Category Archives: Midwest

Interpreting the Midwest Economy Index

by Scott Brave, senior business economist

On March 31, 2011, the Chicago Fed will begin releasing on a monthly basis an index designed to measure growth in nonfarm business activity in the Seventh Federal Reserve District states of Illinois, Indiana, Iowa, Michigan, and Wisconsin. This monthly index, called the Midwest Economy Index (MEI), will serve as a regional counterpart to the Chicago Fed’s National Activity Index (CFNAI), available here, and allow for a comparison of national and regional growth trends.

This blog serves as a source of background information on the MEI, detailing its construction and interpretation. In the future, this information will be available at To receive email updates on the MEI as well as future releases, you can sign up at beginning March 31.

Background on the MEI

The MEI is a weighted average of 128 state and regional indicators encompassing the entirety of the five states in the Seventh Federal Reserve District. It measures growth in nonfarm business activity from four broad sectors of the Midwest economy: 1) manufacturing, 2) construction and mining, 3) services, and 4) consumer spending.

As with similar indexes of regional economic activity, the majority of the indicators in the MEI are based on data from the Payroll and Household Employment surveys and State Initial Unemployment Insurance claims.[1] However, for the manufacturing and construction and mining sectors, the MEI also captures production indicators, while for consumer spending it additionally includes data on personal income and home and retail sales.

The MEI incorporates indicators that are observed at both a monthly and quarterly frequency. To express the monthly index at a quarterly frequency, we translate all 128 indicators into a common frequency by taking a three-month moving average of the monthly indicators. In this sense, the MEI’s closest national counterpart is the three-month moving average of the CFNAI (the CFNAI-MA3). Every indicator is then given a stationary transformation and standardized to have a zero mean and unit variance.

The weight each indicator receives in the MEI depends upon the relative degree to which it explains the overall variation among all the indicators. In this fashion, greater influence in the index is given to those indicators that are able to best explain broader fluctuations in the Midwest economy. The degree to which this is true for any individual indicator is captured in the absolute value of its weight. A full list of indicators and their weights can be found here.

To be able to incorporate indicators that differ in originating date and reporting frequency, we follow the estimation strategy outlined by Stock and Watson.[2] This strategy is based on a statistical method called “principal component analysis” and is used to create a system of relative rankings, or weights, for the indicators. These weights are re-estimated each month, but in practice change very little given the substantial history of the index.

To illustrate the role played by each of the four sectors and five states, the pie charts below show what percentage of the variation in the 128 indicators explained by the MEI can be attributed to each sector (figure 1) or state (figure 2). Broad fluctuations in Midwest nonfarm business activity have historically been explained by the manufacturing sector and, to a lesser extent, the service sector, as well as by the three largest District states (Illinois, Michigan, and Wisconsin).

Interpreting the MEI

Our motivation in creating the MEI is to better understand the relationship between growth in national economic activity and growth in Midwest economic activity. The MEI is a measure of regional economic activity in much the same way as the CFNAI is a measure of national economic activity. CFNAI values above zero indicate growth in national economic activity above its historical trend, and values below zero indicate growth below trend. Similarly, MEI values correspond to deviations of growth in Midwest economic activity around its historical trend.

Over long periods, Midwest economic activity has tended to track national economic activity—as shown in figure 3, which compares the MEI and CFNAI-MA3. Both indexes in this figure have been expressed in standard deviation units, so that a value of –1 corresponds with growth that is 1 standard deviation below trend.

However, over shorter periods this has not always been the case, particularly around the beginnings and ends of recessions (the shaded regions in the figure as defined by the National Bureau of Economic Research, or NBER). To highlight such differences, we construct two separate index values: an absolute value and a relative value. The MEI (absolute value) captures both national and regional factors driving Midwest growth, while the relative MEI (relative value) provides a picture of Midwest growth conditions relative to those of the nation.

A positive value of the relative MEI indicates that regional growth is further above its trend than would typically be suggested based on the current deviation of national growth from its trend, while a negative value indicates the opposite. To obtain this interpretation for the relative MEI, we use the standardized residuals from linear regressions of each of the 128 indicators on the CFNAI-MA3 to construct the index.[3] This construction accounts for differences in national and regional growth trends and volatility that prohibit comparisons of magnitudes in the figure above.

Figure 4 shows the relative MEI in comparison to the CFNAI-MA3. The unit of measurement is again standard deviation units, so that a value of 1 for the relative MEI indicates that Midwest growth is 1 standard deviation greater than would typically be suggested given the level of the CFNAI-MA3. This figure shows that the Midwest business cycle was particularly pronounced during and after the recessions of the late 1970s, early 1980s, and 2007–09.

Other significant periods in which Midwest growth deviated substantially from national growth include the 2001 recession, which more adversely affected the Midwest region, and the 1990–91 recession, which was milder regionally but was preceded by a period of relative weakness.

Contributions to growth by sector and state

Additional information on the sources of growth in Midwest economic activity can be found by decomposing the MEI and relative MEI into contributions from the four broad sectors of the Midwest economy. The figure below plots the time series of these contributions over the history of the index.

