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Record W4312806340 · doi:10.3982/ecta18773

Achieving Scale Collectively

2022· article· en· W4312806340 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

VenueEconometrica · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInnovation and Socioeconomic Development
Canadian institutionsKellogg's (Canada)
Fundersnot available
KeywordsRentingProductivityIndustrial organizationProduction (economics)SubsidyScale (ratio)WorkaroundBusinessPoint (geometry)EconomicsMicroeconomicsLabour economicsMarket economyEconomic growth

Abstract

fetched live from OpenAlex

Many firms in developing countries could be too small to adopt modern technology embodied in expensive production machines. This paper shows that rental market interactions allow these small firms to increase their effective scale and mechanize production. We conduct a survey of manufacturing firms in Uganda, which uncovers an active rental market for large machines between small firms in informal clusters. We then build an equilibrium model of firm behavior and estimate it with our data. We find that the rental market is quantitatively important for mechanization and productivity since it provides a workaround for other market imperfections that keep firms small. The rental market also shapes the effectiveness of development policies to foster mechanization, such as subsidies to purchase machines. Overall, our results point to the importance of taking into account firm‐to‐firm interactions within informal clusters to understand technology adoption in low income countries: focusing on the small scale of firms in isolation might be misleading.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.522
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0100.001

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 head0.015
GPT teacher head0.189
Teacher spread0.175 · 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