As part of Sustainable Development Goals, the United Nations have set targets of upgrading slums and reducing poverty in all its dimensions by 2030. Policies towards improving the living conditions of slum-dwellers require proper assessment of their standard of living as well as understanding the associated characteristics. In this paper, using slum-level primary household survey data from three largest Indian cities, we, first, assess the standard of living of slum dwellers using both monetary and non-monetary approaches and then explore how various household and spatial characteristics are consistently or differently associated with both forms of assessments. We use standard monetary indicators, but to assess non-monetary standard of living, use a counting approach framework and justify the selection of specific indicators in the context of slums. Our analysis yields some interesting observations as some characteristics are differently associated with monetary and non-monetary living standards, which should affect policy designs in slums.
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The information has been accessed in April 2016 at https://sustainabledevelopment.un.org/sdgsproposal.html.
According to the 2011 Census, the total population and the proportion of slum population of Mumbai, Delhi and Kolkata were 12.5 million and 52%, 11 million and 15.5%, and 4.5 million and 32%, respectively. See Bag et al. (2016) for further discussions about these slums’ colonial history.
The need for looking at indicators beyond income to understand and assess living standards has been widely discussed. See, for instance, Stiglitz et al. (2009) and in the Indian context Swaminathan (1995). Whether we should combine monetary and non-monetary indicators into a single SoL measure, is a subject of debate. In this paper, we are interested in comparing monetary SoL with non-monetary SoL and thus we do not pursue that route. For a novel effort to integrate monetary and non-monetary indicators into a single multidimensional measure, see Santos and Villatoro (2016).
Locally, a tenement housing settlement is referred to as Basti in Kolkata, Chawl in Mumbai, and Katra in Old Delhi; whereas a squatter settlement is referred to as Jhupri in Kolkata, Zopadpatti in Mumbai, and Jhuggi Jhopri in Delhi. For related discussion on types of slums see (Risbud 2003, pp. 2; O’Hare et al. 1998, pp. 270).
The Slum Area Improvement and Clearance Act of India (a Union Act) was brought in 1956 (accessed in April 2017; web: http://lawmin.nic.in/ld/P-ACT/1956/A1956-96.pdf). In Kolkata, the first Calcutta Thika Tenancy Act was brought in 1949, the Calcutta Slum Clearance Bill was proposed in 1957 offering subsidized flats to evictees, and the Calcutta Thika Tenancy (Acquisition and Regulation) Bill was brought in 1981 to enhance the protection status further by enabling provision of basic amenities to the dwellers. In Mumbai, the Maharashtra Slum Areas (Improvement, Clearance and Redevelopment) Act was passed in 1971, by which most tenements housing settlement constructed before 1956 were censused and declared as slums. In Delhi, the Union Act of 1956 declared the Old City areas as slum designated area, but by a recent order in 2004 these areas were de-notified to be slums ceasing tenement settlements in this area to be legally recognised as slum.
According to the local municipal corporation data of 2001, nearly 63% of all slum clusters in Mumbai were protected from eviction threats. The Delhi Urban Shelter Improvement Board (DUSIB) Act (Delhi Act 07 of 2010) although recognizes most of the squatter settlements (and maintains a list of Jhuggi Jhopri Cluster settlements), it does not necessarily confer any protection from eviction.
It is worth noting that pavement dwellers are treated differently from slum dwellers by most civic authorities, census and national surveys. While DUSIB maintains a list of daily attendees at various night shelters, it does not provide any data on pavement dwellers. Ironically, civic bodies of Kolkata and Mumbai fail on both counts. Our survey does not collect data on pavement dwellers.
The Maharashtra Slum Areas Act 1971 classified registered squatter settlements into two further categories: protected and notified (web: http://www.sra.gov.in/data/Maharashtra_Slum_Areas_Improvement_Clearance.pdf; accessed in April 2017). The Slum Rehabilitation Act (1995), passed by the state government to promote the development of slum areas and to protect slum dwellers’ rights, protects anyone from eviction who could produce a city-residency status document prior to January 1995, regardless of previously living in that slum. Dwellers in notified slums only deserve basic shelter requirements.
