Financial Sector Innovation and Economic Growth in the Context of Botswana
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it