Discussion Paper No. 2003/17 Donor Funding of Multilateral Aid Agencies Determining Factors and Revealed Burden Sharing Tony Addison, Mark McGillivray and Matthew Odedokun * February 2003 Abstract The paper reports an empirical study of the factors affecting burden sharing among OECD’s 22 DAC members in ‘bankrolling’ the multilateral aid agencies. These are the UN agencies, World Bank’s IDA and non-IDA programmes, regional development banks, European Community, and other multilateral agencies that include the Global Environmental Facility and the Montreal Protocol on environment. Annual data over 1970-2000, pooled across the donor countries, form the basis for the empirical estimation of each donor’s share in the ODA aid receipts for each multilateral agency. Our findings suggest the existence of reverse exploitation, i.e., the financial burden of the agencies is disproportionally carried by the smaller donors. The study also finds that …/. Keywords: burden sharing, ability to pay, exploitation hypothesis, UN agencies, IDA, non-IDA, regional development banks, European Community, Global Environmental Facility, Montreal Protocol JEL classification: F02, F35, H40, H87, O19 (cid:1) Copyright UNU/WIDER 2003 * UNU/WIDER, Helsinki; emails: [email protected]; [email protected]; [email protected] This study has been prepared within the UNU/WIDER project on the Sustainability of External Development Financing, which is directed by Matthew Odedokun. This paper was presented at the project meeting in Helsinki, 23-24 August 2002. UNU/WIDER gratefully acknowledges the financial contribution to the project by the Ministry for Foreign Affairs of Finland. factors such as inherent donor generosity, donor concern for domestic egalitarianism, and the extent to which donors are pro-poor in their bilateral aid policies have an impact on their readiness to support multilateral agencies financially. Size of the donor government and its budgetary balance positively influence burden sharing of contributions to other multilateral agencies. But neither the phase of economic cycle nor the rate of economic growth affects the burden-sharing responsibility of donors. It was also observed that contributions by EU members to the EC do not appear to crowd-out their contributions to other multilateral aid agencies and that right-wing donor governments are generally more parsimonious with regard to financial assistance to multilateral aid agencies. The preferred alternative, particularly among EU member countries, appears to be the EC. The World Institute for Development Economics Research (WIDER) was established by the United Nations University (UNU) as its first research and training centre and started work in Helsinki, Finland in 1985. The Institute undertakes applied research and policy analysis on structural changes affecting the developing and transitional economies, provides a forum for the advocacy of policies leading to robust, equitable and environmentally sustainable growth, and promotes capacity strengthening and training in the field of economic and social policy making. Work is carried out by staff researchers and visiting scholars in Helsinki and through networks of collaborating scholars and institutions around the world. www.wider.unu.edu [email protected] UNU World Institute for Development Economics Research (UNU/WIDER) Katajanokanlaituri 6 B, 00160 Helsinki, Finland Camera-ready typescript prepared by Liisa Roponen at UNU/WIDER Printed at UNU/WIDER, Helsinki The views expressed in this publication are those of the author(s). Publication does not imply endorsement by the Institute or the United Nations University, nor by the programme/project sponsors, of any of the views expressed. ISSN 1609-5774 ISBN 92-9190-416-3 (printed publication) ISBN 92-9190-417-1 (internet publication) 1 Introduction Multilateral aid agencies need adequate funding to meet the ever-demanding requirements for accomplishing their objectives. This is particularly true in the current era of increasing globalization and recognition of the need to centralize the delivery of global public goods. Looking at the UN