Covid 19: Global Data, Analyses, and Policy Implications.

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Coronavirus By Prof Ian Inkster

Introduction: Lazy Media and Damned Statistics

Is it unfair or disingenuous to write of the Western media as lazy? Do we not get the media that we deserve, and so is it not our own fault that much reportage in the west concerning the Covid 19 pandemic often appears both shallow and partial?

I think we are particularly ill-served by the conjuncture of calamitous virus and faltering media. The former is surely irreversible bad luck, but the latter results from several intertwined elements moving together in the same direction.

First, the greatest fall-out from (or companion of) failing democracies has been the rise of a form of populism that has destroyed the older diplomacy. Indeed, we really do live in a post-diplomatic world in which the ‘leaders’ of our nations feel it perfectly reasonable to twitter and televise their confused and vapid statements about major political decisions prior to much in the way of any diplomatic hard-work or silent homework. This has happened before of course – President John F. Kennedy and his television broadcast to the world at a most crucial time in the Cuban crisis of 1962 springs to mind. But even well-known earlier examples seem to have emerged from some reasonable process of quick and quiet preparedness based on discussions across a range of interests. And that was precisely when journalists foraged high and low, built their pictures of likely events, educated the public in salient features of a situation, and did so with mindfulness of the repercussion of what they wrote – the Watergate fiasco was in fact unfolded by a wary and informed press. Post-Diplomacy knows far less of such phases of investigative elucidation, as ministers, Prime Ministers, and Presidents drop thunderbolts from out of the blue and events move in such a way that the media become mere followers and consumers of official releases.

Secondly, related to this is the recent decline in investigative journalism. We rarely see the equivalent of John Pilger standing on a pile of skulls and human remains in Cambodia in 1979, with all the dangerous work, personal passion and political radicalism that this entailed in his Year Zero. Investigation was part and parcel of independence and integrity. These two elements, of post-diplomacy and failures of investigation, lean upon each other in the evolution of lazy journalism, but are they themselves direct functions of mass social media and technology? This is the frequent argument – the rise of populism itself being seen as some function of the true mass-ness of social media and the technology that permits and closets unregulated irresponsibility throughout our world, indisputably stemming from major developments in Western capitalist organisation during this century.

So, some fairly deep underlying elements of global society have created a lazy traditional media, technological changes have produced an irresponsible and mundanely vacillating social media, and they now meet headlong against a global pandemic that takes no notice of anyone but gives these media a ready mode of production for sensation, confusion and material gain. Ease of entry and understanding is paramount in a populist world. Thus, we have our damned statistics.

Following from this the Covid world is one of spikes and latest trends, pick-your-day statistics lead the major stories, and none of this really helps. It’s certainly not entertainment and, as we shall show, it’s certainly not knowledge, never mind information. Noise dominates the Covid world. Even the universal assumption that in any and every nation it is Covid policy, management or governance that really matters is never challenged by the headliners and the celebrity gurus. Again, as we shall show, policy may lie towards the lower end of the diverse elements involved in Covid infection and mortality. Of greater importance might be the basic elements of income and wealth and associated age structures of national populations. Next, other socio-economic factors such as health expenditure (not merely a direct function of national income), air pollution, industrial organisation, rural-urban locations, and population density, through to the character of migrant worker employment. Third, the elements of what we might best label ‘connectivity’, from tourism to foreign trading and other commercial activities: And finally, the environment has profound effects on the spread and mortality of Covid, from the nature of physical frontiers between nations to weather, climate and topography.

Now this is a very complex listing of feasible elements involved in explaining the pattern of Covid infection or mortality for either one nation or territory or for the globe at large. It is not possible to deal with such breadth and seeming complexity in every press release, in every journalistic enquiry, in every scholarly research project or in every government policy document. But it is blindingly obvious that a lazy media that spends over 90% of its time and precious resources focussing on insights and advances or blunders and retreats in government policies and regulations, does not really approach the nub of our problems or hasten their mitigation.

Thus, at present in the global north a major media furore is over the way in which governments have not properly foreseen or acted to reduce the impacts of the so-called ‘Second Wave’ of the pandemic. But little has been noted in that media about attempts at understanding it. In particular, is it broadly epidemiological (linked to the natural character of the virus itself) or is it sociological – related more to such things as loosening of earlier restrictions especially lockdowns, better and increased testing regimes, a weakening of social compliance with policy measures. Our present inability to distinguish between all such elements is never actually the focus of media interest and illustrates the global confusion displayed across the whole range of Covid discourse or chatter.

