Informal Loans in Thailand: Stylized Facts and Empirical Analysis
This paper examines informal loans in Thailand using household survey data covering 4,800 individuals in 12 provinces across Thailand's six regions. We proceed in three steps. First, we establish stylized facts about informal loans. Second, we estimate the effects of household characteristics on the decision to take out an informal loan and the amount of informal loan. We find that age, the number of household members, their savings, and the amount of existing formal loans are the main factors that drive the decision to take out an informal loan. The main determinations of the amount of informal loan are the interest rate, savings, the amount of existing formal loans, the number of household members, and personal income. Third, we train three machine learning models, namely K–Nearest Neighbors, Random Forest, and Gradient Boosting, to predict whether an individual will take out an informal loan and the amount an individual has borrowed through informal loans. We find that the Gradient Boosting technique with the top 15 most important features has the highest prediction rate of 76.46 percent, making it the best model for data classification. Generally, Random Forest outperforms the other two algorithms in both classifying data and predicting the amount of informal loans.