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

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Published on January 20, 2009

Author: guest3d1f1d

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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

REVIEW OF NATIONAL AND STATE SCHEMES FOR WSS INVESTMENT Aashish Mishra, Consultant Water and Sanitation Program – South Asia October 21, 2004

Objective and Methodology Challenges Background Findings Recommendations AGENDA

Objective and Methodology

Challenges

Background

Findings

Recommendations

Objectives and Methodology

Objective To conduct a rapid assessment of GoI Centrally-Sponsored Schemes (CSS) as a vehicle for supporting the provision of water supply and sanitation Methodology: study at two levels National level water and sanitation transfers Design of CSS Distribution of CSS to State governments Efficacy of transfer system for watsan service support State and local level water and sanitation transfers Visits to sample States of A.P., Maharashtra and Kerala Intergovernmental transfer financing mix for watsan sector assistance Alignment of CSS and other intergovernmental transfers OBJECTIVE AND METHODOLOGY

Objective

To conduct a rapid assessment of GoI Centrally-Sponsored Schemes (CSS) as a vehicle for supporting the provision of water supply and sanitation

Methodology: study at two levels

National level water and sanitation transfers

Design of CSS

Distribution of CSS to State governments

Efficacy of transfer system for watsan service support

State and local level water and sanitation transfers

Visits to sample States of A.P., Maharashtra and Kerala

Intergovernmental transfer financing mix for watsan sector assistance

Alignment of CSS and other intergovernmental transfers

Unavailability of Primary Data Sources Conflicting Secondary Data Sources within GoI Weak tracking systems No composite picture Limitations of Available Data Project-based or scheme-total transfer data Discrepancy in Reporting of CSS Transfers! Between GoI Ministries and National Agencies Between GoI and State Governments Exclusion of non-cash transfers in value of CSS distribution makes the picture incomplete! Land and capital transfers Government Guarantees for Loans Labour inputs Biased Sample A.P., Maharashtra and Kerala not representative CHALLENGES

Unavailability of Primary Data Sources

Conflicting Secondary Data Sources within GoI

Weak tracking systems

No composite picture

Limitations of Available Data

Project-based or scheme-total transfer data

Discrepancy in Reporting of CSS Transfers!

Between GoI Ministries and National Agencies

Between GoI and State Governments

Exclusion of non-cash transfers in value of CSS distribution makes the picture incomplete!

Land and capital transfers

Government Guarantees for Loans

Labour inputs

Biased Sample

A.P., Maharashtra and Kerala not representative

Background

NUMEROUS SCHEMES TO CHANNEL FUNDING FOR WSS Water 1 Urban: AUWSP 1 Rural: ARWSP Sanitation 2 Urban: SWM, ILCS 1 Rural: TSC Cross-sector infrastructure 1 Rural: PMGY 2 Urban: Megacities, IDSMT Poverty Alleviation 3 Rural: NSAP & Annapurna, SGSY, SGRY Slum and basic services 2 Rural: SAY, IAY 4 Urban: VAMBAY, Night Shelter, NSDP, SJSRY

Water

1 Urban: AUWSP

1 Rural: ARWSP

Sanitation

2 Urban: SWM, ILCS

1 Rural: TSC

Cross-sector infrastructure

1 Rural: PMGY

2 Urban: Megacities, IDSMT

Poverty Alleviation

3 Rural: NSAP & Annapurna, SGSY, SGRY

Slum and basic services

2 Rural: SAY, IAY

4 Urban: VAMBAY, Night Shelter, NSDP, SJSRY

SCHEMES’ TARGET GROUP & ALLOCATION CRITERION Water AUWSP: Towns under 20,000 population ARWSP: Habitations without safe drinking water Sanitation ILCS: Households with dry or no latrines, manual scavengers SWM: 10 pilot airfield towns TSC: Sanitation uncovered rural areas Cross-sector infrastructure PMGY: Rural sector budget support MEGACITIES: 5 Metros IDSMT: Towns under 500,000 Poverty Alleviation NSAP & Annapurna: Pop. >65 years SGSY: Rural entrepreneurs SGRY: or rural un/underemployed Slum and basic services SAY: SRP pilot districts IAY : Rural homeless VAMBAY, Night Shelter, NSDP, SJSRY: Slum, homeless or urban poor