The remainder of this essay looks at available global and regional data in some detail and is the basis of an analysis that combines the cumulative data from February 2020 to the present into an analysis that hopes to put the mess of media pottage about Covid policies and regimes into some sort or order and perspective. Policy matters, but so too do key underlying global elements.

Global Context- The 21 June Data Analysis

We might begin by intervening on a date by which most large nations of our world already exhibited a significant history of Covid 19. China announced its first case on 1 December, Japan on 16 January, US on 20 January, Nepal on 24 January, India on 30 January, Egypt on 14 February, Pakistan on 26 February, and Nigeria on 27 February. So, the virus had become widespread in all major regions long before 21 June. Omitting small and isolated nations, the analysis is based on 86 nations representing 96% (7,266 million) of the world’s population.

In order to focus on major socio-economic variables in explaining the distribution of Covid infection and mortality, the 86 nations were divided into 4 income groups based on World Bank categories and measurements – 19 nations and over one billion people in group 1 ‘high income’; 22 nations and 2.7 billion people in group 2 ‘upper middle income’; 16 nations and 2.3 billion people in group 3 ‘lower income’; and 29 nations and 1.2 billion people in group 4 ‘low income’.

Although by this time there had already been innumerable media explosions concerning sharp spikes, emergencies and disasters, most emanating from the western part of our globe, this analysis is more concerned with uncovering evidence concerning underlying patterns and their possible causes. Table 1 is a cumulative summary of the mass of basic Covid data up to 21 June[1]:

Table 1: 86 Nations on 21 June 2020, Income Groups, Covid Statistics, Age Distributions.

 123456
GROUPsCmDmD/C %PPP US$0-1465+
1 (19)32082516.7252.221.017.6
2 (22)203766.84.220.528.610.8
3 (16)52327.93.08.532.96.7
4 (29)1653.783.242.543.83.13

Column 4 shows the per capita income groups in terms of the World Bank measures of purchasing power parity per capita in US$000s.[2] Thus, group four average income is only 4.7% of that of group 1. Cm measures Covid cases per million as officially registered, and the first startling finding is that this measure falls dramatically as we move from the rich world of group 1 to the very poor world of group 4. Cases per million in poor nations are 5% of those in the richest nations, 8% of those in middle-income nations. This result is very clear but starkly counter-intuitive. Column Dm measures deaths per million (mortality) and the figures are equally polarised – mortality in rich group 1 is 66 times as great as in poor nations when based on per capita measures and official figures recorded over months.

The mortality measure D/C, of deaths over total cases, is another check on reality for it may proxy for effects of Covid over time in any one nation, since Covid inception and the impact of Covid management policies – most Covid policy, especially in richer nations, is designed to reduce mortality of the especially vulnerable older people or those with reduced immunity as a result of prior or continuing illness. Again, the results are stark and require no sophisticated probability tests – whatever the policies selected in rich nations, their mortality rates are approximately twice those of poor nations as a proportion of their total infection registrations.[3]

This analysis gives some precision to cruder estimates for regions of the sort summarised below in table 2.

Table 2: Covid Cases and Mortality by Region and Income 24 June 2020

REGION1 Cases millions2 Cm3 Dm4 D/C %5 PPP US$6 Population millions
Europe2.331042558.143,398741
North America2.773174285.761,308579
Asia2.0444112.57,0844,463
S America1.739531644.016,590422
Africa0.323062.66,9061,216
WORLD9.01322625.217,9487,594
East Asia0.132461.756,680216
China0.15735.616,6001,400

It is obvious that Europe and North America (dominated by USA) have far higher Covid incidence and mortality than the rest of the world, and that this is probably a result primarily of income levels – as with table 1, the higher the income the more likely the Covid devastation. Europe’s mortality rate (column 3) on 24 June was 4 times greater than the world average, and 23 times that of all-Asia. But it is also notable that the five rich nations and territories of East Asia (Japan, South Korea, Taiwan, Singapore and Hong Kong) stand as an anomaly, with average incomes (column 5) above those of Europe yet very low Covid rates, and it has been argued that this is indeed due to superior Covid management policies and higher levels of civil society compliance there than in other regions, to which we shall return at a later point.[4] China, the first known focal point of the virus, also seems exceptional, and its size weighs heavily in all overall global statistics. With a per capita income level just lower than that of the world average, Chinese Covid experience as measured in columns 1-3 is far superior. Column 4 for China, measuring deaths as a proportion of total cases registered, may hint at a low case-count as the main real anomaly but this has yet to be shown. It is conceivable that historical and cultural features shared amongst East Asian nations have served to dampen Covid effects. Once nations are considered in situ within their income groups (as in table 1) these outlier cases become of less importance, but they require explanation.

