The Hiring Forecasts of Small Firms: Will the Pace of Employment Growth Pick Up?
The U.S. Bureau of Labor Statistics (BLS) announced today that the U.S. labor market added 175,000 payroll jobs in May, continuing a trend of steady but disappointingly slow employment growth. The employment recovery has been even slower among small firms. Will it pick up in the coming 12 months? Results from the Atlanta Fed's latest survey of small businesses in the Southeast suggest that employment growth among small firms will continue but not necessarily at a faster pace.
Since the recession began, changes in employment have been asymmetric across firm size. In contrast to large firms, employment at small and medium sized businesses began decreasing earlier, declined more, and, by last March, was a little further from its prerecession level. As of the first quarter of 2012, employment at firms with fewer than 500 employees was 5 percent below prerecession levels, compared to just 2 percent for firms with more than 500 employees. So why is employment at small firms not recovering as quickly as employment at large firms? Is it poised to accelerate and perhaps catch up?
While the Business Employment Dynamics data series from the BLS only go through first-quarter 2012 (chart 1), we can use our semi-annual survey of small business in the Southeast to find out a little more about the experiences of small firms through first-quarter 2013 as well look at their forecasts through the first quarter of 2014. Four-hundred-seventy-eight firms across the industry and age spectrum participated in the first-quarter 2013 survey, which was conducted during the first three weeks in April. Although the survey is not a random sample, the results are weighted to make them more representative of a national distribution.
When asked about changes in employment over the period Q1 2012 to Q1 2013, employer firms on net said there was almost no change. Slightly more than 40 percent of firms said they had not altered employment levels. The remainder of the responses were distributed pretty evenly between "expansion" and "contraction". As you can see in chart 2, the distribution of firms creating jobs was almost a mirror image of the distribution of firms shedding jobs in terms of the magnitude of change.
In addition to asking about changes during the past 12 months, the survey probed small firms about their expectations for the coming 12 months. Using the power of our panel data set, we can compare the expectations of firms that took the survey exactly one year ago with their actual hiring activity during that time period to determine how accurately firms predict what the future holds and whether these hiring plans are indeed good forecasts of future activity.
As it turns out, the 184 firms participating in both surveys came pretty close to meeting their hiring expectations. However, they did tend to overestimate the extent to which employment would increase (or underestimate the extent to which it would decrease), regardless of how well firms were performing at the time they made their forecast (see chart 3). For example, firms that had recently experienced reductions in their workforce expected the greatest positive change in the pace of hiring, and in fact went on to report the highest actual change during this period. Firms that had not changed their employment levels recently or had changed them by up to 10 percent expected very little growth—on average, they achieved just slightly less than expected. Regardless of how well the firm had recently performed (in terms of employment growth in the previous period), the degree to which hiring increased or downsizing decreased was less pronounced than anticipated.
Small firms are reasonably good at predicting the direction and relative magnitude of their employment growth, but on average tend to overestimate. For this reason, it might be useful to examine changes in the hiring expectations index (as opposed to changes in the pace of employment growth) when trying to understand how the forecast of firms participating in the survey might translate into actual employment growth of small firms in the Southeast.
Chart 4 shows the hiring index of firms across four broad industry groups. In the first quarter of 2013, the index for hiring in the coming 12 months was essentially unchanged from the Q3 2012 survey, and significantly below that of the Q1 2012 survey. The only industry whose employment forecast was notably positive was the construction and real estate industry. Firms in that category have been steadily increasing their hiring forecasts since the third quarter of 2011.
The fact that hiring expectations did not improve in the first-quarter survey leads to another, perhaps more important question: Why didn't they?
One contributing factor that could be having a particularly large impact on hiring expectations is rocky sales. Firms may be less willing to hire if they are uncertain about the future or if they do not expect consistent sales growth. Indeed, by looking at the experiences of firms in the past 12 months, we can tell that there is a clear correlation between rising sales and rising employment. As chart 5 shows, half of employer firms reported a recent rise in sales, and the more sales had risen, the more likely firms were to have increased their workforce.