Water

AUWSP: Towns under 20,000 population

ARWSP: Habitations without safe drinking water

Sanitation

ILCS: Households with dry or no latrines, manual scavengers

SWM: 10 pilot airfield towns

TSC: Sanitation uncovered rural areas

Cross-sector infrastructure

PMGY: Rural sector budget support

MEGACITIES: 5 Metros

IDSMT: Towns under 500,000

Poverty Alleviation

NSAP & Annapurna: Pop. >65 years

SGSY: Rural entrepreneurs

SGRY: or rural un/underemployed

Slum and basic services

SAY: SRP pilot districts

IAY : Rural homeless

VAMBAY, Night Shelter, NSDP, SJSRY: Slum, homeless or urban poor

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

FINDINGS FROM NATIONAL REVIEW

INEFFICIENT TARGETING Targeting gaps Misdirected Channeling of Grant Assistance Weak correlation with State Population Weak correlation to States’ Income Level Weak correlation with States’ Poverty Level Weak correlation with States’ WATSAN service coverage INEFFICIENT VEHICLE FOR IMPROVING SERVICE DELIVERY Fragmentation of schemes creating opportunity cost Unpredictability of transfers Volatility Inconsistent Institutional Arrangements and Funding Patterns Limited monitoring & evaluation and reward for performance KEY FINDINGS FROM NATIONAL REVIEW

INEFFICIENT TARGETING

Targeting gaps

Misdirected Channeling of Grant Assistance

Weak correlation with State Population

Weak correlation to States’ Income Level

Weak correlation with States’ Poverty Level

Weak correlation with States’ WATSAN service coverage

INEFFICIENT VEHICLE FOR IMPROVING SERVICE DELIVERY

Fragmentation of schemes creating opportunity cost

Unpredictability of transfers

Volatility

Inconsistent Institutional Arrangements and Funding Patterns

Limited monitoring & evaluation and reward for performance

TARGETING GAPS Vague allocation criterion, poor baseline data and too narrowly/broadly defined outcomes SECTORAL PROVISION GAPS i.e., no urban water supply CSS for ULBs between 20,000 and 5 million population no urban sanitation CSS for general urban habitations (non poor) or enhancement of services to public such as sewerage or solid waste management TARGET POPULATION GAPS broad CSS allocation criterion, such as States’ population, fails to target needy or service uncovered populations 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 1) INEFFICIENT TARGETING: Targeting Gaps

TARGETING GAPS

Vague allocation criterion, poor baseline data and too narrowly/broadly defined outcomes

SECTORAL PROVISION GAPS

i.e., no urban water supply CSS for ULBs between 20,000 and 5 million population

no urban sanitation CSS for general urban habitations (non poor) or enhancement of services to public such as sewerage or solid waste management

TARGET POPULATION GAPS

broad CSS allocation criterion, such as States’ population, fails to target needy or service uncovered populations

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

Distribution of CSS Analysis of grant transfer distribution reveals ad-hoc release of water and sanitation-related grant funding to states Correlated with several allocation criterion to determine linear relationship States’ total population, urban population and rural population States’ income level measured through State GDP States’ poverty level measured through State “Below Poverty Line” (BPL) population States’ water and sanitation services uncovered population INEFFECTIVE TARGETING OF GRANT ASSISTANCE 1) INEFFICIENT TARGETING: Ad-Hoc Distribution of Transfers

Distribution of CSS

Analysis of grant transfer distribution reveals ad-hoc release of water and sanitation-related grant funding to states

