Impacts of the Universal Primary Education Policy on Educational Attainment

and Private Costs in Rural Uganda

August 25, 2005

Mikiko Nishimura*, Takashi Yamano** and Yuichi Sasaoka***

*Teachers College, Columbia University, USA **Foundation for Advanced Studies on International Development, Japan

***National Graduate Institute for Policy Studies, Japan

Abstract

While some governments in Sub-Saharan Africa have abolished tuition to achieve universal primary education (UPE), few studies have examined the impacts of the UPE policy beyond school enrollment. This study estimates the impact of the UPE policy in Uganda on overall primary education attainments by using data including 940 rural households. We find that UPE has decreased delayed enrollments and increased grade completion rates up to the fifth grade and its effects are especially large among girls in poor households. Yet, schools in Uganda still face further challenges in terms of low internal efficiency and the unequal quality of education. Keywords: educational policy, universal primary education, Uganda Corresponding Author: Mikiko Nishimura Email: YuMikiko@aol.com Address: 1-12-5-501 Asagaya-minami, Suginami-ku, Tokyo 166-0004, Japan

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Impacts of the Universal Primary Education Policy on Educational Attainment and

Private Costs in Rural Uganda

1. Introduction

Sub-Saharan African (SSA) countries have experienced slow progress in achieving universal

primary education (UPE) in the last three decades. Between 1980 and 1995, SSA was the only

region that experienced a decline in the average gross enrollment rate (GER) for primary

education, while other regions experienced substantial increases (UNESCO, 1998). Public

expenditure on primary education also fell by six percent in per capita terms between 1985 and

1995, while it increased approximately threefold in all other developing regions (UNESCO,

1998). International aid agencies and researchers share a common concern that SSA will not

achieve UPE by 2015, unless the progress is to be accelerated rapidly (Carceles, 2001; Bennell,

2002).

Responding to this concern, many SSA governments have abolished school fees for public

primary education, under the name of the UPE or Free Primary Education policy (Avenstrup,

et al., 2004). The UPE policy has been well received by various stakeholders including

politicians, aid agencies, and the beneficiaries as a pro-poor policy.1 Uganda was one of the

first SSA countries to adopt the UPE policy in 1997 and experienced a robust increase of

primary enrollment from 2.8 million in 1997 to 7.6 million in 2004 (UNESCO, 2000; MOES,

2005). The evidence of its actual effect, however, is mixed. While studies indicate that the

UPE policy effectively improved access to primary education for children of poor families by

removing tuition for public primary education (e.g., Deininger, 2003), others reveal that

various fees are still charged under the UPE policy (e.g., Suzuki, 2002). For instance, a

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governmental report shows that 55 percent of primary dropouts left school due to the costs of

schooling (MOES, 2003). These existing studies, however, conducted research a few years

after the implementation of UPE, and there have been no empirical study in recent years.

Since the aim of the UPE policy was primarily to increase the overall educational attainments

of children, it is important to examine the impacts of the UPE policy beyond school

enrollment.

This article, therefore, estimates the impacts of the UPE policy on overall educational

attainments at the primary education level in rural Uganda. In particular, by taking advantage

of using data collected six years after the adoption of UPE, we estimate the impacts of the

UPE policy on delayed enrollments and the completion rates of upper grades in primary

schools. The data include 940 rural households interviewed in 2003. The next section reviews

the existing literature on the UPE policy and the private costs of education in Uganda. It also

sets the conceptual framework for the following analyses. Section 3 outlines the research

questions, data, and methodology, followed in Section 4 by the estimation results. Finally,

Section 5 draws conclusions.

2. UPE in Uganda and the Private Cost of Education

UPE in Uganda

The main question to be investigated is how the reduction of the direct private costs of

education contributes to equality and equity in education. The UPE policy aims at expanding

access, enhancing equity, and increasing efficiency in education systems (Inter-Agency for

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Basic Education for All, 1990). The government faces a particular challenge in providing

quality basic education to marginalized populations such as the poor, ethnic minorities, and

sometimes girls. As far as education financing is concerned, public education in Uganda was

under-funded prior to the introduction of the UPE policy in 1997 (Mehrotra and Delamonica,

1998). The direct costs of education were heavily dependent on private resources. Students’

families paid more than 80 percent of the total direct costs of public primary schooling, while

the government paid the rest. The largest part was allocated to the salaries of teachers and

administrators. The share of private resources in the total direct costs of education in Uganda

was high compared to other low-income countries such as Burkina Faso (41.3%), Bhutan

(27.2%), Myanmar (58.5%), and Viet Nam (40.0%) (Mehrotra and Delamonica, 1998).

In 1997, the government pledged to meet the costs of schooling for four children per family,

which was amended to benefit all children in 2003, while parents meet the costs of school

uniforms, meals, exercise books, local materials for building classrooms, and physical labor

(Mehrotra and Delamonica, 1998; Black, et al., 1999). The role of the government increased

under the UPE policy to provide more resources and ensure the quality and equity of

education, supported by the mobilized resources through the Highly Indebted Poor Countries

(HIPCs) initiative as well as other donor funds (Tan, et al., 2001). The overall education

budget increased from 1.6 to 3.8 percent of GDP, with the share of the primary education

sub-sector of the total education expenditure increased from 40 percent in 1996 to 65 percent

in 2004 (Deininger, 2003; MOES, 2005). As a consequence, the number of primary school

teachers increased by 41 percent from 103,331 in 1997 to 145,703 in 2004 and the number of

schools also increased by 41 percent from 10,490 in 1997 to 14,816 in 2004 (MOES, 2005). In

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addition, the decentralization of responsibilities and massive publicity campaigns with a

particular focus on girls’ education were undertaken and the coordination among donors was

strengthened in targeting investments in primary education. Since then, each school has been

receiving the capitation grant based on the number of pupils in the school and subsequently

spending the grant by following a guideline.

Deininger (2003) found that the introduction of UPE was associated with a significant

expansion of attendance in primary education by the poor and that the school fees decreased

significantly. He used the national household surveys in 1992 and 1999/2000 to compare the

enrollments and private costs of 1992, 1997, and 1999. He also found that the school

attendance increased dramatically for girls aged 6 to 8 years and that the household

expenditure on primary schooling decreased by about 60 percent between 1992 and 1999.

Although his study indicated a significant increase in enrollments just after the adoption of

UPE, it was too early to evaluate the impacts of the UPE on the overall educational

attainments. Also, Deininger (2003) did not examine the impacts of UPE on delayed

enrollments. In contrast, we use data taken six years after the adoption of UPE and are in a

better position to measure the impacts of UPE on overall educational attainments in Uganda

than Deininger (2003) and other previous studies.

