Economic survival duration of Thai workers during COVID-19

This study examines the impact of the COVID-19 pandemic on the livelihood and economic survival of Thai citizen workers, using The Asia Foundation’s survey data which were conducted in May 2020 (first round), August 2020 (second round) and November 2020 (third round). We adopt the Cox proportional-hazards regression with lasso estimation to estimate the coefficients and perform variable selection simultaneously. The model allows us to identify the vulnerable groups with risks of consumption inadequacy. The empirical results show that those workers characterized as low-educated, unemployed, unskilled, working in the tourism sector and living in the northeastern or southern regions are less likely to sustain their consumption. However, our study highlights that higher education is a crucial factor influencing the survivability of Thai workers. Regarding the role of government schemes, the result shows that that a set of cash assistance programs is less likely to increase the survivability of the non-agricultural workers

Structural and predictive analyses with a mixed copula-based vector autoregression model

DATA AVAILABILITY STATEMENT: The data used to support this analysis are available from https://www.dallasfed.org/institute/dgei/gdp.aspx and https://worlduncertaintyindex.com/data/, and code of the proposed model generated during the study is available from the corresponding author upon reasonable request.In this study, we introduce a mixed copula-based vector autoregressive (VAR) model for investigating the relationship between random variables. The one-step maximum likelihood estimation is used to obtain point estimates of the autoregressive parameters and mixed copula parameters. More specifically, we combine the likelihoods of the marginal and mixed copula to construct the full likelihood function. The simulation study is used to confirm the accuracy of the estimation as well as the reliability of the proposed model. Various mixed copula forms from a combination of Gaussian, Student's t, Clayton, Frank, Gumbel, and Joe copulas are introduced. The proposed model is compared to the traditional VAR model and single copula-based VAR models to assess its performance. Furthermore, the real data study is also conducted to validate our proposed method. As a result, it is found that the one-step maximum likelihood provides accurate and reliable results. Also, we show that if we ignore the complex and nonlinear correlation between the errors, it causes significant efficiency loss in the parameter estimation in terms of |Bias| and MSE. In the application study, the mixed copula-based VAR is the best fitting copula for our application study.http://wileyonlinelibrary.com/journal/forhj2023Economic

Time-varying predictability of labor productivity on inequality in United Kingdom

In this paper, we analyze time-varying predictability of labor productivity for growth in income (and consumption) inequality of the United Kingdom (UK) based on a high-frequency (quarterly) data set over 1975:Q1 to 2016:Q1. Results indicate that the growth rate of an index of labor productivity has a strong predictive power on growth rate of income (and consumption) inequality in the UK. Interestingly, the strength of the predictive power is found to be higher towards the end of the sample period in the wake of the global financial crisis. In addition, based on time-varying impulse response function analysis, we find that inequality and labor productivity growth rates are in general negatively associated over our sample period, barring a short-lived positive impact initially.BMK, BMDW and the Province of Upper Austria in the frame of the COMET Programme managed by FFG.http://link.springer.com/journal/11205hj2022Economic

Continuous Monitoring and Future Projection of Ocean Warming, Acidification, and Deoxygenation on the Subarctic Coast of Hokkaido, Japan

As the ocean absorbs excessive anthropogenic CO2 and ocean acidification proceeds, it is thought to be harder for marine calcifying organisms, such as shellfish, to form their skeletons and shells made of calcium carbonate. Recent studies have suggested that various marine organisms, both calcifiers and non-calcifiers, will be affected adversely by ocean warming and deoxygenation. However, regardless of their effects on calcifiers, the spatiotemporal variability of parameters affecting ocean acidification and deoxygenation has not been elucidated in the subarctic coasts of Japan. This study conducted the first continuous monitoring and future projection of physical and biogeochemical parameters of the subarctic coast of Hokkaido, Japan. Our results show that the seasonal change in biogeochemical parameters, with higher pH and dissolved oxygen (DO) concentration in winter than in summer, was primarily regulated by water temperature. The daily fluctuations, which were higher in the daytime than at night, were mainly affected by daytime photosynthesis by primary producers and respiration by marine organisms at night. Our projected results suggest that, without ambitious commitment to reducing CO2 and other greenhouse gas emissions, such as by following the Paris Agreement, the impact of ocean warming and acidification on calcifiers along subarctic coasts will become serious, exceeding the critical level of high temperature for 3 months in summer and being close to the critical level of low saturation state of calcium carbonate for 2 months in mid-winter, respectively, by the end of this century. The impact of deoxygenation might often be prominent assuming that the daily fluctuation in DO concentration in the future is similar to that at present. The results also suggest the importance of adaptation strategies by local coastal industries, especially fisheries, such as modifying aquaculture styles

