Journal of African Economies

Journal of African Economies Advance Access originally published online on August 7, 2008
Journal of African Economies 2009 18(2):183-211; doi:10.1093/jae/ejn015

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Universal Primary Education and School Entry in Uganda

Louise Grogan^*

Department of Economics, University of Guelph, Guelph, ON, Canada

^* Corresponding author: Louise Grogan, Department of Economics, University of Guelph, MacKinnon Building, Room 743, Guelph, ON, Canada. Telephone: +1 (519) 824 4120 ext. 53473. Fax: +1 (519) 763 8497. e-mail: lgrogan{at}uoguelph.ca

JEL classification: H41, H43, J18, O55

This paper examines the initial effects of the introduction of Universal Primary Education (UPE) in January 1997 on school entry in Uganda. Given that advanced age at school entry has historically been associated with primary school dropout, the paper focuses on the the effects of fee elimination on the age at which a child enters school. Data from the 2000 Uganda Demographic and Health Survey and 2001 Education Data Survey are employed to examine the effects of UPE on the probability that a child begins attending school before age nine. School fee elimination under UPE is found to cause a 3% increase in this probability on average. Effects are found to be particularly pronounced for girls and children living in rural areas.

	1. Introduction

TOP 1. Introduction 2. Background 3. Data 4. Fee Elimination and... 5. Conclusions Funding Notes Acknowledgements References

 
A major aim of the United Nations' Millenium Development Goals (United Nations, 2000) is to reduce the number of uneducated African youth. This manifesto sets 2015 as the target year for all children in the world to complete primary school, and for boys and girls to have equal access to education at all levels. In the past ten years, several Sub-Saharan African countries have instituted measures aimed at this goal by eliminating primary school fees in government-aided (public) schools. Malawi eliminated these fees in 1994, Uganda in 1997, Tanzania in 2000, and Cameroon, Burundi, Ghana, Rwanda and Kenya in 2003.

The elimination of school fees has been undertaken differently in different African countries. While Universal Primary Education (UPE) was introduced for all grades of primary schooling in Malawi in 1994, prior to this it had been introduced on a grade-by-grade basis since 1991. In Uganda, UPE was introduced for all primary grades simultaneously in 1997.¹ Lesotho began eliminating school fees gradually in 2000, phasing in UPE by first eliminating fees at the first year of primary school. Kenya, like Uganda, eliminated school fees at all levels of primary schooling simultaneously in 2003.

In all countries in which UPE was instituted, the elimination of the direct costs of schooling created an instantaneous large increase in school enrolment. Enrolment increased by nearly 70% in Malawi in the first year of implementation, and by 58% in Uganda (Uganda Ministry of Education and Sports, 1999). When school fees were eliminated in Lesotho for students in the first year of primary school, enrolment increased by 75%. In Kenya, enrolment increased by 22% in the first year of its programme. These aggregate increases in enrolment after the elimination of fees reflect both increases in school attendance among the primary school-age population and increases due to adults and teenagers attending school for the first time.

Whereas school fees and subsidies have been much examined in the US education literature, to date few studies have examined the impact of the elimination of school fees in countries of Sub-Saharan Africa on pupil enrolment, learning outcomes or retention in schooling. Primarily, this is because the elimination of school fees is such a recent phenomena that data are not yet available.² The literature on school vouchers in the US (see, for example, Epple and Romano, 1998; Ladd, 2002; Neal, 2002) has documented the large behavioural effects of reducing the cost of schooling. Several papers have used natural experiment techniques to identify the effects of subsidising post-secondary education on enrolment at the college level (see, for example, Kane, 1995; Heckman et al., 1998; Ichimura and Taber, 2002). To date, only one published study has examined school fee elimination in any African country. Deininger (2003) shows that there were substantial increases in overall enrolment rates in Uganda following school fee elimination, and that fee elimination reduced socio-economic disparities in access to primary schooling. However, Deininger finds that there were noticeable reductions in the quality of education following the influx of UPE entrants.

This paper focuses on one margin on which the elimination of school fees might be expected to have had an impact: the age at which children enter schooling. This is a very important margin in the Ugandan case. As will be demonstrated, school entry at ages above eight is very strongly associated with early school dropout. The Uganda DHS Survey and EdData Survey, which were undertaken in late 2000 and in the first half of 2001, together comprise one of the first sources of data with which an examination of potential effects of school fee elimination on this margin is feasible.

