THE INNOVATIVE ACTIVITY OF FIRMS OVER THEIR LIFE CYCLE
EVIDENCE FROM FRENCH MICRO-DATA1

Claire Lelarge2
OECD DSTI/EAS and CREST-INSEEFirst Draft, Very Preliminary, Comments Most Welcome
June 2009

Abstract
In this paper, we provide descriptive evidence about the innovative behaviour of firms over their life cycle.
We first provide novel descriptive statistics about the age description of French patenting firms in the main
high-tech industries. Then, we analyze the patenting propensities of firms across age bands, andspecifically try to assess the magnitude of potential financing constraints at various stages of the firms‟ life
cycle. Last, we provide evidence that the characteristics and more precisely the technical content of theinnovations introduced by firms evolve when ageing.
Keywords: Entrepreneurship, innovation, patents, firm age.
JEL Classification: G32, L26, L22, O31, O34.
Introduction [TBC]
The empirical research on industry evolution has shown that entry and exit at the industry level are
typically high, and that new businesses usually start at a very small scale3. Less is known about thedeterminants of young firms‟ survival and growth, and more specifically about the relation between
survival and innovative performance over the firms‟ life cycle.
The analysis of the innovative activities of high-technology firms at early stages of their life has been at thecenter of an important literature though, both in economics and in management. Shane (2001) analyses
under which conditions is new technology exploited through firm formation. The contributions of
1

This research has been conducted in relation with the broader OECD WPIA micro-data project about
entrepreneurship (“An International Quantitative Framework for the Analysis of Entrepreneurship and the Growth ofYoung Firms”).
2
E-mail: claire.lelarge@ensae.fr
3
See Davis et al. (2008) for a synthesis and a survey of the literature.

1

Audretsch (1995 and joint with Agarwal, 2001) emphasize the importance of entry size jointly withtechnology as a determinant of firm survival. At the macro-economic level, Hunt and Nakamura (2006)
state that corporate R&D intensity nearly doubled in the US between 1980 and 2000. Most of the aggregate
increase is due to a substantial increase in the R&D intensity of small firms, which the authors relate tofalling barriers to entry – and therefore most probably to the innovative effort of younger ventures.
However, not much is known about the evolution of the ability to innovate at later stages of the firms‟ life
cycle, and about the potential impact of ageing on the content (e.g. technological features or quality) of
these innovative activities. Sørensen and Stuart (2000) and Huergo and Jamandreu (2004a and 2004b) areamong the few important contributions in this respect. Both underline the fact that investigating the effectof ageing on the innovation activity requires a further reflexion on the concept of firm age. For Sørensen
and Stuart, firm age is a measure of the organizational maturity, rather disconnected from technologicalconcerns. For the Huergo and Jamandreu first authors on the contrary, “age must be assumed to be relativeage, measured in relation to an unobservable total length of the specific product life cycle”. However, thisview partly contradicts the previously quoted literature about start-ups, which generally suggests (e.g.
Nerkar and Shane, 2003) that new firms should exploit radical technologies with broad scope patents inorder to compete with established firms which dominate existing technologies.
In the present contribution, we precisely propose to provide a more systematic analysis of the innovative
behaviour of firms over their life cycle, thus shedding light on the dynamics of technological change inhigh-tech industries. For that purpose, we rely on a unique, quasi-exhaustive dataset constructed fromadministrative files which enables to track the innovative behaviour of French firms in the main high-techindustries over the 1998 to 2004 period. We also take advantage of information about acquisitions andmergers to be able to control for this type of external knowledge acquisition.
We first provide descriptive evidence about the age distribution of French patenting firms in the main hightech industries. Then, we analyze the patenting propensities of firms across age bands, and specifically try
to assess the magnitude of potential financing constraints at various stages of the firms‟ life cycle. Last, we
provide evidence that the characteristics of the innovations introduced by firms evolve with their age,which suggests that barriers to entry may alter the direction as well as the rhythm of technological change.
The remainder of the paper is organized as follows. Section 1 presents the data4 as well as our main
empirical indicators. Section 2 describes the empirical strategy for each of the two topic, while theobtained results are synthesized in section 3. Section 4 briefly concludes.

4

The statistical subtleties concerning the measure of firm age are explained in greater details in appendix A.

2

1. Data and Indicators
1.1. Data
We relied mainly on matched exhaustive administrative datasets in order to be able to track the innovative
behaviour of the entire population of firms, whatever their age. More specifically, we were able to match:
The SIRENE files (French business registers) providing information about the date of creation,
employment and main industry affiliation at creation.



The FICUS files, which are constructed by the INSEE using the fiscal files (BRN, RSI) provided
by the tax administration. These files contain basic employment and accounting information, andenable to construct various indicators of performance and of the structure of financing: cash flows
and cash holdings, share of internal finance, financial costs, capital intensity, etc. Industry levelindicators of competition and capital intensity were also constructed from this source.

Patent data (“Inventors” file), as constructed and provided by the INPI5. This file provided us with
the French applications to the French patent office over the 1995 to 2004 period (application dateand identifying code, SIREN code of firm applicants). This file was enriched using the PATSTAT
database6 to gather information about priority date, IPC classes as well as forward and inward
citations.

The output of the matching procedure is a quasi-exhaustive file, which was restricted to relatively hightech industries (OECD manufacturing high-tech industries, KIBS and ICT industries – see appendix B)
which are the main providers of patent applicants. Observations lost in the matching procedure typicallyconsist of very small firms choosing an alternative taxation regime (personal income tax) and coding
errors. Of particular concern here was the matching procedure between patenting information andcomplementary datasets, since the SIREN firm level identifying code is not directly available in the patentdocument. However, this task was carefully performed by the INPI with help of the INSEE firm
demography department7 on a yearly basis, thus avoiding major stock sampling biases: only less than 2%of the obtained matched observations show incompatible priority years and firm creation dates.

5

Many thanks to Laurence Joly and her team (INPI) for help with this file. The INPI has launched a long term project
consisting of complementing their information system with the SIREN code of firm applicants for analytical
purposes.
6
Many thanks to Hélène Dernis (OECD) for the corresponding SQL extraction.
7
The “Sirénage” procedure is based on name and address (street name and number, zip code and city) matching
between the patent file and the INSEE business registers. However, an important difficulty of this task is that patentapplicants are not always identified as “firms” (may be e.g. a firm sub-division, or a conglomerate of different firms).
In other words, there is no straightforward mapping between the statistical units in each file.

3

The main advantages of this type of data are twofold. First, due to legal constraints, the population of very
young firms is covered since their initial economic activities. Indeed, firms need to provide their “SIREN”
identification code in order to apply for a bank loan or hire new employees, and would typically register
beforehand, thus appearing in the SIRENE files. This is an important advantage over standard surveys,
which most often do not cover the population of very young firms8.
Second, exhaustiveness enables to build several types of data sets while avoiding selection issues, andtherefore to provide insights on various aspects of the innovative behaviour of firms:


We were able to reconstitute the entire population of patenting firms over a given time period, atleast within specific industries. This allows describing the main features of this population, inparticular its age distribution9.



We were also able to reconstitute the exhaustive population of firms active in a given (high-tech)industry over a given (though short) time period, with information about their patenting status.
This enables to analyze patenting propensities at different stages of the firms‟ life cycle in a given
industry10. This step also enables to take account of selection biases when analyzing thecharacteristics of patent portfolios (e.g. across age bands of applicants).

Last, longitudinal information about specific cohorts of firms, from birth to patenting, death oralternative demographic events (acquisition or mergers, bankruptcy filing, etc.) will enable
dynamic or event analyses of patenting over the firms‟ life cycle (forthcoming).

These administrative based dataset was complemented using additional information systems, such as the
LIFI-DIANE (INSEE) dataset providing information about group ownership, and the CITRUS (INSEE)
files providing information about acquisitions and mergers experienced by the sample firms.
Note that the main limit of the French statistical system for the purpose of our analyses is that nolongitudinal and direct information is available to assess the R&D effort of very young and small
ventures11. This induces us to rely solely on patents as an indicator of innovation, and to use reduced form
estimation in our empirical strategy (see below).

8

Surveys specifically targeted at the population of newly created firms (such as SINE in the case of France) are
important exceptions. However, they do not enable to compare young and more mature firms (which are typically notcovered) in these targeted surveys.
9
𝑃 𝑥 Pat = 1 is identified.
10
𝑃 Pat = 1 𝑥 is identified without „„rare disease‟‟ (Manski, 1995) assumption.
11
This is because (most European) R&D surveys as typically designed to assess the aggregate business R&D effort –since small firms do not contribute much to the total, the sampling schemes typically allocate low inclusion
probabilities to these firms.

4

1.2. Descriptive statistics: age (and size) distribution of patenting firms
Figures 1 to 5 report the average age distribution of firms and patenting firms, as well as patenting
propensities, while figures 6 to 9 provide benchmark descriptive statistics across employment bands. All ofthese figures are annualized data computed over the 1995 to 2004 period, and refer to high-tech
manufacturing (Manufacturing HT) industries, knowledge intensive business service (KIBS) industries orInformation and Communication Technology (ICT) related industries only.
The overall age distribution of patenting firms (fig. 2) shows two main peaks, located at the “6 to 10” years
and “40 and more” age bands. The larger density mass is located at 6 to 10 years band; however, thesituation is reversed when weighting by the number of patent applications, since most patents are applied
for by more mature firms (fig. 3).
The breakdown by main high-tech industries also shows important differences. Most patenting firms in the
KIBS are young and were most often created since less than 15 years, while this is not the case in the
Manufacturing HT and ICT industries. However, when weighting by the number of patent applications,this specificity disappears in the KIBS, while ICT industries show a disproportionate share of quite young
firms (6 to 15 years old) among total patent applications.
In all industries, the gross patenting propensities (fig. 4) are highly, more than linearly increasing with firmage, which may result either from higher innovative ability at later stages of the firms‟ life cycle, possibly
fostered through acquisitions of younger ventures, or from selection of the most innovative firms overtime12. A closer look at patenting propensities over the first eight years (fig. 5) shows that the share ofprimo-patenting firms is very low and even decreasing after the first three years, be they weighted by theiremployment level or not. The share of persistent innovators increases rapidly over the first to third years,
and then remains quite stable, and in any case much higher than the rate of primo-patenting firms,
especially in ICT industries. This suggests that selection effects may be particularly important over time.
This statement is further strengthened by the fact that the share of employment weighted patent holders
appears surprisingly high in all industries, on average as large as 20% for eight year old firms. However, it
is impossible to disentangle at this stage whether it is mainly driven by the fact larger firms patent more; orby the fact that patenting firms survive and grow more often. Figure 9 indeed shows that the firms‟
patenting propensity is exponentially increasing with firm size, although the employment distributions ofpatenting firms are rather flat, be they un-weighted (fig. 7) or weighted by the number of patentapplications (fig. 8).

12

See fig. 1 for the shape of industry level average age distributions.

