The effect of firm turnover on innovation strategies:
Panel data evidence∗
Alex Armand
University of Navarra
Pedro Mendi
University of Navarra
PRELIMINARY AND INCOMPLETE
DO NOT CIRCULATE
March 2016
Abstract
Different studies focused on the effect of changes in the market environment on innovation investments, little attention has been given on the effect of changes in the number of firms
operating in a specific sector over firms’ decision to invest on innovation. This paper studies
whether industry- and size-specific entry and exit rates affect firms’ choices to invest on internal R&D or other innovation activities such as external R&D, acquisition of machinery, or
licensing. To identify the effect of changes in entry and exit rates, this paper makes use of a
Spanish panel of firms yearly surveyed on their innovation activities for the period 2005-2013.
We find that exit rates have a negative effect, especially on internal R&D activities and among
smaller firms. We find no statistical significance for the effect among larger firms and on the
acquisition of external innovation inputs.
Keywords: R&D, Innovation, Firm entry, Firm exit.
JEL codes: L22, O31, O32.
∗
Armand: University of Navarra, NCID - Edificio de Bibliotecas, 31009 Pamplona, Spain, Institute for Fiscal
Studies (e-mail: [email protected]); Mendi: University of Navarra, Department of Economics, Edificio Amigos, 31009
Pamplona, Spain (e-mail: [email protected]).
1
1
Introduction
The understanding of the determinants and consequences of firms’ innovation strategies has received wide attention in the Economics and Management literatures, being the focus of a large
number of studies. This understanding is essential in order to better design both government policies and business strategies that boost innovation and thus, ultimately, total factor productivity and
growth. While different studies focused on the environment in which firms are operating, little
attention has been given on the effect of changes in the number of firms in a specific sector over
firms’ decision to invest on innovation.
While internal characteristics have important consequences on this decision1 , external factors are also central in determining whether a firm invest on innovation. Different studies have
focused on the role of the business cycle on R&D expenditures (Barlevy (2007); Arvanitis and
Woerter (2014); Min (2011). The pro-cyclical behavior of R&D is explained by liquidity constraints, whereas counter-cyclicality is based on a lower opportunity cost of R&D during recessions. Aghion et al. (2012), using a panel of French firms, find that liquidity constraints are an
important determinant of the level of expenditure on R&D. Paunov (2012) also analyses the effect
of a recession on firms’ innovation strategies. Understanding how market characteristics affect
this decision can have important consequences on the overall economy.
In this paper, we study how changes in the number of firms operating in a specific sector affects
firms’ choice of innovation strategy. Specifically, we study the impact of entry and exit rates
on firms’ choices of internal and external activities conductive towards the introduction of new
products and processes2 . We make use of a panel of Spanish manufacturing firms yearly surveyed
during the period 2005-13 and we exploit detailed information about innovation strategies and
input of these firms. We focus specifically on firms that were actively doing internal R&D at the
beginning of the period, which allow studying the effect on investment decisions among firms
that were already investing in innovation.3 To measure the effect of changes in the number of
firms operating, we exploit time, industry and firm size variation and we supplement the panel
of Spanish manufacturing firms with industry- and size-specific entry and exit rates, which are
supplied by the Spanish Central Business Register.
The panel dimension of the dataset allow controlling for time-invariant unobservable firm
characteristics, which is an important contribution given that several studies in literature focus
on cross-section analysis. In addition, the level of disaggregation of entry and exit rates allow us
introducing time and (macro) industry dummies to control for unobserved effects that are common
to firms within the (macro) industry and that vary over time. We find that entry and exit rates
1
A branch of literature has focused on internal factors driving innovation. For instance, the study of how firm
characteristics, such as absorptive capacity or being a subsidiary of a foreign multinational, affect firms’ decisions to
engage in internal R&D activities or to procure external knowledge has been studied in contributions such as Argyres
and Silverman (2004); Cohen and Levinthal (1990); Zahra and George (2002); Sadowski and Sadowski-Rasters (2006).
2
Among external activities, we consider external R&D, acquisition of machinery that embodies new technology, or
disembodied technology in the form of licensing.
3
This is a data-driven decision. The dataset is a panel of firms conditional on doing internal R&D at the beginning
of the panel.
2
indeed affect firms’ choices of innovation strategies, and that the effect depends on firm size, with
the overall effect being driven mostly by smaller firms.
Our results suggest that the likelihood of firm exit is indeed a determinant of its choice of
innovation strategy. Most empirical studies, such as Dunne et al. (1988), show that exit is most
likely within smaller and younger firms, a pattern that we indeed find in our data. We find that
a higher exit rate negatively affects innovation activities among small firms, but not among large
firms. The latter tend to be more diversified and less likely to be liquidity constrained. We also
find that the effect of a higher exit rate is larger for internal R&D activities. These are typically
investments with a larger time horizon and subject to a higher degree of uncertainty than external
sources, which tend to be more ready to use, see Pindyck (1991) for theoretical foundations for
investment under uncertainty.
This paper contributes not only to the literature aiming at understanding the effect of the market
environment on innovation strategies, but also to the literature studying the innovation behaviour in
periods of recessions. The period of analysis is of particular interest because the Spanish economy
as a whole encountered wide changes.4 In this period, industrial production dropped by roughly
30 percent and unemployment rate rose from 8 percent in 2006 to 26 percent in 2013, a huge
increase in just 7 years. These conditions are expected to have produced strong pressures to firms
investing in innovation and more specifically to smaller firms, which have less capacity to face
periods of recessions.
The remainder of the paper is organized as follows. Section 2 describes the data used in this
paper. Section 3 discusses the empirical strategy, whose results are presented in Section 4. Finally,
Section 5 presents some concluding comments.
2
Data
The paper combines information from different sources, which are presented in this section. We
combine the PITEC dataset, which provides detailed information about innovation inputs and
outputs of Spanish firms, with data about market characteristics supplied by the Central Business
Register at the Spanish National Statistics Institute and the Spanish Statistical Institute’s Industrial
Survey.
2.1
The PITEC database
The PITEC (Panel de Innovación Tecnológica) database is a panel of firms yearly surveyed by the
Spanish National Statistics Institute (INE)5 . It supplies detailed information about firm’s characteristics and about all inputs and outputs related to innovation, such as activities in R&D, purchase
of services, other activities linked to innovation, factors limiting investments in R&D, intellectual
4
5
See section B.1 in Appendix for a discussion.
In Appendix, Table A1 presents the definitions of the variables used in the empirical analysis.