Table 1 also contains data on age distributions, and we now argue that these are basic to an explanation of the general Covid pattern – of low-income nations suffering a smaller Covid impact than is evident in rich nations.

Here we turn to an explanation that follows the seeming character of this particular virus. It discriminates between youth and age in a fundamental manner. As shown very clearly in columns 5 and 6 of table 1, as income rises amongst nations so too the proportion of youth in their populations falls and the proportion of the elderly even more markedly rises. The 4 income groups show this very clearly indeed. As we move from rich to poor the proportion of youth under 15 years of age doubles, but the proportion of those over 65 years of age falls to around 18%.

Thomas Malthus (1766-1843) was almost right in his famous work of 1798. He laid down simply that as income rises population increases in geometrical proportion but that the ‘means of subsistence’ (particularly food) increased only arithmetically. So, his Essay on Population, argued that any developing economy would soon run into a problem of scarcity and starvation as population inevitably outgrew incomes. Wages could not rise in any sustained manner. On the other hand, he was wrong. As nations increased in wealth beyond cultural subsistence their populations did not escalate, rather they changed in age structure.

The mass of people now on higher incomes began to have less children as they chose better life-styles and needed less children for any work or income they might obtain from them. In rich nations families got smaller, the proportion of children in the population declined and the proportion of older people rose as better incomes, medicines and easier work extended life expectancy. Poorer nations remained nearer the Malthusian assumptions of possessing many children and few older people. This thesis of a ‘demographic transition’ – substituting for Malthus’s own demographic crisis and decline – is now a major proposition for all analysts, and is fleshed out by evidence in every direction, including that presented here.[5]

This is what we are seeing in table 1. And because Covid generally does not attack the young but can be fatal to the old, poor nations have age structures that in themselves minimise the disastrous impacts of the pandemic. This generalisation is all but independent of government policy and management but has direct relationship to income levels. How much further this very strong argument can be pushed depends on further medical research. In particular, do youngster not only escape the virus but also have a lower propensity to pass it on to older members of the population? Are those who are infected, but asymptomatic, less infectious? If these two queries were to be answered with a strong affirmative, then these ‘demographic transition’ effects, ameliorating impacts on the poor, would be even stronger explanations of our global pattern.

What we can suggest in the interim is that the strong global direct relationship between national per capita income and Covid impacts is related primarily to the systematic differences in age structures between rich and poor. The high-income regions of Europe and North America have exceptionally high mortality, in fact weighty enough to drag up the average for the world to 5.2%. (table 2 column 4) The rich world of the global north, over 1 billion people, provides 55.5% of world Covid cases. The poor world of the global south, over 6.2 billion people, provides 44.5% of world Covid cases.

The close relationship globally between levels of income and levels of Covid 19 is the exact reverse of more economistic expectations. A normal consideration of governance, income, infrastructure and political control over civil society would expect the rich nations of our world to have much lower Covid – and especially mortality – numbers than the poor nations, if only for reasons of medical facilities and knowledge, and better diets, transport and communications facilities. That is, the relations between income levels and Covid mortality should be directly inverse or near enough. It is not.

India and South Asia in Global Perspective (3 May-20 August)

By focusing on India and South Asia more broadly it is possible to lay the global trend across a hugely important and populous region of low income, as well as to begin an extension of our explanation of the global pattern. By 3 May, of the South Asian nations, Sri Lanka and Maldives contributed only 2% of the South Asian total of 72,466, a total then representing 21% of world Covid cases. The four major nations (India, Pakistan, Bangladesh and Afghanistan) showed Dm levels of around 1-2%, and D/C levels of from 1.8% (Bangladesh) to 3.3% (India), all very low indeed when compared to quantities in tables 1 and 2 above. By 29 June the Covid data for the four large nations were as follows:

Table 3: Covid Cases and Mortality by South Asian Nations and Income.

