The contribution of railways to economic growth in Latin America before 1914: a growth accounting approach
Bibliographic record
Abstract
Railways are usually considered as one of the most important innovations that fostered the transition of Latin America to economic growth before 1914. The social saving estimates that are available for several Latin American countries seem to confirm that view. However, the interpretation of the results of the social saving literature is not straightforward, since the comparison among social savings calculated for different countries and years may be troublesome, and the actual meaning of the social saving estimates is not clear. This paper suggests an alternative approach to the economic impact of railways in Latin America. It presents estimates of the direct growth contribution of the railway technology in Argentina, Brazil, Mexico and Uruguay before 1914, which are calculated on the basis of the growth accounting methodology. The outcomes of the estimation indicate that railway effects on Uruguayan economic growth were very low. By contrast, in the other three cases under study (Argentina, Mexico and Brazil) the railways provided huge direct benefits. In Argentina and Mexico, these amounted to between one fifth and one quarter of the total income per capita growth of the period under analysis. By contrast, in the case of Brazil, the outcomes of the analysis indicate that the direct contribution of railways to growth would have been higher than the whole income per capita growth of the Brazilian economy before 1914. This unexpected result might suggest that the national level is not the most adequate scale to analyse the economic impact of network infrastructure in the case of large, geographically unequal and insufficiently integrated developing economies.
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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".