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Wang, Hung‐Jen; Schmidt, Peter

doi:10.1023/a:1016565719882

Cited by 705 publications

(99 citation statements)

References 9 publications

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“…Wang [33] provided some theoretical insights into the bias problem in estimating the stochastic frontier function and farm-specific technical efficiency separately using a two-step approach. In the estimation of the technological parameters of the production function, the ignorance of the dependence of inefficiency on its sources can produce biased estimates of these parameters if the explanatory variables in the production function and those in the technical efficiency model were correlated.…”

Section: Analytical Frameworkmentioning

confidence: 99%

Impact of Erratic Rainfall from Climate Change on Pulse Production Efficiency in Lower Myanmar

et al. 2018

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Erratic rainfall has a detrimental impact on crop productivity but rainfall during the specific growth stage is rarely used in efficiency analysis. This study focuses on this untapped point and examines the influence of rainfall specifically encountered during the sowing stage and early vegetative growth stage and the flowering stage of pulses on productivity and efficiency in Lower Myanmar using data from 182 sample farmers. The results of a stochastic frontier production function reveal that rainfall incidence during the flowering season of pulses has a negatively significant effect on yield while replanting crops after serious damage by rain increases productivity. Controlled rainfall variables, seed rate, human labor and land preparation cost are important parameters influencing pulses yield. In the efficiency model, levels of yield loss have a negative impact while being a male household head, access to government credit, access to training, locating farms in the Bago Region and possessing a large area of pulses have a positively significant effect on technical efficiency. Policy recommendations include the establishment of a safety network, such as crop insurance to protect farmers from losses due to unpredictable weather conditions, promoting training programs on cultural practices adapted to climate change, wide coverage of extension activities, giving priority to small-scale farmers and female farmer participation in training and extension activities and increasing the rate of credit availability to farmers.

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Section: Analytical Frameworkmentioning

confidence: 99%

Impact of Erratic Rainfall from Climate Change on Pulse Production Efficiency in Lower Myanmar

et al. 2018

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“…Linearbilinear models could be graphically represented through biplots (Gabriel, 1971;Yan and Tinker, 2006) or enhanced biplots (Gauch, 2006), where genotypes and environments are plotted in a single graph. Later, mixed models were introduced to allow more flexibility and to model different correlation structures among environments (Piepho, 1998;Burgueño et al, 2008;Kelly et al, 2009;Cullis et al, 2010), increasing statistical power (Wang and Schmidt, 2002). A number of correlation structures can be modeled with mixed models, from a single correlation among environments (i.e., compound symmetry models; Piepho, 2005), heterogeneous variances (Piepho, 1998;Malosetti et al, 2004;Burgueño et al, 2008;Cullis et al, 2010), up to a unique correlation for each pair of environments (i.e., unstructured variance-covariance; Burgueño et al, 2008;Malosetti et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Modeling Genotype × Environment Interaction for Genomic Selection with Unbalanced Data from a Wheat Breeding Program

et al. 2016

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Genomic selection (GS) has successfully been used in plant breeding to improve selection efficiency and reduce breeding time and cost. However, there is not a clear strategy on how to incorporate genotype × environment interaction (GEI) to GS models. Increased prediction accuracy could be achieved using mixed models to exploit GEI by borrowing information from other environments. The objective of this work was to compare strategies to exploit GEI in GS using mixed models. Specifically, we compared strategies to predict new genotypes by borrowing information from other environments modeling the correlation matrix across environments and to design sets of environments aiming for low GEI to predict genomic performance in new environments. We evaluated 1477 advanced wheat (Triticum aestivum L.) lines for yield in 35 location–year combinations genotyped with genotyping‐by‐sequencing (GBS). Mixed models were used to obtain either overall or by‐environment predictions for different sets of environments. Overall accuracy was high (0.5). Borrowing information from relatives evaluated in multiple environments and modeling the correlation matrix across environments was the best strategy to predict new genotypes. On the other hand, the best strategy for predicting the performance of genotypes in new environments was either to predict across locations for single years or to predict within defined mega‐environments (MEs) for any year or location. In summary, higher predictive ability was obtained by characterizing and by modeling GEI in the GS context.

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“…The debate on whether to use a one-step or two-step approach introduced important developments into the investigation of how exogenous factors influence the one-sided inefficiency effect. The extensive Monte Carlo results presented by Wang and Schmidt (2002) favor the one-step approach. Among the different models developed, perhaps the most well-known model of the one-step approach is the Battese and Coelli (1995) model.…”

Section: Literature Review Technical Efficiency (Te)mentioning

confidence: 96%

“…In this model, the function which explains inefficiency is estimated as a single step with production technology, which avoids the problem of inconsistency of a two-stage estimation process. Wang and Schmidt (2002) have cautioned against the two-step procedure to calculate the effect of the measured covariates, the 'z's', on estimates, arguing that the omission of the covariates at the 'first step' is tantamount to the omitted variable problem in ordinary regression. Nonetheless, this procedure is common and, indeed, is routine in the DEA literature.…”

Section: Stochastic Frontier Analysis Strategy To Measure Technical Ementioning

confidence: 99%

From the Boom to the Collapse: a Technical Efficiency Analysis of the Spanish Construction Industry during the Financial Crisis

Fernández-López

Coto-Millán

2015

CEB

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Despite its contribution to the Spanish economy, as far as the authors are aware, the technical efficiency of the Spanish construction industry has neither been measured nor have the factors influencing it been analyzed. This paper measures the technical efficiency of the Spanish construction sector before and during the current financial crisis and investigates the degree to which factors influencing efficiency levels in this sector have changed. Stochastic frontier analysis (SFA) methods are applied to firm-level data (692 constructions firms) over the period 1996-2011. The results show that the average Technical Efficiency of the sector is 0.85. Results also indicate that variables affecting Technical Efficiency in the construction sector must be analyzed depending on real state cycle. Based on the findings, policy recommendations to improve the sector efficiency were developed.Important factors affecting efficiency change have been identified, and some managerial recommendations to increase the sector efficiency have been developed. The main recommendations for construction companies include: (1) cooperation and company mergers have a positive impact on companies’ efficiency; (2) accumulation of excessive financial burden damages the business long-term stability; and (3) business strategy should be tailored to economic prospects.Paper Type: Viewpoint

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Untitled

Cited by 705 publications

References 9 publications

Impact of Erratic Rainfall from Climate Change on Pulse Production Efficiency in Lower Myanmar

Impact of Erratic Rainfall from Climate Change on Pulse Production Efficiency in Lower Myanmar

Modeling Genotype × Environment Interaction for Genomic Selection with Unbalanced Data from a Wheat Breeding Program

From the Boom to the Collapse: a Technical Efficiency Analysis of the Spanish Construction Industry during the Financial Crisis

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