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3.3 Analysis
3.3.1 Model I
The equation of Model I is shown below:
Model I : FAit = α+ ß1Pit + ß2Git + ß3Tit+ ß4Fit+ ß5Eit +εit (1)
In this model, FAit (i=1,…,N, t=1,2,..,T) stands for the amount of foreign aid, while α means a constant value. ß represents unknown parameters, while Pit stands for population of recipient country, Git means GDP of recipient country, Tit is representative of trade volume between the donor and the recipient country, Fit
represents FDI inflow from the donor to the partner and Eit is total energy production of the recipient country. Lastly, εit means error.
3.3.1.1 China
The statistical outputs are as Table 6 follows97: Under the Random Effects Model which assumes each difference of the African countries results from random deviation from some mean difference, population (t-ratio: 2.446), trade with China(4.993),and FDI from China (2.279) all have significant positive relationships with the amount of Chinese foreign aid to the African recipient country, while GDP (-2.762) has a
97 We assume that if the t-ratio is more than 1.96 or less than -1.96, we can reject the arguement that the independent variable has not affect the aid amount (so-called null hypothes) aid at 95% confidence level (significance level 0.05).
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negative relationship with the aid amount. However, energy production (.283) does not show any significant impact to the Chinese foreign aid under the Random Effects.
This result implies the Chinese government tends to provide more substantial aid to bigger countries in terms of population and GDP, or countries which have closer FDI and trade ties with more its aid but its aid policy is not closely related to energy-oriented policy.
In the meantime, when it comes to the Fixed Effects Model which assumes that every African recipient country has its own intercept factor, we can find that population (2.911) still has a positive relation with the amount of aid, but other factors such as GDP, trade with China, FDI from China, and energy production do not have a significant influence on the aid.
Table 6: Model I – China’s case: Random Effects and Fixed Effects
VARIABLE RANDOM EFFECTS MODEL’S
COEFFICIENT (T-RATIO)
FIXED EFFECTS MODEL’S
COEFFICIENT (T-RATIO)
CONSTANT -36.2337262(-.773) -3243.52721(-3.259)*
POPULATION .416449D-05(2.446)* .00015187(2.911)*
GDP -.484052D-08(-2.762)* .554611D-08(.875)
TRADE WITH CHINA .00014993(4.993)* .363001D-04(.516)
FDI FROM CHINA .00447400(2.279)* .00362926(1.348)
ENERGY PRODUCTION 10.2841848(.283) 127.739843(.335)
R-SQUARED .4878156 .5130149
ADJUSTED R-SQUARED .2924910 .3097515
Note: t-ratios are in parentheses, * Significant at 5%.
Regarding Fixed Effects shown in Table 7, 35 countries among 41 countries show significant Fixed Effects. It means that there are some other underlying factors such as
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political events or natural environment affecting the amount of foreign aid consistently regardless of population, GDP, trade with China, FDI from China and energy production. In every case, there is a unique set of reasons, potentially including some special political tensions or partnership with the donor, the recipient country’s domestic situation and weather. If we check some representative cases having the highest t-ratios here, Eritrea (3.21544) and Liberia (3.21228) have a tendency to receive a disproportionately large amount of Chinese foreign aid, while South Africa (-3.36470) and Egypt (-3.40222) show opposite tendencies.
As for time, year 2005 (-2.28737) has a negative Fixed Effect as shown in Table 7.
The year 2006 is well-known as a turning point in the expansion of China’s foreign aid volume. FOCAC, a huge political event marking China’s generous aid package to Africa, was held at that time. Before the announcement of this big jump, China still needed to adjust itself to its newly-settled aid environment. We believe that the Chinese government tried to put new projects on hold to maximize the effect of President Hu Jintao’s announcement. In light of this background, we infer that Beijing chose to modify a certain part of the volume toward African countries and the year 2005 was a time period during which China adjusted its foreign aid just before going public with its landmark decision.