3
property and property rights, introduced innovations in production processes and in products.6
The questionnaire is largely comparable with the Community Innovation Survey, used in many
other European countries; see for instance Archibugi et al. (2013); Ballot et al. (2015); Cassiman
and Veugelers (2006, 2002); Mohnen and Roller (2005); van Beers et al. (2008) to cite a few.
Each firm is linked to the industry of its main activity. These industries are based on CNAE-93
and CNAE-09 industry classifications, respectively, which are the industry classifications used by
the Spanish National Institute of Statistics7 . In order to uniform the panel of firms, in absence of a
direct conversion of the two classifications, we have created a new industry variable.8 In addition,
in some of our econometric specifications we have included time-by-industry fixed effects, in order
to control for unobserved time-varying factors at the industry level. When this is the case, we have
grouped some of the industries into macro-sectors. Table 1 presents the list of selected sectors and
the aggregation code identifying each macro sector. The table also provides the correspondence
between the CNAE-93 and CNAE-09 industry classifications.
The PITEC panel includes four subsamples, with firms being assigned to each subsample according to whether they met the required criteria at the time of their addition to the panel. Specifically, the first subsample includes large firms (in excess of 200 employees), with and without
intramural R&D expenditures. The second subsample includes firms with fewer than 200 employees with intramural R&D activities at the time of inclusion in the panel. In 2003 the PITEC panel
was launched including the subsample of large firms and a small sample of smaller firms with
internal R&D activities. The third subsample includes small (fewer than 200 employees) firms
with external R&D activities. Finally, the fourth subsample includes small without innovation
activities. Most of the observations for the latter two samples were added in 2004 and 2005.
Therefore, the PITEC database contains a stable panel of large firms for 2003-13, whereas for
smaller firms for the period is 2005-13. As pointed out above, firms were classified in these subsamples at the time of inclusion in the database, and thus a given firm’s status may have changed
along time. For instance, a small firm that was initially doing internal R&D might have chosen to
discontinue its internal R&D activities later on, or grow to become a large firm.
In this paper, in line with most empirical studies, we focus on the manufacturing sector9 .
The reason to exclude services industries is that the role of formal internal R&D activities is less
relevant. Furthermore, the purchase of external services related to innovation might emcompass
activities presenting deep differences compared to manufacturing.
6
In order to preserve data confidentiality, some of the firm characteristics, such as turnover, capital investment or
the number of employees, have been anonymized. This process consists on a ranking of the values of the variable in
question and computation of the averages of three or five consecutive observations, depending on the specific variable.
For a given firm, the actual realization of the variable in question has been replaced by this average. Given the large
number of observations, this anonymization process is expected not to introduce significant measurement error.
7
The CNAE-09 classification system was introduced in 2008.
8
The PITEC database identifies 56 industries up to 2008 (acti variable in the original dataset), and 44 industries
from 2008 on (actin variable in the original dataset). Starting from 2008, the new industry classification variable,
sector, is based on actin. For years up to 2008, since both acti and actin are reported in 2008, if acti is the same in
periods prior to 2008 and in 2008, sector equals the value of actin in 2008. If there are changes in acti prior to 2008,
sector equals the value of actin such that the overlap between acti and actin is maximum in 2008.
9
The manufacturing sector comprises industries 3 to 25 according to the CNAE-09 classification.
4
Table 1: List of industries and macro-sectors
Manufacturing sector
Food, beverages, and tobacco
Textiles
Wearing apparel
Leather and footwear
Wood and cork
Paper and paper products
Printing and reproduction of recorded media
Chemical products
Pharmaceutical products
Rubber and plastics
Other non-metallic mineral products
Manufacture of basic metals
Fabricated metal products
Computer, electronic and optical products
Electrical equipment
Other machinery and equipment
Motor vehicles, trailers and semi-trailers
Building of ships and boats
Aircraft and spacecraft and machinery thereof
Other transport equipment
Furniture
Other manufacturing
Repair and installation of machinery and equipment
Acti
(CNAE-93)
2, 3
4
5
6
7
8
9
11
12
13
14, 15
16, 17
18
20, 22, 23, 24
21
19
25
26
27
28
29
30, 31
∗
Note: Fabricated metal products excludes machinery and equipment.
classified as a service activity under CNAE-93.
∗
Actin
(CNAE-09)
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
Macro
sector
1
2
2
2
3
4
4
5
5
3
6
7
7
8
8
9
10
10
10
10
4
4
9
Repair and installation of machinery and equipment was
We supplement the dataset with industry-level information by matching the industry classification reported in the PITEC database with aggregated data supplied by the Spanish Statistical
Institute’s Industrial Survey (Encuesta Industrial de Empresas). We have included data about the
level and growth rates of total profits, inventory variation, as well as the total sales, personnel
expenditures, total employment, hours worked, and investment in tangible assets. Specifically,
lnhours is the logarithm of the total number of hours worked in the industry, lntaninvest is the
logarithm of total tangible investment, and gr_prof its is the growth rate of the profits of firms
in the industry. All these variables are observed at the same level of aggregation as sector. Also
recall that some of our specifications include macro-sector dummies interacted with time fixed
effects.
2.2
Firm characteristics and innovation strategies
Table 2 presents summary statistics of the different variables used in the econometric analysis,
distinguishing between small and large firms. Over 70% of the firms overall have innovation inputs
(internal R&D and/or technological purchases), with the percentage being almost 90% within the
subsample of large firms. Disaggregating into internal R&D and external sources of technology,
the proportion of large firms that do at least one of the two activities is also larger. Large firms
also have more patent applications on average than smaller firms, and are more likely to be part of
a foreign multinational.
5
We also provide descriptive statistics by type of innovation strategy and firm size. Regarding
innovation strategy, we consider four categories, namely firms that do both internal R&D and
buy, internal R&D only, buy only, and none. Summary statistics are presented in Table 3 Not
surprisingly, firms that do both internal R&D and buy are larger, have more patent applications,
and are more likely to export than firms that neither do internal R&D nor buy.
Table 2: Firm characteristics, by firm size quintile
Size quintile
Small firms
0.679
(0.467)
Firm has innovation inputs in t
R&D
0.614
(0.487)
Buy
0.371
(0.483)
Subsidiary of foreign MNC
0.076
(0.265)
Log number employees t-1
3.565
(1.033)
Percentage of female employees t-1
0.261
(0.208)
N of patent appl. t-1
0.403
(2.845)
Biotech
0.040
(0.197)
Local market
0.962
(0.191)
National market
0.955
(0.207)
EU market
0.803
(0.398)
Rest of the World
0.640
(0.480)
Part of group of firms
0.303
(0.460)
Note. Standard deviations in parenthesis. Size quintiles are determined over the sample distribution of the number of
employees.
