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Title: WASH Availability, Accessibility and Inequalities in Select Metro Cities of

India

First Author:

Shashi Kala Saroj PhD Scholar, Geography

Centre for the Study of Regional Development (CSRD)

School of Social Sciences

Jawaharlal Nehru University

New Delhi- 110067, India

ORCiD: orcid.org/0000-0002-2829-7446

Email: shashikala.saroj52@gmail.com

Second Author:

Md Juel Rana

PhD Scholar, Population Studies

Centre for the Study of Regional Development (CSRD)

School of Social Sciences

Jawaharlal Nehru University

New Delhi-110067, India

ORCiD: orcid.org/0000-0001-8830-492X

Email id: jranajnu@gmail.com

Third Author:

Bikramaditya K. Choudhary Assistant Professor, Geography

Centre for Study of Regional Development

School of Social Sciences Building

Jawaharlal Nehru University

New Delhi-110067, India

Phone +91 - 1126704693; 919935467846; 919650018631

ORCiD: orcid.org/0000-0001-5630-7026

Email bkchoudhary@mail.jnu.ac.in

Fourth Author:

Srinivas Goli Assistant Professor, Population Studies

Centre for the Study of Regional Development (CSRD)

School of Social Sciences

Jawaharlal Nehru University

New Delhi-110067, India

ORCiD: orcid.org/0000-0002-8481-484X

Email id: sirispeaks2u@gmail.com

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WASH Availability, Accessibility and Inequalities in Select Metro Cities

of India

Abstract

We examined the availability, accessibility of water, sanitation and hygiene (WASH),

and overall ‘WASH’ performance in terms of levels, trends and inequality in the six cities

of India using data from two rounds of India Human Development Surveys conducted in

2004-2005 and 2011-2012. Findings summarise that accessibility and overall WASH

performance in 2011-2012 were better than it was in 2004-2005. Nevertheless, the

change was not significant across cities in terms of WASH availability during survey

years. The WASH availability was poor, but accessibility was better in Mumbai. Chennai

reported a reverse trend, which is availability was better, but accessibility was poor. The

overall level of WASH become significantly better in Mumbai in 2011-2012 compared to

2004-2005, but changes are not notable in other selected cities. The poor performing

cities in terms of WASH viz. Kolkata, Hyderabad, and Chennai exhibited more inequality

compared to better performing cities viz. Mumbai, Bangalore, and Delhi. The intra-city

inequality is attributable to housing types, economic status, educational level, socio-

religious groups and occupational status. Efficiency with equity in WASH performance

both between and within cities should be the priority issue for urban policies to make

cities more inclusive and sustainable and to achieve SDGs by 2030.

Key words: WASH Performance; Inequality; Availability; Accessibility; Metro cities;

India

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Introduction

Water, Sanitation, and Hygiene (WASH) are essential components of the healthy and

dignified life of every human being. The poor WASH might lead to several diseases such

as cholera, typhoid, infectious hepatitis, polio, and ascariasis. It has been identified as one

of the leading distal factors for infant and child mortality in the developing countries

(Coffey et al., 2014; Hathi et al., 2017). Pneumonia and worm infestations are also

associated with unsafe WASH which results in stunted growth and impaired cognitive

and mental development especially among the children (Buttenheim, 2009; Ezeh et al.,

2016; Magalhaes et al., 2011). Major part of the global burden of diseases attributable to

the environmental factors are shared by diarrhea (57 per cent), which is highest in the

world followed by the cardiovascular diseases (42 per cent) and lower respiratory

infections (includes pneumonia, bronchitis, and bronchiolitis) (35 per cent); mainly

caused by household air pollution, water, sanitation and hygiene practices and

environmental drivers (Pruss-Ustun et al., 2016). Diarrheal disease is the leading cause of

global child mortality; causing 20per cent of all deaths in the children below the age of

five (Gunther & Gunther, 2010; Pruss et al., 2002). A recent study found that densely

populated areas with poor sanitation facilities are more harmful to child health (Hathi et

al., 2017). If the major cities being densely populated perform poorly in WASH, it may

result in worst child health outcome. This may be one of the reasons for higher infant

mortality rate among the urban poor as compared to their rural counterpart.

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Beyond the health concern, household members including women are forced for open

defecation in the condition of absence of sanitation facilities within the premises. This

makes them more vulnerable to physical insecurity and sexual abuse, and many of such

cases go unreported and unnoticed by the authorities. Further, they have lower self-

esteem compared to those who can access the facility within the premises especially with

heightened media campaign that intentionally or unintentionally ultimately turns to

‘victim blaming’ (Dreze & Sen, 2013).

At the global level, the Millennium Development Goals (2000) had targeted to reduce the

proportion of world’s population who lives without access to improved water and

sanitation by half before the end of 2015. In continuation, Sustainable Development

Goals (SDGs) have focused on the sustainable management of the availability and

accessibility of WASH. To meet the needs of SDG-11, at the global level, Habitat III has

launched the New Urban Agenda of inclusive, clean and green cities (Biswas & Jamwal,

2017). The Report stated that around one-quarter of the world’s urban population

continues to live in slums and over 90 per cent of urban growth is occurring in the

developing world. Over last two decades, the international communities have been

consistently advocated for water and sanitation to be identified as a fundamental human

right. In July 2010, as a historic landmark, the United Nations (UN) General Assembly

passed a resolution confirming the water and sanitation as an independent human right

(United Nations, 2010). The states are responsible for availability and access to safe

potable water and sanitation facilities (Shreyaskar, 2016). The National Water Policy

(1984) in India brought some aspects of consumer’s rights of drinking water, but it was

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not considered as an independent human right. Cullet (2013) identified that there are

significant gaps in the legal framework for the provision of safe potable water and

sanitation, although several judgments of the Supreme Court observed the pollution free

water and air as a human right. For example, the National Water Policy (2012) and the

National Urban Sanitation Policy (2008) aimed for universal access to water and

sanitation facilities, but did not explicitly identify as human rights.

