Friday, 27 December 2013

Census 2011 Data on Slums and Its Policy Implications

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


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