Presentation on theme: "Ana Revenga Director, Poverty Reduction Group World Bank Stockholm"— Presentation transcript:
1Challenges of Measuring Poverty Reduction and Equality: Using statistics to assess results Ana RevengaDirector, Poverty Reduction GroupWorld BankStockholmNovember 20, 2008
2Overview MM&E systems and the results agenda Measuring poverty Income/Consumption poverty: new global estimatesNon-income poverty: MDGs, voice and empowermentAreas of new analysisMeasuring equality of opportunitiesMeasuring service deliveryPoverty mapsCountry level monitoring systemsTracking program effectiveness & poverty impactsIntegrating M&E into government processesWas asked to talk more broadly about the use of measurement, monitoring & evaluation tools to assess progress on poverty reduction and increased equality more broadly.Brief overview of what I will try to cover:A few words about the central role that statistics play in enhacing the effectiveness of development programs and ensuring results.Challenges of measuring poverty – both in the tarditional sense, when focus is on incomes/consumption as a ‘means’ to better welfare outcomes. And also, about the challenges of measuring non-income dimensions of poverty – in many cases the outcomes we are interested in.Areas of new analysis/new methodologies and their growing importance to the design and implementation of policies.Importance of country-level monitoring and evaluation systems to the country-led development process and to evidence based policy making.
3Using MM&E to enhance development outcomes Objectives:National process:MM&E toolsBetter diagnostics on binding constraints to poverty alleviation and equity.Strategies, allocation and designPoverty DiagnosticsBetter ex-ante understanding of the distributional impacts of reforms, better design.Ex-ante Impact Modeling(PSIA)Measurement, monitoring and evaluation systems are key instrument to build evidence—based policies and to achieve better development results.How? By assuring:Better diagnostics on causes and correlates of poverty and inequalityBetter ex-ante understanding of distributional impacts of reformsBetter ability to track progress and feed-back into policy makingBetter understanding of which interventions reduce poverty and enhance equalityProviding a foundation to build evidence-based policy makingMM&E to improve:- Quality and development impact of national poverty reduction strategies- Quality and policy relevance of poverty diagnostics which underpin interventionsBetter ability to track progress and feedback into policy making.Poverty Monitoring/ M&EImplementationBetter understanding of which interventions reduce poverty; Building evidence-based policy.ResultsImpact Evaluation
4Challenges of Measuring Global (Income) Poverty How do we talk meaningfully about “global poverty”?Poverty lines across countries vary in terms of their purchasing powerTo measure global poverty, we need to apply a common standard, anchored to what “poverty” means in the world’s poorest countriesInternational comparisons of poverty require PPP, but previous estimates (1993 PPPs) biasedCost of living underestimated in poor countries; quality and price differences confusedOther weaknesses: country coverage (no China), urban bias2005 International Comparison Program (ICP) improves PPP and poverty estimatesCoverage increased to 146 economies (many more Africa + China)Revised international poverty line = $1.25 / dayGlobal headcount poverty revised upward (1.4 billion), but trend in poverty reduction still robustIn assessing poverty in a given country, and how best to reduce poverty, one naturally focuses on a poverty line that is considered appropriate for that country. For domestic policy making, this is the relevant line.But how do we talk meaningfully about “global poverty"? Poverty lines across countries vary in terms of their purchasing power, and they have a strong economic gradient, such that richer countries tend to adopt higher standards of living in defining poverty.To measure poverty globally, we need to apply a common standard, anchored in a poverty line that is relevant for world's poorest countries (the $1/day line). Two people with the same purchasing power over commodities are treated the same way—both are either poor or not poor—even if they live in different countriesPPP estimates are necessary to make international comparisons of income/consumption welfare.PPP is a conversion rate for a given currency into a reference currency ($US) that aims to assure parity in terms of purchasing power of commodities – both traded and non-traded – between countriesWhy not just use market exchange rates?Many commodities that people consume are not internationally tradedIn developing countries, low real wages entail that labor-intensive non-traded goods are relatively cheapEvaluation of 1993 ICP (Ryten Report) pointed out fundamental problems with lists of products to be priced and quality of price data previous estimates biasedDue to a wide range in the quality of commodities consumed, quality and price differences were confused. For example: High quality Basmati or Jasmine rice vs. low quality 35% broken grain riceAs a result, cost of living was underestimated in poor countries.2005 ICP implemented methodological and operation improvements to improve PPP estimates.The principal outputs of the ICP are estimates of Purchasing Power Parities (PPPs) benchmarked to the year The new PPPs replace previous benchmark estimates, some dating back to the 1980s.The new PPPs are based upon national surveys that priced nearly 1,000 products and services.The ICP data collection was conducted in 100 economies. When combined with a Eurostat-OECD PPP program, the total number of countries is 146 economies.Based on the new data, the familiar international (extreme) poverty line of $1 a day was revised to $1.25 a day. Using the new poverty line, global headcount poverty estimates have also been revised.
