optimal collective action clause thresholds

8/6/2019 Optimal Collective Action Clause Thresholds

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Optimal Collective Action Clause Thresholds

May 14, 2007

Jenna Seki

Department of EconomicsStanford UniversityStanford, CA 94305

[email protected]

under the direction ofProf. John B. Taylor

ABSTRACT

Major financial crises in the previous decadeMexico in 1994, East Asia in 1997, and Russia in1998have drawn greater attention to the importance of organized sovereign debt resolution.Collective action clauses (CACs) are stipulations in bond contracts that allow for greatercertainty and flexibility in the debt restructuring process in the event of a sovereign default. Byallowing restructuring to occur without a unanimous vote by all bondholders and insteadrequiring a specific supermajority consensus, there is greater probability that restructuring canoccur and the investors compensated. Previous literature has studied the consequences of CACinclusion on borrowing costs. In contrast, this thesis examines the ex ante utility optimizingdecision making process of sovereign borrowers as they decide the voting threshold in theirCACs for bondholders approval of a restructuring. In addition, we empirically examine issue-specific characteristics, domestic economic conditions, and global and emerging market creditconditions to determine the factors that affect a sovereigns decision to issue with a specificvoting threshold.

Keywords: collective action clause, majority action clause, emerging market debt, sovereigndebt, International Monetary Fund, Sovereign Debt Restructuring Mechanism

Acknowledgements: Jenna Seki thanks Professor John Taylor and Professor Geoffrey Rothwellfor their guidance. In addition, she would like to thank Anthony Richards of the Reserve Bank

of Australia and Ashoka Mody of the International Monetary Fund for sharing their data and fortheir assistance.


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Introduction

The bond contracts that sovereign countries make with their domestic and foreign

creditors have come under increased scrutiny in recent years. Previously of concern mainly for

lawyers, investment bankers, and perhaps a few investors, these contracts have been the focus of

recent attempts to prevent and resolve emerging market financial crises. Many financial crises in

the past occurred when countries defaulted or were expected to default on their debt. Such

defaults might have been avoided or better resolved if the bond contracts had allowed for orderly

workouts between the sovereigns and the creditors. Most bond contracts, however, did not

allow for such workouts because they required 100 percent unanimity of the bondholders to

change the financial terms of a bondan impossibly high hurdle. If more contracts had

collective action clauses (CAC), which require less than unanimity (75 percent, for example),

sudden defaults could be avoided and crisis resolution could be swifter, more organized, and

more transparent.

Borrower-creditor contractual agreements are therefore important because they are at the

center of recent attempts to improve the terms of sovereign borrowing that allow for more

organized restructuring methods. The collective action clauses in the agreements allow creditors

to interact collectively with the borrower to follow predetermined procedures in the event of

default. The key factor in the CACs is the specific voting majority threshold (e.g. 75 percent) of

creditors that is required to approve the adoption of the restructuring procedure.

More generally, the ease of an orderly restructuring rests on the voting majority threshold

that must be attained. Higher majority voting thresholds are more difficult to achieve because

holdout creditors exist. Lower thresholds, however, carry moral hazard risk as they allow

sovereigns to restructure too easily.


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But what determines this key threshold? What fraction of bondholders is the optimal

requirement for the sovereign to mandate in its CAC? This thesis examines the determinants of

sovereigns threshold selections.

Recent bond issues indicate that sovereigns have different preferred thresholds, despite

the Group of Ten recommendation that the majority threshold be standardized at 75 percent.

Mexico issued bonds in February 2003 with a 75 percent CAC threshold. Soon after Brazil,

Belize, Guatemala, and Venezuela issued sovereign bonds with 85 percent thresholds, more

closely following the recommendations of the private sectors Emerging Market Credit

Association, which was 95% (EMCA, 2002). Since Mexicos landmark $1 billion February

2003 issue, inclusion of CACs in sovereign debt has become a widespread practice, but

sovereigns issue with different thresholds.

The theory developed in this thesis is based on the idea that restructuring procedures

stipulated in a CAC are a type of insurance for the sovereign. In the case of default, there would

be costly legal proceedings and debtors may be able to seize government assets. With a CAC,

the downside for a sovereign is less severe due to the payment plan stipulated in the contract.

CACs raise borrowing costs (particularly for the lowest rated borrowers), and in effect, the

sovereign is paying a premium to carry the CAC insurance plan. This creates a moral hazard

dilemma. The riskiest debtors (sovereigns with the lowest credit ratings) may find lower CAC

thresholds attractive and be more willing to restructure because the downside-capped

restructuring plan will be easier to secure. From an investors perspective, however, bonds

issued with low CAC thresholds by low-rated borrowers may be unattractive because the

sovereign has less incentive follow good macroeconomic policies to prevent restructuring.

Therefore it is a balance of perceived creditworthiness and risk preferences that influences the


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threshold decision. We will empirically test this theory by examining the decisions of sovereign

borrowers.

New York Versus London Governing Law

The Mexican crisis in 1994 created a contagion that affected many emerging market

economies, especially in Latin America, and similarly the East Asian crisis in 1997 and the

Russian crisis in 1998 created contagion. Global credit tightened during these times and

policymakers began looking for alternative ways to prevent or reduce the probability of such

occurrences. Few CACs were used prior to these crises. The G-10 first recommended the use of

CACs as a method of reducing uncertainty in the emerging market debt arena in 1996, an

argument echoed in G22 and G7 reports (Group of Ten 1996, Group of Twenty-Two 1998,

Group of Seven 1998).

