BLS WORKING PAPERS 

 U.S. Department of Labor 

U.S. Bureau of Labor Statistics 

Office of Employment and Unemployment Statistics 

Job Flows and the Recent Business Cycle: Not All 

“Recoveries” Are Created Equal 

R. Jason Faberman, U.S. Bureau of Labor Statistics 

Working Paper 391

February 2006

All views expressed in this paper are those of the author and do not necessarily reflect the views or policies of the 

U.S. Bureau of Labor Statistics. 
Job Flows and the Recent Business Cycle: Not all “Recoveries” are Created Equal 

January 2006 

R. Jason Faberman 

U.S. Bureau of Labor Statistics 

2 Massachusetts Ave NE, Suite 4945 

 Washington, DC 20212 

Faberman.Jason@bls.gov

Phone: (202) 691-5192 

Fax: (202) 691-7425 

Abstract: 

The last two economic downturns are notable for their slow labor market recoveries. Yet, 

the behavior of their underlying gross job flows is quite different. The 1990-92 period 

had a relatively slow decline in job destruction, while the 2001-03 period had a large, 

persistent decline in job creation that occurs across most industries. The dynamics of the 

latter period run counter to the conventional wisdom that large movements in job 

destruction drive business cycles. Evidence spanning the entire postwar period suggests 

that job creation is at a historic low, and that its recent patterns are part of decades-long 

decline in the magnitude and volatility of job reallocation. 

Keywords: job reallocation, business cycles, employment fluctuations 

JEL Codes: E24, E32, J36 

Acknowledgements 

I thank John Haltiwanger, Steve Davis, Scott Schuh, Dan Sullivan and seminar 

participants at the Federal Reserve Board of Governors, the Bureau of Labor Statistics, 

and Lehigh University for helpful comments. Any remaining errors are my own. The views 

expressed are solely those of the author and do not necessarily reflect the official positions or 

policies of the U.S. Bureau of Labor Statistics or the views of other staff members.
1. Introduction 

The two latest downturns appear remarkably similar. Unlike previous downturns, 

they each have a relatively brief recession followed by steady GDP growth but stagnant 

employment growth. This paper shows that, despite these apparent similarities, the 

underlying labor dynamics, particularly during the “jobless” recoveries, are quite 

different. Previously, others had found that large, episodic movements in gross job 

destruction and only small changes in gross job creation characterized cyclical 

fluctuations in the labor market.1

 This implied that job reallocation was countercyclical,

and that job destruction rather than job creation was relatively more responsive to the 

business cycle. Using a new data source on job creation and destruction from the Bureau 

of Labor Statistics (BLS) and extending it back several years, I find that the dynamics of 

the 1990-92 downturn are consistent with this pattern, but that the dynamics of the 2001-

03 downturn are not. During the latter period, employment losses are driven just as much 

by a dramatic decline in job creation as they are by a spike in job destruction. The drop in 

job creation is pervasive across nearly all industries. Previous studies, which find little 

cyclical movement in job creation, use different data sources, look at earlier periods, and 

focus on certain sectors of the economy, like manufacturing. Nevertheless, I find that the 

recent drop in job creation persists even within manufacturing, and the low creation rates 

during 2001-03 are the lowest on record for this industry during the postwar period. Put 

simply, from a labor market standpoint, the last economic downturn was unlike any other 

in the postwar period. Furthermore, the low rates of job creation appear to be part of a 

 1

 See Davis and Haltiwanger (1990, 1992), and Davis, Haltiwanger, and Schuh (1996). 

1
long-term decline in the magnitude and volatility of job reallocation that began in the 

early 1960’s. 

The data I use come from the Business Employment Dynamics (BED) program of 

the BLS. The BED uses administrative establishment data from state unemployment 

insurance records to produce quarterly estimates of job creation and destruction for the 

private sector and major industries. Publicly available estimates begin in late 1992, and 

illustrate the large drop in job creation discussed in this paper.

2

 Yet, without comparable 

data available for earlier recessions, it is difficult to ascertain whether the BED evidence 

deviates from previous findings because of a change in the cyclical patterns of job flows 

or because different data sources and scope are used. State administrative data exist back 

to 1990 (i.e., the start of the previous recession), but data reporting issues do not allow 

the BLS to release estimates using these data. To rectify this, I create linkage algorithms

for the earlier microdata and estimate job flows back 1990-92 period. The constructed 

time-series runs spans 1990 through 2005 and allows a comparison of the last two 

recessions and “jobless” recoveries. To put the evidence for the last two downturns into a 

cyclical perspective, I also splice the manufacturing data from my extended series with 

job creation and destruction estimates created by Davis and Haltiwanger (1999) from the 

Longitudinal Research Database (LRD) of the Census Bureau and the discontinued Labor 

Turnover Survey (LTS) of the BLS. The final series spans 1947 through 2005, covering 

the entire postwar period. 

Davis and Haltiwanger (1990, 1992) show that, within manufacturing, job 

destruction is relatively more volatile than job creation over the business cycle, leading to 

a negative correlation between job reallocation and employment growth. Foote (1998), 

 2

 The data are available at http://www.bls.gov/bdm/home.htm. 

2
however, argues that this finding may be specific to manufacturing and other contracting 

industries. Other research finds that job creation exhibits greater variation in the crosssection—Baldwin, Dunne, and Haltiwanger (1998) find this pattern across industries, and 

Eberts and Montgomery (1995) and Faberman (2005) find this pattern across geographic 

areas. The evidence in this paper goes a step further and finds that, even within 

manufacturing, the relative volatility of job creation has increased over time. The 

volatility of both job flows decline over the postwar period, but the volatility of job 

destruction falls faster. This coincides with a decline in the trend rates of job reallocation 

for manufacturing. While noting that the latest downturn was different is important in its 

own right, the fact that its sharp decline in job creation is part of an overall decline in job 

reallocation and job flow volatility has implications for several ongoing lines of research. 

