Content of abstract This paper provides a robust multidimensional evaluation of intra -urban differences. The hypothesis that joint consumption of public goods of individuals in non slum urban India dominates those of individuals living in slums is accepted while the hypothesis that consumption of private goods of individuals in non slum urban India dominates those of individuals living in slums is rejected.Urban, poverty, Slums, India, Intra Urban Differences, Multidimensional, Dominance

Abhiroop Mukhopadhyay

S Chandrasekhar

English

Research Papers in Economics

WP-2007-008 
 
 
Multidimensions of Urban Poverty: 
Evidence from India  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
S Chandrasekhar and Abhiroop Mukhopadhyay 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Indira Gandhi Institute of Development Research, Mumbai 
June 2007   2
Multidimensions of Urban Poverty: 
Evidence from India
1 
 
S Chandrasekhar 
Indira Gandhi Institute of Development Research   
General Arun Kumar Vaidya Marg 
Goregaon (E), Mumbai-400065, INDIA 
Email: chandra@igidr.ac.in  
 
Abhiroop Mukhopadhyay 
Planning Unit, Indian Statistical Institute (Delhi Centre) 
7 SJS Marg, New Delhi-110016, INDIA 
Email: abhiroop@isid.ac.in 
 
 
Abstract 
 
Content of abstract This paper provides a robust multidimensional evaluation of intra -urban 
differences. The hypothesis that joint consumption of public goods of individuals in non slum urban 
India dominates those of individuals living in slums is accepted while the hypothesis that 
consumption of private goods of individuals in non slum urban India dominates those of individuals 
living in slums is rejected. 
 
Key words: Urban, Poverty, Slums, India, Intra Urban Differences, Multidimensional, Dominance 
 
JEL Code(s): D31, H42, I32 
                                                 
1 Mukhopadhyay received a grant facilitating his visit to IGIDR from the ‘Capacity Development Programme: 
Strengthening State Plans for Human Development’. The authors thank Nicolas Gravel, Mark Montgomery and 
participants at the Workshop on Poverty Measurement and Analysis, African Population and Health Research 
Centre, Kenya, 2007 for useful comments and discussions. The usual disclaimer applies.
   3
Multidimensions of Urban Poverty: 
Evidence from India
2 
 
S Chandrasekhar and and Abhiroop Mukhopadhyay 
 
 
1. Introduction 
 
This paper contributes to the empirical literature on robust measurement of multidimensions of 
urban poverty by taking the discussion beyond unidimensional measures like head count ratio. In 
particular, using a unique nationwide data set from India, our emphasis is on differences in well being 
across individuals living in slum and non slum urban areas.  
Understanding intra urban differences in well being is important in light of urbanization of 
poverty in developing countries (Ravallion et. al. 2007) and the growth of slum population fuelled by 
migration. It is projected that by 2025 nearly 40 percent of India would be urban. In India, between 
1983 and 2004-05, while poverty (head count ratio) declined, the total number of rural poor declined 
by 12.31 percent while the total number of urban poor increased by 13.89 percent.     
Slums are visual manifestations of urban poverty, viz. poor households deprived along multiple 
dimensions. The UN-Habitat defines a slum household as a group of individuals living under the same 
roof in an urban area lacking one or more of the following: durable housing, sufficient living space, 
easy access to safe water in sufficient amounts at an affordable price, access to adequate sanitation, 
and security of tenure that prevents forced evictions. 
In this paper we compare outcomes in slum and non slum households by using the equivalence 
established between welfare analysis and stochastic dominance (Atkinson and Bourguignon 1982). 
We first establish that if utility is defined over access to public goods like water and sanitation, 
then residents in non slum urban areas are unambiguously better off than slum dwellers. Moreover, a 
household’s utility depends not only on access to these public goods but also on its private 
consumption. We reject the hypothesis that the distribution of monthly per capita expenditure 
(MPCE) in non slum urban areas stochastically dominates that in slums. We find a similar result if we 
consider per capita area of the dwelling, a measure for sufficient living space. 
Our key finding is that we reject the dominance of outcomes in non slums over slums in the case 
of private goods (MPCE, area of dwelling) while finding dominance in the case of public goods 
(water and sanitation). This implies that when we examine the joint distribution of a household’s 
consumption of private goods and access to public goods we do not find that non slum dwellers are 
unequivocally better off than slum dwellers.  
2. Data 
 
We use a data set collected by National Sample Survey Organisation (NSSO) in 2002 covering a 
total of 41,916 households from urban areas: 6,138 slum households and squatters, 35,703 households 
from non slum urban areas and 75 households without a house (National Sample Survey Organisation 
                                                 