5

The aim of the remaining analyses is to try and further disentangle what are the economic determinants
which causally shape the age distribution of patenting firms. Is this distribution mainly determined by firmsize, which is partially collinear with firm age13, or by pure demographical (and institutional) aspects? Is it
determined by other factors such as the features of technology itself, and evolving organizational capacity
over the firm‟s life cycle? What may explain that innovations are introduced by new entrants, new firms or
new organizations, rather than by incumbents, or more mature firms?
2. Empirical strategy and measurement issues
2.1. Empirical strategy
In order to shed light on these aspects, we adopt a two-step empirical strategy:

We first analyze simple patenting propensities over the firms‟ life cycle, with the aim ofinvestigating whether the main determinants of patenting evolve in relation with firm age. This
simple multivariate approach will in particular allow disentangling the respective impacts of size
and age14.

Second, we relate these patenting propensities to the characteristics of the patents themselves.
Insofar as patent characteristics truly reflect the features of the patented innovation, these analyses
enable to shed light on potential differences in organizational abilities of more mature firms
relative to younger ventures.

2.1.1. Patenting propensities
We first run descriptive regression explaining the firms‟ patenting propensity using a reduced form
specification which is standard in the literature (e.g. Crépon, Duguet and Mairesse, 1998 or Bond et al,2003). In the main specification, patenting is regressed against lagged firm (𝑖) and industry (𝑙) level
controls:
emp𝑎,𝑘𝛽𝑘𝑎 . 𝛿𝑖,𝑡+

𝑃𝐴𝑇𝑖,𝑡 =
𝑘

𝛽𝑘

emp,𝑘

. 𝛿𝑖,𝑡−1 + 𝑋𝑖,𝑡−1 . 𝛽1 + 𝑋𝑖,𝑙 . 𝛽2 + 𝑋𝑖,𝑙,𝑡−1 . 𝛽3 + 𝛿𝑡 + 𝜀𝑖,𝑡

(1)

𝑘
emp,𝑘

𝑎,𝑘
Where 𝛿𝑖,𝑡stand for a full set of age band dummies, 𝛿𝑖,𝑡stand for a set of employment band

dummies, 𝑋𝑖,𝑡 stands for additional time varying firm level controls describing in particular the firm‟scapital intensity, its lagged structure of financing, group affiliation and past experiences of acquisitions andmergers. Time varying industry level controls 𝑋𝑖,𝑙,𝑡 include the average capital intensity, and variables
13

Older firms would be on average larger if firm growth is in some way a cumulative process.
However, in absence of a relevant instrumental variable akin of determining survival without directly impacting theability to innovate, these analyses do not provide “structural” estimates of the determinants of patenting, but rather anassessment of the short-term determinants of both selection through survival and patenting. This aspect would require
further investigation using e.g. duration analysis (competing risk models), see section 2.1.1 below.
14

6

proxying product as well as technology competition. Last, constant control variables such as industry
affiliation at creation are stacked in 𝑋𝑖,𝑙 . This equation is estimated using probit or simple linear probability
specifications. In each case, marginal effects (at the sample mean when relevant) are reported, with robust
standard errors corrected for clustering at the industry 4-digit level15.
We also take advantage of the richness of our data to estimate the previous equation separately over eachage band, so as to provide evidence about the stability of this propensity equation over the firms‟ life cycle,
and most particularly about the potential magnitude of credit constraints at various stages of the firms‟
development.
We recognize that this descriptive approach is standard, but does not provide directly interpretableindication about the determinants of patenting. This is due to the fact that in absence of a convincing
instrumental variable, we were not able to explicitly take account of selection processes occurring throughsurvival16. We argue that this analysis might however provide interesting insights. First, it shows thatstandard practice miss the ageing dimension, and is therefore also plagued with the previously described
selection issues. Second, the obtained coefficients do not have a direct, structural interpretation, but they
however provide insight about e.g. the severity of credit constraints on both (indistinguishably17) survival
and patenting, though conditionally upon survival until the specified age band. This latter statement means
more specifically that changes in the selection process at some point in the firms‟ life cycle would mostprobably induce a re-scaling of all of the coefficients obtained for later age bands, even if no change occurs
in terms of economic (patenting or survival) behaviour at these later stages. The instability of coefficientswould be explained by the fact that the set of considered firms would have been modified since it wasdifferently selected at some earlier point in time. In this sense, the obtained coefficients do not have any
external validity, e.g. do not allow in any case assessing the response of firms to a specified “shock” (e.g.
introduction of a targeted public program).
This limitation being acknowledged, we however propose a refinement of the previous baseline
specification with the aim of both investigating dynamic specifications of the age/patenting relationshipand controlling for the potential endogeneity of some of the covariates. We follow the existing literature(e.g. Bond et al, 2003 for a recent example) and rely on a standard GMM setting18. The estimated equation
at this stage can be written as:
15This correction is necessary because of the industry-level controls, which may generate correlations within industry
clusters.
16
Relying on survival analysis (competing risk models) would enable to provide various estimates under different
assumptions, but would not solve the problem. Furthermore, it is not straightforward how survival of older firms
should be analyzed, both ideally and in practice since the statistical information available for these firms is not thesame as for new ventures. The existing literature does not provide much insight on this aspect.
17
In econometrics terms: the separate impacts are not identified in our setting.
18
More specifically, we rely on moment conditions based on the differenced equation, using differences of exogenous
controls and suitably lagged differences of variables suspected of endogeneity as IVs. The relevance of the

7

emp

𝑎,𝑘
𝛽𝑘𝑎 . 𝛿𝑖,𝑡+

𝑃𝐴𝑇𝑖,𝑡 = 𝛽. 𝑃𝐴𝑇𝑖,𝑡−1 +
𝑘

𝛽𝑘

emp,𝑘

. 𝛿𝑖,𝑡+ 𝑋𝑖,𝑡 . 𝛽1 + 𝑢𝑖 + 𝑋𝑖,𝑘,𝑡 . 𝛽3 + 𝛿𝑡 + 𝜔𝑖,𝑡(2)𝑘and is estimated in differences. Following Branstetter et al. (1999)19, we also apply the same methodology
to firm level indicators of internal financing in order to test for Granger causalities between internalfinancing and patenting performance.
It should however be noted that due to the use of lagged values of controls and outcome variables, thissetting does not allow to provide any insight for the specific case of very young firms. Due to the scarcityof the available information, the proper statistical treatment of this specific population of firms remains
largely an open question at this stage.
2.1.2. Investigating the characteristics of patent portfolios
In the last step of our proposed analyses, we propose to describe the features of the patents applied for
depending on the age and size of firms. The previous propensity analysis enables to compute Mill‟s ratios
which are introduced in the regression as a coarse control for selectivity20. The estimated regression at thisstage is therefore:
emp𝑎,𝑘𝛽𝑘𝑎 . 𝛿𝑖,𝑡+

𝑃𝐴𝑇_𝐶𝐻𝐴𝑅𝑗 ,𝑖,𝑡 =
𝑘

𝛽𝑘

emp,𝑘

. 𝛿𝑖,𝑡−1 + 𝜆.

𝑘

𝜑𝑖,𝑡−1+ 𝑋𝑖,𝑙 . 𝛽2 + 𝑋𝑖,𝑙,𝑡−1 . 𝛽3 + 𝜔𝑖,𝑡Φ𝑖,𝑡−1(3)

𝜑Where 𝑃𝐴𝑇_𝐶𝐻𝐴𝑅𝑗 ,𝑖,𝑡 is a well-defined21 characteristic of the considered patent and Φ𝑖,𝑡−1 is the inverse𝑖,𝑡−1Mill‟s ratio. At this stage, 𝑋𝑖,𝑙 only include the firm level two-digit industry affiliation, while 𝑋𝑖,𝑙,𝑡−1
includes the industry level patenting rate (average number of patent per firm) and the share of preemptive
patents (see below for a precise description). All these regressions are estimated at the patent (𝑗) level using
OLS, and robust standard errors are corrected for clustering at the firm level.
2.2. Description of the various empirical indicators
We describe below in details the various indicators introduced in the empirical analysis.
At this stage, the patent indicator is a simple dummy variable indicating whether the firm had applied for atleast one patent over the considered year.

specification is tested thanks to a standard Sargan test, while the order of the auto-correlation of residuals is also
checked up to order 4 with the Arellano-Bond (1991) test for serial correlation.
19
The methodology was first exposed in Holtz-Eakin et al. (1988) but applied to the financing / R&D investment by
Branstetter et al. (1999).
20
This set of regressions relates to the sub-population of patenting firms, which may be the most « efficient » ones (ina broad sense). As explained above, it is necessary to take account of this aspect in order to retrieve more structuralparameters.
21
See below in section 2.2 for a detailed list of indicators.

8

Second, age and employment bands have been defined using the same convention as in OECD (2008) andrelated work22.
More importantly, the firm level controls were defined as follows. The financial indicators were sourced
from the firms‟ balance sheets:

The indicator of cash flow follows Almeida, Campello and Weisbach (2004) and is defined as theratio of earnings before extraordinary items and depreciation (minus dividends) to total assets.

The share of internal finance is the ratio of equity to total assets.



The indicator of intensity of financial costs aims at assessing the direct cost of external finance andis computed as by the ratio of interests paid by the firm over its total debt.

Complementary indicators were computed at the corporate group level, using matched information fromfiscal balance sheets and LIFI files:
Group ownership is a simple dummy variable indicating whether the firm is part of a large
corporate group.

The size of the corporate group was measured as the total employment of all firms having the same
headquarters.



The group level indicator of cash holdings also follows Almeida, Campello and Weisbach (2004)
and is defined as the ratio of holdings of cash and marketable securities to total assets.



The indicator of group specialization is computed as the concentration index of the group‟s sales
over its various four-digit activities.

Further firm level indicators of demographic events were retrieved from the CITRUS files:“Downsizing” is a dummy variable indicating that part of the firm (e.g. one or several divisions)
was acquired by another corporation.

“Acquisition” is a dummy variable indicating that the symmetric event occurred, i.e. that theconsidered firm acquired a division from another corporation.

Last, additional controls were added at the firm or industry levels in order to control for competition, bothon the product and the technology markets.
Capital intensity is defined at the firm level as capital assets over total assets, and at the industry
level as total capital assets over total employment.



H. Herfindahl index is computed as the standard 4-dig. industry level concentration index of sales
among all active firms: 𝐻𝐻𝐼𝑙,𝑡 =



𝑖∈𝑙

2

𝑆𝑖
𝑖′ ∈𝑙 𝑆𝑖′

The industry level indicator of patenting intensity is defined as the average 4-digit level number ofpatent applications per firms, in the considered year.

22

On-going projects led by FORA, etc. See below in table 2 the precise break-downs that were used.

9The variable labelled “Share of XY citations” corresponds to the industry average of citations oftype XY received within two year by patents having a priority date corresponding to theconsidered year, within total number of citations received within the same two-year window.

