The Atlanta Fed's macroblog provides commentary and analysis on economic topics including monetary policy, macroeconomic developments, inflation, labor economics, and financial issues.
- BLS Handbook of Methods
- Bureau of Economic Analysis
- Bureau of Labor Statistics
- Congressional Budget Office
- Economic Data - FRED® II, St. Louis Fed
- Office of Management and Budget
- Statistics: Releases and Historical Data, Board of Governors
- U.S. Census Bureau Economic Programs
- White House Economic Statistics Briefing Room
April 29, 2016
Is the Number of Stay-at-Home Dads Going Up or Down?
A recent Wall Street Journal post observed that most of the recession's "stay-at-home dads" are going back to work. Specifically, data from the U.S. Labor Department shows that the share of married men with children under 18 who are not employed (but their spouse is) rose during the recession and has since given back much of that increase, as the Journal's chart below indicates.
Of course, being a stay-at-home dad in the sense defined in the previous chart (that is, not employed) can be either involuntary because of unemployment, or it can be the result of a voluntary decision to not be in the workforce. Most of the variation in the previous chart is cyclical, suggesting that it is related to the rise and fall in unemployment. But it also looks like the share of stay-at-home dads is higher now than it was a decade or so ago. So perhaps there is also an increasing trend in the propensity to voluntarily be a stay-at-home dad.
To explore this possibility, the next chart shows the annual average share of married men ages 25–54 who have children and who say the main reason they do not currently want a job is because of family or household responsibilities. (This reason doesn't necessarily imply that they are looking after children, but it is likely to be the leading reason.) The fraction is very small—about 1.3 percent in 2015, or 285,000 men—but the share has more than doubled during the last 15 years and would account for about half of the elevated level of the stay-at-home rate in 2015 relative to 2000.
So although large numbers of unemployed stay-at-home dads have been going back to work, it also appears that there's a small but growing group of men who are choosing to take on household and family responsibilities instead.
April 15, 2016
Labor Force Participation: Aging Is Only Half of the Story
The labor force participation rate (LFPR) is an important ingredient in projecting employment growth and the unemployment rate. However, predicting the LFPR has proven difficult. For example, in 2011 the Congressional Budget Office (CBO) projected that the LFPR in 2015 would be about 64.3 percent. In reality, the LFPR turned out to be 62.6 percent. Based on the CBO projection, the economy would have needed to create about 4 million more jobs to reach the 2015 unemployment rate of 5.3 percent.
Why is the LFPR so hard to predict? Leaving aside the challenge of projecting the size of the population, movements in LFPR primarily reflect shifts in the age distribution of the population as well as a number of behavioral factors. Although the aging trends are largely baked in, the behavioral factors vary over time. According to our estimates, about half of the 3.4 percentage-point decline in the LFPR between 2007 and 2015 is the result of the aging of the population, while behavioral factors account for the rest.
The complication is that the specific behaviors can change. The following chart shows a decomposition of the change in LFPR from 2007 to 2011 and from 2011 to 2015. Though the aging of the population contributed about the same amount to the decline in LFPR in both periods, the contributions from other factors have varied a lot. (We delve into the changes in the factors following the chart.)
Aging: The single largest factor contributing to the decline in the overall LFPR has been the rising share of older Americans in the population. In 2007, about one in five Americans were over 60 years old. In 2015, almost one in four were over 60. Moreover, this demographic force will continue to suppress the overall LFPR as the share of older Americans increases further in coming years. (For an in-depth discussion of the economic implications of an aging population—including changes in the labor market—please read the Atlanta Fed's 2015 annual report.)
Later retirement: One countervailing factor to an aging population has been the rising LFPR of older individuals. The retirement rate of those over 60 declined between 2007 and 2011 by a similar amount as it had before the recession. However, the trend toward later retirement has slowed considerably in recent years. The reason for this slowing is a puzzle and has important implications for the future course of overall LFPR.
Schooling among the young: The enrollment in educational programs by American youth has been generally rising over several decades, and this trend has put downward pressure on the overall LFPR. During the 2007–11 period, the share of 16- to 24-year-olds who do not want a job because they were in school or college accelerated relative to the prerecession trend. However, enrollment rates have since flattened out. The slowing may reflect enrollment rates catching up to the longer-term trend or may be a result of changes in the opportunity cost of education.
Not in the labor force but want a job: The share of the population saying they want a job but are not classified as unemployed by the U.S. Bureau of Labor Statistics definition is countercyclical—it tends to go up during bad times and down during good times. The relative size of this group increased between 2007 and 2011 and has since retraced about half of that increase as the economy has strengthened. We expect that this category will continue to shrink some more as the economy continues to expand.
