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The Global Competitiveness Index: an alternative measure with endogenously derived weights

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Abstract

We present an alternative method to compute the Global Competitiveness Index (GCI) by means of a partial least squares path model. In particular, making use of the same set of variables defined by the World Economic Forum we compute the composite indicator GCI by means of a structural equations model with endogenously derived weights. World Economic Forum, instead, defines GCI as a combinations of subindexes with weights that are fixed but vary according to the stage of development a country belongs to. The main issue we address is whether the weights of the subindexes change according to different stages of development.

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Notes

  1. We have checked that different possibilities of grouping countries of the transitions stages do not change the following discussion.

  2. For the measurement model, the manifest variables reported in Table 8 were removed from the final model because both corresponding weights and loadings were not validated by the bootstrap procedure.

  3. Authors upon request can provide the measurement model results.

References

  • Abdi, H., Esposito Vinzi, V., Russolillo, G., Saporta, G., Trinchera, L.: The multiple facets of partial least squares methods. In: Springer Proceedings in Mathematics and Statistics, Springer Proceedings in Mathematics and Statistics, Switzerland (2016)

  • Becker, J.M., Rai, A., Rigdon, E. E.: Predictive validity and formative measurement in structural equation modeling: Embracing practical relevance. In: Proceedings of the International Conference on Information Systems (ICIS) (2013)

  • Bollen, K.A.: Structural Equations with Latent Variables. Wiley, New York (1989)

    Book  Google Scholar 

  • Bollen, K.A., Bauldry, S.: Three Cs in measurement models: causal indicators, composite indicators, and covariates. Psychol. Methods 16(3), 265–284 (2011)

    Article  Google Scholar 

  • Chin, W.W.: Multi-group analysis with PLS (2000). http://disc-nt.cba.uh.edu/chin/plsfaq/multigroup.htm

  • Chin, W.W.: A permutation procedure for multi-group comparison of PLS models. Invited presentation. In: Vilares, M., Tenenhaus, M., Coelho, P., Esposito Vinzi, V., Morineau, A. (eds.) PLS and Related Methods, PLS03 International Symposium “Focus on Customers”, Lisbon, pp. 33–43 (2003)

  • Chin, W.W., Dibbern, J.: An introduction to a permutation based procedure for multi-group PLS analysis: results of tests of differences on simulated data and a cross cultural analysis of the sourcing of information system services between Germany and the USA. In: Esposito Vinzi, V., Chin, W.W., Hensler, J., Wang, H. (eds.) Handbook of Partial Least Squares, Springer Handbooks of Computational Statistics. Springer, Berlin (2010)

    Google Scholar 

  • Esposito Vinzi, V., Chin, W., Henseler, J., Wang, H. (eds.): Handbook of Partial Least Squares (PLS): Concepts, Methods and Applications, Springer Handbooks of Computational Statistics. Springer, Berlin (2010a)

    Google Scholar 

  • Esposito Vinzi, V., Trinchera, L., Amato, S.: PLS path modeling: from foundations to recent developments and open issues for model assessment and improvement. In: Esposito Vinzi, V., Chin, W.W., Hensler, J., Wang, H. (eds.) Handbook of Partial Least Squares, Springer Handbooks of Computational Statistics. Springer, Berlin (2010b)

    Chapter  Google Scholar 

  • Guinot, C., Latreille, J., Tenenhaus, M.: PLS path modeling and multiple table analysis: application to the cosmetic habits of women in Ile-de-France. Chemom. Intell. Lab. Syst. 58(2), 247–259 (2001)

    Article  Google Scholar 

  • Hair, J.F., Hult, G.T.M., Ringle, C.M., Sarstedt, M.: A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM). SAGE, New York (2014)

    Google Scholar 

  • Hanafi, M.: PLS path modelling: computation of latent variables with the estimation mode B. Comput. Stat. 22(2), 275–292 (2007)

    Article  Google Scholar 

  • Kaplan, D.: Structural Equation Modeling: Foundations and Extensions. Sage, Thousands Oaks (2000)

