All posts by Thomas Haasl

The Fiscal Performance of Seventh District States in the 2000s

In a recent Chicago Fed Letter, Thom Walstrum examined the fiscal performance of Illinois’s state and local governments beginning in the late 1980s. His analysis showed that since at least the late 1980s, Illinois’s governments (as a whole) have consistently run a budget deficit. His analysis also revealed that the degree of overspending (or alternatively, undertaxing) by Illinois was greater than that of the average U.S. state and that growing pension liabilities have contributed significantly to Illinois’s budget deficit.

In this blog post, we expand the analysis to the other states in the Seventh Federal Reserve District.[1] Specifically, we document the expenditure and revenue patterns of District states since the early 2000s and compare them to those of the typical U.S. state.[2] We also examine the effect of the Great Recession on the fiscal performance of District states because it plays an outsized role in the overall fiscal performance of certain states over the period we examine.

As in the Fed Letter, we combine the expenditure and revenue data for state and local governments because states differ in which activities they fund at the state or local level. Also, as in the Fed Letter, to account for differences in the sizes of states’ economies, we report expenditure figures as percentages of gross state product (GSP) and revenues.[3]

Our analysis yields a number of interesting results. First, we find that the size of state and local governments (in terms of spending as a percentage of GSP) varies quite a bit among District states. Second, we find that the fiscal performance of state and local governments (in terms of spending as a percentage of revenues) also varies quite a bit. And finally, we find that though the Great Recession had a large negative impact on the fiscal performances of all District states, Illinois and Wisconsin were especially affected, primarily because the value of their pension systems’ assets declined sharply.

We first look at the size of state and local governments in District states in terms of spending as a percentage of GSP. Figure 1 shows total government expenditures as a percentage of GSP for the average U.S. state and for Seventh District states during fiscal years (FY) 2002–13. Indiana is consistently the lowest spender during this span, and it is well below the U.S. average. Iowa and Illinois are also below the national average for most of this period, though they catch up to it by FY2012. In contrast, Wisconsin’s spending is roughly the same as the typical U.S. state. Michigan tracked the national average closely until FY2007, but has been consistently above average since then. Figure 1 also shows a ramp-up in spending across all states in FY2010–11. This is the largely the result of states spending federal funds received through the American Recovery and Reinvestment Act.

fiscalperformance_figure1

Table 1 summarizes figure 1 by taking the average of the percentages over FY2002–13. It also shows a breakdown of average spending by category. We now discuss the unique features of each state’s spending (as a percentage of GSP).

  • Illinois’s total spending was below the U.S. average largely because of lower expenditures on education services and social services (and income maintenance). That said, Illinois spent more than the typical U.S. state on its insurance trust and pension liability increases, both of which are compensation for government workers, including those providing education and social services.
  • Indiana’s total spending was below the U.S. average because of lower spending on most categories, though it spent a particularly low amount on pension liability increases compared with other states.
  • Iowa’s total spending was below the national average (in spite of above-average spending on education and social services) because of below-average spending on its insurance trust and pension liability growth.
  • Michigan’s spending was above the U.S. average largely because of higher spending on education services and its insurance trust.
  • Wisconsin’s spending was quite close to the U.S. average; compared with the typical state, Wisconsin spent more on education services and its insurance trust, but less on pension liability growth.

fiscalperformance_table1

Next we look at each District state’s fiscal performance, which we define as total expenditures as a percentage of total revenues. We interpret lower percentages as better performance. It is important to note here that fiscal performance is independent of the overall size of a state’s governments, because all that matters is that the governments have enough revenues to cover their expenses. While small governments generally do not require the level of revenues that large governments do, small governments could still perform worse than their large counterparts if their revenues are not high enough. Figure 2 shows the time trends for expenditures as a percentage of revenues for each District state and the typical U.S. state. Two features of the figure stick out: First, with the exception of Illinois, District states are quite close to the U.S. average in terms of spending as a percentage of revenues. Second, while most states’ governments were hurt by the Great Recession (FY2008–09), Illinois’s and Wisconsin’s were hit particularly hard, while Indiana’s was not hit that bad.

fiscalperformance_figure2

The first row of table 2 summarizes figure 2 by taking the average of the percentages over FY2002–13. Illinois and Wisconsin spent more out of their revenues than the typical U.S. state during this period, while Indiana, Iowa, and Michigan spent less. Because FY2009 was such an anomaly on account of the Great Recession, we also calculate the averages excluding it (second row). This changes the story quite a bit for Wisconsin governments, which then perform better than the U.S. average. (With this adjustment, Michigan governments perform slightly worse than the U.S. average.)

