Relative Industry Concentration and Customer-Driven IT Spillovers
Bibliographic record
Abstract
We examine how one industry’s productivity is affected by the IT capital of its customers and how this effect depends on industries’ relative concentration. These customer-driven IT spillovers result from customers’ IT investments in various information systems that reduce transaction costs through information sharing and coordination and lead to more efficient production and logistics upstream. The magnitude of IT spillovers depends on relative industry concentration because customers in more concentrated industries relative to those of their suppliers are better able to retain the benefits from their IT investments. We model customer-driven effects based on production theory and empirically test the model using two industry-level data sets covering different and overlapping time periods (1987–1999 and 1998–2005), different scopes of the economy (manufac- turing only versus all industries), and different levels of industry aggregation. We find that, given an increase in a downstream industry’s IT capital, there is a significant increase in downstream industry output as well as significant increases in upstream industry output. Moreover, the magnitude of IT spillovers is related to relative industry concentration: A 1% decrease in a customer’s relative industry concentration increases spillovers by roughly 1%. Thus, further increases in IT capital can be justified along the supply chain, and an industry’s relative concentration—which can reflect market power—in part determines the distribution of productivity benefits.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".