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| 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.
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)
- 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.
- One of the tasks of the meetings referred to above is to try and develop an accepted definition of IFFs.
- “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?
- 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.
- 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?
- 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.
- 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.
- 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.
- 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.
- 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.
- And then from observable street prices estimate the gross sales proceeds.
- 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”.
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