1. PUBLIC SPENDING ON EDUCATION IN UGANDA: A BENEFIT INCIDENCE ANALYSIS
Madina Guloba, EPRC
IAFFE 8th Annual Conference
22-24, July, 2010

2. Background & motivation
Investing in human capital is one of the Ugandan government’s core poverty reduction strategies.
Expenditure on education has been averaging about 30 percent of annual public expenditure (MoFPED, 2008/09) and is significantly higher than that of other pro-poor sectors such as health, transport, water & sanitation, food security and agriculture.
The Ugandan Education Sector Strategic Plan (ESSP II), which is part of the government’s broader Poverty Reduction Strategy framework, has paid particular attention to reducing inequalities in public education.
Initiatives in place, enrolments increased across levels but most noticeably enrolments at primary levels increased tremendously.
Primary school enrolment has expanded rapidly from a gross enrolment total of about 5.8million in 1998 to about 8.0 million pupils in 2008 for schools that responded (MoES, 2008).
Although secondary school gross enrolment rate was still low, it had more than tripled by2008 (0.265 million in 1998 to 1.08m students in 2008).

3. Cont…
In particular, there has been a significant improvement in girls’ primary enrolment rates (over the same period indicating progress towards a key Millennium Development Goal (MDG) and the gender component of the Global Education for All Campaign.
If such progress is sustained, Uganda is on track to eliminate the gender gap in primary education by 2009/10.
Given the prominent role of education in Uganda’s Poverty Eradication Action Plan (PEAP) it s thus critical to evaluate whether current public expenditure patterns in education have been pro-poor.
We estimate enrolment rates for different income quintile groups, and then public spending per student using audited Ministry of Finance Planning & Economic Development (MoFPED) public expenditure accounts (recurrent and development expenditure data).

4. Figure 1: Percentage of total education expenditure allocations by facility level

5. Data and method
Data
Enrolment by wealth categories-used Uganda National Household Survey data of 2005/06 (UNHS III), conducted from May 2005-April 2006,
Survey covered 7,426 households but only 7421 households are used as five households with zero consumption expenditures were dropped.
Uganda Bureau of Statistics (UBoS) followed a multi-stage stratified sampling approach.
Information gathered included household roster and demographic characteristics (sex, age, area of residence, region among others), household level information includes household consumption expenditures (food expenditures and non-food expenditures), housing conditions and welfare among others.
All estimates are weighted using sample weights provided by UBoS.

6. Methodology
Estimating and calculating BIAs is well documented in literature
The per-student subsidy is calculated as total government recurrent expenditures (separately for primary, secondary and tertiary) for 2005/06 divided by the total education users per facility levels estimated from the UNHS III data set.
The explicit assumption behind this analysis is that the differences in unit subsidies received by users are accounted for by the differences in government education expenditures across districts. This assumption is justified on the basis of the budget allocation, according to which allocations are largely driven by the number of enrolments at various facility levels.
Unit of analysis is the household.
We group households by income percentiles
The study used benefit concentration curves to enrich the analysis.

7. Cont…
Table 1: Percentile categorization by welfare in Uganda shillings (Ush)

8. Assumptions made
Government subsidy for one unit of education service is the same for all individuals, regardless of income/expenditure level and geographic location within the population area.
It was assumed that all the data on expenditures in education were reliable given that the data was obtained from audited accounts of sectoral allocations from the MoFPED

9. Findings- US$21,34,168
Table 2: Per-Pupil/student government subsidy for enrolment by facility Level: 2005/06

10. Cont…
Table 3: Estimated school enrolment by percentile and facility level

11. Cont…
Table 4: Benefits of education expenditures by percentile and facility (%)

12. Wealth distribution of benefits Figure 2 & 3: Distribution of benefits of education expenditures by subcategory (%) and Concentration curve of education subsidy by percentile and facility

13. Gender distribution of benefits
Table 5: Benefits of education subsidy by percentile and facility (%)-males & females

14. Figure 4 & 5: Concentration curves for education subsidy by percentiles, (%)-males and females

15. Rural & Urban areas of residence
Table 6: Estimated school enrolment by percentile and facility level for rural and urban

16. Figure 6 & 7: Concentration curves for education subsidy by percentile (%)-rural and urban

17. Regional distribution of benefits

18. Figures: Concentration curves for education subsidy by percentile (%)-By Region

19. Summary & Conclusions
Expenditures have been more in primary education than in secondary and tertiary levels of education.
Only the incidence of primary education is found to be pro- poor (10 percent) and the pro-poor bias of primary education is largely driven by the demographic pattern of poor households tending to have more children.
Expenditure on primary education is pro-poor, while expenditure on higher education levels tends to be pro-rich
The increase in favour of the poorest percentiles can be at least partly attributed to the commencement of ESSP II 2004, which introduced a number of important pro-poor components.

20. Cont…
Same results generally hold for both urban and rural areas, although secondary education is less likely to be pro-poor in rural areas than in urban areas due to low attendance rates by the poor in rural areas.
Benefit recipients are also particularly concentrated in urban areas, perhaps because of lower delivery costs.
Uganda’s education reforms have been gender responsive. Generally, more females than males were found to benefit from the government subsidy at higher levels of household income percentiles.
This was attributed to increased female enrolments especially at primary level and tertiary levels. At the tertiary level, it shows that the government programme of an additional 1.5 points for every female joining university institutions has been of great benefit hence the high government subsidy.

21. Cont…
Again, the benefit was more pro-rich than pro-poor given that the probability for rich households to send their female students to school was higher than the poor households.
On the regional aspect, the government subsidy was found to be more beneficial in the Northern Region at the primary level and pro-Central at the tertiary level.
Again, the Northern region has been in conflict for over 20 years and they are now in recovery hence, households in the poorest percentile are most likely to benefit from the government programme of free primary education and given that their expenditure patterns are down thus, it is not surprising that they spend less at higher levels of education.
They have benefited more on the infrastructure programmes and government subsidy per pupil at primary level.

22. Gracias/Thank you