Journal Issue
Share
Article

Enhancing Development Assistance to Africa: Lessons from Scaling-Up Scenarios

Author(s):
Matthew Gaertner, Laure Redifer, Pedro Conceição, Rafael Portillo, Luis-Felipe Zanna, Jan Gottschalk, Andrew Berg, Ayodele Odusola, Brett House, and José Saúl Lizondo
Published Date:
March 2012
Share
  • ShareShare
Show Summary Details

Chapter 1 Introduction

Much of sub-Saharan Africa (SSA) has grown strongly in recent years. Since about 1995, and for the first time in decades, low-income countries (LICs) in the region have been growing faster, on average, than developed countries and some other regions of the world (Figure 1.1). Many of these countries have also made great strides toward achieving the Millennium Development Goals (MDGs). Progress on reducing poverty and other MDG targets has been rapid since the late 1990s. The share of people living in extreme poverty (on less than US$1.25 a day in purchasing power parity terms) increased slightly in the 1990s, peaking at more than 58 percent in 1999, but dropped to 51 percent in 2005. The Human Development Index (HDI) for Africa confirms the remarkable progress since 2000. In 1990, the HDI for Africa stood at 0.35. By 2000, it had deteriorated to 0.32. In 2010 it reached 0.40. This corresponds to an average annual rate of improvement in the HDI in Africa of more than 2 percent between 2000 and 2010, by far the most rapid compared with other regions over the last 10 years.

Sub-Saharan Africa Is Catching Up to Developed Regions

Source: World Economic Outlook database.

Although the food price shock of 2008 and the global recession of 2008–09 hit the SSA region hard, growth in 2009 for the average country in this region was better than it was, on average, from 1980 through 1995. Assuming the global economy continues to recover, prospects for the coming years are good, and most of the region is expected to bounce back fairly well.4

Despite rapid development progress in the last 10 years, the HDI remains too low and the gains too uneven, both within and across countries. Many countries remain mired in persistent poverty, and even in the most successful countries, progress has been unequal. For instance, Ghana and Uganda have had rapid rates of poverty reduction, but their 2010 MDG reports affirmed that large regional, occupational, and gender disparities remain.5

Both the successes and the failures of growth and development reflect many factors, including domestic policies. Success requires macroeconomic and political stability combined with prodevelopment structural policies and improved governance. Policies have improved in recent years in many SSA countries and—along with higher aid—have already yielded important gains. But this policy achievement needs to be deepened within even the most successful cases and broadened to the rest.

The international community can contribute to future success by living up to its aid commitments and providing more coordination in aid delivery. Scaling up aid is critical to helping SSA countries achieve the MDGs by 2015. But aid also needs to become more predictable so that African countries can implement sustained national development programs for reaching these goals. Similarly, collaboration among development partners operating in Africa must be enhanced to bolster aid efficiency.

This report analyzes the implications of scaling up aid to Africa in line with international commitments and in support of national development strategies. The 10 pilot cases (Benin, the Central African Republic, Ghana, Liberia, Niger, Rwanda, Tanzania, Togo, Sierra Leone, and Zambia) were selected by the MDG Africa Steering Group, in consultation with national authorities.6

The goal was to support the governments in preparing scenarios outlining how they could implement existing national development plans if additional resources were made available to double aid to Africa to US$50 billion.7 The studies were done in 2008–09, and this consolidation was undertaken in 2011. Although some of the “projections” discussed in the specific case studies are now in the past, the underlying messages of the case studies remain relevant.

The case studies investigate the impact of raising aid to $85 per capita in each country (in 2004 prices).8 This amount corresponds to the average aid per capita in SSA if the $50 billion Gleneagles commitments were met.9 The equal per capita allocation is a simplifying assumption that was judged to be a better benchmark than other alternatives.

The contribution of the report derives from the collaboration of country authorities, the UNDP, and IMF staff in developing the country studies using a common framework. The report looks at what each of the 10 countries might do at the sectoral level if aid were available on the scale committed to by the Group of Eight (G-8) in 2005 at the Gleneagles summit. It also looks at the macroeconomic implications of scaling up development assistance on growth, inflation, the real exchange rate, and other macroeconomic indicators. The goal is to ensure that scaled-up development assistance can be delivered in a manner consistent with macroeconomic stability. Macroeconomic analysis is only one step toward the ultimate objective—achievement of the MDGs—but looking at specific MDG outcomes is beyond the scope of this paper.

Both micro- and macroeconomic analyses are necessary to ensure that projects and policies are identified to accelerate MDG progress. The critical question at the project level is whether the additional aid will educate more people, deliver more critical health services, and build much-needed infrastructure. The key macroeconomic questions are, will public investment stimulate private sector activity? Will economy-wide effects such as higher inflation and real exchange rate appreciation—“Dutch disease”—cause unintended negative consequences? How can these effects best be managed?

The micro- and macroeconomic analyses are also interrelated. Project-by-project analysis can predict great promise, but if the projects that flow from such analysis in aggregate impede private investment or shrink productivity growth in the export sector, overall outcomes will disappoint. Similarly, macroeconomic projections need to rely on sectoral and project-level assumptions, notably about the ability of the country to execute well-designed projects successfully.

The report is intentionally long on forward-looking analysis and short on ex post assessments. It does not reopen the debate about the average effectiveness of aid in a large sample of countries and time periods, nor does it reexamine the wealth of microeconomic studies that appraise specific types of projects or interventions. The focus, instead, is on aggregating and analyzing the plausible effects of well-executed plans for scaled-up aid and development efforts. It is hoped that this analysis will illustrate what is possible with determined efforts by donors to fulfill aid commitments and by recipients to craft careful development plans and implement them in the context of supportive macroeconomic policies. In the future, systematic assessment of what has worked and why will remain important.

As a result of the global financial crisis, the international environment has become somewhat less favorable for LICs. At the same time, donors face sharply increased budget challenges of their own, making their commitments more challenging, though no less important. In this environment, attention has turned to nonconcessional finance for public investment in LICs. The relative merit of such funding is beyond the scope of this report, except with regard to two important points. First, many of the conclusions about the need for scaled-up public investment and the requirements for it to succeed apply equally well to investment financed by nonconcessional lending. Second, however, debt-led scaling up raises a host of additional issues, such as the fiscal challenges of financing even very productive projects and the risk of a return to debt distress that could undermine the achievements of the Heavily Indebted Poor Countries process and the Multilateral Debt Relief Initiative. In sum, higher nonconcessional lending is at best a very risky and imperfect substitute for more aid.

The main conclusions of the report are

  • A further increase in aid is still necessary to meet the Gleneagles commitments. Although aid to SSA has risen in recent years, it remains well below the goals set in 2005.
  • Existing development plans are underfunded across the pilot countries, and fulfillment of the Gleneagles commitments would go a long way toward closing these gaps.
  • Existing development plans could be used effectively to shape the spending of increased aid. Such plans generally integrate public investment programs for the use of additional aid with the following: (i) MDG-based development priorities, which are drawn from countries’ Poverty Reduction Strategy Papers (PRSPs); and (ii) multiyear budgets, which are based on Medium-Term Expenditure Frameworks. This approach ensures consistency with existing spending plans, domestic revenue efforts, and nonconcessional borrowing, where such borrowing is available.
  • Scaled-up aid will be most efficient if it is well integrated with recipient budget and implementation systems. It is also important that recipient countries emphasize continued improvements in public financial management and complementary mobilization of domestic resources in line with their potential.
  • The sectoral focus of existing national development plans is on infrastructure and human development, both critical to meeting the MDGs.
  • The macroeconomic analysis conducted here suggests that scaling up to meet the Gleneagles commitments can have a substantial positive influence on growth, as long as the projects are well designed and well implemented.
  • Macroeconomic management needs to avoid counteracting the benefits of aid, while still preserving overall macroeconomic stability. Aid, if sufficiently concessional, allows scaled-up spending along with minimized risks to debt sustainability. Some temporary real exchange rate appreciation—through inflation in a pegged exchange rate regime—and temporary adjustment in the size of tradables compared with nontradables sectors can be expected. The duration of this adjustment varies, but in some cases could be years.
  • Over the longer term, the extent to which growth is enhanced by scaled-up spending depends critically on the volume, efficiency, effectiveness, and sectoral allocation of public investment. Much MDGs-related spending likely has to take place in the nontradables sector. However, if the tradables sector is an especially strong driver of productivity growth, the stakes are higher for the effective use of aid. If aid helps build public capital and raises productivity in the tradables sector, aid can produce even greater gains in overall growth, causing “Dutch vigor.” If aid is wasted, however, the diversion of scarce resources from the tradables sector could reduce productivity growth over time, causing “Dutch disease.” The key is to use aid well, and, if the role of the tradables sector in productivity growth is deemed especially strong, devote more aid to promoting that sector by investing in, for example, ports, roads, and electric power.

The paper is organized as follows. Chapter 2 reviews the evolution of aid in recent years and compares it with commitments. Chapter 3 describes the sectoral and microeconomic foundations of the pilot studies. Chapter 4 presents the macroeconomic implications of scaling up aid and spending as outlined in Chapter 3, discusses some of the policy challenges implied, and provides a synthesis of the insights from the 10 country case studies. The Appendix, at the end of the paper, summarizes each of the country studies.

Chapter 2 The Evolution of Official Development Assistance: Commitments and Delivery

Official development assistance (ODA) remains essential to continued progress toward the Millennium Development Goals (MDGs) in many of the poorest countries.10 ODA has helped many countries accelerate growth and allowed some progress toward the MDGs. It complements domestic resources and private flows from outside the country, including foreign direct investment and remittances. Although the proportion of private resource flows into developing countries has increased over time, ODA still remains one of the most important sources of external finance for development. It supports investments in health, education, and infrastructure, helping to improve living and social conditions, and helping to make private investment more productive and lucrative in these countries. ODA also provides support for countries to cope with and respond to adverse shocks such as negative commodity price swings, civil conflicts, and natural disasters. Emergency and humanitarian aid, for instance, have been crucial in saving lives and rebuilding infrastructure and institutions.

ODA has increased in recent years in real terms but remains far below the levels to which the international community has committed. The real volume of net ODA declined throughout much of the 1990s (Figure 2.1).11 In 2010, net ODA flows reached an all-time high in real terms of $128.7 billion in 2010 prices (OECD, 2011). The negative trend of the 1990s reversed roughly around the time of adoption of the MDGs in 2000, and was reinforced by the 2002 Monterrey Consensus to support developing countries that were making progress toward these goals. The consensus urged donor countries to undertake concerted efforts to increase their net ODA flows to 0.7 percent of each donor country’s gross national product (GNP), reiterating a target that has been widely accepted since the early 1970s.

Net ODA Flows in Volume: Much of the Increase in ODA Flows during 2000-05 Was Accounted for by Debt Relief

Sources: OECD International Development Statistics Online; and IMF staff calculations.

Since then, levels of net ODA have hit record highs (Figure 2.1). However, as a percentage of donors’ gross national income (GNI), net ODA remains at levels seen in the early 1990s and roughly half of the target from the Monterrey Consensus (Figure 2.2).

Net ODA as a Share of Donor GNI Is Still below Earlier Levels and International Commitments

Source: OECD International Development Statistics Online.

Note: Combined net ODA flows and GNI from member countries of the OECD Development Assistance Committee.

In 2005, the international community renewed its commitment to increase aid at the G-8 Summit in Gleneagles, Scotland, with a special focus on sub-Saharan Africa (SSA). The G-8 Gleneagles communiqué underscored the need for a substantial increase in ODA, in addition to other resources, to achieve the internationally agreed-on development goals and objectives, including the MDGs by 2015. It referenced commitments made by the G-8 and other countries to provide an increase of $50 billion in annual ODA by 2010 to $130 billion, compared with the 2004 level of $80 billion (in 2004 prices), and an increase in annual ODA to Africa of $25 billion (in 2004 dollars), also by 2010.12

Nonetheless, aid to Africa as of 2011 is far from meeting the Gleneagles commitments. Although ODA to Africa increased sharply in 2005 and 2006, this increase was accounted for mostly by debt relief to Nigeria and has not been sustained. Excluding debt relief and humanitarian aid, ODA to Africa has been increasing gradually since 2005, but not by enough to meet the Gleneagles targets (Figure 2.3). In fact, now that the 2010 volume of development aid is available, the OECD Development Assistance Committee (DAC) has confirmed that although ODA increased by 37 percent in real terms since 2004, this corresponds to an increase to $30 billion (in 2004 prices), thus resulting in a shortfall of about $20 billion (in 2004 prices) from the Gleneagles commitments. In addition, preliminary estimates by the OECD DAC at the time of writing indicate that Africa only received an additional annual $11 billion of the $25 billion pledged at Gleneagles (OECD, 2011). As a result, large increases in aid would be necessary to achieve the Gleneagles commitments.

Net ODA Flows to Africa Have Been Volatile since 2004, with the Sharpest Increases in 2005 and 2006 Resulting from Large Debt Relief Grants

Source: Authors’ elaboration from OECD International Development Statistics Online.

Aid should grow in a consistent and predictable manner to maximize its efficient use. ODA to Africa has been volatile in the aggregate, and, despite donors’ efforts, it remains unpredictable for many recipient countries. In fact, commitments cannot be relied upon as a robust guide for disbursements. During 1990–2005, annual aid disbursements in SSA deviated from aid commitments by 3.4 percent of recipients’ GDP (Bulir and Hamann, 2008; and Celasun and Walliser, 2008). The unpredictability of aid flows weakens the ability of recipient governments to plan for and meet expenditure targets. More important, it undermines aid spending efficiency and thereby reduces its intended impact, including on long-term capital accumulation and growth.