Firm level descriptive statistics describing the sample variation of all these indicators are reported in table
1A, and figures 10 to 16 provide more details for the main indicators. Patenting firms, especially young
ones, are characterized by a lower level of cash-flow but higher shares of internal financing, although thesegross differences are not statistically significant due to high heterogeneity. The most striking feature is thatpatenting firms are far more often part of a larger corporate group; figure 13 shows furthermore that theprobability of group affiliation for a patenting firm is steeply increasing with age. The corresponding
groups are also on average larger and more diversified. Last, table 1A shows that patenting firms havemore often been involved in firm level demographic events such as downsizing and even more frequently,
acquisitions.
Last, we rely on various indicators proposed by the literature in order to analyze the firms‟ patentportfolios.
-

The indicator of patent scope is derived from Lerner (1994) and is computed as the number offour-digit IPC classes a patent is classified into.

-

The indicator of patent radicalness was initially proposed by Nerkar and Shane (2003) and isdefined as the count of the patent four-digit classes cited in prior art outside of the patent's own
four-digit IPC classes.

-

Last, we measure the measure the quality of patents as the number of citations received within a
two-year window (Trajtenberg [1990]). We follow Grimpe and Hussinger (2008) or Guellec et al.
(2008) among others to assess the preemptive power of a patent as the count of citations classified
as “XY” received within a two-year window23, and its technological content as the count of A
citations.

The descriptive statistics relating to these indicators are reported in Table 1B. The distributions of patentscope, radicalness and of the patent level number of forward citations are highly skewed. In particular, as
was previously shown in the literature, most patents do not receive any citation. However, among thosecited patents, very few receive both types of (A and XY) citations, which suggests that the status of eachkind of patent (or the strategy of the firm when applying for a patent) is indeed differentiated across
categories.
23

Patent applications are cited by the EPO in search reports to determine the patentability of other applications undervarious categories, notably: as general state of the art (citation category A),
 as compromising the patentability of at least part of the citing application (categories X, Y, E).

103. Estimation results
3.1. Patenting propensities over the firms’ life cycle
3.1.1. Static specifications
Table 2 reports the main results obtained for various specifications of the simple, static modelling ofpatenting propensities proposed in equation (1). The main result is that age dummies appear to beremarkably stable whatever the specification: small or larger set of controls, fixed effect specification, etc.
Very young firms (0-2 years) appear to be on average more innovative than firms aged 3 to 15 years. At
later stages of the life cycle, the patenting propensity increases again and is maximal in the last age band
(40 years and more). Controlling for employment therefore significantly alters the gross profiles plotted on
figure 4.
Furthermore, the specification reported in column (5) enables to check that the increase at later stages in
not driven by the acquisition of younger, more innovative divisions. Indeed, acquisitions do not produce
any significant impact on the probability of patenting, at least in the short term, even when they relate todivisions of patent holding firms24.
The increasing profile of patenting propensities according to size (employment) is preserved whencontrolling for age effect and additional controls. Significance remains even in the fixed effectspecification, although coefficients are lower. This is a standard result of the empirical literature on
innovation, where it is often related to a “deep-pocket” effect.
This assumption related to financing constraints is further scrutinized in columns (2) to (4), where
additional controls describing respectively the firm‟s financing structure and the structure of the corporategroup (when relevant) have been included. (Lagged) firm level cash flow is negatively correlated topatenting. However, this result is not robust to the inclusion of firm fixed effects. Figure 10 reports thecoefficients obtained for this explanatory variable on various sub-samples defined according to firm age
and shows that that the negative correlation with cash flow is mainly driven by very young firms.
The indicator of the strength of internal financing is positively correlated to patenting, and this positive
correlation is robust to the inclusion of fixed effects (though lower). It also appears to be significant at allstages of the firm‟s life cycle (figure 11) although, “in elasticity terms 25”, its economic relevance ismaximal at early stages. The indicator of financial burden has the expected negative sign in all
specifications, and seems to hampers more particularly the innovative activities of younger firms (fig. 12).

24

Results not reported to save space, available upon request.
In figures 10 to 16, “elasticity” means that the marginal effect has been normalized with the baseline probability ofpatenting. In the case of continuous control variables, we report the normalized (in percent) increase of the patenting
probability induced by an increase of on sample standard deviation in the considered explanatory variable.
25

11However, the interpretation of these last two variables remains difficult in such a reduced form setting;further insights are provided in section 4.1.2 below.
In all specifications (except with fixed effects due to insufficient “within” variability) and over all age
bands (see figure 1326), being backed by a larger corporate group appears to enhance innovative
performance. This result is often interpreted in the following way: firms facing financing constraints may
rely on more efficient internal capital markets for their risky (innovative) investments when they are part ofa business group. This assumption is also further investigated in column (4), where various group levelindicators were included. Patenting appears to be positively though weakly correlated with group size, andfigure 14 shows that being backed by a larger group is particularly important at the beginning of the firm‟slife. The indicator of (lagged) group wealth as measured by cash holdings is positive and highly
significant. The economic impact of this variable appears quite stable over the life cycle, as shown infigure 1527. Last, group diversification appears to be significantly and positively correlated with the firm
patenting performance.
As a final refinement, the specification reported in column (5) includes additional firm level indicators of
demographic events (“restructuring”). As previously stated, acquisitions are not associated with higher
probabilities of patenting. On the contrary, downsizing events seem to be associated to efficient
rationalizing strategies of re-centering towards core activities since lagged events seem to positively affectthe probability of patenting.
3.1.2. Dynamic specifications and Granger causalities
Table 3 reports the estimates obtained with various specifications of equation (2). Columns (1) and (2)describe the results obtained using the same specifications as in table 2 columns (2) and (3) respectively,
but over the sub-sample of firms older than 6 years only, for homogenous comparison with the experiments
we describe below28. It is however interesting to note that, consistently with figures 13 to 16, the indicatorsof internal finance and the dummy variable indicating group affiliation only remain significant for thispopulation of older firms, while the indicators of cash flow and of the intensity of financial costs are nolonger significant.
In column (3), we estimate a dynamic version of our model, using a GMM estimation of the differenced
equation. Instrumental variables at this stage are the differences of all explanatory variables (under theassumption they are all exogenous), and the two-year and more lagged values of the patenting dummy,with the assumption that the first-differenced errors show no serial correlation. No explanatory variable26

In figure 13, the “group ownership” dummy in the last two categories has another interpretation as in the previous
categories: it seems to denote headquarters rather than group affiliates at these later stages.
27
The obtained marginal effects are less often significant due to a loss in accuracy.
28
In what follows, lagged values of the patenting indicator or of the explanatory variables are used, so that younger
firms were mechanically excluded from the analysis.

12remains significant, however, both the identifying restrictions and the assumption of no serial correlation
are rejected by the Sargan and the Arellano-Bond tests respectively.
Column (3) reports the results obtained using the five-year and more lagged values of the patenting dummy
as IVs, as well as all differenced values of all explanatory variables. This specification is valid if all
explanatory variables can be considered as exogenous, and if the serial correlation of the differenced errors
is of order lower than 3. These two assumptions are not statistically rejected by the tests reported at thebottom of the table. Furthermore, columns (5) and (6) show that the alternative assumptions consisting inconsidering the financing indicators as either predetermined or endogenous are statistically rejected, so thatour preferred specification is that of column (3).
Actually, these experiments validate the simpler (fixed effect) estimation strategy adopted in table 2, since
the lagged indicator of patenting is not significant as soon as the model is not statistically rejected, whereas
the assumption of exogeneity does not seem to be hurting. In this setting and for this population of older
firms, only the indicator of cash flow remains significant, while the indicator of internal financing drops.

Table 4 proposes a further investigation of the causal relationships (in the sense of Granger) between thesethree variables: patenting, cash flow, and structure of financing. We use a similar specification for each ofthese three variables, but rely on the adequate instrumental variables depending on the order of serial
correlation and on the acceptable exogeneity assumptions29.
The obtained results are quite clear-cut: cash flows (Granger) cause patenting, which is compatible with theexistence of financing constraints, while the reverse is not true, i.e. patenting does not cause cash flows, atleast in the short term. However, patenting seems to “causally” allow firms to rely less on their internalfinancing resources, but this effect is only weakly significant. The other most striking finding is that the
regressions show some sort of a trade-off (equivalence) between relying on internal finance and having agreater ability to attract cash flows. Moreover, group ownership seems to reduce the cash-flow (financing)
constraint, but these aspects would require further investigation30.
Figures 17 to 19 show that the incidence of cash flows over patenting is relevant for medium ages (remindthat young firms were excluded from the analysis), but is not relevant any more past 35 years. The
structure of financing does not appear to cause patenting at any stage, while the result about patenting
causing cash flows seems mainly driven by the first age bands (6 to 15 years).

29

More specifically, in column (2) of table 4, the patenting and group affiliation indicators have to be considered as
endogenous. The implied IVs are the three-years and more lagged levels of these variables (since all base controls are
already lagged). Also, the lower serial correlations in columns (2) and (3) allow using the lagged values of thecorresponding left-hand side variables up to order 2 as IVs.
30
Applying the same kind of empirical strategy to explain group ownership is always statistically rejected.

13

3.1. Characteristics of the patent portfolio over the life cycle
As a last step of the empirical analysis, we investigate whether the contrasted patenting propensities overthe firms‟ life cycle are associated with different types of actual patents, or innovations. Tables 5 and 6 aswell as figure 20 summarize the results obtained from the simple OLS estimation of equation 3.
The age profile of patent scope does not show any clear pattern. However, patent radicalness appears to besignificantly and negatively correlated with firm age. This finding is to be related with the previous resultof Nerkar and Shane (2001) who showed that technological radicalness (and also patent scope) reduce new
firm failure.
Panel 4 of table 5 shows that patent quality, as measured by the number of forward citations, appears to be
declining with firm age. In this sense, the first age band (0 to 2 years) is an outlier, which may be explainedby the fact that very young firms are not well-known and may therefore be less often cited since
competitors (or patent examiners) may not be aware of their existence and technological activities.
Forward A citations are also more frequent for patents held by younger firms; the age profile in this caseshows a threshold after 30 years rather than a steadily declining trend. Last, more preemptive patents areapplied for by quite mature firms (16 to 20 years old), and the trend is subsequently rather decreasing withfirm age.
Table 6 reports the coefficients obtained for employment marginal effects in the same regressions aspreviously. We obtain no clear pattern for any indicator, except that firms reporting no employee always
appear as outliers. This may show that these ventures may have a very particular, mostly juridical profile –although their main industry affiliation always belongs to the high-tech manufacturing, KIBS or ICT
industries31.
4. Conclusion
In this paper, we shed light on the dynamics of technological change in industries and analyze theinnovative behaviour of firms over their life cycle. We first provided novel descriptive evidence about theage distribution of French patenting firms in the main high-tech industries, and then analyzed the patenting
propensities of firms across age bands, with a particular emphasis on potential determinants related to thefirms‟ financing conditions. We provided evidence of possible financing constraints, especially at earlystages of the life cycle. Last, we provided precise evidence that the characteristics of the innovations
introduced by firms evolve with their age, which suggests that barriers to entry may alter the direction as
well as the rhythm of technological change.
31

Figure 20 presents the coefficients obtained for the variable indicating the (lagged) industry share of XY citationsin the total amount of forward citations, which was introduced as an additional control in all regressions. A high shareof preemptive patents seems to induce more “focused” patents (lower scope), more technological radicality, and on
average higher quality patents in terms of forward citations or forward A citations.

