REVIEW OF NATIONAL AND STATE SCHEMES FOR WSS INVESTMENT Aashish Mishra, Consultant Water and Sanitation Program – South As...
<ul><li>Objective and Methodology </li></ul><ul><li>Challenges </li></ul><ul><li>Background </li></ul><ul><li>Findings </l...
Objectives and Methodology
<ul><li>Objective </li></ul><ul><li>To conduct a rapid assessment of GoI Centrally-Sponsored Schemes (CSS) as a vehicle fo...
<ul><li>Unavailability of Primary Data Sources </li></ul><ul><li>Conflicting Secondary Data Sources within GoI </li></ul><...
Background
NUMEROUS SCHEMES TO CHANNEL FUNDING FOR WSS <ul><li>Water </li></ul><ul><li>1 Urban: AUWSP </li></ul><ul><li>1 Rural: ARWS...
SCHEMES’ TARGET GROUP & ALLOCATION CRITERION <ul><li>Water </li></ul><ul><li>AUWSP: Towns under 20,000 population </li></u...
Avg. Urban CSS = INR 791 Crore Avg. Urban CSS Per-Capita = INR 28 Avg. Rural CSS = INR 10,911 Crore Rural CSS Per-Capita =...
FINDINGS FROM NATIONAL REVIEW
<ul><li>INEFFICIENT TARGETING </li></ul><ul><li>Targeting gaps </li></ul><ul><li>Misdirected Channeling of Grant Assistanc...
<ul><li>TARGETING GAPS </li></ul><ul><li>Vague allocation criterion, poor baseline data and too  narrowly/broadly defined ...
<ul><li>Distribution of CSS </li></ul><ul><li>Analysis of grant transfer distribution reveals ad-hoc release of water and ...
<ul><li>INEFFICIENCT TARGETING: Weak Correlation with State Population </li></ul>Figure 1: Linear Relationship between CSS...
<ul><li>INEFFICIENCT TARGETING: Weak Correlation with State Population </li></ul><ul><li>Weak Correlation with State Popul...
<ul><li>INEFFICIENCT TARGETING: Weak Correlation with State Population </li></ul>** NE States excluded from the ranking Ta...
<ul><li>States with greater population receive smaller grant transfers per capita! </li></ul><ul><ul><li>Most populated St...
<ul><li>INEFFICIENT TARGETING: Weak Correlation with State’s Income Level </li></ul><ul><li>0.18% R-Sq. suggests no Correl...
Figure 3: Linear Relationship between CSS Transfers and State BPL Population <ul><li>INEFFICIENT TARGETING: Weak Correlati...
Figure 4: State BPL Population and CSS Transfer Per-BPL Person <ul><li>INEFFICIENT TARGETING: Weak Correlation with State’...
<ul><li>13% R-Sq. reveals weak relationship between CSS distribution and States’ “Below Poverty Line” (BPL) population </l...
<ul><li>Correlation with States’ WATSAN service uncovered population is stronger but still inconclusive </li></ul>Table 2:...
Table 3: Average Sector Grant Transfer per WATSAN Uncovered Person Correlation with State’s Service Coverage Level 0.00 25...
<ul><li>Few CSSs are majority of transfers and assistance skewed towards rural sector assistance  --  5X greater transfers...
<ul><li>Opportunity costs generated by many similar & unaligned sector assistance CSS </li></ul><ul><li>Unnecessary Admini...
Annual CSS Transfers
<ul><li>Haphazard Disbursement Pattern Causes Lack of Predictability and Continuity in Grant Support </li></ul><ul><ul><li...
<ul><li>Similar sector grants with inconsistent intergovernmental institutional arrangements and financing pattern </li></...
<ul><li>CSS based on “Traditional” Measures such as Grant Expenditure and Physical Progress.  </li></ul><ul><li>Design of ...
Key Findings -  State/local level
<ul><li>3) POOR MONITORING AND EVALUATION (M&E) </li></ul><ul><li>Mismatch between GoI and State data on CSS transfers </l...
<ul><li>GoI data on CSS transfers to sample States does not match data collected from State government departments </li></...
<ul><li>Non-Cash transfers not factored in the value of intergovernmental system </li></ul><ul><li>Land and capital goods ...
Govt. Guarantee land and capital transfers STATE GOVT. (UDD, PRD) ULBs PRIs HUDCO/LIC and other lenders CENTRAL GOVT. (MoU...
Table 3: Average Annual Transfers from FY 2000/01 to 2003/04 in INR Crore 4) CSS NOT ALIGNED: KEY FINDINGS FROM STATE LEVE...
