Asha Kapur Mehta

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Poverty and Chronic Poverty: An Overview Aasha Kapur Mehta Professor of Economics IIPA, New Delhi and CPRC

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Day 1 Asha Kapur Mehta, Professor of Economics, Indian Institute of Public AdministrationPoverty and Chronic Poverty in India

Transcript of Asha Kapur Mehta

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Poverty and Chronic Poverty: An Overview

Aasha Kapur Mehta

Professor of Economics

IIPA, New Delhi and CPRC

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Chronic Poverty in India

 Over the last five decades, systematic efforts

have been made to alleviate poverty through

•increasing economic growth,

•direct attacks on poverty using targeted programmes,

•land and tenancy reforms,

•participatory and empowerment based approaches and

•provision of basic minimum services.

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As a result of these efforts, the incidence of poverty has declined from

•54.9 per cent in 1973-74 to

•26 to 30 per cent in 1999-2000.

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Despite plans and poverty alleviation strategies we have:

    260 to 300 million people in poverty

  many of whom don’t get two square meals a day

  these are unacceptably high levels

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Incidence of Poverty in India – % of Population and No of People Below the

Poverty Line 1973-74 to 1999-2000

 Year % population below

the poverty line Number of poor (millions)

1973-74 54.9 321.3

1977-78 51.3 328.9

1983 44.5 322.9

1987-88 38.9 307.1

1993-94 36 320.3

1999-2000 26.1 ?? 260.2 ??

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Spatial concentration of poverty

• 7 states of India or 10 out of 28 new states account for 75% of those in poverty.

• These 7 states have had a very high proportion of their population in poverty over decades (over 35 percent till 1993-94).

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Incidence and Concentration of Income Poverty in Selected States of India

State share of India's

State Poor Popula Percentage of the Population of the state that is in poverty

1999-2000 2001 1973-74 1993-94 1999-2000

Assam 3.63 2.59 51.21 40.86 36.09 Bihar* 16.36 10.69 61.91 54.96 42.6 Madhya Prad* 11.47 7.91 61.78 42.52 37.43 Maharashtra 8.76 9.42 53.24 36.86 25.02 Orissa 6.50 3.57 66.18 48.56 47.15 Uttar Pradesh* 20.36 17 57.07 40.85 31.15 West Bengal 8.20 7.81 63.43 35.66 27.02 7 state total 75.28 58.99 All India 100.00 100.00 54.88 35.97 26.1

Source: Mehta and Shah ,World Development, March 2003

*including the newly formed states

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71.65% of India’s poor and more than half of India’s population are located in the following states.

•Uttar Pradesh (including Uttaranchal), •Bihar (including Jharkhand) and •Madhya Pradesh (including Chhatisgarh) •Maharashtra•West Bengal • Orissa•Assam•Source: Mehta and Shah CPRC-IIPA working paper 2

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These 7 states had

•50% to 66% of their population below the poverty line in 1973-74.

• 35% to 55% of their population still in poverty in 1993-94.

•47% of Orissa’s population was in poverty in 1999-2000.

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5 of these states (Bihar, Orissa, Madhya Pradesh, Assam and Uttar Pradesh) have had more than 30% of their people in poverty over several decades. 

So high poverty incidence has existed in these states over a long duration.

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•It is important to recognise that:

•the poor are a heterogeneous group

•use of the term “the poor” actually refers to “different sociological realities”.

•there is entry and exit or “mobility” for some people into and out of poverty

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•However there is a hard core that remains poor over time and extended duration poverty refers to this group.

•Extended duration poverty will be the focus of our attention in our primary research as will the question do those in extended duration poverty also suffer severe poverty and multidimensional deprivation.

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We view chronic poverty in terms of

 

     long duration

     severity and

     multidimensional deprivation.

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Panel Data AnalysisPanel Data Analysis (for over 3000 rural households) shows that there is both •substantial persistence •and substantial mobility into and out of poverty   •-  47% of the poor were chronically poor as per the NCAER national rural panel for the late 1960s. •-  52% of the poor were chronically poor based on the NCAER 1970 and 1981 national rural panel.

