A comparative analysis of multi-output frontier models

Recently, there have been more debates on the methods of measuring efficiency. The main objective of this paper is to make a sensitivity analysis for different frontier models and compare the results obtained from the different methods of estimating multi-output frontier for a specific application. The methods include stochastic distance function frontier, stochastic ray frontier, and data envelopment analysis. The stochastic frontier regressions with and without the inefficiency effects model are also com-pared and tested. The results indicate that there are significant correlations between the results obtained from the alternative estimation methods.

Jinzhai County Household Technical Change and Efficiency Change Analysis: Stochastic Frontier Production Function Approach

Stochastic frontier production function approach is adopted, 93 farmer samples have been collected, pure efficiency, technical efficiency, technical change and scale efficiency and the institutional contribution have been calculated. The results indicated that increasing productivity is the sole measurement to reduce poverty, institution and technical change are the two key factors. Therefore, stable institution, improving technical changes are required. At present, it is urgent to make technical progre...

Mechanization and efficiency in rice production in China

Agricultural mechanization and custom machine services have developed rapidly in China,which can influence rice production efficiency in the future.We calculate technical efficiency,allocative efficiency,and scale efficiency using data collected in 2015 from a face-to-face interview survey of 450 households that cultivated 3096 plots located in the five major rice-producing provinces of China.We use a one-step stochastic frontier model to calculate technical efficiency and regress the efficiency scores on socio-demographic and physical land characteristics to find the influencing variables.Variables influencing technical efficiency are compared at three different phases of rice cultivation.We also calculate technical efficiency by using the Heckman Selection Model,which addresses technological heterogeneity and self-selection bias.Results indicate that:(1)the average value of technical efficiency using a one-step stochastic frontier model was found to be 0.74.When self-selection bias is accounted for using the Heckman Selection Model,the average value of the technical efficiency increases to 0.80;(2)mechanization at the chemical application phase has a positive effect on technical efficiency,but mechanization does not affect efficiency at the plowing and harvesting phases;(3)machines are overused relative to both land and labor,and high machine input use on the small size of landholding has resulted in allocative inefficiency;(4)rice farmers are overwhelmingly operating at a sub-optimal scale.Future policies should focus on encouraging farmland transfer in rural areas to achieve scale efficiency and allocative efficiency while promoting mechanization at the chemical application phase of rice cultivation to improve technical efficiency.

Agricultural Policy, Climate Factors and Grain Output: Evidence From Household Survey Data in Rural China

This paper estimates a stochastic frontier function using a panel data set that includes 4 961 farmer households for the period of 2005-2009 to decompose the growth of grain production and the total factor productivity （TFP） growth at the farmer level. The empirical results show that the major contributor to the grain output growth for farmers is input growth and that its average contribution accounts for 60.92% of farmer’s grain production growth in the period of 2006-2009, whereas the average contributions sourced from TFP growth and residuals are only 17.30 and 21.78%, respectively. The growth of intermediate inputs is a top contributor with an average contribution of 44.46%, followed by the planted area （18.16%）, investment in fixed assets （1.05%）, and labor input （-2.75%）, indicating that the contribution from the farmer’s input growth is mainly due to the growth of intermediate inputs and that the decline in labor inputs has become an obstacle for farmers in seeking grain output growth. Among the elements consisting of TFP growth, the contribution of technical progress is the largest （32.04%）, followed by grain subsidies （8.55%）, the average monthly temperature （4.26%）, the average monthly precipitation （-0.88%）, the adjusted scale effect （-5.66%）, and growth in technical efficiency （-21.01%）. In general, the contribution of climate factors and agricultural policy factor are positive and significant.

