How Destructive is Innovation?
Daniel Garcia-Macia
Chang-Tai Hsieh
International Monetary Fund
University of Chicago and NBER
Peter J. Klenow ∗
Stanford University and NBER
September 28, 2016
Abstract
Entrants and incumbents can create new products and displace the products of competitors. Incumbents can also improve their existing products.
How much of aggregate growth occurs through each of these channels?
Using U.S. Census data on non-farm private businesses from 1976-2013, we
arrive at three main conclusions: First, most growth appears to come from
incumbents. We infer this from the modest employment share of entering
firms (defined as those less than 5 years old). Second, most growth seems
to occur through improvements of existing varieties rather than creation
of brand new varieties. Third, own-product improvements by incumbents
appear to be more important than creative destruction. We infer this because the distribution of job creation and destruction has thinner tails than
implied by a model with a dominant role for creative destruction.
∗
We thank Ufuk Akcigit, Andy Atkeson, Michael Peters, and Paul Romer for very helpful
comments. For financial support, Hsieh is grateful to Chicago’s Polsky Center and Klenow to
the Stanford Institute for Economic Policy Research (SIEPR). Any opinions and conclusions
expressed herein are those of the authors and do not necessarily represent the views of the
U.S. Census Bureau. All results have been reviewed by the U.S. Census Bureau to ensure no
confidential information is disclosed.
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GARCIA-MACIA, HSIEH, KLENOW
1.
Introduction
Innovating firms can improve on existing products made by other firms, thereby
gaining profits at the expense of their competitors. Such creative destruction
plays a central role in many theories of growth. This goes back to at least Schumpeter (1939), carries through Stokey (1988), Grossman and Helpman (1991),
and Aghion and Howitt (1992), and continues with more recent models such
as Klette and Kortum (2004). Aghion et al. (2014) provide a recent survey of
the theory and Acemoglu and Robinson (2012) provide historical accounts of
countries that stop growing when creative destruction is blocked.
Other growth theories emphasize the importance of firms improving their
own products, rather than displacing other firms’ products. Krusell (1998) and
Lucas and Moll (2014) are examples. Some models combine creative destruction and quality improvement by existing firms on their own products — see
chapter 12 in Aghion and Howitt (2009) and chapter 14 in Acemoglu (2011). A
recent example is Akcigit and Kerr (2015), who also provide evidence that firms
are more likely to cite their own patents and hence build on them.
Still other theories emphasize the contribution of brand new varieties to
growth. Romer (1990) is the classic reference, and Acemoglu (2003) and Jones
(2014) are some of the many follow-ups. Studies such as Howitt (1999) and
Young (1998) combine variety growth with quality growth.
Ideally, one could directly observe the extent to which new products substitute for or improve upon existing products. Broda and Weinstein (2010) and
Hottman et al. (2014) are important efforts along these lines for consumer nondurable goods. Such high quality scanner data has not been available or analyzed in the same way for consumer durables, producer intermediates, or producer capital goods – all of which figure prominently in theories of growth.1
Similarly, when new products substitute for existing products, the ideal would
1
Gordon (2007) and Greenwood et al. (1997) emphasize the importance of growth embodied
in durable goods based on the declining relative price of durables.
HOW DESTRUCTIVE IS INNOVATION?
3
be to directly measure whether the new product is owned by another firm (“creative destruction”) or by the firm that also owns the product that is being replaced (“own innovation”). Based on a small number of case studies, Christensen (1997) argues that innovation largely takes the form of creative destruction and that such innovation almost always comes from new firms. However,
as with all evidence based on case studies, it is difficult to know whether the
case studies are representative of the broader economy.
We pursue a complementary approach. We infer the sources of growth indirectly from empirical patterns of firm dynamics of all private sector firms in
the U.S. economy. The influential papers by Baily et al. (1992) and Foster et
al. (2001) document the contributions of entry, exit, reallocation, and withinplant productivity growth to overall growth in the manufacturing sector using
an accounting framework without any model assumptions. We also measure
the contribution of entry, exit, and reallocation to aggregate growth but we differ from Baily et al. (1992) and Foster et al. (2001) in that we filter the data
through the lenses of a specific model of growth. Like us, Lentz and Mortensen
(2008) and Acemoglu et al. (2013) conduct indirect inference on growth models
with manufacturing data (from Denmark and the U.S., respectively). However,
they assume the only source of growth is creative destruction, whereas our goal
is to measure empirically how much growth comes from creative destruction
vs. other sources of innovation (own-variety improvements by incumbents and
creation of new varieties).
We use data on firms in the U.S. Longitudinal Business Database from 1976
to 1986 and 2003-2013. Over this period, we calculate aggregate TFP growth, the
exit rate of firms by age and employment, employment by age, aggregate rates
of job creation and destruction rates, the distribution of job creation and destruction across firms, the dispersion of employment across firms, and growth
in the total number of workers and firms. The parameter values which best fit
these moments lead to three conclusions. First, most growth appears to come
from incumbents rather than entrants. This is because the employment share
4
GARCIA-MACIA, HSIEH, KLENOW
of entrants is modest. Second, most growth seems to occur through quality
improvements rather than brand new varieties. Third, own-variety improvements by incumbents loom larger than creative destruction (by entrants and
incumbents).
The rest of the paper proceeds as follows. Section 2 lays out the parsimonious exogenous growth model we use. Section 3 describes the U.S. census
data we exploit and presents the data moments we use to infer the sources of
innovation. Section 4 presents the model parameter values which best match
the moments from the data. Section 5 concludes.
2.
Models of Innovation
This section lays out a model where growth can be driven by creative destruction, own innovation, and by the creation of new varieties. Although all three
types of innovation have the same effect on aggregate growth, the goal is to
illustrate how they leave different telltale signs in the micro-data.
Static Equilibrium
Aggregate output is a CES aggregate of quality-adjusted varieties:
"
Y =
M
X
σ
# σ−1
1− σ1
(qj yj )
j=1
where yj denotes the quantity and qj the quality of variety j. Labor is the only
factor of production and output of variety j is given by yj = lj where lj is labor
used to produce variety j.
