out empirical evidence to show the existence of the vanguard effects of ODA on ..... GMM model is known to fit for the dataset that has many panel groups (i.e., ...
Does Korea’s Official Development Assistance Promote Its Exports?: Theoretical and Empirical Analyses1 Gil Seong Kang
Abstract This paper aims to contribute to narrowing the knowledge gap of aid effects on donor’s exports through theoretical and empirical analyses of various ODA types. To explain how aid affects donor’s exports, we constructed a theoretical framework that considers aid as "public goods" with externality effects. We also estimate augmented gravity equations using Korea’s bilateral exports and ODA data, employing Hausman-Taylor Method and system-GMM to address the endogeneity problem. Through the long-term analysis with aggregated data, we confirmed the positive effects of total ODA, loans, grants and economic aid but no such effect in non-economic (humanitarian) aid. At the sub-sector level, we found out that in some cases infrastructure aid had no effects and humanitarian aid showed positive effects, which was contrary to the results obtained analyzing aggregated data. These conflicting results imply that the effects of ODA on exports depend on both ODA types and industrial sectors. By comparing two distinct periods, we found out that the vanguard effects of ODA on donor exports have lasted even after untying aid was introduced. These results show that donors are not necessarily anxious about expanding untying aid, since they are highly likely to continue to benefit if aid packages are well designed. The government efforts to sophisticatedly make the combination of various ODA types would not only enhance the trade creation effects of ODA but also secure domestic supports for scaling up ODA; hence eventually helping the international community to mobilize financial resources. Keywords: ODA, Exports, Aid Effectiveness, Korea, Gravity Model, Externality Effects
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Substantial part of this Chapter was published on Journal of Korea Trade, Vol.18, No.4, November 2014.
1
1. Introduction In these days emerging economies such as China, India and Brazil have raised up their ODA 2 volume to Least Developed Countries, supported by their economic success. The Republic of Korea (hereafter Korea), once one of the poorest countries in the world, newly joined the DAC (Development Assistance Committee) in 2010 and has led this new trend. Korea, as a member of DAC, has faced challenges to make advance in ODA in terms of scale and effectiveness. Korea has a plan to increase its ODA/GNI ratio from 0.14% as of 2012 to 0.25% by 2015. Korea is also required to adjust its ODA policy, moving toward recipient-orientation. This means changes in the composition of aid packages - the higher portion of untied aid. Tying ODA which binds recipients to purchase materials or services produced by donors has been commonly used because it is expected to promote donor's exports. However, the international community has continued to recommend donors to expand untying aids, considering recipients' autonomy and implementation costs3. Korea also as a member of the DAC has a plan to increase untying aid ratio from 55% in 2012 up to 75% by 2015 4. The higher proportion of untied aid and the greater amount of ODA are goals that are not easily achieved at the same time. Korea has faced growing demands for the domestic welfare. Therefore, the rapid ODA expansion in scale comes as a considerable burden, making it difficult to persuade the domestic public opinion and politicians. Thus, the economic impacts of ODA on donors are of special importance to ensure political easiness and the persistence of ODA policies. Against this backdrop, we investigate the relation between ODA and the exports of donor to recipient countries. A series of studies have been carried out to investigate the relation. For instance, Djajić et al. (2004) theoretically argued that ODA has stimulating effects on exports (vanguard effect), while Nilsson (1997) and Martínez‐Zarzoso et al. (2009) proved it through the empirical studies on the cases of the EU and Germany. Lee and Lee (2012) confirmed that the ODA has positive impacts on Korea's exports to recipient countries. However, the precedent research provides researchers and policy-makers with limited knowledge. In particular, given the increasing importance of the composition of ODA, there is hardly information about difference in vanguard effects among various types such as grants, loans, infrastructure investment, technical cooperation and humanitarian aid. In addition, our understanding about difference in aid effects by industrial sectors is incomplete. In particular, the services sector is little covered by any serious study with regard to aid effectiveness,
2
ODA (Official Development Assistance) is economic resources that are transferred from donors to developing countries for social and economic development and poverty reduction. According to the definition by OECD, the support of noneconomic objectives such as military assistance for the pursuit of national security interests, support for religious purposes and academic and cultural exchange are included. More than 25% of grant element is also required. In this study, we use the terms ―ODA‖ and ―aid‖ interchangeably to simplify the text. 3 In this context, the 2001 DAC High Level Meeting adopted an advisory resolution on untying aids to the least developed countries, which was reiterated in the Accra (2008) and Busan (2011) High Level Forums on Aid. 4 More details for Korea‘s ODA trend are presented in Appendix 2
2
despite its importance5. Among others, no theoretical framework has been developed yet to explain the relationship between ODA and donor exports in a reasonable and systematic manner. This study makes methodological and empirical contributions to the existing literature. We construct a conceptual and theoretical framework to effectively explain difference in aid effects on donor‘s exports among various aid types and the industrial sectors. Through this framework, we find out empirical evidence to show the existence of the vanguard effects of ODA on exports, as well as their varieties in terms of direction and intensity. As a typological approach, the effects of ODA are examined, divided into tying vs. untying, grants vs. loans and economic vs. non-economic aid. The relation between aid and exports is also investigated at the sub-sector level - machinery, electronics, vehicle and foreign construction. It is also quite a new attempt to estimate the effects on donor exports in the service sector. Last but not least, the existence of ODA effects is also examined during the period that untying aid ration is relatively high. The configuration of study reports is as follows. First, section 1 explains the background and purpose of the study. Section 2 presents theoretical and empirical perspectives on the relation between ODA and exports. In section 3, we derive the theoretical model that is based on Gravity Model. Section 4 presents the empirical analysis and interpretation of the results. In section 5, a summary and policy implications are presented.
