The Swachh Bharat Mission, globally the biggest public programme to support sanitation, has focused on subsidised construction of household toilets in rural areas, while aiming for solid waste disposal and behavioural changes in urban areas. In spite of that, India witnesses a huge gap from the SDGs related to usage of safely managed sanitation associated with more environment- responsive technology and hygiene management. The hierarchical sanitation ladder identifies fivecategories of toilets, namely, safely managed, basic, limited, unimproved, and open defecation. This study attempts to contribute to the existing literature by locating the empirical estimation of the sanitation ladder in India across both rural and urban settings, using nationally representative unit-level National Sample Survey data. Results find that only 38% of urban and 6% of rural households in India use safely managed toilets. Also, using logistic regression, the study posits that receiving subsidy for toilet construction can significantly improve usage of safely managed toilets along the ladder only among the poorest urbanclass and the factor is generally more potent in rural areas. The findings identify that there is hardly any benefit in subsidizing the middle-income class and hence highlight a need to redesign the subsidy-driven sanitation policy.

  • Sustainable sanitation indicates importance of technologically improved toilets with exclusive use by the households and environmentally safe management.

  • Estimation of sanitation ladder in rural and urban areas in India indicates low coverage of sustainable sanitation, particularly in the central geographic region.

  • Receiving construction benefits as a one-off investment cannot ensure smooth transition to safely managed toilets in urban India. Though in rural areas sanitation subsidy push them up along the sanitation ladder, most of the households settle down for middle rungs of categories.

  • The original perceived role of sanitation subsidy was to make a smooth passage to open defecation-free zones. However, given the SDG 6.2.1 of ensuring sustainable sanitation management and excreta removal, the current subsidy system needs to be revisited and changed by public policy planners.

The Swachh Bharat Mission (SBM) of India, the biggest-ever public programme in the world, launched in October 2014 to support sustainable sanitation and subsequent reduction of open defecation (OD) by 2019, has invested huge amounts of public funds in construction of toilets and awareness generation. There was a steady rise in public expenditure devoted to the field of rural sanitation in real terms even before SBM was launched. The budgeted expenditure was Rs. 3,500 crores (US$ 472 million) in 2012–2013, amounting to a more than three times increase since 2009–2010. An additional Rs. 1,000 crore (US$135 million) budget allocation was made in 2014–2015 after the launch of the SBM (GOI, Union Budget 2009-10 to 2018-19). Additionally, the actual expenditure exceeded the budgeted expenditure in the first 3 years of the SBM (PRS India 2019). The total budget of India's sanitation campaign appears to be more than 40 times that of the Mars mission budget (The Hindu 2021, November 16). The programme was introduced simultaneously in both rural and urban areas in 2014, although there were substantial differences in the programme components of the rural (Gramin) and urban counterparts. Owing to differential status in sanitation coverage (Ghosh & Cairncross 2014; Chandana & Rao 2021) at the time of the introduction of the programme, the policy matrix of SBM had been substantially different too: while the focus was on constructing more household toilets in rural areas, solid waste management, construction of community and shared toilets, and behavioural changes were targeted in urban and peri-urban locations1. Although OD declined by 26% within 4 years of launching the SBM (Yadavar 2019), India continues to suffer from the persistent gap from the Target 6.2.1. of Sustainable Development Goal (SDG) related to the universal usage of safely managed sanitation (SMS) services, which goes beyond just coverage of households with toilets. Only building and owning a toilet, or even a technologically sound water-sealed flush toilet, does not appear to guarantee sustainable sanitation procedure instrumental to improving health conditions in the neighbourhood. Heijnen et al. 2014 suggested removal of shared latrines from the improved sanitation categories as latrines shared among households often induce negative health outcomes, partially offsetting the advantages that sanitation with improved technology can garner for the society. Joint Monitoring Programme (JMP) (WHO & UNICEF 2008, 2017) introduced the measurement tool called sanitation ladder identifying categories of household toilets with hierarchical positions being defined jointly by technological, social, and managerial concepts.

Sanitation ladder

The first sanitation ladder was constructed of four rungs of sanitation facility types (JMP 2008), where the top-most position was identified as improved sanitation with exclusive use by the households, while the next category was termed as shared, where households enjoyed improved technology, but not exclusively. The last two categories were technologically unimproved and OD. But with time, fulfilment of the SDG target (6.2.1) became one of the top priorities for universal well-being, which called for a modification in the old sanitation ladder by including a new top-most category named safely managed sanitation (SMS) services that also guaranteed safe disposal of excreta through proper drainage and was considered as the most sustainable solution for a sanitation facility (JMP 2017). This ladder goes beyond just engineering and technology characteristics (like improved and unimproved) and involves the social (sharing) and micro-management (safely managed) of toilets. The safely managed sanitation services are defined as improved facilities that are not shared with other households and where excreta are safely disposed and treated. Thus, the revamped or new sanitation ladder has five categories where safely managed toilet is the highest rung followed by basic, which is nothing but the improved and not shared category per the old sanitation ladder. The shared category is renamed as limited and the last two categories remain the same in both versions of the sanitation ladder, namely, unimproved and OD. The hierarchy of this ladder is predefined in such a way that upward movement in the ladder will indicate more hygienic sanitation technology and reduced risk of diseases with lesser environmental damage.

Even though a cost-benefit analysis of quality vis-à-vis quantity is inevitable in this context of a new sanitation ladder raising the question which one is more beneficial: sustainable toilets for few or less than ideal toilets for many; against the background of the ever-changing understanding of long-term sustainability, policies should recognize the sustainable management of sanitation, rather than just creating improved toilets. Without that, the positive impact of using improved toilets might get muted because of the poor removal of excreta, solid waste management, and drainage facilities.