As in Klette and Kortum (2004), we assume a firm potentially produces multiple varieties. We further assume that there is an overhead cost of production
that must be expended before choosing prices and output. This assumption
allows the highest quality producer to charge the standard markup
σ
σ−1
over the
5
HOW DESTRUCTIVE IS INNOVATION?
wage, as the next lowest quality competitor will be deterred by zero ex post profits. Without this assumption, firms would engage in limit pricing and markups
would be heterogeneous as in Peters (2013).
Assuming firms face the same wage, the profit maximizing quantity of labor
employed in producing variety j is:
lj =
σ−1
σ
σ−1
L W 1−σ qjσ−1
where W is the wage and L is aggregate employment.2 Employment of a firm Lf
is then given by:
Lf ≡
Mf
X
j=1
lj =
σ−1
σ
σ−1
LW
1−σ
Mf
X
qjσ−1
(1)
j=1
where Mf denotes the number of varieties produced by firm f . The distribution
M
Pf σ−1
of firm employment depends on
qj . Larger firms control more varieties
j=1
and produce higher quality products. In the special case of σ = 1 assumed by
Klette and Kortum (2004), the same amount of labor is used to produced all
varieties and a firm’s employment is proportional to the number of varieties it
controls. We will find it important to allow σ > 1 so that firms can be larger when
they have higher quality products rather than just a wider array of products.
After imposing the labor market clearing condition, the wage is proportional
to aggregate labor productivity, which is given by
"
W ∝ Y /L = M
1
σ−1
M
X
qjσ−1
j=1
M
1
# σ−1
.
(2)
The first term captures the benefit of having more varieties, and the second term
is the power mean of quality across varieties.
2
We set the price of aggregate output to one. We assume no misallocation whatsoever of
labor in the model.
6
GARCIA-MACIA, HSIEH, KLENOW
Innovation
Aggregate growth in the model comes from the creation of new varieties and
from growth in average quality. In Klette and Kortum (2004), the number of
varieties is constant and quality growth can only come when a firm innovates
upon and takes over a variety owned by another firm (“creative destruction”).
We will also allow quality growth to come from innovation by firms to improve
the quality of the products they own (“own innovation”). Lastly, we also allow
growth to come from the creation of brand new varieties.
We make the following assumptions about the innovation process. First, we
assume a constant exogenous arrival rate of each type of innovation. Second,
we assume that the probability of innovation scales linearly with the number
of products owned by a firm. For example, a firm with two products is twice as
likely to creatively destroy another firm’s variety compared to a firm with one
product. Third, in the case of an existing product, we assume that innovation is
proportional to the existing quality level of the product.3 Specifically, the quality
of an innovation follows a Pareto distribution with shape parameter θ and a
scale parameter equal to the existing quality level. The average improvement
in quality of an existing variety (conditional on innovation), weighted by em1/(σ−1)
θ
ployment, is thus sq = θ−(σ−1)
. Finally, we assume that entrants have
one product, which they obtain by improving upon an existing variety or by
creating a brand new variety. Incumbent firms, on the other hand, potentially
produce many varieties.
The notation of the innovation probabilities is given in Table 1. Time is
discrete and innovation rates are per existing variety. The probability of each
type of innovation thus increases linearly with the number of varieties owned by
the firm. The probability an existing variety is improved upon by the firm that
owns the product is λi . If a firm fails to improve on a variety it produces, then
3
If innovation was endogenous, there would be a positive externality to research unless all
research was done by firms on their own products. Such knowledge externalities are routinely
assumed in the quality ladder literature, such as Grossman and Helpman (1991), Aghion and
Howitt (1992), Kortum (1997), and Acemoglu et al. (2013).
HOW DESTRUCTIVE IS INNOVATION?
7
that variety is vulnerable to creative destruction by other firms. Conditional on
not being improved upon by the incumbent, δi is the probability the product
is improved upon by another incumbent. Conditional on not being improved
upon by any incumbent, δe is the probability the product will be improved by
an entrant.
In short, a given product can be improved upon by the current owner of the
product, another incumbent firm, or an entrant. The probability a product will
be improved upon by the owner is λi . The unconditional probability of innovation by another incumbent is δ˜i ≡ δi (1 − λi ). The unconditional probability the
product will be improved by an entrant is δ˜e ≡ δe (1 − δi )(1 − λi ). The probability
an existing product is improved upon by any firm is thus λi + δ˜i + δ˜e . And
conditional on innovation, the average improvement in quality is sq .
The rate at which new varieties are created is parameterized by κe and κi .
Brand new varieties arrive at rate κe from entrants and at rate κi from incumbents. The arrival rates are per existing variety and independent of other innovation types. The quality of the new variety is drawn from the quality distribution of existing products multiplied by sκ < 1.
The last parameter we introduce is overhead labor, which pins down the
minimum firm size. We set overhead labor such that the minimum size firm has
1 unit of labor for production plus overhead. The overhead cost endogenously
determines the cutoff quality of varieties as products with a quality below the
threshold do not produce. This cutoff, which we denote as ψ percent of average
quality of existing varieties, rises endogenously with wage growth. We denote
the exit rate of existing varieties due to overhead as δo . Net growth of varieties is
therefore κe + κi − δo .
We can now express the growth rate of the wage and output per worker (in
8
GARCIA-MACIA, HSIEH, KLENOW
Table 1: Channels of Innovation
Channel
Probability
Own-variety improvements by incumbents
λi
Creative destruction by entrants
δe
Creative destruction by incumbents
δi
New varieties from entrants
κe
New varieties from incumbents
κi
Note: The average step size for quality improvements for own
innovation and creative destruction, weighted by employment, is sq =
1/(σ−1)
θ
≥ 1. Quality of new varieties are drawn from the quality
θ−(σ−1)
distribution of existing products multiplied by sκ ≤ 1.
expectation) as a function of the parameters in Table 1:
1
 σ−1


− 1 (3)
g = 1 + sκ (κe + κi ) + sq σ−1 − 1 λi + sq σ−1 − 1 δ˜e + δ˜i − δo ψ 
|
{z
} |
{z
} |
{z
}
new varieties

own innovation
creative destruction
The contribution of new varieties is given by sκ (κe + κi ) which is increasing in
the arrival rates κe and κi and quality of the new varieties sκ . The contribution
of own innovation is the product of the probability of own innovation λi and
the quality improvement sq σ−1 − 1. The contribution of creative destruction is
the product of the probability of creative destruction δ˜e + δ˜i and corresponding
quality increase. Finally, the loss from the exit of low quality varieties due to
overhead costs is captured by δo ψ.
