Category Archives: Iowa

Understanding the Seventh District’s economic slowdown in 2015

As I noted on this blog in February 2015, 2014 was a pretty good year for the Seventh District. Real District gross state product (GSP) grew 1.2%, the unemployment rate fell from 7.3% to 5.8%, and payroll employment grew 1.5%. The strong finish to 2014 led me to feel quite optimistic for how 2015 would turn out. Unfortunately, it has become increasingly clear that economic activity in the Seventh District has steadily slowed as 2015 has progressed. While the District is certainly not in recession, it is now likely growing at a below-trend pace. In this blog post, I provide evidence of the slowdown and explore how the fortunes of District states’ signature industries have both contributed to and helped mitigate the slowdown.

While we wait for the GSP data for 2015 to be released (due out in June), arguably the best overall indicator we have for 2015 District economic activity is our Midwest Economy Index [1] (I should note here that we will be releasing a new survey-based activity index later this month). Figure 1 shows values for the MEI from 2014 to the present. The index was well above zero throughout 2014, indicating that growth was consistently above trend. Just as 2015 began, the index began to decline, and it entered negative territory in June. The most recent reading of the MEI (for November 2015) indicates that District growth is somewhat below trend.

1-MEI

Some important indicators included in the calculation of the MEI are payroll employment, the regional Purchasing Manager Indexes (PMIs) [2], and per capita personal income. Not surprisingly, they also largely suggest that economic activity in the District slowed in 2015. Figure 2 shows that while District payroll employment grew by an average of 23,000 jobs per month in 2014, the pace of growth slowed to only about 12,000 new jobs per month in 2015. Figure 3 shows the simple average of the five PMIs available for the Seventh District. This average also indicates that economic activity declined notably starting in 2015. As a counterpoint, figure 4 shows that the pace of growth in real personal income per capita has not slowed much in 2015: The annualized growth rate for 2014 was 3.08% and the available data for 2015 (through Q3) indicate that the annualized growth rate has only slowed to 2.94%.

2&3-Emp&PMIs

4-RIPC

While the preponderance of evidence suggests that Seventh District economic activity slowed in 2015, it turns out that the experiences of individual states within the District have been quite different. Figure 5 shows the sum of the contributions to the MEI for the eastern states of the District (Indiana and Michigan) and the sum for the western states of the District (Illinois, Iowa, and Wisconsin). Growth in 2014 was above the District’s long run trend in both sub-regions, but the western states outperformed the eastern states. The pace of activity in the eastern states picked up steadily through the first half of 2015 and has since slowed to near the District’s trend. This experience contrasts quite notably with that of the western states. Activity in these states began to slow at the end of 2014 and continued to slow until the middle of 2015, at which point conditions improved some.

5-WEMEIs

One approach to understanding the different experiences of eastern and western District states is to do an economic base analysis for each state. Such an analysis identifies the industries whose employment is especially concentrated in a state (and therefore likely quite important for the state’s economy) by calculating a location quotient (LQ). A location quotient is the ratio of the share of employment in an industry in a state to the share of employment in an industry in the U.S. as a whole:

Formula

As an example, if the machinery industry’s share of employment in Michigan is 1.3% and the machinery industry’s share of employment in the U.S. is 1%, then the location quotient is 1.3, and we say that the machinery industry is 30% more concentrated in Michigan than in the U.S. as a whole.

For this blog post, I calculate location quotients for each state for each of the 3-digit NAICS industries that are available from the Bureau of Labor Statistics’ (BLS) payroll employment survey.[3] I then consider the industries in each state with a location quotient greater than 1.5. This approach successfully identifies the signature industries one typically thinks of for each state in the District. For example, the analysis picks up Michigan’s auto industry, Indiana’s steel industry, and Illinois’s, Iowa’s, and Wisconsin’s machinery industry.

Table 1 shows the high-location quotient industries for Indiana and Michigan, along with the percentage of overall employment the industry represents and the year-over-year employment growth rate of the industry from November 2014 to November 2015. With the exception of the primary metals industry (where employment fell by 0.93%), employment grew for all of Indiana’s high-LQ industries and was solid for most of them. The story is even clearer in Michigan, where the auto industry dominates. Employment in the transportation equipment industry grew 4.59% over the past year.

