Assessment of Employment Trends at Cooperative Banks and Non-Cooperative Banks (2011-2023)
Notice bibliographique
Résumé
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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Prédiction machine sur la base complète
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Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,004 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».