Showing posts with label Illicit Financial Flows. Show all posts
Showing posts with label Illicit Financial Flows. Show all posts

Monday 29 July 2019

Difficulties in Modelling the Volume of Illicit Financial Flows

AA's Done the Math So You Don't Have to

In a previous post, AA discussed some of the shortcomings in and misunderstandings about currently accepted estimate for money laundering.
Today we’ll look at issues surrounding modelling these flows.
What can be done to add more precision to the process and to what extent?
Models in General and Their Problems
What follows is a discussion of “direct” modelling.  That is, mathematical models consisting of equations that (a) estimate a current state of affairs or (b) predict one in the future using observable data.
These models are based on assumptions about fundamental processes underlying events. For example, according to most economic theories, lowering interest rates spurs capital investment.  Capital investment spurs increased production, employment, and thus, higher GDP.
Once these relationships are identified the modeler’s job is to quantify the impact of a particular action or development by reducing it to a mathematical relationship (equation).
One example might be a 1% decrease in interest rates will result in a 5% increase in capital investments.  As more and more of these relationships are reduced to equations, a model is constructed.
Observable data are the inputs for the models.
Predictions from the models can be retroactively compared to actual results, providing a feedback loop of sorts.
It sounds very scientific but it is not.
Assumptions about underlying economic processes are often little more than conjectures based on the teaching whether real or imagined of some economic prophet. There’s an interesting book “Economics as Religion”  that describes this process.
It is scarcely better with modeling the value of firms.  Here assumptions are made about growth rates, the risk free rate, risk premia, etc.  If you know this discipline, you know that these models are very sensitive to slight changes in growth and discount rates.
And despite the best efforts of modelers, no model has yet been developed that reliably predicts GDP several years out or the value of a firm.
The point of this is to emphasize that these models and their results are not infallible. 
IFF Models and Their Additional Problems
When we look at modelling IFFs, we see that much of what is available to economic or financial models is not available.
That means that IFF modelling is going to be more difficult and is likely to result in less reliable results.
We don’t have a satisfactory theory or theories that explain the volume of volume of various types of IFFs that can be expressed in mathematical terms.
What drives corruption?
We might say that it is directly related to cupidity and opportunity and inversely related to morality.  Corruption is also dependent on the bribe payer’s cupidity and inversely related to its morality. For both the risk of being caught is a negative factor.
How do we model this?  What is the equation that describes this?
If we could specify these relationships in equations, we don’t have data on the variables in the equations.
How much cupidity is there in the elites in Country A?  If average cupidity in a Country A is X, what is the standard deviation?  It may just be a subset of the elite that engages in corruption.
We have some estimates of data, e.g., ranking of countries for corruption by Transparency International.  But you’ll notice TI call their assessment “Perceptions of Corruption”.
Perceptions seem a slim reed to build a case on.
According to an unscientific poll that AA recently saw, 84% of Twitter users who responded didn’t like the new format.  Should AA have the “perception” that overwhelmingly Twitter users don’t like the new format? NBL! 
We have some single point data from discovered IIF transactions.  But there is no robust set of available statistics.
No actuals we can compare the predictions from of our model to.
In fact, if we had that data, we wouldn’t need the model.
But there are more difficulties.
UNDOC had a working meeting in 2017 to refine its methodology.  Here’s a link to the gateway page with a brief overview and links to more detailed material.
UNCTAD has created a “task force” that held its last meeting this July.
That material outlines two key problems with estimating IIFs with AA’s commentary on each in the “bullet” points immediately below each boldfaced sentence.
There is no single accepted definition of Illicit Financial Flows (IFFs)
  1. IIFs range from tax avoidance schemes on legally earned profits to the movement of money arising from illicit activities, e.g., drug and human trafficking, corruption, embezzlement.
  2. One of the tasks of the meetings referred to above is to try and develop an accepted definition of IFFs.
  3. “Tax minimization” is technically legal. Presumably, transfer pricing transactions between MNC do not belong in IFFs. But how or where does one draw the line between “tax minimization” and “tax avoidance” (clearly illegal)?  Is it a difference of degree or difference of kind?
  4. Can we separate out the various subtypes of IFFs from the aggregate total? We want information on IFFs not out of academic curiosity, but to craft policy and further enforcement.  One deals with tax avoidance schemes with a particular set of policies and with drug trafficking or corruption with others.
  5. We want to make sure that we don’t double count sub-types in our gross IFFs totals. Are bribes to law enforcement officers and politicians part of the drug trade IFFs? Or do we account for these as part of corruption?
  6. Within these high level definitional problems there are some other problems.  When we consider IFFs do we look both at intra-country and inter-country flows?
There is no single accepted method for estimating IFFs.
  1. Examples of methods that have been used are (a) analyzing discrepancies in Balance of Payment data and in trade statistics (value differences in the goods traded between two countries), (b) cash to GDP measures (assumption is that cash is the preferred payment method for crime and the informal economy).  Sometimes multifactor models are used in an attempt to compensate for shortcomings in a single factor model.
  2. Each of these methods used has drawbacks. We’ll take a look at this topic in a subsequent post.
Alternative Approaches
There are alternatives to direct modelling to estimate IFFs.  But these are likely to be less precise than direct models--if available--would produce.
  1. One could for example, focus on a handful of countries which are the largest consumption markets in the world for illicit drugs and estimate what percent of world drug trade these countries represented.
  2. Then estimate the annual physical flow of drugs to those countries by making estimates that interdictions are x% of the total shipments to those countries.
  3. And then from observable street prices estimate the gross sales proceeds.
  4. Then make an assumption about the profit margin to the overseas cartel as well as costs associated with the drugs outside the country of consumption/final sale, making as well  foundational assumptions about the costs the local distributor in the country defrays, e.g. transport, protection, sales and marketing, plus its profit margin.  
As you can appreciate from the chain of assumptions, this sort of alternative model is likely to be less accurate than a direct model.   
The problems with direct modelling and alternative approaches suggests that if new models are developed, we treat their results with healthy skepticism.  They will most likely give directional rather than locational results.
To be clear, that doesn’t mean that we should ignore such models or not try to create such models.
But rather that we not treat their results as incontrovertible “fact”.

