Water Poverty Analysis
IGB Basin Focal project
Upali Amarasinghe
Stefanos Xenarios, Rajendran Srinivasulu,
Madar Samad

Water-Poverty Analysis
Setting the Context
IGB Riparian countries IGB
• 1.3 billion people in IGB • 605 million live in IGB in
riparian countries in 2000 2000
– 29% or 380 million are poor – 32% or 191 million are poor
• 72% or 942 million in rural • 75% or 454 million in rural
areas in 2000 areas in 2000
– 36% or 340 million are poor – 33% or 151 million are poor

Water-Poverty Analysis
Setting the Context
In IGB - 150 million rural population are poor!
• Many depends their livelihood on agriculture
• Natural resources, especially renewable water resources are
under tremendous pressure
• Droughts and floods are recurrent phenomenon
• Spatial variation of poverty is high
• Spatial variation of natural resources is also high
• What is the extent of water-land-poverty nexus in the IGB?

Water-Poverty Analysis
Setting the Context
Water-Land-Poverty Nexus in the IGB
• Extent of adequate access to land and water resources helped
poverty alleviation?
• Extent of inadequate access to water and land are constraints to
poverty alleviation?
• Extent of degradation of natural resource base due to extensive
irrigated agriculture, causes poverty?
• The coping mechanisms in places under such adversity?

Water-Poverty Analysis
Setting the Context
Objectives of Water-Poverty Analysis in the IGB Basin
Focal project:
• Map sub-national poverty in the IGB
• Identify the determinants of poverty, with a special focus on water,
land and poverty nexus, and
• Identify the coping mechanisms of the people living under poor
conditions of water and land.

Water-Poverty Analysis
Setting the Context
Agreed outputs and progress
• Literature synthesis (Completed. Upali A.)
• Poverty mapping (In progress)
– Small area estimation method (R. Srinivasulu)
– Non-parametric density estimation method (Upali A.)
• Analysis of water-land-environment poverty nexus and coping
mechanisms in the IGB (In progress)
– District level (Upali A.)
– Household level (Stefanos Xenarios)

Water-Land-Poverty Nexus in
the IGB
Upali Amarasinghe
Rajendran Srinivasulu, Dhrubra Pant

Water-Land-Poverty Nexus in the IGB
Literature Synthesis
Outline
• Framework
• Spatial variation of poverty in the IGB
• Linkages of agriculture growth, water and land with poverty
• Econometric analysis of the water-land-poverty nexus
• Future activities

Water-Poverty Analysis- Framework
Water for
agriculture
Water for
Land for
agriculture WLPN domestic
purposes
Agriculture for
livelihood and
nutritional security

Water-Land-Poverty Nexus in the IGB
Literature Synthesis
Agriculture and rural poverty Water for agriculture and poverty
To what extent does agriculture What are the linkages of water and
contributes to income? rural poverty?
• Availability?
Where are the potential locations? • Access?
• Quality?
Land for agriculture and poverty Water for domestic purposes
What are the linkages of land What are the linkages of drinking
and rural poverty? water/health and rural poverty?
• Access (Tenure)? • Access?
• Availability (Size)? • Availability?
• Quality (Type/soil)? • Quality?

Trends of poverty
India Pakistan
70 70
60 60
50 50
HCR (%)
HCR (%)
40 40
30 30
20 20
10 10
0 0
1940 1950 1960 1970 1980 1990 2000 2010 1990 1995 2000 2005 2010
Survey period Survey period
Bangladesh Nepal
70 70
60 60
50 50
HCR (%)
HCR(%)
40 40
30 30
20 20
10 10
0
0
1995-1996 2003-2004
1980 1985 1990 1995 2000 2005 2010
Survey period
Survey period
Rural Urban Total Rural Total Urban

Spatial variation of rural poverty
• Low poverty in the north to north-west
• High poverty in the east to north-east and west
• IGB has the both the least and the highest poverty areas in south Asia

