Debt Affordability

8/14/2019 Debt Affordability

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Debt Affordability: A Stochastic Model

Kenneth A. Kriz1

DRAFT

Draft Date: 17 April, 2007

JEL classification codes: H74, H63, H68

1 Associate Professor, School of Public Administration, University of Nebraska at Omaha; Mailing Address: Annex27, 6001 Dodge Street, Omaha, NE 68182. Phone: 402.554.2058; Email:[email protected]. The authorwould like to thank Qiushi Wang for his research assistance and Fred Thompson for his comments on the paper aswell as his encouragement in pursuing the topic.
mailto:[email protected]:[email protected]:[email protected]:[email protected]


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Debt Affordability: A Stochastic Model

Introduction

In December 1994, the Board for Orange County, California announced one of

the largest municipal bankruptcies of all time. The renunciations of the actions of the

County Treasurer in precipitating the default on over $1.6 billion dollars of municipal

debt, and the fallout associated with the event, were immediate and drastic. Many

individuals lost their jobs in the resulting budget cuts, political careers were cut short or

at least changed inexorably, and citizens had services reduced or eliminated (Baldassare,

1998). Unfortunately, Orange County was neither the first nor the last municipal

bankruptcy. Beyond explicit bankruptcies which occur at infrequent intervals, there have

been a host ofde facto bankruptcies where spending patterns of subnational jurisdictions

(and even a few sovereign nations) had to be changed dramatically in order to

accommodate debt payments.

Default on debt presents a crisis for most borrowers. In the private sector, default

can cause the loss of control over firm assets. In the public sector, control obviously has a

different meaning. However, in a public default situation citizen owners may lose

control over their jurisdiction, with receivership or loss of access to capital markets as

potential penalties. The political penalties for elected leaders may involve loss of controlthrough loss of access to their office. Needless to say, there is a lot at risk when

jurisdictions choose to borrow. However, there is also risk that comes from not

borrowing. Lack of borrowing may produce underinvestment in capital, resulting in

slower than potential economic growth (though there is much debate on this point, see

Munnell (1990) for a review of the seminal literature and Zou (2006) for a recent

contribution). The question thus becomes one of affordable debt levels.

At its heart, the central debt affordability question is: Does the issuance of a bond

issue of size b significantly increase the likelihood that the jurisdiction will not be able to

make payments on its total debt B? A related question is whether the issuance of a bond

issue b makes the level of debtB politically unsustainable. In order to answer these

questions, one needs to realize that there is tremendous uncertainty regarding future


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revenues and expenditures. The more uncertain are future revenues and expenditures, the

more likely that the jurisdiction will not be able to service its debt burden. Therefore, the

question of debt affordability is a question of probabilities.

Despite the inherently stochastic nature of the debt affordability question,

previous models of debt affordability have largely taken a benchmarking approach

comparing debt levels of a jurisdiction either against some fixed standard or against other

jurisdictions. But there are several reasons why a benchmarking approach may not reflect

the true nature of the debt affordability question. This paper takes a different approach,

one that attempts to model more fully the financial implications of issuing debt. We will

produce a model that takes more fully into account specific financial, economic, and

demographic realities of the jurisdiction. Using this model, jurisdictions will be able to

say not only if a certain level of debt will be affordable, but also the reasons why or why

not this is the case.

Literature Review

The literature on debt affordability is relatively well developed.2 Discussion of

debt affordability issues began with Bernards work on meeting the financing needs of

government. He noted that while the private sector finance literature was replete with

articles dealing with debt affordability, public sector researchers had yet to take up the

challenge (Bernard, 1982).

As noted in Ramsey, Gritz & Hackbart (1988), techniques in assessing debt

affordability have generally developed into three approaches: the debt ceiling approach,

the benchmarking approach, and the regression approach. In the debt ceiling approach, a

limited number of measures of debt burden (for example, debt as a percentage of assessed

property value) are calculated, and then compared to some maximum level. Often times

this technique is formalized into statute or constitutionally mandated. For example, in the

2 As the principal purpose of this research is to develop a radically different method for assessing debtaffordability, the review of past literature will be necessarily brief to leave room for the theorydevelopment. Recent and much more detailed literature reviews can be found in Bartle, Kriz and Wang(2006)from which this review was derivedand in Denison and Hackbart (2006).


