Water poverty analysis in the Indo-Ganges Basin


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Presented at the 2nd Phase Planning and Review Workshop of the Indo-Ganges BFP, 24-25 February, 2009, Haryana, India

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Water poverty analysis in the Indo-Ganges Basin

  1. 1. Water Poverty Analysis IGB Basin Focal project Upali Amarasinghe Stefanos Xenarios, Rajendran Srinivasulu, Madar Samad
  2. 2. 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
  3. 3. 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?
  4. 4. 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?
  5. 5. 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.
  6. 6. 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)
  7. 7. Water-Land-Poverty Nexus in the IGB Upali Amarasinghe Rajendran Srinivasulu, Dhrubra Pant
  8. 8. 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
  9. 9. Water-Poverty Analysis- Framework Water for agriculture Water for Land for agriculture WLPN domestic purposes Agriculture for livelihood and nutritional security
  10. 10. 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?
  11. 11. 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
  12. 12. 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
  13. 13. 2025-2050 IGB has one ofof IGB has one the highest population growth in Asia
  14. 14. 2025-2050 IGB has the most densely populated areas in south Asia
  15. 15. 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
  16. 16. 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
  17. 17. 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
  18. 18. 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
  19. 19. 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
  20. 20. 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
  21. 21. End of the Literature Review Thank you
  22. 22. Poverty Mapping of the IGB Using Small Area Estimation Rajendran Srinivasulu PhD Student
  23. 23. 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
  24. 24. 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
  25. 25. 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
  26. 26. 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
  27. 27. 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)
  28. 28. 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