The Evolution of Okun’s Law and of Cyclical Productivity Fluctuations in the United States and in the EU-15

Robert J. Gordon

Northwestern, NBER, CEPR, OFCE

Seminar Presentation at OFCE

Paris, September 14, 2011

Themes of Paper with Broader Implications

Procyclical productivity shocks are not a fundamental object in macroeconomics; they are residual artifacts of lagging hours– Productivity’s lead does not prove causation– The productivity residual varies across time and

places (US vs EU) as a result of labor-market institutions.

– Procyclical productivity fluctuations have nothing to do with technology, and the phrase “technology shocks” should be banished from macroeconomics

Further Broad Themes EU vs. US differences in cyclicality of

productivity reflects American Exceptionalism and European institutions

Among these are – Rising inequality in US, shift in power from labor to

management– Much less increase in EU inequality (outside of

UK), corporatist constraint on executive compensation. Germany management and union agreement on work sharing and kurzarbeit.

– Long history of EU corporatism and government policy to share work by reducing hours

Changes in Cyclical Labor-market Behavior

Point of Departure: Okun’s Law (1962)– In response to a 1 percent change in the output gap,

procyclical responses of hours 2/3, of which employment 1/3, LFPR 1/6, hours/employment 1/6

– Procyclical productivity fluctuations make up remaining 1/3 US study: A new approach to detrending data

– Contrasts H-P and Kalman filters– uses “outside information” from inflation to

determine the unemployment rate gap– Feeds U gap into Kalman filter to eliminate cyclical

component of trend in output and aggregate hours.

Other New Findings: Unconventional US Data and

Analysis of EU-15 Quarterly Data

For US only: a new approach to data– US: Total Economy not NFPB Sector– US: Conventional vs. Unconventional Measures

For EU, an attempt to create quarterly data for EU-15 aggregate that duplicate those long available for US– First quarterly data series back to 1963 on employment and output

with consistent aggregation– Merged post-2000 quarterly hours with earlier annual hours data to

create a series back to 1970 Main finding: in the US, productivity no longer exhibits procyclical

fluctuations. But in the EU, productivity is still procyclical by about the same amount as before.

A key finding for the US: hours gap ~= output gap in 2008-09. But as predicted by Okun’s Law, hours gap < output gap in US in 1980-82 and in EU-15 for 08-09.

Preview of Substantive Hypotheses to Explain Changes

Joint explanation of US and EU behavior based on “American Exceptionalism”

US shift toward greater labor input response is explained by the “Disposable Worker” hypothesis– Increased managerial power, new emphasis on

maximizing shareholder value, decreased power of labor groups and employees

Preview of Substantive Hypotheses (Continued)

Europe has not experienced a parallel shift in market power between labor and management

Also, several important EU countries have developed institutions and policies that explicitly or implicitly restrict the responsiveness of labor to output changes, e.g. “work sharing”– These policies shift the impact of output changes from

employment level onto hours per employee and consequently output per employee

– Work sharing in Europe in the form of shorter hours per employee does not show up in our results

Simple Version and Conventional U. S. Data Version for the Total Economy

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Introducing the Alternative “Unconventional” Identity

Nalewaik’s 2010 Brookings Paper:– GDP and GDI are conceptually identical– But they differ (statistical discrepancy)– GDI is more procyclical– When GDP is revised, it tends to be revised toward what GDI already

shows Hours

– All existing work uses hours based on payroll employment– There is a little-known series on hours based on the household survey

In principle 2 numerators, 2 denominators = 4 possible productivity measures, here we simplify by comparing only two combinations, Conventional and Unconventional

Conventional Compared to Unconventional Identity

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First Part of Paper: Detrending the

Full-Period US Data

Uses Kalman detrending, which allows use of an outside feedback variable.– Avoids excessive cyclicality of H-P trends– For this outside information, turn to a technique for estimating the

time-varying natural rate of unemployment (TV-NAIRU) When possible, we prefer to use Kalman over HP or Bandpass

filters, which use only a univariate series to detrend itself. Last part of paper: study of US vs. EU redoes US results

in a restricted format to match data availability for EU. Uses H-P filter as a stopgap variable for both US and EU.