Available evidence on sanitation facilities across rungs of the ladder

According to SDG (6.2.1), every country should achieve universal usage of safely managed toilets by 2030. However, till date, there is a dearth of evidence-based analysis of households using sanitation categories due to the paucity of data. Available evidence indicates that in most of the lower middle-income countries (LMICs), sanitation policies merely focus on provision of sanitation infrastructure without adequate emphasis on its use, cleaning, drainage, and management (Kwiringira et al. 2014; Donacho et al. 2022), thus not effectively controlling the spread of pathogens. For India, there is hardly any analysis of sanitation ladders across rural and urban locations, although Dasgupta et al. (2021) pointed out that policymakers must focus on on-site sanitation (OSS) management to ensure scaling up the ladder. The recent literature (Augsburg & Rodríuez-Lesmes 2020; Nyambe & Yamauchi 2020) studied household sanitation decision making, exploring the association of household characteristics with preference for toilets in different geographical and developmental settings. All the studies are based on primary data from selected parts or districts of the developmental setting. Although Prakash et al. (2022) attempted to explore different categories of sanitation coverage, the focus of the study remained on correlates of sanitation benefits under SBM. Also, while creating steps along the sanitation ladder, the paper used handwashing behaviour, which, although potentially crucial, was never considered by JMP (2017) and the estimation of the ladder did not follow the standard protocol. Therefore, a nationally representative holistic study covering all the states of the country including the all India figures (rural and urban areas separately) are still not available.

Research gap and objective of the study

According to the WHO/UNICEF JMP report (2021, pg. 134), at least basic sanitation services in India have increased coverage by 14% points from 57% in 2015 to 71% in 2020; such a rate of progress is highest among the large2 countries of the world. However, sustainability of this success heavily depends on a country's progress towards safe management of excreta generated by newly constructed latrines. The transition from ‘access to basic’ toilets to ‘sustainable sanitation’ is the need of the hour as a precautionary measure to alleviate escalating future cost of unprocessed waste management. As urban India enjoys better access to improved toilet facilities at home, it is also expected that it should have the highest proportion of households in the highest bracket of safely managed toilets, thus making urban sanitation sustainable and safe for health. However, there is a large gap in the research related to evidence of detailed sub-regional sustainability of sanitation coverage in urban India. Hence, the paper attempts to carry out an estimation of the sanitation ladder in urban and rural areas of India and then identify whether receipt of SBM subsidy can push a household up to the highest category of the ladder to sustainable sanitation. The present study for the first time, per our knowledge, contributes to the existing literature by including all the rungs of the sanitation ladder, to capture the exhaustive set of household sanitation choices in the rural and urban sectors, in India as a whole and separately in the Indian states. Furthermore, the paper attempts to examine the potency of construction subsidy influencing the type of household sanitation choices separately for the rural and urban areas.

In this context, it must be added that the sanitation subsidy in India was originally designed to reduce instances of OD across regions in India, reflecting upon the Millennium Development Goal 7.C (ensuring improved sanitation facilities). However, the SDG 6.2.1. outlined the importance of sustainable sanitation management, focusing on the vulnerable population. The question that automatically arises is whether the present sanitation subsidy model can fit in the new goal of sustainable management ensuring movement towards the new sanitation ladder. This is where the paper attempts to contribute in the literature. Rather than pointing out the failure of the policy, the study highlights the need for redesigning the subsidy in the current context of the changed targets.

After the introduction, the paper is organized in the following way. The ‘Data and methods’ section outlines the details of the dataset, construction of the sanitation ladder, and the methodology used. The ‘Result’ section discusses the statistical and econometric analysis, while the ‘Conclusion’ outlines notes for policy modifications.

The paper utilized unit-level nationally representative NSSO (National Sample Survey Office) 76th round data on Drinking Water, Sanitation, Hygiene, and Housing Condition in India (July–December 2018) schedule 1.2 published by the Ministry of Statistics & Programme Implementation, Government of India. The entire dataset is available on the public domain. This dataset reports not only the availability and use of sanitation facilities, but also includes other crucial information such as the places of excreta disposal and the hygienic practices followed by households, which can provide valuable insights into the monitoring indicators of the related SDG.

Household utilization of sanitation services is a choice of either ‘safely managed’, ‘basic’, ‘limited’, ‘unimproved’, or ‘open defecation’ on a hierarchical sanitation ladder; ranked in the order from most environment friendly and sustainable to the least one, causing pollution and health hazards defined as follows:

  • i. Safely managed: Use of improved facilities that are not shared with other households and where the excreta are safely disposed3(coded 0)

  • ii. Basic: Use of limited facilities that are not shared with other households (coded 1)

  • iii. Limited: Use of improved facilities (flushed toilet, septic tank, twin leach pit, single pit, ventilated improved pit) shared between two or more households (coded 2)

  • iv. Unimproved: Use of pit latrine without slab or platform or hanging latrines or bucket latrines (coded 3)

  • v. OD: Disposal of human feces in fields, forests, bushes, open bodies of water, beaches, or other spaces (coded 4)

These categories are statistically created from unit-level data from NSSO (2018) regarding types of sanitation facilities owned at the household level and their social use (exclusive/shared among households). Additional information on excreta disposal and treatment is crucial for construction of the complete sanitation ladder to track the progress of individual states towards achieving the SDGs. This process includes several tabulation measures (shown in Figure 1) to reflect the abovementioned definitions of each of the rungs of the ladder.
Figure 1

Construction of the sanitation ladder from NSSO data. Note: Colour code is used to identify the individual rungs of the ladder. Source: Authors' representation.

Figure 1

Construction of the sanitation ladder from NSSO data. Note: Colour code is used to identify the individual rungs of the ladder. Source: Authors' representation.

