Measuring the Fiscal Health of Municipalities

Full text

May 2017
The findings and conclusions of this Working Paper reflect the views of the author(s) and have not been
subject to a detailed review by the staff of the Lincoln Institute of Land Policy. Contact the Lincoln Institute
with questions or requests for permission to reprint this paper.
help@lincolninst.edu
© 2017 Lincoln Institute of Land Policy
Measuring the Fiscal Health of Municipalities
Working Paper WP17BM1
Bruce McDonald III
North Carolina State University

Abstract
One of the difficulties faced in the effective and efficient management of public organizations is
the understanding of when the organization is experiencing financial distress. Administrators and
researchers alike have typically relied upon ratio analysis for this determination, but too heavy of
a reliance on ratios can produce misleading results. Using 150 municipalities from the Fiscally
Standardized Cities (FiSC) database for the period of 1977 to 2012, this study reconsiders the
measurement of fiscal health through an exploration of several predominate approaches. The
efficacy of the measurement approaches is tested with a series of event history analyzes that
captures their utility in predicting municipal bankruptcy.

About the Author
Bruce D. McDonald III is an assistant professor of public budgeting and finance in the
Department of Public Administration at North Carolina State University. He is also an associate
editor with the Journal of Public and Nonprofit Affairs. A graduate of Florida State University
and the London School of Economics, his current research focuses on issues of defense finance
and human capital, the measurement of fiscal health within local governments, and the
intellectual history of public administration field. His research has appeared in the Annual
Review of Political and Military Sociology, Journal of Public Administration Research and
Theory, Public Administration Review, and the Journal of Public and Nonprofit Affairs.
Bruce McDonald
Department of Public Administration
School of Public and International Affairs
North Carolina State University
(919) 515-5178
bmcdona@ncsu.edu

Table of Contents
Introduction ......................................................................................................................................1
What is Fiscal Health .......................................................................................................................2
Measuring Fiscal Health ..................................................................................................................2
Ratio Analysis ............................................................................................................................3
Brown’s 10-Point Test ...............................................................................................................4
Wang, Dennis, and Tu’s Solvency Test .....................................................................................6
Model, Data, and Methodology .......................................................................................................7
Results and Discussion ..................................................................................................................10
Conclusion .....................................................................................................................................12
References ......................................................................................................................................14

Page 1
Measuring the Fiscal Health of Municipalities
Introduction
A key challenge facing the administration of municipalities in the United States is their ability to
meet their service commitments and obligations (Jacob and Hendrick 2013; McDonald 2015).
This challenge became prevalent in 2008 with the commencement of what has been termed the
Great Recession. During this period, administrators were faced with declining revenue while
simultaneously addressing an increase in demand for goods and services in areas such as
unemployment, healthcare, and housing (Kiewier and McCubbins 2014; Scorsone, Levine, and
Justince 2013). Constraints on the ability of a municipality to provide services create not only
hardships in the daily management of the government but also tension between public
administrators, who seek to sustain the organization, and the citizenry, who seek to reap the
benefits of public goods. Evidence of the problem of constraints and the resulting tension can be
seen in the decision of municipalities to file for bankruptcy, with more than 50 municipal
bankruptcy filings in the United States since 2010 (Governing 2015).
Despite the growing concern about the constraints of local municipalities, we have very little at
our disposal to understand when the constraints become too much and the fiscal health1 of the
municipality is in jeopardy. This is largely due to an uncertainty within the public finance
literature on the most appropriate means of measuring fiscal condition (Honadle, Costa, and
Cigler 2004; Jacob and Hendrick 2013; Trussel and Patrick 2013). Administrators and
researchers alike have typically relied upon ratio analysis for this determination, but too heavy of
a reliance on ratios can produce misleading results (Altman 1968). In response to the failure of
ratio analysis, a number of other approaches have been undertaken, but these are largely ad hoc
and are wrought with inconsistencies in the conclusions they provide (see Bird 2015; Justice and
Scorsone 2013). Without a good approach to the measurement of a government’s financial
condition, how are administrators to know if the government is in jeopardy?
The aim of this paper is to reconsider how we measure the financial condition of a government
through an exploration of several predominate approaches. The efficacy of the measurement
approaches is tested with a series of rare event history analyzes that captures their utility in
predicting an extreme financial event: municipal bankruptcy. To complete the analyses, a panel
of 150 municipalities from the Fiscally Standardized Cities (FiSC) database for the period of
1977 to 2012 is adopted.
The main finding is that our current approaches to understanding fiscal health are not sufficient
predictors of fiscal distress. In testing the utility of ratio analysis, this study found mixed results
with only half of the ratios having a meaningful influence on municipal bankruptcy. Wang,
Dennis and Tu’s Solvency Test also produced mixed results with significance appearing in some
the dimensions, but all influence going away when considered from the perspective of their
financial condition index. Lastly, Brown’s 10-point Test had no distinguishable influence on the
bankruptcy status of a municipality.
1 For the purpose of this study, fiscal health and financial condition are used interchangeably.

