Determinants of foreign aid: the case of South Korea.

Kim, Eun Mee ; Oh, Jinhwan

South Korea, the newest member to join the OECD'S Development Assistance Committee, has signaled that it will become a major donor of official development assistance (ODA). Having had its own history of being a large recipient of ODA, South Korea claimed that it will provide aid from the recipient's perspective. Using panel data covering twenty-three years (1987-2009) and 154 recipient countries, we examine whether South Korea's ODA reflects the recipient nation's humanitarian needs more than the donor's interests. We ask three questions: (1) What are the major determinants of South Korea's ODA allocation? (2) Has South Korea's ODA policies changed over different time horizons--that is, years, political regimes? (3) Does South Korea exhibit different standards of allocating ODA for different groups of recipient countries? We find that South Korea provides more aid to higher-income developing countries with higher growth rates, which shows the tendency to serve the donor's economic interests. When we examine the data by time periods, we do not find significant differences over decades or political regimes. However, when we reexamine the data based on recipients" income levels, we find that the relationship between per capita income of the recipient country and ODA allocation is negative only for the middle-income or lower-middle-income group recipients and positive for the rest. This finding suggests the possibility that South Korea's ODA policy may have a dual-track structure.

KEYWORDS: ODA determinants, South Korea, emerging donor, Tobit model

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SOUTH KOREA'S EFFECTIVE USE OF FOREIGN AID TO ERADICATE EXTREME poverty and attain economic development is important in the history of foreign aid, where there are many critical studies on aid dependence and aid fatigue. South Korea declared its global role as a donor of foreign aid in 2009 when it ascended to the Development Assistance Committee (DAC) of the Organization for Economic Cooperation and Development (OECD), members of which provide more than 50 percent of global official development assistance (ODA). In 2010, South Korea hosted the G-20 summit meeting and introduced the development agenda. In late 2011, it hosted the High-Level Forum on Aid Effectiveness (HLF-4), which is the premier global forum to discuss various issues related to aid. South Korea has assumed the role of promoter of poverty reduction and development at these major global forums.

The recent global financial crisis has hit the least developed countries the hardest, although it originated in a developed country, the United States. The least developed countries were faced with additional difficulties since the major donors were severely affected by the crisis and could have reduced their ODA. There is growing concern that the fallout from the global financial crisis coupled with rising food prices and climate change could jeopardize the attainment of the Millennium Development Goals of reducing the world's poverty by half by 2015 (UNDP 2011).

Fearing a reduction of ODA from traditional donors, emerging and nontraditional donors (such as China, India, and South Korea) (1) and private foundations (such as the Bill and Melinda Gates Foundation) have increased their development assistance. Although these new donors play a critical role in assisting developing nations, there are few quantitative analyses that examine in detail the determinants and impact of such assistance.

In this article, we critically examine South Korea's ODA. South Korea's history as a donor goes back to 1963, when it was asked by the United States Agency for International Development (USAID) to conduct a training session. However, it was not until the Economic Development and Cooperation Fund (EDCF) was established in 1987 to handle concessional loans and the Korea International Cooperation Agency (KOICA) was created in 1991 to handle grant aid that South Korea emerged as a donor. South Korea's ODA has soared since then. Table 1 shows the amount of ODA South Korea provided annually (commitment and disbursement, bilateral and multilateral, loan and grant) between 1987 and 2009.

In the early-twenty-first century, Presidents Roh Moo Hyun and Lee Myung Bak both emphasized that South Korea's ODA would reflect its experiences as a recipient. South Korea would provide ODA based more on the recipient nation's humanitarian needs than on the interests of the donor--that is, South Korea's economic and political interests.

Thus, the South Korean case will be used to test the theories on the determinants of aid based primarily on the experiences of traditional donors. There have been few studies that have employed rigorous quantitative analysis of nontraditional emerging donors such as South Korea. Using a Tobit model, we analyze data from South Korea's ODA activities covering twenty-three years (1987-2009) and a total of 154 recipient countries to examine whether its aid reflects the interests of the recipients, as its presidents have claimed, or whether, like many traditional donors, South Korea pursued primarily its domestic interests in providing aid.

Earlier studies on foreign aid have developed in two directions: aid allocation and aid effectiveness. The former focuses on the motivations and determinants behind aid by examining the allocation of aid. The latter addresses the issue of how to better manage aid so that it delivers its goal--poverty reduction/eradication and economic development in the recipient nations. For relatively new donors like South Korea, with a small volume of aid, the latter is very difficult to examine. Thus, our study examines South Korea's aid allocation as a first step in examining its aid activities.

The literature on aid allocation is divided into studies focused on the donor's interests (DI) and those looking at recipient nations' needs (RN). The DI perspective is a realist view on foreign aid, which argues that governments use aid to enhance their national interests (Black 1968; Eberstadt 1988). Other studies examine aid from a humanitarian perspective (Kegley 1993; Lumsdaine 1993; Cigranelli 1993). Most of these studies have dealt with traditional donors in Nordic countries, Western Europe, the United States, and Japan, and there are relatively few that have dealt with emerging donors. There are some critical studies on Chinese aid, but these are based more on qualitative evidence, since reliable quantitative data on Chinese aid is difficult to find (Lancaster 2007; Ortiz 2007; Lum et al. 2009). And there are only a few studies that have examined South Korea's aid using a rigorous quantitative analysis (Koo and Kim 2011). The South Korean case is important since it is the first in which a country successfully graduated from being a major recipient of ODA to becoming an emerging donor and member of the OECD/DAC. The question is whether a more recent donor of ODA would reflect the recipient's needs more than the donor's interests given its history.

