Turn The Nut Counterclockwise: Examining the root cause of systemic racism

The belief in and appearance of systemic racism may be an ineluctable outcome in a diverse society that only allows a limited set of ideas about how human inequality works.

Written by Aldo Rustichini.

The thesis of this essay is that the belief in and appearance of systemic racism may be an ineluctable outcome in a diverse society that only allows a limited set of ideas about how human inequality works. Systemic racism may be a mirage, an illusion produced necessarily by the social dynamics of a dogmatic society, that is, a society that rules out some possibilities, some causes of inequalities, as unthinkable. 

This paper's explanation is necessarily informal, as it doesn't provide strict definitions or theorems. However, it maintains rigorous analysis in line with the theory of statistical discrimination, including all essential definitions and theorems. The full paper can be provided upon request.

Let's start with an elementary example.

Learning in a simple task.

Foreign tourists in Italy might encounter an unexpected challenge with the following basic task. They have bought a new gas stove and need to connect the gas tank to it. A metal pipe extends from the tank. A rubber pipe leads gas to the stove, ending in a nut. The two pipes must be joined using this nut.

On the first try, a person usually carefully aligns the metal pipe and the nut, then attempts to screw in the nut clockwise. When the first attempt fails, it's typically assumed that the alignment is incorrect. With extra care, the alignment is perfected for the second try. After the second and third attempts also fail, a second diagnosis is considered: a flaw in the pipes or nut hinders the connection. So, the next attempt involves extra force. However, the fourth and fifth attempts also fail.

At about this time, usually sooner if the person is clever, he contemplates a radical change of the model used to interpret this simple reality of pipes and nuts. Perhaps, he must screw it counterclockwise. Once this small but revolutionary change of view is adopted, the task is easy. 

This straightforward story offers two key takeaways: (1) learning might necessitate a profound shift in our perception of reality, rather than just fine-tuning the details, and (2) the experiences accumulated while working towards a goal might suggest to the observant that the existing model is flawed. In our example, if the screwing was meant to be done clockwise, one would have succeeded after a few tries. Therefore, persistent failure indicates it's time to question the fundamental assumption.

The Minnesota Department of Education: clockwise, clockwise

This example leads us to explore a significant issue that Minnesota politicians are currently dealing with. We're using it as a case study in our exploration of the fundamental causes of racism. It's a representative example of a broader problem, and the same reasoning can be applied to understanding causes and effects, just like in the Citibank "evaluation" of potential gains from narrowing wage, education, wealth, housing, and investment gaps.

The problem for the Minnesota Department of Education is the achievement gap. We define this gap as a simple statistical regularity, namely the significant and persistent difference in performance, measured in school test scores, between groups, for example, between Black and White students. This simple statistical fact per se does not help determine how to address the problem because we have yet to learn what is causing it. But what does the data say?

Minnesota has been a progressive state for a long time, particularly regarding public education policies. As early as 1869, the state legislature passed a law prohibiting school segregation in St. Paul. Currently, four separate initiatives are aimed at tackling this problem: World's Best Workforce; Achievement and Integration for Minnesota; American Indian Education, and Regional Centers of Excellence. The achievement gap is at the heart of the Department's mission.

These initiatives have successfully reduced disparities in the educational opportunities offered to students. A report by the Federal Reserve of Minneapolis concluded that "Minnesota has successfully reduced variation in education inputs, such as per capita expenditures across districts and class sizes across schools." However, the report also states that the primary goal, which is closing achievement gaps across racial and socioeconomic status, has yet to be achieved and may even be worsening. Intriguingly, Minnesota has some of the most substantial such gaps in the country. An investigation into why the achievement gap is wider in Minnesota reveals that while White students perform close to national averages, Black students underperform significantly, as seen in mathematics comparisons from 2005 to 2022.

What explains these persistent failures? Most likely, a comprehensive understanding of the problem is lacking. A key critique in a March 2022 legislative audit report investigating the Department's role in addressing the achievement gap highlighted that even after 153 years of efforts to mitigate the achievement gap, politicians have failed to define it clearly. According to the report, Minnesota law doesn't clearly articulate what constitutes the "achievement gap," how it should be measured, or the role of the Department of Education in addressing it. Other problems noted in the report, including unrealistic timelines, unclear expectations, and lack of contingency plans in case of failure, all stem from this crucial shortcoming.

We now provide such an understanding, relying on economic theory and the simple standard model used to think about these problems. 

How discrimination may arise: a model

A potential future employee has to decide whether to pursue a professional qualification before joining the workforce. This decision is not visible to others: while the degree earned is observable, the quality of that degree, which hinges on the effort put forth during school and college years, is difficult to assess. The cost of acquiring this qualification is also only known to the individual. This cost varies among individuals, depending on factors like cognitive ability, ambition, interest in intellectual pursuits, patience, etc. For instance, a highly intelligent individual might earn a degree with minimal effort, while someone with less intellectual capacity might need to study tirelessly.

Upon completing their education, these individuals take a test which provides some (though incomplete) insight into their qualifications. It's logical to assume that if an individual is highly qualified, it's more likely for their test score to be high.

