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An Application of the Dea Double Bootstrap to Examine Sources of Efficiency in Bangladesh Rice Farming

Balcombe, Kelvin
Department of Agricultural and Food Economics University of Reading

Fraser, Iain Mcpherson
Applied Economics and Business Management Kent Business School University of Kent

Latruffe, Laure
INRA-Unité ESR Rennes

Rahman, Mizanur
Bangladesh Rice Research Institute, Gazipur 1701, Bangladesh

Smith, Laurence
Centre for Environmental Policy Imperial College, London

Abstract/ Executive Summary:

In this paper we examine sources of technical efficiency for rice farming in Bangladesh. The motivation for the analysis is the need to close the rice yield gap to enable food security. We employ the DEA double bootstrap of Simar and Wilson (2006) to estimate and explain technical efficiency. This technique overcomes severe limitations inherent in using the two-stage DEA approach commonly employed in the efficiency literature. From a policy perspective our results show that potential efficiency gains to reduce the yield gap are greater than previously found. Statistically positive influences on technical efficiency are education, extension and credit, with age being a negative influence.

Keywords: DEA, Double bootstrap, Bangladesh, Rice Farms

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Program Area: Socio-economic and Policy

Non-commodity: Efficiency

Objectives:

The structure of our paper is as follows. In Section 2 we explain and detail the double bootstrap methodology of Simar and Wilson (2006) that we employ this paper. Next we detail our farm survey instrument and sample data. Section 4 we present our results. Finally, in Section 5 we provide a summary and conclusions.

Methodology:

A recent methodological development by Simar and Wilson (2006) identifies serious limitations with the two-step DEA approach. They argue that the two-step procedure takes no account of the underlying data-generating process (DGP), casting doubt statistically on the meaning of the estimates produced to explain technical efficiency. Simar and Wilson argue that DEA efficiency estimates are serially correlated. As such standard inference approaches used in the conventional two-step DEA procedure are statistically invalid. These limitations lead them to develop the double bootstrap procedure that enables consistent inference within DEA models estimating and explaining efficiency scores, while simultaneously producing standard errors and confidence intervals for these efficiency scores. In this paper we employ the Simar and Wilson (2006) DEA double bootstrap procedure to estimate and explain technical efficiency for a sample of Bangladesh rice farms. The rationale for estimating the degree of technical inefficiency in Bangladesh farming can be traced to the apparent large gap between experiment stations’ and highest ‘profit maximising’ yields achievable on farms. In the efficiency literature shortfalls in yields relative to best practice are commonly attributed to socio-economic and institutional constraints and deficiencies in the management practices of farmers (De Datta et al., 1978). These reasons are frequently characterised by several stylised facts. First, rice producers are relatively older people with minimal formal education and limited extension service support, and assumed to be conservative and less receptive to new technology and practices. Second, farm size is very small and fragmented and much of the land is not cultivated by owners but by tenants with fewer resources and lower incentives for investment. Third, working capital in the form of credit is limited in availability. Four, much of the rice seed used is of poor quality and too infrequently replaced. Finally, many farmers are diverted from attention to farming by engagement in off-farm activities. These stylised facts suggest a potentially short term and practical solution of narrowing the yield gap by improving farm level efficiency and as such merit investigation and attention. Our analysis adds to the literature that examines the efficiency of rice farming generally (e.g., Thiam et al., 2001) and in Bangladesh specifically (e.g., Banik, 1994; Sharif and Dar, 1996a,b; Wadud and White, 2000; Coelli et al., 2002; Wadud, 2003). Average DEA estimates of technical efficiency in the Bangladesh literature range between 0.66 (Coelli et al., 2002) and 0.91 (Wadud, 2003). Although all the studies indicate that there is a degree of technical inefficiency, the relative levels of technical inefficiency are not all markedly lower than those typically reported in studies of developed economy agriculture. Based on these results, the potential to close the yield gap may be less than anticipated. However, we need to be careful when attempting to draw broad policy implications from a specific set of related but independent studies. First, all estimates of technical efficiency are sample specific. This is then compounded by the fact that sample size and number of variables included in model specifications impact the estimates (Zhang and Bartels, 1998). In addition, it is now understood that DEA yields biased estimates of efficiency. Simar and Wilson (2000) explain that traditional DEA methods will yield sample estimates efficiency that will positively exaggerate the level of efficiency within a sample of data. By employing the double bootstrap DEA approach of Simar and Wilson (2006) we are able to report bias corrected estimates of technical efficiency. As a result of estimating the bias corrected measures of technical efficiency our results can be viewed by policy makers with increased confidence.

Source of Information: Applied Economics, 40(15), 1919-1925.

Article Link (Online Journal): https://doi.org/10.1080/00036840600905282

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Achievement/Findings:

First, this study has revealed that there is substantial room for improvements in technical efficiency in the sample of farms analysed. The potential improvement, albeit difficult to directly compare with existing estimates in the literature, is almost certainly bigger than previous research has revealed using DEA. Indeed it would be interesting to investigate the extent to which previous estimates of technical efficiency would need to be revised. As a result improvements in technical efficiency amongst these farmers can help to reduce the existing yield gap between the most and the least efficient farmers. Second, the analysis revealed factors that enhance technical efficiency to be education, extension services, owner-operated farms, availability of credit and bigger farms. Although these factors had been identified in earlier research we can, as a result of the methods employed in this paper, feel very secure in presenting these findings. Similarly, we have identified that age and off-farm activity as being negatively related to technical efficiency. The results reported here suggest that access to technical information and to credit, are the leading constraints to improved productivity in Bangladesh rice farming. Also that attempts to alleviate these constraints are likely to be more effective when targeted at younger and better-educated farmers or perhaps farm family members. Finally, it can be stressed once again that the developments in DEA methodology examined in this paper have been applied by only very few researchers to date. There is clearly a need for greater adoption and consideration of the methods employed here to provide increased insight into their potential. Although our findings do not contradict previous studies, it is advisable to use the Simar and Wilson (2006) double bootstrap procedure in further applied research on technical efficiency, as it can increase the confidence that policy makers can place on results generated.

Knowledge Management: Journal