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Technical, Allocative, Cost and Scale Efficiencies in Bangladesh Rice Cultivation: a Non-parametric Approach

Tim Coelli
Professor, School of Economics, University of Queensland, Brisbane, Australia.

Sanzidur Rahman
Hallsworth Fellow in the School of Economics at the University of Manchester

Colin Thirtle
Professor ofAgricultural Economics, Department of Environmental Science and Technology, Imperial College of Science,Technology and Medicine, Prince Consort Road, London, SW7 2BP,

Abstract/ Executive Summary:

Applying programming techniques to detailed data for 406 rice farms in 21 villages, for1997, produces inefficiency measures, which differ substantially from the results of simple yield and unit cost measures. For the Boro (dry) season, mean technical efficiency was69.4 per cent, allocative efficiency was 81.3 per cent, cost efficiency was 56.2 per cent and scale efficiency 94.9 per cent. The Aman (wet) season results are similar, but a few points lower. Allocative inefficiency is due to overuse of labour, suggesting population pressure,and of fertiliser, where recommended rates may warrant revision. Second-stage regressions show that large families are more inefficient, whereas farmers with better access to input markets, and those who do less off-farm work, tend to be more efficient. The information on the sources of inter-farm performance differentials could be used by the extension agents to help inefficient farmers. There is little excuse for such sub-optimal use of survey data, which are often collected at substantial costs.

Keywords: Technical, Allocative, Cost and Scale, Efficiencies, i Bangladesh, Rice Cultivation, Non-parametric Approach

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Program Area: Socio-economic and Policy

Non-commodity: Efficiency

Objectives:

This study will also provide useful information for policy formulation in Bangladesh and serve as an illustration of the output that may be derived from survey data using these methods.

Methodology:

The efficiency measurement methods used in this paper are derived from those presented in Fä re et al. (1985, 1994), which are based upon the work of Farrell (1957), Afriat (1972), C harnes et al. (1978) and others. A comprehensive introduction to DEA methods is provided in Coelli et al. (1998). The four efficiency measures used here (technical, allocative, cost and scale) are briefly described next. Technical Efficiency Technical efficiency relates to the degree to which a farmer produces the maximum feasible output from a given bundle of inputs, or uses the minimum feasible amount of inputs to produce a given level of output. These two definitions of technical efficiency lead to what are known as output-oriented and input-oriented efficiency measures, respectively. These two measures of technical efficiency will coincide when the technology exhibits constant returns to scale, but are likely to differ otherwise. In this study, we use input-oriented efficiency measures because they lead to a natural decomposition of cost efficiency into its technical and allocative components. We do not expect this choice to have a large bearing on our results, given that the farmers in our sample have very small areas of land and hence the technology is unlikely to be significantly affected by non-constant returns to scale. The DEA production frontier is constructed using linear programming techniques, which give a piece-wise linear frontier that “envelopes” the observed input and output data. Technologies produced in this way possess the standard properties of convexity and strong disposability, which are discussed in Färe et al., 1994). The DEA model is used to simultaneously construct the production frontier and obtain the technical efficiency measures. The model is presented for the case where there are data on K inputs and M outputs for each of N farms. For the i-th farm, input and output data are represented by the column vectors xi and yi, respectively. The K, N input matrix, X, and the M, N output matrix, Y, represent the data for all N farms in the sample.

Source of Information: Journal of Agricultural Economics, Volume 53, Number 3 , November 2002, Pages 607-626

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Achievement/Findings:

This study uses data envelopment analysis (DEA) to analyse the efficiency of Bangladeshi rice farmers. Using detailed survey data for 1997, for 406 rice farms spread over 21 villages, measures of efficiency are estimated, which differ substantially from those obtained using simple yield and unit cost measures. The Boro (dry) season results indicate mean technical efficiency of 69.4 percent , mean allocative efficiency of 81.3 per cent and mean scale efficiency of 9 4 . 9 per cent. The Aman (wet) season results are similar, but a few points lower. The majority of allocative inefficiency can be attributed to overuse of labour and fertiliser, pointing towards a disguised unemployment problem, and a set of fertiliser rate recommendations that warrant revision.Second-stage regressions attempt to explain variations in efficiencies between farms. The results obtained indicate that large families are likely to be more inefficient, further highlighting the hidden unemployment problem. We also find that those farmers who have better access to input markets, and those who do less off-farm work, tend to be more efficient. Age, education, experience,soil fertility, extension and training do not have a large influence on efficiencylevels.Overall, we have clearly demonstrated that analyses of agricultural performance that focus on the use of simple yield per hectare and unit cost figures are inadequate on two fronts. First, they may be providing seriously misleading measures of relative farm performance. Second, there is a wealth of information on the sources of inter-farm performance differentials, which is hidden in such simple measures. There is little excuse for such sub-optimal use of survey data, which is often collected at substantial cost. The DEA methods outlined in this paper are easy to calculate and in terpret.1 0 The se methods can also accommodate multiple-output farms and can also be applied to almost any industry.

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