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Bringing Data to the Model: Firm-to-Firm Learning in a Structural Model

Author

Listed:
  • Wyatt Brooks

    (University of Notre Dame)

  • Kevin Donovan

    (Yale University)

  • Terence Johnson

    (University of Notre Dame)

Abstract

We consider a general equilibrium model in which knowledge diffusion generates positive spillovers in the economy. With some probability, firms receive a random draw from the existing distribution of firm types, some portion of which is diffused to the firm's new productivity. The parameters governing the diffusion process -- the likelihood of a draw and the extent to which higher productivity is internalized -- are critical for the magnitude of these benefits. We prove that they can be identified with exogenous and random variation in the likelihood and productivity of matches. We then estimate these parameters using the results of a randomized controlled trial among Kenyan firms, in which firm owners from the left tail of the profit distribution are randomly matched one-to-one with owners from the right tail of the profit distribution. Our quantitative results imply an important role for knowledge diffusion. Removing a labor market distortion increases real income by 63 percent at our estimated parameters, compared to 36 percent in an identical model with no productivity transmission.

Suggested Citation

  • Wyatt Brooks & Kevin Donovan & Terence Johnson, 2018. "Bringing Data to the Model: Firm-to-Firm Learning in a Structural Model," 2018 Meeting Papers 1168, Society for Economic Dynamics.
    • Handle: RePEc:red:sed018:1168
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    References listed on IDEAS

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