Private Costs, Equality, and Equity of Education

The literature regarding the economics of education has long explained the incentive for

public and private investments in education. Psacharopoulos’ studies reveal that public and

private rates of returns to education are generally the highest at the primary level and that this

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trend is most evident in low-income countries (e.g., Psacharopoulos, 1994). The equalization

and pro-poor policy perspectives also support increased public investments in primary

education. The conventional knowledge suggests that income inequality leads to educational

inequality, and vise versa (Carnoy and Levin, 1985; Birdsall, et al., 1997). Because acquiring

education requires substantial individual investments, educational opportunities are limited for

the poor due to credit constraints. In SSA, the limited capacity and low quality of public

schools induced additional private spending on education and led to inequality based on the

socio-economic capacity of the people (Bray and Lillis, 1988; Colclough and Lewin, 1993;

Kitaev, 2001).

Some empirical literature, however, also shows that even under the UPE policies, the

remaining private costs of education are still impediments for enrollment and equality in the

quality of education (Tsang and Kidchanapanish, 1992; Avenstrup, et al., 2004). The UPE

policy normally subsidizes tuition fees only, leaving other direct and indirect costs to be borne

by parents and families. Thus, the equality and equity of education remain as a concern under

the UPE policy. The empirical evidence also challenges the adequacy of the cost intervention.

Some studies argue that the unit costs of schooling at a given quality for marginalized

populations can be quite different from those for non-marginalized groups (Tsang, 1994; Kitaev,

2001). In Kenya, for instance, official fee-abolition did not affect the enrollment of the nomadic

population because it was simply served by spontaneous ‘bush’ schools, largely funded through

contributions in kind (Kitaev, 2001). A more recent study also finds that public policies that

promote the expansion of primary education tend not to benefit the poor (Govinda, 2003).

These empirical studies indicate the importance of considering the adequate costs of schooling

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for different groups to maintain vertical and horizontal equity. The case of Uganda provides us

with a unique opportunity to examine the effect of UPE.

3. Key Research Questions, Data and Methodology

3.1. Key Research Questions

This study takes advantage of using a recent rural household survey that covers the enrollment

patterns in 2003, six years after the implementation of UPE in Uganda. Since the data include

children who were aged six and younger when the UPE was implemented, we can evaluate the

impacts of the UPE on primary educational attainments by comparing the pre- and post-UPE

cohorts. In particular, we ask the following questions:

1. How much did the UPE increase the grade completion rates in primary education in rural

Uganda?

2. How much did the UPE decrease delayed enrollments in rural Uganda?

3. What factors determine private spending on education under UPE in rural Uganda?

By answering these questions, we believe that we can obtain better assessments of the UPE

policies not only for Uganda but also for other countries that have adopted the UPE policy

recently, such as Kenya, or those that are planning to adopt it.

3.2. Data

The data used in this article come from 940 households in rural Uganda surveyed in 2003 as

part of the Research on Poverty, Environment, and Agricultural Technology (REPEAT) project,

which was conducted by Makarere University and the Foundation for Advanced Studies on

International Development (Yamano et al., 2004). The survey was conducted in

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August-October 2003, and the sample for the REPEAT project largely builds upon and

complements a completed research project on policies for improved land management in

Uganda, conducted by the International Food Policy Institute (IFPRI) and Makerere

University between 1999 and 2001 (Pender et al., 2001).

The 2003 REPEAT survey covers 94 Local Council 1s2 that are located across most regions in

Uganda, except the North where security problems exist. From each LC1, ten households are

selected, resulting in a total of 940 households and 3,121 school-age children aged 6 to 18

(Table 1). Of them, 72.5 percent attended primary school, 12.7 percent attended secondary

school, and 16.6 percent did not attend school at the time of survey. The net enrollment rate

(NER), which is the proportion of school-going-aged children attending school over the total

number of school-going-aged children, is 86.1 percent for boys and 86.9 percent for girls at

the primary level and 27.6 percent for boys and 29.5 percent for girls at the secondary level.3

The enrollment rates for girls slightly exceed those for boys both at the primary and secondary

levels.

Educational Attainment Profile

To investigate the impacts of the UPE policy on educational attainments, we present the

educational attainment profiles for pre- and post-UPE cohorts, separately for girls and boys, in

Figure 1. We select late-teens aged 15 to 19 in 2003 (thus aged 9 to 13 in 1997) as the

post-UPE cohort because they were primary-school-aged when the UPE policy was adopted in

1997. Although early-teens aged below 14 could also be included in the post-UPE cohort, we

do not include them in Figure 1 because some of them are still attending primary school. As a

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comparison group, we choose young adults aged 20 to 24 in 2003 (thus aged 14 to 18 in 1997)

as the pre-UPE cohort because they were too old to receive the full benefits of the UPE policy.

The completion rates are higher for the post-UPE cohort at every grade, except grade seven.

Especially, we find large increases in the completion rates at the fourth and fifth grades. The

increases are larger for girls than boys. For instance, the proportion of those who completed

grade five increased by 12 percentage points, from 74.0 to 86.0 percent, for girls and by 7.2

percentage points, from 76.5 to 83.7 percent, for boys.

When we stratify the sample by wealth, measured by the per capita expenditure in 2003, a

remarkable improvement, a 16.3 percentage point jump from 59.3 to 75.6 percent, is found

among girls of the households whose per capita expenditure is in the bottom 40 percent. On

the other hand, the changes are much smaller among girls and boys from households in the top

20 percent of the per capita expenditure. It should also be noted that the UPE policy does not

seem to retain its effect on the sixth and seventh grades in 2003. This could be because the

UPE policy does not have impacts on these grades or because some of teenagers aged 15 to 19

are still attending primary schools and will complete the sixth and seventh grades in the future.

We need to wait for a few more years to make a complete evaluation of the impacts of the

UPE policy on the overall educational attainments in primary education.

Delayed Enrollment

In poor countries like Uganda, delayed enrollment in school is common and considered to

lower the overall educational attainment. To investigate the impacts of UPE on delayed

enrollments, we plot the distribution of the age that pupils started primary school. Again we

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created the post- and pre-UPE cohorts. We choose children aged 6 to 12 in 2003 (0 to 6 in

1997) as the post-UPE cohort because they were younger than 6 years old when the UPE was

adopted. The pre-UPE cohort includes children aged 13 to 18 in 2003 (7 to 12 in 1997) as a

comparison group. As we can see in Figure 2, the entry age into primary school has become

younger, indicating that the delayed-enrollment has decreased, for both girls and boys. Even

when we stratify the sample by the wealth, we still find a sizable shift toward the left after the

adoption of UPE for children in both poor and non-poor households. For instance, those who

enrolled in primary school after the legally set age 6 declined by 22.2 percentage points from

58.0 to 35.8 percent for girls and by 25.1 percentage points from 60.9 to 35.8 percent for boys.

The significant reductions in delayed enrollment could be caused not only by the abolishment

of school fees but also by the construction of additional schools, up to 4,000 schools after the

adoption of UPE (MOES, 2005). Easy access may have been beneficial especially for young

children who might have delayed enrollment due to security issues and physical distance.