A couple of questions that arise from chart 5 are: What about the firms that recently experienced sales growth but didn't hire? Are they planning to hire in the coming 12 months? About one-third of firms say "yes". One driving factor in that decision appears to be sustained sales growth; another is reduced uncertainty. As chart 6 makes apparent, the sales expectations of firms in this group is higher on average for the one-third of firms that say they do plan to hire in the coming 12 months than for the two-thirds who do not. All the firms in the hiring group also expect sales growth to continue, with the most common response being greater than 10 percent growth. In contrast, while 77 percent of firms in the not-hiring group anticipate sustained sales growth, the group’s most common response was lower than that of the hiring group: 1 percent to 5 percent.
Another factor that may be related to hiring is reduced uncertainty. Employer firms experiencing sales growth in the past 12 months are more likely to anticipate hiring if they perceive a decrease in uncertainty compared to six months ago. Seventy percent of firms that had a recent increase in sales and decreased uncertainty concerns relative to six months ago anticipate hiring in the coming year. In contrast, 46 percent of those who had experienced a recent increase in sales but also perceived heightened uncertainty anticipate hiring.
For now, the results suggest that uncertainty and rocky sales growth are negatively affecting the hiring plans of small firms and, unfortunately, that small firms are not likely to increase their rate of hiring in the next 12 months. However, if uncertainty eases and sales growth continues, small firms will likely revisit their hiring plans and the pace of hiring just might improve.
By Ellyn Terry, a senior economic analyst in the Atlanta Fed's research department
The labor force participation rate ticked up in May, as did the rate of unemployment. As we have noted in the past, the near-term trajectory of the unemployment rate depends critically on what happens to the participation rate. So the question is, can we expect further upward changes in the participation rate? The answer depends a lot on the labor market attachment of those that are currently out of the labor force.
A few weeks ago, my frequent coauthor, Julie Hotchkiss, wrote about what we can gain from detailed labor market data about the activities of people who have exited the labor force. In her posting, she discussed the overall increase in exits from the labor force, with a focus on 25–54 year olds. Her work concluded that while people identified "Household Care" as the dominant activity for those not in the labor force, there has been a significant upward shift since the recession in those indicating "School" or "Other" as their primary reason for not being in the labor force. A supposition is that at least those that indicated they were in school would reenter the labor force at some point, doing so with a higher level of skills or, at least, with skills that are better aligned with labor demand. However, because we know little about those in the Other category, the future labor market attachment for them is less clear.
This post explores data on transitions into the labor force, primarily for those in the Other category. As in the earlier blog, the focus is on individuals aged 25–54, as retirement dominates the activity of older individuals not in the labor force and schooling dominates the activity of younger individuals not in the labor force.
One indicator of whether those in the Other group are planning to reenter the labor force is whether the individuals in this group are classified as marginally attached to the labor force. A nonparticipant who is marginally attached indicates they want employment or are available for employment. Also, they indicate having looked for a job in the previous year but not actively looking for a job at present. Using monthly data from the Current Population Survey (CPS) that are matched year over year, we see that the marginally attached workers do transition back into the labor force at twice the rate of all individuals who are not in the labor force, as chart 1 illustrates. These rates are relatively stable over time.
As chart 2 shows, a much higher proportion of individuals in the Other category are marginally attached to the labor force, compared to other types of nonparticipants. Moreover, the percentage of these marginally attached nonparticipants has increased from around 20 percent to 30 percent over the last three years.
This higher probability of marginally attached workers returning to the labor force combined with the significantly increased share of marginally attached workers in the Other category suggests that we should expect to find a higher share of those in the Other group returning to the labor force than we've seen in the past. But it turns out that this expected development is not what has happened. The Other group also includes individuals who are not marginally attached to the labor market, and their transition rates into the labor market have declined. On net, while the transition rate to employment is highest for the Other category (reflecting the large of share of marginally attached), the transition rate into the labor force does not fully reflect the increased level of marginal attachment to the labor force.