2 Mukhopadhyay received a grant facilitating his visit to IGIDR from the ‘Capacity Development Programme: 
Strengthening State Plans for Human Development’. The authors thank Nicolas Gravel, Mark Montgomery and 
participants at the Workshop on Poverty Measurement and Analysis, African Population and Health Research 
Centre, Kenya, 2007 for useful comments and discussions. The usual disclaimer applies.
   4
2005). This data is unique in that, unlike standard data sets that have information on rural and urban 
households, it identifies whether a household lives in the slum or in the non slum urban area. This 
allows us to analyze intra urban differences. In addition, the data has an identifier to distinguish two 
types of slums: notified and non-notified slums. Unlike non-notified slums, in the case of notified 
slums a notification has been issued by the respective municipalities, corporations, local bodies or 
development authorities. In Indian cities, the act of notification leads to improved provision of public 
goods including water and sanitation. 
The definition of a slum used by NSSO is similar in spirit to that of UN-Habitat. In this study, we 
focus on individuals from non slum urban areas, notified slums and non-notified slums. We ignore the 
squatter settlements and the homeless. Our analysis is at the all India level because the number of 
observations on slum households will be greatly reduced if we undertake the analysis at the level of 
city.  For purpose of this analysis, we focus on the twenty six states of India (we exclude the north 
east states and the union territories).  
In line with the literature on poverty measurement, our unit of analysis is the individual. We 
ascribe to each individual the MPCE of the household to which the individual belongs. We smooth the 
MPCE variable in the following way. We have 122 MPCE classes starting with Rs 0 – 50 and in 
increments of Rs 50. Each individual is assigned the midpoint consumption of his or her class. As a 
measure of sufficient living space, we use the per capita floor area (the sum of area of living room, 
area of other rooms, covered veranda and uncovered veranda divided by the household size). With 
regard to availability of public goods, we examine the drainage facilities available to the household 
and hence to the individual (no drainage, open katcha, open pucca, covered pucca and underground – 
ranked from the worst to the best), the drinking water facility (community use, common use of 
households in the building, and household’s exclusive use - ranked from the worst to the best). For 
purposes of distribution analysis we need to be able to rank these facilities. As is evident, our choice 
of variables is driven by the definition of slum proposed by UN-Habitat
3.  
3. Methods 
 
In order to compare the joint distribution of a set of K goods across individuals living in slum and 
non slum urban areas
4, we test whether the distribution in the non slum areas ( () .
A F ) dominates the 
distribution in the slums ( () .
B F ). In the theoretical literature, the equivalence between welfare 
analysis and stochastic dominance has been well established. The robust criteria used for welfare 
analysis are generalizations, to more than two attributes, of the first and second order stochastic 
dominance criteria of Atkinson and Bourguignon (1982). These are known to correspond to the 
unanimity of results of utilitarian welfare evaluations taken over a specific class of individual utility 
functions.    
We use the Union-Intersection method
5 to test for stochastic dominance of 
A F  over 
B F  (Bishop 
and Formby 1999).  
 
                                                 
3 In the absence of data on housing quality or security of tenure we do not focus on these in our analysis. 
4 This is the approach to undertake pair wise comparisons of non slum urban, notified slums and non-notified 
slums. 
5 We do not use the Intersection Union method as these tests have low power.   5
Let 
^ ^
, B A F F be the empirical distributions. We calculate
^
^ ^
)) ( ) ( (
) ( ) (
i
B
i
A
i
B
i
A
i
t F t F V
t F t F
T
−
−
= , for all  i t in 
the K dimensional grid (over all values of the K goods observed in the data) and the variance 
^
V is the 
variance estimator derived by Davidson and Duclos (2000). Then 
A F dominates 
B F iff min 
< ) ,..., ( 1 K T T α C − and max  < ) ,..., ( 1 K T T  α C , where  α C is the critical value (at significance levelα ) 
given by the Studentized Modulos Distribution. 
A necessary condition for  () .
A F  to dominate () .
B F   when we consider K attributes is that no 
region in the marginal distribution of A must be dominated by the marginal distribution of B for each 
of the K attributes.  
4. Results  
 
