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This project is funded by the European Union under the 7th Research Framework Programme (theme SSH) Grant agreement nr 290752. The views expressed in this press release do not necessarily reflect the views of the European Commission.
Working Paper n°5
Multi-dimensional poverty index- a state level
analysis of India
Apara Banerjee, Basudeb Chaudhuri, Edouard Montier,
Ahana Roy
CNRS (India)
Enhancing Knowledge for Renewed Policies against Poverty1 MULTI-DIMENSIONAL POVERTY INDEX- A STATE LEVEL
ANALYSIS OF INDIA
Basudeb Chaudhuri1 , Namrata Gulati2, Apara Banerjee3 ,Ahana Roy3 ,Imdadul Halder3 and Safayet Karim3 , Paul Vertier4
ABSTRACT: The concept of poverty, over the years, has been analysed only in a
uni-dimensional way based on income and consumption but this fails to give a real picture of the problem. Therefore in recent time many authors have put forward the idea of analysing poverty in a multidimensional way taking into consideration various aspects like well-being, employment, nutrition, health, education, etc. This article is an extension of Alkire and Santos’s 2008 work on Measuring Multidimensional Poverty in India. Using their methodology for calculating Multidimensional Poverty Index this study takes into account different variables of Standard of Living, Health and Education and calculates MPI for India. For the calculation three rounds of National Family and Health Survey data has been used and an index has been formulated for each state of India. Poverty disparity existing between rural and urban areas of the country has been highlighted along with intra-urban imbalances. Further the paper tries to link the overall multi-dimensional poverty scenario in India with that of Female multi-dimensional deprivation. Finally the study tries to compare the results of MPI calculated with other published reports and datasets and find out the reasons for existing discrepancies.
CHANGING CONCEPTS OF POVERTY:
Poverty can be defined as a state or condition in which a person or community lacks the financial resources and essentials to enjoy a minimum standard of life and well-being that's considered acceptable in society. The definition and methods of measuring poverty differs from country to country.Since 1971, the Planning Commission has based its classification of poverty on the cost of calorie consumption in rural and urban India. In 1990 Human Development Index (HDI) was
1
Department of Economics, University of Caen & CNRS. E-mail: [email protected]
2Faculty of Economics, South Asian University, New Delhi. E-mail: [email protected]
3Centre de Sciences Humaines, New Delhi, E-mail :[email protected].
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created as a summary composite index that measures a country's average achievements in three basic aspects of human development: health, knowledge, and income.
But it is widely accepted that poverty is a multifaceted phenomenon. Although inability to consume certain basic commodities generates from the root cause of shortfall in income, but sometime income may not be translated into basic needs (Sen 1980). But poverty is typically associated with deprivation in other aspects like health, education social status and political power, which are harder to price. In other word, market may be missing or imperfect to measure the price of the need, or prices need revision to adjust the imperfection of the market. Recent advances in multidimensional poverty analysis seek to marriage between Sen’s (1993) view of poverty as capability deprivation and Atkinson’s (2003) view of counting measure of deprivation.Researchers and economists started emphasizing on multidimensional aspect of poverty and in 2000 the concept gained importance (Tsui 2002 ; Bourguinon and Chakravarty 2003).. Multidimensional Poverty Index (MPI) was designed to portray both incidence and intensity of deprivation as a result takes care of the shortcomings of measuring poverty only on the basis of income and consumption level.
ADVANTAGES OF MPI OVER HDI:
The MPI has also advantages over the HDI. The HDI is a macro phenomenon which measure wellbeing at country level. It uses country averages to reflect aggregate deprivations in health, education, and standard of living. Although it can be disaggregated at micro group or regions such as district level or state level but the main problem is that it relies on macro figures. These are aggregated in a manner such that the observations directly are not used to calculate the index. It could not identify specific individuals, households or larger groups of people as jointly deprived. But the MPI uses household-level data, which is then aggregated to country level. The MPI addresses the poverty situation by capturing how many people experience overlapping deprivations (prevalence) and how many deprivations they face on average (intensity). From the way it is constructed, available information can be used efficiently and loss of information is minimised. The MPI can be broken down by indicator to show how the composition of multidimensional poverty changes for different regions, ethnic groups, etc.with useful implications for policy.
3 THEORETICAL REVIEW:
The concept of Multi-dimensional poverty emerged only in the last few decades and has been dealt in various literatures.
After publication of seminal paper by Tsui(2002), the approach gained importance. This approach for poverty measurement is important because a state may be progressed economically but it may have low level of attainment in some other aspects like health, education or gender parameters. Mehta(2002) tried to identify the chronic poverty at the district level by using multi-dimensional indicators that reflect persistent deprivation such as illiteracy, infant mortality, low level of agricultural productivity and poor infrastructure.
In BPL methodology, implemented by Indian Government in 2002, rural households lying below poverty line were determined taking into account thirteen item questionnaire. Aspects such as ownership of assets, nutrition, women empowerment, access to sanitation and others were considered. Thus Alkire and Seth (2008) developed an Index of Deprivation through the multi-dimensional approach using this BPL 2002 methodology and NFHS data and compared the result with income poverty.Oxford Poverty & Human Development Initiative (OPHI) used MPI (Alkire and Santos 2010) to analysed poverty situation of 104 countries which has a combined population of 5.2 billion (78 per cent of the world total). It was found that half of the world’s poor live in South Asia (51 per cent or 844 million people) and over one quarter in Africa (28 per cent or 458 million). Within South Asia Nepal constitutes for the highest MPI. 65% of population of the country is MPI poor followed by Bangladesh (58%), India (55%) and Pakistan (51%). In Bangladesh, India and Nepal deprivation in Standard of Living was found to be the highest contributor of poverty.
Following Alkire Foster’s Adjusted Headcount Ratio methodology (2007), Gravel and Mukhopadhyay(2007) in their paper provides a multidimensional normative evaluation of the growth episode that India has experienced in the last 15 years. Specifically, the paper compares the evolution, between 1987, 1995 and 2002, of the distribution of several individual attributes on the basis of dominance criteria.
Different national level data sets have been used by various economists to develop multi-dimensional indices. National Sample Survey (NSS) and National Family Health Survey (NFHS)
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datasets has been used by Ranjan and Mishra(2010). They considered a wider range of deprivation dimensions to provide a comprehensive and wide ranging assessment of changes to living standards in India during the period, 1992/93-2004/5.This covers the reforms and the immediate post reforms time periods. The study is conducted both at regionally disaggregated levels and by socio economic groups.NFHS3 data set has also been used by Mohanty (2011) to examine the linkages of multi-dimensional poverty and child survival in India.
Sandip Sarkar (2012)in his paper tried to develop Multi-dimensional poverty index taking into consideration eight indicators such as Highest Educational attainment in households, Mean per capita expenditure, protein, calorie, employment, land, electricity and cooking fuel. Considering all the indicators he calculated Multidimensional Poverty Index and analyzed the poverty situation in rural India comparing rural NSSO quinquennial rounds. There were two methods of defining poverty line as proposed by Task Force. One corresponds to minimum calorie requirements and another was obtained using the Consumer Price Index for Agricultural
Labour of Rural India. Sarkar merged these two methods by considering recent (for 61st round)
Tendulkar Committee report poverty line and Consumer Price Index for agricultural labour. These methods of poverty measurement by Indian Government has been criticised by many. It is believed that Indian government emphasized growth over poverty removal. Planning Commission have reduced the Tendulkar Committee poverty line which resulted in showing accelerating reduction in poverty but in reality it was not reduced. Panagariya(2012) opined that growth alone cannot help in poverty reduction and therefore health and education needs attention. It is also necessary to uplift the socially disadvantaged and backward people and thus they concluded that poverty reduction needs to be tackled with a multi-dimensional approach.