<ul><li>GoI CSSs less Than 25% of financing for basic watsan-related infrastructure provision </li></ul><ul><ul><ul><li>15...
<ul><li>States have own resources for supporting water supply, sanitation and slum / rural poor services.  </li></ul><ul><...
<ul><li>Disparity in level of community ownership and O&M of infrastructure under CSS </li></ul><ul><li>Local contribution...
<ul><li>HUDCO/LIC play a dominant role in financing of basic services </li></ul><ul><ul><li>A.P. (31%), Maharashtra (30%) ...
4) CSS NOT ALIGNED:   INTERGOVERNMENT FINANCING OF WATER SUPPLY  Table 5: Average Annual Transfers for Water Supply Provis...
<ul><li>WS services are financed through variety of sources other than CSS </li></ul><ul><li>ANDHRA PRADESH – ULB/PRI cont...
Table 6: Average Annual Transfers for Sanitation Provision  (from FY 2000/01 to 2003/04) in INR Crore 4) CSS NOT ALIGNED: ...
4) CSS NOT ALIGNED:   Sanitation Not Properly Addressed <ul><li>Intergovernmental financing pattern for sanitation support...
<ul><li>With exception of Megacities Scheme, State Officials and Local Bodies felt a serious deficiency in Urban WS fundin...
Recommendations
4 SETS OF RECOMMENDATIONS <ul><li>Targeting gaps must be addressed </li></ul><ul><ul><li>Socio-economic groups </li></ul><...
<ul><li>A clearer system of incentives must be created </li></ul><ul><ul><li>Better alignment of incentives between CSS </...
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Evaluation of Indian Water Supply & Sanitation Fiscal Transfers and Subsidies, 2004

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Final Presentation made in New Delhi in 2004 for Short-term Consultancy commissioned by the Water and Sanitation Program - South Asia, which is administered by the World Bank

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Evaluation of Indian Water Supply & Sanitation Fiscal Transfers and Subsidies, 2004

  1. 1. REVIEW OF NATIONAL AND STATE SCHEMES FOR WSS INVESTMENT Aashish Mishra, Consultant Water and Sanitation Program – South Asia October 21, 2004
  2. 2. <ul><li>Objective and Methodology </li></ul><ul><li>Challenges </li></ul><ul><li>Background </li></ul><ul><li>Findings </li></ul><ul><li>Recommendations </li></ul>AGENDA
  3. 3. Objectives and Methodology
  4. 4. <ul><li>Objective </li></ul><ul><li>To conduct a rapid assessment of GoI Centrally-Sponsored Schemes (CSS) as a vehicle for supporting the provision of water supply and sanitation </li></ul><ul><li>Methodology: study at two levels </li></ul><ul><li>National level water and sanitation transfers </li></ul><ul><ul><li>Design of CSS </li></ul></ul><ul><ul><li>Distribution of CSS to State governments </li></ul></ul><ul><ul><li>Efficacy of transfer system for watsan service support </li></ul></ul><ul><li>State and local level water and sanitation transfers </li></ul><ul><ul><li>Visits to sample States of A.P., Maharashtra and Kerala </li></ul></ul><ul><ul><li>Intergovernmental transfer financing mix for watsan sector assistance </li></ul></ul><ul><ul><li>Alignment of CSS and other intergovernmental transfers </li></ul></ul>OBJECTIVE AND METHODOLOGY
  5. 5. <ul><li>Unavailability of Primary Data Sources </li></ul><ul><li>Conflicting Secondary Data Sources within GoI </li></ul><ul><ul><li>Weak tracking systems </li></ul></ul><ul><ul><li>No composite picture </li></ul></ul><ul><li>Limitations of Available Data </li></ul><ul><ul><li>Project-based or scheme-total transfer data </li></ul></ul><ul><li>Discrepancy in Reporting of CSS Transfers! </li></ul><ul><ul><li>Between GoI Ministries and National Agencies </li></ul></ul><ul><ul><li>Between GoI and State Governments </li></ul></ul><ul><li>Exclusion of non-cash transfers in value of CSS distribution makes the picture incomplete! </li></ul><ul><ul><li>Land and capital transfers </li></ul></ul><ul><ul><li>Government Guarantees for Loans </li></ul></ul><ul><ul><li>Labour inputs </li></ul></ul><ul><li>Biased Sample </li></ul><ul><ul><li>A.P., Maharashtra and Kerala not representative </li></ul></ul>CHALLENGES
