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economic caste census (sECC) and there is no longer any
notion of a poverty line. who is going to develop this poverty
line and how? Add to that indicators for target 1.2. If you re-
read target 1.1, you will see that this is nothing but a multi-
dimensional poverty index. there is no consensus about
what such a multi-dimensional poverty index should look
like. Constructing one for the purpose of writing an academic
paper is easy. Constructing one for the purpose of tracking
the impact of policy is easier said than done.
Let’s move on to target 1.3, which is far more tractable.
the indicators for target 1.3 are discussed below. Indicator
1.3.1 is ‘percentage of households with any usual member
covered by a health scheme or health insurance’. For 2015-
2016, the baseline number is
28.7 per cent. Indicator 1.3.2 is
‘number of beneficiaries under
Integrated Child Development
scheme’. the 2015-2016 baseline
number is 102.1 million.
Indicator 1.3.3 is ‘proportion
of the population (out of total
eligible population) receiving
social protection benefits under
mahatma Gandhi national
Rural Employment Guarantee
Act (mGnREGA)’. we don’t
yet have a baseline number for
this. (I have a problem with an
indicator like this. whether we
explicitly admit it or not, every
indicator has an implied value
judgement. If the mGnREGA
numbers decline, is that good or
bad? I don’t think the answer is
clear.) Indicator 1.3.4 is ‘number
of self Help Groups (sHGs) formed and provided bank
credit linkage’. the baseline figure for 2015-2016 is 1.
million. Indicator 1.3.5 is ‘proportion of the population
(out of total eligible population) receiving social protection
benefits under maternity Benefit’. this figure is also not
ready yet. Indicator 1.3.6 is ‘number of senior citizens
provided institutional assistance through old Age Homes/
Day Care Centers funded by the Government’. the baseline
number for 2016-2017 is 22,050. notice that in two instances
the denominator is total eligible population, not total
population. the indicators for target 1.3 are much more
tractable. Data are almost always annual, obtained through
the ministries of Health and Family welfare, women
and Child Development, Rural Development and social
Justice and Empowerment. yes, data often come up from
below, from states. And yes, there are time-lag issues. But
otherwise, no serious monitoring issues.
that’s not true of target 1.4. Here are the indicators:
1.4.1—‘percentage of population (rural) living in
households with access to safe drinking water and
sanitation (toilets)’; 1.4.2—‘proportion of population
(urban) living in households with access to safe drinking
water and sanitation (toilets)’; 1.4.3—‘proportion of
population (urban/rural) living in households with access
to electricity’; 1.4.4—‘proportion of homeless population to
total population’; 1.4.5—‘proportion of population having
bank accounts’; 1.4.6—‘number of mobile telephones
as percentage of total population’. A few of the baseline
numbers haven’t yet been compiled. For 1.4.4, the 2011
figure is 0.15 per cent. For 1.4.5 and 1.4.6, we have data for
modified indicators. In 2015-2016, the number of accounts
(including deposit and credit accounts) of scheduled
commercial banks per 1,
people was 1,425. In 2015-
2016, the number of telephone
subscriptions as a percentage of
the total population was 83.4 per
cent. Indicators 1.4.5 and 1.4.6 will
always be reasonably easy and
reasonably current. But think of
1.4.1-1.4.4. such numbers usually
come through the Census. we can
get 1.4.1 through the ministry of
Drinking water and sanitation,
1.4.2 through the ministry of
Housing and Urban Affairs and
1.4.3 through the ministry of
Power. But there is no guarantee
that these will be consistent with
Census numbers. that apart, 1.4.
will only be available through the
Census, and such numbers are
only available once every 10 years.
not quite good enough. I will skip
targets 1.5, 1.a and 1.b, because they won’t add much to
what I have already said.
If you think of the 17 sDG goals, several of them,
understandably, are about social sectors, health and
education, especially the former. Are our data good enough
for either? yes, there are surveys. How reliable are those
surveys? why don’t different findings tally with one
another? what is the process of vetting data? the national
statistical Commission (nsC) has often got involved in
controversies over national income data. For national
income and the Central statistics office-data, the nsC is
indeed the vetting authority. But for other sources, it is the
nsC’s mandate to examine generation of data. now that
we have a newly constituted nsC, perhaps this is what
the nsC will look at. Indeed, the 2001 Rangarajan report
on India’s statistical system highlighted blemishes in the
data-generation system and that is why the nsC was set
up. without better social-sector data, monitoring progress
towards sDGs will be close to impossible. nSaurabh Singh