Minnesota’s Fiscally Healthiest City Is….

It’s not a beauty pageant.  Ratio analysis offers a way to extract more understanding out of government transparency efforts.

Mix a teaspoon of Great Recession hangover, a quart of stiff general fund competition for LGA dollars, a dash of unfunded legacy costs, and the usual gallons of levy reluctance, and you have some of the primary ingredients going into the preparation of city budgets for 2018.  The good news is that the majority of city officials currently appear to find the state of municipal finance relatively stable, and cities are now somewhat better able to meet their needs compared to years past.  According to the League of Minnesota Cities’ 2017 “State of the Cities” survey, two-thirds of respondents said their city was better able to meet its needs in 2016 than in 2015.  However, as the report is quick to note, “better able” is a relative measure.  It does not necessarily suggest that a city’s fiscal health is good; it may mean simply that meeting service needs and balancing the budget is a bit easier now than before.

Evaluating the fiscal condition of governments has been a growing area of interest and study over the last couple of decades.  In the same way business financial statements yield many types of ratios to evaluate business performance and facilitate cross-business comparisons, scholars and government finance professionals have similarly mined governments’ comprehensive annual financial reports (CAFR) to derive metrics to evaluate and compare their fiscal conditions.

Several approaches have been developed over the years.  They include the seminal “10 Point Test” proposed by a government finance scholar and revisited through the years1; a methodology based on 14 “fiscal solvency” metrics published by George Mason University researchers that annually evaluate and rank states’ fiscal conditions2; the Government Accounting Standards Board’s own Analyst’s Guide to Government Financial Statements; and independent initiatives such as the City and County of Denver’s Financial Sustainability and Benchmarking Project.  Unsurprisingly, there are strong similarities across these approaches but all feature their own tweaks and unique points of emphasis.

We examined these different methods and assembled a hybrid approach selecting what we believed to be the most meaningful indicators in order to identify Minnesota’s fiscally healthiest city.  Given the time and effort required, our analysis is limited exclusively to Minnesota’s 30 largest cities.  In addition, our findings are based on 2015 financial reports since the 2016 CAFRs were not available for each of these cities at the time we conducted this investigation.

A Mercifully Brief (But Necessary) Introduction to the Methodology

In reviewing the metrics government finance professionals and academics have developed, the common theme defining fiscal health is risk management and financial sustainability.  Governments are exposed to several types of risk involving their operations including:

  • The risk that a period of financial adversity would negatively impact service delivery
  • The risk that short-term or long-term obligations will become unmanageable
  • The risk associated with consistently financing operating costs using savings
  • The risk associated with having operating expenses funded by revenue sources over which the city exercises little or no control
  • The risk associated with financial difficulties arising out of business-type operations managed by the city
  • The risk associated with inadequate attention to maintenance and replacement of physical assets

Governments’ fiscal health and security depends heavily on understanding the risks they face and managing them appropriately.  The metrics government finance professionals and academics who work with governments have developed offer insights into how well governments are accomplishing that.

We selected ten ratios covering four different areas that provide a broad picture of the overall financial health and security of our pool of cities.  As part of our analysis, we created a “z-score” for each city on each of the 10 metrics.  A z-score simply measures how many standard deviations a city’s result for any single metric is from the overall (unweighted) average for that metric.  Z-scores provide a much better sense of scale than simple ordinal rankings because they capture both clustering and outlier effects. 

Unlike some of the efforts we reviewed, our final rankings do not weight metrics differently according to their relative importance.  In consulting with public finance officials to gain their professional thoughts and perspectives on this issue, it was clear that some metrics are more important than others.  But it was also clear that opinions on which metrics to weight and how much to weight them will differ.  Any weighting decisions inevitably add an element of subjectivity to the title of “fiscally healthiest city”.  Given that our effort is a preliminary exercise, we have left any potential weighting schemes for the future.

There are a couple of additional things to keep in mind when reviewing our results:

  • This is a benchmarking exercise.  Z-scores offer a relative perspective – how financially secure these 30 Minnesota cities are compared with one another.  Objectively, “best in class” performance on any individual metric may still be wanting.  Conversely, “worst in class” performance on any individual metric may not necessarily be a major cause for concern.  It is possible that efforts have been made to set general standards for what constitutes “good” financial ratios for government similar to the way generally accepted standards have been established for what constitutes “good” financial ratios in business.  However, in our investigation we did not come across any such efforts.
  • Our methodology strives to be agnostic with regard to the overall size of a city and its government.  Cities are not explicitly penalized or rewarded for being large or small.  We have standardized each metric using a control variable to minimize differences in city size.
  • The methodology is also agnostic with respect to total amounts of spending or revenues.  Our focus is instead on a city’s ability to finance the levels of services it provides to residents and businesses.

