Can we predict deaths in the Mediterranean using the number of war victims?
By: Francesco Piccinelli
Tags: Data Analysis, Visualization, Rstudio, Data Journalism, Migration

The chart above shows it very well: theoretically, the points you see should float randomly around the red horizontal line at zero. They even follow a declining pattern. This is a red flag showing that, from a strictly statistical point of view, there is little chance to predict how many people would die in the Mediterranean starting from victims of conflict.

There are a few caveats I want to make before leaving. The datasets I used have been collected with a methodology that allows inaccuracies ad errors to be made. Although they really are the best we have (and imply a remarkable organizational effort from those who manage them) they can’t collect all the shipwrecks in the Mediterranean and all the violent clashes all over the world: there are simply too many.

Secondly, migrants ARE running away from wars. A few years ago, using another (yet similar) dataset on conflict managed by the Uppsala University, I estimated that in Africa there were around 7 deaths per armed clash: very little, if we think about the death tolls of WWI, for example. My hypothesis back then was that migrants run away from places where they perceive their overall wellbeing is in such jeopardy that the perspective of at least trying to run away with no guarantee of arriving was more desirable.

Although in this exercise I couldn’t find a statistical backup to my theory from 2018, I am afraid that we are in the eternal re-edition of a caveat by Howard Becker. In his social research methodology handbook, Becker tries to warn social scientists (not journalists…) about asking questions like ‘What’s a nice girl like you doing in a place like this?’. Well, this work tried to find a data-driven answer to one of such questions. But, maybe someone could fix that flaw in three ways.

First, someone could run the analysis I ran by year. Then, someone could select the most ideal-typical country in the most ideal-typical year regarding war and migration, run an OLS (provided that it turns out to be significant), deploy this model to other countries and see if it works.

Second, given that I have worked with absolute values, the dependent variable of future work could be not the number of deaths in the Mediterranean but the weekly (or monthly) differential. This could lead to more significant results (maybe, also, starting with a better theoretical framework).

Third, go Bayesian. Although I still have to figure out what to do with things like ‘prior distributions’, a Bayesian approach could lead to a more probabilistic outcome that could answer a better question than my frequentist approach asked. We could go from ‘how many deaths in the Mediterranean are connected to wars in Africa and the Middle East’, to ‘how likely is that deaths in the Mediterranean increase, given this number of conflict victims?’

Generally speaking, there is plenty to investigate here. It would be great to organize a hackathon to study migration data, once the COVID pandemic is over, but maybe this is just a dream.

Here you can find the code I used.