The Effect of Adjustment Costs and Organizational Change on Productivity in Canada: Evidence from Aggregate Data
Bibliographic record
Abstract
A basic neoclassical model of production is often used to assess the contribution of investment to output growth. In the model, investment raises the capital stock and output growth increases in proportion to the growth in capital. It has been argued, however, that computers, as a "general purpose technology," lead to process innovations and facilitate organizational coinvestments. Since there may be a learning period before firms realize the full potential of the new technology and begin to implement new processes, there may be a lag between the growth in investment and its benefits. In fact, during periods of rapid adoption of new technologies and equipment, firms may incur adjustment costs and struggle to maintain previous levels of output. Using aggregate annual Canadian data from 1961 to 2001, the author explores the magnitude of the effect that investment in new technology, in the form of new computer hardware, can have on output growth. He finds that such investment has a positive effect on output growth that cannot be explained by growth in inputs. This effect, however, is not instantaneous and is strongest only three years after the initial investment. Furthermore, the author's findings suggest that the effect of computer hardware investment has grown over time.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.013 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".