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Measuring the Effect of Corruption onSovereign Bond RatingsMichael Connolly aa University of Miami , USAPublished online: 01 Oct 2007.

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Journal of Economic Policy ReformVol. 10, No. 4, 309–323, December 2007

ISSN 1748–7870 Print/ISSN 1748–7889 Online/07/040309-15 © 2007 Taylor & FrancisDOI: 10.1080/17487870701552053

Measuring the Effect of Corruption on Sovereign Bond Ratings

MICHAEL CONNOLLY

University of Miami, USATaylor and FrancisGPRE_A_255064.sgm10.1080/17487870701552053Journal of Economic Policy Reform1748-7870 (print)/1748-7889 (online)Original Article2007Taylor & Francis104000000December 2007Professor MichaelConnollymickaelconnolly1@juno.com

ABSTRACT Instrumenting for sovereign corruption, we find that TransparencyInternational’s Corruption Perceptions Index which ‘ranges from 10 (highly clean) to0 (highly corrupt)’, is a significant predictor of the Standard and Poor’s sovereign bondratings ranging from 1 (Sovereign Default) to 22 (AAA) in panel data from 52 coun-tries from 1993 to 2002. Corruption downgrades the creditworthiness of sovereignbonds by diverting loan proceeds from productive projects to less productive ones, ifnot to offshore accounts. In particular, a one point worsening of the corruptionperception index leads to an estimated one-notch reduction out of 22 in the sovereignbond rating.

KEY WORDS: Corruption, creditworthiness, sovereign bond rating

JEL CODES: F34

1. Introduction

Fundamental economic and financial variables are used to predict sovereigncredit ratings with success in Cantor and Packer (1996), Bhatia (2002), andAfonso (2003). Cantor and Packer (1996) predict ratings and analyze theimpact of market reactions to rating announcements and confirmations.Afonso (2003) finds that GDP per capita, external debt, the level of economicdevelopment, default history, real growth and the inflation rate explain 87%of the variance in debt ratings. Bhatia (2002) proposes a definition of ratingsfailure based on the ratings stability of the ratings agencies, pointing to fall-ing failure rates, consistent upside bias, and strong interagency correlation.

Correspondence Address: Michael Connolly, Professor of Economics, School of BusinessAdministration, University of Miami, 5250 University Drive, Coral Gables, FL 33124-6550,USA. Tel: (305) 284-4898; Email: mconnolly@miami.edu. Professor Connolly is also ChiefScientist, Project 985, Globalization and Foreign Trade, Hunan University.

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310 M. Connolly

These models overlook, however, the issue of corruption. New models ofgrowth emphasize the importance of institutions to growth and develop-ment. In this vein, the IMF World Economic Report (Chapter 3, 2003)takes the North (1990) view that institutions set the ‘rules of the game’providing an incentive structure to promote growth. In that view, a set of‘aggregate governance’ indicators are drawn from indices on underlyingmeasures:

(1) voice and accountability – the extent to which citizens can choosetheir government, political rights, civil liberties, and independent press;(2) political stability and absence of violence – the likelihood that thegovernment will be overthrown by unconstitutional or violent means;(3) government effectiveness – the quality of public service delivery andcompetence of the civil service, including the degree of its politicization;(4) regulatory burden – the relative absence of government controls ongoods markets, the banking system, and international trade; (5) rule oflaw – the protection of persons and property against violence or theft,independent and effective judges, contract enforcement; and (6) free-dom from graft – absence of the use of public power for private gain orcorruption (North, 1990, p. 119).

These measures are instrumented and positively correlated with economicperformance, particularly level and growth of income.

Corruption is found to deter foreign direct investment (Wei, 2000) and tosignificantly reduce real growth rates (Mauro, 1995) by discouraginginvestment rates. Besley (1995) finds that the absence of firm propertyrights in Ghana lowers investment significantly. Lower growth and invest-ment would imply, in general, less ability to repay loans. Sung Wook Joh(2003) observes that legal restrictions on election expenses in Koreainduced firms to make secret campaign donations in exchange for loans,favorable tax treatment, procurement and construction projects, as well asspecial loans.

