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Aid Flows in Times of Crises Andrew Mold, Annalisa Prizzon, Emmanuel 

Frot, Javier Santiso

CONFERENCE ON DEVELOPMENT COOPERATION

IN TIMES OF CRISIS AND ON ACHIEVING THE MDGs

IFEMA Convention Centre (Madrid)

9-10 June 2010

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Contents

0. INTRODUCTION.................................................................................................................. 3

0.1. Putting ODA in Context – Foreign Financing and the post-crisis Economic Situation in Developing Countries ............................................................................................................. 3 0.2. Challenges and opportunities for bilateral and multilateral aid budgets.......................... 6 0.3.-Road Map ........................................................................................................................ 8

1. TRENDS IN DEVELOPMENT FINANCE ........................................................................ 11

1.1 Net flows of resources to developing countries .............................................................. 11 1.2 Private International Financial Flows ............................................................................. 14 1.3 Foreign direct investment ............................................................................................... 15 1.4 Workers’ Remittances..................................................................................................... 16 1.5 Financing Requirements For Developing Countries And Prospects For Official Development Assistance....................................................................................................... 17

2. OFFICIAL DEVELOPMENT ASSISTANCE .................................................................... 21

2.1 Aid flows in previous times of economic crisis.............................................................. 21 Aid Effort: Evidence from previous studies ...................................................................... 25 Econometric analysis ......................................................................................................... 28 Conclusions ....................................................................................................................... 30

2.2 Fragmentation of Official Development Assistance....................................................... 31 Fragmentation by donor..................................................................................................... 32 Fragmentation by sector .................................................................................................... 39

2.3 Volatility ......................................................................................................................... 43 2.1 A global fragmentation index ......................................................................................... 46

How to summarise recipient fragmentation: a graphical tool............................................ 49 A world map of fragmentation .......................................................................................... 51

2.2 Budget support vs. project aid?....................................................................................... 53 Types of Conditionality ..................................................................................................... 53 Conditionality and Country Ownership: How Far Does Budget Support Resolve the Dilemma?........................................................................................................................... 54

CONCLUSIONS...................................................................................................................... 59

REFERENCES......................................................................................................................... 61

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0. Introduction  

0.1.  PUTTING  ODA  IN  CONTEXT  –  FOREIGN  FINANCING  AND  THE  POST‐CRISIS ECONOMIC SITUATION IN DEVELOPING COUNTRIES 

The economic crisis which struck initially in the summer of 2007, but which took a much more serious turn in September 2008 with the collapse of Lehman Brothers, has been the most serious challenge to global economic prosperity since the 1930s. The OECD countries themselves have suffered seriously, with major falls in output, investment, trade and employment. GDP for the OECD as a whole was estimated to have contracted by -3.5 percent in 2009. But in the developing world, the story has been somewhat more chequered. Initial predictions that developing countries would suffer disproportionately seem to have been unfounded. For the majority of developing countries, their response to the crisis could be characterised as resilience under difficult external circumstances, with a marked decline in average GDP growth but still posting positive figures (on average for all developing countries 1.2 percent in 2009 vis-à-vis 5.6 percent in 2008) (Figure 1).

Figure 1: Real GDP Growth in 2009

 Source: OECD Development Centre, based on OECD, WDI and IMF data 

That does not mean however that many developing countries have not seen their economies disrupted by the turbulence stemming from the financial markets in the industrialised countries. Major sources of income dried up for many developing countries. Most developing countries suffered sharp year-on-year falls in their exports in the last quarter of 2008 and first quarter of 2009, especially those with either strong trading links with the most affected OECD countries (e.g. Mexico, -28.7 percent or Morocco -37 percent in the first quarter 2009) or those dependent on commodities whose prices declined sharply in world markets during that period (e.g. Bolivia -56.2, Chile -40.6 and Nigeria -50.8).1 In the second half of 2009, trade

1 WTO data http://www.wto.org/english/res_e/statis_e/quarterly_world_exp_e.htm

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flows began to recover, especially for developing countries which benefited both from the rapid rebound in commodity prices and the strong demand for commodities. But for the major emerging economies trade flows had still not recovered pre-crisis levels by the beginning of 2010 (even in the case of China, which by January 2010 was still -10 percent down). On a regional level, too, trade flows were still 20-30 percent down on the second quarter of 2008 in the third quarter of 2009.

Figure 2 Monthly trade flows of the BASIC countries and US, 2008m8-2010m1 (2008m8=100)

Figure 3 Quarterly regional trade flows 2008Q2-2009Q3 (2008Q2=100

 Source: OECD DEV, on the basis of WTO figures. Monthly trade data based on a three‐monthly moving average 

Other important sources of income for developing countries through tourism and remittances have all been affected by the crisis, although again not to the extent that many observers suggested at its outbreak. Worker’s remittances in particular proved much more resilient to downturns than expected. Despite the fact that total world remittances are expected to fall to 317 billion dollars in 2009, after reaching 338 billion in 2008, the year-over-year drop is

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relatively less than what was feared earlier in 2008, and less than other financial flows. Moreover, most believe that the bottom has been reached. The global drop also hides the important heterogeneity in the different migration corridors of the world, with the countries and regions dependent on remittances dependent on OECD countries hit harder. Latin America, for instance, was hit relatively harder, while many Southeast Asian countries even enjoyed growth in remittances. Many Southeast Asian countries receive remittances from other Southern countries, particularly from the Middle East. In fact, both Pakistan (21.53%) and Bangladesh (20.89%) saw increases in remittances in the first half of the current fiscal year (July –January) in comparison to the same period in the prior year. In comparison, El Salvadorians remitted 8.5% less in 2009 than in 2008 and in Mexico a 14.4% drop was recorded in November 2009 from November 2008. These drops are serious in countries where remittances typically make up more than 15% of GDP.

Table 1: Weathering the Storm? Remittance Flows, 2009 Country Remittance

Inflows (USD, mln)

Year-on-year Change (%)

Year-to-date Change (%)

Bangladesh 1051 38 19.8 Pakistan 698 3.7 23.9 Philippines 1459 11.3 5.1 El Salvador 337 -0.3 -8.5 Honduras 212 -8.0 -10.8 Mexico 1495 -14.4 -16.3

  Source: Ratha et al. (2009)  Regarding tourism (an increasingly important source of income for many developing countries and regions),2 international tourist arrivals for business, leisure and other purposes are estimated to have declined worldwide by 4% in 2009 to 880 million, but with the last quarter of 2009 beginning to show an upswing. Africa was the only continent to buck these trends, with a +5% increase in the numbers of tourists, with sub-Saharan destinations doing particularly well.3 Despite concerns of a sharp fall in Official Development Assistance (ODA) at the outbreak of the crisis, in 2008 aid flows actually reached a record high of $119 billion. However, it is expected that aid figures fell in 2009, knocking donors further off track in their commitments promised five years ago at the Gleneagles and Millennium + 5 summits. We will discuss these trends in much more depth later in this report. Through its impact on growth and employment, the crisis will certainly impact negative on poverty – estimates produced by the World Bank suggest that the number of poor, measured by the USD 1.25/day baseline, will be 50 million higher in 2009 and 64 million in 2010 compared to what would have been the case without the damaging effects of the crisis, impacting negatively on the achievement of the first MDG (‘reduce global poverty by half by

2 See Honeck (2008) 3 http://www.unwto.org/media/news/en/press_det.php?id=5361&idioma=E

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2015). For African countries, the crisis has been particularly inopportune, as they struggle to reach the Millennium Development Goals. Asia, it is true, still has a much higher number of absolute poor people – more than double the number that live in Africa. But the economic context in Asia is very different, and on balance much more positive. It is often argued that, for Sub-Saharan African countries to keep on track to achieve the MDGs, they need to obtain a growth rate of 7 percent.4 Before the crisis, they were not far short of that target, with an annual growth rate of 5.6 percent of 2008. But the crisis brought down average African GDP growth rates to a paltry 1.9 percent in 2009 – insufficient, in per capita terms, to maintain incomes at the levels of 2008.

0.2.  CHALLENGES  AND  OPPORTUNITIES  FOR  BILATERAL  AND MULTILATERAL AID BUDGETS 

Despite the enormous increase in private flows to the emerging markets in recent years, the poorest developing countries are still heavily dependent on aid flows (Table 2). Africa is most at risk on this score where aid averages around 9 per cent of GDP (compared for instance with South Asia which has reduced its dependency on aid flows to only 1 per cent of GDP). There are, however, wide variations across countries even within Africa, with some like South Africa receiving only a small amount of aid as a share of GDP, while others are still highly aid dependent (e.g. Mali (13 per cent), Malawi (20 per cent), Sierra Leone and Burundi (over 30 per cent)) (Glennie, 2008:22).

Table 2 The relative importance of aid for SSA

Average Percentage of Net Capital Flows (2000-06)

Developing Countries

Sub-Saharan Africa

Private flows 84.9 38.4 Overseas development aid 19.5 65.4 Other official flows -4.4 -3.9

Source: McCulloch (2008)  Since the Gleneagles G8 Summit in July 2005, the major debate within donor circles has been about the scaling up of aid, especially for Africa. In reality, however, progress has not lived up to expectations. In real terms, aid in 2007 was only 15 per cent higher than in the 2004 Gleneagles base year, compared to the 60 per cent required by 2010 to meet the Gleneagles commitments (OECD, 2008). Roodman (2008) has argued that in the aftermath of the financial crisis even existing aid budgets are at risk. He points to some particular examples (Finland, Japan, Norway, and Sweden) of sharp falls in aid during previous financial crises. For instance, Japan's aid (measured as net disbursed ODA in USD at 2006 constant prices) fell 12 per cent between 1990 and 1996. In Finland, according to Roodman’s figures, the fall was even more dramatic; during its banking crisis between 1991 and 1993 when GDP

4 See, for instance, the Millennium Development Goals Monitor website http://www.mdgmonitor.org/

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dropped by nearly 11 per cent, development aid fell by 60 per cent (OECD-DAC figures showing a smaller, but still major drop of nearly 30 per cent). On the basis of national data, historical evidence does indeed seem to show that during sharp economy-wide contractions, aid budgets are vulnerable. In the light of these examples, are all hopes of scaling-up now dashed? And are Roodman’s case studies generalisable? What do we know about the impact of economic cycles in the donor countries themselves on the scale of aid disbursements? Despite the fall of aid during the Finnish financial crisis in the early 1990s, there is no clear pattern between GDP growth and aid. In the case of Japan, the negative growth of -2 per cent in 1998 was actually accompanied by a 40 per cent increase in aid flows. For the United States, the trends between economic growth and the size of the aid budget seem to be similarly ambiguous — while aid dropped in 1990 during the recession of 1990-91, in the 2000-1 recession aid was accompanied by a sharp increase. The simple correlation between aid flows and GDP growth in the US case is just 0.06 over the period 1960-2007. Decisions on allocations to the aid budget do not appear to be strongly affected by the business cycle, as we shall discuss and analyse later. One important dimension to this question is the fiscal balance — one would expect, ceteris paribus, that governments with large deficits would be more prone to cut aid. And by massive intervention to prop up the banking and credit system, OECD governments are currently taking on huge financial commitments, already amounting to several trillion US dollars. For example, the Emergency and Economic Stabilization Plan in the United States alone is currently worth USD 700 billion. As Robert Zoellick, President of the World Bank, has recently affirmed, “at USD 100 billion a year, the amount spent on overseas aid is a drop in the ocean compared to the trillions of dollars that are now being spent on financial rescues in the developed world.” Clearly governments are going to have to take some tough fiscal choices in the coming years. Against this backdrop, the a priori reasons for expecting significant aid cuts during a recession are strong ones — after all, the recipients of ODA are not members of the domestic political constituency — and maintaining the aid budget during a crisis is not necessarily a vote-winner. Under pressure to reduce expenditures, it would therefore seem logical to expect aid flows to be one of the first areas to be affected by cuts.  Once more, however, the empirical evidence on this point is mixed. According to a study by Faini (2006), countries with a healthy fiscal situation tend to be more generous donors. Bertoli et al. (2008), on the other hand, come to exactly the opposite conclusion — that donors with larger fiscal deficits deliver larger aid flows. The findings of both studies can be plausibly interpreted — Faini´s study implies that countries with a better fiscal stance are likely to be more generous in their allocations to development aid, while the Bertoli et al. (2008) study could reflect the fact that fiscally more conservative governments might be less likely to give to development aid! Again, the analysis of individual donors seems to support the viewpoint that there is no systematic relationship between fiscal position and aid allocations. In the case of the United States, for instance, there is no statistical relationship between net bilateral ODA and either tax receipts, deficits or total government expenditures (Kharas, 2008).  To sum up, then, the existing evidence lends support to a rather agnostic interpretation of trends for aid budgets during the course of the current crisis. Severe depressions and financial crises have sometimes been accompanied by aid cutbacks in the past, but at the present time, without knowing how deep the recession will be, it is difficult to be sure. Nevertheless, it is

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not encouraging that countries such as France, Ireland and Italy have already announced cutbacks in their bilateral aid programmes. We are, at present, in uncharted waters. Official 2009 ODA data by donor are to be released in April 2010. On the other hand, DCD/DAC presented in mid February 2010 based on donor countries’ budget plans for 2010. The picture is mixed. Aid to developing countries in 2010 will reach record levels in dollar terms after increasing by 35 per cent since 2004. But it will still be less than the world’s major aid donors promised five years ago at the Gleneagles and Millennium + 5 summits. Though a majority of countries will meet their commitments, the underperformance of several large donors means there will be a significant shortfall, according to a new OECD review. Nevertheless aid has increased strongly as 16 donors have honoured their commitments. But underperformance by the others, notably Austria, France, Germany, Greece, Italy, Japan, and Portugal, means overall aid will still fall considerably short of what was promised. Aid flows – which achieved their highest level ever of 119 USD billion in 2008 - are estimated to plunge to 107 USD billion in 2010. These commitments were made and confirmed repeatedly by heads of governments and it is essential that they be met to the full extent. Figure 4: 2010 ODA projection based on 2005 commitment and DCD/DAC 2010 ODA

estimation based on donors’ budget (million of USD)

 Source: OECD (2010) 

0.3.‐ROAD MAP 

This report begins by providing an overview of the current stylised facts for development finance, with a particular focus on ODA. Geographically, we pay special attention to the challenges of development finance for Africa, the continent with the greatest concentration of poor low income countries confronting major developmental challenges. We then proceed to analyse questions regarding the improvement of aid efficiency. Despite the difficult context of the economic crisis and its possibly profound implications for aid budgets, we argue that there are potentially many promising policy responses in order to enhance aid efficiency – indeed, it is argued that a hard-budget constraint may paradoxically help focus the mind of policy- and

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decision-makers in a positive way, leading to greater efficiency in the allocation and implementation of aid, as outlined in the Paris Declaration and the Accra Agenda for Action.

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1. Trends in development finance

1.1 NET FLOWS OF RESOURCES TO DEVELOPING COUNTRIES 

Before elaborating on the relation between aid flows and economic downturns, it is worth considering recent trends of different flows of development finance to developing countries, in a comparative perspective. First, official grants excluding technical cooperation have soared in this decade, from 29.1 billion in 2001 to 86.2 billion in 2008, surging by percent, or by an average annual 196 increase of 14 per cent. Second, net FDI flows to developing countries jumped from 164.6 USD billion in 2001 to 593.6 USD billion in 2008, with a leap of 260 per cent in 8 years. Third, workers’ remittances progressively increased from 93.8 USD in 2001 up to 326.7 USD million in 2008.

Figure 1.1: Net flows to developing countries (current USD billion)

Source: Authors’elaboration based on Global Development Finance 2010. 

Nevettheless the relative importance of each source of development finance sharply differ from region to region, as depicted in Figure 1.2, with Africa being the region most dependent on ODA, and least on private capital flows.