First, it alters the conventional wisdom in the job flow literature that episodic movements 

in job destruction drive cyclical employment fluctuations by illustrating that such 

movements have become relatively less important in recent years. Second, it adds new 

evidence for ongoing research on the observed decline in aggregate volatility discussed 

by Blanchard and Simon (2002) and Stock and Watson (2003). Finally, the fact that the 

latest downturn is so different has implications for the ongoing debate on the cyclicality 

of separations and the job-finding rate discussed by Hall (2006) and Shimer (2005). 

The next section details the data and defines the relevant job flow concepts. 

Section 3 presents the evidence on job creation and destruction and its variation (or lack 

thereof) across industries during the last two recessions. Section 4 discusses the relevance 

of the latest recession and the decline in job flow volatility in the context of the entire 

3
postwar period, as well as their relation to the recent behavior of hires and separations.

The final section concludes. 

2. Definitions and Data

For this study, I use the BLS definitions of “gross job gains” and “gross job 

losses” as my definitions of job creation and job destruction, which are collectively 

referred to as job flows. These are also the measures used by Davis and Haltiwanger

(1990, 1992) and Davis, Haltiwanger and Schuh (1996). I measure a change in 

employment between the third months of each quarter. Job creation is the sum of all 

employment gains at (i) continuous establishments expanding their employment, and (ii) 

“opening” establishments reporting either positive employment for the first time or after 

reporting zero employment in the previous quarter. Job destruction is the sum of all 

employment losses at (i) continuous establishments contracting their employment, and 

(ii) “closing” establishments either disappearing or reporting zero employment after 

reporting positive employment in the previous quarter.3

 The more familiar net change in 

employment is simply the difference between all jobs created and all jobs destroyed. Job 

reallocation is the sum of all jobs created and all jobs destroyed. Excess reallocation is 

job reallocation less the absolute value of the net change; it is a measure of labor market 

churning in excess of employment growth. Where reported, job flow rates are the 

percentages of employment, where employment is defined as the average of third-month 

employment for the current and previous quarters. 

The BED data are a longitudinal version of the Quarterly Census of Employment 

and Wages (QCEW), or ES-202, program, which is a virtual census of employment that 

 3

 Given these definitions, openings and closings include re-openings and temporary closings, as well as

births and deaths. 

4
includes all establishments covered under state unemployment insurance (UI) programs.

4

The BED links establishment records over time, primarily using UI identification codes. 

Pivetz, Searson, and Spletzer (2001) and Spletzer et al. (2004) detail this linkage process. 

While the public estimates begin in 1992, the available microdata go back to 1990. The 

QCEW program underwent major changes in the collection of data for multi-unit firms in 

the early 1990’s, making it difficult for the BLS to produce job flow estimates that meet 

official publication standards for this period. These changes, carried out by state 

agencies, led to discontinuities in the coding of many UI accounts, and an overstatement 

of establishment openings and closings (and thus an overstatement of job flows) for these 

accounts during this time. Luckily, the nature of the administrative change allows me to 

identify problem records and match them using several identifying assumptions. I 

describe the assumptions and methodology in detail in the appendix. 

My extended data series spans the second quarter of 1990 through the first quarter 

of 2005, and has job flow estimates for all private-sector 3-digit North American Industry 

Classification System (NAICS) industries—the industry detail is comparable to the 2-

digit level of the older Standard Industrial Classification (SIC) system. The estimates are 

seasonally adjusted using the X-12 ARIMA process on the individual component series 

of job creation and destruction statistics (i.e., employment changes at expanding, 

opening, contracting, and closing establishments). Private sector and industry aggregates 

are sums of the seasonally adjusted 3-digit estimates. The data range in coverage from 5.0 

million establishments representing 89.3 million employees in March 1990 to 6.5 million 

establishments representing 108.4 million employees in June 2003. On average, 

 4

 The government, self-employed, and private households are the primary exclusions from the BED. 

5
establishment expansions and contractions at continuous establishments make up about 

80 percent of quarterly job creation and destruction, respectively. 

To put job flows during the last two recessions into a long-run perspective, I 

splice the manufacturing data of my extended series with the job flow estimates created 

by Davis and Haltiwanger (1999). Their estimates are a merged series of job creation and 

job destruction rates for manufacturing from the Census LRD and BLS LTS.5

 The LRD 

has estimates of quarterly job flow rates for manufacturing from 1972-1998, while the 

LTS has estimates of accessions and separations from the early 1930’s through 1982. 

Davis and Haltiwanger merge the two econometrically and get job flow estimates for the 

LTS by using data during the period where the two series overlap. While their merged 

series and my BED manufacturing estimates have the same scope and measure the same

employment flows, differences in data collection methods lead to subtle differences in the 

trend rates and volatilities of each series. To create a single, consistent, long time series 

of job flow estimates, I splice their final merged series to the BED manufacturing 

estimates. I use a GMM procedure that forces the trend estimates of the earlier period to 

be consistent with the BED estimates, while preserving key moments of the earlier data. 

The GMM splicing estimation proceeds as follows. Let Ct and Dt represent the job 

creation and job destruction rate estimates, respectively, at time t from the BED, and let 

POSt, NEGt, and NETt represent the job creation, job destruction, and net growth rate 

estimates, respectively, from the Davis-Haltiwanger spliced series. Also, let be the 

net growth rate calculated from the Current Employment Statistics (CES, also known as 

the payroll survey). The CES net growth rate is a useful part of the estimation strategy 

C Nett

 5

 I thank Steve Davis and John Haltiwanger for providing an updated series of these estimates. Davis and 

Haltiwanger (1999) discuss the two data sources and their merging methodology in detail. 

