Discussion Paper on
Development Assistance
No. 1

Commitment to Development Index(CDI):
Critical Comments

Yasuyuki Sawada
Hirohisa Kohama
Hisaki Kono
Munenobu Ikegami

March 2004

Commitment to Development Index (CDI):
Critical Comments*

FASID Discussion Paper on Development Assistance No.1

March 2004
Yasuyuki Sawada
University of Tokyo, Japan(sawada@e.usawada@e.u-tokyo.ac.jp)
tokyo.ac.jp)
Hirohisa Kohama
University of Shizuoka, Japan(hiro.kohama@nifty.com
hiro.kohama@nifty.com)
com)
Hisaki Kono,
University of Social Sciences and Humanities, Vietnam(hisakijapan@yahoo.co.jp
hisakijapan@yahoo.co.jp)
jp)andMunenobu Ikegami
University of WisconsinWisconsin-Madison, USA(munenobu@fh.freeserve.ne.jp)
munenobu@fh.freeserve.ne.jp)* We gratefully acknowledge financial assistance from the Foundation for Advanced Studies on
International
International Development (FASID). We would like to thank Craig Burnside and David
Roodman for helping us to replicate
replicat e their results,
results, and Yujiro Hayami, Shigeru Ishikawa,
Michiko Yamashita,
Yamashita, and seminar participants at FASID for their constructive comments. Of
course,cours e, we are fully responsible for any remaining errors in this paper.

Table of Contents
1. Introduction････････････････････････････････････････････････････････････････
1
Introduction
2. Six Conceptual Problems of the CDI ･･･････････････････････････････････････････ 1
3. Problems in Computing Category Scores ･････････････････････････････････････････ 2
3.1 Aid Category ･･････････････････････････････････････････････････････････ 3
3.2 Trade Category･････････････････････････････････････････････････････････
4
Category
3.3 Investment Category ････････････････････････････････････････････････････ 4
3.4 Migration Category ･････････････････････････････････････････････････････ 5
3.5 Peacekeeping Category ･･････････････････････････････････････････････････ 5
3.6 Environment･･･････････････････････････････････････････････････････････
6
Environment
4. Toward the Alternative CDI ･･･････････････････････････････････････････････････ 6
4.1
4.1 CDI and Policy Coherence: Policy Coherence Index (PCI) ･･････････････････････ 6
4.2 Contribution of Aid to Economic Growth ････････････････････････････････････ 7
4.3 Derivation of the Optimal Weights ････････････････････････････････････････ 10
4.4 Refined Commitment to Development Index (RCDI) ･･･････････････････････････11
11
4.5 Contribution to Growth Index (CGI) ･･･････････････････････････････････････ 12
4.6 Introducing New Categories: Comprehensive Commitment toDevelopment Index (CCDI) ･････････････････････････････････････････････ 14
4.7 Using Absolute Values: Absolute Commitment to Development Index (ACDI)
15(ACDI)･･････
I)
5. Conclusion ･･････････････････････････････････････････････････････････････ 15
References･･････････････････････････････････････････････････････････････････
16
References
Tables ････････････････････････････････････････････････････････････････････ 19
Figures ････････････････････････････････････････････････････････････････････ 33

Abstract
In this paper, we critically rere-examine the robustness of the CDI ranking. First, we demonstrate thelimitations of the CDI in a constructive manner by closely examining the methodologies
methodologies to computethe main and category scores of the CDI. Particularly, we identify remaining problems of the CDI
such as selection criteria of weights for the category scores and methodological problems of
constructing each category score. The second aim of our paper is to propose alternative indicessuch as the Policy Coherence Index (PCI), Refined Commitment to Development Index (RCDI),
Contribution to Growth Index (CGI), Comprehensive Commitment to Development Index (CCDI),
and Absolute Commitment to Development
Development Index (ACDI). According to our analyses, the CDI
ranking can change significantly depending on weights selected, data used for constructing category
scores, and the addition of new category scores. Moreover, it is also necessary to link the CDI
CDI toother important policy concepts and objectives such as the concept of "policy coherence" and theMillennium Development Goals (MDGs). Especially, the relationship between the CDI and MDGs
should be established in future research by incorporating explicit
explicit categories to capture contributions
to poverty reduction as well as improvements of education and health in developing countries.

1. Introduction
The Commitment to Development Index, hereafter the CDI, constructed by the Center
for Global Development
Development quantifies the contributions of the world’world’s 21 richest countries to poverty
reduction in the world (Table 1; CGD/FP 2003). The CDI can be regarded as a numerical targetingstimulated
mulated a variety ofindicator for the 8th goal of Millennium
Millennium Development Goals
oals (MDGs). It has sti
intensive policy discussions regarding the role of developed countries in helping the world’world’s poor
(Table 2). However, as we will discuss in detail, there is large room for quality improvements in
the index.
The aims of this paper are twofold. First, we demonstrate in a constructive manner thelimitations
limitations of the CDI by closely examining the methodologies used to compute the main andcategory scores of the CDI. Particularly, we identify three outstanding problems of the CDI,
CDI, whichmeasuresmeasures development commitment through a composite index of six categories (aid, trade,investment, migration, peacekeeping, and the environment.) The first problem is that the weights
attached to the category scores are arbitrary.

Instead, these weights should be constructedaccording to the relative contribution of categories to poverty reduction. The second
second problem is in
the methodologies used to compute each category score. The final problem is that there are various
difficulties in aggregating catego
categoryry scores into the CDI. As a result, the second aim of our paper isto propose
propose alternative indicesindices such as the Policy Coherence Index (PCI),(PCI), Refined Commitment toDevelopment Index (RCDI)(RCDI), and Contribution to Growth Index (CGI).
(CGI).
The remainder of the paper
paper is organized as follows. Sections
Sections 2 and 3 examine the limitations
and problems of the CDI.
CDI.

Section 4 presents our alternative indicators.

Finally, Section 5

discusses the results of the new indices.

2. Six Conceptual Problems of the CDI

While thethe CDI’s
CDI’s quantification of poverty reduction contributions by developed countries
may prove meaningful to reducing poverty, the index has several conceptual problems. In the firstplace, the CDI attempts to create a composite index of some indic
indices concerning
concerning contributions
contributions by
developed countries.
countries. In this sense, the CDI is closely related to the 8th goal of MDGs and the conceptof “policy coherence.
oherence." Yet, strictly speaking, the CDI’s
CDI’s construction is not sufficient to capture
appropriately “policy coherence”
coherence” because
because it is an
an arithmetic average of category indices. An index ofthe "variation" in the computation of CDI category
category scores may be a more suitable “p“policycoherence" index.
Second,Second, the appropriateness of the selected categories in the CDI is not clear and there
are valid arguments against including additional criteria such as immigration,
immigration, peacek
eacekeeping1operations, and the environment. For example, if we agree that the MDGs are a desired objective,then the CDI should include categories related to countries’
countries’ contributions to education and health in
the world instead.
Third,
Third, creating an "arithmetic average" is based on the assumption that the unit of eachindex has the same unit of account.
account. However, in the CDI’s
CDI’s calculation,
calculation, GDP or population is used
for normalization
normalization interchangeably. Moreover,Moreover, category indiceindicesices are calculated with or without
adjusting variances and the rules of the withinwithin-category weights
weights for raw information are not unified
and are arbitrary.
Fourth,Fourth, the CDI’
CDI’s "ranking" technique itself may be problematic.
problematic. Although the score ofeach category and the comprehensive score are "quantitative" measures,
measures, "ranking" is a “qualitative”
indicator. The score differential between the Netherlands,
Netherlands, which ranks
ranks first,
first, and Denmark,Denmark, ranked
second,0.1.
1. Yet, the score differential between Australia,
second, is only 0.
Australia, ranked 19th,
19th, and the United States,
States,ranked 20th,
20th, is 0.6. The latter score differential is six times larger than the former, but the “ranking
1differential”
differential” is the same.

Fifth,
Fifth, the criteria need to be carefully
carefully considered when interpreting the CDI ranking.
ranking. The
CDI is based on "inputs
index.. Hence, the CDI should be"inputs" of policies, but it is not an "outcome" index
reconstructed as an index that quantifies the contributions
contributions to an outcome, such as poverty reductionin ddeveloping
eveloping countries.
countries.
Sixth, the practicability of the CDI in actual policy implementations is questionable.
The CDI is related loosely to the MDGs and PRSP
PRSP,, but actual
actual policy decisions of advanced nations
and developing countries are based on various dom
domestic
estic and international political negotiations,where the CDI might be of little relevance.