5Measuring income poverty: New global estimates higher, but poverty falling The % below $1.25 a day was halved, falling from 52% to 26% over Trend decline of one % point per year. At this rate, the developing world as a whole is on track for attaining the first MDG.Number of poor fell by 500 million, from 1.9 billion to 1.4 billionPoverty rate fell in all yearsRobust to choice of poverty lineBased on new PPP data, the international poverty line and global poverty estimates have been revised upwards.Using the new $1.25/day international poverty line, the number of poor living in extreme poverty was revised to 1.9 billion in 1981 and to 1.4 billion in Over this 25 year period, the number of poor fell by an estimated 500 million.This corresponds to a fall in headcount poverty rates from 52% to 26% – halving the proportion of people living in extreme poverty. Despite the upward revision in the number of poor, the developing world as a whole is on track for attaining the first MDG of halving the share of the population living in extreme poverty.
6Measuring income poverty: Progress uneven across regions Revised Poverty EstimatesHowever, progress has been uneven. Most of the poverty reduction took place in East Asia (i.e. China) while the number of poor increased in Sub-Saharan Africa.With more poor people in the world than we once thought and uneven progress across regions, there is clearly more that needs to be done, in particular in Africa.
7Challenges of Measuring Non-Income Dimensions of Poverty More difficult than using more traditional income/consumption based metrics“Non-monetary” indicatorsmay change more slowly than monetary indicatorscan be more difficult (and costly) to collectmay require special surveysmore context-specific and less “universal”may be less tangible and quantifiable…hence perceived as less objective and rigorousNon-monetary indicators more useful for:Medium or longer-run assessmentsaddress policy outcomes or “ends” (being educated or health) rather than inputs or “means” (having more income)Slower, more expensive to collect, but amenable to disaggregation
8Non-Income Measures: Malnourished Children (%) These complexities are reflected in lower availability of high quality data measuring non-income dimensions of poverty:Start with key MDG: fighting hunger and malnutrition. There are serious shortfalls in fighting hunger and malnutrition, which has long been the “forgotten MDG.” The prevalence of undernourishment (percent of the population that is undernourished) has only declined very slowly – from 20% in 1992 to 16% in Total numnber of malnoursihed worldwide projected to increase to nearly 1 billion by end 2008, driven in part by the recent rise in food prices.Nearly one-third of all children in developing countries are estimated to be underweight or stunted, and an estimated 30 percent of the total population in the developing world suffers from micro nutrient deficiencies. Under-nutrition is the underlying cause of over 55 percent of all child deaths, linking nutrition directly to reduction of child mortality (MDG4). The highest rates of malnutrition are found in SAR: underweight prevalence is estimated between 38 percent and 51 percent in the large countries—Afghanistan, Bangladesh, India and Pakistan—none of which is on track to meet the nutrition goal.SSA is estimated to have a 26 percent prevalence of child malnutrition, and in some countries such as Burkina Faso and Zambia, trends are worsening. East Asia, Latin America, and Eastern Europe and Central Asia (ECA) show better performance although all have some countries that are off-track. Showed in this map is percentage of children under the age of five who are underweight (weight-to-age ratio is more than two standard deviations below the international median). The data covers 97 percent of developing countries’ total population and suggests that many countries in Sub-Saharan Africa and the Middle East and North Africa are seriously off track in meeting MDG1 – reducing the prevalence of hunger.Source: Online Atlas of the MDGs (World Bank)
9Non-Income Measures: Malnourished Children (number) Resized based on number of children under 5 who are malnourishedWhen we consider another metric, the number of malnourished children in absolute numbers rather than percentages, India stands out. Despite fast growth rates, India has double the rates of stunted children (47%) than Sub Sahara Africa (24%) and nearly five times that of China. GDP growth not enough to reduce malnutrition.Source: Online Atlas of the MDGs (World Bank)
10Non-Income Measures: Access to Education Africa: Enrollment rates have risen, but male-female gap has not significantly narrowed.SA and MENA: Male-female enrollment gap narrowed. Progress in enrollments for ‘last’ 10-20% is slow.EAP: Net enrollment rates for male and female children decreased slightlyLAC & ECA: Fairly stableA critical dimension of –non-income’ poverty is access to education and educational opportunities. Captured by MDG2: Ensure that children everywhere, boys and girls alike, are able to complete a full course of primary schoolingPrimary school net enrollment rates measure the proportion of children of official school age who are enrolled in school, but the rates ignore effective attendance, repetitions, or the fact that children can start school above the official age (as long as they enter school before the official age of completion).
