Financial Sector Innovation and Economic Growth in the Context of Botswana
Bibliographic record
Abstract
The objective of this study is to examine the role of financial sector development on economic growth using quarterly time series data for the period 2006-2014. We used Autoregressive Distributed Lag (ARDL) model to estimate the impact of technological innovation (Automated Teller Machines {ATMs} and Electronic Funds Transfer at Point of Sale{EFTPOS}), business innovation (bank deposits and credit to private sector) and other determinants of economic growth (inflation, trade and interest rate) on economic growth. The results show that both the technological and business innovation variables have a positive impact on economic growth. Therefore, policies aimed at promoting more distribution and nationwide spread of ATMs and EFTPOS more particularly in rural areas where they are scarce would boost the growth of the economy. In addition, The Global Competitiveness Report (GCR) asserted that Botswana’s financial market is still undeveloped and fall short to the development level of middle income countries. GCR identified the quality of the education system as the main factor dragging the development of the financial sector down. It is focused more on academic achievement rather than equipping learners with practical skills and work experience that can support the national innovative initiatives.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".