The Third Answer: How Market-Creating Innovation Drives Economic Growth and Development
Bibliographic record
Abstract
The second school of thought acknowledges that ideas may be the seeds of growth but points out that such seeds cannot, and will not, grow in poor soil.The most fertile soil for growth is quality institutions-the lack of which is the ultimate limiting factor in most places.Institutions refers to a nation's "soft" infrastructure and includes entities that make up the financial, judicial, legal, political, and even some social systems.Institutions can be formal (nation-states, schools, hospitals) or informal (practices and structures of authority that derive from custom and culture rather than laws and policies).This line of argument has been so persuasive that some international organizations, such as the United Nations and the World Bank, collectively spend billions of dollars trying to help people in poor countries develop new institutions or fix existing ones.2.Both of these perspectives have evident merit-indeed, they are historically linked.Economies expanded at a snail's pace globally until the 18 th -century Age of Enlightenment, when the simultaneous emergence of scientific methods and procedures of modern democracy propelled humanity into an era of learning and discovery far beyond any previously known.3.So, which is it-do ideas or institutions fundamentally drive long-term economic growth?In this essay, we propose that the most historically accurate and practically useful answer to this question is, in fact, neither.In the place of these two conjectured fundamental drivers of long-term economic growth we propose a third: market-creating innovation.What supports this assertion?First, ideas result in economic growth and development only when they are realized through market-creating innovation.(We explain below why we emphasize "market-creating" innovation).The actual process of market-creating innovation is nothing like the zero-cost transfer of ideas-knowledge spillovers-that are the
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.017 |
| Scholarly communication | 0.008 | 0.014 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".