11Non-Income Measures: Primary Completion (%) Completion rates are a much better measure of real progress in this area, but harder to collect.Universal primary completion (MDG2). Globally the primary school completion rate rose between 2000 and 2005 from 78 percent to 83 percent and the pace of progress in many countries has accelerated. Gains are especially strong in North Africa, SSA, and SAR. But more than a third of developing countries are unlikely to reach 100 percent primary completion by 2015 and another 20 percent of countries which lack adequate data to track progress are also likely to be off-track. The groups facing the biggest obstacles to completing primary school are those that are ‘doubly disadvantaged’: girls from ethnic, religious, or caste minorities—amounting to three quarters of the 55 million girls who remain out of school.New research on learning levels suggests that the rapid expansion in enrollment in developing countries has not always led to increases in learning. While increasing access to education remains important, the quality of outcomes is critical.Source: Online Atlas of the MDGs (World Bank)
12Non-Income Measures: Primary Completion (number) Resized based on number of children completing last grade of primaryThe groups facing the biggest obstacles to completing primary school are those that are ‘doubly disadvantaged’: girls from ethnic, religious, or caste minorities—amounting to three quarters of the 55 million girls who remain out of school.New research on learning levels suggests that the rapid expansion in enrollment in developing countries has not always led to increases in learning. While increasing access to education remains important, the quality of outcomes is critical.Source: Online Atlas of the MDGs (World Bank)
13Non-Income Measures: Gender Equality in Education One aspect of acces to education than is especially important is diferentials in access between boys and girls, which we capture here as ratio of grils to boys in primary and secondary education. Africa and South Asia are the two regions where gaps are the largest.Source: Online Atlas of the MDGs (World Bank)
14Non-Income Measures: Women in wage employment Another important monitorable aspect of gender equality is equality of employment. Persistent gender disparities limit women’s opportunities in the labor market. Women are more often concentrated in more precarious and lower quality employment, are more likely to be employed as unpaid family workers, or occupy low-paid, low-status jobs. We don’t have good global measures of ‘quality’ of employment, but one proxy is access to wage employment (usually associated with higher pay, some access to basic benefits).Source: Online Atlas of the MDGs (World Bank)
15Non-Income Measures: Measuring Empowerment Empowerment: expansion of capabilities of poor to participate in, negotiate with and influence institutions that affect their livesInstitutional ClimateSocial and political structuresIndividual assets and capabilitiesCollective assets and capabilitiesEmpowerment is difficult to measure quantitatively and benefits from a mixed method approach:Access to most assets can be measured by indicators (but qualitative methods better at evaluating psychological, social assets)Institutional context can be only partially measured by indicators, and is better grasped through use of qualitative/mixed methods.Empowerment refers broadly to the expansion of freedom and choice to shape one’s life. Implies control over resources and decisions. For poor people, that freedom is severely curtailed by their powerlessness in relation to a range of institutions, both formal and informal.Key factors that constraint/affect poor people’s efforts to affect their well-being include:Institutional ClimateSocial and political structuresIndividual assets and capabilitiesCollective assets and capabilitiesEmpowerment is difficult to measure quantitatively and benefits from a mixed method approach. Mixed methods can include questionnaires, focus groups, Community Score Cards, individual interviews, and ethnography
16Measuring women’s empowerment in Bangladesh Empowerment indicators (results further explored through focus groups) included:Control over assets (husband, self, joint, others)Participation in village meetings and elections (& if not, why not)Participation in household decision making (husband, self, joint, others) about expenditures, children, joining organizationsAutonomy (visiting & purchases)Domestic violenceExercise aimed to answer the question:Do safety net progrmas (key 7 program targeted mainly at women) have an empowerment effect on recipient women?Silghtly empowering efectFocus groups revealed that brining cash into the family increased women’s sense of welf-respect and self-esteem
