I applauded the addition of equity to the 2007 Standards because the public deserves fair treatment by its government, and auditors are in a unique position to find patterns of inequities. In our audit work, we come to understand the intent and objectives of the program, its procedures, the mechanics of service delivery, and the content of its records.
“Government officials entrusted with public resources are responsible for carrying out public functions legally, effectively, efficiently, economically, ethically, and equitably.” -2007 Standards
I had always asked those equity questions of audit teams. Yet not every complaint or situation traces back to system causes that are amenable to an audit. To address that gap, I introduced the office of the Ombudsman in Portland to specifically hear complaints and address equity and fairness issues.
Then I was surprised and disappointed to discover that equity was removed from the 2018 Standards.
“Management and officials entrusted with public resources are responsible for carrying out public functions and providing service to the public effectively, efficiently, economically, and ethically within the context of the statutory boundaries of the specific government program.” -2018 Standards
Equity was still in the 2011 Standards but I think it was an unfortunate excision in 2018, especially with our new increased awareness of disparate treatment and institutional racism. The closest I could find in the 2018 Standards was an example audit objective:
“…determining whether government services and benefits are accessible to those individuals who have a right to access those services and benefits…”
(The GAO responded to the heightened racial justice concerns after George Floyd was killed and reinstated the equity language in 2021.) I don’t know the rationale for dropping this auditing priority, but I do know that it is one of the most difficult of areas to audit. The work itself is more challenging, and the results are often immeasurable. I hope, and believe in my heart, that audits can help to end racism, but its roots are deeper and more widespread than any recommendations we can make.
When I think about preliminary ideas gathered in an audit, I ask myself “why?” Why are minority children showing higher incidence of health problems? Perhaps because they are living near industrial sites? Why are they living near industrial sites? Perhaps because housing is more affordable? Why is housing more affordable? Perhaps because it is less desirable? Why is it less desirable? Perhaps because of the industrial noise and pollution?
My answers may be wrong but the whys force me to think ahead. The health problems may be related to nutrition, or that people choose to live near where they work, and I maintain skepticism about any preliminary chain of causality. Other factors often come into play, like gentrification that displaced people, and implicit racism that limited their education and employment prospects. Yet a series of hypothetical connections gets me thinking about possible recommendations. We’ve reached for root causes and solutions beyond children’s health into air quality, zoning decisions, and housing costs—all before we get to societal root causes like systemic racism.
As auditors, we have access to health data and if we discover a cluster of disease we should—no, we must—report it. I could never justify ignoring that kind of problem and its causes, even if it was outside the intended scope of the audit, and even if there were no ready solutions.
Criminal justice data is also plentiful and ripe for analysis, and has its own set of challenges. While an analysis may show racial disparities, the causes are just as complex. We have heard and seen disparate treatment, ranging from unfounded suspicions to likelier incarceration to fatal interactions with the police. The flaws in our justice system often magnify the underlying conditions that minorities suffer in a society that is obviously or covertly racist. An audit that shows the disparities may produce institutional changes that break the cycle for some individuals, but the socioeconomic causes are more insidious, and beyond our reach.
Your data analysis may indicate service disparities within a program. These may result from a “pattern and practice,” as the attorneys call it. Procedures and training may establish expectations for clients that are insensitive to some cultures, religions, races, disabilities, sexual orientation or gender identities. Sometimes the reasons can be identified and you may recommend modifications to training, written procedures, food choices, or facilities. Other times, you may only be able to point out the disparity.
We audited the application of sentencing guidelines in the early 1990s and found that people who were Latino were more likely to be incarcerated than other people when we controlled for crime severity and criminal history. During the review stage of our audit the presiding judge called me into his chambers. (This was the judge who would later complain to me about shared bathrooms in a proposed general courthouse design.) He had me sit on a low couch in front of his raised desk, as he would some unfortunate miscreant. He pointed a finger at me and said, “You did exactly what I told you not to do.” This was after the district attorney said that judges were responsible for sentencing, even though 90% of cases were decided by negotiated pleas between the prosecutor and defense attorney that the judges merely agreed to. The judges knew they were responsible but didn’t want to admit the decisions were inconsistent. We couldn’t identify why Latinos were more likely to serve jail time than their white or Black counterparts. A state supreme court judge later congratulated us on our work, assembled a commission to travel the state, which found many causes for racial injustice everywhere.
Twenty years later we analyzed placement decisions for juvenile offenders by the courts throughout the state, looking for signs of disparate treatment. Given the differences in settings and race, we thought we would find some problems, but we didn’t. While it didn’t change anything, I felt the audit was worthwhile to acknowledge good and fair decision-making that would affect the future for some of Oregon’s youth. It was an extremely complex statistical analysis and the lead auditor tapped his graduate school professor for assistance on our methodology and interpretation of results. However, this analysis, even of subpopulations, was at a scale that could not detect whether there was a racist counselor.
This gets back to a fundamental caution: correlation does not imply causation. We need to be very careful in our interpretation and the language we use to describe the pattern we identify. Just as we must be careful of the words we use to describe the groups of people who might be discriminated against, we must be scrupulous in our use of descriptive and inferential statistics. We should be examining data that can reveal systemic patterns of differential treatment and reporting any troubling results, but the solutions can be difficult to identify and even more challenging to achieve.
