OECD Banking Statistics, 1979-2009
Bibliographic record
Abstract
The OECD Banking statistics database includes data from 1979 to 2009 on classification of bank assets and liabilities, income statement and balance sheet and structure of the financial system for OECD countries. The OECD have discontinued this dataset, so no further updates will be made. The OECD Banking Statistics are presented in the following tables (some tables will include missing data): Classification of bank assets and liabilities This dataset provides the composition of bank assets and liabilities of residents and non-residents denominated in domestic and foreign currencies based on financial statements of banks in each OECD member country and Russia. Data are reported at current prices in millions of national currency and in millions of Euros for OECD countries. The data covers the years starting from 2005 extending until 2009. The countries covered are Austria, Belgium, Canada, Chile, Czech Republic, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Israel, Italy, Japan, Korea, Luxembourg, Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovenia, Spain, Sweden, Switzerland, Turkey, United Kingdom, and Russian Federation. Income statement and balance sheet This comparative tables comprises statistics on country’s financial profiles by presenting their respective extensive income statements, balance sheets and capital adequacy by banking group that can be further analyzed by type of financial institution such as commercial banks, savings banks co-operative banks and other monetary institutions. This dataset provides information on income statements, balance sheets and capital adequacy by banking group. Data are reported at current prices in millions of national currency. The data covers the years starting from 1979 extending until 2009. The countries covered are Austria, Belgium, Canada, Chile, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Israel, Italy, Japan, Korea, Luxembourg, Mexico, Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovenia, Spain, Sweden, Switzerland, Turkey, United Kingdom, United States and Russian Federation. Structure of the financial system This dataset provides information on the overall structure of the financial system per country by type of institution and their components: Central banks, other monetary institutions, other financial institutions and insurance institutions. Data relate to number of institutions, number of branches, number of employees, total assets and liabilities and total financial assets. The data covers the years starting from 1979 extending until 2009. The countries covered are Austria, Belgium, Canada, Chile, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Israel, Italy, Japan, Korea, Luxembourg, Mexico, Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovenia, Spain, Sweden, Switzerland, Turkey, United Kingdom, United States and Russian Federation. These data were first provided by the UK Data Service in December 2014. The UK Data Service web site includes further information on its OECD Banking Statistics holdings, including a dataset user guide and details of latest database updates. Citation: The bibliographic citation for the database is: Organisation for Economic Cooperation and Development ({YYYY}): Banking Statistics ({Ed. Data download: YYYY-MM}). Mimas, University of Manchester. DOI: {edition specific doi - e.g. DOI: http://dx.doi.org/10.5257/oecd/bank/2012}. Alternative DOIs: 10.1787/bank-data-en (to access via OECD.Stat subscription).
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.021 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.041 | 0.052 |
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".