When Racism Is a Behavior, Not a Feeling: An Institutional Economics Framework for Measurable Change

Police chiefs do not call social psychologists because they want therapy. They call because they have a problem their existing institutional toolkit cannot solve. When Dr. Phillip Atiba Solomon stands before a room of law enforcement executives, he is not there to diagnose their childhood trauma or probe their unconscious biases. He is there because he offers something rare in the long, exhausted history of American racial reform: a definition of racism that makes the problem amenable to institutional intervention.
 

This is not a semantic quibble. It is an institutional economics argument of the first order.
 

For fifteen years, I have watched institutional reform efforts collapse under the weight of their own definitional vagueness. We pour resources into diversity training, implicit bias workshops, and community dialogue sessions—and then we measure success by how participants feel afterward. This is not reform. This is ritual. And rituals, however well-intentioned, do not change the incentive structures that produce discriminatory outcomes.
 

Solomon’s central insight—that racism should be defined as “an accumulated pattern of behaviors that disadvantage one racial group and advantage another, as well as the systems that facilitate that”—is not merely a psychological reframe. It is an institutional one. It moves racism from the domain of unobservable preferences (tastes, as Gary Becker would call them) into the domain of observable, measurable, and therefore manageable institutional outputs.

 

The Measurement Problem as a Transaction Cost Problem

New institutional economics begins with a simple premise: institutions matter because they reduce transaction costs. Ronald Coase taught us that the firm exists because markets are costly to use. Douglass North extended this insight to explain why some societies grow and others stagnate: institutions that lower the costs of exchange enable specialization, investment, and prosperity.
 

But what happens when the institution itself generates transaction costs for certain groups?
 

Consider the policing institution. When a Black driver is stopped for a broken taillight at twice the rate of a white driver in the same jurisdiction, that disparity is not merely a moral embarrassment—it is a transaction cost imposed selectively on one segment of the population. The cost is measured in time, in anxiety, in the risk of escalation, in the erosion of trust that makes future cooperation with law enforcement more expensive for everyone.
 

Solomon’s work at the Center for Policing Equity operationalizes this insight. By combining police behavioral data with demographic and socioeconomic data, his team estimates not just racial disparities in outcomes like stops and use of force, but the portion of those disparities for which law enforcement is directly responsible. This is not activism. This is accounting. And accounting, as any institutional economist will tell you, is the prerequisite for cost reduction.
 

The traditional approach to racism—treating it as a defect of character, a sickness of the heart—makes measurement impossible. You cannot audit a feeling. You cannot put a price tag on a prejudice that someone may not even acknowledge having. But you can measure how many times officers use force against Black civilians versus white civilians in comparable circumstances. You can track whether stops in predominantly Black neighborhoods produce contraband at the same rate as stops in predominantly white neighborhoods. You can calculate the institutional transaction costs imposed by discretionary policing practices.
 

When you can measure it, you can manage it. When you can manage it, you can change it.
 

Property Rights and the Allocation of Enforcement

Property rights theory, in its canonical formulation, holds that well-defined and enforced property rights are the institutional foundation of economic growth. But this formulation begs a crucial question: property rights for whom?
 

The policing institution is, in essence, a property rights enforcement mechanism. It allocates the right to stop, search, detain, and use force. When that allocation is systematically skewed, we are not witnessing individual prejudice—we are witnessing a property rights regime that confers differential enforcement burdens on different populations.
 

Solomon’s data reveal that Black citizens are two to four times more likely to experience use of force by police. This is not random variation. This is a pattern. And patterns, in institutional economics, are evidence of underlying rules—formal or informal—that structure behavior.
 

The formal rules are clear: the Fourth Amendment prohibits unreasonable searches and seizures. The problem lies in the implementation of these rules—the informal norms, the discretionary practices, the organizational culture that determines how formal rules are applied in practice. This is what North meant when he distinguished between formal institutions (laws, constitutions) and informal institutions (norms, conventions, codes of behavior). The gap between the two is where institutional failure lives.
 