TV-NAIRU: The Kalman Feedback Variable

The TV-NAIRU provides the outside information for the Kalman trends– Calculated through an established procedure of regressing the

growth of inflation on lagged inflation, unemployment, and supply shock variables.

The standard NAIRU is too volatile to plausibly represent trend employment, so we take a 20-quarter centered moving average

The time period of 2008-11 distorts the entire NAIRU, because there is a large output gap without a steady decline in core inflation

We cut off the NAIRU at its 2005:Q1 value of 5.0 percent This is consistent with the subsequent decision to cut off changes in

estimated trends at 2007:Q4 in both the U. S. and Europe

The Unemployment Gap, Fed Back to the Kalman

Trend

19551955.251955.51955.7519561956.251956.51956.7519571957.251957.51957.7519581958.251958.51958.7519591959.251959.51959.7519601960.251960.51960.7519611961.251961.51961.7519621962.251962.51962.7519631963.251963.51963.7519641964.251964.51964.7519651965.251965.51965.7519661966.251966.51966.7519671967.251967.51967.7519681968.251968.51968.7519691969.251969.51969.7519701970.251970.51970.7519711971.251971.51971.7519721972.251972.51972.7519731973.251973.51973.7519741974.251974.51974.7519751975.251975.51975.7519761976.251976.51976.7519771977.251977.51977.7519781978.251978.51978.7519791979.251979.51979.7519801980.251980.51980.7519811981.251981.51981.7519821982.251982.51982.7519831983.251983.51983.7519841984.251984.51984.7519851985.251985.51985.7519861986.251986.51986.7519871987.251987.51987.7519881988.251988.51988.7519891989.251989.51989.7519901990.251990.51990.7519911991.251991.51991.7519921992.251992.51992.7519931993.251993.51993.7519941994.251994.51994.7519951995.251995.51995.7519961996.251996.51996.7519971997.251997.51997.7519981998.251998.51998.7519991999.251999.51999.7520002000.252000.52000.7520012001.252001.52001.7520022002.252002.52002.7520032003.252003.52003.7520042004.252004.52004.7520052005.252005.52005.7520062006.252006.52006.7520072007.252007.52007.7520082008.252008.52008.7520092009.252009.52009.7520102010.252010.52010.7520112011.25-4

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Figure 2. Actual Unemployment Rate, Time-Varying NAIRU, and Implied Unem-ployment Gap,

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TV NAIRU

Unemployment Gap

The Wild Differences inHours Trends

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Figure 1a. Annualized Trend Growth Rate of Conventional Aggregate Hours, Al-ternative Methods, 1955:Q1 - 2011:Q2

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Equally Sharp Differencesin Output Trends

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Figure 1b. Annualized Trend Growth Rate of Conventional Output, Alternative Methods, 1955:Q1 - 2011:Q2

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Figure 1

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Alternative Productivity Trends

Special Problem Posed by 2008-11 Cycle

Hours and employment gaps for the US respond roughly as much as output gap

The unemployment gap drives the trend adjustment which treats the entire post-1954 interval as homogeneous

Estimated through 2011:Q2, the Kalman procedure thinks that the long term trend hours growth must be implausibly low to generate the observed decline in hours

We avoid making judgments on 2008-11 cycle by constraining all growth trends as equal to 2007:Q4 growth rates throughout 2008-11– Thus the paper “dodges” the hot current (as yet

unanswerable) topic of the new normal

Conventional (C) vs. Unconventional (U): Medium-run Growth

Trends Major findings in Table 1 The mysterious upsurge in LP growth 2001-07

in C data does not exist in U data Big differences in AAGR of LP growth Conventional 96-01: 2.11, 01-07: 2.10 Unconventional 96-01: 2.33, 01-07: 1.24

– This supports the view that the late 1990s US productivity revival was a one-shot event, not a permanent change in the trend

