12 Financial Condition Analysis
LEARNING OBJECTIVES
After studying this chapter, you should be able to:
• Understand what financial condition analysis is
• Determine measures to assess financial condition
• Identify any warning trend of a deteriorating financial condition
• Specify relationships in a financial condition analysis
• Explain relationships in a financial condition analysis
• Write a financial condition analysis report
Why do you see a doctor? You are either sick or go for a regular physical checkup. If you
are sick, the doctor often asks you a few questions about your symptoms, does some lab tests,
and then prescribes medicines or recommends further treatments. In this process of diagnosis
and treatment, the doctor identifies the causes of the problem and, more important, develops
a strategy to improve your health.
Doing a financial condition analysis is like seeing a doctor. Heads of an organization have
concerns or want to know about its health; they want to know what factors influence its
health and what to do to improve it.
CONCEPTS AND THE TOOL
WHAT IS FINANCIAL CONDITION ANALYSIS?
Financial condition analysis (FCA) is a thorough evaluation of the financial health of an
organization. You can use the analysis to determine the financial condition of your
organization, but more important, you can use it to improve the financial condition. The
ultimate purpose of FCA is to identify the factors that impact financial condition and to provide
recommendations to improve it.
What are the differences between FCA and the analysis of financial statements discussed
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in the previous three chapters? Perhaps the most salient difference is that FCA stresses the
importance of socioeconomic and organizational factors in the analysis, while the analysis of
financial statements has a narrower focus on the financial information. FCA considers
socioeconomic and organizational factors the causes of financial condition.
What are the differences between FCA and the financial performance monitoring
discussed in Chapter 7? Financial monitoring is conducted more frequently than FCA.
Monitoring can be done daily or monthly on a very limited number of financial indicators.
FCA is a more thorough assessment that requires more time and resources for data collection
and analytical designs; it may not be conducted as often as financial monitoring.
When should FCA be conducted? An analysis may be performed at the beginning of a
fiscal period, when a budget is developed, or at the end of the period, when a financial report
is prepared. It can also be implemented during a financial crisis, emergency, or distress.
Finally, it can be part of an organization’s strategic planning process in which the
organization’s financial capabilities to support its mission and goals are examined.
Who conducts FCA? An analysis can be performed by an organization’s internal
management team or outside consultants and independent auditors. The internal approach
has the advantage of ready accessibility to the information needed for the analysis, while
outside consultants or auditors may be more objective in analyses and presenting critical
recommendations.
How difficult is FCA? FCA can be rather complex. Measures and data for the analysis may
not be available. In general, the difficulty level of an analysis is determined by three factors.
First, the scope of the analysis determines analytical complexity. There are four financial
condition dimensions, defined as cash solvency, budget solvency, long-run solvency, and
service solvency. An analysis can focus on any single dimension or combinations of
dimensions. Obviously, an FCA that examines all four dimensions is more complex than an
FCA limited to one dimension. Second, the availability of measures and data also affect the
difficulty of the analysis. If measures or data are not available or not accessible, surrogates or
replacements must be found and used. Third, an FCA requires the specification and testing
of how a financial condition is affected by socioeconomic/organizational factors. The process
of specification is called FCA modeling, which can be a rather complex process. The
complexity is augmented by the lack of quality theories in the FCA literature.
DETERMINING MEASURES IN FCA
After the scope of the FCA is determined, necessary measures need to be developed and
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related data should be collected. Since the purpose of the analysis is to identify
socioeconomic/organizational factors that affect financial condition, measures of financial
condition and socioeconomic/organizational factors should be developed.
Measuring Financial Condition
Financial condition (also known as economic condition) is defined as the ability of an
organization to meet its financial obligations. During the process of providing goods and
services, an organization incurs financial obligations in the form of expenses, expenditures,
and debt that must be paid sooner or later. If the organization can pay these obligations
without incurring much financial hardship, we say that the organization’s ability to pay is
high and the organization is in good financial condition.
The ability to pay is commonly called solvency in finance. There are four levels of solvency.
Cash solvency is the ability to generate sufficient cash to pay for current liabilities. Budgetary
solvency refers to the ability to collect sufficient revenues to pay for expenditures or expenses.
The ability to pay off long-term obligations is the concept of long-run solvency. Finally, service
solvency refers to the ability to financially support a desirable level of services.