6
because a) its growth rates match those of the BED by construction (the CES is 

benchmarked to the BED’s employment levels) and b) it has a time series that spans the 

full 1947-2005 period that I study. Finally, let and represent the GMM estimates of 

job creation and destruction, respectively, of the final spliced series. I define the estimates 

as 

Ct

ˆ Dt

ˆ

(1) . d

t

C

t t t

c

t

C

t t t

D NEG Net

C POS Net

β β β ε

α α α ε

= + + +

= + + +

0 1 2

0 1 2

ˆ

ˆ

Thus, I need to estimate six parameters for the model. I match seven moments to 

minimize the GMM value function. This overidentifies the model, but is necessary given 

highly nonlinear nature of the value function. The seven moments and their values in the 

data are listed in Table 1. They span different periods and different data sets and include 

the means of Ct and Dt, the variance of , the variance of the excess reallocation rate, 

XR

C Nett

t (calculated from the Davis-Haltiwanger series), the relative volatility of creation to 

destruction (defined as the variance of POSt divided by the variance of NEGt), the 

correlation between POSt and NETt, and the correlation between NEGt, NETt. I choose 

these moments to make the estimated series consistent with the trend job flow rates from

the BED (via the first two moments) while preserving the volatility (both relative and 

absolute) and covariation of the original job flow estimates. The parameter estimates 

obtained from GMM estimation represent a unique solution, and the resulting job 

creation and destruction series are robust to variations on the moments used, whether it 

be changes in their time periods or in the moments themselves. I discuss the methodology 

and robustness in more detail in the appendix. 

7
3. Job Flows and the Most Recent Downturn 

3.A. Basic Evidence 

The 1990-2005 period contains a prolonged expansion sandwiched between two 

economic downturns. Based on the National Bureau of Economic Research dating, the 

first downturn begins after a business cycle peak in the third quarter of 1990, reaching its 

trough in the first quarter of 1991. Figure 1 illustrates net employment growth over the 

period. Relative to previous recessions, employment losses are mild, but employment 

gains during the recovery are slow to materialize.6

 The second downturn begins as the 

economy peaks in the first quarter of 2001. The initial drop in employment is comparable 

to the previous recession, but losses persist over a longer period, making the total decline 

are larger relative to previous recessions. These declines include a continuation of losses 

through the trough at the fourth quarter of 2001 until the second quarter of 2003. 

Unlike previous downturns, the labor market is slow to recover after each of the 

last two recessions, with these periods often referred to as “jobless recoveries”. Based on 

the net employment changes over these two periods, one might conclude that there are 

many similarities in the behavior of the labor market over these two periods. The job 

flows underlying these net changes, however, show that the two periods are quite 

different. Figure 2 shows the job creation and destruction rates over the same period. 

Both recessions have relatively high rates of job destruction, with large spikes of job loss 

at or near each trough. The spike in job destruction in the first quarter of 1991 

encompasses 9.5 percent of employment. The spike in job destruction in 2001 is not as 

great (8.2 percent of employment), but persists over three quarters. Job destruction 

 6

 Based on calculations from the CES, private employment declines 1.1 percent during the 1990-91 

recession, as compared to an average decline of 2.7 percent for the previous postwar recessions.

8
declines in both recovery periods, but remains relatively high for a longer period in the 

earlier recovery. After the 1991 trough, the job destruction rate stays above its average 

rate (7.5 percent) during the subsequent expansion until the third quarter of 1992. 

Following the 2001 trough, the job destruction rate falls this rate after only two quarters, 

and continues to fall thereafter. Job creation is markedly different during the two 

recessions and recoveries. Between 1990 and 1992, the job creation rate deviates little 

from 8.2 percent of employment—its average during the subsequent expansion. Job 

creation peaks at the end of 1999, over a year before the onset of the recession. As the 

business cycle peaks, the job creation rate continues to decline throughout the recession 

period, and save for a brief increase in early 2002, continues its slide until net 

employment growth picks up again in mid-2003. Thus, the two “jobless” recoveries are 

quite different, with the earlier period characterized by a relatively slow return of job 

destruction rates to expansion-era levels and the later period characterized by a large,

persistent drop in job creation. Furthermore, the job flow movements during the latest 

downturn depart dramatically from the conventional wisdom that employment 

fluctuations are driven primarily large episodes of job destruction. In this sense, the last 

recession appears unique. 

3.B. Patterns across Industry 

While the comparison of job flows during the last the last two recessions is

interesting in its own right, it is difficult to draw broader conclusions about the labor 

market from the findings. The data only span the last 15 years, so one cannot make 

comparisons to earlier periods without appealing to research with other data sources 

(such as the LRD). Appealing to such research, however, is not a trivial comparison, 

9
since most earlier work focuses on manufacturing. My evidence thus far suggests that the 

latest recession differs from others with its long persistent drop in job creation, but this 

finding could be entirely an artifact of using nonmanufacturing data—research by Foote 

(1998) lends credence to this worry. Thus, it is important to know whether the drop in job 

creation occurs primarily in manufacturing or nonmanufacturing industries. 

Figure 3 presents the most basic evidence to answer this question. The two panels 

plot manufacturing versus nonmanufacturing job creation and job destruction, 

respectively. In short, while the magnitudes rates may be different, both manufacturing 

and nonmanufacturing industries display similar job flow patterns over the sample period. 

In both cases, there is a spike in job destruction during each recession, a slow decline in 

job destruction following the 1990-91 recession, and large, persistent drop in job creation 

between 2001 and 2003. The latter finding is the most notable, illustrating that the recent 

drop in job creation is not predominantly a nonmanufacturing phenomenon. 

The next question, then, is whether the drop in job creation occurred among a 

group of industries (regardless of manufacturing or nonmanufacturing designation) or 

occurred across a broad range of sectors. To answer this question, I pool job creation 

rates over this period for the 92 three-digit NAICS industries. I then regress the log of 

these rates on an industry fixed effect and an interaction of the fixed effect with a dummy 

variable, Tt, equal to 1 for quarters between 2001:1 and 2003:2 and zero otherwise. I use 

the log of the job creation rate because wide variations in trend rates across industries 

make it difficult to compare equal percentage point changes for different industries. 

Formally, the regression is 

(3) Cit i i

Tt it ln = α + β + ε . 

10
Given this specification, the βi coefficients estimate the log point deviation in job 

creation from its mean for industry i during the 2001-03 period. One concern is that 

movements in an industry’s job creation trend may bias βi upward or downward. To 

control for such changes, I rerun the regression using the (log) deviation of job creation 

from its Hodrick-Prescott trend as the dependent variable.7

 Finally, as a comparison, I 

rerun the regression to estimate the change in job creation during the 1990-92 period by 

letting Tt equal 1 for the quarters 1990:2-1992:3 rather than for 2001:-2003:2. 