agencies, for example, the refugee issues of the UNHCR are not abating; neither are the demands for WHO’s epidemic relief (including HIV/AIDS) or WFP’s disaster relief. Multilateral aid agencies outside the UN system are also involved. The number of countries eligible (on the basis of per capita income) for World Bank’s IDA has been increasing because the poorest developing countries have not been able to improve their relative position. Neither has the scale of the World Bank’s non-IDA operations decreased, nor the range of operations of the regional development banks. To top it all, new, possibly competing outlets for multilateral contributions—such as funding multilateral global public goods, i.e., Global Environmental Facility (GEF) and Montreal Protocol—have emerged. Policy analysts have even recommended establishing a ‘common pool’ (Kanbur and Sander 1999 and Zedillo 2000) for donor government contributions which would then be allocated to potential recipient (developing country) governments. Relevant policies require an understanding of the various factors that motivate donors to continue funding existing multilateral agencies. Donors will also need to contribute generously to new agencies now and in the future. This, in turn, pre-supposes an understanding of the factors that have in the past influenced donor support to existing multilateral aid agencies. Following the seminal paper by Olson and Zeckhauser (1966), studies have focussed on the economic theory of collective action and alliances. But most of the extensions have been in the realm of burden sharing NATO’s defence budget (e.g., see Sandler 1993, Siqueira and Sandler 2001, Oneal 1990, Sandler and Hartley 2001). Similarly and understandably, most empirical tests of these burden-sharing theories have been limited to NATO (Hartley and Sandler 1999 and Khana and Sandler 1997). At the same time, parallel but more recent literature on burden-sharing propositions and empirical studies (particularly, post-Kyoto Agreement) have been characterized by environmental issues such as financing the abatement of climatic changes, CO emissions, etc. (e.g., Cardenas 2 et al. 2002 and Kohn 2001). But the financing of aid agencies has scarcely been accorded the same attention. While Olson-Zeckhauser’s theoretical framework has been extended to international organizations generally and empirically tested by Kwon (1998) (using the extended framework), this is an exception. On the other hand, Officer’s (1994) study of UN membership assessment is prescriptive in nature, merely suggesting that UN expenses could be ‘better’ shared by making poor member countries pay more! The present study focuses on the funding of multilateral aid agencies. The approach is essentially empirical. The findings from existing theoretical and applied studies on collective action and alliances with respect to NATO and environmental issues will have a bearing on the present tests. Specifically, the study aims to undertake the following: i) Presentation and discussion of stylized facts on historical donor funding of multilateral aid agencies; 1 ii) Econometric tests of the relevance of the traditional ability-to-pay criteria, particularly, relative size (or GDP) in determining relative historical donor contributions to agencies, in order to shed light on the ‘exploitation’ hypothesis; iii) Econometric tests to determine whether donor-specific factors (e.g., size of government, the country’s overall generosity ratio, and ideological orientation of the government in power) have affected donor’s relative contributions in the past; and iv) Policy recommendations based on the findings. The rest of the paper is organized into 4 sections. In section 2, we present the stylized facts. The statistical framework is described in section 3, while section 4 presents the empirical results. The summary and conclusion are in section 5. 