Understanding Cooperative Membership through Big Data Analysis: Evidence from Philippine Consumer Data Using Double-Selection Lasso Logistic Regression

Purpose – This study identifies key factors influencing cooperative membership in the Philippines using largescale consumer data. By applying advanced statistical techniques to big data, it offers a nuanced understanding of how financial behaviors—rather than demographic traits—drive cooperative participation, providing actionable insights for policy and cooperative development. Design/Methodology/Approach – Using data from the 2014 Consumer Finance Survey of 15,501 respondents nationwide, the study employs a Double-Selection Lasso Logistic Regression (DSlogit) model to isolate significant socioeconomic and financial behavioral predictors of cooperative membership. Findings – Financial engagement—such as savings accounts, long-term banking relationships, and credit card ownership—along with formal employment status, significantly increases cooperative membership. In contrast, sole proprietorship and informal borrowing are negatively associated. Research Implications – The results underscore the importance of financial inclusion strategies and suggest that cooperatives should innovate to better serve entrepreneurs and underserved populations. Future research should explore digital financial platforms and conduct comparative studies across cooperative types and regions to deepen understanding of membership dynamics.