Effects of Exchange Rate, Inflation, and Interest Rates on Tea Exports in Kenya
Bibliographic record
Abstract
The international tea market exhibits significant price volatility, creating challenges for household incomes, food security, and government revenues in tea-producing nations. As a leading global tea exporter, Kenya faces similar risks due to fluctuations in tea export prices. This study explored the impact of selected macroeconomic variables, namely inflation rate, interest rates, and exchange rates, on Kenyan tea export prices, aiming to understand how these factors influence prices in international markets. Its objectives were to examine the effect of exchange rate changes, assess inflation’s influence, and evaluate the impact of interest rates on tea export prices. Using a quantitative research design, the study analyzed quarterly time-series data from 2000 to 2022, incorporating quarterly dummy variables (with the fourth quarter as the reference) to account for seasonal variation. Descriptive statistics and the Augmented Dickey-Fuller (ADF) test ensured stationarity, while Ordinary Least Squares (OLS) regression estimated the relationships between each macroeconomic variable and tea export prices. Findings indicated that exchange rates and inflation significantly drove short-term price fluctuations, with exchange rate depreciation linked to price changes, and inflation showing a positive same-quarter effect but a negative effect in later quarters, reflecting delayed market adjustments. Interest rates had no significant impact. The study also noted price persistence and seasonal trends, particularly higher price changes in the third quarter. It concludes that stabilizing exchange rates and inflation, especially during high-demand quarters, is crucial for sustaining tea export earnings, while recognizing the influence of unobserved factors on price variability.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".