Does intellectual property lead to economic growth? Insights from a novel IP dataset
Bibliographic record
Abstract
Abstract While policymakers often make bold claims as to the positive impact of intellectual property (IP) rights on both developed and developing country economies, the empirical literature is more ambiguous. IP rights have both incentive and inhibitory effects that are difficult to isolate in the abstract and are dependent on economic context. To unravel these contradictory effects, this article introduces an index that evaluates the strength of IP protection in 124 developing countries for the years 1995 to 2011. We illustrate the value of this index to economics study and show evidence that is consistent with IP leading to increased growth. Our results are further consistent with two causal pathways highlighted in the literature: that IP leads to greater levels of technology transfer and increased domestic inventive activity. Yet other aspects of our study fit uneasily with this simple story. For example, we find evidence suggesting that increased levels of growth lead to greater levels of IP protection, contradictory evidence in the literature linking IP with growth, a lack of evidence that increased levels of IP protection lead to actual use of the IP system, and problems with what IP indexes measure. Because of this, we suggest another – and so far undertheorized – explanation of the links between IP and growth: that IP may have few direct effects on growth and that any causality is a result of belief rather than actual deployment of IP.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".