We can also rearrange this expression to express growth as a function of the
HOW DESTRUCTIVE IS INNOVATION?
9
contribution of innovation by entrants and by incumbents:
1
 σ−1


− 1 (4)
g = 1 + sκ κe + sσ−1
− 1 δ˜e + sκ κi + sσ−1
− 1 λi + δ˜i − δo ψ 
q
q
|
{z
} |
{z
}

entrants
incumbents
The contribution of entrants is an average of the arrival rate of new varieties
κe and creative destruction δ˜e , where the arrival rates are weighted by the step
sizes associated with each type of innovation. The contribution of entrants is
a weighted average of the arrival rate of new varieties κi , own innovation λi ,
and creative destruction δ˜i , where again the arrival rates are weighted by the
− 1 for own
corresponding step sizes (sκ in the case of new varieties and sσ−1
q
innovation and creative destruction).
In sum, the parameters for the innovation probabilities (κi , κe , λi , δi , and δe )
along with the parameters for the quality steps (θ and sκ ) pin down the share
of growth driven by own innovation, creative destruction, and new varieties.
Equivalently, the same parameters also determine the share of growth driven by
innovation by incumbents vs. entrants. We will measure these parameters from
the patterns in the micro-data. Once we have these parameters, we can then
provide a decomposition of the contribution of the three types of innovation as
well as the contribution of incumbents vs. entrants to aggregate growth.
Firm Dynamics
The key idea we exploit is that firm employment in the model is the outcome
of all three types of innovation. Remember firm employment is proportional
to the number of products the firm produces and the average quality of these
products. A firm that is successful in innovating, either by improving its product quality, by improving the quality of a product made by another firm, or by
coming up with a brand new variety, will grow in employment. A firm whose
products are taken over by another firm will shrink in employment. At the ex-
10
GARCIA-MACIA, HSIEH, KLENOW
treme, a firm will exit when all its products are creatively destroyed by other
firms.
Creative Destruction
Consider a polar model where the only source of innovation is creative destruction.4 Furthermore, assume σ = 1 so quality has no effect on firm employment.
This is simply the Klette and Kortum (2004) model. In this polar model, the
employment share of entrants is simply the share of products that entrants improved upon. The employment share of entrants is therefore pinned down by
the unconditional probability of creative destruction by entrants δe (1 − δi ).
Incumbent firms grow when they take over another firm’s product. Growth
in firm employment by age is driven by the accumulation of varieties by incumbents over the life-cycle which is pinned down by the creative destruction rate
of incumbents δi . This polar model can thus completely “explain” the employment share of entrants and the growth in firm employment with age. The rate
of creative destruction by entrants δe is pinned down by employment share of
entrants and the rate of creative destruction by incumbents δi by differences in
average employment between incumbent and entrant firms.
In this polar model, job destruction in the model is the outcome of innovation by other firms. Firms where jobs are destroyed are firms whose products
have been taken away by other firms. Exit is nothing more than the extreme
manifestation of job destruction. For example, a one variety firm exits when it
loses its one variety to creative destruction and when it does not innovate on
another firm’s variety. The probability a one variety firm exits is (1 − δi )(δi +
δe (1 − δi )), which is increasing in δe and δi .5
The exit rate of a firm with n varieties can then be expressed as a function
of the exit probability of a firm with one variety. A firm with n varieties exits
4
We assume κi = κe = λi = 0.
The probability a one variety firm does not innovate on another firm’s variety is 1 − δi . The
probability a one variety firm loses its one variety to another firm is δi + δe (1 − δi ). The exit rate
increases with δi only if δe < .5.
5
HOW DESTRUCTIVE IS INNOVATION?
11
Figure 1: Model Exit by Size in Model of Creative Destruction
when it loses all n varieties and does not innovate on another firm’s variety,
which is simply the exit rate of a one variety firm to the nth power. In turn,
since employment of a firm with n varieties is simply n times the employment
of a firm with one variety, the model predicts that a n fold difference in firm
employment will change the exit rate by the power of n.
Figure 1 illustrates the relationship between firm exit and firm employment
predicted by this polar model (look at the figure labeled σ = 1). The intuition
behind this stark prediction is that product quality in the polar model has no
effect on firm employment. To get quality to matter for firm employment, we
need to drop the assumption σ = 1. Figure 1 illustrates the effect of changing
σ = 1 to σ = 3 on the exit-size slope. However, this change will also increase the
thickness of the tails of the distribution of employment growth. This is shown
in Figure 2. In this figure, following Davis et al. (1998), the percent change in
employment on the x-axis is measured as the change in employment divided
by the average of last period and the current period’s employment. The rates
are thus bounded between -2 (exit) and +2 (entry) and the distribution on the
vertical axis is the percent of all job creation or destruction contributed by firms
in each bin of employment growth. For visual clarity, the figure omits firm exit
(-2) and firm entry (+2).
12
GARCIA-MACIA, HSIEH, KLENOW
Figure 2: Distribution of Job Creation and Destruction in Model of Creative
Destruction
Note: Entry (+2) and exit (-2) are omitted in the figure.
Intuitively, more quality heterogeneity across firms implies that more firms
will experience large changes in employment when they grab a high quality
variety from another firm. Similarly, firms will experience a sharp drop in employment when they lose their high quality varieties. In summary, two data
moments that speak to the share of innovation that take the form of creative
destruction are the elasticity of firm exit with firm size and the thickness of the
tails in the distribution of employment growth.