Wednesday 24 July 2019

Estimating the Volume of Illicit Financial Flows – Definitely Not a Science Probably Not Yet an Art

Ainsi parlait Michel

There’s no central authority that keeps track of and publishes details of illicit financial flows.  No national central banks of crime.  No international (criminal) organization akin to the OECD or the IMF.
No major listed criminal enterprises that report their annual results of operations including sector information, costs of doing business, including those related to bribes and threats from which we might construct estimates.
By their natures these flows are undisclosed.

Parties to illicit transactions, the intermediaries they use, and parties they co-opt don’t self-report for obvious reasons.
But you do see figures for these flows.
For example, UNDOC states: 
“The estimated amount of money laundered globally in one year is 2 - 5% of global GDP, or $800 billion - $2 trillion in current US dollars.”
You’ll often see that latter number USD 2 trillion cited in press reports.  Here’s one from January this year in which Bloomberg states that “shady transactions continue to reach as much as $2 trillion a year.”
That wording implies that amount of money laundering is capped.  Apparently, once they reach USD 2 trillion in a year, criminals have to stop money laundering.  Unclear how this information is communicated.
Let’s stop for a minute and reflect.
Amounts and Percentages
UNDOC states the amount of money laundering in one year is estimated as a percentage of global GDP.  It then goes to give a range of USD estimates.
What do those estimated amounts work out to in terms of global GDP?  To USD 40 trillion.
According to World Bank data, that’s roughly the estimated world GDP in  2003.
Are we blindly repeating 16 year old estimated amounts?
Referring to the same World Bank source above 2017 world GDP was some USD 80.886 trillion and in 2018 some USD 85.791 trillion.
That would make
  1. 2017 money laundering USD 1.687 trillion to USD 4.044 trillion and
  2. 2018 money laundering USD 1.716 trillion to USD 4.290 trillion.
Now it could perhaps be that money laundering is not a growth business.  It’s capped at USD 800 billion to USD 2 trillion.  More Sears than Amazon.
AA doubts that.
So, is the answer that we just need to update the amounts?
It’s not that simple.
Origin of the Estimated Range
Before we do, we should know where and when this 2% to 5% estimate came from.
As near as AA can tell, it was first mentioned in a speech by then IMF Managing Director Michel Camdessus in 1998:
“While we cannot guarantee the accuracy of our figures—and you have certainly a better evaluation than us—the estimates of the present scale of money laundering transactions are almost beyond imagination—2 to 5 percent of global GDP would probably be a consensus range.”
Note the words “we cannot guarantee the accuracy” and “would probably be a consensus range”.
Clearly, models are estimates so they are not 100% accurate. Hard to quibble with that statement, though it does serve to warn that one should treat the model’s results with caution.
But “would probably be”.  On its face, that means we really don’t know if it is a consensus or not.  Or who the parties to the consensus might be. Or how they achieved consensus.
Or whether Mike pulled this out of his hat.
Age of the Model
But let’s assume there was a formal model of some sort, which is unclear, and this range is not based on graph drawn on a cocktail napkin in a bar somewhere.  Or a discussion in a bar.
Are we working with a model from 1998?  Should we be?
Is the world the same as it was some 20 years ago?
Results of the Model
