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The 4th International Conference
on Public Management in the 21st Century:
Opportunities and Challenges
第四屆 21 世紀的公共管理：機遇與挑戰 國際學術研討會
The United Nations Development Programme and Standard and Poor's Credit Rating Partnership: Outcomes and Challenges
Alexey Dorofeev
(Park University)
22/10 – 23/10/2010
Macau, China 中國 澳門
The United Nations Development Programme and Standard and Poor’s Credit
Rating Partnership: Outcomes and Challenges
Key-words: global governance, sovereign rating, capital market, Sub-Saharan Africa
Mr. Alexey Dorofeev
Park University/Development Gateway Foundation
9226 NW 60th St, Parkville, MO 64152 United States
Phone: +1 (917) 515 3754
Fax: + 1 (816) 505-5478
2
Abstract
The purpose of this paper is to provide analysis and evaluate the outcomes of the credit rating
partnership between the United Nations Development Programme (UNDP) and Standard and
Poor’s (S&P). The study attempts to assess the impact of economic and governance indicators on
selected S&P ratings. It also discusses the success of the program and estimates the extent to
which ratings contain country’s reliance on Official Development Assistance (ODA). Results
vary country by country. For most countries, external debt stocks and governance indicators
prove to be in strong correlation with the assigned S&P ratings. Conversely, the ratings have
only negligible effect on the share of ODA for all of the sampled countries. The paper concludes
with practical implications to policy makers.
3
Introduction
Most of the countries in Sub-Saharan Africa, with the exception of South Africa, suffer from
inability of central government and local private businesses to tap into global capital markets.
The difference in borrowing cost of credit for sovereigns in this region vis-à-vis most of the
emerging economies, is truly striking. It is estimated that the average SSA country would have
paid “about 3 percent more than the average emerging market borrower or 9.60 percent as of
end-2009” (Sy and Amadou 2010). A study of borrowing patterns by developing countries finds
that countries without stable access to global capital markets “suffer from greater political
instability and worse perceptions, and are more vulnerable to external shocks”(Gaston et al 2004,
p. 3). On the other hand, clearly, countries with serious political risks are more likely to have
lower ratings. In addition, Kaminsky and Schmukler provide evidence of contagion effect
spreading to neighboring countries in instance of investment rating downgrade (2009, p.19).
Financial markets in the poorest countries of the world, mostly in Sub-Saharan Africa, are either
absent or are in a nascent form. There is no mechanism for the funding, if it does become
available, to invest it within the local economy. Foreign direct investment is largely either short
term or goes to resource rich economies. Strikingly, local companies are virtually deprived of the
prospect to raise capital both locally and abroad. Creation of capital markets in African
economies would provide options for investing money in the local economy, and, hence, would
prevent the scarce financial resources from leaving the country. Another crucial benefit of local
capital markets is the increased role of local decision makers and the increased leverage of local
stakeholders (Applegarth 2004, p.6). Some of the other likely consequences of stronger financial
markets, according to Applegarth, include higher level of competition between local banks,
opportunities for locals to invest money received through remittances, and lastly, but very
4
importantly creates tools for African countries to conduct monetary policy. Since the cost of
sovereign borrowing is linked to economic conditions in the country, raising funds through
capital markets contrasts relying on ODA. Good economic policies implemented by central
government mean lower cost of borrowing in the capital market. Conversely, such economic
policies mean lesser availability of ODA funding, and hence gaps in budget that are difficult to
predict.
The UNDP has been in negotiations with S&P since early 1990s regarding an initiative that
would assist the sovereigns with acquiring ratings. Usually, the sovereign country would a pay a
fee to the rating agency for the rating assessment to take place. In the example of Sub-Saharan
Africa, many sovereigns lack the financial capacity to pursue the application. In addition, since
the country does not have a say with regards to the final rating, there is little incentive for the
government to approach the rating agency. Credit ratings are essential to the private sector’s role
in Africa’s economic development and the growth of capital markets at sovereign and regional
level. Ratha et al. vouch in favor of assisting poor countries obtain credit ratings not only for
sovereign borrowing, but for sub-sovereign entities' access to international capital markets (2007,
p.1).
Under the arrangement, the UNDP “plays a catalytic role in explaining the potential benefits of
ratings to interested governments” and in “providing Standard & Poor's with its own insights on
the key economic, financial, and political factors in the region” (Standard and Poors 2004). The
role of UNDP in determining the ratings remains unclear due to the lack of publicly available
5
relevant documentation. Most certainly, it has a very minimal impact on the actual ratings, if
any.
Up to this date, the following Sub-Saharan countries have acquired their sovereign ratings
through the UNDP initiative: Benin, Burkina Faso, Cameroon, Ghana, Kenya,
Madagascar (rating withdrawn) Mali (rating withdrawn), and Mozambique. This study looks at
all these countries, except for Cape Verde, which acquired its rating only in 2009, and thus
would not provide a sufficient amount of observations. Sub-Saharan Africa is of great
importance since many of the countries are clustered and are similar with respect to local capital
market deficiencies. Sovereigns from other regions, which have acquired their ratings through
the UNDP-S&P initiative are left aside, since political and economic environments in these
countries vary drastically from those in Sub-Saharan Africa. Nigeria, Senegal and South Africa,
also from Sub-Saharan Africa, are excluded from the study since their ratings were assigned
without the UNDP involvement.