  The results of the pair wise comparisons are reported in Table 1. When we compare the 
univariate distributions of MPCE or per capita area for non slum urban with each type of slum, the 
minimum value of  i T  is lower than  α C − but the maximum value of  i T is greater than  α C  (Table 1). 
This implies that neither distribution in non slums dominates the corresponding distribution in either 
type of slum. In fact, at very low values of these variables, the univariate distribution of MPCE and 
per capita area in the notified slums and non-notified slums dominates the corresponding distribution 
in non slum urban areas. The domination at the lower end of the distribution implies that the joint 
distribution of MPCE, per capita area, drainage and rights to water source for individuals living in non 
slum urban areas cannot dominate those for individuals living in either type of slum. 
The significance of these findings can be gauged by the fact that if one used a fixed poverty line 
for the year 2002, the only conclusion one can arrive at is that the head count ratio is lower at 20.66 
percent in the non slums compared to 34.23 (40.62) percent in the notified (non-notified) slums
6 . The 
presence of individuals living way below the poverty line in the non slum urban areas, a fact that gets 
shrouded by looking at just the head count ratio, leads to the non dominance result. In similar vein, 
despite the fact that the average per capita area of dwellings is higher (131.61 sq meters) in the non 
slums than in either the notified slums (70.15 sq meters) or the notified slums (67.44 sq meters) we do 
not find that distribution of per capita area in non slum urban dominates either notified or non-notified 
slums. 
The joint distribution of drainage facilities and rights to water source in the non slum urban areas 
does dominate the corresponding distributions in notified slums and non-notified slums. This 
corroborates the observation that the provision of water and sanitation services lags in the slums. Our 
findings imply the need for a concerted focus in improving such amenities as part of slum 
development and upgradation programmes. 
We accept the hypothesis that the univariate distribution of MPCE and per capita area of dwelling 
in notified slums dominates the distribution in non-notified slums. Further, the joint distribution of 
                                                 
6 Using an all India poverty line of Rs 496.36 for the year 2002, we estimate that 22.40 percent of people are 
poor in urban India. The poverty line was calculated by taking the simple average of the official poverty line for 
urban India for 1999-00 and 2004-05. Since this data is not the quinquennial consumption schedule, our 
estimate of urban poverty is different from the official estimates; it is lower by around 3 percent.     6
drainage facilities and rights to water source in the notified slums dominates the corresponding 
distributions in non-notified slums. This result can be explained by the fact that the act of notification 
leads to a greater share of funds under the development programs going to the notified slums thereby 
leading to improved living conditions. 
Further, we find that the joint distribution over consumption of public and private goods, viz. 
MPCE, per capita area, drainage and rights to water source of individuals living in notified slums 
dominates the corresponding distribution in non-notified slums. This result implies that individuals 
living in notified slums are unequivocally better off than dwellers in non-notified slums. 
5. Conclusion 
 
An understanding of intra urban differences is important given that the urban poor are not 
necessarily spatially concentrated. An examination of the joint distribution of a household’s 
consumption of private goods and access to public goods reveals that non slum dwellers are not 
unequivocally better off than slum dwellers.  
Reference 
 
Atkinson, A. B. and F. Bourguignon, 1982, The Comparison of Multi- dimensioned Distribution of 
Economic Status, Review of Economic Studies 49, 183-201. 
Bishop J. A. and J.P. Formby, 1999, Tests of Significance for Lorenz Partial Orders, in: J. Silber, ed., 
Handbook of Income Inequality Measurement, (Kluwer Academic Press, London), 315-339. 
Davidson, R., and J.-Y. Duclos, 2000, Statistical Inference for Stochastic Dominance and for the 
Measurement of Poverty and Inequality, Econometrica 68(6),1435–65. 
National Sample Survey Organisation, 2005, Housing Condition in India, Report No. 489 
Ravallion Martin et. al., 2007, New Evidence on the Urbanization of Global Poverty, World Bank 
Policy Research Working Paper 4199 
 
 
 
 
 
 
   7
 
 
Table 1: Results of First Order Stochastic Dominance Tests  
Null Hypothesis  Minimum Maximum 
Critical 
Value  
() α C  
Result 
Monthly Per Capita Expenditure        
Non Slum Urban Dominates Notified Slum  -90.11  15.64  3.93  Reject 
Non Slum Urban Dominates Non-notified Slum  -105.18  10.10  3.93  Reject 
Notified Slum Dominates Non-notified Slum  -16.31  -0.52  3.87  Accept
        
Per Capita Area of Dwelling        
Non Slum Urban Dominates Notified Slum  -98.28  5.49  4.35  Reject 
Non Slum Urban Dominates Non-notified Slum  -106.12  6.24  4.35  Reject 
Notified Slum Dominates Non-notified Slum  -21.03  4.05  4.34  Accept
        
Drainage and Access to Water Source        
Non Slum Urban Dominates Notified Slum  -84.90  -2.54  3.52  Accept
Non Slum Urban Dominates Non-notified Slum  -107.81  -2.54  3.52  Accept
Notified Slum Dominates Non-notified Slum  -38.46  -16.67  3.52  Accept
        
MPCE, Per Capita Area of Dwelling, Drainage 
and Access to Water Source        
Notified Slum Dominates Non-notified Slum  -38.8  5.15 5.49  Accept
        
 
  
 

Multidimensions of Urban Poverty - Evidence from India

Abstract

Similar works

Full text

Available Versions

Research Papers in Economics