Correlated with several allocation criterion to determine linear relationship

States’ total population, urban population and rural population

States’ income level measured through State GDP

States’ poverty level measured through State “Below Poverty Line” (BPL) population

States’ water and sanitation services uncovered population

INEFFECTIVE TARGETING OF GRANT ASSISTANCE

INEFFICIENCT TARGETING: Weak Correlation with State Population Figure 1: Linear Relationship between CSS Transfers and State Population

INEFFICIENCT TARGETING: Weak Correlation with State Population

INEFFICIENCT TARGETING: Weak Correlation with State Population Weak Correlation with State Population Suggested by 23% R-sq. in Linear Regression 1 Crore in population Rs. 30 Transfers Per Capita NE States Receive Disproportionate Share of Transfers After discounting NE States due to special status, weak correlation stands!

INEFFICIENCT TARGETING: Weak Correlation with State Population

Weak Correlation with State Population

Suggested by 23% R-sq. in Linear Regression

1 Crore in population Rs. 30 Transfers Per Capita

NE States Receive Disproportionate Share of Transfers

After discounting NE States due to special status, weak correlation stands!

INEFFICIENCT TARGETING: Weak Correlation with State Population ** 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

INEFFICIENCT TARGETING: Weak Correlation with State Population

States with greater population receive smaller grant transfers per capita! Most populated States of U.P. and Bihar with the lowest average annual CSS transfers per-capita Least populated States of Goa, Uttaranchal, H.P. and J&K receive greatest CSS transfers per-capita Rural-Urban Divide in grant transfers per-capita Over 50% of States have greater rural CSS transfers per-capita than urban transfers per-capita INEFFICIENCT TARGETING: Weak Correlation with State Population

States with greater population receive smaller grant transfers per capita!

Most populated States of U.P. and Bihar with the lowest average annual CSS transfers per-capita

Least populated States of Goa, Uttaranchal, H.P. and J&K receive greatest CSS transfers per-capita

Rural-Urban Divide in grant transfers per-capita

Over 50% of States have greater rural CSS transfers per-capita than urban transfers per-capita

INEFFICIENCT TARGETING: Weak Correlation with State Population

INEFFICIENT TARGETING: Weak Correlation with State’s Income Level 0.18% R-Sq. suggests no Correlation between CSS distribution and States’ GDP levels No consistent relationship between variables. No pattern of increasing transfers in States’ with lower State GDP across the distribution Figure 2: Linear Relationship between CSS Transfers and State GDP

INEFFICIENT TARGETING: Weak Correlation with State’s Income Level

0.18% R-Sq. suggests no Correlation between CSS distribution and States’ GDP levels

No consistent relationship between variables. No pattern of increasing transfers in States’ with lower State GDP across the distribution

Figure 3: Linear Relationship between CSS Transfers and State BPL Population INEFFICIENT TARGETING: Weak Correlation with State’s Poverty Level

INEFFICIENT TARGETING: Weak Correlation with State’s Poverty Level

Figure 4: State BPL Population and CSS Transfer Per-BPL Person INEFFICIENT TARGETING: Weak Correlation with State’s Poverty Level

INEFFICIENT TARGETING: Weak Correlation with State’s Poverty Level

13% R-Sq. reveals weak relationship between CSS distribution and States’ “Below Poverty Line” (BPL) population States with highest average annual transfer per BPL person have lowest State BPL population Arunachal Pradesh, Mizoram, Himachal Pradesh, J&K and Goa Inversely, States with lowest transfer per BPL person have highest State BPL population Bihar, Orissa, Uttar Pradesh, Madhya Pradesh and West Bengal CSS DISTRIBUTION NOT LINKED TO PREVELANCE OF POVERTY IN STATES! INEFFICIENT TARGETING: Weak Correlation with State’s Poverty Level

13% R-Sq. reveals weak relationship between CSS distribution and States’ “Below Poverty Line” (BPL) population

States with highest average annual transfer per BPL person have lowest State BPL population

Arunachal Pradesh, Mizoram, Himachal Pradesh, J&K and Goa

Inversely, States with lowest transfer per BPL person have highest State BPL population

Bihar, Orissa, Uttar Pradesh, Madhya Pradesh and West Bengal

CSS DISTRIBUTION NOT LINKED TO PREVELANCE OF POVERTY IN STATES!

