The study used annual time series data from secondary sources. The main source was the Handbook of Agricultural Statistics, December 2007 (Ministry Agriculture, 2007). Other sources include BBS (2007) and Bangladesh Economic Review (Ministry of Finance, 2009-2015). The study measured total factor productivity (TFP)-growth of rice. TFP-growth shows the relationship between the growth of output and the growth of input with the influence of technology and technical efficiency. It is generally calculated as a residual (Englander, 1988, p. 6; Hisali & Yawe, 2011, p. 14). Solow (1957) introduced the measurement of productivity growth and technical progress which was associated with a production function/cost function/profit function. For the TFP-growth measurement, economists developed many techniques such as index number approaches including the Malmquist productivity index (Caves, Christensen, & Diewert, 1982, p. 1394; Färe & Grosskopf, 1992, p. 158), Solow’s residual (Raa & Shestwova, 2006, p. 3; Solow, 1957, p. 312), Törnqvist productivity index (Caves, et al., 1982, p. 1394), and Fisher ideal index (Färe & Grosskopf, 1992, p. 158); stochastic production frontier estimation techniques (Sharma, Sylwester, & Margono, 2007, p. 218); Monte Carlo simulation techniques (Slade, 1986, p. 76); translog production function (T. Chang & Hu, 2010, p. 3263); growth accounting matrix (Griliches, 1996, p. 1324); and Durenberger productivity indicator (Barros, Guironnet, & Peypoch, 2011, p. 642). Both mathematical and econometric models are used to measure TFP-growth. Using mathematical models, there are four main approaches to the measurement of TFP-growth namely: (a) Solow’s residual analysis, (b) the index number approach, (c) input-output analysis, and (d) Data Envelopment Analysis (DEA) (Raa & Shestwova, 2006, p. 1). The Malmquist productivity index is a widely-used index number approach because it is simple to measure, easy to understand, and produces reliable results. It provides high accuracy, has minimum restrictions for model specification, and is easy to decompose into two major components: technical efficiency change, and technological change – the main sources of TFP-growth. Similarly, the DEA method is a commonly used technique for the measurement of TFP-growth. The main advantage of using the DEA method is that it avoids model misspecification (Cook & Zhu, 2005, p. 1). This is a scale-neutral method using the measurement of inputs and outputs based on linear programming techniques. (T. Chang & Hu, 2010, p. 3263). This study used the DEA method to calculate the Malmquist productivity index (TFP) with a view to identifying sources of productivity growth and efficiency in rice production. The advantage of the DEA-based Malmquist productivity index is that it calculates the efficiency of factors or inputs. The output-oriented factor-efficiency measures the maximum output from a given input. Similarly, input-oriented efficiency measures the use of minimum input to produce a given output. It is related to returns to scale such as increasing, constant, and decreasing return to scale. The successive production sets are essentially independent from each other. However, there is a certain form of dependence between sequential production sets across time. This dependence is based on the assumption that production units can always produce the same amount of outputs given the same amount of inputs that they have done before in the production processes (Färe & Grosskopf, 1992, p. 159; Färe, et al., 1994, p. 68; Yuk-Shing, 1998, p. 7). Thus, the construction of the latest set requires information on the previous period’s inputs and outputs for measuring productivity performance.