Using panel data from 31 provinces in China,covering the period from 2003 to 2017,this article analyzes the threshold effect of factor price distortion on the technological content of exports.The results show that fac...Using panel data from 31 provinces in China,covering the period from 2003 to 2017,this article analyzes the threshold effect of factor price distortion on the technological content of exports.The results show that factor price distortion does not necessarily impede improvement in the quality of the technological content of exports.Instead,the adverse ejfect can be weakened when the value of per capita GDP is higher than RMB13,154 or the value of FDI goes beyond RMB480.9 billion.This is because a high regional economic development level alleviates the adverse effect of factor price distortion on the technological content of exports.Our results are robust when the dependent variable and sample years are changed.This article also addresses the endogeneity issue.We also consider the underlying mechanism through which factor price distortion affects the technological content of exports.展开更多
To make grain price stable is an important goal for the Chinese government.The paper compared the grain supply elasticity and demand elasticity to determine the grain price stability in China;used "k value" ...To make grain price stable is an important goal for the Chinese government.The paper compared the grain supply elasticity and demand elasticity to determine the grain price stability in China;used "k value" method to analyze the grain price fluctuation from 1985 to 2010;divided the grain price volatility into three stages;and analyzed the factors in each phase.On the base,it put forward some countermeasures to guarantee the stability of the grain price.展开更多
Regular and available supply is the prerequisite of an effective and efficient commercialization process. Using multivariate regression analysis on field data, this research appraises the production and marketing fact...Regular and available supply is the prerequisite of an effective and efficient commercialization process. Using multivariate regression analysis on field data, this research appraises the production and marketing factors that influence cassava market price. The production factors include cultivated area, planting material, yield, and farmers’ field schools;while farmers access to a paved road, having a telephone, the transportation costs of fresh roots, the level of root perishability, and the prices of rice and maize stand as marketing factors. The results show that farmers who attended farmers’ field school adopted improved planting materials, propagated them in their localities and the yields in these communities increased significantly. The farm size also has a significant influence on the availability of fresh roots. On the marketing side, transportation costs, access to a paved road, the prices of rice and maize significantly affect cassava’s market price and tighten the relationship between producers and marketers. We conclude that to increase fresh roots supply, roads leading to cultivating areas should be paved, better transportation provided, communication costs reduced, even distribution of planting materials and appropriate warehouses.展开更多
Real-time electricity price( RTEP) influence factor extraction is essential to forecasting accurate power system electricity prices. At present,new electricity price forecasting models have been studied to improve pre...Real-time electricity price( RTEP) influence factor extraction is essential to forecasting accurate power system electricity prices. At present,new electricity price forecasting models have been studied to improve predictive accuracy,ignoring the extraction and analysis of RTEP influence factors. In this study,a correlation analysis method is proposed based on stochastic matrix theory.Firstly, an augmented matrix is formulated, including RTEP influence factor data and RTEP state data. Secondly, data correlation analysis results are obtained given the statistical characteristics of source data based on stochastic matrix theory.Mean spectral radius( MSR) is used as the measure of correlativity.Finally,the proposed method is evaluated in New England electricity markets and compared with the BP neural network forecasting method. Experimental results show that the extracted index system comprehensively generalizes RTEP influence factors,which play a significant role in improving RTEP forecasting accuracy.展开更多
文摘Using panel data from 31 provinces in China,covering the period from 2003 to 2017,this article analyzes the threshold effect of factor price distortion on the technological content of exports.The results show that factor price distortion does not necessarily impede improvement in the quality of the technological content of exports.Instead,the adverse ejfect can be weakened when the value of per capita GDP is higher than RMB13,154 or the value of FDI goes beyond RMB480.9 billion.This is because a high regional economic development level alleviates the adverse effect of factor price distortion on the technological content of exports.Our results are robust when the dependent variable and sample years are changed.This article also addresses the endogeneity issue.We also consider the underlying mechanism through which factor price distortion affects the technological content of exports.
基金Supported by the National Natural Science Foundation Project (71173035)the National Soft Science Research Plan (2010GXQ5D330)the Plan for Key Teachers of Heilongjiang Province (GC10D206)
文摘To make grain price stable is an important goal for the Chinese government.The paper compared the grain supply elasticity and demand elasticity to determine the grain price stability in China;used "k value" method to analyze the grain price fluctuation from 1985 to 2010;divided the grain price volatility into three stages;and analyzed the factors in each phase.On the base,it put forward some countermeasures to guarantee the stability of the grain price.
文摘Regular and available supply is the prerequisite of an effective and efficient commercialization process. Using multivariate regression analysis on field data, this research appraises the production and marketing factors that influence cassava market price. The production factors include cultivated area, planting material, yield, and farmers’ field schools;while farmers access to a paved road, having a telephone, the transportation costs of fresh roots, the level of root perishability, and the prices of rice and maize stand as marketing factors. The results show that farmers who attended farmers’ field school adopted improved planting materials, propagated them in their localities and the yields in these communities increased significantly. The farm size also has a significant influence on the availability of fresh roots. On the marketing side, transportation costs, access to a paved road, the prices of rice and maize significantly affect cassava’s market price and tighten the relationship between producers and marketers. We conclude that to increase fresh roots supply, roads leading to cultivating areas should be paved, better transportation provided, communication costs reduced, even distribution of planting materials and appropriate warehouses.
基金National Natural Science Foundation of China(No.61701104)the “13th Five Year Plan” Research Foundation of Jilin Provincial Department of Education,China(No.JJKH2017018KJ)
文摘Real-time electricity price( RTEP) influence factor extraction is essential to forecasting accurate power system electricity prices. At present,new electricity price forecasting models have been studied to improve predictive accuracy,ignoring the extraction and analysis of RTEP influence factors. In this study,a correlation analysis method is proposed based on stochastic matrix theory.Firstly, an augmented matrix is formulated, including RTEP influence factor data and RTEP state data. Secondly, data correlation analysis results are obtained given the statistical characteristics of source data based on stochastic matrix theory.Mean spectral radius( MSR) is used as the measure of correlativity.Finally,the proposed method is evaluated in New England electricity markets and compared with the BP neural network forecasting method. Experimental results show that the extracted index system comprehensively generalizes RTEP influence factors,which play a significant role in improving RTEP forecasting accuracy.
