Sources of Total-Factor Productivity and Efficiency Changes in China’s Agriculture

The core of agricultural development depends on agricultural production efficiency improvement, and total-factor productivity growth is its significant embodiment. Hence, it is essential to address the question of “how to improve China’s agricultural productivity and efficiency in order to achieve growth and sustainability of agriculture in the future”. This paper estimates indices of China’s agricultural technical efficiency (TE) scores, total-factor productivity (TFP), and its two components, technological change/progress (TC) and technical efficiency change (EC), using provincial-level panel data of 30 provinces from 2002 to 2017 by applying a stochastic frontier approach (SFA). The paper also identifies determinants of TE, TC, and TFP using selected indicators from four hierarchical levels of the economy, i.e., farm level, production environment level, provincial level, and the state level, by applying a system-GMM method. Results reveal that agricultural labor, machinery, agricultural plastic film, and pesticides are the significant drivers of agricultural productivity, with no significant role of land area under cultivation. Constant returns to scale exist in China’s agriculture. The agricultural technical efficiency level fluctuated between 80% and 91% with a stable trend and a slight decline in later years, while TFP improved consistently over time, mainly driven by technological progress. Among the determinants, government investment in agricultural development projects significantly drives TC and TE, while the experienced labor force significantly increases TE. The disaster rate significantly reduces TE but promotes TC and TFP. The literacy rate significantly improves TC and TFP. However, government expenditures in “agriculture, forestry, and water” significantly reduce TE, TC, and TFP. Policy recommendations include (1) increased levels of mechanization and agriculture film use while avoiding an increase in pesticide use, (2) a continued increase in government expenditure in agricultural development projects, R&amp;D to improve technological progress, and diffusion of modern agricultural technologies, and (3) investment in education targeted at the farming population in order to continue the growth in the productivity and sustainability of China’s agriculture.</jats:p

Agricultural Productivity Growth and Its Determinants in South and Southeast Asian Countries

Improving agricultural productivity is a priority concern in promoting the sustainable development of agriculture in developing countries. In this study, we first apply stochastic frontier analysis (SFA) to analyze the growth of agricultural total factor productivity (TFP) and its three components (technical change—TC, technical efficiency change—TEC and scale change—SC) in 15 south and southeast Asian countries covering the period 2002 to 2016. Then, the determinants of agricultural TFP growth are identified using dynamic panel data models. The results reveal that the south and southeast Asian countries witnessed an overall decline in agricultural productivity during the sample period, thereby creating concerns over sustaining future agricultural growth. Technical progress was the major source of TFP growth, but its contribution has slowed in recent years. On the other hand, declining scale change and technical efficiency change resulted in the deterioration of productivity over time. Variable levels of productivity performances were observed for individual countries, mainly driven by technological progress. Overall, southeast Asia achieved a more stable and sustained agricultural growth as compared to south Asia. Among the determinants, human capital, level of urbanization, and development flow to agriculture positively influenced agricultural TFP growth, while the level of economic development and agricultural import were negatively associated with TFP growth. Policy recommendations include the suggestions that south and southeast Asian countries should increase investment in human capital, focus on technological innovation and make use of financial assistance and development flow to agriculture to increase and sustain agricultural productivity. In addition, frontier countries of the two regions (e.g., India and Indonesia) should take the lead on regional agricultural development ventures by enhancing cooperation with neighboring countries on technological innovations, and countries facing diseconomies of scale (i.e., Afghanistan and Iran) should consider the rational reallocation of agricultural inputs.</jats:p

Measurement of Systemic Risk in Global Financial Markets and Its Application in Forecasting Trading Decisions

No embargo requiredThe global financial crisis in 2008 spurred the need to study systemic risk in financial markets, which is of interest to both academics and practitioners alike. We first aimed to measure and forecast systemic risk in global financial markets and then to construct a trade decision model for investors and financial institutions to assist them in forecasting risk and potential returns based on the results of the analysis of systemic risk. The factor copula-generalized autoregressive conditional heteroskedasticity (GARCH) models and component expected shortfall (CES) were combined for the first time in this study to measure systemic risk and the contribution of individual countries to global systemic risk in global financial markets. The use of factor copula-based models enabled the estimation of joint models in stages, thereby considerably reducing computational burden. A high-dimensional dataset of daily stock market indices of 43 countries covering the period 2003 to 2019 was used to represent global financial markets. The CES portfolios developed in this study, based on the forecasting results of systemic risk, not only allow spreading of systemic risk but may also enable investors and financial institutions to make profits. The main policy implication of our study is that forecasting systemic risk of global financial markets and developing portfolios can provide valuable insights for financial institutions and policy makers to diversify portfolios and spread risk for future investments and trade.</jats:p

Stochastic Dominance Analysis of CTA Funds

In this paper, we employ the stochastic dominance approach to rank the performance of commodity trading advisors (CTA) funds. An advantage of this approach is that it alleviates the problems that can arise if CTA returns are not normally distributed by utilizing the entire returns distribution. We find both first-order and higher-order stochastic dominance relationships amongst the CTA funds and conclude that investors would be better off investing in the first-order dominant funds to maximize their expected utilities and expected wealth. However, for higher-order dominant CTA, riskaverse investors can maximize their expected utilities but not their expected wealth. We conclude that the stochastic dominance approach is more appropriate compared with traditional approaches as a filter in the CTA selection process given that a meaningful economic interpretation of the results is possible as the entire return distribution is utilized when returns are non-normal

Retracted: Understanding the relationship of tourism demands connecting with economy and tourism stock index in an extreme case of Thailand bivariate extreme value copula approach

This article was withdrawn and retracted by the Journal of Fundamental and Applied Sciences and has been removed from AJOL at the request of the journal Editor in Chief and the organisers of the conference at which the articles were presented (www.iccmit.net). Please address any queries to [email protected]

The Effects of Production Inputs, Technical Inefficiency and Biological Risk on Jasmine and Non-Jasmine Rice Yields in Thailand

Both Jasmine and non-Jasmine rice yields in Thailand are substantially lower than their respective average world rice yields. Sources of yield variations in both kinds of rice, i.e., production inputs, technical inefficiency and other factors, are investigated in this study. Factors affecting technical inefficiency of production are analyzed simultaneously with the estimation of the production frontiers using the method of maximum likelihood. Cobb-Douglas stochastic yield frontiers are used to investigate policy implications. The crucial factors influencing Jasmine rice yields are technical inefficiency, chemical fertilizer, labor, transplanting, irrigation, severe drought and neck blast, whereas for non-Jasmine rice, the same factors are significant, except for labor, neck blast, and transplanting, but other chemicals had a significantly positive effect. Factors negatively affecting technical inefficiency for non-Jasmine rice are the ratio of male labor to total labor and experience reflected by the age of the farmers, while labor influences in the positive direction. For Jasmine rice, only the male-labor ratio significantly influences technical inefficiency