All politics starts local: Liability of stateness and subnational labor markets
Bibliographic record
Abstract
Abstract Research Summary State‐controlled acquirers face a liability of stateness (LoS) because host country stakeholders consider them less legitimate and as representatives of foreign political power. We argue that due to LoS, state‐owned enterprises (SOEs) face more regulatory scrutiny in cross‐border acquisitions than comparable private‐owned enterprises (POEs). Applying a voting behavior perspective, we further posit this increased regulatory scrutiny is reduced when acquisitions occur via intermediaries, and in host communities less averse to state ownership due to local labor conditions. Using a sample of cross‐border acquisitions with acquirers from 44 economies and targets in 50 US states, we find that SOEs are 9% more likely to attract additional regulatory scrutiny than POEs. However, this likelihood decreases with indirect acquisitions and in host regions with high unemployment. Managerial Summary State‐owned enterprises experience challenges in their cross‐border acquisitions because people in host societies do not trust them. As a result, regulatory authorities, such as CFIUS in the United States, subject foreign SOE acquirers to greater scrutiny. However, by acquiring foreign firms through subsidiaries rather than through parent organizations, the state influence becomes less visible, resulting in less regulatory scrutiny. Moreover, local stakeholders are concerned with economic opportunities in their local area, which they prioritize over ideological concerns at time of economic crisis. Consequently, SOE acquirers face less additional scrutiny in local communities with high unemployment. Thus, SOE acquirers can work with local communities to overcome the negative perception they encounter when entering foreign markets.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".