J. Serv. Sci. & Management, 2008, 1: 215-226
Published Online December 2008 in SciRes (www.SciRP.org/journal/jssm)
Copyright © 2008 SciRes JSSM
Innovation in the Financial Sector: Persistence and
Schumpeterian Hypotheses
——Econometric Evidence in Germany
Roberto Napoli
Faculty of Economics, University of Trento, Italy
Email: Roberto.napoli@unitn.it
Received August 22
, 2008
revised November 4
, 2008; accepted November 21
, 2008.
The paper analyses innovation features in the German financial sector. The first topic is persistence of innovation. Our
research question is: Do innovators plan further innovation for the subsequent year? In addition, since the sector is so
far poorly researched, very basic questions are investigated in the paper: the relationship between firm size and
innovation (both linear and quadratic), as well as the impact of market structure on innovation (i.e. Schumpeterian and
neo-Schumpeterian hypotheses). Finally, Suttons argument of R & D sunk costs is investigated as a possible
explanation for persistence. Basing on the CIS IV survey, our empirical evidence is consistent with the results of similar
researches carried out in different sectors.
financial innovation, CIS, schumpeterian hypothesis
1. Introduction
Modern evolutionary economics sees the development and
diffusion of innovations as a complex and unsteady process.
Periods of radical changes that cause shifts in the technological
paradigm alternate with phases of incremental innovation
of given technologies. In trying to understand the drivers
of such phenomena, much attention has traditionally been
paid to the manufacturing sector, while only in the last
few decades the interest of researchers has been devoted
to services. Specifically, the financial sector is gaining
centrality in the innovation process, and it has been
recently described as crucial in influencing technological
trajectories. In a neo-Schumpeterian framework, Perez
[1,2] sheds new light on the role of financial intermediaries.
She recalls the clear separation between borrower and
lender, i.e. between entrepreneur and banker, which can
be traced back to Schumpeter [3,4]. However, she argues
that the role of financial intermediaries has been formally
stated, but substantially not recognized from the neo-
Schumpeterian literature, and from Schumpeter himself.
Instead, she considers the banker as capable of true
innovative commitment, just like the Schumpeterian
This paper is understood as a first step in the analysis of
the innovative dynamics going on in the financial sector.
As such, basic research questions are analysed, with the
aim of providing some consistent answers which may then
serve as basis for future, more detailed research. Questions
involved in the analysis are mainly concerned with
persistence of innovation, firm size and market structure
effects on innovation (Schumpeterian Hypotheses), as well
as the neo-Schumpeterian hypothesis of an inverse U
shaped relationship between firm size and innovation.
Furthermore, Suttons argument of R&D sunk costs is
investigated as a possible explanation for persistence. The
focus is on the financial sector and the analysis is carried
out on a sample of 242 German firms. This sector is
worldwide still poorly researched, as stressed by
numerous studies, which makes it interesting to analyse
very basic questions.
The first section defines innovation and addresses the
problematic issue of measuring innovation. In the second
section, theoretical issues and the main empirical findings
about persistence of innovation are highlighted, and two
different approaches based on patent statistics on the one
hand, and on the Community Innovation Survey (CIS) on
the other hand, are analyzed. Furthermore, Schumpeterian
and neo-Schumpeterian hypotheses are briefly described,
as well as the controversial empirical results recovered in
the literature. This part serves as the theoretical
framework for the subsequent analysis. The third part
briefly describes some characteristics of the German
financial sector. In the fourth section the data used in the
model are described, as well as the model developed to
The author thanks the Center for European Economic Re
search (ZEW)
in Mannheim, the seminar participants at the University of Strasbourg
(BETA) 2007 DIMETIC Doctoral Training “Micro Approaches to Inno-
vation and Innovation Networks”. The views ex
pressed here are the
author’s. Any remaining errors are the author’s responsibility.
216 Roberto Napoli
Copyright © 2008 SciRes JSSM
investigate persistence and the different hypotheses
highlighted in earlier paragraphs; subsequently, the results
of the model are presented. The final part discusses the
findings and concludes with some suggestion for possible
extensions of the model.
2. Defining and Measuring Innovation
In the present work, a firm having introduced a new or
improved product or service or a new or improved process
during the period covered by the survey, is considered an
innovator. This means that we consider as an innovator a
firm which reported innovative activities in the last three
years, in terms of new products/services/processes
introduced into the market. However it may be
problematic to identify these innovations. In fact, the
intangible nature of services, as well as the close
interaction between production and consumption, makes
the distinction between product and process innovation
unclear. In addition, there is no clear cut between what
should be considered true innovation and, on the other
hand, what should be viewed as mere product
differentiation. Unfortunately, incremental innovation,
which is typical for the service sector and is highly
interesting when analysing innovation, is difficult to
distinguish from mere product customization, which in
turn has to be excluded from the analysis. The more,
radical innovation in the Schumpeterian sense occurs very
rarely and is often little more than a theoretical eventuality.
This makes it quite difficult to identify financial
innovations in terms of single events. For the purposes of
the present work, three definitions are relevant
- If the innovation involves new or significantly
improved characteristics of the service offered to
customers, it is a product innovation.