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affordability, which seems strange given the dynamic nature of revenue, expenditures,

and economic condition. Last, debt affordability measures are seen by BKW as

mechanical: In essence the city is seen as having zero probability of encountering

financial difficulties as long as its benchmarking calculations fall below some point, then

the probability increases to certainty of difficulties when some magic point is reached

(Bartle, Kriz, & Wang, 2006).

Model

In order to address these difficulties in assessing debt affordability, we take a

different approach than any of those papers discussed above. We model the likelihood of

default on debt given different combinations of revenue base growth, the tax rate of the

jurisdiction, desired growth of expenditures other than capital expenditures, the impact on

revenue base growth of the expenditures that debt will be issued to fund, interest rates,

and the willingness of citizens in a jurisdiction to make large expenditure cuts or tax

increases in the event of a budget shortfall. Our model looks most like that of Thompson

and Gates (Thompson & Gates, 2006). They address a different question in their paper.

They solve for the sustainable growth in expenditures given debt, revenues, and other

variables. We solve for maximum debt, which is a distinct policy question.

We use a median voter model with an existence theorem regarding the

compensation of a government official to motivate our model setup. Assume that a single

rational, utility-maximizing decision maker rules a jurisdiction consisting of a single

citizen whose utility is a function of government operating and capital expenditures,

along with private consumption from an initial allocation of wealth. Our model initially

occurs across two time periods with decisions made at three points in time. At time t0 the

citizen employs the decision maker and provides him with a fixed wage W0. The wage is

an increasing function of the expected amount of utility generated by government

spending. At time t1, after the first time period, the citizen provides the decision maker

with a fixed wage W1 that holds during the second period. At time t2, the decision maker

pays the decision maker a fixed amount W2which is in essence payment for retention of

the decision makers services over another set of time periods. Since the wage and final


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payoff to the decision maker are based on expected future utility, there will be a set of

information and actions by the citizen that will create an incentive for the decision maker

to act in the best interest of the citizen when making choices. However, it is not readily

apparent either to the decision maker or to the citizen what those choices should be. We

turn to the dynamics of the revenue and expenditure generation model for the jurisdiction

to try to reason a set of principles for the determination of optimal debt levels.

Assume that at time t0, the jurisdiction has a balanced budget with no debt

outstanding, so revenues equal operating expendituresR0 = EO0. The decision maker

formulates a first period budget based on projections of future revenuesR1 and future

expenditure needsEO1.

Given this formulation, the required revenues at time t1 are equal to the desired

future expenditureEO1. Assuming that the government cannot run an operating deficit,

required revenues will be equal to actual revenues such thatR1 = EO1. Now assume that

the government wishes to borrow an amountB to finance capital expenditures at time t0.

Also assume that the debt is one-period debt issued at par value at an exogenous and

fixed interest rate r. While borrowing effectively raises revenue in the first period, the

debt must be repaid in the second period. Now the budgetary constraint in the second

period becomes:

rBEOR 1011 (1)

Next we recognize the simple relationship between first period revenue and second

period revenue:

1,001 RRR (2)

and the relationship between first period expenditure and second period expenditure:

1,001 EOEOEO (3)


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1,1,1,1,1 ttttttttttttt TBtrTBtrTBtrTBtrR (7)

The change in revenue is the difference between revenue in period tand in period t+1.

Substituting from (6) and (7), taking the difference and simplifying yields:

1,1,1,1,1,

1,1,1,1,1,

11,

tttttttttttt

tttttttttttttttt

tttt

TBtrTBtrTBtrR

TBtrTBtrTBtrTBtrTBtrR

RRR

(8)

Thus the change in revenue between periods can be broken down into changes strictly

attributable to changes in the tax base (the first term in (8)), changes strictly attributable

to changes in tax rates (the second term), and changes attributable to both factors (the last

term). Given this and our notation we can rewrite (5) as:

r

EOTBtrTBtrTBtrB

1

][ 1,01,01,001,01,000 (9)

One method of analyzing the maximum amount of debt that a jurisdiction can haveoutstanding is to use (9) directly. A common way of expressing debt outstanding is to

standardize the debt figure in relation to some base. Dividing through by the tax base in

the base period standardizes the debt burden to a percentage of tax base in the

jurisdiction:

rTBEOTBtrtrTBtr

TB

B

1

][

0

1,01,01,01,01,00

0

0 (10)

Equation (10) shows that the maximum ratio of debt to tax base is a function of the

jurisdictions initial tax base and tax rate, changes in the tax base, tax rate, and operating

expenditures between periods and the interest rate on the jurisdictions debt. If we denote


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the maximum ratio of debt to tax base asD, analysis of the comparative statics of (10)

shows the following relationships:

Table 1. Determinants of the Maximum Debt to Tax Base Ratio

?