Until 2003, sovereign debt issued under New York governing law rarely included CACs,

hence requiring a unanimous decision among bondholders to restructure the payment plan.

Sovereign debt issued under UK governing law normally included CACs. The divergence

between the British and American approaches to bond contracts can be dated back to the 19 th

century. At that time, both nations underwent significant infrastructure development, pushing

railroads and industrial companies to issue bonds in larger numbers. Both bondholders and

issuers realized the inefficiencies associated with allowing a single creditor to force other parties

to engage in buyouts to forestall liquidation, and changes were made to address these issues. The

English solution was to include CACs in bonds. Beginning in the 1870s these clauses allowed a

supermajority of bondholders to make a binding decision for all bondholders to reduce the

amount due under a bond in the event of default. In contrast, in the United States, majority


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action clauses were not used because they were far too general to address the unique capital

structures of American corporations. It was also believed that the bonds marketability would be

impaired if payment terms had the potential to be amended.

Our study focuses on the determinants of a sovereigns optimal threshold. To do so we

face a comparison of New York versus London governing laws. In other words, in order to test

for different optimal thresholds, we will use governing law as a proxy for the sovereigns

threshold decision.

Previous Research

The majority of previous empirical studies has examined the impact of CACs on the bond

interest rate, and has placed little emphasis on the optimal threshold. Eichengreen and Mody

(2004) found that more creditworthy borrowers are less likely to abandon their debts; thus,

including CACs in their bond contracts does not raise yields. For less creditworthy borrowers,

in contrast, the presence of collective-action clauses significantly aggravates moral hazard and

increases borrowing costs (Eichengreen and Mody, 2004 p. 257). Though not the major basis

of their study, they found that under the English law low rated borrowers pay a premium

compared with US law, while higher rated borrowers pay at a discount.

Gugiatti and Richards (2003) looked at the impact of CACs on yields. They analyzed

whether decisions on CACs on new issues affected secondary market spreads for existing issues.

Their test rests on the assumption that the decision to include or exclude CACs is significant

information to the investor, and that it not only affects the value of the stock of debt at the time

of issue, but also that of previously issued bonds (p. 9). If a borrower has previously issued

bonds without (with) CACs, then the decision to issue bonds with (without) CACs signals a


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decline (improvement) in the creditworthiness of the borrower. This should be reflected not only

in higher (lower) borrowing costs for the bond that contains the change in contractual terms, but

also higher (lower) yields on the outstanding stock of bonds (p. 9) . According to their logic, it

is the most recent issuance decision that is relevant in valuing all the sovereigns bonds in the

market. Although they found that CAC inclusion has little or no effect on abnormal returns (i.e.

borrowing costs) and the value of the total stock of debt, they raise the point that previous

decisions on CAC inclusion may be relevant to the current decision a sovereign faces. It may be

that sovereigns are more likely to repeat the decision from the last issue.1

Eichengreen and Mody (2004) constructed a multinomial logit model of choice of

governing law as the dependent variable. Transaction-specific explanatory variables included

nationality of the investment bank bookrunner and the market in which the bond is issued, while

global credit condition indicators included the US Treasury 10-year rate, US high-yield spreads,

the difference between US and the Japanese treasury two-year rates, and the standard deviation

of the daily Emerging Market Bond Index (EMBI) change during the same quarter (Eichengreen

and Mody, 2004 p. 255). They also examined factors that influenced sovereigns decision to

include CACs (using UK governing law as the proxy for CAC inclusion).

In sum there has been little study of threshold selection processes among sovereigns,

though past research has shed light on factors that may affect the sovereigns decision, such as ex

ante interest rate costs.

The Theory of Optimal CAC Thresholds

The most relevant theory to our endeavor of identifying determinants of CAC threshold

selection was published by Haldane, Penalver, Saporta and Shin in 2005. They set out to answer

1 For this reason we will include a dummy variable for preference change in the regression


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the question, Are there valid reasons why different issuers may want to set different, but

country-specific thresholds? In the consideration of this problem they focus on three metrics

debtors are most concerned about. First, debtors care about the probability of a crisis (i.e.

default), which depends on their fundamentals and expected rollover (reinvestment) behavior of

creditors. Second, they care about their payoff in the event of a non-crisis, which is dependent

on the market interest rate, or how much they must compensate the bondholder for bearing their

risk. Third, they care about their payoff in the event of a crisis, which is determined by the

voting of bondholders to restructure the payment plan, as well as the stipulations dictated in the

CAC.

With these assumptions Haldane et al. (2005) determined that threshold preferences are

based on the debtors risk preferences and creditworthiness. They assume that low voting

thresholds allow debtors to get away with an easily-approved restructuring plan, which would

reduce the principal and interest owed to bondholders. These low thresholds, however, come

with a cost, particularly for less creditworthy borrowers. Creditors often view low thresholds as

a moral hazard problem in which the debtor may be more likely to default given the easily-

approved reduced payment structure. Thus, the sovereign weighs ex ante costs (interest rates) to

ex post costs (in the event of crisis or non-crisis).

Threshold preferences, therefore, weigh risk preferences and creditworthiness, reflecting

whether the sovereign is more concerned about payout costs in the event of crisis or non-crisis.