  6. 6. Background
  7. 7. NUMEROUS SCHEMES TO CHANNEL FUNDING FOR WSS <ul><li>Water </li></ul><ul><li>1 Urban: AUWSP </li></ul><ul><li>1 Rural: ARWSP </li></ul><ul><li>Sanitation </li></ul><ul><li>2 Urban: SWM, ILCS </li></ul><ul><li>1 Rural: TSC </li></ul><ul><li>Cross-sector infrastructure </li></ul><ul><li>1 Rural: PMGY </li></ul><ul><li>2 Urban: Megacities, IDSMT </li></ul><ul><li>Poverty Alleviation </li></ul><ul><li>3 Rural: NSAP & Annapurna, SGSY, SGRY </li></ul><ul><li>Slum and basic services </li></ul><ul><li>2 Rural: SAY, IAY </li></ul><ul><li>4 Urban: VAMBAY, Night Shelter, NSDP, SJSRY </li></ul>
  8. 8. SCHEMES’ TARGET GROUP & ALLOCATION CRITERION <ul><li>Water </li></ul><ul><li>AUWSP: Towns under 20,000 population </li></ul><ul><li>ARWSP: Habitations without safe drinking water </li></ul><ul><li>Sanitation </li></ul><ul><li>ILCS: Households with dry or no latrines, manual scavengers </li></ul><ul><li>SWM: 10 pilot airfield towns </li></ul><ul><li>TSC: Sanitation uncovered rural areas </li></ul><ul><li>Cross-sector infrastructure </li></ul><ul><li>PMGY: Rural sector budget support </li></ul><ul><li>MEGACITIES: 5 Metros </li></ul><ul><li>IDSMT: Towns under 500,000 </li></ul><ul><li>Poverty Alleviation </li></ul><ul><li>NSAP & Annapurna: Pop. >65 years </li></ul><ul><li>SGSY: Rural entrepreneurs </li></ul><ul><li>SGRY: or rural un/underemployed </li></ul><ul><li>Slum and basic services </li></ul><ul><li>SAY: SRP pilot districts </li></ul><ul><li>IAY : Rural homeless </li></ul><ul><li>VAMBAY, Night Shelter, NSDP, SJSRY: Slum, homeless or urban poor </li></ul>
  9. 9. Avg. Urban CSS = INR 791 Crore Avg. Urban CSS Per-Capita = INR 28 Avg. Rural CSS = INR 10,911 Crore Rural CSS Per-Capita = INR 136 SIZE OF OVERALL CSS TRANSFERS CSS Transfers from FY 2000/01 to 2002/03
  10. 10. FINDINGS FROM NATIONAL REVIEW
  11. 11. <ul><li>INEFFICIENT TARGETING </li></ul><ul><li>Targeting gaps </li></ul><ul><li>Misdirected Channeling of Grant Assistance </li></ul><ul><ul><li>Weak correlation with State Population </li></ul></ul><ul><ul><li>Weak correlation to States’ Income Level </li></ul></ul><ul><ul><li>Weak correlation with States’ Poverty Level </li></ul></ul><ul><ul><li>Weak correlation with States’ WATSAN service coverage </li></ul></ul><ul><li>INEFFICIENT VEHICLE FOR IMPROVING SERVICE DELIVERY </li></ul><ul><li>Fragmentation of schemes creating opportunity cost </li></ul><ul><li>Unpredictability of transfers </li></ul><ul><ul><li>Volatility </li></ul></ul><ul><ul><li>Inconsistent Institutional Arrangements and Funding Patterns </li></ul></ul><ul><li>Limited monitoring & evaluation and reward for performance </li></ul>KEY FINDINGS FROM NATIONAL REVIEW
  12. 12. <ul><li>TARGETING GAPS </li></ul><ul><li>Vague allocation criterion, poor baseline data and too narrowly/broadly defined outcomes </li></ul><ul><li>SECTORAL PROVISION GAPS </li></ul><ul><ul><li>i.e., no urban water supply CSS for ULBs between 20,000 and 5 million population </li></ul></ul><ul><ul><li>no urban sanitation CSS for general urban habitations (non poor) or enhancement of services to public such as sewerage or solid waste management </li></ul></ul><ul><li>TARGET POPULATION GAPS </li></ul><ul><ul><li>broad CSS allocation criterion, such as States’ population, fails to target needy or service uncovered populations </li></ul></ul><ul><ul><li>i.e., although NSDP and VAMBAY’s intention is to target service uncovered urban poor, funds are allocated to States based on urban slum or BPL population and do not factor level of service coverage </li></ul></ul>1) INEFFICIENT TARGETING: Targeting Gaps