Table 2 also shows the percentage of total revenues that each spending category represents (calculated excluding FY2009). Examining expenditures in terms of revenue, as opposed to GSP, tells a different story for several states.

  • Illinois’s total expenditures percentage is well above the U.S. average. Spending out of revenues on education is above that of the typical U.S. state, though it remains below that of the other District states. Illinois also spends more than the national average on public safety, environment and housing, interest on general government debt, its insurance trust, and pension liability growth.
  • Indiana’s total expenditures percentage is below the U.S. average. It spends less than the national average on transportation, utilities, its insurance trust, and pension liability growth.
  • Iowa’s total expenditures percentage is not only below the U.S. average but also the lowest among District states. Notably, its spending on public safety, utilities, its insurance trust, and pension liability growth is lower relative to the national average.
  • Michigan’s total expenditures percentage is slightly above the U.S. average. Its education spending is the highest among District states and markedly higher than that of the typical U.S. state. But its spending on transportation, utilities, and pension liability growth is lower than the national average.
  • Wisconsin’s total expenditures percentage is below the U.S. average. While its expenditures for education, public safety, and its insurance trust are above average, its expenditures for pension liability growth are below average.

fiscalperformance_table2

Table 2 shows that Illinois and Wisconsin were hit hardest by the Great Recession. After excluding FY2009, Illinois’s spending as a percentage of revenue decreases 6 percentage points and Wisconsin’s decreases 11 percentage points. These decreases are much larger than those for other District states and the typical U.S. state, which range from 1 to 4 percentage points. What is behind the substantial differences in fiscal performances in FY2009? We found that the source was not changes in expenditures, but changes in revenues. Table 3 shows revenues as a percentage of GSP for the typical U.S. state and states in the Seventh District. The first row is the average value during FY2002–13 excluding FY2009, the second row is the value for only FY2009, and the third row is the difference between the two. All states had lower-than-normal revenues in FY2009, but Illinois and Wisconsin fared particularly poorly. To understand why, we calculated the difference between FY2009 values and the average values of the other fiscal years for all revenue categories. General revenues were actually higher in FY2009 for the typical U.S. state and all District states. The source of the revenue declines was states’ insurance trusts. Most states saw the value of the assets in their insurance trusts fall during the Great Recession, and such declines are treated as negative revenues in the U.S. Census’s accounting framework. The insurance trust funds for Illinois and Wisconsin fared particularly badly in FY2009, which is why their expenditures-to-revenues ratios were so high over the period FY2002–13 (see the first row of table 2). That one bad year made a huge difference in Wisconsin’s overall fiscal performance over the period FY2002–13.

fiscalperformance_table3

Our exploration of the size and performance of District state governments reveals a surprising number of differences among them. There are states with relatively small governments that perform poorly (Illinois) and well (Indiana) and states with relatively large governments that perform poorly (Wisconsin) and well (Michigan). Some states were hit much harder than others during the Great Recession (compare Wisconsin and Indiana), and Wisconsin’s terrible performance in FY2009 shifted the state from being a good fiscal performer to being a bad one over our study period (FY2002–13). The most important reason for the differences in fiscal performance across states is differences in pension system management. Illinois would be closer in performance to the national average if its pension spending matched the national average, and Wisconsin would be better than average if its pension system’s assets hadn’t lost so much value during the Great Recession.

[1] The Seventh Federal Reserve District (which is served by the Chicago Fed) comprises all of Iowa and most of Illinois, Indiana, Michigan, and Wisconsin. In this blog post, we analyze the entirety of each state that falls within the District.

[2] Unlike for the analysis of just Illinois, we are limited to the period after 1999 because we do not have pension system data for other states before 2000.

[3] For more details on the methodology, see the Fed Letter. Note that data on pension liabilities for the Seventh District states, excluding those for Illinois, come from the Board of Governors of the Federal Reserve System.