 

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Figure 1.2: Average yearly net flows, by region, Mln USD

Source: Authors based World Bank World Development Indicators and Global Development Finance 

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All regions have benefited from the increasing flows of external finance, but with large discrepancies. Sub-Saharan Africa and Other Asia and Oceania were very similar in the sixties and the seventies. They diverged dramatically after this period to the point of having very little in common nowadays. The graph for ODA shows how this flow actually stayed at a rather stable level in all the regions since the seventies, except in Sub-Saharan Africa. A large share of the increase in ODA during the last 40 years has been absorbed by this region. Cogneau and Lambert (2006) have shown that ODA actually acts as a compensatory transfer for countries who do not have access to other flows. Moreover, ODA is progressive while FDI and remittances are more regressive transfers, in terms of their distributional consequences. Regardless of these concerns, official flows are expected to be less volatile and so to provide a safe and rather constant source of income, while private capital flows may be subject to sudden changes (on volatility of private capital flows see in particular Nunnenkamp, 2001). For instance FDI to Latin America fell by more than 50 per cent between 1999 and 2003, and bonds to Other Asia and Oceania fell by more than 90 per cent between 1997 and 1998 after having increased by more than 200 per cent between 1991 and 1997. Portfolio equity and bond flows tend to be also pretty volatile as documented by several studies focusing on emerging markets, in particular the ones realised by Wang (2007), Bekaert and Harvey (2000; 2003), Bekaert et al. (2002), and Froot and Donohue (2002). While ODA reached a record high level of USD 44 billion in 2008 for the whole of Africa according to preliminary figures – of which USD 40 billion was destined for sub-Saharan Africa -- slower growth together with a tightening fiscal situation in OECD countries will add to the challenge of increasing development assistance or indeed, even maintaining existing commitments. One positive development has been the substantial progress made since 2002 in the implementation of the HIPC Initiative; 21 out of 29 HIPC countries in Africa have now reached the completion point and qualified for irrevocable debt relief. Another eight countries are benefiting from interim assistance while four others are being considered as potential candidates for debt relief under the initiative. Debt stocks (on a net present value basis) in the 31 eligible HIPCs are projected to decline by about 90% after the application of the MRDI and their debt service ratios are estimated to have declined from around 17% in 1998-99 to 4% in 2006 (APF, 2009). The innovative financing mechanisms launched in 2006 are also starting to generate additional resources for development. Particularly important is the USD 1.5 billion committed by a group of bilateral donors and the Bill and Melinda Gates Foundation under the Advanced Market Commitments (AMCs) to support the development of vaccines. Another USD 1.6 billion have been raised through the issuance of bonds in the capital markets under the International Finance Facility for Immunisation (IFFIm) which converts long-term government pledges -- in servicing and redeeming the bonds – into immediately available cash resources for immunisation programmes. The Solidarity Air Ticket Levy is expected to generate more than USD 500 million in 2009 for the purchase of drugs. Additional resources are also being generated through clean development mechanism projects and other emissions trading schemes to support African countries in addressing climate risks (APF, op. Cit.). There is also a dynamic and growing array of private philanthropic foundations increasingly active both globally and in Africa (Kharas, 2009). Private international giving as reported to the OECD shows a strong upward trend, rising to USD 18.6 billion in 2007. Although large, this number does not capture the full extent of

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private giving. US private foundations, which account for two-thirds of the total, are estimated to have provided three times the figure reported to the OECD.  Although the data is imperfect on this point, ODA from non-DAC donors is also growing. Arab donors provided over USD 2.5 billion in 2007, led by Saudi Arabia. The Republic of Korea (which is soon to become a DAC member) and Turkey are providing roughly about USD 500 million each in aid on an annual basis. Together, non-DAC donors (excluding China, India and Brazil that providing larger amounts of aid but do not report official numbers) totaled $4.7 billion in 2007. Lastly, the growth of sovereign funds holds the promise of further large development finance. At present, their portofolios are concentrated very much in the industrialised countries (UNCTAD, 2009). However, such was the scale of the losses of many of these funds during the financial crisis that they are likely to reallocate their portfolio away from the industrialised countries and more towards emerging and other developing countries in the future.  The current financial and economic crisis is bound to have important impact on all sources of development finance, precisely at a time when they are most needed as a countercyclical instrument. But contrary to past experience, prudent macroeconomic policies in recent years have helped developing regions in Africa and Latin America avoid the major macroeconomic instabilities that followed previous crises. For instance, according to the most recent IMF Regional Economic Outlook for Africa (IMF, 2009), the more solid macroeconomic conditions have made it possible for several African countries to implement counter-cyclical fiscal measures to alleviate the impact of the crisis. Moreover, many countries have effectively used improved policy space –larger revenue base and better fiscal positions—to allow some degree of counter cyclical fiscal policy, through automatic stabilizers and other means to counter the effects of the economic slowdown (APF, 2009).

1.2 PRIVATE INTERNATIONAL FINANCIAL FLOWS 

The Institute of International Finance (2010) now notes that “the macroeconomic outlook for capital flows to (most) emerging economies has never been so propitious”. In January 2010 they revised their forecast at the beginning of 2009 for capital flows to emerging markets upwards by a factor of three – from $165 billion to $435 billion, conceding that they had been too ‘pessimistic’. Direct investment and portafolio investment represents the bulk of these inflows, with private sector credit still being negative. The fundamental change is to perceptions. In the past, emerging markets were associated with high risk investments - macroeconomic instability was linked to unsustainable deficits and debt, which frequently manifested itself in inflation, currency, government debt, or banking crises, or indeed some combination of all four (IFF, 2010). To be sure, there are still emerging economies with potential problems regarding unsustainable fiscal deficits (e.g. Argentina, Ukraine, and Venezuela). Nevertheless, the broad averages tell a story of a marked improvement. But what about the prospects for lower income countries? After several years of continuous increase, private international flows to Africa declined in 2008. Nevertheless, partly on account of continuing strong foreign direct investment flows, private capital flows to Africa suffered a much more moderate decline in 2008, declining by 28% while worldwide the decline was much

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more dramatic, falling by some 70% (APF, 2009). It also needs to be born in mind that the years prior to the crisis had already registered major increases in flows - net private capital flows to Africa were up sharply in 2007 by USD 18 billion to reach USD 77 billion, the highest level on record. The rise was mostly due to a surge in FDI (+USD 9 billion) and sovereign bond flows5 (+USD 8 billion). Prior to the crisis, then, Africa was enjoying renewed access to commercial bank lending. But developing countries’ easy access to global capital markets deteriorated in late 2007 and in 2008 in the wake of the U.S. subprime mortgage crisis. Besides reducing capital flows to developing countries, the turmoil has increased borrowing costs, although less so than in previous periods of economic turmoil (World Bank, 2010).

1.3 FOREIGN DIRECT INVESTMENT 

FDI has been one of the principal beneficiaries of the liberalisation of capital flows over recent decades and now constitutes the major form of capital inflow for many developing countries, including some low-income ones like Chad, Mauritania, Sudan and Zambia. According to UNCTAD (2009: 6), for instance, FDI flows to Africa have been estimated at USD 62 billion in 2008, up from USD 53 billion in 2007, despite the slowdown in global economic growth and its negative consequences for the region. The year 2008 was also good for Mergers and Acquisitions (M&As) in Africa, which rose by an estimated 157 per cent to USD 26 billion. It is often argued that countries are less vulnerable to external financing difficulties when current account deficits are financed largely by FDI inflows, rather than debt-creating capital flows. It is true that FDI inflows generally provide a more stable source of external financing than private debt and portfolio equity flows. And there is no gainsaying the importance of FDI both for its contribution to sustaining current account imbalances and for its contribution to broader economic growth, through technological spillovers and competition effects. But this is only part of the story. There are several reasons for adopting a more cautious stance regarding the potential contribution of FDI finance. Firstly, if profit remittances are taken as a proxy for its “price”, FDI can be an “expensive” form of financing, specially for low-income countries. In Zambia, for instance, profit remittances ran at an average of -4 per cent of GDP between 2004-7 (UNCTAD, 2008: 30). On average World Bank data shows profit remittances exceeding new FDI inflows for every year in low-income countries between 1999-2005. Moreover, although profits will be squeezed because of the crisis, it is not inconceivable that in some cases the rate of profit remittances accelerates, as parent companies try to strengthen their own balance sheets. Secondly, the ease with which multinational enterprises can shift financial resources from one country to another may add to the current instability. For instance, FDI investors often use derivative products such as currency forwards and options, which may put local currencies under pressure and increase instability (Griffith-Jones and Persaud, 2008). Similarly, some components of FDI are more pro-cyclical than others. In particular, reinvested earnings and intra-company 5 Ghana became the first heavily indebted poor country (HIPC) to issue an external bond, with a USD 750 million Eurobond issue in September 2007. The bond issue was oversubscribed several times, despite being launched as international financial markets became more unsettled5. Gabon issued its inaugural sovereign bond in December 2007 when it launched a USD 1 billion 10-year Eurobond with a yield of 8.25% that was used to prepay its Paris Club creditors.

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loans are likely to be curtailed sharply during the current crisis, as companies repatriate financial resources towards parent companies. This was very much the case during previous crisis, such as the Thai crisis (1997) and the Argentinean crisis (2001). Finally, although not to the same extent as other private capital flows, FDI itself is still pro-cyclical. This was particularly evident during the downturn in 2000-01 when global FDI outflows fell by almost 50 per cent. Once the crisis is over, FDI might actually be one of the forms of cross-border flows that will be privileged (as it has been in the aftermath to previous crises). Indeed, there are some early signs that South-South investments may come out of the crisis strengthened over the long term (OECD Development Centre, 2010). In a deleveraged world, FDI could become one of the few ways in which low- and middle-income countries can access capital for development. But in the meanwhile policy makers in developing countries need to monitor trends carefully and adapt policy accordingly. FDI is in itself no panacea and can sometimes compound problems during times of financial crisis. It is certainly no substitute for enlarging tax bases and promoting better mobilisation of domestic resources.

1.4 WORKERS’ REMITTANCES 

Remittance flows are becoming an increasingly important source of finance for the developing world, reaching an estimated USD 327 billion in 2008 or almost 3 times the level of ODA. Between 2000 and 2008, recorded remittance flows to Africa increased from USD 10 billion to USD 33.4 billion – USD 13.6 billion for North Africa and USD 19.8 billion for sub-Saharan Africa. These figures could be even higher if unrecorded flows were included. Although remittances are particularly sensitive to economic conditions in the developing world, as noted in the introduction, in some regions remittances have held up well, despite the depth of the economic downturn. In some cases, this is due to the rising importance of south-south flows. Whereas in 2001 the United States was the major source for remittances, accounting for more than 30 per cent of the money sent home by migrants among countries sending USD 700 million a year or more, just under a decade later (and even though absolute flows rose) the United States now represents only 18 per cent of remittances among the same cohort (Mohapatra et al., 2009). The rest of the world has caught up, with developing countries like Malaysia and Indonesia all now generating material outward flows.

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Table 1.1: Remittances in Africa – Ten Largest Recipients and Sub-totals (USD billion)  

2000  2001  2002  2003  2004  2005  2006  2007  2008 

Nigeria  1.4  1.2  1.2  1.1  2.3  3.3  5.4  9.2  10.0 Egypt  2.9  2.9  2.9  3.0  3.3  5.0  5.3  7.7  9.5 Algeria  0.8  0.7  1.1  1.8  2.5  2.0  1.6  2.1  2.2 Sudan  0.6  0.7  1.0  1.2  1.4  1.0  1.2  1.8  1.9 Tunisia  0.8  0.9  1.1  1.3  1.4  1.4  1.5  1.7  1.8 Kenya  0.5  0.6  0.4  0.5  0.6  0.8  1.1  1.6  1.8 Senegal  0.2  0.3  0.3  0.5  0.6  0.8  0.9  1.1  1.3 South Africa  0.3  0.3  0.3  0.4  0.5  0.7  0.7  0.8  0.8 Uganda  0.3  0.3  0.4  0.3  0.3  0.4  0.4  0.4  0.5 Lesotho  0.3  0.2  0.2  0.3  0.4  0.3  0.4  0.4  0.4                    Sub‐Saharan A.   4.6  4.7  5.0  6.0  8.0  9.4  12.6  18.6  19.8 

North Africa        4.4  4.5  5.0  6.0  7.3  8.5  8.5  11.6  13.6 Africa  9.0  9.2  10.0  12.0  15.3  17.9  21.1  30.2  33.4 

Source: World Bank, Migration and Remittances Fact Book, 2008 plus updates, cited by APF (2009).  One of the factors militating against enhanced remittance flows to low income countries is the high cost of transfers. Partly for a lack of competition, the cost of money transfer to Africa averages about 10% compared to 5.6% for other developing regions. A series of initiatives are underway to help reduce its costs. They include making available information about the cost of transfer in originating countries, promoting the use of postal services in recipient countries for the withdrawal of funds and using mobile phones for fund transfers (APF, 2009).

1.5  FINANCING  REQUIREMENTS  FOR  DEVELOPING  COUNTRIES  AND PROSPECTS FOR OFFICIAL DEVELOPMENT ASSISTANCE 

In the introduction, we documented the effects of the financial crisis on the drying up of development finance as well as the decline in export revenue and a loss of access to international capital markets. Under these exceptional circumstances resources provided by IFIs (International Financial Institutions) and other bilateral and multilateral donors become even more critical to sustain the positive growth record that characterised Sub-Saharan Africa in the last decade and the support to the achievement of the Millennium Development Goals. The three international multilateral financial institutions (AfDB, IMF and World Bank) have responded to the crisis in Africa by sharply increasing concessional financing (APF, 2010): a) The AfDB has taken steps to accelerate resource transfers to its member countries by frontloading its allocations and speeding up disbursements, by restructuring portfolios to release moment project resources and has created a $1.5 billion Emergency Liquidity Facility b) As of September 2009, new commitments to sub-Saharan Africa by the IMF reached $3 billion, compared with $1.1 billion for the whole of 2008 and $0.2 billion in 2007. Through a special allocation of Special Drawing Rights (SDR), Africa received additional financial support

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worth some $17 billion, of which $12 billion for sub-Saharan Africa. In addition to providing additional resources, the IMF has also increased the flexibility of its intervention in low income countries.

Table 1.2 DAC Members’ Commitments and Performance: Summary Table of OECD Secretariat Projections

2005 projection Current projection Assumptions (ODA/GNI

ratios) Current

projection Country for 2010 for 2010 for 2010 Austria 1 673 1 178 0.51% in 2010 0.37% Belgium 2 807 2 620 0.7% in 2010 0.70% Denmark 2 185 2 299 Minimum 0.8% 0.83% Finland 1 475 1 112 0.51% in 2010 0.55%

France 14 110 9 955 0.51% in 2010 and 0.7% in

2015 0.46% Germany 15 509 11 300 0.51% in 2010 0.40% Greece 1 196 525 0.35% in 2010 0.21%

Ireland 1 121 842 0.6% in 2010 and 0.7% in

2012 0.52% Italy 9 262 3 426 0.51% in 2010 0.20%

Luxembourg 328 313 0.93% in 2010 and 1% in the

following years 1.00% Netherlands 5 070 5 323 Minimum 0.8% 0.80% Portugal 933 576 0.51% in 2010 0.34%

Spain 6 925 5 652 0.56% in 2010 and 0.7% in

2012 0.51% Sweden 4 025 3 915 1.00% 1.03%

United Kingdom 14 600 12 975 0.56% in 2010-11 and 0.7%

in 2013 0.56% DAC EU members, total 62009 0.48%

Australia 2 460 2 460 0.37% in 2010-11 and 0.38%

in 2011-2012 0.35%

Canada 3 648 3 542 5.1 billion Canadian dollars in

2010 0.33%

Japan 11 906 9 546

Increase by USD 10 billion in aggregate over 2005-2009

compared to 2004 0.20% New Zealand 289 324 0.35% in 2010 0.34% Norway 2 876 2 995 1% over 2006-09 1.00% Switzerland 1 728 1 820 0.40% 0.47%

United States 24 000 24 705 USD 5 billion nominal per

annum 0.20% DAC members, total 128 128 107 401 0.33 %

Source: OECD  (2009b) and OECD  (2010)  c) In fiscal year 2009 (July 2008 to June 2009), through front-loading and fast-tracked interventions to the most affected country, the International Development Association (IDA) of the World Bank Group posted record lending of $7.8 billion, an increase of 37% over previous year’s level and 15% above target. Seventeen countries front-loaded their IDA allocations. For middle-income countries in sub-Saharan Africa, the Bank provided $400 million in IBRD lending.

