Comments on: “Unemployment and Productivity in the Long Run: The Role of Macroeconomic Volatility” by Pierpaolo Benigno, Luca Antonio Ricci and Paolo Surico Julio J. Rotemberg Prepared for Sveriges Riksbank conference “The Labor market and the Macroeconomy,” Stockholm, September 2-3, 2010.

Great Issues - What long run conditions lead to worse recessions – and hence higher average unemployment. and where - Focus on extending the “monopoly union” model of Dunlop (1944) to the case of potentially asymmetrically rigid wages with random productivity. Substantive contributions - A new fact: Volatility in productivity raises unemployment. - Demonstration that, under certain conditions, the monopoly union model can explain this as well as the negative correlation between unemployment and mean productivity growth.

A neat modeling aspect: employment is constant when wages are flexible Let household i’s utility be: It faces labor demand: First order condition for flexible wages:

Comments on the assumptions - Why does a union view wage changes as costly? Fear of upsetting its membership? - We know wages are quoted in units of currency. Why are wages “perfectly indexed” to inflation but not at all to productivity? -This is clearly better for the fit of the model than 0% indexing to inflation. Perhaps one could say something about what indexing fits best.

Uncomfortable implications of the model 1 - Constant labor share – and yet the VAR includes both Y/L and W/P…

Uncomfortable implications of the model 2 Quadratic cost model is close to “superneutral” Minimize FOC So, asymmetries essential for getting big effects of g In favored specification, there is only a cost of wage declines Then: recessions occur only when the exogenous component of productivity actually declines.

Comment 1: how should one measure (trend) mean and variance of productivity growth, and mean unemployment? They show moving averages - Still very correlated with cycles And VAR estimates with varying coefficients - Lots of possible specification errors - Lots of nuisance parameters

My favorite method for measuring trends ensures temporary trend changes are uncorrelated with cycles

Comparison of trends: Unemployment

Comparison of trends: Mean productivity growth

Comparison of trends: Variance of productivity growth Using my variables: u = 9.4 – 1.2 g - 1.5e^4 variance R 2 =.71 (.06) (1.3e^3) DW=.004

Main concern: Why is this a model of unemployment? Model has clear implications for L (employment) and unemployment is defined as (1-L). Recast model as Gali, Smets and Wouters (2010)? Shouldn’t this model (also) explain the employment to population ratio?

Results using trend Employment/Population ratio Using CO detrending: Correlation(u, g) = -.48 Correlation(e/p, g) = -.06 Using my explanatory variables: e/p = g - 1e^5 variance R 2 =.97 (.05) (1.3e^4) Using BSR’s e/p = 84 – 5.7 g variance R 2 =.76 (.41) (.20)

Conclusions Ambitious – provocative paper that raises lots of interesting questions. Measuring long run variances appears to be tricky. Unemployment is only partly non-employment - and this distinction seems particularly importance at low frequencies