Own Innovation
We can get thinner tails in the employment growth distribution and a flatter
exit size slope if some innovation takes the form of firms improving the quality
of their own products. This generates a flatter exit size slope by introducing
quality heterogeneity. Furthermore, since the quality of an innovated product is
proportional to its previous quality, employment of a firm that has successfully
innovated is proportional to the firm’s previous employment. Because of this,
more own innovation implies that the mass of the employment growth distribution will be concentrated around small employment changes, and the tails
HOW DESTRUCTIVE IS INNOVATION?
13
Figure 3: Distribution of Job Creation and Destruction in Model of Own
Innovation
will be correspondingly thinner.
However, a model where the only source of innovation also has stark empirical predictions. First, the share of entrants is zero in a model with only
own innovation. Second, the exit rate is zero as there is no creative destruction. Third, firms grow when they innovate on their products, which results in a
proportional change in employment share. Firms that do not improve on their
products shrink due to the general equilibrium effect of a rising wage. There is
therefore no mass in left tail of the employment growth distribution implied by
this model. This is shown in Figure 3.
We need to have some creative destruction in order to generate job destruction and exit. The next model we thus consider is a hybrid where incumbent
firms only innovate by improving the quality of their products and entrants
only engage in creative destruction. This hybrid model has the following empirical implications. First, the average size of incumbents is the same as that
of entrants. This is because when incumbents improve on their own products,
this raises the quality that entrants firms are drawing from when they innovate.
When incumbents improve on their products, the quality of products made
by entrants rise at the same rate. Second, this hybrid model assumes that in-
14
GARCIA-MACIA, HSIEH, KLENOW
cumbent firms can only innovate by improving the quality of their one product.
Therefore, this model implies that large firms are larger only because of higher
quality, not because they produce more varieties. The exit size slope therefore
will be completely flat. The probability of exit of a large firms is simply the probability that its one variety is taken away. Finally, the tails of the distribution of
employment growth predicted by this hybrid model are thin. This is because the
probability of creative destruction in this hybrid model is pinned down by the
employment share of entrants in this model. This is because of the assumption
that only entrants engage in creative destruction.
If the data is not consistent with these predictions, then we will also need
some creative destruction from incumbents. Creative destruction from incumbent firms will generate accumulation of varieties with age when incumbent
firms improve upon the varieties of other firms. It will generate heterogeneity
in the number of varieties across firms which implies that larger firms will have
lower exit rates. Finally, it will also thicken the tails of the distribution of firm
employment growth (relative to a model where only entrants engage in creative
destruction).
New Varieties
In the models we have considered up to now, growth in firm employment with
age is entirely pinned down by the decline in exit rates with age. In addition,
allowing for quality heterogeneity from own innovation does not change this
prediction. This is because an entrant is just as likely to grab a high quality
variety as an incumbent. Therefore, differences in average employment between entrants and incumbents still largely reflects accumulation of varieties
by incumbents with age.6
If we find that this prediction is not borne out in the data, allowing for some
6
Entrant quality will be slightly higher than incumbents because some of the incumbent
products will not have been improved upon. Specifically, the average quality of incumbents
1
relative to entrants is δi +(1−δ
> 1.
i )sq
HOW DESTRUCTIVE IS INNOVATION?
15
innovation to take the form of new varieties can help. Intuitively, if new varieties
are of lower quality compared to the quality of existing varieties, then some of
the difference in average employment between incumbents and entrants can
be driven by differences in the average quality of products produced by incumbents vs. entrants. Furthermore, this does not change the relationship between
exit rates and firm age since this empirical pattern reflects the accumulation in
the number of products with age (in the model).
The key empirical question is whether the difference in average employment
between incumbents and entrants is larger or smaller implied by the difference
in exit rates between incumbents and entrants. If the gap in average employment is larger than implied by the observed gap in exit rates (as will be the
case in the US data), then it suggests that entrants also create new lower quality
varieties. On the other hand, if the gap in average employment is smaller than
implied by the observed difference in exit rates, then one explanation could be
that incumbents are the ones that create new low quality varieties.7
Having entrants come up with relatively low quality varieties also has implications for the shape of the quality distribution across firms. Low quality
entrants tend to increase quality dispersion in the steady state by making the
left tail longer. Since firm employment is proportional to the product of average
firm quality and the number of firm varieties, the parameter sκ can be adjusted
to target the empirical dispersion in employment.
When we have creation of new varieties, the total number of varieties grows
at a rate given by the arrival rate of new varieties.8 Furthermore, in a steady
state with a stationary firm size distribution, the distribution of the number of
varieties per firm is also stationary. This implies that the growth rate in the total
number of varieties is equal to the growth rate of the number of firms. In turn,
the growth rate of the number of firms is equal to the growth rate of the product
7
This is likely to be the case in Mexico. Hsieh and Klenow (2012) show that the decline in
exit rates with age is higher in Mexico compared to the US, but the increase in firm employment
with age is lower relative to the US.
8
There is also a small loss in varieties due to overhead costs.
16
GARCIA-MACIA, HSIEH, KLENOW
of total aggregate employment and average firm employment. In the US data,
there is no trend in average firm employment so the arrival rate of new varieties
is pinned down by the growth rate of total employment.
In short, new variety creation gives us three more facts. First, we can match
a stationary firm size distribution with aggregate employment growth. Without
the creation of new varieties, aggregate employment is either constant or average firm size falls. Second, new variety creation breaks the tight relationship
between firm employment with age and firm exit with age. Specifically new
variety creation can explain why the growth of firm employment with age may
be larger or smaller than predicted by the decline in firm exit with age. Third,
the quality of new varieties can help us generate additional dispersion in employment across firms.
Finally, as with the other polar models, a model with only creation of new
varieties has stark implications that will not be borne out in the data. Perhaps
the most important is that a polar model with only new varieties has no exit or
job destruction.
