what drives local service delivery performance in...

What Drives Local Service Delivery Performance in Indonesia?

Blane Lewis, Neil McCullochand Audrey Sacks

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AUSTRALIA INDONESIA PARTNERSHIP FOR DECENTRALISATION (AIPD) WORKING PAPER 3, DECEMBER 2014

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AIPD WORKING PAPER 3, DECEMBER 2014

II Challenges to Measuring Local Government Service Delivery Performance in Indonesia

Disclaimer

The Australia Indonesia Partnership for Decentralisation (AIPD) publishes the AIPD Working Paper Series for swift dissemination of AIPD program results. The work published under this Series may be subsequently revised for publication in other publication series, professional journals or chapters in books. The manuscript of this paper has therefore not been prepared in accordance with the procedures appropriate to formally edited texts. Some sources cited in the paper may be informal documents that are not readily available. The views expressed within this paper are those of the author(s) and not necessarily reflect the views of AIPD. AIPD does not endorse its content and accepts no responsibility for any loss, damage or injury resulting from reliance on any of the information or views contained within it. All rights reserved. Reproduction and dissemination of material in this paper for educational or other non-commercial purposes are authorised without any prior written permission from the copyright holders provided the source is fully acknowledged. Reproduction of material in this paper for resale or other commercial purposes is prohibited without written permission of the copyright holders. Applications for such permission should be addressed to: Jessica Ludwig-Maaroof Program DirectorAustralia Indonesia Partnership for DecentralisationCyber 2 Tower 18th Floor, Suite M.10Jl. H.R. Rasuna Said Blok X-5, Kav. 13, Jakarta, 12950Telp. +62 21 5799 8932 Or by email to:[email protected] ©AIPD 2014

Challenges to Measuring Local Government Service Delivery Performance in Indonesia IIIi

Acknowledgements:

The authors would like to thank Daan Pattinasarany, Cut Dian Agustina and Indira Hapsari in the World Bank’s Indonesia Database for Policy and Economic Research (INDO-DAPOER) team for their patient in explaining the data. We would also like to thank Samer Al-Samarrai, Daim Syukriyah, Darren Dorkin, Eko Pambudhi, and Matt Wai Poi for their help in constructing district level aggregate data and their numerous insights on data quality and analysis. Our gratitude also goes to the Ministry of Education, the Ministry of Health and the Ministry of Public Works for access to their data, as well as to Central Statistics Agency (BPS) for the data which provides the core of the DAPOER dataset. We thank Jessica Ludwig-Maaroof and the entire Australia Indonesia Partnership for Decentralisation (AIPD) team for their support and encouragement. Special thanks to the team at the Directorate General of Fiscal Balance in the Ministry of Finance led by Pak Ubaidi, as well as Dewa Sutisna, for their suggestions and advice. Last and most we would like to thank Diego Fosatti for tireless research assistance.

Table of Contents

Abstract

I. Introduction

II. Data

III. Results

IV. Conclusions

References

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1

4

11

12

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IV Challenges to Measuring Local Government Service Delivery Performance in Indonesia

Challenges to Measuring Local Government Service Delivery Performance in Indonesia

Blane Lewis, Neil McCulloch and Audrey Sacks1

Abstract

There is a strong sense in popular debate that decentralisation in Indonesia has failed to deliver the anticipated results. With a few exceptions, assessments of decentralisation in Indonesia have relied on qualitative assessments of district performance in specific sectors. We use a newly available public dataset which contains measures of district performance in education, health and infrastructure for all districts from 2001-2012 to investigate the determinants of service delivery performance at the district level. We explore the extent to which exogenous factors, such as land area and population influence the ability of districts to deliver better services. We also examine the influence of exogenous but slowly changing variables including regional GDP, poverty and inequality, as well as an endogenous factor, district expenditures. We not only find that there are strong correlations between some structural factors and levels of performance, but also that expenditure is of importance. We conclude with the implications of our findings for policymaker’s attempts to enhance the accountability of district leaders by measuring district level performance on education, health and infrastructure service delivery.

1 Lewis, B: Consultant, Public Finance Management (Department of Foreign Affairs and Trade (DFAT) and Australia Indonesia Partnership for Decentralisation (AIPD); McCulloch, N: Lead Country Economist (DFAT); and Sacks, A: Consultant, Political Economy, Procurement and Anti-corruption (The World Bank Group). 26 May 2014.

