The Size Distribution of Farms and International Productivity Differences
Why this work is in the frame
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Bibliographic record
Abstract
Using internationally comparable data from the World Agricultural Census, we document a factor of 36 difference in average farm size between rich and poor countries. Small farms of less than 2 hectares represent more than 70 % of farms in poor countries but only 15 % in rich countries, whereas large farms of more than 20 hectares represent none of the farms in poor countries and almost 40 % in rich countries. Two questions emerge. First, what explains the striking differences in farm size across countries? Second, are farm-size differences important in understanding agricultural and aggregate productivity gaps across countries? We develop a two sector model with agriculture and non-agriculture that features a non-degenerate size distribution of farms. The theory embeds a Lucas (1978) span-of-control model of farm size into a standard sectoral model with non-homothetic preferences. In the model calibrated to the United States, a reduction in economy-wide productivity from 1 to 1/4 produces an increase in the share of employment in agriculture from 2.5 % to 53%, a 21-fold reduction in average farm size, and a 25-fold reduction in agricultural labor productivity. These results are broadly consistent with data on the sectoral allocation of labor and the size distribution of farms across countries.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it