00074918.2014.980378
Bulletin of Indonesian Economic Studies
ISSN: 0007-4918 (Print) 1472-7234 (Online) Journal homepage: http://www.tandfonline.com/loi/cbie20
Indonesian Intergovernmental Performance
Grants: An Empirical Assessment of Impact
Blane D. Lewis
To cite this article: Blane D. Lewis (2014) Indonesian Intergovernmental Performance Grants:
An Empirical Assessment of Impact, Bulletin of Indonesian Economic Studies, 50:3, 415-433,
DOI: 10.1080/00074918.2014.980378
To link to this article: http://dx.doi.org/10.1080/00074918.2014.980378
Published online: 03 Dec 2014.
Submit your article to this journal
Article views: 205
View related articles
View Crossmark data
Full Terms & Conditions of access and use can be found at
http://www.tandfonline.com/action/journalInformation?journalCode=cbie20
Download by: [Universitas Maritim Raja Ali Haji]
Date: 17 January 2016, At: 23:33
Bulletin of Indonesian Economic Studies, Vol. 50, No. 3, 2014: 415–33
INDONESIAN INTERGOVERNMENTAL
PERFORMANCE GRANTS: AN EMPIRICAL
ASSESSMENT OF IMPACT
Downloaded by [Universitas Maritim Raja Ali Haji] at 23:33 17 January 2016
Blane D. Lewis
The Australian National University
The government of Indonesia has now piloted two different outputbased performance grants to regions. One focuses on increasing the amount and quality of local
government capital spending. The other provides incentives for local governments
to augment equity investments in their water enterprises and for the enterprises to
use those investments to increase the number of household water connections to
the poor. Impact evaluations of the two grants suggest some reasonably positive
outcomes against the stated objectives. While the assessed impacts may not match
the expected outputs (as argued by many performance grant enthusiasts), these
impacts provide a plausible basis for the sustained development and use of such
grants. The alternative would be to continue to rely exclusively on the equitybased
approaches that have dominated intergovernmental iscal relations in Indonesia
and have led to rather weak local public service outcomes.
Keywords: regional development, decentralisation, intergovernmental iscal relations, performance grants, impact evaluation
JEL classiication: H77, H79, R51
INTRODUCTION
Practitioners in intergovernmental iscal relations increasingly advocate the use of
outputbased performance incentives in grant design (for example, Boadway and
Shah 2007). The general argument is that positive incentives purposely embedded in intergovernmental performance grants can help encourage improvements
in local performance across a wide range of dimensions, including as related to
governance, iscal, or servicedelivery outcomes. Such grants may be particularly
attractive to central governments in cases where vertical or horizontal accountability is weak—that is, where such mechanisms do not play their expected role
of assuring quality outcomes (Dumas and Kaiser 2010; Lewis and Smoke 2014).
Research on outputbased incentive schemes is relatively limited for developing countries. And most reviews of local government incentiverelated approaches
do not focus exclusively on the role of transfers (Lewis and Smoke 2009, 2012).
The few publications on performancebased grants are largely forwardlooking
or draw predominantly on anecdotal evidence in their evaluations of past and
ongoing eficacy (Steffensen and Larson 2005, Shah 2010, Steffensen 2010, Rojas
ISSN 0007-4918 print/ISSN 1472-7234 online/14/000415-19
http://dx.doi.org/10.1080/00074918.2014.980378
© 2014 Indonesia Project ANU
Downloaded by [Universitas Maritim Raja Ali Haji] at 23:33 17 January 2016
416
Blane D. Lewis
2011, Mathur 2012). Most of the literature on performance grants offers something
less than an objective assessment of impacts; the research seems really more of a
brief for the idea that incentive methods must necessarily be effective.
The practical emphasis on performance grants departs somewhat from earlier
perspectives on intergovernmental iscal relations in developing countries. Much
of the previous work in this area focused on the rationales for and the design
and implementation of iscal equalisation grants. The principal view was that
the centre’s role was mainly to assure equal access to iscal resources across local
governments and that vertical and horizontal accountability would make certain
the desired outcomes. However, growing disappointment with the weak service
delivery associated with iscal equalisation and, more broadly, with equitybased
approaches (which might also consider fairness from a historical, geographical, or
servicedelivery point of view) has turned attention to mechanisms that concentrate more directly on service outcomes, such as performance grants (Lewis and
Smoke 2012).
The Indonesian system of intergovernmental iscal relations is primarily equity
based. The most important transfer, Dana Alokasi Umum (DAU), is a iscal equalisation grant, whereby allocations are made as a positive function of iscal needs
and a negative function of iscal capacities of local governments. The speciic
purpose capital grant, Dana Alokasi Khusus (DAK), also focuses to a large extent
on distributing funds to local governments with few iscal resources, especially
those governments that are geographically isolated. Indonesian government
policymakers, however, like those in many countries, have become dissatisied
with what they see as weak service outcomes deriving from the current intergovernmental system and have begun to experiment with alternative, performance
based mechanisms (Lewis 2014). This article analyses the impact of two recently
piloted performance grants: DAK Reimbursement (Proyek Pemerintah Daerah
dan Desentralisasi [P2D2]) and Water Hibah (WH).1
BACKGROUND
P2D2 and WH were piloted during 2011–12 and 2010–11, respectively. P2D2 concentrates on encouraging more and better local government (kabupaten and kota)
capital spending,2 whereas WH intends to inspire kabupaten and kota to make
equity investments in their water enterprises (Perusahaan Daerah Air Minum
[PDAMs]) and, in turn, stimulate PDAMs to establish household water connections for the poor. P2D2 initially covered 68 local governments, while WH
included 35 local governments and their PDAMs. Both programs have since been
1. Hibah means ‘grant’ in Indonesian.
2. P2D2 speciies both intermediate performance indicators and (inal) eligibility criteria.
Performance indicators relate to local government counterpart funding, inancial and technical reporting, and capital spending. Eligibility criteria comprise local government provision of reference unit costs, compliance with national procurement guidelines, compliance
with environmental safeguards, realisation of planned spending outputs, and compliance
with technical standards. Incentive payments are made against eligibility criteria only.
Data are not available on the irst three eligibility criteria; as such, this article focuses just
on capital spending outputs.
Downloaded by [Universitas Maritim Raja Ali Haji] at 23:33 17 January 2016
Indonesian Intergovernmental Performance Grants: An Empirical Assessment
417
rolled out to incorporate a larger number of kabupaten and kota, and the central
government is considering expanding its use of such instruments to cover additional regions and sectors.3
P2D2 focuses on improving local capital spending outputs associated with
the DAK.4 The program deines capital spending performance in terms of the
extent to which DAK allocations were actually spent as planned and the degree to
which spending outputs complied with basic technical standards. Capital spending objectives concentrate on speciic infrastructure subsectors: roads, irrigation,
water, and sanitation. Local government performance against program eligibility
targets is veriied by the central government’s Agency for Finance and Development Supervision (Badan Pengawasan Keuangan dan Pembangunan) after DAK
allocations have been spent.
Successful kabupaten and kota are awarded about 10% of the initial infrastructure grant for relevant subsectors—that is, for their counterpart contributions to
DAK funding, hence the use of the term ‘reimbursement’. The reimbursement is
typically made in the iscal year that follows the satisfaction of project objectives.
In 2011 and 2012, respectively, 84% and 89% of participating local governments
achieved program targets.
WH design is similarly straightforward. On the promise of a grant from the
Ministry of Finance, a kabupaten or kota makes an equity investment in its PDAM;
the PDAM then uses the invested funds to create household water connections.
The promised grant connotes the maximum amount of funds that the ministry is
willing to transfer. The kabupaten or kota is required to invest at least that much
in its PDAM. The maximum investment is associated with a targeted number
of connections to be established by the PDAM. Once the investments have been
made and the water connections have been veriied as operational, the Ministry
of Finance transfers funds to the kabupaten or kota: Rp 2 million per connection for
the irst 1,000 connections and Rp 3 million per connection thereafter, until the
maximum is reached.
WH project participants receive the transfers in the year in which connections
are created and veriied. Local governments and PDAMs participating in the program may have established connections and received transfers in 2010 or 2011 or
both. In WH’s irst two years of operation, 97% of all participating local governments and their PDAMs met equity investment and household water connection targets. During 2010–11, the WH program disbursed Rp 199 billion in grants,
which led to the creation of 77,000 household water connections. This implies that
the cost of a water connection was Rp 2.6 million, on average.
This article empirically assesses the impact of P2D2 and WH, according to
questions about each program’s stated objectives. For P2D2, the examination
investigates two main concerns. First, does P2D2 stimulate local governments to
spend more on capital, in general? Second, and more speciically, do P2D2 local
3. Both pilot projects were developed by international aid agencies. P2D2 is a World Bank
project and WH is an effort led by the Indonesian Infrastructure Initiative, a facility of the
Australian Government Department of Foreign Affairs and Trade.
4. See Lewis’s (2013) article for a discussion of DAK design and the effects of the grant on
local government capital spending.
Downloaded by [Universitas Maritim Raja Ali Haji] at 23:33 17 January 2016
418
Blane D. Lewis
governments use their DAK to generate greater capital spending spillovers than
nonP2D2 local governments?5
The WH analysis focuses on three basic questions. First, does the WH program increase local government equity investment in PDAMs? Second, do equity
investments, in general, stimulate the creation of household water connections?
Third, do WH program participation and attendant performance grants, speciically, encourage the creation of household connections?6
In addressing the above research questions, the analyses are conducted across
treatment and control group kabupaten and kota. The treatment group was deined
as a function of P2D2 and WH program participation. The control group was
derived using propensity score matching (PSM) procedures, which are discussed
further below. Incorporating a control group in the analysis reduces biases associated with the nonrandom (and endogenous7) nature of program participation; at
the same time, it produces a ‘counterfactual’ that provides a useful indication of
the outputs that might have been achieved by local governments in the absence
of program participation.
The data used to conduct the analyses are not abundant. Both pilots were small,
generating a limited number of observations. A dearth of data is not uncommon
in applied analysis in developing countries; in fact, it is more likely to be the rule.
But given the lack of solid empirical evidence on the effects of intergovernmental
performance grants across developing countries, in general, and the speciic signiicance of such grants in Indonesian policy discussions at present, it is arguably
useful to proceed with the examination, even in the absence of ideal data. Still, the
analysis here should be considered preliminary.