All the assigned sovereign ratings are quite low and range between CCC assigned to Cameroon
on November 1, 2005 to B+ assigned to Ghana on March 19, 2009. With ratings being so low,
the question arises whether a junk status rating is better than no rating at all. Most of the
academic literature on the subject does confirm the following premise. Ratha et al. find unrated
countries to be perceived as riskier than they are, and, definitely, riskier than default risk
sovereigns (2007, p. 3). The same study also finds ratings to be sticky and to be unlikely to
change over time, unless precipitated by major events or drastic economic improvements (Ratha
et al. 2007, p.7). Hence there is very little deviation from original ratings for the majority of
6
countries. Standard and Poor’s does not disclose the methodology used to assign the ratings; in
public documents it only vaguely discusses some of the main factors considered.
External debt, domestic public debt, political risk and broad money as a share of GDP are found
to be the most important determinants of sovereign’s investment grade status for emerging
economies (Jaramillo 2004, p.3). Analysis was conducted based on the information provided by
all major rating agencies, and found inflation to be an additional statistically significant variable
for Standard and Poor’s. Cantor and Packer find per capita income, GDP growth, inflation, fiscal
balance, external balance, external debt, economic development are listed to have a strong
impact on sovereign ratings (1996, p.39). In another study, per capita income, government
income and changes in the real exchange rate are found to be the most important variables
(Mellios and Paget-Blanc 2006, p. 19). This study attempts to determine the most important
determinants of ratings for UNDP-S&P ratings in Sub-Saharan Africa.
The relevant data, on Sub-Saharan Africa, unfortunately, is very scarce. This study makes an
attempt to identify the most important determinants of sovereign ratings in the UNDP-S&P
Partnerships based on the information available, with a particular emphasis on governance
indicators. Gaston et al. do not find a significant relationship between the share of FDI as a
percentage of GDP and the likelihood of access to the capital market for the studied countries.
Since GDP per capita, appears to be one of the strongest determinants of a rating, many African
countries won’t be able to achieve a credit ranking beyond junk bond years to come.
7
Methodology
There are two models developed in this study: one to estimate the variables with the most
significant impact on sovereign ratings in Sub-Saharan Africa and another one to determine
whether ODA share in relation to GNI decreases as the country receives a sovereign rating. The
data was obtained from the World Bank Data and the World Bank Governance indicators and
covered the following years: 1996, 1998, 2000 and 2002-2008. 1997, 1999 and 2001 were
excluded since the governance indicators did not provide information for these years. Out of the
6 available governance indicators, political stability and government effectiveness were selected
to be included in this study, since both of these variables, in theory, are correlated with both
sovereign ratings and country’s reliance on ODA. Due to the scarcity of information on Sub-
Saharan Africa in comparison to most other regions of the world, the number of variables to pick
from for the study was extremely limited. The ratings are quantified as follows: No rating or
rating withdrawn = 0, CCC = 1, B - = 2, B = 3, B= = 4, BB - = -5. For years, when country’s
rating was changed, a mean of all the rating values for that specific year is calculated and
assigned as numeric value.
In the first model, rating is regressed over the share of external debt stocks (% to GNI), the
government effectiveness indicator, annual GDP Growth, inflation (GDP deflator) and the
political stability indicator. Regression is run by Ordinary Least Squares technique. Log function
is applied to external debt, GDP growth and inflation. With the exception of Gabon and Kenya,
models are quite robust.
8
The second model attempts to assess the impact of ratings on the country’s share of ODA in
GNI. The theory behind is that as the access of sovereigns to international capital markets
increases, they are less likely to rely on ODA. Other variables, such as financing via international
capital markets (gross inflows) would have been a better variable, but could not be included due
to virtual absence of capital market statistics for Sub-Saharan Africa. ODA (as a log function) is
regressed over GDP growth (as a log function), government effectiveness, inflation, political
stability and ratings. Inflation and government effectiveness are taken out for a number of
countries to have a better fit of the model. The model is not nearly as robust the first one, but to
some extent helps to understand the magnitude to which sovereign ratings affect country’s
dependency on foreign aid flows.
Findings
Below is the summary of statistically significant variables for each of the countries for the first
model (Annex II):
Benin – External Debt, Government Effectiveness, GDP Growth, Inflation, Political Stability
Burkina Faso – External Debt, Government Effectiveness
Cameroon – External Debt, Government Effectiveness, Inflation
Gabon – External Debt
Ghana – GDP Growth
Kenya – External Debt, Inflation
Madagascar – External Debt, GDP Growth, Inflation
Mali – Inflation
Mozambique – External Debt, Government Effectiveness, Inflation, Political Stability
9
In the example of Benin, the sign for all of the independent variables, except for the GDP
growth, is consistent with the economic theory. In Cameroon, government effectiveness index is
negatively correlated with the sovereign rating. A similar finding for Mozambique could be
attributed to the past challenges in governances, which nevertheless did not seem to have a
strong negative impact on economic growth. This could be explained by significant number of
instances when its rating was revised. As it appears to be, improvements in the quality of
governance are not always translated into better economic indicators. In the examples of larger
economies, such as Ghana and Kenya, macroeconomic indicators, such as GDP and Inflation,
clearly become prevalent over the governance indicators. External debt stocks variable,
according to the model, has the greatest impact on sovereign ratings. These two variables are
inversely related. Out of the governance indicators, government effectiveness seems to be more
relevant than political stability, though its impact on ratings is not always consistent with the
economic theory.