Spatial Spillover Effects of Internet Development on Foreign Trade in China

This study analyzes the spatial spillover effects of internet development on China’s foreign trade using panel data of 31 provinces in China covering 2003–2016. First, the global Moran’s I is employed to check for spatial autocorrelation in internet development. The results demonstrate a positive correlation between the internet development of the local province and the neighboring provinces during the sample period. Then, we validate the accuracy and performance of the spatial Durbin model by comparing it with two other spatial models: spatial error and spatial autoregression. The Wald and Likelihood Ratio tests confirmed the superiority of the SDM model. According to the direct and indirect effects results obtained from SDM, internet development plays an essential role in promoting local foreign trade and generates a positive spatial spillover effect on the foreign trade of neighboring provinces. The key findings suggest that China should continuously strengthen its internet infrastructure and expand its internet popularity, especially in the tertiary sector, to enhance the advantage of the internet on international trade development

Research capacity strengthening in low and middle income countries - an evaluation of the WHO/TDR career development fellowship programme

Between August 2012 and April 2013 the Career Development Fellowship programme of the Special Programme for Research and Training in Tropical Diseases (World Health Organization) underwent an external evaluation to assess its past performance and determine recommendations for future programme development and continuous performance improvement. The programme provides a year-long training experience for qualified researchers from low and middle income countries at pharmaceutical companies or product development partnerships. Independent evaluators from the Swiss Tropical and Public Health Institute and the Barcelona Institute for Global Health used a results-based methodology to review the programme. Data were gathered through document review, surveys, and interviews with a range of programme participants. The final evaluation report found the Career Development Fellowship to be relevant to organizers' and programme objectives, efficient in its operations, and effective in its training scheme, which was found to address needs and gaps for both fellows and their home institutions. Evaluators found that the programme has the potential for impact and sustainability beyond the programme period, especially with the successful reintegration of fellows into their home institutions, through which newly-developed skills can be shared at the institutional level. Recommendations included the development of a scheme to support the re-integration of fellows into their home institutions post-fellowship and to seek partnerships to facilitate the scaling-up of the programme. The impact of the Professional Membership Scheme, an online professional development tool launched through the programme, beyond the scope of the Career Development Fellowship programme itself to other applications, has been identified as a positive unintended outcome. The results of this evaluation may be of interest for other efforts in the field of research capacity strengthening in LMICs or, generally, to other professional development schemes of a similar structure

A Convex Combination Approach for Artificial Neural Network of Interval Data

As the conventional models for time series forecasting often use single-valued data (e.g., closing daily price data or the end of the day data), a large amount of information during the day is neglected. Traditionally, the fixed reference points from intervals, such as midpoints, ranges, and lower and upper bounds, are generally considered to build the models. However, as different datasets provide different information in intervals and may exhibit nonlinear behavior, conventional models cannot be effectively implemented and may not be guaranteed to provide accurate results. To address these problems, we propose the artificial neural network with convex combination (ANN-CC) model for interval-valued data. The convex combination method provides a flexible way to explore the best reference points from both input and output variables. These reference points were then used to build the nonlinear ANN model. Both simulation and real application studies are conducted to evaluate the accuracy of the proposed forecasting ANN-CC model. Our model was also compared with traditional linear regression forecasting (information-theoretic method, parametrized approach center and range) and conventional ANN models for interval-valued data prediction (regularized ANN-LU and ANN-Center). The simulation results show that the proposed ANN-CC model is a suitable alternative to interval-valued data forecasting because it provides the lowest forecasting error in both linear and nonlinear relationships between the input and output data. Furthermore, empirical results on two datasets also confirmed that the proposed ANN-CC model outperformed the conventional models