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For the second objective of the paper, it is appropriate to involve the ordered logistic function so that the different codes of the sanitation ladder can be put in order. This type of encoding permits the identification of factors that influence the choice of households passing from a higher quality sanitation facility to a lower one. Apart from the focal variable, that is, the subsidy receipt, other control variables include socioeconomic status of the household [religion, caste, monthly per capita expenditure (MPCE) quartile, highest educational qualification in the household], availability of drinking water facility, percentage of women residing in the household, and elderly and infant members of the household. For estimation, the ordered logistic model (Abrudan et al. 2020) has been used, with the following structure:
where i = (1, N) is the index of each household from the data, j = (1, 5) is the index of the values of categories of y, xi is the vector of the exogenous variables, b is the coefficient vector, and cj, j = 1, 4, are cut points of the distribution (technical coefficients of the model). Religion has three categories (Hindu, Muslim, and others), caste has four categories [general caste, Scheduled caste (SC), Scheduled tribe (ST), and Other Backward Castes (OBC)4. Six categories are created for education levels of households, namely, illiterate, below primary, primary, above primary, secondary, above secondary; the access to drinking water assumes three distinct groups, namely, source of drinking water located within dwelling, outside dwelling but inside premises, outside premises.

We calculate the magnitude of the marginal impact (predicted probability) of an independent variable on a dependent variable by estimating how much the dependent variable shifts when the independent variable is changed by a specified amount while keeping the remaining independent variables constant. Calculating predicted probabilities is important owing to the non-linear relationship between independent and dependent variables in logistic regression.

Estimation of sanitation ladder

This section first attempts to identify the nature of the categories along the sanitation ladder in each Indian state along with India's cumulative status. Owing to the existence of significant difference in sanitation choices between rural and urban areas (as identified in earlier studies), the paper discusses the state of sanitation service distribution among rural and urban households separately. Although urban areas have significantly higher share of households in the SMS category compared to rural areas as shown in Figure 2, only 37.91% of urban households actually use SMS services, which is critically low even after big public policies like SBM. The figure is far lower than that of India's neighbours such as Nepal (42%), Bhutan (63%), Myanmar (53%), and China (86%) (JMP 2021); while developed countries such as the USA, the UK, and Canada are very close to reaching the goalpost of 100% SMS. Using this, the approximate shares of the population in urban and rural India with access to safely managed toilets are estimated to be 37.7 and 5.6%, respectively.
Figure 2

Percentage share of households in different categories of the sanitation ladder in India, Rural and Urban (2018). Source: Author's calculation using NSSO 76th round (2018) data by STATA.

Figure 2

Percentage share of households in different categories of the sanitation ladder in India, Rural and Urban (2018). Source: Author's calculation using NSSO 76th round (2018) data by STATA.

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Supplementary material, Tables A1 and A2, presents the percentage distribution of each category of the sanitation ladder across the households (which sums up to 100%) of individual states, respectively, for rural and urban areas based on the NSSO 76th round data (2018). States with higher shares of households using SMS facility are considered to be closer to the SDG target 6.2.1.

It should be noted that an SMS facility is associated with safe disposal of excreta either through access to treatment plants or by behaviours of burying human excreta underground. In urban areas, access to treatment plants is available to 13.3%, while covering faeces (by pit toilets and/or underground) is followed by only 16.5% households. The corresponding figures are 4.5 and 14.5%, respectively, in rural areas (NSSO 2018). This actually proves why achieving SMS, the top-most step in the sanitation ladder, has remained elusive, despite the construction subsidy.

Supplementary material, Table A1, identifies that in more than 40% (15 of 38) of the Indian states and Union Territories (UTs), less than one-fifth of the urban households have access to SMS services, the worst performers being Tripura, Manipur, Bihar, and Chhattisgarh. However, Chandigarh seems to have achieved the sustainable sanitation tag with its 100% safely managed toilets in urban households. Other top performers in this criterion are Delhi, Haryana, Gujarat, and Punjab, where more than three-fourths (75%) of urban households enjoy the sustainable solution for sanitation. Apart from achieving higher percentage of SMS, reduction of OD to zero is another goal of the SDG. Only 10 states and UTs out of 36 have recorded zero OD, whereas, still there are some states like Odisha where percentage of urban households practising OD is as high as 24.65%.

Figure 3 clearly depicts that southwestern parts of India and Haryana, Punjab, have higher percentage of sustainable toilets. However, the central part of the country contributes towards OD in urban India (Figure 4). Unexpectedly, the only state outside north-central India with a high OD level is Tamil Nadu.
Figure 3

Quantile map of safely managed sanitation facility by Households across States & UTs in Urban India. Note: The above quantile maps are based on sorted values of safely managed sanitation percentage across the states & UTs grouped in five bins generated by Geoda software. Source: Author's calculation using NSSO 76th round and presented by Geoda software.

Figure 3

Quantile map of safely managed sanitation facility by Households across States & UTs in Urban India. Note: The above quantile maps are based on sorted values of safely managed sanitation percentage across the states & UTs grouped in five bins generated by Geoda software. Source: Author's calculation using NSSO 76th round and presented by Geoda software.

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Figure 4

Quantile map of incidence of open defecation by Households across States & UTs in Urban India. Note: The above quantile maps are based on sorted values of open defecation percentage across the states & UTs grouped in five bins generated by Geoda software. Source: Author's calculation using NSSO 76th round and presented by Geoda software.

Figure 4

Quantile map of incidence of open defecation by Households across States & UTs in Urban India. Note: The above quantile maps are based on sorted values of open defecation percentage across the states & UTs grouped in five bins generated by Geoda software. Source: Author's calculation using NSSO 76th round and presented by Geoda software.

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For rural India, the main challenge was to overcome the huge burden of OD. In spite of improvement located in earlier studies, more than one-fourth of rural households still defecated in the open per NSSO (2018). Such figures for Uttar Pradesh (48%) and Odisha (54%) presented in Supplementary material, Table A2, are disturbing. The smaller hilly states such as Nagaland, Manipur, Mizoram, Sikkim, and Himachal Pradesh have done exceptionally well. But, the coverage of SMS among rural India is terribly poor except in Chandigarh and Delhi. Approximately 90% of states and UTs have only 10% or lesser households that have an SMS facility in their rural areas.