Apart from multidimensional poverty measurement approach some authors have tried to link different aspects like health or education while dealing with poverty. Though growth tends to reduce poverty, significant improvements in health status are also necessary for poverty to decrease.Mitra and Gupta (2004) analysed the linkages between economic growth, health and poverty using panel data for the Indian states. They found economic growth and health status are positively correlated and have a two-way relationship, suggesting that better health enhances growth by improving productivity, and higher growth allows better human capital formation. Health expenditure is an important determinant of both higher growth and better health status,
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and is therefore a key tool available to policy-makers. Among other exogenous variables, literacy and industrialization seem to improve both health outcomes and growth, and reduce poverty. The intricate interplay between poverty; female literacy, child malnutrition, and child mortality was studied by Singh et al (2011).This paper suggests that the geographical regions from eastern and central India were marked by substantially high level of child malnutrition , infant and under 5 mortality rates. Such geographic regions were concentrated in the states of Rajasthan, Madhya Pradesh, Uttar Pradesh, Bihar and Orissa and are likely to be underprivileged in terms of wealth, female literacy, child nutrition, or safe delivery which eventually increased the risk of child mortality in these areas.
Chaudhuri et al (2011) also emphasized on issues of lack of health care, limited access to quality schooling and opportunity cost of participation in education from the perspectives of child poverty and participation in or exclusion from school education. They used household level data for 2004–05 (NSS 61st Round) and 1993–94 (NSS 50th Round) for India and also major states for analysis. Direct proportional relation between higher education and poverty reduction has been established by Tilak (2007). Using most recent statistics, his study attempted to show that the general presumption on the weak or negligible role of secondary and higher education in development is not valid and that post elementary education is important for reduction in poverty, in improving infant mortality and life expectancy, and for economic growth.
Considering these literatures this paper tries to measure poverty in India through multi-dimensional deprivation index taking into account scenario of three dimensions- Health, Education and Standard of Living for each household in both rural and urban areas as per data available in three rounds of National Family and Health Survey records. It progresses further
from Alkire and Santos’s paper by taking into consideration even the 1st
round of NFHS data. The paper also explores the rural urban disparity in poverty scenario giving special focus to intra-urban differences. Women MPI have also been developed in this study.
METHODOLOGY:
Different countries have used the method of multidimensional poverty index, by Alkire and Santos (2008), to analyse their poverty scenario emphasizing on different contexts. For example Mexico have developed a Multidimensional Poverty Index based on Mexican labour laws and
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ILO regulations. Their index covers eight dimensions – one monetary and seven non-monetary – in the form of labour income, hours worked, social security, family care, sufficient work, protection of labour rights, respect for labour rights and job stability. Mexicans have given importance to the concept of Decent Work while calculating the index.
To measure the multidimensionalpoverty for India we have used Alkire and Foster (2011) method. Before presenting the results it is necessary to explain the method in a nutshell. Suppose
we have a data for individual with dimensions. Let be the matrix of
achievements. Each element denotes the achievement of individual in
dimension where Let be the cut off point ( or criteria) for each dimension .
Define an identification function . In particular
if
if
where and So we need to first identify the set of
individual who are deprived in dimension .
We can consider three cases here i) All s are ordinal (categorical).5 Let dimension has
order (or category) and let is the subset which denote deprivation set. In that case
if
if
ii) All s are cardinal. We defined is the cut-off point of dimension where In that
case
if
if
5
For example an indicator variable “type of cooking fuel” – we have 10 choices 1) Wood 2) Crop residues 3) Dung Cake 4) Coal/Coke/Lignite 5) Charcoal 6 ) Kerosene 7) Electricity 8) LPG 9) Biogas 10 ) Others We have defined an individual as deprived if (s)he falls under anyone of the category 1,2,3,4,5, and 10. Here and
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iii) Most importantly, some s are cardinal and some ordinal, which is more commonly
observed in survey data. We will use both to identify the poverty. Once the dimensional poverty
is defined we will have a matrix , such that
if when j is cardinal or if when j is ordinal and
= 0 otherwise.
Our next step is to aggregate those information to derive a single value which distinguish a person poor and non-poor. Two very popular process is worthwhile to mention here a) union approach b) intersection approach. According to union approach a person is said to be poor if (s) he is deprived at least one dimension i.e.
where is the ith row vector of the matrix . On the other hand a person is said to be
poor, according to intersection approach if (s)he is deprived in all dimensions i.e
And the intermediate situation we could also define poor as
In practice we use weighted mean where we attach weight for dimension . Then if
for union approach
for intersection approach
, for intermediate case.
When dimensions are equally important then is a plausible choice. For our purpose we
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Let us define an indicator vector whose element is defined by if and
otherwise. Then the column sum is nothing but the headcount of the poor. And
is the headcount ratio. Note that each entry of the vector denote the count of
deprivation of individual. Hence average deprivation share across the poor is given by
Hence the adjusted headcount ratio using multidimension is
This is precisely the measure of multidimensional poverty index using ordinal data only. For cardinal data we will have additional information on deprivation which we will not discuss it here.
The calculation for multidimensional poverty measurement in this paper has been done with the three rounds of datasets of NFHS data of years 1992-93, 1998-99 and 2005-06. For computing the index ten indicators are chosen that can be regrouped into 3 dimensions-Health, Education and Standard of Living. . And for each indicator, there is a cut-off to define whether a household is deprived in the given indicator.
If the household is deprived in an indicator, it has the score 1, and 0 if not. Each indicator is given a weighted score following the rule that each dimension is equally weighted 1/3, and each indicator within a same dimension is equally weighted. Therefore, the weighted score of the indicators of the Education and Health dimensions all equal 16.7% = (1/3 * 1/2), where as for the Living Standards dimension, it equals 5.6% = (1/3 * 1/6).
The indicators used for the index calculation are as follows-
Dimensions Indicators Variables Weights
Water Source of drinking Water
Source of non-drinking water
Time to get water and return 5.6 each
Sanitation Type of Toilet facility
9 Standard of
Living
household
Electricity Households having electricity or
not
5.6
Assets Has radio
Has television Has refrigerator Has bicycle Has motorcycle/bike Has car Has telephone Has mobile phone
5.6 each
Main Floor
Material
Type of floor material 5.6
Cooking Fuel Type of cooking fuel 5.6
Health
Nutrition Body Mass Index women
Body Mass Index Men Body Mass Index Child
16.7 each
Mortality Number of Daughters Died
Number of sons died Child is still alive
16.7 each
Education
School Attendance
School Attendance Status 16.7
Years of
Schooling
Years of schooling 16.7
For each household, one must observe whether there is deprivation for each indicator, in which case the household is given a weighted score for this indicator. Then by summing the weighted scores for which the household is deprived, if the sum exceeds 30% then it is considered multi-dimensionally poor.
