In Bazemore v. Friday, 478 U.S. 385 (1986), a case involving pay discrimination in the North Carolina Extension Service, the plaintiff, a group of black agents, submitted a multiple regression model showing that, on average, the black agents' salary was lower than that of their white counterparts. When the case reached the court of appeals, it rejected the plaintiff's case on the grounds that their regression had not included all the variables thought to have an effect on salary. The Supreme Court, however, reversed the appeals court. It stated:
The Court of Appeals erred in stating that petitioners' regression analyses were "unacceptable as evidence of discrimination," because they did not include all measurable variables thought to have an effect on salary level. The court's view of the evidentiary value of the regression analysis was plainly incorrect. While the omission of variables from a regression analysis may render the analysis less probative than it otherwise might be, it can hardly be said, absent some other infirmity, that an analysis which accounts for the major factors "must be considered unacceptable as evidence of discrimination." Ibid. Normally, a failure to include variables will affect the analysis' probativeness, not its admissibility.
Do you think the Supreme Court was correct in this decision? Articulate your views fully, bearing in mind the theoretical consequences of specification errors and practical realities.