    Google Scholar 

  • Krämer, N., (2007), Analysis of high-dimensional data with partial least squares and boosting. Ph.D. Thesis, Technischen Universität Berlin, Berlin, Germany

  • Lamberti, G., Banet, T.A.: Invariance test: detecting difference between latent variables structure in partial least squares path modeling. Int. J. Stat. Probab. 6(2), 54 (2017)

    Article  Google Scholar 

  • Lohmoöller, J.B.: Latent Variable Path Modeling with Partial Least Squares. Springer, Heildelberg (1989)

    Book  Google Scholar 

  • Noonan, R., Wold, H.: PLS path modeling with indirectly observed variables. In: Jöreskog, K.G., Wold, H. (eds.) Systems Under Indirect Observation: Causality, Structure, Prediction Part II. North-Holland, Amsterdam (1982)

    Google Scholar 

  • Rigdon, E.E.: Choosing PLS path modeling as analytical method in European management research: a realist perspective. J. Manag. Eur. (2016). https://doi.org/10.1016/j.emj.2016.05.006.

  • Sanchez, G., Trinchera, L., Russolillo, G.: PLS Path modeling with R (2013). http://cran.r-project.org/web/packages/plspm/index.html

  • Sarstedt, M., Hair, J.F., Ringle, C.M., Thiele, K.O., Gudergan, S.P.: Estimation issues with PLS and CBSEM: Where the bias lies!. J. Bus. Res. 69(10), 3998–4010 (2016)

    Article  Google Scholar 

  • Tenenhaus, A., Tenenhaus, M.: Regularized generalized canonical correlation analysis. Psychometrika 76(2), 257–284 (2011)

    Article  Google Scholar 

  • Tenenhaus, M., Esposito Vinzi, V., Chatelin, Y.M., Lauro, C.: PLS path modeling. Comput. Stat. Data Anal. 48(1), 159–205 (2005)

    Article  Google Scholar 

  • Welzels, M., Oderkerken-Schröder, G., Van Oppen, C.: Using PLS path modeling for assessing hierarchical construct models: guidelines and empirical illustration. MIS Q. 33(1), 177–195 (2009)

    Article  Google Scholar 

  • Wold, H.: Soft modeling: the basic design and some extensions. In: Jöreskog, K.G., Wold, H. (eds.) Systems Under Indirect Observation: Causality, Structure, Prediction, vol. 2, pp. 1–54. North Holland, Amsterdam (1982)

    Google Scholar 

  • Wold, H.: Partial least squares. In: Kotz, S., Johnson, N.L. (eds.) Encyclopedia of Statistical Sciences, vol. 6, pp. 581–591. Wiley, New York (1985)

    Google Scholar 

  • World Economic Forum: The global competitiveness. Full Data Edition, Editor Klaus Schwab (2013). http://www3.weforum.org/docs/WEF_GlobalCompetitivenessReport_2013-14.pdf

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Authors and Affiliations

  1. Istat - Italian National Institute of Statistics, Via Cesare Balbo 16, 00184, Rome, Italy

    Francesca Petrarca

  2. Department of Economics, Roma Tre University of Rome, Via Silvio D’Amico 77, 00145, Rome, Italy

    Silvia Terzi

Authors
  1. Francesca Petrarca
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  2. Silvia Terzi
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Corresponding author

Correspondence to Francesca Petrarca.

Appendix

Appendix

In this appendix, we report the complete list of manifest variables considered in this work. In particular, in Table 8 we reported variables which have not been validated by the bootstrap procedure of our hierarchical models while in Table 9 there is the list of validated variables.

Table 8 List of not validated variables grouped with respect to components and subindexes
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Table 9 List of validated variables grouped with respect to components and subindexes
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Petrarca, F., Terzi, S. The Global Competitiveness Index: an alternative measure with endogenously derived weights. Qual Quant 52, 2197–2219 (2018). https://doi.org/10.1007/s11135-017-0655-8

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