November 09, 2010
Entrepreneurs of necessity
On October 26–27, the Atlanta Fed's Community and Economic Development team, in partnership with the Bank's Center for Human Capital Studies, the Ewing Marion Kauffman Foundation, and the Federal Reserve Bank of Dallas, sponsored a conference titled "Small Business, Entrepreneurship, and Economic Recovery: A Focus on Job Creation and Economic Stabilization." The conference covered a large range of topics including employment, financing, and public policy issues, and material summarizing the findings from the conference and related information will be published in the coming weeks. (You can find the conference papers here.)
One of the things that struck me during the conference is the challenge of simply defining and measuring entrepreneurial activity. For instance, a paper presented by Leora Klapper from the World Bank described recent World Bank efforts to systematically collect country-level data on business formation using data on the number of new domestic corporations—private companies with limited liability each year.
Klapper presented a cross-country chart of this measure, by which countries are grouped into relative income buckets, with the United States being in the "high income" bucket. What chart 1 shows is that entrepreneurial activity declined in all categories of countries in 2009. For high-income countries (including the United States), entrepreneurial activity came to a standstill in 2008 and declined 10 percent in 2009.
This evidence is consistent with measures of job creation from opening employer firms (firms with a payroll) in the United States, such as those contained in the Business Employment Dynamics data. These data are from government administrative unemployment insurance records. On the first day of the conference, John Haltiwanger from the University of Maryland gave a fascinating presentation using the data on firms with a payroll and longitudinally linked versions of these data (the Business Dynamics Statistics) to illustrate a decline in job creation in recent years at businesses that have payrolls and, importantly, a decline in job creation at opening employer firms.
However, another paper at the conference by Robert Fairlie from UC-Santa Cruz showed a measure of entrepreneurial activity that has been on a rapid increase in recent years. Chart 2 shows a picture of Fairlie's measure, which is also published by the Kauffman Foundation as the Index of Entrepreneurial Activity.
This measure is based on the Current Population Statistics survey, which among other things asks respondents the question "Do you have a business?" Dr. Fairlie matches this response with the response in the previous month to identify the number of new businesses created (subject to meeting criteria, such as devoting at least 15 hours per week to this business, and restrictions, such as the exclusion of adults over age 65). Importantly, Fairlie's measure of new businesses picks up new nonemployer businesses, many of which are not incorporated.
What is particularly interesting about Fairlie's research is that he shows not only that this measure of entrepreneurial activity has surged, but that it is closely related to movements in local unemployment rates. That is, he has potentially uncovered an "entrepreneur of necessity" effect caused by high unemployment. For many unemployed workers, the benefits of starting a business during a weak economic environment outweigh the costs. It is noteworthy that the largest proportionate increase in this measure of entrepreneurial activity is by people with less than a high school diploma. This group has been especially hard hit by the recession and weak recovery, and it appears that many have responded by starting their own business.
If entrepreneurial activity is a source of economic growth generally, then a surge in entrepreneurial activity is good news for the economic outlook, right? Indeed, Fairlie cites a 2009 Kauffman Foundation study by Dane Stangler that finds over half of the current Fortune 500 firms started during recessions or bear markets. Also, a 2010 Kauffman study by Michael Horrell and Robert Litan find that, on average, start-ups are not affected in the long term if they start in a recession. However, Horrell and Litan also find negative impacts when the recession is prolonged. To the extent that historical patterns are repeated, one implication of the latter finding is that cohorts starting businesses right before or at the start of the 2007–09 recession may have worse outcomes relative to firms starting more recently.
More generally, this study raises questions about the current economic recovery. For example, if the number of new firms with payrolls is down but the number of nonemployer businesses is up, then what could be expected to happen over time? At what rate do new businesses with no employees become employers, and how fast do they tend to grow? This question is especially important because a new business with no employees generates fewer jobs than a new business with employees unless the nonemployer is purchasing labor services through some other means. As noted here, some researchers are skeptical of the economic importance of growth in nonemployer businesses without controlling for possibly important factors such as the industry they are in or their revenues. According to the U.S. Census Bureau, most nonemployers are self-employed individuals operating very small unincorporated businesses. It also seems reasonable to think that many of them are independent contractors providing labor services to other firms. Clearly, there is no shortage of need for more research on these topics.
