The DOI matches its Crossref record.
The Learning Curve and Pricing in the Chemical Processing Industries
Abstract
Data on 37 chemical products are used to test a number of hypotheses about the learning curve and industrial price behavior. The results document a strong and consistent learning effect. Learning is found to be a function of cumulated industry output and cumulated investment rather than calendar time. Standard economies of scale appear significant but small in magnitude relative to the learning effect. Variations in the slope of the learning curve are linked to differences in R&D expenditures and capital intensity. Market concentration is found to be a strong influence on price flexibility and the timing of learning-related price changes.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
Innovation and Learning: The Two Faces of R & D
Cohen, W. M., & Levinthal, D. A. (1989) · The Economic Journal · doi:10.2307/2233763 · Cited by 7,530
Environmental and technology policies for climate mitigation
Fischer, C., & Newell, R. G. (2008) · Journal of Environmental Economics and Management · doi:10.1016/j.jeem.2007.11.001 · Cited by 1,157
Learning to Contract: Evidence from the Personal Computer Industry
Mayer, K. J., & Argyres, N. S. (2004) · Organization Science · doi:10.1287/orsc.1040.0074 · Cited by 915
The Effect of General and Partner-Specific Alliance Experience on Joint R&D Project Performance
Hoang, H., & Rothaermel, F. T. (2005) · Academy of Management Journal · doi:10.5465/amj.2005.16928417 · Cited by 784
Learning curve models and applications: Literature review and research directions
Anzanello, M. J., & Fogliatto, F. S. (2011) · International Journal of Industrial Ergonomics · doi:10.1016/j.ergon.2011.05.001 · Cited by 508
Metadata
- Authors
- Lieberman, M. B.
- Venue
- The RAND Journal of Economics
- Publisher
- Wiley
- Published
- 1984
- Volume
- 15
- Issue
- 2
- Pages
- 213
- ISSN
- 0741-6261 (ISSN-L)
- OpenAlex
- W2076098586
- Cited by
- 563
- References
- 0
- FWCI
- 15.08
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: The Learning Curve and Pricing in the Chemical Processing Industries. https://app.cochat.ai/papers/10.2307/2555676
- The paper
- Lieberman, M. B. (1984). The Learning Curve and Pricing in the Chemical Processing Industries. The RAND Journal of Economics. https://doi.org/10.2307/2555676
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
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.
