Gross domestic product, savings, investment and inflation, an ARDL approach and Toda-Yamamoto causality: Evidence from Zimbabwe
Bibliographic record
Abstract
This study examined the causal relationships between inflation, Gross Domestic Product (GDP), domestic savings, and investment in Zimbabwe using Toda-Yamamoto causality tests and the Autoregressive Distributed Lag (ARDL) approach with secondary data spanning from 1990-2022. The Granger causality analysis revealed a bidirectional causal effect between inflation and GDP, indicating that inflation significantly impacts the country's economic growth. Additionally, the analysis showed a unidirectional causal relationship from inflation to domestic savings, suggesting that high and persistent inflation can erode the value of existing savings and discourage individuals from saving. Furthermore, the study found a distinct causal flow from savings to investment, without feedback in the opposite direction, highlighting the crucial role of a robust savings culture in providing the necessary foundation for sustained investment and economic growth. The ARDL approach provided further insights into the dynamic relationships between these variables. In the short run, lagged GDP and current and lagged savings positively influenced GDP, while the second lag of savings had a negative impact, supporting the Carroll-Weil hypothesis that savings typically follow, rather than precede, economic growth in the short run. The analysis also found a positive short-run and long-run relationship between investment and GDP, supporting the view that investment is an important factor of economic growth. The study recommends that the policy makers can leverage the synergies between savings, investment, and inflation management to foster sustained economic growth and development in line with the government development policies. Developing policies to attract savings and reduce the cost of savings, as well as promoting long-term savings over transactional savings, can increase the country's overall savings base.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".