Supanee Harnphattananusorn’s research while affiliated with Kasetsart University and other places

The objective of this study is to explore the relationship between changing weather conditions and tourism demand in Thailand across five selected provinces: Chonburi (Pattaya), Surat Thani, Phuket, Chiang Mai, and Bangkok. The annual data used in this study from 2012 to 2022. The estimation method is threshold regression (TR). The results indicate that weather conditions proxied by the Temperature Humidity Index (THI) significantly affect tourism demand in these five provinces. Specifically, changes in weather conditions, such as an increase in temperature, generally result in a decrease in tourism demand. However, the impact of weather conditions varies according to each province’s unique characteristics or highlights. For example, tourism demand in Bangkok is not significantly affected by weather conditions. In contrast, provinces that rely heavily on maritime tourism, such as Chonburi (Pattaya), Phuket, and Surat Thani, are notably affected by weather conditions. When the THI in each province rises beyond a certain threshold, the demand for tourism in these provinces by foreign tourists decreases significantly. Furthermore, economic factors, particularly tourists’ income, significantly impact tourism demand. An increase in the income of foreign tourists is associated with a decrease in tourism in Pattaya. This trend possibly occurs because higher-income tourists tend to upgrade their travel destinations from Pattaya to more upscale locations such as Phuket or Surat Thani. For Thai tourists, an increase in income leads to a decrease in domestic tourism, as higher incomes enable more frequent international travel, thereby reducing tourism in the five provinces. Additionally, the study found that the availability and convenience of accommodation and food services are critical factors influencing tourism demand in all the provinces studied.

The objective of this study is to analyze the spillover effects among the returns of oil, gold, the stock market, and exchange rates in Thailand. Using the time-varying parameter vector autoregression model (TVP-VAR) with extended joint connectedness and data from April 2002 to March 2024, our analysis reveals a moderate level of the dynamic linkage among these returns. We observe that the dynamic connectedness among all returns varies over time, influenced by global economic events and country situations. Notably, in Thailand's landscape, stock market and gold returns act as net shock transmitters, with the stock market exhibiting the highest volatility among all variables, while oil and exchange rate markets function as net recipients. These insights significantly contribute to understanding asset and commodity markets and offer valuable policy implications for effectively managing these markets.

This study investigates whether generation diversity in workforce can influence economic growth. Using a panel data of 37 OECD countries over the years 1979–2019, it is found that the effect of generation diversity on economic growth depends on the development level of a country. Essentially, the generation diversity has a positive impact on economic growth in developed countries, but a negative impact on economic growth in developing countries.

This paper aims to investigate asymmetric relationship between exchange rate volatility and oil price using a nonlinear auto-regressive distribution Lag (NARDL) developed by Shin et al. (2014). This technique allow us for estimating asymmetric long-run as well as short-run coefficients in a cointegration framework. For exchange rate volatility measurement, GARCH (1,1) model is applied. We use monthly data from January 2000 to June 2021. The results show that there are asymmetric impacts of oil price shocks on Thailand exchange volatility both in the long run and short run. Moreover, both positive and negative shocks on stock price index increase exchange volatility in the short run.

Workers of different generations often complain about one another as their opinions, values, attitudes, and approaches to work differ. This might lead to a reduction in labor productivity that can negatively impact the economic growth of any nation. In this paper, we used generation mix indices to analyze whether generation gap has any impact on economic growth. Using Thailand’s data between 1961 to 2019, we found that when generations were intensely mixed, economic growth did suffer.

Pollution cost is an important variable in the calculation of Green GDP, which is an indicator of economic development under the green economy concept. This paper relies on the SFA to estimate the cost of air and water pollution in Thailand. During the past 20 years, Thailand has been subject to an average pollution cost of about 2,209,936 million baht ($63,141 million). The average air pollution cost is about 827,383.11 million baht ($23,640 million) and average water pollution costs approximately 1,382,552.84 million baht ($39,501 million). This study also confirms that the pollution problems affecting health, quality of life, and human capital are worsening. Therefore, the government should take the necessary measures through regulations and strict penalties to control pollution. It should also raise awareness of the long-term impact of pollution on human capital and economic development.

The research aims to investigate the relationship between the exchange rate of Thai Baht against USD and oil price using daily data from January 1999 to March 2019. To test whether three is the long-run relationship between selected variables, the Johansen cointegration method is employed. The results indicate the evidence of long-run relationship between oil price and the Thai Baht. Then, Artificial neural networks (ANN) technique is employed for estimation. For ANN estimation, the results suggest that the most influential variables for the Thai Baht is gold price and oil price is third influential variable for Thai Baht. The research used the Mean Squared Error (MSE), Root Mean Square Error (MSE) and the Mean Absolute Percentage Error (MAPE) to measure the error. The results suggest that ANN estimation is more efficient than linear model in term of error. Keywords: Thai Baht, ANN, Cointegration JEL Classification: A1 DOI: https://doi.org/10.32479/ijeep.8612