Financial Crises & Financial Derivatives

Financial Crises & Financial Derivatives: Government Use of Interest Rate Swaps From 2003 - 2012

DISSERTATION

Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy in the Graduate School of The Ohio State University

Akheil Singla

Graduate Program in Public Policy and Management

The Ohio State University

2015

Dissertation Committee:

Professor Charlotte Kirschner, Advisor

Professor Robert Greenbaum

Professor Trevor Brown

Professor Martin Luby

Copyrighted by

Akheil Singla

2015

Abstract

Despite getting coverage in the financial press, government use of financial derivatives such as interest rate swaps remains a fairly understudied area of public financial management research. Indeed, press accounts emphasizing the downside of these transactions are some of the only sources of information on the performance of these instruments. However, while headlines about termination payments totaling hundreds of millions of dollars may generate reader interest, they are far from systematic evaluations of these instruments. This research aims to remedy this situation by exploring how governments are using these instruments, the contexts in which these instruments are used, and how to develop systems that evaluate the use of these instruments as policy tools.

The first chapter of the dissertation works to identify the scope of use of these instruments over a 10 year period. Broadly, it addresses three questions: 1) How did the largest city governments use debt-related derivatives over time? 2) How, if at all, did state and city use of debt-related derivatives differ over time? 3) How did government use of debt-related derivatives evolve over time? It finds that many cities were active participants in the debt-related derivatives markets. Second, it demonstrates that while there were some differences between state and city government use, these do not necessarily appear to be systematic. Third, it suggests that the evolution of use of debt- related derivatives was altered by the Great Recession, as many state and city

ii governments ratcheted up their use of these instruments from 2003 to 2008 only to swiftly exit the market during and after the financial crisis.

The second chapter builds on the analysis and data from the first chapter to explore the following question: What are the motivating factors for city governments choosing to use debt-related derivative instruments? It does so by using the data gathered and presented from the previous chapter in concert with additional data from city government Comprehensive Annual Financial Reports to examine the relationship between new use of debt-related derivatives and other characteristics of governments.

The chapter finds that the characteristics of government most associated with new debt-

related derivative use are size (as measured by total assets) and prior use of the agreements, with external market conditions also being an important factor.

The third chapter takes a different approach than the first two chapters, instead discussing a framework for evaluating individual debt-related derivative transactions. The chapter leverages an extensive evaluation conducted by The Chicago Tribune, both as a lens for what to do, and as a means to identify additional areas for analysis. In sum, the chapter describes a method by which financial managers and policy makers alike can evaluate whether the instruments saved money in the long run and whether the decisions to use the instruments made sense at the time the decisions were made.

iii

Acknowledgments

I would like to thank my friends and family. Without your help and support, this would have not been possible.

Vita

2010 ...... B.S. Government, Management & Business,

Skidmore College

Publications

“A Descriptive Analysis of State Government Debt-Related Derivatives Policies” (Akheil

Singla and Martin Luby) Public Budgeting & Finance, Summer 2014.

“Blind, Broke, and Bedlam: Differentiating Fiscal Stress from Bankruptcy in California”

(Akheil Singla, James Comeaux, and Charlotte Kirschner) Public Finance and

Management, 14(3), 2014.

“The Legacy of Dwight Waldo: Taking a Broader View of Public Administration”

(Akheil Singla and Kristin Harlow). Book chapter in Mastering Public Administration by

Raadschelders & Fry, 2013

Fields of Study

Major Field: Public Policy and Management v

Table of Contents

Abstract ...... ii

Acknowledgments...... iv

Vita ...... v

List of Tables ...... ix

List of Figures ...... xi

Introduction ...... 1

Chapter 1: Riding the Roller Coaster: Evolutions in Government Use of Debt-related

Derivatives from 2003 – 2012 ...... 5

Introduction ...... 5

Background ...... 8

Debt-related Derivatives ...... 9

Design of Swaps ...... 10

Government Use of Debt-related Derivatives ...... 16

Data & Methods ...... 17

Results ...... 19

Discussion ...... 26

Conclusion ...... 32 vi

Chapter 2: Fair or Foul: Motivations for Government Use of Debt-related Derivatives.. 35

Introduction ...... 35

Background: Justification for Debt-related Derivatives ...... 36

Hypotheses ...... 40

Data & Methods ...... 45

Results ...... 53

Discussion ...... 59

Conclusion ...... 63

Chapter 3: A Framework for Assessing Government Use of Debt-Related Derivatives . 66

Introduction ...... 66

Program Evaluation ...... 68

Application of Framework to Debt-related Derivatives ...... 70

Evaluating the Treated Outcome ...... 73

Estimating the Untreated Counterfactual Outcome ...... 75

Program Effects and Causes of Effect Variation ...... 77

Limits to Traditional Program Evaluation of Swaps & Alternative Methods...... 79

Conclusion ...... 83

Conclusion ...... 84

References ...... 87

vii

Appendix A: Notional Values ...... 91

Appendix B: Additional Results from Chapter 2 ...... 95

viii

List of Tables

Table 1. Types of Debt-related Derivatives ...... 14

Table 2. Types of Risk Associated with Debt-related Derivatives ...... 15

Table 3. Data Collected & Description ...... 19

Table 4. Total Notional Value of Debt-related Derivatives, 2003-2012 ...... 20

Table 5. Termination Payments and Notional Value Terminated (in 000s) ...... 25

Table 6. Government Motivations for Using Debt-related Derivatives ...... 40

Table 7. Financial Condition Ratios ...... 48

Table 8. Characteristics of Explanatory and Dependent Variables ...... 50

Table 9. Correlation Matrix of Independent Variables ...... 51

Table 10. Comparison of Means (t Test): Differences Between New Swap Users and

Non-Users ...... 53

Table 11. Tobit Estimation of New Swap Use ...... 54

Table 12. Methods Comparison, P-Values in Parentheses ...... 58

Table 13. Support for Hypotheses, via Sign and Statistical Significance ...... 60

Table 14. Tribune Method for Estimating Unobserved Counterfactual ...... 75

Table 15. City Notional Value in Millions ...... 91

Table 16. State Notional Value in Millions ...... 93

Table 17. VIF Test for Multicollinearity ...... 95

Table 18. Univariate Tobit Models ...... 96

Table 19. Restricted Tobit Models Comparison ...... 97

Table 20. Tobit Estimation of New Swap Use, 2003-2007 ...... 98

Table 21. Tobit Estimation of New Swap Use, Non-Users in States with No Other Users

Excluded ...... 98

Table 22. Tobit Estimation of New Swap Use, All Non-users Excluded ...... 99

List of Figures

Figure 1. Plain Vanilla Swap ...... 11

Figure 2. Converting to a Synthetic Fixed Rate ...... 12

Figure 3. City and State Notional Value as Percent of Total Debt, 2003-2012 ...... 21

Figure 4. Type of Debt-related Derivative Used in Cities and States over Time ...... 22

Figure 5. Ratio of Fair Value to Notional Value for Cities and States, 2003-2012 ...... 24

Figure 6. City and State Notional Value, 2010-2012...... 28

Figure 7. Floating-to-Fixed Interest Rate Swap ...... 72

Introduction

In recent years, there have been a number of critical developments in the world of

state and local government finance. The Great Recession, of course, is the main driver of

most of these changes, as it resulted in resource limitations that still reverberate in the

choices legislators, mayors, and other policy makers are faced with today. While the

outcomes of these developments are still very much in flux, what is clear is that the

landscape of municipal finance is very different today than it was 10 or 15 years ago.

This dissertation endeavors to trace some of the changes that have occurred over that time

frame and their effects on a relatively new area of municipal debt: the use of debt-related

derivatives at the state and local government level.

Derivatives, broadly, can be thought of as anything that derives its value from

something else. While the specifics of the definition are discussed more throughout the

rest of the chapter, a good example from everyday life might be a promotion that many

professional sporting events run: if the home team scores a certain number of points, say

five goals in a hockey game, any purchaser of a ticket to the game is entitled to some prize, like free chicken wings at a local restaurant. The value of the ticket after the game, then, is based on an outcome in the game, but has no effect on the game itself (Luby,

2012; Javers, 2010).

Debt-related derivatives are financial instruments that governments enter into that derive their value, at least in part, based on government-issued debt. The motivations for using these instruments are numerous, but in the broadest sense, the goal is to generate

1 cost savings for the tax payer. These instruments have simultaneously been described as time bombs by some and important risk management tools by others. The Chicago

Tribune, for instance, published a series of articles in 2014 discussing how a few floating- to-fixed interest rate swaps created problems for Chicago Public Schools and potentially lost the city $100 million dollars. Nevertheless, financial experts still suggest that these instruments are useful in the right contexts. While this divergence in thought is fairly well documented, there remains fairly little in the way of comprehensive study of these instruments. I endeavor to understand how governments are using these instruments, how we can better understand the contexts in which these instruments are used, and how we might go about developing systems that evaluate the use of these instruments as policy tools.

By addressing these questions, the dissertation provides insight on relatively complex instruments by describing how they were used and discussing ways to evaluate that use. More broadly, the information contained here endeavors to advance knowledge in the larger area of public debt management. In general, this area of research deals with establishing best practices about how, when, and why to use debt to finance government projects or services. At the highest level, the field might be best summarized by three ideas: securing required funds in a timely fashion; minimizing the cost associated with securing the funds; and ensuring the ability to repay borrowed funds in a way that does not jeopardize the first two (Justice and Miller, 2011). Correspondingly, Singla and Luby

(2014) note that state derivative policies typically recommend that debt-related derivatives can reduce interest rates, allow increased flexibility in timing the market, and provide the opportunity to hedge risks. Thus, more systematic study of these instruments,

2 while narrow in focus, has implications that are wide in scope. Put another way, more knowledge of debt-related derivatives is needed to understand their use as tools to manage subnational debt portfolios. To date, there has been precious little academic study in this area. Two studies (Steward and Cox, 2008; Luby and Kravchuk, 2013) have assessed the extent to which these instruments were used, but the first is limited to a single year and the second only looks at state governments. Other work is more focused on case studies of particular transactions (Luby, 2012) or addresses the policies in place to manage the use of these instruments (Singla and Luby, 2014). This dissertation endeavors to add to this literature by developing more information on the use of the instruments, empirically assessing the characteristics of governments using debt-related derivatives, and discussing a framework for evaluating government use of the instruments.

The first chapter of the dissertation works to identify the scope of use of these instruments over a 10 year period. Broadly, it addresses three questions: 1) How did the largest city governments use debt-related derivatives over time? 2) How, if at all, did state and city use of debt-related derivatives differ over time? 3) How did government use of debt-related derivatives evolve over time? I find that many cities were active participants in the debt-related derivatives markets. Second, I demonstrate that while there were some differences between state and city government use, these do not necessarily appear to be systematic. Third, I suggest that the evolution of use of debt- related derivatives was altered by the Great Recession, as many state and city governments ratcheted up their use of these instruments from 2003 to 2008 only to swiftly exit the market during and after the financial crisis.

The second chapter builds on the analysis and data from the first chapter to

explore the following question: What are the motivating factors for city governments

choosing to use debt-related derivative instruments? It does so by using the data gathered

and presented from the previous chapter in concert with additional data from city

government Comprehensive Annual Financial Reports to examine the relationship

between new use of debt-related derivatives and other characteristics of governments.

The chapter finds that the characteristics of government most associated with new debt-

related derivative use are size as measured by total assets and prior use of the agreements,

with external market conditions also being an important factor.

The third chapter takes a different approach than the first two chapters, instead

discussing a framework for evaluating individual debt-related derivative transactions. If the first two chapters are more focused on identifying both the scope of use and the characteristics of governments using the instruments, the final chapter is an exploration of what information and analysis one would need to undertake to determine the outcomes of these instruments. The chapter leverages an extensive evaluation conducted by The

Chicago Tribune, both as a lens for what to do, and as a means to identify additional areas for analysis. In sum, the chapter endeavors to describe a method by which financial managers and policy makers alike can evaluate whether the instruments saved money in the long run and whether the decisions to use the instruments made sense at the time the decisions were made.

Chapter 1: Riding the Roller Coaster: Evolutions in Government Use of Debt-related

Derivatives from 2003 – 2012

Introduction

Warren Buffet famously once referred to derivatives as “financial WMDs”

(Berkshire Hathaway, 2002). In the wake of the 2008 global financial crisis, these words

might appear prescient to some. Many have argued that the pervasive use of derivatives,

instruments such as credit default swaps, made a linear finance problem geometric. When

the housing market collapsed, the cumulative exposure of many banks and large financial

institutions to each other via derivatives threatened to collapse the global economy.

Despite these fears not being realized, the resulting recessions in many countries still

demonstrated the risks associated with derivatives. Increased regulation of these

instruments was a central part of the 2010 Congressional response, the Dodd–Frank Wall

Street Reform and Consumer Protection Act (commonly referred to as Dodd-Frank),

reflecting a general acknowledgement of the widespread corporate use of these

instruments. Inherent in this legislation was a central idea: while these instruments are

risky, as evidenced by their role in the financial crisis, they are also important risk- management tools for any financial entity.

While this tension came to the fore for corporate use of debt-related derivatives and their subsequent regulation, government use of these instruments has received comparatively little attention. Bankruptcies in Orange County in 1994 and Jefferson

County more recently have increased the attention paid to the subject, but that attention

has been largely focused on the negative outcomes these instruments can have. For

instance, in Jefferson County the extensive use of interest rate swaps and subsequent

termination payments have been blamed for driving the county to file for Chapter 9

protection. In less extreme cases, swap termination fees and basis mismatch can result in

economic losses for cities expecting economic gains. Chicago, for instance, entered into

an agreement to refund debt using a swap and expected $31 million in savings in 2004.

Just a few years later, that same deal can be projected as an $11.8 million dollar loss

(Luby, 2012). Nevertheless, many finance experts have and would still suggest that

derivatives are an integral part of an overall debt-management strategy that can generate real cost-savings for governments. Singla and Luby (2014), for instance, note that state derivative policies typically recommend that these instruments can reduce interest rates, allow increased flexibility in timing the market, and provide the opportunity to hedge risks. Overall, this divide between attention focused on the negative repercussions and theoretical ideas about the benefits of these instruments reflects a lack of systematic study about the actual uses of these instruments by governments. This research begins to address the issue by describing in much more detail state and local government use of derivatives.

While recent work has charted state and local government use in a particular year

(Stewart and Cox, 2008) and state government use over time (Luby and Kravchuk, 2013), it is unclear how local and state government use of these instruments differs. Recently, there has been a swath of articles in the financial press about interest rate swap portfolios for several cities (Guyette, 2014; Mihalopoulos, 2014; Karlamangla, 2014; Varghese,

2012), but these articles lack any sort of comparison across cities, focusing instead on

negative repercussions. For instance, the Chapter 9 proceedings in Detroit have spurred

significant discussion about the value, legality, and prudence of a series of floating-to-

fixed interest rate swaps associated with pension obligation certificates of participation.

At the time of the transaction, the deal won acclaim from The Bond Buyer, but recently

the transaction has been cited as a major contributing factor in the city’s bankruptcy

(Carvlin, 2005; Stone, Singla, Comeaux, and Kirschner, 2014). State use of these

instruments, which has been more recently documented in academic literature, has not

received the same sort of attention from the financial media. Thus, there is not clear

evidence of the amount of local government use of debt-related derivatives or how this use might compare with state usage.

In addition to open questions about the differences between state and city use of

debt-related derivatives, the effect of the recent economic crisis is also unclear. The

decline in the economy has had a significant effect on most financial markets, but there is

little information addressing how it has altered state and local government use of debt-

related derivatives. Estimates of state use totaled $43 billion in notional value in 2009,

but it is unclear how declines in government revenue, changes in the interest rate

environment, and broader economic pressures might have altered state and city use of

these instruments.

Using comprehensive annual financial reports, I gather data on debt-related

derivative transactions for the 50 most populous cities from 2003 to 2012. I also collect

the same information for all 50 states from 2010 to 2012 and combine it with existing

data on states from 2003 to 2009. Using this information, I explore three questions: 1)

How did the largest city governments use debt-related derivatives over time? 2) How, if

at all, did state and city use of debt-related derivatives differ over time? 3) How did

government use of debt-related derivatives evolve over time? I first find that many cities

were active participants in the debt-related derivatives markets. Second, I demonstrate

that while there were some differences between state and city government use, these do

not necessarily appear to be systematic. Third, I suggest that the evolution of use of debt-

related derivatives was altered by the Great Recession, as many state and city

governments ratcheted up their use of these instruments from 2003 to 2008 only to

swiftly exit the market during and after the financial crisis.

This chapter is organized in five sections. The first section provides background

on the conceptual foundation for understanding derivatives, debt-related derivatives, and

the costs and benefits associated with their use. The second section presents the data and

methods for analysis. The third section presents the analytical results with aggregated

information about state and local government use of debt-related derivatives. A fourth section analyzes the evolutions in the debt-related derivatives market with a particular

eye toward explaining why the market developed the way it did. Finally, a fifth,

concluding section sums up and offers some remarks about future directions for research

in this area.

