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Study on Efficacy of Watsan Subsidies in India
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REVIEW OF NATIONAL AND
STATE SCHEMES FOR WSS
INVESTMENT
Aashish Mishra, Consultant
Water and Sanitation Program – South Asia
October 21, 2004
2
AGENDA
Objective and Methodology
Challenges
Background
Findings
Recommendations
3
Objectives and Methodology
4
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
5
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
6
Background
7
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
8
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
9
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
10
FINDINGS FROM NATIONAL REVIEW
11
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
12
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
13
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
14
1) INEFFICIENCT TARGETING:Weak Correlation with State Population
y = -29.574x + 316.49R2 = 0.2341
y = 60.294x + 105.97R2 = 0.5868
-400.00
-200.00
0.00
200.00
400.00
600.00
800.00
1000.00
1200.00
0.00 2.00 4.00 6.00 8.00 10.00 12.00 14.00 16.00 18.00
State Population (INR Crore)
Allo
cati
on
Per
Cap
(IN
R)
& T
ota
l Allo
cati
on
(IN
R C
rore
)
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
15
• 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!
1) INEFFICIENCT TARGETING:Weak Correlation with State Population
16
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
1) INEFFICIENCT TARGETING:Weak Correlation with State Population
17
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
1) INEFFICIENCT TARGETING:Weak Correlation with State Population
18
•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
0.00
200.00
400.00
600.00
800.00
1000.00
1200.00
0.00 5000.00 10000.00 15000.00 20000.00 25000.00 30000.00 35000.00 40000.00 45000.00 50000.00
State GDP Per Capita
Gran
t Allo
catio
ns P
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
19
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
0.00
50.00
100.00
150.00
200.00
250.00
300.00
350.00
0.00 0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00 4.50
State BPL Population (Crore)
Ave
rage
Ann
ual C
SS
Tra
nsfe
r
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
20
0.00
1000.00
2000.00
3000.00
4000.00
5000.00
6000.00
Bih
ar
Oris
sa
Utta
r P
rade
sh
Mad
hya
Pra
desh
Ass
am
Wes
t Ben
gal
Man
ipur
Tam
il N
adu
Mah
aras
htra
Trip
ura
Pun
jab
Meg
hala
ya
Har
yana
Kar
nata
ka
Ker
ala
Nag
alan
d
Guj
arat
Sik
kim
Raj
asth
an
And
hra
Pra
desh
Aru
nach
al P
rade
sh
Miz
oram
Him
acha
l Pra
desh
Jam
mu
& K
ashm
ir
Goa
Gra
nt
Per
BP
L P
erso
n (
INR
)
0.00
0.50
1.00
1.50
2.00
2.50
3.00
3.50
4.00
4.50
Sta
te B
PL
Po
pu
lati
on
(C
rore
s)
Grant Per BPL Person State BPL Population
Figure 4: State BPL Population and CSS Transfer Per-BPL Person
1) INEFFICIENT TARGETING:Weak Correlation with State’s Poverty Level
21
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!
1) INEFFICIENT TARGETING:Weak Correlation with State’s Poverty Level
22
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
23
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
24
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
25
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
26
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
27
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
28
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
29
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
30
Key Findings - State/local level
31
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
32
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
33
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
34
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
35
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
36
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
37
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
38
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
39
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
40
4) CSS NOT ALIGNED: INTERGOVERNMENT FINANCING OF WATER SUPPLY
Andhra Pradesh Maharashtra Kerala
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
41
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
42
Andhra Pradesh Maharashtra Kerala
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
43
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
44
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
45
Recommendations
46
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
47
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
48
Questions?