Land Titling, Local Governance and Investment: An Empirical Investigation in Tanzania
Bibliographic record
Abstract
The role of property rights in resource allocation has been one of the central themes in development economics. Empirical clarity has been lacking; however, due to possible endogeneity of titles, unobserved heterogeneities and the non-experimental nature of the data. In addition, local political arrangements could encroach on the legitimacy and security that government titles provide. This study introduces new information that captures plot owners’ (dis)approval of local governance structures and its implications on the titling-investment relationship. We find that titles will have the expected outcomes when there is higher level of approval of local administrative units by plot owners. Using the 2010/2011 Tanzania Living Standards Measurement Survey data, we show that the effects of titling on investment is positive and sizable. However, the investment return from titling is either negative or nonexistent when there is a higher level of disapproval of the local governing units by plot owners. Simply providing titles might not help investment without underlying changes in local governance and hence perceptions and legitimacy of local governance structures. Investment on land depends not on titling per se, but the future security titling might provide which in turn depends on the sense of approval (or disapproval) owners accord to the local the administrative structure and their functions.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".