The Atlanta Fed's macroblog provides commentary and analysis on economic topics including monetary policy, macroeconomic developments, inflation, labor economics, and financial issues.
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February 14, 2019
Trends in Hispanic Labor Force Participation
Although the labor force participation (LFP) rate has fallen significantly for the overall population during the past two decades, the trends can differ a great deal depending on which demographic group you examine. One way to view these varied, ever-changing patterns is to use the Atlanta Fed's Labor Force Participation Dynamics tool. We recently redesigned the tool, adding a new interface and more options for understanding specific demographic groups' LFP. The tool also allows users to see what factors (such as disability/illness, being in school, retirement, or family responsibilities) influence changes in the LFP rate for different groups.
While the tool shows us many stories, a particularly interesting one is the experience of people of Cuban, Mexican, Puerto Rican, South or Central American, or other Spanish culture or origin regardless of race (henceforth referred to as Hispanic). Hispanics are a growing share of the U.S. population (the U.S. Bureau of Labor Statistics [BLS] projects that nearly a fifth of the people in the labor force will be Hispanic by 2024, up from a tenth in 1994), and therefore Hispanics' differences in attitudes and preferences for work will exert an increasingly great effect on headline LFP numbers.
Let's parse the LFP rate among Hispanics. First, the Hispanic population is more likely to engage in the labor force than non-Hispanics. (In 2017, the LFP rate of Hispanics was 66.1 percent, compared to 62.2 percent for non-Hispanics). Second, their LFP has fallen by less during the past two decades.
The pictures below come from the redesigned tool. The first two charts compare the decline in the LFP rate for all ethnicities (Chart 1) versus Hispanic (Chart 2) as the combination of six nonparticipation categories. Each colored bar represents how much a particular category of nonpartipation has changed since the fourth quarter of 1998. The red line shows the summation of the change in each nonparticipation category, or the net change in the LFP rate. For example, the LFP rate overall has declined 4.2 percentage points (ppts) during the past two decades. However, among Hispanics, it has fallen significantly less—just 1.0 ppt. A comparison of the size and direction of each of the nonparticipation categories between the two charts shows many differences in the factors affecting the decline in the LFP rate of each group.
Because differences across ethnicity could reflect differences in their age distributions—Hispanics are younger on average than the population as a whole—it is important to control for this difference. Using the tool, it's easy to narrow this comparison to look specifically at 26–55 year olds.
In particular, the LFP rate for women of all ethnicities from 26 to 55 years old has declined by 1.0 ppt since 1998. In sharp contrast, the LFP rate for Hispanic women 26 to 55 years old has actually increased by 3.8 ppts. Compared to 20 years ago, this group is less likely to say they don't want a job because of disability/illness (1.1 ppt) and family responsbilities (1.4 ppt). This group is also less likely to be part of the shadow labor force (1.4 ppt) compared to two decades ago. (The shadow labor force, as we define it, is made up of individuals who say they want a job but are not considered unemployed by the BLS.) This article from the BLS delves into more detail about Hispanics in the labor force.
The LFP tool allows you to explore many other labor force stories. Users can cut the LFP data by three education categories (less than a high school degree, high school or some college, or associate's degree or higher), two age groups (26–55 or all ages), three race/ethnicity categories (white non-Hispanic, black non-Hispanic, and Hispanic) and for men and women. One thing that the tool makes clear is that the factors that influence individual decisions to work, look for work, or to pursue other activities vary across demographic groups, and each group's experience contributes to our understanding of movements in the overall LFP rate.
January 16, 2019
I didn't coin the title of this blog post. It was the label on a chart of the Federal Reserve's balance sheet that appeared in an issue of The Wall Street Journal last week. I've led with this phrase because it does seem to capture some of the sentiment around what has become the elephant in the monetary policy room: Is the rundown in the size of the Fed's balance sheet causing an unanticipated, and unwarranted, tightening of monetary policy conditions? I think the answer to that question is "no." Let me explain why.
In June 2017, the Federal Open Market Committee (FOMC) determined that it was appropriate to begin the process of reducing the size of the Fed's balance sheet, which had more than quadrupled as a result of efforts to combat the financial crisis and support the subsequent recovery from a very deep recession.
As I noted in a speech last November, I see the Committee's strategy for shrinking the balance sheet as having two essential elements.
- First, the normalization process is designed to be gradual. It was phased in over the course of about a year and a half and is now subject to monthly caps so the run-down is not too rapid.