Employers review these test scores and make hiring decisions based on them. It's both intuitive and accurate to suggest that an employer might adopt a simple hiring strategy: they hire the candidate if their score surpasses a specific threshold.

Groups and discrimination

Let's now consider a society consisting of two groups, denoted as B and W in this study, distinguished by a readily observable characteristic. In our simplified employment model, the data available to society includes the candidate's appearance, their test score, the employer's hiring decision, and the candidate's performance if hired.

We might identify discrimination if two individuals from different groups achieve identical test scores, but only one is hired. To propose effective solutions, we first need to understand the reasons behind this discrepancy, as different causes will necessitate different solutions.

The root cause could be hidden within any of the factors we've considered in our simplified employment model: (1) The cost of effort might vary between the two groups due to various reasons, particularly due to differing social and economic conditions or characteristics inherent to the groups; (2) The test may carry inherent bias; and (3) The employer's decision could be biased due to a firm (racist) preference for one group over the other.

Society can learn

It can be proven that if a society remains open-minded and doesn't rule out any potential explanations from the outset, the true cause will be uncovered. This is because the data is so abundant (with millions of observations available). A society that maintains this open-mindedness will not only identify but also have a genuine chance to rectify the root causes of the issues.

Statistical discrimination

A second observation typically drawn from these models is that discrimination can occur even without any of the three factors we've listed being present. This insight, first proposed by Arrow (1973) and later expanded by Coate and Loury, is ingenious but can also be perplexing. Let's delve into it more carefully.

Firstly, it's important to note that the crux of this argument is not group-specific. We can illustrate this by imagining a society without distinct groups. In this society, if the employer's hiring threshold is too stringent (only those scoring one point above the highest possible score will be hired), then no one will exert any effort, since it won't make a difference. Conversely, if the threshold is too lenient (any score suffices), no one will make an effort either, as it's unnecessary. With moderate threshold levels, some effort will be exerted.

Examining the model, we can see that there are two potential long-term outcomes: one with low effort (the undesirable outcome), and another with high effort (the favorable outcome). This model allows for multiple equilibrium outcomes.

Now, if we consider a situation involving two groups, it's possible that if one group falls into the low-effort outcome and the other into the high-effort outcome, it would appear as if discrimination is happening. However, given that the mechanism driving these outcomes is the same for both groups, the system isn't inherently biased. According to this theory, a group ending up in the discriminated position is merely due to unfortunate circumstances or, colloquially, bad luck.

To explain why in reality the group that has historically suffered discrimination and injustice, the B group, is the one experiencing the undesirable outcome, we must factor in historical context and slavery. Yet, if the real explanation lies in historical events and slavery, then what is the value of a complex model? And what does this particular version of the model reveal about the true causes of the observed outcomes?

What is not known 

We now arrive at a critical point: what happens to society's ability to learn and understand if it dismisses certain possible explanations? Clearly, our overarching assertion that an open-minded society will discover the truth won't hold true. The answer is straightforward: if an explanatory factor is dismissed, and that factor plays a role in the observed outcomes, then the learning process will fabricate an artificial explanation. Let's delve deeper into this.

Imagine that there's even a minor difference in the distribution of characteristics among students in the two groups. We know that if society maintained an open mind, it would ultimately pinpoint the cause. In a society where no one is intent on stoking racial discord for political purposes, such a difference wouldn't cause alarm. For instance, there are racial differences in health predispositions, which can be attributed to both socioeconomic and biological factors. These differences are rationally considered in policy-making to achieve the most universally satisfactory outcome.

What occurs to societal learning if these differences are disregarded or deliberately excluded? There will be a mismatch between what the model predicts and the actual data. As the real explanation is dismissed, the most likely artificial explanation will be considered. If even a tiny difference in population characteristics is not allowed in the analysis, then the default explanation becomes systemic discrimination; being the only explanation permitted, it will be adopted. In the long run, it becomes the sole available explanation.

Worse yet, if the range of alternative (or rather, artificial) explanations that society is willing to consider is wide enough, then the artificial explanation can fit the data, including the repeated failures. Hence, if one is willing to entertain even vague or improbable hypotheses, then the artificial explanation will never be discarded.

Even more problematic, these artificial beliefs (such as the pervasive belief in systemic discrimination) influence individual decisions. In our example, the widespread belief that systemic discrimination is the real cause will reduce the efforts of students who are believed to be victims of discrimination, thereby enlarging the disparity in outcomes. This self-fulfilling prophecy feeds into a stronger belief that discrimination is the true cause.

These features are blatantly evident in our example of the long-standing failure to address the achievement gap in Minnesota. Firstly, we see persistent failures to improve the situation, despite redoubling efforts in the same direction. Secondly, we observe the relative decline in the performance of Black students in a progressive state: the stronger the emphasis on systemic racism, the greater its detrimental effect on effort, and the wider the achievement gap.

It is time to consider the possibility that the nut must be turned counterclockwise.

Aldo Rustichini is an Italian-born American economist. He is a professor of economics at the University of Minnesota, where he researches, among other topics, decision theory, game theory, general equilibrium theory, and bounded rationality. He has degrees in philosophy, economics, and mathematics.

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