Similarly, Figures 5 and 6 for rural India depicts that the most critical zone with high OD and less safely managed toilets is again the central part of India. Whereas northern states like Delhi, Punjab, Haryana, Chandigarh, Jammu and Kashmir, Uttarakhand perform better than southern India except Kerala, many rich states (in terms of state domestic product) like Maharashtra, Tamil Nadu, Karnataka perform worse in terms of OD. In short, the distribution of sanitation ladder is heterogeneous across states and also between rural and urban areas of individual states. The result of this section is extremely important as it serves the very first statistics of rural and urban sectors of Indian states that describes the relative position with respect to sustainability of sanitation.
Figure 5

Quantile map of safely managed sanitation facility by Households across States & UTs in Rural India. Note: The above quantile maps are based on sorted values of safely managed sanitation percentage across the states & UTs grouped in five bins generated by Geoda software. Source: Author's calculation using NSSO 76th round and presented by Geoda software.

Figure 5

Quantile map of safely managed sanitation facility by Households across States & UTs in Rural India. Note: The above quantile maps are based on sorted values of safely managed sanitation percentage across the states & UTs grouped in five bins generated by Geoda software. Source: Author's calculation using NSSO 76th round and presented by Geoda software.

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Figure 6

Quantile map of incidence of open defecation by Households across States & UTs in Rural India. Note: The above quantile maps are based on sorted values of open defecation percentage across the states & UTs grouped in five bins generated by Geoda software. Source: Author's calculation using NSSO 76th round and presented by Geoda software.

Figure 6

Quantile map of incidence of open defecation by Households across States & UTs in Rural India. Note: The above quantile maps are based on sorted values of open defecation percentage across the states & UTs grouped in five bins generated by Geoda software. Source: Author's calculation using NSSO 76th round and presented by Geoda software.

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Next, it is important to explore the socioeconomic gradients of the sanitation ladder (Table 1). First, the richest households naturally have greater safely managed toilets and least defecating in open field. Second, better educational qualification also displays a more sustainable choice of sanitation. Third, households belonging to the general caste tend to have more sustainable toilets and low OD in both rural and urban areas. In fact, OD is alarmingly high among SC households, significantly higher than the ST or OBC households.Finally, religion has an interesting differential connect to sustainable sanitation in the rural and urban sectors. While Hindu households have greater percentage of SMS services compared to households beholding to any other religions in urban areas, the opposite is true in rural sectors. However, this does not indicate that the Hindu households are in general better placed in the sanitation ladder even in urban areas. On the contrary, OD is practised the most among Hindu households, indicating serious non-homogeneity across the Hindu households, which supports the existing literature (Spears et al. 2013).

Table 1

Sanitation ladder distribution (% share) across different socioeconomic variables in India across urban and rural households, respectively