Most of the city specific plans over last decade failed to address the issue of water and

sanitation as these urban renewal plans like Jawaharlal Nehru National Urban Renewal

Mission (JNNURM) and Atal Mission for Rejuvenation and Urban Transformation

(AMRUT) remained primarily focused on large scale infrastructure projects. Jacobs

(1961) in her influential book ‘The Death and Life of Great American Cities’ argued that

in the renewal process, rights of the city-dwellers are not given due importance. Well

after five decades, the basic rights of urban-dwellers in the form of safe drinking water

and hygienic sanitation facilities remain a distant dream. Smart Cities Mission aims to

promote housing opportunities for all and at introducing smart solutions to improve the

basic infrastructure and identity of the selected city and to improve the universal supply

of water and sanitation facilities in 109 selected cities in India by 2019 (Ministry of

Urban Development, 2015). However, unveiling blueprints of interventions make clear

that basic facilities including safe drinking water and sanitation is not the prime gainer in

the Smart City Mission. Kundu (2015) also has noted that the Smart City project had no

place for poor urban people, who cannot afford high-quality services and consequently

remained excluded in the design of mission. The city level regeneration plans rather than

considering intra and inter-city inequalities of WASH talks about the financial

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sustainability of the operating agencies. The priority seems to be the financial

sustainability of providers rather than the people of the cities as promised in SDG 2030.

The national level programmes need a city specific agenda and priorities. In the light of

the above mentioned scenario, this study aims to set policy priorities for urban WASH

agenda by analysing twofold specific objectives: 1) To assess the recent change and

existing inequalities in WASH performances in selected major cities of India, and 2) To

assess the factors contributing for poor WASH performance.

WASH in Cities

A long-standing credence in literature shows that living conditions are far better for city

dwellers than for those living in smaller cities or villages (Coffey & Hathi, 2017; Rezvani

& Mansourian, 2013; Węziak-Białowolska, 2016). In other words, in the early 20th

century large cities have emerged as the “islands of privilege” as evident by greater

income-earning opportunities and better access to publicly conferred entitlements such as

education, health care, water, sanitation and hygiene (Harrison, 1982). The cities are

owing to their economic importance and the need to keep them livable to attract business

and finance are expected to have better services including WASH. However, the evidence

suggests that urban advantage is misleading as it obscures enormous differences in

WASH performance between and within urban areas (Harpham et al., 1988;

Montgomery, 2009). The cities of the neoliberal era are growing more for the gated

communities than for the entire population. The intra-city variation in basic facilities like

drinking water supply which is often referred as the primary and secondary circuit of

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water-supply adequately substantiate the intra-city island of affluence (Lahiri-Dutt, 2008;

Smith, 2001).

During the past two centuries, the global population living in cities increased from 5 per

cent to more than 50 per cent. As per the UN urbanisation prospects about 54 per cent of

world’s population were living in urban areas in 2014, the share would be 66 per cent by

2050 (United Nations, 2014). India, China and Nigeria would contribute the highest share

that is 37 per cent of the world’s population between 2014 and 2050. India would add

404 million, China 292 million and Nigeria 212 million urban dwellers in the world’s

projected urban population (United Nations, 2014). According to UN estimation, around

4 per cent of land surfaces are the home of more than half of the world population and

produce three-quarter of world’s pollution and waste. The rapid explosion of the

population is one of the big challenges for the researchers and policy makers for

sustainable growth of basic infrastructures and the supply of essential services in the

cities. The larger cities, particularly the slum areas, are the home for urbanised rural

poverty (Kundu, 2014a). By 2050, about 66 per cent population in the world’s cities

would have limited access to water and sanitation services along with poverty, pollution

and congestion (Pruss-Ustun et al., 2016). In India, the urban population was 286 million

in 2001 which rose to 377 million in 2011 at an annual growth rate of 2.76 per cent

(Bhagat, 2011; Kundu, 2011). The emerging pattern of urbanisation in the country is

known as metropolitisation of the urban population where 35 million plus cities

contribute around 50 per cent of the total urban population (Haque & Patel, 2017).

Previous studies have reported huge inter and intra-city inequality in terms of basic

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services (Elledge & McClatchey, 2013; Gandy, 2008; Goli et al., 2011; GOI, 2009;

Haider, 2016). The majority of the urban population in the Indian cities does not have

access to basic urban amenities, especially the access to WASH.

Need for the Study

The process of systematic city centric migration has been witnesing in India. Two

specific reasons viz. stagnation in the rural economy and desire of people to avail urban

amenities are commonly known in different reports as well as in mainstream literature.

Census (2011) data showed that it is rural to urban migration and not the natural growth

which is a leading cause of urban population growth over a decade. Agriculture is fleeing

and shift towards urban-based occupations such as construction workers, and other blue-

collar jobs have raised the number of urban poor. Such increase in a number of city

dwellers as a consequence of urbanisation of rural poverty also means challenges for

urban authorities especially to the civic bodies responsible to provide basic amenities and

services. The absence of regulatory mechanism to monitor prevailing rental system

resulted in skyrocketing of rents at the hands of private ownership, especially in the

metropolitan cities. This informal settlement system as a consequence has pushed urban

poor to informal settlements more often to unauthorised colonies, which are devoid of

basic privileges of the broad urban system (such as clean drinking water and sanitation).

