macroblog

About


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.


August 15, 2016


Payroll Employment Growth: Strong Enough?

The U.S. Bureau of Labor Statistics' estimate of nonfarm payroll employment is the most closely watched indicator of overall employment growth in the U.S. economy. By this measure, employment increased by 255,000 in July, well above the three-month average of 190,000. Yet despite this outsized gain, the unemployment rate barely budged. What gives?

Well, for a start, there is no formal connection between the payroll employment data and the unemployment rate data. The employment data used to construct the unemployment rate come from the Current Population Survey (CPS) and the payroll employment data come from a different survey. However, it is possible to relate changes in the unemployment rate to the gap between the CPS and payroll measures of employment, as well as changes in the labor force participation (LFP) rate, and the growth of payroll employment relative to the population.

The following chart shows the contribution of each of these three factors to the monthly change in the unemployment rate during the last year.

Contributions to the 1-month change in the unemployment rate

A note about the chart: The CPS employment and population measures have been smoothed to account for annual population control adjustments. The smoothed employment data are available here. The method used to compute the contributions is available here.

The black line is the monthly change in the unemployment rate (unrounded). Each green segment of a bar is the change in the unemployment rate coming from the gap between population growth and payroll employment growth. Because payroll employment has generally been growing faster than the population, it has helped make the unemployment rate lower than it otherwise would have been.

But as the chart makes clear, the other two factors can also exert a significant influence on the direction of the unemployment rate. The labor force participation rate contribution (the red segments of the bars) and the contribution from the gap between the CPS and payroll employment measures (blue segments) can vary a lot from month to month, and these factors can swamp the payroll employment growth contribution.

So any assumption that strong payroll employment gains in any particular month will automatically lead to a decline in the unemployment rate could, in fact, be wrong. But over longer periods, the mapping is a bit clearer because it is effectively smoothing the month-to-month variation in the three factors. For example, the following chart shows the contribution of the three factors to 12-month changes in the unemployment rate from July 2012 to July 2013, from July 2013 to July 2014, and so on.

Contributions to the 12-month change in the unemployment rate

Gains in payroll employment relative to the population have helped pull the unemployment rate lower. Moreover, prior to the most recent 12 months, declines in the LFP rate put further downward pressure on the unemployment rate. Offsetting this pressure to varying degrees has been the fact that the CPS measure of employment has tended to increase more slowly than the payroll measure, making the decline in the unemployment rate smaller than it would have been otherwise. During the last 12 months, the LFP rate turned positive on balance, meaning that the magnitude of the unemployment rate decline has been considerably less than implied by the relative strength of payroll employment growth.

Going forward, another strong payroll employment reading for August is certainly no guarantee of a corresponding decline in the unemployment rate. But as shown by my colleagues David Altig and Patrick Higgins in an earlier macroblog post, under a reasonable range of assumptions for the trend path of population growth, the LFP rate, and the gap between the CPS and payroll survey measures of employment, payroll growth averaging above 150,000 a month should be enough to cause the unemployment rate to continue declining.

August 15, 2016 in Employment, Labor Markets, Unemployment | Permalink | Comments (0)

August 11, 2016


Forecasting Loan Losses for Stress Tests

Bank capital requirements are back in the news with the recent announcements of the results of U.S. stress tests by the Federal Reserve and the European Union (E.U.) stress tests by the European Banking Authority (EBA). The Federal Reserve found that all 33 of the bank holding companies participating in its test would have continued to meet the applicable capital requirements. The EBA found progress among the 51 banks in its test, but it did not define a pass/fail threshold. In summarizing the results, EBA Chairman Andrea Enria is widely quoted as saying, "Whilst we recognise the extensive capital raising done so far, this is not a clean bill of health," and that there remains work to do.

The results of the stress tests do not mean that banks could survive any possible future macroeconomic shock. That standard would be an extraordinarily high one and would require each bank to hold capital equal to its total assets (or maybe even more if the bank held derivatives). However, the U.S. approach to scenario design is intended to make sure that the "severely adverse" scenario is indeed a very bad recession.