Page 2
To explore this issue, the remainder of this paper is structured as follows: The first section, What
is Fiscal Health, introduces the subject of fiscal health in the context of governments, paying
particular attention to how it is defined and what the condition intends to reflect. Next,
Measuring Fiscal Health provides an overview of key measurement systems. Then an event
history analysis model of municipal bankruptcy is developed to test the validity of the
measurement systems in Model, Data, and Methodology. Data and methodological issues are
also addressed. The results of the analysis and discussion of the findings are provided in the
section that follows. The paper closes with a series of comments on the importance of the
findings, their implications for practitioners and researchers, and a recommendation for future
work.
What is Fiscal Health
The financial resources of municipalities are important. They are central to the capacity of a
government to provide or expand a program or service. Concern within the field of public
administration as to the adequacy and efficient management of these resources dates to the start
of the twentieth century with the work of the New York Bureau of Municipal Research as it
sought to clean up New York City’s financial position as a means to improve the city’s services
(McDonald 2010). More mainstream attention to the condition of government finances began in
the 1970s with the looming financial crisis of New York City (Arnett 2013). The experience of
New York City and others who faced bankruptcy in the 1970s and 1980s shaped that concept of
fiscal health that is currently used in the literature. Simply stated, financial condition is
understood as the ability of a government to balance its financial obligations with its available
revenue streams (Arnett 2013; Hendrick 2004; Jacob and Hendrick 2013). A government is
considered fiscally healthy if its resources meet its obligations; if it does not have the resources
then it may be experiencing fiscal stress.
In general, the fiscal condition of a municipality can be viewed through the lens of four
dimensions. These are: (1) the ability of the municipality to meet its immediate or short-term
financial obligations; (2) the ability of the municipality to meet its financial obligations over a
budgeted fiscal year; (3) the ability of the municipality to meet its long-term financial
obligations; and, (4) the ability of the municipality to finance the base level programs and
services as required by law. A multi-dimensional approach allows for the consideration of where
the municipality is in meeting the needs of the citizenry while considering the demands that are
placed on the organization in the future. Innate to this is the ability of the municipality to
withstand unforeseen disruptions, such as an economic recession or change in the demographics
of the residents.
Measuring Fiscal Health
While the concept of fiscal health is straightforward, the determination of a government’s
financial position is not. Public finance researchers attributed this to a poorly defined concept
and one that is inherently difficult to measure (Bahl 1984; Benson, Marks and Raman 1988). The
measurement systems that arose were dependent on the preferences of the researcher, their unit

Page 3
of analysis, and the data available to them. As a result, considerable disagreement exists within
the literature on how it can best be measured (Maher and Nellenberger 2009; McDonald 2015;
Wang, Dennis, and Tu 2007).
Dozens of measurement approaches have been used within the literature and still more have been
developed by practitioners, often as an ad hoc version of one from the literature that has been
modified to fit the government’s needs. A number of states have developed their own systems of
measuring the fiscal health of their local governments (Pew Charitable Trusts 2016). North
Carolina, for example, monitors the audits of its local governments for signs of distress and
approving the issuance of all debt. Ladd and Yinger (1989) developed a measure that
incorporated the capacity of a government to raise revenue relative to the tax burden and
expenditure needs. The International City/County Management Association developed its
Financial Trend Monitoring System (FTMS) which incorporates 36 different financial indicators
across 11 areas (Groves, Godsey, and Shulman 1981). In recent years FTMS has been updated to
include 42 indicators that incorporate environmental and organizational factors as well as
financial (Nollenberger, Groves, and Valente 2003). Despite the utility these approaches provide,
they are cumbersome to estimate and provide little guidance on the actual condition of the
government.
While no single measure of financial condition has emerged (Jimenez 2009), several have
become more prominent in use. The analysis conducted here chooses to focus on three of these
systems. These include ratio analysis, Brown’s (1993) 10-point test, and Wang, Dennis, and Tu’s
(2007) solvency test. The decision to focus on these three is based on the prominence of the
system, such as ratio analysis which rests as the foundation of most approaches.
Ratio Analysis
Behind all measures of fiscal health is the use of ratio analysis. Ratio analysis is the examination
of a financial relationship between items as a means of identifying trends in financial behavior or
position (Kieso, Weygandt, and Warfield 2001). Functionally, it expresses the relationship as a
percentage, rate, or proportion. An example using the current ratio—the ratio of current assets to
current liabilities—is provided in table 1. The use of ratios provides a degree of standardization
for a government to capture its results over time or against other organizations. As a process for
examining trends, however, ratio analysis is not a direct measure of financial condition but rather
a series of indicators on where the government is heading. A ratio that is trending upwards, for
example, may indicate that the government is heading in a financially positive direction.
Table 1: Expressions of Ratio Analysis
Expression
Description
Percentage
Current assets are 105% of current
liabilities
Rate
Current assets are 1.05 times as great as
current liabilities
Proportion
The relationship between current assets and
current liabilities is 1.05:1.

