While one can argue with some of the interpretations, Matthew Hindman’s new study on how local online news is consumed gives us interesting data on reader behavior. And beyond the headline — people don’t consume a lot of local online news — there’s also an interesting data set that goes beyond national generalizations to individual markets.
Below, I’ve broken out three slices of that data set that get at the same question: How does local online news consumption differ from community to community?
Hindman’s analysis looked at 100 metro areas in the United States and examined sampled data from comScore on how web users in those metro areas use the Internet. He then tried to identify all noteworthy local news sites in those cities. (You can read the full paper for his methodology, but he was looking in part for any local news site that was viewed at least once by at least 1 percent of the comScore sample in a particular month.) Armed with that list, he was then able to see how much of a typical web user’s Internet time was spent at local news sites.
The first slice of that data: How many pageviews did a typical web user of each metro area generate per month on local news sites? A higher number means more clicks and more engagement on local news sites. So, for example, in Baton Rouge, a typical web user generated 26.1 pageviews on Baton Rouge-area news sites. On the other end of the spectrum, a typical Los Angeles web user generated only 3.9 pageviews on L.A.-area news sites.
There’s a wide spectrum of results from the top 100 markets. You’ll notice that No. 1, far and away, is Salt Lake City — specifically, at KSL-TV, whose big investment in online classifieds generates a remarkable 250 million pageviews a month. It’s a big outlier in this dataset.
The next slice is of something called the Herfindahl-Hirschman Index. It’s a statistical measure of the vibrancy of competition and the distribution of market power in a given market. It uses a scale of 0 to 10,000, with 10,000 representing a perfect monopoly and 0 representing perfect competition and the absence of market power on the part of any of the competing businesses. (I am not a statistician, so I hope I’m doing this justice.)
Speaking of justice, though, the Justice Department uses the HHI as a tool to help determine when a particular market may need anti-trust intervention. As Hindman puts it in his paper:
Though developed for a somewhat different application, the U.S. Department of Justice and Federal Trade Commission’s joint antitrust guidelines can provide some context in interpreting these numbers. According to revised DOJ and FTC rules, markets with an HHI between 1500 and 2500 are classified as moderately concentrated, while markets with an HHI greater that 2500 are classified as highly concentrated. The HHI statistic serves as an initial screen for heightened scrutiny, while the full test examines other factors — such as entry conditions — that might allow a firm to produce a “significant and non-transitory increase in price.”
Here are the top 100 markets, then, ranked in order by HHI — by how concentrated the market power is in each. Cities dominated by one media outlet like Atlanta or Shreveport show high levels of concentration, while cities like New York, Boston, and Philadelphia show low levels.
Finally, here’s a simple list of how many local news websites Hindman’s analysis found by market. (Again, he’s looking for sites that reached at least 1 percent of the market’s web users in a month’s time. There are many smaller blogs and other sites in these markets.) We here in Boston win out, with 28 outlets — 21 newspaper websites, 5 TV station websites, and 2 radio station websites. Meanwhile, Ft. Smith, Ark., El Paso, and Shreveport bring up the rear with just five total news websites showing up in Hindman’s comScore data.
To a certain degree, the number of outlets is a factor of each market’s population, but not entirely — Cleveland has more local news websites than New York, by Hindman’s measure, Columbus more than Los Angeles, and Little Rock more than Dallas-Fort Worth. Again, one can argue with the methodology, but it’s an interesting dataset nonetheless.