Missing Public Funds and Targeting:
Evidence from an Anti-Poverty
Transfer Program in Indonesia
November 24, 2011
Daniel Suryadarma, ANU and
Chikako Yamauchi, ANU and GRIPS
• Loss of public resources due to corruption
or mismanagement can hinder targeting
performance & public spending efficacy
• Only a fraction of public funds are spent on
the intended purposes – Direct measure of disbursement and receipt
– Reinikka and Svensson (2004) [80%] and Olken
(2006) [18%]
Introduction
• A local government in poor areas plays an
important role in distribution
– Increasingly popular usage of community-
based distribution scheme
• Involve beneficiaries in local resource management
• Shift in the attitude towards collective action (e.g., Ostrom)
• Local capture, incompetent local government
• Relatively little is known about local
characteristics - particularly those of local
government - associated with more
successful delivery of public resources
Issue 1: Local government
• Missing public funds are often not
accounted for, when targeting performance
is studied
– Targeting problem = how much public
resources are received by intended
beneficiaries (the poor)?
– Measure = [funds received by the poor] / [total
funds received by all]
– Total (denominator) might be already reduced
by corruption or mismanagement
Issue 2: Targeting problem
• Quantify the amount of missing funds in
Indonesia’s Inpres Desa Tertinggal (IDT)
• Examine targeting performance with and
without consideration of missing funds
• Investigates pre-existing local conditions
associated with more successful
distribution of public funds
This Paper
• On average 70 percent of public funds reached
intended beneficiaries
• Without taking this into account, conventional
targeting measure suggests pro-poor distribution,
while with the loss, it implies slightly pro-rich
distribution
• Districts which initially had many organized
village governments exhibited high receipt
Overview of the Findings
• First evidence linking the literature on
targeting and the literature on public
resource delivery
Contribution 1
• First evidence empirically showing
association between pre-existing local
government’s capability and the efficiency
of public resource delivery
– 3rd evidence using objective measure • Previous studies used subjective corruption indicators
• Perception may underestimate the true level of corruption (Olken, 09)
– Rich explanatory variables • Pre-existing indicators for village governance
– Panel outcome variable
Contribution 2
• Confirm the importance of lost public
resources to explain that:
– Effect of public spending on growth and other
outcomes is insignificant (Landau (1986) , Filmer and Prichett (1999)
– Countries perceived to be corrupt show worse
outcomes (Barrow (1991), Mauro (1995), Azfar and Gurgur (2007))
– Effect of public spending depends on
perceived level of corruption (Rajkumar and Swaroop (2008) and
Suryadarma (2008))
– Other explanations
• limited redistributive capacity (La Porta et al. (1999) )
• differential budget allocation (Mauro (1998) and Gupta, et al. (2001))
Contribution 3
Background
Indonesia
• Indonesia
– 2008 Corruption Perception Index (Transparency
International)
– 126th out of 180 countries
• tie with Eritrea, Ethiopia, Honduras, Uganda..