Page 4
How the measurement of a government’s condition is carried out with ratio analysis is largely
determined on the type of financial obligation a researcher is interested in. A researcher
interested in the immediacy of a government’s capacity to provide services may be interested in
the measurement of its efficiency ratio (the ratio of total expenditures to total revenue) whereas
someone that is interested in the government’s ability to pay for its pension programs may be
interested in coverage ratios (ratios that reflect the capacity of an organization to meet its short-
term and long-term liabilities). In their textbook on financial management for public
administrators, Finkler, Smith, Calabrese and Purtell (2017) recommend 19 different ratios to be
used in analysis, though many other ratios are also available for use. This can be seen in the
FTMS, which relies on 29 ratios for part of its calculation (Groves, Godsey, and Shulman 1981).
The freedom to choose which ratios to utilize is a benefit in many ways as it allows for the
analysis to be customized to the unique circumstances of a government. The freedom, however,
also highlights a downside of ratio analysis. Without an established set of measures there is no
assurance that a true picture of fiscal health is being captured. Further, the results produced by a
ratio are not necessarily consistent across other ratios. This allows for gaming, such that
administrators can selectively choose ratios that give the desired appearance of a government’s
condition. How to address these short comings are the focus of most measurement systems,
including two of those included in this paper.
Brown’s 10-Point Test
One of the most commonly used measurement systems is Brown’s 10-point test (Honadle, Costa,
and Cigler 2004; Mead 2013). Brown (1993), in conjunction with the Government Finance
Officers Association, had intended to establish a means of measuring fiscal health that was both
easy to use and easy to understand. Ratio analysis provided useful information, but there is
variability in terms of which ratios governments should use and the interpretation of ratios can
provide uncertain results.
Functionally, the 10-point test is dependent upon ratio analysis for its results, but it centers on
only those that are most commonly used. Each of the points is a financial ratio that captures one
of five dimensions of financial position. However, rather than facing the same difficulties present
in ratio analysis, Brown adds an approach for indexing and comparability based on
benchmarking. This is accomplished by calculating each of the ratios used in the test for both the
government of interest and a group of similarly sized localities. Ratios are then quartiled to see
which quartile relative to its peers the government falls into. Each quartile is given a score of “–
1” for the first quartile to a “2” for the fourth across all of the ratios. The result is a score of fiscal
health that ranges between “–10” and “20”. The interpretation of the score is relative to the peer
group included in the study, such that a score of “10” or more places the government among the
best and a score of “–5” or under among the worst. 2 An overview of the test and its points is
provided in table 2.
2 For an example of calculation and application of Brown’s test, see Maher and Nollenberger (2009).

Page 5
Table 2: Brown’s 10-Point Test
Indicator
Type
Measurement
Total Revenues per Capita
Revenue
Total revenues for all governmental
funds (excluding capital project funds)
divided by population
Intergovernmental Revenues/Total
Revenues Percentage
Revenue
Intergovernmental revenues for the
general fund divided by total general
fund revenues
Property Tax or Own Source Tax
Revenues/Total Revenues Percentage
Revenue
Total tax revenues levied locally for
the general fund divided by total
general fund revenues
Total Expenditures per Capita
Expenditure
Total expenditures for all
governmental funds (excluding capital
project funds) divided by population
Operating Surplus or Deficit/Operating
Revenues Percentage
Operating
Position
General fund operating surplus or
deficit divided by total general fund
revenues
General Fund Balance/General Fund
Revenues Percentage
Operating
Position
General fund unreserved fund balance
divided by total general fund revenues
Enterprise Funds Working Capital
Coverage Percentage
Operating
Position
Current assets of enterprise funds
divided by current liabilities of
enterprise funds
Long Term Debt/Assess Value
Percentage
Debt
Long term general obligation debt
divided by total general fund revenues
Debt Service/Operating Revenues
Percentage
Debt
General obligation debt service
divided by total general fund revenues
Postemployment Benefit
Assets/Liabilities Percentage
Unfunded
Liability
Funded ration (i.e. actuarial value of
plan assets/actuarial accrued liability)
Source: Maher and Nollenberger (2009, p. 62)
One of the biggest arguments for the test is its simplicity (Crosby and Robbins 2013; Maher and
Nollenberger 2009). Not only are the ratios easy to calculate, but the data necessary for the
analysis can be found in the financial reports of any local government. This allows any
government to determine where they fall in comparison to others and to render an interpretation
of that position as one of financial condition. The test, however, is not without its faults. The
ratios utilized may have been those that are most commonly used at the time, but there is no
research to support whether they were the correct ratios. The ratios utilized focus on the
government’s general fund, ignoring the broader financial picture. Nor is there any evidence to
support the structure of the grading scheme. Perhaps more importantly, the test does not establish
a true understanding of fiscal health, but rather only produces an indication of how well the
government is doing in comparison to others. This can allow for the test to be gamed by
choosing comparison municipalities that are having financial difficulty.

