Job growth in the Seventh District in 2016—A post-benchmarking update

Here at the Chicago Fed, we closely track one of the most important regional economic indicators, the U.S. Bureau of Labor Statistics’ (BLS) payroll employment survey (also known as the Current Employment Statistics, or CES). The survey is important because it provides a good picture of the overall state of an economy and its initial results are released quickly (unlike some other regional data that are released with a lag of a month or more). Unfortunately, the relatively quick turnaround also means we must exercise caution. While we get an initial estimate only three weeks after the end of the reference month, the estimate can sometimes be revised substantially later on. The BLS makes minor revisions to the data one month after the initial release and major revisions once a year, when the survey is benchmarked to the unemployment insurance census (released as the Quarterly Census of Employment and Wages, or QCEW).[1]

The BLS recently released newly benchmarked data, so we now have an update on how well the Seventh Federal Reserve District[2] did in 2016. Table 1 shows that, for the most part, the job growth numbers that the BLS had been reporting before the most recent benchmarking held true. Overall, Seventh District employment grew at a pace of about 1 percent in 2016, with the BLS reporting a 0.9% increase before benchmarking and a 1.1% increase after. In addition, the most recent benchmarking further affirmed the division in performance between the eastern and western parts of the District over the past few years. Job growth for Indiana and Michigan in 2016 was revised up, whereas job growth for Illinois, Iowa, and Wisconsin was revised down. So now, the job growth numbers for Michigan and Indiana in 2016 easily exceeded the numbers for Illinois, Iowa, and to a lesser extent, Wisconsin.

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The most recent benchmarking by the BLS also gives us the opportunity to see how well we did at predicting the benchmark revisions using a method known as “early benchmarking.”[3] The main idea behind early benchmarking is that it’s possible to use the QCEW data, which are released quarterly, to predict the major annual revisions to the job growth numbers.

This year we found that, unlike last year, the early benchmarked estimates (which we produced in January 2017) were further from the newly released job growth numbers than the March 2016 benchmarked CES estimates were. Our only consolation is that the early benchmarked estimates did get closer to the newly released numbers for the first half of 2016. I’ll explain what happened a little later.

Let’s look first at how the early benchmarking procedure did for the District as a whole. Figure 1 shows three versions of the Seventh District’s CES data, where the solid portion of a line represents data that have been benchmarked using the QCEW and the dashed portion represents data that have not.[4] The black line is the version released in March 2017 (with benchmarked data through September 2016), the red line is the early benchmarked version that we calculated in January 2017 (with benchmarked data through June 2016), and the blue line is the version released in March 2016 (with benchmarked data through September 2015). Surprisingly, in spite of nine months of additional benchmarked data, the March 2016 benchmarked CES appears to track the newly released data better than the early benchmarked CES.

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The first row of table 2 summarizes figure 1 in terms of December-to-December job growth for 2016. For this period, the early benchmarking method performed notably worse: It underestimated District job growth by 83,000, while the March 2016 benchmarked data underestimated job growth by only 25,000. The remaining rows of table 2 summarize 2016 job growth for District states (figures plotting the data for District states are at the end of the post). This year, Indiana was the only state for which the early benchmarked growth estimates had a smaller error than the March 2016 benchmarked data, though the errors for Michigan were very close. The early benchmarking method did particularly poorly for Illinois, estimating that employment fell by 13,000 for the year when it actually grew by 19,000.

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The poor performance of the early benchmarking method was surprising. We expected the early benchmarking method to do better for the first half of 2016 because it could take full advantage of the available QCEW data. And for the second half of the year, we knew by definition that the method would perform the same as the March 2016 benchmarked data because it used that version’s growth numbers. So, given these half-year results, how could the early benchmarking method do worse for the full year? Table 3 shows how. The early benchmarking method’s errors were negative in both halves of the year, while the errors of the March 2016 benchmarked CES were positive in the first half and negative in the second half. So the early benchmarking method’s errors built on each other, whereas the errors in the March 2016 benchmarked data cancelled each other out.

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Even though the early benchmarking method did not do well this year, we still think it is a useful tool for predicting benchmark revisions because it can take full advantage of the available QCEW data. We will see what happens in 2018.

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[1] For more information on the BLS’s benchmarking process, go here.

