# The UN's proposed Multidimensional Vulnerability Index

## At a glance:

I make some visualisations of the country scores of the UN's proposed Multidimensional Vulnerability Index

30 Sep 2023

The United Nations President of the General Assembly’s High-Level Panel on the development of a Multidimensional Vulnerability Index have (September 2023) released an Advanced Unedited Version of their Final Report, and along with it the scores on that index for 142 countries. This is relevant for my work; it’s pretty complex; and I wanted to understand it fully so I downloaded it and drew some charts.

A good measure of “vulnerability” has been sought for at least 30 years as a supplement to measures of economic prosperity, with a number of parties (particularly small island developing states) advocating for its inclusion in eligibility decisions for financial and other forms of assistance. Vulnerability has formally been a factor in determining Least Developed Country status for some time now. This High Level Panel was commissioned to resolve the contradictions in the various measures out there and address the “lack of international consensus on how to characterize and, by extension, mitigate their (small states’) vulnerabilities”.

There is (I think) an awkward contradiction between acknowledging the multidimensional nature of vulnerability and the desire for a single index that can be used for financing decisions. The High Level Panel deal with this by creating an elegant hierarchy where increasingly abstract indexes are created out of other indexes, which themselves are made out of actual indicators that have been scaled from 0 to 100, with 100 meaning the most vulnerable (or least resilient). All the details are in the report linked to above.

Numbers are combined to form the index in the next level up of the hierarchy by taking the square root of the mean of the squares of the original numbers, which has the effect of pushing the average a bit towards the higher numbers in the inputs; this is acknowledgement of the common judgement that you don’t need to be vulnerable against all the inputs to be vulnerable overall. Consider that the arithmetic mean of (2, 6) is 4; the geometric mean is 2.8 (pushed downwards); but the square root of the mean of the squares is 4.5 (pushed upwards). If that “6” meant you were vulnerable to something and the “2” you weren’t to something else, using 4.5 as your combined measure is giving a bit more importance to the variable that you are vulnerable to.

## Visualising the results

First lets look at some results for the Pacific, the area I’m working on in my day job. Here are the values on the final, single Multidimensional Vulnerability Index (MVI) for the 12 Pacific Island countries in the dataset, compared to other countries in grey:

Unlike most of my posts, I haven’t interspersed my text and output with code creating it, because I want the results to be easy to focus on without distraction in talking about how the visualisations are done. But that code is still available in one spot here.

A few surprises in that first plot, for example that Vanuatu (often used as a case study of a vulnerable country, that has had a bad run recently with cyclones) is only very slightly above world median vulnerability.

Next level down from the main index we have two important component indexes - “Structural Vulnerability” and “Lack of Structural Resilience”. The final index for each country is their average of scores on these two, so let’s look again at the Pacific countries on the two indexes at once, again with the rest of the world’s countries shown in grey:

Hmm ok, that’s interesting; for example we can see that Tuvalu gets it’s relatively high overall score (that we saw in the first plot where it was ranked second most vulnerable of Pacific countries) almost entirely from “lack of structural resilience” and in contrast is actually slightly below the median score for “Structural vulnerability”.

The next two charts let us see what the concepts are behind these two indexes.

First, here’s “structural vulnerability”. There are two levels of concepts I’ve merged into one chart here; the first three (economic, environmental and social vulnerability) are themselves made up of the more detailed remaining nine. We’re still dealing with abstracts, not down to any actual data here. But at least we can see what it is that conceptually is leading to high or low scores. In the case of Nauru for example we see a particularly high score for “exposure to entrance of international forced displacement of people”. Hmm, there’s some complicated history behind that one.

And here’s the concepts making up “lack of structural resilience”:

We can see that these “resilience” concepts are about the ability of a society to respond to shocks they will get as a result of vulnerability; for example the lack economies of scale, adequate water supply, or a resilient agricultural system.

Finally, one more level down, here are the actual measureable indicators that are turned into those concepts, and ultimately the index itself:

## Visualising the hierarchy of concepts

But which variables feed in to which concepts, and how to grasp this all at once? To help me understand how the variables get aggregated into concepts, then into higher level concepts, then into two indexes and finally one index, I constructed a network graph to illustrate the hierarchy:

This visualisation works for me - at least on a reasonable sized screen. But this, and all of the following network charts, are a bit fiddly to read in-line in the blog so I’d recommend opening them in another tab (with right mouse button) or clicking on them to navigate straight there (and then you can use the back key to come back here to the main text).

This layout suggests a nice option - we can colour-code the nodes (representing variables, concepts or index scores) according to an actual country’s scores, and see what’s pushing the country’s vulnerability scores up and down and how they add up to the overall score. Here’s such visualisations for each of our 12 Pacific countries, grouped by subregion:

### Melanesia

#### Papua New Guinea

Most vulnerable scores (bright yellow) for PNG coming from gender equity, and lack of access to sanitation. Non-vulnerable scores coming from no problem with drylands, coastal zones, and access to freshwater.

#### Fiji

Fiji scores higher for vulnerability on exposure to natural hazards, rainfall shocks, low connectivity and low integration wutih international markets.

#### Vanuatu

Vanuatu also scores high on vulnerability to natural hazards, low connectivity, and gender equity. But it is scored with a fairly high level of enviornmental resilience, and not too socially vulnerable.

… and so on. I won’t write commentary on each individual country, but for completion they are all here:

### Polynesia

#### Tonga

OK, that was interesting. I certainly feel I understand both a) how the index is constructed and b) the ratings it is giving to Pacific Island countries.

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