00074918.2014.980378

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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

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INDONESIAN INTERGOVERNMENTAL

PERFORMANCE GRANTS: AN EMPIRICAL

ASSESSMENT OF IMPACT

Blane D. Lewis

The Australian National University

The government of Indonesia has now piloted two different outputbased perfor

-mance 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, per-formance 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 embed

-ded 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 account

-ability 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 develop

-ing 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 on going eficacy (Steffensen and Larson 2005, Shah 2010, Steffensen 2010, Rojas

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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 concen

-trate 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 equal

-isation 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 intergov

-ernmental 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 con

-centrates 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 con

-nections 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 tech

-nical reporting, and capital spending. Eligibility criteria comprise local government provi

-sion 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.

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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 addi -tional 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 spend

-ing 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 Develop

-ment Supervision (Badan Pengawasan Keuangan dan Pembangunan) after DAK allocations have been spent.

Successful kabupaten and kota are awarded about 10% of the initial infrastruc

-ture 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 pro

-gram 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 govern

-ments and their PDAMs met equity investment and household water connec

-tion 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.

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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 pro

-gram 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, specii

-cally, 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 associ

-ated 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 sig

-niicance 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 Initia

-tive. 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, lo

-cal infrastructure spending makes up between twothirds and threequarters of total lo-cal capital spending; but available data do not permit the separation of infrastructure spend

-ing from total capital spend-ing 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 partici

-pation may also explain outcomes of interest. As such, treatment may be presumed to be endogenous (Greene 2012, 888).

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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 gov -ernments 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 ‘dou

-bly robust’, since they attempt to correct for the inherent biases involved in empir

-ically 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.

TABLE 1 Variable Summary Statistics

Variable Mean Std Min Max N

P2D2

Local government capital spending 151,982 199,194 4,239 1,565,788 222

DAK (speciicpurpose capital grant) 48,559 53,309 174 321,706 218

Local government revenue 722,623 446,679 210,376 2,834,593 222

Population 451,906 435,580 43,809 2,459,960 256

% population that is urban 35.7 28.0 3.4 100.0 256

% population that is poor 10.8 6.6 1.4 39.5 254

Gross regional domestic product 6,603,166 3,482,494 1,327,098 19,400,000 254

Water Hibah

Equity investment 1,630 2,627 0 14,349 105

Water connections per 1,000 persons 3.2 3.8 0.1 23.5 105

Hibah 602 1,240 0 7,283 105

Local government revenue 505,574 370,471 159,928 2,224,253 105

Population 1,010,122 801,481 117,493 4,858,514 105

% population that is urban 58.0 31.1 9.1 100.0 105

% population that is poor 12.4 5.7 3.2 28.1 105

% population with access to water 62.2 18.9 4.8 99.1 105

Gross regional domestic product 7,382,343 4,395,347 2,505,224 26,200,000 105

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 vari -ables are measured in constant (2000) per capita terms, converted using the implicit GDP delator from Indonesia’s national accounts.

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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 remain

-ing) 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 prov} -inces 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. Vari

-ables 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 (with

-out 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 unneces

-sary.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 dis

-tricts 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 contempo

-raneous 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.

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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 vari

-able 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 standard

-ised 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

TABLE 2 Analysis of Balance, P2D2, and Water Hibah Propensity Score Matching

Mean

Log total revenue per capita Unmatched 13.3 13.4 –9.0

Matched 13.4 13.4 0.4 95.8

Log population Unmatched 12.8 12.6 15.4

Matched 12.6 12.6 2.2 85.7

% pop. that is urban Unmatched 38.0 36.9 31.5

Matched 37.3 36.9 15.9 64.0

% pop. that is poor Unmatched 11.1 9.7 22.6

Matched 10.1 9.7 6.7 70.3

Log GRDPPC Unmatched 15.6 15.6 –1.4

Matched 15.8 15.6 –6.6 –370.2

Water Hibah

Log total revenue per capita Unmatched 12.8 12.9 –12.8

Matched 12.8 12.9 –8.2 36.0

Log population Unmatched 13.5 13.3 24.3

Matched 13.5 13.4 10.8 55.6

% pop. that is urban Unmatched 56.6 45.6 34.4

Matched 56.2 58.5 –7.0 79.7

% pop. that is poor Unmatched 13.3 13.4 –1.6

Matched 13.4 13.5 –0.6 59.6

% pop. with access to water Unmatched 59.2 56.9 13.0

Matched 59.8 58.3 8.1 37.6

Log GRDPPC Unmatched 15.5 15.6 –0.9

Matched 15.5 15.6 –5.9 –580.6

Source: Author’s calculations.