In this paper, we measure the effect of endogenized corruption as a deter-minant of sovereign credit ratings in panel data for 52 countries from 1993to 2002. In particular, the paper uses panel random effects regression withinstrumental variables for corruption. A pooled 2SLS regression is consistentwith the results: a one point decrease in the sovereign corruption index isassociated with a one-notch decrease in the S&P credit rating.

2. Why Would Corruption Reduce Credit Ratings?

Corruption diverts loans from productive to unproductive uses. Loans aresquandered or deposited abroad rather than productively spent. Conse-quently, their return-on-investment is less than the cost of borrowing.Ultimately, corruption may violate the no Ponzi game condition, leadingto a necessary sovereign default. Consequently, severe corruption causesreturn on investment to be less.

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Measuring the Effect of Corruption on Sovereign Bond Ratings 311

The no Ponzi game condition (NPG) simply stated is:

where D(t) is external debt in foreign currency and r is the real rate of interest,assumed constant. The NPG condition requires real debt to be paid off.Otherwise, debt to income would grow under rollover conditions to infinity,an impossibility (McCallum, 1984; Blanchard and Weil, 2002). Default,either partial or full, occurs. Unlike domestic sovereign debt in local currency,external debt in foreign currency cannot be inflated away by the issue of newseignorage, i.e. the printing press.

In corrupt settings, foreign direct investment requires bribes, bribes, andmore bribes to corrupt officials. A higher internal rate of return is requiredto support the scale of bribery. This deters both Foreign Direct Investment(FDI) (Wei, 2000) and investment rates (Mauro, 1995), leading to lowerinvestment and growth to service external debt. As always, real growth andrising tax receipts are the basis for sustainable debt. Bribes are also part ofthe cost of doing business in underwriting the sovereign bonds of corruptgovernments. The higher implicit ‘costs’ are part of the ‘all-in-cost’ of under-writing. The higher the cost of borrowing, the more difficult it is to repay. Acorrupt government may be, by its very nature, less likely to repay (Cole andKehoe, 1998). Agency problems arise: politicians (the agents) are in officetemporarily while the government (the principal) remains to collect taxesfrom the taxpayer to service debt. Though not exhaustive, these factors playan important role in creditworthiness.

3. Corruption as an Explanatory Variable

Transparency International (www.transparency.org) publishes a CorruptionPerceptions Index (CPI) of governments worldwide, ranging from 0 to 10.In their terms: ‘The CPI score relates to perceptions of the degree of corrup-tion as seen by business people, risk analysts and the general public andranges between 10 (highly clean) and 0 (highly corrupt)’. We focus on sover-eign corruption because the issue being dealt with is the underwriting ofsovereign bonds. Clearly, however, corruption is not exogenous. The chal-lenge is to find instrumental variables that are not endogenous that mightexplain corruption. Instrumented corruption may then be used as an explan-atory variable, among others that might capture credit worthiness. Similarly,sovereign credit ratings can be converted from letters to a correspondingscalar index from 1 (sovereign default) to 22 (AAA), one unit corresponds toone notch in the credit rating. Table A1 in the Appendix displays theconversion of letter ratings to scalar ratings. When appropriate instrumentalvariables are found to explain corruption, a simple scatter diagram plottingsovereign bond ratings on the vertical axis (higher numbers implying higherratings) and the corruption perceptions index on the horizontal index

lim ( ) ( )t

rtD t e→∞

− = 0 1

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312 M. Connolly

(higher implying cleaner government), suggests a positive relationship (seeFigure 1).Figure 1. Corruption and sovereign bond ratings (1993–2002).

4. Benchmark Explanatory Variables

A number of other factors have proven significant in explaining sovereigncredit ratings (Afonso, 2003). Most concern the ability to pay relative to debtburden and previous credit history. We add instrumented corruption to theexplanatory variables. The index of credit rating is therefore the dependentvariable, while the benchmark explanatory variables are as given in Table 1.