Much of what we see in this figure can be summed up by the following: When manufacturing has thrived, so has the region. However, the contributions of the service sector to Midwest growth over time have become increasingly important. Consumer spending indicators show a similar pattern, making sizable contributions at business cycle peaks and troughs. Finally, the region has historically been less prone to large fluctuations in growth coming from the construction and mining sector than other parts of the nation.

Looking at regional versus national growth, the manufacturing and service sectors explain the vast majority of movements in the relative MEI. However, the contribution of services to the relative MEI is larger than it is to the MEI and nearly equal to that of manufacturing. This feature of the relative MEI reflects the importance of the service sector during periods where the Midwest economy has expanded or contracted faster than the nation. A good example of the former is the early to mid-1990s, while the early to mid-2000s exemplify the latter.

Using only the indicators for the respective states in the Seventh District, we construct state-level contributions to the MEI and relative MEI.[4] Figure 7 plots the time series of these contributions over the history of the index. No single state dominates growth in Midwest economic activity, although Illinois tends to make the largest contribution to both; and growth trends in nonfarm business activity across states are similar, with the exception of the weakness of the Michigan economy over the past decade.

During the recent recovery, all five Seventh District states have made positive contributions at one time or another to the MEI and relative MEI, suggesting that the manufacturing-driven recovery has benefited the region disproportionately. In the future, we invite you to track these developments through the charting utility for the Seventh Federal Reserve District found here.


[1] See, for instance, Theodore M. Crone and Alan Clayton-Matthews, 2005, “Consistent economic indexes for the 50 states,” Review of Economics and Statistics, Vol. 87, No. 4, pp. 593–603. (Return to text)

[2]J. H. Stock and M. W. Watson, 2002, “Forecasting using principal components from a large number of predictors,” Journal of the American Statistical Association, Vol. 97, No. 460, pp. 1167–1179.(Return to text)

[3]Every indicator is regressed on the contemporaneous value of the CFNAI-MA3. Some indicators are also regressed on the lagged value of the CFNAI-MA3. These indicators are chosen based on the Bayesian Information Criterion, which balances the explanatory power gained by including an additional lag of the CFNAI-MA3 in the regression against the uncertainty introduced from the estimation of an additional parameter.(Return to text)

[4]A handful of indicators exist only at a regional level. To construct state contributions, we omit these variables. Therefore, the state contributions do not sum to the overall index in each period.(Return to text)

Can the Great Lakes Region Break Free of its Long-term Slide?

How can we best understand and adapt to the Region’s long term changes? There are three trends that are fundamental to assessing the Region’s economic behavior and prospects. One is the region’s sharp sensitivity to the national business cycle. The region continues to specialize in manufacturing, especially durable goods sectors such as automotive and machinery. For this reason, the region exhibits above-average swings in employment and business activity as the U.S. economy falls into a recession or recovers afterward. The second trend behavior concerns the Region’s long term re-structuring out of manufacturing. Here, it is not so much that the Region’s manufacturing specialization has abated. Rather, since manufacturing productivity gains give rise to fewer workers, and because consumer demand growth for manufactured goods does not compensate for rising labor-saving productivity, the region’s economic base does not keep pace with the nation. Finally, from decade to decade, the Great Lakes Region has experienced pronounced deviations from its overall growth trend. Quite independently of the long term manufacturing trend, and independently from the two recessions of the 2000s, the boom years of the 1990s have given way to longer term realities. In retrospect, the 1990s experience are an aberrantly prosperous time from which to extrapolate the Region’s future. In order to accurately gauge the Great Lakes Region’s challenges and prospects, all three perspectives must be integrated. As a whole, the Regions outlook remains challenged, but there are also opportunities and possibilities for revived prosperity.

Great Lakes and the Business Cycle

Both the nation and the Region’s employment base have been shifting out of manufacturing and toward service sectors. However, this does not mean that the Region’s economic base no longer rests firmly in manufacturing. In relation to the United States, the Region’s employment specialization in manufacturing has perhaps sharpened in comparison. As shown in Table 1, construction of an index of specialization shows some surprising evidence to this effect. Because farm employment in the region consolidated prior to regions such as the Southeast, farm concentration has sharpened here in relation to the nation. The same can be said of manufacturing—both the durables and non-durables sectors. By 2009, both lie well north of average, with durables concentration at 79 percent above the nation. Nondurables has meanwhile climbed to 34 percent above the nation. Here, food processing employment is the Region’s bulwark while, elsewhere, nondurable staples such as textiles have tended to shed jobs.

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Watching for Job Rebounds

According to economists’ reckoning, the recession very likely ended this past summer, meaning that the U.S.’s national output has begun to grow once again (from a deep trough). But for many households, the direction of recovery has much more to do with job growth, job opportunities, and hours worked. And so, the job watch is on. We continued to lose jobs during 2009, but employment is expected to begin to grow again by mid-year 2010. At some point soon, businesses will no longer be able to expand their production of goods and services without hiring. In anticipation, analysts and some households alike are watching current indicators of labor market activity, such as monthly payroll job counts, along with early or leading indicators of labor market turnaround that might foreshadow rising job opportunities.

There are several such indicators available to states and regions. As a measure of current net job growth, the Bureau of Labor Statistics (BLS) releases state estimates with a three-week lag from the previous month, and metropolitan area estimates with a four- to five-week lag. Recent total nonfarm payroll job trends (below) show that job growth in the U.S. has generally been stronger than in the Midwest (and declines have been more severe).

