MétaCan
Menu
Back to cohort
Record W2311541644 · doi:10.1111/1911-3838.12090

Corporate Social Responsibility, Tax Aggressiveness, and Firm Market Value

2016· article· en· W2311541644 on OpenAlexaffvenueabout
Tao Zeng

Bibliographic record

VenueAccounting Perspectives · 2016
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Taxation and Avoidance
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsCorporate social responsibilityRanking (information retrieval)BusinessProfit maximizationCorporate governanceIndex (typography)Value (mathematics)ReputationEnterprise valueSocial responsibilityMarket valueBusiness administrationAccountingProfit (economics)EconomicsMicroeconomicsFinancePolitical sciencePublic relations

Abstract

fetched live from OpenAlex

This paper examines the relationship of corporate social responsibility (CSR), tax aggressiveness, and firm market value. An economic model has been developed to show that profit-maximization firms are willing to incur additional costs in CSR, such as paying more taxes, as long as they can differentiate their products from non-CSR firms, and that socially conscious consumers will buy products from CSR firms at prices higher than those of non-CSR firms. The empirical study in this paper indicates that the higher the CSR ranking of a firm, the less likely a firm is to engage in tax aggressiveness. It also indicates that a reputation of higher CSR will enhance firm market value. Using Canadian companies listed in the S&P/TSX 60 index, I find that both firms’ five-year effective tax rates and annual effective tax rates are positively associated with their overall CSR scores as well as with their social scores. Firms’ five-year effective tax rates are also positively associated with their governance index. I also find that firms’ overall CSR ranking and governance scores are positively associated with their market value.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.023
GPT teacher head0.239
Teacher spread0.216 · 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 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

Citations89
Published2016
Admission routes3
Has abstractyes

Explore more

Same venueAccounting PerspectivesSame topicCorporate Taxation and AvoidanceFrench-language works237,207