Trade Liberalization and Institutional Change
Bibliographic record
Abstract
Opening up to global trade and investment is often thought to trigger institutional improvement by raising the expected benefits of institutional reform and reducing incumbents' incentives and ability to preserve the status quo. However, recent experience is not entirely consistent with this conventional wisdom. We suggest an explanation based on variation across countries in firms’ reliance on ambient institutions. Large, well established, or state controlled firms depend less on an economy’s institutions than do small, incipient, or purely private sector firms. Multinational firms likewise can use their global organizations to sidestep weak local institutions. Firm heterogeneity of this sort can thus contribute to markedly different institutional responses to liberalization. Our framework also suggests that institutional development might occur in stages. In an economy whose basic institutions are sound, individuals rationally invest in entrepreneurial capability and firms rationally invest less in institution substitutes. Economies with firms that rely more on ambient institutions or with more potential entrants who would rely on those institutions are more likely to experience further institutional improvement following accession to the global economy. Economies with fewer firms or potential entrants dependent on sound institutions, in acceding to the global economy, may exhibit scant institutional improvement, and perhaps even institutional deterioration. Political rent-seeking is not necessary for the latter outcome, but expands the range of conditions under which it ensues.
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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 | 0.006 |
| 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 | 0.010 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".