19

In addition, the IFC committed $300 million in its new infrastructure crisis facility to top up financing for viable privately funded infrastructure projects experiencing financial distress. Looking at bilateral ODA, the picture seems less optimistic. As also presented in the Introduction of this document, in mid February 2010 the Development Assistance Committee released perspective donor countries’ budget plans for 2010. As we pointed out earlier, some donors are on track to almost fully meet commitments (Denmark, the Netherlands, Sweden, Australia, Canada, New Zealand, Norway, Switzerland and the United States) while other donors are falling far behind (especially France, Greece and Italy). The 0.7 per cent ODA/GNI target is far from being met by most of the donors. The only exceptions are given by Belgium, Denmark, Luxembourg, the Netherlands, Sweden and Norway. On average, DAC donors will disburse 0.33 per cent of ODA/GNI where large donors such the United States and Japan are supposed to limit their ODA delivery at an ODA/GNI value of 0.20 per cent. Finally, total ODA in 2010 are estimated at 107 USD billion dollars, around 12 billion dollars lower than ODA figures in 2008, which achieved their highest level ever in 2008 at 119 USD billion. What would be the financial requirements for developing countries to sustain their growth rates and poverty reduction in the aftermath of the financial crisis? Estimates by the IFIs converge about the existence of a substantial remaining financing gap, from complementary perspectives. In September 2009, the IMF projected external financing needs of low-income countries to be on average in 2009-10 about USD 25 billion higher than in 2008, very little of which comes so far from fresh bilateral money. The World Bank, looking at critical MDG safeguarding public spending not covered by domestic resources and existing aid, came to figures of a similar magnitude (OECD, 2010). We move a step further and we assess the net financing requirements for Sub-Saharan African countries in 2009 to sustain economic growth so as to achieve the golden-rule of 7 per cent of GDP growth. This is the widely acknowledged threshold above which a poor country would be able to timely satisfy the MDGs by 2015. We estimate financial requirements based on the savings-gap approach following Gottschalk (2000). The savings-investment gap is a rough indicator of the amount of external finance that developing countries need in order to sustain growth and development6. For this purpose, we consider a two-step analysis. First, we assess the incremental-output ratio (ICOR) over the period 2000-2007 - based on OLS estimation and excluding severe troughs in GDP growth - in other to quantify the investment rate required to achieve sustain a 7 per cent GDP growth rate. Second, based on average national savings ratios over the period 2000-2007 we estimate the savings gap for each SSA country. Finally, we calculate external net external financing (NEF) requirements based on the following formula as in Gottschalk (2000), in order to fill the savings gap: 6 Main criticisms, especially of two-gap models, come, for example, from Findlay (1973) and Van Wijnbergen (1986) and more recently from Easterly (1997, 2006). First, Findlay (1973) shows that more saving is never redundant but it can become increasingly less effective in rising the rate of growth where gaps theory argues that an increase in the propensity to save may have no effect in rising the growth rate of output when the foreign exchange is binding. Second, Van Wijnbergen (1986) introduces relative prices developing a model with short run wage-price rigidities and exchange rate misalignment. Third, according to Easterly (2006), gap models would fail all theoretical checks and empirical tests performed upon them. Nevertheless, even if the methodology presents limitations, it is still useful to estimate long-run term needs for developing countries in a simplified framework.

20

(1.1)

where i* is the investment rate that would be required to achieve a 7 per cent growth rate based on ICOR estimates, s is the domestic savings rate, is the country’s GDP at constant prices, corresponds to profit remittances on foreign direct investment, measures interest payments on non-concessional external debt - where is the external debt stock net of international reserves, is the real interest rate and is the share of non-concessional debt on total debt -

are unilateral transfers which include only workers’ remittances. We estimate NEF based on 2007 data for each variable in equation (1.1) based on both World Development Indicators and Global Development Finance 2009. By assuming that international reserves do not change over time, the net financing requirements represent a rough measure of the current account balance7. We estimate that SSA countries would require about USD 37 billion of financial resources in 2009 – in the form of debt flows, foreign direct investment and official development assistance in order to sustain the growth performance towards the MDGs8. These findings are not directly comparable with IFI estimations mentioned above as our sample represents only a –albeit a large - share of low-income countries; moreover we assessed the total amount of financing required rather than additional resources required. Looked at from a different perspective, the amount is far bigger than the current account deficit observed for 2008 (World Bank, 2009a) - a USD 18.7 billion deficit for Sub-Saharan Africa – and almost close to the net private and official inflows to the whole region in 2008 – USD 38.7 billion. Our calculations clearly provide a lower bound estimate as a baseline scenario for portfolio equity flows, workers’ remittances and debt stocks and undervalues financing requirements for 2009 onwards as it assumes pre-crisis values. Nonetheless, this exercise gives a rough estimation of the resources required to help the region sustain the remarkable average GDP growth performance achieved until the outbreak of the financial crisis and to contribute towards keeping on track to meet the Millennium Development Goals.

7 In other words NEF needs essentially correspond to the projected current account deficits. 8 Based on data availability and the existence of a savings gap, the countries included in the estimation of the USD 38 billion are Cameroon, Comoros, Gabon, Gambia, Guinea-Bissau, Kenya, Liberia, Madagascar, Mauritania, Senegal, Seychelles, South Africa and Swaziland.

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2. Official development assistance

The context described above suggests that developing countries as a group have become less dependent on official aid flows as a financing mechanism, and are increasingly capable of attracting private capital flows. Whether these trends continue post-crisis remains to be seen, but so far the impact of the financial crisis has not been as dire on forms of private financing as many observers were initially forecasting. Nonetheless, it is still true that the poorest countries will remain for the foreseeable future dependent on official development finance. To generate a more effective system of development assistance, in this report we argue that four basic complementary changes are necessary:

a) Higher level of development assistance b) Reducing the fluctuations in development assistance c) Increase the efficiency in aid delivery (less fragmentation, less herding behaviour) d) Revision of the practise of policy conditionality

In the second half of this report, we will elaborate on each of these policy issues.

2.1 AID FLOWS IN PREVIOUS TIMES OF ECONOMIC CRISIS 

Since the Gleneagles G8 Summit in July 2005, the major debate within donor circles has been about the scaling up of aid, especially to Africa9. In that occasion the G8 countries and other donors commit themselves by 2010 to more than doubling Official Development Assistance (ODA) disbursed to Africa countries in 2004, i.e. of about USD 25 billion. Looking at the data available until 2008, there are genuine concerns about the achievement of this target. As pointed out in the Introduction, there are mixed results where the positive results of a group of donors are undermined by the large negative performance of big donors, namely Austria, France, Germany, Greece, Italy, Japan, and Portugal which are evidently far from being able to meet the commitments assumed. Cleary, the financial crisis started in September 2008 poses serious threats to the timely achievement of the ODA target for Africa as public budget are under stress and social protection programmes in donors’countries have been progressively extended to counterweight the effects of the economic slowdown. Among other dimensions, the 2008-2009 financial crisis has been unique in the sense that its epicentre laid in advanced rather than developing countries as in the crisis characterising Asia and Latin America in the 1990s. As we have seen in Introduction, at least at the aggregate level, emerging and developing countries have been escaping the economic downturn more rapidly and with limited damages compared to advanced economies (and also to 9 More specifically, in the Gleneagles communiqué, the G8 agreed to increase aid to Africa by USD 25 billion per year by 2010, increase aid to all developing countries by USD 50 billion per year by 2010, and cancel 100 per cent of debts for eligible Heavily Indebted Poor Countries (HIPCs) to the International Monetary Fund (IMF), the World Bank and the African Development Fund (ADF).

22

early stage predictions). This has been accelerating the catching-up effect already started in the early 2000s (OECD Development Centre. 2010) where OECD countries’ GDP growth is estimated at – 3.5 per cent for 2009, while East Asia and South Asia at 6.8 and 5.7 per cent respectively (OECD, 2009a). Nevertheless, this does not imply that recipient countries have not been hit by the crisis and, most impotent, the global dimension of the financial crisis hampers the usual countercyclical action of ODA flows in recipient countries. With the onslaught of the credit crisis, talk about up scaling of aid has suddenly become more muted. In November 2008, DAC donors made an aid pledge reaffirming earlier commitments to increase the volume of aid and to maintain aid flows at levels consistent with those commitments (OECD, 2008). In March 2009 the Development Assistant Committee within the OECD released the ODA figures for 2008, the first year of the crisis: development aid achieved its highest level ever in 2008 where the current commitments imply an ODA level of USD 121 billion in 2010, expressed in 2004 dollars, or an increase of USD 20 billion from the 2008. This should not be surprising as aid budgets are usually defined at the end of the previous fiscal year. One important dimension to this question is the fiscal balance – one would expect, other things being equal, that governments with large deficits would be more prone to cut aid. And by massive intervention to prop up the banking and credit system, OECD governments are currently taking on huge financial commitments. As Robert Zoellick, President of the World Bank, has recently affirmed, “at $100 billion a year, the amount spent on overseas aid is a drop in the ocean compared to the trillions of dollars that are now being spent on financial rescues in the developed world.” (World Bank, 2008). There may be some reasons for expecting further aid increases, despite the economic downturn. First, some donors may feel morally responsible to achieve at least their Gleneagles commitments. To support this point, a survey of donors’ forward spending plans of ODA donors in early 2009 suggests an 11 per cent increase in programmed aid between 2008 and 2010, including larger disbursements by some multilateral agencies. Second, as highly-indebted countries may face more binding budget constraints - because, on the one hand, the need to sustain social spending to cushion the effects of the crisis on household income and, on the other hand, because of lower government revenue – debt service capacity might be compromised. Debt relief is one the options for donors which can allow some breathing space for debtors – evidently only when it cuts actual debt service and not payments already defined in arrears – and the ODA targets achievement for donor countries. As recently argued by Powell and Bird (2010), recent debt relief disbursements have not violated the aid additionality condition, i.e. the donor commitment to consider debt relief additional, and not substitute, to official development assistance. Indeed, very significant progress has been achieved in implementing the HIPC (Heavily Indebted Poor Countries) Initiative and the MDRI (Multilateral Debt Relief Initiative_ during 2009. With 35 of 40 eligible countries reaching the decision point by end June-2009—of which 26 have reached the completion point — the HIPC Initiative has provided much needed debt relief to most HIPCs. A number of the remaining interim HIPCs are also well placed to progress towards completion point in the period ahead, and benefit from irrevocable debt relief under the Initiatives (IMF and IDA, 2009). Third, as pointed out by Zimmermann (2008), the public support to Official Development Assistance was still extremely high despite the financial crisis.

23

However, the current outlook suggests that at least USD 10-15 billion must still be added to current forward spending plans if donors are to meet their current 2010 commitments. Africa, in particular, is likely to get only about USD 12 billion of the USD 25 billion increase envisaged at Gleneagles. According to OECD (2010) a first group of countries – among them Sweden, with the world’s highest ODA as a percentage of its GNI at 1.03%, Luxembourg (1%), Denmark (0.83%), the Netherlands (0.8%), Belgium (0.7%) – that will meet the ODA target, i.e. the ODA flows to GNI target of 0.7 per cent in 2010 10. A second group of DAC donors, - , the United Kingdom (0.56%), Finland (0.55%), Ireland (0.52%) and Spain (0.51%) – fall short behind. Finally in the rear a group of countries - France (0.46%), Germany (0.40%), Austria (0.37%), Portugal (0.34%), Greece (0.21%), and Italy (0.20%) – which will unlikely meet their ODA commitments in 2010. In reaction to this figures, Kharas (2010) suggests a classification of donors countries based on the relation between their ambitiousness – i.e. the different between actual ODA disbursement in 2004 and ODA commitment for 2010 as set at the G8 Gleneagles meeting in 2005 – and their prospective ODA delivery increase from 2004 to 2010, the latter figure as estimated by OECD DCD/DAC in February 2010. What emerges from the overall picture is that countries that promised to give more, and were more ambitious in setting targets for higher aid, actually were able to deliver more aid even if they fell short of their targets. There are two groups of countries: first DAC countries which are ‘modest’ in their commitments and achieve ‘modest’ results in terms of ODA mobilisation - Japan, Switzerland and the United States; second, donors who are high-flyer compared to previous commitments fail to keep their promises – France, Greece and Italy. In the words of Homi Kharas these two groups should be scrutinized as to whether they are bearing their fair share of the global poverty reduction effort. While there is extensive literature on the relation between recipients characteristics and aid delivery (see for example Alesina and Dollar, 2000) – e.g. income level and political system – as well donors motivations11, the determinants of ODA disbursement from the donor’s perspective have only been partially investigated. The outbreak of the financial crisis – which immediately posed some doubts about whether ODA pledges would have been still honoured – has revitalised this debate. In particular, soon after the initial wave of the financial crisis, Roodman (2008) remarked that in the wake of some recent financial and banking crisis12 directly involving DAC countries (i.e. Finland, Japan, Norway, and Sweden), their aid budgets sharply fell. In Finland, according to Roodman’s figures, the fall was even more dramatic: during its banking crisis between 1991 and 1993 when GDP fell by nearly 11 per cent, development aid fell by 60 percent. Japan's aid – measured as net disbursed ODA in UDS at 2006 constant prices (OECD, 2008) - fell 12 per cent between 1990 and 1996, and has never returned to its pre-crisis level13. 10 See Clements and Moss (2005) for a discussion on the origins and the motivations of ODA GNI targets. 11 See Mold (2009) on this point. 12 Roodman (2008) adopts Laeven and Valencia (2008) classification of systemic banking crisis, i.e. in a systemic banking crisis, a country’s corporate and financial sectors experience a large number of defaults and financial institutions and corporations face great difficulties repaying contracts on time. 13 In the case of Norway and Sweden, Roodman (2008) estimates that after r the Nordic crisis of 1991, Norway’s aid fell 10 per cent and Sweden’s 17%, from peak to trough after adjusting for inflation, considering the measure of Net Aid Transfers (NTA, henceforth) which, despite net ODA, is net of interest payments on loans to donor countries as well as debt cancellation on old non-ODA loans.

24

Figure 2.1 Evolution of ODA and GDP flows in Finland and Japan 1960-2007 (constant 2006 USD)

Finland 

02.

00e+

084.

00e+

086.

00e+

088.

00e+

08O

DA

flow

s in

US

D

5.00

0e+1

01.

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+11

1.50

0e+1

12.

000e

+11

2.50

0e+1

1G

ross

Dom

estic

Pro

duct

in U

SD

1960 1970 1980 1990 2000 2010year

Gross Domestic Product in USD ODA flows in USD

 

Japan 

2.00

e+09

4.00

e+09

6.00

e+09

8.00

e+09

1.00

e+10

OD

A fl

ows

in U

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01.

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+122.

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+123.

000e

+124.

000e

+125.