2025-2050
IGB has one ofof
IGB has one the
highest population
growth in Asia

2025-2050
IGB has the most
densely populated
areas in south Asia

70
y = 3132 x -1.05 JH
Hypotheses 1:
60 R2 = 0.37 Strong potential for
50
y = 4398 x -1.19
CH
BI
OR poverty alleviation in
Rural HCR (%)
40 R2 = 0.59
JH MP
the IGB with agriculture
OR UT WB
30 BI CH UP
UT
growth
MP UP
MH
20 TN MH WB
TN
GU GU
10 RJ RJ HR
PU
KE HP HP HR PU
0
0 50 100 150 200 250
Agriculture GDP/person (US$ in 2000 prices)
Rural HCR - 1999/00 Rural HCR - 2004/05
60
y = 109568 x -1.43
R2 = 0.42
50 OR
BI JH
40 y = 5812 x -0.96 MP
Total HCR (%)
CH OR
R2 = 0.31
UP MP UT
30 BI CH UT
WB MH
UP TN MH
20 WB
TN KE
10 HP HR GUPU
HR
HP PU
0
0 100 200 300 400 500 600 700 800 900
GDP/person (US$ in 2000 prices)
1999/2000 2004/2005

Hypotheses 2: Rural HCR (HCR) vs average rainfall
Head count ratio vs Ranfall
80
Water is still a strong OR
70
determinant in rural poverty BI WB
60
alleviation TN
MP
50 UP
MH MH SI
HCR (%)
AS
40 KA
RJ
GU
30 AP
HY
20 HP
PU
10
0
400 600 800 1000 1200 1400 1600 1800 2000
Average rainfall (mm)
Rural HCRcountGroundwater capita Groundwater availability
Head vs ratio (HCR) vs per availability/pc
HCR 1987-88 HCR-1999-2000
80
70 OR
WB
60
TN
50 UP MP AS
MH ARP
HCR (%)
KE
40 KA No
RJ
30
GJ
relationship
AP
20
HP HY
PU
with water
10
availability
0
0 200 400 600 800 1000 1200 1400
Replenishable groundwater resources/person ( m 3)
HCR 1987-88 HCR-1999-2000

Hypotheses 2:
Water is still a strong
But rural
determinant in rural poverty
poverty has a
alleviation
strong linkage
with access to
irrigation
Rural HCR vs access to irrigation
100 HCR 1999-
2000
80
HCR and % Area (%)
60
Net
irrigated
40 area-% of
net sown
area
20
Groundwat
er irrigated
0
area - % of
Haryana
Madhya Pradesh
Gujarat
Bihar
Punjab
Kerala
Andhra Pradesh
Karnataka
Maharashtra
Arunachal Pradesh
Sikkim
West Bengal
Rajasthan
Tamil Nadu
Uttar Pradesh
Assam
Himachal Pradesh
Orissa
total

Hypotheses 3:
Access to land is still a strong determinant in rural poverty alleviation
Rural HCR vs land holding size
60
Rural poverty
50
India has strong
40
linkages with
HCR (%)
30
Pakistan access to Land
20
and land holding
10
size
0 Banglade
sh
e
m
m
al
ss
l
e
al
rg
rg
iu
n
iu
Sm
le
la
gi
La
ed
ed
nd
ar
ry
l-m
M
La
M
Ve
al
60
Sm
Orissa
Land holding size 50
Bihar
Assam
Madhya Pradesh
40 Uttar Pradesh
HCR (%)
West Bengal
Strong linkage in 30
Maharashtra
TamilNadu
the poor parts of Karnataka
20 Rajasthan
the IGB Gujarat
Andhra Pradesh
10
Haryana
Kerala
0 Punjab
Small Small-medium Medium Large
Land holding size

Hypotheses 4:
Access to domestic water supply is a cause and effect of poverty
HCR vs access to safe sanitation and drinking water supply
90
75
60
%
45
30
15
0
a r du
H jab
Be h
n g tan
h
as t
m ka
Pr al
O r
sh
As s t
al
G h
r P htra
na
Pr l a
Ka than
sa
m
R jara
ha
N de s
es
es
y a ng
a
ep
ea
ra
sa
Ta ta
de
ri s
n
ya
M il N
Ba k is
Bi
W rad
ad
Pu
d h Ke
as
u
N
a
a
th
la
ar
rn
Pa
aj
or
M st
ah
ra
e
tta
ad
U
An
Head count ratio •No apparent
% population using latrine
linkages
% population with drinking water supply within the premices
•Data are too
aggregate to find
any relationship