In Delhi, we interviewed households from Resettlement and Relocation colonies and in Mumbai we interviewed households residing in resettlement lands (during 1970s) and in buildings constructed by the Slum rehabilitation authority (SRA) since 1995. Studying these households is out of the scope for this paper. For further details on the sampling design and the entire sample collection process, see Bag et al. (2016).
We acknowledge that many pagri tenants in Mumbai may appear to claim the ownership of the house, perhaps due to the prospect of getting a house under resettlement schemes.
For a detailed discussion of historical migration pattern please refer to Bag et al. (2016).
Kolkata’s fortune changed dramatically with the shift of capital to New Delhi in 1921 and with partition in 1947, once thriving industries started to stagnate in post-independence era. However, Kolkata registered a large influx of refugee migration twice: around 1950 due to the partition and during 1970s due to civil conflict in Bangladesh. In Mumbai, World War II introduced a number of incentive schemes for industrial expansion coupled with the diversification of the manufacturing sector and the construction of the port in late 1950s.
Between 1960 and 1970, Delhi witnessed a substantial drive to evict squatter settlements as well as rehabilitate them into Resettlement colonies. After a break of close to two decades, the rehabilitation programme was resumed in late 1990s.
The six geographical regions consist of the following states. North-Central: Uttar Pradesh, Bihar, Jharkhand and Uttarakhand; North-Western: Rajasthan, Haryana, Punjab, Himachal Pradesh, Jammu and Kashmir and Delhi; Central: Madhya Pradesh, Chhattisgarh; Western: Maharashtra, Gujarat, Goa, and Daman-Diu; Southern: Tamil Nadu, Kerala, Karnataka, Andhra and Telengana; Eastern: West Bengal and Orissa. The native state of Kolkata, Mumbai and Delhi are West Bengal, Maharashtra and Delhi itself, respectively.
Our survey data show that over 98% of Delhi’s squatters are on public land and in Mumbai, about half of all houses are on public land, 5% are on private land, and 45% are on land of unknown status.
The caste composition is created irrespective of the household head’s religious affiliation. For example, an OBC (or SC/ST) family could have their religious belief in Hinduism, Islam or any other faith. It should be noted that these scheduled categories are constitutionally mandated categories that are entitled for affirmative action. However, benefits of affirmative action can be availed provided the appropriate state authorities have issued a caste certificate to the individuals. We have noticed two issues while interviewing households belonging to non-general caste categories: (a) many households do not have caste certificates despite belonging to scheduled sub-castes (44–52% for SC/STs, 57–72% for OBCs); (b) many households preferred to represent themselves belonging to the general caste category either because they were not able to get respective caste certificates from their place of origin or because they found it embarrassing to reveal their true caste category.
Income data were reported for 98.5% of households in Kolkata and 99.7% of households in Mumbai and Delhi. Income data are difficult to collect, and are likely to be subject to error (possibility of both over- and under- reporting). The figures from these surveys can be considered approximations of incomes earned by households in the month prior to the survey.
Total expenditure of households does not include the cost of clothing, water, and sanitation, the transport cost for the employed persons, cost of Cable TV connection, Mobile and Internet recharges, cost of treatment for illness and chronic diseases, and the expenditure on food consumed outside of the house.
To ensure comparability of monetary aggregates across cities and across the duration of the survey, incomes and expenditures have been adjusted for price differences using consumer price indices obtained from http://labourbureau.nic.in/indtab.html. Delhi’s price index for October 2014 (the final month and city of our survey) has been used as the base price.
When we refer to higher or lower average, we imply statistically significantly higher or lower at 95% level of significance. In order to test whether CDFs are statistically distinguishable, we compute 95% confidence interval for each pairwise difference using the Distributive Analysis Stata Package (DASP) (Araar and Duclos 2013). In majority of the cases, pairwise differences are not statistically significant throughout the support.