14Several aspects are let for future or planned research. The analysis of patenting would for example benefitof a better treatment of selection processes induced by firm survival. In particular, primo-patenting
behaviour may be further analyzed using duration analysis in order to assess the temporal dynamics ofinnovative behaviour and alternative demographic events (see e.g. Audretsch (1995) or Sørensen andStuart (2000)). Also, the analysis of the future of patenting firms, in terms of growth or group acquisition,and the question of the potential “criticality” of innovative performance at early stages remain largely open
questions at this stage.

15

ReferencesAcs, Z. J., and Audretsch, D. B. (1988), “Innovation in Large and Small Firms: An Empirical Analysis”, American Economic
Review, 78(4), 678-690.
Agarwal, R. and Audretsch, D. (2001), “Does Entry Size Matter? The Impact of the Life Cycle and Technology on Firm Survival”,
Journal of Industrial Economics, 44(1), 21-43.
Ahuja, G. and Katila, R. (2001), “Technological Acquisitions and the Innovation Performance of Acquiring Firms: a Longitudinal
Study”, Strategic Management Journal, 22, pp. 197-220.
Almeida, H. and Campello, M. (2007), “Financial Constraints, Asset Tangibility, and Corporate Investment”, Review of Financial
Studies, 20, pp. 1429-1460.
Almeida, H., Campello, M. and Weisbach, M. (2004), “The cash flow sensitivity of cash”, Journal of Finance, 59, pp. 1777-1804.
Arellano, M. and Bond, S. (1991), “Some tests of specification for panel data: Monte Carlo evidence and an application toemployment equations”, Review of Economic Studies, 58, pp. 277-297.
Audretsch, D. (1995), “Innovation, Growth and Survival”, International Journal of Industrial Organization, 13, 441-457.
Audretsch, D. B., and Mahmood, T. (1995), “New Firm Survival: New Results Using a Hazard Rate Function”, Review of
Economics and Statistics, 77(1), 97-103.
Balasubramanian, N. and Sivadasan, J. (2008), “What Happens When Firms Patent? New Evidence from US Economic Census
Data”, US Census Bureau Center, Economic Studies Paper no CES-WP-0803.
Bond, S., Harhoff, D. and Van Reenen, J. (2003), “Investment, R&D and Financial Constraints in Britain and Germany”,
http://cep.lse.ac.uk/pubs/download/dp0595.pdf, forthcoming, Annales d'Economie et de Statistique.
Branstetter, L., Crépon, B., Hall, B. and Mairesse, J. (1999), “Does Cash-Flow Cause Investment and R&D? An Exploration Using
Panel Data for French, Japanese, and United-States Scientific Firms”, in Innovation, Industry Evolution, and Employment, D. B.
Audretsch and R. Thurik eds., Cambridge University Press, Cambridge, p.129-156.
Davis, S., Haltiwanger, J. and Jarmin, R. (2008), “Young Businesses, Economic Churning and Productivity Gains”, Kauffman
Foundation, June 2008.
EMC (2006), “Trends and drivers of change in the European KIBS sector”, Mapping Report.
Eurostat (2007), Eurostat-OECD Manual on Business Demography Statistics. Luxembourg: Office for Official Publications of theEuropean Communities.
http://eur-lex.europa.eu/LexUriServ/LexUriServ.do?uri=OJ:L:2008:097:0013:0059:EN:PDF
Gans, J. S., and Stern, S. (2003), “The Product Market and the Market for Ideas: Commercialization Strategies for Technology
Entrepreneurs”, Research Policy, 32(2), 333-350.
Grimpe, C. and K. Hussinger (2008), “Pre-empting Technology Competition through Firm Acquisitions”, Economics Letters, 100,
189-191.
Guellec, D., Martinez, C. and Zuniga, P. (2008), “Blocking patents: What they are and what they do”, OECD mimeo.
Harhoff, D., Hoisl, K., and C. Webb (2005), “Analysing European and International Patent Citations: a Set of EPO Patent
Database Building Blocks”, OECD STI Working Paper, 2005/9.
Hatzichronoglou, T. (1997), “Revision of the High-Tech Sector and Product Classification”, OECD Science, Technology andIndustry Working Papers, 1997/2.
Hausman, J., Hall, B. and Griliches, Z. (1984), “Econometric Models for Count Data with an Application to the Patents-R&D
Relationship”, Econometrica, 52, pp. 909-938.

16

Holtz-Eakin, D., Newey, W., and Rosen, H. (1988), “Estimating Vector Autoregressions With Panel Data”, Econometrica, vol.
56(6), pp. 1371-1395.
Huergo, E. and J. Jamandreu (2004a), “How does Probability of Innovation Change with Firm Age?”, Small Business Economics,
22, 193-207.
Huergo, E. and J. Jamandreu (2004b), “Firms‟ Age, Process Innovation and Productivity Growth”, International Journal ofIndustrial Organization, 22, 541-559.
Hunt, and Nakamura (2006), “The Democratization of US R&D after 1980”, mimeo, Federal Reserve Bank of Philadelphia.
Jaffe, A (1986), “Technological Opportunity and Spillovers of R&D: Evidence from Firm Patents, Profits and Market Value”,
American Economic Review, 26, 1023-1046.
Klepper, S. (1996), “Entry, Exit and Innovation over the Product Life Cycle”, American Economic Review, 86, 562-583.
Lee, L.-F. (1983), “Generalized econometric models with selectivity”, Econometrica, 51, 507-512.
Lerner, J. (1994), “The Importance of Patent Scope: An Empirical Analysis”, RAND Journal of Economics, 25(2), 319-333.
McGahan, A. and B. Silverman (2001), “How does Innovative Activity Change as Industries Mature?”, International Journal ofIndustrial Organization, 19, 1141-1160.
MacGarvie, M. (2006), “Do Firms Learn from International Trade?”, Review of Economics and Statistics, 88(1), pp. 46-60.
Manski, C. (1995), Identification Problems in the Social Sciences, Cambridge: Harvard University Press.
Miles, I., European Monitoring Centre on Change, “The knowledge-intensive business services sector – What future?”, Sector
Futures, Dublin, June 2005a, available at:
http://www.emcc.eurofound.eu.int/content/source/eu05016a.html
Nerkar, A. and Shane, S. (2003), “When do Start-Ups that Exploit Patented Academic Knowledge Survive?”, International
Journal of Industrial Organization, 21, pp. 1391-1410.
OECD (2008), Measuring Entrepreneurship: A digest of indicators. OECD-Eurostat Entrepreneurship Indicators Program. OECD:
Paris.
Shane, S. (2001), “Technology Regimes and New Firm Formation”, Management Science, 47(9), 1173-1190.
Sørensen, J. and T. Stuart (2000), “Aging, Obsolescence and Organizational Innovation”, Administrative Science Quarterly, 45,
pp. 81-112.
Trajtenberg, M. (1990), "A Penny for Your Quotes: Patent Citation and the Value of Innovations", RAND Journal of Economics,
21, 172-87
Wiklund, J. and Shepherd, D (2003), Knowledge-based Resources, Entrepreneurial Orientation, and the Performance of Small andMedium-sized Enterprises, Strategic Management Journal, 24(13).
Winter, S. (1984), “Schumpeterian Competition in Alternative Technological Regimes”, Journal of Economic Behavior andOrganization, 5, pp. 287-320.

17

Appendix A: Concepts of firm age
Our main source for statistical standards related to the measure of firm age is the Eurostat-OECD Manualon Business Demography Statistics.
The two most relevant concepts are those of firm birth and firm creation, which are defined in close
relation. The first one has a straightforward economic definition, while the second has a straightforwardadministrative and statistical content.
Firm births are defined as follows32:“A birth amounts to the creation of a combination of production factors with the restriction that no otherenterprises are involved in the event. Births do not include entries into the population due to: mergers,
break-ups, split-off or restructuring of a set of enterprises. It does not include entries into a sub-population
resulting only from a change of activity.”
The aim is to produce data on the creation of new enterprises that have started from scratch (green-fieldcreation) and that have actually started activity. An enterprise creation can be considered as an enterprisebirth if new production factors, in particular new jobs, are created. Conversely, events leading to a creationof a new enterprise, but which should be excluded from the statistics on enterprise births are:1.

Enterprises that are created by merging production factors or by splitting them into two (ormore) enterprises (breakups, mergers, split-offs, restructuring)2.

Newly created enterprises that simply take over the activity of a previously created enterprise(take-over)3.

Any creations of additional legal units/enterprises solely for the purpose of providing a single

production factor (e.g. the real estate or personnel) or an ancillary activity (see note below) for an
existing enterprise.
4.

An enterprise that is registered when an existing enterprise changes legal form. E.g. a

successful sole proprietor moves operations from his home to another location and at the sametime changes the legal form of the enterprise to a limited liability company.
5.

Reactivated enterprises if they restart activity within 2 calendar years.

6.

Temporary associations and joint ventures that do not involve the creation of new factors of

production.
The identification of births is carried out by eliminating creations due to events other than births from the
population of new enterprises, that is, break-ups, split-offs, mergers and one-to-one take-overs.

32

The definition is directly derived from the European Commission Regulation (EC) No 2700/98 of 17 December
1998 concerning the definitions of characteristics for structural business statistics

18

In this paper, we exclusively rely on the statistical concept of firm creation, which is the moststraightforward to be empirically defined. The corresponding events are also most frequently quite easily
tracked in business registers.
One of the main advantages of this concept is that it covers the entire universe of all firms – as opposed tothe concept of firm birth – and may therefore be used in order to define a first notion of “firm age” as thetime elapsed between the date of creation and the date considered. It is also the concept which is most often
used in administrative documents and in the definition of eligibility criteria of public programs33.
The alternative concept of firm birth is closer to the economic idea of green-field creation. However, as
previously stated, one drawback of this concept is that the implied notion of firm age does not cover theentire population of firms: what age should be affected to firms whose creation is excluded from the set ofbirths34? Another drawback is obviously data availability since excluding creations that are not births is notstraightforward, and the corresponding information is seldom indicated in business registers35.

In any case, there is no straightforward concept of firm age as soon as a considered firm is affected by a restructuring event such as: acquisitions or mergers (either as an absorbing/acquiring or as an acquiredorganization), split-offs, or even change of ownership. In particular, this implies that age is more difficult
to define in a meaningful way for more mature structures. It is also crucial to take group ownership36 into
account, since firms created or born within group structures may benefit from various (organizational,
financial, legal…) competencies of more mature structures which are part of the group. These both aspects
are taken into account in all of the regression analyses.