Where are the Jobs? Midwest Jobs Recovery by State and Sector

The Great Recession, which lasted from December 2007 through June 2009, had a profound effect on U.S. employment. At its height, U.S. unemployment reached 9.5% and job losses totaled approximately 7.3 million. The current recovery is now entering its eighth year, so it may be a good time to take a look at how many jobs lost during the recession have been recovered as one measure of the nature of the labor recovery. In this blog, we examine the pattern of recovery for all five Seventh District states (Iowa, Illinois, Indiana, Michigan, and Wisconsin) in four industry sectors that were heavily impacted by the recession—construction, finance (finance, insurance and real estate or FIRE), manufacturing, and professional and business services.

Figure 1 provides evidence of the job recovery through September 2016 across industries for the U.S. and the five Seventh District states. The first column for each state shows the number of jobs lost in that sector during the Great Recession (in thousands). The second column shows the number of jobs added during the current recovery (in thousands). And the third column shows the recovery rate (jobs added divided by jobs lost in percent). To provide some perspective on the concentration of each industry’s employment within the state relative to the U.S. total, the fourth column shows an employment location quotient. For example, if you look at Illinois, employment in professional and business services is 1.10 (or 10%) above the U.S. total.

figure1

The first thing to notice is that there is considerable variation in job loss and recovery both within sectors and across states. Two industries, health and education and government, actually added jobs during the recession (except in Michigan, where government employment decreased). However, while job gains have been sustained for the health and education sector, government has actually contracted during the recovery. At the other end of the spectrum, job losses were largest in manufacturing, construction (mining, logging, and construction), and transportation (trade, transportation, and utilities). While transportation has seen a strong recovery (143.6% nationally), the job recovery in manufacturing is only 29.7%, and in construction the figure is 48.1%. On a state level, total non-farm employment recovery ranges from a low of 114.2% to 240.9%. Based solely on this measure, the best job recovery rate among Seventh District states would appear to be in Iowa and the worst in Illinois.

Iowa has consistently outperformed the region and the U.S. in job recovery, both in aggregate and by sector, since the end of the Great Recession (figure 1). A clear contributor was the role of agricultural production and rising farm incomes that the state experienced up until 2015. Currently, low crop prices are weighing on farm incomes, so it may be that this pattern of growth is starting to flatten out. As figure 2 shows, over the longer time horizon, Iowa tracked the rate of U.S. job growth coming out of recession but shows a noticeable divergence starting in 2014.

figure2

Michigan entered the Great Recession significantly weaker than other states in the region and the U.S. as a whole, having continued to lose jobs and suffer economic decline through the recovery from the 2000–01 recession. However, since the end of the Great Recession, the state’s job recovery has accelerated. Particularly noticeable is that Michigan has almost fully recovered its lost manufacturing jobs (98.2%), far exceeding the U.S. (29.7%) and significantly better than Indiana, the most manufacturing-intensive state in the region (73%). Clearly, the auto recovery has been a boon to the state.

Illinois has clearly underperformed both the region and the nation in post Great Recession job recovery, both in aggregate and across industry sectors. While Illinois has more than recovered the total number of jobs it lost during the recession (114.2%), only two sectors of the state’s economy have significantly gained jobs. The leisure and hospitality sector is up 482.3% and the professional and business services sector is up 191.5%. While wages in professional and business service jobs are relatively good, the gains in leisure and hospitality are likely to be lower paying jobs. Now, we look at job performance since January 2000 in four key industries—construction, finance, manufacturing, and professional and business services.

Construction

As we see in figure 3, construction employment in the Seventh District states has underperformed the U.S. with the clear exception of Iowa, which has seen a gain of 40% over the 2000 level. U.S. construction employment has returned to its 2000 level, while the remaining four Seventh District states have yet to reach their January 2000 level, with Michigan particularly hard hit—off roughly 25% over the period. The loss in jobs was particularly sharp in 2009 through 2012. Since then, jobs have gradually but steadily come back.

figure3

Finance, Insurance and Real Estate

Like construction, finance experienced a boom running up to the Great Recession, followed by a sharp contraction during the recession. Figure 4 indicates that the employment declines in this sector were most pronounced in Illinois, Indiana, and Michigan.  Recovery rates are now above the 2000 level for the U.S., Wisconsin, Iowa, and Michigan. Where recovery has clearly lagged is in Illinois and Indiana.