How to measure financial condition? A good financial condition measure should satisfy at
least three criteria. First, a measure must assess a specified element of financial condition (i.e.,
measurement validity). For example, a revenue/expenditure ratio is a valid measure for
budgetary solvency, which assesses the sufficiency of revenues to cover expenditures. It is not
a valid measure for cash solvency, because not all revenues are in the form of cash. Second,
the elements used in formulating a measure should be consistent and objective (i.e.,
measurement reliability). If the unit of a measure is the general fund (i.e., general fund
revenues, general fund expenditures, the fund balance of the general fund), this unit should be
used consistently. Changing the unit will lead to measurement inaccuracy and, worse,
incorrect results for the FCA. Finally, the measure and supporting data should be affordable
to obtain (i.e., measurement affordability). The cost of obtaining measures and data should be
considered in selecting measures. Everything else being equal, the less costly measure is
always a better measure.
Based on these criteria, in this chapter a list of example measures is developed to assess
financial condition. Efforts are made to select only two measures for each dimension of
financial condition. Two ratios are used to measure cash solvency. The cash ratio relates cash,
cash equivalents, and marketable securities to current liabilities. The ratio indicates the extent
of assets available to pay off current liabilities. A higher ratio indicates a better level of cash
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solvency.
Another ratio of cash solvency is the quick ratio. Compared with the cash ratio, the quick
ratio is a more lenient measure, because it includes noncash assets, such as receivables, as
assets to pay off current liabilities. A higher ratio indicates a better level of cash solvency.
Budgetary solvency can be measured by the operating ratio, which assesses the sufficiency of
revenues to cover expenditures. A higher value of the ratio indicates a better level of
budgetary solvency.
Another measure of budgetary solvency is the own-source ratio, which indicates the level of
revenue that comes from a government’s own sources, such as taxes, charges, fees, and other
revenues. Since these revenues are considered more stable and controllable by the government
than revenues from intergovernmental financial assistance, a higher own-source ratio
indicates a higher level of budgetary solvency.
Two measures can be used for long-run solvency. The net asset ratio (or net position ratio)
assesses the extent of a government’s ability to withstand financial emergencies during
economic slowdowns, the loss of major taxpayers, and natural disasters. A higher ratio
indicates a better state of long-run solvency. Note that, after 2012, net position is reported in
lieu of net assets in US state and local governments (see Chapter 9 for this change).
The numerator in the above equation, “Net Assets (or Net Position),” includes capital
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assets that may not be available to pay off long-term obligations. So a modified net asset ratio
can be used:
Another measure of long-run solvency is the long-term debt ratio, which assesses an
organization’s ability to pay off its long-term debt. A higher ratio of this measure indicates a
worse level of long-run solvency. The numerator in the equation “Total Long-Term Debt,” is
also known as the long-term debt outstanding.
Service solvency can be assessed by net assets (net position) per capita, which indicates the
level of net assets (net position) in relation to population. A higher ratio indicates a better
level of service solvency.
Another measure of service solvency is the long-term debt per capita, which assesses the level
of long-term debt for each resident. A higher ratio indicates that a government carries more
long-term debt per capita and suggests a deteriorating state of service solvency.
Measuring Socioeconomic/Organizational Factors
There are a large number of socioeconomic/organizational factors that influence financial
condition. Including all of them in an FCA is impossible or very costly. Selecting proper
factors is critical. In addition to the above-mentioned principles of measurement validity,
reliability, and affordability, two other criteria should also be considered in selecting
socioeconomic/organizational factors in an FCA.
First, a theoretical cause-effect relationship must be developed to indicate how a
socioeconomic/organizational factor impacts financial condition. For example, because
population growth (a demographic factor) can bring more taxpayers and revenues, it is
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justifiable to include population data in an analysis that uses revenues to measure financial
condition. On the other hand, the relationship between the number of school-age children
and revenues is rather difficult to develop; therefore the number of school-age children
should not be included in the analysis. This criterion is called the theoretical justification of
measurement. It is the most important measurement selection criterion in FCA.
The second criterion in measurement selection is that a measure is better if it is more
controllable. “Controllable” means that a measure is sensitive to policies or managerial
operations or other human actions of the organization. For example, a city may find that its
residents’ income level improves its financial condition (i.e., higher-income residents pay
more taxes). However, it is rather difficult for the city to improve residents’ incomes quickly
and it often takes years, therefore the city may not be able to use income growth to improve
its financial condition quickly. On the other hand, if the city finds that higher educational
levels among city financial employees also improve its financial condition (a higher
educational level suggests a higher level of professionalism), the city can relatively quickly
improve its financial condition by hiring people with higher-level degrees. This measurement
selection criterion is called measurement controllability, which should be considered for an
FCA to be more meaningful to decision or policy makers. In general, organizational factors
have higher measurement controllability than socioeconomic factors.