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Estimates of total payroll employment are derived from a sample of business establishments that report on employees, hours, and earnings of their workers. The estimates are revised one month following their initial release to include information from late-reporting firms. Since the estimates are based on just a sample of firms, they are also benchmarked (once per year) to a near-universe of business establishments. For example, BLS reports that “the average absolute benchmark revision at the state total nonfarm (jobs) level was four-tenths of a percent (0.4%) in March 2008. The range of percentage revisions across states were –2.8 percent to 1.5 percent.”

Given such imperfections in accuracy and timing, alternative indicators are eagerly watched, especially if they have exhibited some previous power in “leading” or predicting past job market turnarounds.

Among the most timely current indicators is “initial claims for unemployment insurance”. After a one-week “waiting period,” newly terminated workers may file for umemployment insurance (UI). These claims are compiled weekly at the state level and reported in a news release by the U.S. Department of Labor. UI claims are an incomplete picture of the job market in several ways. For one, not all workers are covered by, or eligible for, UI insurance. But most importantly, from a conceptual standpoint, UI claims reflect only “job destruction” and not “job creation.” Newly hired workers are not counted in this administrative-type data.

For the U.S. overall, UI claims have generally been falling. Initial UI claims peaked above a weekly average of 650,000 (seasonally adjusted) for March of 2009. By January of 2010, UI claims had fallen to 467,000. Still, these numbers remain higher than in the recent past. For all of 2007, UI claims averaged 322,000 per month.

The charts below illustrate the recent labor market improvement, as measured by initial UI claims, for the Seventh District states of Illinois, Indiana, Iowa, Michigan, and Wisconsin.[1]
The lines show the time path of UI claims filed for each of the years 2007–09 and January of this year.

State experiences have varied somewhat, though with a similar general pattern. All states experienced a significant surge in claims during the second half of 2008 (blue line), especially during the fourth quarter. Job destruction worsened into the first half of 2009 (red line). But by the fourth quarter of 2009, UI claims had fallen below those of 2008 in every District state. Nonetheless, for the fourth quarter of 2009 and into the first month of January, UI claims in Iowa, Wisconsin, and Illinois remained well above those experienced during in the same period in 2007.

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Other indicators also help to presage rising job creation. Observations of “help wanted advertising” measure the demand for labor, indicating soon-to-be-filled employment transactions. The Conference Board tracks national and state online job vacancies on a monthly basis. Their recent release reports a third consecutive month of strong national gains in advertised vacancies. The gains were widespread across U.S. regions, including the Midwest. Ohio was among those states recording the strongest January over December gain since the data series began in 2005. Seventh District states Illinois, Michigan, and Wisconsin all recorded monthly gains of 10 percent or more.

Analysts also follow the “contingent worker” or so-called temporary help employment series to foreshadow an eventual upswing in total nonfarm employment. In situations where general business conditions are improving but still somewhat weak, and before employers are willing to commit to permanent hiring, firms may begin to contract for workers from specialized employment services firms. Employment at such firms is sufficiently prevalent in four of our District states so that monthly data are reported by the BLS. As shown below (red line), employment of temporary workers grew in the second half of 2009 in Michigan, Indiana, Illinois, and Wisconsin.[2]

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As another measure of potential labor market tightening, the BLS also reports average weekly hours worked of production and nonsupervisory workers for states and metropolitan areas. During slack production times, firms may choose to ratchet back their employees’ hours rather than to lay them off entirely. If so, once production begins to pick up, and before any new hiring takes place, firms will tend to add back work hours for existing employees. Currently, the BLS reports these data for manufacturing workers only.

As seen in the charts below, average weekly hours worked has begun to rise in the U.S. (blue line) and in each District state (red line) except Illinois. Though the manufacturing sector has experienced sharp declines over the past two years, it has begun to recover early in the aftermath of the 2008–09 recession.

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Local chapters of the Institute of Supply Management (ISM) also track manufacturing employment through their monthly survey of purchasing managers. ISM Indices are directional only (they do not measure levels), indicating expansion versus contraction of various categories of business activity at their establishments. A reading of 50.0 indicates that, during the previous month, activity was equally balanced between expansion and contraction; readings above 50.0 indicate a greater reported tendency of establishments to expand. The Chicago ISM survey reported at the end of January (for December) that its reading on employment “leapt to the highest level in nearly five years.” In contrast, the ISM survey for Southeast Michigan reported “declines for the third straight month, continuing its downward spiral to (a reading of 36.0).

What to make of these many indicators? An understanding of the wide array of labor market indicators can provide both workers and the unemployed with a better “read” on when labor market opportunities are arising or when they will improve in their region. In contrast, the most followed labor market indicator, local unemployment rates, can be misleading. As more jobs open up, some workers who have been waiting on the sidelines (and not hunting for jobs) will actively seek employment once again. Once they are actively looking, the survey from which the unemployment rate is calculated begins to count them as “unemployed,” thereby raising the unemployment rate even as net job growth is taking place.