  13. 13. <ul><li>Distribution of CSS </li></ul><ul><li>Analysis of grant transfer distribution reveals ad-hoc release of water and sanitation-related grant funding to states </li></ul><ul><li>Correlated with several allocation criterion to determine linear relationship </li></ul><ul><ul><li>States’ total population, urban population and rural population </li></ul></ul><ul><ul><li>States’ income level measured through State GDP </li></ul></ul><ul><ul><li>States’ poverty level measured through State “Below Poverty Line” (BPL) population </li></ul></ul><ul><ul><li>States’ water and sanitation services uncovered population </li></ul></ul><ul><li>INEFFECTIVE TARGETING OF GRANT ASSISTANCE </li></ul>1) INEFFICIENT TARGETING: Ad-Hoc Distribution of Transfers
  14. 14. <ul><li>INEFFICIENCT TARGETING: Weak Correlation with State Population </li></ul>Figure 1: Linear Relationship between CSS Transfers and State Population
  15. 15. <ul><li>INEFFICIENCT TARGETING: Weak Correlation with State Population </li></ul><ul><li>Weak Correlation with State Population </li></ul><ul><li>Suggested by 23% R-sq. in Linear Regression </li></ul><ul><li>1 Crore in population Rs. 30 Transfers Per Capita </li></ul><ul><li>NE States Receive Disproportionate Share of Transfers </li></ul><ul><li>After discounting NE States due to special status, weak correlation stands! </li></ul>
  16. 16. <ul><li>INEFFICIENCT TARGETING: Weak Correlation with State Population </li></ul>** NE States excluded from the ranking Table 1: State Population Classification and Average Annual Transfers Per-Capita 24 5 Greater than 7 Crores IV 38 5 4 Crores > 7 Crores III 21 7 2 Crores > 4 Crores II 86 4 Less than 2 Crores I Average Annual Transfer Per-Capita (INR) No. of States State Population Population Quartile
  17. 17. <ul><li>States with greater population receive smaller grant transfers per capita! </li></ul><ul><ul><li>Most populated States of U.P. and Bihar with the lowest average annual CSS transfers per-capita </li></ul></ul><ul><ul><li>Least populated States of Goa, Uttaranchal, H.P. and J&K receive greatest CSS transfers per-capita </li></ul></ul><ul><li>Rural-Urban Divide in grant transfers per-capita </li></ul><ul><ul><li>Over 50% of States have greater rural CSS transfers per-capita than urban transfers per-capita </li></ul></ul><ul><li>INEFFICIENCT TARGETING: Weak Correlation with State Population </li></ul>
  18. 18. <ul><li>INEFFICIENT TARGETING: Weak Correlation with State’s Income Level </li></ul><ul><li>0.18% R-Sq. suggests no Correlation between CSS distribution and States’ GDP levels </li></ul><ul><li>No consistent relationship between variables. No pattern of increasing transfers in States’ with lower State GDP across the distribution </li></ul>Figure 2: Linear Relationship between CSS Transfers and State GDP
  19. 19. Figure 3: Linear Relationship between CSS Transfers and State BPL Population <ul><li>INEFFICIENT TARGETING: Weak Correlation with State’s Poverty Level </li></ul>
  20. 20. Figure 4: State BPL Population and CSS Transfer Per-BPL Person <ul><li>INEFFICIENT TARGETING: Weak Correlation with State’s Poverty Level </li></ul>
  21. 21. <ul><li>13% R-Sq. reveals weak relationship between CSS distribution and States’ “Below Poverty Line” (BPL) population </li></ul><ul><li>States with highest average annual transfer per BPL person have lowest State BPL population </li></ul><ul><ul><li>Arunachal Pradesh, Mizoram, Himachal Pradesh, J&K and Goa </li></ul></ul><ul><li>Inversely, States with lowest transfer per BPL person have highest State BPL population </li></ul><ul><ul><li>Bihar, Orissa, Uttar Pradesh, Madhya Pradesh and West Bengal </li></ul></ul><ul><ul><li>CSS DISTRIBUTION NOT LINKED TO PREVELANCE OF POVERTY IN STATES! </li></ul></ul><ul><li>INEFFICIENT TARGETING: Weak Correlation with State’s Poverty Level </li></ul>
