FROM HONEYMOON TO DIVORCE: INSTITUTION QUALITY AND FOREIGN INVESTORS' OWNERSHIP CONSOLIDATION IN CHINA
Bibliographic record
Abstract
In China, joint ventures (JVs) between foreign investors and Chinese local firms were the most popular form of foreign affiliates before 2001. Over time, with policy space to operate as foreign wholly owned (WOs), many foreign investors in JVs chose to consolidate ownership and turned JVs into their WOs. Here, we examine how institution quality affects foreign investors' JV‐to‐WO ownership consolidation odds. For each province‐year, we construct an institution quality index from the business and judicial quality indicators, and further compute a relative quality index to highlight provincial variations. Using more than 43,000 JVs operating in China's 30 provinces over 1998–2007, we find that increases in institution quality decrease the odds of foreign investors to divorce their Chinese local partners. The odds for foreign investors in JVs to consolidate ownerships are significantly higher if they operate in provinces with relatively weaker institution quality. The odds of foreign investors' JV‐to‐WO decision vary with JVs' local firms being state‐owned enterprises (SOEs) and non‐SOEs, with foreign investors' origins from Hong Kong, Macao, and Taiwan (HMT) and other regions (Foreign), and with foreign investors' initial equity positions. Our results are not driven by foreign direct investment policy shocks, and are robust to alternative measures of institution quality. (JEL F23, L23)
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".