All posts by Bill Testa

District Housing Update

By Bill Testa

The housing sector has made halting progress throughout the five-year recovery from the Great Recession. Beginning in June 2013, progress began to slow as mortgage rates jumped, thereby hampering affordability and lending viability. Even as home mortgage rates and lending standards were beginning to ease, this past winter’s unusually cold and stormy weather dealt another setback to sales and construction activity in several regions, including the Midwest, Northeast, and parts of the South.

In an effort to analyze residential real estate market developments in the Seventh District, I have developed an index that monitors its metropolitan statistical areas (MSAs).[1] The index combines observations of each District MSA’s housing market on a year-over-year basis. Any index value greater than 50 (indicating that more MSA observations are positive than negative) signals expansion for the Seventh District’s residential real estate sector; index values less than 50 indicate contraction.

As of the first quarter of 2013, the Index entered positive (expansionary) territory for the first time since 2005, where it remained throughout 2013, although the pace of expansion eased during the second half of the year. However, the most recent reading for Q1:2014 shows that downward momentum from 2013 coupled with the depressing effect of a harsh winter pushed the index into contractionary mode once again.

In observing individual MSAs (below), scattered contractionary trends are evident in each District state, but especially in smaller MSAs. In contrast, large MSAs continued to expand (e.g., Chicago and Des Moines) or at least showed neutral growth trends (e.g., Detroit, Indianapolis, and Milwaukee).

A look back to the fourth quarter of 2013 (above) shows that, to a greater extent, local housing markets continued to display improvement before the onset of winter, which raises the question of whether forward momentum will soon be reestablished. During the past couple of months, housing indicators suggest that activity has bounced back to some extent. Nationally, three major housing activity indicators—new housing sales, existing home sales, and pending home sales—have all flashed positive in May.[2] Though these measures are not recorded for the particular geography of the Seventh District, all four major U.S. regions expanded by these measures in May—including the Midwest.[3] Seemingly, Midwest housing is back on the road to recovery. Still, strong activity in the second quarter of 2014 may partly reflect pent-up demand from last winter’s stall.

Note: thanks to Thom Walstrum for assistance.


[1]The number of MSA observations varies slightly as MSA boundaries change and some observations must be temporarily dropped from the sample. This index is built from two distinct data measures of housing market activity in each metropolitan area. The first measure is residential building permits. Permits are obtained prior to the construction of both single-family homes and multi-family buildings, such as apartments and condos; and data on the issuance of these permits are collected on a monthly basis. The second measure is the Federal Housing Finance Agency’s House Price Index (HPI), which is a quarterly measure that tracks the movement of single-family house prices. For a discussion of methodology see this earlier blog post. (Return to text)

[2]See;; (Return to text)

[3]The Midwest region consists of 12 states—Ohio, Indiana, Michigan, Illinois, Wisconsin, Minnesota, Iowa, Missouri, North Dakota, South Dakota, Nebraska, and Kansas. Though positive, growth in Midwest new home sales was weak. (Return to text)

Seventh District R&D: Manufacturing the Leader

By Bill Testa

Few would take issue that the U.S. economy is propelled by innovation. To stay ahead of their competitors, virtually all enterprises engage in innovation of one form or another. Such innovations take the form of improvements to products, services, and internal processes of production and delivery. In the case of start-ups or new enterprises, the proportion of activity devoted to innovation can be the dominant activity for years prior to its actual operation and revenue generation. Start-up firms have captured the imagination of cities that are encouraging entrepreneurs in their pursuits.[1] Recently, the State of Illinois has offered funds to expand Chicago’s prominent new business incubator, which is named “1871” in reference to re-building from the great fire of that year. Similarly, the City of Detroit will seek to designate and boost its “TechTown” as a major part of its economic redevelopment.

Many established businesses also engage in innovation, but they do so in a more formal way, that is by budgeting for and performing research and development (R&D). The National Science Foundation tracks R&D funds across all sectors, including the U.S. business sector. Their preliminary estimates for 2012 report that the business sector overall performed 70 percent of the nation’s R&D, amounting to $316.7 billion, followed by federal government (12.2 percent), and universities and colleges (13.9 percent).[2]

In tracking R&D performance as measured in dollars that can be allocated across states, the table below ranks Seventh District states by the dollar amount of R&D for each of four major categories for the latest year available, 2011.[3] The business sector dwarfs others in 2011, accounting for almost 70 percent of R&D performed. By this measure, each District state is ranked above the national average, with Michigan’s sixth place and Illinois’ eighth place figuring very prominently. Based largely on the strength of their performance in the business sector, these states also rank highly in overall R&D performed, at seventh for Michigan and eighth for Illinois. Significant contributions to their rankings are also evident from universities and colleges and federally funded R&D Centers (Illinois), and in the case of Michigan, universities and federal government operations.

Within the business sector, manufacturing companies continue to conduct the lion’s share of R&D. As shown below, manufacturing performed 68.5 percent of private sector R&D in 2011. This is down from previous decades, as several service sectors have grown rapidly. In particular, the software publication, computer systems design, and scientific services sectors now comprise, in aggregate, 19.2 percent of R&D performed.

But rather than these service sectors, manufacturing remains the primary contributor to the Seventh District’s R&D prominence. The far right columns in the table below display the District’s relative employment concentration in leading R&D sectors by individual industry.[4] The first three rows present the employment concentration of leading service industries in R&D performance. With a few exceptions, such as Wisconsin’s high concentration in software publishing at 39 percent above the national average, District state concentrations tend to fall below national levels. In contrast, the manufacturing leaders in R&D activity are much more concentrated in District states. For example, concentrations in non-medicinal chemicals such as industrial chemicals exceed national levels in every District state, as does the machinery industry concentration. Pharmaceuticals and medicinals are strong in Indiana and Illinois, while electronic equipment employment is especially concentrated in Illnois and Wisconsin. Meanwhile, employment concentrations in the motor vehicle industries are off the charts in Indiana and Michigan. And as previously discussed, automotive employment and spending for R&D have become much more concentrated there than the total employment numbers might suggest, as the state has held onto its R&D even as production activities have moved to other states and regions.

Among major R&D performers in manufacturing, the only area in which the Seventh District does not have a significant employment concentration is the computer and electronic products sector. This sector’s products and components are distinguished by “the design and use of integrated circuits and the application of highly specialized miniaturization technologies (which) are common elements….”[5] Manufacturing activity and employment in this sector have tended to concentrate in California, Texas, Massachusetts, and other states outside of the Midwest region.

As regions look to innovation as the wellspring of their economic development, they may be well advised to build on their existing sources of innovation activity. For the states of the Seventh District, the traditional base of manufacturing industries is clearly an important candidate.