3. Problems in Computing Category Scores

In this section,section, we examine the problems
problems with the category score calculation procedures indetail.
detail. A fundamental issue is that the CDI category scores are computed based on resource inputs,
rather than contribution to outcome
outcomes,comes, such as poverty reduction in poor countries. Hence, thissection identifies contributions to economic growth as an important performance criteri
criteria,a,considering the important role of economic growth in poverty reduction (Besley and Burgess 2003;

1

The same problem is pointed out for the Human Development Index (HDI) of the UNDP. Ray
28--29) suggested(1998, 28
suggested that the comparison of HDI ranking with GDP per capita ranking is
informative. Hence, a comparison
comparison of different category rankings in the CDI, such as aid and tradecategory rankings
rankings, might be useful.
2Dollar and Kraay 2002; Ravallion 2001).

3.1 Aid Category

Can official development assistance (ODA) promote economic growth in countries
receiving the aid?
(2000),, only
aid? According
According to the research results
results of Burnside and Dollar (2000)
only when therecipient country
country’’s economic policies are appropriate can development assistance effectively
contribute to economic growth
growth..

However, Easterly, Levine, and Roodman (2003a(2003a,, 2003
2003b)

questioned Bunside and Dollor’
Dollor ’s (2000) results, finding that aid does not work effectively even in
the countries with good policies once the data set used by Burnside and Dollar (2000) is extended.
This lack of consensus in academic research about whether
whether aid has contributed to poverty reduction
in developing countries
countries thus implies that the rationale for including
including the aid category in the CDI is notstrong.
Second,Second, it may not be appropriate to quantify aid only from the viewpoint of pure resource
transfertransfers,
ers, ignoring the different modalities
modalities of assistance (Chang, FernandezFernandez-Arias, and Serven 1998).
1998).
Aggregating the measurements of grantgrant-inin-aid, technical cooperation, and loans may be problematic(in other words, the aggregation of the numbers
numbers of apples
apples (grant)(grant) and oranges
oranges (loans) could be
misleading) as there may exist different incentive effects in grantgrant-inin-aid, technical cooperation, andloans.
Third, it may not be appropriate to standardize the amount of ODA by GDP for donor
countries. An alternative variablecountry,, sincevariable to consider may be the ODA per capita of a donor country
this variable can capture contributions
contributions per person more accurately. Furthermore, in the case of aminimum required quantity of ODA
ODA,, there could be a nonlinear relationshiprelationship between the amount of
ODA and outcomes such as economic growth and/or poverty reduction. Accordingly, the scale of
ODA itself provides important information about donors’
nformation such as thedonors’ contributions. IInformation
total amount of ODA and/or ODA per capita of a recipient country is needed in addition to the ODA
to GDP ratio.
Finally, strategic aspects of ODA should be properly corrected when we use the aid
variable in the CDI. According to recent studies, donor countries largely seem motivated bystrategic considerations,considerations, rather than altruism or the real needs of the recipient countries (Alesina andDollar 2000). This may be due to lobbying efforts by ethnic groups in donor countries (Lahiri and
RaimondosRaimondos-Moller 2000). Moreover, aid is likely to enhance a government’government’s consumption or evenresult in corruption in the recipient country (Boone 1999; Alesina and Weder 2002).22

On the other hand, motivations
motivations for multilateral aid can be said to be more transparent.
33.2 Trade Category

Existing empirical studies demonstrate a positive contribution of international trade
trade toeconomic growth and poverty
poverty reduction (Harrison(Harrison 1996; Collier and Dollar 2001).
2001). Through aneyeball test, we can verify a positive correlation between long
long--term economic growth rates and thelonglong-term enhancement of external openness
openness (Figure(Figure 1).
1).

Nevertheless, there are a couple oftheoretical studies,studies, particularly in relation to the existence of increasing returns to scale or strong
learning effects, which challenge the positive relationship between trade and growth.3 Yet, incomparison to other category indices, the overall problems of using trade category data inconstructing the CDI are not critical.

3.3 Investment Category

There is an emerging consensus in academic literature that direct investment significantly
raises the economic growth rate,
Borenzstein,
rate, and by which poverty is reduced
reduced significantly
significantly ((Borenzstein
Borenzstein, De
Gregorio,
Gregorio, and Lee 1998).
1998). While this appears to justify the inclusion
inclusion of FDI in the investment
category, there are two problems to including the regulation to pension fund investment in the
category. FirstFirst,
irst, if the index of pension fund
fund investment is included as an indirect investment, bankloans
loans and other portfolio investments
investments should be included as well.
well.
The second
second problem is related to the robustness of the positive correlation between theseinternational indirect capital flows and economic growth. On the one hand, the negative correlation
between thethe black market premium in foreign exchange markets and the economic growth ratesuggests that there is a positive correlation between international capital flows and economic growth(Harrison
1996).. However(Harrison 1996)
However,owever, after the financial crises in the 1990s, there is an emerging view thatshortshort-term capital movements are associated with higher risks of sharp recessions. Hence, we may
conclude that larger international capital flows will lead to higher
higher average economic growth andlarger variances of future income levels.

Multilateral agencies are largely apolitical and more exclusively concerned with development and/or
poverty reduction (Cassen et al. 1986,
1986, 281;
281; Sawada 1996).
3

The
pessimism,
simism,”The ideas of “infant industry protection,
rotection,” “export pes
simism,” and “immiserizing growth”
growth” are also
related with this view.
43.4 Migration Category

Theoretically, the relationship between immigration and poverty is not monotonic.
Although for developing countries,
countries, emigration to developed countries createscreates a "brain drain" forthem, there is no consensus about the directional effect of the "brain drain" on developing countries.
On the one hand, "brain drain" has a brain creation effect (“brain effect”) since people makeeducational investments
investments in considering
considering a higher income through emigration.
emigration. These investments
will enhance the economic growth rate of developing countries. On the other hand, the “brain
drain"
drain" also contains an outflow effect,effect, namely a “drain effect,”effect,” because human resources actuallyflow
flow out of the country through emigration. Thus, the total effect of the “brain drain”
drain” is ambiguous.
While a recent empirical analysis supports a beneficial brain drain (BBD)(BBD) effect (Beine, Docquier,and Rapoport 2001), it is difficult to derive a definit
definitee conclusion from the existing literature.
Accordingly, it would be problematic
problematic to attach a significant weight to a migrationmigration-related variable
when computing developed countries’
countries’ contributions to poverty reduction.

3.5 Peacek
Peacekeeping Category

Compared wit
withh the other four categories in the CDI, "peacek
eacekeeping" and the"environment" are considered extraneous categories when we try to capture contributions to poverty
reduction. Therefore
Therefore,
herefore, it is not clear whether these two categories can be treated in the same way as
the other four categories. With respect to using peace
peacekeacekeeping as a category, there are two problems.
First, selecting
selecting a raw data set for this category is arbitrary and may not be justifiable. For example,
the "human contribution to NATO" in Kosovo
Kosovo is considered to have contributed to the high rankingof Greece, while other important peace
peacekeacekeeping operations such as in East Timor are not taken into
account.
Second, as is pointed out by the CGD/FP (2003), the extent of developed countries’
countries’ arms
sales
sales to developing countries should be considered since such sales will ultimately undermine
security in developing countries. As shown in Table 6,6, there is a crosscross-country data set concerningarms exports that can be used for the peacekeeping category index.
index. If we assume fewer arms
exports
exports leads to higher security in developing countries,
countries, Australia and Japan,
Japan, whose CDI indicesthstrank them
respectively, would be ranked 1st for a peacekeeping category that is purely
them 19 and 21 , respectively,

based on arms exports.

5

3.6 Environment

In the "environment" category, the contribution to global commons is used as theevaluation criteria. However
However,owever, when outcomes such as poverty reduction are taken as the criteria,
criteria,
we may need to consider whether each country’s
country’s ODA contributes directly to environmental
improvements
improvements in developing countries.
countries.

For example, while we can evaluate the amount oftechnical assistance in transferring clean energy technology though a micromicro-level analysis, it still
may be difficult to construct an aggregate indicator.
Second, including only fishery subsid
subsidiesies per GDP is not persuasive because marine
resources are fundamentally reproducible.
reproducible. A better approach would appear to include
include the subsidies
subsidiesin connection with the consumption of nonnon-reproducible and exhaustive
exhaustive resources such as fossil
fuels
subsidies.
ies. Fromfuels and mineral resources,
resources, instead of fishery subsid
From such a viewpoint,
viewpoint, variables like theratio of agricultural subsidies
subsidies to GDP and/or
and/or the ratio of industrial subsidies
subsidies to GDP would provide
more appropriate andand comprehensive information to construct the environment category index.
Finally, there is no justification to including the amount of electricity generated by wind
power only. The environment category index should reflect the amount of electric power generated
generatedby other clean methods, such as solar energ
energies
ies and geothermal energies
energies.
ies.

