PRELIMINARY AND INCOMPLETE
FOR DISCUSSION ONLY
Local level governance and schooling
in decentralizing Indonesia
Vivi Alatas
Deon Filmer
The World Bank
February 2004
Introduction
In 2000 Indonesia embarked on a broad agenda of decentralization—affecting
resource mobilization and resource deployment in a number of sectors. In particular,
responsibility for the delivery of all basic education services was transferred from
formerly centralized and deconcentrated structures to the district (Kabupaten) level.1
Importantly, districts are now responsible for determining the level of recurrent and
investment expenditures to allocate to education.
Through its effects on the level and allocation of resources, as well as the newly
devolved
responsibilities for managing schools and administering education,
decentralization will likely affect the quantity and quality of education services delivered.
Two fears stand out. First, that faced with tradeoffs between sectors districts will spend
only minimal amounts on education—in the extreme funding only civil service salaries—
with potentially dire consequences for the quality of education. Second, that due to their
limited resources, poor districts will suffer even more than the average leading to an
increase in between-district inequality.
Decentralization was added on top of an education system that was already under
stress. Despite the large gains in enrollment at the primary level—in Indonesia over 90
percent of children of primary school age are in school—the quality of education at that
level, as well as transition rates to, and the quality of learning in, secondary schools have
remained a constant problem.2 For example in the last available international
comparison, the Third International Mathematics and Science Study 1999 which
compares test scores on similar test in many countries Indonesia performed significantly
worse than its neighbors (e.g. Singapore, Malaysia, Thailand). Moreover, the legacy of
the economic crisis of the late-1990s has squeezed the resources that the central
government could allocate to schooling. Scores on the national standardized test (NEM)
are typically considered to be unacceptable at levels below 6. Figure 1 illustrates the
distribution of average provincial scores: no province has an average of over 6—with
some at the extreme low average of less than 5.
This paper studies the cross-sectional association between school quality and
indicators of local level governance, community characteristics, school characteristics—
including aspects of school management—as well as teacher and student characteristics.
The purpose is to provide a sense of how learning outcomes might change with emerging
variability in districts and school administration and management. As new data are
compiled over time, the ultimate goal is to supplemented the analysis with time-series
information so that inferences about the impact of decentralization can be drawn from
actual changes over time rather than predicted from variation across space.
1
2
“Deconcentrated” administration is that done through local branches of centralized line ministries.
See Moegiadi and Suryadi (); World Bank (2003).
The paper is organized in 3 main sections. A first section reviews some of the
background theoretical and empirical findings around decentralization and schooling
outcomes—ending with the main research questions for this paper. A second section
describes the research design, empirical model, and data used. A third section describes
and discusses empirical results. A final section concludes.
I. Research on decentralization
Both centralized (e.g. France, Japan) and decentralized (United States, New
Zealand) school systems around the world have been able to deliver education
opportunities for their students. There is general recognition that decentralization has
potential benefits but that implementation and the kinds of accountability that it actually
produces yields few fast rules for the impact in any given situation (see for example
Winkler 1993).
Potential benefits of decentralization
Decentralization brings with it substantial promise. Traditional economic models
emphasize the ability of local level governments to more closely reflect the demands of
local populations—a particularly important factor in a large and heterogeneous country
such as Indonesia. As local level democratic institutions take hold, voters are argued to
be able to better have their preferences reflected in the resource allocations made by local
level governments. In addition, local level governments are potentially more accountable
to local level populations since the latter are arguably in a better position to monitor and
sanction the former.
Another strand emphasizes the potential for efficiency gains if local decisionmaking entities, such as local governments or schools, have the flexibility to combine
inputs in ways that maximize outcomes subject to local conditions, thereby achieving
maximum impact for fixed (and limited) budgets. That is, imposing a one-size-fits-all
solution through central decision-making is unlikely to be the most efficient approach if
constraints differ across districts. If decentralization results in flexibility then districts
will be able to adapt and be more efficient.
In addition, local level innovation is likely to lead to successes that can be
replicated, and failures that can be dropped—promoting efficiency across districts in the
long run. As long as districts feel under some pressure—for example because people or
capital is mobile, or because they feel embarrassed by being upstaged by their
neighbors—competition between districts will act to promote local governments to try
and find ways to do things better.
Decentralization may also bring increased flexibility in school-level decisionmaking as well as a renewed sense of ownership (and participation in school affairs) by
parents and local communities. Both of these have been associated with improved
outcomes in prior empirical research.
Risks of decentralization
But decentralization brings with it many risks as well. There are four main fears
that are frequently heard in the Indonesian context.

Central government is asserted to prioritize basic education more than local
governments. The fear is that local governments will allocate only the minimum
amount of resources to education that they can get away with—perhaps only
enough to cover mandatory expenditures such as civil-servant (teacher) salaries—
with resulting negative consequences on the quality of education.

Local elites will be less responsive to local populations than central elites—
perhaps because of historical local level inequalities—which will result in local
“capture”. The fear is that even if resources are allocated to education, local elites
will redirect them for their own profit, effectively starving the education sector of
those resources.

The lack of experience in education administration (or administration more
generally) on the part of local government staff is feared to result in bad choices,
again resulting in poor quality.

Regional variation in endowments will lead to increasing disparities in outcomes.
Some districts have access to substantial natural resources and can tap these to
mobilize funds for all sectors, including education. Poor areas will be unable to
do so, and without compensating transfers from central government will be at a
serious disadvantage which will get passed down through generations.
Selected past empirical findings
There are few studies of the impact of decentralization per se on education
outcomes. Galiani and Schargrodsky (2001) find that decentralization of secondary
schools in Argentina improved the performance of public school students test scores on
average. However, the effect was positive in “fiscally ordered” provinces but negative in
provinces that had run significant deficits.
Typically, however, the argument is sequential—if decentralization leads to X,
and if we find that X leads to better outcomes then decentralization has the potential to
improve education.
James, King, and Suryadi (1996) find that in Indonesia the greater the extent of
local finance (i.e. user charges) the better school performance. This is interpreted as
evidence that greater sense of ownership, greater ability to hold schools managers
accountable, that is greater parental voice can yield good outcomes. Jimenez and Paqueo
(1996) find similar results for the Philippines.
Jimenez and Sawada (1999) find that schools that are managed by parents in El
Salvador, as a part of a government program called Educo that contracted with parent
associations, achieve similar results to ones that are traditionally managed—but with
fewer resources and in more remote and poor areas. The results are consistent with the
greater role that parents take in monitoring the behavior of teachers (e.g. teacher
absenteeism is lower with more parent supervision).
