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Jobless Recovery: A Time Series Look at the United States James DeNicco and Christopher A. Laincz October 21, 2014 Abstract This paper furthers the empirical studies on the time series properties of the United States unemployment rate. Using vector auto regression models and controlling for log differences in GDP, the unemployment rate, and changes in the unemployment rate, we show that fol- lowing a recession, the rate of decrease in the unemployment rate significantly slowed over time. We split our data sample to isolate the recovery portion of the unemployment cycle and find two structural breaks. Controlling for GDP growth rates, the two structural breaks indicate weaker recoveries in the unemployment rate over time, i.e. recoveries that are in- creasingly “jobless.” The first break is in 1959, and the second is in 1984 coinciding with the usual timing of the “Great Moderation.” Using the 7.85 percent unemployment rate at the end of 2012, recovery back to historical long run averages of 5.5 percent unemployment rates after the first structural break will take at least four additional quarters of 2.0 percent GDP growth. After the second structural break it will take at least another four additional quarters of the same growth to get back to the same unemployment rate. This paper substantiates the phenomenon of jobless recovery in the United States and uses the timing of the structural breaks to review the possible causes and related theory, including industry composition, par- ticipation rates and social benefits. 1 Introduction The phrase “jobless recovery” became popular in the United States during the 2000 recession, when it took seven straight quarters of GDP growth to result in decreases in the unemploy- ment rate. With the 2008 recession, the phrase found new life in the beginning stages of recovery. The term is actually first found in print in The New York Times during the de- pression era of the 1930’s. 1 The unemployment rate peaked just below twenty-five percent and took a decade to return to pre-depression levels. One definition of the concept refers to “An economic recovery, following a recession, where the economy as a whole improves, but the unemployment rate remains high or continues to increase over a prolonged period of 1 http://www.npr.org/templates/transcript/transcript.php?storyId=113847257 1

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Jobless Recovery:A Time Series Look at the United States

James DeNicco and Christopher A. Laincz

October 21, 2014

Abstract

This paper furthers the empirical studies on the time series properties of the United Statesunemployment rate. Using vector auto regression models and controlling for log differencesin GDP, the unemployment rate, and changes in the unemployment rate, we show that fol-lowing a recession, the rate of decrease in the unemployment rate significantly slowed overtime. We split our data sample to isolate the recovery portion of the unemployment cycleand find two structural breaks. Controlling for GDP growth rates, the two structural breaksindicate weaker recoveries in the unemployment rate over time, i.e. recoveries that are in-creasingly “jobless.” The first break is in 1959, and the second is in 1984 coinciding with theusual timing of the “Great Moderation.” Using the 7.85 percent unemployment rate at theend of 2012, recovery back to historical long run averages of 5.5 percent unemployment ratesafter the first structural break will take at least four additional quarters of 2.0 percent GDPgrowth. After the second structural break it will take at least another four additional quartersof the same growth to get back to the same unemployment rate. This paper substantiates thephenomenon of jobless recovery in the United States and uses the timing of the structuralbreaks to review the possible causes and related theory, including industry composition, par-ticipation rates and social benefits.

1 Introduction

The phrase “jobless recovery” became popular in the United States during the 2000 recession,when it took seven straight quarters of GDP growth to result in decreases in the unemploy-ment rate. With the 2008 recession, the phrase found new life in the beginning stages ofrecovery. The term is actually first found in print in The New York Times during the de-pression era of the 1930’s.1 The unemployment rate peaked just below twenty-five percentand took a decade to return to pre-depression levels. One definition of the concept refersto “An economic recovery, following a recession, where the economy as a whole improves,but the unemployment rate remains high or continues to increase over a prolonged period of

1http://www.npr.org/templates/transcript/transcript.php?storyId=113847257

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time.”2 In this paper we study jobless recovery as a relative term looking at the relationshipof GDP growth and unemployment rates over time. We consider a post-recessionary periodas a jobless recovery if the speed at which the rate of unemployment declines is statisticallyand significantly slower than prior recessions.

The statistical evidence found in this paper lends credence to the applicability of thephrase when describing changes in the U.S. labor dynamics over time. We find that as U.S.GDP growth recovers after a recession, the size of decreases in the unemployment rate havelessened over time. Others have written papers dealing with jobless recovery but withoutisolating recoveries or looking at the long time series VAR relationship between actual un-employment rates and GDP growth. Goshen and Potter (2003) look at differences in therecovery from the 2001 recession compared to the recovery from the 1990-91 recession andfind the summary evidence points to possible structural changes. They find the data suggestan increase in permanent job losses over temporary layoffs and inter-industry relocation ofjobs may have created the 2001 jobless recovery.

Aaronson, Rissman and Sullivan (2004) also focus on comparisons to the 2001 recessionlooking at the effects of self-employment on jobless recovery. They use panel data of U.S.state unemployment rates from the Current Population Survey (CPS) back to 1979 to esti-mate predicted self-employment rates for the periods following the 2001 recession until thefourth quarter of 2004. They find the predicted estimates are well below the actual valuefor that time frame. They conclude that the joblessness of the recovery after the 2001 re-cession may be attributed to the temporary nature of the self-employed jobs. If these jobsare indeed temporary in nature, then not until the labor markets recover and real wages risewill there be a shift back from self-employment to employment, keeping unemployment ratespersistently high.

Faberman (2008) addresses jobless recovery using relatively new Business EmploymentDynamics (BED) data from the Bureau of Labor Statistics (BLS) covering the period from1990-2006. He uses flow data to study job creation and job destruction as defined by Davis,Haltiwanger and Schuh (1996), again focusing mainly on the recoveries from the 1990-91 and2001 recessions. Faberman extends his data back to 1947 using the BED and the previousestimates to create GMM predicted estimates of job creation and job destruction. Muchin line with the structural breaks found in here, Faberman observes the magnitude of jobflows began to steadily decline in the 1960’s and the volatility of job flows dropped sharplyin the mid 1980’s. He attributes the jobless recovery from the 2001 recession to a persistentdecline in the job creation rate and the recovery from the 1990-91 recession to an increasein the job destruction rate. He links them both to the reduction in volatility and increasedpersistence of job flows in the presence of aggregate shocks as seen in the Great Moderationperiod. (See Kim and Nelson (1999) and McConnell and Perez-Quiros (2000).)

The novelty of this paper is that it addresses the phenomenon of jobless recovery directlyby isolating the recovery portion of the unemployment cycle using a long time series ofquarterly data available from the BLS and BEA for national unemployment rates and GDPgrowth back to 1948. This approach allows us to quantitatively substantiate the statisticalsignificance of the comparative joblessness of recoveries over time. In addition, we look atother macro variables that are changing over time in coordination with jobless recovery to

2http://www.investopedia.com/terms/j/jobless-recovery.asp

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begin to discern its causes.In order to better understand how unemployment dynamics have changed, it is worth-

while to generally describe the different post recession recovery periods over time. We dothis in Table 1.3 We look at 7 recessions over time from the 1950’s to the early 2000’s anddescribe the growth necessary for unemployment rates to peak and begin coming down. 4

Over time it appears that a cessation and reversal of the upward cycle of unemploymentrequires more persistent and greater growth. In addition, visual inspection of Figure 1, theunemployment rate for the US, also suggests the speed of decrease in unemployment slowedover the past four post-recession periods.

Using vector autoregression models with two endogenous structural breaks and control-ling for changes in GDP, we show that following a recession, decreases in the unemploymentrate significantly slowed over time. Specifically, the coefficients on the two structural breaksare positive and successively larger. Thus, holding GDP growth constant yields weaker re-covery in the unemployment rate over time, i.e. “jobless recoveries.”

The first structural break supports the notion from Blanchard and Simon (2001) thatthe Great Moderation is part of a trend that goes as far back as the 1950’s with a possibledeviation in the 1970’s. The second structural break coincides with the period most oftencited as the beginning of the Great Moderation in the 1980’s. These breaks suggest thatthe most recent changes in unemployment dynamics are linked to the documented changesin output and productivity dynamics. A large body of work examines both statistically andtheoretically the various macroeconomic shifts that occurred throughout the Great Modera-tion in the US. We utilize the evidence presented here to discuss a number of the hypothesesput forward for jobless recoveries in the Great Moderation era.

The rest of the paper proceeds as follows: Section 2 summarizes the main data; Section3 presents the empirical model and results; Section 4 contains robustness checks; Section 5illustrates the impact of jobless recovery through forecasting; Section 6 examines inflows tounemployment; Section 7 discusses existing theory in light of the empirical evidence; andSection 8 concludes.

2 US Data: Preliminary Analysis

We first present a preliminary look at both unemployment and GDP behavior over time.The unemployment data come from the BLS and the real GDP data (chained 2005 dollars)is from the BEA. Both series are seasonally-adjusted, quarterly, and span 1948 to 2012. Themean unemployment rate over the time period is 5.81% and the mean quarterly real GDPgrowth rate is 0.78%. We split the data into three periods: 1) before the fourth quarterof 1959 (1959Q4); 2) after 1959Q4 but before the fourth quarter of 1984 (1984Q4); and 3)after 1984Q4. The divisions stem from endogenous structural break points detailed in thefollowing section. Both breaks indicate a statistically significant slowdown in the rate atwhich the unemployment rate falls post-recession.

3We do not always follow the NBER official recession classification, but note the instances where wedeviate from the NBER in the table.

4There is actually a recession ending in Q1-1970, but the time period following it is so volatile that thereis not enough of a recovery period to really examine.

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The average unemployment rate before 1959Q4 was 4.56%, 6.05% after the break until1984Q4, and 6.11% thereafter. Looking at the periods following a recession for unemploy-ment rates, the average negative change was -0.41% before 1959Q4, -0.25% after 1959Q4until 1984Q4, and -0.17% after 1984Q4. These numbers demonstrate that the average neg-ative movement in unemployment is smaller in absolute terms after each structural break.The average change during expansions was -0.20% before 1959Q4, -0.08% after 1959Q4 un-til 1984Q4, and -0.06% after 1984Q4. The slower mean rates of decline are informativeconfirming the flatter linear trend observable in Figure 1.

