Stochastic Frontier Approach and Data Envelopment Analysis to Total Factor Productivity and Efficiency Measurement of Bangladeshi Rice

Abstract

The objective of this paper is to apply the Translog Stochastic Frontier production model (SFA) and Data Envelopment Analysis (DEA) to estimate efficiencies over time and the Total Factor Productivity (TFP) growth rate for Bangladeshi rice crops (Aus, Aman and Boro) throughout the most recent data available comprising the period 1989–2008. Results indicate that technical efficiency was observed as higher for Boro among the three types of rice, but the overall technical efficiency of rice production was found around 50%. Although positive changes exist in TFP for the sample analyzed, the average growth rate of TFP for rice production was estimated at almost the same levels for both Translog SFA with half normal distribution and DEA. Estimated TFP from SFA is forecasted with ARIMA (2, 0, 0) model. ARIMA (1, 0, 0) model is used to forecast TFP of Aman from DEA estimation.

What determines irrigation efficiency when farmers face extreme weather events? A field survey of the major wheat producing regions in China

“…The DEA requires neither any specific functional form, nor a particular distributional form of the one-sided inefficiency term. However, the DEA method is highly sensitive to outliers, and it does not consider random factors and attributes that lead to inefficiencies, which may result in inconsistent estimation (Johansson 2005;Hossain et al 2012). On the other hand, the SFA is less sensitive to outliers and it permits consideration of random factors.…”

Growth in Agricultural Productivity and Its Components in Bangladeshi Regions (1987–2009): An Application of Bootstrapped Data Envelopment Analysis (DEA)

“…This finding is supported by Hossain et al (2012) and Rahman and Barmon (2018). Rahman and Barmon (2018) reported that the total factor energy productivity was mainly driven by the technological progress of the gher farming system in Bangladesh.…”

“…Rahman and Barmon (2018) reported that the total factor energy productivity was mainly driven by the technological progress of the gher farming system in Bangladesh. Hossain et al (2012) reported that technological change was the main driving force for the improvement of TFP of rice in Bangladesh, whereas Rodriguez and Elasraag (2015) reported that the technological change negatively contributes to the TFP growth. It can be argued that the upward shift in technological progress is the result of diffusion of Green Revolution (GR) technology and other factors, such as significant increase in use of chemical fertilizer, extended the irrigated area, infrastructural development, research and extension expenditure, farmers' accessibility to high yielding varieties (HYV) of seed, and availability of market information.…”

Value Addition and Productivity Differentials in the Nigerian Cassava System

“…The strengths of the non-parametric measure of DEA include the ability to estimate models without having to specify a functional form, estimate models with insufficient degrees of freedom, overcome extreme variability within the data, as well as determine productivity estimates from purely quantity data. However, it is easily affected by outliers and there is no statistical inference to be made to determine the significance of the results [30,31]. This study therefore used the DEA-Malmquist productivity measure to assess the productivity of cassava production systems over the 2015-2017 period in Nigeria.…”