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.

Based on the canonical vine (C-vine) copula approach, this paper examines the interdependence between the exchange rates of the Chinese Yuan (CNY) and the currencies of major Association of Southeast Asian Nations (ASEAN) countries. The differences in the dependence structure and degree between currencies before and after the Belt and Road (B&R) Initiative were compared in order to investigate the changing role of the Renminbi (RMB) in the ASEAN foreign exchange markets. The results indicate a positive dependence between the exchange rate returns of CNY and the currencies of ASEAN countries and show the rising power of RMB in the regional currency markets after the B&R Initiative was launched. Besides this, the Malaysian Ringgit proved to be most relevant to the other ASEAN currencies, thus playing an important role in the stability of regional financial markets. Moreover, evidence of tail dependence was found in the returns of three currency pairs after the B&R Initiative, which implies the presence of asymmetric dependence between exchange rates. The results from time-varying C-vine copulas further confirmed the robustness of the results from the static C-vine copulas.

The paper develops a simultaneous equations stochastic frontier model (SFM) with dependent random noise and inefficiency components of individual equations as well as allowing dependence across all equations of the model using copula functions. First, feasibility of our developed model was verified via two simulation studies. Then the model was applied to assess cost efficiency and market power of the banking industry of China using a panel data of 37 banks covering the period 2013–2018. Results confirmed that our simultaneous SFM with dependent random noise and inefficiency components outperformed its predecessor, which is a simultaneous SFM with dependent composite errors but with independent random noise and inefficiency components of individual SFMs as well as the conventional single-equation SFM. Apart from the statistical and computational superiority of our developed model, we also see that Chinese banks in general have a high level of cost efficiency and that competition in the banking industry of China mainly exists in state-owned banks and joint stock banks. Presence of economies of scales as well as diseconomies of scales were found in different banks. Also, the state-owned banks embraced most sophisticated technologies thereby allowing them to operate with the highest level of cost efficiency.