The multi-dimensional poverty index has been calculated and analysed household wise which has been aggregated to the state level. Finally the state level MPI has been ranked and mapped accordingly for the three quinquennial years showing the change in poverty scenario in India over the years. The percentage contribution of each indicator to the overall MPI has been also computed to analyse their impact on overall deprivation. The formula used is as follows-
Contribution of Indicator to MPI =
10
Where CH = Censored Headcount Ratio (CH has been calculated by adding up the number of poor households deprived in a particular indicator and then dividing by the total number of households surveyed)W = Weights given to each indicators
Further the poverty analysis has been carried on for the rural and urban areas in order to find out the existing rural urban differences in deprivation. Women poverty has also been calculated across the states and correlated with overall multi-dimensional poverty and its different dimensions through linear regression analysis. The indicators taken are as follows-
Indicators for measuring women deprivation:
Dimensions Indicators Weights
Standard of Living
1)Source of Drinking water 2)Type of Toilet facilities
3)Frequency of watching television and listening to radio
4)Place of delivery
Weight= 8.3 = (1/3*1/4)
Health 1)BMI of women
2)Number of Daughters Died
3)Type of contraceptive method used 4)Knows about contraceptive method
Weight= 8.3 (1/3* 1/4)
Education 1)Women’s highest education level
2)Education in single years
Weight= 16.7 (1/3* 1/2)
The indicators are taken from Individual recode of NFHS dataset as it has been specified for the women.
RESPONSE TO WEAKNESS OF MPI:
One of the most appealing properties of MPI is that it is easy to calculate. However this simplicity of its approach gives birth to several methodological weaknesses. MPI has been criticised mainly on the following aspects:
1. It is said that MPIsimply counts the number of items lacked by households. As a result of this no correlation exists between the indicators chosen.
2. Moreover questions are raised on the procedure for selection of indicators. It varies giving different indices
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3. Some point out that MPI cannot capture inequality. It cannot differentiate households which are the poorest from the slightly better offs. As a result it is difficult to identify the neediest section of the society.
4. The basis of choosing the cut off as 30% is arbitrary. As a result change in cut offs would give different results.
But these arguments against MPI calculation method can be contested. The assumption that there is no correlation between the indicators chosen for MPI calculation is not true. In fact they are very much correlated. For example proper sanitation and safe drinking water are related to health as well as education indicators. Secondly the flexibility in choosing the indicators should not be termed as a drawback. It is actually useful at the country level where measurement decisions can be made locally to embody prevailing norms of what it means to be poor.The calibration choices should depend upon the purpose of the measure, such as the space in which poverty is evaluated. Thirdly for the argument that MPI cannot identify the neediest part of the society, it can be answered that a household is counted as poor if it is deprived in over 30 percent of the 10 indicators. It indicates that a person who is deprived in 75 percent of indicators is clearly worse off than someone who is deprived in 45 percent of indicators. On the basis of this demarcation, researchers can easily identify the worst of section of the society. Alkire’s response to the questions raised against the choice of cut off as 30% is that the poverty cut off was determined comparing the poverty estimates between all possible pairs of countries. He used poverty cutoff of 20%, 30% and 40%. It has been found 95% of pair wise comparisons, one country has higher (lower) poverty than the other regardless of the poverty cutoff. These results suggest that the particular cutoff of 30% as used by MPI is not a critical choice that dramatically affects results. Moreover for any measure of poverty a question can be raised as to what could be ideal cut off of poverty line. Is it $ 1 a day or $1.5 a day or $ 2 a day per person? This is also true for calorie measure of poverty. One could raise question whether 2400 kcal or 2200 kcal is required for subsistence. Not only that how about other nutrients vitamins or minerals? Should we incorporate these too, or should we believe on calorie only? These are debatable questions. There exist always a trade off in any methodology. The proponent of MPI believe that marginal benefit of multidimensionality is much higher to portrait the real picture of the poverty compare to marginal cost of discretion in selecting cutoffs.
12 ANALYSIS
An analysis of where the poorest live in the country of India has been carried out using this method of Multidimensional Index. Poverty scenario in India shows that 23% of the population is poor as of 2005-06. The maps below show how the poverty scenario has changed over the years in the country.The calculation has been made on the basis of data published in NFHS Round 1 (1992-93),NFHS Round 2 (1998-99) and NFHS Round 3 (2005-06).
Source: NFHS Round 1992-93, 1998-99, and 2005-06
. The scenario changed in 1998-99 with average MPI for India coming down to 25 %. MPI for all the states went below 40% except Bihar. Bihar continued to be the leading poor state with 47 % of MPI. In 2005-06 the average MPI recorded shows a further decline. It is measured to be 23 %. But Bihar still remained the leading poor state with 45% of MPI. Therefore a systematic reduction in poverty can be seen over the three rounds.
During NFHS Round 1, high poverty in states likeRajasthan, Bihar, Madhya Pradesh, Uttar Pradesh, Orissa, Andhra Pradesh, West Bengal and Assamcan be attributed to low standard of living. It is seen that in almost all these poor states around 35% of the households are deprived
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in different indicators of standard of living. They do not have access to own toilet facilities and use indigenous materials like cow dung, wood etc as cooking fuel. Moreover few states like Tamil Nadu and Uttar Pradesh do not have access to proper health facilities leading to high MPI. Above 40% of the households has reported high child mortality in these states leading to high multidimensional poverty.
NFHS Round 2, shows that Bihar accounted for the highest MPI of 47 %.
Approximately 49% of the households in the state were reported to have poor Standard of Living mainly due to lack of proper sanitation facilities and use of inferior type of cooking fuel. Body mass Index was also found to be lower than the national standard i.e. 18.5 for 15% of the surveyed. Therefore poor standards of Living along with improper access to health facilities have resulted in high multidimensional poverty. Bihar is followed by Orissa (40%) and Madhya Pradesh (38%). In both the states Nutrition remains a major issue.
The states which have scored least poverty index are Delhi (7%), Goa (12%) and Kerala (13%).The main reason for a better state of affairs in these areas is due to better education facilities and good Standard of Living status. As for Delhi it is seen that more than 90% of the surveyed households have access to proper toilet facilities and about 98% of the surveyed households are connected by electricity and use LPG connection for cooking. As for Kerala and Goa it is the health scenario along with educational facilities which help to push down the MPI Index. Only about 10-11% of the surveyed households have reported case of child mortality thus indicating a better health status. However the main reason for a low MPI in these states is due to better access to educational facilities. Around 95% of the surveyed households are attending schools.
NFHS Round 3 records that Bihar still occupies the first position in the MPI Index with
poverty as high as 45%. During the latter half of 2000, Jharkhand and Chhattisgarh became newly formed states which earlier were part of Bihar and Madhya Pradesh respectively. As they were a part of already poor states the situation did not improve during 2005-06 and these states
rank 2nd and 3rd in the list of MPI Index after Bihar. Deprivation in Living Standard and poor
health condition are the main reasons behind these states having high MPI Index. However Education index shows a positive sign as all over India deprivation in education is lower when compared with the other two dimensions. Among better fairing states we find Delhi topping the
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list with only 4.7% of the surveyed households under poverty followed by Kerala (6%) and Goa (8%).
The magnitude of decline in poverty in India over a decade is significant but not dramatic. In between NFHS 1 and NFHS 2 average poverty reduced by 8 %. However transition between NFHS 2 and NFHS 3 has been modest as poverty came down by 2-3 % on an average for the entire country.
