As we reflect on the COVID-19 pandemic, we start to wonder how certain countries and regions are able to manage the pandemic better than others. By better, I mean fewer cases and containing the spread within their countries for a sustained period. Through science, we now know that effective containment of COVID-19 relies on preventive measures of wearing facemasks, washing hands, adherence to strict social distancing, monitoring daily health and avoiding public gathering to contain the spread. All of these mean that there is a definite economic loss in the short term due to the businesses closing, restrictions in outdoor events and travel and increase testing and quarantine. In certain countries and regions, this could mean revenues lost in trillions of dollars and thousands of jobs. Investors do not like these short-term closure of the economy and hence you would have seen unfavorable responses from the global stock markets between March and April. To avoid these losses, leaders in these countries favor reopening the economies and loosening the restrictions, which may seem like a sensible strategy. However, this may result in long-term complications including increase in cases, resurgence of a second wave and beyond. This classic short-term vs. long-term economic trap paradox has been extensively studied by academics. I myself found this paradox of focusing on short term results over long-term growth strategies to be true in a study of over 40 technology companies. Are countries falling into similar traps? I try to explore this issue in this blog.
To do this, I first compiled data on countries that are doing well in containing the spread as well as countries that are struggling in this journey. The following website gives real-time data on how the spread is happening globally. Note that the list changes every day as some countries move around these categories. There are three categories of countries:
- Those that have gotten COVID-19 spread under control with the active cases going down
- Those that are in the process of getting COVID-19 spread under control with active cases showing signs of going down
- Those that are struggling to get COVID-19 spread under control with active cases going up.
A quick look at these countries across the three categories suggest they come from similar regions (e.g. continents) and have comparable population size and governance structure (e.g. democratic vs. oligarchy). So why did certain countries contain the spread better than others?
To understand this, I ended up collecting data on countries from (1) and (3). I excluded (2) since they are still not complete in containing the spread. There are 23 countries in category 1 and 27 in category 3. I also collected some information regarding the leaders of the government in these countries. In my previous studies looking at organizational effectiveness, I found that the functional background of the leaders are very important in managing healthcare related issues. For instance, in a study of hospital leaders managing patient experience, we found that leaders with medical background are better equipped to manage communication related aspects of care delivery. In another study of hospital leaders, we found that their leadership style is extremely important in effectively managing quality related issues. There is ample evidence in the field of management on the background of leaders when managing complex issues in their organization. Following these existing ideas, I specifically collected information regarding their backgrounds (education), age, and gender of the leaders (e.g. Presidents or Prime Ministers) of each of these countries. The table below gives these details for these countries. Category =1 represents the set of countries that have contained the spread while Category = 3 represents the set of countries that failed to contain the spread. As seen from table, there are no statistical differences in terms of the regions, size in terms of population between these two categories.
What are some differences?
We do find that countries containing the spread in category 1 have a slightly higher GDP per capita ($29,877) when compared to countries (category 3) that are failing to control ($19312). This does suggest some trends that richer countries are containing the spread better.
It is interesting to see that 40% of leaders (9 out of 23) from category 1 (i.e. countries containing spread) are female while only 3.70% of leaders (1 out of 27) from category 3 are female. These proportions are statistically significant (p<0.01) and this trend about female leaders are better managing COVID-19 crisis has been previously reported in the business press. For instance, management researchers have also found similar kind of response patterns when studying recalls and safety.
I also investigated two other characteristics of these leaders – namely Age and Education. When looking at the age of these leaders, we do find that leaders from category 1 are on an average 6 years younger (Average Age = 57 years) than those in category 3 (Average age = 63 years). This is in fact statistically significant (p<0.05). One possible explanation is that with increase in age, leaders tend to focus more on short-term economic traps when compared to long-term view. There is some support in the psychology and management literature on the relationship between risk taking tendencies and age. For instance, research shows that older CEOs are less likely to invest in R&D (long-term health of the firm) and are more likely to make diversified acquisitions to manage short-term health.
