Empirical Assessment of COVID-19 and Global Education (I)

By Ebenezer M. ASHLEY (PhD.)

Most societies throughout the world have been plagued with epidemics and pandemics at different time periods since time immemorial. Adverse effects of epidemic and pandemic outbreaks on human societies could be categorised into social, economic and educational effects. The underlying objective of this research was to examine the extent of influence that pandemics such as COVID-19 could have on the successful implementation of intended annual academic programmes and activities in various countries and territories across the globe. To achieve the foregoing objective, the research sought to test causal relationship between total learners’ population and total national population. The latter helped to measure the extent to which academic institutions could contribute to the spread of the pandemic, controlling for other internal and external environmental factors.


The quantitative approach to scientific inquiry formed the basis of the research. Specifically, a cross-sectional design, an example of survey design, was adapted and used in the study. This design allowed the researchers to gather relevant research data over a specific period of time (Ashley, Takyi & Obeng, 2016; Creswell, 2009; Frankfort-Nachmias and Nachmias, 2008).

Data required for the conduct of the current research were obtained mainly from secondary sources. These included textbooks, peer-reviewed articles published in journals, research papers, newspaper publications; Google Search Engine including worldometers.info, africanews.com, ghanahealthservice.org, who.int and weforum.org; and electronic databases of international bodies such as the United Nations Education, Scientific and Cultural Organisation (UNESCO), among other significant sources.

Respective data on pre-primary through secondary levels, and tertiary level for one hundred and eight (108) countries across the globe; population data for each of these 108 countries; and available data on COVID-19 were used in the study.

Analytical Tools

Descriptive statistics and regression models were used to describe the research variables; and to evaluate their behaviour over the stated time frame on global education. Measures such as standard deviation and range were employed to describe the extent of dispersion about the central tendency (Ashley et al., 2016; Creswell, 2009; Frankfort-Nachmias & Nachmias, 2008). These measures were used to describe trends in global education drawing on data for the 108 countries; and data for COVID-19 during the research period.

Research Variables

The independent research variable was total learners’ population while the dependent research variable was the relative effect of total learners’ population on total national population.

Regression Model

Regression statistical model was adapted to measure the effect and level of interaction of total learners’ population on total national population over the research period. Specifically, the research sought to measure the extent to which respective global governments’ decisions to re-open schools; while effective medication had not been discovered could negatively impact spread of COVID-19 among their respective total national populations, especially when most school children were found to be asymptomatic; and transmission of the virus from asymptomatic patients to others was prevalent and severe.

The research sought to measure the extent to which in a given academic year, pandemics such as COVID-19 could significantly impact on successful completion of planned educational programmes and activities in countries and territories across the globe, controlling for other internal and external environmental factors such as Tsunamis, hurricanes, earthquakes and bush fires. The Microsoft Excel analytical software was adapted and used in the research. Diagrams and tables were derived from Microsoft Excel to explain the research data.


The study tested causal relationship between total learners’ population and total national population values using the following null and research or alternative hypotheses:

Ho:µ1 = µ2; this implies total learners’ population has no strong effect on total national population

H1:µ1 ≠ µ2; this implies total learners’ population has strong effect on total national population.

Findings from the research and related discussions are presented in the ensuing section. The section presents findings based on review of existing and related literature on the phenomenon; and based on empirical analysis of sampled data. The discussions are categorised into sub-themes and presented as follows.

Descriptive Statistics

A statistical description of the total learners’ population for each of the 108 countries included in the analysis is presented in Figure 5. Analysis in this section drew on the data in Table 2, column 2. The figure indicates the respective sample variance (7.94325) and skewness (8.174115) for the distribution. Skewness explains the distortion or asymmetry of the random variable around the mean in the distribution. Statistical data in the figure depict respective Kurtosis and standard error values of 75.748975 and 2711984.136. The extent to which the coefficients are significantly different from zero is explained by the standard error value. The minimum value in the figure is 9182. This represents total learners’ population value for Cayman Islands (9,182 total learners).


 Figure 5: Total Learners’ Population for Selected Economies

Mean 9366541.944
Standard Error 2711984.136
Median 2428342
Mode 396782
Standard Deviation 28183765.88
Sample Variance 7.94325E+14
Kurtosis 75.74897528
Skewness 8.174115111
Range 275426903
Minimum 9182
Maximum 275436085
Sum 1011586530
Count 108
Largest(1) 275436085
Smallest(1) 9182
Mean 3540.35


The maximum value (275436085) is representative of the total learners’ population for China, including Hong Kong and Macao (275,436,085 total learners). The range explains the difference between the maximum and minimum values for the distribution. Value for the range (275426903) explains the substantial difference (275,426,903) between the respective total learners’ population values for China, including Hong Kong and Macao (275,436,085) and Cayman Islands (9,182) during the research period. The value for sum (1,011,586,530) in Figure 5 depicts the total number of learners included in the analysis. This value is significant relative to the estimated total number of learners across the globe.