In the scenario of high economic growth, the Government is better positioned to supply

adequate resources to develop inclusive and equitable cities. Schemes and flagship

programme like ‘Housing for All’ (Pradhan Mantri Awas Yojana) and ‘Slum Free India’

(Rajiv Awas Yojana) do indicate towards this trend, but the slum dwellers and other

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categories of urban poor such as homeless footpath dwellers and housing-shelter dwellers

have witnessed little improvements in their living conditions. There abode is still

characterised by overcrowded living, lack of basic sanitation, poor waste collection and

removal and scarce water facility. Despite the articulated ‘inclusive urban planning’, the

socio-economic spatial exclusion in accessing WASH is still widespread in informal

settlements threatening the health of urban poor. Against this backdrop, we consider that

there is a strong need to measure the levels and trends in WASH performances and also

its inter-city and intra-city inequality; as well as to examine the socioeconomic correlates

in order to set the policy priorities to improve WASH performance in major cities,

namely Mumbai, Delhi, Kolkata, Chennai, Bangalore and Hyderabad.

Data and Methods

We used information from the two rounds of India Human Development Surveys

(IHDSs). These two rounds of this survey have been conducted in 2004-2005 (IHDS-I)

and 2011-2012 (IHDS-II), with the collaboration of University of Maryland by the

National Council of Applied Economic Research (NCAER), New Delhi. IHDS has

covered multi-topic panel survey of 41,554 households in 1,503 villages and 971 urban

blocks of 276 towns and cities (Desai et al., 2010). In these surveys, a special

representative sample design is adopted for six largest populated cities in India, namely,

Mumbai (N=12.4 million, n=524), Delhi (N=11.0 million, n=1266), Bangalore (N=8.4

million, n=1079), Hyderabad (N=6.4 million, n=259), Chennai (N=4.6 million, n=351)

and Kolkata (N=4.5 million, n=433). Hence, the sample sizes in the survey allow

conducting a rigorous statistical analysis. Both the surveys have collected the information

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on income, consumption, the standard of living, employment, education and various

aspects of gender and family relationships. These surveys also collected the information

on water, sanitation, and hygiene along with the educational, medical and village

infrastructure. A good range of variables was used in this study for both surveys on

availability and accessibility of WASH. The select six cities are from four regions:

Mumbai from the West, Delhi from the North, Kolkata from the East and Chennai,

Bangalore and Hyderabad from the South. Thus, the sample represents the geographical

variation and urban characteristics of the country.

In the study, WASH availability, accessibility and overall performance were estimated. A

good range of indicators were included in the availability which represent the source of

drinking water, sanitation facility and materials for hygienic condition and practices of a

household, while accessibility comprises reachability to drinking water sources, use of

sanitation facilities and actual hygienic condition and practices (Table 1). The availability

and accessibility indicators were dichotomized as 0 for disadvantageous and 1 for the

advantageous group. Then, the availability and accessibility score for each household of

WASH are estimated separately for both survey rounds using Principal Component

Analysis (PCA). The overall performance of WASH is the average of availability and

accessibility scores. The scores for availability, accessibility, and overall performance are

ranked in ascending order and divided into three equal proportions for both rounds of the

survey. These three groups are labelled as poor, middle and better-off. Thus, the city wise

levels of availability, accessibility and overall performance of WASH are estimated for

both the years of survey rounds. The items used for the construction of both availability

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and accessibility were tested for their validity, reliability and suitability both

quantitatively.

To measure the city level inequality in the overall performance of WASH for both survey

rounds, the Gini coefficients and the parameters of inter and intra-city inequalities are

estimated using Theil and Atkinson methods of decomposition (technical proof of the

models is reported elsewhere, see Atkinson, 1970; Cowell, 2000; Pyatt, 1976). The Gini

coefficients give city wise households level inequality estimation. Along with the city

level inequality estimates, the Theil and Atkinson indices allow estimating intra- and

inter-city inequalities. The Theil’s index is the single parameters of the General Entropy

class (GE a=1); while the Atkinson index incorporates the social value judgment of the

people about inequality in the society. All the inequality measure are computed using the

statistical software STATA, version 13 (ineqdeco).

To assess the association between the levels of WASH and their socioeconomic factors of

the households, the order logit regression was applied, because wash as the outcome

variable is categorised into the order of poor (1), middle (2) and better-off (3). The three

separate regression models were carried out for availability, accessibility, and overall

performance of WASH using the software, STATA (ologit). Besides housing types,

economic status, educational level, socio-religious groups and occupational status, the

predictors of these multivariate analyses include years of survey and the cities, where

temporal and spatial effects on WASH could be identified. The proportions of sample

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size in the cities and different categories of independent variables are presented in Table

2.

Results

The levels of WASH in the selected six metro cities

The levels of WASH availability, accessibility, and overall performance are shown in

Figure 1, 2 and 3 respectively for the years 2004-2005 and 2011-2012 in six metro cities

of India. Figure 1 shows that Bangalore had the best performance in WASH availability

having less than 20 per cent poor in both the years, while around half of the cities are in

better-off availability in 2011-2012. Unlike Bangalore, another city of South India,

Hyderabad had the worst performance in WASH availability for both the year. In

particular, the city had more than 55 per cent poor and less than 15 per cent better-off

availability in both survey rounds. Although there is not much variation among the rest of

the four cities, Chennai and Mumbai had better performance in WASH availability as

compared to Kolkata. The WASH availability in Mumbai was better in 2011-2012 than

that in 2004-2005. During the same period, the availability was vice-versa in Delhi.