The group with the next highest transition rate to employment is in the School category, which reflects the inherent transitory nature of that activity. However, it is noteworthy that the school transition rate is lower than it was before the recession. This development reflects an increase in the share of individuals continuing to indicate that school is their primary reason for not participating in the labor force from one year to the next. And it suggests that the lower opportunity cost of attending school is influencing the decision to remain in school longer.
While these trends suggest that we could expect to see higher rates of return to the labor force going forward, this potential development will likely require a much better showing of jobs numbers than were seen today before kicking in.
By Melinda Pitts, research economist and associate policy adviser
Labor force nonparticipants: So what are they doing?
As Dave Altig, Atlanta Fed research director, pointed out earlier this week in this blog post, there is a great deal of interest these days in the labor force participation rate—particularly its level and the direction it's going. The question that seems to be on everyone's mind is how many of the nonparticipants in the labor force can we expect to return to the market. The answer to this question has immediate implications for the unemployment rate (especially if all these nonparticipants were to return to unemployment rolls), and longer-term implications for economic growth—our economy needs workers to fuel production.
The analyses that I can find to date are all primarily focused on a statistical detangling of demographic versus behavioral changes, structural versus cyclical changes, and employment trend versus employment gap debates. But all of this discussion begs the question that my colleague, Melinda Pitts, and I have been investigating: What are these labor force nonparticipants doing? Perhaps an answer to that question will help us get a better handle on which nonparticipants are likely to return to the labor force in the near future.
The Current Population Survey (CPS), administered by the U.S. Bureau of Labor Statistics (BLS), asks labor force nonparticipants about their reason for absence (details of the CPS questionnaire are available from the NBER). The reason given by nonparticipants that gets most of the attention is "discouraged over job prospects." In April 2012, these people accounted for only 1.1 percent of all nonparticipants (41 percent of the marginally attached—those who want a job, are available to work, and searched in the previous year). The vast majority of nonparticipants are absent because of retirement, disability, going to school, caring for household members, or other reasons.
Using the latest survey data we have available (November 2011), we find that most nonparticipants are retired (48 percent); the share who are in school, disabled, or taking care of household members are 18 percent, 16 percent, and 15 percent, respectively; and the share in the category termed "Other" comes in at about 2 percent.
For purposes of better understanding the decline in labor force participation, however, we look at the reasons for absence given by people who leave the labor force. Those who have left the labor force are arguably more likely to return (depending on the reason, of course) than those who have never been in the labor force. A feature of the CPS allows us to track certain individuals from one year to the next, so we are able to identify people who leave the labor force. Chart 1 illustrates how individuals who are not in the labor force—but who were employed or unemployed the previous year—are distributed across the reasons for nonparticipation. The raw data are not seasonally adjusted, of course, so we plot the numbers as a 12-month moving average—this approach does not affect the overall observed trends in the data. In addition, we restrict our analysis here to those between the ages of 25 and 54, since retirement overwhelmingly dominates the nonparticipation decisions of older workers, and schooling dominates the nonparticipation decisions of younger workers.
Chart 1 illustrates what the labor force participation rates have been telling us. For every reason given for absence, except perhaps "Retired," the number of people leaving the labor force has increased during or after the recession of 2008. The most dramatic increases are seen among those people giving "School" and "Other" as a reason. However, since we are in search of changes in reasons that might be out of the ordinary, especially any significant upward shifts in nonparticipants giving a particular reason during and after the recession, we also look at how these folks leaving the labor force are distributed across the different reasons. This information will tell us whether the number of people giving one particular reason increased disproportionately compared with the other reasons.
Chart 2 plots the shares of all of those leaving the labor force (ages 25–54) giving each reason for their absence. Since the beginning of the recession, there has been a significant shift toward the reasons of "School" and "Other" among nonparticipants who have left the labor force within the previous year. The share levels attained by the reasons of "School" and "Other" are historically unprecedented by the end of the data series. These shifts also appear to have come mostly from a decline in the share of people leaving the workforce to take care of household members (HHcare). This is evidenced through the dramatic drop in the share giving the "HHcare" reason at the same time.