Existing studies on aid determinants on DI or RN focused on two determinants: (1) whether the donor's interests are more in line with DI or RN models; and (2) whether the donor's interests changed over time from DI to RN. In these studies, the assumption is that donors would have one policy that focuses either on DI or RN, and the variation is over time and between nations. However, in the case of South Korea, we employ a more nuanced approach at two levels: we left open the possibility that different aid determinants may coexist at the same time, and we divided the recipients based on their income levels. This novel approach could help with future analyses of emerging donors that may not have had developed a uniform policy on aid because of their short donor history.

Considering that South Korea's policies on aid have been recently formalized and may change considerably with different time horizons or recipient groups, we ask here not only what the major determinants of South Korea's ODA allocation are, but also how South Korea's ODA policies have changed over time. Is there a continuity in the ODA policies over the years and over political regimes? Does South Korea exhibit different standards of allocating ODA for different groups of recipient countries?

We organize the discussion as follows. In the next section we review different studies on the determinants of ODA and propose how to proceed with the empirical analysis of South Korea's ODA. In the subsequent section we discuss research design, data, and methodology. We devote the last two sections to a discussion of main findings and conclude with a summary of the findings and directions for further research.

Different Perspectives on the Determinants of ODA and Studies on South Korean ODA

Foreign aid began to be provided in great volume in 1945, when the United States established the Marshall Plan to help Europe recover from World War II. Research on the determinants of foreign aid flourished in an effort to understand the motivations of donor nations. Studies examined donor nations' interests as based either on recipients' needs or donors' interests, respectively, until the 1980s. The DI studies focused on how the donor nations were pursuing their own national strategic interests in foreign aid during the Cold War (Black 1968; Eberstadt 1988). These studies can be understood in the broad discussions of the realist perspective in international relations. While earlier realist studies on foreign aid examined the direct impact of foreign aid on the donor's national interests, neorealist scholars such as R. Gilpin (1987) began to see the indirect nonsecurity effects, such as the economic dimensions of foreign aid and national security. A. Maizels and M. Nissanke (1984) studied bilateral and multilateral aid flows, analyzing recipients' needs and donor's interests separately. They found that the donor interest model fits bilateral aid, while the recipient need model explains multilateral flow.

The RN studies are critical of the DI perspective and examine how foreign aid can be provided based on humanitarian goals such as poverty reduction and economic development of the recipient nations (Kegley 1993; Lumsdaine 1993; Cigranelli 1993). Here the recipients' needs are seen as more important than the donors' interests, and hence this perspective is called the RN perspective. RN studies have spurred the growth of studies focusing on the effectiveness of aid in recipient nations compared to earlier DI studies that tended to focus on aid determinants in donor nations.

More recent studies on ODA tend to combine the DI and RN perspectives and examine multiple determinants of foreign aid, including economic interests, foreign relations (political interest), and humanitarian concerns. Economic interests include promotion of trade and foreign direct investment (FDI). Foreign relations include the prestige of being a donor in international society, enhancing national security as well as influencing the recipient nation's political and institutional systems stemming from relationships derived from past colonial ties.

The combined RN-DI approach on aid was spearheaded by R. D. McKinlay and R. Little (1977, 1978a, 1978b, 1979) with a series of empirical studies that centered on Germany, France, the United Kingdom, and the United States. The findings from these studies supported the argument that donors provide aid based on both RN and DI perspectives and not exclusively on one at the expense of the other. Their studies allowed us to examine how donors' policy directions on aid may change over time in emphasis between the two different interests. A. Alesina and D. Dollar (2000) and Jean-Claude Berthelemy and A. Tichit (2004) made this approach richer by adopting quadratic forms. For example, according to Alesina and Dollar, the positive coefficient of income and negative coefficient of the quadratic form of income reveal that the amount of ODA increases proportionally to recipient income but at a decreasing rate.

Another way to categorize the studies in foreign aid is to determine whether the study deals with a single donor or multiple donors. Regarding single donor studies, B. Mak Arvin and Torben Drewes (2001) examined Germany's bilateral aid flows to eighty-five countries between 1973 and 1995. Their main interests were "biases," and they found that population bias exists, while a middle-income bias does not. M. McGillivray (2003) compared "political criteria" and "development criteria" using the US case and found that development criteria had little impact on ODA allocation during the Cold War, particularly during the 1970s and 1980s. J. P. Tuman and A. S. Ayoub (2004) found that humanitarian perspectives as well as US strategic interests were major determinants of Japan's ODA allocation in Africa. Tuman and J. R. Strand (2006) and Tuman, Strand, and C. Emmert (2009) examined the determinants of Japanese ODA from different perspectives. Multiple donor studies include S. Shishido and N. Minato (1994:G7 countries); Alesina and Dollar (2000: twelve countries); Berthelemy and Tichit (2004: twenty-two countries); Dollar and V. Levin (2004: OECD/DAC members); and D. Potter and D. Van Belle (2009: United States and Japan). An interesting structure of Alesina and Dollar (2000) and Berthelemy and Tichit (2004) is that, after analyzing the entire dataset, they broke it down by time horizon as well as by donor country.

Empirical studies on foreign aid can also be classified according to the analytical tools used. McGillivray and E. Oczkowski (1991), Berthelemy and Tichit (2004), Dollar and Levin (2004), and J. Koo and D. Kim (2011) used the Tobit model, Tuman and Strand (2006) used the OLS model, and Alesina and Dollar (2000) utilized both models.

Although there have been few studies on foreign aid in South Korea, we have seen a growing number of studies since the country began to increase its aid volume in the twenty-first century. K. Lee and G. Park (2007) examined twenty years of South Korea's ODA with a focus on the effect of aid in recipient nations. Due to the small volume of aid to each recipient nation, the study found that South Korea's aid did not have much impact on the recipient nations' economic growth. H. Chun, H. Lee, and E. Munyi (2010) suggested the need for reform of South Korea's ODA policy by arguing that it showed "low ODA/GNI ratio, a high concessional loans compared to grants, a high portion of tied aid, regional bias, and a large number of recipients," which were due to lack of consensus on the fundamental goals of ODA.