[1]See to text)

[2]Funding patterns differ from R&D performer patterns; the federal government funds almost 30% of overall R&D, with large proportions allocated to the business sector (especially defense contractors) and colleges and universities. The character of R&D also differs across sectors, with colleges and universities typically engaging in “basic” research, an activity that advances science with no specific application. In contrast, businesses more often fund development and applied R&D, activities that are intended to introduce new products or services into commercial use. See InfoBrief, NSF 140307, December 2013.(Return to text)

[3]For individual state profiles with many measures, see,14&year=0.(Return to text)

[4]Employment concentrations are measured here across all occupations of firms in the sector, not solely R&D activities.(Return to text)

[5] (Return to text)

Note: Thanks to Timothy J. Larach for assistance.

Industrial Cities Initiative Profiled in New Report

By Emily Engel and Jere Boyle (via)

Community Development and Policy Studies at the Chicago Fed recently published profiles of a group of 10 cities that experienced significant manufacturing job loss in recent decades.

The Industrial Cities Initiative (ICI) includes, Aurora and Joliet in Illinois; Fort Wayne and Gary in Indiana; Cedar Rapids and Waterloo in Iowa; Grand Rapids and Pontiac in Michigan; and, Green Bay and Racine in Wisconsin. While each city has been blogged about before (see the “BLOG” tab), a complete set of more detailed profiles are now compiled into one report.

Collectively, the profiles provide insights from local economic development leaders on the cities’ actions in the wake of the job loss that have either helped or hindered redevelopment efforts.

The authors and contributors to the ICI do not pass judgment on individual cities. So, while we understand the temptation to simply link directly to just one city’s profile, we encourage readers to start their exploration of the ICI with the Summary.

The ICI looked at cities’ conditions, trends and experiences and concluded that efforts to improve their economic and social well-being are shaped by:

  • Macroeconomic forces: Regardless of their size or location, these cities are impacted by globalization, immigration, education, job training needs, demographic trends including an aging population, and the benefits and burdens of wealth, wages, and poverty;
  • State and national policies: State and national policies pit one city against another in a zero-sum competition for job- and wealth-generating firms; and
  • The dynamic relationship between the city and the region in which it is located: Regional strengths and weaknesses to a large extent determine the fate of the respective cities.

The ICI homepage provides access to the full ICI report, individual ICI city profiles and related research, and blogs from around the country about cities that share a manufacturing legacy.

Is Something Ailing the Illinois Economy?

By Bill Testa

As the US economic recovery approaches the five-year mark, a look back shows that it has been far from a smooth and upward ride. Since the end of the Great Recession, the economy has grown at a generally disappointing pace with fits and starts due to repeated setbacks. Many parts of the U.S. economy are still working their way through the effects of the financial crisis that accompanied the recession. For instance, the labor market has been healing quite slowly. And many households and businesses are still repairing their balance sheets after having suffered steep losses in asset values. Also, the overhang in housing inventory has been slow to clear. Meanwhile, global economic recovery has faltered several times—first, in Europe and, most recently, in East Asia.

As the U.S. economy began to recover in mid-2009, Illinois and other states in the Great Lakes region bounced back at a quick pace, albeit from a very low point. The Great Lakes region’s strong industrial orientation—that is, its heavy involvement in durable goods production—translated into a steep economic recovery as the nation’s businesses sought to rebuild their depleted inventories of capital goods and equipment while households similarly began to replace automobiles and other consumer durable goods. Moreover, since the global recovery was quite strong back then, exports of machinery and foodstuffs from the Great Lakes region also contributed to the economic climb. However, the Great Lakes region’s pace of growth began to decelerate two years into the recovery. The aforementioned growth impetus of inventory rebuilding and exports abroad eased. Among the major sectors, only the automotive industry continued to grow quickly.

Following the Great Recession, Illinois began to recover and even gain ground on the nation, but its economic performance began to alarm many observers in 2011. As seen below, Illinois’s unemployment rate fell quickly in 2010 and into early 2011. However, the state’s unemployment rate then failed to show much improvement, even as the nation’s unemployment rate continued to fall more.

It could be that Illinois’s deviation from the national trend in unemployment is related to the economic performance of the broader Great Lakes region. The Illinois economy is highly integrated with the other industrial states of the Great Lakes region—Wisconsin, Indiana, Michigan, and Ohio. Accordingly, the Illinois economy regularly rises and falls along with the economies of these states. If Illinois’s performance differs from its neighbors, it would be a cause for concern—and the degree of concern would be higher as Illinois fell further behind its neighbors. As the chart below suggests, the aggregate unemployment rate of the Great Lakes states (less Illinois), has continued to decline since 2011; Illinois progress has been much less. In what follows, I discuss possible sources of the deviation, including Illinois tax structure and Illinois industrial structure. In addition, I examine an alternative measure of labor market performance, namely the growth in payroll jobs.

Illinois’s seemingly poor economic performance compared with that of its neighboring states has sparked a policy debate as to whether the state’s recent hikes in statewide income taxes may be deterring investment and hiring in the state. Beginning in January 2011, the state’s personal income tax rates were hiked from 3.0 percent to 5.0 percent for the period 2011–14; they are scheduled to go down to 3.75% for the period 2015–23 and then to 3.25% from 2024 onward. (Similar hikes were enacted to the state’s corporate income tax—also with a schedule of phasing out the higher rates). These tax hikes were enacted to help the state pay down a rising stack of short-term debt for operating expenditures and to make progress on a much larger amount of unfunded public employee obligations (such as pensions). To date, the state’s finances have improved only modestly with respect to both short-term debt obligations and its longer-term pension-related debt. For this reason, some observers believe that Illinois tax rates will not be allowed to (fully) phase out as planned.

Are tax rate hikes discouraging hiring and investment in Illinois? It may come as no surprise that the effects of state and local tax differences on state economic growth are far from a settled science. Among the difficulties for settling the debate are that states seldom allow their business climates to get very far out of line with those of their neighbors, thereby making it difficult to find the growth effects of tax differences. However, in the case of Illinois, there is ample cause for concern. The state and its local governments face the possibility of having to pay down very large debt obligations—on the order of $100 billion or more—for employees covered by statewide pension systems. Moreover, the City of Chicago and other overlapping units of local government within the city’s limits face similar amounts of liabilities when measured on a per capita basis, while other Illinois local governments also carry very large unfunded liabilities. As discussed previously, depending on how fast these liabilities are amortized, they could give rise to tax rate differences between Illinois and neighboring states that are very sizable.