In short, the data moments that speak to the relative importance of the sources
of innovation are:
1. Aggregate Employment Growth
2. Aggregate rate of job creation and destruction (the difference between job
creation and destruction is employment growth).
3. Entrant Share of Employment
4. Distribution of employment growth. The specific data moment is the share
of job creation where the employment growth rate ≤ 1.
5. Employment by Age
6. Exit by Age
7. Exit by Employment
HOW DESTRUCTIVE IS INNOVATION?
17
8. TFP Growth
9. Standard Deviation of Firm Employment
Note that there are 9 data moments on this list, and we only need to estimate
7 parameters. Since we have more moments than parameters, there are many
ways to proceed with the estimation. Before we discuss what we do to estimate
these parameters, the next section presents the data moments on the above list.
3.
U.S. Longitudinal Business Database
We use the micro-data from U.S. Longitudinal Business Database (LBD). The
LBD is based on administrative employment records of every establishment in
the US economy. The key advantages of the LBD is its near universal coverage of
the US economy and that measurement error is likely to be small. The variables
we use are the establishment’s employment, the year the establishment appears
in the LBD for the first time, and the ID of the firm that owns the establishment.
We use the year the establishment appears in the LBD to impute the establishment’s age (the LBD does not provide the establishment’s age).
We drop establishments in the public, educational, and agricultural and mining sectors. Furthermore, we focus on firms rather than establishments because
the theories of innovation we examine are theories of firm innovation. In our
baseline sample, we aggregate establishment level data of all establishments to
the firm (using the firm identifier). Firm employment is thus the sum of employment of all the establishments owned by a firm. Firm age is defined as the
age of the oldest establishment owned by the firm. An “entrant” is a firm where
the oldest establishment was created within the last four years (inclusive). An
“incumbent” is a firm where the oldest establishment of the firm was created
five or more years earlier. A firm “exits” when it loses all its establishments.
This definition of a firm treats firm dynamics due to mergers and acquisitions in the same way as employment dynamics of a given establishment. One
18
GARCIA-MACIA, HSIEH, KLENOW
justification for this is that employment dynamics due to mergers and acquisitions may also be the result of the innovation forces we model in this paper.
That is, a firm acquires another firm when it innovates on a product owned
by another firm. Similarly, a firm sells itself or sells one of its establishment
when another firm innovates upon its product. To assess robustness, we will
also work with two different definitions of firms. First, we restrict the sample
to establishments that belong to the same firm throughout the establishment’s
lifetime, and aggregate establishment level data to firms based on this sample.
Second, we simply define a firm as an establishment.
We use the ten year window surrounding 1981 and 2008 (1976-1986 and
2003-2013). The first year the LBD data is available is 1976. We thus define
entrants in 1981 as firms in 1981 that were not in the data in 1976. Likewise, we
define entrants in 2008 as firms in 2008 created after 2003. The last year the LBD
is available is 2013. We thus define exiting firms in 2008 as firms we observe in
the data in 2008 that are no longer in the data by 2013. Similarly, exiting firms in
1981 are firms in 1981 that no longer exist by 1986.
The summary statistics of the sample are shown in Tables 2 and 3. Table
2 shows that total employment and the number of firms increased from 1981
to 2008. At the same time, average employment per firm and dispersion of
firm size are roughly constant. Table 3 shows that the growth rate of aggregate
employment from 2003 to 2013 dropped relative to the growth rate from 1976 to
1986. Since average employment per firm is roughly constant, the growth rate
in the number of firms also fell. At the same time, the growth rate of laboraugmenting TFP (as calculated by the BLS) increased between the two time
periods. In sum, the three aggregate facts we will need to fit are: 1) declining
growth rate of employment; 2) constant average firm size and; 3) increasing
growth rate of TFP.
Figure 4 summarizes the employment share of entrants and Figure 5 the
average employment of entrants and incumbents in 1981 and 2008. Note the
10 percentage point decline in the employment share of entrants between 1981
19
HOW DESTRUCTIVE IS INNOVATION?
Table 2: Summary Statistics in LBD
Employment
Firms
Average Employment
S.D. (log Employment)
1981
78.9
3.307
23.9
1.26
2008
125.6
5.134
24.5
1.29
Note: Total employment and number of firms in millions.
Table 3: Growth Rate of Aggregate Employment and TFP
Employment
TFP
1976-1986
1.93%
1.03%
2003-2013
0.38%
1.44%
Note: Growth rate of total employment calculated from LBD
micro-data. Growth rate of TFP from BLS.
Figure 4: Employment Share of Entrants
20
GARCIA-MACIA, HSIEH, KLENOW
Figure 5: Employment per Firm, Young vs. Old
and 2008 in Figure 4. Decker et al. (2014) point to the decline in the employment
share of entrants as evidence of the decline in “business dynamism.” According
to Hsieh and Klenow (2014), rapid growth of surviving plants is a robust phenomenon across years in the U.S. Census of Manufacturing. Figure 5 suggests
that the same is true for the entire U.S. private sector. As discussed earlier, the
model can explain this fact if older firms have more products and higher quality
products compared to young firms.
Next, we show the exit rate of young vs. old firms (figure 6) and large vs.
small firms (figure 7). Here the exit rate is the annualized probability that the
firm exits within the next five years, “young” firms are those established within
the last four years (inclusive), and “old” firms are those with age ≥ 5. Note that
younger firms and smaller firms have higher exit rates compared to older and
larger firms. The model of innovation can explain this fact if older and larger
firms produce more varieties compared to younger and smaller firms. Note as
well that, in contrast to the prediction of the polar model with only creative
destruction, the decline in exit rates with firm size is very small. A ten-fold
increase in firm size is associated with only about a 1 percentage point decrease
in the exit rate.
The last set of figures present the rate of job creation and destruction, where
HOW DESTRUCTIVE IS INNOVATION?