Table 12.1 presents a list of possible socioeconomic/organizational measures that can be
used in FCA. The list is by no means exhaustive. It serves only as an example of possible
measures in FCA. Other measures are available and should also be considered.
Table 12.1
Socioeconomic/Organizational Factors in FCA
Measure
Description or examples
Socioeconomic factors
Population
The number of residents
Income
Median or mean household income, or median or
mean personal income
Property values
Total assessed property values, or total taxable
property values
Education level
The average number of school years completed by
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residents
Age
Median or mean resident age
Employment rate
Percentage of employed population to total
employment-eligible population
Commercial development or
The number of businesses, the value of commercial
business activities
property, or the number or value of new businesses
during a certain period
Organizational factors
Budget management
Budgetary systems and practices on budget formation,
implementation, and evaluation (e.g., tax rates)
Cash management
Systems and practices in managing cash (e.g.,
availability of mandated cash management policy)
Investment management
Systems and practices in investment (e.g., availability
of periodic review of investment policies)
Fallback management
Systems and practices in upholding and using financial
reserves (e.g., availability of mandated “rainy day
fund”)
Accounting and reporting
Accounting and reporting systems and practices (e.g.,
use of cost accounting)
Internal control
Systems and practices in decentralizing budgeting and
procurement (e.g., availability of a decentralized
procurement system)
Professionalism and leadership
Qualification or behaviors of financial personnel (e.g.,
mean years of education of financial personnel)
Note: This table is derived partly from the Ph.D. dissertation of Lynda M. Dennis, “Determinants of Financial
Condition: A Study of U.S. Cities,” University of Central Florida, 2005.
IDENTIFYING ANY WARNING TREND OF A DETERIORATING
FINANCIAL CONDITION
After financial condition measures are developed and related data are collected, we should
examine the data to identify any possible warning trend of a deteriorating financial condition.
This step requires an examination of at least three periods of data for a specified financial
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condition measure. A three-period continuing deterioration of a measure constitutes a
financial warning trend. For example, if the cash ratio at the ends of the past three months
was 0.75, 0.60, and 0.55 respectively, this downward turn indicates a continuing deterioration
of the measure and constitutes a financial warning trend. Microsoft Excel provides good
graphing functions that can be used for the visual representation of a warning trend.
It is important to note that, although a warning trend provides a strong reason to conduct
an FCA, it is not the only one. The fluctuation (rather than continuation) of a financial
condition measure may also deserve a close look. At other times, managers may simply want
an FCA to explore the possibilities of a continually improving financial condition or to gain
insight into the financial capacity of the organization.
SPECIFYING THE RELATIONSHIP
At the beginning of this chapter we said that the purpose of FCA is to find out the factors
that impact financial condition. In this section, we discuss how to identify the impact. A basic
principle in logic is that in order to say that Event A impacts Event B, both events must first
be related. In other words, to prove that a factor impacts financial condition, this factor and
financial condition must be related. There is a relationship between the factor and the
financial condition. Although a relationship does not mean the impact actually occurs, it does
serve as a necessary condition for the impact to happen. In other words, without the
relationship, the impact cannot happen.
Statisticians have developed tools to assess relationships. They call these tools measures of
associations (association is a synonym for relationship). One measure of association is the
correlation coefficient. Let us look at an example of how to use this statistic in an FCA. Table
12.2 shows a city’s population and revenues for the last five years.
Table 12.2
Population and Revenues
Year
Population
Revenues ($)
Five years ago
173,122
198,837,119
Four years ago
176,373
265,927,499
Three years ago
180,462
249,374,988
Two years ago
184,639
265,884,544
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One year ago
188,013
272,805,096
The Excel Insert Function (fx) can be used to obtain the correlation coefficient easily.
Select “Statistical” in the function category window and choose “CORREL” (correlation) in
the function window. Click the “OK” button. Select the population data in the “Array 1”
window (i.e., A2:A6) and the revenue data in the “Array 2” window (i.e., B2:B6). The
calculation process is shown in Excel Screen 12.1.