One may wonder why Mumbai slum dwellers encounter higher non-food expenditure and earn higher income than their counterparts in the other two cities. Regarding non-food expenditure, Mumbai slum dwellers incur much higher expenditure on house rents, electricity and cooking fuel bills, and outward remittances (refer to Tables 10 and 11 of Bag et al. (2016). Regarding income, average education levels among Mumbai slum dwellers are higher than in other two cities for every age group, irrespective of gender (Table 15 of Bag et al. 2016). Moreover, Mumbai slum dwellers have comparatively much higher participation in formal contractual jobs (30%) yielding significantly higher salary than informal jobs (Table 16 of Bag et al. 2016).
Mismatches between monetary poverty and non-monetary poverty have been documented in various studies. Across nine European countries, Whelan et al. (2004) found mismatches between income poverty and material deprivation. Using longitudinal data for Vietnam, Van Tran et al. (2015) observed that the overlap between income poverty and multidimensional poverty was even less than 50%. For further discussions on relevant studies, see Alkire et al. (2015), Ch 1.
There exist several competing multidimensional approaches, ranging from statistical techniques, such as principal component analysis, factor analysis, and structural equation models to the Fuzzy sets approach to numerous axiomatic approaches. Most of these approaches either do not distinguish well between cardinal and ordinal variables or may not be intuitive for policy purposes. See Chapter 3 of Alkire et al. (2015) for an in-depth discussion on these approaches.
The most well-known application of the counting approach is the global Multidimensional Poverty Index (Alkire and Santos 2010, 2014), which was created with the purpose of cross-country comparisons. This framework however has been adopted for poverty assessment at the national and regional level in various countries. For an application of the MPI in the Indian context, see Alkire and Seth (2015).
For an attempt to revise the well-known Multidimensional Poverty Index in the urban context, see Lucci et al. (2016).
We acknowledge that the indicator may underestimate the level of deprivation, as the questionnaire does not directly inquire if the sanitation facilities are improved or not. However, barring a few cases of no access to a facility (which is less than 2% in Kolkata and Mumbai but around 10% in Delhi), majority of slum households (67–73%) access shared facilities (these are either improved flush toilets constructed by local bodies or private charitable trusts or mobile toilet vans, as verified during surveys).
According to the Millennium Development Goals “A house is considered to provide a sufficient living area for the household members if not more than three people share the same habitable (minimum of four square meters) room.” The website http://mdgs.un.org/unsd/mdg/Metadata.aspx?IndicatorId=0&SeriesId=711 was accessed in August 2016.
Worth noting an important omission from our set of indicators is households’ access to electricity, lacking which may cause being deprived of other important facilities. However, we observed that the proportion of sampled slum dwellers not having access to any electricity is barely statistically significantly different from zero. Moreover, more than 95% of slum dwellers had access to electricity for 18–24 h. We thus decided to not include this indicator in order to avoid redundancy in estimation.
In fact, the completion of secondary education or 10 years of schooling is the first recognized education ‘degree’ one may accomplish. The Indian government is trying to improve the quality of secondary education with targets of achieving gross enrolment ratio of 100% by 2017 and universal retention rate by 2020. For further details, see GoI (2015).
The average-incidence of deprivations in all indicators is equivalent to the union approach based adjusted-headcount-ratio when all indicators are equally weighted (Alkire and Foster 2011).
We have also compared cities using unequal weights across indicators and the comparisons are robust to alternative weights.
Pair-wise association between indicators are reported in "Appendix 2" to reflect the bivariate joint distributions. None of the 11 indicators are perfectly associated with any other indicator.
For the sake of brevity, we only report the coefficients of each correlates and suppress the standard errors.
For example, the years of schooling is expected to increase monetary SoL (say, income) of household, but we refrain from using it as a correlate since this may lead to endogeneity issue in the non-monetary regression model where the left-hand-side variable includes education as an indicator.
