The Economic Impact of Entrepreneurship: Comparing International Datasets
Bibliographic record
Abstract
Abstract Manuscript Type Empirical Research Question/Issue What is the impact of entrepreneurship on GDP /capita, unemployment, exports/ GDP , and patents per population across countries? Is the impact of entrepreneurship mitigated by legal and cultural differences across countries? Do different international datasets provide different answers to these questions? We empirically compare the impact of entrepreneurship on GDP /capita, unemployment, exports/ GDP , and patents per population across countries by examining three datasets from the W orld B ank, the OECD , and C ompendia. Research Findings/Insights Based on a comprehensive sample of all available countries and years, with the W orld B ank data being the most comprehensive, we find entrepreneurship has a significantly positive impact on GDP /capita, exports/ GDP , and patents per population, and a negative impact on unemployment. Inferences from the C ompendia data are very consistent. By contrast, inferences from the OECD data are not supportive of any of these propositions. Theoretical/Academic Implications Our findings point to institutional and cultural impediments to the effectiveness of entrepreneurship. Most notably, the impact of entrepreneurship is significantly mitigated by excessively strong creditor rights that limit entrepreneurial risk‐taking. Furthermore, the data indicate that cultural attitudes associated with low risk‐taking limit the effectiveness of entrepreneurship. By contrast, the impact of entrepreneurship on exports/ GDP does not appear to be directly tied to costs of exporting, which is perhaps best explained by the new economy goods and services created by entrepreneurs that depend less on such costs. For some subsets of the data we find evidence consistent with the view that top tier venture capital funds enhance the impact of entrepreneurship on GDP /capita. Finally, our results show how different definitions of new business entry matter for empirical analysis of entrepreneurship across countries. Practitioner/Policy Implications The data highlight the importance of access to finance without downside costs so that entrepreneurs are encouraged to take risk. Further, the data highlight institutional differences in risk attitudes that more generally inhibit risk‐taking and thereby limit the effectiveness of entrepreneurship. Moreover, the data highlight a central role for careful measurement of entrepreneurial activities and for inclusion of as many countries and years as possible in order to effectively analyze the impact of entrepreneurship.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.013 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".