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Appendix: Additional Regression Results
Stijn Claessens is at the IMF, University of Amsterdam, and CEPR. Danny Cassimon and Bjorn Van Campenhoutare both from the Institute of Development Policy, University of Antwerp. Earlier versions of the paper werepresented at the GARNET conference, September 27–29, 2006, Amsterdam; two ESRC workshops (October 5–62006 and May 2–3, 2007); Oxford/Cornell conference, New Directions in Development Assistance, Oxford, June 11–12 2007; and the ESRC/WEF conference: Finance and Development, London, June 27–29, 2007. We would like to thank those participants, especially Badi Baltagi and our discussant Adeel Malik, as well asAntonio Spilimbergo, Oya Celasun, and Rodney Ramcharan for their comments. We would also like to thank Ying Lin for his help with preparing the data. The work has been supported by the U.K. ERSC under its World Economy and Finance (WEF) Programme, National and International Aspects of Financial Development, Award RES–156–25–0009.
Easterly, Levine and Roodman (2004), for example, find that the Burnside and Dollar (2000) results do not stand up to a longer time period when using exactly the same specifications. Others have also questioned these results (Easterly, 2006 and Radelet, 2006 take a critical look at this research). Rajan and Subramanian (2005) argue that it is hard to find a robust effect of aid on the long-term growth of poor countries, even those with good policies, because aid inflows have systematic adverse effects on a country’s competitiveness, as reflected in a decline in the share of labor intensive and tradable industries in the manufacturing sector, stemming in part from real exchange rate appreciation triggered by aid inflows.
This is also reflected in the frequent policy seminars in the last few years on the “new international aid architecture,” for example, two such seminars during the 2007 Annual Meetings of the World Bank and IMF.
Seminal early studies include Little & Clifford (1965), OECD (1969), Bhagwati (1972), and Dudley and Montmarquette (1976). McKinlay and Little (1977) introduced econometrics; Trumbull and Wall (1994) and Wall (1995) introduced panel data econometric techniques.
Kuziemko and Werker (2006) find that U.S. aid increases by 59 percent and its UN aid by 8 percent when a country rotates onto the UN security council, suggesting bribery. Eisensee and Strömberg (2007) find that U.S. relief is less likely when the disaster occurs at the same time as other newsworthy events, for example, the Olympic Games, suggesting that relief decisions are driven by news coverage of disasters and less by need.
Dollar and Levin (2006) also find consistent sensitivity for democracy.
Berthélemy and Tichit (2004) investigate aid flows over 20 years (1980–1999) for 22 donors and 137 recipients. Comparing aid allocation policies in the 1980s with the 1990s, they find that the bias towards former colonial links has declined and instead donors tend to favor trade partners. Moreover, donors reward good economic policy outcomes more since 1990. Roodman (2005) finds donors’ selectivity standings by the 2005 Commitment to Development Index methodology to be relatively stable since 1995, but this refers more to the ranking of countries and not to their absolute degree of selectivity. Sundberg and Gelb (2006) show that poverty and policy selectivity of aid to Sub-Sahara Africa has improved over time, both for bilateral and multilateral donors. Other recent research (Amprou, 2007) confirms.
Note that we do not try to review the very large literature on the impact of aid: see rather Easterly (2003), McGillivray and others (2006), and Rajan and Subramanian (2005); an earlier review is Mark McGillivray (2004). See also Anderson and Waddington (2006), Radelet (2006) and Easterly (2007).
Collier and Dollar (2002), for example, ask how to allocate a given amount of aid according to countries’ policies that create a better environment for effective aid so as to reduce as much as possible world poverty.
The literature now considers among those potentially driving optimal aid factors such as sound institutions and human rights record (these factors may in part be motivated by their indirect impacts on the MDGs, especially those other than poverty). Gates and Hoeffler (2006), for example, find that the Nordic countries differ significantly from other donors as they give more to democracies and do not give to political allies. See also Amprou, Guillaumont, and Guillaumont-Jeanneney (2007).
This may be in part because donors like the U.K. DFID and the Netherlands development ministry have been using these models since the mid-1990s to help allocate their aid. For longer time, multilateral institutions have used such models to drive their aid programs (see Easterly, 2003 and Wood, 2006). This may have improved the patterns of aid allocations. Obviously, results remain dependent on the criteria used for optimal behavior—for example, growth, poverty, the flow of foreign direct investment received, gross primary school enrolment, infant mortality rate—and on the specific estimation methods regarding the impact of aid. For example, one can optimize with respect to aid impact on current poverty, given policy choices in the country. Or one can optimize with respect to aid impact on policy, given policies’ impact on future poverty in the country. The first, say, may lead donors to give more aid to countries where there is more poverty and where the impact of aid on poverty is larger (because the policy environment is better). The second may lead donors to take a longer-term view and try, through their aid-policies, to induce better policies, such as better governance and greater accountability that lead to lower poverty in the longer term.
Another study is Marchesi and Missale (2004). They examine grants and net loans made to a panel of 55 both HIPC-and non-HIPC countries during the last two decades and find that the total amount of net transfers to HIPCs, as compared to non-HIPCs, has been increasing with their debt level as greater net transfers in the form of net loans from multilaterals and grants more than offset lower bilateral loans.
