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on South East Asia |
| By: | Dzung Nguyen-Le; Long Trinh-Tuan; Thanh Nguyen-Xuan; Tung Nguyen-Duy; Thanh Ngo-Duc |
| Abstract: | The wet-bulb globe temperature (WBGT) is a widely used index for assessing heat stress. However, many studies on heat stress under climate change rely on simplified WBGT calculations, which may introduce biases. In this study, high-resolution climate data and the physically-based WBGT model are employed to provide a more reliable assessment of future heat stress impacts across Vietnam and its seven sub-climatic regions. Projected changes are analyzed for three future periods — the near future (2021–2040), mid-future (2041–2060), and far future (2081–2100) — relative to the baseline period (1995–2014) under three Shared Socioeconomic Pathways (SSPs): SSP1-2.6, SSP2-4.5, and SSP5-8.5. Additionally, changes are assessed across different global warming levels (GWL), ranging from 1.5°C to 4°C above the pre-industrial level. Long-term trends throughout the studied period are also examined. The findings reveal significant increases in heat stress across Vietnam in the future. A major concern is the substantial increases in the number of days exceeding impact-relevant heat stress thresholds, most notably in the Red River Delta and Mekong River Delta, two most densely populated and agriculturally critical subregions of Vietnam. Heat stress emergence and intensity are closely linked to the radiative forcing of SSP scenarios and the GWLs, with higher forcing scenarios and warmer GWL producing more severe conditions and a greater frequency of exceedance days. The most severe impacts are projected under SSP5-8.5 as well as GWLs of 3°C and 4°C, indicating the urgent need to limit future warming to mitigate the risk of heat stress. Biases in simplified WBGT calculations are also discussed, suggesting significant overestimations of exceedance days in most of Vietnam. Such biases could lead to misleading assessments, unnecessary alarms, and potentially flawed adaptation strategies, highlighting the critical need for accurate WBGT modeling in climate impact research. |
| Keywords: | Vietnam |
| JEL: | Q |
| Date: | 2025–05–13 |
| URL: | https://d.repec.org/n?u=RePEc:avg:wpaper:en18118 |
| By: | Shinozaki, Shigehiro (Asian Development Bank); Miyakawa, Daisuke (Waseda University and UTokyo Economic Consulting Inc.) |
| Abstract: | Understanding the business environment and structural issues that limit growth is critical when designing an effective national policy framework for private sector development—especially for micro, small, and medium-sized enterprises (MSMEs). Given the limited MSME data available, this paper employs probabilistic principal component analysis to develop a new way to quantitatively assess what affects MSME development nationally, by using granular firm-level panel data for 49, 565 MSMEs in Thailand as a case study. The estimation results found a potential disproportionate effect of MSME policy interventions during and after the coronavirus disease pandemic. Government assistance for MSMEs likely helped Bangkok-based firms ease the negative pandemic effects, especially in manufacturing. However, it did not help local MSMEs— regardless of sector—as their operational performance deteriorated both during and after the pandemic. This underscores the importance of using a focused approach when designing policies for MSME development to facilitate more sustainable, resilient private sector growth. |
| Keywords: | SME development; access to finance; financial inclusion; SME policy; probabilistic principal component analysis; Thailand |
| JEL: | D22 G20 L20 L50 |
| Date: | 2025–06–11 |
| URL: | https://d.repec.org/n?u=RePEc:ris:adbewp:0785 |
| By: | Katikar Tipayalai; Nattasit Chittavimongkhon; Panjapon Sattayanurak |
| Abstract: | In today’s world, where digital transformation offers numerous benefits, its uneven distribution—often driven by socioeconomic and demographic factors—can exacerbate social inequalities. This study explores the digital divide among vulnerable elderly populations in Thailand, drawing on survey data collected in Lampang province, a region with one of the highest proportions of older persons relative to its population. Focusing on their digital skills and access to government welfare services, we assess digital competence across five key domains: information literacy, communication, online safety, problem-solving, and confidence in engaging with online activities. The findings reveal significant gaps in digital literacy, with limited device ownership and internet access identified as critical barriers. Logistic regression analysis indicates that education, income, and personal access to technology are significant predictors of digital competence. While the results are region-specific, they provide important insights into the challenges faced by older populations in similar socioeconomic contexts. The study underscores the urgent need for targeted interventions, such as digital skills training and increased access to affordable technology, to promote inclusion and enhance the quality of life for older adults. These efforts are crucial for reducing disparities and ensuring equitable participation in Thailand’s increasingly digital society. Therefore, implementing policies and interventions that effectively address this divide is essential to fostering greater social and digital inclusion. |