Excel Screen 12.1 Calculating Correlation Coefficients
Table 12.3
Interpretation of the Correlation Coefficient
Correlation coefficient value
Interpretation
0
No relationship
Larger than 0 but smaller than 0.500
Weak positive relationship
From 0.500 to 0.699
Moderate positive relationship
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From 0.700 to 0.999
Strong positive relationship
1.000
Perfect positive relationship
?1.000
Perfect negative relationship
From ?0.700 to ?0.999
Strong negative relationship
From ?0.500 to ?0.699
Moderate negative relationship
Smaller than 0 but larger than ?0.500
Weak negative relationship
The “Formula Result” in the screen shows a correlation coefficient of 0.755. What does
that mean? Two pieces of information are needed to interpret a correlation coefficient—its
direction and magnitude. A positive value of a coefficient indicates that both factors move in
the same direction. In other words, when the value of one factor increases, the value of the
other increases too. A negative value of a coefficient indicates that the factors move in
opposite directions. The magnitude of a relationship is measured on a scale from ?1.000 to
1.000. A zero (0) would mean no relationship between the two factors, while 1.000 indicates
a perfectly positive relationship and ?1.000 a perfectly negative relationship. Table 12.3 can
be used as the reference in explaining the correlation coefficient value.
In our example, since the correlation coefficient is 0.755, we say that the relationship
between population and revenues is a strong positive relationship, or that they are strongly
positively associated. The establishment of this relationship provides some evidence that
population may impact revenues.
Now, as an exercise, you can use the expenditure data in Table 2.1 in Chapter 2 of this
book to run a correlation analysis. The correlation coefficient between population and total
expenditures should be 0.953.
EXPLAINING THE RELATIONSHIP
With a correlation coefficient, we can tell if a relationship exists, and if it does, how strong it
is. Nevertheless, we still do not know the exact form of the relationship. For example, we
know that population and revenues are strongly associated, but we cannot tell how much
revenue will be brought in if the population increases by, say, 1,000. So, after a strong
relationship is identified, the next step in FCA is to further explore the exact form of the
relationship. Realize that the exact form of a relationship is important for making meaningful
policy or management recommendations. Many methods can be used to identify the exact
form of a relationship. One method uses per capita statistics. Let us use the data in Table
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12.4 as an example.
Table 12.4
Population, Revenues, and Revenue Per Capita
Year
Population (1)
Revenues ($) (2)
Revenue per capita ($)
(2)/(1)
Five years ago
173,122
198,837,119
1,149
Four years ago
176,373
265,927,499
1,508
Three years ago
180,462
249,374,988
1,382
Two years ago
184,639
265,884,544
1,440
One year ago
188,013
272,805,096
1,451
Average
1,386
The exact form of the relationship between population and revenues can be described as
one resident on average brings in about $1,386 in revenues. If an FCA indicates the
government needs to have $2,000,000 in revenues to improve its financial condition to a
certain degree in the next year, it will need to bring in an estimated $2,000,000/$1,386 =
1,443 residents.
When per capita statistics are not available, the growth rate and percentages can also be
used in specifying the exact form of a relationship. For example, if we know that a 1 percent
increase in the tax rate will bring in $1,000,000 in tax revenues, and if $2,000,000 in revenues
is needed to improve financial condition to a certain degree, then the tax rate should be
increased by 2 percent.
Notice that, in our analysis, we examined the impact of one socioeconomic/organizational
factor on financial condition at a time. For instance, in the above example, we examined the
impact of population trend on financial condition. In reality, it is very likely that more than
one factor impacts financial condition. For example, it is possible that both population and
household income affect financial condition jointly. Advanced statistical tools are needed to
analyze this joint impact, and a discussion of these tools is beyond the scope of this book.
FCA REPORT WRITING
The purpose of an FCA report is to make recommendations to improve financial condition.
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The report should first present the rationale of the analysis, describe the existing financial
condition, and discuss the process of the analysis. It should present the key findings by
specifying any important socioeconomic/organizational factors that influence financial
condition. It should also discuss possible policy/management options that can improve
financial condition in the near term and in the long term. If needed, the cost and benefit of
each option should be examined and presented to specify the feasibility of each option.
A CASE STUDY
The city of Lucille (population 313,611) is located in a major metropolitan area in the
southeastern United States. The city provides a wide range of public services to its citizens,
such as policing, fire protection, parks and recreation, city road/street construction and
maintenance, library services, and many other municipal services. Lucille is located within the
boundaries of Osorio County. The county provides services in the areas of correction, court
services, property assessment, county road/street construction and maintenance, and many
other county-level services.