Haldane, et al. theorized that strongly risk-averse borrowers place greater weight on payoffs in

the event of a crisis, and thus choose lower thresholds than less risk-averse debtors. Of these

risk-averse debtors, however, less-creditworthy ones are more likely to issue with high voting


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thresholds. This is because the fear of liquidity problems from creditors not rolling over

(especially short-term creditors) which stem from moral hazard worries.

They also theorized that risk-neutral borrowers prefer high thresholds because the ex ante

benefits of lower interest rates (and investor confidence) and lower probability of a liquidity run

outweighs the ex post cost of easy restructuring (due to low threshold). Low voting thresholds

raise the probability of a liquidity run, in which potential creditors do not rollover on their debt

and new creditors do not purchase the debt.

Figure 1: Variation of Interest rates with Fundamentals

In Figure 1 (Haldane et al. 2005) above, kappa denotes voting threshold. Observe that

interest rates increase at an increasing rate as creditworthiness declines. The lower-rated debtors

need to offer creditors more compensation ex ante for a given threshold. In addition, the less

creditworthy the debtor the more the higher interest rate affects the solvency constraint. The

solvency constraint refers to situations in which the debtor is not able to service its debt burden

(interest and principal). According to this figure, the higher threshold (kappa = 0.85) reduces the

ex ante interest rate cost compared to the lower threshold (kappa = 0.65) while maintaining a


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similar relationship between creditworthiness and interest rate. Notice, however, that as

creditworthiness increases, the interest rate spread between the two bonds with different

thresholds narrows. The theory implies that thresholds are less of an issue in terms of borrowing

costs for the highest-rated borrowers. This seems rational because high-rated borrowers do not

need the insurance that a low voting threshold provides. They also are not hurt by including a

high threshold because of their very low probability of crisis. Due to this feature of spreads

narrowing for the most creditworthy countries, we have focused our study on emerging market

sovereign debt, and examined a range of less-creditworthy borrowers.

Other research suggests additional factors that influence the threshold decision. Catao

and Kapur (2004) examine why many countries with moderate debt-to-income ratios

systematically face higher spreads and more stringent borrowing constraints than others with far

higher debt ratios, finding that macroeconomic volatility is the key. They found that volatility

is associated with higher default probability. According to the theory of Haldane et al. (2005),

creditor rollover is dependent on creditworthiness of the borrower and perceived default

probabilities. Volatility increases the need for borrowing to help smooth domestic consumption,

yet simultaneously the ability to borrow is constrained by the higher default risk that volatility

causes. Catao and Kapur (2004) conducted logit estimates of default probabilities indicate that

output and trade volatility are significant in analyzing sovereign risk. In addition, they found

that ex ante probability of default is increasing in volatility (p. 13). Since volatility affects

probability of default, it may also affect the CAC threshold decision of sovereign borrowers.

Although Eichengreen and Mody (2004) included credit rating in their logit regression to

determine factors that influence the sovereign CAC inclusion decision, this measure of

creditworthiness be more directly accounted for by other variables. Credit ratings may absorb


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explanatory power from issue-specific, country-specific, and global credit condition indicators.

Cantor and Packer (1996) studied the determinants of sovereign credit ratings, with explanatory

variables per capita income, GDP growth, inflation, fiscal balance, external balance, external

debt, economic development, and default history (p. 39). By assigning numerical values B3/B- =

1 and Aaa/AAA = 16 they created a linear rating scale. The explanatory variables per capita

income, inflation, external debt, indicator for economic development, and an indicator for default

history were significant at the 1 percent level, and GDP growth was significant at the 10 percent

level. Thus, when Eichengreen and Mody (2004) constructed the logit regression on UK versus

US governing law by including credit ratings there may be some overlap in the explanatory value

of the individual macroeconomic indicator metric. As a result, our analysis will consider both

inclusion and exclusion of the credit rating term to determine if it has an impact on threshold

selection.

In summary, the optimal thresholds theory presents three main categories of factors that

influence a sovereigns threshold selection. First, issue-specific characteristics such as maturity

length and dollar amount of total issuance may in part affect the threshold decision. Second,

sovereign-specific characteristics such as inflation, debt burden levels and region may also have

some influence. Third, credit condition indicators for both global and emerging markets may

reveal political or economic conditions that may help or hinder the marketability of the debt

issue. Gleaned from the theory, these three categories of influential factors will be identified in

the data set we used and tested for significance.


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Selecting the Data for the Empirical Tests

The data we use for our tests is drawn from Eichengreen and Mody (2004), which

includes all fixed and floating rate bonds issued between 1991 and 2000 by emerging market

sovereigns. Of these 3,295 bonds, 1,588 bonds are governed under UK law, and 1,103 are

governed under US law. The remaining bonds are governed under other laws. For several

reasons, we consider a sub-sample of these bonds in this study.

First, Eichengreen and Mody (2004) include private bonds in their data set. They

acknowledge that their model could be more robust if they control for sovereign issues, which

could produce even larger estimates of the impact of CACs on borrowing costs if moral hazard

concerns apply mainly to low-quality borrowers and case of restructuring is relevant principally

for high-quality borrowers (Eichengreen and Mody, 2004 p. 259). Private borrowers may be

subject to outside influences and different laws that offer incentives to choose a particular

governing law and would introduce bias to the data. As a result, we restricted our data to only

sovereign issues.

Second, for the bonds governed under laws other than NY and London (which comprise

18 percent of their sample) the threshold cannot be determined; hence their inclusion draws into

question the precision of the model.