[2] The Seventh District, which is served by the Chicago Fed, comprises all of Iowa and most of Illinois, Indiana, Michigan, and Wisconsin.

[3] An earlier post discussing the method in detail is here.

[4] As is clear in figure 1, the BLS also revises already benchmarked data, though the revisions are typically small.

Seventh District Update, March 2017

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

  • Overall conditions: Growth in economic activity in the Seventh District picked up to a moderate pace in January and early February, and contacts expected activity to continue rising at a moderate pace over the next six to twelve months.
  • Employment and Wages: Employment growth picked up to a moderate pace, and contacts continued to indicate that the labor market is tight. Wage growth was also moderate.
  • Prices: Prices again rose modestly. Retail prices increased slightly, and contacts reported moderate increases in materials prices.
  • Consumer spending: Growth in consumer spending remained modest. Light vehicle sales slowed somewhat, but the pace was still strong.
  • Business Spending: Growth in business spending remained moderate. Retail and manufacturing inventories were generally at desired levels. Current capital expenditures grew at a moderate pace.
  • Construction and Real Estate: Construction and real estate activity increased slightly. Residential and nonresidential building increased slightly, and homes sales increased modestly. The pace of commercial real estate activity picked up some and remained robust.
  • Manufacturing: Manufacturing production again grew at a moderate pace. Growth was widespread across sectors, and even picked up for some long-struggling sectors.
  • Banking and finance: Conditions were little changed. Market participants reported steady growth in equity prices and low volatility. Loan demand from middle-market businesses increased slightly and consumer loan demand was little changed.
  • Agriculture: Prospects for farm incomes improved slightly. Futures prices moved up enough so that – given expected costs – some corn and most soybean operations could lock in small profits for 2017.

The Chicago Fed Survey of Business Conditions (CFSBC) Activity Index increased to +4 from –13, suggesting that growth in economic activity picked up to a moderate pace in January and early February. The CFSBC Manufacturing Activity Index rose to +31 from +12, and the CFSBC Nonmanufacturing Activity Index moved up to –11 from –27.

The Fiscal Performance of Seventh District States in the 2000s

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Our First Look at Job Growth in the Seventh District in 2016—New Estimates Using Early Benchmarking

Last week we received the December 2016 report from the U.S. Bureau of Labor Statistics’ (BLS) state payroll employment survey (also known as the Current Employment Statistics, or CES), so it’s our first opportunity to look at how well the Seventh Federal Reserve District[1] did in 2016. The recent report is not the final word on job growth in 2016 because the data will eventually be benchmarked against more complete data, primarily those from the Quarterly Census of Employment and Wages (QCEW).[2] Data for January through September 2016 will be benchmarked by the BLS in the middle of March of this year, while data for October through December 2016 will not be benchmarked by the BLS until March of next year.

In June 2015, I wrote a blog post detailing a method called early benchmarking, which predicts how the BLS will revise the CES data (the method was first introduced by our colleagues at the Dallas Fed). The BLS rebenchmarks the CES using QCEW data only once a year. However, QCEW data are released quarterly, so it’s possible to use the QCEW data to predict how the BLS will revise the CES (this process is explained in detail in my earlier post). The benchmark revisions to the CES can be quite large, and last year, I found that for the District and most District states, the early benchmarked jobs numbers were closer to the final benchmarked numbers than the non-benchmarked numbers were.

Table 1 shows that for 2016, the early benchmark procedure is predicting employment in the District grew by 106,000 rather than the 164,000 that the BLS’s current estimates indicate. This difference is largely the result of lower job growth numbers for Illinois and Wisconsin, though Iowa’s job growth number is also lower. The early benchmark procedure also suggests that Indiana’s job growth will be revised up, and Michigan’s will be unchanged.

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Figures 1 through 6 show the employment series as currently published by the BLS (in blue) and the early benchmarked series (in red) for the District and District states. The dashed portions of the series represent data that have not yet been benchmarked using the QCEW. The lines are identical through September 2015, but then the lines follow different paths because the early benchmarked series use growth rates from the QCEW until June 2016. While their levels differ, both series have the same growth rates starting in July 2016 (again, for more details on the early benchmarking procedure, see my earlier post).

After the BLS releases newly benchmarked data in March, I will review how well the early benchmarking procedure performed at predicting job growth in the District.

