Depth & Severity of Poverty In Rural Odisha: A District ... and severity of... · Odisha (rural) has declined by 24.6(25.1) percentage points that is from 57.2 % (60.8%) to 32.6 %(

Depth & Severity of Poverty

In Rural Odisha:

A District Level Estimation

Directorate of Economics & Statistics, Government of Odisha

Depth & Severity of Poverty

In Rural Odisha:

A District Level Estimation

Directorate of Economics & Statistics

Odisha

At present no reliable poverty estimate is available at district level of

Odisha.This study is an attempt to derive district level reliable estimates for

rural Odisha on poverty incidence along with depth and severity of poverty

using NSS data and official poverty line (Rs 695.00) for the reference year

2011-12 derived by Planning Commission, Govt. Of India. The Poverty

Head count Ratio (HCR), Poverty Gap Index (PGI) and Squared Poverty

Gap Index (SPGI) have been computed for all districts of Odisha.To

eliminate poverty from each districts annual monetary estimation has been

computed. The positions of districts in terms of depth and severity have been

shown. District wise poverty trend from 2004-05 to 2011-12 has been

analysed using NSS data and official poverty line (Rs 407.78) for the

reference year 2004-05.

PREFACE

Planning Commission, Govt of India has released its latest poverty estimates for

2011-12. Always the estimates were state specific and had been derived using

the state specific official poverty line and NSS central sample data. As the

sample size of NSS is inadequate, the district level poverty estimates are not

computed for less reliability. This study “Depth and Severity of poverty in rural

Odisha: A district level estimation” has been prepared using the data of a

larger sample (central and state sample pooled data) of NSS 68th round (2011-

12) after testing its reliability at district level.

Three different measures of poverty i.e. Poverty Head count

Ratio (HCR), Poverty Gap Index (PGI) and Squared Poverty Gap Index (SPGI)

have been computed for all districts of Odisha.These measures HCR,PGI and

SPGI have shown the district wise incidence, depth and severity of Poverty

respectively.

The results derived in this paper may be helpful for the planer

and policy maker for poverty elimination from Odisha.It may be helpful for

researcher for further study.

Finally, I would like to express my deep sense of

appreciation to Dr. Sujata Priyambada Parida, Asst. Director for

her sincere effort to prepare this paper.

Valuable suggestions for improvements regarding this paper are most

welcome.

Bhubaneswar.

Sri D. Behera 14.3.17

DIRECTOR

List of abbreviations used

FSU First Stage Unit

HCR Head Count Ratio

HCI Head Count Index

LCL Lower Confidence Limit

UCL Upper Confidence Limit

MPCE Monthly Per-capita Consumer Expenditure

NSS National Sample Survey

NSSO National Sample Survey Office

PGI Poverty Gap Index

PPSWR Probability Proportional to Size With Replacement

MSE Mean Squared Error

RSE Relative Standard Error

SPGI Squared Poverty Gap Index

CONTENTS

1 Introduction 1-2

2 Objective 2

3 Data and Sources 3

4 Tools and Methodology 3-9

4.1 Estimates of Relative Standard Error (RSE) 3-4

4.2 Measures of Poverty 4-7

4.2.1 Head Count Index 5

4.2.2 Poverty Gap Index (PGI) 6

4.2.3 Squared Poverty Gap Index (SPGI) 7

4.3 Formation of a new sample with double sample size (Pooling of Central and State samples)

7-9

4.3.1 Testing the poolability of central and state sample 8-9

5 Distribution of Sample Size (2011-12) 9-10

5.1 Sample size distribution in Rural Odisha 9

5.2 District Level Sample size distribution in Rural Odisha (2011-12)

9-10

6 Result And Analysis 11-28

6.1 Poolability Test 11-12

6.2 Reliability of Samples for district level analysis (2011-12) 12-16

6.3 Incidence of poverty (2011-12) 17-22

6.3.1 District Level Poverty Trend from 2004-05 to 2011-12 20-22

6.3.2 Limitation of HCR 22

6.4 Depth of Poverty (2011-12) 23-26

6.4.1 Cost estimation for eliminating poverty or to bridge the poverty Gap

24-26

6.4.2 Limitation of PGI 26

6.5 Severity of Poverty (2011-12) 26-28

6.5.1 Limitation of SPGI 28

7 Conclusion 28-30

8 Suggestion 30

9 Limitation Of The Study 31

List of Tables and Figures

Table no. Title Page no.

1 District Level Distribution Of Sample Size (2011-12) 10

2 District Wise Poolability Test 11

3 Relative Standard Error of Poverty Ratio for different samples

in % (2011-12)

15

4 District wise poverty HCR within 95% confidence limit 16

Figure no. Title Page no.

