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Water User Associations in the Southwestern Kyrgyzstan

Water User Associations in the Southwestern Kyrgyzstan. Lilia Verchinina Wenting Cheng Yong Joon Lee. Purpose. Evaluation of the effectiveness of water distribution in Southwestern Kyrgyzstan and role of infrastructure intervention. Data description. In the study: rayons 1-18 .

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Water User Associations in the Southwestern Kyrgyzstan

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  1. Water User Associations in the Southwestern Kyrgyzstan Lilia Verchinina Wenting Cheng Yong Joon Lee

  2. Purpose Evaluation of the effectiveness of water distribution in Southwestern Kyrgyzstan and role of infrastructure intervention

  3. Data description In the study: rayons 1-18

  4. Data description • Data set • From 2004 to 2009, 966 observations (WUA) with 32 variables # of observation for years # of observation for rayons

  5. Data description • Important variables • Response variable: proportion of debts paid by WUA • Program : whether a WUA was involved in the WUASP “capacity building program” • Water_sources : main water source for a WUA; the government, natural source, and mixture • Rayon : the district where a WUA is located • Postwbstart : indicator for WUA that received the World Bank’s “heavy infrastructure” • Taria : a measure of the “price” that a WUA charges its members for water delivery and staff salaries

  6. About Kyrgyzstan

  7. About Kyrgyzstan • Topography • Approximately 94% of the country is located at more than 1,000 meters above the sea level and 40% is above 3,000 meters • Agriculture • Accounts for 1/3 of GDP • 65% of the population • 90% of water used for agriculture United Nations Development Assistance Framework of the Kyrgyzstan, 2005-2010

  8. About Kyrgyzstan Worldclim 1960-1990 Averages

  9. About Kyrgyzstan Worldclim 1960-1990 Averages

  10. About Kyrgyzstan • Natural hazards • About 3000-5000 earthquakes every year, with large-scale catastrophes taking place every 5-10 years • Drought, hailstorms, and windstorms • Arable land • Only 7% of Kyrgyzstan land is arable • Mountainous topography and harsh weather conditions Food and Agriculture Organization in Kyrgyzstan

  11. Checking variables stability over years • Proportion of debt paid by WUA

  12. Checking variables stability over years • Condition of WUA’s canals - kdp

  13. Objectives • Client’s assumption • No agreement on the best indicator of water distribution effectiveness • People may be more willing to pay their fees for water if they are actually getting water -> proportion of debts paid by WUA • What factors will influence proportion of debts paid?

  14. What about Random Forest? First Step: Variables Importance • Random Forest classification algorithm • Seeks to estimate E[Y|W], i.e. the prediction of Y given a set of variables {W} • Calculate a measure of importance for the variables using sample with replacement samples • Permutation to determine variable importance • Final output: increase in classification error for each variable if this variable is randomly permuted • Response: proportion of debts paid • Group 1: relatively poor (0-64%), group 2: good (64%-100% )

  15. Variable Importance Result Table of variable importance in year 2006 obtained form random forest: first 2 columns are the mean decrease in accuracy for specific classes last 2 columns are the mean decrease in accuracy over 2 classes and of the Gini index

  16. What about Random Forest? First Step: Variables Importance • Most important covariate: rayon: consistent for each year • Other important variables: past population benefit from WUA, condition of water canal and the “price” that a WUA charges its members for water delivery and staff salaries • Objectives based on the results of the first step: • Control for geography • How can we form groups based on the geographical features?

  17. Second Step: Controlling Hydro-geographical Influence • Form 5 districts based on hydrobasin map • In each district the WUAs share the same water resource

  18. Variable variations in 5 districts • Boxplot • For each variable, compare median values of that variable in 5 water districts • Hypothesis testing • Multi-group hypothesis testing shows that the mean and median values in the 5 districts are significantly different

  19. Variable variations among 5 districts

  20. Variable variations among 5 districts • Conclusion: Statistical analysis agrees with our geographical decision to form 5 water districts

  21. Step 2. The WUAs in Each District • In each water district: what variables are important to determine the effectiveness of water distribution? • Methods • Look at a correlation between a quantitative variable and proportion of debts paid • Analyze tendency using boxplots to find the association between a binary variable and proportion of debts paid • Hypothesis testing

  22. The situation in each districtCorrelation Coefficient • High correlation • District 1: proportion of water requested from the government: 0.49 • District 2: the condition of water canals: -0.46 • District 4: “price” that a WUA charges its members for water delivery and staff salaries: -0.43 • Low correlation • District 1: total population benefits from WUA: 0.05 • District 2: land size: -0.001 • District 3: measure of how good WUA is at paying debt “on time”: 0.01

