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Record W2895988722 · doi:10.1111/1911-3846.12464

The Effects of Competition from S Corporations on the Organizational Form Choice of Rival C Corporations

2018· article· en· W2895988722 on OpenAlexvenueno aff
Michael P. Donohoe, Petro Lisowsky, Michael Mayberry

Bibliographic record

VenueContemporary Accounting Research · 2018
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Taxation and Avoidance
Canadian institutionsnot available
FundersSloan School of Management, Massachusetts Institute of TechnologyUniversity of Illinois at Urbana-ChampaignUniversity of Florida
KeywordsCompetition (biology)BusinessCorporationCorporate taxMonetary economicsMetropolitan areaRevenueEconomicsTax avoidanceAccountingFinanceDouble taxation

Abstract

fetched live from OpenAlex

ABSTRACT Subchapter C of the U.S. Internal Revenue Code levies an entity‐level tax on corporate profits, whereas Subchapter S allows corporations meeting specific criteria to elect out of this tax. Despite these differences, C and S corporations regularly compete for customers and capital. We examine whether and the extent to which competition from S corporations influences the future organizational form choice of rival C corporations and explore outcomes of this choice. Using data for 4,462 private U.S. commercial banks grouped by Metropolitan Statistical Area during 1997–2010, we find that greater competition from S corporation banks increases the likelihood that rival C corporation banks convert to Subchapter S status. We estimate that the aggregate first‐year tax savings from S conversion exceed $372 million. Consistent with these savings being used to maintain competitive parity with rivals, we find that converting banks increase their interest rates on customer deposits and advertising intensity. Our findings provide insight into whether competition from tax‐advantaged firms influences the organizational form choice of rival tax‐disadvantaged firms.

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)0.002
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.537
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.064
GPT teacher head0.299
Teacher spread0.236 · 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 designTheoretical or conceptual
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

Citations24
Published2018
Admission routes1
Has abstractyes

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