Sanzidur Rahman
School of Geography, Earth and Environmental Sciences, University of Plymouth, UK
Basanta K. Barmon
Department of Economics, East West University, Dhaka, Bangladesh
Nesar Ahmed
Department of Fisheries Management, Bangladesh Agricultural University, Mymensingh, Bangladesh
Diversification economies, Technical efficiency, Gher, Blue-green revolution, Bangladesh
Bilpabla located in southern Bangladesh
Socio-economic and Policy
Cropping System
Data and the study area This study is based on farm-level cross-sectional data for the crop year 2006 collected from Bilpabla located in southern Bangladesh. Bilpabla is one of the typical villages in the Dumuria sub-district of the Khulna District and is located 310 km south from the capital Dhaka. The village is divided by a small river and the households are mainly located on both sides of the river. The demographic characteristics of the village are very similar to other villages where ‘gher’ farming is practiced. A total of 90 ‘gher’ farmers were randomly selected. The survey was conducted for a period of six months from November 2006 to April 2007. Questionnaire interviews with gher farmers were preceded by preparation and testing of the questionnaire and the use of enumerators to fill in the questionnaires. 3.2 Analytical framework To examine the existence of diversification economies and diversification efficiencies, a multi-output, multi-input production technology specification is required as opposed to the commonly used single-output, multi-input production technology. The use of a distance function approach (either output-orientated or input-orientated) circumvents this problem and can be analyzed using either parametric or non-parametric methods. Also, the main advantage of a distance function approach is that the production frontier can be estimated without assuming the separability of inputs and outputs (Kumbhakar et al., 2007). We have selected the use of an input-orientated stochastic distance function to address these research questions. This is because, in an economy like Bangladesh, on the one hand, inputs are highly scarce, particularly the land input, and on the other hand, farmers are often constrained by cash/credit (Rahman, 2009). Therefore, it is logical to assume that cost minimization is the prime concern. 3.3 Economies of diversification and diversification efficiencies A number of performance measures can be developed from an input distance function. We adopt a measure of economies of diversification developed by Coelli and Fleming (2004) also applied by Rahman (2009) which, in principle, can be conceived of as the lower-bound estimate of the traditional cost function measure of scope economies. In this formulation, the second cross partial derivative of the input distance function, with respect to output, needs to be positive, to provide evidence of economies of diversification. 3.4 Other factors explaining technical efficiency In addition to variables representing diversification (or specialization) of enterprises, a number of other explanatory factors representing farmers’ socio-economic circumstances may affect efficiency. These are the ‘gher’ area of the farmer, farmers’ education, farming experience (proxied by age of the farmer), dependency ratio (family size/number of working members), and share of female labour in total labour. Choice of these variables is based on the existing literature and the justification for their inclusion is briefly discussed as follows. In Bangladesh, land ownership serves as a surrogate for a number of factors as it is a major source of wealth and influences crop production (Hossain et al., 1990). The size-productivity relationship in Bangladesh varies across regions depending on the level of technological development and environmental opportunities. The relationship is positive in technologically advanced regions, whereas the classic inverse relationship still exists in backward areas (Toufique, 2001). We included the ‘amount of ‘gher’ area operated’ to test whether the size of operation in this farming system influences technical efficiency. This is because Islam et al. (2005) reported that gher size has an influence on total production with smaller ghers managing to yield higher production. Use of the education level of farmers as a technical efficiency shifter is fairly common (e.g., Asadullah and Rahman, 2009; Wadud and White, 2000; Wang et al., 1996). The education variable is also used as a surrogate for a number of factors. At the technical level, access to information as well as capacity to understand the technical aspects related to production is expected to improve with education, thereby, influencing technical efficiency. The justification for including age is straightforward as older, and hence experienced, farmers are more likely to be wiser in decisions regarding the use and allocation of scarce inputs (e.g., Coelli and Fleming, 2004; Llewelyn and Williams, 1996). According to the Chayanovian theory of the peasant economy, higher subsistence pressure increases the tendency to adopt new technology and this has been found to be the case in Bangladesh (Hossain et al., 1990). The subsistence pressure variable (defined as the dependency ratio = family size per household/number of working members) was incorporated to test whether it influences technical efficiency as well (e.g., Wang et al., 1996; Ali et al., 1994).
University of Plymouth PEARL https://pearl.plymouth.ac.ukFaculty of Science and EngineeringSchool of Geography, Earth and Environmental Sciences
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