Abstract
Firms in the agricultural biotech and seed sectors have increased their R&D spending exponentially over the last three decades. The number of patents secured by major integrated biotechnology and seed firms also increased exponentially over this period. We find no evidence of strategic patenting to explain the increase in volume; the increased number of granted patents, therefore, most likely indicates accelerating product innovation in the industry. Technology transfer among private firms in this sector has been increasing as well, as reflected in a large number of licensing and cross-licensing agreements for the commercialization of patented biotech traits and seed germplasm across different suppliers. New product introductions and variety (new biotech traits and hybrids) increased significantly over the last two decades, while the average product life cycle of hybrid seeds declined. All these indicators point to accelerating product innovation and augmented product choices in this market segment.
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Notes
- 1.
This structure is characteristic of the corn as well as the soybean seed industries. The US rice, cotton, and canola seed markets are generally smaller in size and have fewer firms.
- 2.
We constructed this series of R&D expenditures through information and data we collected from financial reports of publicly traded companies, financial analyst reports, consulting reports, trade journals, and information provided directly by individual firms. Because our data on licensing agreements in the biotech and seed industries is incomplete from 2013 on, we use R&D investment and other data until 2012, for consistency.
- 3.
Phillips McDougall (2011) also found that in recent years, large integrated biotech/seed firms have been able to increase manyfold the number of genetic constructs they evaluate at their early R&D stages while cutting the time required to do so by almost 20%. These productivity improvements are likely reflective of the same type of efficiency gains in research brought about improvements in sequencing and other enabling technologies.
- 4.
There are more than 100 firms in our focal set, and these firms have been the primary locus of our R&D in the agricultural biotechnology and seed industries for the period of analysis. As such, patent acquisition for this set of firms paints a fairly complete industry-wide picture.
- 5.
Patent families include both patents that protect the same invention across different jurisdictions and patents in the same jurisdiction that cover different parts of the same invention.
- 6.
Agricultural biotech traits were first introduced in 1996, so the data set we use for our analysis provides an almost complete picture of the commercial use of the technology.
- 7.
When this domino effect occurs in industries where the technologies offered by the different suppliers are similar, the value of the industry will typically move downward since technology suppliers cannot act strategically upon the technology they possess (Dierickx and Cool 1989; Arora and Gambardella 2010; Gambardella and McGahan 2010). Such distributional effects could continue to encourage biotech firms to vertically integrate through the ownership of seed assets and could encourage entry of new firms into the seed industry.
- 8.
Monsanto’s market share of proprietary seeds was initially limited. It has increased over the years through acquisitions and organic growth.
- 9.
The data is collected through annual surveys of corn producers in the USA. There are almost 250,000 farmer responses about annual purchases of seed corn for the period of interest.
- 10.
Accelerated failure time models are one of the two main types of models used for survival analysis, the branch of statistics dealing with the duration of an event; the other being proportional hazard models. The proportional hazard model is simpler to specify because it is nonparametric model, while a distribution needs to be chosen in the case of the accelerated failure time model. However, the results of accelerated failure time models are often easier to interpret because partial effects represent expected change in duration, while proportional hazard models produce hazard ratios whose partial effects are relative and therefore more difficult to translate into expected life time. Overall, the two types of models produce very similar results.
The accelerated failure time model we use takes the form ln(T) = X β + σ ε, where β represents the set of parameters to be estimated, X is a vector of covariates, σ is scale parameter, and ε is a random disturbance term which is normally distributed. The explanatory variables include the average acreage across the lifetime of hybrid, a categorical variable to account for the size of the seed firm marketing the hybrid, trait-specific dummy variables (e.g., insect resistant, herbicide tolerant), and a set of variables which indicate the first year of commercialization of the hybrid. All parameter estimates except a few of the year of introduction dummies were statically significant at the 99% level, and all estimates had the expected sign. While we do not report the statistical results here to keep the manuscript at a manageable size, the results are available from the authors.
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Kalaitzandonakes, N., Magnier, A., Kolympiris, C. (2018). Innovation and Technology Transfer Among Firms in the Agricultural Input Sector. In: Kalaitzandonakes, N., Carayannis, E., Grigoroudis, E., Rozakis, S. (eds) From Agriscience to Agribusiness. Innovation, Technology, and Knowledge Management. Springer, Cham. https://doi.org/10.1007/978-3-319-67958-7_9
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