Kalman Trends: Conv vs. Unconv Output & Hours

Unconventional Productivity: New Story for 1994-2007

Basis of U. S. Analysis: Kalman Trends for

Conventional Definitions

Continuing with U. S.-Only Data:What We Learn from Cyclical

Deviations from Trend (“Gaps”) The most interesting results

– Okun’s 2/3 hours vs. 1/3 productivity result worked perfectly in late 1960s and early 1980s but at almost no other time

The 2008-09 cycle has been as big for hours than for output, while 1980-82 saw a much larger response of output than hours

Correlation of productivity gap with output gap changes timing and disappears after mid-1980s

US: Gaps for C & U Average: Output, Hours, Productivity

US: Gaps for Three Components of Aggregate Hours

Regression Analysis for US-Only 1954-2011

• All variables expressed as QUARTERLY FIRST DIFFERENCES OF DEVIATION FROM TREND, i.e. Δ log gap in X

• Changes in gaps for output identity components explained by – Changes in output gap (current, four lags, and four leads)– Lagged dependent variable (lags 1-4)– Error correction term– Interactive dummy on output gap (for full period)– End-of-expansion dummies

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End of Expansion Variable

End of Expansion Effect– Productivity slows late in expansion, hours grow too

fast (“overhiring”)– Constrained to be completely offset by faster

productivity growth early in recovery (“Early Recovery Productivity Bubble”

Implementation

– Not 0,1 dummies. They enter in the form 1/M, -1/N– M is the number of quarters in late expansion, N in

early recovery– These sum to zero

Error Correction Term

Error Correction Term– Linked to the concept of cointegration

Informal Definition: A linear combination of two series is stationary. See Engle and Granger (1987)

– A regression using only differenced data is misspecified, and one using only levels will ignore important constraints.

– Solution: Add an error correction variable to the regression consisting of the lagged log ratio of the gdp gap to the dependent variable gap, allowing for separate coefficients on the numerator and denominator

For further reference on the concept of error correction in a bivariate model, see Hendry, Pagan, and Sargan (1984)

Regression Results for US-Only, 1954-86 vs. 1986-2011

Hours gap lags output by roughly one quarter Productivity leads output by roughly two and half quarters End-of-expansion dummies (8 recessions)

– To simplify tables, constrained to be equal within subsample– Significant in Hours and LP equations pre 1986 and in full-period

regressions Split sample: 1954-86 vs 1986-2011

– Big change in long-run responses– They don’t pass Chow tests, which are too demanding– Interactive dummy shows a statistically significant change in the sum

of the output coefficients To simplify paper, regressions are presented only for

conventional concept of hours & LP– Unconventional data are noisier due to household hours

US: Long-Run Responses, Before and After 1986

Implications of Regression Analysis

Okun’s Law is overturned, Hours now respond by >1 to output deviations, not <1

Productivity no longer responds procyclically to output fluctuations– No more Okun’s Law– No more SRIRL– No more RBC– No more procyclical “technology shocks,” i.e.,

productivity fluctuations as exogenous inputs in DSGE and other modern macro theories

The “Early Recovery Productivity Bubble”

On average since 1970 LP has grown 1.4 percent AAGR faster than trend in first four quarters of recovery

0.00 percent faster in following eight quarters 2002-03 was unusual because fast growth continued

in the subsequent 8 quarters– (but not in the unconventional data)

EOE effect explains about 2/3 of first four quarters

Actual and Fitted, Early and Late Equations for Hours

Actual and Fitted, Early and Late Equations for Productivity

Now We Turn toComparison of US and EU

The motivating puzzle, shown on the next slide, is that conditional on the decline in output, U rate increased more in U. S. than in EU

To avoid keeping track of 15 countries, we study only the EU-15 aggregate

Some of data are for Euro Area, not EU-15 Reason horizontal axis is change in output, not

output gap (OECD gaps are implausible)

Relationship of Unemployment and Output in US and EU

The first graph shows the relationship between the cumulative growth rates of output and unemployment in the recession.