2.2.1
Sector- and size-specific market entry and exit rates
To measure the effect of entry and exit rate on innovation inputs, we supplement each firm with
sector- and size-specific market rates using information provided by the Central Business Register,
also managed by the Spanish National Statistics Institute. In particular, the Central Business
6
7
Small firms
Buy
Both
(2)
(3)
0.072
0.084
(0.259)
(0.278)
3.546
3.865
(0.980)
(0.951)
0.246
0.282
(0.206)
(0.200)
0.198
0.725
(0.893)
(4.196)
0.028
0.069
(0.165)
(0.254)
0.952
0.961
(0.213)
(0.194)
0.946
0.975
(0.225)
(0.157)
0.781
0.881
(0.413)
(0.323)
0.591
0.745
(0.492)
(0.436)
0.297
0.389
(0.457)
(0.487)
None
(4)
0.056
(0.231)
3.146
(1.057)
0.242
(0.221)
0.092
(0.612)
0.018
(0.134)
0.960
(0.195)
0.925
(0.263)
0.687
(0.464)
0.488
(0.500)
0.217
(0.412)
R&D
(5)
0.384
(0.487)
5.924
(0.735)
0.261
(0.186)
1.315
(6.809)
0.044
(0.206)
0.912
(0.284)
0.988
(0.109)
0.938
(0.241)
0.861
(0.347)
0.828
(0.377)
Large firms
Buy
Both
(6)
(7)
0.451
0.329
(0.499)
(0.470)
6.068
6.199
(0.889)
(0.807)
0.247
0.276
(0.206)
(0.188)
0.172
4.360
(0.752)
(26.462)
0.017
0.100
(0.130)
(0.300)
0.867
0.928
(0.340)
(0.259)
0.961
0.988
(0.193)
(0.108)
0.923
0.948
(0.268)
(0.221)
0.845
0.872
(0.362)
(0.334)
0.845
0.871
(0.362)
(0.335)
None
(8)
0.405
(0.491)
5.552
(0.807)
0.248
(0.229)
0.200
(1.425)
0.023
(0.151)
0.864
(0.343)
0.922
(0.268)
0.821
(0.384)
0.689
(0.463)
0.815
(0.389)
Total
0.120
(0.325)
3.954
(1.349)
0.262
(0.206)
0.789
(8.529)
0.045
(0.208)
0.954
(0.208)
0.959
(0.198)
0.823
(0.382)
0.672
(0.469)
0.389
(0.488)
Note: Standard deviations in parenthesis. The sample is divided in the following grous: “R&D” indicates firms investing uniquely in internal R&D, “Buy” indicates firms investing uniquely in external
R&D, purchasing machinery and licensing, “Both” indicates firms investing in both strategies, while “None” indicates firms not investing in any innovation strategy.
Part of group of firms
Rest of the World
EU market
National market
Local market
Biotech
N of patent appl. t-1
Percentage of female employees t-1
Log number employees t-1
Subsidiary of foreign MNC
R&D
(1)
0.088
(0.284)
3.707
(0.954)
0.264
(0.201)
0.450
(2.836)
0.037
(0.189)
0.967
(0.178)
0.969
(0.173)
0.851
(0.356)
0.704
(0.457)
0.310
(0.463)
Table 3: Firm characteristics and innovation, by type of investment
Register provides information on registrations of companies, companies remaining in business,
and exiting companies, by legal status, wage-earner stratum and main economic activity. In terms
of number of employees, entry and exit rates are available for the following strata: between 1 and
5; between 6 and 9; between 10 and 19; 20 or more. Each firm is then matched using the number
of employees and the sector variable discussed in section 2.1.
To measure how the market environment changed ifor a firm, a potential indicator is the change
in the number of firms operating at year t for a firm in industry j and of size group s, the net exit
(or entry) rate. This should capture how individual firms are affected by a change in a very specific
market environment, rather than an aggregate change, and specifically by whether the number of
firms in the sector and size stratum has increased or decreased. Assuming that ∆jst is the decrease
in the number of firms at year t for a firm in industry j and of size group s, we can compute the
net exit rate as:
netexitjst =
n_exitjst − n_entryjst
∆jst
=
n_surjst−1 + n_entryjst−1
n_surjst−1 + n_entryjst−1
(1)
where n_surjst , n_entryjst and n_exitjst are the number of firms surviving, entering and exiting
industry j in size group s in year t, respectively. Therefore, the sum n_surjst−1 + n_entryjst−1
equals the total number of firms in the industry-size group at the beginning of year t.
Since the interest of the paper is to look specifically at the effect of entry and exit rate, we can
decompose the net exit rate in its two main components, distinguishing the entry of new firms and
the exit of firms. We can therefore write:
exitjst =
entryjst =
n_exitjst
n_surjst−1 + n_entryjst−1
n_entryjst
n_surjst−1 + n_entryjst−1
(2)
(3)
where exitjst is the exit rate at year t for a firm in industry j and of size group s and entryjst
is the entry rate.
Evidence suggests that exit and entry rates also depend on firm characteristics, with smaller
and younger firms affected by higher exit rates (Malerba and Orsenigo, 1996; Abbring and Campbell, 2010). In our sample, in order to compare the average entry and exit rates affecting firms
of different size, we compare the average rates at different quintiles of the distribution of number
of employees.10 The left panel of Figure 1 shows how the average exit rate differs across size
quintiles. Firms in the first quintile are affected by a much larger exit rate (around 3 percent),
compared to larger firms (around 1 percent). This pattern is similar for the entry rate (right panel
of Figure 1) with smaller firms again affected by a larger entry rate (around 2 percent versus 1
percent for larger firms).11
10
Since we observe the number of employees, we create the discrete variable q_sizeijt that takes values {1, 2, 3, 4, 5}
if firm i is in the first, second, third, fourth and fifth quintile of the sample observations in industry j at time t. This
allows us to determine what specific quintile in the size distribution of firms in the sample a particular firm belongs to.
11
In Appendix, we discuss the overall situation of the Spanish economy in the period of reference and we provide
additional descriptive analysis of the entry and exit rates. For example, figure B2 shows the distribution of the exit and
8
.04
.04
.03
.03
Average entry rate
Average exit rate
Figure 1: Average exit and entry rates by size quintile
.02
.01
0
.02
.01
1
2
3
4
0
5
1
2
Size quintile
3
4
5
Size quintile
Note. The left panel shows the distribution of exit rates by firm quintile, while the right panel shows the distribution of
entry rates. Size quintile is determined on the basis of the distribution of number of employees in a certain year.