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[1] The Seventh District comprises all of Iowa and most of Illinois, Indiana, Michigan, and Wisconsin.

[2] For more information on the BLS’s benchmarking process, go here.

Seventh District Update, January 2017

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

  • Overall conditions: Growth in economic activity in the Seventh District continued at a modest pace in late November and December, though contacts expected it to move up to a moderate pace over the next six to twelve months.
  • Employment and Wages: Employment growth slowed to a modest rate, though contacts continued to indicate that the labor market is tight. Wage growth picked up to a moderate pace.
  • Prices: Prices again rose modestly. Retail prices increased only slightly, but contacts reported rallies in energy and metals prices.
  • Consumer spending: Growth in consumer spending picked up to a modest pace. Sales of new light vehicles strengthened further and many dealers reported record sales for 2016.
  • Business Spending: Growth in business spending remained at a moderate pace overall. Retail and manufacturing inventories were generally at desired levels. Current capital expenditures grew at a moderate pace.
  • Construction and Real Estate: Construction and real estate activity edged up. Demand for residential construction, residential real estate, nonresidential construction, and commercial real estate all increased slightly.
  • Manufacturing: Growth in manufacturing production picked up to a robust pace. Growth continued to be strong in autos and aerospace (though it slowed a bit in autos) and was moderate overall among other industries.
  • Banking and finance: Conditions improved on balance. Financial market participants reported broad-based growth in equity prices and low volatility. Loan demand from middle-market businesses increased slightly and consumer loan demand was little changed.
  • Agriculture: Farm incomes were little changed. Corn sales picked up some, but inventories remained high. Soybean sales were up moderately, and exports remained strong.

The Chicago Fed Survey of Business Conditions (CFSBC) Activity Index increased to –8 from –19, suggesting that growth in economic activity remained at a modest pace in late November and December. The CFSBC Manufacturing Activity Index rose to +20 from a neutral reading, and the CFSBC Nonmanufacturing Activity Index moved up to –23 from –30.

The Midwest Economy Index (MEI) decreased slightly to –0.01 in November from a neutral reading in October. The relative MEI decreased to +0.20 in November from +0.22 in October. November’s value for the relative MEI indicates that Midwest economic growth was somewhat higher than what would typically be suggested by the growth rate of the national economy.

Seventh District Update, November 2016

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First, a different special announcement: The Federal Reserve announced on November 30, 2016, changes that will be incorporated into its Beige Book report starting in 2017. For more information, see the press release.

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

  • Overall conditions: Growth in economic activity in the Seventh District slowed to a modest pace in October and early November, but contacts expect growth to return to a moderate pace over the next six to twelve months.
  • Consumer spending: Consumer spending increased slightly over the reporting period, primarily reflecting gains at middle-market retailers. Sales of new light vehicles remained strong in the District.
  • Business Spending: Growth in business spending continued at a moderate pace. Retail and manufacturing inventories were generally at desired levels. Current capital expenditures and employment both grew at a moderate pace.
  • Construction and Real Estate: Activity increased slightly overall. Demand for residential construction, residential real estate, nonresidential construction, and commercial real estate all edged up.
  • Manufacturing: Growth in manufacturing production continued at a moderate pace in October and early November, with strong increases in autos and aerospace (though slowing a bit again in autos) and modest gains overall among other industries.
  • Banking and finance: Conditions were little changed. While U.S. Treasury bond yields were up after the U.S. elections, corporate bond spreads declined. Loan demand from small and middle market businesses was little changed, as was consumer loan demand.
  • Prices and Costs: Cost pressures increased modestly, but remained mild. Energy prices remained low, but industrial metals prices rallied. Retail prices changed little, wage pressures were steady overall, and non-wage labor costs picked up some.
  • Agriculture: Record corn and soybean yields, combined with stable corn prices and rising soybean prices, implied that more crop operations than previously expected would at least break even this year.

The Midwest Economy Index (MEI) rose to −0.01 in October from −0.11 in September. The relative MEI increased to +0.25 in October from +0.16 in September. October’s value for the relative MEI indicates that Midwest economic growth was somewhat higher than what would typically be suggested by the growth rate of the national economy.

