Scientific Papers Series Management, Economic Engineering in Agriculture and Rural Development Vol. 22, Issue 4, 2022 PRINT ISSN 2284-7995, E-ISSN 2285-3952 141 HAPPINESS AND PROFITABILITY UNDER PHILIPPINE RICE TARIFFICATION LAW: REGRESSION AND K-MEANS CLUSTERING APPROACH Leomarich F. CASINILLO Visayas State University, Department of Mathematics, Visca, Baybay City, Leyte, Philippines; E-mail: leomarichcasinillo02011990@gmail.com Corresponding author: leomarichcasinillo02011990@gmail.com Abstract Good economic profitability in rice farming is known to have a positive influence on the happiness or well-being of farmers. This study investigated the relationship between profit and happiness of rice farmers in Leyte, Philippines under the carrying out of Rice Tariffication Law (RTL) in the country. The study employed cross-sectional and secondary data from an existing study from rice farming literature that measures the profit and corresponding happiness of a farmer in one cropping season. Regression modeling was used to elucidate the correlation between profit and happiness, and K-means clustering was employed to categorize a group of farmers that have more or less the same characteristics. Results showed that, on average, profit and happiness are relatively low during the implementation of RTL. The bivariate linear regression model has shown that there is a positive relationship between profit and happiness. This implies that as profit increases, the happiness of a farmer also increases. In addition, the logistic regression has revealed that the likelihood of a farmer being happy increases by 0.324% when the profit increases by 1%. Moreover, the ordered logistic regression has shown that as profit increases by 1%, farmers' log odds of being happy increase by 0.0129. Furthermore, by K-means clustering, the dominant of the farmers (45.76%) are grouped as low profit and happiness, and only 7.91% are categorized as high profit and happiness under RTL. Hence, the study recommends that the Philippine government must subsidize the local farmers' needs to increase their economic profit and improve their well-being as a farmer. Key words: happiness, economic profit, rice tariffication law, regression model, k-means clustering INTRODUCTION Happiness is not only defined as the individual’s conditions of economic prosperity but also refers to the condition of a great and meaningful life [7], [8], [24]. Measuring happiness is scrutiny of subjective well-being and meaning of life of an individual and it is highly studied in the area of social sciences. In particular, there are researches in economics that deals with the relationship between happiness and income [9], [10], [11], [19], [23]. In fact, some social scientists are puzzled about the correlation between these two variables since their relationship is very dynamic across different demographic profiles and life management [12], [19], [21]. On the face of it, happiness research is considered intriguing and interesting to social scientists due to its fluctuating behavior as a function of income inequality. Apparently, studying the economic predictors of happiness will understand the nature of an individual's well-being which is a function of different life events and life profiles [12]. In particular, in the study of Kumar et al. [13], income and satisfaction in life events are strong predictors of farmers' happiness. Income or profit is the main reason why an individual is motivated to work. In fact, good business performance is determined by higher economic profitability. However, during the time that Rice Tariffication Law (RTL) was implemented in the Philippines, profitability (or income) and satisfaction in rice farming has an inverse effect on each other [4]. This means that a farmer with a high income tends to be more unhappy due to corresponding high agricultural expense that leads them to access credit. Apparently, the country Philippines is an agricultural economy where rice is the main crop and main source of income for many Filipinos. In fact, the Philippine government is focusing on the agricultural sector, especially for rice as