Distributive Justice in Aid for Development

Abstract

How should the aid financial burden be distributed across donor governments? This article discusses the “distributive justice” of the current aid-financing pattern, and advocates a progressive modality in which citizens from donor countries with higher living standards contribute proportionally more than citizens from countries with lower living standards. For this purpose, we conceive public foreign aid as a tax mechanism for redistributing income on a worldwide scale. The progressivity analysis for 45 bilateral donors (28 DAC countries and 17 non-DAC donors) using concentration curves and Suits indexes between 2000 and 2012 shows that the current distribution of the aid burden is insufficiently progressive (mainly due to the limited contributions of the richer donors). Finally, we argue that a progressive exaction scheme will improve the distributive justice of the aid system.

Keywords::

• F35
• O19
• I30
• C5

Disclosure statement

No potential conflict of interest was reported by the author.

Notes

¹ As claimed, among others, by Lumsdaine (1993), Noel and Therien (1995), Therien (2002) and Tezanos (2008a).

² In the remainder of this paper, “aid” means “public foreign aid for development”.

³ These are Norway, Denmark, Sweden, Luxembourg and the United Kingdom. However, until 2012 the Netherlands also exceeded the 0.7% aid ratio, and since 2013 the United Kingdom started to fulfil this commitment.

⁴ Indeed, the 0.7% target has constituted an effective tool to raise public awareness of the need for international aid.

⁵ The aid geographical allocation has been researched since the late 1960s in an attempt to identify the variables which govern the allocation decisions of bilateral and multilateral donors. See the literature reviews of McGillivray (2004) and Tezanos (2008b).

⁶ A limited number of studies have proposed “normative” procedures for allocating aid. According to the literature review done by Tezanos (2009), two main distributive approaches have been applied: the “marginalist approach” which allocates aid in order to maximise poverty reduction (such as Collier & Dollar, 2001, 2002; Guillaumont & Chauvet, 2001); and the “equal opportunity approach” which proposes a distributive criteria based on post-welfarist principles (such as Cogneau & Naudet, 2007; Llavador & Roemer, 2001).

⁷ The detailed review by Jones (2009) on “equity in development” offers a clear definition of the concept of equity:

Equity is based on the idea of moral equality, the principle that people should be treated as equals. This is the idea that, despite many differences, all people share a common humanity or human dignity and, as a result of this, we must consider how each of them should be treated. This is not the same as treating people equally […] rather, it is the idea that all count in the moral calculus. (p. 3)

⁸ This utilitarian approach has been strongly criticised. For example, Conybeare (2007) states that even if the marginal utility of income was higher for the poor than for the rich, this is not sufficient to justify an aid transfer: it needs to be shown that the poor would use the money more productively and add more to the aggregate wealth.

⁹ In fact, Rawls (1993, pp. 227–228) specifies that “the features of a tax system are not constitutional essentials”.

¹⁰ Nevertheless, Rawls does not clearly advocate a progressive tax system. See Sugin (2004) for a clear explanation.

¹¹ However, Conybeare (2007, p. 406) explains that the post-welfarist theories are also easily criticised for their assumptions (e.g. risk preference), their invocation of self-evident values (e.g. helping the worst-off) and their internal consistency (e.g. with respect to free choice within contract theory). In particular, Conybeare states that without risk aversion the prudential reason to favour the least well-off disappears, and, furthermore, that risk attitudes are likely to be greater internationally than within nations.

¹² More precisely, for this assumption to hold, public aid must be collected in “similar” progressive ways in each donor country. This assumption is reasonable within the European Union, but is less likely to be met outside this region.

¹³ Two clarifying and complementary review articles on the complex debate on “international distributive justice” can be found in Caney (2001) and Conybeare (2007).

¹⁴ Milanovic (2008) proposes a practical rule in order to minimise the likelihood of a globally regressive aid transfer. This implies taking into account countries' national income distributions and penalising countries with highly unequal distributions since a non-trivial probability exists that the transfers may be received by people richer than rich countries' taxpayers.

¹⁵ In contrast to a conventional Lorenz curve, the aid concentration curve uses three variables to depict a two-dimensional graph. The additional variable is the ranking variable (a measure of living standards), which allows the concentration curve to cross the leading diagonal (45 degree line).

¹⁶ The Appendix explains how to calculate the Suits index.

¹⁷ A zero value of the Suits index can also occur when a first progressive (regressive) section of the curve is offset by a second more regressive (progressive) section.

¹⁸ In contrast, the Gini coefficient varies between 0 and +1.

¹⁹ In accordance with DAC's criteria, ODA consists of grants and loans which meet the following four conditions: (i) they are disbursed to developing countries, (ii) they are granted by the official sector, (iii) their main objective is the promotion of economic growth and welfare, and (iv) in the case of loans, they are granted on concessional financial terms, with a grant element of at least 25%.

²⁰ Other south-south donors are not included in the analysis due to the lack of yearly information.

²¹ It would have been preferable to use the GNI and the GNI per capita PPP in order to make a more direct comparison with the aid effort ratio (ODA/GNI). However, the World Development Indicators' database does not offer complete data on GNI per capita PPP for three DAC donors (Hungary, Slovak Republic and Slovenia). In order to avoid the exclusion of these three countries we took the practical decision of using GDP per capita PPP (instead of GNI per capita PPP), which is available for all countries in the sample. However, a close evaluation of these two indicators for the available countries in the period 2000–2013 shows very limited variations, and no changes in the ranking of per capita incomes (which is the most important aspect in our analysis) so the consequences of not using GNI data for all indicators are negligible.

²² See Table 2 for detailed values. The only exceptions are Turkey and Portugal, which actually had aid shares higher than their respective world income shares, despite being the fourth and the 11th donors, respectively, with lower per capita incomes.

²³ This result disagrees with the analysis of Round and Odedokun (2004) who ran a panel data regression in order to explain DAC donors' aid efforts. They found that there was progressivity in aid efforts in the sense that the higher the real income of the donor, the greater the fraction of real income given as aid. However, there are important differences between these two types of analyses: Round and Odedokun's regression analysis estimates one donor's “average” elasticity of income per capita in relation to its aid effort. On the other hand, the concentration curve and the Suits index compute different slopes for each donor country, and use the ranking of per capita incomes to measure the overall progressivity.

²⁴ Specifically, we estimate the linear regression by the ordinary least squares method, specifying an exponential function that is characteristic of a progressive exaction principle. As this regression method draws the closet line to the observations, it is actually the “easiest” feasible progressive scheme.

²⁵ In fact, there are three “outliers” (Kuwait, Luxembourg and Liechtenstein) which explain the high dispersion across observations (and hence the low R²). Without these outliers, the R² statistics rise from 0.36 to 0.46.

²⁶ It is worth mentioning that this tentative regression is strongly influenced by the existence of the three outliers: Kuwait, Luxembourg and Liechtenstein. Running the regression without these outliers (obviously) renders higher aid efforts to the remaining countries, but especially to the wealthiest ones (such as the Nordic countries, for whom the regression in Figure 4 predicts lower aid efforts than their actual contributions).

²⁷ See, among others, the recent literature reviews on aid effectiveness conducted by McGillivray, Feeny, Hermes, and Lensik (2006) and Tezanos (2010).