Applied Financial Economics
ISSN: 0960-3107 (Print) 1466-4305 (Online) Journal homepage: https://www.tandfonline.com/loi/rafe20
Project finance loan spreads and disaggregated
political risk
Claudia Girardone & Stuart Snaith
To cite this article: Claudia Girardone & Stuart Snaith (2011) Project finance loan spreads
and disaggregated political risk, Applied Financial Economics, 21:23, 1725-1734, DOI:
10.1080/09603107.2011.577006
To link to this article: https://doi.org/10.1080/09603107.2011.577006
Published online: 27 Jul 2011.
Submit your article to this journal
Article views: 655
View related articles
Citing articles: 5 View citing articles
Full Terms & Conditions of access and use can be found at
https://www.tandfonline.com/action/journalInformation?journalCode=rafe20
Applied Financial Economics, 2011, 21, 1725–1734
Project finance loan spreads
and disaggregated political risk
Claudia Girardone and Stuart Snaith*
Essex Business School, University of Essex, Wivenhoe Park, Colchester,
CO4 3SQ, UK
This article provides novel evidence on project finance loan pricing using
economic and disaggregated political risk determinants. As expected, our
findings suggest that the presence of loan guarantees and lower levels of
aggregate political risk results in cheaper project finance loans. The
evidence in support of disaggregated political risk as a pricing determinant
is negligible for developed countries, but significant for developing
countries. For the latter we find that loan spreads are negatively related
to the effectiveness, quality and strength of a country’s legal and institutional systems whilst lower levels of government stability and democratic
accountability are associated with lower loan spreads. Our results are
consistent with a risk allocation approach to project finance deals.
Keywords: project finance; banking; loan pricing; political risk
JEL Classification: F34; G21; G32
I. Introduction
In recent years project finance has become an
increasingly popular method of funding long-term
capital intensive infrastructure projects worldwide.
The nature of modern project finance is to use limited
or nonrecourse syndicated loans via a Special
Purpose Vehicle (SPV), where such debt typically
represents the lion’s share of the capital structure.
The SPV is a standalone legal entity that usually has
one objective such as to build a dam or a pipeline.
Project finance lending techniques are often
employed in developing countries, typically in assetrich industrial sectors such as oil and gas, mining, and
utility and energy. Project finance loans are usually
preferred if the economy of a country is poor, the
corporate governance system is weak, political risk
high and bank influence over the host government is
strong (Hainz and Kleimeier, 2007). For developed
countries project finance is viewed as one financing
alternative among many; for the developing country
it can often be one of the few sources of finance
available. We therefore posit that developing countries, which tend to have high levels of political risk
relative to developed countries, have a weaker negotiating position when entering into contracts with the
SPV. This permits higher levels of risk transference in
such economies, making project finance investing in
such a climate viable.1
The main contribution of this article is to examine
the determinants of project finance loan spreads by
adopting a disaggregated approach to political risk.
To the best of our knowledge this is the first study to
explore in detail these issues. The advantage of our
*Corresponding author. E-mail: ssnaith@essex.ac.uk
1
There are voluntary standards (Equator Principles) in place to ensure socially and environmentally responsible practices.
In the developing country case these principles may help in part to mitigate the stronger negotiating position of the SPV.
These principles were first endorsed in 2003, and in January 2011 there were 69 ‘official adopters’.
Applied Financial Economics ISSN 0960–3107 print/ISSN 1466–4305 online ß 2011 Taylor & Francis
http://www.informaworld.com
DOI: 10.1080/09603107.2011.577006
1725
1726
analysis is that it allows the identification of the
specific elements of political risk that affect loan
pricing, rather than relying on aggregate measures
used elsewhere in the literature. This approach yields
novel results that dovetail with the notion of risk
allocation between the SPV and host government.
More specifically, our disaggregated analysis using
1190 project finance loans and controlling for economic determinants yields two main findings. First is
that the relationship between disaggregated measures
of political risk and project finance loan spreads
differs as a function of country development, with
little evidence found to support a role for political
risk in developed countries. Second is the novel
relationship between specific disaggregated measures
of political risk and loan price for developing
countries. On the one hand, the cost of funds is
negatively related to the effectiveness, quality and
strength of a country’s legal and institutional systems,
and on the other hand, lower levels of government
stability and democratic accountability are more
likely to be associated with lower loan spreads. We
argue that both these findings are consistent with the
manner in which project finance is conducted. For the
first finding we posit that this is indicative of a
difference in host government bargaining power at
the contractual stage of the project finance deal. The
second finding fits well with the risk transference
rationale of the SPV, in this case from the project to
the host country.