	2. Background

TOP 1. Introduction 2. Background 3. Data 4. Fee Elimination and... 5. Conclusions Funding Notes Acknowledgements References

 
Uganda eliminated school fees amid the background of a spiralling AIDS crisis and civil war in the north of the country. However, several previous crises since the country's first elections as an independent nation in 1962 had shattered the economy and education system. The successful coup d'état launched by Idi Amin in 1971 stalled the educational progress achieved since independence. By 1985, government expenditures on education amounted to about 27% of the levels of the 1970s (World Bank, 1993).

Recognising the education crisis, the Government of Uganda in 1987 convened an Education Policy Review Commission (EPRC), which had the mandate to make policy recommendations for all levels of education. In their report to the government in 1989, the Commission recommended the universalisation of primary education as soon as feasible, stating:

‘Only when every child is enrolled at the right age and does not leave school without completing the full cycle of primary education it would be possible to ensure that all the citizens have the basic education needed for living a full life. Also, it will help in achieving a transformation of the society leading to greater unity among the people, higher moral standards, and an accelerated growth of the economy’. (Uganda Ministry of Education and Sports, 1999).

In 1993, the Government of Uganda and the United Nations Children's Fund (UNICEF) began a series of initiatives to increase school enrolment in Uganda. The Primary Education and Teacher Development Project was begun in 1993. It comprised the following major goals: (i) to reform the education of primary teachers, (ii) to prepare for reforms in the primary school curriculum, (iii) to reform the pupil examination system, (iv) to improve the provision of textbooks and reading materials in classrooms, (v) to introduce a system of assessing the quality of education provided and (vi) to introduce a framework for country-wide assessments of the overall progress in education.

The rapid elimination of school fees at the primary level was likely accelerated by the first direct elections for President of Uganda, which took place in the spring of 1996. The eventual winner of these elections, the current president Yoweri Museveni, made a campaign promise to provide free primary schooling to four children per Ugandan family. In December 1996, after being elected, President Musveni announced that school fees would be eliminated in January 1997, coincident with the new school year. An enumeration and advertising campaign was undertaken, and the new school entrants began learning within two months of the presidential announcement. In practice, school fees were waived for all primary school students, regardless of how many siblings were also attending school.

The announcement of UPE in late 1996 committed the government to paying tuition fees at the rate of 5000 Ugandan shillings per pupil per annum in the first three years of schooling, and 8100 Ugandan shillings for the fourth to the seventh classes. Other costs of schooling, such as transportation and uniforms, remained the responsibility of families. To put these fees in the context of local salaries, in 1999 a teacher in a government-aided school in Uganda earned about 75,000 shillings per month (Uganda Ministry of Education and Sports, 1999).

The elimination of school fees removed one of the main sources of funding for government-aided schools and replaced it with a commitment to very substantial increases in funding from the government. According to the Uganda Ministry of Education and Sports (1999), parental contributions were providing up to 90% of recurrent and capital expenditures made by schools just prior to the elimination of school fees. The introduction of UPE initially increased the reliance of government-aided schools on the receipt of money from Kampala.

Evidence suggests that funding from the Ugandan government was relatively unlikely to reach schools in the mid-1990s, before the introduction of UPE. Reinikka and Svensson (2004) examine the receipt of school grants in Uganda using school-level panel data. They find that only 13% of capitation grants for non-wage expenditures actually reached schools in Uganda during 1991–5. Most of the grant was absorbed by local politicians and administrators. As well, they find that schools in better-off communities received larger fractions of the original grant money. These results suggest that, prior to the introduction of UPE, government-aided schools in poorer areas of Uganda were more dependent on revenue from school fee collection than were those in wealthier areas.

The central government devolved responsibilities for schools to District Councils during 1997 and began publicising the level of capitation grants through local newspaper campaigns. This resulted in an increase from 13 to about 80% of capitation grants reaching schools by 1999, according to the Public Expenditure Tracking Survey (Ojoo, 2005; Reinikka and Svensson, 2005).

