Discussion of Kahn-Rich on Tracking the New Economy

1. Discussion of Kahn-Rich on Tracking the New Economy Robert J. Gordon Northwestern University and NBER Federal Reserve of San Francisco, November 7, 2003

2. Productivity Growth is the Hot Topic in Macroeconomics • Look at the numbers, 6.8 in 2003:Q2, 8.1 in 2003:Q3 • How much of this is an unusual cyclical event? How much of this is an acceleration of trend? • Advertisement: Read another paper, “Exploding Productivity Growth: Historical Context, Possible Causes, Future Implications” BPEA 2003:2, forthcoming • Brookings disallows NBER Yellow-covered Papers • Will be on my web site by Monday morning. Search Google (not that Scottish University). • 97 double-spaced pages

3. Two New Ideas in Kahn-Rich Paper • Idea #1. Productivity growth shifts between regimes. “High” and “Low” productivity growth. • Idea #2. We can do better in estimating the trend of productivity growth by using outside information going beyond productivity growth itself • Info A: Real compensation per hour • Info B: Real consumption per hour

4. Does their Approach Signal Increase in late 90’s Trend Earlier than Other Methods? • Their claim: • “One could not decisively conclude that there was a return to a higher growth regime on the basis of productivity alone.” • “Only the corroborating evidence from other cointegrated series can swing the balance strongly in favor of a regime switch.” • Does Their Series Detect Post-1995 Acceleration Faster than Alternatives?

5. Motivations for the Kahn-Rich Reevaluation of Growth Trends • One can be skeptical about both ideas • “We treat that trend as a stochastic process whose mean growth rate has two `regimes’”. • Why two? • Why not high medium low? • Why not four regimes? Ten regimes? • Use two additional series to provide additional information • Skeptic: Why do these two series provide independent information? Why do the authors not take us through the arithmetic of labor’s income share?

6. Additional justifications for their approach • Criterion for use of two additional variables, consumption and real wage: • #1 “We show that aggregate productivity data alone do not provide as clear . . . a signal of changes in trend growth as does the joint signal from the series we examine.” • What aspect of time-series dynamics provide an additional contribution from those two series? • #2 We do not have to choose break dates • Nor do H-P filter nor Kalman time-varying coefficient • #3 How long regimes last (contingent on only two regimes?)

7. Arithmetic of Labor’s Share • Does their real compensation variable provide additional information? • S = (WH/PY) • Change in log labor’s income share • Δs = Δw – Δp – (Δy – Δh) • Usual cyclical behavior, labor’s share rises in recessions, shrinks in recoveries (like now). • In using compensation per hour as a proxy for the productivity trend, they are making a statement about the cyclical behavior of labor’s share. But they have no model.

8. Consumption/Hour • How is Consumption/Hour related to productivity? • C/H = (C/Y) * (Y/H) • So now we need a model of the share of consumption in GDP. Lots of models – Keynesian, RBC, but not in this paper.

9. Alternative Productivity Trends to Compare to Kahn-Rich • #1 Hodrick-Prescott (H-P) filter • Parameter value: meaning • Everyone uses 1600. This is the square of 5/(1/8) =40. When detrending GDP, a 5 percent GDP gap causes the trend to decelerate by 1/8 percent per quarter. • Great Depression: 25 percent GDP gap, trend decelerates at 5/8 percent per quarter, 2.5 percent per year, 10 percent after four years. • Starting at 3 percent per year in 1929, trend by 1933 is growing at 3 – 25 percent or -22 percent per year. • H-P parameter 6400 is much better than 1600

10. #2 Kalman Filter with Time-Varying Coefficients • The Kalman filter explains the change in productivity growth (Δpt) by a time-varying constant and any set of other explanatory variables (βXt): • (5) Δy - Δh(t) = α(t) + βX(t)+ w(t) • The next step is to specify a time-series process for the time-varying productivity trend, and the most straightforward is a random walk: • (6) α(t) = α(t-1) + v(t)

11. Implementation of Kalman Filter Productivity Trend with Time-Varying Coefficient • Replace βX(t) by change in output deviation from trend (or change in unemployment rate). • Could also use supply-shock variables, e.g., change in oil prices, that cause temporary changes in productivity • Kalman filter can use more info than H-P • Compare K-filter with H-P filter • Without output variable they are identical, same smoothness coefficient

12. Kalman with Output vs. H-P

13. Kahn-Rich Compared

14. Summary: Problems with their Productivity Growth Trend • Problem #1. Why is it so Jagged? • Problem #2. Why must there be only two regimes? • Problem #3. Why does the behavior of the real wage tell us something about the productivity trend? • What if super-normal productivity growth goes into profits? How long does it take for real wage growth to catch up? • Problem #4. Why does consumption tell us anything about the productivity trend. What is the theory of the behavior of the C/Y ratio?

15. Thanks to the Authors • For bringing the Productivity Growth Trend to the Attention of this Audience one day after the announcement of 8.1 • For helping to focus attention on the strengths and weaknesses of Hodrick-Prescott and Kalman • And for reminding us that there is a lot of macroeconomics devoted to explaining the change in labor’s income share and the C/Y ratio