Farm Production Growth in the Upper and Middle Parts of the Yellow River Basin,China,During 1980-1999

The impact of inputs on farm production growth was evaluated by analyzing the economic data of the upper and middle parts of the Yellow River basin, China for the period of 1980-1999. Descriptive statistics were employed to characterize the temporal trends and spatial patterns in farm production and five pertinent inputs of cultivated cropland, irrigation ratio, agricultural labor, machinery power and chemical fertilizer. Stochastic frontier production function was applied to quantify the dependence of the farm production on these inputs. The growth of farm production was decomposed to reflect the contributions by input growths and change in total factor productivity.. The change in total factor productivity was further decomposed into the changes in technology and in technical efficiency. The gross value of farm production in the region of study increased by 1.6 fold during 1980-1999. Among the five selected farm inputs, machinery power and chemical fertilizer increased by 1.8 and 2.8 fold, respectively. The increases in cultivated cropland, irrigated cropland, and agricultural labor were all less than 0.16 fold. The growth in the farm production was primarily contributed by the increase in the total factor productivity during 1980-1985, and by input growths after 1985. More than 80% of the contributions by input growths were attributed to the increased application of fertilizer and machinery. In the change of total factor productivity, the technology change dominated over the technical efficiency change in the study period except in the period of 1985-1990, implying that institution and investment played important roles in farm production growth. There was a decreasing trend in the technical efficiency in the region of study, indicating a potential to increase farm production by improving the technical efficiency in farm activities. Given the limited natural resources in the basin, the results of this study suggested that, for a sustainable growth of farm production in the area, efforts should be directed to technology progress and improvement in technical efficiency in the use of available resources.

Technical efficiency and its determinants of the various cropping systems in the purple-soiled,hilly region of southwestern China

This study examines the technical efficiency(TE) differences among typical cropping systems of smallholder farmers in the purple-soiled hilly region of southwestern China.Household-,plot-,and crop-level data and community surveys were conducted to explore TE levels and determinants of typical cropping systems by using a translog stochastic frontier production function.Results indicate significant difference in TE and its determinants among cropping systems.The mean TEs of the rice cropping system(R),the rice-rape cropping system(RR),the rice-rape-potato cropping system(RRP),and the oil cropping system(O) are0.86,0.90,0.84,and 0.85,respectively,which are over 1.17 times higher than those of the maize-sweet potato-other crop cropping system(MSO) and the maize-sweet potato-wheat cropping system(MSW) at0.78 and 0.69,respectively.Moreover,Technical inefficiency(TIE) of different cropping systems is significantly affected by characteristics of the household as well as plot.However,the impact of land quality,mechanical cultivation conditions,crop structure,farming system,farm radius,household type,cultivated land area per capita,and annual household income per capitalon TIE vary by cropping system.Additionally,output elasticity of land,labor,and capital,as a group,is greater than the one of agricultural machinery and irrigation.Finally,when household-owned effective agricultural labor is at full farming capacity,optimal plot sizes for the R,RR,RRP,MSO,MSW,and 0 cropping systems are 1.12hm^2,0.35 hm^2,0.25 hm^2,2.82 hm^2,1.87 hm^2,and 1.17hm^2,respectively.

Efficiency and sustainability of the tourism industry in Latin America

The tourism industry is economically very important.According to the World Travel Tourism Council,in 2019,the tourism industry accounted for a quarter of all new jobs created worldwide,10.3%of all jobs,and 9.6×1012 USD of the global gross domestic product.This study aimed to calculate the tourism efficiency index for different Latin American countries from 2010 to 2021 using data envelopment analysis,which analyzes the relationships between input variables(including the number of employees in the tourism industry and the number of hotel-type establishments)and output variables(including tourism expenditures in other countries and public social expenditures in recreation and culture per capita).Additionally,this study aimed to identify the countries with greater tourism development and the factors that may affect the development of the tourism industry through the stochastic frontier production function.The results of the tourism efficiency index for Central America(including Costa Rica,Dominica,El Salvador,Honduras,Mexico,and Panama)and South America(including Argentina,Brazil,Chile,Colombia,Ecuador,Paraguay,Peru,and Uruguay)exhibited different trends.However,after the global health crisis,the tourism industry recovered,showing new opportunities to promote sustainability.The results of the stochastic frontier production function demonstrated that countries with higher levels of inbound and outbound tourism,contribution of tourism to the economy,natural resources,and literacy rate exhibited more efficient tourism industry,whereas countries with higher pollution levels exhibited less efficient tourism industry.The findings of this study could allow us to formulate suitable public policies to promote tourism,maintain natural resources,and diversify these sectors with more inclusive programmes that can facilitate growth and benefit vulnerable communities.