Health: The share of individuals who do not want a job because they were too ill or disabled to work has increased over time. The relative size of this group increased between 2007 and 2011. Since 2011, the rate of increase has slowed, and it actually declined in 2015. It is not clear what drove the larger increase during the 2007–11 period, but there is some literature linking weak labor market conditions to poor health outcomes.
Prime-age reasons for not wanting a job (other than health): During the recession, the share of prime-age (ages 25 to 54) women not wanting a job because of household or family responsibilities decreased. One explanation is that some women entered the labor force to help make ends meet. At the same time, there was an offsetting effect from a rise in educational enrollment. Since the recession, nonparticipation because of household or family responsibilities has returned to near prerecession levels, and educational enrollment has leveled off.
To summarize, we find that relative to the 2007–11 period there has been a:
- flattening in the rate of retirement by older individuals,
- flattening in the rate of educational program enrollment by younger individuals,
- declining share of the population saying they want a job but not officially counted as unemployed,
- smaller drag from nonparticipation because of health, and
- larger drag for reasons other than health among prime-age individuals.
Where will LFPR be by the end of 2016? What about five years from now?
During the first three months of 2016, there has been an increase in the overall LFPR. This was largely the result of a decline in the share of prime-age people citing health reasons for nonparticipation, with some contribution from a decline in the share who want a job but are not "unemployed."
Though these boosts to participation may offset the effect of an aging population in the short term, most forecasts have the LFPR declining over the next several years. How much participation will actually decline depends on the answers to several difficult questions, such as: Will older individuals push retirement out even farther? Will school enrollment rates rise more rapidly again? Will the health status of the population improve? The difficulty of answering these questions helps explain why making accurate labor force projections is challenging.
April 13, 2016
Putting the MetLife Decision into an Economic Context
In a recently released decision, a U.S. district court has ruled that the Financial Stability Oversight Council's (FSOC's) decision to designate MetLife as a potential threat to financial stability was "arbitrary and capricious" and rescinded that designation. This decision raises many questions, among them:
- Why did MetLife sue to end its status as a too-big-to-fail (TBTF) firm?
- How will this decision affect the Federal Reserve's regulation of nonbank financial firms?
- What else can be done to reduce the risk of crisis arising from nonbank financial firms?
Why does MetLife want to end its TBTF status?
An often-expressed concern is that market participants will consider FSOC-designated firms too big to fail, and investors will accord these firms lower risk premiums (see, for example, Peter J. Wallison). The result is that FSOC-designated firms will gain a competitive advantage. If so, why did MetLife sue to have the designation rescinded? And why did the announcement of the court's determination result in an immediate 5 percent increase in the MetLife's stock price?
One possible explanation is that the FSOC's designation guarantees the firm will be subject to higher regulatory costs, but it only marginally changes the likelihood it would receive a government bailout. The Dodd-Frank Act (DFA) requires that FSOC-designated firms be subject to consolidated prudential supervision by the Federal Reserve using standards that are more stringent than the requirements for other nonbank financial firms.
Moreover, the argument that such designation automatically conveys a competitive advantage has at least two weaknesses. First, although Title II of the DFA authorizes the Federal Deposit Insurance Corporation (FDIC) to resolve a failing nonbank firm in certain circumstances, DFA does not provide FDIC insurance for any of the nonbank firm's liabilities, nor does it provide the FDIC with funds to undertake a bailout. The FDIC is supposed to recover its costs from the failed firm's assets. Admittedly, DFA does allow for the possibility that the FDIC would need to assess other designated firms for part of the cost of a resolution. However, MetLife could as easily have been assessed to pay for another firm as it could have been the beneficiary of assessments on other systemically important firms.
A second potential weakness in the competitive advantage argument is that the U.S. Treasury Secretary decides to invoke FDIC resolution only after receiving a recommendation from the Federal Reserve Board and one other federal financial regulatory agency (depending upon the type of failing firm). Invocation of resolution is not automatic. Moreover, a part of any decision authorizing FDIC resolution are findings that at the time of authorization:
- the firm is in default or in danger of default,
- resolution under other applicable law (bankruptcy statutes) would have "serious adverse consequences" on financial stability, and
- those adverse effects could be avoided or mitigated by FDIC resolution.
Although it would seem logical that FSOC-designated firms are more likely to satisfy these criteria than other financial firms, the Title II criteria for FDIC resolution are the same for both types of firms.
How does this affect the Fed's regulation of nonbank firms?
Secretary of the Treasury Jack Lew has indicated his strong disagreement with the district court's decision, and the U.S. Treasury has said it will appeal. Suppose, however, that FSOC designation ultimately does become far more difficult. How significantly would that affect the Federal Reserve's regulatory power over nonbank financial firms?
