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From Imitation to Innovation: Where is all that Chinese R&D going?

  • Series
  • Speaker(s)
    Fabrizio Zilibotti (Yale University, United States)
  • Field
  • Location
    University of Amsterdam, Roetersstraat 11, Room B3.09
  • Date and time

    February 14, 2020
    12:00 - 13:15

We construct a dynamic model where firms are heterogenous in productivity and are subject to distortions. The productivity distribution evolves endogenously as the result of the decisions of individual firms that seek to upgrade over time their productivity. Firms can adopt two strategyto improve their productivity: imitation and innovation. The theory bears predictions about the behavior of firms and the aggregate equilibrium. We perform the structural estimation of the stationary state of the dynamic model using a Simulated Method of Moments approach which targets moments of the empirical distribution of R&D and productivity growth. We estimate the model using data from China in the period 2007-2012. The estimation highlights some interesting findings. The model predictions align well with the data, and the estimated model also yields a good quantitative fit to the data. In particular, the model produces good quantitative predictions about the growth rate of TFP and the productivity distribution across Chinese firms. We also compare the estimates with those obtained using data for Taiwan. There are some important differences between Taiwan and mainland China, where R&D investments appear to be less productive. On the one hand, the evidence is consistent with a significant extent of distortions and, possibly, overreporting of R&D in China. On the other hand, the speed of technology diffusion is similar across the two economies. We performs counterfactuals to study the effect of alternative policies. Joint with Michael König, Kjetil Storesletten and Zheng Song.
JEL Codes: O31, O33, O47
Keywords: China, Imitation, Innovation, Misallocation, Moral Hazard, Productivity, R&D, Subsidies, Taiwan, Traveling Wave.

Please view full paper here.