Opinion | The gap in covid severity between rich and poor states
5 min read . Updated: 30 Jun 2020, 08:28 PM ISTData analysis of the pandemic’s impact on India reveals a divide that seems set to weaken as time progresses
George Bernard Shaw famously suggested in a 1907 preface of a play that “The greatest of evils and the worst of crimes is poverty." This is generally true, but not necessarily in all situations, as our analysis of severity of covid-19 reveals. Indeed, as of now, those living in rich states are more likely to die from the covid pandemic than those living in poor states.
Following a recent World Bank study (COVID-19 Mortality in Rich and Poor Countries: A Tale of Two Pandemics?, June 2020), we constructed two measures of covid-19 severity: one is the cumulative severity ratio, and the other is the daily severity ratio. The cumulative ratio is measured as the number of covid deaths from the first death to a cut-off date, divided by all-cause deaths in a pre-covid period of the same length. The daily severity ratio is the number of deaths on a particular date divided by deaths on a single day in the pre-covid phase. The cumulative severity ratio shows whether covid fatalities are a significant deviation from the past mortality, and, if so, denotes a pressure point on the health system, which may have adapted to it. The daily severity ratio on specific dates (eg, closure of a lockdown phase) allows us to capture whether the progression of covid has intensified, weakened or remained unchanged over such phases.
These ratios are used to throw light on: one, whether there is a rich-poor divide in the risk of dying from covid-19; two, whether the progression of the pandemic has intensified in some states, weakened in some others and remained unchanged in the rest; three, what the contributory factors are; and four, whether there is a likelihood of convergence of the risk of (relative) fatality between rich and poor states.
The period used for the present analysis is from the date of the first death in an Indian state to the cut-off date of 13 June 2020. We have relied on official data on covid cases, deaths, per capita income, and other explanatory variables.
As of 13 June, the total confirmed cases were 308,993, and the total deaths were 8,884. Although both are suspected to be underestimates and need scrutiny, for the present analysis, we take the official estimates at face value.
Let us first consider the variation in the cumulative severity ratio. The highest severity ratio during the pandemic is observed in Delhi, Maharashtra and Gujarat (all three are relatively rich states). During the first lockdown (lifted on 14 April 2020), the highest severity was observed in Maharashtra, Madhya Pradesh and Delhi (with Madhya Pradesh a poor state); during the second lockdown (lifted on 3 May 2020), the highest severity was observed in Maharashtra, Gujarat (both rich) and Madhya Pradesh (poor); during the third lockdown (lifted on 17 May 2020), the highest severity was observed in Maharashtra, Gujarat and Puducherry (all rich states); and, finally, during the fourth lockdown (lifted on 31 May 2020), the highest severity was observed in Maharashtra, Delhi and Gujarat (all rich).
To further examine the rich–poor divide in (relative) covid fatalities, we compared two extremely poor states (Madhya Pradesh and Uttar Pradesh) with two rich states (Delhi and Gujarat). In Madhya Pradesh, the average cumulative severity ratio was 0.34%, and the daily ratio was 0.47%, and the latter also exhibited greater dispersion. The average cumulative ratio (0.12%) and the daily ratio (0.25%) are much higher in Uttar Pradesh, with much higher dispersion in both cases relative to Madhya Pradesh. In Delhi, the cumulative ratio was 0.61% and the daily ratio was 3.54%, while the dispersion was much higher in the latter. In Gujarat, the cumulative ratio was 0.67% and the daily ratio was 1.45 %, with the former higher than in Delhi. In both cases, the dispersion was lower in Gujarat. So, richer states fared worse than poor states.
Has the progression of (relative) daily covid fatality intensified? Among rich states, a few such as Delhi, Gujarat, Maharashtra and Tamil Nadu show intensification of daily severity, while Kerala shows a largely unchanged ratio at a low value. Among poor states, too, there is a mixed progression. While Bihar shows intensification in May and June, Odisha displays weakening, and Rajasthan displays intensification of (relative) daily fatalities.
Controlling for the effect of (lagged) covid cases, our econometric analysis shows that income is positively associated with covid daily fatalities, but at a diminishing rate, implying that the higher the state’s income, the weaker will be the positive association. However, higher urban population density is likely to partly offset the weakening of the divide. But, given the density, more rapid urbanization is likely to result in a lower fatality rate, presumably because of easier access to healthcare facilities. The higher the dependency of the old on the young, the higher the expected covid fatalities. Rural youth have been migrating increasingly to urban areas, and while the pandemic has seen a reversal, the trend is likely to resume. However, if income growth in high-income states slows and low-income states catch up, as recent evidence suggests, it may more than offset other positive effects and lead to convergence of covid fatality to a rate between the high and low rates.
Summing up, although there is convincing evidence of a rich-poor divide in covid fatalities, it is not implausible that the divide weakens considerably, if not disappear altogether, over a period.
Nidhi Kaicker and Raghav Gaiha are, respectively, assistant professor of management, Ambedkar University Delhi, and research affiliate, population studies centre, University of Pennsylvania, USA
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