October 3, 2025Journal of Philippine development

Profiling Poverty with Multivariate Adaptive Regression Splines

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MARS accurately identifies household poverty correlates, demonstrating high efficiency in model predictions.
The study highlights that MARS models are more parsimonious compared to traditional logistic regression models.
Data from the 2003 Family Income and Expenditure Survey informs the analysis of poverty in Philippine households.
Using MARS enhances the proxy means test for selecting beneficiaries of poverty alleviation programs.

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Implication

Analysis using multivariate adaptive regression splines reveals significant poverty correlates in households, suggesting improved poverty identification methods.

Cite This Study

Cabanilla et al. (2014) studied this question.

synapsesocial.com/papers/68e03505f0e39f13e7fa3efc https://doi.org/10.62986/pjd2013.37.2d

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Implication

Analysis using multivariate adaptive regression splines reveals significant poverty correlates in households, suggesting improved poverty identification methods.

Profiling Poverty with Multivariate Adaptive Regression Splines

Key Points

Discussion

Implication

Cite This Study

Profiling Poverty with Multivariate Adaptive Regression Splines

Key Points

Discussion

Implication

Cite This Study

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