Although the obvious answer would be that it would greatly reduce the Fed's regulatory power, recent experience casts some doubt on this view. Nonbank financial firms appear to regard FSOC designation as imposing costly burdens that substantially exceed any benefits they receive. Indeed, GE Capital viewed the costs as so significant that it had been selling large parts of its operations and recently petitioned the FSOC to rescind its designation. Unless systemically important activities are a core part of the firm's business model, nonbank financial firms may decide to avoid undertaking activities that would risk FSOC designation.
Thus, a plausible set of future scenarios is that the Federal Reserve would be supervising few, if any, nonbank financial firms regardless of the result of the MetLife case. Rather, ultimate resolution of the case may have more of an impact on whether large nonbank financial firms conduct systemically important activities (if designation becomes much harder) or the activities are conducted by some combination of smaller nonbank financial firms and by banks that are already subject to Fed regulation (if the ruling does not prevent future designations).
Regardless of how the courts and the FSOC respond to this recent judicial decision, the financial crisis should have taught us valuable lessons about the importance of the nonbank financial sector to financial stability. However, those lessons should go beyond merely the need to impose prudential supervision on any firms that are systemically important.
The cause of the financial crisis was not the failure of one or two large nonbank financial firms. Rather, the cause was that almost the entire financial system stood on the brink of collapse because almost all the major participants were heavily exposed to the weak credit standards that were pervasive in the residential real estate business. Yet if the real problem was the risk of multiple failures as a result of correlated exposures to a single large market, perhaps we ought to invest more effort in evaluating the riskiness of markets that could have systemic consequences.
In an article in Notes from the Vault and other forums, I have called for systematic end-to-end reviews of major financial markets starting with the origination of the risks and ending with the ultimate holder(s) of the risks. This analysis would involve both quantitative analysis of risk measures and qualitative analysis of the safeguards designed to reduce risk.
The primary goal would be to identify and try to correct weaknesses in the markets. A secondary goal would be to give the authorities a better sense of where problems are likely to arise if a market does encounter problems.
April 11, 2016
The Rise of Shadow Banking in China
China's banking system has suffered significant losses over the past two years, which has raised concerns about the health of China's financial industry. Such losses are perhaps not all that surprising. Commercial banks have been increasing their risk-taking activities in the form of shadow lending. See, for example, here, here, and here for some discussion of the evolution of China's shadow banking system.
The increase in risk taking by banks has occurred despite a rapid decline in money growth since 2009 and the People's Bank of China's efforts to limit credit expansions to real estate and other industries that appear to be over capacity.
One area of expanded activity has been investment in asset-backed "securities" by China's large non-state banks. This investment has created potentially significant risks to the balance sheets of these institutions (see the charts below). Using the micro-transaction-based data on shadow entrusted loans, Chen, Ren, and Zha (2016) have provided theoretical and empirical insights into this important issue (see also this Vox article that summarizes the paper).
Recent regulatory reforms in China have taken a positive step to try to limit such risk-taking behavior, although the success of these efforts remains to be seen. An even more challenging task lies ahead for designing a comprehensive and sustainable macroprudential framework to support the healthy functioning of China's traditional and shadow banking industries.
April 04, 2016
Which Wage Growth Measure Best Indicates Slack in the Labor Market?
The unemployment rate is close to what most economists think is the level consistent with full employment over the longer run. According to the Federal Open Market Committee's latest Summary of Economic Projections, the unemployment rate is currently only 15 basis points above the natural rate. Yet, average hourly earnings (AHE) for production and nonsupervisory workers in the private sector increased a paltry 2.3 percent in March from a year earlier (as did the AHE of all private workers), and is barely above its average course of 2.1 percent since 2009.In contrast, the Atlanta Fed's Wage Growth Tracker (WGT) suggests that wage growth has been increasing. The February WGT reading was 3.2 percent (the March data will be available later in April), considerably higher than its post-2009 average of 2.3 percent.
Why is there such a large difference between these measures of wage growth? Besides differences in data sources, the primary reason is that they measure fundamentally different things. The WGT is an estimate of the wage growth of continuously employed workers—the same worker's wage is measured in the current month and a year earlier.
In contrast, the AHE measure is an estimate of the change in the typical wage of everyone employed this month relative to everyone employed a year earlier. Most of these workers are continuously employed, but some of those employed in the current month were not employed the prior year, and vice versa. These changes in the composition of employment can have a significant effect.
A recent study by Mary C. Daly, Bart Hobijn, and Benjamin Pyle at the San Francisco Fed shows that while growth in wages tends to be pushed higher by the wage gains of continuously employed workers, the net effect of entry and exit into employment tends to put a drag on the growth in wages. Moreover, the magnitude of the entry/exit drag can be relatively large, varies over time, and differs by the type of entry and exit.
For example, older workers who have retired and left the workforce tend to come from the higher end of the wage distribution, and their absence from the current period wage pool exerts downward pressure on the typical wage. The greater number of baby boomers starting to retire is having an even larger depressing effect on growth in wages than in the past. Because the WGT looks only at continuously employed workers, it is not influenced by these net entry/exit effects.