2. The Relation between ODA and Donors’ exports 2.1 Theoretical Background Whilst earlier scholars had controversial views on aid effects on donors‘ overall welfare, there seems to be a consensus on its effects on donor‘s exports. Keynes (1929a, b), as a part of study on Germany's war reparation, argued that capital transfer increases the export of Germany but the level of welfare in Germany would be lower as the value of exports falls. On the other hand, Leontief (1936) and Bhagwati et al. (1983, 1984) claimed that large wealth transfers from developed countries to developing countries have positive impacts on the donors with increases in exports and economic growth. More recently, Djajić et al. (2004) studied the welfare implications of temporary foreign aid in the inter-temporal context. According to them, a temporary transfer of income from donors to recipients in the first period improves the welfare of the recipients and lowers that of the donors; but in the presence of ―habit-formation effect‖ or ―goodwill effect‖, aid in period one may serve to shift preferences of the recipients in favor of the donors‘ export goods in period two, offsetting the welfare loss of the donors. A sub-set of the literature has discussed aid effects in association with the problem of choice among aid types. In 1990, tied aid was a main issue since donors used to exploit it as an
5
Today service industry accounts for 25 percent of total exports (the OECD / WTO Database on Services) and has attracted great interest in terms of job creation covering developed and developing countries.
3
export policy. Most of the studies analyze aid effects from recipient‘s perspective, since tied aid is considered to essentially promote donor‘s exports. Some scholars such as Tajoli (1999), however, argued tied aid can reduce the donor‘s exports or market share, by worsening the terms of trade of recipients and intensifying competition among donors. In earlier 2000s, loans were in the middle of debate as ‗IFAC report 2000‘ strongly recommended grants rather than loans. For example, Bräutigam (2000) preferred loan-type aid in that it would drive recipient countries to try to increase tax revenues and establish sound fiscal discipline. On the other hand, Cordella and Ulku (2007) favor grant-type aid with argument that it is less burdensome to recipients and more effective in relieving the poverty of developing countries. From the perspective of donors that face fiscal constraints, loantype aid is known to be more appropriate than grants to induce the some acts of recipients that donors desire (Odedokun 2004). That is, loans are much larger in terms of nominal amounts compared to grants with the same amount of fiscal burden to donors. For example, given a 25% grant element of loans, donor can choose between $X grants and $4X loans within the same budget; it is natural to see it that $4X would much impress recipients‘ governments and people than $X grants although net benefits going to them are same. Accordingly, ―habit-formation effect‖ or ―goodwill effect‖ could be large in loans than grants. Loans allow donors to implement as many projects as possible given limited budget; thus leading to alleviate their financial burden. 2.2 New Approach The explanations in the existing literature, however, are insufficient to describe the different effects of the different types of aid. In particular, they are of little use to investigate the aid effects of furtherdivided types of ODA: infrastructure (loans), technical cooperation (grants) and humanitarian aid (grants). Infrastructure investments include activities to expand economic infrastructure such as roads, ports and power plants, as well as social infrastructure such as workforce training and regulation system. Technical cooperation is related to activities to increase the human capital such as the dispatch of experts, vocational training and research collaboration. Humanitarian aid is to provide the needy with food, medical aid and any other staple materials and services. Differences in aid effects over these categories have been hardly discussed in the existing literature. Nevertheless, it is intuitively clear that ―habit-formation‖ or ―goodwill effects‖ would apply to all these categories6. However, differences in the intensity of aid effects among the categories still remain in doubt. Therefore, in this study, we see ODA as a kind of "public goods" or ―externalities‖ so as to introduce the additional explanation. ODA for physical infrastructure such as roads and ports or intangible infrastructure such as human resources and social institutions takes the nature of public goods with externalities7. Our new framework to analyze the aid effects from donor‘s perspective is presented in 6
It is human nature to have a good feeling toward someone who gives a favor (aid).
