Comments on IMF Working Paper on Estimating the Size of Shadow Economy in Europe

This post is a continuation of a series of the difficulties of modelling illicit financial activity and the need to understand that the results of such models are estimates not facts when we use those results.

Earlier posts here. Transparency International’s Corruption Perceptions Index, Difficulties in Modelling Illicit Financial Flows, The Origin of the Estimate of Global Money Laundering.

On 13 Dec 2019 the IMF published Working Paper 19/278 Explaining the Shadow Economy in Europe: Size, Causes and Policy Options written by B. Kelmanson, K. Kirabaeva, L. Medina, B. Mircheva and J. Weiss.

Before discussing this publication, just a note that IMF WPs do not represent the IMF’s official position, but report on research in progress and are designed to elicit comment and feedback.

While the WP is “rich” in content, I’d like to highlight a few points for comment. Because as usual AA has an axe to grind.

Defining and Measuring the “Shadow Economy”  (Pages 5-6)

There are several key take-ways here.

1. Different Definitions - There are different definitions of the “shadow economy”.  And so, it’s important to know which definition is being used in an estimate.  It’s like Transparency International’s Corruption Perception Index.  Are we measuring all corruption in a country or just a subset?
2. Estimates Not Measurement - What is actually going on is not measurement in a formal sense.  But an attempt to estimate the size. A critical difference.  One can directly measure AA’s weight.  Or the distance from Bayt Meri to Beirut. But one can’t directly measure this or that person’s intelligence.  One has to estimate it.
3. There is No Single Infallible Method for Such Estimations – in the authors’ words “There is no ideal or leading method to measure the shadow economy, each of them have some conceptual or practical strengths and weaknesses. The choice of the methodology can be governed by data availability, or the research objectives. Multiple methods can be employed to improve accuracy of the estimations.”
4. Each Model has its Limitations - In discussing the model they used (MIMIC), the authors state: “The shortcomings of this method include sensitivity to changes in data and specifications, the sample used, calibration procedures, and starting values (Breusch 2005).”

Results – False Precision

1. The tables of results are on page 25 and 26.
2. The results appear very precise.
3. The authors have come up with results to the tenth of one percent.  Or in non percentage terms .001.  That is some pretty fine parsing for estimates of the size of something that is unknown.
4. Not only that but they have been able to order the sizes of the shadow economies among various countries.
5. In the first table (page 25), France is 15.3% while in Germany is at  15.9%.  In the second table, the scores are 15.0% and 16.7% respectively.
6. Is this precision really possible?
7. What about the caveats about models and estimates in the previous section?
8. Shouldn’t we expect to see less precision?
9. Use of ranges?  Grouping of countries into similar baskets.  For example, Germany and France have roughly the same size shadow economies.
10. AA has his axe out now. I made the argument in an earlier post on Transparency International’s CPI that when valuing the credit worthiness of an issuer rating agencies place similar firms in broad categories, AAA, AA, A etc.  They don’t parse creditworthiness of individual firms within a category. And since I have a habit of repeating myself made the same arguments in another post about modelling illicit financial flows.
11. Similarly in valuing firms, stock analysts come up with a range for the value of stock, not a single point estimate.
12. The point I want to make yet again is that we need to treat results from such modelling efforts as directional not locational. They are not precise but only give an indication of the real state of affairs. And when we use them, we need to keep that in mind.

Implications: Likely Underreporting of “Total” GDP

Ignoring the shadow economy means that we are likely underreporting the total or “true” GDP of a country – that is the GDP from its formal sector and that from its informal or shadow sector.  

Where the shadow economy is relatively small, this probably doesn't make much of a difference. 

But if the authors’ estimates that the shadow economy in the CIS countries is around 40% of formal GDP (page 7) are correct, then typical characterizations of CIS “dismal” economic performance based on only formal GDP are probably less true than they appear.

This does not of course “excuse” the fact that a good portion of this activity is taking place “off the books”.   

5 Fundamental Misperceptions about Transparency International’s Country Corruption “Rankings”

Plenty of Cake to Go Around: Eat Your Fill, Sleep Well

Probably the most well-known source for “rankings” of country corruption is Transparency International.

Well-known as the source, but less so for the contents.

AA believes that most people who use or quote TI’s rankings do not know what they mean and operate with some or all of the following misperceptions.

To be very clear upfront, this post is not arguing that we should not use TI’s CPI.  But rather than we should understand what it is, what are its limitations, and how to use it intelligently.

FIVE COMMON MISPERCEPTIONS ABOUT TI’S CPI

1. TI’s rankings assess the overall level of corruption in a country.
2. While not “facts”, the analytic process behind the rankings results in fairly accurate assessments.
3. TI performs the analysis behind the rankings or at the very least directs it.
4. Every country is rated using the same common set of standards.
5. The rankings are sufficiently precise that we can use them to distinguish the level of corruption in one country from the level in another.

TI provides extensive disclosure about the CPI at its Methodologies page.

Those who read this material carefully will not hold any of the first four misconceptions.  The problem is it appears that TI’s disclosures are infrequently read.