King and Özler (2000) find that more de facto autonomy in Nicaraguan schools as
a result of an education reform was associated with better test scores. Interestingly they
find that schools that are formally designated as being “autonomous” are no better than
other schools—it is only for the measure of actually more autonomously made decisions
that a difference is found.
Eskeland and Filmer (2002) find that in Argentina school autonomy and parent
participation raise student test scores for a given level of inputs in a multiplicative way.
Autonomy has a direct effect on learning (but not for very low levels of participation),
while participation affects learning only through the mediation of the effect of autonomy.
Di Gropello (2002) finds that in Chile, pedagogical and curricular decentralization
to schools (i.e. school autonomy) and the level of school involvement in local financing
decisions both had significantly positive impacts on educational achievement. In
addition, some municipal level wage incentives that decentralization enabled positively
affected educational achievement.
On the other hand, measures of municipal
administrative autonomy and local financial decentralization were found to be negative
Research questions for this paper
Given the limited past literature on the impact of decentralization on education
outcomes, and even on the proximate determinants (such as autonomy and participation)
this paper proposes to assess the role of decentralization in Indonesia on learning
outcomes. Because of the limited time that decentralization has actually been
implemented in Indonesia, and the obvious lags involved in both the realization and
measurement of impacts, this paper is an interim assessment of decentralization so far.
The ultimate goals of the study are to answer the following questions.


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How has the impact of the devolution of administration and financing authority
differed across local governments?
How have local level changes in funding and organization of the education
sector—controlling for local characteristics—interacted with changing
governance and capacity to determine outcomes?
In sum, has decentralization helped or hurt learning outcomes?
What are the main channels of change?
The approach used in the paper is to explore indicators that are likely to be
affected by the move to greater decentralization of the administration and management of
education—including both decentralization across administrative regions as well as
increased school autonomy as part of the move to school-based management. The
selection of indicators is guided by the main accountability relationships outlined in the
World Development Report 2004: Making Services Work for Poor People: the voice
relationship between service beneficiaries and the politicians/policymakers that determine
the level and allocation of resources; the compact relationship (made up of financing,
delegation, monitoring) between politicians/policymakers and the providers who actually
deliver services; and the client power relationship between the users of services and
providers. All of these are likely to be affected by decentralization.
The paper therefore uses baseline cross-sectional variation to explore existing
patterns relating variability in learning outcomes to local level funding, governance and
management indicators, school level characteristics, parent involvement, as well as local
level characteristics. These patterns, it is hoped, will form baseline information that can
be updated over time. Once new data have been collected the study will be able to rely
on changes over time to identify impacts rather than use cross-sectional data to explore
them.
II. Research Design, Empirical Model and Data
The change in the law and structures accompanying decentralization are being
implemented equally across all local governments in Indonesia which rules out a research
strategy on a counterfactual of what would have happened in the absence of
decentralization. The approach used in this paper is therefore to focus on how the
devolution of administration and financing authority has played itself out differently in
different local governments, how this has manifested itself in variation in a variety of
education inputs, and how this variation subsequently affected outcomes.
Empirical specification
The education outcome yit of school i observed at time t is assumed to depend on
the observed student and school characteristics (Z); the school’s budget (X1); a set of
measures of the accountability relationships between client, local governments and
schools (X2) which may affect outcomes directly, or though affecting the level and use of
resources; and unobserved characteristics ,  . These are related to outcomes through the
“returns” to characteristics in terms of education outcomes ()
yit Y ( X 1it , X 2it , Z it ,  it ; t )
(1)
Given the cross-sectional data available at this point, equation (1) will be the
model of interest. As data are collected at multiple points in time, changes can be
decomposed into changes in each of the elements (X1, X2, Z, ). This is an item for
future work though.
Data
Data can be combined from a variety of sources for this analysis. The main novel
source are data collected through the recently fielded Governance and Decentralization
Survey (GDS). These data are combined with other sources, typically collected through
Indonesia’s regular statistical system such as regional national accounts data, data from a
census of schools, and regular, detailed household surveys. The specific datasets used for
this study are:
Governance and Decentralization Survey data
The GDS is being conducted by The World Bank together with CPPS-GMU
(Centre of Public and Policy Studies-Gajah Mada University). It covers 177 local
governments (51 percent of the 348 kabupaten/kotamadya existing in 2002), thereby
providing one of the most extensive resources on governance and decentralization.
GDS was conducted in 2002 to assess the quality of governance and service
delivery just before and right after the 2001 decentralization. The data cover various
thematic areas:







participation,
effectiveness and efficiency,
transparency,
equity,
rule of law,
responsiveness,
accountability and conflict management.
The GDS includes instruments given to local administration officers (including the
education Dinas office) as well to school head teachers allowing for analysis of variables
derived from both levels.
GDS data do not provide details on school characteristics such as teachers
background, financial resources, achievement scores, nor do they have information on
student and parent characteristics. The data can be linked, however, at the school level to
data from the Ministry of National Education’s (MONE) school census, and at the
regional and sub-regional level to data from household surveys conducted by the Central
Bureau of Statistics (BPS) and national fiscal data.
School Census data from the Ministry of National Education
The MONE conducts an annual census-type survey distributed to each district and
to every primary, junior and senior secondary school, approximately 176,000 primary
schools and 33,000 junior and senior secondary schools.3 The detailed questionnaire
covers items such as revenues, expenditure, school resources, teacher background, as
well as learning outcomes as measured by school average results on national standardized
tests (NEM).
The school census is administered as a mail-in survey with schools required to fill
in the form and return copies to the local ministry representatives, who send raw and
processed data up the bureaucratic channels. The most complete data currently available
to us are data from the secondary level from 1999 which cover almost 15,000 schools.
Data from the primary level are available only in aggregated form and lend themselves to
only a limited set of analyses. Data from subsequent years — post-decentralization —
suffer from severe non-response. It appears that without the formal centralized
structures, schools no longer had the incentive to complete their forms.4
Regional budget data (APBD)
The Regional Fiscal Information System (SIKD) of the Minsitry of Finance’s
Directorate General of Center-Regional Fiscal Balance (DJ-PKPD) collects recent annual
and quarterly budget data for Indonesia’s regions (30 provinces and currently 348 local
governments). We use the data from 1994 to 1999 to construct measures of development
(investment) and routine (operational) public expenditures.
