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«Tobias Schmidt (Deutsche Bundesbank) Wolfgang Sofka (Department of Organisation and Strategy, CentER, CIR, Tilburg University) Discussion Paper ...»

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We estimate several additional empirical specifications to demonstrate the robustness of our findings. shows the results. First, we investigate potential effects from the model choice. We replace the Tobit specification with an ordinary least squares regression in Model 4. The core findings on the effects of main bank market share and sector specialization remain intact. All other consistency check models are again based on the original Tobit model for comparison with the main model. Secondly, we follow up on the results of the control variables regarding significant differences for savings and cooperative banks. We have dedicated controls for bank size and geographic scope in every model. However, banks may also differ in their structure. Especially large private banks with a national branch system can benefit from accessing the knowledge pool/risk diversification of the group as a whole. We recalculate all portfolio variables at the group level and estimate separate models for savings and cooperative banks (Model 5) as well as private and national banks (Model 6). Our core findings remain stable in both groups indicating that the effects are not dependent on assumptions about the aggregation level of the banks or limited to certain bank types. Thirdly, our sample split in Table 3 shows that the impact of the positive effects originating from information externalities on firm R&D investment are limited to sectors in which science is an important source of innovation. In an alternative operationalization, we capture the importance of science as a source of innovation based on the OECD classification for the R&D intensity of certain sectors (Appendix A). Within this framework, sectors are classified based on the ratio of firms that perform R&D continuously. Continuous R&D activities imply that the value of knowledge production is long term and accumulative in certain sectors but rather transitory in others. We assume that the sectors with accumulative knowledge production are closer to the scientific knowledge production than other sectors and test it by splitting the sample based on the OECD classification. Model 7 shows the estimation results for low technology industries encompassing low-tech manufacturing as well as low knowledge-intensive services. Model 8 presents the equivalent estimation results for hightech industries, which include medium- as well as high-tech manufacturing and knowledgeintensive services. The results support our main finding that the described effects in hypotheses 1 and 2 are largely confined to high-tech sectors. We can no longer identify the significant negative effect of main bank industry specialization on firm R&D outside of science-based industries (Table 3) when compared with the low-tech industry classification in Model 8. Finally, our theoretical argument is strictly comparative in nature and combinations of firms and their main bank are assumed as given. This is largely in line with our descriptive statistics indicating that 85% of the firms in our sample never switch their main bank during the six year observation period. Nevertheless, firms and banks have made choices about this relationship at a certain point in time but this selection is unobservable to us. In order to test, whether the bank choice is correlated with the error term and may therefore bias the results, we estimate an additional instrument variable tobit model. The only bank choice variable available to us is whether the firm has changed its main bank in the preceding three years. We use this variable as the dependent variable in the first stage equation of the instrument variable estimation (Model 9). We rely on two regional variables (district level) as instruments. We argue that the economic development of a region provides firms as well as banks with more options. Hence, we use district GDP and then number of bank branches within a district (in logs) as instruments. The instruments are jointly significant at the 98% significance level; the number of bank branches is also individually significant. We conduct an additional Sargan test on potential overidentification which is rejected. Model 10 therefore contains the Tobit estimation results with the bank change being instrumented. Estimation results on our main hypothesis tests remain stable. In sum, we are confident that our empirical analyses provide reliable tests of our theoretical framework.

7 Conclusions and future research

In this study we question the general assumption that all banks suffer to the same degree from information deficits or asymmetries when providing funds for R&D intensive clients.

We concede that banks cannot produce the necessary technological information themselves.

They are, however, uniquely positioned to benefit from the information production of the firms in their client portfolio. Hence, they can benefit from an information externality which becomes important if the information is relevant to the technological and market environment of its clients, i.e. the bank has a large market share in the client’s industry. We compare and contrast this effect with portfolio considerations and predict a negative relationship between a firm’s R&D investment and the industry specialization of its main bank. These theoretical predictions are supported by our empirical test for Germany based on a dataset that is, to our knowledge, unique in terms of variable coverage, representativeness, and comprehensiveness.

The dataset also allows us to test whether the effects are equally pronounced in all industries.

We find that the positive effect of information externalities is confined to high-tech industries in which academic research is a major source of knowledge. We have argued theoretically that this is due to increased uncertainties if innovation activities are further removed from commercial application. With regard to the signaling that the firm can provide itself about the quality of its R&D, we find that only patents provide valuable signals to banks, and not government R&D subsidies or venture capital investments. These findings have major implications for academic research as well as for practical management and policy development.

From an academic perspective, our contribution consists in connecting research in finance and strategic management. On the finance side, there has been angrowing interest recently in the financing of firm innovation and growth (Herrera & Minetti, 2007; Benfratello et al., 2008), in which findings from current strategy literature have largely been absent.