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Table 7: Model I – China’s case: Fixed Effects by country and year
Country Coefficient (t-ratio)
Algeria -3391.17252 (-1.42960)
Angola 1432.33495 (2.22779)*
Botswana 2911.58523 (3.06041)*
Burundi 2135.04175 (3.19593)*
Cameroon 721.86478 (2.42306)*
Cape Verde 3172.08163 (3.19143)*
Central African Rep. 2609.63581 (3.20058)*
Congo, Dem. Rep. -5650.01239 (-2.74853)*
Congo, Rep. 2611.74558 (3.12178)*
Cote d’Ivoire 222.64226 (.74600)
Djibouti 3117.90884 (3.19419)*
Egypt, Arab Rep. -9334.68849 (-3.40222)*
Equatorial Guinea 2964.37205 (2.99474)*
Eritrea 2575.93289 (3.21544)*
Ethiopia -7998.95363 (-2.78223)*
Gabon 2905.24531 (3.02042)*
Ghana -114.16983 (-.31921)
Guinea 1803.80858 (3.04015)*
Guinea-Bissau 3022.28152 (3.20225)*
Kenya -2210.02940 (-2.48496)*
Lesotho 2939.02763 (3.19239)*
Liberia 2740.32902 (3.21228)*
Libya 1633.50591 (1.10428)
Madagascar 555.14820 (1.65401)
Mali 1464.62371 (2.93199)*
Mauritania 2780.20464 (3.18037)*
Mauritius 2990.68251 (3.08390)*
Morocco -1646.45599 (-2.62132)*
Mozambique 751.49864 (2.51722)*
Namibia 2894.46830 (3.10485)*
Niger 1233.22454 (2.82909)*
Nigeria -18518.98154 (-3.09040)*
Senegal 1499.99023 (2.89325)*
Sierra Leone 2467.50728 (3.18363)*
South Africa -6176.55615 (-3.36470)*
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Sudan -2699.74141 (-2.61857)*
Tanzania -2694.68194 (-2.52148)*
Togo 2308.61048 (3.15708)*
Tunisia 1529.88354 (2.53302)*
Uganda -1092.19157 (-1.86089)
Zambia 1532.44904 (3.08985)*
Year Coefficient (t-ratio)
2003 102.16224 (1.56561)
2004 49.60837 (.88221)
2005 -128.77980 (-2.28737)*
2006 -22.99081 (-.34690)
Note: t-ratios are in parentheses, * Significant at 5%.
Diagram 4 exhibits some examples of China’s Fixed Effects cases in Model 1. In the case of Eritrea, China has a close relationship with the African country because Beijing had supported the Eritrean independence movement (e.g. Eritrean Liberation Front) from Ethiopia. After its independence in 1993, China was the first country to establish its diplomatic ties with Eritrea98 and has financed various projects such as communication infrastructure and energy development. In 2007, the two countries signed economic deals to remove tariffs on Eritrean products imported to China and partially cancel Eritrea's debt with China. The relations between China and Liberia are a little complex in that they broke off diplomatic ties several times in checkbook diplomacy between China and Taiwan. Finally, Liberia reestablished its relationship with China in 2003. The deployment of China’s peacekeeping force to Liberia99 is seen as one of the major reasons affecting the aid amount.
98 Source: http://www.shaebia.org/artman/publish/article_5779.shtml (accessed on May 28, 2011)
99 According to Chinese official websites, 558 Chinese troops are in Liberia in 2010.
http://eng.mod.gov.cn/DefenseNews/2010-12/07/content_4212718.htm (accessed on May 28, 2011)
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However, South Africa and Egypt seem to hesitate to accept Chinese offer of aid in open manners. Both countries are among the most influential African countries in terms of politics and economics. South Africa and Egypt rank as the largest and second largest economies100 in Africa and they have taken important roles in African politics such as liberation and democratization movements. Also, these two nations have been traditional US allies in the region. In this sense, we tend to conclude that they do not want to be regarded as disadvantaged countries which need Chinese good will in the field of international development.
Diagram 4: Model I – China’s case: Fixed Effects
3.3.1.2 South Korea
In the case of the Random Effects model of South Korea in Table 8, no significant factors affecting the amount of Korean foreign aid can be observed. Population (.069), trade with Korea (1.201), and energy production (.742) are positive, but not
100 Source: http://www.clickafrique.com/Magazine/ST014/CP0000002788.aspx (accessed on May 28, 2011)
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significant enough, while GDP (-.449)and FDI from Korea (-.047) have a negative effect, but still not significant. In light of Random Effects of Model I, South Korea does not seem to be captured by the specific parameters.
However, if we take Fixed Effects into consideration, we can see that energy production (4.550) has a strong positive relationship with the amount of Korean foreign aid. Other factors such as population (-7.06), GDP (-.917), trade with Korea (-.085), FDI with Korea (.464) still do not have a meaningful impact on the aid.
