This paper aims to analyze the dynamics of technical production efficiency of the manufacturing sector in Bangladesh using the cross-sectional data collected under the Survey of Manufacturing Industries (SMI) conducted in 2006 and 2012. Based on the dynamics of mean efficiency scores among the industries derived using Stochastic Frontier Analysis(SFA) techniquewith Cobb-Douglas technology with half-normal distribution during the considered period three most efficiency gainer industries are ((i) Jute textile,(ii) Dying and bleaching of textiles, and(iii) Bidies respectively. On the other hand, under SFA specification with Translog production function top three efficiency gainers are (i) Jute textile,(ii) Bidies, and(iii) Fish, Crustaceans and Molluses respectively. Under constant returns to scale in Data Envelopment Analysis(DEA), based on the mean efficiency score top three efficiency gainers are(i) Fibre textile,(ii) Embroidery of textile and apparel, and(iii) Wooden furniture and fixture respectively while undervariable returns to scale top three gainers are(i) Fibre textile,(ii) Embroidery of textile and apparel, and(iii) Wooden furniture and fixture respectively. Whatever technique we employ, we find that most cases garments or garments related industries remain among top performers in terms of efficiency gain. This indicates that garments industries have improved significantly in terms of efficiency to survive in world competition. Moreover, our results suggest that firm characteristics, location factors as well as ownership features are more important jointly rather than individually to enhance efficiency. Locational and ownership characteristics jointly, in most cases, are also not so influential in pulling the efficiency measures up. However, the firm characteristics are very important in raising the technical efficiency of the firms, especially in case of stochastic frontier analysis. And firm characteristics shows stronger impacts in interaction with other locational and/or ownership characteristics.

Education develops human skills, raises human productivity and, consequently, enables them with higher monetary incentives and better jobs. But the realisation of benefits may differ across income groups due to various limiting factors to achieve it. This article estimates the impacts of education on income and consumption of rural households in Bangladesh, using mean differential approach and unconditional quantile regression approach. It utilises Bangladesh Integrated Household Survey (BIHS) data for the years 2012 and 2015 to estimate the impact of education on the income and consumption of rural households. To address the potential endogeneity problem in impact estimation, ‘total distance from school’ is used as an instrumental variable (IV) in the case of the fixed-effect regression model applied here. Though education affects mean differentials of income and consumption positively, the fixed-effect regression coefficients are surprisingly insignificant. However, quantile regression results suggest that education contributes to income and consumption of lower quantile households more than that of uppermost quantile households. Consequently, these indicate a decline in inequality in rural areas of Bangladesh. Interestingly, education has diminishing positive returns for lower quantiles, implying a declined inequality with an increase in education, but at a diminishing rate, confirming that the impact is non-linear in nature.