As income per capita rises, tax receipts rise as the population grows,financing the revenues of the government. Income per capita is also an indexof the ‘average’ standard of living and level of development in institutions,barring major inequality in the distribution of income. The inflation rate isan index of macroeconomic instability and the reliance on seignorage tofinance a fiscal deficit. A budget surplus on a cash basis implies that thegovernment is able to cover its expenditures, including interest and principalon internal and external debt. A primary surplus alone would not be suffi-cient. Previous default reduces credibility – it is a bit like Adam’s first bite of

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Figure 1. Corruption and sovereign bond ratings (1993–2002).

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Measuring the Effect of Corruption on Sovereign Bond Ratings 313

Eve’s apple – the original sin. The higher external debt to exports, thegreater the debt burden relative to the flow of foreign exchange receipts.Instrumented corruption is our main explanatory variable of interest –higher corruption indices should lead to lower credit ratings. Foreignreserves of the central bank are the ‘liquidity of last resort’ for the paymentof sovereign debt. Creditors will seek payment out of any remaining foreignreserves when current receipts are unable to cover sovereign debt.

5. The Regression Specification

The basic random effects regression equation is specified in (2).

where R is the Standard and Poor’s rating (from 1 to 22), and the µi is a coun-try specific random element, and εit varies by country and time period. Thus,it is a linear regression model with a compound disturbance term – that israndom effects – which may be consistently estimated (see Greene, 2003,p. 285). The corruption index is instrumented using absolute latitude, reli-gious variables, and former colony status.

Absolute latitude has often been found to affect income levels as well asgrowth. Bloom and Sachs (1998) relate closeness to the equator with favor-able conditions for malaria, such as temperature conditions fostering to fullgestation of the mosquito larva, and other tropical maladies, as well as torren-tial rain seasons that lend a geographical explanation of relatively low income

RGDP

NP

PGDP

GDPT GGDP

DfltFrDebtExports

CrrptFrRsGDP i it

= +

+

+

+

−

+ +

+ ( ) +

+ +

α β β β β

β β β β µ ε

1 2 3 4

5 6 7 8 2

$ $

$

$

$( )

∆ ∆

Table 1. Explanatory variables

Explanatory variable Motivation

Income per capita (GDP$/N, USD thousands) Ability to pay through the levying of taxesInflation rate (∆P/P) Measure of macroeconomic volatilityGDP growth rate (∆GDP/GDP) Growth in ability to payBudget surplus as ratio of GDP ((T–G)/GDP) Higher surplus reduces sovereign financing

requirementsPrevious default (Dflt 1=previous default, 0 otherwise)

Previous default reduces creditworthiness

External debt to exports (FrDeb$/Exports$) Debt burden relative to foreign exchange earnings

The (instrumented) TI Corruption Perceptions Index (Crrpt)

Highly corrupt (0) less likely to pay than highly clean (10)

Foreign reserves to GDP (FrRs$/GDP$) Liquidity of last resort

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314 M. Connolly

near the equator. Acemoglu et al. (2001) develop another theory for thecommon empirical importance of absolute latitude. They find that Europeansgenerally located extractive industries near the equator, where the likelihoodof survival of settlers was low, neglecting property rights and institutions. Onthe other hand, in climates more hospitable to European settlers, they estab-lished ‘neo-Europes’ with better institutions. So here the use of absolute lati-tude is likely capturing aspects of both disease and institutions.

6. Regression Results

Our hypothesis is that corruption reduces the creditworthiness of nationsby the diversion of loans to unproductive uses or offshore accounts.Consequently, increased corruption should reduce credit ratings correspond-ingly. However, corruption cannot be treated as exogenous. Accordingly, weinstrument corruption with a set of instrumental variables including absolutelatitude, former colony status, and religious variables, as explained in TableA3.

Random effects regression results in Table 2 explain the S&P sovereignratings. A description of the variables and their sources is given in Tables A6and A7. Using the full set of instruments for the corruption perception index,a unit rise in corruption (i.e. a unit fall in the index) yields nearly a one notchworsening in rated creditworthiness. GDP per capita, growth in GDP andprevious default are highly significant. External debt as a percent of exportsis not. A Pooled Two-Stage Least Squares regression reported in Table A4and a G2SLS with instruments and dummy variables reported in Table A5yield similar results.