Previous studies have also found that there is a relationship between entry and exit rates12 .
Figure 2 presents a scatter plot for the entry and exit rates in the period of study and a linear
prediction. Observations are at firm level to capture variation on entry and exit rates at our main
unit of observation. Similar to previous studies exit and entry rate a strongly correlated, but the
correlation decrease with size. We observe a correlation of roughly 0.7 for firms in the first quintile
and 0.4 for larger firms.
Figure 2: Correlation between entry and exit rate exposure at firm level
1st quintile
2nd-5th quintiles
Fitted values
Entry rate
.15
.1
.05
0
0
.05
.1
.15
Exit rate
The correlation between entry and exit rate for 1st (2nd-5th) quintile is equal to .702 ( .404).
Note. Size quintile is determined on the basis of the distribution of number of employees in a certain year. Fitted values
are computed using a linear regression of the entry rate on the exit rate.
the entry rate for the whole period.
12
For instance, Dunne et al. (1988) find that entry and exit rates are positively correlated across industries.
9
3
Empirical strategy
The time and firm-level variation in terms of innovation strategy and market conditions allows
exploiting a fixed effect estimation method. This allows controlling controlling for firm-specific
unobservable characteristics that are constant over time and affects the choice of innovation strategy. This is particularly important in our setting as it eliminates the possibility that firms in a
specific sector might be affected by different market conditions at time t just due to the peculiarity
of the firm or the sector.
We are interested in measuring the effect of entry and exit rates on the innovation strategy
of the firm i at time t, yit .13 . In our first specification, we focus on the effect of the net exit
rate, netexitit , and for interpretation purposes we use the standardized variable. We estimate the
following equation:
yit = α0 + αN netexitit + Xit β +
T
X
γt dt +
t=2
T X
J
X
ωtj dt sj + ci + uit
(4)
j=2 t=2
where Xit is a matrix of time-varying firm characteristics14 , dt are year fixed effects, si are
macro-sector-specific dummy variables, ci are unobserved firm characteristics that are constant
over time and uit are idiosyncratic error terms. In some specifications we introduced a set of interaction terms between year and macro-sector dummies to control for sector year-specific effects.
We estimate equation (5) using fixed effects estimation and controlling for year aggregate effects.
In equation (4), our parameter of interest is αN , which captures the effect of changes in the
number of firms operating in the sector and in the size stratum of the firm, once we control for
the other observable characteristics and for the firm-specific unobservable characteristics ci . More
specifically, the effect captures the response in terms to innovation inputs to a decrease in the
number of firms operating.
Since the objective of the paper is to differentiate between the effect of firms exiting and
firms entering the sector, we extend equation (4) by measuring the effect of exit (exitit ) and entry
(entryit ) rates in the sector where the firm is operating and in the same size group. Similarly to
equation (4), we focus on the standardized effect of these variables. Specifically, we estimate the
specification:
yit = α0 + αEX exitit + αEN entryit + Xit β +
T
X
t=2
γt dt +
T X
J
X
ωtj dt sj + ci + uit
(5)
j=2 t=2
where Xit is a matrix of time-varying firm characteristics, dt are year fixed effects, si are
macro-sector-specific dummy variables, ci are unobserved firm characteristics that are constant
over time and uit are idiosyncratic error terms. Our parameters of interest are αEX and αEN ,
13
We focus on the contemporaneous effect of exit and entry rates. We have extended our analysis by replacing
the contemporaneous effect with the one-period lages and the results are similar. The effect of entry is positive and
statistically significant for the full sample of firms and for the subsample of smaller firms.
14
The Xit matrix may contain variables observed at the industry level, according to the classification in Table 1.
10
which captures the effect of changes in the exit and entry rates once we control for the other
observable characteristics and for the firm-specific unobservable characteristics ci .
4
Results
In this section we present estimates of the effect in changes in the number of firms operating in a
sector on different indicators of innovation inputs. Specifically, we focus on two types of inputs in
the innovation process. Firstly, we focus on R&D, which includes all activities incorporating research and development and carried out internally. Secondly, we look at external investments (we
indicated this type of investment by “Buy” strategies), which include external R&D, acquisition of
machinery that embodies new technology and acquisition of disembodied technology in the form
of licensing. We analyse both the extensive margin (section 4.1), i.e. whether a firm invests in
these inputs, and the intensive margin (section 4.2), i.e. how large are the investments in these
inputs.
4.1
Propensity to invest on innovation
To understand whether changes in the number of firms operating in a specific industry and size
stratum affect these innovation strategies, we start focusing on the effect of changes in net exit
rate15 , defined as the difference between entry and exit rates. Table 4 reports estimated coefficients
for equation (4) where the dependent variables are indicator variables equal to one if the firm
invested in internal R&D (columns 1-3) or in Buy strategies (columns 4-6) at time t and zero
otherwise. To test for the robustness of the estimate, we include different sets of controls. In
columns 1 and 4, the specification includes controls only for time-varying firm characteristics and
for time fixed effects, in columns 2 and 5 it includes interaction terms between time indicators
and macro-sector indicators, and in columns 3 and 6 it includes controls for industry-level time
varying characteristics. Specifically, the firm-level controls are the first lag of the logarithm of the
number of employees, share of female employees, and the number of patent applications. We have
also included indicators of the firm being a subsidiary of a foreign multinational, biotechnology
activities, belonging to a group of firms, and presence in the local, national, EU and other foreign
markets. At the industry level, he have included the logarithms of hours worked and tangible
investment, as well as the growth in profits.
The net exit rate in the sector and size stratum where a firm is operating has a negative and
statistically significant effect on the probability of the firm engaging in internal R&D, while the
effect on buying technology is negative but not statistically significant. The negative effect on
R&D is robust under different specifications and shows that one standard deviation increase in the
net exit rate, which is linked to a decrease in the number of firms in the stratum-sector, reduces by
around 2 percent the probability to invest.
Since we observe an effect on innovation inputs of the net exit rate in the sector, we extend
15
As discussed in section 3, we use the standardized transformation for this variable.
