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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.


September 30, 2016


A Quick Pay Check: Wage Growth of Full-Time and Part-Time Workers

In the last macroblog post we introduced the new version of the nominal Wage Growth Tracker, which allows a look back as far as 1983. We have also produced various cuts of these data comparable to the ones on the Wage Growth Tracker web page to look at the wage dynamics of various types of workers. One of the data cuts compares the median wage growth of people working full-time and part-time jobs. As we have highlighted previously, the median wage growth of part-time workers slowed by significantly more than full-time workers in the wake of the Great Recession. The extended time series allows us to look back farther to see if this phenomenon was truly unique.

The following chart shows the extended full-time/part-time median wage growth time series at an annual frequency.

Median-nominal-hourly-wage-growth-01

The chart shows that the median wage increase for part-time workers is generally lower than for full-time workers, with the average gap about 1 percentage point. The reason for the presence of a gap is a bit puzzling. Could it be that part-time workers have lower average productivity growth than full-time workers? It is true that a part-time worker in our data set is more likely to lack a college degree than a full-time worker, and the median wage level for part-time workers is lower than for full-time workers. But interestingly, a reasonably systematic wage growth gap still exists after controlling for differences in the education and age of workers. So even highly educated prime-age, part-time workers tend to have lower median wage growth than their full-time counterparts. If it's a productivity story, its subtext is not easily captured by observed differences in education and experience.

Changes in economic conditions might also be playing a role. The wage growth gap exceeded 2 percentage points in the early 1980s and again between 2011 and 2013, both periods of considerable excess slack in the labor market, as we recently discussed here. In fact, in each of 2011, 2012, and 2013, half of the part-time workers in our dataset experienced no increase in their rate of pay at all.

To explore this possibility further, it's useful to separate part-time workers into those who work part-time because of economic conditions (for example, because of slack work conditions at their employer or their inability to find full-time work) from those who work part-time for noneconomic reasons (for example, because they have family responsibilities or because they are also in school). The following chart shows the median wage growth for full-time, voluntary part-time, and involuntary part-time workers.

Median-nominal-hourly-wage-growth-02

Admittedly, there are not that many observations on involuntary part-time workers in our data set. But it does appear that their median wage growth has tended to slow by more after economic downturns than those working part-time for a noneconomic reason—at least prior to the Great Recession. After the last recession, however, the wage growth gap was just about as large for both types of part-time workers. In that sense, the impact of the last recession on the median wage growth of regular part-time workers was quite unusual.

Since 2013, median wage growth for part-time workers has been rising, which is good news for those workers and consistent with the labor market becoming tighter. With the unemployment rate reasonably low, employers might have to worry a bit more about retaining and attracting part-time staff than they did a few years ago.

September 30, 2016 | Permalink | Comments (0)

September 27, 2016


Back to the '80s, Courtesy of the Wage Growth Tracker

Things have been a wee bit quiet in macroblog land the last few weeks, chiefly because our time has been devoted to two exciting new projects. The first is a refresh of our labor force dynamics website, which will feature a nifty tool for looking at the main reasons behind changes in labor force participation for different age groups. More on that later.

The other project has been adding more history to our Wage Growth Tracker. The tracker's current time series starts in 1997. The chart below shows an extended version of the tracker that starts in 1983.

Nominal-wage-growth-tracker

Recall that the Wage Growth Tracker depicts the median of the distribution of 12-month changes of matched nominal hourly earnings. In the extended time series, you'll notice two gaps, which resulted from the U.S. Census Bureau scrambling the identifiers in its Current Population Survey. For those two periods, you'll have to use your imagination and make some inferences.

As we have emphasized previously, the Wage Growth Tracker is not a direct measure of the typical change in overall wage costs because it only looks at (more or less) continuously employed workers. But it should reflect the amount of excess slack in the labor market. This point is illustrated in the following chart, which compares the Wage Growth Tracker with the unemployment gap computed from the Congressional Budget Office's (CBO) estimate of the long-run natural rate of unemployment.

  02-nominal-wage-growth-and-excess-labor-market-slack


As the chart shows, our measure of nominal wage growth has historically tracked the cyclical movement in the unemployment rate gap estimate fairly well, at least since the mid-1980s. We think this feature is potentially important, because the true unemployment rate gap is very hard to know in real time and hence is subject to potentially large revision. For example, in real time, the unemployment rate was estimated to have fallen below the natural rate in the fourth quarter of 1994, but it is now thought to have not breached the natural rate until the first quarter of 1997—more than two years later. The Wage Growth Tracker is not subject to revision (although it is subject to a small amount of sampling uncertainty) and hence could be useful in evaluating the reliability of the unemployment rate gap estimate in real time.