The list of statistically significant variables for the second model (estimating the most significant
determinants of country’s dependence on ODA) is as follows (Annex III):
Benin – GDP Growth, Political Stability, Rating
Burkina Faso – Government Effectiveness, Political Stability, Rating
Cameroon – None
Gabon – Political Stability
Ghana – GDP Growth, Government Effectiveness, Political Stability
Kenya – GDP Growth, Government Effectiveness
Madagascar – Government Effectiveness, Political Stability
10
Mali – External Debt, GDP Growth, Government Effectiveness, Political Stability, Inflation
Mozambique – None
Mali is the only country with a good regression model where most of the independent variables
have a relationship with ODA share and consistent with economic theory. While both
government effectiveness and political stability are statistically significant, political stability in
Mali has a greater impact on country’s dependence on ODA. Rating is found not to be
statistically significant. Revealingly, political stability turns out to have the strongest positive
impact on ODA for Burkina Faso. This relationship contradicts economic theory. For Ghana,
government effectiveness is the most statistically significant variable and does have a strong
negative impact on ODA. The rating is statistically significant only for Burkina Faso and Benin,
but has only a negligible effect on ODA. Overall, the model proves that up to 2008 sovereign
ratings have had a minimal impact on country’s reliance on ODA. The second model, however,
is not as robust as the first one.
Policy Recommendations
The study of ratings in this paper does not mean that the sovereigns would get an actual access to
capital markets. According to statement by Standard and Poor’s, “many of the governments rated
under the new initiative will not use their ratings to international bond markets (Standard &
Poor’s 2006)”. Yet, acquisition of ratings is a good starting point and a serious incentive for
central governments to have well-designed economic policies and to consider tapping into the
capital markets in the future. Further, local private companies, in the long run, are better off due
to their increased prospects of raising capital abroad.
11
Quantitatively, based on this study, there is little evidence that the ratings are economically
beneficial to the sovereigns at present. Yet, in the long run, their potential positive impact should
not be neglected. On the other hand, credit ratings do serve as an important mechanism to
provide strong incentives for central governments to support better economic policies at home.
The publicly available evaluations by Standard & Poor’s offer a somewhat clear picture to
central governments on what needs to be improved for the rating to be upgraded. For example,
Standard and Poor’s states that the country’s “credit standing could benefit if tax and customs
administration was significantly strengthened (Standrad and Poor’s 2006)”. Some of the other
policy recommendations for Benin include faster reforms aimed at diversifying the economy and
promotion of industrial and agricultural development. Nothing that specific is found in donor’s
pledges or project documents of the World Bank. On the other hand, ratings are not always
conducive to long-term economic development. Elkhoury, for instance, contends that developing
countries adopt short-sighted policies in order to avoid rating downgrades, even when there is a
conflict with development needs (2008).
It is important for the UNDP to continue providing assistance to sovereigns seeking ratings.
Ratha et al. contend that countries without sovereign ratings are not necessarily at the bottom of the rating
scale (2007). They go further to predict that Equitaorial Guinea, Angola, Swaziland, Zambia and
Tanzania, if rated, would have above B+ ratings by Standards and Poor’s Scale in 2008. If these
countries receive sovereign ratings, their accessibility to capital markets would be boosted even
further. On the contrary, efforts to make capital markets more accessible to Sub-Saharan Africa
should not only be limited to the efforts of UNDP and S&P. There is a serious need to develop
the institutional framework that would allow sovereigns to borrow in the global capital markets.
This would be more beneficial to the sovereigns in the long run vis-à-vis relying heavily on
12
donors’ funding. The Global Emerging Markets Local Currency (GEMLOC) Bond Fund, a
partnership between the World Bank Group and the private sector, which aims to raise $5 billion
from international capital markets to invest in local-currency bond markets in developing
countries, is an example of such arrangement. A similar arrangement, but larger in scale and
targeting specifically Sub-Saharan Africa, would be another promising policy tool. Clearly,
countries in Sub-Saharan Africa would for a very long time be incapable of accessing capital
markets on par with other developing economies. A mechanism is needed that would create a
separate capital market for sub-Saharan Africa and allow sovereigns to borrow at rates
comparable to the cost of capital for most developing economies. A portion of donor’s money
could hypothetically provide liquidity to this capital market.
The UNDP-S&P partnership should be noted as an innovative form of public-private partnership
arrangement. As such, this arrangement is touted as a breakthrough in capacity development
efforts by an international organization (Muhlen-Schulte 2009). Historically, capacity
development efforts by international organizations were largely qualitative in nature. Thus, the
UNDP-S&P partnership is an example of how this could change in the future. This collaboration
serves as a precedent for other UN bodies, international organizations and development agencies.
For example, a multilateral development bank and one of the rating agencies could work together
to provide municipalities in a developing economy with ratings. This would allow sub-national
entities to access capital markets. This is a very far away prospect, but multilateral development
banks could partner with consulting companies when advising governments of middle- and high-
income developing countries on innovation driven growth.
13
Conclusion
UNDP-S&P sovereign ratings are greatly determined by countries’ external debt stocks and, in
many instances, by the quality of government effectiveness. The impact of sovereign ratings on
country’s reliance on ODA is very minimal. This however, by no means signifies that the
partnership arrangement was not successful. The initiative has set the ground for capital market
growth in countries and contributed to capacity building in selected countries. Remarkably, many
of the sovereigns would not applied for ratings had they not had the outside support. Looking
into the future, there is a great need for international organizations to foster government’s
capacity to tap into international capital markets and decrease their reliance on ODA. This would
give more leverage to the local stakeholders. This study would benefit from more robust
regression models, by accounting for a broader range of capital markets indicators for the studied
sovereigns and an estimate on whether ratings have had any effect on foreign capital outflows.