The regression explains 87% of the variance in the Standard and Poor’srating. Further, the estimated coefficient of 0.83 on corruption suggests thata one-point increase in perceived corruption lowers the Standard and Poor’srating nearly a notch. The implication is that reducing corruption wouldimprove a country’s sovereign credit rating, thus saving a significant numberof basis points on its external borrowing.1

7. Conclusion

We find that higher governmental corruption is associated with lower sover-eign bond ratings. This is likely due to the diversion of funds from productiveuses to unproductive uses and/or to private accounts abroad. By implication,efforts to make underwriting sovereign bonds more transparent and lesscorrupt would improve ratings and lower the cost of sovereign borrowing.China in particular is taking extreme measures to curb public corruption,including executions of corrupt public officials. As their corruption leveldeclines, an improvement in Chinese sovereign bonds is to be expected.

However, because less sovereign corruption is closely associated withhigh income and regulatory frameworks, the cure will take years of growthand development of institutions.2 Sovereign corruption is also closely associ-ated with being near the equator, lending credence to both the institutional

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Measuring the Effect of Corruption on Sovereign Bond Ratings 315

Tab

le 2

.D

epen

dent

var

iabl

e: S

tand

ard

and

Poor

’s s

over

eign

cre

dit

rati

ng (

1 –

SD t

o 22

– A

AA

). I

nstr

umen

ted

expl

anat

ory

vari

able

: cor

rupt

ion

perc

epti

ons

inde

x (0

– m

ost

corr

upt

to 1

0 –

leas

t co

rrup

t

Var

iabl

eC

orru

ptio

n pe

rcep

tion

s in

dex

GD

P pe

r ca

pita

(U

SD t

hous

ands

)In

flat

ion

rate

Gro

wth

in

GD

PB

udge

t sur

plus

to

GD

PPr

evio

us

defa

ult

Ext

erna

l deb

t to

exp

orts

Res

erve

s to

GD

PC

onst

ant

Coe

ffic

ient

0.84

30.

122

−0.0

263.

657

2.56

5−3

.708

−0.0

59−1

3.02

710

.85

z-st

atis

tic

4.62

5.57

−0.6

82.

241.

52−5

.51

−1.1

2−1

.22

9.75

R-s

q0.

87N

o. o

f ob

s.45

9

Ran

dom

eff

ects

: cor

rupt

ion

perc

epti

ons

inde

x in

stru

men

ted

on a

bsol

ute

lati

tude

, sub

-Sah

ara

Afr

ica,

for

mer

Bri

tish

col

ony,

for

mer

Fre

nch

colo

ny, f

orm

er S

pani

sh

colo

ny, f

orm

er P

ortu

gues

e co

lony

, for

mer

War

saw

Pac

t cou

ntry

, per

cent

Bud

dhis

t, p

erce

nt C

atho

lic, p

erce

nt C

onfu

cian

, per

cent

Hin

du, p

erce

nt P

rote

stan

t, p

erce

nt

Mus

lim.

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316 M. Connolly

approach to corruption in Acemoglu et al. (2001) as well as the healthapproach in Bloom and Sachs (1998).3

Acknowledgements

I am indebted to Michelle Connolly, Brian Barrett, Pat Fishe, AndreaHeuson, David Kelly, Keisuke Hirano, Ed Tower, and Tie Su for helpfulsuggestions. I would also like to gratefully acknowledge the research supportof Project 985 Hunan University, and the helpful comments of the referees.

Notes1. A worsening in the credit rating leads to a higher yield to maturity on sovereign bonds (i.e. a higher

risk premium to offset greater expected losses), as shown in Erb et al. (1996, 1999).2. In fact, regressing the natural logarithm of the Corruptions Perceptions Index on the natural loga-

rithm of income per capita yields an elasticity of 28% – as income per capita rises 10%, there is areduction in perceived corruption of 2.8% (adjusted R2=70%). Countries with higher GDP percapita are able to put mechanisms in place to reduce corruption. Clean government appears to be a‘superior’ good. Of course, reverse causation could suggest that better institutions (i.e. less corrup-tion) cause higher levels of real GDP and higher corruption impedes GDP.