Note: P2D2 = Proyek Pemerintah Daerah dan Desentralisasi. DAK = Dana Alokasi Khusus. GRDPPC = gross regional domestic product per capita.

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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 gov

-ernment 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 cov

-ered 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 intro

-duction. The irst question concerns the impact of the P2D2 program on the capi -tal spending of kabupaten and kota. The second relates to the possible effect of

program participation on capital spending spillovers associated with local gov

-ernment 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 varia

-ble 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 treat

-ment local govern-ments 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 num

-ber of observations this proved unfeasible. All regressions in this analysis were estimated by pooling the data.

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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 spend

-ing 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 gov

-ernments than it does in nonP2D2 local gov-ernments—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.

TABLE 3 Explaining Capital Spending

Independent variables Coeficient t-statistic

P2D2 dummy variable –0.058 –0.86

Log of DAK per capita 0.510** 6.60

Log of other revenues per capita 2.148** 5.28

Log of population 0.770** 4.00

% of population that is urban 0.001 0.67

% of population that is poor 0.010 1.57

Log of gross regional domestic product per capita –0.021 –0.19

Constant –31.934** –4.85

Observations 218

R2 _0.784

Note: The dependent variable is the log of per capita local government capital spending across all sec -tors, 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.

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This section of the article explores the three research questions for the WH pro

-gram that were set forth in the introduction. These questions relate to the inlu

-ence 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 posi

-tive 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 vari

-able 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 distribu

-tion 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, treat

-ment—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 endog} -enous 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 var

-iable 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 instrumen

-tal variable approach, such as that employed here.

20. All variables together explain 42% of the variation in per capita WH transfers. The re

-gression output is available from the author on request.

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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 participa

-tion 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 trans

-fers 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 un

-transformed 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.

TABLE 4 Explaining Capital Spending via Program Participation and DAK Interactions

Independent variables Coeficient t-statistic

P2D2 dummy variable –2.279** –2.41

Log of DAK per capita 0.439** 5.78

P2D2 dummy variable × log of DAK per capita 0.216** 2.43

% of population that is urban 0.001 0.72

% of population that is poor 0.014** 2.04

Log of gross regional domestic product per capita 0.034 0.33

Constant –29.609** –4.58

Observations 218

R2 _0.794

Note: The dependent variable is the log of per capita local government capital spending across all sec -tors, 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 -sta-tistic is based on robust standard errors. DAK = Dana Alokasi Khusus. P2D2 = Proyek Pemerintah Daerah dan Desentralisasi.

** p < 0.05.

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TABLE 5 Explaining Equity Investments (Probit Equation)

Independent variables Coeficient z-statistic

Water Hibah dummy variable 1.048** 3.38

% of population that is urban –0.009

% of population that is poor –0.047 –1.46

% of population with access to water, lagged 0.026** 2.01

Log of GRDP per capita –1.051** –2.66

Constant –39.885** –2.66

Observations 105

R2 _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 Coeficient z-statistic Coeficient z-statistic

Water Hibah per capita 0.0003** 4.21 0.0005** 2.98

Log of other revenues per capita –1.1978** –2.25 –1.2250** –2.17

Log of population –0.9202** –2.71 –0.9613** –2.68

% of population that is urban 0.0013 0.21 0.0020 0.30

% of population that is poor –0.0204 –0.84 –0.0223 –0.87

% of population with access to water,

lagged –0.0003 –0.03 0.0011 0.10

Log of GRDP per capita 0.7850** 2.49 0.7383** 2.49

Constant 22.8479** 2.34 24.2593** 2.35

Observations 73 73

R2 0.364 _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.

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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 addi

-tional 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 func

-tion of a program participa-tion 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 (Boad

-way and Shah 2007).

TABLE 7 Total Impact of Program Participation and Water Hibah Transfers on Equity Investments

Water Hibah exogenous Water Hibah endogenous

Independent variables Coeficient z-statistic Coeficient z-statistic

Water Hibah dummy variable 733.7** 3.19 739.1** 3.20

Hibah per capita 0.578** 2.81 0.815** 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.