Because school fees were eliminated before infrastructural improvements in the school system had been carried out, the access shock created by the elimination of fees resulted in a substantial initial decrease in resources available per pupil and a large increase in the pupil–teacher ratio. To address this, the Ugandan goverment approved an Education Sector Investment Plan (ESIP) in December 1998. According to The Uganda Millenium Development Goal Report (2003), the textbook-to-pupil ratio in Ugandan schools had risen to 1:4 in 2002.³ However, by 2003, the textbook-to-pupil ratio had risen to 1:3, the classroom-to-pupil ratio to 1:55 and the desk-to-pupil ratio to 1:3 (Ministry of Education and Sports of Uganda, 2005). Aside from further large-scale infrastructure investments, the government of Uganda has committed itself to training large numbers of new teachers under the Primary Education and Teacher Development Project operating since 1995 and to achieving a textbook-to-pupil ratio of 1:1.

Clearly, the elimination of fees in government-aided schools in Uganda had substantial consequences across the education system and at several levels of government. Household survey data, such as the DHS and EdData surveys, can clearly not speak of school quality or funding issues associated with UPE. However, the overarching goal of school fee elimination was to get Ugandan children into school. The DHS and EdData surveys offer the opportunity to provide a first assessment of the effectiveness of UPE in furthering this goal.

	3. Data

TOP 1. Introduction 2. Background 3. Data 4. Fee Elimination and... 5. Conclusions Funding Notes Acknowledgements References

 
The 2000 DHS and 2001 DHS EdData surveys were, respectively, the third and fourth DHS surveys undertaken in Uganda. Previously, surveys had been undertaken in 1988 and 1995 (Uganda Bureau of Statistics and ORC Macro International, 1988a, 1995b). The 1995 survey provides useful information on the educational situation in Uganda just before the introduction of UPE. This survey suggests that, in 1995, 70% of school-age boys attended primary education, compared with 67% of girls (Uganda Bureau of Statistics and ORC Macro international, 1995b). Owing to the prevalence of grade repetition, common to many countries in the Sub-Saharan Africa, and to high drop-out rates, only 11% of males and 9% of females of secondary school age were found to attend school. About 26% of the population aged 15 or above had successfully completed primary school. In the 1995 DHS, 32% of primary school children were in the appropriate grade for their age, with the vast majority being too old for their grade.

The main DHS survey is a stratified random sample of Ugandan households. This survey was conducted in late December 2000, and January and February 2001. After employing appropriate weights, the survey can be considered representative at the national level. The primary purpose of this survey, common to most DHS surveys, is to provide information on education, nutrition, child and adult mortality, fertility, maternal and child health and knowledge of HIV-AIDS. Health questionnaires were administered to women and men, and detailed information on the living circumstances of each household was recorded.

Within six months of the completion of the main DHS survey, the specially constructed Ugandan EdData survey was administered. Households containing individuals aged 5–18 in the main survey were targeted for this second survey, with some exceptions. Households were excluded if an under-19 member had been identified as the household head in the main DHS survey. Also excluded were households in which children were not de jure. The EdData survey collected information on the age of children at the beginning and end of their schooling, educational attainment and reasons for non-attendance. From parents and guardians, the information on their knowledge of UPE was collected. Adults were asked to give their assessments of the qualities and failings of the schools in the local area.⁴ Using the sample weights constructed for the second survey, the sample is representative of Uganda as a whole. In total, 4,217 households were re-surveyed for the EdData survey, and the information on the education of children was reported by 4,246 parents or guardians. The sample weights of the EdData survey, which make the sample nationally representative, are employed throughout these analyses.

Given that UPE affected primarily children who were at risk of attending government-aided schools in Uganda, it is of interest to understand the basic structure of the education sector in the country. Table 1 presents summary statistics on the percentage of children attending different types of primary schools. Column 2 presents results for the full DHS sample. More than 85% of those who have attended primary school attended government-aided schools. The second most commonly attended school type is private non-religious (10.5%), followed by private religious schooling (3.8%). Columns 3 and 4 of Table 1 reveal, however, that private non-religious schooling is very concentrated in urban areas, where 28% of students attend such schools. In contrast, only 4% of children in rural areas attend private non-religious schools. Similarly, a majority of private religious education takes place in urban areas. In rural areas, nearly 92% of children who have attended primary schools attended the government-aided schools, for which fees were eliminated in 1997. This suggests that the elimination of school fees in government-aided schools might be expected to have a greater impact in rural areas.

