Macro determinants of shadow banking in Central and Eastern European countries
Bibliographic record
Abstract
Our study aims to quantitatively assess some of the determinants of shadow banking dynamics in 11 European Union (E.U.) countries from Central and Eastern Europe (C.E.E.) over the period 2004–2017. Using panel data estimation techniques and a quarterly data set compiled from several publicly available data sources, we alternatively evaluate the impact of six macroeconomic and financial variables on two dependent variables corresponding to two different measures of the shadow banking sector, namely the broad one (including all non-monetary financial institutions, except insurance corporations and pension funds) and the narrow one (excluding from the above one the investment funds, other than money market funds [M.M.F.]). Our findings confirm that shadow banking is sensitive to overall macroeconomic conditions and that economic growth positively influences the expansion of this segment of the financial sector. In addition, a higher demand for funds from institutional investors, which also reveals a more developed financial system, supports the expansion of the shadow banking sector. Moreover, in a low interest rate environment, the search for yield makes investors turn to shadow banks, while the development of the shadow banking sector is also found to be complementary to the development of the rest of the financial system, in particular, traditional banks.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".