NATION or Group1 PPP US$ 000s2 Cm3 Dm4 D/C %5 0-146 65+
India7.830612327.86.0
Pakistan5.480016234.84.5
Bangladesh4.26829128.45.1
Afghanistan1.974115243.22.6
4-Nations AVERAGE4.863213233.64.6
5 Adjacent Nations6.527840.829.05.9
19 group 1 richest52.23,2082516.721.017.6
29 group 4 poorest2.51653.83.243.83.1

By focusing on the South Asian averages, we can place the group of four in the context of the 5 adjacent nations (Nepal, Sri Lanka, Myanmar, Tajikstan, Uzbekistan) and with the group 4 poorest 29 nations on earth. As might now be expected from the income levels, all these nations have a very low Covid presence in comparison with the 19 richest nations of group 1. The differences between the poor nations are of interest. The very poorest 29 nations have incomes around half that of the South Asian and a significantly lower Covid presence, but the surrounding nations have somewhat higher incomes (particularly Sri Lanka, Uzbekistan, and Myanmar) and also a significantly lower Covid presence. This acts as a limit to the Malthusian or age-group approach; large differences in income are determinant, small differences are generally not. This is because large differences of income spell measurable contrast in age groups (especially of the elderly) as in our model, but small income differences are commensurable with almost no differences in age group distributions as shown in table 3 columns 5 and 6. Compared to the huge age group differences between the 4 income groups of table 1, those of table 3 are minor. The ‘demographic transition’ has to be truly marked in order to have a really visible impact upon Covid infection and mortality.

Table 4 looks at India in more detail to further clarify how different elements might relate in explaining Covid patterns. As western media have highlighted, measured by crude infection rates India has been recently suffering badly from Covid. In the last few days (22-24 August) new infections in India have reached around 28% of the global number of new infections. As we have seen, there is only limited knowledge to be gained from the spike furore that has followed publication of the figures. Table 4 analyses Covid impacts and demographic data from 7 Indian states each with Covid cases of over 100,000 by 20 August. The south western state of Kerala is added for reasons that will become clear below.

Table 4: Covid Cases, Mortality and Age Distribution in 7 Indian States, 20 August 2020

NATION/region and cases 000s1 PPP US$ 000s2 Cm3 Dm4 D/C %5 0-146 65+
INDIA 2,9236.72,117401.930.45.2
Maharashtra 62910.55,5171813.326.66.6
Tamil Nadu 35010.64,166711.723.66.6
Andhra P. 3168.35,852520.925.86.0
Karnataka 25011.53,571611.726.26.1
U. Pradesh 1723.6746111.636.04.9
W. Bengal 1265.91,272262.027.15.5
Bihar 1122.490340.640.14.9
KERALA 5211.21,49150.423.48.3
WORLD 22,92017.92.9411023.526.09.0

The 7 major states represent 1.96 million cases, 66% of India’s total; deaths assigned to Covid were 39,687 or 72% of India’s total on that day. The populations of the 7 states represent 56% of the Indian total, so their Covid incidence is significantly higher than the Indian per capita average. Four of the seven states have per capita incomes above those of the Indian average of $6,700, but three have lower incomes. These lowest income states – Uttar Pradesh, West Bengal, and Bihar also have significantly lower Covid impacts than the others – Bihar’s income level is only 22% that of Maharashtra, its Dm or Covid deaths per million ratio is only 2.2% that of Maharashtra.[6] This strongly follows the argument above.

There is some limited evidence here that even within one nation state, the age distribution effects focused on above as operating between nations and national groups, may be having some impact within India. This is especially true at columns 5 and 6 for Uttar Pradesh and Bihar, lesser so for West Bengal.

With its lower Covid cases of 52,199, Kerala in the far south-west is included because it has a relatively high income but low Covid outcomes similar to that of the three lower income states, indeed its deaths per million and D/C% are remarkably low. Also, its age distributions are much closer to those of the four wealthier states, with the highest proportion in India of older people. In addition, Kerala has the lowest population of youth aged 15-24 in India (16%). So, for reasons that are not certain, Kerala breaks the mould. We return to this below.

From Malthus to Governance: Types of Explanation

At the present point in the spread of the Covid world there are some grounds for optimism as the very high rates of infection spill over from the rich north into the mainly poor south. The intuitive fear that things will be much worse as the virus further grips the south is governed by such elements as a lack of income, infrastructure, and skills, a contrary abundance of poverty, communal living, and ill-health, that in all should spell a very significant increase of mortality beyond that of the northern hemisphere. This paper has rejected this intuition.

