Missions, Fertility Transition, and the Reversal of Fortunes: Evidence from Border Discontinuities in the Emirates of Nigeria
Bibliographic record
Abstract
Abstract What are the origins of differences in fertility and economic transition across African societies? This paper addresses this question by causally estimating average and heterogeneous effects of colonial-era Christian missions on human capital accumulation, fertility decline, and household wealth in Nigeria. Our identification strategy exploits discontinuities in mission stations around the borders of the Emirates of Northern Nigeria, where missionary activities were restricted by the colonial administration. We find that areas with greater historical missionary activities have higher levels of schooling, lower levels of fertility, and higher household wealth today. The long-run effect of missions is not found in areas with early access to government schools, and is larger for population subgroups—women and Muslims—that have historically suffered disadvantages in access to education. Importantly, we show that the restriction of missions from the Emirates of Northern Nigeria has led to a reversal of fortunes, wherein areas that were more prosperous and institutionally developed in the past are relatively poorer today. The analysis provides novel insights into the differential pace of demographic transition between Northern and Southern Nigeria, and highlights patterns consistent with the prediction of Unified Growth Theory, whereby an improved technological environment leads to greater demand for education, triggering a fertility decline, resulting in higher incomes per capita.JEL Classification: I20, N30, N37, N47, O15, Z12.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".