  22. 22. <ul><li>Correlation with States’ WATSAN service uncovered population is stronger but still inconclusive </li></ul>Table 2: Correlation Between Annual Sector Transfers and State Uncovered Population <ul><li>INEFFICIENT TARGETING: Correlation with State’s Service Coverage Level </li></ul>10% 3.7 AUWSP States’ Urban Water Supply Uncovered 27% 17.3 ARWSP States’ Rural Water Supply Uncovered 39% 2.1 TSC States’ Rural Sanitation Uncovered 52% 8.6 ILCS and NBA of VAMBAY States’ Urban Sanitation Uncovered R-Squared Slope Annual Grant Transfers Target Population
  23. 23. Table 3: Average Sector Grant Transfer per WATSAN Uncovered Person Correlation with State’s Service Coverage Level 0.00 256.68 0.68 1.79 Punjab 6.79 109.86 77.91 40.00 Kerala 18.51 171.83 6.03 1.02 Rajasthan 29.45 149.30 0.00 11.56 Assam 13.28 132.44 8.71 0.38 Gujarat 10.52 131.26 5.36 0.20 Karnataka 5.76 134.37 3.49 2.98 Maharashtra 54.06 77.69 0.00 4.17 Haryana 2.12 89.04 17.26 16.69 Andhra Pradesh 5.36 75.03 5.34 21.78 Tamil Nadu 12.20 62.19 11.45 12.63 Madhya Pradesh 26.00 52.81 9.41 6.49 Uttar Pradesh 2.07 57.16 11.11 9.93 West Bengal 7.91 55.49 0.31 6.55 Orissa 3.87 16.26 0.00 4.84 Bihar Urban Rural Urban Rural PER WATER SUPPLY UNCOVERED PERSON (INR) PER SANITATION UNCOVERED PERSON (INR)
  24. 24. <ul><li>Few CSSs are majority of transfers and assistance skewed towards rural sector assistance -- 5X greater transfers per cap </li></ul>2) INEFFICIENCT VEHICLE: QUANTUM & FLOW OF CSS TRANSFERS Avg. Urban CSS = INR 791 Crore Avg. Urban CSS Per-Capita = INR 28 Avg. Rural CSS = INR 10,911 Crore Rural CSS Per-Capita = INR 136
  25. 25. <ul><li>Opportunity costs generated by many similar & unaligned sector assistance CSS </li></ul><ul><li>Unnecessary Administrative Costs </li></ul><ul><li>Misdirection of Grants to Unintended Groups </li></ul><ul><li>No “Economies of Scale” in Sector Assistance </li></ul><ul><li>Setbacks for State and local governments </li></ul><ul><ul><ul><li>volatility of fiscal flows, delay in CSS release and rigid guidelines lead to difficulty in multi-year planning and multi-sector assistance strategy </li></ul></ul></ul><ul><li>No Intergovernmental Grant Facilitation Agency </li></ul>2) INEFFICIENCT VEHICLE: Fragmentation of Schemes
  26. 26. Annual CSS Transfers
  27. 27. <ul><li>Haphazard Disbursement Pattern Causes Lack of Predictability and Continuity in Grant Support </li></ul><ul><ul><li>Volatile Actual Annual Release </li></ul></ul><ul><ul><ul><li>i.e., urban CSS, Night Shelter – 51% & then +432% and rural CSS, ARWSP +13% & then – 11% </li></ul></ul></ul><ul><ul><li>Variance in Budgeted and Actual Releases </li></ul></ul><ul><ul><ul><li>in many cases, grant transferred is higher than budgeted allocation (1140% higher for Night Shelter in 2003) </li></ul></ul></ul><ul><ul><ul><li>also released funds much lower than budgeted allocation (86% lower for SJSRY in 2002) </li></ul></ul></ul><ul><ul><li>Bottlenecks for </li></ul></ul><ul><ul><ul><li>Multi-year and business planning </li></ul></ul></ul><ul><ul><ul><li>Comprehensive sector development strategy </li></ul></ul></ul>2) INEFFICIENCT VEHICLE: Unpredictability of Transfers: Volatility
  28. 28. <ul><li>Similar sector grants with inconsistent intergovernmental institutional arrangements and financing pattern </li></ul><ul><ul><li>e.g., Similar slum improvement programmes, such as VAMBAY, 50% GoI & 50% state share while NSDP 30% GoI & 70% local loan </li></ul></ul><ul><li>CSS not responsive to States’ unique institutional arrangement & political climate </li></ul><ul><ul><li>Felt by most States officials interviewed </li></ul></ul><ul><li>Weak design of CSS leads to limited incentives for State matching share or local contribution or loan </li></ul><ul><ul><li>Deterrent to access grant funds at State- or local-level </li></ul></ul>2) INEFFICIENCT VEHICLE: Inconsistent Institutional Arrangements