DATA AND METHODS
Data
Data on inancial and technical reporting outcomes used in the examination of
P2D2 impact come from the Ministry of Finance and were supplied by the World
Bank. Equity investment and household water connection data employed in
the analysis of WH effects were collected directly from local governments and
their PDAMs and were made available by the Indonesian Infrastructure Initiative. All other data on variables used in both the matching procedures and in
5. It would be preferable to examine capital spending and capital spending spillovers for
roads, irrigation, water, and sanitation infrastructure only, since the project focuses on
those subsectors. Unfortunately, the data are insuficient to allow that. In the aggregate, local infrastructure spending makes up between twothirds and threequarters of total local
capital spending; but available data do not permit the separation of infrastructure spending from total capital spending at the local government level. As such, local government
capital spending across all sectors is taken as a broad proxy for local government capital
spending on infrastructure.
6. Given the lack of data, this analysis is unable to assess the impact of investment on the
establishment of connections for poor households.
7. It is reasonable to assume that some of the same factors that determine project participation may also explain outcomes of interest. As such, treatment may be presumed to be
endogenous (Greene 2012, 888).
Indonesian Intergovernmental Performance Grants: An Empirical Assessment
419
TABLE 1 Variable Summary Statistics
Std
Min
Max
N
P2D2
Local government capital spending 151,982
DAK (speciicpurpose capital grant) 48,559
Local government revenue
722,623
Population
451,906
% population that is urban
35.7
% population that is poor
10.8
Gross regional domestic product
6,603,166
199,194
53,309
446,679
435,580
28.0
6.6
3,482,494
4,239
174
210,376
43,809
3.4
1.4
1,327,098
1,565,788
321,706
2,834,593
2,459,960
100.0
39.5
19,400,000
222
218
222
256
256
254
254
Water Hibah
Equity investment
1,630
Water connections per 1,000 persons
3.2
Hibah
602
Local government revenue
505,574
Population
1,010,122
% population that is urban
58.0
% population that is poor
12.4
% population with access to water
62.2
Gross regional domestic product
7,382,343
2,627
3.8
1,240
370,471
801,481
31.1
5.7
18.9
4,395,347
0
0.1
0
159,928
117,493
9.1
3.2
4.8
2,505,224
14,349
23.5
7,283
2,224,253
4,858,514
100.0
28.1
99.1
26,200,000
105
105
105
105
105
105
105
105
105
Downloaded by [Universitas Maritim Raja Ali Haji] at 23:33 17 January 2016
Variable
Mean
Note: P2D2 = Proyek Pemerintah Daerah dan Desentralisasi. DAK = Dana Alokasi Khusus. P2D2 and
Water Hibah data are pooled across 2011–12 and 2010–11, respectively. All iscal and economic variables are measured in constant (2000) per capita terms, converted using the implicit GDP delator from
Indonesia’s national accounts.
the subsequent impact analyses were provided by the World Bank and initially
sourced from the Ministry of Finance and Badan Pusat Statistik (BPS), Indonesia’s
central statistics agency. Summary statistics for variables used in the examination
of P2D2 and WH treatment and control kabupaten and kota are shown in table 1.
Methods
The analysis employs PSM procedures to derive control groups for both P2D2 and
WH, in order to produce samples for which local government participation can be
taken to be randomly (exogenously) assigned.8 Given such samples of local governments for the two programs, this article uses regression analysis to examine
the research questions outlined in the introduction. Combining propensity score
and regression methods is quite common in investigations of program impacts
across treatment and control groups. Such procedures are sometimes called ‘doubly robust’, since they attempt to correct for the inherent biases involved in empirically analysing nonrandomly drawn samples in two complementary ways (Ho
et al. 2007).
8. For a concise introduction to the use of PSM techniques in a policy context, see Heinrich,
Mafioli, and Gasquez’s (2010) technical note.
Downloaded by [Universitas Maritim Raja Ali Haji] at 23:33 17 January 2016
420
Blane D. Lewis
PROPENSITY SCORE MATCHING
For P2D2, a twostage PSM procedure was used. First, ive treatment provinces
(that is, those originally selected by the program and containing the participating
local governments) were matched to ive control provinces from all (28 remaining) provinces.9 Provinces were matched using 2010 data on the number of local
governments, log population, the share of the population that is urban, the share
of the population that is poor, and log gross regional domestic product (GRDP)
per capita.10 Second, 68 treatment local governments in the ive treatment provinces were matched to 68 control local governments (of 81) in the ive control
provinces, also using 2010 data. Variables used in the matching procedure at the
local government level included those used at the provincial level (except the
number of local governments) as well as the log of local government revenues per
capita (net of ownsource revenue).11
A singlestage local government level procedure was employed for WH:
35 treatment local governments were matched to 35 control local governments,
where the latter were selected from 65 local governments that were to be added to
the program in 2013. As such, the derived control group was more likely to satisfy
apparently existing but unspeciied ‘qualitative’ program selection criteria. Variables used in the matching procedure included log total revenue of kabupaten and
kota (net of own sources), log population, the share of the population that is urban,
the share of the population that is poor, the share of the population with access to
clean water (lagged),12 and log GRDP per capita. The procedures used 2009 data
(except for access to clean water, which used 2008 data).
Both cases employed onetoone nearest neighbour PSM procedures (without replacement),13 which identify those treatment cases that do not meet the
socalled common support condition—that is, those with propensity scores that
suggest matching is infeasible. One treatment case in each P2D2 and WH dataset
was thus recognised and therefore dropped from subsequent analyses, leaving
67 P2D2 treatment and control local governments in P2D2 and 34 in WH. An
inspection of propensity score distributions across treatment and control groups
for both P2D2 and WH suggested further trimming of observations was unnecessary.14 Postmatching analysis was then carried out to assess balance conditions.15
9. The actual selection process involved irst choosing ive provinces and then offering the
program to all kabupaten and kota within the provinces. All but one local government in the
ive provinces participated in the project.
10. Variables used in PSM procedures are those on which data are readily available and
which might reasonably be expected to inluence both the selection of participating districts and the outcomes of interest.
11. Ownsource revenues are not included, since they should be considered endogenous.
They are a negligible source of local government revenues, so their exclusion is unlikely to
matter much.
12. The analysis uses the lagged value of access to clean water in order to avoid contemporaneous simultaneity with one of the main outcome variables of interest—the creation of
household water connections.
13. PSM procedures were implemented in Stata 13 using the psmatch2 command.
14. Propensity score frequency distributions for treatment and control groups are available
from the author on request.
15. Balancing analysis was implemented in Stata 13 using the pstest command.
Indonesian Intergovernmental Performance Grants: An Empirical Assessment
421
TABLE 2 Analysis of Balance, P2D2, and Water Hibah Propensity Score Matching
Mean
Downloaded by [Universitas Maritim Raja Ali Haji] at 23:33 17 January 2016
Variable
Treatment Control
Standardised Reduction
bias
in bias (%)
P2D2
Log total revenue per capita Unmatched
Matched
Log population
Unmatched
Matched
% pop. that is urban
Unmatched
Matched
% pop. that is poor
Unmatched
Matched
Log GRDPPC
Unmatched
Matched
13.3
13.4
12.8
12.6
38.0
37.3
11.1
10.1
15.6
15.8
13.4
13.4
12.6
12.6
36.9
36.9
9.7
9.7
15.6
15.6
–9.0
0.4
15.4
2.2
31.5
15.9
22.6
6.7
–1.4
–6.6
Water Hibah
Log total revenue per capita Unmatched
Matched
Log population
Unmatched
Matched
% pop. that is urban
Unmatched
Matched
% pop. that is poor
Unmatched
Matched
% pop. with access to water Unmatched
Matched
Log GRDPPC
Unmatched
Matched
12.8
12.8
13.5
13.5
56.6
56.2
13.3
13.4
59.2
59.8
15.5
15.5
12.9
12.9
13.3
13.4
45.6
58.5
13.4
13.5
56.9
58.3
15.6
15.6
–12.8
–8.2
24.3
10.8
34.4
–7.0
–1.6
–0.6
13.0
8.1
–0.9
–5.9
95.8
85.7
64.0
70.3
–370.2
36.0
55.6
79.7
59.6
37.6
–580.6
Source: Author’s calculations.
Note: P2D2 = Proyek Pemerintah Daerah dan Desentralisasi. DAK = Dana Alokasi Khusus. GRDPPC
= gross regional domestic product per capita.
The examination of balance focuses on the reduction in bias associated with the
matching process. Table 2 summarises the results of the balance analysis.
Attention is focused on the standardised bias (the difference between the variable means of treatment and control groups, divided by the standard deviation
of the variable). There are no formal rules indicating tolerable levels of post
matching bias. Rosenbaum and Rubin (1985) state that the (absolute value of)
postmatching standardised bias should not exceed 10% for any variable. Ho et
al. (2007) suggest that 25% appears to be a common rule of thumb. For P2D2, only
one variable, the share of the population that is urban, has a standardised bias
greater than 10% (16%). For WH, only log total revenue per capita has a standardised bias less than 10% (11%).
It is also useful to consider the extent of bias reduction after matching. As table
2 shows, bias reduction ranges from 64% to 96% for P2D2 variables and from
36% to 80% for WH variables. Note that the bias of log GRDP per capita actually
422
Blane D. Lewis
Downloaded by [Universitas Maritim Raja Ali Haji] at 23:33 17 January 2016
increased after matching for both P2D2 and WH; its postmatching bias, however,
is still very low—just 7% for P2D2 and 6% for WH. More generally, the balancing
analysis demonstrates that the average bias for P2D2 decreased from 10.4% to
3.4% and for WH from 14.5% to 6.8% (not shown in the table).16
Overall, these results suggest that PSM procedures have substantially reduced
biases. It seems reasonable to conclude that treatment can now be taken as
(approximately) randomly assigned across respective P2D2 and WH local government samples.
DAK REIMBURSEMENT
The DAK is a relatively small source of revenue for local governments; it amounts
to only 7% of total local budgets, on average. In the iscal year 2014, the DAK covered 19 sectors. DAK for infrastructure makes up about 30% of the total transfer
(Lewis 2013).
This section examines the two research issues for P2D2 outlined in the introduction. The irst question concerns the impact of the P2D2 program on the capital spending of kabupaten and kota. The second relates to the possible effect of
program participation on capital spending spillovers associated with local government use of DAK.
Capital Spending
The hypothesis to be tested here is that program participation leads to increased
local government capital spending, all else remaining the same. It is posited that
local government per capita capital spending is a function of a dummy variable indicating program participation and per capita DAK, along with a number
of control variables. Data on all treatment and control local governments over
2011–12 are pooled and a standard ordinary least squares (OLS) regression (with
clustered error terms) is run.17
Table 3 provides the regression results, which indicate that the coeficient of
the program participation dummy is statistically insigniicant. This suggests that
P2D2 program participation has no impact on the level of capital spending, all
else remaining the same. As such, the speciied hypothesis is rejected. The table
also shows that DAK (among other variables) is a signiicant determinant of local
government capital spending, as would be expected of a capital grant. The output
implies that the elasticity of per capita capital spending with respect to per capita
DAK is 0.51.