7
―Tying‖ aid is also a kind of public goods for donor‘s firms.
4
. According to the concept, ODA as public goods, aid effects on donor‘s exports would be twofold: (1) ODA has a positive effect on donor's exports. For instance, the companies of donors that have invested in recipient countries more vigorously do business activities enjoying the externality effects of ODA investments and import intermediate goods from their original countries (donors). Accordingly, the trade flows of the donors to the recipients increase. (2) On the contrary, ODA could have a negative effect. It could lower the trade barrier of recipients or local prices, hence intensifying competition among suppliers. Besides, the range of externality beneficiaries would vary case by case: (1) The benefits of national port and airport facilities could go to all exporting countries; (2) donorspecific language training programs would go to the donor exclusively; (3) benefits of local roads in industrial zones where some donors invested at plants would go to the investors of the donors. With more beneficiaries, the competition in the recipient country becomes more intense; hence resulting in the stronger negative effects. < Table 1> Various effects of ODA to exports Effect Types income effect habit-formation effect/ goodwill effect externality effect
Descriptions the overall effect of increasing the purchasing power of the recipient countries as aid promotes economic growth tendency to continue to consume aid donors‘ products or services or benefits as recipients‘ governments favor donors with lower trade barriers the effects as donors enjoy the utility of ODA investments as public good including physical and social infrastructure
The new conceptual framework is used to analyze aid effects in the following ways. First two categories, infrastructure and technical assistance, are classified into economic aid while the rest is classified into non-economic aid. Then, it is intuitively clear that humanitarian aid has little externality effect as public ―goods‖ unless it takes the form of tied aid. On the contrary, we can easily imagine the cases that exports are boosted by economic aid - infrastructure investment and technical assistance: the extension of port capacity allows recipients to trade more goods; advanced custom systems also help recipients to import more efficiently. Differences in effects between infrastructure (loans) and technical assistance (grants) can be examined in sectoral or temporal contexts. For instance, the imports of large-volume goods such as agricultural or mining products would highly depend on the quality of transportation infrastructure (that is, the externality effects of infrastructure aid), while the imports of service such as retails and construction depend on the quality of labor forces (that is, the externality effects of technical cooperation). In addition, considering relative long-period of time required to set up, the externality effects of physical and social infrastructure would take longer time to emerge than technical assistance such as labor-training program. Hence, the externality effects of infrastructure aid are likely to be observed in a long-term perspective; for the short period of time, ―tying‖ would be the main source of effects. 2.3 Empirical Precedents 5
A number of empirical studies have proved the positive effects of ODA on donor‘s exports but the degree of elasticity to donor‘s exports vary over regions and time. For example, Nilsson (1997) who analyzed aid and exports of the EU countries from 1975 to 1992 by the gravity model estimated the elasticity of donor‘s exports to aid at 0.23. Wagner (2003) also used the gravity model with aid and exports of the 20 donors for 1970∼1990 and estimated the elasticity at 0.062 (for fixed effect method) and 0.195 (pooled OLS). Martínez‐Zarzoso et al. (2009), using dynamic panel model and Germany's ODA data for 1991∼2005, estimated the elasticity of 0.078 to 0.165. In the case of Korea, Lee and Lee (2012) estimated the elasticity of 0.116 to 0.143 for 1991∼2008 using the visits of the President of Korea as an instrumental variable. The notable finding of this study is that aid effectiveness to donors could be different by industries: the aid effect is stronger in the paper and textile sectors (labor-intensive) than the machinery sector (capital-intensive). Zarin-Nejadan et al. (2008) also affirmed that the effects of ODA on exports vary over sectors and periods. On the other hand, Tajoli (1999) who studied the case of Italy made conclusion contrary to these researcher, arguing that ODA could reduce donor‘s the exports or market share, by worsening the terms of trade of recipients and intensifying competition among donors. Although these studies produced a lot of knowledge about the relation between ODA and export, there are still many limitations. These studies handled ODA only as a whole and thus failed to investigate type-specific effects to answer the questions: ―what kinds of types would make more effects on donor‘s exports in certain situations?‖. Among others, there hardly exists study on the effects of aid in service industry that is increasingly attracting attention for creating jobs in both developed and developing countries. As for time-specific effects, target intervals should be sophisticatedly chosen considering difference in aid compositions. Methodology is also to be further elaborated. For instance, aid commitments are often inappropriately used as dependent variable and endogeneity issue is not addressed in a proper manner8. We attempt to address the deficiencies in the previous studies. We drew a gravity model reflecting the various aspects of aid effects. Through the model, we empirically test differences in the effects over various types of aid - for instance, the long-term effects for loans (or infrastructure aid) and the externality effects for economic aid - at the aggregate and sectoral level including service. The comparison is made between time periods that show distinct difference in ODA composition. To address endogeneity problem of ODA, Hausman-Taylor method and system-GMM are applied.
