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May 22, 2017
GDPNow's Second Quarter Forecast: Is It Too High?
Real gross domestic product (GDP) growth slowed from a 2 percent pace in 2016 to an annual rate of 0.7 percent in the first quarter of 2017. The Federal Open Market Committee viewed this slowdown in growth "as likely to be transitory," according to its last statement.
Indeed, current quarter GDP forecasting models maintained by the Federal Reserve Banks of New York, St. Louis, and Atlanta have been pointing toward stronger second quarter growth (2.3 percent, 2.6 percent and 4.1 percent, as reported on their respective websites on May 19, 2017).
The Atlanta Fed's model—GDPNow—is at the high end of this range and is also high relative to other professional forecasts. The median forecast for second quarter real GDP growth in the May Survey of Professional Forecasters (SPF) was 3.1 percent, for instance, and recent forecasts from Blue Chip Publication surveys displayed on our GDPNow page show some divergence from our model as well.
We encourage—and frequently receive—feedback on our GDPNow tool, and some users have suggested that our forecast for second quarter growth is too high. In fact, some empirical evidence supports that view. The evidence considered here correlates differences between consensus Blue Chip Economic Indicators Survey and GDPNow forecasts for growth about 80 days before the first GDP release with the GDPNow forecast errors (see the chart below).
A note about the chart: The horizontal axis shows the difference between the Blue Chip consensus forecasts and GDPNow's forecast. The vertical axis measures the 80-day-ahead GDPNow forecast error, defined as the difference between the first published estimate of real GDP growth and the GDPNow forecast at the time of the mid-quarter Blue Chip survey.
As the chart shows, there is a positive relationship between the Blue Chip-GDPNow discrepancy and the GDPNow forecast error. A simple linear regression would predict that the GDPNow forecast of 3.7 percent growth on May 5 was too high by nearly 1.0 percentage point. Moreover, the chart suggests that there has been a bias in GDPNow forecasts since the fourth quarter of 2015 of between 0.9 and 2.0 percentage points at the time of these mid-quarter Blue Chip surveys. If you are inclined to think the GDPNow forecast for second quarter growth is a bit too high, then this evidence will not change your mind.
Given this evidence, you might think that putting relatively little stock in the GDPNow forecast at this point in the quarter would be prudent. Indeed, if we calculate the weighted average of the historical Blue Chip consensus and GDPNow forecasts that produced the most accurate forecast of the first estimate of real GDP growth, then the optimal weight of the GDPNow forecast lies somewhere between 0.34 and 0.55 (see the chart below). The weight depends on the number of days until the first GDP release.
For example, the optimal weight of 0.55 on GDPNow about 54 days before the first GDP release means that 0.55 times the GDPNow forecast plus 0.45 times Blue Chip consensus survey forecast has been more accurate, on average, than any other weighted average of the two forecasts. The lowest weight on GDPNow corresponds to forecasts made about 83 days before the first GDP release—the time when GDPNow's bean-counting algorithms have the least amount of source data to work with.
A weighted average of the Blue Chip consensus and GDPNow forecasts at that time would put the GDP forecast about 0.6 to 0.7 percentage points below the current GDPNow forecast. However, the confidence bands around these estimates are wide, so the positive weight placed on GDPNow early in the quarter could just be the result of chance.
Let's cut to the chase—why, exactly, is the GDPNow forecast for second quarter GDP growth so high? The details of the GDPNow forecast provide some clues. We can compare the GDPNow forecasts of GDP components with those from the SPF. (The Blue Chip forecast does not provide detail on all the GDP components.) The following table translates the median SPF forecasts into contributions to second quarter real GDP growth. These contributions are shown alongside GDPNow's forecasted contributions as well as the average contributions to real GDP growth over the prior four quarters.
Clearly, more than half of the difference between the GDP growth forecasts from GDPNow and the SPF is due to inventories. For both forecasts, inventory investment also accounts for over half of the pickup in second quarter growth from the trailing four-quarter average.
