A Data Envelopment Analysis Approach to Portfolio Selection: An Application to the Blue Chip Stocks in the Philippine Stock Exchange (2010-2019)

There has been a growing interest in the application of data envelopment analysis (DEA) as a nonparametric approach in portfolio optimization due to its flexibility in overcoming the limitations of the conventional mean-variance portfolio (MVP) model. Therefore, this study aims to validate the allocative efficiency of the DEA cross-efficiency model using blue chip stocks in the Philippine Stock Exchange from 2010 to 2019. This study finds that the proposed model is able to distinguish a unique set of best-performing stocks across each holding period and outperforms the MVP more consistently. The results of this study suggest that the proposed DEA cross-efficiency model can encourage more Filipinos to invest because it can provide an allocatively-efficient manner of selecting optimal stocks and incorporate other factors that affect the return and risk of a portfolio. Finally, this study suggests that future studies can examine this model using the entire Philippine stock market with an alternative set of criteria that affect stock returns and, ultimately, the stock’s performance

The Differential Impact of 4Ps on Child Labor Before and During the COVID-19 Pandemic

Poverty is widely considered the root cause of child labor in the Philippines. Thus, the Pantawid Pamilyang Pilipino Program (4Ps) was designed to alleviate poverty and reduce child labor through financial assistance, conditional on welfare investments like children’s education. Using 2017, 2019, and 2020 data from the Annual Poverty Indicators Survey (APIS), the study estimated the differential impact of 4Ps on child labor before and during COVID-19 across varying demographic, socio-economic, and locational characteristics. Results from the propensity score matching (PSM) and difference-in-differences (DID) model indicated that since cash transfers cannot fully offset the opportunity cost of children’s education, children of beneficiaries are still likely to work. The findings also revealed that female-led families, households residing in rural areas, and households with family sizes greater than four have a higher likelihood of engaging in child labor. Moreover, despite living above the poverty threshold, households could still be forced to engage in child labor due to increasing inflation rates and declining purchasing powe