Abstract
Obesity has become an increasingly important public health issue in the USA and many other countries. Hypothesized causes for this increase include declining relative cost of food and a decreasing share of the population working in labor-intensive occupations. In this paper, we suggest another factor: the Internet. Increasing Internet access could affect body weight through several channels. First, more time spent using the Internet, a sedentary activity, could lead to increases in body weight. Second, the prior literature has shown that economic activity (and income) increase with Internet access: given a positive health-income gradient, obesity rates could likewise increase, although the empirical evidence on the income-obesity gradient is mixed. Third, the Internet increases information and creates the possibility for online peer networks. Theoretically, increases in information should lead to more optimal consumer choices. At the same time, greater networking opportunities may result in peers having greater influence over positive or negative health behaviors. While we are unable to fully test these mechanisms, we are able to use the rollout of broadband Internet providers as a plausibly exogenous source of variation in Internet access to identify the reduced form effect of Internet use on body weight. We show that greater broadband coverage increases the body weight of white women and has both positive and negative effects on modifiable adult health behaviors including exercise, smoking, and drinking.
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Notes
Additionally, without expert guidance, the large quantity of information available could lead consumers to accidently misuse the information they do receive.
We describe this work in more detail in the Background section.
For example, Bessière et al. (2010) use a random sample of the US population, but their study only covers 2 years.
Amante et al. (2015) provide evidence that individuals search for health information online, especially when it is difficult to access this information from health care providers.
There is a growing body of work examining the effectiveness of smart phone applications and wearable technology (for example, Jakicic et al. 2016). These technologies, however, were largely developed after the period we consider.
Data available from http://www.pewInternet.org/files/2014/01/Usage-Over-Time-_May-2013.xlsx. Accessed June 20, 2016.
The issue of health information quality is so pervasive that the US National Institutes of Health has a webpage with resources to help consumers evaluate the quality of health-related websites. See https://nccih.nih.gov/health/webresources. Accessed August 18, 2016.
The issue of quality is particularly salient in the work of Culver et al. (1997), who analyze messages from an online medical discussion group. They find 89% of the messages were authored by users without professional training, one third of the messages were inconsistent with conventional medical practices, and only 9% of the medical information provided by those without professional training contained a published citation. Similarly, Biermann et al. (1999) find 35% of websites with medical information about Ewing’s sarcoma did not contain peer-reviewed sources, and some pages contained incorrect or misleading information.
Additionally, the Internet facilitates illegal drug transactions via the “dark web.” http://www.newsweek.com/drugs-dark-web-silk-road-488957. Accessed October 13, 2016.
The cross-sectional relationship suggests that higher income is associated with lower levels of obesity. However, economic recessions have been known to reduce body weight in the severely obese (Ruhm 2005). Similarly, income transfers to low-income Native American adults through a casino opening increased obesity in their children (Akee et al. 2015).
http://www.who.int/mediacentre/factsheets/fs311/en/ Accessed September 1, 2016.
Some of these costs appear to be shifted to obese individuals. Bhattacharya and Bundorf (2009) find that obese individuals earn lower wages and that this serves to shift the cost of higher premiums onto the individual.
A shift to a sedentary lifestyle is partially evidenced in the decreased availability of recreation spaces such as sidewalks and other open spaces.
Lakdawalla et al. (2005) explore the role of welfare-improving technological change as underlying the drop in the relative price of food and the move to more sedentary occupations, and suggest that obesity is a side-effect of these technological changes.
Data can be downloaded from http://transition.fcc.gov/wcb/iatd/comp.html. The documentation from the FCC indicates that these are “lists of geographical zip codes where service providers have reported providing high-speed service to at least one customer as of December 31, [of the relevant year]. No service provider has reported providing high-speed service in those zip codes not included in this list. An asterisk (*) indicates that there are one to three holding companies reporting service to at least one customer in the zip code. Otherwise, the list contains the number of holding companies reporting high-speed service. The information is from data reported to the FCC in Form 477.”
We would have liked to have examined childhood obesity, but the BRFSS does not survey individuals younger than 18. We chose to focus on the under 65 population since those age 65 and older are more likely be retired, and less likely to use the Internet in the same way as younger age groups; therefore, those who are 65 and older are likely to have a very different relationship between broadband introduction and health. Our analysis sample includes pregnant women, but our results are largely robust to excluding them from the sample.