- Second, the normalization process is designed to be as predictable as possible. The schedule of security retirements was announced in advance so that uncertainty about the pace of normalization can be minimized. (In other words, "quantitative tightening" is decidedly not on the QT.) As a result, the normalization process also reduces complexity. Balance-sheet reduction has moved into the background so that ongoing policy adjustments can focus solely on the traditional interest-rate channel.
In his recent remarks at the annual meeting of the American Economic Association, Chairman Jerome Powell was very clear that the fairly mechanical balance-sheet strategy adopted by the FOMC thus far should not be interpreted as inflexibility in the conduct of monetary policy or an unwillingness to recognize that balance-sheet reduction is in fact monetary policy tightening.
I will speak for myself. Balance-sheet policy is an element of the monetary policy mix. The decision to adopt a relatively deterministic approach to balance-sheet reduction is not a decision to ignore the possibility that it has led or might lead to a somewhat more restrictive stance of monetary policy. It is a decision to make whatever adjustments are necessary through the Fed's primary interest-rate tools to the greatest extent possible.
I maintain that there is still wisdom in this approach. The effects of our interest-rate tools are much more familiar to both policymakers and markets than balance-sheet tools are. That, to my mind, makes them the superior instrument for reaching and maintaining our dual goals of stable inflation and maximum employment. It is my belief that reducing the number of moving pieces makes monetary policy more transparent and predictable, which enhances the Committee's capacity for a smooth transition toward those goals.
It should now be clear that nothing is written in stone. Whether the FOMC uses active interest-rate policy with passive balance-sheet policy or uses both instruments actively, policy decisions will ultimately be driven by the facts on the ground as best Committee members can judge, and by assessments of risks that surround those judgments.
In my own judgment, it is far from clear that the ongoing reduction in the balance sheet is having an outsized impact on the stance of monetary policy. I think it is widely accepted that one of the ways balance-sheet policies work is by affecting the term premia associated with holding longer-term securities. (There are many good discussions about balance-sheet mechanisms, including this one by Edison Yu, which can be found in the first quarter 2016 edition of the Philadelphia Fed's Economic Insights, or this article by Jane Ihrig, Elizabeth Klee, Canlin Li, Min Wei, and Joe Kachovec in the March 2018 issue of the International Journal of Central Banking.)
Lots of things can push term premia up and down. But one of the factors is the presumed willingness of the central bank to purchase long-term securities in scale—or not. The idea that running down the balance sheet tightens monetary policy is that, in so doing, the FOMC is removing a crucial measure of support to the bond market. This makes longer-term securities riskier by transferring more duration risk back to the market, which raises term premia and, all else equal, pushes rates higher.
Although estimating term premia is as much art as science, I don't think the evidence supports the argument that these premia have been materially rising as a result of our normalization process. The New York Fed publishes one well-known real-time estimate of the term premia associated with 10-year Treasury securities. But isolating and quantifying the effect of balance-sheet changes on term premia is challenging. It is possible that a number of factors, such as the continued high demand for U.S. Treasuries by financial institutions and a low inflation risk premium, might have dampened the independent effect of balance sheet run-off. But if the term premia channel is a critical piece of what makes balance-sheet policy work, I'm hard pressed to see much evidence of financial tightening via rising term premia in the data so far.
Lest anyone think I am overly influenced by one particular theory, I will emphasize that I am not taking anything for granted. In addition to my monitoring of developments on Main Street, I will be watching financial conditions and term premia as I assess the outlook for the economy. My view is that a patient approach to monetary policy adjustments in the coming year is fully warranted in light of the uncertainties about the state of the economy, about what level of policy rates is consistent with a neutral stance, and about the overall impact of balance-sheet normalization. This patience is one of the characteristics of what I mean by data dependence.
January 11, 2019
Are Employers Focusing More on Staff Retention?
Many people are quitting their current job. According to data from the Job Openings, Layoffs, and Turnover Survey (JOLTS) from the U.S. Bureau of Labor Statistics, workers are voluntarily leaving their current workplace (for reasons other than retirement or internal company transfers) at rates not seen since the late 1990s.
A high rate of quits is consistent with a tightening labor market. In such a market, the promise of more pay elsewhere lures an increasing share of workers away from their current employer. The potential benefit of changing jobs is also evident in the Atlanta Fed's Wage Growth Tracker, which typically exhibits a positive differential between the median wage growth of people changing jobs versus those who don't.
However, this Wage Growth Tracker pattern has changed in recent months. Since mid-2018, the median wage growth of job stayers has risen sharply and is at the highest level since 2008. As the following chart shows, the gap between the wage growth of job switchers and stayers has narrowed.
