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Learning to learn and productivity growth: Evidence from a new car-assembly plant

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  • Sáenz-Royo, Carlos
  • Salas-Fumás, Vicente

Abstract

This paper models learning by experience beyond the experience curve, including the possibility of “learning to learn”: the pace of learning increases over time by building on what has already been learned. We compare the extended deterministic learning model with Jovanovic and Nyarkos' [26] stochastic learning. The theoretical models are tested with data on the total factor productivity of a car-assembly plant in its first months of operation. We find that the deterministic “mixed learning model”, where the speed of learning is equal to a constant plus a learning to learn effect, is the one that best fits the empirical data. The mixed learning model results in a time pattern of total factor productivity growth, first increasing and later decreasing, different from the always decreasing rate of growth of the learning curve, opening new perspectives on the study of learning by experience.

Suggested Citation

  • Sáenz-Royo, Carlos & Salas-Fumás, Vicente, 2013. "Learning to learn and productivity growth: Evidence from a new car-assembly plant," Omega, Elsevier, vol. 41(2), pages 336-344.
  • Handle: RePEc:eee:jomega:v:41:y:2013:i:2:p:336-344
    DOI: 10.1016/j.omega.2012.03.010
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    References listed on IDEAS

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    Cited by:

    1. Womer, K. & Li, H. & Camm, J. & Osterman, C. & Radhakrishnan, R., 2017. "Learning and Bayesian updating in long cycle made-to-order (MTO) production," Omega, Elsevier, vol. 69(C), pages 29-42.

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