It is difficult to interpret the implications of the rise in share of "Other" as a reason for nonparticipation among those leaving the labor force, although this category may be capturing some of the discouraged workers. The implication for the rise in "School" is unmistakable, however. With reasonable expectations, these individuals should re-enter the labor force with enhanced—or at least better-aligned—skills that will be able to make a positive contribution to overall economic growth.
By Julie Hotchkiss, research economist and policy adviser in the Atlanta Fed's research department
The March to April decline in the unemployment rate from 8.2 percent to 8.1 percent was arithmetically driven by yet another decline in the labor force participation rate (LFPR).
The decline in the LFPR, now at its lowest level since the early 1980s, is itself being influenced by a confounding mix of demographic change and other behavioral changes that nobody seems to understand—a point emphasized by a gaggle of blogs and bloggers such as Brad DeLong, Carpe Diem, Conversable Economist, Free Exchange, and Rortybomb, to name a few.
With respect to the first observation, in a previous post my colleague Julie Hotchkiss described how to use our Jobs Calculator to get a ballpark sense of what the unemployment rate would have been had the LFPR not changed. If you follow those procedures and assume that the LFPR had stayed at the March level of 63.8 percent instead of falling to 63.6 percent, the unemployment rate would have risen to 8.4 percent instead of falling to 8.1 percent.
It is clear that interpreting this sort of counterfactual experiment depends critically on how you think about the decline in the LFPR. The aforementioned post at Rortybomb cites two Federal Reserve studies—from the Chicago Fed and the Kansas City Fed—that attempt to disentangle the change in the LFPR that can be explained by trends in the age and composition of the labor force. These changes are presumably permanent and have little to do with questions of whether the labor market is performing up to snuff.
The following chart, which throws our own estimates into the mix, illustrates the evolution of the actual LFPR along with an estimate of the LFPR adjusted for demographic changes:
As the header on the chart indicates, our estimates suggest that roughly 40 percent of the change in the LFPR since 2000 can be accounted for by changes in age and composition of the population—in essentially the same range as the Chicago and Kansas City Fed studies. (If you are interested in the technical details you can find a description of the methodology used to generate the chart above, based on work by the University of Chicago's Rob Shimer.
In other words, 0.9 percentage points of the decline in the LFPR since the beginning of the past recession can be explained by demographic trends (as the baby boomers age, the labor force will grow more slowly than the total population [ages 16 and up]). Subtracting the demographic trends still leaves 1.5 percentage points to be explained, a number right in line with Brad DeLong's back-of-the-envelope calculation of "cyclical" LFPR change.
As DeLong's comments make clear, the interpretation of the nondemographic piece of the LFPR change requires, well, interpretation. And the consequences of connecting the dots between changes in the unemployment rate and broader labor market performance are enormous.
In the recently released Summary of Economic Projections following the last meeting of the Federal Reserve's Federal Open Market Committee, the midpoint of the projections for the unemployment rate at the end of 2013 is 7.5 percent. Turning again to our Jobs Calculator, we can get a sense of what sort of job creation over the next 20 months will be required given different values of the LFPR. For these estimates, I consider three alternatives: The LFPR stays at its April level, the LFPR reverts to our current estimate of the demographically adjusted level (that is, increases by 1.5 percentage points), and an intermediate case in which the LFPR increases by 0.7 percentage points—the lower end of DeLong's estimate of "people who really ought to be in the labor force right now, but who are not."
"Are [people who really ought to be in the labor force right now, but who are not] now part of the 'structurally' non-employed who we will never see back at work, barring a high-pressure economy of a kind we see at most once in a generation?"
As you can see, the answer to that question matters a lot to how we should think about progress on the unemployment rate going forward.
By Dave Altig, executive vice president and research director at the Atlanta Fed
What if...? Looking beyond this month's jobs numbers
Today's employment numbers for February illustrate that while mathematically simple, the relationship between employment, unemployment, and the labor force participation rate is complicated.