A macroblog post I wrote last year showed that the growth-forecast contribution of mid-quarter inventory investment produced roughly equivalent accuracy in the SPF and GDPNow models, but it was much less accurate than the contribution forecasts of the other GDP components. Based on experience, we can't be confident that either forecast of inventory investment is likely to be very accurate or that one is likely to be much more accurate than another.
With very little hard data in hand for the second quarter for most of the GDP components—and for inventories in particular—we will continue to closely monitor if the data are as strong as GDPNow is anticipating or if they hew more closely to other forecasts. Check back with us to see.
May 11, 2017
Are Small Loans Hard to Find? Evidence from the Federal Reserve Banks' Small Business Survey
The Federal Reserve Banks recently released results from the nationwide 2016 Small Business Survey, which asks firms with 500 or fewer employees about business and financing conditions. One key finding is just how small the financing needs of many businesses are. One-fifth of small businesses that applied for financing in the prior 12 months were seeking $25,000 or less. A further 35 percent were seeking between $25,001 and $100,000.
The data also show that firms seeking relatively small amounts of financing (up to $100,000) receive a significantly smaller fraction of their funding than firms who applied for more than $250,000. Chart 1 shows the weighted average of the share of financing received by the amount the firm was seeking.
So what explains this variation in financing attainment across the amount requested? We've heard reports from small business owners that smaller loans are relatively more difficult to obtain, especially from traditional banks. One often-cited rationale is that the administrative burden associated with originating and managing a small loan is often just not worth the bank's time. However, this notion is not entirely consistent with data on the current holdings of small business loans on the balance sheets of banks. As of June 2015, loans of less than $100,000 made up about 92 percent of the number of business loans under $1 million.
So it seems originating a loan for less than $100,000 is not uncommon for a bank after all. So why, then, do business owners say that smaller loans are more difficult to get? Using data from the 2016 Small Business Survey, we can investigate the reason for this apparent disconnect.
Much can be explained by looking at the characteristics of those who borrow small amounts versus large amounts. Firms seeking $25,000 or less are more likely to be high credit risk and younger, have fewer employees, and have smaller revenues than firms applying for more than $250,000. The table below summarizes the differences:
Of particular importance is the credit risk associated with the firm. Controlling for differences in this factor, it turns out that smaller amounts of financing are not more difficult to obtain. Charts 2 and 3 show the weighted average share of financing received by amount sought for low credit risk firms and for middle to high credit risk firms separately.
As charts 2 and 3 demonstrate, low credit risk firms are able to obtain a similar share of the amount requested, regardless of how much they applied for. The same is true for higher risk firms. We also see that medium and high risk firms get less of their financing needs met than low credit risk firms that apply for similar amounts.
From this evidence, it seems that credit approval has more to do with the attributes of the firm than the amount of financing for which the firm applied. These results also highlight the potential importance of alternatives to traditional bank financing so that riskier entrepreneurs—including important contributors to the dynamism of the economy such as startups—have somewhere to turn. A later macroblog post will explore how low and high credit risk firms use financing differently, including where they apply and where they receive funding.
May 05, 2017
Slide into the Economic Driver's Seat with the Labor Market Sliders
The Atlanta Fed has just launched the Labor Market Sliders, a tool to help explore simple "what if" questions using actual data on employment, the unemployment rate, labor force participation, gross domestic product (GDP) growth, and labor productivity (GDP per worker).
We modeled the Labor Market Sliders after the popular Atlanta Fed Jobs Calculator. In particular, the sliders take the rate of labor productivity growth and the rate of labor force participation as given (not a function of GDP or employment growth) and then asks questions about GDP growth and labor market outcomes. Like the Jobs Calculator, the sliders require that things add up, a very useful feature for all those backyard economic prognosticators (we know you're out there).
Let's look at an example of using the sliders. The Congressional Budget Office (CBO) projects that the labor force participation rate (LFPR) will maintain roughly its current level of 62.9 percent during the next couple of years, as the downward pressure of retiring baby boomers and the upward pressure from robust hiring hold the rate stable. The CBO also projects that labor productivity growth will gradually increase to almost 1 percent over roughly the same period.