Background

This section provides a broad discussion of debt-related derivatives, beginning

with an overarching examination of derivatives and moving to the narrower debt-related

derivative. From there, it provides an overview of the types of agreements governments

typically use and offers some information about the motivations for using them.

Derivatives

Derivatives can be broadly defined as investments deriving their value from

another asset or index. This definition is useful for the purposes of casual conversation,

though the lack of specificity becomes problematic when trying to isolate one type of

financial contract from another. As a result, for the purposes of this chapter I will rely on

the Government Accounting Standards Board definition. The GASB definition, edited for

formatting, reads:

A derivative instrument is a financial instrument or other contract with all three of the following characteristics: A) It has (1) one or more underlyings and (2) one or more notional amounts or payment provisions or both. Those terms determine the amount of the settlement or settlements, and, in some cases, whether or not a settlement is required. B) It requires no initial net investment or an initial net investment that is smaller than would be required for other types of contracts that would be expected to have a similar response to changes in market factors. C) Its terms require or permit net settlement, it can readily be settled net by a means outside the contract, or it provides for delivery of an asset that puts the recipient in a position not substantially different from net settlement. (GASB, 2003, p. 1)

It is important to note, however, that not all government-use of derivatives should

be thought of in the same way. Pension funds and other investment pools frequently

invest in derivatives, for instance. However, as I will demonstrate, the motivations for

owning these instruments are different than the motivations for engaging in a debt-related derivative transaction. As a result, I do not include these derivatives in my analysis, as I focus on the debt-related derivatives associated with bonded debt.

Debt-related Derivatives

After a government issues a bond, it might also choose to complete a separate transaction related to the underlying debt, resulting in some additional exchange of payments. This agreement can a variety purposes, including hedging, lowering interest

costs, or avoiding debt limits. If it also meets the definition of a derivative, it can be

referred to as a debt-related derivative. These usually fall into one of three broad

structures: swaps, forwards, and swaptions. A swap is an exchange of future cash flows

based on particular indices or variables. In the broadest context, this includes foreign

currency swaps and commodity swaps, wherein the indices are either currency or some

sort of commodity like gas. However, these agreements are not debt-related derivatives, because the underlying asset is not debt. Debt-related derivatives are agreements between two groups to trade interest payments based on some outstanding loan. The interest rate swap, further described below, is a transaction wherein two parties exchange interest payments based on the principal of some debt. Forwards and swaptions build upon the standard interest rate swap by altering the starting date, often called the effective date.

Whereas an interest rate swap would start immediately, a forward is an agreement for the exchange to begin at some future point. A swaption is a put option where one party has the option, but not an obligation, to enter into a swap at a future date. Because swaptions provide one party with the choice, they are usually associated with some upfront exchange as well (Stewart and Cox, 2008; Luby and Kravchuk, 2013).

Design of Swaps

As interest rate swaps serve as the backbone for most debt-related derivatives –

forwards and swaptions are just interest rate swaps with modified effective dates – it is worth exploring them in more detail. Figure 1 describes the most simple of these agreements, or what is called a plain vanilla swap. In the figure, which similar to a figure in Kolb (1997), we see that two parties, A and B, have agreed to exchange payments.

Party A has agreed to make floating payments to Party B based on the London Interbank

Offered Rate (LIBOR) plus three percent, while Party B has agreed to make fixed

payments to Party A at 12 percent of the principal. If we assume that LIBOR is 10 percent, then Party A owes 13 percent of principal, whereas Party B owes 12 percent of principal. The difference between these two payments, or one percent of principal in this case, would be exchanged (Kolb, 1997). This net payment occurs monthly, semi-

annually, or annually over a predetermined period (Gray and Cusatis, 1995).

Figure 1. Plain Vanilla Swap

In Figure 2, a type of plain vanilla swap – a floating-to-fixed interest rate swap –

between the City of Austin and JP Morgan is illustrated, demonstrating how a swap can

be used as a debt-related derivative.1 In the example, Austin has issued a bond with a

variable interest rate. Austin then enters into a swap with JP Morgan where it agrees to

pay a fixed rate to JP Morgan in exchange for a variable rate payment based on LIBOR.

Much like in Figure 1, one can determine the net payment fairly simply. If we assume

that LIBOR is 5.75 percent, then the city is owed an interest payment from JP Morgan of

3.91 percent. Given the city’s fixed payment of 3.657, then the net difference between the

1 Figure 2 was inspired by a similar figure in Stewart and Cox (2008). 11 two rates, 3.91 percent and 3.657 percent, will be exchanged. This agreement is a debt- related derivative because the principal amount used to determine the interest payments is a variable rate revenue bond for water and wastewater systems issued in 2004.

Figure 2. Converting to a Synthetic Fixed Rate

In this specific instance the state is actually receiving a payment from the counterparty, meaning its net interest cost on this particular issue is being lowered by the swap. However, in many instances, the opposite will be the case, meaning the state will make an interest payment to the counterparty per the swap agreement on top of the interest payment on the bond. Counterparties agree to this exchange either in the hope that they will come out on top of this arrangement, or they are ‘swap dealers’ that typically can manage the risk through an additional agreement or third party. In that scenario, the dealer profits via the administrative fees associated with the transaction.

Governments, however, do not necessarily enter into the agreement in order to make an accounting profit by coming out ahead on the exchange. Rather, they view the transaction as a way to obtain the benefits of both a fixed and floating interest rate. Typically, this means that the floating rate on the bond plus the swap payment will net to a lower interest cost than is available via fixed rate debt at the time of issue. In this sense, the government

is aiming for an economic profit, even if that comes with an accounting loss on the swap

itself.

In the example described in Figure 2, the city makes a fixed payment to JP

Morgan, receives a floating payment, and is responsible for making a floating payment.

As a result, the city has synthetically converted floating-rate debt to fixed-rate debt. Not

surprisingly, these transactions are often called floating-to-fixed interest rate swaps,

because the agreement has artificially altered a floating rate bond into an effectively fixed

one.

Governments can also choose to be the fixed-rate payor in these agreements,

resulting in a fixed-to-floating swap. These agreements are entered into in concert with

fixed rate debt and allow the government to synthetically alter the rate to a floating one.

A government may choose to enter into one of these agreements for numerous reasons,

including hedging, getting to market quickly, or even avoiding statutory limits on floating

rate debt. A synthetic floating rate may also be a way for a government to create variable

rate debt without the costs associated with letters of credit or liquidity facilities.

Basis swaps use a similar framework as a floating-to-fixed or fixed-to-floating swap, only both parties are exchanging floating rates based on some underlying debt.

These instruments are thought of much more as direct hedging tools; a basis swap might allow a government to hedge against certain types of risk (e.g. basis risk), or it may allow the government access to terms the government believes to be more favorable. Typically, basis swaps are considered more complex than vanilla swaps because there is uncertainty on both sides of the exchange, perhaps resulting in the idea that they carry additional risk.

Similarly, forwards and swaptions are also considered more complex than vanilla swaps.

Unlike the other agreements described, a forward or swaption does not require any

particular style of payment exchange – they can be floating-to-floating, floating-to-fixed,

or fixed-to-floating. Rather, these agreements involve alterations to the effective date of the transaction. A forward simply involves an exchange of payments that will begin at some future date, whereas a swaption gives one party the option to enter into a swap at a future date. Correspondingly, a swaption is usually associated with a payment from the party receiving the option to the counterparty. Table 1 describes these types of derivatives in more detail.

Type of Derivative Definition Floating-to-fixed swap An exchange of payments, wherein the government pays a fixed rate in exchange for a floating rate. Used in concert with floating rate debt to synthetically convert it to a fixed rate Fixed-to-floating swap An exchange of payments wherein the government pays a floating rate in exchange for a fixed rate. Used in concert with fixed rate debt to synthetically convert it to a floating rate Swaption An agreement wherein one party (usually the government) sells a counterparty the option, but not the obligation, to enter into a swap agreement of some sort at a future date Forward An agreement wherein two parties agree to a swap of some sort to start at some future date Basis swap An exchange of payments wherein both the government and the counterparty pay a floating rate Interest rate cap An agreement wherein the government pays a counterparty to cover the excess if an interest rate rises above some established point Interest rate floor An agreement wherein the government pays a counterparty to compensate the government if an interest rate falls below some established point Table 1. Types of Debt-related Derivatives

Risks Associated with Swaps Of course, in addition to the potential benefits of reduced interest costs and

hedging, these instruments are also associated with additional risks. Table 2, compiled

from information in Singla and Luby (2014), lists the most common of these cited in

state-level debt management policies. Two of these risks have particular application to

the market from 2003 to 2014: basis risk and termination risk. 14

Types of Risk Definition Counterparty risk The risk that the counterparty will fail to comply with the terms of the agreement Termination risk The risk that the agreement will be terminated prior to maturity Interest rate risk The risk that interest rates differ dramatically from expectations Basis risk The risk that there is a mismatch between any two rates being exchanged Amortization risk The risk associated with mismatch between amortization of the agreement and the underlying debt Price risk The risk that the agreement might not be priced competitively Credit risk The risk that the agreement might alter the credit of either party Market access risk The risk created by a derivative agreement being reliant on a future bond issuance Tax risk The risk created by potential tax events that might alter the relationship between the derivative and the payment Operational risk The risk that the state does not have the administrative capacity to monitor and assess the derivative transaction Rating agency The risk that a ratings agency altering its rating criteria has an effect on an criteria risk agreement Rollover risk The risk that renewing or extending an agreement in the future may not be feasible Source: Singla and Luby (2014) Table 2. Types of Risk Associated with Debt-related Derivatives

Basis risk, broadly, concerns when there is a mismatch between payments being

exchanged (Singla and Luby, 2014; Buchanan, 2005). Typically, in a floating-to-fixed

interest rate swap, the government will try to ensure that the floating rate received

matches the floating rate it must pay to its bond holders. If this happens, then basis risk

does not materialize, and the net payment from the government is simply the fixed

payment to the counterparty. However, if the floating rate on the bond and the floating

rate received from the swap counterparty diverge, the government can either stand to gain

or lose. For instance, when the floating rate on the bond differs from the floating rate

received from the swap counterparty, the net payment from the government becomes the

fixed swap payment and the difference between the two floating rates. If the floating rate

on the bond exceeds the floating rate via the swap, then the government’s interest costs

go up; the opposite is true if rates diverge in the other direction.

Termination risk refers to the early cancelation of any debt-related derivative

(Buchanan 2005). Most agreements have termination provisions describing when one

party may seek early termination of an agreement. Many governments require that

agreements allow for government unilateral termination at any point during the

agreement, though it is not clear that all agreements have this option. The most common

termination provision is a credit rating downgrade, which states than when one of the

parties to the agreement suffers a downgrade below some point, the other party may

require additional collateral or initiate termination (Singla and Luby, 2014). If

termination is initiated, the agreement is canceled, with one party paying the other the fair

value of the agreement at the time of termination.

Government Use of Debt-related Derivatives

Because the present study endeavors to examine state and local government use of

debt-related derivatives, an important consideration is what previous studies have

determined about the extent to which these instruments are used. There have been two

studies that explore this topic. First, Stewart and Cox (2008) examined the CAFRs of all

50 states and the 100 largest cities in 2003 and recorded all debt-related derivative transactions. They found evidence that 23 cities and 23 states had entered into some of these agreements, with interest rate swaps being far and away the most common type of agreement. Luby and Kravchuk (2013) followed up on the previous study and, using the same method of examining CAFRs, examined all 50 states use of debt-related derivatives from 2003 to 2009. They found similar results, estimating the size of the state-level debt-

related derivatives market to have peaked in 2008 at approximately $43.2 billion.2 In

2 It is important to note that when discussing the size of the debt-related derivatives market, the common parlance is to state things in terms of notional value. This estimates the amount of underlying-debt that has 16

addition, both studies found that the most common type of transaction was a floating-to-

fixed interest rate swap.

While each of these two studies yielded important information about government

use of derivatives, they also left several important questions unanswered. First, while

Stewart and Cox (2008) established that municipalities are participants in the derivatives

market, they only examined one year. Luby and Kravchuk (2013) looked at a much wider

swath of time but did so only for state governments. This suggests a sizeable gap in

information; namely, how did municipal government use of derivatives evolve over the

past decade? If previous findings hold, then one might suggest that it is likely that

municipal derivative use expanded greatly over the pre-financial crisis period. Moreover,

while the later work discussed some of the implications of the global financial crisis, not

enough data were analyzed to provide a thorough analysis. Indeed, Luby and Kravchuk

(2013) find that the use of derivatives dropped from 2008 to 2009, but they have no data

from the following years. Thus, the present study fills the gaps by describing the

evolution of both the municipal and state government debt-related derivatives market

over the past decade.

Data & Methods

The research for this chapter took place in three steps: 1) assembling data from

previous authors, 2) gathering comprehensive annual financial reports for the appropriate

governments and extracting the relevant information, and 3) aggregating these data for

the purposes of analysis.

been used in debt-related derivatives transactions, not the exchange of payments between counterparties. In other words, while the market might be estimated at $43.2 billion, the actual cash flows being exchanged are much smaller in size. 17

In step one, I started with the data collected for Luby and Kravchuk (2013). As

noted earlier, that paper charted debt-related derivative use for all 50 states from 2003 to

2009. In step two, I first gathered all CAFRs from the 50 largest cities by population from

2003 to 2012, and then for all 50 states from 2010 to 2012. I choose to explore the 50

largest cities because there was evidence that municipal issuers were engaged in this

market in 2003 (Stewart and Cox, 2008), but there was no follow up information on how

city use changed over time. In addition, because the largest 50 cities all have sizeable

populations (e.g. New York City has a population of eight million), they are much more

directly comparable to states, where more work has been done (Luby and Kravchuk,

2013). Finally, on an anecdotal basis, city governments with larger populations seemed

more likely to have the resources to hire more sophisticated financial officers, which one

could argue would be associated with increasingly sophisticated financing techniques,

chief among them using debt-related derivatives.

From the CAFRs, I extracted information about debt-related derivative

transactions initiated or outstanding during each year. GASB reporting requirements

specified in 2003 require governments engaged in a debt-related derivative transaction to report the objective of the transaction, the terms of the agreement, the associated debt, the fair value, and the risks associated with the deal (GASB, 2003; Stewart and Cox, 2008).

Based on these requirements, I recorded the terms of each agreement as reported in the

CAFR and charted changes over time. For instance, Baltimore entered into a floating-to-

fixed interest rate swap in May of 2003 for a notional value of just over $106 million. I

recorded the effective date of the transaction, the notional and fair values, the associated

debt, and the terms of the agreement in 2003 and then made note of any changes in

18 subsequent years by examining the associated CAFR. As a result, and as shown in Table

3, I have a log of changes in fair value, notional value, and any renegotiations in terms.

Finally, in the third portion of the research, I aggregated the information so that I could see trends across individual governments, types of governments, and time.3 The next section presents this information.

Information Collected Description Associated Debt The underlying debt serving as the principal for the derivative Notional Amount The principal amount, or amount used to calculate payments Effective Date The date at which payments are first exchanged Type of Transaction The style of derivative; can be floating-to-fixed, fixed-to-floating, forward swap, basis swap, swaption, or interest rate cap Fair Value The net present value of net payments exchanged, usually estimated via the zero-coupon method Counterparty The party making payments to the government as a part of the agreement Termination Payment Any payment the government makes or receives upon the termination of a derivative agreement; usually the fair value of the swap at the time of termination Table 3. Data Collected & Description

Results

Over the period from 2003 to 2012, 32 of 50 cities and 41 of 50 states used some sort of debt-related derivative; in 2012, however, only 25 cities and 33 states had some sort of agreement outstanding. This represents a decline of over 20 percent in the number of cities and states utilizing debt-related derivatives. For detailed results about each city and state’s use of debt-related derivatives, please see the Appendix.4

3 I do not perform hypothesis testing or significance tests because I have collected what amounts to a population rather than a sample. As a result, the aggregated numbers provide population means rather than sample means. 4 Any debt-related derivative transaction appearing on the general purpose government (i.e. city or state) CAFR was included. As a result, the data for states often include component units, as these are frequently presented in the CAFRs for states. 19

Cities States Total 2003 $8,117,896,503 $19,630,897,000 $27,748,795,506 2004 $13,169,099,704 $27,797,018,000 $40,966,119,708 2005 $17,964,269,705 $32,853,593,000 $50,817,864,710 2006 $23,389,551,709 $39,072,304,000 $62,461,857,715 2007 $24,777,208,710 $41,645,123,000 $66,422,333,717 2008 $28,347,552,708 $43,245,982,000 $71,593,536,716 2009 $24,811,054,599 $43,039,642,000 $67,850,698,608 2010 $22,602,569,210 $36,022,463,421 $58,625,034,641 2011 $21,350,915,211 $29,880,315,232 $51,231,232,454 2012 $16,683,756,212 $28,089,648,333 $44,773,406,557

Table 4. Total Notional Value of Debt-related Derivatives, 2003-2012

Table 4 shows the overall size of the market for both cities and states, while

Figure 3 provides a graphical representation of how the market evolved over time.5 Table

4 demonstrates that while the market for debt-related derivatives grew considerably prior

to the onset of economic stress in 2008, it also shrank considerably during and in the

aftermath of the crisis. In fact, the total market for debt-related derivatives more than doubled from 2003 to 2008, before declining by more than 35 percent from 2008 to 2012.

