Service-Led Catch-Up in the Indian Economy: Alternative Hypotheses on Tertiarization and the Leapfrogging Thesis
Bibliographic record
Abstract
The experience of India in economic catch-up is unique when compared to other countries. First, the catch-up process of India was not only service-led, but also accompanied by a decoupling between manufacturing and services. Second, productivity performance in the service sector was higher than in the manufacturing sector in terms of the level as well as growth rate. Finally, exports in IT services led the tertiarization of the Indian economy. From this perspective, the trajectory of the Indian catch-up can be characterized as path-creating. Existing hypotheses on tertiarization do not fully account for such aspects of the uniqueness of the Indian experience. \n\nThe leapfrogging argument in Neo-Schumpeterian economics provides a more plausible explanation of the Indian experience. The ICT revolution and the shift from hardware systems to client-server systems have created new markets for the global services trade. This paradigm shift lowered the costs of entry, including fixed investments, for Indian IT service firms and helped close the experience and skill gaps quickly. The industry-specific characteristics of the IT services industry and the country-specific advantages of India further lowered the costs of entry. With steady strategic and organizational innovations, Indian IT service firms succeeded in securing competitive advantages in the global market.
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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".