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NCAER Panel Data for 3139 households from 260 villages of India shows that:

  More than half (52.61%) of the households remained in poverty

  47.39% of poor households escaped from poverty. 25.74% of non poor households entered poverty.  “therefore the persistently poor are by no means a

small subset of the poor.” Source: Bhide and Mehta CPRC-IIPA working papers 6 and 15

Chronic Poverty, Exit and Entry: 1970-71 and 1981-82

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Chronic Poor, Transient Poor and Non Poor

Poor Non-Poor 1981-82 1981-82 Poor 52.61 47.39 1970-71 Non-Poor 25.74 74.26 1970-71

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Incidence of Poverty in Panel

Year % Poor % Non Poor 1970-71 48.13 51.86 1981-82 38.67 61.33

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A recent wave of the NCAER Rural Panel Data Set (1981-1998) confirms:

•substantial persistence and

•substantial mobility

•into and out of poverty

Between 1981-98, of those who were poor in rural areas, 38.6% of the households remained in poverty or were chronically poor.Source: Bhide and Mehta CPRC-IIPA working paper 28 )

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 ,

Important determinants of poverty are: Caste, Tribe and Household Demographic Composition

The probability of being chronically poor is greater for:          Casual Agricultural Labour          Landless households          Illiterate households          Larger households with more childrenSource: Bhide and Mehta CPRC-IIPA working papers 6 and 15

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Factors that drive escape from poverty are

• increased income earning opportunities –• proximity to urban areas, • improved infrastructure • initial literacy status of the household head• ownership of or access to income from

physical assets –cropland, livestock, house

Source: Bhide and Mehta CPRC-IIPA working papers 6 and 15

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Perceived reasons for decline into poverty

Shocks such as• crop failure• high health care costs • adverse market conditions• loss of assets• high interest from private money lenders• social expenses on deaths and marriages.

Entry into poverty can be prevented by policies that reduce health care related shocks or costs and high interest debt.

Source: Anirudh Krishna JHD 2004; Bhide and Mehta JHD 2004

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Who are the chronically poor

Casual agricultural labourers were the largest group Most chronically poor • were landless or near- landless• had higher dependency burden and illiteracy.• depended on wages. Therefore the chronically poor are critically dependent on

changes in wages. Many of those in Chronic long duration poverty tend to be

stuck in a low wage-high drudgery-tough job groove with little opportunity for escape.

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Low wages and drudgery maintain casual

agricultural labourers in chronic poverty

Ratnapandi is a labourer who climbs date palm trees every day to tap them for juice. (Sainath 1996)

     He works 16 hours a day      climbs date palm trees he does not own,      risks his neck,      shins up using his hands and legs and      earns as little as Rs.5 a day. These are • the toughest jobs • with the lowest pay and • the maximum danger

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State Rankings: HDI and Population below the Poverty Line Rank 1993-94 State

rank BPL Population

HDI 1991 Rank

HDI 2001 Rank

Change in HDI Rank 1991-01

1 Punjab Kerala Kerala 0 2 Andhra Pradesh Punjab Punjab 0 3 Gujarat Tamil Nadu Tamil Nadu 0 4 Haryana Maharashtra Maharashtra 0 5 Kerala Haryana Haryana 0 6 Rajasthan Gujarat Gujarat 0 7 Karnataka Karnataka Karnataka 0 8 Tamil Nadu West Bengal West Bengal 0 9 West Bengal Andhra Rajasthan +2 10 Maharashtra Assam Andhra -1 11 Uttar Pradesh Rajasthan Orissa +1 12 Assam Orissa MPradesh +1 13 M Pradesh M Pradesh U Pradesh +1 14 Orissa U Pradesh Assam -4 15 Bihar Bihar Bihar 0 Source: Planning Commission Press Release, March, 1997 and Planning Commission, National Human Development Report, (2002)

This reflects convergence of deprivation in multiple dimensions or multidimensional poverty.

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Multidimensional deprivation

• Several states with high incidence of income poverty also have poor multidimensional indicators.