Challenges to Measuring Local Government Service Delivery Performance in Indonesia v

I. Introduction

After the collapse of the Suharto regime in 1998, Indonesia undertook one of the world’s most rapid and dramatic processes of decentralisation. Legislation that was passed in 1999 (Laws 22 and 25, 1999) and implemented in 2001, devolved responsibility of the main elements of service delivery – health, education and infrastructure provision – to almost 300 districts. This was a bold change from the previous era, in which all of the relevant central ministries provided these services through a system of top-down, deconcentrated control.

Conversely, there were also major hopes for decentralisation. First, it was hoped that decentralisation would increase accountability for the provision of public goods by bringing elected representatives closer to citizens. Second, through decentralisation, sub-national governments could increase revenue mobilisation and tap resources that the central government is incapable of reaching (Bird and Vaillancourt, 2008). Third, in ethnically or culturally divided states decentralisation is expected to reduce conflicts by satisfying local demands for autonomy and thereby mitigating fears of political exploitation and inter-group violence (Lake and Rothchild, 2005; Hechter, 2000). Several scholars have documented examples in which decentralisation has promoted better service delivery. In the four southern Indian states of Andhra Pradesh, Karnataka, Kerala and Tamil Nadu, Besley et al (2007) link decentralisation at the lowest level (the Panchayat system) with better outcomes including better targeting of welfare program beneficiaries.

At the same time, the empirical evidence on the extent to which decentralisation meets these expectations is mixed. Bardhan and Mookherjee (2006) argue that sub-national institutions are more vulnerable to elite capture than national institutions, resulting in the under-provision of public goods and services and to greater exploitation of groups without power, resources and connections. In his review of the fiscal decentralisation literature, Wibbels (2006) presents cases in which decentralisation in many sub-national contexts has appeared to cause poor economic performance, the aggravation of redistributive and ethnic conflicts. Similarly, Devarajan, Khemani and Shah (2009) argue that when decentralisation is only partial, citizens continue to place their demands for service delivery at the hands of national governments, which weakens local governments’ incentives to allocate budgetary resources optimally across competing public needs. Instead, public goods and resources are channelled to narrow interest groups or converted to rents. Even when fiscal decentralisation improves local service delivery, the cost of coordination across multiple levels of government may outweigh the benefits (Treisman, 2000).

1 Challenges to Measuring Local Government Service Delivery Performance in Indonesia

Challenges to Measuring Local Government Service Delivery Performance in Indonesia 2

Given the mixed theoretical predictions and empirical results on the impact of decentralisation on service delivery, it is important for policymakers to be able to measure performance empirically. Over the last 13 years there have been several attempts to assess various different dimensions of local government performance in Indonesia. Many of these measures have focussed on economic, political and fiscal performance. For example, in 2007 the Regional Autonomy Watch (Komite Pemantauan Pelaksanaan Otonomi Daerah - KPPOD 2008) undertook a local business level survey on the quality of economic governance in half of the districts in the country, with the remaining districts covered in another survey in 2010. Surprisingly, this showed a remarkably weak association between the quality of economic governance as perceived by local business and district level growth (McCulloch and Malesky, 2011). Similarly, the national NGO, Partnership for Governance Reform (Kemitraan), has constructed an Indonesia Governance Index which attempts to measure the quality of government, bureaucracy; civil society and “economic society” using a set of indicators reflecting participation; fairness; accountability; transparency; efficiency and effectiveness in each of these sectors.1

On service delivery, there have been a range of attempts to measure performance. The Government of Indonesia mandated Minimum Service Standards (MSS) for public services (Law 25/2009 on Public Services) but has failed to define what constitutes a public service or a public service provider (Buehler (2011). As a result, the Ministry of Home Affairs (MoHA) is mandated to collect data on a long list of indicators of service performance as part of its monitoring of the MSS (refer Government Regulation 6/20082). Most of these indicators are collected either from secondary sources or from the implementing district governments themselves with data is incomplete and of unreliable quality.

