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Organizational Flexibility and E m p l o y m e n t
D y n a m i c s at Y o u n g and Old Plants

Jeffrey R. Campbell and Jonas D. M. Fisher

Working Papers Series
Research Department
Federal Reserve Bank of Chicago
December 1998 (WP-98-24)

■

FEDERAL RESERVE BANK
OF CHICAGO

O rg a n iz a tio n a l F le x ib ility a n d E m p lo y m e n t
D y n a m ic s a t Y o u n g a n d O ld P la n ts

Jeffrey R. C am p bell**

Jonas D .M . Fisher*

O ctober, 1998*

A bstract
There are significant differences in the dynamics of employment over the business cycle
between young and old manufacturing plants. Young plants are more sensitive to aggre­
gate disturbances, and they respond to them along different margins. We interpret these
differences as reflecting greater organizational flexibility at young plants due to the chang­
ing nature of a plant’s environment as it ages. In the presence of aggregate uncertainty,
differences between young and old plants’ organizational flexibility allows the model to re­
produce their distinct cyclical characteristics. Previous empirical studies show that small
firms generally respond by more to aggregate shocks than do large firms. To the extent
that small firms tend to operate young plants, our analysis suggests an alternative to con­
ventional explanations of this evidence which appeal to imperfections in credit markets.
JEL Classification: E24, E32, L16.

^University of Rochester and NBER
^Federal Reserve Bank of Chicago.
*For their helpful comments, the authors would like to thank conference participants at the 1998 Society
for Economic Dynamics and Econometric Society North American Summer Meetings and seminar participants
at the 1998 NBER Summer Institute. We especially thank Glenn MacDonald for his discussion of an earlier
version of this paper at the 1998 Econometric Society North American Winter Meetings. The National Science
Foundation provided research support through grant SBR-9730442. This paper was completed while Campbell
visited the Bradley Center for Policy Research at the University of Rochester’s William E. Simon Graduate
School of Business, and we thank this institution for its generous research support. Any views expressed herein
are those of the authors and not necessarily those of the Federal Reserve Bank of Chicago or the Federal Reserve
System.







1. I n t r o d u c t i o n
The well known stylized fact that in the aggregate the rate of job creation is less variable than the
rate of job destruction is not a feature of job creation and destruction at young manufacturing
plants. The job creation and destruction rates tabulated separately for young and old plants by
Davis, Haltiwanger, and Schuh (1996), which Section 2 of this paper considers in more detail,
reveal that the job creation and destruction rates for young plants are approximately equally
variable. In contrast, for older plants the job creation rate fluctuates much less than the job
destruction rate. Other significant differences between the two groups of plants emerge from
our analysis. The average job creation and destruction rates for young plants are higher than
the averages for old plants. Moreover, young plants’ job creation, job destruction, and net
employment rates are more variable than their counterparts for old plants. Evidently, gross job
flows at young plants behave quite differently than those at old plants.
Davis and Haltiwanger’s (1990, 1992) original finding regarding the relative variability of
job creation and destruction challenged standard business cycle theory because it is difficult
to account for in models where establishments can be aggregated into a single representative
producer, as in Campbell and Fisher (1997). Similarly, the evidence on young and old plants
challenges the body of theory that has emerged to account for the behavior of aggregate job
creation and destruction. In this paper we address this challenge by exploring the implications
of employment adjustment costs, which have been considered by Caballero and Hammour (1994)
and Campbell and Fisher (1998) to account for the aggregate evidence on gross job flows.1 Our
analysis is founded on an organizational interpretation of employment adjustment costs. In this
we follow Lucas (1967), who interprets costs of adjusting a firm’s capital stock as reflecting
the necessity of planning the integration of new capital goods into the production process, and
1Other theoretical approaches have been taken to address the empirical shortcomings of the representative
producer paradigm and these are also challenged by the empirical evidence on young and old plants. One
approach, exemplified by Mortensen and Pissarides (1994) and Caballero and Hammour (1996), focuses on
search and contracting frictions between workers and employers. A second approach shows how asymmetries
in aggregate driving processes can cause job destruction to be more volatile than job creation. Foote (1997)
Caballero (1992), Davis and Haltiwanger (1996) and Campbell and Kuttner (1996) are representative of that
literature. For a further discussion of these approaches and their relationship to the adjustment cost approach,
see Campbell and Fisher (1998).




2

Hamermesh and Pfann (1996) and Campbell and Fisher (1998) who interpret costs of adjusting
employment in a similar organizational light. An explanation for the many observed differences
between the employment dynamics at young and old plants suggests itself when one considers
the organizational nature of adjustment costs: plants find it optimal in their youth to employ
an organization which is more flexible and entails lower adjustment costs than when they are
mature.
The changing nature of a plant’s environment as it ages motivates the organizational differ­
ences between young and old plants in our model. Two features of this change are the learning
by young plants documented by Bahk and Gort (1993) and the decline in the cross sectional
variance of the growth rates of plants as they age, as shown by Dunne, Roberts, and Samuelson
(1989). The theoretical literature on organizational choice predicts that the greater opportuni­
ties for innovation which young plants possess and the greater risk they face should both induce
their managers to choose more flexible organizations than older plants. For example, Athey and
Schmutzler (1995) show that the return to an investment in organizational flexibility increases
with an enterprise’s opportunities for innovation, while de Groote (1994) shows that the de­
gree of flexibility of the optimal production process is increasing in the risk an enterprise faces.
Young plants learning through either production or direct experimentation about an uncertain
production process are therefore more likely to invest in organizational flexibility than mature
plants that have exhausted the learning curve and operate a stable technology.2 The forms of
organizational flexibility which Athey and Schmutzler (1995) cite as useful for implementing
new process and product innovations, such as using more educated workers or using few job
classifications, can also lower the cost of changing the scale of a plant’s activity.3 These consid­
erations suggest that, when they are confronted with shocks to their environments, young and
flexible plants will respond by more and along different margins than old and inflexible plants.
2Choices regarding organizational flexibility axe distinct from choices regarding the technological flexibility
of plant design as studied by Chang (1993), He and Pindyck (1992), Fuss and McFadden (1978), Gilchrist and
Williams (1998) and Stigler (1939). The ex-ante plant design decision may influence the scope for later changes
in the organization of production at a plant. However, the incentives to change the organization of production
at a plant over its life cycle should be present regardless of the initial plant design decision.
3Simon (1991) observes that one function of organizations is to coordinate the expectations of many agents
participating in a complicated production process. The extent to which the plant exploits this opportunity is
another margin upon which a manager can vary the flexibility of a plant’s organization.




3

We develop our explanation for the pattern of gross job flows at young and old plants by
constructing a simple model of job creation and destruction over the plant life-cycle in which
young plants’ organizations tend to be more flexible than those of old plants. Our analysis
builds on the employment adjustment cost model of Campbell and Fisher (1998). In that model
aggregate gross job flows are explained by the choices made at a fixed population of plants. Since
plant entry and exit are not responsible for the empirical results which motivate our analysis, we
introduce a plant life-cycle into this model in the simplest way by assuming that plants exit at
a constant rate and are replaced at that same rate by entrants. The life-cycle of a plant consists
of two stages. After a plant is born, it experiments with the production process. During this
lea rn in g

stage, the plant can costlessly change its employment between periods. This assumption

embodies the idea that learning plants employ a flexible organization. In any given period a
learning plant’s manager can decide to end the learning stage and enter the

m a tu r e

stage by

adopting the process used in the most recent experiment. This decision is irreversible and it
leads to generally higher productivity which is less uncertain from period to period than in the
learning stage. Changing the employment of a mature plant requires paying adjustment costs.
This assumption reflects the idea that mature plants do not invest in organizational flexibility.
As in Bentolila and Bertola (1992), Bertola and Rogerson (1997) and Hopenhayn and Rogerson
(1993), these costs are proportional to the number of jobs created or destroyed. We interpret
them as costs of reorganizing the production process to accommodate a larger or smaller scale.
In the presence of aggregate uncertainty, the difference between learning and mature plants’
organizational flexibility allows the model to reproduce the observed cyclical differences between
young and old plants. The source of aggregate fluctuations we consider is changes to the real
cost of a job, which includes real wages and the cost of complementary materials inputs, such
as oil. As Campbell and Fisher (1998) show, adjustment costs of the kind considered here tend
to induce contracting plants to respond by more than expanding plants to aggregate shocks.
This microeconomic asymmetry can cause the job destruction rate to fluctuate by more than
the job creation rate in the aggregate. Here, this kind of behavior will only be observed at
mature plants, which tend to be older. Learning plants, which tend to be younger, do not
face adjustment costs, and because of this their job creation and destruction rates axe roughly




4

equally variable. In the model, job creation and destruction among young and old plants reflect
the differences between learning and mature plants. Hence, the model is capable of reproducing
the facts that job creation and destruction rates for young plants are equally volatile while for
old plants the job destruction rate is more variable than the job creation rate. Moreover, the
model is also successful at reproducing the other main differences in gross job flows between
young and old plants emphasized above.
The empirical differences between young and old plants which motivate this paper axe similar
to, but distinct from, those which motivated the theoretical literature on industry dynamics
and the plant life-cycle. Using the same data which underlies Davis, Haltiwanger, and Schuh’s
(1996) time series on job creation and destruction, Dunne, Roberts, and Samuelson (1989) found
significant cross sectional differences between young and old plants. Young plants’ grow faster,
their growth rates have a higher cross sectional variance, and they exit more frequently than their
older counterparts. Jovanovic (1982) explains the higher cross sectional variance of young plants
and their larger exit rate as the outcome of a selection process in which young plants learn about
their plant specific productivity as they age. In our model, learning through experimentation
causes young plants’ fast growth. Our work also complements Jovanovic’s (1982) explanation
for young plants greater cross sectional variance by documenting and modeling their greater
time series variance.
Our analysis is also related to the macroeconomics literature which studies the behavior of
small and large firms. Gertler and Gilchrist (1994) and Bernanke, Gertler and Gilchrist (1996)
document differences in the responses of small and large firms to monetary shocks. These
empirical studies show that output is generally more responsive to monetary shocks at small
firms than at large firms, due to a relatively large decline in inventories at small firms and
essentially no decline in inventories at large firms. A common interpretation of this pattern of
responses is that small firms are impeded in their access to credit markets while large firms are
not.4 In our analysis there are no differences in credit market access across plants. All differences
in the cyclical behavior of plants are due to differences in their organizational flexibility. To
the extent that small firms tend to operate young plants, our analysis suggests an alternative
4See F ish er (1998) for a form al artic u latio n an d evaluation of th is view.