Suppose we want to know what GDP growth would be over the next couple of years (other things equal) if labor productivity, which has been sluggish lately, returned to 1 percent, as projected by the CBO. By moving the Labor Productivity slider in the tool to 1 percent and the Months slider to 24, you will see how productivity alone affects GDP growth: it increases to about 2 percent (see the image below). In this experiment, the unemployment rate, average job growth, and LFPR are constrained to current levels.
However, there's more than one way to achieve GDP growth of 2 percent over the next two years. Let's take a look.
Hit the reset button, and productivity, GDP growth, and months revert to their starting values. Then move the Months slider to 24 and the GDP Growth slider to 2 percent. You then see that—at current levels of labor force participation and labor productivity growth—achieving 2 percent GDP growth over the next two years would require the economy to create about 200,000 jobs per months (see the image below), which would push the unemployment rate down to 3.1 percent (a rate not seen since the early 1950s).
Hit the reset button again. Achieving 2 percent GDP growth over the next two years is also realistic with a higher LFPR, some other things equal. First, move the Months slider to 24, then move the Labor Force Participation Rate slider to 63.7 percent. The higher LFPR is consistent with about 2 percent growth in GDP and roughly 200,000 additional jobs added each month (see the image below). (This scenario constrains the unemployment rate and labor productivity growth rate to their current levels.) Of course, we haven't seen the LFPR at 63.7 percent since 2012, but that's another discussion.
What if we wanted something a bit more ambitious, such as averaging 3 percent GDP growth over the next couple of years? Hit the reset button again, and try this scenario. Keep Labor Force Participation Rate at its current level (consistent with the CBO's projection), set Labor Productivity growth to 1 percent (also using the CBO projection as a guide), move the Months slider to 24, and the GDP Growth slider to 3 percent. The Labor Market Sliders allow us to see that the economy would need to add an average of about 240,000 jobs each month for those two years. This scenario, the tight-labor-market method of achieving 3 percent GDP growth, would bring the unemployment rate down to 2.6 percent.
However, suppose the United States were somehow able to recapture productivity growth of around 2 percent, which we experienced in the late 1990s and early 2000s. In that case, 3 percent GDP could be achieved at the current employment growth and unemployment rate.
I encourage you to play around and devise your own "what if" scenarios—and use the Labor Market Sliders to make sure they add up.
April 19, 2017
The Fed’s Inflation Goal: What Does the Public Know?
The Federal Open Market Committee (FOMC) has had an explicit inflation target of 2 percent since January 25, 2012. In its statement announcing the target, the FOMC said, "Communicating this inflation goal clearly to the public helps keep longer-term inflation expectations firmly anchored, thereby fostering price stability and moderate long-term interest rates and enhancing the Committee's ability to promote maximum employment in the face of significant economic disturbances."
If communicating this goal to the public enhances the effectiveness of monetary policy, one natural question is whether the public is aware of this 2 percent target. We've posed this question a few times to our Business Inflation Expectations Panel, which is a set of roughly 450 private, nonfarm firms in the Southeast. These firms range in size from large corporations to owner operators.
Last week, we asked them again. Specifically, the question is:
What annual rate of inflation do you think the Federal Reserve is aiming for over the long run?
Unsurprisingly, to us at least—and maybe to you if you're a regular macroblog reader—the typical respondent answered 2 percent (the same answer our panel gave us in 2015 and back in 2011). At a minimum, southeastern firms appear to have gotten and retained the message.
So, why the blog post? Careful Fed watchers noticed the inclusion of a modifier to describe the 2 percent objective in the March 2017 FOMC statement (emphasis added): "The Committee will carefully monitor actual and expected inflation developments relative to its symmetric inflation goal." And especially eagle-eyed Fed watchers will remember that the Committee amended its statement of longer-run goals in January 2016, clarifying that its inflation objective is indeed symmetric.
The idea behind a symmetric inflation target is that the central bank views both overshooting and falling short of the 2 percent target as equally bad. As then Minneapolis Fed President Kocherlakota stated in 2014, "Without symmetry, inflation might spend considerably more time below 2 percent than above 2 percent. Inflation persistently below the 2 percent target could create doubts in households and businesses about whether the FOMC is truly aiming for 2 percent inflation, or some lower number."