Recently, several initiatives have challenged donors to provide aid such that it can be used more effectively by recipient countries. Under the 2005 Paris Declaration, countries and multilateral entities committed to increase efforts in harmonization, alignment, and management of aid for better development results using a set of monitorable actions and indicators that could ensure aid effectiveness. This was followed by the 2008 Accra Agenda for Action, which focused on aid predictability, the use of partner-country project management and implementation systems, a switch from donor-determined conditionality to recipient-country development objectives, and the relaxing of requirements for the tying of aid. Implementation of the key promises from these declarations and agendas remains a challenge (OECD, 2009).

However, better progress toward the MDGs requires more than higher aid levels and its effective delivery and use. Success also requires that macroeconomic and political stability be combined with prodevelopment structural reforms and improved governance at the country level. In this respect, stronger policies in many SSA countries—along with higher aid—have already yielded important gains. But these policy achievements need to be deepened within even the most successful countries and broadened to others.

More spending on investment in productive infrastructure could help improve growth outcomes. Since 2000, and coinciding with the resurgence of ODA, aid has shifted toward social concerns (e.g., health, education, and water supply), and away from broader economic growth and development issues that factored more prominently in the 1990s (UNCTAD, 2006). The share of total aid spent on social sectors steadily rose from 33 percent during 1990-94 to 47 percent and 60 percent during 1995–99 and 2000–04, respectively (IDA, 2007). During the same periods, the share of ODA going toward infrastructure investment declined from 29 percent to 26 percent and 19 percent, respectively. Support for social spending through ODA is critical if developing countries are to reach the MDGs, but ODA is also important to help fill infrastructure gaps in these countries, given that these gaps sharply increase input costs for private sector activity and constrain growth.13

The recent global financial crisis has created downside risks to the outlook for development aid. The crisis has raised widespread concerns that higher public expenditures aimed at supporting financial systems and stabilizing advanced economies, coupled with revenue shortfalls, would strain donor budgets. The sovereign debt crisis in several European countries may add to the challenges. Despite these concerns, in 2009, net ODA excluding debt relief increased by 6.8 percent in real terms, while net bilateral ODA to SSA rose by 5.1 percent (OECD, 2010). In 2010, net ODA increased yet again, by 6.5 percent compared with 2009; the increase to SSA in bilateral net ODA was similar (6.4 percent), but excluding debt relief grants it was only 1.7 percent (OECD, 2011). Nevertheless, empirical evidence shows that, in the context of crises, bilateral aid declines more sharply in the aftermath of large output contractions in donor countries when these countries have higher public debt burdens—an effect that displays some persistence (Dabla-Norris, Miniou, and Zanna, 2010). Any decrease in development aid, were it to occur, would be deeply regrettable at a historic time when progress is finally being made and—with the aid that has been committed—much more could be achieved in the final stretch toward the 2015 MDG targets.

Chapter 3 The Scaling-Up Scenarios: Sectoral and Micro Analysis

Human development indicators across the 10 pilot countries show an urgent need to enhance development efforts. All 10 countries were grouped within the low human development status by the 2010 UNDP Human Development report and ranked among the 40 least-developed countries in the world (UNDP, 2010a). Three of them (the Central African Republic, Liberia, and Niger) were among the bottom 10 countries. Although many have shown signs of improvement in recent years, human development outcomes still remain very weak.

The Basis for the Micro Analysis

The underlying premise is that aid can finance good projects that improve health and education outcomes, build useful infrastructure, and in general have a high rate of return. This presumption is based on a large amount of analysis and empirical evidence, which is not reviewed here.14 Specifically, African infrastructure lags that of the rest of the developing world and is holding back economic growth by as much as 2 percentage points each year (African Union, AfDB, and World Bank, 2010). Scaling up aid to address this infrastructure deficit will stimulate long-term growth and accelerate progress toward achievement of the MDGs.

However, it is also clear that aid spending may not always have these beneficial effects. Failure can come about because of poor project planning and implementation, insufficient capacity, or inadequate supporting environments. Examples include incomplete coverage of maintenance costs for new infrastructure, import restrictions that keep productive enterprises from reaching their potential, pairing of medicine with inadequate delivery systems, and complex donor-mandated procedures that slow implementation. That these elements are important is illustrated by current low rates of execution of budgeted capital expenditure, ranging from 61 percent in Benin to 38 percent in the Central African Republic.15 Individually valuable projects might also fail to deliver the intended private sector growth responses or affect competitiveness meaningfully. (The next chapter addresses these issues.)

For these reasons, scaled-up aid will be most effective if it is predictable and well integrated with national budget and implementation processes and if recipient countries emphasize continued improvement in public financial management. Past reforms in the pilot countries have set the foundation for enhancing the developmental impact of scaled-up external resources. Strong macroeconomic and budgetary reforms have taken place in these 10 countries, especially in the 1980s and 1990s. Although more work remains, public financial management systems have been strengthened in a number of African countries as part of their reform processes, improving the links between strategies, plans, and outcomes.16

This chapter reports on recent efforts to develop business plans for the use of additional aid in the 10 pilot countries. These plans are designed to show that well-constructed, underfunded development plans are ready for financing. The plans provide sectoral details on the uses to which an additional $25 billion of annual aid to Africa could be put, drawing on existing work over recent years. They are thus designed to allay at least some of the concerns that these countries are not ready for additional aid and thus to provide a practical basis for scaling up aid.

Substantial work has been done in many countries in recent years to identify the sectors to which resources could be allocated to greatest effect and the amounts to be externally financed. Most African countries have relied on two tools for determining how aid, and additional aid, can be best directed and incorporated into the policy formulation and budget planning processes: Poverty Reduction Strategy Papers (PRSPs) and Medium-Term Expenditure Frameworks (MTEFs).

Most countries in SSA, including all 10 pilot countries, have produced PRSPs. The PRSPs identify priority public sector spending designed to promote growth and reduce poverty and provide the basis for determining additional external financing needs. They have been prepared by the countries themselves, in broad consultation with local stakeholders, including civil society, as well as external development partners such as the World Bank, the IMF, and the UNDP. PRSPs have provided a foundation for the provision of multilateral and bilateral financial assistance, as well as for debt relief under the Heavily Indebted Poor Countries (HIPC) Initiative.

PRSPs are generally intended to be the primary instrument for defining and monitoring national development agendas. In most countries, detailed priority action plans were prepared, development programs were costed, resource gaps identified, and resource mobilization strategies developed, paying special attention to what would be needed to achieve the MDGs. The PRSPs, through their associated annual implementation reports, have also been important for enhancing governments’ systems for monitoring MDG achievement. The National Strategy for Growth and Reduction of Poverty (known as the MKUKUTA initiative) in Tanzania, for instance, allows for regular analysis of progress toward the country’s development agenda (including the PRSP) goals and provides an overview of the performance, challenges, lessons learned, and the next steps in the agenda.

Many African countries have also adopted MTEFs. MTEFs are multiyear frameworks for determining public spending priorities (Box 3.1). These frameworks have become an important instrument through which resource requirements can be projected and resource gaps identified. They frame revenue forecasting and expenditure decision making and provide a central pillar for evidence-based budgeting and development management in Africa.

The ABCs of MTEFs

The MTEF is a planning tool for estimating the resource envelope available for public expenditure and for constructing indicative plans for allocating the resources among competing priorities. PRSPs and MTEFs are related. The PRSP lays out the development strategy over the medium to long term, while the MTEF is the vehicle for operationalizing this strategy and determining priorities within a given resource envelope. MTEFs are tools for translating PRSPs into public expenditure programs within a coherent multiyear macroeconomic and fiscal framework.

In operational terms, the MTEF consists of a three-stage approach: (i) projection of a likely resource envelope; (ii) a bottom-up estimation of the current and medium-term costs of intended policies and projects; and (iii) the matching of these costs with available resources. The approach helps to link planning, budgeting, and policy, thereby allowing an MTEF to be a strategic framework for budget preparation and the expenditure process. Essential to the approach are the costing exercise by line ministries and the preparation of a detailed program budget, which contributes to translating government goals into concrete budgetary proposals. The MTEF, therefore, provides the critical link between strategic planning and annual budgeting, thus ensuring that budget execution is more firmly in line with desired development outcomes.

Drawing, where possible, on existing PRSPs and MTEFs, the 10 countries participating in the pilot studies provided detailed expenditure plans for the use of additional aid in line with the Gleneagles commitments. In collaboration with international partners, especially the UNDP, the IMF, the AfDB, and the World Bank, these governments produced detailed estimates of the current and projected expenditure patterns for specific MDG-related programs, including how they would allocate scaled-up official development assistance (ODA) flows, and their domestic and external financing resources. As a result, the potential scaling up is matched on the recipient side by a careful costing and planning mechanism.17

A challenge for this exercise was the sometimes imperfect alignment of existing MTEFs with PRSPs. In the past, the link between such planning exercises and budgeting processes (both MTEFs and annual budgets) has been weak. The pilot studies undertaken as part of this project provided a good opportunity to encourage a stronger alignment between MTEFs and PRSPs. This closer alignment should help ensure that the use of the scaled-up financing is based on the principles and priorities outlined in the PRSPs.18 In addition, costing information produced as part of the PRSP can be integrated better into the MTEFs. The alignment of these instruments should also help harmonize donors’ interventions and provide evidence-based plans and programs to absorb scaled-up ODA more effectively for better development outcomes.

The plans for the use of scaled-up aid also need to take into account simultaneous efforts to enhance domestic resources. Only in this way can the plans to use aid be embedded in a comprehensive MTEF that takes into account the overall picture of expenditure priorities and recurrent costs and the implications for the annual budget.

Most low-income countries (LICs) have made substantial progress in revenue mobilization, and they are continuing their efforts (IMF, 2010b). LICs in Africa have increased tax-to-GDP ratios, on average for nonfragile states, from about 9 percent in 1992 to about 15 percent in 2009 (see Figure 3.1). Efforts to increase revenues in line with potential continue, but raising revenues is more challenging in LICs, which have large informal sectors and relatively weak public sectors. Thus, although higher domestic revenues are an important long-term goal, they cannot be expected to substitute for aid in the near term.

Progress with Domestic Revenue Mobilization in SSA LICs

Source: IMF, Sub-Saharan Africa Regional Economic Outlook database, 2010.

Note: Lines represent simple averages. Other LICs are Benin, Ethiopia, Ghana, Kenya, Madagascar, Malawi, Mali, Mozambique, Niger, Rwanda, Senegal, Tanzania, Ugačnda, and Zambia. Fragile LICs are Burundi, Central African Republic, Comoros, Democratic Republic of Congo, Cote d’Ivore, Eritrea, Gambia, Guinea, Guinea-Bissau, Liberia, Sao Tome and Príncipe, Sierra Leone, Togo, and Zimbabwe.

A final factor that needs to be taken into account in designing plans for scaling up spending is the possible availability of nonconcessional borrowing. Most LICs have limited scope for domestic borrowing to finance scaling up without risking crowding out private sector activity, given the small stock of domestic saving and financial markets that are less well developed. Borrowing externally is a possibility. Such borrowing can enable more ambitious scaling-up plans, particularly in countries in which debt-management capacity is strong and prospects are good that projects will eventually “pay for themselves.” Lower debt burdens resulting from debt-relief initiatives provide some space for pursuing these options. However, care must be taken to ensure that this does not overly burden the country down the road, as has been typical in the past. Certainly, large-scale borrowing raises the stakes; although it can speed scaling up, if the pay-off is too low, the repayment obligation will be burdensome. Some of the pilot cases discussed the possibility of nonconcessional financing options.19

Summary of Results from the Sectoral Analysis

In the exercises, aid increases to a uniform $85 per capita in 2004 terms—in line with the Gleneagles commitments.20 This amount provides a degree of scaling up that varies substantially across the 10 pilot countries, because countries as of 2007 received widely varying amounts of aid (Figure 3.2), according to any measure. The allocation of increased aid to achieve a uniform $85 per capita per country was deemed to be more equitable in the distribution of aid to countries than other notional approaches that were considered. For example, equal increases in U.S. dollar terms would not account for already-existing variations in the current distribution of ODA. Allocating as a percentage of GDP would ignore large variations in GDP per capita among recipient countries.

Per Capita ODA Flows Differ Greatly by Country in 2007

PRSPs in the pilot countries have generally been substantially underfinanced. Although the rate at which PRSPs are financed varies across countries (Figure 3.3) and the precision of their underlying costing estimates differs substantially, the shortfall between projected needs and budgetary realities are high across the board. However, meeting the Gleneagles commitments would greatly reduce or even eliminate the PRSP funding gap in most countries. For example, in Ghana, the Gleneagles commitment would triple the share of ODA-financed expenditure, and thus could finance the entire PRSP.

Many PRSPs in Africa Are Underfinanced

Source: Authors’ analysis based on pilot studies.

The pilot studies show how scaled-up ODA could provide resources to unfinanced projects and programs already contained in national development strategies. Lack of funds remains, on the whole, the crucial constraint to implementing more of the outlined projects and programs.21 The sector strategies are based on needs assessments that lay out the different sources of financing, including the financing gap needed to be filled by the scaling up of aid. Proposed outlays for each sector are based on government prioritization. With regard to sectoral allocation, the studies underscore the need to boost funding for much-needed infrastructure. In Benin, Rwanda, and the Central African Republic, more than a fourth of the incremental ODA would be directed to finance infrastructure. Human-development-related spending, however, is the second leading sector in the studies, averaging about 10 percent of the incremental ODA.

