Bilateralism and Multilateralism in Official Development Assistance Policies
Jean-Claude Berthélemy* University Paris 1 Panthéon Sorbonne, TEAM – CNRS 106-112 Bd de l’Hôpital, 75647 Paris Cedex 13, France email :
[email protected]
October, 2004
*Paper presented at the “Gains and Pains of Multilateralism” Conference, Johns Hopkins University, School of Advanced International Studies, Washington DC, October 21-22, 2004. I thank Derek Beach, Charles Doran, Fen Hampson and other participants for helpful comments.
Résumé
J’examine en détail les motivations de l’aide bilatérale, telles qu’elles sont révélées par les données sur la distribution géographique des engagements d’aide bilatérale. J’identifie à la fois les déterminants liés à l’intérêt propre des bailleurs de fonds et ceux associés aux besoins et mérites des pays bénéficiaires. Les motifs liés à l’intérêt propre des bailleurs sont associés aux liens économiques et politiques qui unissent les pays donateurs et les pays bénéficiaires. Ces liens permettent de définir un « biais bilatéraliste » dans les décision d’allocation de l’aide. De façon attendue, la distribution géographique de l’aide bilatérale, nette du biais bilatéraliste, est très corrélée avec la distribution de l’aide multilatérale. De façon plus surprenante, le biais bilatéraliste est défavorable à l’Afrique sub-saharienne, en dépit de ses liens post-coloniaux avec des donateurs européens, parce que les relations commerciales jouent en fait un rôle plus important que les liens post-coloniaux. Une conséquence du rôle majeur joué par les liens commerciaux est que le biais bilatéraliste n’est pas nécessairement défavorable à la sélectivité de l’aide, étant donné que les principaux partenaires commerciaux sont aussi en moyenne des économies ouvertes et présentant de bonnes performances économiques. Mots-clés: distribution internationale de l’aide JEL classification: F35; C23; C24
Abstract
I examine in detail the motives of bilateral aid allocation decisions, as they are revealed by data on bilateral aid commitments. I identify both self-interest and recipient needs and merits motives in aid allocation. Self-interest motives are related to economic and political ties between donors and recipients. Such variables can be used to define a “bilateralism effect” in aid allocation decisions. Unsurprisingly, aid allocation net of the bilateralism effect is highly correlated with multilateral aid pattern. Perhaps more surprisingly, the bilateralism effect is adverse to the Sub-Saharan African region, in spite of its strong post-colonial ties with European donors, because trade linkages play actually a greater role than political ties. A consequence of the major role played by trade linkages is that the bilateralism effect is not necessarily adverse to aid selectivity, given that major trading partners are also on average open and relatively well performing economies.
Keywords: international aid allocation JEL classification: F35; C23; C24
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1- Introduction
The starting point of this paper is the debate opened by the World Bank (Craig Burnside & David Dollar, American Economic Review, 2000) on aid efficiency. Their conclusion was the absence of any significant tendency for bilateral aid to favor good policy, while they found the opposite result for multilateral aid: “We found no significant tendency for total aid or bilateral aid to favor good policy. On the other hand, aid that is managed multilaterally (about onethird of the total) is allocated in favor of good policy” (Burnside & Dollar, 2000, p.854).
These results were interpreted as the consequence of a bias of bilateral aid allocation decisions towards the self-interest of donors – in particular their geopolitical interests – while multilateral institutions would be more motivated by the needs and merits of potential recipients, and therefore would allocate more efficiently their development assistance.
This does not necessarily mean that bilateral donors make the wrong decisions. Rather, they may pursue different goals, which correspond to their own interest. This is of course acknowledged by Burnside and Dollar. To take this into account, they include as determinants of aid received several dummy variables for Sub-Saharan Africa, Franc Zone, Egypt and Central America. This approach has however two shortcomings. First of all, if aid is given for self-interest reasons, this should be tested on a bilateral aid model, while Burnside and Dollar consider only the aggregate amount of aid received by each recipient. For instance, being a Franc-Zone member is relevant for aid received from France, but not from the USA or the UK; in the aggregate, France being only one among many other donors, being a France Zone member should not count that much. Second, if aid is given for self-interest reasons, one should also include commercial motives in its analysis. For instance, South Africa receives a significant amount of aid from France because it is a major trading partner of France, not because of any particular geopolitical link.