Figure 6: Exit Rate, Young vs. Old
Figure 7: Exit Rate, Large vs. Small Firms
21
22
GARCIA-MACIA, HSIEH, KLENOW
Figure 8: Job Creation and Destruction Rates
the rates are calculated over five years. Specifically, the job creation rate in 1981
is the sum of employment of firms in 1981 that did not exist in 1976 and the
change in employment of firms that exist in both 1976 and 1981 divided by
average total employment in 1976 and 1981. The job destruction rate in 1981
is the sum of employment of firms in 1981 that no longer exist by 1986 and the
change in employment of firms that exist in both 1981 and 1986 divided by the
average of total employment in 1981 and 1986.
Figure 8 shows that the overall rate of job creation exceeds the rate of job
destruction, which is simply another way to illustrate that aggregate employment is growing throughout this period (Table 2). Note also that the aggregate
job creation rate fell by 10 percentage points between 1981 and 2008, while
the job destruction rate was unchanged. The decline in “business dynamism”
highlighted by Decker et al. (2014) is thus entirely driven by the decline in the
job creation rate. In turn, since the growth rate of aggregate employment fell (as
shown in Table 3), the gap between the rate of job creation and job destruction
necessarily has to fall as well.
Finally, following Davis et al. (1998), figure 9 plots the distribution of annual job creation and destruction rates in the LBD in the two time periods. As
discussed earlier, these rates are bounded between -2 (exit) and +2 (entry) and
HOW DESTRUCTIVE IS INNOVATION?
23
Figure 9: Distribution of Job Creation and Destruction
the vertical axis shows the percent of all creation or destruction contributed by
firms in each bin. The empirical moment we use from Figure 9 is the share of job
creation where the growth rate of employment is lower than 1, which is about
35% in the two years.
4.
Sources of Growth
We now present the estimated parameter values chosen to match moments
from model simulations to the data moments in the U.S. LBD. Remember that
we need to estimate 5 parameters for the innovation rates (δi , δe , λi , κi , and
κe ) and 2 parameters for the change in quality (θ and sκ ). If we use all 9 data
moments to estimate these 7 parameters, then the estimation is over-identified.
Instead, we will use the minimum set of data moments necessary to estimate
the parameters, and then “test” the predictions of the model using the moments
that were not used for the estimation.9
We first pick the overhead cost such that the minimum firm size is one. The
overhead cost then pins down the cutoff quality threshold ψ and the rate at
which existing varieties disappear due to obsolescence δo . We then pick the
9
The appendix provides details on the simulation method we use.
24
GARCIA-MACIA, HSIEH, KLENOW
arrival rate of new varieties κi +κe net of δo to exactly match the aggregate growth
rate of employment in the data.
Second, conditional on the estimate of κi +κe and δo , we then pick κi , λi , δi , δe ,
and sκ to provide the best fit to the following five data moments: the aggregate
rate of job creation and destruction, the entrant share of employment, the share
of job creation where the employment growth rate < 1, employment by age, and
the standard deviation of firm employment. Here, although we have five data
moments and five parameters, we will not perfectly fit these five data moments.
The last parameter we need is the shape parameter of the Pareto distribution
θ of the quality draws. Remember that, conditional on innovation, the average
1/(σ−1)
θ
improvement in quality of an existing variety is sq = θ−(σ−1)
. Since we
already have an estimate of the growth contribution of new varieties (which is
pinned down by sκ , κe and κe ) and the arrival rate of quality improvement of
existing varieties, we pick θ such that the sum of the growth contribution of new
varieties and quality improvements of existing varieties is exactly equal to the
TFP growth rate in the data.
To be clear, we do not use exit by age and by employment to estimate the
model. As discussed earlier, exit by age is affected by the rate of creative destruction by entrants. In turn, the relative importance of creative destruction
vs. own innovation among incumbents affects exit by employment. We could
also have used these two data moments for estimation, either in addition to the
7 data moments we chose or instead of some of these data moments. Instead,
we chose to use exit by age and exit by employment as over-identifying tests.
That is, we will compute exit by age and exit by employment predicted by the
estimated parameters and compare them to the data.
Table 4 presents the parameter values inferred from the data using the procedure described above. Based on the data moments from 1976 to 1986, we infer
a 68% arrival rate for own-variety quality improvements by incumbents. Conditional on no own-innovation, quality improvements through creative destruction occur 45% of the time by other incumbents. And conditional on no own-
25
HOW DESTRUCTIVE IS INNOVATION?
Table 4: Inferred Parameters Values
1976-1986
2003-2013
Own-variety improvements by incumbents λi
67.9%
69.7%
Creative destruction by incumbents δi
45.4%
68.6%
Creative destruction by entrants δe
100.0%
100.0%
New varieties from incumbents κi
0.0%
0.0%
New varieties from entrants κe
10.0%
2.2%
Pareto shape of quality draws θ
28.2
16.3
Relative quality of new varieties sκ
0.32
0.65
innovation and creative destruction by another incumbent, quality improvement through creative destruction by entrants occurs with probability one. The
unconditional probability that quality of a given product improves due to creative destruction by an incumbent is thus 14.6%, and the corresponding probability of creative destruction by an entrant is 17.5%.10 The unconditional probability any product is improved upon is thus 100%, of which 68% is from own
innovation and 32% from creative destruction.
The step size of the quality improvement of existing varieties is governed by
the shape parameter θ of the Pareto distribution of the quality draws. Given
that θ = 28, the average improvement in quality (conditional on innovation)
is 3.7 percent. New varieties are only created by entrants and arrive at 10%
per year, with an average quality that is 32% of the average quality of existing
varieties. Overhead costs imply that the cutoff quality threshold ψ is 5 percent of
the average quality of exiting varieties. This cutoff implies that the probability a
10
(1 − 0.679) · 0.454 = 0.146 and (1 − 0.679) · (1 − 0.454) = 0.175.
26
GARCIA-MACIA, HSIEH, KLENOW
Table 5: Sources of Growth, 1976-1986
Entrants
Incumbents
Creative destruction
12.5%
10.4%
22.9%
New varieties
29.3%
0.0%
29.3%
Own-variety improvements
-
47.8%
47.8%
41.8%
58.2%
variety exits due to overhead cost δo is essentially zero.11 Net growth in varieties
is therefore 10% a year, which matches the growth rate of total employment and
number of firms in the 1976-1986 period.
