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:

  1. Those that have gotten COVID-19 spread under control with the active cases going down
  2. Those that are in the process of getting COVID-19 spread under control with active cases showing signs of going down
  3. 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

Alejandro Giammattei

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

 

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