figure4

Manufacturing

Manufacturing has been seen as a relatively good news story during the recent recovery. Particularly in the automobile industry, production levels have been consistently high. In addition, export-led capital goods had strong sales growth until the past couple of years. The question is has the rebound in output been reflected in employment? Figure 5 shows that manufacturing continues to follow a stair-step path when it comes to employment. Since 2000, after a shock, employment steps down; during recovery it tends to level off and not fully recover. Manufacturing continues to see a trend toward higher production with fewer labor inputs. For example, even in Michigan, with record-level auto production, manufacturing employment is still 30% below its 2000 level. Similarly in Indiana, the most manufacturing-intensive state in the Seventh District, employment is still roughly 25% below the 2000 level.

figure5

Professional and Business Services

The sturdiest employment performance emerging from the recession has been in professional and business services. Figure 6 shows that all five Seventh District states (and the U.S.) now have professional and business service employment levels above the 2000 level. Admittedly, the sector was not hit as hard by the recession as many others. Indeed, in many cases employment levels remained above the 2000 level during the worst months of the recession. The clear laggard is Michigan. As the figure shows, Michigan was shedding professional service jobs during the expansionary period emerging from the 2000 recession and then faced an even sharper downturn during the Great Recession. As such, it had had more ground to make up, and by early 2016 it had moved back up to its January 2000 level.

figure6

Conclusion

There is little doubt that aggregate employment growth has rebounded from the depths of the Great Recession. However, as we see in the data, the distribution of those gains has been uneven across states and industry sectors.

Measuring Tax Capacity for Municipalities in Cook County

Illinois’s fiscal situation will likely require tax and revenue increases. How might we assess a municipality’s tax capacity, or ability to “absorb” a larger tax bill? In our previous blog, we reviewed various methods of assessing tax capacity. Now, we use the municipal-gap method to estimate tax capacities for Cook County municipalities.

The municipal gap is the difference between revenue capacity and expected expenditures. We estimate revenue capacity by multiplying the total equalized assessed property values (EAVs) in each municipality by a standard tax rate-the rate required to bring the total property tax revenues of Cook County municipalities in line with their total expenditures on non-school municipal services. Our measure of (own-source) revenue capacity excludes intergovernmental transfers. We predict expected expenditures based on estimates of each municipality’s socioeconomic and physical characteristics. Finally, we subtract expected expenditures from revenue capacity to get the municipal gap. A positive gap implies that a municipality has a larger tax capacity, and a negative gap implies a smaller tax capacity.

In practice, this analysis need not be restricted to municipalities located within one county. However, differences across counties in property assessment practices, as well as the quality and composition of services provided, may distort differences in the municipal gap. Restricting the sample to Cook County municipalities should limit differences due to these extraneous characteristics. To this end, we also exclude municipalities with smaller populations (those with populations below the 25th percentile for Cook County). Consequently, results from our analysis can be generalized to a subset of Cook County municipalities and expenditures, namely general government, which largely consists of public safety expenditures.

Data Sources and Calculations

Non-school municipal expenditures and EAVs were obtained from the Illinois Comptroller’s Fiscal Year 2014 Annual Financial Report.1 In the report, expenditures are categorized according to their reported functions (e.g., public safety) and governmental fund (e.g., general fund). We calculated non-school expenditures by combining all reported expenses across functions and funds. Revenue capacity was calculated by multiplying each municipality’s EAVs by a standard tax rate, which is the aggregate Cook County expenditures divided by aggregate EAVs; the resulting rate is 12.8%. Both expenditures and revenue capacity are normalized across municipalities by expressing them in dollars per capita.

Estimates of socioeconomic and physical characteristics were obtained from the Census Bureau 2014 American Community Survey 5-year estimates.2 We separated characteristics into two groups: environmental and control variables. Environmental variables are assumed to be more exogenous to the choices of local officials and are used to predict expected expenditures. They include the unemployment rate, poverty rate, population density, and population (logged). In contrast, control variables are assumed to be less exogenous, and either failing to control for these factors or using them to predict expenditures may bias our predictions of expected expenditures. Control variables include the percentage of the population ages 25 and older with a bachelor’s degree or higher, income per capita, the percentage of housing units that are owner-occupied, the median age of the population, and whether the municipality owns or operates a public utility company (this last measure comes from the Comptroller’s report).