000e

+12

Gro

ss D

omes

tic P

rodu

ct in

US

D

1960 1970 1980 1990 2000 2010year

Gross Domestic Product in USD ODA flows in USD

Source: OECD (2009b) and World Bank (2009) 

Figure 2.2: US ODA and GDP Growth, 1960-2007

 Source: OECD (2008) and US Department of Commerce (2008) 

There is no statistical relationship between US net ODA and either tax receipts, deficits or total government expenditures (Kharas, 2008). The simple correlation in the US case is just 0.06. While aid dropped in 1990 during the recession of 1990-1, in the 2000-1 recession aid was accompanied by a sharp increase. This is borne out by other cases too. In the case of Japan, the recession in 1996-1997 did not imply a reduction in aid flows. The Finnish banking crisis determined a two-year recession with the already mentioned share reduction in aid flows that recovered only in 2005. As far as Sweden

25

and Norway are concerned, aid flows did not sharply decrease after the eruption of the financial crisis. Finally, neither France nor Germany faced a financial crisis over the last thirty years. During the 1993 recession, France increased its aid disbursement whereas Germany cut them. Despite the German downturn in the early years of 2000, the country expanded its contributions after 2004.

Aid Effort: Evidence from previous studies

The literature on “aid effort” or “aid generosity” from donor countries, i.e. the determinants of aid supply, is rather limited and recent (Pallage and Robe, 2001, Round and Odedokun, 2004; Faini, 2006; Boschini and Olofsgard, 2007, Bertoli et al., 2008 and Chong and Gradstein, 2008). Shared concerns about maintaining ODA commitments in the aftermath of the financial crisis (of global nature) reawakened interest in research in this specific field (Mendoza et al. 2009, Hallet 2009, Dang et al., 2010). In such a sense, the financial crisis has shifted attention to the donor side of the aid relation. Nevertheless, decisions on allocations to the aid budget do not appear to be strongly affected by the business cycle. Empirical evidence based on aggregate data for all DAC donors on this point is rather thin on the ground. But the aggregate studies that do exist (e.g. Pallage and Robe, 2001, Round and Odedokun, 2004; Faini, 2006, Bertoli et al. 2009, Chong and Gradstein, 2008, Mendoza et al. 2009, Dang et al. 2010) contradict one another sufficiently to confirm that there is no trenchant evidence on the nature of the relationship between GDP growth and aid flows. Pallage and Robe (2001) focus on business cycle characteristics of aid flows both from the recipient and the donor’s side. Through an analysis of the detrended business cycle using the HP filter (deviations from output trend), they find little evidence that aid is procyclical with respect to the donors’ economic performance (and even find it is negatively correlated in the case of France and Italy). Round and Odedokun (2004) investigate the decline in aid flow from 1970 to 2000 looking at the gross disbursement of ODA loans and grants as a proxy for aid generosity. The sample is composed of all 22 DAC countries from 1970 to 1999. Based on fixed-effects panel data estimation, they suppose that aid generosity is determined by both non-political and political factors. Among the first group of factors, they include the level per capita income, the phase of the economic cycle (residuals derived from regressing the logarithm of real GDP index on a trend variable), the fiscal balance, ‘peer pressure’ (ODA loans and grants of all other donors expressed as a fraction of their total GDP), domestic pro-poor spending and the policy stance (the Gini coefficient and income share of the poorest 20 percentile), the country size, international strategic and military interests and temporal factors, e.g. the end of the cold war. In the second group, they take into account the government ideological orientation, the constitutional checks and balances and the polarization within the government itself. They find firstly that aid is a superior good (i.e. it increases with per capita income). Secondly, aid shares to GDP decrease with the growth of the donor country population. Thirdly, mixed results are obtained for the time trend (i.e. there is no clear increase in aid budgets overtime). Fourth, aid generosity increases with peer pressure, the greater the number of checks and balances, polarization and fractionalization within the

26

government. Finally the fiscal balance as well as the political orientation do not any significant effect on the aid effort. Faini (2006) analyses the macroeconomic variables determining the amount of aid for 15 donor countries over the period 1980-2004. He proxies total aid flows with net official ODA (grants and loans whose grant element is above 25 per cent), total official flows and Net Aid Transfers (NTA) measure proposed by Roodman (2006). Covariates are the aggregate fiscal deficit, the stock of gross debt, the output gap and political orientation. The study finds firstly that aid flows are a positive function of the donor’s fiscal surplus (controlling for government’s political orientation, the cyclical position of the donor economy, and its income per capita level). Secondly, the debt stock negatively affects the amount of aid flows. Thirdly, the fiscal surplus as well as the output gap have no statistically significant effect. Based on a political economy model, Chong and Gradstein (2008) investigate the factors affecting support for foreign aid among voters in donor countries. They consider a sample of all 22 DAC countries over the period 1973-2002, exploring both fixed-effects panel data techniques and Arellano-Bond dynamic estimator, including the real GDP per capita, the Gini coefficient, the tax revenues, a proxy for government inefficiencies and a dummy for left-wing governments. Their main finding is that foreign aid flows are positively correlated with satisfaction of own government performance and individual relative income. Bertoli et al. (2008) concentrate on the determinants of aid effort, proxied by the net aid disbursements, net of debt relief, over GDP, focusing on the Italian case. They consider four sets of factors: structural variables (real income per capita, income inequality, population size and government intervention and redistribution), historical factors (past colonial history), macroeconomic determinants (fiscal deficit, trade balance over GDP and the output gap), institutional features (independent aid agency, peer effect and the political orientation) and finally alternative sources of foreign finance (remittances). The sample is composed of all the 22 DAC members over the period 1970-2004. They find that the output gap and the trade balance variable, government intervention have a positive and significant impact on aid effort as well as redistribution and political orientation (i.e. conservative government raises aid effort) and fiscal deficit. Negative and significant impacts are found for growing income inequality and population size. These results have been updated in Allen and Giovannetti (2009) extending the analysis of the 22 DAC donors up to 2008. Compared to Bertoli et al. (2008), the output gap variable becomes statistically significant, indicating that severe output fluctuations can have a more than proportional impact on aid budgets. Based on these results, Allen and Giovannetti (2009) predict a fall of total DAC by 22.22 per cent from 2008 to 2009. Hallen (2009) finds that only a weak correlation between aid and OECD donors’ growth between 1971 and 2008, where aid cuts involved about half of the episodes of all deep or protracted recessions with a one year time-lag. Mendoza et al. (2009) concentrate their analysis on the case of the United States. By use of a misery index (an equally-weighted index of inflation and unemployment) they show that a one per cent increase is associated with decline of ODA of 0.01 point as a percentage of GDP. They estimate that ODA from the United States could fall from 13 to 30 percent, depending on the depth of the economic recession.

27

Finally, based on a sample of 24 donor countries over the period 1977-2007, Dang et al. (2010) show that aid flows are significantly correlated with episodes of banking crises in donor countries. In particular aid flows would fall by an average from 20 to 25 per cent with an elasticity of 3 per cent of aid flows to donors’ incomes. According to the aid determinants literature, one would expect, ceteris paribus, that governments with large deficits would be more prone to cut aid. It is worth stressing that the empirical evidence on this point is mixed. According to a study by Faini (2006), countries with a healthy fiscal situation tend to be more generous donors. Bertoli et al. (2008), on the other hand, come to exactly the opposite conclusion — that donors with larger fiscal deficits deliver larger aid flows. The findings of both studies can be plausibly interpreted — Faini´s study implies that countries with a better fiscal stance are likely to be more generous in their allocations to development aid, while the Bertoli et al. (2008) study could reflect the fact that fiscally more conservative governments might be less likely to give to development aid! Again, the analysis of individual donors seems to support the viewpoint that there is no systematic relationship between fiscal position and aid allocations. In the case of the United States, for instance, there is no statistical relationship between net bilateral ODA and either tax receipts, deficits or total government expenditures (Kharas, 2008). How can we justify this lack of evidence of a clear-cut relationship between aid flows and government revenue and donor performance respectively? Firstly, previous financial crisis and economic recessions might have not been so severe to justify excessive cuts and revision in aid budgets (something which may be different when the recession is deep and global in nature).Secondly, the optimistic interpretation is that aid as not determined by the economic cycle in donor countries, but rather by the needs of the developing countries themselves 14 as well as the willingness to accomplish international commitments. Thirdly, aid may not be determined by either the economic cycle or needs in the developing country itself, but rather is principally motivated by geo-strategic objectives of the donor countries (Alesina and Dollar, 2000 Maizels and Nissanke, 1984). Indeed, the beginning of the 1990s was a period of cautious optimism. For the developing world, there was talk of a peace dividend following the end of the Cold War. A substantial cut in military budgets was foreseen, and hopes were pinned on a corresponding increase in the amount of financial resources dedicated to enhancing the ‘soft power’ of the major donors through development aid programmes. Nevertheless nor did the international community step into the breach—far from reaping the hoped-for peace dividend real net ODA declined over the decade by nearly a third.   This is a controversial point of course, but there is a fair amount of econometric support that political motivations do indeed explain a major part of aid flows. Within the limitations of cross-sectional studies, econometric studies show quite clearly that political considerations still play a major role as a determinant of aid flows. For instance, Alesina and Weder (2002) find that there is no significant correlation between the level of corruption and the allocation of foreign aid, regardless of the time period under consideration. They note no apparent change in donor behaviour since the 1990s, though there is some evidence that political interests might play a smaller role in the 1990s than previously (the variables ‘years as a colony’ and ‘Israel’ are no

14 See for example Bandyopadhyay and Wall (2006) and Nunnenkamp et al. (2006) .

28

longer significant).15 At the same time, the indicator of ‘openness’ (which the authors interpret as a proxy for desirable economic policies) is no longer significant in the 1990s, whereas it was previously. Easterly (2007) runs similar regressions on data from 1980 to 2003 and finds a slightly significant increase of democracy as a determinant of aid flows after 1990, as well as more significant coefficients on corruption after 1995, which the author interprets as a response to a speech in 1996 by the then President of the World Bank James Wolfensohn (which invites donors to take the issue of corruption as a more central determinant of their aid allocation. An interesting dimension to all this is when econometric analyses distinguish between donors. Easterly (2007:658) for instance, finds that the World Bank itself shows no sign of increased sensitivity towards policies or corruption despite the policy revolution it led after 1980. And, according to Alesina and Weder (2002), whereas the Scandinavian countries and Australia give more to less corrupt governments, corruption is positively correlated with aid received from the United States. Berthelemy (2006) provides a breakdown of donor motivations for aid allocation, and distinguishes between ' altruistic' and 'egotistical' countries, in terms of the extent to which trade and aid are interrelated, on the one hand, and the degree to which a donor's priorities differ from those of an ‘average’ donor (having previously established that the average donors still allocate aid according to principles of self-interest). An altruistic behaviour is one that implies that aid decisions are made independently of the specific relation that may exist between the donor and the different recipients. The author finds that the most ‘altruistic’ donors are Switzerland, Norway, Austria, Ireland, the Netherlands, New Zealand, Norway and Switzerland, with an egotistical cluster compromising of Austria, France, Italy, the USA and Japan. There is also empirical evidence to show that even the IFIs, who pride themselves on their supposedly apolitical and technocratic approach to the concession of aid, are often closely aligned with the foreign policy objectives of their major benefactors (see, for instance, Andersen, Harr and Tarp, 2006). Little evidence, then, in favour of a shift towards a more ‘ethical’ or discriminating aid policy. The existence of conditionality has, in other words, had little or no impact on actual outcomes, in terms of what we would expect to be desirable a priori and what would be coherent with the contemporary aid discourse. We will return to this point later in this report.

Econometric analysis

After having explored the scope for correlation between aid flows, Gross Domestic Product and fiscal balances we empirically estimate the relation between aid flows and donors’ performance considering the following model:

(2.1)

where i indicates the country and t goes from 1960 to 2007. In order to eliminate the bias in our time series attributed to inflation and exchange rate movements, where reasonable, amounts have

15Similarly, the authors study the factors which impinge on debt relief (an increasingly important component of overall aid flows) and find no evidence that debt relief programs have been targeted to less corrupt countries.

29

been transformed into 2006 constant prices as well as constant 2006 USD exchange rate16. We base our model on previous studies, such as Faini (2006), focusing on the macroeconomic determinants. First, we assume that aid to GDP x is determined by the amount allocated by the donor country in the previous year. Aid flows are measured in terms of net bilateral ODA and are taken from OECD.Stat. GDP figures come from World Bank World Development Indicators. Second, y represents our independent variable of interest, i.e. the GDP growth rate of the donor’s country also taken from World Development Indicators. The vector Z includes a set of control variables: the share of donor’s GDP over world GDP, the share of military expenditure to GDP ratio and the fiscal balance to GDP ratio. The share of world GDP is a proxy for the size of the donor and is expected to have a negative sign since smaller countries tend to allocate a greater share of GDP to aid compared to larger economies. The share of military expenditure on GDP proxies for the strategic interests of the donor countries and the political purposes of aid. These last three variables are all taken from World Development Indicators. The fiscal balance to GDP ratio is expected to have a positive sign, since countries with a larger fiscal surplus may be in a better situation to dedicate more resources to aid. The fiscal balance is inclusive of grants. The source is OECD.Stat (2008) and various national statistics offices. Finally is the country effect and 7.

the error term. The panel is unbalanced but consistency is not compromised by sample selection since it is due to non-response of country units uncorrelated with our regressors (Wooldridge, 2002). We test our dynamic panel data model in equation (2.1) considering the system-GMM estimator (columns 1 and 2 in Table 2.1)17 because our data present values for beta coefficients close to unity: under these circumstances Blundell and Bond (1998) show that the instruments used in the Arellano and Bond (1991) first difference GMM estimator become less informative. Our model reasonably satisfies the sequential exogeneity assumption, under which only past and present observations need to be uncorrelated with unobserved heterogeneity. We use the second lag of the dependent variable as an instrument, under the assumption that the country specific effect is correlated with the error term.

Table 2.1: Econometric Analysis – Dependent variable: ODA to GDP ratio Blundell-Bond Fixed effects (1) (2) (3) (4) Lagged Dependent Variable 0.681*** 0.414*** 0.872*** 0.745*** 0.032 0.051 0.01* 0.036 GDP growth -0.001* -0.003*** -0.001* -0.003* 0.000 0.000 0.00 0.001 Share of World GDP -0.008 0.003 0.006 0.008

Fiscal Balance as a percentage of GDP 0.003*** 0.001 0.000 0.001

16The 2006 GDP deflator has been calculated from World Bank (2008) and the 2006 USD exchange rate from OECD (2008). 17 Robustness check considers the FE estimation in columns (3) and (4) of Table 2.1.

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Military expenditure as a share of GDP 0.001*** 0.000 0.000 0.000

Number of obs 769 407 769 407 R-sq overall 0.9304 0.9618 F-stat/Wald chi2 619.89 974.25 1890.67 94.73 P-value 0.000 0.000 0.000 0.000 Sargan test 18.06 19.37 P-value 1.0000 0.9893

Notes: standard errors are reported under parenthesis. The levels of significance are as follows:  *** 99 per cent level, **95 per cent level, *90 per cent level. 

From the econometric results presented in Table 2.1 (columns (1) and (2)), the lagged dependent variable – aid to GDP ratio – is strongly significant and positive supporting the hypothesis of path dependence in aid disbursement by donors (aid levels are very much a function of the preivous year’s aid level). Second, both the fiscal balance and military expenditure variables (both as a percentage of GDP) present the expected sign and are statistically significant, confirming on the one hand the larger scope for allocations to aid when fiscal circumstances allow and, on the other hand, the importance of geostrategic and political purposes of aid disbursement. Neither parameter value is large however. Contrary to the studies reviewed in previous sections, we find a statistically significant negative correlation between GDP growth and aid to GDP ratio in all our estimations. This coefficient is in any case extremely low, suggesting that economic growth in donors countries did not play a decisive role in aid allocation in the past. A key issue if of course whether these conclusions are still valid in the context of the 2008-2009 financial crisis, as most of the DAC donors have been experiencing contemporaneously negative GDP growth rates – a unique event in recent history.