And so, in the coming months, the unemployment rate in both District states and the U.S. may continue to climb, even as the labor market situation may be improving in some locales and industry sectors.[3]


[1] It is also possible to gather UI claims at the local level of geography. For example, initial claims for metropolitan areas are gathered and reported in Ohio. (Return to text)

[2] These data are also reported for metropolitan areas. These data are also subject to annual revision in March of every year. The data are benchmarked to March of the previous year and projected forward from that benchmark. Hence, the data are re-benchmarked once again the following year, once another benchmark is established (Return to text)

[3] Both the Bureau of Labor Statistics and state and local government agencies provide additional indicators for specific occupations, industry sectors, and local areas. (Return to text)

Midwest in Recession: Then and Now

By Bill Testa and Vanessa Haleco-Meyer

Longtime Midwest residents may be befuddled by ongoing comparisons of the current national recession with those of 1974-75 and 1981-82. While the headlines suggest this recession compares, so far, with the deepest recessions of the past 50 years[1], we in the Midwest have a somewhat different perspective. For us, the recessions of 1974-75 and 1981-82 were far worse, at least so far. An exception may be made here for Michigan, which has been experiencing a recession of sorts all decade long.

Statistical comparisons of regional recessions with the nation are difficult for a number of reasons. Arguably, the best basis of comparison can be made using payroll employment data which are available monthly from the Bureau of Labor Statistics.[2] In the charts that follow, we index job levels in states, the Seventh District (Illinois, Iowa, Indiana, Michigan, and Wisconsin), and the U.S. to a beginning value of 1.0. We begin the time series at the quarter in which employment levels peaked in the state, region, or nation. Since employment peaks may differ between a state or region and the U.S., we sometimes begin comparative series at slightly different dates. For example, employment in the Seventh District last peaked in the second quarter of 2007, but the U.S. peaked in the fourth quarter of 2007. (On the charts, the indexed lines will appear to begin in the same quarter). We use seasonal adjustment to iron out variations in employment that typically occur every year.

The chart below compares payroll job growth for the Seventh District versus the U.S. during the 1974-75 downturn, the 1980s downturn(s), and the 2008 downturn. The U.S. economy officially recorded two back-to-back recessionary periods in the early 1980s. Since the episodes took place so close together, and since the Midwest experienced virtually no pause between downturns, we index jobs beginning from the previous peak (1980-Q1 for the U.S. and 1979-Q2 for the Seventh District) through to the final trough.

In examining payroll job performance during these recessionary periods, the first thing to note is that payroll employment dropped more rapidly in the 1974-75 recession (blue lines) than in subsequent recessions. Seventh District payroll job levels fell by 4% in the four quarters following their peak in the third quarter of 1974 (before turning upwards). In comparison, and despite the dramatic declines over the past few months, the current recession has experienced a shallower and slower decline from the previous employment peak (green lines).

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Recent job declines have also been shallower so far than the fairly dramatic declines the Midwest experienced in the 1980s (red lines). After reaching a peak in 1979, payroll jobs in the District fell for four years, reaching bottom in the first quarter of 1983 at 10% below the peak. The U.S. experience of that time was quite different. Following a slight decline in 1980, national employment growth resumed briefly before falling 3% during the 1981-82 recession. Over the entire length of both recessions, the pace of job decline in the Seventh District was more than five times that of the nation.

The dismal experience of having no post-recession recovery is one that the state of Michigan is now experiencing. The chart below indexes payroll job decline and growth circa the 2001 recessionary period. From its second quarter peak in year 2000, Michigan’s employment has fallen by over 10% (green line). The remaining states of the Seventh District—Indiana, Illinois, Wisconsin, and Iowa—have fared somewhat better, but in the aggregate the four-state region only recently regained its previous peak. In contrast, national employment had regained its previous peak by the end of 2004.

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The final charts (below) display the employment experiences of each Seventh District state for the three aforementioned periods. In each state, the 1980s look worse than the current recession. This is even true for Michigan, which underwent a 15% job decline from its peak in the second quarter of 1979 to the fourth quarter of 1982. However, Michigan and its troubled automotive industry enjoyed a big bounce in 1982 when U.S. consumers returned to auto showrooms and began to buy cars at a rapid pace as gasoline prices eased. This time around, Michigan and much of the surrounding Midwest automotive belt hope for a repeat performance. However, Michigan’s current automotive challenges are surely more structural and deeply rooted. It will take more than an upturn in national automotive sales to pull along the state’s employment and income.

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[1]The nation also experienced less serious downturns, during 1969-70, 1990-91, and 2001. See (Return to text)

[2]Payroll employment numbers are subject to revision in March of every year. See (Return to text)

Manufacturing Headwinds Strengthen

The manufacturing sector exerts an outsized impact on the Midwest economy—especially during cyclical downturns. Regional jobs and income are approximately 30 percent more concentrated in manufacturing in the Seventh District than in the nation as a whole. The District’s economy is even more concentrated in durable goods production–both capital goods, such as machinery, and consumer durables such as autos and appliances. Both capital and consumer durables are highly sensitive to cyclical swings. In times of economic contraction, businesses slow their purchases of capital equipment as they struggle with production overcapacity in relation to their current sales. Meanwhile, households slow their purchases of durable goods as they increase their precautionary savings to meet possible loss of jobs and income.