The model has a range from 2% to 5%.
At the risk of understatement (which AA delightfully accepts, the risk not the understatement), that’s a wide range.
Imagine you came to AA Investment Advisors (AAIA) and asked AA, the firm’s Chief of Research and Head Strategist, what the value of a single share of Company XYZ and of Company DFE were worth.
If you got the answer about Company XYZ from “USD 20 to USD 50” and Company DFE “from USD 40 to USD 100”, what would you think?
You’d probably not think this was particularly useful, nor something that should be relied on for your investment decisions. Or that the nature of these stocks made it impossible to perform a more precise valuation.
Now if you went to Goldman Sachs and got the same answer, what would you think?
Would you think that AA wasn’t much of a financial analyst, but the analysis of the good folks at the Goldmine was spot on and highly useful simply because they were at GS?
In responding, don’t overlook the fact that  you're unlikely to be able to get a decent cup of Turkish coffee at all of GS's global offices.  But at all of our one office in the world at AA Investment Advisors, you can get what is probably one of the best cups of Turkish coffee in the world.
100% organic Arabica coffee hand-ground fresh for each cup using only the finest Turkish grinders. As long as you promise not to spill it on Madame Arqala’s antique furniture or rugs. There are some severe penalties for such infractions as AA can testify.
Hopefully not, you should think the same in both places and with respect to this model.
The right conclusion is that model is pretty much a rough estimate.  Another understatement.  Probably given the task it has set for itself:  to estimate IFFs that are deliberately hidden.
Don’t be dazzled by the source of the model.  “Aristotle says” or “the IMF, UNDOC, UNCTAD, FATF say” doesn’t make it true. Nor confirm precision where none is possible.
Don’t be More Royalist than the King
If you’re still not convinced by AA, if you dig a bit deeper, you’ll see that UNCTAD believes less in the model than many outsiders appear to.
In UNCTAD’s 2018 Annual report go to Goal 16 and look for the dropdown menus.  Pick the one about illicit financial flows.  There you’ll find this quote:
“In close cooperation with the United Nations Office on Drugs and Crime (UNODC) and the United Nations Economic Commission for Africa (UNECA), UNCTAD is working on developing a measurement framework for Goal indicator 16.4.1. This is a complex project that involves defining and designing measurement tools to capture both illegal and illicit activities which, by their very nature, are hidden deliberately.  As a co-custodian of the Goal indicator 16.4.1, we are striving to define, estimate and disseminate statistics on IFFs in the context of developing economies in Africa, some of the most affected by this developmental challenge. Through a series of implementation guidelines, pilot activities and technical assistance, by 2020 NCTAD, UNODC and UNECA will have developed the capacity to measure IFFs in several participating countries in Africa.  The result will be the capacity to more accurately estimate IFFs in participating countries. The lessons learned will inform the national monitoring of IFFs and will guide policy actions in affected countries to curb these flows. As such, it will also increase the likelihood of developing countries achieving the 2030 Agenda for Sustainable Development.”
This post marks the start of series on Illicit Finance.
In following posts, we’ll take a closer look at  difficulties in modeling IFFs, in measuring the probability of corruption, etc.