Utah v. Evans, 536 U.S. 452, 7 (2002)

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458

UTAH v. EVANS

Opinion of the Court

cide "imputation" represents the most practical way to resolve remaining informational uncertainties.

The Bureau refers to different kinds of "imputation" depending upon the nature of the missing or confusing information. Where, for example, the missing or confused information concerns the existence of a housing unit, the Bureau speaks of "status imputation." Where the missing or confused information concerns whether a unit is vacant or occupied, the Bureau speaks of "occupancy imputation." And where the missing or confused information concerns the number of people living in a unit, the Bureau refers to "household size imputation." In each case, however, the Bureau proceeds in a somewhat similar way: It imputes the relevant information by inferring that the address or unit about which it is uncertain has the same population characteristics as those of a "nearby sample or 'donor' " address or unit—e. g., its "geographically closest neighbor of the same type (i. e., apartment or single-family dwelling) that did not return a census questionnaire" by mail. Brief for Appellants 7-8, 11. Because the Bureau derives its information about the known address or unit from the current 2000 census rather than from prior censuses, it refers to its imputation as "hot-deck," rather than "cold-deck," imputation.

These three forms of imputation increased the final year 2000 count by about 1.2 million people, representing 0.4% of the total population. But because this small percentage was spread unevenly across the country, it makes a difference in the next apportionment of congressional Representatives. In particular, imputation increased North Carolina's population by 0.4% while increasing Utah's population by only 0.2%. And the parties agree that that difference means that North Carolina will receive one more Representative, and Utah will receive one less Representative, than if the Bureau had not used imputation but instead had simply filled relevant informational gaps by counting the related number of individuals as zero.

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