In a recent management meeting, City Manager Wendy Higgins told Finance Director
Jeff Boiling that a recent issue of PA Times published the results of a survey indicating that
about 67 percent of US cities had experienced difficulty in collecting sufficient revenues to
pay for their services. In other words, three out of five cities said they had a budget solvency
problem. Wendy wants to know where Lucille stands in budget solvency. Wendy knows that
the city’s Finance Department conducts an annual FCA; but the analysis has always focused
on cash solvency—whether the city has enough cash to meet its short-term financial
obligations. Jeff explained that budget solvency is different from cash solvency in that it
reflects different aspects of financial condition. Wendy then asked Jeff to prepare an FCA on
the city’s budget solvency that focuses on two specific questions: What is the city’s current
budget solvency status? And what can be done to improve the status, if needed? She wanted
to see the analysis in a week so she could present it to the city commission in a budget
workshop.
STEP 1: DEFINING THE SCOPE OF THE ANALYSIS
Jeff knew the scope of the analysis would be limited by several factors, and he discussed these
factors with Wendy to ensure that she understood these limitations. First, the analysis would
focus on budget solvency only. There would be no attempt to address issues of long-term
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solvency and service solvency, which could be done later if needed. Also, there would be no
need to repeat the analysis on cash solvency, which is always conducted at the end of a fiscal
year. The main results of last year’s cash solvency analysis can be seen in the CAFR. Second,
the analysis would focus on governmental funds only. No attempts would be made to address
the issues in other funds. This is because the data of governmental funds are readily available,
and the majority of the city’s expenditures are for governmental activities. The city’s
governmental-activity expenditures are 67 percent of the total primary government expenses.
Third, the analysis would be limited by available measures and data. As the analysis is
needed in a short time, only measures and data available in the CAFR would be used. There
is no time or budget to collect data beyond the city’s possession, which would be needed for a
more comprehensive analysis. Fourth, the analysis would be guided by the financial condition
literature and experiences of city financial personnel. The latter is particularly important, as
the current literature is often not specific enough for the financial environment faced by the
city. Last, any recommendation made in the analysis would be valid for a time frame of no
more than three years.
STEP 2: DETERMINING MEASURES AND COLLECTING DATA
Jeff decided to use two indicators to measure the budget solvency of the city. The first was the
operating ratio (Total Revenues/Total Expenditures), which assesses the extent of revenues to
cover expenses. The revenues include program revenues and general revenues. A higher ratio
indicates a higher budget solvency. More specifically, a ratio of 1.000 indicates all revenues
are used to cover all expenditures. A ratio greater than 1.000 indicates revenues exceed
expenditures, and a ratio less than 1.000 suggests a deficit of revenues over expenditures.
The second was the own-source ratio (Revenues from Own Sources/Total Revenues),
which measures the proportion of total revenues that comes from the city’s own revenue
sources. A higher ratio indicates less reliance on vulnerable intergovernmental revenues and a
higher level of budget solvency.
Jeff also collected information about the following socioeconomic/organizational factors
that could influence budget solvency. (1) Taxable property values can affect the property tax
revenues—one of the largest revenues of the city. Taxable value increases should lead to an
increase in tax revenues, and therefore improve the operating ratio and the own-source ratio.
(2) Population fluctuation may influence both revenues and expenditures, and thus budget
solvency. Population increase can provide more revenues through increased taxes and fees;
population increase may also lead to increased spending to support more public services. (3)
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Income per capita in the city may also affect budget solvency. Income growth may indicate an
increase in the tax base that results in improved budget solvency. But such increases may also
suggest a larger demand for a high quality of public services, which leads to higher spending
and potentially deteriorating budget solvency. (4) A higher unemployment rate may suggest a
greater need for public assistance and services, which could negatively affect budget solvency.
(5) As for the property tax rate, the millage can influence the amount of revenues collected.
Given the size of taxable property values, a higher millage produces a larger amount of
property taxes and a higher level of budget solvency. Jeff collected the data for expenditures,
revenues, and all five socioeconomic/organizational factors for the last ten years from the
city’s CAFR, as shown in Table 12.5.