View this table: [in this window] [in a new window]   Table 1: Types of Schools Attended by Children in 2000 DHS Survey

 
Next we examine summary statistics for the sample of children aged 5–18 at the time of the EdData survey. These are presented in Table 2. Columns 2 and 3 compare the individual and household characteristics of children who have attended school with those who have not. Slightly more boys than girls have attended school. In this sample, children who have attended school are slightly more likely than those who have not to have deceased parents. This is likely because the children who have not attended school are, on average, younger than those who have. In comparing the presence of key household consumer durables across these two groups, it is apparent that children who have not attended school are also more economically disadvantaged. For example, about 8% of school attendees' households have electricity, versus about 1% of non-attendees. Non-attendees are also far more likely to reside in rural areas. Whereas about 11% of attendees live in urban areas, only about 4% of non-attendees do.

View this table: [in this window] [in a new window]   Table 2: Summary Statistics (Means)

 
It is also of interest to contrast the living circumstances of children who attend private schools versus government-aided (state) schools. This is done in columns 4 and 5 of Table 2. Here, the term ‘private’ refers to all schools which are not government-aided, including religious schools. To summarise, the greatest differences among these two groups of children appear to be in housing quality and consumer durables possessed by the household. In general, children who attend private school are more likely to live in households with indoor toilets, telephones, floors and electricity. Private school attendees are also far more likely to reside in urban areas than those attending government-aided schools.

Late school start in Uganda (particularly after age 8) is associated with greater risks of dropping out before the completion of primary school.⁵ In Table 3, the association between late school start and primary school dropout is demonstrated for the 1982 and 1983 cohorts interviewed in the 2001 Uganda EdData Survey. These cohorts were aged 10 and 11, respectively, by January 1994. This implies that those who began school by age 10 should have finished at least seven years of school at the time of the EdData interviews in early 2001.

View this table: [in this window] [in a new window]   Table 3: Probability of Completing At Least Seven Years of Schooling (Probit Marginal Effects)

 
Table 3 presents the probit marginal effects of regressions examining the probability that individuals born in 1982 or 1983 have completed at least seven years of schooling at the time of the EdData survey, conditional on having started school by age 10.⁶ About 87% of the sample had completed this amount of schooling. Panel A presents results in which dummies are used to denote the age at which the child began school. The reference age at school start is 6, the normal age of school entry in Uganda. Column 2 presents results which do not control for individual or household characteristics other than the child's age at school start. Column 3 includes a full set of controls for these characteristics. The results clearly show that school entry between ages 5 and 8 is not associated with differential probabilities of completing at least seven years of schooling. However, beginning school at age 9 or 10 is associated with strongly reduced probabilities.

Clearly, there may be unobserved factors contributing both to later ages at school entry and to higher dropout propensities among children who enter school later. However, the coefficients relating to these ages of school start tell a similar story whether or not controls for other observable characteristics are included (compare columns 2 and 3, and the t-tests of equality of coefficients presented in column 4). Coefficients relating to the age at school entry are statistically the same across specifications in which only age at school start is controlled for and those in which all observable features of individuals, their households and fixed effects are controlled for. Although this by no means proves that later school entry causes dropout, it does strongly suggest that unobserved hetergeneity is not the main factor behind the observed association. One would expect that, if unobserved heterogeneity were driving these results, coefficients would have changed significantly with the inclusion of such an extensive list of controls.⁷

Panel B of Table 3 presents estimates in which dummies representing age at school start are now replaced by a single dummy indicating whether or not the child began school before age 9. Column 2 presents results without controls for other observable characteristics of the individual, while column 3 presents results which include a full set of controls. As in Panel A, results are essentially the same across the two specifications. Starting school before age 9 is associated with a 16–26% increase in the probability that an individual completes at least seven years of schooling.