Conclusions

Regardless of our results here, the reduction in aid flows may not be the only consequence of the financial crisis - the composition of Official Development Assistance may also be radically affected. In particular, there has been a sharp rise in multilateral contributions, as resources are channeled through the IMF and World Bank. The danger is that much of this will bypass the poorer, most vulnerable countries – and instead be destined almost exclusively for the emerging markets and middle income countries. Yet, as suggested by Prizzon (2008), many of the poorest countries are still vulnerable in the context of the current crisis. A major challenge then is for donor nations to maintain their commitments to low-income countries, in particular by making sure that country project and programmable aid does not suffer from cutbacks.

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Figure 2.3: Bilateral ODA by type, 2007 constant prices, net disbursement

0

5000

10000

15000

20000

25000

30000

1960

1964

1968

1972

1976

1980

1984

1988

1992

1996

2000

2004

2008

Project and Programme AidTechnical Co-operationDevelopmental Food AidHumanitarian Aid

 Source: OECD  (2009b) 

Another dimension to all this is its relevance to the debates on aid efficiency. One of the key recommendations in recent years, encapsulated in the Accra Agenda for Action, is the importance of increasing aid efficiency. Even in the face of the possible stagnation of aid budgets, there might be a pay-off if donors react in a way that is pro-poor. Indeed, a hard-budget constraint may help reduce some of the inefficiencies that have become inherent in the international aid system. There are significant portions of aid budgets which have grown enormously over the last 10-20 years – particularly technical cooperation – and yet the rationale for supporting such a large expansion of these expenditures, in terms of aid efficiency, is more doubtful. To sum up, in 2008 the OECD Secretary-General Angel Gurria urged nations to join an “Aid Pledge” that would confirm existing aid promises and avert cuts in budgets for development aid: “Unless we act decisively now, we may not be able to prevent the financial crisis from generating an aid crisis.” This is particularly so for low-income countries. Among the different sources of external finance to developing countries, only foreign aid tends to be stabilizing, in the sense that its value rises when economies contract. So aid is currently at a premium, especially for low income countries with few alternatives to finance for development.18 Now, more than ever, policy makers need to protect aid volumes, and allocate them in a way which is pro-poor.

2.2 FRAGMENTATION OF OFFICIAL DEVELOPMENT ASSISTANCE 

Aid efficiency thus comes to the fore during times of financial crisis. In this section we study different characteristics of donors’ portfolios of recipients. There is growing concern that donors give aid to too many recipients - in other words that aid allocation is too fragmented. Fragmentation creates costs on both sides of the relationship. The recipient has to deal with many donors which have different administrative procedures. The bureaucratic burden can be heavy for developing countries, whose administration has already limited resources. On the other hand the  

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donor must keep active relationships with many recipients. Development agencies must keep track of hundreds of projects in many countries with different organisations. Donors started to acknowledge the inefficiency of aid dispersion and calls have been made for greater co-ordination. SIDA, the Swedish aid agency, for example, has decided in 2007 to halve its number of aid recipients. It made it clear that the aid budget would remain constant but that having too many recipients was too costly, and harmful for developing countries. For this report, we measure aid dispersion by looking first at donors, and then at recipients and finally at sector level.

Fragmentation by donor

A crude measure of fragmentation is the average number of recipients a donor gives aid to. We split the sample into two donor categories: DAC donors and multilateral donors. Figure 2.4 shows this index for each year in the sample. A recipient is considered to be in a donor portfolio in a given year if it receives a positive amount of gross ODA from the donor. We use gross ODA net of emergency aid and debt relief as this measure gets as close as possible with our data to the concept of country programmable aid (CPA) used by the DAC to assess fragmentation19. First, the average number of recipients in a donor portfolio rapidly increased. It was less than twenty in 1960, and is now above 100, reaching the record value of 109 recipients in 2006.

Figure 2.4: Donor portfolio size

Source: Authors based on OECD (2009b) 

Figure 2.5: Portfolio size in 2006, by donor

19 Any flow that is unpredictable by nature is not included in CPA. Around 60 per cent of unpredictable aid is made up by emergency aid and debt relief, but other items that do not imply transfers in the recipient countries (research, administrative costs) are also taken into account. We are not able to use the direct measure of CPA as it is available only for 2005 and cannot be recovered on a disbursement basis for other years. Debt relief grants started to be reported as a separate category in 1988, and emergency aid in 1995 (either because they were inexistent before that date or because of reporting directives). We estimate that our measure that excludes emergency aid and debt relief is a good variable to measure fragmentation at the recipient level.

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ECJapanUNTA

United StatesFranceCanada

GermanyUNDP

KoreaUNICEF

SpainGreeceUNFPANorwaySwedenUnited Kingdom

SwitzerlandBelgiumAustriaFinlandNetherlandsItaly

Global FundOther Bilateral Donors

IrelandCzech Republic

DenmarkUNHCRAustralia

Arab AgenciesTurkeyNew ZealandGEF

LuxembourgWFP

IDAIFAD

PolandArab Countries

ThailandPortugal

AfDFIMF

AsDFIDB Spec. Fund

Nordic Dev. FundSlovak Republic

EBRDHungaryCarDBMontral Protocol

IcelandUNRWA

0 20 40 60 80 100 120 140 160 Source: Authors based on OECD (2009b). 

Second, multilateral donors seem to be more focused but this actually hides large differences among them. As indicated in Figure 2.5, there are some small donors, some of them with a clear regional focus, with ten or twenty recipients (Caribbean Development Bank, EBRD, Nordic Development Fund), and some very large (the European Commission gave aid to 149 developing countries in 2006, more than any bilateral donor; UNDP, UNICEF, UNTA all have more than 100 countries in their portfolios). In particular, even donors that do not allocate large aid quantities can spread them over a large number of countries - Greece, for instance, represents less than 0.2 per cent of total gross disbursements in 2006 but still reaches 115 countries. We can measure dispersion more precisely by considering the Hirschman-Herfindahl (henceforth HH) index on portfolios, rather than only the number of recipients. The index ranges from 1/N to 1, where N is the number of developing countries, with a higher value indicating more concentration. Nevertheless the Hirschman-Herfindahl index misses the portfolio size dimension. Two donors can have the same concentration index with very different portfolio sizes. We first use the simple Hirschman-Herfindahl index but we then complement the analysis to take into account the number of countries in the portfolio. Fragmentation is calculated using gross aid minus emergency aid and debt forgiveness grants. We distinguish between DAC bilateral and multilateral donors20. From Table 2.2, we see that donors fragmentation has been increasing on average. Among bilateral donors, even small countries can have very fragmented portfolios. Luxembourg (but also Norway or Sweden) allocates small aid quantities compared to large donors but has a 20 Data on some non DAC bilateral donors is also available upon request.

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concentration index lower than the two largest donors. On the other hand it means that some small donors have more equal aid allocations across countries. Luxembourg has around 75 countries in its portfolio, but a low fragmentation index. The US gives aid to 135 countries but has a less fragmented portfolio. We will come back to this point later but we should keep in mind at this point that a lower concentration index only means that the recipients’ portfolio weights are more equal. It is also worth stressing that multilateral donors are a heterogeneous group, with some small, specialised organisations and some large aid agencies (EC, IDA) whose portfolios are highly fragmented.

Table 2.2: Fragmentation of aid, by donor and decade DAC donors 1960-1969 1970-1979 1980-1989 1990-1999 2000-2006 2006 Australia 0.57 0.51 0.27 0.17 0.13 0.16 Austria 0.35 0.23 0.20 0.09 0.05 0.06 Belgium 0.59 0.25 0.16 0.04 0.04 0.05 Canada 0.27 0.10 0.04 0.03 0.03 0.04 Denmark 0.40 0.08 0.09 0.05 0.05 0.05 Finland 0.25 0.09 0.05 0.05 0.05 France 0.37 0.05 0.03 0.04 0.04 0.04 Germany 0.14 0.05 0.04 0.04 0.03 0.03 Greece 0.23 0.25 0.14 Ireland 0.29 0.22 0.10 0.09 0.07 Italy 0.19 0.12 0.06 0.06 0.05 0.11 Japan 0.20 0.13 0.07 0.07 0.06 0.06 Luxembourg 0.05 0.05 0.06 Netherlands 0.46 0.14 0.05 0.03 0.03 0.04 New Zealand 0.10 0.12 0.07 0.05 0.06 Norway 0.47 0.11 0.08 0.05 0.03 0.03 Portugal 0.49 0.27 0.31 0.28 0.16 Spain 0.18 0.07 0.04 0.04 Sweden 0.22 0.13 0.08 0.05 0.04 0.03 Switzerland 0.24 0.09 0.04 0.03 0.03 0.03 United Kingdom 0.09 0.11 0.06 0.04 0.06 0.08 United States 0.10 0.09 0.09 0.09 0.09 0.14 Average 0.31 0.15 0.10 0.08 0.07 0.07 Multilateral donors 1960-1969 1970-1979 1980-1989 1990-1999 2000-2006 2006 AfDF (African Dev.Fund) 0.14 0.05 0.05 0.06 0.06 Arab Agencies 0.31 0.06 0.07 0.03 0.03 AsDF (Asian Dev.Fund) 1.00 0.23 0.19 0.16 0.13 0.12 CarDB (Carribean Dev. Bank) 0.24 0.15 0.14 0.16 0.22 Council of Europe 1.00 1.00 1.00 EBRD 0.11 0.12 0.13 EC 0.13 0.05 0.03 0.02 0.02 0.02 GEF 0.13 0.06 0.04 Global Fund (GFATM) 0.23 0.03 IBRD 0.22 0.31 IDA 0.48 0.18 0.13 0.06 0.05 0.05 IDB 0.34 IDB Spec. Fund 0.15 0.08 0.08 0.11 0.17 0.16 IFAD 0.35 0.06 0.03 0.03 0.03 IMF Trust Fund 0.07 0.50 Montreal Protocol 0.24 0.45 0.36 Nordic Dev. Fund 0.08 0.09 0.07 SAF+ESAF+PRGF(IMF) 0.14 0.11 0.10 0.09

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UNDP 0.03 0.02 0.02 0.02 0.02 0.02 UNFPA 0.05 0.05 0.03 0.02 0.02 UNHCR 0.13 0.20 0.11 0.08 0.03 0.03 UNICEF 0.05 0.06 0.05 0.03 0.03 0.03 UNRWA 0.54 0.54 0.36 0.42 0.49 UNTA 0.02 0.02 0.01 0.01 0.01 0.01 WFP 0.08 0.05 0.04 0.05 0.05 0.07 Average 0.29 0.18 0.15 0.09 0.10 0.10

Source: Authors based on OECD (2009b).  Moreover, portfolios have become more fragmented over time. This is true for most donors, but some stand out. The US has kept the same level of fragmentation for 45 years. Donors such as Australia and Japan, that are quite specialised geographically, have less fragmented portfolios. Turning to multilateral donors, we observe that some of them also have highly fragmented portfolios. The European Commission and UN agencies give aid to most countries and so have extremely low indices. Figure 2.6 uses data for 2006 and shows the level of fragmentation for each donor in 2006 where a low value indicates high fragmentation. The most fragmented portfolios are held by multilateral institutions, especially UN agencies and the European Commission.

Figure 2.6: Portfolio fragmentation in 2006, by donor

UNTAUNDPUNFPAECUNICEFUNHCRGermanySwitzerlandArab AgenciesIFADGlobal FundNorwaySwedenNetherlandsSpainFranceCanadaGEFBelgiumIDADenmarkFinlandKoreaAfDFLuxembourgAustriaNew ZealandJapanWFPIrelandNordic Dev. FundCzech RepublicArab CountriesUnited KingdomIMFTurkeyItalyAsDFEBRDGreeceUnited StatesOther Bilateral DonorsAustraliaIcelandIDB Spec. FundPortugal

CarDBMontreal Protocol

ThailandUNRWA

Slovak RepublicPoland

Hungary

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Source: Authors based on OECD (2009b) Fragmentation by recipient.

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In this section we mirror the analysis from the recipients’ perspective. Put schematically, the developing country decides how much money it requires and then tries to find the necessary financing (Kharas, 2008). Because negotiating with each lender entails transaction costs (administrative resources, meetings, human capital, etc.), and that administrative cost is often large (see Acharya et al. 2006, Knack and Rahman 2007), the country decides its sources of finance taking these costs into account, the relative price of the different financing sources, and their corresponding risks. It must be mentioned that some recipients started to take measures against fragmentation. India decided in 2003 to progressively phase out aid from all but six large donors, arguing that the benefits were too small compared to the bureaucratic costs associated with small sums of money (Financial Times 2008). In order to reduce the administrative burden of aid Tanzania also announced in 2003 that the period April-August each year would be a “quiet time” and that it would meet only the most urgent donor missions during these months (Roodman, 2006).

Figure 2.7 plots the average recipient portfolio size in each year.

Source: Authors based on OECD (2009b). 

A developing country received aid on average from less than 2 donors in 1960, but by 2006 this had risen to more than 28 in 2006. This can be further divided into 15 DAC donors, 9 multilateral donors, and 4 bilateral non-DAC donors. The median number of donors was even higher at 33. The increase has been gradual and continuous, and shows few signs of abatement. This trend has not been confined to a particular region. Recipients in all regions experienced a large increase in the number of donors (Table 2.3). However there are some disparities between regions, with Other Asia and Oceania being less afflicted by the problem than other regions.

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Table 2.3: Average number of donors per recipient, by region and decade 1960-1969 1970-1979 1980-1989 1990-1999 2000-2006 2006 Europe 9.20 16.60 17.59 20.90 33.72 34.40 Latin America and Caribbean 4.23 12.04 17.82 22.18 23.96 25.06 Middle East and North Africa 5.81 15.46 18.99 23.17 28.14 29.86 Other Asia and Oceania 3.21 9.69 14.71 17.69 20.45 21.33 South and Central Asia 6.36 18.98 25.93 26.05 34.18 36.94 Sub-Saharan Africa 5.04 15.59 23.95 27.73 30.64 32.62

Source: Authors based on OECD (2009b).  The next table identifies the ten recipients with the highest average portfolio size in each decade, and in 2006. In particular, India and Tanzania both acted against fragmentation have always been present in this list.

Table 2.4: Ten largest recipient portfolio sizes, average by decade 1960-1969 1970-1979 1980-1989 1990-1999 2000-2006 2006

India 13.1 India 25.7 Bangladesh 31.6 Mozambique 36 Ethiopia 42.1 Sri Lanka 44 Pakistan 11.5 Pakistan 25.2 Pakistan 31.3 Uganda 35.2 Mozambique 40.3 Afghanistan 43 Turkey 11.4 Tanzania 25.1 India 31.2 India 35 China 40.1 Indonesia 43 Chile 11.3 Kenya 24.2 Tanzania 31 Ethiopia 34.8 Pakistan 40.1 Zambia 43 Tanzania 10.8 Egypt 24 Kenya 30.5 Philippines 34.6 Ghana 40.1 Malawi 43 Nigeria 10.4 Ethiopia 23.8 Philippines 30.5 Ghana 34.4 Tanzania 40 Vietnam 43 Indonesia 10.4 Sri Lanka 23.8 Uganda 29.8 Nepal 34.3 Uganda 40 Bangladesh 43 Kenya 10.3 Sudan 23.5 Sudan 29.8 Bangladesh 34.2 India 39.9 Tanzania 43 Tunisia 10.2 Indonesia 22.8 Ghana 29.7 Kenya 34.1 Senegal 39.9 Uganda 43 Brazil 9.8 Turkey 22.7 Ethiopia 29.6 Tanzania 34 Kenya 39.6 Nepal 42

Source: Authors based on OECD (2009b).  For each recipient we then compute its Hirschman-Herfindahl index: a large value means that a large share of the aid the country receives comes from a small number of donors: a low index can be interpreted as a high dispersion of its aid among many donors. Aid allocation was highly concentrated before 1970, but quickly became much more fragmented (Figure 2.8). This process has slowed down but is still ongoing. Aid from multilateral donors is usually less fragmented than aid from DAC countries (Figure 2.8). By using data on portfolio size and concentration we are able to uncover the process of fragmentation: an ever-increasing portfolio size that brings in only small aid quantities.