W. You (2009), G. Kim (2009), and Koo and Kim (2011) empirically examined South Korea's ODA pattern. Among them, Koo and Kim's study rigorously found that South Korea's economic interests are far more influential than recipients' needs in determining its ODA allocation. Using random effect Tobit models, they analyzed a panel dataset covering nineteen years and 142 countries to examine South Korea's ODA determinants. According to their study, South Korea's ODA disbursement has a positive relationship with its trade and FDI flows with recipient countries, arguing that donor interest is strongly presented. However, they argued that recipient need is also shown after finding negative regression coefficients of per capita gross domestic product (GDP). The variable on human rights, which was also used to examine recipients' needs, did not show statistically significant results. Finally, they used aid-related international meetings, aggregate aid amount worldwide, total aid amount of recipient countries, and INGO membership rate as measures of World Polity Theory. Most of these variables showed positive relationship with the dependent variable.

Koo and Kim (2011) concluded that South Korea's ODA reflected the donor's interests as well as the world political discourse on aid. Thus, their study made an important contribution to the studies on emerging donor aid and specifically on South Korea. However, there are some shortcomings, which we have tried to overcome in this study. We examined the data according to different time horizons as well as in different groups of recipient nations. We examined data from 1987 to 2009, which includes all years of South Korea's aid activities, while Koo and Kim (2011) examined data from 1989 to 2007. Since aid policies began to take greater political priority in the Roh and Lee regimes, we felt it was important to add the political time horizon and utilize the most recent data. Last, this study uses per capita flows of ODA and trade to avoid biased results stemming from country size, uses aid commitment in which donors have fuller control than disbursement, uses constant prices instead of current prices to neutralize inflation effects, and uses quadratic terms of some variables to examine the rate of change.

Research Design

We follow in this article the common structure of Alesina and Dollar (2000) and Berthelemy and Tichit (2004), whose studies showed results for all the years and all the countries as well as decade by decade (the 1980s and the 1990s). (2) However, we divide the years not only into two decades (the 1990s and the 2000s), but also into three political regimes (Kim Dae Jung, Roh Moo Hyun, and Lee Myung Bak (3)) to check whether each decade or government shows different ODA allocation patterns. While the earlier papers divided the dataset by donors, we cannot do so because we deal with a single donor country. Instead, we divide recipient countries into two or three groups by their income level. (4) This will allow us to provide a more nuanced analysis of South Korea's ODA given its relatively short history of aid activities. With this structure in mind, we test the following hypotheses.

Hypothesis 1: South Korea's ODA policies have changed over time and with political regimes (the 1990s and the 2000s; Kim Dae Jung, Roh Moo Hyun, and Lee Myung Bak governments), and moved from donor's interests toward the humanitarian needs over time.

This is an important hypothesis to test, as each government may have different objectives for its ODA policies. For example, after the Rob Moo Hyun administration took office in February 2003, South Korea's pattern of ODA disbursement began to change rapidly toward grant aid as opposed to concessional loans, and its ODA volume continued to increase rapidly. The Roh government began to push for more aggressive ODA policies, including the Policy Framework for ODA in 2005; policies and programs to increase ODA volume and shift geographical orientation of South Korea's ODA from Asia to Africa in 2006; and the application for membership in the OECD's DAC in 2007. Additionally, ODA policies may have moved toward the humanitarian side as the current Lee regime emphasized the importance of "giving aid with two hands." This is an expression in Korean to reflect South Korea's humility toward its recipients, to respect the recipients' ownership. This hypothesis will allow us to test whether this is merely a political slogan or reflects actual aid activities.

Hypothesis 2: South Korea's ODA policy has a dual-track structure on the basis of the income level of recipient countries with DI toward middle-income developing countries and with RN toward least developed countries.

ODA is composed of loans and grants. As the name suggests, grants are a type of ODA that does not have the obligation of repayment and that targets the least developed countries. The South Korean government

is trying to expand the portion of grant aid of total ODA (MOFAT 2009). However, South Korea is also providing low-interest, long-term concessional loans, which are spent on improving developing countries' economic and social infrastructure. Disbursement of grant aid to the least developed countries and concessional loans to developing countries may describe South Korea's dual-track policies--providing humanitarian aid to poorer countries and investing strategically in relatively higher-income developing countries that are economically and socially close to South Korea. The Korean International Cooperation Agency's (KOICA) new policy of differentiating priority recipient countries (5) from general recipient countries and concentrating on the former in terms of providing grant aid confirms this hypothesis.

To test this hypothesis, we break down the recipient countries into three groups, depending on their income level, conduct a set of regressions, and compare results among the different groups.

Data and Methodology

We base our study on a comprehensive dataset covering South Korea's 154 recipient countries between 1987 and 2009; 1987 is the year that South Korea established the EDCF and the first year for which data is available, and 2009 is the most recent available year. As a dependent variable, we use South Korea's ODA commitment downloaded from the OECD database. As Berthelemy and Tichit (2004) correctly pointed out, aid commitments better reflect donors' decisions, because "donors have total control of the commitments, compared to disbursements which depend in part on the recipients' willingness and administrative capacity to get the money." In addition, like Berthelemy and Tichit (2004), we use ODA per capita instead of total flows in order to effectively control for the fact that larger countries (in terms of population) tend to receive more ODA. We also use constant price (with a base year 2009) to adjust the effect of inflation.

The independent variables are as follows. Income and population variables, which are provided both linearly and quadratically, are transformed into log to deal with extreme values.

* Ln_Income is the log of per capita GDP, which is the essential variable measuring whether ODA allocation meets recipients' needs; a negative sign is evidence of humanitarian aid and a positive sign is evidence of donor's interest. (6) These data are from the World Bank's World Development Indicators and are given in constant prices with a base year 2000.

