An Industrial Organization Approach to the Study of Export Intensity: Strategic Market Interactions and Export Intensity
Bibliographic record
Abstract
In developing countries characterized by relatively small domestic markets, local firms may have to internationalize in order to realize their growth potential.Despite the formidable challenges that may accompany the internationalization process, globally-oriented managers and domestic policymakers may effectively craft coherent export-promotion strategies and policies, respectively, if they have a solid understanding of the determinants of export performance.While the empirical export performance literature in the field of international business (IB) has the potential to contribute towards this end, it appears to be hampered by a paucity of rigorous theoretical frameworks.In the virtual absence of a well-articulated direction on how to fill this theoretical void, this paper makes a case for the application of industrial organization (IO)-based modeling in this line of research.It formulates a model of exporting in the context of market structures characterized by a monopoly, and a (symmetric linear) Stackleberg duopoly with price discrimination.Under this theoretical framework, it is found that the Stackleberg leader has an export intensity of zero, while the Stackleberg follower has an export intensity of one-half.But at an export intensity of two-thirds, the price-discriminating monopolist has the largest export intensity.These analytical results provide insights into the so-called "industry effects" phenomenon that has been noted in empirical export performance studies, and strengthens the theoretical argument for the conventional use of industry-dummy variables to control for hypothesized industry effects.More generally, this paper signals a potentially fruitful direction for IO-based modeling in the extant empirical export performance literature.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".