Book Commentary
The Why Axis reminded me why I love to learn cool stuff. The book “switched on” my curiosity juices. For those that enjoy Behavioral Economics or Behavioral Psychology, this book should be on your reading list. Even if just curious about these topics, the book is written in a very accessible way. Each chapter takes a different behavioral economics-related journey, with easy-to-understand narratives and testing descriptions.
As motivated by the book, next are examples of my takeaways and curiosity explorations. This is based on the chapters shining a bright light on discrimination.
The Why Axis - a discrimination example
First, I was reminded that “discrimination” is a word carrying different meanings for different people. For example, discrimination may relate to:
A person that uses data analysis to understand the difference between groups.
A person that has a highly tuned taste. (like a sommelier)
A lender that uses a predictive score (e.g., FICO) to provide the correct loan product. [i]
A person that is good at sales and sizing up the buyer.
A person harboring animus toward others, triggered by a labeling characteristic. (like skin color, gender, or sexual orientation)
These are discrimination examples. You may have noticed, my 5 examples are in a discrimination “badness” order. With the 5th example being considered truly “bad” discrimination.
The authors take us through the subtle differences, using well narrated randomized control trial (RCT) results. They focus on economic discrimination, as related to examples 3 and 4. They show how economic discrimination may be bad but is not always bad. My takeaway is the degree of perceived economic discrimination “badness” depends on whether the subject of economic discrimination had some control over that being discriminated against.
For example, compare how you feel about these 2 acts of discrimination:
a person getting a higher loan rate because they didn’t make past payments
a person getting a higher car repair price because they are disabled.
If like most people, you probably thought "1 is more ok than 2." To this end, the authors conclude
“…individuals hold more prejudice toward those when they feel they have a choice in conditions...”
In summary:
Understanding and influencing the motivation of those with an opportunity to discriminate is key to anticipating a) the likelihood to discriminate and b) the nature of the discrimination.
The authors show that animus-based discrimination, like example 5, is on the decline. They also show that economic discrimination, like examples 3 and 4, is on the rise. Policymakers will better serve society by focusing on economic discrimination.
Discrimination and the impact of Fair Lending
This book reminded me that the U.S.’s Fair Lending mortgage and consumer lending-based regulatory regime (broadly defined) is a very useful and exemplar anti-discrimination model. Admittedly, it is far from perfect. But its efforts to create a fulsome framework are admirable. The framework includes:
Fair Lending law defines applicable protected classes as protected from discrimination,
It defines what discrimination is via disparate treatment and disparate impact,
It has a testing regime to identify mortgage lending discrimination via HMDA testing, and
It has a discrimination enforcement mechanism via the CFPB and the U.S. Treasury / Office of Civil Rights and Diversity.
Fair Lending opportunities
Throughout the book, the authors used several car sales cases describing discrimination. The authors explain why car buying is such a good case study for discrimination. In a companion article, Cutting through complexity: An auto buying approach, we provide an approach to reduce or eliminate car buying economic discrimination.
The book also reminded me of Fair Lending’s opportunities. Not everyone will follow a car buying discipline reducing the potential for discrimination. As such, having a legal framework is helpful to reduce the potential disparate impact on all car buyers. For example, portions of auto lending through “indirect auto channels” remain outside Fair Lending’s legal scope. (The indirect auto channel is where 1) a bank provides auto financing and 2) the auto dealer participates as a loan origination agent to the indirect transaction.) The key is, the auto dealer has different loan options from multiple banks. These loan options provide differing financial incentives to both the borrower and the auto dealer. The auto dealer is aware of the value of the incentives to both itself and the borrower. HOWEVER, the auto dealer is not obligated to disclose the auto dealer’s incentives and there is no obligation for the dealer to share all loan options with the borrower.
This is a tremendous information asymmetry that greatly benefits the auto dealer during sales negotiations.
It is possible a higher rate, higher auto dealer incentive loan would be presented to a car buyer and a lower rate, lower auto dealer incentive loan would not.
In the eyes of Fair Lending regulation, because indirect auto loan originations are out of scope, an auto dealer may legally discriminate and steer an unwitting car buyer toward higher-priced auto loans. The “out of scope” definition is technical, relating to the fact that an auto dealer is not itself a bank. To help influence lawmakers, it doesn’t hurt that the auto industry also has a strong lobbying arm looking after its interests. The practice of loan product steering has proven to have a disparate impact on minority communities. [ii] The majority of all auto loans are originated via the indirect auto channel.
In 2013, the CFPB issued a rule to extend fair lending to auto dealers. This was rationalized since auto dealers are often loan originating agents. In 2018, that rule was invalidated by congress. Since then, some states, like New York, have stepped in to implement indirect auto-based discrimination rules.
It is a long game. The good news is, our laws and social attentions are moving in the right direction. It would not surprise if congress' 2018 invalidation action was merely a speedbump on the longer-term road to reduce discrimination. Finally, the tools of social justice are improving. This is occurring via:
The availability of data and testing,
The application of behavioral economics, and
Research like that provided in The Why Axis.
Notes
[i] Hulett, Resolving Lending Bias - a proposal to improve credit decisions with more accurate credit data, The Curiosity Vine, 2021
[ii] Butler, Mayer, Weston Racial Discrimination in the Auto Loan Market, Consumer Financial Protection Bureau, 2021
The authors’ analyzed data from before and after the implementation and repeal of the 2013 CFPB rule extending the reach of Fair Lending to auto dealers. The rule was invalidated in 2018. They find a significant reduction in race-based discrimination when Fair Lending is extended to auto dealers.
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