Data from the Household Socio-Economic Survey (SUSENAS)
Indonesia’s Central Bureau of Statistics (BPS) annually collects the SUSENAS a
nationally representative household survey covering a variety of social and economic
themes.5 Since 1993 sample sizes were increased to over over 200,000 households
annually in order to provide statistics representative at the district level (Surbakti 1995).
We use SUSENAS 1999 to construct district (Kabupaten) average per capita household
expenditure and the variability of that indicator (as measured by the standard deviation).
Combining the data: The World Development Report 2004 framework
The recent World Development Report on Making Services Work for Poor People
provides a framework for analyzing how decentralization might play itself out and
ultimately affect learning outcomes. The framework is structured around four
3
An estimated 42% of junior and senior secondary schools are public, while an estimated 84% of primary
schools are classified as public schools.
4
After decentralization, the return rate significantly declined to roughly 26 percent: only around 11,000 out
of 33,000 junior and senior secondary surveys were returned for the 2000/01 school year. Even the
offering of a block grant incentive of 10 million rupiah to each school that returns the questionnaire, failed
to increase the return rate. If the situation does not improve with time, the lack of this data source will be a
serious impediment to future analyses of the impact of decentralization on education.
5
For more about SUSENAS refer to “Indonesia’s National Socio-Economic Survey after Thirty Years of
Development”, BPS, 1994
relationship of accountability: Voice, Compacts, Management and Client Power. The
indicators used in the empirical specification are organized around these relationships.6
Voice is defined as the ability of citizens to hold politicians and policy makers
accountable for mobilizing and allocating resources, and ultimately for performance.
When citizen voice with respect to services is weak (either because voice is ineffective or
because the political process focuses purely on ideology and not on the quality of
services) then services such as education suffer—often becoming the currency of political
patronage. That is, rather than being managed for performance, they are managed to
extract rents for politicians and those connected to them.7
To examine the extent of voice and the link between client and policy makers, we use
the following set of indicators:
District level
 Number of meeting between government and the NGOs
 The extent of community complaints through NGOs about education services
 The extent of frequency education problem published in local newspapers
 Per capita average education development expenditure during the period 1994 to
1999
School level
 Shares of school income from local government
The data suggest relatively weak voice channels. About 60 percent of NGOs said that
they had never received any complaint from communities about education services.8
About 60 percent of media outlets described the frequency of coverage of education
problems as “low to moderate”. Of course these could be driven by a general satisfaction
with the provision of education—an interpretation at odds with the indicators of
education quality, as well as any discussion with a concerned citizen.
Local governments, at the time of the survey, were minimally involved in the
financing of education. The 1999 data show that the share of school income from local
government is on average less than 0.1 percent for Junior high school and about 0.15
percent for senior high schools. Clearly local governments had not seen it as their role to
finance secondary education prior to decentralization, raising fears about their readiness
to take on the responsibility with decentralization.
Compacts relate to the extent to which responsibilities and objectives are clearly
communicated to schools, and the ability of policymakers to track progress and assess
performance—and hold providers accountable for that performance. The basic
6
The cross-sectional variation in the pre-decentralization setting is what this paper analyzes . When the
next round of data are available we will be able to compare the changes that occur on those four
accountability relationships and assess how these changes affect outcomes.
7
The recent debates and demonstration of National Education System Law illustrate this point.
8
Tables with summary statistics on these, and subsequent, indicators are in the Appendix.
decentralization laws were vague and created many ambiguities.9 To examine the extent
of the compact link between policy makers and providers, we use the following set of
indicators:
District and School level
 The number of times school was invited to attend meetings with local education
administrators (Education Dinas).
School level
 The fact that a head teacher was himself involved in setting the vision and mission
of a school.
The average number of meetings between local government administrators and school
held in a year is on the order of once a month (around 11 times a year). Of course this is
a crude measure of the interaction between policymakers and providers, if for no other
reason than the quality of these meetings can be highly variable.
More than 70 percent of head teachers report themselves as being involved in
establishing the school’s vision and mission. Having a clear vision and mission is of
course just one part of clarity in objectives.
Management is the set of actions and incentives that create effective frontline
providers. It involves drawing in the right people, equipping them with the right
knowledge, giving them the right incentives, and choosing the right mix of
complementary inputs (that is the full set of proximate determinants of outcomes). To
examine the extent of management capabilities we use the following sets of proxies:
School level
 The extent of autonomy of schools in decision making such as hiring and firing
staff, general school-based management, budget allocation, and curriculum and its
local content,
 Proximate determinants of schooling production function such as expenditure per
pupil, student teacher ratios, teaching material resources, teachers background.
Almost 90 percent of secondary level head teachers report being involved in decisionmaking regarding disciplining teachers. On the other hand, only about 40 percent report
being involved in recruitment and promotion of teachers. Likewise, less than half claim
involvement in decisions regarding the allocation of the education budget, and the
procurement of books and instructional tools.
A common problem faced in education management is that teacher salaries crowd out
all other inputs. Teachers salary absorb about 60 percent of a secondary school’s overall
budget, with an additional 12 percent allocated for officer salary.
This set of issues has been highlighted as a particular area of concern in the World Bank’s recent
assessment of the education sector (Education Sector Review, 2003).
9
Client power relates to the ability of the beneficiaries of services—students and their
parents—in holding the school and school system accountable. The long route of
accountability, that is accountability from beneficiaries to policymakers to providers,
often breaks down. Directly involving clients in monitoring and disciplining providers
can be a way of overcoming this breakdown. To explore the ability of clients to hold
schools accountable we use the following set of proxies:
School level
 Number of Parent Teacher Association (PTA) meetings during last year
 Involvement of PTA in decision making such as budget allocation, curriculum
implementation, teacher discipline and school based management
 Share of school income from parent contributions
When asked directly, PTA representatives typically report that their involvement is
mainly in setting PTA fees, and in helping to collect those fees. These groups, by all
accounts, play only a minimal role in helping to monitor school activities. Nevertheless,
they are the only formal body representing parents to interact with the school on a regular
basis.
The average share of income raised from parent contributions is around 20 percent—
much higher than what they get from any other source other than central government.
III. Empirical Results
There are two main specifications used to explore the cross-sectional data:
 First: an unconditioned estimation of the relationship between budget,
accountability mechanisms and results on the standardized exams test. The
model includes all the indicators of voice, compact, management and client
power described above. In addition, household survey data are used to derive
the level and variability in district household per capita expenditures. School
characteristics such as teacher background and proximate determinants—
which might be functions of budgets and accountability—are excluded from
this basic specification.