To the extent that firms adjust the pay for incumbent workers in response to labor market pressures to attract and retain workers, the WGT should reasonably capture changes in the tightness of the labor market.
Economists at the Conference Board modeled the relationship between different wage growth series and measures of labor market slack. One of the slack measures they use is the unemployment gap—the difference between an estimate of the natural rate of unemployment and the actual unemployment rate.To illustrate their findings, the following chart shows the WGT and AHE measures along with the unemployment gap lagged six months (using the Congressional Budget Office estimate of the natural rate).
The WGT appears to move more closely with the lagged unemployment gap than does the growth in AHE, and a comparison of the correlation coefficients confirms the stronger relationship with the WGT. The correlation between the lagged unemployment gap and the change in average hourly earnings is 0.75.
In contrast, the correlation with the wage growth tracker is higher at 0.93. Moreover, the unemployment gap-AHE relationship appears to be particularly weak since the Great Recession. The correlation since 2009 falls to just 0.08 for the AHE, whereas the WGT correlation is still 0.93.
Our colleagues at the San Francisco Fed concluded their analysis of the effect of flows into and out of the employment on wage growth by suggesting that:
"... wage growth measures that focus on the continuously full-time employed are likely to do a better job of gauging labor market strength, since they are constructed to more clearly capture the wage dynamics associated with improving labor market conditions. The Federal Reserve Bank of Atlanta's Wage Growth Tracker is an example."
That assessment is consistent with the Conference Board study, and suggests that labor markets may be tighter than is commonly believed based on sluggish growth in measures of average wages such as AHE.
March 15, 2016
Collateral Requirements and Nonbank Online Lenders: Evidence from the 2015 Small Business Credit Survey
Businesses can secure a bank loan by offering collateral—typically a business asset such as equipment or real estate. However, the recently released 2015 Small Business Credit Survey (SBCS) Report on Employer Firms,conducted by seven regional Reserve Banks, found that 63 percent of business owners who had borrowed also used their personal assets or guarantee to secure financing. Surprisingly, the use of personal collateral was common not only among startups. Older and relatively larger small firms (see the following chart) also relied heavily on personal assets.
Source: 2015 Small Business Credit Survey
Note: "Unsure", "None", and "Other" were also options but are not shown on the chart.
Alternative lending options also exercised
Not every small business owner has sufficient hard assets, such as real estate or equipment, that can be used as collateral to secure a traditional bank loan or line of credit. For these circumstances, there are options such as credit cards and products offered by nonbank lenders (mostly operating online) that have less stringent underwriting requirements than banks. Many online nonbank lenders advertise unsecured loans or require only a general lien on business assets, without valuing those business assets.
In the 2015 SBCS, 20 percent of small firms seeking loans or lines of credit applied at nonbank online lenders. These lenders have a good reputation for quick application turnaround, and the collateral requirements can be looser than those applied by traditional lenders. But when borrowers were asked about their overall experience, only a net 15 percent of businesses approved at nonbank online lenders were satisfied (40.6 percent were satisfied and 25.3 percent were dissatisfied). In contrast, small banks received a relatively high net satisfaction score of 75 percent (see the chart).
Source: 2015 Small Business Credit Survey Report on Employer Firms
1 Satisfaction score is the share satisfied with lender minus the share dissatisfied.
2 "Online lenders" are defined as alternative and marketplace lenders, including Lending Club, OnDeck, CAN Capital, and PayPal Working Capital.
3 "Other" includes government loan funds and community development financial institutions.
The survey also showed that high interest rates were the primary reason for dissatisfaction at nonbank online lenders (see the chart).
Source: 2015 Small Business Credit Survey Report on Employer Firms
Note: Respondents could select multiple options. Select responses shown due to low observation count.
Merchant cash advances make advances
Most applicants to nonbank online lenders were seeking loans and lines of credit, but some were seeking a product that tends to be particularly expensive relative to other finance options: merchant cash advances (MCA). MCAs have been around for decades, but their popularity has risen in the wake of the financial crisis. Typically a lump-sum payment in exchange for a portion of future credit card sales, the terms of MCAs can be enticing because repayment seems easier than paying off a structured business loan that requires a fixed monthly payment. Instead, the lender is paid back as the business generates revenue, in theory making cash flow easier to manage.
One potential challenge for users of MCA products is interpreting the repayment terms. Instead of displaying an annual percentage rate (APR), MCAs are usually advertised with a "buy rate" (typically 1.2 to 1.4). For example, a buy rate of 1.3 on $100,000 would require the borrower to pay back $130,000. However, a percentage of the principal is not the same as an APR. The table below compares total interest payments made on a 1.3 MCA versus a 30 percent APR business loan repaid over 12 months and over six months. With a 12-month business loan, a 30 percent APR would equal total interest payments of roughly $17,000. With a six-month business loan, repayment would include about $9,000 in interest.
