Economic Development in Des Moines, Iowa

By Rick Mattoon

In a forthcoming article in the bank’s Economic Perspectives, I profile the economic development efforts underway in the five largest cities in the Seventh District—Des Moines, Indianapolis, Milwaukee, Detroit, and Chicago. (For a complete profile of all five cities see, Industrial clusters and economic development in the Seventh District’s largest cities.) Each city faces its own unique set of challenges and has a distinctive economic base that has influenced its growth path. In a series of blogs, I would like to summarize some of the major trends in each metropolitan economy, starting with Iowa’s capital city—Des Moines.

The Des Moines MSA (metropolitan statistical area) economy has developed a strong mix between financial and professional service firms and manufacturing. In addition, the city benefits from being the capitol of the state, leading to a high concentration in state government employment. Large employers in the area include Wells Fargo (banking), Principal Financial (financial services), Mercy Medical and United Point Health (both health care), DuPont Pioneer (agribusiness), John Deere (agricultural machinery), Marsh (insurance), and UPS (shipment and logistics).

Des Moines MSA Industry Structure

To get a sense of which industries are most important to the metropolitan area’s economy, we can look at its employment concentration in industries relative to the U.S. Table 1 shows the employment percentage for each industry for both the U.S. and the Des Moines MSA. For example, Des Moines has only two industries where the share of local employment is above the national share of employment—wholesale trade and management of companies. In addition, the table provides location quotients (LQs)[1] that demonstrate the relative concentration of each industry in the Des Moines MSA compared with the U.S. A reading of 1 indicates that Des Moines has the same industry employment concentration as the U.S. As the table shows, Des Moines has significantly above average employment concentrations in two industries—wholesale trade at 1.27 ( or 27% above the U.S. average) and management of companies at 1.17. Additionally, Des Moines’s industry concentrations are roughly in line with the U.S. averages for such important industries as construction, retail trade, administrative and waste services, and arts and entertainment. The industries that are much less represented in Des Moines are agriculture, mining, and utilities (although clearly agriculture is of key importance to Iowa as a whole). Interestingly, two sectors that the city targets for growth, professional and business services and manufacturing, have relatively low concentrations (0.74 and 0.64, respectively). In the case of professional and business services, an issue with the data is that nondisclosure rules do not permit an LQ to be calculated for the important finance and insurance sector, which is likely to have high levels of professional employment.

Des Moines MSA Economic Development Strategy

The Greater Des Moines Partnership led an effort to develop a five-year plan for Des Moines and the capital region. The plan aims to position Des Moines as a midsized city with a specialized industry base. It focuses on an industry and demographic comparison with other similar regions, including Omaha, Nebraska, Madison, Wisconsin, and Denver, Colorado. The plan identifies key clusters in which the region is most competitive and recommends that the region market itself specifically to these sectors: finance and insurance; information solutions; health and wellness; agribusiness; manufacturing; and logistics.

The other elements of the plan are similar to most of the other cities’ development plans in stressing appropriate human capital development and work force training. In particular, the Des Moines plan emphasizes developing an employment and training pipeline that meets the needs of local businesses. There is also a geographic component to the plan, targeting growth along the I-35 corridor.

If one reviews the strategy relative to the data on industry structure, it becomes clear that the targets for development consist of a mix of large employment centers (finance and insurance) and logistics-related wholesale trade, as well as historically important industries, such as manufacturing and agribusiness. Manufacturing does not currently represent a high employment concentration in Des Moines, so its inclusion may signal a hope to revive the sector. Given recent speculation that manufacturing is seeing favorable conditions for reshoring of jobs and activities (due to factors such as lower energy costs), many midwestern cities are hoping to restore some manufacturing activity. Finally, Des Moines also benefits from a stable fiscal situation. While the city’s credit rating was recently downgraded by Moody’s due to unfunded pension obligations, it still has an Aa2 rating. And the state government’s fiscal condition is relatively solid.

Finally, Des Moines’ recent economic performance has been quite strong relative to much of the Seventh District. The following chart shows the year-over-year growth in payroll employment for Des Moines versus the Seventh District average. With the exception of a brief period coming out of the Great Recession, the MSA’s employment growth rate has been favorable. In particular, Des Moines has opened a significant gap with the District since 2013.

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[1]The U.S. Bureau of Labor Statistics (BLS) defines LQs as “ratios that allow an area’s distribution of employment by industry to be compared to a reference or base area’s distribution”. (Return to text)

Seventh District Update, July 2014

By Thom Walstrum and Scott Brave

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A summary of economic conditions in the Seventh District from the latest release of the Beige Book and from other indicators of regional business activity:

Overall conditions: Growth in economic activity remained moderate in June and contacts maintained their optimistic outlook for the rest of the year.

Consumer spending: Consumer spending increased in June, but the overall pace of growth remained modest. In most cases, retail sales met or fell slightly below expectations. Light vehicle sales rose as consumers continued to enjoy favorable incentives and credit conditions.

Business spending: Business spending continued to grow at a moderate pace in June. Capital expenditures and spending plans continued to increase, with expenditures still concentrated on industrial and IT equipment. Hiring picked up and hiring expectations continued to increase, with the gains more pronounced in the service sector than in manufacturing.

Construction and real estate: Construction and real estate activity increased at a moderate pace in June. Residential construction increased, but home sales declined modestly. Nonresidential construction strengthened considerably and commercial real estate activity continued to expand.

Manufacturing: Manufacturing production continued to grow at a moderate pace in June. The auto, aerospace, and energy industries remained a source of strength for the District. Steel service centers reported improving order books, as did many specialty metal manufacturers. Demand for heavy machinery grew at a slow but steady pace, weighed down by the weakness in mining.

