Regional Science and Urban Economics 40 (2010) 574–582
Contents lists available at ScienceDirect
Regional Science and Urban Economics
j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / r e g e c
Industrial innovation: Direct evidence from a cluster-oriented policy
Oliver Falck a,b,1, Stephan Heblich c,⁎, Stefan Kipar a,2
a
b
c
Ifo Institute for Economic Research, Poschingerstr. 5, D-81679 Munich, Germany
CESifo, Germany
Max Planck Institute of Economics, Entrepreneurship, Growth, and Public Policy Group, Kahlaischestr. 10, D-07745 Jena, Germany
a r t i c l e
i n f o
Article history:
Received 18 September 2009
Received in revised form 20 March 2010
Accepted 22 March 2010
Available online 30 March 2010
JEL classification:
R38
R11
O32
Keywords:
Difference-in-differences
Difference-in-difference-in-differences
Cluster policy
Industrial policy
a b s t r a c t
Can local industrial policies increase local firm competitiveness? Cluster-oriented policies targeted at
selected industries have just such a goal and are very popular among politicians, despite the controversy
surrounding these policies in academia. Thus, it would appear useful to discover if cluster-oriented policies
are effective. This paper evaluates the cluster-oriented policy introduced in Bavaria, Germany, in 1999. The
policy's goal was to foster innovation and regional competitiveness by stimulating cooperation. Using
difference-in-difference-in-differences estimates, we find for different innovation measures that the clusteroriented policy increased the likelihood of becoming an innovator in the target industries by 4.6 to 5.7
percentage points. At the same time, R&D expenditures decreased by 19.4% on average for firms in target
industries, while access to external know-how, cooperation with public scientific institutes, and the
availability of suitable R&D personnel increased.
© 2010 Elsevier B.V. All rights reserved.
Paradoxically, even as the world becomes increasingly globalized,
with decreasing transportation and transaction costs, diminishing
distances, and global sourcing, there is a growing body of literature on
the renaissance of regional economics. Most firms can now easily
spread their activities around the world, and yet they choose to cluster
some activities in certain regions. This phenomenon leads Porter
(1998, p.90) to the conclusion that “enduring competitive advantages
in a global economy are often heavily localized, arising from concentrations of highly specialized skills and knowledge, institutions,
rivalry, related businesses, and sophisticated customers.” Porter calls
a regional concentration of certain firms or industries that benefit
from the local environment a “cluster.”
Porter's concept of clusters arises from the wide and well-developed
literature in the field of regional and urban economics. Originating from
Marshall's (1920) initial idea that the costs of moving goods, people, or
ideas cause industries to concentrate, the cluster concept integrates
different theoretical considerations about external scale economies.
In a nutshell, agglomerative forces arise from pecuniary externalities
resulting from saved transportation and transaction costs, a pooled labor
market, and shared public goods, or from knowledge externalities as
“intellectual breakthroughs must cross hallways and streets more easy
than oceans and continents” (Glaeser et al., 1992, p. 1127).3 One major
implication from these agglomeration theories is that regional differences in agglomeration factors explain differences in regional industry
structure and, hence, regional performance (see Ciccone and Hall, 1996;
Duranton and Puga, 2005; Rosenthal and Strange, 2003). Thus,
politicians, in their desire to increase local firm competitiveness, are
interested in regional industry policies that will lead to the creation of
agglomeration factors, and this is how Porter's cluster approach became
a leading analytical concept in regional development (Martin and
Sunley, 2003).
The cluster concept has been, for the most part, enthusiastically
embraced by policymakers, but is viewed with more distrust by
economists and geographers, who are not wholly convinced that it is
a complete panacea for regional woes (see Duranton, 2010; Martin and
Sunley, 2003). The chief criticism of the latter is directed to the general
and vague formulation of the cluster concept—there is too much room
for interpretation. This lack of specification makes it difficult, if not
⁎ Corresponding author. Tel.: +49 3641 686 733; fax: +49 3641 686 710.
E-mail addresses: [email protected] (O. Falck), [email protected] (S. Heblich),
[email protected] (S. Kipar).
1
Tel.: +49 89 9224 1370; fax: +49 89 9224 1460.
2
Tel.: +49 89 9224 1696; fax: +49 89 9224 1460.
3
Research on pecuniary externalities is a major focus of the new economic
geography that analyzes interregional trade flows (Krugman, 1991; Fujita et al., 1999),
whereas research on knowledge spillovers analyzes the regional knowledge stock
comprised of formerly produced knowledge embodied in patents (Jaffe et al., 1993) or
people (Audretsch and Feldman, 1996) as basis for further knowledge creation.
1. Introduction
0166-0462/$ – see front matter © 2010 Elsevier B.V. All rights reserved.
doi:10.1016/j.regsciurbeco.2010.03.007
O. Falck et al. / Regional Science and Urban Economics 40 (2010) 574–582
dangerous, to formulate concrete policies because an incompletely
specified framework does not allow for the identification of causal
relationships. Accordingly, nothing guarantees that political action
based on a cluster concept actually has the desired results. So if a policy's
justification depends on whether its existence and implementation is an
improvement on its absence, there is no direct justification for a cluster
policy. However, this does not mean that every political action defined
to be a cluster policy is ineffective; it simply means that pursuing a
cluster policy is not guaranteed to be a success just because it is a cluster
policy. The policy will require a detailed description of its concrete
objectives before its effectiveness can be evaluated.