Source:NFHS Round 1, 2 and 3
However contrary to this systematic decrease in poverty scenario in India there are few states which have recorded an increase in MPI over the years. From 1992-93 to 1998-99 north-eastern states like Nagaland and Meghalaya shows an increase by 10 % and 1% respectively but from 1998-99 to 2005-06 the situation for these states improved with decrease in poverty index. The main reason behind increase of poverty in these states is due to deterioration in the standard of living indicators. Percentage of surveyed households deprived from access to drinking water has increased in between these two rounds by 2%. During 1998-99 to 2005-06 states namely Arunachal Pradesh, Manipur and Tripura showed an increase in poverty scenario by 4.7%, 0.7 % and 5 % respectively. Deterioration in the Health indicator has resulted in increase in poverty in these states. Households below BMI standard have increased from 8% in NFHS Round 2 to 16% in NFHS Round 3 for Arunachal Pradesh and from 20 % to 24 % for Tripura. There has also
-20 -15 -10 -5 0 5 10 15 An d h ra Pra d es h Aru n ach al Pra d es h As sam Biha r Chh at is garh De lh i G o a G u ja ra t H ar yan a H im ach al Pra d es h Jamm u a n d Ka sh m ir Jh ar kh an d K arn at ak a K erala Mad h ya Prad es h Mah ara sh tra Ma n ip u r Me gh ala ya Mizo ra m N aga lan d Oris sa Pu n ja b Raj asth an Sikk im Tamil N ad u Trip u ra U tt ar Pra d es h U tt ar an ch al Wes t Be n gal Ch an ge in % States
CHANGE IN MULTIDIMENSIONAL POVERTY INDEX 1992-93 TO 2005-06
NFHS1-NFHS2 NFHS2-NFHS 3
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been a slight increase of around 1% among the surveyed households recording child mortality cases.
Highest poverty reduction has taken place in Andhra Pradesh. Between 1998-99 and 2005-06 MPI have decreased by 8%. This decrease in poverty is mainly attributed by the sharp decline in percentage of households deprived in Education. In 1998-99, 23% (approx) households were deprived in Education but during 2005-06 it was seen that the percentage has decreased to15%.
CONTRIBUTION OF DIFFERENT DIMENSIONS TO MPI:
Calculation of MPI includes what makes the people poor, deprivation of which indicator leads to their poverty. Thus to identify how people are poor-the composition of deprivations they
experience, MPI has been decomposed into its component-censored indicators. The overall picture shows that during. NFHS Round1 child mortality had the highest contribution to Multidimensional Poverty Index of India. Around 14 % surveyed households reported mortality cases during that time. However in the next two rounds India shows an improvement of health facilities which helped to the decrease of poverty. In NFHS Round 2 and 3 all indicators
2,1 2,1 1,1 2,2 2,7 2,2 1,6 1,7 1,2 2,5 3,2 2,4 2,3 2,7 1,9 1,6 2,0 4,3 5,2 14,3 3,0 2,6 3,5 2,9 7,0 2,5 1,6 0,0 5,0 10,0 15,0 20,0 25,0 30,0 35,0 40,0 NFHS 1 NFHS 2 NFHS3 Ce n sor e d H e ad co u n t R atio (i n % ) Year
Contribution of Different Dimensions to MPI INDIA, (1992-93 to 2005-06)
School Attendance Education in single years Mortality Nutrition Floor Material/Type of House Asset Cooking Fuel Electricity Sanitation Water
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considered under the three dimensions of Standard of Living, Health and Education equally contribute to the poverty index.
Source: NFHS Round 1, 2 and 3
It is seen that in 1992-93, Northern and North-eastern states record a higher MPI than the rest of the country and this is mainly due to poor health facilities in these areas. On an average more than 20% of the households do not have access to proper health facilities leading to low nutrition level and higher number of mortalities.
There has been a change in the scenario in 1998-99 across the states where access to good standard of living shows a decrease recording more than 40 % of deprived households. Improper toilet facilities and use of inferior quality of cooking fuel mainly leads to low standard of living. Scenario remains same in 2005-06 with very modest decrease in all the three dimensions. Thus the decomposition of indicators shows that how India is poor because of not only economic indicators but because of deprivation of social indicators.
17 RURAL AND URBAN POVERTY
In India poverty, over the years, has always been predominantly high in rural areas than urban areas. Poverty has reduced for both rural and urban areas from 1992-93 to 2005-06 but the rate of poverty reduction in urban areas is much faster than the rural areas.
State wise analysis of rural poverty shows that from 1992-93 to 2005-06 rural poverty has reduced for most of the states. But form 1998-99 to 2005-06 poverty in rural areas has increased in some north-eastern states like Arunachal Pradesh, Tripura and Nagaland and also in the
central state of Madhya Pradesh. During all the three rounds of NFHS highest rural poverty is found in Bihar followed by Madhya Pradesh. High rural poverty in these areas can be attributed to the fact that the central belt of the country has a huge concentration of tribal people (Tribal Sub-plan,2003). 37 31 29 23 12 10 0 20 40 60 80 NFHS 1 NFHS 2 NFHS 3 C hange in % Year
Change in Rural Urban Poverty in India (NFHS Round 1-3)
Urban Rural
Source: NFHS Round 1, 2 and 3
0 0,1 0,2 0,3 0,4 0,5 0,6 A N D H R A … A R UN A C H A L … ASSAM BIH A R C H H A TT IS G A R H D ELHI GOA G UJ A R A T H A R YA N A H IMAC H A L … JA M M U & … JH A R KH A N D KA R N A TA KA KE R A LA M A D H YA … M A H A R A SH TR A M A N IP UR M EG H A LAY A M IZ O R A M N A G A LAN D O R IS SA P UN JA B R A JA ST H A N SIK KI M TA M IL N A D U TR IP UR A UT TA R P R A D ES H UT TA R A N C H A L W ES T B EN G A L M P I STATES
Change in Rural Poverty from 1992-93 to 2005-06
NFHS 1 NFHS 2 NFHS 3
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The urban scenario on the other hand shows that there has been an overall reduction of poverty in urban areas with the exception in states like Arunachal Pradesh, Tripura, Nagaland, Punjab, and Uttar Pradesh where poverty has increased from 1998-99 to 2005-06.
Source: Datasets of NFHS 1(1992-93), NFHS 2(1998-99) and NFHS 3(2005-06)
During NFHS 1 the highest urban poverty is recorded in Orissa (32%) followed by Assam (30%). But during NHFS2 there has been a marked reduction in poverty scenario in Assam where poverty went down from 30% to 13% in 1998-99 which could be explained by favourable socio-economic characteristics in 1990s like very high level of literacy rate. Urban poverty has however increased in Tripura and Arunachal Pradesh. This highlights the fact that the process of economic and social development and state sponsored alleviation programmes are not working efficiently in these states to tackle the
problems of poor people here as also pointed out by Srivastava in 2010.