 Second: an augmented version that is closer to what is typically estimated in
the “education production function” literature. To the basic set, this
specification adds the usual proximate determinants such as class size,
teachers education and experience, and indicators of school infrastructure.
Each specification is estimated on two samples. The first sample is the set of schools
that can be matched between the GDS and School Census. Many of the variables—in
particular those collected from the head teacher—are defined at the school level in this
sample. The sample consists of about 140 junior secondary and 120 senior secondary
schools.
The second sample consists of all the schools in the districts in which the GDS was
implemented. For this specification, all school level information collected from the GDS
is averaged at the district level, and interpreted as an indicator for all schools in that
district. This sample consists of about 3,300 junior secondary schools and about 680
senior secondary schools.
Results from the matched samples
The sample sizes in the matched sample are small, which makes it hard to identify
effects. Indeed, almost none of the proxies used to describe the four accountability
relationships yield statistically significant coefficients. For junior secondary schools,
only two statistically significant results emerge from the augmented models. More
community complaints through NGOs are associated with more test scores—suggesting a
positive link between voice and learning. Head teacher involvement in setting the school
vision and mission is also associated with higher test scores—suggesting that a clarity in
objectives contributes to more learning.
For senior secondary schools the estimates are even weaker. Only one variable is
statistically significant—more school expenditures per student are associated with more
learning. The effect operates through observed characteristics as it disappears in the
augmented specification—that is after controlling for teacher characteristics, school
infrastructure and so on.
Interpreting the results from the matched sample is problematic. When the variables
from the GDS are removed from the analysis (results not shown here) the coefficients on
the other variables in the specification change substantially. Clearly the GDS sample (the
matched sample) is not representing the distribution of the population of schools
suggesting that the sample needs to be enlarged if national representation is being
sought.10
Results from the unmatched samples
Voice
The unmatched data—which assume that the GDS indicators are indicative of district
averages—yield more compelling findings on the indicators of accountability. More
NGO involvement—measured by the number of meetings between NGOs and local
government as well as the use of NGOs to channel complaints about education services—
10
In addition, with a substantial number of coefficients estimated at least 5 percent are going to be
determined statistically significant (at the 95 percent confidence level) even when they are not. The three
significant findings discussed above may therefore be spurious.
is associated with higher test scores (the latter is insignificant at the senior secondary
level).
While information is hypothesized to increase accountability—and ultimately
learning—more media publication of problems in the education sector is associated with
lower test scores. Of course this result could be driven by reverse causation: that is lower
test scores prompting more negative stories in the media (future work will need to
explore this issue).
The share of school income from local government is not associated with higher test
scores at the junior secondary level—perhaps not surprising given the overall low level
that these resources play. At the senior secondary level, a relatively higher share of local
government resources appear to positively affect effectiveness in the use of inputs: the
variable is statistically significant and positive in the augmented model.
Compact
Clarity of the schools objectives—as measured by the head teacher’s involvement in
setting the school’s vision and mission—is positively associated with learning outcomes.
The other indicator of compact—meetings between local government education
administrators and head teachers—is not associated with higher test scores. As
mentioned before, however, the quality of those meetings might be so low as to not be of
much value to promoting accountability.
Management
Some of the results on the extent of school autonomy are compelling. Greater head
teacher involvement in teacher recruitment and in school based management is
consistently associated with better test scores. However, their involvement in curriculum,
and even budget allocation, decisions are negatively associated with learning. In
addition, involvement in teacher discipline is negatively associated with test scores
(although this may be influenced by reverse causation whereby worse discipline—
associated with worse test scores—requires greater head-teacher oversight and
involvement).
Client Power
In general the data are consistent with client power remaining a weak force in
Indonesian schools. The involvement of Parent Teacher Associations in teaching tools
procurement and in curriculum are associated with higher test scores in junior secondary
schools—but none of the other indicators are (teacher discipline, school based
management, textbooks, …) and none are significant at the senior secondary level. While
James and others (1996) found that schools that relied heavily on parental (or other local
sources) to be more efficient, our results indicate that higher share of school income from
parent contributions are negatively correlated with scores (again, this could be because of
reverse causation).
Proximate determinants
While the focus of this paper is not on the proximate determinants of increased test
scores, the third and fourth columns of Tables 1 and 2 report specifications that include a
set of teacher and school characteristics. The messages are similar to those in other
papers on education production functions:
 Student characteristics explain most of the variation in learning outcomes. The
lowest primary leavers exam score accepted (a measure of student quality at
entry) is the only variable that is significantly associated with current test
scores in all the specifications for junior high schools (matched, unmatched,
basic or augmented).
 There is no significant relationship between teacher salary and achievement
score.
 Rural areas lag behind urban areas. However, after controlling for teacher and
school characteristics and other proximate determinants the difference
becomes small (and in some cases insignificant).
 Schools in which teachers have more experience tend to have higher test
scores, as do those where more teachers hold a sarjana degree.
 More student per teacher (and larger class sizes) appear to be negatively
associated with test scores, but the statistical significance of this result is not
robust.
Conclusions and Further Studies
Decentralization can result in both good or bad outcomes for education—on both
theoretical and empirical grounds. If decentralization exacerbates the limited capacity of
(now) local governments to set up clear accountability targets for schools—backed by
adequate funding—and to monitor and hold schools accountable for those targets then it
is likely to result in worse learning outcomes. If, at the same time, students and their
parents are not wiling or able to get involved and hold schools accountable, again
learning outcomes should not be expected to improve. And if (now) local politicians do
not listen to the voices of the citizens they represent—especially the poor—then one
should not expect decentralization to result in a better, and more pro-poor, allocation of
expenditures.
Because of the limited time that decentralization has actually been implemented
in Indonesia, and the obvious lags involved in both the realization and measurement of
impacts, this paper is an interim assessment of the cross-sectional association between
some of the indicators that one might expect decentralization to affect and test scores.
The preliminary data analysis suggests that some of the indicators of voice and compact
are associated with higher test scores, that client power is weak and unable to elicit
quality from providers, and that financial resources are not strongly associated with
learning outcomes.
To be able to evaluate the relationship between local government and schooling
outcomes in a decentralizing setting, we can not overemphasize the importance of the
availability of complete and extensive data that allows schools to be tracked before and
after the change in regulations. In addition, sufficient sample sizes are necessary to
detect impacts.