In this paper, we evaluate the impact of a cluster-oriented policy–the
High-Tech Offensive–pursued in one German state, Bavaria. The policy
was targeted at five distinct technology fields and its goal was to
increase cooperation between science, business, and finance in these
industries in order to increase agglomeration economies by way of what
Griliches (1992) describes as “working on similar things and hence
benefiting from each other's research.” Government, of course, cannot
force firms and other actors to cooperate, but it can provide incentives
that may have the same effect. Accordingly, the Bavarian High-Tech
Offensive mainly focused on improving the public infrastructure, e.g., by
providing different firms access to joint research facilities. The underlying mechanism that transforms the provision of public research
infrastructure into increased cooperation is the trust between agents
that develops through the frequent face-to-face contact and informal
meetings that take place within this infrastructure (Dei Ottati, 1994;
Williamson, 1999). Repeated interaction may result in a deeper division
of labor and R&D cooperation between firms, leading to innovation and
regional competitiveness (Von Hippel, 1987; Feldman and Audretsch,
1999).
Our empirical identification strategy initially uses difference-indifferences (DD) analyses to identify the effect of the Bavarian HighTech Offensive on firm competitiveness. The DD specification compares
the innovation performance of Bavarian firms in target industries to
other states' firms in the same industries before and after the policy was
introduced in Bavaria. In a second step, we extend our analyses by
looking at both different states and a control group within the treatment
state, i.e., Bavaria. This difference-in-difference-in-differences (DDD)
design is intended to control for two kinds of potentially confounding
trends: (1) changes in the innovation performance of target-industry
firms across states that are unrelated to the policy and (2) changes in all
firms' (target-industry and non-target-industry) innovation performance within the policy-change state Bavaria. These changes could be
due to other state policies that affect all firms' performance or to statespecific changes in the economy that affect all firms equally.
Given that the Bavarian cluster-oriented policy targeted high-tech
firms, we consider innovation activity to be the appropriate outcome by
which to measure the success of the policy. We capture innovation
activity with a binary output measure for innovation, additionally distinguish patent-protected innovations, and finally consider R&D expenditures as an input measure for innovation. Based on this framework,
we find that, depending on the type of innovation, the Bavarian HighTech Offensive increased the likelihood of firms becoming innovators
in the target industries by 4.6 to 5.7 percentage points. By contrast, R&D
Fig. 1. Introduction of the High-Tech Offensive in Bavaria.
575
expenditures decreased by 19.4% on average for firms in target
industries in Bavaria. At the same time, the possibility of obtaining
access to external know-how, the possibility for cooperation with public
scientific institutes, and access to suitable R&D personnel increased.
Together, these findings point toward the effectiveness of the policy in
terms of fostering cooperation as firms produce more innovation output
with less costly innovation input.
The remainder of the paper is organized as follows. Section 2 provides
a more detailed description of the Bavarian High-Tech Offensive. We
introduce our empirical identification strategy in Section 3, and describe
the data stemming from the Ifo Innovation Survey in Section 4. Section 5
contains our results, explores several transmission channels through
which the Bavarian High-Tech Offensive might have an effect on a firm's
innovativeness, and demonstrates the robustness of our results on the
basis of a second dataset. Section 6 concludes.
2. The Bavarian High-Tech Offensive
Starting in the early 1990s, Bavaria began to privatize state-owned
firms, including DASA, Bayernwerk, and Versicherungskammer,
which, by 1993, garnered the state more than €4 billion. The state
then reinvested about €2.9 billion in the initiative Offensive Zukunft
Bayern (Future of Bavaria Offensive), a program launched in 1994, the
first phase of which continued until 1999. The program's goal was to
foster economic growth by creating an overall innovation-friendly
atmosphere. Specifically, the program channeled €1.4 billion into the
educational system, research and development infrastructure, qualification programs, vocational colleges, and universities; spent another
€436 million to improve transportation and telecommunication
infrastructure, support small and medium-sized enterprises and
start-ups, and promote exporting; allocated €371 million for labor
market and social programs; budgeted €356 million for environmental
protection and the development of new energy sources; and poured
€345 million into cultural programs.
These expenditures suggest that the first phase of the initiative was
not targeted at specific industries, but was instead aimed at improving
hard and soft locational factors of the Bavarian economy with the goal
of attracting successful firms. Once these basic investments were
underway, the state government launched the High-Tech Offensive, a
follow-up program focused on firms active in five key technology
fields: life science, information and communication technology, new
materials, environmental technologies, and mechatronics.4 Since its
introduction in 1999, the program has attempted to enhance the
inherent strengths of companies in these five fields through the
formation of tightly woven regional cooperation networks in the form
of clusters. Therefore, we regard the Bavarian government's High-Tech
Offensive as one example of governmental action undertaken to foster
industrial cooperation and refer to it as “cluster-oriented policy.”
Specifically, the policy contained a wide variety of measures
especially intended to foster these key technologies. One of the
initiative's chief goals was to link science, business, and finance in
order to foster innovation activity and development in Bavaria. About
50% of the program's budget was allocated to improve public research
infrastructure and institutions, thereby reinforcing existing strengths
of universities and research units. For instance, it is the initiative that
made it possible for private firms in the key technologies to use the
research reactor in Garching (near Munich) for corporate research.
Funds were also allocated to foster and improve the coordination of
already existing regional clusters within Bavaria. Furthermore, the
program aimed at linking competencies at different locations by
supporting the emergence of state-wide networks, e.g., the Bavarian
network for mechatronics or the virtual Innovation and Entrepreneur
4
The program was displaced by the Allianz Bayern Innovative (Bavaria's Clusters) in
2004. This new program focused on 19 key technologies.
576
O. Falck et al. / Regional Science and Urban Economics 40 (2010) 574–582
Network. The state's own venture capital organization was infused
with sufficient funds to be able to provide venture capital to firms
planning new projects. The state also created science parks in which
firms could occupy space rent free to carry out new research, and
fostered the connection of universities, science parks, and institutes to
high-speed Internet networks.