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Figure 2.8: Recipient concentration index H

Source: Authors based on OECD (2009b). 

Table 2.5 gives the list of countries with the most fragmented aid allocation, measured as the average of their concentration index. Some recipients have suffered from fragmentation for a long time. Tanzania topped the list during three decades. It implemented its new policy in 2003 and actually ranked lower in the following years. Mozambique, Lesotho, Gambia, Kenya are also often in the list. At the other end of the ranking we find small countries, mainly islands (St Helena, Tokelau, Mayotte, Niue) which get most aid from a single donor.

Table 2.5: 10 Most fragmented recipient, all donors, aid net of emergency aid and debt forgiveness grants, by decade

1960-1969 1970-1979 1980-1989 1990-1999 2000-2006 2006 Sri Lanka 0.29 Tanzania 0.10 Tanzania 0.076 Mozambique 0.069 Mozambique 0.072 Mozambique 0.073 Argentina 0.33 Sri Lanka 0.11 Kenya 0.081 Tanzania 0.076 Nicaragua 0.091 Nicaragua 0.079 India 0.34 Bangladesh 0.13 Zambia 0.087 Zimbabwe 0.081 Nepal 0.093 Zambia 0.083 Indus Basin

0.34 Ethiopia 0.13 Mozambique 0.087 Cape Verde 0.083 Burkina Faso

0.095 Rwanda 0.085

Indonesia 0.34 India 0.14 Lesotho 0.092 Gambia 0.087 Kenya 0.096 Bosnia-H 0.086 Sudan 0.35 Pakistan 0.15 Rwanda 0.094 Ethiopia 0.089 Cambodia 0.096 Malawi 0.088 Togo 0.35 Tunisia 0.15 Cape Verde 0.095 Sudan 0.090 Tanzania 0.098 Mali 0.089 Ghana 0.35 Peru 0.16 Bangladesh 0.097 Lesotho 0.092 Cuba 0.10 Benin 0.089 Somalia 0.38 Nepal 0.16 Gambia 0.099 Kenya 0.092 Mali 0.10 Somalia 0.091 Ethiopia 0.38 Nigeria 0.16 Sri Lanka 0.099 Angola 0.095 Bolivia 0.10 Cambodia 0.092

Source: Authors based on OECD (2009b). 

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Fragmentation by sector

In this section, we use DAC’s Country Programmable Aid (CPA) classification, which includes flows that are defined as ODA (Official Development Assistance) but discounting humanitarian aid, food aid, donor administrative costs, debt relief, budget support to NGOs, aid to refugees in donor countries and unallocated flows. Each time we refer to aid in the text, we mean CPA and not ODA. CPA is meant to better capture programmable development projects not motivated by emergency situations. It also explicitly excludes activities not located in the developing country (donor administrative costs, aid to refugees in the donor country) and debt relief that does not imply an actual cash transfer. Many authors, for instance Kharas (2007), consider that CPA is a better measure of development efforts than ODA. It is also the quantity that DAC OECD uses in its studies of fragmentation (OECD 2008). We exploit the Credit Reporting System (CRS) Aid Activities database of the OECD. This reports a sectoral breakdown of aid data for the 22 member countries of the OECD’s Development Assistance Committee (DAC), the European Commission and other international organisations. Countries that are non-DAC but OECD members report their aggregate, but not their sector aid disbursements, and so are not included in the analysis. “New” donors, such as Brazil, China, India, Russia, Saudi Arabia or Venezuela are becoming increasingly important and any study on fragmentation would greatly benefit from their inclusion. However, aid data for these countries is scarce and virtually non-existent at the sector level. If anything, we believe that adding these donors to the analysis would make fragmentation even worse than it is described here. Readers should therefore take numbers presented in this paper as a lower bound on how severe fragmentation is. The CRS database includes all aid recipients, but we do not consider multi/regional recipients (for example, funding to the African Union). An aid project is defined as an entry in the CRS database, as identified by its CRS identification number and with a strictly positive flow (some observations are zeros)21. By imposing these conditions, we aim to capture flows that correspond to projects in the field, and not in the donor country; that represents financing available to the developing country. There is serious under-reporting in the data, such that any trend must be interpreted with caution. Disbursement data is virtually absent before 1990 and commitment data, though available since 1973, is also incomplete in the early years. To avoid this issue, we focus primarily on the last year of available data (2007) and refrain from exploiting time variation. We start by simply counting the number of aid projects in the world. There were 93 517 projects in 2007, based on disbursement data. Because there is under-reporting, this is a lower bound. If instead of using CPA we simply refer to ODA projects, we find 132 326 aid projects in 2007. Nevertheless, the number of aid projects is arguably a crude indicator of the extent to which aid allocation is fragmented. An important limitation of counting aid projects is that it does not take into account when different projects are inter-related and are therefore part of a bigger, coordinated project. The CRS data does not reflect these subtleties.  

21 See Appendix for more details about the method we used to count the number of projects.

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Figure 2.9. Number of aid projects, 1973-2007

 Source: Authors, 2009, based on OECD DAC databases, 2009. 

This large increase in aid projects has been accompanied by a fall in the average project size, as shown on Figure 2.10. The expansion of partnerships has not been met by a similar expansion in aid budgets, therefore resulting in more, but smaller, projects22.

Figure 2.10: Average project size, 1990-2007

 Source: Authors, 2009, based on OECD (2009b). 

22 The Appendix reports the number of projects and average project size for each donor in 2007.

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These figures so far show a sharp increase in the number of projects, but we do not know if that increase has been equally distributed across sectors. To answer this question, Figure 2.11 shows the plot of the number of projects in each sector as a percentage of the total number of projects. Aid sector definition follows that of DAC. Because pre-1990 data for disbursements is hardly existent we rely on commitment data to have a more consistent long-term view.

Figure 2.11 Project sector repartition, 1973-2007, commitment data

 Source: Authors calculation based on OECD (2009b). 

We observe a major shift in priorities from Production (agriculture, forestry, fishing, industry, mining, construction, trade, and tourism) and Economic (transport, communication, energy and banking) sectors to Social (health, education, population, water supply, government and conflict prevention) sectors. Social sectors now represent more than 60% of the total number of projects, up from 30% in the 1970’s (disbursement data would show a very similar picture). A finer breakdown confirms these results. Instead of using broad sectors, sub-categories are not aggregated. Social sectors are the seven bottom ones on the picture. The Government & civil society and Population sectors have gained the most projects over time. Agriculture and Energy sectors have gained much less. Social sectors have benefited the most from the expansion in project numbers. Looking at quantities committed or disbursed, instead of number of projects, would yield the same conclusion. The shift in priorities from donor countries towards these sectors also implies that we should expect them to be most fragmented.

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Figure 2.12. Project subsector repartition, 1973-2007, commitment data

 Source: Authors calculation based on OECD (2009b). 

Aid agencies are also prone to recurring fads and fashions that are reflected by shifts in sector allocations. Donors used to invest heavily in infrastructure before moving towards social issues. This trend has been observed elsewhere (see Easterly 2009). In the early days of aid, the emphasis was on increasing the quantity of physical infrastructure and, to a lesser extent, agriculture. The theoretical rationale behind giving aid to raise the stock of capital was provided by the two-gap model. This model stated that developing countries lacked investment, and so that aid had to finance large projects (dam, power station, highways, steel mills, etc.). However, by the 1990s, donors realised that low maintenance on these large scale projects made them ineffective. From the 1980s onwards, donors favoured an agenda of structural adjustments and macroeconomic reforms. The subsequent disillusionment with this agenda led to a focus on institutional reform, corruption and democracy, as shown by the quick expansion of aid to the Government and Civil Society sector. The importance of institution quality, property rights, and the rule of law was emphasised by North (1991). Acemoglu et al. (2001) also initiated a vast literature on the long-term consequences of institutions. Agriculture has been a victim of the comparative attractiveness of social sectors for aid agencies. OECD (2008) argues that transaction costs are lower in social sectors and that funds in these sectors are easily channelled through large public sector entities. Moreover, social sector aid leads to the delivery of well identified basic services that have a direct impact on development targets such as the Millennium Development Goals. Easterly (2009) notes that, despite some clear

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successes in this sector, like the Green Revolution in India, and the recognition that it is important for development, African agricultural aid is widely seen as a failure. He also remarks that, as a share of total aid, aid to agriculture has sharply fallen, and that the World Bank and USAID have been severely criticised for neglecting the sector. Since in the poorest countries virtually everyone works in the agricultural sector, this lack of consideration must have been quite damaging. Caselli (2005) shows that, looking at sectoral data, one of the main reasons why poor countries are poor is their much lower labour productivity in the agricultural sector23. He quantifies this effect by computing cross-country income differences, had all countries had the same agricultural labour productivity as the USA, he finds the stunning result that, world income inequality would virtually disappear. Improvements in agricultural productivity would therefore bring sizable benefits. The small number of projects and low stakes in the sector seem to imply that aid donors failed to recognise this potential, or at least had other reasons not to exploit it. Foreign aid to farming fell dramatically between 1980 and 2004, but also public spending halved in the sector during the same period. The neglect of traditional donors and developing countries’ governments has led new donors like China or oil exporters from the Persian Gulf to invest in the sector. An official at Sudan’s agriculture ministry said investment in farming in his country by Arab states would rise almost tenfold from USD 700 million in 2007 to a forecast USD 7.5 billion in 2010, representing half of all investment in the country when it was a mere 3% in 2007. These new investors bring with them seeds, marketing techniques, jobs, schools, clinics and roads. We do not enter here into the debate of whether these investments will succeed where other Western initiatives failed, or whether aid from “new donors” carries costs that reduce its value, but it seems that the neglect of agriculture by traditional donors has opened up the way for emerging donors in this sector.

2.3 VOLATILITY 

Aid volatility is usually considered to be a serious problem, and is known to be higher than for domestic sources of finance. A direct consequence of volatility is that recipient governments find it difficult to plan ahead. This is even more complicated when aid is not only volatile but also unpredictable. In a recent paper, Celasun and Walliser (2008) show that between 1990 and 2005, there was a significant absolute difference between aid promised and aid given, equal to 3.4 per cent of each sub-Saharan African nation’s GDP (in the case of countries like Sierra Leone the swings have been equivalent to 9 per cent of GDP). Agénor and Aizenman (2007) show that aid volatility may have permanent costs in terms of output and growth, and may create a poverty trap. Large projects require sustained capital inputs and a sudden shortfall may seriously jeopardise their achievement. This is especially true for recipients where ODA represents a large share of their total revenue. For instance in 2007 grants represented 30 per cent of the total tax revenue in Tanzania. The figure reached 47 per cent in Rwanda. For these countries shocks to aid supply correspond to significant variations in their revenues. Arellano et al. (2009) find that a fall in aid volatility would imply significant welfare

23 The other two reasons are that they are also less productive in the non-agricultural sectors, and that much of their labour force is in the agricultural sector, where labour productivity is lower.

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gains. Borensztein et al. (2008) explain that aid is volatile and fails to smooth economic shocks. They call for a new role for aid as a stabilising financial instrument. Our study of volatility contributes to this debate by investigating the relative volatilities of official and private flows. In view of the increasing role of capital flows for developing countries, the need for a counter-cyclical instrument that shields against the high volatility of these new flows is becoming even more pressing. Volatility is defined as the coefficient of variation (the standard deviation of the quantity divided by the mean of its absolute value). This normalisation avoids finding larger volatilities for larger flows. It is calculated for each recipient and then averaged over all the developing countries in the sample.

 Table 2.6: Volatility of flows, 1960-2006

Total ODA

Total ODA net of emergency aid and debt relief

Bilateral ODA

Multilateral ODA

FDI Bond Equity Remittances

Mean 0.78 0.76 0.85 0.98 1.26 2.65 3.30 0.74 Standard deviation

0.44 0.37 0.48 0.44 0.39 1.13 1.38 0.41

Minimum 0.26 0.26 0.29 0.37 0.53 0.49 1.32 0.08 Maximum 3.71 2.94 3.99 4.11 2.38 6.08 6.08 1.80 Number of observations

152 152 152 152 126 71 71 124

Source: Authors based World Bank (2009b) and OECD (2009b).  Table 2.6 presents the volatility of each flow for the period 1960-2006 (for private flows it is measured on the period 1970-2006). First, total ODA has a volatility of 0.78 which means that its standard deviation is on average 78 per cent of its mean. It confirms the result that aid is quite volatile for developing countries, in line with the findings of previous studies already mentioned on aid volatility. Second, we consider net ODA net of emergency aid and debt relief. These two categories are by nature volatile, because of natural disasters for emergency aid, and because debt relief is usually granted in large amounts. Nevertheless, the figures show that aid volatility is actually not created by these. Third, ODA can be further split up into bilateral aid from DAC countries and multilateral aid. The former is less volatile than the latter, such that DAC donors appear to provide aid on a more stable basis. Fourth, as expected, private flows are more volatile than aid, with the exception of remittances. Unsurprisingly equity and bond flows are the most volatile. Remittances are not only a major source of funds but they are also a stable source.

Table 2.7: Volatility of flows, pair-wise comparison, 1970-2006 Total ODA Bilateral ODA Multilateral ODA FDI Bond Equity Remittances Mean 0.67 0.72 0.94 1.23 2.35 3.06 0.74 Standard 0.25 0.30 0.30 0.39 0.95 1.32 0.38

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deviation Minimum 0.27 0.29 0.54 0.69 0.49 1.32 0.08 Maximum 1.41 1.64 1.68 2.38 5.27 6.08 1.80

Source: Authors based World Bank (2009b) and OECD (2009b). 

Table 2.8 Volatility of flows, by decade, 1960-2006 Total

ODA Total ODA net of emergency aid and debt relief

Bilateral ODA

Multilateral ODA

FDI Bond Equity Remittances

1960-1969 0.73 0.73 0.72 1.65 n.a n.a n.a n.a 1970-1979 0.61 0.61 0.65 0.79 0.99 1.79 2.20 0.36 1980-1989 0.37 0.37 0.44 0.50 0.97 1.74 2.19 0.38 1990-1999 0.46 0.48 0.52 0.62 0.90 1.83 1.86 0.50 2000-2006 0.46 0.43 0.53 0.65 0.88 1.72 1.83 0.41

Source: Authors based World Bank (2009b) and OECD (2009b).  Quite surprisingly, despite explicit policy recommendations such as those contained in the Paris Declaration, aid has not become more stable over time (Table 2.7). Both bilateral and multilateral donors increased the variability of their aid compared to the period 1980-1989. On the other hand private flows are now more stable than they used to be. Only remittances are now slightly more volatile (although they are still the most stable source of funds). Table.8 restricts the sample to non-missing observations for ODA and remittances to avoid losing too many observations. It confirms that ODA volatility has substantially increased for both types of donors and that remittances and ODA have similar volatilities.