However, one might suggest that total notional amount may be misleading as an indicator of the growth in the market, as more debt affords governments more opportunities to use these instruments. In other words, it is possible that the growth in notional amount outstanding is simply a reflection of increased total debt. Figure 3 demonstrates, though, that this is not the case. It shows declines in notional value as a percent of total debt that are similar to those in aggregate notional value.

5 It should be noted that some CAFRs for cities – particularly in the years 2003, 2004, and 2005 – were unavailable, meaning the notional value for cities reported in Table 5 is likely understated. The Appendix denotes which CAFRs were unavailable. 20

16%

14%

12%

10%

0% 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

States Cities

Figure 3. City and State Notional Value as Percent of Total Debt, 2003-2012

Figure 3 also shows an additional point about the market for debt-related

derivatives and differences between state and city use. Namely, while states are larger

users in aggregate, cities had larger shares of their debt portfolios associated with debt-

related derivatives. In other words, for any particular debt issue, a city was more likely to

use some sort of synthetic rate financing than a state was. If one assumes that debt-related derivatives are relatively riskier than traditional bond financings, then this may suggest that cities are taking more risks as an overall portion of their debt management strategies.

Alternatively one could argue that using swaps is simply more complex than not doing so, and correspondingly, debt-management practices at the city level are more complex than they are at the state level. In order to check this, I explored the types of derivatives cities and states used over this time frame. This check rests on the notion that vanilla

swaps – floating-to-fixed or fixed-to-floating – are less risky than swaptions, forwards,

and basis swaps.

Figure 4. Type of Debt-related Derivative Used in Cities and States over Time

Figure 4 shows the percentage of agreements – as measured by percentage of notional value – that cities and states had in each type of debt-related derivative. It

demonstrates some evidence of a systematic difference in the types of debt-related derivatives that cities and states use. While both cities and states rely upon the vanilla floating-to-fixed swap for the majority of agreements, city portfolios are much more diverse on average. In particular, the use of forward swaps and basis swaps is far more prevalent in cities than in states. Furthermore, state portfolios became considerably less diverse over time, as 96 percent of all debt-related derivatives were floating-to-fixed interest rate swaps by 2012.

In addition to the above information, I also gathered data on fair values and termination fees. While one might be tempted to consider this information about the performance of these instruments, it is essential to note that it only demonstrates accounting changes rather than economic profits or losses. In other words, while the fair value of a particular agreement in a given year might be negative $1 million, it is possible that when placed into proper context with a government’s overall debt-related derivatives strategy that this agreement is actually generating an economic profit. This is because fair values of swaps only take into account the exchange of payments between the government and swap counterparty; a government can expect to pay more than it receives via a swap, resulting a negative fair value, while still netting a lower synthetic rate on the debt than would have been available at the time of issue, which would be a positive economic profit overall. Figure 5 presents changes in fair values of agreements over time.

The figure shows the ratio of fair value to notional value. Because states and cities did not always report fair values for particular instruments, and some instruments appeared and disappeared from CAFRs from year-to-year, the ratio allows for consistent comparison across time.

0.05

0 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 -0.05

-0.1

-0.15

-0.2

-0.25

Cities States

Figure 5. Ratio of Fair Value to Notional Value for Cities and States, 2003-2012

As the figure shows, state and city fair value tracked very closely with each other

over time. More specifically, in the early portion of the decade prior to the financial

crisis, the average agreement generated a modest accounting loss. However, in the later

years during and following the recession, the average agreement became much more of a

liability.6

An additional way that fair value can be used is to estimate the termination risk

the government is liable for at any given time. This is because the fair value of a debt-

related derivative is the net present value of all future cash flows as a part of the

agreement, and when an agreement is terminated, it is terminated at fair value. So, when

a government terminates an agreement that has a negative fair value, it must make a

payment equal to that fair value to the counterparty. As a measure of how much

6 Perhaps in response to this decline, GASB changed reporting guidelines in 2008 (GASB, 2008), saying that the fair value of derivatives that could be considered hedging instruments (e.g. most debt-related derivatives) must be reported as an asset or liability rather than as an investment gain or loss. 24

termination risk governments realized, I also collected information about terminations

over the 10 year period. However, the specifics of terminations were not always

presented in the CAFRs, as many governments did not disclose termination payments

made over time. Thus, the data presented here are likely an understatement of both the

notional amount of terminations and termination payments.7

Termination Payments Notional Terminated Cities States Cities States 2004 $9,316 $0 $164,760 $0 2005 ($20,062) $0 $223,200 $0 2006 $40,932 $2,120 $903,600 $50,000 2007 $0 ($1,028) $0 $59,440 2008 ($23,419) $0 $587,835 $0 2009 ($193,833) ($5,410) $1,963,326 $82,900 2010 ($252,174) ($188,000) $2,695,495 $3,700,000 2011 ($110,027) ($350,150) $635,408 $4,461,413 2012 ($640,050) ($61,535) $4,885,596 $219,875 Table 5. Termination Payments and Notional Value Terminated (in 000s)

Table 5 shows termination payments and the notional amount terminated for both

cities and states over time.8 In the table, negative amounts are payments from the government to the counterparty. Similar to the changes in fair value, terminations were relatively modest prior to the recession, likely reflective of agreements performing up to expectation. However, from 2009 onward, terminations increased dramatically from pre-

7 Estimates by Bloomberg suggest aggregate termination payments by governments amounted to $4 billion from 2008 to 2010 (McDonald, 2010). However, these estimates include smaller local and special purpose governments. Furthermore, there is no information about how Bloomberg compiled their results. 8 Because I began data collection in 2003, I have no information on the agreements outstanding from 2002. As a result, I do not have information on any agreements that were ended in 2003, meaning I cannot discuss termination events in 2003. Correspondingly, the table is from 2004 to 2012. 25

financial crisis levels, with notional amounts terminated exceeding two billion dollars

annually for four consecutive years and payments ranging from $200 to $700 million

annually. In fact, in 2011, states terminated nearly 15 percent of all agreements, while

cities terminated more than 29 percent of agreements in 2012. From 2004 to 2012, states

terminated 2.51 percent of agreements, while cities terminated 5.99 percent of

agreements.

Discussion

With the information presented in the previous section, I can now begin to make

some statements about the market for government debt-related derivatives from 2003 to

2012. First, I turn to the question of differences between state and city use of debt-related derivatives. Based on the information above, the most substantial difference was that, on average, cities appeared to be more active market participants than states were, given the relative sizes of debt portfolios. For instance, on average cities had a larger notional value to debt ratio, used more exotic agreements, and terminated a larger share of agreements.

However, it is important to note that these averages are driven in large portion by individual governments. For instance, Detroit terminated nearly $3 billion worth of agreements in 2012, accounting for 61 percent of terminations in 2012. Furthermore, while on average cities used more debt-related derivatives as a percentage of notional value than states did, when looking at the median state versus the median city, the opposite is true. This suggests that the averages for cities are being skewed by particular outliers who are much more active in the marketplace than most other cities. Figure 6, a bar chart of notional amount outstanding from 2010 to 2012, indicates a very similar trend for both city and state use of these instruments. In fact, while this figure suggests

26 that states are more engaged in the market, one might draw the opposite conclusion from the earlier aggregations. Given this, I suggest that while there may be differences between particular cities and states, in general, these differences do not appear to be systematic. Based on this result, I now assess the trends of the combined market of both state and city government debt-related derivatives.

$4,500,000,000 $4,000,000,000 $3,500,000,000 $3,000,000,000 $2,500,000,000 $2,000,000,000 2010 $1,500,000,000 2011 $1,000,000,000 2012 $500,000,000 $0 Tulsa Mesa Dallas Austin Miami Fresno Seattle Boston El Paso Tucson Detroit Denver Omaha Atlanta Raleigh Wichita Chicago Phoenix Oakland San Jose Houston Portland Nashville Louisville Arlington Charlotte Memphis New York Las Vegas Cleveland Baltimore San Diego Columbus Milwaukee Fort Worth Kansas City Long Beach Los Angeles Jacksonville Sacramento Washington Indianapolis San Antonio Philadelphia Minneapolis Albuquerque San Francisco Virginia Beach Oklahoma City Colorado Springs 28

$5,000,000,000 $4,500,000,000 $4,000,000,000 $3,500,000,000 $3,000,000,000 $2,500,000,000 $2,000,000,000 2010 $1,500,000,000 2011 $1,000,000,000 2012 $500,000,000 $0 Ohio Iowa Utah Texas Idaho Maine Alaska Illinois Hawaii Kansas Florida Indiana Oregon Virginia Nevada Arizona Georgia Missouri Alabama Vermont Arkansas Colorado Montana Kentucky Michigan Louisiana Nebraska Delaware California Maryland Wyoming New York Wisconsin Oklahoma Tennessee Minnesota Mississippi New Jersey Connecticut Washington New Mexico Rhode Island Pennsylvania West Virginia South Dakota North Dakota South Carolina North Carolina Massachusetts New Hampshire

Figure 6. City and State Notional Value, 2010-2012

Perhaps the most important trend is the precipitous decline in the use of debt- related derivatives from 2008 onward. Based on the evidence in the previous section, there are two clear drivers of this decline: a lack of new agreements, and the termination of existing ones. I discuss each of these in turn.

The lack of new agreements by governments seems likely to have been driven by the larger forces affecting the overall market for municipal debt during and after the economic crisis. First, the onset of the crisis placed many governments under considerable fiscal stress, due to a combination of declining revenues and relatively steady or even increasing expenditures. As a result, governments began deferring capital investment, preferring instead to use resources to cover operating expenses (Martell and

Kravchuk, 2012).

Second, the collapse of the monoline bond insurance industry increased yields for many issuers. The monoline bond insurance industry has historically been thought of as a way for municipal issuers to increase access to capital by receiving a third party guarantee of payment. The companies providing this insurance were also thought of as being low-risk, as they typically only insured municipal securities, and default rates in the municipal market are very low (Wells Fargo; Moldogaziev, 2013; Martell and Kravchuk,

2012). However, in the lead up to the financial crisis, these companies increasingly invested in insurance contracts outside of the municipal market; when the crisis hit, they suffered rating declines and the market ceased to value their insurance in the same way.

As a result, municipal issuers were no longer able to rely on the guarantees of highly rated insurers, and instead were assessed on their own creditworthiness. Spreads between 29

AAA rated issuers and BBB rated issuers spiked from an average of 35 basis points from

2002 to 2007 up to 250 basis points in 2008 (Martell and Kravchuk, 2012; Stone and

Youngberg, 2009). All of this meant that many municipal issuers were unable to secure

credit as cheaply as they were prior to the onset of the crisis.

Finally, during the crisis, forms of credit enhancement such as letters of credit

became increasingly expensive. As financial institutions suffered rating declines, the

number of institutions able to provide credit enhancement dwindled, resulting in both

fewer opportunities and increased cost for those opportunities. Related to both the lack of

credit enhancement and the collapse of the bond insurance market, governments began to

curtail issuing variable rate debt, as the markets for variable rate demand obligations and

auction rate securities contracted (Martell and Kravchuk, 2012).

The combination of these factors – fewer issues of debt, increased cost of issuing

debt particularly for lower rated issuers, and less variable rate debt – likely drove the lack

of new debt-related derivative agreements by raising costs and reducing the opportunities

for new agreements. In particular, the lack of variable rate debt is important because the

vast majority of government debt-related derivatives are associated with variable rate

debt. Even beyond vanilla floating-to-fixed swaps, many swaptions, forwards, and basis

swaps are associated with variable rate underlying debt. Thus, with fewer variable rate

issues, the potential for new debt-related derivatives was curtailed.

The second clear driver of the decline in the use of debt-related derivatives was that governments terminated many of their outstanding agreements starting in 2008.

Financially, the most viable explanation for termination is basis risk. More specifically, 30

during and following the crisis, interest rates like LIBOR and the SIFMA swap rate

declined to near-zero levels. Most floating-to-fixed agreements specified that government receipts were based on these rates. Had floating bond payments followed historical trends and matched these indices, there would not have been an issue; governments would have continued to pay the fixed rate and whatever small difference there was between the swap and bond rates. However, during the crisis, LIBOR and SIFMA began to diverge considerably from variable bond rates, particularly during 2008 and 2009 when there were large numbers of failed auctions (Luby and Kravchuk, 2012). The resulting basis mismatch likely created problems for governments, as they had to pay the fixed rate on the swap and the large difference between the two floating rates, thereby increasing interest costs.

Given the fiscal stress many governments were under, terminating the agreements may have been a way to save in the short-term, particularly considering that many governments financed large termination payments by refinancing the underlying debt on the swap including the termination payment to the counterparty. Philadelphia, for instance, used this strategy in several of its swap terminations from 2010 to 2012. Detroit also terminated the vast majority of its $4 billion debt-related derivatives portfolio prior to filing for bankruptcy protection. However, without further study probing the specifics of each particular transaction, it is difficult to say with any sort of certainty that terminations were driven by cash flow issues.

Going forward, it is important to consider whether governments will continue to forgo debt-related derivatives. Many, if not all, of the conditions that explain the decline 31

in the use of these instruments are easing or disappearing altogether. The mismatch

between SIFMA or LIBOR and variable bond rates, for instance, has subsided

considerably since the crisis, and credit constraints in the broader financial market are

easing as well. A persistent decline in government use of debt-related derivatives, then, may be indicative of a systematic change in risk preferences of state and city financial managers, or perhaps an instance of a market over-correcting. There is some anecdotal evidence of this, as lawmakers in Pennsylvania and Tennessee have proposed banning debt-related derivatives for local governments, and entities like the City of Houston and

Delaware River Port Authority have stated they will not enter into these agreements in the future (McDonald, 2010). Unfortunately, though, a clear answer for the entire market is not readily available based on the present study, though other scholars will be more able to address the question as time progresses.

Conclusion

This research has endeavored to address three questions: how active were the largest cities in the debt-related derivatives market, to what extent do state and city use of debt-related derivatives differ, and how has their use evolved over time? In regard to the first and second questions, I find that while cities seem to be more active market participants than states when total debt levels are controlled for, these differences also appear to be driven by outliers. The median city and state are much more similar in their use of these agreements than the aggregates may otherwise suggest. On the second question, the evolution of the government debt-related derivatives market over the past 32

decade might seem like a bit of a roller coaster ride – lots of movement up, down, and all

around, but in the end you end up where you started. The specific factors driving the

rapid increase and precipitous decline in the use of these instruments were the lack of

new agreements and the termination of existing ones. In charting city and state use of

these instruments over time and offering an explanation for the changes, the present study

makes a contribution to the existing literature on government use of debt-related derivatives.

Nevertheless, it is also important to note the limitations of this research. Chiefly, I have only collected information on the 50 largest cities and the 50 states. Given the nearly 90,000 local governments, it would be difficult to suggest that I can generalize findings to all subnational use of debt-related derivatives. In particular, the present study does not address use of these agreements by special purpose governments (e.g. port authorities, school districts, water districts, transportation authorities). This is noteworthy given estimates in the financial press of the total size of the government debt-related derivatives market inclusive of special purpose entities being between $250 and $500 billion9 (McDonald, 2010; Dodd, 2010), which is several times larger than what I have found for states and the largest cities.

In addition to addressing the limitations of the present study, future research on

the topic of debt-related derivatives might explore several areas. First, a more in depth

look at whether there are particular characteristics of governments that were using these

9 These figures come from an IMF report (Dodd, 2010), but unfortunately there is no information about how they were calculated. 33 instruments would be useful. Second, continued explorations of how the market for these instruments is evolving in the wake of the financial crisis may yield information about government risk aversion. Finally, there remains a lack of information about whether city and state use of debt-related derivatives over the past decade generated savings for tax payers. It is my hope that the present study provides a foundation from which future researchers may build to answer these and other questions.

Chapter 2: Fair or Foul: Motivations for Government Use of Debt-related Derivatives

Introduction

Municipal swaps and other debt-related derivatives have come up in the news with some frequency over the past few years (Guyette, 2014; Mihalopoulos, 2014;

Karlamangla, 2014; Varghese, 2012). The preponderance of these stories focus on the

negative outcomes that can be associated with these instruments, particularly in terms of

termination payments or other financial losses. While some reporting on the issue stops

there, many take a step further and call into question the use of these instruments by

governments at all, suggesting that governments are not financially sophisticated enough

to engage in these deals, or that governments are being taken advantage of by financial

institutions on Wall Street (Gillers and Grotto, 2014; Grotto and Gillers, 2014). These

stories harken to a broader point – why would a government choose to enter into a

complex financial instrument like a debt-related derivative?