Table 8: Model I – South Korea’s case: Random Effects and Fixed Effects
VARIABLE RANDOM EFFECTS MODEL’S
COEFFICIENT (T-RATIO)
FIXED EFFECTS MODEL’S
COEFFICIENT (T-RATIO)
CONSTANT .79434721(1.233) -2.09640356(-.208)
POPULATION .157519D-08(.069) -.362849D-06(-.706)
GDP -.104124D-10(-.449) -.618677D-10(-.917)
TRADE WITH KOREA .130636D-05(1.201) -.170545D-06(-.085)
FDI FROM KOREA -.154214D-05(-.047) .204153D-04(.464)
ENERGY PRODUCTION .35580524(.742) 15.0837917(4.550)*
R-SQUARED .5036589 .5103526
ADJUSTED R-SQUARED .3143763 .3059780
Note: t-ratios are in parentheses, * Significant at 5%.
We can interpret the output of energy production (4.550) in Table 8 as showing that energy production of African countries has affected the Korean aid. According to EIA’s database101, South Korea’s gap between total primary energy production and consumption is bigger than that of China as shown in Table 9. We can infer from this
101 Source: http://www.eia.gov (accessed on May 28, 2011)
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fact that the Korean government should stabilize its energy supply in various channels including foreign aid.
Table 9: Total Primary Energy Production & Consumption of China and Korea
(Unit: Quadrillion Btu) 2003 2004 2005 2006 Total Energy
Production (A)
China 49.44333 59.3806 64.44684 66.78357 S. Korea 1.34121 1.34367 1.49622 1.51193 Total Energy
Consumption (B)
China 51.15543 62.91903 68.24567 72.890649 S. Korea 8.65312 8.91069 9.22774 9.34094 Total Energy Gap
(A-B)
China -1.7121 -3.5384 -3.79883 -6.10703 S. Korea -7.31191 -7.56702 -7.73152 -7.82901 Source: EIA’s database.
Concerning Fixed Effects in Table 10, 11 countries of the total have significant Fixed Effects. Ghana (3.21898) and Cote d’Ivoire (2.89174) mark the highest positive Fixed Effects as shown in Diagram 5. Ghana has been one of the most traditional partners with South Korea since the two countries signed a contract of EDCF loans in oil refinery storage complex project in 1991.102 In addition, Ghana received a debt cancellation of 4billion USD which accounts for two third of the total debt from the aid community so-called the Paris Club in 2001 due to its successful economic efforts and democratization. Korea also joined the debt cancellation activities. In the case of Cote d’Ivoire, there has been severe political unrest following the military coup in
102 Economic Development Cooperation Fund 20 years, 309.
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1999.103 Developed countries provided huge humanitarian help to resolve such humanitarian needs and Korea also participated in the aid activities. The Fixed Effects of the above two African countries can be explained by these political considerations.
On the other hand, Algeria (-4.53801) and Libya (-3.84289) show some significant negative Fixed Effects. Algeria has produced and accumulated funds by exporting oil and ranks as the fourth largest economy on the African continent.104 It is known that they are sensitive to being a borrower of concessional loans from foreign countries in that they have a lot of foreign reserves. Libya also has a similar tendency. In the past, Korea had relied on oil money from Libya construction projects for a long time until the 1990s. As a result of this situation, Algeria and Libya do not want to receive aid from Korea.
Regarding Fixed Effects of time in the Korean case, no significant Fixed Effect can be observed. T-ratios of Year 2003, 2004, 2005 and 2006 are -.92025, .98441, .10835, and -.00971 respectively.