The Federal Reserve's Policy Statement on the Scenario Design Framework for Stress Testing indicates that the severely adverse scenario will have an unemployment increase of between 3 and 5 percentage points or a level of 10 percent overall. That statement observes that during the last half century, the United States has seen four severe recessions with that large of an increase in the unemployment rate, with the rate peaking at more than 10 percent in last three severe recessions.

To forecast the losses from such a severe recession, the banks need to estimate loss models for each of their portfolios. In these models, the bank estimates the expected loss associated with a portfolio of loans as a function of the variables in the scenario. In estimating these models, banks often have a very large number of loans with which to estimate losses in their various portfolios, especially the consumer and small business portfolios. However, they have very few opportunities to observe how the loans perform in a downturn. Indeed, in almost all cases, banks started keeping detailed loan loss data only in the late 1990s and, in many cases, later than that. Thus, for many types of loans, banks might have at best data for only the relatively mild recession of 2001–02 and the severe recession of 2007–09.

Perhaps the small number of recessions—especially severe recessions—would not be a big problem if recessions differed only in their depth and not their breadth. However, even comparably severe recessions are likely to hit different parts of the economy with varying degrees of severity. As a result, a given loan portfolio may suffer only small losses in one recession but take very large losses in the next recession.

With the potential for models to underestimate losses given there are so few downturns to calibrate to, the stress testing process allows humans to make judgmental changes (or overlays) to model estimates when the model estimates seem implausible. However, the Federal Reserve requires that bank holding companies should have a "transparent, repeatable, well-supported process" for the use of such overlays.

My colleague Mark Jensen recently made some suggestions about how stress test modelers could reduce the uncertainty around projected losses because of limited data from directly comparable scenarios. He recommends using estimation procedures based on a probability theorem attributed to Reverend Thomas Bayes. When applied to stress testing, Bayes' theorem describes how to incorporate additional empirical information into an initial understanding of how losses are distributed in order to update and refine loss predictions.

One of the benefits of using techniques based on this theorem is that it allows the incorporation of any relevant data into the forecasted losses. He gives the example of using foreign data to help model the distribution of losses U.S. banks would incur if U.S. interest rates become negative. We have no experience with negative interest rates, but Sweden has recently been accumulating experience that could help in predicting such losses in the United States. Jensen argues that Bayesian techniques allow banks and bank supervisors to better account for the uncertainty around their loss forecasts in extreme scenarios.

Additionally, I have previously argued that the existing capital standards provide further way of mitigating the weaknesses in the stress tests. The large banks that participate in the stress tests are also in the process of becoming subject to a risk-based capital requirement commonly called Basel III that was approved by an international committee of banking supervisors after the financial crisis. Basel III uses a different methodology to estimate losses in a severe event, one where the historical losses in a loan portfolio provide the parameters to a loss distribution. While Basel III faces the same problem of limited loan loss data—so it almost surely underestimates some risks—those errors are likely to be somewhat different from those produced by the stress tests. Hence, the use of both measures is likely to somewhat reduce the possibility that supervisors end up requiring too little capital for some types of loans.

Both the stress tests and risk-based models of the Basel III type face the unavoidable problem of inaccurately measuring risk because we have limited data from extreme events. The use of improved estimation techniques and multiple ways of measuring risk may help mitigate this problem. But the only way to solve the problem of limited data is to have a greater number of extreme stress events. Given that alternative, I am happy to live with imperfect measures of bank risk.

Author's note: I want to thank the Atlanta Fed's Dave Altig and Mark Jensen for helpful comments.


August 11, 2016 in Banking, Financial System, Regulation | Permalink | Comments (1)

July 29, 2016


Men at Work: Are We Seeing a Turnaround in Male Labor Force Participation?

A lot has been written about the long-run decline in the labor force participation (LFP) rate among prime-age men (usually defined as men between 25 and 54 years of age). For example, see here, here, here, and here for some perspectives.