From an academic perspective, while there has been some work characterizing government use of debt-related derivatives (Stewart and Cox, 2008; Luby and Kravchuk,

2013; Luby, 2012; Singla and Luby, 2014), there has been very little that explores potential motivations for their use. Luby (2012) suggests that an empirical examination of the characteristics of debt-related derivative users would be useful as a lens to examine public financial managers’ decisions. 35

This chapter seeks to address these very issues. Specifically, the chapter explores

following question: What are the motivating factors for city governments choosing to use

debt-related derivative instruments? It does so by using the data gathered and presented

from the previous chapter in concert with additional data from city government

Comprehensive Annual Financial Reports to examine the relationship between new use

of debt-related derivatives and other characteristics of governments. The chapter finds

that the characteristics of government most associated with new debt-related derivative

use are size as measured by total assets and prior use of the agreements, with external

market conditions also being an important factor. Based on this, the chapter concludes

that much of the discussion suggesting improper or poor motives for government use of

debt-related derivatives is unfounded.

The rest of the chapter proceeds as follows: first, a background section explores

the motivations for using debt-related derivatives; second, I develop testable hypotheses

based on the background from the previous section; third, I describe the data and methods

used to test these hypotheses; fourth, I discuss the results of the various models; fifth, a

discussion section interprets the results; and finally, a sixth section concludes.

Background: Justification for Debt-related Derivatives

There are numerous theoretical and empirical studies that discuss why private

entities might choose to use debt-related derivatives.10 These studies typically suggest

that companies use derivatives as hedges, particularly to maximize firm value. More

10 For a broader discussion of debt-related derivatives, including the mechanics of how the agreements work, please refer to the first chapter. 36

specifically, derivatives allow corporations ways to deal with things like information

asymmetry, imperfect capital markets, taxes, costs associated with financial distress,

transaction costs, and agency costs (Brailsford, Heaney, and Oliver, 2004; Stewart and

Trussel, 2006). However, the extent to which these factors also apply to government

entities is unclear, largely because of the broader differences between governments and

the private firms. For instance, governments do not have to generate returns for

shareholders in order to survive, whereas a corporation does. Nevertheless, governments

must still find a way to pay for the services they provide, meaning they cannot simply

ignore costs or opportunities for gains.

Two studies have made important strides in addressing the motivations for public

or nonprofit use of derivatives. The first, by Brailsford, Heaney, and Oliver (2004),

explores the motivation for government use of derivatives in Australia. The authors

suggest that derivatives use is primarily motivated by a principal-agent problem, wherein

public managers fear budget discrepancies – which are theorized to be associated with

negative consequences like demotion or firing for public managers in charge of finances

– and use derivatives to mitigate risks. The paper empirically tests whether derivative use is associated with a number of government financial characteristics, including assets, liabilities, cash flows, and volatility of cash flows. The findings are mixed, with total assets and liabilities both being associated with increased use of derivatives. The other study in this area is focused on non-profit hospital use of derivatives, and while it tests different variables, it only finds consistently positive relationships for variables

measuring size and growth (Stewart and Trussel, 2006), both of which are similar to the

findings of the first paper.

While these studies have attempted to empirically explore the relationship

between certain factors and use of debt-related derivatives outside of the private sector

context, both have substantial issues. First, Brailsford, Heaney, and Oliver (2004) relies

on survey data to construct its measure of derivatives use, which while reliable, lacks the

certainty of data drawn from audited financial statements. More problematic, though, is

that the dependent variable – use of derivatives – is binary, meaning there is no

accounting for using more or less derivatives. Stewart and Trussel (2006) avoid this issue

by relying on notional value outstanding for their dependent variable. However, this

construction yields another potentially confounding factor: namely, the study relies on the

ratios that measure current financial condition to explain decisions that may have been made several years prior. For instance, a debt-related derivative entered into five years

earlier may still have a substantial notional value in later years, but the organization’s

liability structure may have changed dramatically. In the present study, I address both of these concerns by constructing a measure of newly issued debt-related derivatives in a particular year.

It is also important to note that both of the prior studies address different contexts: one explores governments in Australia, while the other discusses U.S. non-profit hospitals. Subnational governments in the United States, like cities, counties, or states, are not explored. Governments, for their part, tend to identify different motivations for using these instruments. Using state policy documents as a guide, Singla and Luby (2014) 38 identify four allowable purposes for using debt-related derivative instruments and four justifications that are considered invalid. Along the allowable purposes side are the ideas of flexibility, interest rate savings, responding to market conditions, and hedging.

Flexibility is the idea that agreements are acceptable when they give the government additional options to manage its debt portfolio. Interest rate savings suggests that if a swap agreement will generate cost savings, it may make sense to pursue the agreement.

Responding to market conditions suggests that if a swap allows a government to react to the changing financial markets, then it may make sense to use a swap. Finally, hedging as a justification indicates that the government believes that a swap agreement will allow the government to balance against other risks in the debt portfolio. On the invalid justifications front are the ideas of speculation, illiquidity, lack of transparency, and inconsistency with policy. Broadly, these ideas suggest that governments should not use swap agreements for speculative purposes or to generate investment returns. For more information on these justifications or restrictions, see Table 6. Drawn from state-level policies that govern behavior, these allowable purposes or restricted justifications demonstrate the common ways that governments likely think about these instruments.

Purpose Explanation Flexibility Agreements are acceptable when they allow the state alternative options when managing its debt portfolio Interest Rate Savings Agreements are acceptable when they lower the cost of capital for the state Responding to Market Agreements are acceptable when they allow the state to respond to Condition advantageous changes in the market Hedging Agreements are acceptable when they allow the state to balance other risks in the broader debt portfolio Restricted Purpose Explanation Speculation Agreements solely designed to generate trading profits are disallowed Illiquid Agreements that are not easily converted to cash or other liquid assets are disallowed Not Transparent Agreements that are difficult to price or otherwise understand are disallowed Inconsistent with Policy Any agreement that is inconsistent with the spirit of policy, even if not specifically addressed, is disallowed Source: Singla and Luby (2014)

Table 6. Government Motivations for Using Debt-related Derivatives

In the next section, I discuss the how these studies inform my analysis of the

factors associated with city government use of debt-related derivatives.

Hypotheses

As stated earlier, this chapter explores the following research question: What are the motivating factors for city governments choosing to use debt-related derivative instruments? In this section I draw on the motivations identified above to explore the potential relationships among key concepts. Before entering into that discussion, though, it is important to ground the discussion using theory. Specifically, in conducting this analysis, I draw broadly on the idea of policy innovation. This aspect of policy studies focuses on the adoption of new policies, as this is an important aspect of policy change and therefore is important to understanding the policy process. Here, I draw on the policy diffusion literature to explore the motivations for a government choosing to use debt- 40 related derivatives. This choice is not, in the strictest terms, the same as a policy innovation. Nevertheless, in many ways, the decision to use these instruments shares many characteristics of a policy – the agreements extend out over a long period and they are costly to change once enacted – so I believe that this literature offers strong guidance on how to explore my research question. Furthermore, the question of policy innovation implies a deviation from the norm, which is clearly the case for debt-related derivatives, given the standard practice of traditional bond finance as a consistently available and more widely-used option.

In general, studies of policy innovation can be divided into two categories: diffusion models and internal determinants models. The former suggests that the dominant factor in policy innovation is what the surrounding areas choose to do. For instance, if Delaware, Pennsylvania, New Jersey, Virginia, and West Virginia adopt a new policy on education finance, a diffusion model might suggest that Maryland is also likely to adopt a similar change. Internal determinants models, however, suggest that while policies clearly move from place to place, the largest determinants are the social, political, and economic characteristics of the government (Berry and Berry, 2007). While recent scholars have attempted to combine the two, my analysis will rely on the internal determinants models. This is because diffusion models often focus on adoption dates and are more reliant on a single government making a single choice. Internal determinants models, however, do not suffer from these same problems. As such, I characterize the

potential motivations for using debt-related derivatives as characteristics of a government.11

First, I begin with the most commonly agreed upon factor associated with

increased use of derivatives: size. Generally, there is consistent agreement, both

theoretical and empirical, that larger organizations, measured by constructs like assets,

are more likely to use debt-related derivatives (Brailsford, Heaney, and Oliver, 2004;

Stewart and Trussel, 2006). The theoretical justification for this usually relies on an

economies-of-scale argument: larger organizations have more capacity to deal with the

complexities of swaps or other debt-related derivative agreements, and thus are more

likely to use them. Implicit in this argument is that larger organizations tend to be more

financially complex, and therefore financial managers in these organizations are more

financial sophisticated, so as to deal with this complexity. Based on this argument, I

hypothesize the following relationship:

H1: Larger governments will be more likely to enter into debt-related derivative

agreements.

Of course, as the previous section demonstrated, there are other factors that might

motivate a government to use a debt-related derivative agreement. In particular, the budgetary situation of a government might be a compelling motivation for a swap

11 In addition, one might suggest that there are professional networks (e.g. Government Financial Officers Association) along which diffusion might occur. However, access to these networks is likely substantively similar across the cities explored in this analysis, meaning it is not necessary to incorporate it into the models. 42

agreement. Some argue that this is a function of public managers being self-interested

and wanting to minimize the chance of negative personal consequences – such as

reductions in salaries or termination of employment due to budgetary constraints – arising

from unbalanced budgets or unexpected shortfalls. Debt-related derivatives, then, could

be a way to remedy cash flow issues by offering quick injections of cash (Luby, 2012).

Cash flow problems are traditionally captured using some measure of free cash or liquid

assets. Others suggest that debt-related derivative agreements are motivated by informational issues, namely an attempt to address information asymmetries or deal with agency concerns. These constructs are operationalized using traditional financial condition ratios like assets to liabilities or current liabilities to total liabilities. As a result, the empirical tests of the motivations to use debt-related derivatives tend to use ratio analysis to capture a variety of theoretical ideas.

In considering the measures used to test these results, though, one might take a different point of view. Typically, the ratios used to address the budgetary or informational motivations are derived from financial condition analysis. These ratios are intended to capture broad points about a government entity’s ability to remain solvent over varying time frames: short-run, over a budget cycle, and long-run (Singla, Comeaux,

and Kirschner, 2014; Wang et al. 2007). It makes sense, then, to consider how the

financial condition of a government over varying time frames might alter the motivations

to use debt-related derivatives.

In the short run, cash flow concerns might be mitigated by a swap agreement that

generates a cash payment. Similarly, budgetary imbalances might be corrected via swap 43

agreements that provide short-term savings. Long-run issues, however, are not so easily corrected via swaps. Nevertheless, governments using more debt are also more likely to have more expertise in debt transactions, which may make them more likely to engage in the more exotic debt-related derivatives market. As a result, I hypothesize the following three relationships:

H2: Governments with worse cash positions will be more likely to use debt-related

derivatives.

H3: Governments with budgetary imbalances will be more likely to use debt-related

derivatives.

H4: Governments with more long term obligations relative to total assets will be more

likely to use debt-related derivatives.

Finally, it is important to consider the information gathered from government

policy documents on motivations to use swaps. As noted in the prior section, policy

guidance in this area tends to stray fairly far from the theoretical motivations presented in

the empirical studies of this topic. Policy guidance is focused on market conditions as a

motivating factor for use of debt-related derivatives, whereas the empirical studies tend to

eschew this in favor of factors like budgetary concerns or informational issues. At a most

basic level, the financial motivations for a debt-related derivative agreement are about 44

cost minimization or hedging interest rate risk. These principles are strongly related to the

movements of different financial indices, which in turn capture changes in financial

markets (e.g. SIFMA swap index captures movements in the Variable Rate Demand

Obligation market). As a result, any attempt to measure use of debt-related derivatives should capture changes in external financial markets. From a more theoretical sense, policy diffusion models frequently suggest that external forces are important factors that influence outcomes (Berry and Berry, 2007). Given this information, I hypothesize the following relationship:

H5: Governments will be more likely to enter into debt-related derivative transactions

given favorable market conditions.

Lastly, it is important to note that there may be an argument that one of the principal motivating factors for use of a swap are familiarity with the concept and with the actors involved. Put another way, it is not difficult to imagine that a government that has used one of these instruments before may be more likely to enter into an agreement in the future. Based on that, I hypothesize the following:

H6: Governments that have previously entered into a swap agreement will be more likely

to enter into a new agreement in the future.

Data & Methods

In this section, I discuss how I operationalize the constructs discussed in the

previous section, as well as the methods used to address my research questions. The data

cover the years 2003 to 2010 and are from a variety of sources: the dataset collected in

the first chapter, the United States Census Bureau, and from Comprehensive Annual

Financial Reports (CAFRs).

Given my research question, I use a dependent variable that captures use of debt-

related derivatives. I define this variable as Newly Issued Swaps. Rather than using

aggregate notional value of the entire swap portfolio, this variable captures the notional

value of new agreements that a city entered into during a given year. For instance, though

a city may have a swap portfolio with a total notional value of $100 million in 2006, only

$25 million of that may have been entered into in that year. In this scenario, Newly

Issued Swaps would equal $25 million, meaning the variable is an aggregate measure of

the value of new transactions in a given year.12 I constructed this information for the

years 2003 through 2010. The majority of the time, city governments did not enter into

new swap agreements. Across the 50 cities and over the eight year window, there were 70

instances of new swaps being issued and 280 instances of no new swaps being issued.13

The new issues ranged from a peak of more than $2 billion in new agreements by New

York City in 2004 to a relatively small $8 million issue by Nashville, Tennessee in 2008.

The remaining 280 observations take a value of 0, reflecting the lack of new agreements.

12 It is important to note that this variable is constructed by using new transactions as reported in the CAFRs, rather than taking the difference between notional value outstanding from year-to-year. The former approach avoids the problem of agreements maturing or declining in notional value over time. 13 There are approximately 50 missing values due to the unavailability of CAFRs on government websites. 46

The first chapter describes in much more detail how I gathered these data, as well as trends in the data across cities and over time.

From the hypotheses outlined in the previous section, I construct several explanatory variables: Total Assets, Current Ratio, Operating Ratio, Long Term Liability

Ratio, Population, Previous Use and Year. Total Assets and Population are used as measures of size, in accordance with the first hypothesis. These measures capture different aspects of size, however, as one indicates the financial size of the government and the other indicates the number of citizens under the jurisdiction of the government. In addition to the measures of size, I also construct a binary variable – Previous Use – taking the value of 1 if a government has entered into a debt-related derivative agreement in a prior year. This measure is intended to capture previous experience with these instruments.

The three financial ratios – current ratio, operating ratio, and long term liability ratio – are measures of financial condition over the short, medium, and long term (Wang et al. 2007). Table 7 shows the calculation of each of these ratios, as well as a description of how to interpret the numbers. The Current Ratio is intended to capture a government’s cash position, so very liquid assets like cash or inventories are summed in the numerator while any outstanding obligations due within a year are in the denominator. Operating

Ratio divides total revenues by total expenditures, which means that it is a measure of budget surplus or deficit. Long Term Liability Ratio attempts to capture a government’s long run debt burden. Unlike short term obligations, though, long term debt is repaid over many years, meaning that traditionally illiquid assets could be used to aid in repayment in 47 the future. As a result, non-current liabilities are divided by total assets. For the first two indicators, higher numbers are thought to be indicative of stronger financial condition, while a higher number is indicative of reduced financial condition for the latter case.

Combining this with the hypotheses from the previous section, the expected direction of the relationship is negative for both current and operating ratio and positive for long term liability ratio.

Ratio Calculation Interpretation Current Ratio Current Assets / Current Liabilities Lower number indicates weaker cash position Operating Ratio Total Revenues / Total Lower number indicates budget deficit Expenditures Long Term Non-current Liabilities / Total Higher number indicates higher liability Liability Ratio Assets burden Table 7. Financial Condition Ratios

While there are a variety of measures that can be used to capture these constructs, the three selected here are advantageous for a number of reasons. First, each comes from government-wide financial data presented in the Statement of Net Assets or Statement of

Activities required by the Government Accounting Standards Board Statement 34. This makes them better measures of government-wide financial condition than ratios focused on the general fund. Second, these ratios have been demonstrated to be more reliable predictors of financial distress and less prone to year-to-year swings than other financial ratios attempting to measure the same construct (Stone, Singla, Comeaux, Kirschner,

2015). Finally, these measures are similar to those used to explain motivations for the use

of derivatives in past research. Total Assets, Current Ratio, Operating Ratio, and Long

Term Liability Ratio were all collected from the CAFRs for each city. Table 8 provides

descriptive statistics for these variables.14

Before moving forward, it is important to note that there are alternative measures

of financial condition that are fairly common in the literature. Credit ratings, for instance,

are a standard metric that are often used in research on municipal bonds. Using credit

ratings as an explanatory variable, however, is problematic in this case because it is a

single financial measure of creditworthiness that does not distinguish between short-term

and long-term solvency. The approach I take here is more granular, allowing me to

distinguish between governments with short-term and long-term issues. In addition, credit

ratings are designed to be stable over time, which does not necessarily make them the

most useful explanatory variable in a longitudinal study such as this one. For these

reasons, I use the financial condition ratios identified above.