The remainder of this article is organized as
follows: Section II provides a brief introduction to
project finance; Section III reviews the main literature; Section IV presents the data and the main
methodological issues; Section V discusses the main
results and a final section concludes.
C. Girardone and S. Snaith
There are three main distinguishing features that
differentiate project finance lending from standard
deals in other types of syndicated lending. Firstly,
such financing includes the creation, by the project
sponsors (equity-holders, who are often large multinational corporations), of a standalone SPV where
nonrecourse debt forms the majority of the capital
structure. Secondly, the structure of the SPV allocates
risk to those participants who are best able to handle
it via legally binding contracts. Thirdly, such financing is predominantly used in developing countries.2,3
It is through the formation of the SPV that there is
no recourse to the project sponsors, as the debt is held
by the newly created SPV. In the event of a project
failure this means that the bank has no or at most
limited recourse to the project sponsors. Large
international banks fund the majority of the loan,
often with a smaller contribution from multilateral
development banks, such as the International
Finance Corporation (IFC, 1999). The participation
of such institutions is one method by which political
risk is addressed in project finance.4
The importance of contracts to project finance is
highlighted by Esty (2004), who finds that a standard
project contains 40 or more contracts between the
input supplier and the output buyer involving more
than 15 different parties in the process. In particular,
project finance makes considerable use of political
risk guarantees and loan covenants that typically
restrict or encourage various actions to enhance the
probability of repayment. The contract between the
project and the host state is particularly crucial. This
host government agreement (concession agreement)
will give the right to the SPV to build and operate the
project, state the infrastructure the government is
required to undertake to ensure the success of the
project, as well as have measures to protect against
expropriation.
II. Project Finance and the SPV
III. Literature Review
Over the past 20 years project finance has experienced
considerable growth that was mainly brought about
by the globalization of product markets, an extensive
process of deregulation of key industrial sectors
worldwide, and the privatization of state-owned
entities. Project finance deals tend to be tangibly
asset-rich, and are relatively well-diversified across
industrial sectors (Esty, 2004).
2
Despite the growing international importance of
project finance to fund large-scale projects, there is
a paucity of studies in this area. Esty (2004) argues
that the field of project finance is ‘relatively
unexplored territory for both empirical and theoretical research’. The literature that offers an economic
treatment of project finance (Brealey et al., 1996; Esty
For more background on the specifics of project finance and the SPV see Brealey et al. (1996) and Esty (2002).
Benjamin Esty’s project finance portal is an excellent research tool for project finance research. See Esty (2011).
4
For example, a host country would be less likely to expropriate a project as it would risk losing the future support of the
development bank that was a party to the loan.
3
Project finance loan spreads and disaggregated political risk
and Megginson, 2003) emphasizes a number of
features that make project finance a ‘special case’
compared to alternative methods of financing. This
relates to the distinguishing features of project
finance mentioned in the previous section, such as
the high levels of debt-to-equity (typically averaging
around 60–70%), and the use of the SPV and
attendant legal contracts.
Much of the extant empirical literature has examined project finance lending from three perspectives.
The first focuses on the determinants of loan pricing
(e.g. Kleimeier and Megginson, 2000; Sorge and
Gadanecz, 2008). The second examines what factors
might affect the syndicate structure of the SPV (Esty
and Megginson, 2003). Lastly, the final approach
examines what factors might affect the project finance
proportion of syndicated lending (Altunbas and
Gadanecz, 2004; Hainz and Kleimeier, 2007). The
purpose of this review is not to focus on these latter
two perspectives, but rather on the former, the
determinants of the loan price with particular emphasis on political risk.
An early study by Kleimeier and Megginson (2000)
use a standard Ordinary Least Squares (OLS)
regression to identify what factors are important in
assessing loan spreads for a sample of 1824 project
finance loans. Unexpectedly project finance loans are
found to have lower credit spreads over the London
Interbank Offered Rate (LIBOR) than any other
comparable nonproject finance loans. Moreover, the
main results generally suggest that project finance
loans should be considered separately from other
types of syndicated loans because they have a longer
average maturity, are more likely to have third-party
guarantees and to be extended to borrowers in riskier
countries.
In particular they find that higher maturity, the
availability of third party guarantees and the presence
of collateralizable assets significantly reduce the
average project finance loans spreads. Finally, project
finance loans involve more participating banks and
are more likely to be extended to borrowers in assetrich industries.
Kleimeier and Meggison’s results were later
confirmed by Altunbas and Gadanecz (2004) who
investigate the microeconomic and macroeconomic
determinants of bank lending and find that project
finance loans have lower spreads than other forms of
syndicated lending. The authors use a broad sample
of syndicated loans, including project finance, and
take into account additional pricing factors such as
fees that are typically charged in loan syndications.
