Abstract
This paper investigates the importance of bureaucratic inertia in foreign aid allocation. Inertia puts a limit on the ability of foreign aid to alleviate poverty and promote growth. We exploit the 9/11 attacks as a natural experiment that provides a lower bound on the effects of inertia. We make use of a dynamic panel data model combined to a proper treatment of the sample selection problem inherent to virtually all models on aid decisions. The paper moves beyond existing studies and provides an ordering of the donor’s motivations. Interestingly, merit-based motivations have sizable effects on aid decisions. However, inertia is found to drive most of the aid distribution. This may provide a rationale for the weak enforcement of aid contracts. Inertia introduces a time inconsistency problem on the side of the donor. Ex-post, the donor of conditional aid has incentives to deliver it regardless of reforms’ implementation or recipient’s discipline. Anticipation that this will happen destroys the recipient’s incentive to carry out costly policy reforms. This puts the whole aid architecture under strain. Coordination among donor agencies, donors’ investment in reputation and institution building as well as changing aid modalities rather than volumes seems to be promising routes toward re-establishing efficiency.
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McGillivray and White (1993), state that “a commonly identified influence is the tendency for aid bureaucracies, like other spending agencies, to use the preceding year’s allocation as a benchmark for the current year’s aid allocation in a process of marginal incrementalism or bureaucratic inertia. In a more general context, Wildavsky (1964) states that the principal influence on the budget for any spending agency in the current year is last year’s budget. Mosley (1985) states that this is, even stronger in the case of aid than of other categories of public expenditure, since most of the aid announced consists of money committed several years in advance to the support of particular projects.
We restrict attention to official development assistance (ODA) defined by the OECD as those flows of official financing administered with the promotion of the economic development and welfare of developing countries as the main objective, and which are concessional in character with a grant element of at least 25%.
In addition, Furuoka (2008) does not report on the number of instruments used which casts doubt on the quality of the results as instruments proliferation is another limitation of differenced-GMM (Roodman 2009b). The lack of information relates also to whether a one-step or a two-step GMM estimator was used. Likewise, it is not reported whether tests use the Windmeijer (2005) finite-sample correction to the reported standard errors. Without this correction, the standard errors tend to be highly downward biased.
Recall that the Washington Consensus refers to a set of broadly free market economic prescriptions developed in 1989 by economist John Williamson and supposed to be in line with policy advises by Washington, D.C.-based international organizations.
Although many alternative explanatory variables could be considered to capture recipient’s need, GDP per capita is the most commonly used due to its availability and its strong correlation with other need variables such as life expectancy, infant mortality, or literacy. Neumayer (2003) shows that these other need variables are statistically non-significant once income is controlled for.
The list of countries eligible for ODA is established by the Development Assistance Committee (DAC) of the OECD.
Alesina and Dollar (2000) made a comment in the same vein, while discussing the appropriateness of applying OLS versus the usage of a Tobit procedure that accounts for the truncated nature of the aid data.
As discussed in Sect. 2, we follow Alesina and Dollar (2000), Berthélemy (2006), Neumayer (2003) among others in classifying donor’s motivations as altruistic when they are driven by recipients economic needs. Likewise, when aid is used as a tool to protect and promote donor’s political, geo-strategic or economic and commercial interests, it is guided by self-interest. Therefore, we classify donor’s motivations as opportunistic. However when aid is allocated among recipient countries based on their respective records in terms of good governance and implementation of market liberalization policies, it is said to be driven by merit-based motivations.
For obvious reasons, we do not carry the log transformation for qualitative ordinal scores (Democracy, PTS, etc.). Taking the log would not have much sense.
In our model, using all available lags results in a number of instruments that is still lower than the country count. This is a first indication that instruments proliferation would not be an issue here
Kanbur (2006) gives striking examples of real experiences showing how difficult it is for the donors to suspend the release of aid, even if the recipient fails in meeting the conditions for giving funds
Aid conditionality means that donors attach conditions for entering into an aid agreement with the recipient or for keeping up aid.
In the literature, the relationship between ODA and the level of GDP per capita is controversial both in terms of its sign and its significance. Fielding (2014) studying humanitarian aid provided by the USA as well as Apodaca and Stohl (1999) studying US bilateral economic and military aid found a (negative but) non-significant relationship. Furuoka (2008) in a study on ODA from all sources, whether bilateral or multilateral found that relatively wealthy developing countries have received larger amounts of aid.
Neumayer (2003), finds that personal integrity rights are statistically non-significant at best, and exert a negative influence on aid allocation, at worst.
The PITF is funded by the Central Intelligence Agency, meaning that if the US administration bases its ODA allocations on a political instability score, the PITF would be the most likely candidate.
The classification of countries as low-income or middle-income countries is defined by the World Bank, based on gross national income GNI per capita
The authors apply the MMSC and downward testing procedures to dynamic panel data models. The criteria select the correct model specification and moment conditions for GMM estimations. The method is based on the J statistic for testing over-identifying restrictions and has parallel with the likelihood-based selection criteria Akaike information criteria (AIC), Bayesian information criteria (BIC) or Hannan-Quinn information criteria (HQIC).
Alesina and Dollar (2000) use a similar logic by introducing different variables into the regression sequentially, as a way to look at their relative importance. The difference here is that Alesina and Dollar (2000) compare the coefficients of determination (they are using the OLS technique) and they are interested in the contribution of individual variables to the explanatory power of the model. Our focus is rather the vectors of motivations (need, merit, self-interest)
A detailed explanation of the dimension along which each of these three pillars is measured are available on MCC’s website. For instance, “investing in people” is measured by scores on (i) immunization rate, (ii) public expenditure on health, girls’ primary education, child health, natural resource protection, etc. Moreover, MCC publishes the list of data sources for each criterion. These include the World Bank, UNICEF, UNESCO, WHO, etc.
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Sraieb, M.M. The dynamics of US foreign aid decisions. Empir Econ 63, 1859–1886 (2022). https://doi.org/10.1007/s00181-022-02200-0
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DOI: https://doi.org/10.1007/s00181-022-02200-0