Third and most important, our test is based on the idea that the choice of governing law

should be treated as endogenous. Hence the sample should be further reduced to countries that

have issued both under UK and US governing law. There are sovereigns who, by tradition, only

issue under one of the governing laws. By only including countries which have issued both

under UK and US law in this time span, there is an implicit decision made between the two

governing laws. As a result, further research can be done to assess the contributing factors of


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why a given sovereign issuer may switch between UK and US governing laws, issue to issue.

While Eichengreen and Mody (2004) found that low rated issuers tend to use UK law more than

US law, followed by a greater use of US laws in the middle and then a shift back to UK laws for

the higher-rated emerging market issuers, a restricted sample may produce different results.

After removing these problematic observations from our sample, the number of bonds falls to

241 from 15 different countries, 131 under UK governing law and 110 under US governing law.

Table 1 and Figure 2 summarize the data. Notice in Table 1 Argentina issued the most

bonds during this time, with a greater tendency to issue under UK governing law. Turkey, the

second most represented country, issued 21 bonds under US law and 15 under UK law. The

countries that issued fewer bonds fairly evenly split their bonds between the two governing laws.

The countries that issued more bonds, however, tended to favor one governing law over the

other. The percentage of issues with CACs (under UK law) is presented in Figure 2.

Table 1: Governing Law by Country Figure 2: CAC Inclusion by Country

0% 20% 40% 60% 80% 100%

Argentina

Brazil

China

Hungary

Kazakhstan

Lebanon

Malaysia

Mexico

Philippines

Poland

South Africa

Trinidad & Tobago

Tunisia

Turkey

Venezuela

Country US UK TotalArgentina 15 55 70

Brazil 4 15 19

China 6 2 8

Hungary 5 12 17

Kazakhstan 1 5 6

Lebanon 12 7 19

Malaysia 1 1 2

Mexico 15 2 17

Philippines 10 7 17

Poland 2 2 4

South Africa 5 4 9

Trinidad & Tobago 4 2 6

Tunisia 3 1 4

Turkey 21 15 36Venezuela 6 1 7

Total 110 131 241

Governing Law


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Figure 3 Figure 4

0

2

4

6

8

10

12

A3/A

-to

Baa1/B

BB+

Baa2/BB

B

to

Ba1/B

B

Ba3/BB-to

B3/B-

Caa1/CCC

+to

Ca1/CC

+

Credit Rating and

Years to Maturity

Average

Years to

Maturity

0%

20%

40%

60%

80%

100%

A3/A

-to

Baa1/B

BB+

Baa2/BB

B

to

Ba1/B

B

Ba3/BB-to

B3/B-

Caa1/CCC

+to

Ca1/CC

+

Credit Rating

%UK

There appears to be a negative relationship between years to maturity of the bond and

CAC inclusion. Figure 4 above indicates that the most creditworthy borrowers are less likely to

issue with CACs than the lowest rated borrowers. In addition, Figure 3 indicates that the longest

maturity bonds are granted to the most creditworthy borrowers. Therefore we might expect to

observe a negative relationship between maturity length and CAC inclusion. On one hand this

does not make sense because longer maturity bonds are riskier, and riskier bonds should have

CACs as backup plans, but there may be another explanation. It may be that countries that need

CACs because of their low creditworthiness and high risk levels are not granted long maturity

bonds. We will consider maturity length as an issue-specific potential determinant of optimal

thresholds.

Econometric Methodology

Our goal is to test the theory that certain factors determine a sovereigns optimal CAC

voting threshold. We will use the three categories of influential factors stated in the theory


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section of this thesis (issue-specific characteristics, country-specific characteristics, and credit

condition indicators). We regress the governing law of our 241 observations on these

explanatory categories. The bond data spans issuance dates from 1991 to 2000, all of which are

prior to Mexicos first-mover issuance in 2003. Mexicos $1 billion February 2003 issuance was

landmark because it was the first large issuance under US governing law that included a CAC.

We assume, that prior to February 2003 sovereign debt issued under US governing law did not

include CACs, and those issued under UK governing law did include them.

We assume that bonds issued under US governing law have CAC voting thresholds of

100 percent and those under UK law have thresholds of 75 percent. Due to data constraints we

focus on these binary observations (UK governing law or US governing law) rather than the full

spectrum of thresholds.

We make the assumption that all sovereign issuers in the data set were utility-optimizing.

In other words, we assume that the governing law selection of the sovereign for that specific

issue was the optimal one, and not a random decision.

We will identify specific characteristics of the bond, country, and credit condition

indicators. We use probit and linear probability models to estimate optimal thresholds. The

basic equation is:

(y) = 0 + 1x1 + 2 x2 + 3 x3 + 4 x4 + 5 x5 +

where y = 1 if UK law, y=0 if US lawx1 = Maturity lengthx2 = Issue Sizex3 = Inflationx4 = External Debt / GDPx5 = US High Yield Spread

where is the residual. We use both the probit and linear probability models to test for

robustness. We will be most concerned with the sign and significance of the coefficients of the


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explanatory variables when relating them to the theory. With our model, given data of maturity,

issue size, inflation, debt burden and credit conditions, sovereigns can determine how different

factors affect their optimal threshold selections. Our methodology heavily rests on the

assumption that sovereigns in our data set have selected their optimal governing law.

We have added a dummy variable for whether the sovereign used UK governing law in

its previous issue to test if sovereigns are more inclined to repeat their previous selection. As

mentioned above in Footnote 1, this variable may indicate the reluctance demonstrated by the

sovereign to change governing law or voting threshold. We will test both with and without this

variable, because we are concerned not with whether the sovereign chose the governing law of

its previous issue, but why it chose that governing law to begin with.