By John Robertson, a vice president and senior economist in the Atlanta Fed's research department
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August 18, 2010
Just how curious is that Beveridge curve?
A few weeks back I made note of the following:
"Since the second quarter of last year, the unemployment rate has far exceeded the level that would be predicted by the average correlation between unemployment and job vacancies over the past decade."
The focal point of that comment was the so-called Beveridge curve described by the Cleveland Fed's Murat Tasci and John Lindner as follows:
"The Beveridge curve is an empirical relationship between job openings (vacancies) and unemployment. It serves as a simple representation of how efficient labor markets are in terms of matching unemployed workers to available job openings in the aggregate economy."
Since my last post, the U.S. Bureau of Labor Statistics (BLS) published the June edition of its Job Openings and Labor Turnover Survey (JOLTS). Just as not much changed in June relative to May, either with respect to job openings or the unemployment rate, not much changed with the Beveridge curve:
(A monthly version of this picture can be found in the JOLTS graphs and highlights published on the BLS Web site.)
One of the observations made in my previous post was that the apparent shifting of the Beveridge curve—in other words, the observation that given recent experience the number of unemployed individuals seems high relative to the number of available jobs—might be explained by extended unemployment benefits, but only if you are willing to accept estimates of the policy's impact that are on the high end. I referenced a few Federal Reserve papers—here and here—but they only included estimates on the lower end. Several people have asked (in the comments section of my earlier post and in private e-mails) where the higher-end estimates come from. One of these is from an article titled "The Economic Effects of Unemployment Insurance" by Shigeru Fujita, which is forthcoming (but not yet published) in the Philadelphia Fed's Business Review. (Shigeru estimates that extended unemployment benefits raise the unemployment rate by 1.5 percentage points, enough to explain the lion's share of the Beveridge curve shift.)
Tasci and Lindner, in the article mentioned earlier, offer up a few other observations. First, in the last several months labor market statistics have in general been distorted by the entry and exit of significant numbers of temporary Census workers. Second, it does appear to be the case that the current rise in the unemployment relative to job openings is just a standard characteristic of the early phases of a recovery. On this point they provide this chart …
… along with this explanation:
"One important observation is that a longer-term look at the Beveridge curve shows that the dynamics we have seen recently are not an exception, but are common during the recovery phase of business cycles. As the economy starts improving, it takes time to deplete unemployment, even though job openings are relatively quick to adjust.
"Hence, cyclical changes may not necessarily present themselves as… a neat movement along the curve. During and after recessions in the postwar period, the Beveridge curve has generally followed a pattern of shifting to the right during a recovery. One potential reason for this could be that even though some unemployed workers start filling the available job openings, workers who had left the labor force might get encouraged by the recovery and start looking for a job, thereby keeping the unemployment high. While the Census may have skewed the data for this recovery, the path of the curve going forward looks poised to follow in the footsteps of previous recessionary periods."
"Firm conclusions will only be able to be drawn as more data are generated."
By Dave Altig, senior vice president and research director at the Atlanta Fed
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August 03, 2010
What makes forecasting tough
Bloomberg's Caroline Baum recounts her recent conversation with the Atlanta Fed's own Mike Bryan under the headline For Good Economic Forecasts, Try Flipping a Coin:
"How do economists fare when it comes to real forecasting, to predicting [gross domestic product] GDP growth and inflation one year out? About as good as a coin toss, according to Bryan's research. Less than half the economists did better than the naive forecast, which is based on no understanding of the economy and merely assumes next year's outcome will be the same as this year's. It's what you'd expect if the results were purely random."
A case in point could be found yesterday on Bloomberg, which featured a "chart of the day" that looked something like the one below (though I've updated the data for manufacturing inventories, given today's factory orders report):
The chart was accompanied by this commentary:
"U.S. business inventories are so low relative to demand that any increase may act as a catalyst for larger companies to add workers, according to Nicholas Colas, chief market strategist at BNY ConvergEx Group."