1 RSE (%) Of Poverty Ratio For State, Center And Pooled Sample

And Comparison With The Minimum RSE

13

2 Poverty Head Count Ratio (State And Central Sample Result In

95% Confidence Limit Of Pooled Result

14

3 RSE (%) Of Poverty Head Count Ratio (Adjusted Pooled Sample) 14

4 Poverty Head Count Ratio Within 95% Confidence Limit

(Adjusted Pooled Sample)

16

5 District Level Poverty Head Count Ratio (%) Of Rural Odisha 18

6 District Wise Estimated BPL Population ('000) Of Rural Odisha

19

7 District Wise RSE(%) Of Poverty Head Count Ratio (Combined

Sample) For 2004-05

20

8 District level Poverty Trend of Rural Odisha from 2004-05 to

2011-12

21

9 District Level Poverty Gap Index (%) Of Rural Odisha 23

10 District wise Annual Cost Estimation (Rs. In Crores) To Bridge

The Poverty Gap In Rural Odisha

25

11 District Wise SPGI(Squared Poverty Gap Index) in(%) 27

Depth & Severity of Poverty in Rural Odisha:A District Level Estimation 1

1 INTRODUCTION

Poverty is the state of one who lacks a certain amount of material possessions or money.

Poverty estimation is the fast and foremost step before designing and implementation

of any appropriate anti-poverty policies. Two basic ingredients of poverty estimation

are Poverty Line and Data on size distribution of consumption. Poverty line is a cut-

off point separating the poor from the non-poor.

The Planning Commission of India, had been estimating official poverty line for

all states as well as India using Household Consumer Expenditure data of NSS

(National Sample Survey) up to 2011-12.According to the latest measure poverty in

Odisha (rural) has declined by 24.6(25.1) percentage points that is from 57.2 %

(60.8%) to 32.6 %( 35.7%) between 2004-05 and 2011-12.All these results had been

derived using the central sample data of NSSO, Govt of India. But there is no poverty

estimation available for below state level officially. The main obstacle to obtain district

level poverty estimation is the inadequate sample size at district level. But on behalf of

Govt of Odisha, Directorate of Economics and Statistics has been participating in NSS

in equal matching sample basis to Govt. of India. The sample surveyed by DES, Odisha

is called as state sample.


This study has taken an initiative to obtain district level poverty estimates using

three sets of samples i.e. central, state and combined (by pooling state and central)

samples using technical procedure. This study is an attempt to investigate whether the

poverty has been declined so significantly from all over rural Odisha uniformly or

otherwise, whether the declined rate is uniform for all districts of rural Odisha or

otherwise. Whether the position of districts suffering from poverty in normal measure

is same as the case of depth and severity of poverty or otherwise.

2 OBJECTIVE

The study was undertaken with following objectives

To derive district wise poverty ratio i.e Head Count Ratio (HCR) of rural Odisha

using official poverty lines (poverty lines derived by using Tendulkar

methodology).

To analyse the district wise poverty trend from 2004-05 to 2011-12.

To derive district wise depth of poverty i.e poverty gap and to estimate an annual

monetary outlay for all districts of rural Odisha to bridge the poverty gap.

To identify the districts with severe poverty i.e having more people suffering from

severe poverty.


3 DATA AND SOURCES

Household Consumer Expenditure data of NSS 68th round (2011-12) and 61st round

(2004-05) relating to schedule1.0 (type1) have been used for this study. The central

sample data are available from NSSO, Govt. of India and the state sample data are

available from DES, Odisha. The official poverty lines of Planning

Commission(Tendulkar Methodology) have been used for computation of all poverty

measures. For the year 2011-12, the poverty line is Rs. 695/- and for 2004-05 it is Rs.

407.78 for rural Odisha. The households with MPCE (Monthly Per-capita Consumer

Expenditure) less than the poverty line are considered as BPL (Below Poverty Line)

households. The population of these BPL households are stated as poor population for

this study.

4 TOOLS AND METHODOLOGY

For this study different methodology and analytical tools have been described to handle

the data.

4.1 Estimates of Relative Standard Error (RSE)

In this present study RSE of Poverty Head Count Ratio (HCR) has been computed. As

HCR is a ratio estimates of poor population to total population the following methods

of calculation has been used.


Let Y and X be the overall estimates of the aggregates Y and X for two

characteristics y and x respectively at district and all Odisha level. Then the combined

ratio estimate )ˆ(R of the ratio )(X

YR will be obtained as

X

YR

ˆ

ˆˆ . The Mean Squared

Error (MSE) of R is as follows

s t

ststststststststXXYYRXXRYY

XRESM

2121

2

21

22

212

ˆˆˆˆˆ2ˆˆˆˆˆˆ4

1)ˆ(ˆ

Where 1ˆstY and 2

ˆstY are the estimates 1for sub-sample 1 and sub-sample 2 respectively

for stratum‘s’ and sub-stratum‘t’.

RSE for the ratio estimate R

4.2 Measures of Poverty

The most important Foster-Greer-Thorbecke (FGT) method (FGT, 1984) has been used

for calculating incidence, depth and severity of poverty in the present study. This FGT

method includes three indices as Head Count Index (HCI), Poverty Gap Index (PGI),

1Sample design and estimation procedure of 68th round prepared by SDRD,NSSO, GOI.

100ˆ

ˆˆˆˆ

R

RESMRESR


and Squared Poverty Gap Index (SPGI).HCI measures the incidence of poverty, PGI

measures the gap or depth of poverty and SPGI measures the severity of poverty.

Foster-Greer-Thorbecke (FGT) index mathematically expressed as:

FGT = 𝟏

𝒏∑ (

𝒁−𝒀𝒊

𝒁)∈

𝒎𝒊=𝟏

Where, Yi =per capita expenditure of ith poor, Z = poverty line, n = total population

and m = number of poor population.

