# Global murder rate extrapolation from population trends

The graph shows the evolution of worldwide murder rate until 2100, if the murder rate in each country were to remain the same, and population grow as in the medium variant of the UN World Population Prospectus prediction. The current crime rates are taken from the the UN Global Study on Homicide. The code and data used to generate the plot can be found in this git repository.

Of course, it’s unrealistic to assume that murder rate will stay constant through time in a given country. And it’s also possible that their deviation follow a global trend, instead of canceling out, affecting them significantly the accuracy of the numbers in this extrapolation. For example, if one were to do such a chart using IQ, it seems quite likely that the Flynn effect would make it deviate strongly from the actual behavior. For murder rate there is a potential similar effect, more famously discussed in a recent book by Steven Pinker, but I remain less convinced about it representing a trend as likely to be projected in the future as the one in the Flynn effect.

Relatedly, one could argue that as many countries improve in “general well-being”, they will improve along these lines. I picked murder rate for this graph since economically developed countries already have values that differ on orders of magnitude from each other, and the same happens with really underdeveloped countries (compare Malawi with Madagascar, for example) so it seems like something that might not be so coupled with “general well-being” as other metrics.

Also, of course, even if the number was to increase for the next century there’s no reason to assume that it would just continue increasing for the next one – if it has decreased in the past, it seems possible that it would decrease in the future as well.

Note also that the number described in the Wikipedia page for the current global murder rate doesn’t agree with the beginning of the line in the graph. This seems to be because the UN Global Study on Homicide source doesn’t take the sum of the most recent murder totals for all countries in its data report tool (which would be 379845), but instead uses a more refined analysis, with a low estimate of 324000, a high estimate of 518000, and a chosen summary value of 437000. In any case, divergences about the starting point shouldn’t affect the trend itself noticeably.
By Abel Molina

# Datapoint: Interest in ethical phone in 5 European countries via Twitter ads

It seemed interesting to study how does attraction to an idea vary between some adjacent countries. The Fairphone project seemed like something worth promoting, so went with that. A choice of European countries seemed reasonable given that the project has mostly been active in that area, and considering also the ability to either write in the relevant languages or get some help to do so. And indeed, after much harassing of friends knowledgeable in the relevant languages, the messages were the following:

Looking at the results, the vast majority of engagements (98.7%) were clicks, with 2 retweets and 5 follows as well.

Engagement rates are the following:

• Italy: 1.20%  (standard deviation 0.15%)
• England: 1.21% (standard deviation 0.17%)
• Germany: 1.86% (standard deviation 0.24%)
• Spain: 2.13% (standard deviation 0.17%)
• French: 1.64% (standard deviation 0.19%)

The formula for the standard deviation is $\sqrt{ \dfrac{engagement rate* (1 - engagement rate)}{num views} }$. As a guideline, I’d ignore differences between points that are closer to each other than the corresponding standard deviations, and start taking things more and more seriously with ~1.5-3 standard deviations. It depends also on other factors, such as the number of options available – the more options you have, the more likely it is that noise gives rise to a large difference between two of them, so I’d suggest higher standards.

As for the engagement cost, we have

• Italy: $0.09 (noise level$0.01)
• England: $0.12 (noise level$0.02)
• Germany: $0.10 (noise level$0.01)
• Spain: $0.04 (noise level$0.003)
• French: $0.08 (noise level$0.01)

The method to compute the noise level here is a bit sketchy. It’s based on computing the standard deviation for the number of engagements (according to the observed probability), looking at the extremes of the corresponding interval, dividing the budget by each of these extremes, and then looking at the length of the corresponding interval. Would say it’s way more standard here (and maybe for the engagement rate as well) to build confidence intervals instead.

Some factors to take into account are the following:

• The wording might be a bit off in some of the languages, altering the engagement rate.
• Translating the message literally might not be the best way to obtain comparable results, since style of advertising varies in different countries. The fact that Spain has the highest engagement rate, as well as the original copy, suggests this might be going on.
• Different countries might have a different rate of Twitter users that speak the language used in the ad. Don’t think this is much of a relevant factor, and expecting that number to be > 98% in all countries.
• And more generally, there can be a multitude of reasons why Twitter population cannot necessarily be extrapolated to general population.
• EDIT: As pointed out by @cosenalgo (thanks a lot!), there is a huge factor going on, which is that the Tweet appears as tweeted by “Abel Molina”. It seems quite reasonable to expect engagement rate to increase upon more familiarity of the name (this article presents some possible evidence for this). That would help explain as well the higher engagement rate in Spain.
By Abel Molina

# Datapoint: Difference in engagement rate for Twitter ads from adding a picture (target in South Africa)

Made a Twitter campaign with the same tweet with and without picture, found more difference in engagement than expected…

The click-through rate difference seems to be 5.13±0.6% with picture vs 1.24±0.9% with no picture (target is South Africa). Interesting…