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Record W3002154064 · doi:10.1111/1911-3846.12588

R&D Investments and Tax Incentives: The Role of Intra‐Firm Cross‐Border Collaboration*

2020· article· en· W3002154064 on OpenAlex
Jing Huang, Linda K. Krull, Rosemarie Ham Ziedonis

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueContemporary Accounting Research · 2020
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicInnovation Policy and R&D
Canadian institutionsnot available
Fundersnot available
KeywordsMultinational corporationIncentiveIntellectual propertyBusinessMonetary economicsInternational economicsIndustrial organizationEconomicsMicroeconomicsFinancePolitical scienceLaw

Abstract

fetched live from OpenAlex

ABSTRACT U.S. multinational corporations increasingly use intra‐firm, cross‐border research collaboration to disperse R&D across different countries. This paper investigates the implications of such collaboration on the abilities of firms to garner benefits from R&D tax incentives. We find that the association between R&D intensity and tax incentives is three to five times larger when firms have extensive cross‐border collaboration connected to a country. We also find that the effect is stronger when local intellectual property protection is weaker and when local innovation resources are higher. Our results suggest that cross‐border collaboration helps firms achieve more tax‐efficient R&D investments both by reducing the nontax frictions posed by weak intellectual property protection and by increasing the nontax benefits of foreign R&D.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.891
Threshold uncertainty score0.378

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.105
GPT teacher head0.376
Teacher spread0.271 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it