16. Standardised biases of variables for all local governments compared with those for treatment local governments may also be of interest. For P2D2, these standardised biases for log
total revenue per capita, log population, the share of the population that is urban, the share
of the population that is poor, and log GRDP per capita are 7.0, –3.0, –3.1, 24.0, and –116.6,
respectively. For WH, the biases for log total revenue per capita, log population, the share of
the population that is urban, the share of the population that is poor, log GRDP per capita,
and the share of the population with access to water are 32.5, –42.8, –34.8, 12.8, –13.3, and
–37.1, respectively.
17. An attempt was made to estimate a panel speciication but because of the limited number of observations this proved unfeasible. All regressions in this analysis were estimated
by pooling the data.
Indonesian Intergovernmental Performance Grants: An Empirical Assessment
423
TABLE 3 Explaining Capital Spending
Downloaded by [Universitas Maritim Raja Ali Haji] at 23:33 17 January 2016
Independent variables
P2D2 dummy variable
Log of DAK per capita
Log of other revenues per capita
Log of population
% of population that is urban
% of population that is poor
Log of gross regional domestic product per capita
Constant
Observations
R2
Coeficient
–0.058
0.510**
2.148**
0.770**
0.001
0.010
–0.021
–31.934**
t-statistic
–0.86
6.60
5.28
4.00
0.67
1.57
–0.19
–4.85
218
0.784
Note: The dependent variable is the log of per capita local government capital spending across all sectors, infrastructure and otherwise. All iscal and economic variables are measured in constant (year
2000) terms, converted using the the implicit GDP delator from Indonesia’s national accounts. The
tstatistic is based on robust standard errors. P2D2 = Proyek Pemerintah Daerah dan Desentralisasi.
DAK = Dana Alokasi Khusus.
** p < 0.05.
Capital Spending Spillovers
Here it is postulated that local governments participating in the project use their
DAK to ‘crowd in’ additional capital spending to a greater extent than nonproject
local governments. It is speciied that local government per capita capital spending is a function of a dummy variable for program participation; per capita DAK;
the interaction between the program participation dummy and per capita DAK;
and other variables, including the control variables speciied above.
Table 4 provides the relevant regression output. Consider the impact of DAK
on capital spending for nonP2D2 and P2D2 local governments. The regression
results suggest that the elasticity of capital spending with respect to DAK is 0.439
for nonP2D2 participants and 0.655 (0.439 + 0.216) for P2D2 participants. As the
table shows, the difference is statistically signiicant.
The estimated elasticities can be transformed into marginal effects in the usual
way (that is, at the point of means). In marginal terms, the results imply that an
additional rupiah of DAK leads to an extra Rp 2.05 of capital spending for P2D2
kabupaten and kota yet only an extra Rp 1.34 of capital spending for nonP2D2 local
governments. DAK seems to stimulate more capital spending in P2D2 local governments than it does in nonP2D2 local governments—that is, the crowdingin
hypothesis appears to be conirmed.
WATER HIBAH
The Hibah mechanism has yet to be used much by the central government as a
means of transferring funds to the regions. The WH, speciically, is very limited
in size. It made up signiicantly less than 1% of the total revenue budgets of local
governments that received the grant in 2010 or 2011 or both.
424
Blane D. Lewis
Downloaded by [Universitas Maritim Raja Ali Haji] at 23:33 17 January 2016
This section of the article explores the three research questions for the WH program that were set forth in the introduction. These questions relate to the inluence of the program equity investments of local governments in their PDAMs, the
impact of those investments on the creation of household water connections, and
the direct effect of the program on the establishment of water connections.
Equity Investments
The hypothesis here is that both program participation and transfers have a positive effect on local government equity investment, everything else being equal.
It is postulated that kabupaten and kota per capita equity investment is a function
of a program participation dummy variable, WH per capita transfers, and other
variables.
The multivariate examination in this case immediately faces two dificulties.
The irst concerns the nonnormal probability distribution of the dependent variable local government per capita equity investment. Inspection of the distribution
shows a sizable collection of zero observations (that is, censoring) and a relatively
large gap between the zero and nonzero observations. The nature of the distribution suggests that using a hurdle model might be a suitable approach.18 A hurdle
model would explain the local government’s choice of whether to invest or not
(by way of a probit speciication) and, given a positive decision in that regard,
how much to invest (by way of a more typical linear regression).
The second problem concerns the nature of WH transfers. More speciically, it
seems reasonable to contend that such transfers are endogenous. WH transfers
are in the irst instance determined by whether a local government participates
in the WH program. Project participants receive the grant (at some point over the
two years); nonproject participants do not. Project participation—that is, treatment—was originally considered to be endogenous, but that endogeneity was
accommodated by using PSM. (PSM did not address the linked endogeneity of
transfers, however.19) In the analysis below, the WH grant is speciied as endogenous and its predicted value is used in the estimation procedures (and standard
errors are bootstrapped).
The predicted value of WH transfers is estimated by irst regressing the variable in per capita terms against the program participation dummy; the log of
other revenues per capita; the log of population; the share of the population that
is urban; the share of the population that is poor; the share of the population with
access to clean water (lagged); the log of GRDP per capita; and dummy variables
for the on or offJava location of kabupaten and kota location, and the year. The
analysis assumes that the two inal variables (that is, the excluded instruments)
are exogenous and important in determining WH transfers but not investments.20
18. The other possibility would be to employ a tobit model. However, as Greene (2012,
855) explains, a tobit model is more appropriate when data are distributed with a cluster of
zeroes and a grouping of observations near zero.
19. Standard (for example, OLS) estimation of a model with endogenous righthandside
variables will result in inconsistent parameter estimates. Such cases require an instrumental variable approach, such as that employed here.
20. All variables together explain 42% of the variation in per capita WH transfers. The regression output is available from the author on request.
Indonesian Intergovernmental Performance Grants: An Empirical Assessment
425
TABLE 4 Explaining Capital Spending via Program Participation and
DAK Interactions
Downloaded by [Universitas Maritim Raja Ali Haji] at 23:33 17 January 2016
Independent variables
P2D2 dummy variable
Log of DAK per capita
P2D2 dummy variable × log of DAK per capita
Log of other revenues per capita
Log of population
% of population that is urban
% of population that is poor
Log of gross regional domestic product per capita
Constant
Observations
R2
Coeficient
–2.279**
0.439**
0.216**
1.980**
0.750**
0.001
0.014**
0.034
–29.609**
t-statistic
–2.41
5.78
2.43
5.11
4.12
0.72
2.04
0.33
–4.58
218
0.794
Note: The dependent variable is the log of per capita local government capital spending across all sectors, infrastructure and otherwise. All iscal and economic variables are measured in constant (year
2000) terms, converted using the implicit GDP delator from Indonesia’s national accounts. The t-statistic is based on robust standard errors. DAK = Dana Alokasi Khusus. P2D2 = Proyek Pemerintah
Daerah dan Desentralisasi.
** p < 0.05.
WH is then predicted in the usual way, by using the estimated equation, but
insisting that the predicted values of WH grants be nonnegative.
Tables 5 and 6 show the regression results from the estimation of the hurdle
model.21 Table 5 provides the output for the probit equation in which the dummy
variable for program participation is used to explain the decision of whether to
invest or not. WH transfers are used to explain the level of equity investment. Two
cases are considered: one where WH transfers are taken as an exogenous variable
and the other where WH grants are endogenously speciied. The argument here is
that the latter is the correct speciication; results based on the former are included
just for comparison.22
Tables 5 and 6 provide a partial indication of the impact of program participation and WH transfers on investment; the results need to be combined to assess
total impact. Table 7 provides the relevant output. The empirical results indicate
that project participation leads to an increase in per capita investment of Rp 734
in constant 2000 terms (or Rp 2,124 in current prices for 2010–11) when WH transfers are taken to be exogenous and Rp 739 in constant 2000 terms (or Rp 2,141
21. The hurdle model is estimated using the tpm procedure in Stata 13.
22. In the second regression, (predicted) untransformed real per capita WH transfers are
used as an explanatory variable and not their log. The log transformation of the variable
is bimodal and its employment creates dificulties for the estimation procedures. The untransformed variable is skewed, of course; in this case, however, using a variable with a
skewed distribution is preferable to using one with a bimodal distribution.
426
Blane D. Lewis
TABLE 5 Explaining Equity Investments (Probit Equation)
Downloaded by [Universitas Maritim Raja Ali Haji] at 23:33 17 January 2016
Independent variables
Coeficient
Water Hibah dummy variable
Log of other revenues per capita
Log of population
% of population that is urban
% of population that is poor
% of population with access to water, lagged
Log of GRDP per capita
Constant
z-statistic
1.048**
2.615**
1.625**
–0.009
–0.047
0.026**
–1.051**
–39.885**
Observations
R2
3.38
3.11
3.44
–1.46
2.01
–2.66
–2.66
105
0.238
Note: The dependent variable is a categorical variable indicating whether the local government made
investments in its PDAM. All iscal and economic variables are measured in constant (year 2000)
terms, converted using the implicit GDP delator from Indonesia’s national accounts. The z-statistic is
based on robust standard errors. GRDP = gross regional domestic product.
** p < 0.05.
TABLE 6 Explaining Equity Investments (Regression Equation)
Water Hibah exogenous Water Hibah endogenous
Independent variables
Water Hibah per capita
Log of other revenues per capita
Log of population
% of population that is urban
% of population that is poor
% of population with access to water,
lagged
Log of GRDP per capita
Constant
Observations
R2
Coeficient
z-statistic
Coeficient
z-statistic
0.0003**
–1.1978**
–0.9202**
0.0013
–0.0204
4.21
–2.25
–2.71
0.21
–0.84
0.0005**
–1.2250**
–0.9613**
0.0020
–0.0223
2.98
–2.17
–2.68
0.30
–0.87
–0.0003
0.7850**
22.8479**
–0.03
2.49
2.34
0.0011
0.7383**
24.2593**
0.10
2.49
2.35
73
0.364
73
0.355
Note: The dependent variable is the log of per capita local government equity investment in PDAM.
All iscal and economic variables are measured in constant (year 2000) terms, converted using the
implicit GDP delator from Indonesia’s national accounts. The z-statistic is based on robust standard
errors. GRDP = gross regional domestic product.