4.2.1 Head Count Index

The headcount is the simplest and best known poverty measure. The Head Count Index

measures the incidence of poverty which defined as the share of population that cannot

afford to buy a basic basket of goods. Since when we substitute ε = 0, in the general

expression of the FGT indices, then the equation:

𝑯𝑪𝑰 =𝟏

𝒏∑(

𝒁− 𝒀𝒊𝒁

)𝟎

=𝒎

𝒏

𝒎

𝒊=𝟏

Where, n is the total population and m is the number of poor population.

The measure HCI is easy to compute and easily understood. It is also called as HCR

(Head Count Ratio). HCR (%) is same as the percentage of population lying below

poverty line.


4.2.2 Poverty Gap Index (PGI)

The Poverty Gap Index (PGI) is superior to the Head Count Index (HCI) in so far, as it

provides a better indication of the depth of poverty. This provides information

regarding how far households are from the poverty line. It is defined by the mean

distance below the poverty line expressed as a proportion of the line, where the mean

is taken over the whole population, counting the non-poor as having zero poverty gaps.

This measure reflects not only the incidence of poverty, but also the depth of poverty.

Poverty Gap Index can be calculated by substituting ε = 1, it turns out to be the

poverty gap measure:

𝑷𝑮𝑰 =𝟏

𝒏∑

(𝒁 − 𝒀𝒊)

𝒁

𝒎

𝟏

Where Yi is the expenditure of the ith individual, and the sum is taken only those

individuals who are poor. ‘Z’ is the poverty line, ‘n’ represents total population and

‘m’ represents number of poor.

From the above equation we can also reach at the definition of poverty gap i.e. PG

𝑷𝑮 =∑(𝒁 − 𝒀𝒊)

𝒎

𝒊=𝟏


4.2.3 Squared Poverty Gap Index (SPGI)

The Squared Poverty – Gap Index is defined as the mean of the squared proportionate

poverty gaps. It is often described as a measure of the severity of poverty. It reflects the

severity of poverty. It is also described as a measure of severity of poverty. It takes into

account the inequality among the poor. It is obtained by substituting

ε = 2, as follows:

𝑆𝑃𝐺𝐼 =1

𝑛∑(

𝑍 − 𝑌𝑖𝑍

)

𝑚

𝑖=1

2

The squared poverty gap index attributes more weight to the poorest among the poor.

4.3 Formation of a new sample with double sample size (Pooling of Central

and State samples)

A multi-stage stratified sampling design has been adopted for both 61st and 68th round

of National Sample Survey. The first stage units (FSU) are the census villages and the

ultimate stage units (USU) are the households in rural sectors. Normally the rural

sector of each district of Odisha constitutes a separate stratum and for each stratum

the number of sample villages had been selected by probability proportional to size with

replacement (PPSWR), size being the population of the village as per latest Population

census. The Second stage units are the households of the selected sample village by

forming three separate second-stage strata (SSS) based on the affluence and principal


earning of the households. Using a scientific Method2 pooled estimate at first stage

stratum level has been calculated as the weighted average of central and state sample

estimates with number of sample villages as the weights.

4.3.1 Testing the poolability of central and state sample

Though the central sample and state sample are drawn independently following

identical sampling design with same concepts, definitions and instructions to collect the

state sample data. There is also expected agency bias in the two sets of data generated

by different agencies. So two sets of samples cannot be merged to get a new set of sample

without poolability test. Since the parametric distribution of the sample mean is

unknown the nonparametric test Wald-Wolfowitz run test has been used to test

poolabilty at district levels. Suppose X and Y are independent random samples with

cumulative distribution function (CDF) as Fs(x) and Fc(y). Null Hypothesis to be tested

is H0: Fs(x) = Fc(y) for all x and y against alternative Hypothesis is H1: Fs(x) <=

Fc(y) for all x and Fs(x) < Fc(x) for some x. Let x1, x2, ….., xm be iid observation from

state sample with distributive function Fs and y1,y2,…..,yn be iid observation from

central sample with distributive function Fc. Two sets of data have been pooled and

ordered with respect to comparable characteristic under consideration say monthly per

capita expenditure (MPCE). U has been taken as the total runs observed where 'run' is

2The method of Meenha and Sardana published in Sarvekhyana July-Sept(1990)


a sequence of adjacent equal symbols. The number of runs U is a random variable

whose distribution for large sample can be treated as normal with:

𝑴𝒆𝒂𝒏(𝑼) = 𝑬(𝑼) =𝟐𝒎𝒏

𝒎+ 𝒏

𝑽𝒂𝒓𝒊𝒂𝒏𝒄𝒆(𝑼) = 𝑽(𝑼) =𝟐𝒎𝒏(𝟐𝒎𝒏−𝒎− 𝒏)

(𝒎+ 𝒏)𝟐

As this study is based on large sample the variable U is to be normalized to Z i.e.

𝒁 =𝑼 − 𝑬(𝑼)

√𝑽(𝑼)

Finally Z has been used as test variate for poolabilty of two samples.

5 DISTRIBUTION OF SAMPLE SIZE (2011-12)

5.1 Sample size distribution in Rural Odisha

5.2 District Level Sample size distribution in Rural Odisha (2011-12)

The following table shows district wise sample villages and sample households of rural

Odisha which have been canvassed for collection of consumer expenditure data.