The results of Table 3 underline the importance of providing a disaggregated analysis of the effects of school fee elimination on school enrolment. If a majority of new school entrants are young, the eventual effects on educational attainment in the population will likely be very different from the case in which a majority of them are older. This makes sense according to standard theories about incentives to invest in education: as the opportunity cost of being in school rises in age (labour market opportunities improve in age), so do the incentives to drop out when families face income shocks. Because people become stronger as they approach adulthood, their productivity in physical labour rises. Children's ability to make up family income lost due to the sickness of an adult in the household rises in age. It is possible that this relationship has been somewhat altered in Uganda by the advent of UPE. However, it is unlikely that the labour market factors which raise the opportunity cost of time spent in school with age have changed much. For this reason, the remainder of this paper focuses on the effects of school fee elimination under UPE on the probability of entering school before age 9.

	4. Fee Elimination and School Entry Ages

TOP 1. Introduction 2. Background 3. Data 4. Fee Elimination and... 5. Conclusions Funding Notes Acknowledgements References

 
Did the elimination of school fees cause a discontinuity in the probability of a child entering school before his or her ninth birthday? As has been demonstrated, an increase in this probability should have positive implications for primary school completion rates. Figure 1 provides a graphical presention of the results of a simple regression of the probability that a child begins school before age 9, by year of birth. Marginal effects of a probit regression including only year of birth and a gender dummy as regressors are graphed, as is the 95% confidence interval.

View larger version (11K): [in this window] [in a new window] [Download PowerPoint slide]   Figure 1: Probability of School Entry Before Age 9

 
The results show that, relative to the reference birth year of 1982, there were no significant differences in the probability that a child born in 1983 through 1987 entered school before age 9. However, those born between 1988 and 1992 experienced significantly greater probabilities of entering school before this age than those born in the reference year. Individuals born in 1988 would have been eight years old when school fees were eliminated in January 1997. They were thus the first birth cohort who could have been affected on this margin. However, to show that this association is a causal effect of school fee elimination under UPE, more controls are clearly necessary.

Table 4 presents the probit marginal effects of an examination of the probability that an individual begins attending school before age 9, by birth year. The oldest cohort in the sample was born in 1982 (the reference year) and the youngest in 1992. The results show that individuals born in the years 1988 through 1992 have significantly greater probabilities of beginning school before age 9 than those in the reference group. Those born between 1983 and 1987, in contrast, do not have different probabilities than the reference group of beginning school before age 9.

View this table: [in this window] [in a new window]   Table 4: Probability of Beginning School before Age 9 by Birth Cohort (Probit Marginal Effects: Probability of Beginning School before Age 9)

 
Like Figure 1, these results show that the elimination of school fees under UPE caused discontinuity in the probability that an individual attends school before age 9. An individual who was born in, say, October 1988 would have been eight years old at the onset of UPE in January 1997. The fact that those born in this year and later have higher probabilities of entering school before age 9 appears attributable to school fee elimination under UPE. In the language of the programme evaluation literature, such children would have been eligible for the ‘treatment’, which is entering school under UPE before his or her ninth birthday. In contrast, a child born in October 1987 would not have been eligible for this treatment due to his or her earlier birth. Year of birth is clearly exogenous, and while it is possible that individuals born in different years have different characteristics, these are now controlled for in the regression. The initial treatment effect of UPE can be identified by comparing the probability of entering school before age 9 among students born close to the cutoff of January 1988.

The results in Table 4 are also robust to the type of household wealth controls used. Column 2 presents results using consumer durables dummies and column 3 employs a linear term in the DHS/World Bank-constructed household wealth index. This household wealth index was created in cooperation with the World Bank for the purpose of comparing living standards across households in circumstances in which income measures are either unavailable or unreliable. The index employs principal components analysis to divide the population into quintiles of wealth distribution on the basis of assets possessed by the household. Each household asset, such as a car, radio, television or dwelling characteristic such as type of roof or floor, is assigned a factor score. This enters into a household's total asset score. Households with higher wealth index scores generally have more assets and better living conditions than do those with lower wealth index scores.⁸

As can be seen, results are essentially the same when the DHS/World Bank wealth index is employed to control for household wealth instead of the asset dummies.