To summarize the overall growth of District states’ flagship industries, I calculate the average growth rate for the industries, weighted by their relative size. Employment in Indiana’s flagship industries grew 1.35% over the past year, while employment in Michigan’s flagship industries grew 3.38%. Thus, even though the pace of growth in economic activity slowed in Indiana and Michigan in the second half of 2015, it was still a good year for both states.

6-Table 1

The story is more mixed for the states in the western part of the District (table 2). Machinery (and the fabricated metal producers who support them) has not faired well in the past year: Illinois, Iowa, and Wisconsin all saw notable declines in machinery and fabricated metal employment (with the exception of Wisconsin’s machinery employment, which was flat). However, Iowa and Wisconsin were helped by strong growth in other flagship industries (food products in both Iowa and Wisconsin and finance in Iowa). Illinois has few other flagship industries to help it, though it’s worth noting that Chicago has fared much better than downstate Illinois because of its concentration in business services and finance. Average employment growth for Illinois’s high-LQ industries was dismal (-2.04%), while growth was solid for Iowa’s (1.58%), and slow for Wisconsin’s (0.73%). Thus, although some flagship industries have done well in the western states in the District, the struggles of the machinery industry appear to have put quite a damper on their economic performance.

7-Table 2

So we see that the overall slowdown in the District in 2015 was not a shared experience across District states. The eastern states (Michigan and Indiana) did notably better than the western states (Illinois, Iowa, and Wisconsin) and these differences are relatively consistent with the performance of states’ flagship industries. What does the future hold for these flagship industries? At the moment, it’s hard to find much evidence that there will be a significant reversal of fortunes in 2016. The auto industry is likely to continue to benefit from steady growth in the U.S. economy and low gasoline prices, while the machinery industry is likely to continue to suffer from weaker global growth and depressed commodities prices (which hurt demand for both mining and agriculture machinery).

That said, while flagship industries certainly play an important role in a state’s economy because of all the related industries that support them, there are still many industries that are not closely related to them. For example, Iowa’s contribution to the MEI has been negative for most of 2015 (not shown), likely because of the struggles in the farming industry (see the Chicago Fed’s latest AgLetter for more details). The converging trends in the MEI (figure 5) suggest that these other factors are also making their presence known.

[1] The MEI is a weighted average of 129 Seventh District state and regional indicators measuring growth in nonfarm business activity from four broad sectors of the Midwest economy: manufacturing, construction and mining, services, and consumer spending.

[2] The PMIs included in the index are for Chicago, Iowa, Detroit, and Milwaukee.

[3] Data are not available for all 3-digit NAICS industries because there is not sufficient employment in some industries in some states for the BLS to be able to cover them accurately.

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)

Assessing the Midwest Floods of 2008 (and 1993)

By Rick Mattoon

As water levels recede, the region is beginning to take stock of impact from some its worst flooding since 1993. The geographic footprint of this year’s flooding (depicted below) is less extensive than the nine states and 504 counties affected 15 years ago. And as always, an assessment of the short- and long-term impact of this natural disaster on the national and regional economy will be difficult. In this blog, I will look at the effect of natural disasters on economies and contrast current flood conditions with those the region faced in 1993.

Click to enlarge.

How to think about the losses to the U.S. economy

From a conceptual viewpoint of our economy, natural disasters impact our economic well-being in two basic ways. First, they destroy what we have produced in the past—our “capital stock”—including lives, homes, commercial buildings, public infrastructure and property. Second, they often interrupt normal commercial activity and production. Transportation and deliveries do not take place, people cannot get to work and work places become dysfunctional until normalcy is restored.

In a December 1993 Chicago Fedletter, Bill Testa, Gary Benjamin and I wrote, “Perhaps the most meaningful definition of economic loss due to a disaster is the value of output of goods foregone—that is, the total net output that would have been produced had it not been for the disaster. Foregone output results for two reasons. First, natural disasters destroy productive capital stock such as roads, bridges and factories, thereby reducing output until such time as the capital stock is restored. Second, natural disasters can interrupt day-to-day business activity.”

As that article points out, the impact of the natural disaster tends to have a somewhat unusual affect on the national income accounts—the official way in which we measure the nation’s economic output and income from quarter to quarter. Following the 1993 floods, estimates for the third quarter reduced personal income by $9 billion and forecasted uninsured losses to be $2 billion. Losses to proprietors’ incomes were estimated at another $1 billion.

