Rural Policies, Price Change and Poverty in Tanzania: An Agricultural Household Model-Based Assessment
Bibliographic record
Abstract
Exogenous shocks to farmers' consumption, production and labour market decisions are rarely considered accurately. For farm households, under labour market imperfections, such decisions are often interlinked. This calls for non-separable agricultural household models. According to this framework, second-order (or behavioural) effects include a direct (i.e., supply or demand reactions due to an exogenous shock) and an indirect (i.e., supply or demand adjustments to the endogenous variations in the shadow wage generated by the exogenous shock) component. Under large price changes or following structural interventions, such as those concerning land redistribution or mechanisation practices, neglecting such second-order effects on consumption and production can bias the final impact on household welfare. The main objective of this study is thus to develop a robust and comprehensive tool to evaluate the effect on household welfare of different agricultural policies in Tanzania and food price changes. A two-stage estimation strategy is adopted: the shadow price of labour is first estimated and then used to estimate production and demand systems as well as labour market functions. These models are subsequently used to simulate the effect on household welfare of a hypothetical 40% increase in the price of cereals and other crops and a hypothetical 10% increase in the hectares of arable land and in the use of ox-ploughs. The results are finally compared with the case in which a separable model is adopted.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".