Do such doubts actually exist? In a follow-up to our question about the numerical target, in the latest survey we asked our panel whether they thought the Fed was more, less, or equally likely to tolerate inflation below or above its targe. The following chart depicts the responses.
One in five respondents believes the Federal Reserve is more likely to accept inflation above its target, while nearly 40 percent believe it is more likely to accept inflation below its target. Twenty-five percent of firms believe the Federal Reserve is equally likely to accept inflation above or below its target. The remainder of respondents were unsure. This pattern was similar across firm sizes and industries.
In other words, more firms see the inflation target as a threshold (or ceiling) that the Fed is averse to crossing than see it as a symmetric target.
Lately, various Committee members (here, here, and in Chair Yellen's latest press conference at the 42-minute mark) have discussed the symmetry about the Committee's inflation target. Our evidence suggests that the message may not have quite sunk in yet.
April 11, 2017
Going to School on Labor Force Participation
In the aftermath of the Great Recession, labor force attachment declined. However, that pattern has been reversing itself lately. In particular, the labor force participation rate (LFPR) of the prime-age (25 to 54 years old) population, the core segment of the workforce, has been moving higher since late 2015. While this is good news, the prime-age LFPR remains well below prerecession levels, meaning that there are more than two million fewer prime-age people participating in the labor force. What factors have contributed to that decline? Where did those people go?
The Atlanta Fed LFP dynamics web page has an interactive tool that allows users to drill down into the drivers of the change in LFPR. The tool breaks the change in LFPR into two parts. The first part is the effect of shifts in the share of the population in different age groups (we use five-year age groups). The second part is the change attributable to shifts in the rate of nonparticipation. Using a methodology described here, we can drill deeper into the second part to learn more about the reasons for not participating in the labor force.
The U.S. Census Bureau will make the first quarter 2017 microdata on the reasons for nonparticipation available in a few weeks, so the following chart shows a decomposition of the 1.8 percentage point decline in the prime-age LFPR (not seasonally adjusted) between the fourth quarters of 2007 and 2016.
In this chart, "residual" pertains to the part of the total change in the LFPR that is attributable to the simultaneous shifts in both age-group population shares and age-group participation rates. In the present case, the residual is zero.
Because we are examining changes in prime-age participation, and all age groups within prime-age have reasonably similar participation rates, a change in the composition of ages tends to have little impact on the overall prime-age LFPR. Instead, the decline is due to shifts in the nonparticipation rate within age groups (the orange bar). In particular, the decline could indicate an increased likelihood of being in school, having family responsibilities that prevent participation, being in the shadow labor force (wanting a job but not actively looking), and a disability or poor health.
Although all these factors put downward pressure on participation, an important countervailing influence is that the education level of the population has been rising over time, and participation tends to increase with more education. In 2007, 41.0 percent of the prime-age population had a college degree, and they had an 88.3 percent participation rate versus 79.5 percent participation for those without a degree. By the end of 2016, the fraction with a degree had increased to 47.3 percent, and that cohort's participation rate had declined 1 percentage point, to 87.3 percent, versus a drop of 3.5 percentage points, to 76.0 percent, for those without a degree.
To see the importance of rising education on participation, the following chart shows the decomposition of the 1.8 percentage point decline in prime-age LFPR based on education-group population shares (degree and nondegree) instead of age-group shares.
In this chart, "residual" indicates the part of the total change in LFPR due to the simultaneous shifts in both education-group population shares and education-group participation rates.
As the chart shows, the shift in the education distribution of the prime-age population from 2007 to 2016 by itself would have increased the prime-age participation rate by about 0.7 percentage points (the green bar). Conversely, if education levels had not increased then the participation rate would have decreased by even more than it actually did. The nonparticipation effect would be larger for most nonparticipation reasons and especially for reasons of disability or poor health (−0.8 percentage points versus −0.5 percentage points). See the charts and analysis in the "health problems" section of the Labor Force Dynamics web page for more information on health-related nonparticipation by education.
