Portfolios of Political Ties and Business Group Strategy in Emerging Economies
Bibliographic record
Abstract
Using data on 290 business groups, this study examines how ties with rival political parties maintained by Taiwanese firms from 1998 through 2006 affected business strategies, specifically the unrelated diversification into new industries. Taiwan’s recent democratization and emerging economy provide an ideal setting for studying the economic impact of firms’ ties with rival political parties. By focusing on a firm’s entire portfolio of ties instead of strictly dyadic business–government ties, we offer a novel model that demonstrates how the interplay of various ties affects a firm’s strategy differently under different forms of government. Our analysis shows that under a united government, ties to the ruling party facilitate entries of business groups into unrelated industries, while ties to the opposition parties inhibit such moves. Portfolios of ties to both the ruling and opposition parties impose additional obstacles to market entry. Under a divided government, however, ties to the ruling party are conducive to market entry, and portfolios of ties to both the ruling party and the opposition party with legislative authority offer a further boost. Regardless of type of government, the effect of having a portfolio of political ties tends to be mitigated by a firm’s internal resources and capabilities: a firm with sufficient resources and market entry experience has a better chance of achieving its goals even when a dominant political party withholds its support. Our study highlights the tradeoffs that politically connected firms confront in emerging economies with underdeveloped political and market institutions.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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 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".