Effect of demographic transition on economic growth: does economic freedom matter? Evidence from ECOWAS countries
Bibliographic record
Abstract
According to the economic literature, for a country to benefit from the demographic dividend, it must first undergo a demographic transition, which describes the shift of a population from a stage of high fertility and mortality to one of low fertility and mortality. This transition, which results in a temporary increase in the share of the working age population, opens up a huge window of opportunity if the sound policies are implemented. Indeed, the literature indicates that local conditions can limit the expected effects of the change in age structure on economic growth. In this study, we focus on the role of economic freedom institutions in ECOWAS region by analysing the consequences of the interaction between economic freedom indicators and the growth rate in the share of the working age population on economic growth over the period 1996–2018. To do so, the study uses a robust technique, namely the Augmented Mean Group (AMG) method, which takes into account both the dependence and the heterogeneity of the individuals in the panel. The estimation shows that an increase in the share of the working age population only has a positive effect on economic growth when countries have better economic freedom institutions. This contribution is made in particular through improvements in indicators of investment freedom, financial freedom and government integrity. These results call on policy makers in the region to improve these dimensions in particular to enable their economies to benefit from the demographic transition dividend.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".