Brief Data Summary

Table 1 provides descriptive statistics on expenditures, revenue capacity, and the environmental and control variables. On average, a municipality spends $2,545 per capita on non-school services. Cook County municipalities allocate, on average, 33% of non-school expenditures to public safety, and only 2% to social services. The large gap between average revenue capacity ($4,199 per capita) and average expenditures underscores the fact that we exclude a large portion of all expenditures municipalities face, such as those appropriated to overlapping governments. The sizes of standard deviations reflect substantial heterogeneity in the compositions of expenditures across municipalities, in particular for debt and capital outlay. The minimum value for the other expenditures/expenses category in part reflects reimbursements.

table1_descriptivestatistics

Calculating Municipal Gaps

We use regression analysis to derive expected expenditures. First, we estimate the effects of environmental variables on the dependent variable, actual municipal expenditures, while holding constant the control variables. Second, we predict expected expenditures using a municipality’s actual values of the environmental variables and the estimated effects.

Table 2 provides results from estimating the effects of environmental and control variables on non-school municipal expenditures. Altogether, we could find no evidence that the environmental variables affect municipal expenditures, controlling for additional factors. In addition, the adjusted R-squared value suggests that we explain roughly 40% of the variation in expenditures (around its mean). Table 3 provides an example of calculating expected expenditures for the City of Chicago (values were rounded to 2 decimal places). Multiplying a municipality’s actual values for the environmental variables by each variable’s corresponding effect results in that variable’s contribution to expected expenditures; summing all the contributions leads to expected expenditures. The final step in the analysis is subtracting expected expenditures from revenue capacity, resulting in the municipal gap.

table2_regression

table3_examplecalc

Results and Conclusion

Table 4 displays the results for municipalities with the largest and smallest gaps. Glencoe Village is assigned the largest positive gap, with an additional $15,846 in revenue per capita available after appropriating funds for expected expenditures. In contrast, Park Forest Village would require an additional $1,802 in revenue per capita to fund its remaining expected expenditures. How do the results of the municipal gap analysis using expected expenditures compare to those using actual expenditures? Table 5 provides findings for the latter scenario. Results for all municipalities included in our study may be obtained here.

table4_expected

table5_actualgaps

Mapping the municipal gaps illustrates the geographic discrepancies in tax capacity. Figure 1 displays the municipal gaps calculated using expected expenditures. For comparison, figure 2 displays the municipal gaps calculated using actual expenditures. Results are largely consistent between the two; one primary difference is that tax capacity is larger for several southern Cook County municipalities in the analysis with actual expenditures. Perhaps one unsurprising result is that, in general, northern Cook County municipalities have greater tax capacity than more central and southern Cook County municipalities. However, there are “pockets” of municipalities with smaller tax capacity located within regions that have greater tax capacity, and vice versa.

Figure 1: Gap with Expected Expenditures

weighted-municipal-gap

Figure 2: Gap with Actual Expenditures

actual-municipal-gap

In sum, identifying the ability for municipalities to absorb larger tax bills is becoming increasingly crucial to estimating local governments’ capacity to generate additional own-source revenues. Several methods exist that rely on comparisons between each government’s revenue and expenditures under hypothetical conditions. Here, we utilized the municipal-gap method to identify tax capacities for a subset of Cook County municipalities. Among the limitations of our analysis is the fact that we exclude important information on both the revenue and expenditure sides. Our analysis relies on non-school expenditures and property values to derive expected expenditures and revenue capacity. Local officials with more complete information on expenditures (e.g., for overlapping governments and schools) and revenues from additional sources (e.g., intergovernmental transfers) may benefit from estimating tax capacity using the municipal-gap approach, or one of the other methods we talked about in our previous blog.

  1. FY2014 Annual Financial Report data procured from the financial database website.
  2. U.S. Census Bureau; 2014 American Community Survey 5-Year Estimates, http://factfinder.census.gov.

What is Illinois’s Tax Capacity?

A recent study ranked Illinois 47th among U.S. states and Puerto Rico for its fiscal health.1 Particularly concerning was the report’s finding that the combination of total debt, unfunded pension liabilities, and underfunded other post-employment benefits amounts to 61% of total state personal income. In contrast, the same figure for other Seventh District states ranges from a high of 38% in Michigan to a low of 16% in Indiana. Given the magnitude of Illinois’s debt, any plan aimed at improving the state’s fiscal solvency will likely require both expenditure cuts and tax and revenue increases.

So what is the taxable capacity of Illinois? Two broad issues arise. First is the issue of fairness: Would further taxation violate society’s notions of imposing undue burdens on those who can least afford it? Second is the issue of impairing economic activity: Would further taxation discourage economic activity or otherwise drive out taxable wealth to an unacceptable degree?

