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Socio-economic Sustainability in Poland. SEM Analysis at Regional Level

Author

Listed:
  • Michal Bernard Pietrzak

    (Nicolaus Copernicus University, Poland)

  • Adam P. Balcerzak

    (Nicolaus Copernicus University, Poland)

Abstract

Improving conditions for sustainable development has been a major aim of every Polish government since the beginning of transformation process. Thus, the aim of the paper is to analyse the level of sustainable development in Poland at re-gional level. The research was conducted at NUTS 3 level for the years 2010-2013. The analysis was based on the data from Central Statistical Office of Poland. It was assumed that the phenomenon of socio-economic sustainability at regional level should be considered as a multivariate latent variable. As a result, it was measured with application of Structural Equation Modeling (SEM). The proposed model was based on eight observable variables that are often pointed in the literature as measures of socio-economic development and human welfare. Application of SEM model allowed to verify the usefulness of the observable variables for analysis of the phenomenon of sustainable development in Poland at regional level. The conducted research confirmed serious disparities at regional level in the sphere of socio-economic sustainability that must be the subject of special interest and counteractions of Polish government.

Suggested Citation

  • Michal Bernard Pietrzak & Adam P. Balcerzak, 2016. "Socio-economic Sustainability in Poland. SEM Analysis at Regional Level," Working Papers 20/2016, Institute of Economic Research, revised May 2016.
  • Handle: RePEc:pes:wpaper:2016:no20
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    Keywords

    Structural Equation Modeling (SEM); regional analysis; sustainable de-velopment; Poland;
    All these keywords.

    JEL classification:

    • Q01 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - General - - - Sustainable Development
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis

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