Tourism and Economic Growth in Kenya: A Time Series Analysis

Aims: To investigate the dynamic relationship between international tourism inflows and economic growth in Kenya over the period 2012-2023 using time series analysis.

Methodology: Annual data on real Gross Domestic Product (GDP) and international tourist arrivals were analyzed using unit root tests, Johansen cointegration analysis, vector error correction models (VECM), Granger causality tests, and forecast error variance decomposition techniques. The optimal lag order was chosen based on the Akaike Information Criterion.

Results: The Johansen cointegration test revealed one cointegrating equation (trace statistic = 35.52, p < 0.05), indicating a long-run equilibrium relationship between GDP and tourism arrivals. The VECM showed a significant error correction term for GDP (-3.1857, p < 0.01), confirming long-run causality. However, the Granger causality test failed to reject the null hypothesis that tourism arrivals do not Granger-cause GDP (F-statistic = 0.1626, p = 0.8543). Forecast error variance decomposition showed that by the 10th period, 99.9% of tourism arrivals variance was attributed to its shocks, while GDP shocks contributed only 0.1%.

Conclusion: While a long-run equilibrium relationship exists between tourism and economic growth in Kenya, there is no evidence of short-run causality from tourism arrivals to GDP. The results suggest that factors specific to the tourism sector, rather than broader economic conditions, have a more substantial influence on tourism arrivals in the long run. Policymakers should focus on developing targeted strategies to enhance the tourism sector's linkages with other economic sectors to maximize its potential for driving sustainable economic growth. Practical recommendations include diversifying tourism products, improving tourism infrastructure, and implementing marketing strategies to attract high-value tourists.