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Natural Hazards and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Volume 14, issue 7 | Copyright

Special issue: Flood ris​k analysis and integ​rated management

Nat. Hazards Earth Syst. Sci., 14, 1731-1747, 2014
https://doi.org/10.5194/nhess-14-1731-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 15 Jul 2014

Research article | 15 Jul 2014

Evaluating the effectiveness of flood damage mitigation measures by the application of propensity score matching

P. Hudson1, W. J. W. Botzen1, H. Kreibich2, P. Bubeck3, and J. C. J. H. Aerts1 P. Hudson et al.
  • 1Institute for Environmental Studies, VU University Amsterdam, Amsterdam, the Netherlands
  • 2GFZ German Research Centre for Geosciences, Section Hydrology, Potsdam, Germany
  • 3adelphi, Caspar-Theyss-Straße 14a, Berlin, Germany

Abstract. The employment of damage mitigation measures (DMMs) by individuals is an important component of integrated flood risk management. In order to promote efficient damage mitigation measures, accurate estimates of their damage mitigation potential are required. That is, for correctly assessing the damage mitigation measures' effectiveness from survey data, one needs to control for sources of bias. A biased estimate can occur if risk characteristics differ between individuals who have, or have not, implemented mitigation measures. This study removed this bias by applying an econometric evaluation technique called propensity score matching (PSM) to a survey of German households along three major rivers that were flooded in 2002, 2005, and 2006. The application of this method detected substantial overestimates of mitigation measures' effectiveness if bias is not controlled for, ranging from nearly EUR 1700 to 15 000 per measure. Bias-corrected effectiveness estimates of several mitigation measures show that these measures are still very effective since they prevent between EUR 6700 and 14 000 of flood damage per flood event. This study concludes with four main recommendations regarding how to better apply propensity score matching in future studies, and makes several policy recommendations.

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