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State Rankings: HDI, GDI, GEM and HPI Rank HDI GDI GEM HPI 1 Kerala Kerala Kerala Kerala 2 Punjab Maharashtra Maharashtra Tamil Nadu 3 Maharashtra Gujarat Himachal Punjab 4 Haryana Himachal Gujarat Maharashtra 5 Gujarat Punjab Karnataka Haryana 6 West Bengal Karnataka Haryana Gujarat 7 Himachal Tamil Nadu West Bengal Karnataka 8 Karnataka West Bengal Tamil Nadu West Bengal 9 Tamil Nadu Andhra Rajasthan Andhra Pradesh 10 Andhra Haryana Madhya Pradesh Orissa 11 Assam Assam Punjab Madhya Pradesh 12 Orissa Orissa Andhra Rajasthan 13 Rajasthan Madhya Pradesh Uttar Pradesh Assam 14 Bihar Rajasthan Bihar Uttar Pradesh 15 MadhyaPradesh Bihar Orissa Bihar 16 Uttar Pradesh Uttar Pradesh Assam 15 States Source: For HDI and GDI - AK Shiva Kumar, Gender Equality and Political Participation: Implications for Good Health', mimeo, 1996 For GEM - Aasha Kapur Mehta, Recasting Indices for Developing Countries, EPW, 1996 For HPI - K. Seetha Prabhu and Sangita Kamdar (1997) On Defining Poverty from a Human Development Perspective, University of Mumbai, mimeo.

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Deprivation at the District Level: Identifying

the 50 most deprived districts in India

• Multidimensional indicators were estimated for 379 districts in 15 large states of India based on data for the early 1990s.

• Choice of Variables was based on:– reflection of long duration deprivation to the

extent possible– Data availability at the district level. Source: CPRC-IIPA Working Paper 9

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For example, persistent spatial variations in the infant mortality rate could reflect persistent deprivation to the

means of accessing good health

– due to inability to get medical care

– due to lack of income or

– lack of available health care facilities in the vicinity or

– poor quality of drinking water resulting in water borne diseases that cause mortality

– or lack of roads and public transport that enable quick transportation to hospitals in case of emergency or

– all of the above.

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Similarly, illiteracy could be considered to be a persistent denial of access to information,

knowledge and voice.

• Low levels of agricultural productivity may reflect poor resource base,

• low yields due to lack of access to irrigation and other inputs,

• poor quality of soil resulting from erosion or • lack of access to resources for investment

because of lack of collateral or • adverse climatic or market conditions.

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• Poor quality of infrastructure reflects persistent denial of opportunities for income generation and growth.

• We use various combinations of multidimensional indicators that could reflect persistent deprivation, such as illiteracy, infant mortality, low levels of agricultural productivity and poor infrastructure

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Three groups of indices are computed.

• 1) An average of three indicators representing education, health and income, with equal weights of one third each assigned to each. These are:

• a.   An average of female literacy and percent population in the age group 11-13 years attending school

• b.   Infant mortality rate• c.   Agricultural productivity

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2) An average of four indicators representing education, health, income and development of infrastructure with equal weights of one fourth each assigned to each. These are:

a.   An average of female literacy and percent population in the age group 11-13 years attending school

b.  Infant mortality ratec.   Agricultural productivityd. Infrastructure development

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3)An average of four indicators representing education, health, income and development of infrastructure with equal weights of one fourth each assigned to each. These are:

• An average of literacy and percent population in the age group 11-13 years attending school

• Infant mortality rate• Agricultural productivity• Infrastructure development

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• Each of these sets of three indices are computed on the basis of three different methods with a view to determining robustness of the results. The three methods are:1) the method used by the UNDP with the minimum-maximum range given below:

• a. For literacy, female literacy and percent population in the age group 11-13 years attending school – 0 to 100 in each case b. Infant mortality rate - 0 to 200 c. Agricultural productivity – 0 to 30d. Infrastructure development – 0 to 500

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• 2) calculating an Adjusted value of each index so that the values obtained are not sensitive to changes in the ranks with changes in the minimum – maximum limits used. The method for calculating the AHDI is given in a footnote in working paper 9. The minimum-maximum used is the same as in the UNDP method in (1) above.

