Measuring the Effect of Covid-19 on Bank Lending: Empirical Evidence from Albania
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study aims to empirically contribute to the identification and evaluation of microeconomic and macroeconomic indicators at the level of lending in Albania. It identifies a number of important factors, such as the level of gross domestic product, return on assets, unemployment rate, inflation rate, non-performing loans rate, capital adequacy, liabilities and regulatory capital to assets risk weighted. Quantitative analysis and econometric models will study the quantitative impact of each of these factors on both the level of net credit stock and the level of new credit. The creation of these 2 econometric models will serve us to measure and evaluate the changes encountered in the dependent variable over a given period of time, as a result of shocks from other variables. Also, a current and important contribution to this thesis relates to the impact assessment of COVID-19. In order to maintain the simplicity and usefulness of the model, some realistic features of the current economy have been left out, such as the level of loan repayment etc. The study period is from the first quarter of 2009 to the fourth quarter of 2020. The data used were obtained from the Bank of Albania and the Albanian Association of Banks, which were presented in the form of a time series. Despite the limited number of data considered regarding the impact of COVID-19 as well as their temporal distribution, this study with the work it performs, serves as a good starting point for further studies in this field.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 it