* (Ln_Income) (2) is the quadratic form of Ln_Income, which was introduced in Alesina and Dollar (2000) and also used in Berthelemy and Tichit (2004). This variable captures rate of change as a second order effect. For example, if Ln_Income shows a positive coefficient and (Ln_Income) (2) shows a negative coefficient, it means that South Korea's ODA is proportional to recipient income at a decreasing rate.

* Ln_Pop is the log of population of recipient countries.

* (Ln_Pop) (2) is the quadratic form of Ln_Pop, again introduced in Alesina and Dollar (2000) and Berthelemy and Tichit (2004).

* Trade refers to bilateral trade flows (export and import) between South Korea and the recipient country; this is one of the most frequently used variables in this literature. (7) If the sign is positive, it means that South Korea provides more ODA to stronger commercial ties, which is the evidence that the country pursues donor's interests. These data are from the IMF's Direction of Trade Statistics. It would be preferable to have data with a constant price, but this database provides only current prices. We normalized the data by scaling to population, as we did for the dependent variable. Additionally, assuming that last year's trade data may affect this year's ODA allocation, we use a one year lagged variable. Using a lagged variable may allow us to avoid any possible endogeneity problems as well.

* Japan is a dummy variable equal to 1 if the recipient is ranked in the top thirty countries receiving Japanese ODA and zero otherwise. (8) This dummy measures Japan's influence on South Korea's ODA allocations. It would not be too unrealistic to assume that Japan, as South Korea's neighbor and as an early donor, has influenced South Korea's ODA policies.

* Growth is the growth rate of a recipient country, which is again lagged by a year. This variable was used by Berthelemy and Tichit (2004).

Using these variables, we analyze data in the following order: (1) an aggregate dataset with all the years and countries, (2) data for all the countries over different years (the 1990s, the 2000s; Kim Dae Jung, Roh Moo Hyun, and Lee Myung Bak administrations), and (3) data for all the years over different countries on the basis of income level (9) (top half (1/2); bottom half (2/2); top one-third (1/3), middle one-third (2/3), bottom one-third (3/3), top one-third plus middle one-third (or upper-middle group in another term in this study) ((1+2)/3), and middle one-third plus bottom one-third (or lower-middle group in this study) ((2+3)/3).

Regarding methodology, we use the random-effect Tobit model, as used by Berthelemy and Tichit (2004) and in part by Alesina and Dollar (2000). The advantage of Tobit over OLS is that Tobit regards a zero value in the dependent variable not just as a number but as a code, which is a censored random variable as a lower limit. If the dependent variable contains many zeros, which is the case in this study, it is better to use Tobit. In our Tobit regressions, we included the countries (10) that have not received aid from South Korea to avoid a selection bias problem, which may be a key issue in the literature.

Results

Results on the Basis of Different Time Horizons

Table 2 provides the result of several regressions. Column 1 reports the most complete sample with all the years and all the recipient countries, which is our "base specification," using the term in Alesina and Dollar (2000). Columns 2-6 report reduced samples to see the changes over time. Berthelemy and Tichit (2004) compared the 1980s and 1990s, but our study compares a variety of years. Columns 2 and 3 compare two decades--the 1990s and 2000s, and columns 4-6 compare three political regimes.

Following Alesina and Dollar (2000), Berthelemy and Tichit (2004), and Cooray, Gottschalk, and Shahiduzzaman (2005), we use recipient countries' per capita income and population in both linear and quadratic forms. The relationship between per capita income and ODA is linearly positive but quadratically negative in most cases, meaning that South Korea's ODA allocation is diminishingly increasing (increasing at a decreasing rate) to recipients' income. This pattern is consistent throughout all the regressions over various groups of years, implying that South Korea's ODA allocations, regardless of decades and the political orientation of the government, are basically representing the donor's interests. Additionally, combined with the finding that signs for population are all negative and statistically significant, it can be concluded that, overall, South Korea is providing aid to relatively higher-income developing countries with smaller populations.

Next, bilateral trade flows between South Korea and recipient countries are introduced. Except in column 2, this variable shows a negative sign, which does not seem very intuitive, as one could expect that South Korea would provide more ODA to countries with which it has stronger commercial ties. It should be noted that this variable is scaled down in terms of the population of recipient countries. In other words, this is trade per capita, which could explain the negative coefficient. In fact, when we conduct the same regressions with total instead of per capita trade flows, we get positive coefficients. It can thus be understood that South Korea provides more aid to countries with which it has higher trade flows; however, these countries are mostly large countries in terms of population, and this table suggests that a larger population will lead to reduction of South Korean ODA. Therefore, the positive effect of trade on South Korean ODA could be crowded out and dominated by a negative relationship between population and ODA.

The next variable, Japan, measures politically strategic consideration. This variable is similar to the "Friends of Japan" of Alesina and Dollar (2000), who calculated countries' UN voting patterns toward Japan. We used here the amount of ODA that a recipient country receives from Japan and made a dummy variable with 1 for the top thirty recipient countries and 0 otherwise. From South Korea's perspective, a positive coefficient could mean that South Korea strategically allocates ODA to compete against Japan. From the recipients' perspective, the positive relationship (a country receiving ODA from Japan would like to get more ODA from South Korea) implies that ODA between Japan and South Korea are either complementary or in completely different sectors. For example, if a country receives cars as a part of ODA from Japan, it may want to receive car navigators from South Korea (complementary). Or, if the country receives cars from Japan, it may want to receive computers from South Korea (different sectors). If it shows a negative relationship, the ODA of Japan can be interpreted as a substitute for South Korea's ODA. According to Table 2, this coefficient shows a positive sign in columns 1, 2, and 4, and a negative sign in the rest.