Altogether, between 1999 and 2004, the High-Tech Offensive
expended €1.35 billion for four major purposes: €663.6 million were
directed to the creation of globally competitive hi-tech centers,
€179 million to the development of regional technology concepts,
€267.4 million to support qualification measures, start-ups, and
research and development environments, and €65.5 million to support
international link-ups.
When the initiative started in 1999, Bavaria was the first German
state to initiate a highly visible, state-wide policy targeted at distinct
industries and it was not until 2001 that other German states followed
Bavaria's lead and introduced their own state-wide policies (Hesse
and Saarland in 2001; Thuringia followed in 2002) (Fig. 1). However,
the Bavarian program dwarfs the other states' programs, both in
visibility and scope. Considering additionally that Bavaria is one of the
largest German states, it should be easier to discern the effects of a
state-wide policy within its borders, as compared to more narrowly
defined local cluster policies that exist in all German states.5 This gives
us confidence in our investigation of the effect of the Bavarian state
government's cluster-oriented policy.
Here, Ift equals unity if firm f has introduced an innovation in year t,
0 otherwise. αf are firm fixed effects. cp99ft is a binary variable that
equals unity for firms located in Bavaria after the policy was
introduced. Thus, β is the coefficient of interest, indicating the effect
of the Bavarian High-Tech Offensive on a firm's propensity to innovate
or a firm's R&D spending, respectively.
Interpreting β as a causal effect of the Bavarian policy on a firm's
innovation activity requires controlling for any systematic shocks to
the innovation behavior of Bavarian firms that might be correlated
with the policy. Therefore, we add a full set of year dummies, αt, to
capture any national trends that could drive the innovation behavior of
Bavarian firms in the target industries. We further include a dummy
ip94ft that equals unity from the year 1994 onwards for firms located in
Bavaria. As described in Section 2, the Bavarian state government
launched a first initiative in 1994 that did not target specific industries.
However, if there was any impact of the 1994 general industrial policy
on firms that were later targeted by the 1999 Bavarian High-Tech
Offensive, the dummy should capture these effects.
One shortcoming of the DD approach is that it only considers the
subsample of firms in target industries and thus cannot control for
systematic shocks that may have affected all Bavarian firms. To deal
with state-specific shocks, we additionally consider a control group
within Bavaria and use changes in the innovation behavior of firms in
industries not targeted by the Bavarian High-Tech Offensive, resulting
in a difference-in-difference-in-differences (DDD) estimator (see
Hamermesh and Trejo, 2000):
3. Identification strategy: from DD to DDD
The goal of the Bavarian High-Tech Offensive was to foster
innovation and regional competitiveness by stimulating cooperation.
Thus, we are primarily interested in estimating the causal effect of this
cluster-oriented policy on the innovation activity of firms. In a first
step, we focus on firms in industries targeted by the Bavarian policy
using a difference-in-differences (DD) approach (see Campbell, 1969;
Card and Sullivan, 1988; Card, 1990):
2
After
Before
After
Before
ΔTI = IBav;TI −IBav;TI − INonBav;TI −INonBav;TI :
ð1Þ
Here, TI denotes target industries and Itr,TI represents a firm's
innovation activity in region r (Bavaria or other German states) at
time t (before and after the introduction of the High-Tech Offensive in
Bavaria in 1999). We focus on the subsample of firms f in those
industries targeted by the Bavarian High-Tech Offensive. Thus, we
compare Bavarian firms in target industries with firms engaged in the
same industries in other German states. We use three different outcome
measures. First, we use a binary variable that equals unity if a firm has
introduced an innovation, 0 otherwise. We subsequently refine this
basic measure with information on whether a patent was filed for the
innovation introduced, thereby capturing innovations that are worthy
enough to the firm to be protected by a patent. Finally, we use a
continuous measure, the log of a firm's R&D expenditures, to evaluate
the effects of the policy on the input side of the innovation process.
Based on firm-level panel data, we estimate Eq. (1) by linear
models irrespective of whether the outcome measure is binary or
continuous. We choose linear specifications throughout the paper
because the linear probability framework is more robust to misspecifications and it allows us to include fixed effects in a simple and
easily interpretable framework (see Angrist, 2001). In the case of the
binary outcome measure, the linear probability model is
Prob Ift j ⋅ = αf + αt + βcp99ft + γip94ft + εft :
ð2Þ
5
For an overview of the cluster policies in German states, see Kiese and Schaetzl
(2008).
3
2
2
Δ = ΔTI −ΔNTI :
ð3Þ
Again, TI denotes target industries and NTI denotes non-target
industries. The first differences are identified by the different
innovation behavior of firms over time, i.e., pre and post 1999. The
second differences are differences in the innovation behavior between
all Bavarian and non-Bavarian firms independent of the industry to
which they belong. The third and final difference is that between the
above-mentioned differences in target and non-target industries. In
the case of a binary outcome measure for innovation, we estimate
Eq. (3) with the following linear probability model:
Prob Ift j ⋅ = αf + αt + αTI;94 + αTI;99 + αBav;94 + αBav;99 + βcp99ft
+ γip94ft + εft :
ð4Þ
We estimate Eq. (4) on the basis of all firms, regardless of whether
they belong to industries that are targeted by the Bavarian High-Tech
Offensive or to non-target-industries. ip94ft and cp99ft are now binary
variables that equal unity for firms in the target industries that were
located in Bavaria from 1994 onwards and 1999 onwards, respectively. In addition to firm fixed effects αf and a full set of year dummies
αt, the DDD approach allows us to add target-industry-specific time
dummies (αTI,94 equals unity for the years from 1994 onwards and
αTI,99 equals unity from the year 1999 onwards) and Bavaria-specific
time dummies (αBav,94 equals unity from the year 1994 onwards and
αBav,99 equals unity from the year 1999 onwards) to control for
industry-specific and state-specific time trends. Thus, in order to
interpret β as a causal effect of the Bavarian cluster-oriented policy on
a firm's innovation activity, the identifying assumptions are fairly
weak in this DDD approach. It only requires that there is no contemporaneous shock that specifically affects the innovative behavior of
firms in the target industries in Bavaria relative to their Bavarian
counterparts in non-target industries in the same year that the
Bavarian High-Tech Offensive was introduced (see Gruber, 1994).
