Pricing to Market Behavior: Evidence from Albanian Exporting Firms
Bibliographic record
Abstract
This paper delves into the pricing behavior of Albanian exporting firms, analyzing their response to market conditions and exchange rate fluctuations.Drawing inspiration from Krugman's seminal work in 1986, on pricing to market dynamics, the study investigates whether Albanian firms employ similar adaptive pricing strategies observed in larger economies.Recent empirical research suggests that such practices are prevalent in small, open economies like Albania, contrary to earlier assumptions.Utilizing a linear econometric model and quarterly data spanning from 2005 to 2023, the analysis aims to ascertain the extent to which Albanian exporting firms act as price setters in response to market dynamics.Through rigorous empirical analysis, including descriptive statistics, unit root tests, ARDL cointegration analysis, model estimation, hypothesis testing, and robustness checks, the study provides insights into the relationship between pricing behavior and exchange rate movements.The findings highlight the significance of adaptive pricing strategies in global markets and offer implications for policymakers, businesses, and academic researchers.Policymakers can use these insights to formulate effective economic policies, while businesses can make informed decisions regarding pricing strategies and currency risk management in the global marketplace.Moreover, this study contributes to the econometrics and macroeconomics literature, paving the way for future research into pricing behavior and exchange rate dynamics.Overall, it serves as a foundational resource for informed decision-making and further academic inquiry into pricing behavior and exchange rate dynamics in emerging economies like Albania.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".