A few days back, in The Wall Street Journal, you could find this:
"Until recently, businesses had helped supercharge economic growth by restocking inventories. Now the oomph from inventories is waning.
"In the second quarter, the change in private inventories added slightly more than one percentage point to the 2.4% increase in gross domestic product from the first quarter, measured at a seasonally adjusted annual rate, the Commerce Department said Friday.
"That is a big change from the first quarter, when inventory-building contributed 2.6 percentage points to GDP growth of 3.7%, and the fourth quarter of last year, when it contributed 2.8 percentage points to GDP growth of 5%....
"But Friday's report suggests companies are nearly done restocking their shelves.
" 'Our sense is current inventories are about where they need to be globally, both in industrial distribution and with the large North American retailers,' John Lundgren, chief executive of Stanley Black & Decker Inc., said in a July 21 call with analysts discussing the tool and hardware maker's second-quarter results."
But, on the same topic, Seeking Alpha opined:
"Inventory increases added 1.05% to second quarter GDP. Based on the annual revision, they added 2.64% to first quarter GDP or 71% of the total increase. Inventories were also responsible for approximately two-thirds of the GDP increase in the fourth quarter of 2009. The entire economic 'recovery' has essentially been an inventory adjustment [emphasis theirs]. This does not bode well for the future."
So one analysis suggests that the latest readings on inventories portend a boost to GDP, one foresees a drag on GDP, and yet another divines that inventories are basically played out as an economic story for the balance of the year.
Again from the Baum piece:
"Bryan said it's not just about getting the number right. 'It's about the narrative.' "
For comparison, it's also useful to take a longer look at what effect inventories have on GDP growth coming out of a recession; see the graph below. It charts the percentage point contributions of various components to real GDP growth in the first four quarters following the end of a recession (the current recession is assumed to have ended in second quarter of 2009). I've shown on the graph the percentage contribution of inventories to the last seven recoveries, beginning with the one in 1971.
Regarding the point made in Seeking Alpha, inventories have contributed around 70 percent to the economic recovery recently, but in the recovery that began in 2002 inventories contributed 75 percent in the first four quarters. So the last two recovery periods stand out for large inventory components. But looking across the data, it's hard to say what an ordinary inventory contribution would be. Regardless of whether inventories are an unusually large part of this recovery, in absolute levels the scale of the recent inventory cycle—the initial liquidation and the subsequent restocking—has been unprecedented.
By Andrew Flowers, senior economic research analyst in the Atlanta Fed's research department
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June 25, 2010
Increasing hours worked versus increasing hiring
The current recovery has been characterized by increasing production and sales without an associated expansion in employment. Part of the explanation for the lack of hiring has to do with increased productivity of workers (output per hour worked)—either by improved production methods or simply requiring more effort from staff per hour worked. Another reason why firms have been relatively slow to hire is that, in addition to slashing payrolls during the recession, many firms also cut the work hours of the remaining staff to levels well below prerecessionary norms. As a result, these firms have some scope to increase the hours worked by their current staff before hiring additional workers. This fact is evident in the often-cited increase during the recession in the number of people working part time for economic reasons (see here and here, for example). That number has remained relatively stable at around nine million people over the last year, but it is still more than twice its prerecessionary average.
Another perspective on the part-time issue can be gleaned from data on average work week obtained from the U.S. Bureau of Labor Statistics (BLS) Current Population Survey. Chart 1 shows the pattern of average weekly hours (not seasonally adjusted) for all nonfarm wage and salary workers during the period of January 2008 through May 2010. For ease of comparison, the chart is scaled to be relative to the 2002–07 average. Compared with prerecession levels, average hours worked declined during the recession although they really didn't begin falling until the second half of 2008. As of May 2010, average hours worked were still about 1.5 percent below the prerecession average but have been trending higher in recent months. (Note that the sharp drop in September 2009 is a quirk of Labor Day falling during the survey week and hence cutting the work week one day shorter than usual.) The fact that average hours worked has moved higher is an encouraging sign for employment growth going forward if the historical norm is any guide. Of course, a firm may need to hire new workers even when hours per worker are below average. For example, the decision to start an additional manufacturing production line will probably require hiring new staff even if existing staff on other lines are working fewer hours than usual.
