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The World’s 19 Most Powerful Countries Account for 71% of the World’s New Coronavirus Cases

The World’s 19 Most Powerful Countries Account for 71% of the World’s New Coronavirus Cases

On August 31st this year, 6 months have passed since the current pandemic forcefully started, on March 1st. It is an important opportunity to examine how the world’s great powers have developed effective government programs to reduce the number of cases. The power structure of the world is defined by nodal analysis as the number of hierarchically descending countries that account for 80% of the world nominal GDP. According to the latest definitive data available in the UN database in 2018, the power structure of the world consists of 19 countries presented in Table 1, each country being characterized by the share of total GDP and the share of total cases.

The data in Table 1 confirm the similarity of concentration between the world economy and total Covid-19 cases and reinforce the conclusion that the volume of the global recession induced by pandemic will be determined by the 19 countries. It is verified that the crises and booms of the world economy are mainly determined by the power structures of the world of those times. As it is known, the number of cases recorded by a country determines the potential of the economic recession of that country. Also, from Table 1 results the great variability of the share of cases, which requires an objective analysis of the effectiveness of government programs to reduce the number of contaminations.

In the following, I show a model that can largely characterize the effectiveness of countries in reducing the number of cases. The model uses a reference size that consists of the average value of global cases reported per million of inhabitants.

For each country, the number of cases per million inhabitants is calculated, then is divided by the world average and is denoted by R. Two situations may be found.

  • The situation in which R is above unit. It indicates the inefficiency of the government’s program to reduce the number of cases as compared to the world average.
  • The situation in which R is below unit. It indicates the effectiveness of government programs to reduce the number of contaminations relative to the world average.

The summary of August 31st is:

Table 2 shows in descending order the cases per 1 million inhabitants and the value of the R indicator.

Table 2 provides very relevant conclusions on the proposed topic.

  1. Out of a total of 19 countries, 11 have above unit value of the R indicator, so the government programs are inefficient in terms of the number of cases as compared to the world average. 8 countries have below unit value of the R indicator, so they have efficient government programs in relation to the world average.
  2. The above unit values of the R indicator are dominated by the US and Brazil, which have by far the most inefficient government programs to reduce the number of cases. In my previous analyses, I have highlighted the leadership of the US in terms of global inefficiency in relation to the Covid-19 cases.
  3. Regarding the below unit values of the R indicator, they have the peculiarity that 6 countries are from Asia, one from Europe and one from Oceania. In fact, the only continent that is not present in the power structure of the world is Africa. However, among these 8 countries, a special case is India. The below unit value of the R indicator may undergo an essential change in the next 3 months, which could lead us in the next quarter to the surprise of the country’s transition to the inefficiency zone. I emphasize that India has a daily average of cases of 70,000. In three months, the number of Indian cases would increase in this situation by 63,000,000!

Particular mention should be made of the success of the government’s program to reduce the number of contaminations in Germany, which, together with Japan, are the only countries in the G7 group with below unit value of the R indicator.

  1. A special case is Brazil, which in the last two months seems to have lost control of reducing the number of cases. Very important is the contagion effect in the South American countries, which, on the whole, seem to have lost control of reducing the number of cases, similar to Brazil.

In the last part of August, Eurostat published data on the decline of GDP in the second quarter of 2020 as compared to the same period of the previous year. We would have expected the values of the reductions to be proportional to the values of the R index. This is not the case, the statistical analysis showing that the influence of the R index on the value of the GDP reduction we referred to is about 30-40%. The explanation of this situation consists in the fact that the value of the GDP reduction at the level of a country depends, apart from the value of the R index, also on other specific factors, such as:

  • the high share of contamination in people over the age of 65 years;
  • high share of the number of cases of the unemployed;
  • relatively low number of outbreaks of contamination in the economic organizations leading to temporary cessation of activity.
  • the relatively high share of an ethnic minority with lower influence in the economic process (the typical case of African Americans in the USA).

The factors listed above are likely to diminish the reduction in GDP, which would result from the value of the R indicator.

Compared to those presented above, for the next period it is necessary that all the countries of the world evaluate every two months the trend of the values of the R index in order to establish the efficiency of the elaborated programs and the way in which the population complies with the pandemic prevention rules.

As for me, I shall return in early December with a comparative analysis of the next 3 months regarding the effectiveness of government programs to reduce the number of Covid-19 cases. 

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OEconomica No. 1, 2016