The data here go some long way towards arguing that demographic features of poverty over-ride the disadvantages of low incomes and lack of resources and have for some time been acting as a break on the rampage of Covid 19. This gives a window for public policy to resolve how best to work at specific local sites utilising local knowledge and expertise in identifying the infected, tracing networks and isolating very precise social spaces in order to cut down infection, relying far less than the north on central government-dominated complete lockdowns and expensive infrastructures of hospitalisation and sheltering of the vulnerable

However, it is always possible that the major income and demographic forces could eventually be overridden by the conjunction of those other factors operating to the detriment of the poor that were listed above – poverty is hardly a good thing and means a lack of infrastructure and of health, dietary, sanitary and transport assets with which to fight the virus. Here, however we argue that most of the other feasible elements involved in fighting against Covid may in fact also work in favour of the poor. These range from basic socio-economic factors (health, air pollution), through to degrees of connectivity (with other nations and regions), and types of natural environment (climate, density and topography). Apart from wealth itself, we claim that most features of our world favour the poor when it comes to the ultimate infection and mortality impacts of Covid 19.

We might begin with the global summary table 5 below which centres on general social environments and health facilities.

Table 5: World Covid, Income, and Social Indicators 1 June 2020

National Groups by income1 PPP in US$0002 Cm3 Dm4 D/C5 HDI6 Health US$
Group 152.232082516.7290.94,307
Group 220.52037674.278.0996
Group 38.5523283.867.2418
Group 42.516543.350.6120

Here, standard global measures of general welfare and health provision are aligned with the 4 income groups of table 1 onwards. The human development index (HDI) of column 5 is a UN measure of human or societal development incorporating life expectancy, education, and income, and so is designed as some indication of health and social infrastructure. Each nation is awarded a mark out of 100.[7] Health expenditure of column 8 is an exceptionally useful measure provided by the OECD for it in fact measures health expenditure per capita PPP in international US$. That is, it measures how much a nation spends on health in terms of not just a dollar figure but incorporating a PPP measure of income that takes account of cost of living in each country.[8] It may be taken as a good indicator of how any nation might be prepared for response to pandemic, and what proportion of resources is used for health.

Table 5 illustrates that lower incomes spell lower virus potency as measured by both cases and deaths per million, but also mean dramatic falls in human development measures and health per capita. So, in fighting the Covid 19 virus the poor nations do not have the background infrastructural development nor the financial resources to fight Covid 19 at a level of anything approaching those of group 1 and even group 2. A playing field is not visible. In particular, the fall in health expenditures per capita as we move from group 1 to group 4 is appalling, suggesting a massive inability to follow the high-resource expenditures on health that can be undergone in rich nations.

But the question arises as to how far the knowledge, infrastructural capabilities or large funds of the rich compensate for the counteracting forces of demography. All the evidence given here shows that up to this precise time the Malthusian effects have swamped the socio-economic factors in determining Covid patterns of incidence and (even more clearly) mortality. Governance and management of the virus may well be affecting patterns within the four income groups, but this is likely to be more observable at the levels of groups 1 and 2 and not much (if anything) between groups.[9] Group 4’s broader measures of wealth and individual self-help capacity are calculated in the HDI index as being far below those of nations in Group 1.

But in other respects poorer nations have advantages. Table 6 below considers population densities and levels of urbanism.

Table 6: World Covid Regions, Urbanism and Population Densities 2020

Regions and NationsPPP US$Pop. Density kmUrban% Total popDmCm
WORLD17,94814.7551022941
ASIA7,08415050281347
EUROPE43,39834742764,455
N AMERICA61,303198269018,408
USA64,426348053617,381
AFRICA6,906454321895
S AMERICA16,590258542812,963
      
INDIA6,75438234402,117
MAHARASHTRA10,477370451815,517
TAMIL NADU10,58755048714,166
ANDHRAN P8,26130830525,852
KARNATAKA11,52431939613,571
U. PRADESH3,6358282211746
W. BENGAL5,9831,02932261272
BIHAR2,3951,106114903
KERALA11,1538604851491

There seems to be little relationship between general density of population settlement and levels of Covid activity. Asia, with the highest regional level of density in terms of people per square kilometre – five times that of Europe – has very low Covid activity as measured by columns Cm and Dm, and so too of course has India. However, the levels of urbanism, the proportion of the population living in cities, seems strongly associated with Covid – in Europe and America high levels of urbanism are strongly related to high levels of Covid infection and mortality. In the poor nations of Asia and Africa low urbanism associates with low Covid activity. South America is of great interest – a region of low incomes compared to Europe and the US but one which has very high measures of Covid activity, well above the European level and the highest levels of urbanism in the world at 85%. This juxtaposition suggests that urbanism matters at a global comparative level. There can be one or two reasons for this. First, that high urbanity accounts more for close human proximities, and thus high contagion, than does general high density spread more evenly in a nation or region. Second, that air pollution is more concentrated in places of high urbanity, and thus might be a strong conditioning factor on both contagion rates and the weakening of immunity systems, especially amongst older people. India’s high density has not meant high Covid activity, its low levels of urbanism may well contribute strongly to both low infection and low mortality.