The aggregate picture in Chart 1 masks considerable variation across industries. For example, Chart 2 shows the normalized average weekly hours reported by workers in the education and health services industries and in the financial industry. For these workers, although average hours worked per week declined mildly during the second half of 2009 weekly hours worked have since returned to prerecessionary levels. This performance suggests that, other things equal, additional demand for hours of work in these industries is likely to be met by additional hiring.
Chart 3 shows the evolution of average weekly hours reported by workers in the manufacturing and transportation/warehouse industries. In these industries, average hours worked began to decline in the fall of 2008, but they have recovered much of the decline in recent months and are now about 1 percent below their prerecession averages. As with the aggregate picture, the fact that average hours worked has been trending higher recently is encouraging news for future employment growth in these industries.
In contrast, Chart 4 shows the pattern of average hours reported by workers in the construction industry, the wholesale and retail trade industry, and the leisure and hospitality industry. For these workers, average weekly hours started to decline in the fall of 2008 and have shown no clear signs of recovery—still sitting some 3 percent to 4 percent below their prerecession averages and not trending higher. Thus, there appears to be more scope for firms in these industries to increase hours without necessarily having to hire additional workers.
This analysis does have some caveats. For one thing, it is based on worker-reported data about hours worked drawn from the BLS's Current Population Survey. An alternative would be the employer-reported measurements in the BLS's Establishment Survey.
Probably more importantly, this analysis uses the prerecession history as a guide to what is "normal." If, for example, firms decide to keep average weekly hours lower by increasing the use of part-time workers, then the fact that average hours are below prerecession levels does not imply that firms won't hire when demand increases. Some industries already make heavy use of part-time employment. For example, in May 2010 the reported average weekly hours by workers in the leisure and hospitality industry was 33.3 hours compared to 42 hours in manufacturing. Absent an offsetting increase in wage rates, a permanent shift toward increased part-time employment would lower a worker's income relative to full-time employment and probably result in an increased propensity for multiple job-holding by individuals and households.
By Amy Ellingson, economic analyst at the Atlanta Fed
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June 11, 2010
Another view of the structural versus cyclical unemployment question
One of the key functions of labor markets is matching firms looking for workers who have particular attributes (or skills) with individuals looking for work who have those attributes. What economists have been worrying a lot about recently is the potential for a substantive mismatch between the skills of those looking for work and the skills that firms want. This type of labor reallocation friction is one of many potential structural problems affecting the U.S. labor market at present (see, for example, here, here, and here).
A 2003 New York Fed article by economists Erica Groshen and Simon Potter examined the issue of structural rigidities in labor markets during the recovery from the 2001 recession. Their idea was to identify the share of employment in industries that had either continued to lose or gain jobs on net after the recession versus the share of employment in industries that had responded cyclically (gaining jobs after having lost them during the recession or losing jobs after gaining them during the recession) to the recession. The New York Fed researchers used industry of employment as a proxy for industry-specific skills, though it's not a perfect measure. For example, the skills of construction workers are generally different from the skills of health care workers. The more often that employment is accounted for by industries that are continuing to gain or lose employees, the more the potential exists for skill mismatch going forward.
Using the first 12 months of the recovery as a basis, Groshen and Potter found that in the 1974–75 recession and the recessions of the early 1980s the share of employment in industries continuing recession employment trends was around 50 percent. That share increased to 57 percent for the 1990–91 recession and rose sharply to 79 percent for the 2001 recession. The researchers took these findings as evidence of structural change playing a more significant role in influencing the labor market recovery from the 2001 recession than earlier recessions saw.
