Bibliographic record
Abstract
This dissertation investigates the returns to education for households in rural Peru. In these and similar areas of the developing world, economic activity is typically observed at the household rather than at the individual level. Estimating the returns to education and other labour supply parameters becomes a complicated task of identification when the majority of income is derived from household level ventures such as farming and non-farm self-employment activities. This task is further complicated by imperfections in local markets, such as job rationing and poor access to markets. As a result, wages are not observed for a substantial proportion of economic activity. After a description of education in rural Peru in the first essay, the analysis turns to an investigation of the channels through which education affects earnings at the household level. Issues of aggregation over household members are discussed in the second essay. The analysis reveals that an allocative effect dominates education's effect on household earnings: households with more educated members are more likely to diversify their sources of income and generate more income from non-subsistence and non-farm activities. The last essay decomposes household earnings returns to schooling into various structural parameters of labour supply behaviour. In particular, this essay develops a simple methodology that allows the estimation of wage-dependent parameters, such as the elasticity of labour supply and the wage returns to education, when wages are unobservable. This analysis exploits regional variation in access to and the development of local markets in the problem of identification. Results indicate that education affects earnings disproportionately to its effect on hours. Thus, the results provide evidence of wage returns to education in rural Peru. Furthermore, the evidence provided in this dissertation quantifies the role of local market development in the determination of the returns to education. In more developed areas, education facilitates access to lucrative opportunities. These opportunities provide better wages and better hours than those in more isolated areas that depend more on subsistence agriculture. As a result, the interaction between education and local market development improves household welfare by increasing earnings and the consumption of leisure.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 source (direct Gemma or distilled Codex), 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".