  29. 29. <ul><li>CSS based on “Traditional” Measures such as Grant Expenditure and Physical Progress. </li></ul><ul><li>Design of CSSs does not promote good performance on any level, including ineffective or no incentives for: </li></ul><ul><li>Monitoring of grant assistance Rudimentary M&E </li></ul><ul><li>Basic expenditure tracking </li></ul><ul><li>Evaluation of programme outcomes </li></ul><ul><li>Reform milestones Reform Agenda </li></ul><ul><li>Incentives for locally driven reform </li></ul><ul><ul><ul><li>no rewards for superior performance </li></ul></ul></ul><ul><ul><ul><li>does not foster of innovation or “Best Practice” </li></ul></ul></ul><ul><ul><ul><li>no incentives for improving local contribution effort and O&M of Infrastructure </li></ul></ul></ul>2) INEFFICIENCT VEHICLE: Limited M&E and Incentives for Performance
  30. 30. Key Findings - State/local level
  31. 31. <ul><li>3) POOR MONITORING AND EVALUATION (M&E) </li></ul><ul><li>Mismatch between GoI and State data on CSS transfers </li></ul><ul><li>Non-cash transfers not taken into account </li></ul><ul><li>4) CSS NOT ALIGNED WITH OTHER FUNDING MECHANISMS </li></ul><ul><li>GoI CSS transfers are only part of the picture </li></ul><ul><li>States have own financial resources for supporting WSS </li></ul><ul><li>Uneven level of local government contribution </li></ul><ul><li>Institutional borrowing available </li></ul><ul><li>Targeting Gaps on Urban Side </li></ul>KEY FINDINGS FROM STATE LEVEL
  32. 32. <ul><li>GoI data on CSS transfers to sample States does not match data collected from State government departments </li></ul><ul><li>One example is ARWSP transfer for 2003 to A.P. -- State Department’s reported CSS receipt was 66% lower than GoI reported transfer – a difference of 43 Crores for one CSS that year! </li></ul><ul><li>Another example, discrepancy in Kerala of 50% from GoI records and Kerala’s State Poverty Alleviation Department, for NSDP transfer during 2001/02 – a difference of 5 Crores for that year. </li></ul><ul><li>Numerous examples of discrepancies in both urban and rural transfers </li></ul><ul><li>Indicates Budget Management issues at several levels: </li></ul><ul><li>within GoI Ministries </li></ul><ul><li>intergovernmental fiscal transfers </li></ul><ul><li>within State Government Departments </li></ul>3) POOR M&E: Mismatch between GoI and State Data
  33. 33. <ul><li>Non-Cash transfers not factored in the value of intergovernmental system </li></ul><ul><li>Land and capital goods transfers and acquisition </li></ul><ul><ul><li>essential to water supply grants such as ARWSP & AUWSP </li></ul></ul><ul><ul><li>Discussions in sample States revealed that these water supply CSS have implicit transfers (land and equipment) but not factored into value of transfer </li></ul></ul><ul><li>GoI and State government guarantees for loans and market access </li></ul><ul><ul><li>necessary for loans under most grants </li></ul></ul><ul><ul><li>defaulted loans as de facto grants </li></ul></ul><ul><ul><li>A.P. and Maharashtra’s active guarantee policies </li></ul></ul><ul><ul><li>State water agencies, MJP in Maharashtra and KWA in Kerala, have history of default </li></ul></ul><ul><li>Labour inputs for programmes (i.e. SJSRY and SGRY) </li></ul><ul><li>Technical training </li></ul><ul><ul><ul><li>Value-added to local resources base </li></ul></ul></ul><ul><li>Necessary to approximate the market value of these transfers to reveal “real” quantum and distribution of grant funding across States </li></ul>3) POOR M&E: Non-Cash Transfers Not Addressed
  34. 34. Govt. Guarantee land and capital transfers STATE GOVT. (UDD, PRD) ULBs PRIs HUDCO/LIC and other lenders CENTRAL GOVT. (MoUD, MoEU, MoRD, HUDCO) CSS Grant Plan Grant CSS Matching Grant Local Loan / Contribution Parastatal Agency (Water Authority) Institutional Borrowing De facto Grant Percent Contribution to Intergovernmental Transfer Mix for Local government Services Financing  = Andhra Pradesh  = Maharashtra  = Kerala  = 14.4%  = 25.6%  = 25%  = 12.3%  = 14.2%  = 16.9%  = 12%  = 26.1%  = 47.8%  = 31.2%  = 30.1%  = 10.3%  = 30.1%  = 4.5% Figure 4: Intergovernmental Transfer Mix for Basic Services in Sample States 4) CSS NOT ALIGNED: KEY FINDINGS FROM STATE LEVEL