Figure 2 demonstrates the WASH accessibility in the selected cities for both the years.

The results show that Mumbai was the best performing city having 18 per cent and 3 per

cent in poor, and 29 per cent and 52 per cent in better-off categories for the year 2004-

2005 and 2011-2012 respectively. Afterwards, the levels of accessibility were followed

by Bangalore and Delhi. Bangalore had 29 per cent poor and 32 per cent better-off

accessibility in 2011-2012, while Delhi had 29 per cent and 30 per cent poor, and 20 per

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cent and 16 per cent better-off for the years 2004-2005 and 2011-2012 respectively. On

the contrary, Chennai was the worst performing city having 65 per cent and 52 per cent in

the poor, and 5 per cent and 8 per cent in the better-off groups for the subsequent survey

years accordingly. After Chennai, the poor accessibility level was followed by Hyderabad

and Kolkata. In Hyderabad, the poor percentages were 61 per cent and 48 per cent, and

the better-off performance were 22 per cent and 18 per cent in those years subsequently.

While in Kolkata, these were 43 per cent and 47 per cent in the poor, and 35 per cent and

28 per cent in the better-off during the same years.

The overall WASH performance of the selected cities for both survey years is displayed

in Figure 3. The figure reveals that Mumbai had 6 per cent in poor and 55 per cent in

better-off groups of overall WASH performance securing the best performing city in

2011-2012. It was followed by Bangalore with 18 per cent for the poor and 49 per cent

for the better-off categories in the same year securing the second best position, although

Bangalore had better overall performance than Mumbai in 2004-2005. The overall

performance of WASH was worst in Hyderabad followed by Kolkata for both years.

Particularly in Hyderabad, the poor were 60 per cent and 53 per cent in the subsequent

years, and the better-off were 1per cent and 20 per cent in the same years. Similarly, in

Kolkata, the shares were 37 per cent and 43per cent in the poor class, which were 26 per

cent and 30 per cent in the better-off in the same time periods. There were not much

overall variation observed between Delhi and Chennai.

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Inequalities in WASH in the selected six metro cities

Table 3 presents the results of average, within and between city inequality in WASH

performance in 2004-05 and 2011-12. The total inequality has increased from 2004-2005

(Gini = 0.248) to 2011-2012 (Gini = 0.286) in the entire sample of the selected cities. In

both the survey years, the within inequalities among the cities were much higher than the

inequalities between the cities. The inequality between the cities has slightly declined

from 2004-2005 (Theil = 0.009; Atkinson = 0.006) to 2011-2012 (Theil = 0.006;

Atkinson = 0.004). Interestingly, the absolute inequality measure suggests that it has

slightly increased from 2004-2005 (Theil = 0.130) to 2011-2012 (Theil = 0.136), but the

welfare measure of inequality exhibits a meagre decline in within inequality for the same

duration (Atkinson = 0.083 and Atkinson = 0.083). The increase of inequality as reflected

by Theil’s Index is a cause of concern even though the quantum of rising is very small.

Hyderabad and Kolkata showed higher inequalities in both the years compared to other

cities. Contrary to this, Bangalore and Delhi showed the relatively low level of inequality

in the same years. Mumbai is the only city where inequality was found to be reduced

considerably primarily due to its in-situ slum development initiatives.

Determinants of WASH in the selected six metro cities

The results from order logit regression analyses of WASH availability, accessibility and

overall performance across selected metro cities of India are presented in Table 4. House

types, economic status, educational level, socio-religious groups and occupational status

are considered as independent variables on which WASH availability, accessibility and

performance are dependent. The overall WASH performance was significantly better in

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2011-2012 as compared to 2004-2005. It is interesting to note that while accessibility has

become better during the study period, availability does not show a significant

improvement.

The results suggest that there is significant city level variation in WASH overall

performance and the differences are also found in availability and accessibility in

particular. Mumbai and Hyderabad (Odds Ratio [OR hereafter]: 0.28, Interval at 95per

cent Confidence level [CI hereafter]: 0.24, 0.34) are the best and worst overall WASH

performing cities correspondingly. After Mumbai (OR: 1.00, CI: 0.13, 0.15), Bangalore

(OR: 0.87, CI: 0.70, 1.02) and Delhi (OR: 0.84, CI: 0.70, 1.02) were better in overall

performance than Kolkata (OR: 0.53, CI: 0.46, 0.62) and Chennai (OR: 0.51, CI: 0.42,

0.63). Unlike overall performance, the availability was highest in Bangalore (OR: 1.37,

CI: 1.13, 1.65) followed by Delhi (OR: 1.36, CI: 1.18, 1.56), Chennai (OR: 1.05, CI:

0.85, 1.28) and Mumbai, while it was lowest in Hyderabad (OR: 0.39, CI: 0.32, 0.43)

followed by Kolkata (OR: 0.89, CI: 0.77, 1.03). Mumbai and Chennai (OR: 0.10, CI:

0.08, 0.12) were the most and least performing cities in accessibility respectively.

