Climate Change, Fertility and Sahelian Demographics
Bibliographic record
Abstract
Climate change, especially in Africa’s central Sahel region, is occurring in the context of exponential population rise with countries like Chad and Niger still in the “early expanding phase” of demographic growth. While many experts predict a mid-century climate and demographic ‘mega crisis’ for the region; our paper looks at the effect of the rising temperature, through the medium of increased temperature and precipitation variability upon fertility and hence demographic trends as we advance into the 21st century. The paper uses climate data and DHS (Demographic and Health Survey) data from Chad, which has demonstrated significant warming since the late 1960’s. We create a weather shock variable that is defined as a t>2 departure from the post-1960 mean of temperature and precipitation by month, year, and GIS location. We regress the following years’ human fertility outcomes by month and GIS location upon these shocks when occurring in the growing months of June, July, and August. We find that the effect is highly negatively significant with a one-year lag. Then we go on to look for the mechanism behind this significance; the literature suggesting that climate effects fertility through biological or food security related channels. We then regress the male/female sex ratio on the same weather shocks to see if there is a rise in miscarriages among male fetuses due to either the direct effect of heat or as an effect of increased female malnutrition. By running both these models with weather shocks from Chad’s dry season months of December, January, and February, we discern whether the significance is driven by pure temperature or by some sort of food security/household income channel. Though we see some dry season effect, most of the significance is driven by the shocks in the growing season and with the significant effect of these shocks on the sex ratio, we can assume that increased female malnutrition is a key driver of both the rise in miscarriages and the drop-in fertility. We then run these models within each of the three Chadian climate zones: the Sahara, the Sahel, and the Sudan. Seeing that the Sahel zone is the major driver for the significance of our models, we discuss if this can have implications for the wider African Sahel.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".