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Selectivity on aid modality: Determinants of budget support from multilateral donors

Abstract

Since the late 1990s a selection on policy approach to aid was advocated such that more aid should be allocated to countries with good policies, but there is little evidence that this has occurred. This paper argues that donors may exercise selectivity over the aid modality. Specifically, multilateral donors will cede more recipient control over aid by granting more budget support to those recipients with better expenditure systems and spending preferences (towards the poor) aligned with the donor. We test this for European Commission and World Bank budget support over 1997–2009 and find some support. Both donors have given budget support to almost half of the countries they give aid, and it is usually a significant share of their aid. The principal determinants of receiving budget support are having a poverty reduction strategy in place, which can be considered a good indicator of aligned preferences, and indicators of government efficiency. These variables did not, however, influence the amount of budget support given. Multilateral donors have been more likely to give budget support to countries with aligned spending preferences and better quality systems, even if they have not reallocated the total aid envelope in that way.

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Notes

  1. The Appendix is available on this journal’s webpage.

  2. Budget support only increases spending on development goods under conditionality so it imposes a cost on recipients as ‘an inefficiency may emerge if donors are forced to impose higher levels of expenditure on the more controllable components of the budget’ (Cordella and Dell'Ariccia 2007: 1261).

  3. The game theoretic approach is less suitable to describe the situation we have in mind because although, as in this framework, the utility of one player depends on the decision of the other player involved, in the game no delegation of tasks is involved and no transfer against outcome usually occurs.

  4. Although Hagen (2006a) critizises this approach given the limited enforcement of aid contracts, Fafchamps and Mintens (1999) argue that in developing countries, even in the absence of legal institutions, enforcement of contracts is based on trust generated by repeated interactions. Donors may accept the contract as enforceable (even if not fully).

  5. The assumption that A is pre-determined allows us to focus on the choice of the composition of aid (note that it does not make the modelling easier). A static model is obviously an over simplification with respect to reality, where usually there are repeated interactions between donors and recipients. However, ‘the one shot relationship is rather typical of aid projects, since the employees of donor agencies find themselves frequently moving from country to country and function to function’ (Murrell 2002, p. 79, fn 14). In a similar vein, donor officials deciding on the composition of aid may view it as one shot (it is the donor agency rather than an official that has repeated interaction).

  6. As with CDA, the model we present has a multilateral donor in mind on the basis that they are more likely to be able to exercise selectivity and where we can more confidently assume that the donor is entirely altruistic. Svensson (2000) argues that international organizations have less inequality aversion and are therefore less susceptible to the Samaritan’s Dilemma. Although Hagen (2006b) argues that this would not resolve the dilemma if aid efficiency varies across recipients and it is not evident that multilaterals have less inequality aversion or are better able to enforce conditionality, it supports the tendency for multilaterals to be more selective.

  7. The complete information case does not require the incentive compatibility constraints but gives essentially similar results (the proof is available on request).

  8. This is independent of the model assumption that donor costs are the same for PA and GBS (δ does not appear in the equilibrium conditions). Although in principle more donors could imply more to coordinate with in providing GBS, in practice only a few donors provide GBS together.

  9. Booth et al. (2006) argue that while PRSPs have motivated budget support alignment of donors, the donor evaluation processes for PRSP implementation and budget support performance are not themselves aligned very well.

  10. This is corroborated by alternative estimates where IPRSP is set at 1 for all years after it is agreed (i.e., not turned off if a PRSP is agreed): education spending and number of donors (positive) and effectiveness (negative) are significant and 70% of cases are correctly predicted (see online Appendix B3, Table B6).

  11. Contrary to the results in Table 1, the coefficients on education spending (for both) and number of donors (for EC) are now negative and significant, and government effectiveness is now positive and significant. Countries with higher education spending are more likely to have a PRSP; given this, those without a PRSP that receive GBS have relatively low education spending. This suggests that if donors do not see education spending being addressed under a PRSP they may use GBS conditionality to achieve this. Another implication is that low government effectiveness is no bar to having an PRSP, so the interpretation for GBS would then be either that a recipient is in the PRSP process (that may improve efficiency) or have relatively high efficiency.

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Acknowledgements

This is a revised version of a paper presented at the 4 th Annual Conference on the Political Economy of International Organizations, Zurich, 27–29 January 2011. The authors are grateful to Christopher Kilby, Steven Knack, Axel Dreher, conference participants and two anonymous referees for constructive comments. We retain responsibility for the views expressed and any errors.

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Correspondence to Oliver Morrissey.

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Appendices

Appendices

An appendix with further detail on the data and supplementary estimates is available online at this journal’s webpage (Appendix B). The data used and Stata do files are available at https://sites.google.com/site/paulclist/data and on this journal’s webpage.

