| Abstract: |
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. |