One might expect that we would have seen a drop in the unemployment rate in February, given the addition of an estimated 227,000 payroll jobs for the month (see the U.S. Bureau of Labor Statistics' Employment Situation for February 2012). However, the share of the working-age population in the labor force (or, rather, the labor force participation rate, LFPR) is estimated to have increased from 63.7 percent in January to 63.9 percent in February. A 0.2 percentage point increase in the LFPR is not unprecedented, but after a year of flat and declining labor force participation, it's notable. There are a lot of reasons why the supply of labor, as represented by the LFPR, rises and falls over time. In the short run, a decision of someone to enter (or re-enter) the labor force could be driven by a reassessment of job prospects. This sort of situation is why the LFPR might rise as an economy improves from a very weak position.
While not its primary purpose, the Federal Reserve Bank of Atlanta's Jobs Calculator, which was introduced last week, can help figure out roughly what the unemployment rate would have been if the LFPR had remained at its January level of 63.7 percent.
From the Jobs Calculator web page, first set the number of months to one. Then set the labor force participation rate to 63.7 percent. Next, adjust the unemployment rate until the average monthly change in payroll employment gets close to 227,000. (For example, an unemployment rate of 7.9 percent results in an estimated change in employment of 250,743, using data from the U.S. Bureau of Labor Statistics's Current Employment Survey. This calculation necessarily assumes that people enter and leave the labor force from unemployment and is only approximate because it's using February data.)
So, if the LFPR had remained at the 63.7 percent it was in January, the unemployment rate would have been roughly 8 percent in February.
Look for enhancements to the Jobs Calculator in the coming months that will make this sort of calculation more straightforward.
By Julie Hotchkiss, a research economist and policy adviser in the Atlanta Fed's research department
How many jobs does it take? Introducing the Atlanta Fed's Jobs Calculator
When I began my career at the Atlanta Fed in 2003, the U.S. labor market had not yet started creating jobs on net again after the 2001 recession. The question being asked over and over was, "How many jobs does the U.S. economy need to create in order to lower the unemployment rate by a certain amount?" I even participated in the discussion by writing an Economic Reviewarticle on the subject.
Of course, the Federal Reserve's interest in how many jobs it takes to lower the unemployment rate comes directly from Section 2A of the Federal Reserve Act, which states:
"The Board of Governors of the Federal Reserve System and the Federal Open Market Committee shall maintain long run growth of the monetary and credit aggregates commensurate with the economy's long run potential to increase production, so as to promote effectively the goals of maximum employment, stable prices, and moderate long-term interest rates."
This passage is often referred to as the Fed's "dual mandate" for monetary policy. Put simply, the Fed wants to achieve (1) stable prices and (2) maximum employment. Reduction in the unemployment rate is commonly used as a measure of the progress toward the goal of maximum employment.
In a technical sense, answering the question "How many additional jobs over the next Y months are needed to lower the unemployment rate by X percentage points?" does not require a difficult calculation. But it does require some knowledge about the U.S. Bureau of Labor Statistics's (BLS) Household Survey, which gives us the official measure of the U.S. unemployment rate. This survey is based on estimates of the size of the labor force and the number of people employed that are inferred from a survey of individual households. The Household Survey differs from the BLS's Payroll Survey, which provides another estimate of employment from a survey of the payroll of individual businesses. Early each month, the estimate of employment from the Payroll Survey shares the spotlight with the Household Survey's estimate of the unemployment rate when the BLS releases its monthly employment report.
To calculate the change in employment needed to achieve a particular unemployment rate requires an assumption about how much the labor force will grow or an assumption about labor force participation given a particular population growth rate. The more the labor force grows (or the participation rate increases), the more jobs the economy needs to create, on net, to absorb the larger labor force.
In recent months, economists again (here and here) are asking (or pontificating on), "How many jobs does it take...?" To help answer that question, we at the Atlanta Fed have developed a new tool that will make the calculation for you. The tool—called the Jobs Calculator—is available on the Atlanta Fed's Center for Human Capital Studies' web page. (Readers should note that the calculator currently uses data from the January employment report, the most recent one available. When the February report is released on March 9, the data the calculator uses will be updated.)