INEFFICIENT TARGETING: Weak Correlation with State’s Poverty Level

Correlation with States’ WATSAN service uncovered population is stronger but still inconclusive Table 2: Correlation Between Annual Sector Transfers and State Uncovered Population INEFFICIENT TARGETING: Correlation with State’s Service Coverage Level 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

Correlation with States’ WATSAN service uncovered population is stronger but still inconclusive

INEFFICIENT TARGETING: Correlation with State’s Service Coverage Level

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)

Few CSSs are majority of transfers and assistance skewed towards rural sector assistance -- 5X greater transfers per cap 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

Few CSSs are majority of transfers and assistance skewed towards rural sector assistance -- 5X greater transfers per cap

Opportunity costs generated by many similar & unaligned sector assistance CSS Unnecessary Administrative Costs Misdirection of Grants to Unintended Groups No “Economies of Scale” in Sector Assistance Setbacks for State and local governments volatility of fiscal flows, delay in CSS release and rigid guidelines lead to difficulty in multi-year planning and multi-sector assistance strategy No Intergovernmental Grant Facilitation Agency 2) INEFFICIENCT VEHICLE: Fragmentation of Schemes

Opportunity costs generated by many similar & unaligned sector assistance CSS

Unnecessary Administrative Costs

Misdirection of Grants to Unintended Groups

No “Economies of Scale” in Sector Assistance

Setbacks for State and local governments

volatility of fiscal flows, delay in CSS release and rigid guidelines lead to difficulty in multi-year planning and multi-sector assistance strategy

No Intergovernmental Grant Facilitation Agency

Annual CSS Transfers

Haphazard Disbursement Pattern Causes Lack of Predictability and Continuity in Grant Support Volatile Actual Annual Release i.e., urban CSS, Night Shelter – 51% & then +432% and rural CSS, ARWSP +13% & then – 11% Variance in Budgeted and Actual Releases in many cases, grant transferred is higher than budgeted allocation (1140% higher for Night Shelter in 2003) also released funds much lower than budgeted allocation (86% lower for SJSRY in 2002) Bottlenecks for Multi-year and business planning Comprehensive sector development strategy 2) INEFFICIENCT VEHICLE: Unpredictability of Transfers: Volatility

Haphazard Disbursement Pattern Causes Lack of Predictability and Continuity in Grant Support

Volatile Actual Annual Release

i.e., urban CSS, Night Shelter – 51% & then +432% and rural CSS, ARWSP +13% & then – 11%

Variance in Budgeted and Actual Releases

in many cases, grant transferred is higher than budgeted allocation (1140% higher for Night Shelter in 2003)

also released funds much lower than budgeted allocation (86% lower for SJSRY in 2002)

Bottlenecks for

Multi-year and business planning

Comprehensive sector development strategy

Similar sector grants with inconsistent intergovernmental institutional arrangements and financing pattern e.g., Similar slum improvement programmes, such as VAMBAY, 50% GoI & 50% state share while NSDP 30% GoI & 70% local loan CSS not responsive to States’ unique institutional arrangement & political climate Felt by most States officials interviewed Weak design of CSS leads to limited incentives for State matching share or local contribution or loan Deterrent to access grant funds at State- or local-level 2) INEFFICIENCT VEHICLE: Inconsistent Institutional Arrangements

Similar sector grants with inconsistent intergovernmental institutional arrangements and financing pattern

e.g., Similar slum improvement programmes, such as VAMBAY, 50% GoI & 50% state share while NSDP 30% GoI & 70% local loan