Table 2.9: Volatility of flows, by decade, pair-wise comparison Total ODA Bilateral ODA Multilateral ODA Remittances 1970-1979 0.46 0.48 0.61 0.37 1980-1989 0.33 0.39 0.46 0.38 1990-1999 0.40 0.46 0.59 0.50 2000-2006 0.46 0.52 0.62 0.45

Source: Authors based World Bank (2009b) and OECD (2009b).  It is known that aid flows follow the recipients’ business cycles rather than being counter-cyclical. Borensztein et al. (2008), among others, document this result by computing the correlation between aid and income shocks, defined by the gap between the variable and its five year moving average (so at least five years of observations are required for the shock variable to be defined). We follow their strategy but using aid and capital flow data. Table 2.10 shows that the correlation between aid and private capital flow shocks is extremely low. We knew from the literature that aid was not disbursed during a shortfall in GDP, and have now established that it is neither used as a buffer against a shortfall in capital flows. We complement this result by computing the correlation between FDI and other capital flow shocks. The correlations are larger, though still quite small, and all positive. It indicates that the different capital flow shocks tend to happen simultaneously, thus making the counter-cyclicality of aid even more crucial during an unexpected negative shock. Our results on volatility show that compared to private capital flows

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aid volatility is rather limited, but that aid is not used to act as a compensatory mechanism for capital flow shocks.

Table 2.10: Correlation between aid and capital flow shocks Coefficients of correlation

FDI-5-year moving average of FDI

Bond-5-year moving average of Bond

Equity-5-year moving average of Equity

Remittances-5-year moving average of Remittances

ODA-5-year moving average of ODA

0.009 -0.04 -0.03 0.008

FDI-5-year moving average of FDI

0.12 0.09 0.19

Source: Authors based World Bank (2009b) and OECD (2009b).3. Towards greater aid efficiency   In a time of economic crisis, the incentives and motivation of donors to improve the quality and efficiency of aid giving should be correspondingly greater. Put another way, there is nothing like a hard budget constraint to concentrate the mind on the efficient use of resources. As our discussion is made evident here, the delivery of aid currently suffers from several important deficiencies. Among these is the multiplication of actors, the lack of coordination, the fragmentation of aid, and the volatility in the delivery. The Paris declaration addresses these issues but unfortunately so far objective evaluations of the degree of implementation of the Paris declaration have so far been fairly critical of the slow rate of progress. Why is this? The persistence of the problems despite a widespread recognition and consensus of their importance suggests that there are some structural problems which perhaps the donor community in themselves are unable to address. This has led some authors to go as far as to suggest that the aid system is unreformable in its current configurations and that the only people in a plausible position to tackle some of these problems are the recipients themselves (Whitfeld, 2009). This idea that aid coordination should be carried out by recipient countries rather than the donor community is an idea which is fully compatible with the ideas of the Paris declaration on ownership. Another important issue not tackled so far is the composition of aid flows. Currently around one third of aid flows is dedicated to technical cooperation. The worse case so far documented was Tanzania the end of the 1990s when technical cooperation approach 70% of all aid received by the country. This is difficult to justify by any measure. There is of course an important role to play in providing technical expertise and knowledge. But it is a more question of getting the balance right. There are similar problems related to the lack of aid going to the productive sectors. The Chinese example surely shows the demand amongst recipients are more investment in infrastructure roads buildings and factories. There is some econometric support of the idea that aid to the productive sectors has a strong impact on subsequent growth performance (UNCTAD, 2008).

2.1 A GLOBAL FRAGMENTATION INDEX  

Tracking progress regarding the degree of fragmentation is also a clear challenge. For that purpose, we propose first a donor social sector fragmentation index. For each donor we count the

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number of its significant partnerships in all subsectors of the social sector (education, health, etc) and divide this number by the total number of partnerships in the social sector that involves the same donor. This is repeated for each sector. We also define a global donor fragmentation index as the fraction of significant partnerships across all sectors. The indexes are presented for year 2007 in Table 3.1. They indicate which donors have the most fragmented portfolios in each sector. The global donor index is not based on the sector figures, but on a finer decomposition of subsectors (the social sector is decomposed into education, health, population, etc.). Sector indices confirm the higher fragmentation of the social sectors, according to both measures, and which donors are the most fragmented. The United States often has the most fragmented allocation, a possible explanation being that the donor usually disburses a very large share of its aid to a handful of countries. Its other recipients therefore appear as insignificant, even though the United States are still an important donor for them.  An alternative global index is to consider recipient countries instead of recipient-sectors. This decomposition was the first used by OECD (2008) and Frot (2009). Using the same CRS data, the last column of the table presents a fragmentation index that is the proportion of significant recipients for each donor. The global index based on sectors is almost always much higher than when it is based on countries. It shows donors tend to specialise. Their aid share in the recipient may be low, and so the recipient is counted as insignificant, but their aid share in a sector in the same recipient may be very high, and the recipient-sector is counted as significant. Fragmentation is over-estimated by looking at aggregate country data. It shows that taking into account the sectoral nature of aid matters significantly in measuring fragmentation. This is true for the first definition of fragmentation, based on global shares, but less for the second definition. The “top” fragmentation measures for country-sectors and countries are quite close, except for the largest donors, whose fragmentation appears to be much lower with the country index. One could also argue that the country-sector index underestimates fragmentation because it does not take into account that sectors are in a same country. The country-sector index is neutral with respect to recipients whereas a portfolio where significant sectors are grouped in a few recipients could be considered to be less fragmented than when they are dispersed over many recipients24.

24 It is possible to modify the index to take into account the fact that sectors are in the same or different countries. However this degree of substitutability between country-sectors must be arbitrarily imposed and here we limit ourselves to the simple case where country-sectors contribute equally to the fragmentation index regardless of their being in the same or different countries.

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Table 3.1. Donor fragmentation index, 2007

Social Economic Production Multisector Programme

Assistance Global (country-sectors

Global (countries)

Global

Top

Global

Top

Global

Top

Global

Top

Global

Top

Global

Top

Global

Top

Australia 53 47 61 44 38 49 56 40 75 50 52 24 26 27 Austria 41 13 80 29 59 8 69 23 50 9 27 11 Belgium 49 27 59 28 49 29 60 40 50 0 52 17 29 17 Canada 46 36 50 31 59 17 41 32 75 50 47 19 29 28 Denmark 67 43 64 56 67 48 73 70 80 60 68 25 41 36 EC 45 70 68 74 46 76 54 82 82 97 51 37 50 87 Finland 51 13 77 20 42 20 64 29 100 0 54 9 27 5 France 47 55 73 66 36 54 55 68 92 83 49 36 34 53 Germany 43 58 45 60 43 71 54 54 89 56 46 32 39 74 Global Fund 63 94 63 94 53 62 Greece 51 13 92 15 55 9 94 35 57 8 26 4 Ireland 41 21 85 11 36 17 72 38 100 50 48 15 29 14 Italy 41 18 67 42 51 22 55 34 60 40 47 11 28 20 Japan 44 53 47 57 19 39 48 66 75 100 44 25 23 71 Luxembourg 60 23 92 29 65 19 71 32 65 16 39 13 Netherlands 61 55 62 40 45 60 67 52 81 100 61 27 35 41 New Zealand 68 28 87 70 59 19 82 42 100 100 71 17 32 15 Norway 54 44 64 41 43 50 55 38 100 100 55 26 31 33 Portugal 39 30 88 56 70 70 100 38 100 0 52 22 22 16 Spain 55 53 59 40 54 62 62 54 100 90 57 31 38 45 Sweden 39 33 47 29 33 28 71 58 100 100 42 21 39 38 Switzerland 50 22 79 38 36 21 66 43 100 33 53 12 32 18 UNAIDS 65 38 65 38 54 2 UNDP 68 35 77 37 56 47 77 42 67 19 60 20 UNFPA 77 82 77 82 62 5 UNICEF 53 29 45 26 52 25 48 28 United Kingdom 42 57 67 52 27 33 57 62 100 100 46 35 25 43

United States 30 67 27 54 38 74 29 51 100 100 31 36 30 75 Source: Authors calculation based on OECD (2009b). 

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How to summarise recipient fragmentation: a graphical tool

The sector decomposition allows further comparisons between recipients and across sectors within recipients. Tanzania is known to have a highly fragmented aid allocation. According to the World Bank aid to Tanzania is disbursed through more than 700 projects managed by 56 parallel implementation units25. Tanzania received 541 donor missions during 2005 of which only 17% involved more than one donor. Frot and Santiso (2008) confirm, using a Hirshman-Herfindahl index, that Tanzania has one of the most fragmented aid portfolios when one looks at the total aid donor allocations. They also confirm that this has been the case for many years though the situation slightly improved after the Tanzanian government took some preventive actions. Sector data also confirm that Tanzania has, on average, a fragmented aid allocation. To illustrate graphically this property, we construct a “radar” plot with the total number of donors active in the sector, and the number of donors that collectively represent less than 10% of total aid disbursed in the sector.

Figure 3.1. Fragmentation, Tanzania, 2007, disbursement data

 Source: Authors calculation based on OECD (2009b). 

The graph indicates which sectors attract most aid donors. As already shown, social sectors concentrate the majority of donors. Productive and infrastructure sectors receive little attention.

25 See http://siteresources.worldbank.org/IDA/Resources/Aidarchitecture-4.pdf.

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The interior line gives the number of donors that represent less than 10% of total aid. Fragmentation is severe when there are lots of donors in a sector and many disburse small quantities. For instance the education sector is quite fragmented. On the other hand, 75% (3 out of 4) of the donors collectively disburse less than 10% of total aid in the transport and communications sector. This proportion is high, but given that there are only 4 donors, one cannot really say that aid to the sector is very fragmented. Tanzania is often considered as a case of an aid darling (i.e. a country that is perceived in an extremely positive light by most donors, and thus one that receives very significant donor support). For aid orphans, the radar plot is quite dissimilar. As an example, consider the case of Belize. The radius of the radar plot is the same as for Tanzania. Belize has very few donors in each sector. This is only revealed by a sector analysis: if we count total disbursements to Belize, we find 16 donor countries. Many sectors do not receive any attention at all.

Figure 3.2 Fragmentation, Belize, 2007, disbursement data

 Source: Authors calculation based on OECD (2009b). 

We use Papua New Guinea as a final example. According to Figure 3.4, Australia enjoys a monopoly in the country. The characteristic of Papua New Guinea is that the two lines are often very close, especially in sectors with many donors. Australia is the dominant donor in most

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sectors and sometimes even represents more than 90% of total aid (for instance in the Government & Civil Society sector, Australian aid is USD 92 million and total aid to the sector is USD 99 million; the second biggest donor in the sector, New Zealand, disburses USD 3.3 million).

Figure 3.4. Fragmentation, Papua New Guinea, 2007, disbursement data

 Source: Authors calculation based on OECD (2009b). 

A world map of fragmentation

Finally, we present maps of fragmentation in the education and energy sectors, using the number of donors that collectively represent less than 10% of total aid as an indicator of fragmentation. The same colours are used on both maps. The darker the colour, the higher the fragmentation. Fragmentation in the education sector is severe in many countries, but never in the energy sector.

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Figure 3.5. Recipient fragmentation in the education (top) and energy (bottom) sectors, 2007, disbursement data

 

 Source: Authors calculation based on OECD (2009b). 

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2.2 BUDGET SUPPORT VS. PROJECT AID? 

What about aid modalities? How can this play a role in a potential improvement in the quality and efficiency of aid allocation? Since the 1980s, donor preferences have changed markedly, increasingly favouring programme over project aid. Budget support in particular has been one of the most favoured modalities of delivering aid in recent years. But paradoxically, despite its professed objectives of enhancing recipient ownership of policies, budget support has often inadvertently led to a situation whereby donors try to control the development agenda more extensively than they have in the past. As argued in Mold (2009), donors should look again at the relative merits of less ambitious forms of programme aid (e.g. SWAps) and project aid. We will elaborate on these arguments in this section.

Types of Conditionality

Contemporary donor discourse stresses development co-operation as a partnership. Yet in reality nearly all aid conditionality uses negative incentives (sticks) – threats of aid cuts, sanctions of various kinds, military intervention and commercial or diplomatic retaliation – and positive ones (carrots) – promises of more aid, trade concessions, seats at international negotiating tables or protection by foreign troops (Edwards, 1999, p. 114). This is a classic principal-agent type problem, to disburse aid in such a manner as to motivate an agent (the recipient) to act in ways that the principal (the donor) wishes (Killick, 1998). Edwards claims that, despite much disagreement on the impact of conditionality, the carrots generally have had more success than the sticks. Although the point is debatable, recalcitrant governments rarely change their behaviour owing to external pressure; indeed, such pressure can intensify their resolve to resist. The literature on the impact of sanctions is highly relevant here. Sanctions applied to Cuba by the United States since the 1960s have been singularly unsuccessful in catalysing regime change. Indeed it might be argued that they have, perversely, strengthened the position of the Cuban government, little impact on the Iraqi regime (although by all accounts it had a devastating impact on the Iraqi population). On the other hand, in the case of South Africa, although initially sanctions had little impact on the apartheid government, when the United States started really to apply pressure on the South African government with harsher economic sanctions from the mid-1980s onwards, the apartheid regime was eventually forced to capitulate (Davis and Engerman, 2003; Cortright and Lopez, 2000). An alternative way of looking at the question distinguishes whether conditionality affects mainly actions, outcomes or processes (Buiter, 2004). In recent years, policy conditionality (action-based conditionality) has been increasingly supplanted by outcome conditionality. Through its “MDG Contracts”, the European Commission has been especially active in promoting a results-based approach whereby a proportion of its general budget support is conditioned on the rate of progress towards the Millennium Development Goals. Linking conditionality more clearly to improvements in human development indicators has a lot of potential benefits. In practice, however, key outcomes tend to lag far behind actions, making it very difficult to donors to evaluate fairly the performance of the aid recipient. Worse still from the point of view of evaluating performance, the contribution of the particular action demanded by donors to the

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eventual outcome may be hard to identify measure and verify. Precisely because of this, conditions should affect only policy instruments genuinely under the recipient government’s control and demonstrably linked to the policy targets at which they aim (Mosley, 1987, p. 34). The existence of external shocks (e.g. climatic disasters or a sharp reduction in the price of a country’s exports) complicates matters further still – if targets are not met it becomes unclear whether this is due to reasons beyond the control of the recipient government (force majeure) or the poor implementation of the agreed policies. Put bluntly, donors often simply do not know enough about whether conditionalities have been satisfied by the recipient governments. Finally, process conditionality tries to predetermine the instruments of policy implementation, rather than the policies themselves. In a strict sense, this is governance conditionality, on which more will be said later. Whether the shift towards new aid delivery modalities (particularly budget support) has helped or hindered progress is also open to debate. Killick (2005, p. 95) argues that:“A major subject of concern with the shift to programme aid is its potential for adding substantially to conditionality. It could be that the relationships that have developed on a partnership basis are evolving as a substitute for conditionality, but it could easily result in more old-style policy stipulations. It could go either way.” In  some  dimensions  of  aid  policy,  it  is  clear  that  conditionality  is  still  onerous,  as witnessed  by  the  extensive  conditionality  associated with  initiatives  like  the MCA.  In any case, donors still place many constraints on host government policy execution and broader political governance, both through IFI and bilateral conditionalities.  

Conditionality and Country Ownership: How Far Does Budget Support Resolve the Dilemma?