• 3) calculating an Adjusted value of each index so that the values obtained are not sensitive to changes in the ranks with changes in the minimum – maximum limits used. The minimum-maximum used are the actual minimum and maximum for each of the variables.

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• The 9 sets of results were then sorted to identify the 52 to 60 most deprived districts out of 379 districts in 15 large states of India. These are:

• 1 district in Assam,

• between 5 to 8 districts in Bihar,

• 11 to 12 districts in Rajasthan,

• 21 to 26 districts in Madhya Pradesh,

• 4 districts in Orissa, and

• 6 to 10 districts in UP.

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• What clearly emerges is the constancy of districts regardless of indicators used and method of computation. The same 52 to 60 districts are identified as the most deprived in almost all 9 cases listed below.

• Identification of districts that reflect chronic deprivation in multidimensional parameters is the first step in determining strategies to correct such imbalances.

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Most deprived 50 or so districts. 3 variables 4 variables 4 variables 3 variables 4 variables 4 variables 3 variables 4 variables 4 variables

Felit &sch Felit &sch Lit & sch Felit &sch Felit &sch Lit & sch Felit &sch Felit &sch Lit & sch

imr, agrlpro imr, agrlpro imr, agrlpro Imr, agrlpro imr, agrlpro imr, agrlpro imr, agrlpro imr, agrlpr imr, agrlpr

infrastr infrastr Infrastr Infrastr infrastr infrastr

ADJ HDI1 ADJ HDI2 ADJ HDI3 HDI1 HDI2 HDI3 ADJ HDI1 ADJ HDI2 ADJ HDI3

UN min-max UN min-max UN min-max UN min-max UN min-max UN min-max Actual min-

max Actual min-max Actual min-max

Range 0.09-0.16 0.09-0.15 0.08-0.14 0.24-0.32 0.21-0.28 0.23-0.30 0.03-0.09 0.03-0.07 0.03-0.08 No. of districts 56 54 60 56 55 55 52 52 59

Index 1 2 3 4 5 6 7 8 9

State

Assam Dhubri Dhubri Dhubri Dhubri Dhubri Dhubri Dhubri Dhubri Dhubri

Bihar Araria Araria Araria Araria Araria Araria Araria Araria Araria

Bihar Deoghar Deoghar Deoghar Deoghar Deoghar

Bihar Katihar

Bihar Kishanganj Kishanganj Kishanganj Kishanganj Kishanganj Kishanganj Kishanganj Kishanganj Kishanganj

Bihar Palamu Palamu Palamu Palamu Palamu Palamu Palamu Palamu Palamu

Bihar Purnia Purnia Purnia Purnia

Bihar Sahibganj Sahibganj Sahibganj Sahibganj Sahibganj Sahibganj

Bihar Sitamarhi Sitamarhi Sitamarhi Sitamarhi Sitamarhi Sitamarhi Sitamarhi Sitamarhi Sitamarhi

MP Bastar Bastar Bastar Bastar Bastar Bastar Bastar Bastar Bastar

MP Betul Betul Betul Betul Betul Betul Betul Betul Betul

MP Chhattarpur Chhattarpur Chhattarpur Chhattarpur Chhattarpur Chhattarpur Chhattarpur Chhattarpur Chhattarpur

MP Damoh Damoh Damoh Damoh Damoh Damoh Damoh Damoh Damoh

MP Datia Datia Datia

MP Dhar

MP East Nimar East Nimar East Nimar East Nimar East Nimar East Nimar East Nimar East Nimar East Nimar

MP Guna Guna Guna Guna Guna Guna Guna Guna Guna

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MP Jhabua Jhabua Jhabua Jhabua Jhabua Jhabua Jhabua Jhabua Jhabua

