The J-Curve Effect in An Undiversified Economy: The Case of Botswana
Bibliographic record
Abstract
According to the J-curve proposition, a devaluation of the exchange rate causes a short-run deterioration of the trade balance, followed by long-run improvement. This thesis contributes to the literature by evaluating the J-curve effect in Botswana. Mining is the largest economic sector in Botswana and diamonds are the main export product. Other mineral exports are copper-nickel, soda ash and gold. Botswana is a unique country because its economic growth, trade balance and government revenue are significantly determined by diamond sales. As a result, the trade balance of the country balance is volatile and follows the dynamics of the diamond market. Economic shocks in the trading partners of Botswana such as Belgium, Switzerland and Canada, determine the demand for diamonds produced in Botswana. Botswana has previously devalued and revalued its currency to boost exports and control inflation. The COVID-19 pandemic significantly affected the economy of Botswana and caused notable disruptions in economic activities and an 8% decline in domestic output in 2020. As the economy is undiversified, the COVID-19 pandemic caused a decline in total factor productivity, high levels of unemployment, inequality and a decline in export growth. Thus, examining the J-curve effect in resource-dependent economies like Botswana will help us understand the J-curve effect better. This thesis comprises three empirical studies. The first study evaluates the asymmetric effects of exchange rates on the trade balance. The investigation contributes to the literature by examining the asymmetric effects of exchange rates on the trade balance in the face of exchange rate volatility, using a GARCH (1,1)-M SVAR model. In determining the impact of exchange rate asymmetry and volatility, previous studies assume that volatility and asymmetry are independent phenomena, even though the concepts are indivisible. The J-curve effect was found in the Botswana economy in the prepandemic period, and its trade balance responded asymmetrically to exchange rate shocks. Exchange rate volatility negatively impacts Botswana's trade balance in the short and long run. These results are important for policymaking, because they explain why devaluation is not always positive and, furthermore, highlight the role played by exchange rates in improving the trade balance. The second study contributes to the literature by examining the causality between the trade balance and its determinants using a novel time-varying Granger causality (TVGC) approach. The TVGC approach enables temporal fragilities in causal relationships to be examined through intensive subsample data analysis. Botswana is dependent on developed nations for diamond trade and the time-varying causality approach provides insights into how this dependency fluctuates over the years. The results will assist policymakers to restructure the economy, promote domestic products and measure the effectiveness of macroeconomic policies. The TVGC approach detected causality from the real exchange rate to the trade balance of Botswana spanning 2008M03-2010M07. The third empirical study forecasts Botswana's trade balance using a new signal-processing technique (Fast Iterative Filtering [FIF]) that is adaptive to structural breaks. Understanding the different movements of the trade balance over time will assist policymakers to adjust or implement policies that support export growth and trade surplus. The results indicate that fast iterative filtering outperforms the benchmarks for the trade balance of Botswana, regardless of the presence of the COVID-19 pandemic shock. In general, the extant literature on the J-curve theory focuses only on bilateral trade relations and has limited research generalisability. All the empirical studies in this project offer research generalisability by including other countries in the analysis. Subsequently, this project offers robust results and significant contributions to the literature.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".