Upali Amarasinghe

Stefanos Xenarios, Rajendran Srinivasulu, Madar Samad

Water Poverty Analysis IGB Basin Focal project

Water-Poverty Analysis Setting the Context

IGB Riparian countries

• 1.3 billion people in IGB riparian countries in 2000

– 29% or 380 million are poor

• 72% or 942 million in rural areas in 2000

– 36% or 340 million are poor

IGB

• 605 million live in IGB in 2000

– 32% or 191 million are poor

• 75% or 454 million in rural areas in 2000

– 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)

Upali Amarasinghe

Rajendran Srinivasulu, Dhrubra Pant

Water-Land-Poverty Nexus in the IGB

Water-Land-Poverty Nexus in the IGBLiterature 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

WLPN

Agriculture for livelihood and

nutritional security

Water for domestic purposes

Water for agriculture

Land for agriculture

Water-Land-Poverty Nexus in the IGBLiterature Synthesis

Agriculture and rural poverty

To what extent does agriculture

contributes to income?

Where are the potential locations?

Water for agriculture and poverty

What are the linkages of water and

rural poverty?

• Availability?

• Access?

• Quality?

Land for agriculture and poverty

What are the linkages of land

and rural poverty?

• Access (Tenure)?

• Availability (Size)?

• Quality (Type/soil)?

Water for domestic purposes

What are the linkages of drinking

water/health and rural poverty?

• Access?

• Availability?

• Quality?

Trends of poverty

India

0

10

20

30

40

50

60

70

1940 1950 1960 1970 1980 1990 2000 2010

Survey period

HC

R (

%)

Pakistan

0

10

20

30

40

50

60

70

1990 1995 2000 2005 2010

Survey period

HC

R (

%)

Bangladesh

0

10

20

30

40

50

60

70

1980 1985 1990 1995 2000 2005 2010

Survey period

HC

R(%

)

Rural Urban Total

Nepal

0

10

20

30

40

50

60

70

1995-1996 2003-2004

Survey period

HC

R (

%)

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 of the highest population

growth in Asia

IGB has one of highest population

growth in Asia

2025-2050

IGB has the most densely populated areas in south Asia

Hypotheses 1: Strong potential for poverty alleviation in the IGB with agriculture growth

y = 109568 x-1.43

R2 = 0.42

y = 5812 x-0.96

R2 = 0.31

0

10

20

30

40

50

60

0 100 200 300 400 500 600 700 800 900

GDP/person (US$ in 2000 prices)

Tota

l H

CR

(%

)

1999/2000 2004/2005

OR

JH

CHMP OR

MPCH

UT

BI

BI

UP

UP

HP PUHR

MH

MHUT

TN

TN

GU

KE

HRHP PU

WB

WB

y = 3132 x-1.05

R2 = 0.37

y = 4398 x-1.19

R2 = 0.59

0

10

20

30

40

50

60

70

0 50 100 150 200 250

Agriculture GDP/person (US$ in 2000 prices)

Rura

l H

CR

(%

)

Rural HCR - 1999/00 Rural HCR - 2004/05

ORCH

MP

ORMP

CHUT

BI

BIUP

UP

PUHR

MHMH

UT

TN

TN

GU

KE

HR

HPPU

WB

WB

RJ

JH

JH

GURJ

HP

Hypotheses 2: Water is still a strong determinant in rural poverty alleviation

Head count ratio (HCR) vs average rainfall

0

10

20

30

40

50

60

70

80

400 600 800 1000 1200 1400 1600 1800 2000

Average rainfall (mm)

HC

R (

%)

HCR 1987-88 HCR-1999-2000

MH

TN

OR

MH

KA

BI

UPMP

HY

PU

WB

RJ

HP

AS

AP

GU

SI

Head count ratio (HCR) vs per capita Groundwater availability

0

10

20

30

40

50

60

70

80

0 200 400 600 800 1000 1200 1400

Replenishable groundwater resources/person ( m3)

HC

R (

%)

HCR 1987-88 HCR-1999-2000

TN

OR

KE

UP MP

HYPU

WB

ARP

RJ

MH

HP

AS

AP

KA

GJ

No relationship with water availability

Rural HCR vs Groundwater availability/pc

Rural HCR vs Ranfall

Hypotheses 2: Water is still a strong determinant in rural poverty alleviation

Rural HCR vs access to irrigation

0

20

40

60

80

100

Pu

nja

b

Him

ach

al P

rad

esh

Ha

rya

na

Ke

rala

An

dh

ra P

rad

esh

Gu

jara

t

Ra

jasth

an

Ka

rna

taka

Ta

mil N

ad

u

Ma

ha

rash

tra

Utta

r P

rad

esh

We

st B

en

ga

l

Ma

dh

ya

Pra

de

sh

Sik

kim

Aru

na

ch

al P

rad

esh

Assa

m

Bih

ar

Ori

ssa

HC

R a

nd %

Are

a (

%)

HCR 1999-2000

Netirrigatedarea-% ofnet sownarea

Groundwater irrigatedarea - % oftotal

But rural poverty has a strong linkage with access to

irrigation

Hypotheses 3: Access to land is still a strong determinant in rural poverty alleviation

Rural HCR vs land holding size

0

10

20

30

40

50

60

Land

less

Mar

gina

l

Smal

lSm

all-m

ediu

m

Med

ium

Larg

eVer

y la

rge

Land holding size

HC

R (

%)

India

Pakistan

Bangladesh

Rural poverty has strong

linkages with access to Land

and land holding size

0

10

20

30

40

50

60

Small Small-medium Medium Large

Land holding size

HC

R (

%)

Orissa

Bihar

Assam

Madhya Pradesh

Uttar Pradesh

West Bengal

Maharashtra

TamilNadu

Karnataka

Rajasthan

Gujarat

Andhra Pradesh

Haryana

Kerala

Punjab

Strong linkage in the poor parts of

the IGB

Hypotheses 4: Access to domestic water supply is a cause and effect of poverty

HCR vs access to safe sanitation and drinking water supply

0

15

30

45

60

75

90

Punja

bH

arya

naKer

ala

Andhr

a Pra

desh

Guj

arat

Raj

asth

anKar

nata

kaTam

il N

adu

Mah

arash

tra

Utta

r Pra

desh

West

Beng

al

Mad

ya P

rade

shN

orth

eas

tAss

am

Bihar

Oris

sa

Pakis

tan

Bangl

adesh

Nep

al

%

Head count ratio

% population using latrine

% population with drinking water supply within the premices

•No apparent linkages

•Data are too aggregate to find any relationship

Determinants of rural poverty

1. Water productivity, 2. irrigation quantity, 3. Reliability of irrigation, 4. Land holding size, 5. agriculture dependent population

Econometric analysis

75%R2

0.3*0.58Ln (% rural population)

0.09*-0.19Ln (Net sown area/person)

0.1*-0.18Ln (% of groundwater irri. area)

0.08*-0.17Ln (% CWU from irrigation)

0.3*-1.52(Ln (Water productivity))2

0.5*-3.42Ln (Water productivity)

1.3-1.60Constant

Standard

Error

Coefficient

Dependent variable- Ln (Rural head count ratio)

End of the Literature Review

Thank you

Rajendran SrinivasuluPhD Student

Poverty Mapping of the IGB Using Small Area Estimation

• 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), Indiraet 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

Issue

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 censusdivision

• 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 (Ghoshand 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