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Determinants of Technical Efficiency of Rice Farms in North-central and North-western Regions in Bangladesh

Stefan Bäckman
University of Helsinki, Finland

K. M. Zahidul Islam
University of Helsinki, Finland

John Sumelius
University of Helsinki, Finland

Abstract/ Executive Summary:

This paper estimates a quadratic stochastic frontier production function to examine the determinants of technical efficiency in rice farming in Bangladesh using the computer program FRONTIER 4.1. Primary data has been collected using a multi-stage random sampling technique from twelve villages in north-central and north-western regions in Bangladesh. Rice cultivation displayed much variability in technical efficiency ranging from 0.16 to 0.94 with mean technical efficiency of 0.83 which suggested substantial gains in output with available resources and existing technologies. The analysis of the determinants of technical efficiency revealed that the age and education of the household heads, availability of off-farm incomes, land fragmentation, access to microfinance, extension visits, and regional variation were the major factors that caused efficiency differentials among the farm households studied. Hence, the study proposes strategies such as providing better extension services and farmer training programs, ensuring access to agricultural microfinance, reducing land fragmentation and raising educational level of the farmers to enhance technical efficiency.

Keywords: Quadratic stochastic frontier production function, Technical efficiency, Bangladesh, Multi-stage random sampling, Agricultural microfinance

Location: In Bangladesh

Start Date:

End Date:

Program Area: Knowledge Management

Commodity: Rice, Rice Farming

Objectives:

To provide the policymakers with information about the nature of the problems facing the rice farms in Bangladesh and to design programs that improve efficiency.

Methodology:

According to Farrell's (1957) model, technical efficiency (TE) is defined as the ability of a farm to obtain the best products from a given set of inputs (output-increasing oriented), or alternatively as the measure of the ability to use the minimum feasible amount of inputs to produce a certain level of output (input-saving oriented) (Greene, 1980; Atkinson and Cornwell, 1994). Consequently, technical inefficiency is defined as the extent to which firms fail to reach optimal production. Farrell (1957) proposed to measure TE of a farm by comparing its observed output to that output which could be produced by a fully efficient farm, given the same bundle of inputs. Aigner et al. (1977) and Meeusen and van den Broeck (1977) independently proposed the stochastic frontier (SF) production function to account for the presence of measurement errors and other noise in the data, which are beyond the control of managers. Farmers, in general, operate under uncertainty and therefore, the present study employs a stochastic production frontier approach for measuring TE. Following Battese and Coelli (1995), the following stochastic frontier production function and inefficiency effects model are estimated simultaneously using single stage with the computer program, FRONTIER 4.1, developed by Coelli (1996). Data were collected from twelve villages in northwest and north-central regions in Bangladesh through a survey conducted in June-August 2009. These regions were selected due to their high levels of poverty and good agricultural potential. For microfinance users, data were collected with the help of Microfinance Institutions? (MFIs) clients? lists. Personal interviews were conducted for both the users and non-users of microfinance to collect the data. We interviewed 180 agricultural microfinance borrowers and 180 non-borrowers (the control group) of agricultural microfinance who operated farm land between 0.2 to 1 hectare. This land holding criteria was set by the microfinance institutions while granting agricultural microfinance loan. Non-borrowers are selected based on similar land holdings and socio-economic background to provide a control group for comparison with borrowers. As the entire sample is used in explaining efficiency among the sampled farms, there are no sample-selection issues as well. Farmers having land more than 1 hectare but taking microfinance exclusively for agriculture were also considered. Data were collected from the farmers producing Boro, Aman, and Aus rice crop from the selected areas. As most farmers in Bangladesh are illiterate, most of them, with some exceptions, do not keep any vouchers or written record of input prices as well as do not maintain any written documents about input-output data. With a view to minimizing errors stemming from reliance on farmers' memory, data were collected immediately after the harvest from the three growing seasons. Output is defined as the market value of the aggregated rice production in the survey period. Rice output prices were gathered from individual farms. All rice (Boro, Aman, Aus) produced on the sample farms were aggregated into one output value (Taka3). Land represents the total amount of land (own-cultivated land, sharecropping land, and rented/leased land) used for rice production and was measured in hectares. Labor includes both family (imputed for hired labor) and hired labor utilized for pre and post-planting operations and harvesting excluding threshing. It was measured in annual labor-days used for rice production. Fertilizers include all sorts of organic and inorganic fertilizers used by farm households for rice production. It represents the total cost of fertilizer measured by market prices. Seeds included all seeds used in rice production and was measured in Taka. If seedlings were purchased, it was converted into equivalent amount of seeds to compute the seed price.

Source of Information: The Journal of Developing Areas, January 2011

Article Link (Online Journal): DOI: 10.1353/jda.2011.0001

Funding Source:

Budget:

Total Budget (Tk.) :

Achievement/Findings:

This study uses a quadratic stochastic frontier production function on survey data (2009) to determine the technical efficiency and its determinants in rice production in the north-central and north-western regions of Bangladesh. First, we draw a conclusion on the methodology choice of production technology. This was based on some selected hypotheses and we concluded that the traditional production function model was not adequate for farm-level analysis. Consequently, we proposed the quadratic stochastic production function. Second, the results revealed that the mean technical efficiency of farms was 0.83, indicating that there are opportunities to gain substantial additional output or decreases the inputs, given the existing technology and resource endowments of rice farmers in the study areas.

Knowledge Management: Journal