On estimating efficiency effects in a stochastic frontier model

Paul, Satya; Shankar, Sriram

doi:10.1016/j.ejor.2018.05.052

Cited by 26 publications

(22 citation statements)

References 28 publications

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“…This provides, indeed, the missing 2K additional equations. To accomplish this, we depart from the assumption that U it is a (vector) random variable, and, instead, we make use of the following device originally proposed by Paul and Shankar (2018) and further developed by Tsionas and Mamatzakis (2019):…”

Section: Endogeneity Issuesmentioning

confidence: 99%

Constraints in models of production and cost via slack-based measures

Khan

Tsionas

2021

Empir Econ

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In this paper, we propose the use of stochastic frontier models to impose theoretical regularity constraints (like monotonicity and concavity) on flexible functional forms. These constraints take the form of inequalities involving the data and the parameters of the model. We address a major concern when statistically endogenous variables are present in these inequalities. We present results with and without endogeneity in the inequality constraints. In the system case (e.g., cost-share equations) or more generally, in production function-first-order conditions case, we detect an econometric problem which we solve successfully. We provide an empirical application to US electric power generation plants during 1986–1997, previously used by several authors.

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Section: Endogeneity Issuesmentioning

confidence: 99%

Constraints in models of production and cost via slack-based measures

Khan

Tsionas

2021

Empir Econ

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“…We therefore added advertising expense (Compustat, DATA 45) as an input in the SFE. Our SFE modelling of marketing capability adopts a Cobb-Douglas type formulation common in the economics production and consumer demand literature (Cobb and Douglas, 1928;Douglas, 1976; Barros and Athanassiou, 2015;Chen et al, 2015;Paul and Shankar, 2018 where i refers to firm i in year t. The SFE results indicate that all three inputs are significant: SGA (α 1 = 0.36, p < 0.01), REC (α 2 = 0.61, p < 0.01), ADV (α 3 = 0.05, p < 0.01). We computed marketing capability from the generated efficiency scores and compared its distribution between 1996 and 2004.…”

Section: Marketing Capabilitymentioning

confidence: 99%

Analysing the relationship between idiosyncratic risk and strategic capabilities using penalty-based selection and shrinkage methods

Sun

Raju

2019

IJBDA

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Recent research has documented the dramatic increase in idiosyncratic risk and the under-diversification of portfolios. We provide a unique marketing perspective to the financial risk management literature by suggesting that idiosyncratic volatility can be reduced by enhancing marketing, operational and R&D capabilities. We investigate the relationship between idiosyncratic risk, firm capabilities and financial control variables using the least absolute selection and shrinkage operator (LASSO)-a penalty-based variable selection and shrinkage technique developed in the context of 'machine learning' and 'big data' that has not been much used in the empirical marketing literature. Our results differ from those reported in the literature. Using the more stringent criterion imposed by the LASSO, we find that whereas R&D, marketing and operational capabilities have no statistically significant individual effects, the interactive effects between marketing capability and R&D intensity have a significant effect on reducing idiosyncratic risk.

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“…Some factors related to production efficiency issues are (i) farmers economizing behavior, (ii) factors contributing to technical efficiency, and (iii) socioeconomic factors influencing technical efficiency. The research related to farm efficiency including research by (Feng 2008;Nezhad et al 2012;Coventry et al 2015;Addison et al 2016;Zhang et al 2016, Nguyen 2017Achandi 2018;Paul & Shankar 2018;Chandio et al 2019;Kouyate et al 2019). Gong et al (2019); investigate technical efficiency in crop production comparing family farms that are members of cooperatives, either as core members who have taken investment stakes or common members who have not, with their nonmember counterparts.…”

Section: Introductionmentioning

confidence: 99%

Production efficiency of Inpago Unsoed-1 and Situbagendit rice farming in Central Java, Indonesia

et al. 2020

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Abstract. Mulyani A, Darwanto D, Widodo S, Masyuri. 2020. Production efficiency of Inpago Unsoed-1 and Situbagendit rice farming in Central Java, Indonesia. Biodiversitas 12: 3276-3286. Inpago Unsoed-1 rice is a new superior rice variety that is not as familiar as Situbagendit variety for farmers in Indonesia. As one of the new superior rice variety, Inpago Unsoed-1 is expected to improve farm business efficiency so that it can increase farmers' income. This study was conducted to figure out the factors influencing the production of Inpago Unsoed-1 and Situbagendit rice in Province of Central Java, and to recognize the level of technical, allocative and economic efficiencies of Inpago Unsoed-1 and Situbagendit rice farming in Province of Central Java. Research sites included selected regencies in Province of Central Jawa, such as Banyumas, Purbalingga, and Cilacap. Meanwhile, the selected districts were Kalibagor (Banyumas); Kalimanah, Kemangkon, Bukateja, Kutasari, Kaligondang, and Bobotsari (Purbalingga); and Gandrungmangu (Cilacap). Data were collected by direct observation and interviews using semi-structured questionnaire. The sampling technique used non-probability sampling using a purposive sampling method. Data were collected during December 2018-May 2019, 147 farmers were sampled, consisting of 71 and 76 farmers for Inpago Unsoed-1 and Situbagendit, respectively. Data were analyzed by using Cobb-Douglas production function and function of Stochastic Frontier. The results showed that production factors of land acreage (ha) and P fertilizer (kg) positively affected the production of Inpago Unsoed-1 rice, whereas seeds (kg), N fertilizer (kg) and pesticide (l) had negative impact on its production. On Situbagendit rice production, the positive and negative effects were revealed by land acreage (ha) as well as pesticide (l) and N fertilizer (kg), respectively. Technical, economic, and allocative efficiencies on Inpago Unsoed-1 rice farming were 0.75, 0.65, and 0.88, respectively, which were higher than those of Situbagendit, i.e. 0.73, 0.54 and 0.75, respectively.

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On estimating efficiency effects in a stochastic frontier model

Cited by 26 publications

References 28 publications

Constraints in models of production and cost via slack-based measures

Constraints in models of production and cost via slack-based measures

Analysing the relationship between idiosyncratic risk and strategic capabilities using penalty-based selection and shrinkage methods

Production efficiency of Inpago Unsoed-1 and Situbagendit rice farming in Central Java, Indonesia

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