Crafting Intellectual Property Rights: Implications for Patent Assertion Entities, Litigation, and Innovation

Feng, Josh; Jaravel, Xavier

doi:10.1257/app.20180361

Cited by 36 publications

(34 citation statements)

References 50 publications

(75 reference statements)

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“…Recent examples of examiner designs includeChetty et al (2011),Maestas et al (2013),Doyle et al (2015),Feng and Jaravel (2017), andDobbie et al (2018). Notably,Kolesar et al (2015) study theChetty et al (2011) design under the assumption that examiner groups are invalid instruments for treatment, leveraging an orthogonality condition similar to ours.…”

mentioning

confidence: 99%

Quasi-Experimental Shift-Share Research Designs

Borusyak

Hull

Jaravel

2018

Self Cite

282

473

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Many empirical studies leverage shift-share (or "Bartik") instruments that average a set of observed shocks with shock exposure weights. We derive a necessary and sufficient shock-level orthogonality condition for these instruments to identify causal effects. We then show that orthogonality holds when observed shocks are as-good-as-randomly assigned and growing in number, with the average shock exposure sufficiently dispersed. Quasiexperimental shift-share designs may be implemented with new shock-level procedures, which help visualize the identifying variation, correct standard errors, choose appropriate specifications, test identifying assumptions, and optimally combine multiple sets of quasi-random shocks. We illustrate these ideas by revisiting Autor et al. (2013)'s analysis of the labor market effects of Chinese import competition.

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mentioning

confidence: 99%

Quasi-Experimental Shift-Share Research Designs

Borusyak

Hull

Jaravel

2018

Self Cite

282

473

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“…More specifically, to the best of my knowledge there is no information on the patent attorney or agent and, before 1965, no information on the patent examiner associated with each patent for most years; there is no equivalent to the USPTO's Patent Application Information Retrieval (PAIR) system for historical patents 42. This makes analysis that exploits assignment of patents to examiners, as in Sampat and Williams (2019), Gaulé (2018), Righi and Simcoe (2019), or Feng and Jaravel (2020), infeasible with historical patent data.…”

Section: What Historical Patents Don't Havementioning

confidence: 99%

Historical patent data: A practitioner's guide

Andrews

2021

Economics Manag Strategy

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I provide a primer on six recent large‐scale historical patent data sets for use in innovation research. I discuss how each data set is constructed, the types of patent information included in each, and the quality and completeness of each. Throughout, I emphasize when our knowledge of the history of invention is dependent on the data source used and provide recommendations about which data set is most likely to be best for different contexts. Overall, these data sets paint a remarkably consistent picture of the history of U.S. invention. When the data sets do disagree, these differences tend to be minor, although I highlight some important exceptions. I further describe several “niche” historical patent data sets that allow researchers to study institutional contexts that cannot be studied using modern data. Finally, I discuss features of patent data that are not available for the historical patents but are available for modern patents.

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“…What becomes clear through this analysis is the potential pitfalls of composite patent quality measures. The obvious exemplar here is the relationship between International and KPSS value, aspects of both of which can be related to the financial value of a patent (Harhoff et al, 2003;Fischer and Leidinger, 2014;Kogan et al, 2017) and, as such, have been used directly as proxies for this value (Yang et al, 2015;Poege et al, 2019;Feng and Jaravel, 2020). Due to the generally negative relationship between these two metrics shown in Figure 5, 16 a composite measure where both were included could be empirically problematic.…”

Section: Dominance Analysismentioning

confidence: 99%

Patent Quality: Towards a Systematic Framework for Analysis and Measurement

Higham¹,

Rassenfosse²,

Jaffe³

2020

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The 'quality' of novel technological innovations is extremely variable, and the ability to measure innovation quality is essential to sensible, evidence-based policy. Patents, an often vital precursor to a commercialised innovation, share this heterogeneous quality distribution. A pertinent question then arises: How should we define and measure patent quality? Accepting that different stakeholders have different views of this concept, we take a multi-dimensional view of patent quality in this work. We first test the consistency of popular post-grant outcomes that are often used as patent quality measures. Finding these measures to be generally inconsistent, we then use a raft of patent indicators that are defined at the time of grant to dissect the characteristics associated with different post-grant outcomes. We find broad disagreement in the relative importance of individual characteristics between outcomes and, further, significant variation of the same across technologies within outcomes. We conclude that measurement of patent quality is highly sensitive to both stakeholder viewpoint and technology type. Our findings bear implications for scholarly research using patent data as well as for policy discussions about patent quality.

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Crafting Intellectual Property Rights: Implications for Patent Assertion Entities, Litigation, and Innovation

Cited by 36 publications

References 50 publications

Quasi-Experimental Shift-Share Research Designs

Quasi-Experimental Shift-Share Research Designs

Historical patent data: A practitioner's guide

Patent Quality: Towards a Systematic Framework for Analysis and Measurement

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