Songsak Sriboonchitta

China’s transition from strict fertility control toward a pronatalist regime raises important questions regarding how institutional regime changes are associated with wage outcomes across different policy stages. While previous studies primarily examine contemporaneous labor-market outcomes, this study evaluates wage associations across fertility-policy regime stages using a dynamic panel specification. Using five waves of the China Family Panel Studies (CFPS) from 2014 to 2022, the analysis constructs a short, unbalanced panel and estimates a wage equation using two-step System Generalized Method of Moments (System GMM) with collapsed instruments and restricted lag depth to address dynamic endogeneity and unobserved heterogeneity. To improve representativeness and mitigate observed wage-observability differences, survey weights are combined with inverse-probability weighting. The preferred baseline estimates indicate a positive but modest lagged-wage coefficient. Monte Carlo and sensitivity analyses further suggest that the persistence estimate is fragile and may overstate the degree of true persistence in this short-panel setting. Accordingly, the findings do not support strong intertemporal wage persistence and instead indicate only limited dependence of current wages on past wage realizations. The dynamic specification is therefore informative primarily as a diagnostic framework for assessing whether regime-stage wage associations exhibit meaningful persistence. Additional exposure-based heterogeneity analyses show negative interaction coefficients for Female and childbearing women (CBW). Married women aged 20–39 experience additional negative wage associations during fertility-policy regime stages, with similar results obtained under a narrower CBW20–35 robustness definition. These findings suggest that positive aggregate regime-stage associations may conceal relative wage disadvantages among women in demographic groups more plausibly exposed to fertility-policy-related labor-market conditions. The CBW indicator is interpreted as a demographic-exposure proxy rather than as a direct measure of employer expectations, fertility intentions, or discrimination. Overall, the results highlight exposure-based heterogeneity in regime-stage wage associations while emphasizing that the estimates should be interpreted as conditional associations embedded within broader institutional transitions rather than as causal fertility-policy effects.

This study proposes a hybrid forecasting framework that integrates Kalman Filtering (KF), Markov Switching (MS), and nonlinear recurrent learning for stock-index prediction. The KF component smooths short-term price noise, the MS model identifies latent return–volatility regimes, and the LSTM/GRU components learn nonlinear temporal patterns from regime-conditioned information. The framework is evaluated using the CSI 300, S&P 500, and Nikkei 225 indices through forecasting-accuracy measures, Bootstrap Diebold–Mariano tests with Modified Bayes Factor evidence, out-of-sample trading simulations, and robustness checks. The empirical results show that regime conditioning is the primary source of forecasting and economic improvement. KF–MS–LSTM performs best for the CSI 300 and Standard MS performs strongest for the S&P 500, while KF–MS–LSTM and KF–MS–GRU are more competitive for the Nikkei 225. In contrast, models without regime information, including pure LSTM/GRU and the standalone Transformer, generally exhibit weaker forecasting and trading performance. The findings suggest that latent market-state information is more important than neural-network complexity alone for robust financial forecasting, while the incremental value of Kalman filtering and recurrent learning remains market dependent. Overall, the results support regime-aware forecasting as an interpretable and economically meaningful approach for stock-index prediction under heterogeneous market environments.

Predicting financial stock returns remains challenging due to their inherent nonlinearity, non-stationarity, and sensitivity to market microstructure noise. Existing approaches typically rely on either econometric filtering techniques or deep learning models in isolation, limiting their ability to jointly capture latent dynamics and complex temporal dependencies. This study proposes a hybrid Extended Kalman Filter–Long Short-Term Memory (EKF–LSTM) framework that integrates nonlinear state-space filtering with deep sequential learning. The EKF component performs nonlinear state estimation and denoises to extract latent signals from noisy observations, while the LSTM network models nonlinear temporal dependencies in the filtered series. The proposed framework is evaluated using data from multiple international markets, including China, the United States, and Europe, providing cross-market evidence of model robustness. Empirical results show that the EKF–LSTM model consistently outperforms benchmark models (ARIMA, standalone EKF, LSTM, and GRU) across standard statistical metrics, including RMSE, MAE, and mean directional accuracy (MDA). In addition, the model delivers economically meaningful improvements under a long-only trading strategy, achieving higher risk-adjusted returns and lower maximum drawdowns relative to benchmark strategies. Diebold–Mariano tests further confirm that these performance gains are statistically significant. Overall, the findings demonstrate that integrating nonlinear state-space filtering with deep learning provides a robust and effective framework for financial time-series forecasting. However, the results should be interpreted with caution due to the limited sample size and the simplifying assumptions underlying the trading strategy.