11
Table 4: Effect of net exit rates on innovation strategies
Std industry net exit rate t
R&D
(1)
FE
-0.018***
(0.005)
Firms with internal R&D in 2005
R&D
R&D
Buy
Buy
(2)
(3)
(4)
(5)
FE
FE
FE
FE
-0.021***
-0.020***
-0.012**
-0.008
(0.005)
(0.005)
(0.005)
(0.005)
Buy
(6)
FE
-0.007
(0.005)
Ln of hours worked
-0.017
(0.020)
0.005
(0.025)
Investment in tangible assets
0.007
(0.015)
0.008
(0.019)
Profits growth rate
0.000
(0.000)
28436
0.608
Yes
Yes
Yes
Yes
-0.000
(0.000)
28436
0.443
Yes
Yes
Yes
Yes
Observations
rho
Time-varying controls
Time fixed effects
Time*Sector fixed effects
Industry-level controls
28511
0.601
Yes
Yes
No
No
28511
0.609
Yes
Yes
Yes
No
28511
0.433
Yes
Yes
No
No
28511
0.442
Yes
Yes
Yes
No
Note: Standard errors in parenthesis are clustered at firm level. *** p < 0.01, ** p < 0.05, * p < 0.1. The dependent variables
are indicator variables for investments in internal R&D (columns 1-3) or purchase of external R&D, machinery or licensing (columns
3-6). Rho is the share of the overall variance explained by the firm-level unobserved fixed effect.
our analysis to understand whether exit and entry rates (as defined in section 2.2.1) have an asymmetric effect. To this purpose, we estimate equation (5) focusing on the same outcomes. More
specifically, table 5 presents estimates focusing on whether firms have invested on internal R&D
and on the purchase of external R&D resources, machinery and franchising. All the specifications
include time-varying controls at the firm level, in addition to time fixed effects.
Firm exit, but not entry, has a negative effect on the probability of carrying out internal R&D.
The effect on buying external technology is also negative, but its statistical significance is lost once
we include the time-macro-industry dummies, and the industry-level controls. We can observe that
the effect presented in table 4 is therefore driven by variation over time and across sectors of the
exit rate, while entry rate has no effect on innovation strategies, once we condition for the exit
rate. An increase of one standard deviation in the exit rate is decreasing the propensity to invest in
R&D by roughly 3 percent.
Up to now we have focused on variation of exit and entry rate not only at the level of sector of
the firm, but also in the same size stratum. It is possible that the decision to invest in innovation
inputs might depend not only on the change of number of firms operating in this specific stratum,
but also on the sector-wide variation. To test whether this sector-wide entry and exit rates affect
innovation inputs we proceed to estimate equation (5) where the entry and exit rates are computed
at the industry level. Therefore, the exit and entry rates for all firms in the same industry are the
same, regardless of the size of the firm. We are thus reducing the variation in entry and exit rates.
The estimates are reported in table 6. The signs of the estimated coefficients are similar to the
previous cases, although the coefficients on internal R&D are much smaller in absolute value, and
12
Table 5: Effect of standardized exit and entry rates on innovation strategies
Standardized exit rate t
Standardized entry rate t
R&D
(1)
FE
-0.028***
(0.006)
-0.004
(0.008)
Firms with internal R&D in 2005
R&D
R&D
Buy
Buy
(2)
(3)
(4)
(5)
FE
FE
FE
FE
-0.029***
-0.029***
-0.016**
-0.010
(0.007)
(0.007)
(0.006)
(0.007)
-0.000
(0.010)
-0.002
(0.010)
0.010
(0.009)
0.004
(0.010)
Buy
(6)
FE
-0.009
(0.007)
0.000
(0.010)
Ln of hours worked
-0.016
(0.020)
0.006
(0.025)
Investment in tangible assets
0.005
(0.015)
0.007
(0.019)
Profits growth rate
0.000
(0.000)
28436
0.608
Yes
Yes
Yes
Yes
-0.000
(0.000)
28436
0.443
Yes
Yes
Yes
Yes
Observations
rho
Time-varying controls
Time fixed effects
Time*Sector fixed effects
Industry-level controls
28511
0.600
Yes
Yes
No
No
28511
0.609
Yes
Yes
Yes
No
28511
0.433
Yes
Yes
No
No
28511
0.442
Yes
Yes
Yes
No
Note: Standard errors in parenthesis are clustered at firm level. *** p < 0.01, ** p < 0.05, * p < 0.1. The dependent variables
are indicator variables for investments in internal R&D (columns 1-3) or purchase of external R&D, machinery or licensing (columns
3-6). Rho is the share of the overall variance explained by the firm-level unobserved fixed effect.
no longer statistically significant. The coefficient on buy is statistically significant at the 5 percent
level. These results suggest that R&D investment are more sensitive to exit rates in the size stratum
of the firm, while external investments are more influenced by sector exit rates.
To understand the differences on the effect of exit rate and the choice of investment strategies
between small and large firms, we foucs again on sector-size variation and we study how such
effect varies depending on firm size. Specifically, we built indicator variables specifying which
quintile firms are representing in the distribution of the number of employees across all firms in
a specific year. To capture how the effect of exit rate varies accordingly to firm size, we have
extended equation (5) by interacting the exit and entry rate variable with indicators of the firm
being in the second, third, fourth and fitfh quintiles of the size distribution.16
We start by focusing on internal R&D. Figure 3 plot marginal effects for exit and entry rate
on the probability to invest on internal R&D at different quintiles of the firm size distribution.
We observe that the negative effect of exit rates captured in previous tables is mainly driven by
small firms, specifically in the two smallest quintiles. This is not surprising since, as we have seen
before, these are the firms affected by a higher rates, while largest firms are affected by smaller exit
rates.17 In terms of entry rates, the right panel shows that the effect is roughly zero for most firms
16
The estimated coefficients are reported in table B5.
In literature, smaller and younger firms are observed to be affected by higher exit rates (Malerba and Orsenigo,
1996).