Table 5 presents the sources of growth in the 1976 to 1986 period implied
by the parameters in Table 4. The rows decompose aggregate TFP growth into
the contribution of growth from creative destruction, new varieties, and ownvariety improvements (see equation (3)). About 23% of growth comes from
creative destruction. Own-variety improvements by incumbents account for
48%. New varieties a la Romer (1990) are the remainder at 29%.
The columns in Table 5 decompose aggregate TFP growth into the contribution of entrants vs. incumbents (see equation (4)). Incumbents are the main
source of growth, accounting for 58% of aggregate TFP growth, with entrants
contributing under the remaining 42%. Aghion et al. (2014) provide complementary evidence for the importance of incumbents based on their share of
R&D spending.
Note in Table 6 that innovation by entrants in the 2003-2013 is significantly
lower than in 1976-1986 period. The innovation rates by entrants Table 4 indicate why. The arrival rate of new varieties κe fell from 10% to 2% a year. The unconditional probability an existing product is creatively destroyed by an entrant
fell from 17.5% to 10% per year. Remember the ten percentage point decline
11
The exact number is δo = 0.0073%.
27
HOW DESTRUCTIVE IS INNOVATION?
Table 6: Sources of Growth, 2003-2013
Entrants
Incumbents
Creative destruction
8.8%
19.1%
27.9%
New varieties
9.3%
0.0%
9.3%
Own-variety improvements
-
62.8%
62.8%
18.1%
81.9%
in the employment share of entrants between 1981 and 2008 shown in Figure
4. The model explains the decline in the employment share of entrants as the
result of a decline in innovation by entrants.
The declining importance of entrants can also be seen in Table 6, which decomposes the sources of growth for the 2003 to 2013 period. Entrants contribute
18% of aggregate TFP growth during the 2003-2013 period, down from 42% in
the first ten years of the LBD. After we filter the data through the lenses of our
model, this is our answer to the question how much does the observed decline
in “business dynamism” matter for aggregate growth.
Yet remember from Table 3 that the growth rate of aggregate TFP increased
from 1% at year from 1976 to 1986 to 1.44% a year from 2003 to 2013. How can
the growth rate of aggregate TFP increase when innovation by entrants falls?
The answer of course is that innovation by incumbents must have increased,
and this increase more than offsets the effect of less innovation by entrants. Table 6 shows that contribution of incumbents, primarily via own innovation, was
the main source of innovation in the most recent period of the LBD data. And
remember that more own innovation implies that there will be less employment
volatility in the data.
Remember our estimation procedure chose parameter values such that the
model exactly matched aggregate TFP and employment growth in the data. Table 7 shows the fit of the model for the subset of the data moments we used
28
GARCIA-MACIA, HSIEH, KLENOW
Table 7: Model Fit, 1976-1986
Data
Model
19.9%
20.1%
3.6
1.8
Job creation rate
40.4%
40.5%
Job destruction rate
30.4%
30.5%
Share of job creation from employment growth < 1
35.8%
19.5%
1.26
1.26
Employment share of entrants
Average employment incumbents
Average employment entrants
SD(log employment)
Note: Table presents predicted values for the moments used for estimation that were not
forced to exactly fit the data. The estimation also used employment and TFP growth rates,
which we force the model to match exactly.
for the estimation but did not force the model to fit exactly. As can be seen,
the model almost perfectly matches the employment share, the aggregate rate
of job creation and destruction, and the standard deviation of log employment.
However, the model’s predicted value of average employment of old vs. young
firms is lower than in the data. The model predicts a ratio of 1.8 in the model
while the ratio of employment of incumbents to entrants is 3.6 in the data. The
model also performs poorly in terms of the share of job creation due to firms
where employment growth is less than 1. The model predicts that the share is
20% whereas the share is 36% in the data.
To generate more employment with age, we need more creative destruction
by incumbents or new varieties created by entrants to have lower quality. The
quality of new varieties by entrants is constrained by the size dispersion. The
rate of creative destruction by incumbents is constrained by also needing to fit
the tails of the distribution of job creation. More creative destruction will lower
the share of job creation around small changes in employment. And remember
HOW DESTRUCTIVE IS INNOVATION?
29
that the model’s predicted share of job creation due to small changes in employment is already too low. Similarly, we can do better in terms of matching
the distribution of job creation by lowering the rate of creative destruction by
incumbents. However, this has the cost of lowering growth in employment by
age.
Finally, remember we did not use the elasticity of exit rates with firm employment and firm age to estimate the model’s parameters. However, these
moments are useful because they also depend on the relative magnitude of
the different sources of innovation. The elasticity of exit rates with firm employment depends on the relative importance of creative destruction vs. own
innovation by incumbents. The elasticity of exit rates with respect to age also
depends on rate of creative destruction by incumbents.
Figure 10 presents the relationship between firm exit and employment predicted by the model parameters in Table 4 for the 1976-1986 period. As can be
seen, the exit rate predicted by the model as well as the elasticity of exit with
respect to firm size predicted by the model is lower than in the data. Intuitively,
this is because the model generates too little heterogeneity in the number of
varieties across firms relative to the overall heterogeneity in firm size. A higher
rate of creative destruction by incumbents will increase the exit rate and the
slope of the exit rate with respect to firm size, but this parameter is constrained
by the need to fit the share of job creation driven by firms with small changes
in employment. And a higher rate of creative destruction by incumbents will
further lower the share of job creation among firms with small employment
changes (which is already too low relative to the data.)
The model does better in terms of the fit of the exit rate with respect to firm
age. The model predicts that the ratio of exit rates among incumbent firms
relative to entrants is 86%. In the data this ratio is 81%.
30
GARCIA-MACIA, HSIEH, KLENOW
Figure 10: Exit by Employment, Model vs. Data
5.