- If the innovation involves new or significantly
improved methods, equipment and/or skills used to
perform the service, it is a process innovation.
- If the innovation involves significant improvements in
both the characteristics of the service offered and in the
methods, equipment and/or skills used to perform the
service, it is both a product and a process innovation.
These definitions are reported in the Community
Innovation Survey (CIS) questionnaire. Since CIS data are
used in the present paper to test the empirical model, we
adopt the same definitions of innovation. This seems
reasonable, given that respondents to the CIS survey are
asked to self-identify as an innovator or as a non innovator
basing on the same definition.
Measurement of innovation is a strongly debated issue
in the economic literature. There are many different
instruments to measure innovation. Input indicators like
R&D expenditures belong to the first generation of
See Oslo Manual (OECD 2005), p. 53
innovation indicators. They relay on the assumption of
linear relationship between inputs and outputs of
innovation, which has been rejected from the literature
especially since evolutionarists like e.g. Nelson, Winter,
Dosi began to influence heavily the scientific community
in the early eighties. Patent statistics are one of the most
traditional indicator for firm innovativeness: as an output
indicator, they may work properly for manufacturing
sectors (however with strong and well known limitations,
see e.g. Malerba et al. [5] but fail completely in capturing
innovation in most services, where patents are not an
effective instrument to prevent imitation. Interestingly,
Lerner [6] analyzes the dramatic increase in financial
patents, observed in the US financial market between
1996 and 2001, and explains it as a consequence of
changes in the federal law. However, financial formulas
cannot be patented in most countries outside the US,
especially in Europe. Furthermore, financial formulas are
often developed in Universities. All this factors make
patents an unfit tool to measure innovation in the financial
A further group of measuring instruments, composed by
those indicators capturing both inputs and outputs of
innovation, as well as the process inbetween, overcome
the drawbacks of “pure input” and “pure output”
indicators, in that they recognize the complexity of the
innovative phenomenon, at the cost of being often quite
complicated themselves. Finally, a recently established
instrument is the Community Innovation Survey (CIS),
which has been introduced in Europe in the early nineties.
Outcomes of the CIS approach are also highly disputed,
due to the fact that self-definition of managers as an
innovator is often considered too “soft” a tool to measure
3. Previous Findings
3.1. Persistence of Innovation
Schumpeter distinguished between two market situations,
known as Schumpeter Mark I and Mark II. The idea of
persistence can be found in Schumpeter Mark II, also
called “deepening pattern of innovation” in Malerba and
Orsenigo [7], as opposed to the “widening pattern” (Mark
I). In Schumpeter Mark II a few well established firms
with large R&D divisions accumulate knowledge and
innovative capabilities, which results in continuous innovation.
Similarly, Winter [8] defined two technological regimes:
the entrepreneurial regime, characterized by small firms,
low entry barriers and high mortality; and the routinized
regime, where bigger firms establish solid R&D
departments with structured innovative activity. Much of
the literature investigating innovation persistence aims at
identifying the one or the other innovative pattern in the
analyzed sector.
See Tether [9] for an extensive analysis of advantages and drawbacks
of CIS analysis
Innovation in the Financial Sector: Persistence and Schumpeterian Hypotheses 217
Copyright © 2008 SciRes JSSM
The idea of persistence is embedded in the concept of
cumulation, defined as “the fact that existing innovators
may contribute to be so also in the future with respect to
non innovators” [7]. Malerba and Orsenigo [7] consider
cumulativeness, and hence persistence of innovation, as
directly linked to appropriability conditions: market power
enables effective appropriability of innovation benefits,
which in turn imply high cumulativeness conditions and
hence ensure persistence of innovative behaviour in large
and well established firms. In this perspective, innovation
protection mechanisms build up a shield against imitation
and allow profits (and rents) to innovations. This view,
however, depicts rather extreme situations, which are
more common in the manufacturing sector than in services.
Specifically, the financial sector shows some features of
the “widening pattern of innovation”, in that only 1,8%
firms use patents as a protection mechanism
and imitation
is amongst the biggest worries of managers, making the
sector quite turbulent. At the same time it is characterised
by high concentration and large firms, which makes it
more similar to the sectors characterised by a “deepening
pattern of innovation”. Consequently, it would be hard to
forecast some specific features of persistence in the
financial sector if we follow this classification. In fact, the
Schumpeterian argument that firms have an advantage in
R&D in the markets in which they have high market
shares because market power enables them to capture the
returns to innovation, doesn’t seem to hold for the
financial sector, according to the widespread agreement
that imitation is difficult to avoid and innovation returns
difficult to capture. In sum, this view seems to rest on the
core idea that innovation protection mechanisms, which
can be enforced by large and well established firms, are
effective in fostering innovation. However, innovation
protection mechanisms is a much disputed theme on
which traditional neoclassical views are challenged by the
evolutionarist view [10], so that no assumption is made in
the present paper as to how appropriability conditions
work in the financial sector.
