Capital markets and capital allocation: Implications for economies in transition*
Bibliographic record
Abstract
Abstract Recent work showing that a sounder financial system is associated with faster economic growth has important implications for transition economies. Stock prices in developed economies move in highly firm‐specific ways that convey information about changes in firms’ marginal value of investment. This information facilitates the rapid flow of capital to its highest value uses. In contrast, stock prices in low‐income countries tend to move up and down en masse, and thus are of scant use for microeconomic capital allocation. Some transition economy markets are coming to resemble those of developed economies, others those of low‐income countries. Stock return asynchronicity is highly correlated with the strength of private property rights in general and public shareholders’ rights in particular. Other recent work suggests that small entrenched elites in low‐income countries preserve their sweeping control over the corporate sectors of their economies by using political influence to undermine the financial system and deprive entrants of capital. The lack of cross‐sectional independence in some transition economies’ stock returns may be a warning of such economic entrenchment. Sound property rights, solid shareholder rights, stock market transparency, and capital account openness appear to check this, and thus contribute to efficient capital allocation and economic growth.
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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 | 0.003 |
| 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 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".