Disentangling neutral, directional, and complementarity effects of technological change: evidence from 32 OECD countries, 1994–2019
Bibliographic record
Abstract
This paper develops and applies a novel growth-accounting methodology based on a Constant Elasticity of Substitution (CES) production function to disentangle the total effect of technological change on GDP into three components: neutral (level), directional (bias), and complementarity (substitution). While the neutral effect corresponds to Solow’s Total Factor Productivity (TFP), the directional effect captures changes in output elasticity, and the complementarity effect reflects variations in the elasticity of substitution between capital and labor. Applying the framework to a balanced panel of 32 OECD countries over 1994–2019 reveals that neutral technological change alone contributed little, and in many cases negatively, to GDP growth, in line with the ‘productivity paradox’. By contrast, the directional effect emerges as the dominant positive channel in nearly all countries, while the complementarity effect, though typically smaller, plays a substantial role in Canada, South Korea, and the UK; has been historically important in France, Italy, and Spain; and is increasingly salient in Australia, Japan, and the US. These results highlight the need to go beyond standard TFP measures to fully capture the economic impact of innovation, as well as the policy relevance of influencing both the direction and the complementarity of technological change.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".