Aashish Mishra WSP-SA Presentation 2004

REVIEW OF NATIONAL AND

STATE SCHEMES FOR WSS

INVESTMENT

Aashish Mishra, Consultant

Water and Sanitation Program – South Asia

October 21, 2004

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AGENDA

Objective and Methodology

Challenges

Background

Findings

Recommendations

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Objectives and Methodology

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OBJECTIVE AND METHODOLOGY

ObjectiveTo 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

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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

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Background

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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

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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

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ILCS3%

IDSMT8%

SJSRY11%

AUWSP13%

VAMBAY14%

Megacities15%

Night Shelter Scheme0% SWM in Airforce Tow ns

1%

NSDP35%

CRSP/TSC1%

SAY0%

SGSY5%

NSAP/Anna.8%

SGRY33%

ARWSP/MoU Program14%

IAY15% PMGY

24%

Avg. Urban CSS = INR 791 Crore

Avg. Urban CSS Per-Capita = INR 28Avg. 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

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FINDINGS FROM NATIONAL REVIEW

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KEY FINDINGS FROM NATIONAL REVIEW

1) 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

2) 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

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TARGETING GAPSVague 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

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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

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1) INEFFICIENCT TARGETING:Weak Correlation with State Population

y = -29.574x + 316.49R2 = 0.2341

y = 60.294x + 105.97R2 = 0.5868

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State Population (INR Crore)

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& T

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Allocation Per Cap (INR) Total Allocation (INR Crore) Linear (Allocation Per Cap (INR)) Linear (Total Allocation (INR Crore))

U.P.

Arunachal Pradesh

Mizoram

Sikkim

U.P.

Maharashtra

A.P.

Bihar

Rajasthan

Karnataka

Figure 1: Linear Relationship between CSS Transfers and State Population

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• 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!


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Population Quartile

State Population No. of States Average Annual

Transfer Per-Capita (INR)

I Less than 2 Crores 4 86

II 2 Crores > 4 Crores 7 21

III 4 Crores > 7 Crores 5 38

IV Greater than 7 Crores

5 24

** NE States excluded from the ranking

Table 1: State Population Classification and Average Annual Transfers Per-Capita


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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


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•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

State GDP Per Capita & Grant Allocations Per Capita

y = -0.0014x + 233.2R2 = 0.0018

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State GDP Per Capita

Gran

t Allo

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er C

apita

(INR

)

Goa

Arunachal Pradesh

Mizoram

Sikkim

Bihar Punjab

1) INEFFICIENT TARGETING:Weak Correlation with State’s Income Level

Figure 2: Linear Relationship between CSS Transfers and State GDP

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Figure 3: Linear Relationship between CSS Transfers and State BPL Population

y = 34.292x + 88.989

R2 = 0.1338

y = 15.579x + 138.95

R2 = 0.0344

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State BPL Population (Crore)

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SS

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BPL Population - all States BPL Population -- NE excluded

Linear (BPL Population - all States ) Linear (BPL Population -- NE excluded)

1) INEFFICIENT TARGETING:Weak Correlation with State’s Poverty Level

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Grant Per BPL Person State BPL Population

Figure 4: State BPL Population and CSS Transfer Per-BPL Person


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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!


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

Table 2: Correlation Between Annual Sector Transfers and State Uncovered Population

Target Population Annual Grant Transfers

Slope R-Squared

States’ Urban Sanitation Uncovered

ILCS and NBA of VAMBAY

8.6 52%

States’ Rural Sanitation Uncovered

TSC 2.1 39%

States’ Rural Water Supply Uncovered

ARWSP 17.3 27%

States’ Urban Water Supply Uncovered

AUWSP 3.7 10%

1) INEFFICIENT TARGETING: Correlation with State’s Service Coverage Level

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Table 3: Average Sector Grant Transfer per WATSAN Uncovered PersonPER SANITATION

UNCOVERED PERSON (INR)PER WATER SUPPLY

UNCOVERED PERSON (INR)

Rural Urban Rural Urban

Bihar 4.84 0.00 16.26 3.87

Orissa 6.55 0.31 55.49 7.91

West Bengal 9.93 11.11 57.16 2.07

Uttar Pradesh 6.49 9.41 52.81 26.00

Madhya Pradesh 12.63 11.45 62.19 12.20

Tamil Nadu 21.78 5.34 75.03 5.36

Andhra Pradesh 16.69 17.26 89.04 2.12

Haryana 4.17 0.00 77.69 54.06

Maharashtra 2.98 3.49 134.37 5.76

Karnataka 0.20 5.36 131.26 10.52

Gujarat 0.38 8.71 132.44 13.28

Assam 11.56 0.00 149.30 29.45

Rajasthan 1.02 6.03 171.83 18.51

Kerala 40.00 77.91 109.86 6.79

Punjab 1.79 0.68 256.68 0.00

Correlation with State’s Service Coverage Level

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

ILCS3%

IDSMT8%

SJSRY11%

AUWSP13%

VAMBAY14%

Megacities15%

Night Shelter Scheme0% SWM in Airforce Tow ns

1%

NSDP35%

CRSP/TSC1%

SAY0%

SGSY5%

NSAP/Anna.8%

SGRY33%

ARWSP/MoU Program14%

IAY15% PMGY

24%

Avg. Urban CSS = INR 791 Crore

Avg. Urban CSS Per-Capita = INR 28Avg. Rural CSS = INR 10,911 Crore

Rural CSS Per-Capita = INR 136

2) INEFFICIENCT VEHICLE:QUANTUM & FLOW OF CSS TRANSFERS

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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