Page 6
Wang, Dennis, and Tu’s Solvency Test
More recently, Wang, Dennis, and Tu (2007) sought to produce an approach to understanding
financial condition that addressed some of the short comings present in earlier attempts. They
argued that the focus should be on the measurement of a government’s financial condition rather
than on the factors that drive or determine it. It was hoped that the result would be an expression
of a government’s actual condition rather than an indication of where it was headed. Central to
their approach was an understanding of financial condition as a reflection of the government’s
financial solvency; that is, the ability of the government to meet its long-term financial
obligations.
To develop a method of estimating fiscal health, Wang, Dennis, and Tu (2007) began with the
four dimensions of solvency: cash, budget, long-run, and service. For each dimension, a series of
indicators were adopted, producing a total of 11 indicators that were typically based on the
government’s financial ratios and were in the context of the Governmental Accounting Standards
Board (GASB) Statement No. 34. An overview of the dimensions and their respective indicators
is provided in table 3.
Table 3: Wang, Dennis, and Tu’s Solvency Test
Indicator
Type
Measurement
Cash Ratio
Cash
Solvency
(Cash + Cash Equivalents +
Investments)/Current Liabilities
Quick Ratio
Cash
Solvency
(Cash + Cash Equivalents +
Investments + Receivables)/Current
Liabilities
Current Ratio
Cash
Solvency
Current Assets/Current Liabilities
Operating Ratio
Budget
Solvency
Total Revenues/Total Expenses
Surplus (Deficit) per Capita
Budget
Solvency
Total Surplus (Deficit)/Population
Net Asset Ratio
Long-Run
Solvency
Restricted and Unrestricted Net
Assets/Total Assets
Long-Term Liability Ratio
Long-Run
Solvency
Long-Term (Non-Current)
Liabilities/Total Assets
Long-Term Liability per Capita
Long-Run
Solvency
Long-Term (Non-Current)
Liabilities/Population
Tax per Capita
Service
Solvency
Total Taxes/Population
Revenue per Capita
Service
Solvency
Total Revenues/Population
Expenses per Capita
Service
Solvency
Total Expenses/Population
Source: Wang, Dennis, and Tu (2007, p. 8–9)

Page 7
To transition the results from a series of indicators and into measurement of condition, they
followed the premise of Brown’s point based test but moved it away from peer-comparison
towards standardization. After adjusting the direction of the results for the indicators to ensure
that they do not cancel each other out, the results were averaged across each dimension. The
result was a score for each dimension that could then be summed together for a single measure of
health referred to as a Financial Condition Index (FCI). Arnett (2014) later expanded the
approach by using the average of the z-scores for the indicators and adding a weight to each
dimension. Weighted dimensions could then be added together for an indexed measure of
condition.
Adopting a standardized approach allows for the comparison of a government’s FCI across time,
but adding a weighted component also allows for comparability across governments (Arnett
2014; Hummel 2015; Wang, Dennis, and Tu 2007). In many ways, this addressed the short
comings of a ratio analysis and Brown’s test. Additionally, the data necessary for the estimation
comes from the annual financial reports of the government, making such comparisons easy to
produce. With the utility comes uncertainty regarding the validity as indicators were chosen
based on common use and face-validity and attempts to test the validity was based on the FCI’s
relationship to socioeconomic variables.
Model, Data, and Methodology
The primary goal of the present study is to establish the validity of the systems of measuring
fiscal health in predicting extreme occurrences of fiscal activity, specifically municipal
bankruptcy. To accomplish this goal, I utilize an event history analysis (EHA) approach.3 EHA
is concerned with patterns and causes of qualitative changes (or “events”) at a given point in
time. The intent is to determine how a variable, or set of variables, affected the probability that
an organization would transition into a new social state.
The EHA is accomplished in two steps: First, the risk set of the analysis must be defined.
Second, the event model must be established. The definition of the risk set for this study is based
on the 150 municipalities represented in the Lincoln Institute of Land Policy`s Fiscally
Standardized Cities (FiSC) database for the period of 1977 to 2012. During this period, 12 of the
municipalities declared bankruptcy or had an emergency board imposed as an alternative to
filing. The final risk set comprises 5,400 observations, of which 114 observations (about 2.1%)
involve financially distressed cities.
Turning attention towards the event model, the extant literature provides no clear model of
government bankruptcy behavior. To move beyond this limitation, I follow the literature on
fiscal health and adopt a systems approach (see Hendrick 2004; McDonald 2015; and
Nollenberger, Groves, and Valente 2003). Within this approach, cities are viewed as open
organizations that not only influence their environment, but whose policies and decisions are also
influenced by their environment (Cyert and March, 1963; March and Simon, 1985). Accordingly,
the behavior of a government and the policies it produces are viewed as a function of the
3 For more information about event history analysis, see Berry and Berry (1990), Box-Steffenmeier and Jones (1997),
and McDonald and Gabrini (2014).