Despite some partial reversal over the last year and a half, the prime-age LFPR is still lower than it had been prior to the recession. However, the decline in participation could have been even larger if the education level of the population had not also increased. Rising education is associated with a lower incidence of nonparticipation than otherwise would be the case, and it's principally associated with less nonparticipation attributable to disability or poor health. While researchers agree on the positive association between education and health, pinning down the specific reasons for this remains somewhat elusive. Factors such as income, informational, and occupational differences—as well as public policy choices—all play a role. Recent research by Nobel laureate Angus Deaton and Anne Case suggests that both education and racial differences are important considerations—emphasizing the sharply rising incidence of health problems among middle-age, white families with lower levels of education—and this Washington Post article highlights rising disability rates in rural America.
March 30, 2017
Bad Debt Is Bad for Your Health
The amount of debt held by U.S. households grew steadily during the 2000s, with some leveling off after the recession. However, the level of debt remains elevated relative to the turn of the century, a fact easily seen by examining changes in debt held by individuals from 2000 to 2015 (the blue line in the chart below).
Not only is the amount of debt elevated for U.S. households, but the proportion of delinquent household debt has also fluctuated significantly, as the red line in the above chart depicts.
The amount of debt that is severely delinquent (90 days or more past due) peaked during the last recession and remains above prerecession levels. The Federal Reserve Bank of New York reports these measures of financial health quarterly.
In a recent working paper, we demonstrate a potential causal link between these fluctuations in delinquency and mortality. (A recent Atlanta Fed podcast episode also discussed our findings.) By isolating unanticipated variations in debt and delinquency not caused by worsening health, we show that carrying debt—and delinquent debt in particular—has an adverse effect on mortality rates.
Our results suggest that the decline in the quality of debt portfolios during the Great Recession was associated with an additional 5.7 deaths per 100,000 people, or just over 12,000 additional deaths each year during the worst part of the recession (a calculation based on census population estimates found here). To put this rate in perspective, in 2014 the death rate from homicides was 5.0 per 100,000 people, and motor vehicle accidents caused 10.7 deaths per 100,000 people.
It is well understood that an individual experiencing a large and unexpected decline in health can encounter financial difficulties, and that this sort of event is a major cause of personal bankruptcy. Our findings suggest that significant unexpected financial problems can themselves lead to worse health outcomes. This link between delinquent debt and health outcomes provides more reason for public policy discussions to take seriously the nexus between financial well-being and public health.
March 20, 2017
Working for Yourself, Some of the Time
Self-employment as a person's primary labor market activity has become much less commonplace in the United States (for example, see the analysis here and here ). This is a potentially important development, as less self-employment may indicate a decline in overall labor market mobility, business dynamism, and entrepreneurial activity (for example, see the evidence and arguments outlined here ).
Recessions can be particularly bad for self-employment, with reduced opportunities for potential business entrants as well as greater difficulty in keeping an existing business going (see here for some evidence on this). However, the rate of self-employment has been drifting lower over a long period, suggesting other factors are also playing a role in the decision to enter and exit self-employment.
One especially troubling development is the decline in the rate of self-employment for those in high-skill service providing jobs (management, professional, and technical services)—the people you might expect to be particularly entrepreneurial. For example, for workers aged 25 to 54 years old, the self-employment rate has declined from 13 percent in 1996 to 9 percent in 2016, and for those 55 years of age or older, the rate has dropped from 27 percent to 19 percent (using data from the Current Population Survey).
Not only are people in high-skill service jobs less likely to be self-employed than in the past, those who are self-employed are also less likely to be working full-time. The fraction usually working full-time has decreased from about 79 percent in 1996 to 74 percent in 2016. (The full-time rate for comparable private sector wage and salary earners has remained relatively stable at around 88 percent.) One possible explanation for the decline in hours worked is the last recession's lingering effects, which made it harder to generate enough work to maintain full-time hours. Another possibility is that more of the self-employed are choosing to work part-time.