In this blog post, we describe several methods for identifying a community’s tax capacity. In general, researchers have attempted to measure and compare capacities for specific places by calculating hypotheticals that rely on norms or averages across all places. For example, how much revenue might we expect to raise in a community if we imposed average tax rates there? And how much should a community be spending on public services, given its population characteristics and its need for services? And importantly, how do the two estimates differ? Are there obvious gaps between resources and needs?

Gordon, Auxier, and Iselin (2016)2 used such a representative revenue and expenditure approach to estimate this hypothetical gap in funds a state has available for government operations. The study documents that there is enormous variation in the amount of revenue states collect and what they spend on public goods and services. To drill down to tax capacity, the study measures what each state would collect in revenues and spend on government services if it followed national averages, adjusted for state-specific economic and demographic factors. Table 1, section (1) shows the actual gap, or the difference between actual revenues and expenditures, in FY2012, assuming that states rely only on revenues that they raise themselves through taxes and fees. The gaps are sizable, but adding in federal transfers largely erases the gaps. Turning to the hypotheticals as they relate to tax capacity, Table 1, section (2) compares representative revenues, or revenues that a state would raise if it had an average tax structure, to representative expenditures, or expenditures if the state had an average spending per capita. In contrast to the actual gap, the representative gap remains even after the addition of federal transfers in all of the Seventh District states other than Iowa. In this hypothetical case, if Seventh District states (other than Iowa) adopted a nationally representative tax structure, they would not have sufficient resources even after federal transfers to provide a representative level of public expenditures.

table1

Haughwout et al. (2003)3 developed a more refined analysis, termed the “revenue-hill” method, that estimates the deterioration in tax capacity that takes place as higher tax rates discourage taxable activity. The revenue hill builds a hypothetical schedule of tax rates and revenues that demonstrates how fully a city is utilizing its tax base. The goal is to build a “Laffer Curve” that allows policymakers to estimate the economic effects of the next tax dollar (e.g., effect on employment). The closer the measure is to the top of the hill, the closer the city is to exhausting its tax capacity. Once a city is over the top of the hill, increases in tax rates will become so unproductive that revenues actually decline. These measures can be constructed for each tax base a city might use. Therefore, while a city may have reached capacity for one tax base (e.g., sales tax), it may still have capacity in another (e.g., property tax). Haughwout et al. (2003) examined four cities—New York, Philadelphia, Houston, and Minneapolis—and found that only Minneapolis was “comfortably” below its revenue hill, and thus had additional tax capacity. In the case of Minneapolis, additional taxes could provide net benefits to property owners. New York and Houston were at the top of their revenue hills, implying that additional taxes would have a negative impact on employment.

Finally, Bo Zhao and Jennifer Weiner of the Boston Fed suggested the “municipal-gap” method for measuring tax capacities across municipalities in Connecticut, by recognizing that taxable capacity can only be measured in the context of a government’s particular needs and resource costs in providing adequate services.4  For example, limited tax capacity can exist when a community faces higher costs or fewer resources for providing public services (or both). In both cases, it can be driven by economic, topographic, and demographic factors specific to the community (e.g., a relatively high rate of poverty or significant risk of extreme weather).

First, one identifies revenue capacity. Revenue capacity is defined as the ability of municipalities to raise revenue from all of the sources they are authorized to tax, even if they choose not to tax a particular base. Capacity is calculated using the “representative tax system” approach, where all communities use a standard uniform tax rate against the tax base. The rate is determined by ensuring that the statewide rate raises enough revenue to cover existing expenditures. Second, one identifies expected expenditures–the average level of spending based on the municipality’s underlying socioeconomic and physical characteristics. This number is is calculated using regression analysis to predict a municipality’s expenditures based on actual values of the underlying characteristics. One purpose of deriving expected expenditures is to remove the variation in expenditures due to the choices of local officials who may favor particular government programs.

The study focused on the costs of providing largely non-educational local services, primarily public safety. It found that large fiscal disparities in Connecticut were primarily driven by their differences in revenue raising capacity. The uneven distribution of the property tax base coupled with the relative dependence on property tax revenues in the state meant that resource-rich municipalities had, on average, per capita revenue capacity eight times that of resource-poor towns. The cost of providing municipal services was less dispersed, with the highest-cost municipalities spending 1.3 times that of the lowest-cost towns. Importantly, the study also found that non-school revenue grants from the state had limited effect in reducing fiscal disparities.