4. Toward an alternative CDI
In this subsection, we employ crosscross-country data sets to refine the CDI and develop thefollowing alternative indices:（11） Policy Coherence Index (PCI)（22） Refined Commitment to Development Index (RCDI(RCDI)CDI)（33） Contribution to Growth Index (CGI
(CGI)
CGI)
（4
4） Comprehensive Commitment to Development Index (CCDI(CCDI)CDI)（55） Absolute Commitment to Development Index (ACDI)
4.１ CDI and Policy Coherence: Policy Coherence Index (PCI)The CDI is closely related with the concept of policy
policy coherence.
oherence. Yet, to capture theconsistency of a developed country’country’s policies, we need to quantify the crosscross-category variability ofindices for each category. The following PCIs for developed countries areare considered to be such an
attempt:
6

PCI A＝

(1)

1




Var
(
score
)
j 

 1 N
 +1

∑ score j 
 N j =1
,where j represents a category. For example, if a country has perfectly equal category scores, thisindex takes the maximum value, 1, and would represent perfect policy coherence. The estimatedestimated
results are reported in Table 4. According to this table, the Netherlands,
Netherlands, whose CDI score is theAsthighest,highest, also receives the highest PCI score. In the case of Japan, its ranking improves from 21

for the CDI to 17th for the PCIA.

receivess the lowest PCIA ranking while the UK
The US receive

dramatically improves its ranking from 11th to 2nd.
This PCI is an extreme measurement tool since the “levels”
levels” of the category scores are not taken
into account at all. A more appropriate index, therefore, is a combination
combination of the CDI and PCIA.
For example, we can consider the following policy coherence index:4

PCI B＝CDI +

(2)

1




Var
(
score
)
j 

 1 N
 +1

∑ score j 
 N j =1
.

The estimated results of PCIB are reported in Table 3. By construction, the PCIB ranking is closer
to the CDI than is the PCI
PCIA.
4.2 Contribution of Aid to Economic Growth5

To refine the CDI in the direction of an outcomeoutcome-oriented index, we examine therelationship between aid and growth by using crosscross-country data. This relationship has beco
become thefocal point of research and policies on international aid. One of the most influential papers in thisarea is that of Burnside and Dollar (2000)(2000),, hereafter BD, who found that the impact of aid on the

45This formula is related with the concept with certainty equivalence.
This section is based on Sawada, Kohama, and Kono (2004).
7

growth of recipients is positive and conditional on good policies. However, this coconditional
nditional linkage
between aid and poverty reduction through economic growth breaks down once the data is extended.
Easterly, Levine, and Roodman (2003), hereafter ELR, who augmented their data from 1970–
1970–93 to1970–
original
inal period of 1970–
1970–97 and filled in missing data for the orig
1970–93,
93, found that there is no
significant relationship between the amount of aid and economic growth of recipient countries, evenafter controlling for policy variables. Consequently,
Consequently, BD’s
BD’s finding is not as robust once the data set isrefined.
refined.
By breaking down the aid variable into loans and grants,
grants, we further investigated the reason
why this conditional linkage between aid and growth disappeared.
Reconsidering
Reconsidering ELR’s
ELR’s findings

Our data set is identical to the one employed by ELR with the only difference being that we
broke down the effective development aid (EDA) variable compiled by Chang et al. (1998) into thevalues of loans and grants. With this aim,
aim, we took the following twotwo-step procedure. First, using the
1975–
1975–95 data, we regressed the loan variable from EDA’s
EDA’s data set on other variables such as net
loans
loans, grants
grants, and technical assistance. Then, with the estimated regression coefficients, we
extrapolated the loan data for 1970–
1970–74 and 1996–
1996–97. With respect to grant data, we employed thetheOrganisation for Economic CoCo-Operation and Development’s
Development’s (OECD) International Development
Statistics data directly because EDA’
EDA’s grant data is the same as the OECD data.
data.6 All variables apartfrom these aid variables are the same as ELR’s
ELR’s data set, and we
we have excluded the outliers identified
by ELR (Table 5).
5).
Using the data set for 19701970-97, we can replicate ELR’
ELR’s regression results. The first threecolumns in Table 6 show our replication of ELR’s
ELR’s results. As we can see, the coefficients on aid andaidaid-policy interaction are positive and negative, respectively, but both are statistically insignificant.7
However, the ways in which grants and loans work to generate economic growth are likely
to be quite different. Columns 4–6 of Table 6 show the results th
thatat include the separate effects ofloans per GDP and grants per GDP on economic growth. We found that the coefficients of loans are
positive and statistically significant when we include the loanloan-policy interactions (columns 5 and 6).8

6

EDA’s
EDA’s data does not contain data on technical assistanceassistance (TA) since donors benefit from payments
received in return for the TA supplied.
supplied. This may greatly reduce the donor’s net financial cost. We
also estimate the effect of TA on economic growth explicitly by using the OECD data [Table 6,
specifications (8) an
andd (9)].
7
We also conduct the two
two--stage least squares estimations to cope with a possible endogeneity biasof the aid variables. The results, which are not reported here,
here, are also consistent with ELR’s
ELR’s results.
8
Loans are typically used for infrastructure
infrastructure projects; hence, their impacts are realized with time lags.
The fact that the data for loans are disbursementdisbursement-based might also introduce these lagged effects. To
investigate further the effectiveness of loans, we also included the lagged variable of loans.
loans. The
estimation results, which are not reported here, are also consistent with the results of Table5
Table5.
8

Hence, we
we may attributeattribute the disappearance of conditional aid effectiveness in the ELR study to an
omitted variable bias caused by the assumption of the same coefficient on loan and grant variables.
Yet, all the coefficients of thethe loanloan-policy interaction terms shown in Table
Table 6 are negativeand mostly insignificant. The total effect of loans on economic growth will be positive in therelevant range, at least if we evaluate the total effect at the average policy level (i.e., 1.423)
1.423).
However, the negative coefficients of the ininteraction
teraction terms imply that the impact of loans on income
growth is lower in a country with good policies.
We suspect, nevertheless, that this
this finding is spurious due to the characteristics of the policy
variable constructed by BD and ELR. Their policy variablevariable is based on the regression results ofincome on fiscal surplus, the inflation rate, and openness. The existing studies on fungibility issues
in foreign aid suggest that there is a greater “flypaper effect” with concessional loans than with
grants (Feyzioglu,
Feyzioglu, Swaroop, and Zhu 1998). Therefore, the policy variable correlates positively withthe loan variable, and as a result, the loanloan-policy interaction variable can capture squaredsquared-loan
amounts. In
interactions
tions imply a decreasing andIn this case, negative coefficients of loanloan-policy interacmarginal impact of loans on growth. In fact, this interpretation also corresponds with the finding ofBD (2000(2000),
2000), which showed that there are diminishing returns for the impact of aid on growth.
growth.9

Aid Modality and Growth

Empirically,
Empirically, iitt is widely known that aid increases consumption rather than investments(Burnside and Dollar 2000; Boone 1999). Easterly (2003) attributed this finding to the classic
Samaritan’Samaritan’s dilemma, where aid could actually worsen incentives to invest if the recipient believes
that future poverty will call forth future aid. This disincentive’s
disincentive’s effects will be smaller in the case ofloans
loans than grants,
grants, since future repayments will be required for loans and the net resource transfer ismuch smaller than the nominal
nominal amount. In fact, in the context of Japanese foreign aid, it
it has beencommonly argued that the repayment obligations prevent recipient countries
countries from investing in
ineffective projects,
projects, thus resulting in discipline being imposed on project selection and management(Kohama 1995). We can find in the literature relevant arguments on the optimal design of public
transfers under asymmetric information. For example, the work requirements of workfare programs(Besley and Coate 1992) or inin-kind transfers (Blackorby(Blackorby and Do
Donaldson 1988) can be used as
selfself-sorting and incentive enhancement devices
devices. Similarly, repayment requirements for subsidizedloans should be an effective solution for incentive problems. On the other hand, there could be an
opposite disincentive effect of loan provisions on investments,
investments, particularly when the amount of

9

To test our interpretation, we estimated a model with a squaredsquared-loan variable instead of theloan--policy interaction variable. The coefficient of the squaredloan
squared-loan variable is -0.168, with astandard deviation of 0.418. While this coefficient is not statistically significant, its direction isconsistent with our interpretation.
9

outstanding debt becomes excessive. This is known as the debt overhang problem in the literature on
sovereign debt (Krugman 1988). Our empirical results suggest that the former incentive
incentive effectdominates the latter disincentive effect,effect, in the context of foreign aid.

Reconsidering BD’
BD’s Findings

We examined BD’
BD’s findings during the period 19701970-73 by breaking down the total loanvariable into loans and grants. The descriptive statistics
statistics of each variable for the period 19701970-93 arerepresented in Table 7.
As in the previous case, we first replicate BD’
BD’s findings. As can be seen in specification(2) of Ta
Table 10, the coefficients of the aid and policy interaction term is positive and statisticallystatisticallysignificant.