I summarize the results by presenting the employment-weighted distribution of 

the βi coefficients across industries for each specification. Figure 4 illustrates the 

distributions in three panels. In each panel, the horizontal axis represents the log point 

change, so that a reading of -0.10 represents a 10 percent decline in the job creation rate. 

The top panel presents the kernel density function of the coefficients overlaid upon the 

histogram of their distribution. While a few industries exhibited above-average job 

creation during the downturn, most experienced declines between 3 and 35 percent. For 

the entire private sector, the decline in job creation was 12.5 percent, and in 

manufacturing, the decline was 22.1 percent. The middle panel compares the kernel 

density of the 2001-03 downturn to that of the 1990-92 downturn. The distribution of 

changes in industry job creation is well to the right for the earlier period, with most 

industries experiencing above-average job creation rates. Job flows, however, have a 

declining trend over the sample period, so a better comparison lies within the bottom 

panel, which illustrates the distribution of industry job creation changes relative to their 

H-P filtered trends. For the 1990-92 period, the distribution is practically degenerate, 

 7

 To estimate these trends, I use a relatively high smoothing parameter value of λ = 105, given that a lower 

parameter value may over-predict changes in the trend over such a short time series. 

11
with nearly all industries exhibiting job creation rates close to their trend. For the 2001-

03 period, though, it is still the case that most industries exhibit a decline in job creation 

relative to trend, with the average drop being 3.3 percent below trend. 

In summary, the large, persistent decline in job creation during the last downturn 

occurs across a wide variety of industries in both manufacturing and nonmanufacturing. 

The declines are pervasive, and persist even after controlling for a declining trend in job 

flows. This suggests that the decline in job creation during the 2001-03 downturn, which 

contrasts with earlier findings that recessions are generally driven by large movements in 

job destruction, is not merely an artifact of examining newer, nonmanufacturing data. 

4. The Relevance of the Recent Downturn

4.A. Long Run Declines in Aggregate Volatility 

While the drop in job creation during 2001-03 appears unique and pervasive 

across most industries, the relatively short time series makes it unclear whether or not the 

decline represents a dramatic shift in the cyclical behavior of job flows. Ideally, one 

would want comparable private sector job flows that date back over several business 

cycles. Such data does not exist for the private sector, but is available for manufacturing. 

With my spliced series of manufacturing data from the BED and Davis-Haltiwanger 

estimates, I have a time series of comparable job flow estimates for manufacturing that 

span the entire postwar period and allow me to put the recent job flow dynamics into a 

historical perspective. Given the strikingly similar patterns observed across industries, 

particularly the drop in job creation, one can generalize (with a degree of caution) the 

following manufacturing results to the broader labor market. 

12
The spliced manufacturing estimates are in Figure 5. Several findings stand out in 

the figure. First, job destruction in the latest recession exhibits a pattern similar to earlier 

recessions, though the magnitude of its spike is lower than in previous recessions. 

Second, the drop in job creation in 2001-03 is unique compared to the rest of the postwar 

period. Job creation rates are at their lowest during this time. In addition, they remain low 

well after the end of the recession. This is in contrast to recessions prior to 1990, where 

the beginnings of a recovery were characterized by sharp increases in job creation. The 

absence of such a spike is likely related to a declining importance of temporary layoffs 

discussed by Groshen and Potter (2003) and Aaronson, Sullivan and Rissman (2004). A 

decreased role for temporary layoffs implies there will be less recalls during the recovery 

period, and hence, a lower likelihood of a sharp rise in job creation. Finally, the overall 

pace and volatility of job flows decline throughout the entire postwar period. 

Figure 6 highlights this latter point. It plots the time series of trend and actual 

excess reallocation.8

 Excess reallocation is the sum of job creation and destruction less 

the absolute value of net growth, so it measures the churning of employment in excess of 

what one would need to generate the observed growth rate. Excess reallocation 

consistently trends downward from the early 1960’s forward, from a peak of 12.5 percent 

in 1961 to a low of 8.0 percent by 2005. 

Table 3 quantifies the decline in job flows and job flow volatility over the period. 

The decline in volatility is particularly interesting—the standard deviations of job 

creation and job destruction both decline over time, implying that decreasing volatility 

accompanies the declining job flow rates. More notable, however, is that the volatility of 

job destruction falls faster than that of job creation. As a consequence, the relative 

 8

 The trend uses a Hodrick-Prescott filter with a smoothing parameter of λ = 1600. 

13
volatility of job creation increases and the negative correlation between growth and 

reallocation weakens over time. This suggests that, at least in manufacturing, the relative 

importance of job creation over the business cycle has increased, and that the image of 

recessions as large episodes of job destruction amid a period of overall turbulence is less 

applicable for more recent years. 

Thus, while the large, persistent drop in job creation during the 2001-03 downturn 

is unique among the postwar period, it appears to be part of a broader trend of decreasing 

labor market volatility and diminished occurrences of large episodes of job destruction. 

All of this ties into the research on the declining volatility of aggregate growth by Kim 

and Nelson (1999), McConnell and Perez-Quiros (2000), Blanchard and Simon (2001), 

and Stock and Watson (2003). These studies find that the volatilities of aggregate 

measures of output, consumption and employment growth have declined in the United 

States since the mid 1980’s. There is a debate, however, on whether this decrease occurs 

as a break in the pattern of volatility or as part of a declining trend. This debate is 

complicated by a finding of an increase in firm-level volatility over the same period by 

Comin and Philippon (2005). In terms of employment growth, my findings only further 

complicate the debate. Figure 7 shows the net employment growth for my spliced 

manufacturing series during the postwar period. Consistent with the research on 

aggregate volatility, there is a noticeable and particularly abrupt drop in the volatility of 

growth in the mid-1980’s. Yet, my evidence on the underlying job flows shows a 

declining trend in both their magnitude and volatility beginning in the early 1960’s. It is 

unclear why there exists such a disparity in the timing. It is also unclear how one can 

reconcile the declining trend in job reallocation with the increase in firm-level volatility, 

14
though changes in the patterns of entry and exit and the behavior of small versus large 

firms no doubt play a role.9 Reconciling the aggregate, firm-level and job flow evidence 

is beyond the scope of this paper, and remains an interesting avenue for future research. 

