LES High Tech Deal Term and Royalty Surveys: Descriptive Statistics and Analysis of the Financial Terms (2008 to 2017)
Bibliographic record
Abstract
The 2017 Licensing Executives Society (USA Canada) (LES USA Canada) High Tech Deal Term and Royalty Survey was launched on July 28, 2017 and closed on September 30, 2017. The Survey received 155 complete deals from 70 companies and organizations. Since the inaugural Survey in 2011, LES High Tech Royalty Surveys have collected a total of 477 samples. This paper presents the descriptive statistics and analysis of the financial terms stipulated in the samples from the 2017 Survey and from the combined samples of all three surveys. Several different payment methods have been adopted by the collected transactions, including flat and tiered percentage rates, flat and tiered unit rate, upfront lump sum payment, and milestone payments, among others. Our analysis focuses mainly on flat running royalty rates as percentages of sales and lump sum payments, which together account for more than two thirds of the combined samples. The 2017 Survey reports an average royalty rate of 5.69% and a median rate of 5%. Over the 10-year period from 2008 to 2017, the average rate is calculated to be 5.73%, and the median rate, 5%. Average and median rates are tabulated for major features and characteristics of the reported deals, including the organization type and size of licensors/licensees, technology features such as technology field and development stage, field of use, type of IP, and exclusivity, among various others. Similar analysis is conducted and presented for the deals with lump sum payments. Our analysis also sheds light on certain specific issues arising from the surveys. As an example, to explain the seemingly counterintuitive royalty rate behavior across exclusive and non-exclusive deals, we take a deeper dive into the data and calculate average royalty rates by exclusivity and by licensor organization types, which offers a tentative explanation to the initially puzzling rate pattern.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".