COMPARISON OF MIXED EFFECT REGRESSION TREE (MERT) AND LINEAR MIXED MODEL (LMM) FOR CLUSTERED DATA ON CASE STUDY HOUSEHOLD POVERTY IN WEST JAVA PROVINCE
Abstract
This study compares the performance of the Linear Mixed Model (LMM) and the Mixed Effect Regression Tree (MERT) in analyzing the determinants of household consumption expenditure in West Java Province. The LMM integrates fixed and random effects to account for both individual and regional variations, while MERT extends this approach by incorporating a regression tree framework to capture nonlinear relationships and complex interactions among socio-economic variables. Using data from the 2023 National Socioeconomic Survey (SUSENAS), household consumption expenditure is modeled as an indicator of poverty. The results show that key determinants across both models include the gender and age of the household head, highest educational attainment, household size, land and car ownership, and welfare card ownership. Education and asset ownership consistently emerge as major factors influencing household welfare. The MERT model demonstrates superior predictive performance, with lower RMSE and MAE values compared to the LMM, while offering greater interpretability by identifying specific household profiles. Female-headed households with higher education and no car ownership tend to have higher expenditure in the high-income group, whereas female-headed households with welfare cards remain vulnerable in the low-expenditure group. From a policy perspective, these findings highlight the importance of improving educational access, enhancing asset ownership, and strengthening targeted social protection for vulnerable groups. Overall, while both models contribute valuable insights, the MERT model provides a more flexible and powerful framework for identifying and interpreting the determinants of household welfare in West Java.
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