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This study examines the determinants of planned retirement age of informal workers in Chiang Mai Province, Thailand. The least absolute shrinkage and selection operator estimator regression (LASSO) and the ordinary least squares estimator regression (OLS) are employed to determine variables important for planned retirement age equation. The computational cost of this method is we need to take into account 2 times fewer variables compared with standard approaches in the simple regression literature. A cross-sectional study is conducted among informal workers in Chiang Mai Province, Thailand. A total of 400 informal workers are enrolled. A number of important participants, demographic characteristics, financial variables and health factors are considered in the planned retirement age equation. The study compares the results of two methods, namely the LASSO and the OLS. According to the minimum value of The out-of-sample Root Mean Squared Error, the LASSO performs better than the OLS. The results suggest that age of respondent, number of children, own accommodation, sex of respondent, and marital status (single) have positive impact on planned retirement age. In contrast, cost of bone disease treatment, daily expenses, caretaker after retirement, chronic disease, education level, cost of treatment for diseases, income after retirement, financial burden after retirement, and savings group deposits have negative impact on planned retirement age.