It turns out that both explanations have played a role. The following chart shows the percent of part-time self-employment in high-skill service jobs. The blue lines are for unincorporated businesses and the green lines are for incorporated businesses. In order to distinguish cyclical and noncyclical effects, the chart shows the part-time rate for those who want to work full-time but aren't because of slack business conditions or their inability to find more work (part-time for economic reasons, or PTER), and those who work part-time for other reasons (part-time for noneconomic reasons, or PTNER).
In the chart, I classify someone as self-employed when that person's main job is working for profit or fees in his or her own business (and hence it does not capture people whose primary employment is a wage and salary job but are also working for themselves on the side). The self-employed could be sole proprietors or own their business in partnership with others, and the business may assume any of several legal forms, including incorporation. The chart pertains to the private sector, excluding agriculture, and part-time is usually working less than 35 hours a week.
On the cyclical side, the PTER rates (the dotted lines) rose during the last recession and have been slowly moving back toward prerecession levels as the economy has strengthened. In contrast, the PTNER rates (the solid lines) have moved higher since the end of the recession, continuing a longer-term trend. Choosing to work part-time has been playing an increasingly important role in reducing full-time self-employment in high-skill jobs. Note that there is not an obvious long-term trend toward greater PTNER for those self-employed in middle- or low-skill jobs (not shown).
Shifting demographics is one important factor contributing to the decline in average hours worked. In particular, the PTNER rate for older self-employed is much higher than for younger self-employed, and older workers are a growing share of part-time self-employed, a fact that reflects the aging of the workforce overall. (For more on the self-employment of older individuals, see here .) The net result is a rise in the fraction of self-employed choosing to work part-time. The higher rate of PTNER for the older self-employed appears to be mostly because of issues specific to retirement, such as working fewer hours to avoid exceeding social security limits on earnings.
The last recession and a relatively tepid economic recovery reduced the hours that some self-employed people have been able to work because of economic conditions. However, there has also been a longer-term reduction in how many hours other self-employed people (especially those in occupations requiring greater education and generating greater hourly earnings) choose to work. This increased propensity to work only part-time in their business is another factor weighing on overall entrepreneurial activity.
March 02, 2017
Gauging Firm Optimism in a Time of Transition
Recent consumer sentiment index measures have hit postrecession highs, but there is evidence of significant differences in respondents' views on the new administration's economic policies. As Richard Curtin, chief economist for the Michigan Survey of Consumers, states:
When asked to describe any recent news that they had heard about the economy, 30% spontaneously mentioned some favorable aspect of Trump's policies, and 29% unfavorably referred to Trump's economic policies. Thus a total of nearly six-in-ten consumers made a positive or negative mention of government policies...never before have these spontaneous references to economic policies had such a large impact on the Sentiment Index: a difference of 37 Index points between those that referred to favorable and unfavorable policies.
It seems clear that government policies are holding sway over consumers' economic outlook. But what about firms? Are they being affected similarly? Are there any firm characteristics that might predict their view? And how might this view change over time?
To begin exploring these questions, we've adopted a series of "optimism" questions to be asked periodically as part of the Atlanta Fed's Business Inflation Expectations Survey's special question series. The optimism questions are based on those that have appeared in the Duke CFO Global Business Outlook survey since 2002, available quarterly. (The next set of results from the CFO survey will appear in March.)
We first put these questions to our business inflation expectations (BIE) panel in November 2016 . The survey period coincided with the week of the U.S. presidential election, allowing us to observe any pre- and post-election changes. We found that firms were more optimistic about their own firm's financial prospects than about the economy as a whole. This finding held for all sectors and firm size categories (chart 1).
In addition, we found no statistical difference in the pre- and post-election measures, as chart 2 shows. (For the stat aficionados among you, we mean that we found no statistical difference at the 95 percent level of confidence.)
We were curious how our firms' optimism might have evolved since the election, so we repeated the questions last month (February 6–10).
Among firms responding in both November and February (approximately 82 percent of respondents), the overall level of optimism increased, on average (chart 3). This increase in optimism is statistically significant and was seen across firms of all sizes and sector types (goods producers and service providers).
The question remains: what is the upshot of this increased optimism? Are firms adjusting their capital investment and employment plans to accommodate this more optimistic outlook? The data should answer these questions in the coming months, but in the meantime, we will continue to monitor the evolution of business optimism.
