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

Stefan Bäckman
University of Helsinki, Finland

K.M. Zahidul Islam
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 the 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: 00-06-2009

End Date: 00-08-2009

Program Area: Farm Mechanization

Commodity: Rice, Rice Farming

Objectives:

To estimates a quadratic stochastic frontier production function to examine the determinants of technical efficiency in rice farming in Bangladesh.

Methodology:

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 nonusers of microfinance to collect the data. We interviewed 180 agricultural microfinance borrowers and 180 non-borrowers (the control group) of agricultural microfinance who operated farmland between 0.2 to 1 hectare. This landholding criterion was set by the microfinance institutions while granting agricultural microfinance loan. Non-borrowers are selected based on similar land holdings and socio-economic backgrounds 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. In conducting the research, multistage sampling technique was used. The first stage was the purposive selection of two districts (Mymensingh and Sherpur) from northcentral and four districts (Rajshahi, Naogoan, Dinajpur, and Gaibandha) from the northwest region in Bangladesh. The second stage involved the identification of those farmers who had taken microfinance specially allocated for agricultural production. Finally, a multi-stage proportional random sampling method was used to select 60 households (30 from microfinance borrowers and 30 from non-borrowers of microfinance) from each district, thus a total of 360 households were surveyed. 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 hectare. 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. Irrigation represents the total irrigation costs for rice production. This cost is estimated from total rice land irrigated and it was measured in Taka. Tilling includes the total land tilled with tractor and or bullocks. It represents the total cost of tilling measured in Taka. Other costs include pesticide, seedbed preparation, and crop transportation costs and it was measured in Taka. Capital is the sum of farm tools, machinery and animal power used in rice production and was measured in Taka. A large set of data were also collected about the farmers? socio-economic characteristics and other aspects such as the farmer's age, years of schooling, access to credit, numbers of contacts with extension agents, wealth, investment, institutional constraints to get a loan, land ownership etc. Some basic characteristics of the sample farms are presented. It is evident that farms were small in terms of output and total area farmed. On average each farm produces rice worth Taka 88326 and it is highly variable ranging from Taka 4200 to Taka 795000. Farm operators averaged 42 years old and it ranged from 17 years to 85 years. Approximately 98% of the farm households were adult. Their experience in rice farming was vast and it ranged from 1 year to 70 years while their education level was moderate.

Source of Information: The Journal of Developing Areas, 45(1), 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 northcentral 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. The empirical results revealed that inefficiency exists in the rice production systems and we found farmers? experience and extension visits negatively affected technical inefficiency whereas factors such as access to microfinance, regional dummy, off-farm income, age, education, and land fragmentation positively affected technical inefficiency. In particular, policies leading to granting access to microfinance, raising the educational level of farmers, ensuring land preservation for agricultural purposes, and ensuring sufficient returns to the farmers could be beneficial for reducing inefficiency in rice production in Bangladesh. The findings of the relationship between microfinance and technical efficiency suggest that improving greater access of farmers to agricultural microfinance will improve production efficiency. Consequently, streamlining the microfinance to the credit constraints farmers would be a vital factor in increasing farm technical efficiency and revenue. However, this is a multi-disciplinary work that needs to be addressed out more rigorously by the government policymakers in collaboration with Non-Government Organizations (NGOs) and donor agencies. recommended. These measures, if addressed in national agricultural policy formulation, may direct the farmers? production frontier upward in the long run, which may in turn, reduce technical inefficiency on the one hand and lead to food security through increased production on the other hand.

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