** p < 0.05.
Indonesian Intergovernmental Performance Grants: An Empirical Assessment
427
TABLE 7 Total Impact of Program Participation and Water Hibah Transfers on
Equity Investments
Water Hibah exogenous
Independent variables
Downloaded by [Universitas Maritim Raja Ali Haji] at 23:33 17 January 2016
Water Hibah dummy variable
Hibah per capita
Water Hibah endogenous
Coeficient
z-statistic
Coeficient
z-statistic
733.7**
0.578**
3.19
2.81
739.1**
0.815**
3.20
2.50
Note: The dependent variable is the log of per capita local government equity investment in PDAMs.
All iscal and economic variables are measured in constant (year 2000) terms, converted using the
implicit GDP delator from Indonesia’s national accounts. The z-statistic is based on robust standard
errors.
** p < 0.05.
in 2010–11 terms) when WH grants are assumed endogenous. Furthermore, the
calculations suggest that an extra rupiah of WH transfers leads to Rp 0.58 of additional investment in PDAMs when grants are treated as exogenous and Rp 0.81
extra investment performance when transfers are speciied as endogenous.
The last result would seem to be especially reasonable in light of program
procedures. That is, since many local governments make equity investments in
their PDAMs even without receiving the program grant, it might be expected that
an additional rupiah of WH to participating local governments would result in
less than one rupiah of investment—that is, that local governments would divert
some of their planned (pregrant) investments to other purposes.23
Household Water Connections
The proposition under investigation here is that both program participation and
local government equity investments positively inluence the creation of water
connections, all else remaining the same. It is posited that connections are a function of a program participation dummy; local government per capita equity
investments (whether inanced by Hibah or by other sources of revenue); and
other control variables.
Given the preceding analysis, it is apparent that investments should be treated
as endogenous. As such, the predicted value of equity investments from the earlier
analysis is used in the examination. An inspection of the probability distribution of
(log) household water connections implies that a simple linear least squares model
would sufice in this instance (where standard errors are again bootstrapped). As
table 8 shows, program participation is a statistically signiicant determinant of
household water connections when investment is taken to be exogenous but not
when it is considered to be endogenous—the preferred speciication. Conversely,
investment is a signiicant explanator of household water connections in either
23. Nonmatching speciicpurpose grants typically have an impact of less than one (Boadway and Shah 2007).
TABLE 8 Explaining Household Water Connections
Downloaded by [Universitas Maritim Raja Ali Haji] at 23:33 17 January 2016
Independent variables
Water Hibah dummy variable
Investment per capita
Log of population
% of population that is urban
% of population that is poor
% of population with access to water,
lagged
Log of GRDP per capita
Constant
Observations
R2
Investment exogenous
Investment endogenous
Coeficient
t-statistic
Coeficient
t-statistic
0.4915**
0.0001**
–0.4173**
–0.0019
0.0349*
2.77
4.07
–4.23
–0.41
1.72
0.3816
0.0002**
–0.3612**
–0.0030
0.0378
1.58
2.30
–3.46
–0.61
1.61
0.0135
2.12
0.0126*
1.71
0.6325**
–5.3520
3.59
–1.63
0.5964**
–5.5244
2.97
–1.56
105
0.472
105
0.413
Note: The dependent variable is the log of number of new household connections per 1,000 people. All
iscal and economic variables are measured in constant (year 2000) terms, converted using the implicit
GDP delator from Indonesia’s national accounts. The t-statistic is based on robust standard errors.
* p < 0.1; ** p < 0.05.
TABLE 9: Explaining Connections: Treatment and Control Group Investments
Independent variables
Water Hibah dummy variable
Investments per capita
Water Hibah dummy × investments
per capita
Log of population
% of population that is urban
% of population that is poor
% of population with access to water,
lagged
Log of GRDP per capita
Constant
Observations
R2
Investment exogenous
Investment endogenous
Coeficient
Coeficient
t-statistic
t-statistic
0.4779**
0.0001**
2.19
2.08
0.5523**
0.0005*
1.90
1.75
0.0000
–0.4166**
–0.0019
0.0354*
0.15
–4.16
–0.41
1.68
–0.0002
–0.3170**
–0.0017
0.0439*
–0.99
–2.73
–0.33
1.77
0.0134**
0.6372**
–5.4324
2.11
3.52
–1.60
0.0090
0.5780**
–5.9780*
1.07
2.96
–1.64
105
0.474
105
0.418
Note: The dependent variable is the log of the number of new household connections per 1,000 people.
All iscal and economic variables are measured in constant (year 2000) terms, converted using the the
implicit GDP delator from Indonesia’s national accounts. The t-statistic is based on robust standard
errors.
* p < 0.1; ** p < 0.05.
Downloaded by [Universitas Maritim Raja Ali Haji] at 23:33 17 January 2016
Indonesian Intergovernmental Performance Grants: An Empirical Assessment
429
case. These results are taken to infer that project participation in and of itself is
probably not important for the establishment of water connections; what matters
for the creation of connections is local government equity investment in PDAMs,
at least in the present framework.
As before, the estimated elasticities in table 8 can be transformed into marginal effects, which in this instance would provide an estimate of the change in
the number of new household connections per 1,000 persons given a onerupiah
change in per capita equity investments. The marginal effects themselves can be
transformed into ‘unit investment costs’ simply by taking the inverse. The relevant calculation suggests that the investment cost required to increase household
water connections by one unit is Rp 6.3 million when investments are treated as
exogenous and Rp 4.3 million when investments are speciied as endogenous.
(Rupiah igures are in 2010–11 terms.)
In this context, are treatment group investments more costeficient in creating household water connections than control group investments? This question
can easily be examined econometrically by specifying an interaction term—the
project participation dummy multiplied by equity investments. Table 9 gives
the relevant regression results. The output shows that the coeficient of the interaction variable is not signiicantly different from zero, regardless of how investments are speciied. That is, the results suggest that investment by WH local
governments is no more costeficient than investment by nonproject kabupaten
or kota.
In the analysis above, WHinanced investments are subsumed within total
investments. As such, the speciic impact of WH transfers on the creation of
household water connections is obscured. In order to assess the direct effects of
WH transfers on household connections, investments inanced by the performance grant must be separated from investments inanced by other sources of
local government revenue. With this objective in mind it is posited that per capita
household water connections are a function of the standard program participation dummy, WHinanced investments, investments inanced from other sources
of revenue, and the usual control variables.
As table 10 shows, program participation is not statistically signiicant in determining water connections, irrespective of assumptions about the endogeneity of
transfers. In contrast, both per capita WHinanced investments and other revenueinanced investments are important in explaining household water connections, regardless of speciication. While program participation in and of itself does
not appear to matter for the creation of household water connections (a similar
result as found before), WH performance transfers do have a signiicant inluence.
The table shows the estimated elasticities, as usual. The usual calculation
implies that the cost of creating an additional WHinanced household water connection is Rp 2.9 million when WH is taken to be exogenous and Rp 2.5 million
when it is taken to be endogenous. Furthermore, the results suggest that the cost
of an additional connection inanced by other sources of revenue costs about Rp
8.2 million when WH is taken to be exogenous and Rp 6.9 when WH is taken to be
endogenous (all igures in 2010–11 terms). The usual statistical test shows that the
differences between the effects of WHinanced investments and other revenue
inanced investments are statistically signiicant, regardless of assumptions about
the endogeneity of WH transfers.
Blane D. Lewis
430
TABLE 10 Explaining Connections as a Function of
Water Hibah–Financed Investments
Downloaded by [Universitas Maritim Raja Ali Haji] at 23:33 17 January 2016
Independent variables
Treatment dummy
Hibah inanced investments
per capita
Other revenueinanced investments
per capita
Log of population
% of population that is urban
% of population that is poor
Log of GRDP per capita
% of population with access to water,
lagged
Constant
Observations
R2
Investment exogenous
Investment endogenous
Coeficient
t-statistic
Coeficient
t-statistic
0.3034
1.62
0.2892
1.04
0.0003**
6.16
0.0004*
1.71
0.0001**
–0.3955**
–0.0008
0.0373*
0.6862**
3.42
–4.05
–0.19
1.93
3.88
0.0001*
–0.3612**
–0.0021
0.0352
0.6457**
1.69
–3.44
–0.41
1.49
2.93
0.0103**
–6.3512*
1.71
–1.95
0.0117
–6.2424
1.58
–1.62
105
0.506
105
0.417
Note: The dependent variable is the log of per capita local government equity investment in PDAM.
All iscal and economic variables are measured in constant (year 2000) terms, converted using the
World Bank’s national GDP delator. The t-statistic is based on robust standard errors.
* p < 0.1; ** p < 0.05.
SUMMARY, DISCUSSION, AND CONCLUSIONS
The empirical results in this article suggest that the amount of P2D2 local government capital spending is not signiicantly different from that of nonP2D2 kabupaten and kota, on average, all other things being equal. This implies that P2D2 has
no impact on the level of participating local government capital spending.
However, DAK is apparently more stimulative of capital spending by P2D2
kabupaten and kota than it is of that by nonP2D2 local governments. The regression results imply that an additional rupiah of DAK leads to an extra 2.05 rupiah
of capital spending for P2D2participating local governments yet only an extra
1.34 rupiah of capital spending for nonP2D2 places.
As such, project participation apparently leads to a supplementary 0.71 rupiah
of capital spending from each rupiah increase in the DAK, all else remaining the
same. This implies that while capital spending by P2D2 local governments is not
greater, on average, than that by nonP2D2 kabupaten and kota, as DAK increases,
capital spending rises more quickly for local governments participating in the
project. And at some level of DAK, (approximately 30 thousand rupiah per capita), capital spending of P2D2 local governments begins to exceed capital spending of nonP2D2 local governments.
Otherwise put, the results indicate that local governments participating in
P2D2 use their DAK to crowdin additional capital spending to a larger extent
than nonprogram participants do. There are two possible means through which
Downloaded by [Universitas Maritim Raja Ali Haji] at 23:33 17 January 2016
Indonesian Intergovernmental Performance Grants: An Empirical Assessment
431
this effect might be accomplished. First, given a marginal increase in DAK, P2D2
kabupaten and kota are more apt to use other (endogenous) revenues—from own
sources or reserve funds—to further increase spending on capital.24 Or, second,
they are more likely to reallocate spending from current items—personnel or
goods and services—to capital, as DAK transfers increase at the margin (that is,
without using additional endogenous revenues for capital spending).
The speciic empirical result under discussion here necessarily implies that
treatment local governments do one or both of these two things. It is not obvious, however, how the P2D2 program might actually lead to such changes in
behaviour. P2D2 incentives concentrate on encouraging local governments to
spend all of their DAK (at a reasonable level of quality) and not to link DAK
inanced spending with capital spending funded from other sources or to incite a
reallocation of current to capital spending. It is possible that the program has such
effects, of course, but it would be useful to at least verify and explain the apparent
impacts through additional ield research.