RURAL AND URBAN DIFFERENCE:
The average difference in rural and urban poverty in overall India is 18 % according to 2005-06. The difference has increased from 13 % in 1992-93 to 18 % in 2005-06 which was attributed to the fact that the situation in urban areas has been improved
0 0,05 0,1 0,15 0,2 0,25 0,3 0,35 A N D H R A … A R UN A C H A L … ASSAM BIH A R C H H A TT IS G A R H D ELHI GOA G UJ A R A T H A R YA N A H IMAC H A L … JA M M U & … JH A R KH A N D KA R N A TA KA KE R A LA M A D H YA … M A H A R A SH TR A M A N IP UR M EG H A LAY A M IZ O R A M N A G A LAN D O R IS SA P UN JA B R A JA ST H A N SIK KI M TA M IL N A D U TR IP UR A UT TA R P R A D ES H UT TA R A N C H A L W ES T B EN G A L M PI STATES
Change in Urban Poverty from 1992-93 to 2005-06
NFHS 1(1992-93) NFHS 2(1998-99) NFHS 3(2005-06)
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with urban biased policies. These policies lead to larger gaps between rural and urban areas in terms of many development indicators, such as education, health, nutrition, per capita income, and poverty. During the first round of NFHS i.e. during 1992-93 Bihar had the highest rural urban difference in poverty. In the consecutive round of 1998-99 West Bengal overtook Bihar.2005-06 saw highest rural-urban poverty difference in Rajasthan.
During 1992-93 Rural-Urban poverty difference was found to be highest in Bihar followed by Madhya Pradesh. This is because the rate in reduction of rural poverty is much slower than that of urban poverty in the state. Rural areas do not have access to proper health and educational facilities and standard of living is very low. Industrial development has taken place only in selected areas of the state like in the districts of Patna, Munger and Begusara and this has attracted further development in and around these areas leading to rural-urban disparity.
Across the years state wise analysis shows that the rural and urban difference increased in the states of Madhya Pradesh, Maharashtra, Rajasthan, West Bengal and North Eastern States from 1992-93 to 2005-06. The sharpest increase has been noted in West Bengal, where rural urban difference from 1992-93 to 2005-06 has increased by 12%. Apart from this NFHS2 in 1998-99 also records highest rural –urban poverty difference in West Bengal. This is mainly because of the capital city centric development in West Bengal. Major developments in health, social and economic sectors have taken place revolving the main urban and capital city i.e. Kolkata and in the adjacent areas of Howrah. The rural areas have very poor education facilities. As a result of this uni-polar development rural-urban difference is high in the state with 72% of the population living in rural areas.
The second in list with high rural urban difference is Rajasthan where the difference between rural and urban poverty increased by 11% in between 1992-93 to 2005-06.Rajasthan also topped the list of states with highest rural-urban disparity during 2005-06. Though over the years there has been a visible improvement in the poverty scenario of Rajasthan but there has been a regional dimension to this development pattern. According to Human Development Update 2008 – Rajasthan, slow progress in social, economic and health indicators have been noticed in the northern and a few eastern districts and most urban areas of the state but the rural areas in southern districts and some eastern areas lag behind due to low literacy level and high infant mortality rate. This has led to an increase in rural urban difference in the state. High rural urban
20
difference in Maharashtra and Gujarat is mainly due to regional imbalance in development (Kumar et.al. 2008). High industrialisation in these states has led to development of social, economic and health infrastructure in and around the urban areas while the rural areas continue to lag behind.
A low variation in rural-urban poverty has been registered in Kerala and Delhi over all the three rounds of NFHS. Reason for low regional disparity in Kerala can be attributed to the well-equipped health facilities provided across the state and also to the high literacy level. Rural Urban poverty does not differ much as the state has followed a balanced development mechanism. As for Delhi major part of the state falls under the urban area and as a result the rural areas of Delhi also have access to better health and education facilities along with a high standard of living. This is one of the major reasons for low rural-urban poverty difference in Delhi.
INTRA-URBAN DISPARITY IN POVERTY SCENARIO:
Urban India has recorded a deprivation index of 24 % in the year 2005-06. This paper studies the intra-urban differences in poverty
taking into consideration NFHS classification of urban areasinto capital city, small city and town. In NFHS cities and towns with more than 5million population is classified as Mega City.
Large City are the ones which have population
between 1 to 5 million. Small City have been termed for those whose population varies between 1 lakh to 1 million. Town have less than 1 lakh population. Calculation shows that towns are the largest contributors to urban poverty with 48 % of MPI.
The state wise analysis for NFHS 1 shows that mostly the poverty is concentrated in the town area i.e. the suburbs of the large cities. The main reason behind this is inability of economic reforms to
22,3 29,6 48,1
Intra-Urban MPI
2005-06
capital/ large city small city town21
create jobs in small and medium towns as well as rural areas. Initial development of urban areas
had attracted poor people from the rural areas. The graph below shows how poverty in towns is
dominating over small cities and large cities.
Source: NFHS Round 1
During NFHS 2 there has been an overall decrease in the urban poverty scenario from 53 % in 1992-93 to 30 % in 1998-99. But the situation remained same with towns dominating in poverty but the poverty in capital cities showed an increase for some states like Goa and some north eastern states like Nagaland, Manipur, Meghalaya, Mizoram where in 1992-93 capital cities shows no poverty but in 1998-99 it shows 3% to 5 %of MPI. This is mainly because of the growth of population in small cities which leads them to fall in the category of large cities.
0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 0,9 1 an d h ra … as sam bihar goa gu ja ra t h ar yan a h im ach al … ja m m u ka rn at ak a kerala m ad h ya … m ah ar asht ra m an ip u r m egh ala ya m izo ra m n aga lan d o ris sa p u n ja b ra ja sth an ta m il n ad u w es t b en gal u tt ar … n ew d elh i ar u n ach alp … trip u ra M PI States
Statewise Intra-urban MPI 1992-93
town small city capital, large city
22 Source :NFHS Round 2
The urban poverty is further decreased in 2005-06 from 30 % in 1998-99 to 24 % in 2005-06. During NFHS 3 it is interesting to note that urban poverty is only recorded in the towns and
0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 an d h ra p ra d es h ass am b ih ar goa gu ja ra t h ar yan a h im ach al … ja m m u ka rn at ak a kerala m ad h ya p rad es h m ah ar asht ra m an ip u r m egh ala ya m izo ra m n aga lan d o ris sa p u n ja b ra ja sth an sikk im ta m il n ad u w es t b en gal u tt ar p rad es h n ew d elh i ar u n ach alp ra d … trip u ra M PI States
Statewise Intra-Urban MPI
1998-99
town small city capital/large city Source : NFHS Round 3 0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 Jamm u a n d … H im ach al p ra d es h Pu n ja b U tt ar an ch al Ha ry an a De lh i Raj asth an U tt ar p ra d es h Biha r Sikk im Aru n ach al … N aga lan d Ma n ip u r Mizo ra m Trip u ra Me gh ala ya As sam We st b en gal Jh ar kh an d Oris sa Chh at tis garh Ma d h ya p ra d es h G u ja ra t Ma h ar asht ra An d h ra p ra d es h Karn at ak a G o a Ke ra la Tamil n ad u M PI STATESStatewise Intra-Urban MPI2005-06
town small city capital/ large city
23
except for Bihar none of the capital cities of any state have recorded significant poverty index.
MULTI-DIMENSIONAL POVERTY INDEX FOR WOMEN
The women deprivation has been calculated for all the three rounds of NFHS. The maps below show the relation between Multidimensional poverty for women with that of overall Multidimensional Poverty Index for the year 1992-93 to 2005-06
Source :NFHS Round 1, 2 and 3
Comparison for three rounds show that poverty for women in few states have gone down from NFHS 1 to NFHS 2 i.e. from the year 1992-93 to 1998-99 but it recorded a slight increase from NFHS 2(1998-99) to NFHS 3(2005-06). The states which recorded such phenomenon are Bihar, Rajasthan, Madhya Pradesh, Uttar Pradesh and some north-eastern states like Nagaland and Manipur.