Because an MCA is structured as a commercial transaction instead of a loan, it is regulated by the Uniform Commercial Code in each state instead of by banking laws such as the Truth in Lending Act. Consequently, the provider does not have to follow all of the regulations and documentation requirements (such as displaying an APR) associated with making loans.
Converting a buy rate into an APR is not straightforward for many potential users, as was made clear in a recent online lending focus group study with small business owners conducted by the Cleveland Fed. When asked what the APR was on a $40,000 MCA that required a repayment of $52,000 (the same as a 1.3 buy rate), their answers were the following: (Product A is the MCA type of product; see the study for exactly how it was presented to respondents.)
Source: Federal Reserve Bank of Cleveland
The correct answer is that "it depends on how long it takes to pay back." For example, if the debt is repaid over six months, the APR would be 110 percent (as this calculator shows).
Nonbank online lenders can fill gaps in the borrowing needs of small business. But there may also be a role for greater clarity to ensure borrowers understand the terms they are signing up for. In a September 2015 speech, Federal Reserve Governor Lael Brainard highlights one self-policing movement already well under way:
Some have raised concerns about the high APRs associated with some online alternative lending products. Others have raised concerns about the risk that some small business borrowers may have difficulty fully understanding the terms of the various loan products or the risk of becoming trapped in layered debt that poses risks to the survival of their businesses. Some industry participants have recently proposed that online lenders follow a voluntary set of guidelines designed to standardize best practices and mitigate these risks. It is too soon to determine whether such efforts of industry participants to self-police will be sufficient. Even with these efforts, some have suggested a need for regulators to take a more active role in defining and enforcing standards that apply more broadly in this sector.
Many, but not all, nonbank online lenders have already signed the Small Business Borrower Bill of Rights. Results from the 2015 Small Business Credit Survey Report on Employer Firms can be found on our website.
February 17, 2016
Are Paychecks Picking Up the Pace?
From the minutes of the January 26–27 meeting of the Federal Open Market Committee, it's clear that many participants saw tightening labor market conditions during 2015:
In their comments on labor market conditions, participants cited strong employment gains, low levels of unemployment in their Districts, reports of shortages of workers in various industries, or firming in wage increases.
Based on the Atlanta Fed's Wage Growth Tracker (WGT), the median annual growth in hourly wage and salary earnings of continuously employed workers in 2015 was 3.1 percent—up from 2.5 percent in 2014 and 2.2 percent in 2013. That is, the typical wage growth of workers employed for at least 12 months appears to be trending higher.
However, wage growth by job type varies considerably. For example, the WGT for part-time workers has been unusually low since 2010. The following chart displays the WGT for workers currently employed in part-time and full-time jobs. For those in part-time jobs, the WGT was 1.9 percent in 2015, versus 3.3 percent for those in full-time jobs. The part-time/full-time wage growth gap has closed somewhat in the last couple of years but is still large relative to its size before the Great Recession. Note that full-time WGT is similar to the overall WGT because most workers captured in the WGT data work full-time (81 percent in 2015).
In addition to hours worked, median wage growth also tends to vary across occupation. The following chart plots the WGT for workers in low-skill jobs, versus those in mid- and high-skill jobs. (We define low-skill jobs as those in occupations related to food preparation and serving; building and grounds cleaning; and maintenance, protection, and personal care services.)
Notably, after lagging during most of the recovery, median wage growth in low-skill occupations increased 2.8 percent in 2015, versus 2.0 percent in 2014 and compared to 3.2 and 2.7 percent for other occupations in 2015 and 2014, respectively.
The improvement in wage growth for low-skill occupations seems mostly attributable to full-time workers; wage growth for people in low-skill jobs working part-time was about half that (1.6 percent versus 3.0 percent) of those working full-time (see the chart).
This pickup in low-skill wage growth fits with some anecdotal reports we've been hearing. Some of our contacts in the Southeast have reported increasing wage pressure for workers in lower-skill occupations within their businesses. One can also see evidence of growing tightness in the market for low-skill jobs in the help-wanted data. As the following chart shows, the ratio of unemployed to online job postings for low-skill jobs is always higher than for middle- and high-skill occupations. But the ratio for low-skill jobs is now well below its prerecession level, and the tightness has increased during the last two years.
The take-away? Wage growth for continuously employed workers appears to have picked up some steam in 2015, and the recent trend in wage growth is positive across a variety of job characteristics. Wage growth for people in lower-skill jobs has increased during the last couple of years, consistent with evidence of increasing tightness in the market for those types of jobs. The largest discrepancy in wage growth appears to be among part-time workers, whose median gain in hourly wages in 2015 still fell well short of those in full-time jobs.