Table 12.5
CAFR Data for the City of Lucille
STEP 3: IDENTIFYING WARNING TRENDS
How is the city’s budget solvency? Is there a warning trend? To answer these questions, Jeff
used the data in Table 12.5 to compile data for the operating ratio and the own-source ratio
for the past ten years, as shown in Table 12.6. Notice that the operating ratio for this year is
1.086, which is the ratio of this year’s revenues ($285,705,000) to expenditures
($263,139,000). This year’s own-source ratio, 0.875, is calculated from this year’s own-source
revenues ($249,941,000) divided by total revenues ($285,705,000).
The operating ratio shows the city had sufficient revenue to pay its bills for the past ten
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years. Jeff used a ratio value of 1.000 (Revenues = Expenditures) as the benchmark to evaluate
and explain the operating ratio. The ratio has been greater than the benchmark, indicating a
satisfactory status of budget solvency in that indicator. The own-source ratio shows that more
than 85 percent of the city’s revenues came from its own sources, more than the national
average of about 70 percent for local governments. This measure shows that the city does not
appear to over-rely on intergovernmental revenues. As intergovernmental revenues can
fluctuate over time and are not considered a reliable source of revenue, the absence of
overreliance on intergovernmental revenues suggests a satisfactory status of budget solvency
for the city.
Nevertheless, two issues concern Jeff. First, the operating ratio has fluctuated over the last
five years. This year’s ratio (1.086) is particularly low in comparison with those of previous
years (the average of the past ten years is 1.124). Thus, there may be a need to stabilize the
ratio. Second, although the own-source ratio is higher than the national average for local
governments, it is still lower than that of cities with populations greater than 100,000 in the
state. It is also lower than several adjacent cities that have socioeconomic characteristics that
are similar to Lucille’s. These concerns indicate there is still room for improvement in the
city’s budget solvency. Jeff then decided to continue the analysis to explore possible ways to
improve the budget solvency of the city.
Table 12.6
Operating Ratio and Own-Source Revenue Ratio
Year
Operating ratio
Own-source revenue ratio
1
1.127
0.863
2
1.113
0.858
3
1.135
0.854
4
1.093
0.849
5
1.120
0.848
6
1.114
0.855
7
1.152
0.861
8
1.149
0.869
9
1.154
0.872
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10
1.086
0.875
(this year)
Table 12.7
What May Affect the Operating Ratio?
Correlation with operating ratio
Taxable property values
0.114
Population
0.072
Income per capita
0.086
Unemployment rate
?0.307
The millaqe rate
?0.266
Note: The figures are correlation coefficients.
Table 12.8
What May Affect Revenues?
Correlation with revenues
Taxable property values
0.986
Population
0.988
Income per capita
0.971
Unemployment rate
?0.537
The millage
?0.985
Note: The figures are correlation coefficients. Strong relationships are highlighted in bold.
STEP 4: SPECIFYING THE RELATIONSHIPS
In this step, Jeff wanted to specify the impact of socioeconomic/organizational factors on the
budget solvency of the city. The socioeconomic/organizational factors include taxable
property values, population, income per capita, the unemployment rate, and the millage.
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Among them, the millage is considered an organizational factor, as it can be determined by
the city’s policy makers. All of the others are socioeconomic factors.
The Operating Ratio
Using
the
data
in
Table
12.5,
Jeff
first
ran
a
correlation
analysis
of
socioeconomic/organizational factors with the operating ratio. Table 12.7 shows the result.
According to our rule of thumb to judge the relationship, none of these relationships is
strong (i.e., greater than 70 percent or less than ?70 percent). None of these factors appears
to influence the ratio directly. However, since the operating ratio is the division of revenues
by expenditures (i.e., Revenues/Expenditures), one way to improve the ratio is to increase
revenues. So Jeff decided to examine factors that could influence revenues. Table 12.8 shows
the correlation between revenues and the socioeconomic/organizational factors.
The results show that revenues are strongly and positively associated with taxable property
values, population, and income levels, which suggests that an increase in these factors leads to
the increase in revenues and, therefore, the improvement of the operating ratio. The results
also show that the millage is strongly and negatively associated with revenues. Revenue
increase is associated with millage decline. This bewildering relationship is the subject of a
later discussion in this analysis.
The Own-Source Ratio
Jeff
then
ran
a
correlation
analysis
to
assess
the
possible
influences
of
socioeconomic/organizational factors on the own-source ratio. Table 12.9 presents the
results.
Table 12.9
What May Affect the Own-Source Ratio?
Correlation with the own-source ratio
Taxable property values
0.812
Population
0.704
Income per capita
0.551
Unemployment rate
0.063
The millage rate
?0.721
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Note: Th