Econometric analysis
Dependent variable- Ln (Rural head count ratio)
Coefficient Standard
Error
Constant -1.60 1.3
Ln (Water productivity) -3.42 0.5*
(Ln (Water productivity))2 -1.52 0.3*
Ln (% CWU from irrigation) -0.17 0.08*
Ln (% of groundwater irri. area) -0.18 0.1*
Ln (Net sown area/person) -0.19 0.09*
Ln (% rural population) 0.58 0.3*
R2 75%
Determinants of rural poverty
1. Water productivity, 2. irrigation quantity, 3. Reliability of irrigation,
4. Land holding size, 5. agriculture dependent population

End of the Literature Review
Thank you

Poverty Mapping of the IGB
Using Small Area Estimation
Rajendran Srinivasulu
PhD Student

Issue
• Can we estimate poverty mapping at district level?
Yes! But it requires more time and sufficient econometric model
• Do we have sufficient data sources?
Yes!
• What are the data sources are available? and time period?
NSS, Census and other secondary sources
• Is there any study?
India – Bigman and Srinivasan (2002), N S Sastry (2003), Indira
et al, (2002), Bigman & Deichmann, (2000), Dreze and Srinivasan
(1996)
• What are the methodology has been adopted by the literature?
Pooling Data from NSS and Census, Small Area Estimation
(SAE), other secondary data set at regional level and Primary
survey
• The present study’s methodology and future plan

Methodology Available
• Small Area Estimation (SAE)
• Pooling Data from Census, NSS,
Agricultural Survey, Cost of Cultivation
Survey and various Geographical
Surveys (Bigman and Srinivasan, 2002)
• Pooling Data from Census and NSS
• Region-wise Analysis

Small Area Estimation
• The term small area usually denote a small geographical area,
such as a county, a province, an administrative area or a census
division
• From a statistical point of view the small area is a small domain,
that is a small subpopulation constituted by specific
demographic and socioeconomic group of people, within a larger
geographical areas
• Sample survey data provide effective reliable estimators of
totals and means for large areas and domains. But it is
recognized that the usual direct survey estimators performing
statistics for a small area, have unacceptably large standard
errors, due to the circumstance of small sample size in the area

Small Area Estimation (SAE)
• The small area statistics are based on a collection of
statistical methods that “borrow strength” form
related or similar small areas through statistics
models that connect variables of interest in small
areas with vectors of supplementary data, such as
demographic, behavioral, economic notices, coming
from administratvive, census and specific sample
surveys records
• Small area efficient statistics provide, in addition of
this, excellent statistics for local estimation of
population, farms, and other characteristics of
interest in post-censual years

Type of Approaches
• The most commonly used tecniques for small area estimation are the
empirical Bayes (EB) procedures, the hierarchical Bayes (HB) and the
empirical best linear unbiased prediction (EBLUP) procedures (Rao,
2003)
• Some utilization of this tecniques in agrigultural statistics are related
to the implementation of satellite data, and, in general, of differently-
oriented sumpley surveys in model-based frameworks
• There are two types of small area models that include random area-
specific effects: in the first type, the basic area level model,
connection through response and area specific auxiliary variables is
established, because the limited availability at such type of data at unit
level
• The second type are the unit level area models, in which element-
specific auxiliary data are available for the population elements (Ghosh
and Rao, 1994; Rao, 2002)

Bigman and Srinivasan (2002) Model
• Step 1: Econometric Estimation of the Impact of
district-specific characteristics based on the
probability that the households residing in a given
district are poor
• Step 2: predictions of the incidence of poverty in all
the districts of the country based on the
characteristics of these districts.
• Step 3: First validation of the prediction – predicted
and actual value from NSS
• Step 4: Ranking and Grouping
• Step 5: second validation of the prediction:
comparison of predicted values and actual values