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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: [email protected] 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
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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
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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