Asset Inequality without Markets: Evolution of Household Land Holding in a Peasant Community, Peruvian Amazon
Bibliographic record
Abstract
Observers often point to markets as a major cause of asset inequality; markets, however, need not be the only cause of the gap between richer and poorer households. In this paper, we report on the determinants of land inequality in a peasant community from the Peruvian Amazon where land and labour markets are all but absent. The data gathered in 1994/5 through in-depth interviews with agroforestry-reliant households (n=36) -on farming practices, land holding, demographic characteristics, incomeexpenditures and household non-land wealth -allow us to create a panel data set to study land accumulation and inequality since 1960. During this period, new forest land became increasingly scarce around the community as the frontier closed. Land holdings in 1994 were highly unequally distributed with 20% of households holding 58% of the land. Results of regression analyses indicate that households with larger land holdings tend to be older (to a point), richer in in-house labour, access to more fertile land (yarinal) and who began with a larger initial endowment of land. Analysis of Gini coefficients over time indicate that land inequality fell sharply between the late 1960s and the Agrarian Reform of the early 1970s but then rose thereafter, reaching pre-Reform levels by the early 1990s. Decomposition of overall land inequality through time points to the importance of household acquisition of more fertile land, of holding land in forest fallow and of means other than claiming (e.g., gifts, state-transfers, bribes) in contributing to land inequality, especially since the early 1980s. Our findings suggest the importance of competition for scarce land through traditional allocative institutions and means rather than markets in understanding land inequality in traditional agrarian societies.
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How this classification was reachedexpand
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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".