Bibliographic record
Abstract
The innovation of new technologies is fundamental for driving aggregate economic growth. The spread (or lack of) of these technologies across countries helps explain growth in developing countries and why large productivity gaps persist. My thesis studies the contribution of technological progress and diffusion for explaining cross-country productivity gaps and aggregate growth. The first chapter studies how the diffusion of new technologies affects growth. I develop a model of endogenous growth, in which firms decide how to invest in the adoption of a new technology and innovation. Knowledge embodied in products is more complementary to future innovations using the same technology. Consequently, as firms adopt the new technology it becomes harder to innovate with the old technology. I calibrate to empirical evidence using patent data on the diffusion of information-communication technology (ICT). Diffusion is driven by adoption early on (about 1/3) and innovation later (about 2/3). Despite large firm-level gains from adoption, aggregate growth falls by 15% over the transition because firms scale back innovation. The second chapter studies how institutional distortions prolong the adoption of new technologies in developing countries. I build a model in which firms choose technology and resource inputs given their underlying productivity and exposure to the institutional environment. I calibrate to US data on the distribution of employment and adoption pattern of new technologies. Increasing distortions to be consistent with low-income countries increases the adoption lag of new technologies by 19 years (43% of the data) and decreases productivity by 65%. The third chapter (with Faisal Ibrahim, Gaelan MacKenzie and Swapnika Rachapalli) studies the role of knowledge embodied within traded products for diffusion. We use patent data to construct a measure of embodied technology. Using this measure, we find that increases in high-knowledge traded goods are associated with higher productivity and R\ growth. To quantify the importance of this channel, we develop an endogenous growth model with trade and knowledge links. We find that a large fraction of long-run growth is attributable to spillovers with developing countries benefiting relatively more.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.032 | 0.016 |
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".