However, there remain quite a number of early entries in primary school prior to age six. Thus,

unlike previous predictions, made just after the implementation of UPE, that untimely entries

were only a transitional phenomenon (Deininger, 2003), the UPE policy has not yet

established timely entries into primary school.

Repetition

UPE can have both positive and negative impacts on repetition. Although UPE may have

reduced the number of dropouts, it may also have encouraged low-score students, who would

drop out of school without UPE, to repeat grades. To investigate the impacts of UPE on

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repetition, we also would like to identify the pre- and post-UPE cohorts. However, we only

have repetition information on children aged 18 and younger. Some of them are still in

primary school, or others who are not in school currently may go back to school later. Thus, it

is still premature to investigate the issues of repetition.4 Nonetheless, our data show that the

internal inefficiency, which is partly caused by repetition, is persistent. When looking at GER

and NER by grade, we note that the proportion of children of the grade-aged children in each

grade gradually reduces and becomes less than 10 percent at the seventh grade in primary

school for both boys and girls. More than half of our sample pupils have repeated the same

grade at least once in primary education, and about a quarter of the pupils have repeated at

least twice. Thus, it seems that primary education is still suffering from internal inefficiency

under UPE.

3.3. Methodology

Although Figures 1 and 2 are informative, we need to control for various factors to measure

the impacts of UPE on educational attainment. Thus, in the following section, we estimate the

determinants of enrollment, delayed enrollment, and the completion of the fourth and fifth

grades. We use the same pre- and post-UPE cohorts as defined in Figures 1 and 2. In addition,

we estimate the determinants of education expenditure to investigate what factors influence

education expenditure under UPE.

Determinants of Enrollment

To examine the determinants of enrollment, we use a dummy variable, Eij, which takes one if

child i of household j attends school and zero otherwise, and estimate the following model

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with Probit at the child level:

Prob (Eij=1) = f (Cij, Hj, Xj, Rj), (1)

where Cij is a set of characteristics of child i of household j; Hj is a set of household head

characteristics; Xj is a set of household characteristics; and Rj is a set of two regional dummies

where household j resides. We estimate equation (1) for children aged 6 to 12, i.e. primary

school age, and for children aged 13 to 18, i.e. secondary school age, as well as boys and girls

separately.

Determinants of Delayed Enrollment

To examine the determinants of delayed enrollment, we use a dummy variable, Dij, which

takes one if child i of household j delays enrollment to primary school beyond the age of 6 and

zero otherwise, and estimate the following model with Probit at the child level for children

aged 6 to 18:

Prob (Dij=1) = f (UPEij, Cij, Hj, Xj, Rj), (2)

where UPEij is a dummy variable for the post-UPE cohort, i.e. children aged 6 to 12 in 2003,

and all the other explanatory variables are the same as in equation (1). We estimate equation

(2) for children aged 6 to 18 for boys and girls separately.

Determinants of Educational Attainment up to Grades 4 and 5

Next, to examine the determinants of educational attainment up to grades 4 and 5, we use

dummy variables, A4ij and A5ij, which take one if child i of household j completed grade 4 and

grade 5, respectively, and zero otherwise. To estimate the overall impacts of UPE on primary

educational attainment, we need data from the population that has already completed primary

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education. Thus, we estimate equation (3) for young adults aged 15 to 24 and regard those

who are aged 15 to 19 in 2003 (thus aged 9 to 13 in 1997) as the post-UPE cohort and those

who are aged 20 to 24 in 2003 (thus aged 14 to 18 in 1997) as the pre-UPE cohort. Then, we

estimate the following model with Probit at the individual level:

Prob (A4ij or A5ij=1) = f (UPEij, Cij, Hj, Xj, Rj), (3)

where the UPE cohort dummy variable in this model takes one for late-teens aged 15 to 19 in

2003. The variables on the education of parents and a dummy variable for orphans are

removed because such information is collected only for children aged 18 and younger. Instead

of the education of parents, we include the maximum education level of household members.

We estimate equation (3) for boys and girls separately.

Determinants of Education Expenditure

Finally, by using the Ordinary Least Square (OLS) regression model, we estimate the

education expenditure at the household level:

ln (EXj) = f (Hj, Xj, Rj, Zj) (4)

where EXj is the education expenditure in log for household j, while the explanatory variables,

Hj, Xj, and Rj are defined as in equations (1) through (3). The explanatory variable, Zj includes

two variables of child characteristics of household j to examine the effect of households with

only girls or households with orphans. We estimate equation (4) for primary level and

secondary level separately.

Furthermore, the economic burden of education, defined as the proportion of education

expenditure in the total household expenditure, Bj, is included as a dependent variable with the

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same explanatory variables as equation (4) to examine the determinants of the economic

burden of education:

ln (Bj) = f (Hj, Xj, Rj, cj), (5)

where Bj is the proportion of the education expenditure out of the total expenditure in

household j, while the explanatory variables, Hj, Xj, Rj and cj are the same as stated in equation

(4). We estimate equation (5) for the primary level and secondary level separately.

Variables

In the following analyses, we use five dependent variables (Eij, D ij, Aij, EXj, and Bj), one set of

child characteristics (Cij), three sets of household characteristics (Hj, Xj,, Zj), and the regional

dummy (Rj). The dependent variables have already been defined earlier. The child

characteristics, Cij, include age and a dummy variable for orphans. There is a high possibility

that orphans are AIDS orphans who are more prone to be under difficult circumstances (see

Yamano, Shimamura, and Sserunkuuma, 2005).5 We also use a dummy variable for the UPE

cohort (between 6 and 12) for delayed enrollment and a dummy variable for the UPE cohort

(between 15 and 19) for educational attainment.

The household head characteristics, Hj, include age and dummy variables for the gender and

the religion of the household head. For the household characteristics, Xj, we use the

socio-economic status of the household, including the years of parental education, the number

of siblings aged 6 to 18, per capita household expenditure in US$ in logs, the value of asset

holdings in logs, and the size of land in acres that is owned or operated by the household in

logs. Another set of household characteristics, Zj, includes one dummy variable for the

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households having only girls and another variable that is a ratio of orphans to the total number

of children in the household.

Because we conduct the education expenditure analysis at the household level and the analysis

on educational attainment includes information on adults, the maximum schooling years of

men and women were used instead of parental education for these analyses. In addition, we

create a dummy variable for children whose parental education is unknown for the analyses on

enrollment and delayed enrollment, since they are likely to be children who are separated from

parents and would thus need particular attention.6 We also include regional dummies, Rj, to

control for regional characteristics.

4. Results

4.1. Determinants of Enrollment

The regression results in Table 2 suggest that different factors affect the school enrollment of

boys and girls aged 6 to 12. Among girls, we find that the age of the child and the mother’s

education are the only factors that have significant impacts on enrollment. Younger girls tend

to be out of school, which will result in delayed enrolments, while the mother’s education has

a positive impact on enrollment. For boys aged 6 to 12, younger boys also tend to be out of

school but the education levels of both father and mother do not have impacts on enrollment.