CategoriesSectorSafely managedBasicLimitedUnimprovedOpen defecation
Highest education in the household 
Illiterate Total 5.63 38.09 11.07 1.20 43.99 
Urban 20.71 36.78 17.76 1.18 23.57 
Rural 1.62 38.44 9.29 1.21 49.44 
Below primary Total 8.46 41.52 13.54 1.38 35.09 
Urban 25.43 33.67 24.35 1.23 15.32 
Rural 2.09 44.47 9.49 1.44 42.52 
Primary Total 9.68 46.51 11.8 2.04 29.96 
Urban 27.12 37.94 21.38 2.00 11.56 
Rural 3.29 49.66 8.29 2.06 36.7 
Upper primary/middle Total 10.13 52.84 10.55 2.19 24.3 
Urban 27.26 44.47 18.79 1.87 7.61 
Rural 3.73 55.97 7.47 2.31 30.53 
Secondary Total 15.19 58.25 8.58 1.38 16.6 
Urban 33.76 48.23 12.74 0.96 4.31 
Rural 5.54 63.46 6.42 1.6 22.99 
Higher secondary and above Total 27.1 58.05 6.29 0.74 7.81 
Urban 43.42 47.61 7.08 0.67 1.22 
Rural 8.06 70.23 5.37 0.83 15.51 
Monthly per capita income 
Poor Total 12.2 50.79 8.87 1.76 26.37 
Urban 24.75 49.03 13.7 1.54 10.98 
Rural 3.43 52.02 5.5 1.92 37.13 
Lower middle Total 15.9 54.44 9.13 1.53 18.99 
Urban 31.31 50.65 12.78 1.02 4.24 
Rural 4.57 57.23 6.45 1.91 29.84 
Upper middle Total 20.12 54.58 8.99 1.12 15.19 
Urban 40.29 45.67 11.26 0.86 1.92 
Rural 5.23 61.15 7.32 1.31 24.99 
Rich Total 23.56 56.8 7.29 0.85 11.5 
Urban 52.24 39.77 6.75 0.53 0.7 
Rural 7.29 66.47 7.59 1.03 17.63 
Caste 
General Total 30.24 52.91 1.24 6.61 
Urban 49.09 40.02 8.86 0.83 1.19 
Rural 8.17 67.99 9.17 1.72 12.95 
Scheduled Tribe (ST) Total 9.28 60.28 6.32 3.01 21.09 
Urban 23.45 56.87 12.9 0.86 5.92 
Rural 4.7 61.39 4.2 3.71 26.01 
Scheduled Caste (SC) Total 13.22 47.36 9.9 1.02 28.49 
Urban 31.93 41.9 14.49 1.24 10.43 
Rural 4.46 49.92 7.75 0.91 36.95 
Others Backward Class Total 15.64 56.4 8.31 0.81 18.84 
Urban 32.4 50.9 11.38 1.01 4.31 
Rural 4.56 60.03 6.28 0.68 28.45 
Religion 
Hindu Total 18.23 52.93 8.13 0.87 19.84 
Urban 39.18 44.45 11.06 0.83 4.48 
Rural 4.55 58.46 6.22 0.9 29.87 
Muslim Total 19.2 54.42 12.71 2.3 11.37 
Urban 35.19 47.88 11.76 1.59 3.58 
Rural 5.37 60.08 13.53 2.91 18.11 
Others Total 19.51 65.18 6.07 2.93 6.31 
Urban 32.53 55.34 9.2 1.93 
Rural 11.14 71.51 4.06 4.17 9.12 
Location of source of drinking water 
Within dwelling Total 31.99 54.7 4.49 0.73 8.09 
Urban 48.31 45.03 4.61 0.81 1.24 
Rural 9.77 67.87 4.32 0.62 17.42 
Outside dwelling but within premises Total 12.34 58.05 13.45 1.32 14.84 
Urban 26.34 48.5 21.08 0.55 3.53 
Rural 4.72 63.25 9.3 1.74 20.99 
Outside premises Total 7.6 50.63 8.9 1.88 30.99 
Urban 23.05 45.72 16.08 1.88 13.27 
Rural 2.8 52.15 6.67 1.88 36.5 
Receipt of toilet construction subsidy from SBM 
SBM subsidy received Total 3.97 86.76 5.41 0.93 2.93 
Urban 15.93 73.15 9.42 1.14 0.36 
Rural 2.15 88.83 4.8 0.9 3.32 
SBM subsidy not received Total 20.74 49.26 9.01 1.33 19.67 
Urban 38.86 44.84 11.06 0.95 4.28 
Rural 6.14 52.82 7.35 1.63 32.07 
CategoriesSectorSafely managedBasicLimitedUnimprovedOpen defecation
Highest education in the household 
Illiterate Total 5.63 38.09 11.07 1.20 43.99 
Urban 20.71 36.78 17.76 1.18 23.57 
Rural 1.62 38.44 9.29 1.21 49.44 
Below primary Total 8.46 41.52 13.54 1.38 35.09 
Urban 25.43 33.67 24.35 1.23 15.32 
Rural 2.09 44.47 9.49 1.44 42.52 
Primary Total 9.68 46.51 11.8 2.04 29.96 
Urban 27.12 37.94 21.38 2.00 11.56 
Rural 3.29 49.66 8.29 2.06 36.7 
Upper primary/middle Total 10.13 52.84 10.55 2.19 24.3 
Urban 27.26 44.47 18.79 1.87 7.61 
Rural 3.73 55.97 7.47 2.31 30.53 
Secondary Total 15.19 58.25 8.58 1.38 16.6 
Urban 33.76 48.23 12.74 0.96 4.31 
Rural 5.54 63.46 6.42 1.6 22.99 
Higher secondary and above Total 27.1 58.05 6.29 0.74 7.81 
Urban 43.42 47.61 7.08 0.67 1.22 
Rural 8.06 70.23 5.37 0.83 15.51 
Monthly per capita income 
Poor Total 12.2 50.79 8.87 1.76 26.37 
Urban 24.75 49.03 13.7 1.54 10.98 
Rural 3.43 52.02 5.5 1.92 37.13 
Lower middle Total 15.9 54.44 9.13 1.53 18.99 
Urban 31.31 50.65 12.78 1.02 4.24 
Rural 4.57 57.23 6.45 1.91 29.84 
Upper middle Total 20.12 54.58 8.99 1.12 15.19 
Urban 40.29 45.67 11.26 0.86 1.92 
Rural 5.23 61.15 7.32 1.31 24.99 
Rich Total 23.56 56.8 7.29 0.85 11.5 
Urban 52.24 39.77 6.75 0.53 0.7 
Rural 7.29 66.47 7.59 1.03 17.63 
Caste 
General Total 30.24 52.91 1.24 6.61 
Urban 49.09 40.02 8.86 0.83 1.19 
Rural 8.17 67.99 9.17 1.72 12.95 
Scheduled Tribe (ST) Total 9.28 60.28 6.32 3.01 21.09 
Urban 23.45 56.87 12.9 0.86 5.92 
Rural 4.7 61.39 4.2 3.71 26.01 
Scheduled Caste (SC) Total 13.22 47.36 9.9 1.02 28.49 
Urban 31.93 41.9 14.49 1.24 10.43 
Rural 4.46 49.92 7.75 0.91 36.95 
Others Backward Class Total 15.64 56.4 8.31 0.81 18.84 
Urban 32.4 50.9 11.38 1.01 4.31 
Rural 4.56 60.03 6.28 0.68 28.45 
Religion 
Hindu Total 18.23 52.93 8.13 0.87 19.84 
Urban 39.18 44.45 11.06 0.83 4.48 
Rural 4.55 58.46 6.22 0.9 29.87 
Muslim Total 19.2 54.42 12.71 2.3 11.37 
Urban 35.19 47.88 11.76 1.59 3.58 
Rural 5.37 60.08 13.53 2.91 18.11 
Others Total 19.51 65.18 6.07 2.93 6.31 
Urban 32.53 55.34 9.2 1.93 
Rural 11.14 71.51 4.06 4.17 9.12 
Location of source of drinking water 
Within dwelling Total 31.99 54.7 4.49 0.73 8.09 
Urban 48.31 45.03 4.61 0.81 1.24 
Rural 9.77 67.87 4.32 0.62 17.42 
Outside dwelling but within premises Total 12.34 58.05 13.45 1.32 14.84 
Urban 26.34 48.5 21.08 0.55 3.53 
Rural 4.72 63.25 9.3 1.74 20.99 
Outside premises Total 7.6 50.63 8.9 1.88 30.99 
Urban 23.05 45.72 16.08 1.88 13.27 
Rural 2.8 52.15 6.67 1.88 36.5 
Receipt of toilet construction subsidy from SBM 
SBM subsidy received Total 3.97 86.76 5.41 0.93 2.93 
Urban 15.93 73.15 9.42 1.14 0.36 
Rural 2.15 88.83 4.8 0.9 3.32 
SBM subsidy not received Total 20.74 49.26 9.01 1.33 19.67 
Urban 38.86 44.84 11.06 0.95 4.28 
Rural 6.14 52.82 7.35 1.63 32.07 

Note: Total number of households surveyed was 1,06,838 (63,736 in rural areas and 43,102 in urban areas). Total number of households in India is estimated as 27,11,055, i.e. 271.10 million, with 178.38 million in rural and 92.72 million in urban areas. (Statement 1 and 1.1 of NSSO Report.)

