The Lion on the Move Towards the World Frontier: Catching Up or Remaining Stuck?
Bibliographic record
Abstract
The remarkable growth spurt reported by the Sub-Saharan African (SSA) economy since the mid-1990s offers the opportunity to revisit the narrative of its economic development experience. We investigate whether the SSA economy has initiated a gradual process of convergence which reverses the long-term fall so far behind the U.S. frontier. Our framework begins with a top-down approach that performs a nested development accounting exercise. This aggregate analysis complements a bottom-up approach that tracks the sectoral origins of the SSA aggregate relative labor productivity performance. The application of this framework to a representative sample of the SSA economy over the 1970-2010 period suggests the following set of results. After one-quarter of a century of falling behind the U.S. level of real income per capita, the SSA economy observed a swift turnaround towards the end of the 1990s, yet without showing any sign of catch-up. Second, parallel to favorable demographic developments, SSA reports a startling relative labor productivity gap which accounts for much of its relative income per capita gap. Third, the use of the concept of cognitive skills reveals that human capital considerations have worsened o_ over time, making total factor productivity no longer the biggest part of the story underlying relative labor productivity differences. Fourth, the sectoral evidence points to the coexistence of headwinds (negative within- and reallocation-effects) and tailwinds (between-effects) which tend to cancel out each other, thus preventing SSA aggregate economic performance to get anywhere closer to the world frontier even during the growth spurt period.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.011 | 0.016 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".