In a recent analysis of Illinois’s economic performance since the beginning of the hike in its income tax rates, Andrew Crosby and David Merriman examine several labor market measures of performance of the state versus the rest of the Midwest region.[1] Similar to the charts above, the authors note that the unemployment rates diverge in a striking fashion right around the time that Illinois hiked its income tax rates. However, given the high variability of unemployment rate measurement at the state level, the authors think it best to consider other measurements. In examining payroll job growth, they show that growth in payroll employment displays a far less prominent deviation between Illinois and the rest of the Midwest region. In addition, the timing of the growth difference between Illinois and its neighbors does not develop until 2013—two years beyond the income tax hike.

While there is some evidence, then, that Illinois’s fiscal problems are weighing down its recovery, such problems are more of a long term concern. Illinois’s slow recovery may have more to do with its industrial structure. To further the analysis, I draw on data from the U.S. Bureau of Labor Statistics that are called the Quarterly Census of Employment and Wages (QCEW). These data are reported for states and the nation from the comprehensive reporting of those firms and establishments that are covered by the Federal-State Unemployment Insurance Program. One clear advantage of using such data is that specific industry employment data are reported by firms and establishments, which allows us to investigate the possible effects of differences in industry mix. On the downside, the data are compiled and released with a time lag of one half year or more.

The chart of the QCEW data for Illinois versus the four remaining Great Lakes states (Indiana, Michigan, Ohio, and Wisconsin) are shown below. Illinois’s employment outperformed the remaining states of the Great Lakes region in the years prior to the recession and during the recession. As a matter of interpretation, I would argue that Illinois’s relative superior performance prior to the recession likely reflected Michigan’s collapsing auto industry employment from 2003 onward, along with the unsustainable residential property construction boom that took place in the Chicago area prior to the onset of the recession in December, 2007. Illinois also outperformed the region during the recession, and this is somewhat typical. Illinois is the domicile of highly compensated professional and business service workers who are not as readily laid off during economic downturns.

However, the period following the recession—from mid-2009 onward—contrasts mildly but unfavorably from the previous periods. After the recession, the Great Lakes region’s employment recovers faster than Illinois’s in each year. This trend is again somewhat consistent with the possible pernicious effects of the 2011 tax hike. While Illinois’s employment performance lagged in the year prior to the tax hike, which seems counterintuitive, it is possible that firms began curtailing investment and hiring prior to the tax hike itself in anticipation of an inferior climate in which to do business.

That said, it is notable that, as opposed to the unemployment rate gap that was observed, the payroll job growth difference seen here is small.[2] More importantly, there are alternative possible causes for Illinois’s lagging payroll job growth. In particular, Illinois’s mix of industries, while similar in some respects to those of other Great Lakes states, differs as well. It is possible that the small differences in job growth between Illinois and its neighbors are due to its somewhat different industry mix rather from disinvestment and a reluctance to hire in the state.

To investigate further, I compiled the QCEW data covering the five Great Lakes states from third quarter of 2007 to the third quarter of 2013, with detailed counts of jobs for each of 88 private sector industries. In the table below, the first row displays the actual job growth in Illinois for three two-year periods, as well as the entire period 2007:Q3–2013:Q3. During 2007:Q3–2009:Q3, Illinois experienced a net loss of 377,000 private sector payroll jobs, and gained back all but 158,000 by the third quarter of 2013.

As an analytic exercise, I further ask how the Illinois economy would have fared 1) if it had the same industry composition as the four other Great Lakes states combined and 2) if its industries had the same job growth rates as those in the other states. The second row of the table reports the results of this exercise (based on the two hypothetical scenarios, as well as an interaction of the two); the final row is the difference in hypothetical growth from actual growth. As shown above, Illinois hypothetically outpaced the region by 89,300 jobs in the 2007:Q3–2009:Q3 period by having a different industry mix and employment growth performance, but it gave back those jobs (and more) in the four years afterward.

To examine the results of this exercise in a different way, I decompose the differences in actual and hypothetical job growth in the table below. The first component shows the effects of maintaining Illinois’s actual industry-by-industry rates of employment growth but then hypothetically imposing the Great Lakes mix of industries. In the first row of the table below, one can see that during 2007:Q3–2009:Q3, Illinois’s industry mix was favorable to that of the remaining Great Lakes region, because it accounted for a 40,300 hypothetical gain in jobs. Seemingly, there are noteworthy differences in Illinois’s mix of industries from its neighbors’ that account for some of the year-to-year performance differences that we observe.[3] For the subsequent two periods of the recovery, the mix of industries in Illinois (below) shows a hypothetical employment loss of 11,000 (from 2009 – 2011), and a further loss of 16,000 (2011 – 2013).

What are some of the industry mix differences that are notable between Illinois and other Great Lakes states? The large professional and financial services employment base in the Chicago area has already been noted. Further, in relation to other states, Illinois is now much more services oriented overall rather than goods producing. Manufacturing’s share of employment for 2013 clocks in at 11.4 percent of private sector payroll jobs in Illinois, versus 16.4 percent for the other four states. (See the appendix below for a more detailed illustration of Illinois employment base versus the GL region).

The schism in manufacturing employment share between Illinois and the Great Lakes region is wholly attributable to the Chicago area. As of 2013, the Chicago MSA employment base recorded only a 9.5 percent share in manufacturing, while the remainder of Illinois recorded 15.9 in manufacturing. From a geographic perspective, these differences may also explain part of the overall performance difference between Illinois and the remaining Great Lakes states. As the graphic below suggests, annual payroll employment growth in the Chicago MSA has kept pace with the remainder of the Great Lakes region while Illinois (non-Chicago) has fallen behind since 2011.

And within manufacturing, Illinois tends to lean more toward food processing and farm, construction, mining machinery relative to the other Great Lakes states. In contrast, while there are important auto assembly operations in the Bloomington–Normal and Rockford areas of Illinois, as well as important links to the automotive supply chain throughout the state, Illinois’s ties to the automotive industry are much less prominent than those of Michigan, Indiana, and Ohio.

Despite such industry differences between Illinois and the other Great Lakes states, an extension of the analysis suggests that the state’s competitive job performance did not kept pace during the recovery. For the same time periods, a second hypothetical component shows the effect of holding Illinois’s actual industry mix constant, but imposing the average job growth rates of the same industries from the neighboring Great Lakes states (second row below). Here, because the industries that make up Illinois’s mix tended to grow more rapidly (decline more slowly) than they did in the Great Lakes region, the state hypothetically gained another 44,100 during the 2007–09 period, but subtracted 63,000 and 50,000 jobs in the subsequent periods. (The final component is the interaction of two hypothetical effects).

