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Published in: Social Indicators Research 2/2013

01-09-2013

Identifying Synergies and Complementarities Between MDGs: Results from Cluster Analysis

Authors: Maria Carmela Lo Bue, Stephan Klasen

Published in: Social Indicators Research | Issue 2/2013

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Abstract

The MDGs are interlinked: acceleration in one goal is likely to speed up progress in others. Nevertheless, these synergies are not always visible, and may differ across countries. Using bivariate cluster analysis, this paper investigates whether distinct groups of developing countries can be identified, using statistical methods, on the basis of the correlation of changes in main MDG indicators over the 1990–2008 period. Identified groups include: (1) “good performers”, characterized by strong positive synergies in MDGs indicators; (2) “poor performers”, where there are synergies in poor progress towards the MDGs and (3) “partial performers” countries where progress in one MDG went along with regress or stagnation in another. We then study the determinants of cluster membership. While growth in GDP per capita is, unsurprisingly, best able to distinguish between “good” and “poor” performers, a poor institutional framework and deteriorations in the income distribution is a notable correlate of partial progress, thus apparently undermining synergies in reaching the MDGs. In light of the current discussions about the post-MDG system, our results suggest that synergies between MDG progress can be achieved but they cannot be taken for granted. Improving institutional performance and reducing inequality appear particularly important drivers of promoting such synergies.

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Footnotes
1
Looking at pairs of MDGs indicators-instead of opting out for some overall achievement index-allow us to have a closer inspection on possible synergies which may uniquely derive from achievements in two different human development dimensions. Hence, we avoid the risk (intrinsic in more complex multidimensional indices) of losing precious information on the direction of improvements and on the strength of complementarities between related spheres of human development.
 
2
Because of data availability constraints we were not able to include in our analysis any indicator for child malnutrition which is also been proven by the related literature to exhibit strong connections with the other MDGs indicators.
 
3
Analogous conclusions have been conveyed by Deininger (2003).
 
4
It is important to underline that our study relies on a narrow definition of synergy i.e. on the simultaneous achievements occurring in two dimensions which, deriving from strong complementarity in those two dimensions, would ensure that progress in one dimension will facilitate progress in another.
 
5
For other ways to define progress, see Clemens et al. (2007) and Lange and Klasen (2011). The results here are largely robust to defining changes in these ways.
 
6
See Fig. 1 and 2 in the Appendix provided in the working paper version of this article (Lo Bue and Klasen 2012).
 
7
With respect to this last point, it should be noted that, if absolute changes are regressed against initial levels a positive relationship is found (i.e. the greater the initial level, the larger is the associated rate of reduction). In other words, in absolute terms, in high mortality countries larger achievements are due to the immediate effect of some basic, cheap and easy-available policies whereas to reduce mortality rates even further would require much more efforts. While this affects the expected improvements, the residuals from these regressions are not dramatically different and do not materially affect the results of the cluster analysis.
 
8
Cluster analysis is mainly an exploratory data-analysis technique. As stated by Everitt (1993) “Clustering methods are intended largely for generating rather than testing hypotheses”.
 
9
In a simulation study, Milligan and Cooper (1985) found that the Calinski-Harabasz criterion recovered the correct number of groups most frequently.
 
10
For each analysis, Figs. 27 provided here below show the results in a graphical form. For further details, see the tables in the appendix of the working paper version of this article (Lo Bue and Klasen 2012) where we report for each cluster the means and the range of changes in the two indicators as computed under the conditional approach (i.e. standardised residuals).
 
11
As a robustness check, we have repeated these bivariate cluster analyses using standardised residuals from regressions with absolute annual changes in given MDG indicators (instead of relative annual changes). Also, we have performed multivariate cluster analyses (with three indicators per each). Our original results appear quite robust even though the composition and the location of groups changes slightly.
 
12
One needs to bear in mind that we are using our relative performance measure. So these countries with negative residuals in education might still have improved enrolment rates, but less than expected given initial conditions.
 
13
Here we only show countries for which we were able to do all six cluster analyses; for all other countries, please see the individual cluster analyses in the appendix.
 
14
While the identification of a group of bad performers is pretty clear in these last two cases, defining a group of best performers to be used as a reference category is not straightforward.
 
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Metadata
Title
Identifying Synergies and Complementarities Between MDGs: Results from Cluster Analysis
Authors
Maria Carmela Lo Bue
Stephan Klasen
Publication date
01-09-2013
Publisher
Springer Netherlands
Published in
Social Indicators Research / Issue 2/2013
Print ISSN: 0303-8300
Electronic ISSN: 1573-0921
DOI
https://doi.org/10.1007/s11205-013-0294-y

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