A number of independent surveys of the coverage and quality of service delivery have been conducted. The World Bank’s Governance and Decentralisation Survey team interviewed households and surveyed health and education facilities in 32 districts in 2004 and 134 districts in 2006 (World Bank 2007), thus providing useful information on the micro-determinants of performance. The Indonesian Demographic and Health Surveys (1997, 2002, 2007, 2012) and Central Statistics Agency (2013) have provided national and provincial representative data on health outcomes for several years, but the data is not representative at the district level. Conversely, the Basic Health Research(Riskesdes)is a large household survey which provides detailed information on health inputs, outputs and outcomes; it is only available for 2007 at the district level.3 There is, therefore no source of independent, high quality data on service delivery performance that is both comprehensive, regularly available, and representative at district level. As a result, policymakers have had to rely on analysis of existing surveys, as well as qualitative evidence from a wide range of case studies (Winters et al, 2014; Rosser et al, 2011; Rosser and Joshi, 2013.

1 See http://www.kemitraan.or.id/igi/ for details.2 Peraturan Pemerintah No. 06 Tahun 2008 tentang Pedoman Evaluasi Penyelenggaraan Pemerintahan Daerah3 Riskesdes 2010 was only representative at the provincial level; Riskesdes 2013 was not available at the time of writing.

The National Socio-economic Survey (Susenas) implemented by the Central Statistical Agency (BPS) annually is representative of district level and covers around 250,000 households each year. However, it is not designed as a survey on service delivery, but does contains a number of variables which relate to health, education and infrastructure inputs, outputs and outcomes. Annually, the World Bank office in Indonesia aggregates several individual-level Susenas and service-delivery variables at the district-level and merges them into the Indonesia Database for Policy and Economic research (DAPOER).4 DAPOER also includes district level aggregates taken from the PODES village level census, which takes place every three years. The DAPOER dataset includes a wide range of district level structural variables available from BPS (e.g. on population, regional GDP, area of the district) as well as financial data from the Ministry of Finance indicating both recurrent and capital expenditure at the district level and sector. We then use the combined dataset to test the extent to which changes in the quality of service delivery at the local level are driven by the structural characteristics of the district, or by the level of district expenditure.

To preview our results, we find that structural determinants including regional GDP per capita, population and poverty prevalence influence service delivery improvements. Increased expenditure can improve services up to a point, however, increased expenditure that exceeds an optimal level may correspond to a deterioration of service performance.

Our paper is structured as follows. The next section describes the data in more detail and provides descriptive statistics on service delivery changes. The following section outlines our analytical model and identification strategy. Our results are followed up with a range of robustness tests. We conclude with some implications for policies and suggestions for further work.

4 See http://data.worldbank.org/data-catalog/indonesia-database-for-policy-and-economic-research.



II. Data

A number of data issues had to be addressed in the construction of our dataset. First, although Indonesia comprised of 292 districts in 2000/2001, districts have been allowed to split over time (see Fitriani et al., 2005 on the determinants of split; also Pierskalla, 2012). Unfortunately, when a district splits, the BPS retains the same code for the mother district and the “son” district (i.e. the district that retains the same name as the mother), only supplying a new code for the new “daughter” district. However, the son district may be substantially smaller in area, population and other characteristics than the mother.

We have therefore drawn on a dataset showing the years in which districts split, if ever, and have used this to construct a new dataset of all of the districts that ever existed between 2001 and 2012. After a split, a new district code is assigned to the mother and daughter districts and they are treated as separate entities. This creates an unbalanced panel with entry and exits of units. This method ensures that data on our explanatory variables always refers to the same geographical area.5

Second, we rely heavily on the annual household survey, Susenas. There were two substantial changes that affected Susenas during the period that we analyse. First, in 2008, BPS realised that it was using different population weights for the consumption module (undertaken in March) and the main survey (undertaken in July). As a result, the population weights were changed from July 2008 onwards. Thus, we need to be cautious about district level changes between 2007 and 2008. Our analysis employs year dummies that deal with any effects which are common across districts in any year.6

The second change in Susenas poses more serious implications for our work. From 2011 onwards, Susenas switched to using population weights from the 2010 population census, which resulted in some significant changes. Moreover, BPS changed the sampling methodology, the recall period in the questionnaire and also switched from one large annual survey to four quarterly surveys. We therefore believe that making comparisons between data from 2011 onwards with data prior to 2011 is unwise and have dropped 2011 and 2012 from our analysis.

