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Is the ‘Developing World’ Changing? A Dynamic and Multidimensional Taxonomy of Developing Countries


Existing classifications of developing countries have been based on – or dominated by – income per capita. Even those deemed to be ‘alternatives’ to the income classification include income per capita as a main component. This article proposes an alternative approach to classifying countries based on cluster analysis that allows us to identify the key development characteristics of each cluster of countries. We build five clusters of developing countries and consider changes over time since the late 1990s. We find that there is neither a simple ‘linear representation of development levels’ (from low- to high- development countries) nor a ‘linear dynamic of development’ (as if groups were ‘immutable’ and countries were just trying to accommodate themselves to the ‘established’ groups), which implies that the dominant international classification needs review. Instead our multidimensional and dynamic taxonomy offers a more nuanced understanding of the diversity of challenges of developing countries and their evolution over time.


Les classifications existantes des pays en développement ont été fondées sur – ou dominées par – le revenu par habitant. Même celles qui sont considérées comme des « alternatives» à la classification du revenu par habitant incluent le revenu par habitant comme une composante qui est fortement pondérée. Cet article propose une approche alternative à la classification des pays, basée sur l’analyse de cluster qui nous permet d’identifier les caractéristiques clés du développement de chaque groupe de pays. Nous construisons cinq groupes de pays en développement et nous considérons les changements au fil du temps depuis la fin des années 1990 au sein des groupes eux-mêmes, au sein des pays de chaque groupe et dans le monde en développement dans son ensemble.

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  1. For this reason we did not claim our taxonomy was the final word, but rather an illustration of the enduring weakness of income per capita in capturing the many dimensions of development.

  2. The World Bank itself has recently opened a review of the thresholds. In a similar vein, the United Nations’ Development Cooperation Forum of the Economic and Social Council (ECOSOC) is currently reviewing the subject of country classifications (Alonso et al, 2014). And the OECD’s Development Centre is carrying out its own multidimensional well-being classification (Boarini et al, 2014).

  3. See, respectively, for Brookings and Carlton: and

  4. OECD (2015) introduced a more sophisticated analysis of fragile states.

  5. We are not proposing that ‘environmental sustainability’ is a ‘definition’ of ‘development’ but rather that it is a constraint on the inter-temporal feasibility of paths of development and hence a trade-off between current and future development.

  6. See Appendix A for descriptive statistics of the data set.

  7. This section draws upon Tezanos (2012) and Tezanos and Quiñones (2012), who previously used cluster analysis for classifying the middle-income countries of Latin America and the Caribbean.

  8. Regarding the standardization method, we use the ‘range −1 to 1’, which is deemed to be preferable than other methods ‘in most situations’ (Mooi and Sarstedt, 2011, p. 247). The analysis was conducted using SPSS software.

  9. The two additional countries included in the later period are Serbia and Montenegro, which were not independent states in 2000. The countries not included in the analysis are either insular states with fewer than one million inhabitants (Antigua and Barbuda, Dominica, Fiji, Grenada, Kiribati, Maldives, Marshall Islands, Mauritius, Mayotte, Palau, Samoa, Sao Tome and Principe, Seychelles, Solomon Islands, St Kitts and Nevis, St Lucia, St Vincent and the Grenadines, Tonga, Tuvalu and Vanuatu) or countries with limited statistical information (Afghanistan, Bosnia and Herzegovina, Cuba, Eritrea, Kosovo, Lebanon, Libya, Mongolia, Myanmar, North Korea, Somalia, Sudan, Timor-Leste, Uzbekistan, the West Bank and Gaza, and Zimbabwe).

  10. If highly correlated variables are used for cluster analysis, specific aspects covered by these variables will be overrepresented in the outcome. Everitt et al (2011) and Mooi and Sarstedt (2011) argue that absolute correlations above 0.9 are problematic.

  11. See the dendrogram plots for both periods in Appendix B. SPSS re-scales the distances to a range of 0 to 25. Therefore, the last merging step to a 1-cluster solution takes place at a (re-scaled) distance of 25.

  12. See the VRC in Appendix C.

  13. See the ANOVA outputs for each period in Appendix D.

  14. The taxonomy should not depend on the order of the data set, unless there are outliers that influence the results.

  15. Appendix E shows the complete set of countries classified by periods, clusters, GNI per capita and income groups.

  16. The overall number of LICs decreased from 51 to 29, and – in contrast – the number of middle-income countries (both LMIC and UMIC) increased (see Table 2).

  17. It is worth noting that the WGI are designed to have a world average value of zero (across all countries and in each year). Thus, if the average WGI for all developing countries remains virtually static across the two analysed periods this means that it has not varied much in relation to the world average (which includes also developed countries).


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Appendix A

Table A1

Table A1 Descriptive statistics of the data set

Appendix B

Figure B1

Figure B1
figure 1

Dendrograms of developing countries. 1995–2000. 2005–2010

Appendix C

Table C1

Table C1 Variance ratio criterion (VRC)

Appendix D

Table D1

Table D1 ANOVA outputs of the development clusters. 1995–2000. 2005–2010

Appendix E

Table E1

Table E1 Cluster membership of developing countries

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Tezanos Vázquez, S., Sumner, A. Is the ‘Developing World’ Changing? A Dynamic and Multidimensional Taxonomy of Developing Countries. Eur J Dev Res 28, 847–874 (2016).

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  • development classifications
  • developing countries
  • development studies
  • income per capita classification
  • cluster analysis