Although we find that the household expenditure increases boys’ enrollment, but not girls’

enrollment, in this age group, we obtain the opposite results in the older age group in the last

two columns in Table 2. Thus, it is not clear if there is a gender preference for boys over girls,

or vice versa.

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In the survey, we have asked the reasons for the non-enrollment. The most frequently cited

reason for dropouts or non-enrollment by parents or guardians of children aged 6 to 12 is ‘not

yet schooling age’ for both boys and girls, which accounts for 69.7 percent for boys and 78

percent for girls. “Can’t pay school fee” was the second most frequently cited reason for both

boys (8.2 percent) and girls (10.1 percent). These results are different from the previous

national household surveys that indicated school fees as the most important reason for

non-enrollment (MOES, 2001; Deininger, 2003). Regarding the starting age, as many as 112

parents or guardians of children aged 6 claimed that it is too early to send their children to

school. In addition, more than 10 parents or guardians of children over age 10 also responded

that their child is not yet school going age. The distance to school in part may explain why

younger children of school age do not still attend school due to security and physical reasons

(MOES, 2001). However, the fact that some parents consider it too early to send their children

aged over 10 raises some questions on parental awareness on schooling.

Among children aged 13 to 18, socio-economic factors have strong impacts on enrollment.

Because some of them attend secondary schools, which are not free, the results are as expected.

Gender differences in the estimated coefficients appear strong among this age group. While all

demographic and socio-economic factors similarly show a statistical significance for both

boys and girls, Catholic religion is a negative factor only for girls.7 Furthermore, a girl is

likely to be out of school when her household head is young (in the 20s), while boys are not

affected by the age of household head.

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Among this age group, the most frequently cited reason for non-enrollment is “Can’t pay

school fee” for boys, 37.7 percent, and “Married or pregnant” for girls, 29.4 percent. “Can’t

pay school fee” was the second most frequently cited reason for girls, 23.5 percent. Thus, it is

clear that school fees are the most important constraint for boys and that school fees and

marriage/pregnancy are the two most important constraints for girls in this age group.

4.2. The Impacts of UPE on Delayed Enrollment and Attainment

Delayed Enrollment

The results in Table 3 indicate that the UPE policy has reduced the delayed enrollment by 24.3

percentage points for girls and 25.8 percentage points for boys. We also find that orphans and

children in female-headed households are more prone to delay enrollment. In contrast,

children in Muslim households, children with educated parents, and children in high

expenditure households are less likely to delay enrollment. We note that the education level of

mothers seems to have a large impact on preventing delayed enrollment for both girls and boys,

and that boys in female-headed households are more likely to delay enrollment. In short,

socio-economic factors influence delayed enrollment in primary school, and the UPE policy is

not sufficient to eliminate delayed enrollment by itself.

Educational Attainment

Finally, the results in Table 4 indicate that the UPE policy has significantly increased

educational attainment in primary school. According to the results, the completion rates of the

fourth and fifth grades increased more than 11 percentage points for young female adults aged

15 to 19 in the post-UPE cohort. In contrast, the UPE policy has rather limited impacts on

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young male adults: the completion rates of the fourth grade increased by 4.4 percentage points

but the completion rate of the fifth grade did not increase significantly for male young adults

in the post-UPE cohort. We attempted the same equations for the sixth grade, but the UPE

cohort did not show a statistical significance for both male and female young adults. Thus, we

conclude that the UPE policy has large positive impacts on the completion rates of primary

education up to the fifth grade for female students but only up to fourth grade for male

students, and the sizes of the impacts are larger for female students than male students.

We find both in Tables 3 and 4 that many socio-economic factors, such as educational

resources and wealth variables, influence delayed enrollment and educational attainment.

Therefore, the results suggest that socio-economic factors still have a significant influence on

overall education attainment in primary education even when the tuition is free under the UPE

policy. This is why we examine the education expenditure next.

4.3. Private Cost of Education and Its Determinants

Private Cost of Education

Because our data do not have information on education costs for each child on each item, we

calculate the education cost per pupil by obtaining the average per pupil spending on

education from households that have children either in primary or secondary school. We then

calculate the average per capita spending for the primary and secondary levels and obtain the

ratio of primary to secondary education costs at 1:8.7. Then, we apply this ratio to the

education spending of households that have children in both primary and secondary schools.

After obtaining the education cost for each child in all households, we find the overall primary

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to secondary cost ratio at 1:9.8, which is a little higher than the previous ratio of 1:9.6 found

by Appleton (2001).

As noted in the previous section, households bear the direct costs of primary education, e.g.,

meals and uniform, even under UPE. In Uganda, the estimated cost per pupil varies

tremendously by child. We find that the average annual spending on education is

approximately 41,800 shillings (US$21) per pupil in primary education and 409,800 shillings

(US$206) per pupil in secondary education, while the median spending is at about 15,000

shillings (US$7.5) and 205,000 shillings (US$103) for primary and secondary, respectively. In

terms of the proportion of education expenditure out of the total household expenditure, it is

2.7 percent for each primary pupil and 15.5 percent for each secondary pupil. Compared to

other countries, the proportion is low for primary education but high for secondary education

(World Bank, 2002).

In Figure 3, we present the education expenditure per pupil in Uganda Shillings (Ushs) by the

per capita household expenditure quintile. It is clear that the absolute expenditure level differs

significantly across the expenditure quintiles. The amount of education expenditure of

households in the highest quintile is 7.9 times and 8.3 times as much as that of households in

the lowest quintile for a primary pupil and a secondary pupil, respectively. In Figure 4,

however, we find that the proportions of education expenditure out of the total expenditure are

remarkably similar across expenditure quintiles: the proportion of education expenditure per

pupil is about 2 to 3 percent at the primary education level but is 13 to 16 percent at the

secondary education level. The low proportions for the primary education level may be due to

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the UPE policy as found by earlier studies (MOES, 2001; Deininger, 2003).

Determinants of Private Expenditure on Education

According to the regression results in Table 5, the education expenditure at the primary

education level is positively associated with asset-value, per capital household expenditure,

and the number of children in the household. Households with young household heads in the

20s and 30s spend significantly less on primary education than older household heads in their

40s. Furthermore, households with only female children spend significantly less on primary

education. This suggests that households spend less for girls’ education. Without expenditure

information at the child level, however, we are not able to draw a concrete conclusion on this

issue.

Turning to orphans, we find that the ratio of orphans out of the total number of children does

not affect the education expenditure at the primary education level but increases it at the

secondary level. By using the same data as in this paper, Yamano, Shimamura, and

Sserunkuuma (2005) found that orphans are likely to stay with relatively wealthy households.

This may imply that they may tend to stay in households who can afford to spend more, in

both absolute and relative expenditure, at the secondary level. We need further investigation,

however, on the causality of the adoption of orphans and education spending.