As measured by labor market indicators, then, the Illinois economy has not fared as well as neighboring states during the economic recovery that began in mid-2009. Measurements of the state’s unemployment rate show Illinois in the least favorable light. In contrast, other labor market indicators, such as payroll employment growth, suggest that the state’s underperformance is much more mild. Nonetheless, even payroll employment trends suggest that Illinois is underperforming when examined on an industry-by-industry basis. Accordingly, recent changes in public policies that influence the investment climate, such as tax rate hikes, cannot be ruled out entirely,though such policy effects are unlikely to be exerting such a large and immediate effect.

In looking for alternative or contributing explanations, the state’s particular mix of industries is likely contributing to underperformance. For example, the state’s high concentration in construction and mining machinery stands out, as does its lower concentration in automotive as compared to Great Lakes states located to the east. The downstate Illinois economy is highly concentrated in manufacturing, and downstate areas have seen slower payroll employment growth than the Chicago area. And so, Illinois’s performance may yet converge with its neighbors as the automotive boom settles down, and as global economic recovery revives exports of machinery and equipment.

In considering other structural causes, the Chicago area experienced super-normal growth prior to the recession due to excessive home-building and related activities. Accordingly, part of Chicago’s recent performance may derive from a slow healing of residential real estate and related activity following the boom period.[4]

Appendix 1: More on Illinois employment base as compared to the remaining Great Lakes region

The table below constructs an “industry dissimilarity index” between Illinois and other Great Lakes states (using the QCEW database as above) for the year 2013. Illinois is dissimilar to Indiana, Michigan, and Wisconsin, more or less, to the same degree when all industries are accounted for. However, Illinois is more dissimilar to Indiana and Michigan—the two most auto-intensive states in the region—and less dissimilar to Wisconsin in the case when the index is constructed to account for manufacturing industries alone.

Appendix 2: Selected Illinois industries comparison (index based on wages)

Here, the indexes of concentration shown in columns two and three relate Illinois and the four remaining Great Lakes states to the nation. An index value of one indicates parity with the nation, for example, while an index value of two indicates that industry wages in Illinois (or the Great Lakes region) are twice the national average. For example, the first row indicates that Illinois payroll wages in the Agriculture, Construction, and Mining Machinery sector lies at 2.88 times the national average while, in the four remaining Great Lakes states, the sector’s payroll lies at less than the national average—80 percent.

Thank you to Wenfei Du and Thom Walstrum for assistance.


[1]The authors use the U.S. Census definition of Midwest, which comprises Illinois, Indiana, Iowa, Kansas, Michigan, Minnesota, Missouri, Nebraska, North Dakota, Ohio, South Dakota, and Wisconsin. (Return to text)

[2]Some observers have questioned the veracity of Illinois’ high unemployment rates for the post-2011 period to date, citing concerns about measurement error and possible changes in survey methodology. However, some corroboration of the reported unemployment rates is offered by reported first-time claims for unemployment insurance. Over the period from 2011 to date, the annual average of Illinois claims as a share of the national total increased from 3.6 percent to 4.1 percent. At the same time, the Wisconsin share fell from 3.4 to under 3.2, while Ohio, Indiana, and Michigan also fell. Similarly, these same data on UI claims within Illinois corroborate local area unemployment patterns within the state. That is, over the two initial years the recovery, the Chicago area unemployment rate gave ground to the remainder of the state; while gaining ground during the latter half of the recovery.(Return to text)

[3]See the appendix table at end for examples of some of the large industry employment sectors that differ between Illinois and the remainder of the Great Lakes region.(Return to text)

[4]As measured by permits filed to construct residential units, the Chicago MSA recovery has been weaker than other large MSAs in the region including Detroit, Des Moines, and Indianapolis.(Return to text)

Differences in State Safety Net Spending

By Jacob Berman, Associate Economist

The social safety net in the United States consists of dozens of anti-poverty programs on the local, state, and federal level that provide benefits to low-income households. Although anti-poverty programs are generally funded by the federal government, most are administered by states. State governments have broad discretion over the generosity of programs, so the level of benefits for any given household varies widely across regions. For example, the cut off for a single-parent household with three children to be eligible for Medicaid ranges from an annual income of $50,868 in Washington D.C. to $2,652 in Alabama. Similarly, the maximum weekly benefit for unemployment insurance ranges from $674 in Massachusetts to $235 in Mississippi.

One technique for comparing safety nets across states is to use eligibility rules to determine the benefits a hypothetical low-income household is likely to receive. However, as the number of states and programs under consideration grows, this calculation becomes more difficult because eligibility rules can be extremely complicated. For example, a full description of eligibility for the Temporary Assistance for Needy Families (TANF) program, sometimes referred to as welfare, requires a 250 page document that needs to be updated every year. Instead, I compare safety net programs using data on expenditures from the national accounts, and household income data from the Census Bureau’s American Community Survey (ACS). I find that real benefits for low-income households in the most generous area, Vermont, are about two-and-a-half times greater than in the least generous area, Georgia.

Safety-net programs come in many different forms. Some programs (such as TANF) provide cash benefits which allow households to consume anything they choose, while others (such as Medicaid) provide in-kind benefits which only permit households to consume specific goods or services. Short-term programs (such as unemployment insurance) provide temporary aid, while others (such as disability insurance) are designed to provide more long-term support. Safety nets are meant to guarantee a minimum level of consumption and insure households against the risk of a large drop in market income.

My method for measuring the generosity of safety net programs is to add up the total amount spent on benefit transfers targeted at low-income households, and to divide it by the number of persons living in households below a given market income threshold. This approach has several strengths. First, my approach is comprehensive. The national accounts are the only data that include programs that are unique to all states and localities. Also, these data are derived from state outlays so they reflect households that actually collect benefits. Because take-up rates vary widely, some households do not receive benefits even though they are eligible. Second, my approach uses survey data for market income, which are accurate relative to survey data on transfers. Data on labor and capital income come from the ACS, which is the largest survey conducted by the federal government with over 3 million observations per year. Although using survey data on transfers would provide a clearer picture of which households receive benefits, the data are less reliable since the sample is much smaller and more likely to be affected by underreporting.