5

explanation for the greater adjustment of inventories by small firms. That is, organizational
flexibility at small firms reduces any motive to smooth production.

The remainder of the paper is organized as follows. The next section presents a study of
Davis, Haltiwanger, and Schuh’s (1996) job creation and destruction data broken down by plant
age. Section 3 describes the model economy. Section 4 studies various parameterized versions
of the model, and Section 5 contains concluding remarks.

2. E v i d e n c e o n G r o s s J o b F l o w s b y P l a n t A g e
In this section we document how the cyclical behavior of gross job flows differs across plants
in the US manufacturing sector depending on their age. Our analysis is based on quarterly
and annual data constructed by Davis, Haltiwanger, and Schuh (1996) (hereafter DHS).5 The
sample period is 1972:11 to 1988:IV for the quarterly data and 1973-1988 for the annual data. We
find that there are significant differences in the behavior of gross job flows for young versus old
plants.6 As a basis for comparison, we begin our discussion by reviewing the cyclical properties
of gross job flows for the US. manufacturing sector as a whole. This is followed by our analysis
of plants by age.
2.1. The US M anufacturing Sector
We follow DHS by studying gross job flows in terms of rates of job creation, destruction, growth
and reallocation. For a given population of plants, the rate of job creation (destruction) in a
period, is defined as the total number of jobs added (lost) since the previous period at plants
which have higher (lower) employment in the current period compared to the previous quarter,
divided by the average of total employment in the current and previous period. The rate of job
growth is given by the difference between the rates of job creation and destruction, and the rate
of job reallocation is given by their sum. Figures 1A and IB plot these variables at the quarterly
frequency for the US manufacturing sector as a whole.7 The vertical lines in Figure 1 indicate
5See th e technical ap p en d ix of DHS for detailed inform ation a b o u t th e co n stru ctio n of th is d a ta .
6DHS briefly discuss som e of th e differences betw een young and old p lan ts described here. See T able 4.5,
p.77, T able 5.4, p.97, and th e surrounding te x t of DHS.
7A11 q u a rte rly series stu d ied here have been seasonally ad ju sted by rem oving q u arterly m eans.




6

business cycle peaks and troughs, as defined by the NBER.
These figures illustrate several well known facts about gross job flows (see, for example DHS).
First of all, the indicated rates of job creation and destruction illustrate that on an ongoing basis
relatively large numbers of jobs are either created or destroyed. In fact, as Figure IB shows,
between 8 and 14 percent of all jobs are created or destroyed every quarter. Second, the creation
rate is less variable than, and tends to co-move negatively with, the destruction rate. Third,
the reallocation rate co-moves negatively with the rate of job growth, th at is job reallocation is
countercyclical.
The statistics (standard errors in parenthesis) reported in the first column of Table 1 confirm
these impressions. For the four variables of interest this table includes means, standard devia­
tions and selected correlation coefficients. Also included in the table is the ratio of the variances
of the rates of job destruction and creation, a statistic emphasized by Davis and Haltiwanger
(1992). Notice that the excess volatility of the destruction rate relative to the creation rate
is statistically significant, as is the negative correlation between these two variables and the
negative correlation between job growth and job reallocation.8 Not surprisingly, job creation is
significantly procyclical and job destruction is significantly countercyclical.
2.2. Young and Old Plants
To study the relationship between establishment age and gross job flows, DHS measure job cre­
ation and destruction rates for plants in three different age categories. DHS (p. 225) recommend
aggregating the two categories which include the youngest establishments and we do this here.
We refer to this combination as ‘young’ plants. These plants axe typically less than about 10
years old and account for 22.5% of total employment on average over the sample period.9 We
refer to the remaining plants as ‘old’ plants.10 The bottom two rows of Figure 1 display gross
8N ote th a t th e larger variability of jo b d estru ctio n relative to jo b creation a n d th e definitions of th e jo b grow th
ra te an d th e jo b reallocation ra te im ply th a t th e covariance betw een th e la tte r two variables is negative.
9
W e aggregate th e two youngest catagories by adding th e ir jo b creation an d d e stru c tio n ra te s a fte r w eighting
th e m by th e ir to ta l em ploym ent shares. We th e n divide th e results by th e sum of th e tw o g ro u p s’ em ploym ent
shares.
10B ecause of th e sam ple design of th e underlying d a ta source th e th resh o ld betw een young a n d old changes
over tim e. T h e m inim um age of a n old establishm ent is a t least 9 years a n d a t m ost 13 years. T h ese changes
only occur a t th e end of a year.




7

Figure 1: Gross Job Flows in the Manufacturing Sector, 1972:II-1988:IV




G row th a n d R e a l l o c a t i o n
B: All P l a n t s

C: Y o u n g P l a n t s

D: Y o u n g P l a n t s

Percent

C r e a t io n a n d D e s t r u c t i o n
A: All P l a n t s

Percent

\ J ' ; \f'-.\
*Q
-)•oo

O
C
C
bD

/V
v
72

74

76

78

80

82

84

86

88

*72

74

76

E: Old P l a n t s

'ercent
4 8 1

Percent

0*

76

78

80

'V»/.
•

0yv

Vv
74

82

84

80

82

84

86

88

F: Old P l a n t s

o
N
CO
N

72

78

86

V
'''

\

"'AVvy.

pfw\

If V,
1
eo 1
*72 74 76 78 80 82 84 86 88

88

8

job flows for the young and old plants.
Figures IB and ID reveal striking differences between the creation and destruction rates
of young and old plants. The creation and destruction rates appear to be larger and more
volatile for young plants than for old plants. Among young plants, the creation rate varies
about the same amount as the job destruction rate, while for old plants job destruction is much
more variable than job creation, as in the manufacturing sector as a whole. These differences
' in variability between young and old plants are particularly evident during non-recessionary
periods. During the recoveries of 1975 —76 and 1982 —83, the creation rate for young plants
reached the levels the destruction rate did dining the preceding recession, while job creation at
older plants rose only modestly. Figures 1C and IE confirm that the differential behavior of
job creation and destruction between young and old plants carries through to job growth and
reallocation. The reallocation rate is higher and more volatile for younger plants. In addition
the rate of job growth at young plants is more volatile than at old plants. Generally, job growth
at young plants varies consistently throughout the business cycle, while job growth at old plants
seems to vary most around recessions and is relatively smooth outside of recessions.
The last two columns of Table 1 summarize this evidence. The sample statistics verify that
gross job flows are more volatile at young plants. Furthermore, they reveal that job creation
and destruction at young plants do not conform to the well-known regularities from the manu­
facturing as a whole. We cannot reject the hypotheses for young plants that job creation and
destruction axe equally variable, that these variables are uncorrelated, and that job growth and
reallocation are also uncorrelated. In contrast, the qualitative pattern of statistics for the old
plants seem in line with the evidence for all of the manufacturing sector.
Table 2 reports the results of formal tests of these differences between young and old plants.
The first row displays Wald-type statistics which test for the equality between young and old
plants of the summary statistics indicated in the column headings. This table indicates that
the average rate of job reallocation, the excess variability of job destruction compared to job
creation, the variability of job growth, the correlation between creation and destruction, and the
correlation between job growth and reallocation for young plants are all significantly different
than the analogous statistics for old plants, at very high levels of confidence.




9

2.3. Robustness
The foregoing discussion is based on data which include plant startups and shutdowns. Due
to idiosyncracies in how the data are collected, the contributions of these plants to aggregate
measures of gross job flows at the quarterly frequency is not based on direct observations but is
derived from the information on continuing plants (plants which continue to operate in successive
sample periods.) Hence, it is reasonable to be concerned about whether the differences in the
cyclical behavior of gross job flows between young and old plants just discussed are genuine
and not artifacts of the data construction procedure implemented by DHS. To address this
concern we have analyzed the analogous annual data on gross job flows by plant age, in which
direct observations on startups and shutdowns are available. We find that all the differences
highlighted above appear in the annual data as well and that these differences continue to be
statistically significant at very high levels of confidence (not shown).
It is also of interest to examine the behavior of continuing plants separately from startups and
shutdowns. This provides some guidance on how the differences between young and old plants
might be explained. For example, if the differences were primarily due to job reallocation at plant
startups and shutdowns, then an explanation based on the determinants of plant entry and exit
would seem promising. On the other hand if the young-old plant differences are present among
continuing plants then an explanation based on the determinants of employment decisions at
incumbent plants would seem more appropriate. We have analyzed gross job flows at continuing
plants using both quarterly and annual data. With two exceptions all the differences highlighted
above are reflected in the gross job flows of continuing plants. First, at the quarterly frequency
there does not appear to be a significant difference between the job growth rates of young and
old plants. However, this difference remains statistically significant at the annual frequency.11
Second, at the annual frequency the differences in the correlation between job creation and job
destruction evident in the quarterly and annual data when startups and shutdowns are included
in the data disappear when continuing plants are studied separately. The difference is still
11A t th e q u a rte rly frequency th e point estim ate of th e sta n d a rd deviation of th e ra te of jo b grow th is slightly
lower for young p la n ts th a n for old p lan ts - 1.68 (0.21) versus 1.70 (0.24). A t th e an nual frequency th e young-old
com parison is 5.05 (0.15) versus 4.17 (0.13).




