Shadow Banking in Spain

The Wall Street Journal had a really interesting piece yesterday about the rise of "time banks" in Spain.  The idea being that, in an economy as thoroughly broken as the Spanish economy, many unemployed attempt to use these intermediaries to facilitate a barter economy.

So, a bunch of people join together and offer to work on various odd jobs for each other, banking the time and using those collected hours to request services from others.  Thus, Silvia Martin

...has relied on other time-bank members to give her lifts around town for her odd jobs and errands, as well as to help with house repairs.  In return, she has cared for members' elderly relatives, organized children's parties and even hauled boxes for a member moving to a new house

Of course, the unemployment rate in Spain for 25-34 year-olds is an absurdly high 27%, while the rate for 16-24 year-olds is an even more absurdly, dangerously, high 53%.  At least they aren't in a recession!

But it is interesting to observe that people are willing to enter these very small economies rather than keep attempting to make the regular economy work.  At some level, they must be making the decision that trading without specialization (all hours are valued equally) and among a very small geographical group are costs worth paying in order to begin making some trades for labor.  Amazing, really.

But, as you might expect, the idea of a time bank, where the unit of exchange is measured in hours, is just a very small step away from turning it into a real bank which uses its own currency.

Which is exactly what has happened in some cases. 

One bank launched something called an "eco," a currency they just made up.  Turns out, not only have dozens of local businesses decided to accept it as a basis for trade, so have two town governments!  

There are some ways in which this is a very positive story: people working to solve problems the government is unwilling to solve.  On the other hand, this represents groups of people choosing to work in a parallel economy with a non-convertible currency (and non-convertible even in, like, the next town over).  That just gives you an idea of how desperate things are in Spain. 

Hard information and cheap talk

It is pretty typical for economists to view information as either “hard” or else “soft.” The difference being the ability of the person receiving the information to verify the claim made. Soft information is unverifiable and is believed only when the person receiving it figures his interests and the interests of the person sending it are aligned.

So, when my wife suggests we meet for lunch at our favorite place in Carytown, I just go ahead and believe her. Probably, she really does want to meet for lunch instead of wanting to design an elaborate ruse to waste time. Soft information can be useful in cases like this.  This is cheap talk -- unverifiable and untrustworthy information, unless the interests of all the parties are sufficiently aligned.


Hard information is verifiable. Receivers of hard information are more likely to accept it as true even when the sender’s interests are not perfectly aligned with the receiver’s interests because false statements could be exposed (though, of course, the receiver might still verify statements whenever interests are very poorly aligned). UPS used to tell a story about a good employee who does not get a promotion and a great employee who does. Turns out, the difference was that when the good employee was asked a question, he returned with a verifiable answer to it; when the great employee was asked the same question, he returned with a boatload of verifiable information about it: shipment s left at time t, weighing w pounds and driven by driver d, etc., etc.

Of course, information is often neither hard nor soft, but somewhere in-between. Lots of information requires an effort by the person receiving it to turn it from soft information into hard information. A great example is a mathematical proof you might come across in a paper. Most of us, even if we are good at math, would have to make an effort to confirm that, in fact, for every epsilon there really is a delta such that…

Otherwise, and the way I usually do it, I just go ahead and believe the guy writing the proof. Here, information that could be verified is simply left as soft information and accepted based on the idea that the writer is probably not trying to get me to believe something about Proposition 26 that isn’t true.

But it is also generally true that soft information can be turned into verifiable statements when both the sender and the receiver make investments (time, effort, education, whatever). So, the “hardness” of information is variable and, more than that, a variable the listener can control. And that makes things more complicated.

Communicating Sophisticated Analytics

I've got a problem with our client group...


Marketing analytics must include, finally, communication with other parts of the firm. As anybody who performs sophisticated analytics can attest, communication is not necessarily the easiest part of the process, not least because it involves a strategic interaction between the analysts (Senders) and the client groups (Receivers). This strategic interaction is filled with places where even the best analytics can wind up being discarded. So, we want to avoid that. How?

The communication effort should be built to manage the incentive alignment concerns of the recipients of the analysts and also take into account the fact that there is a moral hazard in this sort of communication.

One intuitive example of incentive alignment is when new analysts show up and begin looking at a process that has been active for a while. Newbies tend to find all sorts of places where the process seems sub-optimal to them and make recommendations on how to improve the process. It is very difficult for others to evaluate these recommendations because the incentive for new employees is to over-state the degree to which old processes need to be fixed. Finding problems puts them on the map, so they tend to find more than they should.


Well, we aren't newbies.  So what's the real problem?

But the moral hazard is the real problem. 


Side explanation: moral hazard

Moral hazard is the name economists use to talk about the problem that creeps up whenever costs are borne privately but benefits are shared: people tend to not want to invest in those costs.  So, to use a typical example, car insurance is subject to moral hazard: we'd all benefit if I drove carefully, but since my personal cost of an accident is low, I might drive faster than I ought.  Similar thing with communication: we both benefit if a good project gets implemented, but I might want to skimp on the cost of understanding the analysis that lets us understand the project.