February 05, 2016
Introducing the Refined Labor Market Spider Chart
In January 2013, Atlanta Fed research director Dave Altig introduced the Atlanta Fed's labor market spider chart in a macroblog post.
In a follow-up post that June, Atlanta Fed colleague Melinda Pitts and I introduced a dedicated page for the spider chart located at the Center for Human Capital Studies (CHCS) webpage. It shows the distribution of 13 labor market indicators relative to their readings just before the 2007–09 recession (December 2007) and the trough of the labor market following that recession (December 2009). The substantial improvement in the labor market during the past three years is quite evident in the spider chart below.
As of December 2012, none of the indicators had yet reached their prerecession levels, and some had a long way to go. Now, many of these indicators are near their prerecession values—and some have blown by them.
To make the spider chart more relevant in an environment with considerably less labor market slack than three years ago, we are introducing a modified version, which you can see here. Below is an example of a chart I created using the menu-bars on the spider chart's web page:
In this chart, I plot the May 2004 and November 2015 percentile ranks of labor market indicators relative to their distributions since March 1994. As with the previous spider chart, indicators such as the unemployment rate, where larger values indicate more labor market slack, have been multiplied by –1. The innermost and outermost rings represent the minimum and maximum values of the variables from March 1994 to January 2016. The three dashed gray rings in between are the 25th, 50th, and 75th percentiles of the distributions. For example, the November 2015 value of 12-month average hourly earnings growth (2.26 percent) is the 23rd percentile of its distribution. This means that 23 percent of the other monthly observations on hourly earnings growth since March 1994 are lower than it is.
I chose May 2004 and November 2015 because they had the last employment situation reports before "liftoffs" of the federal funds rate. November 2015 appears to be stronger than May 2004 for some indicators (job openings, unemployment rate, and initial claims) and weaker for others (hires rate, work part-time for economic reasons, and the 12-month growth rate of the Employment Cost Index).
The average percentile ranks of the variables for these two months are similar, as the chart below depicts:
Also shown in the chart is the Kansas City Fed's Level of Activity Labor Market Conditions Indicator. It is a sum of 24 not equally weighted labor market indicators, standardized over the period from 1992 to the present. In spite of its methodological and source-data differences with the average percentile rank measure plotted above, it tracks quite closely, especially since 2004. However, as shown in the spider chart that I referred to above, there is quite a bit of variation within the indicators that may provide additional information to our analysis of the average trends.
We made a number of other changes to the spider chart to ensure it reflects current labor market issues. These changes are documented in the FAQs and "Indicators" sections of the new spider chart page. Of particular note, users can choose not only the years for which they wish to track information, but also the period of reference that provides the basis of the spider chart. The payroll employment variable is now the three-month average change rather than a level. Temporary help services employment has been dropped, and two measures of 12-month compensation growth and the employment-population ratio (EPOP) for "prime-age workers" (25 to 54 years) have been added.
Some care should be taken when comparing recent labor market data values with those 10 or more years ago as structural changes in the labor market might imply that a "normal" value today is different than a "normal" value in, say, 2004. The variable choices for the refined spider chart were made to mitigate this problem to some extent. For example, we use the prime-age EPOP as a crude adjustment for population aging, putting downward pressure on the labor force participation rate and EPOP over the past 10 years (roughly 2 percentage points). This doesn't entirely resolve the comparability issue since, within the prime-age population, the self-reporting rate of illness or disability as a reason for not wanting a job has increased about 1.5 percentage points since 1998 (see the macroblog posts here and here and the CHCS Labor Force Participation Dynamics webpage). If this increase in disability reporting is partly structural—and a Brookings study by Fed economist Stephanie Aaronson and others concludes it is—some of the decline in the prime-age EPOP since the late 1990s may not be a result of a weaker labor market per se.
Other variables in the spider chart may have had structural changes as well. For example, a study by San Francisco Fed economists Rob Valleta and Catherine van der List concludes that structural factors explain just under half of the rise in the share of workers employed part-time for economic reasons over the 2006 to 2013 period.
To partially account for structural changes in trends, we allow the user to select one of 11 time periods over which the distributions are calculated. The default period is March 1994 to present, which is what was used in the example above, but users can choose a window as short as five years where, presumably, structural changes are less important. A trade-off with using a short window is that a "normal" value may not produce a result close to the median. For example, the median unemployment rate is 5.6 percent since March 1994 and 7.3 percent since February 2011. The latter value is much farther away from the most recent estimates of the natural rate of unemployment from the Congressional Budget Office and the Survey of Professional Forecasters (both 5.0 percent).
In our June 2013 macroblog post introducing the spider chart, we wrote that we would reevaluate our tools and determine a more appropriate way to monitor the labor market when "the labor market has turned a corner into expansion." The new spider chart is our response to the stronger labor market. We hope users find the tool useful.
January 29, 2016
Shrinking Labor Market Opportunities for the Disabled?