Empirical Results

Our results are presented in Tables 2 and 3. Table 2 is the probit model and Table 3 is

the linear probability model. We focus on regression 1, which strongly supports our hypothesis

that the vector of issue-specific, country-specific, and credit condition indicators determines

optimal CAC voting thresholds. The probit and linear probability models give similar results.

The probit model in Table 2 regression 1 demonstrates that maturity length is negatively related

to the probability of use of a lower threshold, as hypothesized in the theory section of this paper.

The intuition that the highest rated borrowers face no additional cost by using lower thresholds

and thus are more likely to include them, however, does not fit this rationale. The highest rated

borrowers are granted the longest maturity bonds, which the model indicates makes them more


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Table 2Probit Model: Dependent Variable = UK governing law

241 Observations

Log Likelihood -141.884 -121.977 -120.731 -139.656 -99.532 -122.285 -153.322 -151.374 -132.487

Years to Maturity -0.102 *** -0.092 *** -0.092 *** -0.104 *** -0.105 *** -0.108 ***

Inflation -1.448 *** -1.075 *** -0.772 -1.127 ** -0.228 -0.783 *

External Debt / GDP 0.008 ** 0.008 ** 0.010 ** 0.011 *** 0.004 0.006Credit Rating -0.374 *** -0.147 -0.303 **

Credit Rating2

0.0158 *** 0.009 0.0127 **

Previous Issue: UK gov law 1.216 *** 1.185 *** 1.4094 ***

US High Yield Spread -1.688 ** -1.528 ** -1.195

Latin America -0.061 -0.198 -0.424

Middle East -0.473 -0.644 ** -0.652 **

Euro 1.356 *** 1.016 *** 1.092 ***

Mark -6.939 -6.450 -6.083

Yen 0.704 ** 0.776 ** 0.699 **

Other Currency 1.566 *** 1.280 *** 1.253 ***

*** 1% significance ** 5% significance * 10% signficance

91 3 5 87642

Table 3Linear Probability Model: Dependent Variable = UK governing law

241 Observations

R-Square 0.179 0.321 0.329 0.197 0.433 0.304 0.101 0.116 0.246

Adj R-Sq 0.166 0.309 0.312 0.176 0.414 0.280 0.094 0.101 0.226

F Value 12.900 27.820 19.140 9.550 22.160 12.680 13.380 7.760 12.700

Pr > F


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inclined to issue with a higher threshold. The coefficient on the years to maturity is small

(although significant) compared to that of inflation in Table 2. Thus, while its explanatory

power is significant, it is limited in influencing the actual threshold decision. This may be a

result of a limited data set which does not have a wide and normal distribution of maturities and

credit ratings. Maturity length is significant to the 1 percent level in all regressions of the probit

regressions, indicating that is a major determinant of voting threshold.

The probit model presented in Table 2, regression 1 indicates a negative relationship

between inflation and CAC inclusion in which the inflation factor is significant at the 1 percent

level. Greater inflation usually indicates greater uncertainty and volatility in the country, which

Catao and Kapur (2004) wrote likely leads to higher default probabilities. One may conclude

that the riskier the country, the less likely the sovereign will issue low thresholds because of the

moral hazard problem. Higher inflation leading to higher CAC thresholds demonstrates the

moral hazard dilemma facing these low-rated borrowers. The nature of these results draws

attention to the tradeoff between low threshold insurance and investor confidence.

We scaled external debt to reflect the level of the burden compared to the real GDP of the

country. The coefficient of external debt to GDP is consistently positive, and in Table 2

regression 2 it is significant at the 5 percent level. The linear probability model (Table 3,

regression 2) indicates that for every percent change in external debt to GDP, the probability of

CAC inclusion is raised by 0.3 percent. While this relationship proves significant at the 5

percent level, the magnitude of the variables influence is small. The positive coefficient on the

variable suggests that higher debt levels encourage sovereigns to issue with lower thresholds

because the greater burden requires greater insurance in the case of default.


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The US High Yield Spread is an index of representative high yield bonds that depicts the

spread to risk-free Treasury notes. It is an indicator of global credit conditions for lower rated

borrowers and in particular may indicate investor confidence in risky assets such as emerging

market debt. In Table 2 regression 1 the coefficient of this variable is negative and significant at

the 5 percent level. The wider the index spread trades to Treasuries, the greater uncertainty and

perceived risk there is in the high yield market. As credit tightens, it becomes more expensive

for high yield borrowers to obtain and maintain financing. The model indicates that the riskier

the environment, the less likely the sovereign will issue with low thresholds. One reason may be

that investors are fleeing to higher quality investments and may shun bonds with built-in

insurance due to the potential for moral hazard. We found similar results in terms of magnitude

and coefficient sign in the linear probability model, demonstrating the robustness of our test.

We estimated the effects of credit rating and credit rating2

separate from the other

explanatory variables in regressions 7, 8 and 9 because these variables absorbed the explanatory

power of the other country-specific indicators. Because credit rating agencies take into account

inflation and debt burdens, this makes sense that their explanatory power would overlap. The

negative coefficient on credit rating and positive coefficient on credit rating2 confirms the

quadratic relationship between CAC inclusion and creditworthiness found by Eichengreen and

Mody (2004). At the extremes, that is for the highest and lowest rated sovereigns, there is

incentive to include lower thresholds as insurance. For the mid-rated borrowers, however, there

is greater ambiguity as to the costs and benefits of CAC inclusion and fewer opt to include this

contractual approach. In our analysis we find it more useful to examine the individual issue and

country specific characteristics to better estimate the sovereigns optimal CAC voting threshold.