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As a dependent variable, disbursements must be used (Martínez-zarzoso et al., 2009). Otherwise, commitments with 2 or 3 year lags should be used (Stuckler et al. 2013). The considerable part of loan commitments is not disbursed due to lack of readiness of recipients even after 3-5 years. Besides, a cited study used the President‘s visit as an instrumental variable (IV), but the President's visit is often made to developing countries with economic consideration (i.e., it is also likely correlated with exports).
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3. Model and Data 3.1 Theoretical Model In this study, we derive a theoretical model incorporating aid effects into the traditional gravity model of Anderson and van Wincoop (2003). To this end, we introduce monopolistic competition model where each country specializes in unique varieties of goods. We let the amount of aid be ―L” from a certain donor ―d” to recipient ―j”. For simplification, we make the following assumptions9: 1) the effects occur to only country d (donor); 2) only externality effects exist in the aid flows; 3) the varieties of d‘s goods that are consumed in j increase by aid effects10. ‗Assumption 1‘ looks so strict but it can be very natural in the situation that ODA is made with 100% tying condition or tailor-made designed for donor. As shown in the ‗assumption 2‘, we strategically consider only externality effects at this stage for simplification. According to the ‗assumption 3‘, the following relation holds: N*d = edjNd (Nd denotes the varieties of d‘s good, N*d is for the varieties after affected by aid and edj is for aid effects to donor‘s exports). To derive gravity model, we suppose there is a representative consumer, in county j, with a CES utility function and budget constraint as the following11:
𝒰 ∗j = Y ∗j =
C 𝑖=1
N∗i c ij
C 𝑖=1
σ−1/σ
N∗i pij c ij ,
c : the consumption of i‘s goods in country j, ij
pij: the CIF prices of i‘s goods in j (i.e., pij=Tij×pi, Tij is bilateral trade cost, pi is FOB price), σ: the CES elasticity of substitution (σ > 1), asterisk (*): notation for variables affected by externality effects. By solving the utility maximization problem under the budget constraint, we can derive the following optimal consumption:
c ∗ij = pij 𝐏 ∗j
−σ
Y ∗j 𝐏 ∗j ,
(1)
P*j: the overall price level of j (or the multilateral resistance of j). Then, from the identity condition of total imports from country d to country j, X*dj≡N*dpdjc*dj, we can derive the following gravity equation under the market-clearing condition.
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In the latter part of this section, we will ease the assumption so as to cover more realistic cases. For example, let‘s imagine the case that a training program for CAD (Computer Aided Design) is carried out in country ―j‖ financed by ODA from country ―d‖ - PC (Personal Computer) exporter. To trainee who has learnt how to use CAD application through the program, PC is more than internet-surfing tool: it is equipment for industrial design. This leads to an increase in varieties of goods produced country d and consumed in j. 11 Appendix 1 presents the detailed derivation of equation (1) through (4). 10
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X where, 𝐏 d = s ∗d N ∗d 𝐏 ∗j
1−σ
=
1 1−σ
C ∗i i=1 s
∗dj
=
pd (s ∗d = T ij 𝐏 i
Y ∗d Yw
1−σ
Y ∗d Y ∗j
Td j
Yw
𝐏d 𝐏∗j
=
N ∗d p d y d Yw
1−σ
,
(2)
, pd is market − clearing price),
.
To separate the ODA effects (edj) from (2), we use the following relation: Y ∗j = edj Nd pdj c dj + Y j = b edj Y j and 𝐏 ∗j = p edj 𝐏 j (p′ < 0 if
T dj 𝐏d
b′ > 0,
edj = 1 + edj
,
< 𝑜𝑡ℎ𝑒𝑟 countries′ average, p′ > 0 otherwise).