2 Some stylized facts 2.1 Trend and structure of multilateral ODA The ODA multilateral contributions by donor countries to various aid agencies and programmes during 1970-2000 are shown in Chart 1. It can be seen that while some agencies—World Bank IDA programmes and UN aid agencies—exhibit positive trends, at least in nominal terms, contribution volumes are characterized with ‘cyclical’ swings. IDA, for example, shows a downward movement since its all-time peak in the early 1990s. Furthermore, there is no discernible pattern in the trend of contributions to the Bank’s non-IDA programmes. The regional development banks drastically pummelled from the all-time peak attained around 1998 (coinciding with the Southeast Asian crisis) to below the 1994 pinnacle (coinciding this time with the Mexican episode). Contributions to other or ‘residual’ agencies or programmes rose to a peak around 1978 but fell steadily until around 1994 when they started to rise again to surpass the 1978 figure. This is being attributed to the start of contributions in 1994 to GEF and the Montreal Protocol multilateral environmental programmes. The EC development programme, another component of ODA multilateral (not shown), has been increasing, mainly due to EC’s ever-expanding membership. Total multilateral ODA contributions, including grants and loans, are shown in Chart 2, together with total bilateral grants.1 As shown in Chart 1, multilateral ODA has always been less than bilateral ODA (at least, since 1970) and the gap has been increasing over time. The chart also shows that both have been decreasing since the mid-1990s from the all-time peaks attained earlier in the decade. 1 The combined bilateral ODA (grants and loans) is not shown in order to facilitate comparison with multilateral ODA, which included in grants. Some ODA multilateral loans do exist (mostly from Japan), but they are not analysed according to aid agencies and programmes and consistent statistics became available only from the mid-1990s. Furthermore, net resource transfers to multilateral recipients of ODA loans over the years are no doubt due to re-flows being generated to donor countries. Thus ODA multilateral loans are excluded in Charts 1 and 2 and bilateral loans excluded in Chart 2. 2 2.2 Historical pattern of ‘burden’ sharing in the finance of multilateral aid agencies and programmes Table 1 gives the statistics on the historical burden-sharing contributions of OECD’s 22 DAC members versus total contributions, as well as for each multilateral aid agency and programme. We also give each country’s relative share of the DAC members’ combined GDP as an indication of the ability to contribute or ‘pay’. As explained later, the so- called ‘exploitation’ hypothesis is supported if the relative share of the financial burden for these agencies increases faster than the ability to contribute or pay, so that larger members are faced with a disproportionate share. The hypothesis is contradicted if burden sharing is proportionate to relative ability-to-pay. However, if burden sharing increases slower than the ability-to-pay, this could be called ‘reverse exploitation’, a term not in the lexicon of existing theoretical works on the subject, as a similar situation has not been contemplated. A cursory look at the table does not indicate clearly whether exploitation or reverse exploitation exists. The balance of evidence, however, supports the existence of reverse exploitation, especially with regard to total multilateral ODA. If GDP is considered an indicator of the size and ability of members to pay, the above-average members2 would be Italy, UK, France, Germany, Japan, and USA, in ascending order. Only in the case of UK (the second smallest) does the relative share of multilateral ODA clearly exceed its share of the combined GDP. The individual share of multilateral ODA for Italy, France, Germany and Japan falls only slight short of each country’s share of combined GDP. On the part of the USA, the dominant and largest single member, the share of its GDP is almost twice its share of official aid. On the other hand, certain small countries— notably Denmark, Netherlands, Norway and Sweden3—display relative shares of multilateral ODA that are several multiples of their relative shares of the combined GDP of DAC. But this is not to overlook some mixed aspects of the evidence. For