• Inpres Desa Tertinggal (IDT, 1994-97)
– “Presidential Instruction for Left-Behind Villages”
Treasurers of groups of
eligible households in
“Poor” villages (20,000) Non-poor villages (40,000)
Indonesian Government
Grant of 20 million rupiah
A fund for business loans
IDT: grants to poor villages
Selection of eligible households
• National guideline
– Select poor households (eligible for IDT
loans) in each selected village
– A village head and a local government agency
(LKMD, Village Community Resilience Board)
facilitate the selection
– We explore characteristics of these local
institutions
– Eligible households are formed into
community groups “pokmas”
Treasurers of pokmas
Central Government
Flow of money
Province Government
District Government
Subdistrict Government Financial institution
[1] proposal [2] approval
certificate [3]
[4] receipt
– Flow of documents
– Flow of money
Village head
Take-up rate was high Province 1994 1995
Aceh 100.00 92.40
Sumatera Utara 100.00 100.00
Sumatera Barat 100.00 100.00
Riau 100.00 100.00
Jambi 100.00 100.00
Sumatera Selatan 100.00 99.10
Bengkulu 100.00 100.00
Lampung 100.00 100.00
DKI Jakarta 100.00 100.00
Jawa Barat 100.00 100.00
Jawa Tengah 100.00 100.00
Yogyakarta 100.00 100.00
Jawa Timur 100.00 100.00
Kalimantan Selatan 100.00 100.00
Kalimantan Barat 100.00 100.00
Kalimantan Tengah 100.00 100.00
Kalimantan Timur 100.00 100.00
Sulawesi Selatan 100.00 92.42
Sulawesi Utara 100.00 92.47
Sulawesi Tengah 100.00 100.00
Sulawesi Tenggara 100.00 100.00
Bali 100.00 100.00
NTB 100.00 100.00
NTT 100.00 100.00
Timor Timur 100.00 99.73
Maluku 100.00 100.00
Irian Jaya 99.71 71.38
Average 99.99 98.06
Scope
• Scope
– 41% of communities were funded at least once
– Total grant value per village = Rp.45 million
– 90 percentile annual PCE = Rp. 999 thousand
– 28% of households received a loan
– Average yearly loan size (Rp.124 thousand) =
27% of recipients’ annual PCE (Rp.462
thousand)
Village-level corruption
• Major allocation decisions were made at the village
level
• The share of receipt measures a loss which
occurred at the village level
• between financial institutions and households in
designated communities
• treasurers and village officials can be involved • compulsory ‘saving’ or ‘fees’, collective purchase
Data &
Amount of Missing Funds
• SUSENAS 1993 & 1997 – Household socio economic survey
– Repeated cross-section
– representative at the district level
• IDT data – indicate which villages received IDT between 1994 and
1996
• PODES 1993 – Census of villages
Data Sources
• % entitled funds actually received
= Value of funds received/ Value of funds disbursed
• Value of funds disbursed to each district
= 20 million rupiah * number of villages funded
• Value of funds received in each district
= Total value of loans reported to have been received by households in IDT villages – Measure of resource delivery
• Any households in IDT villages are regarded as beneficiaries
• Targeting within IDT villages is evaluated in the next section
• Households’ creditworthiness is not taken into account
% entitled funds actually received
• Has anyone in the household been a member of pokmas?
• Has anyone received a loan from IDT?
• How much?
• What year?
• E.g.1: a rich household without a pokmas member receives funds -> unlikely reported in SUSENAS
• E.g.2: a rich household with a pokmas member receives funds -> likely reported
More about value of funds received
Estimation of value of receipt
• Sampling weight for households in IDT villages
= reciprocal of the probability for a household of being selected for interview given being in IDT village
= Total number of households in the 1993 PODES / Number of sampled households in the 1997 SUSENAS
• Poorer districts contain a larger number of sampled households • Weighted regressions are used with the number of
sampled households as a regression weight
0
.05
.1.1
5
Fra
ction
0 1 2 3 4 5 6 7 8 9 10Overall share of funds received as IDT benefits (1994-96)
Overall share of receipt
Mean = 0.71 (N = 263)
Log of overall share of receipt * 100 0
.02
.04
.06
Fra
ction
0 2 4 6 8Log of overall share of funds received as IDT benefits * 100 (1994-96)
Targeting Performance
• Predict per capita household expenditure
– PCEhat97 = βhat
9394*X97
• Rank households in a district
• Define the poor as the bottom 20%
• Measure the share of funds received by
the bottom quintile
• If 20% -> universal allocation
• If > 20% -> better targeting than universal
allocation
Targeting performance measure
Targeting w. & w/out missing funds
(1) Baseline (2) Scenario 1 (3) Scenario 2
Σi=Poor ri
/ Σi=All ri
[Σi=Poor ri +
0.2*(D-Σi=All ri)]
/ D
Σi=Poor ri
/ D
27.4% 24.9% 20.1%
p-value=.107 p-value=.000
Average share of overall IDT funds received by each district’s
poorest 20% households (N=251)
Significantly worse than (1),
the mean indicates worse than
universal distribution
ri = value received by
household i
D = value of disbursement
Correlates of Receipt:
Conceptual Framework
• General level of political awareness – Living standard
• Median PCE
– Village residents’ education & information exposure • Average number of years of education among adults aged 20-60
• Share of adults aged 20-60 who read newspaper last week
• Share of adults aged 20-60 who listened a radio last week
– Village government’s capability • LKMD’s self-reported organization skills
• Share of villages with female heads / educated heads
• Average age / tenure of village heads
Factors Related to Public Resource Delivery
• Inequality in political awareness / bargaining power – Within village coefficient of variation in PCE
– Homogeneity in citizenship (Hafindahl index)
• Demand for credit – Average village population (number of households)
– Shares of villages which had a bank, other public credit programs, good inter-village road
– Share of villages which were funded for infrastructure
• Geographical characteristics – Average distance from district capital
– Density (population per hectare)
– Share of urban communities
Factors Related to Public Resource Delivery
• Village Community Resilience Board – Lembaga Ketahanan Masyarakat Desa (LKMD)
• National institution in charge of the implementation of national programs at the village level – members = local residents, appointed by the village head
• “Less organized” – (1) does not exist
– (2) only exists in very basic form
• “Organized” – (3) exists and is able to develop and conduct work projects utilizing
grants from the national government matched with contributions of community members
– (4) exists and forms village development plans, keeps reports in order, and has well-functioning sections
What’s organized LKMD?