33

However, another less desirable feature of this concept (and of the related concept of firm age) is that these have alarger “institutional” or “historical”, country specific component than firm creation. For example, in the French case,the distribution of firm age is partly driven by the large waves of “nationalizations” and “privatizations” experienced
since WWII.
34
Note that this problem even amounts to put the underlying concept of firm into question.
35
This is at least not the case in the French SIRENE files.
36
It should be also noted that the notions of firms as opposed to corporate groups are not fully harmonized (e.g. US
vs. Europe)…

19Appendix B: Definition of the Manufacturing High-Tech, Knowledge Intensive Business Services
and Information and Communication Technologies industries

ISIC 2
3845

ISIC 3.1
3511

3845

3530

3825

2919

3825

3000

3825

7250

3522

2423

3832
3832

2213
2230

3832

3110

3832

3120

3832

3190

ISIC 4
3011
3315
2829
3030
3315
2819
3250
3312
3320
2610
2620
2817
3320
3312
9511
2100
3250
5920
2011
2100
2420
3812
3822
2610
2710
2790
2811
3312
3314
3320
2610
2710
2733
2790
3314
2599
2630
2651
2733
2740
2790
2822
2930
3020

3832

3210

3313
3314
2610
2790
3314

High-Tech Industries (OECD, 1997)
Except repair and maintenance of ships
Repair and maintenance of ships
Manufacture of aircraft launching gear, catapults etc.
Manufacture of air and spacecraft; except manufacture of aircraft seats and lighting fixtures, repair of aircraft
Repair and maintenance of aircraft
Manufacture of general-purpose machinery (see other links for exceptions)
Manufacture of laboratory centrifuges
Repair of other general-purpose machineryInstallation of other general-purpose machinery
Manufacture of computer modems
Manufacture of office machinery and equipment, except computers and peripheral equipment
Installation of mainframes and similar computers, other office machinery
Repair of office machinery and equipment, except computers and peripheral equipment
Excluding manufacture of radioactive in-vivo diagnostic substances and manufacture of dental fillings andbone reconstruction cements
Manufacture of dental fillings and bone reconstruction cements
Publishing of recorded media
Enrichment of uranium and thorium ores
Manufacture of radioactive in-vivo diagnostic substances
Production of uranium metal from pitchblende or other ores; smelting and refining of uranium
Transfer stations for spent nuclear fuels (e.g. temporary storage)
Treatment and disposal of radioactive nuclear waste
Manufacture of electronic transformers (solid state), coils, chokes, and other inductors
Manufacture of electric motors, generators and transformers, except turbine-generator sets
Manufacture of battery chargers, rectifiers, solid-state inverters
Manufacture of turbine-generator sets
Repair of turbine-generator sets
Repair of electric motors, generators and transformers, battery chargers etc.
Installation of electric motors, generators and transformers
Manufacture of electronic connectors such as cylindrical, rack and panel, printed circuit, etc.; electronic
switches, such as snap, toggle, rocker, etc., for use in electronic apparatus
Manufacture of electricity distribution and control apparatus, except wiring devices
Manufacture of wiring devices
Manufacture of surge suppressors
Repair of electricity distribution and control apparatus
Manufacture of permanent metallic magnets
Manufacture of burglar and fire alarm systems
Manufacture of pulse generators
Manufacture of indicator panels; electrical conduit tubing and joints for such tubing, of base metal lined withinsulating material
Manufacture of lighting equipment for motor vehicles, electrical fireplace logs, electrical insect lamps
Manufacture of other electrical equipment
Manufacture of electroplating machinery
Manufacture of motor vehicle electrical equipment
Manufacture of mechanical and electromechanical signaling, safety and traffic control equipment for railways,
tramways, inland waterways, roads, parking facilities, airfields etc.
Repair of pulse generators
Repair of other electrical equipment
Manufacture of electronic components and boards
Manufacture of solid-state fuel cells, power supplies; manufacture of non-electronic electric components, suchas non-electronic capacitors, resistors etc. (e.g. power capacitors)
Repair of solid-state fuel cells, power supplies; non-electronic electric components, such as non-electronic

20

3832

3220

3832

3230

3832

3311

2630
2651
3313
3320
9512
2610
2630
2640
2670
2817
3312
3313
9521
2660
3250
3290
3313

3832

ISIC3.1
7210
7221
7229
7230
7240
7240
7240
7240
7240
7240
7240
7240
7240
7250
7290
7310
7320
7411
7412
7414
7414
7421

3312

ISIC4
6202
5820
6201
6202
6202
5811
5812
5813
5819
5820
5920
6010
6020
6312
3312
9511
6209
7210
7220
6910
6910
7010
7020
0910

3319
3320
2651
2670
2819
2829
3250
3313
3319

capacitors, resistors etc. (e.g. power capacitors)
Manufacture of communication equipment, except remote controls, pagers, burglar and fire alarm systems,
installation of communications equipment
Manufacture of GPS equipment
Repair of GPS equipment
Installation of communication equipment
Repair of communication equipment, industrial-type (e.g. commercial TV cameras)
Manufacture of heads (pickup, recording, read/write, etc.), phonograph needles
Manufacture of infrared devices (e.g. remote controls), pagers
Manufacture of consumer electronics, except video game consoles and digital cameras
Manufacture of digital cameras
Manufacture of dictating machines
Repair of dictating machines
Repair of digital cameras
Repair of public address systems and components"Manufacture of medical and surgical equipment and orthopaedic appliances"
Manufacture of orthopedic and prosthetic devices, artificial teeth, splints, crutches; orthopedic shoes; boneplates and screws; syringes, catheters etc.; medical, surgical dental or veterinary furniture (operating tables,
dentists' chairs etc.); medical thermometers; dental laboratory furnaces; laboratory sterilizers, ultrasoniccleaning machinery; mechanotherapeutic appliances; surgical and medical instruments
Manufacture of gas masks
Repair of electro-diagnostic apparatus, X-ray and similar apparatus, irradiation apparatus and tubes (medical
and other), hearing aids, pace makers, mirrors, reflectors
Repair of orthopedic and prosthetic devices, gas masks
Installation of X-ray machines etc.
Manufacture of measuring, testing, navigating and control equipment
Manufacture of optical range finders
Manufacture of machinists precision tools (except optical), levels, tape measures etc.
Manufacture of tire alignment and balancing equipment; balancing equipment (except wheel balancing)
Manufacture of laboratory type distilling apparatus
Repair of measuring, testing, navigating and control equipment
Repair of laboratory sterilizers and laboratory type distilling apparatus

Knowledge-Based Intensive Sectors (Miles, 2005)
Hardware consultancy
Software consultancy and supply
Computer programming activities
Computer consultancy and computer facilities management activities
Data processing
On-line publishing of books etc.
On-line publishing of directories and mailing lists
On-line publishing of newspapers etc.
On-line publishing of other information (greeting cards, statistics etc.), database publishingOn-line software publishingOn-line music publishing
Internet radio broadcasting
Internet TV broadcasting
Web portals
Maintenance and repair of office
Repair of computers and peripheral equipment
Other computer related activities
Research and experimental development on natural sciences and engineering
Research and experimental development on social sciences and humanities
Legal activities
Accounting, book-keeping and auditing activities; tax consultancy; Market research and public opinion polling
Business and management consultancy activities
Management activities of holding companies
Oil and gas field exploration

21

7421
7421
7421

0990
7110
7410

7421
7422
7430
7491
7491
7491
7494

7490
7120
7310
7810
7820
7830
7420

ISIC3.1
3000
3000
3000
3000
3130
3130
3130
3130
3130
3210

ISIC4
2610
2620
2817
3320
2610
2731
2732
2790
3314
2610

3210

2790

3210

3314

3220
3220
3220
3220
3220
3230
3230
3230
3230
3230
3230
3230
3230
3312
3312
3312
3312
3312
3312
3312
3313
3313
3313
5151
5152
7123
6420
6420
6420
6420

2630
2651
3313
3320
9512
2610
2630
2640
2670
2817
3312
3313
9521
2651
2670
2819
2829
3250
3313
3319
2651
3313
3320
4651
4652
7730
6110
6120
6130
6190

Exploration of mine sites, except oil and gas
Architectural and engineering activities and related technical consultancy
Industrial design of tools, seating, automobiles, packaging etc.
Weather forecasting activities; quantity surveyors; activities of technical consultants (other than engineers
etc.)
Technical testing and analysis
Advertising
Activities of employment placement agencies
Temporary employment agency activities
Other human resources provision
Photographic activities

OECD Definition of the ICT sector
Manufacture of computer modems
Manufacture of computers and peripheral equipment
Manufacture of office machinery and equipment, except computers and peripheral equipment
Installation of mainframes and similar computers, other office machinery
Manufacture of printer cables, monitor cables, USB cables, connectors etc.
Manufacture of fibre optic cable for data transmission
Manufacture of other electronic and electric wires and cables
Manufacture of extension cords
Repair of electric and electronic wires and cables (except computer cables etc.)
Manufacture of electronic components and boards
Manufacture of solid-state fuel cells, power supplies; manufacture of non-electronic electric components, such as nonelectronic capacitors, resistors etc. (e.g. power capacitors)
Repair of solid-state fuel cells, power supplies; non-electronic electric components, such as non-electronic capacitors,
resistors etc. (e.g. power capacitors)
Manufacture of communication equipment, except remote controls, pagers, burglar and fire alarm systems, installation ofcommunications equipment
Manufacture of GPS equipment
Repair of GPS equipment
Installation of communication equipment
Repair of communication equipment, industrial-type (e.g. commercial TV cameras)
Manufacture of heads (pickup, recording, read/write, etc.), phonograph needles
Manufacture of infrared devices (e.g. remote controls), pagers
Manufacture of consumer electronics, except video game consoles and digital cameras
Manufacture of digital cameras
Manufacture of dictating machines
Repair of dictating machines
Repair of digital cameras
Repair of public address systems and components
Manufacture of measuring, testing, navigating and control equipment
Manufacture of optical range finders
Manufacture of machinists precision tools (except optical), levels, tape measures etc.
Manufacture of tire alignment and balancing equipment; balancing equipment (except wheel balancing)
Manufacture of laboratory type distilling apparatus
Repair of measuring, testing, navigating and control equipment
Repair of laboratory sterilizers and laboratory type distilling apparatus
Manufacture of industrial process control equipment
Repair of industrial process control equipment
Installation of industrial process control equipment
Wholesale of computers, computer peripheral equipment and software
Wholesale of electronic and telecommunications equipment and parts
Renting and leasing of other machinery, equipment and tangible goods
Wired telecommunications activities
Wireless telecommunications activities
Satellite telecommunications activities
Other telecommunications activities

22

7210
7221
7229
7230
7240
7240
7240
7240
7240
7240
7240
7240
7240
7250
7290

6202
5820
6201
6202
6202
5811
5812
5813
5819
5820
5920
6010
6020
6312
3312
9511
6209

Hardware consultancy
Software consultancy and supply
Computer programming activities
Computer consultancy and computer facilities management activities
Data processing
On-line publishing of books etc.
On-line publishing of directories and mailing lists
On-line publishing of newspapers etc.
On-line publishing of other information (greeting cards, statistics etc.), database publishingOn-line software publishingOn-line music publishing
Internet radio broadcasting
Internet TV broadcasting
Web portals
Maintenance and repair of office
Repair of computers and peripheral equipment
Other computer related activities

23

Source: Exhaustive business registers (INSEE) and patent applications to the national patent office (file constructed at the INPI).