Source: Author's calculation using NSSO 76th round data by STATA. Sampling weights are applied during estimation.

Sanitation benefit and sanitation ladder

Officially, the poor households are offered sanitation benefits to deal with their lack of affordability to construct toilets. Thus, with subsidies, they are supposed to overcome their economic vulnerability and have access to better sanitation facilities. In both rural and urban areas, better transition from basic to safely managed toilets have been associated with non-receipt of SBM subsidy (last rows of Table 1). From that table, we find that in India, 3.97% of recipient households of the sanitation subsidy enjoy safely managed toilets, while for the non-recipients the share is 20.74%, thus indicating that subsidy benefit cannot transform their toilets to the sustainable level. The excreta management and practice of safe disposal of excreta remains inadequate, which limits the household from climbing to the highest ladder.

Next, we move on to the ordered logistic regression results to identify whether receipt of sanitation benefits from government in 3 years preceding the survey has successfully pushed the households towards usage of sustainable sanitation. Although the percentage of people getting such a benefit is very low (only 4%) in urban India, the corresponding figure is very high in rural India (more than 90%). Hence, how far sanitation benefits from the current subsidy for the urban counterparts becomes the pertaining question, the answer to which will be clearer from the results of the nested ordered logistic regression as shown in Table 2. In Model 1, we use only the variable capturing the sanitation benefit (benefit received taken as reference). Model 2 includes household characteristics (religion and caste), while in Model 3 the highest education in the household and their income category are added as controls. In Model 4, the characteristics of the water, sanitation, and health (WASH) facilities of the households and demographic characteristics are added. Nested regression with adding control variables in phases is used to identify the robustness of the focal variable's (sanitation benefits) effects. The full regression results are added as Supplementary material, Table A3. An odds ratio (OR) value greater than 1 indicates possibility of choosing a sanitation system lower down the ladder compared to the reference category, which is safely managed toilets.

Table 2

| Results of nested ordered logistic regression analysis for urban and rural India (odds ratios) (safely managed 0, basic 1, improved 2, unimproved 3, open defecation 4)

Urban
Rural
Sanitation categories as in ladderModel 1Model 2Model 3Model 4Model 1Model 2Model 3Model 4
Sanitation benefit (Ref: received) 
Sanitation Benefit not received (Ref received) 0.623*** (0.025) 0.715*** (0.029) 0.952 (0.040) 1.015 (0.044) 3.265*** (0.069) 3.850*** (0.084) 4.392*** (0.098) 4.893*** (0.111) 
Household social characteristics (Religion, Caste) applied as controls No Yes Yes Yes No Yes Yes Yes 
Highest education and income category of household applied as controls No No Yes Yes No No Yes Yes 
WASH facilities & demographic status applied as controls No No No Yes No No No Yes 
Number of Observations 42,935 42,935 42,935 42,648 63,626 63,626 63,626 63,260 
LR χ2 136.33 1,128.73 5,229.62 7,767.10 3,398.51 7,670.10 12,951.5 15,957.3 
Prob> χ2 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 
Pseudo R2 0.001 0.019 0.053 0.080 0.025 0.057 0.096 0.119 
Urban
Rural
Sanitation categories as in ladderModel 1Model 2Model 3Model 4Model 1Model 2Model 3Model 4
Sanitation benefit (Ref: received) 
Sanitation Benefit not received (Ref received) 0.623*** (0.025) 0.715*** (0.029) 0.952 (0.040) 1.015 (0.044) 3.265*** (0.069) 3.850*** (0.084) 4.392*** (0.098) 4.893*** (0.111) 
Household social characteristics (Religion, Caste) applied as controls No Yes Yes Yes No Yes Yes Yes 
Highest education and income category of household applied as controls No No Yes Yes No No Yes Yes 
WASH facilities & demographic status applied as controls No No No Yes No No No Yes 
Number of Observations 42,935 42,935 42,935 42,648 63,626 63,626 63,626 63,260 
LR χ2 136.33 1,128.73 5,229.62 7,767.10 3,398.51 7,670.10 12,951.5 15,957.3 
Prob> χ2 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 
Pseudo R2 0.001 0.019 0.053 0.080 0.025 0.057 0.096 0.119 

Note: Odds ratio and the robust standard error (in parenthesis) are reported. The reference category of dependent variable is safely managed sanitation = 0, other categories are coded as basic = 1, limited = 2, unimproved = 3, open defecation = 4. Thus, OR value greater than 1 indicates more tendency to choose sanitation categories with higher numerical codes, i.e., of lower rungs in the sanitation ladder. OR value less than 1 stands for tendency to opt for toilets in higher rungs of the ladder.

***Significant at 1% level. OR > 1 indicates propensity to choose lower rungs along the sanitation ladder. Source: Author's calculation using NSSO 76th round data.

Although Model 1 (including only sanitation benefit as the predictor variable) and Model 2 (including social characteristics along with sanitation subsidy as control variables) find the sanitation benefit as an important predictor in determining household choice of sanitation, the impact is actually negative in a way that households without sanitation benefit tend to have higher probability of choosing better toilets from the upper rungs of the sanitation ladder than that of households with the sanitation benefit. However, in the rest of the models with added control variables, the impact of the sanitation benefit turned out insignificant stating the fact that sanitation benefit hardly plays any role in urban India to ensure future sustainability. That means, after receiving subsidy, households show no significant difference from non-receivers in accessing sustainable sanitation. This result should be taken with a pinch of salt. The insignificant difference can be interpreted as the more vulnerable households that receive subsidy can at least reach a similar position to the more privileged non-recipients. Conversely, the huge social investment of construction subsidy keeps their movement truncated below optimal level.