Visually, this observation can be presented as a four-quadrant "bubble chart" that measures job growth during the recession on the horizontal axis and job growth in the first 12 months of recovery on the vertical axis (the size of the each bubble reflects the relative employment size of the industry). We replicated Groshen and Potter's work with minor data definitional changes and find that for the first 12 months of recovery from the 2001 recession 81 percent of employment was in industries continuing recession employment trends (the top right and bottom left quadrants in the chart).
Using the same approach as Groshen and Potter, how does the 2001 recession compare with the most recent recession? To make that determination, we used data available from the 11 months of recovery coming out of the most recent recession (assuming the recession ended in June 2009). We calculate that 65 percent of employment is in industries either still losing or gaining jobs. This share is less dramatic than the 2001 experience but a bit more than the 1990–91 experience.
The positioning of certain industries within the four quadrants is not too surprising given the nature of the most recent recession. For instance, construction and related industries are deep in the continued job-loss quadrant. In contrast, the temporary help sector has behaved procyclically. Jobs in federal government and health care have continued to grow, with the former boosted by temporary hiring of census workers. Of the 79 industries examined, about a third of them have landed in a different quadrant compared with the 2001 recession.
Of particular interest is the share of employment in industries that are continuing to lose jobs. For unemployed workers from those industries, there is less prospect of being reemployed in that industry and hence a greater chance that skill mismatch will be an issue for those workers. Interestingly, the share of employment in industries experiencing continued net losses is similar to that seen during the 2001 recession (45 percent versus 41 percent).
This most recent recession was especially deep, and the large share of unemployed workers reporting they were permanently separated from their employers suggests that many of those jobs in all likelihood will not come back. If new jobs come with different skill requirements, then skill mismatch could become a significant factor once labor demand increases. However, the relatively disappointing May private-sector payroll jobs numbers released last Friday and the improving but low level of job openings reported in the JOLTS data for April are reminders that weak labor demand is still the dominant factor inhibiting the overall employment recovery.
By Menbere Shiferaw, senior economic research analyst, and John Robertson, vice president and senior economist, both in the Atlanta Fed's research department
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June 03, 2010
The recovery: Job rich or job poor?
Gross domestic product (GDP) is growing, supported by strong labor productivity numbers and modest employment growth. Even after today's downward revision to first quarter labor productivity measures, growth in labor productivity in the business sector has averaged 5.6 percent over the last three quarters—more than twice the long-run average. Okun's law has underpredicted the rise in unemployment, and some commentators call for a jobless recovery. Today, Federal Reserve Bank of Atlanta President Dennis Lockhart gave a speech on the prospects for the future of employment and labor productivity that posed the question:
"… how long can firms ride this productivity growth before having to yield to new hiring to support greater activity?"
To provide some context, below is a chart that shows a decomposition of GDP growth during post-WWII recessions. This decomposition does not explain growth, but it does show the relative contribution of various factors such as average labor productivity, average hours worked, the unemployment rate, and other labor market variables.
Note: The decomposition is based on the identity.
Where Y is GDP, H is total hours worked from nonfarm payrolls, E* is employment from the nonfarm payroll survey, E is employment based on the household survey, L is labor force, and N is working age population.
As this chart shows, relatively high labor productivity growth during a recession is not a phenomenon isolated to the 2007–09 and 2001 recessions (for present purposes, the end of the most recent recession is identified with the trough in GDP in the second quarter of 2009). All recessions from WWII through 1970 also featured sizable growth in labor productivity. Notice also that these recessions experienced large declines in the employment rate (rise in the unemployment rate). Hence, the productivity gains were attributable to outsized reductions in hours rather than gains in output. The same also holds true of the 2007–09 recession. As President Lockhart noted:
"Many employers reacted to the downturn by aggressively cutting their workforces, reorganizing remaining workers, and cutting other costs."
Of course, the big question centers on the recovery. Specifically, does the outsized contribution from labor productivity during the last recession portend continued outsized gains (and hence subdued employment prospects) during the recovery and expansion as well?
Perhaps history can provide a guide. The next chart provides the same GDP decomposition, but this time for the first three quarters of the recovery (we chose three quarters to allow a common comparison across recoveries). This chart reflects at least two salient features. First, the fastest way to employment growth is faster GDP growth—recessions that are immediately followed by very strong GDP growth also tend to have strong employment growth. Second, productivity remains an important contributor to growth, and that contribution has been especially large during the early phases of the past three recessions.