It’s like this: getting knowledge from the head of the researcher into the head of the group that needs it requires investments from both sides. The analyst needs to make a good and effective presentation (which might very well involve multiple presentations, background conversations, multiple presentations given to multiple groups, etc.), but the group receiving the analysis needs to make investments, too. Understanding sophisticated analysis isn’t easy, especially if it uses unfamiliar techniques.

So, the payoff to the communication depends on the effort the other team puts in to understanding the analysis. And that creates room for moral hazard.

So, what do you do? Dewatripont and Tirole (2005) have a very interesting paper on this problem, which develops a model for this sort of communication. Like a lot of papers which deal with cooperative solutions, they wind up with a fairly large set of sub-cases, depending on the receiver’s assumptions about the degree to which his interests are aligned with the sender’s, the type of oversight (supervisory or executive) the receiver will ultimately have to exercise over the recommendation, the level of certainty the receiver has about the sender’s payoff associated with the project, etc.


Sounds complex

It gets dicey, for sure. The key insights are that a decrease in either party’s stake in the project will lessen the total communication effort. If there are communication “set-up costs” (new analytical techniques being introduced to the discussion, for example, that not everyone is familiar with), then we can see sudden and discontinuous breakdowns in communication. Senders of information should invest in positive cues about their credibility; that way, Receivers will be more likely to invest in the joint communication.

These cues come from sources you might expect: people who know the history of the Sender, people who have done some investigation of the analytical claims, etc. The goal of obtaining these cues is to convince the client to engage and evaluate the analysis. Once that happens, the analyst’s job is made much easier.



So,  need other people to endorse the analysis?  Where have I heard that before?

OK. Sounds obvious, I know. But it is a mistake I’ve made in the past and I’d bet it’s one others have made as well. Client can’t / won’t engage the analysis because he doesn’t have the skills to do so and doesn’t want to just rubber-stamp my project. So what do you do? If you are naïve, you ask him again, only using more flattery this time. Won’t work. What you need is some borrowed credibility – that’s the key.  Without it, you simply can't expect to win -- communication is broken and can't be fixed.

Borrowed Money

Some interesting time series charts from the Federal Reserve in St. Louis I've been looking at recently.  They involve consumer revolving credit.  In the aftermath of the Great Recession, Americans are re-balancing their accounts and, for the first time in forever, reducing their revolving debt levels.  Check this:

-- Per-capita revolving debt in 2005 constant dollars (shaded regions represent recessions)

We can see the big increase beginning in the 1990s, with the development of considerably more sophisticated credit models.  These models allowed credit card companies to separate the unsecured credit market into correct risk categories.  Because of this, we should probably interpret this big increase as both an increase in credit demand as well as in increase in credit supply: the equilibrium where we are able to separate credit riskiness probably has lots more loans being offered than the equilibrium where we have to pool the risky households with the safe ones.

But since the recession, we see decreases in these borrowing amounts.  It is important to note that these are separate from the loans that have been written off or otherwise defaulted; similarly significant changes also live in time series that look at cohorts of stable accounts that have not defaulted.

There are several interesting possible explanations for this shift in consumer behavior.  The two that I find most interesting are the changes in government spending and the lack of change in personal disposable income.  A couple more charts should make the point.


Here is the Federal spending as a percent of GDP.  Note that, during the Clinton years, this percent was decreasing ("The era of big government is over.") and consumer borrowing was increasing rapidly.  Meanwhile, as government spending has increased significantly, consumers perhaps grasp that all that spending has to be repaid by somebody and so you'd better start saving for the time when taxes increase to pay that debt off.

So, this has some plausible affect on the level of consumer borrowing.  The other -- and to my mind more interesting -- time series is the constant dollar household disposable income, per capita.  Check this nightmare out:

Here is the per-capita disposable (that is, after tax) income series since 1991.  This measure has stalled entirely since 2006.

This gives some support to the feeling that consumers perceive that their lifetime income profile might not be as good as they used to think.  We can match this picture up with all sorts of poll data that suggests people think the next generation won't be as well off, or that the country is on the wrong track, or that consumers are gravely concerned about the economy.  This chart provides a pretty good explanation.

In an environment where real disposable incomes are increasing, real wealth is increasing rapidly (we don't need a picture of housing prices!) and credit supply is expanding (because the companies can tell good risks from bad ones), it shouldn't surprise us that consumers want to pull lots of future consumption into right now.  Our borrowing should have increased in that situation (maybe not as much as it actually did, but still..).

I suspect that people interpret the decrease in wealth associated with the housing bust, the significant increase in government debt, and the six years of no income increases as strong signals that the future income will be small enough that they don't want to pull as much of it into today.  