24

0

50

100

150

31/35

36/40

>40

births

3/5

6/10

11/15

16/20

21/25

26/30

31/35

36/40

>40

births

3/5

6/10

11/15

16/20

21/25

26/30

31/35

36/40

>40

births

26/30

21/25

16/20

0

0

0

0

5,000

40,00020,00060,00010,00015,00040,00010,00020,0005,000

20,00025,00060,00015,00080,000HT Manufacturing Industries
KIBS Industries
Fig. 1 Average age distribution of the entire population of firms, 1995-2004

11/15

6/10

3/5

0/2

0/2

0/2

>40

births

26/30

21/25

16/20

11/15

6/10

3/5

0/2

births

>40

36/40

31/35

26/30

21/25

16/20

11/15

6/10

3/5

0/2

births

>40

36/40

31/35

26/30

21/25

16/20

11/15

6/10

3/5

0/2

births

500

150

150

250

births

400

100

100

200

36/40

300

0

150

>40

26/30

21/25

16/20

11/15

6/10

3/5

0/2

50

50

100

>40

200

600

births

>40

36/40

31/35

26/30

21/25

16/20

11/15

6/10

3/5

0/2

0

50

31/35

400

800

births

>40

36/40

31/35

26/30

21/25

16/20

11/15

6/10

3/5

0/2

0

36/40

100

200

600

1,500

Fig. 3 Average age distribution of patenting firms, patent weighted, 1995-2004

36/40

0

0

400

1,000

Fig. 2 Average age distribution of patenting firms, 1995-2004

31/35

31/35

26/30

21/25

16/20

11/15

6/10

3/5

0/2

births

>40

36/40

31/35

26/30

21/25

16/20

11/15

6/10

0

200

500

Total: HT+KIBS+ICT

3/5

0/2

births

>40

36/40

31/35

26/30

21/25

16/20

11/15

6/10

3/5

0/2

0

Descriptive statistics: Age distribution of French patenting firms in the main high-technology industries
ICT Industries

Descriptive statistics: Age distribution of French patenting firms in the main high-technology industries (cont.)

2.5

>40

births

36/40

31/35

26/30

21/25

16/20

11/15

3/5

0/2

6/10

0
>40

births

36/40

31/35

26/30

21/25

16/20

11/15

3/5

6/10

0

0/2

>40

births

36/40

31/35

26/30

21/25

16/20

11/15

3/5

6/10

0

0/2

births

>40

36/40

31/35

26/30

21/25

16/20

11/15

3/5

6/10

0/2

0

.5

1

1

2

1

2

2

1.5

4

3

3

2

4

6

4

Fig. 4 Average patenting propensities across age bands, 1995-2004 (in %)Fig. 5 Primo-patenting propensities (black), persistent patenting propensities (grey), and patent owners (transparent)
over the first 8 years of the firms’ life cycle and as a percentage of firms (left) or employment weighted firms (right)

0

8

8

8

8

7

7

7

7

6

6

6

6

5

5

5

5

4

4

4

4

3

3

3

3

2

2

2

2

1

1

1

1

0

0

0

5

10

15

20

-10

0

10

20

0

Source: Exhaustive business registers (INSEE) and patent applications to the national patent office (file constructed at the INPI).

25

0

5

10

15

20

25

0

5

10

15

26

Source: FICUS Fiscal files (DGI Tax Administration and INSEE) and patent applications to the national patent office (file constructed at the INPI).
0

50

100

150

200

500/999

250/499

100/249

50/99

20/49

10/19

1/9

no emp>10th

5000/10th

1000/4999

500/999

250/499

100/249

50/99

20/49

250/499

500/999

20/49

50/99

100/249

250/499

20/49

50/99

100/249

250/499

1/9

1000/4999

5000/10th>10th

no emp500/999

1000/4999

5000/10th>10th

no emp500/999

1000/4999

5000/10th>10th

no emp100/249

0

0

0

0

10,00020,00020,00040,00010,00050,00030,00060,00040,00080,00020,00010000050,00010000030,000150000HT Manufacturing Industries
KIBS Industries
Fig. 6 Average size (employment) distribution of the entire population of firms, 1995-2004

50/99

20/49

10/19

1/9

10/19

1/9

10/19

5000/10th

>10th

400

150

1/9

10/19

1000/4999

100

150

no emp>10th

5000/10th

1000/4999

500/999

250/499

100/249

50/99

20/49

10/19

1/9

no emp

300

0

50

100

250

1000/4999

500/999

250/499

100/249

50/99

20/49

10/19

1/9

no emp>10th

5000/10th

1000/4999

500/999

250/499

100/249

50/99

20/49

50

200

no emp

200

1/9

10/19

0

150

no emp

100

600

800

0

100

5000/10th

400

600

no emp>10th

5000/10th

1000/4999

500/999

250/499

100/249

50/99

20/49

10/19

1/9

50

>10th

200

400

800

Fig. 8 Average size (employment) distribution of patenting firms, patent weighted, 1995-2004
5000/10th

0

0

200

600

Fig. 7 Average size (employment) distribution of patenting firms, 1995-2004>10th

1000/4999

500/999

250/499

100/249

50/99

20/49

10/19

1/9

no emp>10th

5000/10th

1000/4999

500/999

250/499

100/249

50/99

20/49

0

0

400

Total: HT+KIBS+ICT

10/19

1/9

no emp>10th

5000/10th

1000/4999

500/999

250/499

100/249

50/99

20/49

10/19

1/9

200

Descriptive statistics: Employment distribution of French patenting firms in the main high-technology industries
ICT Industries

Descriptive statistics: Employment distribution of French patenting firms in the main high-technology industries (cont.)
HT Manufacturing Industries
KIBS Industries
Fig. 9 Average patenting propensities across size bands, 1995-2004 (in %)ICT Industries
60

20

27

>10th

no emp5000/10th

500/999

1000/4999

250/49950/99

100/249

20/49

1/9

10/19

>10th

no emp5000/10th

500/9990

Source: FICUS Fiscal files (DGI Tax Administration and INSEE) and patent applications to the national patent office (file constructed at the INPI).

1000/4999

250/49950/99

100/249

20/49

1/9

10/19

0
>10th

no emp5000/10th

500/999

1000/4999

250/49950/99

100/249

20/49

1/9

10/19

0
no emp>10th

5000/10th

500/999

1000/4999

250/49950/99

100/249

20/49

10/19

1/9

0

5

10

20

20

10

20

40

15

30

40

60

40

25

50

80

Total: HT+KIBS+ICT

Tab. 1A.: Firm-Level Sample Descriptive Statistics
High-Tech Manufacturing, KIBS and ICT Industries, 1998-2004
Patenting firms

Non-patenting firms

MeanMedianStd Dev.

Mean

MedianStd Dev.

(1)

(2)

(3)

(4)

(5)

(6)

Cash flow

0.050

0.066

0.186

0.139

0.094

0.322

Share of internal finance

0.358

0.360

0.268

0.257

0.272

0.382

Intensity of financial costs

0.082

0.052

0.107

0.081

0.050

0.112

Group ownership

0.557

-

-

0.090

-

-

Size of the corporate group

5881

997

13296

3357

180

17469

Group cash holdings

0.160

0.154

0.143

0.137

0.135

0.170

Group specialization

0.628

0.607

0.280

0.723

0.794

0.260

Downsizing

0.012

-

-

0.001

-

-

Down.: 2 years lag

0.013

-

-

0.001

-

-

Down.: 3 years lag

0.011

-

-

0.001

-

-

Acquisition

0.024

-

-

0.002

-

-

Acq.: 2 years lag

0.030

-

-

0.001

-

-

Acq.: 3 years lag

0.027

-

-

0.001

-

-

Capital intensity, firm level

0.347

0.157

0.373

0.333

0.147

0.393

Capital intensity, industry level

882

91

2181

1491

118

2767

H. Herfindahl index

0.051

0.021

0.075

0.025

0.009

0.052

Ind. patenting intensity (per firm)

0.140

0.033

0.405

0.026

0.009

0.133

Share of XY citations

0.376

0.368

0.215

0.401

0.386

0.253

Financial aspects:

Acquisitions and mergers:

Firm and ind. controls:

4265

Observations

548242

Source: Exhaustive business registers (INSEE) and accounting information sourced from fiscal files (INSEE-DGI), patent applications to thenational patent office (file constructed at the INPI), LIFI survey and CITRUS information system (INSEE). The descriptive statistics relating to thesize, cash holdings and specialization index of the corporate group have been computed on the subpopulations of firms belonging to a larger
corporate group (2379 observations in the case of patenting firms, 49502 in the case of non-patenting firms).

Tab. 1B.: Patent-Level Sample Descriptive Statistics
High-Tech Manufacturing, KIBS and ICT Industries, 1998-2004
Mean

MedianStd Dev.

Min

Max

(1)

(2)

(3)

(4)

(5)

Patent scope

1.642

1

0.920

1

11

Radicalness

2.277

2

2.278

0

26

Dum. Fwd citations

0.146

-

-

-

-

Dum. Fwd A citations

0.070

-

-

-

-

Dum. Fwd XY citations

0.074

-

-

-

-

Dum. Both types

0.017

-

-

-

-

# Fwd citations

0.271

0

0.834

0

14

# Fwd A citations

0.108

0

0.466

0

11

# Fwd XY citations

0.105

0

0.43

0

9

21816

Observations

Source: Patent applications to the national patent office (file constructed at the INPI) and complementary patent information sourced from the
PATSTAT database.

28

Tab. 2.: Patenting propensities in the main manufacturing and service high-tech industries,
1998-2004

Basic
Specification(1)
Firm Age:
0/2 years
3/5 years

ExtendedSpecification(2)

ExtendedSpecification,
Fixed
Effects(3)

Internal
CapitalMarkets(4)

Acquisitions
andMergers
(5)

ref

ref

ref

ref

ref

-0.125***(0.043)-0.148***(0.038)-0.114**(0.050)-0.147***(0.038)-0.145***(0.040)6/10 years-0.163***(0.055)-0.212***(0.050)-0.155*(0.082)-0.216***(0.049)-0.144***(0.043)

11/15 years-0.109*(0.065)-0.058(0.077)-0.172***(0.060)-0.100(0.083)-0.257**(0.172)0.325*(0.172)-0.108**(0.056)-0.044(0.083)-0.212**(0.106)0.017(0.230)0.320(0.202)0.185(0.222)-0.265**(0.115)-0.105(0.226)0.193(0.197)0.025(0.220)-0.571**(0.230)-0.757**(0.313)-0.683(0.443)-0.639(0.510)-0.184***(0.056)-0.107(0.078)-0.277(0.106)-0.120(0.213)0.193(0.193)0.011(0.216)1.874***(0.546)

1.526***(0.495)-0.879(0.726)1.465***(0.482)

1.376***(0.502)-0.114**(0.046)ref-0.154***(0.050)ref-0.095**(0.043)ref-0.149***(0.051)ref-0.153***(0.055)
ref0.461***(0.109)0.361***(0.096)0.200**(0.094)0.349***(0.092)0.419***(0.097)20-49 emp.