Y*d is exactly what we observed as GDP of country d. So, we can simply express Y*d as Yd. Suppose that b(edj )=(edj )η ; p(edj )=(edj )
θ
(η > 0, θ < 0) for simplicity. Substituting b edj Y j and
p edj 𝐏 j for Y ∗j and 𝐏 ∗j , and taking natural logarithm, the following equation is derived for exports of donor country d to country j. X ∗dj = ln Y d Y j + 1 − σ ln T dj + σ − 1 ln 𝐏 d + σ − 1 ln 𝐏 j + [η + σ − 1 θ ]ln edj
(3)
We suppose τ, a perpetual benefit flow from a project financed by ODA ―L‖, then the market value of the project is τ/γ (γ is a market interest rate). We now consider the effects by ODA to exports, for simplification, just by multiplying benefit τ by edj; then the project value that is perceived by donor ―i‖ is edjτ/γ (or ―Ĺ‖). If donor country pursues its interest maximization, the supply size of ODA for the project is Ĺ. Therefore, we can rewrite L as the following: L = Ĺ = edjτ/γ or (γ/τ)×L = φL = edj. Substituting φL for edj in (3), we can derive the flowing gravity equation that incorporates ODA ―L‖ as explicit explanatory variables. X ∗dj = ln Y d Y j − σ ln T dj + σ ln 𝐏 d + σ ln 𝐏 j + [η + (σ − 1)θ ]ln L + C,
(4)
where, η > 0, σ > 1, θ < 0, and C = lnφ + ln T dj − ln 𝐏 d − ln 𝐏 j .
Equation (4) shows that the ODA effects on exports from d to j can be divided into two parts: the positive one (i.e., η) from increases in varieties; negative one (i.e., θ) from decreases in multilateral resistance level of j (or increases in relative bilateral trade barrier – Tdj/ PdPj). The actual direction and intensity of effects will be determined by the combination of the two forces. Now, easing the assumption, we suppose that other exporter country ―f” also benefits from ODA from country d to country j. If bilateral trade barrier between f and j is lower than that of country d (i.e., Tfj/Pf < Tdj/Pd),
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Pj will fall further, strengthening the negative forces12. Next, we ease ‗assumption 2‘ so as to consider habit-forming and goodwill effects13. The two effects are reflected through bilateral trade costs. Then, Tdj in equation (4) is replaced by T*dj. Habitforming effect is a kind of enhancing product familiarity, leading to cutting trade costs to search for information. Goodwill effects act through lower tariff or non-tariff barrier and also contribute to reducing trade costs. Therefore, if we denote two effects as hij and suppose that the intensity of the effects depends on the size of ODA ―L‖, the simple expression of the effects by ODA can be set as hij = L-ρ (ρ > 0). Trade costs affected by two effects can be expressed as T*ij = hijTij = L-ρTij. Substituting L-ρTij for T*ij, the following equation is derived: X ∗dj = ln Y d Y j − σ ln T dj + σ ln 𝐏 d + σ ln 𝐏 j + [η + (σ − 1)(θ + ρ) ]ln L + C, (4′) Where, η > 0, θ < 0, ρ > 0, σ > 1 and C = lnφ + ln T dj − ln 𝐏 d − ln 𝐏 j .
In equation (4′), the overall effects including habit-forming and goodwill effects depend on the magnitude and sign of the coefficient of lnL, η + (σ-1)(θ+ρ). Finally, by replacing multilateral resistance with country fixed effect, the equation (4′) can be revised as the following general gravity equation: X ij = β0 + β1 ln Y i Y j + β2 ln T ij + β3 ln Lij + β4 dummyi + β5 dummyj + εij , (5) where, ln Lij is ODA flows from country i to country j. 3.2 Econometric Model 3.2.1 Static Analysis – Aggregate Level Analysis Econometric model is constructed based on the theoretical model in section 4.1.1. To this end, we refer to Martinez-zarzoso, et al. (2009) but modified their specification replacing country dummy with tariff variable14. ln(Expit ) = β0 + β1 ln YYit +β2 ln disti + β3 ln(Tariffit ) + β ln(FEXit ) + β5 ln(FDIit ) + 4
β6 ln(ODAit )+β7 yrt + ϵit ,
(6)
Expit: export volume from Korea to country i at time t, 12
This is in line with Tajoli (1999)‘s argument that tied aid could reduce the market share of donor‘s firms in recipient country by deteriorating the term of trade and intensifying competition among donors . 13 Besides, the income effect is β1ΔYj, if ODA lifts up recipient‘s income by ΔYj. 14 Country dummy is to replace Multilateral Resistance Term (MRT) in Anderson and Wincoop (2003). For a long-term period study, it is realistic to consider MRT is time-variant. Therefore, tariff would be more appropriate as proxy for MRT rather than time-invariant dummy.
