Does the Volcker Rule fix finance?

One of the main governmental responses to the 2008 crisis was the Dodd-Frank Wall Street Reform and Consumer protection act. This act includes changes such say “say on pay” which requires shareholder approval on executive compensation and empowering the SEC by allowing it to exercise judgment on proxy voting and protecting the customer by capping interchange fees in banks with over 10 billion in assets. However the most important part of this reform is likely to be the Volcker Rule.

Named after Paul Volcker, the Volcker Rule is essentially a reincarnation of the Glass Steagall act. This rule prohibits deposit taking institutions from directly engaging in proprietary trading or speculation, this is also done by the prohibition of over 3% ownership in any given hedge fund or private equity fund. One point of view is that this reduces the market risk of retail institutions and shifts it to the investment banks, the theoretical subsequent result being that there is less market-making and a less efficient system that disrupts allocation of capital throughout the economy. Before the rule, banks held securitized assets on their balance sheets for weeks which would put them at risk of swings in the products value. In practice however investment banks will now take a much more cautious approach as they would now be using borrowed money from the retail banks.

An example of what we should expect to see if more risk reduction is taking place is a more cautionary approach by investment banks. At the next booming period we will be able to juxtapose investment banks before and after the Volcker Rule. The norm so far has been to create securitized mortgages and hold in the bank’s balance sheets typically for a couple of weeks until they could be sold, this was the “warehouse” function of banks in the securitization process. If a more cautionary approach is taken, a reduction in the “warehouse” aspect of securitization will take place and there will be a greater incentive to directly link buyers and sellers beforehand. This could be an indication that the previous system was run on moral hazard of investment banks not bearing their own risks.

Although there are claimants that this rule will greatly cripple markets, by crippling access to liquid, if the investment opportunities are truly there, then other industries will be able to pick up the slack without putting as much risk on the consumer(lets not forget how much money companies are sitting on today). Without citizens bearing the risk it’s probable that risk-taking will be reduced and the dropping of standards would not occur without sufficient reason. This bearing of risk will likely result in a demand for greater transparency, the benefit will be a decline in cloudy activities, such as shadow banking.


Do hospitals make you sicker?

Selection bias is often a very destructive force when trying to determine what works and what doesn’t, it’s often impossible to evaluate if programmes are a success or not unless you’re under something extreme like a totalitarian regime.

Let’s take hospitalization for instance; if you were to ask people coming out of the hospital how their health status was, it would probably be worse for people coming out of the hospital rather than randomly selected people from the population.

So let’s assign some simple terms here then.  First let’s have the treatment dummy:

So its 0 if they don’t get treated and 1 if they do get treated. Then we observe the actual outcome (health status) for each individual with Y.

So to actually measure how much of an effect treatment has we need to compare people receiving treatment to those not receiving it.

Yet the world presents problems since what we actually observe in the real world is:

So there is no one number that represents what actually happens. We can measure the, the average effect of treatment on the treated (ATT), average effect of treatment on the untreated (ATU) or the average treatment effect (ATE).

In public institutions there is this top down selection problem and the more private an institution is the more bias comes from self-selection.

The ATT can tell us the effect of going to the hospital on the people who went to the hospital. The first part of the equation is the observed part, where we measure the health status of individuals who went to the hospital had they went to the hospital. The second part is unobserved because it measures the potential health status of patients who went to the hospital had they not gone to the hospital.

ATT can show us how much people gain from going to the hospital. In the real world private enterprises are much more likely to survive if people notice that there is a positive effect and so the market eliminates a low output hospital. A government hospital on the other hand might have trouble eliminating waste because it will receive customers who might not necessarily think the hospital is any good but will still go just because it is free.

The ATU tells us the expected effect of going to the hospital on someone who did not go to the hospital. The unobserved portion is the effect of the hospital on those who did not go. Second part which we observe is the effect of the hospital on those who went to the hospital.

So it seems pretty obvious that in the real world ATU and ATT are very close to impossible to measure accurately. However what we can measure accurately is the average treatment effect. This is represented by:

To accurately measure this we must make sure to apply a RCT (randomized control trial), in other words randomly allocate if people will go to the hospital or not, and here a paradox arises. We can understand if something works if we randomly allocate it, but if we randomly allocate it we are not maximizing the use of the hospital since we are sending in healthy people. Yet if we don’t randomly allocate it we cannot observe if the hospital is working.

This also is the case with control areas if we decide to do something differently in one country/state/city and leave the rest untouched we can then compare them to see if the area where we applied the new method is better off. So what’s the next step? If they are better off then we mass produce this method to the control areas and we can no longer see if it’s working(think long term effects), and if we don’t mass produce it then those control areas are not benefiting from this new method that could be improving their standard of living.

So the paradox here is between knowing something works versus making it work for everyone. Whether we apply a randomized control trial or use control groups, chances are we are not helping out the best we can, and if we do not apply these methods then we are ignorant as to whether the program is helping people at that specific period in time. To properly understand treatment effects we need a sacrificial lamb to exist. Though what’s generally done is to assume that if something worked in the past, it will keep doing so.