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Annual CSS Transfers (INR Crore) Annual Transfer Change FY

2000/01 FY 2001/02

FY 2002/03

Average Transfer

2001-2002

2002-2003

Average Change

Urban CSS Transfers AUWSP 64 94.98 121.94 93.64 48% 28% 38% NSDP 365.81 385 202.53 317.78 5% -47% -21% Megacities 85.9 115.4 119.9 107.07 34% 4% 19% IDSMT 56.17 75.68 85.43 72.43 35% 13% 24% ILCS 29.33 9.84 29.83 23.00 -66% 203% 68% Night Shelt. 1.36 0.66 3.51 1.84 -51% 432% 190% SWM Airfce 0.74 5 5 3.58 576% 0% 288% SJSRY 85.09 38.31 101.13 74.84 -55% 164% 55% VAMBAY 0 73.56 217.6 145.58 196% 98% Urban Sub. 688.40 798.43 886.87 791.23 16% 11% 14%

Rural CSS Transfers ARWSP 1523 1723 1534.3 1593.43 13% -11% 1% CRSP/TSC 130.85 129.5 140.08 133.48 -1% 8% 4% IAY 1664.17 1527 1725 1638.72 -8% 13% 2% NSAP/Anna 800.35 1135 680 871.78 42% -40% 1% PMGY 2500 132.25 2747 1793.08 -95% 1977% 941% SAY 6.31 6.31 6.31 6.31 0% 0% 0% SGRY 2804.39 3250 4440 3498.13 16% 37% 26% SGSY 544.94 500 710 584.98 -8% 42% 17% Rural Sub. 9974.01 8403.06 11982.69 10119.92 -16% 43% 13%

TOTAL 10662.41 9201.49 12869.56 10911.15 -14% 40% 13%

Annual CSS Transfers

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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

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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

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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&EBasic expenditure tracking

Evaluation of programme outcomesReform milestones Reform AgendaIncentives 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

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Key Findings - State/local level

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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

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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

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Non-Cash transfers not factored in the value of intergovernmental systemLand 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 transferGoI 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 baseNecessary 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

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Govt. Guarantee

land and capital transfers

STATE GOVT. (UDD, PRD)

ULBsPRIs

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

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Table 3: Average Annual Transfers from FY 2000/01 to 2003/04 in INR Crore

Andhra Pradesh Maharashtra Kerala

% State Total

% State Total

% State Total

GoI CSS Transfers

195.07 14.43% 207.43 25.15% 90.03 24.96%

State Matching Share

166.66 12.32% 116.91 14.18% 61.08 16.93%

State Grant Transfers

162.50 12.02% 214.87 26.06% 172.31 47.77%

Local Government Contribution

406.43 30.06% 37.40 4.54% N/A

Institutional Borrowing

421.60 31.18% 248.05 30.08% 37.31 10.34%

Total 1352.27 824.68 360.74

4) CSS NOT ALIGNED:KEY FINDINGS FROM STATE LEVEL

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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

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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

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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

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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

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4) CSS NOT ALIGNED: INTERGOVERNMENT FINANCING OF WATER SUPPLY


Megacities 50.42 7.89% 60.39 8.62% N/A

AUWSP 8.27 4.89% 6.33 0.90% 3.96 2.10%

ARWSP 83.82 13.13% 110.18 15.73% 95.24 50.52%

Swajaldhara 16.16 2.53% 24.22 3.46% 2.62 2.10%

State WS Program 31.25 4.89% 49.4 26.20%

WS Bonds 214.87 31.12%

Local Contribution 286.08 44.80% 37.41 5.34%

HUDCO/LIC Loan 162.63 25.47% 247.08 35.27% 37.31 19.79%

Total 638.63 700.49 188.5

Table 5: Average Annual Transfers for Water Supply Provision (from FY 2000/01 to 2003/04) in INR Crores

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WS services are financed through variety of sources other than CSSANDHRA 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

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ILCS 3.33 0.85% 0.75 2.62%

NBA / VAMBAY 14.83 3.81% 8.31 28.89% 5.14 39.21%

TSC 44.79 11.50% 19.68 68.48% 7.98 60.79%

Rural SanitationContribution

120.36 30.91%

Rural Sanitation HUDCO Loan

206.12 52.93%

Total 389.43 28.74 13.12

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

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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

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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

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Recommendations

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4 SETS OF RECOMMENDATIONS

A. 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 sewerageB. 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

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C. 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

strategyD. 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

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Questions?

Aashish Mishra WSP-SA Presentation 2004

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