MétaCan
Menu
Back to cohort
Record W4362471988 · doi:10.1257/mac.20170229

Land Misallocation and Productivity

2023· article· en· W4362471988 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

VenueAmerican Economic Journal Macroeconomics · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicLand Rights and Reforms
Canadian institutionsUniversity of TorontoYork University
Fundersnot available
KeywordsTotal factor productivityEconomicsProductivityRentingCapital (architecture)Agricultural productivityPovertyAgricultureInequalityAgricultural economicsNatural resource economicsLabour economicsMacroeconomicsEconomic growthGeography

Abstract

fetched live from OpenAlex

Using detailed household-level data from Malawi on physical quantities of agricultural outputs and inputs, we measure farm total factor productivity (TFP), controlling for land quality, rain, and transitory shocks. We find that operated land size and capital are essentially unrelated to farm TFP, implying substantial factor misallocation. The agricultural output gain from a reallocation of factors to their efficient use among existing farmers is a factor between 1.7- and 2.8-fold. We provide suggestive evidence connecting misallocation with the extent of land markets and illustrate how an efficient allocation via rental markets can substantially reduce agricultural income inequality and poverty. (JEL D24, D31, I32, O13, O15, Q12, Q15)

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.872
Threshold uncertainty score0.294

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
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 head0.009
GPT teacher head0.208
Teacher spread0.199 · 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