A STUDY OF REGRESSION ANALYSIS ON FOOD PRICE FLUCTUATON IN DEVELOPING COUNTRIES—A CASE STUDY OF KENYA

Discussions on the state of food price fluctuations comes hand in hand with the challenge of food insecurity. Food insecurity is a systemic problem all over the world. Data provided by the Food and Agriculture Organization, according to Global Report on Food Crises (GRFC) 2025, show that an estimated number of 295 million people worldwide experienced some form of food insecurity in 2024. The report highlights that acute food insecurity and malnutrition are worsening, with a rise in the number of people experiencing severe hunger. The Government of Kenya has in the past deployed some interventions to solve food insecurity through efforts mainly on supply-side food production issues, such as subsidized farm inputs, affordable farm machinery, pesticides, and fertilizers. There is scarce empirical research conducted on food price instability, especially in the developing world. Furthermore, there is also an urgent need to develop a system that tracks and catalogs efforts made to tackle food price fluctuations. Other factors affecting food inflation are still unresolved such as fuel costs, currency exchange rates, climate change, low investments in R&D, high cost of agricultural inputs, high cost of fuel, punitive export policies, an increased capacity of food banks for relief efforts, etc. This study investigated these factors contributing to food price fluctuations in Kenya. This paper expounds on the use of regression analysis to analyze food price movements in Kenya with an intention of predicting and implementing policies beforehand to avoid food price shocks. Food prices are at the heart of food security, which is a challenge in the developing world. Progress in food price fluctuations can only be realized by comparing how the situation has changed over time, and statistical techniques such as regression analysis provide a good starting point for the data-driven decision-making processes required in carefully evaluating food price volatility to solve this systematic problem.