4.B. Cyclical Movements in Hires and Separations 

Closely related to job flows are worker flows, i.e., worker hires and separations. 

In fact job flows, which measure the net employment change at the establishment level 

for a given period, are a subset of worker flows, which measure all employment changes 

at an establishment. Consequently, the cyclical behavior of hires and separations are an

important part of labor market dynamics. Recent work by Hall (2006) and Shimer (2005) 

studies the cyclical patterns of separations and the job-finding rate. Using data on gross 

worker flows, they conclude that since separations appear acyclical, employment 

fluctuations are primarily driven by movements in the job-finding rate. Given the close 

relation of the job-finding rate to hires, and the close relation of hires to job creation, one 

might conclude that my evidence only reinforces these findings. Two important caveats 

are in order, however, when interpreting my results in the context of worker flows. 

First, the last recession appears truly unique. This is important to note for worker 

flows because arguably the most comprehensive data source on hires and separations 

(which Hall, Shimer, and others use as part of their studies), the BLS Job Openings and 

Labor Turnover Survey (JOLTS), begins in December 2000, just prior to the start of the 

last recession. Thus, while the JOLTS present a thorough picture of worker flows during 

the last downturn, it is difficult to make inferences from it for earlier periods. Figure 8 

plots the quarterly rates of hires and separations calculated from publicly available 

 9

 Comin and Philippon (2005) perform their analysis using a sample of publicly traded firms from the 

Compustat database. This sample excludes smaller firms, which tend to account for much of the volatility 

observed in job flow data. 

15
JOLTS data.10 Hiring, like job creation, drops considerably during the recession. It 

remains low until mid-2003, and then, like job creation, increases from late 2003 on. It 

makes sense that that both job creation and hiring would move together because the 

measures are closely related. It also suggests that hiring, like job creation, had a dramatic 

shift in its cyclicality during the last recession. Separations and job destruction, however, 

have somewhat different patterns. Both increase during the 2001 recession, though 

separations do not exhibit the large spike seen with job destruction. Both also remain low 

throughout the “jobless recovery” period. From there on, however, the two series move in 

opposite directions—job destruction continues its decline, while separations increase 

considerably. 

This decoupling of separations and job destruction brings me to the second

caveat: while hires may be analogous to job creation, separations are not analogous to job 

destruction. Why does the latter analogy not hold? For one, the majority of separations 

(averaging 54 percent in the JOLTS data) are worker quits, which are procyclical. Only 

about a third of separations are layoffs, which, given their propensity for episodic 

increases during downturns, correspond better with the notion of job destruction. The rise 

in separations during the 2001 recession is driven by a rise in layoffs, while the rise in 

separations beginning in 2003 is driven by a rise in quits. This distinction explains the 

observed acyclicality of separations. Quits tend to be procyclical because of on-the-job 

search, and consequently often reflect the employer-to-employer transitions studied by 

Nagypal (2004) and Fallick and Fleischman (2004). Both studies show that these 

transitions, and their cyclicality, are large part of labor market dynamics, though their 

 10 The data are available at https://www.bls.gov/jlt/home.htm. 

16
evidence does not span back further than the mid-1990’s.11 Thus, it is difficult to make 

inferences about the cyclicality of separations for earlier periods. Given my evidence on 

job destruction, and the evidence of Groshen and Potter (2003) and Aaronson et al. 

(2004), it is likely that layoffs have become less volatile over time, and their relative 

importance for cyclical employment fluctuations has declined. The behavior of quits is 

less clear. Have quits, and for that matter employer-to-employer transitions, become more 

cyclical, as seems to be the case with hiring and job creation? If so, the observed 

acyclicality of separations may be a recent phenomenon, with earlier periods 

characterized by countercyclical movements in separations. Instead, what if the 

cyclicality of quits and employer-to-employer transitions has not changed, but their 

magnitudes and volatility have undergone declining trends? If this were true, then 

separations are likely acyclical in earlier periods. Understanding the behavior of quits 

and, more importantly, employer-to-employer transitions will shed light on the relative 

importance of separations, the job-finding rate, and other labor market variables in 

accounting for cyclical employment fluctuations. Further research on these dynamics will 

also aid in linking my evidence on job flows to a broader picture of labor market 

dynamics. 

5. Conclusions 

On the surface, employment patterns during the latest economic downturn seem

very similar to those of the previous downturn—there are large losses during the 

recession followed by a stagnant, “jobless” recovery. Yet, the underlying patterns of job 

creation and destruction show two different stories. Following the 1990-91 recession, 

 11 The gross flows data of the Current Population Survey only have estimates of employer-to-employer 

transitions back to 1994. 

17
employment growth is slow to recover primarily because of a slow return of job 

destruction to expansion-period rates. Following the 2001 recession, job destruction 

returns to pre-recession levels relatively quickly, but a large decline in job creation 

persists well into 2003. This evidence comes from a previously unexploited source of job 

flow data that, unlike most sources for previous research, includes both manufacturing 

and nonmanufacturing establishments. The drop in job creation occurs in both sectors,

and a more-detailed industry analysis shows that nearly all industries have job creation 

rates below trend for the 2001-03 period. 

Such a large, pervasive drop in job creation during this time is notable because it 

departs from previous evidence (and the conventional wisdom) that cyclical fluctuations 

in employment are driven primarily by large, episodic increases in job destruction. With a 

spliced series of manufacturing job flows dating back to 1947, I show that this was 

indeed the case in every recession prior to 2001—in this sense, the last recession is truly 

unique. Furthermore, it appears that the decline in job creation is part of a long-term

decline in both the magnitude and volatility of job reallocation where the relative 

volatility of job creation to job destruction has increased over the period. 

The decline in job reallocation is closely related to research on an observed 

decline in aggregate volatility. This literature finds a drop in the volatility of output, 

consumption, and employment growth in the mid-1980’s. I find that while the volatility 

of net growth in manufacturing drops abruptly in the mid-1980’s, the level and volatility 

of the underlying job reallocation begins their decline in the early 1960’s. This only adds 

to the puzzle of understanding the decline in aggregate volatility, particularly when one 

considers the finding of increasing firm-level volatility by Comin and Philippon (2005). 