The UNDP-S&P arrangement itself is an example of innovative partnership between an
international organization and a private entity.
14
References
Applegarth, Paul V. 2004. “Capital Market and Financial Sector Development in Sub-
SaharanAfrica”, A Report of the Africa Policy Advisory Panel, Center for Strategic and
International Studies, Washington, DC
Cantor, R., and Packer, F. 1996. “Determinants and Impact of Sovereign Credit Ratings”.
Federal Reserve Bank of New York Economic Policy Review, October 1996, pp. 37-54.
Chimni, B., 2004. “International Institutions Today: An Imperial Global State in the Making”,
EJIL, Vole 15, No.1, pp. 1-37.
Credit FAQ: The Future of Sovereign Credit Ratings, 23 March, 2004. Standard & Poor’s
Elkhoury, Marwan 2008. “Credit rating Agencies and Their Potential Impact on Developing
Countries”, Report # 186, UNCTAD, January 2008
Gelos, Gaston, Ratna Sahay and Guido Sandleris, 2004. “Sovereign Borrowing by Developing
Countries: What Determines Market Access?. The International Monetary Fund, Working
Paper # 221.
Gueye, Cheick, Sy, Amadou, June 2010. “Beyond Aid: How much should African countries pay
to borrow?” IMF Working Paper
Jaramillo, Laura, 2010. “Determinants of Investment Grade Status in Emerging Markets”, IMF
Working Paper
Kaminsky, Graciela, Schmukler, Sergio. 2001. “Emerging Markets Instability: Do sovereign
Ratings Affect Country Risk and Stock Returns?”, February 28, 2001
Mellios, Constantin, Paget-Blanc, Eric, 2006. “Which Factors Determine Sovereign Ratings?”
The European Journal of Finance, Vol. 12, No.4 (June), pp. 361-377
Mohapatra, Sanket, Plaza, Sonia, Ratha, Dilip, April 2008. “New Sources and Innovative
Mechanisms for Financing Development in Sub-Saharan Africa”, Policy Research Working
Paper, The World Bank Development Prospects Group, Migration and Remittances Team
Muhlen-Schulte, Arthur, 2009. “Full Faith in Credit? The Power of Numbers in Rating Frontier
Sovereigns and the Global Governance Development by UNDP”, Working Paper, Center
for the Study of globalization and Regionalization, Department of Politics and International
Studies, University of Warwick
Ratha, Dilip, De, Prabal, Sanket, Mohaparta, , June 2007. “Shadow Sovereign Ratings for
Unrated Developing Countries”. Policy Research Paper, World Bank
Standard and Poor’s Commentary, September 5, 2006
15
Annex I
UNDP-S&P Partnership Ratings (Source: S&P)
Benin
Dec. 19, 2007 B/Positive/B B/Positive/B BBB
April 10, 2007 B/Stable/B B/Stable/B BBB
Sept.7, 2006 B/Negative/B B/Negative/B BBB
Nov. 1, 2005 B+/Stable/B B+/Stable/B BBB
Dec. 29, 2003 B+/Stable/B B+/Stable/B
Burkina Faso
Aug. 6, 2008 B/Stable/B B/Stable/B BBB
July 6, 2006 B/Positive/B B/Positive/B BBB
Nov. 1, 2005 B/Stable/B B/Stable/B BBBMarch 5, 2004 B/Stable/B B/Stable/BBB
Cameroon (Republic of)
Feb. 26, 2007 B/Stable/B B/Stable/B BBB
May 3, 2006 B-/Stable/C B-/Stable/C BBB
Nov.1, 2005 CCC/Stable/C CCC/Stable/C BBB
Dec. 3, 2004 CCC/Stable/C CCC/Stable/C
Nov. 26, 2003 B/Stable/B B/Stable/B
Gabonese Republic Nov. 8, 2007 BB-/Stable/B BB-/Stable/B
Ghana (Republic of)
March 16, 2009 B+/Negative/B B+/Negative/B 3 BB
Sept. 19, 2007 B+/Stable/B B+/Stable/B 3 BB
April 6, 2006 B+/Stable/B B+/Stable/B BB
Nov.1, 2005 B+/Stable/B B+/Stable/B B+ Sept. 4, 2003 B+/Stable/B B+/Stable/B
Kenya (Republic of)
Aug. 4, 2008 B/Positive/B B/Positive/B BB
March10, 2008 B/Stable/B B/Stable/B BB
Feb. 4, 2008 B/Negative/B B/Negative/B BB
Jan2, 2008 B+/Watch Neg/B B+/Watch Neg/B BB
Sept. 8, 2006 BB-/Stable/B B+/Stable/B
Madagascar (Republic of)
May 11, 2009 NR/NM/NR NR/NM/NR
March 18, 2009 B-/Negative/B B-/Negative/B B
Feb. 2, 2009 B/Negative/B B/Negative/B B
Nov. 1, 2005 B/Stable/B B/Stable/B B
May 25, 2004 B/Stable/B B/Stable/
16
Mali (Republic of)
July 3, 2008 NR/NM/NR NR/NM/NR
Nov. 1, 2005 B/Stable/B B/Stable/B BBB
May5, 2004 B/Stable/B B/Stable/B
Mozambique (Republic of)
Dec. 21, 2007 B+/Stable/B B+/Stable/B B+
April 6, 2006 B/Positive/B B/Positive/B B+
Nov. 1, 2005 B/Positive/B B/Positive/B B July 7, 2004 B/Positive/B B/Positive/B
17
Annex II
Determinants of Sovereign Ratings
Benin
Dependent Variable: RATING Method: Least Squares Date: 08/23/10 Time: 14:39 Sample: 1 10 Included observations: 9 Excluded observations: 1
Variable Coefficient Std. Error t-Statistic Prob.
