Nattapong Puttanapong

This study introduced a new approach for monitoring regional development by applying satellite data with machine learning algorithms. Satellite data that represent physical features and environmental factors were obtained by developing a web-based application on the Google Earth Engine platform. Four machine learning methods were applied to the obtained geospatial data to predict provincial gross domestic product. The random forest method achieved the highest predictive performance, with 97.7% accuracy. The constructed random forest model was extended to conduct variable importance and minimal depth analyses, enabling the quantification of a factor’s influence on the prediction outcome. Variable importance and minimal depth analyses generated similar results, indicating that urban area and population are the most influential factors. Moreover, environmental and climate indicators exert medium-level effects. This study showed that integrating available satellite data and machine lear...

The agricultural and food processing sectors have been one of major production activities in Thailand because their employment are among the largest portions in labor market. Also both sectors have a high magnitude of Leontief multipliers, showing their substantial degrees of spillovers to other industries. To examine the network of both sectors' contributions to Thai economy, this study constructed the Social Accounting Matrix (SAM) by extending the official Input-Output table of 2010. The computational technique of Structural Path Analysis (SPA) was then applied to the constructed SAM. The results generated by SPA unveiled details of supply chains connecting agricultural activity, food processing sector, employment of factors of productions and household's incomes. Specifically, based on the most detailed classifications of the official Input-Output table, the SPA results revealed and quantified the broad and in-depth details of supply chains of tapioca milling, rice milli...

This study statistically identified the localised association between socioeconomic conditions and the coronavirus disease 2019 (COVID-19) incidence rate in Thailand on the basis of the 1,727,336 confirmed cases reported nationwide during the first major wave of the pandemic (March-May 2020) and the second one (July 2021-September 2021). The nighttime light (NTL) index, formulated using satellite imagery, was used as a provincial proxy of monthly socioeconomic conditions. Local indicators of spatial association statistics were applied to identify the localised bivariate association between COVID-19 incidence rate and the year-on-year change of NTL index. A statistically significant negative association was observed between the COVID-19 incidence rate and the NTL index in some central and southern provinces in both major pandemic waves. Regression analyses were also conducted using the spatial lag model (SLM) and the spatial error model (SEM). The obtained slope coefficient, for both...

To formulate and monitor the progress of development policies, acquiring data with sufficient spatiotemporal details is inevitable. With the increasing availability of open remote-sensing data and open-source software packages, this research suggested the novelty integration of satellite data and spatial analytical methods, enabling a timely and costless framework for assessing the nationwide socioeconomic condition. Specifically, the spatial statistical and spatial econometrical methods were applied to geospatial data to identify the clustering patterns and the localized associations of inequality in Thailand. The spatial statistical results showed that Bangkok and its vicinity had been a cluster of high socioeconomic conditions, representing the spatial inequality of development. In addition, results of the spatial econometrical models showed that the satellite-based indicators could identify the socioeconomic condition (with p-value < 0.010 and R-squared ranging between 0.345 ...

Continued urban expansion undergone in the last decades has converted many weather stations in Thailand into suburban and urban setting. Based on homogenized data during 1970-2019, therefore, this study examines urbanization effects on mean surface air temperature (Tmean) trends in Thailand. Analysis shows that urban-type stations register the strongest warming trends while rural-type stations exhibit the smallest trends. Across Thailand, annual urban-warming contribution exhibits a wide range (< 5% to 77%), probably manifesting the Urban Heat Island (UHI) differences from city to city resulting from the varied urban characteristics and climatic background. Country-wide average urban warming contribution shows a significant increasing trend of 0.15 oC per decade, accounting for 40.5% of the overall warming. This evidence indicates that urban expansion has great influence on surface warming, and the urban-warming bias contributes large fraction of rising temperature trends in Thai...

This study examines an alternative approach in estimating poverty by investigating whether readily available geospatial data can accurately predict the spatial distribution of poverty in Thailand. It also compares the predictive performance of various econometric and machine learning methods such as generalized least squares, neural network, random forest, and support vector regression. Results suggest that intensity of night lights and other variables that approximate population density are highly associated with the proportion of population living in poverty. The random forest technique yielded the highest level of prediction accuracy among the methods considered, perhaps due to its capability to fit complex association structures even with small and medium-sized datasets.

