Short Term Economic Traps and COVID-19 Response in Countries – Is there a trend?

 

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
Bhutan 1 Asia 0.754 9426 Lotay Tshering 0 51 Medicine 0
Burma 1 Asia 53 6707 Win Myint 0 68 Science 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
Estonia 1 Europe 1.3 37605 Kersti Kaljulaid 1 46 Business 1
Finland 1 Europe 5.5 46559 Sanna Marin 1 35 Administrative Science 0
Georgia 1 Europe 37 12409 Salome Zourabichvilli 1 68 Sciences 0
Hungary 1 Europe 9.7 35941 Janos Ader 0 61 Law Degree 0
NewZealand 1 Pacific/Australia 5 40226 Jacindra Arden 1 39 Communication 0
Cyprus 1 Europe 1.18 41572 Nicos Anastsiader 0 64 Law Degree 0
Iceland 1 Europe 0.364 54743 Guoni Johnanesson 0 52 Historian 0
Ireland (N. Ireland) 1 Europe 1.8 35000 Brandon Lewis 1 49 Law 0
Norway 1 Europe 5.6 79638 Erna Solberg 1 59 Economics 1
Malaysia 1 Asia 32 34567 Muhyiddin Yassin 0 72 Literature 0
Niger 1 Africa 22 1213 Mahamadou Issoufou 0 68 Engineering 0
Taiwan 1 Asia 23 55078 Tsai Ing-Wen 1 54 Law 0
Vietnam 1 Asia 96 8066 Nguyen Trong 0 76 Philosophy 0
Afghanistan 3 Asia 32 2024 Ashraf Ghani 0 71 Anthropologist 0
Albania 3 Europe 2.85 14866 Ilir Meta 0 51 Economics 1
Algeria 3 Africa 43.6 15765 Abdelmadjid Tebbounse 0 75 MBA 1
Argentina 3 South America 40.17 20055 Alberto Fernandez 0 61 Law 0
Australia 3 Pacific 25 54799 Scott Morrison 0 52 Economics 1
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
Kenya 3 Africa 47 4071 Uhru Kenyatta 0 59 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
Indonesia 3 Asia 267 34567 Joko Widodo 0 60 Forestry 0
Bangladesh 3 Asia 161 5453 Sheik Hasina 1 73 Political Science 0
Iraq 3 Asia 38 17952 Barham Salih 0 60 NA 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
Israel 3 Asia 9.27 40336 Benjamin Netanyahu 0 71 Architecture 0
Mexico 3 North America 128 21362 Andre Obrador 0 67 Public Administration 0
Poland 3 Europe 38 35651 Mateusz Morawiecki 0 52 Economics 1
Panama 3 Central America 4.2 28456 Laurentino Cohen 0 67 BBA 1
Ukraine 3 Asia 41 10310 Denys Shmyhal 0 45 Economics 1