nep-ino New Economics Papers
on Innovation
Issue of 2020‒10‒26
six papers chosen by
Uwe Cantner
University of Jena

  1. Linguistic Metrics for Patent Disclosure: Evidence from University versus Corporate Patents By Nancy Kong; Uwe Dulleck; Adam Jaffe; Shupeng Sun; Sowmya Vajjala
  2. Entrepreunial Orientation : Open Innovation Concept and Risk Management Measurement By mosse, Michelle veren
  3. In knowledge we trust: learning-by-interacting and the productivity of inventors By Matteo Tubiana; Ernest Miguelez; Rosina Moreno
  4. Uncertainty, Intangible Capital, and Productivity Dynamics By Edoardo Palombo
  5. Organizing Crisis Innovation: Lessons from World War II By Daniel P. Gross; Bhaven N. Sampat
  6. Medical Research and Health Care Finance: Evidence from Academic Medical Centers By Pierre Azoulay; Misty L. Heggeness; Jennifer L. Kao

  1. By: Nancy Kong; Uwe Dulleck; Adam Jaffe; Shupeng Sun; Sowmya Vajjala
    Abstract: Encouraging inventors to disclose new inventions is an important economic justification for the patent system, yet the technical information contained in patent applications is often inadequate and unclear. This paper proposes a novel approach to measure disclosure in patent applications using algorithms from computation allinguistics. Borrowing methods from the literature on second language acquisition, we analyze core linguistic features of 40,949 U.S. applications in three patent categories related to nanotechnology, batteries, and electricity from 2000 to 2019. Relying on the expectation that universities have more incentives to disclose their inventions than corporations for either incentive reasons or for different source documents that patent attorneys can draw on, we confirm the relevance and usefulness of the linguistic measures by showing that university patents are more readable. Combining the multiple measures using principal component analysis, we find that the gap in disclosure is 0.4 SD, with a wider gap between top applicants. Our results do not change after accounting for the heterogeneity of inventions by controlling for cited-patent fixed effects. We also explore whether one pathway by which corporate patents become less readable is use of multiple examples to mask the “best mode” of inventions. By confirming that computational linguistic measures are useful indicators of readability of patents, we suggest that the disclosure function of patents can be explored empirically in a way that has not previously been feasible.
    Keywords: patent disclosure, computational linguistic analysis, readability
    JEL: K11 O31 O34
    Date: 2020
  2. By: mosse, Michelle veren
    Abstract: Entrepreneurial Orientation is an orientation part of entrepreneurship which consists of a process of making strategies and policies that form the basis of entrepreneurship (Rauch, Wiklund, Lumpkin & Frese). Entrepreneurial Orientation is an important thing as a basis for strength in an organization to improve the course of the entrepreneurial process and can lead to a pattern of business behavior that is applied if you want to maintain a business. There are 5 dimensions of entrepreneurial orientation, namely Innovation, fgProactiveness, Risktaking (Miller, 1983), Autonomy Orientation and Competitive Aggressiveness according to (Lumpkin & Dess, 1996). A company is said to be able to apply an entrepreneurial orientation if the company has characteristics such as first in market product innovation, has the courage to take risks, is proactive in making innovations.
    Date: 2020–10–01
  3. By: Matteo Tubiana; Ernest Miguelez; Rosina Moreno
    Abstract: Innovation rarely happens through the actions of a single person. Innovators source their ideas while interacting with their peers, at different levels and with different intensities. In this paper, we exploit a dataset of disambiguated inventors in European cities to assess the influence of their interactions with co-workers, organizations’ colleagues, and geographically co-located peers, to understand if the different levels of interaction influence their productivity. Following inventors’ productivity over time and adding a large number of fixed effects to control for unobserved heterogeneity, we uncover critical facts, such as the importance of city knowledge stocks for inventors’ productivity, with firm knowledge stocks and network knowledge stocks being of smaller importance. However, when the complexity and quality of knowledge is accounted for, the picture changes upside down, and closer interactions (individuals’ coworkers and firms’ colleagues) become way more important.
    Keywords: inventors; productivity; stock of knowledge; interactions
    JEL: O18 O31 O33 O52 R12
    Date: 2020
  4. By: Edoardo Palombo (Queen Mary University of London)
    Abstract: Following an unparalleled rise in uncertainty over the Great Recession, the US economy has been experiencing anaemic productivity growth. This paper offers a quantitative study on the link between uncertainty and low productivity growth. Firstly, using micro level data I show that uncertainty accounts for half of the drop in intangible capital stock during the Great Recession. Secondly, to investigate the effect of uncertainty on productivity growth dynamics, I present a novel general equilibrium endogenous growth model with heterogeneous firms that undertake intangible capital investment subject to non-convex costs and time-varying uncertainty. I show that uncertainty can generate slow recoveries and a persistent slowdown in productivity growth when accounting for the empirical discrepancy between the realised and expected changes to the second-moment of fundamentals.
    Keywords: Uncertainty, R&D, Innovation, Productivity, Great Recession, Intangible Capital, Slow Recoveries
    JEL: O40 O41 O51
    Date: 2020–06–25
  5. By: Daniel P. Gross; Bhaven N. Sampat
    Abstract: World War II was one of the most acute emergencies in U.S. history, and the first where the mobilization of science and technology was a major part of the government response. The U.S. Office of Scientific Research and Development (OSRD) led a major research effort to develop technologies and medical treatments that not only helped win the war, but also transformed civilian life, while laying the foundation for postwar science policy. Scholars and policymakers often make broad appeals to the World War II innovation model in other crises. In this essay we describe exactly how it worked. We do so first through a general overview of how OSRD approached several questions that may confront any crisis innovation effort: priority setting, selecting and engaging researchers, a funding mechanism, coordinating research efforts, and translation to practice. Next we present case studies of the radar, atomic fission, penicillin, and malaria research programs, illustrating how the principles applied in specific contexts, but also heterogeneity. We conclude by discussing lessons from OSRD, such as what makes crisis innovation policy different, how crisis innovation policy approaches may vary, and the limits to generalizing from World War II for contemporary crises.
    JEL: H12 H56 N42 N72 O31 O32 O38
    Date: 2020–10
  6. By: Pierre Azoulay; Misty L. Heggeness; Jennifer L. Kao
    Abstract: Academic Medical Centers (AMCs)—comprising medical schools, teaching hospitals, and research laboratories)—play an important role in US biomedical innovation. The Balanced Budget Act of 1997 (BBA) changed the formula used to reimburse Medicare inpatient claims and subsidies for medical residents. We study the effect of changes in the generosity of clinical care reimbursements on the rate and direction of research performed within these institutions. We compare AMCs’ relative exposure to the reform and how these differences affect their researchers’ ability to attract NIH grant funding, as well as the quantity, impact, and content of their publications. We find that in response to the BBA, research activity increased by 10% among the average teaching hospital and 20% among major teaching hospitals, with larger effects observed for “translational” and clinical research. We find little evidence of concurrent changes in clinical outcomes.
    JEL: I13 I18 I23 O30
    Date: 2020–10

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