Skip to main content
Log in

Body weight and Internet access: evidence from the rollout of broadband providers

  • Original Paper
  • Published:
Journal of Population Economics Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2

Similar content being viewed by others

Notes

  1. Additionally, without expert guidance, the large quantity of information available could lead consumers to accidently misuse the information they do receive.

  2. We describe this work in more detail in the Background section.

  3. For example, Bessière et al. (2010) use a random sample of the US population, but their study only covers 2 years.

  4. 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.

  5. 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.

  6. http://investor.shareholder.com/wbmd/releasedetail.cfm?releaseid=249537&CompanyID=HLTH and http://investor.shareholder.com/wbmd/releasedetail.cfm?releaseid=274852&CompanyID=WBMD, accessed April 26, 2018.

  7. Data available from http://www.pewInternet.org/files/2014/01/Usage-Over-Time-_May-2013.xlsx. Accessed June 20, 2016.

  8. 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.

  9. 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.

  10. Some argue there is a glut of disorganized health-related information online (Donald et al. 1998; Berland et al. 2001; Purcell et al. 2002).

  11. 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.

  12. 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).

  13. http://www.who.int/mediacentre/factsheets/fs311/en/ Accessed September 1, 2016.

  14. 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.

  15. A shift to a sedentary lifestyle is partially evidenced in the decreased availability of recreation spaces such as sidewalks and other open spaces.

  16. 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.

  17. 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.”

  18. 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.

  19. 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.

  20. Calculations made using Census county population estimates for 2000.

  21. 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.

  22. We have also run models with month by year fixed effects (rather than separately controlling for year fixed effects and month fixed effects), which produced similar results to our baseline specification. These results are in Appendix Tables 14 and 15.

  23. 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.

  24. 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)

  25. This is consistent with women engaging with online health information to a greater degree than men, as we mention in the Conceptual Framework section.

  26. While the coefficient on obesity loses statistical significance, the magnitude of the coefficient is qualitatively similar.

  27. 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.

  28. These estimates are available upon request.

  29. 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.

  30. See for example, https://www.huffingtonpost.com/kelly-coffey/a-trainer-comes-clean-abo_b_5977286.html, accessed March 28, 2018.

  31. We use 1999 county income as a proxy that is correlated with yearly county income but that is not directly affected by broadband rollout.

  32. For example, individual observations from Middlesex County, MA in the 1996, BRFSS are matched to the 1999 broadband measure for Middlesex County, MA.

  33. 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.

  34. The most straightforward approach is the Bonferroni correction, but it is viewed as overly conservative (Christensen and Miguel, 2016; Ross et al., 2008)

  35. 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.

References

  • Akatsu H, Kuffner J (1998) Medicine and the Internet. West J Med 169(5):311–317

    Google Scholar 

  • Akee R, Spilde K, Taylor J (2015) The Indian gaming regulatory act and its effect on American Indian economic development. J Econ Perspect 29(3):185–208

    Article  Google Scholar 

  • Akerman A, Gaarder I, Mogstad M (2015) The skill complementarity of broadband Internet. Q J Econ 130(4):1781–1824

    Article  Google Scholar 

  • Almond D, Hoynes H, Schanzenbach D (2011) Inside the war on poverty: the impact of food stamps on birth outcomes. Rev Econ Stat 93(2):387–403

    Article  Google Scholar 

  • Amante DJ, Hogan TP, Pagoto SL, English TM, Lapane KL (2015) Access to care and use of the Internet to search for health information: results from the US National Health Interview Survey. J Med Internet Res 17(4):e106

    Article  Google Scholar 

  • Anderson ML (2008) Multiple inference and gender differences in the effects of early intervention: a reevaluation of the Abecedarian, Perry preschool, and early training projects. J Am Stat Assoc 103(484):1481–1495

    Article  Google Scholar 

  • Atasoy H (2013) The effects of broadband Internet expansion on labor market outcomes. Ind Labor Relat Rev 66:315–345

    Article  Google Scholar 

  • Bailey MJ, Collins WJ (2011) Did improvements in household technology cause the baby boom? Evidence from electrification, appliance diffusion and the Amish. Am Econ J: Macroecon 3:189–217

    Google Scholar 

  • Bailey MJ, Goodman-Bacon A (2015) The war on poverty’s experiment in public medicine: community health centers and the mortality of older Americans. Am Econ Rev 105(3):1067–1104

    Article  Google Scholar 

  • Baker LB, Wagner TH, Singer S, Bundorf MK (2003) Use of the Internet and e-mail for health care information: results from a national survey. J Am Med Assoc 289(18):2400–2406

