Market power of selected German food and beverage exporters on international markets
Bibliographic record
Abstract
In this paper we applied two well known empirical models, the 'pricing-to-market' and the 'residual demand elasticity' approach, to test for market power of German food and beverage exporters on the main international markets (beer, cocoa powder, chocolate, and sugar confectionary). We compared the estimation results and considered the time series properties of the model variables, which both had an impact on the conclusions drawn from the model estimates.On many international food markets conditions for the existence of market power are fulfilled, such as concentrated supply and/or demand structures, or specific product qualities. For the products under study we have shown that the supply side is highly concentrated. For instance, for beer, cocoa powder, chocolate, and sugar confectionary the six biggest importers hold a significant share of total imports of the respective destinations. In addition, many products are well known for their specific quality characteristics.The 'pricing-to-market' model identifies market power by the estimation of the exchange rate transmission elasticity. A significant transmission elasticity above minus one indicates the use of market power. The 'residual demand elasticity' approach signalizes market power if the estimates for the elasticity lie in the range of minus one to less than infinity. Both frameworks utilize the effects of exchange rate variations on export volumes and prices.On four markets, US and Canadian beer market, UK sugar confectionary, and Italian cocoa powder market, we find significant parameters for the exchange rate transmission elasticity which indicate a 'pricing to market' behavior with similar estimates around -0.7. Thus, the use of market power by German exporters cannot be rejected for these products and destinations. Because the RDE model results do not indicate market power at all, both approaches partly result inconsistent conclusions, which further do not fit to the fact that the law of one price is significantly violated for all products. Even though markets obviously indicate to some extent market power, the applied model approaches do not consistently detect it. Therefore, the observed price discrimination must be caused by some other factors than the deviation of demand elasticities between international markets. The explicit consideration of competitors' behaviour on the international markets can be one factor that might be worthwhile to enhance the understanding of the pricing process on these markets.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".