What Solomon’s CompStat for Justice tool effectively does is make the informal visible. By tracking disparities in real time, it transforms implicit organizational patterns into explicit data points. This is the institutional equivalent of bringing hidden property rights claims into the light: once you know who is bearing the cost of enforcement, you can begin to reallocate the burden.

 

The Cost of Institutional Change

Here is where the analysis gets hard. Identifying a problem is not the same as solving it. Institutional change is expensive, path-dependent, and politically treacherous.
 

Solomon acknowledges this directly. Police chiefs do not call him because they enjoy being told their departments are racially biased. They call because they are stuck. The problem feels impossible. And the reason it feels impossible is that the conventional reform toolkit—training, sensitivity, “combating ignorance”—has failed, repeatedly and expensively.
 

From an institutional economics perspective, the failure is predictable. Training programs that target individual attitudes are attempting to change informal institutions (beliefs, norms) without altering the formal institutional structures (incentives, accountability mechanisms, resource allocations) that shape behavior. This is like trying to change the direction of a river by painting the water. The underlying channel remains the same.
 

Real institutional change requires altering the cost-benefit calculus of the actors within the institution. This is what Solomon’s data-driven approach accomplishes. When a police department knows that its use-of-force data will be analyzed, compared across jurisdictions, and made visible to the public, the cost of continuing discriminatory practices rises. When officers see that disparities in stops are being tracked and that commanders are paying attention, the informal norm that “this is just how we do things” begins to crack.
 

The evidence suggests this works. Police departments that have engaged with the Center for Policing Equity and measured changes over time have seen a 26 percent decrease in use of force incidents, a 13 percent decrease in injuries to officers, and 25 percent fewer arrests—all without an attendant crime surge.
 

These are not trivial numbers. They represent real reductions in institutional transaction costs—fewer confrontations, fewer injuries, fewer arrests that produce no public safety benefit. They represent, in short, a more efficient institution.

 

The Formal-Informal Coordination Problem

But the data alone are not the intervention. The intervention is the use of the data within a framework of institutional accountability.
 

Solomon describes his work as creating a “data-driven vaccine against racial disparities in policing”. The vaccine metaphor is instructive. Vaccines do not eliminate the pathogen from the environment; they alter the host’s response to it. Similarly, data-driven interventions do not eliminate racism from society; they alter the institutional response to behaviors that produce racial disparities.
 

This requires coordination between formal and informal institutions. The formal institution—the police department, with its hierarchy, its rules, its accountability structures—must be willing to receive and act on the data. The informal institution—the culture of policing, the norms of the street, the unwritten rules about who gets stopped and why—must be willing to change in response.
 

This coordination is the hardest part of institutional reform. Formal institutional change is relatively straightforward: you can rewrite a policy, issue a new directive, create a new oversight committee. Informal institutional change is slow, contested, and unpredictable. Norms do not change because someone issues a memo.
 

What Solomon’s approach does is create a feedback loop between the formal and the informal. The data make visible what was previously invisible. The visibility creates pressure for change. The change, if sustained, gradually alters the informal norms. This is institutional change from the inside out—not imposed by external reformers, but generated by the institution’s own data, its own metrics, its own accountability mechanisms.
 

This is why police chiefs talk to him. He is not telling them they are bad people. He is telling them they have a management problem—and he has the data to prove it.

 

Beyond Policing: A General Institutional Framework

The logic extends beyond law enforcement. Any institution that produces systematically disparate outcomes can be analyzed through the same framework.
 

Consider hiring. When a company’s recruitment practices consistently produce a workforce that does not reflect the available talent pool, that is not necessarily evidence of conscious prejudice. It may be evidence of institutional friction—networks that transmit job information unevenly, screening criteria that correlate with race without intending to, interview processes that reward cultural familiarity over competence.
 