Correlates of Receipt:
Empirical Strategy
Yij = α1 + β1 Xij + γ1 Dj + uij
i: district
j: island
Xij: Pre-determined covariates
Dj: island fixed effects
uij: error term, assumed to be independent across
districts
– Weighted by the original sample size
– Tobit and linear models are used
Correlates of overall share of received
funds: island-level fixed effects model
I
Sumatera, Java, Sulawesi, Kalimantan, the group of Bali and
Nusa Tenggara islands, and the group of Eastern islands
Islands?
Changes in correlates of recept: island
fixed effects model
Yijt = α2 + α296 Dt
96 + α297 Dt
97
+ β2 Xij + β296 [Xij*Dt
96] + β297 [Xij*Dt
97]
+ Dj + γ296 [Dj*Dt
96] + γ297 [Dj*Dt
97] + uijt
t: year ( t = 1995-1997)
Dt96
= 1 if year = 1996
Dt97
= 1 if year = 1997
• Weighted by the original sample size
• Tobit and linear models are used
Yijt = α2 + α296 Dt
96 + α297 Dt
97
+ β296 [Xij*Dt
96] + β297 [Xij*Dt
97]
+ γ296 [Dj*Dt
96] + γ297 [Dj*Dt
97] + µi + uijt
t: year ( t = 1995-1997)
Dt96
= 1 if year = 1996
Dt97
= 1 if year = 1997
µi: district-level fixed effects
• Weighted by the original sample size
• Linear model only
Changes in correlates of receipt:
district-level fixed effects model
Correlates of Receipt: Results
Correlates of overall receipt
1SD increase (0.3) in the share of organized villages: 15 ppt increase (21 % of the mean)
Significant level: + = 10%, * = 5%, ** = 1%.
Outcome = Overall share of funds received by households (1) (2)
in designated communities Tobit Island FE
Log of median PCE (in thousand Rp) -0.378 -0.360
Average coef of var within village in household PCE [standardized] 0.056 0.050
Average year of education among adults aged 20-60 0.081* 0.078
Log of average number of households -0.066 -0.041
Log of average density (population per hectare) -0.049 -0.061
Share of urban communities -0.960** -0.695*
Log of average distance from the district capital -0.220*** -0.208***
Share of villages with organized government 0.549** 0.505**
Average age of village heads -0.015 -0.012
Average tenure of village heads 0.039 0.029
Share of villages with female heads 1.186 0.408
Share of villages with heads who attained high school or above -0.630* -0.540
Share of villages funded under infrastructure programs -1.555** -1.527**
Number of districts 263 263
Number of districts censored at zero 15 15
Log likelihood -289.41 -252.62
F-stat 2.25
Changes in correlates of receipt
Significant level: + = 10%, * = 5%, ** = 1%.