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Learning? Endogenous School Quality and Student Performance in Nicaragua.”
Development Research Group, World Bank, Washington, D.C. Processed.
Pritchett, Lant, and Deon Filmer. 1998. “What Education Production Functions Really
Show: A Positive Theory of Education Expenditures.” Economics of Education
Review 18: 223–39.
Winkler, Donald R. 1993. “Fiscal Decentralization and Accountability in Education:
Experience in Four Countries.” In Jane Hannaway and Martin Carnoy, eds.,
Decentralization and School Improvement: can we fulfill the promise? San
Francisco: Jossey-Bass.
Figure 1. Average Standardized Exam Scores by Provinces
Mean
5.5-5.8
5.5
5.8
5.2-5.5
5.2
.5
to5
4 (7
4.9-5.2
46
.9
5
to4.2
6
(6
4.6-4.9
41
.6
.9
)
8
to
1
Not
in8 (5
)(7
4Notodata
Sample))
(1)
Source: Ministry of National Education data, 1999.
Table 1 Correlates of the log of test scores (NEM): Junior Secondary Schools
Basic Specification
Matched Sample
Voice:
# Loc.gov.meet NGO
# community complain about edu.service
published education problem during the last year
Per capita routine expenditure devoted for teacher salary 94-99
Per capita education expenditure
headmaster: involved in setting vision and mission
Management:
Inv. in recruitment decision
Inv. in budget allocation
Inv. in curriculum
Inv. in teacher discipline
Inv. in school based management
Expenditures per pupil
Getting DBO
Client Power:
bp3 member: involved in recruitment decision
bp3 member: involved in BP3 decision
bp3 member: involved in decision on budget allocation of
education
bp3 member: involved in curriculum
bp3 member: involved in setting vision and mission
bp3 member: involved in books procurement decision
Bp3member: involved in tools procurement
bp3 member: involved in decision on teacher discipline role
PTA inv. in school based mng.
bp3 member: involved in decision on socialization of Gov.'s
program
# of PTA meetings
Share of income from parents contribution
Augmented Specification
Matched Sample
Unmatched Sample
-0.00173
(0.98)
0.07806
(1.25)
-0.10846
(1.81)
0.00146
(1.44)
0.00224
(0.42)
0.00081
(2.20)*
0.04195
(3.30)**
-0.03858
(2.77)**
-0.00012
(0.58)
0.00008
(0.09)
-0.02735
(0.31)
-0.00011
(0.06)
0.13213
(2.14)*
0.02563
(0.42)
0.00056
(0.53)
0.00267
(0.51)
0.00037
(1.04)
0.04453
(3.61)**
-0.02675
(1.98)*
-0.00052
(2.49)*
-0.00006
(0.07)
0.03732
(0.45)
0.00007
(0.13)
0.02006
(0.61)
0.00043
(1.33)
0.02370
(1.29)
-0.00021
(0.39)
0.07148
(2.16)*
0.00044
(1.40)
0.04236
(2.36)*
-0.00600
(0.35)
-0.00740
(0.34)
-0.02721
(1.15)
0.00919
(0.29)
-0.03285
(1.17)
0.00000
(1.57)
0.00183
(0.10)
0.04116
(4.80)**
-0.04977
(4.60)**
0.00426
(0.38)
-0.04993
(2.94)**
0.03968
(2.93)**
0.00000
(0.54)
0.00206
(0.67)
-0.00998
(0.61)
0.00015
(0.01)
-0.02889
(1.21)
-0.03317
(1.05)
0.00273
(0.09)
0.00000
(1.49)
-0.00393
(0.23)
0.03761
(4.55)**
-0.05597
(5.32)**
-0.00234
(0.22)
-0.04488
(2.72)**
0.01947
(1.47)
-0.00000
(2.76)**
0.00435
(1.47)
-0.03663
(1.15)
-0.02839
(1.17)
0.01094
0.01764
(1.08)
-0.02928
(2.30)*
-0.00741
-0.04898
(1.61)
-0.01676
(0.71)
0.00497
0.03043
(1.94)
-0.02717
(2.19)*
-0.00703
(0.43)
0.00858
(0.37)
0.01090
(0.56)
-0.02024
(0.88)
0.00876
(0.43)
-0.03577
(1.65)
0.01039
(0.53)
-0.00274
(0.61)
0.03854
(3.22)**
-0.01150
(1.18)
0.00728
(0.56)
0.07887
(7.01)**
-0.01229
(1.19)
-0.05205
(5.28)**
-0.00203
(0.19)
0.02514
(1.15)
-0.00653
(0.32)
-0.00825
(0.38)
0.00144
(0.07)
-0.01905
(0.91)
0.00319
(0.17)
-0.01364
(0.60)
0.04183
(3.62)**
-0.01294
(1.38)
0.00566
(0.45)
0.07290
(6.73)**
-0.00531
(0.53)
-0.05570
(5.79)**
-0.01419
(0.15)
0.00216
(0.47)
-0.08855
(1.36)
(0.21)
0.00064
(0.26)
-0.08154
(8.93)**
(0.75)
0.00076
(0.18)
-0.09519
(1.29)
(1.51)
0.00009
(0.04)
-0.03099
(3.23)**
Share of income from local government
Compact:
# times invited by Dinas
Unmatched Sample
…continued
Table 1 continued
Student Characteristics:
Lowest National Exams test score accepted
Scholarship for the poor
Geographic Locations:
Rural area
Java & Bali
Sumatera
Kalimantan
Sulawesi
Average Expenditure per capita in the Kabupaten
Standard Deviation of expenditure per capita in the Kabupaten
Per capita routine expenditure 94-99
Basic Specification
Matched Sample
Unmatched Sample
Augmented Specification
Matched Sample
Unmatched Sample
0.00298
(3.62)**
0.06206
(1.77)
0.00118
(9.04)**
0.05414
(3.26)**
0.00307
(3.65)**
0.05264
(1.60)
0.00091
(7.13)**
0.02622
(1.63)
-0.07546
(4.94)**
0.05169
(1.77)
-0.05627
(1.85)
-0.11456
(3.06)**
0.03181
(1.06)
0.00000
(1.98)*
-0.00000
(0.10)
-0.00139
(1.43)
-0.03126
(6.97)**
-0.00331
(0.42)
-0.04509
(5.93)**
-0.08812
(9.28)**
0.04601