The Chicago Fed Survey of Business Conditions (CFSBC) Activity Index decreased to −20 from +9, suggesting that growth in economic activity slowed to a modest pace in October and early November. The CFSBC Manufacturing Activity Index remained at +3, and the CFSBC Nonmanufacturing Activity Index decreased to −33 from +12.

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

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

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

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

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

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

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

Construction

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

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Finance, Insurance and Real Estate

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

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Manufacturing

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

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Professional and Business Services

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

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Conclusion

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

The Midwest Feels the Sting of an Extended Agricultural Downturn

On November 29, 2016, the Federal Reserve Bank of Chicago will hold a conference to examine the agricultural downturn in the Midwest and discuss future directions for farming, concluding with a panel discussion on the evolution of agricultural lending. (Check https://www.chicagofed.org/events/2016/ag-conference for more details on the conference, including the agenda, and to register.) Experts from academia, industry, and policy institutions will explore the implications of the downturn for both the farm sector and the broader regional economy. The goals of the conference include understanding key trends in farm income, product prices, and input costs; assessing the primary factors behind the sector’s downturn; examining policies that provide support to farm operations and promote risk management; and discussing the role of agricultural lending under these challenging circumstances, as well as in the next phase for agriculture.

Bountiful harvests since 2012’s drought have replenished crop stockpiles to such an extent that supply has outstripped demand, leading to significant reductions in crop prices for midwestern farmers (see charts 1 and 2). Moreover, livestock product prices have tumbled in recent years (see chart 3). Depressed revenues for crop and livestock operations have translated into lower profits as costs of production have been slower to adjust, particularly for crop farms. This downturn in farm profitability followed on the heels of historically high levels of real net farm income[1] (see chart 4). In addition, diminishing income for farm households and operations negatively affected businesses on the Main Streets of towns across the Seventh Federal Reserve District (all of Iowa and most of Illinois, Indiana, Michigan, and Wisconsin).

This latter point was reinforced through a special question included in the Chicago Fed’s latest Agricultural Land Value and Credit Conditions Survey, to which banks responded during October (results were recently released in AgLetter). Bankers were asked: “Is a weakening agricultural economy leading to weaker Main Street business activity in your area?” In all five states of the District the consensus answer was “Yes,” with varying degrees of certainty—30% said definitely and 43% probably, while only 12% did not agree with this take on current business activity in their areas (15% were not certain of any impacts). Based on these observations, the downturn in farm country appears to have slowed the economy of the rural Midwest on Main Street as well.

As for farm households and farm operations themselves, the U.S. Department of Agriculture (USDA) forecasted real net farm income for 2016 to be $64.3 billion, a decrease of 12% from 2015 and the lowest result since 2009 (the most recent reading that was below the median of $66.9 billion from 1950 to 2016).[2] In 2009 dollars, net farm income for the U.S. averaged $95.2 billion over the previous five years, dipping down from the prior five-year period ($95.7 billion, a level that was the highest since the period from 1949 to 1953). With farm incomes even higher than during the 1970s farm boom, the just-concluded era of high farm incomes looks likely to have set the bar quite high for the current generation of farmers.

Furthermore, looking at the District, farm incomes were even bleaker. Real net farm income for each state is available through 2015. Using these USDA data, we estimate that the net farm income for the five states of the Seventh Federal Reserve District was $9.1 billion in 2015, a decrease of 31% from 2014 and the lowest outcome since 2003. The District’s value was also below that of the median year from 1950 to 2015 of $12.8 billion. District states accounted for only 12.4% of U.S. net farm income in 2015, after hitting 22% as recently as 2013.

The spillover impacts of farm income declines on Main Street activity in the region can be attributed to the relatively large role played by farmland rentals in the District—39% of net rent received by non-operator landlords nationwide. Such rental transactions provided $5.5 billion for non-operator landlords in 2015, down 27% from a peak of $7.5 billion in 2013 (adjusted for inflation). These payments lift the direct economic impact of agriculture on the District, although some landlords reside elsewhere. Also, $3.1 billion in 2015 was paid out to hired labor by farms in the District (down 8.8% from $3.4 billion in 2014). Additionally, District agricultural operations spent $23.5 billion in real terms on purchased inputs that did not originate on farms in 2015 (declining 10% from 2013’s peak of $26.1 billion), implying that farms reduced their purchases from cooperatives, equipment dealers, and other businesses. These negative effects on income and business activity reinforce a pullback in spending by farm households. Hence, the economic fortunes of agriculture radiate out into communities and the businesses of the District, which are sharing the pain of the current downturn.