The execution of development strategies needs to take into account country-specific labor rigidities and implementation capacity. Scaling-up plans, too, need to consider potential bottlenecks in skilled labor and implementation capacity. In many cases, aid itself can help to relieve these bottlenecks. For example, if engineers are in short supply for infrastructure projects, alleviating the shortage could be the focus of aid intervention. A shortage of medical doctors in rural areas is another serious constraint in many countries. However, careful sequencing of projects is necessary, for instance, to ensure that education and training initiatives can deliver the skilled labor needed for other investments.22

A country example illustrates how scaled-up aid could help achieve development strategy objectives. The Poverty and Social Impact Analysis Capacity Development report for Benin suggested that addressing limited funds and funding mechanisms would be critical for achieving the overriding objectives of the country’s national development strategy (Adjovi and Mensah, 2007). For instance, between 2004 and 2006, the average annual aid flow as a percentage of GDP was less than 9 percent for Benin. Thus, the scaling up of aid, if implemented in 2010, would have helped the government to increase its capital expenditures from $412 million in 2007 to $613 million in 2010 and its expenditures on goods and services from $178 million in 2007 to $321 million by 2010 (Figure 3.4). As a result, education expenditure per capita would have more than doubled from $3 in 2008 to $7 in 2010, health expenditure per capita would have increased from $19 in 2008 to $25 in 2010, and infrastructure spending would have increased from $19 in 2008 to $25 in 2010. The impact of the scaled-up aid is greater if other economy-wide constraints are effectively managed, including the use of skilled manpower to manage critical sectors and efforts to increase the absorptive capacity of the public sector.

The Mechanics of the Micro Analysis: Benin

Source: UNDP staff calculations.

Chapter 4 The Macroeonomic Impact of Scaling Up Aid

A few questions stand out when examining the macroeconomic impacts of scaled-up aid. The first is overarching: can aid-financed public investment lead to higher growth? The question of whether and to what extent foreign aid helps poor countries grow remains controversial. The aid-growth literature provides mixed conclusions on the effectiveness of aid in stimulating GDP growth, as opposed to other microsocial outcomes, for which the aid impact is clearer.23 Recent studies include Rajan and Subramanian (2008), which concludes that “it is difficult to discern any systematic effect of aid on growth” (p. 660). Clemens, Radelet, and Bhavnani (2004) argue that aid intended for investment purposes does show a growth impact. More recently, Arndt, Jones, and Tarp (2009) claim that “aid has a … significant causal effect on growth over the long term…. [and] remains an important tool for enhancing the development prospects of poor nations” (p. 1).

Aid can lead to growth, depending on how it is used and on country-specific factors. In general, aid is used, in part, for public investment in capital that will increase the marginal productivity of capital (both private and public), and thus stimulate higher growth. However, the impact of aid-financed public investment on growth depends on country-specific factors, including, among others, (i) the extent to which aid-associated spending is directed to projects that, if implemented well, yield a sound return; (ii) the degree to which spending is relatively efficient ($1 spent does not necessarily buy $1 more of roads or schoolbooks, but some fraction that depends on administrative costs, corruption, and the like); and (iii) the existence of supporting institutions, such as the legal system and financial services, that enable a private sector response to public investment.24 These country-specific conditions may play a significant role in determining the actual growth impact, as suggested by the empirical literature about the link between public investment and growth. (Box 4.1).

Public Investment and Growth

Public investment (in roads, education, health care, and so on) is clearly critical to development. But how much will overall output increase in response to a given increase in public investment spending? The evidence from the empirical literature does not provide clear-cut answers.a Although many studies seem to suggest a positive relationship, particularly for infrastructure investment, the magnitudes of these contributions vary considerably from one study to another, presumably because of differences in the econometric methodology employed, the level of data aggregation, and the type of public investment considered. A majority of recent studies—especially those using physical indicators such as miles of road and phones per capita to measure investment in infrastructure—find significantly positive effects of public capital on growth. In contrast, the findings are less robust in those studies that use public investment flows (or their cumulative value), likely a reflection that investment spending may be a poor proxy for the accumulation of productive assets, owing to waste, inefficiencies in public procurement, corruption, or inclusion of current expenditures (e.g., wages and salaries) in reported investment figures.b

The lack of unanimity in the results may be explained by country-specific factors. The empirical literature suggests that the impact of public investment on growth may depend on, among others, (i) the financing source of public investment; (ii) the institutional context within which investment decisions are undertaken; (iii) the quality of project evaluation, selection, and management; and (iv) the regulatory and operational framework in which infrastructure services are provided.

aSee the recent surveys of this literature by Romp and de Haan (2007) and Straub (2008a, 2008b) and the references therein.bSee Easterly and Rebelo (1993) and Pritchett (2000). The recent work by Arslanalp and others (2010), however, finds a positive impact for public capital—built from investment flows—on economic growth, using a data set that includes high-, middle-, and low-income countries

The second question is a corollary to the first: can aid inflows hurt growth through real exchange rate appreciation that undermines growth-promoting export industries, the so-called Dutch disease? Another question is, if the Dutch disease phenomenon is present, would it diminish the positive effects of public investment temporarily or permanently? These effects have traditionally been linked to revenue inflows associated with discoveries of oil or other natural resources, but interest has risen about the similar impact of increased aid inflows (Box 4.2).

Dutch Disease

One of the major issues in the debate on scaling up aid has been the possible adverse macroeconomic consequences. An increase in aid is similar to a commodity windfall in that it results in large flows into the economy, which can cause an appreciation of the real exchange rate, directing resources into the more profitable nontradables sector and adversely affecting the tradables sector. The adverse impact on the tradables sector is often dubbed “Dutch disease.”

Whether a real appreciation is, in fact, a disease—that is, whether it is harmful—depends in large part on the role played by the tradables sector in productivity growth. Some microeconomic evidence indicates that exports growth may have a larger positive impact on productivity than other types of growth, generating positive externalities that increase overall growth.a These externalities might arise, for example, as exporting firms “learn by doing” and the new know-how spreads to other domestic firms. As a general matter, the experience of many countries with export-led growth, as well as the empirical evidence on the related natural resource curse, suggests caution in discarding this concern too rapidly. However, public investment in infrastructure, health, and education is also critical to growth, and aid can help finance this spending, so even with the positive externalities of the tradables sector, a balanced approach to investment is important.

Critically, the real exchange rate appreciation and contraction of the tradables sector need not be a “disease.” These occurrences may be just a necessary counterpart to freeing up the resources required to make aid-financed investments. Even if exports create positive externalities, if aid is spent well and the policy response is appropriate, the impact on competitiveness would be temporary, with the medium-term returns to growth outweighing any negative externalities associated with a temporary decline in the tradables sector. Indeed, if the tradables sector creates positive externalities and public capital is invested to further stimulate its productivity (for example, through better transportation networks), the increased aid would have synergistic effects on competitiveness, exports, and productivity over the medium term, a phenomenon Berg and others (2010) label “Dutch vigor.”b

The literature regarding the potential impact of aid on real exchange rates and manufacturing is growing. But the empirical record is mixed. Rajan and Subramanian (2011), for instance, conclude, by measuring the relative growth rates of exportable industries, that aid inflows seem to have systematic adverse effects on a country’s competitiveness. It underscores that a channel for these negative effects is the real exchange rate appreciation caused by aid inflows. Christiansen and others (2009), however, find that foreign aid is progressively absorbed over time through net imports, and is associated with a more depreciated real exchange rate in the long term. The mixed empirical results are not surprising given the country-specific factors that condition the effects of aid, such as the efficiency and nature of investment, the responses of policymakers and the private sector, and so on, as emphasized in the text.

aSee, for instance, Van Biesebroeck (2005), who finds that exporting has raised productivity in manufacturing firms in SSA.bSee also Torvik (2001) and Adam and Bevan (2006).

Can fiscal and monetary policy responses shape the macroeconomic effects of aid surges? Concerns about competitiveness and real appreciation have frequently caused authorities to accumulate some portion of the aid flows as international reserves—partial aid absorption—while still spending the local currency counterpart of these flows (Berg and others, 2007). When public investment is productive, partial aid absorption helps limit short-term real appreciation of the currency but at the expense of lower medium-term growth because private consumption and investment are crowded out. Similarly, countries that seek to maintain the level of the nominal exchange rate to avoid losing competitiveness are likely to still experience (i) real appreciation through inflationary pressures, when aid inflows are not sterilized; or (ii) crowding out of the private sector, thus constraining growth, when there is full sterilization.25 Therefore, policies can exert an important influence on the macroeconomic impact of aid and, to that extent, should be designed to help reap the growth benefits of aid. However, macroeconomic policies cannot compensate for poor investments or absorptive capacity constraints.

The macroeconomic analysis undertaken by the IMF for these 10 countries focused on these questions. In general terms, by setting country-specific parameters, the studies traced how an aid increase would affect public investment spending, how public investment spending would translate into an accumulation of public capital (spending efficiency), how this would affect the marginal productivity of public and private capital, and how private investment would respond. Public and private capital accumulation would drive growth in the medium term. At the same time, based on specified fiscal and monetary policy responses, the studies assessed how an aid shock might affect inflation and the real exchange rate, and thus move private investment toward the nontradables sector, away from the potentially more dynamic tradables sector. Special attention in the adjustment process was given to the possibility of having Dutch disease or Dutch vigor effects.

Design and Features of the Model

Most of the analyses of the pilot countries were conducted using a common framework developed at the IMF by Berg, Gottschalk, Portillo, and Zanna (Berg and others, 2010). The framework is based on a small open-economy quantitative model in the tradition of dynamic stochastic general equilibrium (DSGE) models, in which short-term growth is driven by changes in aggregate demand, while longer-term growth is driven by accumulation of capital (Box 4.3).

DSGE Models

Dynamic stochastic general equilibrium (DSGE) models provide a coherent framework for policy discussion and analysis. Thus, they are playing an important role in the formulation and communication of monetary policy at many central banks in both developed and developing economies.a

The benchmark DSGE model for an open or closed economy is micro-founded, includes real and nominal rigidities, and emphasizes agents’ intertemporal choice (Christiano, Eichenbaum, and Evans, 2005; and Smets and Wouters, 2003). Because current agents’ choices depend on future uncertain outcomes, the model is inherently dynamic and allows for agents’ expectations to influence current macroeconomic outcomes.

The basic structure is built around three interrelated blocks: a demand block, a supply block, and a policy block. The blocks capture the behavior of the economic agents of the economy—households, firms, and the government. These agents interact in markets that clear every period, reflecting general equilibrium features. The demand block describes households’ decisions. They consume, decide how much to invest, and are monopolistic suppliers of labor, which allows them to set wages. The supply block captures firms’ behavior. Firms hire labor, rent capital, and set prices because they are monopolistic suppliers of goods. The policy block models government decisions. Fiscal policy is usually restricted to a Ricardian setting, while monetary policy is conducted through an interest rate feedback rule, in which the interest rate is set in response to deviations from an inflation target and an output gap. This basic structure is enriched with a stochastic structure of demand, supply, and policy shocks.

The DSGE-type model used for most of the Gleneagles scenarios is an open-economy version with tradable and nontradable goods that has been specifically developed to analyze scenarios in which aid is scaled up. The model has a number of low-income-country-specific features. The economy includes “hand-to-mouth” consumers without access to financial markets, amplifying the effects of demand shocks. Domestic and foreign assets are imperfect substitutes, so the capital account can be closed. And as described in the text, the model contains a number of features designed to capture the role of separate and uncoordinated monetary and fiscal policy responses to aid surges, public investment efficiency, absorptive capacity constraints, and the risks of Dutch disease.

This category of models is well suited for macroeconomic analysis and for addressing policy issues of interest in low-income countries. The models may be helpful for analyzing the macroeconomic effects of particular shocks in these countries such as those to terms of trade, remittances, and aid. Admittedly, these models, as complicated as they are, contain many rough approximations and shortcuts, and will not on their own produce accurate forecasts. However, they can help organize thinking, offer a way to systematically incorporate various sorts of empirical evidence, and provide a vehicle for transparently producing alternative macroeconomic scenarios, which can be compared across countries. These features make them suitable for analyzing aid scaling-up scenarios.

aFor a review of the issues and challenges surrounding the use of DSGE models at central banks, see Tovar (2009).

The model was designed to analyze both the short- and medium-term macroeconomic effects of aid surges by capturing the main mechanisms and policy issues of interest in low-income countries. The model incorporates a role for public capital in production so that government spending can raise output directly as well as induce private investment. Specifically, an increase in public investment from an aid increase leads to an increase in the public capital stock, which results in higher GDP growth. Firms respond to the positive impact of public investment on the marginal productivity of private capital by increasing investment, thus expanding private capital and growth.

But the model allows for varying degrees of public investment efficiency, allowing for less than full conversion of investment into useful public capital. This, in turn, affects both the private investment response and growth.

The model includes a learning-by-doing externality in the tradables sector to capture the notion that real exchange rate appreciation may harm productivity in this sector and hinder overall growth. However, even if such externalities exist, the gains from well-invested aid can outweigh these negative effects. Indeed, if aid is invested well, the externalities raise the productivity of the tradables sector, such that aid can produce even greater gains in growth—higher public capital accumulation induces crowding-in effects on private capital accumulation, which, over the medium term, helps raise tradables output above its trend, producing Dutch vigor. In contrast, when aid is not invested well, these externalities induce declines in productivity in the tradables sector that suggest that aid can harm growth—less public and private capital accumulation might not fully offset the negative effects of real appreciation on productivity, resulting in an adverse impact on growth.

On the policy front, the model allows for separate and possibly uncoordinated fiscal and monetary policy responses to an aid surge, permitting a variety of policy combinations to help contain inflation and real exchange appreciation. The fiscal authorities’ decision corresponds to a view on whether to spend or save the aid. The monetary authorities’ decision, however, is about whether to “absorb” the aid and finance a higher current account deficit by selling the aid-associated inflows in the foreign exchange market, or to accumulate the aid as international reserves with resulting crowding out of the private sector (Box 4.4). The model also allows for issuance of central bank bonds to sterilize increases in the domestic money supply that may occur as a result of reserves accumulation. Such sterilization policies lead to increases in the real interest rate and, therefore, to crowding out of private sector activity.