103 Source: https://www.cia.gov/library/publications/the-world-factbook/geos/iv.html (accessed on May 28, 2011)
104 Source: http://www.clickafrique.com/Magazine/ST014/CP0000002788.aspx (accessed on May 28, 2011)
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Diagram 5: Model I – South Korea’s case: Fixed Effects
Table 10: Model I – South Korea’s case: Fixed Effects by country and year
Country Coefficient (t-ratio)
Algeria -90.83056 (-4.53801) *
Angola -11.40384 (-1.69632)
Botswana 3.03463 (.31786)
Burundi 4.86031 (.71694)
Cameroon 6.99107 (2.45989) *
Cape Verde 2.32866 (.23154) Central African Rep. 3.69022 (.44647) Congo, Dem. Rep. 21.93491 (1.10003) Congo, Rep. -4.01866 (-.46780) Cote d’Ivoire 8.18384 (2.89174) *
Djibouti 2.62069 (.26496)
Egypt, Arab Rep. -6.68590 (-.23884) Equatorial Guinea -2.63723 (-.26209)
Eritrea 3.83331 (.47175)
Ethiopia 31.15550 (1.11092)
Gabon -5.05449 (-.52350)
Ghana 9.89229 (3.21898) *
Guinea 5.60473 (.94188)
Guinea-Bissau 2.67438 (.27964)
Kenya 21.92937 (2.57128) *
Lesotho 2.86925 (.30771)
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Liberia 3.68780 (.39583)
Libya -51.23366 (-3.84289) *
Madagascar 8.51464 (2.70568) *
Mali 6.57739 (1.31305)
Mauritania 3.24710 (.36627)
Mauritius 2.92199 (.29922)
Morocco 18.16728 (2.67384) *
Mozambique 7.76374 (2.90063) *
Namibia 3.02250 (.32177)
Niger 7.01727 (1.62352)
Nigeria -33.11365 (-.54900)
Senegal 7.03496 (1.35430)
Sierra Leone 4.04201 (.51453) South Africa -57.81266 (-3.39995) *
Sudan 8.83965 (.92598)
Tanzania 18.91694 (1.85366)
Togo 4.43963 (.59723)
Tunisia 8.70848 (1.46062)
Uganda 12.77479 (2.35364) *
Zambia 5.51133 (1.13909)
Year Coefficient (t-ratio)
2003 -.58397 (-.92025)
2004 .53106 (.98441)
2005 .05901 (.10835)
2006 -.00609 (-.00971)
Note: t-ratios are in parentheses, * Significant at 5%.
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3.3.2 Model II
The equation of model II is shown below.
FACit = α+ ß1GC it + ß2T it + ß3F it + ß4E it +εit (2)
It is designed to test the relationship between aid per capita of recipient countries and independent variables such as GDP per capita, trade and FDI with the donor country, and energy production of the recipient country. We presented Model II considering the amount of foreign aid per capita as a dependent variable. Model II has the advantage in that we can make exact comparisons in terms of per capita amount by country, while Model I which focuses on the total aid amount of individual African country is appropriate for checking country to country relations in terms of macro volume of the funds.
In this model II, FACit (i=1,2,…,N, t=1,2,…,T)stand for the amount of foreign aid per capita from the donor in different recipient countries and times. α represents a constant value and ß means unknown parameters. Meanwhile, GC it signifies GDP per capita of recipient country, T it is representative of trade volume between the donor and the recipient country, F it, stands for FDI inflow from the donor to the partner and
E it, means total primary energy production. Finally, εit stands for error.
3.3.2.1 China
As for the statistical outputs of China, trade with China (5.830) shows a significant positive relation with the aid, while energy production (-2.230) has a significant
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negative impact on aid under the assumption of Random Effects in Table 11. Other factors such as GDP per capita (-1.263), FDI from China (.002) do not have any effects. In the case of the Fixed Effects model, we cannot observe any apparent relationship between the amount of aid and the other independent variables. It is interesting that both of the statistical outputs contradict many arguments suspicious of connections between African countries and China providing aid in return for energy resources.
Table 11: Model II – China’s case: Random Effects and Fixed Effects
VARIABLE RANDOM EFFECTS MODEL’S
COEFFICIENT (T-RATIO)
FIXED EFFECTS MODEL’S COEFFICIENT (T-RATIO)
CONSTANT 3.91983096(1.754) -15.6222590(-1.431)
GDP PER CAPITA -.00098017(-1.263) -.00353742(-1.634)
TRADE WITH CHINA .741610D-05(5.830)* .401596D-05(1.494)
FDI FROM CHINA .155387D-06(.002) .496107D-04(.448)
ENERGY PRODUCTION -2.79607551(-2.230)* 29.9532068(1.933)
R-SQUARED .5155592 .5314182
ADJUSTED R-SQUARED .3364382 .3415618
Note: t-ratios are in parentheses, * Significant at 5%.
With regards to details of Fixed Effects, 9 countries of the total 41 African countries have significant Fixed Effects as illustrated in Table 12. Diagram 6 summarizes our interpretations of these statistical outputs. Mozambique (3.49318), Cape Verde (2.63198), South Africa (-2.45158) and Egypt (-2.30046) are countries representative of significant Fixed Effects.