This leads us to conclude that in countries where there is a favorable policy

environment, aid has a promotional effect.
Next, we examine the case in which total amount of aid is split into loans, grants, andtechnical assistance. As can be seen from specification (5), the coefficient of the grant and policy
interaction term is positive and statistically significant.

Moreover, in specification (9), theinteraction term of squared technical assistance and policy variable has a positive
positive and statisticallysignificant coefficient. These results suggest that grants and technical assistance may promote
growth under a relatively favorable policy environment.
The above findings demonstrate that the results of the shorter period imply a sample
sampleselection bias or a structural change in the latter half of the 1990s.

4.3 Derivation of the Optimal Weights

In this subsection, we estimate crosscross-country growth regression with aid variables as wellas variables related to CDI category scores. Then, with economic growth as our criteria, we derive
optimal weights for each catecategory
gory of the CDI. As Chowdhury and Squire (2003) clearly point out,
such optimal weights are desired
desired for computing a more accurate CDI.

We will utilize theaugmented BDBD-ELR regression model:(3)g r = α 0 + α 1 x1r + α 2 x 2r + ⋅ ⋅ ⋅ ⋅ α N x Nr + X r β + u r .

In this equation, g represents the economic growth rate of a country, r. Variables x1 , x1, …, xN
represent explanatory variables to compute weights and X is a set of other control variables. The
last term, u, is a wellwell-behaved error term. From equation (3), we can obtain an optimal weight tothe variable xi by using the following formula:

10

wi =

(4)

α̂ˆi ×α

1 R∑ xir
R r =1

1 R∑ gr
R r =1In this equation (4), R is the number of samples and α̂αˆ i is the coefficient derived from estimating
equation (3).
In the actual estimation, we employ
employ the total shortshort-tem and longlong-term capital inflows forthe investment score variable, remittances from overseas migrants for the migration score variable,
and CO2 emissions for the environment score variable. These variables are extracted from theWorld
Indicators.. With respect to the trade score, we employ theWorld Bank’
Bank’s World Development Indicators
updated Openness Index used for the policy variable by BD and ELR. Recalling that in ELR’
ELR’ssmodel, the policy variable is constructed by:(5)+1.46×openness,,
Policy = 5.92×budget surplus -1.89×inflation +1.46×openness

we compute thethe optimal weight for the trade category by multiplying the coefficient of the policy
variable in equation (3) by the openness coefficient, 1.46, in equation (5), and further byby the average
Openness Index.
With respect to the peacekeeping category, we assume that the number of assassinations
assassinations
variable in the BDBD-ELR regression is the inverse indicator of peace.
peace. Then we compute the fitted
value of this indicator as the negative weight of peacekeeping.
Table 9 summarizes the descriptive statistics of variables and Table 10 summarizes theestimation results of regressions. In these specifications for Table 10, the coefficients on aid
become statistically insignificant, unlike ffor
or BD and ELR. On the other hand, policy and FDI
variables are consistently positive and significant. While the coefficients on the short
short--term capital
inflow are consistently positive, they are not statistically significant. Assassinations and CO2
coefficientscoefficients are mostly negative, although they are not statistically significant.
From the above estimation results, the optimal weight will be setting estimated weights
for the trade and investment categories. We will consider an extended version of this optimal
optimalweight by adding the weights for aid or for all variables, although they are not necessary significant.
For the following computations, we employ Table 10, specification (5).

4.4 Refined Commitment to Development Index (R(RCDI)In Table 11
11, we present the Refined Commitment to Development Index (R(RCDI). The
RCDI is a slightly revised version of the original CDI, which uses equal weights for each of its six
11categories. As the original CDI has errors in the score of the investment category, we first report the
total ranking after correcting those errors [[Table
Table 11, specification (A)].
(A)].
If we presume that each category ranking in the CDI is accurate,accurate, there are some countries
whose ranking
rankingss are remarkably changed by using the optimal weights
weights described above [Table[Table 11,
specification (B)]. For example, Denmark (2nd), Portugal (3rd), Switzerland (5th), Sweden (8th),
Norway (10th), Greece (13th), Australia (19th), and the United States (20th) all experienced widemovements in their rankings. The reason for this is that these countries all have uneven scores acrossthe six categories. On the other hand, since Japan ranks
ranks below 15th in all the categories except
investment (7th), using different weightings
weightings has only a limited influence on its ranking
ranking.
ing. For instance,
if we put equal weights
trade,, and investment, Japan's ranking
weights on only aid, traderanking slightly rises to 20th, andGreece is 21st.
Two comments can be made about the aid category in the CDI.
CDI. First, the aid score isbased on the donors' aid values to their GDP ratio.

Thiss variable could be interpreted as
Thi

Burden,,” where rich countries should pay more. Second, the aid scores in“Contribution to Worthily Burden
the CDI exclude interest payments and thus countries such as Japan, whose ODA consists of a large
proportion of loans,loans, have
have a lower ranking by nature.
Considering these problems, we develop
develop another two indices.
indices. The first index uses aid per
population instead of aid per GDP. In this case, though Japan's rank
ranking
ing in the aid category improves
to 13th, its rank
ranking
ing in the total sco
score
re is still the lowest,
11,
1, specification (Clowest, as shown in Table 1(C). The
second index uses net disbursements for the aid variables, which do not deduct interest payments
payments
from ODA,
ODA, to compute the aid score. Table 11,11, specification (D(D) shows this index does not change
Japan's ranking
ranking.
ing.
Table 11
11 suggests that Japan’
Japan’s policy coherence is at a low level among the developed
countries no matter which concept we use.
4.5 Contribution to Growth Index (CGI)

As a next step, we use the “optimal weight” derived from the results of crosscross-country
regression discussed in Section 4.3 and reported in Table 10, specification (5) to calculate theContribution to Growth Index (CGI), which is an index based on the degree of contribution toeconomic growth in developing countries.
We should note
note that the aid index used in the CDI is imperfect as the proxy variable forcountries..” Hence, to overcome this issue
issue,, first“the degree of contribution to growth of developing countries
we should define a variable that quantifies the degree of contribu
contribution.
tion. Ideally, w
what
hat donors should
maximize is the weighted sum of economic growth of the aid recipient countries, as:
R

(6
(6)

Weighted sum of economic growth in developing countries =∑w g ,
r =1

12

r

r

where r expresses
expresses the aid recipient countries
countries and w is the weight on that country based on its
population. As the results of BDBD-ELR and our Tables 6 and 8 show,
show, the amount of aid received
relative to the GDP of recipient countries is an important policy variable.
variable. Hence, the variable that
capturescaptures the contribution of donor d's aid to growth in developing countries can be expressed by:

AIDrd

R

(7)

Index of contribution of donor d's aid to growth＝growth∑ w GDP ,
r =1

r

r

where AIDrd is the aid value of donor d to recipient country r. Note that since grants
grants and loans
loans havedifferent effects
effects on economic growth, we set the interinter-aid variable weights of grants
grants and loans
loans to0.45 and 0.55, respectively, to derive the weight of aid, wr. Further, to derive worthily the burden ofeach donor, we divide thethe value derived by (7(7) by a donor's GDP to yield the aid index. In this case,
Japan’
Japan’s aid ranking is 14th and the lowest ranked country is the United States.
As for investment, we set the weights of foreign direct investment and regulation on
pension funds
funds, which is the proxy for foreign indirect investment, as 0.82 and 0.18,
0.18, respectively,
based on the results of the regression.
On trade, migration, and the environment, we employ the original CDI category variables
without any change.
On peacekeeping, we rereplaceplace the category index in the CDI by the donor's arms export tototal export ratio. We extract data from the World Bank’
Bank’s World Development Indicators andutilize an average variable over 1998 and 1999.
In Table 12, specification (1), we show the ranking
ranking where we replace the index in eachcategory by the CGI described above,
above, while putting
putting equal weight on all the six categories like in the
CDI. This modification raises Japan's ranking
ranking to 17th, mainly because its nonnon-export of arms gives
Japan the highest peacekeeping score.
Second, in
14,, specification (2), we compute the CGI with the assumption that thein Table 14
optimal weight is:is:(8) (Aid, Trade, Investment, Migration, Peacekeeping, Environment)= (0.27, 0.26, 0.31, 0.07, 0.09, 0.01),0.01),
based on the results
results of Table 10,
10, specification (5). In this case, Japan's ranking
ranking becomes lower than
Table 12,12, specification (1), due to a decrease in the weight on peacekeeping.
Third,
ird, we also take into account the statistical significance of the variable. According
to the results from Table 5, specification (5),(5), only the trade policy variable, which is contained in thepolicy variable,variable, and foreign direct investment are statistically significant.
significant. Hence, we utilize thefollowing weights:
13

(9)

(Aid, Trade, Investment,
Investment, Migration, Peacekeeping, Environment) = (0, 0.26, 0.25, 0, 0, 0)

as the optimal weights
weights. The
The results with these weights are reported in specification (3). In this case,
Japan ranks
ranks 20th.
Finally,
Finally, we compute the CGI when we put the positive weights
weights on the importanteconomic variables of aid and international indirect capital flows in addition to trade and FDI:

(10)10) (Aid, Trade, Investment, Migration, Peacekeeping, Environment) = (0.27, 0.26, 0.31, 0, 0, 0).
The results are reported in Table 14, spec
specification
ification (4). Japan still ranks
ranks 20th, reflecting the country’country’s
low aid category ranking.