These facts lend support to the idea that recovery from high unemployment will takelonger after the structural breaks, but does not address the relationship between GDP andunemployment. The simple averages, of course, could coincide with differences in the lengthand/or strength of growth periods. Before 1959Q4, the average period of uninterruptedgrowth was 4.3 quarters, 6.4 quarters after the break until 1984Q4, and 17.0 quarters ofgrowth after that. Thus, the flattening of the unemployment rate recovery does not coincidewith an increase in the frequency of recessions. The overall average growth rates duringthe three time periods were 0.91%, 0.87% and 0.65%, and the average growth rates duringexpansions in the three time periods were 1.53%, 1.21% and 0.78%. These figures indicatethere has also been a decrease in quarterly growth rates over all and during recoveries thatcoincide with the structural breaks. There is no link, however, between GDP and unemploy-ment rates in these summary statistics. The idea of a jobless recovery is that unemploymentrates are slow to recover despite GDP growth. If slower recoveries in unemployment rates aredue merely to slow downs in GDP growth, then we do not have a story of jobless recovery.In Section 3 we test for jobless recovery using a VAR.

Calculating changes in employment per billion dollars of GDP growth during each timeperiod allows us to look at the relationship between growth and at least one determiningfactor in changes in the unemployment rate. The economy grew in real terms by about$962 billion between 1948 and 1959Q4 with an increase in employment of 7.7 million. Thatequates to approximately 8,000 jobs for every billion dollars of GDP growth. Between 1959Q4and 1984Q4 the economy grew in real GDP terms by $3.9 trillion with almost 41 millionjobs added on net, or 10,600 jobs for every billion dollars of growth. From 1984Q4 through2012Q4 real GDP increased $7.0 trillion with 37 million added in employment, resulting in5,300 jobs added for every billion dollars in growth. While no causal relationship can bediscerned from this simple exercise, it is quite striking that GDP growth after the structuralbreak in 1984Q4 was about half as effective in increasing the number of employed than beforethe structural break.

The prima facie evidence certainly suggests a case for increasingly “jobless recoveries”in the United States. However, the basic statistical presentation here does not accountfor the non-linearity and asymmetry in unemployment rate movements. The higher theunemployment rate above long-run equilibrium, the greater the potential for rapid declines.Moreover, the changes to the simple averages could also be explained if a single outlierrecession, the first oil shock being the most obvious candidate, behaved dramatically differentfrom the long-run. The issue is important because if “jobless recoveries” are one time eventsassociated with characteristics specific to certain recessions, that suggests the causes are thenlinked to those underlying features of specific recessions. On the other hand, if the trendis towards jobless recoveries in all recessions over time, it points towards broader, systemic

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changes in the labor market.

3 Analysis

In order to account for the asymmetry in unemployment dynamics, we separate the differentphases of the unemployment cycle. We isolate the downward portion of the cyclical move-ments in the unemployment rate by splitting the sample. We split the data into periods ofOutflows and Inflows. Our definitions differ from Merz in that we care about net movements.Outflows are periods of negative changes in the unemployment rate. Inflows are periods ofpositive changes in the unemployment rate. As a robustness check we also split the datainto two samples using changes in real GDP. One subsample consists of positive or expansionperiods and the other consists of negative or contraction periods. The results support themodel using Inflows and Outflows. However, the regression equation from our system ofequations, specified below, with DUR as the dependent variable consistently showed betterfits through the standards of AIC, SIC and adjusted R2, when using cyclical phases char-acterized as Outflows instead of Expansions. In other words, using the positive and negativemovements of unemployment obviously describes the movements of unemployment betterthan the positive and negative movements of real GDP. Running a number of robustnesscheck regressions in Section 4, interchanging unemployment rates, changes in unemploymentrates, changes in GDP, participation rates, structural breaks and time variables, the over-whelming evidence shows that over time, changes in the unemployment rate have slowedduring the downward phase of unemployment cycles. Including interaction terms only addssupport to the basic regressions.

In order to avoid biasing the regression results, we allowed the data to dictate the properpoints for our structural breaks. We rely on the widely used Quandt-Andrews (QA) test foran unknown breakpoints. With jobless recovery as the focus of this paper, we apply the QAtests to the portion of our VAR model with DUR, or changes in unemployment rates, as thedependent variable. We are looking for the breaks with regard to changes in unemploymentrates, controlling for the level of the unemployment rate and GDP growth. We begin withno structural breaks in the specification and apply the AQ test to the entire (whole daterange) split sample to find the best place for a single structural break. If the result of theQA test is significant we consider that date as a potential structural break. We then testthat date for significance in the single regression equation from our VAR with DUR as thedependent variable.5 If the structural break is significant in the regression, we then split thedata around the break and repeat the test until we can no longer find statistically significantstructural breaks. For the Outflows subsample, we find two structural breaks, which areboth significant at the 1% level.

The core approach of the analytical framework has its roots in Evans (1989), who de-scribes US unemployment dynamics through the use of VARs. Evans’ paper addresses thebehavior of the labor market from 1950 to 1985, asserting a structural break in 1974, whichwas chosen because it had the smallest standard error in his regressions. Evans specifica-tion incorporated the unemployment rate, changes in growth and a structural break of thefollowing form:

5For significance of the QA test , we use Andrews’ (1993) critical values in Table 1 on page 840.

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∆Yt = ay +3∑i=1

byi∆Yt−i +3∑i=1

cyiUNt−i + dyD74 + eyt (1)

UNt = au +3∑i=1

bui∆Yt−i +3∑i=1

cuiUNt−i + duD74 + eut (2)

where ∆Y is real output growth, UN is the unemployment rate, and D74 is the structuralbreak.

Evans found a positive and significant coefficient for his structural break variable, modeledabove, as well as for a time trend variable. However, Evans model is not adequate tostudy changes in the speed of recovery from high periods of unemployment. Because thespecification above does not discern between the phases of the business cycle, it cannotcapture the asymmetric dynamics of unemployment. (See Moosa et. al, 2004.)

We analyze our times series data using VARs. Unlike Evans two variable system, weuse three variables in order to adequately capture labor dynamics. We use both levels anddifferences of unemployment rates in our VAR with real changes in GDP. Because our focusis “jobless recovery,” it requires using differences in the unemployment rate. However, theunemployment level is an important factor in the size of the difference in unemploymentrates. There are more likely larger declines in unemployment rates at the peak of a recessionas opposed to near long run averages.

Below are the main specifications for our VAR regressions in this paper for both struc-tural breaks and a time trend:

Structural Breaks

DUROFt = α+ β1URt−1 + β2DURt−2 + β3DGDPt−1 + β4SB594t + β5SB844t + εt (3)

UROFt = θ + γ1URt−1 + γ2DURt−2 + γ3DGDPt−1 + γ4SB594t + γ5SB844t + εt (4)

DGDPOFt = ψ + φ1URt−1 + φ2DURt−2 + φ3DGDPt−1 + φ4SB594t + φ5SB844t + εt (5)

Time Trend

DUROFt = α+ β1URt−1 + β2DURt−2 + β3DGDPt−1 + β4QUARTERt + εt (6)

UROFt = θ + γ1URt−1 + γ2DURt−2 + γ3DGDPt−1 + γ4QUARTERt + εt (7)

DGDPOFt = ψ + φ1URt−1 + φ2DURt−2 + φ3DGDPt−1 + φ4QUARTERt + εt (8)

where DUROF is the change in the unemployment rate, UROF is the unemployment rateand DGDPOF is logged differences in GDP for the outflows subsample. SB594, SB844and QUARTER represent the structural break in the fourth quarter of 1959, the structuralbreak in the fourth quarter of 1984, and the quarterly time trend.

In Table 2, we present unit root tests for the different lag lengths and model specifications.We test the variables using the Augmented Dickey-Fuller (ADF) test and the Dickey-Fuller

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Generalized Least Squares (DF-GLS) test. In all of our regressions we include either a con-stant or a constant and a time trend. The regressions are all run with between one and eightlag lengths for our endogenous variables. At a lag length of eight, we do not reject the unitroot for the unemployment rate. However, we firmly reject the unit root at a lag length ofone, which is what we use in this paper to capture the convexity of the unemployment ratecycle described in the paragraph above. The only other variable where we fail to reject theunit root in some tests was log differences in total factor productivity (DTFP), which wasused as a robustness measure. We failed to reject the unit root for DTFP in the DF-GLStest with a constant for between five and eight lag lengths. Again, for that variable we onlyuse a one period lag length for which we soundly reject the unit root in all tests. For thedifferences in the unemployment rate (DUR) and log differences in GDP (DGDP) we are ableto reject the unit root in all tests performed. In order to ensure stationarity in everythingthat follows, DUR and DGDP are the only two variables for which we include up to eightlag lengths.

Merz (1999) examines the time series properties of unemployment rate dynamics in dif-ferent pieces. Merz defines “Inflows” as those who become unemployed, while Outflows aredefined as those who leave the state of unemployment. The idea gives insight into the differ-ent moving parts of the unemployment cycle, but still does not address the parts in separatephases of a cycle. For instance, Merz finds that Outflows have become more strongly coun-tercyclical, but does not show which part of the asymmetric unemployment cycle drives theresult. Therefore while the results from Merz (1999) lend support to Hall’s (2005) findingthat the large majority of labor dynamics are due to fluctuations in the hiring rate as op-posed to layoffs, it does not address the phenomenon of a “jobless recovery.”

Table 3 presents the results for our main specifications. While coefficients on the laggedvariables in this VAR structure are biased, we see the expected signs on our coefficients.URt−1 represents a one period lag of the unemployment rate, and has the expected negativesign. The higher the unemployment rate the more likely there will be a negative changein the unemployment rate. DGDPt−1 represents a one period lag of log differences in realGDP, and has the expected negative sign on the coefficient. Stronger GDP growth increasesthe likelihood of declining unemployment rates. DURt−2 represents a two period lag of thedifference in the unemployment rate, and has the expected positive sign. The larger thechange in the unemployment rate from two quarters past the larger will be this period’schange in the unemployment rate.

