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Learning-by-Doing and Market Performance
Abstract
This article studies the implications of learning-by-doing for market conduct and performance. We use a general continuous-time model to show that output increases over time in the absence of strategic interactions, and that a monopolist learns too slowly, compared with the social optimum. We then specialize to a two-period model with linear demand and learning to analyze the case in which firms do consider the effect of their learning on the actions of their rivals. We show that strategic incentives can induce firms to choose decreasing output paths, and that a little diffusion of learning across firms increases output if firms are naive, but decreases output if firms play strategically. We further show that increased learning improves welfare, and that welfare can be improved by using a balanced-budget tax-subsidy scheme which transfers production incentives to the less competitive "mature" phase of the industry.
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Cited by
Competing Technologies, Increasing Returns, and Lock-In by Historical Events
Arthur, W. B. (1989) · The Economic Journal · doi:10.2307/2234208 · Cited by 7,489
Increasing Returns and Path Dependence in the Economy
Arthur, W. (2026) · doi:10.3998/mpub.10029 · Cited by 4,547
Do firms learn to create value? The case of alliances
Anand, B. N., & Khanna, T. (2000) · Strategic Management Journal · doi:10.1002/(sici)1097-0266(200003)21:3<295::aid-smj91>3.0.co;2-o · Cited by 1,625
Abrahamson, E., & Rosenkopf, L. (1993) · The Academy of Management Review · doi:10.2307/258906 · Cited by 1,047
Social Network Effects on the Extent of Innovation Diffusion: A Computer Simulation
Abrahamson, E., & Rosenkopf, L. (1997) · Organization Science · doi:10.1287/orsc.8.3.289 · Cited by 803
Metadata
- Authors
- Fudenberg, D., & Tirole, J.
- Venue
- The Bell Journal of Economics
- Publisher
- JSTOR
- Published
- 1983
- Volume
- 14
- Issue
- 2
- Pages
- 522
- ISSN
- 0361-915X (ISSN-L)
- OpenAlex
- W1605228868
- Cited by
- 343
- References
- 0
- FWCI
- 25.76
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.