March 2, 2017 in Books , Business Inflation Expectations , Economic conditions , Economic Growth and Development , Forecasts , Inflation Expectations , Saving, Capital, and Investment , Small Business | Permalink | Comments ( 0)
February 28, 2017
Can Tight Labor Markets Inhibit Investment Growth?
One of the most vexing developments of the current expansion has been the long and persistent reduction in the pace of business fixed investment (see chart 1).
The slide in investment spending evident in this chart has had a substantial impact on the pace of gross domestic product (GDP) growth in recent years and is also behind the slow pace of capital accumulation that has been a major factor in the slow labor productivity growth postrecession .
The other notable aspect of chart 1 is that employment growth has been robust during most of the recovery, and that growth remains robust. That sustained performance has taken the economy to the point where measures of labor market performance can be reasonably described as "close to a state of full employment."
Continued strong employment growth could sensibly support a relatively bullish story on investment going forward. As the table below shows, "high-pressure" labor markets—defined as periods when the official unemployment rate falls below the Congressional Budget Office's estimate of the "natural unemployment rate"—tend to be associated with strong levels of business fixed investment spending.
That said, we are taking note of some cautionary sounding from a special question about investment constraints on the most recent Federal Reserve Small Business Credit Survey, whose full results will be released in April. (The Small Business Credit Survey is a collaboration among Federal Reserve Banks and collects information from small businesses throughout the country. The 2016 survey was open from mid-September to mid-December, and generated more than 16,000 responses—about 10,000 of which were from employer firms.)
One of the survey's special questions was the following: What factors constrained your investment decisions over the past 12 months? The respondents were allowed to check as many factors as they deemed relevant and, perhaps not surprisingly, the collective answer was "a lot of things," as chart 2 shows.
Though there are a lot of contenders in that chart, it was interesting to us that the modal response (though admittedly by a hair) was an inability to find or retain qualified staff. It gets even more interesting when you focus on stable, growing firms—those that were profitable in 2016, are increasing payrolls and revenues, and have been in business for at least six years (see chart 3).
For this group—by definition, the most dynamic firms in the sample—perceived constraints on talent acquisition and retention is easily the largest issue when it comes to investment spending headwinds, independent of the size of the firm (measured by annual revenues). Indeed, more than 50 percent of the businesses with revenue in excess of $10 million identified the labor market as a problem.
We want to be sufficiently modest about interpreting these survey results. (The survey's full results will be released in April.) We have only asked this question once and therefore have no ability to compare with historical data. We also don't know for sure if firms truly are being constrained by their ability to find or retain qualified staff, or if respondents were simply identifying with that option as an issue with their business in general. But the idea that business investment could be constrained by access to talent is important for thinking about the growth potential of the economy. The possibility that education and workforce development efforts could have spillover effects into investment growth is intriguing.
February 23, 2017
More Ways to Watch Wages
The Atlanta Fed's Wage Growth Tracker slipped to 3.2 percent in January from 3.5 percent in December. The Wage Growth Tracker for women was 3.1 percent in January, down significantly from what we saw in late 2016, when gains topped 4 percent. For men, the January reading was 3.4 percent, very close to its average for the past 12 months. As I noted last month, I did not think the unusually high female wage growth was sustainable, and that proved to be the case. Since 2009, the Wage Growth Tracker for women has averaged about 0.3 percentage points below that for men—the same as the gap in the latest data.
Understanding why the Wage Growth Tracker slowed last month highlights the importance of being able to look beyond the top-line number. To provide Wage Growth Tracker users with more information, we have now added several additional cuts of the data to the Wage Growth Tracker web page. The amount of detail we can provide is limited by sample size considerations, and as a result, the additional data are reported as 12-month moving averages. The new data provide more detailed age, race, education, and geographic comparisons, as well as comparisons across broad categories of occupation, industry, and hours worked. As an example, here is a look at the (12-month average) median wage growth data for those who usually work full-time versus those who usually work part-time.
Have fun with these new tools, and we encourage you to comment and let us know what you think.
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