O. Falck et al. / Regional Science and Urban Economics 40 (2010) 574–582
4. Data on firm innovation in Germany
Industry-specific innovation activities of manufacturing firms are
derived from the Ifo Innovation Survey (for a detailed description of
the dataset, see Lachenmaier, 2007). In this survey, more than 1000
firms report yearly on whether or not they have introduced an
innovation, i.e., a product or process innovation, and if a patent was
filed for the innovation introduced. As an input measure for the
innovation process, R&D expenditures are reported and defined as the
sum of internal and external costs of R&D, expenditure for construction and design or patent registration and licenses, and costs of
preparing for production or market introduction.
The surveyed firms are a subsample of firms that are surveyed
monthly for business cycle research.6 Because these firms participate
regularly in the Innovation Survey, the panel character of the data is
guaranteed. It is important to stress that the survey sample is not
designed to evaluate the High-Tech Offensive specifically, but has
covered this subject in its methodology for more than 20 years.
Therefore, the survey can be viewed as a random sampling of firms
with respect to the policy and there is no problem of selection bias.
The voluntary character of the Ifo Innovation Survey does not
result in a sample that is necessarily representative of Germany as a
whole. Therefore, we compare the distribution of firms in our sample
with the population of firms provided by the Federal Statistical Office,
which reveals that our data oversample large firms and undersample
small firms throughout the period under consideration (see Fig. A1 in
the Appendix). This occurs because business cycle research surveys
tend to include a larger number of large firms that represent a large
share of the economy in terms of employees and/or sales. Furthermore, the two-digit-NACE industries “Fabricated metal products”
(NACE code 28) and “Radio, TV, communication” (NACE code 32) are
notably underrepresented in the Ifo Innovation survey, whereas the
industry “Pulp, paper, and paper products” (NACE code 21) is overrepresented (see Fig. A2 in the Appendix). Still, we can control for firm
size in terms of employees and industry of the firm by including firm
fixed effects, which, according to Winship and Radbill (1994), is both
appropriate and more efficient than weighing the data.
We assign the firms in our sample to the five fields defined by the
Bavarian policy according to their two-digit NACE code (see Table A1
in the Appendix). The data from the Ifo Innovation Survey are
available from 1982 to 2007. Nevertheless, we do not want to extend
the timespan beyond the year 2001 as state-wide cluster policies were
introduced in other German states at that time. Furthermore, we
remove all observations before the year 1991.
To disentangle the effects of the non-targeted 1994 policy from the
targeted 1999 policy, we restrict our sample to firms with at least one
observation in each policy period. More precisely, we include only
those firms with at least one observation before 1994, one observation
between the years 1994 and 1998, and one observation from 1999
onwards. This leaves us with a panel of incumbent firms each having
at least three observations, thus enabling us to estimate our model
with firm fixed effects in order to control for time-invariant unobserved firm characteristics. This means, however, that we can
analyze the effect of the Bavarian High-Tech Offensive only on incumbent firm innovation activity, not on domestic or foreign entry.7
We eliminate a possible problem of endogeneity that could arise if
firms moved from some other state to Bavaria in order to benefit from
the policy by only including in the final sample those firms that did
not move between states during the sample period. Our final sample
consists of 1039 firms, each observed at least at three points in time.
6
Specifically, the unit of observation in the Ifo Innovation Survey is the product
range. In the case of a firm with a wide range of products, the firm would be divided
into different units. For the sake of simplicity, however, we will adhere to the notion of
the firm throughout the paper.
7
For the location decision of foreign multinationals in Germany, see Spies (2010).
577
Three-hundred-one of the firms are located in Bavaria; 738 are
located in other German states. Out of the 301 firms located in Bavaria,
185 belong to industries targeted by the High-Tech Offensive. Of these
185 firms, 43 (40) switched from no innovation in the years 1994 to
1998 to innovation (to patent-protected innovation) in the years 1999
to 2001. In the next section of the paper, this variation is used to
identify the effect of the High-Tech Offensive on firm innovation. Fig. 2
shows the size distribution (by employment) of the switching firms in
target industries in Bavaria.
Fig. 3 provides a preliminary look at the evolution of innovation
across industries and states. Yearly innovation rates for our sample are
simply calculated as the number of firms that have introduced an
innovation over all firms in a state's industry. Fig. 3 plots average
innovation rates in each policy spell: without industrial policy for the
years 1991 to 1993, with the non-targeted industrial policy for the
years 1994 to 1998, and with the targeted High-Tech Offensive for the
years 1999 to 2001. Innovation rates are calculated separately for
industries targeted by the policy and for industries that were not. A
further distinction is made between Bavarian industries and industries in states that have not introduced a state-wide cluster-oriented
policy.
Three things stand out in Fig. 3. First, innovation rates in industries
targeted by the Bavarian High-Tech Offensive are higher than those
for other industries, both in Bavaria and in the other German states.
Second, innovation rates in the industries targeted by the Bavarian
policy increase after the policy's introduction in 1999, whereas the
same industries outside of Bavaria show a decrease in innovation.