17
13
Table 6: Effect of sector-level exit and entry rates on innovation strategies
Std industry exit rate t
Std industry entry rate t
R&D
(1)
FE
-0.006
(0.011)
R&D
(2)
FE
-0.019
(0.016)
0.006
(0.014)
0.012
(0.022)
Firms with internal R&D in 2005
R&D
Buy
Buy
(3)
(4)
(5)
FE
FE
FE
-0.016
-0.028**
-0.044**
(0.017)
(0.012)
(0.018)
0.018
(0.022)
0.028*
(0.015)
0.040*
(0.024)
Buy
(6)
FE
-0.040**
(0.019)
0.033
(0.025)
Ln of hours worked
-0.019
(0.021)
0.009
(0.025)
Investment in tangible assets
0.007
(0.016)
-0.004
(0.020)
Profits growth rate
0.000
(0.000)
28437
0.609
Yes
Yes
Yes
Yes
0.000
(0.000)
28437
0.442
Yes
Yes
Yes
Yes
Observations
rho
Time-varying controls
Time fixed effects
Time*Sector fixed effects
Industry-level controls
28512
0.601
Yes
Yes
No
No
28512
0.609
Yes
Yes
Yes
No
28512
0.432
Yes
Yes
No
No
28512
0.441
Yes
Yes
Yes
No
Note: Standard errors in parenthesis are clustered at firm level. *** p < 0.01, ** p < 0.05, * p < 0.1. The dependent variables
are indicator variables for investments in internal R&D (columns 1-3) or purchase of external R&D, machinery or licensing (columns
3-6). Rho is the share of the overall variance explained by the firm-level unobserved fixed effect.
apart from the largest quintile, for which we observe a negative slightly significant coefficient.18
In terms of external investments, Figure 3 presents instead the marginal effects of exit and
entry rates on the probability to purchase external R&D, machinery and licensing. Exit rates tend
to affect negatively firms in the first and the third quintile, but for both group of firms the coefficient
is marginally significant. For entry rates, we observe instead a positive significant effect for the
firms in the smallest quintiles, while for the others the effect is not significant.
4.2
Innovation expenditures
In section 4.1 we observed that changes in the number of firms operating in one sector influence
the decision to invest in innovation inputs. However, these results are not informative about the
change in firms’ investments in innovation. To this purpose, we focus on the amount spent by firms
on internal R&D on the one hand and on external R&D, machinery and licensing on the other.
Table 7 presents the estimates of the effect of the exit and entry rate on innovation expenditures19
Similarly to previous results, all specifications include time-macro-sector fixed effects, as well as
industry-level controls.
18
Results in this section assumes that coefficients other than the ones on entry and exit rate are constant across size
strata. In Appendix, we present estimates for equation (5) estimated separately for small (table B2) and large firms
(table B3). The results are consistent with the results presented in this section.
19
Since expenditures are equal to zero if the firm is not investing in innovation, we report innovation expenditures as
the logarithms of expenditures plus one. Results are robust if we look at level outcomes.
14
Figure 3: Marginal effects of exit and entry rates on the buy strategy, by size
A. Internal R&D
Exit rate
Entry rate
.1
Marginal effect of Standardized Entry rate
Marginal effect of Standardized Exit rate
.04
.02
0
-.02
-.04
-.06
.05
0
-.05
-.1
1
2
3
Size quintile
4
5
1
2
3
Size quintile
4
5
2
3
Size quintile
4
5
B. Buy
Exit rate
Entry rate
Marginal effect of Standardized Entry rate
Marginal effect of Standardized Exit rate
.05
0
-.05
.1
.05
0
-.05
-.1
1
2
3
Size quintile
4
5
1
Note. Coefficients are estimated using Fixed Effects estimation for equation (5) and using interaction terms between exit
(entry) rates and indicator variables for firms’ size quintile.
15
The estimated effect of the exit rate on R&D expenditure is negative and statistically significant, while that on buy is also negative, but statistical significance is not robust to all specification.
A one standard deviation increase in the exit rate is decreasing expenditures in internal R&D by
40 percent. No effect is observed for the entry rate.
Table 7: Effect of exit rates on innovation expenditures, using stdexitnet, contemporaneous effect
Standardized exit rate t
Standardized entry rate t
R&D
(1)
FE
-0.410***
(0.096)
-0.078
(0.137)
Firms with internal R&D in 2005
R&D
R&D
Buy
Buy
(2)
(3)
(4)
(5)
FE
FE
FE
FE
-0.429***
-0.416***
-0.245***
-0.133
(0.105)
(0.106)
(0.094)
(0.101)
-0.020
(0.154)
-0.039
(0.157)
0.148
(0.135)
0.029
(0.153)
Buy
(6)
FE
-0.119
(0.102)
-0.021
(0.154)
Ln of hours worked
-0.318
(0.331)
-0.033
(0.393)
Investment in tangible assets
0.085
(0.255)
0.203
(0.305)
Profits growth rate
0.006
(0.006)
28436
0.633
Yes
Yes
Yes
Yes
-0.004
(0.007)
28436
0.473
Yes
Yes
Yes
Yes
Observations
rho
Time-varying controls
Time fixed effects
Time*Sector fixed effects
Industry-level controls
28511
0.627
Yes
Yes
No
No
28511
0.634
Yes
Yes
Yes
No
28511
0.466
Yes
Yes
No
No
28511
0.472
Yes
Yes
Yes
No
Note: Standard errors in parenthesis are clustered at firm level. *** p < 0.01, ** p < 0.05, * p < 0.1. The dependent variables
are expenditures on R&D (columns 1-3) or on purchases of external R&D, machinery or licensing (columns 3-6). Expenditures are
reported in logarithms. Rho is the share of the overall variance explained by the firm-level unobserved fixed effect.
5
Conclusions
In this paper we have analyzed whether exit and entry rates have an impact on firms’ innovation
strategies, specifically engaging in internal R&D, procuring external R&D, acquiring machinery
that embodies new technology, or purchasing disembodied technology in the form of a license.
We hypothesize that a higher exit rate indicates a lower likelihood of survival, which affects a
firm’s decision to engage in innovation-related activities. Since small firms are more likely to
be hit by involuntary exit, we expect the effect to be stronger among smaller firms. Furthermore,
since internal R&D involves a longer investment horizon and thus a greater commitment level than
external sources of knowledge, we expect firms’ decisions to engage in internal R&D to be more
intensely affected by exit rates.
Bearing these considerations in mind, we analyse a panel of Spanish manufacturing firms
yearly surveyed over the 2005-13 period. We find that exit rates indeed affect firms’ choices of
innovation strategies, with the effect being driven mainly by smaller firms. We also find that
16
the effect of exit rates on internal R&D is stronger than that on external R&D, acquisition of
machinery, and licensing.
17
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19
Appendices
A
Variable description
This Appendix presents additional information about the variables selected for the analysis. Table
A1 presents the list of variables used and a short description.