However, the story is completely different in case of rural India, where those households who did not receive sanitation subsidy tend to have OR values greater than 1, leading towards lower categories. In other words, the sanitation benefit significantly pushes the rural households to choosing sanitation facilities of higher rungs in the sanitation ladder (mostly basic toilets as percentage of sustainable sanitation is meagre in rural areas). Households without sanitation benefits are nearly five times more likely to have basic to lower categories of sanitation along the ladder compared to SMS. The positive effect of the sanitation benefit holds true for all the models in the rural areas, hence proving to be robust with the addition of the control variables.

Looking at the coefficients of cut points (which reflect the expected ratios of cases across the boundary values in the distribution of ordered outcomes when all Xs, i.e. predictor variables, are zero) of the full specification regression in Model 4 (reported in Supplementary material, Table A4), it is clear that compared to the urban case, the probabilistic distribution of the sanitation categories is far skewed towards the lower rungs in rural areas (for example, cut1 for urban area is −1.672, while it is −3.061 for rural areas). Thus, in rural areas although sanitation subsidy is potent to shift households towards better quality of sanitation along the ladder, the OR turned out greater than 1 in all the regression models for rural India, which means rural households are more likely to construct a comparatively poor-quality toilet along the sanitation ladder being devoid of sanitation benefit, but they tend to settle in the middle rungs, i.e. up to basic sanitation, given the meagre percentage of SMS in rural India. Thus, sanitation subsidy in rural regions provides impetus to reach up to the basic level along the sanitation ladder. However, the probability to have an SMS, that is the highest level of sanitation along the ladder, remains insignificant even after receiving sanitation benefits.

Summary:

  1. Majority of the household toilets in rural India reached the category of Basic sanitation along the sanitation ladder. Therefore, transition from basic to safely managed toilets is still missing, which was important to achieve SDG. 6.2.1

  2. In urban India, no significant differences appear between the recipients and non-recipients of the sanitation subsidy.

  3. However, in rural India the sanitation benefit turned out significant in promoting better (mostly basic) sanitation facilities for beneficiary households.

  4. Households without sanitation benefits (irrespective of income category) in rural India are more likely to construct toilets that are of lower grade along the sanitation ladder irrespective of their income profiles.

On deeper checks, the study attempts to compute predicted probabilities based on the regression results. The four panels of Figure 7 depict results vis-à-vis four control variables: namely, income groups (quartiles), distance from drinking water sources (DWdistance), social caste groups, and highest education in household. It is observed that the predicted probabilities of safely managed toilets (outcome = 0) keeping other control variables (i.e., blue line) at their means is upward rising in case of income and highest education. The predicted probabilities for safely managed toilets for all income categories lie lower than that of basic toilets (red line with Outcome 1) and. interestingly, they are very close to each other for rich households, keeping other variables constant at their sample means.
Figure 7

Adjusted predictions at means for income class (quartiles), drinking water source distance (DWdistance), social group and highest education respectively in urban households. Source: Author's calculation using NSSO 76th round data.

Figure 7

Adjusted predictions at means for income class (quartiles), drinking water source distance (DWdistance), social group and highest education respectively in urban households. Source: Author's calculation using NSSO 76th round data.

Close modal

Conversely, the general or upper caste (social group = 0) possesses the highest predicted probability of safely managed toilets (more than 0.4) compared to other social groups. Similarly, coverage of safely managed toilets is declining as the source of drinking water tends to get further away, i.e., from being within the dwelling to outside the premises.

Figure 8 narrates a very different story for rural households. The predicted probabilities for SMS toilets (i.e., outcome = 0) are significantly low (close to zero) in rural areas, as a result hardly any variation could be noticed in the coverage of SMS toilets across the different socioeconomic groups. Only if the household belongs to the highest MPCE class (quartiles = 4), and if the education level in the household crosses secondary level, does the probability of having a sustainable toilet become different from zero. Most of the rural households are likely to construct a basic toilet (red line with outcome = 1) and the probability of such toilets is rising with increase in both education and income levels. Strikingly, rural India still portrays enough preference for OD. For illiterate rural households, the probability of OD (outcome = 4 the grey line) is positioned higher than that of coverage of having improved or safe toilets, indicating that the choice of OD is widespread in rural areas. This analysis hints that SMS toilets are still not preferred by those households that are better off in terms of endowments (income, caste, education), and hence more awareness generation campaigns are called for on the importance of sanitation management skills.
Figure 8

Adjusted predictions at means for income class (quartiles), drinking distance from water source (DWdistance), social group and highest education, respectively, in Rural Households. Source: Author's calculation using NSSO 76th round data.

Figure 8

Adjusted predictions at means for income class (quartiles), drinking distance from water source (DWdistance), social group and highest education, respectively, in Rural Households. Source: Author's calculation using NSSO 76th round data.

Close modal
Since household level per capita income groups appear to play differential roles in rural and urban areas, the paper examines the inclusion of interaction terms of income quartile and sanitation receipt. Although officially only poor categories are expected to receive sanitation benefits, data identify that even in the richest quartile 16.42% households receive subsidies in rural sector. The results of the ordered logistic regression on interaction between income quartiles and sanitation benefit nullifies the role of sanitation benefits in urban India (Figure 9 and Supplementary material, Table A4) except among the most economically unprivileged households. Thus, it identifies that there is no benefit in subsidizing the middle-income class. Those who belong to the upper middle class and did not receive construction subsidy are less likely (OR being 0.784 < 1) to choose sanitation categories in the lower rungs, while the similar propensity is stronger for the rich (OR being 0.557), indicating stronger income effect for economically better-offs. In urban India, sanitation benefit incurs less remarkable results in enabling decision towards household sanitation services.
Figure 9

ORs of ordered logistic regression analysis with interaction between income quartiles and sanitation benefit (both rural and urban India) with reference category being poor and received sanitation subsidy with all control variables. Note: reference category of dependent variable is Safely Managed Sanitation = 0, other categories ranked as basic = 1, limited = 2, unimproved = 3, and open defecation = 4; ***significant at 1% level. OR > 1 means propensity to choose lower rungs along the sanitation ladder. Source: Author's calculation using NSSO 76th round data.

