By Hans Bader.
"It is now dangerous for an academic to conduct or even discuss research
that shows an absence of racial bias in the criminal justice system. An
Asian-American college official was forced to resign his position after
discussing such research, as The College Fix reports in the article,
"Scholar forced to resign over study that found police shootings not
biased against blacks." As it notes:
Michigan State University leaders have
successfully pressured Stephen Hsu to resign from his position as vice
president of research...The main thrust to oust Hsu came because the
professor touted Michigan State research that found police are not more
likely to shoot African-Americans....
'I interviewed MSU Psychology professor Joe Cesario, who studies police shootings,' he wrote in an email to The College Fix... Cesario is the Michigan State psychology professor who co-authored the study
published July 2019 that debunked the notion that police are more
likely to shoot African-Americans. Hsu wrote on his blog that the paper concluded 'there is no widespread racial bias in police shooting.'
Cesario’s research had been cited in a widely shared Wall Street Journal
op-ed headlined “The Myth of Systemic Police Racism” that was published
June 3 amid racially charged protests against the death of George Floyd
in police custody.
As Professor Hsu notes, "Cesario’s work (along with similar work by others, such as Roland Fryer at Harvard) is essential to understanding deadly force and how to improve policing.”
The reprisals against Professor Hsu help explain why there are fewer
and fewer new studies finding an absence of bias in the criminal justice
system -- even as societal racism continues to diminish,
according to surveys like the General Social Survey. Researchers now
have an incentive to conduct misleading studies, that deliberately
cherrypick data and omit relevant variables, in order to reach a
conclusion that is less risky to their career: that discrimination is
widespread.
Researchers used to regularly find that the criminal justice system was
fair to racial minorities, in arrests and sentencing. In 1994, federal
statistician Patrick Langan looked at the nation’s 75 largest counties
and found “no evidence that, in the places where blacks in the United
States have most of their contacts with the justice system, that system
treats them more harshly than whites.” As he noted
in “No Racism in the Justice System,” “Many studies have been conducted
that show no bias in the arrest, prosecution, adjudication, and
sentencing of blacks.”
Similarly, statistical expert Stephen P. Klein of the RAND Institute studied California’s state criminal justice system and found
that criminal sentencing in California was racially fair and
non-discriminatory. (See Stephen P. Klein, et al., “Race and
Imprisonment Decisions in California,” 247 Science 812 (1990)). That was
the opposite of what Dr. Klein expected to find. He had served as an expert witness for civil-rights groups in landmark cases such as Serrano v. Priest--
and studied criminal justice on the recommendation of the liberal
California ACLU, which sees racism everywhere. But his statistical
analysis debunked claims that the criminal justice system was
systematically racist.
These studies involved painstaking statistical analysis that sought to
take all relevant factors into account. The more factors a researcher
takes into account, the more accurate a statistical analysis becomes.
But the more factors a study takes into account, the more time and
money it takes to do the study. So researchers are tempted to cut
corners by omitting factors or variables that are hard to measure, or
that the researcher suspects may not be all that important.
There is another, even bigger reason for researchers to wrongly omit
relevant variables or rely on incomplete data: Taking into account more
data or variables can end up debunking claims of discrimination, rather
than providing the "proof" of discrimination that progressive officials
and journalists want. Studies frequently allege discrimination precisely
by ignoring key variables. Their authors are rewarded by being given
tons of favorable publicity; or having their studies lead to social
change.
A classic example is the gender-bias study used to give female faculty pay raises in Smith v. Virginia Commonwealth University (1996).
The study claimed female faculty were being paid less than men by VCU
due to sex discrimination. But it turned out that the study ignored
relevant factors actually used by the university to set pay -- such as
scholarly productivity, and whether a faculty member had previously
served as an administrator. If these important variables had been
included in the statistical analysis, there would almost certainly have
been no finding of discrimination.
But these important variables were excluded, leading to the university
giving its female faculty pay raises to compensate for the non-existent
discrimination. Male faculty then sued, alleging that because there was
no discrimination against women to remedy, the gender-based pay raises
discriminated against men.
A federal appeals court ruled that the male faculty could sue the
university over the gender-based pay raises. It concluded that the
omission of these "major" variables (such as productivity and
administrative experience) meant that the gender-bias study was flawed.
After its ruling, the university paid off the male faculty to settle the
lawsuit, because it was fairly obvious the university would lose.
The appeals court was interpreting the Supreme Court's murky and vague decision in Bazemore v. Friday, which says a study should include the "major" factors and variables to be admissible in a race or sex discrimination case.
But what makes a variable major versus minor? The Supreme Court's vague
decision itself gave little guidance as to what is a "major" variable
that must be included, versus a minor one that can be excluded.
That vagueness has been the source of endless mischief, and
incentivized countless bad studies. A researcher who wants to engineer a
false finding of discrimination can just omit variables that are
supposedly "minor," but which, if included, would show that a racial or
sexual disparity isn't due to discrimination, but rather, something else
(like fewer minorities than whites having the qualifications needed for
a job, or more blacks than whites having a prior criminal record).
The researcher's false finding of discrimination can then be used to
justify doing things that progressive officials are eager to do, like
creating an affirmative action plan, or awarding gender-based pay
raises.
For every flawed study alleging discrimination that is successfully
challenged in court because it omitted major variables, there are
countless others that are never even challenged, because such a
challenge is just too costly. To challenge such a study, it is often
necessary to pay an expert witness to explain to the court why the
omitted variables are major rather than minor, and thus should have been
excluded. Such experts usually charge at least $750 per hour for their
work, and take many hours to complete and write up their analysis.
If you are a researcher, why conduct a painstaking statistical analysis
that takes all relevant factors into account, and thus finds no racism
or sexism, when you can make your job easier and reduce your workload by
deliberately omitting relevant factors, and thus reach the politically
less risky conclusion of "discrimination"?
The challenge to the flawed gender-discrimination study in Smith v. VCU
was only successful because the challengers lucked out, and received
hundreds of thousands of dollars worth of free legal assistance from the
Center for Individual Rights."
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