Increased female poverty in Rajasthan can be attributed to a dominantly poor agrarian economy, where strict social rules define women’s marital and reproductive roles and relationships, great social pressure for producing children, and a high infant and maternal mortality rate (Kumar, McNay, Castaldo, 2008). Higher incidence of women poverty in the rest of the states is a result of gender bias existing in the socio-economic sphere. Preference for a male child over a female child leads to low nutrition, increased health issues and low literacy level among women.
24
COMPARISON OF DEPRIVATION OF NFHS 3 DATA MPI AND PLANNING COMMISSION DATA
The headcount ratio of Multi-dimensional poverty has been compared with the headcount ratio of income poverty. The Indian Planning Commission has been measuring absolute poverty in the consumption dimension. Absolute (private) consumption poverty line is taken to convey the inability of an individual or a household to afford a socially perceived normative minimal basket of basic human needs that is expected to be reflected in some normative minimal standard of living that should be assured to every individual/household.
Therefore on the basis of income and calorie intake poverty line is fixed and people unable to meet the income and calorie intake standard are listed below the poverty line. On the basis of this income poverty Planning Commission has calculated Headcount ratio. 2004-05 calculation of this Headcount Ratio shows Orissa as the poorest state with headcount ratio of 57 % followed by Bihar (54%) and Chhattisgarh (49%).
However the method of Planning Commission has been criticized over the years from the point of view that poverty is multi-dimensional and cannot be measured only on the basis of income and consumption. Alkire and Seth in their paper “Measuring Multi-dimensional Poverty in
India: A new Proposal” develops an Index of Deprivation through the multidimensional
approach using BPL 2002 methodology and NFHS data.
Following this method MP has been calculated S.Alkire and M.E.Santos have chosen ten indicators that can be regrouped into 3 dimensions: Health, Education and Living Standard, which are the same than for the Human Development Index. And for each indicator, there is a cut-off to define whether a household is deprived in the given indicator. The MPI can be decomposed into two indexes H, the headcount, which is the ratio of households multi-dimensionally poor, and A the average intensity of poverty among the poor households.
In order to compare the results of MPI with that of Planning Commission Headcount Ratio of MPI has been taken and compared with the headcount ratio of Income Poverty as calculated by Tendulkar Committee Report under Planning Commission. According to the Headcount Ratio calculation under MPI, again Bihar is the poorest state (77%) followed by Chhattisgarh (71%) and Jharkhand (70%).Both Planning Commission and Headcount Ratio of MPI yields more or
25
less similar results with Bihar, Orissa, Chhattisgarh, Jharkhand as the poor states of India in 2004-05.
NFHS 3 Planning Commission
State
Headcount
(MPI) Rank State
Headcount (Income Poverty) Rank Bihar 77.31679 1 Orissa 57.2 1 Chhatisgarh 71.2543 2 Bihar 54.4 2 Jharkhand 70.00321 3 Chhattisgarh 49.4 3
Uttar Pradesh 68.78083 4 Madhya Pradesh 48.6 4
Madhya Pradesh 68.67281 5 Jharkhand 45.3 5
Orissa 66.81078 6 Uttar Pradesh 40.9 6
Rajasthan 61.89786 7 Tripura 40.6 7
Assam 58.87935 8 Maharashtra 38.1 8
Tripura 55.91415 9 Manipur 38 9
West Bengal 55.48344 10 Assam 34.4 10
Andhra Pradesh 50.44138 11 Rajasthan 34.4 11
Meghalaya 50.27226 12 West Bengal 34.3 12
Arunachal Pradesh 49.53491 13 Karnataka 33.4 13 Nagaland 48.86626 14 Uttaranchal 32.7 14 Karnataka 45.0693 15 Gujarat 31.8 15 Manipur 42.69115 16 Arunachal Pradesh 31.1 16 Gujarat 41.24627 17 Sikkim 31.1 17
Uttaranchal 40.87414 18 Andhra Pradesh 29.9 18
Maharashtra 39.94735 19 Tamil Nadu 28.9 19
Haryana 39.25688 20 Goa 25 20
Jammu and
Kashmir 38.8595 21 Haryana 24.1 21
Tamil Nadu 37.48956 22 Himachal Pradesh 22.9 22
Sikkim 32.27421 23 Punjab 20.9 23 Himachal Pradesh 31.94728 24 Kerala 19.7 24 Punjab 24.52777 25 Meghalaya 16.1 25 Mizoram 23.56803 26 Mizoram 15.3 26 Goa 18.88621 27 Jammu and Kashmir 13.2 27 Kerala 13.93843 28 Delhi 13.1 28 Delhi 10.91721 29 Nagaland 9 29
26
Only discrepancies can be seen in case of Maharashtra and few north-eastern states like Manipur, Meghalaya, and Nagaland. According to Planning Commission report Maharashtra ranks as 8th poorest state in India which shows it as relatively poor state however according to MPI
calculations Maharashtra is in the 19th position marking it as well-off state. In case of Nagaland
and Meghalaya they stand in better position ranking, last in the deprivation of income poverty.
The states lie in the 29th position and 25th position respectively in terms of deprivation in income
poverty whereas they are in 14th and 12th position respectively in terms of deprivation of health,
education and standard of living.
This discrepancy can be explained on the basis that if only income and calorie intake is considered North Eastern states manages to remain above the basic standard but when other indicators like access to public goods like water, sanitation, health and educational facilities are considered it fails to perform well and hence ranks higher in the list of poor states under MPI calculation.
CONCLUSION:
The overall study on Multi-dimensional Poverty Index in India using Alkire and Santos method of calculation applied on data of National Family Health Survey shows that there has been an imbalanced development with poorer states continuing to be poor. Bihar remains the most deprived state over the three rounds of NFHS data. Contrary to the results of income poverty that shows a systematic decrease of poverty in all states in India, the MPI calculations shows an increase in few states like Arunachal Pradesh, Tripura and Manipur by 4.7%, 5% and 0.7 % respectively during 1992-93 and 2005-06.
Majority of the Indians are rural and this paper attempts to understand the extent of rural-urban disparity and its reasons.The number of poor people in India, according to the country’s Eleventh National Development Plan, amounts to more than 300 million. The major reason behind concentration of poor in the rural areas is urban biased policies of the government. The average difference in rural and urban poverty in overall India is 18 % according to 2005-06 and calculation shows that the rural-urban gap is increasing. Within the urban areas there is no balanced development with the towns contributing more to the poverty index over small and large cities.
27
The study also focuses on the important issue that poverty in India is not only location specific but also gender specific. Women in general are the most disadvantaged people in Indian society, though their status varies significantly according to their social and ethnic backgrounds. Over the last decade poverty index amongst women have gone down except in some North and North-eastern States of India. The social notion of a male child being superior to a female child is the root cause for deprivation of women in India.
The Multidimensional Poverty Index tries to capture poverty in India from different aspects which will help in better understanding of the root causes of poverty and finding out policy measures to curb the issue.