(6.02)**
0.00000
(2.65)**
0.00000
(0.10)
-0.00005
(0.27)
-0.03598
(1.98)
0.01344
(0.44)
-0.06077
(2.14)*
-0.10780
(3.05)**
0.01970
(0.65)
0.00000
(1.42)
-0.00000
(0.67)
-0.00069
(0.69)
-0.01035
(2.29)*
-0.00995
(1.30)
-0.04042
(5.49)**
-0.08789
(9.48)**
0.03751
(5.00)**
0.00000
(1.78)
-0.00000
(1.16)
0.00025
(1.38)
-0.00208
(1.46)
-0.00274
(0.07)
0.00164
(1.21)
-0.00482
(0.41)
0.00099
(0.85)
0.00922
(2.99)**
0.00521
(0.12)
0.00935
(2.91)**
-0.00000
(0.97)
0.00013
(0.42)
0.00034
(0.04)
0.00070
(2.97)**
-0.00068
(0.39)
0.00032
(1.08)
0.00089
(1.82)
-0.00690
(0.69)
0.00605
(12.20)**
0.00000
(1.01)
0.00006
(0.09)
0.00202
(0.83)
0.00267
(1.45)
0.04904
(1.80)
-0.00320
(0.10)
0.04165
(2.57)*
-0.03990
(0.91)
-0.07901
(2.21)*
-0.00267
(1.71)
-0.00014
(0.34)
0.00922
(2.99)**
1.36218
(11.54)**
140
0.74
-0.00012
(1.71)
-0.00165
(4.55)**
0.00239
(5.42)**
0.00044
(0.12)
0.00110
(0.26)
0.00414
(1.42)
-0.01168
(2.28)*
-0.00675
(1.15)
-0.00014
(0.72)
-0.00003
(0.39)
0.00089
(1.82)
1.67841
(85.49)**
3218
0.35
Teacher Background:
# of teachers with diploma 1-2
# of teachers with master/doctoral
# of teachers with s1 from ikip
# of teachers with s1 from non ikip
# of teachers with diploma 3-4
Additional books per student
Average hours of trainings
Average length of teacher's exp.
Average teacher salary
Other Proximate Determinants
Class size
Pupil teacher ratio
# of computer
Dummy having laboratory facilities
Dummy having library
Dummy having photocopy machine
Dummy having typewriters
Proportion class in bad condition
Student books per student
Text books per teacher
Additional books per student
Constant
Observations
R-squared
Absolute value of t-statistics in parentheses
* significant at 5% level; ** significant at 1% level
1.63446
(20.81)**
143
0.60
1.68741
(94.65)**
3292
0.28
Table 2 Correlates of the log of test scores (NEM): Senior Secondary Schools
Basic Specification
Augmented Specification
Matched Sample
Unmatched Sample Matched Sample
Unmatched Sample
Voice:
# Loc.gov.meet NGO
0.00512
(1.39)
-0.17213
(1.22)
0.04100
(0.30)
0.00055
(0.32)
0.00498
(0.69)
1.53298
(1.40)
0.00362
(1.97)*
0.00326
(0.05)
-0.04817
(0.63)
0.00116
(1.06)
0.00720
(1.48)
1.08208
(1.39)
0.00366
(1.01)
-0.16950
(1.17)
0.01798
(0.14)
0.00329
(1.88)
-0.00202
(0.30)
1.00543
(0.91)
0.00349
(1.99)*
0.06609
(0.97)
-0.07437
(1.00)
0.00023
(0.22)
0.00420
(0.89)
1.57744
(2.15)*
0.00190
(1.22)
0.01530
(0.27)
0.00141
(0.83)
0.18165
(2.01)*
0.00175
(1.14)
-0.02968
(0.50)
0.00184
(1.14)
0.24622
(2.83)**
0.01601
(0.37)
-0.01697
(0.40)
0.03779
(1.00)
-0.04564
(0.81)
-0.01972
(0.34)
0.00000
(3.91)**
-0.02273
(0.65)
0.17330
(3.77)**
-0.04129
(0.72)
-0.10883
(1.89)
-0.25959
(3.08)**
-0.02066
(0.28)
0.00000
(2.29)*
-0.00157
(0.10)
0.01950
(0.46)
-0.03226
(0.76)
0.02752
(0.75)
-0.06855
(1.20)
0.01468
(0.25)
0.00000
(0.86)
0.00019
(0.01)
0.18847
(4.18)**
-0.10358
(1.87)
-0.15657
(2.81)**
-0.33286
(3.99)**
0.05344
(0.75)
0.00000
(1.35)
0.01149
(0.76)
-0.02704
(0.41)
Bp3 member: involved in setting BP3 fee
0.02462
(0.49)
Bp3 member: involved in decision on budget allocation of education 0.08099
(1.74)
Bp3 member: involved in curriculum
0.13980
(2.23)*
Bp3 member: involved in setting vision and mission
-0.01225
(0.28)
Bp3 member: involved in books procurement decision
0.00967
(0.18)
Bp3 member: involved in tools procurement decision
-0.03653
(0.84)
Bp3 member: involved in decision on teacher discipline role
-0.03719
(0.70)
BP3 inv. in school based management
-0.06247
(1.35)
Bp3 member: involved in decision on socialization of Gov.'s program 0.01611
(0.38)
# of PTA meetings
0.00242
(0.22)
Share of income from parents contribution
-0.09204
(0.72)
-0.07565
(0.89)
0.08866
(1.37)
0.07052
(1.06)
0.09287
(1.43)
-0.04671
(0.91)
0.09912
(1.36)
0.01210
(0.20)
-0.09213
(1.72)
-0.12709
(2.32)*
0.11544
(2.30)*
0.01490
(1.24)
-0.15280
(3.15)**
0.00212
(0.03)
-0.04139
(0.76)
0.05336
(1.10)
0.09415
(1.44)
-0.00987
(0.21)
0.06424
(1.12)
0.02777
(0.63)
-0.08518
(1.66)
0.00628
(0.13)
0.02804
(0.66)
0.00184
(0.18)
0.02115
(0.16)
-0.08237
(1.00)
0.07575
(1.21)
0.08158
(1.27)
0.09830
(1.54)
-0.01693
(0.35)
0.05785
(0.81)
0.01842
(0.31)
-0.06416
(1.23)
-0.19685
(3.73)**
0.10723
(2.24)*
0.01321
(1.14)
-0.04352
(0.88)
# community complain about edu.service
Published education problem during the last year
Per capita routine expenditure devoted for teacher salary 94-99
Per capita education development expenditure
Share of income from local government
Compact:
# times invited by Dinas
headmaster: involved in setting vision and mission
Management:
Inv. In recruitment decision
Inv. In budget allocation