3. We regressed using the lagged corruption perception index as the sole instrument for Crrpt (reportedin Table A2). While this does not offer the best instrumentation for CPI, it does provide for a timevarying instrument, which allows us to complete a Hausman test for the presence of correlationbetween the explanatory variables and the latent individual effects. We find that in this regression(using lagged corruption as an instrument), the Hausman test does not indicate the presence of suchcorrelations and hence, we present the random effects regressions in the tables.

ReferencesAcemoglu, D., Johnson, S. & Robinson, J. (2001) The colonial origins of comparative development: an

empirical investigation, American Economic Review, 91(5), December, pp. 1369–1401.Afonso, A. (2003) Understanding the determinants of sovereign debt ratings: evidence for the two lead-

ing agencies, Journal of Economics and Finance, 27, Spring, pp. 56–74.Besley, T. (1995) Property rights and investment incentives: theory and evidence from Ghana, Journal of

Political Economy, 103(5), October, pp. 903–937.Bhatia, A. V. (2002) Sovereign Credit Ratings Methodology: An Evaluation, IMF Working Paper WP/

02/170.Blanchard, O. J. & Weil, P. (2002) Dynamic Efficiency, the Riskless Rate and Debt Ponzi Games Under

Uncertainty, MIT Department Economics Working Paper: 01/41, November.Bloom, D. E. & Sachs, J. D. (1998) Geography, demography, and economic growth in Africa, Brookings

Papers on Economic Activity, 2, pp. 207–295.Cantor, R. & Packer, F. (1996) Determinants and impact of sovereign credit ratings, FRBNY Economic

Policy Review, Federal Reserve Bank of New York, 2(2), pp. 37–53.Cole, H. & Kehoe, P. (1998) Models of sovereign debt: partial versus general reputations, International

Economic Review, 39, pp. 55–70.Erb, C. B., Harvey, C. R. & Viskanta, T. E. (1996) The influence of political, economic, and financial

risk on expected fixed-income returns, Journal of Fixed Income, pp. 7–31.Erb, C. B., Harvey, C. R. & Viskanta, T. E. (1999) New perspectives on emerging market bonds, Journal

of Portfolio Management, 25(2), pp. 2–13.Greene, W. H. (2003) Econometric Analysis, 5th edn (Upper Saddle River, NJ: Prentice Hall).Hausman, J. (1978) Specication tests in econometrics, Econometrica, 46(6), pp. 1251–71.International Monetary Fund (2003) Growth and institutions, in: World Economic Outlook, Chapter 3,

pp. 95–128.

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Measuring the Effect of Corruption on Sovereign Bond Ratings 317

Joh, S. W. (2003) Corporate governance and firm profitability: evidence from Korea before theeconomic crisis, Journal of Financial Economics, 68(2), pp. 287–322.

Kalinina, O. & Alexeeva, D. (2002) Sovereign ratings history since 1975, Standard and Poor.Mauro, P. (1995) Corruption and growth, Quarterly Journal of Economics, 110(3), pp. 681–712.McCallum, B. T. (1984) Are bond-financed deficits inflationary? A Ricardian analysis, Journal of Politi-

cal Economy, 99(1), February, pp. 123–135.Mitton, T. (forthcoming) A cross-firm analysis of the impact of corporate governance on the East Asian

financial crisis, Journal of Financial Economics.North, D. C. (1990) Institutions, Institutional Change, and Economic Performance (New York:

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Websiteshttp://devdata.worldbank.org/data-query/http://www.cia.gov/cia/publications/factbook/index.htmlhttp://www.imf.orghttp://www.standardandpoor.comhttp://www.transparency.org/cpi/index.html)i

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318 M. Connolly

Appendix

In Table A1, each notch in the S&P rating corresponds to one unit. Clearly, thehigher the sovereign bond rating, the greater the index, ranging from 1 to 22.The same index is used by Afonso (2003) and Bhatia (2002), among others.