These findings suggest that regression discontinuity analysis is an appropriate technique for identifying the magnitude of this discontinuous effect. Regression discontinuity analysis is a programme evaluation technique in which sample members are assigned to treatment or control groups on the basis of a cutoff score, or pre-programme measure. In these data, there was a discontinuous increase in the probability of entering school by age 9 coincident with the introduction of UPE. Although there may be a secular trend in the probability of entering school by age 9 across people of different birth years, it is the treatment cutoff in January 1998 which provides identification. Similar types of regression discontinuity analyses have been used widely in the evaluation of educational programmes and school quality (see, for example, Thistlethwaite and Campbell, 1960; Angrist and Lavy, 1999; Black, 1999). Where randomised trials are inappropriate, regression discontinuity design can provide robust identification.⁹

There are two key variables used for identification in the regression discontinuity estimates: a continuous, linear variable for the year of birth (YBIRTH) and a dummy variable indicating whether or not school fees had been eliminated before a child attained the age of 9 (UPELEQ8). The first of these variables (YBIRTH) accounts for any potential secular, linear trend across cohorts in the probability of entering school before age 9.¹⁰ Given that both the Ugandan government and UNICEF had been working for several years to increase school enrolment by the time school fees were eliminated, allowing such a secular trend seems appropriate. The second variable, UPELEQ8, assigns sample members to treatment and control groups on the basis of the year of birth. While the assignment to the treatment or control group might here be considered exogenous to the individual, controls for observable personal and household characteristics are also included in the regression.

Formally, define y_i as the age of school entry of a respondent in the DHS EdData survey. In the probit model, the variable y_i is not observed. An observation rule defines the relationship between the latent variable y_i and the observed variable y*_i.

Given the observation rule, y*_i takes the form:

(1)

Using the regression discontinuity estimator, the effects of being in a UPE-affected cohort on the probability of school entry before age 9 are identified by the UPELEQ8 term.

(2)

Table 5 presents regression discontinuity estimates of the effect of school fee elimination in January 1997 on the probability that a child entered school before age 9. The reported coefficients are the marginal effects of a probit regression in which the dependent variable is a binary variable indicating that a child entered school at age 8 or earlier. Results for the full sample are presented in columns 2 and 3. Column 2 uses dummy variables to control for household wealth, whereas column 3 includes a linear term in the DHS/World Bank wealth index. Results are similar across the two specifications. Both indicate that the discontinuous, positive effect of school fee elimination on the probability of entering school before age 9 is about 3%. As well, the coefficients on the YBIRTH term show a statistically significant secular trend in this probability across the cohorts included in the DHS.

View this table: [in this window] [in a new window]   Table 5: Regression Discontinuity Estimates of the Probability of Beginning School Before Age 9 (Probit Marginal Effects)

 
The effects of school fee elimation likely differed across population subgroups. Columns 4 through 7 of Table 5 present results which disaggregate the sample by gender and area of residence. In column 4, results are presented for females, and in column 5 for males. Comparing the two estimates, it is apparent that the effects of school fee elimination on this margin are concentrated on females. Among girls for whom school fees were eliminated before the ninth birthday, the probability of entering school before this age is 5% higher. No such effect of UPE is found for boys.

Columns 6 and 7 of Table 5 present results for children in urban and rural areas, respectively. In both areas, a positive secular trend is observed across cohorts. The probability of entering school before age 9 is increasing significantly over time. However, the effect of school fee elimination appears to be concentrated in rural areas. In rural areas, which comprise two-thirds of Uganda's population, a 3.4% increase in the probability of attending school before age 9 is associated with the advent of UPE in January 1997. In urban areas, no significant jump in this probability is associated with the introduction of UPE. There are several possible explanations for the observed lack of effect of UPE on the probability of a child entering school before the ninth birthday in urban areas. This may reflect the fact that school enrolment in urban areas was much higher than in rural areas before the elimination of school fees. There is also some anecdotal evidence that urban schools continued to charge fees after 1997.

The final two specifications in Table 5 divide the sample at 50th percentile of the household wealth index. The goal is to ascertain whether or not the introduction of UPE had differential effects on the probability of a child entering school before age 9 across the wealth distribution. The results provide evidence of a greater effect of the introduction of UPE on the probability of children from a poorer household entering school before age 9. The coefficient on UPELEQ8 is larger for the poorer 50 percentile than for the richer 50 percentile (0.0386 versus 0.0284), and a t-test of the equality of coefficients rejects the null hypothesis at the 10% level (|t| = 4.24). This finding is consistent with the finding of Deininger (2003), that UPE acted to reduce the gap in school in attendance across socioeconomic groups.