5 The alternative way of handling this would be to condense the districts back to the original 292 districts. We have conducted our analysis both ways (results available on request). However, we prefer our unbalanced panel since the reasons for district splits may be correlated with the drivers of district service delivery performance.. Also, there are districts that split more than once, further complicating the meaning of any split variable. 6 We plan to run robustness checks to see whether an analysis for 2000-2007 yields broadly similar results


Service delivery performance in Indonesia

Table 1 shows the coverage of our service delivery variables. For education we use net enrolment rates for junior secondary school.7 To measure educational quality we employ data from the national Ministry of Education on national test results.8 For health we use the following indicators as measures of the quality of public health services: immunisation coverage; and the percentage of births attended by skilled staff.9

For infrastructure service delivery we use the share of households in a district with access to safe water and safe sanitation.10 The other main forms of infrastructure of importance to local populations are electricity and telecommunications infrastructure neither of which falls under local governments’ domain. Electricity is a national level responsibility under the state-owned National Electricity Company (PLN) while, telecommunication coverage is predominantly private.

Table 1: Service Delivery Variables

Note: all data comes from Susenas except for the national test data (Ministry of Education and Culture).

To give an indication of the enormous variation in service delivery performance, Table 2 shows the minimum, lower quartile, median, upper quartile and maximum values for each of the variables in Table 1 for 2011. The enormous variation in performance is immediately evident. Junior secondary school enrolment rates vary from 3.8% (in Nduga in Papua) to over 86% (in Madiun district in East Java); similarly almost no children go to senior secondary school in Yalimo (again in Papua), whilst well over half do so in Blitar city.

Service Delivery Variables Variable Name Description Availability School Enrollment Net enrollment rates for junior secondary school 2001 to 2011 Examination Scores National exam scores for junior and senior secondary

school education 2008 to 2011

Immunization Coverage Immunization coverage for children under 5 years old 1996 to 2011 Birth by Skilled Staff Births attended by skilled health workers 1996 to 2011 Safe Water Share of households with access to safe water 2001 to 2011 Safe Sanitation Share of households with access to safe sanitation 2001 to 2011

7 As enrolmentrates for primary school are extremely high, this variable is not a good performance measure of service delivery performance in most districts.8 The national test results have been heavily criticised since almost all children pass the national test (will add references). However, the test consists of two parts; a national test and a local evaluation. Generally, it is the local evaluation which is adjusted to ensure that pupils pass the overall test. Hence, we only use the results from the more objective national test as a measure of educational quality.9 We are also looking into whether we can include data on malnutrition from Susenas.10 We also exploring using data from the PODES surveys on road infrastructure, specifically, the share of villages that report having an asphalt main road, as well as data from the Ministry of Public Works on the length of good quality roads in each district. However, this paper does not report on these results.


The enormous variation across the country is illustrated in Figure 1 which shows a map of the share of attended births by district across the country in 2011. Not only is the relative poverty of Eastern Indonesia evident in this indicator (and many others), but Figure 1 also shows that there is considerable variation, even within the relatively well-off provinces.

Table 2: Service Delivery Outcomes in 2011

The prevailing view is that local public service delivery has improved little since decentralisation began — despite a very substantial transfer of funds to local governments to discharge their new-found responsibilities (Lewis, 2014).11 Table 3 shows the median district performance for each year from 2001 to 2011, where a mixed picture of performance emerges. Net Enrolment Rates for both junior secondary school and, particularly, for senior secondary school have been increasing

Variable min p25 p50 p75 max NER junior 3.8 61.4 67.2 72.0 86.2 NER senior 1.4 41.8 49.0 58.0 80.3 Immunisation 11.5 74.3 78.7 81.5 89.8 Attended births 2.9 63.3 80.6 93.6 100.0 Safe water 0.7 40.2 54.7 69.7 99.8 Safe sanitation 2.2 48.2 63.6 74.6 96.4

Births Attended by Skilled Staff (Percent of Population), 2011

11 An exception is Skoufias et al (2014) who find that the introduction of direct elections (Pilkada) is positively associated with improvements in health service delivery.

