The economics of missionary expansion: evidence from Africa and implications for development
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract How did Christianity expand in Africa to become the continent’s dominant religion? Using annual panel census data on Christian missions from 1751 to 1932 in Ghana, and pre-1924 data on missions for 43 sub-Saharan African countries, we estimate causal effects of malaria, railroads and cash crops on mission location. We find that missions were established in healthier, more accessible, and richer places before expanding to economically less developed places. We argue that the endogeneity of missionary expansion may have been underestimated, thus questioning the link between missions and economic development for Africa. We find the endogeneity problem exacerbated when mission data is sourced from Christian missionary atlases that disproportionately report a selection of prominent missions that were also established early.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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 it