Uganda BOOST
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This document analyzes health and education spending in Uganda before and after public expenditure reviews conducted using the BOOST methodology. It finds that: 1) After the reviews, the composition of Uganda's national budget shifted spending away from wages and transfers towards capital investment and goods/services. 2) However, at the district level nearly all increased health spending went to wages, despite rising demand for health services. A similar pattern occurred for education spending. 3) There was wide variation in efficiency and outcomes across Ugandan districts that was comparable to differences between countries worldwide.
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Uganda BOOST
- 1. The first BOOST: Lessons about Health Expenditures in Uganda The World Bank Group
- 2. Uganda: Budget Composition - Before and After the BOOST-based Public Expenditure Reviews 1,800,000,000 Wages 1,600,000,000 BEFORE 1,400,000,000 AFTER Transfers/Grants 1,200,000,000 '000 Ugandan Shillings 1,000,000,000 Non-Wage Employee Costs 800,000,000 Gross Fixed Capital 600,000,000 Formation 400,000,000 Goods and Services 200,000,000 0 2003/04 2006/07 2009/10
- 3. Agency Grants Compared To Social Service Delivery (Last 4 years' cumulative, USh Bn) 1,800.0 1,600.0 1,400.0 1,200.0 1,000.0 800.0 600.0 400.0 200.0 - Schools Front Line Health Agency / Grants District Development
- 4. In Districts … all of the increase in health was wage growth… District Health Budgets 2003/04 - 2006/07 (Bn) (During this period, UNHS data suggests the number of people seeking care from public health centers and dispensaries increased by over 50%) 120.0 100.0 District Public Health Wage 80.0 District Public 60.0 Health Non-Wage 40.0 District PHC Buildings 20.0 - 2003/4 2004/05 2005/06 2006/07
- 5. The Same was true for wages in District-level School Budgets District School Budgets 2003/04 - 2006/07 600.0 School Salaries 500.0 School Construction (SFG) School Non-salary 400.0 300.0 200.0 100.0 - 2003/04 2004/05 2005/06 2006/07
- 6. Within Districts, there were large differences in relative Efficiency in Education P7 Completion Rate (vertical axis) Adult Literacy Rate (horizontal axis) and Primary Education 120.00%Expenditure Per Student (Avg. 2004 -2008) (size of bubbles) 100.00% P7 Completion Rate (%) Bukwo 80.00% 52,317.24 UShs Wakiso Abim 41,137.49 UShs (2008) Kumi 60.00% 49,388.93 UShs Kampala 40.00% 20.00% Kotido Kalangala 55,237.01 UShs 135,180.90 UShs 0.00% 0.00% 20.00% 40.00% 60.00% 80.00% 100.00% 120.00% Adult Literacy Rate (%) (2007)
- 7. Benchmarking revealed that the spread in indicators within Uganda was a wide as for countries in the world 140 140 Primary Completion Rate (%)… Pupil Teacher Ratio… Kaabong Romania 120 120 100 100 Central African Republic 80 Cape Verde Maracha-Terego 80 Rwanda Koboko 60 Namutumba Sub-Saharan Africa 60 Uganda Katakwi 40 40 20 Central African Republic Pader 20 Colombia Kampala Kotido Liechtenstein 0 0 Countries in the World Countries in the World Uganda Districts Uganda Districts
- 8. Relationship between Life Expectancy (vertical axis) and GDP Per Capita (PPP$) (horizontal axis) and Health Expenditure Per Capita (US$) (2006-2007 average) (size of bubbles) 100.00 90.00 Life Expectancy (2007) 80.00 70.00 Uzbekistan 60.00 Namibia 50.00 Jinja Kampala 40.00 30.00 20.00 0.00 1,000.00 2,000.00 3,000.00 4,000.00 5,000.00 6,000.00 GDP Per Capita (PPP$) (2007) Uganda Districts Countries in the World
- 9. Health spending across Uganda
Hinweis der Redaktion
- In 2007/2008 the World Bank Uganda PREM team created the world’s first “BOOST” data tool. BOOST Analysis allowed the team to see the big picture in terms of trends in spending trends in Uganda. During the period 2003/04 to 2006/07 the focus of public expenditure analysis and dialogue in Uganda was on fixing budget implementation processes (see PERs 2003, 2004). Budget composition was deteriorating - fewer and fewer non-staff resources were available for front-line services, and infrastructure bottlenecks were emerging (See CEM 2007). However, our WB budget analysis tended to be forward looking, and sector based, following multi-donor dialogue around the MTEF. There was no thorough backward look at budget trends, and no single disaggregated database was available to de-compose the budget by economic, sector, or organizational levels. BOOST changed this. Created under the 2007 PER and developed under the 2008 PER, BOOST shone more light on damaging trends which were emerging in budget composition. These trends had not been clearly identified under 4 annual multi-donor budget support operations. Government subsequently moved to address underspending on infrastructure, and sought efficiency gains from staff budgets, eg by reducing absenteeism.
- Despite all the Budget Support that donors were providing, Central Agency grants were rising faster than front-line health service funding. Additional funding for agencies was almost twice the increase in District Development Budgets from 2003/4 to 2006/7
- One of the striking findings was that even though numbers of people seeking care from public health centers and dispensaries had increased by 50%, non-salary funding at District level was frozen in nominal terms.
- Similarly, all of the increase in District school funding was going to teacher salaries.
- BOOST allowed the PER team to link spending to results in Ugandan Districts for the first time This revealed wide differences in relative performance, even for similar Districts with similar endowments in adult literacy. This allowed the team to discuss possible management reasons for the differences, in addition to seeking more funding for services, Government started looking to increase the efficiency of funding
- Benchmarking revealed that the spread of indicators within Uganda was as wide as for countries around the world
- This was even truer in health,where life expectancy varied as much between Districtin Uganda as it did countries in the world.
- Connecting spending to these wide ranging results by District under the BOOST allowed the PER team in the health sector to consider spending and efficiency on a spatial basis The resultant map made it clear that spending was lowest and relative efficiency was highest in the poorest Districts of the country (North and East)