Post on 05-Jul-2020
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THE EFFICIENCY OF PUBLIC
EDUCATION IN UGANDA
MARCH 2008
This paper was written by Donald Winkler (Consultant) and Lars Sondergaard (AFTP2)
with the very generous assistance of the Ministry of Education and Sports, Government
of Uganda, and the helpful guidance of Harriet Nannyonjo (Task Team Leader). James
Habyarimana was responsible for conducting a school survey to gather information on
the sources and uses of funds at the school level. Maria Shkaratan did much of the
analysis of EMIS data. The Planning Department, MoES, generously facilitated the
provision of information and data for analysis. Several organizations provided very
helpful comments on an earlier draft of the report, including MoES, UNICEF, GTZ, DCI,
and Irish Aid.
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TABLE OF CONTENTS
THE EFFICIENCY OF PUBLIC EDUCATION IN UGANDA
EXECUTIVE SUMMARY: KEY FINDINGS AND RECOMMENDATIONS
A. INTRODUCTION
B. AN EFFICIENCY FRAMEWORK
C. EXTERNAL EFFICIENCY
D. INTERNAL EFFICIENCY OF PRIMARY EDUCATION
E. EFFICIENCY OF PRIMARY TEACHER EDUCATION
F. INTERNAL EFFICIENCY OF SECONDARY LEVEL EDUCATION.
G. INTERNAL EFFICIENCY OF TERTIARY LEVEL EDUCATION
H. NEXT STEPS
I. SUMMARY AND CONCLUSION
J. REFERENCES
K. ANNEXES
1. Statistics
2. School Grants
3. Teacher Absenteeism
4. Formula Funding
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ABBREVIATIONS
BOG Board of Governors
CC Coordinating Centers
CCT Coordinating Center Tutor
DEO District Education Office
DIS District Inspector of Schools
EMIS Annual School Census of MoES
ESA Education Standard Agency
ESC Education Service Commission
JSE Junior Secondary Education
MoES Ministry of Education and Sports
MoSP Ministry of Public Service
NAPE National Assessment of Progress in Education
NTC National Teachers College
PETS Public Expenditure Tracking Survey
PLE Primary Leaving Exam
PSC Public Service Commission
PTA Parent Teachers Association
PTC Primary Training College
SFG School Facilities Grant
SMC School Management Committee
SSE Senior Secondary Education
TSC Teachers Service Commission
UBOS Uganda Bureau of Statistics
UNEB Uganda National Examinations Board
UPE Universal Primary Education
UPPET Universal Post Primary Education & Training
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EXECUTIVE SUMMARY AND KEY ISSUES
This is a study of the efficiency of Uganda’s public education system. Since this
type of study is relatively new for Ugandan education, the study begins by defining the
basic concepts, terminology, and methods for analyzing efficiency.
The scope of the education sector—from pre-primary through university post-
graduate; it’s magnitude—representing over 7 percent of GDP; the lack of basic financial
and resource information for some sub-sectors; and the limited time and resources
available for carrying out this study has resulted in some sub-sectors receiving more
attention than others. In particular, given the lack of any recent cost or efficiency
analysis of primary education, and the fact that this sub-sector absorbs almost two-thirds
the government’s education expenditure, special emphasis was put on primary education.
On the other hand, some sub-sectors—BTVET and teacher training, for example—have
not received much attention, mainly due to the lack of data and the need to carry out
original surveys to obtain the information that would have been required to analyze these
relatively small sub-sectors. In addition, since recurrent expenditures represent more
than 95 percent of the Government’s education budget, this study focuses on recurrent
expenditures, although the future growth of enrollments and school infrastructure argues
for a subsequent, separate analysis of the efficiency of development spending.
Uganda is very fortunate to have a large number of studies and evaluations that
have been carried out in the education sector over the past decade. In addition, Uganda
has rich data bases that provide much of the information required for the analysis of
education efficiency—census data, household surveys, demographic and health surveys,
service delivery surveys, and an Education Management Information System [EMIS]
whose quality has been significantly improved in recent years. What Uganda lacks is
the kind of finance, expenditure, and resource information required for analyzing
efficiency. Hence, this study carried out a rapid unit cost survey of 180 public and
private primary schools in six districts across three regions to provide this information1.
While the survey is not nationally representative, it is as least representative of those six
districts, which collectively reflect much of the nation.2
Issue: Cost and expenditure information is essential for monitoring the efficiency
of Government spending. A nationally representative survey of expenditures,
finance, resources, and outcomes at the primary and secondary school levels,
including BTVET secondary level schools, would help provide the information
needed for assessing efficiency. The MoES could contract a firm to carry out
such a survey and provide cost and efficiency indicators.
1 A cost survey was recently carried out for tertiary education, and Shinyekwa (2006) recently did a cost
survey of secondary schools, leaving the largest sub-sector—primary—as the one having the least cost and
finance information. 2 While the sample was 180, survey data was collected on only 160 schools due to school holidays and
some primary schools having been upgraded to secondary schools
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FINDINGS AND RECOMMENDATIONS
This study has generated a large number of findings and recommendations, as
well as a number of areas where additional information needs to be gathered in order to
improve the analysis. The findings and recommendations are summarized below,
followed by a discussion of the highest priority actions in the sector for improving
efficiency.
This study documents the magnitude and extent of the leakage and misuse of
educational resources. When possible, it identifies the principal causes of inefficiencies.
However, in general, further research is needed in order to pinpoint causes and thus
identify cost-effective solutions. For example, the study documents the problem of an
inequitable and inefficient assignment of teachers across districts and schools.
Determining the multiple reasons for poor deployment and developing programmatic and
policy options for treating those reasons is beyond the scope of this work and requires a
study of its own.
External Efficiency.
Uganda has done an admirable job of increasing access to primary education over
the past decade. However, increased access has come at the expense of the quality of
instruction. International evidence generally shows that improvements in quality—in
terms of student knowledge--are more strongly related to economic growth than are
improvements in access. Uganda needs to make a very serious effort to improve
quality at all levels, while maintaining its impressive accomplishments with respect to
coverage. Improvements in internal efficiency can help Uganda achieve both quality and
quantity.
Issue: In terms of facilitating economic growth, improvements in quality—at all
levels of education but especially in lower primary—are likely to have a high
payoff. A careful assessment of the costs and benefits of raising quality at the
lower primary level versus raising access at the post-primary level would help
guide MoES resource allocation.
Internal Efficiency of Primary Education.
The internal efficiency of primary education is low. There are four principal
sources of inefficiency. The first is the leakage of resources between the central
government and the school through ghost teachers, misuse of UPE grants to district
governments, etc. The second is the leakage of resources within the school, mainly
attributable to high rates of student, teacher, and headmaster absenteeism. The third is
the deployment of teachers both across and within districts, which appears to be unrelated
to measures of need. The fourth is the allocation of resources within government
schools, where class sizes are largest in the early grades and smallest in the later grades.
While it is difficult to precisely quantify the overall magnitude of inefficiency, this study
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calculates that at least one-third of the expenditures on primary education are wasted
or used inefficiently. However, it’s important to note that several types of leakage—
ghost teachers, UPE capitation grants, and teacher absenteeism--have all decreased over
time.3
Teachers are the most valuable resource in improving educational outcomes.
Uganda’s main efficiency problem is the poor utilization of its teaching staff. Three
pieces of evidence to support this conclusion. First, over three-quarters of teachers are
not in class teaching when unannounced school visits are conducted, and many of them
are not even at work. Second, across districts, teachers are not deployed to the regions
where there is greatest need for them. [see Figure D2 in the paper]. Third, within
schools, teachers are not being assigned in such a way that class sizes across grades are
the smallest possible: rather, the early grades [P1-P3] have large class sizes, and the the
later grades [P4-P7] have much smaller class sizes [see Figure D9].
High levels of teacher, headmaster, and student absenteeism is the most
important source of leakage at the school level. The magnitude of teacher absenteeism,
in particular, is so large that reducing it should be a principal focus of Government efforts
to improve efficiency in primary education. Since Government actions to reduce
absenteeism are relatively recent, they may not have yet had much impact. [See annex 3
for analysis of the determinants of teacher absenteeism.] This report uses an
internationally recognized methodology to measure absenteeism and presents an option
of policy measures to reduce absenteeism.
Issue: In Uganda, a 20 percent reduction in teacher absenteeism alone would be
the equivalent of hiring 5,000 more teachers (at a cost of Ush 12 bn). Policy
measures to reduce teacher, headmaster, and student absenteeism could thus have
a very high payoff. A careful assessment of the costs and benefits of specific
policy measures would be useful to guide MoES policies to reduce absenteeism.
Government teachers are not deployed in sufficient numbers to the neediest
districts, where their presence is likely to have the biggest impact on improving
educational outcomes. Analyzing EMIS [Education Management Information System]
data, the deployment of government-paid teachers across districts is perverse, with
student teacher ratios in government schools being the highest in the poorest districts.
[See Figure D3] . In addition, there is no relation between the current teacher
deployment and measures of educational outcomes4.
Issue: Since the MoPS already has an explicit rule for the deployment of teachers
across districts, it would be useful to know why the actual deployment differs.
The district teacher service commissions should establish and implement a
transparent, explicit rule for the deployment of teachers across schools within
3 See the World Bank (2007b) Public Expenditure Review for Uganda for greater detail on changes in
leakages over time. 4 There are questions as to the accuracy of EMIS data on teacher employment, so this analysis should be
repeated using the Ministry of Public Service [MoPS] data base
7
districts and establish procedures to regularly monitor deployment. Since much
of the poor deployment of teachers appears to be linked to teacher transfers,
consideration might be given to grant schools rights over teacher reassignments to
prevent the movement of teachers from unpopular to popular schools without a
replacement satisfactory to the school.
Issue: A more radical proposal would eliminate district deployment of teachers
altogether. Each school would be given an annual formula-driven budget
determined by student enrollments and special needs and would recruit as many
teachers as allowed by that budget.5
Within schools, students at the lower primary level receive too few resources,
which contributes to P3 children having low achievement levels and being poorly
prepared for English only instruction beginning at P4. Government schools allocate
fewer teachers (measured on a per student basis) to the early grades of primary school
relative to the later grades. The large class size in the early grades constrains teachers in
individualizing instruction. The low percent of total hours that teachers are actually
present teaching in the classroom also contributes to low student achievement. Another
factor contributing to low achievement, high repetition and high dropout is the low
percentage of students entering primary school at the appropriate age.
Issue: MoES could create and enforce a norm that requires that class sizes be no
larger in the early grades [P1 – P3] than in the later grades [P4 – P7]. In schools
where there are a sufficient number of classrooms, this would require creating
additional streams at the lower grades. In schools where there are an insufficient
number of classrooms to create additional streams, this would require
constructing new classrooms and/or introducing double shifts.
Issue: MoES might consider the following policy change: Put at least as high a
priority and as much emphasis on learning achievement levels at P3 as on the
percent of children passing the P7 school leaver’s examination, and create
incentives [e.g., school merit pay, public recognition] that reward schools that
show annual gains and better than expected performance on the P3 test.
Issue: School communities could play an important role in reducing teacher
absenteeism from the classroom if there were transparent rules establishing clear
expectations about teacher presence in the classroom. Absenteeism might be
reduced if the MoES were to publicize norms around the number of hours that
teachers should be in the classroom teaching each day, and engage communities
to monitor compliance with the norms.
5 This is the method Chile uses to fund its privately managed, publicly funded schools; Georgia and
Armenia follow similar procedures to fund public schools. In most countries [but not Chile] a common
salary scale and benefit structure applies to all teachers irrespective of where they are employed, and the
central government maintains the pension scheme.
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Issue: Strong incentives could reduce student absenteeism. MoES might
consider establishing public information campaigns and incentives [e.g. school
lunch] to encourage parents to enroll students at the proper age, and create
incentives to schools and districts to adopt pro-active policies to enroll students in
P1 at the appropriate age. Capitation based funding is one means of providing an
incentive to schools to actively try to enroll students.
The lack of an effective inspection system at the district level combined with the
limited powers of school management committees [SMCs] to hire and fire school
personnel contribute to an almost complete lack of accountability by districts and schools
to parents, the public, and the ministry for compliance with MoES norms and guidelines
and for adequate educational performance.
Issue: There are several options MoES might consider adopting to strengthen
local accountability. It might Increase the capacity of SMCs to develop school
budgets, including the UPE grant, and monitor expenditures and involve the SMC
in the annual performance evaluation of headmasters. It might also consier
developing district level report cards which give district residents the information
required to assess the performance of DEOs, as well as school level report cards
that include the school budget, school outcomes, student and teacher absenteeism,
and the level of resources that parents have a right to expect in their schools.
Creating school-wide incentives for unexpectedly good performance that bring
the community and teaching faculty together in pursuit of a common goal is also a
good idea.
Efficiency of Primary Teacher Education.
Pre-service teacher training occurs in too many poorly resourced Primary Teacher
Colleges [PTCs], while in-service training takes place in Coordinating Centers [CCs]
dispersed throughout the country and affiliated with 23 core PTCs. The experience of
other countries shows that using in-service training to produce qualified teachers is more
cost-effective than traditional, pre-service training, and Uganda’s experience appears to
be consistent with that finding. However, more careful analysis needs to be carried out to
determine the cost-effectiveness of producing qualified teachers via the two modes of
training. In addition, more analysis is required to determine what capacity is required for
producing qualified teachers via traditional pre-service training, how many PTCs should
be upgraded to provide higher quality and more cost-effective pre-service training, and
what is the minimum size required for a PTC to be both effective and efficient.
Issue: MoES might consider carrying out a survey of PTCs and CCs to estimate
the unit costs and the classroom effectiveness of qualified teachers produced by
these two different modalities. It could determine the future demand for teachers,
taking account of teacher attrition, demographic change, and policies concerning
the pupil-teacher ratio and other factors that affect demand. On the basis of the
demand and cost studies, MoES could determine how many PTCs should remain
operating, and identify means of adequately resourcing both PTCs and CCs.
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Internal Efficiency of Secondary Education.
The internal efficiency of public secondary education is low and unit costs are
high. The reasons for low efficiency include low workloads, poor teacher deployment,
and high teacher salaries. A significant portion of secondary school teachers are under-
utilized. The reasons include an overly prescriptive curriculum, constraints on classroom
space, and small schools in terms of student enrollment. In addition, the salaries of
public secondary school teachers, and especially of public secondary school headmasters,
are high relative to per capita GDP, high relative to primary school teachers, and high
relative to salaries paid teachers in private secondary schools [see Table F2 and Figure
F3]. If the Government carries through with its plans to significantly increase secondary
school net enrollment rates, these high salaries may very well be unsustainable.
Issue: Simplifying the secondary school curriculum by requiring fewer courses,
and mandating that all teachers should be required to have the skills to teach at
least two subject matters could reduce the unit costs of secondary schooling..
Isuse: The MoPS might consider contracting independent analysts to study the
teacher labor market in Uganda to determine appropriate salary levels of
secondary school teachers and headmasters and to propose options for reducing
average salaries [e.g., protecting currently employed teachers and headmasters
while employing new teachers using a new pay scale].
The distribution of Government secondary education expenditures, as proxied
by teacher deployment across and within districts, appears to bear no discernible
relationship with the measures of need and cost most commonly used in education
funding formulas. Furthermore, this distribution shows no relationship between funding
and proxies for educational outcomes. Prima facie, these findings provide evidence of an
inefficient allocation of Government monies.
MoES: The Government might consider establishing a clear, transparent teacher
deployment formula for secondary education. As with the UPE grant, the funding
and deployment formula would be public information so each school would know
its staffing entitlement, and schools and communities would have to give approval
for teacher transfers which would reduce staffing.
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The role of the private sector in the finance and provision of secondary
education in Uganda is critically important, including the sizeable fees paid by
households to public secondary schools. As shown in Figure 1, Uganda is among the
countries in Africa with the highest percentage of secondary school enrollments in private
schools. In total, household expenditures on secondary education are triple those of
Government. It is critical to protect and sustain household financing levels, most of
which is provided by high income households, to permit the expansion of more heavily
subsidized educational opportunities to lower income households. The present allocation
of Government subsidies is not transparent and does not offer explicit incentives to
private schools and households to sustain and increase private provision and finance.
Issue: The MoES might consider designing and implementing a transparent
capitation grant to private institutions with clear, explicit incentives for sustaining
household finance of secondary education [e.g., cost-sharing up to some desired
level of expenditure]. Such a grant could include adjustments for differences in
school location or type of school which may affect the school’s cost structure and,
also, adjustments for the income levels of students [perhaps proxied by region or
rural/urban location], to ensure equity. [In general, see La Rocque (2006) for
further suggestions on strengthening public-private partnerships.]
Accountability by schools to either parents or the MoES is weak. School
inspection is infrequent enough to be ineffective, thereby seriously weakening
accountability to the MoES. The local BOGs and PTAs have unclear and sometimes
competing roles and usually lack the capacity and information to effectively manage
school budgets.
FIGURE 1. AVERAGE PERCENT OF PRIVATE ENROLLMENT
IN SELECTED SSA COUNTRIES
0 10 20 30 40 50 60
Uganda
Comoros
Madagascar
Niger
Togo
Senegal
Benin
Botswana
Congo
Eritrea
% private enrollment
PE
JSE
SSE
Source: UIS, World Bank; latest years available.
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Issue: The BOGs and PTAs might be more effective if they were to have clearly
delineated and strengthened roles, be given training to improve their governance
capacity, and be provided the information they need to hold schools accountable,
possibly in the form of school report cards that allow schools to assess their
relative performance.
Internal Efficiency of Tertiary Education.
Higher [tertiary] education in Uganda is monitored and managed by three
different organizations with the Ministry—the Teacher Education Department, BTVET,
and Higher Education plus the newly created National Center for Higher Education
[NCHE]. This division of management and oversight responsibilities is an obstacle to
rational and efficient resource allocation within the sector.
Public higher education—especially the universities—is funded from both public
and private sources. Among universities, the government funds only the most
meritorious applicants, who also happen to mainly come from the highest income
households in Uganda. At other tertiary institutions, Government funds specific inputs
rather than students and allows those institutions very little management autonomy.
Many public tertiary institutions could not survive without the revenue from student
tuition fees. Both public and private institutions have difficulty accessing the credit
markets to fund expansion, and the effects of UPE and UPPET will soon be felt at the
tertiary level in the form of significantly increased demand.
Issue: Government might consider reforming government funding of universities
by eliminating full-funding of government sponsored university students and
replacing it with capitation grants and student loans targeted on financially needy
students. Government funding of other tertiary institutions could be reformed by
replacing input-based funding by capitation grants. Government might also
support lines of credit to both public and private institutions to ensure they have
adequate funding to support expansion.
Information for Decision-making.
Public education is one of Uganda’s largest industries. A private sector business
with the number of employees and the budget of public education would typically have a
sophisticated information system that would provide accurate and up to date information
on organizational performance, expenditures, and efficiency on at least a weekly basis.
In contrast, the public education system provides only some of this information and
typically on an annual basis. Furthermore, the information provided to decision-makers
is not always accurate. A good example is information on the single most important
educational input—teachers. As shown in Figure 2, there are large discrepancies in the
total number of payroll primary teachers as reported in the EMIS, as reported in the
Education Sector’s Annual Performance Review (June 2006), and the records of the
Ministry of Public Service [MoPS]. The EMIS data underestimate the number of total
payroll teachers by about 10 percent per year relative to the MoPS data, which is thought
12
to be the more reliable . For an educational system to operate efficiently, it is absolutely
essential that the managers of that system have reliable and up to date information on its
key inputs.
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FIGURE 2. HOW MANY TEACHERS
ARE ON THE GOVERNMENT
PAYROLL?
105,000
110,000
115,000
120,000
125,000
130,000
02/03 03/04 04/05 05/06
(nu
mb
er o
f te
ach
ers
on
pa
yro
ll)
Number of teachers on payroll (ESAPR 2005)
Number of teachers on payroll (MPS, end-June figures)
Number of teachers on payroll (EMIS 2005)
Issue: MoES could contract an external consultant to carry out an independent
assessment of the quality and coverage of data provided by the MoES’s EMIS.
The assessment could include revising the survey instrument and survey
procedures to improve accuracy and to improve coverage, especially of non-
public secondary schools and of school budgets and expenditures. To the extent
possible include measures of school achievement like PLE and SLE results.
NEXT STEPS
The next steps in the analysis of education efficiency in Uganda are to fill in the
gaps where cost and financing information is lacking, e.g., BTVET and Primary Teachers
Colleges and school infrastructure, and to systematically develop and evaluate some of
the recommendations made in this report in terms of their likely cost, impact, and
administrative and political feasibility.
HIGH PRIORITY ISSUES
This study has identified a number of issues or problems of education efficiency
and possible actions or policies which might be undertaken to correct those problems.
Several issues appear to be of especially high priority, either due to the magnitude of the
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underlying problem or because the proposed actions are so fundamental. These issues
are summarized in the table below:
TABLE: HIGH PRIORITY ISSUES
SUB-SECTOR ISSUE/RECOMMENDATION
Primary Reduce headmaster absenteeism.
Increase teacher classroom time.
Improve accountability arrangements.
Rationalize teacher deployment.
Prioritize grades 1-3.
Secondary Reduce teaching costs.
Ensure high quality.
Facilitate privately-funded expansion.
Tertiary Reform government finance.
Stimulate privately-financed supply.
MoES Strengthen information and analysis.
Carry out specific efficiency studies.
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A. INTRODUCTION
In 2004 the Ministry of Education and Sports [MoES] adopted the Education
Sector Strategic Plan for 2004-2015. The previous Education Strategic Investment Plan
[ESIP] for 1998-2003 had put top priority on getting children in school through
implementation of the Universal Primary Education [UPE] program. Since this objective
had been largely accomplished by 2004, this new plan set out new priorities focused on
raising the quality and relevance of education and improving the efficiency and
effectiveness of the education sector. The purpose of this paper is to contribute to the
accomplishment of this latter priority—improving the efficiency and effectiveness of the
education sector.
Through both Government and household expenditures, Uganda already allocates
over 7 percent of its GDP to the education sector6. This exceeds the average 6 percent of
GDP that OECD countries spend on education. Meanwhile, the coverage of post-primary
education remains relatively low, and the quality of all levels of education needs
improvement. To significantly increase post-primary enrollments at current unit cost
levels would require an unsustainably large increase in education expenditures. While
Government and household education expenditures may increase in future years, the
Government’s goals to increase the coverage of post-primary and to improve the quality
of all levels of education cannot be attained in the absence of improvements in efficiency
in the use resources.
To fully analyze the efficiency of an education system is a very large task well
beyond the limited resources and time available for this study. It was simply not possible
to carry out an in-depth analysis of each sub-sector, and decisions had to be made as to
how best to focus the study. Three factors guided these decisions: [a] the existence of
recent analytic work on costs and efficiency; [b] the size or budget share of the sub-
sector, and [c] the availability of the basic information required to carry out an efficiency
analysis. First, in some cases—e.g., tertiary education—recent in-depth analytic work
had already been undertaken, and the findings having to do with efficiency only had to be
summarized and updated with recent information. Second, in other cases—e.g., primary
education and secondary education—the mere size of the sub-sectors, which in the case
of primary represents two-thirds of all government education expenditures, demanded
careful attention. Third, in other areas—e.g., teacher training and secondary level
BTVET—the information base is exceptionally weak, while in other areas—e.g., primary
and general secondary—the information base is relatively rich as a result of the annual
information census carried out by the EMIS. As a result of these considerations, this
study put greatest emphasis on the primary and secondary sub-sectors and put least
emphasis on BTVET and teacher training. There is an urgent need for much more
intensive work on these latter sub-sectors, but that will require original data collection
and the involvement of specialists from those sub-sectors.
6 The 7 percent figure includes both government and household spending. See Annex 1 for detailed
statistics on government spending disaggregated by level of education.
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Several years have passed since the last efficiency study of Ugandan education.
For this reason, we start off this study by defining the basic concepts, terminology, and
methods of analyzing education efficiency . The reader who is already familiar with the
economic analysis of efficiency may wish to skip this section and proceed directly to the
analysis sections.
Uganda is very fortunate to have a large number of studies and research that have
been carried out in the education sector over the past decade. In addition, Uganda has
rich data bases that provide much of the information required for the analysis of
education efficiency—census data, household surveys, demographic and health surveys,
service delivery surveys, and an Education Management Information System [EMIS]
whose quality has been significantly improved in recent years. What Uganda lacks is an
institutional survey that provides the kind of finance, expenditure, and resource
information required for analyzing efficiency. Hence, this study carried out a rapid unit
cost survey of 180 public and private primary schools in six districts across three regions
to provide this information7. At the secondary level, a separate cost survey was carried
out as part of the concurrent analysis of the cost implications of UPPET.8 And at the
tertiary level, a similar cost survey had been recently carried out by the National Council
for Higher Education [NCHE] with support from the Rockefeller Foundation.9 As noted
above, similar surveys need to be carried out for secondary level BTVET schools and for
Primary Teacher Colleges.
7 While the sample was 180, survey data was collected on only 160 schools due to school holidays and
some primary schools having been upgraded to secondary schools. While the survey is not nationally
representative, it is at least representative of these six districts, which collectively reflect much of the
nation. 8 Shinyekwa (2006).
9 NCHE (2005).
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B. AN EFFICIENCY FRAMEWORK
What is the meaning of efficiency in education?
Efficiency is measured by comparing education expenditures with education
outcomes. Governments make expenditures at all levels of education, and two of the
most basic efficiency questions are whether government is spending the appropriate
amount on each level or type of education, and whether government is making the
appropriate choices on quantity versus the quality of education. These questions are
answered by looking at the success that the graduates of different levels of education
have in the labor market relative to the costs of their education. This is called the
external efficiency of the educational system.
It is important to have indicators of external efficiency not only to guide the
allocation of government spending across levels of education, say pre-primary vs. upper
secondary, but, also, across types of education. Thus, knowing the returns to general
secondary vs. vocational secondary education would help guide a policy decision about
whether or not to upgrade and expand vocational instruction.
A different type of efficiency question concerns the use of resources in producing
the outcomes of education, which is called the internal efficiency of the educational
system. Assessments of internal efficiency are typically done for a specific level of
education, say primary education, and the simplest indicator of internal efficiency is the
unit cost of producing one unit of educational output, which may be a student enrolled, a
graduate of that level of education, or a student who has attained some minimum level of
knowledge. Other things equal, an educational system which can produce a unit of
output at lower unit cost than another educational system is said to be more efficient.
The economist’s emphasis on unit cost is frequently criticized by educators who
mistakenly believe the economist is saying that improving efficiency is the same thing as
reducing costs in general. This is not true. Indeed, increasing the unit cost of enrollment
may actually reduce the unit cost of a graduate. The confusion between economists and
educators results from a failure to carefully specify the unit of output.
What are some common indicators of efficiency in education?
External Efficiency. The most common indicators of external efficiency in
education are estimates of the private and social rates of return to expenditures on
education at the different levels or types [e.g., academic vs. vocational secondary] of
education. Unfortunately, at this point there is no good country level indicator of the
appropriate levels of access and quality of education.
The private rate of return shows the financial returns in terms of increase
incomes accruing to individuals as a result of investing their own time and money in a
given level of education. An individual who can attain a higher return to “investing” in,
18
say, secondary education than she could by making some alternative investment should
make the decision to attend secondary school. To do otherwise would be an inefficient
use of that individual’s own resources. Government policies—e.g., decisions to charge
low tuition at the tertiary level—affect the private rate of return and, also, individual
education decisions. Thus, the elimination of user fees in the PTCs should have
increased the number of applicants for teacher training. Supply-side restrictions on
enrollments may make it impossible for individuals to further their education even if their
private rates of return are high. In addition, estimates of the private rate of return can
influence cost recovery policies. Thus, if Kyambogo University finds the private rate of
return to a bachelors degree in accounting and finance is very high, it will know it has the
option of charging users a high percentage of their instructional costs and still be able to
attract the number of students it wishes to enroll. On the other hand, if it finds the private
rate of return to, say, a bachelors degree in arts is very low, it will know that it may not
have the option of any cost recovery if it wishes to attract students and retain the
program.
The social rate of return shows the financial returns to society resulting from
investing society’s resources [i.e., government plus individual costs] by enrolling
students in a given level or type of education. If calculated correctly, the social rate of
return should guide government decisions about the supply and finance of education.
Thus, a high social rate of return to tertiary education may influence the government to
expand enrollment capacity this level.
There are two serious problems with estimates of rates of return that argue for
using them with caution to guide public policy. First, the estimates are calculated based
on what individuals with different education levels have earned in the past, which may
not necessarily be a good predictor of future earnings. Thus, while the social rate of
return to secondary education in Uganda is relatively low, evidence from surveys of
national competitiveness suggest that future returns may be considerably higher. Also,
these estimates ignore the non-monetary, social benefits of additional education, such as
better health and nutrition, better child-rearing, reduced poverty, and scientific
advancement.
Finally, we lack information on the rate of return in which many countries are
most interested these days—the rate of return to investing in quality improvements in the
classroom. Raising quality, and thus student learning or achievement, in most cases
requires additional resources. In principal, one could calculate the increased income that
results from the additional learning resulting from additional investments. In practice
these are extremely difficult calculations to make, but the little evidence we have
suggests the returns may be very high indeed10
. Many parents appear to know this and
make sizeable private investments to raise their child’s learning and future educational
and career prospects. .