14 There are some missing observations for some variables in years in which CAFRs were unavailable. These observations are dropped in the regressions described in the later sections of the chapter. 49

Std. Variable Observations Mean Dev. Min Max Newly Issued Swaps (Millions) 354 69.81 216.43 0 2086.30 Population 354 948022 1272149 332728 8391881 Log (Population) 354 13.45 .637 12.72 15.94 Total Assets (Millions) 354 8220 10520 1021 74832 Log (Total Assets) 354 22.4 .867 20.74 25.04 Current Ratio 354 4.65 2.55 1.28 15.30 Operating Ratio 353 1.06 0.13 0.51 1.97 Long Term Liability Ratio 354 0.49 0.26 0.10 2.14 Previous Use 354 0.58 0.49 0 1 Table 8. Characteristics of Explanatory and Dependent Variables

Finally, in addition to these independent variables, I also use dummies for year in

order to capture market conditions. These dummies serve as year fixed effects, which will

soak up many of the external market forces that make a debt-related derivative agreement

more or less attractive for a government, as these forces are frequently the same across

governments. For instance, swap rates – often the SIFMA municipal swap index or some percentage of LIBOR in a floating-to-fixed transaction – are based on the same indices for all governments. As a result, the motivations like ‘responding to market conditions’ that are stated by governments in policy documents ought to be captured by year dummies. In addition, external forces like the broader economic conditions of the year will be captured by year dummies. However, there are aspects of external market forces that year dummies do not capture. The credit rating of an individual government, for instance, might make a debt-related derivative transaction more or less attractive.15 In addition, variation in conditions within a given year will not be addressed. This may be

15 As stated earlier, however, this approach introduces its own problems. 50 particularly problematic, as pricing conditions often can vary from hour to hour, so yearly averages do not necessarily capture the conditions a government faces when making a decision to enter into an agreement. Unfortunately, a more refined approach is not feasible given the nature of the data available on swap transactions, as the information is only presented in the CAFRs on an annual basis. So, while it would be too broad of a statement to suggest that these yearly dummies capture the exact pricing conditions a government faces when making a decision, they serve as a reasonable proxy for the variation in external market conditions faced by government.

Log (Total Current Operating Long Term Log Previous Assets) Ratio Ratio Liability Ratio (Pop.) Use Log (Total Assets) 1

Current Ratio -0.1085 1

Operating Ratio -0.1441 0.2605 1

Long Term Liability 0.4687 -0.1346 -0.4599 1 Ratio Log (Population) 0.7645 -0.1145 -0.3361 0.6255 1

Previous Use 0.3016 0.0946 -0.0336 0.3208 0.2109 1 Table 9. Correlation Matrix of Independent Variables

Further, Table 9 shows correlations among the independent variables. Of note are the relatively low correlations among each of the financial condition variables, which suggest that each is measuring a different aspect of financial condition. Total Assets,

Long Term Liability Ratio, and Population are fairly highly correlated, however, which requires a check for multicollinearity issues in the next section.

In order to address the research questions and explore the hypotheses presented earlier, I first use simple comparison of means tests to examine whether there are significant differences between issuers of new swap deals and non-issuers. This approach provides information about the hypotheses without holding other variables constant, which is both useful and potentially misleading. In order to address the potentially misleading aspects of this, I use a Tobit estimation technique. Tobit models are useful for censored data, wherein there is a minimum or maximum value that alters the distribution of the data (Gujarati, 2011). A commonly used example is test scores – the data may show a distribution that is mostly normal, but due to maximum and minimum scores, there will be more observations at the tails than one would expect. For instance, two students scoring an 800 on the verbal section of the SAT may have different verbal abilities, but the test does not allow for a higher score to differentiate between the two students (UCLA: Institute for Digital Research and Education). In effect, the ceiling – or in some cases, floor – creates a limited dependent variable. For the case of newly issued swaps, the floor is 0, because a government cannot choose to issue negative amounts of swaps. One might suggest that ending agreements or termination may be ways that the value of a variable capturing newly issued swaps could be negative, but it is fallacious to imply that the motivations for this action are the same as the motivations for entering into a new agreement. Further, treating the maturation of a swap agreement the same as a termination would also be problematic, as the former is a passive action and the latter is a proactive decision. For these reasons, it is necessary to censor the dependent variable with a lower bound of 0. As a result, Tobit, rather than ordinary least squares, makes the 52 most sense.16 The next section discusses my Tobit model, as well as the processes I used to account for heteroscedascity and multicollinearity in the data.17

Results

In this section, I discuss the results of my analysis. First, I present the results of the comparison of means tests, which are shown in Table 10. The t test here assesses the differences in mean value of the explanatory variables by group: governments that issued a new swap, and governments that did not issue a new swap.

No New Swaps, n = 282 New Swaps Issued, n = 72

Variable Mean Std. Error Std. Dev. Mean Std. Error Std. Dev. t stat P-value Current Ratio 4.696 0.158 2.654 4.496 0.25 2.126 0.593 0.554 Operating Ratio 1.065 0.008 0.135 1.041 0.014 0.12 1.41 0.16 Long-term Liability Ratio 0.474 0.156 0.262 0.576 0.025 0.211 -3.08 0.002 Log (Total Assets) 22.301 0.05 0.847 22.789 0.099 0.843 -4.361 0.000 Log (Population) 13.401 0.035 0.592 13.615 0.912 0.774 -2.481 0.014 Previous Use 0.5 0.03 0.501 0.889 0.037 0.317 -6.272 0.000 Table 10. Comparison of Means (t Test): Differences Between New Swap Users and Non-Users

16 It is also worth noting that limited dependent variable estimation techniques like negative binomial or Possion regression are inappropriate for this scenario, as those techniques require count data (Gujarati, 2011). In this scenario, the data on number of swap transactions could be modeled using these techniques. However, number of transactions may be a misleading indicator of use of swap transactions: one deal worth $100 million in notional value is a larger use of swaps than two deals with an aggregate notional value of $45 million, but count data would assume the opposite. 17 The data, as analyzed in this chapter, are longitudinal, necessitating the use of time-based fixed effects. However, because I have observations for different cities, I could also include city-based fixed effects as well. In doing so, though, I would lose out on time insensitive characteristics such as population. 53

As seen in the table, four of the six explanatory variables have statistically significant differences in means between the two groups. Current Ratio and Operating Ratio, however, do not appear to have meaningful differences in means.18

Next, I move to the results of the Tobit estimation in Table 11.19

New Swaps (In Millions) Coefficient Robust Std. Error P-Value 95% Confidence Interval Current Ratio -15.91 16.88 0.347 -49.12 17.30 Operating Ratio -476.83 396.68 0.230 -1257.09 303.43 Long Term Liability Ratio 56.94 217.48 0.794 -370.84 484.70 Log (Total Assets) 280.59 102.78 0.007 78.42 482.74 Log (Population) -158.06 125.58 0.209 -405.08 88.96 Previous Use 682.97 138.35 0.000 410.83 955.11 2003 940.70 264.84 0.000 419.75 1461.65 2004 978.18 288.70 0.001 410.31 1546.04 2005 820.13 252.51 0.001 323.45 1316.81 2006 662.33 277.32 0.017 116.86 1207.80 2007 423.52 261.51 0.106 -90.86 937.90 2008 573.44 245.56 0.020 90.44 1056.44 2009 243.17 244.04 0.320 -236.85 723.19 Constant -5184.35 1637.96 0.002 -8406.16 -1962.54

Observations 280 left censored at New Swaps = 0, 73 uncensored Pseudo R-squared 0.07 Table 11. Tobit Estimation of New Swap Use

18 I also ran univariate models to check whether there was a statistically significant relationship between the explanatory variables and the dependent variable. The results, available in the Appendix, are substantively similar. 19 It should be noted that many studies attempting to explain debt rely on a per capita measure of debt outstanding rather than the nominal figure. While I would suggest that a similar swaps per capita variable is a fundamentally different measure than total new swap use (it captures relative use rather than absolute use), I have estimated a Tobit model using the per capita figure and the results are substantively similar. 54

Tobit estimators are interpreted in similar ways to standard OLS estimations, with one important caveat: because there is a limited dependent variable due to censoring in the data, the coefficients represent the expected change in the dependent variable, regardless

of censoring (Gujarati, 2011). As such, one would interpret the coefficient of 682.97 on

the Previous Use variable as an indication that previous experience with debt-related

derivatives is associated with an increase of newly issued swaps of around $680 million,

all else constant. For the Total Asset variable, the interpretation is a bit trickier because

the variable is log transformed. As a result, one should interpret the coefficient of 280.59

as implying that a one percent increase in total assets is associated with an increase of

$2.81 million in new swaps in a given year, all else equal. For the year dummy variables, there is statistical significance at the 0.05 level in all years aside from 2007 and 2009.

The reference year for the analysis is 2010, meaning that while it is excluded from the analysis due to multicollinearity issues, the remaining year values should be interpreted relative to 2010. In the earlier years of the analysis the coefficients are larger, indicating that market conditions were more favorable for debt-related derivatives during those years relative to 2010. In the later years, however, the coefficients decline, suggesting that conditions were less favorable for new debt-related derivatives, again relative to

2010.20 Finally, most of the year dummies, as well as Total Assets, and Previous Use have p-values below 0.05, indicative of statistical significance in the traditional sense.21

20 As a check to see whether the decline in use of debt-related derivatives was driving the results, I ran the analysis excluding the years 2008 through 2010. The direction of the coefficients and statistical significance do not change. The full results of this model are available in the appendix. 21 It is important to note, however, that because the data here may be considered a population of large governments (I have data on the 50 largest city governments) that the p-values ought to be taken with care, 55

As I indicated in the previous section, there is some concern about

multicollinearity due to the correlation among Population, Total Assets and Long Term

Liability Ratio. In looking at the Tobit estimation results, there is room for concern, as

two of the variables do not have statistical significance and the sign on the Population

variable is the opposite of what I hypothesized. As a further check, I estimated the same

model using the Ordinary Least Squares approach and then ran a VIF test. None of the

variables have a tolerance level below 0.3 and the mean VIF is 1.83, suggesting that

multicollinearity is not a problem. However, when estimating the model using only

Population, the sign of the coefficient changes, which suggests that multicollinearity may

be an issue. Thus, there is conflicting evidence. In the results below, I interpret the full

model including both Population and Total Assets, as I believe that it is important to

control for both financial size and citizens within the jurisdiction. However, in case

multicollinearity is an issue, I also estimate restricted models in the Appendix;

substantively, when either Population or Total Assets are dropped from the model, the

remaining variables have similar effect sizes and statistical significance. For full results of the VIF test, as well as the results of restricted models, please see Tables 17 and 19 in the Appendix.

One of the potential downsides of Tobit models is an inability to deal with heteroscedastic error terms. While heteroscedascity is a problem for OLS estimators, it

as statistical hypothesis testing for regression estimates of a population is not the same as it is with a sample. Rather, because there is no sampling error, the estimates are not affected by random chance, meaning the p-values may offer little to no insight on the actual significance of these estimates. Nevertheless, to make results easier to read, I present traditional p-values here and discuss ‘significance’ in accordance with the norms of public finance literature. 56

only affects efficiency. The common correction for this is to use robust standard errors. In

Tobit models, however, heteroscedascity makes the estimator neither consistent nor

efficient (Gujarati, 2011). To check for heteroscedascity, I ran the same model using OLS

and then used the Breusch-Pagan/Cook-Weinberg test, which indicated that the error term

was heteroscedastic. To adjust for this, I use robust standard errors in the estimation of

the Tobit model.

In addition to using Tobit, I also provide the results of the same model using

Logistic and OLS regression. First, I estimate the same model using Logistic regression

(Wooldridge, 2006). This requires transforming the dependent variable from continuous but censored to dichotomous. In this case, any value above zero in the previous dependent variable becomes a 1, with all other values remaining 0. The second check I do is to run a traditional OLS regression on the original dependent variable. Both the

Logistic and OLS regressions use robust standard errors, so as to deal with the heteroscedascity. The purpose of these approaches is not to necessarily provide estimates, but rather to check whether the results are consistent across methods. The results are shown in Table 12.

New Swaps (In Millions) Tobit Logistic OLS -15.91 .967 -4.52 Current Ratio (0.347) (0.559) (0.261) -476.83 .050 54.19 Operating Ratio (0.230) (0.052) (0.596) Long Term 56.94 .747 83.26 Liability Ratio (0.794) (0.689) (0.267) 280.59 3.44 41.52 Log (Total Assets) (0.007) (0.000) (0.060) -158.06 0.359 8.44 Log (Population) (0.209) (0.019) (0.856) 682.97 4.96 71.02 Previous Use (0.000) (0.000) (0.000) 940.70 53.40 93.14 2003 (0.000) (0.000) (0.009) 978.18 37.25 156.93 2004 (0.001) (0.000) (0.011) 820.13 32.58 89.24 2005 (0.001) (0.000) (0.010) 662.33 12.93 92.00 2006 (0.017) (0.006) (0.067) 423.52 7.56 34.70 2007 (0.106) (0.028) (0.308) 573.44 13.27 57.19 2008 (0.020) (0.003) (0.102) 243.17 4.12 11.71 2009 (0.320) (0.112) (0.659) -5184.35 4.50e-17 -1154.09 Constant (0.002) (0.000) (0.014) Observations 353 353 353 Table 12. Methods Comparison, P-Values in Parentheses

For the Logit model, the results are presented as odds ratios, meaning that a one unit increase in the independent variable is associated with an increase or decrease in the odds of the dependent variable equaling one, all else equal (UCLA: Institute for Digital

Research and Education). In assessing whether the confirmatory models conflict with the

Tobit model, the two most important factors are statistical significance and direction of coefficient (Wooldridge, 2006). The Logit model generally confirms the findings of the 58

Tobit model, as the only substantive difference is in the coefficient of the Long Term

Liability Ratio variable, which the Logit model suggests is negatively associated with the new use of swaps. The next section discusses this in more detail.

An important final note is the potential that some of these results are being driven by legal or statutory factors individual to the cities in question. For instance, a state may forbid the use of these agreements at the municipal or even state level (Singla and Luby,

2014). Unfortunately, data were not available indicating whether a particular government had statutory authority to enter into this agreement. The preceding analysis assumes that all governments do have this authority. To account for the potential that this is not the case, I estimate the model two additional ways, based on two different assumptions: first,

I exclude all cities that did not use swaps during this period, assuming that all governments choosing to not use these instruments did not have the legal authority to do so; second, I exclude city governments choosing not to use these instruments only in states without another city using these agreements (e.g. Mesa, AZ does not use swaps, but is included in the analysis because Phoenix, AZ does use swaps), on the assumption that the legal framework in question is at the state level. The full results appear in Tables 21 and 22 of the Appendix, but are largely substantively similar under both sets of assumptions. The only major difference is that in the first scenario, the Previous Use variable does not have statistical significance.

Discussion

Table 8 provides an overview of what the models discussed in the previous section tell us about the hypotheses from earlier in the chapter. Recall that there were four 59 general ideas being tested: the effect of government size, the effect of financial condition, the effect of previous use of debt-related derivatives, and the effect of market condition.

Government size was measured by total assets and population; financial condition is assessed via three ratios – current ratio, operating ratio, and long term liability ratio – designed to gauge solvency over the short, medium, and long run; a binary variable called

Previous Use captured whether a government had entered into a debt-related derivative agreement in the past; and year dummy variables served as proxies that capture much of the external forces driving market conditions.

Hypothesis t-Tests Tobit Logit OLS H1A: Larger governments (as measured by Total Supported Supported Supported Supported Assets) will be more likely to enter into debt- related derivative agreements H1B: Larger governments (as measured by Supported Not Not Not Population) will be more likely to enter into debt- Supported Supported Supported related derivative agreements H2: Governments with worse cash positions (as Not Not Not Not measured by Current Ratio) will be more likely to Supported Supported Supported Supported use debt-related derivatives H3: Governments with budgetary imbalances (as Not Not Not Not measured by Operating Ratio) will be more likely Supported Supported Supported Supported to use debt-related derivatives H4: Governments with increasing reliance on debt Supported Not Not Not (as measured by Long Term Liability Ratio) will be Supported Supported Supported more likely to use debt-related derivatives H5: Governments will make decisions about debt- N/A Supported Supported Supported related derivatives based on market conditions (as captured by Year dummies) H6: Governments that have previously entered into Supported Supported Supported Supported a swap agreement (as captured by Previous Use) will be more likely to enter into a new agreement. Table 13. Support for Hypotheses, via Sign and Statistical Significance

As Table 13 shows, there is consistent evidence on most of the hypotheses. For

the first two hypotheses, which assess the effect of government size on use of new swap

agreements, there is clear evidence of an effect of financial size, as measured by Total

Assets, and mixed support for the effect of Population. This suggests that the principal

size characteristic for users of debt-related derivatives is financial size, rather than

jurisdictional size. An alternative interpretation of these results is that the full models are affected by multicollinearity. However, by estimating restricted versions of the Tobit model, excluding either Total Assets or Population and exploring the results, one avoids

this problem and can determine whether the previous interpretation holds. The restricted

model without Total Assets increases the coefficient on Population, but the result is still

statistically insignificant. In the restricted model without Population, though, the

coefficient on Total Assets decreases and the result remains statistically significant.22

Based on this, I suggest that it is not simply that all larger governments are more likely to

use debt-related derivatives, but rather that governments with more financial resources

are more likely to use these instruments.

For the three financial condition hypotheses, I find a consistent lack of support for

both the short and medium term hypotheses, and mixed evidence on the long-term

hypothesis. However, the divergence on the long-term hypothesis is driven by the

comparison of means results, which do not attempt to hold the other variables constant.

