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

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  • Ayoe Hoff
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    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.

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    Bibliographic Info

    Article provided by Taylor & Francis Journals in its journal Journal of Applied Statistics.

    Volume (Year): 33 (2006)
    Issue (Month): 9 ()
    Pages: 891-907

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    Handle: RePEc:taf:japsta:v:33:y:2006:i:9:p:891-907

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    Keywords: Total factor productivity change; Malmquist index; data envelopment analysis; bootstrap;

    References

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    1. SIMAR, Léopold & WILSON, Paul, 1995. "Sensitivity Analysis to Efficiency Scores : How to Bootstrap in Nonparametric Frontier Models," CORE Discussion Papers 1995043, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    2. Simar, Leopold & Wilson, Paul W., 1999. "Estimating and bootstrapping Malmquist indices," European Journal of Operational Research, Elsevier, vol. 115(3), pages 459-471, June.
    3. 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.
    4. Simar, L. & Wilson, P.W., 1999. "Statistical Inference in Nonparametric Frontier Models: the State of the Art," Papers 9904, Catholique de Louvain - Institut de statistique.
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    Cited by:
    1. 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.
    2. 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.
    3. Halkos, George & Tzeremes, Nickolaos, 2012. "Measuring seaports’ productivity: A Malmquist productivity index decomposition approach," MPRA Paper 40174, University Library of Munich, Germany.

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