It is worth noting that the choice of the innovation
measure may heavily affect outcomes of the analysis. As
Gerosky et al. [11] point out, an overestimation of
persistent innovative behaviour may be expected if R&D
expenditures are used to measure innovation, as they
occur on a routine basis. On the other hand, using patents
as an innovation measure may be problematic too, as the
link between patents and innovation outputs is still unclear.
Roper et al. [12] argue that patent activity and firms’
innovation are only weakly related, whilst Dosi et al. [10]
point out that the relationship between patents and
innovation tends to differ between sectors and depends on
industry-specific knowledge basis. Furthermore, patents
may be registered on an irregular basis by the Patent
Offices, which may not reflect the periodicity of firms’
Mannheim Innovation Panel, ZEW, year 2004.
decision to patent: this would heavily affect outcomes if
persistence is to be analyzed [11]. Moreover, an
underestimation of innovative activity may occur if
patents are used as a measure for innovation. If firms
undertake single innovative projects that last longer than
one year, then their persistent innovative behaviour may
turn into irregular patterns of innovations [11]. In this
cases, firms may well be persistent innovators if their
stream of innovative activity continues after the first
multiple-year project, but in fact a year-by-year survey
would misleadingly identify them as non-persistent
For the purposes of the present work, it seems useful to
distinguish two groups of studies about persistence: in the
first group patent statistics or R&D expenditures are used
as a measure of innovation, while the second group is
based on the CIS survey.
3.1.1. Patents and R&D As a Measure of Innovation
Common view of the first group of studies is that a small
core of persistent innovators exist in most manufacturing
sectors. As Cabbagnol and Le Bas [13] point out, big
oligopolistic firms are more likely to carry out their
innovative activities continuously and for long periods.
Studying the British market, Geroski et al. [11] find that
very few firms are persistently innovative, and that a
critical mass of patens at firm level is necessary to pursue
continuous innovative activity. Furthermore, even
persistent innovators are so for short periods of time. It is
noteworthy that Gerosky et al.’s results are rather extreme,
as they tend to exclude altogether any influence of past
innovation activity on the actual innovative behaviour of
. Le Bas et al. [14] as well as Le Bas and Latham
[15] find similar results for French firms, suggesting that
the size of innovation activity (measured, for instance, by
the volume of R&D expenditures) be the main factor
fostering persistence. Furthermore, on the background of
previous studies (Malerba and Orsenigo [16,17,18],
Malerba et al. [5]), Cefis and Orsenigo [5] ask if
persistence of innovation is determined by the existing
technological regime (as defined by Nelson and Winter
[20], Dosi [21]) or rather is industry-specific. They also
analyze cross-country differences in the degree of
innovation persistence and find some degree of
persistence both in innovators and in non innovators.
Interestingly, non-innovators have a high probability to
remain in the same innovative state over time.
Furthermore, Cefis and Orsenigo [19] find relevant cross-
country differences, while intersectoral differences do not
vary substantially across countries, which leads to the
conclusion that persistence is up to a certain extent a
technology-specific variable. Malerba et al. [5]
suggest in
“It is very hard to find any evidence at all that innovative activity can
be self-sustaining over anything other than very short periods of time, at
least for the kind of innovative activity we have focused on here.”
(Gerosky et al. [11], p. 45).
Malerba et al. [5] link innovation persistence to industry heterogeneity,
arguing that firms having a competitive advantage in some field tend to
enhance their commitment to innovation in the specific field and by this
218 Roberto Napoli
Copyright © 2008 SciRes JSSM
their patent-based cross-country analysis that a minimum
threshold of innovative activity is necessary to become a
persistent innovator. Cefis [22] analyzes in a more
systematic way the nature of this threshold, and finds that
the probability to switch from non-innovator to innovator
by introducing one patent is much lower than the
probability to increase the number of patents if this is not
zero. Furthermore, Cefis [22] suggests that once the
threshold is crossed, innovative activities may enjoy
economies of scale, hence leading to persistent innovation.
Bottazzi et al. [23] choose a slightly different approach,
however still based on patent statistics. In order to study
innovation in the pharmaceutical sector, they analyse the
distribution of innovative drugs, both “New Chemical
Entities” and patented products, into the US market
Interestingly, they find that the introduction of different
innovations in the market cannot be considered as
independent events. Spill-over effects, as well as firm-
specific learning effects of innovative activity may spread
across research projects and influence subsequent
innovation, which can be interpreted as a hint to
persistence of innovative activity at firm level.
It is worth noting that most of the cited studies, show
that innovation (in terms of number of patents) is
persistent in a small number of firms only, which are
normally characterized by large size and market power,
hence showing features similar to the ones described in
Schumpeter Mark II. As Malerba and Orsenigo [18] further
point out, around this core of big and persistent innovators,
a fringe of turbulent, occasional innovators, primarily
composed by small firms, enter and exit the market,
surviving only for short periods in the innovators group.
3.1.2. The CIS-Based Studies
The second group of studies uses the CIS approach to
analyse innovative patterns related to persistence. In fact,
patent statistics used from the first group tend to
underestimate innovative activity, and hence persistence,
since they capture only innovation first introduced in the
market by the firm. As Duguet and Monjon [24] point out,
this means that patent data could measure persistence of
innovative leadership rather than persistence of innovation.
