Navigating the Governance Gap: Is Africa's Informal Sector Holding Back Economic Transformation?
Bibliographic record
Abstract
ABSTRACT Understanding how the proliferation of informal economic activities influences Africa's transformation efforts is gaining popularity. Yet, empirical evidence documenting the informality‐led structural transformation lag remains scarce. In this paper, we examined the impact of informality on African transformation while accounting for the moderating role of governance. We used a novel dataset on Africa's economic transformation sourced from the African Centre for Economic Transformation from 2000 to 2018 for 24 African countries. Estimations were done using fixed effects and instrumental variables with high‐dimensional fixed effects (IV‐HDFE) techniques, which account for bias from omitted variables and endogeneity problems. Two key findings emerged from our analysis. First, we found that informality has a significant negative impact on African economic transformation. Secondly, we found that when interacting with governance, the effects of informality on economic transformation become positive, thus showing that effective governance attenuates the negative impact of informality on economic transformation in Africa. These findings are robust to various estimations and sample splitting and have important implications for policy on African structural transformation. We concluded by emphasising the importance of government effectiveness for economic transformation in the face of high informality on the continent.
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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 | 0.002 | 0.009 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".