The regression discontinuity specification presented in Table 5 assumes that any secular trend across birth cohorts in the probability of a child entering school before age 9 is linear. In fact, this assumption is more strict than is necessary for the identification of effects using regression discontinuity (Hahn et al., 2001). However, more flexible specifications allowing for a quartic trend in the date of birth result in statistically unchanged coefficients of the variable of primary interest, UPELEQ8.¹¹ This is likely because the data span only 11 different birth years, so trends across cohorts are adequately captured by the linear YBIRTH term.

The presence of information on school type in the EdData survey provides an opportunity to check the identification strategy employed above. The results of Table 5 suggest that school fee elimination mainly affected children whose financial backgrounds put them on the margin for school attendance before January 1997. The elimination of school fees at the primary level in January 1997 was undertaken only in government-aided schools. In general, children who attend private schools come from wealthier backgrounds, as was seen in Table 2. If the identification strategy used in Table 5 is indeed valid, fee elimination in government-aided schools should have had a negligible impact on children whose families could afford private schooling. That this is indeed the case in the DHS EdData is shown in Table 6.

View this table: [in this window] [in a new window]   Table 6: The Effect of School Fee Elimination on Private School Entry Probit Marginal Effects: Probability That School Attended Is Private—includes unschooled

 
Table 6 presents marginal effects from probit estimation of the probability that a child attends a private school. Both school attendees and non-attendees are included in the sample. Estimates for the full sample and for each of the subsamples of Table 5 are presented. To summarise, it is found that there is no discontinuity in the probability of attending private school associated with school fee elimination. This is true for the groups whose probability of attending school before age 9 increased under UPE: poorer students, girls and those in rural areas. This finding suggests that school fee elimination affected only children who were sufficiently financially disadvantaged to not have been at risk of attending private school, either before or after the elimination of fees in government-aided schools. It also suggests that UPE did not initially lead to significant numbers of parents switching children from public to private schooling because of fears about the quality of public education under UPE.

The findings in Table 6 may be also related to anecdotal evidence that fee elimination was not, in fact, undertaken in urban areas. Most private schools are located in urban areas and the incentives to attend them would not have changed much if UPE were not actually being implemented. Resources per student would not have diminished much in government-aided schools if few new students attended school and they were still collecting fees. In rural areas, there are very few private schools, so even parents worried about quality under UPE would not have had many local alternatives. One might expect that over time new alternatives to overcrowded government-aided schools will emerge if some parents are willing to pay more. However, these results pertain only to the first four years of UPE, and thus cannot inform us about the longer term effects of UPE on the private education sector in Uganda.

	5. Conclusions

TOP 1. Introduction 2. Background 3. Data 4. Fee Elimination and... 5. Conclusions Funding Notes Acknowledgements References

 
Overall, the results of this paper suggest significant positive effects of school fee elimination on the timely enrolment of girls and children living in rural areas of Uganda. Given the strong historical association between age at school start and retention in schooling in Uganda, school fee elimination should promote the completion of primary education among these two disadvantaged groups. The results of this analysis show that looking at gross or net enrolment in primary education may provide a very limited picture of both the quantitative effects of the elimination of school fees and of the effects specific to socioeconomic groups.

Clearly, however, fee elimination under UPE changed more than ages at school entry. In all countries which eliminated fees, including Uganda, the sudden increase in enrolment led to shortages of teachers and textbooks. Wherever UPE was instituted, large fractions of new students in the first year were adults or far above the normal age at school entry. Classrooms became overcrowded, sometimes necessitating multiple school ‘shifts’ during the day. These factors are likely to have had negative effects on retention in schooling. Nakibuuka (2004) reports that 2003 registrations for the Primary School Leaving Examination, which should have been written then by the first UPE cohort, were far below the levels that UPE enrolment figures would predict. A Poverty Elimination Action Plan revision paper, written by Uganda Ministry of Education (2003) found that only 33% of the 1997 UPE cohort had reached Primary 6 by 2002, and only 22% had reached Primary 7 by 2003. Massive investments in teacher education, textbooks and school construction do, however, appear to have markedly improved the resources available to students and teachers in more recent UPE cohorts.