instance, shares of small countries like Australia, Ireland and Switzerland in the combined ‘group’ GDP are no smaller than their respective shares in total multilateral ODA contributions. The fact that some countries carry less than their expected GDP-based share while others assume a greater proportion is, without a doubt, a reflection of domestically- related economic and political circumstances or factors. While many of these factors can be hypothesized, their proper identification is an empirical issue—and the main objective of the present study. The methodology and econometric framework of this study are described below. 2 Those with about 5 per cent or above as their relative share of group GDP total, since there are 22 members, giving an average of just above 4.5 per cent or 100/5. 3 The so-called ‘G4’ countries that have earned reputation for reaching and even surpassing the UN aid target of 0.7 per cent of GDP. 3 Chart 1: Donors' Grants and Loans to Multilateral Aid Agencies, 1970-2000 (US$ million) 7000 Resid. Multi. Grants 6000 World Bank (Non-IDA) UN Agencies Regional Dev. Banks 5000 IDA n 4000 o milli $ US 3000 2000 1000 0 1970197119721973197419751976197719781979198019811982198319841985198619871988198919901991199219931994199519961997199819992000 Year Chart 2: Bilateral and Multilateral Grants, 1970-2000 (US$ million) 40000 35000 Multilateral Grants Bilateral Grants 30000 25000 n o milli 20000 $ S U 15000 10000 5000 0 1970197119721973197419751976197719781979198019811982198319841985198619871988198919901991199219931994199519961997199819992000 Year 4 Table 1: Relative Shares of Donor Countries in Different Multilateral ODA and GDP (percent), 1970-99. Aust- Aust- Bel- Canada Den- Fin- France Ger- Greece Ire- Italy Japan Luxe- Nether- N. Zea Norway Port- Spain Sweden Switz- UK U.S. Total ralia ria gium mark land many land mbourg lands land ugal erland (in %) 1. United Nations Agencies 1970-79 1.44 0.67 1.72 9.08 6.08 0.98 1.93 7.11 n.a. 0.20 1.26 4.38 n.a. 7.74 0.37 4.94 n.a. n.a. 10.64 2.13 7.18 32.23 100 1980-89 2.92 0.65 1.04 8.13 5.25 2.11 3.15 6.40 n.a. 0.18 4.25 11.78 0.01 6.30 0.16 6.41 0.01 0.64 7.52 2.21 4.57 26.63 100 1990-99 1.88 0.71 1.03 5.08 6.89 2.43 3.37 7.57 0.29 0.27 5.02 15.37 0.12 6.55 0.20 5.83 0.12 0.99 6.82 2.65 5.31 21.67 100 1970-99 2.08 0.68 1.26 7.43 6.07 1.84 2.82 7.03 0.29 0.22 3.51 10.51 0.10 6.86 0.24 5.73 0.10 0.87 8.33 2.33 5.69 26.85 100 2. The World Bank IDA Programme 1970-79 2.36 0.69 1.85 7.38 1.18 0.43 6.46 13.42 n.a. 0.17 2.44 9.92 n.a. 2.65 0.10 1.31 n.a. n.a. 5.10 n.a. 15.93 28.68 100 1980-89 2.26 0.84 1.61 5.53 1.24 0.71 7.02 11.50 n.a. 0.12 5.54 22.35 0.07 3.85 0.09 1.59 n.a. 0.65 2.78 n.a. 7.49 25.21 100 1990-99 1.92 0.85 1.79 4.28 1.66 0.65 8.22 13.66 0.07 0.15 5.40 21.59 0.09 4.74 0.15 1.65 0.09 0.92 2.85 2.18 6.55 21.03 100 1970-99 2.18 0.79 1.75 5.73 1.36 0.60 7.23 12.86 0.07 0.14 4.46 17.95 0.09 3.75 0.11 1.51 0.09 0.84 3.58 2.18 9.99 24.97 100 3. The World Bank Other (i.e., non-IDA) Programmes 1970-79 -0.87 2.18 3.22 3.56 4.18 1.73 7.90 8.79 n.a. n.a. 7.36 7.05 n.a. 1.65 0.05 4.83 n.a. n.a. 1.29 n.a. 33.87 13.27 100 1980-89 2.20 0.29 3.49 5.35 2.92 1.87 9.43 7.43 n.a. 0.52 4.84 17.12 n.a. 5.33 0.76 1.62 0.72 6.08 0.40 n.a. 6.67 26.50 100 1990-99 2.38 1.57 1.55 3.57 8.31 0.40 2.51 1.31 1.74 0.16 4.87 37.63 0.44 4.30 0.29 2.70 0.41 3.05 1.03 5.55 3.50 14.93 100 1970-99 1.24 1.34 2.75 4.16 5.14 1.30 6.62 5.85 1.74 0.44 5.69 20.60 0.44 3.76 0.37 3.05 0.48 4.06 0.91 5.55 14.68 18.23 100 4. Regional Development Programmes 1970-79 n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a. n.a. 1980-89 3.42 0.89 1.11 9.39 0.45 0.99 5.73 6.15 n.a. n.a. 5.67 29.53 n.a. 2.04 0.21 1.75 0.27 1.30 2.34 2.32 4.44 22.85 100 1990-99 2.85 0.81 0.80 6.72 1.65 1.14 7.97 7.51 0.16 n.a. 4.35 36.26 n.a. 3.07 0.12 2.41 0.25 1.73 2.68 1.95 3.21 14.48 100 5 1970-99 3.14 0.85 0.96 8.05 1.05 1.06 6.85 6.83 0.16 n.a. 5.01 32.89 n.a. 2.55 0.16 2.08 0.25 1.59 2.51 2.13 3.83 18.66 100 5. Global Environmental Facilities, Montreal Protocol and Other Multilateral Institutions 1970-79 3.30 0.41 0.66 9.98 0.98 0.91 2.37 6.97 n.a. 0.02 2.53 17.28 n.a. 