  35. 35. Table 3: Average Annual Transfers from FY 2000/01 to 2003/04 in INR Crore 4) CSS NOT ALIGNED: KEY FINDINGS FROM STATE LEVEL 360.74 824.68 1352.27 Total 10.34% 37.31 30.08% 248.05 31.18% 421.60 Institutional Borrowing N/A 4.54% 37.40 30.06% 406.43 Local Government Contribution 47.77% 172.31 26.06% 214.87 12.02% 162.50 State Grant Transfers 16.93% 61.08 14.18% 116.91 12.32% 166.66 State Matching Share 24.96% 90.03 25.15% 207.43 14.43% 195.07 GoI CSS Transfers % State Total % State Total % State Total Kerala Maharashtra Andhra Pradesh
  36. 36. <ul><li>GoI CSSs less Than 25% of financing for basic watsan-related infrastructure provision </li></ul><ul><ul><ul><li>15% in Andhra Pradesh </li></ul></ul></ul><ul><ul><ul><li>25% in Maharashtra and Kerala </li></ul></ul></ul><ul><li>State Plan Transfers and institutional borrowing comprise majority share of intergovernmental fiscal flows </li></ul><ul><ul><ul><li>State officials interviewed felt that CSS funds could have greater impact as “untied” budgetary support for ongoing State sector assistance programmes </li></ul></ul></ul>4) CSS NOT ALIGNED: GoI CSSTransfers Only Part of the Picture
  37. 37. <ul><li>States have own resources for supporting water supply, sanitation and slum / rural poor services. </li></ul><ul><li>Ready access to tax resources, institutional borrowing, bonds and local govt. contribution that some feel serve as substitute to CSS </li></ul><ul><ul><li>In contrast, CSS “Plagued” by paperwork, delays, “unachievable” targeting, lack of responsiveness </li></ul></ul><ul><ul><li>ANDHRA PRADESH - 60% of funding from institutional borrowing and local contributions </li></ul></ul><ul><ul><li>MAHARASHTRA - 60% of funding from institutional borrowing and State Plan transfers </li></ul></ul><ul><ul><li>KERALA – 48% from state plan transfers and 10% from institutional borrowing </li></ul></ul>4) CSS NOT ALIGNED: States have own Resources for WSS
  38. 38. <ul><li>Disparity in level of community ownership and O&M of infrastructure under CSS </li></ul><ul><li>Local contributions high as 30% in A.P. and low as 5% in Maharashtra </li></ul><ul><li>Found that service providers with greater community contribution have more sustainable O&M </li></ul>4) CSS NOT ALIGNED: Local Contribution for WSS
  39. 39. <ul><li>HUDCO/LIC play a dominant role in financing of basic services </li></ul><ul><ul><li>A.P. (31%), Maharashtra (30%) and Kerala (10%) </li></ul></ul><ul><ul><li>A.P. and Maharashtra have urban infrastructure development funds and rural borrowing through State Guarantees </li></ul></ul><ul><ul><li>Kerala has limited State Guarantees and now relies on land mortgage for institutional borrowing </li></ul></ul><ul><ul><li>High rate / risk of default and hence “De Facto” grants to Local Bodies </li></ul></ul><ul><ul><li>Need for alternate private institutional lenders </li></ul></ul>4) CSS NOT ALIGNED: Role of Institutional Borrowing
  40. 40. 4) CSS NOT ALIGNED: INTERGOVERNMENT FINANCING OF WATER SUPPLY Table 5: Average Annual Transfers for Water Supply Provision (from FY 2000/01 to 2003/04) in INR Crores 188.5 700.49 638.63 Total 19.79% 37.31 35.27% 247.08 25.47% 162.63 HUDCO/LIC Loan 5.34% 37.41 44.80% 286.08 Local Contribution 31.12% 214.87 WS Bonds 26.20% 49.4 4.89% 31.25 State WS Program 2.10% 2.62 3.46% 24.22 2.53% 16.16 Swajaldhara 50.52% 95.24 15.73% 110.18 13.13% 83.82 ARWSP 2.10% 3.96 0.90% 6.33 4.89% 8.27 AUWSP N/A 8.62% 60.39 7.89% 50.42 Megacities Kerala Maharashtra Andhra Pradesh