Appendix A: Proof, data and additional results

Appendix A1: Proof of proposition 1

The relevant constrains for the maximization problem in (A1) are: the incentive compatibility constraint for the \( \underline \theta \) type (A4), the participation constraint for the \( \bar{\theta } \) (A3) and the budget constraint (A6).

$$ \mathop {max}\limits_{{a_{BS}};{a_{PA}}} V :p\{ \lambda [\gamma {a_{BS}} - \underline \theta \underline q ] - \delta a_{BS}^2\} + (1 - p)\{ \lambda [\gamma {a_{PA}} - \bar{\theta }\bar{q}] - \delta a_{PA}^2\} $$
(A1)
$$ sub \quad \gamma {a_{BS}} - \underline \theta \underline q \geqslant 0 $$
(A2)
$$ \gamma {a_{PA}} - \bar{\theta }\bar{q} \geqslant 0 $$
(A3)
$$ \gamma {a_{BS}} - \underline \theta \underline q \geqslant \gamma {a_{PA}} - \underline \theta \bar{q} $$
(A4)
$$ \gamma {a_{PA}} - \bar{\theta }\bar{q} \geqslant \gamma {a_{BS}} - \bar{\theta }\underline q $$
(A5)
$$ A = p{a_{BS}} + (1 - p){a_{PA}} $$
(A6)

Given that (A6) is satisfied as an equality we can rewrite it as:

$$ {a_{BS}} = \frac{A}{p} - \frac{{(1 - p){a_{PA}}}}{p} $$
(A7)

Substituting (A7) into (A4) and rearranging we get:

$$ \frac{\gamma }{p}[A - {a_{PA}}] - \underline \theta \Delta q \geqslant 0 $$
(A8)

where \( \Delta q = \underline q - \bar{q} > 0 \). Substituting Eq. A7 into Eq. A1 we can then maximize with respect to \( {a_{PA}} \), \( {\mu_1} \) (the Lagrange multiplier associated with (A4)) and \( {\mu_2} \) (the Lagrange multiplier associated with (A2)).

The first order conditions with respect to \( {a_{PA}} \), \( {\mu_1} \) and \( {\mu_2} \) are given by:

$$ 2\delta \frac{{(1 - p)}}{p}[A - {a_{PA}}] - {\mu_1}\frac{\gamma }{p} + {\mu_2}\gamma = 0 $$
(A9)
$$ \frac{\gamma }{p}[A - {a_{PA}}] - \underline \theta \Delta q = 0 $$
(A10)
$$ \gamma {a_{PA}} - \bar{\theta }\bar{q} = 0 $$
(A11)

To analyze under which conditions \( {\mu_1} > 0 \) or \( {\mu_1} = 0 \) and \( {\mu_2} > 0 \) or \( {\mu_2} = 0 \) assume that \( {\mu_1} = {\mu_2} = 0 \). Then Eq. A9 can be rewritten as:

$$ A = {a_{PA}} $$
(A12)

Given \( p \in (0,1) \), (A12) can never be realized and this implies that \( {\mu_1} = {\mu_2} = 0 \) can not be an equilibrium of the model.

Assume now \( {\mu_2} = 0 \). From Eq. A9 we then get:

$$ {\mu_1} = \frac{{2\delta (1 - p)}}{\gamma }[A - {a_{PA}}] $$
(A13)

Given \( p \in (0,1) \) and the budget constraint being always satisfied as an equality, \( A > {a_{PA}} \), which implies \( {\mu_1} > 0 \). The optimal marginal amount of \( {a_{PA}} \) and \( {a_{BS}} \) will be given by:

$$ a_{BS}^* = \frac{{(1 - p)\underline \theta \Delta q}}{\gamma } $$
(A14)
$$ a_{PA}^* = - \frac{{p\underline \theta \Delta q}}{\gamma } $$
(A15)

To check if the assumption of \( {\mu_2} = 0 \) still holds, rewrite (A9) as:

$$ {\mu_2} = {\mu_1}\frac{1}{p} - 2\delta \frac{{(1 - p)}}{{\gamma p}}[\tilde{A} - {a_{PA}}] $$
(A16)

Substituting the value for \( {\mu_1} \) in (A13) into (A16) gives \( {\mu_2} = 0 \). Therefore, Eqs. 10 and 11 in the text are the final equilibria (it can be shown that the two remaining cases, \( {\mu_1} = {\mu_2} > 0 \) and \( {\mu_1} = 0;{\mu_2} = > 0 \), cannot be solutions for this model).

Appendix A2: Data sources

The data used for the analysis and the Stata files are available on this journal’s website and also at https://sites.google.com/site/paulclist/data. The data on GBS timing and value, total aid and number of donors are from the Creditor Reporting System (CRS) of the OECD-DAC. Information on the year in which a PRSP or IPRSP was agreed is taken from the IMF. All other data are from the World Bank. A detailed discussion of the data and sources is available in online Appendix B2.

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Clist, P., Isopi, A. & Morrissey, O. Selectivity on aid modality: Determinants of budget support from multilateral donors. Rev Int Organ 7, 267–284 (2012). https://doi.org/10.1007/s11558-011-9137-2

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Keywords

  • Aid Modality
  • Budget Support
  • Project Aid
  • Aid Selectivity

JEL Classification

  • F35
  • O19