TI’s apparently precise ranking system does give the impression that Misconception #5 is correct.  It is not.

MISPERCEPTION #1 – Overall Level of Corruption in a Country

Here’s a quote from a TI FAQ that on its rankings:

“Is the country/territory with the lowest score the world's most corrupt nation?  No. The CPI is an indicator of perceptions  public sector corruption, i.e. administrative and political corruption. It is not a verdict on the levels of corruption of entire nations or societies, or of their policies, or the activities of their private sector.”

What does TI rank then?  What is its definition of “corruption”?

Why should we care?

It’s very important to understand TI’s focus if one is to use their rankings intelligently.

If you read the FAQs in the Methodologies material (page 2), you will find a list of what is included and what is not.

Money laundering, IFFs, informal markets, the private sector are NOT included.

Broadly speaking, TI’s CPI focuses on the public sector only.

TI is very clear on this but AA wonders how many users of TI’s CPI understand this.

What this means then is that a private sector member’s actions do not affect the ranking of its respective country.

This is very important because if one is using TI rankings to construct assessments of money laundering and terrorism finance, one might be mis-specifying the risk, if one assumes that TI rankings assess the overall level of corruption in a country.

Why?

Private sector enterprises are probably the major channels through which ML and TF take place in most jurisdictions.

MISPERCEPTION #2 – Rankings as “Facts”

TI’s annual ranking for 2018 is here.

The first thing to note is that this is described as the “Corruption Perceptions Index”.

The key word here is “perceptions”.    “Opinions” not “facts”.

That makes sense.

There are no formal reports filed on bribes paid or bribes accepted.

One has to infer the extent of corruption in a country from very limited hard data – corruption cases that have come to light—and other indirect indicators.

The first takeaway then is that a ranking for a specific country is an estimate. 

Likely a very rough estimate.

Similar to the 2% to 5% of global GDP (usually mis-stated as amounts from USD 800 billion to USD 2 trillion) estimate bandied about as the annual flows of money laundering, corruption rankings are often treated as scientific fact.  They are not and should not be treated as such. 

MISPERCEPTION #3 - The rankings are based on TI’s research.

TI uses the published assessments of 13 sources.

Each of these sources prepares reports for its own or its clients’ use using its own criteria and methodology.

TI does not do the research itself. It does not set the focus, criteria or methodology for these sources’ studies.

Rather TI repurposes the 13 sources’ reports to create the CPI.  In 2015, one source, IHS Global, stopped providing data to TI.  TI now accesses some IHS data via information published by the World Bank.

MISPERCEPTION #4 – Common Standards and Methodologies

Who are the experts? What are their methodologies?

For a detailed answer click on “Methodologies”. Here you will find a discussion about each expert and its methodology.

Click here to see the sources used in ranking a specific country.

The first thing you will notice is that not every source rates every country.

In a situation where some countries are rated by some experts and other countries are rated by other experts should we automatically assume that all the experts use an identical single common standard and methodology?

Clearly we need to look a bit deeper because if the experts don't have a single common standard, then which experts rate a country will impact that country's rating.

AA has read this material and encourages everyone who uses TI’s CPI to read it as well.

Why?

First, this is quite a heterogeneous group.

It includes multi-lateral institutions (2), NGOs/Foundations (5), companies selling country risk or business information services (4), university affiliated entities (2).

Each of these has a specific purpose for its study motivated by its stated “mission” or, in some cases, perhaps by its ideology.

That is not meant as a pejorative remark. But as a practical one.  We need to be sensitive to conscious and unconscious factors that may influence a rating, particularly in the case where “perceptions” play a key role in determining rankings.

AA argued in another post that the collapse of Abraaj seemed to be treated in some circles as evidencing a more serious failure by regulators and markets than scandals in certain OECD countries that had a much greater impact on the world economydid.

Are there other geographical biases? Is corruption in African Country G more heinous than Baltic Country L?

Without taking a stand on the issue, AA would note that there is some controversy about the independence of Freedom House from US foreign policy. The FH study that TI uses rates former Soviet bloc states.

Second, the experts’ focus is also heterogeneous.

Not all of these sources focus on corruption itself: bribes paid, bribes taken.

Rather a number of them focus on legal/institutional capacity.  Whether the country has an adequate framework to prevent/punish corruption, e.g., legislation, staffing and independence of investigative and legal bodies, administrative practices, e.g., professional independent civil service, open bidding, whether information is available to the public, etc. 

These indicators by themselves are not indicators of corruption but rather perhaps indicators of opportunities for corruption.

Very big difference.

Laws and frameworks are fine but as experience shows repeatedly they do not prevent crime from occurring.

That’s not to say that these elements aren’t important.

They are necessary but not sufficient elements.

The question is how much weight they should be given when assigning corruption perceptions to a particular country.

AA would be in the camp where actual corruption rather than opportunities for corruption would be given more weight in “rankings”.