Table 1 provides an overview and description of the measures employed.  Following is a discussion of each category and our results.

Table 1

Financial Health Metrics

City Financial Position

Two of our five financial position metrics are balance sheet-oriented measures providing different perspectives on the risks associated with cities’ ability to meet their financial obligations.  Table 2 shows the top five performers in each of the five metrics.  The most basic measure is the cash ratio, which indicates whether there is adequate cash and easily convertible to cash assets to cover short-term liabilities.  A broader perspective is offered by the debt-to-assets ratio that reflects the amount of financial leverage the city employs.6  This perspective is important because, unlike other liabilities debt covenants offer very little flexibility.  Not only will a city employing a higher degree of financial leverage find funding ongoing operations more difficult during a recession than one with low leverage, higher interest rates typically accompany these higher levels of repayment risk.

The remaining three financial position measures mostly use information from the statement of revenues, expenditures, and changes in fund balances  (a.k.a. the “income/expense statement”) to address other forms of exposure.  The operating reserves ratio compares the fund balances a city can spend at its discretion7 in relation to its annual spending – a measure of ability to withstand unanticipated revenue shortfalls and address emergency needs.  The operating surplus/deficit ratio examines changes in reserves over a two-year period instead of at a single point in time, to see if they are growing or being used to finance current operations.  The debt coverage ratio provides perspective on a government’s capacity to finance debt payments by comparing them to its overall operating revenues.8  Importantly, the debt coverage ratio looks only at the debt a city accounts for in its governmental funds – mostly “general obligation” debt, or debt backed by a government’s power to tax.9

The financial position metrics demonstrate one of the significant advantages of using a z-score approach to benchmarking – mitigating issues the timing of the CAFR financial snapshot can create.  While cities’ operating expenditures are regular, the timing of many major revenue sources is not.  Cities in Minnesota receive large lump sum payments four times a year – including one-half of their property tax dollars in November and one-half of their LGA payments in December.  Because the CAFR measures a government’s financial status as of December 31 in any year, cities tend to be at a high point with regard to assets – for example, the average cash ratio of the 30 cities in our pool was 11 times current liabilities – skewing perceptions about financial position.  Since z-scores offer the perspective of relative position, all cities are skewed in a similar fashion and the effect gets largely washed out.

As the table indicates, there is some overlap among the top performing cities in these five metrics.  Three cities – Brooklyn Park, Eagan, and Minnetonka – are in the top five in three metrics, while four other cities – Andover, Coon Rapids, Plymouth and Woodbury – are in the top five in two metrics.  The remaining eight spots are filled with eight different cities.  Cities ranking high in these four metrics tend to be very large suburbs located in the seven-county metro, with medium growth and high commercial activity.10

Table 2

Top Five Cities: Financial Position Metrics

Revenues

The three revenue metrics all address the influence and reliability of city revenue sources.  The profitability ratio simply examines whether city business-like enterprises11 (golf courses, ice arenas, liquor stores, etc) are run at a profit – since unprofitable operations can require a subsidy, which could create a significant drag on city finances.  The external exposure ratio measures the potential risk that revenue streams completely outside a city’s control present.12  This ratio compares the amount of outside financing used to support general operating expenses – including aid payments from other governments, interest and investment earnings, and dollars generated by business-like enterprises – with the total property tax levy.  The risks associated with outside decisions or economic conditions increases as the ratio increases – cities with higher ratios need proportionately larger property tax increases to make up any shortfalls in these revenues.

The final revenue measure also addresses revenue exposure but focuses specifically on the reliability and predictability of a city’s own-source revenues.  Compared to property taxes and special assessments, collections from other sources such as fines, fees for services and permits are more variable and are often heavily influenced by economic conditions and circumstances.  The own source revenue control ratio provides a measure of this exposure.

Table 3 presents the top performers on the revenue metrics.  With respect to business-like operations the average city in this group collected 5.7% more in revenues than it spent, but Rochester stands out as having a level of “profitability” with respect to these operations that is nearly three standard deviations above the 30 city average.  Perhaps not surprisingly, higher wealth metro-area suburbs that tend to receive little, if any, Local Government Aid score best on external exposure while Brooklyn Center and Eagan lead the way with respect to the predictability and reliability of their own-source revenues.

