Econometric Vectorization of Rural E-Commerce: A Serverless Cloud Architecture for Analyzing Agri-Digitalization and Household Welfare

The rapid expansion of rural digitalization and agricultural e-commerce presents transformative opportunities for poverty alleviation and farm productivity in emerging economies. However, measuring the socio-economic impacts of Information and Communication Technology adoption relies heavily on delayed, cross-sectional survey data, limiting the efficacy of advanced microeconometric models. This paper proposes a cloud-native architectural framework utilizing Amazon Web Services to construct a real-time data ingestion pipeline for agricultural economics. By deploying distributed Python middleware, the proposed system programmatically extracts unstructured telemetry from rural e-commerce endpoints, normalizing logistics, pricing, and adoption variables into structured panel-data vectors. Preliminary architectural evaluations demonstrate that automating the data synthesis layer drastically reduces the friction of preparing datasets for treatment-effect models and quantile regressions. This methodology provides agricultural economists with a deterministic, highly scalable technological foundation for conducting empirical, high-frequency evaluations of rural commercialization and household welfare.