5
1727
Overall, the results show an inverse relationship
between the cost of borrowing and a country’s
economic strength, whereas ceteris paribus higher
political risk results in larger loan spreads.
In a related study on the nature of credit risk in
project finance Sorge (2004) looks at the term
structure of loan spreads in project finance using a
set of microeconomics variables including maturity,
loan guarantees and a measure of corruption as a
proxy for political risk. Here the aim is to examine
the nature of the relationship between maturity and
the cost of the loan, specifically whether it is linear or
nonlinear. The main finding is that the relationship
between project loan spreads and maturity is humpshaped. As one would expect, Sorge finds that while
corruption increases the cost of borrowing, the
involvement of multilateral development banks or
export credit agencies decreases it. Sorge and
Gadanecz (2008) extend the analysis by examining
both microeconomic and macroeconomic factors.
The main conclusions are that for poorer countries
in particular, the presence of political risk and
political risk guarantees play an important role in
the pricing of project finance, with the latter having a
significant impact in reducing credit spreads. Other
important factors are the host country’s creditworthiness and the syndicate size.
Overall, this review has shown that only a handful
of studies have empirically investigated the determinants of spreads specifically for project finance loans.
In addition, none of the reviewed studies provides an
in-depth analysis of the effects of disaggregate political risk components on spreads. This constitutes the
main novelty of this article and the details of our
chosen empirical models and variables are described
in the following section.
IV. Data and Methodology
Data
We employ the Loan Analytics database (formerly
Loanware) from Dealogic which contains extensive
information on loans made in the international
syndicated loans market signed from January 1980.
Specifically, financial information for a panel of 1190
project finance loans worth over $205 billion over
1996–2003.5 All spreads are measured as the margin
over LIBOR. Since the chosen time period covers
various international financial crises (South-East
The sample of 1190 project finance loans represents 49% of the available observations downloadable from the database.
Observations can be lost for a number of reasons, including missing information or being based on non-LIBOR rates.
C. Girardone and S. Snaith
1728
Asia, Russia, Brazil and Turkey) it is reasonable to
expect that spreads have increased over the period of
study as the availability of credit decreased particularly for the affected countries. Given the variables
included in our model (see below), much of these
effects should be captured by the macroeconomic and
political risk variables.
The dataset is split into three sub-samples: developed, emerging and developing countries, according
to the World Bank’s data on country development.
Two proxies for disaggregated political risk are used:
the International Country Risk Guide (ICRG, 2011)
political risk index and the World Bank’s Worldwide
Governance Research Indicators Dataset. The span
of the dataset is dictated by data availability for both
the World Bank’s data on governance and on
economic development. The latter uses Gross
National Income per capita and defines: ‘high
income’ countries as developed; ‘upper middle
income’ countries as emerging and finally, ‘low
income’ and ‘lower middle income’ countries as
developing.
The political risk rating included in the ICRG
database is calculated for 140 countries and is based
on 100 points. It includes 12 weighted variables
covering both political and social attributes and can
be broadly divided into two levels of risk: low risk
(80–100 points) and high to moderate risk (0–79
points). The aim of this political risk rating is to offer
a method of measuring the political stability of the
countries covered by the ICRG on a comparable
basis. Table A1 in the appendix provides definitions
of each variable.
Also included in Table A1 are the World Bank’s
aggregate governance indexes that specify six dimensions of governance that relate to political risk. Data
are available for the years 1996, 1998, 2000, 2002 and
2004 for 209 countries and territories.6 As with the
ICRG index, high values are associated with low
political risk.
Methodology
Our analysis of the data is based upon applying a
statistical model to our sample of project finance
loans. The estimation procedure is a simple crosssectional OLS regression that takes the form of
Equation 1, where 0 s refer to coefficients on microeconomic variables, 0 s on macroeconomic variables
and on the political risk variable.7 Equation 1 treats
with one political risk index variable, though when
using disaggregated political risk data this will be
6
7
replaced by 12 variables and coefficients in the ICRG
case, and six in the World Bank case (see Table A1):
LOAN SPREAD
¼ þ 1 MATURITY þ
þ
þ
2 DEAL VALUE
3 GUARANTEE þ
5 CURR RISK þ
4 BANKS
CLUB
6
þ 7 ENVIR RISK þ 1 RESERVES
þ 2 INVEST þ 3 CREDPRIV þ 4 GDPGR
þ 5 ACCBALANCE þ 6 INFLATION
þ 7 IMPEXP þ 8 PPP SHARE þ 9 WORLDTR
þ 10 USTREAS þ POL RISK
ð1Þ
Taking each variable in turn, MATURITY is the
loan maturity in years. Although one could expect
that longer term loans cost more, results on this
relationship in the empirical literature are mixed.