D

Sign

TB -

TB +

tr +

tr +

r -

EO -

The story behind equation (10) is that the jurisdiction at the beginning of theperiod makes an estimate of the change in the tax base and then sets the proper borrowing

level and changes in tax rates and operating expenditures in order to avoid default. Of

course, relying on a point estimate of changes in tax base exposes the jurisdiction to

significant risk. If the realized change in tax base is far below that which was predicted,

significant cuts in operating expenditures and/or increases in tax rates may be required.

Assume temporarily that the change in the tax base and the interest rate were

known perfectly. Then determining the maximum borrowing would simply reduce to a

mathematical model of the effects of the choice oftrand the change inEO. The

government could simply set trandEO so as to avoid default in each period. Viewed this

way, it can be seen that default for a bond is some combination of debt burdens, tax base

deficiencies, and unwillingness either by the public or their elected officials (or both) to

make necessary changes in tax rates or expenditures. So a default is at least in some

respects a political event. Jurisdictions could absorb relatively large shortfalls in revenue

through revenue rate and spending adjustments or they can default. Another conclusion

of this analysis is that there is hardly ever a case where one debt-to-tax base figure will

capture the maximum amount that a jurisdiction can safely borrow.

This becomes even more apparent when one relaxes the assumption that the

change in tax base is known. In most cases the change in tax base is wholly stochastic.

The decision to issue debt is now a decision based at least in part on expected growth in


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the tax base. In an ex ante sense, that expectation will only be realized within a given

confidence level. In order to analyze the maximum amount of debt, officials must now

assess the likelihoodthat the growth in the tax base will be sufficient to cover debt

payments. To look at this decision, we can rewrite (9) in terms of the change in the tax

base necessary to support debt service:

1,01,00

01,01,00 1TB

trtr

TBtrEOrB

(11)

Dividing (11) through by tax base to standardize, we see that the minimum percentage

change in tax base necessary to support a debt of sizeB is a function of that debt size, the

interest rate, the desired expenditure change, and the change in tax rate, standardized to a

percentage of future tax base:

0

1,0

1,000

1,01,00 1

TB

TB

trtrTB

trEOrB

(12)

Comparative statics of (12) tell us something about the relationships between variables.

Jurisdictions with higher debt loads and greater expenditure demand as well as those thatpay higher rates of interest need greater changes in tax base to sustain comparable debt

burdens. And jurisdictions with bigger tax bases and higher tax rates, along with those

that can raise their tax rates more, have an ability to sustain debt loads with smaller

percentage changes in tax base. If we call the right-hand side of (12) Tthe comparative

statics are:


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Table 2. Determinants of the Minimum Change in Tax Base Needed to Support Debt of Size B

?

T

Sign

B +TB -

tr -

tr -

r +

EO +

Equation (12) can be generalized for multiple-period debt. The only thing that changes

will be the required debt payment in each period. Given fixed rate debt, in all periods t

prior to maturity m, the required payment P on the bond will be the interest payment

equal to the face value of the bond times the interest rate and at maturity the payment will

be the last interest payment, or:

rBP

mtrBP

m

t

1

0

0(13)

The required tax base increase necessary to pay the debt without cutting expenditures or

increasing taxes can then be written as:

mt

TB

TB

trtrTB

trEOP

mtTB

TB

trtrTB

trEOP

m

m

mmm

t

t

ttt

0

,0

,000

,0,0

0

,0

,000

,0,0

(14)

The question of interest for rational utility-maximizing decision makers is thereforewhether the growth in the jurisdictions tax base is going to be sufficient to service the

debt load without causing a cut in expenditures and/or an increase in tax rates. This

requires some type of modeling of the future growth in tax base. Following Merton

(1974) and others, we model the growth of the tax base as a diffusion-type Markov


