This study investigates the gender-specific short-term impacts of the COVID-19 lockdown policy on labor market outcomes enforced in the Philippines in April 2020. More specifically, we employ the triple difference estimator, focusing on the ex-ante and ex-post differences between men and women working in areas subjected to Enhanced Community Quarantine (ECQ) and those who do not. The findings indicate that the lockdown policy had a more detrimental impact on male workers than on females, partly attributed to the higher number of men engaged in sectors forced to suspend operations due to the lockdown measures. Additionally, the study identified significant variations across demographic groups regarding age, educational attainment, and the number of children. The insights gathered can help policymakers create targeted interventions to lessen the gender-specific impacts of economic downturns.
This study looks at the statistical properties and predictability using deep learning methods of the U.S. aggregate bond index in daily observations spanning 2018 to February 2026. We first establish that index levels are extremely persistent and consistent with unitroot behavior (Dickey and Fuller), while log returns are covariance-stationary with weak linear dependence and pronounced volatility clustering characteristic of ARCH-type processes (Engle; Bollerslev). Motivated by the trade-off between stationarity and information retention, we construct a "stationary but maximally persistent" representation via fractional differencing (Granger and Joyeux; Hosking) following the procedure of López de Prado, and evaluate shorthorizon forecast using two neural paradigms: (i) Multilayer Perceptrons (MLPs) trained on lagged vectors with joint lag-length and hyperparameter tuning (Hornik et al.; Rumelhart et al.); and (ii) Convolutional Neural Networks (CNNs) trained on Gramian Angular Field (GAF) image encodings (Wang and Oates). Empirically, MLPs match the strong naive persistence benchmark on levels, collapse toward near-zero forecasts on returns, and achieve the strongest incremental performance on the fractionally differenced series, where moderate dependence remains but unit-root drift is attenuated. In contrast, CNN-GAF models deliver consistently negative out-of-sample R 2 across all three representations. Overall, the results imply that, for short-horizon forecasting of broad bond indices, the primary determinant of predictive performance is the transformation of the series-its degree of stationarity and memory-rather than architectural complexity. Lag-based models remain competitive under persistence, while GAFbased CNNs are better suited to pattern-based tasks than to persistence-dominated next-step prediction.
Systemic risk remains a key concern for financial authorities, especially in emerging economies where traditional, low-frequency balance sheet indicators often lag rapidly changing market conditions. This study develops a high-frequency Systemic Risk Sentiment Index (SRSI) for the Philippines using news headlines from 2011 to 2025 and an ensemble of domain-specific financial sentiment models. Results show that negative sentiment is mainly driven by external-sector developments, market volatility, and equity-related news, with surges aligning with global and domestic stress episodes. Event study analysis demonstrates that the SRSI captures sharp deteriorations in sentiment several weeks before major financial stress events, while Granger causality results indicate modest predictive power for domestic equity market movements. Overall, the SRSI is best viewed as a responsive, real-time barometer that complements conventional systemic risk measures. This study represents one of the initial efforts to construct a sentiment-based systemic risk indicator tailored to the Philippine financial system and offers a scalable, low-cost framework that other central banks may adopt to enhance real-time macro-financial surveillance.
Over the past decade, women’s participation in corporate boards has grown, yet their association with firm performance remains inconclusive, particularly in family-controlled firms where director independence may be constrained. This study decomposes female board representation by family affiliation and examines its association with the firm performance of publicly traded, nonfinancial family-controlled firms in the Philippines from 2003 to 2023. Using the generalized method of moments to address endogeneity concerns, we find that female family directors are positively associated with firm performance but only when measured using Tobin’s Q, a market-based valuation, suggesting that investors value stewardship. However, the lack of significant results for accounting-based measures—ROA and ROE—indicates that actual performance may not align with investor perception. These results suggest the potential relevance of contextual factors such as patriarchal biases and limited succession opportunities in Philippine family-controlled firms. Meanwhile, when testing the critical mass theory, we find that the relationship between female board participation and firm performance does not vary once female representation reaches a particular threshold, whether for female family or non-family directors.
This study examines the interconnectedness of the Philippine banking system across three contagion channels: interbank loans, interbank deposits, and payment systems. Using network data from 481 banks supervised by the Bangko Sentral ng Pilipinas (BSP), the study applies topology-based measures to assess the structure and strength of interbank linkages. It introduces two metrics: the Overall Interconnectedness Index (OII), which measures the level of connectedness of the network, and the Core Connectivity Index (CCI), which identifies robustly linked banks within the system. The results show that payments are more interconnected than loans and deposits, but the overall interconnectedness remains very low across all channels. For the full banking system, OII values range from 0.06 to 0.65%, indicating a sparse network structure. In the core network of universal and commercial banks, loans and deposits show modestly higher interconnectedness, while payments display a much stronger core–periphery pattern. The CCI results are consistent with these findings, confirming weak connectedness in the loans and deposits networks and relatively stronger connectedness in the payments network. These findings suggest that the Philippine interbank network has limited potential for contagion through small shocks, but its sparse structure may also reduce risk-sharing capacity and weaken the system’s ability to absorb larger shocks. The proposed measures offer a useful framework for monitoring systemic risk and identifying banks that contribute most to interconnectedness. They also provide policy implications for financial regulators, like BSP, in strengthening financial stability through improved market access, payment system participation, and macroprudential surveillance.
