The DOI matches its Crossref record.
E conometric A nalysisof C ollusive B ehaviorin a S oft‐D rink M arket
Abstract
This paper proposes an empirical methodology for studying various (implicit or explicit) collusive behaviors on two strategic variables, which are price and advertising, in a differentiated market dominated by a duopoly. In addition to Nash or Stackelberg behaviors, we consider collusion on both variables, collusion on one variable and competition on the other, etc. Using data on the Coca‐Cola and Pepsi‐Cola markets from 1968 to 1986, full information maximum likelihood estimation of cost and demand functions are obtained allowing for various collusive behaviors. The collusive hypothesis is not rejected, and the best form of collusive behavior is selected via nonnested testing procedures. Using the best model, Lerner indices are computed for both duopolists to provide summary measures of market power. Finally, our approach is contrasted with the conjectural variation approach and is shown to give superior results.
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
Network effects and competition: an empirical analysis of the home video game industry
Shankar, V., & Bayus, B. L. (2003) · Strategic Management Journal · doi:10.1002/smj.296 · Cited by 486
Andreyeva, T., Kelly, I. R., & Harris, J. L. (2011) · Economics & Human Biology · doi:10.1016/j.ehb.2011.02.004 · Cited by 378
Ciliberto, F., & Williams, J. W. (2014) · The RAND Journal of Economics · doi:10.1111/1756-2171.12070 · Cited by 186
Metadata
- Authors
- Gasmi, F., Laffont, J. J., & Vuong, Q.
- Venue
- Journal of Economics & Management Strategy
- Publisher
- Wiley
- Published
- 1992-06
- Volume
- 1
- Issue
- 2
- Pages
- 277-311
- ISSN
- 1058-6407 (ISSN-L)
- 1530-9134
- OpenAlex
- W2088108260
- Cited by
- 249
- References
- 0
- FWCI
- 6.97
- 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: E conometric A nalysisof C ollusive B ehaviorin a S oft‐D rink M arket. https://app.cochat.ai/papers/10.1111/j.1430-9134.1992.00277.x
- The paper
- Gasmi, F., Laffont, J. J., & Vuong, Q. (1992). E conometric A nalysisof C ollusive B ehaviorin a S oft‐D rink M arket. Journal of Economics & Management Strategy. https://doi.org/10.1111/j.1430-9134.1992.00277.x
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.