Growth is the one year lagged variable of growth rate of recipient countries. This variable has a positive coefficient in almost all columns, meaning that South Korea would like to provide ODA to rapidly growing countries.

Findings in Table 2 show that most columns have similar results. This indicates that there are no significant structural breaks in South Korea's ODA scheme based on decade or government. Coefficients of income, population, and growth rate are very consistent throughout all the regressions. Coefficients for Trade and, Japan show mixed signs, but they do not look very important in terms of statistical significance.

Overall, given positive coefficients of Ln_Income and Growth, it seems that South Korea basically gives aid to higher-income countries with higher growth rates. Negative coefficients for Trade indicate that a positive link between trade and ODA seems to be crowded out by a negative effect of population. In general, we can state that South Korea's ODA allocation shows a pattern consistent with its economic interests. This finding might be contradictory to the current government's policies of minimizing donor interest and emphasizing humanitarian views, rejecting Hypothesis 1. However, Hypothesis 2 may suggest different results if we divide the country group into two or three by income level. These results are provided in the next section.

Results on the Basis of Different Group of Recipients

Unlike in the previous section, where we examined difference between years, in this section, we compare differences among recipient nations. Table 3 shows the overall results. The most interesting variable is Income. In the previous section, when we use a full set of countries, this variable is significantly positive throughout all the regressions. However, in this section we show difference among groups. For example, when we break down countries into three groups based on income level, the income coefficients of the middle-income group (the second group, or column 4, and the middle- plus low-income group (the second group plus the last group, or column 7) are negative. Other groups, including the high-income group (the first one-third group, or column 3) and the low-income group (the last one-third group or column 5), show positive coefficients. We do not see this difference when we divide the countries into two groups.

We find in general a positive relationship between the income of recipients and South Korean ODA allocation to them. However, when we look at the middle one-third or bottom two-thirds groups (all recipients minus the top one-third), this relationship becomes negative. This result suggests the possibility that South Korea's ODA policy may have a dual standard in terms of DI-RN. When allocating ODA, South Korea considers its own economic interests in the richest group (the first group, or column 3), the upper-middle group (the first and the second group, or column 6), or even the poorest group (the third group, or column 5). However, South Korea considers recipients' needs in the middle-income group (the second group, or column 4) or the lower-middle group (the second plus third group, or column 7); (11) within this group, South Korea tends to provide more aid to the relatively poorer countries, but with a caveat that these are not the poorest countries in the world.

This finding is consistent with KOICA's policy for priority recipient countries, which are more or less above the category of the least developed countries (see note 5 for a complete list of these countries in 2009). As a matter of fact, KOICA has provided more aid to Low Middle Income Countries (LMICs) than to the Least Developed Countries (LDCs) (12)--for example, 36.5 percent to LMICs and 28.3 percent to the LDCs in 2009, and 40.9 percent to LMICs and 26.9 percent to the LDCs in 2008 (KOICA 2008, 2009). This finding is consistent with our finding of "dual-track structure." If South Korea truly pursued humanitarian aid and considered recipients' needs, it should have actively expanded aid to the least developed countries and/or heavily indebted poor countries in the sub-Saharan region, most of which belong to the bottom one-third group in this study. However, the fact that almost half of South Korea's priority recipient countries do not belong to the poorest one-third group signals that South Korea's ODA does not entirely reflect recipients' needs. (13)

In sum, our findings show mixed support for Hypothesis 2. That is, South Korean ODA shows a dual-track structure, where its aid is based more on DI for high-income recipient countries, while its aid for the middle-income group showed RN. More importantly, the results showed that South Korea does not provide much assistance to the bottom one-third of least developed recipients, which desperately need development assistance from the RN approach.

Conclusion

We have examined in this article South Korea's ODA allocation for 154 recipient countries from 1987 to 2009 by breaking the panel dataset into different groups of years and recipient countries. We conducted the first series Tobit regressions for all the years, for the 1990s, for the 2000s, and for the Kim Dae Jung, Roh Moo Hyun, and Lee Myung Bak administrations, respectively. As the second series of regressions, our analyses focused on a smaller group of countries depending on their income levels.

The findings from the first series of Tobit regressions, where the dataset was broken into different groups of years, but not into recipient countries, show that South Korea's ODA allocation was basically directed toward higher-income developing countries. This result was consistent irrespective of decade or political regime. However, when we examined the data based on recipient nations' income, the findings presented more mixed signs and suggested the possibility that South Korea's ODA policy may have a dual-track structure--that is, that it may pursue donor interest in the group of relatively higher-income developing countries but adopt a humanitarian approach to the group of lower-income developing countries (but excluding the bottom one-third, which are the least developed countries). In other words, South Korea's ODA volume is proportional to a recipient's income level when the recipient's income is relatively higher (top one-third or, at least, top half) or absolutely lower (bottom one-third), but counterproportional to the same variables when the recipient's income level is somewhere in the middle or slightly lower than average. Overall, as shown in Table 2, South Korea's ODA allocation for all recipient countries was positively related to per capita GDP, meaning that the proportional effect may dominate the counterproportional effect.

In this study, we were able to advance our knowledge of an emerging donor, South Korea, which recently joined the OECD/DAC. The study provided an important opportunity to examine how an emerging donor allocates aid and whether that aid reflected a recipient's needs. With a breakdown of recipients into different groups, the study allowed us to develop a methodology to examine relatively new donors with a short history of aid. The findings did not support the hypothesis that South Korea as an emerging donor with its own history of being a recipient of aid and of experiencing extreme poverty would follow recipient need more than donor interest. This is particularly salient given that the South Korean government has made a big claim that it would pursue a "Korean ODA model." Although its exact meaning is yet to be determined, the implication is that it would be a different model from what traditional donors have adopted, and that it would be more respectful of the sovereignty, dignity, and ownership of recipient nations. However, when we examined the aid allocation among different income groups of recipients, we found that South Korea has in fact a dual-track structure, showing a DI perspective toward the higher-income group and a RN perspective toward the second group. In other words, as the traditional donors' aid allocation changed over time from DI to RN, and earlier DI-RN studies assumed this type of transition over time, the South Korean case study suggests that DI and RN interests may coexist. And, this may be more pronounced in donors with a relatively short history of aid, which has not allowed enough time to gradually mature into more RN-based aid.