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The story of China and Mozambique’s relationship is quite similar politically to that of China and Eritrea. China supported Mozambique’s independence movement against Portuguese rule. China established diplomatic ties with this country just after its independence in 1975. Based on such traditional friendship, China not only relieved some of the expired debt of Mozambique which should have been repaid by the end of 1999, but also signed the Promotion and Reciprocal Protection of Investment trade agreement with Mozambique.105 Cape Verde has also been a traditional partner for China in that this country has not been affected by Taiwan’s efforts to disconnect the ties with China and the economic relations between the two countries have been strengthened since the mid 1990s.106 These specific situations are likely to result in such Fixed Effects. As for South Africa and Egypt, these two African giants do not want to have the reputation of being as recipient nations as has been interpreted already in the section on Model I. As for time, no significant Fixed Effect can be observed in Table 12.
Diagram 6: Model II – China’s case: Fixed Effects
105 Source: http://www.china.org.cn/english/features/focac/183432.htm (accessed on May 28, 2011)
106 Source:
http://www.opensourcesinfo.org/journal/2008/1/4/china-in-cape-verde-the-dragons-african-paradise.ht ml (accessed on May 28, 2011)
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Table 12: Model II – China’s case: Fixed Effects by country and year
Country Coefficient (t-ratio) Algeria -204.06378 (-2.03325) *
Angola 4.19047 (.17631)
Botswana 40.36997 (2.34521) *
Burundi 16.04901 (1.16909)
Cameroon 26.95376 (2.30217) * Cape Verde 37.24001 (2.63198) * Central African Rep. 18.03269 (1.31522) Congo, Dem. Rep. 11.11875 (.86381) Congo, Rep. 18.83955 (1.75886) Cote d’Ivoire 12.99196 (1.04947)
Djibouti 22.13188 (1.59952)
Egypt, Arab Rep. -81.30558 (-2.30046) * Equatorial Guinea 31.40269 (1.45260)
Eritrea 18.83948 (1.37169)
Ethiopia 14.54540 (1.04740)
Gabon 25.64446 (1.91839)
Ghana 13.84837 (1.01152)
Guinea 16.14541 (1.15839)
Guinea-Bissau 17.39369 (1.26706)
Kenya 14.38070 (1.06003)
Lesotho 20.21482 (1.47229)
Liberia 18.39492 (1.31467)
Libya -80.42886 (-1.74564)
Madagascar 15.57903 (1.12133)
Mali 16.45503 (1.18766)
Mauritania 18.49826 (1.34671)
Mauritius 32.72776 (1.93609)
Morocco 21.21386 (1.51208)
Mozambique 42.75648 (3.49318) *
Namibia 27.10107 (1.81008)
Niger 16.29281 (1.18403)
Nigeria -170.25696 (-2.12662) *
Senegal 17.54501 (1.27154)
Sierra Leone 16.51705 (1.19818) South Africa -176.35639 (-2.45158) *
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Sudan -3.98423 (-.29127)
Tanzania 14.37879 (1.04143)
Togo 14.69703 (1.02843)
Tunisia 17.03721 (1.43106)
Uganda 16.07436 (1.17980)
Zambia 30.79405 (2.27219) * Year Coefficient (t-ratio)
2003 2.73901 (1.06217)
2004 .59485 (.25354)
2005 -4.54407 (-1.91784)
2006 1.21022 (.47196)
Note: t-ratios are in parentheses, * Significant at 5%.
3.3.2.2 South Korea
As for Korea’s Random Effects under model II which are summarized in Table 13, GDP per capita (5.426) shows strong positive relationships with the amount of Korean ODA. Other factors such as trade with Korea (-.101), FDI from Korea (-.449), and energy production (-.878) do not have any significant influence on the amount of aid. When it comes to Fixed Effects, the result is not much different. GDP per capita (4.133) still has a significant impact on the Korean ODA, while trade with Korea (-.263), FDI from Korea (-.012) and energy production (-.320) do not show such an effect. It means that if the income level of African countries is higher, they tend to receive more money from the Korean side.
This result might reflect the Korean government’s risk-avert tendency to make safe use of funds. As explained in Chapter 2 dealing with comparisons between Chinese and Korean foreign aid, Korean government tries to utilize their funds efficiently and
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safely. Providing aid to African countries which have relatively higher income and less debt can be an effective way to achieve this aim.