SAMPLE TYPES

CENTRAL STATE POOLED

VILLAGES HOUSEHOLDS VILLAGES HOUSEHOLDS VILLAGES HOUSEHOLDS

372 2973 371 2965 743 5,938


TABLE 1: DISTRICT LEVEL DISTRIBUTION OF SAMPLE SIZE (2011-12)

DISTRICT__ NAME SAMPLE TYPE

CENTRAL STATE POOLED

VILLAGES HOUSEHOLDS VILLAGES HOUSEHOLDS VILLAGES HOUSEHOLDS

BARGARH 16 128 16 128 32 256

JHARSUGUDA 8 64 8 64 16 128

SAMBALPUR 8 64 8 64 16 128

DEOGARH 8 64 8 64 16 128

SUNDARGARH 16 128 16 128 32 256

KEONJHAR 16 128 16 128 32 256

MAYURBHANJ 16 128 16 128 32 256

BALASORE 16 128 16 128 32 256

BHADRAK 16 128 16 128 32 256

KENDRAPARA 16 126 16 128 32 254

JAGATSINGHPUR 12 96 12 96 24 192

CUTTACK 16 128 16 128 32 256

JAJPUR 16 128 16 128 32 256

DHENKANAL 12 96 12 96 24 192

ANGUL 12 95 12 93 24 188

NAYAGARH 12 96 12 96 24 192

KHURDA 12 96 12 96 24 192

PURI 16 128 16 128 32 256

GANJAM 20 160 20 160 40 320

GAJAPATI 8 64 8 64 16 128

KANDHAMAL 8 64 8 64 16 128

BOUDH 8 64 8 64 16 128

SUBARNAPUR 8 64 8 64 16 128

BALANGIR 12 96 12 96 24 192

NUAPADA 8 64 8 64 16 128

KALAHANDI 16 128 15 120 31 248

RAYAGADA 8 64 8 64 16 128

NABARANGPUR 12 96 12 96 24 192

KORAPUT 12 96 12 96 24 192

MALKANGIRI 8 64 8 64 16 128

ODISHA 372 2973 371 2965 743 5,938


6 RESULT AND ANALYSIS

6.1 Poolability test

Before pooling central and state samples Wald-Wolfwitz run test has been used to test

the poolability. The poverty line used for this study is based on the monthly percapita

consumer expenditure (MPCE) for mixed reference period (MRP) of NSS. So

poolability test has been done for the indicator MPCE (MRP). The Z value at 1% level

of significance is -2.33[one sided test]. The following table shows district wise

poolability test.

TABLE 2:( DISTRICT WISE POOLABILITY TEST)

Name of the District MPCE(MRP) Name of the District MPCE(MRP)

Zvalue ACCEPT Zvalue ACCEPT

Baragarh -1.42 Yes Nayagarh 0 Yes

Jharsuguda 0.5 Yes Khurdha -1.23 Yes

Sambalpur -0.25 Yes Puri 0.89 Yes

Deogarh -1.51 Yes Ganjam -0.79 Yes

Sundargarh -0.71 Yes Gajapati -0.76 Yes

Keonjhar -1.77 Yes Kandhamal -0.25 Yes

Mayurbhanja 0.18 Yes Boudh 1.76 Yes

Balasore 0.35 Yes Sonepur -0.76 Yes

Bhadrak -0.53 Yes Bolangir 0 Yes

Kendrapara -1.43 Yes Nuapada -1.26 Yes

Jagatsinghpur 1.44 Yes Kalahandi -0.18 Yes

Cuttack 0.71 Yes Rayagada -0.25 Yes

Jajpur -1.6 Yes Nabarangapur 0.41 Yes

Dhenkanal 0.41 Yes Koraput 2.87 No

Anugul -1.55 Yes Malkangiri -0.5 Yes


From the table2 it is clearly noticed that all districts have satisfied poolability test

except Koraput. As poolabilty test has been satisfied in majority cases i.e 29 districts

out of 30 districts the pooled sample can be taken for this study.

6.2 Reliability of Samples for district level analysis (2011-12)

The RSE (Relative Standard Error) (%) computed for poverty head count ratio for three

sets of sample has been taken as the measures of reliability for this study.

From figure1 and table 3, it is observed that RSE for state and central samples

are more than 25% for some districts. But for the pooled sample the RSE is less than

21% for all districts. For 24 districts RSE for pooled sample is less than 15%. Also in

maximum districts RSE of pooled estimates is less than that of central and state samples.

So it may be interpreted that pooled sample is more reliable than central and state

samples. But in some districts RSE is minimum for state sample or central sample than

pooled sample.

Again from figure 2 the variation of central and state sample estimates from the

confidence limits of pool estimates is clearly observed. The variation in Nuapada

district is highest. The detail figures of RSE are given in table 3. For more reliable

estimate one adjusted factor has been introduced. When RSE of pooled estimates is

more than 15% and variation between central and state sample RSE is nearly 20% or

more, the sample of minimum RSE has been taken for further analysis of study. Hence


for district level analysis of Nuapada, Jagatsinghpur and Malkangiri the sample with

minimum RSE has been adjusted. RSE of adjusted sample along with three sets of

samples have been given in table 3. Figure 3 represents the district level RSE for the

adjusted pooled sample which has been preferred as most reliable for this study.

