In network science, it’s fairly common to take a graph of something and be able to apply techniques like neighbourhood analysis or clustering which then reveal something about the data. This comes up in all kinds of real world examples from social networks to music preferences etc. What the authors are saying (I think) is that they tried the same tricks on the Panama data and got nothing useful out of it. The standard graph techniques don’t explain anything useful about what’s going on. I would guess (from previous experience working on similar things at opencorporates.com a few years ago) that this is because these networks aren’t constructed according to the real world - instead they’re a response to specific tax rules. For example if you use a specific tax avoidance scheme like the Irish dog leg then the company structure will be defined by the laws of the countries involved. Another reason would be that intermediaries like company formation agents are likely to show connections where they don’t exist in reality. If I use the same company formation agent as say, Donald Trump, it doesn’t follow that we’re related financially. Finally the Panama papers were just about hiding assets in many cases, so you’ll have these chains of ownership going through as many countries as they could be bothered to work with. This is just to give law enforcement etc. a hard time. Again I wouldn’t expect graph analysis to make sense of that on its own. If anyone is interested, banks in the US are required to document their corporate structures so there’s good data available on their structures. It suffers from the same issues though - it’s hard to tell from looking at a graph of companies where the bad stuff is happening.
>What the authors are saying (I think) is that they tried the same tricks on the Panama data and got nothing useful out of it. The standard graph techniques don’t explain anything useful about what’s going on.
The impression I got was not that they found nothing useful, but that they didn't find broadly interconnected networks within the data.
If that's the case, it's likely that the transactions in the data aren't tightly linked.
Which is useful, in that it tells us that those involved in such activities are not a cabal of evil elites, rather they're disparate groups with little in common except their desire to avoid taxes/hide assets.
That conclusion certainly implies that addressing these activities will be more difficult as they are decentralized and widespread, but it's certainly not useless IMHO.
It just means that they didn’t find useful network structures with the specific network analysis methods they used and the specific networks that they constructed using the data.
It leaves room for tons of other network analysis and data analysis.
> that they didn't find broadly interconnected networks within the data.
That seems to agree with what the parent said. It sounds to me like if a broadly interconnected network was found, that would be a failure on the part of the people trying to facilitate the tax avoidance schemes - the closer to homogenous noise the graph looks, the harder it is to trace. Based on that, I'd say that there's no utility in trying to prove the negative, i.e., that larger networks do not exist.
If the people hiding their assets actually did their job well, they would expect and hope that you (as an outside analyst) would come to exactly the conclusion that you have come to.