CSS not responsive to States’ unique institutional arrangement & political climate

Felt by most States officials interviewed

Weak design of CSS leads to limited incentives for State matching share or local contribution or loan

Deterrent to access grant funds at State- or local-level

CSS based on “Traditional” Measures such as Grant Expenditure and Physical Progress. Design of CSSs does not promote good performance on any level, including ineffective or no incentives for: Monitoring of grant assistance Rudimentary M&E Basic expenditure tracking Evaluation of programme outcomes Reform milestones Reform Agenda Incentives for locally driven reform no rewards for superior performance does not foster of innovation or “Best Practice” no incentives for improving local contribution effort and O&M of Infrastructure 2) INEFFICIENCT VEHICLE: Limited M&E and Incentives for Performance

CSS based on “Traditional” Measures such as Grant Expenditure and Physical Progress.

Design of CSSs does not promote good performance on any level, including ineffective or no incentives for:

Monitoring of grant assistance Rudimentary M&E

Basic expenditure tracking

Evaluation of programme outcomes

Reform milestones Reform Agenda

Incentives for locally driven reform

no rewards for superior performance

does not foster of innovation or “Best Practice”

no incentives for improving local contribution effort and O&M of Infrastructure

Key Findings - State/local level

3) POOR MONITORING AND EVALUATION (M&E) Mismatch between GoI and State data on CSS transfers Non-cash transfers not taken into account 4) CSS NOT ALIGNED WITH OTHER FUNDING MECHANISMS GoI CSS transfers are only part of the picture States have own financial resources for supporting WSS Uneven level of local government contribution Institutional borrowing available Targeting Gaps on Urban Side KEY FINDINGS FROM STATE LEVEL

3) POOR MONITORING AND EVALUATION (M&E)

Mismatch between GoI and State data on CSS transfers

Non-cash transfers not taken into account

4) CSS NOT ALIGNED WITH OTHER FUNDING MECHANISMS

GoI CSS transfers are only part of the picture

States have own financial resources for supporting WSS

Uneven level of local government contribution

Institutional borrowing available

Targeting Gaps on Urban Side

GoI data on CSS transfers to sample States does not match data collected from State government departments 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! 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. Numerous examples of discrepancies in both urban and rural transfers Indicates Budget Management issues at several levels: within GoI Ministries intergovernmental fiscal transfers within State Government Departments 3) POOR M&E: Mismatch between GoI and State Data

GoI data on CSS transfers to sample States does not match data collected from State government departments

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!

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.

Numerous examples of discrepancies in both urban and rural transfers

Indicates Budget Management issues at several levels:

within GoI Ministries

intergovernmental fiscal transfers

within State Government Departments

Non-Cash transfers not factored in the value of intergovernmental system Land and capital goods transfers and acquisition essential to water supply grants such as ARWSP & AUWSP Discussions in sample States revealed that these water supply CSS have implicit transfers (land and equipment) but not factored into value of transfer GoI and State government guarantees for loans and market access necessary for loans under most grants defaulted loans as de facto grants A.P. and Maharashtra’s active guarantee policies State water agencies, MJP in Maharashtra and KWA in Kerala, have history of default Labour inputs for programmes (i.e. SJSRY and SGRY) Technical training Value-added to local resources base Necessary to approximate the market value of these transfers to reveal “real” quantum and distribution of grant funding across States 3) POOR M&E: Non-Cash Transfers Not Addressed

Non-Cash transfers not factored in the value of intergovernmental system

Land and capital goods transfers and acquisition

essential to water supply grants such as ARWSP & AUWSP

Discussions in sample States revealed that these water supply CSS have implicit transfers (land and equipment) but not factored into value of transfer

GoI and State government guarantees for loans and market access

necessary for loans under most grants

defaulted loans as de facto grants

A.P. and Maharashtra’s active guarantee policies

State water agencies, MJP in Maharashtra and KWA in Kerala, have history of default