Page 8
environment from the community in which it resides, as well as the institutions that frame its
structure and guide its governance.
From this literature, the following model of an event history analysis of municipal bankruptcy is
suggested:
,=󰇕, +, +, +,
where the dependent variable Bi,t is the hazard rate of the probability that municipality i will file
for bankruptcy in year t. The fiscal health of the municipality is reflected as H. The variables G,
D, and E capture the political/governmental, demographic, and economic conditions of the
municipality in the given year.
Furthermore, Ψ denotes the cumulative normal distribution of the model. The hazard rate is
measured as a dummy variable where a “0” or a “1” were used to signify whether the
municipality filed for bankruptcy. Assuming a municipality in bankruptcy is likely to remain in
the state for more than the year of bankruptcy filing, a “1” is assigned to all years in which the
municipality remains in bankruptcy. As a result, the estimation of the model takes a logit form.
To accommodate for the small number of bankruptcies in the dataset, the Firth approach is
utilized. The Firth approach counteracts the bias of a small sample in maximum likelihood
estimation by adding a term to deal with the first-order term of the asymptotic expansion of the
bias (see King and Zeng 2001, and Wang 2014). Having previously established the risk set, data
on municipal bankruptcies were obtained from the financial archives of the individual
municipalities and the Municipal Securities Rulemaking Board. The simple statistics for all study
data are offered in table 4.

Page 9
Table 4: Simple Statistics
Variable
Mean
Std. Dev.
Min.
Max
Dependent Variable
Filed for Bankruptcy
0.0211
0.1437
0
1
Independent Variable
Efficiency Ratio
1.0291
0.4495
-8.6571
22.3618
Debt Service Ratio
1.2941
0.9606
-17.3023
23.6038
Cash Ratio
0.0000
1.0000
-0.0382
64.9709
Current Ratio
0.0167
1.2898
-0.0382
76.9894
Brown’s Test
4.5
3.2539
-6
14
FCI
0.0911
1.1036
-0.2446
68.9512
Cash Solvency
0.0055
1.0926
-0.0381
68.9770
Budget Solvency
-0.0396
0.0148
-0.2451
0.3008
Long-Run Solvency
0.0007
0.0939
-0.0763
1.6021
Service Solvency
0.1244
0.1111
-0.0018
1.3723
Council-Manager
0.4383
0.4962
0
1
Mayor-Council
0.4962
0.5000
0
1
Authority
0.6800
0.4665
0
1
Governor
0.5000
0.5000
0
1
Midwest
0.2133
0.4096
0
1
Northeast
0.1400
0.3470
0
1
Pacific
0.1600
0.3666
0
1
South
0.3666
0.4819
0
1
Hispanic
0.1042
0.1382
0.0025
0.8218
Male
0.4924
0.0428
0.4627
0.5474
Aged 0 to 17
0.2604
0.0361
0.1336
0.4762
Aged 18 to 65
0.6202
0.0466
0.3793
0.7539
Ages 66+
0.1193
0.0407
0.0186
0.3926
Following the discussion of fiscal health measurement systems provided in the previous section,
three systems are tested here. These include ratio analysis, Brown’s (1993) 10-point test, and
Wang, Dennis, and Tu’s (2007) solvency test, measured as both the individual dimensions and as
the FCI. The data necessary to estimate these approaches is from the FiSC database. For the
purposes of estimating the quartiles necessary for the 10-point test, the municipalities included in
the database will serve as the peer governments.
Accounting for political and governmental influence on bankruptcy decisions, three variables are
reported. Broadly, these are: Form, Authority, and Governor Party. In the model, form represents
the form of government that the municipality adheres to. Measurement for the variables follows
the IMCA’s typology and is retrieved from their municipal yearbooks. In the model, form is
reflected as two dummy variables (Council–Manager and Mayor–Council) with cities adhering
to the commission form serving as the reference group. Authority reflects a series of dummy
variables that control for whether a state specifically authorizes municipal bankruptcies,
conditionally authorizes, authorizes under limitations, or does not have specific authorization.
Information on the authority is obtained from state statutes. In these variables, a “1” reflects the

Page 10
presence of the authority and a “0” its absence. To account for political influences, we follow the
extant literature and rely upon the political affiliation of the governor as a reflection of local
political beliefs. The political affiliation of the governor’s from 1977 to 2011 is drawn from
Klerner’s (2013) Governors Dataset. Data on 2012 affiliations is obtained from websites of each
states. The variable is measured as a dummy, where a “0” was assigned for a Republican
controlled governorship and a “1” for when it was Democratic controlled. In eight observations
the control transitioned from one party to another during the year. In these instances, party
affiliation was assigned based on which party maintained control during the majority of the year.
Next, three variables are included to control for other variations of municipalities throughout the
dataset. Hispanic is the log of Hispanic share of the municipality’s population and Male is the
log of its male share of the population. Three variables account for the respective age of the
population within the municipality. These are 0 to 17, 18 to 65, and 66 and older, where each
variable reflects the log of the share of the population aged within the variables brackets. The
population estimates necessary for these measurements are obtained from the Economic
Database of Woods and Poole Economics. Lastly, I include the Census region of each
municipality. These regions are reflected as four dummy variables (Midwest, Northeast, Pacific,
and South), with municipalities placed in the West region servicing as the reference group.
Results and Discussion
The results of estimating the event history analyses for each of the approaches to understanding
fiscal health are presented in table 5. Overall, the open-systems model of municipal bankruptcy
was statistically significant; with the log likelihoods indicating the model fit the data well. The
influence of the control variables remained relatively stable across each of the models,
suggesting that the model, and its subsequent results, is robust. The results point to two sets of
interesting results. The first set of interesting results relates to the focus of this paper: the utility
of the extant approaches to measuring fiscal health. The second set of interesting results relates
to the causal factors behind a municipality’s decision to declare bankruptcy.

