R&D, Innovation and The Business Cycle
Bibliographic record
Abstract
Technological Innovation is a major driver of long-run economic development. It increases wealth \ngeneration capacity, productivity, and rewards innovators with temporary monopoly profits. Part of the \ninnovation effort, investments in R&D are uncertain and long-run oriented, and absent market frictions, \nshould not be impacted by current economic conditions. This thesis investigates whether firms’ R&D \nexpenses respond to changes in short-run macroeconomic activity in the G7 countries, while considering \nthe endogenous relation between innovation and economic growth. Empirical studies attribute the procyclical behavior of R&D, especially in the U.S, to financial constraints that drive firms away from the \noptimal allocation of resources. Moreover, substantial evidence indicates that managers manipulate \ndiscretionary expenses to meet their current earnings targets, compromising other dimensions of the \nbusiness, such as future growth. We study whether macroeconomic conditions affect firm-level R&D \ninvestments beyond what can be explained by financial factors and growth opportunities. Our results show \nthat contemporaneous macroeconomic conditions, as well as access to financing, are positively associated \nwith firm-level R&D intensity, most notably in young and high-tech firms. We find a positive association \nwith the 1-year lagged economic conditions in the U.S and Canada, but not in the other countries of the \nG7. Recessions are especially significant in explaining cuts in R&D investments in all countries. Therefore, \nR&D seems to be pro-cyclical regardless of financial constraints, suggesting that the latter relation with \nearnings management activity might be complimentary, rather than mutually exclusive. We address the \nendogenous nature of innovation and economic growth by estimating R&D and GDP in a panel Vector Auto \nRegression (PVAR) and find no evidence that reverse causality impacts our results. Furthermore, although \naggregate level and the weighted average change in R&D of our sample are reasonably correlated, we do \nnot find a relevant long-run relation between firm-level R&D intensity and economic activity, which calls \nfor further investigation.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".