Earlier in the current recession, manufacturing activity had been having a somewhat subdued regional impact. The chart below compares the year-over-year growth of industrial production in the District and the nation during the last two recessions (recessions are indicated by the shaded vertical bars). By way of contrast, in the months leading up to the 2001 recession, production and manufacturing employment began to fall in advance of the recession. During the period leading up to that recession, very strong national and global investment in IT equipment and plant capacity took place. Specifically, the so called “Y2K” effect, coupled with buoyant world growth in response to emerging Internet innovation, spurred investment across many durable goods sectors. During the current recession, manufacturing activity held up relatively well through the first two quarters of 2008 before dropping sharply later in the year.

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A closer look at this decline by broad sector would show that 2008’s production strength resided in the capital goods and machinery sectors. In both the Chicago Fed Midwest Manufacturing Index and the national index of production, machinery production remained almost flat. In contrast, slides in automotive production coincided with the general downturn in business activity. In fact, District automotive production fell sharply during the first half of 2008 in response to the gasoline price spike, which depressed sales of the larger vehicles in which the District’s producers tend to specialize.

Meanwhile, ongoing growth in the global economy bolstered capital goods and machinery purchases. Developing nations such as China and India have become important customers for U.S. capital goods, and their continued growth contributed to U.S. export growth. At the same time, high prices for farm, energy, and other commodities also spurred foreign demand for U.S. manufactured mining, construction, and farm equipment. As commodity prices fell off of their mid-summer peaks, so did both domestic and foreign demand for such equipment. So too, as the financial crisis worsened in the fall, and spread to other parts of the world, U.S. exports abroad began to ease. This can be seen below in the two charts of manufactured exports. Year over year through the fourth quarter of December of 2008 (3 month smoothed), exports fell by 4.2% in the nation and by 3.6% in the District.

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Note: Emily Engel contributed to this blog entry.

Seventh District Labor Markets at Year-end

by Bill Testa and Vanessa Haleco-Meyer

Government agencies regularly report statistics that reflect state and local labor market conditions. These measures are far from perfect in their accuracy, and they often seem to conflict. Yet, these measures currently agree to a negative view of the labor markets in the Seventh Federal Reserve District.

State unemployment rates, using a household sample survey, measure those people of working age who are actively looking for work as a fraction of the work force (both employed and unemployed). Since it is sample based, the measure is imprecise, especially readings for a single given month. The chart below shows that the unemployment rates for the nation and the Seventh District began to move up moderately off of their cyclical lows throughout 2007. During 2008, the unemployment rates accelerated primarily because of net job destruction. The gap between the Seventh District’s higher unemployment rate and that of the nation remained fairly steady in recent years, even as unemployment rates were climbing in each of the District’s states and in the nation as a whole.

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Not all states of the District maintained a higher-than-average unemployment rate over the past few years. As measured in the fourth quarter of 2006 (chart below), Michigan’s high unemployment rate accounted for the bulk of the gap between the District’s rate and the nation’s. By the fourth quarter of 2008, Illinois’ unemployment rate had climbed above that of the nation, and Indiana’s unemployment rate also topped the national average. In contrast, Iowa’s and Wisconsin’s rates of unemployment in 2008 were seemingly lower than those of the overall District and the nation.

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Federal and state government agencies also track and report payroll employment. These data, released on a monthly basis, are drawn from a sample survey of firms that provide information on their employees; and so, unlike the unemployment figures, the data are not counting those in the work force who are self-employed. Since it is only sample-based, the payroll survey, too, contains measurement error. These errors tend to be more pronounced during times of sharp turns in economic direction (such as the present). During economic downturns, some firms may drop out of the sample as they cease operations. This has tended to understate net job declines, since the sampling methods cannot distinguish between a failed firm and one that is simply late or negligent in reporting. State payroll figures are adjusted for such biases during the first quarter, but even with such adjustments, revised figures do not cover the recent months, but are rather “re-benchmarked” up to a point early in the previous year.

The chart below displays the change in total payroll jobs in the fourth quarter of 2008 relative to fourth quarter in 2006. All states except Iowa lost jobs on net. Over much of this two-year period, Iowa continued to enjoy a boom in farm commodity prices and strong production and sales of related equipment. In the chart, job losses in Michigan and Indiana are especially prominent, reflecting their troubles with their automotive sectors. Using this measure, Wisconsin’s job losses seem to be more severe than what its unemployment rate may have suggested.

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Labor market indicators often conflict both because of inherent measurement error and because they measure different features of the labor market.[1] Accordingly, it is often best to gather an array of indicators in assessing labor market conditions. Reported figures from each state’s unemployment insurance (UI) system are also followed. Each state’s UI system records weekly data on new applications or claims for insurance by those who have recently lost their jobs. (Data also report the number of people who have lost jobs and continue to receive unemployment benefits.) These data do not comprehensively reflect labor market conditions. That is because layoffs or other job separation events are only part of the process of net job gain or loss. In particular, job hires or emerging self-employment may be taking place in a state at the same time that job separations are on the rise. The chart below displays changes in initial claims for UI in the fourth quarter of 2008 relative to the fourth quarter of 2006. As compared with the final quarter of 2006, layoffs and other involuntary unemployment events were emerging much more rapidly in late 2008. This is so in the nation and in each of the District’s states. Indiana’s job separations were running especially high late in 2008 as compared with the fourth quarter of 2006—well in excess of the increase experienced nationally. And separations in Iowa have also begun to rise sharply in the fourth quarter.

