Assessment of Employment Trends at Cooperative Banks and Non-Cooperative Banks (2011-2023)
Bibliographic record
Abstract
Banking is in a state of flux and influenced by many trends. What has been relatively underexposed so far is how employment has developed at banks with different ownership structures. This research letter aims to fill this gap by examining employment trends at 16 European co-operative banking groups (henceforth, CBGs) from 13 European countries on the one hand, and 90 other banks in their home countries on the other for the period 2011-2023. To this end, employment data have been collected from multiple data sources. In our sample, CBGs and all other banks employed around 696,000 and 1,931,000 staff, respectively, in 2023. According to our calculations, average staff reduction at CBGs was about 0.6 per cent per year for the period 2011-2023, whereas the staff volume at other banks declined by around 1.3 per cent per year. That means that CBGs’ share in total employment has increased from around 25 per cent in 2011 to about 26.5 per cent in 2023. In 9 of the 13 countries, the employment index (2011=100) for CBGs is substantially higher than for all other banks in 2023. We also find a higher degree of variability in employment changes within the group of non-cooperative banks. Since the standard deviation of employment swings is significantly less among CBGs, one could argue that they are more stable employers. In the last years of the period, we detected a remarkable shift from employment decline to employment growth. This turnaround happened in CBGs one year earlier. We tentatively attempted to search for explanations for these - diverse - employment trends. Based on an econometric analysis, we conclude that, in any case, employment fluctuations are statistically unrelated to real GDP growth. Moreover, we discovered that purchases and sales of subsidiaries are in certain periods more common at larger and internationally active (non-)cooperative banks. It seems reasonable to assume that this partly explains the higher volatility in employment at other banks. The employment decline in most years of the sample can probably be attributed to IT developments. Virtualization often went hand in hand with branch closures as well as departures of employees. In this context, it is possible that the less pronounced employment decline in CBGs is partly due to their business model and ownership structure. The annual reports consulted suggest that stricter compliance requirements, and partly linked, geopolitical developments have contributed to an increasing number of employees tasked with financial security, fraud prevention and customer protection in recent years. The whole range of these ‘gatekeeper’ activities is undeniably important, but on the other hand it could represent relatively large costs for smaller (non-)cooperative banks in particular that are not offset by financial revenues. Finally, the implications of Artificial Intelligence (AI) for banking employment are obviously far-reaching but uncertain. We anticipate that AI will not merely replace employment, but mainly transform it.
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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 | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".