The traditional response is diversity training. The institutional economics response is measurement. Track the pipeline at every stage. Identify where disparities emerge. Intervene at the points where the data say the problem is, not where your intuition says it should be.
 

Consider healthcare. When Black patients receive lower-quality care than white patients with the same conditions, that is not necessarily evidence of malicious providers. It may be evidence of institutional patterns—where clinics are located, how appointment systems work, which specialists are accessible, how medical decisions are made under time pressure.
 

The traditional response is cultural competency training. The institutional economics response is measurement. Track outcomes by race, control for clinical factors, identify the specific decision points where disparities emerge, and redesign the institutional processes that produce them.
 

This is not soft. It is hard. It requires data infrastructure, analytical capacity, and the organizational will to act on uncomfortable findings. But it is also solvable—not in the sense of easy, but in the sense of tractable. You can make progress. You can measure whether you are making progress. You can adjust your approach when you are not.

 

The Limits of the Framework

No institutional framework is a panacea, and this one has real limits.
 

First, measurement is not neutral. What you choose to measure shapes what you choose to see. Solomon’s focus on policing behaviors is powerful, but it does not address the deeper historical and structural forces that produce the neighborhoods, the schools, the economic conditions that generate police contact in the first place. Institutional reform can reduce the harm of policing, but it cannot replace the need for broader social change.
 

Second, data-driven reform requires institutional capacity that many organizations lack. Small police departments, under-resourced school districts, cash-strapped healthcare systems cannot simply build their own CompStat for Justice. The scalability of these interventions depends on external investment and technical assistance—which brings us back to the political economy of reform.
 

Third, and most fundamentally, the framework assumes that institutions want to change—or at least that they can be induced to change by the right combination of data and pressure. This is not always true. Some institutions are organized around the preservation of advantage. Some benefit from the transaction costs they impose on others. For these institutions, more data is not a solution; it is a threat.
 

Solomon is aware of this. His work is not naive. He is not arguing that data will melt the hearts of racists. He is arguing that data can change the calculus of institutional actors—that when the cost of continuing discriminatory practices becomes higher than the cost of changing them, institutions will change.
 

This is the core insight of institutional economics applied to racial justice: change happens when the incentives line up. The job of the reformer is to make them line up.

 

Conclusion: From Impossible to Solvable

The genius of Solomon’s framework is that it transforms the question. Instead of asking, “How do we change hearts and minds?”—a question that has no good answer and no measurable progress—it asks, “How do we change behaviors and the systems that produce them?”
 

This is the difference between a moral problem and an institutional one. Moral problems are infinite. Institutional problems are finite. They have boundaries, costs, trade-offs, and solutions.
 

The data from Solomon’s interventions are encouraging but not definitive. A 26 percent reduction in use of force is meaningful progress, but it is not victory. The path from where we are to where we need to be is long, and the transaction costs of institutional change are real. But for the first time in a long time, we have a framework that makes the problem tractable—that tells us what to measure, how to measure it, and what to do with the measurement.
 

That is what institutional economics does at its best. It takes problems that feel impossible and shows us the levers we can actually pull.
 

The lever here is not love. It is not forgiveness. It is not a change of heart. The lever is data, accountability, and the slow, grinding work of institutional reform. It is not glamorous. It is not quick. But it is real.
 

And real is what we need.
 

Source Reference Link

https://www.ted.com/talks/dr_phillip_atiba_solomon_how_we_can_make_racism_a_solvable_problem

Link Brief

Social psychologist Phillip Atiba Solomon redefines racism as measurable harmful behaviors rather than inner hatred. His team uses data to track racial bias in law enforcement, creating actionable tools for police departments to eliminate discriminatory policing practices. This speech offers scientific, data-driven solutions to systematically reduce institutional racism.
 

Content Disclaimer

This article is for general reference only and does not constitute professional R&D guidance, production process advice or quality certification. All material performance data has specific test premises; readers should verify parameters against actual equipment and working conditions.