Investments in Information Technology: Indirect Effects and Information Technology Intensity
Bibliographic record
Abstract
Many studies measure the value of information technology (IT) by focusing on how much value is added rather than on the mechanisms that drive value addition. We argue that value from IT arises not only directly through changes in the factor input mix but also indirectly through IT-enabled augmentation of non-IT inputs and changes in the underlying production technology. We develop an augmented form of the Cobb-Douglas production function to separate and measure different productivity-enhancing effects of IT. Using industry-level data from the manufacturing sector, we find evidence that both direct and indirect effects of IT are significant. Partitioning industries into IT-intensive and non-IT-intensive, we find that the indirect effects of IT predominate in the IT-intensive sector. In contrast, the direct effects of IT predominate in the non-IT intensive sector. These results indicate structural differences in the role of IT in production between industries that are IT-intensive and those that are not. The implication for decision-makers is that for IT-intensive industries the gains from IT come primarily through indirect effects such as the augmentation of non-IT capital and labor.
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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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.001 |
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".