Inv. In curriculum
Inv. In teacher discipline
Inv. in school based management
Expenditures per pupil
Getting DBO
Client Power:
Bp3 member: involved in recruitment decision
…continued
Table 2 Continued
Basic Specification
Augmented Specification
Matched Sample
Unmatched Sample Matched Sample
Unmatched Sample
Student Characteristics:
Lowest National Exams test score accepted
Scholarship for the poor
Geographic Location:
Rural area
Java & Bali
Sumatera
Kalimantan
Sulawesi
Average per capita expenditure in the Kabupaten
Standard deviation of per capita expenditure
Per capita routine expenditure 94-99
0.00105
(0.82)
-0.13244
(1.70)
0.00157
(2.43)*
0.00498
(0.06)
-0.00017
(0.12)
-0.06816
(0.92)
0.00087
(1.39)
-0.00484
(0.06)
-0.08015
(2.38)*
0.11993
(1.52)
-0.02318
(0.33)
-0.14677
(1.73)
0.08323
(1.04)
-0.00000
(0.71)
0.00000
(1.56)
-0.00136
(0.93)
-0.07631
(4.08)**
-0.13593
(3.28)**
-0.23163
(5.65)**
-0.33928
(6.76)**
-0.01191
(0.28)
0.00000
(0.68)
0.00000
(1.33)
-0.00155
(1.60)
-0.00154
(0.04)
0.04738
(0.52)
-0.00946
(0.12)
-0.11616
(1.23)
0.02901
(0.34)
0.00000
(0.22)
-0.00000
(0.46)
-0.00199
(1.34)
-0.01322
(0.70)
-0.17129
(4.19)**
-0.21084
(5.27)**
-0.28187
(5.65)**
0.00879
(0.20)
-0.00000
(0.39)
0.00000
(1.60)
-0.00068
(0.75)
0.01311
(0.63)
0.07345
(1.83)
0.00514
(2.42)*
0.02518
(1.76)
-0.00033
(0.13)
0.04227
(0.46)
0.01980
(2.73)**
0.00000
(0.44)
0.00165
(0.27)
0.02269
(0.78)
0.00406
(5.43)**
-0.00087
(0.19)
0.00223
(1.66)
0.00495
(0.12)
0.01388
(4.26)**
-0.00000
(1.92)
-0.00429
(1.93)
0.00528
(0.69)
0.00087
(0.37)
0.09316
(1.14)
0.06397
(0.82)
-0.03302
(0.83)
-0.25855
(2.97)**
-0.05989
(0.71)
-0.00242
(0.61)
-0.00165
(0.65)
-0.00548
(0.62)
1.38369
(5.42)**
116
0.77
-0.00025
(0.43)
0.00123
(0.47)
0.00247
(1.90)
-0.00918
(0.46)
-0.01035
(0.53)
-0.02074
(1.32)
-0.00343
(0.10)
-0.02116
(0.52)
0.00053
(0.42)
0.00090
(1.40)
-0.00165
(0.60)
1.30611
(11.93)**
665
0.44
Teacher Background:
# of teachers with diploma 1-2
# of teachers with master/doctoral
# of teachers with s1 from ikip
# of teachers with s1 from non ikip
# of teachers with diploma 3-4
Average hours of trainings
Average length of teacher's exp.
Average teacher salary
Other Proximate Determinants
Class size
Pupil teacher ratio
# of computer
Dummy having laboratory facilities
Dummy having library
Dummy having photocopy machine
Dummy having typewriters
Proportion class in bad condition
Student books per student
Text books per teacher
Additional books per student
Constant
Observations
R-squared
Absolute value of t-statistics in parentheses
* significant at 5% level; ** significant at 1% level
1.46697
(8.65)**
122
0.57
1.49642
(16.30)**
683
0.33
Appendix Tables
Table 1a. Junior High School : Student's Scores
Bahasa and Sastra Indonesia
Bahasa Indonesia
English
Biology
Physics
IPA SLTP
Chemistry
Economics
IPS SLTP
History and Culture
Mathematic
PPKN
Mean
Standard Deviation
5.82
6.09
5.66
5.14
4.84
4.81
3.80
5.15
5.46
5.93
5.71
6.52
0.67
0.63
0.67
0.65
0.77
0.74
1.01
0.68
0.58
0.65
0.63
0.62
Table 1b. Senior High School : Student's Scores
Mean
Standard Deviation
IPA:
Bahasa Indonesia
English
Biology
Physics
Chemistry
Mathematic
PPKN
5.80
4.66
4.39
3.61
4.70
3.67
5.75
0.80
1.45
0.97
1.09
1.36
1.31
0.80
IPS:
Bahasa Indonesia
English
Economics
Sociology
Tata Negara
Math
PPKN
5.35
4.01
4.20
4.88
4.79
3.17
5.46
0.95
1.44
1.16
1.00
1.03
1.24
0.90
Language:
Other foreign language
Indonesian and Literary
Indonesian
English
History and Culture
Mathematic
PPKN
5.70
5.58
5.31
5.00
5.23
3.26
5.62
1.49
0.79
1.10
1.30
0.97
1.20
0.83
Table 2. Teacher Characteristics
Junior High School
Senior High School
Average length of teacher's experience
11.15
[5.77]
10.51
[5.00]
Average hours of teacher's trainings
0.06
[0.14]
0.08
[0.17]
Number of teachers with diploma 1-2
6.47
[5.67]
0.42
[1.18]
Number of teachers with diploma 3-4
6.27
[5.31]
10.11
[8.63]
Number of teachers with S1 from IKIP
10.35
[7.83]
27.90
[15.31]
Number of teachers with S1 from non IKIP
0.26
[0.79]
0.58
[1.65]
Number of teachers with master/doctoral
0.02
[0.13]
0.09
[0.39]
4,554,503
[2,797,112]
5,167,276
[2,715,436]
Average of teacher's salary in education year
1999/2000 (in Rupiah)
Note: Number in bracket are standard deviation.
Table 3. Schools Infrastructure
Junior High School
Senior High School
Total teacher
29.20
[15.02]
39.15
[17.87]
Pupil and teacher ratio
17.50
[6.77]
15.74
[4.51]
Class size
43.30
[28.81]
44.48
[20.33]
Proportion class in bad condition
12.64%
[0.23]
9.07%
[0.20]
Has Laboratory
57.88%
[49.38%]
70.89%
[45.44%]
Has Computer
36.14%
[48.04%]
92.20%
[26.82%]
Note: Number in bracket are standard deviation.