Figure3 shows the range of RSE (%) of the sample taken for poverty

analysis of the study. For 25 districts, RSE (%) is less than 15 and for rest 5 districts

RSE (%) varies from 15 to 20.9.Figure 4 shows the Poverty HCR (Head Count Ratio)

within 95% confidence limit for the sample taken for analysis.

Figure 1

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RSE (%) OF POVERTY RATIO FOR STATE,CENTER AND POOLED SAMPLE AND COMPARISION WITH THE MINIMUM RSE

rse_c rse_s rse_p Minimum_RSE


Figure 2

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POVERTY HEAD COUNT RATIO(State and Central sample result in 95% confidence limit of Pooled result)

HCR_center HCR_state HCR_pooled LCL UCL


Table 3: (Relative Standard Error of Poverty Ratio for different samples in %) (2011-12)

DISTRICT RSE_Central RSE_State RSE_Pooled RSE_Adjusted_P

BARGARH 7.7 12.6 5.8 5.8

JHARSUGUDA 28.3 13.1 6.5 6.5

SAMBALPUR 12.1 17.8 11.2 11.2

DEOGARH 1.9 5.9 4.3 4.3

SUNDARGARH 9.4 4.7 3.0 3.0

KEONJHAR 7.4 9.3 6.7 6.7

MAYURBHANJ 5.5 5.6 3.3 3.3

BALASORE 25.2 12.5 18.5 18.5

BHADRAK 12.4 13.0 9.0 9.0

KENDRAPARA 26.7 4.9 9.7 9.7

JAGATSINGHPUR* 15.4 35.6 17.0 15.4

CUTTACK 22.7 26.9 20.3 20.3

JAJPUR 19.7 9.8 8.5 8.5

DHENKANAL 11.6 20.9 19.5 19.5

ANGUL 20.9 17.5 12.9 12.9

NAYAGARH 14.4 23.9 14.0 14.0

KHURDA 20.4 29.1 20.9 20.9

PURI 15.8 21.3 13.0 13.0

GANJAM 19.0 15.0 11.5 11.5

GAJAPATI 14.8 0.4 4.1 4.1

KANDHAMAL 11.6 2.9 7.0 7.0

BOUDH 2.8 12.9 6.1 6.1

SUBARNAPUR 10.4 17.1 12.4 12.4

BALANGIR 14.9 0.9 3.4 3.4

NUAPADA* 13.2 40.3 17.2 13.2

KALAHANDI 5.5 9.2 5.2 5.2

RAYAGADA 6.2 5.2 2.0 2.0

NABARANGPUR 3.7 4.1 1.2 1.2

KORAPUT 2.2 3.1 1.9 1.9

MALKANGIRI* 10.1 29.7 19.7 10.1

MAX_RSE 28.3 40.3 20.9 20.9

MIN_RSE 1.9 0.4 1.2 1.2

*For these three districts sample has been adjusted with minimum RSE. The detail description is

in last para of heading 6.2 at page 12-13.


Figure 4

Table 4: (District wise poverty HCR within 95% confidence limit)

DISTRICT HCR LCL UCL DISTRICT HCR LCL UCL

BARGARH 0.35 0.31 0.39 NAYAGARH 0.33 0.24 0.43

JHARSUGUDA 0.29 0.25 0.32 KHURDA 0.14 0.08 0.19

SAMBALPUR 0.47 0.37 0.58 PURI 0.18 0.13 0.22

DEOGARH 0.67 0.61 0.73 GANJAM 0.25 0.19 0.30

SUNDARGARH 0.52 0.49 0.55 GAJAPATI 0.75 0.69 0.81

KEONJHAR 0.47 0.41 0.53 KANDHAMAL 0.59 0.51 0.67

MAYURBHANJ 0.61 0.57 0.65 BOUDH 0.62 0.55 0.69

BALASORE 0.3 0.19 0.41 SUBARNAPUR 0.42 0.32 0.53

BHADRAK 0.17 0.14 0.20 BALANGIR 0.67 0.63 0.72

KENDRAPARA 0.29 0.23 0.34 NUAPADA 0.62 0.46 0.78

JAGATSINGHPUR 0.17 0.12 0.22 KALAHANDI 0.49 0.44 0.54

CUTTACK 0.15 0.09 0.21 RAYAGADA 0.7 0.67 0.73

JAJPUR 0.2 0.17 0.23 NABARANGPUR 0.56 0.54 0.57

DHENKANAL 0.16 0.10 0.23 KORAPUT 0.78 0.75 0.80

ANGUL 0.22 0.17 0.28 MALKANGIRI 0.61 0.49 0.73

*LCL-Lower confidence Limit *HCL-Higher Confidence Limit

00.10.20.30.40.50.60.70.80.9

1

BA

RG

AR

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JHA

RSU

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SAM

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DEO

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KEO

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KEN

DR

AP

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ATI

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LKA

NG

IRI

POVERTY HEAD COUNT RATIO WITHIN 95% CONFIDENCE LIMIT

(ADJUSTED POOLED SAMPLE)

HCR LCL UCL


6.3 Incidence of poverty (2011-12)

Head Count Ratio (HCR) is always taken as Normal measure of poverty. When it is

expressed in percentage (%) it is nothing but the percentage of poor population lying

below poverty line. The district level poverty percentage along with district positions

for rural Odisha for the period 2011-12 has been shown in figure-5. Poverty HCR (%)

varies from 14% to 78%.Poverty percentage is below 20% in six districts i.e. Khurda,

Cuttack, Dhenkanal, Bhadrak, Jagatsighpur and Puri but it is 70% or above in

Rayagada, Gajapati and Koraput districts.Figure5 shows district wise percentage of

poverty as well as rank in HCR (%).