This paper is based on an attempt to correct these two limitations. To this end, the whole information on bilateral aid flows is considered, i.e. for each year the observations are bilateral aid commitments granted by the different bilateral donors to the different recipients. Second, it introduces not only geopolitical variables to capture the donor-interest motives, but also the intensity of bilateral trade between the different donors and the different recipients. 2
More precisely, in this paper, I examine in detail the motives of bilateral aid allocation decisions, as they are revealed by data on bilateral aid commitments, using a yearly panel dataset on bilateral aid commitments granted by 22 OECD/DAC bilateral donors to 137 developing country recipients, over the 1980s and 1990s decades. I identify both self-interest motives and recipient needs and merits motives in aid allocation. Self-interest motives are by definition best described by bilateral variables, which are related both to economic and geopolitical ties between donors and recipients. Such variables can then be used to define a “bilateralism effect” in aid allocation decisions, i.e. the reverse shift in aid allocation that would be observed if all bilateral preference variables were neutralized in the estimated aid allocation equation.
Observing this bilateralism effect can then be used to compare the pattern of assistance that the different recipients would receive in absence of bilateral preferences, with what they actually receive, and with what they receive from multilateral donors. The end result is that the pattern of distribution of bilateral aid exhibits the same correlation with economic performances (measured by economic growth) as the pattern of distribution of multilateral aid. Moreover, the correlation of bilateral aid distribution with economic growth is entirely due to the component that is associated with the bilateralism effect. This result suggests that pursuing self-interested bilateral goals does not necessarily imply the inefficiency of the aid allocation system. When donors target countries with high trade intensity linkages, they target also on average good performers, given that economic performance is generally linked to openness and therefore also to trade intensity.
I discuss in section 2 which variables can be introduced to capture the effect of both the donor self-interest and the recipient needs and merits on bilateral aid allocation patterns. I review in section 3 some econometric issues to be solved and provide a summary of my econometric estimations of the aid allocation equation. Such estimates are used in section 4 to evaluate the bilateralism effect, to discuss its consequences on overall aid patterns, and to compare such patterns with multilateral aid distribution. I conclude in section 5 with some considerations on the architecture of the international aid system.
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2- Self interest of donor and recipient needs/merits variables
Since the contributions of McKinley and Little (1979) and Dudley and Montmarquette (1976), as well as Maizels and Nissanke (1984) and many others, there has been a long debate in the development finance literature on the question of the true motives of development assistance: do bilateral donors grant their assistance to recipient countries in view of improving the development perspectives of those recipients and of reducing poverty, or is this assistance driven by self-interest motives?
There is a growing consensus in the most recent literature (see, e.g., Berthélemy & Tichit, 2004 and, for a recent survey, Neumayer, 2003) saying that both types of variables contribute to explain the aid allocation decisions. Conversely, multilateral aid may be viewed as exempt of self-interested behaviors.
The self-interest of donor argument may be linked to several objectives pursued by the donors. One of them is geopolitical. The usual assumption in the previous literature is that providing aid to a recipient may influence its attitude in favor of the donor. In this context, it is usually assumed that a donor will provide assistance to recipients who are like-minded, or at any rate who are potential political allies. Alesina and Dollar (2000) use data on votes at the UN to measure such a political alliance effect. However, political alliance may be a result as well as a determinant of aid allocation. Another possibility is to link this political alliance factor to the colonial past of the donors. A related argument suggests a link to internal politics: Lahiri and Raimondos-Møller (2000) propose a theoretical model in which the lobbying by ethnic groups may influence aid pattern. They illustrate this analysis with data showing the significant amounts of aid given by the UK to India, the USA to Israel, Germany to Turkey and France to Cameroon. They conclude that, assuming that there is a positive correlation between ethnic composition and the colonial experience of a donor country, this may explain why former colonies are usually major recipients of official development assistance from their former colonial rulers.
In this paper I use, to catch these effects, a combination of dummy variables for former colonial ties and for other broad geopolitical interests of the donors:
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Dummy variables for former colonies of Belgium, France, Portugal, Spain and the UK. 4
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A dummy variable for the couple USA-Egypt, because Egypt has received large amounts of assistance from the United States since its peace accords with Israel. Would Israel be in our database, we would need obviously to introduce a similar dummy variable for its link with the United States, but Israel is not anymore a developing country, and therefore does not belong to our database.
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A dummy variable catching the close ties that exist between the USA and Latin American countries.
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A dummy variable catching the geopolitical interest of Japan in assisting Asian developing countries.
