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Nonparametric measurement of the overall shift in the technology frontier: an application to multiple-output agricultural production data in the Brazilian Amazon

Listed author(s):
  • Tsunehiro Otsuki

    ()

This article develops a simple, but informative, approach to measure technical changes by applying the Malmquist index framework using data envelopment analysis (DEA). A set of directional vectors is used to capture complete information regarding the overall shift in the technology frontier; we then visualize the DEA frontiers in the output space. This approach deals with the problems of sample dependence and non-circularity of technical change measures. It allows us to measure output-by-output technical change. Furthermore, the geometric mean of the technical change measures derived using this approach satisfies circularity. The application of Malmquist indices to the panel datasets of agricultural production in the Brazilian Amazon during the period 1975–1995 indicates a non-Hicks-neutral technical change, with intersection of frontiers during both the 1975–1985 and the 1985–1995 sub-periods. We find moderate progress in cattle production and annual crops, and a decline in perennial crops (bananas). Technological progress is a modest 0.7% during the period 1975–1995 on average. In addition, comparisons with the sample-dependent measures in the preceding studies demonstrate that these indices are usually sensitive to the distribution of actual sampled data points. Copyright Springer-Verlag 2013

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File URL: http://hdl.handle.net/10.1007/s00181-012-0582-4
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Article provided by Springer in its journal Empirical Economics.

Volume (Year): 44 (2013)
Issue (Month): 3 (June)
Pages: 1455-1475

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Handle: RePEc:spr:empeco:v:44:y:2013:i:3:p:1455-1475
DOI: 10.1007/s00181-012-0582-4
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Order Information: Web: http://www.springer.com/economics/econometrics/journal/181/PS2

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  1. Pastor, Jesus T. & Lovell, C.A. Knox, 2005. "A global Malmquist productivity index," Economics Letters, Elsevier, vol. 88(2), pages 266-271, August.
  2. Caves, Douglas W & Christensen, Laurits R & Diewert, W Erwin, 1982. "The Economic Theory of Index Numbers and the Measurement of Input, Output, and Productivity," Econometrica, Econometric Society, vol. 50(6), pages 1393-1414, November.
  3. Fulginiti, Lilyan E. & Perrin, Richard K., 1997. "LDC agriculture: Nonparametric Malmquist productivity indexes," Journal of Development Economics, Elsevier, vol. 53(2), pages 373-390, August.
  4. Berg, Sigbjorn Atle & Forsund, Finn R & Jansen, Eilev S, 1992. " Malmquist Indices of Productivity Growth during the Deregulation of Norwegian Banking, 1980-89," Scandinavian Journal of Economics, Wiley Blackwell, vol. 94(0), pages 211-228, Supplemen.
  5. Chen, Yao & Iqbal Ali, Agha, 2004. "DEA Malmquist productivity measure: New insights with an application to computer industry," European Journal of Operational Research, Elsevier, vol. 159(1), pages 239-249, November.
  6. David R. Lee & Christopher B. Barrett & John G. McPeak, 2006. "Policy, technology, and management strategies for achieving sustainable agricultural intensification," Agricultural Economics, International Association of Agricultural Economists, vol. 34(2), pages 123-127, March.
  7. Nin, Alejandro & Arndt, Channing & Preckel, Paul V., 2003. "Is agricultural productivity in developing countries really shrinking? New evidence using a modified nonparametric approach," Journal of Development Economics, Elsevier, vol. 71(2), pages 395-415, August.
  8. Simar, Leopold & Wilson, Paul W., 1999. "Estimating and bootstrapping Malmquist indices," European Journal of Operational Research, Elsevier, vol. 115(3), pages 459-471, June.
  9. Mette Asmild & Fai Tam, 2007. "Estimating global frontier shifts and global Malmquist indices," Journal of Productivity Analysis, Springer, vol. 27(2), pages 137-148, April.
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