Since I am interested in the variance across states, I focus only on programs in which states have some discretion over benefits. These programs are as follows:

  • Medicaid
  • Children’s Health Insurance Program (CHIP)
  • Earned income credits
  • Unemployment insurance
  • Supplemental Security Income (SSI)
  • Temporary Assistance for Needy Families (TANF)
  • Supplemental Nutrition Assistance Program (SNAP)
  • Special Supplemental Nutrition Program for Women, Infants, and Children (WIC)
  • Worker’s compensation
  • Temporary disability insurance

Social Security and Medicare, the two largest transfer programs, are not included since benefit eligibility is uniform across states and not targeted to low-income households. I define low-income households as any household in the bottom quartile of the national market income distribution. Using the 2012 ACS data, that cutoff is about $14,000. (Modest changes in the low income threshold do not affect the results.)

Following Census’ methodology, I drop persons living in group quarters since the concept of a household is not well-defined in this instance. In this exercise I am primarily interested in nonelderly adults and children, so I omit elderly, childless households from the sample. The real value of a transfer payment depends on the quantity of goods and services a household can purchase within their state. Since the price level varies across regions, the outlay data and the low-income threshold are adjusted using regional price parity multipliers for each state. This correction tends to make the safety net more generous in states dominated by rural communities, such as South Dakota, and less generous in states dominated by urban centers, such as New York.

Table 1 shows the average real transfer for a low-income person in the five most generous and least generous states. Vermont ranks as the most generous state with the average low-income person receiving about $26,000 in benefits. This is due largely to the fact that, using my measure, Vermont has the most generous Medicaid program and Medicaid accounts for about half of all of the programs I consider. Vermont also has its own refundable earned income credit and SSI program. Conversely, Georgia is at the bottom of the ranking since it has some of the most restrictive laws for Medicaid and TANF.

Table 2 highlights the results for states in the Seventh District. Iowa ranks as the most generous state and Michigan as the least generous. Overall, though, the differences between states in the region are small. Medicaid accounts for much of the difference, but income support programs also play a role. All states in the region offer a refundable earned income credit ranging from 34% of the federal credit in Wisconsin to 6% in Michigan. In Iowa, unemployment insurance replaces a high percentage of previous earnings, federal SSI recipients receive additional state funding, and SNAP benefits are not subject to household asset limits.

Figure 1 plots the relationship between the percentages of persons in a state defined to be “low-income” with the natural log of the average benefit. Average benefits are shown on a logarithmic scale since the marginal utility of benefits is assumed to decline as benefits increase. The blue line is the fit of an ordinary least squares (OLS) regression. The two variables are negatively correlated and statistically different from zero. That is, states with a large percentage of households earning low market income are also states that give the least generous benefits. Since the average poor person in high poverty states will tend to have less income than the average poor person in lower poverty states, we might expect a positive correlation since most programs tend to increase benefits as market income declines. Another reason we might expect a positive correlation is if more generous benefits strongly disincentivize work. Instead, these factors appear to be outweighed by the treatment of social insurance as a normal good; richer states are more willing to pay for the benefits that safety nets provide.

It is important to remember that there are many other types of state and local government policies that influence the welfare of low-income households. Tax policies also vary widely across states and can have powerful redistributive effects, particularly consumption taxes, which are regressive. Additionally, direct government purchases, such as the provision of education or transportation services, are not included in this exercise. Outside of the budget process, regulations influence the prices households pay for goods and services. For example, restrictive zoning laws tend to increase housing costs. Transfer payments are only part of the story. Developing a more complete accounting of the redistributive effects of state and local policies would be a valuable area for further research.

Freight movement slows in January, while freight rates remain high—Is it the weather or something else?

By Paul Traub and Bill Testa

The severity of this winter season has had a noticeably negative impact on everything from retail sales to industrial production. Roadway freight operations are no exception.

The effects of the extreme cold and heavy snow, which started last December and has continued into March of this year, seem to be showing up in some recent economic data on freight services. Chart 1 below contains the Transportation Services Index (TSI)[1] for freight in the United States. The TSI contains freight data for most modes of freight transportation, including truck, rail, inland water, air, and pipeline. This index shows that on a seasonally adjusted basis, freight movement dropped in January by 2.8%. Since the data are adjusted for seasonality, the drop in January looks to be even more significant.

Though all modes of transportation have been affected by this winter’s weather, trucking arguably experienced the worst of it. Many firsthand reports (including my own) have indicated that ice and snow shut down routes in states that do not normally face such harsh wintry conditions. Extremely cold weather also made the loading and unloading of trucks more difficult, causing delays and disrupting normal schedules.

This winter’s disruptions to trucking operations were also accompanied by price spikes. According to DAT Solutions, spot rates (excluding long-term contractual prices) for dry vans, which account for the majority of long-haul freight, are up 17.6% from October 2014. These price spikes could be partially due to the severe winter weather and may only be temporary; however, some evidence points to shifting fundamentals that may be contributing to rising cost trends in the industry. Since the U.S. economy reached the bottom of the Great Recession (in mid-2009), the U.S. Bureau of Economic Analysis’s producer price index for long haul truck-borne freight has climbed at an average annual pace of 3.9%.

Many industry experts argue that tightening capacity together with rising costs in the trucking industry are driving up freight prices. As chart 2 shows, according to ACT Research, the so-called active population of heavy-duty (class 8) trucks has been declining steadily since 2007, even while the economic recovery has been ongoing.

ACT Research defines the active population of trucks as those trucks still in service that are 15 years of age or younger. The reason for this distinction is that once a vehicle reaches 15 years of age, it becomes much less likely to be used for hauling meaningful amounts of freight over long distances. So, at the same time the number of freight loads has been increasing on account of the recovering economy, the number of trucks available to carry those loads has been declining.

Another factor affecting freight rates has been the significant increase in truck prices. Truck prices started increasing in 2002 because of federally mandated diesel emission standards that required the costly development of new engine technologies. ACT Research analysts contend that since 2002 the cost of meeting these standards has added an estimated $30,000 to the cost of a new truck—a price increase of about 31%. Rising prices for new trucks have, in turn, made used trucks more attractive, causing their prices to go up as well. The average price for a used class 8 truck was higher in January of 2013 than ever before.[2]

There is yet another factor that is likely to drive up costs for the trucking industry: the projection for a severe shortage of qualified truck drivers. The effects of the shortage, which has been in the making for some time, were somewhat mitigated during the most recent economic downturn. Since then, as freight activity has recovered, the driver shortage has become a more serious problem. A shortage of drivers, coupled with fewer trucks on the road, has tightened freight utilization rates, which are said to be approaching uncharted territory: Some estimates now have capacity utilization rates in the trucking industry in excess of 95%.

If, as I would argue, the recent slowdown in freight activity is due primarily to the severe winter weather, then missed deliveries will need to be managed. But this will not be easy. In the trucking industry, backlogs can be difficult to make up because there is only so much the trucking industry as a whole can ship—and only so much any one truck can haul (due to legal weight limit restrictions on most highways). Making up for the backlogs will result in added demands on a truck fleet that is already running at near-full capacity.