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Investment exogenous Investment endogenous

Independent variables Coeficient t-statistic Coeficient t-statistic

Water Hibah dummy variable 0.4915** 2.77 0.3816 1.58

Investment per capita 0.0001** 4.07 0.0002** 2.30

% of population that is urban –0.0019 –0.41 –0.0030 –0.61

% of population that is poor 0.0349* 1.72 0.0378 1.61

lagged 0.0135 2.12 0.0126* 1.71

Constant –5.3520 –1.63 –5.5244 –1.56

Observations 105 105

R2 _0.472 _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

Water Hibah dummy variable 0.4779** 2.19 0.5523** 1.90

Investments per capita 0.0001** 2.08 0.0005* 1.75

Water Hibah dummy × investments

per capita 0.0000 0.15 –0.0002 –0.99

% of population that is poor 0.0354* 1.68 0.0439* 1.77

lagged 0.0134** 2.11 0.0090 1.07

Constant –5.4324 –1.60 –5.9780* –1.64

R2 _0.474 _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.

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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 mar

-ginal 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 rele

-vant 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 creat

-ing 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 inter

-action variable is not signiicantly different from zero, regardless of how invest

-ments 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 perfor

-mance 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 participa

-tion 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 deter

-mining water connections, irrespective of assumptions about the endogeneity of transfers. In contrast, both per capita WHinanced investments and other reve

-nueinanced investments are important in explaining household water connec

-tions, 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 con

-nection 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.

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SUMMARY, DISCUSSION, AND CONCLUSIONS

The empirical results in this article suggest that the amount of P2D2 local govern

-ment capital spending is not signiicantly different from that of nonP2D2 kabu-paten 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 regres

-sion 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 cap

-ita), capital spending of P2D2 local governments begins to exceed capital spend

-ing 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

TABLE 10 Explaining Connections as a Function of Water Hibah–Financed Investments

Treatment dummy 0.3034 1.62 0.2892 1.04

Hibah inanced investments

per capita 0.0003** 6.16 0.0004* 1.71

Other revenueinanced investments

per capita 0.0001** 3.42 0.0001* 1.69

% of population that is poor 0.0373* 1.93 0.0352 1.49

lagged 0.0103** 1.71 0.0117 1.58

Constant –6.3512* –1.95 –6.2424 –1.62

R2 _0.506 _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.

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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 obvi

-ous, 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 PDAMs than they other

-wise would. The results imply that program participation leads to an increase in per capita equity investment of Rp 2,141 (in 2010–11 terms), all else remaining the same. WH transfers speciically also seem to be reasonably successful in stimulat

-ing such investments. In this regard, it is estimated that an extra rupiah of WH leads to an added 0.81 rupiah in investment.

The analysis also shows that equity investments are, in general, positively associated with the creation of new household water connections. The estimated investment cost of an additional household connection is Rp 4.3 million rupiah (in 2010–11 terms). The econometric results also imply, however, that treatment group local government investment is no more eficient in creating water connec

-tions than control group investment. But WHinanced investments do seem to be more costeficient than investments inanced by other sources of local govern

-ment revenue. The estimated marginal unit cost of WHinanced invest-ments is Rp 2.5 million, while that of other revenueinanced investments is Rp 6.9 million (both in 2010–11 terms).

At any rate, WH transfers seem to have led participating PDAMs to create a greater number of household water connections than would have otherwise been the case—a clear beneit of the program. The principal question in this context is whether the derived beneits are worth the cost. The costs include a payment of about Rp 2.5 million per household connection, which, according to WH project documents, is intended to represent 45% of the total actual cost of making a con

-nection. This is an especially important issue for policymakers; while the program started out using foreign grant funds, it would eventually have to be taken on budget—that is, funded by rupiah (murni)—if it were to be expanded nationally. More analysis would be needed before the government could make an informed beneit–cost decision.

24. In theory, P2D2 participation may also induce local governments to borrow for capital spending as well. As is well known, however, local government borrowing is very limited in Indonesia, so this seems a distant possibility. See Lewis’s (2007) article for a review of local governments’ borrowing experiences.

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In conclusion, outputbased performance transfers in Indonesia have led to some reasonably positive outcomes. The results are perhaps especially notewor

-thy given that the period over which impacts were assessed was a mere two years. One might sensibly expect even better effects in the future as the grants gain more traction. The results achieved so far, however, may pale somewhat in compari

-son with the expected impacts, as based on typical arguments of incentive grant enthusiasts within international development agencies, the government, and else

-where. Nevertheless, the evidence suggests that the general approach of intergov

-ernmental performance transfers merits further consideration and development. The other option, of course, would be to do nothing, but this would most likely mean continued weak local public service delivery. As this article was being written, the Ministry of Finance was discussing and designing a broad range of reforms to the intergovernmental iscal, legislative, and regulatory framework, with a view to improving service outcomes. In this context, it would certainly be useful for policymakers to think hard about adopting more farreaching performance grant initiatives—as a counterweight to continued reliance on the various equitybased approaches that dominate the intergovernmental system— while at the same time being realistic about expected impacts.

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