Duguet and Monjon base instead on the Community
Innovation Survey (CIS), where detailed data at firm level
is provided and innovation is measured as the percentage
of firms that self-identify as innovators. Duguet and
Monjon find a high rate of persistence, and that size
effects are in fact important in explaining persistence.
way they reproduce initial asymmetries and end up with generating
further heterogeneity. In their paper, persistence is not really investi-
gated, but rather used as an explanatory variable to describe firm-level
innovative activities across sectors and countries. Malerba et al. [5] also
investigate implications of persistence and firm-heterogeneity on con-
centration, market entry and exit, firm size. However, these interesting
relationships go beyond the purposes of the present work.
This approach reduces the limitation of the traditional patent-statistics
approach, in that it considers also new products introduced in the mar-
ket without being patented.
Specifically, smaller firms are motivated by dynamic
increasing returns in the production of innovations,
whereas persistence of innovation in larger firms, as also
explained by the patent-statistics approach, originates
from continuous R & D investments. Interestingly, Peters
[25] involves in her analysis also the service sector, and
finds that German manufacturers show higher rates of
persistency than services, whereas in both cases true state
dependence exists, in the sense that the decision to
innovate in one period positively influences the
probability to innovate in the subsequent period(s). Peters
introduces in her model Suttons view of R&D investments
as sunk costs [26]. The fact that R&D costs cannot be
recovered, and that they are incurred to implement long
term research departments, commit the firm to employ
them over time. This may translate into persistent
innovation. More recently, Roper and Hewitt-Dundas [12]
analyze persistence in Ireland and Northern Ireland using
both a quantitative approach and a qualitative case-studies
analysis to get deeper insights about innovation patterns in
persistently innovative plants. They distinguish between
product and process innovation. They find high rates of
persistence both in innovators and in non-innovators;
moreover, they find a positive relationship between plant
size and product as well as process innovation.
The first point which seems worth stressing is the
effectiveness of the CIS approach to analyze innovation.
Admittedly, patents are an objective measure of
innovative activity, while CIS surveys are based on a self-
identification as innovator by the respondent. Yet it is not
easy to see how else to measure innovation in services, if
not using CIS surveys. The second point is the focus of
the studies belonging to the second group, which in most
cases is on the manufacturing sector. Peters however
compares persistence of innovation in the manufacturing
and the services sectors, which is only possible using the
CIS database. Finally, and most importantly, it is worth
noting that whenever CIS analyses are concerned, each
observation of the panel covers innovative activities over
a 3-year period and data are collected with a four-year
. This implies that a firm is considered as a
persistent innovator if it introduced one or more
innovations, say, in the period 1996-1998, and again in
the period 2000-2002
. However, this seems to provide a
too weak definition of persistent innovator. In fact, one
should consider the dynamics going on in services and
even more in the financial sector, where new products are
quickly replaced by newer ones. Service firms introduce
regularly new products, which may differ from old ones
only through slightly changed characteristics or added
CIS I (1990-92), CIS II (1994-96), CIS III (1998-2000), CIS IV (2002-
04). E.g. the survey of 2001 refers to years 1998-2000, next survey of
2005 refers to 2002-2004.
In Germany instead, where data are collected yearly, a further overlap-
ping problem arises, since e.g. data collected in 2001 refer to the period
1998-2000, and data collected in 2002 refer to the period 1999-2001.
Innovation in the Financial Sector: Persistence and Schumpeterian Hypotheses 219
Copyright © 2008 SciRes JSSM
services. In this sense, they appear to be persistent
innovators over short periods of time. Interestingly,
prevailing in the timing of the launch of new products into
the market is the most important strategy of German
financial firms to overperform competitors (Napoli [27])
This suggests that financial innovations “expire” very
quickly, and firms react by replacing them quickly with
new innovations. As a consequence, the analysis based on
subsequent waves of three-year periods, may lead to an
artificial overestimation of persistence. Instead, the period
under analysis should be kept as short as possible to
correctly identify persistent innovators.
One way to overcome this problem is proposed by
Peters [25], who uses input measures (innovation expenditures),
which are available on a yearly basis, rather than output
measures. However, this point is problematic too, as it
assumes that innovation inputs transform linearly into
innovation outputs, thereby denying much of the
evolutionary literature dealing with learning effects,
human capital contribution, complexity of the whole
innovative process etc. In the present work a further
solution is proposed. The idea is to keep the time lag as
short as possible
, so as to capture firms that innovate in
the three-year period and plan to innovate immediately
thereafter, i.e. in the subsequent year. These firms would
be then defined as persistent innovators. Admittedly, this
may not suffice to assess persistence in longer periods.
However it allows a stronger assessment of persistence of
innovation in the short run, which seems interesting given
the short life-cycle of innovations in the financial sector.
In contrast, a different approach which would identify as a
persistent innovator a firm which introduced innovations
in the period, say, 1998-2000 and then again in 2002-
2004, seems less adequate given the mentioned
characteristics of the financial sector, where products are
quickly replaced and easily imitated (see e.g. Tufano [28],
Roper and Hewitt-Dundas [12]).