This suggests that, while long-term solvency may be a characteristic associated with

increased use of debt-related derivatives, its effect is washed away when controlling for

22 The full results for these models – restricted models 2 and 3 – are available in the appendix. 61

other factors. For the short and medium term hypotheses, the findings suggest that

governments are not broadly using debt-related derivatives to patch short term holes in

budgets or deal with cash flow problems. Recall that current and operating ratios are measures of government solvency over the short and medium term, or timelines ranging from the immediate future to two years down the road.

Taken with the findings on the long-term hypotheses, these findings suggest that

the financial condition of a government has no substantive effect on the decision to use

debt-related derivatives. Given the discussions in the financial press (Gillers and Grotto,

2014; Grotto and Gillers, 2014) and even somewhat in the academic literature (Luby,

2012) about how these instruments could be used to address short-term budget gaps, this

may be surprising. However, as these reports were all based on individual cases, the most

likely explanation is that while some governments may have entered into swap

agreements for short-term reasons, most did not; the cases reported in the press may

simply be evidence of the old adage ‘if it bleeds, it leads.’

The fifth hypothesis was in regard to the effect of market conditions on the use of

these instruments. However, as noted earlier, these variables might be best thought of as

measures of external market forces, which while an important component of the overall

market condition that an individual government would face, are not the entire picture.

Each of the approaches finds support for the notion that these yearly measures of market

conditions are important factors in a government’s choice to use a debt-related derivative.

From this, I suggest that there is strong support for the notion that governments are

responding to external market conditions when deciding whether to pursue a debt-related 62

derivative agreement, even when controlling for the internal financial characteristics of a

government. In a broader sense, this finding is important because it lends what might be

thought of as face validity to the model: had the yearly dummies been consistently

insignificant, government use of debt-related derivatives would have been considerably more difficult to decipher. Instead, there is strong evidence that a principal motivating

factor was the external market conditions, likely driven by the inviting interest rate

environment of the mid-2000s (i.e. increasing interest rates following the 2001

recession).

Finally, the last hypothesis suggested that governments with prior experience

using debt-related derivatives would be more likely to use the instruments in the future.

The findings, across all methods, were consistently in support of this hypothesis. In addition to showing consistent support, the magnitude of the effect is worth noting: based on the comparison of means results, nearly 90 percent of new swap issuers from 2003 to

2010 had prior experience with a debt-related derivative. This is perhaps indicative of

governments building the institutional capacity to deal with debt-related derivatives over

time. Alternatively, or perhaps concurrently, one might argue that the relationships with

key financial institutions built during an initial transaction would be important in future

deals, reducing transaction costs for all parties.

Conclusion

This chapter has sought to empirically investigate the motivations of governments that choose to enter into debt-related derivative agreements. After tracing the potential motivations from both the academic and practitioner realms, I used the dataset from the 63

first chapter in combination with data pulled from CAFRs to test these motivations. I find

that the characteristics of governments most associated with use of debt-related

derivatives are total assets and previous use of debt-related derivatives, and also find a consistent effect of external market conditions.

An important takeaway from this research appears to be that despite myriad suggestions that governments were being taken advantage of, that less sophisticated governments were being bamboozled, or that governments were lured into deals that traded short term savings for long run losses, there is little evidence of these things occurring systematically. The evidence here is that, on the whole, governments were not more likely to use the instruments when they were in poor financial shape, and that the most likely governments to use these instruments were in fact the ones with prior experience using these instruments. Put another way, the findings here can be taken as a fairly strong rebuke of the argument that governments behaving recklessly by sacrificing long-run stability for short-term savings.

Of course, it is necessary to note that while government motivations do not seem

to appear to be problematic, it is still an open question as to whether governments were

actually sophisticated enough to be using these instruments. Reckless behavior is not

simply limited to short-sighted financial behavior; governments lacking the skill

necessary to adequately assess the costs and benefits of these transactions but still using

debt-related derivatives would also be reckless. In order to address this concern, future

research ought to assess the extent to which these deals generated cost savings, and the

expected benefits that a deal could have been reasonably been expected to produce when 64 it was made. The third chapter of this dissertation explores both of these ideas by discussing a framework one might use to analyze each aspect of an individual transaction.

Chapter 3: A Framework for Assessing Government Use of Debt-Related Derivatives

Introduction

In the previous chapters of this dissertation, I have worked mostly in the realm of identifying and questioning the scope of a potential problem: namely, how much are governments using debt-related derivatives, and what are the characteristics of governments choosing to use these instruments? While these are important questions, there is another fundamental aspect of the issue that I have yet to address – what have been the outcomes for governments choosing to use these instruments? In this chapter, I discuss a framework for addressing that question. More specifically, I explore the extent to which traditional approaches to program evaluation are useful with regard to government use of debt-related derivatives, and provide a discussion of an alternative way of thinking about the issue, particularly as it pertains to the risk preferences of a

government.

At first glance, it may not seem as though program evaluation and government

use of debt-related derivatives are compatible. However, when the choice to use debt- related derivatives is viewed as a policy decision, program evaluation program evaluation provides a lens to assess that decision. Program evaluation addresses the extent to which government programs provide better outcomes for citizens. As applied to debt-related derivatives, program evaluation can identify the aspects of the complex financial deals that are in need of study for governments to know, in retrospect, whether their use of 66

derivatives is optimal. Put another way: program evaluation provides a framework

through which governments can determine whether their use of a debt-related derivative

saved citizens money.23 With this in mind, this chapter aims to demonstrate how one might go about conducting a program evaluation of government use of debt-related derivatives.

However, while program evaluation is certainly a useful framework for assessing the costs and benefits of using debt-related derivatives, it does not necessarily form the best way to assess the decision to use a swap or other derivative. Much in the same way that a poker player playing Texas Hold ‘Em might make a large bet if he or she is dealt two aces to start the game and then go onto lose, a government could make a financially sound decision regarding a swap and still lose money. This chapter also explores how to go about evaluating financial decision makers, as opposed to the tools themselves, and the challenges associated with that process.

Before I apply these tools to the topic, however, I delve into the mechanics and ideas behind program evaluation. Then, I discuss how those concepts apply to debt- related derivatives, with a particular emphasis on how the concepts can serve as a way to measure the outcomes of these programs. After that, I explore how outcome-based evaluations might be limited, as they involve the use of hindsight, instead suggesting evaluations of the decision in the context in which it was made. Taken together, this

23 Saving money via reduced interest costs is likely the most substantial policy objective for the use of these instruments, though some might suggest that hedging or managing overall interest rate risk in a debt portfolio are other motivators. Nevertheless, I will focus on this objective for this chapter. 67 chapter offers a coherent framework for judging the use of debt-related derivative instruments, both from an outcome and a process perspective.

Program Evaluation

One of the cornerstones of policy analysis is program evaluation, which is “the systematic assessment of the operation and/or the outcomes of a program or policy, compared to a set of explicit or implicit standards, as a means of contributing to the improvement of the program or policy” (Weiss pg. 4, 1997). In so far as government use of municipal swaps might be considered either a program or policy choice, one can use the tools of program evaluation to explore the subject. In general, program evaluation seeks to assess the extent to which a policy is successful in achieving some goal. This goal is usually referred to as the outcome of interest, or treated outcome. For instance, a needle exchange program may have a reduction in communicable diseases among drug- using populations as an outcome of interest. Measuring the effect of the program on this outcome – called program effects – is the goal of program evaluation.

In practice, measuring program effects poses a number of challenges. First, simple measures of the outcome are not enough to establish the effects of the program. Consider the needle exchange example: a reduction in communicable diseases among drug-using populations could be driven by a number of factors unrelated to the implementation of a needle exchange program. A reduction in drug-use overall, for instance, could lower the instances of disease. Simply measuring what happened after the implementation of the needle exchange program may lead one to draw incorrect conclusions about program 68

effects. Instead, evaluators must attempt to measure the untreated counterfactual

outcome, which is the outcome in the absence of the policy intervention, and compare it

to the observed treated outcome. However, the central challenge to this is that both

outcomes cannot be directly observed, because one group cannot both receive the

treatment and simultaneously not receive the treatment (Weiss, Bloom and Brock, 2014).

Governments implementing a needle exchange program, for instance, cannot directly

observe what would have happened had they not implemented the program. As a result,

evaluators must effort to measure the counterfactual, commonly referred to as the

untreated counterfactual outcome. In natural science scenarios, randomized controlled

trials are often used to assess this outcome. In social science situations, when randomized

assignment of treatment is often unrealistic or undesirable, evaluators often have to resort

to less precise methods like natural experiments or pre-and-post treatment measurements of the treated outcome. Once an evaluator has measured both the treated and untreated effects, the program effect is the difference between the two.

Beyond program effects, however, there are other important factors that ought to

be explored. For instance, program effects may be different across varying subgroup

populations, which can have important policy ramifications. In the needle exchange

example, younger age groups may have different outcomes than older age groups do.

Subgroup analysis is an attempt to measure this effect variation among different groups

(Weiss, Bloom and Brock, 2014). Potential explanations for subgroup variation, and

more broadly, explanations for differing program effects are differences in treatment,

differences in the recipient characteristics, and differences in the environment of the 69

program (Weiss, Bloom and Brock, 2014). Evaluators ought to explore whether these

factors play a role in differing program effects.

The next section will explore how the above program evaluation framework can be applied to government use of swaps and other debt-related derivatives.

Application of Framework to Debt-related Derivatives

To begin, the first step one must take is to establish both the specifics of the

program being evaluated and the goals of that program. In the case of government debt- related derivatives, the program might best be thought of as government use of these instruments. From an evaluation standpoint, then, a government choosing to use a debt- related derivative like an interest rate swap is choosing to receive the treatment. It bears stating that there are numerous types of debt-related derivatives and that even among the same type of agreement terms can differ, but at the programmatic level, it makes sense to group these things together. As I discuss later, variations in these aspects might be considered treatment variation, and thereby evaluated as such.

While the goals of a debt-related derivative agreement are complex and each specific agreement likely has different specific purposes, broadly, governments are usually looking to achieve interest rate savings via these instruments. As such, the most readily available outcome measure is the interest rate the government pays as a result of the debt-related derivative agreement. This is what I will refer to as the outcome of interest or treated outcome.

Because use of a swap agreement or other debt-related derivative is a choice,

assignment to the treated or untreated group is based on self-selection, which traditionally can flummox analysis of program effects. That will not be the case here. As detailed in the previous section, one of the core ideas behind program effects is that evaluators need to measure the difference between the observed treated outcome and the unobserved counterfactual outcome. Usually, estimation of the unobserved counterfactual outcome is reliant on some kind of experiment or statistical technique that creates equivalent comparison groups that do not receive the treatment. For an evaluation of a debt-related derivative transaction, however, the construction of a comparison group is not necessarily the best way to estimate the unobserved counterfactual outcome. Rather, using information about broader financial markets (e.g. yield curves), one can estimate what the government would have paid had it decided to issue fixed-rate debt. Put another way, one can estimate the unobserved counterfactual outcome for a government choosing to receive the treatment, so no control group or experiment is necessary.

To understand this procedure, consider the example of a floating-to-fixed interest rate transaction. Recall that this transaction involves three separate payments: a floating rate from the government to the bond holders, a fixed rate from the government to the swap counterparty, and a floating rate from the swap counterparty to the government.

Figure 7 shows the movement of funds visually.

Figure 7. Floating-to-Fixed Interest Rate Swap

In the transaction, the government is essentially taking advantage of differences in the yield curves for fixed and floating rate debt by issuing floating rate debt – which, due to its frequent rate resets, functions like short-term debt – at a lower rate than available via a fixed-rate obligation. Then, using a swap transaction, the government synthetically fixes the rate. This gives the government both cost savings and cost certainty, assuming that the two floating rates do not diverge substantially. From a program evaluation standpoint, then, the first step is to evaluate the net payments made by the government over the life of the agreement, which effectively become the net payment made by the

72 government. This is the treated outcome, which, when compared with the untreated counterfactual outcome – discussed later in this section – yields program effects.

Before moving onto the specifics about how to evaluate the treated and unobserved counterfactual outcomes, it is worth taking a brief detour to discuss a recent analysis in The Chicago Tribune that essentially applies the standard program evaluation framework to a the interest rate swap portfolio of Chicago Public Schools. The Tribune’s analysis looked at both the actual costs of the swap transaction and the floating rate debt, and compared it to an estimate of what the district would have paid had it simply issued fixed rate debt. The newspaper estimates that the district lost nearly $100 million on the deal (Gillers and Grotto, 2014). Several different articles describe the analysis in some detail, and the next two subsections draw on these articles where appropriate.

Evaluating the Treated Outcome

Evaluation of the net interest payment involves three steps, each of which is a determination of a separate rate: the fixed rate paid by the government as a part of the swap, the floating rate received by the government as a part of the swap, the floating rate paid by the government on its floating rate debt. Algebraically, one determines the net payment at a particular period via Equation 1.

Equation 1: Net Payment = (N*A) + (N*B) – (N*C)

In Equation 1, N is the notional principal, A is the floating rate on the bond, B is the fixed rate paid to the swap counterparty, and C is the floating rate received from the swap counterparty. By performing this calculation for each pay period and then discounting for time value of money accordingly, one has the treated outcome. 73

Practically, however, the above equation is too simplistic, as it ignores the myriad fees that a government likely pays to execute this transaction. When issuing floating rate debt, via an auction rate security or variable-rate demand obligation, governments usually purchase some form of credit enhancement, via some combination of a letter of credit, a liquidity facility, or bond insurance. Each of these has a substantial cost associated with it. As a result, one needs to collect information about the fees associated with the swap transaction and the bond. While this may seem to be a trivial matter, in practice it can be complicated. Obtaining information on these costs requires access to records that, while technically public record, are not readily available. As a result, researchers and reporters alike are usually required to use public records requests to obtain access, which can be both costly and time consuming. Further, governments may not fully comply with these requests when made, as The Chicago Tribune experienced (Gillers and Grotto, 2014).

Nevertheless, this information is essential to establishing the true costs associated with any variable rate bond issue.

In addition to neglecting fees, the previous equation assumes that the evaluation is being conducted after the completion of the deal. However, as swap deals often extend over the life of long term debt, it may be unrealistic to wait until the conclusion of a deal to conduct an evaluation. In these instances, one must calculate the net payments the government has already made using the previous method and also estimate what the government will pay going forward. This requires assumptions about the future directions of the floating rates, which can come from a variety of futures indexes or yield curves, including the U.S. dollar swap curve (Gillers and Grotto, 2014). 74

Estimating the Untreated Counterfactual Outcome

In order to estimate the untreated counterfactual outcome, one needs to estimate what the government would have paid had it chosen not to pursue an interest rate swap.

For a floating-to-fixed interest rate swap, this would be the use of traditional fixed-rate bonds to finance the project. Estimating this cost can be accomplished in a variety of ways. First, one could simply look at the yield on the most recent fixed-rate bond issued by the government. However, as bond issues can be relatively rare, this method is likely to ignore changes in both the external market and internal credit changes that can take place over time. A more complex method would be to compare the spread between the most recent fixed-rate bond issue by the government and a composite AAA bond yield, and then use that spread to estimate the cost of fixed-rate debt going forward. Finally, one could estimate the costs using Thompson Reuters MMD yield curves, a method used by

The Chicago Tribune when conducting this type of program evaluation. Table 14 describes this process in more detail.

Step Detailed Action 1. Get yield information for fixed rate debt from Thompson Reuters MMD Yield Curve. 2. Use Microsoft Excel function to convert fixed rate yield to price. 3. Resulting price is the percent of face value investors pay to purchase bond. 4. Multiply price by the face value of the actual variable rate debt used to get the face value of the equivalent fixed rate debt. 5. Payment is equal to the face value of the fixed rate debt multiplied by the yield from the Thompson Reuters MMD curve. Source: Gillers and Grotto (2014) Table 14. Tribune Method for Estimating Unobserved Counterfactual

This approach involves estimating the return that investors would demand from a

fixed-rate security via the MMD yield curves. Then, using the information on the

expected yield, one can estimate the price of the security and use it to estimate equivalent

face value of fixed-rate alternatives. Last, using the yield and face value of the fixed rate

alternative, one can calculate the payment by the government (Gillers and Grotto, 2014).

Another aspect of the untreated counterfactual is the value of the call option on

fixed-rate debt. This refers to the notion that in most cases, fixed-rate bonds include the

option for the government to refinance the debt after 10 years if market conditions are

superior. This advanced refunding option has value for the government, because it

represents a potential future benefit for the government via the reduced costs associated

with potentially refinancing at a lower interest rate in the future. However, it is essentially

foregone if the government chooses to pursue a floating-to-fixed interest rate swap, which requires termination prior to refunding (Luby, 2012; Gillers and Grotto, 2014). As a result, an aspect of the untreated counterfactual outcome that ought to be factored in is the value of the advanced refunding option. However, these options are surprisingly complex in terms of valuation, which makes this a difficult proposition (Kalotay and

May, 1998; Zhang and Li, 2004; Luby, 2012). In its analysis, The Chicago Tribune largely eschewed this complexity by simply assuming that the government in question would refinance at the most optimal time, thereby achieving optimal cost savings and applying those savings to the counterfactual outcome (Gillers and Grotto, 2014).

