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The Atlanta Fed's macroblog provides commentary and analysis on economic topics including monetary policy, macroeconomic developments, inflation, labor economics, and financial issues.

Authors for macroblog are Dave Altig, John Robertson, and other Atlanta Fed economists and researchers.


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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.

Rise and Decline of the Stay-at-Home Dad

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.

Share of 25- to 54-year-old married men with children who don't currently want a job because of family or household responsibilities

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 29, 2016 in Employment, Labor Markets, Unemployment | Permalink

Comments

what happens if we actually model this explicitly? Using controls for cyclical effects? My concern is that you are assuming that the most recent point on the chart is some kind of long run point. Not that I have substantial evidence to the contrary, but it appears to me the trend may well continue

Posted by: g | April 30, 2016 at 02:39 PM

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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 15, 2016 in Labor Markets | Permalink

Comments

Excellent as always. But can the behavioral factors in the 25-54 main cohort and its specific lfpr be shown in a separate post. Do behavioural factors of the older cohorts effect the 25-54 cohort and its lfpr.

Posted by: am | April 15, 2016 at 04:03 PM

I compared YoY changes in the labor participation rate with YoY changes in real median household income (as a proxy for wages). The relationship appears to be a close one. Apparently, the idea of opportunity cost is a major part of the explanation for changes in the labor participation rate.

See http://www.philipji.com/item/2016-04-16/what-explains-changes-in-the-labour-participation-rate

Posted by: Philip George | April 16, 2016 at 08:11 AM

If I'm retired (which I am), I'm not in the labor force anymore. Why am I counted in the LFPR? Retirees shouldn't impact LFPR one bit.

Posted by: Mac McVicker | April 28, 2016 at 08:58 PM

http://www.bls.gov/opub/btn/volume-4/people-who-are-not-in-the-labor-force-why-arent-they-working.htm
Dated but useful showing the change in age distribution from 2000 to 2014.
By a bls economist.

Posted by: am | June 17, 2016 at 05:56 AM

http://www.bls.gov/opub/btn/volume-4/people-who-are-not-in-the-labor-force-why-arent-they-working.htm
Dated but useful showing change in age distribution from 2000 to 2014.
By BLS economist.

Posted by: am | June 17, 2016 at 05:58 AM

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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).

Lessons learned?
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 13, 2016 in Banking, Regulation | Permalink

Comments

Looking at market micro-structure is an excellent idea. One might even be able to look at the incentives of the different participants, ala Ashcraft and Schuermann

https://www.newyorkfed.org/medialibrary/media/research/staff_reports/sr318.pdf

Posted by: Brian Peters | April 13, 2016 at 04:23 PM

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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 11, 2016 in Asia, Banking, Regulation | Permalink

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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.

Wage Growth

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).

Wage Growth and the Unemployment Gap

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.

April 4, 2016 in Economic conditions, Employment, Labor Markets, Wage Growth | Permalink

Comments

There are some similar issues in the UK. On the statistics the difference between the price change (here nominal wages KA25 ) and the quantity changes (here in employment KA26) is set out in UK's Av Weekly Earnings series http://www.ons.gov.uk/employmentandlabourmarket/peopleinwork/earningsandworkinghours/datasets/averageweeklyearningsbysectorearn02

Comparisons of the stats suggest number of differences between the countries. First, UK nominal wage wrt to labour market variables is (much) less than in UK.

Secondly, in both countries stats on 'insiders' (eg WGT) tends to be more responsive to recessions - perhaps because the link between pay and jobs is more immediate so they accept lower pay - than in recoveries where they are not as interested in the need to attract outsiders.

Thirdly, there has been a very different effect of 'outsiders' employment growth on total current wage growth in the countries. A caricature until recently is that the UK there has been growth in employment of people with below average wages and vice versa in the US.

One reason for this might be the relative success of welfare to work policies. In UK there has been a successful 'activation' policy including for new groups such as lone parent and those aged 60-64. Whereas in the US there has been increased welfare dependency - including the extension of unemployment benefit duration in existence until the of 2013.

Posted by: Bill Wells | April 05, 2016 at 07:19 AM

Maybe also check the distribution of wage income, as trophy earners may be seeing greater growth, dragging up the average, but perhaps not the median.

john

Posted by: john | April 05, 2016 at 08:27 AM

Is there data that shows reduction in pay for a post when the previous occupant of the post retired rather than quit.

Posted by: am | April 09, 2016 at 03:06 AM

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