The model facilitates an examination of the impact of the efficiency of public investment on growth. First, it is important to distinguish between the efficiency of public investment based on aid-surge funds and the historical efficiency of public investment. It is the efficiency of the aid-surge-related public investment relative to historical investment efficiency that determines how much impact aid-financed public investment has on growth. Inefficient aid-surge-related investment will create only a small amount of additional public capital—however, historically low efficiency also means that previous public investment spending created very little public capital to begin with, so even a small addition to public capital can make a big difference to output. This conclusion has some important and perhaps surprising implications. If a country has a lot of trouble converting investment spending into useful capital—in other words, both historical and aid-related investment efficiency are low—the growth effects of a given aid surge are hard to determine.

Spending and Absorption

The management of aid flows occurs through the interaction of fiscal and monetary policies, through spending and absorption. Spending is defined as the widening of the fiscal deficit associated with the use of an increment of aid. Absorption is defined as the widening of the current account deficit (excluding the inflow of the aid) resulting from foreign exchange purchases associated with the aid.

Absorption measures the extent to which aid engenders a real resource transfer through higher imports. For a given amount of fiscal spending, absorption depends on exchange rate and monetary policies. The government spends some aid dollars on imports and can sell the remainder of the aid dollars to the central bank in exchange for a local currency counterpart to finance spending on domestic goods. The degree of absorption depends on the central bank’s response. The central bank can sell the foreign exchange to mop up the local currency counterpart spent by the government, thus allowing the private sector to import commensurately.

If the central bank accumulates the foreign exchange as international reserves instead of selling it, the real exchange rate is unlikely to appreciate to the same extent. However, the authorities will have attempted to use the same aid flow twice—once to finance spending and once to increase reserves—and this can have unintended negative effects. Once the aid money is accumulated as reserves, it is not available to finance the aid-related spending. As this spending takes place, the money supply increases. If the monetary authorities do not react, the resulting inflation will in effect tax domestic residents to pay for the spending. Central bank efforts to issue bonds instead to soak up (“sterilize”) the increase in the money supply will potentially raise interest rates and crowd out sector activity.

Berg and others (2007) provide evidence of SSA countries that, when confronted with aid surges, spent most of the aid but limited aid absorption in an effort to contain the impact on the real exchange rate and competitiveness. In several of the countries studied, aid was spent, but was not fully absorbed, to buffer reserves and out of concerns about exchange rate appreciation. This “double use” of the foreign exchange from aid inflows—for both spending and to buffer reserves—led to higher inflation in some cases and to an increase in interest rates and the domestic debt burden in others. The simple lesson is that the reactions of monetary, fiscal, and reserves policy must be coordinated to avoid unintended negative consequences from aid surges.

But if a country’s investment efficiency either declines with the aid surge (perhaps because it cannot handle the larger aid volume), or increases (for example, because of improved investment planning and financial management practices), a measurable effect will result. Finally, the model can be calibrated to each country. Because the model is micro-founded, some parameters can be based on microeconomic evidence, such as the efficiency of investment. Other parameters depend on steady-state ratios that can be determined from national income accounts, public and private sector balance sheets, and input-output matrices, or can be informed by more or less structural macroeconometric estimates, for example, money demand parameters. Other parameters describe the policy response to aid or the policy regime in place and can, therefore, be treated as free parameters and modified according to the policy experiment.

Synthesis of Country Case Studies

Simulation results from the 10 country studies suggest that increased aid can have a significant positive long-term impact on economic growth, and that negative effects on inflation and real exchange rates should be manageable. However, this outcome depends crucially on the macroeconomic policy response, as well as on the efficiency of public investment and the response from the private sector. Effective use of increased aid presents the main challenge, given constraints on absorptive and administrative capacity in most of the countries.

In most of the case studies, a significant increase in aid flows would be required for the Gleneagles commitments to be met. Benin, Liberia, Rwanda, and Zambia are currently receiving relatively high levels of aid. Ghana, Tanzania, and Togo would require a scaling up of aid of at least 5 percent of GDP, and more than 10 percent of GDP would be necessary in the Central African Republic, Niger, and Sierra Leone.26 The case studies assumed that the increases in aid were used to scale up public spending on investment, broadly defined, as prioritized by each country’s national poverty reduction strategy. Most of the case studies assumed that the additional aid was in the form of grants, with no additional debt created as a result of the scaling up. This is a critical assumption—if additional debt were to be created, the fiscal implications of future interest payments as well as debt sustainability would need to be taken into account, lowering the growth payoff of the higher aid.

All country cases showed a positive growth impact from an aid surge, although the scale of the macroeconomic effects varied according to the specific country circumstances and to the magnitude and duration of the aid increase required to meet an ODA level of $85 per capita (2004 prices). The assessments for Benin, the Central African Republic, Niger, Rwanda, Sierra Leone, Tanzania, and Togo show a significant effect on GDP growth due to the higher investment in public infrastructure and the resulting positive impact on private sector investment. Even in countries with lower aid increments, the results are nonnegligible: in Benin, for example, the incremental increase in aid in 2008-15 is estimated to boost growth by 0.8 percent of GDP on average relative to the benchmark; consequently, per capita income would be 29.4 percent higher in 2015, reaching $574 in 2007 constant prices.

The positive growth return occurs despite higher inflation and real exchange rate appreciation in several cases in which increased aid has a temporary adverse impact on the tradables sector (Table 4.1). In 7 of 10 cases, an aid shock resulted in real exchange rate appreciation of more than 2.5 percent (and up to 15 percent for Niger and the Central African Republic) against the steady state, but in all cases the tradables output recovered over the long term and GDP was permanently higher than in the steady state. In Togo, for example, the aid surge was quite large, about 9.5 percent of GDP. The real exchange rate appreciated by about 9 percent and inflation was 5 percent higher than in the steady state, with nontradables production higher and tradables production lower. However, over the long term, the tradables sector recovers with production higher than in the steady state despite the appreciated exchange rate, showing evidence of Dutch vigor—higher productivity resulting from public investment outweighed the effect of the real exchange rate appreciation, adding about 1 percentage point to the real GDP growth rate. A similar effect is seen in one of the scenarios for Tanzania, for which aid increased by 6 percentage points of GDP and the real exchange rate appreciated by 4 percent: the tradables sector was initially adversely affected, but tradables output was higher over the long term, again adding about 1 percentage point to the real GDP growth rate.

Table 4.1.Summary of Baseline Scaling-Up Scenarios for 10 Pilot Countries(percentage deviation from benchmark, unless otherwise noted)
CountryAid shock

(% of GDP)
REER

appreciation

t+2
Tradables

production

t+2
Nontradables

production

t+2
Annual average

increase in real

GDP level

t+2 to t+12
Sierra Leone14.66.0-6.06.02.7
Niger14.015.0n.a.n.a.2.5
Central African Republic14.015.0-11.010.03.5
Togo9.59.0-9.011.01.5
Tanzania6.04.0-2.03.01.0
Liberia14.91.50.00.50.2
Ghana4.52.70.01.01.0
Benin2.45.9n.a.n.a.0.8
Zambia0.90.6-0.61.10.3
Rwanda0.91.4-1.30.50.1
Source: Authors’ calculations.Note: n.a. = Not applicable; REER = Real effective exchange rate; t+2 = 3 years from the latest year for which data were available in the respective country.

Because Liberia already receives more than $85 per capita in ODA, an ODA of 4.9 percent of GDP was assumed.

Source: Authors’ calculations.Note: n.a. = Not applicable; REER = Real effective exchange rate; t+2 = 3 years from the latest year for which data were available in the respective country.

Because Liberia already receives more than $85 per capita in ODA, an ODA of 4.9 percent of GDP was assumed.

The magnitude of the real exchange rate appreciation, inflation, and the growth impact depended significantly on the macroeconomic policy response to increased aid. The Niger, Rwanda, Tanzania, and Zambia cases contain scenarios with partial absorption policies (Box 4.4). These cases show that partial absorption could help limit short-term real appreciation as compared with full absorption of the aid increase, but at the expense of lower growth over the long term because private sector activity is crowded out. The Tanzania case goes further to show a scenario with partial absorption in which the increase in the domestic money supply is not fully sterilized; in this case, inflation and interest rates spike sharply with a further adverse impact on private sector investment.

Fixed exchange rate regimes can present difficult issues for macroeconomic policymakers. In cases in which the use of the aid requires a real appreciation, a fully absorbed aid surge will lead to a period of inflation: with the stable nominal exchange rate, the real appreciation requires a rise in the price of nontradable goods. The Central African Republic (fixed exchange rate regime) and Sierra Leone (floating exchange rate regime) cases show similar magnitudes of increased aid in their scaling-up scenarios, with almost full absorption in both cases. In the Central African Republic, the real exchange rate appreciates by 14 percent, with a temporary increase of 10 percentage points in the inflation rate. There is a pronounced but relatively temporary effect on tradables output. In Sierra Leone, more is spent on imported goods, resulting in lower exchange rate appreciation (6 percent). The exchange rate regime also makes a difference for inflation—with a nominal appreciation of the rate, inflation initially falls rather than rises, but stabilizes quickly.

Policymakers should be careful not to misinterpret—and overreact to—the inflation that may result from an aid surge in a fixed exchange rate regime. If they attempt to fight it, for example, by keeping the money supply stable in the face of the aid inflow, they may succeed in narrow terms in keeping inflation low, and thus in stabilizing the real exchange rate. But like any partial absorption strategy, higher domestic interest rates and crowding out of private investment and consumption would ensue, as if the additional spending were domestically financed. In the end, inflation may be a necessary corollary to the effective use of the aid. Of course, policymakers need to be aware that inflation may increase for other reasons, such as excessively loose fiscal and monetary policies, in addition to the aid surge.

The country cases reinforce the point that higher aid efficiency is important for maximizing the return to growth and mitigating the effects on inflation and the real exchange rate, both in magnitude and speed. Inefficient use of aid raises the risk that Dutch disease effects would dominate, limiting the positive impact on medium-term growth. Most of the country studies contained scenarios examining the impact of a decline in aid efficiency. Almost all of these scenarios, however, showed that the growth return to public investment eventually outweighed temporary negative impacts on inflation and competitiveness.

A decline in the marginal productivity of public investment or the diversion of aid to finance unproductive government consumption would reduce the boost to medium-term growth from aid, and raise the risk that the effect of a real exchange rate appreciation could outweigh the impact of the increased capital stock on growth, creating a Dutch disease scenario. Indeed, the Niger and Togo studies showed scenarios in which efficiency was lowered to a point such that Dutch disease effects dominated and the tradables sector was unable to recover, yielding an outcome in which productivity and growth were lower than in the baseline aid shock scenario (where parameters were set at historical and/or standard levels). Whether Dutch disease effects are important—or even whether there is a disease at all—depends on two factors: the extent of externalities in the tradables sector and the productivity of the aid-financed investment spending. Gains in productivity associated with growth in the tradables sector can amplify the positive effects of aid on tradables output and real GDP (the aforementioned Dutch vigor), but this effect can also magnify the negative impact of a contraction in the tradables sector due to real appreciation resulting from increased aid inflows (Dutch disease). Whether the learning-by-doing externalities intensify the positive or negative impact of aid depends on the efficiency of public investment and on the extent to which increased public capital accumulation is successful in crowding in private investment. Such crowding in helps ensure that the initial fall in tradables output fades quickly rather than becoming protracted. Given the problems across the region with effective execution of public investment projects in the past, public financial management reforms must occur ahead of the scaling up of aid to ensure effective use of the increased resources.

The impact of scaled-up aid on growth is likely to be reduced in countries in which the private sector remains underdeveloped or constrained by an unfavorable business climate. Although the case studies did not incorporate a more muted private sector response to public capital accumulation—one proxy in the DSGE model would be to restrict access to financial services even further, or to raise the cost private firms face in adjusting their investment rate—several cases pointed to the investment climate as an important factor in the response of growth to aid. The medium-term growth impact of increasing aid is likely to be stronger if the increase in public investment is reinforced by an expansion of private capital in response to increased marginal productivity; a muted response leads to a higher risk of Dutch disease.

The caveats in interpreting the results of the country studies should be considered. Data limitations remain a critical constraint in calibrating the model for each country, requiring that a number of assumptions be made in setting the country-specific parameters (Box 4.5). In addition, the region is undergoing rapid structural change, and many of the parameters that describe key relationships in these economies may be changing over time. Finally, although the model is designed to reflect the dynamics of low-income countries as closely as possible, the analysis might not sufficiently capture channels of transmission relevant to Africa.27

Assumptions for Calibrating the DSGE Model

The DSGE model was calibrated using a number of assumptions for country-specific parameters. The broad categories included historic averages of GDP components; basic macroeconomic variables; public sector assets; other basic characteristics of the economy; and the size, duration, and use of the aid increase. After the model was calibrated, a baseline aid shock was examined. Thereafter, parameters were modified to simulate different aid and policy scenarios.

For example, the baseline calibration for Tanzania is depicted in the table below.a

GDP components (% of GDP)
Consumption83
Consumption of tradable goods50
Investment27
Government spending25
Government consumption16
Government spending on tradable goods9
Government spending on nontradable goods16
Trade balance-35
Exports14
Imports49
Inflation, growth, interest rate (%)
Annualized inflation5
Trend growth7
Annualized real interest rates2
Aid process (% of GDP)
Benchmark aid8
Persistence of aid shock (fraction of previous year’s shock that persists in current year)0.95
Aid increase5
Duration of aid increase (number of quarters)20
Policy rule parameters (%)
Amount of increased aid spent100
Persistence of real debt accumulation90
Portion of aid shock spent on nontradables60
Portion of aid shock spent on investment50
Sale of foreign exchange from aid surge100
Weight of exchange rate target1.5
Inflation-targeting coefficient28
Assets (% of GDP)
Real money balances10
Private sector net foreign assets4
Reserves13
Government deposits at the central bank7
Government debt held by the central bank4
Government debt held by the private sector4
Total government debt8
aGiven data constraints, parameters on basic characteristics of the economy (e.g., technology, elasticity of substitution, wage adjustment, capital mobility, financial market access) were kept at standard levels for low-income countries in the model for Tanzania.

