Measuring Economic Vulnerability: A Structural Equation Modeling Approach

  • Ambra Altimari
  • Simona BalzanoEmail author
  • Gennaro Zezza
Conference paper
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)


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.


Hierarchical component model Partial least squares Structural equation modeling Vulnerability index 


  1. 1.
    Dijkstra, T.K., Henseler, J.: Consistent and asymptotically normal PLS estimator for linear structure equations. Comput. Stat. Data Anal. 81, 10–23 (2015)CrossRefGoogle Scholar
  2. 2.
    Dijkstra, T.K., Henseler, J.: Consistent partial least squares path modeling. MIS Q. 39(2), 297–316 (2015)CrossRefGoogle Scholar
  3. 3.
    Esposito Vinzi, V., Chin, W.W., Henseler, J., Wang, H.: Handbook of Partial Least Squares. Springer, Berlin (2010)CrossRefGoogle Scholar
  4. 4.
    Guillaumont, P.: An economic vulnerability index: its design and use for international development policy. Oxf. Dev. Stud. 37(3), 193–228 (2009)CrossRefGoogle Scholar
  5. 5.
    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)zbMATHGoogle Scholar
  6. 6.
    Jöreskog, K.G., Sörbom, D., Magidson, J.: Advances in Factor Analysis and Structural Equation Models. Abstract Books, Cambridge (1979)zbMATHGoogle Scholar
  7. 7.
    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)zbMATHGoogle Scholar
  8. 8.
    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)CrossRefGoogle Scholar
  9. 9.
    Tenenhaus, M., Esposito Vinzi, V., Chatelin, Y.M., Lauro, C.: PLS path modeling. Comput. Stat. Data Anal. 48, 159–205 (2005)MathSciNetCrossRefGoogle Scholar
  10. 10.
    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)CrossRefGoogle Scholar
  11. 11.
    Wold, H.: Path models with latent variables: The NIPALS approach. In: Blalock, H.M., et al. (eds.) Quantitative Sociology, pp. 307–357 (1975)CrossRefGoogle Scholar

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Economics and LawUniversity of Cassino and Southern LazioCassinoItaly

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