In future research, it would be interesting to examine whether this pattern of aid allocation would be extended even with a longer time frame or change to a clearer pattern toward RN. This would help us examine whether an emerging donor changes its donor activity to conform to global norms for donor activity once it becomes a member in a community of advanced donors such as the OECD/DAC. Furthermore, this longer time frame would allow us to examine with empirical data whether there are distinct effects of political regimes for ODA activity in an emerging donor.

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Notes

This work was supported by the World Class University program through the National Research Foundation of Korea, funded by the Ministry of Education, Science and Technology of the Republic of Korea (grant no. R32-20077). We wish to thank Stephan Haggard and two anonymous reviewers for their constructive suggestions for revision. We would also like to express our gratitude to Ji Hyun Kim of the Graduate School of International Studies at Ewha Womans University for her research assistance.

(1.) The term traditional donors refers to the United States, Western and Northern European nations, and Japan, which have provided development assistance since the end of the World War II and belong to the OECD's Development Assistance Committee (DAC). The term emerging donors refers to a group of donors that often do not belong to OECD's DAC and that began to provide ODA much later than the traditional donors. However, China has been providing ODA to sub-Saharan Africa since the late 1950s and thus rejects the term, using instead the term south-south cooperation. In this article, we refer to relatively new donors--China, South Korea, India, Brazil, and Middle Eastern nations--as emerging and nontraditional donors (Kim and Lee 2009).

(2.) In fact, Alesina and Dollar (2000) showed these time-specific results in the first series of regression assuming that this is a part of "aggregate results."

(3.) Kim Young Sam's presidency (1993-1997) is not considered because it was during an incipient stage of Korean ODA. The size of the ODA was very small and allocation was to only a few countries. Regarding the current Lee administration, we have data for only two years, since the most recent available year is 2009.

(4.) This study uses per capita GDP for the year 2007 to divide recipients into two or three groups. The reason for using 2007 is that it is the most recent year for which almost all the recipient countries' data are available. We have data up to 2009, but a number of countries show missing values in 2008 and 2009. Breaking down the dataset into different groups is important in investigating dual-track policies of Korean ODA.

(5.) Bangladesh, Cambodia, Indonesia, Laos, Mongolia, the Philippines, Sri Lanka, Vietnam, Iraq, Kahakhstan, Uzbekistan, Egypt, Ethiopia, Nigeria, Senegal, Tanzania, Guatemala, Paraguay, and Peru, as of 2009. In 2010, KOICA made a big shift in selecting its priority recipient countries by dropping six and adding thirteen (KOICA 2011).

(6.) Trade is one of the most frequently used variables measuring donor interest. However, given that a number of gravity model studies confirm a positive relationship between per capita income and trade flows, it would not be too unrealistic to assume that per capita income could be a variable measuring donor interest.

(7.) FDI is also commonly used in measuring donor interest. See Berthelemy and Tichit (2004) and Koo and Kim (2011). However, we did not use this variable because trade and FDI often move in a similar direction with strong correlation, creating a multicollinearity problem.

(8.) Given that Japan's ODA policy most resembles that of Korea (Jeong, 2010), it is sufficient to consider Japan only instead of considering multiple donors, including the United States.

(9.) As explained in note 4, per capita GDP for 2007 is used in this study. When we use two groups, the top half is a group of countries with per capita GDP of $1,618 or above, and the bottom half is the rest. When we use three groups, the top one-third is a group of countries with per capita GDP of $2,725 or above, the middle one-third is a group of countries with per capita between $703 and $2,727, and the bottom one-third is the rest. This is different from the World Bank's classification of country income groups, which has five groups: low-income ($1,005 or less), lower-middle-income ($1,005-$3,975), upper-middle-income ($3,975-$12,275), high-income non-OECD and high-income OECD ($12,275 or more).

(10.) In the original manuscript, we did not include countries that have not received aid from South Korea. Based on a reviewer's suggestion, we included additional countries, making the total number of recipient countries 154 (it was 134 in the original manuscript). As long as we could obtain data for the independent variables, we included any country that was listed as a recipient of South Korean ODA in the OECD database.

(11.) As specified in note 10, this group classification is different from that of the World Bank.

(12.) KOICA's latest reports on ODA statistics include four categories of recipient nations as follows: the Least Developed Countries (LDCs), Low Income Countries (LICs), Low Middle Income Countries (LMICs), and Upper Middle Income Countries (UMICs) (KOICA 2008, 2009, 2010), which differs from our classification.

(13.) We have noted with interest that in 2010 South Korea's ODA share to the LDCs became the largest (40.4 percent), followed by the share to the LMICs (30.4 percent), which suggests a possibility that South Korea's ODA policies may have started to be directed toward humanitarian interests even to the LDCs (KOICA 2010).

Eun Mee Kim is dean and professor in the Graduate School of International Studies and director of the Institute for Development and Human Security at Ewha Womans University, Seoul. She received her PhD from Brown University. Her research focuses on the political economy of development, development cooperation, globalization, and multiculturalism. Her publications include Multicultural Society of Korea (2009); The Sociology of the Economic Crisis: Transformation of the Developmental State and Business Group Networks (2005); and Big Business, Strong State: Collusion and Conflict in South Korean Development, 1960-1980 (1987).