Most notable are the results for the structural break and time trend variables. We findpositive signs with high levels of statistical significance in the VARs for unemployment rates.Therefore, controlling for changes in GDP and the level of the unemployment, during peri-ods of Outflows changes in the unemployment rate have become smaller in absolute valueover time. It follows directly that recovery from elevated levels of unemployment following arecession will be slower as time goes on, using either structural breaks or time variable rep-resenting the process. All of the above provide statistically significant evidence supportingthe trends observed in the previous section.6

6These results hold with two significant structural breaks for DUR or UR as the dependent variable withup to at least eight lags of DUR and DGDP . These results hold with two significant structural breaks forDGDP as the dependent variable up to three lags of DUR and DGDP and for the 1984 structural break

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We repeat the same exercise explained above using the QA tests to find unknown break-points for our Expansion subsample. Below are the VAR specifications:

Structural Breaks

DUREt = α+ β1URt−1 + β2DURt−2 + β3DGDPt−1 + β4SB601t + β5SB813t + εt (9)

UREt = θ + γ1URt−1 + γ2DURt−2 + γ3DGDPt−1 + γ4SB601t + γ5SB813t + εt (10)

DGDPEt = ψ + φ1URt−1 + φ2DURt−2 + φ3DGDPt−1 + φ4SB601t + φ5SB813t + εt (11)

Time Trend

DUREt = α+ β1URt−1 + β2DURt−2 + β3DGDPt−1 + β4QUARTERt + εt (12)

UREt = θ + γ1URt−1 + γ2DURt−2 + γ3DGDPt−1 + γ4QUARTERt + εt (13)

DGDEt = ψ + φ1URt−1 + φ2DURt−2 + φ3DGDPt−1 + φ4QUARTERt + εt (14)

Table 4 presents the results. We still find two structural breaks, however, in slightlydifferent quarters. The first break in 1960Q1 is significant at the 1% level, but the secondbreak in 1981Q3 is only significant at the 10% level. Even though the second structuralbreak was weaker, the fact that the unknown structural breaks are found in such closeproximity to the Outflows regressions add support to the idea that something occurred inthese time frames causing slower recovery of unemployment after a recession. If we use the1959 and 1984 structural breaks in this Expansion subsample, both breaks are significant atthe 1% threshold with the same signs as the Outflows subsample regardless of the dependentvariable.

4 Robustness

The first robustness test uses a more traditional approach. The following VAR is for theOutflows subsample and includes only changes in the unemployment rates and GDP growthrates, but not levels. This specification therefore ignores the potential for non-linearity inthe rate of recovery, specifically the convex decline in the unemployment rate following arecession. We present a specification with four lags, but the results are robust from one toeight lags. Again we repeat our exercise of finding unknown breakpoints using this specifi-cation. The resulting VAR models are below:

Structural Breaks

DUROFt = α+4∑i=1

γiDURt−i +4∑i=1

βiDGDPt−i + β5SB602t + β6SB951t + εt (15)

up until at least eight lags.

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DGDPOFt = θ +4∑i=1

φiDURt−i +4∑i=1

ωiDGDPt−i + ω5SB602t + ω6SB951t + εt (16)

Time Trend

DUROFt = α+4∑i=1

γiDURt−i +4∑i=1

βiDGDPt−i + β5QUARTERt + εt (17)

DGDPOFt = θ +4∑i=1

φiDURt−i +4∑i=1

ωiDGDPt−i + ω5QUARTERt + εt (18)

We still find two breaks, but again the specific quarters change. The first break pointchanged from the first to the second quarter of 1960. However, when disregarding the level ofthe unemployment rate and focusing only on changes in the unemployment rate, the secondstructural break shifts about a decade to the first quarter of 1995. We put more weight onthe main specification, however, due the importance of capturing the convexity of recoveriesin unemployment rates as previously discussed. The 1990’s were generally a period of lowerunemployment rates and one would expect smaller changes in the unemployment rate, con-trolling for GDP growth.7

Table 5 shows the results are indeed robust to the more conventional time series specifi-cation. On both structural breaks and the time trend there is still a positive and significantcoefficient indicating that negatives changes in the unemployment rate have become smallerover time. We also want to know if these results are unique to a VAR including GDP growthand if they are still significant with alternative measures. We replace GDP growth withestimated quarterly TFP growth rates from John Fernald at the San Francisco Federal Re-serve Bank. Again using QA tests to find unknown break points, we have our resulting VARspecifications below:

Structural Breaks

DUROFt = α+ β1URt−1 + β2DURt−2 + β3DTFPt−1 + β4SB594t + β5SB844t + εt (19)

UROFt = θ + γ1URt−1 + γ2DURt−2 + γ3DTFPt−1 + γ4SB594t + γ5SB844t + εt (20)

DTFPOFt = ψ + φ1URt−1 + φ2DURt−2 + φ3DTFPt−1 + φ4SB594t + φ5SB844t + εt (21)

Time Trend

DUROFt = α+ β1URt−1 + β2DURt−2 + β3DTFPt−1 + β4QUARTERt + εt (22)

UROFt = θ + γ1URt−1 + γ2DURt−2 + γ3DTFPt−1 + γ4QUARTERt + εt (23)

DTFPOFt = ψ + φ1URt−1 + φ2DURt−2 + φ3DTFPt−1 + φ4QUARTERt + εt (24)

Convincingly, the structural breaks are in the exact same place for the Outflows subsam-ple when using this measure of TFP growth as they were for GDP growth. Table 6 shows the

7The results are robust to using the 1959Q4 and 1984Q4 structural breaks.

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results are indeed robust to this VAR specification, and are in fact stronger. Increases in pro-ductivity result in successively smaller changes in the unemployment rate at the structuralbreaks. Fernald calculates a number of different productivity measures along with this TFP,which he defines as output growth less the contribution of labor and capital. We also testthe VAR using Fernald’s measure of utilization of capital and labor and utilization-adjustedTFP. The results are robust to both specifications and support the conclusion that the U.S.is indeed experiencing jobless recoveries when compared to historical data.

5 Forecasting

The key results for the VARs are strong and robust and here we use the main specification toillustrate the economic impact of the jobless recoveries. First, in order to show the predictivepower of the model we perform an out of sample forecast using the Outflows model to predictthe unemployment rate. We repeat the process of finding unknown breakpoints with QAtests, but with the data cutoff before 2000. We then use the actual growth rates to predictthe recovery period following the 2001 recession, which resulted in a peak unemploymentrate of 6.3% in June 2003. Table 7 and Figure 2 present the results. Visually, the predictedvalues of the forecast model are very close to the actual values and, in fact, the averageforecast error for the recovery from the 2001 recession is .08%.

In order to understand the scale of the impact of slowing recovery times, Figure 3 usesthe whole data range for the Outflows subsample. This forecast starts at the 7.85% unem-ployment rate at the end of 2012. Using an uninterrupted annual growth rate of 2.0%, thedifference in the time for the unemployment rate to return to the historical long run averageof about 5.5% before and after the structural break in 1959Q4 is about 4 quarters.8 Thedifference in the time for the unemployment rate to return to levels of about 5.5% beforeand after the structural break in 1984Q4 is 8 quarters. The model predicts that before thestructural break in 1959Q4 the unemployment rate would return to 5.5% in the first quarterof 2014. The model predicts that it will now take until the first quarter of 2016, a two yeardifference.

The forecast using the Outflows subsample might be overly optimistic. Using the regres-sion model with the Outflows subsample ignores the fact that there are periods of increasingunemployment rates during expansions. The Expansion subsample includes periods of in-creasing unemployment rates during recovery from a recessions. Repeating the forecastexercise using the Expansion subsample reveals a much more dire scenario. Again startingat the 7.85% unemployment rate at the end of 2012, Figure 4 shows the prediction from themodel with the Expansion subsample is that the U.S. will never really be able to get backto a 5.5% unemployment rate with 2.0% annual GDP growth. In order to get back to thehistorical long run average, the U.S. would need to experience more robust growth. Theimplications for longer unemployment recovery range from political with voting implicationsto various cost implications in terms of lost tax revenues, extended unemployment bene-fits, decreases in consumption and countless other examples. It is imperative to find causesand policy prescriptions for this negative trend. This paper is aimed at starting that process.

82.0% is the average annualized quarterly growth rate during the most recent recovery.

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6 Inflows to Unemployment

While this paper is primarily concerned with the recovery portion of the unemploymentcycle, we also investigate inflows to unemployment. If there have been shifts in the economythat slow down the hiring process due to increased costs of screening, then these same shiftscould slow down the separation process. If a firm is already employing a productive workerthen changes that make finding another equally productive worker harder may cause thefirm to hold onto the current worker for a longer period of time, or until further declinesin output. Performing the same procedures for Inflows to unemployment as we did forOutflows, we do not find a significant structural break through the QA test. Thus, we donot present VAR results. The lack of significance may simply be a result of a much smallersample size. Alternatively, this result may indicate we are observing differences in the firmsdecision making process when it comes to letting go of an employee rather than hiringone. This result supports two possible theories. One possible theory is that the separationprocess has been less affected than the hiring process. This outcome could be due to ashortsightedness of firm’s in the firing process. The other possible theory is that effects onInflows during contractions and expansions cancel each other out. This outcome could bedue to a calculation that during recession firms can unload less productive workers with lesschance of workers claiming wrongful discharge or being successful if they do. Firms maycarry unproductive workers during good times to avoid these possible separation costs, andthen dump them along with necessary cutbacks during recessionary periods. Regardless ofthe reason, we see the results are not symmetric for Outflows and Inflows.

7 Potential Contributing Factors to Jobless Recoveries

This section discusses potential explanations for jobless recovery and gives empirical evidenceon the validity of those explanations. First we investigate whether the the changes in therelationship between unemployment rates and GDP, represented by our structural breaks,are driven by changes in employment or changes in unemployment. We then investigatethe possible contribution to our structural breaks from participation rates, the percentageof women in the workforce, a changing industry composition, wages, and social benefits.For the empirical evidence, we calculate the percentage of the structural breaks that canbe explained by the different factors for our specifications with changes in employment andchanges in unemployment. While can find no formal test for statistical significance acrossour regressions, we look to see if the changes are outside of the standard errors for the struc-tural breaks and by how much.9

9There are other possible contributing factors to these structural breaks with no direct data thatwe can use to test explanatory power, including changes in labor laws. Of note is the passingof The Work Hours Act of 1962, The Age Discrimination Act in Employment (ADEA) of 1967, TheOccupational Safety and Health Act, of 1970, and The Employee Retirement Income Security Act (ERISA)of 1974. The structural breaks also coincide with increases in worker protections with regard to wrongfuldischarge through the state court systems with the adoption of Employment-At-Will Exceptions. DeNicco(2012) uses U.S. state panel data going back to 1976 and finds significant contributions from two out of threeEmployment-At-Will Exceptions on jobless recovery.

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7.1 Unemployment and Employment

Like Merz (1999), Shimer (2005) and Hall (2005) challenge the conventional view on thedynamics of the unemployment rate with relatively new empirical evidence. Traditionaldescriptions of unemployment dynamics would begin with a negative shock causing masslayoffs (or separations) that increase the number of unemployed. In turn, job-finding ratesdecline and the duration of unemployment rises. However, the Job Openings and LaborTurnover Survey (JOLTS) greatly advanced our knowledge of separations. Hall and Shimer,backed by this empirical evidence, argue that separations play much less of a role in theunemployment dynamics than previously believed. They find that unemployment is high,at least since 2000, during a recession more due to firms reducing their hiring rates ratherthan increased separation rates. While there are changes in separation rates that accompanyrecessions, they are insignificant compared to regular, aggregate worker flows out of jobs.