The labor force participation rate (LFPR) among prime-age (25–54 years old) people averaged 80.8 percent in 2015, down 1.8 percentage points (2.6 million people) from 2009, according to the U.S. Bureau of Labor Statistics. According to our calculations from the Current Population Survey (CPS), a drop in LFPR among individuals with disabilities accounts for about a fifth of that decline.
Many people with disabilities are active in the labor force, working or looking for work. But the disabled LFPR has fallen a lot in recent years—it's down from 39.3 percent in 2009 to 34.5 percent in 2015. In other words, for some reason, more prime-age individuals with disabilities have opted out of the labor market.
A rising share of the prime-age population with a disability is not the culprit. In fact, the 2015 average disability rate was 6.4 percent, the same as in 2009. It is possible that the severity rather than incidence of disabilities has increased in recent years; labor market attachment does vary with type of disability, as this report shows.
But we suspect that the relatively large decline in disabled labor market attachment probably has also to do with shifts in employment opportunities for those with a disability. Some insight into this issue can be seen by looking at the change in employment shares in occupations that tend to have relatively low pay.
Workers with a disability tend to make less than nondisabled workers. We estimate the median wage of a worker with a disability in 2009 to have been 76 percent that of a nondisabled worker. In 2015, the relative median wage had declined to 74 percent. This drop is partly related to a relative increase in the share of employment for workers with a disability in low-paying occupations (which we define as jobs in personal care, food services, janitorial services, etc.), as the chart shows. The employment news has not been all bad for workers with a disability, however. There has been a rise in the share of employment of people with disabilities in higher-paying occupations since 2009, although they do tend to earn less than other workers in those types of occupations.
For some workers with disabilities, the financial return to employment versus nonemployment may have become somewhat less attractive in recent years. One factor related to the decision to engage in the labor market is the ability to collect Social Security Disability Insurance (SSDI). SSDI claims rose notably when the unemployment rate was high, which is consistent with the idea that the expected return to labor market activity for some individuals with a disability declined.
Job seekers with a disability have also struggled to find jobs offering the hours they desire. For example, the share of unemployed people finding full-time or voluntary part-time employment within a month, or "the hours-finding rate," is much lower for the prime-age disabled than for the nondisabled, and this share has improved relatively less over the recovery. Between 2009 and 2015, the average disabled hours-finding rate improved 4.7 percentage points, from 9.5 to 14.2 percent. During the same period, the nondisabled hours-finding rate increased 6.3 percentage points, from 13.0 to 19.3 percent.
The incidence of disability among prime-age individuals has not increased in recent years. But the labor market attachment of the disabled has declined, and this decline accounts for about one-fifth of the 1.8 percentage point fall in prime-age labor force participation between 2009 and 2015. Those with disabilities already have a harder time finding well-paying jobs, but that difficulty appears to have increased in that time span.
By John Robertson, a senior policy adviser in the Atlanta Fed's research department, and
January 15, 2016
Are Long-Term Inflation Expectations Declining? Not So Fast, Says Atlanta Fed
"Convincing evidence that longer-term inflation expectations have moved lower would be a concern because declines in consumer and business expectations about inflation could put downward pressure on actual inflation, making the attainment of our 2 percent inflation goal more difficult."
—Fed Chair Janet Yellen, in a December 2, 2015, speech to the Economic Club of Washington
To be sure, Chair Yellen's claim is not controversial. Modern macroeconomics gives inflation expectations a central role in the evolution of actual inflation, and the stability of those expectations is crucial to the Fed's ability to achieve its price stability mandate.
The real question on everyone's mind is, of course, what might constitute "convincing evidence" of changes in inflation expectations. Recently, several economists, including former Treasury Secretary Larry Summers and St. Louis Fed President James Bullard, have weighed in on this issue. Yesterday, President Bullard cited downward movements in the five-year/five-year forward breakeven rates from the five- and 10-year nominal and inflation-protected Treasury bond yields. In November, Summers appealed to measures based on inflation swap contracts. The view that inflation expectations are declining has also been echoed by the New York Fed President William Dudley and former Minneapolis Fed President Narayana Kocherlakota.
Broadly speaking, there seems to be a growing view that market-based long-run inflation expectations are declining and drifting significantly away from the Fed's 2 percent target and that this decline is troublingly correlated with oil prices.
A problem with this line of argument is that the breakeven and swap rates are not necessarily clean measures of inflation expectations. They are really better referred to as measures of inflation compensation because, in addition to inflation expectations, these measures also include factors related to liquidity conditions in the markets for these securities, technical features of the inflation protection in each security, and inflation risk premia. Here at the Atlanta Fed, we've built a model to separate these different components and isolate a better measure of true inflation expectations (IE).
