Roads, Electricity, and Jobs: Evidence of Infrastructure Complementarity in Sub-Saharan Africa
Bibliographic record
Abstract
Evidence for road expansion and electrification as drivers of job creation is limited and mixed, with most studies having considered either one or the other, and only in isolation. This paper estimates the average and heterogeneous impacts of road and electricity investments and the interaction of the two on job creation over the past two decades in 27 countries of sub-Saharan Africa. Exploiting the exogenous location of ancestral ethnic homelands, a new instrumental variable is created for road accessibility, inspired by post-independence leaders' agenda of building roads to extend authority over the entire expanse of their country, and to promote nation building. Topography and lightning strikes—a key source of damage to electric lines and disruption of service—are used to instrument electricity supply. The paper finds positive and significant effects on employment from enhancing proximity to roads and to electric grids. Moreover, the interaction of the two enhances the effects, making them complementary investments. The impacts of both individual and bundled investments are positive, but with differences between men and women, workers of various ages, and countries at different stages of development. In urban areas, better access to roads and electricity promotes all types of employment. In rural areas, greater access induces a transition from low- to high-skilled occupations. These differential effects suggest that the structural transformation brought about by road and electricity expansion is primarily a rural phenomenon.
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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 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".