On a not seasonally adjusted basis, the Bureau of Labor Statistics estimates that the LFP rate among prime-age males is down from 90.9 percent in the second quarter of 2007 to 88.6 percent in the second quarter of 2016—a decline of 2.3 percentage points, or around 1.4 million potential workers.

Many explanations reflecting preexisting structural trends have been posited for this decline. But how much of the decline also reflects cyclical effects and, in particular, cyclical effects that take a while to play out? We don't really know for sure. But one potentially useful approach is to look at the Census Bureau's Current Population Survey and the reasons people give for not wanting a job. These reasons include enrollment in an educational program (especially prevalent among young individuals), family or household responsibilities (especially among prime-age women), retirement (especially among older individuals), and poor health or disability (widespread). In addition, there are people of all ages who say they want a job but are not counted as unemployed. For example, they aren't currently available to work or haven't looked for work recently because they are discouraged about their job prospects.

To get some idea of the relative importance of these factors, the following chart shows how much each nonparticipation reason accounted for the total change in the LFP rate among prime-age males between 2012 and 2014 and between 2014 and 2016. The black bars show each period's total change in the LFP rate. The green bars are changes that helped push participation higher than it otherwise would have been, and the orange bars are changes that helped hold participation lower than it otherwise would have been.

160728

A note on the chart: To construct the contributions derived from changes in nonparticipation rates, I held constant the age-specific population shares in the base period (2012 and 2014, respectively) in order to separate the effect of changes in nonparticipation from shifts in the age distribution.

Notice that the decline in the prime-age male LFP rate between 2012 and 2014 has essentially fully reversed itself over the last two years (from a decline of 0.53 percentage points to an increase of 0.55 percentage points, respectively). The positive "want a job" contribution in both periods clearly reflects a cyclical recovery in labor market conditions. But the most striking change between 2012–14 and 2014–16 is the complete reversal of the large drag attributable to poor health and disability. Other things equal, if nonparticipation resulting from poor health and disability had stayed at its 2012 level, prime-age male participation in 2014 would have only declined 0.10 percentage points. If nonparticipation due to poor health and disability had stayed at its 2014 level, prime-age male participation in 2016 would have increased only 0.14 percentage points.

The incidence of self-reported nonparticipation among prime-age men because of poor health or disability has been declining recently. According to the Current Population Survey data, this reason represented 5.4 percent of the prime-age male population in the second quarter of 2016. Although this is still 0.7 percentage points higher than in 2007, it is 0.3 percentage points lower than in 2014. Some of this turnaround could be the result of changes in the composition of the prime-age population. But not much. Around 90 percent of the LFP rate change because of poor health and disability is due to age-specific nonparticipation rather than shifts in the age distribution, suggesting that some of the turnaround in the incidence of people saying they are "too sick" to work is a cyclical response to strengthening labor market conditions. We've yet to see how much longer this turnaround could continue, but it's an encouraging development.

For those interested in exploring the contributions to the changes in the LFP rate by gender and age over different time periods, we're currently developing an interactive tool for the Atlanta Fed's website—stay tuned!

July 29, 2016 in Employment, Labor Markets | Permalink | Comments (1)

July 18, 2016


What’s Moving the Market’s Views on the Path of Short-Term Rates?

As today's previous macroblog post highlighted, it seems that the United Kingdom's vote to leave the European Union—commonly known as the Brexit—got the attention of business decision makers and made their business outlook more uncertain.

How might this uncertainty be weighing on financial market assessments of the future path for Fed policy? Several recent articles have opined, often citing the CME Group's popular FedWatch tool, that the Brexit vote increased the probability that the Federal Open Market Committee (FOMC) might reverse course and lower its target for the fed funds rate. For instance, the Wall Street Journal reported on June 28 that fed funds futures contracts implied a 15 percent probability that rates would increase 25 basis points and an 8 percent probability of a 25 basis point decrease by December's meeting. Prior to the Brexit vote, the probabilities of a 25 basis point increase and decrease by December's meeting were roughly 50 percent and 0 percent, respectively.