There are some counterarguments to the existence of
persistence, like e.g. standardization. Once a new
technology has been successfully introduced and
sufficiently imposed as a standard in the market, some
conservative-rather than innovative-forces can be at work in
the firm, and make continuous innovation or persistency
less likely. In this direction work path dependence, learning
processes and network externalities, thus reinforcing
and perhaps discouraging further
innovation from the innovator itself, which may now be
more concerned with establishing a market for its new
product rather than developing new ideas.
“Timing advantage” (‘Zeitlicher Vorsprung’) is seen as the most effec-
tive way to protect IPR in the German financial sector (MIP, 2005 sur-
Needless to say, this contrasts with the necessity to measure persis-
tence over a longer period.
See e.g. Teece [29].
Furthermore, firms could cannibalise rents of their own
innovations by introducing new products, hence having a
negative incentive towards persistent innovation
(“replacement effect”, see Le Bas, Latham 2004).
However, the opposite may hold as well: new products
introduced in period t may complete or improve the
performance of products introduced in period t (Gilbert
and Newberry [30]).
3.2. Schumpeterian and Neo-Schumpeterian
There is a broad literature dealing with the so called
Schumpeterian hypotheses
, i.e. with the relationship
between market structure and innovation on the one hand,
and firm size and innovation on the other hand. There is
no doubt that the search for consistent findings in this area
failed in coming up with general results (see e.g.
Symeonidis [31], Teece [29]). Still, some firm-size or
market structure effects on innovation may be relevant in
subsectors, and failing in capturing them may lead to
incomplete explanations. Gellatly and Peters [32] for
example, analysing three service subsectors, find higher
innovation rates in more concentrated segments (financial
services) than in less concentrated ones (communication
and technical business services).
Our data suggest that a size effect exists in the German
financial sector. The 242 analyzed firms have been divided
into 10 subgroups, each with approximately 24 firms. The
first group (1-53 employees) shows innovation rates
which are lower than the average; the second group of
firms (54-600 employees) moves around the mean, while
the last group of large firms show the highest innovation
rate. This figures suggest some positive relationship
between size and innovation rate, which will hence be
tested in the model.
Figure 1. Source: Mannheim innovation panel
Kamien and Schwartz [33] summarize the neo-Schumpeterian hy-
potheses and the inconclusive empirical work on these arguments. See
also Cohen [34] and Cohen and Levin [35] and, more recently, Vaona
and Pianta [36] for a literature review on the relationship between size
and innovation.
Innovating financial
intermediaries by size class,
(number of employees, 2004)
63% 65%
67% 81% 73% 75% 71%
1-45-89-2627-53 54-
105 105-
219 220-
305 306-
600 601-
Innovationg financial intermediaries by size class,
Germany (number of employees, 2004)
220 Roberto Napoli
Copyright © 2008 SciRes JSSM
The idea of the firm size being related to the innovative
activity can be further expanded. It may well be that a
positive relationship, which we expect to find between
size and innovation, is quadratic rather than linear. It
seems reasonable that the positive effect on innovation of
one additional employee expires at a certain firm size
level. This may be due to inefficiencies or to organizational
problems, which may arise when the firm size grows. This
is the so called “Neo-Schumpeterian hypothesis”, which is
understood as an extension of the Schumpeterian hypothesis.
In order to test it, the relationship between the squared
size and innovation activity is analyzed. There are a few
examples in the literature, where higher degree relationships
have been found between firm size and R&D. Acs and
Audretsch [37] and Siddharthan [38] report a quadratic U-
shaped relationship, while further studies found also
evidence of a cubic relationship between firm size and
R&D activities (see Kumar and Aggarwal [39] for more
details). The idea of a cubic relationship however, seems
too extreme, and some doubts may arise as to how to
interpret results. The quadratic relationship instead, seems
interesting in terms of management issues: an inverse-U
relationship, as argued by neo-Schumpeterians, would
mean that expanding the firm size may ensure advantages
in terms of innovative activity only up to a certain level,
and may turn into an hampering factor if the firm becomes
too large. To test this hypothesis in the model discussed
later the square of firms size (number of employees) will
be used as a regressor. It seems appropriate to keep in the
model both measures of the firm size
, so as to
investigate both the linear and the quadratic relationship
of size with the probability to innovate. In fact, the
outcome (which we expect) of a positive linear relationship
between size and innovation would fall short of a
complete explanation about the extent of this relationship
(does size effects indefinitely foster innovation or do they
expire once a certain level is reached?). In this case,
introducing the second degree variable could add useful
insights on that. In turn, the squared relationship alone
would explain the relationship in a poor way, as the linear
relationship cannot be inferred from the quadratic one
As far as known, no studies have yet analysed persistence
of innovative activities in financial firms, while only a few
studies have recently tested Schumpeterian hypotheses in
the financial sector [40,41]. None of them, however,
concentrated on the neo-Schumpeterian hypothesis. More
in general, the lack of empirical literature on the
determinants of financial innovation has been repeatedly
I.e. the logarithm of employees and the squared logarithm of employ-
As an example, if we find a negative quadratic relationship, but don’t
know anything about the linear relationship, we are not able to under-
stand if size has a positive or negative effect on innovation, as the nega-
tive quadratic relationship contains both effects and does not allow, on
its own, to understand which one prevails.
stressed (see e.g. Frame and White [42], Heffernan et al.