I also looked at the educational background of these leaders. In particular, I coded their education to be 1 if the leaders had an economics and/or business degree and 0 otherwise. It is interesting to find that a vast majority of leaders, around 42% (11 out of 26) from category 3 had an economics or business degree while only 8% of the leaders (2 out of 23) from category 1 had a business or economics degree. This was also statistically significant (p<0.05) suggesting that leaders with economic or business degree favor short-term needs from the market over long term.
While these trends are interesting to write a blog, I would like emphasize correlation is not causation and these are mere correlations observed in a small sample of data. Obviously, more analyses that are rigorous is required to make bold claims on these directional relationships. Nevertheless, it makes us wonder on some factors that may come into play as we think about these discussions and leaders locally.
|Countries||Category (1-under control, 3 – not under control)||Region||Size (million of people)||Per Capital Gdp (in $)||Leader of the State||Gender (1= Female, 0= Male)||Age (in years)||Background||Education|
|Andorra||1||Europe||0.077||42305||Xavier Zamora||0||40||Master of Law (ESADE)||0|
|Bahamas||1||Central America||0.385||33494||Hubert Minnis||0||66||Doctor of Medicine||0|
|Barbados||1||Central America||0.287||18798||Mia Mottley||1||54||Law Degree||0|
|Belize||1||Central America||0.404||8576||Dean Barriw||0||69||Law Degree||0|
|Cameroon||1||Africa||26||3820||Joseph Ngute||0||66||Law Degree||0|
|China||1||Asia||1400||20984||Xi Jinping||0||67||Chemical Engineering||0|
|Cuba||1||Central America||11.19||8822||Miguel Diaz Canel||0||50||Electronics Engineer||0|
|Denmark||1||Europe||5.8||51643||Mette Frederisken||1||44||Social Science||0|
|Finland||1||Europe||5.5||46559||Sanna Marin||1||35||Administrative Science||0|
|Hungary||1||Europe||9.7||35941||Janos Ader||0||61||Law Degree||0|
|Cyprus||1||Europe||1.18||41572||Nicos Anastsiader||0||64||Law Degree||0|
|Ireland (N. Ireland)||1||Europe||1.8||35000||Brandon Lewis||1||49||Law||0|
|Argentina||3||South America||40.17||20055||Alberto Fernandez||0||61||Law||0|
|Brazil||3||South America||210||17016||Jair Bolsonaro||0||65||Military Academy||0|
|Cote d’Ivoire||3||Africa||26||6201||Alassane Ouattara||0||78||Economics||1|
|Egypt||3||Africa||100||14023||Abdul Fatttah- el-Sisi||0||66||Military Academy||0|
|Ecuador||3||South America||17||11701||Lenin Moreno||0||67||Psychology||0|
|Guatemala||3||Central America||17||8413||Alendra Falla||0||64||Economics||1|
|Haiti||3||Central America||11||1819||Joseph Jouthe||0||59||Engineer||0|
|Dominican Republic||3||Central America||10.7||20625||Danllo Medina||0||68||Economics||1|
|US||3||North America||328||67426||Donald Trump||0||74||MBA||1|
|Venezuela||3||South America||28||2900||Nicolas Maduero||0||58||NA||0|
|Uzbekistan||3||Asia||34||9595||Shavkat Mirziyoyek||0||62||Technology Sciences||0|
|India||3||Asia||1352||9595||Narendra Modi||0||70||Political Science||0|
|Bangladesh||3||Asia||161||5453||Sheik Hasina||1||73||Political Science||0|
|Kazahstan||3||Asia||18.7||30178||Kassym-Jomrat Tokakye||0||67||International Relations||0|
|Colombia||3||South America||50.37||16267||Ivan Marquez||0||43||Law||0|
|Mexico||3||North America||128||21362||Andre Obrador||0||67||Public Administration||0|
|Panama||3||Central America||4.2||28456||Laurentino Cohen||0||67||BBA||1|