It should be noted that of the surveyed households in Kolkata only 7% have BPL + (i.e. BPL, Annapurna or Antyodaya) cards, 84% are APL and 9% have no card. In Mumbai, 21% households have BPL + card, 70% are APL and 9% have no card. In Delhi, however, 49% households have BPL + cards, only 19% are APL and 32% have no card.
However, Marx et al. (2013) noted that in the slums of Kenya, Bangladesh, the living standard of households do not seem to be improving over time (i.e. the number years either spent in slum or the household first leaving the countryside).
In the absence of any official regional categorization for Kolkata, we create six regional divisions consisting of different boroughs and wards as follows. North-West: boroughs 1, 2 and 4; North-East: borough 3 and wards 57 and 58; South-West: boroughs 9, 14 and 13; South-East: boroughs 10, 11 and 12; West: borough 15; Central: rest of the areas.
For Delhi, regrouping 11 revenue districts as follows creates regional divisions. East: districts North-east, Shahdara, and East; Central: district Central; South: district South; South-east: district South-east; New Delhi: district New Delhi; North and West: districts South-west, West, North-West and North. It should be noted that areas in Central, South and New Delhi are either close to or are better integrated to the heart of the city, and are thus expected to be relatively well off areas. We observe slum dwellers in these areas to enjoy better economic prospects.
The city of Mumbai has six official zonal divisions by combining different wards (See, Risbud 2003, pp. 2). Inner island city: A, B, C, D and E; Outer island city: F/S, F/N, G/S and GN; Inner Western Suburbs: H/E, H/W, K/E and K/W; Outer Western Suburbs: P/N, P/S, R/S, R/C and R/N; Inner Eastern Suburbs: L, M/E, M/W; and Outer Eastern Suburbs: T, S and N.
This finding supports the findings of Mohan (1979) in the developing country contexts in general.
Our findings based on per-capita expenditures for Delhi partially supports Mitra’s (2005) findings in terms of intra-city spatial differences, female-headed households, and castes, but contradicts Mitra’s findings related to household size or child dependence (the proportion of children). We observe households with large size and higher child dependence to have lower per-capita consumption expenditure.
Mitra (2005) observed SC/ST households usually resided in poor quality houses in Delhi slums. We however do not observe SC/ST households to be usually worse off non-monetarily than general Hindu households but SC/ST households without caste certificates are non-monetarily worse off than general Hindu households.
We should also point that the households in legally protected slums are non-monetarily worse off than those whose tenure status is unavailable. Note that those slums for which the legal status is unavailable are quite diversified collection, some of which are on central government land and thus are not covered in any slum development program under the Slum Act. One important factor, commonly noticed across these slums, is that there’s hardly any deprivation in water facility indicator.
To test the robustness of our main findings in non-monetary regressions, we reconstruct the deprivation scores using other weighting schemes and for different combinations of deprivation cut-offs of indicators. Our main findings discussed above are mostly robust to these changes.
In the Inner Eastern suburb (wards L, MW and ME) area of Mumbai has relatively higher non-monetary SoL. There are two striking features of this zone. First, 30% of households are Muslim and 29% are SC/ST. This is the largest concentration of these two communities here among all zones of Mumbai. Notably, one-third of Mumbai’s general Muslim households reside here. Secondly, in this area, almost 84% of surveyed households reside in either protected or declared slums and many of these slums were created by resettling the households from various parts of the city. Almost 56% of the surveyed households reside in non-pucca houses and about 43% of households report some kind of leaking (mostly through roof).
Various state and central governments’ schemes are available at the following sources, accessed in September 2016: http://www.wbdma.gov.in/HTM/MUNI_AtaGlance.htm (tab: programmes).