Figure 5 demonstrates this point in this most recent recession. While there was an in-crease in layoffs and separations in 2007, they are much smaller than the drop in both jobopenings and hirings. Furthermore, we see the job openings and hiring rates respond muchsooner to the economic downturn than layoffs and separations. The results from Fujita andRamey (2009) dampen the findings of Hall and Shimer with their statistical analysis showingthat between 40 and 50 percent of fluctuations in the unemployment rate are due to changesin separation rates. However that still leaves between 50 and 60 percent of the fluctuationsin unemployment rates to the job hiring rate. Without Jolts data going back further than2000, however, it is not possible to directly test whether jobless recoveries are due to changesin separation rates or hiring rates. We can test whether our results are driven by changes inunemployment or changes in employment levels. The following specifications replace changesin the unemployment rate with its two components, changes in unemployment and changesin employment:

Log Difference in Unemployment: Outflows Subsample

UROFt = α+

4Xi=1

γiURt−i +

4Xi=1

λiDUt−i +

4Xi=1

βiDGDPt−i + β5SB594t + β6SB844t + εt (25)

DUOFt = ψ +

4Xi=1

κiURt−i +

4Xi=1

σiDUt−i +

4Xi=1

δiDGDPt−i + δ5SB594t + δ6SB844t + εt (26)

DGDPOFt = θ +

4Xi=1

φiURt−i +

4Xi=1

$iDUt−i +

4Xi=1

ωiDGDPt−i + ω5SB594t + ω6SB844t + εt (27)

Log Difference in Employment: Outflows Subsample

UROFt = α+

4Xi=1

γiURt−i +

4Xi=1

τiDEt−i +4X

i=1

βiDGDPt−i + β5SB594t + β6SB844t + εt (28)

DEOFt = χ+

4Xi=1

υiURt−i +

4Xi=1

ζiDEt−i +4X

i=1

δiDGDPt−i + δ5SB594t + δ6SB844t + εt (29)

DGDPOFt = θ +

4Xi=1

φiURt−i +4X

i=1

µiDEt−i +4X

i=1

ωiDGDPt−i + ω5SB594t + ω6SB844t + εt (30)

where DU is the log difference in unemployment and DE is the log difference in employment.

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By replacing DUR with its components, we run more traditional VARs while still includingthe unemployment rate. We include four lags of each variable and use the structural breaksfound previously for each subsample. Table 8 suggests the main results in this paper aremore due to both changes in unemployment and changes in employment, controlling forGDP. Both structural breaks are significant for log differences in unemployment while onlythe 1984 structural break is significant for log difference in employment. The results forboth, however, show contributions to jobless recovery.

The positive coefficients on the structural breaks for DU mean that the structural breaksare correlated to “less negative” decreases in unemployment during Outflows, controllingfor the unemployment rate and for log differences in real GDP. The negative coefficient forthe 1984 structural break with DE means that the structural break is correlated with “lesspositive” increases in employment during Outflows, controlling for the unemployment rateand for log differences in real GDP.

7.2 Participation Rates and Women in the Workforce

Figure 9 shows appreciable changes in the labor participation rate over the time period weare investigating. The labor participation rate rose from 59% in 1948 to a peak of 67% inthe late 1990’s. Most of this increase comes from women entering the labor market. In thistime frame the participation rate for women rose from 32% to 60%, while the participa-tion rate for men fell from 86% to 75%. In a search and matching setting (Mortensen andPissarides, 1994), events that affect participation margins will affect the unemployment func-tion. As people become discouraged and stop looking for work or find encouragement andenter the labor force to look for work, the participation rate will vary. As longer periods ofunemployment persist, workers become discouraged and give up searching, which takes themoff the unemployment roles completely and speeds up the recovery of unemployment rates.Working in the other direction, recovery from high rates of unemployment will be slowed asthe number of participants increase. Changing societal norms making it more common forwomen to enter the workforce could have resulted in non-working spouses joining the laborforce to replace lost wages during periods of high unemployment, resulting in slower recov-eries in unemployment rates. Peretto (2006) specifically models the effect of participationmargins on the unemployment rate. The paper investigates the effects of product and labormarket frictions in a dynamic general equilibrium model with a three-states representationof the labor market: 1) Working or Employed, 2) Looking for work and cannot find it orUnemployed, and 3) Not participating in the labor market (maybe discouraged or content).One of the conclusions from Peretto’s model is that participation margins amplify the effectsof labor market frictions generating unemployment. With increased participation margins,there will be higher numbers of people who cannot find work.

Looking at the evidence in Figure 7, higher participation rates may be one of the plausiblecontributors to jobless recovery. Figure 7 shows 20 quarter rolling correlations of participa-tion rates and unemployment rates over the business cycle. From about 1960 until the early2000’s, participation rates and unemployment rates became more positively correlated whenunemployment rates were high. In fact during this time period, the correlation betweenparticipation rates and unemployment rates was often positive when unemployment rateswere high. This observation indicates that between 1960 and 2000 during periods of high

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unemployment there were often increases in the number of people entering the labor force,which could slow recoveries. After the early 2000’s, however, this relationship breaks downand completely reverses. The timing of these observations follows observed changes in par-ticipation rates. After the participation rates of women peak in the early 2000’s and declinealong with the participation rates of men, the correlation between participation rates andunemployment rates became more strongly negative during periods of high unemployment.It may be the case that after 2000, changes in the participation rate are working to bringunemployment rates down during recovery periods because people are exiting the labor force.

From the peak of 9.76% in 2009, the quarterly unemployment rate fell to 7.85% by theend of 2012, 14 quarters later. This is a drop of 1.91 percentage points. The drop wasaccompanied by a decrease in the participation rate from 65.09% to 63.63%, or 1.46 per-centage points. During that time period, the U.S. experienced a decrease in unemploymentof about 2.8 million people or about 18.7% and an increase in Employment of about 4.5million people, or 3.27%. However, there was an increase in the working age population ofabout 8 million people or 3.4%, leaving the employment to population ratio little changed.10

Combing the decrease in unemployment and the increase in employment leaves an increasein the labor force of about 1.7 million people, or only 21.25% of the increase in the workingage population. To contrast this, we will compare these numbers to the only comparablepeak in the unemployment rate in our sample in 1982, which is before the change in therelationship of the correlation of unemployment rates and participation rates to the businesscycle.

From the peak of a 10.85% in 1982, the quarterly unemployment rate fell to 7.15% overthe next 14 quarters. That is a drop of 3.7 percentage points. As opposed to our comparisontime period, the drop was accompanied by an increase in the participation rate from 64.14%to 65.13%, or 0.99 percentage points. That is 2.45 percentage points greater than during the14 quarters following the peak unemployment rate in 2009. During the 14 quarters followingthe peak unemployment rate in 1982, the U.S. experienced a decrease in unemployment ofabout 3.0 million people or about 30.4% and an increase in employment of about 9.8 millionpeople, or 9.90%. There was an increase in the working age population of about 6.8 millionpeople or 3.92%, resulting in an increase in the employment to population from 57.18% to60.47%, or 3.29 percentage points. This is 3.39 percentage points larger than during the14 quarters following the peak unemployment rate in 2009. Combing the decrease in un-employment and the increase in employment leaves an increase in the labor force of about6.1 million people, or 90.44% of the increase in the working age population, which is 69.19percentage points larger than during the 14 quarters following the peak unemployment ratein 2009.

The behavior of participation rates during these two specific recoveries was quite dif-ferent, which may mean the effects on jobless recovery may be very different even in twotime periods that both occur at least partly after the second structural break. To test theexplanatory power of changes in participation rates on jobless recovery, we calculate thepercentage of the structural breaks explained by changes in the participation rate for ourVAR specifications including changes in unemployment and changes in employment. Wererun both VAR’s in the “Unemployment and Employment” subsection, but we also include

10The employment to population ratio fell from 58.72% to 58.65%.

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differences in the participation rate, DPR, as a fourth endogenous variable.11 The resultsin Table 9 show that changes in the participation rate have very little explanatory powerfor our structural breaks indicating increasingly jobless recovery either through changes inunemployment or changes in employment. This finding does not mean that changes in par-ticipation rates do not affect the speed of recovery in unemployment rates, but rather thatwhile controlling for the level of the unemployment rates and GDP growth, changes in par-ticipation rates cannot explain the increasingly jobless recoveries during the time periodsindicated by our structural breaks.

Next we want to see if the increase of women in the workforce may have explanatorypower for our structural breaks other than through participation rates. In our sample, thepercentage of the labor force made up from women increased by 18.63 percentage points from28.28% to 46.91%. There may be adjustment costs associated with large demographic shiftsin the labor force. These adjustments costs can be as simple as installing bathrooms that areequipped for women or more complicated such as instituting gender sensitivity training. Itmay be that the rise in the percentage of women making up the labor force is just correlatedwith other events, such as a transition towards a larger percentage of the workforce in theservice industry, driving jobless recovery. We examine this possibility in the next subsection.

We rerun both VAR’s in the “Unemployment and Employment” subsection, but we alsoinclude differences in the percentage of women making up the labor force, DPLFW , as afourth endogenous variable. The results in Table 10, show that the increase in the percentageof women may have some explanatory power for our structural breaks indicating increasinglyjobless recovery through changes in unemployment. With the inclusion of DPLFW , the 1959structural break for changes in unemployment goes down by 0.005, or about 9.43%, and the1984 structural break goes down by 0.011, or about 18.03%. For changes in employment,including the changes in the percentage of women in the labor force makes the structuralbreak in 1984 even more negative by about 33.33%. This result indicates that withoutthe increases in the percentage of women in the labor force, changes in employment wouldhave become even smaller than before 1984. However, only the change to the structuralbreak in 1984 for log differences in unemployment is actually outside the standard error forthe original structural break in Table 8 . Again, the evidence is mixed as to whether thepercentage of women in the labor force is correlated with jobless recovery, depending on thetime period and whether we include changes in employment or changes in unemployment.