Remarkably, such initial losses soon appear to translate into economic gains as business and households rebuild. The rise in construction activity and the resumption of business activity often boost gross domestic product (GDP) estimates for future quarters, as households and businesses attempt to rebuild their physical capital and, in the case of businesses, to fill order backlogs. For example, following Hurricane Andrew, annualized GDP growth hit 5.7% in the fourth quarter of 1992, spurred by rebuilding activities.

However, such rebuilding does not reflect an actual economic gain in the broad long-term perspective. In most cases the rebuilding merely replaces lost capital stock—meaning that, in the long term, the nation’s product will not exceed what would have been produced without the disaster. While the immediate burst of economic activity is quite evident, the losses from the foregone output of interrupted and diminished business activity may go largely undetected because the diminished growth takes place in small amounts spread over many years.

Regional economic losses

The ultimate extent of the damage to the region’s economy will in large part depend on who pays for the rebuilding. If the losses are in large part covered by the national government and insurance companies, and if reimbursement is prompt, the region can conceptually restore output and even increase its levels of economic growth. However, if the 1993 flood experience is a guide, it is more likely that the region will absorb a significant share of the disaster-related cost. Because flood insurance was not extensively used, it was estimated that 15% to 25% of the flood costs were borne by state and local governments, not to mention the costs to uninsured homeowners who were forced to rebuild using their own resources. In the most recent floods, it was estimated that only about 1% of Iowans owned flood insurance. In hard hit Cedar Rapids, only 777 of the 4,000 homes damaged or destroyed by flooding were covered. Despite efforts after the 1993 floods to expand coverage, the cost of the policy and the limits of coverage still deter homeowners from purchasing polices. It is estimated that the average cost of a policy in Iowa is $500 per year with coverage only including direct flood damage and not related damage such as water that enters the house through a backed up basement drain. Even if property owners choose to be fully insured, insurance must be paid for. Thus, residents of these regions do bear at least some of the costs in choosing to live and work in disaster prone areas. Currently $2.7 billion in federal flood relief has been approved to aid 2008 flood victims. This does not include the value of low-interest loans and small business assistance as well as the value of crop insurance and private insurance.

Specific categories of losses–Agriculture

In both floods, the greatest concern focused on flooded crop land. In the 1993 floods, nine million acres were submerged by the flood. The lost acreage had been expected to produce 6% of the region’s harvest that year. The estimated crop losses were $7 billion. The states with the largest percentage loss were Missouri 12%, Minnesota 11%, South Dakota 8% and Iowa 7%. In this year’s flooding, damage is heaviest in Iowa where 2 million to 3 million acres of corn and 2 million acres of soybeans were flooded. The American Farm Bureau estimates crop losses at $8 billion for the region, with $4 billion of the total in Iowa. Other states with significant estimated losses are Illinois ($1.3 billion), Missouri ($900 million), Indiana ($500 million) and Nebraska ($500 million). The Bureau points out that it is not only the flooding that will impact crops but also the excessive rainfall that occurred this year.

A June 30 estimate by the USDA projected this year’s corn harvest to be down from 86.5 million acres to 78.9 million, or 8.7 percent. However, the impact on prices may be softened if a robust corn harvest occurs, since supplies should be sufficient to meet demand for food, feed and ethanol. Following the USDA report, corn futures fell from $7.55/bushel to $7.25/bushel, significantly off the $8/bushel price recorded on the Chicago Board of Trade in the immediate wake of the flooding. Still, this price is significantly elevated over the early June $6/bushel price.

One big difference between this year’s floods and that of 1993 was the preexisting stocks and prices of corn and soybeans. In 1992, a bumper crop had been harvested. For example, the stock-to-use ratio for corn hit 25% in 1992 and even in the flood year of 1993 ended at 11%. While prices rose, the increase was a modest hike from $2/bushel to $2.50/bushel. Today the corn stock-to-use ratio is only 6%; prices spiked accordingly to $8/bushel immediately after the flood before retreating to the current price. This tightness reflects the increasing demand for corn both for export and for ethanol. Given this, even if the number of acres lost is smaller than in 1993, the impact on prices will need to be closely monitored.

Spillover issues into other agriculture markets also need to be considered, as livestock feed prices are affected. The condition of the fields for next year’s planting will need to be assessed as well.