I have also tested whether EU countries were giving more assistance across the board to ACP countries (Associated states from Africa, the Caribbean and the Pacific Ocean), with which the European Community has established from 1963 a preferential treatment, but this variable is never significant in my regressions. As shown by Grilli and Riess (1992), there is possibly a bias in the aid budget managed by the European Commission towards ACPs, but, according to my estimations, this bias does not show up in bilateral aid data, when all other factors are controlled.
Aid may be used also to deepen commercial linkages with a recipient, and not only political alliances. Not all donors have strong geopolitical interests, but all of them have trade interests. A donor’s foreign assistance policy based on his self-interest will typically be biased toward countries that tend naturally to have more trade with him. This is after all the clear motive of tied aid, which persists in spite of continuous efforts from the OECD/DAC to keep it under control. Therefore, following Berthélemy and Tichit (2004), I have also introduced commercial interest motives, as measured by the flow of bilateral trade (imports+exports) with the recipient country, expressed as a percentage of the donor GDP. There might be a simultaneity bias when aid is tied, since more tied aid will imply more imports from the donor. However, the risk is limited since I am working on aid commitment flows, and aid disbursements usually lag behind commitments, particularly for project loans or grants, which require building new equipment. In order to be of the safe side, I have lagged this variable.
The combination of the geopolitical dummies and of the trade intensity variable will define what I call the “bilateralism effect” in my aid allocation equations.
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As a complement, it is useful to consider financial motives, particularly for my period of observation, during which a large number of recipients has been affected by a debt crisis. In the debt crisis literature (see, e.g., Birdsall, Claessens and Diwan, 2003), this is known as the “defensive lending” argument. Donors could be locked in a “debt game”, in which they have to provide new resources to highly indebted countries simply to avoid that these debtors fall in arrear, whatever the quality of economic management of these debtors and their responsibilities in the debt crisis. However, it is not possible to include this argument in the bilateralism effect, for two reasons: first, theoretically speaking, a donor cannot protect its own financial interest alone through defensive lending, because refinancing and other financial relief mechanisms are usually subjected to a burden-sharing rule, for instance under the auspices of the Paris Club; second, bilateral debt data are hardly accessible, when they exist.
Nevertheless, I will
introduce a debt burden variable, defined as the ratio of net present value of debt over export, as an explanatory variable in my aid allocation equations. Although this variable cannot be taken into account in the “bilateralism effect”, it may be interpreted to some extent as a selfinterest variable. There is however some irreducible ambiguity in this interpretation, since one could also argue that a heavy debt burden increases the needs of the debtor, which could motivate donor assistance.
Let me turn now to the development motives of aid. These development motives are of course, according to most donor statements, the actual motives for their assistance programs. Such motives can be captured by the introduction of two different categories of variables. A first category is based on the argument saying that aid is granted to the neediest countries, for the sake of poverty alleviation. A second category takes into account the issue of aid efficiency: if the objective is poverty alleviation, aid should be given to recipients where it can have an impact on poverty, which may depend on the quality of economic policies and on the governance of these countries.
The most straightforward indicator of beneficiary needs is income per capita, measured at international prices (in purchasing power parity terms). If aid is to be allocated on the basis of recipient needs, the poorest countries should receive more, and the richest countries less.
The quality of economic policies is more difficult to measure. I have tried several policy variables similar to those introduced by Burnside and Dollar, such as openness, government deficit and inflation. None of these variables was significant. However, the outcome of these 6
policies, measured by real GDP growth rates, is positively linked to aid allocation. In order to avoid simultaneity bias, I have introduced this variable with a lag, so that the estimation can clearly be interpreted as showing the impact of past growth on new aid allocation. I have also tried social outcome variables, such as life expectancy at birth, child mortality, literacy rate and school enrolment ratios, but none of these variables showed any robust correlation with aid allocation, possibly because their introduction reduces drastically the number of available observations, for lack of complete data.
Concerning governance, I have used the civil liberty and political freedom evaluation provided by Freedom House. This variable is a multinomial qualitative variable, which takes values from 1 (highest quality of democracy) to 7 (lowest quality of democracy). Introducing this variable directly in the regression would introduce possible bias, since there is no reason, for instance, to assume that the marginal impact of a shift of this variable from 1 to 2 would be the same as the impact of a shift from 2 to 3, or half the impact of a shift from 1 to 3. The only proper treatment of this variable is to decompose it into as many dichotomic dummy variables as it has occurrences, and to introduce each and every of these dummy variables in the regression. In principle, since I use the average of two indices (civil liberty, and political freedom), each with seven occurrences, I would have to deal with 13 occurrences. However, the differences of parameter estimates on these 13 dummy variables are not all significant. Tests of differences of such parameters suggest actually that countries can be regrouped into only two categories: those that have an index equal or below 4, and the others. This amounts to simply define only a democracy/non-democracy dummy variable, based on this threshold.
