ferences in teleworking propensity by region. In their models of teleworking behavior, they included dummy variables to capture state-level effects (controlling for individual characteristics); classifying and mapping these state-level effects, he said the regional differences are striking. In general, teleworking rates are considerably higher in the West—likely because “hubs of technology” and places with high concentrations of the industries most likely to enable telework are on the West Coast; high levels of telework are also found in the extremes of the Eastern Seaboard (New England, Georgia, and Florida), while the lowest rates are in the Deep South and the Rust Belt.

Of this examination of telework behaviors, Levanon made clear that this is not especially complex work—indeed, he said, this is a quite “simple usage of the microdata.” But he said that he thinks that it speaks to an important base of potential data users—namely, people in the human resources field interested in trends in the workforce. Levanon said that, as best he knows, the Conference Board’s specific analysis of teleworking using the ACS data had not been done before, and he said that the range of interesting kinds of things that can be learned from ACS microdata might spur additional work—and interest in using the microdata—among parties new to the data.

The second ACS-related Conference Board project that Levanon described is one that is still in progress, but one that again makes use of the ACS’s strength in providing consistent measures across a broad range of geography. Specifically, the Conference Board is studying wage inequality, by any number of factors. As an example of the work and the ACS’s utility in it, he displayed the graph shown in Figure 6-1. The graph provides a general sense of geographic differences in wage inequality through a relatively straightforward metric; making use of ACS microdata, he calculated the ratio of the 90th percentile of total wages for fulltime workers in each state to the 10th percentile. Printed at a small size, he noted that it is hard to read, but some of the states at the top—with the largest ratios and hence the greatest spread in wages—include California, Texas, New Jersey, the District of Columbia, Georgia, and Virginia. At the bottom—smaller ratios and less spread across wages—are South Dakota, Maine, Vermont, North Dakota, Wisconsin, and Iowa. The dark vertical bar shows the ratio for the nation as a whole. He said that this fairly simple univariate slice from ACS microdata spurs questions and areas for future probing; based on the states on the high and low ends of the spectrum, “one can speculate that ethnic diversity” might be an important determinant of the inequality, and that is something that can be examined at finer levels of aggregation. The national-level vertical line—and the fact that so many states are below the line—suggests interesting trends as well; in early looks at the change in the ratios over time, Levanon said that shifts in the national-level ratio are more pronounced than are shifts in the distribution of inequality across the states. Again, he observed, these are interesting phenomena that “I don’t think can be done [using] any other data source” with such a level of confidence.

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