4.6 Introducing New Categories:
ategories: Comprehensive Commitment to Development Index (CCDI)

As we already pointed out, CDI is interpreted as a trial to quantify and evaluate
evaluate theinternational contribution of each developed country from a synthetic point of view. International
contribution of developed countries, however, is not restricted to aid, trade, investment, migration,
peacekeeping, and the environment. In this su
subsection,bsection, we propose a Comprehensive Commitment
to Development Index (CCDI) by introducing the new categories of culture and social development.
The provision of aid by developed countries to promote cultural
cultural activities in developing
countries should be considered
considered an important form of international cooperation.
cooperation. Since available dataon aid for the promotion of cultural
cultural activities is limited, we yield a new index that computes thecontribution of developed countries to the United Nations Educational, Scientif
Scientific
ic and Cultural
Organization (UNESCO),(UNESCO), which plays an important role in preserving legacy all over the world andsupporting international cultural exchange. Note that the contribution of the United States to
UNESCO is zero as it only returned to UNESCO in October 2003,
2003, after an absence of 19 years.
In addition, to take into account developed countries’
countries’ contributions to achieve the MDGs,
we include another new category index for contributions to social sector development by eachdonor’
infrastructure
tructure and service in 2001, didonor ’s aid for social infras
dividing it by the donor's GDP in the same
year.10
As for the migration index, the possibility of a brain drain implies that using the numberof migrants as an index for international contribution is misleading.

Therefore, we focus onrefugees to make a new index for migration
migration,, computed by dividing each donor's contribution to the
UNHCR by its GDP.
10

It will be possible to interpret the usage of this information as capturing its contribution to the
HDI made by the UNDP.

14

We summarize the results of introducing these new categories and new migration index
in Table 13
13. We call these indices the Comprehensive
Comprehensive Commitment to Development Index (CCDI).
Table 13, specification (5) shows
shows the ranking when we take an arithmetic mean of the eightcategories,
categories, with equal weights for all the categories.
categories. As Japan contributes a great deal to UNESCO,
its ranking
ranking improves
improves to 13th. Table 13,
13, specification (6) is the result when we reduce the weights forthe new cultural cooperation and social development categories by 20 percent.
percent. In specification (7),
we use the CDI's original category index for migration with optimal weightweights
ights for the CDI's six
categories by adding the two new categories with 12.5 percent weights. In those cases, Japan still
has a low rank
ranking
ing.
ing.
4.7 Using Absolute Values
Values:: Absolute Commitment to Development Index (ACDI)

Finally, we show the rankings
rankings when we use a country’country’s absolute values of aid, foreign
direct investment, contribution
contributionss to UNESUNESCO and the UNHCR, and aid for social infrastructure,
infrastructure,
instead of dividing these category totals by the country’country’s GDP. According to equation (6), these
these
indices are supposed
supposed to quantify a donor's contribution to economic development of lowlow-income
countries in absolute terms.
terms. In Table 14, we
we present the absolute value version of the CDI, in which
we do not divide category values by GDP. Table 14, specifications (8) ~ (10) show that Japan islistedlisted among the highly
highly ranked
ranked countries.
countries.
Judgingrankingss described above,
Judging from the overall
overall ranking
above, Japan has a strong presence in thecontribution to growth of developing countries in absolute values,
values, but it ranks at a lower
lower level if we
adjustadjust for the size of its economy as CGD/FP (2003) pointed out.

5. Conclusion

In this paper, we have critically rere-examined the robustness of the CDI ranking. The
ranking of the CDI can be changed significantly depending on weights selected, data used forconstructing category scores, and the addition of new category scores.
scores.
It is also necessary to link the CDI to other important policy concepts and objectives suchas “policy coherence” and the MDGs. Especially, the relation
relationshiplationship between the CDI and MDGs is
not necessarily clear. Although poverty reduction as well as improvements
improvements to education and healthare explicitly included in the MDGs, these categories
categories are not incorporated into
into the CDI. Possible
future refinements of the CDI should be directed toward quantifying
quantifying developed countries’
countries’
contributions to achieve the MDGs.

15

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18

Table 1 Basic Sources of the CDI
1. Magazine Articles
CGD/FP (2003), “Ranking the Rich,
Rich,” Foreign Policy,
Policy, May/June 2003,
2003, propro-poor,
poor, 5656-66.
http://www.foreignpolicy.com/story/story.php?storyID=13656
2. Ba
Basic Data: http://www.cgdev.org/rankingtherich/details.html
Technical Paper: Nancy Birdsall
Birdsall and David Roodman, Center for Global Development
Background Papers
Aid component: David Roodman
The aid component was influenced by an earlier paper by William Easterly, Center for Global
Development.
Development.
Trade component: William Cline, Center for Global Development
Investment component: John Williamson and Josh Catlin, Institute for International Economics
Migration component: Kimberly Hamilton and Elizabeth Grieco, Migration Policy Institute
Peacekeeping component: Michael O'Hanlon, B
Brookings
rookings Institution
Environmental performance: David Roodman, Center for Global Development
Data Sets
Summary background data: compiled by David Roodman
Detailed background data: compiled by David Roodman

19

Table 2 Related Articles in English or Japanese
1. “Tojokoku Shien Nihon SaikaSaika-i – Enjo, Toshi nado 6 Bunya no Sougoten”
Sougoten” (“(“Japan in the lowest place foraid to developing countries – looking from overall six categories including aid and investment”
investment”), Asahi
Asahi
Shimbun,Shimbun, April 30, 2003, 2.
http://www.asahi.com/business/update/0429/026.html
2．Nancy
Nancy Birdsall and Moises Naim,
Naim, “Ranking donor nations - When the rich talk aid, the poor don't always
alwaysget it.
it.”
International Herald Tribune,
Tribune, April 29, 2003, 6.
http://www.iht.com/articles/94678.html
3．Economics
Economics focus: “Gauging generosity - Which rich countries do most to help poor countries?”
countries?”
May 1,
1, 2003.
2003.
The Economist (print edition)
edition)http://www.economist.com/printedition/displayStory.cfm?Story_ID=1748607
４．“An“An Economist in Search of a 'Global Social Contract' to Reduce Poverty.
Poverty.”
By Nora Boustany
Post,, Friday, May 9, 20
2003
03,, A32
A32..
Washington Post03
http://www.washingtonpost.com/wphttp://www.washingtonpost.com/wp-dyn/articles/A32760dyn/articles/A32760-2003May8.html
5．LEADERSLEADERS & LETTERS: “A focus on effective aid for the poor”
By Robert Picciotto
Financial Times,
Times, May 17, 2003.
2003.
http://search.ft.com/search/article.html?id=030517001240&query=Picciotto&vsc_appId=totalSearch&state=Form
6.
Furuta, Hajime (2003)(2003),, “Tojokoku Enjo – Nihon no Koken, Tadashiku Hyoka wo”wo” (“(“Aid
Aid todeveloping countries – Correct evaluation of Japan’
Japan’s contribution”
contribution”), Asahi Shimbun,
Shimbun, July 16, 2003, 14.
7. Furuta, Hajime (2003), “The Rich Respond,”
Respond,” Foreign Policy,
Policy, October 2003, 6 and 8.

20

Table 3
Total Arms Exports, 1997
Total Arms
Arms Export
Exports
xports (US dollars)
United States
95,524,597,287
United Kingdom
17,474,744,644
France11,728,089,259
Italy
1,779,248,555
Canada1,677,373,436
Spain
1,648,297,711
Sweden1,640,168,844
Netherlands
1,199,553,737
Germany
941,247,834
Belgium
310,196,881
310,196,881
Greece200,967,216
Switzerland
126,754,279
Finland76,104,324
Australia
0Austria0
Denmark0
Ireland0
Japan
0
New Zealand0
Norway0Portugal0
Sources: World Bank (http://www.fas.org/asmp/profiles/WBarmsexports2.htm
http://www.fas.org/asmp/profiles/WBarmsexports2.htm)
)
for
the
total
t
otal
tp://www.fas.org/asmp/profiles/WBarmsexports2.htm
export of arms to total trade ratio, and Penn World Table Mark 6（http://pwt.econ.upenn.edu/
http://pwt.econ.upenn.edu/）
http://pwt.econ.upenn.edu/
for the total trade data.