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The severity and speed with which labor markets deteriorated during the final three months of 2008 has been especially disconcerting. In the District, jobs declined at a 5.1% pace. Nationally, payroll jobs declined at a 3.7% annualized rate during the fourth quarter of 2008 (1.3 million). Since 1960, payroll job decline in the nation has exceeded this pace in only one quarter, that being the first quarter of 1975 (-6.1%). And currently, most forecasts predict economic output to bottom out no sooner than the second half of this year.

Such concerns are especially acute because job markets recovered slowly in the aftermath of the past two national recessions. Slowly recovering job markets often reflect structural imbalances that have preceded and accompanied recessionary periods. The 2001 recession partly reflected the fallout from overspending on technology-oriented enterprises, such as telecommunications, and other capital equipment. Workers displaced from these sectors might have found it difficult to find jobs in new industries, or the impacted sectors themselves were slow to recover and begin hiring anew. This time around, sharp structural imbalances in housing construction and financial services are underway.

Imbalances that can emerge among different multistate regions in the U.S. can also play a role in achieving “full employment.”[2] An industry shock to a particular sector that is highly concentrated in one region may displace workers whose job opportunities may be emerging in another region. Past Midwest experiences are a case in point. The region suffered inordinately through the double-dip national recessions of 1980 and 1981–82. The chart below compares the District’s unemployment rates with those of the nation from two periods: the 1980s and the current decade. By the end of 1982, the nation’s unemployment rate approached 11%, while the District’s unemployment exceeded 13%.

This wide gap of the early 1980s came about from underlying currents having distinct geographical accents. In particular, high oil and natural gas prices were buoying energy exploration activities in many parts of the West and Southwest; rapidly expanding federal spending to rebuild national defense stocks were lifting many regions of the South and West; and the rapidly rising value of the U.S. dollar contributed to moribund exports of farm products and manufactured goods from the Midwest (as well as to stiff import competition).

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In contrast, the recession of 2001 and its immediate economic aftermath had fewer inter-regional differences. As seen above, unemployment rates between the District and the nation were very similar. As the remainder of the decade unfolded, however, the profound structural changes going on in the automotive industry did begin to negatively affect District labor markets; the District’s unemployment rate began to rise higher relative to the national average. The Detroit Three automakers (Chrysler LLC, Ford Motor Co., and General Motors Corp.) and their suppliers experienced significant losses to foreign-domiciled auto plants located in other regions and to imported automotive products as well. While (post-2001) job levels largely recovered in the District, Michigan experienced continuous year-over-year job losses.

Now, amid a sharp regional downturn, employment statistics will be keenly watched to help guide our decisions regarding job search, education and training, local investment, home sales, and migration.


[1]Note: Unemployment rates do not necessarily reflect job trends because working age people can drop out of the work force in response to a lack of job opportunities, thereby lowering the unemployment rate, even though payroll jobs and job vacancies are falling. A worker who drops out of the labor force no longer reports as being “unemployed” in the survey. The reverse can also take place by the same reasoning: Even with a rising number of jobs and employed persons, there can be rising unemployment. (Return to text)

[2]Note: Comparing levels of unemployment between periods can be somewhat difficult. The “natural rate of unemployment,” or normal benchmark for a “full employment economy,” is thought to have been higher in the 1980s than today—by about 1 to 1.5 percentage points. The natural rate depends on demographics of the population, such as age and education (affecting labor force participation rates by age). For a discussion, see study by David Brauer, among others. (Return to text)

Growth and Great Lakes Cities

For half a century or more, the industrial belt of the Great Lakes and Midwest has lagged counterpart regions in much of the South and West. Large midwestern metropolitan areas arguably offer the best prospects for relief from this historical pattern. The reasons are rooted in a fundamental restructuring of the global economy that favors cities. In underdeveloped countries, rapid urbanization and the emergence of large cities have gone hand in hand with economic growth and progress. And in developed countries on all continents, two factors have lifted growth opportunities for large cities. Foremost, technological gains in transmission of information have intensified the productivity of cities because of their role as meeting places. Face-to-face communication complements digital information flows. As business people can more easily transmit and receive information via electronic devices, their time has been freed so that they can engage more intensely and broadly in in-person dialog and social interaction. In other words, carrying one’s office in the palm of one’s hand allows one to leave the physical office to better explore opportunities and ideas. Cities tend to maximize these encounters in person. Enhanced and cheaper air travel lends a helping hand.

A second factor, the opening of global trade and capital markets, has increased the possible scale and opportunities for large cities. Cities tend to function best in managing and administering far-flung markets. More open and intensive global trade has tended to broaden the reach and scale by which successful cities can perform such functions in finance, advertising, research and development, law, and company management. For this reason, some analysts believe that they can identify the emergence of “global cities” that have succeeded in such opportunities.