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ANNEXURE 1: STATEWISE MPI FOR 3 ROUNDS OF NFHS DATA
STATE NFHS 1 NFHS 2 NFHS 3 Andhra Pradesh 0.43 0.32 0.24 Arunachal Pradesh 0.38 0.21 0.25 Assam 0.48 0.33 0.30 Bihar 0.52 0.47 0.45 Chhatisgarh 0 0 0.37 Delhi 0.23 0.07 0.05 Goa 0.25 0.12 0.08 Gujarat 0.36 0.27 0.20 Haryana 0.30 0.22 0.18 Himachal Pradesh 0.30 0.16 0.13
30
Jammu and Kashmir 0.28 0.25 0.18
Jharkhand 0 0 0.40 Karnataka 0.37 0.28 0.21 Kerala 0.22 0.13 0.06 Madhya Pradesh 0.47 0.38 0.37 Maharashtra 0.32 0.25 0.19 Manipur 0.27 0.19 0.20 Meghalaya 0.34 0.35 0.26 Mizoram 0.20 0.15 0.10 Nagaland 0.14 0.24 0.24 Orissa 0.52 0.40 0.36 Punjab 0.29 0.17 0.11 Rajasthan 0.42 0.38 0.33 Sikkim 0 0.21 0.15 Tamil Nadu 0.37 0.22 0.16 Tripura 0.38 0.22 0.28 Uttar Pradesh 0.48 0.38 0.37 Uttaranchal 0 0 0.19 West Bengal 0.41 0.35 0.29
ANNEXURE 2: STATEWISE HEADCOUNT FOR 3 ROUNDS OF NFHS DATA
STATE Headcount(NFHS 1) Headcount(NFHS 2) Headcount(NFHS 3)
Andhra Pradesh 0.657 0.640 0.504 Arunachal Pradesh 0.592 0.456 0.495 Assam 0.734 0.644 0.589 Bihar 0.755 0.810 0.773 Chhatisgarh 0 0 0.713 Delhi 0.472 0.172 0.109 Goa 0.476 0.281 0.189 Gujarat 0.593 0.544 0.412 Haryana 0.533 0.478 0.393 Himachal Pradesh 0.516 0.394 0.319
Jammu and Kashmir 0.496 0.533 0.389
31 Karnataka 0.597 0.562 0.451 Kerala 0.405 0.316 0.139 Madhya Pradesh 0.713 0.717 0.687 Maharashtra 0.545 0.535 0.399 Manipur 0.475 0.421 0.427 Meghalaya 0.559 0.676 0.503 Mizoram 0.366 0.360 0.236 Nagaland 0.247 0.517 0.489 Orissa 0.750 0.740 0.668 Punjab 0.493 0.377 0.245 Rajasthan 0.645 0.718 0.619 Sikkim 0 0.454 0.323 Tamil Nadu 0.608 0.502 0.375 Tripura 0.610 0.484 0.559 Uttar Pradesh 0.724 0.723 0.688 Uttaranchal 0.000 0.000 0.409 West Bengal 0.674 0.642 0.555
ANNEXURE 3: STATEWISE RURAL URBAN MPI FOR 3 ROUNDS OF NFHS DATA
State NFHS 1(1992-93) NFHS 2(1998-99) NFHS 3(2005-06) RURAL URBAN RURAL URBAN RURAL URBAN
Andhra Pradesh 0.488302 0.251022 0.387498 0.134346 0.298539 0.115208 Arunachal Pradesh 0.394049 0.268014 0.222767 0.113201 0.287256 0.162564 Assam 0.50323 0.306452 0.356004 0.130544 0.351093 0.116956 Bihar 0.567677 0.272293 0.498808 0.230852 0.48956 0.227346 Chhatisgarh 0 0 0 0 0.439081 0.138644 Delhi 0.291567 0.221676 0.100815 0.068005 0.07714 0.045649 Gujarat 0.415076 0.269043 0.142104 0.088077 0.288766 0.077432 Goa 0.282992 0.211602 0.37203 0.140167 0.126666 0.046827 Himachal Pradesh 0.31043 0.179541 0.28173 0.088147 0.149424 0.025227 Haryana 0.325579 0.225481 0.173294 0.046102 0.227659 0.073172
32
Jharkhand 0 0 0 0 0.48975 0.121007
Jammu and Kashmir 0.307115 0.170219 0.303827 0.080654 0.234512 0.057082 Karnataka 0.439988 0.244773 0.365145 0.123769 0.287798 0.094212 Kerala 0.228453 0.192636 0.146071 0.069133 0.069501 0.03046 Meghalaya 0.374365 0.192158 0.400004 0.126314 0.326463 0.078456 Maharashtra 0.395631 0.222476 0.348214 0.128189 0.304578 0.060038 Manipur 0.302338 0.202283 0.2217 0.121901 0.234822 0.118629 Madhya Pradesh 0.534077 0.279088 0.450659 0.191458 0.453075 0.154999 Mizoram 0.239396 0.158021 0.228437 0.08313 0.181074 0.039008 Nagaland 0.15774 0.077544 0.26646 0.123231 0.27746 0.12613 Orissa 0.553951 0.319699 0.417663 0.230907 0.398788 0.160518 Punjab 0.316592 0.209951 0.22658 0.039666 0.138834 0.067193 Rajasthan 0.46029 0.260664 0.44419 0.199796 0.41553 0.106531 Sikkim 0 0 0.234468 0.074835 0.18165 0.032901 Tamil Nadu 0.433399 0.257081 0.275132 0.114254 0.215226 0.096649 Tripura 0.406399 0.274494 0.255486 0.114992 0.302295 0.148585 Uttaranchal 0 0 0 0 0.238814 0.073585 Uttar Pradesh 0.528129 0.296263 0.440221 0.173765 0.429875 0.176647 West Bengal 0.468684 0.295668 0.422368 0.135973 0.385205 0.089841
ANNEXURE 4: STATEWISE INTRA-URBAN MPI FOR NFHS ROUND 1
place of residence(NFHS 1)
state capital, large city small city town countryside
andhra pradesh 0.2231501 0.226225 0.299697 0.4883021 assam 0 0.284087 0.323386 0.5032297 bihar 0.1225905 0.194496 0.39648 0.5676767 goa 0 0 0.211602 0.2829921 gujarat 0.2575795 0.230726 0.310205 0.4150759 haryana 0 0.209481 0.249118 0.3255793
33 himachal pradesh 0.1613206 0.168235 0.205018 0.3104301 jammu 0 0.136998 0.218848 0.307115 karnataka 0.1946822 0.219961 0.322756 0.439988 kerala 0.1760278 0.206376 0.180665 0.2284534 madhya pradesh 0.2259005 0.240331 0.328264 0.534077 maharashtra 0.2168687 0.212304 0.258472 0.395631 manipur 0 0.132836 0.25298 0.3023379 meghalaya 0 0.201735 0.172424 0.3743646 mizoram 0 0.146244 0.171083 0.2393955 nagaland 0 0 0.077544 0.1577404 orissa 0 0.32807 0.314232 0.553951 punjab 0.177 0.182195 0.253515 0.3165923 rajasthan 0.18513 0.218163 0.317084 0.4602895 tamil nadu 0.2438648 0.277129 0.259397 0.4333989 west bengal 0.2764215 0.345398 0.282353 0.4686842 uttar pradesh 0.3074174 0.249328 0.363929 0.5281286 new delhi 0.2216761 0 0 0.2915667 arunachalpradesh 0 0 0.268014 0.394049 tripura 0 0.204141 0.305713 0.4063987
ANNEXURE 4: STATEWISE INTRA-URBAN MPI FOR NFHS ROUND 2
states MPI by place of residence(NFHS 2)