18
Finally, the decline in job reallocation and the uniqueness of job flows in the 

latest downturn have implications for the current debate on the role of separations versus 

the job-finding rate in driving cyclical employment fluctuations. Limited knowledge on 

the behavior of quits and employer-to-employer transitions in earlier periods makes it 

difficult to infer what the decline in job reallocation and the uniqueness of the latest 

recession imply about the cyclicality of separations (and whether this cyclicality has 

changed over time). Further research, especially studies on the employer-to-employer 

transitions that lie at the center of the relationship between job flows and worker flows, 

will aid in the understanding of the long-term behavior of worker flows and provide a 

better understanding of the labor market overall. 

Appendix

A. Linking the 1990-92 BED Data 

Implementation of the “Multiple Worksite Report” (MWR) to state administrative 

records in the early 1990’s caused serious complications to the BED linkage process. The 

BLS implemented the MWR so that multi-establishment firms could easily report the 

employment and payroll of their separate establishments. Prior to the MWR, firms in 

many states reported their multiple establishments as a single record. The MWR changed 

that, but when it did so, its restructuring of administrative records (which involved 

breaking out the single records into their individual establishments) was not fully 

recorded by every state. Consequently, the BLS did not have the necessary information to 

link what were in reality continuous units. This created large overstatements of opening 

19
and closing establishments in the first quarter of 1991, the second quarter of 1992, and 

the first quarter of 1993. 

I correct for these overstatements by using unique characteristics of the MWR 

implementation process. First, since the MWR implementation occurs at the state level, I 

only need to focus on the affected states. Second, firm identifying codes do not change 

during the MWR implementation, only the codes for the individual reporting units 

change (this is not necessarily true of other administrative changes). Third, since these 

changes are theoretically only changes in paperwork, there should be no movement of 

employment across industries or locations, which sometimes occurs in the data during 

corporate mergers and other account restructurings. Finally, the administrative data have 

a fine level of geographic and industry detail (county level, and either 4-digit SIC or 6-

digit NAICS, respectively). Large employment fluctuations at these levels of detail are 

relatively rare and thus easily identifiable in the data. 

Given these characteristics, I use a three-step process. The first step calculates job 

flows using the standard BED methodology. From this, I take the subset of 

establishments identified as openings or closings. The second step uses a grid search for 

openings and closings with identical firm identifiers by county and detailed industry I 

assume that these records are the result of the MWR implementation and match them. In 

the cases where there are multiple openings and multiple closings within the same cell (as 

opposed to one closing and one or multiple openings), I match probabilistically based on 

the employment level of each record. The final step recognizes that, in practice, some

new records will have different industry codes than their predecessor. It takes the 

20
remaining unlinked records with identical firm identifiers and attempts to match within 

counties only. This last step produces less than 10 percent of the total matches I identify. 

My approach is not without risks. First, there exists the possibility of producing 

false matches of truly opening and closing establishments. I am not too concerned with 

this possibility since the false match would have to occur among opening and closing 

establishments within both the same firm and the same county, an occurrence that is 

extremely rare. Second, there exists the possibility that I miss links that occur either 

within firm accounts and across counties or across entirely different firm account 

identifiers. Without predecessor or successor record information, I cannot identify these 

matches without increasing the chances of a false match among other records, so some 

small potential for missed links remains. 

Table A.1 lists the results of my matching strategy for the three quarters of 

interest. The matches significantly reduce employment changes at opening and closing 

establishments, and slightly increase employment changes at continuing establishments, 

(newly-matched records often have legitimate changes in employment during these 

quarters). The first quarter of 1991 has the largest reduction in openings and closings, 

while changes to the first quarter of 1993 are relatively modest. 

B. GMM Estimation of Early Manufacturing Job Flows 

In order to produce a long time-series of manufacturing job flows, I splice the 

manufacturing estimates from the BED to a merged series of LRD and LTS data created 

by Davis and Haltiwanger (1999), using a GMM estimation strategy that matches key 

moments of the estimated and original data. The estimation minimizes the standard 

quadratic function, 

21
(A.1) ′ min( ( ) − ) ( ( ) − ) s d s d M θ M W M θ M

θ . 

In this specification, θ represents the matrix of α and β parameters from equation (1), 

(θ )is the vector of moments for the spliced series to be estimated, and s M d M is the 

vector of moments from the actual data listed in Table 1. I use the parameter estimates to 

produce predicted values for the 1947-90 period. For 1990 forward, I use the original 

BED manufacturing estimates. 

The relatively complex nature of the variances and correlations used lead the 

minimization function to be highly nonlinear in its parameters. Finding a unique solution 

is difficult, so I use an overidentified model with seven moments to optimize its six 

parameters. I list the final parameters in Table A.2. The estimates are unique and match 

the original moments exactly. I check their robustness, in terms of how well the estimated 

job flow series preserve the cyclical patterns of the original series, using several 

variations on the moments I chose. These variations include moments calculated only 

from the LRD portion of the Davis-Haltiwanger series (i.e., 1972-1998), moments 

calculated only from the period of BED-LRD overlap (i.e., 1990-98), and an exactly 

identified model of six moments that excludes the variance of excess reallocation. The 

exactly identified model fails to produce a unique solution, implying that at least one of

the moments chosen provides no power to the estimation procedure. The other checks

have unique solutions, but produce job flow estimates that deviate dramatically from the 

original series. In contrast, the estimates from the specification I use closely match the 

behavior of the original job flow series. Figure A.1 illustrates this with panels depicting 

the estimated time series overlapped with the original Davis-Haltiwanger series for job 

creation and job destruction, respectively.

22
References

Aaronson, Daniel; Rissman, Ellen R. and Sullivan, Daniel G. “Can Sectoral Reallocation 

Explain the Jobless Recovery?” Economic Perspectives, Federal Reserve Bank of 

Chicago, Second Quarter 2004, 28(2), pp. 36-49.

Baldwin, John; Dunne, Timothy and Haltiwanger, John C. “A Comparison of Job 

Creation and Job Destruction in Canada and the United States.” Review of Economics and 

Statistics, August 1998, 80(3), pp. 347-356. 