MP Mandla Mandla Mandla Mandla Mandla Mandla Mandla Mandla Mandla

MP Panna Panna Panna Panna Panna Panna Panna Panna Panna

MP Raisen Raisen Raisen Raisen Raisen Raisen Raisen Raisen Raisen

MP Rajgarh Rajgarh Rajgarh Rajgarh Rajgarh Rajgarh Rajgarh Rajgarh Rajgarh

MP Rajnandgaon Rajnandgaon Rajnandgaon Rajnandgaon Rajnandgaon

MP Ratlam Ratlam Ratlam Ratlam Ratlam Ratlam Ratlam Ratlam Ratlam

MP Rewa Rewa Rewa Rewa Rewa Rewa Rewa Rewa Rewa

MP Sagar Sagar Sagar Sagar Sagar Sagar Sagar Sagar Sagar

MP Satna Satna Satna Satna Satna Satna Satna Satna Satna

MP Sehore Sehore Sehore Sehore Sehore Sehore Sehore Sehore Sehore

Seoni

MP Shahdol Shahdol Shahdol Shahdol Shahdol Shahdol Shahdol Shahdol Shahdol

MP Shajapur Shajapur Shajapur Shajapur

MP Shivpuri Shivpuri Shivpuri Shivpuri Shivpuri Shivpuri Shivpuri Shivpuri Shivpuri

MP Sidhi Sidhi Sidhi Sidhi Sidhi Sidhi Sidhi Sidhi Sidhi

MP Surguja Surguja Surguja Surguja Surguja Surguja Surguja Surguja Surguja

MP Tikamgarh Tikamgarh Tikamgarh Tikamgarh Tikamgarh Tikamgarh Tikamgarh Tikamgarh Tikamgarh

MP West Nimar West Nimar West Nimar West Nimar West Nimar West Nimar West Nimar West Nimar West Nimar

Orissa Ganjam Ganjam Ganjam Ganjam Ganjam Ganjam Ganjam Ganjam Ganjam

Orissa Kalahandi Kalahandi Kalahandi Kalahandi Kalahandi Kalahandi Kalahandi Kalahandi Kalahandi

Orissa Koraput Koraput Koraput Koraput Koraput Koraput Koraput Koraput Koraput

Orissa Phulbani Phulbani Phulbani Phulbani Phulbani Phulbani Phulbani Phulbani Phulbani

Rajasthan Banswara Banswara Banswara Banswara Banswara Banswara Banswara Banswara Banswara Rajasthan Barmer Barmer Barmer Barmer Barmer Barmer Barmer Barmer Barmer

Rajasthan Bhilwara Bhilwara Bhilwara Bhilwara Bhilwara Bhilwara Bhilwara Bhilwara Bhilwara

Rajasthan Dholpur Dholpur

Rajasthan Dungarpur Dungarpur Dungarpur Dungarpur Dungarpur Dungarpur Dungarpur Dungarpur Dungarpur Rajasthan Jaisalmer Jaisalmer Jaisalmer Jaisalmer Jaisalmer Jaisalmer Jaisalmer Jaisalmer Jaisalmer

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Rajasthan Jalor Jalor Jalor Jalor Jalor Jalor Jalor Jalor Jalor

Rajasthan Jhalawar Jhalawar Jhalawar Jhalawar Jhalawar Jhalawar Jhalawar Jhalawar Jhalawar

Rajasthan Nagaur Nagaur Nagaur Nagaur Nagaur Nagaur Nagaur Nagaur

Rajasthan Pali Pali Pali Pali Pali Pali Pali Pali Pali

Rajasthan Sirohi Sirohi Sirohi Sirohi Sirohi Sirohi Sirohi Sirohi Sirohi

Rajasthan Tonk Tonk Tonk Tonk Tonk Tonk Tonk Tonk Tonk

UP Bahraich Bahraich Bahraich Bahraich Bahraich Bahraich Bahraich Bahraich Bahraich