Third, the experts’ methods are not identical.  Some use in-house experts to make assessments.  Others reach out to local contacts, and other outside experts, e.g., academics, lawyers, accountants, etc.  In some cases like EIU they use in-country free-lancers at least in part.

Some of the experts appear to ask a single or a couple of questions as part of a larger study on more than just corruption.

Others have a more robust set of questions on corruption.  Or survey a wider set of contacts.

For example, in 2018 The World Economic Forum Executive Opinion Survey (WEF-EOS)--one of TI’s sources—received 12,274 responses from executives in 140 countries in 2018 about corruption.

Fourth, some of the experts—primarily the 3 firms that sell political risk and country assessments to businesses -- assess all levels of corruption from the petty to “grand” corruption.  Varieties of Democracy, another of TI's expert sources does as well. 

As a practical matter, their 3 firms' clients (businesses) are likely to be most interested in the need to pay ongoing bribes to ensure their daily operations run unhindered if they invest in Country X.

So smaller recurring cash payments to facilitate clearance through customs of imports and exports, to secure connection to and maintenance of utilities, to deal with tax authorities, to obtain licenses, etc. are of prime concern.

Finding out about them is fairly easy.  One can ask businesses in the country. They will be more likely to report such occurrences because they are imposed on them as opposed to grand corruption where they may be a willing participant.

Because it’s harder to find out the true level of grand corruption, there is a risk that corruption ratings based on petty or moderate corruption may skew the rating for a country.

Fifth, unlike the countries in the CPI, the 13 experts are unranked.  Their perceptions are accorded equal weighting.  Each expert’s score is added and a simple arithmetic mean is calculated.

They are all presumed to be all equally smart and informed and use equally valid methods to evaluate corruption.  It doesn’t matter whether an expert asked a single question or sent a questionnaire and got 12,274 responses.

It doesn’t matter if the expert is expert in a limited geographical area or covers the world.  The Economist Intelligence Unit who use in-country free lancers in part to do their assessments and rated 131 countries in 2018 are presumed to know as much about each of those countries as the African Development Bank which uses in-house economists knows about the 54 African countries it rated. Or PERC which contacts a wide range of potential respondents to ask a single question and rated 15 Asian countries.

As you might expect, not every country is rated by all 13 experts.  Some of this is because of geographical specialty. The experts from the African Development Bank don’t rate Switzerland, the USA, or France. PERC’s focus is a slice of Asia.

It’s not unreasonable to say then that the rating standards across all countries are not uniform given the diversity of focus, methodology, level of detail, etc. of the 13 experts and the fact that the same 13 experts do not rate each country.

The full data set shows the score, the standard error (think standard deviation but for a sample), the Upper CI and Lower CI.

There is a wealth of information here.  If you use the TI CPI, then you should be familiar with this information so you can use it intelligently.

For example, should we treat a rating with only 3 experts (the minimum required for a rating) as being as valid as one with 10?

If the standard error is large, should we assess that the rating is less accurate than one which has a smaller standard error?  For example, the SE for Switzerland is 1.57, Bahrain and the Philippines are at 1.81, Saudi is at 6.34, Qatar at 8.08, and Oman at 9.46.

MISPERCEPTION #5 - Ratings are Precise Measures

TI ranks some 180 countries.  100 is the theoretical “best” score.  0 the worst.

Denmark in the first rank with 88.

New Zealand is at 87.

Then four countries follow at 85.

All the way down to Syria (13) and Somalia (10).

This is some very precise parsing of differences in corruption.

Let’s stop and reflect for a moment.

We started with “perceptions” but we seem to have wound up with “precision”.  AA would argue “false” precision.

On a hundred point scale, NZ would appear to be 1% more corrupt than Denmark.

Can we really parse gradations this fine?

More importantly is there really a practical difference in corruption between Denmark (ranking #1 with 88) and Germany (ranking #11 with a score of 80)?

The answer to both questions is no. 

TI agrees with this at least in part.

In their FAQs, they answer a hypothetical question from a reader about changes of 1 or 2 points in a specific country’s rating year-on-year with:

“It is unlikely that a one or two point CPI score change would be statistically significant.”

AA would argue that even larger differences among countries are not significant either.

Let’s look at an endeavor that has more data and more rigorous mathematical analysis of the data, though one which is not devoid of opinion:  credit ratings. 

S&P, Moody’s, and Fitch rank issuers.

But they don’t assign them individual ranked ratings.  Rather they group them into categories of similar risk.

Those issuers least likely to default are rated (placed in category) AAA.  If distinctions are made, a “+” or “–“sign is used.

AA doesn’t think it’s a sensible proposition that corruption analysis is more scientific than credit analysis and hopes you do too.

AA suggests that TI adopt a similar approach in an effort to prevent misunderstanding and misuse of its rankings.  That is, divide countries into broad categories of risk of corruption like credit ratings or S&P's BICRA.

This will have the immediate effect of preventing users from plugging the current “precise” ratings into their models and coming up with equally imprecise results in theirs.

Some even more impressive with results to two digits to the right of the decimal point, though admittedly not on a 100 point scale.

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”.

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.