Statistical Analysis of Effect of Population on Economic Growth in Uganda (2000-2020)
Bibliographic record
Abstract
This study was aimed at examining the effect of population on economic growth in Uganda from 2000 to 2020. Specifically, the objectives were to; examine the Effect of age dependency ratio on economic growth in Uganda, to establish the effect of total fertility rate on economic growth in Uganda, and to assess the effect of enrollment in primary schools on economic growth in Uganda. A longitudinal study design was used to study the effect of population increase in Uganda from years 2000 to 2020 and relevant data were sourced World Bank Development Indicators database. Augmented Dickey Fuller test was applied for test of stationarity. Similarly, the test for classical linear regression model (CLRM), autocorrelation and heteroscedasticity assumptions was done. Multiple linear regression model was employed to model the effect of the increase in population on economic growth in Uganda. The study found a statistically significant negative effect of age dependency ratio on economic growth (β = ?1.201591, P-value = (0.029) β = ?6.465372, P-value = (0.046) have a significant effect on economic growth in Uganda. Conclusively, growth in age dependency ratio and increase in total fertility rate significantly reduces the economic growth in Uganda.
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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".