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YYit: GDP of Korea multiplied by GDP of country i at time t, disti: Geographical distance between Korea and country i, Tariffit: Importer‘s average tariff rate for all exporters at time t, FEXit: Exchange rate (LCU per 1 USD / Korean Won per 1 USD), FDIit: FDI stock from Korea to country i at time t, ODAit: ODA stock or flows from Korea to country i at time t (total or for various types), yrt: year dummy to control for deflator, business cycle, etc. To estimate the equation (6), we employ the Hausman-Taylor (HT) method to address the endogeneity of ODA, as well as Fixed Effect (FE) and Random Effect (RE) methods. The expected signs of coefficient estimates are as follows. As the basic idea of gravity model, the coefficient of economic size (β1) is expected to have positive sign while the one for distance (β2) would have negative sign. Since the higher tariff adversely affects exports, β3 is inferred to have negative sign. Higher exchange rate would lift up export prices nominated in USD, resulting in reduced exports (β4 is negative). However, if export firms response it by increasing the quantity of exports or cutting profits, exports in USD would be unchanged or rather increase (β4 is positive). The coefficient of FDI is uncertain a priori. For export-oriented FDI, the coefficient β5 would be positive while FDI as substitute for domestic investment is expected to have negative sign. The coefficient of ODA (β6), our main concern, is expected to have positive sign if it has vanguard effects for donor‘s exports, while β6 could have negative sign if ODA crowds out private exports or investments. 3.2.2 Static Analysis – Sectoral Level Analysis In this section, we investigate aid effects for various industries. The regression model is the same as the previous section except for adding differences in per capita income between Korea and importers (Ydiffit) to control for industrial characteristics originated from target consumers. The regression model is as follows. ln(Expit ) = β0 + β1 ln YYit +β2 ln disti + β3 ln(Tariffit ) + β4 ln(FEXit ) + β5 ln(FDIit ) + β6 ln(Ydiffit )+β7 ln(ODAit ) + β8 yrt + ϵit
(6′)
Target industries are four manufacturing industries including beverages, paper, textile, general industrial machinery, telecommunication equipment, electronic machinery, vehicle, footwear and one service industry - foreign construction. Among the manufacturing ones, beverages, paper, textile and vehicle are consumer goods, while industrial & electric machinery, telecommunication equipment are capital goods (intermediate goods). The consumer goods are divided into the inferior goods of beverages, paper and textile and the normal goods of automobiles. Although service industries run a wide gamut including tourism, telecommunications, logistics, healthcare, business services and 10
construction, etc., we choose foreign construction among them since its time series data are available corresponding to the ones of the manufacturing sectors. Construction service also runs a wide range from the civil engineering to high-value plants, deeply related to ODA in the sense that its final outputs are infrastructure. Many studies about service exports, which have investigated mainly the determinants of service exports such as human capital and institutions, have employed the gravity model that is known to have explanation capability for service trade flows (Mattoo et al. 2008). Sectoral analysis also employed the Hausman-Taylor Method to address the endogeneity problem of ODA. Most of coefficient signs are expected to be similar to those of the aggregate level analysis. Nevertheless, some differences are expected: the coefficient signs of FDI (β5) are unpredictable but for textiles it is expected to be positive since FDI in the sector should be considered as export-oriented. Textile industry has moved plants to product input materials into developing countries to seek cheap labors, while automobiles are the current prime sector that have still plants in the Republic of Korea. The FDI of foreign construction also plays a complementary role because the exports are made with suppliers moving to consumers, necessarily accompanied by investments in importer country (Mattoo et al, 2008). The coefficients sign of income differences variables (β6) are expected as positive for inferior goods and negative for normal goods. 3.2.3 Dynamic Analysis – Comparison between Periods In this study, dynamic panel analysis is also attempted in order to consider the cases where trade moves with inertia – affected by the trade flows of the previous period – and at the same time to address the endogeneity problem of ODA. To this end, the system-GMM method by Arellano and Bover (1995) and Blundell and Bond (1998) use lagged levels as well as lagged differences as instruments. GMM model is known to fit for the dataset that has many panel groups (i.e., large ―N‖) and short time series (i.e., small ―T‖). If period is prolonged, instrument number soars, causing the over-identification problem. Therefore, we choose two short periods of 7 or 8 years and compare them in terms of the effect of ODA on donor exports. Taking into account the characteristics of the actual ODA expenditures, we chose two periods of 8 years from 1996 to 2003 and 7 years from 2006 to 201215. The features of both periods are presented in
. < Table 2> Comparison between periods years
grants
economic aid†
tied aid‡
period I
1996-2003
33.2%
80.9%
98.3%
period II
2006-2012
54.1%
66.0 %
68.1 %
† Physical and social infrastructure and technical cooperation. ‡ Partially tied aid is included
15
2004 and 2005 are excluded because of a drastic change particularly in humanitarian aid, which could mislead the overall trend. Humanitarian aid spiked by 1,270% in 2004 and 100% in 2005, allegedly, as an effort to enhance country‘s image ahead of joining OECD DAC.