President Lockhart's interpretation of these observations is that many employers:
"… have reacted to the upswing by holding employment at or near recession levels, seeking efficiencies in supply chains, investing in labor-saving automation, and generally tweaking their business models to operate more efficiently than before the recession. We've heard this story frequently in anecdotal accounts of our directors and business contacts across the Southeast.
"As long as efficiency and productivity gains can be achieved in this way, employers may remain hesitant to hire."
Going forward, President Lockhart concludes that:
"I do not expect the recent outsized productivity growth to continue indefinitely and become a new, permanently higher trend rate. Some degree of 'wait and see' behavior is at work and is no doubt reflected in the productivity numbers. With growing economic momentum, deferral of hiring will become riskier.
"Some employment gains should result as labor productivity levels out and falls back over time to something resembling the historic trend rate. But the pace of hiring is likely to be gradual. Current data on the use of part-time workers suggest that businesses have some scope to increase hours without hiring new full-time employees. And there are other, more structural obstacles to the rapid reemployment of the jobless. Some jobs in the construction sector and certain manufacturing industries are likely permanently lost, requiring some amount of migration of workers to other sectors. And, for a time, skill and geographic mismatches may frustrate employers willing to hire.
"Also, the weight of uncertainty about the future business environment makes a gradual pace of employment progress a reasonable assumption. I hear often from members of the business community that uncertainty regarding federal, state and local fiscal fundamentals and regulatory rules-of-the-game are feeding reticence to pull the trigger on new ventures, new hires and new investments. The recent European sovereign debt and banking pressures have added to uncertainty in financial markets.
"Sizing all this up, I expect recovery in the medium term to be neither jobless nor job rich."
The current Blue Chip consensus for second quarter GDP is about a 3.2 percent annual rate. Payroll employment expanded by an estimated 290,000 in April, and with an employment report out Friday that will likely feature very sizable gains (some forecasts calling for an addition of more than 500,000 jobs), the relative contribution of labor productivity to GDP growth in the second quarter is almost certain to decline sharply. However, most of the surge in May's number will be temporary U.S. Census workers, and this increase will reverse itself in pretty short order. Hence, it will probably take awhile to see how President Lockhart's forecast of continued modest employment growth pans out.
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April 14, 2010
The inventory question
"I hadn't looked at this for awhile—should I interpret the return of the inventory-sales ratio to near normal levels as good news?"
Here's the picture Thoma was looking at, updated to incorporate today's U.S. Census Bureau release on February manufacturing and trade inventory and sales:
Tim Duy has taken a look at these data and comes to this conclusion:
"Increasingly, the recovery looks sustainable—sustainable in the sense that a double dip recession looks unlikely. As Bloomberg reports, this is the message of the inventory cycle, which appears to have largely run its course. Inventories surged as the recession intensified, leaving firms scrambling to bring output in line with the new level of sales. Now, firms have inventories under control."
I have been pondering those data as well, ever since the advance fourth quarter gross domestic product report indicated that 3.4 percentage points of the then-reported 5.9 percent annualized growth rate was accounted for by a slowing in the pace of inventory decumulation. (The numbers have subsequently been revised to 3.8 percentage points of a 5.6 percent growth rate.) It certainly appears that inventory-sales ratios have reverted to the prerecession norm, justifying Duy's sense that inventories will not be a big part of the economic story as we move through 2010.
That conclusion does rest, of course, on the likelihood that a downward trend in the ratio truly did break in the middle part of the decade. As the chart shows, the same pause in the trend occurred in the mid-1990s, only to commence its southward trek on the other side of the 2001 recession.
But the situation is even more curious than that. If you dig a little deeper, you find that not all inventory-sales ratios tell the same story. In particular, inventory-to-sales ratios at the retail level look very lean relative to prerecession levels while manufacturer's inventories still appear to be relatively bloated.