In more details it can be stated that 13 districts have poverty HCR (%) less than

all Odisha HCR whereas 16 districts are with more poverty incidence. But the district

Baragarh has same poverty incidence to all Odisha. It is also observed from the figure

that all KBK districts along with Gajpati, Deogarh, Boudh, Mayurbhanj, Sundargarh

Keonjhar, Kandhamal and Sambalpur districts have more incidence of poverty than

that of all Odisha.


Figure 5 : Poverty percentage or HCR (%)

Figure 6 : Estimated BPL Population

14

15 1617 17 18

2022

25

29 29 3033

35 35

42

47 4749

52

5659

61 61 62 62

67 6770

75

78

KH

UR

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K

DH

EN

KA

NA

L

BH

AD

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JAG

ATS

ING

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AR

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AR

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OD

IS

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AR

NA

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SAM

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EON

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T

DISTRICT LEVEL POVERTY HEAD COUNT RATIO(%) OF RURAL ODISHA

Khurda,Cuttack and Dhenkanal districts are with lowest incidence of poverty

where as Koraput , Gajapati and Rayagada are with highest incidence of poverty.


Figure 6: Estimated BPL Population ('000)

The district wise poverty percentage (figure5) has been used in district wise census

population of 2011 for rural Odisha to estimate the BPL population (‘000).The

estimated BPL population has been shown in Figure-6.As BPL population depends on

poverty percentage along with total rural population of the districts, order(rank) of

districts in figure-6 differs from that of figure-5.

101.02

163.43

172.05

173.57

194

.3

9

224.48

234.8

235.3

258.08

260.86

283.26

291.41

338.42

343.84

344.51

357.32

380.36

389.89

393.48

465.9

574

.6

6

620.17

634.66

690.26

704.78

712.89

727.88 899.71

972.58

1419.37

0

200

400

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1000

1200

1400

1600

JH

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NJ

DISTRICT WISE ESTIMATED BPL POULATION ('000) OF RURAL ODISHA


6.3.1 District Level Poverty Trend from 2004-05 to 2011-12

For this part of study the HCE pooled data of NSS 61st round (2004-05) has been used

to get the trend of poverty from 2004-05 to 2011-12. So to test the reliability of the

pooled sample (central and state) of 61st round, district wise RSE(%) of poverty HCR

has been computed and presented in figure-7. RSE (%) of all the districts are less

than 10%. We may assume that the district level poverty estimates of 2004-05 are

reliable for comparison with that for the year 2011-12.

Figure 7: RSE (%) of Poverty HCR (2004-05)

0.00

1.00

2.00

3.00

4.00

5.00

6.00

7.00

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9.00

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JA

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RS

E(%

)

DISTRICT WISE RSE(%) OF POVERTY HEAD COUNT RATIO (COMBINED SAMPLE)

FOR 2004-05


Figure 8: Poverty Trend from 2004-05 to 2011-12 in term of HCR (%)

69

53

52

64

89

48

63

46

78

63

79

86

58

70

48

63

50

80

35

83

45

74

71

26

58

84

25

82

73

59

61

22

14 1

6

29

56

18

33

17

49

35

52

62

35

47

25

42

29

61

17

67

30

61

59

15

47

75

20

78

70

62

67

47

3936 35

3330 30 29 29 28 27

24 23 23 2321 21

19 1816

1513 12 11 11

9

54 3

-3-6

-20

-10

0

10

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District level Poverty Trend of Rural Odisha from 2004-05 to 2011-12

2004_05 2011_12 % of poverty declined


Figure-8 shows the district wise poverty incidence of rural Odisha for 2004-05 and

2011-12 along with the percentage of poverty declined during these seven years.

According to this study, poverty has declined 23% from 2004-05 to 2011-12. Poverty

has declined highest i.e. (47%) in Angul district during these seven years. The reduction

in poverty percentage (%) is more in twelve districts (including three KBK districts i.e.

Nabarangpur, Kalahandi and Nuapada) than that of all Odisha. The poverty reduction

percentage (%) for Sambalpur and Ganjam districts is same to State. Poverty

declination in fourteen districts (including four KBK districts) less than that of all

Odisha. But it is noticed that poverty has inclined in Bolangir and Boudh districts. For

four districts i.e. Rayagada, Koraput, Jajpur and Gajapati the declined percentage is

very less i.e. less than 10%.

6.3.2 Limitation of HCR

The head-count ratio does not indicate how poor the poor are, and hence does not

change if people below the poverty line become poorer. Moreover, the easiest way to

reduce the headcount index is to target benefits to people just below the poverty line,

because they are the ones to move across the poverty line. If concentration of people is

more just below the poverty line then the HCR will indicate poverty is more and poverty

will be declined faster if these people will just cross the poverty line. It never affects the

depth of poverty or gap of poverty from poverty line.


