Bibliographic record
Abstract
We model and estimate the effects to downstream productivity from information technology (IT) investments made upstream. Specifically, we examine how an industry’s productivity is affected by the IT capital stock of its suppliers. These supplier-driven IT spillovers occur because, due to competition in the supplying industry, quality benefits from suppliers’ IT investments can pass downstream. If the output deflators of supplying industries (consequently the intermediate input deflator of the using industries) do not capture the quality improvement from IT, then the output productivity of the supplying industries is mismeasured or misassigned. We develop and empirically test a model capturing these supplier-driven effects using data on 85 manufacturing industries at the three-digit SIC code level. We find that for a 10.5% increase in suppliers’ IT capital, the suppliers’ output increases by 0.63%–0.70%, which is more than covering the cost of the increase in suppliers’ IT capital. In addition, this increase in suppliers’ IT capital increases the average downstream industry’s output by $66–$72 million, thereby confirming substantial supplier-driven IT spillovers downstream. We also infer the magnitude of the measurement error of the price deflator of the intermediate input resulting from the failure to account for IT-related quality improvement, finding that the measured price deflator overestimates the true deflator by approximately 30% at the mean level of IT capital.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".