Industrial innovation: Direct evidence from a cluster-oriented policy

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 − INon
Bav;TI −INon
Bav;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)

References Angrist, J.D., 2001. Estimation of limited dependent variable models with dummy endogenous regressors: simple strategies for empirical practice. J. Bus. Econ. Stat. 19, 2–16. Audretsch, D.B., Feldman, M.P., 1996. R&D spillovers and the geography of innovation and production. Am. Econ. Rev. 83, 630–640. Bellmann, L., 2002. Das IAB Betriebspanel: Konzeption und Anwendungsbereiche. Allgemeines Statistisches Arch. 86, 177–188. Campbell, D.T., 1969. Reforms as experiments. Am. Psychol. 24, 409–429. Card, D.E., 1990. The impact of the Mariel boatlift on the Miami labor market. Ind. Labor Relat. Rev. 43, 245–257. Card, D.E., Sullivan, D., 1988. Measuring the effect of subsidized training programs on movements in and out of employment. Econometrica 56, 497–530. Ciccone, A., Hall, R., 1996. Productivity and the density of economic activity. Am. Econ. Rev. 86, 54–70. Dei Ottati, G., 1994. Trust, interlinking transactions and credit in the industrial district. Camb. J. Econ. 18, 529–546. Duranton, G., 2010. California Dreamin’: The feeble case for cluster policies. Review of Economic Analysis, forthcoming.

582

O. Falck et al. / Regional Science and Urban Economics 40 (2010) 574–582

Duranton, G., Puga, D., 2005. From sectoral to functional urban specialization. J. Urban Econ. 57, 343–370. Feldman, M.P., Audretsch, D.B., 1999. Innovation in cities: science-based diversity, specialization and localized competition. Eur. Econ. Rev. 43, 409–429. Fischer, G., Janik, F., Müller, D., Schmucker, A., 2008. The IAB Establishment Panel - from Sample to Survey to Projection. FDZ Methodenreport No.01/2008. Fujita, M., Krugman, P., Venables, A., 1999. The Spatial Economy: Cities, Regions, and International Trade. MIT Press, Cambridge, MA. Glaeser, E., Kallal, H., Scheinkman, J.A., Shleifer, A., 1992. Growth of cities. J. Political Econ. 100, 1126–1152. Griliches, Z., 1992. The search for R&D spillovers. Scand. J. Econ. 94, 29–47. Gruber, J., 1994. The incidence of mandated maternity benefits. Am. Econ. Rev. 84, 622–641. Hamermesh, D., Trejo, S.J., 2000. The demand for hours of labor: direct evidence from California. Rev. Econ. Stat. 82, 38–47. Jaffe, A., Trajtenberg, M., Henderson, R., 1993. Geographic localization of knowledge spillovers as evidenced by patent citations. Q. J. Econ. 63, 577–598. Kiese, M., Schaetzl, L., 2008. Cluster und Regionalentwicklung. Verlag Dorothea Rohn, Dortmund.

Krugman, P., 1991. Geography and Trade. MIT Press, Cambridge, MA. Lachenmaier, S., 2007. Effects of Innovation on Firm Performance. Ifo Contributions to Economic Research, Munich. Marshall, A., 1920. Principles of Economics. MacMillan, London. Martin, R., Sunley, P., 2003. Deconstructing clusters: chaotic concept or policy Panacea. J. Econ. Geogr. 3, 5–35. Porter, M., 1998. On Competition. Harvard Business School Press, Cambridge, MA. Rosenthal, S.S., Strange, W.C., 2003. Geography, industrial agglomeration, and agglomeration. Rev. Econ. Stat. 85, 377–393. Spies, J., 2010. Network and border effects: where do foreign multinationals locate in Germany? Reg. Sci. Urban Econ. 40, 20–32. Von Hippel, E., 1987. Cooperation between rivals: informal know-how trading. Res. Policy 16, 291–302. Williamson, O.E., 1999. The Economics of Transaction Costs. Edward Elgar, Cheltenham. Winship, C., Radbill, L., 1994. Sampling weights and regression analysis. Sociol. Meth. Res. 23, 230–257. Wooldridge, J.M., 2006. Introductory Econometrics: a Modern Approach. Thomson, Mason, OH.