Blanchard, Oliver J., and Simon, John “The Long and Large Decline in U.S. Output 

Volatility.” Brookings Papers on Economic Activity, Vol. 1, 2001, pp. 135-164. 

Comin, Diego, and Philippon, Thomas, “The Rise in Firm-Level Volatility: Causes and 

Consequences.” National Bureau of Economic Research (Cambridge, MA) Working 

Paper No. 11388, May 2005. 

Davis, Steven J. and Haltiwanger, John C. “Gross Job Creation and Destruction: 

Microeconomic Evidence and Macroeconomic Implications,” in Oliver Jean Blanchard 

and Stanley Fischer, eds., NBER Macroeconomics Annual 1990. Cambridge, MA: MIT

Press, 1990, pp. 123-168. 

__________. “Gross Job Creation, Gross Job Destruction and Employment

Reallocation.” Quarterly Journal of Economics, August 1992, 107(3), pp. 819-63. 

__________. “On the Driving Forces behind Cyclical Movements in Employment and 

Job Reallocation.” American Economic Review, December 1999, 89(5), pp. 1234-1258. 

Davis, Steven J.; Haltiwanger, John C. and Schuh, Scott. Job creation and destruction. 

Cambridge, MA: MIT Press, 1996. 

Eberts, Randall W. and Montgomery, Edward. “Cyclical versus Secular Movements in 

Employment Creation and Destruction.” National Bureau of Economic Research 

(Cambridge, MA) Working Paper No. 5162, June 1995. 

Faberman, R. Jason. “What’s in a City?: Understanding the Micro-Level Dynamics 

Underlying Urban Growth.” Bureau of Labor Statistics Working Paper No. 386, 

December 2005. 

Fallick, Bruce and Fleischman, Charles A. “Employer-to-Employer Flows in the U.S.

Labor Market: The Complete Picture of Gross Worker Flows,” Federal Reserve Board of 

Governors, mimeo, 2004. 

Foote, Christopher. “Trend Employment Growth and the Bunching of Job Creation and 

Destruction,” Quarterly Journal of Economics, August 1998, 113(3), pp. 809-834. 

23
Groshen, Erica L., and Potter, Simon. “Has Structural Change Contributed to a Jobless 

Recovery?” Current Issues in Economics and Finance, August 2003, 9(8). 

Hall, Robert E., “Job Loss, Job Finding, and Unemployment in the U.S. Economy over 

the Past Fifty Years,” forthcoming, 2005 NBER Macroeconomics Annual. Cambridge, 

MA: National Bureau for Economic Research, 2006. 

Kim, Chiang-Jin, and Nelson, Charles R. “Has the U.S. Economy Become More Stable?

A Bayesian Approach Based on a Markov-Switching Model of the Business Cycle.” 

Review of Economics and Statistics, November 1999, 81(4), pp. 608-616. 

McConnell, Margaret M., and Perez-Quiros, Gabriel. “Output Fluctuations in the United 

States: What has Changed Since the Early 1980’s?” American Economic Review, 

December 2000, 90(5), pp. 1464-1476. 

Nagypal, Eva. “Worker Reallocation over the Business Cycle: The Importance of Job-toJob Transitions.” Northwestern University, mimeo, 2004. 

Pivetz, Timothy R.; Searson, Michael A. and Spletzer, James R. “Measuring Job Flows 

and the Life Cycle of Establishments with BLS Longitudinal Establishment Microdata,” 

Monthly Labor Review, April 2001, 124(4), pp. 13-20 . 

Shimer, Robert, “Reassessing the Ins and Outs of Unemployment,” University of 

Chicago, mimeo, 2005. 

Spletzer, James R.; Faberman, R. Jason; Sadeghi, Akbar; Talan, David M. and Clayton, 

Richard L. “Business Employment Dynamics: New Data on Gross Job Gains and 

Losses,” Monthly Labor Review April 2004, 127(4), pp. 29-42. 

Stock, James H., and Watson, Mark W. “Has the Business Cycle Changed and Why?” in 

Mark Gertler and Kenneth Rogoff, eds., NBER Macroeconomics Annual 2002. 

Cambridge, MA: MIT Press, 2003, pp. 159-218. 

24
Figure 1. Private Sector Net Employment Growth Rates 

-1.5

-1

-0.5

0

0.5

1

1.5

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005

Notes: Net growth rates come from author’s calculations using the BED. Shaded areas represent NBERdated recessions.

Figure 2. Private Sector Job Creation and Job Destruction Rates 

6.4

6.8

7.2

7.6

8

8.4

8.8

9.2

9.6

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005

Job Destruction Rate

Job Creation Rate

Notes: Job flow rates come from author’s calculations using the BED. Shaded areas represent NBER-dated 

recessions.

25
Figure 3. Manufacturing vs Nonmanufacturing Job Flows 

(a) Job Creation Rates

3.0

3.5

4.0

4.5

5.0

5.5

6.0

6.5

7.0

7.5

8.0

8.5

9.0

9.5

10.0

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005

Nonmanufacturing

Manufacturing

(a) Job Destruction Rates 

3.0

3.5

4.0

4.5

5.0

5.5

6.0

6.5

7.0

7.5

8.0

8.5

9.0

9.5

10.0

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005

Nonmanufacturing

Manufacturing

Notes: Job flow rates come from author’s calculations using the BED. Shaded areas represent NBER-dated 

recessions.

.