This study analyzes the temporal pattern and spatial clustering of leptospirosis, a disease recognized as an emerging public health problem in Thailand. The majority of those infected are farmers and fishermen. Severe epidemics of leptospirosis in association with the rainy reason have occurred since 1996. Still, an understanding of the annual variation and spatial clustering of the disease is lacking. Data were collected from the Center of Epidemiological Information, Bureau of Epidemiology, Ministry of Public Health, covering the nationwide incidence of leptospirosis during the period 2013-2015. Clustering techniques, including local indicators of spatial association and local Getis-Ord Gi* statistic, were used for the analysis and evaluation of the annual spatial distribution of the disease. Both these statistics revealed similar results for the areas with the highest clustering patterns of leptospirosis. Specifically, there were persisting hotspots in north-eastern and southern ...

Background: Hypertension (HT) has been one of the leading global risk factors for health and the leading cause of death in Thailand for decades. The influence of socioeconomic factors on HT has been varied and inconclusive. The aim of this study was to determine the association between socioeconomic determinants and HT in Thailand. Methods: This study used data from the National Socioeconomic Survey, a cross-sectional study that was conducted by the National Statistical Office of Thailand in the years 2005, 2006 and 2007. In our analysis, data were collected on gender, age, marital status, smoking status, education, status of work, occupation, current liability (short-term debt), household monthly income, residential area, region and previously diagnosed HT by a physician. Results: The odds of having HT were significantly higher among those who had household monthly income, education, residential area and region. The participants who had monthly income of <10001 baht (2005: AOR =...

This study aimed to determine the association between socioeconomic determinants and Chronic Respiratory Diseases (CRDs) in Thailand. The data were used from the National Socioeconomics Survey (NSS), a cross-sectional study conducted by the National Statistical Office (NSO), in 2010 and 2012. The survey used stratified two-stage sampling to select a nationally representative sample to respond to a structured questionnaire. A total of 17,040 and 16,905 individuals in 2010 and 2012, respectively, were included in this analysis. Multiple logistic regressions were used to identify the association between socioeconomic factors while controlling for other covariates. The prevalence of CRDs was 3.81% and 2.79% in 2010 and 2012, respectively. The bivariate analysis indicated that gender, family size, geographic location, fuels used for cooking and smoking were significantly associated with CRDs in 2010, whereas education, family size, occupation, region, geographic location, and smoking wer...

Spatial pattern detection can be a useful tool for understanding the geographical distribution of hypertension (HT). The aim of this study was to apply the technique of local indicators of spatial association statistics to examine the spatial patterns of HT in the 76 provinces of Thailand. Previous studies have demonstrated that socioeconomic status (SES), economic growth, population density and urbanization have effects on the occurrence of disease. Research has suggested that night-time light (NTL) can be used as a proxy for a number of variables, including urbanization, density, economic growth and SES. To date, there has not been any study on spatial patterns of HT and there is no information on how NTL might correlate with HT. Therefore, this study has investigated NTL as a parameter for detection of hotspots of HT in Thailand. It was found that HT clusters occurred in Bangkok and in metropolitan areas. In addition, significantly low-rate clusters were seen in some provinces in...

The prevalence of Diabetes Mellitus (DM) is increasing, globally. However, studies on the association between Socioeconomic Status (SES) factors and DM have mostly been conducted in specific areas with rather small sample sizes or not with nationally representative samples. Their results have also been inconclusive regarding whether SES has any influence on DM or not. To determine the association between SES and DM in Thailand. This study utilized the data from the National socioeconomics survey, a cross-sectional study conducted by the National Statistical Office (NSO) in 2010 and 2012. A total of 17,045 and 16,903 participants respectively who met the inclusion criteria were included in this study. The information was collected by face-to-face interview with structured questionnaires. Multilevel mixed-effects logistic regression analysis was performed to determine the potential socioeconomic factors associated with DM. The prevalence of DM was 3.70% (95% CI: 3.36 to 4.05) and 8.11...

In recent years, issues of climate change and CO2 mitigatation have become more and more important. Since the electric power generation is one of the key major greenhouse polluters. Measures and technologies to mitigate its emissions are of interest to most countries. This study aims to investigate effects and economic implications of levying carbon tax on electricity generation in Thailand by using the dynamic Computable General Equilibrium (CGE) model. In this study, economic activities of Thailand were categorized into 40 sectors, with 49 commodities. Four scenarios were assumed: Business as usual (BAU), Low carbon tax rate (LT), 150 baht per ton CO2 , Average carbon tax rate (AT), 450 baht per ton CO2 , and High carbon tax rate (HT), 750 baht per ton CO2. The result shows that, although the imposition of the high carbon tax rate can yield a greater impact on the economic growth, particularly for a short term, its effect is moderate. In addition, its impact on CO2 is much more ef...