Conclusion
How much innovation takes the form of creative destruction versus firms improving their own products versus new varieties? How much innovation occurs
through entrants vs. incumbents? We try to infer the sources of innovation
from manufacturing plant dynamics in the U.S. We conclude that creative destruction is vital for understanding job destruction and accounts for something
like 20 percent of growth. Own-product quality improvements by incumbents
appear to be the source of 80 percent of growth. Net variety growth contributes
little, as creation of new varieties is mostly offset by exit of low quality varieties.
Our findings could be relevant for innovation policy. The sources of growth
we identify can alter business stealing effects vs. knowledge spillovers, and
hence the social vs. private return to innovation. The importance of creative
destruction ties into political economy theories in which incumbents block entry and hinder growth and development, such as Krusell and Rios-Rull (1996),
Parente and Prescott (2002), and Acemoglu and Robinson (2012).
It would be interesting to extend our analysis to other sectors, time periods,
and countries. Retail trade experienced a big-box revolution in the U.S. led
by Wal-Mart’s expansion. Online retailing has made inroads at the expense
of brick-and-mortar stores. Chinese manufacturing has seen entry and expan-
HOW DESTRUCTIVE IS INNOVATION?
31
sion of private enterprises at the expense of state-owned enterprises (Hsieh and
Klenow (2009)). In India, manufacturing incumbents may be less important for
growth given that surviving incumbents do not expand anywhere near as much
in India as in the U.S. (Hsieh and Klenow (2014)).
Our accounting is silent on how the types of innovation interact. In Klette
and Kortum (2004) a faster arrival of entrant creative destruction discourages
R&D by incumbents. But, as stressed by Aghion et al. (2001), a greater threat of
competition from entrants could stimulate incumbents to “escape from competition” by improving their own products. Creative destruction and own innovation could be strategic complements, rather than substitutes.
Our conclusions are tentative in part because they are model-dependent.
We followed the literature in several ways that might not be innocuous for our
inference. We assumed that spillovers are just as strong for incumbent innovation as for entrant innovation. Young firms might instead generate more knowledge spillovers than old firms do – Akcigit and Kerr (2015) provide evidence for
this hypothesis in terms of patent citations by other firms.
We assumed no frictions in employment growth or misallocation of labor
across firms. In reality, the market share of young plants could be suppressed
by adjustment costs, financing frictions, and uncertainty. On top of adjustment
costs for capital and labor, plants may take awhile to build up a customer base,
as in work by Foster et al. (2013) and Gourio and Rudanko (2014). Irreversibilities could combine with uncertainty about the plant’s quality to keep young
plants small, as in the Jovanovic (1982) model. Markups could vary across varieties and firms. All of these would create a more complicated mapping from
plant employment growth to plant innovation.
32
GARCIA-MACIA, HSIEH, KLENOW
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HOW DESTRUCTIVE IS INNOVATION?
35
Appendix: Simulation algorithm
Our simulation algorithm consists of the following steps:
1. Specify an initial guess for the distribution of quality across varieties.
2. Simulate life paths for a large number of entering firms.
3. Each entrant has one initial variety, captured from an incumbent or newly
created. In every year of its lifetime, it faces a probability of each type of
innovation per variety it owns, as in Table 1. A firm’s life ends when it loses
all of its varieties to other firms or when it reaches age 200.
4. Based on an entire population of simulated firms, calculate the moments
of interest listed in Table 8. Simulating life paths for many entering firms
produces a joint distribution of firm age, number of varieties, quality of
each variety, employment and exit probability. Take into account growth
in average quality and aggregate employment over time when computing
relative qualities and employment of varieties for a firm at different ages.
Set aggregate employment in each year so that employment per firm in
the simulation is the same as in the data.12
5. Repeat steps 1 to 4 50 times so that all moments converge. Step 1 takes
the quality distribution across varieties from step 4 as the starting point.
The converged moments imply a stationary joint distribution of all the
variables of interest (age, varieties, quality, employment and exit) across
firms.
6. Repeat steps 1 to 5, searching for parameter values to minimize the percentage distance between the simulated and empirical moments.
12
This requires the number of firms and aggregate employment in the model to grow at the
same rate as aggregate employment in the data.
36
GARCIA-MACIA, HSIEH, KLENOW
(a) Use theoretical restrictions from the model to pin down some parameters analytically. First, κi + κe − δo must equal empirical aggregate employment growth. Second, θ must be such that expected TFP
growth is the same as in the data. Third, set κi = 0 to maximize
growth in employment by age. Fourth, set λi as high as possible given
δi , or equivalently set δe = 1, to maximize the share of job creation
below 1. Size by age and the share of job creation below 1 are the only
moments we cannot fit precisely, as we reach corner solutions for the
related parameters. 13
(b) Update each of the remaining parameters using the most related moments as follows. Increase (decrease) δe if the entrants share is too
low (high). Increase δi if the job destruction rate is too low. Increase
δo if the fraction of varieties affected by overhead costs is lower than
δo . Increase ψ if minimum employment is below 1. Increase sκ if the
standard deviation of log firm employment is too high.
(c) To gain speed, for a given initial set of parameters, simulate less entrant firms and perform less iterations when the distance between
simulated and data moments is still large (e.g. more than 4 percent).
(d) Stop searching parameters when all simulated moments fall within a
1 percent distance of the empirical value. 14
13
We choose to assign lower lexicographical priority to size by age and the share of job
creation below 1 when matching moments. There are alternative sets of parameters which fit
these two moments exactly but fail on other moments which we deem more relevant, such as
the entrants share.
14
To speed up the code, one may allow for a slightly larger margin of error in moments related
to parameters with a small impact on growth contributions, such as δo and ψ.
37
HOW DESTRUCTIVE IS INNOVATION?
Table 8: Targeted Moments
Moment
Fit
TFP Growth Rate
Exact
Employment Growth Rate
Exact
Average Firm Employment
Exact
Employment Share of Entrants
Very Good
Job Creation and Destruction Rates
Very Good
Std. Dev. of Log Firm Employment
Very Good
Minimum Size
Very Good
Size by Age
Corner Solution
Share of Job Creation below 1
Corner Solution