There is a clear need to gather longitudinal data at the individual level on the educational trajectories of students and on the quality of learning outcomes under UPE. In order to ascertain the specific effects of the elimination of school fees on AIDS orphans, a large and particularly disadvantaged socioeconomic group in Uganda, data must be collected which include specific questions on the timing of parental sickness and death. There is also a need for survey data which permits an examination of the effects of the elimination of school fees on the resources available at the school level in Uganda. With data of this nature, it would be possible to provide a comprehensive picture of the costs and benefits of school fee elimination in Uganda.

	Funding

TOP 1. Introduction 2. Background 3. Data 4. Fee Elimination and... 5. Conclusions Funding Notes Acknowledgements References

 
The author is grateful to the Social Sciences and Humanities Research Council of Canada (SSHRC) for financial support.

	Acknowledgements

TOP 1. Introduction 2. Background 3. Data 4. Fee Elimination and... 5. Conclusions Funding Notes Acknowledgements References

 
The author is grateful for helpful comments of participants at the 2005 Centre for the Study of African Economies (CSAE) Conference in Oxford, to the editors and referees of this Journal for very helpful comments and to ORC Macro for making the Demographic and Health Survey (DHS) data and Education Survey (EdData) for Uganda publicly available.

	Notes

TOP 1. Introduction 2. Background 3. Data 4. Fee Elimination and... 5. Conclusions Funding Notes Acknowledgements References

 
¹ In both Uganda and Malawi, the elimination of school fees at all levels of primary schooling was prompted by a national-level election campaign.

² The most recent Demographic and Health Survey (DHS) and Educational Data Survey (EdData) for Malawi, which adopted UPE first in 1994, were carried out on a different sample than the main DHS survey. This means that important household information cannot be related to pupil information.

³ The report states, ‘Although the sectoral budget allocation increased from 20.6 billion Ugandan shillings at the start of UPE to 46.7 in 2003, this increase has not resulted in a proportional improvement in the pupil teacher ratio, or the quality of education’. However, international evidence does not show conclusively that pupil–teacher ratios are a key factor in learning outcomes. See, for example, Hanushek (1986) and Hanushek (1997) for evidence against strong effects of school resources on learning outcomes, and Card and Kreuger (1990) and Case (1999) for counter-evidence.

⁴ For more on the 2000 DHS and 2001 EdData surveys, the reader is referred to the website of DHS Macro, www.measuredhs.com

⁵ Education in Uganda follows the British system. The normal age for school entry in Uganda is 6, although many students begin at age 5 or 7. It consists of seven years of primary school, four years of secondary school (O-level), two years of upper secondary school (A-levels), followed by university. Before an individual can enter secondary school, he or she must successfully complete the Primary school Leaving Examination, or PLE.

⁶ In the 2001 EdData, only 1.3% of school attendees began school after this age.

⁷ For example, child ability may be an unobservable driving the association between late entry and early dropout. It is argued that this cannot explain the association for the following reason. Assume that parental education were correlated with child ability, which seems reasonable. If unobservable differences in ability across children were actually driving the observed association between age at school entry and school dropout, including dummies for whether the mother and father have completed primary education should have significant effects on the coefficients of interest. As shown in column 4 of Table 3, this is not the case.

⁸ For more information on the DHS/World Bank household wealth index, the reader is refered to Filmer and Pritchett 1998 and Filmer 2001 , who show that this asset-based index compares well with expenditure data in measuring household living standards.

⁹ For a detailed analysis of the restrictiveness and robustness of regression discontinuity design as a programme evaluation mechanism, see, for example, Hahn et al. (2001).

¹⁰ Note that this is not the case in the results presented in Table 4 or Figure 1.

¹¹ These specifications are not presented here but are available on request from the author. Given the lack of difference in coefficients on UPELEQ8 across specifications employing a linear trend in YBIRTH and a quartic in YBIRTH, it is unlikely that allowing for a fully non-parametric specification would yield different results. See, for example, Ludwig and Miller (2007) for recent work employing non-parametric regression discontinuity estimators.

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