7.3 Changing Industry Composition of the Labor Force

While Stock and Watson (2003) find at most a small contribution from sectoral shifts in thelabor force to the overall decline in macroeconomic volatility seen in the “Great Modera-tion,” a transition to a service oriented economy may play a role in jobless recovery. Lookingat Table 12, since 1948 the industry makeup of the United States has changed dramatically.The makeup of the U.S. labor force has moved away from being a goods producing, manu-facturing heavy economy towards a service providing economy. We see distinct employmentshare increases in Professional and Business services, Education and Health services andLeisure and Hospitality services. The efficiency wage model (Shapiro and Stiglitz, 1984)

11We reject the null hypothesis of a unit root for changes in the participation rate at the 1% level

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can help explain how this shift may be contributing to jobless recovery. In this asymmetricinformation model, involuntary unemployment stems from employers not being able to ob-serve worker efforts. MacLeod and Malcomson (1998) look at the labor market conditions inwhich efficiency wages versus merit pay will be endogenously determined as optimal. Theyfind that in highly capital intensive labor markets such as goods-producing and especiallymanufacturing sectors, the cost of vacancy is high and therefore efficiency wages will beoptimal compared to merit pay. Efficiency wages will keep workers in their posts. An exam-ple of merit pay is a per item payment for goods produced or quantities harvested. Usingmerit pay in the service sector, however, is difficult because of firms’ inability to verify andquantify productivity. Often times in the service sector, firms depend solely on measures ofquality. Quality is much more difficult to measure than quantity. The difficulty in verifyingand quantifying productivity in the service sector makes shirking easier and more likely thanin goods-producing industries. Firms in the service sector will choose to pay higher wagesto prevent shirking.

Following the logic of MacLeod and Malcomson (1998) this shift towards a service dom-inated labor force may be contributing to jobless recovery in two ways. First, as demandincreases after a recession, capital intensive industries hire quickly to keep costly capitalfrom sitting idle. But with the large majority of the U.S. labor economy moving away froma manufacturing and goods-producing workforce, we may be seeing firms stretch employeeproductivity. Looking at Figure 10 we see the goods-producing sector is much more respon-sive to both the initial recession and the beginning stages of recovery. Second, if efficiencywages are being paid in the service sector, the higher marginal cost of labor will drive downemployment.

We look to see how much of our structural breaks can be explained by this transition byrerunning both VAR’s in the “Unemployment and Employment” subsection, but now alsoincluding differences in the percentage of those employed in the service industry, DPCESS,as a fourth endogenous variable.12 The results in Table 13 show that the increase in thepercentage of employment in the service industry may have some explanatory power for ourstructural breaks indicating increasingly jobless recovery through changes in unemployment.With the inclusion of DPCESS, the 1959 structural break for changes in unemploymentgoes down by 0.003, or about 5.66%, and the 1984 structural break goes down by 0.008, orabout 13.11%. However for changes in employment, including the changes in the percentageof employment in the service industry makes the structural break in 1984 more negative byabout 33.33%. This result indicates that without the increases in the percentage of employ-ment in the service industry, changes in employment would have become even smaller thanbefore 1984. Again, the evidence is mixed as to whether the percentage of employment inthe service industry contributes to jobless recovery, depending on the time period and if weinclude changes in employment or changes in unemployment.

These results, while showing smaller contributions to jobless recovery through changesin unemployment, are similar to the results for the percentage of women in the labor force.Women disproportionally make up a larger percentage of employment in the service sector ascompared to the goods producing sector. From 1964 to present, women averaged 48.47% ofprivate service providing employment and only 24.85% of goods producing employment. By

12This data comes from the Current Employment Statistics (CES) survey from the BLS

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the end of 2012 women made up 53.03% of private service providing employment and only22.11% of goods producing employment. The shifts in the percentage of the labor force madeup by women and the percent of employment in the service industry both took place over thesame time frame and were of similar magnitudes. It may be that some of the changes in themagnitude of the structural breaks in our VARs when the differences in the percentage ofwomen in the labor force are included are due to correlations with changes in the percentageof employment in the service industry. To test this idea we include both DPCESS andDPLFW in our VAR specifications so that we now have five endogenous variables.

The result is a structural break for log differences in unemployment of 0.046 in 1959and 0.045 in 1984, which is a decline of 0.007 or 13.21% and 0.016 or 26.23% from thestructural breaks found in Table 8. The combined decreases in magnitude for the 1959 and1984 structural breaks for log differences in unemployment when DPCESS and DPLFWwere individually included in the our VAR specifications were 0.008 or 15.09% and 0.019 or31.15%. These results indicate that little or none of the increase in the magnitude of thestructural break with the sole inclusion of changes in the percentage of women in the laborforce to our VAR can be explained due to a correlation with changes in the percent of em-ployment in the service industry. The result for the 1984 structural break for log differencesin employment when we include both variables is -0.005, indicating much the same as forthe log differences in unemployment.

7.4 Social Benefits, Reservation Wages and Search Intensity

Hiring in a search and matching setting will also be affected by changes on the supply side.The increases in social benefits in the U.S. over time may have led to a negative income effecton labor. That effect could result in a decrease in job search intensity through an increasein reservations wages. Using data from the Bureau of Economic Analysis, total governmentspending on social benefits to persons as a percentage of GDP has risen from 4.6% in 1960to 9.7% in 1980 to 15.43% in 2010. Government assistance increased in a number of areas,including healthcare, housing, food, and income levels below certain poverty thresholds.From the U.S. Census Bureau, for example, the percentage of people on medicaid increasedfrom 8.4% in 1987 to 15.9% in 2010 and the percentage increase in people on food stampshas outpaced the population growth rate 34.4% to 21.1% from 1990 to 2008. From the SocialSecurity Administration, the number of people receiving Supplemental Security Income fromthe federal government has increased by 26.6% from just 2003 to 2010, while the populationonly increased by 6.2%.

One of the more striking increases in entitlements comes from disability payments. Usingdata from the Social Security Administration and the BLS, there were about 152 people inthe labor force per every worker receiving disability benefits in 1960. By 1970 that numberquickly went down to 55, then 41 by 1990 and 18 by 2010. Workers on disability can stillwork and receive benefits as long as they make under a certain threshold or are not workingtoo many hours. In bad times, disabled workers may be more willing stay at home or workpart time, and accept less income supplemented by their benefits. In boom times there couldbe larger numbers of disabled workers either drawn into the labor force or moving from parttime to full time work if the increase in wages and benefits is large enough. Looking at

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Figure 8, we see that real earnings have been generally increasing since the early 1980’s.However, it has been a volatile climb, and the real earnings at the end of 2012 were belowthe real earnings level in 1979. If wages have not sufficiently kept up with the increases insocial benefits, then a decrease in search intensity and an increase in reservation wages couldbe contributors to jobless recovery.

In order to test the explanatory power on our structural breaks of social benefits in thecontext of reservation wages, we construct a variable as the ratio of total U.S. wages andsalaries divided by total U.S. social benefits paid.13 Looking at Figure 9, this ratio declineddramatically over our sample period.14 In the mid 1950’s there were about $18 in wages andsalaries earned for every $1 paid out in social benefits. This ratio declined rapidly to about$5 in wages and salaries earned for every $1 paid out in social benefits in the mid 1970’s,and then steadily declined to around $3 in wages and salaries earned for every $1 paid outin social benefits in 2012.

We look to see what percentage of our structural breaks can be explained by changesin this ratio by again rerunning both VAR’s in the “Unemployment and Employment” sub-section, but also including log differences of the ratio, DWSSBP , as a fourth endogenousvariable. In Table 11, we see that changes in this ratio may have some explanatory powerfor our structural breaks indicating increasingly jobless recovery through changes in un-employment. With the inclusion of DWSSBP , the 1959 structural break for changes inunemployment goes down by 0.006, or about 11.32%, and the 1984 structural break goesdown by 0.004, or about 6.56%. These changes are not outside the standard error for thestructural breaks found in Table 8, but the results in Table 11 indicate the decrease in wagesand salaries earned per social benefit paid out, may play small role in the observed increasein the joblessness of recoveries over time.

7.5 Combined Effect

We take this exercise one step further and include all of the variables with possible ex-planatory power that we have examined in our VAR specifications for log differences inunemployment and log differences in employment. The result is a structural break for logdifferences in unemployment of 0.042 in 1959 and 0.045 in 1984, which is a decline of 0.011or 20.75% and 0.016 or 26.23% from the structural breaks found in Table 8. So while addingthese variables of interest to our VAR specifications individually does not always result in achange to the structural breaks outside of the standard errors found in Table 8, the additiveeffects on the structural breaks in 1959 and 1984 are outside of the standard errors by 37.50%and 100%.15

From these results, we can say that while much of the explanation for the increase injobless recovery in the United States is yet to be found, we can explain some of the storywith the factors we have presented here. It seems there are many possible contributors to theincrease in the joblessness of recoveries in the United States, each playing some small role.Complicating the matter further for some factors, the effects on jobless recovery depend on

13These data come from the BEA National Income and Personal Income accounts.14The large decline and rapid increase in the early 1950’s dealt with payments to soldiers following WWII15The result for the 1984 structural break for log differences in employment when we include all the

variables is -0.004, which is not outside the standard error.

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the time frame investigated.

8 Conclusions

Using vector auto regression models and controlling for log differences in GDP, the un-employment rate and changes in the unemployment rate, we show that the United Statesentered into an era of “jobless recoveries.” We split our data sample into positive and nega-tive changes in the unemployment rate, which we refer to as Inflows and Outflows, to isolatethe recovery portion of the unemployment cycle. We find structural breaks for changes in theunemployment rate in the Outflows subsample in the fourth quarter of 1959 and 1984 usingthe Quandt-Andrews test for unknown breaks. When we incorporate the structural breaksinto our split sample VAR’s, the coefficients with changes in the unemployment rate as thedependent variable are statistically significant, positive and successively larger. This resultindicates that controlling for the unemployment rate and GDP growth, the U.S. is experienc-ing weaker recovery in unemployment rates. Using the 7.85 percent unemployment rate atthe end of 2012, recovery back to historical long run averages of 5.5 percent unemploymentrates after the first structural break will take four additional quarters of 2.0 percent GDPgrowth. After the second structural break it will take eight additional quarters of the samegrowth to get back to the same unemployment rate. When we replace the structural breakswith a time trend, we find the coefficient for the time trend is also statistically significantand positive.

We conduct a number of robustness tests and the results support our main specifica-tion. The results are robust to splitting the data into periods of positive and negative GDPgrowth, using a more conventional VAR with only changes in the unemployment rate andlog differences in the unemployment rate, and replacing GDP growth with estimated TFPgrowth. Then, after substantiating jobless recovery in the United States, we separate changesin unemployment rates into changes in unemployment and changes in employment and findboth contribute to jobless recovery. Changes in unemployment play a significant role in boththe 1959 structural break and the 1984 structural break. Changes in employment seem onlyto play a significant role in the 1984 structural break. Without more data, however, wecannot determine whether the significant structural breaks for both changes in employmentand changes in unemployment are due to changes in hiring or changes in separations.