Chapter 5 Conclusion

This paper analyzes the implications of scaling up aid to Africa in line with the international commitments made at the 2005 G-8 Gleneagles Summit. It reflects the results of 10 pilot studies conducted jointly by the country authorities, the UNDP, and IMF staff using a common framework. From a sectoral perspective, the report assesses what each of the 10 countries might do with aid made available at the levels committed to by the G-8 in 2005. It also explores the macroeconomic implications of scaling up development assistance through what might be expected of growth, inflation, real exchange rate developments, and other macroeconomic indicators.

Both micro- and macroeconomic analyses are necessary and interrelated in making aid effective. The core challenge is to execute productive investments efficiently. If this challenge can be met, the macroeconomic challenges are manageable. However, poor macroeconomic responses can still thwart intended results.

The main conclusions of the paper follow:

  • An increase in aid is necessary to meet the Gleneagles commitments. Although aid to SSA has risen in recent years, it remains well below the goals set in 2005.
  • Existing development plans are underfunded in the pilot countries, and scaling up development assistance in line with the Gleneagles commitments would go a long way toward closing the gap.
  • Existing development plans should be used to shape the spending of increased aid. These plans should integrate public investment programs for the use of additional aid with the following: (i) MDG-based development priorities, which are drawn from the countries’ Poverty Reduction Strategy Papers; and (ii) multiyear budgets, which are based on Medium-Term Expenditure Frameworks. This ensures consistency between existing spending plans, domestic revenue efforts, and nonconcessional borrowing, where available.
  • Scaled-up aid will be most efficient if it is well integrated with recipients’ budget and implementation systems. Recipient countries must also emphasize continued improvements in public financial management and complementary mobilization of domestic resources in line with potential, underpinned with results-based monitoring and evaluation systems.
  • The sectoral focus of countries’ spending plans is on infrastructure and human development, both of which are critical to meeting the MDGs.
  • The macroeconomic analysis suggests that scaling up to meet the Gleneagles commitments can have a substantial positive effect on growth, as long as the projects financed are well implemented, and consequently on track toward achieving the MDGs.
  • Macroeconomic management needs to avoid counteracting the benefits of aid while still preserving overall macroeconomic stability. Aid, if sufficiently concessional, allows scaling up while avoiding risks to debt sustainability. Some temporary real exchange rate appreciation—through inflation in a pegged exchange rate regime—and temporary adjustment in the size of tradables versus nontradables sectors can be expected. The duration of this adjustment varies, but in some cases could be years.
  • Over the longer term, the extent to which growth is enhanced depends critically on the volume, efficiency, and effectiveness of public investment. If the tradables sector is an especially strong driver of productivity growth, the stakes are higher for the effective use of aid. If aid helps build public capital and raises productivity in the tradables sector, aid can produce even greater gains for overall growth, causing Dutch vigor. If aid is wasted, however, the diversion of scarce resources from the tradables sector could reduce productivity growth over time, causing Dutch disease. The key is to use aid well, and if necessary, devote it to investment, such as in ports, roads, and electric power to promote productivity, including in the tradables sector.

Appendix: Summaries of Country Studies*

Benin

Meeting the Gleneagles commitment for Benin would require a moderate scaling up of aid by approximately 2 percent of GDP over the next few years. Aid inflows were 5.9 percent of GDP in 2007, equivalent to $42 per capita (Table A1.1). To reach $85 per person by 2010, aid would have had to increase to 7.7 percent of GDP by 2010 and average 7.5 percent of GDP annually during 2011–15. This would have increased overall ODA from $330 million in 2007 to $732 million in 2010.

Table A1.1.Benin: ODA Scaling-Up Summary ($US million)
Measure2007200820092010Average,

2008-10
Total,

2007-10
ODA (current projections)329.9382.6401.2428.4404.11,542
ODA as % of budget (current projections)27.119.521.721.020.7
ODA per capita (current projections)42.147.348.149.748.4
ODA (with Gleneagles)329.9456.1590.2732.6593.02,109
ODA as % of increased budget (with Gleneagles)27.022.029.031.027.3
ODA per capita (with Gleneagles)42.156.470.785.070.7
Additional ODA under Gleneagles scenario0.073.6189.0304.1188.9567
Source: Authors’ calculations based on case study.
Source: Authors’ calculations based on case study.

The government of Benin has identified education, health, agriculture, infrastructure, gender, and statistics as priority areas for scaled-up outlays. These priorities cover programs and projects that were identified in the MDG needs-assessment exercise, but that could not be integrated in the Medium-Term Expenditure Framework of the Growth and Poverty Reduction Strategy (2007–09) because of funding constraints.

Under the Gleneagles scaling-up scenario, priority is given to infrastructure and health, accounting for three-fourths of spending and including investments to strengthen health systems and improve maternal and child health. Approximately one-fourth of the additional resources would be allocated to agriculture, and 10 percent to the education sector, with emphasis on technical and professional training (see Figure A1.1).

Benin: Average Spending by Sector in the Gleneagles Scenario

Source: Authors’ illustration based on case study.

The additional aid inflows are projected to have a positive impact on GDP growth, with some short- to medium-term pressures on inflation and moderate appreciation of the real exchange rate. The incremental increase in aid in 2008–15, assuming it is effectively used, would boost annual growth by an average of 0.8 percentage point relative to the steady state. As a result, per capita annual income would increase to $574 by 2015 in constant 2007 prices, 6 percent higher than in the baseline scenario. As a result of the allocation of about one-third of the additional aid inflows to education and health, human capital investment would rise to 4.7 percent of GDP in 2015 from 2.2 percent in 2008, also suggesting higher potential growth for Benin beyond 2015 as higher skilled and healthier individuals enter the labor force (see Figure A1.2).

Benin: Macroeconomic Impact of Aid under the Gleneagles Commitment, 2007-15

Source: Authors’ estimates.

Note: x-scale measures t-years after the first year (t0) in the period of study.

The impact on growth and poverty reduction could be lower if Benin’s absorptive and administrative capacity is not strengthened. The simulations are based on the critical assumption that structural reforms will continue to be implemented to address any significant bottlenecks in the economy that could jeopardize the effective use of the additional aid. Structural reforms that will make Benin’s economy more competitive and flexible include the divestiture of key public enterprises, effective management of the port, and improved public financial management. With such reforms, the additional aid inflows could have a more profound impact on growth over the medium and long terms. In addition, higher inflation and real exchange rate appreciation are risks because spending of the additional aid on nontradables could strain already limited absorptive capacity.

Central African Republic

The Gleneagles scenario would suggest a significant boost in foreign assistance to the Central African Republic. Foreign assistance in the form of grants and concessional loans is still very low by regional standards, with ODA per capita of only $13 in 2007. Meeting the Gleneagles commitment would require a scaling up of aid of 13 percent of GDP, with an increase in overall ODA from $55 million in 2007 to $389 million in 2010 (Table A1.2).

Table A1.2.Central African Republic: ODA Scaling-Up Summary ($US million)
Measure2007200820092010Average, 2008–10Total, 2007–10
ODA (current projections)54.696.789.797.194.5338.1
ODA as % of budget (current projections)14.218.514.713.815.7
ODA per capita (current projections)12.722.020.021.221.1
ODA (with Gleneagles)54.6161.5272.8388.5274.3877.4
ODA as % of increased budget (with Gleneagles)14.227.434.439.133.6
ODA per capita (with Gleneagles)12.736.860.985.060.9
Additional ODA under Gleneagles scenario0.064.8183.1291.4179.8539.3
Source: Authors’ calculations based on case study.
Source: Authors’ calculations based on case study.

The government of the Central African Republic has identified education, health, agriculture, infrastructure, gender, and crisis prevention as priority areas for scaling up (Figure A1.3). These areas include programs and projects identified in the MDG needs-assessment exercise that could not be integrated in the PRSP (2008–10). Half of the additional projected aid would be invested in the infrastructure sector, making more rural roads available and increasing access to safe drinking water. About one-quarter of the Gleneagles aid would be dedicated to the health sector, helping to improve indicators on child mortality, malaria, and HIV/AIDS prevalence. With supplementary interventions in nutrition, the population below the minimum level of dietary energy consumption could drop to 45 percent (compared with 60 percent under current projections) and the prevalence of underweight children would likely be reduced by an additional 8 percentage points. Finally, with additional investments in the implementation of the Global Peace Accord and Disarmament, Demobilization and Reintegration interventions, 20,000 additional excombatants could be disarmed and reintegrated.

Central African Republic: Average Spending by Sector in the Gleneagles Scenario

Source: Authors’ illustration based on case study.

The additional aid inflows would have a large impact on GDP growth, with some short-term pressures on inflation and real exchange rate appreciation (Figure A1.4). The simulations indicate a substantial positive impact on growth and per capita GDP, reflecting investment in much-needed public infrastructure and the resulting positive impact on private sector investment. This outcome would also serve to reinforce the recent economic recovery following several years of political instability and a deterioration in living standards. The positive impact of higher aid on the accumulation of public and private capital is relatively large for the Central African Republic, given the country’s low starting point and seriously depleted capital stock. Although inflation would rise and the real exchange rate would appreciate in the short term, the adverse impact on the tradables sector would likely be relatively limited.

Central African Republic: Macroeconomic Impact of Aid under the Gleneagles Commitment

Source: IMF staff estimates.

Note: x-scale measures t-years after the first year (t0) in the period of study.

There are risks that the growth impact of additional aid inflows could be constrained by limited absorptive and administrative capacity and an underdeveloped private sector. Because of limited administrative capacity and weak public financial management, additional aid could be diverted to unproductive government consumption or investment, and the supply response to the aid-financed increase in demand for nontradables and services may be much weaker than the model assumes. In addition, the numerical simulations must be interpreted with some caution, given considerable data limitations for the Central African Republic.

Ghana

The Gleneagles scaling up would result in a significant increase in foreign assistance to Ghana. In 2008, ODA was 9 percent of GDP, or about US$61 per capita; a projected scaling up to $85 per capita by 2010 would have required an increase in ODA of about 4½ percent of GDP per year, increasing the overall ODA from $1,139 million in 2007 to $2,036 million in 2010 (Table A1.3).

Table A1.3.Ghana: ODA Scaling-Up Summary ($US million)
Measure2007200820092010Average,

2008–10
Total,

2007–10
ODA (current projections)1,138.71,406.51,596.61,461.41,488.25,603.2
ODA as % of budget (current projections)2826332728.7
ODA per capita (current projections)5161686163.3
ODA (with Gleneagles)1,138.71,425.41,723.92,035.91,728.46,323.9
ODA as % of increased budget (with Gleneagles)2826343431.3
ODA per capita (with Gleneagles)5162748573.7
Additional ODA under Gleneagles scenario018.9127.3574.4240.2720.6
Source: Authors’ calculations based on case study.
Source: Authors’ calculations based on case study.

The government of Ghana has identified energy, education, health, technology/communication, and agriculture as key areas for scaling up (Figure A1.5). These priority sectors are derived from the Growth and Poverty Reduction Strategy, 2006–09. Under the Gleneagles scenario, priority would be given to energy, to which 32 percent of the additional resources made available under the Gleneagles envelope would be dedicated, while 26 percent of the additional resources would be allocated to the education sector in an effort to raise the gross enrollment rate to 100 percent by 2010. The health sector would receive 16 percent of the additional resources; attention would be given particularly to nutrition, sexual and reproductive health, health systems, and sector governance. A similar share of the Gleneagles envelope would be dedicated to communication and technology. Finally, 8 percent of the additional ODA would be invested in agriculture, with particular emphasis on food security and emergency preparedness.

Ghana: Average Expenditure of Additional Resources by Sector in the Gleneagles Scenario

Source: Authors’ illustration based on case study.

The model simulations suggest that the macroeconomic impact of scaled-up ODA to Ghana, taken alone, should be readily manageable. Higher investments would boost growth by about 1 to 1½ percent per year, with increased demand for nontradables generating a real exchange rate appreciation of about 2 percent over the short term, declining to about half that amount over the medium term. Although tradables-sector production declines slightly at the outset of the scaling-up exercise, production in this sector rises in subsequent years, suggesting that Dutch disease effects would be modest or absent. Inflation is boosted by about 1 percent initially, and by less than ½ percentage point over the medium term (Figure A1.6).

Ghana: Macroeconomic Impact of Aid under the Gleneagles Commitment

Source: IMF staff estimates.

Note: x-scale measures t-years after the first year (t0) in the period of study.

The challenges of managing scaled-up ODA could be complicated by Ghana’s shift to oil producer status. Oil production will generate additional foreign currency inflows starting in 2011 that, with scaled-up ODA, could exacerbate Dutch disease risks. The inflationary impact and real exchange rate appreciation are minimized when oil revenues are partly saved. Under a high-case scenario, in which oil revenues are used to boost government spending by a further 6 percent of GDP, the impact on inflation is calculated to be less than 3 percent per year, while the decline in tradables production on account of Dutch disease effects would be limited to several years. In a scenario in which increased spending is focused on the nontradables sector (such as public service wages, rather than importation of capital goods), the Dutch disease effects are more pronounced, with tradables production depressed below steady-state levels for a decade.

The results of the scaling-up exercise are sensitive to existing shortcomings in public expenditure management and absorptive constraints linked to limited manpower skills in key areas (for example, health and education). These considerations would need to be taken into account in the design of a potential scaling-up program.

Liberia

Liberia already receives more aid per capita than the Gleneagles commitment of $85 in light of its particularly elevated need for external financing.

