The impact of South African Rand exchange rate volatility on Botswana’s long-haul inbound leisure tourists’ arrival
Bibliographic record
Abstract
In recent years, African Continental cooperation and integration have gained momentum with a renewed push by establishing the African Continental Free Trade Area (AfCFTA). However, the continent is still confronted by multiple regional economic communities (RECs) with fragmented trade policy dynamics that provide limited “within and between” bloc integration. Structural regional setup challenges characterize the Southern African Development Community; amongst those, the regional bloc is highly anchored to the South African (SA) economy. Therefore, as the regional economic hub, SA has an enormous policy spillover effect with severe economic repercussions. As the AFCFTA is built around the liberalization of goods and services across the continent, the studypaid particular attention to the South African exchange rate policy dynamics' effect on the tourism sector in SADC, using Botswana as a case study. The main objective of the study was to investigate the impact of South African Rand volatility on Botswana’s inbound leisure tourists’ arrival using annual tourists’ arrival data of 8 significant overhaul tourists' source markets for Botswana, namely, USA, Canada, UK, Germany, France, Netherlands, Australia and New Zealand for a period of 2005-2019. The study findings are expected to provide insight and unearth key negotiation points in regional and continental tourism market reform for improved international market competitiveness and access for all. The novelty of this study is that it utilized annual longhaul inbound leisure tourism data and an efficient Pseudo-Poisson Maximum Likelihood (PPML) analytical method to examine the impact of South African Rand exchange rate volatility on Botswana tourism. The study findings revealed that ZAR volatility has a negative effect on Botswana's inbound leisure tourism demand. Further, the results suggest that an increase in South African long-haul inbound leisure tourists' arrival consequently translates to increased inbound leisure tourists’ arrival in Botswana. These findings provide critical policy implications; first, the ZAR stability is vital for the realization of improved Botswana’s international tourism market competitiveness, and second, collaborative tourism destination branding and marketing for the two countries is crucial for enhanced tourist' arrival in Botswana
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".