6.4 Depth of Poverty (2011-12)

Poverty Gap Index (PGI) is a measure of the depth of poverty of the poor population

from poverty line. This can be interpreted as the average shortfall in income for the

population, from the poverty line. It is an important measure beyond the commonly used

headcount ratio. Suppose two districts have the similar headcount ratio, but may have

distinctly different poverty gap indices. A higher poverty gap index means that poverty

is more severe.

Figure 9: Poverty Gap Index (%)

1.7

3

2.2

4

2.4

6

2.9

7

3.0

2

3.1

4

4.0

9

4.1

6

4.5

7

4.8

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4.9

1

5.0

4

6.4

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6.8

3

7.7

4

7.91

8.5

5

9.0

4

9.4

0

10

.08

10

.08

10

.46

12

.17

12

.54

15

.61

15

.75

16

.76

17

.06 2

2.8

9

25

.6

3

34

.0

9

0.00

5.00

10.00

15.00

20.00

25.00

30.00

35.00

40.00

45.00

50.00

KH

UR

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I

DISTRICT LEVEL POVERTY GAP INDEX (%)OF RURAL ODISHA


From figure-9 it can be stated that the depth of poverty is highest in Malkangiri than

Koraput whereas Koraput has highest poverty as per the HCR in figure-5.Rank of

almost all the districts of figure-5 have been changed in figure-9.As per HCR the

position of Malkangiri and Mayurbhanj are same but depth of poverty is highest in

Malkangiri whereas Mayurbhanj is in ninth position in depth of poverty. Similarly

Koraput is in second position as per PGI and first as per HCR. So Koraput is in critical

position in both cases. While comparing to All Odisha, all KBK districts (except

Subarnapur) are with more poverty gap. Each individual district can be analysed in

this way. As interpretation PGI for Malkanagiri 34.09 indicates that the expenditure

shortfall of poor of this district is on an average 34.09%.

6.4.1 Cost estimation for eliminating poverty or to bridge the poverty gap

The measure PGI reflects the average distances of the poor below the poverty line so it

gives PCPG (per capita poverty gap). Using per capita poverty gap and estimated BPL

population of each district the cost of eliminating poverty has been computed. It shows

how much money would have to be transferred to the poor to bring their expenditure

up to the poverty line. However this interpretation is only reasonable if the transfers

could be made perfectly. At the other extreme, one can consider the maximum cost of

eliminating poverty, assuming that the policy maker knows nothing about who is poor

and who is not. Figure10 represents district wise annual cost estimation to eradicate


poverty from rural Odisha. The amount depends upon distance of the poor people from

poverty line along with their numbers.

Figure 10 : Estimated Cost to bridge the poverty gap

The result of figure-10 is self-explanatory. It indicates minimum requirement is for

Jagatsinghpur district (Rs. 14.37 Crores) where as it is maximum for Koraput district

14.37

16.29

17.78

24.1

6

27.91

34.6

4

35.2

2

38.40

41.74

43.67

44.04

44.61

48.67

54.61

54.84

55.25

74.30

83.52

92.84

94.34

94.41

95.0

6

97.12

100.55

108.03

117.81

129.82

190.74

236.76

247.03

0.00

50.00

100.00

150.00

200.00

250.00

300.00

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T

DISTRICTWISE ANNUAL COST ESTIMATION(Rs. IN CRORES)

TO BRIDGE THE POVERTY GAP IN RURAL ODISHA


(Rs.247.03 crores).For rural Odisha the total annual cost estimation to bridge the

poverty gap is Rs.2358.56 crores.

6.4.2 Limitation of PGI

Poverty gap index (PGI) ignores the effect of inequality among the poor. It does not

capture differences in the severity of poverty amongst the poor. This index is an

incomplete improvement over poverty headcount ratio (HCR).A serious shortcoming of

this measure is that it may not convincingly capture differences in the severity of poverty

amongst the poor.

6.5 Severity of Poverty (2011-12)

Squared Poverty Gap Index (SPGI) is a measure which attributes more weight to the

poorest among poor. The magnitude of measure is larger for poorer households. It is

stated as the measure of severity of poverty among poor households. Also it takes the

inequality among poor.

Unlike the head-count and poverty-gap indexes, the absolute value of the poverty

severity index has no intuitive interpretation and is not easy to interpret. For

comparisons, however, the key point is that a ranking of the districts in terms of SPGI

has reflected their ranking in terms of the severity of poverty.Figure-11 shows SPGI in

% for all the districts of Odisha along with their ranks.


Figure 11 : Index of Severe poverty for 2011-12

The severity of poverty in Malkangiri is highest and much higher comparing to

any other districts. Koraput and Gajapati are in second and third position for severe

poverty. Also Severity is more in Deogarh, Rayagada, Bolangir, Kandhamal, Boudh,

Mayurbhanja, Nuapada, Nabarangpur and Keonjhar .The figure indicates that the

severity of poverty still exist in many districts of Odisha.

