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Paper short abstract:

This study explores how GMO labeling impacts consumer behavior, pricing, and nutrition using retail data and machine learning. We identify key drivers of preferences, price sensitivities, and dietary shifts, offering insights for policymakers to refine strategies and promote sustainable food systems

Paper long abstract:

Genetically modified (GM) products offer immense potential to address global food security and sustainability by boosting agricultural productivity, reducing resource use, and mitigating environmental impacts. Research shows that GMOs can significantly increase crop yields, lower reliance on pesticides, and improve farmer incomes. With the introduction of mandatory GMO labeling in the U.S., understanding consumer responses to GM products has become critical for shaping effective policies and market strategies. This study investigates the economic and nutritional impacts of GMO labeling on consumer behavior, market trends, and dietary health. The retail and purchase datasets, such as Nielsen Retail Scanner Data and household purchase panel data, provide valuable insights into sales and pricing trends while capturing complex patterns of purchasing behavior. To analyze these datasets and better understand consumer behavior, we employ machine learning techniques, including predictive and generative models to identify key drivers of consumer preferences, estimate price sensitivities for GM and non-GM products, and simulate market responses to price fluctuations. Furthermore, we assess how shifts in purchasing behavior affect dietary quality by analyzing nutritional profiles and product substitutions using machine learning techniques. The findings offer actionable insights into the influence of GMO labeling on consumer decision-making. These insights help policymakers and the food industry optimize pricing, refine marketing strategies, and promote informed choices to support more sustainable food systems.