DEAL VALUE is the loan size in $m: this variable
should be negatively related to spreads as only
borrowers with a good credit history should be able
to obtain large size loans. GUARANTEE is a
dummy variable that takes the value of one if there
is an implicit or explicit third-party repayment
guarantee and zero otherwise. For this variable, the
expectation is that the availability of guarantees will
reduce the cost of borrowing. BANKS is the number
of banks in the syndicate and is included to test to
what extent syndicate structure affects the spread; it is
reasonable to expect that a higher number of participants might have a significant risk mitigating effect.
Whilst this is certainly true in the case of strategic
default, Esty and Megginson (2003) offer another
possibility. They posit that a weakening of creditor
rights, which would result in an increase in risk,
would require a greater need for monitoring of cash
flows and an increased need for re-contracting
resulting from economic distress. This type of risk
they argue is best handled by a smaller syndicate.
CURR_RISK is a dummy variable measuring currency risk that takes the value of one if a loan is
exposed to currency risk and zero otherwise. This
specific risk arises when the currency of the loan is
different from the currency of the project’s host
country, and it is therefore expected to affect the loan
spread positively. CLUB is a dummy taking the value
of one according to whether the type of deal is in the
form of a club of banks or bilateral. A final dummy
variable, ENVIR_RISK, takes the value of one if the
industry of the borrower is typically perceived to be
high risk environmentally and zero otherwise. This
factor may be important because it reflects the level
Following the extant literature we interpolate to get data for 1997, 1999, 2001 and 2003.
White’s (1980) corrected SEs used.
Project finance loan spreads and disaggregated political risk
of potential or actual environmental risks for each
industrial sector included in our sample.8
Turning
to
the
macroeconomic
factors:
RESERVES is the ratio of international reserves to
Gross Domestic Product (GDP); INVEST is the level
of investments to GDP; CREDPRIV is the level of
domestic credit to private sector as a % of GDP;
GDPGR is real GDP growth; ACCBALANCE is the
level of current account balance as a % of GDP.
INFLATION is the percentage annual inflation rate;
IMPEXP is the ratio of imports to exports;
PPPSHARE is the Purchasing Power Parity (PPP)
share of world GDP and, finally WORLDTR is a
proxy for the growth in world trade (in %) and is
calculated as absolute sum of imports plus exports for
all countries.
For all indicators of actual or potential economic
strength expectations are for a negative relationship
with loan spreads (INVEST, CREDPRIV, GDPGR,
ACCBALANCE and PPPSHARE). In contrast,
INFLATION should be positively related to spread
because it is likely associated with weaknesses associated with the country’s finances. Similarly, high
IMPEXP and WORLDTR variables could signal a
strong dependence from abroad and more competition for funds, respectively, and are thus expected to
be positively related to loan spreads. Expectations for
RESERVES are mixed because while on the one
hand if high they may signal a strength of a country
in the form of a safety net, on the other hand it may
mean the contrary as in various instances developing
countries have preferred to rebuild their reserves
rather than servicing their debts (Altunbas and
Gadanecz, 2004). USTREAS is the US Treasury
rate calculated at constant maturities three-year
middle rate. Altunbas and Gadanecz argue that this
variable should control for the price of alternative
risk-free investments and gives an indication of the
‘riskier borrowers’ appetite for risk’. Finally, the
aggregate political risk variable is POL_RISK; high
(low) values for the POL_RISK index are associated
with low (high) political risk levels. In line with the
extant literature the coefficient on POL_RISK should
be negative.
With regards to the disaggregated data our a priori
view is not uniform. This can be seen by referring to
the political risk measures provided in the appendix
and the key features of project finance. For example,
it is perfectly plausible to expect the quality of law
and order to have a negative impact on the loan
spread. However, given claims that project finance is
often conducted in countries where political risk and
influence over the host state is high, for the
8
1729
Table 1. Regression results for economic factors and ICRG
political risk index, 1996–2003
Abbr
1
2
3
4
5
6
7
1
2
3
4
5
6
7
8
9
10
Abbreviations
Aggregated
POL RSK index
MATURITY
DEAL VALUE
GUARANTEE
BANKS
CURR_RISK
ENVIR_RISK
CLUB
0.1977
0.0234***
66.031***
0.2992
16.942
8.9780
3.9936
RESERVES
INVEST
CREDPRIV
GDPGR
ACCBALANCE
INFLATION
IMPEXP
PPP SHARE
WORLDTR
USTREASURY
POL_RISK
0.6167
4.5902***
0.3843***
4.3091**
0.8230
0.0038
0.9812***
1.5145**
2.0142***
5.5873**
2.3573***
Adjusted R2
15.1%
Notes: High (low) levels of the political risk index variable
POL_RISK correspond to low (high) levels of risk.
