Estimating Heterogeneous Effects of Land Titling on Rural Household’s Agricultural Productivity: Evidence From the Southern Highland Regions of Tanzania
Bibliographic record
Abstract
This paper analyses the effect of land titles on agricultural productivity in the southern highland regions of Mbeya and Ruvuma and assesses the potential mediating effect of access to credit. The contribution of this paper to the existing literature is threefold. First, it contributes to the general literature on the impact of land titling on agricultural performance. Second, it investigates whether access to credit is an important mediating variable. Third, it assessed whether households respond differently depending on farmer and land characteristics. To contribute to the evidence on the impact of land titling four hypotheses were tested: Since the study is based on observational data, propensity score matching technique was employ to determine the land titling effects. The findings suggest that land titles have a statistically significant positive effect on productivity. This can at least partially be explained by an increase in credit access for titled households. The study results further suggest heterogeneous effects of titles, which vary with age of the head of household and size of land cultivated.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".