    Article  Google Scholar 

  • Bellou A (2015) The impact of Internet diffusion on marriage rates: evidence from the broadband market. J Popul Econ 28(2):265–297

    Article  Google Scholar 

  • Berland G, Elliot M, Morales L, Algazy J, Kravitz R, Broder M, Kanouse D, Muñoz J, Puyol J, Lara M, Watkins K, Yang H, McGlynn E (2001) Health information on the Internet: accessibility, quality, and readability in English and Spanish. J Am Med Assoc 285(20):2612–2621

    Article  Google Scholar 

  • Bessière K, Pressman S, Kiesler S, Kraut R (2010) Effects of Internet use on health and depression: a longitudinal study. J Med Internet Res 12(1):e6

    Article  Google Scholar 

  • Bhattacharya J, Bundorf MK (2009) The incidence of the healthcare costs of obesity. J Health Econ 28:649–658

    Article  Google Scholar 

  • Bhuller M, Havnes T, Leuven E, Mogstad M (2013) Broadband Internet: an information superhighway to sex crime? Rev Econ Stud 80:1237–1266

    Article  Google Scholar 

  • Biermann J, Golladay G, Greenfield M, Baker L (1999) Evaluation of cancer information on the Internet. Cancer 86:381–390

    Article  Google Scholar 

  • Carrell SE, Hoekstra M, West JE (2011) Is poor fitness contagious? Evidence from randomly assigned friends. J Public Econ 95(7-8):657–663

    Article  Google Scholar 

  • Case A, Paxson C (2008) Stature and status: height, health and labor market outcomes. J Polit Econ 116(3):499–532

    Article  Google Scholar 

  • Cawley JH (2011) The economics of obesity. In: Cawley JH (ed) The Oxford handbook of the social science of obesity. Oxford University Press, New York, pp 303–312

    Chapter  Google Scholar 

  • Cawley J (2015) An economy of scales: a selective review of obesity's economic causes, consequences, and solutions. J Health Econ 43:244–268

    Article  Google Scholar 

  • Cawley J, Meyerhoefer C (2012) The medical care costs of obesity: an instrumental variables approach. J Health Econ 31:219–230

    Article  Google Scholar 

  • Cawley J, Ruhm CJ (2011) The economics of risky health behaviors. Handbook of health economics. Vol. 2. Elsevier, 95-199.

  • Christakis N, Fowler J (2007) The spread of obesity in a large social network over 32 years. N Engl J Med 357:370–379

    Article  Google Scholar 

  • Christensen GS, Miguel E (2016) Transparency, reproducibility, and the credibility of economics research. National Bureau of Economic Research Working Paper No. 22989

  • Courtemanche C (2009) Rising cigarette prices and rising obesity: coincidence or unintended consequence? J Health Econ 28(4):781–798

    Article  Google Scholar 

  • Culver JD, Gerr R, Frumkin H (1997) Medical information on the Internet: a study of an electronic bulletin board. J Gen Intern Med 12(8):466–470

    Article  Google Scholar 

  • Cunningham SA, Vaquera E, Maturo CC, Venkat Narayan KM (2012) Is there evidence that friends influence body weight? A systematic review of empirical research. Soc Sci Med 75:1175–1183

    Article  Google Scholar 

  • DellaVigna S, Ethan K (2007) The Fox News effect: media bias and voting. Q J Econ 122(3):1187–1234

    Article  Google Scholar 

  • Dettling LJ (2017) Broadband in the labor market: the impact of residential high-speed Internet on married women’s labor force participation. Ind Labor Relat Rev 70(2):451–482

    Article  Google Scholar 

  • Dhurandhar EJ, Kaiser KA, Dawson JA, Alcorn AS, Keating KD, Allison DB (2015) Predicting adult weight change in the real world: a systematic review and meta-analysis accounting for compensatory changes in energy intake or expenditure. Int J Obes. 39(8):1181–1187

    Article  Google Scholar 

  • Donald A, Lindenberg B, Humphreys L (1998) Medicine and health on the Internet: the good, the bad, and the ugly. J Am Med Assoc 280(15):1303–1306

    Article  Google Scholar 

  • Finkelstein EA, Trogdon JG, Cohen JW, Dietz W (2009) Annual medical spending attributable to obesity: payer- and service-specific estimates. Health Aff 28(5):w822–w831

    Article  Google Scholar 

  • Fletcher J (2011) Peer effects and obesity. In: Cawley JH (ed) The Oxford handbook of the social science of obesity. Oxford University Press, New York, pp 303–312