Table 3

Top Five Cities: Revenue Metrics

Capital Assets and Retirement

The final set of metrics focus on two distinct areas that complement metrics emphasizing current operating budgets and take a longer term perspective on fiscal health.  The capital asset replacement ratio looks at the extent to which governments are maintaining their asset base.  Note that this metric looks only at the capital assets that support governmental functions – and not those that support business-type operations (such as a golf course clubhouse).

Last, but certainly not least, is the financing of long term retirement obligations.  City budgets finance two retirement-related costs.  One is retirement income, which cities support through contributions to Social Security and the state’s public pension systems.  The other is retirement health care, which they finance through contributions to Medicare and for what’s known as “other post-employment benefits”, or “OPEB” – a catch-all phrase for retirement benefits other than pensions.

On a relative basis, differences between cities in pension, Social Security, and Medicare costs aren’t particularly meaningful.  These programs are financed by assessing cities based on some percentage of their payroll.  Because cities participate in all these programs, and because the rates don’t vary, any differences in costs are largely a function of differences in payroll.  More importantly, none of the choices about what kind of benefits to offer or how much to spend for them are being made by local elected officials.  While these retirement obligations can have a major impact on government financial health these decisions are made further up the food chain, by the federal or state government.

Where cities do have considerable control over their retirement costs is in the OPEB area.  OPEB costs are almost always related to health insurance, and subsidize retirees’ healthcare costs.  Unlike retirement income programs or Medicare, cities have wide latitude to negotiate these benefits with their retirees as part of their collective bargaining process.

Cities have options about how to pay for these costs.  One way is to pay for them as the bills come – known as a “pay-as-you-go” basis.  The other payment method is based on the idea that since the city is essentially creating future costs now by making these promises, it should set aside money now that can be used to pay for the costs as they come due in the future.  Pre-funding these benefits is considered a best practice, because it pays for the costs of these benefits as employees earn them – matching the cost of the benefit with the taxpayers and businesses that benefit from the services they provide.  Alternately, the pay as you go system pushes the costs of the benefits being earned now onto future taxpayers.

Government finance standards now require that cities work with actuaries to determine the current value of all the OPEB liabilities they owe to their workforce – both past and present – even if they only finance these costs on a pay-as-you-go basis.  This allows governments to understand what their “unfunded liabilities” are – essentially how much these benefits can be expected to cost in future years.

The change in OPEB liabilities ratio takes this information to determine the risks associated with a city’s decisions regarding both the level of OPEB benefits it offers (value of benefits) and the share of those benefits it pays for during the year (annual cost).  The main risk is that increasing OPEB liabilities will generate increasing costs – and because these are also fixed costs, additional OPEB costs essentially mean a choice between more revenues, redirecting money away from providing services, or some combination of the two.  Our ratio measures the annual change in unfunded OPEB liabilities relative to the city’s operating budget.13

Table 4

Top Five Cities: Capital Asset and Replacement Cost Metrics

Table 4 presents the top performers on the revenue metrics.  Brooklyn Center’s score in the capital asset replacement ratio is worth noting because its outlier status flags the need for an explanation for such a large relative change.  Major changes in this ratio will generally be the result of the purchase or sale of a major asset, and taxpayers will want to understand why the purpose behind such decision-making.  With regard to OPEB, on average these cities’ unfunded liabilities grew by about 0.5% of their operating revenues – not surprising considering that most of the cities we analyzed finance their OPEB costs on a pay-as-you-go basis.  Table 4 highlights the cities with the best practices, with Duluth and Eagan standing out.  Duluth is the only city in this group whose OPEB liabilities declined during the course of 2015, giving it a z-score of roughly 2.5.  Eagan is the only city in this group that has fully pre-funded its OPEB liabilities, and so we have set its score equal to the best-performing city that has unfunded liabilities (Duluth) to provide a better sense of the low risks associated with a fully funded OPEB plan.  Other high performers included the city of Blaine, which reported no additional OPEB liabilities in 2015, and Cottage Grove and Brooklyn Park where their growth was minimal.