10

apparent, though less pronounced, in the quarterly data for continuing plants.
To summarize, there appear to be significant differences in the behavior of gross job flows
between young and old plants. Furthermore, these differences generally do not depend on the
frequency of the data considered or on whether startups and shutdowns are included in the
analysis. The main differences between young and old plants, which we will emphasize in what
follows, are (i) the rate of job reallocation is larger at young plants, (ii) job destruction and
job creation are equally variable at young plants but job destruction is more variable than job
creation at old plants, (iii) the rate of job growth is more variable at young plants, and (iv) job
reallocation is acyclical at young plants but countercyclical at old plants.12

3. T h e M o d e l E c o n o m y
Addressing the observed differences between young and old plants’ aggregate employment dy­
namics requires a model with ongoing aggregate uncertainty as well as heterogeneity of plants
across and within age cohorts. In this section we describe a competitive equilibrium industry
dynamics model which incorporates these features in a simple way. As in Caballero (1992) and
Campbell and Fisher (1998), a fixed measure of plants which experience both idiosyncratic and
aggregate shocks populates the industry. To introduce an age distribution, we assume that
plants exit at a constant rate and are replaced at the same rate. New plants learn through
experimentation about the production process. They operate in the context of a flexible organi­
zation to facilitate the learning process. This flexibility manifests itself in it being costless for a
learning plant to change its scale of production. In any period the manager of a learning plant
can choose to adopt its most recent experiment and take her plant into the mature phase of
its life-cycle in which no more learning takes plane. In the absence of learning, mature plants
operate with a less flexible organization in which it is costly to change the scale of production.
12It is in terestin g to n o te th a t, w ith one exception, th e differences we em phasize here regarding young a n d old
p la n ts also hold if th e m anufactu rin g d a ta is divided by p la n t size (sm all p lan ts w ith less th a n 100 em ployees,
a n d large p la n ts) an d by ow nership characteristics (plants ow ned by single-plant firm s versus p la n ts ow ned by
m u lti-p lan t firm s). T h e one exception is th a t th ere is m uch less evidence in favor of differences in jo b grow th
ra te s w hen th e m an u factu rin g sector is divided along these lines. T h e sim ilarity in th e findings for p la n t size
co m pared to p la n t age are consistent w ith th e D unne, R oberts, an d Sam uelson (1989) finding th a t young p lan ts
te n d to b e sm aller th a n average.




11

The formal description of the model proceeds in three steps. We first describe the environ­
ment facing individual plants. Then we characterize and discuss the solution to the problem
faced by the manager of a

m a tu re

plant, that is a plant which has stopped learning. Here we

make extensive use of results in Campbell and Fisher (1998) (hereafter CF). Finally, we consider
the problem of a

lea rn in g

plant’s manager who must decide whether to take her plant into the

mature phase of its life-cycle by adopting the production process from its latest experiment.
3.1. The Environment
The industry is composed of a continuum of plants which produce an homogeneous good for sale
in a competitive goods market and purchase inputs in competitive factor markets. We assume
that the industry is small relative to these markets, so that its product demand and factor
supply curves are all infinitely elastic. As a consequence, equilibrium prices do not depend on
the industry’s scale of production and aggregate quantities are irrelevant for the decisions of
individual plants. This simplifies the analysis considerably since it means that gross job flows,
which are the focus of our analysis, can be calculated easily by aggregating the decisions of
individual plants that have been computed by taking prices as given.13
Plants are operated by risk neutral managers who value future profits with the constant
discount factor /5. After every period, a fraction 6 of all operating plants exit. They are replaced
at the beginning of the next period by an identically sized mass of new plants. Each plant uses
two variable factors of production which must be combined in fixed proportions: labor, which
comes in fixed shift lengths, and materials. We refer to an employee plus her accompanying unit
of materials as a job. The per-period cost of a job at time
output price, is denoted by

W t.

Although

Wt

t,

measured in units of the constant

reflects both labor and materials costs, we refer

to it simply as the wage. It is the only source of aggregate uncertainty and follows a Markov
chain over the set j w 1, W 2, . . . ,
managers are made after observing
the fact that changes in

Wt

with transition matrix n . All time
W t.

t

decisions by plant

Note that we include materials in the model to highlight

can reflect changes in the prices of inputs which complement labor

13A lth o u g h we present our m odel in p a rtial equilibrium , it can be rein terp reted in a general equilibrium
fram ew ork w ith p a rtic u la r assum ptions on tastes an d technology along th e lines considered by C am pbell an d
F ish er (1998).




12

as well as changes in the direct price of labor.
Let the output, idiosyncratic productivity level, and employment at date t of a representative
plant be denoted by

and nt, respectively. Output is produced according to

yt, z t,

Vt

where

a

=

z tn ? ,

is assumed to be strictly between 0 and 1. The strict concavity of the production

function reflects the presence of a fixed factor at the plant or a limit to a manager’s span of
control, as in Lucas (1978).
The process governing

zt

depends on whether the plant is in its learning phase or has pro­

gressed to the mature phase. As long as a plant is in its learning phase, realizations of z t are an
sequence of random variables. However, a learning plant’s manager does not observe

i.i.d .

zt

before choosing how many workers to employ, nt, and production takes place. Instead, she views
a signal of

zt, zf,

and learns

zt

only after production takes place. Let zf denote the part of

learned through production and define

z t — zfzf.

We assume that

zf

zt

and zf are independently

distributed log-normal random variables:

In

~ N (//s, of)

and

In zf ~ N (//p, of)

.

Hence,
lnzt ~ N (/^ ,o f) ,
where

=

fJ-s

+ /xp and of = of

+

of.

At the beginning of the next period, with knowledge of

zt

in hand, the manager of an

incumbent learning plant must decide whether to continue learning or to adopt the most recent
experiment and cease learning, that is take the plant into the mature phase of its life-cycle. If the
manager decides to continue learning about the production process, then it draws a new value
of

zf

and chooses a new level of employment. The flexibility of learning plants’ organizations

implies that there are no costs to changing the scale of production at these plants. If the
manager decides to take the plant into its mature phase, then the value of zt+i will equal zt. In




13

all subsequent periods the process for zt is a random walk in logarithms:

lnzt+i = lnzt + et+1.

The innovation, et+i, is assumed to be

over time and have a normal distribution trun­

i.i.d .

cated above and below by very large numbers, with mean, /zm, and variance, er^.14 At the
time of adoption, the manager also chooses employment. Because learning plants use flexible
organizations, this choice is not constrained by costs of adjusting the plant’s scale. However,
it has dynamic consequences since thereafter it is costly for the plant to change scale between
periods. If a mature plant expands, it incurs the job creation cost r c for every job added, and
if it contracts, it incurs the job destruction cost

tjl

for every job lost.15

3.2. M ature Plants
The manager’s problem is to choose employment at all dates to maximize the present discounted
value of plant profits. To study this problem, we cast it as a dynamic program. There are three
state variables: the plant’s current productivity, its employment in the previous period, and
the wage. Each period, the manager observes the state variables and chooses current employ­
ment. Using m to denote employment in the previous period, and dropping time subscripts, the
dynamic program is

g ( z ,m ,W )

=

max z n a

— W n — t (n , m ) (n — m )

(1)

+ ( 3 ( l- 6 ) E [ g ( z ',n ,W ') \z ,W } .

Here E [-(-] is the conditional expectation operator, and we use the

notation in the usual

fashion. In forming the expectation the manager understands the law of motion for

z

and

W.

Also, r (n, m) represents the per-job adjustment costs, which are measured in units of the output
14T ru n catio n is a technical condition necessary for th e analysis of th e p la n t’s dynam ic program m ing problem .
15To avoid th e possibility th a t a p la n t m anager would choose to hoard e ith er m aterials or lab o r in excess of
th e o th er, we assum e th a t th e jo b creation and d estruction costs are incurred w henever th e m i n i m u m of labor
or m aterials changes. T h is is consistent w ith our in terp retatio n of these costs as organizational.




14

Figure 2: Illustration of Optimal Employment Policies for Mature Plants

ln(z)

good:
r (n, m) =
CF show that two functions, a
d e s tr u c tio n sch edu le, n ( z , W )

=

y

n

(z,

IF) so that n

(z,

jo b c re a tio n sch ed u le , n ( z , W ) =

y (IF)

and a

jo b

(IF) z l ^ x~a \ characterize the optimal employment policy. If

lagged employment, m, is between
destroys jobs. If m is less than

r cI { n > m } — t ^ I { n < m } .

n (z,

IF) and

n (z, W )

n (z,

IF), then the plant neither creates nor

then the optimal policy specifies employment equals

IF) —m jobs are created. Similarly, if m is greater than n

optimal policy specifies employment equals

n (z , IF)

and

m —n (z,

(z,

IF) then the

IF) jobs get destroyed.

Figure 2 illustrates the optimal employment policy for a given value of IF. The job creation
and destruction schedules are both log linear in

z

common slope 1/(1 —a ).16 Since y(IF) <

the job creation schedule lies below the job

y (W ),

with intercepts lny(IF) and lny(IF) and

destruction schedule. Consider the employment decisions at three plants with identical lagged
employment but different realizations of technology in the current period. These plants are
denoted

A, B ,

and

C

in the figure. Plant

A

lies above the job destruction schedule. It chooses

current employment to be the value implied by the destruction schedule at its current level of
16T h e fact th a t th ese schedules are log linear in technology is du e to th e ran d o m w alk a ssu m p tio n on th e
evolution o f technology a t m atu re p lants. A m ean reverting process w ould intro d u ce no n lin earities in to th e
schedules w hich w ould com plicate th e analysis considerably.