Regarding WH, the empirical analysis suggests that the program encourages
local governments to make larger investments in their PD
ISSN: 0007-4918 (Print) 1472-7234 (Online) Journal homepage: http://www.tandfonline.com/loi/cbie20
Indonesian Intergovernmental Performance
Grants: An Empirical Assessment of Impact
Blane D. Lewis
To cite this article: Blane D. Lewis (2014) Indonesian Intergovernmental Performance Grants:
An Empirical Assessment of Impact, Bulletin of Indonesian Economic Studies, 50:3, 415-433,
DOI: 10.1080/00074918.2014.980378
To link to this article: http://dx.doi.org/10.1080/00074918.2014.980378
Published online: 03 Dec 2014.
Submit your article to this journal
Article views: 205
View related articles
View Crossmark data
Full Terms & Conditions of access and use can be found at
http://www.tandfonline.com/action/journalInformation?journalCode=cbie20
Download by: [Universitas Maritim Raja Ali Haji]
Date: 17 January 2016, At: 23:33
Bulletin of Indonesian Economic Studies, Vol. 50, No. 3, 2014: 415–33
INDONESIAN INTERGOVERNMENTAL
PERFORMANCE GRANTS: AN EMPIRICAL
ASSESSMENT OF IMPACT
Downloaded by [Universitas Maritim Raja Ali Haji] at 23:33 17 January 2016
Blane D. Lewis
The Australian National University
The government of Indonesia has now piloted two different outputbased performance grants to regions. One focuses on increasing the amount and quality of local
government capital spending. The other provides incentives for local governments
to augment equity investments in their water enterprises and for the enterprises to
use those investments to increase the number of household water connections to
the poor. Impact evaluations of the two grants suggest some reasonably positive
outcomes against the stated objectives. While the assessed impacts may not match
the expected outputs (as argued by many performance grant enthusiasts), these
impacts provide a plausible basis for the sustained development and use of such
grants. The alternative would be to continue to rely exclusively on the equitybased
approaches that have dominated intergovernmental iscal relations in Indonesia
and have led to rather weak local public service outcomes.
Keywords: regional development, decentralisation, intergovernmental iscal relations, performance grants, impact evaluation
JEL classiication: H77, H79, R51
INTRODUCTION
Practitioners in intergovernmental iscal relations increasingly advocate the use of
outputbased performance incentives in grant design (for example, Boadway and
Shah 2007). The general argument is that positive incentives purposely embedded in intergovernmental performance grants can help encourage improvements
in local performance across a wide range of dimensions, including as related to
governance, iscal, or servicedelivery outcomes. Such grants may be particularly
attractive to central governments in cases where vertical or horizontal accountability is weak—that is, where such mechanisms do not play their expected role
of assuring quality outcomes (Dumas and Kaiser 2010; Lewis and Smoke 2014).
Research on outputbased incentive schemes is relatively limited for developing countries. And most reviews of local government incentiverelated approaches
do not focus exclusively on the role of transfers (Lewis and Smoke 2009, 2012).
The few publications on performancebased grants are largely forwardlooking
or draw predominantly on anecdotal evidence in their evaluations of past and
ongoing eficacy (Steffensen and Larson 2005, Shah 2010, Steffensen 2010, Rojas
ISSN 0007-4918 print/ISSN 1472-7234 online/14/000415-19
http://dx.doi.org/10.1080/00074918.2014.980378
© 2014 Indonesia Project ANU
Downloaded by [Universitas Maritim Raja Ali Haji] at 23:33 17 January 2016
416
Blane D. Lewis
2011, Mathur 2012). Most of the literature on performance grants offers something
less than an objective assessment of impacts; the research seems really more of a
brief for the idea that incentive methods must necessarily be effective.
The practical emphasis on performance grants departs somewhat from earlier
perspectives on intergovernmental iscal relations in developing countries. Much
of the previous work in this area focused on the rationales for and the design
and implementation of iscal equalisation grants. The principal view was that
the centre’s role was mainly to assure equal access to iscal resources across local
governments and that vertical and horizontal accountability would make certain
the desired outcomes. However, growing disappointment with the weak service
delivery associated with iscal equalisation and, more broadly, with equitybased
approaches (which might also consider fairness from a historical, geographical, or
servicedelivery point of view) has turned attention to mechanisms that concentrate more directly on service outcomes, such as performance grants (Lewis and
Smoke 2012).
The Indonesian system of intergovernmental iscal relations is primarily equity
based. The most important transfer, Dana Alokasi Umum (DAU), is a iscal equalisation grant, whereby allocations are made as a positive function of iscal needs
and a negative function of iscal capacities of local governments. The speciic
purpose capital grant, Dana Alokasi Khusus (DAK), also focuses to a large extent
on distributing funds to local governments with few iscal resources, especially
those governments that are geographically isolated. Indonesian government
policymakers, however, like those in many countries, have become dissatisied
with what they see as weak service outcomes deriving from the current intergovernmental system and have begun to experiment with alternative, performance
based mechanisms (Lewis 2014). This article analyses the impact of two recently
piloted performance grants: DAK Reimbursement (Proyek Pemerintah Daerah
dan Desentralisasi [P2D2]) and Water Hibah (WH).1
BACKGROUND
P2D2 and WH were piloted during 2011–12 and 2010–11, respectively. P2D2 concentrates on encouraging more and better local government (kabupaten and kota)
capital spending,2 whereas WH intends to inspire kabupaten and kota to make
equity investments in their water enterprises (Perusahaan Daerah Air Minum
[PDAMs]) and, in turn, stimulate PDAMs to establish household water connections for the poor. P2D2 initially covered 68 local governments, while WH
included 35 local governments and their PDAMs. Both programs have since been
1. Hibah means ‘grant’ in Indonesian.
2. P2D2 speciies both intermediate performance indicators and (inal) eligibility criteria.
Performance indicators relate to local government counterpart funding, inancial and technical reporting, and capital spending. Eligibility criteria comprise local government provision of reference unit costs, compliance with national procurement guidelines, compliance
with environmental safeguards, realisation of planned spending outputs, and compliance
with technical standards. Incentive payments are made against eligibility criteria only.
Data are not available on the irst three eligibility criteria; as such, this article focuses just
on capital spending outputs.
Downloaded by [Universitas Maritim Raja Ali Haji] at 23:33 17 January 2016
Indonesian Intergovernmental Performance Grants: An Empirical Assessment
417
rolled out to incorporate a larger number of kabupaten and kota, and the central
government is considering expanding its use of such instruments to cover additional regions and sectors.3
P2D2 focuses on improving local capital spending outputs associated with
the DAK.4 The program deines capital spending performance in terms of the
extent to which DAK allocations were actually spent as planned and the degree to
which spending outputs complied with basic technical standards. Capital spending objectives concentrate on speciic infrastructure subsectors: roads, irrigation,
water, and sanitation. Local government performance against program eligibility
targets is veriied by the central government’s Agency for Finance and Development Supervision (Badan Pengawasan Keuangan dan Pembangunan) after DAK
allocations have been spent.
Successful kabupaten and kota are awarded about 10% of the initial infrastructure grant for relevant subsectors—that is, for their counterpart contributions to
DAK funding, hence the use of the term ‘reimbursement’. The reimbursement is
typically made in the iscal year that follows the satisfaction of project objectives.
In 2011 and 2012, respectively, 84% and 89% of participating local governments
achieved program targets.
WH design is similarly straightforward. On the promise of a grant from the
Ministry of Finance, a kabupaten or kota makes an equity investment in its PDAM;
the PDAM then uses the invested funds to create household water connections.
The promised grant connotes the maximum amount of funds that the ministry is
willing to transfer. The kabupaten or kota is required to invest at least that much
in its PDAM. The maximum investment is associated with a targeted number
of connections to be established by the PDAM. Once the investments have been
made and the water connections have been veriied as operational, the Ministry
of Finance transfers funds to the kabupaten or kota: Rp 2 million per connection for
the irst 1,000 connections and Rp 3 million per connection thereafter, until the
maximum is reached.
WH project participants receive the transfers in the year in which connections
are created and veriied. Local governments and PDAMs participating in the program may have established connections and received transfers in 2010 or 2011 or
both. In WH’s irst two years of operation, 97% of all participating local governments and their PDAMs met equity investment and household water connection targets. During 2010–11, the WH program disbursed Rp 199 billion in grants,
which led to the creation of 77,000 household water connections. This implies that
the cost of a water connection was Rp 2.6 million, on average.
This article empirically assesses the impact of P2D2 and WH, according to
questions about each program’s stated objectives. For P2D2, the examination
investigates two main concerns. First, does P2D2 stimulate local governments to
spend more on capital, in general? Second, and more speciically, do P2D2 local
3. Both pilot projects were developed by international aid agencies. P2D2 is a World Bank
project and WH is an effort led by the Indonesian Infrastructure Initiative, a facility of the
Australian Government Department of Foreign Affairs and Trade.
4. See Lewis’s (2013) article for a discussion of DAK design and the effects of the grant on
local government capital spending.
Downloaded by [Universitas Maritim Raja Ali Haji] at 23:33 17 January 2016
418
Blane D. Lewis
governments use their DAK to generate greater capital spending spillovers than
nonP2D2 local governments?5
The WH analysis focuses on three basic questions. First, does the WH program increase local government equity investment in PDAMs? Second, do equity
investments, in general, stimulate the creation of household water connections?
Third, do WH program participation and attendant performance grants, speciically, encourage the creation of household connections?6
In addressing the above research questions, the analyses are conducted across
treatment and control group kabupaten and kota. The treatment group was deined
as a function of P2D2 and WH program participation. The control group was
derived using propensity score matching (PSM) procedures, which are discussed
further below. Incorporating a control group in the analysis reduces biases associated with the nonrandom (and endogenous7) nature of program participation; at
the same time, it produces a ‘counterfactual’ that provides a useful indication of
the outputs that might have been achieved by local governments in the absence
of program participation.
The data used to conduct the analyses are not abundant. Both pilots were small,
generating a limited number of observations. A dearth of data is not uncommon
in applied analysis in developing countries; in fact, it is more likely to be the rule.
But given the lack of solid empirical evidence on the effects of intergovernmental
performance grants across developing countries, in general, and the speciic signiicance of such grants in Indonesian policy discussions at present, it is arguably
useful to proceed with the examination, even in the absence of ideal data. Still, the
analysis here should be considered preliminary.
