On November 2, 1983, President Ronald Reagan signed a law establishing a federal holiday for the birthday of Martin Luther King Jr., to be celebrated each year on the third Monday in January. As the legislation that passed Congress said: “such holiday should serve as a time for Americans to reflect on the principles of racial equality and nonviolent social change espoused by Martin Luther King, Jr..” Of course, the case for racial equality stands fundamentally upon principles of justice, with economics playing only a supporting role. But here are a few economics-related thoughts for the day clipped from posts in the previous year at this blog, with more detail and commentary at the links.

1) “Some Economics of Black America” (June 18, 2021)

The McKinsey Global Institute has published “The economic state of Black America: What is and what could be” (June 2021). Much of the focus of the report is on pointing out gaps in various economic statistics. In terms of income. While such comparisons are not new, they do not lose their power to shock. For example:

Today the median annual wage for Black workers is approximately 30 percent, or $10,000, lower than that of white workers … We estimate a $220 billion annual disparity between Black wages today and what they would be in a scenario of full parity, with Black representation matching the Black share of the population across occupations and the elimination of racial pay gaps within occupational categories. Achieving this scenario would boost total Black wages by 30 percent … The racial wage disparity is the product of both representational imbalances and pay gaps within occupational categories—and it is a surprisingly concentrated phenomenon.

2) “The Broken Promises of the Freedman’s Savings Bank: 1865-1874” (January 18, 2021)

The Freedman’s Savings Bank lasted from 1865 to 1874. It was founded by the US government to provide financial services to former slaves: in particular, there was concern that if black veterans of the Union army did not have bank accounts, they would not be able to receive their pay. In terms of setting up branches and receiving deposits, the bank was a considerable success. However, the management of the bank ranged from uninvolved to corrupt, and together with the Panic of 1873, the combination proved lethal for the bank, and tens of thousands of depositors lost most of their money. 

Luke C.D. Stein and Constantine Yannelis offer some recent research on lessons the grim experience and its long-lasting effects on the trust level of African-Americans for finacial institutions in in “Financial Inclusion, Human Capital, and Wealth Accumulation: Evidence from the Freedman’s Savings Bank” (Review of Financial Studies, 33:11, November 2020, pp. 5333–5377, subscription requiredhttps://academic.oup.com/rfs/article-abstract/33/11/5333/5732662). Also, Áine Doris offers a readable overview in the Chicago Booth Review (August 10, 2020). 

3) “Interview with Rucker Johnson: Supporting Children” (October 19, 2021)

Douglas Clement interviews Rucker Johnson about his research in the Fall 2021 issue of For All, published by the Opportunity & Growth Institute at the Minneapolis Fed (“Rucker Johnson interview: Powering potential,” subtitled “Rucker Johnson on school finance reform, quality pre-K, and integration”). One of the themes of the interview is the potential for gains to both equity and efficiency from supporting socioeconomically disadvantaged children along a variety of dimensions. Johnson notes:

On disparities in spending and opportunity across K-12 schools:

Today, about 75 percent of per pupil spending disparities are between states (rather than between districts within states). And we’ve witnessed that inequality in school spending has risen since 2000. After three decades of narrowing—the ’70s, ’80s, and ’90s—primarily due to the state school finance reforms emphasized in my work with Kirabo Jackson and Claudia Persico, there has been a significant rise in inequality, especially sharply following the Great Recession.

What I want to highlight here is the current disparities nationwide in school resources. School districts with the most students of color have about 15 percent less per pupil funding from state and local sources than predominantly White, affluent areas, despite having much greater need due to higher proportions of poverty, special needs, and English language learners.

Teacher quality is often the missing link that people don’t consider directly when thinking about school resource inequities. For example, schools with a high level of Black and Latino students have almost two times as many first-year teachers as schools with low minority enrollment. And minority students are more likely to be taught by inexperienced teachers than experienced ones in 33 states across the country. … Part of it is that the invisible lines of school district boundaries are powerful tools of segregation. It’s a way of segregating and hoarding access to opportunity. And when I say access to opportunity, I mean quality of teachers, I mean curricular opportunity. For example, only a third of public schools with high Black and Latino enrollment offer calculus. Courses like that are gateways to majoring in STEM in college and having a STEM career. Or simply the fact that less than 30 percent of students in gifted and talented programs are Black or Latino.

4) “The Confidence of Americans in Institutions” (July 24, 2021)

In early July, the Gallup Poll carried out an annual survey in which people are asked about their confidence in various institutions. Here are some of the results, as reported at the Gallup website by Jeffrey M. Jones, “In U.S., Black Confidence in Police Recovers From 2020 Low” (July 14, 2021) and by Megan Brenan, “Americans’ Confidence in Major U.S. Institutions Dips” (July 14, 2021).

5) “The Problem of Automated Screening of Job Applicants” (September 24, 2021)

Employers need to whittle down the online job applicants to a manageable number, so they turn to automated tools for screening the job applications. In “Hiring as Exploration,” by Danielle Li, Lindsey R. Raymond, and Peter Bergman (NBER Working Paper 27736, August 2020). They consider a “contextual bandit” approach. The intuition here, at least as I learned it, refers to the idea of a “one-armed bandit” as a synonym for a slot machine. Say that you are confronted with the problem of which slot machine to play in a large casino, given that some slot machines will pay off better than others. On one side, you want to exploit a slot machine with high payoffs. On the other side, even if you find a slot machine which seems to have pretty good payoffs, it can be a useful strategy to explore a little and see if perhaps some unexpected slot machine might pay as well or better. A contextual bandit model is built on finding the appropriate balance in this exploit/explore dynamic.

From this perspective, the problem with a lot of automated methods for screening job applications is that they do too little exploring. In this spirit, the authors create several algorithms for screening job applicants, and they define an applicant’s “hiring potential” as the likelihood that the person will be hired, given that they are interviewed. The algorithms all use background information “on an applicant’s demographics (race, gender, and ethnicity), education (institution and degree), and work history (prior fims).” The key difference is that some of the algorithms just produce a point score for who should be interviewed, while the contextual bandit algorithm produces both a point score and a standard deviation around that point score. Then, and here is the key point, the contextual bandit algorithm ranks the applicants according to the upper bound of the confidence interval associated with the standard deviation. Thus, an applicant with a lower score but higher uncertainty could easily be ranked ahead of an applicant with a higher score but lower uncertainty. Again, the idea is to get more exploration into the job search and to look for matches that might be exceptionally good, even at the risk of interviewing some real duds. They apply their algorithms to actual job applicants for professional services jobs at a Fortune 500 firm.

They find that several of the algorithms would have the effect of reducing the share of selected applicants who are Black or Hispanic, while the contextual bandit approach looking for employees who are potentially outstanding “would more than double the share of selected applicants who are Black or Hispanic, from 10% to 23%.” They also find that while the previous approach at this firm was leading to as situation where 10% of those interviewed were actually offered and accepted a job, the contextual bandit approach led to an applicant pool where 25% of those who were interviewed were offered and accepted a job.