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This work is part of the Nopoor Research Project supported by the European Commission (www.nopoor.eu) under Seventh Framework Programme (FP7/2007–2013) under Grant Agreement No. 290752. We are grateful to the participants of the Development Studies Association Conference held in September 2015 at Bath, UK; the NOPOOR Policy Conferences held in March 2016 at Delhi, India, in May 2016 at Accra, Ghana and in July 2016 at Mexico City, Mexico; at TERI University, Delhi, India in August 2016 and at Keele Management School, Keele University in March 2017 at Staffordshire, UK. The paper has also benefitted by the comments received from Araceli Ortega, Bharat Ramaswami, Gerardo Leyva Parra, Himanshu, Anirban Kar, Abhijit Banerji, and the anonymous referee. We also thank to Tanu Gupta for valuable research assistance, and Anish Gupta for his support.
Appendix 1: List of Sampled Slums in Kolkata, Delhi and Mumbai
|Borough no.||Slum name|
|1||Chiria More, Dumdum Road|
|Churipara, Ghosh Bagan|
|Seth Pukur Road|
|2||27, Munshi Para Lane, Canal West Road|
|93\1a, 440, Masjid Bari Street, Hatibagan|
|Krishna Ram Bose Street|
|3||1 No Dr. Panchanan Mitra Lane, Narkeldanga|
|1-6, 67-71, Surah 2nd Lane, Narkeldanga|
|15\H\6\1 8\A, 8\C 7\7 Chamru Singh Lane|
|4||1, Ramesh Dutta Street, Rabindra Sarani|
|2,3a, 3b, 3c, 3d Gas Street|
|2b, Brojo Kumar (B.K.) Seth Street|
|5||1 No Srinath Babu Lane|
|3 & 12 Gour Dey Lane|
|3,3\1,3\4,15,16,14, Beliaghata Rd|
|6||1-4, Chhatu Babu Lane|
|14 Elliot Rd, Ripon Street|
|14, Niyogi Pukur Lane|
|15 Convent Lane. 114-120 Ananda Palit Rd.|
|29\2, 51\1, Doctor (Durga Charan) Lane|
|Islam St; 1-3,29,30,47 Phool Bagan Rd.|
|7||1,2,5-14, Tiljala Road|
|12\4\7,12\4, Gobind Khatick Raod|
|13, Mahendra Chatterjee Road|
|13-14, Paymental Garden Lane|
|15-16, Gobra Road|
|17,19, 21, 105, Beck Bagan Rd.|
|17, New Tangra Road|
|2, New Tangra Road|
|28-30, Christopher Road|
|4-9, Ram Mohan Bera Lane|
|41, Pulin Khatik Road|
|6-16, Guri Para|
|Muslim Camp, 69 D.C. Dey Road|
|8||19, Ashutosh Mukherjee Road|
|39, Beltala Road. Pyara Bagan Basti|
|9||16, Ibrahim Road|
|Jayananda Mistri Lane|
|10||Chetla Rd., Mandal’s Temple|
|Dr. Deodar Rahman Road|
|Kumor Para Bustee|
|11||Atabagan, Sheikh Para|
|H.L. Sarkar Road|
|12||EM Bypass, Purba Rajdanga|
|Safui Road, Naskar Para|
|Shahid Smriti Colony|
|Uttar Purba Panchanna Gram|
|13||Agarwal Garden Road, Behala|
|Manjhi Para Road, Behala|
|Motilal Gupta Road, Behala|
|14||33, Pathak Para Road|
|Dr.A.K. Paul Road|
|Ram Narayan Mukherjee Rd|
|15||Fatehpur Village Road|
|Gazipara, Akra Road|
|Paharpur Road I|
|Paharpur Road Ii|
|Panch Para Basti|
|Revenue district||Slum name|
|Dhobi Ghat, Press Road|
|Shakoor Ki Dandi|
|East||Indira Camp Trilok Puri|
|Pandit Bismil Camp|
|North East||CPJ Block, New Seelam Pur|
|Imbira Pushtha Basti|
|New Delhi||Indira Gandhi Camp|
|Saraswati Camp RK Puram|
|Sewa Camp Vasant Vihar|