1.522***(0.272)

1.234***(0.244)0.612***(0.176)

1.195***(0.239)1.304***(0.251)50-99 emp.

3.659***(0.596)3.064***(0.548)1.156***(0.410)2.915***(0.548)3.263***(0.629)100-249 emp.

7.230***(1.098)6.345***(1.024)1.624**(0.673)

6.066***(1.020)6.742***(1.185)250-499 emp.

13.364***(1.722)

12.221***(1.658)4.798***(1.374)11.778***(1.645)13.168***(1.976)500-999 emp.

23.957***(2.245)22.618***(2.151)

7.453***(2.241)21.995***(2.157)24.467***(2.292)

1000-4999
emp.
5000-10th
emp.
>10th
emp.

31.427***(3.425)29.939***(3.327)8.986**(3.662)29.314***(3.303)33.881***(3.936)40.491***(13.336)

39.145***(13.347)

38.574***(13.513)36.286**(14.290)64.445***(8.128)63.294***(7.969)20.984***(8.182)33.651(23.864)

62.677***(8.120)65.246***(6.653)

16/20 years
21/25 years
36/30 years
31/35 years
36/40 years>40 years
Size (employment):
0 emp.
1-9 emp.
10-19 emp.

-0.197*(0.118)-0.041(0.238)0.202(0.184)-0.170(0.226)

Note: Marginal effects at the sample mean (multiplied by 100) reported, robust standard deviations have been clustered at the industry 4-digit level.
Estimation sample is restricted to firms which were active in manufacturing HT industries, KIBS services and ICT industries over the estimation
period (1998-2004).
Source: Exhaustive business registers (INSEE) and accounting information sourced from fiscal files (INSEE-DGI), patent applications to thenational patent office (file constructed at the INPI), LIFI survey and CITRUS information system (INSEE). All explanatory variables are lagged.

29

Tab. 2. (cont): Patenting propensities in the main manufacturing and service high-tech industries,
1998-2004

Basic
Specification(1)

ExtendedSpecification(2)

ExtendedSpecification,
Fixed
Effects(3)--0.335***(0.096)-0.039(0.056)-0.344***(0.096)-0.278***(0.093)

Share of internalfinance
Intensity of financial
costsGroup ownership-

0.329***(0.075)0.184***(0.072)0.327***(0.068)0.312***(0.073)--0.329***(0.075)-0.320**(0.162)-0.407**(0.191)-ln Size of thecorporate group
Group cash
holdings
Group specialization-

1.280***(0.222)--0.300**(0.148)0.069(0.136)-1.674**(0.661)0.148*(0.086)1.626***(0.340)----3.520***(1.344)

-

-

-

-

-2.233***(0.564)-

Acquisitions and mergers:
Downsizing

-

-

-

-

1.195
(1.721)

Down.: 2 years lag

-

-

-

-

4.316**(1.743)

Down.: 3 years lag

-

-

-

-

Acquisition

-

-

-

-

Acq.: 2 years lag

-

-

-

-

Acq.: 3 years lag

-

-

-

-

4.026**(1.869)-1.370(1.371)0.934(1.126)0.209(1.328)-0.241*(0.133)0.271**(0.131)0.282**(0.139)--0.118**(0.056)0.075(0.069)-0.026(0.024)2.702**(1.288)0.026(0.088)yes-0.091**(0.043)0.094(0.065)5.146**(2.186)-0.163(0.136)yes-0.120**(0.056)
0.117*(0.072)4.539**(2.173)-0.123(0.129)yes

552507552507367030Financial aspects:
Cash flow

Firm and ind. controls:
Capital intensity,
firm level
Capital intensity,
industry levelln H. Herfindahl index
Ind. patenting intensity(per firm)
Share of XY citations-

Time and industry(3 dig.) dummies

yes-0.095**(0.044)0.098(0.064)5.189**(2.169)-0.166(0.138)yes

552507552507

Observations--

Internal
CapitalMarkets(4)

Acquisitions
andMergers(5)Note: Marginal effects at the sample mean (multiplied by 100) reported, robust standard deviations have been clustered at the industry 4-digit level.
Estimation sample is restricted to firms which were active in manufacturing HT industries, KIBS services and ICT industries over the estimation
period (1998-2004).
Source: Exhaustive business registers (INSEE) and accounting information sourced from fiscal files (INSEE-DGI), patent applications to thenational patent office (file constructed at the INPI), LIFI survey and CITRUS information system (INSEE). All explanatory variables are lagged.

30Regression analyses across age bands: indicators of financing constraints

Descriptive Statistics

Marginal Effects

Elasticities.2

patenting-.2-.4

age bandGraphs by patenting status

>40

36/40

31/35

26/30

21/25

16/20

11/15

3/5

6/10

0/2

>40

36/40

31/35

26/30

21/25

16/20

6/10

3/5

-.8

-.04

0/2

>40

36/40

31/35

26/30

21/25

16/20

3/5

11/15

0/2

excludes outside values

11/15

excludes outside values

6/10

>40

36/40

31/35

26/30

21/25

16/20

3/5

6/10

11/15

0/2

-1

-.6

-.5

-.02

cash flow0
cash flow0

cash flow

.5

0

1

non patenting.02

Fig. 10 Cash flow

age band.2.4

share of internal finance

.6

.04

26/30

31/35

36/40

>40

31/35

36/40

>40

21/25

26/30

age bandGraphs by patenting status

16/20

11/15

3/5

6/10

0/2

>40

36/40

31/35

26/30

21/25

16/20

6/10

11/15

3/5

0/2

>40

36/40

31/35

26/30

21/25

16/20

11/15

3/5

6/10

0/2

>40

36/40

31/35

26/30

21/25

16/20

3/5

6/10

11/15

0

0

.01

.02

.03

share of internal finance

.5
0
0/2

-.5

internal finance.8patenting1

non patenting.05

Fig. 11 Internal financeage bandGraphs by patenting status

age band

21/25

16/20

11/15

3/5

6/10

0/2

>40

36/40

31/35

26/30

21/25

16/20

6/10

11/15

3/5

0/2

>40

36/40

31/35

26/30

21/25

16/20

11/15

3/5

6/10

0/2

>40

36/40

31/35

26/30

21/25

16/20

3/5

6/10

11/15

-.04

-.2

-.1

0

magnitude of financial costs

.02
0
-.02

.1
0
-.1

0/2

internal finance.2

magnitude of financial costs.3

patenting.1.04

Fig. 12 Magnitude of financial costs
non patenting

age bandNote: Each point represents the marginal effect at the sample mean or the elasticity (see text for definition) obtained from a regression specified as
in tab. 2 col. 2 and run over the specified age band. Robust standard deviations have been clustered at the industry 4-digit level. Estimation sample
is always restricted to firms which were active in manufacturing HT industries, KIBS services and ICT industries over the estimation period (19982004).
Source: Exhaustive business registers (INSEE) and accounting information sourced from fiscal files (INSEE-DGI), patent applications to thenational patent office (file constructed at the INPI), LIFI survey and CITRUS information system (INSEE). All explanatory variables are lagged.

31Regression analyses across age bands: indicators of internal capital markets

Descriptive Statistics

Marginal Effects

Elasticities.3

patenting105group ownership.1

age bandGraphs by patenting status

>40

36/40

31/35

26/30

21/25

16/20

11/15

3/5

6/10

0/2

>40

36/40

31/35

26/30

21/25

16/20

6/10

11/15

3/5

0/2

>40

36/40

31/35

26/30

21/25

16/20

3/5

11/15

0/2

6/10

>40

36/40

31/35

26/30

21/25

16/20

3/5

6/10

11/15

0/2

0

-5

-.1

.2

0

0

.4

group ownership.6group ownership.2.8

non patenting

15

Fig. 13 Group ownership (dummy variable)

age bandpatentingage bandGraphs by patenting status

>40

36/40

31/35

26/30

21/25

16/20

11/15

3/5

6/10

0/2

>40

36/40

31/35

26/30

21/25

16/20

6/10

excludes outside values

11/15

3/5

-.01

0/2

>40

36/40

31/35

26/30

21/25

16/20

11/15

3/5

6/10

0/2

>40

36/40

31/35

26/30

21/25

16/20

3/5

6/10

11/15

0/2

excludes outside values

-1

0

1

size of the group

0.01

size of the group10,0005,000
0group size (emp.)

15,0002.02

20,000non patenting

3

Fig. 14 Size of the corporate group (employment)

age band.2

Fig. 15 Total cash holdings within the group structurepatenting0

1

cash holdings (group)

.1
0

.2
0

26/30

31/35

36/40

>40

31/35

36/40

>40

21/25

26/30

age bandGraphs by patenting status

16/20

11/15

3/5

6/10

0/2

>40

36/40

31/35

26/30

21/25

16/20

6/10

3/5

-1

-.1
0/2

>40

36/40

31/35

26/30

21/25

16/20

11/15

3/5

6/10

excludes outside values

11/15

excludes outside values

0/2

>40

36/40

31/35

26/30

21/25

16/20

3/5

6/10

11/15

0/2

-.2

cash holdings (group).4

cash holdings (group).62

non patenting

age band1patenting0-1

group specialization

-2

-.2

-.1

group specialization

.6
.4
.2

age band

21/25

16/20

11/15

3/5

6/10

0/2

>40

36/40

31/35

26/30

21/25

16/20

6/10

3/5

-3

-.3
0/2

>40

36/40

31/35

26/30

21/25

16/20

11/15

3/5

6/10

>40

36/40

31/35

26/30

0/2

excludes outside values

11/15

excludes outside values

Graphs by patenting status

21/25

16/20

3/5

6/10

11/15

0/2

0

group specialization

.8

0

1

non patenting.1

Fig. 16 Specialization of the corporate groupage bandNote: Each point represents the marginal effect at the sample mean or the elasticity (see text for definition) obtained from a regression specified as
in tab. 2 col. 4 and run over the specified age band. Robust standard deviations have been clustered at the industry 4-digit level. Estimation sample
is always restricted to firms which were active in manufacturing HT industries, KIBS services and ICT industries over the estimation period (19982004).
Source: Exhaustive business registers (INSEE) and accounting information sourced from fiscal files (INSEE-DGI), patent applications to thenational patent office (file constructed at the INPI), LIFI survey and CITRUS information system (INSEE). All explanatory variables are lagged.

32

Tab. 3: Patenting propensities in the main manufacturing and service high-tech industries,
Static and Dynamic Specifications
Firms aged 6 years and more
1998-2004
StaticStaticDynamic

Dynamic

Dynamic

Dynamic

Specification

Specification,

Specification,

Specification,

Specification,

Specification,

Fixed

Differences

Differences

Diff. MA(3)Diff. MA(3)MA(3)

Predet.

Endog.