Although there were little differences among the rest of the four cities, the accessibility

was relatively better in Bangalore (OR: 0.41, CI: 0.34, 0.49), followed by Delhi (OR:

0.34, CI: 0.29, 0.39), Kolkata (OR: 0.28, CI: 0.24, 0.32) and Hyderabad (OR: 0.23, CI:

0.19, 0.27).

Besides intercity differences, the WASH overall performance in general, and availability

accessibility in particular varied significantly across the socioeconomic groups within the

city. Table 4 also suggests that housing types, economic status of the households,

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educational status and occupational status of the head of households, and socio-religious

groups have significant effects on WASH performances. All the three WASH

performances were significantly higher in non-slum and non-poor households as

compared to slum and poor households respectively. The WASH performances were

considerably improved from illiterate heads of the households to primary, secondary and

tertiary education. Among the Hindu community, the general caste had highest WASH

performances, followed by OBCs, SCs, and STs, while the lowest performance was

found among the Muslim community. The head of the households who are engaged in

tertiary economic activities and non-worker had more WASH level as compared to those

in primary activities.

Discussion

A careful examination of levels and inequality trends in WASH and its determinants

suggests that although the accessibility and overall WASH performance has improved

during 2004-5 and 2011-2012, there is no significant change in availability. The

availability was poor in Mumbai, but the accessibility was better, while the level was

reverse in case of Chennai. The overall level of WASH becomes significantly better in

Mumbai in 2011-2012 compared to 2004-2005, but temporal differences are not

considerably observed in other selected cities. Interestingly, the poor performing cities

such as Kolkata, Hyderabad, and Chennai had more intra-city inequality as compared to

better performing cities such as Mumbai, Bangalore, and Delhi. This is reflective of the

hypothesis that if the overall WASH performance improves, the probability of even poor

getting benefit from it is relatively better. It is therefore suggestive that priority of the

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urban local bodies should be to improve the overall performance of WASH elements. For

instance, as observed during the field visit in Hyderabad during December 2016 that

when there is 24-hour running water in public taps, the poor would also get the benefit of

the water; whereas, if city gets only 4 hours water supply, the wealthy would use booster

pump and poor will not get enough water and the public tap will often run dry.

Furthermore, city-specific interpretation of findings suggests that historical cities like

Hyderabad and Kolkata have poor WASH performances and higher inequality. The poor

accessibility in Hyderabad and Kolkata could rightly be attributable to the poor WASH

availability. In particular, Hyderabad has revealed poorest WASH performance out of

the six selected cities. The reason could be the sudden influx of population in the city,

which partially is a result of the global image of ‘HITEC city’ visualised through the

‘Cyber Tower’. It creates a challenge for the distribution of existing resources (Martinez

et al. 2008). Another cyber city, Bangalore was one of the cities designated as a growth

engine from the 1950s, with the establishment of public-sector industries. With private

high-tech industries which came to Bangalore in early 2000. Bangalore owing to

scientific and strategic community hub provided greater public voice as compared to

Hyderabad and thereby, provided a better WASH availability in the city at the aggregate

level. Although Hyderabad is located along the river Moosi and also having two large

lakes one is in heart of city and other is in the outskirts, but, unmindful sanitation system

of the city made both of the water sources polluted beyond permissible limit and water

cannot be used for drinking purpose. Loss of available sources of water is a major reason

for its poor ranking in WASH performance.

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While the poor performance of WASH in Kolkata might be related to the specific

geographical and historical context prevailing in the city. Clean and safe drinking water

gets relatively lower priority in Kolkata compared to drier geographical regions (Keshri

& Bhagat, 2010; Ramaswamy, 2008; Sur, Manna, Deb, Deen, Danovaro-Holliday,

Seidlein, Clemens & Bhattacharya, 2004). Prevailing higher inequalities may be ascribed

to unplanned spatial expansion and resultant uneven service delivery (Bhattacharya,

2002). The appeal of S.K. Mullick to the Governor General of India in the year 1918,

regarding formulation and implementation of by-laws to reduce overcrowding, was one

among numerous such instance to quote here. He stated,

...has the energy and we know from his personal efforts in the crusade against

Malaria, Hookworm disease that question of sanitation occupy his foremost

thought …. We are oriental people and the man in the street understands the

personality and the hookkum of the Lat Sahib far better than the promulgation

of the best meant bye-laws of corporation (Mullick, 1918, quoted in Choudhary,

2008).

In Mumbai, the availability did not significantly improve, but the accessibility improved

substantially in the city and due to in-situ slum development programme. It can be

considered as a model for other large metropolitan cities. Although the two-third

population of Mumbai is living in a slum, the good accessibility and less inequality

suggest that the city has inclusive and facilitated water and sanitation supply system

(Coelho & Raman, 2010; Dupont, 2008; Sheikh & Banda, 2014). On the other hand, the

poor accessibility in Chennai could be marked as a lack of inclusive policies for the

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distribution of water and sanitation, in spite of the fact that the city has relatively better

availability of WASH (Shaw, 2005).

Delhi, the capital of India where almost half of its population living in slums, nearly 20

per cent of its total population do not have access to drinking water and toilet facilities

within the premises and 4 per cent defecate in the open (Population Foundation of India,

2015). The large space of Delhi is occupied for administrative offices and official

residences, therefore, remaining land cover is overcrowded, it led to formation of huge

slums. Population is growing much faster than its resources growth. Also, lack of

inclusive policies in globalizing Delhi is leading to marginalization on the basis of their

socio-economic status (Acharya et al., 2017).

