Covid 19 in Asia: Populations and Borderlands in Global Context.

Image for Representation purpose only

By Ian Inkster, SOAS, University of London.

India is now 4th, and Pakistan 12th on the international lists for Covid cases. And much has been made of this in the Western media as evidence of South Asia having now joined the high-Covid nations of the West, South America and the Middle East. Not so noted is that India’s death’s per million are 12, Pakistan’s 19, Bangladesh’s are 1 and Afghanistan’s 19, in comparison with Europe’s 255 or the USA’s 428. Furthermore, the South Asian nations have now been under virus attach since 27 January, plenty of time for the mortality rates to have soared. Agreeing that all statistics are problematic and fast-moving, we have little reason to believe that South Asia speaks falsely to a quantum level above Europe or Russia or USA, nor do we think that poor nations have anything to gain from deliberate falsifying of mortality data but not of total cases, especially when aid from richer nations might be more readily gained if the exaggerations were upwards rather than downwards.

Paying serious attention to the statistics is therefore well worthwhile. Here we do pay attention and focus on the neglected features of age-distribution and borderlands. The result is a more optimistic conclusion than that suggested by commentators who think that lower wealth and incomes must doom South Asia to a worse fate from Covid 19 than is occurring right now in the richer northern nations.

Table 1, ‘South Asia in Global Context’, sets up the widest of our generalisations based on the statistics available for 29 June. Deceptively composed of small numbers it covers some major features of 75% of the world’s people. On the left the incomes column summarises data on the basis of World Bank calculation of per capita purchasing power parity (PPP) of each individual nation. The figures here are in US$ 000s, showing for instance the average purchasing power of South Asia (here taken as India, Pakistan, Bangladesh and Afghanistan) as less than one-tenth that of the 19 richest nations. Again the 29 poorest large nations on earth have only approximately half the incomes of South Asia. The 5 nations bordering South Asia (see also table 2) have income around 40% above that of South Asia.

But what is of really great significance is that these incomes do not directly correlate with any positive incidence of the presence of Covid 19. In terms of both cases of infection (Cm cases per million) and mortality (Dm deaths per million) from the virus, the relations between income and Covid impact seems right now be directly inverse – greater poverty is associated with lower levels of the incidence and mortality of the virus. South Asia is benefiting from a global trend, with deaths per million just a tiny fraction of those recorded for rich nations, and incidence of the disease at only 20% of that of rich nations. Categorising nations or groups by income frees analysis from any biases concerning culture and forms of governance. However these might vary, or however they may be judged as better or worse than others, the case at present seems to be that there are basic underlying reasons associated with incomes that are acting as a break on Covid 19’s devastating impacts in poorer nations.

The clue to this seeming paradox, where the massive resources of rich nations do not protect them from very high incidence and mortality, lies in the age distribution of the poorer nations. The two middle columns show the percentage of young folk(0-14) and of older folk (aged 65-years and over)in populations as income rises.


Regions (Nations)Income
Cases per MillionDeaths per MillionAge group 0-14
Age 65+HDIHealthPopulation (Million)
South Asia (4)4.86321333.64.6581611,777
Region (5)6.52783.5295.966271148
China (1)16.7583.02312.2757621439
Rich 1952.23,2082512117.690.943071012
Poor 292.51653.843.83.150.61201233

At the lowest levels (the poorest 29 nations in the world) the proportion of youth is double that of the rich, 43.8% on average compared to 21% for the rich. Given that we know that young people are all but untouched by the virus, then other things being equal, many more of the population of poor nations will survive the virus, mostly unscathed. In contrast, the column for distribution of elderly people shows that rich nations have near 18% of their populations over 65-years, poor nations just over 3%. Given that Covid runs amuck amongst the elderly and kills many of them, then the effects on regions such as South Asia are obvious, a lower level of mortality as a percentage of the total population. Combining these two age-group trends means that poor nations have a decided and measurable advantage over the richer nations. Indeed we have an interpretation of the present global distribution of Covid 19 based on income and age-structure of nations.\