| Keywords: | Digital disparity; Logistic regression; Older adults; Aging society; Thailand |
| JEL: | J18 O31 O33 |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:pui:dpaper:234 |
| By: | Gimiseve, Harry; Miamba, Nelson; Na'ata, Bartholomew; Dorosh, Paul A.; Schmidt, Emily; Yadav, Shweta |
| Abstract: | In 2023, Papua New Guinea introduced a partial ban on poultry imports from Australia and Asian countries (representing about 70 percent of total PNG poultry imports) in response to the biosecurity threat posed by Avian Influenza (bird flu). Such a restriction on supply has the potential to lead to sharp price increases, steep reductions in household consumption and greater food insecurity. This memo presents an overview of PNG’s poultry sector and describes an analysis of the ef fects of these trade restrictions on poultry prices, production and consumption using a partial equilibrium model of PNG’s poultry sector. This new analysis builds on earlier work (Dorosh and Schmidt, 2023) that explored the implications of a total ban on poultry imports, by simulating the impacts of a partial poultry ban, including the effects on various household groups within PNG. |
| Keywords: | poultry; imports; biosecurity; avian influenza; supply; prices; household consumption; food security; trade; Papua New Guinea; Asia; Melanesia |
| Date: | 2024–03–14 |
| URL: | https://d.repec.org/n?u=RePEc:fpr:pacerp:140446 |
| By: | Manabu Nose (Faculty of Economics, Keio University.); Yasuyuki Sawada (Faculty of Economics, The University of Tokyo); Tung Nguyen (Graduate School of Economics, Hitotsubashi University.) |
| Abstract: | This paper examines the nonlinear effects of a large-scale highway construction project in the Greater Mekong Subregion, which connects the historically conflict-affected borderlands of northern Vietnam to the country’s industrial core. Employing a market access framework with geo-coded highway network and firm-level panel data, we estimate the causal impact of improved interregional connectivity, while accounting for spillovers via production input-output linkages. To address endogeneity issues arising from non-random route placements, we construct least-cost path spanning tree networks. Our instrumental variable estimates reveal that enhanced market access spurred manufacturing firm agglomeration and employment growth, particularly in peripheral rural areas. We further explore the underlying sources of polycentric development patterns, finding pronounced effects in second-tier cities characterized by less intense competition and better access to national road networks. Our findings are robust to controls for industrial zones, underscoring the pivotal role of the upgraded highway connectivity in transforming previously marginalized regions and supporting economy-wide industrialization over the past decade. |
| Date: | 2025–06 |
| URL: | https://d.repec.org/n?u=RePEc:tky:fseres:2025cf1251 |
| By: | Kerwin Kofi Charles; Jonathan Guryan; Kyung H. Park |
| Abstract: | We revisit the early months of the Covid-19 pandemic to examine whether restaurant foot traffic reveals changes in sentiment towards ethnic groups. Our findings show reduced demand for dining at Asian restaurants located inside Asian enclaves, while outside enclaves, the decline in visits to Asian restaurants was comparable to non-Asian restaurants. In contrast, Italian restaurant enclaves did not experience similar declines in foot traffic after news of the outbreak in Italy and the first U.S. case linked to travel to Italy. We also find suggestive evidence that the shift in consumption was associated with elevated negative sentiment towards Asians rather than efforts to avoid exposure to international travelers. |
| JEL: | I12 I18 J14 J71 |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:33831 |
| By: | Srijuntrapun, Patranit; Ket-um, Pattama; Attavanich, Witsanu |
| Abstract: | The escalating food waste crisis, with millions of tons of food being discarded annually, highlights the pressing necessity to improve household food waste management practices. This complex and multifaceted challenge is a crucial element of a comprehensive national strategy for reducing food waste. This article seeks to examine the diverse demographic and social factors that shape household food waste management practices in Thailand. A substantial national dataset (n = 2, 500) was meticulously gathered through questionnaires, using multi-stage sampling and multiple regression analysis to reveal critical insights. This study reveals that educational attainment (β = 0.299), household size (β = 0.201), and monthly income (β = 0.058) are positively associated with effective household food waste management practices. Notably, the type of housing, such as single houses over 200 square meters (β = .058**) and condominiums/apartments (β = .063**), significantly influence food waste management behaviors. However, townhouses (β = -.074***) are negatively associated with improved food waste management practices. The research also identifies key barriers to effective food waste prevention, including the lack of organizational guidance (29.4%), the perception that waste reduction does not save costs (26.1%), and uncertainty about where to donate surplus food (25.2%). Additional challenges of managing food scraps include the uncertainty about options for donation or sale of food scraps (43.3%) and the limited knowledge of