Internal Efficiency. An analysis of internal efficiency of public education
spending attempts to answer the questions: [1] Can educational outcomes be increased
10
Hanushek [2007].
19
without raising the current level of resources or funding? and [2] Can expenditures be
reduced without adversely affecting the current level of educational outcomes?
There are several ways to proceed in answering these questions. This study uses
three types of analysis to assess internal efficiency of public education in Uganda. The
first is to identify ways in which resources may leak out of the system. Leakages like
ghost teachers, misuse or diversion of education funds, and teacher absenteeism all
reduce the size of the total resources available for the provision of education. Reducing
leakages increases the available resources for delivering education more effectively and,
thus, increases internal efficiency.
A second type of analysis concerns the allocation of education budgets across
inputs. The question here is whether or not an education system is spending its monies
wisely. For example, systems which fail to allocate sufficient funds for building and
equipment maintenance, or systems which fail to provide textbooks to students may be
inefficient because they could be producing a higher level of output without increasing
overall spending. Or, it may be the case that output levels could be increased with a
given budget if the delivery process were altered, e.g., using multi-grade instead of single
grade instruction, or using distance learning instead of traditional classroom construction
for teacher in-service training.
A third type of analysis concerns how public funds leverage private sector
finance of education. For example, when government wishes to expand enrollments by
10,000 at the secondary level it can either directly provide public schools, or it can offer
financial incentives to private schools to increase their enrollments by 10,000. If the
private financial incentives cost less than direct public provision, the government is
leveraging its funding. In some cases where private schools have excess capacity, the
government may have to offer only small financial incentives to achieve its desired result.
In other cases, such as offering financial incentives in remote rural areas where no private
schools already exist, it may not be possible for government to leverage its funding.
Public funds can also leverage private finance through selective targeting of
subsidies. Imagine a university with a fixed budget provided by the Government. That
university has three options in charging tuition fees for students. First, it could charge no
tuition fee whatsoever. Second, it could charge a uniform tuition fee to all students.
Third, it could charge a tuition fee that varies with the student’s ability to pay. If the goal
is to maximize university enrollment with the Government’s fixed budget, the third
option would be the best one.11
Inputs, Outputs, and Outcomes. Since efficiency is defined as the relationship
between inputs [expenditures] and outputs [a student enrolled, a student graduating, etc],
it is necessary to carefully measure both the inputs or costs and the outputs of the
education system. Figure B1 gives a visual representation of how these variables are
used in an efficiency study. Funding from different sources purchases tangible inputs
11
Universities frequently charge a uniform tuition “price” and then provide scholarships (i.e., subsidies)
based on student financial need.
20
FIGURE B1: INPUTS, OUTPUTS, AND OUTCOMES OF EDUCATION
Government
funds
Inp
uts
Teachers Classrooms
MoES leadership,
guidance,
regulations and
non-financial
support
Textbooks
and other
inputs
purchased
Donor funds
and others
Household
contributionsIn
term
ed
iate
ou
tpu
ts
Number of enrolled
children in
government schools
Other factors
influencing
attendance (e.g.
socio-economic
status, health,
etc)
Ou
tpu
ts
Number of
graduates from
government
schools
Number of
enrolled children
in private schools
Number of
graduates from
private schools
Ou
tco
me
s
Educated citizenry
participating in civil
society. Educated
workers with the right
set of skills to increase
competitiveness of the
economy
Number of graduates
from government
schools with the
desired level of
proficiency
Number of graduates
from private schools
with the desired level
of proficiency
Fin
an
cia
l in
pu
ts
Ta
ng
ible
inp
uts
21
which are then used to enroll students [an intermediate output] in order to produce an
educated child [output] who then contributes to society [outcome], including economic
competitiveness.
Perhaps the most difficult task in studying internal efficiency in primary
education is specifying the desired output or outcome12
. Table B1 lists several,
alternative output measures in order of complexity. Some of these measures must be
used with great care. Almost no one would advocate reducing unit costs of enrolling
students if that would also reduce quality. On the other hand, if we observe two regions
of the country, and one has lower unit costs and higher achievement than the other, it may
very well be more efficient. Also, if low unit costs translate into lower quality, higher
repetition, and higher dropout rates, the result may be higher unit costs of attaining
literacy or graduation. Thus what appears to be efficient, or low cost, in terms of
enrollment is actually inefficient, or high cost, in terms of output. Perhaps the most
useful efficiency indicator is the unit cost of graduates having demonstrated some
minimum competency in core subjects. Indeed, government may be able to reduce the
unit costs of this measure by increasing the unit costs of enrollment in order to raise
quality. The differences in these indicators can be large. For example, in Ugandan
primary education for 2005/06, the unit cost per student enrolled is Ush 50,534, the unit
cost per primary school graduate is Ush 923,833, and the unit cost per primary school
graduate achieving some minimum level of knowledge is Ush 4,506,500.13
TABLE B1: MEASURES OF EDUCATION EFFICIENCY
Output/Outcome Measure/Indicator
Enrollment Cost per student enrolled
Increased Enrollment Cost per additional student enrolled
Literacy Cost per student completing grade 4
Graduate Cost per primary school graduate
Achievement Cost per student achieving at least
some minimum level of knowledge
in a specified grade
Learning, or increased
achievement
Cost per additional knowledge or
achievement gained
12
In general, output refers to the concrete results and products which contribute to educational outcomes,
such as, an enrolled child, a child graduating from school, a child graduating with some defined
competency. An output is different from an outcome, the societal or ultimate goal of your education
policy): for instance: build a competitive workforce; ensure that you have the foundation of a democratic
society; empower citizens, etc. In some instances, the distinction between output and outcome is unclear.
For instance, “graduating a student with a specified level of proficiency” can be thought of as both an
output and an outcome. 13
Calculated from fiscal data and the annual MoES Statistical Education Abstract. We estimate the unit
cost of enrollment by dividing recurrent government expenditure by total enrollment in government
schools. We estimate the cost of graduating a student by dividing recurrent government by the number of
graduates (in that particular year) from government schools. Finally, we estimate unit cost of graduating a
student with a desired level of proficiency by dividing recurrent government spending by our estimate of
the number of graduates that obtain the desired level of proficiency.
22
Graduate with
Minimum Achievement
Cost per primary school graduate
demonstrating some minimum
achievement level
Table B1 suggests another unit cost distinction that is extremely important for
policy decisions, and that is the difference between average and marginal costs.
Perhaps this distinction is best seen by looking at UPE. Uganda has had exceptional
success in enrolling students in primary school, attaining a net enrollment rate [NER] of
about 92 percent by 2006. If we add up all the costs of providing primary education and
divide by the number of students, we obtain a unit cost of primary school enrollment of
UShs. 50,534. If we were to devise a program to enroll the remaining 8 percent of
students in primary school, the unit cost for those additional students is likely to be
considerably higher, say 70 thousand, because the students not in school are in especially
remote areas or have learning difficulties which require specialized teachers. In addition,
if parents don’t sufficiently value education to send their kids to school, it may be
necessary to provide incentives—e.g., free school meals or small scholarships—to
parents. In this hypothetical example, the unit cost of increased enrollment (i.e., marginal
cost of enrollment) is 70 thousand.
Table B2 below calculates the three most important unit costs in primary
education: the cost of enrolling a student, the cost of graduating a student, and the cost of
graduating a student with a desired level of proficiency in literacy and numeracy.14
In a
perfectly efficient system, there would be no drop-outs and no students repeating any
classes. Moreover, every student passing through the system would be able to show
proficiency in literacy and numeracy. Thus, in a school system with seven grades to
complete, it would cost exactly 7 times the unit cost of enrolling a student to graduate a
student with the desired level of proficiency
In Uganda’s case, the unit cost of a graduate is more than twice what is should be in a
perfectly efficient system. In particular, it costs approximately $27 to enroll a child.
Therefore, the aspirational target is that the cost of graduating a child with proficiency in
literacy and numeracy should be $189 (7 x $27). In reality (as shown below), the cost is
$2,424. Similarly, the cost of graduating a student from primary seven (ignoring the
quality aspect) is $497
14
We estimate the unit cost of enrollment by dividing recurrent government expenditure by total
enrollment in government schools. We estimate the cost of graduating a student by dividing recurrent
government by the number of graduates (in that particular year) from government schools. Finally, we
estimate unit cost of graduating a student with a desired level of proficiency by dividing recurrent
government spending by our estimate of the number of graduates that obtain the desired level of
proficiency.
23
TABLE B1: MAIN INDICATORS TO MEASURE INTERNAL EFFICIENCY OF
GOVERNMENT RECURRENT SPENDING ON PRIMARY
Estimates Based on
Government Recurrent
Expenditure
(in 2005/06 constant prices)
Intermediate
Outputs, Outputs
and Outcomes
Measure/Indicator 2000/01
(Ush)
2005/06
(Ush)
Enrollment Cost per primary student enrolled 40,470
($23)
50,534
($27)
Graduate Cost per primary school graduate 817,944
($464)
923,833
($497)
Graduate with
Minimum
Achievement
Cost per primary school graduate
demonstrating some minimum
achievement level
2,370,853
($1,345)
4,506,500
($2,424)
Source: Authors’ estimates based on Statistical Education Abstracts (various issues) and fiscal data
24
C. EXTERNAL EFFICIENCY
As noted above, indicators of external efficiency are important as guides for
Government investment in education. Governments need to make critical decisions about
how much to invest in each level of education, each type of education [e.g., different
degree programs or different modalities], and in quality and access.
How much is Uganda investing in education?
As a society, Uganda invests over seven percent of GDP in formal primary,
secondary, and tertiary education, excluding the income foregone by students. As shown
below, in aggregate this investment is funded almost equally by government and private
households. However, at the primary level government bears the larger financing burden,
and at the secondary level households bear the larger financing burden. This in part
reflects the fact that many students, in both public and primary secondary schools, pay
relatively high user fees.
TABLE C1. UGANDA EDUCATION EXPENDITURES AS SHARE OF GDP
Level/Source Government/Donor Household Total
Primary 2.27 1.32 3.59
Secondary 0.63 1.88 2.51
Tertiary 0.55 0.48 1.03
Total 3.45 3.68 7.13 . Source: Calculated on basis of UNHS 2006, Liang (2004) and UBS 2000 DHS
Is public education “free” in Uganda?
In 1996 Uganda adopted a policy of Universal Free Primary Education by
abolishing school fees. As shown in Table C2, school fees are far lower at the primary
level than other levels of education, but at 9,006 UShs, the average school fee paid in
government schools according to the latest household survey, they are far above zero15
.
However, for most students school fees are very low, with the median fee collected in a
rural public school being zero. In 2006, on average, students in public primary schools
paid 4,892 UShs in school fees, and students in the bottom income quintile [in 2002] paid
only UShs. Of course, school fees are only part of the financing burden facing families.
On average, households with students in primary school pay as much for uniforms,
transportation and school supplies as they do for school fees.
15
See Annex 1 for more details on the use of household survey data to calculate household contributions to
education spending. As shown in the annex, there are significant differences in fees paid in government
schools, especially by urban/rural location.
25
TABLE C2. AVERAGE PER PUPIL HOUSEHOLD EXPENDITURES ON
EDUCATION BY LEVEL OF EDUCATION, 2006 [UShs]
School Fees Other School
Expenditures
Total
Primary, All Schools 14,254 14,240 28,494
Government 9,006 15,930 24,936
Non-Government 259,336 167,453 426,789
Secondary, All
Schools 1/
161,432 76,512 237,944
Government 270,123 158,449 428,572
Non-Government 259,336 167,453 426,789
1/ Includes boarding schools
Source: Calculated from UNHS 2006.
Is Uganda allocating its publicly financed education expenditures appropriately
across levels of education?
Government should make its decisions about allocating spending across levels of
education by looking at estimated social rates of return, which compare the incremental
income gains of that level of education to the social costs [government plus household
costs] of providing that education. The social rates of return estimated using the 2000
household survey data are given in Table C3 and show high returns to all levels of
education, especially primary. As noted earlier, these estimated rates of return reflect
past, as opposed to future, labor market conditions. However, the results do support
arguments for continuing and perhaps increasing expenditures at the primary level.
TABLE C3. RETURNS TO EDUCATION IN UGANDA, 2000
Level Private Return Social Return
Primary 30.2 23.7
Secondary 11.5 10.5
Tertiary 24.2 13.4
Source: Appleton (2001) and Liang (2004) calculated from 2000 UNHS.
Absent from Table C3 is evidence on the returns to pre-primary education.
International evidence on early childhood development programs suggests these returns
can be very high, especially for children from disadvantaged backgrounds. Investing in
the health and school preparedness of pre-school children can reduce delayed entry to
primary school and increase the likelihood of success in school.
26
Is Uganda allocating its publicly financed education expenditures appropriately
between quantity and quality?
Uganda has achieved a high net enrollment rate at the primary level16
. However,
there is evidence that the quality of primary education is low although it has improved
since its low point after the introduction of UPE. As shown in the following figure,
Uganda is in the middle of the African countries participating in SACMEQ with respect
to both academic performance and cost-effectiveness. However, its performance is
almost identical to that of South Africa, which scored at the bottom of all countries
internationally participating in the most recent TIMSS. This suggests that the quality of
Uganda’s education, too, lies far below that of international comparators outside of
Africa.
FIGURE C2. PRIMARY SCHOOL SPENDING AS A PERCENTAGE OF PER
CAPITA GDP AND MATHEMATICS TEST SCORES ON SACMEQ
Table C4 below provides further evidence that the quality of education in Uganda
is low even by the country’s own standards. Less than half of P3 and P6 students attain
even minimum competencies in reading and math.
16
Estimates of the primary level NER vary depending on the data source. Using EMIS data, the MoES
calculates an NER in 2006 of 91.7 as reported in the 2006 ESSAPR, but using household survey data, the
NER is estimated at 84.
Primary school spending as percentage of per
capita GDP and math test scores
Kenya
SeychellesMauritius
Botsw ana
Sw azilandUganda
LesothoNamibia
S.AfricaMalaw i
Zambia
0
5
10
15
20
25
30
400 450 500 550 600
scores in math
pri
mary
sch
oo
l sp
en
din
g
as %
of
per
cap
ita G
DP
27
TABLE C4. PERCENTAGES OF PUPILS ATTAINING MINIMUM
COMPENTENCIES IN ENGLISH LITERACY AND NUMERACY
Subject/Grade 1999 2003
Literacy P3 18.2 34.3
Literacy P6 13.2 20.0
Numeracy P3 38.6 42.9
Numeracy P6 41.5 20.5
Source: NAPE
Figure C1 and Table C4, along with other data, provide evidence that the quality
of primary education in Uganda is low. In addition, country cross-sectional evidence
demonstrates that [a] there is a large positive relationship between quality improvements
and economic growth and [b] there is almost no relationship between increases in access
to education and economic growth.
In Figure C3 below, the economic growth rates of countries are plotted against
their scores on international assessments, controlling for other factors which may affect
economic growth. As can be seen, the slope of the line fitted to these observations is
positive and steep, indicating that, controlling for access, higher test scores contribute
significantly to economic growth.
In Figure C4 below, the economic growth rates of countries are plotted against
their average years of education of the populace, again controlling for other factors that
affect growth. The slope of the resulting line fitted to the observations is almost flat, and
the estimated slope of the line is statistically insignificant, indicating that, controlling for
test scores, higher years of educational attainment do not contribute significantly to
economic growth.17
17
Hanushek and Woessman (2007).
28
FIGURE C3. ECONOMIC GROWTH AND ACHIEVEMENT TEST SCORES.
FIGURE C4. ECONOMIC GROWTH AND YEARS OF EDUCATION.
29
What determines the quality of education in Uganda?
There have been several studies of the causes of low student achievement in
Uganda.18
Table C5 presents the results of one such study carried out by the Education
Standards Agency [ESA]. The challenge is not knowing what to do but, rather, how to
do it in a context of poorly trained teachers and budget contraints.
TABLE C5. SCHOOL FACTORS AFFECTING TEACHING AND LEARNING:
QUALITATIVE ASSESSMENT, 2004
Variable Assessment
Teacher Qualifications Lack of qualified teachers, especially in rural schools
Teaching Methods Inadequate lesson preparation
Class Size Overly large classes constrain teachers’ time for class
supervision and marking
Learning Materials Lack of basic materials, especially materials written in
indigenous languages
Teacher Absenteeism High absenteeism attributed to low commitment, poor
school management, lack of accommodation, and low
salaries
Language of Instruction Use of mother tongue constrains supply of teachers Source: Education Standards Agency, Report on Monitoring Learning Achievement in Lower Primary,
2004
There is, in addition, a large international literature on the characteristics of effective
schools, which helps provide a checklist that can be used to self-assess how to improve
instructional quality. A study of Ugandan schools produced the characteristics listed in
Table C6. This list highlights several variables that are analyzed in some detail in this
study: teachers and headmasters are supervised regularly; textbooks are available and
used; students [and teachers] attend school regularly; there is an emphasis on preparing
children to read in the early grades; communities and parents are actively involved in the
school and in their childrens’ education
18
See, for example, Nannyonjo (2006) and Hicks (2005).
30
TABLE C6. CHARACTERISTICS OF EFFECTIVE SCHOOLS AND EVIDENCE
FROM RWENZORI DISTRICT
Characteristics of Effective Schools Evidence from Rwenzori District
Head Teacher monitors and supervises
teachers’ lesson plans and teaching
Head Teacher monitoring and supervision
notably better in high-performing schools
Teachers prepare for teaching through
lesson plans & varied teaching methods
Little variety in teaching methods across
schools, with most teachers prepared but
seldom enriching lessons or encouraging
student-centred work. Very little
difference between trained and untrained
teachers in preparation, variety of teaching
methods, and use of books.
Pupils attend regularly and participate in
class work and homework
Homework is seldom required and
feedback to students is seldom given.
Schools with more regular student
attendance perform better.
Teachers use instructional materials,
especially textbooks
All schools have some textbooks but there
is little evidence of their being used and
students are not permitted to carry books
home. Half the schools have learning aids,
but they are not often on display.
Teachers frequently assess student work
and provide meaningful feedback and
remedial work
Schools rating “high” on pupil assessment
had high PLE results, but most schools
show little evidence of written feedback to
pupils.
Reading and writing are explicitly taught in
the early grades, including use of reading
cards
Reading and writing are not emphasized in
the schools studied. Textbooks are rarely
used, and students infrequently read in
class. P3 students have extremely poor
reading skills in general.
The school and Head Teacher are
externally supervised at least three times
per term
External supervision is not found to be
related to high PLE results because
supervision is not focused on teaching and
learning.
The community is involved in providing
financial and in-kind support to the schools
and parental support to children
A community’s financial and material
support of schools is associated with high
PLE results. Community participation in
school governance is generally very weak.
Source: DCI (2004)
31
As noted above, one option for improving the quality of education in Uganda is to
introduce stronger teacher incentives. Box C1 relates Chile’s program to reward teachers
on the basis of student test performance. Box C2 presents Guinea’s program of school
grants to promote quality through teacher training. Box C3 presents some of the
evidence on the impacts of teacher incentives on quality.
BOX C1: TEACHER PERFORMANCE INCENTIVES IN CHILE
Established in 1994, Chile’s National System of Performance Assessment (SNED) awards
teacher incentive grants to schools based on an index of school excellence measures.
Objectives of School Grant. The SNED creates competition among schools to encourage
teachers to improve their performance.
Design Features. Chile’s National System of Performance Assessment (SNED) program
mandates that schools spend grants in the form of teacher incentive awards and teacher bonuses.
The teacher incentive grants are conditional in that awarded school directors must use 90 percent
of the grant for teacher bonuses based on hours worked. The school director is to allocate the
residual 10 percent to “outstanding” teachers at his/her discretion to avoid the “free-rider”
problem. Another design feature of the SNED program is that the teacher incentive grants are
distributed through a competitive process. Schools are stratified within regions by socioeconomic
status and other external factors that affect school performance. This ensures that the process is
competitive among comparable establishments. Every two years, schools are ranked according to
an index of school performance measures using the national System for Measuring Educational
Quality (SIMCE) test as the basic criterion. Schools can win the teacher incentive grants
repeatedly.
Source: The Authors.
BOX C2.: SCHOOL GRANTS FOR TEACHERS IN GUINEA
Since 1994, Guinea has been implementing a unique and promising World Bank funded program
that integrates school improvement with professional development for teachers known as the
Small Grants Staff Development and School Improvement Program (PPSE). PPSE is a
conditional school grant program that engages primary school teachers to participate in the
process of education quality improvement through competitive small grants of approximately
$1000 that are awarded to school-based teams of teachers.
Objectives of School Grant. The overall objective of Guinea PPSE is to improve the quality and
relevance of specific school inputs, which in this case are teachers. As a means of improving the
quality of primary education, PPSE provides organizational support and the incentives necessary
for teachers to assume primary responsibility for their own professional development and to
determine what is most appropriate in their local context for improving teaching practices.
Furthermore, this program seeks to give teachers greater professional autonomy to analyze
teaching and learning problems at the classroom level, define the problems or issues to be
addressed in a 1-year project, propose and implement solutions, then evaluate and report results.
Design Features. Diverging from the traditional top-down approach of in-service teacher
training where central education authorities mandate workshop contents for large groups of
teachers, PPSE allows teams of teachers to design professional development programs unique to
their local context and compete for grant funds to implement their own programs. Teachers learn
about PPSE grant competition through a series of workshops led typically by a pedagogical
advisor or a regular school teacher, who presents the program’s operational manual and proposal-
32
writing guidelines. Interested teams of teacher then go through a two-cycle, highly structured
competition. First, teacher teams determine the contents of their projects, prepare their own
budget, and then submit preliminary proposals for their own professional development program
to a prefectural jury, which is presided over by the prefectural director of education (DPE) and
composed of retired teachers and local education leaders. Second, once promising proposals are
selected, pre-selected teacher teams are invited to revise their proposals with help from the
facilitators based on critical comments received from a prefectural jury, and then submit their
final proposals to a regional jury who makes final decisions of which team will receive grants.
The regional jury is presided over by the Regional Inspector of Education (IRE) and composed of
local educational leaders. Selected teacher teams are granted full funding, provided with project
implementation support from the project facilitator, and visited by an evaluator, who is typically a
prefectural or regional jury member, three times throughout the 1-year project cycle. In addition,
since PPSE also has performance incentives as one of its design features, teacher teams are
given the option of renewing their grant if they show that their projects attained good results.
School grant schemes can also offer incentive based on performance.
Source: The Authors.
BOX C3. INTERNATIONAL EXPERIENCE WITH TEACHER INCENTIVES.
Teacher incentives can be broadly defined to include instruments that affect: (a) who
becomes a teacher, (b) how long they stay in the profession and (c) what they do in class.
This broad definition of incentives encompasses “general incentives” such as salaries and
benefits, as well as “targeted” incentives such as bonuses given to teachers for their
performance or for undertaking special activities (e.g. teaching in remote schools).
Incentives can be monetary and non-monetary (e.g. status or career stability).
International experience provides fairly robust evidence that general incentives do have
an impact on teaching quality and supply. The level and profile of teacher salaries, both
in absolute terms and relative to the salary of comparable workers, matter. Chile’s more-
than-doubling of average teacher salaries in the past decade is associated with an increase
in the quality of students entering teacher education programs. Similarly, the increased
and more equitable distribution of resources resulting from FUNDEF (Fund for
Maintenance and Development of the Fundamental Education and Valorization of
Teaching) in Brazil led to improvements in student outcomes. In Latin America, low
teacher salaries and a flat wage profile are major factors contributing to the poor
preparedness of teachers. Individuals that choose to become teachers often are not strong
students, are not interested in teaching as a career and do not have the appropriate
characteristics to succeed as teachers.
In theory, targeted incentives can be argued to be a superior policy tool to improve
teaching quality than across-the-board salary increases on the basis of both fiscal and
efficiency considerations. However, there has been very little experience with applying
performance-based incentives. Targeted incentive reforms, such as merit pay, are
relatively rare and existing plans are often small-scale and short-lived. Various teacher-
and school-targeted incentive programs were implemented in the United States. The
evidence on these programs’ effects is inconclusive.
33
Other countries like Chile and Mexico implemented national performance-based teacher
incentive systems. A review of these and other Latin American countries’ experiences
with targeted teacher incentives found that although teachers generally respond to
incentives, they do not always do so in the expected way. Design flaws in performance-
based incentive reforms were likely behind their lack of uniform success. In addition,
many of the gains in student outcomes attributed to targeted incentive reforms have been
small or short-lived. Cambodia has a small program recently introduced to recognize
best teachers. Three teachers in each province receive a one-time award ranging from
R80,000 to R120,000 (USD20-30).
Targeted incentive programs rewarding teacher for undertaking special activities, such as
working in difficult areas are far more common than performance-based incentives.
Beyond financial incentives, several governments have introduced school-based
management reforms giving local communities greater authority over schools, in the
hopes of increasing teacher accountability and, as a result, student achievement. The
general principle is that engaging communities in school matters makes teachers more
accountable for what they do in class and also makes their work more appreciated, thus
creating an incentive for teachers to work harder and better. A review of the evidence on
school-based management reforms in Central America concludes that while the reforms
have improved class size, teacher absenteeism, increased working hours and homework
assigned, they did not have an effect on teaching practices.
References: McEwan and Santibañez (2004), Vegas (2005), and Villegas-Reimers
(1998).
Source: World Bank (2007c)
34
D. INTERNAL EFFICIENCY OF PRIMARY EDUCATION
In 1996 Uganda made a strong political commitment to UPE, and the Education
Strategic Investment Plan [ESIP] of 1998-2003 provided the roadmap for meeting that
goal. Uganda’s ambitious education reform as enshrined in ESIP went well beyond
simply expanding coverage and included curriculum reform, increased provision of
learning materials, use of local languages at the lower primary level, reduced
procurement costs for textbooks and instructional materials, increased use of in-service
training to enhance teacher qualifications, and creation of an Education Standards
Agency [ESA] to improve the system of school inspection. Not all elements of the
reform have been uniformly and fully implemented to date, although there have been
impressive achievements. The implementation of ESIP and the accompanying SWAP
arrangements have been evaluated elsewhere and are not the focus of this study.19
Uganda’s UPE policy was implemented rapidly, leading at least in the short run to
larger class sizes, higher percentages of unqualified teachers, and fewer school supplies
and materials to students. While the Government has attempted to systematically address
each of these resource problems, there is still a gap between desired goals and reality on
the ground. Policies and programs that are implemented rapidly often lead to waste, so it
will not be surprising if this analysis finds inefficiency in how resources are used to
deliver primary education.
As noted earlier, an assessment of internal efficiency has several elements,
beginning with the identification of possible leakages of resources between the
government and the school, leakages within the school itself, and proceeding to analysis
of how resources that reach the school may not be productively used.
How much leakage is there between the release of funding by the central
government and the receipt of resources by the primary school?
Based on expenditure and personnel audits and evaluations, the estimated leakage
of recurrent expenditures between the Ministry of Finance and the schools is Shs. 20 bn ,
or 6 percent of total budgeted recurrent primary education expenditures.20
As
summarized in Table D1, this includes UPE leakages, ghost teachers and MoES
administrative waste [questionable expenditures] identified in agency audits. It does not
include ghost non-teaching personnel, nor does it include administrative waste at the
district level, as there are no available estimates for these items.
Ghost teachers are those who appear inappropriately—for whatever reason—on
the payroll. Weak payroll information systems and fraud are two possible reasons for the
appearance of ghosts, who are normally identified during “payroll clean-up exercises” in
19
For example, see Ward, Penny, and Read (2006). 20
See “Annual Budget Performance Report, 2005/06, MOFPED, p. 55), the latest Public Expenditure
Tracking Survey (“UPE Capitation Grant Tracking Study, FY 2005/06, USAID), analysis of Accountant
General’s detailed budget for the MoES, the Auditor General’s audit of the MoES, and the school survey
undertaken as part of this report.
35
which every teacher on the payroll is verified. A 1993 payroll exercise revealed that 20
percent of primary teachers were ghosts. A 2003 audit undertaken by MoPS found 9
percent of teachers to be ghosts. A conservative estimate of 4 percent is used here.
Hence, the loss is 4 percent of the teacher wage bill.
TABLE D1. EXPLAINING THE LOSS OF CENTRAL GOVERNMENT
RESOURCES FROM THE CENTER TO THE PRIMARY SCHOOL
Percent
Loss
Basis Amount
(Shs.
Bn.)
Source for Loss
Estimate
Expenditure
Reaching the
School
314
UPE Leakage 16% UPE Grants 5 PETS survey carried
out in 2006.
Ghost Teachers 4% Wage Bill 11 MoPS (2003)21
Central
Government
Expenditure
334 Government estimates
of budget releases in
2006.
The single largest source of government to school leakage is the UPE grant. The
most recent estimate of the percent of leakage is 16 percent.22
The leakage is 16 percent
of total UPE grants, or Shs. 5 bn. Leakage has decreased over time as measured by
several Public Expenditure Tracking Surveys [PETS]. In addition to the leakage, there is
a two month delay from the time UPE grants are released by the Central Government to
the time they arrive at schools; this, too, has decreased from the 5 month delay measured
in 2001. The cost of this delay is not included in the leakage or waste reported in Table
D1.