DATA AND METHODS
Data
Data on inancial and technical reporting outcomes used in the examination of
P2D2 impact come from the Ministry of Finance and were supplied by the World
Bank. Equity investment and household water connection data employed in
the analysis of WH effects were collected directly from local governments and
their PDAMs and were made available by the Indonesian Infrastructure Initiative. All other data on variables used in both the matching procedures and in
5. It would be preferable to examine capital spending and capital spending spillovers for
roads, irrigation, water, and sanitation infrastructure only, since the project focuses on
those subsectors. Unfortunately, the data are insuficient to allow that. In the aggregate, local infrastructure spending makes up between twothirds and threequarters of total local
capital spending; but available data do not permit the separation of infrastructure spending from total capital spending at the local government level. As such, local government
capital spending across all sectors is taken as a broad proxy for local government capital
spending on infrastructure.
6. Given the lack of data, this analysis is unable to assess the impact of investment on the
establishment of connections for poor households.
7. It is reasonable to assume that some of the same factors that determine project participation may also explain outcomes of interest. As such, treatment may be presumed to be
endogenous (Greene 2012, 888).
Indonesian Intergovernmental Performance Grants: An Empirical Assessment
419
TABLE 1 Variable Summary Statistics
Std
Min
Max
N
P2D2
Local government capital spending 151,982
DAK (speciicpurpose capital grant) 48,559
Local government revenue
722,623
Population
451,906
% population that is urban
35.7
% population that is poor
10.8
Gross regional domestic product
6,603,166
199,194
53,309
446,679
435,580
28.0
6.6
3,482,494
4,239
174
210,376
43,809
3.4
1.4
1,327,098
1,565,788
321,706
2,834,593
2,459,960
100.0
39.5
19,400,000
222
218
222
256
256
254
254
Water Hibah
Equity investment
1,630
Water connections per 1,000 persons
3.2
Hibah
602
Local government revenue
505,574
Population
1,010,122
% population that is urban
58.0
% population that is poor
12.4
% population with access to water
62.2
Gross regional domestic product
7,382,343
2,627
3.8
1,240
370,471
801,481
31.1
5.7
18.9
4,395,347
0
0.1
0
159,928
117,493
9.1
3.2
4.8
2,505,224
14,349
23.5
7,283
2,224,253
4,858,514
100.0
28.1
99.1
26,200,000
105
105
105
105
105
105
105
105
105
Downloaded by [Universitas Maritim Raja Ali Haji] at 23:33 17 January 2016
Variable
Mean
Note: P2D2 = Proyek Pemerintah Daerah dan Desentralisasi. DAK = Dana Alokasi Khusus. P2D2 and
Water Hibah data are pooled across 2011–12 and 2010–11, respectively. All iscal and economic variables are measured in constant (2000) per capita terms, converted using the implicit GDP delator from
Indonesia’s national accounts.
the subsequent impact analyses were provided by the World Bank and initially
sourced from the Ministry of Finance and Badan Pusat Statistik (BPS), Indonesia’s
central statistics agency. Summary statistics for variables used in the examination
of P2D2 and WH treatment and control kabupaten and kota are shown in table 1.
Methods
The analysis employs PSM procedures to derive control groups for both P2D2 and
WH, in order to produce samples for which local government participation can be
taken to be randomly (exogenously) assigned.8 Given such samples of local governments for the two programs, this article uses regression analysis to examine
the research questions outlined in the introduction. Combining propensity score
and regression methods is quite common in investigations of program impacts
across treatment and control groups. Such procedures are sometimes called ‘doubly robust’, since they attempt to correct for the inherent biases involved in empirically analysing nonrandomly drawn samples in two complementary ways (Ho
et al. 2007).
8. For a concise introduction to the use of PSM techniques in a policy context, see Heinrich,
Mafioli, and Gasquez’s (2010) technical note.
Downloaded by [Universitas Maritim Raja Ali Haji] at 23:33 17 January 2016
420
Blane D. Lewis
PROPENSITY SCORE MATCHING
For P2D2, a twostage PSM procedure was used. First, ive treatment provinces
(that is, those originally selected by the program and containing the participating
local governments) were matched to ive control provinces from all (28 remaining) provinces.9 Provinces were matched using 2010 data on the number of local
governments, log population, the share of the population that is urban, the share
of the population that is poor, and log gross regional domestic product (GRDP)
per capita.10 Second, 68 treatment local governments in the ive treatment provinces were matched to 68 control local governments (of 81) in the ive control
provinces, also using 2010 data. Variables used in the matching procedure at the
local government level included those used at the provincial level (except the
number of local governments) as well as the log of local government revenues per
capita (net of ownsource revenue).11
A singlestage local government level procedure was employed for WH:
35 treatment local governments were matched to 35 control local governments,
where the latter were selected from 65 local governments that were to be added to
the program in 2013. As such, the derived control group was more likely to satisfy
apparently existing but unspeciied ‘qualitative’ program selection criteria. Variables used in the matching procedure included log total revenue of kabupaten and
kota (net of own sources), log population, the share of the population that is urban,
the share of the population that is poor, the share of the population with access to
clean water (lagged),12 and log GRDP per capita. The procedures used 2009 data
(except for access to clean water, which used 2008 data).
Both cases employed onetoone nearest neighbour PSM procedures (without replacement),13 which identify those treatment cases that do not meet the
socalled common support condition—that is, those with propensity scores that
suggest matching is infeasible. One treatment case in each P2D2 and WH dataset
was thus recognised and therefore dropped from subsequent analyses, leaving
67 P2D2 treatment and control local governments in P2D2 and 34 in WH. An
inspection of propensity score distributions across treatment and control groups
for both P2D2 and WH suggested further trimming of observations was unnecessary.14 Postmatching analysis was then carried out to assess balance conditions.15
9. The actual selection process involved irst choosing ive provinces and then offering the
program to all kabupaten and kota within the provinces. All but one local government in the
ive provinces participated in the project.
10. Variables used in PSM procedures are those on which data are readily available and
which might reasonably be expected to inluence both the selection of participating districts and the outcomes of interest.
11. Ownsource revenues are not included, since they should be considered endogenous.
They are a negligible source of local government revenues, so their exclusion is unlikely to
matter much.
12. The analysis uses the lagged value of access to clean water in order to avoid contemporaneous simultaneity with one of the main outcome variables of interest—the creation of
household water connections.
13. PSM procedures were implemented in Stata 13 using the psmatch2 command.
14. Propensity score frequency distributions for treatment and control groups are available
from the author on request.
15. Balancing analysis was implemented in Stata 13 using the pstest command.
Indonesian Intergovernmental Performance Grants: An Empirical Assessment
421
TABLE 2 Analysis of Balance, P2D2, and Water Hibah Propensity Score Matching
Mean
Downloaded by [Universitas Maritim Raja Ali Haji] at 23:33 17 January 2016
Variable
Treatment Control
Standardised Reduction
bias
in bias (%)
P2D2
Log total revenue per capita Unmatched
Matched
Log population
Unmatched
Matched
% pop. that is urban
Unmatched
Matched
% pop. that is poor
Unmatched
Matched
Log GRDPPC
Unmatched
Matched
13.3
13.4
12.8
12.6
38.0
37.3
11.1
10.1
15.6
15.8
13.4
13.4
12.6
12.6
36.9
36.9
9.7
9.7
15.6
15.6
–9.0
0.4
15.4
2.2
31.5
15.9
22.6
6.7
–1.4
–6.6
Water Hibah
Log total revenue per capita Unmatched
Matched
Log population
Unmatched
Matched
% pop. that is urban
Unmatched
Matched
% pop. that is poor
Unmatched
Matched
% pop. with access to water Unmatched
Matched
Log GRDPPC
Unmatched
Matched
12.8
12.8
13.5
13.5
56.6
56.2
13.3
13.4
59.2
59.8
15.5
15.5
12.9
12.9
13.3
13.4
45.6
58.5
13.4
13.5
56.9
58.3
15.6
15.6
–12.8
–8.2
24.3
10.8
34.4
–7.0
–1.6
–0.6
13.0
8.1
–0.9
–5.9
95.8
85.7
64.0
70.3
–370.2
36.0
55.6
79.7
59.6
37.6
–580.6
Source: Author’s calculations.
Note: P2D2 = Proyek Pemerintah Daerah dan Desentralisasi. DAK = Dana Alokasi Khusus. GRDPPC
= gross regional domestic product per capita.
The examination of balance focuses on the reduction in bias associated with the
matching process. Table 2 summarises the results of the balance analysis.
Attention is focused on the standardised bias (the difference between the variable means of treatment and control groups, divided by the standard deviation
of the variable). There are no formal rules indicating tolerable levels of post
matching bias. Rosenbaum and Rubin (1985) state that the (absolute value of)
postmatching standardised bias should not exceed 10% for any variable. Ho et
al. (2007) suggest that 25% appears to be a common rule of thumb. For P2D2, only
one variable, the share of the population that is urban, has a standardised bias
greater than 10% (16%). For WH, only log total revenue per capita has a standardised bias less than 10% (11%).
It is also useful to consider the extent of bias reduction after matching. As table
2 shows, bias reduction ranges from 64% to 96% for P2D2 variables and from
36% to 80% for WH variables. Note that the bias of log GRDP per capita actually
422
Blane D. Lewis
Downloaded by [Universitas Maritim Raja Ali Haji] at 23:33 17 January 2016
increased after matching for both P2D2 and WH; its postmatching bias, however,
is still very low—just 7% for P2D2 and 6% for WH. More generally, the balancing
analysis demonstrates that the average bias for P2D2 decreased from 10.4% to
3.4% and for WH from 14.5% to 6.8% (not shown in the table).16
Overall, these results suggest that PSM procedures have substantially reduced
biases. It seems reasonable to conclude that treatment can now be taken as
(approximately) randomly assigned across respective P2D2 and WH local government samples.
DAK REIMBURSEMENT
The DAK is a relatively small source of revenue for local governments; it amounts
to only 7% of total local budgets, on average. In the iscal year 2014, the DAK covered 19 sectors. DAK for infrastructure makes up about 30% of the total transfer
(Lewis 2013).
This section examines the two research issues for P2D2 outlined in the introduction. The irst question concerns the impact of the P2D2 program on the capital spending of kabupaten and kota. The second relates to the possible effect of
program participation on capital spending spillovers associated with local government use of DAK.
Capital Spending
The hypothesis to be tested here is that program participation leads to increased
local government capital spending, all else remaining the same. It is posited that
local government per capita capital spending is a function of a dummy variable indicating program participation and per capita DAK, along with a number
of control variables. Data on all treatment and control local governments over
2011–12 are pooled and a standard ordinary least squares (OLS) regression (with
clustered error terms) is run.17
Table 3 provides the regression results, which indicate that the coeficient of
the program participation dummy is statistically insigniicant. This suggests that
P2D2 program participation has no impact on the level of capital spending, all
else remaining the same. As such, the speciied hypothesis is rejected. The table
also shows that DAK (among other variables) is a signiicant determinant of local
government capital spending, as would be expected of a capital grant. The output
implies that the elasticity of per capita capital spending with respect to per capita
DAK is 0.51.