Based on this analysis, it doesn’t look like freight rates will be coming back down any time soon, especially if the economy keeps improving. As businesses moved to optimize their supply chains with techniques such as just-in-time inventory,[3] freight has taken on an increasingly important role in their production processes. As a percent of total logistics expense for private business, trucking-related costs comprise 77.4% of transport costs and 48.6% of total logistics spending.[4] Accordingly, when real gross domestic product (GDP) increases by 1%, some analysts estimate that the truck transportation needed to bring this about increases by 2 to 3%.[5] Should the demand for hauling freight by truck grow dramatically, the trucking industry’s capacity would be strained under the current circumstances. When trucking capacity is strained, prices for those freight hauls that are not under long-term contract can jump. Given the changing fundamentals to the trucking industry discussed previously, some analysts argue that the recent price spikes for shipping freight via trucks will ultimately work their way into long-term contractual prices for hauling freight (which are predicted to reset throughout the year). Some estimates have the increase for contractual freight in the coming months to be in the range of 4% to 6%.

Rising capacity utilization for the trucking industry, increases in the costs of new trucking equipment, higher demand for qualified truck drivers, and a declining number of heavy-duty trucks in operation are some of the reasons that freight prices are on the rise. North American heavy-duty truck production is increasing to meet demand, but recently announced fuel economy standards will continue to add costs to the production of new vehicles—and, in turn, increase their sale prices. So while rising freight rates have historically been a good predictor of improved economic activity, there are other factors at work driving up rates at this time. It remains to be seen how all of this will affect consumer prices, but if these expected freight rate increases cannot be readily absorbed, they will have some impact on the consumer. For these reasons we will be keeping an eye on freight and freight rates in the months ahead—long after the snow has melted.


[1]Truck transportation makes up a significant portion of the Transportation Services Index (TSI), accounting for 40% of the data used. (Return to text)

[2]Newscom Business Media Inc., 2014 “Used Trucks Cost More than Ever Before”, Today’s Trucking, February 27. (Return to text)

[3]Just-in-time inventory is an inventory strategy employed by firms to increase their efficiency and decrease waste by receiving goods only as they are needed in the production process; this strategy reduces costs associated with carrying large inventories (of raw materials or finished goods, such as cars). (Return to text)

[4]Dan Gilmore, 2013 “State of the Logistics Union 2013”, Supply Chain Digest, June, 20, 2013. (Return to text)

[5]Jeff Berman, 2014 “Truckload capacity trends in 2014 are worth watching, say industry stakeholders”, Logistics Management, Jan. 10, 2014. (Return to text)

Michigan’s Automotive R&D Part II

By Thomas Klier, Bill Testa, and Thomas Walstrum

The automotive industry is somewhat synonymous with Michigan. This relationship was born of an explosion of technological innovation in Southeast Michigan, including the assembly line and key developments in the internal combustion engine and transmission system. Looking at innovative activity today, a hundred years later, it is not far-fetched to state that the geography of automotive innovation in North America resembles that of yesteryear, with Michigan retaining its dominant role. The state has been highly successful to date in sustaining its leading automotive R&D concentration. Yet, for good reason, policy initiatives in the state are aimed at retaining and building on its strength.

The research and development (R&D) activity of private industry is increasingly being recognized as an important part of the innovation that spurs economic growth and competitiveness. Companies undertake R&D both to improve their production processes for cost and quality and to create wholly new products and services.

Among mainstay U.S. industries, automotive remains one of the most innovative in this regard. R&D that was both financed and performed by U.S. domiciled automotive companies amounted to $11.7 billion in 2011, representing 5.2 percent of total R&D spending. The R&D intensity of automotive manufacturing (as a share of the industry’s value added) is 15.3 percent, compared with 9.2 for all manufacturing, and 1.7 percent for all private industry.[1]

The importance of innovation to automotive companies remains paramount. A recent report by the Boston Consulting Group cited nine automotive companies among the world’s most innovative companies in 2013. The report names several factors behind the innovative burst among automotive companies, including the quickly tightening fuel-efficiency and environmental standards, which have spurred interest in electric and hybrid vehicle technologies. At the same time, auto companies continue to strive to meet ongoing demands for safety, comfort, and performance. Today’s vehicles increasingly comprise advanced electronic and IT components, which are developed both by automotive companies and purchased from technology companies in other industry sectors. By one estimate, “Electronics make up nearly 40% of the content of today’s average new automobile, and their share will continue to grow.” R&D initiatives to enhance the performance and to lower the cost of batteries that may power many of tomorrow’s autos are one example of an important and emerging R&D direction; automatic guidance systems for tomorrow’s (driver-less) cars is another.

Today, Michigan remains the epicenter of automotive R&D in the U.S. The state has maintained its leading place even while production has dispersed throughout the nation. According to data from the National Science Foundation that has been assembled for recent years only, R&D that is both funded and performed by auto companies in Michigan held fast at between 70 and 80 percent of the nation’s total from 1998 to 2011, amounting to $8.87 billion in 2011.

Automotive R&D has propelled Michigan to a leadership position among Midwest states. The table shows Michigan leading the region with $13.7 billion in total business-performed R&D by all industries in 2011, closely followed by Illinois ($12.0 billion), but far ahead of Ohio, Indiana, and Minnesota.[2]

Michigan is also a leader in employment of auto engineers to support long-term R&D and innovation. Drawing on data from the Census and the more recent American Community Survey, we can see how large Michigan’s share of the nation’s automotive engineers is relative to its share of the nation’s work force. Michigan employs over one-half of the nation’s automotive engineers, but its work force overall represents just 3 percent of the nation’s. Granted, Michigan’s share of the nation’s automotive engineers has fallen by ten percentage points since 1980; nonetheless, the state has added 18,000 (two thirds) of its engineers since 1980.

The remarkable importance of automotive technology in Michigan (as represented by engineering workers) can also be understood by comparing it with Michigan’s eroding share of automotive production. By overlaying Michigan’s automotive production workers as a share of the nation on the chart above, the strong role of automotive technology becomes clearer. Since 1950, Michigan’s share of production workers has fallen from 54 to 19 percent, a loss of approximately 255,000 jobs.

And while there are many technologically advanced industries in Michigan—including bio-pharma, medical equipment, industrial chemicals, and office furniture—automotive engineering has come to dominate further in recent decades. As the chart shows, automotive engineers once comprised 30 percent of engineers in all industries in Michigan. By 2012, their share had risen to 51 percent.