However, in practice governments do not know what the most optimal time is, given the 76

lack of knowledge about the future, so while this method is simple, it lacks nuance and is

biased toward lowering the costs of the untreated counterfactual outcome. Better methods

rely on much more sophisticated analyses of interest rate volatility and use simulation-

based methods like Markov Chain Monte Carlo simulation to estimate the value of

advance refunding option (Luby, 2012; Kalotay and May, 1998).

Program Effects and Causes of Effect Variation

After establishing the treated outcome and estimating the untreated counterfactual

outcome, one can capture program effects by taking the difference between the two. In its

analysis, The Chicago Tribune analyzed several floating-to-fixed swap transactions made

by Chicago Public Schools using most of the aspects of the framework described above,

finding that the program effects were a net loss of nearly $100 million. Put in less

technical terms, The Chicago Tribune estimated that the use of floating-to-fixed interest

rate swaps cost the Chicago Public School system $100 million relative to fixed-rate

bonds over the life of the debt (Gillers and Grotto, 2014).

Once program effects are established, one might consider looking at the factors

that might affect these outcomes. Recall that there are three standard causes of variation in program effects: treatment variation, client characteristics, and contextual factors.

First, I consider treatment variation. In the case of municipal swaps and derivatives, it is important to remember that the treatment can vary quite dramatically – there are a number of different types of agreements (e.g. floating-to-fixed interest rate swap, fixed- to-floating interest rate swap, swaption, forward swap, basis swap, interest rate cap), so evaluators ought to be careful not to conflate gains or losses from one type of agreement 77

as indicative of the performance of other types of agreements. Of course, as discussed in

previous chapters, municipal governments predominantly use the floating-to-fixed

interest rate swap, so this sort of treatment variation is not necessarily very common.

However, within floating-to-fixed interest rate swaps, treatment variation can occur via

different terms. For instance, a government may choose to enter into a swap agreement

that pushes basis risk – the risk that the two floating rates diverge – onto the counterparty,

while another may not. Based on market trends over the past decade, a government

choosing the former terms would likely have better outcomes than a government

choosing the latter terms.

A second potential driver of program effects is client characteristics, which are

best thought of as differences in the governments choosing to enter into a debt-related

derivative agreement. This may be related to treatment variation, as certain characteristics

of governments may be more or less likely to pursue certain types of debt-related derivative terms. The second chapter of the dissertation explores the effect of client characteristics like financial condition, albeit not on net payments but rather the choice to use these instruments. Other characteristics which may affect program outcomes are debt- management practices and financial sophistication within the government. Finally, contextual factors are essential to consider when evaluating government use of debt- related derivatives. Things like external market forces, which are beyond the control of municipal governments, are often thought of as the main drivers of the success or failure of a particular agreement.

Limits to Traditional Program Evaluation of Swaps & Alternative Methods

One of the key aspects of program evaluation is that it seeks to explore the

outcomes of a policy or program. On the one hand, this makes sense, as outcome

measures are often the best way policymakers can make informed decisions about

whether to continue or alter existing programs going forward. However, this sort of

analysis does implicitly contain what might best be thought of as hindsight bias. This

idea, which comes out of the psychology literature on judgment and decision making,

suggests that people are more likely to exaggerate the probability of an event occurring

because the event actually happened (Dawson, Arkes, Siciliano, Blinkhorn, Lakshmanan,

Petrelli, 1988). Consider, for instance, a fairly common scenario in No Limit Texas Hold

‘Em, which is a popular poker variant. The first player, Player A, may bet heavily,

thinking that two kings is an excellent hand and the odds of winning are high. However,

Player A does not have all of the information, like what the other players have in their

hands, or what the common pool cards will be. An outcome-based evaluation might use that information – like, for instance, that Player B was dealt two aces, or that the common pool cards will be ace, ace, jack, jack, jack – to suggest that Player A was incorrect to bet heavily early in the hand. Anyone who plays poker knows that this is the incorrect inference to draw from the scenario, and that in the long run, Player A will win far more hands with those initial cards than he or she will lose. This specific scenario was one in which Player A simply got unlucky, but that luck does not alter the underlying probabilities.

The above situation, wherein hindsight bias alters perceptions of decisions made with imperfect information, directly applies to discussions about municipal use of swaps or derivatives. The program evaluation approach I identified earlier uses knowledge about the future directions of financial markets to evaluate whether a swap transaction or other debt-related derivative saved or lost a government money when compared to the non-derivative alternative. While that information is important to have, it does not necessarily provide the fairest context on which to judge the decisions financial managers make. Indeed, when presented with the results of The Chicago Tribune’s outcome-based evaluation approach, one of the financial advisors to Chicago Public Schools responded by saying “Anyone can be a brilliant ‘financier’ with 10 years of hindsight” (Grotto and

Gillers, 2014). The argument that the advisor implicitly makes is that in order to judge the decision to use a debt-related derivative, one should only use information available at the time the decision was made.

The challenge with conducting analysis in this fashion is that it introduces much more uncertainty into the process. In Equation 1, I suggested that for a floating-to-fixed interest rate swap, the Net Payment is essentially a function of the difference between the two floating rates added to the single fixed payment the government makes. This still holds, but for a swap decision to make sense, the Net Payment on the swap needs to be estimated to be less than it will be for the fixed rate alternative. Equation 2 shows this mathematically.

Equation 2: N*Y > (N*A) + (N*B) – (N*C)

In Equation 2, Y is the payment the government would make given the choice to issue fixed-rate debt instead of pursuing a debt-related derivative, N is the notional principal, A is the floating rate on the bond, B is the fixed rate paid to the swap counterparty, and C is the floating rate received from the swap counterparty.

In order to make the assessment required in Equation 2, the evaluators need to explore several things. First, they need to evaluate the swap transaction to establish the value of B. Second, they need to estimate what the government would have expected to pay if it decided to finance the transaction via fixed-rate debt. The methods described in the previous section are similarly effective here.

Third, and more complexly, evaluators need to estimate the value of A and C, or the two floating rates involved in the transaction, based on information available at the time of the transaction. This introduces uncertainty into the transaction, as projecting interest rates for a decade long period is challenging. In theory, the swap is designed such that, if past market conditions are good predictors for future market directions, the two floating rates will net to zero. Thus, the important evaluation here is not necessarily the individual movements of the two rates, but rather their potential to diverge. This essentially means that a government entering into this type of agreement is betting that past market conditions will be accurate predictors of the future differences between the two rates (Luby, 2012). However, a government should also not simply assume that these two rates will net to zero – the risk that they will not is called basis risk, and its manifestation was a substantial problem for many governments during the Great

Recession. This risk, along with several others noted in previous chapters in the 81

dissertation (e.g. termination risk, credit risk, counterparty default risk), is the most

substantially challenging aspect of evaluating the quality of decisions made by financial

advisors with regard to swaps. Evaluators not only need to estimate the likelihood of

these risks manifesting, but they also need to evaluate whether a financial manager was

taking on too much risk. In other words, based on the information available at the time

the decision was made, would the risks of the swap transaction be worth the expected

reward?

There does not appear to be a clear point at which a deal is too risky, meaning

there is not a clear answer to the question. Broadly, the question asks what level of return

is necessary for a given level of risk. So, if a swap deal generates a 0.5 percent interest

cost savings 90 percent of the time and a 2.5 percent increase in interest costs 10 percent

of the time, is it a good deal? For an individual, this would be a question of risk tolerance: how much risk can an individual bear to take, given the potential for a reward. For a government, the issue is much murkier, given that public funds derive from taxation and aggregating the risk preferences of the tax-paying population is impractical. A potential solution to this issue is to develop criteria about the acceptable level of risk and use that level of risk to assess swap decisions. Different governments may choose different levels of acceptable risk, but so long as those levels are agreed upon in advance, judging financial managers’ decisions on these instruments is much more feasible. Some governments have documented statements in their policies governing the use of debt- related derivatives on the rewards of the transaction being worth the risk, but these statements are not particularly common in these policy documents. Combined with the 82

evidence regarding the lack of any policy documentation regarding the use of swaps for

most governments (Singla and Luby, 2014), it does not appear that there is consensus on

this issue.

Conclusion

This chapter has examined how the language and concepts of traditional program evaluation apply to government use of debt-related derivatives. Broadly, program evaluation, which encourages estimation of the unobserved counterfactual outcome, is a strong framework for assessing the performance of the tools themselves. This information is extremely important to assessing the future use of these instruments and their viability as cost-savers going forward. However, one of the flaws of traditional program

evaluation is that it relies on information unavailable when decisions were made to assess

the program. In the context of debt-related derivatives, then, it may not make sense to judge the decision to use a swap based purely on performance. Instead, evaluators should judge financial decision makers using the information available when the decision was made. Doing so, however, requires an assessment of risk tolerance for a government and necessitates the determination of better decision rules for use of swaps or other debt- related derivatives.

Conclusion

As the preceding chapters note, this dissertation has endeavored to explore the use of debt-related derivatives by subnational governments over the past decade. In the first chapter, this entails describing state and city government use of these instruments from

2003 to 2012. The second chapter builds on that foundation by empirically investigating the characteristics of governments choosing to use a debt-related derivative agreement.

Finally, the third chapter discusses a method for evaluating subnational use of these agreements, both in terms of outcomes and in terms of decision making.

The findings of the first two chapters illustrate several important points about government use of debt-related derivatives. First, government use from 2003 to 2007 expanded dramatically, before almost ceasing entirely by 2010. New agreements effectively ceased, likely due to a combination of worse market conditions and risk

aversion from the manifestation of basis risk during the heights of the Great Recession.

Second, despite suggestions in the financial press that use of these agreements was

motivated at least in part by a desire to generate short-term cost savings in order to avoid

more difficult decisions, there is evidence to the contrary. Governments in worse short

and medium term financial condition were no more likely to use these agreements than

governments in better financial condition. Third, there is evidence to suggest that institutional relationships matter, as governments that had used an instrument before were far more likely to use one again. This fits with previous findings that institutional 84

relationships with underwriters and other financial institutions are powerful forces in

debt-management decisions (Simonsen and Hill, 1998). Finally, in the final chapter, the

discussions of how to evaluate these instruments reveals that a two-pronged approach is

necessary: an outcome-based evaluation, and a process-based evaluation. Outcome-based

evaluations will be more focused on cost savings, while process-based evaluations will

focus on whether the decision to use the instrument made sense at the time the decision

was made. Taken together, the information is intended to advance knowledge about how

governments have used these instruments, which are usually intended as debt- management tools.

However, it is important to note several broader limitations about the research.

First, much of the conclusions one might draw based on the first two chapters are based on data coming from only the largest subnational governments. While I believe that these governments are both a sample worth studying on their own and may be the most frequent users of these instruments, it is important to note that there are nearly 90,000 subnational governments in the United States, and many of them may operate differently.

In particular, special purpose governments like water districts or port authorities may behave differently than general purpose governments when it comes to use of these instruments. Second, the presence of the Great Recession here may limit the extent to which the findings of this dissertation are applicable to the future. Put another way, while it is important to understand how subnational government use of debt-related derivatives changes during times of financial crisis, the fact remains that periods of financial crisis are not the norm. Moreover, each crisis has fundamentally different causes, and thereby 85

different effects, meaning it would be unwise to expect the next recession to unfold in the

same way as the Great Recession. For instance, given a financial crisis based around high

inflation, use of a floating-to-fixed interest rate swap would be expected to be increasingly profitable for governments. Future research should address these limitations by collecting data on a wider range of governments over different time frames.

In addition, while the second chapter addresses motivations for use and the third chapter addresses a method by which one can evaluate the outcomes and decisions to use these instruments, there still remains an overarching question: should governments be using these instruments? Future research might work to further understanding in this area by attempting to understand how financial sophisticated governments and financial managers truly are. It may be the case that the answer to the normative question is contingent upon the sophistication of the government in question.

References

Berkshire Hathaway 2002 Annual Report. 2002.

Berry, F. S., & Berry, W. D. (2007). Innovation and Diffusion Models in Policy Research. In Theories of the Policy Process (2nd ed., pp. 223-260). Cambridge: Westview Press.

Brailsford, T., Heaney, R., & Oliver, B. (2004). Use of derivatives in public sector organizations. Accounting and Finance, 45, 43-66.

Buchanan, A. B. (2005). Dealing With Municipal Swap Risks. Municipal Finance Journal, 35-43.

Carvlin, E. (2005). Detroit Uses COPs to Shift Pension Burden and Set a Few Records. The Bond Buyer, December 29.

Dodd, R. (2010, June). Municipal Bombs. Finance & Development.

Gillers, H. & Grotto, J. (2014, November 11). Banks kept CPS in shaky bond market. The Chicago Tribune.

Gillers, H. & Grotto, J. (2014, November 7). CPS Resisted Sharing Financial Data with Public. The Chicago Tribune.

Gillers, H. & Grotto, J. (2014, November 7). How the Tribune Analyzed CPS' Bond Deals. The Chicago Tribune.

Government Accounting Standards Board. (2003). Disclosure Requirements for Derivatives Not Reported at Fair Value on the Statement of Net Assets.

Gray, G., & Cusatis, P. (1993). Understanding and Valuing Municipal Derivative Securities. Municipal Finance Journal, 1-41.

Grotto, J. & Gillers, H. (2014, November 7). Risky Bonds Prove Costly for Chicago Public Schools. The Chicago Tribune.

Gujarati, D. N. (2011). Econometrics by Example. Houndmills, Basingstoke, Hampshire: Palgrave Macmillan. 87

Guyette, C. (2014, August 6). Bad interest rate swaps contribute to Detroit water crisis and financial crisis. The Detroit Metro Times.

Javers, E. (2010, April 20). Debate Tests Members' Financial IQs. Politico.

Justice, J. B., & Miller, G. J. (2011) Accountability and Debt Management: The Case of New York's Metropolitan Transportation Authority. The American Review of Public Administration 41(3), 313-28.

Kalotay, A. J. & May, W. H. (1998). The Timing of Advance Refunding of Tax-Exempt Municipal Bonds. Municipal Finance Journal 19.3, 1-15.

Karlamangla, S. (2014, August 13). L.A. council votes to pressure two banks to redo interest rate swaps. The Los Angeles Times.

Kolb, R. W. (1997). Futures, options, and swaps. Malden, MA: Blackwell.

Luby, M. (2012). The Use of Financial Derivatives in State and Local Government Bond Refinancings: Playing with Fire or Prudent Debt Management. Journal of Public Budgeting, Accounting & Financial Management, 24(1)

Luby, M & Kravchuk, R. (2013). An Historical Analysis of the Use of Debt-Related Derivatives by State Governments in the Context of the Great Recession. Journal of Public Budgeting, Accounting & Financial Management.

Martell, C. R., & Kravchuk, R. S. (2012). The Liquidity Crisis: The 2007-2009 Market Impacts on Municipal Securities. Public Administration Review, 72(5), 668-677.

McDonald, M. (2010, November 10). Wall Street Collects $4 Billion from Taxpayers as Swaps Backfire. Bloomberg.

Mihalopoulos, D. (2014, June 17). Exclusive: A financial time bomb in Chicago. The Chicago Sun Times.

Moldogaziev, T. T. (2013). The Collapse of the Municipal Bond Insurance Market: How Did We Get Here And Is There Life for the Monoline Industry Beyond the Great Recession. Journal of Public Budgeting, Accounting & Financial Management, 21(1), 199-233.

SIFMA. (2010, November 10). About The Municipal Swap Index.

Singla, A., & Luby, M. J. (2014). A Descriptive Analysis of State Government Debt- Related Derivatives Policies. Public Budgeting and Finance, 105-125.

Singla, A, Comeaux, J & Kirschner, C. L. (2014). Blind, Broke, and Bedlam: Differentiating Fiscal Stress from Bankruptcy in California. Public Finance and Management, 14(3): 306-328.

Simonsen, B, & Hill, L. (1998). Municipal Bond Issuance: Is There Evidence of a Principal-Agent Problem. Public Budgeting & Finance. 71-100.

Stone, S. B., Singla, A., Comeaux, J. & Kirschner, C. L. (Forthcoming). A Comparison of Financial Indicators: The Case of Detroit. Public Budgeting & Finance

Stone & Youngberg. (2009). The Municipal Bond Market: One Year Later (Rep.)

Stewart, L. J., & Cox, C. A. (2008). Debt-related Derivative Usage by U.S. State and Municipal Governments and Evolving Financial Reporting Standards. Journal of Public Budgeting, Accounting & Financial Management 20(4), 439-56.

Stewart, L. J., & Trussel, J. (2006). The Use of Interest Rate Swaps by Nonprofit Organizations: Evidence from Nonprofit Health Care Providers. Journal of Health Care Finance, 33, 6-22.

"Annotated Stata Output: Probit Regression." UCLA: Institute for Digital Research and Education. Retrieved from http://www.ats.ucla.edu/stat/stata/output/Stata_Probit.htm

UCLA: Institute for Digital Research and Education. Stata FAQ: How do I interpret odds ratios in logistic regression?