However, given the country’s very low starting point for its stock of human and physical capital, it is assumed that aid needs to rise further to meet the expenditure requirements necessary to reach the MDGs. As a result, the Gleneagles scenario assumes a nominal increase in total ODA of 15 percent over 2008 levels to $108 per capita, equal to an annual additional aid flow of about 4.9 percent of GDP on average from 2009 through 2011. The assumed increase in the Gleneagles envelope would have increased overall ODA from $399 million in 2008 to $483 million in 2011 (see Table A1.4).

Table A1.4.Liberia: ODA Scaling-Up Summary ($US million)
Measure2008200920102011Average, 2009-11Total, 2008-11
ODA (current projections)398.7407.6417.3403.3409.41626.9
ODA per capita (current projections)101.298.796.890.295.2
ODA (with Gleneagles)398.7427.3455.7482.8455.31764.5
ODA per capita (with Gleneagles)101.2103.5105.7108105.7
Additional ODA under Gleneagles scenario019.738.479.545.9137.6
Source: Authors’ calculations based on case study.
Source: Authors’ calculations based on case study.

The government of Liberia has identified infrastructure, education, health, and agriculture as priority areas for scaling up (Figure A1.7). These sectors are derived from Liberia’s Poverty Reduction Strategy (2008-11). Priority would be given to infrastructure, to which 31 percent of the additional resources ($29 million) would be allocated. The emphasis would be on water and sanitation, with the objective being to finance the rehabilitation of all existing damaged drinking water facilities in urban areas, the rehabilitation of Monrovia’s sewage system, and the construction of 700 rural boreholes with hand pumps and 30,000 rural latrines for families. A similar share (29 percent) of the additional Gleneagles resources would go to education. The main goals in this sector would be school construction, curriculum development, teacher training, vocational training, and school feeding programs. In addition, 21 percent of the assumed additional resources ($19 million) would be earmarked for health, and 7 percent ($7 million) for agriculture, mainly for input supply.

Liberia: Average Expenditure by Sector in the Gleneagles Scenario

Source: Authors’ illustration based on case study.

The model results for Liberia indicate that an aid increase of this magnitude would have a positive and long-term impact on economic growth, with manageable effects on inflation and competitiveness (Figure A1.8). The GDP growth impact is initially 0.5 percentage point higher than the benchmark before aid scaling up. The growth impact declines in the following years, averaging 0.16 percent over 10 years. The longer-term impact on growth is modest because the proportion of investment spending, which has persistent effects, is assumed to be relatively small, as is the case for existing aid. Although the real exchange rate would appreciate in the short term, the adverse impact on the tradables sector would be relatively limited. Simulation results also confirm that Dutch disease effects are not likely to outweigh the positive growth effects of aid. Using dynamic stochastic general equilibrium and other models, the growth impact of scaled-up aid in Liberia is similar to that in other IMF country case studies, after controlling for Liberia-specific structural factors, although the results are subject to uncertainty from several factors, including a high level of dollarization. Other factors include a high dependence on imports, which reduces multiplier effects; a relatively low share of aid used for investment; thin financial markets; and a low growth elasticity to existing high levels of aid. Accordingly, a higher GDP growth impact could be reached if incremental aid is tilted more toward priority sectors that have persistent productivity effects—such as investment in physical infrastructure, health, and education—as tradables production increases and financial markets develop.

Liberia: Macroeconomic Impact of Aid under the Gleneagles Commitment

Source: IMF staff estimates.

Note: x-scale measures t-years after the first year (t0) in the period of study.

The simulation results for Liberia are subject to considerable uncertainty. For example, the actual growth response could be higher if the labor market response were stronger than projected, which might occur if participation in the formal labor market increased from currently low levels. The analysis and results for Liberia are also subject to substantial caveats owing to significant data limitations. Notably, national accounts data are estimates by IMF staff pending the compilation of national accounts, scheduled for late 2011.

Niger

The increase in aid required to meet the Gleneagles commitment for Niger requires a significant scaling up, equivalent to 18 percent of GDP by 2010. The Gleneagles envelope would have increased overall ODA to $655 million in 2008, $949 million in 2009, and $1,262 million in 2010 (Table A1.5), nearly double the level of foreign assistance projected in the current program agreed upon between the authorities and the IMF. In light of the magnitude of the increase, the scaling up could increase the risk of debt distress if the grant element of new aid is relatively low.

Table A1.5.Niger: ODA Scaling-Up Summary ($US million)
Measure2007200820092010Average, 2008-10Total, 2007-10
ODA (current projections)378.0520.9520.9536.9526.21956.7
ODA as % of budget (current projections)38.143.743.141.942.9
ODA per capita (current projections)28.137.436.236.236.6
ODA (with Gleneagles)378.0654.6949.01262.1955.23243.7
ODA as % of increased budget (with Gleneagles)38.149.058.063.056.7
ODA per capita (with Gleneagles)28.147.066.085.066.0
Additional ODA under Gleneagles scenario0.0133.7428.1725.2429.01287.0
Source: Authors’ calculations based on case study.
Source: Authors’ calculations based on case study.

The government of Niger has identified education, health, agriculture, and infrastructure as priority areas for scaling up (Figure A1.9). These priority sectors are derived from the “MDG scenario” of the Accelerated Growth and Poverty Reduction Strategy (2008-12). The Gleneagles-assumed composition of aid-funded expenditures is the same as the composition of PRSP-related expenditures in 2007, with 52 percent allocated to health and education programs, and 42 percent assigned to the productive sectors of agriculture and infrastructure. Priority would be given to education—additional investments are expected to raise the gross enrollment rate by 12 percentage points. Equal shares (21 percent) of the additional resources would go to the infrastructure and agriculture sectors, increasing the population’s access to safe drinking water to 80 percent (compared with 75 percent under current projections) and decreasing the level of undernourished children by 11 percentage points. In the health sector, receiving 14 percent of the Gleneagles resources, priority would be given to improving health systems and maternal health. A scaling up in aid of this size would have a significant impact on economic growth in the medium and long terms. The simulation results suggest that average GDP growth would increase by 1.9 percentage points per year relative to the steady state during the following 10 years (Figure A1.10), allowing Niger to increase real income per capita by 60 percent from 2008 to 2020 (as opposed to an increase of about 30 percent under the steady state). This scenario would be altered if a bigger percentage of aid were to be allocated to social expenditures and a smaller share to infrastructure. With a higher share of aid flows being directed to social expenditures, the impact of aid on growth in the short term would be smaller, given that the impact of this spending takes place over a longer time span.

Niger: Average Expenditure by Sector in the Gleneagles Scenario

Source: Authors’ illustration based on case study.

Niger: Macroeconomic Impact of Aid under the Gleneagles Commitment

Source: IMF staff estimates.

Note: The IMF Debt Sustainability Framework for Low-Income Countries considers that external debt may not be sustainable if above the threshold of 150 percent of exports.

The increase in demand as a result of increased aid would also have a sizeable impact on domestic inflation, resulting in a considerable real exchange rate appreciation. However, the simulation projects that this would be relatively short-lived, because the pressure on domestic prices and the real exchange rate would ease up in the medium term as aggregate supply responds to increased foreign aid.

The execution capacity of the public administration must be improved to better manage the increased expenditures funded by scaled-up aid. The results presented in this assessment hinge on a supply response that has no major bottlenecks in the accumulation of factors of production. If, for instance, these additional expenditures are only 80 percent as efficient as current projects in increasing the public capital stock, GDP growth would be 1.4 percentage points above the steady state over the next 10 years, 0.5 percentage points less than in the “efficient” scaling-up scenario.

Rwanda

Rwanda has experienced a significant increase in aid flows in recent years and the additional aid under the Gleneagles scenario would be modest. Almost 50 percent of budget spending between 2004 and 2007 was financed by aid (Table A1.6). The additional aid needed to meet the Gleneagles commitments would be only about 1 percent of GDP, with an increase in overall ODA to $854 million by 2010.

Table A1.6.Rwanda: ODA Scaling-Up Summary ($US million)
Measure2007200820092010Average,

2008-10
Total,

2007-10
ODA (current projections)4756587085166272357
ODA as % of budget (current projections)48.952.747.932.144
ODA per capita (current projections)5169725164
ODA (with Gleneagles)4755957218547232645
ODA as % of increased budget (with Gleneagles)48.950.248.443.847
ODA per capita (with Gleneagles)5162748574
Additional ODA under Gleneagles scenario0-631333796287
Source: Authors’ calculations based on case study.
Source: Authors’ calculations based on case study.

The government of Rwanda has identified education, health, agriculture, and infrastructure as priority areas for scaling up (Figure A1.11). These priority sectors are derived from the Economic Development and Poverty Reduction Strategy, which emphasizes the country’s commitment to achieving the MDGs while implementing Rwanda’s Vision 2020—the country’s strategy for long-term development directly linked to the MDGs. Priority would be given to the infrastructure sector with the construction of rural roads and improvements in energy transmission. The second largest share of investment (30 percent) would go to the health sector in an effort to strengthen health infrastructure and geographical accessibility for the rural population. Agriculture—in particular, land husbandry, hillside irrigation, and water harvesting—would also receive a large share of the Gleneagles aid (28 percent).

Rwanda: Average Expenditure by MDG Sector in the Gleneagles Scenario

Source: Authors’ illustration based on case study.

As a result, the simulations suggest that scaling up of aid to $85 per capita does not have a significant macroeconomic impact, nor does it present major policy challenges for Rwanda (Figure A1.12). Although the model suggests that the additional aid flows would generate some reallocation in resources from the tradables to the nontradables sector, the impact on inflation, the real exchange rate, and growth is short lived and modest, reflecting the relatively low amount of additional aid needed to reach the Gleneagles commitment. In the short term, the results show a positive impact on growth as a result of the ability to draw on unutilized capacity to increase output, but in the longer term the impact is relatively modest because higher production in the nontradables sector is offset by slower growth in the tradables sector relative to the benchmark.

Rwanda: Macroeconomic Impact of Aid under the Gleneagles Commitment

Source: IMF staff estimates.

Note: x-scale measures t-years after the first year (t0) in the period of study.

Sierra Leone

To meet the Gleneagles commitment, foreign aid to Sierra Leone will need to more than triple from its current levels. Aid inflows were projected to be 7.4 percent of GDP in 2008, equivalent to $29 per capita; under the Gleneagles scenario, foreign assistance would have needed to increase by an additional 15 percent of GDP to reach the nominal target of $85 per capita by 2010 (Table A1.7). The Gleneagles envelope would have increased overall ODA from $161 million in 2007 to $451 million in 2010.

Table A1.7.Sierra Leone: ODA Scaling-Up Summary ($US million)
Measure2007200820092010Average,

2008-10
Total

2007-10
ODA (current projections)161146129111128546
ODA per capita (current projections)3229252125
ODA (with Gleneagles)1612543504513521216
ODA per capita (with Gleneagles)3250678567
Additional ODA under Gleneagles scenario0108221340223670
Source: Authors’ calculations based on case study.
Source: Authors’ calculations based on case study.

The government of Sierra Leone has identified infrastructure, education, health, and agriculture as priority areas for scaling up (Figure A1.13). These priority sectors are derived from Sierra Leone’s Interim Poverty Reduction Strategy. Priority would be given to the infrastructure sector, to which 80 percent of the additional investments are expected to be allocated, with an emphasis on investment in roads (85 percent of new infrastructure investments). In addition, 9 percent of the Gleneagles ODA has been earmarked for agriculture, mainly for farm and community-based interventions; 7 percent would go to the education sector, with an emphasis on primary education; and 3 percent would go to the health sector, in particular HIV/AIDS and malaria treatment and prevention.

Sierra Leone: Average Expenditure by Sector in the Gleneagles Scenario

Source: Authors’ illustration based on case study.

The impact on GDP growth from the increase in aid would be considerable because higher public investment would boost productivity and encourage greater private investment (Figure A1.14). As the stock of public capital rises, so does the productivity of private capital, leading to a sustained increase in private capital over six years. Output growth starts to increase after two years as a result, peaking at 10 percent (from a benchmark rate of 6 percent) before gradually returning to the steady-state rate. The nominal and real exchange rates would appreciate as a result of higher aid inflows, but the impact on the export sector would be offset by higher returns to private investment. The nominal real exchange rate appreciation would contribute to lower inflation.

Sierra Leone: Macroeconomic Impact of Aid under the Gleneagles Commitment

Source: IMF staff estimates.

Note: x-scale measures t-years after the first year (t0) in the period of study.

The impact on growth would be constrained if Sierra Leone’s limited absorptive and administrative capacity is not addressed, emphasizing the importance of sustained implementation of the government’s structural reform agenda. Delays in the implementation of public financial management reforms could hamper the productivity of public spending and result in a lower level of capital accumulation than predicted by the model. In addition, the weak business environment coupled with shortages in human capital and the poor state of infrastructure (in particular, electricity supply and the transportation network) could result in a weaker supply response to the increased demand for nontradable goods and services than envisaged under the model.

Tanzania

An increase in aid inflows to levels consistent with the Gleneagles commitments would imply a significant increase in the existing amount of aid to Tanzania, from 8 percent of GDP (2010) to 14 percent. Under the Gleneagles scenario, ODA per capita would reach $85 in 2011/12, increasing overall ODA from $1,704 million in 2008/09 to $3,600 million in 2011/12 (Table A1.8).

Table A1.8.Tanzania: ODA Scaling-Up Summary ($US million)
FY08/09FY09/10FY10/11FY11/12Average, FY09/10-11/12Total FY08/09-11/12
ODA (current projections)1,7042,1052,0402,0302,0587,879
ODA per capita (current projections)4251484749
ODA (with Gleneagles)1,7042,1052,1853,6962,6629690
ODA per capita (with Gleneagles)4251758570
Additional ODA under Gleneagles scenario0014516666041811
Source: Authors’ calculations based on case study.
Source: Authors’ calculations based on case study.