C 22.69699 5.440866 4.171578 0.0251 LOG(EXTDEBT) -2.252400 0.791050 -2.847355 0.0653
GOVTEF 2.379962 2.352331 1.011746 0.3862 LOG(GDPGROWTH) -6.619433 2.124806 -3.115311 0.0527
LOG(INFLATION) -1.401813 0.752872 -1.861953 0.1595 POLSTAB 3.815900 3.469105 1.099967 0.3517
R-squared 0.951254 Mean dependent var 2.000000 Adjusted R-squared 0.870009 S.D. dependent var 1.936492 S.E. of regression 0.698187 Akaike info criterion 2.354061 Sum squared resid 1.462394 Schwarz criterion 2.485544 Log likelihood -4.593274 F-statistic 11.70858 Durbin-Watson stat 2.276750 Prob(F-statistic) 0.034953
18
Burkina Faso
Dependent Variable: RATING Method: Least Squares Date: 08/23/10 Time: 15:44 Sample: 1 10 Included observations: 10
Variable Coefficient Std. Error t-Statistic Prob.
C 17.7646802272
4.59266381907
3.86805586628
0.0117834319489
LOG(EXTDEBT) -3.4823940
6211
0.966370146685
-3.60358199
605
0.015484704493
LOG(GDP) -0.0670705
219556
0.814388060452
-0.08235695
63611
0.93755817828
GOVTEF 4.99683983965
3.64215003034
1.37194783247
0.228434760582
POLSTAB 1.23566902393
2.62838277688
0.470125217225
0.658056990703
R-squared 0.758750663484
Mean dependent var 1.5
Adjusted R-squared 0.565751194272
S.D. dependent var 1.58113883008
S.E. of regression 1.04193186645
Akaike info criterion 3.22688299366
Sum squared resid 5.4281100716
Schwarz criterion 3.37817554016
Log likelihood -11.134414
9683
F-statistic 3.93136140001
Durbin-Watson stat 2.13184823891
Prob(F-statistic) 0.0828123909159
19
Cameroon
Dependent Variable: RATING Method: Least Squares Date: 08/23/10 Time: 15:46 Sample: 1 10 Included observations: 9 Excluded observations: 1
Variable Coefficient Std. Error t-Statistic Prob.
C 6.09663241346
0.161664474637
37.7116396607
4.10153329495e-
05 LOG(DEBT) -
1.24216965493
0.0384762512521
-32.2840613
237
6.53142915569e-
05 LOG(GDP) 0.0940490
402596 0.12060861
7266 0.77978706
9875 0.492381
618098 GOVTEF -
0.270805567245
0.0518778823011
-5.22005824
512
0.0136725455679
LOG(INF) -0.5591177
98723
0.0999452815334
-5.59423906
907
0.011282787002
POLSTAB -0.0150207
942799
0.123368155367
-0.12175584
7246
0.910790192745
R-squared 0.998937146647
Mean dependent var 1.11111111111
Adjusted R-squared 0.997165724393
S.D. dependent var 1.26929551764
S.E. of regression 0.0675746470381
Akaike info criterion -2.316446
70912 Sum squared resid 0.0136989
98767 Schwarz criterion -
2.18496365756
Log likelihood 16.424010191
F-statistic 563.918142157
Durbin-Watson stat 1.59433531407
Prob(F-statistic) 0.00011753665286
7
20
Gabon
Dependent Variable: RATING Method: Least Squares Date: 08/23/10 Time: 15:50 Sample: 1 10 Included observations: 7 Excluded observations: 3
Variable Coefficient Std. Error t-Statistic Prob.
C 36.0463651394
22.9687088422
1.56936836924
0.257125439572
LOG(DEBT) -6.8008127
0865
4.86853552899
-1.39689084
493
0.297263927357
GOVTEF 2.67342841752
2.79574929372
0.956247551785
0.439861971816
LOG(INF) 1.06138595203
2.4027717944
0.44173398177
0.701852758736
POLSTAB -0.2417431
34202
4.1899417112
-0.05769606
13928
0.959236633273
R-squared 0.589880346918
Mean dependent var 1.42857142857
Adjusted R-squared -0.2303589
59246
S.D. dependent var 2.43975018237
S.E. of regression 2.70620864562
Akaike info criterion 5.00478278966
Sum squared resid 14.6471304672
Schwarz criterion 4.96614718184
Log likelihood -12.516739
7638
F-statistic 0.719156400437
Durbin-Watson stat 3.58808303361
Prob(F-statistic) 0.65204117632
21
Ghana
Dependent Variable: RATING Method: Least Squares Date: 08/23/10 Time: 15:41 Sample: 1 10 Included observations: 10
Variable Coefficient Std. Error t-Statistic Prob.