[40]). This makes the topic even more interesting, since
the sector is gaining growing attention. The contribution
of the present study to the literature is twofold. First, it is
one of the few empirical studies of financial innovation.
Second, it identifies some possible factors underlying
financial innovation.
The present study is based on CIS data to study
persistence mainly for two reasons. The first concerns
with the well recognized and already mentioned limitations
of the patent statistics, like e.g. underestimation of
innovative activity, which can be even more effective in
services than in the manufacturing sector. But there is an
even stronger argument that makes it impossible to use
patent data. In fact, patents are not a widespread
mechanism to protect innovations in the financial sector,
since less than 2% of German bankers and insurers use
them to protect innovation
. The neglect of patents as an
effective protection mechanism is likely to hold also in
neighbour States due to common laws at European level,
which e.g. exclude patentability of financial formulas
Furthermore, as Tufano [43] points out, the easily imitated
nature of financial innovation does not lend itself to
models based on patent statistics.
4. The German Financial Sector
As shown in following figure, the incidence of big firms in
the German financial sector is much higher than the
incidence of big firms in German services. In fact, 677 out
of 2.742 financial intermediaries (or 25%) have more than
250 employees, while the percentage falls to 5% if the
whole service sector is considered.
Concentration measures in the German financial sector
are calculated basing on revenues stated by firms and
reported in the 2005 MIP survey. The CR4 Concentration
Ratio (40% for the financial sector) and the CR8 (60%)
Distribution of German firms by size
Financial sectorServices
More than 250
Less than 50
Figure 2. Source: Mannheim innovation panel, 2004
Mannheim Innovation Panel, ZEW, year 2004.
Lerner [6] shows a dramatic increase in the number of U.S. financial
patent awards due to patentability of financial formulas newly intro-
duced in the U. S. law. However, similar patterns are not likely to show
up in Europe.
Innovation in the Financial Sector: Persistence and Schumpeterian Hypotheses 221
Copyright © 2008 SciRes JSSM
show a highly concentrated financial market. Concentration is
even higher if data are disaggregated by sub-sectors.
Furthermore, the Herfindahl-Hirschman Index (HHI) confirms
that the banking sub-sector
is more concentrated than the
insurance sub-sector
and than the financial sector as a
5. Econometric Analysis
The Database
The data used for the analysis are firm level data from the
Mannheim Innovation Panel (MIP) in the German
financial services sector (NACE3 651, 652, 660, 671,672).
The MIP is based on innovation surveys carried out by the
Centre for European Economic Research (ZEW) on
behalf of the German Federal Ministry of Education and
Research. The target population covers all legally
independent firms with 5 or more employees and the
surveys are drawn as stratified random samples (stratified
by firm size, branches of industries and East/West region).
The samples are constructed as panels and about 10.000
firms in manufacturing and 12.000 service firms are
questioned each year. Participation is voluntary and the
response rate varies between 20% and 25%. The survey
methodology is detailed in the OSLO-Manual (OECD
2005). The data which are used to test hypotheses stem
from the 2005 survey. Following table summarizes the
population of German financial firms and the sample used
for the estimation of the model.
1) The first relationship analysed in the proposed model
is the one between innovation activities in the period
2002-2004 and innovative projects for 2005, with the aim
of assessing short-run persistence of innovation at firm
level. The rationale behind this choice is straightforward:
Table 1. Concentration in the German financial sector
Banks Insurances Financial sector
70% 52% 40%
80% 70% 60%
1.739 830 562
Source: Mannheim Innovation Panel, 2004
Table 2. The German financial sector
65 Financial intermediation
except insurance and
pension funding 2.053 117
66 insurance and pension
funding except compulsory
social security 490 55
67 Activities auxiliary to
financial intermediation 199 70
Totale 2.742 242
NACE3= 651, 652, 671
NACE3= 660, 672
persistence of innovative behaviour requires one firm to
be an innovator and to plan new innovation for the
subsequent year. As Malerba et al. [5] point out, “in the
simplest statistical interpretation, the notion of innovative
persistence can be defined as the conditional probability
that innovators at time t will innovate a time t+1” (p. 804).
We expect to find a high rate of persistence in the
financial sector for two reasons. First of all, such a result
would be consistent with prior researches using CIS data
for other sectors (see previous section). Secondly,
persistence at industry level is evident from following
The figure shows persistence at industry level in the
financial sector. Between 1994 and 2005 the rate of
innovating firms moved around 70%, with some lower
values in 2002-2003. However, this figure does not
provide insights about firms identity. One possible
explanation of this figure is that there might be continuous
(or frequent) new entrance in the markets, which
increments the innovation rate. Malerba and Orsenigo [18]
find an extreme turbulence in innovative activities in the
manufacturing sector, and a high turnover of innovative
firms, which would exclude high rates of innovative
persistence at firm level. On the other hand, there might
be a big group of firms that innovate persistently, where
turbulence would concern and a smaller group of non
innovators which steadily enter end exit the market. The
conclusion cannot be drawn from the figure, and firm
level analysis is required.