** and *** indicate significance at 5 and 1% levels,
respectively.
developing country sample the relationship between
some of the political risk measures and price may
have a positive impact.
V. Results and Discussion
Loan price determinants: political risk index
Table 1 shows the empirical results derived from the
estimation of our model described in Equation 1
using the ICRG aggregated measure of political risk.
Focusing on microeconomic factors, the most
striking result is the significance and magnitude of
the variable GUARANTEE. The sign for this variable is negative thereby indicating that the presence
of a risk mitigant in the form of a third party
repayment guarantee is likely to lower the loan
spread. This finding is in line with the main literature:
for example, Sorge and Gadanecz (2008) investigate
the risk mitigating role of explicit and implicit
guarantees from multilateral development banks
and export credit agencies. They find that guarantees
play an important role in project finance particularly
Examples of high environmental risk industries include chemicals, mining and oil and gas.
C. Girardone and S. Snaith
1730
Table 2. Political risk ICRG measures 1996–2003
Variables
Developed countries
Emerging countries
Developing countries
Government stability
Socio-economic conditions
Investment profile
Internal conflict
External conflict
Corruption
Military in politics
Religion in politics
Law and order
Ethnic tensions
Democratic accountability
Bureaucracy quality
7.1114
6.7171
24.3193
5.0075
9.4957
2.2667
21.8786
0.1708
8.1726
5.2228
24.5552
0.3251
0.3759
17.4941
7.5226
15.0658
84.5817***
3.0254
2.8612
30.7002
1.6805
23.3951***
19.5811
33.8795***
29.5173**
14.1824
21.1594
14.0646
0.7182
10.9219
2.5245
17.6367
44.8096***
3.5806
17.7335**
33.2758***
Adjusted R2
9.4%
99.6%
36.7%
Notes: Note that for a given country each disaggregated measure of political risk assigns a higher value the lower
the risk.
** and *** indicate significance at 5 and 1% levels, respectively.
in emerging countries and have a significant impact in
reducing credit spreads (by almost one-third on
average). Our regression results indicate that the
magnitude of the coefficient for GUARANTEE is by
far the largest (66.03) among our variables and
corresponds to almost 30 times that of political risk.
In an attempt to investigate what drives this large
coefficient for the repayment guarantees, we have
re-estimated the model by taking into account the
economic development of the regions using the World
Bank’s data on country development. These unreported results yield a significant coefficients of
56.13 for developed countries and to 62.68 for
the joint sample of developing and emerging countries, showing the strong risk mitigating effect of
these guarantees across all economic groups.9
Focusing on other microeconomic variables, in line
with the literature (e.g. Sorge, 2004), no significant
linear relationship between MATURITY (the term of
project finance loans) and the size of credit spreads
has been found. Moreover, we found that the variable
DEAL VALUE is significantly negative, thereby
confirming our view that banks will tend to lend
larger amounts to those who are more creditworthy,
hence on average the cost of larger loans would be
lower.
Concerning the macroeconomic factors, several
observations can be made: first, typical indicators of
a country’s strength, like GDP growth and domestic
9
credit to the private sector are negatively related to
loan spreads and the coefficients are statistically
significant.10 These results suggest that, as expected,
lenders would charge borrowers less the better the
host country’s economic prospects. On the other
hand, the positive and statistically significant relationship between the variable INVEST and loan
spreads can be interpreted as if high values for
investment over GDP increase the borrower’s credit
risk.11 The only unanticipated result in Table 2 is the
PPPSHARE coefficient that is positively related to
loan spreads. One possible explanation could be that
lenders charge wealthier borrowers a premium.
The IMPEXP variable is positively related to
project finance loan prices suggesting that countries
that are more dependent from abroad will have to
pay a higher spread. This is confirmed by the variable
WORLDTR that is also positive and significant. The
last macroeconomic coefficient is a proxy for
USTREASURY and is found negative and statistically significant. One possible explanation put forward by Altunbas and Gadanecz (2004) is that since
USTREASURY is a proxy for the price of alternative
investments of banks, in times of tight monetary
policy only the less risky developing countries’
borrowers will be able to obtain finance.
The last coefficient to consider is the coefficient on
the ICRG aggregated political risk index. In line with
expectations we find that the coefficient is negative
Coefficients are significant at the 1% level. Unreported results are available from the authors upon request.