21

Table 4 Policy Coherence Index (PCI)
Index A
Rank

Index BCountryCountry

Score

Country

Score

1

Netherlands

0.78

Netherlands

6.66

2

United Kingdom

0.74

Denmark

6.35

3

Belgium

0.72

Switzerland

6.03

4

Austria

0.71

New Zealand

5.78

5

Denmark

0.69

Portugal

5.64

6Ireland0.67

Germany

5.36

7

Sweden0.66

Austria

5.32

8Norway

0.66

Spain

5.32

9

Portugal

0.64

Sweden

5.30

10

Spain

0.64

Norway5.19

11Germany

0.64

United Kingdom

5.00

12

New Zealand

0.64

Belgium

4.82

13

France0.62

Greece4.40

14Finland

0.62

France4.37

15

Canada0.61

Ireland

4.28

16

Switzerland
Switzerland

0.60

Italy

4.18

17

Japan

0.60

Canada

4.14

18

Italy

0.59

Finland4.11

19

Australia

0.58

Australia

3.67

20

Greece

0.53

Japan

3.08

21

United States

0.50

United States

3.07

22

Table 5
Reconsidering the Easterly, Levine, and Roodman (2003) Model:
Model:
Descriptive Statistics of the Variables
Variable name

Obs.
Obs.

Mean

Std. Dev.

Min

Max

GDP/capita growthln (initial GDP)
PolicyEthnic Frac.
Assassinations
Ethnic* Assas.
Institution
M2/GDP
Aid
Loan
Loans
ans
GrantsGrantsTechnical Assistance

344
344
344
344
344
344
344
344
344
344
344
344

1.353
7.499
1.423
0.468
0.468
0.492
0.189
4.324
26.175
1.189
0.366
0.823
0.463

3.435
0.759
1.048
0.297
1.274
0.606
1.543
14.478
1.478
0.490
1.184
0.537

-12.693
5.429-4.740
0.000
0.000
0.000
1.580
4.580
-0.049-1.224-0.0020.009

10.076
9.339
3.720
0.900
11.5007.360
8.140
120.308
7.312
2.382
6.696
2.831

Data sources: See the text.

23

Table 6
Reconsidering the Easterly, Levine, and Roodman (2003)(2003) Model
Replication of ELR
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
-0.42
-0.37
-0.34
-0.38
-0.38 -0.36
-0.39
-0.42
ln (initial GDP)(-1.12) (-0.96) (-0.86) (-1.01) (-0.98) (-0.90) (-1.02) (-1.09)
1.04*** 1.26*** 1.22*** 1.05*** 1.19*** 1.17*** 1.05*** 1.07***
Policy(5.25) (5.54) (5.16) (5.34) (5.49) (5.17) (5.32) (5.00)
0.03
0.06
-0.02
0.09
0.08
-0.03
0.10
-0.01
Ethnic Frac.
(-0.02) (0.04) (0.08) (-0.03) (0.13) (0.12) (-0.04) (0.15)
-0.34
-0.37
-0.37
-0.32
-0.34 -0.33
-0.32
-0.34
Assassinations(-1.34) (-1.45) (-1.43) (-1.27) (-1.31) (-1.27) (-1.26) (-1.31)
0.12
0.20
0.19
0.10
0.15
0.13
0.10
0.14
(0.19) (0.31) (0.30) (0.16) (0.23) (0.21)
Ethnic* Assas.
(0.21) (0.16) (0.22)
0.32*** 0.32** 0.32** 0.33*** 0.33** 0.33** 0.33*** 0.33***
Institution(2.63) (2.56) (2.58) (2.69) (2.60) (2.57) (2.68) (2.66)
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
(0.23) (0.21) (0.20) (0.27) (0.06) (0.00) (0.26) (0.34)
M2/GDP
Sub-1.61*** -1.65*** -1.69*** -1.64*** -1.63*** -1.62*** -1.60*** -1.40**
Sub-Saharan(-2.92) (-2.96) (-2.96) (-2.95) (-2.93) (-2.80) (-2.68) (-2.32)
Africa1.35*** 1.12** 1.16** 1.34*** 1.28** 1.32** 1.34** 1.34**(2.62) (2.18) (2.22) (2.62) (2.51) (2.48) (2.60) (2.56)
East Asia
-0.04
0.24
0.26
Aid
(-0.25) (0.83) (0.88)
-0.18
-0.12
Aid* Policy(-1.24) (-0.54)-0.01
Aid2* Policy(-0.36)0.34
1.29* 1.40*
0.35
1.57*
(0.66) (1.67) (1.69) (0.65) (1.94)
Loans
Loans
-0.88**-0.72* -0.54(-1.72) (-1.43)(-2.03)
Loan* Policy-0.20(-0.79)Loan2* Policy-0.14-0.18 -0.20
-0.13
0.54
Grants(-0.73) (-0.44) (-0.47) (-0.50) (0.96)
Grants
0.02
0.06
-0.40
Grant* Policy(0.11) (0.21)(-1.38)0.00
Gran(-0.05)Grant2* Policy-0.08 -2.25**
Technical
Technical(-0.12) (-1.97)
Assistance (TA)
1.25**(2.17)TA* Policy
TA2* Policy
344
344
344
344
344
344
344
344
Observations
R-squared
0.33
0.33
0.33
0.33
0.34
0.34
0.33
0.35
Note: *, **,**, and *** represent 10%, 5%, and 1% statistical significance, respectively.

24

(9)
-0.52
(-1.35)
1.44***(5.74)-0.12(-0.18)-0.35(-1.29)0.14(0.21)
0.28**(2.27)
0.01(0.48)-1.09*(-1.84)0.96*(1.82)

1.74*(1.95)-0.73*(-1.70)-0.25(-1.01)0.28(0.52)
0.08(0.18)-0.06(-0.88)-2.08*(-1.96)-1.07(-1.32)
1.02***(3.18)
344
0.37

Table 7
Reconsidering the Burnside and Dollar (2000) Model
Descriptive Statistics of the Variables
Variable

Obs.
Obs.

Mean

Std. Dev.

Min

Max

GDP/capita
GDP/capita growthln (initial GDP)
PolicyEthnic Frac.
Assassinations
Ethnic* Assas.
Institution
M2/GDP
Aid
Loans
Loans
GrantsGrantsTechnical Assistance
Assistance

268
268
268
268
268
268
268
268
268
268
268
268

1.328
7.535
1.346
0.459
0.465
0.181
4.396
24.210
1.027
0.366
0.662
0.438

3.477
0.711
1.280
0.300
1.265
0.614
1.519
11.393
1.285
0.486
0.952
0.521

-12.693
5.743-3.910
0.000
0.000
0.000
1.580
4.580
-0.049-1.224-0.0020.009

10.076
9.339
3.720
0.900
11.5007.360
8.140
81.641
6.682
2.382
4.607
2.831

Data sources* See the text.

25

ln (initial GDP)
PolicyEthnic Frac.
Assassinations
Ethnic* Assas.
Institution
M2/GDP
SubSub-SaharanAfricaAfricaEast Asia
Aid
Aid* Policy
Aid2* Policy
Loans
Loans
Loan*Policy
Loan2*Policy
GrantsGrantsGrant* Policy
Grant2* Policy
Technical
Assistance (TA)TA* Policy

Table 8Reconsidering Burnside and Dollar (2000) Model
Model
Replication of BDBD-ELR
(1)
(2)
(3)
(4)
(5)
(6)
(7)
-0.19
-0.25
-0.07
-0.19
-0.22
-0.08
-0.04
(-0.08) (-0.41) (-0.50) (-0.17) (-0.40) (-0.47) (-0.17)
1.00*** 0.70*** 0.74*** 0.99*** 0.72*** 0.78*** 1.02***(5.73) (3.86) (4.05) (5.66) (3.94) (4.25) (5.74)
-0.50
-0.57
-0.53
-0.39
-0.51
-0.55
-0.53
(-0.73) (-0.66) (-0.74) (-0.71) (-0.52) (-0.67) (-0.73)-0.54* -0.51* -0.51* -0.56* -0.54* -0.53* -0.58*(-1.82) (-1.69) (-1.69) (-1.84) (-1.75) (-1.71) (-1.89)
0.85
0.92*
0.82
0.81
0.93*
0.84
0.97*
(1.83) (1.60) (1.59) (1.84) (1.65) (1.62) (1.91)
0.34** 0.37*** 0.35
0.35** 0.34** 0.37*** 0.33** 0.34***(2.56) (2.72) (2.53) (2.53) (2.66) (2.37) (2.63)
0.02
0.03
0.02
0.02
0.02
0.02
0.02
(1.09) (1.40) (1.44) (1.10) (1.07) (1.17) (1.02)-1.45** -1.53** -1.48** -1.49** -1.57** -1.42** -1.18*(-2.28) (-2.39) (-2.29) (-2.37) (-2.47) (-2.19) (-1.75)
1.11*
0.72
0.79 1.17**
0.79 1.28**
1.13*(1.34) (1.98) (1.88) (1.33) (2.14) (1.89) (1.20)
-0.20
-0.33
0.23
(1.03) (-0.64) (-0.86)
0.21
0.35**(2.39) (0.91)
0.05(0.67)
0.48
-0.07
0.56
0.01
(-0.13) (0.59) (0.65) (0.02)
-0.05
-0.19
(-0.57) (-0.13)-0.08(-0.83)
-0.64
0.60
0.35
-0.40
(1.18) (-0.85) (-1.25) (1.52)
0.48***
0.10(2.68) (0.29)
0.16(1.52)-0.78(-1.00)TA2* Policy

