Abstract
We empirically analyze whether support by the parent organization in the early (nascent and seed) stage speeds up the process of commercialization and helps spin-offs from public research organizations generate first revenues sooner. To identify the impact of support by the parent organization, we apply multivariate regression techniques as well as an instrumental variable approach. Our results show that support in the early stage by the parent organization can speed up commercialization. Moreover, we identify two distinct channels—the help in developing a business plan and in acquiring external capital—through which support by the parent organization can enable spin-offs to generate first revenues sooner.
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Notes
For examples of support measures for entrepreneurs at universities around the world see—among many others—Shane (2004), Wright et al. (2007), Astebro and Bazzazian (2011), O’Shea et al. (2004), Lockett and Wright (2005), Phan and Siegel (2006), O’Kane et al. (2015), Hayter and Link (2015), Wright et al. (2006), Moray and Clarysse (2005), Walter et al. (2006).
Max Planck Innovation, the technology transfer office of the Max Planck Society closes on average 150 license agreements a year, about half of them with companies abroad. The nature and extent of the licenses are as varied as the payment modalities. They range from upfront payments and sales-based royalties to additional milestone payments, e.g. in the case of drug development. Furthermore, according to self-reported records, Max Planck Innovation currently oversees more than 1440 inventions and has shareholdings in 16 companies. Since 1979 Max Planck Innovation managed about 3600 inventions and has closed more than 2300 license agreements. The total revenues from Max Planck inventions currently amount to about 280 million Euro.
A license based spin-offs is subject to a license, know-how or option agreement with Max Planck Innovation.
We are aware that asking the scientists about the time of expected first revenues might introduce some imprecisions. However, targeting actual entrepreneurs from the Max Planck Society who have already started and generated first revenues comes typically at high costs of losing many of them, resulting in smaller number of observations. In Sect. 3.1 we provide evidence that our approach unlikely leads to a large and systematic measurement error. In Sect. 3.3 we apply instrumental variable approach that helps in case of selection bias and measurement error in the dependent variable.
We do not believe that the entrepreneurship rate we report in this study is downward biased because scientists conceal their entrepreneurial intentions/activities. First, at the beginning of the interviews, scientists were ensured that the survey is only for research purpose. Second, scientists at universities and PROs in Germany are legally allowed to run own businesses but also legally obligated to report such activities to their employers. Third, Max Planck Society actively supports those scientists who want to become entrepreneurs, however, scientists are absolutely free to ask (or not) for support.
In Sect. 3.4 we will relate the availability of a written business plan and external capital to support of MPI in order to analyze whether the support by the parent institution impacts on the speed of commercialization through these two particular channels.
An IV approach can also help in cases where the dependent variable is measured with error (Angrist and Krueger 1991; Ashenfelter and Krueger 1994; Bound et al. 1994), as in our case, where the time to first revenues is guessed by the respondents. Random measurement error in the dependent variable does not bias the slope coefficient; it does lead to larger standard errors. Assuming this scenario (likelier than systematic measurement error, if at all), it is encouraging that we find statistically significant coefficients for the variable of main interest. However, even when the measurement error in the dependent variable is correlated with the true dependent variable or with the RHS variables, we can still get consistent estimates by using instrumental variables as long as the instrument(s) are only correlated with the true RHS variables but not with the measurement error. That is, the instruments most likely to be helpful in this case are the types of instruments we would be using anyway for other reasons (say to cure selection bias).
There is no reason to believe that our instrument captures the effect possible positive/negative externalities of being located near/far from the prosperous Munich which might influence scientists’ expectations. Although prosperous, Munich does not provide the best possible conditions for scientists and firms in each and every field, because—at regional level—entrepreneurship requires a rather complex and multidimensional ecosystem. For instance, the Max Planck Institute for Marine Microbiology is located in Bremen at the North Sea. Similarly, the institutes focused on medical research—the Max Planck Institute for Biophysical Chemistry, the Max Planck Institute for Experimental Medicine and the Max Planck Institute for Medical Research—are located in Goettingen and Heidelberg that host universities with medical departments among the strongest in Germany. Moreover, though financial institutions—particularly VCs—are concentrated in few major centers, one of which is Munich, there is no evidence whatsoever for a spatial financial gap (Fritsch and Schilder 2008, 2012). Not least, remaining confounding effects should be captured by the local GDP per capita included to account for regional heterogeneity in the conditions for entrepreneurship (Fritsch and Falck 2007).
The average marginal effect of the travel time is ca. −0.002 suggesting that 50 min travel time reduces the likelihood of reporting support by Max Planck Innovation by 10 %.
As there is no straightforward way to account for the fact that the values of MPI support in the second column are the predicted values from the logit regression in the first column (standard errors from the logit regression are not normal as in the linear model), the estimates should be interpreted with caution. However, given the kind of data we have—binary variable for support by the TTO and interval data for the time of commercialization—we prefer this approach over a 2SLS since the precision of estimation of a linear model on such data depends crucially on the number of observations.
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Slavtchev, V., Göktepe-Hultén, D. Support for public research spin-offs by the parent organizations and the speed of commercialization. J Technol Transf 41, 1507–1525 (2016). https://doi.org/10.1007/s10961-015-9443-6
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DOI: https://doi.org/10.1007/s10961-015-9443-6