I have also attempted to introduce some other variables linked to governance. The first one concerns the occurrence of conflicts, be they internal or interstate. I have used for that matter the database built by PRIO (International Peace Research Institute of Oslo), which defines 4 categories of conflicts: extrastate (colonial conflicts – which is not relevant in our case), interstate, internal and internationalized internal. For each category, PRIO defines 3 levels of intensity, from minor conflict to war. I have used a methodology similar to the one used for the Freedom House index. The end result is the introduction of two dummy variables:
one
corresponding to non-minor internal conflicts, and the other corresponding to non-minor interstate conflicts. In the final results, these two variables will be merged into only one single conflict dummy variable.
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Another variable that can be used to check whether aid is granted to recipients considered as well-governed is the per capita amount of assistance that they receive from multilateral donors, given that multilateral assistance acts very often as a catalyst of bilateral assistance. Such multilateral assistance is usually conditional on the implementation of structural adjustment or reform programs and it is therefore used by the bilateral donors as a signal that the recipient is committed to put to good use the external resources that he receives.
In the same spirit, I have also entered the total aid commitments (per capita) provided by other bilateral donors. This variable, utilized for instance by Tarp et al. (1998), is introduced to test whether a donor reacts on average positively to aid allocations decided by other donors. This may happen if a given donor considers that other donors tend to give more aid to countries that deserve assistance. In such a case, the assistance of other donors can be considered as complementary to one’s assistance. This variable must be however considered with caution, for at least two reasons. First of all, the aid granted by other donors may be considered as a substitute, rather than a complement, of one’s assistance, in which case the correlation between a donor’s aid commitment and the other donors’ aid would be negative, instead of positive. Secondly, this variable may create a simultaneity bias.
Finally, I have introduced data on military expenditure, as a share of GDP, available since 1988 from SIPRI (Stockholm International Peace Research Institute). One could argue that “excessive” military expenditure should trigger a reduction of foreign assistance, because it would imply a high risk of utilization of this assistance for non-developmental purposes. However, this variable was never significant.
3- Econometric estimation
I have introduced all the previously mentioned variables in the estimation of an aid allocation equation. An original feature of this exercise is that the estimation is performed on a very large three-dimensional panel dataset, covering yearly data for the 1980s and the 1990s, 22 donors and 137 recipients. The dependent variable is the amount of aid commitment per capita received yearly by each recipient from each donor, converted in constant US dollars at 1985 prices, using the OECD GDP deflator. The explanatory variables are those introduced in the previous section, augmented by two auxiliary variables, as discussed below.
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Following the previous literature, I have included in the list of explanatory variables the population of the recipient. The size of the recipient is not neutral, as initially shown by Dudley and Montmarquette, because there are aid administrative costs, which are not proportional to the amount of aid granted. As a consequence of the presence of sort of fixed costs in aid administration, per capita aid granted to a recipient may depend positively or negatively on its population, depending on the elasticity of administrative costs with respect to the amount of aid granted, and on the elasticity of the expected aid impact with respect to the recipient’s population. Empirically, one usually observes that small countries receive more assistance per capita then large countries.
I have also entered the total amount of aid granted by the donor on the year of observation. This provides a way to take into account the fact that some donors have larger aid budgets than others, and that such aid budgets fluctuate over time. I don’t attempt here to explain the size of donors’ aid budgets, which is usually a decision made prior to aid allocation per se.
The definition of all variables and their sources is provided in Appendix 1.
Another important step is to choose an appropriate specification and method of estimation. In the previous literature, the specification and estimation method of aid allocation equations has been debated at length. Different issues at are stake.
The principal issue is that we deal with a censored variable, given that aid commitment cannot be negative. This implies that there is possibly a selection bias if this feature is not taken into account. There are four different ways of correcting the selection bias: −
A two-part model: in a first step, a probit model determines the probability of receiving assistance, and in a second, a linear model explaining aid commitments is estimated, based only on strictly positive observations. In this procedure, the choice of the recipient and the amount of aid allocated to this country thereafter are supposed independent from each other. This method suffers from the risk of introducing a selection bias in the second step, since the fact that a country receives strictly positive aid flows is not independent from the factors that may influence its selection as recipient.
