Ally Bank Discrimination Case (Sources: Wall Street Journal article posted, Consumer Financial Protection Bureau article posted, ally.com) In Chapter 1, we discussed federalism. Federalism has one particular advantage: That the federal government may be able to guarantee rights are broadly distributed, even though some states may not want to give those rights. This is a very important job of the federal government. Federal regulators may have unique challenges, however, in administering this regulation. This case study will often highlight how government may work imperfectly. Todays example is one that brings up many of these issues. Ally Bank is an extremely large bank (Exceeding $54 billion in customer deposits) that has an online banking presence and is involved in some larger multi-million dollar middle-market corporate transactions. Ally Bank used to be GMAC the GM here is General Motors and is involved in auto financing. The discrimination case is related to a practice of Ally Bank that allowed auto dealers to mark up the interest rate of an auto loan to consumers. Ally had no idea the race of the borrower, but the CPFB claimed/showed that there was discrimination through the dealerships obviously, the dealership has a perception of the race of the borrower since the customer is right in front of them. The discrimination obviously would have been on a wide scale very large auto market mixed with the very large problem of racism in the United States. CPFB claims that over 235,000 minority borrowers would have been harmed by this practice, and the relevant dates of the discrimination case were April 2011-December 2013. CPFB estimated that African Americans would pay on average $300 more for the price of a car over the life of the loan. Since Ally did not collect the race of the applicant, it does not know the individuals who were discriminated against. Federal law prohibits Ally from collecting the race of the applicant you would not want racial information to be used against you in a loan decision, after all. In order to distribute the money, CPFB and DOJ used Bayesian Improved Surname Geocoding (BISG) to determine the likely race of the borrower. This has a particularly interesting problem the method obviously does not determine the exact race of the borrower. Only a probability. For instance, since my last name is Steiner and I grew up in Highland, IL, it is extremely likely I am white (I will show this in class). But you can imagine that other characteristics might give uncertain answers.

BISG was initially developed for health-care research and has been used by insurance companies to measure racial disparities in health-care outcomes. A key developer of the technique, Rand Corp. statistician Marc Elliott, has deployed the method in recent years on a wide range of research in which individuals last names and locations are

known but their actual race or ethnicity isnt. One project included trying to determine which Medicare beneficiaries should receive survey materials in Spanish

and English.

But Mr. Elliott warns that some caution is warranted when using the method, originally developed for drawing conclusions about groups, to identify and send

checks to individuals. This is not the primary intended use, Mr. Elliott said. That said, depending on the situation, it's possible that it will be fairly accurate. (WSJ)

The Wall Street Journal outlines who gets paid if it is 95% likely that you were impacted, you will receive a check, and if it is 50-95% likely that you were impacted, you will receive a check if you confirm that you were part of the impacted group. The Wall Street Journal profiled an anecdote of a person receiving a check who was white and someone who had to apply for the check who was black. These statistical tools have a particular utility in research: If we want to control for race in a linear regression estimation, for instance, but dont have the correct tool, we might want to use an approximate tool. The BISG quote above demonstrates this utility. However, obviously, this methodology makes a lot of controversial assumptions and implements them on a legal basis. Is it OK to administer checks to people even if they were not harmed by discriminatory practices when the larger goal is at stake while some of those who were harmed fall through the cracks? Is it better than nothing (noting that the cost of actually surveying people would be extremely high relative to the reward)? There are also many assumptions about race implicit in the measurements, which are administered by the federal government. I personally cannot answer for you whether many of these assumptions are right or wrong. However, it is clear that it is very difficult to implement federal policy with perfection and that the goals of one regulation may impact the goals of another. We will see this throughout the course.