C -19.059346
2519
22.9022709749
-0.83220333
3579
0.452093066205
LOG(DEBT) 1.56516319428
2.43847059337
0.641862648879
0.555897366716
LOG(GDP) 10.4019031123
4.9801407792
2.0886765201
0.104987652322
GOVTEF 5.9250047196
9.53442683097
0.621432711651
0.567978160442
LOG(INF) -0.9696914
74634
2.26301937405
-0.42849455
2788
0.690357322422
POLSTAB -0.6146189
09613
3.04349084862
-0.20194537
7917
0.849814171339
R-squared 0.865146780155
Mean dependent var 2
Adjusted R-squared 0.696580255348
S.D. dependent var 2.10818510678
S.E. of regression 1.16126319086
Akaike info criterion 3.42060307508
Sum squared resid 5.3941287938
Schwarz criterion 3.60215413088
Log likelihood -11.103015
3754
F-statistic 5.13237596343
Durbin-Watson stat 2.81485549572
Prob(F-statistic) 0.0691083970875
22
Kenya
Dependent Variable: RATING Method: Least Squares Date: 08/23/10 Time: 15:56 Sample: 1 10 Included observations: 10
Variable Coefficient Std. Error t-Statistic Prob.
C 71.6202929635
50.9064675692
1.4068996806
0.232198826209
LOG(EXTDEBT) -9.9464685
1541
7.52303101923
-1.32213578
41
0.256659034026
LOG(GDP) 0.26207482947
1.53834018564
0.170362077202
0.872995173308
GOVTEF 4.49317540523
15.1874699498
0.29584752563
0.782069507912
LOG(INF) -1.5271275
2897
1.29833939374
-1.17621596
967
0.304730354242
POLSTAB 4.88600624191
13.0780591231
0.37360331498
0.727657755421
R-squared 0.67595408651
Mean dependent var 1.375
Adjusted R-squared 0.270896694648
S.D. dependent var 2.23994667595
S.E. of regression 1.91263550376
Akaike info criterion 4.41855060623
Sum squared resid 14.632698281
Schwarz criterion 4.60010166203
Log likelihood -16.092753
0312
F-statistic 1.668785955
Durbin-Watson stat 2.36006286454
Prob(F-statistic) 0.320015348122
23
Madagascar
Dependent Variable: RATING Method: Least Squares Date: 08/23/10 Time: 16:42 Sample: 1 10 Included observations: 9 Excluded observations: 1
Variable Coefficient Std. Error t-Statistic Prob.
C -5.1007494
9441
4.04158116234
-1.26206781
196
0.29611930723
LOG(EXTDEBT) -0.9298351
58783
0.294393487468
-3.15847733
855
0.0509309152721
LOG(GDP) 2.71081090505
0.95054354305
2.8518534736
0.0650100034299
GOVTEF 0.678796554447
1.17196013785
0.579197647192
0.603082013928
LOG(INF) 2.87616150822
0.635590382306
4.52518097863
0.0201852027977
POLSTAB 0.716506829223
0.951049620904
0.753385326564
0.505952346581
R-squared 0.975675471868
Mean dependent var 1.66666666667
Adjusted R-squared 0.935134591647
S.D. dependent var 1.58113883008
S.E. of regression 0.402695320164
Akaike info criterion 1.2534480464
Sum squared resid 0.486490562647
Schwarz criterion 1.38493109795
Log likelihood 0.359483791212
F-statistic 24.066459992
Durbin-Watson stat 3.80064098397
Prob(F-statistic) 0.0126001181971
24
Mali
Dependent Variable: RATING Method: Least Squares Date: 08/25/10 Time: 11:21 Sample: 1 10 Included observations: 8 Excluded observations: 2
Variable Coefficient Std. Error t-Statistic Prob.
C 23.3113770423
66.0216538928
0.353086838451
0.75776563556
LOG(DEBT) -5.3543126
1195
18.8905147193
-0.28343921
2298
0.803486222688
GOVTEF 3.40439602994
40.2421051803
0.0845978612371
0.940287021671
LOG(INF) -0.9633451
14155
0.894803384842
-1.07659976
535
0.3942758926
POLSTAB 14.0190357105
66.4046521126
0.211115264736
0.852355021543
LOG(GDP) 1.57444788939
21.614853673
0.0728410154059
0.948561809228
R-squared 0.654622326875
Mean dependent var 1.125
Adjusted R-squared -0.2088218
55937
S.D. dependent var 1.55264750852
S.E. of regression 1.70708058304
Akaike info criterion 4.02115200549
Sum squared resid 5.82824823398
Schwarz criterion 4.08073316175
Log likelihood -10.084608
022
F-statistic 0.758152454908
Durbin-Watson stat 0.883296325768
Prob(F-statistic) 0.653281485135
25
Mozambique
Dependent Variable: RATING Method: Least Squares Date: 08/23/10 Time: 17:22 Sample: 1 10 Included observations: 8 Excluded observations: 2
Variable Coefficient Std. Error t-Statistic Prob.
C 12.6618889691
2.78805135597
4.54148340634
0.0452212353545
LOG(DEBT) -3.3470746
4503
0.555136852293
-6.02927842
244
0.0264232178994
LOG(GDP) 0.262339902264
0.27339191853
0.959574458801
0.438526810922
GOVTEF -3.2677511
5442
2.37343988313
-1.37679963
063
0.302434759205
LOG(INF) 0.605241433481
0.601931949927
1.00549810249
0.420539320649
POLSTAB -1.6148681
3143
0.890394818222
-1.81365400
875
0.211405647657
R-squared 0.983018970671
Mean dependent var 1.75
Adjusted R-squared 0.94056639735
S.D. dependent var 1.90862703084
S.E. of regression 0.465304334752
Akaike info criterion 1.42145549701
Sum squared resid 0.433016247878
Schwarz criterion 1.48103665327
Log likelihood 0.314178011944
F-statistic 23.1556980829
Durbin-Watson stat 2.33920999661
Prob(F-statistic) 0.0419134404699
26
Annex III
Share of ODA to GNI
Benin Dependent Variable: LOG(ODA) Method: Least Squares Sample: 1 10 Included observations: 9
Excluded observations: 1
Variable Coefficient Std. Error t-Statistic Prob.