(8)
-0.23
(-0.51)0.71***(3.79)-0.42(-0.55)-0.55*(-1.79)0.87*(1.68)
0.37***(2.70)
0.02(0.96)-1.04(-1.50)1.25**(2.08)(9)-0.34(-0.71)
0.97***(4.75)-0.44(-0.59)-0.56*(-1.81)0.86*(1.69)
0.32**(2.34)
0.02(0.88)-0.92(-1.37)0.92(1.54)

0.92(1.03)-0.39(-1.06)1.10(1.15)-0.42(-1.05)-0.06(-0.44)-0.04(-0.06)0.06)
0.71(1.40)-0.10(-0.61)-1.94(-1.57)-1.40(-1.58)0.98**(2.38)
268
0.42

0.19
(0.32)
0.23(0.72)-2.01(-1.61)0.68(1.21)

Observations
268
268
268
268
268
268
268
268
R-squared
0.38
0.39
0.39
0.38
0.40
0.40
0.39
0.41
Note: *, **,**, and *** represent 10%, 5% and 1% statistical significance, respectively.

26

Table 9
Estimation of the Optimal CDI Category Weights
Descriptive Statistics of V
Variable
ariablesariablesVariable
GDP/capita growthln (initial GDP)
PolicyEthnic Frac.
Assassinations
Ethnic* Assas.
Institution
M2/GDP
Aid
Loan
Grant
Technical AssistancePortfolio Investment/GDP
Net FDI/GDP
Remittances
Remittances/GDP
CO2 emissions
emissions/GDP
Openness

CDI Category（ Trade）
Trade）
Peacekeeping

Aid
Aid
Aid
Aid
Investment
Investment
Investment
Migration
Environment(Trade)

Obs.
Obs.

Mean

Std. Dev.

Min

Max

220
220
220
220
220
220
220
220
220
220
220
220
220
220
220
220
220

0.66
7.54
1.41
0.47
0.52
0.19
4.31
28.06
1.35
0.39
0.95
1.47
0.51
0.27
1.06
1.66
1.69

3.49
0.78
1.12
0.30
1.32
0.66
1.59
15.52
1.56
0.51
1.23
1.70
0.58
0.70
1.43
3.26
2.45

-12.69
5.43
-4.74
0.00
0.00
0.00
1.58
6.09
-0.05
-1.22
0.00
0.01
0.01
-0.80
-2.69
0.00
0.04

10.08
9.34
3.72
0.90
11.50
7.36
8.14
120.31120.31
7.00
2.38
6.18
6.69
2.83
3.91
9.69
22.10
15.72

Note: We use portfolio
portfolio investment, net FDI, remittances
emittances, and CO2 emissions
emissions data for 2000, takenfrom the World
World Development Indicators 2002.
2002. These are figures divided by GDP.

27

Table 10
Estimation of the Optimal CDI Category Weights
ln (initial GDP)
PolicyEthnic Frac.
Assassinations
Ethnic* Assas.
Institution
M2/GDP
SubSub-Saharan Africa
East Asia
Aid

(1)
-0.99
(-1.51)
0.94***(3.55)-0.93(-1.03)-0.35(-1.30)0.19(0.28)
0.40**(2.37)-0.01(-0.66)-1.83**(-2.42)
1.59*(1.96)-0.21(-1.06)

Aid* Policy
Aid 2* Policy

(2)
-0.98
(-1.45)0.94***(3.50)-0.93(-1.02)-0.35(-1.30)0.19(0.29)
0.40**(2.34)-0.01(-0.66)-1.83**(-2.41)
1.59**(2.03)-0.21(-0.63)0.00(-0.01)(3)-1.00(-1.40)0.96***(3.26)-0.95(-1.02)-0.36(-1.29)0.19(0.29)
0.40**(2.32)-0.01(-0.62)-1.83**(-2.40)1.56*(1.88)-0.22(-0.65)-0.03(-0.09)0.01(0.12)

Loans
Loans

(4)
-0.90
(-1.38)0.93***(3.64)-0.91(-1.00)-0.34(-1.23)0.18(0.27)
0.42**(2.42)-0.01(-0.69)-1.82**(-2.42)
1.59*(1.97)(5)-1.00(-1.44)0.85***(3.41)-0.85(-0.93)-0.33(-1.16)0.16(0.24)
0.43**(2.39)-0.01(-0.76)-1.85**(-2.45)
1.76**(2.26)(6)-0.98(-1.36)0.83***(3.15)-0.82(-0.88)-0.33(-1.18)0.17(0.25)
0.44**(2.40)-0.01(-0.76)-1.88**(-2.49)1.77**(2.17)(7)-0.90(-1.37)
0.91***(3.54)-0.89(-0.98)-0.34(-1.22)0.18(0.26)
0.43**(2.41)-0.01(-0.55)-2.03**(-2.58)1.60*(1.96)(8)-1.04(-1.53)0.75***(3.06)-0.70(-0.76)-0.31(-1.12)0.14(0.21)
0.45**(2.52)-0.01(-0.54)-1.96**(-2.42)
1.81**(2.29)(9)-1.07(-1.49)1.15***(3.80)-0.80(-0.92)-0.42(-1.46)0.27(0.38)
0.42**(2.40)-0.01(-0.52)-1.72**(-2.12)
1.42*(1.78)

0.25(0.50)

0.70(0.92)-0.36(-0.90)0.12(0.22)

0.94(1.04)-0.58(-1.29)-0.34(-1.33)-0.61(-1.13)0.17(0.64)

0.59(0.78)-0.43(-1.10)0.12(0.67)-0.59(-1.05)0.19(0.56)-0.01(-0.15)-0.41(-1.32)-0.05(-0.06)-0.19(-0.47)0.41(0.59)-1.35(-0.85)1.01(1.38)

Observations

0.41(1.25)
0.46***(3.18)
0.07(0.77)-0.02(-0.15)220

0.41(1.25)
0.46***(3.24)
0.07(0.73)-0.02(-0.15)220

0.40(1.21)
0.46***(3.24)
0.07(0.68)-0.02(-0.16)220

0.39(1.20)
0.48***(3.25)
0.08(0.87)-0.03(-0.19)220

0.39(1.22)
0.45***(3.03)
0.08(0.80)-0.01(-0.07)220

0.39(1.19)
0.45***(3.01)
0.08(0.79)-0.01(-0.10)220

0.40(1.22)
0.47***(3.31)
0.07(0.79)-0.03(-0.20)220

0.45(1.37)
0.42***(2.95)
0.09(0.92)
0.01(0.04)
220

1.12(1.11)-0.75
0.75(-1.57)-0.04(-0.21)-0.27(-0.34)0.35(0.59)-0.07(-0.70)-1.26(-0.87)-1.46(-1.22)
1.04**(2.58)
0.37(1.10)
0.38***(2.73)
0.12(1.09)-0.02(-0.11)220

R-squared

0.42

0.42

0.42

0.42

0.43

0.43

0.42

0.43

0.45

Loan* Policy
Loan2* Policy
GrantsGrantsGrant* Policy
2
Grant
Grant * Policy

Technical Assistance(TA)
TA*Policy
TA2* Policy
Portfolio Investment
Net FDI/GDP
Remittances
Remittances
CO2 emissions
emissions

Notes: We employed the results of specification (5) to compute the optimal weights. In the case
case of the aid
variable, not all figures are significant. Yet, when calculating weights, in Table 8, specification (5) for
the 1970 to 1993 data, the Grant* Po
Policy coefficient is significant. Therefore, we employed the coefficientof Grant* Policy in Table 10. In addition, although the coefficients of loans for 19701970-1993 data are notsignificant, in Table 6 for 1970 to 1997, they become significant.
significant. Accordingly, we conclude thatweights computed using the loan coefficient in Table 10 are valid.
*, **,**, and *** are statistically significant at 10%, 5% and 1%, respectively.