2.20 0.63 1.72 n.a. n.a. 2.07 1.50 1.43 45.38 100 1980-89 3.43 0.74 1.73 7.23 7.19 1.87 5.24 4.45 n.a. 0.13 4.57 6.61 0.78 2.00 0.55 5.16 0.96 4.15 3.65 1.06 5.26 33.60 100 1990-99 2.12 1.21 1.87 11.18 10.05 1.33 8.57 6.18 0.33 0.14 5.93 13.72 0.05 3.54 0.59 1.35 0.17 0.98 2.53 1.48 5.88 21.01 100 1970-99 2.94 0.79 1.42 9.44 6.07 1.37 5.39 5.87 0.33 0.00 4.34 12.37 0.41 2.58 0.59 2.78 0.54 2.56 2.75 1.34 4.19 32.92 100 Combined Multilateral ODA (Grants), i.e. 1 through 5 1970-79 1.82 0.55 2.54 7.07 2.44 0.59 7.48 12.89 n.a. 0.20 4.05 8.34 n.a. 4.82 0.25 2.13 n.a. n.a. 5.12 0.87 9.48 29.44 100 1980-89 2.25 0.64 2.06 5.79 2.69 1.07 8.55 11.71 n.a. 0.26 6.38 15.70 0.06 4.72 0.15 2.77 0.11 0.76 3.40 0.97 8.17 21.79 100 1990-99 1.49 0.88 2.07 4.05 3.68 1.19 9.66 14.02 0.78 0.34 7.13 16.74 0.13 4.98 0.15 2.26 0.40 2.63 3.22 1.54 8.29 14.85 100 1970-99 1.85 0.69 2.23 5.64 2.94 0.95 8.56 12.88 0.78 0.27 5.85 13.59 0.10 4.84 0.18 2.39 0.26 1.69 3.91 1.12 8.65 22.03 100 Nominal Gross Domestic Products, GDP (US dollars) 1970-79 2.21 0.89 1.42 3.93 0.88 0.60 7.74 12.32 n.a. 0.22 4.96 12.31 n.a. 2.08 0.32 0.70 n.a. n.a. 1.66 1.28 5.48 41.11 100 1980-89 2.03 0.86 1.09 3.59 0.73 0.65 6.78 10.09 n.a. 0.24 5.28 16.02 0.05 1.72 0.28 0.71 0.32 2.29 1.30 1.22 5.60 39.18 100 1990-99 1.74 0.97 1.15 2.88 0.77 0.59 6.73 9.98 0.54 0.31 5.58 19.97 0.08 1.75 0.25 0.66 0.47 2.74 1.16 1.26 5.61 35.16 100 1970-99 1.99 0.91 1.22 3.46 0.80 0.61 7.08 10.80 0.54 0.26 5.27 16.10 0.06 1.85 0.28 0.69 0.40 2.52 1.37 1.25 5.56 38.48 100 3 Model framework and statistical methodology 3.1 A review of existing theoretical framework and testing methodologies As mentioned earlier, the economics of collective action can be traced to Olson and Zeckhauser (1966). Although they focused on alliances (military or defence equivalent of collective action), they demonstrate that their theory and empirical test have equal applicability to other forms of collective action, including the financing of UN and OECD’s contributions to ODA. The theory of collective action simply refers to an adaptation of the public goods theory to a setting of organizational cooperation (Kwon 1998). In its pure form, a public good has two distinguishing characteristics: non-rivalrous (in the sense that its consumption by an individual does not diminish the amount available to others) and non-excludable (in the sense that those providing the good cannot exclude others from its benefits, giving rise to the free-rider problem). Therefore, unless organizational arrangements exist for the provision of a public good, it tends to be under-provided. In the international setting, such public goods are referred to as global public goods or regional public goods, depending on the geographical scope of likely beneficiaries. International organizational arrangements have been established to cater for the provision of cross- border public goods, such as military alliances among groups of countries, one of which is NATO. In Olson and Zeckhauser’s (1966) application to military alliances, they utilize a pure public good model, with defence being ‘characterized as deterrence or inhibiting an enemy’s attack on any ally through the threat of an annihilating retaliation’ (Sandler and Hartley 2001: 871). Within such a model, they recognize only disproportionate burden sharing, whereby bigger alliance members bankroll an unequal share. It was on the basis of this that they coined the term ‘exploitation’ and propounded their famous ‘exploitation hypothesis’, whereby smaller members are generally assumed to exploit bigger ones in financing alliances or collective action. However, as reviewed by Sandler and Hartley (2001), recent theoretical studies have shown that the exploitation hypothesis is not applicable within a joint-product model of alliance (i.e., a product encompassing the characteristics of a pure public good and private, excludable good). According to Sandler and Hartley (2001: 878), The collective action implications of the joint product model may be drastically different than those of the purely public deterrence model of alliances. … As the ratio (of excludable