Jinhwan Oh is assistant professor in the Graduate School of International Studies, Ewha Womans University, Seoul. He received his PhD from Cornell University. His research focuses on geographical aspects of economic development, political economy of international trade, and the aid policies of South Korea as an emerging donor. He has published in Korean Journal of Defense Analysis, Review of Urban and Regional Development Studies, and Letters in Spatial and Resource Sciences.

Table 1 ODA of South Korea as a Donor: Commitment and Disbursement,
Bilateral and Multilateral, Grant and Loan

 Commitment

 Bilateral by Region

Year Europe Africa United States Asia Oceania Others

1987 0.02 17.39 0.32 22.57 -- 0.47
1988 0.04 0.14 14.58 1.94 0.34 0.55
1989 0.04 0.25 0.25 1.57 8.4 1.24
1990 0.04 14.74 0.13 25.01 0.41 1.25
1991 0.16 29.23 3.92 54.92 14.39 8.42
1992 16.02 21.91 3.29 22.57 1.45 12.93
1993 0.54 6.37 4.02 24.48 1.15 12.09
1995 0.15 7.37 16.04 150.41 1.2 13.82
1996 1.02 6.94 3.69 341.97 1.32 13.97
1997 0.8 34.61 7.7 120.13 1.7 13.17
1998 41.64 19.48 110.25 44.11 1.12 12.2
1999 36.08 3.7 35.25 208.01 0.78 10.62
2000 1.17 47.96 29.75 195.11 0.54 10.43
2001 1.16 5.19 37.6 154.36 0.87 13.43
2002 35.07 9.28 75.17 158.73 0.85 11.89
2003 27.2 9.38 10.18 258.16 12.22 15.5
2004 20.15 62.14 27.98 342.23 0.89 21.14
2005 47.12 60.46 59.8 382.84 0.9 22.35
2006 2.18 52.05 80.75 376.15 1.42 35.47
2007 1.94 169.3 52.25 554.52 4.36 35.05
2008 53.06 229.7 41.18 921.41 1.82 63.36
2009 16.97 294.5 112.14 953.25 3.35 69.96
1987 -- 0.19 0.21 1.51 -- 0.47
1988 0.04 0.11 0.07 1.75 0.34 0.55
1989 0.03 2.36 0.15 1.41 0.92 1.24
1990 0.03 10.58 0.07 1.84 0.4 1.25
1991 0.16 5.79 3.91 14.9 1.34 8.42
1992 0.18 13.07 3.28 17.8 1.44 12.93
1993 0.54 24.47 3.86 20.4 1.15 12.09
1994 0.09 13.55 3.51 28.6 1.03 11.59
1995 4.11 13.15 3.19 26.4 1.2 13.82
1996 6.77 12.78 9.72 61.6 1.32 13.8
1997 3.56 12.05 10.02 68.2 1.87 13.23
1998 0.21 7.15 6.4 144 1.27 12.37
1999 0.1 11.96 5.77 125 0.81 10.55
2000 0.41 26.85 13.55 91.6 2.44 10.42
2001 16.64 5.32 16.82 152 5.09 13.43
2002 21.64 6.42 10.1 185 1.3 11.89
2003 3.19 19.91 11.68 202 5.07 15.41
2004 6.91 27.62 14.63 254 0.41 21.14
2005 2.87 34.13 17.29 327 0.43 22.35
2006 25.23 38.83 21 185 0.95 34.48
2007 13.13 54.44 42.46 233 2.9 34.96
2008 11.62 93.75 61.89 253 2 63.03
2009 46.36 95.01 55.84 313 1.53 68.91

 Commitment

 Bilateral by Type
 Bilateral Multilateral
Year Grant Loan Total Total

1987 2.75 38.1 40.77 5.41
1988 3.13 14.5 17.59 5.04
1989 4.28 7.48 11.75 105.52
1990 4.02 37.6 41.58 5.32
1991 27.79 83.3 111.04 6.19
1992 33.88 44.3 78.17 8.6
1993 34.39 14.3 48.65 10.9
1995 43.49 146 188.99 16.26
1996 46.27 323 368.91 79.37
1997 54.11 124 178.11 66.93
1998 50.53 178 228.8 217.52
1999 43.73 251 294.44 140.29
2000 52.89 232 284.96 75.1
2001 64.51 148 212.61 107.23
2002 76.22 215 290.99 130.73
2003 152.87 180 332.64 126.71
2004 236.65 238 474.53 291.44
2005 295.59 278 573.47 99.31
2006 256.62 291 548.02 175.66
2007 364.03 453 817.42 255.02
2008 438.79 872 1,310.5 191.74
2009 383.04 1,067 1,450.2 529.93
1987 2.38 0 2.38 37.27
1988 2.86 0 2.86 44.61
1989 3.92 2.16 6.11 34.88
1990 3.72 10.5 14.17 56.84
1991 27.4 7.07 34.48 28.35
1992 33.4 15.3 48.74 34.01
1993 34 28.6 62.55 53.53
1994 37.4 21 58.35 77.65
1995 43.4 19 61.83 38.5
1996 45.9 61.5 105.94 30.79
1997 53.6 58.2 108.92 72.61
1998 51 123 171 79.6
1999 45.7 112 154.25 218.66
2000 52.9 96.6 145.31 89.66
2001 64.5 156 208.87 113.38
2002 76.2 174 236.35 82.36
2003 153 119 257.54 126.9
2004 208 133 324.67 90.81
2005 277 143 403.94 251.95
2006 210 116 305.13 64.25
2007 278 130 380.7 159.55
2008 332 189 485.72 237
2009 367 249 581.1 234.94

Source: OECD Statistics Database.

Notes: Million USD, constant price (base year: 2009). In disbursement,
sum of "grant" and "loan" is not the same as "total" because "loan" is
gross, and "total" is net disbursement. A significant portion of
multilateral aid is grants, so we did not provide columns for
multilateral grants and loans.