Page 11
Table 5: Event History Analysis Results
Model 1
Model 2
Model 3
Model 4
Efficiency Ratio
-0.3492
Debt Service Ratio
0.2676***
Cash Ratio
-96.9439**
Current Ratio
38.6711
Brown’s 10-Point Test
0.0159
FCI
-0.0455
Cash Solvency
-48.7220***
Budget Solvency
4.3266
Long-Run Solvency
-5.3212***
Service Solvency
2.3506***
Council-Manager
5.0010
4.7838
4.7519
4.8027
Mayor-Council
5.2570
4.9654
4.9497
4.6754
Authority
0.1625
0.1159
0.1013
0.2330
Governor
0.3014***
0.3064***
0.3008***
0.2462**
Midwest
5.6934
4.8533
4.8405
5.2448
Northeast
6.1807
5.1569
5.1684
5.6576
Pacific
4.4534
3.5818
3.5890
3.7107
South
4.8826
4.0472
4.0484
4.2663
Hispanic
0.1575**
0.1722***
0.1736***
0.1811***
Male
0.2392**
0.2337**
0.2249**
0.1501
Aged 0 to 17
2.8569**
4.3415***
4.0432***
5.2341***
Aged 18 to 65
15.1124***
18.3672***
17.6352***
19.5780***
Ages 66+
5.8488***
6.5867***
6.3938***
7.2294***
Constant
5.7179***
14.1734***
13.1794***
15.5604***
Log Likelihood
-310.4519
-325.9145
-326.0923
-293.8733
* p < 0.10, ** p < 0.05, *** p< 0.01
Regarding the utility of the measurement approaches, the estimates provide mixed support. In
Model 1, which reflects the utility of ratio analysis in predicting bankruptcy, there are conflicting
results. While two of the ratios included significantly reflect the bankruptcy decision, the other
two do not. Specifically, there is a positive and significant relationship between the debt service
ratio of a government and its likelihood of filing for bankruptcy. As a government’s debt grows
relative to its available revenue, the capacity of a government to continue meeting its fiduciary
responsibility diminishes, ultimately contributing to the bankruptcy decision. The results also
point to a negative and significant relationship between the government’s cash ratio and their
decision to file. Intuitively, the effect of the cash ratio is appropriate given that a smaller ratio
implies that there are more outstanding liabilities in the government and fewer resources to meet
them. Interestingly, neither of the remaining ratios showed an influence on outcomes.
Model 2 reflects the influence of Brown’s 10-Point Test on municipal bankruptcy. Despite the
reliance upon the test by practitioners, the estimates showed that it had no significant influence
on the bankruptcy of the municipalities in the study sample.

Page 12
Models 3 and 4 reflect the financial condition index (FCI) produced by Wang, Dennis, and Tu’s
Solvency Test and the individual dimensions of that test, respectively. The results of the analysis
show two sides of the same picture. On the one hand, the FCI showed no significant effect,
suggesting that the FCI is not a sufficient means of measuring the fiscal health of a government.
On the other hand, the individual dimensions of the index show significant influence. Both the
cash long-run solvency measurements had a negative, but significant effect on bankruptcy
decisions. Conversely, service solvency had a positive and significant effect and budget solvency
had no meaningful influence.
The second set of results that the estimates produce relate to the causal factors behind a
municipality’s decision to declare bankruptcy. As the models control for the
political/governmental, demographic, and economic conditions of the municipality, the estimates
provide us a degree of understanding about the conditions that lead a government to the
bankruptcy decision. In the political/governmental realm, the form of government that a
municipality adheres to has no measurable influence, nor does the presence or absence of the
authority for a municipality to declare bankruptcy. The political party of the governor, which
serves as a reflection of the local political climate of a municipality has a significant effect, with
municipalities in states where the governor is a Democrat as being more likely to experience
extreme financial distress.
The results also point to no geographic influence, with no significant influence appearing on any
of the Census region. They do, however, point to important influences based on the
demographics of the community. As the Hispanic share of population grows, so does the
likelihood of a bankruptcy decision. The gender dispersion of the population also has an
influence, with the share of males serving as a driver to bankruptcy filings. Lastly, there is a
positive and significant influence across each of the measures of the population age. While the
population aged older than 65 has a larger influence than the 0 to 17 population, the biggest
influence is seen on the share of the population that is between the ages of 18 and 65. These
results reflect the expectation of services that different segments of the populations have from the
government. As the expectation for the services increases, so does the likelihood of the
municipality filing for bankruptcy or undergoing control by an emergency financial board.
Conclusion
One of the challenges faced by governments is the understanding of their fiscal health. Given the
increase in demand for public services over the past few years, this challenge has become even
more important as governments struggle to meet their service commitments and obligations
without raising additional revenue. A number of approaches to understanding fiscal health have
been developed within the literature (see Wang, Dennis, and Tu 2007; Justice and Scorsone
2013), some of which have gained widespread use (Honadle, Costa, and Cigler 2004), as an
attempt to address this challenge. Despite their development, however, none of the approaches
have been rigorously tested against instances of fiscal distress.
This paper seeks to reconsider how we measure the financial condition of a government by
investigating the utility of several prominent measurement systems. These systems on which this