Third, the change of innovation rates from the pre-treatment period
1994–1998 to the post-treatment period 1999–2001 is negative for all
control groups, whereas there is an increase for the treatment group,
i.e., target industries in Bavaria. As there is not only a decrease in
innovation rates outside Bavaria in the target industries after 1999 but
also in non-target industries in and outside Bavaria, it is presumably
not only a crowding-out effect that we observe here but a general
downward trend in innovation rates in Germany. However, note that
the results in Fig. 3 are only preliminary and do not include controls
for other influences. This will be part of the following regressions.
5. Direct evidence from the Bavarian High-Tech Offensive
5.1. Evidence from the Ifo Innovation Survey
Table 1 sets out our baseline results. The first three columns report
the results of the standard DD model comparing only target industries
in Bavaria and the other states. The coefficient of interest is the
estimate on the policy 1999 variable (cp99), which equals unity for
Fig. 2. Number of firms in target industries in Bavaria that switch from no innovation in
period 1994–1998 to innovation in period 1999–2001. Notes: Our sample consists of
185 firms in target industries in Bavaria. Firm size classes are in terms of employees.
578
O. Falck et al. / Regional Science and Urban Economics 40 (2010) 574–582
Fig. 3. Average innovation rates. Notes: Innovation rates (on the ordinate) are
calculated as the number of firms that have introduced an innovation over all firms in a
state's industry based on data from the Ifo Innovation Survey. This figure plots average
innovation rates in each policy spell, i.e., (1) without any industrial policy in the years
1991 to 1993; (2) with the non-targeted industrial policy in the years 1994 to 1998; and
(3) with the targeted High-Tech Offensive in the years 1999 to 2001. Innovation rates
are calculated separately for industries targeted by the High-Tech Offensive (TI) and for
industries that were not targeted (NTI). A further distinction is made between Bavarian
industries and industries in states that have not introduced a state-wide policy.
firms in Bavaria after the year 1998 (including 1999) and can be
interpreted as the impact of the state-wide Bavarian High-Tech
Offensive on the three outcome measures. The first column shows the
effect of the policy on firms' propensity to innovate in the target
industries. Neither policy, the 1994 non-targeted policy nor the
targeted 1999 policy, shows any significant effect at this point.
Column 2 shows the results on a firm's propensity to introduce an
innovation for which a patent was filed. This outcome measure should
capture the innovations a firm decides are important enough to
protect. Again, no significant effect can be attributed to any policy in
this specification. Column 3 displays the effect of the policies on firms'
R&D expenditures. Here, we can attribute a negative effect to the 1994
policy and no significant effect for the 1999 High-Tech Offensive.
The size of the firm in terms of employees tends to have a positive
effect on all innovation measures, but this effect is not statistically
different from zero, a result that holds true for all specifications.
Columns 4 to 6 of Table 1 set out the results of the DDD approach, in
which we are able to use non-target industries in Bavaria as an
additional control group. Here, we identify a significantly positive
effect of the 1999 policy (Column 4). Keep in mind that this is an “ontop” effect, meaning that even after the estimated positive effect of the
1994 policy in this specification, the 1999 cluster-oriented policy still
has an additional positive effect on firms' propensity to innovate in the
target industries. Thus, the coefficient must be interpreted as showing
that the High-Tech Offensive led to an increase in the propensity to
innovate by 4.6 percentage points. Column 5 shows an even more
pronounced effect on the propensity to report an innovation for which
a patent was filed. This effect is highly significant and means that the
introduction of the policy in Bavaria in 1999 resulted in an increase in
the propensity to introduce a patent-protected innovation by 5.7
percentage points. The results of these specifications are evidence that
the triple difference approach successfully isolates a negative Bavariaspecific trend after 1999.
Finally, the sixth column of Table 1 reports the results for R&D
expenditures as a dependent variable in our DDD approach. We find
that the High-Tech Offensive had a significantly negative effect on
R&D expenditures. As R&D expenditures are coded in logs, we have to
interpret the coefficients as semi-elasticities. More specifically, as the
policy variable is binary, we need to calculate the correct semielasticity by the formula 100 ⋅ (eβ − 1) (see Wooldridge, 2006). Doing
so suggests that the introduction of the policy measure decreased R&D
Table 1
Results on the basis of the Ifo Innovation Survey.
DD
Policy 1994 (ip94)
Policy 1999 (cp99)
DDD
(1)
(2)
(3)
(4)
(5)
Innovation
Innovation (patent)
R&D expenditures (log)
Innovation
Innovation (patent)
R&D expenditures (log)
0.0264
[1.543]
0.0407
[1.665]
0.00111
[0.0685]
0.0243
[1.517]
− 0.160**
[− 2.565]
− 0.0964
[− 1.031]
0.00109
[1.219]
0.586***
[38.76]
incl.
incl.
5282
709
0.006
0.000989
[0.528]
0.261***
[24.41]
incl.
incl.
5246
709
0.002
0.0825
[1.675]
13.52***
[108.3]
incl.
incl.
2393
570
0.007
0.0914***
[2.961]
0.0461*
[1.859]
− 0.0658**
[− 2.618]
− 0.00579
[− 0.251]
− 0.0272
[− 0.884]
0.00783
[0.312]
0.000842
[1.019]
0.523***
[43.82]
incl.
incl.
7802
1039
0.004
− 0.00843
[− 0.501]
0.0574***
[5.208]
0.00897
[0.509]
− 0.0326*
[− 1.934]
0.0409**
[2.410]
0.00360
[0.326]
0.00115
[0.590]
0.221***
[21.03]
incl.
incl.