This is a praticular risk for firms and individuals who buy consumer debt.  And there are forward-looking indicators that should be of keen interest to them.

Mortgage Defaults

Came across an interesting paper on mortgate defaults the other day while I was, actually, searching for some models of credit card default.

The authors, Campbell and Cocco, develop a microeconomic model of this important behavior and come up with some interesting results.

In particular, they make a clean connection between negative equity and borrowing constraints when it comes to default. Negative equity increases the default risk, of course, but the degree of negative equity that triggers a default depends on the degree to which the household is constrained in the credit markets. pofoundly constrained households can default at low levels of negative equity.

The other interesting result is the breakdown of the particular risks of the various types of home financing. For example, interest-only mortgages have the highest risk of default waves. Even so, there are ranges of risk events for which they have some clear advantages over other forms of financing. For one, these borrowers are less likely to face borrowing constraints than other types of borrowers. This benefit is generally overwhelmed by the equity risk they face -- interest-only homes are the most exposed to decreases in home value.

The paper is pretty deep and will take several readings to get a handle on the arguments. But the idea of approaching the question with a dynamic micro-model is significant.

New Product Launches -- Tobacco

In the last year or so, I’ve had the chance to monitor several product extension launches in the cigarette industry; all the big players have made some moves. For example, Marlboro has sequentially launched several Special Blend sub-brands, while Newport has successfully launched a non-menthol brand. Despite the different success levels we observe in terms of market share for these launches, all of them share a remarkable similarity in terms of their share diffusion in the market. I want (briefly) to look at some of the implications of this fact as it relates to the cigarette industry.

The basic Bass model suggests that the volume (share) of a new product is determined by the interaction of two effects. The first is the innovation/advertising effect, which is taken to drive people to experiment with the product quickly. The second effect is the imitation/word-of-mouth effect, which is assumed to spread the desire to try the product over time.

The sales as a function of time can be expressed:

Nt = Nt-1 + p(m-Nt-1) + q(Nt/m)(m- Nt-1)

Where N is the number of units sold (or, scaled appropriately, market share), m is the total market size for the product, p is the innovative/advertising effect, and q is the imitation/word-of-mouth effect. Typical values for p are said to be around 0.03 and for q around 0.4.

You might be able to see that the last term on the right hand side is the difference term that we see in simple models of population growth with carrying capacity, used to model the size of a colony of bacteria in a Petri dish, for example. So, the “q” term is the logistic or organic growth and the “p” term is the exponential or advertising-related growth. And overall volume is determined by the balance between these two effects.

So far, so good. But here is where it gets interesting. Below are a pair of simulated new product launches, the first from a product with typical coefficients and the second with coefficients that have been calibrated to yield weekly changes in volume that mimic those of the new products in the cigarette industry. Check out the graphs at the top of the page to see what I mean.

The overriding fact of all the new product introductions was a peak in the share change that happens in week #2, and the share changes generally fell to near zero by week #4 (though I haven’t modeled the drift that some products display subsequently). But the coefficients as calibrated are very far outside what is considered typical: the “p” value is 0.3 (up from 0.03) and the “q” value is 0.8 (as compared to a typical value of 0.4).

Now, these numbers are merely the result of a quick and dirty calibration, but they suggest the possibility that cigarettes are an unusual industry with respect to new product introduction.

New Product Inflection Points

New product diffusion is often modeled as an S-curve. The idea being that there is some period where the dare of the product is getting traction very slowly and that, when it hits, the share increases rapidly to some saturation point. The Bass-style diffusion models generally allow for this sort of process.

I'm thinking this is not a particularly good model for sophisticated CPG companies, especially if they are using some brand extension. This is so for two main reasons.

First, companies that rely one an extant sales organization are much better able to establish a wide number of outlets, reducing the search- and word-of-mouth effects.

Second, and more interesting (to me, anyhow), established organizations seem to benefit from a ready understanding by the market of the option value of new product trial.

The intuition is as follows. The market understands that a new product has both risks (overall product liking as well as switching costs, if any exist) as well as benefits (it could be much better). Brand extension serves both to reduce the variance on the prior belief about the liking as well as increase the prior estimate of the expected liking value. Higher expected value plus lower variance equals better deal.

In this case, we would expect the potential for a gain in utility to outweigh the risk for almost everyone who would consider trial of the new product.

It seems to me there are two implications from this thinking. First, such effects must be two-edged: reducing risk for consumers already purchasing something from the line but necessarily increasing risk for outsiders. If so, then trial should be weighted toward cannibalization at a greater than fair share proportion. That's interesting, I think.

Second, this might drastically shorten the length of time that new product promotions should be run: you wouldn't need long promotions if everybody already gets the idea that they should try the product sooner rather than later. In that case, what would matter would be things like watching for inflection points to know when most of the market has already seen the "option" purchasers act.

It would be interesting to look at these sorts of market penetration for line extensions to see when these points happen and how predictive they are of eventual share.