Hypothesis 1: There is a positive relationship between
past innovative activity and innovative (expected)
behaviour in year t+1.
2) The second relationship tests the Schumpeterian
hypothesis of positive correlation between firm size and
innovation. In past paragraphs hints of a positive
relationship between size and innovation have been
highlighted in the German financial sector. However,
there are also counterarguments to this Schumpeterian
hypothesis. Scherer and Ross [44], e.g., argue that small
firms innovate more because too much bureaucracy
Figure 3. Source: MIP, own calculation
222 Roberto Napoli
Copyright © 2008 SciRes JSSM
inhibits innovative activities, and this is more likely to
happen in larger firms. Given the hints of a positive
relationship between German financial firms and innovation,
we expect to provide empirical evidence confirming the
Schumpeterian hypothesis.
Hypothesis 2: Firm size is positively correlated to
3) Furthermore, referring to the mentioned literature,
the so-called neo-Schumpeterian hypothesis of an inverse
U-shaped relationship between size and innovativeness
will be tested, in order to evaluate if size effects of
innovation vanish above a certain firm size.
Hypothesis 3: A negative second degree relationship
exists between firm size and innovativeness
4) The fourth hypothesis can be traced back again to
Schumpeter, as it deals with the relationship between
market structure and innovativeness. The rationale is that
one firm’s market power can be measured by the number
of competitors who market similar products, i.e.
substitutes. In the German CIS questionnaire, firms are
asked about the number of direct competitors they face in
the market. If this number is low (up to five), then a firm
is considered to have high market power. We expect to
confirm that these firms are more innovative. This would
mean that the German financial sector displays features
which are similar to the Schumpeter Mark II scenario
described in previous chapters.
Hypothesis 4: Market power, in terms of small number
of competitors, is positively associated to innovation
5) Finally, empirical evidence is provided to the Sutton
hypothesis of R&D sunk costs and innovation. If the
amount of investments for innovative activities in year t is
positively associated with innovation both in year t and
t+1, this may be due to the lock-in effect caused by R&D
sunk costs. In other words, expenditures in R&D in year t
commit firms to innovate in year t +1.
Hypothesis 5: Innovation in year t+1 is positively
influenced by investments for innovative activities in year t.
Model Specification
The above hypotheses are tested with a data set of 242
German firms in the financial sector from the Mannheim
Innovation Panel. Data refer to year 2004, with the
exception of the dependent variable as explained hereafter.
The variables are defined as follows:
(dummy variable): innovative activities
planned by firm i for 2005. Firms have been requested if
they planned some innovative activity for subsequent
years (2005 and 2006). Since the survey has been carried
out in 2005, the answer is to be considered a forecasted
value, or some sort of “expected innovation”. As such, the
planned innovation rate may differ from the true value.
However, given the short horizon of the forecast, the
“planned value” can be considered as a reliable proxy for
the true value (which of course was unknown in 2004). It
seems realistic that planned innovation transforms into
effective innovation in the subsequent year. Some support
to this belief is provided by the low rate of firms that give
up innovative projects before completing them.
(dummy variable): innovative activities
carried out by firm i in 2004.
: number of employees of firm i. It will be
used in logarithmic form. Also, the squared value will be
tested for its influence on the dependent variable.
: (dummy variable) equals one if firm i has
five or less competitors that market similar products.
: Expenditures for innovative activities
as a proportion of revenues of firm i.
: (dummy variable) firms headquartered in
East Germany.
: Export value (=sales abroad).
In the following, descriptive statistics of the variables
introduced in the model are reported. In order to provide
further relevant insights about the German financial
market, the number of employees is also reported (in non-
logarithmic form), as well as the absolute value (i.e. not as
a proportion of revenues) of expenditures for innovative
Since the dependent variable is dichotomic, a probit
model is used in order to test the influence of independent
variables. Summarising the above discussion and
hypotheses in a functional form:
Econometric Results
The following table reports the estimation results of the
probit model whit all the independent variables including
the control variables. Note that marginal effects are
reported, as well as the p-value (in parenthesis).
The first important result is a positive and significant
relationship between innovation activities carried out in
2002-2004 and plans to innovate in 2005, as predicted by
hypothesis 1. This means that firms that innovated in the
Type Mean Std.dev.
dummy 0.756198
dummy 0.723141
(ln) cont. 4.547479
(ln, squared) cont. 26.136261
dummy 0.474790
cont. 0.038873
dummy 0.165290
cont. 143.0131
Employees (cont., not
in the model)
expenditures (cont., not
in the model)
Innovation in the Financial Sector: Persistence and Schumpeterian Hypotheses 223
Copyright © 2008 SciRes JSSM
Table 3. Marginal effects
Marginal effects
0.203 (0.000)**
(ln) 0.042 (0.010)*
(ln, squared) -0.003 (0.044)*
-0.021 (0.191)
0.635 (0.032)*
0.011 (0.532)
0.001 (0.135)
Constant -3.347 (0.000)**
Observations 138
* significant at 5%; ** significant at 1%
Pseudo R
= 0.3937
(See appendix for correlations)
period 2002-2004 are likely to innovate also in 2005.
