Multinationals, cross-border acquisitions and wage dispersion
Bibliographic record
Abstract
Abstract We examine the impact of cross-border acquisitions on intra-firm wage dispersion using a detailed Swedish linked employer-employee data set including data on all firms and about 50% of the Swedish labour force with information on job-tasks and education. Foreign acquisitions of domestic multinationals and local firms increase wage dispersion but so do also other types of cross-border acquisitions. Hence, it is the acquisition itself rather than foreign ownership that increases wage dispersion. The positive wage effect is concentrated to CEOs and other managers, whereas other groups are either negatively affected or not affected at all. On examine l’impact d’acquisitions d’outre-frontières sur la dispersion des salaires à l’intérieur des firmes à l’aide d’une banque suédoise de données détaillées (reliant employeur et employé) qui fournit des données sur toutes les firmes et sur 50 pourcent de la main d ‘œuvre suédoise (y compris sur les tâches des types de travail et le niveau d’éducation). Les acquisitions étrangères de multinationales domestiques et de firmes locales accroissent la dispersion des salaires, mais c’est le cas aussi pour d’autres types d’acquisitions. Donc, c’est l’acquisition elle-même plutôt que la propriétéétrangère qui engendre la dispersion plus grande des salaires. L’effet positif sur les salaires est concentré sur les PDGs et autres gestionnaires, alors que les autres groupes sont soit négativement affectés soit complètement inaffectés.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".