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# Quantitative Vulnerability Assessment for Economic Systems

## Philipp Willroth

In 2004 tsunami waves caused huge economic losses along the coastline of Southern Thailand. These resulted from direct damages and the following economic downturn. This study investigates the factors that led to this vulnerable situation. One of the greatest challenges in vulnerability research is the quantification. To answer this question, a wide database has been used, encompassing highly accurate remote sensing data, quantitative surveys and qualitative focus group discussion data. These data have been integrated in a structural equation model to reproduce factors and relations leading to the hazard induced effects and the capabilities to cope with. The model showed that the impact was almost completely compensated for by households’ and companies’ internal and external resilience capabilities.

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# 5 Methods of analysis

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5.1 Structural equation modelling As the primary analysis method, the structural equation modelling approach was chosen for this study, since it offers the opportunity to assess causal relations between theoretical constructs. The advantages and disadvantages of these ap- proaches will be discussed in the following chapter. Basically, the method is a combination of different statistical methods which integrates path analysis and factor analysis into a single model. In contrast to exploratory factor analysis, factors and causal relations are specified by the researcher in advance, guided by theoretical assumptions and tested on a specific dataset (ARBUCKLE 2006:45pp). The model is designed by the user in the form of a scheme representing the hy- pothetical relations between latent variables and between the manifest variables and their latent variables (WOLD 1982). In general, a structural equation model consists of an inner and an outer model (cp. Figure 20). The outer model specifies the relations between the con- structs or latent variables and their explaining indicators. The inner model de- scribes the relations between the latent variables. These can be divided into de- pendent latent variables respectively endogenous variables, and in independent latent variables respectively exogenous variables. In the following section, the latent variables will be named � (m=1… 1) and their related indicators x(m) j=1… j. Direction and power of the relations between the latent variables are represented by the path coefficients �(ml). The connections between latent varia- bles and their connected indicators are described as factor loading coefficients �(m)...

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