0.03

0.05

0.06

0.09

0.09

0.1

0

0.17

0.17

0.21

0.23

0.24

0.25

0.41

0.47

0.60

0.63

0.73

0.82

0.88

1.02

1.02

1.09

1.48

1.57

2.44

2.48

2.81

2.91 5.24

6.57

11.6

2

0.00

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16.00

18.00

20.00

22.00

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34.00

36.00

38.00

40.00

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DISTRICT WISE SPGI(SQUARED POVERTY GAP INDEX) in(%)


6.5.1 Limitation of SPGI

The main drawback is that SPGI is difficult to interpret. However this is a very

important measure overcoming the shortfall of HCR and PGI.

7 CONCLUSION

This study showed district level poverty estimates using data of a reliable sample

and official poverty line (Rs 695.00 for rural Odisha)for the reference year 2011-12.The

sample taken has been assumed as reliable for district level poverty estimates on the

basis of Relative Standard Error (RSE). RSE on poverty ratio is less than 15(%) for 25

districts and varies from 15 to 20.9 for rest 5 districts for the selected sample.

According to this study, District level Poverty HCR (%) varies from 14% to 78%.

The HCR is below 20% in six districts i.e Khurda, Cuttack, Dhenkanal, Bhadrak,Puri

and Jagatsighpur but more or equal to 70% in Rayagada, Gajapati and Koraput

districts.

District wise poverty trend from 2004-05 to 2011-12 has been derived using the

data of a reliable sample and official poverty line (Rs 407.78 for rural Odisha) for the

reference year 2004-05.Poverty has declined highest i.e. (47%) in Angul district during

these seven years. Decline of poverty percentage (%) is more in twelve districts

(including three KBK districts i.e. Nabarangpur, Kalahandi and Nuapada) than that of


all Odisha i.e. 23%. But the poverty has inclined in Bolangir and Boudh districts. For

four districts i.e. Rayagada, Koraput, Jajpur and Gajapati the declined percentage is

very less i.e. less than 10%.Hence we can conclude that the poverty has been declined

fast in all over rural Odisha. But the rate is not uniform for all districts.

In addition to the common measure of poverty the study has derived the district

wise depth of poverty which included the per capita poverty gap. It has been derived

that the depth of poverty is highest in Malkangiri whereas Koraput is in second position.

So Koraput is in critical position in both cases i.e. incidence and depth of poverty. All

KBK districts (except Subarnapur) are with more poverty gap. Using percapita poverty

gap and estimated BPL population an annual monetary estimation has been prepared

for all districts. As per this estimation , minimum requirement is for Jagatsinghpur

district is (Rs. 14.37 Crores) where as it is maximum for Koraput district (Rs.247.03

crores).For rural Odisha the total annual cost estimation to bridge the poverty gap is

Rs.2358.56 crores.

The severity of poverty in Malkangiri is highest and much higher comparing to

any other districts. Koraput and Gajapati are in second and third position for severe

poverty. Also Severity is more in Deogarh, Rayagada, Bolangir, Kandhamal, Boudh,

Mayurbhanja, Nuapada, Nabarangpur and Keonjhar .Finally it has been investigated


through the study that many districts of Odisha are affected by the severity of poverty

till now.

8 SUGGESTION

Although this study has estimated different measures of incidence, depth and severity of

poverty for all districts of rural Odisha, it is unable to identify the households suffering

from poverty or severe poverty. Proper identification of poor is one of the important

steps before planning to eliminate poverty from Odisha. Norm of selection should be

prepared on the base of poverty gap of the households. If a common plan or norm will

be applied for all people lying below poverty line then poverty may be declined faster

but can’t be eliminated. Rather the condition of the people suffering from severe poverty

may be remaining unchanged. Hence instead of a common norm, different norm and

planning should be prepared according to the depth of poverty. The facilities given

should not be uniform i.e. the bottom level should be more facilitated. The people

suffering with severe poverty should get more facilities. It is suggested to make the poor

people engaged in such sustainable economic activities that, they can able to cross the

poverty line by earning the required monetary amount annually instead of distributing

money or kinds to them. The poor people should be sensitized regarding their ability to

be engaged in profitable economic activities to overcome the difficulties and painful life

of poverty.


9 LIMITATION OF THE STUDY

For all districts of entire rural Odisha only one poverty line has been used for poverty

analysis where the life style and consumption pattern of districts of different regions

are different. The study will be more effective if district level poverty lines will be used

for respective districts .But the official poverty line has been estimated up to state level

only.

References:

Bahadur, R.R. (1966): “A Note on Quantiles in Large Samples" Annals of Mathematical

Statistics, 37, 77-80.

Deaton, Angus and Jean Dreze (2002): ‘Poverty and Inequality in India: A Reexamination’,

Economic and Political Weekly, Vol. 37, No.36.

Government of India (1993): Perspective Planning Division, Planning Commission, Report

of the Expert Group on Estimation of Proportion and Number of Poor.

Minha B.S. & Sardana M.G. (1990): A notes on pooling of state and central sample data of

NSS, Sarvekshana July –Sept1990,NSSO,Department of Statistics MOSPI, Govt of India.

National Statistical Commission (2011): Report of Committee of pooling of state and

central sample of data of NSS.

Planning commission, Government of India. (2011): Report of expert group to review the

methodology for estimation of poverty under the chairman ship of Prof S. Tendulkar.

SDRD, NSSO, Govt of India (2012): Notes on Sample Design and Estimation procedure for

68th round.

Poverty is the parent of revolution and crime.

-----Aristotle

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