As shown in Table D2, several grant management problems were identified and
these include: [1] delays and uncertainty in funding, which make it difficult to plan and
spend efficiently; [2] inadequate supervision of construction projects; [3] delays in
receiving and damage to textbooks; and [4] failure to use textbooks by teachers and
students in the classroom. In terms of financial loss these problems are of relatively
minor importance. However, the impact of these losses on student learning may be of
considerable importance. Given the very low ratio of books to students [about 1:3] in
Uganda, loss and failure to use textbooks may have a considerable impact on student
learning.
TABLE D2. GRANT MANAGEMENT PROBLEMS IDENTIFIED BY HEAD
TEACHERS
Type of Fund Management Problems
21
This is a conservative estimate. In its efficiency study done for the MoPS, Price Waterhouse Coopers
cites earlier studies estimating between 9.2 and 20 percent of teachers may be irregularly on the payroll. 22
USAID (2006).
36
UPE Delayed and irregular release of funds
SFG Contractors delay implementation of construction
School heads not involved in management [turn-key projects]
IMG Delayed supplies of textbooks
Damaged materials due to storage
Choice of materials do not match user’s interests
Wages Not received and paid promptly into teachers’ bank accounts Source: Northern Uganda PETS (2006), p. 38.
Unfortunately, there are no clear benchmarks as to what level of leakage from
government to school should be expected in a well-managed education system. Even
though Uganda’s leakage is only 6 percent of primary education recurrent expenditures,
this is equal to twice the total public expenditures on primary school instructional
materials in Uganda. In any case, the MoES should undertake aggressive actions to
reduce leakage and invest in monitoring and oversight up to that point where the marginal
returns equal the marginal costs of monitoring.
Issue: The MoES should regularly monitor leakage of all types and set targets
and develop strategies for reducing wastage. This may require strengthening the
internal audit unit of the ministry..
How much leakage is there in the school itself?
According to the calculations in Table D1, about Shs. 314 bn in resources reach
primary schools. Now, the question is how much of these resources actually reach the
pupil. The most obvious leakage at the school level itself is absenteeism. When
headmasters and teachers have unexcused absences, pupils fail to receive their services.
And there is a serious problem of headmaster and teacher absenteeism in Uganda.
A study of teacher absenteeism carried out in 2004 found an average rate of
teacher absenteeism of 27 percent in Uganda23
. As shown in Table D3, this was
considerably higher than most other countries which carried out similar surveys at the
same time. Given the high estimate of teacher absenteeism, this study carried conducted
unannounced school visits to 160 government and non-government schools in November
2006. The schools were randomly selected across three regions (Western, Eastern, and
Central) and six districts.24
The 2006 and 2004 surveys are identical methodologically,
making the results comparable.
TABLE D3. TEACHER ABSENTEEISM RATES, 2002-03
Country Absence rate (%)
Bangladesh 15
23
Estimates of teacher absenteeism from unannounced visits are significantly higher than absenteeism
recorded in official records in most countries. 24
Habyarimana (2007); see Annex 2.
37
Ecuador 14
India 25
Indonesia 19
Peru 11
Papua New Guinea 15
Uganda 27
Zambia 17
Sources: Chaudhury, Hammer, Kremer, Muralidharan, and Rogers 2004 for most countries;
NRI and World Bank 2003 for Papua New Guinea; Habyarimana, Das, Dercon, and Krishnan 2003 for Zambia
Note: Absent staff are fulltime teachers on current shift who were not found anywhere in the
school at the time of an unannounced visit.
As shown in Figure D1, the 2006 survey finds teacher absenteeism has improved
somewhat [possibly due to MoES initiatives] with 19 percent of teachers having
unexcused absences from the school25
. This translates into a loss of Ush 60 bn. of
teacher’s time, almost double the government’s financial annual contribution to Makerere
University. Absenteeism varies by teacher rank with 27 percent of head teachers, 16
percent of senior teachers, and 14 percent of junior teachers being absent.
The magnitude of leakage due to headmaster and teacher absenteeism present a
powerful case for investing in actions to reduce absenteeism. Analysis of variations in
teacher absenteeism across schools finds that strong parental involvement in the school,
competition from a nearby non-government school, and the presence of useable teacher
housing are just some of the factors that contribute to lower absenteeism rates26
. While
statistical analysis suggests policies that may deter absenteeism, Annex A demonstrates
that with the exception of the India experiment described in Box D1, teacher incentive
experiments have not been successful in reducing absenteeism. .
BOX D1. MONITORING TEACHER ABSENTEEISM IN INDIA
Duflo and Hanna (2006) evaluate a randomized intervention in community schools in
which an NGO provides cameras to teachers and institutes attendance-dependent
remuneration. Teachers are expected to take pictures every morning, and teachers will be
paid depending on the number of “full” days attended. The results of this intervention
were surprisingly large. Teacher absence fell by about half from a high of 36% in
comparison schools to 18% in program schools. The authors discuss the political-
economy of this intervention and conclude that it is not a realistic option for national
scale up.
Source: Duflo and Hanna (2006).
There is strong evidence that teacher absenteeism can be reduced by monitoring.
While in most countries, teachers are required to sign an attendance book, this data is
rarely collected and analyzed or used to monitor average attendance for each teacher.
25
This is considerably smaller than the 27% absenteeism rate estimated in the 2002/03 study. 26
See Annex A.
38
Private schools report higher teacher attendance, even when they pay the same or less
than government schools, partly because they have managers at each school who monitor
attendance. In the Gambia, teachers in church schools receive the same pay as those in
government schools, but they report much higher teacher attendance because the church
is responsible for distributing the pay, and can withhold payment to teachers who have
poor attendance. Also in the Gambia, teacher attendance is reported to have increased
since the introduction of a system of cluster monitors, which ensures that each school is
visited regularly.
Some writers have suggested that absenteeism could be improved by community
monitoring. Clearly parents have a strong motivation to ensure that teachers attend, and
their willingness to pay for low cost private schools is often explained by the belief that
teacher attendance is higher there. To date it is difficult to find examples of successful
mechanisms to build on this motivation.
Some teacher absenteeism is caused by the need to travel to collect pay. In
Liberia and Zambia, rural schools can close for up to a week each month as teachers
leave to collect their pay. In Lesotho and the Gambia the government has begun to pay
teachers by electronic transfer to avoid this problem. However this sometimes makes it
more difficult to withhold pay from teachers who have absconded, or moved to another
school without permission. Where the banking system is not sufficiently developed to
allow bank transfer, some countries like Malawi have arranged for district officials to
travel to each school to deliver pay. This is expensive and time consuming, but provides
an opportunity for district officials to check attendance in each school on a monthly basis.
FIGURE D1. WHERE TEACHERS ARE AT TIME OF ENUMERATOR’S VISIT.
In class, teaching,
18.2%
Out of class, break,
17.6%
Out of class, in
school, 34.2%
Can't find teacher,
19.2%
Administrative
work, 8.1%
With surveyor,
0.2%In class, not
teacher, 2.4%
39
As shown in Table D4, the leakage of central government resources due to
headmaster and teacher absenteeism reduce the expenditure reaching students to Shs. 254
bn, or 76 percent of government recurrent expenditures.
40
TABLE D4. EXPLAINING THE LOSS OF CENTRAL GOVERNMENT
RESOURCES IN THE SCHOOL
Percent
Loss
Amount
(bn. Ushs)
Basis for
Estimate
Expenditure
Reaching the
School
314
Loss Due to
Headmaster and
Teacher
Absenteeism
19% 53 Teacher
unexcused
absences from
2006 school
survey.
Expenditure
Reaching the
Classroom
261
Note: The loss due to absenteeism is 19% of the Ush 276 bn wage bill.
The picture becomes still more complicated if one considers student absenteeism.
Student absenteeism is rarely measured accurately by administrative records. Even
asking teachers to recall student absenteeism tends to understate the magnitude of the
problem.27
Only direct observation yields an accurate answer.
TABLE D5. STUDENT ABSENTEEISM PATTERNS
IN KABULASOKE AND NAKASEKE, 2004
DISTRICT P.3 P.6
Kabulasoke Boys 36.4% 31.5%
Kabulasoke Girls 32.1% 29.5%
Nakaseke Boys 25.1% 20.9%
Nakaseke Girls 25.7% 15.7% Source: Education Standards Agency, Report on Monitoring Learning
Achievement in Lower Primary, 2004
Table D5 shows the results of direct observation in two districts. Student
absenteeism rates are between 16 and 36 percent. Other direct observation studies have
also found high absenteeism rates.28
The causes of absenteeism are multiple. One study
found the principal reasons to be illness, household work, and the low value put by
parents on education.29
27
This was the procedure used in the 2000 SACMEQ study, for example. 28
See the Musisi (2006) study, which found pupil absent on average 23 days per year. 29
Musisi (2006).
41
Using statistics reported in observational studies, one can assume an average
primary school student absenteeism rate of about 20 percent. This means that only 80
percent of the resources that reach the classroom in turn reach the student. In the case of
Uganda, Shs. 52 bn are lost as the result of student absenteeism30
. If one adds the losses
associated with student absenteeism to the leakages given in Tables D1 and D4, total
leakage between the central government and the pupil is on the order of Shs. 125 bn, or
37 percent of the government primary education expenditure..31
The MoES should [a] closely monitor leakages due to absenteeism and [b] adopt
measures to attempt to reduce absenteeism. Improved school inspection, community and
PTA monitoring, higher penalties for unexcused absenteeism, and bonuses for high
attendance rates are all measures that could reduce teacher absenteeism. Student
absenteeism could be reduced through stronger incentives to schools to ensure students
attend class by, for example, tying UPE grants to average daily attendance, and stronger
incentives to parents to send their children to school by, for example, providing free
school lunches or cash transfers to households that regularly send children to school.32
The capacity to monitor teacher absenteeism and other leakages is dependent on
better school inspection. Like Uganda, most countries have external supervisory unit,
expected to visit schools to monitor and enhance quality. While these personnel were
traditionally called inspectors, this role has now a wide variety of titles. These
inspection/supervision staff are often based either at district offices or at a central
headquarters. Frequency of inspection visits is usually severely curtailed by transport
and logistical difficulties, and most schools are visited less frequently than once a year.
Where transport is a major problem, the most isolated schools tend to be visited least
frequently.
A few countries have managed to have more frequent supervision by having a
highly decentralized inspection staff. In the Gambia a “cluster monitors” is allocated to
every ten schools, and is expected to live a one of the schools, and travel by motorbike to
visit each school every 2 weeks. There are indications that teacher absenteeism has been
reduced following the introduction of these frequent external visits. In Eritrea, there is a
cluster leader for every 80 teachers, with the expectation that each teacher can be
observed twice a year.
.
30
If as a result of student absenteeism teachers spend larger amounts of time with the remaining students in
the classroom, the Shs. 52 bn ia an overestimate of the true loss. But most evaluations of teaching in
Uganda suggest that having marginally fewer students in the classroom does not affect traditional
pedagogical practices. 31
These estimates do not include books which are sent to the school but are not used by the teacher or other
losses of non-personnel inputs. 32
This is the principal behind Mexico’s Progresa and Brazil’s Bolsa Familia, both of which give small cash
transfers to families that send their children to school. See Box D1.
42
BOX D2. MEXICO: FINANCIAL INCENTIVES TO ATTEND SCHOOL
43
How do high repetition and dropout rates affect efficiency in the production of
primary school graduates?
If the goal is a primary school graduate, students who fail to graduate can be
thought of as having “wasted” the resources expended on them33
. Since the unit cost of
an enrolled student is Ush. 50,534, a student who successfully went through primary
school with no repetition would cost Ush. 353,738. In reality, one primary school
graduate, including repetition and dropout, costs about Ush. 923,833, a difference of Ush.
570,095. In other words, the actual unit cost of a graduate is 2.6 times what would be the
case if there were no repetition or dropout. The large “savings” from reducing repetition
and dropout argue for aggressive efforts on the part of the MoES to improve the quality
of instruction and to enforce automatic promotion.
One of the principal determinants of repetition and dropout is the age at which a
child enters primary school. Students who enter late for their age face low probabilities
of academic success. Unfortunately, Table D6 shows that 62 percent of students entering
P1 are older than age 7, the normal entrance age. Programs to encourage on-time, first-
time enrollment of children could thus have a large impact on repetition and dropout
rates.
TABLE D6. DISTRIBUTION OF HOUSEHOLD MEMBERS
BY CLASS AND AGE
Class 5 6 7 8 9 10 11 12 13-24 Total
P1 4.7 12.9 20.1 23.1 14.2 12.8 4.4 4.0 3.8 100
P2 1.0 3.5 8.8 17.6 17.2 20.9 9.1 10.9 11.0 100
P7 0.9 0.8 5.7 92.5 100 Source: UBOS, 2004 National Service Delivery Survey, p. 15
Is government funding allocated efficiently across districts?
Government primary education spending per pupil often varies across districts or
schools due to differences in cost structure [especially, due to higher costs of delivering
schooling in remote, sparsely populated regions] and differences in poverty levels [with
education spending being higher to compensate for the effects of poverty]. Since the
Ugandan EMIS does not capture all spending at the school level and since salaries
represent approximately ninety percent of all central government funding of primary
education, this analysis uses the personnel wage bill as a proxy for total government
funding. Nationally, the mean wage bill per student is 39,259 UShs, but there is
considerable variation across districts and schools. For example, the top spending
quintile of schools has a mean wage bill per student of 50,526 UShs, almost double the
33
Alternatively, one could define the goal as completion of 4th
grade, which often corresponds to literacy,
or some other measure.
44
figure [26,585 UShs] for the bottom quintile of schools in the country34
. It’s appropriate
to ask whether this variation in wage bill per student is the result of explicit MoES
policy—perhaps to compensate for high costs in sparsely populated, rural areas—or is the
result of policies and practices having nothing to do with the effective delivery of
education to children.
FIGURE D2. GOVERNMENT WAGE BILL PER STUDENT ACROSS
DISTRICTS
05
10
15
20
25
Num
ber
of
dis
tric
ts
mean-1 s.d. +1 s.d.-2 s.d. +2 s.d. +3 s.d. +4 s.d.
20000 30000 40000 50000 60000 70000Government wage bill per student
Source: EMIS (downloaded Oct 2006) and World Bank calculations
Government wage bill per student by district
Variance in the wage bill per student across schools can be disaggregated into the
variance that lies between districts and the variance that lies between schools within
districts. Figure D2 illustrates the distribution of the wage bill per student across
districts. Since the Central Government’s Ministry of Public Service [MoPS] directly
deploys teachers in the country, this distribution is the direct result of its actions and
subsequent teacher transfers.
Most education systems allocate teachers to schools based on the number of
students enrolled in a school. If that were the case in Uganda, the distribution in Figure
D2 would be much more tightly distributed around the mean. So what could explain the
wide variation in wage bill per student.across districts? Perhaps teachers are allocated
according to need as measured by poverty levels of districts. To investigate this
possibility, the district wage bill per student is plotted against district poverty rates in
Figure D3.
34
Calculations based on the 2004-05 EMIS.
45
FIGURE D3. WAGE BILL PER STUDENT AND
DISTRICT LEVEL POVERTY
0
10,000
20,000
30,000
40,000
50,000
60,000
70,000
80,000
0 20 40 60 80 100
(percent of population living in poverty)
(go
v't
wa
ge
bil
l p
er e
nro
lled
ch
ild
)
As is readily seen, the wage bill per student across districts in Uganda is in fact
inversely, not directly, related to need. Furthermore, regressing the wage bill per student
against district poverty rates and the percent of district population that is rural [another
common determinant of expenditures internationally] yields the finding that the percent
rural population is not related to the wage bill, while the poverty rate is strongly
negatively related to the wage bill. In short, primary education expenditures [as proxied
by the teacher wage bill] in Uganda appear not to be distributed according to the types of
variables often found in other countries.35
35
For example, in Chile a local government’s education expenditures are largely based on a capitation
grant from the central government’s education ministry. Children from poor households or children in
rural schools are weighted more heavily than other students such that average expenditures per pupil are
somewhat higher in local governments having high percentages of children from poor households and high
percentages of rural population.
46
FIGURE D4. MEAN CLASS SIZE BY DISTRICT, 2006.
Mean class size by district in public and non-public
schools
0
20
40
60
80
100
Tororo Mayuge Luweero Mukono Kibaale Ntungamonum
ber
of
stu
dents
per
cla
ss
public non-public
Figure D4 also looks at the distribution of central government funding, this time
proxied by average class size, across the six districts used as the sample for the unit cost
survey. Here, too, there is a large difference in average class size in public schools—but
not private schools--across districts. Again, it is not clear what the rationale is behind the
allocation algorithm that determines this distribution.
Of course, teacher deployment may be done on the basis of sound criteria not
immediately evident to the analyst. If so, one would expect a positive relationship
between the wage bill per student and measures of educational outcomes. However, as
shown in Figure D5, there is also no obvious relationship between the wage bill and a
crude measure of educational outcome [the ratio of P5 to P1 students]. For example, for
the expenditure level Shs. 40,000 on the horizontal axis, one finds the measure of
educational outcome to vary between 0.10 to more than 0.90. If government policy were
to increase funding for districts of low educational outcome [a measure of need], one
would expect to find higher expenditures for low outcomes and lower expenditures for
high outcomes.
47
FIGURE D5: WAGE BILL PER STUDENT AND EDUCATIONAL OUTCOMES
ACROSS DISTRICTS IN UGANDA.
0.00
0.10
0.20
0.30
0.40
0.50
0.60
0.70
0.80
0.90
1.00
0 20,000 40,000 60,000 80,000
(gov't wage bill per enrolled child)
(ra
tio
of
P5
stu
den
ts t
o P
1 s
tud
en
ts)
Is government funding allocated efficiently across schools within districts?
While the central government [MoPS] deploys teachers—and thus the wage bill—
across districts, it is the district governments [district teacher service commissions] which
deploy teachers within their districts, presumably following national guidelines.
Figures D6 and D7 show the distribution of school average government wage bills
per student [i.e., excluding community funded teachers] in two districts. Both districts
illustrate very large differences in per pupil allocations, which are unrelated to any stated
objective allocation criteria.
48
FIGURE D6. GOVERNMENT WAGE BILL PER STUDENT ACROSS
SCHOOLS WITHIN LUWERO
DISTRICT0
10
20
30
40
50
60
Nu
mb
er o
f sc
ho
ols
mean-1 s.d. +1 s.d.-2 s.d. +2 s.d. +3 s.d. +4 s.d.
0 20000 40000 60000 80000Government wage bill per student
Source: EMIS (downloaded Oct 2006) and World Bank calculations
Luwero district
Government wage bill per student within a particular district
FIGURE D7. GOVERNMENT WAGE BILL PER STUDENT ACROSS
SCHOOLS WITHIN NTUNGAMO
DISTRICT
01
02
03
04
05
06
0
Nu
mb
er o
f sc
ho
ols
mean-1 s.d. +1 s.d.-2 s.d. +2 s.d. +3 s.d. +4 s.d.
0 20000 40000 60000 80000Government wage bill per student
Source: EMIS (downloaded Oct 2006) and World Bank calculations
Ntungamo district
Government wage bill per student within a particular district
49
As with the central government’s allocation of wage expenditures across districts,
it may be that there is a direct relationship between that allocation and unobserved
measures of need. If so, one would expect a positive relationship between the allocation
of the wage bill and measures of educational outcomes. Figure D8 shows just such a
scatter diagram. Once again there is no obvious relationship.
FIGURE D8. GOVERNMENT WAGE BILL PER STUDENT AND A CRUDE
MEASURE OF SCHOOL OUTCOMES.
0
.2.4
.6.8
1
Rat
io o
f st
ud
ents
en
roll
ed i
n P
7 t
o s
tud
ents
en
roll
ed i
n P
1
5000 10000 15000 20000 25000Gov't wage bill per student
In conclusion, there is no obvious relationship between the distribution of
education expenditures [i.e., the wage bill] across districts or across schools and the usual
measures of need. In addition, there is no relationship between the distribution of
education expenditures and crude measures of education outcomes—in Figure D8 there is
no discernible pattern to the data. The distribution of teachers appears to be unrelated to
educational criteria and is thus inefficient.
Are teachers used productively within schools?
FIGURE D9. MEAN CLASS SIZE BY GRADE
IN PUBLIC AND NON-PUBLIC PRIMARY SCHOOLS
50
Mean class size by grade in public and non-public
schools
0
10
20
30
40
50
60
70
80
90
100
grade 1 grade 2 grade 3 grade 4 grade 5 grade 6 grade 7
num
ber
of stu
dents
per
cla
ss
public non-public
A principal determinant of unit cost is class size, and we find higher class sizes in
the lower than the upper grades for public schools. For private schools the differences
across grades are relatively minor. In other words, children in the lower grades receive
fewer resources than those in the higher grades in public schools36
. Most educators
would argue for the reverse: the lower grades should have smaller class sizes, and higher
unit costs, than the higher grades within primary schools. Large class sizes in the early
grades are more likely to contribute to higher repetition and dropout, especially among
children from lower income homes.
FIGURE D10. RATIO OF ENROLLMENT
IN P7 TO P1 AND STUDENT TEACHER RATIO FOR GRADES 1-3
Ratio of enrollment in P7 to enrollment in P1
and student /teacher ratio for grades 1 to 3
y = -0.0056x + 0.5508
R2 = 0.2321
0.0
0.2
0.4
0.6
0.8
1.0
0 20 40 60 80 100
student/teacher ratio for grades 1 to 3
rati
o P
7/P
1 e
nro
llm
en
t
36
While official education policy as stated in the ESSP is for smaller class sizes in P1 and P2, headteachers
do not allocate teachers across grades this way.
51
As shown in Figure D10, there is a weak correlation between large class sizes in
P1-P3 and the survival rate to P7. According to the regression line, having a student
teacher ratio of 40 instead of 60 would result in a survival rate that was 0.11 points higher
according to this simple regression line. However, the large dispersion around the line
indicates that having a small student-teacher ratio is not a guarantee of high performance.
Some schools with low student-teacher ratios perform poorly but almost no schools with
high student-teacher ratios perform well.
The productivity of primary school teachers appears to be low in general.
Average class sizes are considerably larger than student-teacher ratios. The discrepancy
between class size and student-teacher ratio suggests that teachers have relatively light
workloads. Figure D1 is consistent with this conclusion, showing that only 21 percent of
teachers are physically in the classroom at the time of an unannounced survey.37
The use
of teacher time both within the school and within the classroom needs further
investigation, but the preliminary evidence suggests that teachers are underemployed in
public primary schools.
Other than personnel, are expenditures allocated efficiently across inputs?
Research shows that having an adequate number of textbooks can be very
productive, especially when teachers are not adequately trained. While the Government’s
goal is to increase the ratio of books to students, at present these ratios [0.2 textbooks per
pupil for P1-P3 and 0.33 textbooks per pupil for P4-P6] are far below accepted
international norms. Research evidence tells us that the quality of instruction and the
level of student learning could be improved by employing fewer teachers—especially
given current low student-teacher ratios—and purchasing more textbooks and
instructional materials, assuming the textbooks are in fact used by teachers and students.
Could the way resources are organized to deliver education be improved
substantially [i.e., change the production technology]?
Public primary schools in Uganda are predominantly organized as single shifts
with single grade classrooms. In principle, multigrade classrooms can be as effective as
single grade classrooms, and they can be considerably more cost-effective, especially in
rural areas where the number of pupils in a single grade is low. About one-fifth of
primary schools in Uganda have less then the 7 teachers that would be required to teach
all grades in a monograde setting. Also, the high dropout of students in many schools
means that class sizes are often small in P6 and P7, meaning these grades should have
highest priority for multigrade teaching. In 1998 the MoES initiated a multigrade pilot in
two districts, Kalangala and Sembabule. An evaluation of this experience carried out in
2006 found that multigrade teaching can be as effective as monograde teaching, but
37
This result may be influenced by the timing of the survey, which took place in November, at the end of
the school year. An earlier study carried out in 2003 found 41 percent of teachers physically present in the
classroom, although only 26 percent were actively teaching.
52
success requires continued teacher training and management attention from
headmasters.38
In areas where school facilities and/or the supply of teachers is constrained,
double-shifting is an option for making more intensive use of school facilities while
employing current teachers for a second shift at considerably less than double the wage of
a single shift39
. Double shifts have little impact on instructional quality, but some
students may have difficulty traveling to or from schools when shifts end after sunset.
What is the status of school infrastructure? According to the 2004 National
Service Delivery Survey, the physical facilities of primary schools are woefully
inadequate, especially classrooms, teachers houses, and toilets. Poor infrastructure can
pose health and security risks and thus adversely affect learning . Inadequate toilet
facilities has been shown to contribute to dropout when girls reach puberty. Indeed, in
the 2004 Survey 30.2% of respondents reported inadequate buildings as the most serious
constraint to school performance, while 17.6 percent reported this as a serious constraint,
more than any other item. While the Government has been addressing this problem, it
has not always done so efficiently. The School Facilities Grant has served to decentralize
construction activities to the district level, but SMCs and headmasters have very little
voice and even less responsibility. There’s a need to do a systematic inventory of school
infrastructure with the aim of identifying the highest priorities for investment.
TABLE D7. EDUCATIONAL FACILITIES: PERCENT RESPONDING
AVAILABLE AND ADEQUATE
Facility Available Adequate
Class rooms 98.8 28.5
Teachers houses 51.8 8.3
Library 15.3 26.8
Laboratory 0.5 42.9
Workshop 1.3 33.3
Latrine/Toilets 97.6 30.9 Source: UBOS, 2004 National Service Delivery Survey, p. 19
How can incentives be used to improve school and student performance?
Educational researchers have begun to experiment with and rigorously evaluate how
to use incentives to improve school and student performance. The incentives they
evaluate are located at three main levels:
38
Higgins, et.al. (2006). Effectiveness was judged on the basis of student attendance and retention and
PLE results as well as classroom observations of multi-grade classrooms relative to a control group of
single grade classrooms. 39
Smith (2007)
53
Teacher level incentives such as prizes, remuneration bonuses, promotion and
individual recognition. Outcomes such as average test scores are measured at the
teacher level. This set of incentives can also involve negative interventions such
as naming and shaming or even firing poorly performing teachers.
School level incentivesl: high performing schools (measured at the level of the
school, rather than teacher incentives as above) are given collective rewards.
These can include private rewards to all teachers (sharing a collective pot of
money or other prizes), or a local public good at the school such as a staff room,
library , etc.
Household level incentivesl: Incentives located at the household typically aim at
motivating students to increase the effort dedicated to learning. Prizes are
announced at the pupil level and recipients typically receive scholarships. Other
forms of household level incentives include empowering of parents/local
communities to monitor teachers or manage schools directly.
Teacher level incentives work by directly raising the returns to teacher effort).40
For
these to work, teachers must believe that the costs of increased effort are less than the
expected gains in remuneration (or whatever the form of the performance-based reward).
Western Kenya and India provide examplses of such interventions.
Teacher Incentives in Western Kenya. This study attempted to measure the impact of
a randomized intervention in which high performing teachers would be rewarded with
prizes at the end of the school year.41
Prizes were substantial and included bicycles,
mattresses and other household durables. Performance was determined by the rank of the
school in the district and the degree of improvement relative to the previous year’s exam
results. The authors measured a variety of inputs before and after the intervention. While
the authors find improvements in test scores in ‘treatment’ schools, this improvement is
driven primarily by teachers using different teaching strategies – particularly teaching to
the test. There is no significant different across treatment and comparison schools in
teacher absenteeism (teacher absence rates are around 20% in these schools).
Monitor-less monitors in India. Another teacher incentives paper evaluates the
impact of monitoring and performance-based remuneration on teacher absence.42
It
evaluates a randomized intervention in community schools in which an NGO provides
cameras to teachers and institutes attendance-dependent remuneration. Teachers are
expected to take pictures every morning, and teachers are paid depending on the number
of “full” days attended. Teachers in treatment schools received a base pay as well as
performance-based (measured by ‘full days attended’) component. The results of this
intervention were surprisingly large. Teacher absence fell by about half from a high of
36% in comparison schools to 18% in treatment schools. In addition, test scores are
40
See Jacobson (1989) for an early intervention. 41
See Glewwe, Illias and Kremer (2003). 42
See Duflo and Hanna (2006).
54
higher in treatment schools. The authors discuss the political-economy of this
intervention and conclude that it is not a realistic option for national scale up.
School-based incentives rely on mobilizing peer monitoring/head teacher effort to
support higher teacher effort levels. This India study outlines an innovative approach that
evaluates group (or school-level incentives) vs teacher incentives43
. This paper seeks to
determine the impact of two broad interventions on rural primary schools in India: 1) a
set of smart inputs (an additional volunteer teacher and cash block grants vs 2) group or
individual teacher incentives (performance-based pay). These four interventions are
evaluated in a randomized-control design in Andra Pradesh, India. The four treatment
groups are: (i) individual-incentives schools, (ii) group-based incentives schools, (iii)
schools that get an additional teacher, and (iv) schools that receive block grants
(equivalent expenditure).
This study finds evidence in support of the incentives treatment arms. Learning
outcomes are higher in incentives schools, but the authors do not find any differences in
treatment impact between group vs individual incentives. They conclude that it is likely
that the small size of the schools (three teachers on average) makes it easy for peer
monitoring to have the same effects as individual incentives. However, like the teacher
incentives study in Kenya, teacher attendance is not affected. Instead, teachers choose to
increase “cheap effort” – assigning more homework and practice tests rather than show
up.