Structures

Given the smaller geographic footprint, the potential cost of rebuilding and the infrastructure loss is considerably less in this year’s flooding. While Cedar Rapids and parts of Iowa City were severally impacted, much of the flooding was contained within sparsely populated areas. In 1993, an estimated 45,000 to 55,000 private homes were destroyed, and between 35,000 and 45,000 commercial structures were damaged. Similar to today, most of the homes did not carry flood insurance, making uninsured losses the most significant issue. Estimated property and nonagricultural losses totaled $5 billion before insurance.

Another difference with the 1993 floods was the damage to infrastructure. In 1993, 1,000 miles of road were closed, and 500 miles of railroad track were underwater. Nine out of 25 non-railroad bridges were damaged and closed. This time, some highways were closed for several days due to flooding but damage to bridges, locks and other infrastructure was limited. The exception of course is in cities such as Cedar Rapids where infrastructure losses in the downtown are extensive. Cedar Rapids had 1,300 city blocks underwater, forcing 24,000 residents to evacuate. Preliminary damage estimates have been placed at $736 million or roughly $6,095 per capita. This must also be placed in the context of disruption to Iowa’s second largest city of over 120,000 people with a 2005 gross metropolitan product of $11.2 billion.

Transportation disruptions

One of the more immediate problems that flooding causes is transportation disruptions. The 1993 floods were so extensive that barge traffic on the Mississippi was halted for 2 months. In contrast, barge traffic is expected to be affected for 3 to 4 weeks this time. By July 5, the entire Mississippi was reopened to navigation. Railway disruptions were also more severe in 1993. The destruction of rail bridges added four days to rail shipping times for several months while this time rail disruption was minimal for almost all major freight lines. The effected lines caused temporary delays of 1 to 2 days for most shipments.

What happens next?

For towns that were most directly affected, the question is, what does the path to recovery look like? Unlike individual farms and factories, cities’ and towns’ economies are composed of complex interrelationships that have developed over many years. A natural disaster can upset, disrupt and even destroy those relationships so that restoration is often impossible and sometimes undesirable. And so, the form of rebuilding may require careful consideration and evaluation.

Following the 1993 event, many communities and individuals simply choose not to rebuild. Other communities used natural disasters to redefine themselves. An interesting example of a town rebuilding after a flood was Grand Forks, North Dakota. The Minneapolis Fed chronicled the rebuilding of Grand Forks in a September 2006 Fedgazette article. Grand Forks was the victim of flooding in 1997. In April of that year, 80% of the town was submerged. By 2006, the area was largely restored with the region’s economy growing at a faster pace than before the flood. This was due largely to the influx of $600 million in federal disaster aid (approximately $10,000 per resident).

After much painful disruption, lengthy deliberation and hard planning, the flood eventually spurred a new vision for the area. Roughly 1,200 homes in the 100-year flood plain were bought out by the Federal Emergency Management Agency as part of a flood protection plan. The population rebound remained slow until the Army Corps of Engineers finalized a flood protection plan in 2000. Once this occurred, a building boom was unleashed. The city supported this by providing $10,000 forgivable loans for people staying at the same address for a specific period of time. The new housing was more expensive than what it replaced, with the new larger homes carrying average price tags of $138,000 versus the $85,000 homes that had been destroyed.

For business, the greatest disruption was for restaurants, bars, hotels and any business where discretionary spending is important. Many of these businesses had to lay off workers. Other businesses such as banks, health care and manufacturing suffered lost sales but did not suffer drastic employment declines. In fact given the gains in construction jobs, employment in Grand Forks rebounded to its pre-flood level in five months. To some observers, the newly rebuilt Grand Forks with its improved infrastructure and new capital stock is better positioned for growth than before the flood, but this is only true because of significant government subsidies and 10 years of hard work. And of course, it is not true for every household and business impacted by the flood, as many chose to leave Grand Forks.

Conclusions

The 2008 flood may seem to be milder in its overall economic impact on the larger region and the nation, but it is just as devastating for those who have suffered it as it was for those in previous floodings. The ultimate costs and impacts can only be known over time as damages become known, as the extent of relief is determined and as households, businesses and towns decide how to respond to the disruption. Most though not all of the agricultural costs and recovery will be known by the end of the growing/harvesting season. In contrast, the recovery and rebuilding process for towns, businesses and households will be protracted and laborious.