or private benefits to total benefits) nears one, the exploitation hypothesis is anticipated to lose its relevancy. At the empirical level, Olson and Zeckhauser’s test of their exploitation hypothesis consists of two stages. First, the defence burden of each alliance member is defined and measured as the ratio of defence spending to GDP, giving what is referred to in the literature as within-ally measure of burden (Sandler and Hartley 2001: 883). Second, a simple non-parametric Spearman or rank correlation test between the size of the economy (i.e., GDP) and defence burden is carried out. A positive and statistically significant rank correlation, as reported by Olson and Zeckhauser, is interpreted to support the exploitation hypothesis. Several subsequent studies based on this test framework have supported the theory, except the recent analyses which have been based 6 on data for the 1980s and thereafter (see Sandler and Hartley 2001 for a comprehensive survey). While Olson and Zeckhauser’s empirical tests were also applied to non-military forms of collective action, subsequent studies have been confined to alliances only, particularly NATO. Probably the first to deviate from this tradition was Boyer (1989), who extended the test to OECD’s ODA programme. In order to test his hypothesis that members become specialized in financing collective action according to their respective comparative advantage, i.e., militarily strong members are more inclined to share an alliance burden, Boyer compared the burden sharing (defence/GDP ratio) of NATO members to their ODA contributions (in relation to their GDP). Since then, the only other study to have been extended beyond a military alliances is the one by Kwon (1998). Kwon hypothesized that the two factors broadly influencing UN expense burden sharing by OECD members include domestic politico-economic conditions and international incentives. Thus, apart from performing the traditional Spearman’s rank correlation tests to make an inference on the exploitation hypothesis, Kwon estimated panel regression equations for OECD member countries’ burden sharing of the UN finances. Regressors include lagged value of the dependent variable, GNP, and some variables representing domestic politico-economic conditions (specifically, per capita income, imports-to-trade ratio, and party ideology). The international incentive factors include cold war intensity, third world influence and time-cum-trend variables. But, invariably, only some of these were observed to exert statistical significance. Nevertheless, the application to a non-defence form of collective action is noteworthy as there appears to be a vacuum in existing empirical literature on collective action. As rightly observed by Sandler and Hartley (2001: 870): Insights garnered from the study of alliances can be applied to a broad set of collectives concerned with curbing environmental degradation, controlling terrorism, promoting world health, eliminating trade barriers, furthering scientific research, and assisting foreign development. This essay on alliances has much to offer for understanding a wide range of international organisations such as arms-control regimes, the EU, the United Nations (UN), WTO, and pollution pacts. The above mentioned empirical studies examine the within-ally burden sharing (measured as the contribution share to collective action in relation to contributor’s GDP). But there is a more direct and easily interpreted alternative measure of burden sharing, the among-ally indicator. This is defined in literature as each contributor’s share of the total contribution by all members. According to Sandler and Hartley 2001: 883-4), ‘Another burden-sharing measure, devised by Sandler and Forbes (1980), denotes among-ally burdens by relating an ally’s share of NATO’s total spending … to its derived benefits from being defended’. Sandler and Forbes proxied the benefits derived from defence spending by what was being protected by NATO activities. This was taken to be a simple average of three factors, namely, each ally’s industrial base (approximated by the ally’s share of the combined GDP of NATO members), its population (in relation to total population of all NATO members), and its exposed borders (in relation to exposed borders of all NATO members).4 Within this framework, 4 The result has a semblance to the statistics given in Table 1 if NATO is substituted for each multilateral agency there and the average of the three benefit factors (including GDP) also substitutes for GDP in the same table. 