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Bootstrapping Malmquist Indices for Danish Seiners in the North Sea and Skagerrak

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  • Ayoe Hoff

Abstract

In connection with assessing how an ongoing development in fisheries management may change fishing activity, evaluation of Total Factor Productivity (TFP) change over a period, including efficiency, scale and technology changes, is an important tool. The Malmquist index, based on distance functions evaluated with Data Envelopment Analysis (DEA), is often employed to estimate TFP changes. DEA is generally gaining attention for evaluating efficiency and capacity in fisheries. One main criticism of DEA is that it does not have any statistical foundation, i.e. that it is not possible to make inference about DEA scores or related parameters. The bootstrap method for estimating confidence intervals of deterministic parameters can however be applied to estimate confidence intervals for DEA scores. This method is applied in the present paper for assessing TFP changes between 1987 and 1999 for the fleet of Danish seiners operating in the North Sea and the Skagerrak.

Suggested Citation

  • Ayoe Hoff, 2006. "Bootstrapping Malmquist Indices for Danish Seiners in the North Sea and Skagerrak," Journal of Applied Statistics, Taylor & Francis Journals, vol. 33(9), pages 891-907.
  • Handle: RePEc:taf:japsta:v:33:y:2006:i:9:p:891-907 DOI: 10.1080/02664760600742151
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    References listed on IDEAS

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    1. Leopold Simar & Paul Wilson, 2000. "A general methodology for bootstrapping in non-parametric frontier models," Journal of Applied Statistics, Taylor & Francis Journals, vol. 27(6), pages 779-802.
    2. Léopold Simar & Paul Wilson, 2000. "Statistical Inference in Nonparametric Frontier Models: The State of the Art," Journal of Productivity Analysis, Springer, pages 49-78.
    3. Léopold Simar & Paul W. Wilson, 1998. "Sensitivity Analysis of Efficiency Scores: How to Bootstrap in Nonparametric Frontier Models," Management Science, INFORMS, pages 49-61.
    4. Léopold Simar & Paul W. Wilson, 1998. "Sensitivity Analysis of Efficiency Scores: How to Bootstrap in Nonparametric Frontier Models," Management Science, INFORMS, pages 49-61.
    5. Simar, Leopold & Wilson, Paul W., 1999. "Estimating and bootstrapping Malmquist indices," European Journal of Operational Research, Elsevier, vol. 115(3), pages 459-471, June.
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    Cited by:

    1. George E. Halkos & Nickolaos G. Tzeremes, 2015. "Measuring Seaports' Productivity: A Malmquist Productivity Index Decomposition Approach," Journal of Transport Economics and Policy, University of Bath, vol. 49(2), pages 355-376, April.
    2. Sethi, Amarjit Singh, 2016. "Sources of Growth in India: Evidence from Punjab and Haryana," Journal of Regional Development and Planning, JRDP, pages 15-34.
    3. Solís, Daniel & Agar, Juan J. & del Corral, Julio, 2015. "IFQs and total factor productivity changes: The case of the Gulf of Mexico red snapper fishery," Marine Policy, Elsevier, pages 347-357.
    4. Solis, Daniel & Agar, Juan & del Corral, Julio, 2015. "The impact of IFQs on the productivity of the US Gulf of Mexico Red Snapper Fishery," 2015 Annual Meeting, January 31-February 3, 2015, Atlanta, Georgia 196639, Southern Agricultural Economics Association.
    5. Arjomandi, Amir & Valadkhani, Abbas & Harvie, Charles, 2011. "Analysing Productivity Changes Using the Bootstrapped Malmquist Approach: The Case of the Iranian Banking Industry," MPRA Paper 50397, University Library of Munich, Germany.
    6. Olson, Kent D. & Vu, Linh, 2009. "Productivity Growth, Technical Efficiency and Technical Change on Minnesota Farms," 2009 Annual Meeting, July 26-28, 2009, Milwaukee, Wisconsin 49204, Agricultural and Applied Economics Association.

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