Effects of economic growth, foreign direct investment and internet use on child health outcomes: empirical evidence from South Africa
Bibliographic record
Abstract
This study examines the effects of economic growth and foreign direct investment (FDI) on child health outcomes measured by Infant Mortality Rate (IMR) and Child Mortality Rate Under 5 (CMRU5) with several control variables such as corruption, inequality and HIV among others. It analyzes South Africa's annual time series data for the period 1985–2016. As variables were found with mixed order of integration, Autoregressive Distributed Lag (ARDL) model is applied to determine cointegration and estimate short-run and long-run coefficients.Results indicate that economic growth and FDI have negative significant effects on both indicators of child health outcomes in both the short run and the long run. This implies that both economic growth and FDI contribute towards reducing IMR and CMRU5 in South Africa and thus help improve child health outcomes. Toda and Yamamoto (TY) causality test confirms causal association between these variables. Policy implications are discussed.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".