The Demand for Loans for Major Rice in the Upper North of Thailand

Though Thailand is the largest rice exporting country, its yield is relatively low. This might be a result of the under use of purchased input factors. Amongst other factors, high input prices and capital constraints could be some reasons. The latter could be removed by loans providing favorable market conditions exist. This paper seeks to investigate factors affecting the decision to borrow, and the demand for loans, for rice. The Tobit type-II models are estimated using the survey data collected from 656 rice farmers in the Upper North of Thailand in 2004. It is found that significant factors affecting the decision of borrowing include; the land planted to rice, dummy variable for off-farm income sources, and annual interest rates. In the second step, the farmers who borrowed from the rural financial sources, including 202 and 250 farmers from Chiang Mai and Chiang Rai respectively, are considered. According to the OLS estimation, only the land planted to rice has a positive significant effect on the amount of loans for major rice. Further, the interest rate affects the probability of loans but has no impact on the amount of loans, for rice.Upper North of Thailand, Tobit type-II model, Probability of loans for rice, Amount of loans, Rural financial sources, Agricultural Finance, Crop Production/Industries,

"Time Series Modelling of Tourism Demand from the USA, Japan and Malaysia to Thailand"

Even though tourism has been recognized as one of the key sectors for the Thai economy, international tourism demand, or tourist arrivals, to Thailand have recently experienced dramatic fluctuations. The purpose of the paper is to investigate the relationship between the demand for international tourism to Thailand and its major determinants. The paper includes arrivals from the USA, which represents the long haul inbound market, from Japan as the most important medium haul inbound market, and from Malaysia as the most important short haul inbound market. The time series of tourist arrivals and economic determinants from 1971 to 2005 are examined using ARIMA with exogenous variables (ARMAX) models to analyze the relationships between tourist arrivals from these countries to Thailand. The economic determinants and ARMA are used to predict the effects of the economic, financial and political determinants on the numbers of tourists to Thailand.

Technical Efficiency of Rice Production in the Upper North of Thailand: Clustering Copula-Based Stochastic Frontier Analysis

This study examines the efficiency of rice production in Thailand, especially major rice, which is the main crop of farmers in all regions of Thailand and is still a pressing issue. Analyzing technical efficiency by using the appropriate analytical tools inevitably brings about determining the correct production efficiency measures. In this study, we applied the K-Means algorithm and copula-based stochastic frontier model to cluster farmer groups in order to find the different factors that impact the group, and to relax the assumption of the two components of random error, which is that they are independent to each other; the correlation of the two components of random error is also represented by the estimation of copula. The findings from the K-Means clustering algorithms applied in this study indicate that the production frontiers can be divided into two frontiers, with the number of farmers under the frontiers of such production differing from the number of farmers collected in each area. The production frontiers were obtained with 591 farmers under the first production frontier and 65 farmers under the second. In addition, the results reflected a correlation between the two error components U and V. This suggests inefficiencies and zero-mean, and that the symmetric error is not independent of each other. The findings from the application of the copula-based stochastic frontier production function models indicate that land, cost of chemicals, and labor inputs have significant positive effects on the mean output of major rice in both groups of farmers. Therefore, the results of this study indicate that the financial services in rural areas should be continuously promoted by governmental policy, particularly via agricultural loans, to rural people since the utilization of inputs affects the quantity of rice produced. Timely loans should be encouraged

Modeling the Price Volatility of Cassava Chips in Thailand: Evidence from Bayesian GARCH-X Estimates

Thailand is a significant global exporter of cassava, of which cassava chips are the main export products. Moreover, China was the most important export market for Thailand from 2000 to 2020. However, during that period, Thailand confronted fluctuations in the cassava product price, and cassava chips were a product with significant price volatility, adapting to changes in export volumes. This study aims to analyze the volatility of the price of cassava chips in Thailand from 2010 to 2020. The data were collected monthly from 2010 to 2020, including the price of cassava chips in Thailand (Y), the volume of cassava China imported from Thailand (X1), the price of the cassava chips that China imported from Thailand (X2), the price of the cassava starch that China imported from Thailand (X3), the substitute crop price for maize (X4), the substitute crop price for wheat (X5), and Thailand’s cassava product export volume (X6). The volatility and the factors affecting the volatility in the price of cassava chips were calculated using Bayesian GARCH-X. The results indicate that the increase in X1, X2, X3, X4, and X6 led to an increase in the rate of change in cassava chip price volatility. On the other hand, if the substitute crop price for wheat (X5) increases, then the rate of change in the volatility of the cassava chip price decreases. Therefore, the government’s formulation of an appropriate cassava policy should take volatility and the factors affecting price volatility into account. Additionally, the government’s formulation of agricultural policy needs to consider Thailand’s macro-environmental factors and its key trading partners, especially when these environmental factors signal changes in the price volatility of cassava

Technical Efficiency of Rice Production in the Upper North of Thailand: Clustering Copula-Based Stochastic Frontier Analysis

