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Record W4319313692 · doi:10.1111/1911-3846.12853

The effect of <scp>US</scp> tax reform on the taxation of <scp>US</scp> firms' domestic and foreign earnings

2023· article· en· W4319313692 on OpenAlexvenueno aff
Scott Dyreng, Fabio B. Gaertner, Jeffrey L. Hoopes, Mary Vernon

Bibliographic record

VenueContemporary Accounting Research · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Taxation and Avoidance
Canadian institutionsnot available
FundersUniversity of Illinois at ChicagoUniversity of Illinois at Urbana-ChampaignUniversity of Miami
KeywordsMultinational corporationEarningsTax reformBusinessMonetary economicsTax avoidanceIncome taxIndirect taxEconomicsLabour economicsInternational economicsPublic economicsFinance

Abstract

fetched live from OpenAlex

Abstract We quantify the immediate net effect of the Tax Cuts and Jobs Act (TCJA) on the tax burden of corporate profits for public US corporations. We find similar reductions in effective tax rates for domestic and multinational firms, yet the entirety of multinational tax savings stemmed from tax savings on their domestic, not foreign, earnings. We find no significant change in the federal tax burden on foreign earnings neither on average norspecifically for firms most likely to be subject to new anti‐abuse provisions. We find some evidence that firms not targeted by anti‐abuse provisions saw reductions in their federal tax burden on foreign income. Overall, while the tax burden on domestic income decreased significantly, our findings suggest the tax burden on the foreign earnings of US multinationals is largely unaffected despite the overhaul of the international tax system. Importantly for US multinationals' investment decisions, while foreign income was heavily tax‐favored prior to tax reform, we find that foreign and domestic incomes are similarly taxed after TCJA enactment.

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.

How this classification was reachedexpand

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)0.010
metaresearch head score (Gemma)0.017
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.158
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
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 head0.037
GPT teacher head0.289
Teacher spread0.252 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations30
Published2023
Admission routes1
Has abstractyes

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