Water Poverty Analysis
IGB Basin Focal project
Upali Amarasinghe, Stefanos Xenarios
Rajendran Srinivasulu, Dhrubra Pant,
Madar Samad
BFP: Water-Poverty Analysis
Setting the Context
IGB Riparian countries has:
• 1.3 billion people in 2000
– 380 million (29%) are poor
• 942 million (72%) rural
population
– 340 million (36%) are poor
IGB has:
•605 million people
-191 million (32%)
are poor
•454 million (75%)
rural populatio
-151 million (33%)
are poor
Poverty is a rural phenomena
Poverty trends
Pakistan
70
60
60
50
50
HCR (%)
HCR (%)
India
70
40
30
40
30
20
20
10
10
0
1940
1950
1960
1970
1980
1990
2000
0
1990
2010
1995
Survey period
Bangladesh
70
60
60
50
50
HCR (%)
HCR(%)
2005
Survey period
70
40
30
Nepal
40
30
20
20
10
10
0
1980
2000
0
1985
1990
1995
2000
2005
2010
1995-1996
Survey period
Survey period
Rural
Urban
2003-2004
Total
Rural
Total
Urban
2010
Poverty trends
trends
Spatial
Regional poverty
• A major part of the
poor lives in the
eastern parts
• Evidence of spatial
clustering
Districts
Spatialtrends
trends
Poverty
• Rural poor tends to
live in clusters
• Water and land
factor in livelihood
and food security
• IGB is the hot bed
of poor in South
Asia
Spatial and temporal trends in the Indian IGB
Water poverty nexus-analytical framework
Water for
agriculture
• Water poverty of exists
when:
– household is poor or
incidence of poverty is high
Land for
agriculture
WPLN
Water for
domestic
purposes
– agriculture play a major
role in the rural livelihoods
– access to a reliable water
supply is a key factor in
improving productivity
Agriculture for
livelihood and
nutritional security
Poverty trends
Contribution of agriculture
1% of growth in
agriculture GDP
reduces poverty
by 1.05%
70
y = 3132 x -1.05
R2 = 0.37
60
CH
50
Rural HCR (%)
JH
-1.19
y = 4398 x
R2 = 0.59
40
BI
JH MP
BI
30
OR
CH
20
GU
10
OR
UP
UT WB
UT
MP UP
MH
TN MH
TN
RJ
WB
GU
RJ
KE
HR
HP
HP
PU
PU
HR
0
0
50
100
150
Agriculture GDP/person (US$ in 2000 prices)
Rural HCR - 1999/00
Rural HCR - 2004/05
200
250
How to unravel water land poverty nexus?
• Use a Logit Regression model
• LR estimate probability of a person being in poverty
P
1
1 e
(  X X i X j Z Z i Z j )
• P is the logistic cumulative probability function
• P/(1-P) is the odds ratio of household being poverty
• Combine household and survey data
Data
• Household consumption and expenditure
survey data
– NSSO 55th (1999/00) and 61st (2004/05) rounds
– each has 30,000+ households in the IGB
• District level aggregates of census data
– Population census, agriculture census, annual
agriculture at a glance publications, IWMI water
and climate atlas
– 280 districts in the IGB
Data
•
Household level
•
– Cropped area per person
– Average grain yields,
– Irrigated area- % of cropped
– Fruits and vegetable area
– Rainfall
area
– Gross irrigated area –
%GCA
– Land tenure and holding
size
– % Groundwater irri. area
– Socio-economic
– Irrigated water productivity
• HH SIZE
– Marginal and small land
holdings
• Sex, religion, social class,
education, dwelling type,
• Access to electricity
– Road density
• Type of ration card
•
District level
– Access to electricity
14 indicators
•
13 indicators
Results: Agriculture for rural livelihoods
Results: Agriculture for rural livelihoods
Low
poverty in
2004/05 agricultur
1999/00
e
operators
Household employemnt
Others
Ag operator
Self employed in non-ag.
Non-ag labor
Agriculture labor
0
60
40
20
0
10
20
30
40
Headcount ratio (%)
50
% of households
• Agriculture is an important component of
the livelihoods
Results: Water for agriculture
Results: Water for agriculture
Irrigated area-% of total cultivated area
100
2004/05
1999/00
75-100
50-75
25 - 50
0 - 25
0
60
40
20
% of households
0
0
Headcount
10
20ratio (%)30
• Yes. Access to water is important
• GW irrigation impacts are higher
• Water productivity in irrigation can have significant impacts
40
Results: Water for agriculture
4.8
1.20
4.0
1.00
3.2
0.80
2.4
0.60
1.6
0.40
0.8
0.20
0.0
WP (kg/m3)
Yield
Yield (ton/ha)
Relationships of yield and consumptive water use of
foodgrains
0.00
0
100
200
300
400
500
600
700
CWU (mm)
Yield
Max yield
Max WP
Consumptive water use
• Water productivity in irrigation can have significant impacts
– Supplemental irrigation
– Reducing the gap of irrigation
– Deficit irrigation
Results: Land for agriculture
Results: Land for agriculture
Land holding size
2004/05
1999/00
Large
Medium
Semi medium
Small
Marginal
No land
50
40
30
20
10
% of households
0
0
10
20
30
Headcount ratio (%)
– Marginal/small lands are significant constraints for reducing poverty
– Eastern IGB is besieged with marginal land holdings
40
Results: Infrastructure & Socioeconomic variables
• Other statistically significant explanatory variables
– Road density, access to electricity
– Household head
• female headed HH (10% of the households) has high odds poverty
– Education of the HH head
• 55% has less than primary education
– Number of graduate/postgraduate
– Social groups
• ST,SC (26% of the population) has significantly higher odds
– Religions
• Muslims (15% of the population) has high odds
– Household size
• >=5 has sig. higher odds than <=3
– HH with ration cards
• BPL (22% of the population) has higher odds
Conclusions
• Does water poverty exist?
– Very much!
• Can agriculture growth have further impact?
– Yes. Increase yields!
– Increase rainfed yields, intensify irrigated agriculture.
• Can water related interventions reduce poverty?
– Substantially in the eastern IGB!
– Irrigation matters, and reliability of irrigation is even more significant
– Increase irrigated water productivity!
• Can access to electricity reduce poverty?
– Yes. Odds of poverty of those who do not have access to electricity is 2
to 1
– Increase access to electricity in the eastern IGB can result in better
water use
Conclusions
• Land related interventions help
– Large number of marginal and small sizes are the biggest constraint.
– Land consolidation, where ever is possible, can contribute to productivity
increase and poverty reduction.
• Roads, electricity, education matters.
• Non-farm employment have a significant impact.
• Backward social classes and women headed households have higher
poverty. Need thorough analysis of these groups.
• Impacts of environmental factors needs to be included
Thank you