Table A1: Variable definitions
Variable
innov01
r_n_d
buy
stdexit
stdentry
q_size
f orsub
employees
local
national
eumkt
othermkt
f emale
patnum
lnhours
lntaninvest
gr_prof its
B
B.1
Description
Dummy that takes value 1 if the firm has innovation inputs in t, 0 otherwise
Dummy that takes value 1 if the firm did internal R&D in t, 0 otherwise
Dummy that takes value 1 if the firm purchased new embodied or disembodied technology or purchased external R&D in t, 0 otherwise
Standardized exit rate in t, by industry and size group
Standardized exit rate in t, by industry and size group
Quartile in the size distribution in t, by industry
Dummy that takes value 1 if the firm is a subsidiary of a foreign multinational, 0
otherwise
Logarithm of the number of employees in t-1
Dummy that takes value 1 if the firm sells in the local market from t-2 to t, 0 otherwise
Dummy that takes value 1 if the firm sells in the national market from t-2 to t, 0
otherwise
Dummy that takes value 1 if the firm sells in any EU market from t-2 to t, 0 otherwise
Dummy that takes value 1 if the firm sells in any other foreign market from t-2 to t, 0
otherwise
Percentage of female employees in t-1
Number of patent applications in t-1
Logarithm of the industry total number of hours worked in t
Logarithm of the industry total tangible investment in t
Growth rate of the industry total profits in t
Additional analysis
The Spanish economy in the period 2005-2015
The period of analysis is of particular interest because the Spanish economy encountered wide
changes.
Left panel in Figure B1 shows the Industrial Production Index for the period 2006-2014. If we
consider the year 2010 as base we can observe that a significant drop occurred in the first years
of the period. The Index scored around 125 for 2006 and 2007, then descending to 217 in 2008.
The production continued to decrease in the period, reaching an index value on 90 in 2013. This
clearly translated in a situation where several firms faced pressure from the market as the crisis hit
the economy.
20
Similar trend is observed when looking at unemployment and prices. The right panel in Figure
B1 shows the time series for the unemployment rate and the Consumer Price Index (CPI) for the
same period. The unemployment rate rose from 8 percent in 2006 to 26 percent in 2013, a huge
increase in just 7 years. Inflation decreased over time up to a phase of deflation, with a CPI index
growth of 3.5 percent in 2006 and a decrease of 0.5 percent in 2015.
Figure B1: Industrial Production Index, Unemployment Rate and Consumer Price Index (Spain
2006-2014)
Note. The left panel presents the Industrial Production Index for the period 2006-2014. The base year for
the index is the year 2010. The right panel shows for the same period the time series for the unemployment
rate and for the Consumer Price Index. Source: International Monetary Fund.
This situation translated into a significant variation in the exit and entry rates in the manufacturing sector. Figure B2 shows the distribution of the exit and the entry rate across the sample
firms. The unit of observation is the firm, therefore these variables measure the individual exposure to different exit and entry rates. The left panel of the figure shows the distribution of exit
rates at which firms in the sample are exposed over the period 2005-2013. Similarly the right
panels shows the entry rates. Exit and entry rates show a very similar distribution, with exit rates
presenting higher values, but being more concentrated around zero.
B.2
Heterogeneous effects by firm size
To estimate the heterogeneous effect of exit and entry rates in term of firm size, we follow two
separate strategies. First, we estimate equation (5) presented in the main text separately for small
and large firms. Table B2 and table B3 presents estimates respectively for small and large firms.
We can observe that exit rates affect the investment in internal R&D only for small firms, while
for large firms no significant effect is observed.
Additionally, we extend equation (5) by including interaction terms between the entry and exit
rates in the sector and stratum of the firm with size quintiles. Specifically, to measure the effect of
21
.4
.4
.3
.3
Fraction
Fraction
Figure B2: Distribution of exit and entry rate
.2
.1
0
.2
.1
0
.02
.04
Exit rate
.06
0
.08
0
.02
.04
Entry rate
.06
.08
Note. The left panel shows the distribution of the exit rate as defined by equation (2), while the right panel shows the
distribution of the entry rate as defined by equation (3). The dotted line represent the mean of the distribution. Source:
author’s calculations using the Central Business Register (Spanish National Statistics Institute).
the net exit rate on innovation inputs, we estimate the following specification:
yit = α0 + αN netexitit +
5
X
αN,q netexitit qq +
q=2
5
X
δq qq + Xit β +
q=2
+
T
X
γt dt +
t=2
T X
J
X
(6)
ωtj dt sj + ci + uit
j=2 t=2
where qq is an indicator variable equal to 1 if firm i is part of the q-th size quintile, Xit is a
matrix of time-varying firm characteristics, dt are year fixed effects, si are macro-sector-specific
dummy variables, ci are unobserved firm characteristics that are constant over time and uit are
idiosyncratic error terms. We estimate equation (7) using fixed effects estimation and controlling
for year aggregate effects. Table B4 present the results for this estimation.
In addition, we extend the estimates to consider heterogeneous effects in terms of exit and
entry rate sepately. To this purpose, we estimate the following specification which introduces
interactions between size quintiles and entry and exit rates separately:
yit = α0 + αEX exitit +
5
X
αEX,q exitit qq + αEN entryit +
q=2
+
5
X
5
X
αEN,q entryit qq +
q=2
δq qq + Xit β +
q=2
T
X
t=2
γt dt +
T X
J
X
(7)
ωtj dt sj + ci + uit
j=2 t=2
where again qq is an indicator variable equal to 1 if firm i is part of the q-th size quintile, Xit is
a matrix of time-varying firm characteristics, dt are year fixed effects, si are macro-sector-specific
dummy variables, ci are unobserved firm characteristics that are constant over time and uit are
idiosyncratic error terms. Results are presented in table B5.
22
Table B2: Effect of standardized exit and entry rates on innovation strategies, small firms
Standardized exit rate t
Standardized entry rate t
Log number employees t-1
Observations
rho
Time-varying controls
Time fixed effects
Time*Sector fixed effects
Industry-level controls
R&D
(1)
FE
-0.026***
(0.006)
Small firms with internal R&D in 2005
R&D
R&D
Buy
Buy
(2)
(3)
(4)
(5)
FE
FE
FE
FE
-0.027***
-0.026***
-0.015**
-0.010
(0.007)
(0.007)
(0.006)
(0.007)
Buy
(6)
FE
-0.010
(0.007)
-0.005
(0.009)
-0.003
(0.010)
-0.004
(0.010)
0.006
(0.009)
0.002
(0.010)
-0.001
(0.010)
0.083***
(0.013)
24034
0.593
Yes
Yes
No
No
0.083***
(0.013)
24034
0.601
Yes
Yes
No
No
0.083***
(0.013)
23970
0.600
Yes
Yes
Yes
No
0.029**
(0.012)
24034
0.414
Yes
Yes
Yes
No
0.031**
(0.013)
24034
0.424
Yes
Yes
Yes
Yes
0.029**
(0.013)
23970
0.425
Yes
Yes
Yes
Yes
Note: Standard errors in parenthesis are clustered at firm level. *** p < 0.01, ** p < 0.05, * p < 0.1. The dependent variables
are indicator variables for investments in internal R&D (columns 1-3) or purchase of external R&D, machinery or licensing (columns
3-6). Rho is the share of the overall variance explained by the firm-level unobserved fixed effect.
