Comparing Market Power at Home And Abroad: Evidence from Austrian Banks And Their Subsidiaries in CESEE
Bibliographic record
Abstract
In this study, we examine markups of Austrian banks and their subsidiaries in Central, Eastern and Southeastern Europe (CESEE) on an unconsolidated level. Markups are evaluated by means of the Lerner index by simultaneously estimating a price and a cost function derived from oligopoly theory. For that purpose, we use a novel fixed effects seemingly unrelated regression approach and a unique supervisory dataset covering around 800 banks over the period from the first quarter of 2008 to the second quarter of 2016. We find evidence for positive markups for Austrian subsidiaries in CESEE. These markups are even higher than the markups of Austrian parent banks, which emphasizes the importance of the CESEE markets for the overall profitability of the Austrian banking sector. Looking at the determinants of markups for Austrian subsidiaries in CESEE, we find that higher Lerner indices are associated with better capitalization, higher loan loss provisions and, more generally, greater size – the latter effect is especially true for banks in more developed host countries. Also, there is a negative correlation between the Lerner indices of subsidiaries and parent banks. This implies that opportunity costs in the home country play a role in determining market power in the host country.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".