Table 13: Model II – South Korea’s case: Random Effects and Fixed Effects
VARIABLE RANDOM EFFECTS MODEL’S
COEFFICIENT (T-RATIO)
FIXED EFFECTS MODEL’S
COEFFICIENT (T-RATIO)
CONSTANT -.46565651(-1.584) -1.24525559(-.925)
GDP PER CAPITA .00053726(5.426)* .00126002(4.133)*
TRADE WITH KOREA -.478961D-07(-.101) -.286694D-06(-.263)
FDI FROM KOREA -.755665D-05(-.449) -.265470D-06(-.012)
ENERGY PRODUCTION -.14382265(-.878) -.58417648(-.320)
R-SQUARED .3444936 .3632025
ADJUSTED R-SQUARED .1021215 .1051898
Note: t-ratios are in parentheses, * Significant at 5%.
Regarding details of Korean Fixed Effects, only 3 countries show negative significant Fixed Effects. Botswana (-2.33190), Gabon (-2.98337) and Mauritius (-2.10347) are the cases. Diagram 7 shows the way we interpret it. Botswana, Gabon and Mauritius established formal relations with South Korea in 1968, 1962, 1971 respectively.
However, they seem unlikely to benefit from this close relationship with South Korea in that they also have diplomatic ties with North Korea in 1974, 1974, 1973 respectively. Furthermore, they did not have urgent humanitarian need in the sense that the income levels of these three countries (UMICs) were relatively high. In terms of time Fixed Effects, there is no significant Fixed Effect as shown in Table 14. The t-ratios of year 2003, 2004, 2005 and 2006 are .51932, -.11211, 1.25822 and -1.57475.
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Diagram 7: Model II – South Korea’s case: Fixed Effects
Table 14: Model II – South Korea’s case: Fixed Effects by country and year
Country Coefficient (t-ratio)
Algeria 2.20146 (.18438)
Angola 1.97953 (.59646)
Botswana -5.56247 (-2.33190) *
Burundi 1.13033 (.62939)
Cameroon .33118 (.20881)
Cape Verde -1.36241 (-.71995) Central African Rep. .85504 (.47586) Congo, Dem. Rep. 1.16681 (.70367) Congo, Rep. -.29591 (-.22001) Cote d’Ivoire .32086 (.19627)
Djibouti .39346 (.21721)
Egypt, Arab Rep. 1.90991 (.44168) Equatorial Guinea -3.60082 (-1.16934)
Eritrea 1.00313 (.55815)
Ethiopia 1.10173 (.62451)
Gabon -5.67753 (-2.98337) *
Ghana .74429 (.43201)
Guinea .81043 (.44994)
Guinea-Bissau 1.02183 (.56855)
Kenya .85504 (.49217)
Lesotho .43574 (.24199)
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Liberia 1.66423 (.56523)
Libya -4.22044 (-.80465)
Madagascar .88577 (.48747)
Mali .69483 (.38665)
Mauritania .55408 (.31096)
Mauritius -4.84399 (-2.10347) *
Morocco -1.05012 (-.55945)
Mozambique .96887 (.59614)
Namibia -2.92325 (-1.44391)
Niger .95604 (.53361)
Nigeria 4.23361 (.43826)
Senegal .36697 (.20379)
Sierra Leone .96402 (.53640) South Africa -.55996 (-.06150)
Sudan .88448 (.70447)
Tanzania .92891 (.52796)
Togo .83373 (.46384)
Tunisia -1.61828 (-.98635)
Uganda .88806 (.49978)
Zambia .63081 (.37019)
Year Coefficient (t-ratio)
2003 .19294 (.51932)
2004 -.03750 (-.11211) 2005 .42438 (1.25822)
2006 -.57981 (-1.57475)
Note: t-ratios are in parentheses, * Significant at 5%.
For reference, the cases of China and South Korea’s Fixed Effects as explained above are shown in Diagram 8.
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Diagram 8: Map of China and South Korea’s Fixed Effects countries
Note: The rectangles and circles represent the largest Fixed Effects’ cases of China and South Korea respectively.
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Chapter 4
Panel Data Analysis (II) ：Chinese Foreign Economic Cooperation
As explained above in the part entitled Data Sets in Chapter 3, among the various concepts of Chinese foreign aid，Chinese Foreign Economic Cooperation seems likely to be the only Chinese official data which has details by recipient country. We think that this official data is worthwhile as a reference.