Using the tool is as simple as choosing the target unemployment rate you want to achieve and when you want to achieve it. The Jobs Calculator produces the average number of jobs that need to be created, on net, per month in order to reach the target in the specified time period. You can even make some adjustments in the assumptions about labor force participation and population growth (and hence labor force growth). Of course, the calculator doesn't answer the questions of what numbers to plug in or why. That's up to you.
Please tell us what you think.
By Julie Hotchkiss, a research economist and policy adviser in the Atlanta Fed's research department
Weighing the risks to the inflation outlook: Two views
The Federal Reserve Bank of Atlanta's Survey of Business Inflation Expectations released earlier today showed a continuation of rather modest expectations for unit cost pressures over the coming 12 months. In February, our panel of firms reported a 1.9 percent average expected rise in unit costs over the coming year, still within the very narrow 1.8 percent to 2 percent range the group has been reporting over the past five months.
That's the good news. Now for some (potentially) bad news. In a special question this month, we asked the panel to weigh in on their expectations for annual unit cost increases over the longer term—specifically, the next 5 to 10 years. The group's expectation was a percentage point higher, at 2.9 percent.
The reason for the higher expectation for unit costs over the longer term can be seen in the following chart, which compares how the group assigns probabilities to unit cost changes over the next 12 months to how they judge these probabilities over the longer term.
In both instances, the Atlanta Fed's Business Inflation Expectations panel of firms puts the greatest likelihood that unit costs will rise in the 1 percent to 3 percent range—in a range that matches the Federal Open Market Committee's longer-term inflation objective.
But how does the group assess the risks around that increase? Over the short term, the panel sees a higher likelihood that unit costs may fall short of the 1 percent to 3 percent range. Specifically, the group sees a 36 percent chance that unit costs will rise less than 1 percent compared against only a 26 percent chance that they will rise above 3 percent. Yet when sizing up the next 5 to 10 years, the group sees only a 15 percent chance that unit costs will rise less than 1 percent per year compared with a 46 percent chance that costs will rise by more than 3 percent.
What our panel of firms appears to be telling us is that the risks to the inflation outlook—in both the near term and longer term—aren't particularly balanced. In the near term, they weigh the inflation risks more heavily to the downside. But looking over the next 5 to 10 years, the panel sees the inflation risks leaning decidedly to the upside.
What we can't tell from these data is whether the panel's assessment of the inflation risks is different today than it was before. After all, this is the first time we've asked the question, but you can bet it won't be the last.
Yesterday's wholesale trade report, with its positive surprise in December inventory accumulation, has estimates of fourth quarter gross domestic product (GDP) on the rise again.For the advance GDP release, the U.S. Bureau of Economic Analysis assumed that the book value of merchant wholesale inventories rose by $17 billion (at a seasonally adjusted annual rate, or SAAR) in December. The wholesale trade report suggests the book value instead may have risen by $56 billion SAAR. Our own calculations suggest fourth quarter GDP may be revised up from 2.8 percent to around 3.1 percent.A piece of that revision comes from positive sales activity, which would appear to be an unambiguous plus.
The inventory piece is trickier. Forecasters have a tendency—because the statistics have a tendency—to take a larger-than-expected inventory buildup in one quarter out of growth estimates for the next quarter. The implication in present tense is, of course, that 2012 may start out on the slow side as the fourth quarter inventory swell is run off.
That's not how we see it. Our current read is that it is better to think of the fourth quarter inventory buildup as a payback from a decumulation in the third quarter. Here's a look at overall inventory changes over the recent past, broken down into their various industrial components:
If you look hard, you will see that, though the fourth quarter inventory rise was broad-based, the third to fourth quarter change in wholesale inventories was particularly notable. In fact, the wholesale inventory picture in the back half of 2011 was dominated by a fairly large decumulation of nondurable goods inventories in the third quarter, a decline that was reversed in the last three months of the year:
So, consider two stories that might frame thinking about the role of inventories in GDP growth in the first quarter or first half of this year. One story is inventory-inflated growth in the fourth quarter of 2011, to be followed by payback in the form of a drag on production in the first quarter (or so) of 2012. Another story is that the drag actually emerged in the third quarter of last year, providing a little extra juice in the fourth quarter, with no particular consequences for the current-year growth trajectory.
