A Big Data Analysis of Gasoline Price Information Platform and Market Competition - 이홍재 (정보통신정책연구, 2025)
This study investigates how the introduction and subsequent diffusion of OPINET, a government-operated online platform that provides real-time fuel price information to consumers in South Korea, has influenced the competitive dynamics of the retail gasoline market. Using a comprehensive dataset of 931,460 station-level price observations collected over a five-year period, we analyze whether and how increased information transparency has altered the pricing strategies of gas stations. To overcome the limitations of conventional regression models, we employ machine learning techniques that capture complex interactions and assess price differentials under varying degrees of geographic competition, as measured by the distance to the nearest competitor. Our empirical findings indicate that as OPINET became more widely used and accessible, gas station pricing behavior exhibited increased responsiveness to the local competitive environment, particularly in markets with relatively dense station networks. This pattern suggests that enhanced visibility of price information may, under certain conditions, facilitate more competitive pricing and improve market efficiency. However, the results do not imply that OPINET universally promotes competition in all contexts or regions. Instead, the effects appear to depend on local market structure and consumer engagement with the platform. The study offers nuanced policy implications regarding the design of digital information systems to support competition and improve consumer outcomes in retail markets.
Keywords: Information; Competition; Fuel Price; OPINET; Machine Learning. JEL Classification: D4; L1