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Record W4206703138 · doi:10.3386/w29061

Mechanizing Agriculture

2021· report· en· W4206703138 on OpenAlex

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.

Bibliographic record

VenueNational Bureau of Economic Research · 2021
Typereport
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural Economics and Policy
Canadian institutionsYork University
FundersSloan School of Management, Massachusetts Institute of TechnologyAgricultural Technology Adoption Initiative
KeywordsAgricultureComputer scienceGeographyArchaeology

Abstract

fetched live from OpenAlex

What are the gains from mechanization? We run a randomized control trial that subsidizes access to equipment rental markets to study how the adoption of mechanization shifts farming households' labor supply, farm productivity and labor demand. The intervention induces greater mechanization in the upstream production stage, with labor savings concentrated in downstream, non-mechanized stages. Savings on family labor are concentrated among members engaged in worker supervision and accompanied by an increase in households' non-agricultural income. To assess the welfare implications of the intervention, we build a model of heterogeneous farmers that make joint labor supply and production decisions because incentives to mechanize depend on the opportunity cost of supervising hired labor. The calibrated model predicts a consumptionequivalent welfare improvement of 7.6%, with two-thirds of those gains accruing to leisure. Welfare gains are heterogeneous despite common treatment effects. Through counterfactuals, we show that endogenous productivity gains account for relatively more of the welfare gains for farmers with low-supervision ability.

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)0.002
metaresearch head score (Gemma)0.001
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: Other · Consensus signal: none
Teacher disagreement score0.891
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.439
GPT teacher head0.486
Teacher spread0.047 · 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