A similar relationship is found by Eichengreen and Mody (2000) who examine the determinants of a syndicated lending,
which contrasts with our own study which focuses exclusively on project finance lending.
11
An alternative interpretation considers the variable investments/GDP as a proxy for high potential for future growth, which
would imply a positive relationship with loan spread. However for developing countries the high level of debts and a less
robust financial environment may result in increased risk.
10
Project finance loan spreads and disaggregated political risk
and significant with a value of approximately 2.4.
This indicates that a 1% reduction in the overall
political risk (i.e. an increase in the index) results in a
2.4 basis point reduction in the loan price.12
Loan price determinants and country development:
disaggregated political risk index
Building on the results from the previous section,
Table 2 illustrates the regression results for the 12
disaggregated ICRG political risk components
described in Table A1. The results are presented for
each of the three sub-samples based on country
development as ranked by the World Bank. Note that
in every case the lower the points total, the higher is
the risk (and vice versa).13
Based on the developed countries sample, the first
point to note is that none of the disaggregated
measures of political risk are statistically significant
in explaining the loan spread. This is contrasted by
the finding of several significant variables in our
emerging and developing country sub-samples. This,
in conjunction with higher R2 values in the emerging
and developing country samples suggests that in
determining the loan spread for developed countries
the weighting placed on political risk will be substantially less relative to more risky, less developed
countries. This is in line with the intuition that
developed countries are characterized as countries
where there is very little political risk. Take for
example the ‘level’ of law and order and the quality of
the bureaucracy. For developed countries this has
remained broadly constant and of a reasonable
standard in recent times, negating the need to
attach a premium to the cost of a loan.
Turning to the significant coefficients in the
emerging country sample, we see some evidence of
the anticipated significant negative coefficients,
though only for external conflict, ethnic tensions
and bureaucracy quality. For the developing country
sample we see that law and order and bureaucracy
quality are also significantly negative. An incremental
improvement in these two measures will on average
remove 44.81 and 33.28 basis points from the cost of
a loan. These two results dovetail well with our
understanding of project finance. In the case of the
12
1731
former, the importance of a reliable recourse to a
strong and impartial legal system is crucial for a form
of finance that is governed by so many contracts. In
the case of the latter, given the reliance of large
infrastructure projects on the host country, a lack of
institutional strength and quality in the bureaucracy
will also attract a risk premium.
For the remaining significant coefficients, government stability and democratic accountability, a positive relationship is found with respect to loan spread.
This result, prima facie, can be viewed as somewhat
puzzlingly, implying that the more stable and democratically accountable the government, the more the
loan costs. Taking government stability as an example, the coefficient implies that an increase in stability
will result in (on average) an increase in the cost of a
loan. Based on the definitions of these two variables
(Table A1) we find that in developing countries
project finance deals will on average be cheaper if
countries exhibit lower levels of government unity
and legislative strength and are less responsive to
their people.
This result offers a clear link between the
weakness of a host country government and the
cost of a project finance loan. Based upon our
results we suggest that projects that are conducted
in host countries where governments are in a
weaker negotiating position benefit to a larger
degree from risk transference. Whilst it is unclear
what the ramifications of such transference are
upon the host country, for the project we argue the
reduction in risk results in a reduction of the loan
spread. To better assess this attention would need
to be paid to the tool that enables the majority of
this risk transference – the host government agreement. The relationship we find bears out the
common belief that developing countries will agree
to far broader concessions in such agreements as
opposed to developed countries at the contractual
stage of the project finance deal.14
Our findings complement those of Hainz and
Kleimeier (2007) who find that the percentage of
syndicated lending that is classified as project finance
increases when a country exhibits poor economic
performance, weak corporate governance, high political risk and strong bank influence over host
Although not shown, we also tested the aggregate measure of political risk for the period 1984 to 2004 (with a total of 1883
observations) and found that the coefficient is also negative (0.7125) and statistically significant with an R2 of 14.1%.
13
For example, a high value for ‘Democratic Accountability’ means the democracy is rated as very accountable. Less
intuitively a higher value for corruption is implicit of less corruption. Therefore a low disaggregated value, or index value, is
seen as a measure of high risk (say the absence of law and order and the presence of corruption), whilst high values imply the
presence of that which is nonrisk-inducing: for example, good quality of democracy, low corruption, the absence of military in
politics.
14
This has been confirmed with interviews with leading project finance lawyers. For further discussion of legal aspects see
Leader and Ong (2011).
C. Girardone and S. Snaith
1732
Table 3. Political risk WB measures 1996–2003
Variables
Developed Emerging
Developing
countries
countries
countries
Voice and
0.2325
accountability
Political stability
2.2012
Government
0.4396
effectiveness
Regulatory burden
3.4553
Rule of law
7.7329**
Control of
4.3546**
corruption
Adjusted R2
10.4%
0.6080
coefficient is not significant for developing countries
and is never found significant using the ICRG
definitions (Table 2).