UP Banda Banda Banda Banda Banda Banda Banda Banda Banda

UP Basti Basti Basti Basti Basti Basti Basti Basti Basti

UP Budaun Budaun Budaun Budaun Budaun Budaun Budaun Budaun Budaun

UP Gonda Gonda Gonda Gonda Gonda Gonda Gonda Gonda Gonda

UP Hardoi Hardoi Hardoi Hardoi Hardoi Hardoi Hardoi Hardoi Hardoi

UP Lalitpur Lalitpur Lalitpur Lalitpur Lalitpur Lalitpur Lalitpur Lalitpur

UP Shahjahanpur Shahjahanpur Shahjahanpur Shahjahanpur Shahjahanpur

UP Siddrathnagar Siddrathnagar Siddrathnagar Siddrathnagar Siddrathnagar

UP Sitapur Sitapur Sitapur Sitapur Sitapur Sitapur

Source: CPRC-IIPA Working Paper 9

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Chronic Severity poverty is viewed in three ways:

• those who are chronically or severely below the poverty line or with incomes that are 75% of the poverty line or less; and

•  those suffering hunger or not getting even two square meals a day as an extreme form of deprivation.

• inability to absorb the impact of shocks can also lead to extreme poverty, starvation and suicide.

 

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Severe Poverty• 134 million people were earning incomes that were less than

or equal to three fourths of the poverty line or were chronically below the poverty line in the severity sense based on data for 1993-94.

• Rural poverty was severest or the proportion of those who were very poor was largest in South Western Madhya Pradesh, Southern Uttar Pradesh, Southern Orissa, Inland Central Maharashtra, Southern Bihar, Northern Bihar and Central Uttar Pradesh.

• Urban poverty was specially severe in Inland Central, Eastern and Northern Maharashtra, Southern Uttar Pradesh, Inland Northern Karnataka, South Western and Central Madhya Pradesh and Southern Orissa.

• Source: Datta and Sharma, Planning Commission, 2000

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Regions with very high incidence of very Poor and Poor in Rural Areas : 1993-94

State/Regions Very Poor Poor

South Western M.P. 42.24 68.2

Southern U.P. 39.7 66.74

Southern Orissa 34.08 69.02

Inland Central Maharashtra

28.91 50.02

Southern Bihar 31.57 62.44

Northern Bihar 27.62 58.68

Central U.P. 26.79 50.02

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Hunger

Hunger And Lack Of Availability Of Two Square Meals A Day      Starkest indicator of severe poverty      Within the over 200 million people identified as undernourished in India is a subset that is unable to access even two square meals a day.      Issues of state failure and community failure especially in the context of starvation related deaths.

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Shock: Entry into PovertySevere poverty could result from sudden shocks such as ill

health or crop failure. The impact of such shocks can be transient if households

       could sell assets or        borrow or   generate income from alternative employment opportunities However, if the household has        no assets to sell        or no access to credit, or       is able to borrow at exploitative rates of interest and gets

into a debt trap, shocks can have long duration ramifications in terms of pushing households below the poverty line.

Policy changes such as globalisation can also be sources of shock.

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Globalisation: Suicide to escape chronic poverty

Konda Kistiah a powerloom weaver of Rajivnagar in Sircilla town, was unable to feed his family, including aged parents and two children. His wife had died of tuberculosis. He was unable to get a job for over three months and the debts were increasing.

In despair he committed suicide at the young age of 32, due to lack of hope for himself or in the future for his children,

lack of perceived alternative avenues for employment hunger and poverty, lack of assets, ill health, responsibility for elderly and other dependents, is repeatedly seen in many reports and needs attention in any discussion

of chronic severity poverty.

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Health related shocks

In the context of health related shocks casual labourers cannot afford to take time off from work in case of ill health.

The food that they and their families eat, depends on the money earned from working that day.

  

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Rising Morbidity Based on 30 Day Recall NSS 2 Rounds

1986-87 1995-96

Rural P 64 86

M 64 84

F 63 89

Urban P 31 84

M 30 81

F 33 89

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Drinking Water, Illness and PovertyPoor Quality Drinking Water exists in both rural

and urban areas. Data does not reflect the reality. • This has serious health related ramifications. • High incidence of hepatitis. • Pathogens, excess flouride, arsenic, salinity, iron

and chemical pollutants like pesticides and insecticides in water

• The poor cannot afford either the devices or the health related shocks and loss of work days from diseases caused by poor quality of water.