Testing the Instruments

With only lagged corruption as an instrument, the regression finds thatpredicted corruption is a significant predictor of credit rating, as well as GDPper capita, previous default, and external debt as a percent of exports. A unitrise in corruption (i.e. a unit fall in the index) is associated with a 60% notchworsening in the S&P credit rating. The regressions are reported in Table A2.

Testing Instruments: The Corruption Perceptions Index

To further test our instruments, we regress the corruption perceptions indexagainst instrumental variables which explain about 73% of the total vari-ance. The regression is reported in the text and the fitted corruption indexbelow in Table A3. Absolute latitude appears to reduce corruption, as does

Table A1. A scalar index of ratings conversion of letter ratings to scalar ratings

S&P Index scale

Upper investment grade AAA 22AA+ 21AA 20AA− 19A+ 18A 17A− 16

Lower investment grade BBB+ 15BBB 14BBB− 13

Non-investment grade BB+ 12BB 11BB− 10

Lower non-investment grade B+ 9B 8B− 7

CCC+ 6CCC 5CCC− 4

CC 3C 2

Default SD, D 1

Source: Standard and Poor’s.

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Measuring the Effect of Corruption on Sovereign Bond Ratings 319

Tab

le A

2.G

2SL

S ra

ndom

eff

ects

reg

ress

ion

usin

g in

stru

men

tal v

aria

bles

Dep

ende

nt v

aria

ble:

Sta

ndar

d an

d Po

or’s

sov

erei

gn c

redi

t ra

ting

(1

– SD

to

22 –

AA

A)

Inst

rum

ente

d ex

plan

ator

y va

riab

le: c

orru

ptio

n pe

rcep

tion

s in

dex

(0 –

mos

t co

rrup

t to

10

– le

ast

corr

upt)

Var

iabl

eC

orru

ptio

n pe

rcep

tion

s in

dex

GD

P pe

r ca

pita

(U

SD t

hous

ands

)In

flat

ion

rate

Gro

wth

in

GD

PB

udge

t sur

plus

to

GD

PPr

evio

us

defa

ult

Ext

erna

l deb

t as

% o

f ex

port

sR

eser

ves

as %

GD

PC

onst

ant

Coe

ffic

ient

0.58

40.

1719

−0.0

020.

243

0.61

4−3

.893

−0.1

46−0

.677

11.7

53z-

stat

isti

c5.

067.

96−0

.06

0.02

0.04

3−7

.16

−2.1

6−0

.82

16.5

8R

-sq

0.88

No.

of

obs.

467

Ran

dom

eff

ects

: cor

rupt

ion

perc

epti

ons

inde

x in

stru

men

ted

on c

orru

ptio

n pe

rcep

tion

s in

dex

lagg

ed.

Var

iabl

eC

orru

ptio

n pe

rcep

tion

s in

dex

lagg

edC

onst

ant

R-s

qN

o. o

f ob

s.

Coe

ffic

ient

0.98

80.

067

0.98

468

z-st

atis

tic

149.

991.

67

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320 M. Connolly

being a former British colony, while percent Hindu, percent Muslim, andbeing a sub-Saharan Africa country (although only Botswana is in thesample) appear to increase corruption. The importance of absolute latitudeas a positive factor is also found in growth studies (Sala-i-Martin, 1997), aswell as in studies that link closeness to the equator with health problems suchas malaria and production problems due to heavy rains. The rule of Englishlaw may also provide an institutional framework lessening corruption.

The institutional school would seem to explain better the extent of corrup-tion. While absolute latitude is significant, suggesting distance from the equa-tor is associated with less corruption, the institutional school explains a greatdeal: former British colony promoting less corruption, but Hinduism andIslam being associated with more. A pooled two-stage least squares regressionwas run to check the robustness of the panel regressions. If the pooled 2SLSQyielded significantly different results, it would cast doubt upon them (seeTable A4). While the coefficients change slightly, the results remain essentiallysimilar. In particular, the estimated coefficient of the instrumented corruptionperceptions index is a bit lower, but highly significant. Previous default onceagain is a strong and significant predictor of credit rating. Having defaultedis clearly costly in terms of creditworthiness in terms of all our regressions.

