Cross-Sectional and Time-Series Analysis of Agriculture and GDP Growth: A Ten-Year Comparative Study (2014–2023)

Abstract This study examines the relationship between agriculture value added and economic growth using cross-sectional and time-series panel data for ten selected countries over the period 2014–2023. Agriculture continues to play a significant structural role in many developing economies, while its relative contribution has declined in advanced economies due to structural transformation. Using secondary data obtained from the World Bank World Development Indicators and the International Monetary Fund World Economic Outlook database, the study constructs a balanced panel dataset comprising 100 observations. The independent variable is agriculture, forestry, and fishing value added as a percentage of GDP, while the dependent variable is real GDP growth (annual percent). Descriptive statistics, time-series trend analysis, Pearson correlation, and panel regression techniques were employed to analyze the data. The results indicate a moderate positive correlation (r = 0.28) between agriculture’s share in GDP and economic growth. Regression analysis reveals that agriculture share has a statistically significant positive impact on GDP growth at the 5 percent level (p < 0.05), leading to the rejection of the null hypothesis. However, the explanatory power of agriculture alone remains moderate, suggesting that growth dynamics are influenced by multiple structural factors. The findings confirm that agriculture remains economically relevant, particularly in emerging and developing countries, and plays a stabilizing role during periods of economic disruption. The study concludes that while structural transformation reduces agriculture’s relative GDP share over time, its contribution to economic growth continues to be significant within broader development processes.