Table 2 Tobit Estimation: All Countries with Different Time Periods

 (1) 1987-2009 (2) 1990s (3) 2000s (4) Kim

Ln_Income 1.572 * 0.752 * 2.788 * 1.360 *
 (0.668) (0.326) (1.157) (0.626)

(Ln_Income) (2) -0.111 * -0.058 * -0.206 * -0.101 *
 (0.047) (0.023) (0.081) (0.044)

Ln_Pop -1.889 *** -0.798 *** -2.748 *** -0.868 **
 (0.386) (0.175) (0.588) (0.321)

(Ln_Pop) (2) 0.063 *** 0.025 *** 0.087 *** 0.027 **
 (0.013) (0.006) (0.019) (0.011)

Trade -0.001 *** 0.000 -0.002 *** 0.000
 (0.000) (0.000) (0.001) (0.000)

Japan 0.018 0.051 -0.074 0.279 *
 (0.158) (0.081) (0.276) (0.141)

Growth 0.025 *** 0.006 * 0.031 -0.003
 (0.007) (0.003) (0.018) (0.005)

Constant 8.005 * 3.835 * 12.045 2.282
 (3.926) (1.818) (6.232) (3.357)

No. Obs. 3,264 1,402 1,500 743

Censored 1,106 499 347 204

Rho 0.083 0.116 0.079 0.268

 (5) Roh (6) Lee

Ln_Income 2.583 5.312 **
 (1.656) (1.785)

(Ln_Income) (2) -0.188 -0.400 **
 (0.117) (0.126)

Ln_Pop -3.838 *** -2.357 **
 (0.820) (0.802)

(Ln_Pop) (2) 0.118 *** 0.076 **
 (0.027) (0.026)

Trade -0.003 ** 0.000
 (0.001) (0.000)

Japan -0.485 -0.130
 (0.443) (0.367)

Growth 0.040 0.052
 (0.029) (0.035)

Constant 22.021 * 0.865
 (8.768) (8.938)

No. Obs. 755 296

Censored 147 89

Rho 2.23E-19 0.510

Sources: Dependent variable: per capita ODA commitments, OECD
Database; independent variables: income, World Development
Indicators; population, World Development Indicators; trade per
capita, IMF Direction of Trade Statistics.

Notes: Tobit Random Effect. Normal distribution and censoring at
zero. Standard errors in parentheses. * (.05), ** (.01), ***
(.001). Many coefficients without asterisks are significant at
the 10%n level. Rho: Standard deviation of the random effect
divided by standard deviation of residual.

(1) 1987-2009 (all years); (2) 1990s: (3) 2000s; (4) Kim Dae Jung
regime (1998-2002); (5) Rob Moo Hyun regime (2003-2007); (6) Lee
Myung Bak regime (2008-2009).

Table 3 Tobit Estimation: All Years with Different Recipient Countries

 (1) 1/2 (2) 2/2 (3) 1/3 (4) 2/3

Ln_Income 13.496 *** 0.242 31.166 *** -4.643
 (3.021) (0.923) (6.669) (4.299)

(Ln_Income) (2) -0.827 *** 0.012 -1.840 *** 0.379
 (0.186) (0.076) (0.389) (0.307)

Ln_Pop -2.850 *** 0.597 -4.181 *** 0.044
 (0.690) (0.472) (1.131) (0.564)

(Ln_Pop) (2) 0.096 *** -0.015 0.141 *** 0.000
 (0.024) (0.015) (0.040) (0.018)

Trade -0.001 * 0.000 -0.001 0.000
 (0.000) (0.000) (0.000) (0.001)

Japan -0.048 0.036 -0.179 0.252
 (0.323) (0.088) (0.474) (0.194)

Growth 0.026 0.010 ** 0.025 0.019 **
 (0.016) (0.003) (0.026) (0.007)

Constant -34.906 ** -7.739 -102.448 *** 12.810
 (13.191) (4.808) (30.162) (15.223)

No. Obs 1,650 1,614 1,094 1,109

Censored 648 458 488 330

Rho 0.083 0.236 0.072 0.161

 (5) 3/3 (6) (1+2)/3 (7) (2+3)/3

Ln_Income 0.054 7.046 *** -0.813
 (1.314) (1.947) (0.855)

(Ln_Income) (2) 0.030 -0.448 *** 0.088
 (0.116) (0.124) (0.067)

Ln_Pop 1.208 * -2.157 *** 0.104
 (0.607) (0.582) (0.351)

(Ln_Pop) (2) -0.033 * 0.072 *** -0.001
 (0.017) (0.020) (0.011)

Trade -0.001 -0.001 ** -0.001
 (0.000) (0.001) (0.001)

Japan 0.017 0.056 0.123
 (0.084) (0.248) (0.101)

Growth 0.004 0.032 ** 0.012 **
 (0.004) (0.011) (0.004)

Constant -12.093 * -12.332 -0.067
 (6.060) (8.972) (3.856)

No. Obs 1,061 2,203 2,170

Censored 288 818 618

Rho 0.150 0.098 0.146

Sources: Dependent variable: per capita ODA commitments, OECD
Database; independent variables: income, World Development
Indicators; population, World Development Indicators; trade per
capita, IMF Direction of Trade Statistics.

Notes: Tobit Random Effect. Normal distribution and censoring at
zero. Standard errors in parentheses. * (.05), ** (.01), *** (.001).
Many coefficients without asterisks are significant at the 10%
level. Rho: Standard deviation of the random effect divided by
standard deviation of residual.

(1) high-income recipients (first half); (2) low-income
recipients (second half); (3) high-income recipients (first
one-third); (4) middle-income recipients (second one-third); (5)
low-income recipients (bottom one-third); (6) the first one-third
plus the second one-third; (7) the second one-third plus the
third one-third.