composting or bio-fermentation methods (30.2%). In conclusion, this study provides essential insights for policymakers, ractitioners, and researchers by identifying key demographic, knowledge-based, and behavioral factors that shape household food waste management. The study’s findings underscore the need for targeted educational initiatives and infrastructure enhancements. Policymakers can leverage these insights to develop policies that support public-private partnerships and improve waste management infrastructure. Practitioners can apply this knowledge to implement more effective waste segregation strategies, while researchers are encouraged to explore socio-economic factors influencing food waste at a national scale, thereby addressing critical research gaps. This comprehensive approach is vital for reducing household food waste and promoting sustainable waste management practices across diverse communities. |
| Keywords: | household food waste, socio-demographic drivers, food waste practices, waste hierarchy approach |
| JEL: | Q53 Q56 R2 |
| Date: | 2024–11–13 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:124924 |
| By: | Bahaa Aly, Tarek |
| Abstract: | This study presents a novel hybrid framework that integrated Long Short-Term Memory (LSTM) networks with Daubechies wavelet transforms to estimate Deep Impulse Response Functions (DIRF) for monthly macroeconomic time series, across five economies: Brazil, Egypt, Indonesia, United States, and the United Kingdom. Eight key variables, yield curve latent factors (LEVEL, SLOPE, CURVATURE), foreign exchange rates, equity indices, central bank policy rates, GDP growth rates, and inflation rates, were modeled using the proposed LSTM-Wavelet approach, and were compared against an ANN-Wavelet hybrid, and a traditional Vector Error Correction Model (VECM). The LSTM-Wavelet model achieved a superior overall median R2, outperforming the ANN-Wavelet and VECM. The approach excelled in capturing nonlinear dynamics and temporal dependencies for variables such as equity indices, policy rates, GDP, and inflation. Db4 was superior for capturing short and medium-term patterns in macroeconomic variables like GDP, EQUITY, and FX, cause its shorter filter and moderate smoothing excelled at isolating cyclical patterns in noisy, volatile data. Cumulative DIRFs revealed consistent cross variable dynamics e.g., yield curve shocks propagated to equity, FX, policy rates, GDP, and inflation, in line with economic theory. These findings underscored the hybrid model’s ability to capture non-linearity, multiscale interactions in macroeconomic data, offering valuable insights for forecasting and policy analysis. |
| Keywords: | Deep Impulse Response Function, Long Short-Term Memory, Daubechies Wavelet transform, Macroeconomics, nonlinearity, Forecasting |
| JEL: | C5 C53 C58 |
| Date: | 2025–05–30 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:124905 |
| By: | Ilhyock Shim; Torsten Ehlers; Fredy Gamboa; Han Qiu |
| Abstract: | Regional integration among emerging market economies complements global integration rather than substituting for it, which implies that strong regional ties act as buffers against global fragmentation.Emerging Asia is more integrated than other emerging market regions due to a higher share of manufacturing with complex supply chains and the presence of regional financial centres in Hong Kong SAR and Singapore.Trade and banking integration reinforce each other, and regional payment system integration has positive externalities by reducing the transaction costs of trade and enabling cross-border banking services.Tapping the significant potential for further regional integration requires concerted efforts on cross-border cooperation and the implementation of targeted trade and banking sector liberalisation policies. |
| Date: | 2025–06–05 |
| URL: | https://d.repec.org/n?u=RePEc:bis:bisblt:102 |
| By: | Maarten van Damme; Rishi Sreedhar; Martin Ganahl |
| Abstract: | Pricing of exotic financial derivatives, such as Asian and multi-asset American basket options, poses significant challenges for standard numerical methods such as binomial trees or Monte Carlo methods. While the former often scales exponentially with the parameters of interest, the latter often requires expensive simulations to obtain sufficient statistical convergence. This work combines the binomial pricing method for options with tensor network techniques, specifically Matrix Product States (MPS), to overcome these challenges. Our proposed methods scale linearly with the parameters of interest and significantly reduce the computational complexity of pricing exotics compared to conventional methods. For Asian options, we present two methods: a tensor train cross approximation-based method for pricing, and a variational pricing method using MPS, which provides a stringent lower bound on option prices. For multi-asset American basket options, we combine the decoupled trees technique with the tensor train cross approximation to efficiently handle baskets of up to $m = 8$ correlated assets. All approaches scale linearly in the number of discretization steps $N$ for Asian options, and the number of assets $m$ for multi-asset options. Our numerical experiments underscore the high potential of tensor network methods as highly efficient simulation and optimization tools for financial engineering. |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2505.17033 |