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