It is worth reiterating a significant proportion of the total global population is contributed by the 108 countries included in the analysis. As a result, another descriptive statistical test was conducted to ascertain the magnitude of the total population values for the sampled economies during the period. The population data in Table 2, column 3 were used for the analysis in this section. Figure 6 presents a statistical description for measures of central tendency such as the mean, median, and mode; and measures of dispersion such as the range, minimum, maximum and standard deviation (Ashley et al.; Frankfort-Nachmias and Nachmias, 2008) for the total population values used in the research.

Figure 6: Total Populations for Selected Economies

Mean 44009699.73
Standard Error 14043522.13
Median 10566017.5
Mode #N/A
Standard Deviation 145944563.1
Sample Variance 2.12998E+16
Kurtosis 81.68638723
Skewness 8.594030212
Range 1447404370
Minimum 65722
Maximum 1447470092
Sum 4753047571
Count 108
Largest(1) 1447470092
Smallest(1) 65722
Mean 50.80553333


Figure 6 presents the respective highest (1,447,470,092) and lowest (65,722) total national population values recorded during the research period. The highest value represents the total populations for China, including Hong Kong and Macao while the lowest reflects the total population value for Cayman Islands. The range of total population values during the period is 1447404370 (1,447,404,370). This represents the difference between the highest (1,447,470,092) and lowest (65,722) total population values recorded during the period.

Results in Figure 6 depict respective mean and median of 50.8055and 10566017.5; and standard deviation of 145944563.1. These tell us the extent to which the observations were dispersed around the central tendency. The mode explains the variable with the highest frequency or number of occurrence in the data. The figure shows no absolute value (#N/A) for the mode. This implies no total national population value was repeated. That is, there were no two or more countries with the same total national population values during the period. Coincidentally, a significant number if not all of the 108 countries included in the analysis were greatly affected by the COVID-19 outbreak. This raised concerns about the possibility of meeting universal targets for educational programmes and activities during 2020.


The objective of this research was to test the underlying hypothesis. That is, measure the extent to which a given total learners’ population significantly influences total national population value. Statistics in column 2, Table 2, depict the respective total learners’ population values for the 108 economies sampled for the research. Data in the table show countries such as China, including Hong Kong and Macao (275,436,085), Indonesia (68,265,787), United States (55,100,000), Pakistan (46,803,407) and Bangladesh (39,936,843) have fairly large total learners’ population values affected by COVID-19.


Column 3 in the table presents the respective total national population values for the sampled population. Data used in this section were obtained from the databases of UNESCO and Worldometer. Causal relationship between the independent variable (total learners’ population) and the dependent variable (total national population) was tested using regression analytical tools. Results from the analysis are presented in the following section.

Table 2: Data on Total Learners and Total Population for Selected Global Economies