In terms of the economic burden of education, the Muslim religion and the Central region are

significant factors that have positive association with the economic burden. The household

assets also positively contribute to the economic burden of education. In contrast, the age of

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household head and having only female children are negatively associated with the economic

burden of education. The households with only female children spend less proportion of

household expenditure on education, which suggests a low priority placed on girls’ education.

5. Policy Implications and Conclusions

As shown in the previous section, the UPE policy seems to have decreased delayed enrollment

in primary school and achieved higher educational attainment at least up to grade 4 for boys

and grade 5 for girls in primary education. The UPE policy has also achieved a low economic

burden of education at the primary level for all households, regardless of their household

expenditure level. As a result, the UPE policy has positive impacts on the poor, especially girls,

in improving their access to school. In this respect, the UPE policy has contributed to the access

and equity of education as a pro-poor policy.

However, the study also reveals that there should be more than just the one demand-side policy

intervention of reducing the school tuition in public primary education to achieve universal

primary education. First, internal inefficiency, such as delayed enrollment and repetition,

remains a major problem in primary education in Uganda. Thus, further policy interventions

would be necessary to respond to the reasons for internal inefficiency. The proper

supply-side-policy interventions, such as providing enough school facilities in the nearby

neighborhood, or the demand-side-policy interventions, such as improving parental awareness,

should follow the abolition of school tuition. In response to these challenges, the Ministry of

Education and Sports is currently making efforts to pay special attention to schools in the

“hard-to-reach” areas. Special policy intervention in these areas have recently been accepted in

21

the form of top-up salary and provision of housing for teachers. Also, school construction in

the remote areas is to be facilitated using school mapping based on the recent Geographic

Information Survey (GIS) results. Such targeting strategies are believed to further the benefits

of UPE to marginalized children (Malinga, 2005).

Second, although this study does not investigate the quality of education in detail, quality

improvements would be essential for retaining pupils at upper grades. A government report

indicates that numbers of teachers and schools increased by 41 percent while the enrollment

increased by 171 percent between 1997 and 2004 (MOES, 2005). This raises concerns of

deterioration in the quality of public primary schools.

Finally, low completion rates in upper grades suggest high indirect costs for older children.

While the UPE policy reduces the costs of primary education, more comprehensive rural

development strategies should increase the benefits from primary education so that the

expected benefits exceed the total costs of the direct and indirect costs of education.

From the supply side perspective, the funding scheme for primary schools could provide better

incentives for them to reduce internal inefficiency. For instance, the Government of Uganda

provides each public primary school the capitation grant based on the number of pupils in the

school. Under this financing scheme, schools have incentives to keep as many pupils as

possible. This could also provide an incentive for schools to encourage pupils to repeat grades.

The question remains as to whether this potential incentive is strong enough for schools to

worsen internal inefficiency. Although we do not have empirical evidence to either support or

22

dismiss this incentive, the funding scheme should be modified to avoid such a possibility. For

instance, some sort of reward schemes for schools that achieve high internal efficiency could

be useful as an alternative funding scheme.8

Public resource allocation is a difficult endeavor in countries like Uganda where resources are

extremely limited. Uganda’s UPE has been successful in expanding educational opportunities

to children in poor households. The next step should target marginalized children who have

not received benefits from the current UPE. For the supply-side policy intervention,

improvements in the quality and internal efficiency of public primary education should be

enhanced, coupled with more comprehensive rural development strategies.

23

Note:

1 UPE policies have been adopted in many other Sub-Saharan African countries such as

Malawi, Lesotho, Kenya, and Tanzania.

2 Local Council 1 is the lowest level of administrative unit in Uganda.

3 According to UNESCO (2005), Uganda’s gross enrollment rate (GER) at the primary level

was 139.1 percent for male and 133.7 percent for female, while GER at the secondary level

was 19.1 percent for male and 14.6 percent for female as of 2001.

4 When comparing the repetition experienced by the age group of 13-15 with that of 16-18, the

proportion of pupils who experienced repetition at least once reduced from 75.5 percent to

68.8 percent for girls and 73.3 percent to 72.2 percent for boys. However, since some of the

age group of 13-15 may still be in primary school and will repeat in the future, the definite

change of repetition is not measurable at this stage. A tentative observation is that repetition

has not considerably improved under the UPE policy.

5 A recent report shows that about 14 percent of children aged 0 to 17 are defined as orphans of

any type in Uganda, and about 48 percent of them are estimated to be AIDS orphans

(UNAIDS/UNICEF/USAID, 2004). In Yamano (2005), it was found that among adolescents

aged 15 to 18, adolescents who are either double orphans or single orphans not living with

the remaining parent are significantly less likely to attend school than non-orphans living

with both parents and this phenomenon is more evident among girls than boys.

6 In the sample aged 0 to 17, those whose father’s education is unknown account for 6.1

percent and those whose mother’s education is unknown account for 5.8 percent. These do

not highly correlate with the status of orphan. Thus, those whose parental education is

unknown are likely to be children either whose parent has disappeared for some reason or

24

whose parent was not present when the child was born.

7 This contradicts the conventional knowledge that the Muslim culture prevents girl’s

education (Bennel, 2002). In relation to pregnancy or marriage, that is the major reason for

girls’ dropout of this age group. Restricted birth control of Catholic religion may have some

impact on female child in terms of increasing the chance of dropout due to pregnancy, but

further investigation would be necessary for careful interpretation.

8 Currently, the capitation grant is calculated by multiplying 25 shillings per pupil per day by

the number of pupils in school and 270 school days. In the education sector review meeting

in 2004, the Ministry of Finance and Planning proposed a plan to provide each school with

an additional 100,000 shillings per month. This plan would contribute to assist small schools,

but would similarly result in lowering the performance-based incentive of schools. Thus, a

more preferable incentive scheme would be to reward teachers whose classes or schools

improved repetition or dropout rates so that the inventive scheme would directly link to the

quality improvement of the school.

25

References

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Bennel, P. (2002). Hitting the Target: Doubling Primary School Enrollments in Sub-Saharan

Africa by 2015. World Development, vol. 30(7), 1179-1194. Birsdsall, N, Ross, D. and Sabot, R. (1997). Education, Growth, and Inequality. In Pathways

to Growth: Comparing East Asia and Latin America, eds. N. Birdsall and F. Jaspersen, pp. 93-130. Inter-American Development Bank. Washington, DC. Distributed by the Johns Hopkins University Press.

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Growth in Uganda. International Journal of Educational Development, 19, 111-126. Bray, M. and Lillis, K. (1988). Community Financing of Education: Issues and Policy

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Primary Education in Uganda. Economics of Education Review, 22, 291-305. Govinda, R. (2003). Making Basic Education Work for the Poor: Experiences from the South.