(4)

(5)

(6)

Effects
Past Patenting

(1)

(2)

(3)

-

-

7.183***

49.059

5.185

6.798

(1.620)(46.741)(18.027)(12.678)

Firm Age Bands

yes

yes

yes

yes

yes

yes

Size (employment) Bands

yes

yes

yes

yes

yes

yes

Financial aspects:
Cash flow
Share of internal financeIntensity of financial costs
Group ownership-0.2220.173**

0.296***

0.348**

0.263

0.162

(0.168)(0.085)(0.110)(0.155)(0.218)(0.406)0.472***

0.358***

0.163

0.083

1.268**

1.723**(0.117)(0.107)(0.136)(0.203)(0.514)(0.783)-0.122-0.174

0.218

0.377

0.163

0.165

(0.175)(0.208)(0.258)(0.366)(0.285)(0.290)0.973***

0.008-0.055-0.105-0.0040.535(0.190)(0.114)(0.174)(0.228)(0.474)(0.641)

Firm and ind. controls:
Capital intensity,

0.319**-0.089-0.042-0.032

0.134

0.138

firm level(0.152)(0.077)(0.101)(0.126)(0.122)(0.129)

Capital intensity,-0.128*

0.040

0.010

0.020

0.015

0.046

industry level(0.075)(0.092)(0.117)(0.149)(0.125)(0.125)ln H. Herfindahl index

0.137-0.0450.005-0.040

0.011

0.027
(0.037)(0.107)(0.033)(0.032)(0.040)(0.035)

Ind. patenting intensity

5.488*-3.165*-2.978-4.381-2.586-2.682(per firm)(2.851)(1.758)(2.037)(2.950)(2.256)(2.208)

Share of XY citations-0.246-0.001-0.096-0.159-0.068-0.096(0.214)(0.126)(0.151)(0.198)(0.164)(0.163)

yes

yes

yes

yes

yes

yes

Auto-corr. Test (ord. 1 to 4)--

Ord. 1, 2

Ord. 3

Ord. 3

Ord. 3Sargan Test (pval.)

-

-

0.000

0.079

0.003

0.003

371444371444237287237287237287237287

Time
dummiesObservations

Note: Marginal effects at the sample mean (multiplied by 100) reported, robust standard deviations have been computed. Estimation sample isrestricted to firms which were active in manufacturing HT industries, KIBS services and ICT industries over the estimation period (1998-2004).
Source: Exhaustive business registers (INSEE) and accounting information sourced from fiscal files (INSEE-DGI), patent applications to thenational patent office (file constructed at the INPI), LIFI survey and CITRUS information system (INSEE). All explanatory variables are lagged.
OLS (col. 1 and 2) and GMM 2-step difference (col. 3 to 6) estimation.

33

Tab. 4: Investigating Granger causalities,
Firms aged 6 years and more
1998-2004
PatentingCash-Flow

Share(me*100)(me*100)

Internal Fin.

(1)

(2)

(3)

49.059-0.192-1.620*(46.741)(0.361)(0.969)0.348**-4.156-1.537**(0.155)(7.993)(0.752)0.083-17.770***-1.074(0.203)(2.748)(1.266)-0.105-2.402**-0.683(0.228)(1.252)(0.584)Lagged Indicators:
PatentingCash flow
Share of internal financeGroup ownership
Additional firm and industry controls

yes

yes

yes

Auto-corr. Test (ord. 1 to 4)

Ord. 3

Ord. 1

Ord. 1

Sargan Test (pval.)

0.079

0.130

0.419

Observations

237287235195235518

Note: Marginal effects at the sample mean (multiplied by 100) reported, robust standard deviations have been computed. Estimation sample isrestricted to firms which were active in manufacturing HT industries, KIBS services and ICT industries over the estimation period (1998-2004).
Source: Exhaustive business registers (INSEE) and accounting information sourced from fiscal files (INSEE-DGI), patent applications to thenational patent office (file constructed at the INPI), LIFI survey and CITRUS information system (INSEE). All explanatory variables are lagged.
GMM 2-step difference estimation.

34

Granger causalities across age bands: main parameters of interest
Marginal Effects

Elasticities.4

age band

>40

36/40

31/35

26/30

21/25

16/20

11/15

6/10

>40

36/40

31/35

-.6

-.4

-.2

0

current patenting.2

lagged cash flows as a predictor of
26/30

21/25

16/20

11/15

6/10

-.1

-.05

0

current patenting.05

Fig. 17 PATENTING PROPENSITY EQUATION,
Dynamic Model, CASH FLOW Variable

age band

.5
-.5

0

current patenting

31/35

36/40

>40

36/40

>40

26/30

31/35

age band

21/25

16/20

11/15

6/10

>40

36/40

31/35

26/30

21/25

16/20

11/15

6/10

-1

-.02

0

.02

.04

current patenting.06

lagged internal finance as a predictor of

1.08

Fig. 18 PATENTING PROPENSITY EQUATION,
Dynamic Model, INTERNAL FINANCE Variable

age band.10cash flows

age band

26/30

21/25

16/20

11/15

6/10

>40

36/40

31/35

26/30

21/25

16/20

11/15

6/10

-.04

-.1

-.02

0

cash flows.02

lagged patenting as a predictor of

.2

.04

Fig. 19 CASH FLOW EQUATION,
Dynamic Model, PATENTING Variable

age bandNote: Each point represents the marginal effect at the sample mean or the elasticity (see text for definition) obtained from a regression specified as
in tab. 4 (col. 1 or 2) and run over the specified age band.. Estimation sample is always restricted to firms which were active in manufacturing HT
industries, KIBS services and ICT industries over the estimation period (1998-2004).
Source: Exhaustive business registers (INSEE) and accounting information sourced from fiscal files (INSEE-DGI), patent applications to thenational patent office (file constructed at the INPI), LIFI survey and CITRUS information system (INSEE). All explanatory variables are lagged.
GMM 2-step difference estimation.

35

Panel 2: Patent Scope

0-.05

Age band

>40

36/40

31/35

26/30

21/25

16/20

11/15

6/10

3/5

0/2 (ref)

>40

36/40

31/35

26/30

21/25

16/20

11/15

6/10

3/5

0/2 (ref)

-.1

-.01

0

.01

Marginal effect on scope

.05

.02

Panel 1: Patenting Propensities

Age bandPanel 4: Fwd Citations

.06
.04
.02
0

31/35

36/40

>40

36/40

>40

26/30

21/25

31/35

Age band

16/20

11/15

6/10

3/5

0/2 (ref)

>40

36/40

31/35

26/30

21/25

16/20

11/15

6/10

3/5

0/2 (ref)

-.04

-.02

-.03

-.02

-.01

0

Marginal effect on fwd citations

.01

.08

Panel 3: Radicalness

Age bandPanel 6: Fwd XY Citations

.04
.02
0

0

.02

Marginal effect on fwd XY citations

.04

.06

Panel 5: Fwd A Citations

Age band

26/30

21/25

16/20

11/15

6/10

3/5

0/2 (ref)

>40

36/40

31/35

26/30

21/25

16/20

11/15

6/10

3/5

0/2 (ref)

-.02

-.02

Marginal effect on radicalness

Tab. 5 Characteristics of patent portfolios: age marginal effects

Age bandNote: Each panel corresponds to a different regression were the specified indicator is regressed against age and employment dummy variables.
Patent scope was introduced as an additional control in panel 3. All regressions also include time and industry (ISIC3) dummies, as well as Mill‟sratios computed from the propensity analysis, and additional industry level controls of patenting intensity (log average count of patent applications
per firm, and log average share of XY citations in total forward citations received). Each point represents the coefficient (marginal effect) obtained
from OLS estimation for the specified dummy variable.
Estimation sample is always restricted to firms which were active in manufacturing HT industries, KIBS services and ICT industries over theestimation period (1998-2004).
Source: Exhaustive business registers (INSEE) and accounting information sourced from fiscal files (INSEE-DGI), patent applications to thenational patent office (file constructed at the INPI), LIFI survey and CITRUS information system (INSEE). All explanatory variables are lagged.

36

Tab. 6 Characteristics of patent portfolios: employment marginal effects
Panel 8: Patent Scope

0
-.05

>10th

5000/10th

1000/4999

500/999

250/499

100/249

50/99

20/49

Employment band

Panel 10: Fwd Citations

.05
0
-.05

5000/10th>10th

5000/10th>10th

Panel 12: Fwd XY Citations

.02
0
-.02
-.04

Employment band

500/999

250/499

100/249

50/99

20/49

10/19

1/9 (ref)no emp>10th

5000/10th

1000/4999

Employment band

500/999

250/499

100/249

50/99

20/49

10/19

1/9 (ref)no emp

-.06

-.02

0

.02

.04

Marginal effect on fwd XY citations

.06

.04

Panel 11: Fwd A Citations

1000/4999

Employment band

1000/4999

500/999

250/499

100/249

50/99

20/49

10/19

1/9 (ref)no emp>10th

5000/10th

Employment band

1000/4999

500/999

250/499

100/249

50/99

20/49

10/19

1/9 (ref)no emp

-.1

-.04

-.02

0

.02

Marginal effect on fwd citations

.1

.04

Panel 9: Radicalness

Marginal effect on radicalness

10/19

1/9 (ref)no emp>10th

5000/10th

Employment band

1000/4999

500/999

250/499

100/249

50/99

20/49

10/19

1/9 (ref)no emp

0

-.1

.2

.4

.6

Marginal effect on scope

.05

.8

.1

Panel 7: Patenting Propensities

Note: Each panel corresponds to a different regression were the specified indicator is regressed against age and employment dummy variables. All
regressions also include time and industry (ISIC3) dummies, as well as Mill‟s ratios computed from the propensity analysis, and additional industry
level controls of patenting intensity (log average count of patent applications per firm, and log average share of XY citations in total forward
citations received). Each point represents the coefficient (marginal effect) obtained from OLS estimation for the specified dummy variable.
Estimation sample is always restricted to firms which were active in manufacturing HT industries, KIBS services and ICT industries over theestimation period (1998-2004).
Source: Exhaustive business registers (INSEE) and accounting information sourced from fiscal files (INSEE-DGI), patent applications to thenational patent office (file constructed at the INPI), LIFI survey and CITRUS information system (INSEE). All explanatory variables are lagged.

37

-.1

-.05

0

Marginal effect

.05

.1

Fig. 20 Pre-emptive patents as a hurdle for innovation?

scope

radicalness

fwd cit.

fwd A cit.

fwd XY cit.

Note: Each point corresponds to a different regression were the specified indicator is regressed against the log average share of XY citations in
total forward citations received by total industry level patents. All regressions also include age and employment dummy variables, time andindustry (ISIC3) dummies, as well as Mill‟s ratios computed from the propensity analysis, and an additional industry level control of patenting
intensity (log average count of patent applications per firm). OLS estimation. Estimation sample is always restricted to firms which were active inmanufacturing HT industries, KIBS services and ICT industries over the estimation period (1998-2004).
Source: Exhaustive business registers (INSEE) and accounting information sourced from fiscal files (INSEE-DGI), patent applications to thenational patent office (file constructed at the INPI), LIFI survey and CITRUS information system (INSEE). All explanatory variables are lagged.

38