Posted: 27 Aug 2015 12:15 AM PDT
Joseph Tracy, Robert Rich, Samuel Kapon, and Ellen Fu say "that roughly 90 percent of the labor gap that opened up following the recession has been closed":
Mind the Gap: Assessing Labor Market Slack, Liberty Street Economics, NY Fed: Indicators of labor market slack enable economists to judge pressures on wages and prices. Direct measures of slack, however, are not available and must be constructed. Here, we build on our previous work using the employment-to-population (E/P) ratio and develop an updated measure of labor market slack based on the behavior of labor compensation. Our measure indicates that roughly 90 percent of the labor gap that opened up following the recession has been closed.
An earlier post, "A Mis-Leading Labor Market Indicator," argued that the gap between the E/P ratio and a demographically adjusted version of the same ratio is a useful measure of labor market slack. A challenge in constructing this measure is that it requires a normalization (a level shift) to "re-center" the demographically adjusted E/P ratio. In this earlier post, we normalized by assuming that the average labor gap should be zero over a long period of time. Although this approach was easy to implement, it had the disadvantage of not being linked to wage behavior.
To better motivate an E/P-based approach, we turn to Phillips curve models that relate wage growth to labor market slack. The specification we consider relates nominal wage growth to an E/P gap variable (defined as the difference between a demographically adjusted E/P ratio and the actual E/P ratio), as well as expected inflation and trend productivity growth. Expected inflation is subtracted from nominal wage growth to derive an expected real wage growth series, which is then regressed on a constant, the E/P gap, and trend productivity growth. The E/P gap is normalized so that the estimated intercept of the Phillips curve model is set to zero. This approach implies that when the resulting normalized E/P gap is zero, expected real wage growth adjusted for the return to labor productivity is, on average, zero. That is, a labor market with no slack will have nominal wage growth, on average, equal to expected inflation plus a return to labor productivity.
We estimate Phillips curve models for three wage measures: compensation per hour, average hourly earnings, and the employment cost index. We construct four-quarter-ahead growth rates starting in the first quarter of 1982, the earliest start date for which all three measures are available, and ending in the second quarter of 2015. Expected inflation is measured using survey data on ten-year CPI inflation expectations, and trend productivity growth is a twelve-quarter moving average of (annualized) productivity growth rates. Adopting the same approach we took in another post—"U.S. Potential Economic Growth: Is it Improving with Age?"—we have extended the data sample used to estimate the demographically adjusted E/P ratio back to the early 1960s. This provides us with roughly thirteen million observations on individuals that we divide into 280 cohorts based on decade of birth, sex, race/ethnicity, and educational attainment. For each cohort, we estimate a cohort-specific profile for average employment rates by age that abstracts from cyclical effects. Aggregating these predicted employment rates across individuals produces a demographically adjusted E/P ratio, with the quarterly series derived as an average of the three monthly values.
The three Phillips curve models yield similar normalizations, so we average them instead of selecting one. The chart below shows the actual E/P ratio along with the demographically adjusted E/P ratio based on this new normalization.
The next chart plots the estimated E/P gap (the difference between the two series in the chart above) along with the three expected real wage growth measures adjusted for trend productivity growth. By our definition, a positive E/P gap indicates slack in the labor market. The periods in which the estimated E/P gap is zero line up well with the periods in which our adjusted real wage growth measures are also close to zero. Moreover, periods in which the adjusted wage measures have exceeded zero generally correspond to episodes of tight labor markets (negative E/P gaps), while periods in which the measures are below zero are typically associated with slack in the labor market (positive E/P gaps).
The current normalized E/P gap is estimated to be 32 basis points, which represents an 89 percent reduction from the 283-basis-point gap in November 2010. This finding suggests that the labor market has made considerable progress in its recovery, but is still not yet back to neutral. To gain additional perspective on this finding, we can compare the current gap with those that existed in two earlier tightening episodes. At the time the FOMC began to raise rates in February 1994, the gap was 92 basis points; at the end of that tightening cycle, it was 14 basis points. And when the Committee began to raise rates in June 2004, the gap was -38 basis points; at the end of that tightening cycle, the gap was -125 basis points. To assess labor market slack and understand the behavior of labor compensation in the quarters ahead, it will be particularly important to mind the gap.
Posted: 27 Aug 2015 12:06 AM PDT
Posted: 26 Aug 2015 11:35 AM PDT
Dudley Puts The Kibosh On September, by Tim Duy: Monday's action on Wall Street was too much for the Fed. That day, Atlanta Federal Reserve President Dennis Lockhart pulled back his previous dedication to a September rate hike earlier, reverting to only an expectation that rates rise sometimes this year. But today New York Federal Reserve President William Dudley explicitly called September into question. Via the Wall Street Journal:
Posted: 26 Aug 2015 08:34 AM PDT
The Future of Macro: There is an interesting set of recent blogs--- Paul Romer 1, Paul Romer 2, Brad DeLong, Paul Krugman, Simon Wren-Lewis, and Robert Waldmann---on the history of macro beginning with the 1978 Boston Fed conference, with Lucas and Sargent versus Solow. As Romer notes, I was at this conference and presented a 97-equation model. This model was in the Cowles Commission (CC) tradition, which, as the blogs note, quickly went out of fashion after 1978. (In the blogs, models in the CC tradition are generally called simulation models or structural econometric models or old fashioned models. Below I will call them CC models.)
I will not weigh in on who was responsible for what. Instead, I want to focus on what future direction macro research might take. There is unhappiness in the blogs, to varying degrees, with all three types of models: DSGE, VAR, CC. Also, Wren-Lewis points out that while other areas of economics have become more empirical over time, macroeconomics has become less. The aim is for internal theoretical consistency rather than the ability to track the data.
I am one of the few academics who has continued to work with CC models. They were rejected for basically three reasons: they do not assume rational expectations (RE), they are not identified, and the theory behind them is ad hoc. This sounds serious, but I think it is in fact not. ...He goes on to explain why. He concludes with:
... What does this imply about the best course for future research? I don't get a sense from the blog discussions that either the DSGE methodology or the VAR methodology is the way to go. Of course, no one seems to like the CC methodology either, but, as I argue above, I think it has been dismissed too easily. I have three recent methodological papers arguing for its use: Has Macro Progressed?, Reflections on Macroeconometric Modeling, and Information Limits of Aggregate Data. I also show in Household Wealth and Macroeconomic Activity: 2008--2013 that CC models can be used to examine a number of important questions about the 2008--2009 recession, questions that are hard to answer using DSGE or VAR models.
So my suggestion for future macro research is not more bells and whistles on DSGE models, but work specifying and estimating stochastic equations in the CC tradition. Alternative theories can be tested and hopefully progress can be made on building models that explain the data well. We have much more data now and better techniques than we did in 1978, and we should be able to make progress and bring macroeconomics back to it empirical roots.
For those who want more detail, I have gathered all of my research in macro in one place: Macroeconometric Modeling, November 11, 2013.