11
In period I, ODA was made mainly in the form of loans rather than grants and most ODA is economic and tied aid. Showing a sharp contrast, the half of the total ODA is made in grants format in period II. The increases in the portion of non-economic and untied aid are also notably distinct compared to period I. To verify the break around 2006, we conducted CHOW test (Chow 1960) which is generally used to see if there is a structural change at a given break time. The test is performed by applying a time dummy variable to the equation (6) and testing the significance of the coefficient for an interaction term between the time dummy and aggregate ODA variable. The estimation equation for CHOW test is as follow: ln(Expit ) = α + βln(ODAit ) + γln(ODAit ) × periodIIdummy + δX it + ϵit ,
(6′′)
where periodIIdummy is dummy variable for year 2006-2012 (i.e., periodIIdummy is 1 after 2006 and zero otherwise) and Xit denotes control variables. The null hypothesis that γis zero was rejected at the 5 percent level16 and we found evidence of a structural break in 2006. Then, we constructed the following econometric model for the dynamic panel analysis: ln(Expit ) = β0 ln(Expit−1 ) + β ln YYit +β2 ln disti + β3 ln Tariffit + β4 ln(ODAit ) + β yrt + ϵit 1
5
(7) The difference from the static model is that we add the lagged exports in order to capture the impact of the past trade flows but omit FDI since the effect of FDI varies over industries so that in shortmedium term the overall effect is expected to be ambiguous. In addition, exchange rate variable is also omitted considering the possible noise from the fixed-exchange rate regime and foreign currency crisis. 3.3 Data For dependent variable, bilateral trade flows from South Korea to developing countries, is obtained from UNCOMTRAD. Industrial level trade flows are also extracted from UNCOMTRAD on a basis of SITC-2 digit, including Textile (65), Industrial Machinery (74), Electronic Product (77) and Road Vehicle (78). For foreign construction, the contract amount is gathered on an annual basis from the Korea International Construction Association. As for explanatory variables, ODA disbursement flows was obtained from the OECD's DAC database by year and recipient country. For ODA type-specific effects analysis, ODA data were extracted separately for grants, loans, technical cooperation and humanitarian assistance, as well as the total amount. For income and economic size, GDP data were used from World Development Indicator. Bilateral FDI data were used using OECD data. Tariffs were the country worldwide (simple) average of manufacturing products, extracted from UNCOMTRAD. Distance variable was extracted from the database of the CEPII, measured as the great arc distance between the largest population cities (refer to Appendix 3 for more details).