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stochastic process. Most previous papers have used a Geometric Brownian Motion

process. The Geometric Brownian Motion process is especially attractive for modeling

because the past history of tax base growth has no influence on the next period growth

rate (there is no memory). Given this stochastic process, the diffusion of the tax base

would be:

tTBtTBTB (15)

Dividing through by the tax base yields a form as in (14):

ttTB

TB

(16)

The likelihood of default from a debt issue of a certain size then would become the

probability in period tthat the realized growth of the tax base is less than the required

growth rate. Combining (16) and (14), we find the probability of default P(Def):

mt

trtr

trEOPtt

mttrtr

trEOPtt

PDefP

m

mmm

t

ttt

,00

,0,0

,00

,0,0

)(

(17)

Analyzing the comparative statics in Table 3, we find that the mean growth rate of

the tax base, the tax rate, and the change in tax rate have an inverse relationship with the

probability of default. We also find that the volatility of the tax base, the amount of debt,

the interest rate, and the desired change in operating expenditures have a positive

relationship with that probability.


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Table 3. Determinants of the Probability of Default

?

)(

DefP

Sign

- +

B +tr -

tr -

r +

EO +

Simulation

In order to benchmark the relative influence of different variables on the

maximum debt burden, simulations must be run. Equation (17) and Table 3 show the

various factors that might influence the probability of default, but says nothing about the

magnitude of the risk that they introduce. In order to assess relative magnitudes, we

developed Monte Carlo simulations of (17) to study the effects of changes in the

important variables.

In order to develop the simulation, we had to initially set some parameters that

would not change over the simulation iterations. We chose arbitrarily a desired growthrate of expenditures (EO) of three percent, initial tax rate ( tr) of five percent, average

tax base growth rate () of five percent, tax base volatility () of 2.5 percent, risk-free

interest rate (r) at six percent, and debt maturity of 30 years.

Two important policy variables for the simulation are the disposal of surpluses

during the bond payoff period and the default rule the level of expenditure cuts or tax

increases which would produce a situation where either the official would choose not to

make the payment on the debt or the citizenry would revolt, refuse to make payment, and

fire the official. These are trenchant not only in simulation, but in reality. As Kriz

(2002), Hou (2003) and many others have pointed out, keeping surpluses as savings

(through the use of reserve funds or another mechanism) reduces risk that jurisdictions

will have to reduce expenditures or raise taxes during times of economic downturn. Also


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The results show that at low levels of borrowing, the probability of default is very

low regardless of the tolerance for reductions in expenditures or increases in taxes. But

the probability grows as borrowing increases. This much is probably not unexpected.

However, two more subtle points emerge from Figure 1. First, the increase in default

probability from increases in borrowing is fairly linear until the 50 percent point and then

it becomes exponential. After a certain point, borrowing dominates growth in the tax base

and overwhelms it. We estimate that point to be when borrowing is greater than 50

percent of the tax base. As borrowing approaches 90 percent of the tax base, it becomes

more likely than not that debt service in a given year will force expenditure cuts or tax

increases of more than 15 percent. The second intriguing result is the relative

unimportance of citizen tolerance for changes in budgets, at least at relatively low levels

of debt. There is statistically no difference in probabilities of default regardless of the

default rule for borrowing under 50 percent of the tax base. It isnt until the buildup of

debt becomes relatively large that citizen tolerance for cuts or increases becomes

important.

Our model suggests that the ratio of the mean growth rate of the tax base to its

standard deviation is vital in determining debt affordability. High volatility in the growth

rate of the tax base compared to the mean growth rate increases the probability that in a

given period the growth of the tax base will be insufficient to cover the required debt

payment and normal operating expenditures. Figure 2 shows the results of our simulation

model when the tax base standard deviation is changed.


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Figure 2. Results of Simulation at Different Levels of Tax Base Volatility

0

0.1

0.2

0.3

0.4

0.5

0.6

0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 0.55 0.6 0.65 0.7 0.75 0.8 0.85 0.9

Borrowing as Percent of Tax Base

ProbabilityofDefault

Tax Base Std Dev 1%

Tax Base Std Dev 2%

Tax Base Std Dev 3%

Tax Base Std Dev 4%

Tax Base Std Dev 5%

Figure 2 shows that at low levels of tax base volatility compared to mean growth

(which was kept at its base value of five percent) the debt burden can become quite

high (up to 70 percent of the tax base) without the jurisdiction incurring a large

probability of default. After this point is reached, however, the probability of default

grows exponentially. As tax base volatility grows, so too does the probability of default,

until it becomes high and more linear as the base volatility approaches the mean growth

rate of the base.