Labour inputs for programmes (i.e. SJSRY and SGRY)

Technical training

Value-added to local resources base

Necessary to approximate the market value of these transfers to reveal “real” quantum and distribution of grant funding across States

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

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

GoI CSSs less Than 25% of financing for basic watsan-related infrastructure provision 15% in Andhra Pradesh 25% in Maharashtra and Kerala State Plan Transfers and institutional borrowing comprise majority share of intergovernmental fiscal flows State officials interviewed felt that CSS funds could have greater impact as “untied” budgetary support for ongoing State sector assistance programmes 4) CSS NOT ALIGNED: GoI CSSTransfers Only Part of the Picture

GoI CSSs less Than 25% of financing for basic watsan-related infrastructure provision

15% in Andhra Pradesh

25% in Maharashtra and Kerala

State Plan Transfers and institutional borrowing comprise majority share of intergovernmental fiscal flows

State officials interviewed felt that CSS funds could have greater impact as “untied” budgetary support for ongoing State sector assistance programmes

States have own resources for supporting water supply, sanitation and slum / rural poor services. Ready access to tax resources, institutional borrowing, bonds and local govt. contribution that some feel serve as substitute to CSS In contrast, CSS “Plagued” by paperwork, delays, “unachievable” targeting, lack of responsiveness ANDHRA PRADESH - 60% of funding from institutional borrowing and local contributions MAHARASHTRA - 60% of funding from institutional borrowing and State Plan transfers KERALA – 48% from state plan transfers and 10% from institutional borrowing 4) CSS NOT ALIGNED: States have own Resources for WSS

States have own resources for supporting water supply, sanitation and slum / rural poor services.

Ready access to tax resources, institutional borrowing, bonds and local govt. contribution that some feel serve as substitute to CSS

In contrast, CSS “Plagued” by paperwork, delays, “unachievable” targeting, lack of responsiveness

ANDHRA PRADESH - 60% of funding from institutional borrowing and local contributions

MAHARASHTRA - 60% of funding from institutional borrowing and State Plan transfers

KERALA – 48% from state plan transfers and 10% from institutional borrowing

Disparity in level of community ownership and O&M of infrastructure under CSS Local contributions high as 30% in A.P. and low as 5% in Maharashtra Found that service providers with greater community contribution have more sustainable O&M 4) CSS NOT ALIGNED: Local Contribution for WSS

Disparity in level of community ownership and O&M of infrastructure under CSS

Local contributions high as 30% in A.P. and low as 5% in Maharashtra

Found that service providers with greater community contribution have more sustainable O&M

HUDCO/LIC play a dominant role in financing of basic services A.P. (31%), Maharashtra (30%) and Kerala (10%) A.P. and Maharashtra have urban infrastructure development funds and rural borrowing through State Guarantees Kerala has limited State Guarantees and now relies on land mortgage for institutional borrowing High rate / risk of default and hence “De Facto” grants to Local Bodies Need for alternate private institutional lenders 4) CSS NOT ALIGNED: Role of Institutional Borrowing

HUDCO/LIC play a dominant role in financing of basic services

A.P. (31%), Maharashtra (30%) and Kerala (10%)

A.P. and Maharashtra have urban infrastructure development funds and rural borrowing through State Guarantees

Kerala has limited State Guarantees and now relies on land mortgage for institutional borrowing

High rate / risk of default and hence “De Facto” grants to Local Bodies

Need for alternate private institutional lenders

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

WS services are financed through variety of sources other than CSS ANDHRA PRADESH – ULB/PRI contribution and HUDCO/LIC loans are pivotal to WS provision Municipalities are required to Bear 50% of Cost of water supply schemes under HUDCO/LIC assistance pattern MAHARASHTRA -- WS Bonds and HUDCO/LIC loans comprise greatest share of WS financing Officials felt that Urban WS CSS funding should be devolved to ULBs KERALA – 30-35% of State Plan Budget devolved as “Untied” Transfers to Local Bodies Untied funds broadly earmarked for sector intervention, such as infrastructure and poverty alleviation programmes 4) CSS NOT ALIGNED: Water Supply Not Dependent on CSS