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Inter-Agency for Basic Education for All, (1990). Meeting the Basic Human Needs: A

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Kitaev, I. (2001). Private Education in Sub-Saharan Africa: A Re-Examination of Theories

and Concepts Related to Its Development and Finance. IIEP, UNESCO. Paris. Malinga, F. (2005). Interview notes of authors with Ms. Florence Malinga, Commissioner

Education Planning, Ministry of Education and Sports, Uganda, August 19, 2005 Mehrotra, S. and Delamonica, E. (1998). Household Costs and Public Expenditure on Primary

Education in Five Low Income Countries: A Comparative Analysis. International Journal of Educational Development, 18 (1), 41-61.

Pender, J., Jagger, P, Nkonya, E. and Sserunkuuma, D. (2001). Development Pathways and

Land Management in Uganda: Cases in Uganda: Causes and Implications. EPTD Discussion paper, 85. International Food Policy Research Institute. Washington, DC.

Psacharopoulos, G. (1994). Returns to Investment in Education: A Global Update. World

Development, vol. 22(9), 1325-1343. Suzuki, I. (2002). Parental Participation and Accountability in Primary Schools in Uganda.

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Tan, J, Soucat, A. and Mingat, A. (2001). Enhancing Human Development in the HIPC/PRSP Context: Progress in the Africa Region during 2000. World Bank. Washington, DC.

Tsang, M. C. and Kidchanapanish, S. (1992). Private Resources and the Quality of Primary

Education in Thailand. International Journal of Educational Research, 17 (2), 179-198. Tsang, M. C. (1994). Costs of Education in China: Issues of Resource Mobilization, Equality,

Equity, and Efficiency. Education Economics, 2 (3), 287-312. Uganda, the Ministry of Education and Sports (2001). The 2001 Uganda DHS EdData Survey:

Findings. MOES. Kampala. Uganda, the Ministry of Education and Sports (2003). Technical Note on Primary Repetition,

Survival, and Completion Rates before and after Universal Primary Education (UPE) in

Uganda. MOES. Kampala. Uganda, the Ministry of Education and Sports (2005). The Education Management

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Orphan Estimates and a Framework for Action. United Nations. New York. UNESCO (1998). World Education Report 1998. UNESCO. Paris. UNESCO (2000). The EFA 2000 Assessment: Country Reports: Uganda. UNESCO. Paris. UNESCO (2005). EFA Global Monitoring Report 2005. UNESCO. Paris. World Bank (2002). Community Support for Basic Education in Sub-Saharan Africa. World

Bank. Washington, DC. Yamano, T., Sserunkuuma, D. and Otsuka, K. (2004). The REPEAT Survey in Uganda:

Results. FASID Development Database, 2004-09-01. Foundation for Advanced Studies on International Development (downloadable from http://www3.grips.ac.jp/~21coe/j/index.html). Tokyo.

Yamano, T., Shimamura, Y. and Sserunkuuma, D. (2005). Living Arrangements and Schooling

of Orphaned Children and Adolescents in Uganda. FASID Discussion Paper Series on International Development Strategies, No.2005-02-007.

28

FEMALE

20.0

30.0

40.0

50.0

60.0

70.0

80.0

90.0

100.0

P1 P2 P3 P4 P5 P6 P7

Grade

% c

om

plete

d

after UPE (age 15-19) before UPE (age 20-24)

MALE

20.0

30.0

40.0

50.0

60.0

70.0

80.0

90.0

100.0

P1 P2 P3 P4 P5 P6 P7

Grade% c

om

plete

d

after UPE (age 15-19) before UPE (age 20-24)

Enrollment Pattern by Expenditure Level Before and AfterUPE (Female)

20.0

30.0

40.0

50.0

60.0

70.0

80.0

90.0

100.0

P1 P2 P3 P4 P5 P6 P7

Grade

% C

om

plete

d

After UPE Poor 40% After UPE Rich 20%

Before UPE Poor 40% Before UPE Rich 20%

Enrollment Pattern by Expenditure Level Before and AfterUPE (Male)

20.0

30.0

40.0

50.0

60.0

70.0

80.0

90.0

100.0

P1 P2 P3 P4 P5 P6 P7

Grade

% C

om

plete

d

After UPE Poor 40% After UPE Rich 20%

Before UPE Poor 40% Before UPE Rich 20%

Figure 1. Enrollment Profile before and after UPE1: Educational Attainment

29

Age Enrolled in Primary School Before and After UPE (Female)

0.0

10.0

20.0

30.0

40.0

50.0

60.0

70.0

80.0

90.0

100.0

3 4 5 6 7 8 9 10 11 12 13 14

Age Enrolled in Primary School

% P

ropo

rtio

n

After UPE (age 6-12) Before UPE (age 13-18)

Age Enrolled in Primary School Before and After UPE (Male)

0.0

10.0

20.0

30.0

40.0

50.0

60.0

70.0

80.0

90.0

100.0

3 4 5 6 7 8 9 10 11 12 13 14

Age Enrolled in Primary School

% P

ropo

rtio

n

Male After UPE (age 6-12) Male Before UPE (age 13-18)

Age Enrolled in Primary School by Expenditure Level Beforeand After UPE (Female)

0.0

10.0

20.0

30.0

40.0

50.0

60.0

70.0

80.0

90.0

100.0

3 4 5 6 7 8 9 10 11 12 13 14

Age Enrolled in Primary School

% P

ropo

rtio

n

After UPE Poor 40% After UPE Rich 20%

Before UPE Poor 40% Before UPE Rich 20%

Age Enrolled in Primary School by Expenditure Level Beforeand After UPE (Male)

0.0

10.0

20.0

30.0

40.0

50.0

60.0

70.0

80.0

90.0

100.0

3 4 5 6 7 8 9 10 11 12 13 14

Age Enrolled in Primary School

% P

ropo

rtio

n

After UPE Poor 40% After UPE Rich 20%

Before UPE Poor 40% Before UPE Rich 20%

Figure 2. Enrollment Profile before and after UPE2: Age Entry in Primary School

30

Per Pupil Education Expenditure by per capita Household Expenditure Level

0

100000

200000

300000

400000

500000

600000

700000

800000

900000

1 2 3 4 5

Per Capita Household Expenditure Quintile

Per

Pupi

l E

ducat

ion C

ost

in U

shs.

primary secondary

Figure 3. Education Expenditure per Pupil by Per Capita Household Expenditure Level

Economic Burden of Education by Per Capita Household Expenditure Level

0

2

4

6

8

10

12

14

16

18

1 2 3 4 5

Per Capita Household Expenditure Quintile

Perc

enta

ge o

f Per

Pupi

l Edu

cat

on

Exp

pendi

ture

in t

he T

ota

l H

ouse

hold

Exp

endi

ture

(%)

primary secondary

Figure 4. Economic Burden of Education per Pupil by Per Capita Household Expenditure

Level

31

Table 1. Sampled Households and Children/Adolescents (aged 6-18) in Uganda

Number of Households Number of Children/Young Adults

All With primary school pupils

With secondary school pupils

With no pupils

All In primary school

In secondary school

Not in school

Regions

(1) (2) (3) (4) (5) (6) (7) (8)

East 410 (100.0)

340 (82.9)

114 (27.8)

64 (15.6)

1,414 (100.0)

1,063 (75.2)

166 (11.7)

184 (13.0)

Central 300 (100.0)

230 (76.7)

85 (28.3)

63 (21.0)

972 (100.0)

675 (69.4)

143 (14.7)

154 (15.8)

West 230 (100.0)

181 (78.7)

60 (26.1)

43 (18.7)

735 (100.0)

525 (71.4)

88 (12.0)

122 (16.6)

Total 940 (100.0)

751 (79.9)

259 (27.6)

170 (18.1)

3,121 (100.0)

2,263 (72.5)

397 (12.7)

460 (14.7)

Note: numbers in parentheses are percentages. The total number of pupils, if including all age groups, is 2,341 for primary and 401 for secondary school.