This study examines the efficiency of rice production in Thailand, especially major rice, which is the main crop of farmers in all regions of Thailand and is still a pressing issue. Analyzing technical efficiency by using the appropriate analytical tools inevitably brings about determining the correct production efficiency measures. In this study, we applied the K-Means algorithm and copula-based stochastic frontier model to cluster farmer groups in order to find the different factors that impact the group, and to relax the assumption of the two components of random error, which is that they are independent to each other; the correlation of the two components of random error is also represented by the estimation of copula. The findings from the K-Means clustering algorithms applied in this study indicate that the production frontiers can be divided into two frontiers, with the number of farmers under the frontiers of such production differing from the number of farmers collected in each area. The production frontiers were obtained with 591 farmers under the first production frontier and 65 farmers under the second. In addition, the results reflected a correlation between the two error components U and V. This suggests inefficiencies and zero-mean, and that the symmetric error is not independent of each other. The findings from the application of the copula-based stochastic frontier production function models indicate that land, cost of chemicals, and labor inputs have significant positive effects on the mean output of major rice in both groups of farmers. Therefore, the results of this study indicate that the financial services in rural areas should be continuously promoted by governmental policy, particularly via agricultural loans, to rural people since the utilization of inputs affects the quantity of rice produced. Timely loans should be encouraged

The Demand for Loans for Major Rice in the Upper North of Thailand

Though Thailand is the largest rice exporting country, its yield is relatively low. This might be a result of the under use of purchased input factors. Amongst other factors, high input prices and capital constraints could be some reasons. The latter could be removed by loans providing favorable market conditions exist. This paper seeks to investigate factors affecting the decision to borrow, and the demand for loans, for rice. The Tobit type-II models are estimated using the survey data collected from 656 rice farmers in the Upper North of Thailand in 2004. It is found that significant factors affecting the decision of borrowing include; the land planted to rice, dummy variable for off-farm income sources, and annual interest rates. In the second step, the farmers who borrowed from the rural financial sources, including 202 and 250 farmers from Chiang Mai and Chiang Rai respectively, are considered. According to the OLS estimation, only the land planted to rice has a positive significant effect on the amount of loans for major rice. Further, the interest rate affects the probability of loans but has no impact on the amount of loans, for rice

Production efficiency of jasmine rice producers in northern and north-eastern Thailand

The paper jointly evaluates the determinants of switching to Jasmine rice and its productivity while allowing for production inefficiency at the level of individual producers. Model diagnostics reveal that serious selection bias exists, justifying use of a sample selection framework in stochastic frontier models. Results from the probit variety selection equation reveal that gross return (mainly powered by significantly higher Jasmine rice price), access to irrigation and education are the important determinants of choosing Jasmine rice. Results from the stochastic production frontier reveal that land, irrigation and fertilisers are the significant determinants of Jasmine rice productivity. Significantly lower productivity in Phitsanulok and Tung Gula Rong Hai provinces demonstrate the influence of biophysical and environmental factors on productivity performance. The mean level of technical efficiency is estimated at 0.63 suggesting that 59% [(100 − 63)/63] of the productivity is lost due to technical inefficiency. Policy implications include measures to keep Jasmine rice price high, increase access to irrigation and fertiliser availability, as well as investment in education targeted to farm households which will synergistically increase adoption of Jasmine rice as well as farm productivity

Time Series Modelling of Tourism Demand from the USA, Japan and Malaysia to Thailand

Even though tourism has been recognized as one of the key sectors for the Thai economy, international tourism demand, or tourist arrivals, to Thailand have recently experienced dramatic fluctuations. The purpose of the paper is to investigate the relationship between the demand for international tourism to Thailand and its major determinants. The paper includes arrivals from the USA, which represents the long haul inbound market, from Japan as the most important medium haul inbound market, and from Malaysia as the most important short haul inbound market. The time series of tourist arrivals and economic determinants from 1971 to 2005 are examined using ARIMA with exogenous variables (ARMAX) models to analyze the relationships between tourist arrivals from these countries to Thailand. The economic determinants and ARMA are used to predict the effects of the economic, financial and political determinants on the numbers of tourists to Thailand

Production Efficiency of Jasmine Rice Producers in Northern and North-eastern Thailand

The paper jointly evaluates the determinants of switching to Jasmine rice and its productivity while allowing for production inefficiency at the level of individual producers. Model diagnostics reveal that serious selection bias exists, justifying use of a sample selection framework in stochastic frontier models. Results from the probit variety selection equation reveal that gross return (mainly powered by significantly higher Jasmine rice price), access to irrigation and education are the important determinants of choosing Jasmine rice. Results from the stochastic production frontier reveal that land, irrigation and fertilisers are the significant determinants of Jasmine rice productivity. Significantly lower productivity in Phitsanulok and Tung Gula Rong Hai provinces demonstrate the influence of biophysical and environmental factors on productivity performance. The mean level of technical efficiency is estimated at 0.63 suggesting that 59% [(100  -  63)/63] of the productivity is lost due to technical inefficiency. Policy implications include measures to keep Jasmine rice price high, increase access to irrigation and fertiliser availability, as well as investment in education targeted to farm households which will synergistically increase adoption of Jasmine rice as well as farm productivity. Copyright (c) 2009 The Authors. Journal compilation (c) 2009 The Agricultural Economics Society.