To date, large cities of the Great Lakes have not fully benefitted from these “new economy” trends. Migration to regions with warmer climates has slowed these cities’ work force and population growth—a trend also reflected throughout the remainder of the region. But more fundamentally, many if not most of the region’s large urban economies were built not on the service industries that benefit from the ongoing global changes, but rather on the manufacture of goods and associated freight transportation. These cities’ transition to services and knowledge-based economies has proven difficult because manufacturing-oriented places must overcome and replace larger portions of their economic base. Manufacturing-oriented income in the region has withered because of global competition, falling real prices for manufactured goods, and technical advances that have allowed goods to be produced with less labor. To these obstacles, technical changes in the production processes themselves may be added: Such changes have made the more-densely populated parts of large cities especially difficult places in which to manufacture, compared with those far suburban and rural places, where land is cheap and the transportation of materials is more convenient. The growth-retarding effect from manufacturing on U.S. metropolitan areas over the 1960–90 period has been documented in a statistical study by Edward Glaeser.

Have the relative growth rates of midwestern metro areas coincided with the degree of their original manufacturing orientation? The charts below display employment concentration in manufacturing for the eleven largest metropolitan areas in the industrial belt on the vertical axis. The horizontal axis displays each metropolitan area’s total job growth on the first chart and real per capita income growth on the second chart. The inverse correlation of economic well-being with initial manufacturing concentration is quite evident. A simple correlation between job growth from 1969 through 2006 and the manufacturing orientation in 1969 is a strongly negative 0.8. Similarly, the correlation between manufacturing and per capita income growth is -0.7.

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What might be some other reasons behind varying performance of these metropolitan areas? For one, even within the manufacturing sector, industry mix (and related performance) varies markedly. For example, the Twin Cities’ manufacturing base included emerging medical instruments and computer equipment during this time period, while Detroit hosted sagging domestic auto production.

Other observers wonder about the role that the metro core or central city has played in its relative growth and development. Due to marked suburbanization within metropolitan areas, and fixed central city boundaries, some cities such as Cleveland and St. Louis became relatively small islands of population; today, the city population accounts respectively for only 20.9% and 12.5% of these two metropolitan areas. As such, cities such as these were left largely alone to provide public services to low-income populations—and to do so with a rapidly diminishing tax base. Accordingly, some researchers speculate whether growth and development suffered as a result of this trend—not only in the city but in the entire metropolitan area. In contrast, central city Columbus and Indianapolis began with a broader geography and richer tax base with which to provide public services and development-oriented infrastructure.

While Midwest cities have many challenges to overcome, there are also assets on which to build. As widely shown and increasingly recognized, the most important overall determinant of regional growth performance has been the educational attainment of its population and work force. This is not surprising given the structural changes that have taken place in the emerging economy—changes which place a greater emphasis on information exchange and the development of creative ideas. For Midwest metro areas, and as discussed by Timothy Dunne in a recent Economic Commentary, educational attainment may be more important than for other regions. To succeed in overcoming the shocks that rocked their industrial bases, educational attainment in Midwest metro areas may have been most helpful in adaptation and re-invention. Tim Dunne displays charts similar to those above which indicate a weaker correlation between educational attainment and growth in warm weather metro areas as compared to cold weather climes. In considering educational attainment of the populations, the table below displays the ranks of Great Lakes metropolitan areas among 118 metropolitan areas in 1970 and 2006. The two local leaders in 1970 college attainment, Columbus, Ohio, and the Twin Cities also experienced the fastest employment growth. While Pittsburgh ranked low in college attainment in 1970, its gains in this metric since then have been the most rapid. Perhaps not accidentally, Pittsburgh’s growth in per capita income also outpaced other cities in the region.

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As for policy, while the region’s goods-producing industry mix has left behind a legacy of a slow-growing industrial base, the region also boasts top-notch colleges and universities. With regard to elementary and secondary education, the region maintains a healthy income base with which to support its schools. Similar to most other parts of the country, the region’s educational challenges are to have its students to perform much better, especially in central cities and lower-income communities.

Note: Vanessa Haleco-Meyer contributed to this weblog.

Supplemental Information:

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Autos: A Further Loosening of the Manufacturing Belt?

This year’s Nobel Prize in Economics was awarded to Paul Krugman for his insights into spatial concentration of economic activity and the relationships among industry clusters, firm or industry-specific economies of scale, and patterns of international trade. In illustrating the flavor of his theoretical work at his Nobel Prize lecture, Krugman explained the surprising prevalence of worldwide trade among goods within the same general product category. Such trade can arise from acute economies of scale in production that are achieved by firms or industries that produce slightly differentiated products. If accompanied by the ability to easily transport and widely export its products, the location of a firm’s product or of an industry’s production will often become quite concentrated and rooted in particular countries or regions. By way of illustration, the 1860-1970 era of Midwest-Northeast dominance in manufacturing was said to arise from vast scale economies of mass production that came into play during the 19th century, accompanied by sharply lower transportation costs (via railroad) which allowed the manufacturing belt to export its wares to other U.S. regions and to the world. Krugman ended the lecture by discussing how the manufacturing belt had finally been shifting away from the Midwest, most recently the automotive segment. To do so, Krugman drew on the work of our Bank’s Thomas Klier.