Right now, it looks to us like the latter story might be the right one. Of course, that doesn't mean there aren't significant risks to the outlook for domestic production, and hence inventories. For instance, although today's report on international trade in December was relatively benign in terms of fourth quarter GDP revisions, it did show a substantial further weakening in exports to the euro zone. Weaker demand from Europe will weigh on U.S. export growth. The big unknown is how weak that demand will get.
By Dave Altig, senior vice president and research director at the Atlanta Fed
Comparisons can be useful in determining where the economy is at any given point in time, and today's Employment Situation report from the U.S. Bureau of Labor Statistics provides another opportunity to do just that. According to that report, the U.S. economy added 243,000 nonfarm payroll jobs in January 2012. But total nonfarm employment is still 5.6 million lower than at the start of the last recession (December 2007).
For additional comparisons, more than 1 million fewer people filed initial unemployment insurance claims during the last week of January 2012 than during the last week of January 2009 (at the height of the recession). However, 55,000 more people filed initial claims during the last week of January 2012 than during the last week of January 2007 (before the recession started).
When examining layoffs, more than 400,000 fewer workers were laid off or discharged during November 2011 (according to the most recent data) than during the height of the recession in November 2008. Nonetheless, there were roughly a million fewer job openings and hires than during November 2007 (before the recession started).
Nominal average hourly earnings of wage and salary workers were about two dollars (9.6 percent) higher in January 2012 than around the start of the recession (January 2008). Nonetheless, real average hourly earnings (controlling for inflation) were only eight cents (0.8 percent) higher over the same time period.
Considering everything, there is both good news and bad news in the labor market today.
The median three-digit NAICS (North American Industry Classification System) industry lost 7 percent of its jobs during the most recent recession. In other words, half of the industries lost less than 7 percent of their jobs and half of the industries lost more than 7 percent of their jobs. Industries faring the worst (those in the 75th percentile of job losses) shed 13 percent of their jobs. And what might be considered "fortunate" industries (those in the 25th percentile of job losses) saw only 3 percent of their jobs disappear over this time period.
Chart 1 compares the year-over-year employment growth among industries that experienced below-median and above-median job loss, as well as those industries in the 75th and 25th percentiles of job loss. That chart also shows another potential bit of good news—that is, in spite of the dramatic differences in job losses, all four categories of industries are currently adding jobs at about the same rate. And that overall job growth, at about 0.24 percent per month, is roughly the same as the average monthly job growth seen between 1993 and 2000 and exceeds the average monthly growth before the recession, from 2004 through 2007.
Still, there is more bad news. At the current rate of growth, those industries that experienced above-median job loss during the recession will not regain prerecession employment levels until the end of 2015.
Chart 2 illustrates the employment level for those industries with above median job losses along with projections of employment based on three assumptions of monthly employment growth. To even recover before the end of 2013 the jobs that they lost during the recession, these industries as a group would need to experience extraordinary employment growth.
Does the projected labored employment recovery among these particularly hard-hit industries suggest there are more serious structural impediments to the efficient operation of the labor market today than there were after the previous two recessions? Several posts on this blog (here and here, for example) have addressed this question of structural change without coming to a definitive answer. Returning to chart 1 gives us yet another opportunity to speculate on this point.
Note that the category in chart 1 into which each three-digit industry is placed (above-median or below-median job loss, etc.) is based on job losses between January 2008 and June 2009. Plotting the annual growth rates back to 1990 illustrates that the industries that were hardest hit during the most recent recession were also those with the greatest job losses during the previous two recessions. So there appears to be nothing special about these industries that led to their suffering during the most recent recession.
