Bibliographic record
Abstract
Recent empirical evidence has suggested a positive association between various measures of investor protection and financial market development, and between financial market development and economic growth. We introduce investor protection in a simple extension of the two-period overlapping generations model of capital accumulation. We use such structure in order to develop predictions for the effects of investor protection on economic growth. Young individuals (entrepreneurs) are endowed with investment projects that need to be financed by outside investors. The quality of each project is random and unknown at the time of financing. Ex-post, once production is carried out, entrepreneurs observe their cash-flows, but financiers do not. Thus agents have an incentive to misreport their cash-flows and appropriate part of them. We capture the degree of investor protection as the extent to which this appropriation is possible. For a closed economy, our results show that, contrary to conventional wisdom, better investor protection is generally detrimental to capital accumulation and economic growth. The standard argument says that if investors are risk-averse, better investor protection results in larger demand for capital. In addition to this effect, we show that the aggregate supply of capital decreases with better investor protection, and we find that this second effect generally dominates the first. With international capital mobility, instead, better investor protection does promote financial market development and output growth. The key mechanism is an increase in the net inflow of capital from abroad, suggesting a specific channel through which investor protection affects the economy.
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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 | 0.003 | 0.020 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 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".