The Causality Relationship Between Financial Sector Profitability and the Botswana Economy
Bibliographic record
Abstract
Previous research has lamented on both the importance and the symbiotic relationship between financial sector performance and the state of a country’s economy. Findings of these studies are generally in concert in that the performance of the financial sector is intertwined with macroeconomic indicators. There is, however, a difference in opinion on the precise nature of the relationship between these sets of variables. This difference of opinion has led to the development of three parallel strands of theory: demand-driven relationship; supply-driven relationship; and economic developmental stage. To the extent that an understanding of the precise nature of the relationship between these critical variables would promote economic growth, it was found imperative to investigate the phenomenon in the context of a developing economy. This paper examines the causal relationship between the financial sector index and GDP in Botswana. The study uses data over a period of 10 years (2003-2013). This study timeframe is significant because previous research does not incorporate the critical periods in financial markets history over which the global economy experienced the economic cycle of the boom years (2003-2006), followed by a recession (2007-2010) and finally the recovery period (2010 and beyond). This financial cycle provides a unique opportunity for new insights into how financial sector performance relates to the economy. The findings are suggestive of an existence of a stable long-run relationship between the financial sector and the economy. In addition, the results show that the economy granger-cause the financial sector index with no reverse causality observed. Policy implications of these findings are discussed in the paper.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".