MétaCan
Menu
Back to cohort
Record W4313422466 · doi:10.26509/frbc-wp-202239

Corporate tax cuts and the decline of the manufacturing labor share

2022· report· en· W4313422466 on OpenAlex
Barış Kaymak, Immo Schott

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueWorking paper · 2022
Typereport
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Taxation and Avoidance
Canadian institutionsUniversité de Montréal
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsWage shareLabour economicsEconomicsCapital (architecture)Distribution (mathematics)Corporate taxCapital intensityMarket shareManufacturing sectorBusinessMarket economyTax reformHuman capitalTax avoidanceEfficiency wageWage

Abstract

fetched live from OpenAlex

We document a strong empirical connection between corporate taxation and the manufacturing labor share, both in the US and across OECD countries. Our estimates associate 30 percent to 60 percent of the observed decline in labor shares with the fall in corporate taxation. Using an equilibrium model of an industry where firms differ in their capital intensities, we show that lower corporate tax rates reduce the labor share by raising the market share of capital-intensive firms. The tax elasticity of the labor share depends on the joint distribution of labor intensities and value added at the micro level. Given the empirical distribution in the US manufacturing sector, our quantitative analysis suggests that corporate tax cuts explain a significant part of the decline in the manufacturing labor share since the 1950s. The shift away from traditionally large, labor-intensive production units raised the concentration of market shares and reduced the concentration of employment.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.727
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.044
GPT teacher head0.232
Teacher spread0.188 · 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