Innovation Effects of Information and Communication Technologies: Evidence from Canadian Firms
Bibliographic record
Abstract
The productivity effects of information and communication technology (ICT) as a general-purpose technology, have been extensively researched, but evidence on the impact of ICT on innovation in the economy is limited. ICT can drive innovation through direct and indirect channels. Directly, ICT can deepen capital investment in the knowledge-creation process by lowering its relative prices, leading to complementary investments in innovation. Indirectly, ICT can create spillover effects due to its network characteristics and interactions with other factors that influence innovation, such as human capital and organizational structure. In this study, we investigate the direct and indirect impacts of ICT on product and process innovations using a panel of Canadian Workplace and Employee Survey (WES) data from 1999 to 2005, a period during which ICT was booming. We also examine the impacts of various characteristics of employers and employees, such as training, size, market, gender, education, and experience, on innovation. Our mixed logit model estimation results support the positive effects of ICT on four types of product and process innovations in different industries. Furthermore, the results suggest that ICT has an indirect impact on innovation through its interactions with organizational changes. To address potential endogeneity issue, we also estimate the average treatment effect on treated using the propensity score-matching method.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 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) | 0.002 | 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".