Since the instrumented corruption perceptions index is constant for eachcountry, a fixed-effects regression could not be run. However a randomeffects regression using instrumental variables and a dummy variable foreach country yielded similar results which are reported in Table A5 (withoutthe country dummy variables, eight of which were significant).

Table A3. Testing instrumental variables – regression of the corruption perceptions index on the instruments

Variable Coefficient z-statistic

Absolute latitude 0.051 2.04Sub-Saharan Africa −2.121 −1.84Former British colony 2.356 3.53Former French colony 2.010 1.42Former Spanish colony −0.481 −0.05Former Portuguese colony −1.066 −0.66Former Warsaw Pact country −1.575 −1.43Percent Buddhist 0.940 0.51Percent Catholic −0.268 −0.19Percent Confucian −2.036 −0.50Percent Hindu −4.854 −2.26Percent Jewish −0.045 −0.02Percent Protestant 2.711 1.72Percent Muslim −3.175 −2.04Constant 3.783 2.39R-sq 0.73No. of obs. 460

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Measuring the Effect of Corruption on Sovereign Bond Ratings 321

Tab

le A

4.Po

oled

2SL

SQ

Dep

ende

nt v

aria

ble:

Sta

ndar

d an

d Po

or’s

sov

erei

gn c

redi

t ra

ting

(1

– SD

to

22 –

AA

A)

Inst

rum

ente

d ex

plan

ator

y va

riab

le: c

orru

ptio

n pe

rcep

tion

s in

dex

(0 –

mos

t co

rrup

t to

10

– le

ast

corr

upt)

Var

iabl

eC

orru

ptio

n pe

rcep

tion

s in

dex

GD

P pe

r ca

pita

(U

SD t

hous

ands

)In

flat

ion

rate

Gro

wth

in

GD

PB

udge

t sur

plus

to

GD

PPr

evio

us

defa

ult

Ext

erna

l deb

t to

exp

orts

Res

erve

s as

%G

DP

Con

stan

t

Coe

ffic

ient

0.45

90.

193

−0.0

912.

486

7.17

9−3

.447

−0.2

02−0

.484

12.4

7t-

stat

isti

c5.

3112

.22

−1.5

30.

983.

16−1

5.62

−4.0

5−1

.11

28.0

0R

-sq

0.88

No.

of

obs.

459

Pool

ed le

ast

squa

res:

cor

rupt

ion

perc

epti

ons

inde

x in

stru

men

ted

on a

bsol

ute

lati

tude

, sub

-Sah

ara

Afr

ica,

fro

mer

Bri

tish

col

ony,

for

mer

Fre

nch

colo

ny, f

orm

er

Span

ish

colo

ny, f

orm

er P

ortu

gues

e co

lony

, for

mer

War

saw

Pac

t cou

ntry

, per

cent

Bud

dhis

t, p

erce

nt C

atho

lic, p

erce

nt C

onfu

cian

, per

cent

Hin

du, p

erce

nt P

rote

stan

t,

perc

ent

Mus

lim.

Tab

le A

5.G

2SL

S ra

ndom

eff

ects

reg

ress

ion

usin

g in

stru

men

tal v

aria

bles

Dep

ende

nt v

aria

ble:

Sta

ndar

d an

d Po

or’s

sov

erei

gn c

redi

t ra

ting

(1

– SD

to

22 –

AA

A)

Fitt

ed e

xpla

nato

ry v

aria

ble:

cor

rupt

ion

perc

epti

ons

inde

x (0

– m

ost

corr

upt

to 1

0 –

leas

t co

rrup

t)

Var

iabl

eC

orru

ptio

n pe

rcep

tion

s in

dex

GD

P pe

r ca

pita

(U

SD t

hous

ands

)In

flat

ion

rate

(%

)G

row

th in

G

DP

(%)

Bud

get s

urpl

us

as %

GD

PPr

evio

us

defa

ult

Ext

erna

l deb

t as

% o

f ex

port

sR

eser

ves

as

% G

DP

Con

stan

t

Coe

ffic

ient

0.98

70.

0982

−0.0

2803

0.04

270.

035

−3.0

14−5

.07

−12.