16. Standardised biases of variables for all local governments compared with those for treatment local governments may also be of interest. For P2D2, these standardised biases for log
total revenue per capita, log population, the share of the population that is urban, the share
of the population that is poor, and log GRDP per capita are 7.0, –3.0, –3.1, 24.0, and –116.6,
respectively. For WH, the biases for log total revenue per capita, log population, the share of
the population that is urban, the share of the population that is poor, log GRDP per capita,
and the share of the population with access to water are 32.5, –42.8, –34.8, 12.8, –13.3, and
–37.1, respectively.
17. An attempt was made to estimate a panel speciication but because of the limited number of observations this proved unfeasible. All regressions in this analysis were estimated
by pooling the data.
Indonesian Intergovernmental Performance Grants: An Empirical Assessment
423
TABLE 3 Explaining Capital Spending
Downloaded by [Universitas Maritim Raja Ali Haji] at 23:33 17 January 2016
Independent variables
P2D2 dummy variable
Log of DAK per capita
Log of other revenues per capita
Log of population
% of population that is urban
% of population that is poor
Log of gross regional domestic product per capita
Constant
Observations
R2
Coeficient
–0.058
0.510**
2.148**
0.770**
0.001
0.010
–0.021
–31.934**
t-statistic
–0.86
6.60
5.28
4.00
0.67
1.57
–0.19
–4.85
218
0.784
Note: The dependent variable is the log of per capita local government capital spending across all sectors, infrastructure and otherwise. All iscal and economic variables are measured in constant (year
2000) terms, converted using the the implicit GDP delator from Indonesia’s national accounts. The
tstatistic is based on robust standard errors. P2D2 = Proyek Pemerintah Daerah dan Desentralisasi.
DAK = Dana Alokasi Khusus.
** p < 0.05.
Capital Spending Spillovers
Here it is postulated that local governments participating in the project use their
DAK to ‘crowd in’ additional capital spending to a greater extent than nonproject
local governments. It is speciied that local government per capita capital spending is a function of a dummy variable for program participation; per capita DAK;
the interaction between the program participation dummy and per capita DAK;
and other variables, including the control variables speciied above.
Table 4 provides the relevant regression output. Consider the impact of DAK
on capital spending for nonP2D2 and P2D2 local governments. The regression
results suggest that the elasticity of capital spending with respect to DAK is 0.439
for nonP2D2 participants and 0.655 (0.439 + 0.216) for P2D2 participants. As the
table shows, the difference is statistically signiicant.
The estimated elasticities can be transformed into marginal effects in the usual
way (that is, at the point of means). In marginal terms, the results imply that an
additional rupiah of DAK leads to an extra Rp 2.05 of capital spending for P2D2
kabupaten and kota yet only an extra Rp 1.34 of capital spending for nonP2D2 local
governments. DAK seems to stimulate more capital spending in P2D2 local governments than it does in nonP2D2 local governments—that is, the crowdingin
hypothesis appears to be conirmed.
WATER HIBAH
The Hibah mechanism has yet to be used much by the central government as a
means of transferring funds to the regions. The WH, speciically, is very limited
in size. It made up signiicantly less than 1% of the total revenue budgets of local
governments that received the grant in 2010 or 2011 or both.
424
Blane D. Lewis
Downloaded by [Universitas Maritim Raja Ali Haji] at 23:33 17 January 2016
This section of the article explores the three research questions for the WH program that were set forth in the introduction. These questions relate to the inluence of the program equity investments of local governments in their PDAMs, the
impact of those investments on the creation of household water connections, and
the direct effect of the program on the establishment of water connections.
Equity Investments
The hypothesis here is that both program participation and transfers have a positive effect on local government equity investment, everything else being equal.
It is postulated that kabupaten and kota per capita equity investment is a function
of a program participation dummy variable, WH per capita transfers, and other
variables.
The multivariate examination in this case immediately faces two dificulties.
The irst concerns the nonnormal probability distribution of the dependent variable local government per capita equity investment. Inspection of the distribution
shows a sizable collection of zero observations (that is, censoring) and a relatively
large gap between the zero and nonzero observations. The nature of the distribution suggests that using a hurdle model might be a suitable approach.18 A hurdle
model would explain the local government’s choice of whether to invest or not
(by way of a probit speciication) and, given a positive decision in that regard,
how much to invest (by way of a more typical linear regression).
The second problem concerns the nature of WH transfers. More speciically, it
seems reasonable to contend that such transfers are endogenous. WH transfers
are in the irst instance determined by whether a local government participates
in the WH program. Project participants receive the grant (at some point over the
two years); nonproject participants do not. Project participation—that is, treatment—was originally considered to be endogenous, but that endogeneity was
accommodated by using PSM. (PSM did not address the linked endogeneity of
transfers, however.19) In the analysis below, the WH grant is speciied as endogenous and its predicted value is used in the estimation procedures (and standard
errors are bootstrapped).
The predicted value of WH transfers is estimated by irst regressing the variable in per capita terms against the program participation dummy; the log of
other revenues per capita; the log of population; the share of the population that
is urban; the share of the population that is poor; the share of the population with
access to clean water (lagged); the log of GRDP per capita; and dummy variables
for the on or offJava location of kabupaten and kota location, and the year. The
analysis assumes that the two inal variables (that is, the excluded instruments)
are exogenous and important in determining WH transfers but not investments.20
18. The other possibility would be to employ a tobit model. However, as Greene (2012,
855) explains, a tobit model is more appropriate when data are distributed with a cluster of
zeroes and a grouping of observations near zero.
19. Standard (for example, OLS) estimation of a model with endogenous righthandside
variables will result in inconsistent parameter estimates. Such cases require an instrumental variable approach, such as that employed here.
20. All variables together explain 42% of the variation in per capita WH transfers. The regression output is available from the author on request.
Indonesian Intergovernmental Performance Grants: An Empirical Assessment
425
TABLE 4 Explaining Capital Spending via Program Participation and
DAK Interactions
Downloaded by [Universitas Maritim Raja Ali Haji] at 23:33 17 January 2016
Independent variables
P2D2 dummy variable
Log of DAK per capita
P2D2 dummy variable × log of DAK per capita
Log of other revenues per capita
Log of population
% of population that is urban
% of population that is poor
Log of gross regional domestic product per capita
Constant
Observations
R2
Coeficient
–2.279**
0.439**
0.216**
1.980**
0.750**
0.001
0.014**
0.034
–29.609**
t-statistic
–2.41
5.78
2.43
5.11
4.12
0.72
2.04
0.33
–4.58
218
0.794
Note: The dependent variable is the log of per capita local government capital spending across all sectors, infrastructure and otherwise. All iscal and economic variables are measured in constant (year
2000) terms, converted using the implicit GDP delator from Indonesia’s national accounts. The t-statistic is based on robust standard errors. DAK = Dana Alokasi Khusus. P2D2 = Proyek Pemerintah
Daerah dan Desentralisasi.
** p < 0.05.
WH is then predicted in the usual way, by using the estimated equation, but
insisting that the predicted values of WH grants be nonnegative.
Tables 5 and 6 show the regression results from the estimation of the hurdle
model.21 Table 5 provides the output for the probit equation in which the dummy
variable for program participation is used to explain the decision of whether to
invest or not. WH transfers are used to explain the level of equity investment. Two
cases are considered: one where WH transfers are taken as an exogenous variable
and the other where WH grants are endogenously speciied. The argument here is
that the latter is the correct speciication; results based on the former are included
just for comparison.22
Tables 5 and 6 provide a partial indication of the impact of program participation and WH transfers on investment; the results need to be combined to assess
total impact. Table 7 provides the relevant output. The empirical results indicate
that project participation leads to an increase in per capita investment of Rp 734
in constant 2000 terms (or Rp 2,124 in current prices for 2010–11) when WH transfers are taken to be exogenous and Rp 739 in constant 2000 terms (or Rp 2,141
21. The hurdle model is estimated using the tpm procedure in Stata 13.
22. In the second regression, (predicted) untransformed real per capita WH transfers are
used as an explanatory variable and not their log. The log transformation of the variable
is bimodal and its employment creates dificulties for the estimation procedures. The untransformed variable is skewed, of course; in this case, however, using a variable with a
skewed distribution is preferable to using one with a bimodal distribution.
426
Blane D. Lewis
TABLE 5 Explaining Equity Investments (Probit Equation)
Downloaded by [Universitas Maritim Raja Ali Haji] at 23:33 17 January 2016
Independent variables
Coeficient
Water Hibah dummy variable
Log of other revenues per capita
Log of population
% of population that is urban
% of population that is poor
% of population with access to water, lagged
Log of GRDP per capita
Constant
z-statistic
1.048**
2.615**
1.625**
–0.009
–0.047
0.026**
–1.051**
–39.885**
Observations
R2
3.38
3.11
3.44
–1.46
2.01
–2.66
–2.66
105
0.238
Note: The dependent variable is a categorical variable indicating whether the local government made
investments in its PDAM. All iscal and economic variables are measured in constant (year 2000)
terms, converted using the implicit GDP delator from Indonesia’s national accounts. The z-statistic is
based on robust standard errors. GRDP = gross regional domestic product.
** p < 0.05.
TABLE 6 Explaining Equity Investments (Regression Equation)
Water Hibah exogenous Water Hibah endogenous
Independent variables
Water Hibah per capita
Log of other revenues per capita
Log of population
% of population that is urban
% of population that is poor
% of population with access to water,
lagged
Log of GRDP per capita
Constant
Observations
R2
Coeficient
z-statistic
Coeficient
z-statistic
0.0003**
–1.1978**
–0.9202**
0.0013
–0.0204
4.21
–2.25
–2.71
0.21
–0.84
0.0005**
–1.2250**
–0.9613**
0.0020
–0.0223
2.98
–2.17
–2.68
0.30
–0.87
–0.0003
0.7850**
22.8479**
–0.03
2.49
2.34
0.0011
0.7383**
24.2593**
0.10
2.49
2.35
73
0.364
73
0.355
Note: The dependent variable is the log of per capita local government equity investment in PDAM.
All iscal and economic variables are measured in constant (year 2000) terms, converted using the
implicit GDP delator from Indonesia’s national accounts. The z-statistic is based on robust standard
errors. GRDP = gross regional domestic product.
** p < 0.05.
Indonesian Intergovernmental Performance Grants: An Empirical Assessment
427
TABLE 7 Total Impact of Program Participation and Water Hibah Transfers on
Equity Investments
Water Hibah exogenous
Independent variables
Downloaded by [Universitas Maritim Raja Ali Haji] at 23:33 17 January 2016
Water Hibah dummy variable
Hibah per capita
Water Hibah endogenous
Coeficient
z-statistic
Coeficient
z-statistic
733.7**
0.578**
3.19
2.81
739.1**
0.815**
3.20
2.50
Note: The dependent variable is the log of per capita local government equity investment in PDAMs.