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Region and Currency of Issue

In the linear probability model (Table 3) dummies for the regions Latin America and

Middle East were significant determinants of CAC inclusion. Thus, we included them in the

probit model, and found that while Latin America proved insignificant, the dummy for the

Middle East region was significant to the 5 percent level (Table 2, regression 4). Thus, there is

also a regional bias toward UK governing law that may reflect a trend in the countries located

there.

We included dummies for currency of issue, with the US dollar as the baseline, to

determine if choice of governing law is influenced by a desired currency. There is a problem

with this approach, however, because we cannot determine if it is the desired currency

influencing the sovereigns CAC decision or if the desirability for CAC inclusion leads the

sovereign to choose the currency of issue most conducive to this selection. In addition, there

may be other factors, such as targeting specific investors, that the sovereign elects a specific

currency of issue. As a result we modeled both with and without the currency dummies, and

found that the euro, yen, and other currencies were positively related to CAC inclusion. Japan

was not included in this data set because it is not an emerging market economy, but in practice as

a borrower Japan routinely uses 75 percent thresholds in its bond contracts. Thus, the significant

positive coefficient on the yen is to be expected. Since these coefficients are in comparison to

the baseline dollar, we would expect that issues in euro would be more likely have lower voting

thresholds, as Britain is in the Eurozone and there may be a regional bias. We tested the model

both with and without these region and currency factors, and found that while significant, they

deplete the significance of the country-specific characteristics.


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Conclusion

The theory developed in this paper indicates that a combination of maturity length of the

bond, national inflation, external debt ratios, and high yield spreads dictate optimal thresholds for

sovereigns. Our empirical results confirm the theory. The explanatory variables, which we

categorized into issue-specific, country-specific, and credit condition characteristics, affected

sovereigns optimal thresholds as predicted by the theory. Greater inflation and credit tightening

(US High Yield Spread) signal greater risk associated with the debtor and that higher thresholds

are optimal due to moral hazard concerns. Higher external debt levels and longer maturity bonds

signal that lower thresholds are optimal. Our findings indicate how different determinants affect

the sovereigns optimal threshold selection.

According to our review of the existing literature, research this kind of study has never

been done. Identifying the factors that affect the threshold selection has enabled us to determine

how sovereigns can best employ this contractual method to optimize payment structures.

As the theory hypothesized, a vector of issue-specific factors, country-specific factors,

and global and emerging market credit conditions are determinants of CAC inclusion and

threshold selection. We have considered a wide range of other factors (reported in the

regressions in the Appendix), but the variables we have focused on in the main body of this

theses seem to be the most consistent and robust. To be sure, there may be other contributing

factors that we have not included in the study, such as confidence in the political regime or

aversion of a purchasing a countrys debt based on its human rights practices. In addition, a

greater sample size may lead to more definitive results, and is a goal of future study.

The results in this thesis have broad implications for policy. For example, the alternative

to CACs as a method of restructuring unsustainable debt burdens that minimizes cost to the


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sovereign and the loss to creditors is the sovereign debt restructuring mechanism (SDRM).

SDRM was first introduced in 2003 by the International Monetary Fund (IMF) and is modeled

after bankruptcy frameworks in national economies. This statutory approach has met great

opposition, however, because it calls for the development of an international bankruptcy court.

As nations have different legal systems and rules, in order to maintain credibility this court

would need to possess the power to overrule domestic legal policy, a requirement not well

received by national governments and policymakers. As a contractual approach, CACs offer the

insurance of speedy restructuring in the case of default without encroaching on or contradicting

national laws. Thus, it is important to analyze the costs and benefits of CAC inclusion and

threshold selection, and study what factors motivate sovereigns to choose this contractual

method.

We believe that CACs are the most appropriate and effective method to reduce the

occurrences of crises and speedily restructure in the case of default. Our findings indicate that

the argument for a market standard threshold (as suggested by the G-10) is inappropriate. Our

model suggests that a variety of factors influence a sovereigns optimal threshold, and that

standardization would be sub-optimal. Portes (2003) argues that the lack of standardization may

undermine the effectiveness of CACs as a widely adopted method, but we think that it is the

flexibility and personalization of CACs that makes it such a valuable tool. It is the

individualization that CACs afford by allowing sovereign governments to choose their optimal

threshold that has made it increasingly popular and our study has revealed some factors that can

assist these governments in their endeavor to identify this optimal fraction.


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Appendix

This appendix supplements the main findings presented in the Results section of this

thesis.

Table 4 presents additional specifications for the probit model results, and Table 5

presents additional specifications for the linear probability model results. Notice that the effects

of the key explanatory variables are similar in these models, even when other factors are added

to the equations. In other words the results stand up to any alternative specification. This shows

the robustness of our approach.

In Table 6 the data set is broken into sub-samples by region. Eastern Europe, East Asia

and Pacific, Latin America and the Middle East regions are individually tested with a similar

model to that used for the entire sample. These estimation results are less reliable than for the

full sample due to the limited number of observations for each region, and few of the estimated

coefficients are significant statistically. For this reason we focused on the full sample in this

paper. As more data are obtained future research using this method may be able to shed light on

regional trends in governing law and threshold selections.


