Page 305 - ES 2020-21_Volume-1-2 [28-01-21]

288 Economic Survey 2020-21 Volume 1

9.4 Second, general medicine has been the overwhelmingly major clinical specialty used
since 2018 with its share continuously growing. It is followed by general surgery, obstetrics
and gynaecology. These three categories combine to account for more than half of the claims
received on average. Dialysis – high frequency, low cost procedure that is life-saving for patients
with renal difficulties – accounts for a large chunk (30 per cent) of the general medicine category
claims under PM-JAY.

9.5 Third, the claims for dialysis did not diminish due to CoVID-19 or because of the lockdown
in March-April 2020 even while we can observe a steep fall in claims under the overall general
medicine category during the lockdown. This highlights the users’ reliance on PM-JAY for the
life-saving dialysis procedure. Thus, the critical, life-saving health procedures such as dialysis
seem to have not been severely affected during the CoVID-19 pandemic.

9.6 Fourth, general care-seeking as seen in the PM-JAY claims exhibited a V-shaped recovery
after falling during the lockdown and has reached the pre-CoVID-19 levels in December 2020.

9.7 The final, but the most crucial, analysis in the chapter attempts to estimate the impact of
PM-JAY on health outcomes by undertaking a difference-in-difference analysis. We compare
the health indicators measured by National Family Health Survey 4 (NFHS 2015-16) and the
National Family Health Survey 5 (NFHS 2019-20) to undertake this analysis. As PM-JAY was
implemented in 2018, these two surveys provide before-after data to assess the impact of PM-
JAY with the NFHS-4 serving as the baseline to compare the changes using NFHS-5. To mitigate
the impact of various confounding factors, including but not limited to secular improvements in
health indicators across the country, we undertake this analysis by calculating a difference-in-
difference.

9.8 This analysis is undertaken in two parts. In the first part, we use West Bengal as the state
that did not implement PM-JAY and compare the before-after difference in health outcomes to
its neighbouring states that have implemented PM-JAY – Bihar, Sikkim and Assam. Apart from
all these states being contiguous to each other and therefore being similar on socio-economic
dimensions, we show that the baseline characteristics of these two groups of states were similar.
In the second part, we repeat the same analysis for all states that did not implement PM-JAY
vis-à-vis all states that implemented PM-JAY. of course, the heterogeneity across the entire
group of states in the country is large. The second analysis is less of a like-for-like comparison
than the first one. Combining the findings from both these comparisons ensures that the findings
are robust not only to a more localised, and therefore, more careful comparison but also to a
comparison that spans all the major states in the country. The findings from this analysis are
summarised as follows:

1. The proportion of households that had health insurance increased in Bihar, Assam and
Sikkim from 2015-16 to 2019-20 by 89 per cent while it decreased by 12 per cent over the
same period in West Bengal. When comparing across all the states over this time period, we
find that the proportion of households with health insurance increased by 54 per cent for the
states that implemented PM-JAY while falling by 10 per cent in the states that did not adopt
PM-JAY. Thus, PM-JAY enhanced health insurance coverage.
2. From 2015-16 to 2019-20, infant mortality rates declined by 20 per cent for West Bengal and
by 28 per cent for the three neighbouring states. Similarly, while Bengal saw a fall of 20 per
cent in its Under-5 mortality rate, the neighbours witnessed a 27 per cent reduction. Thus,