A General Purpose Technology at Work: The Corliss Steam Engine in the late 19th Century US
Bibliographic record
Abstract
The steam engine is widely regarded as the icon of the Industrial Revolution and a prime example of a "General Purpose Technology," and yet its contribution to growth is far from transparent. This paper examines the role that a particular innovative design in steam power, the Corliss engine, played in the intertwined processes of industrialization and urbanization that characterized the growth of the US economy in the late 19 th century. Waterpower offered abundant and cheap energy, but restricted the location of manufacturing just to areas with propitious topography and climate. Steam engines offered the possibility of relaxing this severe constraint, allowing industry to locate where key considerations such as access to markets for inputs and outputs directed. The enhanced performance of the Corliss engine as well as its fuel efficiency helped tip the balance in favor of steam in the fierce contest with waterpower. With the aid of detailed data on the location of Corliss engines and waterwheels and a two-stage estimation strategy, we show that the deployment of Corliss engines indeed served as a catalyst for the massive relocation of industry away from rural areas and into large urban centers, thus fueling agglomeration economies, and attracting further population growth. This illustrates what we believe is an important aspect of the dynamics of GPTs, whether it is electricity in the early 20 th century or Information Technologies in the present era: the fact that GPTs induce the widespread and more efficient relocation of economic activity, which in turn fosters long-term growth.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".