research is often concerned with the impact of network disruption and in particular transport disruption (commuting in particular) using transport model based on criticality assessment.<sup>4</sup> They found that the consequences of infrastructure‐related indirect impacts are often larger than the direct impact on firms. West and Lenze (1994) summarize the impact of hurricane Andrew on Florida, including job market con‐ sequences. The Bureau of Labor Statistics (2006) of the US also provides a detailed analysis of the large labor market consequences of hurricane Katrina. Using household survey in three counties and 16 cities after the 2004 hurricane landfalls in Florida, Smith and McCarty (2006) show that households are more often forced to move outside the affected area by infrastructure problems than by structural damages to their home.

The ECLAC methodology (UN ECLAC, 2003) assesses direct indirect and secondary effects of single events by a collection of data and information of various types (physical, monetary, and expert judgment) in each sector. Indirect costs are thus estimated through collection of information from economic agents, governments and experts, taking in consideration particular aspects such as transport disturbances cost, loss of opportunities etc. Of course, the aggregation of data from very different sources should be done with care to avoid gaps and double counting.

# _Econometric approaches_

Econometric approaches are based on statistics and do not investigate a single event. They focus on se‐ ries of events and investigate the “mean” indirect cost of these events, like the average impact on long term economic growth.

Econometrics analyses at national scale have reached different conclusions on the impact of disasters on growth. Alabala‐Bertrand (1993) and Skidmore and Toya (2002) suggest that natural disasters have a pos‐ itive influence on long‐term economic growth, probably thanks to both the stimulus effect of reconstruc‐ tion and the productivity effect (also labeled “Schumpeterian creative destruction effect” or productivity effect, described earlier in Section 4.b.). Others, like Noy and Nualsri (2007), Noy (2009), Hochrainer (2009), Jaramillo (2009), and Raddatz (2009), suggest exactly the opposite conclusion, i.e. that the overall impact on growth is negative. As suggested by Cavallo and Noy (2010) and Loayza et al. (2009), the dif‐ ference between both conclusions may arise from different impacts from small and large disasters, the latter having a negative impact on growth while the former enhance growth.

There are also examples of local‐scale econometric approaches. For instance, Strobl (2008) investigates the impact of hurricane landfall on county‐level economic growth in the US. This analysis shows that a county that is stuck by at least one hurricane over a year sees its economic growth reduced on average by 0.79 percentage point, and increased by only 0.22 percentage point the following year. On Vietnam, Noy and Vu (2009) investigate the impact of disasters on economic growth at the province level, and found that lethal disasters decrease economic production while costly disasters increase short‐term growth. GDP is not the only relevant indicator of disaster economic consequences, and Rodriguez‐Oreggia et al. (2009) focus instead on poverty and the World Bank’s Human Development Index at the municipality level in Mexico. They show that municipalities affected by disasters see an increase in poverty by 1.5 to 3.6 percentage point. Looking at different economic variables, Hallstrom and Smith (2005) assess the

> 4 The Weather project is a 7FAP of the EU concerned with weather extremes and transport working more precisely on these issues.

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