0.5820
3.2212**
4.6744
1.1910
7.2721***
2.5907
4.0766
4.8294**
3.4694**
5.0770***
2.3426
25.7%
35.9%
Note: See notes to Table 2.
governments. One reason for the popularity of
project finance in such situations is the lack of
other investment alternatives available in such economies. As a consequence this would lead to an
increased ability to influence host governments, and
thus gain greater concessions for the project and
change the risk–return relationship in the manner
outlined above.
We now validate our results by employing an
alternative measure of political risk, the World
Bank’s Worldwide Governance Research Indicators
Dataset, in place of the ICRG dataset (see Table 3).
While we consider this comparison an essential
‘robustness test’, comparative conclusions should be
drawn tentatively as the definitions of the indicators
differ across the two datasets.
Overall, Table 3 appears to confirm two main
empirical findings. Firstly, there exists a clear negative relationship between loan spreads and two of the
indicators, government effectiveness and the rule of
law for the developing country sample. These are
associated with the ICRG definition of bureaucracy
quality and law and order respectively. This implies
that the cost of funds in project finance is strongly
related to the effectiveness, quality and strength of a
country’s legal and institutional systems. Secondly,
based upon the R2 values, measures of political risk
seem better able to describe loan spreads of developing countries.
The main difference between the results yielded by
the second dataset relates to the coefficient for
the corruption index that is found significant and
positive for developed and emerging countries. This
implies that a reduction in corruption (an increase
in the measure ‘corruption’) will mean that on average
loans will be more expensive. However, the
VI. Conclusions
In recent years, the relative importance of project
finance investments for long-term infrastructure has
increased remarkably, and forms an important source
of finance for countries that are characterized by a
high level of political risk. As expected, our evidence
suggests that project finance loan spreads are significantly lower when loan guarantees are present, and
that lower levels of aggregate political risk are
associated with lower loan spreads. The importance
of guarantees is explained by the nonrecourse nature
of project finance, and it makes intuitive sense that
when, in general, political risk is lower, the cost of a
loan should be lower.
These findings generally corroborate the extant
(albeit scarce) literature on the determinants of
spreads in project finance loans. Yet, this article’s
main contribution is to examine the disaggregate
components of political risk while controlling for the
economic determinants of project loan spreads. We
also distinguish across groups of countries according
to their level of economic development. As far as we
are aware such an analysis has not been carried out
before and is expected to provide us with a better
understanding of the role of political risk in this
context.
Our results from the developed country sample
indicate a limited role for political risk, which is
contrasted by the results garnered for developing
countries. The findings of developing economies
dovetail well with our understanding of project
finance risk allocation. They indicate that the quality
and strength of a country’s legal and institutional
systems help to reduce the cost of project finance
loans. Similar evidence is obtained for bureaucracy
quality. In the case of the former a project finance
deal in such a climate will be reliant on the host
country’s legal and institutional systems. In the case
of the latter the construction of large infrastructure
project requires the extensive cooperation of host
governments. A positive relationship between the cost
of project finance lending with government stability
and democratic accountability is also observed. This
implies that the weaker the host country government
the lower the loan price. We argue that this is a
consequence of risk transference from project to the
developing host state.
Project finance loan spreads and disaggregated political risk
Finding that the cost of project finance loans is
reduced when dealing with weak governments, in
conjunction with the support of a limited role for
political risk in developed countries, suggests that
future research should focus on how bargaining power
varies between developed and developing host countries, and how this variation manifests itself in the host
government agreements of such countries.
Acknowledgements
The authors thank the ESRC for funding this
research under the World Economy and Finance
Research Programme (ESRC award reference
number: RES-156-25-001), and Jerry Coakley,
Sheldon Leader and John Wilson for helpful comments and suggestions.
References
Altunbas, Y. and Gadanecz, B. (2004) Developing country
economic structure and the pricing of syndicated
credits, Journal of Development Studies, 5, 143–73.
Brealey, R. A, Cooper, I. A. and Habib, M. A. (1996)
Using project finance to fund infrastructure investments, Journal of Applied Corporate Finance, 9, 25–38.
Eichengreen, B. and Mody, A. (2000) Lending booms,
reserves and the sustainability of short-term debt:
inferences from the pricing of syndicated bank loans,
Journal of Development Economics, 63, 5–44.
Esty, B. C. (2002) Returns on project-finance investments:
evolution and managerial implications, Journal of
Applied Corporate Finance, 15, 71–86.