26
27

Figure 4. The Distribution of (log Point) Changes in Job Creation by Industry

(a) 2001q2 – 2003q2 Downturn, Histogram and Kernel Density

-.50 -.45 -.40 -.35 -.30 -.25 -.20 -.15 -.10 -.05 0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.50

beta01

0

5

10

15

D

e

n

s

i

t

y

(b) Kernel Densities of Both Downturns, Unconditional Changes

0.00

0.02

0.04

0.06

0.08

0.10

0.12

-0.5 -0.4 -0.3 -0.2 -0.1 0.0 0.1 0.2 0.3 0.4 0.5

2001q1-2003q2

1990q2-1992q3

(c) Kernel Densities of Both Downturns, Deviations from H-P Trend

0.00

0.02

0.04

0.06

0.08

0.10

0.12

-0.5 -0.4 -0.3 -0.2 -0.1 0.0 0.1 0.2 0.3 0.4 0.5

2001q1-2003q2

1990q2-1992q3

Notes: Figures plot the employment-weighted histogram and kernel densities of the log point changes in the 

job creation rate across 92 3-digit NAICS industries for the noted periods. Estimates are conditional on

industry, and in panel (c) they represent deviations from industry-level Hodrick-Prescott trends that use a 

smoothing parameter of λ = 105

.
28

Figure 5. Job Creation and Job Destruction in Manufacturing, 1947 - 2005 

3.04.05.06.07.08.09.0

10.0

1947 1952 1957 1962 1967 1972 1977 1982 1987 1992 

Job Destruction Rate Job Creation Rate

Notes: Estimates come from author’s calculations from the BED and a spliced series of LTS and LRD manufacturing job flow data. Thtogether using GMM estimation to match key moments of the data. See text and Davis and Haltiwanger (1999) for details. Shaded area

recessions. 
Figure 6. Excess Reallocation in Manufacturing, Actual and Trend, 1947 – 2005. 

7.0

8.0

9.0

10.0

11.0

12.0

13.0

14.0

15.0

1947 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 2002

Excess Reallocation Rate H-P Trend

Notes: Excess reallocation estimates come from the spliced manufacturing series described in the text. The 

trend is Hodrick-Prescott filtered with a smoothing parameter of λ = 1,600. Shaded areas represent NBERdated recessions. 

Figure 7. Net Employment Growth in Manufacturing, 1947 – 2005. 

-6.0

-5.0

-4.0

-3.0

-2.0

-1.0

0.0

1.0

2.0

3.0

4.0

5.0

6.0

1947 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 2002

Notes: Net growth rate estimates come from the spliced manufacturing series described in the text. Shaded 

areas represent NBER-dated recessions.

29
Figure 8. JOLTS Hires and Separations, 2001:1 –2005:1 

8.0

8.5

9.0

9.5

10.0

10.5

11.0

11.5

12.0

2001 2002 2003 2004 2005

Hires Rate

Separations Rate

Notes: Estimates are quarterly rates and come from publicly available data from the Job Openings and

Labor Turnover Survey (JOLTS). The shaded area represents the NBER-dated recession. 

30
Figure A.1. Original vs. Spliced Manufacturing Job Flow Series 

(a) Job Creation 

3.0

4.0

5.0

6.0

7.0

8.0

9.0

10.0

11.0

1947 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 2002

Original LRD-LTS Data Spliced Data

BED Data

(b) Job Destruction 

3.0

4.0

5.0

6.0

7.0

8.0

9.0

10.0

11.0

1947 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 2002

Original LRD-LTS Data Spliced Data

BED Data

Notes: Panels compare series of spliced BED-LRD-LTS estimates derived from a GMM model to the 

original merged LRD-LTS series created by Davis and Haltiwanger (1999). In each panel, the dashed 

vertical line depicts the break in the estimated series where the actual BED data are used from there 

forward. 

31
Table 1. Moments Matched in GMM Splicing Estimation

Moment Data Source Time Period Value 

Mean Ct BED 1990:2 – 1998:4 5.165 

Mean Dt BED 1990:2 – 1998:4 5.235 

Variance of C Nett CES 1947:1 – 1998:4 1.996 

Variance of Excess Reallocation, XRt LTS-LRD 1947:1 – 1998:4 2.899 

Relative Volatility, var( )

var( )

t

t

NEG

POS

LTS-LRD 1947:1 – 1998:4 0.658 

Correlation of POSt, NETt LTS-LRD 1947:1 – 1998:4 0.662 

Correlation of NEGt, NETt LTS-LRD 1947:1 – 1998:4 -0.794 

Notes: Estimates are based on author’s calculations from the noted data source.

Table 2. Job Flow Summary Statistics and Correlations 

Sector Manufacturing 

Private 

Employment

Period 1947 – 2005 1947 – 1972 1972 – 1990 1990 – 2005 1990 – 2005

Data Source Spliced 

Series 

Spliced 

LTS-LRD 

Spliced 

LTS-LRD BED BED

Mean

 Ct 5.7 6.4 5.4 4.8 7.9

 Dt 5.8 6.3 5.7 5.2 7.6

Standard Deviation

 Ct 0.95 0.79 0.57 0.54 0.45

 Dt 1.06 1.09 1.03 0.66 0.52

ρ(Ct, Dt) 0.05 -0.53* -0.25 0.06 0.44* 

ρ(Nett, Realloct) -0.12 -0.35* -0.54* -0.20 -0.17 

Note: Estimates are from seasonally adjusted BED data for the private sector, and the spliced series of 

BED-LRD-LTS data for manufacturing. See text for details. 

* Significant at the 5 percent level.

32
Table A.1. Results of Early BED Match Identification

Initial Estimate Corrected Estimate

Thousands of

Employees 

Percent of 

Employment 

Thousands of

Employees 

Percent of 

Employment 

First Quarter, 1991

 Changes at Openings 5,321 6.0 2,270 2.5 

 Changes at Closings 5,462 6.1 2,103 2.4 

 Changes at Expansions 4,402 4.9 4,685 5.3 

 Changes at Contractions 7,784 8.7 8,376 9.4 

Second Quarter, 1992 

 Changes at Openings 3,156 3.6 1,887 2.1 

 Changes at Closings 2,481 2.8 1,226 1.4 

 Changes at Expansions 6,642 7.5 6,747 7.6 

 Changes at Contractions 4,310 4.9 4,401 5.0 

First Quarter, 1993 

 Changes at Openings 2,111 2.4 1,835 2.1 

 Changes at Closings 2,171 2.4 1,871 2.1 

 Changes at Expansions 4,706 5.3 4,752 5.3 

 Changes at Contractions 6,319 7.1 6,388 7.1 

Note: Listed employment changes are prior to seasonal adjustment. 

Table A.2. Parameter Values Used in GMM Splicing Estimation 

Parameter Estimate Parameter Estimate

α 0 1.424 β 0 2.049

α1 0.774 β1 0.664

α 2 -0.072 β 2 -0.097

Note: See text for description of GMM estimation strategy. 

33