What is the future of automotive R&D in Michigan; will the region’s extreme concentration in the activity continue? There are no hard and fast answers, yet there are identifiable features that will come into play. On the one hand, there are many historical instances of geographically concentrated centers being very cohesive and long-lived. Once established, such “clusters” tend to grow and feed on themselves. Technology activity is drawn to technology activity. Skilled workers are drawn to activity-rich cities, and companies are, in turn, drawn toward pools of skilled workers. For example, global financial centers such as New York and London have held their dominant positions for many decades, even centuries. The San Francisco Bay area has enjoyed a long run of dominance in the areas of IT and biotech. In a similar way, Michigan’s established leadership in automotive R&D may persist.

On the other hand, company reorganizations and the geographical shifting of activities that tend to be interdependent with technological activities represent risks to Michigan’s position. It may be beneficial for some industries to locate technology and production in close proximity to facilitate (and reduce the cost of) communication and transportation between the two activities. Thus, the fact that Michigan has lost automotive production in recent decades may have negative implications for automotive R&D in the state.

Similarly, co-dependence between R&D and company headquarters activities such as marketing and strategic planning has also been seen as important in some industry sectors. Thus, any major shift in corporate headquarters activity away from Michigan would raise the concern that it might be accompanied by a shift in R&D activity.

Finally, mature industries such as automotive are often severely disrupted by the emergence of wholly new and sometimes unexpected technologies that greatly shake up their organization and geography. For example, the development of aerospace technologies for military uses shifted the locus of related U.S. production from the Northeast to the Southwest and West during the course of the twentieth century. So far, this has not yet taken place as Michigan continues as the U.S. leader in automotive innovation and R&D activity.


[1]For 2011, National Science Foundation, National Center for Engineering and Scientific Statistics, Business R&D and Innovation Survey, and U.S. Department of Commerce, Bureau of Economic Analysis. (Return to text)

[2]Latest data from the National Science Foundation, available here. (Return to text)

District Unemployment Rates During this Expansion

By Bill Testa[1]

In assessing economic conditions among states and regions, we often pay a lot of attention to the current unemployment rate. The unemployment rate measures the share of the work force that is out of work and actively seeking employment. The release of the unemployment rate is timely; it is usually available during the third week following the end of the previous month. Accordingly, when the National Council of State Legislatures asked me to address their January meeting in Chicago and to discuss the economy in the Midwest states, the unemployment came to mind as an expected and known indicator.

The current unemployment rate alone, however, tells us little about how a state economy fares in relation to the current and past swings in economic activity—the so-called business cycle. In the states of the industrial Midwest, for example, unemployment rates often fall steeply during national economic downturns, but often recover rapidly afterward. To get a clearer picture of how these states are doing, I compared each one’s unemployment rate now, 18 quarters following the bottom of the national recession, with its rate 18 quarters following the trough of the past recessions of 1974-75, 1980-82, 1990-91, and 2001. In this way, I can control for demographic differences across states, while also creating a consistent benchmark for comparing unemployment rates at similar points in the national business cycle.

As an example, let’s take the interesting case of Illinois[2]. The chart below displays Illinois’s unemployment rate for the fourth quarter of 2013, 18 quarters following the trough of the 2007-09 recession. At 8.8%, it is historically very high for this point during an economic recovery and expansion. Clearly, work force conditions are not good. Only the period following the 1981-82 recession rivals this one.

The recession of 1981-82 and its aftermath were severe in both the nation and the Midwest. The nation’s unemployment rate also remained stubbornly high 18 quarters following the 1982 trough. Yet, 18 quarters following that trough, the Illinois unemployment rate still exceeded the nation’s. And in comparison, today’s gap between Illinois and the nation is larger than the 1980s, and indeed larger than in any subsequent period.

While this gives us a pretty good handle on the current unemployment situation, a look at the broader multi-state regional performance is also telling. Multi-state regions are highly interrelated, perhaps none so closely as the industrial Midwest. The line graph below compares the national unemployment rate with that of the East North Central (aka Great Lakes) region, the region with which Illinois is typically grouped. From the chart, one can see that the Great Lakes’ regional unemployment rate soared far above the nation’s during the 1981-82 recession. As many of us recall from that era, everything that could go wrong for the region’s economy did go wrong. In particular, the region’s hallmark manufacturing industries—including steel, machinery, and automotive—suffered steep declines in both domestic and international markets. At the same time, domestic agriculture contracted following rapid but unsustainable growth during the 1970s. Meanwhile, other U.S. regions enjoyed growth driven by rising defense spending and the emergence of high tech industries—principally in computing equipment and micro-electronics.

But the recovery from the 1981-82 recession was fast for both the region and the nation and, in the decade following, the Great Lakes region fared even better. Its unemployment rate(s) fell below the nation’s during the 1990s as low gasoline prices spurred the recovery of domestic automotive production and as a growing global economy boosted demand for the region’s capital goods machinery and equipment. Meanwhile, many other regions were held back by local financial (savings and loan) crises, as well as by sagging demand for defense-related goods and services.

As the line graph above indicates for our more recent expansionary years, the region’s unemployment rate fell quickly following the 2008-09 recession, so much so that parity was reached with the nation during 2011. Since that time, however, the region’s unemployment has flattened, while the national unemployment rate has continued to decline. As a result, a gap has opened up of more than 1 percentage point between the national unemployment rate and that of the region. By way of explanation, the pace of manufacturing growth eased following the bounce-back of the national and global recovery, which exerted an outsized effect on the industrial Midwest. Slowing global growth in Europe and parts of Asia have further dampened the sector’s growth. And so, in the multi-state regional context, Illinois’s recent unemployment gap with the nation is somewhat consistent with the experience of neighboring states. Yet, in comparing the Illinois-U.S. gap to that of the other Great Lakes states, and to neighboring Iowa (below), the Illinois’ gap is large.


[1]Thank you to Jacob Berman for research assistance.(Return to text)

[2]Charts for the remaining Great Lakes states (and Iowa) are below.(Return to text)


Appendix – Charts for Indiana, Iowa, Michigan, Ohio, and Wisconsin

The Importance of Manufacturing to the Seventh District and Michigan

By Paul Traub

There has been a lot written about manufacturing returning to the United States from abroad, and there are data to suggest that this is happening. Rising wages abroad, falling energy prices in the U.S., and declining willingness of domestic manufacturers to suffer the delays and poorer quality of overseas supply chains are conspiring to shift some production back to the U.S., a trend called onshoring. At a Federal Reserve Bank of Chicago conference last April, Justin Rose of the Boston Consulting Group (BCG) attested that the U.S. still makes over 70% of the manufactured goods it consumes, while its prospects remain bright as global trends are conspiring to encourage onshoring. However, while U.S. manufacturing output remains hefty and onshoring is undoubtedly taking place, there is some debate as to whether manufacturing jobs are returning to the U.S. in a meaningful way. A recent Forbes article, “Reports Of America’s Manufacturing Renaissance Are Just a Cruel Political Hoax” makes the case that even though “some reshoring has taken place,” there hasn’t been enough to offset the continued offshoring of manufacturing jobs along with the job declines that derive from labor-saving advances in technology. This debate is of great importance to the Seventh District and Michigan.