Having established the practical difficulties of allocating aid according to performance, we ask the question about how donors can limit the difficulties associated with conditionality. Arguably, part of the solution entails re-examining the arguments in favour of different aid modalities. OECD (2008, p. 15) points out that most donors mix aid modalities depending on local contexts, with no one preferred way of delivering aid. Many donors still rely on project aid, whether or not as part of sector or programme approaches,  to maintain contact with field realities, to work with non‐traditional actors such as the private sector and civil society, to develop innovative approaches and to compensate for weak national capacity.  The  proliferation  of  decentralised  cooperation,  especially  in  donor  countries such as Spain which have expanded their co‐operation significantly in recent years, has increased the visibility of project aid.  According to Mosley and Eeckhout (2000), problems with project aid became increasingly evident even prior to the debt crisis of the 1980s in the so-called micro-macro paradox, where quite respectable rates of return on individual projects were accompanied with disappointing macroeconomic results. Nevertheless, the switch away from project to programme aid had little to do with these problems, but was rather an expedient reaction by donors to the challenges created

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by the debt crisis: donors needed a quick-disbursing aid instrument that could bring about policy change and would build government capacity depleted by the stabilisation measures of the 1980s. Project aid, it was decided, could do none of these things (Mosley and Eeckhout, 2000, p. 136). Another motive driving the move away from project aid was the objective of reducing the transaction costs of providing aid, which had been rising sharply (Riddell, 2007, p. 201). The traditional critique of project aid is well summarised by Killick (2008, p. 4): “Traditional assistance can lead to a proliferation of projects which is incoherent, non-transparent and inconsistent with the national government’s priorities. Project aid also spawns large numbers of Project Implementation Units (PIUs), often outside the government’s structures, tending to weaken efforts to build capacities within the public service. More generally, assistance programmes are often not well aligned with national development strategies, institutions and procedures.” As a consequence in the last two decades we have witnessed a marked shift towards ambitious and innovative forms of programme aid – that is, any form of contributions made available to a recipient’s country for general development purposes and which is not tied to specific projects, such as balance-of-payments support, general budget support, commodity assistance or debt relief. In the last decade, in particular, many hopes in particular have been pinned on budget support. As one study explains: “There is a wide range of expectations from general budget support. These include: improved co-ordination and harmonisation among donors; alignment with partner country systems and policies; lower transaction costs; higher allocative efficiency of public expenditure; greater predictability of funding; increased effectiveness of the state and public administration as general budget support is aligned with and uses government allocation and financial management systems; and improved domestic accountability through increased focus on the government’s own accountability channels” (IDD and Associates, 2006, p. 1). Yet despite a strong a priori rationale, the empirical evidence for the superiority of some of the new aid modalities over old-fashioned project aid remains quite weak. On budget support, the few impact studies undertaken so far all have conceptual and methodological problems stemming chiefly from the difficulty of tracing the effects on poverty and income levels of adding aid to overall budgetary resources (Riddell, 2007, p. 201). Further complications arise because budget support may constitute only a relatively small proportion of total aid resources; its impact is difficult to disentangle from that delivered through other aid modalities. Some important lessons can be gleaned from past experiences. In spite of the impression often given of budget support as an innovatory modality of aid delivery, it is not by any stretch of the imagination new. The Marshall Plan was essentially an ambitious (and by almost all accounts successful) experiment in budget support, though arguably under very special conditions – the reconstruction of countries which had already attained relatively high levels of institutional and human development. Subsequent experiences have been far more mixed. The United Kingdom provided budget support to a number of former African economies in the wake of independence during the 1960s, but gradually phased this out in favour of projects. In the 1990s, there were a number of experiments in budget support in Bangladesh, Cape Verde, Nicaragua and Tanzania. Again the experiences were mainly mixed and conditionality quite pervasive in the implemented programmes (White and Dijkstra, 2003). Most Australian aid to Papua New Guinea from its independence in 1975 until the mid-1990s also took the form of budget support. The Australian government estimated the total disbursements up to the early 2000s at USD 14 billion, so the

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sums were enormous. Yet an extensive official Australian evaluation carried out in 2003 heavily criticised the results, especially in terms of the accepted primary objective of budget support – increasing the autonomy and capacity of national institutions (AusAid, 2003). The report concluded (p. xi) “In considering the impact of aid since independence, it is relevant to recognise that there was a clear rationale for budget support in the immediate post-independence period but, in time, it was recognised that budget support had an adverse impact on incentives to develop and implement effective economic development policies.” One of the most extensive recent evaluations of budget support has been a three-year study commissioned by a consortium of donors in 2003, at the request of the OECD. It involved development of a methodology to evaluate budget support and case studies in seven countries: Burkina Faso, Malawi, Mozambique, Nicaragua, Rwanda, Uganda and Viet Nam. The published report (IDD and Associates, 2006) endorsed budget support but had mixed conclusions on its effectiveness. On the positive side, it found that funding of basic public services in health and education increased during the provision of budget support and found some evidence of reduced transaction costs of multiple meetings, donor visits and reporting requirements. But it also found under-estimation of the political risks in several countries, with over-optimistic assumptions about the ability of international partners to influence matters deeply rooted in partner countries’ political systems. It also drew attention to some fundamental tensions regarding the desire of donors to establish benchmarks and controls, their temptation to indulge in micromanagement and recipient countries’ wish to enjoy maximum liberty to determine expenditures according to their own priorities. The risk that budget support could paradoxically actually end up strengthening donor interference and control over recipient governments was spelled out very clearly by White and Dijkstra (2003, p. 550): “In most countries there is an enormous scope for improvement in public financial management, in budget reporting procedures and in accountability of budget performance to parliament and to the public at large. If donors succeed in focusing on these issues...and if they succeed in harmonising and simplifying their procedures with regard to budget support, these positive systemic effects become a reality. However, if they attempt to micro-manage the use of the funds and if each donor continues to set is specific requirements for that use or for reporting, budget support will become a drag on development.” It is not clear yet whether donors have managed to avoid these problems. In their study of the Multi-Donor Budget Support for Ghana, Killick and Lawson (2007, p. 4) note that “it has not achieved a sufficient critical mass and it has strayed too far from its initial objective of reducing transactions costs. These flaws have prevented it from minimising the risks of injecting budget support into a still weak fiscal system. While it is seen as having kept reform on the agenda and as having a generally pro-poor influence, it has neither been able to minimise the risks by galvanising more effective Performance Finance Management (PFM) systems nor to maximise the payoff in terms of poverty-reduction.” A UK Parliamentary Committee (Committee of Public Accounts, 2008, pp. 5-7) into the use of budgetary support by DFID arrived at the conclusion that the decision process governing which countries receive budget support is still opaque: “DFID have stated principles which must be met before working with government partners, and have developed useful tools to help appraise prospects for budget support and assess risks. Most budget support proposals, however, do not clearly weigh up the risks and benefits. In addition, the pattern of budget support that has arisen appears arbitrary.”

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Knoll (2008, p. 12) notes that the donor community, which includes bilateral development agencies, the European Commission (EC) and regional development banks, still views recipient compliance with the terms of the IDA´s Poverty Reduction Support Credit (PRSC) and the IMF´s Poverty Reduction and Growth Facility as a precondition for general budget support disbursal. Among the bilateral agencies, for instance, SIDA (Sweden) directly or indirectly (via the EC) attaches its release decision to recipient compliance with PRGF conditionality, and makes decisions on a case-by-case basis. The Belgian budget-support scheme for Burkina Faso is attached to Poverty Reduction Support Credit conditionality, while DFID (United Kingdom) draws its disbursal conditions from the PRSP (or from the terms of the PRGF, if those are considered consistent with the British approach). In the United Republic of Tanzania, DANIDA (Denmark) partly referred to PRSC prior actions but also took into consideration conditions directly from the PRSP. Linking up budget support to IFI programmes means that, despite the intentions of budget support, conditionality is still pervasive. As a consequence of all this, the strengthening of domestic accountability remains an elusive objective (de Renzio, 2006). It would seem reasonable to conclude that budget support is still far from delivering in terms of its promise to reduce conditionality and enhance recipient country ownership. Moreover, it appears to be particularly unsuited to fragile states with weak institutional structures. If that is the case, then there is a danger that it ends up legitimising a new kind of ex ante conditionality, whereby budget support is considered appropriate for some countries, but not for others. In a sense, then, it is open to the same criticisms regarding the Millennium Challenge Account. In addition, because of its all-encompassing nature, budget support, however well designed, almost inevitably leaves an enormous amount of discretion to donors in terms of deciding whether a country is progressing well or not, and thus is worthy of continued support. This puts recipient governments constantly on the defensive, in terms of justifying their performance, and raises the risk, paradoxically, of greater conditionality, not less. It has led to a situation whereby donors demand greater participation in policy discussions and planning in a number of countries, and become deeply involved in core policy processes. Donors have also lacked important political economy insights as to the nature of domestic power struggles. Precisely for these reasons, there has been a growing number of voices questioning the wisdom of a greater emphasis on budget support (de Renzio, 2006; Deaton et al., 2006; Whitfield and Maipose, 2008; de Renzio and Hanlon, 2009). Disappointing or politically complicated experiences of budget support in recent years with previously designated aid “darlings” (countries such as Ethiopia, Rwanda and Uganda) have also convinced some members of the donor community to stay on the sidelines for the time being: they have either set a ceiling to this aid modality (25 per cent in the case of Denmark) or still make little use (Canada, France, Portugal, Spain, US) or in some cases no use of it at all (Greece, Luxembourg) (OECD, 2008; p. 15). What are the alternatives then? Banerjee (2007, p. 21) puts the arguments in a very forceful way: “Donors are unclear about what they should be pushing for. Given that, it is easy to lead them to grandiose and unfocused project designs where none of the details are spelled out clearly and diverting money is a cinch. From this point of view, the current fashion of channelling aid into broad budgetary support (rather than specific projects) seems particularly disastrous. We need to go back to financing projects and insist that the results be measured.” Arguably, however, to dichotomise the whole issue into budget support vs. project lending is to risk simplifying a complex question. And there are many intermediate aid modalities. Different aid modalities will continue to coexist. The argument here is certainly not against programme aid as a concept,

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simply in favour of a more cautious assessment of the implications of budget support as a “low conditionality” aid modality. There are alternatives. For instance, SWAps, where support is given for a particular sector, underpin some of the same objectives as budget support, but arguably within the more modest and achievable context of building up local capacity at a sectoral level. Moreover, none of this implies an automatic endorsement of the current portfolio of projects. Donors have clearly promoted too many small, fragmented projects. The errors of the past need to be avoided – for example, the World Bank’s increased lending orientation since the 1990s towards both huge, nonproject emergency bailout packages and direct private-sector support in such areas as luxury hotels, the alcoholic beverage industry, banking, etc., all sectors with small or negligible impacts on poverty reduction. As Rich (2002, p. 26) notes, “both priorities have even less connection to directly helping the poorest of the poor than do more traditional Bank project loans”. At the same time, many projects in the productive sectors remain without funding: the African instigated New Economic Partnership for African Development (NEPAD), for example, identified many priority infrastructure projects, yet financing has often not been forthcoming2. The United Kingdom-led Commission for Africa (2005) suggested that Africa required an additional USD 20 billion a year in infrastructure investment, in the form of support for African regional, national, urban and rural priorities ranging from rural roads and slum upgrading to information and communication technology, and proposed that donors provide half of the additional funding up to 2010. Regrettably, it has become much easier to mobilise resources for nonproject purposes, such as technical assistance, debt relief, food aid and emergency relief, than for real development projects and programmes. In 2005, debt relief accounted for nearly one-quarter (USD 25.4 billion) of total official aid, including the Paris Club’s extraordinary debt cancellation for Iraq and Nigeria. By providing funds in this fashion, donors bypass the need to have well-designed and well-implemented development projects (Kharas, 2008, p. 12). The rise in technical co-operation, the most dynamic component of spending patterns over the last 20 years, also has stemmed partly from the proliferation of ambitious new aid modalities – particularly budget support – all requiring extensive monitoring and technical capacities. In 2005, the USD 29 billion spent on technical co-operation accounted for some 40 per cent of total ODA net of debt relief and remains largely tied to expensive Northern contractors and donor control. For some recipient countries, the share went much higher. It peaked at 73 per cent in Uganda in the late 1990s (Riddell, 2007, p. 202). As much as 70 per cent of aid for education is spent on technical assistance (The Reality of Aid, 2008, p. 8). In Zambia, more money is received each year in the form of technical assistance than the whole of the government budget for education (Glennie, 2008, p. 69).

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Conclusions

This report began by providing an overview on the different forms of development finance and then looked more specifically at the detail on ODA. As the world economy moves out of the economic crisis, there are serious concerns about future prospects for ODA. The size of the budget deficits in many OECD countries suggests that difficult decisions will have to be made over the short to medium term. While our econometric analysis shows little evidence of a strong relationship between economic growth in the OECD countries and the subsequent budgetary decisions on development aid, it is worth stressing the the depth of the current crisis is unparalleled since the 1930s. This report then reviewed evidence on aid levels and aid efficiency in the wider context of the financing needs of developing countries. We have estimated some of those financing needs for Sub-Saharan African countries, and also established econometrically that there is no clear relationship between economic growth and ODA levels, suggesting that the financial crisis may not have a dramatic negative impact on aid volumes that many analysts predicted when the crisis struck (e.g. Roodman, 2008). Indeed, in the aftermath of the financial crisis, from 2008 to 2009, total net official development assistance (ODA) from members of the OECD DAC did not decrease but rose slightly in real terms (+0.7%) to USD 119.6 billion, and increased 6.8% if debt relief is excluded from the figures. This is not to mimimise concerns over falling aid budgets – clearly, this is still a source of concern. Against this backdrop, our report suggests various ways in which to improve aid delivery and use the financial crisis as an opportunity to rethink the framework for development cooperation. Amongst those, issues related to the fragmentation and lack of coordination of aid should clearly be a priority. A real supply-side problem exists in the sheer number and diversity of new aid players, both public and private. Our index of fragmentation potentially helps monitor this situation.  In this paper, we also propose a shift in emphasis back to a more project-based approach to development aid. Project aid raises old concerns related to fungibility: would the funds provided for project aid really translate into increased investment in development, or would they simply allow more leeway for governments to spend more on less socially desirable goals such as military spending? But the question has equal validity for other aid modalities, and maybe is even more relevant for budget support, where oversight of government expenditures is far more onerous. While some recipient countries profess their enthusiasm for budget support, what they surely have in mind is large scale, no-strings-attached programme aid and not the kind of intrusive conditionality currently inherent in much budget and programme aid.  This is indisputably part of the reason why Chinese aid is so popular with many recipient governments. As a final note, it is worth reflecting a little deeper about the nature of the challenge that Chinese aid presupposes. Arguably, the low conditionality and project-based approach of Chinese aid and the way it is linked with trade and investment policies provides some useful lessons for the donor community. Oya (2006, p. 26) is very explicit about the benefits from

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Chinese vis-à-vis OECD aid and argues that there are four potential advantages: i) the aid is more targeted to important infrastructure projects with long maturity and long-term potential (there is no hurry for disbursements); ii) it is less bureaucratic and with lower transaction costs; iii) it is more efficient, with lower costs and faster, and iv) it allows more policy space (i.e. lower conditionality) and increases the bargaining power of African countries vis-à-vis other donors. In fact, contrary to some reports, it is untrue that the Chinese employ no conditionality to the use of their aid, and on occasions have vigorously expressed their concerns about corruption and the possible diversion of their resources towards illegitimate uses.  Legitimate concerns have been raised about the appropriateness of some projects supported by Chinese financing, and whether it encourages low-income countries to take on more debt in a way which is not sustainable (Manning, 2006). The Chinese are also widely accused of turning a blind eye to human rights abuses in some African countries and of refusing to lay down governance conditions on their African trading partners. Alden (2007, p. 102) notes “the disturbing character of China’s “no conditionalities” is that it succeeded in capturing African elites with ease, irrespective of their lack of democratic credentials. The directness of the Chinese challenge to the G8 vision for partnership and transformation is only just taking root”. An alternative view is that growing Chinese engagement in Africa is laying the ground for a promising new, more workmanlike relationship between recipient governments and donors, one built on mutual respect. Chan (2008, p. 347) puts the point forcefully: “Africans are not naïve and should not be patronised with concerns that they are being taken for a ride. Like Hugo Chavez in Venezuela, they are rejoicing at least in having options and having suitors….The Chinese provide not only an alternative to the West but also leverage to use in continued dealings with the West. Being courted might be the prelude to being taken seriously and, in a long string of African capitals, this is the true sunrise that the Chinese bring.”

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