Open Access Article SciPap-1069
The Technical Efficiency of Transplanted Aman Rice Farms in Bangladesh: A Parametric and Nonparametric Approach of Efficiency
by Kanis Fatama Ferdushi 1 iD icon and Anton Abdulbasah Kamil 2,* iD icon

1 Shahjalal University of Science and Technology, Shahjalal University of Science and Technology, Kumargaon, Sylhet 3114, Bangladesh

2 Faculty of Economics, Administrative and Social Sciences, Istanbul Gelisim University, Cihangir Dist. Petrol Office Str. No:3-5 Gelisim Tower Avcilar/Istanbul, TURKEY, Istanbul 34310, Turkey

* Authors to whom correspondence should be addressed.

Abstract: The comparison of technical efficiency has been estimated by parametric and nonparametric production model for agricultural rice farms. This paper employs a translog stochastic frontier model to explain the source of inefficiencies of rice farm due to socioeconomic and farm-specific variables in seven selected regions of Bangladesh. The motivational point is of “start trade” to international rice market like other Asian countries. The average efficiencies estimates of Transplanted AMAN rice farms are vary 60%-90% in stochastic frontier analysis (SFA) whereas in data envelopment analysis (DEA) the technical efficiencies estimates are vary 64% to 79%. Average technical efficienies of Dhaka, Khulna, Barisal and Rangpur were found higher through SFA than DEA. Kernel density estimates appropriates to the empirical efficiency. The results of distribution of efficiencies indicate that Dhaka, Rajshahi, Khulna, Barisal, Sylhet and Rangpur efficiency were found negatively skewed. The graphical representation of the kernel density estimates indicated that inefficiencies were presented among the farms in the all selected regions.

Keywords: Technical Efficiency, Translog Stochastic Frontier Production Model, Inefficiency, Kernel Density

JEL classification:   B23 - Econometrics • Quantitative and Mathematical Studies

SciPap 2020, 28(2), 1069; https://doi.org/10.46585/sp28021069

Received: 2 May 2020 / Revised: 28 May 2020 / Accepted: 1 June 2020 / Published: 16 June 2020