|Shaheed Arjun Das Camp|
|Shri Ram JJC|
|North||Block- EE, Ring Bund Huts|
|CD Block- Sabzi Mandi|
|Hanuman Mandir, Model Town|
|Sanjay Sudhar Samiti Camp|
|North West||Chandra Shekhar Azad Colony|
|Dhobi Ghat, Y Block, Mangolpuri|
|Kanhaiya Nagar Crossing|
|Shakti Nagar Extension|
|South East||Bhoomiheen Camp Kalkaji|
|Harijan Camp, Lodhi Road|
|Sanjay Colony Tilak Bridge|
|Shahdara||Deepak Colony Dilshad Garden|
|Rajiv Gandhi Camp|
|South||Grah Kalyan Samiti|
|Harijan Camp and Banjara Camp|
|JJC in Front Of B4/B5, Vasant Kunj|
|Jeewan Jyoti Rajeev Camp|
|Mini Subhash Camp|
|Motilal Nehru Camp Jnu|
|Sanjay Colony, Okhla|
|South West||Behind Police Station Vasant Kunj|
|East Sagarpur, Nallapar|
|Sonia Gandhi Camp|
|West||5/35 Industrial Area Kirti Nagar|
|Chara Mandi Jhakira Flyover|
|Harijan Basti, Chuna Bhatti|
|Mayapuri Railway Line|
|Natraj Cinema, Sudama Puri|
|Peeli Kothi,Hari Nagar|
|Sanjay Camp, Chuna Bhatti|
|Ward name||Slum name|
|Dr. Babasahed Ambedkar Nagar|
|Shivaji Nagar, Malabar Hills|
|Sion Koliwada (+Sra)|
|FS||Shivdi Cross Road|
|GN||Janta Naya Nagar|
|Kashinath Dhrurubhai Patra Chawl|
|GS||Mahatma Phule Nagar, Ward G/S|
|Rajiv Gandhi Transit Campus|
|Jaku Club Santacruz|
|Jawahar Nagar, Ward H/E|
|Shastri Nagar, Santacruze|
|Railwayline Service Road|
|KW||Gaon Devi Dongri (+Sra)|
|Umar Bhai Chawl, Behrambagh (+Sra)|
|L||Ambedkar Nagar, Ward-L|
|Mahatma Phule Nagar, Ward L|
|Nehru Nagar Kurla|
|Shivaji Kutir Jhoparpatti|
|Vinoba Bhave Nagar|
|ME||Borla Kamawadi Zopadpatti|
|Kamala Raman Nagar|
|Raman Mama Nagar|
|Shivaji Nagar, Chembur|
|Bhai Bhai Nagar|
|Chhatrapati Shivaji Nagar|
|Pant Nagar Ghatkopar (East)|
|Pitamaha Ramji Nagar|
|PN||Irani Wadi (+Sra)|
|Jawahar Nagar, Ward P/S|
|RC||Holy Cross Road|
|Kasturba Road, 8/9 Kaheri|
|RS||Ganesh Nagar Govt.|
|Ganesh Nagar Kandivali|
|Ganesh Nagar Pvt.|
|Ratna Bai Chawl|
|S||Ambedkar Nagar, S-Ward|
|Shree Datta Mandir|
Appendix 2: Pairwise Association Between Indicators in Kolkata, Delhi and Mumbai
The following table reports the percentage of slum dwellers deprived in each indicator as well as the percentage of slum dwellers that are deprived in every pair of indicators simultaneously, reflecting the pair-wise association between indicators’ deprivations.
|Percentage of slum dwellers simultaneously deprived in row and column indicators|
|Percentage of slum dwellers simultaneously deprived in row and column indicators|
|Percentage of slum dwellers simultaneously deprived in row and column indicators|
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Cite this article
Bag, S., Seth, S. Does It Matter How We Assess Standard of Living? Evidence from Indian Slums Comparing Monetary and Multidimensional Approaches. Soc Indic Res 140, 715–754 (2018). https://doi.org/10.1007/s11205-017-1786-y
- Multidimensional counting approach
- Slums in Indian metro cities
- Monetary well-being
- Regression analysis
- Standard of living in slums