Next we use the timing of the structural breaks to review the possible causes and relatedtheory, including industry composition, participation rates, and social benefits. Our resultsindicate the combined effect of all of these factors play little or no role in explaining the1984 structural break for changes in employment. However, they may explain up to aboutone-fifth of our 1959 structural break and up to about a quarter of our 1984 structural breakfor changes in unemployment. The increased percentage of women in the workforce and theincreased percentage of employment in the service sector play the most prominent roles inexplaining the structural break for changes in unemployment. The rise in social benefitscompared to wages may also play some limited role in both structural breaks. Participationrates seem to have the least amount of explanatory power. However, this does not meanparticipation rates do not contribute to jobless recovery. The evidence appears to indicatethat the role of participation rates in jobless recovery may depend heavily on the time frame

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investigated.The evidence in this paper shows there is no single explanation, or even small number of

explanations, that can account for the observed increase in the joblessness of recoveries inthe United States. This paper provides a road forward with empirical evidence as to whichof the factors we tested are most worthwhile to investigate in future research. It also tells usthat we need to be cognizant that these different factors can have differing effects on joblessrecovery dependent on the time period.

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References

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[2] Aaronson, Rissman and Sullivan (2004), “Can sectoral reallocation explain the joblessrecovery?” Journal of Economic Perspectives, Q2, 36-49.

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Tables

Table 1: Historical Summary of U.S. Recoveries from Recessionary Periods

Recession Summary

Q3-1953 to Q1-1954 (3quarters of negative growth)

It took two quarters of an average of 0.62% real GDPgrowth for the unemployment rate to peak and startcoming down after the recession ending Q1-1954.

Q4-1957 to Q1-1958 (2quarters of negative growth)

It took one quarter of 0.61% real GDP growth for theunemployment rate to peak and start coming downafter the recession ending in Q2-1958.

Q2-1960 to Q4-1960 (actuallytwo quarters of negative

growth with one positive inbetween)

It took one quarter of 1.2% real GDP growth for theunemployment rate to peak and start coming downafter the contraction ending in Q4-1960.

Q3-1974 to Q1-1975 (3quarters of negative growth)

It took two quarters of an average 0.76% of real GDPgrowth for unemployment to peak and start comingdown after the recession ending in Q1-1975.

Q4-1981 to Q1-1982 (2quarters of negative growth)

It took three quarters of an average 0.078% real GDPgrowth (Actually two periods of positive growth sand-wiching one negative quarter.) for unemployment topeak and start coming down after recession ending inQ1-1982.

Q3-1990 to Q1-1991(3quarters of negative growth)

It took five quarters of an average of 0.72% real GDPgrowth for unemployment to peak and start comingdown after the recession ending in Q1-1991.

Q1-2001 to Q3-2001(actuallytwo quarters of negative

growth with one positive inbetween)

It took seven quarters of 0.49% real GDP growth forthe unemployment rate to peak and start coming downafter the contraction ending in Q3-2001.

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Table 2: Unit Root Tests: Null Hypothesis for a Unit Root

Test ADF t-Stat DF-GLS t-StatVariable Lag C C+T C C+TDGDP 1 -8.10*** -8.30*** -5.28*** -7.61***DGDP 2 -7.98*** -8.25*** -4.90*** -7.47***DGDP 3 -7.98*** -8.25*** -4.52*** -7.33***DGDP 4 -7.47*** -7.98*** -4.09*** -7.06***DGDP 5 -6.44*** -6.93*** -3.27*** -5.96***DGDP 6 -6.08*** -6.73*** -3.04*** -5.84***DGDP 7 -5.57*** -6.06*** -2.42** -4.89***DGDP 8 -4.79*** -5.21*** -1.91* -4.07***DTFP 1 -9.45*** -9.61*** -3.00*** -5.69***DTFP 2 -7.72*** -8.12*** -2.47** -4.95***DTFP 3 -8.82*** -9.19*** -2.34** -4.87***DTFP 4 -7.93*** -8.43*** -1.97** -4.31***DTFP 5 -6.91*** -7.46*** -1.56 -3.63***DTFP 6 -6.33*** -6.83*** -1.19 -2.97**DTFP 7 -5.80*** -6.42*** -1.09 -2.89*DTFP 8 -4.92*** -5.28*** -0.57 -1.95DUR 1 -7.50*** -7.49*** -5.48*** -6.80***DUR 2 -7.80*** -7.78*** -5.42*** -6.95***DUR 3 -8.83*** -8.82*** -5.82*** -7.81***DUR 4 -7.48*** -7.48*** -4.76*** -6.62***DUR 5 -5.94*** -5.95*** -3.63*** -5.17***DUR 6 -5.95*** -5.95*** -3.43*** -5.01***DUR 7 -7.25*** -7.26*** -3.83*** -5.85***DUR 8 -5.40*** -5.39*** -2.71*** -4.18***UR 1 -3.72*** -3.80*** -2.51** -3.89***UR 2 -3.92*** -4.26*** -2.67*** -4.16***UR 3 -3.44*** -3.77** -2.26** -3.69**UR 4 -2.60* -3.00 -1.67* -3.02**UR 5 -2.76* -3.27* -1.87* -3.34**UR 6 -3.25** -3.76** -2.21** -3.77***UR 7 -3.04** -3.51** -1.97** -3.51***UR 8 -2.18 -2.62 -1.24 -2.67*

***,**,* denote significance at the 1%, 5%, and 10%threshold respectively. ADF = Augmented Dickey-Fuller; DF-GLS = Dickey-Fuller Generalized LeastSquares. C = Constant; C+T=Constant plus a timetrend.

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Table 3: OLS VARs for Outflows Subsample.

VAR 1 2Specification Structural Breaks Time Trend

Variable DUR UR DGDP DUR UR DGDP

C -0.009 -0.009 0.012*** 0.080 0.080 0.011***(0.062) (0.062) (0.003) (0.063) (0.063) (0.003)

UR(-1) -0.068*** 0.932*** 0.001* -0.067*** 0.933*** 0.001**(0.009) (0.009) (0.0004) (0.010) (0.010) (0.0004)

DUR(-2) 0.105*** 0.105*** -0.001 0.088** 0.088** -0.001(0.037) (0.037) (0.002) (0.039) (0.039) (0.002)

DGDP(-1) -4.287** -4.287** 0.093 -5.228*** -5.228*** 0.088(1.784) (1.784) (0.089) (1.914) (1.914) (0.088)

SB594 0.231*** 0.231*** -0.004**(0.043) (0.043) (0.002)

SB844 0.297*** 0.297*** -0.009***(0.044) (0.044) (0.002)

QUARTER 0.001*** 0.001*** -4.5E-05***(0.0002) (0.0002) (1.0E-05)

N 145 145 145 145 145 145R2 0.435 0.990 0.160 0.349 0.988 0.162

Adj. R2 0.415 0.990 0.130 0.330 0.988 0.138F-Stat 21.40*** 2734*** 5.300*** 18.74*** 2982*** 6.776***

LL 59.601 59.601 494.89 49.303 49.30 495.072DW 2.434 2.434 1.862 2.145 2.145 1.855

***,**,* denote significance at the 1%, 5%, and 10% threshold respectively. () contains standard errors.

Table 4: OLS VARs for Expansion Subsample.

VAR 1 2Specification Structural Breaks Time Trend

Variable DUR UR DGDP DUR UR DGDP

C 0.239*** 0.239*** 0.009*** 0.280*** 0.280*** 0.010***(0.066) (0.066) (0.002) (0.065) (0.065) (0.002)

UR(-1) -0.057*** 0.943*** 0.001*** -0.054*** 0.946*** 0.0008***(0.011) (0.011) (0.0003) (0.011) (0.011) (0.0003)

DUR(-2) 0.120*** 0.120*** 0.001 0.118*** 0.118*** 0.001(0.043) (0.043) (0.001) (0.043) (0.043) (0.001)

DGDP(-1) -14.257*** -14.257*** 0.215*** -13.954*** -13.954*** 0.198***(1.886) (1.886) (0.055) (1.924) (1.924) (0.054)

SB601 0.159*** 0.159*** -0.004**(0.049) (0.049) (0.001)

SB813 0.164*** 0.164*** -0.007***(0.049) (0.049) (0.001)

QUARTER 0.0006** 0.0006** -4.0E-05***(0.0002) (0.0002) (6.7E-06)

N 217 217 217 217 217 217R2 0.360 0.981 0.203 0.338 0.980 0.223

Adj. R2 0.345 0.980 0.184 0.326 0.980 0.210F-Stat 23.71*** 2149*** 10.738*** 27.068*** 2610*** 15.174***

LL 12.666 12.666 780.47 9.059 9.059 783.19DW 2.128 2.128 2.005 2.052 2.052 2.016

***,**,* denote significance at the 1%, 5%, and 10% threshold respectively.( ) contains standard errors.

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Table 5: Robustness Test 1: OLS VARs for Outflows Subsample.

VAR 1 2Specification Structural Breaks Time Trend

Variable DUR DGDP DUR DGDP

C -0.411*** 0.015*** -0.413*** 0.021***(0.061) (0.003) (0.073) (0.003)

DUR(-1) 0.081 -0.009*** 0.090 -0.011***(0.060) (0.003) (0.063) (0.003)

DUR(-2) 0.141** -0.001 0.151** -0.003(0.054) (0.002) (0.058) (0.002)

DUR(-3) 0.097 0.001 0.104 -0.001(0.060) (0.003) (0.063) (0.003)

DUR(-4) -0.189*** 0.006*** -0.182*** 0.006***(0.050) (0.002) (0.052) (0.002)

DGDP(-1) -1.122 -0.106 -1.028 -0.199**(2.238) (0.100) (2.396) (0.100)

DGDP(-2) 2.726 -0.056 3.055 -0.140(2.239) (0.100) (2.407) (0.101)

DGDP(-3) 4.964** -0.066 5.611** -0.132(2.375) (0.106) (2.486) (0.104)

DGDP(-4) 2.344 0.082 3.008 0.028(2.327) (0.104) (2.422) (0.101)

SB602 0.104** -0.002(0.042) (0.002)

SB951 0.214*** -0.007***(0.048) (0.002)

QUARTER 0.001*** -4.8E-05***(0.000) (1.0E-05)

N 145 145 145 145R2 0.391 0.270 0.347 0.312

Adj. R2 0.346 0.216 0.303 0.266F-Stat 8.618*** 4.963*** 7.971*** 6.800***

LL 54.216 505.085 49.111 509.348DW 2.313 1.783 2.200 1.775

***,**,* denote significance at the 1%, 5%, and 10% thresh-old respectively. ( ) contains standard errors.

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Table 6: Robustness Test 2: OLS VARs for Outflows Subsample.

