<i>Givers of Great Dinners Know Few Enemies</i>: The Impact of Food Sufficiency and Food Sharing on Low-intensity Household Conflict in Eastern Democratic Republic of Congo
Bibliographic record
Abstract
Our study establishes a linkage between household food sufficiency and food sharing behaviour with the reduction of low-intensity, micro level conflict using primary data from 1763 households of eastern Democratic Republic of Congo. We develop a theoretical explanation of such behaviour using the seminal theories of dissatisfaction originating from food insecurity and the reciprocity of gifts in economic anthropology. We first examine if food sufficient households are less likely to engage in low-intensity conflict. Following, we investigate possible heterogeneous effects of food sufficiency, conditional on food sharing behaviour. Using propensity score matching, we find that food sufficiency reduces household conflict risk by an average of around 10 percentage points. Upon conditioning on food sharing behaviour, we find that conflict risk in the subpopulation of food sufficient households is 13.8 percentage points lower for households that share their food while the effects disappear for households that do not share their food. Our results hold through a rigorous set of robustness checks including doubly robust estimator, placebo regression, matching quality tests and Rosenbaum bounds for hidden bias. We conclude that food sufficiency reduces low-intensity conflict for households only in the presence of food sharing behaviour and offer explanations and policy prescriptions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Open science | 0.000 | 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".