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Record W1570615037 · doi:10.34989/swp-2002-21

Entrepreneurial Risk, Credit Constraints, and the Corporate Income Tax: A Quantitative Exploration

2021· preprint· en· W1570615037 on OpenAlexaff
Césaire Meh

Bibliographic record

VenueRePEc: Research Papers in Economics · 2021
Typepreprint
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Finance and Governance
Canadian institutionsBank of Canada
Fundersnot available
KeywordsBusinessIncome taxCredit riskCorporate taxMonetary economicsEconomicsDouble taxationPublic economicsTax avoidanceFinance

Abstract

fetched live from OpenAlex

This paper describes the positive effect that corporate income tax has on capital formation in the presence of liquidity constraints and uninsurable risk. The author uses a dynamic general-equilibrium model in which individuals choose whether to become entrepreneurs or workers. Workers save by holding corporate equity and therefore are subject to double taxation, as the return on their savings is taxed at both the corporate and personal level. Entrepreneurs, on the other hand, save by investing in their businesses and are taxed only at the personal level. This differential tax treatment results in an increase in capital accumulation because entrepreneurs must save in response to liquidity constraints and uninsurable risk. A calibrated version of the model is used to quantify the consequences of eliminating the corporate income tax. Interestingly, the removal of the corporate income tax decreases capital formation: by eliminating double taxation, the return on workers' savings increases, which in turn reduces the number of entrepreneurs. Consequently, the stock of capital decreases, since entrepreneurs have a higher marginal rate of saving than workers, as they save not only for life-cycle motives but to self-insure against business risk and to start and finance their businesses.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0030.004
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.000

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 head0.054
GPT teacher head0.276
Teacher spread0.221 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations5
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueRePEc: Research Papers in Economics→Same topicCorporate Finance and Governance→French-language works237,207→