17Areas of New Analysis: Measuring Inequality of Opportunities The Human Opportunity Index (HOI) measures differences in opportunity among children.The HOI synthesizes both the absolute level of basic opportunities in a society and how equitably those opportunities are distributed.As the answers are aggregated across services, children and circumstances, a picture arises of how equitable (or not) a society is.Between one fourth and one half of income inequality observed among Latin America and the Caribbean adults is due to personal circumstances endured during childhood that fell outside of their control or responsibility, such as race, gender, birthplace, parent’s educational level and their father’s occupation. These circumstances reveal the level of inequality of opportunity in the region.The new Human Opportunity Index, developed by a Group of economists from the World Bank, Argentina and Brazil, shows how personal circumstances play in gaining or preventing access to those services needed for a productive life, such as running water, sanitation, electricity or basic education among children in the region. This opens up a whole new field of study dedicated to designing public policy focused on equityThe index recognizes that as long as some children do not have access to specific basic services critical for future advancement in life (such as primary education or running water) and that such access is determined by circumstance, inequality of opportunities will prevail.The first component of the index—the general availability of a given basic opportunity—can be readily determined using household survey data. The second component—the equity of opportunity distribution— are based on how dissimilar are the access to opportunities for people with different circumstances at birth. Hence, an increase in coverage of a basic service at the national level will always improve the index. However, if that increase in coverage is biased toward a disadvantaged group (for example, a poor region), it will further reduce inequality of opportunity, increasing the index more than proportionally.
18Areas of New Analysis: Human Opportunity Index With data representing some 200 million children ages 0–16 and spanning roughly the last decade ( ), team of Bank and Argentine/Brazilian econmists constraucted a Human Opportunity Index for each of the 19 largest Latin American and Caribbean countries. The five basic opportunity variables considered were completing sixth grade on time, school attendance at ages 10–14, and access to water, sanitation and electricity.To construct a single summary indicator that can facilitate the measurement of opportunity in each country, all five different indicators of children’s opportunities were incorporated into an overall Human Opportunity Index (table 1). The results show that for the different opportunities considered, Argentina, Chile, Costa Rica, Uruguay, and República Bolivariana de Venezuela are closest to universality. Guatemala, Honduras, and Nicaragua are the farthest from that target, both due to low coverage and unequal distribution.Progress in the Human Opportunity Index can occur through increases in average access, and increases in equality of the existing opportunities. The analysis shows that two thirds of the improvements in the Human Opportunity Index are driven by an increase in the total supply of available opportunities, and a third by a reduction of inequities in the distribution of the available opportunities. This tendency varies across countries and basic opportunities.When looking at Latin American countries today, income inequality and inequality of opportunity reveal interrelated but distinct stories. Some countries, such as Costa Rica and Uruguay, show relative income equality and low inequality of opportunity for children (table 2). Countries with high income inequality today, for example, Brazil and Chile, might have less inequality in the future because equitable access to basic opportunities is improving as a result of long standing proactive government policies. Other countries, for example, Bolivia and Honduras, might still be trapped in a situation of high income inequality and very unequal opportunities for children, suggesting that stronger equity-oriented policies are needed.Based on this new data, policy makers will be able to put scarce public money to better use. If the inequality of outcomes today reflects past inequality in basic opportunities, it is all the more important for policy makers to be able to track the allocation of basic opportunities among children today to assist them in designing policies to break intergenerational cycles of inequality, and improve future outcomes.The results of the Human Opportunity Index,. Location most determines inequality of opportunity in housing conditions for children. The urban-rural divide clearly is the most important determinant for inequality of opportunity in basic housing infrastructure. Parental education and parental income have a smaller but still important role in explaining why many children do not have access to basic infrastructure services.The basic opportunities considered in the Human Opportunity Index are generally agreed-on aspirations for universal coverage in Latin America and the Caribbean, and indeed the world. However, the Human Opportunity Index can be readily used to examine other opportunities that might be of interest to a particular country. For example, Chile considered access to computers and the Internet to be basic opportunities for children.
19Measuring Equality of Opportunity – within countries Another use of the Human Opportunity Index is to. An analysis made at the sub-national level for Brazil showed that the Human Opportunity Index varied significantly across states, and that progress over time across regions has been uneven. Looking at completion of sixth grade on time, richer Brazilian states have values that are well below the average for Chile, the best performer in the region. At the other end of the scale, the poor states of the northeast are doing worse than Guatemala and Nicaragua, the lowest performing countries in the region. The wealthy states of Santa Catarina and São Paulo perform four times better on the Human Opportunity Index than the poor states of Alagoas and Piaui.