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−
A Heckman’s two-step method: the procedure is the same as for the two-part model, except that in the second step, the inverse Mill’s ratio obtained from the first step is introduced together with explanatory variables, in order to correct the selection bias due to the endogenous nature of the allocation of the selection process.
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A Heckman’s one-step method: the specification is similar to the previous one, but all parameters, including the correlation between the error terms of the selection and allocation equations are estimated in one single maximum-likelihood procedure.
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A Tobit model, which estimates the aid commitments in only one step, and therefore takes also into account the endogenous selection of the recipients. The difference with the Heckman’s method is that the exogenous variables are supposed to have the same impact on the probability of receiving aid and on the amount of aid allocated thereafter: aid received is described as the maximum of zero and of a linear combination of the explanatory variables.
In Berthélemy and Tichit (2004), the last specification was used. Here, with the same database, I use the Heckman and the two-part approaches. Although the results are not qualitatively very different, and therefore confirm Berthélemy and Tichit results, this method has here two merits: −
First of all, it permits using a log-linear specification of the aid allocation equation, given that the logarithm of zeros (the censored observations) are treated properly as missing variables. This introduces more flexibility and allows for an easiest interpretation of parameters. For instance, estimates will not change whether the equation is estimated on aid per capita or on aid volume, once the size of population of the recipients has been introduced in the list of explanatory variables.
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Second, the Tobit procedure, with a very large database, is hardly manageable with a large much number of explanatory variables, for computational reasons. Here, I have been able to introduce a larger number of explanatory variables than in Berthélemy and Tichit.
Another issue concerns the possible introduction of fixed effects. With a very large database, with a lot of heterogeneity among donors and recipients, it is necessary to take account of donor and recipient specificities that would not be taken into account by the explanatory variables. However, in practice, estimates are not consistent when one introduces fixed effect in
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a Probit selection model or in a Tobit model. There is therefore no perfectly satisfying method available. I don’t need here to introduce fixed effects for the donors, given that I have already introduced in the explanatory variables the donors’ total aid budget. I have however to take account of the unobservable recipient specificities. To this end, I have used the following procedure: −
(1) I have checked that the level equation in the Heckman procedure is qualitatively not very different from the one obtained in the second step of the two-part method, which consists simply in a linear estimation based on observations for which aid commitment is strictly positive. Therefore, in this case, the properties of the selection equation do not matter much for the estimation of the determinants of the size of aid allocation. This result is similar to the one obtained with a smaller dataset by Alesina and Dollar (2000), who concluded that a linear estimation on strictly positive observations was as good as a Heckman estimation.
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(2) I have introduced fixed effects in the single equation for aid allocation. Introducing such fixed effects does change a few results significantly, in particular concerning aid commitment from other donors, suggesting that taking account of fixed effects matters for the end result.
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(3) For double-checking, I have introduced recipient dummy variables in the Heckman procedure. Unsurprisingly, this has a major impact on parameters estimated in the selection equation. However, the aid allocation equation provides results that are very similar to those obtained in step (2), confirming the initial conclusion that in our case correcting the potential selection bias does not matter very much. I have therefore kept estimates obtained in step (2).
The results, and the comparison of different methods, are reported in Appendix 2. Most estimates are very significant and robust to changes in method of estimation. They are also very robust to changes in the list of explanatory variables. To summarize, I find that the per capita aid commitment patterns is influenced by the different explanatory variables as reported in Table 1.
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Using these estimates, I can now study the pattern of the bilateralism effects. I report results of this exercise in the next section.
Table 1: Summary of estimation results (final equation) Explanatory variable Self interest of donor variables Bilateral trade/donor GDP (lagged one year) Pot-colonial dummies USA – Egypt dummy USA – Latin America dummy Japan – Asia dummy net present value of debt to export ratio (non-bilateral variable) Recipients needs and merits Real GDP per capita (lagged one year) Real growth rate of the recipient (lagged one year) Civil liberty and political freedom dummy (lagged one year) Dummy for non-minor internal conflict (lagged one year) Dummy for non-minor interstate conflict (lagged one year) Per capita multilateral aid commitment Per capita aid commitment granted by the other bilateral donors Auxiliary variables Donor total aid commitment budget Population of recipient
sign
Comments for interpretation
>0 >0 >0 >0 >0 >0
Commercial interest Geopolitical ties Geopolitical ties Geopolitical ties Geopolitical ties Defensive lending
0 >0
