of defence against Covid-19 and, sadly, many areas of the world still lack even basic soap and water handwashing. Simple equipment like this can make a big difference:

Figure 5: Basic home handwashing facilities with soap and water are the first line of defense against Covid-19 - interactive map

5. Small amount of travel: Travel spreads Covid-19, and the amount of travel varies hugely around the world. Countries that already have very low levels of travel ‒ both domestic trips and international arrivals ‒ are less likely to experience rapid spread of the virus.

Figure 6: Less travel can limit the spread of Covid-19 (overnight tourist trips per capita, domestic and international) - interactive map

6. Small percentage of the population over 65: While Covid-19 does pose a risk to younger, healthy people, the greatest risk appears to be the to the elderly, who represent a very small share of the population in many developing markets.

Figure 7: A lower percentage of the population over the age of 65 reduces Covid-19 risk - interactive map

7. Low incidence of Covid-19 co-morbidities: Where Covid-19 does impact the young, it tends to be a result of co-morbidities. I have modelled this using HIV, tuberculosis, respiratory disease, cardiovascular disease, cancer and diabetes prevalence (for the former two) and mortality (for the latter four) rates.

Figure 8: Higher incidence of co-morbidities increases  risk of Covid-19 (worst co-morbidity score among HIV, TB, and aggregate of cardiovascular disease, respiratory disease, cancer and diabetes) - interactive map

8. High average May temperature: Research on other coronaviruses suggest that they are less likely to be transmitted during warmer weather. Although it is too early to know for sure whether the same with be true of Covid-19, areas with higher average May temperatures could experience a slower spread and lower incidence of the virus.

Figure 9: Higher May temperatures may slow the spread of Covid-19 - interactive map

9. High quality of the health administration at ports of entry: The WHO tracks the implementations of its recommendations for public health administration at points of entry, which indicates which countries have the capability to reduce the importation of Covid-19.

Figure 10: Strong public health capacity at Point of Entry helps to avoid importation of Covid-19 - interactive map

10. Large number of hospital beds: Although many developing countries have limited medical resources, the number of hospital beds per 1,000 people gives an indication of the ability of the health system to respond to rising infections.

Figure 11: More hospital beds per 1,000 people increases a country's ability to cope with Covid-19 cases - interactive map

[1] Two data series ‒ quality of public health at ports of entry and quality of public administration ‒ are already indices, so I used linear rescaling to transform this data.

[2] The index value is the simple average of all rescaled data available for each individual country. Not all data is available for all countries, and I have ignored missing data rather than attempt to impute a value.