VAR 1 2Specification Structural Breaks Time Trend

Variable DUR UR DTFP DUR UR DTFP

C -0.060 -0.060 2.478** 0.027 0.027 1.894**(0.058) (0.058) (1.096) (0.059) (0.059) (1.041)

UR(-1) -0.067*** 0.933*** 0.310** -0.066*** 0.934*** 0.348**(0.009) (0.009) (0.167) (0.010) (0.010) (0.171)

DUR(-2) 0.130*** 0.130*** 0.963 0.117*** 0.117*** 1.024(0.035) (0.035) (0.661) (0.038) (0.038) (0.664)

DTFP(-1) -0.005 -0.005 -0.037 -0.009* -0.009* -0.018(0.005) (0.005) (0.085) (0.005) (0.005) (0.085)

SB594 0.240*** 0.240*** -1.806**(0.044) (0.044) (0.832)

SB844 0.316*** 0.316*** -3.071***(0.045) (0.045) (0.846)

QUARTER 0.001*** 0.001*** –0.013***(0.0002) (0.0002) (0.004)

N 145 145 145 145 145 145R2 0.417 0.990 0.132 0.330 0.988 0.116

Adj. R2 0.396 0.989 0.101 0.311 0.988 0.091F-Stat 19.92*** 2650*** 4.224*** 17.24*** 2898*** 4.582***

LL 57.377 57.377 -367.83 47.254 47.254 -369.16DW 2.343 2.343 1.852 2.076 2.076 1.849

***,**,* denote significance at the 1%, 5%, and 10% threshold respectively.( ) contains standard errors.

Table 7: Forecast Model (Outflows: UR as Dependent Variable)

Variable C UR(-1) DUR(-2) DGDP(-1) SB594 SB844UR 0.056 0.916 0.146 -3.569 0.256 0.310

Stand Error 0.070 0.011 0.039 1.915 0.045 0.048Probability *** *** * *** ***

Statistics N R sqr. Adj. R sqr. Log Likelihood F Stat DW136 0.988 0.988 46.947 1843.251 2.098

Table 8: OLS VARs: Log Differences in Employment and Unemployment.

VAR 1 2Variable DU UR DGDP DE UR DGDP

C -0.062*** -0.096 0.018 0.005** -0.129 0.018***(0.015) (0.079) (0.004) (0.003) (0.086) (0.004)

SB594 0.053*** 0.197*** -0.002*** 0.0002 0.2085*** -0.0032(0.008) (0.043) (0.002) (0.001) (0.047) (0.002)

SB844 0.061*** 0.26*** -0.007*** -0.003*** 0.297*** -0.008***(0.008) (0.043) (0.002) (0.001) (0.045) (0.002)

N 145 145 145 145 145 145RMSE 0.030 0.156 0.008 0.005 0.161 0.008R2 0.457 0.991 0.307 0.306 0.991 0.287χ2 122*** 16803*** 64*** 64*** 15714*** 58***

***,**,* denote significance at the 1%, 5%, and 10% threshold respectively.( ) contains standard errors.

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Table 9: OLS VARs: Log Differences in Unemployment and Employment with Differencesin Participation Rates.

VAR 1 2Variable LDU UR DGDP DPR LDE UR DGDP DPR

C-0.062*** -0.094 0.017*** -0.001 0.004 -0.243*** 0.017*** -0.001(0.015) (0.079) (0.233) (0.001) (0.003) (0.087) (0.005) (0.001)

SB5940.052*** 0.196*** -0.001 0.000 0.000 0.13*** -0.003 0.000(0.009) (0.045) (0.002) (0.001) (0.002) (0.049) (0.003) (0.001)

SB8440.059*** 0.256*** -0.007*** -0.001 -0.003* 0.251*** -0.008*** -0.001(0.009) (0.045) (0.002) (0.001) (0.001) (0.044) (0.002) (0.001)

N 145 145 145 145 145 145 145 145RMSE 0.030 0.157 0.008 0.003 0.005 0.151 0.008 0.003R2 0.469 0.992 0.339 0.331 0.322 0.992 0.307 0.329χ2 128*** 17220*** 74*** 72*** 69*** 18477*** 64*** 71***

Table 10: OLS VARs: Log Differences in Unemployment and Employment with Differencesin the Percentage of Women in the Labor Force.

VAR 1 2Variable LDU UR DGDP DPLFW LDE UR DGDP DPLFW

C-0.052*** -0.073 0.016*** -0.023 0.006** -0.121 0.017*** 0.000(0.016) (0.083) (0.378) (0.097) (0.003) (0.087) (0.163) (0.098)

SB5940.048*** 0.19*** -0.002 -0.028 0.000 0.186*** -0.003 -0.047(0.009) (0.045) (0.002) (0.053) (0.001) (0.048) (0.002) (0.054)

SB8440.050*** 0.24*** -0.006** -0.131** -0.004** 0.267*** -0.007*** -0.146**

(0.01) (0.050) (0.002) (0.059) (0.002) (0.051) (0.002) (0.057)N 145 145 145 145 145 145 145 145

RMSE 0.030 0.156 0.008 0.182 0.005 0.161 0.008 0.181R2 0.485 0.992 0.333 0.311 0.326 0.991 0.296 0.320χ2 137*** 17322*** 72*** 65*** 70*** 16397*** 61*** 68***

Table 11: OLS VARs: Log Differences in Unemployment and Employment with Log Differ-ences in the Ratio of Wages and Salaries Earned over Social Benefits to Persons Paid.

VAR 1 2Variable DU UR DGDP DWSSBP DE UR DGDP DWSSBP

C-0.070*** -0.114 0.017*** -0.081*** 0.004 -0.107 0.016*** -0.084***(0.014) (0.075) (0.129) (0.028) (0.002) (0.080) (0.184) (0.029)

SB5940.047*** 0.177*** -0.002 -0.027* 0.000 0.203*** -0.004* -0.021(0.008) (0.041) (0.002) (0.015) (0.001) (0.044) (0.002) (0.016)

SB8440.057*** 0.246*** -0.007*** -0.010 -0.003** 0.279*** -0.008*** -0.002(0.008) (0.041) (0.002) (0.015) (0.001) (0.042) (0.002) (0.015)

N 145 145 145 145 145 145 145 145RMSE 0.028 0.148 0.008 0.055 0.005 0.151 0.008 0.054R2 0.554 0.993 0.326 0.414 0.407 0.992 0.322 0.434χ2 180*** 19241*** 70*** 102*** 100*** 18480*** 69*** 111***

30

Page 31: Jobless Recovery: A Time Series Look at the United States › ~jpd48 › Jobless Recovery.pdf · growth was 4.3 quarters, 6.4 quarters after the break until 1984Q4, and 17.0 quarters

Table 12: Percent Industry Composition: Bureau of Labor and Statistics

Year 1950 1960 1970 1980 1990 2000 2010

Goods-Producing 37.34 35.91 31.93 27.48 21.97 18.84 13.79

Mining & Logging 2.02 1.44 0.97 1.15 0.70 0.45 0.53

Construction 5.09 5.57 5.08 5.09 4.97 5.16 4.35

Manufacturing 30.23 28.90 25.89 21.24 16.31 13.22 8.92

Service-providing 49.03 48.78 50.51 54.68 61.40 65.43 68.86

Financial Activities 4.12 4.60 4.90 5.46 6.04 5.87 5.89

Profes. & Bus. 6.54 6.76 7.38 8.22 9.87 12.55 12.74

Education & Health 4.84 5.33 6.37 7.64 9.84 11.44 14.95

Leisure & Hosp. 6.24 6.32 6.68 7.39 8.51 8.96 10.03

Other 1.89 2.09 2.49 2.98 3.87 3.93 4.10

Government 13.63 15.31 17.56 17.84 16.63 15.73 17.34

31

Page 32: Jobless Recovery: A Time Series Look at the United States › ~jpd48 › Jobless Recovery.pdf · growth was 4.3 quarters, 6.4 quarters after the break until 1984Q4, and 17.0 quarters

Table 13: OLS VARs: Log Differences in Unemployment and Employment with Differencesin the Percentage of Employment in the Service Industry.

VAR 1 2Variable LDU UR DGDP DPCESS LDE UR DGDP DPCESS

C-0.058*** -0.081 0.016*** -0.045 0.005** -0.117 0.016*** -0.059(0.015) (0.08) (0.307) (0.054) (0.002) (0.086) (0.171) (0.058)

SB5940.050*** 0.194*** -0.001 0.076** -0.001 0.215*** -0.003 0.066*(0.009) (0.047) (0.002) (0.032) (0.001) (0.051) (0.002) (0.034)

SB8440.053*** 0.246*** -0.006** 0.111*** -0.004*** 0.299*** -0.007*** 0.112***(0.010) (0.050) (0.002) (0.034) (0.002) (0.052) (0.003) (0.035)

N 145 145 145 145 145 145 145 145RMSE 0.029 0.156 0.008 0.106 0.005 0.160 0.008 0.108R2 0.503 0.992 0.335 0.434 0.387 0.991 0.314 0.403χ2 147*** 17477*** 73*** 111*** 92*** 16494*** 66*** 98***

Figures

Figure 1: US Unemployment (1948-2012)- BLS

0.00  

2.00  

4.00  

6.00  

8.00  

10.00  

March-­‐48  

March-­‐50  

March-­‐52  

March-­‐54  

March-­‐56  

March-­‐58  

March-­‐60  

March-­‐62  

March-­‐64  

March-­‐66  

March-­‐68  

March-­‐70  

March-­‐72  

March-­‐74  

March-­‐76  

March-­‐78  

March-­‐80  

March-­‐82  

March-­‐84  

March-­‐86  

March-­‐88  

March-­‐90  

March-­‐92  

March-­‐94  

March-­‐96  

March-­‐98  

March-­‐00  

March-­‐02  

March-­‐04  

March-­‐06  

March-­‐08  

March-­‐10  

March-­‐12  

Une

mplym

net  R

ate  

32

Page 33: Jobless Recovery: A Time Series Look at the United States › ~jpd48 › Jobless Recovery.pdf · growth was 4.3 quarters, 6.4 quarters after the break until 1984Q4, and 17.0 quarters