Household incentives. Finally incentives located at the household level typically
work through their effects on student effort or mobilizing parental involvement in
monitoring or the management of schools. Improvements in student effort can have a
profound effect on the learning environment and greatly boost the satisfaction that
teachers get from teaching. As such, teacher attendance and preparation can improve
dramatically under the ‘collective mission’ to win merit-based scholarships. Western
Kenya is an example of such an intervention.
In Western Kenya a randomized intervention was carried out in which a series of
pupil scholarship possibilities are announced at the beginning of the school year44
. About
200 girls are eligible for scholarships in 60 schools in two districts (about 15% of the
eligible enrollment). The scholarships pay for tuition for the next two years and parents
receive an unconditional cash transfer of $12 per recipient. The results of this
intervention were quite dramatic. Performance of all pupils, including non-eligible
scholarship recipients (boys), in treatment schools perform much better than control
schools. In addition, teacher absenteeism falls by 6.5 percentage points in treatment
schools. The study attributes this to an improvement in working conditions engendered
by increases in pupil effort.
The other form of incentives-at-the-household-level includes empowering parents
to monitor teachers. This ranges from establishing school management committees to
43
See Karthik and Sundararaman (2006). 44
See Miguel, Kremer and Thornton (2005).
55
outright hiring, firing and remuneration policy control. A number of studies have looked
at the effects of greater community control in Latin America and show large effects on
school performance.45
)). When control was transferred to the community, so that parents
could hire and fire teachers, teacher attendance and test scores went up.46
While the
associations here are very strong, it is difficult to interpret these results as causal. An
interesting analogy is the community monitoring study in Uganda that focuses on health
care centers. Using report cards as the means to provide information to the community on
the performance of health centers, communities that were randomized to receive this
information have much better performing health care centers. Attendance of health center
staff and quality of service were higher in report card communities.
Are the accountability mechanisms strong enough at the primary level?.
Education systems with strong accountability have transparent financial and
resource flows, provide timely and accurate information to all stakeholders, and provide
incentives, or consequences, for good and bad performance. Accountability in education
is provided by two principal mechanisms in Uganda. The first is community
participation in and monitoring of school budget and performance through participation
in School Management Committees. For the SMCs to play this role effectively they
require active participation by parents and information provided to parents as to [a] the
minimum requirements or standards for learning to take place, [b] the performance of
their schools in terms of budget and school outcomes, and [c] the responsibility and
capacity to hold headmasters and teachers responsible for poor performance.
Table D8 shows that SMCs and PTAs exist in almost all primary schools in the
country, but they offer relatively limited opportunities for broad participation.l
According to the 2004 UBOS National Service Delivery Survey 22.4% of PTA’s and
SMC’s meet monthly, but 58.6% meet only once a term. Research in other countries
demonstrates that one of the variables most strongly related to student performance is
parental participation in the school.
45
See see Jimenez and Sawada (1998) and King and Ozler (2001). 46
See Lewis (2005) for a recent review.
56
TABLE D8 . SCHOOL GOVERNANCE, 2003
Source: Education Standards Agency, Report on Monitoring Learning Achievement in
Lower Primary, 2004
The second mechanism for ensuring accountability is the inspection system.
Each DEO is responsible for the inspection of schools within its district. However, there
are few inspectors, and they seldom have the vehicles and fuel to visit schools47
. The
central government’s Education Standards Agency is in principle responsible for overall
inspection of the school system but lacks the budget, manpower, and authority to carry
through on this mandate.
In the absence of an effective inspection system, and it appears very unlikely that
the funding will be forthcoming in the medium term to support an adequate inspection
system, the best option open to the MoES may be to strengthen local governance, and
give local governing bodies the information and the authority to perform effectively.
47
A 2007 survey by the ESA found that most districts had only three inspectors to cover as many as one
thousand schools. For example, Wailiso district has three inspectors and 1001 primary schools, and
Kampala has three inspectors and 985 primary schools. The number of schools per inspector also varies
greatly across districts. For example, Kalangala district has two inspectors and only 25 primary schools.
Frequency of Meetings Percent
School Management Committee Exists 95.8%
SMC Meets Once per Term 43.5%
SMC Meets Twice per Term 47.8%
SMC Meets Less than Once per Term 8.6%
PTA Exists 70.8%
PTA Meets Once per Term 37.5%
PTA Meets Twice per Term 37.5%
PTA Meets Less than Once per Term 25.0%
57
E. EFFICIENCY OF PRIMARY TEACHER EDUCATION.
Uganda has forty-seven Primary Teacher Colleges [PTCs], forty-five of which are
public and two of which are private. The PTCs use a standard curriculum, defined by
Kyambogo University. Of the forty-five public PTCs, twenty-three are designated core
PTCs. All PTCs provide residential training leading to the Primary Teaching Certificate
[Grade III], which is the minimum required qualification. The core PTCs also offer a
three-year, part-time in-service training program, which is delivered through 539
affiliated Coordinating Centers [CCs], each of which is staffed by a Coordinating Center
Tutor [CCT], who is an employee of the corresponding PTC. On average, each core PTC
staffs about 23 CCs, and each CC serves on average18 schools. In addition to this
training program, the CCs offer in-service training and technical assistance to all staff of
primary schools, including management training of head teachers.
Teacher Supply and Demand.
As of 2006, about 145 thousand teachers were employed in Uganda’s primary
schools, including about 19 thousand privately employed and about 126 thousand
employed by the TSC.. This number represents a large increase over the 89 thousand
teachers employed at the introduction of UPE in 1997. To accommodate the demands
of UPE, many uncertified, untrained teachers were employed, and at the same time pupil-
teacher ratios increased dramatically. The response of the Ministry to the problem of
unqualified teachers has been to put emphasis on in-service teacher training—the Teacher
Development and Management System [TDMS]. However, as shown in Table E1, the
number of unqualified teachers enrolling in the three year in-service program and passing
the Grade III teacher’s examination is only one percent of the stock of teachers. The
number of unqualified teachers achieving passes annually through the in-service program
represents only six percent of the total number of unqualified teachers.48
As shown in Table E1, the number of pre-service teachers passing the Grade III
examination is smaller than the number of teachers leaving the teaching profession,
which means that even if all Grade III passes in fact enter teaching, unqualified teachers
must be employed to fill the gap. Indeed, the percent of licensed teachers appears to be
increasing49
. Also, while the post-UPE enrollment bubble peaked between 2003-6, the
Ministry policy to reduce student-teacher ratios and demographic trends suggest the
demand for teachers will continue to grow, albeit at a more moderate pace than the past
decade50
. At the same time, the current level of output from the PTC pre-service and in-
service programs is not sufficient to increase the overall level of teacher qualifications. If
the goal is to increase the percent of qualified teachers, it’s clear that the annual number
of Grade III passes needs to increase. This can be done by increasing the pass rate or by
increasing enrollments in the pre-service and/or in-service programs.
48
MoES ESSAPR (2006), p. 41. 49
World Bank (2007) reports 89.7 percent of primary teachers are Grade III or higher, but the average
district percentage of qualified teachers is as low as 55.1 percent (Nakapiripirit District in the Northeast). 50
Projected enrollments decrease from 2003-2006 and then increase again. Between 2006 and 2015
enrollments are projected to increase by 2.6 million, or 40 percent over the 2006 base.
58
TABLE E1. STOCK AND FLOW OF PRIMARY SCHOOL TEACHERS, 2004
Number Percent of
Stock
Stock of Teachers 147,291 100%
Teachers at Grade III 93,831 64%
Licensed Teachers 22,756 15%
Transfers 11,760 8%
Annual Departure from Teaching 6,843 5%
Annual Pre-Service PTC Passing Test 5,746 4%
Annual In-Service PTC Passing Test 1,334 1%
Additional Teachers Required by 2015* 42,001 29% * Assuming constant PTR = 55:1
Efficiency of Teacher Education.
In total, the PTCs enroll almost 18 thousand students in the two-year residential pre-
service training . The dropout rate between the first and second year of the program is
about 12 percent, and the failure rate of PTC students taking the Grade III examinations
is about 22 percent51
. This high failure rate represents a significant wastage of resources
that could be reduced by better selection of PTC pre-service students and improved
evaluation and assessment of students throughout the two-year program.
Since no in-depth study of PTC expenditure and finance has been undertaken
since the introduction of UPE, it’s difficult to assess the internal efficiency of the PTCs.
However, unit costs appear to be high, especially for the pre-service program. Teacher
education recurrent expenditures were UShs. 16.6 bn in FY 2005/06, or 2.8 percent of the
total MoES recurrent budget and 6.5 percent of primary education salaries. The UShs.
16.6 bn. in recurrent expenditures compares with UShs. 8.4 bn in development
expenditures for teacher education. Since 17,511 students were enrolled in PTCs in
2004/05, the approximate unit cost of an enrolled student was UShs. 950k, or almost
twenty times the unit cost of primary education. This compares with the estimated unit
cost of UShs. 243k calculated in the 1996 study. The approximate unit cost of a
graduate [of both the residential and in-service programs] was about UShs. 484k in 1996
and about UShs. 2.3 mn in 2005/06. Data do not permit a separate calculation of the unit
cost of a graduate of the pre-service and in-service programs, but a study of similar
teacher training programs in Malawi found that the unit cost of pre-service programs is
about triple that of in-service programs52
. Given the fact that pre-service programs do not
51
ESSAPR (2006). The pass rate varies by year, ranging from 61 percent in 2002/03 to 85 percent in
2005/06. 52
Kunje and Lewin (2000).
59
produce higher Grade III examination pass rates, the cost-effectiveness of the CC in-
service program is likely to be higher and thus argue for expansion of that program.53
A 2004 value for money audit of the CCs carried out by Ernst and Young for the
Auditor General provides additional information on the internal efficiency of teacher in-
service training. Among the problems identified are:
Operational costs are under-budgeted;
Delays in funding required for course delivery;
Lack of funding for equipment maintenance and replacement;
Some supplies (e.g., bicycles) provided but not used.
Two design problems affecting the effectiveness, and thus the cost-effectiveness,
of the CCs have also been identified.54
One problem is mission creep. Since the CCs are
in close proximity to the schools and in regular contact with head teachers, teachers and
communities, they are asked to take on a wide range of activities. As a result, the CCTs
have long and heavy work schedules and sometimes take on tasks for which they are not
qualified. Mission creep prevents the CCT from focusing on its core functions, including
in-service training. Another problem is sometimes a lack of coordination and
clarification of roles between the CCTs and the district inspectors. The CCTs report to
the MoES through the PTCs, but given their proximity to the schools they also serve as
the de facto first line of inspection, which is a district responsibility. When CCTs and
district inspectors work harmoniously they can complement each other’s work, but there
is no administrative requirement that they do so.
Need for Data and Analysis.
The relatively low quality, the high costs, and the lack of recent data and
economic analysis on teacher education makes it difficult to document the magnitude of
efficiency problems, to diagnose their causes, and to recommend options for
improvement. The PTC Cost Effectiveness and Efficiency Study carried out by MoES in
1996 should be repeated to attempt to more rigorously answer the following questions:
1. What are the unit costs of producing a qualified teacher via [a] traditional, pre-
service training and [b] in-service training provided through the CC? Given the
multiple activities of the CC, answering this question will require careful cost
accounting.55
Also, what would be the unit costs of each training modality if all
inputs were provided in adequate amounts?
2. What is the contribution of training to the probability of passing the Grade III
entrance examination, and what is the relative cost-effectiveness of pre-service
and in-service training?
53
Again, the pass rate for in-service teachers varies by year, from 64 percent in 2002/03 to 94 percent in
2003/04. See World Bank (2007) for further information. 54
Burke (2002). 55
See Kunje and Lewin (2000) for a discussion of the difficulties involved.
60
3. Given likely future demand for new teachers, what is the best combination of pre-
service and in-service training in terms of the percent of newly qualified teachers
produced by each?
4. What is the minimum and the optimal size for a PTC to produce high quality
teacher training? Given the answer to this question and the one above, how many
PTCs should be supported by the Government? Which criteria should be used to
select PTCs for closure or conversion to other uses?
5. Aside from training unqualified teachers, what is a feasible and desireable level of
spending on in-service training expressed as a percentage of the total salary bill?
6. What is the expected future need for newly recruited teachers given teacher
retirement and attrition rates, reduced student-teacher ratios, and growth in
primary school enrollments? How may changes in teacher salaries and working
conditions affect teacher retirement and attrition?
61
F. INTERNAL EFFICIENCY OF SECONDARY EDUCATION.
Secondary school enrollments are growing rapidly, and the Government’s
commitment to universal secondary education [UPPET] indicates this growth will
continue indefinitely. At the same time, the unit costs of secondary education are high—
both in absolute terms and relative to per capita GDP. The combination of increasing
enrollments and high unit costs yield future secondary level expenditures that are not
sustainable. Unit costs will need to decrease if UPPET is to be come reality.
Improvements in efficiency are thus critical to the success of UPPET. One option is to
use distance education for the provision of secondary instruction in remote areas. Box F1
relates the experience of countries in Central America with this technology.
Why are Secondary School Enrollments Growing so Rapidly?
The success of the Uganda’s UPE program in increasing primary school
enrollments has led to large increases in enrollments at the secondary level. As shown in
Figure F.1, enrollments in the first year of secondary, S1, and total junior secondary
enrollments, S1-S4, have increased dramatically in the past five years. The growth
reflects a combination of growing numbers of P7 graduates and changes in EMIS data
collection procedures to capture a higher percentage of private school enrollments56
.
FIGURE F1: UGANDA SECONDARY SCHOOL ENROLMENTS.
56
However, even with these improvements the EMIS does not capture all private schools in its survey, so
Figure F1 understates total secondary enrollments. Since accurate secondary school enrollment data are
reported in Uganda’s household survey, the EMIS should develop an algorithm to “correct” for non-
reporting by private schools.
S1 and S1-S4 Enrolments
0
100,000
200,000
300,000
400,000
500,000
600,000
700,000
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
S1
Total S1-S4
62
BOX F1. DISTANCE EDUCATION IN CENTRAL AMERICA.
63
The demand for secondary school is likely to increase even more rapidly in future
years due to demographic growth, an increase in the transition rates from P6 to P7, and
possible improvements in PLE results. Currently there are about 750,000 pupils in P6, of
whom about 500,000 enroll in P7, of which 400,000 take the PLE and about 350,000
qualify for S1. In 2006 about 200,000 students entered S1.57
These numbers indicate the
potential magnitude of growth in secondary school enrollments, if spaces were made
available to all students successfully passing the PLE. Relatively small improvements in
P6 to P7 transition rates and in PLE pass rates could easily double or triple secondary
school enrollments. Future demographic growth and reduced dropout at the primary
level can further increase demand.
At present, as can be seen in Figure F2, demand is tempered by the relatively high
“price” of both public and private secondary education to those passing the PLE. The
share of total costs paid by households is high in Uganda relative to most other countries
of Sub-Saharan Africa.
Even if Government is able to pay the fees for financially needy students,
families will still need to pay the other private costs of secondary schooling, which can be
considerable, especially when children in rural areas need to pay boarding or
57
Lewin (2007).
FIGURE F2: HOUSEHOLD EXPENDITURE ON SECONDARY
EDUCATION AS PERCENT OF TOTAL SECONDARY EDUCATION
EXPENDITURE.
84
68
60
59
59
57
43
31
30
30
30
23
20
15
10
0 10 20 30 40 50 60 70 80 90
Congo, Dem.Rep.*
Malawi
Lesotho
Uganda
Rwanda*
Kenia*
Cameroon
Senegal
Ethiopia
Benin
Togo
Niger*
Mauritania
Tanzania
Mali*
% private expenditures
*Excludes public capital expenditure
Sources: SEIA estimates, Education Sector Reviews (CSRs, 2001-2006), Lewin, 2005, Tanzania PER,
World Bank 2003, World Bank, 2007.
64
transportation costs to access schools distant from their homes. If Government wishes to
pursue a policy of universal secondary education, it would need to address not only the
fees charged by schools but, also, the other private costs which negatively affect the
demand for secondary school.58
The potential total costs of expanding secondary
education coverage are very high and warrant a detailed examination of how unit costs
might be reduced without adversely affecting the quality of instruction.
Are the high unit costs of secondary education sustainable?
TABLE F1: SECONDARY EDUCATION PUBLIC
EXPENDITURE AS PERCENT OF GDP IN SSA
Country Year % country year %
Benin1*
1998 0.98 Niger 2001 0.59
Chad 2003 0.5 Niger 2002 0.58
Cameroon 2001 0.91 Rwanda* 2001 0.59
Cote d'Ivoire 1999 0.98 Senegal 2001 0.63
Kenya* 2003 1.61 Tanzania 2002 0.23
Ethiopia* 2001 0.35 Uganda 2005 0.63
Mali 2004 1.05 Zambia* 2000 0.51
Mozambique 1999 0.2
WEI av. 1999 0.21 OECD av. 1999 0.25
Sources: CSR, Africa Region, World Bank, 2001-2006; Kenya, World Bank 2004; Tanzania, World Bank, 2003; UIS 2002; WB World Development Indicators (GDP
p.c.)
The unit costs of secondary education in Uganda, expressed relative to per capita
GDP, is 0.63, as is shown in Table F1. This figure is about average for Sub-Saharan
Africa, but is much higher than the average for the countries that comprise WEI [World
Education Indicators] or OECD, countries which by and large have near universal access
to secondary schooling.
Table F2 shows that the unit cost of lower secondary is five times that of primary
education, whereas the unit cost of upper secondary is eight times that of primary
education. These differences in costs are mainly driven by the ratio of students to
teacher salaries relative to GDP per capita. As shown in Table F2 student-teacher ratios
are lower at the secondary level, while salary levels are higher. The costs of secondary
education in Uganda are high relative to what one finds in countries with high coverage
at the secondary level. In countries with high secondary education coverage, the unit cost
of secondary education is no more than double that of primary education, and the unit
cost of secondary education is no more than 30 percent of GDP.
58
One means of doing this is to introduce cash transfers to poor households contingent on children
enrolling in school, as Brazil has done with its Bolsa Escola and Mexico has done with its Progresa
program.
65
TABLE F2. SECONDARY EDUCATION UNIT COSTS
Primary Lower
Secondary
Upper
Secondary
Pupil Teacher Ratio 50 19 15
Average Teacher Salary /GDP Per
Capita
3.8 6.9 9.4
Non Teacher Salary/GDP Per Capita 0.5 2 3
Non-Salary Expenditure/GDP Per
Capita
0.5 2 3
Teacher Wage Bill as Percent of
Total Recurrent Expenditure
79% 63% 61%
Total Unit Cost as Percent of GDP
Per Capita
10% 57% 103%
Unit Cost in Thousands of UShs 60 300 500
School age pop as % total pop 22% 11% 6%
GER Government 100% 12% 2%
GER Private 10% 8% 2%
GER Total 110% 20% 4%
Government Expenditure as Percent
of GDP Spent on This Level
2.27% 0.63% *
Household Expenditure as Percent of
GDP Spent on This Level
1.32% 1.88% *
Source: Adapted from Lewin (2007).
* Included in Lower Secondary estimate.
The cost of secondary education varies by type of school, as shown in Table F3.
Boarding school is almost three times as costly as day school. Schools in urban areas are
more costly than those in rural areas. And public schools are more than twice as costly as
private schools. In addition, as was shown in Table F2, unit costs also vary by level of
schooling, with upper secondary costing almost twice as much as lower secondary.
TABLE F3. RELATIVE UNIT COSTS OF SECONDARY SCHOOLS, 2001.
Type of
School
Boarding Day Rural Peri-Urban Urban Public Private
Unit Cost
Relative to
Day School
2.86 1.00 1.64 1.67 1.94 2.13 0.95
Source: Unit Cost Study 2001 as reported in Bennell and Sayed (2002).
As noted earlier, currently Government education spending is about 3.5 percent of
GDP. As calculated by Lewin (2007), if the Government were successful in attaining
100 percent enrollment at the primary and lower secondary level and 50 percent
66
enrollment at the upper secondary level, it would need to spend closer to 13% of GDP on
education, which is clearly not feasible.
The implication of the high ratio of secondary education unit costs to primary
education unit costs and the infeasibility of expanding secondary education coverage at
such high unit costs is that secondary education must become much more efficient.
Secondary schools must begin educating far larger numbers of students with their current
budgets, hopefully without reducing the quality of instruction. Thus, it is important to
analyze why secondary education is so costly at present.
Why are secondary education unit costs so high?
The reasons for higher unit costs at the secondary level than the primary level are
lower pupil teacher ratios, lower teaching loads, and higher teacher salaries. The lower
pupil-teacher ratios and lower teaching loads are in turn partly a product of the secondary
school curriculum59
. The pupil-teacher ratio of 19 in lower secondary [S1-S4] and 15 in
upper secondary [S5-S6] is lower than the Africa-wide average of 25 and far lower than
the primary school average of 52. Clearly, efficiency could be improved through
moderate increases in student-teacher ratios.
Secondary education unit costs are also high because teachers on average teach
only 22.5 periods per week out of a fifty period week, according to a 2001 Teacher
Utilization Study. The average teacher spends less than 15 hours per week in the
classroom and in total works about 29 hours per week. According to the Teacher
Utilization Study most under-utilized teachers teach only one subject, and 45 percent of
teachers teach only one subject. Teacher productivity could be increased significantly by
simplifying and reducing the secondary school curriculum and by requiring all teachers to
teach at least two subjects60
.
Another part of the story as to why secondary education is so costly is the
constraint on classroom space. The MoES deploys teachers to schools based on the
number of streams under the assumption that one stream should have no more than 45
students. However, the lack of classroom space may force the school to combine streams
or sections in any particular grade and thus reduce the number of needed teachers. The
result is that about 34 percent of secondary school teachers are under-utilized.
Finally, small schools may contribute to the high costs of secondary education.
The average enrollment in all rural schools only 232, and that of all urban schools is
414.61
About 92 percent of private rural day schools have fewer than 50 students in the
6th
form, compared to only 6 percent of public urban day schools. Private rural
secondary schools have an average enrollment of only 160, while at 542 students urban
boarding schools have the largest average enrollment. While private schools have fewer
students, their custom of contracting teachers by the hour helps them control costs, in
59
See Bregman et.al. (2007). 60
The topic of secondary school curriculum reform is treated in depth in Bregman, et.al. (2007). 61
Bennell and Sayed (2002), Table 4.1.
67
contrast to Government schools which hire mainly full-time teachers, even for specialized
courses.
What should be done about high teacher salaries?
Currently there are 15 thousand teachers on the Government payroll in
Government sponsored schools plus approximately 4 thousand teachers employed by
PTAs. In addition, there are almost 19 thousand teachers employed by private schools.
As seen in Table F4, secondary school teacher salaries are almost double those of
primary school teachers, and these figures do not include the salary supplements often
funded from PTA contributions. At 6.9 times per capita GDP, junior secondary school
teacher salaries are high by middle income country standards but, as shown in Figure F3,
about average by African standards. The salaries of headmasters are especially high.
Head teachers of junior secondary [O level] schools are usually at Grade U2 of the public
service, which translates to a monthly salary of 921,989 UShs in 2006, which is 2.5 times
the average public secondary school teacher salary.
TABLE F4. PUBLIC SECONDARY SCHOOL TEACHER SALARIES, 2006.
Primary
Teachers
Secondary
Teachers
Number of Teachers
Employed by TSC62
126,000 15,078
Number of Teachers
Employed by PTA
NA 3,953
Average Monthly
Salary
209,534 361,484
Mode Grade Level U7U U5U
Salary at Mode Grade
Level
163,425 361,484
Total Wage Bill 279 billion 77 billion Source: Estimates based on EMIS and Unit Cost Survey, November 2006.
Public secondary school teachers are well paid in Uganda in part because they are
well-educated relative to the general populace. Thirty percent of secondary school
teachers have at least a first graduate degree, and almost all the remaining seventy
percent of teachers have secondary school degrees.63
However, this is not the complete
explanation since the 3,953 teachers funded exclusively out of PTA funds and the 18,576
teachers in private secondary school receive much lower salaries than government-funded
teachers64
. Since the wages paid privately-funded teachers [who outnumber the publicly-
62
Teachers in government schools only. In addition, there are 18,576 teachers in private secondary
schools. 63
Annual School Census, 2000. 64
A survey of 74 schools in 2006 found the average PTA teacher salary to be 178,492 UShs in public day
schools and 130,925 UShs in private schools, both of which are less than half the average government
68
funded teachers] are market-determined and since the supply of teachers to public
secondary schools exceeds the demand, an argument can be made that publicly funded
secondary school teachers are paid more than required by the labor market65
.
.
FIGURE F3. JSE AND SSE TEACHER SALARIES AS MULTIPLES OF PER
CAPITA GDP.
8.8 8.5
7.7
6.9 6.86.1
5.7
4.9 4.8
9.810.2
7.9
12.1
9.2
7.36.8
5.8
7
0
2
4
6
8
10
12
14
Cha
d
Niger
Togo
Moz
ambiqu
e
Uga
nda
Mali
Rwan
da
Zambia
Mad
agas
car
teach
ers
' sala
ry/G
DP
p.c
.
JSE SSE
The high salary levels of Government-funded teachers and headmasters contribute
significantly to high unit costs and are not likely to be sustainable as enrollments and
employed teachers increase under UPPET. MoPS should consider altering the salary
structure for teachers and headmasters who are newly recruited to the public service in
order to reduce unit costs. While this would introduce a two-tier salary structure for
publicly funded teachers, there is already a de facto two-tier structure with publicly
funded and privately funded teachers receiving different pay for similar work.
Is Government Funding for Secondary Education Allocated Efficiently Across
Districts?
Government funding for education should result in roughly equal spending per
student across schools except for special needs which may require higher spending.
These special needs may include low household income (poverty), remote rural location
(more expensive to retain teachers, lack economies of scale), and high cost programs
funded teacher salary. The 2002 Teacher Utilization Study found that both PTA teachers and private
school teachers had salaries ranging from 70 to 100 thousand UShs per month. 65
This argument presumes equal experience and credentials and that private school teachers are not simply
public school teachers working a second job. A more in-depth analysis of teachers’ wages would help to
determine whether public secondary school teachers are paid an appropriate salary.
69
(vocational skills, science). A Unit Cost Study carried out in 2001 found large
differences in average spending between types of schools.66
Contrary to what one would
expect, the unit cost of rural secondary schools was less than 60 percent that of urban
secondary schools.
While data on school budgets and expenditures are not available in the MoES
EMIS, teacher salaries absorb a high percentage of school budgets, and a key determinant
of a school’s wage bill is the number of teachers it employs. Thus, if expenditures are
allocated equally across schools, one would also expect that the ratio of students to
teachers would also be allocated approximately equally. Hence, in Figure F4 the
distribution of the student/teacher ratio is presented for all the Government secondary
schools in Uganda. As shown, the mean student teacher ratio is 17.8 with a standard
deviation of 8.5. About 71.3 percent of all schools fall within one standard deviation of
the mean.
FIGURE F4 . DISTRIBUTION OF TEACHERS
ACROSS SECONDARY SCHOOLS
05
01
00
150
200
250
300
350
Num
ber
of sch
oo
ls
mean-1 s.d. +1 s.d.-2 s.d. +2 s.d.student/teacher ratio/x
0 20 40 60Student/Teacher Ratio
Government Secondary Schools
Source: EMIS 2005
Differences in student-teacher ratio may simply reflect differences in school and
population characteristics across districts. Perhaps the distribution within districts is
more closely concentrated around the average student-teacher ratio. However, in general,
66
Reported by Bennell and Sayed (2002).
70
this is not the case. As shown in Figure F5 for one district, there can be large differences
in student-teacher ratios even within relatively small geographic areas.
FIGURE F5. TEACHER DEPLOYMENT
ACROSS SCHOOLS IN MBALE DISTRICT.
05
10
15
20
Num
ber
of sc
hoo
ls
0 10 20 30 40 50Student/Teacher Ratio
Government Secondary Schools
The fact that the distribution of student teacher ratios is so spread out may simply
reflect the fact that the MoPS allocates teachers according to criteria other than simple
student enrollment. One such criterion might be the poverty rate of the school or district,
since children living in poverty are likely to be more costly to educate. To test this
proposition, in Figure F6 the average student teacher ratio of each district is plotted
against that district’s poverty level.67
The graph shows no relationship between poverty
and student-teacher ratio, the proxy for Government funding.
67
Since information does not exist on the percent of a school’s children who are in poverty, this analysis
cannot be carried out at the school level.
71
FIGURE F6. SECONDARY SCHOOL STUDENT TEACHER RATIO
AND POVERTY RATES ACROSS DISTRICTS.
01
02
03
0
Stu
den
t/Te
ach
er R
atio
0 10 20 30 40 50 60 70 80 90 100Percentage of the Poor
Government Secondary Schools
Differences in student-teacher ratios across schools might also be justified if those
schools having low student-teacher ratios are able to translate that resource advantage
into better performance. One proxy for performance is the grade repetition rate. As
shown in Figure F7, there is no apparent relationship between student teacher ratios and
this proxy for school performance. Another proxy for performance is the ratio of S4 to
S1 students, a measure of the academic survival rate. A plot of this proxy against
student-teacher ratios also shows no apparent relationship.
72
FIGURE F7. STUDENT TEACHER RATIO
AND REPETITION RATE ACROSS SCHOOLS
05
1015
20
Per
cent
of S
tude
nt R
epea
ting
Cla
sses
0 10 20 30 40 50Student/Teacher Ratio
Government Secondary Schools
In short, there is no obvious objective criteria for deploying Government-funded
teachers, especially within districts. This can be seen in one final graph in Figure F8,
showing the distribution of the student-teacher ratio across schools in Mbale district.
