The vision of Slum Free India can be achieved only on the
foundations of sound plans based on sound data.
Newly released data on slums show that over a third of India’s slum
dwellers live in unrecognised slums. Lack
of government recognition, implies entrenched barriers to legal rights and
basic services such as water, sanitation, and security of tenure. The “Primary Census Abstract for Slum”(2011),
published on 30th September, 2013 is of interest to policy makers in multiple
ways, right from its definition of slums to the data on assets and amenities of
slum dwellers.
The Census 2011 data on slums highlights that out of the 4,041 Statutory
Towns, slums were reported in 2,543 Towns (63%). The latest census data is
noteworthy as it includes, for the first time, those slums which are not identified
or notified by the Government. Three
types of slums have been defined in Census, namely, Notified, Recognized and
Identified. Only 34% of the slums were
notified, 29% recognised and 37% identified. As is evident, the largest
category is identified slums which implies they are neither recognised nor
notified, and hence lack many amenities.
While the introduction of a third category called “identified
slums” has definitely led to the inclusion of non-notified and unrecognised
slums, the ones that have less than 60-70 households are excluded. Also, while
there are 7,935 towns in the country, slums were counted only in the 4,041
statutory towns. As many as 3894 towns were ignored while counting slums. Thus there are shortcomings in the 2011
Census on Slums.
Gautam Bhan and Arindam Jana of the Indian Institute for Human
Settlements (IIHS), Bangalore, point out that the slum data should be
approached with caution on three counts:
(i) Correlation between the
definition of ‘slum’ and urban poverty : Many of the newspaper reports treat slums as
a special expression of urban poverty, and hence interpret the increase in
amenities and assets in slums as an indicator for improvement of conditions of
the urban poor. While the Census identifies only slums with at least 60-70
households, there exist a large number of clusters with lesser number of
households and poor living conditions. These smaller and less organised
clusters, created by the breaking down of larger slums through multiple cycles
of eviction and resettlement, have lesser ability to mobilise political or
other patronage to gain access to services. Therefore, it is faulty to conclude
that a narrowing “slum” and “non-slum” gap indicates a reduction of urban
vulnerability or poverty.
(ii) The dimension of quality when
estimating access to basic services: “The all-India figures for access to drinking
water, latrines and electricity suggest a closing gap of service access between
slum households and their non-slum counterparts.” For instance, 65% of slum households have
access to treated tap water as compared to 61% in other non-slum households.
This appears to imply that the delivery mechanism for treated water is better
for slums as compared to other households. However, “access to treated tap
water” does not imply individual household connections. The census data also
suggests that 58% of slum households have a “flush/pour flush latrine” within
the household. Yet only 48% have either treated or untreated tap water within
the household. The possible gap (of nearly 10% or 1.3 million households)
indicates households where a physically built flush latrine may or may not have
sufficient water to function effectively.
(iii) The question of why so few cities and towns report any slums: For
example, only 14.4% of all towns and cities in Jharkhand report having any
slums, 34% for Odisha, 28% for Uttar Pradesh, 14% for Assam, and Manipur, at
the extreme, reports not a single town or city with a slum. We have already
pointed out in the previous section the basis for ignoring 3894 towns while
counting slums.
Relevance
of the findings
Since slum dwellers constitute major segment of the urban poor, it
is important to know their correct count. Non-availability of authentic
statistics on State-wise slum population has lead to faulty planning and
under-estimation of financial requirements.
The Rajiv Awas Yojana (RAY) extends benefits to not just the
notified and recognised slums but identified slums as well. A robust database
on slums and getting a definitive understanding of the magnitude of the problem
is critical for implementation of schemes like RAY. While the new census
exercise has resulted in the inclusion of more towns, the 60-70 household
cut-off and the omission of census towns still results in the exclusion of many
slums. These slums might be ignored in the RAY. Unless there is an authentic
database to assess the magnitude of the problem, it is not possible to
undertake formulation of plans, policies and schemes so that potential
beneficiaries are targeted in a meaningful manner.
Amrutha Jose Pampackal