Infrastructure and education remain priority sectors and, therefore, are the main areas requiring substantial additional external funding (Figure A1.15). Out of external funding requirements averaging $760.8 million annually, infrastructure required $283.9 million, followed by the education sector with $222.4 million. The health sector ranked third, with an annual average of $149.3 million, and $68.9 million was allocated for agriculture.

Tanzania: Average Expenditure by Sector in the Gleneagles Scenario

Source: Authors’ illustration based on case study.

The model-based analysis suggests that aid scaling up of this magnitude would have a significant impact on the level of GDP (Figure A1.16) and help sustain progress toward meeting the MDGs. Tanzania has moved toward achieving the MDGs, especially on universal primary education, reversing HIV/AIDS, and access to safe drinking water. However, Tanzania’s lack of infrastructure is a binding constraint on economic transformation and rapid poverty reduction, not only in Tanzania itself but also in the region—pointing to scope for a sizeable growth payoff if extra aid is focused on removing infrastructure bottlenecks.

Tanzania: Macroeconomic Impact of Aid under the Gleneagles Commitment

Source: IMF staff estimates.

Note: The baseline scaling-up scenario is an aid shock of 5 percent of GDP for five years, with full spending and absorption; scenario 1 shows full spending and partial absorption with full sterilization; scenario 2 shows full spending, partial absorption, and partial sterilization; and scenario 3 shows higher spending efficiency. x-scale measures t-years after the first year (t0) in the period of study.

Simulations indicate that an appropriate monetary policy response would be needed to contain the potentially sizeable, albeit temporary, price and exchange rate effects of the increased aid inflows. To limit the inflationary impact, the portion of the aid inflows spent domestically would need to be sterilized, either through foreign exchange sales that would ensure no crowding out of the private sector or through central bank liquidity instruments.

Efficiency of spending and the responsiveness of the private sector to infrastructure investment would be key factors in determining growth returns. Public spending in Tanzania has already increased substantially since 2000, putting pressure on public financial management systems to deliver strong value for spending, so use of additional aid would need to be tightly managed to ensure maximum benefits. Indeed, the government has a strong emphasis on improving public financial management, including through improving commitment control, expenditure tracking, and monitoring to enable tracking of actual budget execution against broader spending objectives. The government is also taking steps to ensure that annual budgets and short-term planning are better aligned with medium-term spending priorities, as laid out in a prioritized medium-term public investment plan. Similarly, a strong private sector response to public investment would be needed, underscoring that decisive actions to improve the business climate are essential.

Togo

The increase in aid to Togo required to meet the Gleneagles target amounts to 10 percent of GDP over the period under review. The Gleneagles scenario assumes that Togo’s annual aid inflows would have risen to 17 percent of GDP in 2010 and would decline to 14 percent of GDP after 20 years, compared with a benchmark of 7.5 percent of GDP after debt relief through the Heavily Indebted Poor Countries Initiative and the Multilateral Debt Relief Initiative. Under the Gleneagles scenario, ODA per capita would have reached $85 in 2010, increasing overall ODA from $50 million in 2007 to $499 million in 2010 (Table A1.9).

Table A1.9.Togo: ODA Scaling-Up Summary ($US million)
Measure2007200820092010Average,

2008-10
Total,

2007-10
ODA (current projections)49.891.1149.1184.4141.5474
ODA as % of budget (current projections)10.115.122.125.220.8
ODA per capita (current projections)9.116.326.031.424.6
ODA (with Gleneagles)49.8192.7342.4499.2344.81,084
ODA as % of increased budget (with Gleneagles)10.127.339.547.638.1
ODA per capita (with Gleneagles)9.134.459.785.059.7
Additional ODA under Gleneagles scenario0101.6193.3314.8203.2610
Source: Authors’ illustration based on case study.
Source: Authors’ illustration based on case study.

The government of Togo has identified education, health, agriculture, and infrastructure as priority areas for scaling up (Figure A1.17). These priorities cover programs and projects that have been identified in the MDG needs-assessment exercise, but have not been integrated in the Poverty Reduction Strategy Paper. The education sector would receive 30 percent of the additional Gleneagles aid, with the goal of increasing the gross enrollment rate by an additional 10 percentage points and the literacy rate by 13 additional percentage points. A quarter of the Gleneagles resources would be dedicated to infrastructure, with particular attention to water and sanitation. In the health sector, Togo would implement key interventions to strengthen health systems and maternal health, and to address HIV/AIDS and malaria. Some 9 percent of the additional resources would be dedicated to the agriculture sector, with particular emphasis on food security and nutrition interventions.

Togo: Average Expenditure by Sector in the Gleneagles Scenario

Source: Authors’ illustration based on case study.

The model suggests that such an increase in aid would have a significant effect on Togo’s economy, considerably boosting economic growth and GDP per capita (Figure A1.18). The positive impact of higher aid on the accumulation of public and private capital is relatively large, given Togo’s low starting point after more than a decade without donor support and deteriorating capital stock. Government investment would increase from 8 percent of GDP to about 16 percent in two years. As a result, real annual GDP growth is projected to be 1 percent higher on average during the first 10 years. Although inflation and the real exchange rate would rise in the short term, the adverse impact on the tradables sector would be relatively limited. The very positive growth response is explained by the significant impact of higher aid on the accumulation of public and private capital.

Togo: Macroeconomic Impact of Aid under the Gleneagles Commitment

Source: IMF staff estimates.

Note: x-scale measures t-years after the first year (t0) in the period of study.

The achievement of higher GDP growth rates following the aid increase will depend on the economy’s capacity constraints and on the strengthening of the private sector. In addition, the numerical simulations must be interpreted with some caution, reflecting a number of difficult-to-verify assumptions and considerable uncertainty about key macroeconomic relationships in Togo.

Zambia

The projected increase in ODA required to meet the Gleneagles commitment for Zambia is relatively modest. To meet the benchmark of US$85 per capita per year, ODA would have needed to rise by 0.9 percent of GDP in 2010 (Table A1.10), a modest increase compared with that required for many other sub-Saharan African countries. A scaling up of this magnitude would have increased overall ODA from $893 million in 2008 to $1,037 million in 2010.

Table A1.10.Zambia: ODA Scaling-Up Summary ($US million)
200820092010Total 2008-2010
ODA (current projections)8936669122,471
ODA per capita (current projections)765575
ODA (with Gleneagles)8936661,0372,596
ODA per capita (with Gleneagles)765585
Additional ODA under Gleneagles scenario00125125
Source: Authors’ calculations based on case study.
Source: Authors’ calculations based on case study.

The macroeconomic impact on Zambia from the assumed increase in ODA would be positive, but not large (Figure A1.19). An increase in aid of this magnitude would provide an estimated first-year boost to economic growth of 0.3 percentage point, with long-term annual growth rising by 0.15 percentage point. Although inflation would initially rise moderately and the real exchange rate would appreciate, the adverse effect on the tradable-goods sector is projected to be limited.

Zambia: Macroeconomic Impact of Aid under the Gleneagles Commitment

Source: IMF staff estimates.

Note: x-scale measures t-years after the first year (t0) in the period of study.

Model simulations indicate that the impact of higher levels of aid on the GDP path is sensitive to both the efficiency and the composition of the additional government spending. In addition, initial price and exchange rate movements are sensitive to the specific monetary and exchange rate policy responses. The model suggests that spending without selling commensurate foreign exchange would limit growth through crowding out of the private sector; maintaining a fixed exchange rate would raise inflation and output volatility; and increasing spending too rapidly could reduce spending efficiency and, thereby, the positive impact on the marginal productivity of private capital from increased government spending.

References

    AdamC. and D.Bevan2006“Aid and the Supply Side: Public Investment, Export Performance, and Dutch Disease in Low-Income Countries,”World Bank Economic ReviewVol. 20No. 2 pp. 26190.

    • Search Google Scholar
    • Export Citation

    AdjoviN.A. and M.Mensah2007“Poverty and Social Impact Analysis (PSIA) Capacity Development in Benin” (New York: United Nations International Poverty Center).

    • Search Google Scholar
    • Export Citation

    African Union African Development Bank (AfDB) and World Bank2010“Africa’s Infrastructure: An Agenda for Transformative Action,”background paper for the UN MDG Summit Side EventSeptember212010.

    • Search Google Scholar
    • Export Citation

    ArndtC.S.Jones and F.Tarp2009“Aid and Growth: Have We Come Full Circle?”Department of Economics Discussion Paper No. 2009/22 (Copenhagen: University of Copenhagen).

    • Search Google Scholar
    • Export Citation

    ArslanalpS.F.BornhorstS.Gupta and E.Sze2010“Public Capital and Growth,”IMF Working Papers 10/175 (Washington: International Monetary Fund).

    • Search Google Scholar
    • Export Citation

    BergA.S.AiyarM.HussainS.RoacheT.Mirzoev and A.Mahone2007“The Macroeconomics of Scaling Up Aid: Lessons from Recent Experience” IMF Occasional Paper No. 253 (Washington: International Monetary Fund).

    • Search Google Scholar
    • Export Citation

    BergA.J.GottschalkR.Portillo and L.F.Zanna2010“The Macroeconomics of Medium-Term Aid Scaling-Up Scenarios,”IMF Working Paper 10/160 (Washington: International Monetary Fund).

    • Search Google Scholar
    • Export Citation

    BulirA. and A.J.Hamann2008“Volatility of Development Aid: From the Frying Pan into the Fire,”World DevelopmentVol. 36No. 10 pp. 204866.

    • Search Google Scholar
    • Export Citation

    BourguignonF. and M.Sundberg2006“Constraints to Achieving the MDGs with Scaled-Up Aid,”United Nations Department of Economic and Social Affairs Working Paper 15 (New York: United Nations).

    • Search Google Scholar
    • Export Citation

    CelasunO. and J.Walliser2008“Predictability of Aid,”Economic PolicyVol. 23No. 55 pp. 54594.

    ChristianoL.M.Eichenbaum and C.Evans2005“Nominal Rigidities and the Dynamic Effects of a Shock to Monetary Policy,”Journal of Political EconomyVol. 113No. 1 pp. 145.

    • Search Google Scholar
    • Export Citation

    ChristiansenL.A.PratiL.Ricci and T.Tressel2009“External Balance in Low Income Countries,”IMF Working Paper 09/221 (Washington: International Monetary Fund).

    • Search Google Scholar
    • Export Citation

    ClemensM.S.Radelet and R.Bhavnani2004“Counting Chickens When They Hatch: The Short-Term Effect of Aid on Growth,”Center for Global Development Working Paper 44 (Washington: Center for Global Development).

    • Search Google Scholar
    • Export Citation

    ConceiçãoP.S.Mukherjee and ShivaniNayyar2011“Impact of the Economic Crisis on Human Development and the MDGs in Africa,”African Development ReviewVol. 23No. 5 pp. 43960.

    • Search Google Scholar
    • Export Citation

    Dabla-NorrisE.R.AllenL.ZannaT.PrakashE.KvintradzeV.LledoI.Yackovlev and S.Gollwitzer2010“Budget Institutions and Fiscal Performance in Low-Income Countries,”IMF Working Paper 10/80 (Washington: International Monetary Fund).

    • Search Google Scholar
    • Export Citation

    Dabla-NorrisE.C.Miniou and L.Zanna2010“Business Cycle Fluctuations, Large Shocks, and Development Aid: New Evidence,”IMF Working Paper 10/240 (Washington: International Monetary Fund).

    • Search Google Scholar
    • Export Citation

    EasterlyW.2003“Can Foreign Aid Buy Growth?”Journal of Economic PerspectivesVol. 17No. 3 pp. 2348.

    EasterlyW. and S.Rebelo1993“Fiscal Policy and Economic Growth,”Journal of Monetary EconomicsVol. 32No. 3 pp. 41758.

    Economic Commission for Africa African Union Commission African Development Bank and United Nations Development Programme2010Assessing Progress in Africa toward the Millennium Development Goals: MDG Report 2010. Available at http://www.undp.org/africa/documents/mdg/full-report.pdf.

    • Search Google Scholar
    • Export Citation

    FosterV. and C.M.Briceno-Garmendiaeds.2010Africa’s Infrastructure: A Time for Transformation (Paris and Washington: Agence Française de Developpement and World Bank).

    • Search Google Scholar
    • Export Citation

    Group of Eight (G-8). 2005a. “The Gleneagles Communique.”Available at http://www.unglobalcompact.org/docs/about_the_gc/government_support/PostG8_Gleneagles_Communique.pdf.

    • Search Google Scholar
    • Export Citation

    Group of Eight (G-8). 2005b. “G8 Gleneagles Africa Statement.”July8. Available at http://www.global-campaign.org/clientfiles/G8Statement-Africa.doc.

    • Search Google Scholar
    • Export Citation

    HansenH. and F.Tarp2000“Aid Effectiveness Disputed,”Journal of International DevelopmentVol. 12No. 3 pp. 37598.

    International Development Association (IDA)2007Aid Architecture: An Overview of the Main Trends on Official Development Assistance Flows IDA Resource Mobilization (Washington) http://siteresources.worldbank.org/IDA/Resources/Seminar%20PDFs/73449-1172525976405/3492866-1172527584498/Aidarchitecture.pdf.

    • Search Google Scholar
    • Export Citation

    International Monetary Fund (IMF)2007“The Creation of Fiscal Space for Priority Spending: Case Studies in Sub-Saharan Africa” in Regional Economic Outlook: Sub-Saharan Africa World Economic and Financial Surveys (Washington: International Monetary FundOctober).

    • Search Google Scholar
    • Export Citation

    International Monetary Fund (IMF)2008“The Great Sub-Saharan Africa Growth Takeoff: Lessons and Prospects” in Regional Economic Outlook: Sub-Saharan Africa World Economic and Financial Surveys (Washington: International Monetary FundOctober).

    • Search Google Scholar
    • Export Citation

    International Monetary Fund (IMF)2010aEmerging from the Global Crisis: Macroeconomic Challenges Facing Low-Income Countries IMF Policy Paper (Washington: International Monetary Fund).