FIGURE F8. TEACHER DEPLOYMENT
ACROSS SCHOOLS IN MBALE DISTRICT.
05
10
15
20
Num
ber
of sc
hoo
ls
0 10 20 30 40 50Student/Teacher Ratio
Government Secondary Schools
73
While the MoPS undoubtedly has rules for allocating teachers across schools, in
practice the distribution of teachers across schools [see Figure 4] and even across districts
[see Figure 6] is highly unequal. Furthermore, this distribution appears to be unrelated to
the available measures of academic performance. Indeed, it is not obvious that having
high student-teacher ratios is worse then low student-teacher ratios in terms of available
quality measures. This relationship needs to be analyzed in greater depth with better
control measures and better indicators of academic performance. However, if these
results hold true, the efficiency of secondary education could be improved significantly
by [a] increasing the average student-teacher ratio and [b] allowing schools and districts
to deviate from that average only for reasons of special needs, small size due to remote
locations, and special programmatic needs.
How can Government sustain the important private sector role in the finance and
provision of secondary education?
According to the MoES’s Annual School Census, which understates the number
of private schools and private school enrollments, there were 903 private secondary
schools in 2005, representing 41 percent of all secondary schools and 37 percent of all
secondary school enrollments. Private schools are free to set their own tuition fees,
which vary greatly depending on the location and type of school—rural, urban day, and
boarding. As shown in Table F4, the average annual fee is about 163,000 UShs. These
fees cover the salaries of 19,000 private school teachers, in addition to other personnel
and non-personnel recurrent costs.
TABLE F4. HOUSEHOLD EXPENDITURES
ON SECONDARY EDUCATION, 2006.
All Public Schools Public Day Schools Private Schools
Fees 125,296 72,187 163,120
Total Expenses 213,491 120,255 236,960
Source: Calculated from 2006 UNHS. Note: All public schools includes boarding schools. The average fees paid by all secondary
school students is 161,432 UShs.
Public secondary schools are also allowed to charge fees, and while they are
required to receive MoES approval for fee increases, such requests are seldom turned
down. Revenues from fees are used for a number of recurrent expenditures, including the
salaries of PTA contract teachers and salary supplements for government-funded
teachers. The average annual fee in public secondary schools is 125,296 UShs. In
addition, higher income households spend more on both school fees and total school-
related expenditures than do lower income households. Overall, the richest income
quintile spends about three times as much on fees and on total school-related
expenditures as does the bottom income quintile68
.
The role of private sector finance in secondary education in Uganda is thus very
important. Just the fees that households pay to public secondary schools amount to about
68
See Annex Tables.
74
47 billion UShs annually. Since most fees are used for contracting and compensating
teachers, this is equivalent to a 61 percent increase in the secondary education wage bill
funded by Government. As shown in Table F1, households in aggregate spend three
times as much on secondary education as does the Government. While Government
spends 0.63 percent of GDP on secondary education, households spent an additional 1.88
percent of GDP. While this appears to show that Government is successfully leveraging
private sector financing, it is more accurate to say that private sector finance comes about
due to the lack of Government funding and provision.
The challenge facing the Government as it pursues Universal Post Primary
Education and Training [UPPET] is how to expand publicly-subsidized secondary
education [a] without reducing the demand for fee-paid private secondary education and
[b] without reducing household contributions to public sector secondary schools.
Government needs to think carefully about how to use its funding to create incentives for
private supply and finance while proving new opportunities to households which cannot
afford to pay the levels of fees charged currently. This will require creation of some
form of transparent, explicit capitation grant to private institutions with the amount of the
grant conditional on school location, student income, program cost, etc.69
Korea is often
cited as a model where private finance and provision played a key role in educational
development (See Box F1).
Is there strong accountability for academic and financial performance of public
secondary schools?
In 2002 the Government created the Education Standards Agency [ESA] with the
objective of strengthening inspection, especially of secondary schools. At the time
experts agreed that an inspector:school ratio of 25:1 would be necessary to allow
inspectors the time to observe teachers in their classes, to review budgets and
expenditures, and to advise Boards of Governors [BOG]. With almost 2,000 secondary
schools, this would require a staff of about 80 inspectors. At present the ESA has a total
staff of about 50 to cover all secondary schools, tertiary institutions, and teacher training
colleges. Not only is the inspection service seriously understaffed, but it also lacks the
means of transport to regularly visit schools. In addition to ESA inspectors, the districts
have their own inspectors, who don’t have adequate capacity to even inspect the primary
schools, and the CCTs provide advisory and training services to the schools, but they
don’t do inspection.
While Government inspection is deficient, secondary schools have two local
governance institutions which give the community a role in monitoring and holding the
school accountable. These are the BOG and the PTA. Unfortunately, the distinct roles of
these two institutions are not always clear, and the headmaster of the secondary school is
often viewed as untouchable. Still, the fact that parents contribute so significantly to the
funding of both Government and private schools gives them a potentially strong voice in
school governance.
69
See LaRocque (2007) for further elaboration of such proposals.
75
It seems unlikely that in the near future the Government will give ESA the
financial and human resource it requires to be an effective inspection agency. In the
absence of an effective inspection service for secondary schools, there is a need to
strengthen the local governance institutions by training their members, clarifying their
roles, giving them increased responsibility and authority, and providing them with the
information they need to monitor the performance of the schools they govern. If there
role in accountability is to be strengthened, the BOGs and PTAs must extend their current
fund-raising function to a broader one of oversight.
BOX F2. PRIVATE SECTOR ROLE IN EXPANDING ACCESS IN KOREA
Source: World Bank (2005)
76
H. INTERNAL EFFICIENCY OF TERTIARY EDUCATION
A recent major study of tertiary education was jointly carried out by the MoES
Department of Higher Education and the World Bank, resulting in several publications.70
The Uganda National Council for Higher Education has followed up this study with
annual reports on higher education.71
Both the Bank and the NCHE reports analyze the
efficiency of public expenditure on higher education and make a number of policy
recommendations for improvements in higher education finance and efficiency of
resource use. Given this recent, rich information and analysis, the current study mainly
updates statistics and organizes the findings of previous studies to more explicitly
respond to the policy questions being addressed in this report.
As of 2005, the tertiary (higher education) sub-sector in Uganda enrolled over
124,000 students in 157 institutions ranging from universities to specialized training
institutions. This enrollment figure corresponds to 3.8 percent of the 19-25 year old
cohort, about the average for Sub-Saharan Africa, and represents a rapid growth in
enrollments since the year 2000. Table H.1 summarizes the distribution of enrollments
across institutional types.
TABLE H.1. TERTIARY LEVEL ENROLLMENTS, 2005
Institutional Type MoES Budget
Home
Enrollments Share of
Total
Universities & Affiliated
Colleges
Higher
Education*
78,107 62.8%
National Teachers Colleges
[NTCs]
Teacher
Development
12,096 9.7%
Colleges of Commerce BTVET 14,479 11.6%
Management Institutions BTVET 9,411 7.5%
Other Technical BTVET 10,220 8.2% Source: NCHE (2006)
* Public universities have their own budget lines.
Higher education enrollments in Uganda appear to be increasing rapidly. As UPE
translates into higher numbers of primary school graduates and UPPET brings about
larger numbers of secondary school graduates, the demand for higher education can be
expected to increase faster still. It is important to plan for this rapid growth, rather than
having to react in an ad hoc fashion it in future years. In addition, it is important to make
specific plans and projections concerning the growth of publicly managed universities
and non-university institutions [BTVET and NCTs] and the concomitant increase in
public expenditures on higher education.
70
See World Bank (2004). 71
See NCHE (2005).
77
Allocation Across Tertiary Institutions
Tertiary institutions have their budgetary homes in three different MoES
departments-- Higher Education, Teacher Development, and BTVET, and the major
public universities have their own budget lines. While the Higher Education Department
budget is for public universities, the budgets for Teacher Development and BTVET
include expenditures at the primary and secondary levels as well as the tertiary level.
This arrangement makes it difficult to accurately track total tertiary expenditures over
time. For 2004 total public spending on all types of higher education represented about
15 percent of the MoES budget, and the distribution of that spending was 79% allocated
to universities, 30% allocated to BTVET institutions, and 8% allocated to NTCs.72
The
university recurrent expenditure for 2005/06 was UShs. 80 bn., or 15 percent of total
MoES recurrent expenditure.73
Is the budgetary allocation across higher education programs appropriate?
The economic rationale for the public funding of higher education has several
elements. The first one is equity, or to ensure that children from lower income
households are not excluded from higher education for reasons of affordability. This
rationale translates into government policies to provide financial aid or subsidies targeted
to needy students. The second element is to ensure the nation has the type of highly
skilled labor required for innovation and economic growth. This rationale often
translates into subsidies for particular skill areas, such as science or engineering. The
third element is to maximize the nation’s return on its higher education investment.
When this return is measured in pecuniary terms, the public policy response is to ensure
an adequate supply of that instruction which yields the highest rate of return, or the
highest income relative to the costs of instruction. Finally, the fourth element is to ensure
an adequate production of new knowledge, which is a pure public good that merits a high
level of subsidy. The rationale translates into government funding of university research
projects and funding to develop the capacity to carry out such research.
To answer the question about the allocation of the higher education budget across
institutional types, one must at a minimum know how higher education fits in the
country’s development plans and know the pecuniary returns to different types of
instruction. Although there is evidence that the overall social and private rates of return
to higher education are quite high, limited information exists to determine returns to
specific types of instruction.74
Table H.2 shows the unit costs and subsidies for several
degree programs, but there is no corresponding information on earnings by career
program.
72
Calculated from World Bank (2004), Table 29. 73
Calculated from Table 2.1 of the MoES ESSAPR for 2005/06. (Makerere University + Mbarara
University + Kyambogo University + Makarere University Business School + Gulu University + District
Tertiary Institutions) 74
As shown by Appleton (2001), the social and private returns to higher education appear to be relatively
high and to be increasing over time.
78
TABLE H.2. UNIT COSTS AND SUBSIDIES OF DEGREE AND DIPLOMA
PROGRAMS (in US$)
Program Unit Cost
of an
Enrolled
Student
Annual Fees Absolute
Subsidy
Percent
Subsidy
Degree Programs
Medicine (MUST) 4,588 1,535 3,053 66%
Basic Science 1,971 773 1,198 61%
Business/Commerce 1,278 1,085 193 15%
Fine Art 1,434 800 634 44%
Diploma Programs
Education (science) 418 560 -142 -34%
Technical 647 333 314 49%
Health Professional 1,544 836 708 46%
Hotel and Tourism 4,994 214 4780 96% Source: NCHE (2006)
Given the lack of information on returns, one can still ask whether the pattern of
subsidies reported in Table H.2 appears to make sense. The highest income profession—
medicine—has one of the highest subsidies, both in absolute and percentage terms. The
most costly degree program—hotel and tourism—has the lowest fees and highest subsidy
levels. And one program where it could be argued the Government may wish to
encourage enrollments—science education—has a negative subsidy. On the other hand,
business programs are only lightly subsidized, which could be argued is appropriate, and
basic science is quite highly subsidized, which again could be argued is appropriate. On
the face of it, there is plenty of room to improve the allocation of public subsidies across
degree and diploma programs by raising fees and reducing subsidies for those programs
which are especially costly and those which generate high incomes for graduates and to
increase subsidies for those programs which arguably have high social benefits but
relatively low incomes.
In terms of equity, one would hope that higher income students would receive
lower subsidies than lower income students. In general, university students come from
households with higher average incomes [Ush 3.1m] than do students in all tertiary
programs [Ush 1.5m] and young people who never enroll in higher education [Ush
0.4m].75
An expenditure allocation that would improve equity is one that would give
lower subsidies to degree than diploma students. Excluding hotel and tourism, that does
not appear to be the case.
Do higher education financing policies promote internal efficiency?
75
Calculated from the 1999/2000 Integrated Household Survey as reported in World Bank (2004).
79
Another way of answering the question whether the allocation of public higher
education expenditures is appropriate is to analyze whether the mechanisms used to fund
higher education programs promote either efficiency or equity. Do funding mechanisms
provide incentives for universities and other institutions to operate efficiently, or do they
pose obstacles to the wise use of resources?
The public funding mechanism varies by institutional type. For the recurrent
budget, public universities receive a subvention per government-funded student, and the
Government sets both the subvention amount and the number of students to be funded.
This is equivalent to a block grant to the university and provides no specific efficiency
incentives. On the other hand, the equity implications of this grant are almost certainly
negative as it is the students from advantaged homes and advantaged secondary schools
who are most likely to obtain the scores that “win” a scholarship. Furthermore, since
Government-funded students’ attendance is assured, the universities have incentives to
play such students in academic disciplines or career tracks which face low demand from
fee-paying students. While this may appear efficient from the university’s perspective, it
almost certainly is not from society’s perspective.
Public post-secondary institutions managed by the BTVET directorate do not
receive a block grant. Rather, they receive funding based on the inputs authorized by the
MoES and MOF. Input-based funding always carries with it rigidities in terms of how
funds can be spent, and in practice in Uganda is unrelated to enrollments or outputs.76
This funding mechanism provides no incentives to institutions to manage their resources
efficiently and often results in non-personnel inputs being underfunded.
Government funding of public higher education in Uganda should be reformed to
[a] directly tie funding to the total number of students enrolled in an institution, [b] adjust
per student capitations to account for program cost differences, [c] provide incentives to
institutions to generate their own revenues, [d] reward institutions which graduate
students on time, and [e] provide additional per pupil funding for program areas of high
national priority.
Do higher education financing policies leverage public monies?
Two of the basic higher education policy decisions faced by governments are [a]
what percent of costs in public institutions should be covered by fees charged to students
and [b] what incentives should be provided to increase the supply of places offered by
private institutions? The answers to these questions determine the extent to which
Government funding leverages private sector funding. In general, public institutions
should be given the autonomy to set their own fees, but Government can influence the
fees charged different types of students or different types of instruction through selective
subsidies. The trickier question is what emphasis should be given to expanding
enrollment vs. ensuring quality of instruction and research.
76
Kasozi (2003) finds no correlation between public subventions received by institutions and total student
enrollment.
80
By simultaneously restricting public subsidies [e.g., the number and per student
subsidy of sponsored university students], allowing institutions to set student fee levels,
and facilitating the entry of privately managed institutions, higher education finance in
Uganda encourages private finance, at least at the university level.77
As shown earlier in
Table A 2, private finance is almost as important as public finance at the tertiary level.
One result of this policy is that the public subsidy per university student in Uganda [e.g.,
US$ 459 at Makerere University and US$ 591 at Kyambogo University] is lower as a
ratio to GDP per capita [less than 2.0] than is true for Sub-Saharan Africa as a whole,
where the ratio is over 4.0.78
The fees paid by students in public universities is
approximately equal to the recurrent unit cost of educating those students, but the same is
not true of the non-university or “other tertiary institutions”. Of course, encouraging
private finance means more simply charging fees, as illustrated by the case in Box H1.
BOX H1. DAR ES SALAAM MANAGEMENT REFORM.
Source: Mkude (2001) and World Bank (2002)
Despite having an enlightened policy with respect to university fees, there are no
explicit incentives to stimulate private finance and provision. While the number of
77
This is true despite occasional political interference, such as Parliament’s rejection of Makerere’s fee
increase in 2005. Institutional autonomy is in principle ensured by the Universities and Other Tertiary
Institutions Act of 2001. 78
World Bank (2004), Table 35. Unit costs are also low relative to estimates of what they should be to
provide higher education of adequate quality. See Kasozi et.al. (2002) and a recent mimeo update of
Kasozi’s work for evidence.
81
private universities has grown in recent years, especially among universities and colleges
of commerce and management, they enroll a disproportionately small proportion of total
enrollment. For example, private universities represent 81 percent of all universities but
only 29 percent of total enrollments. Selective subsidies to private institutions, especially
for development expenditures and for children from poor households or for skill areas of
high national priority, could be a cost-effective way of rapidly increasing enrollment
rates. While there has been rapid growth of private provision of university education and
business and commerce education in Uganda, the same appears to not be true for some
areas in the non-university [“other tertiary institutions”] sector. Thus, perhaps it is with
the more trade-oriented schools where the Government should experiment with selective
subsidies to encourage private provision and finance. At the university level,
Government currently sponsors and fully pays for a fixed number of students selected on
the basis of merit. Changing this policy to provide partial subsidies rather than full
subsidies to meritorious students attending public institutions would further leverage
private finance, especially if the magnitude of the subsidies were made contingent on
household income79
.
Do higher education financing policies ensure adequate quality?
By allowing public universities to charge fees to non-government sponsored
students, Uganda has ensured that prestigious universities like Makerere have been able
to maintain a reasonable level of quality. The alternative policy—prohibiting tuition fees
while expanding enrollments with fixed government budgets—would have led to rapid
declines in quality, a policy and consequence that is commonly found in some Latin
American countries. Despite its policy regarding the setting of fees, the Government’s
policy does not necessarily ensure an adequate level of quality of instruction and
research.
Relative to per capita incomes, universities in developing countries have
expensive cost structures. The market for university professors is increasingly global in
nature, meaning that universities in poor countries must pay competitive salaries, which
are high relative to per capita incomes, in order to retain their best and brightest faculty
members. For this reason, the unit cost of higher education for countries in Sub-Saharan
Africa is a much higher percentage of GDP per capita [422%] than it is for richer
countries in Europe and Central Asia [36%] or even South Asia [74%].80
At the same
time, low per capita incomes serve to constrain what even high income households can
pay in terms of university fees. And as access to secondary education in Uganda opens
up to lower income households, the average household income of secondary school
graduates will decrease, further constraining what universities can charge in tuition fees.
In short, universities are constrained in terms of their capacity to generate private
financing, meaning that if Uganda wishes to maintain and increase the quality of
universities like Makerere and Kyambogo, Government will in the end need to provide
79
See Johnstone (2004) for examples in Africa of cost-sharing in higher education. Also, the World Bank
[2004] notes that a disproportionate number of sponsored students come from the highest income strata of
families in Uganda and have the capacity to pay full fees. 80
World Bank (2004), Table 35.
82
adequate levels of funding. While it is not fiscally possible to fund high quality for all
tertiary level institutions, it may be feasible to do so for a very limited number.
Actions that Would Improve Efficiency in Government Tertiary Education
Expenditures.
The 2005 NCHE report The State of Higher Education and Training in Uganda
provides detailed recommendations for improving efficiency in the use of resources in
tertiary education81
. Adoption of the following actions are consistent with those
recommendations:
Develop an explicit strategy to accommodate growth in tertiary enrollments over
the next decade, including options for funding both capital and recurrent
expenditures and specifying the role of the private sector in funding and
provision.
Reform government funding of universities to eliminate full-funding of
government-sponsored students and replace it with capitation grants and student
loans targeted on financially needy students and on programs of high national
priority.
Substitute input-based funding of non-university [BTVET and NTCs], other
tertiary education with capitation grants and institutional and managerial
autonomy [and accountability] in the use of funds.
Improve information systems on tertiary education to regularly report unit costs
by program and institution, and track program graduates to monitor the impact of
tertiary programs on earnings and employment.
Stimulate the private provision of tertiary education, and especially the BTVET
institutions, by facilitating access by both publicly and privately managed
institutions to lines of credit for development expenditures aimed at increasing
capacity or quality82
.
81
Further, detailed recommendations are reported in the World Bank (2004) report on tertiary education. 82
One example might be the schemes being developed with the assistance of the International Finance
Corporation {IFC] in Ghana and Kenya to facilitate capital expenditures at the secondary level.
83
84
NEXT STEPS
This efficiency analysis has covered a large number of issues across several sub-
sectors. Still, it has not been possible to treat all sub-sectors in equal depth, and there is a
need to complete the analysis by undertaking additional survey and analytic work on,
especially, BTVET and teacher education. These are sub-sectors where a significant
amount of data collection and survey work will need to be undertaken prior to doing the
analysis itself.
In addition, this study has proffered several recommendations for policies and
programs to address some of the efficiency problems and issues that have been identified.
However, it has not been possible to systematically assess the costs, benefits, and
administrative and political feasibility of adopting and implementing these
recommendations. This kind of careful evaluation is required before setting priorities for
action and before converting the recommendations into programs that can be
implemented.
HIGH PRIORITY ISSUES
For the reasons given above, it is not possible to rank order the various
recommendations made in this report. However, in the absence of detailed financial
information, it is still possible to identify recommendations where the magnitude of
benefits relative to costs should be highly favorable. These are listed below by sub-
sector.
Primary Education.
Reduce Headmaster Absenteeism. Headmasters, who are well paid relative to
teachers, have double the absenteeism rate of teachers. The frequent absence of
headmasters both sets a negative role model for teachers and constrains schools in their
attempts to implement quality-enhancing reforms. Thus, the potential benefits of
reducing headmaster absenteeism are large. There are costs associated with monitoring
absenteeism, but simply improving transparency on absenteeism rates [possibly by
involving PTAs and SMCs] may be effective.
Increase Teacher Classroom Time. Teachers are also excessively absent from
school, and even when at school often absent from the classroom, leading to insufficient
student-teacher contact time and limited learning. The potential benefits of reduced
teacher absenteeism are to some extent conditional on changes in teaching practices, but
attempts to improve learning are not likely to bear fruit so long as teachers fail to teach.
Headmasters are directly responsible for monitoring teacher attendance—both at school
and in the classroom—and they should be held accountable for performing this function.
Improve Accountability Arrangements. The weakness of the district school
inspection system limits the Ministry’s and districts’ ability to hold schools accountable
85
for performance. Given the cost and difficulty of improving inspection, local governance
should be strengthened by [a] giving PTAs and SMCs additional financial and monitoring
responsibilities and [b] providing them with annual school report cards. This will require
strengthening the capacity of the CC’s to provide assistance and training to PTAs and
SMCs and will require reorienting the Ministry’s EMIS to the delivery of district and
school level report cards on financial and academic performance.
Rationalize Teacher Deployment. Large disparities in class size and student
teacher ratios across schools are neither equitable nor efficient. Staffing guidelines and
teacher deployment practices need to be revised to reduce disparities, perhaps by giving
schools the authority to approve or disapprove teacher transfers. This action may be
politically difficult, but its administrative cost is minimal.
Prioritize Grades 1-3. Students who fail to enter school on time and who fail to
achieve mastery over reading by the end of grade 3 are candidates for repetition and
dropout. For reasons of equity and efficiency, greater priority should be given to grades
1-3. Average class sizes should be smaller in grades 1-3 than grades 4-7. At least as
much emphasis should be put on what percent of P 3 students exhibit mastery of reading
as on what percent of P7 students pass the PLE. Incentives to strengthen learning in
grades 1-3 could include linking the headmaster’s annual evaluation to improvements in
P3 indicators.
Secondary Education.
Reduce Teaching Costs. Teaching costs at the secondary level should be
reduced by increasing the student-teacher ratio by simplifying the curriculum and
rationalizing teacher deployment. Double-shifting should also be seriously considered as
an option as the demand for secondary increases under UPPET.
Ensure Quality. As the demand for secondary school places continues to grow
in the face of tightly constrained government spending, there is the risk of lower
standards. MoES will need to be able to quickly identify quality related problems that
may result from double-shifting or other cost-reducing measures. In the face of this risk,
the ESA’s already stretched capacity to monitor quality is critical, and it will be
necessary to significantly increase its capacity with the aim of giving feedback to the
schools and to the MoES for the purpose of quickly identifying and correcting quality-
related problems.
Facilitate Privately Funded Expansion83
. Even with more effective and
efficient use of teachers, it is not feasible for the Government to fully fund a massive
expansion of secondary education. Government will need to continue to rely on the
private sector to both provide and to finance secondary education, and it is important that
it develop a strategy to leverage public funds by facilitating and stimulating private
finance and provision. Among other things, it should consider working with the private
83
See LaRocque (2006) for an in-depth discussion of how Uganda could foster public-private partnerships
in education.
86
capital market to ensure that secondary schools can borrow at reasonable cost for the
purpose of expanding their operations.
Tertiary Education.
Reform Government Finance. Current Government funding of “sponsored
students” is neither equitable nor efficient and should be replaced by financial aid
targeted to qualified students who could not otherwise attend a higher education
institution and by capitation grant funding that reflects national interests. At the same
time, the line-item, input-oriented budgeting of some tertiary level institutions should be
replaced by formula-funding capitation grants along with giving managers greater
resource allocation autonomy along with responsibility for results.
Stimulate Privately-Financed Supply. Government should work with the
private capital market to develop mechanisms that give fee-charging tertiary
institutions—both public and private--access to private funding to develop infrastructure
and expand supply. It should also stimulate the supply of privately managed institutions
by providing an appropriate regulatory framework and selective subsidies.
MoES.
Strengthen Information and Analysis Role. The current EMIS does a good job
of reporting student enrollment and teacher employment data. Its scope needs to be
increased—either on a sample or census basis--to collect and report financial and
expenditure information to provide continuous monitoring of financial indicators for
decision making. Furthermore, this information should be linked to school performance
results such as the PLE, SLE, and UNEB tests. The resulting data should be reported
back to the districts and schools in the form of report cards. In addition, the MoES
should strengthen its capacity to analyze data, possibly locating this function in an
expanded EMIS unit.
Carry out Selected Studies. The basic cost and output information required for
analyzing efficiency is missing for some sub-sectors, such as BTVET and Primary
Teacher Colleges and school construction. Important policy questions—such as whether
BTVET should be expanded along with general secondary education, or whether some of
the PTCs should be consolidated—cannot be answered in the absence of information and
analysis. The MoES should commission special surveys and studies covering these areas.
87
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91
GOVERNMENT BUDGET FOR EDUCATION IN 2005/06 {MTEF
CLASSIFICATION (in Ush bn.)
Total
recurrent Wage Non-Wage
Domestically-
financed
Donor-
financed
Total
expendit
ure
Uganda Management Institute 0.4 0.0 0.4 0.0 0.0 0.4
Education and Sports (incl Prim Educ) 46.9 7.3 39.6 19.3 39.5 105.7
Makerere University 33.4 0.0 33.4 0.1 17.6 51.1
Mbarara University 6.4 4.0 2.4 0.4 0.0 6.8
Kyambogo University 11.0 6.1 4.9 0.3 0.0 11.3
Education Service Commission 2.2 0.5 1.8 0.1 0.0 2.3
Makerere University Business School 4.2 0.0 4.2 0.0 0.0 4.2
Gulu University 3.2 0.0 3.2 1.2 0.0 4.4
District Primary Educ incl SFG 287.5 254.0 33.5 51.0 0.0 338.5
District Secondary Education 82.8 76.3 6.5 0.0 0.0 82.8
District Tertiary Institutions 24.0 15.7 8.3 0.0 0.0 24.0
District Health Training Schools 4.2 2.5 1.8 0.0 0.0 4.2
506.3 366.4 139.9 72.3 72.3 650.9
Recurrent expenditure Development expenditure
Source: MOFPED, MTEF tables
GOVERNMENT EXPENDITURE ON EDUCATION
Education (Ush, billion) 98/99 99/00 00/01 01/02 02/03 03/04 04/05 05/06
Total education expenditure (MTEF number) 326 356 415 512 561 587 638 656
Recurrent 241 258 286 360 396 436 484 538
Wages 158 153 180 238 274 310 358 396
Non-wages 83 106 106 122 123 127 126 142
Development 85 98 129 152 164 151 154 118
Domestic 36 67 87 96 95 81 88 69
Donor 49 31 42 56 69 70 65 49
Primary (WB estimate based on MTEF) 205 237 285 349 366 383 407 415
Recurrent 152 172 196 245 259 285 309 334
Wages 99 101 124 162 179 202 248 276
Non-wages 52 70 73 83 80 83 61 58
Development 53 65 88 104 107 98 98 81
Domestic 22 44 59 66 62 53 56 52
Donor 31 21 29 38 45 46 42 29
Secondary (WB estimate based on MTEF) 72 71 77 98 110 114 126 109
Recurrent 53 51 53 69 78 84 96 100
Wages 35 30 34 46 54 60 71 88
Non-wages 18 21 20 23 24 25 25 12
Development 19 20 24 29 32 29 30 9
Domestic 8 13 16 18 19 16 17 4
Donor 11 6 8 11 14 14 13 5
Tertiary (WB estimate based on MTEF) 49 48 53 64 85 91 104 131
Recurrent 36 35 37 45 60 67 79 104
Wages 24 21 23 30 41 48 39 32
Non-wages 12 14 14 15 19 20 40 72
Development 13 13 17 19 25 23 25 27
Domestic 5 9 11 12 14 12 14 13
Donor 7 4 5 7 10 11 11 15 Source: World Bank attempt to reclassify total government spending on education into private, secondary and “others”
92
NON-WAGE EXPENDITURES PER STUDENT IN PUBLIC AND PRIVATE SCHOOLS
Total non-
wage
expenditure
School
materials Repairs Utility bills
Non-
teacher pay
and
teacher
lunch No. schools
Government 4,728 3,120 696 111 801 130
Private 30,965 20,756 3,399 2,348 4,461 19
Government aided (private and NGO) 6,149 4,127 1,084 217 722 7
Total 7,987 5,314 1,042 388 1,243 156
Mean spending per student (Ush)
93
GOVERNMENT SCHOOLS: MEAN AND MEDIAN PER STUDENT HOUSEHOLD EXPENSES ON EDUCATION, BY
URBAN/RURAL AND EXPENSE ITEM
SCHOOL FEES
UNIFORMS
AND SPORTS
CLOTHES
BOOKS AND
SUPPLIES
BOARDING
FEES OTHER
TOTAL
SCHOOL
EXPENSES
SCHOOL
FEES
UNIFORMS
AND SPORTS
CLOTHES
BOOKS AND
SUPPLIES
BOARDING
FEES OTHER
TOTAL
SCHOOL
EXPENSES
MEANSPRIMARY
urban 32,182 7,784 9,603 1,701 14,751 66,107 161,775 13,331 18,046 19,501 27,117 233,601
rural 6,701 4,102 5,218 993 3,831 20,326 71,223 6,777 8,824 10,445 16,322 112,451
Total 9,006 4,435 5,616 1,055 4,823 24,568 98,066 8,745 11,592 13,075 19,602 149,643
SECONDARY
urban 354,126 20,348 44,812 45,846 82,794 487,519 331,663 22,270 48,281 82,116 57,949 545,547
rural 247,221 18,043 32,334 42,349 55,818 363,633 224,759 16,961 31,545 42,145 55,840 344,247
Total 270,123 18,522 35,053 43,053 61,821 391,512 259,336 18,705 37,040 55,179 56,530 413,703
MEDIANSPRIMARY
urban 7,000 5,500 5,200 - 1,000 26,000 114,000 10,000 11,000 - 5,000 155,800
rural - 4,000 3,600 - 600 11,500 33,500 5,000 5,000 - 2,100 54,000
Total - 4,000 3,600 - 700 12,200 49,200 6,000 6,000 - 3,000 79,000
SECONDARY
urban 270,000 15,000 26,000 - 22,000 405,000 230,000 20,000 28,000 - 20,000 362,000
rural 165,000 15,000 20,000 - 16,000 273,000 150,000 15,000 21,500 - 15,000 227,500
Total 183,000 15,000 20,500 - 20,000 291,000 170,000 15,000 24,000 - 18,000 283,800
PUBLIC SCHOOLS PRIVATE SCHOOLS
94
TREND IN HOUSEHOLD PRIMARY SCHOOL EXPENDITURE: MEAN AND MEDIAN PER STUDENT ANNUAL
HOUSEHOLD SCHOOL EXPENSE BY ITEM*. Uganda Household Surveys 2002 and 2006. In Ugandan Shillings.