15

technology. Therefore it destroys jobs at the rate equal to the vertical distance from
job destruction schedule. Plant

B

lies between the two schedules in the

leaves employment unchanged. Plant

C

A

to the

reg io n o f in a c tio n ,

so it

lies below the job creation schedule. It creates jobs at

the rate equal to the vertical distance from

C

to the job creation schedule. Because adjustment

costs penalize employment changes, we expect that increasing their size will widen the region of
inaction and reduce average gross job flows at mature plants.
We are interested in the dynamics of gross job flows generated by fluctuations in the wage.
Since the wage determines In y ( W

)

and In y ( W ) , it follows that variation in the wage leads

to fluctuations in the creation and destruction schedules of mature plants. The analysis of CF
suggests that lny(VF) will respond by less to a wage change than

In y

(W), so we expect the

creation schedule will be less variable than the destruction schedule. That is, the employment
decisions of job creating mature plants axe expected to be less volatile than the employment
decisions of job destroying mature plants. CF show, in an economy comprised entirely of ma­
ture plants, how this microeconomic asymmetry translates into greater variability of aggregate
job destruction compared to aggregate job creation. Therefore, we expect that aggregate job
destruction at mature plants will be more variable than aggregate job creation at mature plants
here.
Since the microeconomic asymmetry is crucial for the dynamics of gross job flows at mature
plants it is helpful to understand how it arises. To this end, consider the effects of a temporary
change in the wage. For an expanding plant, the total cost of the last job created is greater
than the wage because of the expected adjustment costs incurred. These additional costs, which
include the job creation cost and the expected cost of destroying that job in the future, lower the
elasticity of the total cost of job creation with respect to the wage below unity. For a shrinking
plant, the total cost of the last job retained is less than the wage because by retaining the job
the plant avoids the cost of job destruction and the expected cost of recreating that same job
in the future. The subtraction of avoided adjustment costs increases the elasticity of the total
cost of job retention with respect to the wage above unity. Since expanding and contracting
plants operate on the margins which equate the costs and benefits of adding and retaining a job,
respectively, the asymmetric responses of the total costs of job creation and retention to wage




16

changes can induce contracting plants to respond by more than expanding plants to fluctuations
in the wage.17
3.3. Learning Plants
Each period, the manager of a learning plant has one or two decisions to make, depending on
whether the plant is an entrant or an incumbent. If it is an entrant then the only choice to
make is how many workers to employ. If it is an incumbent then the manager must also decide
whether or not to adopt the technology realized from experimenting in the previous period.
Recall that this adoption decision is made before the manager observes a new productivity
signal and decides how many workers to employ in the period. Below we first describe the
employment decision of an entrant and an incumbent that chooses to continue learning. After
this we describe the employment decision of a plant which has made the decision to adopt its
most recent experiment. Finally, we describe the adoption decision.
If a plant is an entrant or an incumbent that chooses to continue learning, its current em­
ployment choice has no dynamic consequences: optimal employment maximizes only expected
profits for the period, conditional upon the productivity signal of the current experiment,
Let

nl (zs, W )

z s.

denote the expected current profits of a learning plant which receives the pro­

ductivity signal

zs

when the wage equals

W.

The associated employment policy is characterized

by a familiar static labor demand schedule. Therefore we expect the creation and destruction
decisions of entrants and incumbents which choose to continue learning to be equally variable.
Moreover, since there is no region of inaction in the employment policy for these plants we
expect their gross job flows generally to be larger on average and more variable than those at
mature plants.
Now consider the employment decision of a plant which has already decided to adopt its
most recent experiment. Due to the adjustment costs faced by a plant in its mature phase, the
manager of an adopting plant must consider the consequences of its current employment choice
on future profits. The profit maximization problem for a plant which is adopting its most recent
17T h e nondifferentiability o f th e a d ju stm en t costs a t th e p o in t of zero change im plied by p ro p o rtio n a l a d ju st­
m en t costs is crucial to th is result. W ith strictly convex a n d everyw here differentiable a d ju stm e n t costs th e jo b
creatio n a n d re te n tio n m argins are identical a n d so m ust behave identically in response agg reg ate shocks.




17

experiment, z, when the wage is

W

is

max z n a —W n +

where

g

(z, n ,

W)

(3 (1

—6) E

[g (z ' , n , W ') \z, W ] ,

is the solution to (1). CF show that

g

(z, n, W )

= z l/{1~a)v

g

(z, n ,

can be written as

W)

(n /z 1/(1~a), w

(2)

)

,

where «(•,•) is strictly concave in its first argument. Let
n

x

(3)

and rewrite (2) as

max z1/(1- Q) (x a
X

- Wx +

/? (1 - <5) E

where the random variable u is defined to equal
strate that E

[uv ( x / u , W ') | W ]

(

z

'

(4)

[uv ( x / u , W ') \W ] ) ,

j

= exp ( e / (1 —a)). CF also demon­

is a strictly concave and differentiable function of x . It follows

that (4) has a unique solution, characterized by its first order condition. Denote this optimal
choice of

x

with

y a (W ),

where the superscript ‘a ’ stands for ‘adoption’. Rescaling

y a (W )

as

specified by (3) yields y a (W ) z1^ 1-0) as the optimal employment for a plant which is adopting a
technology with productivity z. This policy resembles the policy for nonadopting learning plants
in that there is no region of inaction. Nevertheless, in our simulation analysis described below
we find that the configuration of adjustment costs at mature plants is important for determining
the impact that this policy has on the dynamics of gross job flows. Finally, note that the present
discounted value of following this policy can be written as

z 1^ l ~°^va ( W ) .

That is, the value

function associated with (4) is an increasing, homogeneous of degree 1/(1 —o;) function of z.
We now characterize the optimal adoption decision. Let

vl

(z, W ) denote the value of a

learning plant at the beginning of a period with productivity from its most recent experiment
given by z and current wage




W.

The Bellman equation associated with the optimal adoption

18

decision is then given by

vl

If

W

(z, W ) = max

(W

) ,E

[irl ( z s/, W ) + / 3 ( l - 6 ) v l ( z ', W ’) \w ] } .

(5)

is constant, then (5) is formally identical to the problem of optimal wage search without

recall presented by Sargent (1987, Chapter 2). In the search problem,

z l ^ l ~°^va ( W )

is the

‘wage’ and E [V ( z 3>, W ) | w j is the ‘unemployment benefit.’ Sargent’s arguments can be applied
directly in the constant wage case to demonstrate that the
and weakly increasing in

z.

v l (z, W )

which solves (5) is unique

Therefore, analogous to the search problem, a simple threshold

rule characterizes the optimal adoption decision: adopt any experiments
threshold. In the case where

W

z

which exceed the

is stochastic, the optimality of a time varying threshold rule

can be more easily demonstrated by using the contraction mapping theorem to characterize
v l (z,

W ).18 According to this threshold rule, a plant will adopt a production process if its

productivity from experimentation exceeds a lower bound which depends on the current wage,
z(W).

A key implication of the optimal adoption policy is that the older a plant is, the more

likely it is to have made the transition from the learning phase to the mature phase of its
life-cycle.

4. A g g r e g a t e F l u c t u a t i o n s
Due to the form of the adoption decision, mature plants will tend to be older than average, so we
expect their behavior to have a disproportionate influence on the gross job flows of old plants.
Similarly, learning plants will tend to be younger than average, and so we expect their behavior
will dominate the gross job flows of young plants. W ith this in mind, the intuition regarding
learning and mature plants’ employment policies described in the previous section strongly
suggests that the model can reproduce many of the differences between young and old plants’
gross job flow dynamics. However, the model contains several potentially confounding factors
which the intuition does not address. Caballero’s (1992) observations alert us to the possibility
18T h e p ro o f of th is assertion is available from th e a u th o rs u p o n request.




19

that variation in the distribution of plants across the state space might undo the microeconomic
asymmetries described here. Furthermore, systematic variation in plants’ optimal adoption
decisions could have a significant impact on gross job flows. To demonstrate that the differences
between learning and mature plants’ employment policies drive young and old plants’ observed
differences, we present in this section evidence from simulating several parameterized versions
of the model.
Our discussion is focused around a baseline calibration of the model in which the wage is
assumed to be

i.i.d .

over time. The baseline model reproduces the salient features of the gross

job flow observations discussed in Section 2. The rate of job reallocation is larger at young plants
that at old plants, job destruction and job creation are equally variable at young plants while
job destruction is more variable than job creation at old plants, the rate of job growth is more
variable at young plants than at old plants, and job reallocation is acyclical at young plants but
countercyclical at old plants. Because the baseline model’s driving process is

i.i.d .

over time,

its fluctuations reflect cycles at all possible frequencies equally. To determine how high and low
frequency fluctuations separately contribute to the model’s properties, we also consider several
versions of the model in which

Wt

is specified to follow a deterministic cycle. We also consider

how the magnitude of adjustment costs at mature plants influences employment dynamics in the
model. These two sets of experiments highlight how the adoption decision can have a significant
influence on the dynamics of gross job flows in the model.
4.1. P a ra m e te r Values
To implement our model we need to specify the following parameters

Plant-level parameters : a, /?, 6, fxm , a m , fj,s, a s , fxp , a p , r c, r d
Aggregate Fluctuations :

II, W \ , . . . ,

WN.

A desire for our model to mimic a “representative” manufacturing industry guides our choice of
parameter values. The elasticity of production with respect to labor input, a , is approximately




20

equal to the share of gross output paid to labor and materials.19 We set

a

to 0.85. This is a

typical value for the transportation equipment sector, which is the largest two digit manufac­
turing industry.20 The discount factor /? is set to 1.05-1/4 so that the annual real interest rate
is 5% and a period in the model corresponds to one quarter. Because the event of a plant’s
exit is independent of its characteristics, the model’s exit rate,

6,

equals the fraction of industry

employment at plants which exit between periods. This is set to equal its average value in the
DHS (1996) sample for the manufacturing sector, 0.83%.21
We adopt several different specifications for the transition matrix and support of
the baseline case, In W t approximates a Gaussian
aw -

Our calibration procedure chooses

<Jw

i.i.d .