What, exactly, is that chart trying to tell us? Does it represent some shift in supply-chain management, with inventory holdings being pushed down from the retail level to manufacturers? If not, can we expect some resurgence in retail inventories (as the Duy-cited Bloomberg article suggests), coupled with continued decumulation at the manufacturing level? And what would be the net effect of such developments on aggregate inventory levels?
Those are good questions, too. If you have any insights, I'd love to hear them.
By Dave Altig, senior vice president and research director at the Atlanta Fed
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March 17, 2010
Bad by any measure
A few weeks back, The Economist published a story touting the well-known fact that the "American economy just had its worst decade since the 1930s." Whether looking at gross domestic product (GDP), consumption, income, or nonfarm payrolls, the decade from 2000 to 2010 generally looks bad from an economic perspective. During this decade, of course, the nation has experienced two recessions—the latter being the most severe since the Great Depression. (See the graphs below, reproduced from the February 25 article in The Economist titled "Back to The Crash.")
But given that decades are rather arbitrary economic demarcations, why not examine other time periods? So here's another approach: Using the yearly trough-to-trough periods according to National Bureau of Economic Research (NBER) recession dating, the charts below have replicated the ones shown above from The Economist. (Note: The NBER only designates troughs by quarter. So for a trough ending in first or second quarter, the calendar year in which the trough falls is designated as the "trough year." If the trough falls in third or fourth quarter, the following calendar year is the trough year. For these calculations, we use annual data instead of quarterly because pre-1947 quarterly data are unavailable.) This simple exercise sheds some interesting light on the recent experience of the U.S. economy—namely, that it was bad by any measure.
Looking at real GDP growth over these periods, the 2002–09 era looks very weak, with only 1946–49 having a lower average annual rate of growth (in these years, GDP averaged an annual decline of 2.01 percent). Average annual real GDP growth was 1.72 percent for the 2002–09 period, much lower than the average of 3.97 percent for the previous 10 trough-to-trough periods.
Similarly for real consumption and income growth, the 2002–09 period is also bleak. Average annual consumption and income growth had averaged 3.81 percent and 3.79 percent, respectively, going into 2002. But during this recent trough-to-trough period, income growth was very weak at 1 percent, with only the 1946–49 period doing worse (–1.09 percent). But consumption growth in 2002–09 was the lowest on record, averaging only 2.12 percent growth annually.
Another interesting observation is the spread between average annual consumption and income growth. The 1946–49 and 2002–09 periods are where it's the largest, at 5.9 percent and 1.1 percent, respectively. These large imbalances could possibly reflect growth in household debt and/or lower saving rates, as consumption growth far outstrips income growth. Indeed, debt grew and savings declined notably during 2002–09.
Lastly, a look at the nonfarm payroll growth confirms the most recent trough-to-trough period as one of extraordinary weakness. Given that data prior to 1939 are unavailable, the previous eight bottom-to-bottom periods saw average annual growth of 13.5 percent in payrolls. But for 2002–09, average annual payroll growth of 0.44 percent reaffirms the so-called "jobless recovery" from the 2001 recession and the large decline in payrolls during this current recession. No other previous period comes close.
By Andrew Flowers, economic research analyst, at the Atlanta Fed
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March 12, 2010
A look at the income-side estimates of growth
Last week, a post in the New York Times' Freakonomics blog on Okun's law made note of the statistical discrepancy between the two methods for calculating national output:
"…there are two measures of output growth—the usual measure, which adds up total spending in the economy, and the alternative, which adds up total income. In theory, the two should be exactly the same. In practice, they have been very different during this recession… These GDI [gross domestic income] numbers suggest that output growth actually declined much more sharply than had been widely understood."
Indeed, the recession looks deeper and the recovery seems much less pronounced, looking at the income-side data in this chart.
There has been a good deal of coverage about the discrepancy between the income and expenditure sides of gross domestic product (GDP) calculations in the past couple of years. Jeremy Nalewaik, at the Federal Reserve Board, is often cited for his work arguing that GDI may be a more reliable measure for delineating recessions than GDP (see 1, 2, 3). In fall 2008, Jim Hamilton noted the relatively weak behavior of GDI toward the beginning of the current recession: "It is interesting that while GDP indicates sluggish growth over the last three quarters, GDI looks much more like a recession, with 2007:Q4–2008:Q1 satisfying the traditional rule of thumb of two quarters of falling real output."