The US is an outlier: the increase in unemployment is higher than the decrease in output would predict

Next Figure: The U.S. 2010 level of unemployment is not unusually high; 2007 unemployment was unusually low

US vs EU: Cumulative Change of Output and Unemployment 07-10

US vs EU: Level of Unemployment vs. Growth of Output

Stripped Down Identity for Comparing US and European Data

• No suitable quarterly data (yet) for the EU on Employment Rate or LFPR– Y/H: Output per Hour: labor productivity– H/E and E/N: Components of aggregate hours, the

labor input– H/E problem, a hybrid between payroll survey and

household survey

Comparing the US and the EU, Graphs and Regressions

Uses simplified output identity: only two components of aggregate hours: H/E and E/NOther differences from full US regression

– No EOE variable (not available for Europe)– Shorter time period, 1971:Q2 – 2011:Q1

Early = 1971-1991, Late = 1991-2011– No outside cyclical variable, so we can’t use

Kalman detrending, instead we use Hodrick-Prescott filter with a parameter of 6400, running the trends to 2007:Q4 and extending the trend growth rates to 2011:Q1

US vs EU: Actual Four-Quarter Growth of Output

Observations on the Actual Growth Rates in US and EU

Change in output growth from 2008-2010 is nearly identical for EU and US

Between 1986 and 2006 volatility of output growth is about the same

Pre-1986 the US has a much more volatile business cycle with back-to-back recessions in 1980 and 1981-82

Observations on the Trend Growth Rates in US and EU

European productivity trend growth starts high (4.5 percent), then steadily declines

Difference between the productivity trends is especially high after 1994, when US LP begins to increase steeply

The growth disparity after 1994 has its counterpart in levels: PPP-adjusted productivity in the EU relative to the US reached 92 percent in 1995 then slipped back to about 83 percent

US vs EU: Trend Four-Quarter Growth of Output, Hours

US vs EU: Trend Four-Quarter Growth of Labor Productivity

US vs EU: Trend Four-Quarter Growth of H/E, E/N

Now Come Graphs of Actual vs. Trend Changes

The next two charts show the division of the hours response between Hours per employee (H/E) and employment per capita (E/N).

Notice the lack of a strong difference between US and EU regarding E/H

A much more visible difference in response of E/N

EU vs. US Actual vs. Trend Hours per Employee

EU vs. US Actual vs. Trend Employment per Capita

Observations on Gaps in US and EU for the Main Variables

The gap for a variable is the percent log ratio between actual and trend

A gap is a level variable and thus cumulates changes

If a variable grows slower than trend quarter after quarter, the gap keeps becoming more negative

Can see that the depth of the 2008-2009 recession was virtually identical in US vs. EU

Observations on Gaps in US and EU (cont.)

Output gaps restate the result that the depth of the recession of 08-09 was about equal in US and EU

Hours gap is much more negative for the US, with a trough of -8.7 percent versus -4.4 percent for EU

Leads to a higher productivity gap for the US during the recession, even briefly going positive

US had an earlier and shorter lived drop in productivity gap during the recession, even relative to higher trend productivity growth

Observations on Gaps in US and EU (cont.)

Discrepancy in US and EU hours gap in the 08-09 recession is almost entirely due to employment per capita rather than hours per employee

US E/N gap slipped to almost -8 percent and is yet to show much recovery, while EU gap reached only around -5 percent

The lack of difference between the EU and US on H/E is the most surprising result shown in the data

US vs EU: Gaps for Output, Labor

US vs EU: Gaps for Productivity (Output per Hour)

US vs EU: Gaps for Hours per Employee

US vs EU: Gaps for Employment per Capita

Overall Differences in US and Europe Graphs

In EU, hours tend to respond less than in the U.S. to output changes in the late half of the data (1991-2011)

Difficult to analyze differences before 1991, because both output and hours were more volatile in the US than in the EU during the first 15 years of the data (1971-1986)

Regression Analysis Europe vs. US, 1971-2011

Dependent variables: Hours (H), Productivity (Y/H), Hours per Employee (H/E), and Employment per Capita (E/N)

Independent variables:– 4 lags of dependent variable– Current value, 4 leads, and 4 lags of output– Error correction term

EU vs US Regression Results US: early-period response of hours to output is 0.63 EU: early-period response of hours is 0.70. Mean lag

in hours response of 0.21 quarters vs. 0.29 quarter lag in US

Key Difference: In late interval, the long-run responses shift in opposite directions, increasing to 1.28 for the US and decreasing to 0.61 for the EU.