Why slower growth and high inflation can occur concurrently, while in other cases growth can be non-inflationary? Why did aggregate demand policy sometime fail to work, given an orthogonal shock? This study ponders on these queries by estimating the aggregate supply and aggregate demand curves in four East Asian countries. Applying the Structural Vector Auto-Regression (SVAR) with the restrictions a-la Blanchard and Quah, it is revealed that while the AD and AS curves in most cases follow the textbook definitions, in some countries the AS curve is so flat that demand expansion would have been effective to stimulate growth, and supply-based policies would be more desirable to control prices. We also found that during the crisis the supply shock played a more significant role in the price fluctuations, suggesting that focusing on AD management alone was not the best approach to take.

DESCRIPTION Trade openness has continuously gained its significance on the structure of Thai economy. The empirical evidence shows that the increasing inflows of intermediate, machinery and Foreign Direct Investment (FDI) have simultaneously correlated with the exports of manufacturing products. This evolution indicates the deeper connection of Thai economy to the global supply chain. Based on this fact, this study introduces the new approach of quantifying and tracing the network of impact transmission between the international supply chain and Thai economy. To construct the global input-output table exhibiting Thailand’s international trade linkages, the World Input-Output Database (WIOD) is extended to include Thailand’s domestic and international trade statistics. In order to extract the main structure of international production network, the computational techniques of Leontief backward and forward multipliers and the Structural Path Analysis (SPA)are applied to the newly const...

While there is agreement that inequality is a major challenge to the Thai economy and society, the scope and the roots of the problem are still subject to academic and public discussion. Accordingly, there is also no consensus on the policies needed to address inequality and achieve a path of shared growth. This paper wants to contribute to this important policy debate by examining the possible distributional impact of two obvious important policies that may be important pillars of a shared-growth strategy. The first policy is an increase in agricultural productivity and the second upgrading the skills of the Thai workforce. To address the distributional effects of these two policy options for shared growth, this paper uses a sequential macro-micro approach that links a macroeconomic model, here an applied CGE model, to a behavioral micro-simulation model. The results suggest that shared growth cannot be achieved by “marginal” interventions targeted at poor individuals in traditiona...

One of leading global automobile makers, Thailand is currently ranked first in ASEAN and 15th world’s largest automobile manufacturer. The country aims to be one of the world's top 10 automobile makers in the upcoming years. The automotive industry is a driving force for the Thai economy. It accounts for ten percent of GDP of the country, employs more than 500,000 direct skilled-labor jobs, and creates spillover effects to other industries in the economy. The industry was severely hit by the East Japan Quake and the Great Thai Flood in 2011; however, only a year later, in 2012 it quickly recovers from these disasters. The exceptionally high growth in both production and sales sectors in Thailand in 2012 pushed the car production to the new record. Another positive factor creating high domestic demand for automobile was the government’s tax rebates for first-time car buyers. This tax rebate policy was launched in order to help the automotive industry recover in the wake of 2011 d...

In the past 30 years, the static CGE model has been widely used in the analysis of environmental economics. In the case of Thailand, there are many studies which use static CGE models to explore economy-wide impacts of imposing policies on CO 2 emission. However, the static model has a limitation in its one-period comparative static feature. Hence, in this study, the recursive dynamic process has been included in the model to extend its capability of simulating the growth path of Thai economy and sectoral adjustment in medium-run and long-run. In addition to the dynamic feature, the Monte-Carlo technique is implemented by running the dynamic model with various sets of parameters randomly generated from given distribution properties. This enhanced capability of performing both stochastic and dynamic simulations expands the dimension of impact analysis of carbon tax policies, especially toward the multi-period effects and their stochastic properties over time. This new technique will ...

ABSTRACT Climate change is a major concern for developing countries because changing weather conditions would significantly affect agricultural production output. Employing half of its total labor supply and land in agricultural activities, Thailand is considered one of the major food-exporting countries vulnerable to climate change. Despite the forecast of future weather volatility and its effect on Thailand's major crop yield such as rice, cassava, sugar cane, and corn, there is a lack of studies examining the resulting nationwide economic impact. This study aims to explore the economy-wide impacts of crop yield fluctuation on the Thai economy by using both static and Monte-Carlo CGE models. For effects on agricultural markets, simulation results indicate that prices and quantities of corn and cassava are the most sensitive to weather oscillation. In the nationwide impact, both static and Monte-Carlo CGE models show that rice is the most significant crop because its volatility in price and quantity causes the highest impact and fluctuation on both the macro level and in terms of income distribution. The Monte-Carlo CGE model demonstrates that the fluctuation of impact on government and institutions can be reduced when volatilities of all four crop outputs are pooled. This suggests the possibility to design a crop insurance scheme to minimize the impact of volatility through risk pooling.