Hence, according to our estimation and to our short-run
definition of persistence, German financial firms display a
persistent innovative behaviour over the analyzed period.
Furthermore, expenditures for innovative activities
incurred in 2004 positively and significantly influence the
probability to innovate in the subsequent year, suggesting
some lock-in effect of R&D investments, as argued by
John Sutton [26].
Finally, the number of direct competitors in the market
does not seem to have an impact on the probability to
innovate. The Schumpeterian hypothesis of market
structure influencing innovation could not be confirmed.
Admittedly, this may be due to the fact that OLIG is a bad
proxy for market power. However, different results may
have been obtained focusing only on product innovation.
In fact, OLIG directly refers to product competitors, and
may be a better proxy for market power as far as only
product innovation is concerned. Instead, we analysed
product and process innovations together, hence results
may be biased.
6. Discussion and Concluding Remarks
How can we explain persistence, given the financial
sector’s characteristics so far described (e.g. low
protection mechanisms, high rates of imitation, high
concentration) and the results of the model that suggests
persistence, albeit limited to the short run? According to
Tufano [28], who analyses the first mover advantages in
the financial sector, innovators gain know how advantages
and new knowledge while developing an innovation. This
knowledge capital can be further improved and applied to
develop further innovations, hence leading to persistence.
Similarly, Merton [45] uses the metaphor of “financial
innovation spiral” meaning that one innovation begets the
next. Both these ideas are consistent with our findings that
the most firms showing innovative behaviour in 2002-
2004 have already planned innovations for upcoming
years. In fact, not only they plan innovations for year 2005
as shown in the model. They also have plans for year 2006
(results not shown in the model), which suggests that
persistence may hold also beyond our limited 2-years
horizon. Consistently with this explanation, Tufano [43]
provides examples of financial innovations built upon
recent new products and aimed at improving their
performances or better accomplish their functions. In all
these cases, low appropriability conditions, along with
ease of imitation, seem to play a major role in committing
firms to innovate continuously, in order to offset
competitors’ gains from imitation. By this way a
reinforcing loop may be at work, resulting in persistence
of innovation at firm level. In the same direction may
work past investments in innovative activities, as shown in
the model. The commitment to innovation can be further
reinforced by past R&D expenditures, which have been
found to influence future innovation.
For what concerns firm size and innovation, our results
are straightforward: while the linear relationship shows
that firm size is important in determining innovation, the
negative quadratic relationship suggests that this is only
true up to a certain level. Large firm size can be
detrimental to innovation: one simple explanation suggests
that organisational diseconomies may be at work.
Therefore, medium-sized firms are responsible for the
bulk of the R&D activity. However, an estimation of the
point of inflection could provide useful insights to
understand to what extent large firm size negatively
affects innovation.
Limits of the Model and Further Research
The first concern is about the insights which can be drawn
from the present model about persistence. Given the lack
of data allowing to test for persistence in the services
sector (patent statistics do not exist in many subsectors,
CIS data refer to a too long period and tend to
overestimate persistence), the present approach suggests a
new solution to test persistence, which applies in the short
run. This seems not too unrealistic in the financial sector,
where the financial product’s life cycle is short and
imitation occurs very quickly, forcing competitors to
renew their product lines on a regular basis. Admittedly,
our results on persistence however seem to capture only
one part of the phenomenon and further empirical
evidence is needed on this topic.
In addition, the model, because of the econometric
approach chosen, fails in capturing the unobserved
individual heterogeneity, which Peters [25] has proven to
explain persistence of innovation across sectors.
224 Roberto Napoli
Copyright © 2008 SciRes JSSM
The empirical analysis proposed can be seen as a first
step in the still poorly researched field of financial
innovation. As such, very basic questions have been
addressed, like the relationship between firm size and
innovation as well as evidence about innovation persistent
behaviour at firm level. An interesting point, which would
be worth analysing, would be to find the threshold upon
which positive firm size effects expire, and a further
increase in the firm dimension has negative impact on the
probability to innovate. This relationship emerges in the
model, but the threshold remains unknown.
Furthermore, it could be interesting to distinguish
between banks and insurances in the financial sector, as
well as between product and process innovations.
Significant differences can emerge with respect to firm
size, in the sense, for example, that smaller firms could
chose different strategies of product/process innovation
with respect to larger firms. Also, it can be distinguished
between firms which aim at internalize the results of their
innovation activities, i.e. innovate for themselves, and
firms that innovate for other players. The former are more
likely to develop process innovations, the latter product
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correlation matrix
(ln, squared) OLIG EAST EXP
INNO 1.0000
INNOEXPS 0.1139 1.0000
EMPL (ln) 0.2478 -0.0991 1.0000
( ln, squared ) 0.2569 -0.1026 0.9644 1.0000
OLIG -0.0801 -0.0716 0.0823 0.0185 1.0000
EAST -0.0786 -0.0045 -0.1760 -0.1487 -0.0324 1.0000
EXP -0.0578 -0.0279 0.1634 0.1941 0.0939 -0.0486 1.0000