Banking and finance: Credit conditions improved moderately. Corporate financing costs decreased further. Business lending increased, with contacts noting a pickup in demand for financing of equipment and commercial real estate. Growth in consumer loan demand was steady.

Prices and costs: Cost pressures increased, but remained modest. Energy costs remained elevated. Competition put downward pressure on retail prices, but wholesale prices changed little, compressing margins. Wage pressures increased, primarily for skilled workers. Non-wage labor costs were little changed.

Agriculture: The District’s corn and soybean crops made up ground as favorable weather helped plants emerge more quickly than the five-year average. Corn, soybean, wheat, milk and cattle prices moved down, while hog prices moved higher as disease affected supplies.

Led by improvements in the manufacturing sector, the Midwest Economy Index (MEI) increased to +0.41 in May from +0.11 in April, reaching its highest level since December 2013. However, the relative MEI remained negative for the third straight month, after edging down to –0.38 in May from –0.36 in the previous month. May’s value for the relative MEI indicates that Midwest economic growth was moderately lower than would typically be suggested by the growth rate of the national economy.

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.

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[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 http://www.census.gov/construction/nrs/; http://www.realtor.org/research-and-statistics; http://www.realtor.org/topics/pending-home-sales/data (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.

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[1]See http://www.brookings.edu/about/programs/metro/innovation-districts.(Return 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 http://www.nsf.gov/statistics/states/interactive/show.cfm?stateID=53,14&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] http://www.bls.gov/iag/tgs/iag334.htm (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.

Seventh District Update

by Thom Walstrum and Scott Brave

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A summary of economic conditions in the Seventh District from the latest release of the Beige Book and from other indicators of regional business activity:

Overall conditions: Growth in economic activity in the Seventh District was moderate in April and May. Although contacts were expecting a stronger pick-up in growth, they maintained their optimistic outlooks for the remainder of the year.

Consumer spending: Growth in consumer spending increased slightly, but overall remained modest. Several retail contacts reported higher than normal inventories in anticipation of stronger summer sales. Light vehicle sales decreased slightly.

Business Spending: Business spending grew at a moderate pace, led by higher capital expenditures on equipment and software. Hiring plans changed little from the previous period.

Construction and Real Estate: Growth in construction and real estate activity picked up. Residential construction increased, while nonresidential construction continued to expand at a slow pace. Contacts also noted improvement in residential and commercial real estate markets.

Manufacturing: Manufacturing production continued to grow at a moderate pace. Capacity utilization in the auto and steel industries increased as production levels rose. Demand for heavy machinery grew at a slow but steady pace, weighed down by the weakness in mining.

Banking and finance: Credit conditions improved slightly. Corporate financing costs decreased. Business lending increased, driven by commercial and industrial loan demand from small businesses. Growth in consumer loan demand remained modest.

Prices and Costs: Cost pressures increased, but overall were modest. Energy and transportation costs remained elevated. Contacts reported lingering shipment delays of goods and raw materials from the harsh winter weather earlier in the year. Wage pressures rose slightly and non-wage pressures rose moderately.

Agriculture: Corn and soybean planting progressed quickly after precipitation and cool temperatures slowed fieldwork earlier in the spring. Corn and wheat prices were lower, while soybean prices drifted higher. Livestock prices remained well above the levels of a year ago, although hog prices moved lower.

The Midwest Economy Index (MEI) increased to +0.12 in April from –0.04 in March. However, the relative MEI decreased to –0.23 in April from –0.13 in the previous month, remaining negative for the second consecutive month. April’s value for the relative MEI indicates that Midwest economic growth was somewhat lower than would typically be suggested by the growth rate of the national economy.

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.

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[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.

Seventh District Update

by Thom Walstrum and Scott Brave

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A summary of economic conditions in the Seventh District from the latest release of the Beige Book and from other indicators of regional business activity:

Overall conditions: Growth in economic activity in the Seventh District picked up in March, and contacts generally maintained their optimistic outlook for 2014.

Consumer spending: Growth in consumer spending increased slightly in March, but remained modest. Sales of winter-related items were stronger than normal, while other sales categories, in particular light vehicles, picked up as the weather improved.

Business Spending: Growth in business spending increased to a moderate pace in March. Growth in capital spending picked up. The pace of hiring increased, and while hiring plans decreased slightly, they remained positive.

Construction and Real Estate: Growth in construction and real estate activity was modest in March. Although conditions improved, residential construction and real estate contacts reported that adverse weather continued to restrain growth. Demand for nonresidential construction grew at a moderate pace and commercial real estate activity continued to expand.

Manufacturing: Growth in manufacturing production increased from a mild to moderate pace in March, with contacts from a number of industries reporting increased activity. The auto, aerospace, and energy industries remained a source of strength. Auto and steel production recovered from the weather-related slowdown, while demand for heavy machinery remained soft.

Banking and finance: Credit conditions were again little changed on balance over the reporting period. Corporate financing costs decreased slightly, as bond spreads narrowed. Banking contacts reported moderate growth in business loan demand and modest growth in consumer loan demand.

Prices and Costs: Cost pressures were mild. While energy and transportation costs remain elevated, they were lower than during the previous reporting period. Wage pressures were slightly lower and non-wage pressures moderated.

Agriculture: The slow arrival of spring-like weather delayed fieldwork, but farmers were generally not too worried about the delay. Soybean prices rose relative to corn. The livestock sector moved further into the black, as milk, hog, and cattle prices increased.

The Midwest Economy Index (MEI) decreased to –0.03 in February from +0.32 in January, falling below zero for the first time since June 2013. Moreover, the relative MEI moved down to –0.01 in February from +0.23 in the previous month. February’s value for the relative MEI indicates that the Midwest economy was growing at a rate consistent with national economic growth.

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.

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[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)



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