Additionally, the pattern of recovery of these hardest-hit industries is similar to that experienced after the previous two recessions. Like before, the worst performing industries (those with job losses in the 75th percentile) are adding jobs at a faster rate than industries that did not suffer as much. Industries in the 75th percentile of job losses added jobs in 2011 at an average monthly rate of 0.22 percent; industries with below-median losses added jobs at an average monthly rate of 0.12 percent. This analysis does not suggest to me that unique structural features of this recession or recovery are holding employment growth back—it appears that the culprit is simply the extraordinarily deep hole the economy, and thus the job market, fell into this time around. The bad news, then, is that time may be the only answer for those industries to fully recover.
By Julie Hotchkiss, a research economist and policy adviser in the Atlanta Fed's research department
Where's inflation heading? Well, here's what the minutes of the December meeting of the Federal Open Market Committee (FOMC) had to say on the subject:
"Participants observed that inflation had moderated in recent months as the effects of the earlier run-up in commodity prices subsided . . . many participants judged that the moderate expansion in economic activity that they were projecting . . . would be consistent with subdued inflation going forward."
But not all FOMC meeting participants viewed these trends with equanimity:
"Indeed, some expressed the concern that, with the persistence of considerable resource slack, inflation might run below mandate consistent levels for some time."
According to Reuters, San Francisco Fed President John Williams said it this way:
"The data so far on the inflation front are confirming my view that inflation is ebbing and moving to be too low, and that is an important driver of my thinking about policy."
But as you might expect, some see the inflation risks weighing a bit on the other side of the scale. Again, from the December FOMC meeting minutes:
"Some participants were concerned that inflation could rise as the recovery continued . . . A few participants argued that maintaining a highly accommodative stance of monetary policy over the medium run would erode the stability of inflation expectations."
In fact, Philadelphia Fed President Charles Plosser had this to say in a speech earlier this week:
"I do anticipate that with many commodity prices now leveling off or falling, and inflation expectations relatively stable, inflation will moderate in the near term . . .
"But as a policymaker, my focus is less on the near term and more on the medium term. Looking further ahead, I believe we must monitor the inflation situation very carefully, particularly in this environment of very accommodative monetary policy. Inflation most often develops gradually, and if monetary policy waits too long to respond, it can be very costly to correct. Measures of slack such as the unemployment rate are often thought to prevent inflation from rising. But that did not turn out to be true in the 1970s. Thus, we need to proceed with caution as to the degree of monetary accommodation we supply to the economy."
What doesn't seem to be in dispute is that monitoring the data for any sign that the inflation trend is shifting—either higher or lower—is probably a good idea. And there are a lot of data to watch. In a speech last year to the Calhoun County Chamber of Commerce, Atlanta Fed President Dennis Lockhart had this to say about reading the inflation data:
"To achieve price stability, policymakers must detect inflation in its early stages before it is firmly established, especially in the psychology of consumers and businesses. This early detection is a challenge because inflation is not easily measured in the short term with any precision. No single price statistic enjoys a sufficient vantage point from which to assess inflation in the short term. With imperfect tools, inflation is more easily monitored than precisely measured."
The research department of the Federal Reserve Bank of Atlanta has taken pretty seriously the task of monitoring inflation developments. Where there are gaps in our information, we've been working to fill them with data, and we've aggregated it all into one place: the Inflation Project web page.
On the Inflation Project, we now report a sticky-price CPI statistic calculated from consumer price index data using only those components whose prices are slow to change. Joint research with the Cleveland Fed has shown this measure to be helpful when thinking about inflation expectations. Using Treasury Inflation-Protected Securities data, we now produce a weekly measure of the probability of a sustained deflation. And come January 27, we'll begin reporting the results of a monthly survey of business inflation expectations that examines firms' price-setting environment and the pricing pressures they face. From the responses, we'll generate a monthly measure of respondents' year-ahead unit cost expectations.
But of course, there are already a lot of data to keep an eye on. To make it a little easier to gain some perspective, we're also unveiling our inflation dashboard. The dashboard provides a platform for visualizing some of the data we commonly monitor to keep abreast of emerging inflation developments. It tracks 30 data series grouped into six major categories—retail prices, inflation expectations, labor costs, producer prices, material and commodity costs, and money and credit.