W t.

In

process with a standard deviation of

so that the standard deviation of net employment

growth in the baseline model roughly matches its empirical counterpart from the manufacturing
sector (see Table 1). For this case, the support of W t and its transition matrix are chosen using
Tauchen’s (1984) method for approximating autoregressive processes with Markov chains.22 In
the deterministic cases, the support of

Wt

and the transition matrix are chosen so th at In W t

follows cycles of various frequencies, in each case restricted so that the unconditional standard
deviation matches

crw

from the baseline case. In all examples we study, the average value of W t

equals one.
Now consider the six parameters which characterize the learning process and the evolution
of idiosyncratic productivity. Two of these parameters are redundant, and we utilize four sub­
stantial restrictions to identify the remaining ones. First, note that both

fis

and

fip

only affect

the scale of individual plants, so they have no impact on model statistics which are scale free,
such as those considered in Table 1. We set both of these so that the average employment of a
learning plant equals one.
Our first' substantial restriction is that the employment of mature plants does not tend to
increase or decrease. In the long run, the average growth rate of employment at a mature
19T h is will n o t be exact because of th e adju stm en t costs paid by m a tu re plants.
20See, for exam ple, th e 1977 C ensus of M anufactures.
21In D H S’s n o ta tio n , th is equals th e average value of N E G D , jo b d e stru ctio n a t exiting p la n ts, over th eir
sam ple period.
22 T h e ap p ro x im atio n uses seven possible states which are equispaced over th re e sta n d a rd d ev iatio n s of th e
w age’s logarithm .




21

plant equals exp(/zm/ ( l —a ) + <r^/(2(l —o;)2)). Given a value for

we choose

fj.m

so that

this expression equals one. Holding fixed all other model parameters, the job reallocation rates
for young and old plants increase in the amount of idiosyncratic uncertainty. Our second and
third restrictions exploit this by constraining the average reallocation rates for old and young
plants in the baseline model to equal their empirical averages reported in Table 1. Note that
throughout this section we define the set of young plants to be all those plants less than 40
quarters old, which corresponds roughly to the age cut-offs applicable in the construction of the
data for young plants underlying Table l.23 Our final restriction is that productivity is more
certain among mature plants than among learning plants. We operationalize this assumption
by constraining of = 12o^. That is, three years of productivity innovations have the same
variance as one experimental draw. Our main findings are unaffected by increasing the ratio of
these variances from 12 to 20.
Finally, we need to specify the adjustment cost parameters, r c and r^. Ideally we would like
to take these values from a study of micro data. Unfortunately, this option is not available to
us. Recall that r c and

are costs of changing the number of employees at a plant. These net

adjustment costs involve disruptions to production and all other costs that are not related to the
identity of the workers but depend solely on changing the number of employees. As Hamermesh
and Pfann (1996) emphasize in their review of work on adjustment costs in factor demand, net
adjustment costs are intrinsically difficult to measure because usually they are implicit, in that
they result in lost output, and thus are not measured and reported by firms.24
Without good estimates of these costs, we choose them on

a p r io r i

grounds. Since we are

unaware of compelling evidence that one adjustment cost is significantly larger than the other,
we impose the restriction r c = r^. Since we choose the wage so that its average equals unity,
the adjustment costs can be interpreted as fractions of the flow cost of a job. It follows that the
23For b o th groups an d for th e group com prising all p lan ts in th e industry, we m easure gross jo b flows as th e
p o p u latio n c o u n te rp a rts to th e m easures defined by DHS. In p a rticu lar, th e ra te of jo b creatio n in a given period
a n d for a given gro u p of p lan ts is th e sum of all jo b s created a t job creating p lan ts in th e group divided by th e
average of cu rre n t a n d lagged to ta l group em ploym ent. Similarly, jo b d estru ctio n is th e sum of all jobs destroyed
a t jo b destroying p lan ts in th e group divided by th e average of cu rren t a n d lagged to ta l group em ploym ent.
24 O ur specification of ad ju stm e n t costs is consistent w ith th e view th a t n e t costs to em ploym ent a d ju stm en t
involve lost o u tp u t.




22

common value of the adjustment costs multiplied by the aggregate job reallocation rate provides
an upper bound for total industry adjustment costs incurred as a fraction of total variable costs.
We set r c = Td = 1/2 in our baseline model. Combined with a steady state job reallocation rate
of about 11%, this value implies that adjustment costs incurred by the industry are no more
than 5.5% of total variable costs. Because our choices of r c and

are somewhat arbitrary, we

do report results from several experiments which use different values for these costs.
Table 3 summarizes our parameter choices. The only surprising result from our calibration
exercise is the small value of ers, the standard deviation of the experimental productivity signal.
In the absence of this signal, learning plants only change their employment in response to
aggregate disturbances. The small calibrated value of a s indicates that employment at learning
plants responds so much to aggregate shocks that little idiosyncratic uncertainty is required for
them to reproduce the average job creation and destruction rates for young plants. Indeed, if we
set

ay/ =

0 and recalibrate, the calibrated value of a s triples to 0.021 while the other parameter

values change very little.
4.2. The Baseline M odel
Table 4 reports statistics from simulating the baseline calibrated model.25 The first moments
show that the model captures the salient features of the empirical evidence on average gross
job flows. By construction, the calibrated model exactly matches the average job reallocation
rates for young and old plants, however it also nearly reproduces the overall job reallocation
rate. The model’s steady state value of job reallocation equals 10.31%, while the average of
this statistic in the data equals 11.11%.26 In the data, young plants’ average job creation rate
exceeds their average job destruction rate, and the opposite is true for old plants. The model’s
first moments also display this pattern, although young plants’ grow too quickly and old plants
25 A ll th e m odel s ta tistic s in th is p ap er are based on sim ulating th e m odel for 1100 q u a rte rs, beginning from
th e ste a d y sta te . Before calculating sam ple statistics, we disposed of th e first 100 observations. W e used th e
sam e seed for all experim ents involving draw s from a random num ber generator.
26T h is u n d erp red ictio n of th e in dustry-w ide jo b reallocation ra te reflects th e u n d er rep resen tatio n o f young
p la n ts in th e m odel relative to th e U.S. m anufacturing sector. T h e average share of em ploym ent a t young p lan ts
is a b o u t 11% in th e m odel, while th e analogous sh are m easured by D avis, H altiw anger, a n d Schuh is a b o u t 21%.
T herefore, th e baseline m odel m ay also u n d erp red ict young p la n ts’ influence on o th e r overall sta tistic s.




23

shrink too slowly relative to the evidence reported in Table 1. The reasons for the positive net
growth rate for young plants and negative net growth rate for old plants are twofold. First, exit
effects all plants equally, while entry contributes to employment at young plants only. Second,
while learning plants which continue to experiment with the production process do not grow on
average, the transition from the learning phase to the mature phase typically involves plants
expanding their employment. This effect contributes to positive net growth at young plants.
The model is also successful at reproducing many of the cyclical asymmetries between young
and old plants. By construction, the standard deviation of overall employment growth in the
model is about the same as in the data, about 1.9%. As in the data, the standard deviation
of employment growth among old plants is a small amount below this figure. The analogous
statistic for young plants is considerably higher. The ratio of the variance of job destruction to
the variance of job creation is 1.61 for the industry as a whole. This ratio is lower in the model
than it is for the manufacturing sector as a whole (see Table 1), but it is close to the variance
ratio for several two digit manufacturing industries.27 The statistics for old plants reflect this
asymmetry, but those for young plants do not. The variance ratio for young plants is 1.12 and
the analogous statistic for old plants is 1.79. Finally, notice that job reallocation for young
plants is close to being acyclical, while job reallocation is strongly countercyclical at old plants.
In contrast to these successes, the model counterfactually predicts a nearly perfect negative
correlation between job creation and destruction for both types of plant. In the data this
correlation is essentially zero among young plants, and among old plants, it equals —0.33. This
value is not atypical for the correlation between overall job creation and destruction in two
digit manufacturing industries.28 The excessively strong negative correlation between the two
gross job flows in the model reflects the assumption that a single variable which changes the
industry’s labor supply curve is the sole source of aggregate fluctuations. Including other sources
of aggregate uncertainty which cause job creation and destruction to comove positively, such as
the investment specific technology shocks in Campbell’s (1998) model of entry and exit, would
probably alter this feature of the model.
27In th e ir T able 5.2, DHS re p o rt variance ratios for th e L um ber industry, th e Stone, C lay an d G lass industry,
th e T ra n sp o rta tio n E q uipm ent industry, an d th e In stru m en ts in d u stry of 1.54, 1.61, 1.71 a n d 1.85, respectively.
28See T able 5.2 o f DHS.