With the increase in official debt reduction in the 1990s, some have investigated the motivations for debt relief. Chauvin and Kraay (2006) find that debt relief, particularly from multilateral creditors, has been allocated to countries with better policies in recent years. Somewhat surprisingly, however, conditional on per capita income and policy, more indebted countries are not much more likely to receive debt relief. However, countries that are large debtors, especially vis-à-vis multilateral creditors, are more likely to receive debt relief. Persistence in debt relief is also driven by slowly-changing country characteristics, indicating that it may difficult for countries to “exit” from cycles of repeated debt relief. Although we focus only on the determinants of aid, including debt relief, not on its impact, it is worth noting some papers. Chauvin and Kraay (2005) find little evidence that debt relief affects the level and composition of publicspending, or that debt relief raises growth, investment rates or the quality of policies and institutions. This suggests some skepticism regarding the likely benefits of large-scale debt relief. See also Cassimon andVan Campenhout (2007) who analyze the long-term fiscal response of HIPC debt relief compared to that of donor grants or loans. Aid disaggregation approaches have become more common in general, especially in fiscal response studies (of aid). See Mavrotas (2005) for a recent review of this literature.
There are some 20 multilateral financial agencies, providing about 30 percent of total net aid transfers in2005.
We also used other institutional environment indexes, such as the governance and corruption indexes of Kaufmann, and others (2004, 2005) and the law and order indexes of Freedom House, and found these to have similar relationships with aid flows, but less consistent so. We did not use alternative proxies for aid effectiveness suggested in the recent aid literature (Amprou and others, 2007), such as compliance with MDG targets, vulnerability to external shocks, quality of governance and accountability, or degree of democracy.
While not our focus, one can also analyze whether intra-bilateral debt composition affects flows, for example, when a bilateral engages in defensive lending to a country when it has relatively large debt claims.
The influence of ‘friends of the donor’ is analyzed by Alesina and Weder (2002), which define friends by the number of times the recipient has voted in the same manner as the specific donor in the UN. The data on friends do not cover all donors, however (only Australia, Belgium, Canada, France, Germany, Italy, Japan, Netherlands, Portugal, U.K., and USA) and are time-invariant. We thank though Beatrice Weder for providing us the data.
They argue that the original Matyas model is likely mis-specified, since “it does not span the whole vector space of possible treatments of explaining variations in bilateral trade [aid] and ignoring such bilateral trade [aid] interactions may lead to biased estimation” (see further Baltagi, Egger, and Pfaffermayr, 2003).
The pre-test estimator is based on a sequence of Hausman specification tests.
Grants are total bilateral grants, netted of debt forgiveness grants. Loans equal net loan transfers (corrected for offsetting entries on debt relief), including interest payments, but net of interest payments forgiven. Debt relief sums debt forgiveness grants (net of offsetting entries on debt relief) and interest payments forgiven. “Offsetting entries on debt relief” is the amortization part of debt forgiveness and has to be deducted to avoid counting amortization forgiveness in ODA in future years. See further, Global Monitoring Report 2007 (IMF and World Bank 2007, Box 4.1, p. 153) for details on DAC debt relief accounting.
Figure 2 sums all aid flows and divides that by the sum of all recipient countries’ populations, which means it is a weighted average of the individual countries’ per capita aid flows. It differs from the number in Table 2, since that is the simple average of the individual countries’ average per capita aid flows.
Furthermore, earlier results used a log dependent variable, which means only observations with positive aid flows were used. When using that specification, we also find evidence of defensive lending in our data over the whole period.
A notable exception to this view is Easterly (2007) who argues that in his review of the evidence there is little or no sign of increased selectivity with respect to policy and institutions.
There are some caveats. Specifically, the increase in aid flows may come as debt relief, new debt flows, or grants. To the extent that aid flows are overestimated because debt relief is measured as net transfers, the HIPC debt reduction may led to a measured, but not real increase in aid flows. Furthermore, whether this benefits the country is a separate question. An increase in aid in the form of debt flows following debt reduction may be less beneficial when, as some have argued, the reduction in debt is just creating free headroom for other lenders. See the discussion around the latest debt sustainability analysis, for example, IDA and IMF (2006).
The other exception is the income responsiveness. While statistically significant, the positive sign is counter intuitive.
In a comprehensive assessment of donors’ attitude towards aid, the Center for Global Development’s Commitment to Development Index (see Roodman, 2005)—covering not only aid flows, but also investment, security and migration—shows for 21 major donor countries—the same countries as in oursample except Luxembourg—an average improvement between 2003 and 2007 in the index (which runs from 1 to 10) from 5.1 to 5.3. Over the same period, the Index suggests some convergence among donors, with the standard deviation going from 1.03 to 0.84 between 2003 and 2007.
The correlations are somewhat less strong for the specific quality of aid CDI index. The correlation with the KKM voice and accountability index, for example, is now 0.67. This somewhat lower correlation suggests that aid allocation is not just related to the donors’ overall institutional environments but also to the institutional setup, and consequent behavior, of the specialized agencies involved in aid budgets.