Desired future expenditure growth is yet another important variable in

determining the likelihood of default. To some extent, taking on debt represents a

tradeoff for jurisdictions. They get capital projects in the current period or near future. In

exchange they tradeoff some amount of future expenditures on operating items, replacingthose expenditures with payments on the debt incurred to build the capital infrastructure.

Unless tax base growth is sufficient to generate sufficient revenue to offset the debt

service payments, cuts in expenditures, increases in taxes, or default on the debt becomes

likely. Figure 3 demonstrates the extent of the tradeoff.


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Figure 3. Results of Simulation at Different Levels of Desired Future Operating Expenditure Growth

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 0.55 0.6 0.65 0.7 0.75 0.8 0.85 0.9



Expenditure Growth 1%





Figure 3 shows clearly that if a jurisdiction has a strong preference for capital

spending (and is thus willing to accept much lower expenditure growth) then default is

unlikely. At a level of expenditure growth three percent below the mean growth rate of

the tax base (desired expenditure growth of two percent versus mean tax base growth rate

of five percent), even with debt apprising 90 percent of the tax base, the probability of

default is a mere 13 percent. However, if citizens of a jurisdiction want their cake and

eat it too, demanding high growth rates of operating expenditure along with new capital

projects, they are likely to run into trouble. At a level of desired expenditure growth one

percent below the mean growth rate of the tax base, default is more likely than not for

any debt level in excess of 45 percent of the tax base.Interest rates may also affect debt affordability. As interest rates rise, required

debt service rises. Debt service requirements may overwhelm tax base growth. However,

Figure 4 shows the obvious interaction between interest rates and debt levels. At modest

levels of debt, differences in interest rates have very little influence on the probability of


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default. However, as debt rises, the probability of default becomes much greater at higher

interest rates.

Figure 4. Results of Simulation at Different Interest Rates

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

10% 15% 20% 25% 30% 35% 40% 45% 50% 55% 60% 65% 70% 75% 80% 85% 90%


Proba

bilityofDefault

Interest Rate 4%

Interest Rate 6%

Interest Rate 8%

Interest Rate 10%

The final variable which is predicted to have an effect on the default probability is

the initial tax rate. The initial tax rate amplifies changes in the tax base, so we expect to

see higher tax rates lead to lower probabilities of default. In practical terms, if

jurisdictions have difficulties collecting revenues then they are less likely to be able to

collect enough revenues to service debt payments. Figure 5 shows the results of default

probability simulations where initial tax rates are varied. The effects of tax rates are

dramatic. At low tax rates, the default probability climbs dramatically as debt is added,

reaching an asymptotic high above 90 percent. At higher tax rates, the climb is much lessdramatic, with 7 percent tax rate default probabilities only reaching 25 percent.


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Figure 5. Results of Simulation at Different Initial Tax Rates

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 0.55 0.6 0.65 0.7 0.75 0.8 0.85



Tax Rate 3%

Tax Rate 4%

Tax Rate 5%

Tax Rate 6%

Tax Rate 7%

The final variable which must be considered is the endogenous growth in the tax

base caused by adopting the capital project. Since tax base growth is such a strong

determinant of the probability of default, we anticipate that stronger rates of endogenous

growth should have an equally dramatic effect on default rates. In order to analyze

whether this was the case, we created simulations with debt having a multiplier effect

on tax base growth rates. We simulated our base model varying the multiplier in the

range 0 (no endogenous effect) to 2.0 (a 10 percent increase in debt causing a 20 percent

increase in annual growth rates of the tax base). Below (Figure 6) we present the result

only for multipliers from 0 to 0.5, because the results for multipliers greater than 0.5 are

essentially the same as for a multiplier of 0.5. We also present only debt levels of 50% ofthe tax base, because this shows the general pattern of results. Figure 6 clearly shows the

dramatic effect we predict. As the multiplier approaches 0.5, the probability of default

goes to less than 1 percent. However, as the multiplier gets smaller, default probabilities

climb dramatically.