capital/large city small city town countryside
andhra pradesh 0.0656605 0.1260154 0.162394 0.3874984
assam 0.1283157 0.1598205 0.125808 0.3560039
bihar 0.1800933 0.159509 0.263289 0.4988077
goa 0.0354118 0 0.091099 0.1421042
34 haryana 0 0.0926152 0.079285 0.2817304 himachal pradesh 0.0325555 0 0.052442 0.1732944 jammu 0.0576173 0 0.12383 0.303827 karnataka 0.0318411 0.1165736 0.166671 0.365145 kerala 0.0629608 0.0556145 0.071781 0.1460707 madhya pradesh 0.0485918 0.1615287 0.239913 0.4506588 maharashtra 0.0894898 0.164964 0.157576 0.3482142 manipur 0.06016 0 0.178659 0.2217003 meghalaya 0.1089697 0 0.132642 0.4000041 mizoram 0.0572346 0 0.117865 0.2284366 nagaland 0.0585312 0 0.132847 0.2664604 orissa 0.2766537 0.0790558 0.247644 0.4176631 punjab 0.022766 0.0225693 0.051245 0.2265795 rajasthan 0.16495 0.1525672 0.233905 0.4441899 sikkim 0.0683504 0 0.107741 0.2344682 tamil nadu 0.0804576 0.1048464 0.134458 0.2751324 west bengal 0.092257 0.1481291 0.15277 0.4223683 uttar pradesh 0.1137142 0.1626758 0.198945 0.4402213 new delhi 0.0667831 0.1036569 0.067183 0.1008154 arunachalpradesh 0.049125 0 0.127641 0.2227668 tripura 0 0.0575567 0.151056 0.2554862
ANNEXURE 5: STATEWISE INTRA-URBAN MPI FOR NFHS ROUND 3
state MPI by place of residence
capital/ large city small city town countryside
Jammu and kashmir 0.0394769 0.0559759 0.0692478 0.2345118
Himachal pradesh 0.0147755 0.0342547 0.0221599 0.1494237
Punjab 0.0605049 0.0524462 0.1078649 0.1388342
Uttaranchal 0.047569 0.0874324 0.0827099 0.2388141
35 Delhi 0.0456676 0 0 0.0771402 Rajasthan 0.0749784 0.1096263 0.1223542 0.4155304 Uttar pradesh 0.151842 0.1479251 0.2367335 0.4298748 Bihar 0.2338033 0.182559 0.2737025 0.4895602 Sikkim 0 0 0.0329014 0.1816502 Arunachal pradesh 0 0 0.1625639 0.2872555 Nagaland 0 0 0.1261303 0.2774601 Manipur 0 0.0405909 0.1197477 0.2348218 Mizoram 0 0.0316649 0.045618 0.1810738 Tripura 0.0355 0.2848947 0.1482911 0.3022951 Meghalaya 0 0.0597943 0.082325 0.3264625 Assam 0.0749247 0 0.130654 0.3510929 West bengal 0.0509908 0.0874009 0.1270532 0.3852047 Jharkhand 0 0.0706681 0.1383854 0.4897498 Orissa 0 0.1231451 0.1972788 0.3987879 Chhattisgarh 0.2209639 0.0846404 0.1225639 0.4390812 Madhya pradesh 0.1051291 0.1321414 0.2160051 0.453075 Gujarat 0.0610444 0.0918478 0.1070071 0.2887658 Maharashtra 0.0359527 0.065611 0.1358325 0.3045779 Andhra pradesh 0.1031108 0.0538689 0.1192051 0.2985386 Karnataka 0.0393463 0.0987932 0.1648675 0.2877983 Goa 0 0 0.046827 0.1266657 Kerala 0 0.0280368 0.0324704 0.0695005 Tamil nadu 0.0513037 0.0879923 0.1179357 0.2152263
36 ANNEXURE 7: PERCENTAGE CONTRIBUTION OF INDICATORS TO MPI (NFHS 2)
state Water Sanitation Electricity Cooking Fuel Assets Type of House Health Education
andhra pradesh 2.227757 3.161977 1.757985 3.172624 2.938403 1.722053 15.84278 11.82652 assam 3.061259 2.731411 3.590824 3.808666 3.441521 3.654685 18.51119 9.042748 bihar 3.466833 3.89095 3.863199 3.487758 3.663729 3.422179 17.28783 13.18822 goa 1.405613 1.712483 0.3143544 1.583748 1.366693 0.1317295 14.32066 3.964074 gujarat 2.474116 2.462555 1.11711 2.362839 2.533368 1.358452 16.35092 7.722942 haryana 1.715611 2.457543 0.5979605 2.473108 1.719831 0.3021064 15.75885 4.64685 himachal pradesh 1.770714 2.607393 0.3906616 2.560594 2.215564 0.1088952 15.44955 4.527046 jammu 1.981835 2.434077 0.5618713 2.223759 1.92362 0.6982343 14.17907 4.227912 karnataka 2.058187 2.723261 1.588569 2.801968 2.526493 1.365566 15.81982 8.488873 kerala 1.515204 0.8935491 1.0863 2.019421 1.74625 0.6408024 11.97588 1.971871 madhya pradesh 3.015509 3.493201 1.882357 3.522257 3.069036 3.327045 18.24721 10.71897 maharashtra 1.78351 2.199754 1.137091 2.026089 2.254885 1.502338 14.85449 6.329806 manipur 1.706814 0.8095764 1.361326 2.346225 2.000737 1.598527 13.59374 3.629098 meghalaya 2.337097 2.201613 2.331452 2.907258 2.783064 2.647581 9.595767 8.922379 mizoram 1.514627 0.1957682 0.6954921 1.427047 1.720699 0.973689 9.863294 3.349218 nagaland 0.7290566 0.5494339 0.5283019 1.341887 1.278491 1.051321 4.096227 4.47434 orissa 3.310946 3.901708 3.418649 3.577839 3.36982 2.750242 18.63553 12.68537 punjab 2.569063 2.000871 0.3642702 2.17342 1.453595 0.237037 13.54504 6.205976 rajasthan 2.839091 3.197607 2.179019 3.259035 3.01779 1.858476 15.18787 10.0986 tamil nadu 2.117471 2.770609 1.600187 2.907768 2.626919 1.579286 16.59482 7.183299 west bengal 2.967664 2.715887 2.909276 1.976926 2.827037 2.745 16.37784 8.786405 uttar pradesh 3.263984 3.476918 3.132701 3.654211 3.113028 2.545906 19.49524 8.893424 new delhi 2.205276 0.7691053 0.1934185 0.3045961 1.18031 0.236062 14.46092 3.388143 arunachalpradesh 1.78897 1.182934 1.544225 3.059313 2.808741 2.872841 13.90219 10.35713 tripura 1.912555 1.55856 2.2223 3.225285 2.65496 3.195786 17.06655 6.12871 total 55.7387626 56.0987449 40.3689005 64.2036411 60.23458 42.5258398 371.0133 180.757924 All India 2.143798562 2.157644035 1.552650019 2.469370812 2.316715 1.635609223 14.26974 6.952227846