In a series of journal articles and a recent book, Klier has been documenting and explaining this shifting geography of the North American automotive industry. Such work helps us to understand the situation of the “Detroit” automotive industry today, as does the more general spatial clustering of some industries and firms that has been observed by Paul Krugman and others.

Today in the industrial Midwest, we lament the tarnished star of wealth and income that once elevated our region’s standards of living above those of many other regions. During the glory years of Midwest manufacturing, the Great Lakes region’s share of manufacturing was phenomenally high relative to its population share. The chart below illustrates the rise and sustained dominance of manufacturing activity in the region. It is remarkable that the region sustained this high share of manufacturing, and high per capita income (shown below), even while population was ebbing away to the South and West.

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Per Krugman, manufacturing gained a foothold here as profound economies of scale in industries such as steel and meat packing grew rapidly, along with the ability to export these goods by rail. William Cronon documents these transport advantages for 19th century Chicago for meat packing, farm machinery and lumber in his celebrated book, Nature’s Metropolis. Manufacturing also thrived here due to ready access to abundant natural resources and energy inputs to production, along with the ability to feed, house and transport a large work force to work site factories in the cities.

Once established, the spatial proximity of firms and related industries helped to sustain the region’s manufacturing dominance, as the totality of these firms and industries became greater than the sum of their parts. That is to say, the Midwest’s manufacturing sector became highly productive in part because firms and their suppliers bought and sold to one another in close proximity. Steel makers sold to machinery producers; machinery producers sold to car and truck makers; car and truck makers sold to both steel and machinery producers. These efficiencies played out at much finer detail among many highly specialized suppliers and producers. In this way, transportation costs were minimized and economies of scale and scope were realized within the tight agglomeration of the manufacturing belt. So too, not unlike Silicon Valley of today, mutual proximity created a sharing and dissemination of new ideas and technology that gave producers a leg up in locating within the Midwest.

Klier has researched these spatial relationships within the Midwest automotive industry—both parts and finished vehicles. For much of its history (but with several major eras that either stretched or compacted its geography), North American automotive production has clustered in Michigan and neighboring states, enjoying the insulating benefits of great economies of scale in mass production and mutual proximity of parts suppliers within the industry, as well as proximity to Midwest steel making, machine tooling and other key industries … all the while enriching generations of automotive workers.

Of course, things look very different today, as the Detroit 3 automakers struggle to remain viable in an era of increased competition for dwindling consumer dollars. In part, competition has shaken loose the original industry and its Midwest geography as imported autos finally broke through into the U.S. consumer market during the 1970s gasoline crisis. Foreign-domiciled competitors have since chosen to produce autos on U.S. soil, though not exactly with the same geographic footprint as the original Detroit 3 auto makers. In their recent JRS article, Klier and coauthor Dan McMillen document how the older spatial cluster of automotive parts makers has been giving way to a re-fashioned but densely configured auto cluster sited further South.

This shift in location raises some questions: How do we explain this shift southward? Could (or should) anything have been done about it? Can anything be done about it now? No doubt the cost advantages of spatial proximity and the early economies of scale in automotive manufacturing were highly advantageous. At the same time, however, the very success and unchallenged structure of the domestic industry may have failed to keep the region’s institutions, policies and companies sharp and competitive.

Exports and the 2008 Economic Slowdown

Back in the 1930s, policy makers perhaps contributed to the economic downturn by sharply lifting tariffs on imports into the U.S.—the infamous Smoot-Hawley legislation passed on June 17, 1930 that raised import tariffs on over 20,000 goods. In response to these policy actions, our trading partners raised tariffs (and nontariff barriers) on U.S. exports. If the U.S. intention was to keep jobs at home, the effect was probably to aggravate unemployment here and abroad.

In recent years, trading activity with other nations has been a definite engine of growth for the U.S. Exports are contributing much to an otherwise faltering pace of economic growth. Though exports comprise only 12.4 percent of U.S. output, export growth accounted for one-half of the nation’s (2.0) percent GDP growth in 2007; exports accounted for one-third of GDP growth over 2006 and 2007. Indeed, in every year since 2002, the growth of exports has added at least one-half percentage point to national output growth.

Per the graph below, export growth has similarly lifted incomes and output in the Seventh District. Overall, nominal export value climbed by $54.7 billion, or 58.7 percent, from 2003 through 2007, with every District state joining in the expansion. Per the table below, our NAFTA partners, Canada and Mexico continue to be our largest export destinations, with China growing rapidly over the 1997-2007 period.

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The strong role of our NAFTA partners as an export destination reminds us that growth in trade often comes about from the hard policy work involved in negotiating trade agreements with other countries. The graph below illustrates the growing number of countries that now receive exports from producers in Seventh District states. Each District state has added a fair number of trading partners since 1997.

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Aside from avoiding the (past) mistake of squelching international trade, the U.S. also has the opportunity to expand its export opportunities in the years ahead. Awaiting enabling legislation from the U.S. Congress, bilateral or two-nation agreements have been negotiated with Panama, Columbia and South Korea. To learn more about these agreements and those that have preceded it, one source of information is (this web site is a joint effort between the Departments of Agriculture, Commerce, State, Treasury and the Office of the United States Trade Representative).

Note: Vanessa Haleco-Meyer contributed to this weblog.

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.

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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.


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.


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.