28

Table 11
Refined Commitment to the Development Index (RCDI)(RCDI)
（ A）

（ B）

（ C）

（ D）

Revised CDI afterminor correction
corrections
orrections

Refined CDI (A)with Optimal
OptimalWeights
Country

Index (A)without interest
interest
payments
CountryCountry

Score

Rank

Score

Index (A) revisedby using
using Aid per
population estimate
estimate
Country

1

Netherlands

5.88

1

Netherlands

6.70

Denmark

5.66

Netherlands

5.89

2

Denmark

5.66

2

Denmark

5.64

Netherlands

5.61

Denmark

5.66

3

Switzerland

5.43

2

Portugal

5.55

Switzerland

5.50

Switzerland

5.43

4

New Zealand

5.14

4

Switzerland
Switzerland

5.39

New Zealand

4.97

New Zealand

5.14

5

Portugal

5.00

5

Spain

5.34

Portugal

4.77

Portugal

5.00

6Germany

4.72

6

Sweden4.87

Norway4.76

Germany

4.75

7

Spain

4.68

7

Norway4.51

Germany

4.63

Spain

4.68

8

Sweden4.64

8

New Zealand

4.38

Austria

4.49

Austria

4.65

9Austria

4.61

8

United Kingdom

4.37

Spain

4.47

Sweden

4.64
4.53

Rank

Country

Score

Score

10

Norway4.53

10Austria

4.33

Sweden4.38

Norway11

United Kingdom

4.26

11

Belgium

3.90

United Kingdom

4.14

United Kingdom

4.26

12

Belgium

4.10

11

Ireland

3.86

Belgium

3.94

Belgium

4.10

13

Greece3.88

13Germany

3.71

Greece3.72

Greece3.88

14France3.74

13France3.65

France3.60

France3.76

15

Ireland3.61

15

Canada3.62

Ireland

3.56

Ireland

3.61

16

Italy

3.59

16

Finland

3.43
3.43

Italy

3.51

Italy

3.60

17

Canada

3.53

17

Italy

3.23

Canada3.45

Canada3.53

18

Finland3.50

17

Australia

3.20

Finland3.38

Finland3.50

19

Australia

3.08

19

United States

3.15

Australia

2.98

Australia

3.08

20

United States

2.57

19

GreeceGreece3.05

United States

2.59

United States

2.57

21

Japan

2.48

21

Japan

2.64

Japan

2.50

Japan

2.56

29

Table 12
Contribution to the Growth Index (CGI)

(1)

(2)

(3)

(4)

CGI
with Equal Weights for
All Six Revised
Categories

CGI
with Optimal Weights
for All
All Six Revised
Categories

CGI
with Optimal Weights
for Trade andInvestment Categories
Only / Other Weights
Are Set to ZeroCGIwith Optimal
Weights for Aid,
Trade, andInvestment
Categories Only
/ Other Weights
Are Set to ZeroCountryCountryCountry

Rank Country
Country
1 Switzerland
2 Netherlands
3 Denmark4 Austria
5 Germany
6 Spain
7 Sweden
8 Portugal
9 New Zealand
10 Belgium
11 Norway
12 Ireland
13 Finland
14 United Kingdom
15 Canada
16 Italy
17 Japan
18 France
19 Australia
20 Greece
21 United States

Score
6.83
6.66
5.98
5.62
5.48
5.45
5.38
5.33
5.16
5.16
4.57
4.44
4.44
4.41
4.33
4.02
3.77
3.69
3.69
3.58
2.19

Netherlands
Switzerland
Denmark
Portugal
Spain
SwedenAustria
Belgium
NorwayUnited Kingdom
IrelandGermany
New ZealandFinland
CanadaFranceItaly
Australia
Japan
GreeceUnited States

Score
6.97
6.10
5.81
5.66
5.56
5.09
4.81
4.61
4.22
4.20
4.19
4.12
3.97
3.89
3.77
3.48
3.28
3.21
3.13
2.77
2.76

30

Spain
Portugal
Netherlands
Switzerland
Austria
SwedenUnited Kingdom
United States
Belgium
CanadaDenmark
FinlandFranceNew ZealandGermany
Ireland
Italy
Australia
GreeceGreeceJapan
Norway

Score
7.47
7.07
6.62
6.47
5.19
4.75
4.67
4.56
4.32
4.28
4.25
4.24
4.23
4.19
4.15
3.92
3.91
3.47
3.35
3.11
2.16

Netherlands
Portugal
Denmark
Denmark
Switzerland
Spain
SwedenAustria
Belgium
United Kingdom
NorwayIrelandFinlandFranceGermany
United States
CanadaNew Zealand
Italy
Australia
Japan
Greece

Score
7.00
5.68
5.59
5.56
5.53
4.98
4.22
4.15
4.13
3.70
3.68
3.56
3.45
3.31
3.11
3.08
3.02
2.86
2.68
2.62
2.29

Table 13
Comprehensive Commitment to Development Index (CCDI)
Based on Eight Categories
By Introd
Introducing
ucing Cultural Cooperation and Social Development Categories

(5)

（6）

（7）

With Equal Weights
for the Eight Categories(UNHCR Contribution
for the Migration
Category)

With Equal Weights for
the Six Original
Categories andReduced Weights for the
Two New Categories By
20%

With Optimal Weights for
the Six Original
Categories,
1/8 Weights for the Two
New Categories(The Original CDI
Migration Category
Index)

Rank

Country

Score

Country

Score

Country

Score

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16

Netherlands
Denmark
SwedenSpain
Germany
NorwaySwitzerland
Portugal
France
Italy
United Kingdom
AustriaJapan
Finland
Belgium
Australia

17
18
19

CanadaIreland
New Zealand

6.56
5.39
5.33
5.14
4.52
4.45
4.32
4.25
4.14
4.06
3.93
3.87
3.83
3.75
3.71
3.62
3.60
3.33
3.17

Netherlands
Denmark
Switzerland
Spain
Portugal
SwedenAustria
Belgium
Germany
FinlandFinlandUnited Kingdom
NorwayItaly
IrelandFranceNew ZealandJapan
CanadaGreece

7.02
6.16
6.09
6.03
5.90
5.62
5.21
5.06
4.88
4.87
4.56
4.54
4.53
4.27
4.25
4.21
4.16
3.91
3.80

Netherlands
Spain
Denmark
Switzerland
Portugal
SwedenGermany
NorwayAustria
United Kingdom
Belgium
FranceCanadaIreland
Australia
Italy
New ZealandFinland
Japan

6.94
5.45
5.06
5.03
4.82
4.60
4.43
4.28
4.22
4.12
3.98
3.94
3.87
3.69
3.55
3.52
3.45
3.41
3.39

20
21

GreeceUnited States

2.71
1.62

Australia
United States

3.68
2.08

GreeceUnited States

2.39
2.34

31

Table 14
Absolute Commitment to Development Index (ACDI)
Absolute Values for Category Data with Six or Eight Categories(8)
With Equal Weights
Using the Six Original
CDI Categories
Categories
Rank

(9)
With Optimal Weights
Using the Six Original
CDI Categories

(10)
10)
With Equal Weights
Using Eight Categories

Country

Score

Country

Score

Country

Score

19

Germany
Switzerland
Netherlands
Japan
New Zealand
Spain
Austria
United Kingdom
United States
Denmark
Belgium
SwedenCanadaItaly
FrancePortugal
IrelandFinlandFinland
Australia

6.15
5.38
5.35
5.16
4.91
4.90
4.83
4.81
4.68
4.53
4.39
4.30
4.19
4.04
4.01
3.93
3.82
3.82
3.59

United States
United Kingdom
KingdomJapan
Germany
Netherlands
FranceSpain
SwedenCanadaNew Zealand
Switzerland
Denmark
Austria
Italy
Belgium
Australia
Ireland
Portugal
Finland

7.08
3.85
3.78
3.77
3.17
3.00
2.99
2.06
1.99
1.95
1.91
1.82
1.80
1.75
1.71
1.67
1.58
1.54
1.26

Japan
United States
Germany
Netherlands
United Kingdom
Spain
France
Italy
SwedenDenmark
Switzerland
Austria
Austria
Portugal
Belgium
Finland
CanadaNew ZealandAustralia
Greece

5.71
5.47
4.58
3.92
3.78
3.73
3.66
3.34
3.09
3.07
2.98
2.87
2.85
2.80
2.76
2.68
2.58
2.44
2.43

20
21

GreeceNorway3.48
3.30

GreeceNorway1.17
0.50

Ireland
Norway

2.36
2.10

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18

32

Figure 1
Relations between the Growth Rate of the Degree of Economic Openness
Openness and the
LongLong-Term per Labor Economic Growth Rate

Per labor economic growthrate, 19601960-1990

2.5

2

1.5

1

0.5

0
-1

-0.5

0

0.5

1

-0.5

-1

Growth rate of the degree of economic
openness, 1960-1990

Source: Penn World Table Mark 6 (http://pwt.econ.upenn.edu/(http://pwt.econ.upenn.edu/).
http://pwt.econ.upenn.edu/).

33

1.5

2

T

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