Can Land Title Reduce Low-Intensity Interhousehold Conflict Incidences and Associated Damages in Eastern DRC?
Bibliographic record
Abstract
The broad aim of this study is to evaluate the effectiveness of land reform policy as a sustainable tool for averting low-intensity local conflict and protecting vulnerable households in conflict and post-conflict societies from adverse consequences of conflict. Empirical studies on micro-level conflict have been limited on two fronts - the difficulty of collecting survey data from conflict prone societies, and a general lack of attention to the consequences of low-intensity local conflict. This paper attempts to address both these limitations. Using a survey on violent and non-violent conflict experiences of 1582 farming households from the postwar society of North Kivu, in eastern Democratic Republic of Congo (DRC), I explore whether land title can i) lower the probability of low-intensity conflict between households; and ii) lower the damages for households in the event of a conflict. To address concerns of potential selection bias, I employ the quasi-experimental estimation technique of propensity score matching (PSM). A rigorous set of tests and sensitivity analyses ensures both the quality of matching and reliability of estimates. These findings show that land title reduces a household’s probability of experiencing low-intensity interhousehold conflict roughly between 10 and 18 percentage points. However, I find no evidence that households with land title are subject to lower damages in the event of a conflict. These findings suggest that in vulnerable societies with high exposure to conflict, land reform programs that just grant title to households may reduce conflict to some extent but will not necessarily reduce the adverse consequences associated with conflict. Thus, land title is not a panacea for all conflict related adversities and cannot serve as a stand-alone tool for reducing adversities associated with conflict. Further research is required on whether supplementing land reform programs with policies such as promoting good governance and strengthening local institutions can sustainably promote peaceful societies.
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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 | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".