Table 4. Voice: Media Involvement
Mean
Published education problem during the last year
Publication influences community
Publication influences government
No. Observation
Source: GDS-1
Note: Refer to GDS-Media questionnaire, page 3 and page 7,
variable; MGP1f, MGR3, MGR4.
0.583
0.714
0.757
176
Table 5. Voice: Non-Government Organizations
Mean
# of meeting between Government and Community
6.65
# of meeting between Government and the NGOs
5.29
# of NGO's participation in policy formulation of Local government
2.66
# of NGO's using aspiration facility of Local Government
4.58
Community complain to NGO about education service
0.52
NGO tells community's complain to Local Government
0.70
Local Government respons to community's complaint
0.68
Local regulation for Education
0.29
NGO's opinion about Local regulation of Education
0.64
Interest on community needs from:
- Community leaders
0.66
- University students
0.70
- NGOs
0.73
Source: GDS-1
Note: Refer to GDS-NGO questionnaire, page 3, 7-9
variable; LGP1-LGP4, LGR2, LGR2a, LGR6b, LGR7b, LGR8e-f
Table 6. Participation Headmaster in Decision Making
Primary
Headmaster
Junior
Senior
Recruitment and promotion for teacher and administration
35.98%
staff 45.20%
42.61%
BP3 fee
60.62%
68.36%
68.75%
Education budget allocation
33.71%
36.72%
36.93%
Curriculum and local material
58.36%
75.99%
42.61%
Vision and mission of school
69.41%
86.44%
86.93%
Book procurement
42.49%
44.35%
41.76%
Tools procurement
42.21%
50.28%
48.86%
Teacher discipline role
69.97%
87.29%
86.65%
Implementation of School Based Management
59.49%
85.59%
84.94%
Socialization of Gov. program on education
56.66%
73.73%
73.30%
Source: GDS-1
Note: Refer to GDS-School questionnaire, page 4 - 5 : SGP3a-SGP3j.
Table 7. Responsiveness
Primary
Junior
Senior
Dinas met headmaster to discuss
learning and teaching process
61.54%
64.41%
60.94%
Headmaster told problem to Dinas
53.33%
59.11%
59.38%
Dinas discussed budget allocation
27.90%
30.72%
30.75%
Source: GDS-1
Note: Refer to GDS-School questionnaire, page 7-8 : SGR1, SGR2, SGR3
Table 8. School Capability to run Education programs
Primary
Junior
Senior
Budget
28.05%
29.10%
32.39%
Professional staff/teacher
50.71%
50.28%
51.14%
Infrastructure
23.80%
27.12%
28.69%
Technology and Information
20.11%
37.01%
38.07%
Networking
62.61%
62.99%
70.74%
Authority
88.39%
92.66%
92.05%
School authority to manage its budget
71.16%
76.38%
77.39%
Source: GDS-1
Note: Refer to GDS-School questionnaire, page 11-12 ; SD2.1, SD2.1d, SD2.2-SD2.6
Table 9. Headmaster: Main Problem of Education in District
Primary
Junior
Senior
Lack of teacher
34.84%
38.70%
36.36%
Teachers had low skill
27.76%
33.90%
32.39%
Inequality of teacher distribution
19.83%
21.75%
20.74%
School infrastructure
43.91%
43.50%
44.60%
Salary and incentive for teacher
24.93%
20.06%
18.47%
Book and tools procurement
22.66%
9.89%
7.10%
Juvenil delinquency
5.10%
5.65%
4.55%
The discipline of teacher
5.10%
4.80%
4.26%
School budget
35.69%
39.83%
40.91%
Community participation
13.31%
16.67%
15.91%
Others
12.18%
16.67%
30.11%
Source: GDS-1
Note: Refer to GDS-School questionnaire, page 15 ; SD9
Table 10. Dinas: Main Problem of Education in District
Percentage
Lack of teacher
55.43%
Teachers had low skill
34.29%
Inequality of teacher distribution
28.00%
School infrastructure
51.43%
Salary and incentive for teacher
13.14%
Book and tools procurement
9.71%
Juvenil delinquency
1.14%
The discipline of teacher
2.86%
School budget
36.57%
Community participation
12.00%
Others
12.00%
Source: GDS-1
Note: Refer to GDS-Dinas Pendidikan questionnaire, page 14,
Variable: ED11
Table 11. School: Shares in Income and Expenditure
Junior High School
Mean
Standard Deviation
Senior High School
Mean
Standard Deviation
Income:
Student parent
17.95%
21.88%
21.63%
21.20%
Yayasan
0.17%
2.92%
0.32%
4.35%
Central Government
79.46%
24.23%
75.58%
23.28%
Local Government
0.07%
1.62%
0.15%
1.69%
DBO
2.34%
8.40%
2.32%
6.41%
Teacher's salary
60.63%
25.78%
59.58%
24.49%
Officer's salary
11.56%
15.57%
12.10%
15.71%
Teaching and learning process
11.53%
14.16%
11.67%
12.68%
Maintenance
5.01%
6.86%
4.70%
6.16%
Rehabilitation
1.04%
3.81%
1.02%
2.75%
Infrastructure procurement
4.19%
7.54%
4.37%
7.34%
Expenditure:
Table 12. School and BP3
Frequency of meeting with student
parent for:
Primary
Junior
Senior
BP3 fund management
1.869688
2.149718
2.28125
Repot of BP3 fund
1.478754
1.522599
1.480114
Source: GDS-1
Note: Refer to GDS-School questionnaire, page 6-7 : SGT3-SGT4
Table 13. Participation BP3 member in Decision Making
BP3 member
Primary Junior Senior
Recruitment and promotion for teacher and administration staff 14.45%
8.76%
7.95%
BP3 fee
67.42% 81.64% 82.39%
Education budget allocation
22.95% 20.06% 21.02%
Curriculum and local material
12.46% 18.93% 11.08%
Vision and mission of school
33.14% 49.72% 48.86%
Book procurement
13.60% 16.38% 17.61%
Tools procurement
17.28% 27.68% 29.26%
Teacher discipline role
18.98% 24.29% 23.01%
Implementation of School Based Management
27.20% 50.28% 46.31%
Socialization of Gov. program on education
24.36% 32.77% 31.82%
Source: GDS-1
Note: Refer to GDS-School questionnaire, page 4 - 5 : SGP3a-SGP3j.