24

Further insight into the sources of the model’s behavior can be gained by inspecting the learn­
ing and mature plants’ optimal policies. The static labor demand schedule which characterizes
the optimal employment choice of learning plants is log-linear in
intercept equal to lny8 ( W ) = In ( W

/a ) /

zs

with slope 1/ (1 —O') and

(1 — a ) . 29 The creation and destruction schedules of

mature plants and the employment policy of plants just entering the mature phase are also loglinear with the same slope as the static labor demand schedule and intercepts given by lny (W ),
lny (W), and lnya ( W ) , respectively. These four intercepts along with the optimal technology
adoption threshold,

z(W),

completely characterize plants’ optimal policies. The first column

of Table 5 reports some useful statistics summarizing their behavior in the baseline model. To
aid with their interpretation, it also reports statistics for Pr

[zi > z { W ) ) ,

the probability of an

learning plant adopting its latest experiment. The remaining columns of Table 5 report the
analogous statistics from variations on the baseline model which we discuss below.
The means of the employment schedules reveal that lny* (PF) tends to he between lny (PF)
and lny (PF), so it is impossible to unambiguously sign the impact of job creation and destruction
costs on an individual plant’s employment. However, the fact that In y a (PF) is generally less
than ln y 8 (PF) indicates that the prospect of future adjustment costs tends to lower a plant’s
optimal employment. Because lny“ (PF) also lies between lny (PF) and lny(PF), plants which
just adopted a technology are located in the region of inaction and so will contribute very little
to aggregate employment fluctuations.
The volatility statistics highlight the features of the plant’s decision problems which drive
the results in Table 4. As in CF, lny(PF) is less variable than lny(PF), which indicates th at
the job creation schedule responds less to aggregate shocks than the job destruction schedule.
This policy asymmetry is the direct cause of the variance asymmetry between the job creation
and destruction rates for old plants. High variation in the static labor demand schedule, which
learning plants use, causes most of the employment variation at young plants. Indeed, the
standard deviation of In y8 (PF) is more than double that of In y (PF). Thus, it appears th at even
moderately sized adjustment costs can greatly decrease the variance of employment. In contrast,
changes in the policies of adopting plants contribute very little to aggregate fluctuations. The
29T h e su p e rsc rip t ls’ in y s (it7) sta n d s for ‘s ta tic ’.




25

employment policy of adopting plants is less variable than the job creation schedule, while
the technology adoption threshold barely moves at all. The decision to end a plant’s learning
phase and bring it into maturity has inherent long run consequences, so the invariance of the
technology adoption threshold to temporary factor price changes is not surprising.. When plants
do choose to adopt a technology, the employment level they choose tends to lie between the job
creation and destruction schedules for mature plants. This implies that adjustment on either
margin is not likely in the short to medium run so that transitory disturbances should have
little weight in this decision. In contrast, expanding and contracting mature plants are likely to
be adjusting employment again soon because they are already close to one of the adjustment
schedules. Because the decisions by these plants to delay job creation or destruction can be
very easily reversed in the neax future, their optimal employment decisions are more sensitive
to transitory disturbances.
4.3. Determ inistic Cycles
Because the baseline model’s aggregate driving process is

i.i.d .

over time, its fluctuations reflect

cycles at all possible frequencies equally. By simulating versions of the model in which W t follows
a deterministic cycle, we can determine how high and low frequency fluctuations separately
contribute to the model’s properties. Table 6 reports statistics from the model’s gross job
flows for two such experiments. The first three columns report statistics for all plants, young
plants, and old plants from solving and simulating the model when

Wt

follows an eight quarter

deterministic cycle. The second three columns report the analogous statistics when

Wt

follows a

twenty quarter cycle.30 The second and third columns of Table 5 contain the statistics describing
the plants’ optimal policies. Although we have solved the model using many other deterministic
cycles, these two simulations are representative of our findings.
Table 6 indicates that the gross job flow asymmetries which adjustment costs induce are
strongest when the driving process has relatively high frequency fluctuations. In particular, the
gross job flow statistics from the model with an eight quarter cycle are broadly similar to those
30 R ecall th a t our calib ratio n procedure chooses th e unconditional variance of InW t in these experim ents to
equal its value in th e baseline m odel.




26

from the baseline model and so they accord well with the data. As we noted in Section 2, the
high frequency fluctuations in the data which are not obviously associated with any particular
business cycle episode reveal most acutely the differences between young and old plants’ gross
job flows. This seems consistent with the eight quarter cycle model, since the frequency of the
driving process in this model lies somewhere between seasonal and business cycle frequencies.
The statistics describing the optimal policies in this case are also very similar with those from the
baseline model. The average values of the employment schedules’ intercepts and the technology
adoption threshold are identical in the two parameterizations. The job creation and destruction
schedules’ intercepts have standard deviations which are slightly higher than those from the
baseline model. It is unsurprising that these forward looking employment decisions are more
responsive to

Wt

in this parameterization because the process for

Wt

exhibits more persistence.

For the same reasons, the employment schedule for plants that are entering m aturity is also
more variable. By construction, the first and second moments of the frictionless employment
schedule are identical across all of the model parameterizations we consider.
Figure 3 provides additional insight into establishments’ optimal policies by graphing In W t
and the log intercepts to the four employment schedules over the cycle. So that they will all fit
on the same scale, each of the plotted series has had its average value removed. Naturally, the
employment schedules all comove negatively with In W t . The intercepts of the forward looking
employment policies, the job creation and destruction schedules and the employment schedule
of adopting plants, all lead In W t by one quarter. This lead is economically interesting but not
quantitatively significant because the forward looking employment policies’ intercepts change
very slowly near their peaks and troughs. To confirm this, Figure 4 plots net employment
growth for both young and old plants for a 24 quarter period from the simulation. It is clear
from the figure that employment growth at young and old plants move together.
When a twenty quarter cycle drives the model’s fluctuations, its behavior contrasts sharply
with that for higher frequency cycles. As CF noted, the job creation and destruction schedules of
plants facing proportional adjustment costs display little asymmetry when aggregate fluctuations
primarily reflect low frequency movements. The policy statistics in the third column of Table
5 confirm that this effect operates here as well. The job creation and destruction schedules are




27

Figure 3: Employment Policy Intercepts and the Wage in the Model with an Eight Quarter
Cycle




Time, in Quarters

28

Figure 4: Employment Growth at Young and Old Plants in the Model with an Eight Quarter
Cycle




29

both much more volatile when
quarter cycle, because

Wt

Wt

follows a twenty quarter cycle than when it follows an eight

is much more persistent. The job destruction schedule still moves

more than the job creation schedule, but the difference between the two standard deviations is
small relative to their absolute magnitudes. Therefore, old plants’ gross job flows display no
asymmetry in this case. In contrast to the old plants, job creation at young plants fluctuates
substantially

m o re

than job destruction. This reflects the fluctuations in the employment of

plants which are adopting their most recent experiment (see Table 5). These fluctuations are
more pronounced when low frequency fluctuations change W t because this decision is intrinsically
forward looking. Plants which are adopting a technology tend to be young and growing, so these
fluctuations primarily effect young plants’ job creation.
4.4. Adjustm ent Costs
The existing empirical literature provides very little guidance regarding the values of r c and

Td,

the per-job creation and destruction costs. Therefore, it is desirable to assess the sensitivity of
the model with respect to these choices. We do so with two sets of experiments. In the first
set, we vary both r c and

rd

together between 0 and 1.5 and resimulate the model using the

baseline specification for the other parameters. The results of these experiments (not shown)
are entirely consistent with the intuition developed above. To conserve space, we focus on what
these experiments reveal about asymmetries in gross job flows. When both adjustment costs
equal zero, there is little substantial difference between young and old plants. The overall job
creation and destruction rates have equal variances, as do those for old plants. Job creation at
young plants is slightly more variable than job destruction. If the adjustment costs are both set
above their baseline values of 0.5, then the ratio of job destruction’s variance to job creation’s
increases for old plants and for all plants. Young plants’ gross job flows, however, still exhibit
substantially more symmetry than those of old plants. When both r c and

rd

equal 1.5, the

ratio of job destruction’s variance to job creation’s equals 2.81 for old plants and 1.14 for young
plants.
The second set of experiments were designed to examine the separate contributions of job
creation and destruction costs to the model’s behavior. Here, we simulate the model twice. Each




30

time, we set one of the adjustment costs equal to the extreme value of zero while maintaining
the other at its baseline value. Table 7 reports the job flow statistics from these two simulations.
The first three columns report statistics for the

rc

= 0,

= 0.5 case. Eliminating job creation

costs from the baseline model causes three main changes in its behavior. First, the gross job
flows are larger on average with the lower adjustment costs. Second, both the gross and net
employment flows respond more to aggregate shocks. Finally, the ratio of job destruction’s
variance to job creation’s falls, regardless of whether one considers all plants together or either
age group. The fall of the variance ratio for old plants is not surprising, because decreases in the
magnitude of the adjustment costs tend to decrease the microeconomic asymmetries in mature
plants’ employment policies. W hat is surprising about this result is that the variance ratio for
young plants falls considerably, from 1.12 to 0.72. The last three columns of Table 7, which
report statistics from the case where r c = 0.5 and r<f = 0, shed some light on this finding. The
most noticeable change between these statistics and those of the previous case is in the variance
ratios. The variance ratios for both groups of plants are significantly above one, and th at for
young plants is

h ig h er

than that for old plants.

The difference between these two experiments is surprising since CF find that the results from
their model do not depend on whether adjustment costs are particular to either job creation or
destruction. Indeed, the fourth and fifth columns of Table 5, which report statistics describing
plants’ optimal policies, indicate that mature plants’ job creation and destruction schedules
behave almost identically in the two parameterizations. To explain the difference in their gross
job flow statistics, note that the particular configuration of adjustment costs has a significant
impact on the level of employment chosen by plants adopting their most recent experiment.
Recall that such plants face no adjustment costs for this initial choice of scale. Because of this
assumption, the employment choice of an adopting plant coincides exactly with that from an
expanding mature plant with the same productivity level when r c = 0. Therefore, young plants
which have just adopted a technology are bunched near the job creation schedule. As Foote
(1997) explained, such bunching increases the variability of job creation for these plants while
decreasing it for job destruction. This explains the sizeable fall in the variance ratio for young
plants when r c = 0. The opposite happens when 7-4 = 0 and r c is positive. In this case, the




31

employment choice of an adopting plant coincides exactly with that from a

sh rin k in g

mature

plant with the same productivity level. Young plants are bunched near the destruction schedule
in this case, so the variance of job destruction increases and that of job creation decreases.
These last two experiments reveal that the bunching of plants in the state space which occurs
over the plant life-cycle can impact aggregate statistics as significantly as the aggregate trends
which Foote (1997) considered. Furthermore, explicitly adding plant life-cycle considerations
to the sensitivity analysis of Campbell and Fisher (1998) overturns their finding that the ability
of adjustment costs to reproduce the job creation and destruction evidence does not depend
on whether those costs are incurred with job creation or with job destruction. While their
conclusion still holds if one only considers the aggregate evidence, the model with only job
creation costs cannot reproduce the aggregate dynamics of young plants.