Page 13
paper focuses are: ratio analysis, Brown’s 10-point Test, and Wang, Dennis and Tu’s FCI. Using
a rare events history analysis approach, each system is tested for its efficiency in predicting
extreme fiscal distress: municipal bankruptcy. To complete the analysis, a panel of 150
municipalities from the Fiscally Standardized Cities (FiSC) database for the period of 1977 to
2012 is adopted.
The results of the event history analysis produced mixed results. Ratio analysis, one of the
common approaches to measuring fiscal health, showed a significant effect for some of the ratios
included in the study and no effect for others. This allows for the conclusion that the debt service
ratio and cash ratio directly reflect the financial position of a government and the efficiency ratio
and current ratio do not. Brown’s 10-point test was found to have no effect on the bankruptcy
decision of a government. The FCI also showed no effect as an index, but did show significance
in its deconstructed form.
When looking at all four models, a trend becomes obvious. Measurement systems that are reliant
upon a series of variables to describe the financial condition of a government have a more
meaningful influence than systems reliant upon an index. Although both the field and the
academic literature is encouraging of a single measurement, such efforts might be made in vain.
When data is indexed, the unique information about the condition of a government that the data
provides may be lost or crowded out. This can be particularly seen in the testing of the FCI,
where the index was tested first in its summated form and then retested in its multiple
dimensions.
Several important implications should be taken away from this study. First and foremost is that
we should reconsider how we measure fiscal health. Rather than abandoning the previous work
on the subject altogether, however, our reconsideration should point towards the refinement of
measurement systems that utilize multiple perspectives, such as ratio analysis or the individual
dimensions of the FCI. This would help avoid the loss of utility that an index approach generates
while clarifying what ratios truly affect condition. As noted previously in this study, there are
dozens of financial ratios that can be utilized, but not all of them point towards serious and long-
term problems for an organization. Some of these ratios are utilized directly in ratio analysis and
indirectly in determining the measurement of the FCI dimensions, but there is no certainty of
which ratios matter. Research should be geared towards resolving this uncertainty. Second, we
should reconsider the concept of fiscal health. Traditionally the concept has been thought of as
the generic financial position of an organization. While the understanding might suffice for
healthy governments, does it continue to hold true those undergoing extreme fiscal distress? If
different financial ratios point towards different aspects of an organization’s position, it may be
possible that how we measure the position of a healthy government is different from how we
measure that of an unhealthy government. Without clarity in what we are intending to measure
and what the data is to be utilized for, no single measurement system can emerge.

Page 14
References
Altman, E.I. 1968. “Financial ratios, discriminant analysis and the prediction of corporate
bankruptcy.” Journal of Finance, 23(4):589–609.
Arnett, S. 2014. State fiscal condition: Ranking the 50 states (Working Paper No. 14–02).
Arlington, VA: Mercatus Center, George Mason University
Bahl, R. 1984. Financing State and Local Government in the 1980’s. New York, NY: Oxford
University Press.
Benson, E.D., Marks, B.R., and Raman, K.K. 1988. “Tax effort as an indicator of fiscal stress.”
Public Finance Quarterly, 16(2): 203–218.
Berry, F.S. and Berry, W.D. 1990. “State lottery adoptions as policy innovations: An event
history analysis.” American Political Science Review, 84(2): 395–415.
Bird, R.M. 2015. “Reflections on measuring urban fiscal health.” In Bird, R.M. and Slack, E.
(Eds.), Is Your City Healthy? Measuring Urban Fiscal Health (pp. 11–42). Toronto,
Canada.
Box-Steffenmeier, J.M. and Jones, B.S. 1997. “Time is of the essence: Event history models in
political science.” American Journal of Political Science, 41(4): 1414–1461.
Brown, K.W. 1993. “The 10-point test of financial condition: Toward and easy-to-use
assessment tool for smaller cities.” Government Finance Review, 9(6): 21–26.
Crosby, A. and Robbins, D. 2013. “Mission impossible: Monitoring municipal fiscal
sustainability and stress in Michigan.” Journal of Public Budgeting, Accounting and
Financial Management, 25(3): 522–555.
CQ Press. 2016. Voting and Elections Collection. Retrieved from:
http://library.cqpress.com/elections
Diebold, J., Reitano, V. and McDonald, B.D. 2015. “Sweat the small stuff: Strategic selection of
pension policies used to defer required contributions.” Presented at the Annual Conference
of the Association for Public Policy Analysis and Management. Miami, FL: November 12–
14.
Finkler, S.A., Smith, D.L., Calabrese, T.D., and Purtell, R.M. 2017. Financial Management for
Public, Health, and Not-for-Profit Organizations (5th edition). Washington, DC: CQ Press.
Governing. 2015. Bankrupt Cities, Municipalities List and Map. Retried from:
http://www.governing.com/gov-data/municipal-cities-counties-bankruptcies-and-
defaults.html
Groves, S.M., Godsey, W.M, and Shulman, M.A. 1981. “Financial indicators for local
governments.” Public Budgeting and Finance, 1(2): 5–19.
Hendrick, R. 2004. “Assessing and measuring the fiscal health of local governments: Focus on
Chicago suburban municipalities.” Urban Affairs Review, 40(1): 78–114.
Honadle, B.W., Costa J.M., and Cigler, B.A. 2004. Fiscal Health for Local Governments: An
Introduction to Concepts, Practical Analysis, and Strategies. London, UK: Elsevier.