32

Table 2. Determinants of Enrollment by Gender and Age Group

Enrollment (Eij=1) Primary Level

(Age 6-12) Secondary Level (Age 13-18)

Female Male Female Male

Child Characteristic Age .050**

(9.10)

.046**

(9.67)

-.057**

(-7.24)

-.054**

(-7.70)

Orphan (=1) .028

(0.87)

.005

(0.17)

-.070† (-1.75)

.031

(0.98)

HH Head Characteristics

Head aged 20-29 (=1) -.037

(-0.95)

.012

(0.37)

-.234*

(-2.37)

-.019

(-0.32)

Head aged 30-39 (=1) -.006

(-0.22)

.026

(1.30)

.039

(1.08)

-.012

(-0.34)

Head aged 50-59 (=1) -.030

(-0.96)

.020

(0.90)

-.028

(-0.84)

.051† (1.81)

Head aged over 60 (=1) -.016

(-0.52)

.016

(0.68)

.066*

(2.15)

-.003

(-0.11)

Gender of head (female=1) .046

(1.18)

-.053*

(-2.17)

-.006

(-0.15)

.048

(1.14)

Catholic (=1) -.032

(-1.52)

-.003

(-0.14)

-.075*

(-2.53)

-.007

(-0.31)

Muslim (=1) .003

(0.12)

.011

(0.44)

.023

(0.57)

.012

(0.36)

Household Characteristics

Years of father’s education .004

(1.20)

.004

(1.31)

.009*

(2.32)

.007† (1.84)

Years of mother’s education .006*

(1.98)

.003

(1.06)

.005

(1.17)

-.004

(-1.16)

Father’s education unknown (=1) .014

(0.35)

-.102† (-1.71)

-.054

(-0.90)

-.137† (-1.91)

Mother’s education unknown (=1) -.011

(-0.28)

.010

(0.24)

.005

(1.17)

-.115

(-1.58)

Number of siblings aged between 6-18 -.003

(-0.88)

-.001

(-0.35)

.001

(0.21)

-.002

(-0.40)

ln (Asset-value in UShs.) .011

(1.10)

.009

(0.94)

.003

(0.22)

.029*

(2.50)

ln (per capita expenditure in US$) -.017

(-1.11)

.024† (1.84)

.068**

(3.36)

.019

(1.05)

ln (Land in acres) .003

(0.26)

-.004

(-0.44)

.002

(.015)

.009

(0.67)

Region

West (=1) .002

(0.07)

-.027

(-1.14)

-.015

(-0.45)

-.112**

(-3.05)

Central (=1) -.021

(-0.92)

-.049*

(-2.25)

-.009

(-0.30)

-.086**

(-2.69)

Pseudo R-Square .208 .233 .242 .241

Observations 842 882 678 678

Note: Numbers in parentheses are z-values. ** indicates 1 percent level, * indicates5 percent level, and †indicates 10 percent level.

33

Table 3. Determinants of Delayed Enrollment in Primary School by Gender (Cohort of

age 6-18) Delayed Enrollment (Dij=1)

All Female Male

Child Characteristic UPE cohort (age 6-12=1) -.250**

(-12.25)

-.243**

(-8.12)

-.258**

(-9.12)

Orphan (=1) .079*

(2.29)

.077

(1.54)

.073

(1.50)

HH Head Characteristics

Head aged 20-29 (=1) -.076

(-1.47)

-.157*

(-2.19)

-.002

(-0.03)

Head aged 30-39 (=1) -.003

(-0.11)

-.025

(-0.60)

.012

(0.30)

Head aged 50-59 (=1) -.004

(-0.13)

-.040

(-0.93)

.034

(0.84)

Head aged over 60 (=1) -.014

(-0.47)

-.048

(-1.15)

.021

(0.50)

Gender of head (female=1) .078*

(2.11)

.032

(0.61)

.120*

(2.32)

Catholic (=1) -.022

(-0.96)

-.056

(-1.65)

.007

(0.21)

Muslim (=1) -.056† (-1.87)

-.052

(-1.19)

-.059

(-1.41)

Household Characteristics

Years of father’s education -.007*

(-2.19)

-.012*

(-2.58)

-.001

(-0.30)

Years of mother’s education -.018**

(-5.27)

-.026**

(-5.17)

-011*

(-2.32)

Father’s education unknown (=1) -.055

(-1.02)

-.051

(-0.70)

-.041

(-0.50)

Mother’s education unknown (=1) -.147**

(-2.90)

-.212**

(-3.10)

-.081

(-1.08)

Number of siblings aged between 6-18 .006

(1.33)

.005

(-0.88)

.007

(1.11)

ln (Asset-value in UShs.) -.010

(-0.93)

-.008

(-0.51)

-.014

(-0.90)

ln (per capita expenditure in US$) -.061**

(-3.96)

-.066**

(-2.88)

-.059**

(-2.77)

ln (Land in acres) .018

(1.47)

.011

(0.64)

.024

(1.40)

Region

West (=1) .099**

(3.61)

.104*

(2.59)

.090*

(2.41)

Central (=1) -.008

(-0.31)

.007

(0.19)

-.016

(-0.44)

Pseudo R-Square .083 .106 .073

Observations 2675 1304 1371

Note: Numbers in parentheses are z-values. ** indicates 1 percent level, * indicates5 percent level, and †indicates 10 percent level.