SCHOOL
FEES
SCHOOL
FEES AND
BOARDING/
LODGING UNIFORM
BOOKS
AND
SUPPLIES
TOTAL
SCHOOL
EXPENSES
SCHOOL
FEES
SCHOOL
FEES AND
BOARDING/L
ODGING UNIFORM
BOOKS
AND
SUPPLIES
TOTAL
SCHOOL
EXPENSES
SCHOOL
FEES
SCHOOL
FEES AND
BOARDING/
LODGING UNIFORM
BOOKS
AND
SUPPLIES
MEANSALL SCHOOLS
ALL STUDENTS 8,273 8,720 2,909 2,547 15,658 11,893 12,511 3,447 4,182 23,776 14,254 14,994 4,132 5,012
URBAN 39,509 41,977 4,632 4,480 54,341 52,254 54,553 6,538 8,011 80,211 62,625 65,380 7,836 9,601
RURAL 5,134 5,378 2,735 2,353 11,770 7,227 7,650 3,090 3,739 17,251 8,661 9,168 3,703 4,481
PUBLIC SCHOOLS - - - -
ALL STUDENTS 3,712 3,969 2,749 2,299 10,452 4,892 5,068 3,125 3,696 14,604 5,863 6,074 3,745 4,430
URBAN 21,357 22,492 4,267 3,597 33,942 25,712 26,980 5,063 6,481 47,466 30,816 32,334 6,068 7,767
RURAL 2,521 2,719 2,647 2,211 8,866 3,236 3,326 2,971 3,475 11,991 3,879 3,986 3,560 4,164
NONPUBLIC SCHOOLS - - - -
ALL STUDENTS 34,979 36,466 3,758 3,970 45,576 50,997 53,729 5,339 5,945 73,617 61,118 64,393 6,398 7,125
URBAN 65,690 68,989 4,876 5,575 81,499 94,064 98,607 8,971 9,728 132,740 112,733 118,177 10,751 11,659
RURAL 19,653 20,235 3,199 3,169 27,648 32,498 34,453 3,778 4,320 48,221 38,948 41,290 4,528 5,178
MEDIANSALL SCHOOLS
ALL STUDENTS - - 2,500 1,500 6,000 - 278 2,920 2,670 10,013 - 333 3,500 3,200 URBAN 14,000 15,000 3,750 2,800 26,250 12,516 12,516 4,589 4,172 36,922 15,000 15,000 5,500 5,000 RURAL - - 2,500 1,500 5,800 - - 2,698 2,503 9,262 - - 3,233 3,000 PUBLIC SCHOOLS - - - - - - - - - - - - - - ALL STUDENTS - - 2,500 1,500 5,533 - - 2,781 2,503 8,511 - - 3,333 3,000 URBAN - - 3,333 2,100 12,750 4,172 4,673 4,172 3,504 18,294 5,000 5,600 5,000 4,200 RURAL - - 2,500 1,500 5,400 - - 2,642 2,503 8,205 - - 3,167 3,000 NONPUBLIC SCHOOLS - - - - - - - - - - - - - - ALL STUDENTS 17,500 18,000 3,000 1,800 23,000 25,032 25,032 4,172 3,588 38,215 30,000 30,000 5,000 4,300 URBAN 55,400 55,400 4,000 3,100 67,000 75,096 75,096 6,675 5,757 99,794 90,000 90,000 8,000 6,900 RURAL 9,000 9,000 2,500 1,333 15,200 12,516 13,350 2,920 3,087 25,338 15,000 16,000 3,500 3,700
2006, constant 2002 prices2002 2006, current prices
95
TREND IN HOUSEHOLD SECONDARY SCHOOL EXPENDITURE: MEAN AND MEDIAN PER STUDENT ANNUAL
HOUSEHOLD SCHOOL EXPENSE BY ITEM*. Uganda Household Surveys 2002 and 2006. In Ugandan Shillings.
SCHOOL
FEES
SCHOOL
FEES AND
BOARDING/
LODGING UNIFORM
BOOKS
AND
SUPPLIES
TOTAL
SCHOOL
EXPENSES
SCHOOL
FEES
SCHOOL
FEES AND
BOARDING/
LODGING UNIFORM
BOOKS
AND
SUPPLIES
TOTAL
SCHOOL
EXPENSES
SCHOOL
FEES
SCHOOL
FEES AND
BOARDING/
LODGING UNIFORM
BOOKS
AND
SUPPLIES
MEANSALL SCHOOLS
ALL STUDENTS 123,770 128,524 8,430 8,946 150,794 134,699 142,201 12,333 19,869 198,539 161,432 170,423 14,781 23,812
URBAN 145,034 154,728 7,417 9,609 176,407 166,775 167,421 16,184 23,976 232,387 199,875 200,649 19,396 28,734
RURAL 113,741 116,165 8,909 8,634 138,714 115,625 127,204 10,043 17,426 178,412 138,573 152,450 12,037 20,885
PUBLIC SCHOOLS
ALL STUDENTS 121,185 123,458 7,541 7,991 144,776 104,547 118,709 11,204 14,189 169,404 125,296 142,269 13,427 17,006
URBAN 129,624 130,111 8,122 9,914 153,916 55,055 55,055 13,270 8,070 110,940 65,981 65,981 15,904 9,672
RURAL 118,872 121,634 7,382 7,464 142,269 121,946 141,086 10,477 16,341 189,956 146,148 169,087 12,556 19,584
NONPUBLIC SCHOOLS
ALL STUDENTS 120,243 121,041 8,998 8,756 142,870 136,107 138,128 13,841 22,067 197,718 163,120 165,542 16,588 26,447
URBAN 160,352 160,622 7,112 7,397 178,627 187,360 187,360 19,738 29,123 259,665 224,545 224,545 23,656 34,903
RURAL 94,667 95,802 10,201 9,622 120,069 101,640 105,019 9,875 17,322 156,060 121,813 125,863 11,835 20,759
MEDIANSALL SCHOOLS
ALL STUDENTS 108,000 115,000 6,500 8,000 143,800 111,809 112,644 10,013 14,519 173,137 134,000 135,000 12,000 17,400 URBAN 106,000 144,000 2,500 7,200 189,000 138,927 138,927 16,688 23,085 222,367 166,500 166,500 20,000 27,667 RURAL 108,000 114,000 8,000 8,000 131,500 100,128 101,129 8,344 12,516 147,688 120,000 121,200 10,000 15,000 PUBLIC SCHOOLS
ALL STUDENTS 114,000 115,000 5,000 6,500 129,000 100,128 100,128 10,013 9,011 134,505 120,000 120,000 12,000 10,800 URBAN 120,000 130,000 8,000 6,000 163,500 - - 11,682 2,086 54,236 - - 14,000 2,500 RURAL 108,000 115,000 5,000 7,000 129,000 112,644 112,644 8,344 10,013 147,688 135,000 135,000 10,000 12,000 NONPUBLIC SCHOOLS
ALL STUDENTS 90,000 90,000 6,000 8,000 114,000 120,988 125,160 10,847 16,688 174,556 145,000 150,000 13,000 20,000 URBAN 144,000 144,000 - 6,750 189,000 175,223 175,223 17,940 25,032 249,485 210,000 210,000 21,500 30,000 RURAL 90,000 90,000 10,000 8,000 101,000 87,612 87,612 6,953 12,516 125,160 105,000 105,000 8,333 15,000
2002 2006, constant 2002 prices 2006, current prices
96
TREND IN HOUSEHOLD PRIMARY AND SECONDARY SCHOOL EXPENDITURE: MEAN PER STUDENT TOTAL
ANNUAL HOUSEHOLD SCHOOL EXPENSE BY GROUPS OF HOUSEHOLDS. Uganda Household Surveys 2002 and
2006. In Ugandan Shillings.
households with
at least one
member in
school of any
level
households
with children
only in primary
school
households with
children only in
secondary
school
households
with at least
one member in
school of any
level
households
with children
only in primary
school
households
with children
only in
secondary
school
households
with at least
one member in
school of any
level
households with
children only in
primary school
households
with children
only in
secondary
school
MEANSALL SCHOOLS
ALL STUDENTS 38,686 15,658 150,794 61,443 23,776 198,539 73,637.5 28,494.5 237,943.5 URBAN SCHOOLS 94,505 54,341 176,407 148,816 80,211 232,387 178,351.4 96,130.8 278,509.3 RURAL SCHOOLS 29,519 11,770 138,714 45,913 17,251 178,412 55,025.6 20,674.6 213,821.4 PUBLIC SCHOOLS
ALL STUDENTS 20,251 10,452 144,776 27,759 14,604 169,404 33,267.9 17,503.0 203,025.2 URBAN SCHOOLS 55,576 33,942 153,916 72,771 47,466 110,940 87,213.8 56,886.0 132,958.2 RURAL SCHOOLS 17,449 8,866 142,269 23,685 11,991 189,956 28,386.2 14,370.9 227,656.8 NONPUBLIC SCHOOLS
ALL STUDENTS 66,779 45,576 142,870 114,172 73,617 197,718 136,831.3 88,227.2 236,959.5 URBAN SCHOOLS 101,050 81,499 178,627 178,050 132,740 259,665 213,387.8 159,084.5 311,200.5 RURAL SCHOOLS 46,869 27,648 120,069 80,634 48,221 156,060 96,637.0 57,791.2 187,033.0
MEDIANSALL SCHOOLS
ALL STUDENTS 9,750 6,000 143,800 16,354 10,013 173,137 19,600 12,000 207,500 URBAN SCHOOLS 63,000 26,250 189,000 99,877 36,922 222,367 119,700 44,250 266,500 RURAL SCHOOLS 8,250 5,800 131,500 13,100 9,262 147,688 15,700 11,100 177,000 PUBLIC SCHOOLS
ALL STUDENTS 6,333 5,533 129,000 9,700 8,511 134,505 11,625 10,200 161,200 URBAN SCHOOLS 19,100 12,750 163,500 23,889 18,294 54,236 28,630 21,925 65,000 RURAL SCHOOLS 6,000 5,400 129,000 9,262 8,205 147,688 11,100 9,833 177,000 NONPUBLIC SCHOOLS
ALL STUDENTS 36,480 23,000 114,000 62,163 38,215 174,556 74,500 45,800 209,200 URBAN SCHOOLS 78,500 67,000 189,000 127,329 99,794 249,485 152,600 119,600 299,000 RURAL SCHOOLS 21,667 15,200 101,000 36,296 25,338 125,160 43,500 30,367 150,000
2002 2006, constant 2002 prices 2006, current prices
97
ANNEX 2: SCHOOL GRANTS.
What are School Grants? School grants are transfers of financial resources and authority from
governments or non-governmental organizations directly to schools or small networks of schools.
School grants are managed by the school director, a school council, or parent-teacher association
(PTA) with the legal authority to receive and spend funds. School grants are often supported by
education development projects financed by bilateral and multilateral organizations.
School grants can be either unconditional or conditional. Unconditional school grants are those
that the receiving organization may spend as it wishes. An example is Nicaragua’s Autonomous
School model, where the Ministry of Education transfers a monthly lump sum payment to
secondary schools who then independently decide how to spend funds. Conditional school grants
are financial resources transferred to the school level for the purpose of purchasing specific
school inputs such as textbooks or teacher training or to fund school improvement projects.
What Are the Objectives of School Grants? The objectives of school grants vary widely. It is
precisely this capacity to address multiple and different objectives that makes them an attractive
policy tool. The improvement of the quality and relevance of school inputs—better teacher
performance, increased provision and relevance of textbooks and school materials, improved
school infrastructure—motivate many school grant projects. The Small Grants for School
Improvement Program in Guinea enables teachers to take responsibility for their own professional
development. Teams of up to 10 teachers work together to determine their own professional
development needs, and to compete for small grants.
Some school grant programs also have the objective of improving school access and/or equity and
use a targeting mechanism to meet the needs of populations underserved by the education system.
With the aim of serving the poorest and most isolated communities, El Salvador’s Education with
Community Participation Program channels education funds through parents’ organizations at the
community level to hire teachers and manage educational services first-hand.
Improvement of management and efficient utilization of resources represents a fourth stated
objective of school grant schemes. In most countries, teachers’ salary expenditures eclipse
essential non-salary expenditures. School grants that are earmarked for non-personnel inputs are
one means of ensuring minimum provision of such inputs.
What are Some Design Features of School Grants? School grant funds are often formula-
based, with poverty rates and student population determining the funding amount. Some school
grant plans incorporate a targeting mechanism to reach underserved populations. School grants
can be competitive or simply based on fulfillment of particular criteria. School grant schemes can
also offer incentives based on performance. Ethiopia’s Community-Government Partnership
Program bases the opportunity for continued program participation on approved financial and
subproject management of previous grants, and schools progress through three phases of funding.
Each phase is worth increasingly more funding, and the application criteria become increasingly
more rigorous. Alternatively, Chile’s National Teacher Performance Evaluation System (SNED)
awards its incentive grants based on student achievement.
To increase accountability for funds, a variety of programs include safeguards. Indonesia’s
School Improvement Grants Program requires that two members of the school committee, the
head teacher, and the community representative sign to open the school’s bank account and to
approve each withdrawal and use of funds. At each phase in Ethiopia’s Community-Government
Partnership Program, the school sponsors an open-house to inform the larger community about
school improvement efforts. After completion of the project, the school holds another open-house
to convey its accomplishments.
Examples of International Experience
1. Chile--National Teacher Performance Evaluation System (SNED)
Established in 1994, Chile’s National System of Performance Assessment (SNED) awards
teacher incentive grants to schools based on an index of school excellence measures.
Objectives of School Grant. The SNED creates competition among schools to encourage
teachers to improve their performance.
Design Features. Chile’s National System of Performance Assessment (SNED) program
mandates that schools spend grants in the form of teacher incentive awards and teacher bonuses.
The teacher incentive grants are conditional in that awarded school directors must use 90 percent
of the grant for teacher bonuses based on hours worked. The school director is to allocate the
residual 10 percent to “outstanding” teachers at his/her discretion to avoid the “free-rider”
problem. Another design feature of the SNED program is that the teacher incentive grants are
distributed through a competitive process. Schools are stratified within regions by socioeconomic
status and other external factors that affect school performance. This ensures that the process is
competitive among comparable establishments. Every two years, schools are ranked according to
an index of school performance measures using the national System for Measuring Educational
Quality (SIMCE) test as the basic criterion. Schools can win the teacher incentive grants
repeatedly.
2. Guinea— Programme de Petites Subventions aux Écoles (PPSE)
Since 1994, Guinea has been implementing a unique and promising World Bank funded program
that integrates school improvement with professional development for teachers known as the
Small Grants Staff Development and School Improvement Program (PPSE). PPSE is a
conditional school grant program that engages primary school teachers to participate in the
process of education quality improvement through competitive small grants of approximately
$1000 that are awarded to school-based teams of teachers.
Objectives of School Grant. The overall objective of Guinea PPSE is to improve the quality and
relevance of specific school inputs, which in this case are teachers. As a means of improving the
quality of primary education, PPSE provides organizational support and the incentives necessary
for teachers to assume primary responsibility for their own professional development and to
determine what is most appropriate in their local context for improving teaching practices.
Furthermore, this program seeks to give teachers greater professional autonomy to analyze
teaching and learning problems at the classroom level, define the problems or issues to be
addressed in a 1-year project, propose and implement solutions, then evaluate and report results.
Design Features. Diverging from the traditional top-down approach of in-service teacher
training where central education authorities mandate workshop contents for large groups of
teachers, PPSE allows teams of teachers to design professional development programs unique to
their local context and compete for grant funds to implement their own programs. Teachers learn
about PPSE grant competition through a series of workshops led typically by a pedagogical
advisor or a regular school teacher, who presents the program’s operational manual and proposal-
writing guidelines. Interested teams of teacher then go through a two-cycle, highly structured
competition. First, teacher teams determine the contents of their projects, prepare their own
budget, and then submit preliminary proposals for their own professional development program
to a prefectural jury, which is presided over by the prefectural director of education (DPE) and
composed of retired teachers and local education leaders. Second, once promising proposals are
selected, pre-selected teacher teams are invited to revise their proposals with help from the
facilitators based on critical comments received from a prefectural jury, and then submit their
final proposals to a regional jury who makes final decisions of which team will receive grants.
The regional jury is presided over by the Regional Inspector of Education (IRE) and composed of
local educational leaders. Selected teacher teams are granted full funding, provided with project
implementation support from the project facilitator, and visited by an evaluator, who is typically a
prefectural or regional jury member, three times throughout the 1-year project cycle. In addition,
since PPSE also has performance incentives as one of its design features, teacher teams are
given the option of renewing their grant if they show that their projects attained good results.
School grant schemes can also offer incentive based on performance.
3. Indonesia’s School Improvement Grant Program
Indonesia initiated the School Improvement Grant Program (SIGP) for primary and junior secondary
schools as part of the large school safety net program to mitigate the impact of the economic crisis of
1997.
Objectives of School Grant. The SIGP, funded by the Royal Netherlands Government through a
World Bank Trust Fund, targets large one-off grants to a small number of schools based on the
following three categories:
1. Schools coping with a large increase of displaced students due to civil unrest and social conflict
2. Schools with sufficient damage from natural disaster
3. Schools among the poorest 10 percent in the poorest 10 percent of districts
Design Features. Though the program depends upon district-level education officials for school
selection, funds go directly from the Government of Indonesia to schools. School committees,
composed of teachers, local government authorities, and community members allocate funds
depending on the category in which the school falls. For category one, school grants might be used
towards purchasing furniture or more textbooks to cope with the influx of internally displaced
students. For categories two and three, grant monies might be used towards the repair of water
supplies and improvement of toilet facilities. Before SIGP funds, lack of adequate toilet facilities
forced students to use nearby streams or fields, discouraging parents from sending girls to school and
disrupting classes through frequent requests for toilet breaks.
Many steps incorporated into the implementation of the SIGP seek to enhance grant effectiveness.
For example, with construction as a central component of many subprojects, on each district
committee, the SIGP demands the participation of a member of the district’s department of public
works. At the school-level, the head teacher and one community representative must sign for each
withdrawal of grant funds and inform the community through the school notice board what the school
committee will use the SIGP funds for. Finally, category three grant seeks to fine-tune the allocation
of grant funds so that resources reach those with greatest needs.
4. Tanzania—Community Education Fund (CEF)
Started in 1995, the Community Education Fund (CEF) program is a non-competitive matching
grant program that has been designed to increase the allocation of public funding for non-salary
expenditures at the school level and to empower communities to improve their primary schools.
Objectives of School Grant. The objectives of the CEF program are as follows:
Increase community involvement in the management of school resources and create a
sense of ownership for school inputs.
Overcome lack of public financing and improve quality of school inputs.
Increase access to schooling and improve quality of education performance (test scores).
Ensure funding reach school in a timely manner and resources are available for the
quality control of schools.
Design Features. There are several criteria that schools have to meet in order to be eligible to
participate in the CEF program. These criteria include registration as a primary school to operate
in Tanzania, constitution of a school committee that is comprised of members elected by the
parents, and development of a Three-Year School Plan with a contractual agreement between
the school and the school community. The community decides by majority vote whether they
would like to participate in the CEF program. If they decide to participate in the program, the
community may raise funds by collecting cash contributions from parents and local businesses,
and through various fund-raising activities. The government will then match the money raised by
the community on a 1-to-1 basis, but the amount is not to exceed Tsh. 6,000 per pupil. One of the
salient features of the CEF program is that funds are being allocated directly to primary schools.
The schools, however, may not exclude students from attending if the parents are unable to
contribute to the CEF. Furthermore, students may not, under any circumstances, be used to
generate funds for the schools by providing labor outside the proximity of the school property.
The CEF is a matching fund as well as a targeted fund program. This is because disadvantaged
schools are eligible to receive additional targeted subsidy of 0.5-to-1.
One way of ensuring accountability, the CEF grant program is designed in such a way that not all
schools may continue to participate in the CEF program even if they satisfy the initial eligibility
criteria. There are several requirements that participating schools have to meet in order to
maintain their status. In addition to complying with their own Three-Year Plan, schools must
show consistent improvement in school enrollment without jeopardizing student attendance and
performance.
5. Armenia-School Improvement Program (SIP)
Armenia’s School Improvement Program (SIP) aims at promoting community and parental
participation in school financing and management, and at building capacity for reform
management. The Ministry of Education and Science implemented SIP through support from the
World Bank in 1998, and the program is headed by a SIP Board chaired by the Ministry of
Education and Science. The SIP is a conditional and competitive grant program that supports
implementation of policies for school autonomy and innovative school improvement projects
(micro projects) aimed at increasing education quality by channeling resources directly to
schools.
Objectives of School Grant. The objective of the SIP is to improve education quality by
decentralizing school management, increase school autonomy by formalizing community and
parental participation in school management, and support capacity building at the school level.
Design Features. The SIP makes grant funds available to individual schools based on the budget
determined by their elected parent-teacher board. Schools have to meet a list of specified criteria
to qualify for submitting grant proposals on an annual basis and to receive funding of up to a
maximum of US$ 15,000 for micro-projects. Some criteria for eligibility include (i) autonomous
school, which can be demonstrated through having an active school board composed of elected
teachers and parents following national guidelines; and (ii) an original micro project business
plan for improving school quality, according to locally defined objectives, prepared by elected
parent-teacher board. The SIP has several design features:
Matching—it is a requirement for community to contribute at least 10% of total project costs.
Among schools with similar conditions, the SIP is biased towards schools making a larger
contribution to the total project costs. School contributions may include both monetary and in-
kind, such as provision of school materials, equipments and labor.
Formula based--the central government uses a simple formula based on pupil population to
allocate funds across clusters of schools. Each individual grant provides the school with out-of-
budget resources for further school development as determined by the school council itself. Grant
scope of activities include training of teachers and administrative staff, new organization of
school management with community and teacher participation, and integration of children with
special needs into the school process.
Competitive—presented project must not duplicate the design of school improvement projects
initiated by others or ongoing projects.
Conditional—the micro-project must be implemented by the elected parent-teacher board and the
quantity of work carried out must conform to the timetable submitted with the proposal.
ANNEX 3: TEACHER ABSENTEEISM
Characterizing Teacher Absence in Uganda: Evidence from 2006 Unit Cost Study by James Habyarimana, March 2007.
Introduction and Motivation
The relationship between schooling inputs and educational outcomes continues to receive
wide attention in discussions about how to improve educational outcomes. A
predominant share of educational inputs in developing countries are publicly provided.
Primary among these inputs is a key input in cognitive achievement: teacher instruction.
The importance of teacher instruction is underlined by the fact that it is difficult for
households to find alternative substitutes, particularly in developing countries where the
markets for private instruction is very thin and parental levels of education are too low (to
support significant self-provision).
In addition to being a vital input in the production function for learning, teacher salaries
account for a large fraction of recurrent expenditure in all levels of schooling, and even
more so at the primary level. Bruns et. al. (2003) estimate that primary school teacher
remuneration in developing countries account for between 50-80% of recurrent
expenditure.
Despite the importance of instructional time in the production function and its demands
on meager public resources, recent evidence from a cross-section of studies around the
world has revealed very high levels of teacher absenteeism. The best estimates come
from a multi-country study in which teacher absence is measured using direct observation
(Chaudhry et. al. 2006). The multi-country surveys were conducted at the end of
2002/early 2003 and found absence rates ranging from 11% in Peru to 27% in Uganda.
This chapter presents the results of a follow-up study to measure and characterize teacher
absence in Ugandan schools. The timing of the study is crucial to understand and
possibly evaluate a number of important policies that have been implemented in the
intervening period. Chief among these is the increased control (and experience) that
district authorities are exercising over the management of primary education. Many
proponents of decentralization argue that assigning control to managers with better
information and possibly incentives leads to higher output conditional on inputs.
The main findings reported in this chapter are that:
1. The level of teacher absenteeism is high. Nearly 20% of all teachers could
not be found in the school at the time of enumerator visits.
2. A large fraction of teachers that are present were not in class at the time of
the enumerator verification. In fact nearly one third of teachers were
outside of the classroom when they were found.
3. Teacher absence is very heterogenous. Variation between districts
accounts for less than 2% of variation, while districts and schools account
for only 18% of total variation.
4. We find a strong negative association between parental involvement
(parental contributions of resources and a higher frequency of parent
meetings) and school level absence.
5. We document a strong negative association between the number of
functioning teacher housing units available and the level of teacher
absence
6. The presence of neighboring non-government owned schools is associated
with lower levels of teacher absence. This is potential evidence of the
effects of competition.
7. Individual characteristics such as teaching in the district-of-birth and age
are significantly associated with teacher absence.
8. The results of the study conducted at the end of 2006 tentatively suggest
that absenteeism has fallen relative to the levels measured in 2002/2003.
We discuss the possibility that this result is affected by the composition of
the sample.
Understanding the sources of such high levels of teacher absence is crucial for the design
of more effective systems to produce the cognitive skills required to be productive. A
number of studies have attempted to evaluate the extent to which the measured absence is
a result of weak incentives (salaries) and/or weak enforcement (monitoring by managers
of education or parents). While earlier studies primarily used non-experimental data
(King and Ozler (1999)), recent studies present evidence from randomized interventions
targeted at different aspects of instructional time. (Duflo and Hanna (2006); Glewwe et.
al. (2006); Miguel et. al. (2007) and Karthik (2007)). The results of these studies provide
a measure of confidence on the reliability of different types of interventions that are
likely to generate improvements in teacher attendance.
The rest of this chapter is organized as follows: section 2 discusses data and sampling
issues, section 3 explains the methodology and presents baseline estimates of teacher
absence. Section 4 discusses the reasons for teacher absence. Section 5 compares the
results to estimates from the 2002 survey. Section 6 presents the individual-, school- and
district-level correlates of absence. Section 7 discusses the results and concludes.
Data
The data used for this exercise comes from 160 schools in six districts from three regions
in Uganda. There are no districts from the Northern region in this sample owing to an on-
going parallel and region-specific study. All schools were visited in November 2006. The
districts were selected from the eligible universe (excluding Northern districts) with a
sampling probability proportional to population share. Population estimates were drawn
from estimates provided by the Uganda Bureau of Statistics (www.ubos.or.ug). Care was
taken to ensure that the drawn sample reflected the broad attributes of the population with
respect to socio-economic status (see appendix for details).
Having selected the districts, the list of schools was stratified by ownership into two
categories: government-owned schools and non-government owned schools. The list of
the schools and their attributes comes from the 2005 Education Management Information
System (EMIS) school returns data. The stratification was done in order to generate a
sample of non-government owned schools that was large enough to produce reliably
precise measures of central tendency for non-government owned schools. Within the each
strata, schools were selected with sampling probability proportional to pupil enrollment.
23 government schools and 7 non-government schools were selected for each district. By
design, about ¾ of the schools sampled are government owned and the rest non-
government owned. Non-government owned schools include community schools,
private-owned and schools owned/managed by non-governmental organizations (chiefly
religious organizations). The appendix shows the characteristics of schools in selected
districts and sampled schools relative to non-sampled schools in the country and selected
districts respectively.
