Does Intellectual Property Lead to Economic Growth? Insights from an Improved IP Dataset
Bibliographic record
Abstract
While policymakers often make bold claims on 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 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. We find, for example, 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 due to belief rather than actual deployment of IP.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.003 |
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 teacher head, 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".