UCLA: Institute for Digital Research and Education. Stata Data Analysis Examples

Varghese, R. (2012, January 17). Philadelphia Swaps Cost Taxpayers $331M: Report. Bloomberg.

Wang, X, Dennis, L. & Tu, Y. S. (2007). Measuring Financial Condition: A Study of U.S. States. Public Budgeting and Finance, 27(2), 1-21

Weiss, C. H. (1998). Evaluation: Methods for Studying Programs and Policies. Upper Saddle River, NJ: Prentice Hall.

Weiss, M. J., Bloom, H. S., & Brock, T. (2014). A Conceptual Framework for Studying the Sources of Variation in Program Effects. Journal of Policy Analysis and Management 33(3), 778-808.

Wooldridge, J. M. (2006). Introductory Econometrics: A Modern Approach. Mason, OH: Thomson/South-Western.

Wells Fargo Advantage Funds. Deterioration of Monoline Insurance Companies and the Repercussions for Municipal Bonds (Report).

Zhang, W. D., & Li, J. (2004). The Value of the Advance Refunding Option and the Refunding Efficency of Tax-Exempt Municipal Bonds. Municipal Finance Journal 25(1), 17-31

Appendix A: Notional Values

City 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 Albuquerque X X X $0 $41 $37 $0 $0 $0 $0 Arlington X X $164 $164 $164 $164 $0 $0 $0 $0 Atlanta $2,080 $1,148 $1,148 $1,148 $1,148 $1,674 $1,604 $1,590 $441 $441 Austin $0 $439 $677 $677 $677 $848 $729 $664 $641 $504 Baltimore $858 $1,005 $975 $957 $927 $920 $805 $681 $650 $626 Boston $0 $0 $0 $0 $0 $0 $0 $0 $0 $0 Charlotte X $362 $355 $576 $868 $850 $726 $630 $622 $615 Chicago X X $2,676 $2,670 $2,851 $2,842 $2,832 $2,685 $2,796 $1,941 Cleveland $1,267 $547 $544 $610 $728 $669 $665 $473 $248 $237 Colorado Springs $178 $680 $913 $908 $590 $943 $728 $794 $783 $705 Columbus $0 $11 $10 $10 $10 $0 $0 $0 $0 $0 Dallas $0 $0 $0 $0 $0 $0 $0 $0 $0 $0 Denver $951 $700 $940 $2,034 $2,224 $2,050 $2,039 $1,382 $1,966 $1,945 Detroit $0 $1,822 $2,170 $2,712 $2,893 $3,937 $3,876 $3,866 $3,829 $800 El Paso $0 $0 $0 $0 $0 $0 $0 $0 $0 $0 Fort Worth X X X X $0 $0 $0 $0 $0 $0 Fresno $0 $0 $0 $0 $0 $0 $0 $0 $0 $0 Houston $0 $853 $853 $1,102 $1,351 $1,351 $1,102 $902 $902 $902 Indianapolis $144 $144 $580 $579 $579 $578 $15 $12 $8 $4 Jacksonville $1,023 $1,024 $178 $128 $123 $121 $117 $112 $107 $103 Kansas City X X X X X $236 $236 $236 $233 $101 Las Vegas X X X $0 $0 $0 $0 $0 $0 $0 Long Beach $194 $130 $65 $0 $0 $252 $69 $69 $69 $69 Los Angeles $0 $236 $236 $552 $552 $550 $314 $313 $313 $151 Louisville $0 $0 $0 $0 $0 $0 $0 $1,048 $1,026 $968 Memphis X X $0 $0 $0 $0 $0 $0 $0 $0 Mesa X X X $0 $0 $0 $0 $0 $0 $0 Miami $0 $0 $31 $65 $65 $65 $35 $0 $0 $0 Milwaukee $0 $0 $0 $0 $0 $0 $0 $0 $0 $0

Table 15. City Notional Value in Millions (Continued)

Table 15 continued

Minneapolis $0 $0 $0 $0 $0 $0 $0 $0 $0 $0 Nashville $0 $61 $61 $61 $61 $61 $57 $55 $55 $50 New York $1,286 $2,486 $3,080 $3,080 $3,080 $3,080 $2,730 $2,602 $2,546 $2,521 Oakland $138 $255 $255 $255 $216 $102 $93 $85 $77 $69 Oklahoma City $0 $0 $0 $0 $0 $0 $0 $0 $0 $0 Omaha X X $34 $34 $34 $0 $0 $0 $0 $0 Philadelphia X X X $2,899 $3,186 $4,277 $3,848 $2,295 $2,007 $1,998 Phoenix X X X X $130 $130 $0 $0 $0 $0 Portland X X $0 $0 $0 $0 $0 $0 $0 $0 Raleigh X $50 $238 $388 $388 $338 $338 $338 $338 $338 Sacramento X X X X X X $70 $69 $67 $66 San Antonio $0 $0 $121 $118 $116 $114 $112 $109 $107 $104 San Diego $0 $0 $0 $0 $0 $0 $20 $20 $20 $0 San Francisco $0 $0 $405 $405 $405 $790 $585 $585 $513 $483 San Jose X X $0 $0 $0 $0 $0 $0 $0 $0 Seattle $0 $0 $0 $0 $0 $0 $0 $0 $0 $0 Tucson $0 $0 $0 $0 $0 $0 $0 $0 $0 $0 Tulsa $0 $0 $0 $0 $0 $0 $0 $0 $0 $0 Virginia Beach X $0 $0 $0 $0 $0 $0 $0 $0 $0 Washington X $1,217 $1,255 $1,255 $1,367 $1,367 $1,067 $987 $987 $942 Wichita $0 $0 $0 $0 $0 $0 $0 $0 $0 $0 Total $8,118 $13,169 $17,964 $23,390 $24,777 $28,348 $24,811 $22,603 $21,351 $16,684 * “X” Denotes Missing CAFR

State 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 Alabama $771 $1,121 $1,121 $1,121 $1,139 $789 $789 $911 $231 $230 Alaska $409 $406 $402 $399 $634 $629 $843 $862 $856 $829 Arizona $0 $0 $0 $0 $103 $103 $103 $103 $103 $103 Arkansas $44 $0 $10 $10 $10 $0 $0 $0 $0 $0 California $5,078 $5,876 $6,273 $10,170 $9,998 $9,829 $9,699 $4,731 $3,061 $2,508 Colorado $72 $122 $232 $232 $230 $228 $226 $216 $216 $211 Connecticut $0 $0 $0 $1,733 $1,767 $1,728 $1,636 $1,502 $1,478 $1,436 Delaware $0 $0 $0 $0 $36 $18 $36 $68 $0 $0 Florida $0 $0 $0 $0 $0 $0 $0 $0 $0 $0 Georgia $0 $0 $0 $0 $0 $0 $0 $459 $519 $292 Hawaii $0 $0 $0 $0 $0 $0 $0 $0 $0 $0 Idaho $0 $0 $0 $0 $0 $0 $0 $750 $707 $662 Illinois $357 $1,446 $1,467 $2,068 $1,856 $1,854 $2,579 $2,494 $2,197 $2,249 Indiana $0 $21 $0 $0 $0 $0 $0 $0 $0 $0 Iowa $0 $0 $0 $0 $0 $0 $0 $388 $339 $306 Kansas $429 $580 $727 $727 $727 $802 $802 $738 $716 $671 Kentucky $0 $0 $0 $0 $0 $0 $0 $227 $222 $216 Louisiana $0 $0 $0 $0 $0 $1,111 $979 $685 $678 $662 Maine $45 $0 $0 $0 $0 $0 $0 $0 $0 $0 Maryland $0 $0 $0 $100 $171 $262 $308 $296 $280 $267 Massachusetts $2,421 $2,239 $2,971 $2,584 $3,732 $3,204 $3,485 $3,451 $3,425 $3,306 Michigan $0 $0 $0 $0 $0 $0 $0 $0 $0 $0 Minnesota $885 $779 $0 $0 $0 $0 $0 $733 $411 $398 Mississippi $0 $50 $150 $344 $393 $559 $423 $414 $186 $182 Missouri $0 $0 $0 $0 $0 $400 $0 $0 $0 $0 Montana $0 $0 $73 $0 $0 $98 $98 $51 $48 $47 Nebraska $0 $0 $0 $0 $0 $0 $0 $0 $0 $0 Nevada $0 $0 $0 $0 $0 $0 $44 $44 $44 $0 New Hampshire $0 $0 $0 $0 $0 $0 $0 $0 $0 $0 New Jersey $3,720 $4,434 $4,796 $4,683 $4,446 $4,115 $4,430 $4,165 $2,864 $2,453 New Mexico $0 $200 $420 $420 $520 $420 $420 $560 $557 $553 New York $2,210 $5,458 $5,585 $5,641 $6,642 $6,577 $4,944 $3,200 $2,299 $2,105 North Carolina $704 $1,141 $1,816 $1,994 $1,793 $1,791 $1,782 $911 $403 $528 North Dakota $0 $93 $142 $181 $174 $195 $201 $224 $183 $174 Ohio $226 $382 $782 $849 $849 $782 $678 $354 $346 $337 Table 16. State Notional Value in Millions (Continued) 93

Table 16 continued

Oklahoma $0 $0 $0 $0 $0 $0 $0 $0 $0 $0 Oregon $0 $0 $15 $157 $193 $348 $394 $331 $524 $416 Pennsylvania $754 $903 $2,640 $0 $0 $0 $0 $0 $0 $0 Rhode Island $0 $0 $101 $101 $61 $73 $14 $0 $0 $0 South Carolina $153 $150 $147 $232 $228 $263 $281 $269 $265 $262 South Dakota $0 $0 $0 $254 $252 $336 $378 $398 $396 $393 Tennessee $0 $0 $0 $0 $0 $0 $0 $0 $0 $0 Texas $1,001 $1,170 $1,525 $2,580 $2,916 $3,525 $4,372 $3,134 $3,192 $3,346 Utah $0 $0 $0 $624 $696 $795 $776 $751 $720 $683 Vermont $0 $0 $0 $0 $0 $0 $0 $0 $0 $0 Virginia $0 $0 $0 $0 $0 $0 $0 $375 $367 $425 Washington $0 $0 $0 $0 $0 $0 $0 $0 $0 $0 West Virginia $64 $64 $64 $64 $64 $64 $64 $64 $64 $64 Wisconsin $288 $1,164 $1,394 $1,805 $1,922 $2,219 $2,131 $2,039 $1,864 $1,655 Wyoming $0 $0 $0 $0 $95 $129 $126 $124 $121 $119 $19,63 $27,79 $32,85 $39,07 $41,64 $43,24 $43,04 $36,02 $29,88 $28,09 Total 1 7 4 2 5 6 0 2 0 0

Appendix B: Additional Results from Chapter 2

Variable VIF 1/VIF Log (Population) 3.32 0.301436 Log (Total Assets) 2.73 0.366648 Long Term Liability Ratio 2.02 0.49551 2007 1.79 0.558562 2009 1.75 0.572323 2008 1.75 0.57249 20006 1.73 0.579144 2005 1.68 0.596933 2004 1.62 0.61885 2003 1.56 0.642687 Operating Ratio 1.47 0.67961 Previous Use 1.24 0.808663 Current Ratio 1.11 0.901986

Mean VIF 1.83 Table 17. VIF Test for Multicollinearity

New Swaps Tobit (In Millions) (Full) (1) (2) (3) (4) (5) (6) -15.91 -20.11 Current Ratio (0.347) (0.336) -476.83 -596.22 Operating Ratio (0.230) (0.147) Long Term 56.94 631.72 Liability Ratio (0.794) (0.010) 280.59 247.42 Log (Total Assets) (0.007) (0.000) -158.06 212.34 Log (Population) (0.209) (0.012) 682.97 659.64 Previous Use (0.000) (0.000) 940.70 2003 (0.000) 978.18 2004 (0.001) 820.13 2005 (0.001) 662.33 2006 (0.017) 423.52 2007 (0.106) 573.44 2008 (0.020) 243.17 2009 (0.320) -5184.35 -460.83 -76.27 -853.40 -6083.59 -3340.59 -1012.98 Constant (0.002) (0.000) (0.18) (0.000) (0.000) (0.004) (0.000) Observations 353 353 353 353 353 353 353 Table 18. Univariate Tobit Models

New Swaps Tobit Restricted Restricted Restricted (In Millions) (Full) (1) (2) (3) -15.91 3.45 -16.40 -27.75 Current Ratio (0.347) (0.828) (0.341) (0.105) -476.83 -389.43 -320.69 -51.78 Operating Ratio (0.230) (0.376) (0.385) (0.886) Long Term 56.94 423.70 -54.50 129.65 Liability Ratio (0.794) (0.083) (0.790) (0.569) 280.59 366.03 196.82 Log (Total Assets) (0.007) (0.001) (0.002) -158.06 -262.05 106.45 Log (Population) (0.209) (0.041) (0.188) 682.97 697.88 735.70 Previous Use (0.000) (0.000) (0.000) 940.70 792.01 924.38 890.08 2003 (0.000) (0.005) (0.000) (0.000) 978.18 831.25 961.61 942.08 2004 (0.001) (0.005) (0.001) (0.000) 820.13 768.64 802.95 781.60 2005 (0.001) (0.004) (0.001) (0.001) 662.33 648.62 648.06 627.29 2006 (0.017) (0.025) (0.016) (0.015) 423.52 408.20 413.92 404.06 2007 (0.106) (0.138) (0.103) (0.098) 573.44 567.01 574.20 584.09 2008 (0.020) (0.029) (0.017) (0.012) 243.17 240.08 523.44 283.60 2009 (0.320) (0.354) (0.298) (0.188) -5184.35 -5562.81 -5535.34 -2892.52 Constant (0.002) (0.001) (0.001) (0.019) Observations 353 353 353 353 Table 19. Restricted Tobit Models Comparison

New Swaps (In Millions) Coefficient Robust Std. Error P-Value 95% Confidence Interval Current Ratio -18.99 22.37 0.397 -63.10 25.13 Operating Ratio -61.53 515.03 0.905 -1077.27 954.22 Long Term Liability Ratio 264.03 352.38 0.486 -448.93 940.99 Log (Total Assets) 281.03 100.01 0.005 83.66 478.41 Log (Population) -131.59 140.30 0.349 -408.30 145.12 Previous Use 595.53 144.18 0.000 311.17 879.88 2003 532.80 184.18 0.004 169.56 896.03 2004 558.75 206.58 0.007 151.33 966.17 2005 403.56 161.44 0.013 85.18 721.94 2006 250.62 190.22 0.189 -124.53 925.76 Constant -5597.14 1722.86 0.001 -8994.96 -2199.31

Observations 152 left censored at New Swaps = 0, 53 uncensored Pseudo R-squared 0.06 Table 20. Tobit Estimation of New Swap Use, 2003-2007

New Swaps (In Millions) Coefficient Robust Std. Error P-Value 95% Confidence Interval Current Ratio -22.91 17.63 0.195 -57.63 11.80 Operating Ratio -365.76 388.27 0.347 -1130.21 398.70 Long Term Liability Ratio 129.04 221.44 0.561 -306.95 565.03 Log (Total Assets) 263.34 100.97 0.010 64.54 462.14 Log (Population) -178.69 125.37 0.155 -425.52 68.14 Previous Use 516.36 136.70 0.000 247.21 785.50 2003 914.27 262.69 0.001 397.07 1431.48 2004 956.95 289.14 0.001 387.67 1526.22 2005 808.61 249.88 0.001 316.64 1300.59 2006 659.80 279.83 0.018 114.74 1204.85 2007 413.86 260.30 0.113 -98.65 926.36 2008 570.21 244.69 0.021 88.44 1051.99 2009 243.54 243.16 0.317 -235.22 722.30 Constant -4459.34 1611.65 0.006 -7632.49 -1286.19

Observations 207 left censored at New Swaps = 0, 73 uncensored Pseudo R-squared 0.05 Table 21. Tobit Estimation of New Swap Use, Non-Users in States with No Other Users Excluded

New Swaps (In Millions) Coefficient Robust Std. Error P-Value 95% Confidence Interval Current Ratio -22.19 19.41 0.254 -60.44 16.06 Operating Ratio -384.53 420.94 0.362 -1214.29 445.18 Long Term Liability Ratio 138.57 220.28 0.530 -295.62 572.76 Log (Total Assets) 234.27 103.52 0.025 30.21 438.32 Log (Population) -164.96 124.57 0.187 -410.51 80.58 Previous Use 240.94 156.67 0.126 -67.87 549.76 2003 830.52 268.61 0.002 301.05 1359.99 2004 884.17 297.02 0.003 298.71 1469.63 2005 794.79 250.69 0.002 300.66 1288.92 2006 668.93 279.71 0.018 117.60 1220.27 2007 419.77 262.01 0.111 -96.67 936.22 2008 580.33 245.26 0.019 96.90 1063.75 2009 250.88 243.34 0.304 -228.76 730.52 Constant -3683.69 1667.44 0.028 -6970.40 -396.97

Observations 154 left censored at New Swaps = 0, 73 uncensored Pseudo R-squared 0.03 Table 22. Tobit Estimation of New Swap Use, All Non-users Excluded