Figure 9

ORs of ordered logistic regression analysis with interaction between income quartiles and sanitation benefit (both rural and urban India) with reference category being poor and received sanitation subsidy with all control variables. Note: reference category of dependent variable is Safely Managed Sanitation = 0, other categories ranked as basic = 1, limited = 2, unimproved = 3, and open defecation = 4; ***significant at 1% level. OR > 1 means propensity to choose lower rungs along the sanitation ladder. Source: Author's calculation using NSSO 76th round data.

Close modal

On the contrary, rural India posits excessive dependence on the sanitation subsidy while making a choice of household toilet type, as shown in Figure 7. From the general idea of economics, it is justified to assume that the choice of sanitation service will be upgraded towards a more sustainable one with the rise in household income, which is reflected in Table 2. But, the interaction results for rural households indicate that the lower middle, upper middle, and rich households in rural India are significantly more likely to choose unsustainable toilets (ORs are 5.428, 4.246, 3.057, all being greater than 1) if they are non-recipients of the sanitation benefit compared to a poor household having sanitation benefit. Thus, a rich household cannot enjoy a better quality of sanitation if it does not enjoy the subsidy, which indicates a subsidy-dependent system in India.

Similar analysis for interaction between caste and sanitation subsidy identifies that compared to general caste households receiving a subsidy, households belonging to SC, ST, and OBC castes and not receiving subsidy have lower propensity to move to SMS in both rural and urban areas, and that too with higher values of OR per Supplementary material, Table A6. But there is no difference in the choice of SMS when they receive a subsidy (ORs are insignificant). This indicates that sanitation subsidy works similarly across castes in choosing SMS, although among non-recipients of the subsidy, the higher general caste chooses SMS more than the reserved castes.

The main findings of this paper identified till 2018 (with the latest available dataset of NSSO 76th round) state that the coverage of safely managed sustainable (SMS) toilets in India continues to be limited, even after several years of the globally biggest public programme on sanitation. The sanitation ladder is concentrated in the medium rung of basic toilet (across almost all socioeconomic categories as found in Table 1), which refers to the technologically improved and socially not shared, but environmentally unsustainable toilets. Although over time the share of households using OD fell significantly, access to sewerage and pathogen removal remained low, which is a matter of special concern for densely populated urban areas. The coverage of SMS hovers around less than 40% in urban areas, whereas it is exceptionally low (less than 5%) in rural sectors. The western states (Gujarat, Maharashtra, and Karnataka) and some northern states (Punjab and Haryana) perform better in usage of urban sustainable toilets, while the states in the central region are still lagging.

Additionally, receiving sanitation subsidy in urban areas does not ensure usage of SMS. On the contrary, the coverage of sanitation benefits among rural population is very high and the subsidy system help the households opt for more sustainable types of sanitation. Even if a household is rich, it would opt for better sanitation management only if they receive the subsidy. Thus, compared to a household belonging to the poor income class and receiving sanitation subsidy (reference category in Figure 8), a rich household without benefits opts for lower categories of toilets (the odd ratio being 3.057 > 1), more than offsetting the positive income effect.

This particular result delivers a note for policymakers to develop additional mechanisms of reducing the usage of common and shared toilets in urban slums and congested areas and improving solid waste management and personal hygiene management, moving away from just offering a sanitation subsidy. In rural India, as income of the households ceases to play the crucial role in choosing sanitation services, awareness generation and micro-management of excreta removal should be focused upon.

It may be added that depending on disbursal of subsidy for construction of an in-house toilet may be economically viable at the nascent stage of development. That is why sanitation subsidy could reduce OD, but could not automatically ensure choice of SMS. The recent status in rural sanitation coverage substantiates that the sanitation sector has already overcome that early stage of development and most of the households have access to technologically improved toilets at home. The next stage of overall development in the sanitation sector is to transcend to maturity through sustainability, which has been deserted in SBM (rural). The analysis of cut points of logistic regression identifies that although sanitation subsidy helps households climb along the sanitation ladder, they actually settle more in the middle rung, rather than reaching the highest category of SMS. The focus of SBM seems exclusively on subsidized toilet construction especially in rural India. Again, for greater challenges like sustainability, the policy of the current subsidy mechanism does not turn out as an effective tool for urban India, endangering the pathway to SDGs. The receipt of sanitation subsidy via policy intervention might be identified as a necessary pre-condition for reducing OD, but in no way is it a sufficient condition for ensuring SMS. The paper succinctly puts forward the case that only adoption of improved technology can never ensure social and environmental sustainability unless policies are redesigned for improved sustainable management and hygiene behaviours.

The authors declare there is no conflict.

1

According to the SBM Urban Mission document, construction of shared and community toilets was given priority along with solid waste management. https://www.ielrc.org/content/e1405.pdf

2

Also, third highest among all countries after Nepal (16 % pts.) and Cambodia (18 % pts.)

3

Based on the information of excreta management and place of disposal of excreta last time (recorded only for septic tank/pit/composting latrine) in the survey manual. Excreta disposed in treatment plants or buried in covered twin leach pit/single pit are considered as safe practices.

4

The scheduled castes (SC) and scheduled tribes (ST) are the most disadvantaged socioeconomic groups in India as recognized in the Indian Constitution. The terms are recognized in the Constitution of India (Article 366 (24). Often the word Dalit is synonymously used as SC, although Dalit is actually an umbrella terminology identifying oppressed castes and untouchables, while SC is a legal term. They are offered much affirmative action support. Other Backward Castes (OBC) are considered less disadvantaged than SC and ST, yet vulnerable in terms of 11 educational and socioeconomic criteria and are further subdivided into various categories based on annual income (Ramaiah 1992). The General castes are the forward castes in relation to socioeconomic indicators.

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