All iscal and economic variables are measured in constant (year 2000) terms, converted using the
implicit GDP delator from Indonesia’s national accounts. The z-statistic is based on robust standard
errors.
** p < 0.05.
in 2010–11 terms) when WH grants are assumed endogenous. Furthermore, the
calculations suggest that an extra rupiah of WH transfers leads to Rp 0.58 of additional investment in PDAMs when grants are treated as exogenous and Rp 0.81
extra investment performance when transfers are speciied as endogenous.
The last result would seem to be especially reasonable in light of program
procedures. That is, since many local governments make equity investments in
their PDAMs even without receiving the program grant, it might be expected that
an additional rupiah of WH to participating local governments would result in
less than one rupiah of investment—that is, that local governments would divert
some of their planned (pregrant) investments to other purposes.23
Household Water Connections
The proposition under investigation here is that both program participation and
local government equity investments positively inluence the creation of water
connections, all else remaining the same. It is posited that connections are a function of a program participation dummy; local government per capita equity
investments (whether inanced by Hibah or by other sources of revenue); and
other control variables.
Given the preceding analysis, it is apparent that investments should be treated
as endogenous. As such, the predicted value of equity investments from the earlier
analysis is used in the examination. An inspection of the probability distribution of
(log) household water connections implies that a simple linear least squares model
would sufice in this instance (where standard errors are again bootstrapped). As
table 8 shows, program participation is a statistically signiicant determinant of
household water connections when investment is taken to be exogenous but not
when it is considered to be endogenous—the preferred speciication. Conversely,
investment is a signiicant explanator of household water connections in either
23. Nonmatching speciicpurpose grants typically have an impact of less than one (Boadway and Shah 2007).
TABLE 8 Explaining Household Water Connections
Downloaded by [Universitas Maritim Raja Ali Haji] at 23:33 17 January 2016
Independent variables
Water Hibah dummy variable
Investment per capita
Log of population
% of population that is urban
% of population that is poor
% of population with access to water,
lagged
Log of GRDP per capita
Constant
Observations
R2
Investment exogenous
Investment endogenous
Coeficient
t-statistic
Coeficient
t-statistic
0.4915**
0.0001**
–0.4173**
–0.0019
0.0349*
2.77
4.07
–4.23
–0.41
1.72
0.3816
0.0002**
–0.3612**
–0.0030
0.0378
1.58
2.30
–3.46
–0.61
1.61
0.0135
2.12
0.0126*
1.71
0.6325**
–5.3520
3.59
–1.63
0.5964**
–5.5244
2.97
–1.56
105
0.472
105
0.413
Note: The dependent variable is the log of number of new household connections per 1,000 people. All
iscal and economic variables are measured in constant (year 2000) terms, converted using the implicit
GDP delator from Indonesia’s national accounts. The t-statistic is based on robust standard errors.
* p < 0.1; ** p < 0.05.
TABLE 9: Explaining Connections: Treatment and Control Group Investments
Independent variables
Water Hibah dummy variable
Investments per capita
Water Hibah dummy × investments
per capita
Log of population
% of population that is urban
% of population that is poor
% of population with access to water,
lagged
Log of GRDP per capita
Constant
Observations
R2
Investment exogenous
Investment endogenous
Coeficient
Coeficient
t-statistic
t-statistic
0.4779**
0.0001**
2.19
2.08
0.5523**
0.0005*
1.90
1.75
0.0000
–0.4166**
–0.0019
0.0354*
0.15
–4.16
–0.41
1.68
–0.0002
–0.3170**
–0.0017
0.0439*
–0.99
–2.73
–0.33
1.77
0.0134**
0.6372**
–5.4324
2.11
3.52
–1.60
0.0090
0.5780**
–5.9780*
1.07
2.96
–1.64
105
0.474
105
0.418
Note: The dependent variable is the log of the number of new household connections per 1,000 people.
All iscal and economic variables are measured in constant (year 2000) terms, converted using the the
implicit GDP delator from Indonesia’s national accounts. The t-statistic is based on robust standard
errors.
* p < 0.1; ** p < 0.05.
Downloaded by [Universitas Maritim Raja Ali Haji] at 23:33 17 January 2016
Indonesian Intergovernmental Performance Grants: An Empirical Assessment
429
case. These results are taken to infer that project participation in and of itself is
probably not important for the establishment of water connections; what matters
for the creation of connections is local government equity investment in PDAMs,
at least in the present framework.
As before, the estimated elasticities in table 8 can be transformed into marginal effects, which in this instance would provide an estimate of the change in
the number of new household connections per 1,000 persons given a onerupiah
change in per capita equity investments. The marginal effects themselves can be
transformed into ‘unit investment costs’ simply by taking the inverse. The relevant calculation suggests that the investment cost required to increase household
water connections by one unit is Rp 6.3 million when investments are treated as
exogenous and Rp 4.3 million when investments are speciied as endogenous.
(Rupiah igures are in 2010–11 terms.)
In this context, are treatment group investments more costeficient in creating household water connections than control group investments? This question
can easily be examined econometrically by specifying an interaction term—the
project participation dummy multiplied by equity investments. Table 9 gives
the relevant regression results. The output shows that the coeficient of the interaction variable is not signiicantly different from zero, regardless of how investments are speciied. That is, the results suggest that investment by WH local
governments is no more costeficient than investment by nonproject kabupaten
or kota.
In the analysis above, WHinanced investments are subsumed within total
investments. As such, the speciic impact of WH transfers on the creation of
household water connections is obscured. In order to assess the direct effects of
WH transfers on household connections, investments inanced by the performance grant must be separated from investments inanced by other sources of
local government revenue. With this objective in mind it is posited that per capita
household water connections are a function of the standard program participation dummy, WHinanced investments, investments inanced from other sources
of revenue, and the usual control variables.
As table 10 shows, program participation is not statistically signiicant in determining water connections, irrespective of assumptions about the endogeneity of
transfers. In contrast, both per capita WHinanced investments and other revenueinanced investments are important in explaining household water connections, regardless of speciication. While program participation in and of itself does
not appear to matter for the creation of household water connections (a similar
result as found before), WH performance transfers do have a signiicant inluence.
The table shows the estimated elasticities, as usual. The usual calculation
implies that the cost of creating an additional WHinanced household water connection is Rp 2.9 million when WH is taken to be exogenous and Rp 2.5 million
when it is taken to be endogenous. Furthermore, the results suggest that the cost
of an additional connection inanced by other sources of revenue costs about Rp
8.2 million when WH is taken to be exogenous and Rp 6.9 when WH is taken to be
endogenous (all igures in 2010–11 terms). The usual statistical test shows that the
differences between the effects of WHinanced investments and other revenue
inanced investments are statistically signiicant, regardless of assumptions about
the endogeneity of WH transfers.
Blane D. Lewis
430
TABLE 10 Explaining Connections as a Function of
Water Hibah–Financed Investments
Downloaded by [Universitas Maritim Raja Ali Haji] at 23:33 17 January 2016
Independent variables
Treatment dummy
Hibah inanced investments
per capita
Other revenueinanced investments
per capita
Log of population
% of population that is urban
% of population that is poor
Log of GRDP per capita
% of population with access to water,
lagged
Constant
Observations
R2
Investment exogenous
Investment endogenous
Coeficient
t-statistic
Coeficient
t-statistic
0.3034
1.62
0.2892
1.04
0.0003**
6.16
0.0004*
1.71
0.0001**
–0.3955**
–0.0008
0.0373*
0.6862**
3.42
–4.05
–0.19
1.93
3.88
0.0001*
–0.3612**
–0.0021
0.0352
0.6457**
1.69
–3.44
–0.41
1.49
2.93
0.0103**
–6.3512*
1.71
–1.95
0.0117
–6.2424
1.58
–1.62
105
0.506
105
0.417
Note: The dependent variable is the log of per capita local government equity investment in PDAM.
All iscal and economic variables are measured in constant (year 2000) terms, converted using the
World Bank’s national GDP delator. The t-statistic is based on robust standard errors.
* p < 0.1; ** p < 0.05.
SUMMARY, DISCUSSION, AND CONCLUSIONS
The empirical results in this article suggest that the amount of P2D2 local government capital spending is not signiicantly different from that of nonP2D2 kabupaten and kota, on average, all other things being equal. This implies that P2D2 has
no impact on the level of participating local government capital spending.
However, DAK is apparently more stimulative of capital spending by P2D2
kabupaten and kota than it is of that by nonP2D2 local governments. The regression results imply that an additional rupiah of DAK leads to an extra 2.05 rupiah
of capital spending for P2D2participating local governments yet only an extra
1.34 rupiah of capital spending for nonP2D2 places.
As such, project participation apparently leads to a supplementary 0.71 rupiah
of capital spending from each rupiah increase in the DAK, all else remaining the
same. This implies that while capital spending by P2D2 local governments is not
greater, on average, than that by nonP2D2 kabupaten and kota, as DAK increases,
capital spending rises more quickly for local governments participating in the
project. And at some level of DAK, (approximately 30 thousand rupiah per capita), capital spending of P2D2 local governments begins to exceed capital spending of nonP2D2 local governments.
Otherwise put, the results indicate that local governments participating in
P2D2 use their DAK to crowdin additional capital spending to a larger extent
than nonprogram participants do. There are two possible means through which
Downloaded by [Universitas Maritim Raja Ali Haji] at 23:33 17 January 2016
Indonesian Intergovernmental Performance Grants: An Empirical Assessment
431
this effect might be accomplished. First, given a marginal increase in DAK, P2D2
kabupaten and kota are more apt to use other (endogenous) revenues—from own
sources or reserve funds—to further increase spending on capital.24 Or, second,
they are more likely to reallocate spending from current items—personnel or
goods and services—to capital, as DAK transfers increase at the margin (that is,
without using additional endogenous revenues for capital spending).
The speciic empirical result under discussion here necessarily implies that
treatment local governments do one or both of these two things. It is not obvious, however, how the P2D2 program might actually lead to such changes in
behaviour. P2D2 incentives concentrate on encouraging local governments to
spend all of their DAK (at a reasonable level of quality) and not to link DAK
inanced spending with capital spending funded from other sources or to incite a
reallocation of current to capital spending. It is possible that the program has such
effects, of course, but it would be useful to at least verify and explain the apparent
impacts through additional ield research.
Regarding WH, the empirical analysis suggests that the program encourages
local governments to make larger investments in their PD