Figure 1: Share of Attended Births by District 2011


since 2001.Over two thirds of children were enrolled in junior secondary school in the median district in 2011 and almost half enrolled in senior secondary school. By contrast, since 2006, enrolments in junior secondary school have not improved. Immunisation coverage in the median district has also increased, but only slightly from 74.3% in 2004 to 78.7% in 2011. Attended births have improved substantially from only half in 2001 (if the data is to be believed – see below) to over 80% in the median district in 2011. Overall, access to safe water and safe sanitation have improved with some reversals, for example, the performance of the median district on access to safe sanitation fell between 2004 and 2008 before picking up again. Overall, the data provides evidence of gradual improvement but with occasional reversals.12

Table 3: Performance of the Median District by Year

Although these indicators come from the most reliable sources of data available, they raise questions about the extent to which this data is used to provide reliable measures of performance improvement over time. Figure 1 shows the reason for the concern about data reliability. The vertical axis measures changes in the net enrolment rates at junior secondary schools between 2008 and 2010, whilst the horizontal axis measures the net enrolment rates in 2008. Two features of the data are immediately obvious. First, there is strong absolute convergence; districts that start with poor junior secondary school NER are much more likely to make significant progress than districts that start with a higher NER. Indeed, districts starting with a NER of over 70 are more likely to see a fall in the NER than a rise. Second, some of the changes are completely implausible. It is simply not credible that the junior secondary school NER rose by more than 20 percentage points over two years and yet there are 16 districts that report this taking place. It is equally unlikely that several districts saw their enrolment rates collapse by more than 10 percentage points over two years and yet this is what the data shows. Moreover, these two characteristics of strong absolute convergence and a non-trivial number of implausible changes is true for all of our service delivery indicators to a greater or lesser extent.There are two plausible explanations for the

Note: variables as defined in Table 1.

12 There is an important distinction in definition between access to “safe” and “clean” water. The former includes wells and the latter focuses on piped water. While access to safe water has improved since decentralisation, access to clean water has deteriorated.

Year 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 NER junior 61.0 62.5 64.0 67.6 64.0 68.3 67.5 67.0 67.5 67.8 67.2 NER senior 34.5 34.9 38.1 42.1 39.8 43.6 44.5 45.7 46.1 47.2 49.0 Immunisation

74.3 73.1 74.1 76.3 76.8 78.2 79.3 78.7

Attended births 50.0 69.2 69.4 74.9 71.0 71.9 73.1 74.7 76.1 78.6 80.6 Safe water 41.6 42.5 42.8 43.8 42.6 42.7 46.1 47.6 50.7 53.4 54.7 Safe sanitation 54.1 58.7 59.8 62.8 59.7 58.7 57.8 60.8 62.3 63.1 63.6


absolute convergence and these large variation. First, it is highly likely that there is real absolute convergence in the data; one would expect that improving performance from a very low base is much easier than improving performance from a high level. This suggests that the existing level of performance is an important driver of future changes. Second, if performance data is measured incorrectly, then this will induce convergence since the pool of poor performing districts in any year will consist of two groups; the genuinely poorly performing districts and those registered as poor performance due to measurement error. If the measurement error is random, then the latter group will (on average) revert to their true performance in any future year, giving the impression of convergence. The same is true of districts registering good performance, which will consist of genuinely good performers and the mis-measured mediocre (or poor) performers. Again, the latter will revert to their true performance type in future years, giving the impression of convergence. Hence, our convergence may simply be regression to the mean.

Significant measurement error in the data is entirely possible. Although Susenas draws from a relatively large sample at the district level, many of the measures of service delivery only apply to a limited set of respondents. For example, immunisation coverage only applies to households with children under 5 years old; attended births only applies to women who recently gave birth; and school enrolment, particularly at the higher levels, only applies to those households that have older children at home. It is entirely possible that the standard errors of the estimates of these variables could be substantial, particularly in places with smaller samples.13

Figure 1: Absolute Convergence in NER Junior 2008-2010

13 We are in the process of calculating standard errors for the districts with small samples in the Susenas.


Analytical model

The purpose of the empirical examination detailed below is to explain local government service delivery outcomes. Five local service indicators are considered: primary school net enrolment rate; junior secondary school net enrolment rate; percent of total births assisted by a health professional; percent of households with access to safe water; and the percent of households with access to sanitation. For each of the five indictors the service delivery outcome is defined in terms of standard deviations from the mean. It is posited that the average change in service outcomes across all five indicators is a function of the average level of outcomes in the previous period; total local government per capita spending; population; urbanization; poverty; income inequality; and economic output.

