Economics of technology cycle time (TCT) and catch-up by latecomers: Micro-, meso-, and macro-analyses and implications
Bibliographic record
Abstract
Abstract This paper provides an analytical review of the literature on the role of technology cycle time (TCT) in the catching-up process of latecomers at the firm, sectoral, and national levels. At the national level, latecomer economies follow a detour that consists of economic growth through specialization in short-TCT sectors during the catching-up phase, followed by a shift to long-TCT sectors in the post-catching-up phase. The paper then discusses the double-edged nature of TCT at the sectoral level, such that short TCT can either be a window of opportunity associated with the rapid obsolescence of existing technologies and thus low entry barriers, or another source of difficulty associated with the truncation of learning from existing technologies. Only latecomers with a certain absorptive capacity can benefit from short TCT as a window of opportunity. Finally, at the firm level, this paper discusses the issue of possible convergence in the behavior of catching-up firms towards those of mature firms in advanced economies. At all three levels, the keywords are detours and convergence. Given the barriers to entry in long-TCT sectors, latecomers pursue a strategy of detouring into short-TCT sectors. That is, instead of trying to emulate incumbents by entering long-TCT sectors, latecomers take the opposite route. Subsequently, as latecomers improve their capabilities over time, they shift their specialization from short to long TCT sectors, thereby achieving convergence in behavior and strategy at the firm, sectoral, and national levels.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".