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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- 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.
References
Dijkstra, T.K., Henseler, J.: Consistent and asymptotically normal PLS estimator for linear structure equations. Comput. Stat. Data Anal. 81, 10–23 (2015)
Dijkstra, T.K., Henseler, J.: Consistent partial least squares path modeling. MIS Q. 39(2), 297–316 (2015)
Esposito Vinzi, V., Chin, W.W., Henseler, J., Wang, H.: Handbook of Partial Least Squares. Springer, Berlin (2010)
Guillaumont, P.: An economic vulnerability index: its design and use for international development policy. Oxf. Dev. Stud. 37(3), 193–228 (2009)
Hair, J.F., Hult, T., Ringle, C.M., Sarstedt, M.: A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM). Sage, Thousand Oaks (2014)
Jöreskog, K.G., Sörbom, D., Magidson, J.: Advances in Factor Analysis and Structural Equation Models. Abstract Books, Cambridge (1979)
Tenenhaus, M., Esposito Vinzi, V.: PLS regression, PLS path modeling and generalized Procustean analysis: a combined approach for multi-block analysis. J. Chemometr. 19(3), 145–153 (2005)
Tenenhaus, M., Hanafi, M.: A bridge between PLS path modeling and multi-block data analysis. In: Esposito Vinzi, V., et al. (eds.) Handbook of Partial Least Squares, pp. 99–109. Springer, Berlin (2010)
Tenenhaus, M., Esposito Vinzi, V., Chatelin, Y.M., Lauro, C.: PLS path modeling. Comput. Stat. Data Anal. 48, 159–205 (2005)
Wetzels, M., Odekerken-Schrder, G., van Oppen, C.: Using PLS path modeling for assessing hierarchical construct models: guidelines and empirical illustration. MIS Q. 33(1), 177–195 (2009)
Wold, H.: Path models with latent variables: The NIPALS approach. In: Blalock, H.M., et al. (eds.) Quantitative Sociology, pp. 307–357 (1975)
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Altimari, A., Balzano, S., Zezza, G. (2019). Measuring Economic Vulnerability: A Structural Equation Modeling Approach. In: Greselin, F., Deldossi, L., Bagnato, L., Vichi, M. (eds) Statistical Learning of Complex Data. CLADAG 2017. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Cham. https://doi.org/10.1007/978-3-030-21140-0_10
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