This also is important from a monetary policy perspective if we are worried about the risk of the economy overheating. For example, President Rosengren of the Boston Fed described why he dissented at the most recent Federal Open Market Committee meeting in favor of a quarter-point increase in the target range for the federal funds rate. His dissent, he said, arose partly from his concern that the economy may overheat and drive unemployment below a level he believes is sustainable.

Currently, the CBO estimate of the unemployment rate gap looks like it is plateauing at close to zero. The fact that the Wage Growth Tracker for the third quarter slowed a bit is consistent with that. But it's only one quarter of data, and so we'll closely monitor the Wage Growth Tracker in the coming months to see what it suggests about the actual unemployment rate gap. We'll discuss what observations we make here.

September 27, 2016 | Permalink | Comments (0)

September 08, 2016


Introducing the Atlanta Fed's Taylor Rule Utility

Simplicity isn't always a virtue, but when it comes to complex decision-making processes—for example, a central bank setting a policy rate—having simple benchmarks is often helpful. As students and observers of monetary policy well know, the common currency in the central banking world is the so-called "Taylor rule."

The Taylor rule is an equation introduced by John Taylor in a seminal 1993 paper that prescribes a value for the federal funds rate—the interest rate targeted by the Federal Open Market Committee (FOMC)—based on readings of inflation and the output gap. The output gap measures the percentage point difference between real gross domestic product (GDP) and an estimate of its trend or potential.

Since 1993, academics and policymakers have introduced and used many alternative versions of the rule. The alternative forms of the rule can supply policy prescriptions that differ significantly from Taylor's original rule, as the following chart illustrates.

Effective federal funds rate and prescriptions from alternative versions of the Taylor rule
(enlarge)

The green line shows the policy prescription from a rule identical to the one in Taylor's paper, apart from some minor changes in the inflation and output gap measures. The red line uses an alternative and commonly used rule that gives the output gap twice the weight used for the Taylor (1993) rule, derived from a 1999 paper by John Taylor. The red line also replaces the 2 percent value used in Taylor's 1993 paper with an estimate of the natural real interest rate, called r*, from a paper by Thomas Laubach, the Federal Reserve Board's director of monetary affairs, and John Williams, president of the San Francisco Fed. Federal Reserve Chair Janet Yellen also considered this alternative estimate of r* in a 2015 speech.

Both rules use real-time data. The Taylor (1993) rule prescribed liftoff for the federal funds rate materially above the FOMC's 0 to 0.25 percent target range from December 2008 to December 2015 as early as 2012. The alternative rule did not prescribe a positive fed funds rate since the end of the 2007–09 recession until this quarter. The third-quarter prescriptions incorporate nowcasts constructed as described here. Neither the nowcasts nor the Taylor rule prescriptions themselves necessarily reflect the outlook or views of the Federal Reserve Bank of Atlanta or its president.

Additional variables that get plugged into this simple policy rule can influence the rate prescription. To help you sort through the most common variations, we at the Atlanta Fed have created a Taylor Rule Utility. Our Taylor Rule Utility gives you a number of choices for the inflation measure, inflation target, the natural real interest rate, and the resource gap. Besides the Congressional Budget Office–based output gap, alternative resource gap choices include those based on a U-6 labor underutilization gap and the ZPOP ratio. The latter ratio, which Atlanta Fed President Dennis Lockhart mentioned in a November 2015 speech while addressing the Taylor rule, gauges underemployment by measuring the share of the civilian population working their desired number of hours.

Many of the indicator choices use real-time data. The utility also allows you to establish your own weight for the resource gap and whether you want the prescription to put any weight on the previous quarter's federal funds rate. The default choices of the Taylor Rule Utility coincide with the Taylor (1993) rule shown in the above chart. Other organizations have their own versions of the Taylor Rule Utility (one of the nicer ones is available on the Cleveland Fed's Simple Monetary Policy Rules web page). You can find more information about the Cleveland Fed's web page on the Frequently Asked Questions page.

Although the Taylor rule and its alternative versions are only simple benchmarks, they can be useful tools for evaluating the importance of particular indicators. For example, we see that the difference in the prescriptions of the two rules plotted above has narrowed in recent years as slack has diminished. Even if the output gap were completely closed, however, the current prescriptions of the rules would differ by nearly 2 percentage points because of the use of different measures of r*. We hope you find the Taylor Rule Utility a useful tool to provide insight into issues like these. We plan on adding further enhancements to the utility in the near future and welcome any comments or suggestions for improvements.

September 8, 2016 in Banking, Federal Reserve and Monetary Policy, Monetary Policy | Permalink | Comments (0)

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)

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