China’s shift from strict fertility control to a pro-natalist regime may have unintentionally intensified gender inequality in labor markets. This study investigates the gendered labor-market effects of fertility policy reforms using nationally representative data from the China Family Panel Studies (CFPS). We adopt a weighted nonlinear difference-in-differences (DID) and difference-in-differences-in-differences (DDD) framework to address selection bias and unobserved heterogeneity in binary labor-market outcomes, combining survey weights with causal reweighting to enhance population-level inference. The results indicate that while aggregate DID effects appear neutral to mildly positive, triple-difference estimates reveal substantial penalties for women aged 20–39. Specifically, fertility-policy reforms reduce promotion probabilities by approximately 6–8 percentage points, employment probabilities by 3–4 percentage points, and the likelihood of earning above the median wage by about 6–7 percentage points. These effects are consistent across alternative weighting schemes and remain robust to placebo tests, event-study specifications, and additional controls for differential trends. This study contributes to the literature by demonstrating how demographic policy interventions can generate unintended distributional consequences through employer expectations and statistical discrimination. The findings highlight a policy trade-off between demographic revitalization and gender equality, suggesting that pro-natalist policies may reinforce labor-market disparities in the absence of complementary institutional reforms. These results underscore the importance of aligning demographic objectives with gender-equality policies, particularly in the context of Sustainable Development Goal 5.

Over the past two decades, agricultural exports in Southeast Asia have surged due to trade liberalization and rising global food demand. This study examines the environmental impacts of agricultural exports in ASEAN-5 (Indonesia, Malaysia, the Philippines, Thailand, and Viet Nam) over the period 2001–2021, focusing on biodiversity loss and greenhouse gas (GHG) emissions. Using the Feasible Generalized Least Squares (FGLS) as the main estimator, with difference Generalized Method of Moments (GMM) and bias-corrected Least Squares Dummy Variable (LSDVC) as robustness checks, the analysis assesses the effects of both aggregate and product-specific agricultural exports (rubber, rice, and palm oil) on the Species Habitat Index (SHI) and agricultural GHG emissions, while evaluating the moderating role of agricultural trade openness. The results reveal pronounced heterogeneity across commodities and environmental dimensions. Aggregate agricultural exports are associated with incremental biodiversity loss but do not exert uniform effects on emissions. Rubber exports emerge as the most environmentally consequential, with trade liberalization significantly amplifying negative impacts on both biodiversity and emissions. Rice exports primarily increase emissions, while palm oil exports show weak environmental effects. Real income and renewable energy intensity are associated with worsening biodiversity outcomes but mitigates agricultural GHG emissions. The results also do not support the inverted U-shaped EKC hypothesis. These findings underscore the need for commodity-specific and instrument-based policies in ASEAN that align agricultural export growth with biodiversity conservation and emissions mitigation.

Using a fixed-effects panel threshold regression with Driscoll–Kraay inference, this paper examines how institutional quality shapes the distributional effects of foreign direct investment (FDI) in the ASEAN-5 economies (Indonesia, Malaysia, the Philippines, Thailand, and Vietnam) over 2002–2023. The empirical framework allows the impact of FDI on income inequality (net Gini index) to differ across low- and high-institutional regimes and to vary within regimes through interaction terms. Across governance indicators from the Worldwide Governance Indicators and a composite institutional quality index (IQ) constructed via principal component analysis (PCA), the results reveal pronounced nonlinearities, most clearly for government effectiveness, where the association between FDI and inequality switches sign across institutional regimes. For other governance dimensions, the FDI–inequality relationship is similarly regime-dependent and operates partly through regime-specific interaction effects, underscoring the importance of institutional thresholds in mediating distributional outcomes. Robustness checks confirm the directional consistency of the baseline results. Our findings imply that governance reforms must surpass critical institutional thresholds, particularly in effectiveness and implementation capacity, before FDI can contribute to reducing income inequality, highlighting the central role of deep governance improvements in enabling inclusive growth in ASEAN economies.