34

Table 4. Determinants of Attainment up to Grade 5 by Gender (Cohort of age 15-24)

Grade 4 Attainment (A4ij=1) Grade 5 Attainment (A5ij=1)

All Female Male All Female Male

Child Characteristic UPE cohort (age 15-19=1) .079**

(5.86)

.111**

(5.62)

.044*

(2.52)

.063**

(3.53)

.113**

(4.30)

.013

(0.52)

HH Head Characteristics

Head aged 20-29 (=1) -.063**

(-2.60)

-.089**

(-2.60)

-.021

(-0.66)

-.080*

(-2.45)

-.067

(-1.52)

-.078

(-1.55)

Head aged 30-39 (=1) -.030

(-1.29)

-.048

(-1.49)

-.002

(-0.06)

-.036

(-1.17)

-.029

(-0.71)

-.033

(-0.68)

Head aged 50-59 (=1) .005

(0.31)

-.017

(-0.61)

.017

(0.76)

.009

(0.39)

.025

(0.71)

-.015

(-0.46)

Head aged over 60 (=1) -.009

(-0.52)

-.013

(-0.46)

-.010

(-0.46)

.003

(0.12)

.015

(0.44)

-.012

(-0.36)

Gender of head (female=1) -.034† (-1.74)

.006

(0.20)

-.065*

(-2.52)

-.008

(-0.31)

.008

(0.21)

-.019

(-0.49)

Catholic (=1) -.040**

(-2.75)

-.027

(-1.37)

-.046*

(-2.32)

-.062**

(-3.09)

-.058*

(-2.06)

-.064*

(-2.30)

Muslim (=1) -.043*

(-2.10)

-.040

(-1.43)

-.036

(-1.27)

-.058*

(-2.09)

-.037

(-0.98)

-.078† (-1.95)

Household Characteristics

Max. years of men’s education

.020**

(3.18)

.021*

(2.07)

.018*

(2.48)

.017*

(2.32)

.017

(1.40)

.017† (1.84)

Max. years of women’s education

.024*

(2.38)

.036*

(2.32)

.014

(1.16)

.040**

(3.26)

.051**

(2.80)

.031† (1.90)

ln (Asset-value in UShs.) .019**

(2.74)

.019† (1.93)

.017*

(1.99)

.035**

(3.71)

.034*

(2.37)

.038**

(3.04)

ln (per capita expenditure in US$)

.020*

(2.26)

.027*

(2.15)

.010

(0.90)

.049**

(3.96)

.065**

(3.60)

.030† (1.80)

ln (Land in acres) -.020**

(-3.15)

-.028**

(-2.96)

-.013

(-1.56)

-.036**

(-4.12)

-.042**

(-3.21)

-.033**

(-2.77)

Region

West (=1) -.056**

(-3.27)

-.018

(-0.77)

-.085**

(-3.58)

-.086**

(-3.70)

-.033

(-1.03)

-.136**

(-4.15)

Central (=1) .021

(1.40)

.034† (1.74)

.009

(0.40)

.017

(0.81)

.028

(0.96)

.009

(0.29)

Pseudo R-Square

.123

.153

.121

.097

.115

.096

Observations

1838

939

899

1838

939

899

Note: Numbers in parentheses are z-values. ** indicates 1 percent level, * indicates5 percent level, and †indicates 10 percent level.

35

Table 5. Determinants of Total Private Cost of Education and Economic Burden of

Education: Primary Level (Households with children aged 6-18) Primary Level Secondary Level

Ln (Education Expenditure)

% Economic burden

Ln (Education Expenditure)

% Economic burden

HH Head Characteristics Head aged 20-29 (=1) -1.484**

(-3.204)

-3.321*

(-2.096)

-.927

(-1.394)

-2.582

(-1.481)

Head aged 30-39 (=1) -1.093**

(-3.324)

-2.212† (-1.966)

-.265

(-.556)

-1.551

(-1.242)

Head aged 50-59 (=1) -.068

(-.174)

2.581† (1.931)

-.452

(-.835)

-.344

(-.243)

Head aged over 60 (=1) -.838*

(-2.256)

.703

(.553)

.301

(.589)

1.892

(1.413)

Gender of head (female=1) -.489

(-1.040)

.916

(.570)

-.630

(-.966)

-2.603

(-1.523)

Catholic (=1) -.031

(-.112)

.137

(.147)

-.680† (-1.798)

-3.227**

(-3.254)

Muslim (=1) .195

(.503)

2.700*

(2.033)

-.247

(-.461)

-1.780

(-1.270)

Household Characteristics

Maximum years of men’s education

.121

(1.128)

.085

(.232)

.670**

(4.525)

2.111**

(5.441)

Maximum years of women’s education

.061

(.433)

.077

(.161)

.512**

(2.645)

.971† (1.913)

ln (Asset-value in UShs.) .405**

(3.039)

1.004*

(2.205)

.094

(.512)

-.187

(-.387)

ln (Per capita expenditure in US$)

.852**

(4.589)

-.497

(-.783)

1.476**

(5.830)

3.824**

(5.759)

ln (Land in acres) -.154

(-1.073)

.559

(1.142)

-.367† (-1.846)

-.560

(-1.072)

Household Child

Characteristics

Only girl child/ren (=1) -.934**

(-3.010)

-1.928† (-1.816)

.126

(.304)

.926

(.848)

Orphan-total children ratio .215

(.778)

-1.381

(-1.461)

.862*

(2.226)

3.033**

(2.986)

Number of children (age 6-12)

.655**

(7.151)

.927**

(2.959)

Number of children (age 13-18)

1.640**

(12.603)

3.060**

(8.968)

Region

West (=1) .002

(.006)

1.711

(1.588)

-.667

(-1.532)

.654

(.573)

Central (=1) .113

(.376)

2.932**

(2.861)

.120

(.289)

2.527

(2.329)

R-Square .219 .104 .369 .313

Observations 766 766 766 766

Note: Numbers in parentheses are t-values. ** indicates 1 percent level, * indicates5 percent level, and †indicates 10 percent level.

36

Appendix

Composition of Reasons for Dropout/Non-enrollment(Male, Age 6-12) N=122

69.7%

8.2%

0.0%

0.8%

20.5%

0.8%

Not yet schooling age

Can’t pay school fee

Not enough returns fromfurther schooling

Married/pregnant

Have jobs

Other/unknown

Composition of Reasons for Dropout/Non-enrollment(Female, Age 6-12) N=109

78.0%

10.1%0.0%

0.0%

0.0%11.9%

Not yet schooling age

Can’t pay school fee

Not enough returns fromfurther schooling

Married/pregnant

Have jobs

Other/unknown

Composition of Reasons for Dropout/Non-enrollment (Male, Age 13-18) N=106

0.9%

37.7%

4.7%

0.0%7.6%

49.1%

Not yet schooling age

Can’t pay school fee

Not enough returns fromfurther schooling

Married/pregnant

Have jobs

Other/unknown

Composition of Reasons for Dropout/Non-enrollment(Female, Age 13-18) N=119

2.5%

23.5%

5.9%

29.4%

0.8%

37.9%

Not yet schooling age

Can’t pay school fee

Not enough returns fromfurther schooling

Married/pregnant

Have jobs

Other/unknown

Appendix Figure A1. Reason for Dropout or Non-Enrollment by Gender and Age Group

37

Appendix Table A1. Cost and Economic Burden of Education per Pupil

Mean SD Median Minimum Maximum Number

Cost UShs UShs UShs UShs Primary 41,816 78,960 15,000 28 700,934 2,216 Secondary 409,756 587,358 204,926 243 3,311,782 388

Burden % % % % Primary 2.70 3.62 1.61 .00 44.12 2,216 Secondary 15.45 11.68 14.21 .03 61.11 388