Internet adoption and usage patterns are different: Implications for the digital divide

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Abstract

There is a well-documented “digital divide” in internet connection. We ask whether a similar divide exists for internet usage. Using a survey of 18,439 Americans, we find that high-income, educated people were more likely to have adopted the internet by December 2001. However, conditional on adoption, low-income, less-educated people spend more time online. We examine four possible reasons for this pattern: (1) differences in the opportunity cost of leisure time, (2) differences in the usefulness of online activities, (3) differences in the amount of leisure time, and (4) selection. Our evidence suggests this pattern is best explained by differences in the opportunity cost of leisure time. Our results also help to determine the potential effects of internet-access subsidies.

Introduction

There is a well-documented “digital divide” in the tendency to connect to the internet (e.g., Chinn and Fairlie, 2006, Fairlie, 2004, Fox, 2005, Hoffman and Novak, 2000). Connection alone, however, is not necessarily the best measure of the benefit of using the technology. Instead, usage generally determines how much value individuals derive from the internet. Prior research analyzing the business benefits of information technology has acknowledged this fact (e.g., Devaraj and Kohli, 2003, Astebro, 2004, Zhu and Kraemer, 2005), but there is less research on the importance of usage to households. In this paper, we find little evidence of a digital divide in usage. We argue that the pricing structure of both fixed connection fees and near-zero usage fees leads to a negative correlation between income and time online among those who have connected.

Using a survey of 18,439 Americans from December 2001, we show that the patterns of internet adoption and usage indeed differ by demographics. Specifically, we find that high-income, educated people were more likely to adopt the internet, but they also spend considerably less time online, conditional on adoption.

We then consider four explanations for this pattern: (1) low-income people have a lower opportunity cost of leisure time due to low wages, (2) low-income people find the internet more useful than others, (3) low-income people have more leisure time, and (4) the low-income people who choose to adopt the internet are those who place a particularly high value on it (i.e., selection). We compare these explanations by correcting for selection, controlling for leisure time levels, and analyzing usage of specific applications (e.g., email, telemedicine). Although data limitations mean we cannot completely rule out the possibility that selection drives the results, we argue that the empirical evidence points most strongly to low-income individuals spending more time online due to lower opportunity costs of leisure time.

These results also have implications for policy discussions on access subsidies. We conduct simulations to determine which applications low-income people would use if given internet access. Our findings indicate that this group would spend a great deal of time online and likely use the internet for activities that policymakers often view positively (e.g., news, health information). This suggests the potential benefits of subsidies; however, we also must consider other issues to determine if subsidies are worth the cost.

Among the relevant internet-usage papers, Lambrecht and Seim (2006) show that adoption of online banking depends on the user’s comfort with technology but that usage depends more on the complexity of the user’s banking needs. Goldfarb (2006) finds that internet usage for email and chat (rather than e-commerce and information search) was an important driver for internet technology to diffuse beyond the university setting. Sinai and Waldfogel (2004) indirectly examine usage by looking at the importance of online content in the decision to adopt. Here, we aim to show that, in terms of household demographics, adoption and usage patterns differ. We then examine possible explanations for this difference.

The next section describes the empirical strategy and the data. Section 3 shows that, controlling for many factors, internet adoption and usage have different demographic patterns. It then describes four explanations for why we observe this pattern and empirically compares them. Section 4 discusses some policy implications of our results, and Section 5 concludes.

Section snippets

Empirical framework

We model the adoption/usage decision as a two-stage process. In the first stage, households decide whether to adopt the internet; in the second stage, they decide how much time to spend online. Therefore, in the second stage, households that adopt solve the following problem:MaxI,L,Mu2(I,L,M)s.t.L+ITandM+pSwhere u2(.) is utility from usage. It is increasing in I (leisure time spent on the internet), L (other leisure time), and M (money). T is the total leisure time, p the price of internet

Adoption vs. usage

Columns (1) and (2) of Table 2 show that usage and adoption follow very distinct patterns. These columns contain the results of a Type-II tobit regression where we define usage as “usage for personal reasons.” The coefficient estimates in Column (2) show that internet adoption is increasing in income and education.

Policy implications

In addition to trying to understand the pattern observed in Fig. 1, our second contribution is to provide a better understanding of the effect of subsidizing home internet access. Column (1) of Table 4 contains predicted usage for the entire sample and breaks this down by income. The results show that predicted usage among low-income individuals would be high, even higher than their counterparts, and Columns (2) through (9) illustrate that application usage often follows patterns similar to

Conclusions

We show that internet adoption and usage follow different patterns. While income and education positively correlate with adoption, they negatively correlate with hours spent online. Given our results, we argue that the most likely explanation for this finding is that low-income individuals spend more time online due to their lower opportunity costs of leisure time. In particular, the pricing structure of the internet, with both fixed connection and near-zero usage fees, leads to a negative

Acknowledgements

We thank Forrester Research for the data and Chris Forman, Shane Greenstein, the editor, the associate editor, two anonymous referees, and seminar participants at the University of Toronto and Cornell University for helpful comments. This research was partially supported by SSHRC Grant #538-02-1013. All opinions and errors are ours alone.

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