WS services are financed through variety of sources other than CSS

ANDHRA PRADESH – ULB/PRI contribution and HUDCO/LIC loans are pivotal to WS provision

Municipalities are required to Bear 50% of Cost of water supply schemes under HUDCO/LIC assistance pattern

MAHARASHTRA -- WS Bonds and HUDCO/LIC loans comprise greatest share of WS financing

Officials felt that Urban WS CSS funding should be devolved to ULBs

KERALA – 30-35% of State Plan Budget devolved as “Untied” Transfers to Local Bodies

Untied funds broadly earmarked for sector intervention, such as infrastructure and poverty alleviation programmes

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

4) CSS NOT ALIGNED: Sanitation Not Properly Addressed Intergovernmental financing pattern for sanitation support is different than WS No dedicated State-level grant programmes for sanitation, except State matching share for CSS A.P. rural sanitation assistance mainly through State Guaranteed HUDCO loans Urban Sanitation CSS linked to other sectors, such as housing, and thereby overshadow sanitation assistance Officials felt that convergence of Urban CSS is desirable

Intergovernmental financing pattern for sanitation support is different than WS

No dedicated State-level grant programmes for sanitation, except State matching share for CSS

A.P. rural sanitation assistance mainly through State Guaranteed HUDCO loans

Urban Sanitation CSS linked to other sectors, such as housing, and thereby overshadow sanitation assistance

Officials felt that convergence of Urban CSS is desirable

With exception of Megacities Scheme, State Officials and Local Bodies felt a serious deficiency in Urban WS funding through CSS 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 Urban WS Support should be broadened to cover all ULBs and locally-driven 4) CSS NOT ALIGNED: Targeting Gaps on Urban Side

With exception of Megacities Scheme, State Officials and Local Bodies felt a serious deficiency in Urban WS funding through CSS

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

Urban WS Support should be broadened to cover all ULBs and locally-driven

Recommendations

4 SETS OF RECOMMENDATIONS Targeting gaps must be addressed Socio-economic groups Different categories of local bodies Uncovered sectors of WSS such as expansion of existing WS services, solid waste management and sewerage Intergovernmental transfers should be better designed Clearer role of CSS within context of competing transfers Realistic eligibility requirements and matching shares Better public budget management system Timely release of CSS transfer installments Predictability in annual transfers for multi-year planning Inclusion of non-cash transfers

Targeting gaps must be addressed

Socio-economic groups

Different categories of local bodies

Uncovered sectors of WSS such as expansion of existing WS services, solid waste management and sewerage

Intergovernmental transfers should be better designed

Clearer role of CSS within context of competing transfers

Realistic eligibility requirements and matching shares

Better public budget management system

Timely release of CSS transfer installments

Predictability in annual transfers for multi-year planning

Inclusion of non-cash transfers

A clearer system of incentives must be created Better alignment of incentives between CSS Better alignment of incentives between CSS and other intergovernmental transfers Promote local government contribution for services Greater autonomy at local level for sector development strategy A better framework for monitoring and evaluation is needed Develop more refined grant assessment criterion and milestones, and reward good performance Improve the budget monitoring system (alignment between GoI and State data) Value and monitor non-cash transfers 4 SETS OF RECOMMENDATIONS

A clearer system of incentives must be created

Better alignment of incentives between CSS

Better alignment of incentives between CSS and other intergovernmental transfers

Promote local government contribution for services

Greater autonomy at local level for sector development strategy

A better framework for monitoring and evaluation is needed

Develop more refined grant assessment criterion and milestones, and reward good performance

Improve the budget monitoring system (alignment between GoI and State data)

Value and monitor non-cash transfers

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