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Figure 6. Results of Simulation at Different Tax Base Multipliers

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

0.18

0.2

0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5



Multiplier (0)

Multiplier (0.25)

Multiplier (0.5)

Conclusions/Policy Implications

In this paper we have developed and simulated a model of debt affordability thattakes into account the stochastic nature of revenue growth. Our model has yielded many

important insights into those factors that may go into determining the affordability of

debt. First and foremost, our model strongly refutes the notion that a single measure of

debt affordability can in any way capture the likelihood that a jurisdiction will default on

its debt. Simple benchmarking exercises are not worthwhile when seen in the context of

the tremendous sensitivity of default likelihood to changes in variables such as tax base

volatility. Even regression approaches taken in isolation can only tell us a limited amount

about the likelihood of default. This is because regression results alone can only tell us

about one variable such as the income elasticity of the tax base, and little about other

factors such as the willingness of the jurisdiction to forego current expenditures to sustain

a capital investment program.


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Beyond this, our research indicates several issues regarding the direction and

magnitude of various factors that lead to a jurisdiction being more or less creditworthy.

The first observation yielded by our research is that the political will to sustain debt

service in the face of tax increases or service cuts (as proxied by our default rule variable)

has a negative but very minor effect on the likelihood of default. At low levels of debt in

fact, there is almost no effect of increased political will. At higher levels of debt, the

difference is small but present. This indicates that jurisdictions should worry about

citizen sensitivity to tax and expenditure changes only at higher rates of debt.

The second observation is that revenue base volatility has a strong positive effect

on default probability. The implication here is that borrowing should be primarily

supported by revenue bases which are less volatile. If individual jurisdictions have a

choice, they should borrow more against revenue bases that are stable, such as the

property tax. In a comparative sense, jurisdictions that have lower revenue volatility

should be able to borrow more than those jurisdictions with high volatility.

Another factor that emerges as being very important in determining the likelihood

of default is the desired rate of operating expenditure growth. Operating expenditures

may crowd out the ability of jurisdictions to pay debt service. Jurisdictions that wish to

have high growth rates of operating expenditures can ill afford to burden themselves with

high debt levels. And jurisdictions that have high expectations or requirements for capital

investment must work to restrain other spending pressures to ensure that debt is

affordable.

Interest rates can play an important role in determining debt affordability. This is

especially true for jurisdictions with high debt loads. These jurisdictions become much

less financially viable as the level of interest rates rises. A possible corollary of this result

is that as interest rates increase, the spread between debt interest costs at issuance of

jurisdictions with low debt and high debt will rise. This would suggest the possibility of a

vicious cycle as interest rates rise where jurisdictions with high initial debt levels must

pay more for their new debt, which reduces the likelihood of repayment on all of their

debt, which in turn increases the interest rate on further debt, and so forth.


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Another variable whose impact increases with higher debt levels is the initial tax

rate. The ability to sustain higher tax rates can dramatically affect the likelihood of debt

service payments being made. This suggests that the credit rating practice of trying to

assess the political will to sustain tax rates is sound. Returning to the story of Orange

County, the actual trigger for the countys bankruptcy was the failure to pass a tax

increase in the wake of the pension fund debacle (Baldassare, 1998). This result confirms

our earlier one that jurisdictions with high debt loads must be much more aware of

citizens sensitivity to tax rates.

The final variable which is extremely important in determining the likelihood of

debt repayment is the multiplier effect that is created by the use of debt proceeds. Our

results indicate that public capital investments that are effective create economic growth

that can essentially defease the debt. This would indicate that effective capital budgeting

by jurisdictions is vital to ensure that debt is affordable.

In practical terms, the model which we have developed can be easily analyzed for

a particular jurisdiction and debt issuance decision. Given a set of historical data on tax

base growth, effective tax rates, and debt burdens, debt of a certain level can be analyzed

in terms of how likely it is to be repaid. And the sensitivity of those results can be

calculated with respect to changes in key variables such as the investment multiplier and

tax rate changes. In the end, almost any analytical approach to debt affordability is bound

to make assumptions. Our model makes far fewer assumptions and produces output

which decision makers can use to better guide debt issuance decisions.

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Debt Affordability