7719
1039
0.003
− 0.0562
[− 0.399]
− 0.216*
[− 1.940]
− 0.102
[− 0.726]
0.123
[0.902]
0.0127
[0.0904]
0.0232
[0.211]
0.0819
[1.703]
13.27***
[112.9]
incl.
incl.
3235
804
0.004
αBav,94
αBav,99
αTI,94
αTI,99
Number of employees (in 1000s)
Constant
Year dummies
Firm fixed effects
Observations
Number of firms
Adjusted R2
(6)
Notes: difference-in-differences (DD) and difference-in-difference-in-differences (DDD) estimations are based on the Ifo Innovation Survey, 1991–2001. For each firm in the
estimations, we have at least three observations (one before 1994, one from 1994 to 1998, and one from 1999 on). Linear models are estimated throughout the table. In the DD
estimation, only firms in target industries are considered while non-target industries serve as an additional control group in the DDD. In Columns (1) and (4), the dependent variable
is binary and equals unity if a firm has introduced an innovation. In Columns (2) and (5), the dependent variable is binary and equals unity if a firm has introduced a patent-based
innovation. In Columns (3) and (6), the dependent variable is the log of a firm's R&D expenditures. ip94 equals unity for target-industry firms located in Bavaria from the year 1994
on. cp99 equals unity for target-industry firms located in Bavaria from the year 1999 on. Thus, all “policy 1999” effects are “on top” of the 1994 policy. αBav,94 equals unity for firms in
Bavaria from the year 1994 on. αBav,99 equals unity for firms in Bavaria from the year 1999 on. αTI,94 equals unity for firms in target industries from the year 1994 on. αTI,99 equals
unity for firms in target industries from the year 1999 on. Cluster-robust t-statistics on the state level are reported in brackets. ***, **, * statistically significant at the 1%, 5%, and 10%
levels.
O. Falck et al. / Regional Science and Urban Economics 40 (2010) 574–582
expenditures by 19.4% on average. However, this does not necessarily
point toward a negative effect of the policy; to the contrary, it suggests
that firms are now able to develop innovations at lower costs. The
non-targeted 1994 policy has no significant effect in this specification.
Generally, the DDD estimates are not completely different from
the DD estimates as the coefficients of the interaction terms that
capture state and industry-specific trends, αBav,94, αBav,99, αTI,94, and
αTI,99, are usually not significantly different from zero. This means
that, with the exception of the specifications with patent-protected
innovations as the outcome, we do not have a strongly different trend
in the data between target and non-target industries or between
Bavaria and the other states.
5.2. Transmission channels of the High-Tech Offensive
In this section, we provide some evidence as to the transmission
channels through which the policy took effect by making use of an
additional section of the Ifo Innovation Survey in which firms report
whether they faced any obstacles in the pursuit of innovation activity.
Firms are given a list of obstacles from which to choose and are
instructed to check off those they experienced. By using those
obstacles as alternative outcome measures we are able to shed light
on the channels through which the policy influenced firm action.
Table 2 shows a summary of the DDD regressions performed with
the reported obstacles as outcomes. Since obstacles are negatively
related to innovation activity, a negative coefficient needs to be
interpreted as a positive effect of the policy, seeing it as the propensity
of reporting an obstacle decreased. Interestingly, the High-Tech
Offensive did not have an effect through monetary channels. The
non-targeted 1994 policy shows a negative effect for the reported lack
of equity and external capital; the targeted 1999 High-Tech Offensive
does not. Additionally, the High-Tech Offensive resulted in a
decreased probability of firms reporting insufficient possibility of
cooperating with public scientific institutes. This confirms the success
of the High-Tech Offensive in fostering cooperation between science
and business. Another important finding concerns the obstacles
capturing the information exchange between firms. As a result of the
High-Tech Offensive, the probability of reporting inadequate information about external know-how significantly decreased, as did the
probability of reporting difficulties in obtaining access to external
know-how. Note, again, that the coefficients of the 1999 policy have to
be interpreted as on-top effects on the 1994 policy as the 1994 policy
dummy remains unity after 1999. Finally, the obstacle of “finding
adequate personnel for the R&D sector” decreased due to the HighTech Offensive in Bavaria, which indicates some success of the created
virtual networks and qualification programs.
Taken together, this evidence suggests that there was something
more to the High-Tech Offensive than just throwing money at certain
industrial sectors. For example, looking at the effects on the reported
obstacles to innovation, network effects can be identified as an increase
in cooperation between the science sector and the business world.
5.3. Robustness test: evidence from the IAB Establishment Panel
As a robustness test, we estimate our models using data from a
different source, the IAB Establishment panel. (for a detailed
description of the dataset, see Bellmann, 2002 and Fischer et al.,
2008).8 The IAB Establishment Panel is highly representative of the
German manufacturing sector. However, we are not able to control for
the effect of the 1994 policy because the panel is rather small in scope
before the year 1996. Still, we are confident that this robustness check
offers additional insights and is worth pursuing.
8
Data access was provided via remote data access from the Research Data Centre
(FDZ) of the German Federal Employment Agency (BA) at the Institute for Employment Research (IAB).
579
Table 2
Results on the basis of the Ifo Innovation Survey (channels).