What can tax capacity studies tell us about Illinois’s fiscal problems?

Illinois has a particularly difficult choice when it comes to future tax adjustments. First, the debt overhang at the state level is so large that any future tax increases will necessarily be directed to paying down debt rather than purchasing new services. Incremental tax increases will pay for services already consumed and, as such, it is difficult to see how future taxes will provide governments with resources to support programs that enhance growth. Second, capacity studies, such as Gordon et al. (2016), suggest that Illinois has already reached its capacity limits. While it would be a stretch to adapt this to the revenue-hill concept of actual declines in revenues in response to tax hikes, it does imply that Illinois has little room to increase taxes without reducing economic activity in ways that would be damaging. The depth of the problem increases when recognizing that many Illinois municipalities also face revenue gaps that would make the compound effects of a state tax increase coupled with a local increase that much worse. As these studies show, there tends to be considerable variation in both the level of expenditures and available revenues across any state when it comes to financing government services. The question then becomes what is the geography of tax capacity in Illinois?

In a second blog, we will apply local revenue capacity and service cost to Illinois municipalities. Stay tuned to see what fiscal disparities might exist for municipalities in Cook County.

  1. Norcross, E., & Gonzalez, O. (2016). Ranking the States By Fiscal Condition.
  2. Gordon, T., Auxier, R., & Iselin, J. (2016). Assessing Fiscal Capacities of States.
  3. Haughwout, A., Inman, R., Craig, S., & Luce, T. (2003). Local Revenue Hills: Evidence from Four U.S. Cities.
  4. Zhao, B., & Weiner, J. (2015). Measuring Municipal Fiscal Disparities in Connecticut.

Detroit and Chicago: Real Property Value Comparisons

Both Chicago and Detroit have become poster children for city government financial stress in recent years. Chicago’s city and school district alike have been running structural deficits, meaning that the government has been covering its normal operating expenditures by issuing or over-extending debt and running down its assets. Both Chicago’s municipal government and school district face large shortfalls in required contributions for the future pensions of current and retired employees. Both have raised local property taxes as partial steps toward balancing their budgets. In the case of Detroit, the city has only just emerged from Chapter 9 bankruptcy while its school district teeters on insolvency under state-mandated “emergency manager” operation.

Are these places comparable in terms of their outlooks and situation? The value of real property in each place offers one fascinating indicator of the resources available to their local governments, as well as a look into how private homeowners and commercial property owners perceive the general prospects of Detroit and Chicago.

Why examine real property values? In some sense, real estate and improvements are long-lived assets that are largely fixed in place. In the market for these properties, buyers and sellers must assess and incorporate the government fiscal liabilities and service benefits – present and future – attached to these properties in the prices at which they buy and sell. High (and rising) property values may indicate that home owners and commercial property owners expect that the prospects for value in these locations are good and that they will continue to improve. And from the local government’s perspective, high values indicate that there may be room for further imposition of local taxes to fund government services, if need be.

Nuts and bolts

The estimation of the value of real property—land and improvements– in a locality is far from an exact science. The actual sales prices of property can be thought of as one reflection of an individual parcel’s value. However, as we saw during the financial crisis last decade, sales transaction prices can be very volatile, and sometimes speculative. More practically, parcels of property do not turn over frequently, so that transactions prices of all property parcels are not observed in any one year. In practice, then, local public officials often rely on various estimation methods in assessing the value of property for taxation purposes, most of which involve using a sample of information from similar properties that were sold during a year. From the recorded sales price and a property’s particular features such as size, location, age, and configuration, the property assessment office infers the value of each property and, ultimately, the total value of property against which taxes are levied.1 These taxable values are often termed assessed values or sometimes equalized assessed values and often represent some fixed percentage of market or true value.2

In the charts, we draw on such data from the local and state governments of Detroit, Chicago, and Illinois to estimate the market or true value of real property in both Detroit and in Chicago.3 For Detroit, figures on total assessed property value are published in the city’s Comprehensive Annual Financial Report (CAFR). By law, assessed value must amount to one-half of true value in Michigan. And so, to arrive at our estimates of full value, we simply double the assessed value. We believe that this yields a far upper bound on the value of residential property in Detroit because there is much evidence that, following the steep plunge in Detroit’s property market over the last decade, assessments were not reduced to accord with actual market value in a timely fashion.4

Drawing on prices of homes that recorded sales, the following chart shows that average home prices began to fall in 2004, while assessed value did not begin to fall until 2009.Detroit_Assessed_HomePricesFor Chicago, a prominent local government “watchdog” and research foundation has long been using state and city data on recorded property sales by class of property to estimate full market values.5 The accuracy of these data is believed to be reasonable, though far from exact.6

What do we see?