Responses of productivity also shift in the opposite direction (0.37 to 0.45 for EU, 0.37 to -0.26 for the US)

EU vs US Regression Results (cont.)

US: the response of both components of hours increases. H/E from 0.32 to 0.40 and E/N from 0.39 to 1.05.

EU: both responses decrease, H/E from 0.39 to 0.16 and E/N from 0.72 to 0.70

Most of the US increase in H response is due to E/N, but most of the EU decrease in H response is due to H/E.

Puzzle: the sums of the responses of H components are larger than the direct responses of H

US Long Run Responses

EU Long Run Responses

Implications of Regression Results

In the US there was a distinct shift toward unitary response of labor input to output changes, and zero response of productivity as in the full U.S. regression results.

In Europe there was an opposite shift toward increased responsiveness of productivity and decreased responsiveness of the labor input.

We need an explanation for these opposing trends

Dynamic Simulations

Alternate coefficients from regressions of the early (1971-1991) and late (1991-2011) periods.

Stripped down regression equation using only the leads and lags of the GDP gap.

Figure 15 shows the actual levels of H/N, H/E, and E/N against those predicted by the early and late period simulations

The late period coefficients predict a much greater drop in E/N for the US than the early coefficients do

Simulated vs. Actual H/N

Simulated vs. Actual H/E

Simulated vs. Actual E/N

Unified Explanatory Hypothesis:“American Exceptionalism”

Joint explanation of changes in American and European behavior

American shifts toward greater labor response explained by “disposable worker” hypothesis

Europe’s opposite shift explained by the absence of the conditions of the “disposable worker” idea and by differing institutions and policies that promote work sharing.

Substantive Explanation of U.S. Increased Flexibility of Labor Input

Disposable worker hypothesis Based on increased managerial

power, diminished worker power Separate causes at top and bottom Same set of causes that has been

developed previously to explain rising U.S. inequality

The U. S. Disposable Worker

Explains both rise in cyclical responsiveness and of income inequality

Ingredients in increased management power: exec pay based on stock options, sensitivity to 2000-02 and 2007-09 stock market debacles

Stock options help explain huge increase in share of top 1 percent 1982-2000 and fluctuating share since then. Stock option income treated as labor income

Increased emphasis by management on maximizing shareholder value

Decomposing the Top Decile US Income Share into 3 Groups, 1913-

2008

Not just Strong Management, Weak Workers

Contributions of weak labor bargaining power the same list as the sources of increased income inequality in the bottom 90 percent– Reduced share of work force was unionized– Unions became weaker– Lower real minimum wage– Globalization: low-skilled immigrants and low-cost

imports

Application of this Hypothesis to 2000-04

2001-03, large employment response and long period of employment decline (19 months after NBER trough month, Nov 2001)– Output recovery was so weak that output gap got worse, not

better– Savage corporate cost cutting (intertwined nexus of

executive compensation, stock market, profit collapse)– Why did productivity rise so fast? Delayed spillover of ICT

inventions of the late 1990s Recall that productivity growth slowed down in the

unconventional data during 2001-04 The savage cost-cutting hypothesis has been validated by

industry cross-section results of Oliner-Sichel-Stiroh (2007)

2008-09 Responses: Similarities and Differences to 2001-03

Similar: collapse of stock market and corp. profits (bigger than 2000-02)– Same incentive for savage cost cutting

Different: It was much much bigger– Output gap widened by 5x as much– Apocalypse Now: Fear in late 2008 and early 2009 of

another Great Depression For every deck chair thrown off the Titanic in 2001-02, five

deck chairs were tossed over in 2008-09 Management didn’t just cut labor costs, but also capital