Conversely, strategic management literature has been heavily focused on venture capital investments (Levitas & McFadyen, 2009) for firm innovation while largely ignoring the fact that these selective investments are dwarfed by the importance of bank financing for the vast majority of firms in most modern economies (Phelps & Tilman, 2010). We see our theoretical combination of information externality and portfolio theory as a valuable extension of both research streams because it provides a novel, differentiated perspective on the largely acknowledged root cause of information availabilities and asymmetries. What is more, we identify both opportunities for and boundaries of firm signaling to important external partners such as banks. Especially the boundary conditions of ineffective signals are, to our knowledge, largely unexplored in the literature (e.g. Levitas & McFadyen, 2009).

Our theoretical and empirical findings are strictly comparative in nature, and interpretations of opportunities for active search and selection (either through banks or firms) have to be made carefully. We find clear evidence that a firm’s main bank makes a significant difference in the availability of funds for R&D investment. Hence, it follows only logically that firms should be better off working with highly diversified banks. The positive effects of working with leading banks in a firm’s industry are limited to sectors in which there are great underlying uncertainties. Interestingly, risk considerations stemming from specialization in a bank’s industry portfolio are a less pressing issue for firms with haveopportunities to signal the value of their R&D activities through patents. Venture capital investments or government R&D subsidies are, in themselves, valuable for firms but provide no further signaling effect to a firm’s main bank.

On the policy making side, our results cast doubts on a general call for banks specializing in financing innovation. Banks with a broad industry portfolio are equally valuable because they can manage risks through diversification, especially for firms operating in more stable technological environments. What is more, we find that government R&D subsidies have value for firm R&D investment itself, but their effect as a signaling tool to banks should not be overestimated. A similar logic applies to the potential signaling of venture capital investors.

In sum, our research provides a novel perspective on the relationship between banks, their information availabilities and the R&D investment of their firms. This provides fruitful avenues for future research. First, we develop and test strictly comparative arguments. This is partly due to the fact that the vast majority of the firms in our sample (85%) never change their main bank. However, more detailed insights into how firms and banks select these relationships would be an important addition to our current model both theoretically and empirically. Secondly, we are able to investigate our research question empirically for a large economy with a well-established, diverse financial system. However, European economies have been described as being especially reliant on bank financing. Comparative studies in Anglo-American settings could provide valuable insights into the international generalizability of our findings. Thirdly, we have focused on the firm’s R&D input side.

Banks may influence not just overall R&D investment but also the nature of firm R&D as well as the outcomes. We suspect that this particular research question lends itself more to qualitative research but we are confident that our comprehensive, quantitative analyses provides a reliable basis for it.

–  –  –

Aboody, D. & Lev, B. 2000. Information Asymmetry, R&D, and Insider Gains. The Journal of Finance, 55 (6): 2747-2766.

Aerts, K. & Schmidt, T. 2008. Two for the Price of One? On Additionality Effects of R&D

Subsidies: A Comparison between Flanders and Germany. Research Policy, 37 (5):


Aghion, P., Fally, T. & Scarpetta, S. 2007. Credit Constraints as a Barrier to the Entry and Post-Entry Growth of Firms. Economic Policy, 22 (52): 731-779.

Aghion, P., Harris, C., Howitt, P. & Vickers, J. (2001), Competition, Imitation and Growth with Step-by-Step Innovation.

Agrawal, A. 2006. Engaging the Inventor: Exploring Licensing Strategies for University Inventions and the Role of Latent Knowledge. Strategic Management Journal, 27 (1): 63-79.

Ahuja, G., Coff, R.W. & Lee, P.M. 2005. Managerial Foresight and Attempted Rent Appropriation: Insider Trading on Knowledge of Imminent Breakthroughs. Strategic Management Journal, 26 (9): 791-808.

Ahuja, G., Lampert, C.M. & Tandon, V. 2008. Moving Beyond Schumpeter: Management Research on the Determinants of Technological Innovation. The Academy of Management Annals, 2: 1 - 98.

Amit, R., Glosten, L. & Muller, E. 1990. Entrepreneurial Ability, Venture Investments, and Risk Sharing. Management Science, 36 (10): 1232-1245.

Audretsch, D.B. & Lehmann, E.E. 2004. Financing High-Tech Growth: The Role of Banks and Venture Capitalists. Schmalenbach Business Review, 56: 340-357.

Bank for International Settlement 2005. International Convergence of Capital Measurement and Capital Standards: A Revised Framework. Basel Committee on Banking Supervision: http://www.bis.org/publ/bcbs107.htm.

Baum, J.A.C. & Oliver, C. 1991. Institutional Linkages and Organizational Mortality.

Administrative Science Quarterly, 36 (2): 187-218.

Benfratello, L., Schiantarelli, F. & Sembenelli, A. 2008. Banks and Innovation:

Microeconometric Evidence on Italian Firms. Journal of Financial Economics, 90 (2): 197-217.

Berry, L.L., Shankar, V., Parish, J.T., Cadwallader, S. & Dotzel, T. 2006. Creating New Markets through Service Innovation. MIT Sloan Management Review, 47 (2): 56-63.

Bond, S.R., Harhoff, D. & van Reenen, J. (1999), Investment, R&D and Financial Constraints in Britain and Germany.

Boot, A.W.A. 2000. Relationship Banking: What Do We Know? Journal of financial intermediation, 9 (1): 7-25.

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