1733
Esty, B. C. (2004) Why study large projects? An introduction to research on project finance, European Financial
Management, 10, 213–24.
Esty B. C. (2011) Project finance portal: research, data and
information sources, Harvard Business School.
Available at http://www.people.hbs.edu/besty/projfinportal/ (accessed 5 May 2011).
Esty, B. C. and Megginson, W. L. (2003) Creditor rights,
enforcement, and debt ownership structure: evidence
from the global syndicated loan market, Journal of
Financial and Quantitative Analysis, 38, 37–59.
Hainz, C. and Kleimeier, S. (2007) Project finance as a
risk management tool in international syndicated
lending, Governance and the Efficiency of Economic
Systems Discussion Paper No. 183, University of
Mannheim.
International Country Risk Guide (ICRG) (2011) A
business guide to political risk for international
decisions. Available at www.prsgroup.com (accessed
5 May 2011).
International Finance Corporation (IFC) (1999) Project
Finance in Developing Countries: IFC’s Lessons of
Experience, IFC, Washington DC.
Kleimeier, S. and Megginson, W. (2000) Are project finance
loans different from other syndicated credits?, Journal
of Applied Corporate Finance, 13, 75–87.
Leader, L. and Ong, D. (Eds) (2011) Global Project
Finance, Human Rights and Sustainable Development,
Cambridge University Press, Cambridge.
Sorge, M. (2004) The nature of credit risk in project
finance, Bank for International Settlement Quarterly
Review, 91–101, December.
Sorge, M. and Gadanecz, B. (2008) The term structure of
credit spreads in project finance, International Journal
of Finance and Economics, 13, 68–81.
White, H. (1980) A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity, Econometrica, 48, 817–38.
Appendix
Table A1. Components of political risk indexes
Variables
Definitions
a
ICRG
Government stability
Socio-economic conditions
Investment profile
Internal conflict
External conflict
Government’s ability to carry out its declared programmes and to stay in office.
Subcomponents: government unity; legislative strength; popular support
Socio-economic pressures at work in society that could constraint government action or
fuel social dissatisfaction. Subcomponents: unemployment; consumer confidence;
poverty
Factors affecting the risk to investment not covered by other political, economic or
financial risk components Subcomponents: contract viability/expropriation; profits
repatriation; payment delays
Political violence in a country and its actual and potential impact on governance
Subcomponents civil war/coup threat; terrorism/political violence; civil disorder
Risk to the incumbent government from foreign action, from nonviolent to violent
external pressures. Subcomponents: war; cross-border conflict; foreign pressures
(continued )
C. Girardone and S. Snaith
1734
Table A1. Continued
Variables
Definitions
Corruption
Corruption within the political system particularly in the form of excessive patronage,
nepotism, job reservations, secret party funding and suspicious ties between politics
and business
Involvement of military in politics even at peripheral level including the threat of
military take over
Domination of society or governance by a single religious group that seeks to replace
civil law by religious law and to exclude other religions from the political or social
process
Law is an assessment of the strength and impartiality of the legal system while the order
subcomponent is an assessment of popular observance of the law
It is an assessment of the degree of tension within a country due to racial, nationality or
language divisions
How responsive is government to its people on the basis that the less responsive the
more likely it is to fall
Institutional strength and quality of the bureaucracy refers to the ability to govern
without drastic changes in policy or interruption of government services
Military in politics
Religion in politics
Law and order
Ethnic tensions
Democratic accountability
Bureaucracy quality
World Bank Worldwide Governance Research Indicators Dataset
Voice and accountability
Measures various political, civil and human rights e.g. these indicators measure the
extent to which citizens of a country are able to participate in the selection of
governments and the independence of the media
Political stability
The likelihood of violent threats to, or changes in, government, including terrorism e.g.
acts having direct effect on the continuity of policies, and possibly undermining the
ability of all citizens to peacefully select and replace those in power
Government effectiveness
The competence of the bureaucracy and the quality of public service delivery including
the quality of the bureaucracy, the independence of the civil service from politics and
the credibility of the government’s commitment to policies
Regulatory burden
The incidence of market-unfriendly policies, such as price controls or inadequate bank
supervision, as well as perceptions of the burdens imposed by excessive regulation in
areas like foreign trade and business development
Rule of law
The quality of contract enforcement, the effectiveness and predictability of the judiciary,
the police and the courts, as well as the likelihood of crime and violence. It aims to
measure the success of a society in developing an environment in which fair and
predictable rules form the basis for economic and social interactions, including
property rights protection
Control of corruption
The exercise of public power for private gain, including both petty and grand corruption
and state capture
Note:
a
ß The PRS Group, Inc.