Empirical Analysis of the Transformation of Economic Growth into Social Development at an International Level


This paper takes a look at the countries of the world and their rankings in terms of material wealth. Using the attributes and variables featured in the development of these countries and synthetic indexes, it analyzes the relationship between wealth levels and estimates of social development. First we use the indicators estimated by UNDP. Using UNDP methodology and multivariate statistical methods such as factor analysis, we then turn to complementary estimators. Our results not only allow us to identify which countries are better (or worse) at turning economic growth into social development but they also contribute to detecting the dimensions of development behind position changes across various rankings.

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  1. 1.

    For more details on this indicator consult UNDP (1990, 2013).

  2. 2.

    This indicator uses the average of four dimensions: fourteen variables, including income although per capita consumption is not included.

  3. 3.

    Sanchez-Fernandez and Prada (2015) carried out a similar exercise limited to the countries of the European Union.

  4. 4.

    This work uses the most recent UNDP Statistics Annex (2013, pp. 144–197): http://hdr.undp.org/en/reports/.

  5. 5.

    Standardized geometric average of HDIpc, life expectancy and standardized schooling variable.

  6. 6.

    Objectively speaking, this paper deals with nothing on the “happiness of the countries”.

  7. 7.

    This is the total amount for which the UNDP supplies data.

  8. 8.

    The relation between income growth and Non-income_HDI has proven to be notably weak and statistically non-significant for the group of world-wide countries between 1970 and 2010 (UNDP 2010; Raworth and Stewart 2003).

  9. 9.

    The analysis of Qatar and Liechtenstein has been eliminated given that their GDPpc was over $80,000 and this created a break in the trend of the variable for the rest of the countries.

  10. 10.

    This is a third of the countries under study (34.6 %).

  11. 11.

    We have not included strictly economic indicators (productivity, salary per employee, household expenses, and available income) because the behavior of these indicators is very linked to the level of wealth, income or economic growth of the countries (Villaverde and Maza 2013).

  12. 12.

    Specifically: ecological variables, mortality rates, unemployment, homicides, suicides and inequality.

  13. 13.

    Social Development Index for the 15 variables selected.

  14. 14.

    (UNDP 2010: VI) preface of the 20th edition of the United Nations Report.

  15. 15.

    As in the previous case, Qatar and Liechtenstein have been eliminated given their over $80,000 GDP that created a break in the path of the variable for the rest of the countries.

  16. 16.

    0.23 is the correlation coefficient between GNIpc and SDI15 for all of the countries of the world under study.

  17. 17.

    Australia, Germany, Sweden, US, Canada, Norway and United Kingdom.

  18. 18.

    This means that there is a high intercorrelation among variables.

  19. 19.

    It specifically compares the $30,277 GNIpc of France as opposed to the $828 GNIpc of Madagascar.

  20. 20.

    Its weakness basically stems from infant mortality, university education and homicide rates.

  21. 21.

    For the results obtained with our SDIfa.

  22. 22.

    Situations of the “paradox of opulence” (Hirsch 1976).


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Prada-Blanco, A., Sanchez-Fernandez, P. Empirical Analysis of the Transformation of Economic Growth into Social Development at an International Level. Soc Indic Res 130, 983–1003 (2017). https://doi.org/10.1007/s11205-015-1206-0

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  • Wealth
  • Social development
  • Synthetic indicators
  • UNDP

JEL Classification

  • I31
  • O10
  • O57
  • P24
  • P44
  • R13