C 0.686352344648
0.754007558758
0.910272498831
0.429791872882
LOG(GDPGROWTH) 0.665831906688
0.3351094487
1.9869087824
0.141106828827
GOVTEF -0.1795754
27691
0.403038011605
-0.44555456
9346
0.686110016933
LOG(INFLATION) -0.0848062
158925
0.106059945552
-0.79960644
3801
0.482395500876
POLSTAB 0.615929570866
0.393037383875
1.56710174689
0.215080383307
RATING 0.0789770883463
0.0526221291627
1.50083414721
0.230383736673
R-squared 0.792532571999
Mean dependent var 2.23282534226
Adjusted R-squared 0.446753525331
S.D. dependent var 0.164621869938
S.E. of regression 0.122446635995
Akaike info criterion -1.127561
82581 Sum squared resid 0.0449795
359994 Schwarz criterion -
0.996078774249
Log likelihood 11.0740282161
F-statistic 2.29202023557
Durbin-Watson stat 2.35688376599
Prob(F-statistic) 0.263225063849
27
Burkina Faso
Dependent Variable: LOG(ODA) Method: Least Squares Sample: 1 10 Included observations: 10
Variable Coefficient Std. Error t-Statistic Prob.
C 2.97747682054
0.31153217063
9.55752599972
0.00021229505105
2 LOG(GDP) -
0.0600615800057
0.0878239023743
-0.68388648
6275
0.524447379676
GOVTEF 0.61530759151
0.382274391538
1.60959668011
0.168400230611
POLSTAB 0.907673192056
0.290559280145
3.12388298733
0.0261373186419
RATING -0.0587166
769251
0.0255308547996
-2.29983200
272
0.0697871038662
R-squared 0.712287907583
Mean dependent var 2.23980131733
Adjusted R-squared 0.482118233649
S.D. dependent var 0.156766869032
S.E. of regression 0.112815713633
Akaike info criterion -1.219269
40287 Sum squared resid 0.0636369
262129 Schwarz criterion -
1.06797685638
Log likelihood 11.0963470144
F-statistic 3.09462100462
Durbin-Watson stat 2.81221181171
Prob(F-statistic) 0.123467459801
28
Cameroon
Dependent Variable: LOG(ODA) Method: Least Squares Sample: 1 10 Included observations: 9 Excluded observations: 1
Variable Coefficient Std. Error t-Statistic Prob.
C -4.8739644
7566
10.0070213576
-0.48705446
9207
0.651705270704
LOG(INF) 1.31723150271
1.39858769583
0.941829752
0.39960655808
LOG(DEBT) 1.14870922776
1.9164923002
0.599381081595
0.581215458648
POLSTAB 0.598149292859
0.978096513633
0.611544243867
0.573888872287
RATING 0.935619496361
1.51416571811
0.617910896523
0.570078565383
R-squared 0.244764708032
Mean dependent var 1.56386590621
Adjusted R-squared -0.5104705
83936
S.D. dependent var 0.505051194171
S.E. of regression 0.620713996289
Akaike info criterion 2.18428824807
Sum squared resid 1.54114346076
Schwarz criterion 2.2938574577
Log likelihood -4.8292971
1632
F-statistic 0.324090665035
Durbin-Watson stat 2.48427582815
Prob(F-statistic) 0.84959830406
29
Gabon
Dependent Variable: LOG(ODA)
Method: Least Squares
Sample(adjusted): 1 10 Included observations: 5 Excluded observations: 4 after adjusting endpoints
Variable Coefficient Std. Error t-Statistic Prob.
C 0.195439420417
0.290567538092
0.672612714073
0.62305246158
LOG(GDP) -0.0940628
811246
0.323668158162
-0.29061518
3337
0.819948147521
POLSTAB -1.6637114
5486
0.587709534316
-2.83083965
414
0.216176373684
RATING 0.043057705592
0.0992647134006
0.433766482741
0.739449070241
R-squared 0.92563018843
Mean dependent var 0.219722457734
Adjusted R-squared 0.70252075372
S.D. dependent var 0.491314351676
S.E. of regression 0.26797099364
Akaike info criterion 0.194686079941
Sum squared resid 0.0718084534322
Schwarz criterion -0.117763
590112 Log likelihood 3.5132848
0015 F-statistic 4.148772
05724 Durbin-Watson stat 2.2246967
5791 Prob(F-statistic) 0.342869
826909
30
Ghana
Dependent Variable: LOG(ODA) Method: Least Squares
Sample: 1 10 Included observations: 10
Variable Coefficient Std. Error t-Statistic Prob.