Obstacle to innovation: “Our innovation activity is
hampered by …”
Finance
Lack of equity capital
Lack of external capital
Cooperation
Insufficient possibilities for cooperation with other
enterprises
Insufficient possibilities for cooperation with public
scientific institutes
Inadequate information about externally available
know-how
Difficulties in obtaining access to external know-how
Human resources
Difficulties in finding suitable personnel …
… in the R&D sector
… for production
… in the sales sector
Policy 1994 Policy 1999
(ip94)
(cp99)
− 0.0663***
[− 5.417]
− 0.0236**
[− 2.459]
0.0129
[1.207]
− 0.00616
[− 0.470]
0.00706
[1.480]
− 0.0231***
[− 18.41]
0.0141***
[3.054]
− 0.0317***
[− 4.173]
− 0.00221
[− 0.715]
− 0.0209***
[− 5.218]
− 0.0275***
[− 5.876]
− 0.0174**
[− 2.414]
− 0.00166
[− 0.195]
− 0.0128***
[− 3.008]
− 0.0294***
[− 2.987]
− 0.0146***
[− 3.280]
0.00534
[0.744]
− 0.0180***
[− 3.628]
Notes: difference-in-difference-in-differences (DDD) estimations are based on the Ifo
Innovation Survey, 1991–2001. For each firm in the estimations, we have at least three
observations (one before 1994, one from 1994 to 1998, and one from 1999 on). Linear
probability models are estimated. The dependent variables are binary and equal unity if
a firm has reported the obstacle in question. ip94 equals unity for target-industry firms
located in Bavaria from the year 1994 on. cp99 equals unity for target-industry firms
located in Bavaria from the year 1999 on. Thus, all “policy 1999” effects are “on top” of
the 1994 policy. Only coefficients of the ip94 and the cp99 variables are reported. The
complete model specification corresponds to that in Eq. (4). Cluster-robust t-statistics
on the state level are reported in brackets. ***, ** statistically significant at the 1%, 5%,
and 10% levels.
The IAB survey includes an innovation section every three years
that asks firms whether they introduced an innovation in the
preceding two years. Conveniently, the innovation section was
conducted for the years 1998 and 2001, which enables us to evaluate
the 1999 policy. We therefore construct a balanced panel of firms that
answered the innovation section of the survey in both waves around
the policy period (1998 and 2001), leaving us with a sample of 1188
firms with two observations each.
The IAB Establishment Panel version of the innovation question is
somewhat different than the one asked in the Ifo Innovation Survey. In
the Ifo Innovation Survey, firms are asked whether they introduced
any kind of innovation in the preceding year; the IAB panel
distinguishes between innovations copied from products already on
the market but still new to the firm and products that were completely
new both to the firm and the market. We use only completely new
product innovations as the outcome measure in this section.
Consequently, care must be taken in comparing the resulting estimates
quantitatively across datasets. Furthermore, the unit of observation in
the IAB panel is the establishment, defined as a physical production
plant, which, in the terminology of the Ifo Innovation Survey, could in
principle contain more than one product range. Nevertheless, for the
sake of simplicity, we use the notion of the firm in the remainder of the
paper. R&D expenditures are not available in the IAB panel.
The first column of Table 3 reports the result of the DD estimation
when comparing only target industries in Bavaria to those in the other
German states. The resulting coefficient for the High-Tech Offensive
dummy is statistically significant and positive. The High-Tech Offensive
resulted in an increase of the probability to innovate of 4.4 percentage
points.
Using this dataset clearly reveals the advantages of the DDD specification. After introducing the third difference, Column 2 of Table 3
580
O. Falck et al. / Regional Science and Urban Economics 40 (2010) 574–582
Table 3
Results on the basis of the IAB Establishment Panel.
Policy 1999 (cp99)
DD
DDD
0.0441**
[2.165]
0.166***
[4.655]
− 0.122***
[− 5.471]
0.00192
[0.0553]
− 0.0153
[− 0.715]
0.000077
[1.118]
incl.
0.126***
[3.224]
2370
1188
0.004
αBav,99
αTI,99
Second wave dummy
Number of employees
Firm fixed effects
Constant
Observations
Number of firms
Adjusted R2
− 0.0135
[− 0.656]
0.000072
[1.114]
incl.
0.128**
[2.778]
1787
894
0.001
Notes: difference-in-differences (DD) and difference-in-difference-in-differences
(DDD) estimations are based on the IAB Establishment Panel, where the waves 1998
and 2001 are considered. Each firm enters with two observations (1998 and 2001,
respectively) in the estimation. Linear probability models are estimated. In the DD
estimation, only firms in target industries are considered while non-target industries
are used as an additional control group in the DDD. The dependent variable is binary
and equals unity if a firm has introduced an innovation that is new to the market in the
two years preceding the survey. cp99 equals unity for target-industry firms in Bavaria
from the year 1999 on. αBav,99 equals unity for firms in Bavaria from the year 1999 on.
αTI,99equals unity for firms in target industries from year 1999 on. Cluster-robust tstatistics on the state level are reported in brackets. ***, ** statistically significant at the
1%, 5%, and 10% levels.
shows that Bavarian firms in general experienced a huge drop in the
propensity to innovate (as defined by the IAB survey) from 1999
onwards. By including the interaction αBav,99, we successfully isolate
this trend and are able to attribute a 16.6 percentage point increase in
target firms' innovation probability to the introduction of the HighTech Offensive in Bavaria. Of course, the magnitudes of the estimates
based on the IAB Establishment Panel should not be compared directly
with the results gained from the Ifo Innovation Survey because of the
difference in the innovation question between the two surveys. Still,
we consistently identify a significantly positive impact of the 1999
policy that appears to be robust to different methods and different
data sources.
only the target industries in Bavaria cannot be ruled out, we are
confident that we made significant progress in estimating a reliable
effect of a local industrial policy on the innovation behavior of firms.
Since our preferred outcome variable is binary and only reflects
whether a firm introduced an innovation, we cannot conclude that the
positive effect of the policy measure actually resulted in more
innovations. It is quite possible that the same number of innovations
were made, but that more firms claim that they have introduced an
innovation because certain innovations were developed jointly.
