Statistical Modeling, Causal Inference, and Social Science: Not enough discrimination?

Not enough discrimination?

Aleks pointed me to this article by Stan Liebowitz on the recent financial crisis:

Daniel Lakeland

Moral Hazard has everything to do with this... The way I see it is:

1) Lax rules and enforcement in the equities market leads to a stock bubble in which employees are receiving huge quantities of options, IPOs are set at low prices and zoom high in the first few days of trading, and the main industry making money is the financial services industry.

2) People holding these stocks eventually figure out that they're going out of business, and dump them.

3) The resulting liquidity crunch leads the Fed to bail out financial services by pumping money into the economy.

4) The resulting "free money" and the fact that financial services industries have made out like bandits leads them to write as many home loans as possible.

5) HGTV becomes hugely popular.

6) Interest rates reset and government does what the financial services companies expected... bails them out again.

If there is ANY industry that is actively seeking out an edge and will produce quantitative estimates of the correct moral hazard level they should take on, it is the financial services industry. And, btw, that moral hazard level is very HIGH based on historical government actions.

Kevin

"these policies will have done a disservice to their putative beneficiaries if . . . they are dispossessed from their homes"

There can also be a disservice for some of those who hold on to their bad investment and continue to attempt to service their loans. There is also a disservice to everyone who wisely decided not to become a speculator, take on an ARM, or state their income was more than it actually was. Those who stayed on the sidelines will be paying for those who gambled.

http://www.voiceofsandiego.org/articles/2008/04/23/opinion/01toscano042308.txt

B

You and the author of the article are talking about two different approaches to race, and neither does much to explain the subprime mess.

There is a race-blind approach that simply insists that the race variable be censored, and there is an affirmative-action type approach that insists that race be counted and acted upon.

Your censored math scores are the race-blind approach, but I just can't see how the race-blind approach could cause a credit crisis. If a bank behaves as normal on credit scores and financial history and forces itself to omit the race variable from consideration, subprime loans still won't happen.

No, for an antidiscrimination law to cause a mortgage crisis, we can't just have censoring of one variable, but the forced lending to people with bad credit because they are of a certain race. The article you link to claims that this is what the 1977 Community Reinvestment Act did, and that caused the subprime mess. It's a common theme; I've heard it myself a dozen times. But there's no evidence: the letter of the law is race-blind, not affirmative-action, and what evidence there is of the implementation doesn't find much link between the CRA and lending to bad credit. Here's a further discussion of the data on and polemic uses of the Community Reinvestment Act.

So your argument that the race variable was censored doesn't address why banks developed lax lending standards; the article's affirmative-action argument just doesn't correlate with reality. Race is just not enough to matter.

Banks, mortgage brokers, S&Ls, &c. started lending to people with bad credit and thus induced this mess because they found ways to minimize, profit from, shift, or otherwise deal with the risk. There's just no need to resort to stories of government coercion to explain all this. I mean, how many mortgage brokers did you hear five years ago kvetching about how the government forces them to take on risky loans with high commissions?

C. Zorn

At least constitutionally speaking, the rationale for not allowing some of those additional predictors (such as race) to be used is that they *ought* not be predictive of anything once the other (non-prohibited) predictors have been factored in. One could argue that prohibiting their use will/should, in the long run, reduce their predictive power relative to what it would have been had they been allowed to continue to be used (think about the social/economic/whatever status of African Americans in the South before and after Jim Crow). If we think in these terms, then enshrining a protected class X in the constitution amounts to a societal commitment to drive \hat{\beta}_X toward zero over time.

WInston

A solution to the problem of legal prohibition of such "phobic" variables as race and sex would be to use surrogates for them (or build models to predict them and use the predictions).

For example, in a predicting recidivism of parolees, sex is a strong predictor but can not be used by the authorities because of legal restrictions. Since most prisons house only s single sex, one could get around this by adding prison name as a nominal predictor. Of course one would have to give up several more degrees of freedom, but, hey, there was plenty of data.

I would certainly hope this is what the medical insurance companies Prof. Gelman mentions are doing, otherwise I may be paying too little/much for my insurance.

bccheah

A very entertaining recounting of these events is available in the NYT article by Peter Passell May 10, 1996 called Race, Mortgages and Statistics: The Unending Debate Over a Study of Lending Bias. I don't know how well this link works:
http://query.nytimes.com/gst/fullpage.html?res=9E01E5D91539F933A25756C0A960958260&sec=&spon=&pagewanted=all

Matt

Bond ratings offer about 16 levels, and about 3 major ratings company.

Even then, one can simply look at their performance by looking to see if bond ratings are smoothly distributed. They are not.

Bad estimation is all over the place. But I would start with the ratings companies, and the foolish banks who weren't watching the ratings spread.