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Appendix

Appendix: Table 4aProbit Model: Dependent Variable = UK governing law

241 Observations

Log Likelihood -80.553 -109.601 -111.075 -120.669 -141.371 -120.964 -141.722

Degrees of Freedom 32 28 23 8 7 7 6

Years to Maturity -0.091 *** -0.080 *** -0.079 *** -0.088 *** -0.088 *** -0.087 *** -0.087 ***

Inflation -0.533 -2.136 * -2.022 ** -0.839 -0.863 * -0.755 -0.788

External Debt / GDP 0.068 0.069 0.082 ** 0.006 0.005 0.006 0.005

Credit Rating 0.413 0.298 -0.295 -0.181 -0.293 -0.206 -0.317 **

Credit Rating2 -0.010 -0.008 0.010 0.007 0.011 ** 0.008 0.012

Previous Issue: UK gov law 1.211 *** 1.030 *** 1.063 *** 1.186 *** 1.186 ***

US High Yield Spread -0.452 -0.686 -0.772

US 2 Year Rate -0.337 * -0.086 -0.055 -0.101 -0.104

EMBI Standard Deviation -0.199 -0.106 -0.104 -0.079 -0.056 -0.069 -0.046

Foreign Reserves / GDP 0.110 0.021 0.029

Log (Issue Amount in USD) -0.278 -0.297 -0.242

Domestic Credit / GDP -0.247 3.309 2.064

Total Debt / GDP 0.060 0.109 0.029

Short Term Debt / GDP -0.305 -0.457 -0.344

Imports / GDP -0.209 -0.148 -0.302

GDP Growth 0.130 0.076 0.032

Exports / GDP -0.107 0.021 0.109

Total Debt Service / GDP -0.119 0.010 -0.016

Real GDP 0.104 0.105 -0.058

GNP -0.227 0.463 0.114

Bank Credit Stock / GDP 0.064 -0.047 -0.089

Standard Deviation of US 10 Year 0.429 0.541 0.665

US Industrial Production Growth -0.021 -0.064 -0.065

Latin America -1.736 -0.888

Middle East -1.866 -1.549

Trinidad & Tobago -0.288 -0.447

Africa -0.398 0.037

East Asia & Pacific -0.257 -0.677

Euro 2.157 ***

Mark -6.918

Yen 0.939 **

Other Currency 2.275 ***


1 2 3 4 5 6 7


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Appendix: Table 4bProbit Model: Dependent Variable = UK governing law

241 Observations

Log Likelihood -120.992 -121.174 -139.819 -120.311 -120.443 -138.005 -98.655 -98.6651 -120.404 -1

Degrees of Freedom 7 6 6 9 8 8 11 10 10

Years to Maturity -0.089 *** -0.088 *** -0.094 *** -0.089 *** -0.088 *** -0.093 *** -0 .102 *** -0.102 *** -0.100 *** Inflation -0.818 -0.782 -0.929 * -0.634 -0.598 -0.693 0.067 0.062 -0.278

External Debt / GDP 0.006 0.006 0.005 0.007 0.007 0.006 0.003 0.003 0.003

Credit Rating -0.191 -0.196 -0.285 * -0.064 -0.066 -0.076 -0.242 -0.241 -0.317 *

Credit Rating2

0.007 0.007 0.011 * 0.003 0.003 0.005 0.009 0.009 0.012 *

Previous Issue: UK gov law 1.157 *** 1.182 *** 1.138 1.162 *** 1.394 *** 1.387 ***

US High Yield Spread -0.503 -1.520 ** -0.437 *** -1.510 * 0.145 -1.149

US 2 Year Rate

EMBI Standard Deviation

Foreign Reserves / GDP

Log (Issue Amount in USD)

Domestic Credit / GDP

Total Debt / GDP

Short Term Debt / GDP

Imports / GDP

GDP Growth

Exports / GDP

Total Debt Service / GDP

Real GDP

GNP

Bank Credit Stock / GDP

Standard Deviation of US 10 Year

US Industrial Production Growth

Latin America -0.179 -0.161 -0.484

Middle East -0.474 -0.478 -0.751 *

Trinidad & Tobago

Africa

East Asia & Pacific

Euro 1.392 *** 1.398 *** 1.090 ***

Mark -6.895 -6.907 -6.425

Yen 0.625 * 0.619 * 0.648 **

Other Currency 1.593 *** 1.575 *** 1.270 ***


15 1610 8 9 12 1311 14


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Appendix: Table 4cProbit Model: Dependent Variable = UK governing law

241 Observations

Log Likelihood -121.736 -121.977 -141.884 -120.602 -120.731 -139.656 -144.434 -99.527 -99.532

Degrees of Freedom 5 4 4 7 6 6 3 9 8

Years to Maturity -0.093 *** -0.092 *** -0.102 *** -0.093 *** -0.092 *** -0.104 *** -0.097 *** -0.105 *** -0.105 ***Inflation -1.097 *** -1.075 *** -1.448 *** -0.810 * -0.772 -1.127 ** -1.400 *** -0.225 -0.228

External Debt / GDP 0.008 ** 0.008 ** 0.008 ** 0.010 ** 0.010 ** 0.011 *** 0.009 ** 0.004 0.004

Credit Rating

Credit Rating2

Previous Issue: UK gov law 1.185 *** 1.216 *** 1.163 *** 1.185 *** 1.4144 *** 1.4094 ***