In order to assess possible quadratic effects, both local government per capita spending and its square are included as explanatory variables. Urbanisation is proxied by density (i.e. population divided by area in square kilometres); poverty is represented by the percent of the population that is poor and the poverty gap; income inequality is measured by the gini coefficient for personal household expenditure at the district level; and economic output is defined as per capita gross regional domestic product (GRDP). Log transformations of local government per capita spending, population, density, poverty gap, and GRDP per capita are employed.

Two types of models are estimated: a first order autoregressive (AR (1)) model and a dynamic panel data (DPD) model. The AR (1) model can be written as:

where i and t are local government and time subscripts; x is a vector of exogenous explanatory variables; α and β are the parameters to be estimated; ν are the panel level effects; and ε is the error term:

where |ρ|<1 and η is random noise.

The AR (1) model accommodates the first order serial correlation that results from including the lagged dependent variable as an explanatory variable on the right-hand side. It does not, however, overcome problems related to the potential endogeneity of other right-hand side variables, such as local government spending, for example. A DPD can be used for those purposes.

The DPD model can be expressed as:

itiititit xyy ενβα +++= −1 (1)

(2)

(3)

ititit ηρεε += −1

itiitit

p

jjtijit wxyy ενββα ++++= ∑

=− 21

1,


where i and t are local government and time subscripts, respectively and p is the number of lags of the dependent variable used on the right-hand side; w is the vector of endogenous and predetermined variables ; αj, β1, and β2 are the parameters to be estimated; ν is the panel level effects, and ε is the usual error term, independently and identically distributed for each i over all t.

The DPD model is estimated by systems Generalised Method of Moments (GMM) procedures. The latter is an instrumental variables approach that is especially useful for estimating DPD models that have many groups and few periods and where extensive simultaneity is potentially problematic, both of which apply to the present examination.

In equation (3) the lagged dependent variables are correlated with panel effects, rendering the usual estimators inconsistent. Estimation of equation (3) via systems GMM proceeds by first differencing the equation to remove the panel level effects and then using instruments to form moment conditions. Possible instruments of the differenced equation include lagged levels of the endogenous and predetermined variables (second lags and onward) and differences of the exogenous variables. Instruments for the level equation can also be employed and these comprise of lagged differences of endogenous and predetermined variables, as well as all exogenous variables15.

The validity of the over-identifying restrictions that result from implementing systems GMM is typically tested by the Sargan (or some related) procedure. The latter is essentially a test to see that the instrumental variables are collectively uncorrelated with (i.e. they are orthogonal to) the error terms and therefore exogenous. The Sargan test statistic has a chi-square distribution (with degrees of freedom equal to the number of instruments minus the number of regressors) under the assumption that the instruments are orthogonal to the error terms. So, if the test statistic is smaller than the relevant chi-square critical value (at standard levels of significance) then the instruments can be assumed to be exogenous and therefore valid.

Employment of the systems GMM methodology portends a number of advantages in model estimation beyond providing a solution for simultaneity. It obviates the need for making particular assumptions about the precise distributional form of the error term in the estimating equation, helps to overcome omitted variable bias, and is relatively robust in the presence of measurement error. The methods described briefly above and used in the present investigation are based primarily on the work of Arellano and Bond (1991), Arellano and Bover (1995), and Blundell and Bond (1998). Greene (2011) provides an up-to-date and thorough description of the techniques.

14 For endogenous variables E[wit, εis] ≠ 0 for s ≤ t but E[wit, εis]=0 for s>t; and for predetermined variables E[wit, εis] ≠ 0 for s<t but E[wit, εis] = 0 for s ≥ t.15 GMM estimation was carried out in this paper using the xtdpd procedure in Stata 13.