presents the estimate results of the regression equation (1) for 1988~2012. The results by Fixed Effect model (FE) are shown in the column (1) ~ (3). The total amount of ODA showed the coefficient of 0.033 with statistical significance at 5% level. Loans give rise to the coefficient of 0.029, while the one of grants is 0.027. As for various categories, infrastructure (loans), technical cooperation and humanitarian assistance showed the coefficient of 0.29, 0.028 and -0.013, respectively. Except for humanitarian assistance, all were statistically significant at least at the 10 level. In the case of the control variables, the coefficient of GDP is a positive value and tariff is a negative one as expected and the both were statistically significant at the 1% or 5% level. The coefficient estimate of exchange rate is around 0.15 and significant at the 1% level. A weak negative effect of FDI was identified but there was no statistical significance. Distance variable is not estimated in the fixed effect model because it is time-invariant. The estimation results through Random Effect Model (RE) are presented in the column (4) ~ (6). The total amount of ODA showed the coefficient of 0.027. The coefficient of loans was 0.028 while the estimate for grants was 0.039. Infrastructure (loans), technical cooperation showed the coefficient of 0.028 and 0.040 and significant at the 1% level. The coefficient estimate of humanitarian assistance was -0.014 but statistically insignificant. As for control variables, GDP, tariffs and exchange rates showed results similar to the case of the FE with statistical significance. The sign of FDI coefficient was mixed and no statistical significance was observed. Distance variable is negative as expected and statistically significant at the 1% level. We conducted Hausman-test to determine whether the bias exists in estimates by RE method. As the results, the null hypothesis for consistency of specification (4) and (5) is not rejected but the one of specification (6) is rejected, indicating possible bias. The results from Hausman-Taylor method (HT) with the consideration of endogeneity are presented in a column (7) ~ (9). Total amount of ODA had the coefficient of 0.028 and loans also gave rise to the coefficient of 0.028, while the coefficient of 0.032 for grants was 0.029. As for various categories, infrastructure (loans), technical cooperation and humanitarian assistance showed the coefficient of 0.028, 0.029 and -0.015, respectively. Except for humanitarian assistance, all were statistically significant at the 1% or 5% level. The control variables generally showed a similar elasticity from the FE method, while the estimates for distance variables failed to show statistical significance. From these results, we can confirm the overall positive effects of ODA to Korea‘s exports, except for the type of humanitarian aid. Main findings are summarized as follows: (1) the positive effects of
13
total aid were confirmed17; (2) the positive effects to exports are also found in both loans and grants; (3) humanitarian assistance showed negative coefficients but they are statically insignificant. < Table 3> Regression Results – Static Analysis for 1988-2012 (1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
VARIABLES
lexports
lexports
lexports
lexports
lexports
lexports
lexports
lexports
lexports
lgdp
1.439***
1.415***
1.418***
1.268***
1.258***
1.253***
1.344***
1.329***
1.328***
(0.081)
(0.081)
(0.081)
(0.042)
(0.040)
(0.040)
(0.056)
(0.055)
(0.055)
-0.128**
-0.114**
-0.116**
-0.138***
-0.132**
-0.134**
-0.126**
-0.116**
-0.117**
(0.056)
(0.056)
(0.056)
(0.053)
(0.053)
(0.053)
(0.053)
(0.053)
(0.053)
0.157***
0.151***
0.148***
0.092***
0.079***
0.078***
0.125***
0.117***
0.116***
(0.026)
(0.026)
(0.026)
(0.021)
(0.021)
(0.021)
(0.022)
(0.022)
(0.022)
-0.003
-0.005
-0.004
0.003
-0.001
0.000
-0.000
-0.003
-0.002
(0.006)
(0.006)
(0.006)
(0.006)
(0.006)
(0.006)
(0.005)
(0.006)
(0.006)
ltariff lfex lfdi loda_grants
0.027*
0.039***
loda_loans
0.029***
0.029***
0.028***
0.028***
0.028***
0.028***
(0.010)
(0.010)
(0.009)
(0.009)
(0.009)
(0.009)
(0.014)
Loda_total
(0.013)
0.033**
0.027**
(0.013)
(0.012)
loda_tc loda_ha
0.029** (0.013)
0.028**
0.040***
0.028**
0.029**
(0.013)
(0.013)
(0.013)
(0.013)
-0.013
-0.014
-0.015
(0.010)
(0.010)
(0.010)
ldist
-0.698***
-0.699***
-0.683***
-0.391
-0.405
-0.394
Constant
(0.184) 19.127***
(0.182) 19.120***
(0.272) 21.584***
(0.260) 21.215***
(0.262) 21.295***
27.119***
26.689***
26.751***
(0.197) 19.177***
(1.789)
(1.789)
(1.786)
(2.274)
(2.137)
(2.124)
(3.018)
(2.908)
(2.925)
Observations
1,156
1,156
1,156
1,156
1,156
1,156
1,156
1,156
1,156
R-squared Number of imp
0.680
0.682
0.683
135
135
135
135
135
135
135
135
135
FE
FE
FE
RE
RE
RE
HT
HT
HT
Method
Note: Dependent variable is the log of the amount of exports (lexports). lfdi, ltariff and ldist are control variables indicating FDI stock, tariff rate (importers) and distance in log. lgdp is the log of importer’s GDP times the log of exporter’s GDP. lo\da_total is the log of the summation of total ODA. loda_grants, loda_tc and loda_ha mean grants ODA, loans ODA, technical cooperation ODA and humanitarian aid in log, respectively. loda_loans is the log of loan-type ODA stock. § Standard errors in parentheses, *** p