The core set of questions include a set of fixed core questions asked every year and a set of rotating core questions asked every other year. We focus on weight and health behavior outcomes from the fixed core of questions, but also utilize responses regarding the intensity of exercise that are part of the rotating core of questions in 2001, 2002, 2003, 2005, and 2007.
Calculations made using Census county population estimates for 2000.
See, for example, Utilization of Ambulatory Medical Care by Women: United States, 1997-1998. Vital Statistics and Health Series Report 13, No. 149. 51 pp. (PHS) 2001-1720.
This makes it difficult to do an event study which is ideally done with a balanced panel so as not to pick up compositional changes as counties enter and exit the sample in the graph.
The following obesity-related interventions were prominent: food and cigarette taxes/prices, state mandatory physical education, nutrition and calorie labeling, and advertising of bad health behaviors (Cawley and Ruhm, 2011: pgs 97-109; Cawley 2015: pgs 256-258). For all of these policies, we generally did not see any reason to suggest that they would be correlated with broadband introduction. In addition, for many of these interventions, it was unclear how effective they were at changing obesity and nutrition. The recent research suggests that food taxes do not have an impact on obesity or nutrition (Cawley and Ruhm, 2011: pg 168), and there has been mixed evidence that cigarette taxes affect obesity and weight gain. Specifically, the effect of cigarette taxes and prices on increasing obesity is sensitive to specifications (such as how time is modeled) and could actually decrease obesity when dynamic effects are allowed for (Courtemanche 2009). Likewise, securing causal estimates of the effect of mandatory physical education on obesity has been difficult due to their likely being policy endogeneity biasing those estimates (Cawley and Ruhm, 2011: pg, 175). We consider advertising online of bad health behaviors to be a credible pathway for our effects. However, well-identified evidence on advertising is hard to come by and the literature that does exist shows mixed and often inconclusive results of advertising on risky health behavior (Cawley and Ruhm, 2011: pg 39)
This is consistent with women engaging with online health information to a greater degree than men, as we mention in the Conceptual Framework section.
While the coefficient on obesity loses statistical significance, the magnitude of the coefficient is qualitatively similar.
These noisier effects on the non-white samples are potentially due to the smaller sample. An alternative explanation is that our broadband measure captures access less consistently for these group: though with somewhat larger effects on those who are affected.
These estimates are available upon request.
Since smoking is an appetite suppressant, it can be associated with declines in weight. However, we take the increase in smoking as evidence for a more general story of broadband expansions causing overall worse health behaviors which in turn outweighs the benefits of decreased food consumption from smoking.
See for example, https://www.huffingtonpost.com/kelly-coffey/a-trainer-comes-clean-abo_b_5977286.html, accessed March 28, 2018.
We use 1999 county income as a proxy that is correlated with yearly county income but that is not directly affected by broadband rollout.
For example, individual observations from Middlesex County, MA in the 1996, BRFSS are matched to the 1999 broadband measure for Middlesex County, MA.
We also estimate models for the other race-gender groups and find the current period estimates are congruent with the main paper table estimates (no statistically significant effect of current broadband). We do not report them in the main paper but are happy to share the estimates upon request.
We calculated this by multiplying our estimate of the effect of increasing obesity for white women (a 3.5 percentage point increase in obesity) by the change Internet providers over the years of our sample (a 29.3% increase) by the population of adult white women in the USA in 2005 (68,013,866). This suggests that the Internet pushed 1.2 million white women into obesity. According to Cawley and Meyerhoefer (2012), annual cost estimates for obesity are $3613 (women) or $2739 (white), suggesting an increase in costs of approximately $2.2 billion.
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Acknowledgements
We owe special thanks to Ken Couch, David Slutsky, Lyudmyla Sonchak, and to participants of the 2018 American Economic Association Annual Meeting, 2017 World Congress of the International Health Economics Association, 2017 Eastern Economic Association Annual Meeting, and 2016 Southern Economic Association Annual Meeting and two anonymous reviewers for their helpful comments on this paper.
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DiNardi, M., Guldi, M. & Simon, D. Body weight and Internet access: evidence from the rollout of broadband providers. J Popul Econ 32, 877–913 (2019). https://doi.org/10.1007/s00148-018-0709-9
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DOI: https://doi.org/10.1007/s00148-018-0709-9