But apart from recession dating, how seriously should we take these income-side numbers?
One issue with using GDI data is that they lag the GDP data by a full quarter. That stated, a 2006 study by Fixler and Grimm at the Bureau of Economic Analysis (BEA) argues that GDI data contains valuable information.
"There is evidence that income-side measures contain information about revisions to estimates of GDP. National income is statistically significant in explaining revisions from the final current quarterly to the latest estimates of GDP. Conversely, there is no evidence that product-side measures contain information about revisions to GDI and national income."
In other words, history suggests that when these two measures of national output disagree, GDP tends to get revised in the direction of GDI and not the other way around. So, if this relationship holds, it would be prudent not to dismiss the latest divergence in the two measures because it suggests that the decline in national output has been more protracted, and the recovery (through the third quarter 2009) more modest, than what is being reflected in GDP.
If true, this pattern could raise questions about current levels of productivity and associated labor cost measures. The decline in unit labor costs over the recession has been remarkable by either measure. But the recent drop in unit labor costs by way of the expenditure-side estimate was roughly 1.5 percentage points larger than the labor cost estimate that would be computed from the income side of the accounts. If revisions going forward continue to favor the income-side estimates, then maybe downward wage pressure—while probably still large—may be less than many believe.
By Laurel Graefe, senior economic research analyst, and Jacob Smith, quantitative research analysis specialist, both at the Atlanta Fed
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February 09, 2010
If your economic forecast for the coming year embeds something like robust growth in consumer spending, last Friday's Federal Reserve report on consumer credit should probably give you pause.
At least some folks look at that picture and see a slow slog ahead. Calculated Risk sums up the concern:
"Consumer credit has declined for a record 11 straight months—and declined for 14 of the last 15 months and is now 4.8% below the peak in July 2008. It is difficult to get a robust recovery without an expansion of consumer credit—unless the recovery is built on business investment and exports (seems unlikely to be robust)."
At Angry Bear, the question is a little more pointed:
"Remind me again why all those banks were 'bailed out?' Wasn't it supposed to be to kick-start the economy again?"
Well, here's the thing. That consumer credit picture embeds both the supply of credit and the demand for credit. Though both tighter credit standards and weak loan demand are certainly at play, it is does seem that, at the moment, weak demand is the factor most responsible for slow loan growth in the United States. Recall, for example, this information from the Federal Reserve's January Senior Loan Officer Survey:
"The January survey indicated that commercial banks generally ceased tightening standards on many loan types in the fourth quarter of last year but have yet to unwind the considerable tightening that has occurred over the past two years. The net percentages of banks reporting tighter loan terms continued to trend lower. Banks reported that loan demand from both businesses and households weakened further, on net, over the survey period."
As regular readers of macroblog know, our own Atlanta Fed surveys (here and here) are indicating that soft customer demand, not credit access, is a significant story in business capital expenditure and expansion plans.
Of course, we don't really know whether credit availability will become a more significant problem when demand begins to recover. This uncertainty is behind what is the real back story at this critical point of the recovery. As we peer ahead, we essentially have two competing, and contradictory, economic histories as our guides. First, there is the statistical regularity that deep recessions in the United States have in the post-WWII period been reliably followed by rapid recoveries. But second, there is the Reinhart-Rogoff statistical regularity that recoveries from financial crises are slow and difficult.
A Wall Street Journal interview with Carmen Reinhart provides reasonable arguments as to why slow and painful is a sensible bet. On the other hand, one could argue that the advance fourth quarter gross domestic product figure is consistent with the sharp bounce-back scenario. (If you are looking for that argument, Brian Wesbury and Robert Stein oblige.)
One thing is certain. At least one history is going to be revised.
By Dave Altig, senior vice president and director of research at the Atlanta Fed
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