Examining the four South Asian nations together, we find general low levels of air pollution measured as 2018 emissions of carbon dioxide, methane, nitrous oxide, perfluorocarbon, hydrofluorocarbon and sulphur hexafluoride per capita in metric tons – India at 1.9, Pakistan 1.0, Bangladesh 0.6, and Afghanistan 0.3.[10] Other large low-income nations have comparable results but not generally as low – Brazil 2.4; Indonesia 2.1; Philippines 1.4. These figures contrast strongly with those for large rich regions – 16.1 for the USA, 7.0 for the European Union, 12.1 for Russia and 9.4 for Japan. It seems clear that South Asia is especially benefiting from a combination of age distributions, low urbanism, and low air pollution when compared to other regions, and that the USA and Europe represent the reverse.

As I have suggested in earlier columns for South Asia Express, South Asia benefits also from other socio-economic and environmental features. In particular, as with many parts of Africa, the four major nations of the region have low degrees of connectivity with the rest of the world.[11] Total borders for the four nations amount to 22, with a total length of 30,758 kilometres. In Europe this could be a disaster – border proximities for nations such as Italy or Belgium seem to have been disastrous. But much of South Asian border territory is distant from internal population centres, mountainous or contentious. This reduces connectivity. Of possibly greater importance is the fact that the borders represent a protective belt – rather than a pathway or airway into a contagious mass – of low-Covid nations. Table 7 summarises this.

Table 7: South Asia and its Protective Belt.

REGIONSPPP US$ 000sCmDmNumber of BordersLength Km BordersPopulation millions
4 South Asian Nations4.8632132030,7581,777
5 Border Nations6.527841618,674148

It can be seen that the five nations bordering South Asia, composing a population around only 8% that of the four large nations, represents an extensive belt of very low Covid infection and mortality. With higher but comparable income levels in purchasing power parity terms, the five nations have per capita Covid infection levels less than one-half those of South Asia, and mortality rates of one-third. The outer protective layer of 18,674 kilometres encloses a huge low-Covid region of the world. From a purely geo-political and epidemiological perspective it looks like a region that might be very successful indeed in isolating its ultimate Covid experience from the high rates of infection and mortality experienced in Europe, the USA and South America. The likelihood of this would be increased by two further elements – restrictions on entry into the region and the evolution of a supranational Covid management system.[12] This would allow the natural advantages of this huge region – over 1.9 billion people – to become a systematic basis for Covid resistance

From Patterns to Impacts – Socio-Cultural Analyses

We do not wish to discount the serious, indeed horrific character of the pandemic for India, the way it has terrified communities and regions and exposed the inadequacies of the major physical infrastructures in a nation that is still at the early stages of a fundamental process of economic development. It has already caused enormous disruptions to the lives of those millions who are fearfully locked into their urban neighbourhoods or desperately on the move away from city crowds and pollution towards what they hope will be better conditions for survival in rural areas of less density. Return to home villages and townships might well cause economic losses but it is one way of tapping into more locally-based supply lines for food, water, shelter and basic care – whether through families and communities or local government assistance.

The pandemic has, indeed, exposed social underbellies throughout the world, and in India uncovers what the great economist and humanist Amartya Sen refers to as ‘a person’s capability deprivation’, the exposure of the individual to all those detrimental elements that are integral to – but also beyond – the fact of low incomes per se.[13] Globally, this pandemic reveals weaknesses throughout entire social systems and political frameworks, as shown clearly in the failures of governance to manage the virus in the rich nations of Europe and North America.

It is of great interest to our analysis here that in the 1990s Sen placed Kerala in the small minority of regions that have had high success in raising the length and quality of life despite little success in achieving high economic growth.[14] Sen and Jean Drèze already distinguished ‘support-led’ improvement in health and well-being from that which is ‘growth-mediated’, identifying Kerala as a prime example of the former. Its state health budget as a proportion of the state budget is today 6.2% compared to the Indian national expenditure of 4%[15] Certainly, despite its higher proportion of elderly people and its lower proportion of younger, and higher level of urbanism, as shown in tables 4 and 6 above, its Covid presence now is very low when compared to the 7 major state cases. The obvious contrast in those tables is with Maharashtra, with its high Covid presence, relatively high income per capita but very low present per capita public health expenditure of around 3.6%.[16] Health care, education, and we must also assume sanitary and other provisions, may lead to sustained ‘reductions in mortality rates and enhancement of living conditions, without much economic growth’.[17] This leads to the possibility that poor nations that can focus on institutionalising sustained well-being could reap the age-distribution advantages of low income and also improved medical resources as an effective environ for Covid management. Investing in facilities does not have to wait on economic growth and may indeed promote it. Covid 19 then becomes a laboratory for experimentation in priority-reorientation in public policy that might have lasting positive outcomes on health care and disease prevention.