7 Wilcoxon tests were employed to formally determine whether the distribution of defence burdens is the same as the distribution of average benefit shares. This approach of among-ally burden-sharing analysis has also been adopted by Khana and Sandler (1996) and Sandler and Murdoch (2000), among others. But the simplistic Wilcoxon test has weaknesses. For instance, in the event of a mismatch between relative burden sharing and benefit derived, it is not possible to determine which member countries— the bigger or the smaller—are the exploiters. Nor would it shed light on the factors explaining the observed exploitation. While the econometric approach devised and used in the present study is based on the among-ally framework, it manages to overcome these defects. Specifically, our approach indicates whether in the event of exploitation, smaller members are exploiting the bigger ones (as initially assumed by Olsen and Zeckhauser [1966] and in the studies thereafter) or whether it is reverse exploitation, with bigger members exploiting the smaller ones. It also explains the reasons for whatever form of exploitation is observed in terms of other economic and political factors, along the lines of Kwon’s (1998) study that was, however, based on a within- ally framework. 3.3 Econometric approach adopted in the study 3.3.1 The postulated determinants of burden sharing tested for Our interest is in finding out the extent to which the ability-to-pay explains the contributions to multilateral aid agencies by DAC members, and in identifying other specific domestic factors (beyond the relative ability-to-pay) that have hindered or enhanced their cross-member contributions.5 As in virtually all previous multilateral burden studies, a DAC member’s relative (cid:1)n ability-to-pay x is proxied by its relative share of the group GDP (such that x = 1, i i i=1 i = 1, … n, where n is the number of DAC members). Ideally, i.e. if exploitation or reverse exploitation hypothesis is not supported, the share y of each member i in the i funding of a particular multilateral aid agency should also be equal to x, i.e., y = x. i i i Otherwise, if y > x, there is said to be exploitation of the bigger members by the i i smaller ones while reverse exploitation exists if y < x. But, as pointed out earlier, i i available evidence suggests that the equality does not seem to hold, although the direction of inequality might not necessarily be the same for all multilateral aid agencies. Hence, there is need to explain relative burden sharing in terms of other (particularly, domestic) factors, i.e., after controlling for the effect of ability-to-pay. One such factor is the per capita income level. One would expect this to provide more impetus to the ability-to-pay beyond that induced by the size of the overall GDP. More affluent but small-sized members may be able to bear a disproportionate share of the 5 There is some confusion in the literature as to whether the scale factor, such as the relative size of GDP, is an ability-to-pay or benefit derived variable. At times, both are implied simultaneously. For example, Kwon (1998: 39), in explaining the concept of exploitation, makes references to both benefits and resources (i.e., ability) thus: ‘Those who would benefit most from a collective good and have the greatest resources to provide it will bear a disproportionate share of the costs, while ‘smaller’ members of the group will bear a burden that is less than their share of the benefits and resources, behaving as free (or cheap) riders’. Here, we refer to it simply as ‘ability-to-pay’, without necessarily implying that it could also not be referred to as ‘benefit received’ indicator. 8