In tune with the findings of this study, it is also reported that the inequalities are mostly

within the cities, which could be consequences of the socioeconomic hierarchies and

discrepancies in the spatial service deliveries, particularly for water supply and sewerage

systems within the city spaces (Ali, 2002; Haque, 2016; Kumar, Kumar & Mitra, 2009;

Kundu, 1991; Motiram & Osberg, 2010). Bangalore, Mumbai, and Delhi have better

WASH performance than Hyderabad and Kolkata, yet the inequality among the cities is

much lesser than the within city inequality (Menon, 2015; Singh, 2009). The present

study also indicates that intra-city inequality is a major challenge in WASH

performances. It suggest that the existing inequality in WASH performance is attributed

to housing type, economic and socio-religious background of the households, educational

and occupational status of the household’s head. Similar to our findings, other studies

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reported that SCs and STs among the Hindus and Muslims are segregated in the city

spaces and these segregated areas are characterised by poorly facilitated service

deliveries such as water and sanitation (Bhan & Jana, 2015; Haque, 2016; Hegde, 2013;

Sidhwani, 2015). Along with the availability of water and sanitation, the level of

education is also associated with the hygienic practices in the households (Kuberan,

Singh, Kasav, Prasad, Surapaneni, Upadhyay & Joshi, 2015). The WASH between the

household and the workplace could have a symbiotic relationship although studying this

aspect is not within the scope of this study. For example, the white collar workers are

well facilitated with improved water and sanitation, and more likely to have hygienic

practices as compared to the blue collar worker. Further, the occupational structure is

often correlated with their educational and economic background, which also affects the

WASH performance status in the households (Cohen, 1950; Furlong, Biggart & Cartmel,

1996).

Cities when generative act as ‘growth engines’ for the development of the peri-urban

areas and also for their respective hinterlands. They emanate centripetal forces which

attract various resources, public and private investments. Owing to strong and relatively

better infrastructure these cities provide a base to the various economic activities. The

available economic opportunities result in high migration rate towards the metro cities in

comparison to small and medium cities. There are undesirable though tangential

outcomes including, poor sanitation condition across several residential pockets and

uneven development of the region (Kundu, 2003). In large metropolitan centres, slums

are the first destination of the migrants from the countryside who are mainly poor and

21

unskilled workers. Poor supply of water and sanitation results from the unequal

distributive policy and lack of affordability of these new migrants who though contribute

in city’s population and their economies (Giles and Brown, 1997; Smith, 2001).

The question of availability and accessibility is linked to equitable distributions of water

and provision of sanitation services. These services demand considerable financial

allocation to the governing urban local bodies. The studies related to JNNURM and

AMRUT suggest that while there is enough funding for urban infrastructure, the specific

allocation and expenditure for basic amenities and services are almost negligible

compared to the capital intensive big projects on urban transportation system (Kundu,

2014; Mahadevia, 2007). Congested housing conditions usually characterise slum areas,

lack of safe water, poor sanitation facilities and resultant unhygienic practices within and

around the households in the otherwise ‘thriving cities’. Social and spatial segregation of

city population based on socio-economic status and occupation is a leading factor related

with intra-city inequality (Bhan & Jana, 2015; Murthy, 2012; Goli, Doshi & Arokiasamy,

2013; Goli, Arokiasamy & Chattopadhyay, 2011). Various studies proved that the living

conditions of the population living in slum remain worse than the poor population living

in rural areas is in tune with findings of this study (Shukla, 2015; Sanusi, 2010; Giles &

Brown, 1997).

Conclusion

This study presented a comparative assessment of WASH inequalities and the associated

factors, in the selected major cities of India. The results presented in this paper

22

demonstrated that inequality exists in WASH performance, especially at the intra-city

level. The spatial inequalities are significant across the economic status, housing types,

educational level, socio-religious groups, and the occupational structure regarding access,

availability and overall WASH performance. The study reveals that the existing

inequality in WASH is a major roadblock to sustainable and inclusive urban development

in India. At the policy level, several interventions (that is ‘Housing for All’, ‘JNNURM’,

‘Rajiv Awas Yojana (RAY)’, ‘Swachh Bharat Mission’) are well placed on the paper by

the government to achieve the sustainable growth of cities which is the main focus in

universal access of civic amenities and slum development strategies through increased

resource allocation. However, though these are impressive attempts on the part of the

government to promote urban development, the real challenge, unaddressed yet, lies in

ensuring the availability and equitable access to basic civic amenities, particularly

WASH, by a large proportion of population inhabiting in peri-urban areas. It is critical to

reflect on how effective the urban development and poverty alleviation programmes are

in addressing the intra-city and intercity inequalities and how relentlessly the policies

focus in the direction of improving the living conditions of marginalised urban poor

residing in informal settlements. Most of the ongoing urban policies and programmes in

Indian cities are centralised, following top-down approach (GOI, 1997; GOI, 2002).

Various studies have recommended that the top-down approach is ineffective for

connecting people to centralised water, sanitation facilities, while the bottom-up approach

is participatory in nature and include the local solutions. Engagement of the local

community also influences people’s sanitation and water use behaviour which provides

sustainable solution and connectivity with the policies (Ramachandraiah, 2001; Biswas &

23

Jamwal, 2017). The implementation of ‘Smart Cities’ is also another example of a

centralised policy which do not have any relevance with the grass-root level condition.

Smart cities do not have a place for the poor population in the informal sector and those

who do not have the affordability of the high-quality services, can be consequently

excluded from the access to smart city services. Highly hyped programmes like this

nothing but creating privatised gated communities for people above the upper middle

class under neoliberal approach (Burte, 2014; Kundu, 2015).