Nevertheless, even if only the number of firms reporting an
innovation increased, it still points toward a better diffusion of
knowledge, that is, more firms have access to new technologies. This
is a positive effect from a regional perspective.
Seeing that cluster-oriented policies are globally en vogue, we
hope that our study will steer the discussion of these policies away
from its current focus on correlations derived from well-known case
studies of successful clusters and toward more consideration of the
causal effects of cluster-oriented policies on economic outcomes. The
Bavarian High-Tech Offensive cost an enormous amount—
€1.35 billion. Whether all that money was money well spent is a
question we cannot answer here. A cost–benefit analysis is needed,
one that compares the cost of the program with the economic value of
the innovations it induced and, furthermore, compares the benefits of
the Bavarian High-Tech Offensive with those of programs instituted in
other areas and for other industries.
We can, however, provide a “guesstimate” or some back-of-theenvelope calculations of the potential benefits of target firms' higher
innovation propensity by exploiting another question from the Ifo
Innovation Survey, which asked firms to quantify the effects of the
reported innovation on their sales. We use this question from the
2001 wave of the innovation survey and initially calculate the average
contribution of innovations to target firms' sales by firm size classes
(in terms of employment) in 2001. Multiplying this with the
estimated increased innovation propensity of 4.6 percentage points
(based on all innovations), we then extrapolate this number to the
total number of firms in the target industries in Bavaria. This
calculation, which is obviously very rough and should be viewed
with great caution, suggests that the Bavarian High-Tech Offensive
made it possible for the firms it targeted to generate an additional
€3.3 billion in sales.
6. Conclusions
Acknowledgments
Our goal in this paper is to evaluate the success of a clusteroriented economic policy aimed at increasing the innovation activity
of firms in high-tech industries. We analyze the Bavarian High-Tech
Offensive, introduced in 1999 by the Bavarian state government,
which was designed to increase cooperation between firms, public
research institutes, and financing institutions so as to stimulate
innovation and increase regional competitiveness. Applying difference-in-difference-in-differences methodology, we find that, depending on the innovation measure considered, introduction of the
Bavarian-wide High-Tech Offensive increased the likelihood of an
innovation by a firm in the target industry by 4.6 to 5.7 percentage
points and decreased R&D spending in the target industries by 19.4%
on average. We also find increased opportunity for obtaining access to
external know-how, cooperating with public scientific institutes, and
accessing suitable R&D personnel.
We have made some progress in estimating effects of a clusteroriented policy on innovation that can be causally interpreted under
relatively weak assumptions, i.e., the absence of a shock that solely
affected the target industries in Bavaria. Potential shocks experienced
by all firms in Bavaria are isolated by the DDD approach, as are shocks
that affect only the target industries in both Bavaria and all states. Even
though the possibility of an unobserved, additional shock affecting
This research was based on the project “Effects and Determinants
of Innovation in Germany—A Panel Analysis,” which was funded by
the German Science Foundation. The authors are also indebted to
Matthias Kiese for valuable insights concerning cluster policies in
Germany, as well as to the editor Daniel McMillen, two anonymous
referees, Gilles Duranton, Henry Overman, the participants of a
workshop on cluster policies at the Max Planck Institute of Economics
in Jena in 2008, the participants of a workshop on the econometric
evaluation of public policies at the University of Barcelona in 2008, the
participants of the 2009 Spring Meeting of Young Economists in
Istanbul, the participants of the 2009 meeting of the Scottish
Economic Society in Perth, the participants of a workshop of the
German-speaking section of the European Regional Science Association in Innsbruck in 2009, the participants of the 2009 Econometric
Society European Meeting in Barcelona, the participants of the 2009
meeting of the European Association for Research in Industrial
Economics in Ljubiljana, and the participants of the 2009 meeting of
the North American Regional Science Council in San Francisco for
helpful comments on an earlier version of this paper. We also thank
the Research Data Centre (FDZ) of the German Federal Employment
Agency (BA) at the Institute for Employment Research (IAB) for the
provision of the IAB establishment data.
O. Falck et al. / Regional Science and Urban Economics 40 (2010) 574–582
581
Table A1 (continued)
Appendix A
Regrouped
industries
[4] Environmental
technologies
[5] Mechatronics
Non-target industries
Fig. A1. Distribution of firms in the Ifo Innovation Survey (1991–2001) by size classes.
Notes: This figure compares the distribution of firms in the Ifo
Innovation Survey with the population of firms provided by the
Federal Statistical Office by size classes.
2-digit industries
[25] Rubber and plastic products
[26] Other non-metallic mineral
products
[27] Basic metals
[28] Fabricated metal products
[20] Wood and wood products
[31] Electrical machinery
[29] Machinery and equipment
n.e.c.
[34] Motor vehicles
[35] Other transport equipment
[15] Food products and
beverages.
[16] Tobacco products
[18] Wearing apparel
[19] Leather
[21] Pulp, paper, and paper
products
[22] Publishing and printing
[23] Coke and petroleum products
[36] Furniture, manufacturing
n.e.c.
Fig. A2. Distribution of firms in the Ifo Innovation Survey (1991–2001) by industries.
Notes: This figure compares the distribution of firms in the Ifo
Innovation Survey with the population of firms provided by the
Federal Statistical Office by industries.
Table A1
Regrouping of industries.
Regrouped
industries
Industries targeted by the [1] Life sciences
Bavarian High-Tech
[2] Information and
Offensive
communication
technologies
[3] New materials
2-digit industries
[33] Medical and optical
instruments
[30] Office machinery and
computers
[32] Radio, TV, communication
[17] Textiles
[24] Chemicals
(continued on next page)
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