5. C o n c l u s i o n s
Young plants’ failure to mimic the employment dynamics of their older counterparts presents a
direct challenge to the body of macroeconomic theory which arose to explain aggregate gross job
flows. Our model of organizational differences over the plant life-cycle addresses this challenge
for one class of models, those which rely on organizational frictions to motivate plant level em­
ployment adjustment costs. In our model, the changing nature of a plant’s environment as it ages
underlies organizational differences between young and old plants, and it is these organizational
differences which give rise to the observable differences in their employment dynamics. Because
young plants maintain flexible organizations which entail no adjustment costs, they respond
more to aggregate shocks and on different margins than do old plants which are less flexible
and face costs of job creation and destruction. The microeconomic asymmetries identified by
Campbell and Fisher (1998) cause gross job flows at old plants to respond asymmetrically to
aggregate shocks. Without adjustment costs, employment at young plants responds on both the
job creation and destruction margins equally. Therefore, the model reproduces the finding that
young plants’ job creation and destruction rates are roughly equally variable, but job destruction
at old plants varies considerably more than job creation. Adjustment costs tend to reduce job




32

flows and dampen plants’ responses to aggregate disturbances, so the model also reproduces the
fact that the volume and variability of gross job flows at young plants axe greater than at old
plants. The features of the model we emphasize are most pronounced in the presence of high
frequency aggregate disturbances, so differences between young and old plants in our model
most resemble those in the U.S. data in this case.
Our work has implications for macroeconomics beyond this paper’s focus on the gross job
flows measured by DHS, because it represents the first articulation of an economic rationale
for differences in how groups of producers respond to business cycle shocks which does not rely
on imperfections in credit markets. We have focused on aggregate differences among plants to
connect our work to the existing evidence on gross job flows. However, our plants could be easily
reinterpreted as firms, which may be a more appealing object of study when considering orga­
nizational choice. Under this interpretation, our model implies that young firms respond more
to business cycle shocks than do old firms. Further research into the organizational approach to
adjustment costs should reveal additional implications which will allow us to further differentiate
it from existing credit market theories. For example, the connection we have emphasized be­
tween technological learning and plants’ organizational choices suggests that the pace and form
of

a g g reg a te

technological change could influence the distribution of flexibility among producers

and therefore the magnitude and character of an economy’s response to business cycle shocks.
Although our model can reproduce the salient differences between employment dynamics
at young and old plants, the further development of an organizational theory of adjustment
costs will require a richer framework. One microeconomic limitation of our model is its direct
connection of the activity of learning through experimentation with flexibility in scale. Existing
models of plants’ organizational flexibility choices, such as Milgrom and Roberts (1990) and
Athey and Schumtzler (1995), endogenize similar connections by demonstrating mutual com­
plementarity between investments in flexibility along several dimensions of the firm’s problem.
Extending these models to production problems with an infinite horizon and life-cycle consid­
erations is therefore an important step in developing a more complete macroeconomic theory of
organizationally based adjustment costs.
Two macroeconomic limitations of our model are the assumptions that the relevant labor




33

supply curve is infinitely elastic and that all managers use a constant real interest rate to discount
future profits. These were useful for gaining intuition regarding plants’ optimal employment
decisions and their aggregation, but they rule out any important general equilibrium feedback
to producers’ behavior from consumers’ labor supply, consumption and investment decisions. To
properly understand how investments in organizational structure and the resulting adjustment
costs contribute to business cycle dynamics, the employment choice problem with endogenous
organizational change should be integrated into a standard business cycle framework.




34

R e fe re n c e s
Athey, Susan and Armin Schumtzler. 1995. Product and Process Flexibility in an Innovative
Environment.

R A N D J o u r n a l o f E c o n o m ic s,

26(4), 557-574.

Bahk, Byong-Hyong and Michael Gort. 1993. Decomposing Learning by Doing in New Plants.
J o u r n a l o f P o litic a l E c o n o m y ,

101(4), 561-583.

Bentolila, Samuel and Guiseppe Bertola. 1990. Firing Costs and Labour Demand: How Bad is
Eurosclerosis.

R e v ie w o f E c o n o m ic S tu d ie s ,

57(3), 381-402.

Bernanke, Ben, Mark Gertler and Simon Gilchrist. 1996. The Financial Accelerator and the
Flight to Quality.

R e v ie w o f E c o n o m ic s a n d S ta tis tic s

78(1), 1-15.

Bertola, Guiseppe and Richard Rogerson. 1997. Institutions and Labor Reallocation.
E c o n o m ic R e v ie w ,

E u ro p ea n

41(6), 1147-1171.

Caballero, Ricardo J. 1992. A Fallacy of Composition.

A m e r ic a n E c o n o m ic R e v ie w ,

82(5),

1279-1292.
Caballero, Ricardo J. and Mohamad L. Hammour. 1994. The Cleansing Effect of Recessions.
A m e r ic a n E c o n o m ic R e v ie w ,

84(5), 1350-1368.

Caballero, Ricardo J. and Mohamad L. Hammour. 1996. On the Timing and Efficiency of
Creative Destruction.

Q u a r te rly J o u rn a l o f E c o n o m ic s,

111(3), 805-852.

Campbell, Jeffrey R. 1998. Entry, Exit, Embodied Technology, and Business Cycles.
E c o n o m ic D y n a m ic s,

R e v ie w o f

1(2), 371-408.

Campbell, Jeffrey R. and Jonas D. M. Fisher. 1997. Understanding Aggregate Job Flows.
E c o n o m ic P e r s p e c tiv e s

21(5), 19-37.

Campbell, Jeffrey R. and Jonas D. M. Fisher. 1998. Aggregate Employment Fluctuations with
Microeconomic Asymmetries. Unpublished Manuscript. University of Rochester.
Campbell, Jeffrey R. and Kenneth N. Kuttner. 1996. Macroeconomic Effects of Employment
Reallocation.




C a m e g ie - R o c h e s te r C o n feren ce S e r ie s o n P u b lic P o lic y ,

35

44, 87-116.

Chang, M.H. 1993. Flexible Manufacturing, Uncertain Comsumer Tastes, and Strategic Entry
Deterrence.

J o u rn a l o f In d u s tr ia l E c o n o m ic s

41, 77-90

Christiano, Lawrence J. and Martin Eichenbaum. 1992. Current Real Business Cycle Theories
and Aggregate Labor Market Fluctuations.

A m e ric a n E c o n o m ic R e v ie w

82(3), 430-50.

Davis, Steven J. and John Haltiwanger 1990. Gross Job Creation and Destruction: Microeco­
nomic Evidence and Macroeconomic Implications.
& Fischer, Stanley (eds).

P a g e s 1 2 3 - 1 8 6 of:

Blanchard, Olivier Jean,

N B E R M a c ro eco n o m ics A n n u a l

Davis, Steven J. and John Haltiwanger. 1992. Gross Job Creation, Gross Job Destruction, and
Employment Reallocation.

Q u a rte rly J o u rn a l o f E c o n o m ic s,

107(3), 819-863.

Davis, Steven J. and John Haltiwanger. 1996. Driving Forces and Employment Fluctuations.
NBER Working Paper # 5775.
Davis, Steven J., John Haltiwanger, and Scott Schuh. 1996.

J ob C re a tio n a n d D e s tr u c tio n ,

MIT Press, Cambridge, Mass.
de Groote, Xavier. 1994. The Flexibility of Production Processes: A General Framework.
M a n a g e m e n t S cie n c e,

40(7), 933-945.

Dunne, Timothy, Mark J. Roberts, and Larry Samuelson. The Growth and Failure of U.S.
Manufacturing Plants.

Q u a r te rly J o u rn a l o f E c o n o m ic s,

104(4), 671-698.

Fisher, Jonas D.M. 1998. Credit Market Imperfections and the Heterogeneous Response of
Firms to Monetary Shocks.

J o u rn a l o f M o n e y , C re d it a n d B a n k in g ,

forthcoming.

Foote, Christopher L. 1997. Trend Employment Growth and the Bunching of Job Creation and
Destruction. Working Paper, Harvard University.
Fuss, M.C. and D. McFadden. 1978. Flexiblity versus Efficiency in Ex Ante Plant Design. In
M.C. Fuss and D. McFadden, eds.,
A p p lic a tio n s .

P ro d u c tio n E c o n o m ic s: A D u a l A p p ro a c h to T h e o ry a n d

Amsterdam: North-Holland.

Gertler, Mark and Simon Gilchrist. 1994. Monetary Policy, Business Cycles, and the Behavior
of Small Manufacturing Firms.




Q u a r te rly J o u rn a l o f E c o n o m ic s,

36

109(2), 311-340.

Gilchrist, Simon and John C. Williams.

1998. Putty-Clay Investment: A Business Cycle

Analysis. Unpublished Working Paper, Boston University.
Hamermesh, Daniel S. and Gerard A. Pfann.
J o u r n a l o f E c o n o m ic L ite ra tu re ,

1996. Adjustment Costs in Factor Demand.

34(3) 1264-1292.

He, H. and R.S. Pindyck. 1992. Investments in Flexible Production Capacity.
E c o n o m ic D y n a m ic s a n d C o n tro l,

Jou rn al o f

16, 575-599.