One limitation of using fed funds futures to assess market participant views is that this method is restricted to calculating the probability of a rate change by a fixed number of basis points. But what if we want to consider a broader set of possibilities for FOMC rate decisions? We could look at options on fed funds futures contracts to infer these probabilities. However, since the financial crisis their availability has been quite limited. Instead, we use options on Eurodollar futures contracts.

Eurodollars are deposits denominated in U.S. dollars but held in foreign banks or in the foreign branches of U.S. banks. The rate on these deposits is the (U.S. dollar) London Interbank Offered Rate (LIBOR). Because Eurodollar deposits are regulated similarly to fed funds and can be used to meet reserve requirements, financial institutions often view Eurodollars as close substitutes for fed funds. Although a number of factors can drive a wedge between otherwise identical fed funds and Eurodollar transactions, arbitrage and competitive forces tend to keep these differences relatively small.

However, using options on Eurodollar futures is not without its own challenges. Three-month Eurodollar futures can be thought of as the sum of an average three-month expected overnight rate (the item of specific interest) plus a term premium. Each possible target range for fed funds is associated with its own average expected overnight rate, and there may be some slippage between these two. Additionally, although we can use swaps market data to estimate the expected term premium, uncertainty around this expectation can blur the picture somewhat and make it difficult to identify specific target ranges, especially as we look farther out into the future.

Despite these challenges, we feel that options on Eurodollar futures can provide a complementary and more detailed view on market expectations than is provided by fed funds futures data alone.

Our approach is to use the Eurodollar futures option data to construct an entire probability distribution of the market's assessment of future LIBOR rates. The details of our approach can be found here. Importantly, our approach does not assume that the distribution will have a typical bell shape. Using a flexible approach allows multiple peaks with different heights that can change dynamically in response to market news.

The results of this approach are illustrated in the following two charts for contracts expiring in September (left-hand chart) and December (right-hand chart) of this year for the day before and the day after Brexit. With these distributions in hand, we can calculate the implied probabilities of a rate change consistent with what you would get if you simply used fed funds futures. However, we think that specific features of the distributions help provide a richer story about how the market is processing incoming information.

Prior to the Brexit vote (depicted by the green curve), market participants were largely split in their assessment on a rate increase through September's FOMC meeting, as indicated by the two similarly sized modes, or peaks, of the distribution. Post-Brexit (depicted by the blue curve), most weight was given to no change, but with a non-negligible probability of a rate cut (the mode on the left between 0 and 25 basis points). For December's FOMC meeting, market participants shifted their views away from the likelihood of one additional increase in the fed funds target toward the possibility that the FOMC leaves rates where they are currently.

The market turmoil immediately following the vote subsided somewhat over the subsequent days. The next two charts indicate that by July 7, market participants seem to have backed away from the assessment that a rate cut may occur this year, evidenced by the disappearance of the mode between 0 and 25 basis points (show by the green curve). And following the release of the June jobs report from the U.S. Bureau of Labor Statistics on July 8, market participants increased their assessment of the likelihood of a rate hike by year end, though not by much (see the blue curve). However, the labor report was, by itself, not enough to shift the market view that the fed funds target is unlikely to change over the near future.

One other feature of our approach is that comparing the heights of the modes across contracts allows us to assess the market's relative certainty of particular outcomes. For instance, though the market continues to put the highest weight on "no move" for both September and December, we can see that the market is much less certain regarding what will happen by December relative to September.

The greater range of possible rates for December suggests that there is still considerable market uncertainty about the path of rates six months out and farther. And, as we saw with the labor report release, incoming data can move these distributions around as market participants assess the impact on future FOMC deliberations.



July 18, 2016 in Europe, Interest Rates, Monetary Policy | Permalink | Comments (1)

Google Search



Recent Posts


August 2016


Sun Mon Tue Wed Thu Fri Sat
  1 2 3 4 5 6
7 8 9 10 11 12 13
14 15 16 17 18 19 20
21 22 23 24 25 26 27
28 29 30 31      

Archives


Categories


Powered by TypePad