This paper introduces a new econometric framework for modeling fractional outcomes bounded between zero and one. We propose the Fractional Probit with Cross-Sectional Volatility (FPCV), which specifies the conditional mean through a probit link and allows the conditional variance to depend on observable heterogeneity. The model extends heteroskedastic probit methods to fractional responses and unifies them with existing approaches for proportions. Monte Carlo simulations demonstrate that the FPCV estimator achieves lower bias, more reliable inference, and superior predictive accuracy compared with standard alternatives. The framework is particularly suited to empirical settings where fractional outcomes display systematic variability across units, such as participation rates, market shares, health indices, financial ratios, and vote shares. By modeling both mean and variance, FPCV provides interpretable measures of volatility and offers a robust tool for empirical analysis and policy evaluation.

This study examines how air transport infrastructure and trade flows influence economic growth across ASEAN countries, with a focus on identifying the threshold levels at which these factors begin to enhance growth. Despite increasing investment in regional logistics and connectivity, policymakers often lack evidence-based thresholds to guide infrastructure and trade policy for long-term development. Addressing this gap, this study applies a Dynamic Panel Threshold Model to uncover the tipping points at which improvements in air cargo volume (lnCargo) and air transport infrastructure quality (lnQAir) translate into stronger economic growth. By employing System-GMM and First-Difference GMM estimations, the analysis captures the threshold effects of air cargo volume (lnCargo) and air transport infrastructure quality (lnQAir) on economic growth over varying regimes. The results reveal significant single-threshold effects for both lnCargo and lnQAir, indicating that their contributions to economic growth become substantial after surpassing specific critical levels. When air cargo volume exceeds approximately 267,067 tons per year (lnCargo > 5.5875), its positive effect on economic growth strengthens, particularly when accompanied by high-quality infrastructure. Similarly, air transport infrastructure quality exhibits a significantly stronger impact on economic growth once it exceeds the critical threshold of lnQAir = 1.5476 (≈4.7001 index points). These findings emphasize the complementarity between trade flows and infrastructure, aligning with endogenous growth theory, which suggests that infrastructure investments yield increasing returns when integrated with trade expansion. Policy implications suggest that ASEAN economies should adopt demand-driven infrastructure development aligned with trade dynamics, prioritizing regional connectivity, logistics efficiency, and investment attraction to sustain long-term economic growth.

Is it possible for the imposition of environmental regulation to develop the textile industry? Is it possible to balance quality and efficiency with energy conservation and emission reduction and achieve the sustainable development of the textile industry? The main objective of this study is to analyze the effects of the environmental regulation on industrial agglomeration and industrial efficiency within the textile sector. The findings reveal that, first, environmental regulation has significantly facilitated the agglomeration of the textile industry to regions with less stringent environmental standards. The restructuring and optimization of domestic value chains have also been promoted. Second, environmental policies in the process of the promotion of the industrial agglomeration are accompanied by a significant improvement in industrial efficiency. This improvement has contributed to the achievement of sustainability goals in the textile domain. Third, the influence of environmental regulations on industrial agglomeration and industrial efficiency improvement of the textile industry is strongly heterogeneous in terms of ownership. This impact is more significant in state-owned industries and private industries. This study holds substantial theoretical significance and practical relevance in promoting environmentally friendly and sustainable progress in the Chinese textile industry.

Based on the survey data of five large grain-producing provinces in China, we have studied the promotion effect of the agricultural production trusteeship on the high-quality production of grain by using a propensity score matching method. The empirical results show that the high-quality production of the grain level increases by 0.292, with an increase of 87.4% after farm households participate in agricultural production trusteeship. The level of high efficiency, premiumization, greenization, and branding of grain production increased by 0.234, 0.373, 0.208, and 0.158, respectively. However, there are differences in the facilitation effects of different trusteeship services, with the best promotion effect of agricultural material supply services, followed by post-harvest services, and then land management services and cultivating and harvesting services. The average treatment effect on the treated (ATT) is 0.287, 0.230, 0.158, and 0.139, respectively. Meanwhile, there are differences in the promotion effects for farm households with different factor endowments. The promotion effect is better for small farm households with three laborers or less, a land management scale of 10 mus or less, and one type of agricultural machinery or less. Therefore, in order to give full play to the promotion effect of agricultural production trusteeship on high-quality production of grain, the government should vigorously support its development and guide more smallholders to choose agricultural production trusteeship.