Hopenhayn, Hugo and Richard Rogerson. 1993. Job Turnover and Policy Evaluation: A General
Equilibrium Analysis.

J o u rn a l o f P o litic a l E c o n o m y ,

101(5), 915-938.

Jovanovic, Boyan. 1982. Selection and the Evolution of Industry.

E c o n o m e tr ic a ,

50(2), 649-

670.
Lucas, Robert E. 1967. Adjustment Costs and the Theory of Supply.
E conom y,

J o u rn a l o f P o litic a l

75(4), 321-334.

Lucas, Robert E. 1978. On the Size Distribution of Business Firms.

B e ll J o u r n a l o f E c o n o m ic s ,

9(2), 508-523.
Milgrom, Paul and John Roberts. 1990. The Economics of Modern Manufacturing: Technology,
Strategy, and Organization.

A m e ric a n E c o n o m ic R e v ie w ,

80(3), 511-528.

Mortensen, Dale T. and Christopher A. Pissarides. 1994. Job Creation and Destruction in the
Theory of Unemployment.

R e v ie w o f E c o n o m ic S tu d ie s,

61, 397—415.

Sargent, Thomas J. 1987.

D y n a m ic M a cro eco n o m ic T h eo ry,

Harvard University Press, Cam­

bridge, Mass.
Simon, Herbert A. 1991. Organizations and Markets.

J o u rn a l o f E c o n o m ic P e r s p e c tiv e s ,

5(2),

25-44.
Stigler, George. 1939. Production and Distribution in the Short Run.
E conom y,

J o u r n a l o f P o litic a l

47(3), 305-327.

Tauchen, George E. 1984. Finite State Markov-Chain Approximations to Univariate and Vector
Autoregressions. Working Paper #84-09, Duke University Department of Economics.




37

Table 1: Gross Job Flows in the Manufacturing Sector by Plant Type

Statistic
(creation)

E

E(destruction)
E(growth)
E'(reallocation)
S'(creation)
^destruction)
^'(growth)
S'(reallocation)
V (destruction)
V (creations)

^(creation,destruction)
^(creation,growth)
^(destruction,growth)
/9(reallocation,growth)

Plant, Age
Old
All Plants Young
7.52
4.54
5.39
(0.16) (0.33) (0.12)
5.72
6.56
5.24
(0.28) (0.32) (0.27)
-0.33
0.95 -0.70
(0.36) (0.46) (0.33)
11.11
14.08
9.77
(0.28) (0.45) (0.26)
0.85
1.66
0.71
(0.06) (0.19) (0.06)
1.45
1.65
1.43
(0.21) (0.20) (0.20)
2.32
1.90
1.81
(0.23) (0.25) (0.24)
1.42
2.26
1.35
(0.16) (0.20) (0.15)
2.89
0.99
4.06
(0.87) (0.37) (1.12)
-0.33
-0.07 -0.36
(0.10) (0.13) (0.09)
0.70
0.73
0.68
(0.05) (0.06) (0.05)
-0.91
-0.73 -0.93
(0.03) (0.09) (0.02)
-0.51 -0.002 -0.63
(0.12) (0.19) (0.09)

Notes: V ( x ) denotes the variance of variable x , S ( x ) denotes the standard deviation of variable x
in percent, p ( x , y ) denotes the correlation between variable x and variable y , and E ( x ) the mean
of variable x. Numbers in parentheses axe standard deviations, computed using the procedure
described in Christiano and Eichenbaum (1992). For estimation of the relevant zero-frequency
spectral density, a Bartlett window truncated at lag three was used. See the text for variable
definitions and the data sources.




38

Table 2: Differences in Gross Job Flows by Plant Age

__________£(realloc.)
Test
Statistic 156.1
10.0
p-value (0)
(0.002)

V

(growth)

11.1
(0.001)

p(creation,
destruction)

p(realloc.,
growth)

11.0
(0.001)

16.9
(0.0001)

Notes: V (x ) denotes the variance of variable x , p(x, y ) denotes the correlation between variable
x and variable y , and E ( x ) the mean of variable x . Table entries not in parenthesis are values
of the Wald-type test statistic for the null hypothesis that the statistic indicated in the column
heading is identical for young and old plants. The test statistics are distributed X2(l)- The
number in parenthesis is the probability that a chi-square random variable with one degree of
freedom exceeds the reported value of the associated test statistic.




Table 3: Baseline Parameter Values
Parameter

Value

a

0.85

p

1.05-1/4

6

0.0083

Vm

-0.013

Om

0.062

Vs

-0.152

Os

0.007

Vp

-0.023

op

0.214

Tc

0.5

Td

0.5

ow

0.021

39

Table 4: Gross Job Flows in the Baseline Model
All Plants

Young

Old

^(creation)

5.15

8.86

4.62

£■(destruction)

5.15

5.14

5.16

F (growth)

0.00

3.71

-0.53

10.31

14.00

9.78

^(creation)

0.84

1.57

0.74

■S(destruction)

1.06

1.67

0.99

1.88

3.11

1.72

^'(reallocation)

0.32

0.91

0.29

V (destruction)
V (creations)

1.61

1.12

1.79

Statistic

£?(reallocation)

S

(growth)

^(creation,destruction)

-0.97

^(creation,growth)

-0.84 -0.99

0.99

0.96

1.00

^(destruction,growth)

-0.99

-0.96

-1.00

^(reallocation,growth)

-0.70

-0.11

-0.86

Notes: V { x ) denotes the variance of variable x , S ( x ) denotes the standard deviation of variable
in percent, p ( x , y ) denotes the correlation between variable x and variable y , and E ( x ) the
mean of variable x.

x




40

Table 5: Model Policy Statistics
Cycle of
8 Quarters 20 Quarters
-1.08
-1.08

No Costs of
Creation Destruction
-1.08
-1.08

E Q n y s (W ))

Baseline
Model
-1.08

(lnt/° ( W ) )

-1.39

-1.39

-1.39

-1.73

-0.73

E(]nz(W ))

0.26

0.26

0.27

0.26

0.26

E(]ny(W ))

-2.06

-2.06

-2.06

-1.73

-1.79

E (kiy(W ))

-0.57

-0.57

-0.57

-0.66

-0.73

0.02

0.02

0.02

0.02

0.02

S(\ny*(W ))

14.73

14.17

14.17

14.73

14.73

S (In 1/“ (W0)

2.76

4.09

8.62

5.02

7.02

S(]nz{W ))

0.04

0.89

1.00

0.02

0.07

S(\ny{W ))

3.76

4.30

8.27

5.01

4.99

S(\ny(W ))

6.16

6.79

10.30

7.08

7.02

S ( P i [ Zl > z ( W ) ) )

0.01

0.20

0.22

0.01

0.02

Statistic

E

E (P v[zi>z(W )})

Notes: S ( x ) denotes the standard deviation of variable x in percent, and
of variable x.




41

E(x)

denotes the mean

Table 6: Gross Job Flows in the Model with Deterministic Cycles

Statistic
F1(creation)
i? (destruction)

20 Quarter Cycle
8 Quarter Cycle
Old
All Plants Young
Old All Plants Young
8.62 4.61
5.11
5.11
8.46 4.63
5.12

4.91

5.15

5.11

4.72

0.00

3.71

-0.54

0.00

3.73 -0.53

10.23

13.53

9.75

10.21

13.18

9.79

S'(creation)

0.74

1.21

0.68

0.98

1.42

0.91

S(destruction)

0.90

1.25

0.85

0.94

1.00

0.93

S(growth)

1.62

2.39

1.51

1.89

2.39

1.82

S(reallocation)

0.33

0.61

0.30

0.31

0.58

0.30

V (destruction)
V (creations)

1.47

1.07

1.58

0.94

0.50

1.05

-0.94

-0.88

-0.95

-0.95

0.98

0.97

0.98

0.99

0.99

0.99

^(destruction,growth)

-0.99

-0.97

-0.99

-0.99

-0.98

-0.99

^(reallocation,growth)

-0.49

-0.07

-0.59

0.10

0.72

-0.08

E

(growth)

jE (reallocation)

^(creation,destruction)
^(creation,growth)

5.16

-0.94 -0.95

Notes: V ( x ) denotes the variance of variable x, S ( x ) denotes the standard deviation of variable
x in percent, p ( x , y ) denotes the correlation between variable x and variable y, and E ( x ) the
mean of variable x.




42

Table 7: Gross Job Flows in the Model with Asymmetric Adjustment Costs

Statistic
jEJ(creation)

No Creation Costs
No Destruction Costs
Old
All Plants Young
Old All Plants Young
6.21
9.53
5.77
6.76
11.77
5.99
6.21

5.62

6.29

6.76

8.21

6.54

0.00

3.90

-0.52

0.00

3.56

-0.55

12.43

15.15

12.07

13.52

19.98

12.52

S'(creation)

1.33

2.14

1.23

1.31

1.95

1.22

S(destruction)

1.45

1.82

1.41

1.59

2.44

1.47

S (growth)

2.76

3.86

2.62

2.88

4.30

2.67

S(reallocation)

0.34

0.94

0.32

0.42

1.00

0.36

V (destruction)
V (creations)

1.19

0.72

1.33

1.45

1.56

1.46

-0.98

-0.90

-0.98

-0.98

-0.92

-0.98

0.99

0.98

0.99

0.99

0.98

0.99

p(destruction,growth)

-0.99

-0.97

-1.00

-1.00

-0.98

-1.00

p(reallocation,growth)

-0.38

0.34

-0.58

-0.66

-0.50

-0.70

E

(destruction)

.©(growth)
.©(reallocation)

^(creation,destruction)
p(creation,growth)

Notes: V ( x ) denotes the variance of variable x, S(x) denotes the standard deviation of variable
x in percent, p(x, y ) denotes the correlation between variable x and variable y , and E ( x ) the
mean of variable x.




43