In technical terms, we estimate an affine term structure model—similar to that of D'Amico, Kim and Wei (2014)—that incorporates information from the markets for U.S. Treasuries, Treasury Inflation-Protected Securities (TIPS), inflation swaps, and inflation options (caps and floors). Details are provided in "Forecasts of Inflation and Interest Rates in No-Arbitrage Affine Models," a forthcoming Atlanta Fed working paper by Nikolay Gospodinov and Bin Wei. (You can also see Gospodinov and Wei (2015) for further analysis.) Essentially, we ask: what level of inflation expectations is consistent with this entire set of financial market data? And we then follow this measure over time.
As chart 1 illustrates, we draw a very different conclusion about the behavior of long-term inflation expectations. The chart plots the five-year/five-year forward TIPS breakeven inflation (BEI) and the model-implied inflation expectations (IE) for the period January 1999–November 2015 at a weekly frequency. Unlike the raw BEI, our measure is quite smooth, suggesting that long-term inflation expectations have been, and still are, well anchored.
After making an adjustment for the inflation risk premium, we term the difference between BEI and IEs a "liquidity premium," but it really includes a variety of other factors. Our more careful look at the liquidity premium reveals that it is partly made up of factors specific to the structure of inflation-indexed TIPS bonds. For example, since TIPS are based on the non-seasonally adjusted consumer price index (CPI) of all items, TIPS yields incorporate a large positive seasonal carry yield in the first half of the year and a large negative seasonal carry yield in the second half. Chart 2 illustrates this point by plotting CPI seasonality (computed as the accumulated difference between non-seasonally adjusted and seasonally adjusted CPI) and the five-year breakeven inflation.
Redemptions, reallocations, and hedging in the TIPS market after oil price drops and global financial market turbulence can further exacerbate this seasonal pattern. Taken together, these factors are the source of correlation between the BEI measures and oil prices. To confirm this, chart 3 plots (the negative of) our liquidity premium estimate and the log oil price (proxied by the nearest futures price).
Our measure of long-term inflation expectations is also consistent with long-term measures from surveys. Chart 4 presents the median along with the 10th and 90th percentiles of the five-year/five-year forward CPI inflation expectations from the Philadelphia Fed's Survey of Professional Forecasters (SPF) at quarterly frequency. This measure can be compared directly with our IE measure. Both the level and the dynamics of the median SPF inflation expectation are remarkably close to that for our market-based IE. It is also interesting to observe that the level of inflation "disagreement" (measured as the difference between the 10th and 90th percentiles) is at a level similar to the level seen before the financial crisis.
Finally, we note that TIPS and SPF are based on CPI rather than the Fed's preferred personal consumption expenditure (PCE) measure. CPI inflation has historically run above PCE inflation by about 30 basis points. Accounting for this difference brings our measure of the level of long-term inflation expectations close to the Fed's 2 percent target.
To summarize, our analysis suggests that (1) long-run inflation expectations remain stable and anchored, (2) the seemingly large correlation of market-implied inflation compensation with oil prices arises mainly from the dynamics of the TIPS liquidity premium, and (3) long-run market- and survey-based inflation expectations are remarkably close in terms of level and dynamics over time. Of course, further softness in the global economy and commodity markets may eventually drag down long-term expectations. We will continue to monitor the pure measure of inflation expectations for such developments.
By Nikolay Gospodinov, financial economist and policy adviser; Paula Tkac, vice president and senior economist; and Bin Wei, financial economist and associate policy adviser, all of the Atlanta Fed's research department
- Can Two Wrongs Make a Right?
- Are People in Middle-Wage Jobs Getting Bigger Raises?
- GDPNow and Then
- What's behind the Recent Uptick in Labor Force Participation?
- Is the Number of Stay-at-Home Dads Going Up or Down?
- Labor Force Participation: Aging Is Only Half of the Story
- Putting the MetLife Decision into an Economic Context
- The Rise of Shadow Banking in China
- Which Wage Growth Measure Best Indicates Slack in the Labor Market?
- Collateral Requirements and Nonbank Online Lenders: Evidence from the 2015 Small Business Credit Survey
- May 2016
- April 2016
- March 2016
- February 2016
- January 2016
- November 2015
- October 2015
- September 2015
- August 2015
- July 2015
- Business Cycles
- Business Inflation Expectations
- Capital and Investment
- Capital Markets
- Data Releases
- Economic conditions
- Economic Growth and Development
- Exchange Rates and the Dollar
- Fed Funds Futures
- Federal Debt and Deficits
- Federal Reserve and Monetary Policy
- Financial System
- Fiscal Policy
- Health Care
- Inflation Expectations
- Interest Rates
- Labor Markets
- Latin America/South America
- Monetary Policy
- Money Markets
- Real Estate
- Saving, Capital, and Investment
- Small Business
- Social Security
- This, That, and the Other
- Trade Deficit
- Wage Growth