The Effects of Climate Change in the Poorest Countries: Evidence from the Permanent Shrinking of Lake Chad
Bibliographic record
Abstract
Empirical studies of the economic effects of climate change largely rely on climate anomalies for causal identification purposes. Slow and permanent changes in climate-driven geographical conditions, that is, climate change as defined by the Intergovernmental Panel on Climate Change, have been relatively less studied, especially in Africa, which remains the most vulnerable continent to climate change. This paper focuses on Lake Chad, which used to be the 11th largest lake in the world. Lake Chad, which is the size of El Salvador, Israel, or Massachusetts, slowly shrank by 90 percent for exogenous reasons between 1963 and 1990. While the water supply decreased, the land supply increased, generating a priori ambiguous effects. These effects make the increasing global disappearance of lakes a critical trend to study. For Cameroon, Chad, Nigeria, and Niger—25 percent of Sub-Saharan Africa’s population— the paper constructs a novel data set tracking population patterns at a fine spatial level from the 1940s to the 2010s. Difference-in-differences show much slower growth in the proximity of the lake, but only after the lake started shrinking. These effects persist two decades after the lake stopped shrinking, implying limited adaptation. Additionally, the negative water supply effects on fishing, farming, and herding outweighed the growth of land supply and other positive effects. A quantitative spatial model used to rationalize these results and estimate aggregate welfare losses, which accounts for adaptation, shows overall losses of about 6 percent. The model also allows studying the aggregate and spatial effects of policies related to migration, land use, trade, roads, and cities.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".