At our conference on November 29, the first session will dig deeper into the impacts of the agricultural downturn, especially on farm operations and rural economies. The second session will explore trends in agricultural financing, with a particular focus on bankruptcy research. Following the keynote address by Bill Northey, the Secretary of Agriculture and Land Stewardship for Iowa, there will be a session devoted to the role of farm policy during the downturn and potential directions for future legislation. Finally, a panel discussion will look into the evolution of agricultural financing as distress in the sector builds, as well as innovations in response to the changing farm environment. These sessions should enhance our understanding of the agricultural downturn and provide clues about potential changes that will affect the sector’s future. Visit https://www.chicagofed.org/events/2016/ag-conference for more information and to register.

Chart 1.

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 Chart 2.

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 Chart 3.

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Chart 4.

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[1] Net farm income is a standard way to measure the size of returns from agricultural operations. Basically, net farm income equals the value of agricultural production and net government transactions minus purchased inputs, capital consumption, and payments to stakeholders. See www.ers.usda.gov/topics/farm-economy/farm-sector-income-finances.aspx for details.

[2] See http://www.ers.usda.gov/data-products/farm-income-and-wealth-statistics/data-files-us-and-state-level-farm-income-and-wealth-statistics/ for data.

Measuring Tax Capacity for Municipalities in Cook County

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

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

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

Data Sources and Calculations

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

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

Brief Data Summary

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

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Calculating Municipal Gaps

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

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

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Results and Conclusion

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

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

Figure 1: Gap with Expected Expenditures

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Figure 2: Gap with Actual Expenditures

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

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

Seventh District Update, October 2016

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First, our ongoing special announcement: As a Midwest Economy blog reader, you may also want to sign up to follow our new Chicago Fed Survey of Business Conditions (CFSBC), which is a survey of business contacts conducted to support the Seventh Federal Reserve District’s contribution to the Beige Book. The Chicago Fed produces diffusion indexes based on the quantitative questions in the survey. Click here to sign up for email alerts and click here to view the latest release.

If you are a Seventh District business leader and would like to share your perspective on current economic conditions with us, you are welcome to participate in the CFSBC. Please send an email with your contact information to thomas.walstrum@chi.frb.org.

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

  • Overall conditions: Growth in economic activity in the Seventh District continued at a moderate pace in late August and September, and contacts expect growth to remain moderate over the next six to twelve months.
  • Consumer spending: Growth in consumer spending increased only slightly and store traffic remained low. The sales pace of autos in the District remained strong, but slowed slightly.
  • Business Spending: Growth in business spending remained at a moderate pace. Retail and manufacturing inventories were generally at desired levels. Current capital expenditures and employment both grew at a moderate pace.
  • Construction and Real Estate: Activity increased modestly overall. Residential construction was little changed, while nonresidential construction, residential real estate, and commercial real estate activity all increased slightly.
  • Manufacturing: Growth in manufacturing production picked up to a moderate pace. Activity continued to be strong in autos and aerospace, while gains remained modest overall among other industries.
  • Banking and finance: Conditions were little changed. Equity prices declined slightly and volatility was low. Loan demand from small and middle market businesses continued to rise. Consumer loan demand increased modestly.
  • Prices and Costs: Cost pressures were unchanged and remained mild. Most energy and metals prices were flat and remained low, though steel prices fell some. Retail prices changed little and wage pressures were steady.
  • Agriculture: Low expectations for farm incomes continued, with contacts expecting that a profitable soybean harvest would not be enough to offset an unprofitable corn harvest.

The Midwest Economy Index (MEI) increased to –0.04 in August from –0.16 in July. The relative MEI moved up to +0.11 in August from +0.01 in July. August’s value for the relative MEI indicates that Midwest economic growth was slightly higher than what would typically be suggested by the growth rate of the national economy.

The Chicago Fed Survey of Business Conditions (CFSBC) Activity Index increased to +7 from −16, suggesting that growth in economic activity picked up to a moderate pace in late August and September. The CFSBC Manufacturing Activity Index increased to +6 from +3, and the CFSBC Nonmanufacturing Activity Index increased to +8 from −27.



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