    • Search Google Scholar
    • Export Citation

    International Monetary Fund (IMF)2010bRegional Economic Outlook: Sub-Saharan Africa—’Resilience and Risks World Economic and Financial Surveys (Washington: International Monetary FundOctober).

    • Search Google Scholar
    • Export Citation

    International Monetary Fund (IMF)2010c“Staff Guidance Note on the Application of the Joint Bank Fund Debt Sustainability Framework for Low-Income Countries” IMF Policy Paper (Washington: International Monetary Fund).

    • Search Google Scholar
    • Export Citation

    International Monetary Fund (IMF)2011World Economic Outlook databaseApril. Available at http://www.imf.org/external/pubs/ft/weo/2011/01/weodata/index.aspx.

    • Search Google Scholar
    • Export Citation

    International Monetary Fund (IMF) and World Bank2010Global Monitoring Report 2010: The MDGs after the Crisis (Washington: International Monetary Fund and World Bank).

    • Search Google Scholar
    • Export Citation

    Ministry of Finance Planning and Economic Development of the Government of Uganda2010Millennium Development Goals Report for Uganda 2010: Special Theme: Accelerating Progress towards Improving Maternal Health September 2010 (Kampala, Uganda).

    • Search Google Scholar
    • Export Citation

    MischF. and P.Wolff2006“Pro-Poor Budgeting for PRSP Implementation,”paper presented during the Workshop on Public Expenditure and Service Delivery in Africa: Managing Public Expenditure to Improve Service Quality and AccessLusaka, ZambiaOctober9-11.

    • Search Google Scholar
    • Export Citation

    MishraP. and D.L.Newhouse2009“Health Aid and Infant Mortality,”Journal of Health EconomicsVol. 28No. 4 pp. 85572.

    National Development Planning Commission (of the Government of Ghana) and United Nations Development Programme20102008 Ghana Millennium Development Goals Report April 2010 (Accra, Ghana).

    • Search Google Scholar
    • Export Citation

    Organization for Economic Cooperation and Development (OECD)2009“Aid Effectiveness: A Report on Implementing the Paris Declaration,”Better Aid Series (Paris: Organization for Economic Cooperation and Development).

    • Search Google Scholar
    • Export Citation

    Organization for Economic Cooperation and Development (OECD)2010“Development Aid Rose in 2009 and Most Donors Will Meet 2010 Aid Targets” (Paris: Development Co-operation Directorate). Available at http://www.oecd.org/document/11/0,3343,en_2649_34447_44981579_1_1_1_37413,00.html.

    • Search Google Scholar
    • Export Citation

    Organization for Economic Cooperation and Development (OECD)2011“Development Aid Reaches an Historic High in 2010” (Paris: Development Co-operation Directorate). Available at http://www.oecd.org/document/35/0,3746,en_2649_34447_47515235_1_1_1_1,00.html.

    • Search Google Scholar
    • Export Citation

    PritchettL.2000“The Tyranny of Concepts: CUDIE (Cumulated, Depreciated, Investment Effort) Is Not Capital,”Journal of Economic GrowthVol. 5No. 4 pp. 36184.

    • Search Google Scholar
    • Export Citation

    RadeletSteven2010Emerging Africa: How 17 Countries Are Leading the Way (Washington: Center for Global Development).

    RajanR.G. and A.Subramanian2008“Aid and Growth: What Does the Cross-Country Evidence Really Show?”Review of Economics and StatisticsVol. 90No. 4 pp. 64365.

    • Search Google Scholar
    • Export Citation

    RajanR.G. and A.Subramanian2011“Aid, Dutch Disease, and Manufacturing Growth,”Journal of Development EconomicsVol. 94No. 1 pp. 10618.

    • Search Google Scholar
    • Export Citation

    RompW. and J.de Haan2007“Public Capital and Economic Growth: A Critical Survey,”Perspektiven der WirtschaftspolitikVol. 8No. s1 pp. 652.

    • Search Google Scholar
    • Export Citation

    RoodmanD.2007“The Anarchy of Numbers: Aid, Development, and Cross-Country Empirics,”World Bank Economic ReviewVol. 21No. 2 pp. 25577.

    • Search Google Scholar
    • Export Citation

    SmetsF. and R.Wouters2003“An Estimated Dynamic Stochastic General Equilibrium Model of the Euro Area,”Journal of the European Economic AssociationVol. 1No. 5 pp. 112375.

    • Search Google Scholar
    • Export Citation

    StraubS.2008a“Infrastructure and Development: A Critical Appraisal of the Macro Level Literature,”World Bank Policy Research Working Paper No. 4590 (Washington: World Bank).

    • Search Google Scholar
    • Export Citation

    StraubS.2008b“Infrastructure and Growth in Developing Countries: Recent Advances and Research Challenges,”World Bank Policy Research Working Paper No. 4460 (Washington: World Bank).

    • Search Google Scholar
    • Export Citation

    TorvikR.2001“Learning by Doing and the Dutch Disease,”European Economic ReviewVol. 45No. 2 pp. 285306.

    TovarC.2009“DSGE Models and Central Banks,”Economics the Open-Access Open-Assessment E-JournalVol. 3No. 16 pp. 131.

    United Nations (UN)2010Millennium Development Goals Report 2010 (New York: UN).

    United Nations Conference on Trade and Development (UNCTAD)2006Economic Development in Africa—Doubling Aid: Making the ‘Big Push’ Work (Geneva: UNCTAD).

    • Search Google Scholar
    • Export Citation

    United Nations Development Programme (UNDP)2010aThe Real Wealth of Nations: Pathways to Human Development Human Development Report 2010—20th Anniversary Edition (New York: UNDP).

    • Search Google Scholar
    • Export Citation

    United Nations Development Programme (UNDP)2010bWhat Will It Take to Achieve the Millennium Development Goals? An International Assessment (New York: UNDP).

    • Search Google Scholar
    • Export Citation

    United Nations Development Programme (UNDP)2010cUnlocking Progress: MDG Acceleration on the Road to 2015 (New York: UNDP).

    United Nations Millennium Project2005Investing in Development: A Practical Plan to Achieve the Millennium Development Goals (New York: United Nations).

    • Search Google Scholar
    • Export Citation

    Van BiesebroeckJ.2005“Exporting Raises Productivity in Sub-Saharan African Manufacturing Firms,”Journal of International EconomicsVol. 67No. 2 pp. 37391.

    • Search Google Scholar
    • Export Citation

    WarnerA.M.2010“Cost-Benefit Analysis in World Bank Projects,”Independent Evaluation Group (Washington: World Bank).

1The MDG Africa Steering Group is composed of the heads of the African Union, the African Development Bank Group, the European Commission, the Islamic Development Bank, the International Monetary Fund (IMF), the Organization for Economic Cooperation and Development (OECD), the United Nations (UN) Development Group, and the World Bank Group. The steering group is assisted by a working group chaired by the UN Deputy Secretary-General and composed of technical level representatives.
2The 10 pilot countries were Benin, the Central African Republic, Ghana, Liberia, Niger, Rwanda, Tanzania, Togo, Sierra Leone, and Zambia.
3Many people have contributed to this paper. The underlying country cases (see the Appendix) were the work of teams of IMF and UNDP staff. The main contributors to the paper were Matthew Gaertner, Rafael Portillo, Laure Redifer, and Louis-Felipe Zanna (all IMF); and Brett House and Ayodele Odusola (UNDP). Benedicte Christensen, Daniela Gregr, Kayla Keenan, and Ali Zafar made critical contributions in the early stages of this project. Many other colleagues at the IMF and the UNDP provided helpful comments.
4For sources and for more information, see UN (2010); UNDP (2010a, 2010b, 2010c); IMF (2008); and Radelet (2010). On the effects of the crisis, see, for example, ECA, AU, AfDB, and UNDP (2010); Conceição, Mukherjee, and Nayyar (2011); IMF (2010a, 2010b); and IMF and World Bank (2010).
5See Ghana’s 2010 MDG Report (National Development Planning Commission and UNDP, 2010) and Uganda’s 2010 MDG Report (Ministry of Finance, Planning and Economic Development, 2010) for more information on the level and quality of disparities.
6The countries were selected based on three key factors: (i) government support and interest to ensure ownership; (ii) countries’ development indicators (countries with GDP less than the LIC threshold, off-track on at least half of the MDGs, and in which a new Poverty Reduction Strategy Paper was being developed or being implemented since 2008); and (iii) a mix of countries to ensure broad representation across regional groupings, land-locked or coastal status, and postcrisis transition.
7See Gleneagles communiqué and commitments (G-8, 2005a). The studies vary in focus and structure, partly reflecting different priorities identified by the countries.
8Aid is already about that level for Liberia, so the scenario assumes a 15 percent increase.
9The computation of aid per capita excludes the populations of South Africa and Nigeria. The principal authors of this chapter are Pedro Conceição and Ayodele Odusola, Regional Bureau for Africa, UNDP.
10The Organization for Economic Cooperation and Development (OECD) defines ODA as those flows to developing countries and multilateral institutions provided by official agencies, including state and local governments, or by their executive agencies, each transaction of which meets the following tests: (i) it is administered with the promotion of the economic development and welfare of developing countries as its main objective; and (ii) it is concessional in character and conveys a grant element of at least 25 percent.
11“Net ODA” nets out repayments of the principal of ODA loans as well as debt relief and clearance of arrears. Thus, it reflects actual flows to developing countries. There are a number of more detailed questions about the exact operational definition of ODA, which may vary slightly across country case studies.
12From the Gleneagles communiqué: “The commitments of the G8 and other donors will lead to an increase in official development assistance to Africa of $25 billion a year by 2010, more than doubling aid to Africa compared to 2004” (G-8, 2005b, p. 8, para. 27).
13On Africa’s infrastructure needs, see Foster and Briceno-Garmendia (2010). The principal authors of this chapter are Pedro Conceição, Brett House, Ayodele Odusola, Daniela Gregr, and Ali Zafar (UNDP).
15Warner (2010) reviews these issues in the context of World Bank project assessments.
16In particular, improvements in the different stages of the budget process seem to be associated with good fiscal performance; see Dabla-Norris and others (2010).
17UNDP launched support missions to each country through the spring and summer of 2008, through which it worked collaboratively with governments and local representatives of the AfDB, the IMF, and the World Bank.
18The role of the PRSP is reflected in the institutional setup in each of the case study countries. In particular, there is a central coordinating secretariat or unit in the Ministry of Finance or Ministry of Planning. This helps to ensure a strong alignment with MTEFs and annual budgets.
19See IMF (2007) for a discussion of the different ways countries in Africa can finance public investment programs and some of the considerations involved. IMF (2010c) discusses the opportunities and challenges of debt-led scaling up and some of the ways IMF-supported programs analyze and take this into account.
20Liberia is an exception. Aid was already higher than $85 per capita; the exercise thus analyzed a 15 percent increase.
21This is further reinforced by Misch and Wolff (2006).
22For an analysis of these issues in the context of aid scaling up, see for, example Bourguignon and Sundberg (2006). The principal authors of this chapter are Laure Redifer and Luis-Felipe Zanna (IMF).
23Surveys of this literature include, among others, Hansen and Tarp (2000); Easterly (2003); and Roodman (2007).
24The type of investment also matters for the timing of the return: public investment in transportation, energy, or communications infrastructure would be expected to produce growth returns sooner than public investment in education and health. Judgments were made on a case-by-case basis about what type of investment was included, but in general, cases focused on investment along the lines of that financed by what Clement, Radelet, and Bhavnani (2004) termed “short-impact aid,” typically including investment for infrastructure and in productive sectors such as agriculture and industry.
25Sterilization corresponds to adjusting the domestic money supply to offset additional government spending in local currency terms, either through selling foreign exchange, issuing central government bonds, or both.
26Although the standard used was to raise recipient countries’ ODA levels to $85 per capita (except for Liberia, as noted in the Appendix), for the purposes of calibrating the model and interpreting its results, this amount was converted into GDP terms for each country.
27To take just one example, the cases assume no changes in domestic tax revenues associated with the aid scaling up. As emphasized above, enhanced domestic revenue efforts are critical to raising and sustaining public service provision.
*The IMF papers were written over the course of 2008—10, and the consolidation was undertaken over the course of 2011. As of the time of consolidation, some of the papers’ “projections” are now in the past; however, the thrust of the analysis is still valid and the results relevant. For the IMF, the authors of the country case studies referred to in this paper were Joannes Mongardini and Issouf Samake (Benin); Martin Pietri, Noriaki Kinoshita, Takahiro Hitakatsu, and Mario Carlos Zejan (Central African Republic); Jihad Dagher and Peter Allum (Ghana); Seok Hyun Yoon and Chris Lane (Liberia); Emilio Sacerdoti and Gonzalo Salinas (Niger); Zuzana Murgasova and Stella Kaendera (Rwanda); Norbert Toé and Kadima Kalonji (Sierra Leone); Laure Redifer and Matthew Gaertner (Tanzania); Christian Mumssen and Samuele Rosa (Togo); and Nils Maehle and Jean Noah Ndela Ntsama (Zambia). The country teams at the IMF were assisted by Jan Gottschalk and Rafael Portillo. The summaries of the IMF country cases were prepared by Matthew Gaertner and Laure Redifer. For UNDP, the contributors to the country case studies were Anatole Ayisi, Jean Bogounou, Souleman Boukar, Boubou Camara, Siaka Coulibaly, Idrissa Diagne, Brett House, Usman Iftikhar, Alofa Janvier, Kamil Kamaludden, Lamin Manneh, Theodore Mpatsewenumgabo, Shantanu Mukherjee, Gonzalo Pizaro, Kodzo Sedegah, Yousouffa Sila, and Abdoulie Sireh-Jallow.

Other Resources Citing This Publication