US High Yield Spread -0.573 -1.688 ** -0.433 -1.528 ** 0.104

US 2 Year Rate

EMBI Standard Deviation

Foreign Reserves / GDP

Log (Issue Amount in USD)

Domestic Credit / GDP

Total Debt / GDP

Short Term Debt / GDP

Imports / GDP

GDP Growth

Exports / GDP

Total Debt Service / GDP

Real GDP

GNP

Bank Credit Stock / GDP

Standard Deviation of US 10 Year

US Industrial Production Growth

Latin America -0.077 -0.061 -0.198 -0.198

Middle East -0.466 -0.473 -0.644 ** -0.644 **

Trinidad & Tobago

Africa

East Asia & Pacific

Euro 1.351 *** 1.356 ***

Mark -6.930 -6.939

Yen 0.708 ** 0.704 **

Other Currency 1.579 *** 1.566 ***


23 27242120 252219 26


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Appendix: Table 5aLinear Probability Model: Dependent Variable = UK governing law

241 Observations

R-Square 0.539 0.387 0.378 0.327 0.184 0.326 0.182

Adj R-Sq 0.468 0.306 0.312 0.304 0.160 0.305 0.161

F Value 7.590 4.780 5.730 14.080 7.510 16.070 8.670

Pr > F


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Appendix: Table 5bLinear Probability Model: Dependent Variable = UK governing law

241 Observations

R-Square 0.327 0.325 0.194 0.333 0.331 0.208 0.437 0.437 0.315 0.309

Adj R-Sq 0.307 0.308 0.173 0.307 0.308 0.180 0.410 0.412 0.285 0.283

F Value 16.160 18.790 9.370 12.790 14.350 7.590 16.170 17.840 10.570 11.500

Pr > F


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Appendix: Table 5cLinear Probability Model: Dependent Variable = UK governing law

241 Observations

R-Square 0.323 0.321 0.179 0.331 0.329 0.197 0.184 0.434 0.433 0.304

Adj R-Sq 0.308 0.309 0.166 0.311 0.312 0.176 0.166 0.412 0.414 0.280

F Value 22.410 27.820 12.900 16.450 19.140 9.550 10.570 19.650 22.160 12.680

Pr > F


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Appendix: Table 6: Regression Results by Region

Probit Model: Dependent Variable = UK governing law

Region: Eastern Europe (27 observations)

Log Likelihood -10.937 -10.567 -8.279 -7.979 -8.616 -8.500 -7.346 -7.304

Degrees of Freedom 3 4 4 5 5 6 6 7

Years to Maturity -0.208 * -0.220 ** -0.348 -0.350 -0.325 * -0.312 -0.467 -0.444

Inflation 9.780 * 10.848 * 18.020 ** 19.178 ** 13.318 * 13.375 ** 20.484 ** 20.280 **

External Debt / GDP 0.015 0.015 0.018 0.017 0.455 0.398 0.575 0.526

Credit Rating -21.113 -18.828 -25.529 -23.494

Credit Rating2

0.961 0.857 1.165 1.073

Previous Issue: UK gov law -0.675 -0.672 -0.466 -0.292

US High Yield Spread 10.454 * 10.364 9.206 8.993



Region: East Asia & Pacific (27 observations)

Log Likelihood -11.012 0.000 -10.999 0.000 -4.924 0.000 0.000 0.000


Years to Maturity -0.109 ** -4.242 -0.107 * -2.577 -0.637 * -4.523 -14.927 -2.498

Inflation 17.266 * 458.445 16.660 607.076 35.018 * -39.324 485.313 67.187External Debt / GDP 0.090 2.770 0.074 3.568 -10.366 * -29.530 -272.574 -43.216

Credit Rating 1,566.773 * 4461.993 39500.660 6,384.320

Credit Rating2

-81.883 * -232.992 -2064.520 -333.712

Previous Issue: UK gov law 80.653 76.764 41.692 274.158 16.205

US High Yield Spread 0.659 34.317 44.940



Region: Latin America (113 observations)

Log Likelihood -60.293 -55.538 -47.308 -54.282 -47.053 -49.538 -58.830 -49.311


Years to Maturity -0.070 ** -0.068 ** -0.076 *** -0.070 ** -0.062 ** -0.066 ** -0.071 ** -0.070 **

Inflation -2.335 ** -1.303 -2.014 ** -1.189 -2.989 *** -1.705 -2.562 ** -1.547

External Debt / GDP 0.013 0.010 0.017 0.011 -0.018 -0.006 -0.008 -0.002Credit Rating 1.247 ** 1.051 * 1.355 ** 1.100 *

Credit Rating2

-0.035 * -0.031 -0.039 ** -0.033

Previous Issue: UK gov law 1.362 *** 1.317 *** 1.253 *** 1.205 ***

US High Yield Spread -2.192 * -0.971 -2.123 -1.059



Region: Middle East (59 observations)

Log Likelihood -35.565 -28.410 -32.913 -26.988

Degrees of Freedom 3 4 4 5

Years to Maturity -0.135 * -0.216 ** -0.147 * -0.218 **

Inflation 0.626 -1.198 -0.458 -1.879

External Debt / GDP -0.002 0.014 0.002 0.015

Credit Rating

Credit Rating2

Previous Issue: UK gov law 1.542 *** 1.438 ***

US High Yield Spread -3.675 ** -2.996


5 6 7 81 2 3 4

1 2 3 4 5 6 7 8

5 6 7 81 2 3 4

1 2 3 4 5 6 7 8


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optimal collective action clause thresholds

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