Table B3: Effect of standardized exit and entry rates on innovation strategies, large firms
Standardized exit rate t
Standardized entry rate t
Log number employees t-1
Observations
rho
Time-varying controls
Time fixed effects
Time*Sector fixed effects
Industry-level controls
R&D
(1)
FE
-0.014
(0.015)
Large firms with internal R&D in 2005
R&D
R&D
Buy
Buy
(2)
(3)
(4)
(5)
FE
FE
FE
FE
-0.018
-0.019
-0.006
-0.038
(0.022)
(0.024)
(0.021)
(0.026)
Buy
(6)
FE
-0.032
(0.026)
0.003
(0.018)
0.011
(0.026)
0.013
(0.027)
0.025
(0.029)
0.007
(0.039)
-0.002
(0.040)
0.088***
(0.022)
4477
0.663
Yes
Yes
No
No
0.089***
(0.022)
4477
0.739
Yes
Yes
No
No
0.091***
(0.022)
4466
0.739
Yes
Yes
Yes
No
0.085***
(0.027)
4477
0.492
Yes
Yes
Yes
No
0.079***
(0.027)
4477
0.574
Yes
Yes
Yes
Yes
0.079***
(0.027)
4466
0.581
Yes
Yes
Yes
Yes
Note: Standard errors in parenthesis are clustered at firm level. *** p < 0.01, ** p < 0.05, * p < 0.1. The dependent variables
are indicator variables for investments in internal R&D (columns 1-3) or purchase of external R&D, machinery or licensing (columns
3-6). Rho is the share of the overall variance explained by the firm-level unobserved fixed effect.
23
Table B4: Effect of net exit rates on innovation strategies with interaction with size quintile
Std industry net exit rate t
R&D
(1)
FE
-0.028***
(0.006)
Firms with internal R&D in 2005
R&D
Buy
(2)
(3)
FE
FE
-0.029***
-0.014**
(0.007)
(0.006)
Buy
(4)
FE
-0.012*
(0.006)
q_size3=2
0.013
(0.017)
0.014
(0.017)
0.026
(0.017)
0.026
(0.017)
q_size3=3
0.033
(0.022)
0.033
(0.022)
0.048**
(0.024)
0.048**
(0.024)
q_size3=4
0.078***
(0.028)
0.077***
(0.028)
0.133***
(0.029)
0.132***
(0.030)
q_size3=5
0.132***
(0.033)
0.131***
(0.033)
0.228***
(0.037)
0.229***
(0.038)
q_size3=2 × Std industry net exit rate t
-0.000
(0.013)
0.001
(0.013)
0.007
(0.013)
0.010
(0.013)
q_size3=3 × Std industry net exit rate t
0.033**
(0.014)
0.035**
(0.015)
-0.015
(0.015)
-0.008
(0.016)
q_size3=4 × Std industry net exit rate t
0.020
(0.016)
0.021
(0.016)
-0.005
(0.017)
-0.000
(0.018)
q_size3=5 × Std industry net exit rate t
0.045***
(0.012)
28511
0.601
Yes
Yes
No
0.049***
(0.013)
28511
0.609
Yes
Yes
Yes
0.024
(0.014)
28511
0.426
Yes
Yes
No
0.026*
(0.016)
28511
0.435
Yes
Yes
Yes
Observations
rho
Time-varying controls
Time fixed effects
Time*Sector fixed effects
Note: Standard errors in parenthesis are clustered at firm level. *** p < 0.01, ** p < 0.05, * p < 0.1. The dependent variables are
indicator variables for investments in R&D (columns 1-2) or purchase of machinery or licensing (columns 3-4).
24
Table B5: Effect of exit and entry rates on innovation strategies with interactions with size quintiles
Standardized exit rate t
R&D
(1)
FE
-0.037***
(0.008)
Firms with internal R&D in 2005
R&D
Buy
(2)
(3)
FE
FE
-0.038***
-0.017**
(0.008)
(0.007)
Buy
(4)
FE
-0.013*
(0.008)
Standardized entry rate t
0.002
(0.011)
0.005
(0.011)
0.015
(0.011)
0.012
(0.011)
q_size3=2
0.019
(0.021)
0.021
(0.021)
0.022
(0.021)
0.016
(0.021)
q_size3=3
0.029
(0.026)
0.030
(0.027)
0.033
(0.030)
0.027
(0.030)
q_size3=4
0.076**
(0.033)
0.077**
(0.035)
0.110***
(0.037)
0.103***
(0.038)
q_size3=5
0.130***
(0.036)
0.133***
(0.036)
0.226***
(0.043)
0.223***
(0.044)
q_size3=2 × Standardized exit rate t
0.002
(0.016)
0.004
(0.016)
0.005
(0.017)
0.006
(0.017)
q_size3=3 × Standardized exit rate t
0.033*
(0.020)
0.038*
(0.021)
-0.030
(0.022)
-0.027
(0.023)
q_size3=4 × Standardized exit rate t
0.019
(0.017)
0.022
(0.018)
-0.022
(0.023)
-0.022
(0.025)
q_size3=5 × Standardized exit rate t
0.047***
(0.016)
0.055***
(0.017)
0.027
(0.020)
0.026
(0.021)
q_size3=2 × Standardized entry rate t
0.022
(0.024)
0.020
(0.025)
-0.012
(0.024)
-0.023
(0.024)
q_size3=3 × Standardized entry rate t
-0.024
(0.022)
-0.028
(0.023)
0.004
(0.025)
-0.010
(0.026)
q_size3=4 × Standardized entry rate t
-0.011
(0.030)
-0.012
(0.033)
-0.016
(0.030)
-0.028
(0.032)
q_size3=5 × Standardized entry rate t
-0.042**
(0.020)
28511
0.600
Yes
Yes
No
-0.043*
(0.022)
28511
0.609
Yes
Yes
Yes
-0.028
(0.028)
28511
0.426
Yes
Yes
No
-0.036
(0.030)
28511
0.436
Yes
Yes
Yes
Observations
rho
Time-varying controls
Time fixed effects
Time*Sector fixed effects
Note: Standard errors in parenthesis are clustered at firm level. *** p < 0.01, ** p < 0.05, * p < 0.1. The dependent variables are
indicator variables for investments in R&D (columns 1-2) or purchase of machinery or licensing (columns 3-4).
25