Due to lags in data availability, the estimates below are representative through calendar year 2014. The charts display total property value across all types, as well as the largest category in both Chicago and Detroit, that being residential property.

As shown here, Chicago real property value rose dramatically during the early part of last decade before dropping off just as dramatically. Detroit’s property values remained flat. However, Detroit’s apparent stability belies the fact that assessed values of residential properties have not been allowed to fall off in tandem with actual market transactions price there. As measured by volume of sales, the market for residential real estate in the city of Detroit became almost nonexistent during this period.7 Few homes were sold using conventional financing; almost all of them sold for cash. By some estimates, prices of homes sold fell by many multiples during this time, though they have since been heading back up in parts of the city.

Even using generous measures, and with rising home prices in some neighborhoods, residential property value overall in Detroit have continued to drift lower in recent years. In contrast, following the steep decline, Chicago property values have begun to recover for both residential and commercial (not shown) property.

Most telling, at over $80,000 per resident, the value of overall taxable real property in Chicago remained markedly higher than that of Detroit as of 2014. By even our generous measure, Detroit’s property values were only about $25,000 per resident.Detroit_Chicago_PropValuesDiscussion

It would appear that, as measured by real estate values, Chicago’s economy and prospects remain much stronger than Detroit’s. And from a local government perspective, Chicago’s taxable resources with which to pay down liabilities and fund public services appear to be much larger. Of course, there are myriad political and institutional factors at play that render such a simple assessment of wealth inadequate to characterize the fiscal capacity of these cities. In both places, for example, property wealth is concentrated in a subset of places such as near the downtown areas and along the waterfronts. Accordingly, it may be difficult to tap available property wealth selectively because existing statute largely requires that tax rates be applied uniformly. And voters and their representatives may be reluctant to allow tax hikes at all. Similarly, there may be different levels of sensitivity to taxation in these two places and among different constituencies. For example, the imposition of new and higher taxation may cause economic activity and investment to decline more sharply in one place as opposed to another.

More broadly, we might ask whether property wealth is a good indicator of potential resources that local governments may draw on to fund services. A look at more U.S. cities may be helpful. The next chart undertakes the same exercise for the most populous cities. Here we see that Chicago continues to look fairly robust by this measure, though less so than the cities of San Diego, Los Angeles, Austin, and New York City.MajorCities_ProvValues

  1. Assessed value of property for taxation purposes is often a fixed percentage of market value across all property parcels, or else it is a fixed percentage across all parcels of a certain type or class such as residential, commercial or industrial. In turn, estimates of full value of all property can be made by taking sample average ratios of “assessed value/sales price” and applying these ratios to all parcels’ assessed values.
  2. Equalized assessed values refers to the practice of further adjusting the totalities of assessed value of property across jurisdictions so that each locale’s assessed valuation represents the same or “equalized” value in relation to (percent)  sales price or true value.
  3. The city of Detroit also taxes tangible personal property of commercial enterprises such as computing equipment and furniture; Chicago does not.
  4. Using sales price and assessed values for a sample of 8,650 residential parcels in 2010, Hodge et al find an average assessment to sales price ratio of 11.47, which suggests an average over assessment or property values many times over. See Timothy R. Hodge, Daniel P. McMillen, Gary Sands, and Mark Skidmore, 2016, “Assessment Inequity in a Declining Housing Market: The Case of Detroit,” Real Estate Economics.
  5. See https://www.civicfed.org/sites/default/files/Estimated%20Full%20Value%20of%20Real%20Property%20in%20Cook%20County%202005_2014.pdf
  6. Discrepancies arise because only sample values of real estate transactions are available in any one year. In addition, full value projections derive from the median value of property in each class. However, the median property value may not represent the entire distribution of property values.
  7. See http://www.urban.org/sites/default/files/alfresco/publication-pdfs/2000739-Detroit-Housing-Tracker-Q1-2016.pdf