Can Urbanisation Improve Household Welfare? Evidence From Ethiopia
Bibliographic record
Abstract
Abstract Despite evolving evidence that Africa is experiencing urbanisation in a different way, empirical evaluations of the welfare implications of urban-development programs in Africa remain scant. We investigate the welfare implications of recent urbanisation processes in Ethiopia using household-level longitudinal data and satellite-based nightlight intensity. We also examine the impact of urban growth on the composition of household consumption and welfare. We employ temporal and spatial variations in nightlight intensity to capture urban expansion and growth. Controlling for time-invariant unobserved heterogeneity across individuals and localities, we find that urbanisation, as measured by nightlight intensity, is associated with significant welfare improvement. We find that tripling existing average nightlight intensity in a village is associated with a 42–46% improvement in household welfare. Urbanisation is also associated with a significant increase in the share of non-food consumption, which is a good measure of overall welfare and poverty. In addition, we find significant heterogeneity in urban expansion across major towns and small towns. Urban expansion in rural areas and small towns appears more impactful than similar expansion in major cities. Finally, quantile regression results suggest that better-off households are likely to benefit more from urban expansion, which may translate into higher inequality across households or communities. Our results can inform public policy debates on the consequences and implications of urban expansion in Africa.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".