Outcome = Overall share of funds received by households (3) (4) (5) (6) (7)
in designated communities Island FE District FE
94 bench-
mark
Change in coeff Change in coeff
94 & 95 94 & 96 94 & 95 94 & 96
Log of median PCE (in thousand Rp) -0.392 0.334 0.137 0.596 0.343
Ave coef of var within village in household PCE [standardized] -0.050 0.050 0.146 0.033 0.092
Average year of education among adults aged 20-60 0.132** -0.119** -0.095 -0.025 -0.056
Log of average number of households 0.062 -0.131 -0.382** 0.022 -0.189
Log of average density (population per hectare) -0.063 0.019 0.001 0.022 0.033
Share of urban communities -0.547 0.653* -0.178 0.493 -0.151
Log of average distance from the district capital -0.041 0.019 -0.275 0.046 -0.237
Share of villages with organized government 0.160 -0.082 0.873** -0.137 0.802**
Average age of village heads -0.029 0.006 0.035 -0.012 0.028
Averate tenure of village heads 0.077 -0.033 -0.097 -0.038 -0.101
Share of villages with female heads 2.030 -0.206 -4.366 -0.310 -4.798
Share of villages with heads who attained high school or above -0.264 -0.041 -0.331 -0.149 -0.209
Share of villages funded under infrastructure programs -0.967 0.340 -1.285 1.375 -0.075
Number of districts 771 771
Number of districts censored at zero 85 85
Log likelihood -947.24 -590.31
F-stat 1.76 1.04
What to Take Away?
Summary of Findings Conclusions
• Only 71 percent of public funds reached intended
beneficiaries
• Once missing funds are taken into account, it is revealed
that IDT’s targeting performance was poorer
• Share of receipt was 15 ppt (21%) higher in districts with
higher share of villages with organized governments
– Higher record keeping & organizational capabilities
• Not systematically correlated with other village
government characteristics (heads’ education, gender,
and tenure)
Summary of Findings Implications • It is important to take into account leakage in
evaluating targeting performance
• Imply that training / monitoring of local
government officers might limit the
disappearance of public funds and improve public
spending efficacy
– Cannot distinguish dishonesty and incompetence, but
Olken (2007) suggests dishonesty is not the only factor
• Evaluating impact of different types of training
programs would be fruitful future research
• Responses were not used by the central government for monitoring – Only the list of participants
• Responses were not used by the central government to decide the funding status of the village in coming years – A separate village census was conducted for the purpose
• Repayment obligation was likely to be perceived very weakly – Repayment rate = 19 percent
Accuracy of responses
Treatment of loans from different sources
Data source =
1997 SUSENAS 1994 1995 1996
Treatment in
the upper
bound
Treatment in
the lower
bound
Direct 85.33 73.88 73.21 As it is As it is
Rotated 13.77 23.86 23.64 0 0
DK 0.81 2.12 2.20 As it is 0
Direct & Rotated 0.08 0.13 0.95
Divided by
two
Divided by
three
Direct & DK 0.00 0.02 0.00
Divided by
two
Divided by
three
Rotate & DK 0.00 0.00 0.00 n.a. n.a.
Aligning reference periods
• Value of receipt refers to a calendar year
• Value of disbursement refers to a fiscal year
1994 1995 1996
II III IV I II III IV I II III IV
• 1994/95 receipt = total 1994 receipt + ¼ * 1995 receipt
• 1995/96 receipt = ¾ * 1994 receipt + ¼ * 1996 receipt
• 1996/97 receipt = total 1996 receipt
Adjust for lower take up rates
• Reduced the value of disbursement for provinces where take-up rates were lower than 100 percent
Variable Mean Std Dev
Coef
{R>1}
Citizenship fragmentation 0.670 0.022 0.004
Religion fragmentation 0.561 0.116 -0.001
Share of villages with bank 0.217 0.250 0.011
Share of villages with previous credit program 0.296 0.265 -0.002
Share of villages with year-round roads 0.844 0.217 0.003
Average village density (population / ha) 4.876 9.366 0.946
Share of villages with advanced LKMD 0.767 0.266 -0.010
Median per capita expenditure (PCE) (Rp.1000) 34.986 12.780 0.782
Inequality: 90/10 PCE Ratio 3.373 0.810 -0.084
Data Issue: Excess Receipt
Differences in observables between villages with R<=1 and R>1
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