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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.

Banking system plays an important role in the economic development of any country. Domestic banks, which are the main components of the banking system, have to be efficient; otherwise, they may create obstacle in the process of development in any economy. This study examines the technical efficiency of the Malaysian domestic banks listed in the Kuala Lumpur Stock Exchange (KLSE) market over the period 2005–2010. A parametric approach, Stochastic Frontier Approach (SFA), is used in this analysis. The findings show that Malaysian domestic banks have exhibited an average overall efficiency of 94 percent, implying that sample banks have wasted an average of 6 percent of their inputs. Among the banks, RHBCAP is found to be highly efficient with a score of 0.986 and PBBANK is noted to have the lowest efficiency with a score of 0.918. The results also show that the level of efficiency has increased during the period of study, and that the technical efficiency effect has fluctuated considerably over time.

Bangladesh remains one of the most vulnerable countries in the world to the effects of climate change. Given the reliance of a large segment of the population on the agricultural sector for both their livelihoods as well as national food security, climate change adaptation in the agricultural sector is crucial for continued national food security and economic growth. Using household data from lowland rice farmers of selected haor areas in Sylhet, the current work presents an analysis of the determinants behind the implementation of different climate change adaptation strategies by lowland rice farmers. The first objective of this study was to explore the extent of awareness of climate change within this population as well as the type of opinions held by lowland rice farmers with respect to climate change. To serve this purpose, a severity index (SI) was developed and subsequently employed to evaluate the perceptions and attitudes of 378 farmers with respect to climate change vulnerability. Respondents were interviewed with respect to climate change related circumstances they faced in their daily lives. Attained SI index values ranged from 69.18% to 93.52%. The SI for the perception “Climate change affects rice production” was measured as 93.52%. Using data collected from the same 378 farmers, a logistic regression was carried out to investigate the impact of socio-economic and institutional factors on adaptation. The results show that credit from non-government organizations is highly statistically significant for adaptation, and that rural market structure also has a positive effect on adaptation. Among the studied factors, credit from non-governmental organizations (NGOs) was found to be the most important factor for adaptation. The results of this work further indicate that marginal farmers would benefit from government (GoB) funded seasonal training activities that cover pertinent information regarding adaptation after flash floods. Additionally, the authors of this piece recommend timely issuance of government-assisted credit during early flash floods to afflicted farmers, as such an initiative can aid farmers in adapting different strategies to mitigate losses and enhance their productivity as well as livelihood.

The proper estimation of pedestrian speed-flow-density relationships is of vital importance, because such relationships play an important role in developing useful tools for analysing and improving pedestrian facilities in terms of efficiency and safety. One of the major problems with previous macroscopic studies of pedestrian flow characteristics is that the relationships were established based on a model with specification errors that had been estimated by ordinary least squares (OLS). Thus, the validity of the relationships and conclusions drawn from those studies is open to question and should be examined further. In this study, pedestrian speed-flow-density relationships in Dhaka, Bangladesh, are estimated using a weighted regression method. The flows and speeds generated by the derived flow-density and speed-flow relationships based on the weighted regression method and the OLS method, separately, are compared with empirical values. The root mean square error is used as an evaluation criterion. In addition, the pedestrian characteristics of Dhaka are compared with those of other studies. The results indicate the existence of a probable bias in previous studies and an improvement in predictive power with the use of the weighted regression method. Pedestrian flows on the sidewalks in Dhaka have some particular characteristics that are not similar to the uninterrupted pedestrian flows in other countries. Since the weighted regression estimation techniques can mitigate a part of the OLS bias, such techniques could be incorporated in simulation packages to predict pedestrian flows and speeds as well as to design and analyse the capacity of a pedestrian facility precisely. The study also recommends refraining from the direct adoption of foreign design and parameters for pedestrian facilities in Dhaka.