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2019 | OriginalPaper | Chapter

Measuring Economic Vulnerability: A Structural Equation Modeling Approach

Authors : Ambra Altimari, Simona Balzano, Gennaro Zezza

Published in: Statistical Learning of Complex Data

Publisher: Springer International Publishing

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Abstract

Macroeconomic vulnerability is currently measured by the United Nations through a weighted average of eight variables related to exposure to shocks, and frequency of shocks, known as Economic Vulnerability Index (EVI). In this paper we propose to extend this measure by taking into account additional variables related to resilience, i.e., the ability of a country to recover after a shock. Since vulnerability can be considered as a latent variable, we explore the possibility of using the Structural Equation Model approach as an alternative to an index based on arbitrary weights. Using data from a panel of 98 countries over 19 years, we test our results with respect to the ability of the indices based on weighted averages, or on the SEM, in explaining the growth rate in real GDP per capita.

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previous chapter Evaluating the Quality of Data Imputation in Cardiovascular Risk Studies Through the Dissimilarity Profile Analysis
next chapter Bayesian Inference for a Mixture Model on the Simplex
Footnotes
1
EVI data can be downloaded from http://​byind.​ferdi.​fr/​en/​evi.
 
2
Data sources: UN databases: UNSD–NA, UN–PD, UNCTAD Stat, FAOSTAT; World Bank databases: WDI, WGI; Centre for International Earth Science Information Network (CIESIN); Emergency Events Database (EM–DAT); Centre d’Etudes Prospectives et d’Informations Internationales (CEPII).
 
3
For a detailed description of the variables, see the companion page at http://​gennaro.​zezza.​it/​files/​abz.
 
4
More detailed results are available at http://​gennaro.​zezza.​it/​files/​abz.
 
5
We are aware that this analysis cannot rule out the possibility that GDP growth has an impact on vulnerability, and that therefore our explanatory variables may not be weakly exogenous.
 
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Metadata
Title
Measuring Economic Vulnerability: A Structural Equation Modeling Approach
Authors
Ambra Altimari
Simona Balzano
Gennaro Zezza
Copyright Year
2019
Book
Statistical Learning of Complex Data

Print ISBN: 978-3-030-21139-4

Electronic ISBN: 978-3-030-21140-0

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-3-030-21140-0_10

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