data could charitably be described as “funky,” and “the ACS helps smooth out the rough spots.” Hitting a theme raised in Chapter 4 and that would come up again in the discussion period, Christenson also said that Acxiom’s making use of the ACS imparts credibility to the products—it “carries quite a bit of weight with our customers if we say we are using Census [Bureau] data.”

Christenson closed by noting that, from his and Acxiom’s perspective, the ACS is a uniquely useful data resource because of its breadth, depth, and completeness, and it is useful both as a product that Acxiom can develop and make available to users as well as a source for its own modeling and estimation work. From a business perspective, the ready availability of annual releases of small-area data is greatly beneficial; constantly vying with competitors, it is good to feel current and up to date in estimates and projections (and not to feel like competitors have a leg up somehow).

Like other workshop speakers, he said that he would certainly like “more” from the ACS—but in a slightly different way. He said that he regularly hears from the company’s salespeople and clients who are interested in ever more granular data—frustrated that household-level (or, for that matter, individual-level) data are not readily available from the ACS. From the marketing standpoint, their clients are greatly interested in data down to ZIP+4 Codes—a cut that could be as fine as 2–3 households. The reason why these incredibly fine-grained data are not available is fairly straightforward—respondents’ privacy must be respected and personally identifiable information not disclosed—and so Christenson said that the ACS block group level is very valuable to Acxiom. But one concern that he wanted to express concerned the granularity of the categories used to calculate estimates. Until the arrival of the ACS, he said, Acxiom had been using the 2000 census-based Summary File 3—developed its products around the categories used there—and encountered significant problems when the categories used in variables like income or age ranges were made more coarse in the ACS files. Put bluntly, he said, “we don’t care about confidence intervals”—in the sense of being paralyzed by the reported standard errors. Dealing with uncertainty is an accepted part of the bargain; Acxiom has statisticians, and its clients have statisticians, to sort through those issues, and they would rather be in the position of working with finer categories and making their own conclusions about what numbers are fine to use and which are not.


Though-most people may be familiar with the Conference Board through its highly visible Consumer Confidence Index, Gad Levanon (director of macroeconomic research) emphasized that the Conference Board’s analysis and research

The National Academies | 500 Fifth St. N.W. | Washington, D.C. 20001
Copyright © National Academy of Sciences. All rights reserved.
Terms of Use and Privacy Statement