  23. The situation in each districtBoxplots

  24. Models • 5 models where we control for important geographical and water covariates and then analyze association between infrastructure covariates and “the proportion of debts paid” covariate

  25. Model 1 5 WATER DISTRICTS BASED ON THE HYDROBASIN MAP  3 WATER SOURCES IN EACH WATER DISTRICT 1-NATURAL, 2-MIX, 3-GOVERNMENT  WATER USER ASSOCIATIONS IN PROGRAM/NOT IN PROGRAM  PROPORTION OF DEBTS PAID

  26. Model 2 5 WATER DISTRICTS BASED ON THE HYDROBASIN MAP  3 WATER SOURCES IN EACH WATER DISTRICT 1-NATURAL, 2-MIX, 3-GOVERNMENT  WATER USER ASSOCIATION GETS/DOES NOT GET HEAVY INFRASTRUCTURE FROM WORLD BANK  PROPORTION OF DEBTS PAID

  27. Model 3 5 WATER DISTRICTS BASED ON THE HYDROBASIN MAP  2 LEVELS OF WUA CANALS CONDITION WORSE CONDITION and BETTER CONDITION  WATER USER ASSOCIATIONS IN PROGRAM/NOT IN PROGRAM  PROPORTION OF DEBTS PAID

  28. Model 4 5 WATER DISTRICTS BASED ON THE HYDROBASIN MAP  2 LEVELS OF WUA CANAL CONDITION WORSE CONDITION and BETTER CONDITION  WATER USER ASSOCIATION GETS/DOES NOT GET HEAVY INFRASTRUCTURE FROM WARLD BANK  PROPORTION OF DEBTS PAID

  29. Model 5 5 WATER DISTRICTS BASED ON THE HYDROBASIN MAP  3 WATER SOURCES IN EACH WATER DISTRICT 1-NATURAL, 2-MIX, 3-GOVERNMENT  PRICE CHARGED TO ITS MEMBERS FOR WATER DELIVERY AND STAFF SALARIES  PROPORTION OF DEBTS PAID

  30. Model 5. Controlling for water sources, correlation: prices charged and proportion of debts paid

  31. Models 1, 2, 3 & 4 • Running out of observations we are not able to go through all the steps of the models • Fragments of these models are consistent with the pattern of model 5: • Water districts 1, 2 & 5: positive association between the infrastructure covariates and proportion of debts paid, meaning getting more infrastructure, WUAs pay more debts • Water districts 3 & 4: negative association between the proportion of debts paid and infrastructure covariates, meaning getting infrastructure, WUAs pay less debts, comparing with WUAs in other water districts.

  32. Fragment from Model 1 and 2: District 1 versus District 3 Controlling water sources, proportion of debts paid

  33. Fragment from Model 1: District 1 versus District 3 Controlling program, proportion of debts paid

  34. Fragment from Model 2: District 1 versus District 3 Controlling World Bank Infrastructure, proportion of debts paid

  35. Patterns ObservedAssociation between infrastructure covariates and the proportion of debts paid: • Water Districts 1, 2 and 5: positive association between the infrastructure covariates and proportion of debts paid • Water Districts 3 and 4: negative association between the infrastructure covariates and proportion of debts paid • Why water districts 1,2,5 on one side and 3,4 on the other side are different?

  36. Map, map and map! Let’s see how the districts are formed:Ⅰ Rivers forming the southern borders of the Fergan ValleyⅡ Not defined district by hydrological basinsⅢ Karadarya (Syrdarya)Ⅳ Naryn river (Syrdarya)Ⅴ Rivers forming the northern border of the Fergana Valley

  37. 75.2% of Syrdarya is formed in Kyrgyzstan (Dist. 3 and 4)15.2% of Syrdarya is formed in Uzbekistan

  38. The main source of water of Kyrgyzstan glaciers and snowfields

  39. Lessons learnedStudying the effect of government and World Bank infrastructure: • Control for • geographical location • water sources used • Look for (collect the data) • Weather conditions • Districts 1,2, 5: lack of natural water sourcesdependence on weather • Districts 3,4: glaciers and iceformationsless dependent on weather conditions • Topography • Location appropriate for farming • Political situation • Districts 3,4: conflicts because of the irrational usage of waterAral Sea (Uzbekistan) is drying up

  40. Conclusions • WD with natural sources (big river) • Not interested in infrastructure and intervention • Smaller proportion of debts paid may not indicate lack of well-being • WD with lack of natural water sources • Interested in infrastructure and need intervention • Higher proportion of debts paid may not indicate well-being, but rather dependence on infrastructure

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