Endogeneity in panel data stochastic frontier model with determinants of persistent and transient inefficiency

Lai, Hung-pin; Kumbhakar, Subal C.

doi:10.1016/j.econlet.2017.10.003

Cited by 35 publications

(37 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, endogeneity may occur when there is non-spatially dependent correlation between the inputs and statistical noise or inefficiency. Recent literature about heterogeneity and endogeneity in SFA has developed other ways to account for these issues (Amsler et al, 2016;Lai and Kumbhakar, 2018;Kutlu and Tran, 2019).…”

Section: Discussionmentioning

confidence: 99%

Modeling Spatial Interaction in Stochastic Frontier Analysis

Pede

2021

Front. Sustain. Food Syst.

View full text Add to dashboard Cite

We compare farm level efficiency rankings derived from non-spatial and a variety of spatial model specifications that account for unobserved heterogeneity in both the production and the efficiency sides of the stochastic frontier model in an empirical application on rice farming in the Philippines. We show how not accounting for unobserved spatial heterogeneity affects efficiency estimates and farm efficiency rankings. When not accounting for unobserved spatial heterogeneity efficiency, models show farms to be relatively more inefficient than they actually are (i.e., once unobserved spatial heterogeneity is incorporated in the models). More importantly from a policy perspective, the rankings of the farms in terms of efficiency are altered once unobserved spatial heterogeneity is incorporated in efficiency models. We recommend the use of unobserved effects in both production and efficiency within the stochastic frontier analysis framework to avoid making any misleading recommendations to farmers and policymakers.

show abstract

Section: Discussionmentioning

confidence: 99%

Modeling Spatial Interaction in Stochastic Frontier Analysis

Pede

2021

Front. Sustain. Food Syst.

View full text Add to dashboard Cite

show abstract

“…In Colombi et al (2014), the model is estimated in a single step with the distributional assumptions that and are both iid half-normal; and and are iid normal. The iid assumptions in Colombi et al (2014) and Kumbhakar, Lien and Hardaker (2014) are relaxed in Badunenko and Kumbhakar (2017), and Lai and Kumbhakar (2018). Colombi et al (2014), Badunenko and Kumbhakar (2017), and Lai and Kumbhakar (2018) use a single step ML method to estimate the parameters and the conditional mean (extension of the Jondrow et al 1982 result) and then obtain both persistent and time-varying inefficiency.…”

Section: Methodology: Stochastic Frontier As a Benchmarkmentioning

confidence: 99%

International Benchmarking for Country Economic Diagnostics: A Stochastic Frontier Approach

Norambuena¹

2020

SSRN Journal

View full text Add to dashboard Cite

“…Hence, scaling non-constant returns of those multiple factors is possible to allow for the violation of various situations, which is one of the necessary conditions for the long-run completive equilibrium in productivity measurement [12]. The other benefit of using such an approach is the ability to authorize systematic variation with time [54]. Moreover, using time-series data enable us to account for some heterogenicity that we cannot control in cross-section data.…”

Section: Theoretical Contributionmentioning

confidence: 99%

“…Moreover, using time-series data enable us to account for some heterogenicity that we cannot control in cross-section data. It is the way the current research prefers for SFA to estimate efficiency and cotton growth data considering its efficiency over other approaches in the agricultural economic literature [54]. Second, parametric procedures also allow the functional specific need to accommodate (a) calculating single output from multiple inputs, (b) testing the hypothesis, and (c) the availability of maximum likelihood econometric estimates.…”

Section: Theoretical Contributionmentioning

confidence: 99%

Evolution of Overall Cotton Production and Its Determinants: Implications for Developing Countries Using Pakistan Case

Arshad¹,

Yuan-feng

Hanif

et al. 2022

Sustainability

View full text Add to dashboard Cite

Managing the declining yield of non-food crops has opened new strategic challenges amidst global uncertainties. The COVID-19 scenario has increased awareness of natural lifestyle and eco-friendly products, largely dependent on non-food crop material. This strategic shift requires moving beyond traditional farm practices to improve agricultural production efficiency, and developing countries in particular have shown a consistent loss in their self-sufficiency of industrial crops despite being major exporters of non-food crop materials. However, existing studies analyze production efficiencies of non-food crops from general or theoretical aspects often by virtual estimates from breaking down the multiple factors of crop productivity. This study examined multiple factors of crop production to identify “which crop inputs have been inefficiently used overtime” by tracking efficiency changes and various input issues in overall cotton production from practical aspects, i.e., scaling non-constant returns of those multiple factors would allow for the violation of various situations. Accordingly, a stochastic frontier approach was employed to measure the production frontier and efficiency relationship using time-series data of Pakistan’s cotton production from 1971–2018—a specific non-food crop perspective from a top-ranked cotton-producing country that has recently been shifted towards being a non-exporter of cotton due to low yield. The coefficient of area, seed, and labor indicates the positive relationship with cotton production, while fertilizer, irrigation, electricity, and machinery are statistically negative. This implies that policymakers need priority-based strategies for the judicial use of synthetic fertilizers, irrigation, a subsidy policy, and technology adoption, which could significantly improve the efficiencies of cotton productivity from the same land resources. Being adaptable to other developing economies, the analysis would strategically facilitate designing and developing affordable technology-driven solutions and their customized extensions towards sustainable non-food crop production practices and Agri-Resources efficiencies.

show abstract

Endogeneity in panel data stochastic frontier model with determinants of persistent and transient inefficiency

Cited by 35 publications

References 7 publications

Modeling Spatial Interaction in Stochastic Frontier Analysis

Modeling Spatial Interaction in Stochastic Frontier Analysis

International Benchmarking for Country Economic Diagnostics: A Stochastic Frontier Approach

Evolution of Overall Cotton Production and Its Determinants: Implications for Developing Countries Using Pakistan Case

Contact Info

Product

Resources

About