– GDPI declined at annual rate of -32 percent 2008:Q4-2009:Q2

Explanations for EU Behavior

Three broad differences between the US and Europe offer a point of departure for developing explanations:– 1) Different evolution of inequality– 2) Longstanding European regulations

that protect employment– 3) Explicit European institutions

encouraging work-sharing and reducing hours, both in the long run and during a downturn

Differences in Inequality

The U.S. exhibited a move toward maximizing shareholder value and cost-cutting. This move has the same causes as the increasing income inequality in the U.S. as compared to Europe.

Factors leading to lower European inequality and lower responsiveness of labor to output:– Smaller role of short-term profit maximization in management– Greater power of unions– Corporatist tradition: unions join with management in making

decisions that ultimately effect labor responsiveness

Income share of top 0.1 percent in the US quadrupled from 2 to 8 percent between 1975 and 2000.

Top share in France has remained remarkably stable, increase in U.K. has been relatively moderate compared to U.S.

Gini Coefficients in 2007: EU Average = 0.31, US = 0.45 Cultural customs and institutions (e.g. traditional role of labor of

German corporate boards) play a large role in determining inequality. US unions have very little influence over management, leading to

decisions that can cut jobs and make labor much more responsive to output swings

Differences in Inequality (cont.)

Top 0.1% Income Shares in the U.S., France, and the U.K.,1913-

2007

Pre-1980, EU had consistently lower unemployment than US EU Governments enacted policies that reduced employment per

capita to deal with the hardships of unemployment in 2008 crisis. Employment Protection Legislation (EPL) – An attempt by EU

governments to directly regulate layoffs– Outright bans as well as mandated severance packages. This helps to

explain the small shift toward less elasticity in the response of labor to output swings in Europe.

– Timing question: EPL reached its peak in the early 1990s Backlash against EPL: After 1995 several EU countries

introduced a flexible second tier of employment

Employment Protection Legislation

Legislation and policies by EU countries since 1985 aimed at cutting work hours instead of firing employees– Sweden: reduction in hours is aimed at providing parental leave to

parents of both genders– Netherlands: shift to part-time work to accommodate the cultural

norm that mothers should not work full time– Germany: hours reductions have been achieved through

corporatist negotiations between employers and unions– France: switched to a compulsory 35-hour work week

Work Sharing

Work sharing in Europe represents a link to the responsiveness of labor input – shows that European countries view hours as

an adjustment mechanism to respond to output changes, while US cost-cutting most often takes the form of layoffs

{WARNING} – The regressions for H/E in US vs. EU don’t support this hypothesis– Behavior in countries outside NL and GE may

dominate EU results– Big employment responses in Spain and Ireland

Work Sharing (cont.)

US Changes after 1986– Okun’s Law is Dead– Procyclical productivity innovations are dead– RBC model and “technology shocks” are no

longer relevant as core determinants of business cycles

Europe – Comparisons are tentative under the absence

of a quarterly labor force series– Analysis shows that changes in the

responsiveness of labor and productivity have occurred in U. S. but not in Europe

Conclusions for Macro

Differences between Europe and the U.S. can be traced to institutional differences – Institutions Matter.

As a implication of this importance of institutions, the entire concept of procyclical “technology shocks”, a tenet of modern macroeconomic theory, becomes obsolete. – Why should there be procyclical productivity shocks in Europe but not in

the US? Productivity fluctuations are not an autonomous

representations of random technological change, but an artifact of the fact that employment and hours respond with different elasticities and lags to output changes

Conclusions (cont.)

Much remains to be accomplished in this line of investigation.

Need a data series on European employment rate and labor-force participation

Need to recognize differences among EU countries– Explicit, formalized work sharing programs like

kurzarbeit in Germany are not duplicated in the other EU nations

– Could form EU-15 sub-aggregates of “north” and “south” Europe. Are the PIGS or PIIGS different?

Further Research