Multiple Regression Analysis on the Impact of Agricultural Indicators on GDP Growth in Turkey (2010–2023)

Abstract

The main objective of this study is to evaluate the impact of indicators such as agricultural employment, agricultural inflation, and the agricultural foreign trade balance on Gross Domestic Product (GDP) growth through multiple linear regression analysis. In the economics literature, the agricultural sector is acknowledged to contribute to GDP, particularly in developing countries, via employment and foreign trade. Within this framework, agricultural price stability plays a central role in maintaining macroeconomic stability and growth potential. This study specifically aims to examine the effects of agricultural employment, agricultural inflation, and the agricultural foreign trade balance (defined as the export/import ratio) variables on Turkey’s GDP growth rate during the 2010–2023 period.

The analyses were performed using the Multiple Linear Regression method (utilizing SPSS 27), with the logarithms of the GDP Growth Rate and Agricultural Employment variables being used. The established model was found to have a high explanatory power and was statistically significant. Model reliability was confirmed through diagnostic tests (VIF, Durbin-Watson, Breusch-Pagan). In the coefficient analysis, it was concluded that agricultural inflation has the strongest and a negative effect on GDP growth. In contrast, the agricultural foreign trade balance and agricultural employment variables positively influence GDP growth. These findings indicate that combating inflation in the agricultural sector should be a priority policy area for achieving economic stability and sustained growth.