20Areas of New Analysis: Measuring Service Delivery Service delivery information may be used to increase accountabilityAdministrative data, facility surveys, PETsData may be used to deepen our understanding of poverty and inequality and target policy responsesLinking LSMS and facility surveysCareful evaluation aimed at answering key questions of design and the resulting effects can be used to increase the effectiveness of existing programsRepresent a vehicle for holding service providers accountable for the quality and quantity of services they provide. Serve to obtain feedback from clients back to service providers (customer satisfaction surveys etc.). Also for the government to monitor performance of service providers (admin data, where quality is high enough; supplement with facility surveys, PETs etc).From poverty reduction perspective, accurate measurements of service delivery are an important tool for understanding poverty and inequality and properly targeting the response to these problems. Best option is to have integrated households and Facility data (LSMS, Indonesia Family Life Survey) but linking can also be done ex-post by linking a separate add-on facility survey to an existing living standards survey (e.g. Ukraine school survey).
21Measuring Service Delivery - Teacher Absence Map (Public Schools) Source: Kremer, Muralidharan, Chaudhury, Hammer, and Rogers “Teacher Absence in India.”
22Areas of New Analysis: Poverty Maps Poverty maps can improve policy design:Understanding spatial pattern of poverty and correlatesTargeting programs and fundingMonitoring progress and communicating resultsEach dot is randomlyplaced within a DS unitand represents500 poor personsRecent innovationSmall area estimation poverty maps are a recent innovation that provide detailed estimates of poverty levels in highly disaggregated geographical units (cities, towns, villages). The technique uses census and household survey data to obtain a finer resolution of poverty.Poverty maps can improve policy interventions:Deepen our understanding of spatial distribution of poverty and its correlates. The combination of data from different sources and at various levels of analysis has allowed many countries to better understand poverty and its determinants.Sri Lanka example: Comparing the poverty map (left) and the (middle) map depicting access to nearby markets or cities has demonstrated that poverty incidence is highly correlated with geographical isolation as measure by distance to the nearest market or city. This has prompted a shift to an emphasis on reaching areas that are more isolated. Although this simple visual correlation does not provide conclusive evidence on causal relationships, it does help identify relationships that merit a closer investigation.Comparing poverty headcount map (left) with the poverty density map (on right), it is interesting to note that in the low poverty headcount areas, there is a high density of poor (as represented by the red dots).Targeting social welfare programsGiven budget constraints and the need to prioritize assistance, targeting programs and funding can help reduce leakage and increase bang for the buck. Poverty maps can improve targeting by locating the poor and measuring the extent of deprivation.Several countries have used poverty maps to help target public expenditures. For example, Panama's Social Investment Fund used poverty maps to target investment in schools, health centers, and roads toward the country’s poorest districts.Communication and raising awarenessPoverty maps contain a large amount of information and are easy to understand for decision makers and general public.Monitoring possible when there are maps for more than one point in timeHelps build statistical and analytical capacityLimitationsPoverty map is only as accurate as the data inputs -- surveys and census data (junk in, junk out)Maps show correlations, not causationLong intervals between updatesPoverty HeadcountAccessibility IndexDistribution of the Poor
23Country level statistical and monitoring systems Country’s statistical capacity is criticalNot only for tracking indicatorsBut for supporting rational decision making, policy design and implementationBut for results, must link M&E to strategy and budgetTo have an impact, monitoring and evaluation data must be used for policy formulation and budgetingRequires strong political leadership, coordination, and dissemination of resultsBasics firstFocus on strengthening and harmonizing existing processesDon’t rely on technical fixes aloneCreate demand among policy makers and stakeholdersImportance of country level systemsCountry capacity to measure progress toward core development outcomes is critical to country-led implementation of poverty reduction strategies, and is the foundation for global monitoring of progress toward the Millennium Development Goals. All the various measures and new analysis mentioned in this presentation depend on the statistical and monitoring capacity at the country level.Effective statistical and monitoring systems are not just to monitor progress towards a target, they underpin development and the basis for rational decision making, macro-economic management, and the efficient allocation of scarce resources. Need for an integration systemTo generate results, M&E systems must be well integrated into a government’s strategy formulation and budget processes. Data itself is not useful unless policy makers use it (demand it) for decision making and budget allocations.Building a well integrated system