As Chart 1 shows, the United States lost about 33.2% of its manufacturing jobs between 2000 and 2010 compared with a 1.5% decline in total nonfarm payroll jobs. Given the extent of manufacturing job decline during the recession, it wasn’t surprising to see growth in manufacturing jobs exceed total nonfarm payroll growth through 2012 even though that pace of growth slowed somewhat in 2013. In addition, while nonfarm employment is now close to its prerecession peak, manufacturing employment is still down by more than 30% from its 2000 level and 11.0% below its 2007 level.

Comparing U.S. employment with that of the Seventh District and Michigan (Chart 2), we see a slightly different picture. While total nonfarm employment for the District has improved, it is still 4.9% below its 2000 level and Michigan is still down 12.8%. Manufacturing employment is well below 2000 levels in both the District and Michigan, –28.9% and –38.2%, respectively. However, there has been some progress in the District since the recession, especially in manufacturing jobs. While the District has seen total nonfarm jobs grow by 4.2% since 2010, manufacturing jobs have improved by more than twice that rate, increasing by 8.7%. Michigan has seen total nonfarm employment grow by 5.5% for the same period, with manufacturing jobs increasing an astonishing 17.0%.

Obviously, this is important to the Seventh District and Michigan economies, given the relative heft of the manufacturing sector in the region. Charts 3 and 4 use nominal data to create manufacturing’s share of total output for the United States, the Seventh District, and Michigan. The charts show that while manufacturing has been declining steadily since 1997, the District and Michigan remain more dependent on manufacturing than the nation as a whole. The charts also illustrate that manufacturing has made somewhat of a rebound since the end of the recession. In fact, the manufacturing shares of both the U.S. and the district are very close to where they were just prior to the recession.

Much of the District’s growth in manufacturing output is related to growth in light vehicle sales, which reached their lowest levels in almost three decades during the 2008 recession. Still, the growth is quite impressive. Chart 5, which uses data from the Bureau of Economic Analysis (BEA), shows how the District accounts for almost half of the nation’s motor vehicle and parts manufacturing and this share has remained fairly constant over the last 15 years. Michigan accounts for more than half of the District’s and about 25% of the nation’s motor vehicle and parts output.

Chart 6 shows that U.S. light vehicle production fell about 6.0 million units from the peak in 2000 to just 5.6 million units in 2009. Between 2009 and 2013, light vehicle production rebounded by 5.2 million units or 94%. This would do a lot to explain the District’s manufacturing growth over the past four years.

Motor vehicle and parts employment has been declining for a number of years, due to outsourcing and increased use of automation in vehicle assembly plants. As Chart 7 shows, a simple calculation of dividing the total U.S. motor vehicle and parts employment by the number of light vehicles produced in the U.S. reveals that in 2013, there were only about 74% of the number of employees it took to produce the same number of vehicles in 2006. Since the economy started to recover in 2009, the motor vehicle and parts sector has seen an increase of about 140,000 employees, while increasing total vehicle production by 5.2 million units.

Based on projections from Ward’s Automotive, light vehicle production is expected to increase another 800,000 units by 2015. If these projections are achieved, this will certainly help the District’s overall economic output, but its impact on total employment will be more modest than that seen from 2009 to 2013. Still, we can expect the overall economic impact from the growth in production to be positive for the Seventh District and Michigan.

OECD Chicago Economy Conference

By Emily Engel and Susan Longworth

On Friday September 27, 2013, The Federal Reserve Bank of Chicago hosted the Summit on Regional Competiveness (Summit) with The Alliance for Regional Development (Alliance) and the Chicagoland Chamber of Commerce. The Alliance is an economic development group working to implement recommendations contained in a 21-county, tri-state (Illinois, Indiana, and Wisconsin) analysis completed by the Organization for Economic Co-operation and Development (OECD).

According to the OEDC report, cooperative and regionally oriented economic development offers opportunity for sustainable growth in key industries within the Chicago MSA. Please read our blog from last year to learn more about how ‘Federal Agencies Align to Support Regional Growth’.

The goal of the Summit was to demonstrate that the tri-state region can cooperate to find solutions to the challenges in the OECD report.The agenda included welcome remarks from Daniel Sullivan, Director of Research and Executive Vice President, Federal Reserve Bank of Chicago, and a keynote address from Austan Goolsbee, the Robert P. Gwinn Professor of Economics, University of Chicago’s Booth School of Business (link to The emcee for the event was Theresa E. Mintle, President and Chief Executive Officer, Chicagoland Chamber of Commerce. Governors Scott Walker of Wisconsin; Governor Pat Quinn of Illinois; and Governor Mike Pence of Indiana all attended the Summit. Video from the summit is available here.

The Summit addressed the following topics:

• Matching Skills to Jobs in the Tri-State Region—to explore potential regional approaches and programs that satisfy business needs while closing the skills gap;

• Innovation in the Tri-State Region—to explore how best to enhance the region’s performance in innovation-driven business clusters;

• Transportation and Logistics in the Tri-State Region—to explore how connectivity including, air, road, port, and rail transportation can help grow the region’s future as a mega-logistics hub; and

• Increasing the Region’s Competitiveness through Green Growth—to explore environmental factors impacting economic growth and development.

Takeaways from the Summit included the following points.Panelists highlighted the need to brand the region in a manner that capitalized on regional assets like the Great Lakes and a central location. Concerns were raised about balancing municipal needs with regional ambitions. As municipalities struggle to retain and raise revenue and jobs, there is unresolved tension between collaboration and competition that is most acutely felt by local officials.The region’s significant transportation assets were mentioned frequently, along with the need to address growing congestion and manage natural resources, such as the Great Lakes and other inland waterways responsibly. Workforce development was discussed at length and strategies for engaging and training new workers were emphasized. Panelists recognized the increasingly technical nature of today’s jobs and the challenges faced by those seeking higher education.

For the full agenda, please see the Federal Reserve Bank of Chicago’s website.

For more background information, please read the Chicago Tri-State Metropolitan Area OECD report.