Is it possible that this new pattern is just noise and will disappear in coming months? After all, the wage growth for job stayers has spiked before. But taken at face value, it suggests that the cost of losing staff (recruiting cost, cost of training new employees, productivity loss from having a less experienced workforce, etc.) might be forcing some employers to boost current employees' pay in an effort to retain staff. Interestingly, the pattern is most evident among prime-age workers in relatively high-skill professional occupations.
Firms are always focused on staff retention and employ various strategies to reduce turnover in key positions. One of those strategies—larger pay increases—might now be taking a more prominent role.
We will closely monitor the Tracker for further evidence on this emerging trend. However, that might be later rather than sooner because the source data is provided by the U.S. Census Bureau, which has cut most services because of the current lapse in congressional funding. But when information becomes available, we'll be ready to parse it—so stay tuned!
December 04, 2018
Defining Job Switchers in the Wage Growth Tracker
Among the questions we receive about the Atlanta Fed's Wage Growth Tracker, one of the most frequent is about the construction of the job switcher and job stayer series. These series are derived from data in the Current Population Survey (CPS) and are intended to show how median wage growth differs for those who change their job from last year versus those who are in the same job. However, the monthly CPS does not actually ask if the person has the same job as a year ago.
So how to proceed? The CPS does contain information about the person's industry and occupation that we aggregate into consistent categories that can be compared to the person's industry/occupation reported a year earlier. If someone is in a different occupation or industry category, then we can reasonably infer that the person has changed jobs. To illustrate, for 2017, 18.8 percent of people in the Wage Growth Tracker data are in a different industry category than they were in 2016, and 28.6 percent are in a different occupation category. Of those who remain in the same industry, 23.9 percent changed occupation group, and of those in the same occupation group, 13.5 percent are in a different industry. However, this information doesn't allow us to identify all job switchers because being in the same industry and occupation group as a year earlier does not preclude having changed jobs.
Fortunately, the CPS also has questions based on who a person said their employer was in the prior month. It asks if that person still works there and if the employee's activities and job duties are the same as last month. If someone answers either of these questions in the negative, then it is likely that person is also in a different job than a year earlier. In 2017, 1.5 percent of people in the Wage Growth Tracker data said they have a different employer than in the prior month, and 0.9 percent report having different job responsibilities at the same employer.
Unfortunately, the dynamic structure of the CPS means that the responses to these "same employer/activity" questions can only potentially be matched with an individual's response in the prior two months, and not a year earlier. Moreover, some responses to those questions are blank, even for people whom we identify as being employed in the prior month. For those individuals, we simply don't know if they are in the same job as a month earlier. Of the non-null responses, the vast majority do not change job duties or employer from one month to the next. So if we assume the blank responses are randomly distributed among the employed population, it's reasonable to also treat the blanks as job stayers.
Previously, we had treated the blank "same employer/activity" observations as job switchers, but that approach almost certainly misclassified some actual job stayers as job switchers. Instead, we now define a job switcher as someone in the Wage Growth Tracker data who is in a different occupation or industry group than a year earlier, or someone who says no to either of the "same employer/activity" questions in the current or prior two months. We label everyone else a job stayer.
Does the definition matter for median wage growth? The following chart shows the annual time series of the difference between the median wage growth of job switchers and job stayers based on both the old and new definitions.
As you can see, the results are not qualitatively different. Job switchers have higher median wage growth during strong labor market conditions and lower growth during bad times. Not surprisingly, the gap in median wage growth is generally lower (more negative) using the old definition.
The next chart shows the annual time series of the share of job switchers in the Wage Growth Tracker data based on the new and old definitions. A caveat: we have been unable to construct occupation and industry groupings for 2003 that are completely consistent with the groupings used in 2002. This results in an erroneous spike in measured job switching for 2003.
Notice that the share of job switchers under the new definition peaked prior to the last two recessions, declined during the recessions, and then recovered. That share is now at a cyclical high. A discrete jump in the number of blank responses recorded for the "same employer/activity" questions in the CPS starting in 2009 masked this cyclicality under the old definition.
In about a week from now, the next update of the Wage Growth Tracker data will implement the new and improved definition of job switchers—I hope you'll check it out, and I'll be writing about it here as well.
- Trends in Hispanic Labor Force Participation
- Quantitative Frightening?
- Are Employers Focusing More on Staff Retention?
- Defining Job Switchers in the Wage Growth Tracker
- Cryptocurrency and Central Bank E-Money
- Polarization through the Prism of the Wage Growth Tracker (Take Two)
- Polarization through the Prism of the Wage Growth Tracker
- On Maximizing Employment, a Case for Caution
- Demographically Adjusting the Wage Growth Tracker
- What Does the Current Slope of the Yield Curve Tell Us?
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