 Source: Mehta and Menon, Alternative Economic Survey, 2000-01

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• 21% of all communicable diseases (11.5 % per cent of all diseases) are water related. Every year 1.5 million children under 5 years die in India of water related diseases and the country loses 1800 million person hours (over 200 million person days) each year due to water borne diseases.

• Providing safe drinking water for all in real terms is a key policy concern.

Source: Mehta and Menon, Alternative Economic Survey, 2000-01

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Older Persons• 71 million persons > 60 and 27.1 m > 70 years old• 58% of older females and 45% males in rural areas

and 64% of females and 46% males in urban areas are fully dependent on others.

• 58% of older females and 28% of males had no financial assets and 52% of females and 19% of males owned no property.

• 63% of males and 58% of females continue to work beyond the age of 60. Even 80 year olds: 22% males and 17% females continue to work ostensibly due to poverty.

• Source: Irudaya Rajan CPRC-IIPA working paper 17

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Urban Slums

The urban poor in slums face situations of conflict and contested claims on spaces that provide livelihood earning opportunities and escape from chronic poverty.

 forced relocation to areas that reduce access to

livelihood opportunities increases costs.

fractured claims get reflected in other indicators such as prolonged low incomes, low food intake, and sustained exposure to health risks.

Source: Benjamin CPRC-IIPA working paper 4

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Remote Rural Areas

Livelihoods based on seasonal migration from tribal areas as a coping strategy did not lead to exit from poverty in villages in south western MP.

 • Migrants face hostile situations in urban receiving

areas access to home-based networks and services are ruptured.

• a very basic level of sustenance is assured • indebtedness usually remains • human capabilities hardly change • in destination areas abuse is common • rights are not upheld and it is hard to avoid low self

esteem.Source: Shah and Sah, CPRC-IIPA working paper 5 and JHD 2004

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             A large proportion of the poor in remote areas are both chronically and severely poor and the incidence of this is negatively associated with size of land holding and household population. Remote rural areas are likely to experience chronic poverty on the basis of agro-ecological and socio-economic factors.         Unless efforts are made to develop the deprived areas, out migration from drought prone regions may only shift poverty from rural to urban or from dry land to agro-climatically better endowed regions.

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Rural Infrastructure: Key to Anti Chronic Poverty Strategy

• Superior rural infrastructure accounts for regional differences in poverty among rural casual labourers.

• promotes the shift from low productivity casual labour in agriculture to more productive casual labour in non farm sector

• is the key to higher wages • Focus on improvement of rural infrastructure in

states where poverty ratios are persistently high. Source: Sheila Bhalla CPRC-IIPA working paper 14

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Drivers Maintainers Interrupters Health shock Low employment

opportunities Literacy and Education – Lok Jumbish/Shiksha Karmi

Crop failure Illiteracy/lack of education/skills

Asset transfer – Operation Barga

Borrowing consumption loans at high interest (120%)

Lack of (info about) income earning opportunities

Skills transfer, value addition and linkages with market – Gum Karaya

Macro policy change Social exclusion Access to credit at reasonable rates of interest – SHGs

Loss of Employment Disability Social and political action – MKSS Jan Sunwayi, Right to Information

Conflict Alcohol/addiction Social Protection – Grain Banks e.g. Ralegan

Loss of social support Remote location Growth, increased productivity and higher wages

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Large social expenses Governance Failure Linkages with urban neighbourhood

Drought/flood/earthquake/cyclone/disaster

Poor access to health care facilities

Insurance cover

Investment Failure Forced sale of assets to meet a crisis

Access to Water and Watershed Management – Pani Panchayat, Ralegan

Loss of Productive Assets

Lack of inheritance rights for women

Leadership – e.g. Anna Hazare, Sulankhe, Aruna Roy

Indebtedness Access to Health Care - RKS Bonded labour Infrastructure

Source: Bhide and Mehta CPRC-IIPA Working Paper 15