We have entered at the right two columns that might be expected to yield advantage to the rich. The HDI is a composite measure from the United Nations Development Programme that combines in one index number levels of life expectancy, education, and income for individual nations. It is very clear that the rich have much higher social and health assets than the poor, just the types of infrastructure that are enlisted in the fight against Covid 19. Again, the health figures from the OECD give health spending figures in US$ terms, ones that reflect also the purchasing power parity of individuals. They show that both the level of spending and the proportion of spending to GDP fall considerably as incomes fall. So, the rich have an accumulated earned advantage in infrastructure, but this is not offsetting the natural demographic advantage of poorer nations when it comes to infection and mortality levels from Covid 19.

Table 2 below hones-in on South Asia by considering the region and its bordering neighbours in greater detail. The 5 nations from Table 1, (Nepal, Sri Lanka, Myanmar, Tajikstan and Uzbekistan)along with China have active borders with at least one of the 4 large states of South Asia. The case of Turkmenistan excludes itself as its highly repressive regime admits to no Covid 19 within its territory, though it has closed land borders, tests new arrivals and has cancelled all flights to China.

(US 000$)
Cases per MillionDeath per MillionDeath/CasesAge group 0-14Age group 65+Borders
Borders (km)HDIHealth
Total/ avg4.863213233.64.6203075858161
Sri Lanka12.8950.50.62410.10078353

Table 2: South Asia: Populations and their Borders (29 June)

Borders are a major problem in attempting to assess the dangers from Covid of any one nation. Obviously island nations have some advantage as seen by Australia or Madagascar (though not by the UK). The real problem arises when a low-covid nation borders in crowded terrain other nations of high or higher Covid infection. Northern Italy was surely a prime example – with borders including high-Covid nations such as France and Switzerland – and even worse the borders of Belgium (the worst case of Covid mortality) are directly connected through secondary borders to a chain of high-Covid nations.

In contrast , the nearly 31 thousand kilometers of South Asian land border are linked to large nations of seemingly low-Covid 19 invasion. This might effectively isolate the whole region of 9 nations, that in total is itself bordered by regions of often low population density and mobility, in contrast to the complex and crowded borders of Europe and East Asia. If we take the 9 nations as a demographic bulwark then it is bordered by China (22,147 kilometers of border).

The entire Sino-Indian border (including the western LAC, the small undisputed section in the centre, and the McMahon Line in the east) is 4,056 km, frequently in dispute over Arunachal Pradesh but well away from major population centres and movements. In addition, of course, even though the most likely nation of covid-origin, China has seemingly dampened down the original devastations of Covid and long-declared the low covid figures of table 1. Unfortunately it also shares a demography that is closer to that of the West, with its relatively low proportions of youth and high proportion of elderly. But given that it is a huge region of generally sparse population and that Nepal still registers very low Covid levels, it is quite feasible that greater South Asia will escape many of the ravages of Covid both within and around its border regions. Nepal might well have been a site of original infection into South Asia from China, but spread was slow and dampened by policies of early intervention, border-controls and lockdown.

Our conclusion can be modestly optimistic. Demography works in South Asia’s favour, and bordering nations with a shared low-covid experience so far jointly act as a bulwark from the outside. Whether border tensions or other sources of loss of control from internal movements of population and especially of migrant workers, cause a disturbance in this locational advantage can not be predicted. But we can repeat that despite the guesstimates of sundry media the figures as yet do hold-out and there might soon be a breaking of the expansion of the Covid 19 as a result of demographic elements. Whether this will be aided and abetted by astute policies over the next months remains to be seen. The policy packages of the rich nations, with their contrasting demographic structures are unlikely to be of great salience to the management strategies of South Asian governance.

Professor Ian Inkster is a global historian and political economist who has taught and researched at universities in Britain, Australia, Taiwan and Japan. Author of 13 books on Asian and global dynamics with particular focus on industrial and technological development, and the editor of History of Technology since 2000. Forthcoming books are Distraction Capitalism: The World Since 1971, and Invasive Technology and Indigenous Frontiers. Case Studies of Accelerated Change in History with David Pretel. Twitter: inksterian.


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