CONCLUSIONS – Wealth and Poverty in a Covid World.

In brief, within the income, demographic, and wider environmental environs of South Asia, the direct impact of the Covid 19 virus on sickness and death are mitigated when compared with its impacts in rich nations. More specifically, age distributions, low urbanism, and low connectivity combine to yield a measurable advantage over other, especially richer parts of the globe. But the indirect impacts of Covid 19 on health and well-being in all poor nations are both broad and deep, because poverty lies beyond just income alone, it is embedded within, even defined fully by, the capability deprivation that poverty entails. And just as income-poverty has been found to be a great and long-lasting evil, so does the absence of individual capability remain intrinsic to the life of poverty and the likely long-term impacts of Covid 19. As one result of this, the indirect effects of Covid 19 on health, well-being, and mortality in South Asia, including the possibility of a growing virulence of other illnesses associated with breakdowns in food and water, basic medical facilities, and effective hospital supplies, including nurses and auxiliaries, may over-ride the natural advantages that have been suggested in this essay.

(Views are personal)

References

  1. This global summary of detailed information on 96% of the world’s population, as with the other major tables, is derived from raw data in: Coronavirus Worldometer, https://www.worldometers.info/coronavirus/: Unitd Nations, Department of Social Affairs, World Population Prospects: the 2019 Revision, Population Division, 2020; UN, Human Development Report 2019, Human Development Programme, Washington DC, 2019;  The Economist, Pocket World in Figures, Profile Books, London 2020;  The World Bank Group,  Population by Age Groups, 2020, https://data.worldbank.org/indicator/SP.POP.1564.TO.ZS: Central Intelligence Agency, The World FactBook, https://www.cia.gov/library/publications/the-world-factbook/fields/341.html: OECD, The World Economy on a Tightrope, OECD Economic Outlook, June 2020: The OECD, Tackling Coronavirus: The COVID-19 crisis and state ownership in the economy: Issues and policy considerations, OECD, 2020
  2. PPP statistics adjust for cost of living differences by replacing normal currency exchange rates with rates designed to equalise the prices of a standard ‘basket of goods and services.’ This creates the purchasing power parity PPP. These then are used to obtain PPP estimates of GDP per head. Here they are presented in US$ rather than from an index as this makes much clearer the actual differences between nations and regions. Cheung, Yin-Wong, ‘Purchasing Power Parity’, in Reinert, Kenneth A. et al. (eds.), The Princeton Encyclopaedia of the World Economy, Princeton, 2009.
  3. If deaths did not result from Covid except under most extraordinary circumstances, then the epidemic would have constituted a global scare rather than a global disaster, and the huge financial impacts of the virus would have been far, far smaller. Secondly, given the time and circumstance, such mortality comparisons suffer from spurious accuracy but are no worse than claims to certainty based on even more dubious impressions. They do at least capture the virus effect on nations from the period of its inception until 21 June, rather than attempt to generalise on the basis of the daily or weekly trends. The advantages of the deaths to total cases ratio D/C is that it is a rough but reasonable measure of the extent to which any nation or region has managed the virus effectively enough to reduce mortality over time, and to do so whilst increasing the accuracy of identifying total cases.
  4. But see Ian Inkster, ‘Covid 19:  Japan and ‘the East Asia Edge’, The Japan Times, 15 May https://www.japantimes.co.jp/opinion/2020/05/15/reader-mail/covid-19-japan-east-asia-edge/#.Xw3V7-d7nIU: ‘Covid-19: Global Mortality Rates and Policy, International Policy Digest 26 April https://intpolicydigest.org/2020/04/26/covid-19-global-mortality-rates-and-policy: ‘Covid 19 – Taiwan in East Asia’, Taiwan Insight, 3 April 2020, https://taiwaninsight.org/2020/04/03/covid-19-taiwan-in-east-asia/.
  5. So, population only accelerated at low levels of income; where income could continue to grow and production escaped subsistence methods (that is, technological change and industrial revolution) then long-term development could occur – the best early reading by far is R.R. Nelson, ‘A Theory of the Low-level Equilibrium Trap’, American Economic Review, 46, 1956, pp.894-908.
  6. MOSPI, Net State Domestic Product, Ministry of Statistics and Program Implementation, Government of India, 2 July 2020. Bihar is the poorest of India’s 33 states and territories.