Considering the opportunities of urban revolution with an estimated 600 million urban

population by 2031 and 75 per cent of national income to be derived from the cities

(Tewari & Godfrey, 2016), India has better scope for economic growth through investing

in city-modernization- smart and healthy cities. However, it is an utmost priority for

policies to ensure equality for a huge proportion of the socioeconomically diverse

population in cities, in the first place.

In the light of the study findings and review of the status of present existing urban

policies, we suggest that prioritising equality in WASH performance is indispensable for

inclusive urban development, particularly for improving the urban social and health

indicators such as morbidity, nutrition and mortality. There is a strong need to identify

the pathways to the inclusive development of cities. To this direction, it is crucial to

address the policy-level under representation of the heterogeneous population in urban

areas as well as to identify the governance failure. Strengthening local governance to

improve the living conditions of urban poor would be a significant step towards the

24

decentralized and inclusive growth of cities improving the prospects of achieving SDGs

by 2030.

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Tables

Table 1: Indicators included in the WASH indices

WASH availability index

(2004-05 [AIC: 0.026, Coefficient: 0.55], 2011-12 [AIC: 0.024, Coefficient: 0.02] )

1) The households having improved sources of drinking water are coded as 1 and it is 0 otherwise. The

improved sources of drinking water include piped water, tube well, hand pump, covered well, rain and

bottled water, while unimproved sources are open well, river, pond, truck and others.

2) The households having water supply for more than one hour are considered better availability (1) compared

to the less than one hour (0).

3) The households having toilet facilities are those which have any type of toilet facilities particularly

traditional latrine, VIP latrine, and flush toilet (1), when non-availability of toilet facility is open defecation

(0).

4) The households having vessel with lid for drinking water storage are hygienic (1), while no vessel for

storage and vessels with no lid are unhygienic (0).

5) The households with separate kitchen are categorized as having improved cooking place (1), and

unimproved conditions include cooking in outdoors and in living area (0).

6) The households with better housing space are those which have three and less persons per sleeping room

(1) as compared to more than two persons per sleeping room (0).

7) The households having improved building materials viz. improved wall, roof and floor are considered as

pukka house (1), and the houses are kutcha otherwise (0). The improved materials of wall are burnt bricks,

stone and cement/concrete, while the improved includes grass, thatch, mud, unburnt bricks, plastic, woods,

GI/metal sheets and others. Similarly, the improved roof materials are cement/concrete, bricks and stones,

while the improved materials comprises grass, mud, wood, tiles, slate, plastic, GI/metal sheets, asbestos

and others. The improved floor types incorporate cement/concrete and tiles/mosaic, while the improved

materials are mud, unburnt bricks, wood, bamboo, bricks, stone and others.

WASH accessibility and hygiene practice index

(2004-05 [AIC: 0.024, Coefficient: 0.52], 2011-12 [AIC: 0.027, Coefficient: 0.02] )

(1) The household’s members spend less than and equal to 30 minutes to fetch water per day have better

accessibility to water (1) than those who spend more than 30 minutes (0).

(2) Better accessibility of toilet facilities comprise toilets within the dwelling, shared toilet inside and outside

building, and public toilets (1). Not accessibility of toilet facility is open defecation (0).

(3) Households using soap for washing hands after defecation are grouped into hygienic practice (1), while the

hand wash using other material such as water alone, mud/ash and others are unhygienic practices (0).

(4) The households always purify the drinking water have better hygienic conditions (1) than those which

never, rarely, sometimes and usually purify (0).

(5) Improved methods of pouring drinking water are using long ladle and tap in the vessels (1), while the

unimproved methods include cups and utensils (0).

(6) The households use improved (1) and unimproved (0) cooking fuels. The improved fuels LPG and

kerosene and unimproved are firewood cow dung, crop residue, coal and charcoal.

Note: The advantageous and disadvantageous groups are coded as 1 and 0 respectively.

34

Table 2: Descriptive statistics of the study variables

Background characteristics Proportions (CI)

2004-05 (n = 4133) 2011-12 (n = 3912)

Cities

Mumbai 0.142 (0.131, 0.153) 0.134 (0.124, 0.145)

Delhi 0.321 (0.307, 0.335) 0.324 (0.309, 0.338)

Kolkata 0.270 (0.256, 0.283) 0.276 (0.262, 0.290)

Chennai 0.070 (0.063, 0.079) 0.066 (0.059, 0.074)

Bangalore 0.087 (0.079, 0.096) 0.090 (0.081, 0.099)

Hyderabad 0.111 (0.101, 0.121) 0.111 (0.101, 0.121)

Housing types

Slum 0.232 (0.22, 0.245) 0.100 (0.091, 0.110)

Non-slum 0.768 (0.755, 0.78) 0.900 (0.890, 0.909)

Economic status

Poor 0.101 (0.092, 0.111) 0.071 (0.063, 0.079)

Non poor 0.899 (0.889, 0.908) 0.929 (0.921, 0.937)

Educational statusª

Illiterate 0.445 (0.430, 0.461) 0.498 (0.483, 0.514)

Primary 0.111 (0.102, 0.121) 0.107 (0.098, 0.117)

Secondary 0.350 (0.336, 0.365) 0.310 (0.296, 0.324)

Higher 0.093 (0.085, 0.103) 0.085 (0.076, 0.094)

Socio-religious groupsª

Hindu general 0.357 (0.342, 0.371) 0.327 (0.313, 0.342)