And Our Winner Is...Eagan

Adding the ten individual z-scores together provides a comprehensive perspective of these cities’ relative financial security.  Table 5 shows these results for the ten best-performing cities, with the city of Eagan coming out on top.  We performed some basic statistical analysis to test whether scores correlated with certain characteristics. City type14 seems to have very little relationship to the overall results, with a simple regression analysis indicating a less than 1% correlation between changes in total z-score and type of city.  Our testing suggests that changes in population explain about 8% of the change in scores – hardly a strong causative relationship.

Table 5

Top Ten Fiscally Healthiest Cities

With all due respect and congratulations to the city of Eagan, the primary purpose of this exercise is to reveal another way to extract more value out of government transparency efforts.  Thanks to major investments governments have made in their information systems, all sorts of financial data is now instantly available with the click of a mouse, and on line tools enable this data to be organized, sliced and diced in countless ways.  But context and perspective – the two key ingredients needed to turn data into understanding and allow taxpayers to make informed judgments about the financial condition of governments – don’t just appear with better access to numbers.

Ratios like the ones outlined in our effort use CAFR data to provide this context and perspective.  Not only do such measures present a snapshot of the many dimensions of government fiscal health, the measures can be used to benchmark financial condition against other governments, establish trend lines, and flag signs of fiscal stress or weakness.  In what area of finance is a government underperforming relative to others?  Why?  And most importantly, what is the plan to manage that stress or weakness?  Even if some unique economic or institutional factors justify the reason for being an outlier, getting that explanation out into the public is an important part of government transparency.

The challenge, of course, is that choosing and reporting a consistent and best set of ratios to evaluate and benchmark local government financial health is far beyond the capacity of ordinary citizens.  That is solidly a responsibility of governments and their finance professionals.  History suggests mandating new reporting requirements would not be well received.  It’s up to governments to take the initiative to make an analysis like this available.  Many have done excellent work in making tax and spending data much more accessible to the public.  That commitment needs to carry forward to further that data’s interpretation and use.

Footnotes
  • 1 "The Ten Point Test of Financial Condition: Toward an Easy-to-Use Assessment Tool for Smaller Cities” Government Finance Review, December 1993; and “Revisiting Kenneth Brown’s ‘Ten-Point Test’” Government Finance Review, October 2009.
  • 2 “Ranking the States by Fiscal Conditions”, Mercatus Center at George Mason University, various years.
  • 3 “Governmental” funds include a city’s general fund, any debt service funds, any capital funds (money used to purchase or construct land and buildings, equipment, or infrastructure), and any special revenue funds (money that is dedicated for specific uses).
  • 4 “Governmental Activities” comes from the Statement of Net Position (SNP).  The SNP is a government-wide financial statement that accounts for the same activities as “governmental funds” do, but with one major difference.  Governmental Funds are reported on a modified accrual basis, which provides a short-run financial perspective – effectively looking only at assets that are expected to be used within the current year or liabilities that are expected to be paid with current resources.  Governmental Activities are reported on a full accrual basis, meaning that – unlike the Governmental Funds – the numbers include long-term liabilities and long-lived assets.
  • 5 “Operating” funds include a city’s general fund, any debt service funds, and any special revenue funds.
  • 6 Lower debt to asset ratios are reflected in higher z scores.
  • 7 Includes fund balances that the city has designated as “committed”, “assigned”, and “unassigned”.  Balances in the “committed” and “assigned” categories can be repurposed at the city’s discretion.
  • 8 Lower debt coverage ratios are reflected in higher z scores.
  • 9 This excludes debt associated with enterprises because they typically raise cash through “revenue bonds”, which aren’t backed by a government’s taxing power but instead by the revenues generated by the activity which is being financed (for instance, water and sewer charge revenues will be used to pay for bonds for a water or sewer enterprise)
  • 10 i.e., they belong to the “metro large cities” city cluster in a typology the Minnesota House of Representatives’ nonpartisan Research Department developed in conjunction with their work on the Local Government Aid program.  House Research provides more information on the 15 “clusters” of cities they have developed at http://www.house.leg.state.mn.us/hrd/lgahist.aspx#noaction
  • 11 We exclude utility enterprises because 1) governments have significant pricing power by virtue of the fact that the enterprise is a utility, and 2) Minnesota law gives cities strong recourse to recoup unpaid utility bills by authorizing collection as an assessment on the property tax statement.  These factors help ensure adequate funding for utility enterprises and drastically reduce the risks they pose to city finance.
  • 12 Lower external exposure ratios are reflected in higher z scores.
  • 13 Lower OPEB liabilities ratios are reflected in higher z scores.
  • 14 See footnote 10 for information on the city typology system we used.