Owing to time constraints, only 160 out of the sampled 180 schools could be visited
before the end of the third term. Of the 160 schools that were visited 127 were
government schools and 33 were non-government schools. Table 1 below presents means
of selected characteristics. The average school in the sample had an average of 11.6
teachers and a pupil-teacher ratio of 48.4. Only 5% of the schools in the sample were
multi-grade schools, where more than one grade is taught in the same classroom. 20% of
the schools were located in peri-urban or urban areas, with nearly two thirds in peri-urban
areas. One third of schools is located within 5 kilometers of a main road and nearly two-
thirds are located within 5 km of a taxi stage.
Table 1: School characteristics, selected means by ownership
Non-
government
Government Total
Number of teachers 10.64
(0.98)
11.90
(0.42)
11.63
(0.39)
Pupil teacher ratio 38.42
(3.29)
51.09
(1.23)
48.44
(1.25)
Proportion of mother’s literate 0.72
(0.07)
0.60
(0.04)
0.63
(0.03)
Multi-grade school 0.06
(0.04)
0.05
(0.02)
0.05
(0.02)
Proportion of schools within 5km of main road 0.39
(0.09)
0.32
(0.04)
0.34
(0.04)
Proportion of schools within 5km of taxi stand 0.79
(0.07)
0.62
(0.04)
0.65
(0.04)
Proportion Urban 0.36
(0.09)
0.15
(0.03)
0.20
(0.03)
Number of observations 127 33 160
Since the primary focus of this chapter is on teachers, it is worth pointing out a number of
important teacher characteristics. A total of 1837 teachers were surveyed as part of this
exercise. This includes all teachers in schools with less than 20 teachers and a maximum
of 20 for schools that had more than 20 teachers. A little more than 10% of schools had
20 or more teachers.
As figure 1 shows, 60% of all teachers are class teachers/permanent teachers; 14% are
heads of department and 20% are head-teachers or deputy head teachers. The rest are
volunteer, private or other part-time teachers. Figure 1: Teacher composition
Teacher Rank9%
10%
14%
41%
20%
1%1%0%4%Head Teacher
Deputy/Asst. Head
Director of Studies/Head
Class Teacher
Permanent/Regular
Private
Temporary/Probationary
Volunteer
Other
The demographic structure of our sample of teachers is presented in table 2 below. 58%
of teachers are male. The average age in the sample was 35.6 and they had been teaching
in the school for an average of 4.4 years. Only about a quarter of all teachers had
completed A-levels. More than ¾ of teachers are married and fluent in the language
spoken in the area around the school. More than half the teachers are teaching in their
district of birth. This is in part an outcome of autonomy in hiring by the districts and the
tendency for districts boards to hire ‘locals’. 63% of all teachers live in the same parish as
the school.
In the sections that follow, we restrict our analysis to full time teachers. This involves
dropping about 1.7% of observations that correspond to part-time, private, volunteer
teachers or teachers that have been transferred but are still on the school roster.
Table 2: Teacher characteristics, selected means
Mean
(Standard
Error)
Gender (1=Male) 0.58
(0.01)
Age, years 35.60
(0.32)
Tenure of current posting 4.40
(0.12)
Proportion that have completed A-levels 0.24
(0.01)
Proportion teaching in district of birth 0.54
(0.01)
Proportion that live in the same parish as school 0.63
(0.01)
Proportion fluent in language spoken around school 0.77
(0.01)
Proportion married 0.76
(0.01)
Number of Observations 1843
Measuring Absence
Teacher absence is measured in three ways. Firstly we ask the head teacher or the
primary respondent (typically a deputy head teacher or senior teacher) about the
attendance of up to 20 teachers per school.84
We call this the head teacher spot absence
report. Secondly we ask the teacher about the duration of absence for all teachers over the
last 30 days. We call this the head teacher absence duration report. Finally, we use direct
observation to assign absence status; the enumerator marks a teacher as present, if the
enumerator can find that teacher during his/her visit during working hours. A teacher is
absent if the enumerator cannot find this teacher within the school boundaries. All
measures rely on the fact that each of the school visits is unannounced.85
The head teacher reports of absence are unreliable for a number of reasons:
1. The head teacher/primary respondent has incentives to misrepresent the
absence status of teachers since this reflects on his/her capacity to
effectively manage the school.
2. The head teacher is unlikely to be aware of the absence status of all
teachers at all times.
3. Recall bias is likely to be significant given a 30-day recall period and the
fact that the head teacher must report a duration for every teacher on his
roster.
84
In schools with 20 or few teachers, all teachers are sampled. In larger schools, the 20 teachers are drawn
randomly using a random number table. This random number table differs from school to school. 85
Care was taken to ensure that schools were not informed of when or if enumerators would visit the
school. District education offices were informed of the survey but had no idea which schools had been
selected to participate in the survey.
Both of the head teacher based measures could be improved if there was an attendance
register in which teacher attendance was accurately recorded. In general, it is not clear
how accurately and/or if teacher attendance registers are used. Evidence from this survey
indicates that head teachers in 2 schools reported that no such register existed.
Furthermore, 55 teachers reported as present by the head teacher did not sign the register,
while 50 teachers reported as absent signed the register. It is only coincidental that these
two quantities offset each other. Evidence from the previous survey (Habyarimana,
2004), suggests that the bias associated with a reliance on attendance registers is on the
order of 5 percentage points.
If absence durations over at 30-day recall period were reported with small/no biases, one
could use the estimate as a measure of the probability of a teacher being absent. An
unbiased measure of absence duration also allows us to determine the concentration of
absence by teacher. In the absence of any recall bias, the probability that a teacher was
absent is equivalent to the ratio of monthly absence duration to the total number of days
in the month. As the result below demonstrates, absence durations from head teacher
recall predict a much lower level of spot absence. According to this data and assuming a
teaching month of 22 days, the spot absence rate suggested by head teacher recall is on
the order of 10%, about half the size of the corresponding spot absence measure.
Table 3: Head Teacher Response: Spot Absence and Absence Duration over last 30
days Variable Mean Implied spot absence
Head teacher report, duration 2.08
(0.09)
0.1
Head teacher report, spot 0.21
(0.01)
However, the dispersion in the days absent is potentially informative if recall bias is
independent (or proportional to) of the duration of absence. An examination of the data
suggests that head teachers report that about 50% of all teachers are present throughout
the previous month; 10% are absent for 1 day; 12% for 2 days; 9.5% for 3 days; 5.5% for
4 days. 10% of teachers have absence durations between 5-10 days. 3% of teachers are
absent 10 or more days. To the extent that head teachers have incentives to under-report
absence, this distribution suggests that absenteeism is not restricted to a small fraction of
“ghost” teachers. As has been shown in other surveys (Chaudhry et. al (2006) and
Glewwe et. al. (2003)), between 50-80% of all teachers have taken an absence episode at
least once over the previous 30 days.
Given the shortcomings of both the head teacher reports of absence, we rely instead on
the measure of absence that comes from direct observation of the teacher’s status. This
measure has a number of advantages over other measures that have been used in the
literature. Firstly, it does not suffer from biases resulting either from recall or from mis-
reporting. Secondly, it provides a measure that is of direct policy interest. Since the
enumerator’s arrival in the school is random, the estimate of directly-observed absence
tells us the fraction of teachers one can expect to find in school during working hours. In
addition, this strategy allows us to characterize the allocation of teacher effort for
teachers that are present.
This measure of absence does have an important drawback. With only one school visit,
we are unable to measure teacher-level absence rates with any precision. One visit,
cannot distinguish between teachers who are absent a lot of the time, and teachers who
are only absent a few times. This has implications for the teacher-level analysis that we
will perform – measurement error of teacher absence rates is likely to produce noisy
estimates of associations at the teacher level. As a result, we use the school level estimate
of absence which is measured with greater precision.
18.3 % of teachers could not be found by the enumerator over the duration of the school
visit. This is a non-trivial fraction of the workforce. We investigate the existence of
simple associations in the data in the table below. In particular we look at the extent to
which absence rates vary by ownership, location and district. Surprisingly, there is very
little variation in average absence across ownership and location categories. In fact,
government run schools have a lower rate of absence than non-government owned
schools (albeit not statistically significant). Similarly, absence rates do not differ across
bucolic status. Both peri-urban and rural areas have absence rates of 18% compared to
20% for urban areas (difference not significant). There is however, variation in absence
rates across districts. Average absence rates range from 10% in Mukono to 23% in
Mayuge.
Table 4a: Mean Absence Rates: Ownership, Location and District
Category Mean Absence (std. Error)
Ownership
Government 0.18
(0.01)
Private 0.23
(0.03)
Community 0.21
(0.04)
Other 0.04
(0.04)
Location
Urban 0.20
(0.04)
Peri-urban 0.18
(0.02)
Rural 0.18
(0.01)
District
Kibaale 0.19
(0.02)
Luweero 0.17
(0.02)
Mayuge 0.23
(0.02)
Mukono 0.10
(0.02)
Ntungamo 0.17
(0.03)
Tororo 0.21
(0.02)
Total
0.18
(0.01)
As the table below shows, the absence rate varies significantly across teacher rank. Head
teacher absenteeism stands at 27% compared to the average of 18%. The absence rate of
other teachers is on par with average absence rates.
Table 4b: Teacher Absence rates by teacher rank Teacher Rank Absence Rates
Head Teacher 0.27
(0.04)
Senior Teachers 0.16
(0.02)
Regular Teachers 0.18
(0.01)
Others 0.13
(0.04)
Overall 0.18
(0.01)
We turn now to an examination of the reasons for teacher absence. Direct observation
does not permit the possibility of characterizing the absence episode. In order to establish
reasons for absence, we asked the head teacher/primary respondent to report why a
particular teacher was not in school on the day of the survey visit. The figure below
shows the distribution of primary respondent explanations for teachers that they report as
not being in school.
Figure 2a: Reasons for Teacher Absence
Absence for official reasons accounts for 20% of absences; the bulk of which are
teaching related. Each of the following categories account for 1/6 of all absences
Illness
Authorized leave
Different shift/expected to arrive later
Un-authorized leave
Finally observations for which the head teacher could not determine the reason or where
teachers had left early, account for another 1/6 of all absences. While these reasons are
subject to recall and other biases, they provide a guide to understanding and
characterizing teacher absence.
Firstly, it is instructive to look at the breakdown in absence reasons by teacher rank. We
split the sample of teachers into the head teacher and other teachers (including the deputy
head teachers, heads of department and other teachers). Figure X below provides a
breakdown of teacher absence by teacher rank:
Figure 3:
45.8%
12.5%
6.3%
20.8%
2.1%2.1%
10.4% 13.9%
2.3%2.0%
15.9%
18.1%14.2%
5.7%
6.2%
16.1%
0.3%5.4%
Head Teacher Regular Teacher
Official teaching related duty Official non-teaching duty
Assigned elsewhere/transferred Sick
Authorized leave Expected to arrive later
Left early Don't know
Unauthorized absence Suspended
Other - please specify
Source: Uganda Unit Cost Study, 2006
Reasons for Teacher Absence
Nearly half of all head teachers’ absences are due to official teaching related duties
compared to only about 14% of regular teacher absences. 16% of regular teacher
absences are unauthorized compared to 2.1% of head teacher absences.
Given the absence rates and teaching days per month, this is equivalent to approximately
0.7 teaching days per month devoted to official teaching related duties such as seminars,
exam invigilation and training. As shown in the table AA above, absence rates for head
teachers are 50% higher than other teachers. In addition, nearly half of these absences are
due to official teaching related reasons. Consequently, head teachers are away from
school about 3 days a month for official teaching related reasons. In the multi-country
studies cited above, head teacher absence rates were about 3 percentage points higher
than that of regular teachers.
How much do these reasons tell us about the sources of high teacher absence? Firstly, we
need to point out the need for caution in interpreting head teacher reported reasons for
absence. However, conditional on the reliability of these reports, it is clear that there is no
predominant source for absenteeism. Illness, which has been identified as a major source
of absenteeism in a number of studies (Das et. al. 2004; Bennel (2005) and Bell et. al.
(2003)), only accounts for little more than 3% of all observations. Official reasons
account for only 3.6% of observations. The picture painted by this evidence is one of
weak incentives and an education system that requires teachers, and particularly, head
teachers to be away from school.
Understanding why teachers are absent and what fraction is absent is crucial. But given
that the input of interest is instructional time, it is important to examine the allocation of
teacher effort even for those teachers that are present. The figure below shows the
distribution of activity across a number of categories.
Figure 4: What are teachers doing when the enumeration team finds them?
2006
19%
2%
18%
35%
8%
18%0%
In class, teaching
In class, not teaching
Out of class, break
Out of class, in school
Administrative work
Can't find teacher
With surveyor
18% of teachers are in class teaching; only 2.4% are in class but not teaching. 17.6% are
out of class on a scheduled break. More than 1/3 of teachers are out of class and in school
and 8% of teachers are doing administrative work. While the foregoing has emphasized
absence rates that are very high, figure 4 above suggests that improving teacher
attendance alone is not enough to increase instructional time. One concern with the result
that 1/3 of teachers are out of class is that it reflects the odd timing of the survey (at the
end of term during the examination/marking period). In the figure below we present
evidence from the 2002/3 absence survey.
2002
28%
11%
9%18%
6%
25%
3%In class teaching
In class but not teaching
Out of class on a scheduled break
Out of class, but in school
Doing administrative work
Cant find teacher/absent
Accompanying surveyor
The figure above shows that 18% of teachers are outside the classroom, when they should be teaching. The timing of the 2002 survey provides a potential lower bound of the proportion of time that teachers spend in the classroom.
Robustness of the Absence Estimates
One of the concerns with our measure of teacher absence is that it relies on the effort of
the enumerators to find the teachers. In this case, a teacher will be classified as absent
when he/she is present in school but beyond the reach of an enumerator. The other
concern is that physical verification requires that the enumerator go down his/her list
until all teachers are accounted for. There is a danger that the time it takes to complete all
teachers might be too long enough to invalidate any verification that takes place outside
school hours. Alternatively, teachers that live close by might be alerted that an absence
survey is being conducted and show up.
To address some of these concerns, we provide two pieces of evidence that increase our
confidence in the reliability of the absence estimates. We employ data from the classroom
observation as the first piece of evidence. As part of the survey, classrooms from grades
1-4 were visited to collect information on the classroom environment, including the
number of textbooks, desks and subject being taught at the time of the observation.86
The
grade was chosen depending on whether the first and last digit of the school identification
number is odd/even. The classroom observation collected data on nearly 330 classrooms
in the 160 schools. Data from the classroom observation shows that nearly 20% of all
classrooms observed (during school hours) had no teacher in them. The figure below
shows the distribution of enumerator observations of the classrooms visited.
86
Note that for this piece of evidence to be valid, we require that absence rates of teachers that teach in
these lower grades not be systematically different from the population.
Figure 5: Classroom observation
24.5%
35.7%3.6%
14.8%
3.1%2.6%
15.8%
Quiet, teacher around Orderly, Teacher
Disorderly, Teacher Disorderly, No Teacher
Other Orderly, No Teacher
No class
Source: Uganda Unit Cost Study, 2006
Class room observation
In about ¼ of the classrooms visited, the teacher was quietly conducting his/her class.
36% of classrooms had a teacher in them with the pupils doing an assignment. In about
17.4% of the classrooms there was no teacher (and the class was predominantly
disorderly). This number is close to the level of absence that we obtain (18%). In one
sixth of the classes, the pupils were either not in class or had already gone home. This
fraction is larger for the lowest grades since they typically only study half day (see figure
below)
Figure 5b: Classroom observation by grade
24.2%
35.8%2.1%9.5%
2.1%
26.3% 21.1%
32.1%5.5%
15.6%
4.6%3.7%
17.4%
22.5%
41.3%
6.3%
18.8%
1.3%1.3%8.8%
29.6%
35.2%
0.9%
15.7%
3.7%4.6%
10.2%
P1 P2
P3 P4
Quiet, teacher around Orderly, Teacher
Disorderly, Teacher Disorderly, No Teacher
Other Orderly, No Teacher
No class
Source: Uganda Unit Cost Study, 2006
Class room observation
The figure above shows that there is very little variation in the proportion of classrooms
with no teachers. The lower classes (P1 and P2) are more likely to have ended by the time
the enumerators conducted the classroom observations.
The second piece of evidence involves dropping any observations for which verification
of attendance occurred outside normal school hours. We are able to do this because we
record the exact time that a verification was done. There are two reasons why we this
needs to be done. Firstly, a teacher is not technically absent if he/she cannot be found
outside of school hours. Secondly, it is possible that absent teachers are alerted to the
enumerators presence and return to school. While this can happen at any time, it is more
likely to happen later in the day. Both of these would generate unreliable measures of
teacher absence. We define normal school hours to be between 8AM and 5PM. This is
the modal school time range in our sample. Using this criteria changes absence levels
only by a small fraction (see table 6 below).
Table 5: Absence measures: robustness test
Variable All observations Excludes observations verified
after 5pm.
Proportion absent (excludes head
teachers)
0.174
(0.010)
0.168
(0.010)
Proportion absent (includes head
teachers)
0.183
(0.009)
0.177
(0.009)
The absence rate does not change much when we exclude observations identified after
5pm.
Comparison of 2002 and 2006 Surveys
Both the 2002 and 2006 surveys use the same methodology to measure teacher absence
and visited schools at the same time of the year. However, differences in the estimates
produced by both surveys could be due either to differences in behavior – absence rates
have changed or due to use of different samples.87
We can examine the extent to which
the differences are due to one of these two reasons by restricting our analysis to two
districts that were visited in both surveys.
We begin by comparing the samples of teachers and schools that were visited across the
two surveys. Selected means are presented in the table below to determine if there are
systematic differences across the two samples.
Table 6: Sample comparisons: 2002 vs 2006
Selected Characteristic 2002 October Visit 2006 November Visit
Number of Teachers 13.44
(0.56)
11.63
(0.39)
Pupil to Teacher Ratio 50.52
(1.69)
48.44
(1.25)
Mother's Literacy 0.57
(0.03)
0.63
(0.03)
Proportion government owned 0.89
(0.03)
0.79
(0.03)
Multi-grade teaching 0.11
(0.03)
0.05
(0.02)
Distance to road (< 5km) 0.37
(0.05)
0.34
(0.04)
Distance to taxi stand (< 5km) 0.73
(0.05)
0.65
(0.04)
Urban location 0.16
(0.04)
0.20
(0.03)
94 160
Un-weighted absence rate (%) 25.2a 18.4
Weighted absence rate (%) 23.7a 18.3
a - excludes northern districts from calculation.
An examination of average characteristics of schools visited in both surveys suggests that
the two samples are comparable across most dimensions. Only the proportion of schools
that are government owned is the share significantly different with the former sample
containing 90% of government owned schools compared to 79% of the current sample.
87
The 2006 survey uses a stratified sample (by ownership) while the 2002 survey had no stratification (so
non-government owned schools are over-represented in the 2006 sample).
On the basis of the similarity of these two samples, one can make a quick comparison of
absence across the two samples. A comparison of the simple mean is not particularly
informative. Using information about the design of the sample allows us to produce
national estimates for both samples – note that the 2006 sample is not drawn from a
national sample of districts.
The table above suggests that the weighted mean of absence has fallen from nearly 24%
in 2002 to 18.3 in 2006.
While the weighting does attempt to control for differences in the district-composition of
the sample, it is possible that differences in the districts remain that the weighting scheme
cannot deal with. To control for this we restrict our analysis to two districts that were
visited in both surveys. We look at changes in mean absence rates in Luweero and Tororo
across the two years as check on what is likely driving the differences in national absence
rates.
The chart below shows the results of this analysis.
Figure 6: Changes in Absence rates: 2002-2006
The results show what looks a like a dramatic decline in absence rates in Tororo district
but a much smaller and statistically insignificant decline in the absence rate in Luweero
districts. Absence rates drop by 40% in Tororo between the two surveys. Is this
difference capturing real changes in the provision of instruction time or does it reflect
subtle differences in the timing of the visits (October/early November vs late
November). It is difficult to say. Census data or a repeated panel would be the ideal tool
to determine whether absenteeism has fallen since 2002.88
88
The high absence rates obtained in 2002 are driven by 3 districts where more than 1/3 of teachers are
determined to be absent. This includes Tororo district. In the visit in 2006, the district with the highest
absence rate, Mayuge, had 23% of its teachers absent.
Determinants of Absence
In this section we examine the correlates of absence at the individual, school and district
levels. The objective of this exercise is to identify dimensions of association that could
potentially be affected by policy. It is important to point out that the results that are
reported below are not causal, but rather associations that are potentially causal to
absence.
We begin by presenting a theoretical framework that is a rough model of the teacher’s
attendance decision. In words, a teacher’s chooses not to attend when the benefits of
being away from school exceed the costs of being absent from class.
The benefits and costs of this decision can be driven by three broad categories of factors.
Individual determinants such as the experience and seniority, marital status,
education levels (that determine outside opportunities), gender, residential
location e.t.c
School based factors such as the availability of housing units, the extent to which
parents are involved in monitoring and enhancing school quality, school location
and quality of the schooling environment.
District based factors include poverty rates, the quality of management, and in
particular monitoring capacity of the district and crucially the likelihood that
absenteeism is punished by the controlling authority.
We can model the teacher’s choice in a regression framework that includes each of these
factors. In particular assume that we estimate a model:
yijk = + Xijk + + Sjk + Qk + i + ijk
Where yijk takes on the value of 1 if the teacher is absent and 0 otherwise. Xijk represents
individual factors thought to affect the absence of teacher i in school j and districk k, Sjk
are school level characteristics in school j in district k, and Qk are district level factors
and i is unobserved fixed teacher characteristics.
We estimate the model above using a linear probability model and as a limited dependent
variable probit estimation. Note that both of these models attempt to model the
probability that a teacher is absent on the left hand side. As noted earlier, with one school
visit our measure of teacher absence probabilities is very noisy. As such we would expect
that this will tend to produce imprecise point estimates of the strength of association. We
present below a dependent variable that is measured with less error.
The individual level variables include teacher age and age squared, length of
posting at current school, teacher gender, rank, marital status, proximity of residence to
school, schooling, and ties to the local community. The school level variables include the
number of functioning teacher houses, teacher enrollment, pupil teacher ratios, a dummy
that takes on the value of 1 if the school is government owned and 0 otherwise, an
infrastructure index, school remoteness and location, multi-grade teaching, the degree of
competition, measured by the number of non-government owned schools less than 1km
and 1-5km away.89
We include district level variables such as the proportion of schools in
the district that have been visited in the term of the survey, the district poverty rates (from
the poverty mapping exercise) and the proportion of teachers in the district who are
involved as local council members.
The results are presented in tables A1 and A2 in the appendix. While the results of
the probit analysis are more reliable, the results from the linear probability model are
easier to interpret and are similar to the probit results. I will report the LPM results in this
chapter. Eight specifications are estimated that cumulatively add individual, school and
district controls. Specification (8) includes district fixed effects. As you can see from the
regressions, there are three robust associations at the individual level:
1. There is a ceteris paribus moderate U-shaped relationship of absence with
age. Absence rates fall with age, until a minimum that is reached around
age 25 and then increase therafter
2. Teachers working in their district of origin are about 3.5 percentage points
more likely to be absent. (Decentralization?) ceteris paribus.
3. Teachers that live in the same parish as the school are 9 percentage points
less likely to be absent, ceteris paribus.
At the school level there are three robust associations:
1. Increasing the degree of parental involvement (contributions, and
frequency of parents and PTA meeting, reduces absence rates ceteris
paribus.
2. Increasing the number of non-governmental schools within 1km of a
school reduces absence rates by about 1 percentage point, ceteris paribus.
3. Teachers in schools that practice multi-grade teaching are nearly 10
percentage points more likely to be absent, ceteris paribus.
The other school level controls such as teacher enrollment, pupil-teacher ratios, school
location and measures of school infrastructure are not significantly associated with
teacher absence.
At the district level, there are no robust associations. District poverty rates are positively
but insignificantly related to absence rates. Similarly, measures of district monitoring
capacity do not seem to exert any real influence on absence rates.
One reason to be skeptical of these results is that a single visit to the school does not
provide a consistent estimate of teacher-level absence rates. Measurement error (assumed
to be uncorrelated with the variables of interest) reduces the precision of the estimates,
and possibly conceals relationships of interest. We investigate this by running a
regression at the school level, where the dependent variable
Ajk = + Sjk + Qk + j + ik
Where Ajk is the fraction of teachers in school j in district k that are absent. Sjk are school
level characteristics and Qk are district level factors and j is unobserved fixed school
89
The infrastructure index is the constructed as the leading eigen value of principle component analysis of
the availability in the school of the following factors: electricity, water, library, staff-room, playground and
computers.
characteristics. We estimate this model using ordinary least squares.90
The results are
shown in table A3 in the appendix. 9 specifications are estimated that start by
sequentially adding school level factors that affect teacher behavior directly. There are
three robust associations at this level:
1. Increasing the number of functioning teacher houses is associated with a
reduction in average absence rates, holding other factors, including the
number of teachers constant. In particular, an increase in housing units
from the current average of 1 house for 7 teachers to 1 house for 2
teachers would reduce absence by 5 percentage points.91
2. Increasing the degree of parental involvement in the school is associated
with a reduction in absence rates. A one unit increase in this index
(equivalent to a move from the 10th
to the 90th
percentile) is associated
with a reduction in absence rates of 4.6 percentage points.
3. Finally an increase in the number of neighboring non-government owned
schools is associated with a reduction in average school absence rates. An
increase of 1 non-government owned school within 1km of a school is
associated with a 2 percentage point reduction in absence rates.
The result of this school level analysis does not throw up other correlates of interest as we
had suspected. Instead, it increases our confidence in a number of policy-amenable
factors.
1. School infrastructure. The results of this analysis specifically suggest that the
construction of more teacher housing would potentially reduce teacher absence.
The channels are myriad, and subject to the caveat pointed out above, include
greater proximity to the school that increases the monitoring levels of the
community and peers and greater satisfaction (through high remuneration).
2. Parental/community involvement. As with the results pointed out in the
introduction, increased involvement by parents and communities is associated
with lower absence rates. It is difficult to isolate the sources of this effect. On the
one hand, it is possible that community involvement means greater monitoring by
the community. On the other, it is possible that parents that care will support a
learning environment that increases the utility of teaching and therefore reduces
absence.
3. Competition effects. Being surrounded by well-run schools exerts imperatives for
schools to perform better. It is also possible that this reflects some of the effects
outlined in 2 above (better parents and improved working environment).
90
The most important concern with this formulation is that unobserved factors are likely correlated with
variables of interest and lead to biased inferences. 91
It is possible that this result, like the proximity result in the individual level regressions reflects the ease
with which teachers can respond to an attendance check. Teachers close by will be able to appear as though
they are present simply because there is an enumerator looking for them.
Discussion and Conclusion
This chapter has presented the results of a survey of teacher attendance in six districts in
Uganda at the end of 2006. The results discussed in this study highlight very high levels
of teacher absence. 1 in 5 teachers cannot be found in school at any given time. Given the
importance of instruction time and the share of teacher remuneration in the budget, high
teacher absence represents a gaping source of inefficiency. In addition, an examination of
teachers that are present in the school suggests about a third of teachers are outside of the
classroom. While this is plausibly due to end of year examinations, it suggests that
interventions that simply keep teachers in school are not likely to raise instruction time to
the optimal levels required.92
Even at the lower bounds suggested by the March/April
visits suggests that pupils are deprived of a large fraction of the interaction between
pupils and teachers that comprises learning.
Teacher absence is very heterogenous and reflects a wide array of factors from weak
incentives, poor working conditions and a debilitating disease environment. This chapter
explores the possibility that teacher absence rates have fallen between 2002 and 2006.
We find some evidence in support of this hypothesis, but it is very weak at best. In order
to determine trends in teacher absence, better data will need to be collected.
The correlates of absence that we identify at the individual level are age, residential
proximity and working in the same district of birth/origin. The latter raises an important
question about decentralization. One of the major trends in teacher hiring, and indeed in
other sectors, is that an increasing fraction of new hires are indigenous to the district. Our
results suggest that one possible explanation of this association is that individuals with
strong social ties are more likely to be pulled away from work either for personal benefit
or to help relatives and friends.
School level correlates include greater competition generated by neighboring private
schools, increased participation and involvement of parents and teachers in schools that
practice multi-grade teaching are more likely to be absent. We do not find any strong
evidence that district level factors affect teacher attendance.
What can be done?
This section briefly reviews 4 studies that have attempted to address teacher performance
either directly or indirectly. Two studies are located in India and the other two are located
in Western Kenya.
Teacher Incentives in Western Kenya
This study conducted by Glewwe, Illias and Kremer (2003) attempted to measure the
impact of a randomized intervention in which high performing teachers would be
rewarded with prizes at the end of the school year. Prizes were substantial and included
bicycles, mattresses and other household durables. Performance was determined by the
rank of the school in the district and the degree of improvement relative to the previous
year’s exam results. The authors measured a variety of inputs before and after the
intervention. The authors find improvements in test scores in treatment schools.
92
The corresponding numbers from the previous survey range between 18 and 22.5% (the latter
corresponds to visits in March and April).
However, this improvement is driven primarily by teachers using different teaching
strategies – particularly teaching to the test. There is no significant different across
treatment and comparison schools in teacher absenteeism (teacher absence rates are
around 20% in these schools).
Girls Scholarships in Western Kenya
Miguel, Kremer and Thornton (2005) evaluate a randomized intervention in which a
series of pupil scholarship possibilities are announced at the beginning of the school year.
