The Research on Total Factor Productivity of Soybean in China

This paper uses the panel stochastic frontier model to study the total factor productivity of Chinese soybean. The research shows that the impact of direct cost and labor cost on yield is positive and significant, the impact of indirect cost on yield is not significant, and the impact of cash cost on yield improvement is negative.

Industrial Efficiency Algorithm Based on Spatio-Temporal-Data-Driven

Stochastic frontier model is an important and effective method to calculate industry efficiency. However, when dealing with temporal and spatial data from the industry, it is difficult to accurately calculate the industrial production efficiency due to the influence of spatial correlation and time lag effect. If the traditional spatial statistical method is used, the setting method of spatial weight matrix is often questioned. To solve this series of problems, one possible idea is to design a spatial data mining process based on stochastic frontier analysis. Firstly, the stochastic frontier model should be improved to analyze spatio-temporal data. In order to accurately measure the technical efficiency in the case of dual correlation between time and space, a more effective spatio-temporal stochastic frontier model method is proposed. Meanwhile, based on the idea of generalized moment estimation, an estimation method of spatiotemporal stochastic frontier model is designed, and the consistency of estimators is proved. In order to ensure that the most appropriate spatial weight matrix can be selected in the process of model construction, the K -fold crossvalidation method is adopted to evaluate the prediction effect under the data-driven idea. This set of spatio-temporal data mining methods will be used to measure the technical efficiency of high-tech industries in various provinces of China.

Interpreting Stochastic Frontier Model as Random Coefficient Model and Vice Versa

This note explores the relationship between the stochastic frontier model and the random coefficient regression model. It shows how to interpret the former as a special case of the latter and vice versa. JEL classification numbers: C13, C51, D24. Keywords: Stochastic production frontier, Random coefficient regression, Composite error, Technical inefficiency.

Analysis of technical efficiency of smallholder tomato producers in Asaita district, Afar National Regional State, Ethiopia

The tomato had nutritional, economic and health benefits to the societies, however, its production and productivity were low in developing countries and particularly in Ethiopia. This might be due to technical inefficiency caused by institutional, governmental, and farmers related factors. Therefore this study tried to investigate the factors that affecting technical efficiency and estimating the mean level of technical efficiency of tomato producers in Asaita district, Afar Regional State, Ethiopia. Both primary and secondary data sources were used; the primary data was collected from 267 tomato producers from the study area cross-sectional by using a multistage sampling technique. The single-stage stochastic frontier model and Cobb Douglas production function were applied and statistical significance was declared at 0.05. The maximum likelihood estimates of the stochastic frontier model showed that land, labor, tomato seed, and oxen have a significant effect on tomato output; and education, extension contact, training, and access to credit have a positive and significant effect on technical efficiency, whereas household size, off-farm income, livestock ownership, distance to market, and pesticides have a worthy and significant effect on technical efficiency; and also estimated mean technical efficiency of tomato producer in a study area was 80.9%. In a line with this, the responsible body should prioritize rural infrastructure development in areas such as education, marketplace, and farmer training centers; demonstrate access to credit and extension services; use the recommended amount of pesticides per hectare, and give more intension to mixed farming rather than animal husbandry exclusively.

Transient and Persistent Efficiency and Spatial Spillovers: Evidence from the Portuguese Wine Industry

This paper investigates the presence of spatial spillovers in firms’ productive (in)efficiency. For this purpose, a spatial stochastic frontier model is specified and estimated, accounting for spatial dependence and persistent and transient (in)efficiency. This approach is applied to a panel dataset from 2014 to 2019 of Portuguese wineries. Apart from the traditional input and output quantities used in the estimation of a production function, the novelty of this study is the inclusion of information on the firms’ exact location, which allows incorporating the neighboring dependence in the productive efficiency analysis. Empirical findings show that despite the Portuguese wineries’ technological positive dependence on spatial closeness for both inputs and outputs, the geographic closeness is not strong enough to provide overall productive efficiency gains.