  7. Human Development Index (HDI) | Human Development Report hdr.undp.org. United Nations Development Programme. Retrieved 23 February 2020: Human Development Report 2010 (PDF). New York: United Nations Development Programme. 2010. ISBN 9780230284456. Retrieved 23 February 2020: Human Development Report 2019 (PDF), New York, United Nations Development Programme. 2019. pp. 304–307. ISBN 978-92-1-126439-5. Retrieved 23 February 2020.
  8. See footnote .. above for PPP. OECD, Health Resources- Health Spending, doi:10.1787/8643de7e-en; https://data.oecd.org/healthres/health-spending.htm; Health spending here measures the final consumption of health care goods and services (i.e. current health expenditure) including personal health care (curative care, rehabilitative care, long-term care, ancillary services and medical goods) and collective services (prevention and public health services as well as health administration), but excluding spending on investments. The OECD thereby indicate not just dollars spent but to what degree out of national incomes a nation chooses to spend on health-it is not just a measure of how health expenditure must fall with national income, rather of how much out of any income nations are able to spend on health.
  9. Democracy has been offered up in the media many times as a stumbling block to fast response and civil acquiescence. Yet the variations of socio-economic freedom within nations do not correlate to any particular advantage or authoritarianism in Covid management. Nations of the lowest Covid infection and mortality include ones of very high levels of socio-economic freedoms, such as Singapore, Hong Kong, New Zealand, Australia, Estonia, and Taiwan, all reckoned within the top 11 of 180 nations by the Heritage Foundation Freedom Index: Terry Miller et al eds., 2020 Index of Economic Freedom, The Heritage Foundation, Washington DC, 2020.
  10. EDGAR, Emissions Database for Global Atmospheric Research
  11. Ian Inkster, ‘Covid 19: Strategy for the Global South: why it is unwise to follow the West’, The South Asian Express, 28 May https://sae.news/2020/05/28/covid-19-strategy-and-tactics-for-the-global-south/: ‘Covid 19: South Asia in Global Context’, South Asian Express, May 6 https://sae.news/2020/05/06/covid-19-south-asia-in-global-context/
  12. Meant literally as the coming together of the nine national governments to form an institutional structure to create policies that have authority or jurisdiction over the member nations. Of course, that could only work if it was also agreed that for the duration of the pandemic this project would ride over differences of religion, ideology and so on in order to secure a common goal. Such a project might well develop more innovative and practical procedures against Covid 19 than have been evidenced amongst the far richer nations of the global north.
  13. Amartya Sen, Development as Freedom, Oxford, OUP, 1999, p. 87-110.
  14. Ibid., p. 45. Sri Lanka is of course similar in these respects, but the total of cases (less than 3,000) is too small to lay alongside our other exemplary cases, it is an isolated island with exceptionally low Covid presence – 140 cases per million, 0.6 deaths per million in a total population of 21.4 million (smaller than that of Kerala). The island has maintained relatively strict management regulations – for instance, failure to wear masks in public leads to a mandatory requirement of two weeks quarantine.
  15. The Hindu 12 May 2016, https://www.thehindu.com/news/cities/Thiruvananthapuram/public-health-expenditure-high-in-state/article858801
  16. Times of India, 15 October 2019, https://timesofindia.indiatimes.com/city/pune/maharashtras-per-capita-health-spend-at-bottom-of-pile-with-up-bihar/articleshow/71591286.cms It must be said however, that such comparisons can only support the Sen type of analysis if incorporating fuller measures of expenditure on well-being such as family welfare, water supply and sanitation, public housing, community services and so on. Such data is increasingly reliable and available. Health care expenditure is used here as it is both globally available and most closely linked to possible policies regarding Covid 19.
  17. Very importantly Sen and Drèze argued that there may be a real virtuous cycle in operation – sustaining measures that increase well-being might well lead to growth, this in turn allowing greater welfare expenditure. Furthermore, failure at truly basic economic innovation, especially land reform, as in say Brazil, Pakistan and much of India, reduced choice and opportunity and thus growth. See, Jean Drèze and Sen, Hunger and Public Action, 1989, and Drèze, India. Economic Development and Social Opportunity, 1995.

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