Figure 2: Out of Sample Forecast Results for Recovery from the 2001 Recession

0.00  

1.00  

2.00  

3.00  

4.00  

5.00  

6.00  

7.00  

December-­‐02  

February-­‐03  

April-­‐03  

June-­‐03  

August-­‐03  

October-­‐03  

December-­‐03  

February-­‐04  

April-­‐04  

June-­‐04  

August-­‐04  

October-­‐04  

December-­‐04  

February-­‐05  

April-­‐05  

June-­‐05  

August-­‐05  

October-­‐05  

December-­‐05  

February-­‐06  

April-­‐06  

June-­‐06  

August-­‐06  

October-­‐06  

December-­‐06  

February-­‐07  

Une

mploymen

t  Rate  

DATE  

Recovery  Forecast  for  2001  Recession    

UR  Predicted  

UR  

33

Page 34: Jobless Recovery: A Time Series Look at the United States › ~jpd48 › Jobless Recovery.pdf · growth was 4.3 quarters, 6.4 quarters after the break until 1984Q4, and 17.0 quarters

Figure 3: Forecast Results from 2012

2.00  

3.00  

4.00  

5.00  

6.00  

7.00  

8.00  

9.00  

10.00  

March-­‐08  

July-­‐08  

Novem

ber-­‐08  

March-­‐09  

July-­‐09  

Novem

ber-­‐09  

March-­‐10  

July-­‐10  

Novem

ber-­‐10  

March-­‐11  

July-­‐11  

Novem

ber-­‐11  

March-­‐12  

July-­‐12  

Novem

ber-­‐12  

March-­‐13  

July-­‐13  

Novem

ber-­‐13  

March-­‐14  

July-­‐14  

Novem

ber-­‐14  

March-­‐15  

July-­‐15  

Novem

ber-­‐15  

March-­‐16  

July-­‐16  

Novem

ber-­‐16  

Unemployment  Rate  Forecast  from  2012:  Ou8lows  

UR  Before    1959Q4  

UR  Between  1959Q4  and  1984Q4  

UR  AGer  1984Q4  

Long  Run  Average  

34

Page 35: Jobless Recovery: A Time Series Look at the United States › ~jpd48 › Jobless Recovery.pdf · growth was 4.3 quarters, 6.4 quarters after the break until 1984Q4, and 17.0 quarters

Figure 4: Forecast Results from 2012

0.00  

1.00  

2.00  

3.00  

4.00  

5.00  

6.00  

7.00  

8.00  

9.00  

10.00  

March-­‐08  

February-­‐09  

Janu

ary-­‐10  

Decem

ber-­‐10

 

Novem

ber-­‐11  

Octob

er-­‐12  

Septem

ber-­‐13  

August-­‐14  

July-­‐15  

June

-­‐16  

May-­‐17  

April-­‐18  

March-­‐19  

February-­‐20  

Janu

ary-­‐21  

Decem

ber-­‐21

 

Novem

ber-­‐22  

Octob

er-­‐23  

Septem

ber-­‐24  

August-­‐25  

July-­‐26  

June

-­‐27  

May-­‐28  

April-­‐29  

March-­‐30  

February-­‐31  

Janu

ary-­‐32  

Decem

ber-­‐32

 

Novem

ber-­‐33  

Octob

er-­‐34  

Septem

ber-­‐35  

Unemployment  Rate  Forecast  from  2012:  Expansion  

UR  Before    1960Q1  

UR  Between  1960Q1  and  1981Q3  

UR  AOer  1981Q3  

Long  Run  Average  

35

Page 36: Jobless Recovery: A Time Series Look at the United States › ~jpd48 › Jobless Recovery.pdf · growth was 4.3 quarters, 6.4 quarters after the break until 1984Q4, and 17.0 quarters

Figure 5: JOLTS: Job Openings and Labor Turnover Survey (2001-2012) - BLS

0.0  

0.5  

1.0  

1.5  

2.0  

2.5  

3.0  

3.5  

4.0  

4.5  

Feb-­‐01  

Jun-­‐01  

Oct-­‐01  

Feb-­‐02  

Jun-­‐02  

Oct-­‐02  

Feb-­‐03  

Jun-­‐03  

Oct-­‐03  

Feb-­‐04  

Jun-­‐04  

Oct-­‐04  

Feb-­‐05  

Jun-­‐05  

Oct-­‐05  

Feb-­‐06  

Jun-­‐06  

Oct-­‐06  

Feb-­‐07  

Jun-­‐07  

Oct-­‐07  

Feb-­‐08  

Jun-­‐08  

Oct-­‐08  

Feb-­‐09  

Jun-­‐09  

Oct-­‐09  

Feb-­‐10  

Jun-­‐10  

Oct-­‐10  

Feb-­‐11  

Jun-­‐11  

Oct-­‐11  

Feb-­‐12  

Jun-­‐12  

Oct-­‐12  

Percen

t  

Hiring,  Separa/on,  Job  Opening,  Layoffs  &  Discharge  Rates  (3  month  moving  averages)  

Hiring   Separa>on   Job  Opening   Layoffs  &  Discharges  

36

Page 37: Jobless Recovery: A Time Series Look at the United States › ~jpd48 › Jobless Recovery.pdf · growth was 4.3 quarters, 6.4 quarters after the break until 1984Q4, and 17.0 quarters

Figure 6: Monthly Participation Rate from (1948-2012) - BLS

52.0  

54.0  

56.0  

58.0  

60.0  

62.0  

64.0  

66.0  

68.0  

Jan-­‐48  

Nov-­‐49  

Sep-­‐51  

Jul-­‐5

3  

May-­‐55  

Mar-­‐57  

Jan-­‐59  

Nov-­‐60  

Sep-­‐62  

Jul-­‐6

4  

May-­‐66  

Mar-­‐68  

Jan-­‐70  

Nov-­‐71  

Sep-­‐73  

Jul-­‐7

5  

May-­‐77  

Mar-­‐79  

Jan-­‐81  

Nov-­‐82  

Sep-­‐84  

Jul-­‐8

6  

May-­‐88  

Mar-­‐90  

Jan-­‐92  

Nov-­‐93  

Sep-­‐95  

Jul-­‐9

7  

May-­‐99  

Mar-­‐01  

Jan-­‐03  

Nov-­‐04  

Sep-­‐06  

Jul-­‐0

8  

May-­‐10  

Mar-­‐12  

Percen

t  

U.S.  Par,cipa,on  Rate  

37

Page 38: Jobless Recovery: A Time Series Look at the United States › ~jpd48 › Jobless Recovery.pdf · growth was 4.3 quarters, 6.4 quarters after the break until 1984Q4, and 17.0 quarters

Figure 7: Unemployment Rate and Correlation Between Unemployment Rate and Partici-pation Rate (1953-2012) - BLS

2.00

4.00

6.00

8.00

10.00

UR

-1-.5

0.5

1_corr20_UR_PR

1950q1 1960q1 1970q1 1980q1 1990q1 2000q1 2010q1qtr

_corr20_UR_PR UR

38

Page 39: Jobless Recovery: A Time Series Look at the United States › ~jpd48 › Jobless Recovery.pdf · growth was 4.3 quarters, 6.4 quarters after the break until 1984Q4, and 17.0 quarters

Figure 8: Median Usual Weekly Earnings of Full-Time Wage and Salary workers - BLS

290  

300  

310  

320  

330  

340  

350  

Mar-­‐79  

Jan-­‐80  

Nov-­‐80  

Sep-­‐81  

Jul-­‐8

2  

May-­‐83  

Mar-­‐84  

Jan-­‐85  

Nov-­‐85  

Sep-­‐86  

Jul-­‐8

7  

May-­‐88  

Mar-­‐89  

Jan-­‐90  

Nov-­‐90  

Sep-­‐91  

Jul-­‐9

2  

May-­‐93  

Mar-­‐94  

Jan-­‐95  

Nov-­‐95  

Sep-­‐96  

Jul-­‐9

7  

May-­‐98  

Mar-­‐99  

Jan-­‐00  

Nov-­‐00  

Sep-­‐01  

Jul-­‐0

2  

May-­‐03  

Mar-­‐04  

Jan-­‐05  

Nov-­‐05  

Sep-­‐06  

Jul-­‐0

7  

May-­‐08  

Mar-­‐09  

Jan-­‐10  

Nov-­‐10  

Sep-­‐11  

Jul-­‐1

2  

Median  usual  weekly  earnings  -­‐  in  constant  (1982-­‐84)  dollars,  seasonally  adjusted  

39

Page 40: Jobless Recovery: A Time Series Look at the United States › ~jpd48 › Jobless Recovery.pdf · growth was 4.3 quarters, 6.4 quarters after the break until 1984Q4, and 17.0 quarters

Figure 9: Wages and Salaries Earned Per Dollar in Social Benefits to Persons Paid from(1948-2012) - BLS

40

Page 41: Jobless Recovery: A Time Series Look at the United States › ~jpd48 › Jobless Recovery.pdf · growth was 4.3 quarters, 6.4 quarters after the break until 1984Q4, and 17.0 quarters

Figure 10: Industry Unemployment Rates: BLS

0  

5  

10  

15  

20  

25  

Jan-­‐00  

Apr-­‐00  

Jul-­‐0

0  

Oct-­‐00  

Jan-­‐01  

Apr-­‐01  

Jul-­‐0

1  

Oct-­‐01  

Jan-­‐02  

Apr-­‐02  

Jul-­‐0

2  

Oct-­‐02  

Jan-­‐03  

Apr-­‐03  

Jul-­‐0

3  

Oct-­‐03  

Jan-­‐04  

Apr-­‐04  

Jul-­‐0

4  

Oct-­‐04  

Jan-­‐05  

Apr-­‐05  

Jul-­‐0

5  

Oct-­‐05  

Jan-­‐06  

Apr-­‐06  

Jul-­‐0

6  

Oct-­‐06  

Jan-­‐07  

Apr-­‐07  

Jul-­‐0

7  

Oct-­‐07  

Jan-­‐08  

Apr-­‐08  

Jul-­‐0

8  

Oct-­‐08  

Jan-­‐09  

Apr-­‐09  

Jul-­‐0

9  

Oct-­‐09  

Jan-­‐10  

Apr-­‐10  

Jul-­‐1

0  

Oct-­‐10  

Une

mploymen

t  Rate  

DATE  

6  Month  Moving  Average  Unemployment  Rate  

Service  Average  (SA)  

ConstrucBon  (SA)  

Goods  Producing  without  ConstrucBon  Average  (SA)  

41