  41. 41. <ul><li>WS services are financed through variety of sources other than CSS </li></ul><ul><li>ANDHRA PRADESH – ULB/PRI contribution and HUDCO/LIC loans are pivotal to WS provision </li></ul><ul><ul><ul><li>Municipalities are required to Bear 50% of Cost of water supply schemes under HUDCO/LIC assistance pattern </li></ul></ul></ul><ul><li>MAHARASHTRA -- WS Bonds and HUDCO/LIC loans comprise greatest share of WS financing </li></ul><ul><ul><ul><li>Officials felt that Urban WS CSS funding should be devolved to ULBs </li></ul></ul></ul><ul><li>KERALA – 30-35% of State Plan Budget devolved as “Untied” Transfers to Local Bodies </li></ul><ul><ul><ul><li>Untied funds broadly earmarked for sector intervention, such as infrastructure and poverty alleviation programmes </li></ul></ul></ul>4) CSS NOT ALIGNED: Water Supply Not Dependent on CSS
  42. 42. Table 6: Average Annual Transfers for Sanitation Provision (from FY 2000/01 to 2003/04) in INR Crore 4) CSS NOT ALIGNED: INTERGOVERNMENT FINANCING OF SANITATION 13.12 28.74 389.43 Total 52.93% 206.12 Rural Sanitation HUDCO Loan 30.91% 120.36 Rural Sanitation Contribution 60.79% 7.98 68.48% 19.68 11.50% 44.79 TSC 39.21% 5.14 28.89% 8.31 3.81% 14.83 NBA / VAMBAY 2.62% 0.75 0.85% 3.33 ILCS Kerala Maharashtra Andhra Pradesh
  43. 43. 4) CSS NOT ALIGNED: Sanitation Not Properly Addressed <ul><li>Intergovernmental financing pattern for sanitation support is different than WS </li></ul><ul><li>No dedicated State-level grant programmes for sanitation, except State matching share for CSS </li></ul><ul><li>A.P. rural sanitation assistance mainly through State Guaranteed HUDCO loans </li></ul><ul><li>Urban Sanitation CSS linked to other sectors, such as housing, and thereby overshadow sanitation assistance </li></ul><ul><li>Officials felt that convergence of Urban CSS is desirable </li></ul>
  44. 44. <ul><li>With exception of Megacities Scheme, State Officials and Local Bodies felt a serious deficiency in Urban WS funding through CSS </li></ul><ul><li>AUWSP Scheme -- There are no ULBs in the States that are considered as “Urban Areas” under 20,000 population; and in A.P., AUWSP handled by Rural Development Department </li></ul><ul><li>Urban WS Support should be broadened to cover all ULBs and locally-driven </li></ul>4) CSS NOT ALIGNED: Targeting Gaps on Urban Side
  45. 45. Recommendations
  46. 46. 4 SETS OF RECOMMENDATIONS <ul><li>Targeting gaps must be addressed </li></ul><ul><ul><li>Socio-economic groups </li></ul></ul><ul><ul><li>Different categories of local bodies </li></ul></ul><ul><ul><li>Uncovered sectors of WSS such as expansion of existing WS services, solid waste management and sewerage </li></ul></ul><ul><li>Intergovernmental transfers should be better designed </li></ul><ul><ul><li>Clearer role of CSS within context of competing transfers </li></ul></ul><ul><ul><li>Realistic eligibility requirements and matching shares </li></ul></ul><ul><ul><li>Better public budget management system </li></ul></ul><ul><ul><li>Timely release of CSS transfer installments </li></ul></ul><ul><ul><li>Predictability in annual transfers for multi-year planning </li></ul></ul><ul><ul><li>Inclusion of non-cash transfers </li></ul></ul>
  47. 47. <ul><li>A clearer system of incentives must be created </li></ul><ul><ul><li>Better alignment of incentives between CSS </li></ul></ul><ul><ul><li>Better alignment of incentives between CSS and other intergovernmental transfers </li></ul></ul><ul><ul><li>Promote local government contribution for services </li></ul></ul><ul><ul><li>Greater autonomy at local level for sector development strategy </li></ul></ul><ul><li>A better framework for monitoring and evaluation is needed </li></ul><ul><ul><li>Develop more refined grant assessment criterion and milestones, and reward good performance </li></ul></ul><ul><ul><li>Improve the budget monitoring system (alignment between GoI and State data) </li></ul></ul><ul><ul><li>Value and monitor non-cash transfers </li></ul></ul>4 SETS OF RECOMMENDATIONS
  48. 48. Questions?

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