involves institutional change. Challenges: Institutional change will require strong political leadership, coordination between agencies and actors to align planning and budget processes, capacity not only to compile statistics but incentives to actually use statistics for policy, and timely dissemination of results in an easily accessible format.Start with basics firstBuilding an effective M&E system and linking it to the government planning and budget processes can take time and will often need to be done incrementally. Most countries already possess some form of a M&E system. The challenge is not to develop new systems, but to rationalize and improve existing systems.Don’t focus solely on technical fixes of the supply side (i.e. statistical capacity) because the demand for M&E outputs by policy makers may not exist.If the use of M&E information is low, it is important to identify the reasons why (e.g. low awareness of its existence, a low level of demand, poor quality data, lack of staff able to analyze and act on information). This will help identify the next steps to improve the supply of monitoring information or to increase the demand for M&E information.1. Focus on strengthening and harmonization of existing processes and adopt a gradual approach to reform–don’t rely on technical fixes and parallel processes2. Support from within: High level ownership of policies, a challenge function within the executive (in addition to parliament, civil society) and clear sector roles.3. Foster incentives for integration: Linking reporting to decision-making (clarity, timeliness accessibility of information)4. Keep it simple
24Country level M&E Systems: Lessons from Uganda M&E results can have big impactsPublic Expenditure Tracking Survey (PETS) used in 1996 to identify leakage in funding flow to primary schoolFound only 13% of funds reached schools inGreater transparency increase flow to 80-90% inBuild on existing systemsNational Integrated M&E System (NIMES) created to coordinate and harmonize 16+ existing systemsIntended to relieve data-collection burden and reduce multiplicity of performance indicatorsLink strategy and budget processesPoverty Action Fund (PAF) links Poverty Reduction Strategy priorities to budgetPublic Expenditure Tracking SurveyConcerned with poor performance of public services (e.g. health and education) and a belief that “leakage” of funds was a major reason, Uganda implemented the first Public Expenditure Tracking Survey (PETS) in The survey measured the proportion of funds provided by the central government that reached primary schools and the extent of the leakage.Only 13% of earmarked (nonwage) funds actually reached schools in ; 87% of funds simply disappeared. About 20% of funds allocated to teacher salaries went to people who did not exist or were not working as teachers.These results attracted media attention. Government responded with greater transparency of the amount allocated to, and received by, each school, disseminating this information through local newspapers, radio stations, and publicly displaying at each school.In follow-up PETS, flow of nonwage funds reaching primary schools increased from 13% in to 80-90% inCost of initial 1996 PETS was $60,000, but the increase in funds reaching primary schools estimated at over $18.5 million per annum. As a cost-effective evaluation tool, GoU now conducts PETS for other basic service sectors.National Integrated M&E System (NIMES)Given at least 16 different M&E systems at the sector and subsector level, NIMES was created as an umbrella M&E system, under the aegis of the Prime Minister, to coordinate and harmonize these existing systems. NIMES aims to address the problems of the heavy data-collection burden at district and facility level, an excessive number of performance indicators (e.g ~1,000 for just 3 sectors – health, education, and water & sanitation), and the scarcity of measures of client satisfaction and outcomes.Poverty Action Fund (PAF)A virtual poverty fund was created to protect priority funding areas for poverty reduction efforts. PAF grew from 17.5% to 37% of government budget between 1997/8 and 2002/3. The government guarantees all PAF budgeted resources in full, regardless of resource shortfalls. PAF also reinforces the focus on program results through a system of activity-based budget reporting. By providing donors with greater comfort with regard to allocation, implementation, and transparency, PAF has enabled sector specific donor support to shift from project to budget support.However, there are tradeoffs. When unforeseen economic shocks arise or priorities shift, PAF’s strict protection of priority sectors may limit the flexibility necessary to reallocate resources efficiently. What are the limits? Also, uneven line ministry capacities in performing strategic budget planning and monitoring outcomes have led to a bias in spending allocation with the effect of crowding out weaker sectors (agriculture, infrastructure) that are important for growth and poverty reduction.[Note: There are now some questions about the continuity of Uganda’s progress. While Uganda did a lot of things right, they may not have stuck or remained effective.]While Uganda has made great strides in using M&E information for better policy design and implementation and has been working towards building a more integrated M&E system, it is important to remember that Uganda is still in the process of institutional change and the focus should be on maintaining momentum in this challenging process of change.