8319

99.

72z-

vari

able

6.21

4.36

−0.0

712.

530.

02−5

.56

−0.0

91−0

.14

11.0

4R

-sq

0.98

No.

of

obs.

459

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322 M. Connolly

In Table A6, the average corruption index and average S&P index andletter ratings are reported. The only country from sub-Saharan Africa isBotswana due to data limitations.

Table A6. Data sources and description

Data item (52 countries 1993–2002) Source Mean S.D.

Corruption perception index (CPI)

Transparency International (www.transparency.org)

5.62 2.38

Absolute latitude (degrees)

CIA World Factbook (www.cia.gov)

34.83 16.32

Per capita GDP (USD thousands)

Political risk services group $12,032 $11,572

Inflation rate (percent) Political risk services group 31% 18.7%Real GDP growth rate (percent)

Political risk services group 3.2% 3.9%

(Budget surplus)/GDP (percent)

Political risk services group −20% 5.1%

Foreign reserves ($bn) Political risk services group 24.6 43.6S&P rating Standard & Poor’s sovereign

ratings history15.65 5.00

Total foreign debt ($bn) Political risk services group $80.3 $125.7Exports ($bn) Political risk services group $88.8 $132.2Nominal GDP ($bn) Political risk services group $514.6 $1,344.1Dummy variablesGeographical religious and dummy variables

Xavier Sala-i-Martin database

Previous default www.newyorkfed.org

Note: 1995 values were used for the corruption perception index for the years 1993–1994. Countries with previous sovereign default: Argentina, Bolivia, Brazil, Bulgaria, Costa Rica, Mexico, Peru, Philippines, Poland, Romania, Russia, South Africa, Turkey, Venezuela.

Table A7. Summary average corruption and S&P ratings by country

Average corruption rating

Average S&P rating (numerical)

Average S&P rating (letter)

Argentina 3.8 9.2 B+Australia 8.7 20.4 AAAustria 7.5 22.0 AAABelgium 6.1 21.0 AA+Bolivia 2.8 9.8 BB−Botswana 6.1 17.0 ABrazil 3.5 9.2 B+Bulgaria 3.5 8.4 B

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Measuring the Effect of Corruption on Sovereign Bond Ratings 323

Table A7. (continued)

Average corruption rating

Average S&P rating (numerical)

Average S&P rating (letter)

Canada 9.0 21.0 AA+Chile 7.2 15.7 A−China 2.9 14.2 BBBColumbia 3.0 12.4 BB+Costa Rica 5.8 11.0 BBCzech Republic 4.9 16.0 A−Denmark 9.7 21.2 AA+Egypt 3.0 13.0 BBB−El Salvador 3.7 11.4 BBFinland 9.5 20.0 AAFrance 6.8 22.0 AAAGermany 8.0 22.0 AAAGreece 4.6 14.2 BBBHong Kong, China 7.4 17.3 AHungary 4.8 13.6 BBBIndia 2.8 11.6 BB+Ireland 8.1 20.4 AAIsrael 7.4 15.7 A−Italy 4.2 20.0 AAJapan 6.5 21.8 AAAKazakhstan 2.4 9.9 BB−Malaysia 5.1 15.8 A−Mexico 3.3 11.6 BB+Morocco 3.9 11.0 BBNetherlands 8.9 22.0 AAANew Zealand 9.4 20.5 AA+Norway 8.8 22.0 AAAPeru 4.5 10.8 BBPhilippines 3.0 11.2 BBPoland 4.9 13.2 BBB−Portugal 6.3 19.4 AA−Romania 3.3 8.9 B+Russian Federation 2.4 7.6 BSingapore 9.1 21.8 AAASlovak Republic 3.8 12.1 BB+South Africa 5.2 12.0 BB+Sweden 9.2 21.0 AA+Switzerland 8.7 22.0 AAAThailand 3.1 14.8 BBB+Tunisia 5.1 13.3 BBB−Turkey 3.7 8.9 B+United Kingdom 8.5 22.0 AAAUnited States 7.7 22.0 AAAVenezuela, RB 2.6 8.9 B+

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