III. Results

Table 4 provides the estimation results. The results show a negative coefficient on the lagged dependent variable. This probably reflects the significant measurement error which induces a regression to the mean as discussed above, as well as conditional convergence. However, in addition, a range of other variables are significantly related to service performance. Of particular note, we find an inverted U shaped relationship between overall expenditure and service delivery; more money improves service delivery up to a point, but after that point is reached further funding has a negative impact on service delivery. This reinforces the findings of others that it may be the quality of spending that matters more than the volume (BKF, 2013). Table 4 also shows that districts with larger populations have better services supporting the idea of economies of scale in provision of services. Conversely, districts with greater poverty or a larger poverty gap have significantly worse performance. Finally, in the GMM specification, we find that the overall level of the economy has, as expected, a positive impact on the ability to improve services.

Table 4: Explaining Service Delivery Performance



IV. Conclusions

Measuring service delivery performance at the district level is important for improving the quality of services over time. It will help the GoI to be able to better monitor and, if necessary, target resources to those districts with poor service delivery. Also, the dissemination of information on how districts are performing in service delivery to CSOs and the media can potentially strengthen district-level electoral accountability. Our paper has used a new dataset of district performance on services in education, health and infrastructure between 2001-2010 to explore the determinants of service quality improvements. We find structural determinants, such as regional GDP per capita, population and the extent of poverty do have an important influence on service delivery improvements. Moreover, we find that increased expenditure can improve services up to a point, however, expenditure exceeding an optimal level, may even worsen service performance.

These findings have important implications for the way in which the government tries to monitor service delivery at the district level and encourage better performance. First, we find that existing data on service delivery performance at the district level is subject to substantial measurement error. As a result, some districts record significant and implausible changes from year to year. The extent to which these changes are due to sampling error, non-sampling error or genuine improvements is unclear. However, it does suggest that the government should place greater effort on the collection of accurate and precise estimates of performance. This could be done through increasing sampling sizes in some regions and ensuring the proper implementation of existing surveys. However, the limited number and relevance of the variables available from existing dataset suggest the need for more detailed intermittent surveys on service delivery (such as the 2011 Health Facilities Census).

Second, until the extent of measurement error is clearer, the government would be wise to exercise caution in using the existing data to determine penalties or rewards for service delivery improvements. This is particularly pertinent to our finding that certain structural variables influence outcomes. Since these variables are, by definition, not under the control of the district governments, it is important to take them into account when determining whether a district has performed well or not. Otherwise, it is likely that policymakers would commit to Type 1 and Type 2 errors i.e. penalising districts whose performance was poor, even though it was better than their structural variables might lead one to expect; and rewarding districts with good performance, even though performance was worse than what it should be for a district with their structural characteristics.


References

Besley, T., R. Pande, and V. Rao (2007). Political economy of panchayats in South India. Economic and Political Weekly: 661-666.

Bird, R. and F. Vaillancourt, eds (2008). Fiscal decentralization in developing countries. Cambridge University Press.

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Fitrani, F., B. Hofman, and K. Kaiser. (2005). Unity in Diversity? The Creation of New Local Goverments in a Decentralising Indonesia. Bulletin of Indonesian Economic Studies, Vol. 41, No. 1: 57–79.

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Lewis, B. (2014). Decentralised government: greater demand needed to raise service quality in Indonesia. Available at: http://www.eastasiaforum.org/2014/03/22/ decentralised- government-greater-demand-needed-to-raise-service-quality-in- indonesia/

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McCulloch, N. and E. Malesky (2011). Does Better Local Governance Improve District Growth Performance in Indonesia? IDS Working Paper Vol. 2011 No. 369, Brighton.

Narayan, A., E. Skoufias, B. Dasgupta, and K. Kaiser (2014). Electoral Accountability and Local Government Spending in Indonesia. World Bank Policy Research Working Paper 6782.

Person, T. and G. Tabellini (2004). Constitutions and economic policy. Journal of Economic Perspectives: 75-98.


Pierskalla, J. (2012). Splitting the Difference: Institutional Capacity, Elite Competition and District Creation in Indonesia. Unpublished Manuscript.

Pepinsky, T. (2014). Subnational peripheries and the comparative method. Mimeo, Cornell University.

World Bank. (2007). Sample and coverage. Vol. 1 of Governance and decentralization survey. Washington, DC. Available at: http://documents.worldbank.org/curated/en/ 2007/01/16344870/governance-decentralization-survey-vol-1-3-v-1-sample-coverage

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