C 3.03292739675
1.07215142391
2.82882373618
0.0367254704008
LOG(GDP) -0.6952139
90172
0.690849740609
-1.00631721
966
0.360450697194
GOVTEF -1.8858103
3959
0.661938359318
-2.84892137
319
0.0358690586316
POLSTAB 0.742453935767
0.306792397525
2.42005324042
0.06011346161
RATING 0.0272884037457
0.0552972972016
0.493485308083
0.642579379282
R-squared 0.674487983176
Mean dependent var 2.36972961574
Adjusted R-squared 0.414078369717
S.D. dependent var 0.202226078452
S.E. of regression 0.154794998978
Akaike info criterion -0.586577
363539 Sum squared resid 0.1198074
58542 Schwarz criterion -
0.435284817042
Log likelihood 7.9328868177
F-statistic 2.5901040066
Durbin-Watson stat 2.50366802728
Prob(F-statistic) 0.162389945575
31
Kenya
Dependent Variable: LOG(ODA) Method: Least Squares Sample: 1 10 Included observations: 10
Variable Coefficient Std. Error t-Statistic Prob.
C 1.78566458334
0.796460551047
2.24200003502
0.0884256135001
LOG(GDP) 0.116281377021
0.100127124277
1.1613374284
0.31008388076
GOVTEF 1.14222146965
1.03445682635
1.10417509997
0.331469936012
LOG(INF) -0.0223995
891704
0.119178595709
-0.18794976
5955
0.860065544046
RATING -0.0044421
1243957
0.0334490651527
-0.13280228
9669
0.900762559074
POLSTAB -0.2613255
55893
0.632495786769
-0.41316568
6413
0.70067379059
R-squared 0.642786828294
Mean dependent var 1.37338665689
Adjusted R-squared 0.196270363661
S.D. dependent var 0.171088654836
S.E. of regression 0.153382638062
Akaike info criterion -0.628052
820021 Sum squared resid 0.0941049
34635 Schwarz criterion -
0.446501764224
Log likelihood 9.1402641001
F-statistic 1.43955907387
Durbin-Watson stat 3.0270783831
Prob(F-statistic) 0.372916186739
32
Madagascar
Dependent Variable: LOG(ODA) Method: Least Squares Sample: 1 10 Included observations: 9 Excluded observations: 1
Variable Coefficient Std. Error t-Statistic Prob.
C 2.14833690655
3.91522636613
0.548713332424
0.621400731748
LOG(GDP) -0.0835333
409005
1.16526939533
-0.07168586
18577
0.947363435964
GOVTEF 1.08446480236
0.921474856775
1.176879428
0.32411901188
LOG(INF) 0.401723736092
0.940867310609
0.426971722328
0.698188968026
POLSTAB 0.849301275172
0.72100819013
1.17793568339
0.323755794552
RATING 0.0694512784299
0.225116048345
0.308513226581
0.777874243444
R-squared 0.758017199135
Mean dependent var 2.52478407934
Adjusted R-squared 0.354712531027
S.D. dependent var 0.406514922152
S.E. of regression 0.326552571348
Akaike info criterion 0.834269454317
Sum squared resid 0.319909745562
Schwarz criterion 0.965752505875
Log likelihood 2.24578745557
F-statistic 1.87951506411
Durbin-Watson stat 2.2875866026
Prob(F-statistic) 0.320074524338
33
Mali
Dependent Variable: LOG(ODA) Method: Least Squares Date: 08/25/10 Time: 12:06 Sample: 1 10 Included observations: 8 Excluded observations: 2
Variable Coefficient Std. Error t-Statistic Prob.
C 1.32473195043
0.694394054748
1.90775243735
0.307361063753
LOG(DEBT) 0.453873712384
0.196600464469
2.3086095631
0.260226523353
LOG(GDP) -0.8891516
02792
0.220859673309
-4.02586669
386
0.15499545968
GOVTEF -1.5464385
3471
0.411381882658
-3.75913135
679
0.165519572009
POLSTAB -1.0667486
409
0.685130466772
-1.55700073
582
0.363455878972
LOG(INF) -0.0736845
311225
0.0114757442159
-6.42089347
204
0.0983580005067
RATING 0.00246721166282
0.00721562275012
0.341926365646
0.790256547414
R-squared 0.998175659932
Mean dependent var 2.663693609
Adjusted R-squared 0.987229619526
S.D. dependent var 0.154149074378
S.E. of regression 0.0174197869671
Akaike info criterion -5.591861
54896 Sum squared resid 0.0003034
48977979 Schwarz criterion -
5.52235019999
Log likelihood 29.3674461958
F-statistic 91.1905696358
Durbin-Watson stat 1.64665730701
Prob(F-statistic) 0.0799882025695
34
Mozambique
Dependent Variable: LOG(ODA) Method: Least Squares Sample: 1 10 Included observations: 10
Variable Coefficient Std. Error t-Statistic Prob.
C 2.63467833502
1.30586271737
2.01757680956
0.0996779313785
LOG(GDP) 0.169662195759
0.219139466891
0.774220172046
0.473800424336
POLSTAB -0.3079774
34137
0.498411960423
-0.61791742
2919
0.563684842965
GOVTEF 0.602822655916
1.14579760126
0.526116179029
0.621300067819
RATING 0.0699997014446
0.107652867633
0.650235362826
0.544229784058
R-squared 0.387584615066
Mean dependent var 2.49202812914
Adjusted R-squared -0.1023476
92882
S.D. dependent var 0.397016758684
S.E. of regression 0.41683880111
Akaike info criterion 1.39461848585
Sum squared resid 0.868772930553
Schwarz criterion 1.54591103234
Log likelihood -1.9730924
2923
F-statistic 0.791098298231
Durbin-Watson stat 1.48734279836
Prob(F-statistic) 0.57789969273