Afghanistan 9,979,405 38,928,346
Albania 652,592 2,877,797
Algeria 10,236,182 43,851,044
Argentina 14,202,149 45,195,774
Armenia 540,503 2,963,243
Austria 1,708,540 9,006,398
Azerbaijan 1,983,999 10,139,177
Bahrain 292,429 1,701,575
Bangladesh 39,936,843 164,689,383
Belgium 2,984,458 11,589,623
Bolivia* 2,612,837 11,673,021
Bosnia and Herzegovina 523,241 3,280,819
Bulgaria 1,224,406 6,948,445
Burkina Faso 4,686,723 20,903,273
Cambodia 3,522,262 16,718,965
Cayman Islands* 9,182 65,722
Chile 4,891,092 19,116,201
China** 275,436,085 1,447,470,092
Colombia 11,532,903 50,882,891
Costa Rica 1,317,482 5,094,118
Côte d’Ivoire 6,338,832 26,378,274
Croatia 787,188 4,105,267
Cyprus 180,617 1,207,359
Czech Republic 2,068,763 10,708,981
D. P. Republic of Korea 4,755,570 51,269,185
Denmark 1,497,943 5,792,202
Ecuador 4,783,225 17,643,054
Egypt 26,071,893 102,334,404
El Salvador 1,604,845 6,486,205
Equatorial Guinea* 160,019 1,402,985
Estonia 272,781 1,326,535
Ethiopia 24,686,497 114,963,588
Fiji 453,894 896,445
France 15,462,340 65,273,511
Gabon 478,438 2,225,734
Georgia 883,677 3,989,167
Germany 15,382,695 83,783,942
Ghana 9,696,756 31,072,940
Greece 2,204,532 10,423,054
Grenada 35,288 112,523
Guatemala 4,559,618 17,915,568
Honduras 2,285,249 9,904,607
Hungary 1,791,758 9,660,351
Iceland 98,224 341,243
Indonesia 68,265,787 273,523,615
Iran 18,635,825 83,992,949
Iraq 7,435,696 40,222,493
Ireland 1,319,122 4,937,786
Israel 2,481,467 8,655,535
Italy 10,876,792 60,461,826
Jamaica 627,156 2,961,167
Japan* 16,496,928 126,476,461
Jordan 2,372,736 10,203,134
Kazakhstan 5,060,284 18,776,707
Kenya 14,314,351 53,771,296
Kuwait 749,324 4,270,571
Kyrgyzstan 1,661,618 6,524,195
Latvia 396,782 1,886,198
Lebanon 1,363,393 6,825,445
Lesotho 396,782 2,142,249
Libya 1,885,226 6,871,292
Lithuania 586,120 2,722,289
Luxembourg 109,897 625,978
Malaysia 7,962,033 32,365,999
Mauritania 947,589 4,649,658
Mexico 37,589,611 128,932,753
Mongolia 1,026,210 3,278,290
Montenegro 135,689 628,066
Morocco 8,943,156 36,910,560
Namibia 745,566 2,540,905
Netherlands 4,211,999 17,134,872
North Macedonia 359,623 2,083,374
Norway 1,357,563 5,421,241
Pakistan 46,803,407 220,892,340
Palestine 1,626,357 5,101,414
Panama 998,348 4,314,767
Paraguay 1,744,889 7,132,538
Peru 9,911,513 32,971,854
Poland 7,553,488 37,846,611
Portugal 2,375,217 10,196,709
Qatar 343,524 2,881,053
Republic of Korea  10,181,358 25,778,816
Republic of Moldova 586,158 4,033,963
Romania 2,951,879 19,237,691
Rwanda 3,464,409 12,952,218
Saint Lucia 33,162 183,627
Saudi Arabia 8,410,264 34,813,871
Senegal 3,660,526 16,743,927
Serbia 1,220,968 8,737,371
Slovakia 988,103 5,459,642
Slovenia 412,224 2,078,938
South Africa 14,612,546 59,308,690
Spain 9,706,284 46,754,778
Sri Lanka 5,218,372 21,413,249
Sudan 8,824,167 43,849,260
Switzerland 1,589,837 8,654,622
Syrian Arab Republic 4,188,528 17,500,658
Thailand 15,401,441 69,799,978
Trinidad and Tobago 277,190 1,399,488
Tunisia 2,751,424 11,818,619
Turkey 24,901,925 84,339,067
Ukraine 6,785,004 43,733,762
United Arab Emirates 1,362,359 9,890,402
United States*** 55,100,000 331,002,651
Uzbekistan 7,474,117 33,469,203
Venezuela* 6,866,822 28,435,940
Yemen 6,119,823 29,825,964
Zambia 4,012,617 18,383,955

Data Sources: UNESCO (2020) & Worldometer (2020)

*Data exclude tertiary level learners

**Data include Hong Kong and Macao

***Data exclude breakdowns

Graphical presentation of the data in Table 2 is outlined in Figure 7. Stated in different terms, data on respective total learners’ populations and total national populations for the sampled economies are presented in Figure 7. Data distribution in the figure affirm dominance of China, including Hong Kong and Macao (275,436,085) and Indonesia (68,265,787) in the total number of learning populations adversely impacted by the outbreak of COVID-19. Nearly 72.5% (about 55.1 million learners) of the estimated total learning population in the United States (76 million total learners) were severely impacted by the predatory COVID-19 pandemic during 2020. However, the respective learning populations impacted in Zambia (4,012,617 learners) and Switzerland (1,589,837 learners) were equivalent to 21.83% and 18.37% of their respective national populations: Zambia (18,383,955 people) and Switzerland (8,654,622 people).

The total number of learners affected in the United States (55.1 million learners) was nearly 8.3 million more than the affected learning population in Pakistan (46.8 million learners); and about 17.5 million more than the estimated total learning population negatively impacted in Mexico (37.6 million) during the pandemic period. The computations suggest the affected learning population (37,589,611 learners) constituted nearly 29.15% of the total Mexican population (128,932,753); which is equivalent to 38.95% of the United States’ total population (331,002,651).

In Indonesia, about 24.96% of the total population (273,523,615) is learners who were affected by the outbreak of COVID-19; implying about 29% of the population had the potential of spreading the virus throughout the country, if nationwide school closures had not been adapted and implemented as a timely non-pharmaceutical intervention tool. Similarly, the estimated total learners’ population affected (14,612,546 learners) suggests, about 24.64% of the total population in South Africa (59,308,690 people) served as potential conduits for spread of the Coronavirus across the length and breadth of the country. Lo and behold, the second wave of COVID-19 and its attendant increase in reported cases and deaths in South Africa amply demonstrated how precipitated school re-openings resulted in overwhelming surge in social and economic costs to the country.

Author’s Note

The above write-up was extracted from an earlier publication titled: “Impact of the Coronavirus Pandemic on Global Education” by Ashley (2022) in the International Journal of Business and Management.


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