Page 15
Hummel, D. 2015. “Right-sizing cities: A look at five cities.” Public Budgeting and Finance,
35(2): 1–18.
Jacob, B. and Hendrick, R. 2013. “Measuring and predicting local government fiscal stress:
Theory and practice.” In Levine, H., Justice, J.B., and Scorsone, E.A. (Eds.), Handbook of
Local Government Fiscal Health (pp. 11–41). Burlington, MA: Jones and Bartlett
Learning.
Jimenez, B.S. 2009. “Fiscal stress and the allocation of expenditure responsibilities between state
and local governments: An exploratory study.” State and Local Government Review, 41(2):
81–94.
Justice, J.B. and Scorsone, E.A. 2013. “Measuring and predicting local government fiscal stress.”
In Levine, H., Justice, J.B., and Scorsone, E.A. (Eds.), Handbook of Local Government
Fiscal Health (pp. 43–74). Burlington, MA: Jones and Bartlett Learning.
Kieso, D.E., Weygandt, J.J, and Warfield, T.D. 2011. Intermediate Accounting, Volume 1.
Hoboken, NJ: John Wiley and Sons.
Kiewiet, D.R. and McCubbins, M.D. 2014. “State and local government finance: The new fiscal
ice age.” Annual Review of Political Science, 17: 105–122.
King, G. and Zeng, L. 2001. “Logistic regression in rare events data.” Political Analysis, 9(2):
137–163.
Klamer, C. 2013. “Governors Dataset.” Retrieved from http://hdl.handle.net/1902.1/20408
Maher, C. S. and Nollenberger, K. 2009. “Revisiting Kenneth Brown’s ‘10-point test’.”
Government Finance Review, 25(5): 61–66.
McDonald, B.D. 2015. “Does the charter form improve the fiscal health of counties?” Public
Administration Review, 75(4): 609–618.
McDonald, B.D. and Gabrini, C.J. 2014. “Determinants of county charter decisions: An event
history analysis of Florida counties.” Journal of Public Administration Research and
Theory, 24(4): 721–739.
Mead, D.M. 2013. “The development of external financial reporting and its relationship to the
assessment of fiscal health and stress.” In Levine, H., Justice, J.B., and Scorsone, E.A.
(Eds.), Handbook of Local Government Fiscal Health (pp. 77–124). Burlington, MA: Jones
and Bartlett Learning.
Nelson, K.L. and Svara, J.H. 2010. “Adaptation of models versus variations in form: Classifying
structures of city government.” Urban Affairs Review, 45(4): 544–562.
Nollenberger, K., Groves, S.M., and Valente, M.G. 2003. Evaluating Financial Condition: A
Handbook for Local Government (4th edition). Washington, DC: International City/County
Management Association.
Pew Charitable Trusts. 2016. State strategies to detect local fiscal distress. Washington, DC:
Pew Charitable Trusts.
Scorsone, E.A., Levine, H., and Justice, J.B. 2013. “Introduction.” In Levine, H., Justice, J.B.,
and Scorsone, E.A. (Eds.), Handbook of Local Government Fiscal Health (pp. 1–7).
Burlington, MA: Jones and Bartlett Learning.

Page 16
Trussel, J.M. and Patrick, P.A. 2013. “The symptoms and consequences of fiscal distress in
municipalities: An investigation of reductions in public services.” Accounting and the
Public Interest, 13(1): 151–171.
Wang, X. 2014. “Firth logistic regression for rare variant association tests.” Frontiers in
Genetics, 5: 1–2.
Wang, X., Dennis, L., and Tu, Y.S. 2007. “Measuring financial condition: A study of U.S.
states.” Public Budgeting and Finance, 27(2):1–21.