Hindu OBC 0.266 (0.253, 0.280) 0.263 (0.249, 0.277)

SCs 0.200 (0.188, 0.213) 0.241 (0.228, 0.255)

STs 0.010 (0.008, 0.014) 0.013 (0.009, 0.017)

Muslims 0.132 (0.122, 0.143) 0.132 (0.121, 0.143)

Other religion 0.035 (0.030, 0.041) 0.025 (0.020, 0.030)

Occupational statusª

Primary 0.407 (0.392, 0.422) 0.434 (0.418, 0.449)

Secondary 0.134 (0.124, 0.145) 0.240 (0.227, 0.254)

Tertiary 0.338 (0.324, 0.353) 0.306 (0.291, 0.320)

Non worker 0.121 (0.112, 0.132) 0.020 (0.016, 0.025)

Note: Lower and upper limit of Confidence Interval (CI) at 5% significance level; ª

information for household heads

35

Table 3: WASH inequality in selected metro cities of India (2004-05 to 2011-12)

Cities 2004-05 2011-12

Gini Theil Atkinson Gini Theil Atkinson

Mumbai 0.268 0.152 0.096 0.192 0.067 0.037

Delhi 0.203 0.111 0.073 0.283 0.138 0.078

Kolkata 0.266 0.155 0.100 0.331 0.183 0.100

Chennai 0.209 0.116 0.075 0.300 0.163 0.100

Bangalore 0.147 0.048 0.029 0.245 0.105 0.059

Hyderabad 0.328 0.219 0.135 0.320 0.177 0.102

Within Inequality - 0.130 0.083 - 0.136 0.076

Between Inequality - 0.009 0.006 - 0.006 0.004

Total 0.248 0.139 0.089 0.286 0.142 0.080

36

Table 4: Odds ratios from order logit regression analyses by WASH availability, accessibility and

overall performance in selected metro cities of India, 2011-12

Background variables Availability Accessibility and

Practice Overall performance

Years (2004-05®)

2011-12 0.95 (0.87, 1.04) 1.09 (1.00, 1.18)* 1.25 (1.14, 1.36)***

Cities (Mumbai®)

Delhi 1.36 (1.18, 1.56)*** 0.34 (0.29, 0.39)*** 0.84 (0.73, 0.97)**

Kolkata 0.89 (0.77, 1.03) 0.28 (0.24, 0.32)*** 0.53 (0.46, 0.62)***

Chennai 1.05 (0.85, 1.28) 0.10 (0.08, 0.12)*** 0.51 (0.42, 0.63)***

Bangalore 1.37 (1.13, 1.65)*** 0.41 (0.34, 0.49)*** 0.84 (0.70, 1.02)*

Hyderabad 0.39 (0.32, 0.46)*** 0.23 (0.19, 0.27)*** 0.28 (0.24, 0.34)***

Housing types (Slum®)

Non-slum 3.71 (3.26, 4.21)*** 2.06 (1.82, 2.33)*** 2.84 (2.51, 3.22)***

Economic status (Poor®)

Non-poor 3.60 (3.01, 4.30)*** 2.46 (2.08, 2.91)*** 3.49 (2.93, 4.17)***

Educational status (Illiterate®)

Primary 1.27 (1.09, 1.48)** 1.31 (1.13, 1.53)*** 1.38 (1.19, 1.60)***

Secondary 1.91 (1.72, 2.13)*** 1.93 (1.74, 2.15)*** 2.03 (1.82, 2.26)***

Higher 3.12 (2.61, 3.72)*** 2.76 (2.32, 3.29)*** 3.58 (2.99, 4.29)***

Socio-religious groups (Hindu General®)

Hindu OBC 0.84 (0.75, 0.95)** 0.67 (0.60, 0.76)*** 0.75 (0.67, 0.85)***

SCs 0.59 (0.52, 0.66)*** 0.51 (0.45, 0.57)*** 0.54 (0.48, 0.62)***

STs 0.62 (0.40, 0.96)** 0.47 (0.31, 0.73)*** 0.46 (0.30, 0.71)***

Muslim 0.43 (0.37, 0.50)*** 0.41 (0.35, 0.47)*** 0.40 (0.34, 0.46)***

Other religion 1.13 (0.88, 1.46) 0.64 (0.50, 0.82)*** 0.89 (0.69, 1.14)

Occupational status (Primary®)

Secondary 1.01 (0.89, 1.14) 1.34 (1.18, 1.51)*** 0.99 (0.87, 1.11)

Tertiary 1.52 (1.36, 1.70)*** 1.78 (1.59, 1.99)*** 1.51 (1.35, 1.69)***

No Occupation 1.23 (1.03, 1.47)** 1.58 (1.33, 1.88)*** 1.24 (1.04, 1.47)**

Log likelihood -7588.84 -7647.79 -7701.34

LR Chi2 (19) 2173.52*** 2018.76*** 2167.49***

Pseudo R2 0.125 0.117 0.123

Note: *** p<0.001, ** p<0.05, * p<0.10; Lower and upper limit of Confidence Interval (CI) at 5%

significance level are presented in the parentheses

37

Figures

Figure 1: WASH availability in selected metro cities in India during 2004-05 to 2011-12

Figure 2: WASH accessibility in selected metro cities in India during 2004-05 to 2011-12

38.6

20.827.3

34.8 36.8 38.227.8 25.9 20.0 17.1

59.7 55.4

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38

Figure 3: Overall WASH performance in selected metro cities in India during 2004-05 to

2011-12

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39