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Home | Courses | Data Driven Innovation Strategy (course cancelled for 2022-23)

Data Driven Innovation Strategy (course cancelled for 2022-23)

  • Teacher(s)
    Eric Bartelsman, Michael König
  • Research field
    Management Science
  • Dates
    Period 2 - Oct 24, 2022 to Dec 16, 2022
  • Course type
  • Program year
  • Credits

Course description

Innovation is the engine of growth for firms and economies. Many competing theories have been put forward to guide decisions on scale, scope and timing of innovative investment as well as on strategies for research collaboration. In this class, we will present some of these models but also present the type of evidence that can be used to guide decisions and the methods for analyzing these data.

The available data include from firm-level data on start-ups, innovative investment and growth, inventor-level data on inventor-firm linkages, patent and patent-citation data, firm-to-firm transaction data, data on adoption and diffusion of innovative products and services, and surveys on research collaborations. The course will follow the emerging literature in this area as it uses recent quantitative techniques to answer questions relevant to decision makers.

The course emphasizes techniques for exploring causality in the context of applied problems, but it also connects these tools with concepts from econometrics and randomized trials, including: instrumental variables, propensity scores, differences-in-differences, regression discontinuity, stratification, direct and indirect effects, confounding, and selection. The course provides an exploration of quantitative research methods as used in recent academic literature on innovation strategy.

This course is open for external participants. Research Master and PhD students register here; others register here.

Course literature

The following selected list of readings are mandatory. The full reading list and any changes will be communicated on Canvas.

  • Akcigit, Ufuk, and William R. Kerr. “Growth through Heterogeneous Innovations.” Journal of Political Economy 126, no. 4 (March 20, 2018): 1374–1443. https://doi.org/10.1086/697901.
  • Bartelsman, Eric, George van Leeuwen, and Michael Polder. “CDM Using a Cross-Country Micro Moments Database.” Economics of Innovation and New Technology 26, no. 1--2 (2017): 168--182. https://doi.org/10.1080/10438599.2016.1202517.
  • Crepon, Bruno, Emmanuel Duguet, and Jacques Mairesse. “Research, Innovation And Productivity: An Econometric Analysis At The Firm Level.” Economics of Innovation and New Technology 7, no. 2 (1998): 115–58. https://doi.org/10.1080/10438599800000031.
  • Gentzkow, Matthew, Bryan Kelly, and Matt Taddy. “Text as Data.” Journal of Economic Literature 57, no. 3 (September 2019): 535–74. https://doi.org/10.1257/jel.20181020.
  • Gupta, Somit, Ronny Kohavi, Alex Deng, Jeff Omhover, and Pawel Janowski. “A/B Testing at Scale: Accelerating Software Innovation.” In Companion Proceedings of The 2019 World Wide Web Conference, 1299–1300. WWW ’19. New York, NY, USA: Association for Computing Machinery, 2019. https://doi.org/10.1145/3308560.3320093.
  • Hsieh, Chih-Sheng, Michael D. König, and Xiaodong Liu. “A Structural Model for the Coevolution of Networks and Behavior.” The Review of Economics and Statistics, August 10, 2020, 1–41. https://doi.org/10.1162/rest_a_00958.
  • Hsieh, Chih-Sheng, Michael König, and Xiaodong Liu. “Network Formation with Local Complements and Global Substitutes: The Case of R&D Networks.” SSRN Scholarly Paper. Rochester, NY: Social Science Research Network, February 13, 2017. https://doi.org/10.2139/ssrn.2917560.
  • Klette, Tor Jakob, and Samuel Kortum. “Innovating Firms and Aggregate Innovation.” Journal of Political Economy 112, no. 5 (2004): 986–1018.
  • König, Michael D., Xiaodong Liu, and Yves Zenou. “R&D Networks: Theory, Empirics, and Policy Implications.” The Review of Economics and Statistics 101, no. 3 (July 16, 2018): 476–91. https://doi.org/10.1162/rest_a_00762.
  • Peters, Bettina, Mark J. Roberts, Van Anh Vuong, and Helmut Fryges. “Estimating Dynamic R&D Choice: An Analysis of Costs and Long-Run Benefits.” The RAND Journal of Economics 48, no. 2 (n.d.): 409–37. https://doi.org/10.1111/1756-2171.12181.
  • Pisano, Gary P. “It’s the Only Way to Make Sound Trade-off Decisions and Choose the Right Practices.,” 2016, 12.
  • Trajtenberg, Manuel, Gil Shiff, and Ran Melamed. “The ‘Names Game’: Harnessing Inventors’ Patent Data for Economic Research.” National Bureau of Economic Research, September 8, 2006. https://doi.org/10.3386/w12479.