About 200 girls are eligible for scholarships in 60 schools in two districts (about 15% of
the eligible enrollment). The scholarships pay for tuition for the next two years and
parents receive an unconditional cash transfer of $12 per recipient. The results of this
intervention were quite dramatic. Performance of all pupils, including non-eligible
scholarship recipients, perform much better than control schools. In addition, teacher
absenteeism falls by 6.5 percentage points in treatment schools. The authors attribute this
to an improvement in working conditions engendered by increases in pupil effort.
Monitor-less monitors in India
Duflo and Hanna (2006) evaluate a randomized intervention in community schools in
which an NGO provides cameras to teachers and institutes attendance-dependent
remuneration. Teachers are expected to take pictures every morning, and teachers will be
paid depending on the number of “full” days attended. The results of this intervention
were surprisingly large. Teacher absence fell by about half from a high of 36% in
comparison schools to 18% in program schools. The authors discuss the political-
economy of this intervention and conclude that it is not a realistic option for national
scale up.
Group vs Individual Incentives in India
A suite of interventions is evaluated in a randomized-control design in India. The
incentives include individual and group-based incentives, additional teachers and block
grants (equivalent expenditure). Karthik and Sundararaman (2006) find evidence in
support of teacher incentives. Learning outcomes are higher in treatment schools.
However, like the teacher incentives study in Kenya, teacher attendance is not affected.
Instead, teachers choose to increase “cheap effort” – assigning more homework and
practice tests rather than show up.
A number of studies have been carried out in Latin America and show large effects of
community involvement on school performance. When control was transferred to the
community, so that parents could hire and fire teachers, teacher attendance and test scores
went up (see Lewis (2005) for a recent review). While the associations here are very
strong, it is difficult to interpret these results as causal.
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Table A1: Correlates of Absence: Individual level linear probability model
Dependent Variable: Indicator -- Cannot find teacher/teacher absent
(1) (2) (3) (4) (5) (6) (7) (8)
Gender (1=Male)
-0.004 -0.056 -0.004 -0.006 0.007 -0.019 -0.005 -0.019
(0.018) (0.036) (0.018) (0.020) (0.023) (0.020) (0.022) (0.019) Teacher age, years
-0.008 -0.009 -0.008 -0.011 -0.013 -0.009 -0.012 -0.010
(0.003)** (0.003)*** (0.003)** (0.003)*** (0.004)*** (0.004)*** (0.004)*** (0.003)*** Age squared 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 (0.000)*** (0.000)*** (0.000)*** (0.000)*** (0.000)*** (0.000)*** (0.000)*** (0.000)*** Tenure current posting
0.003 0.003 0.003 0.002 0.005 0.002 0.006 0.003
(0.004) (0.004) (0.004) (0.004) (0.004) (0.004) (0.004) (0.004) Tenure current posting squared
-0.000 -0.000 -0.000 -0.000 -0.000 -0.000 -0.000 -0.000
(0.000)* (0.000)* (0.000)* (0.000)* (0.000)*** (0.000) (0.000)*** (0.000) Completed A-levels
0.023 0.021 0.023 0.031 0.053 0.025 0.033 0.032
(0.021) (0.021) (0.021) (0.024) (0.033) (0.027) (0.035) (0.025) Head teacher 0.033 0.029 0.033 0.042 0.052 0.042 0.060 0.044 (0.034) (0.034) (0.034) (0.041) (0.054) (0.041) (0.054) (0.035) Deputy teacher -0.033 -0.035 -0.033 -0.023 -0.005 -0.017 0.005 -0.016 (0.031) (0.031) (0.031) (0.030) (0.038) (0.030) (0.038) (0.031) Head of dept -0.025 -0.028 -0.025 -0.036 -0.072 -0.001 -0.009 -0.007 (0.027) (0.027) (0.027) (0.028) (0.032)** (0.026) (0.033) (0.030) Teaches in district of origin
0.023 0.021 0.023 0.024 0.033 0.036 0.048 0.035
(0.020) (0.020) (0.020) (0.021) (0.027) (0.022)* (0.028)* (0.021)* Lives in same parish as school
-0.096 -0.096 -0.096 -0.093 -0.054 -0.102 -0.066 -0.099
(0.019)*** (0.019)*** (0.019)*** (0.024)*** (0.028)* (0.024)*** (0.027)** (0.019)*** Teacher fluent in local language
0.033 0.032 0.033 0.029 -0.005 0.017 -0.004 0.018
(0.023) (0.023) (0.023) (0.024) (0.029) (0.022) (0.027) (0.025) Teacher is married
-0.016 -0.050 -0.016 -0.017 -0.033 -0.013 -0.030 -0.011
(0.023) (0.031) (0.023) (0.027) (0.034) (0.027) (0.033) (0.023) Male * married 0.069 (0.042) Government owned
0.013 0.039 0.035 0.059 0.041
(0.031) (0.033) (0.034) (0.032)* (0.027) School infrastructure index
-0.020 -0.020 -0.005 -0.005 0.007
(0.022) (0.025) (0.019) (0.020) (0.019) Distance to transport point index
0.021 0.019 0.031 0.015 0.031
(0.025) (0.030) (0.026) (0.026) (0.021) Parental involvement index
-0.036 -0.038 -0.040 -0.037 -0.044
(0.021)* (0.030) (0.023)* (0.031) (0.016)*** District monitoring index
-0.026 -0.048 -0.003 -0.020 0.008
(0.045) (0.061) (0.043) (0.059) (0.030) Urban 0.011 0.041 0.049 0.036 0.008 (0.027) (0.041) (0.039) (0.042) (0.029) Number of teachers
0.003 -0.001 -0.000 -0.004 -0.002
(0.002) (0.003) (0.003) (0.003) (0.002) Pupil teacher ratio
-0.000 -0.000 0.000 0.000 -0.000
(0.001) (0.001) (0.001) (0.001) (0.001) School practices multi-grade teaching
0.090 0.155 0.133 0.210 0.136
(0.053)* (0.051)*** (0.059)** (0.048)*** (0.060)** Total number of NGO schools within 1km radius
-0.012 -0.010 -0.007 -0.003 -0.004
(0.007)* (0.009) (0.007) (0.008) (0.007) Total NGO number of schools within 1-5 km radius
-0.002 -0.003 -0.003 -0.004 -0.003
(0.002) (0.002)* (0.002)** (0.002)** (0.002) District recognition award
0.026 0.051 0.034
(0.035) (0.047) (0.030) District poverty rates
0.002 -0.000
(0.002) (0.002) District Inspections
-0.233 -0.036
(0.400) (0.435) District local council
-1.041 -1.998
(0.700) (0.851)** Average literacy of grade 4 pupil mums in the school
0.036 0.024
(0.038) (0.035) Constant 0.329 0.365 0.329 0.335 0.395 0.310 0.489 0.361 (0.077)*** (0.080)*** (0.077)*** (0.092)*** (0.115)*** (0.114)*** (0.147)*** (0.089)*** Observations 1643 1643 1643 1574 998 1574 998 1574 R-squared 0.08 0.08 0.08 0.10 0.11 0.11 0.13 0.11
Notes: Standard errors in parentheses significant at 10%; ** significant at 5%; *** significant at 1%. All specifications include controls for day of visit. Specification 8 includes district fixed effects.
Table A2: Correlates of Absence: Individual level probit
Cannot find teacher/teacher absent
(1) (2) (3) (4) (5) (6) (7) (8)
Gender (1=Male) -0.004 -0.056 -0.004 -0.004 0.008 -0.016 -0.002 -0.017
(0.020) (0.039) (0.020) (0.020) (0.022) (0.020) (0.021) (0.020)
Teacher age, years -0.005 -0.005 -0.005 -0.007 -0.009 -0.005 -0.008 -0.006
(0.004) (0.004) (0.004) (0.003)* (0.004)** (0.004) (0.004)* (0.003)*
Age squared 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
(0.000)*** (0.000)*** (0.000)*** (0.000)*** (0.000)*** (0.000)*** (0.000)*** (0.000)***
Tenure current posting 0.004 0.004 0.004 0.003 0.013 0.003 0.012 0.004
(0.005) (0.005) (0.005) (0.005) (0.007)* (0.005) (0.007)* (0.005)
Tenure current posting squared
-0.000 -0.000 -0.000 -0.000 -0.001 -0.000 -0.001 -0.000
(0.000)* (0.000) (0.000)* (0.000) (0.000)** (0.000) (0.001)* (0.000)
Completed A-levels 0.026 0.024 0.026 0.035 0.054 0.029 0.035 0.036
(0.024) (0.024) (0.024) (0.025) (0.033)* (0.027) (0.035) (0.027)
Head teacher 0.027 0.023 0.027 0.039 0.051 0.038 0.055 0.041
(0.040) (0.040) (0.040) (0.040) (0.051) (0.039) (0.050) (0.037)
Deputy teacher -0.034 -0.035 -0.034 -0.022 -0.004 -0.012 0.014 -0.011
(0.029) (0.029) (0.029) (0.029) (0.038) (0.030) (0.039) (0.030)
Head of dept -0.021 -0.024 -0.021 -0.031 -0.068 0.000 -0.020 -0.006
(0.028) (0.027) (0.028) (0.026) (0.027)** (0.027) (0.032) (0.029)
Teaches in district of origin
0.023 0.022 0.023 0.025 0.031 0.038 0.043 0.035
(0.019) (0.019) (0.019) (0.020) (0.025) (0.020)* (0.024)* (0.020)*
Lives in same parish as school
-0.097 -0.097 -0.097 -0.090 -0.055 -0.102 -0.067 -0.098
(0.025)*** (0.025)*** (0.025)*** (0.024)*** (0.027)** (0.024)*** (0.026)*** (0.021)***
Teacher fluent in local language
0.037 0.036 0.037 0.034 0.005 0.025 0.008 0.025
(0.022)* (0.022)* (0.022)* (0.024) (0.029) (0.023) (0.027) (0.025)
Teacher is married -0.022 -0.058 -0.022 -0.026 -0.045 -0.019 -0.037 -0.017
(0.028) (0.039) (0.028) (0.028) (0.034) (0.028) (0.032) (0.024)
Male * married 0.070
(0.048)
Government owned 0.010 0.038 0.029 0.055 0.034
(0.033) (0.033) (0.033) (0.027)** (0.025)
School infrastructure index
-0.019 -0.014 -0.005 0.002 0.006
(0.025) (0.026) (0.021) (0.020) (0.019)
Distance to transport point index
0.023 0.023 0.033 0.020 0.031
(0.025) (0.029) (0.026) (0.025) (0.021)
Parental involvement index
-0.030 -0.032 -0.034 -0.029 -0.037
(0.017)* (0.024) (0.019)* (0.025) (0.015)**
District monitoring index
-0.024 -0.043 -0.003 -0.013 0.006
(0.040) (0.049) (0.037) (0.045) (0.028)
Urban 0.015 0.049 0.058 0.038 0.015
(0.032) (0.049) (0.049) (0.050) (0.032)
Number of teachers 0.003 -0.001 -0.000 -0.005 -0.002
(0.003) (0.003) (0.003) (0.003)* (0.002)
Pupil teacher ratio -0.000 -0.000 0.000 0.000 -0.000
(0.001) (0.001) (0.001) (0.001) (0.001)
School practices multi-grade teaching
0.085 0.153 0.149 0.278 0.160
(0.059) (0.060)** (0.077)* (0.081)*** (0.084)*
Total number of NGO schools within 1km radius
-0.015 -0.012 -0.008 -0.001 -0.004
(0.009)* (0.011) (0.009) (0.010) (0.008)
Total NGO number of schools within 1-5 km radius
-0.003 -0.004 -0.004 -0.005 -0.004
(0.003) (0.003) (0.003) (0.003)* (0.003)
District recognition award
0.035 0.060 0.043
(0.039) (0.049) (0.033)
District poverty rates 0.002 -0.001
(0.002) (0.002)
District Inspections -0.289 -0.317
(0.412) (0.418)
District local council -0.924 -1.847
(0.674) (0.761)**
Average literacy of grade 4 pupil mums in the school
0.053 0.040
(0.042) (0.037)
Observations 1643 1643 1643 1574 998 1574 998 1574
Notes: Standard errors in parentheses significant at 10%; ** significant at 5%; *** significant at 1%. All specifications include controls for day of visit. Specification 8 includes district fixed effects.
Table A3: Correlates of Absence: School level regressions
(1) (2) (3) (4) (5) (6) (7) (8) (9)
No of functioning teacher housing units
-0.013 -0.012 -0.013 -0.012 -0.011 -0.011 -0.006 -0.011 -0.011
(0.005)***
(0.005)***
(0.005)***
(0.005)**
(0.005)**
(0.005)**
(0.007) (0.005)**
(0.005)**
Number of teachers
0.002 0.002 0.002 0.002 0.002 0.002 -0.002 0.001 -0.001
(0.003) (0.003) (0.003) (0.003) (0.003) (0.003) (0.004) (0.003) (0.004) Pupil teacher ratio
-0.001 -0.001 -0.001 -0.001 -0.001 -0.001 -0.001 -0.001
(0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) Multi-grade School
0.075 0.077 0.075 0.084 0.077 0.042 0.094 0.100
(0.060) (0.060) (0.061) (0.062) (0.062) (0.080) (0.065) (0.065) Other teacher conditions index
0.012 0.014 0.013 0.005 -0.010 0.007 0.019
(0.024) (0.026) (0.025) (0.025) (0.029) (0.027) (0.027) Government school
0.004 -0.001 -0.003 0.022 0.006 0.014
(0.035) (0.036) (0.036) (0.041) (0.037) (0.037) Distance to transport points
0.008 0.004 0.020 0.003 0.024 0.024
(0.027) (0.027) (0.028) (0.034) (0.028) (0.029) Urban location
-0.021 -0.016 0.028 0.055 0.053 0.030
(0.040) (0.039) (0.043) (0.053) (0.052) (0.043) PTA/parents involvement index
-0.046 -0.045 -0.042 -0.048 -0.053
(0.022)**
(0.022)**
(0.026) (0.023)**
(0.023)**
District/ministry supervision index
-0.000 -0.002 -0.035 0.008 0.015
(0.040) (0.039) (0.054) (0.040) (0.040) Total number of NGO schools within 1km radius
-0.019 -0.016 -0.018 -0.014
(0.010)* (0.012) (0.011) (0.011) Total NGO number of schools within 1-5 km radius
-0.002 -0.003 -0.003 -0.002
(0.003) (0.003) (0.003) (0.003) District poverty rates
0.001
(0.002) District Inspections average
0.134
(0.424) District local council involvement
-0.921
average (0.751) District recognition award
0.019
(0.041) Average mother literacy
-0.007
(0.051) Constant 0.181 0.209 0.209 0.212 0.208 0.238 0.272 0.257 0.268 (0.033)*
** (0.052)*
** (0.052)*
** (0.056)*
** (0.056)*
** (0.057)*
** (0.080)*
** (0.094)*
** (0.064)*
** Observations 158 155 155 153 153 153 96 153 153 R-squared 0.05 0.06 0.06 0.06 0.09 0.13 0.14 0.15 0.17
Notes: Standard errors in parentheses significant at 10%; ** significant at 5%; ***
ANNEX 4: FUNDING FORMULAS FOR BASIC EDUCATION.
Box 1: New Zealand Funding Formula
Background
New Zealand schools’ Board of Trustees manages educational services and receives resources directly from
the Ministry of Education via a funding formula. Schools’ Boards of Trustees consists mainly of elected
parents and school principal, and they are responsible for the day-to-day administration of the school and
overseeing its resources without other intermediary or regional agencies being involved in the decision-
making process. In order to ensure sound accountability, all Board of Trustees are required to produce an
annual financial report which are to be made available to the Ministry of Education as well as to the school
community.
Schools receive funding to cover their operational activities and most of their required inputs, but the direct
payment of teachers’ salaries in the majority of the schools remains to be the responsibility of the central
government. Since 1996, schools may choose to be funded directly for the costs of teaching personnel
(Directly Resourced Schools Program) or to have the component of teachers’ salaries be paid directly by
the Ministry of Education (Centrally Resourced Schools Program). Although the Directly Resourced
Schools (DRS) program devolve a higher level of resources directly to the school level and thereby give the
Boards of Trustees greater flexibility in making decisions in resource allocations, most schools choose the
Centrally Resourced School (CRS) approach for allocating teachers’ salaries. School budget allocation in
New Zealand is divided into three distinct parts: operational funding, major capital projects, and staffing of
teaching personnel and senior managers. The formula of resource allocation from the central government
to schools consists of both operational and teaching personnel for DRS schools, and only operational
funding for all other schools, but funding for major capital projects is retained at the central level.
The operational funding in New Zealand includes all four components of a funding formula: basic student
allocation, curriculum enhancement, student supplementary educational needs, and school site needs. The
funding framework takes into consideration actual costs of educational resources and develops cost
structures in terms of needs of different groups from various schools.
Component 1: Basic student allocation
Teaching and non-teaching personnel, relief teaching funding (e.g. costs of employing substitute teachers),
and operational funding, are allocated predominantly by the proportion of student enrollment. In the case
teaching personnel, the staffing entitlement is allocated based on a teacher to student ratio of each grade
level, and the allocation of non-teaching personnel, e.g. management staffing, is calculated separately based
on a weighted enrollment factor which varies by different level of education. Costs of employing substitute
teachers may not be a recurrent one, but substitute teacher funding are provided on a teacher-entitlement
basis and rates are differentiated by the size of the core staffing and management component allocated to
each school.
Component 2: Curriculum Enhancement
Additional staffing and resources are provided for schools with advanced curriculum or specialized
programs such as computer technology or Maori language programs. In addition, per-student funding rates
increase with as students get older because funding for school-to-work is built into the per-student funding
component at a rate of grade level because funding for school-to-work program is built into the per-student
funding component at a rate of $17.19 for Y9-10 and $19.06 for Y10-15.
Component 3: Student supplementary educational needs
Targeted Funding for Educational Achievement (TFEA) provides additional per-student funding to schools
that cater to students from socio-economically disadvantaged communities. Further, additional funding is
made available to upper secondary school students for resources on career guidance. This funding is shown
to benefit disadvantaged students most because they have a tendency to leave schools early and are
therefore at a greater risk of unemployment. Disadvantaged students are evaluated on the basis of
household income, the concentration of workface in manual and unskilled occupations, household
crowding, parents’ academic qualifications, family welfare benefit dependency, and the proportion of
school enrollment made up of Maori and Pacific Island students. These indicators were selected because
investigation shows that they have a high correlation with average school achievement scores. Since the
first five indicators are drawn from the Population Census data instead of school-level survey, information
used to construct the composite indicator cannot be manipulated unfairly by schools.
Ongoing Resource Scheme (ORS) ensures a school that caters to students with disability has sufficient
resources and additional teaching staff to support students so that they can participate both in special and
mainstream schools. Students are assessed by independent verifiers on the basis of their learning support
needs rather than type of learning disability. Since students are assessed by an independent entity, this
preserves the integrity of the indicators. Once this group of students has been identified, they are further
categorized into level of needs. Funding allocation is therefore differentiated based on the level of
students’ needs.
Component 4: School site needs
The Ministry of Education is responsible for major property upgrades and the establishment of new
classrooms. However, funding is made available to the schools’ Boards of Trustees for school site
maintenance, utility costs (e.g. heat, light, and water), and minor improvement projects. Specific site
characteristics are taken into consideration for funding allocation, such as the size and location of school
sites. Small schools (with less than 160 student enrollment), for example, benefit from additional cores
staffing allocation as well as a supplementary ‘base funding’, which is allocated on the basis of an
enrollment range designed to offset ‘diseconomies of scale’ for very small schools. This type of assistance
guarantees delivery of education to very isolated areas, however, one of the unintended outcomes is that it
could prevent small schools from amalgamating with other smaller schools situated in the same area. In the
case school site location, schools are compensated for higher costs incurred in the delivery of educational
services as a result of being located in isolated rural communities. Schools are categorized as being
‘isolated’ if it is at least 30 kilometers away from a trade and service center, with a population of 2,000 or
more, and if there is an absence of schools within close proximity offering educational services to students
of the same education level or age group.
Box 2: South Africa Formula-Based Distribution of Resources
National-to-Provinces Funding Allocation
Provinces in South Africa receive unconditional block grants from the national government determined by
the equitable shares formula. The level of funding transferred to each province is determined in a two-step
process. First, the national government decides how to divide the total national revenue among different
levels of government (e.g. national, province, and municipal). Second, the pool of funds available to
provincial government is distributed based on a weighted average demographically driven funding formula,
where the weights reflect the proportion of national spending allocated to each major social service sector,
such as education, health, and welfare. In the case of education, equitable shares for each province are
computed based on a 40 percent weight, and determined by actual student enrollment and school-age
population, with the latter given twice the weight the former. This formula represents a compromise to
overcome the problem of over student enrollment and under student enrollment. For example, a formula
that is based on school-age population alone will not give provinces the incentive to reduce the proportion
of school-age population not enrolled in school. On the other hand, a formula that is based on actual student
enrollment alone may encourage inefficiency and over student enrollment.
Non-Personnel and Non-Capital Recurrent Province-to-Schools Funding
Once each province receives its fiscal transfer, it determines how the cost allocation of various categories
will fit within the overall provincial education budget. School budget allocation in South Africa is divided
into three major categories: non-personnel and non-capital recurrent, major capital, and personnel
expenditures. Financing of schools by provincial governments is determined by the National Norms and
Standards for School Funding, which only imposes distributional requirements to provinces but does not
stipulate a minimum basic allocation per student. This national program was designed to give poorer
schools more non-personnel and non-capital recurrent funding (e.g. utilities, maintenance of building,
teaching materials, and non-emergency building repairs) as a means or promoting greater equal educational
opportunity.93
However, non-personnel funding are limited since it only consist of what is left after each
province has meet all its personnel costs commitments, which usually amounts to an average of about 90
percent of the total provincial education budget.94
In order to distribute the funds in a progressive manner, it
requires each Provincial Department of Education (PED) to first rank all its schools according to a poverty
index, which is calculated by applying a 50 percent weight to the relative poverty of the school community
(as measured by the characteristics of the parents of students attending the school or characteristics of the
community where the school is located in) and a 50 percent weight to the physical conditions of the school
itself (as measured by classroom to student ratio and access to basic services such as water and electricity),
and then dividing the list of schools in five quintiles, from poorest to least poor. Allocation of resources for
each component in the formula will be made on a per-student basis favoring the poorer segment of the
population. Based on this formula, 60 percent of available recurrent non-personnel and non-capital
resources will go to 40 percent of the poorest schools in each province.
Capital Non-Recurrent Funding
Non-recurrent capital resources for construction of new schools and classrooms are retained at the
provincial level and are targeted at the neediest population. The objective of this formula is to eliminate
backlogs of physical facilities and provide sufficient school places in all provinces. Similar to the formula
in which recurrent non-personnel resources are allocated, PED rank geographical areas from neediest to
least needy based on proportion of children who are not enrolled in school or who are in existing
overcrowded schools, then prioritize funding allocation to the neediest and most crowded areas.
(Note: If you think this section is too long already you may choose not to include the paragraph below)
There are four weaknesses to this formula-based distribution of resources. (1) The ability to effectively
implement this program varies across provinces because not all provinces have the technical and analytical
capacity to rank schools according to the specified criteria. (2) Non-personnel funding are limited since it
only consist of what is left after each province has meet all its personnel commitments. (3) Redistribution
of funds had to occur within provinces, but the degree of distribution possible within each province
depended on its mix of schools. For example, a poor province with a few wealthy schools could do much
less than a rich province with a more even distribution of wealthy and poor schools. As a result, provinces
vary tremendously in the funds available for learners in the poorest schools. (4) The program is designed to
redistribute funds for recurrent spending and not on capital spending
Personnel (Teaching and Non-Teaching Personnel)
The PED is responsible for providing teaching and non-teaching personnel (e.g. administrative and support
staff) to the schools. The Ministry of Education is responsible for determining the norms and standards for
the provision of teaching personnel at the school level but not non-teaching personnel. Teachers are
provided to schools on a formula basis driven by type of curriculum, number of students, and schools’
circumstances. The National Norms and Standards recommend achieving personnel to non-personnel cost
ratio of 85:15 by the year 2005 to improve adequacy and equitable finance of non-personnel education
services.
School Fees
Independent schools also receive funding from the PED on a pro-poor basis, and anchored on the per-
student spending level in public schools. The level of subsidy level, however, is reduced for schools
charging high fees since it is indicative of the socio-economic well-being of a school’s community. School
fee level is determined by the School Governing Body (SGB) fee revenue is used to supplement state’s
public funds to improve the quality of education (e.g. recruiting additional staff). Parents qualify for full
exemption or partial exemption of school fees depending on the level of the combined annual gross income
of both parents.
93
Fiske and Ladd (2004, pp. 116). 94
Fiske and Ladd (2004, pp. 116).
Box 3. Chile Per-Capita Funding Formula
Chile decentralized public education to municipalities in 1981, but the central government remained
responsible for financing education. The Chilean education system is mixed, that is, schools may be public
(municipal), private subsidized, or fully private. The central government finances education by transferring
a formula-based per-capita subsidy (Unidad de Subvención Educacional or USE) to both private subsidized
and public schools. However, in the case of public schools, resources are transferred to the Municipal
Department of Education or the Municipal Department who then manages and provides for schools.
Non-Personnel Recurrent Funding
The Chilean per-capita subsidy consists of three main components: basic student allocation, supplementary
educational needs, and school site needs. The per-capita funding is a base subsidy weighted according to
the grade level of students, type of school attended by students (e.g. vocational, adult, and boarding),
special needs of students, and location of schools. The funding weights increase from primary to secondary
school, but the weight is higher for regular schools compared with adult schools (see Table below). Of all
the different categories of students, children with special needs receive the highest weight on per-capita
subsidy. In terms of school site needs, a ‘zone assignment’ factor is included in the per-capita subsidy
since 1974 to take into account high cost or disadvantaged areas of the country. Another factor that
accounts for school site needs is the ‘rural factor’, which was included in the per-capita subsidy since 1987
to provide supplemental funding to small schools (less than 85 students). However, it is included only if
the school is located more than five kilometers away from the nearest urban center and other schools of the
same type. The reason for restricting its application to the rural area only is to avoid funding schools that
have diminishing enrollments because they are underperforming. Further, extending its application to the
urban area would eliminate the incentives to increase student enrollment and insulate schools from
competition.95
The monthly per-capita subsidy that schools receive is based on the average attendance for the previous
three months, and there is a penalty for misrepresenting student attendance report. A school’s monthly
funding amount is calculated as follows: (Average Months Attendance) x (USE) x (student weight) x (zone
weight) x (rural weight). Private and public schools both receive per-capita subsidy on an equal basis, but
if private subsidized schools charge fees, the per-capita subsidy they receive will be reduced accordingly.
In order to prevent private subsidized schools from excluding students who cannot afford to fees, a
compulsory scholarship scheme was introduced since 1997. Private subsidized schools and the government
both contribute to the scholarship fund and the scholarships can be awarded either in full or partial
exemptions.96
The Chilean per-capita funding formula is intended to have both equity and market regulation function. It
has an equity function because it takes into account cost differentials due to level of schooling,
supplementary educational needs, and regional and school-site cost variations. However, there are two
weaknesses to the per-capita funding formula. First, while the weights are intended to reflect variation in
the cost of providing education, they are not based on a detailed analysis of schooling costs (Parry, 1997).
On the other hand, the weight assignment for rural schools has been improved recently through the
development of cost models attempting to repoduce cost structures of schools located in sparsely populated
areas.97
Second, although the per-capita subsidy is supposed to have a market regulation function, the
funding formula does not directly transfer incentive to schools simply because school managers do not have
much financial and managerial discretion, and the municipal administration, in a way, acts as a middleman
between the Ministry of Education and the schools.
Capital Non-Recurrent Funding
95
Gonzalez, (2005). 96
Gonzalez, (2005). 97 For more details on subsidy correction for rural areas in Chile, see Gonzalez (2005).
Since the per-capita subsidy system only covers operating costs of schools but not capital expenditures, the
FNDR (National Fund for Regional Development) was designed for municipal schools to finance capital
facilities. These funds are targeted to regions on a needs-based basis and are for municipal schools only.
Private schools on the other hand finance their own capital facilities from small fees, donations from
parents, business or church congregations, and bank loans (Parry, 1997). Municipalities compete with
other municipalities within their region for funding by submitting a proposal for new schools, health
centers or other related investments on capital facilities to the regional representative (SEREMIS) of the
Ministry of Education.
Table 1. Weights on per-capita subsidy
Categories Weights 1980 Weights 1989 Weights 1992
Pre-primary 0.880 0.909 0.909
Primary Education, Grades 1-6 1.000 1.000 1.000
Primary Education, Grades 7-8 1.077 1.107 1.107
Primary Education, Adults 0.308 0.316 0.474
Secondary Education 1.210 1.245 1.245
Secondary Education, Adult 0.365 0.375 0.563
Special Students (Mentally or Physically Challenged) 2.250 2.312 3.000
Vocational-Agriculture 1.210 1.245 1.970
Vocational-Industrial 1.210 1.245 1.480
Vocational-Commercial 1.210 1.245 1.300
Boarding School (Meals) -- -- 0.125
Source: Parry, Tarry. (1997). “Achieving Balance in Decentralization: A Case Study of Education
Decentralization in Chile.” World Development, Vol. 25, No. 2, pp. 211-225.