The Empirical Economics Of Online Attention
University of California, Davis — Department of Economics
Shane M. Greenstein
Harvard University – Technology & Operations Management Unit; National Bureau of Economic Research (NBER)
Warren Buffett: If You Own A Good Business, Keep It
Indiana University – Kelley School of Business – Department of Business Economics & Public Policy
July 8, 2016
In several markets, firms compete not for consumer expenditure but instead for consumer attention. We model and characterize how households allocate their scarce attention in arguably the largest market for attention: the Internet. Our characterization of household attention allocation operates along three dimensions: how much attention is allocated, where that attention is allocated, and how that attention is allocated. Using click-stream data for thousands of U.S. households, we assess if and how attention allocation on each dimension changed between 2008 and 2013, a time of large increases in online offerings. We identify vast and expected changes in where households allocate their attention (away from chat and news towards video and social media), and yet we simultaneously identify remarkable stability in how much attention is allocated and how it is allocated. Specifically, we identify (i) persistence in the elasticity of attention according to income and (ii) complete stability in the dispersion of attention across sites and in the intensity of attention within sites. We illustrate how this finding is difficult to reconcile with standard models of optimal attention allocation and suggest alternatives that may be more suitable. We conclude that increasingly valuable offerings change where households go online, but not their general online attention patterns. This conclusion has important implications for competition and welfare in other markets for attention.
The Empirical Economics Of Online Attention – Introduction
“…[I]n an information-rich world, the wealth of information means a dearth of something else: a scarcity of whatever it is that information consumes. What information consumes is rather obvious: it consumes the attention of its recipients. Hence a wealth of information creates a poverty of attention and a need to allocate that attention efficiently among the overabundance of information sources that might consume it.” (Simon, 1971).
Herb Simon brought attention to the economic importance of attention, first articulated about information systems, which applies to any situation with abundant information. The observation remains relevant today, even more so for the information supplied by the commercial Internet. A scarce resource, users’ attention, must be allocated across the Internet’s vast supply of web sites. Firms compete for user attention.
At first glance, competition among Internet sites has much in common with other competitive settings. Users make choices about where to allocate their time, and in any household there is only a finite amount of such time to allocate, which translates into a finite budget of time for which firms compete. In some cases (e.g., electronic commerce), the firms try to convert that attention into sales of products. At over $360 billion per year, e-commerce comprises eight percent of total US sales in 2016.1 In other cases (e.g., most media), firms try to convert that attention into advertising sales, which amounts to $67 billion of spending.2 Firms compete for users by investing in web page design, in internal search functions, and in other aspects such as the speed at which relevant information loads. Over time, new firms enter with new offerings, and users can respond by making new choices, potentially substituting one source of supply for another.
However, first impressions mislead. Competition among web sites lacks one of the standard hallmarks of competition. Relative prices largely do not determine user choice among options, nor do prices determine competitive outcomes. Most households pay for monthly service, then allocate online time among endless options without further expenditure. Unless a household faces a binding cap on usage, no price shapes any other marginal decision. Instead, choice depends on the non-monetary frictions and the gains of the next best choice. Present evidence suggests only a small fraction of users face the shadow of monetary constraints while using online resources (Nevo, Turner, Williams, 2015). Relatedly, subscription services also play little role. As we will show below, only one of the top twenty sites (Netflix) is a subscription service, i.e., where the price of a web site plays an explicit role in decision making.
In this study, we use extensive microdata on user online choice to help us characterize demand for the services offered online. The demand for services by a household is the supply of attention for which firms compete. The study characterizes household heterogeneity in allocation of attention at any point in time, and how households substitute between sources of supply over time. We ground the analysis in a specific time period, the allocation of US household attention in the years 2008 and 2013, which was a time of enormous change in the supply of online options for the more than 70% of US households with broadband connections to the Internet. During this five-year period, US households experienced a massive expansion in online video offerings, social media, and points of contact (e.g., tablets, smartphones), among other changes.
Our dataset contains information for more than forty thousand primary home computers, or “home devices,” at US households in 2008 and more than thirty thousand in 2013. These data come from ComScore, a firm that tracks households over an entire year, recording all of the web sites visited, as well as some key demographics. The unit of observation is a week’s worth of choices made by households. We calculate the weekly market for online attention (total time), its concentration (in terms of time) for sites (our measure of breadth, or “focus”), and the weekly fraction of site visits that lasted at least 10 minutes (our measure of depth, or “dwelling”). In addition, we measure shares of attention for different site categories (e.g., social media). Using these measures of online attention, we analyze how they vary both horizontally (across demographics) and vertically (over time, 2008-2013).
We find that demand is comprised of a surprising mix of discretionary and inflexible behavior. First, we find strong evidence that income plays an important role in determining the allocation of time to the Internet. This finding reconfirms an earlier estimate of a relationship between income and extent of Internet use (Goldfarb and Prince, 2008), but does so using a more expansive and detailed dataset, and for later years when broadband access is more prevalent. We find that higher income households spend less total time online per week. Households making $25,000-$35,000 a year spend ninety-two more minutes a week online than households making $100,000 or more a year in income, and differences vary monotonically over intermediate income levels. Relatedly, we also find that the amount of time on the home device only slightly changes with increases in the number of available web sites and other devices – it slightly declines between 2008 and 2013 – despite large increases in online activity via smartphones and tablets over this time. Finally, the monotonic negative relationship between income and total time suggests online attention is an inferior good, and we find that this relationship remains stable, exhibiting a similar slope of sensitivity to income. We call this property persistent attention inferiority. There is a generally similar decline in total time across all income groups, which is consistent with a simple hypothesis that the allocation of time online at a personal computer declines in response to the introduction of new devices.
We also examine how breadth and depth changed with the massive changes in supply (i.e., video proliferation and Internet points of contact) between 2008 and 2013. Our casual expectation was that depth would increase, and more tentatively, that breadth would increase as well, but the findings do not conform to such expectations. Rather, breadth and depth have remained remarkably stable over the five years. While there is a statistical difference in the joint distribution of breadth and depth, it is just that – statistical and driven by our large sample. The size of the difference is remarkably small, with little implied economic consequence. We call this property persistent attention distribution. Despite the evidence that income and other economic variables affect total time online, demographics – perhaps surprisingly – predict little of the variation in breadth and depth. For one, breadth and depth are not well-predicted by income and there is only a limited role played by major demographics, such as family education, household size, age of head of household, and presence of children.
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