    Google Scholar 

  • Gentzkow M (2006) Television and voter turnout. Q J Econ 121(3):931–972

    Article  Google Scholar 

  • Gentzkow M, Shapiro JM (2008) Preschool television viewing and adolescent Test Scores: Historical evidence from the Coleman study. Q J Econ 123(1):279–323

    Article  Google Scholar 

  • Guldi M, Herbst C (2017) Offline effects of online connecting: the impact of broadband diffusion on teen fertility decisions. J Popul Econ 30(1):69–91

    Article  Google Scholar 

  • Hoynes H, Schanzenbach D (2009) Consumption responses to in-kind transfers: evidence from the introduction of the food stamp program. Am Econ J: Appl Econ 1(4):109–139

    Google Scholar 

  • Hoynes H, Page M, Stevens AH (2011) Can targeted transfers improve birth outcomes? Evidence from the introduction of the WIC program. J Public Econ 95(7–8):813–827

    Article  Google Scholar 

  • Impicciatore P, Pandolfini C, Casella N, Bonati M (1997) Reliability of health information for the public on the world wide web: systematic survey of advice on managing fever in children at home. Br Med J 314:1875–1879

    Article  Google Scholar 

  • Jakicic JM, Davis KK, Rogers RJ, King WC, Marcus MD, Helsel DH, Rickman AD, Wahed AS, Belle SH (2016) Effect of wearable technology combined with a lifestyle intervention on long-term weight loss: the IDEA randomized clinical trial. J Am Med Assoc 316(11):1161–1171

    Article  Google Scholar 

  • Jensen R, Oster E (2009) The power of TV: cable television and women's status in rural India. Q J Econ 124(3):1057–1094

    Article  Google Scholar 

  • Jerome GJ, Myers VH, Young DR, Matthews-Ewald MR, Coughlin JW, Wingo BC, Ard JD, Champagne CM, Funk KL, Stevens VJ, Brantley PJ (2015) Psychosocial predictors of weight loss by race and sex. Clin Obes 5(6):342–348

    Article  Google Scholar 

  • Kearney M, Levine P (2015a) Media influences on social outcomes: the impact of MTV’s 16 and pregnant on teen childbearing. Am Econ Rev 105(12):3597–3632

    Article  Google Scholar 

  • Kearney M, Levine P (2015b) Early childhood education by MOOC: Lessons from Sesame Street. National Bureau of Economic Research Working Paper No. 21229

  • Kling JR, Liebman JB, Katz LF (2007) Experimental analysis of neighborhood effects. Econometrica 75(1):83–119

    Article  Google Scholar 

  • Kolko J (2012) Broadband and local growth. J Urban Econ 71:100–113

    Article  Google Scholar 

  • Kremer M, Levy D (2008) Peer effects and alcohol use among college students. J Econ Perspect 22(3):189–206

    Article  Google Scholar 

  • La Ferrara E, Chong A, Duryea S (2012) Soap operas and fertility: evidence from Brazil. Am Econ J: Appl Econ 4(4):1–31

    Google Scholar 

  • Lakdawalla D, Philipson T, Bhattacharya J (2005) Welfare-enhancing technological change and the growth of obesity. Am Econ Rev: Pap Proc 179th Ann Meet Am Econ Assoc 95(2):253–257

    Article  Google Scholar 

  • List, JA, Shaikj AM, Xu Y (2016) Multiple hypothesis testing in experimental economics. National Bureau of Economic Research Working Paper No. 21875

  • Lundborg P (2006) Having the wrong friends? Peer effects in adolescent substance use. J Health Econ 25:214–233

    Article  Google Scholar 

  • McLellan F (1998) Like hunger, like thirst’: patients, journals, and the Internet. Lancet 352:S39–S43

    Article  Google Scholar 

  • Melanson EL, Keadle SK, Donnelly JE, Braun B, King NA (2013) Resistance to exercise-induced weight loss: compensatory behavioral adaptations. Med Sci Sports Exerc 45:1600–1609

    Article  Google Scholar 

  • Miller WC, Koceja DM, Hamilton EJ (1997) A meta-analysis of the past 25 years of weight loss research using diet, exercise or diet plus exercise intervention. Int J Obes Relat Metabol Disord 21:941–947

    Article  Google Scholar 

  • Owen N, Healy GN, Matthews CE, Dunstan DW (2010) Too much sitting: the population health science of sedentary behavior. Exerc Sport Sci Rev 38(3):105–113

    Article  Google Scholar 

  • Potarca G (2017) Does the Internet affect assortative mating? Evidence from the U.S. and Germany. Soc Sci Res 61:278–297

    Article  Google Scholar 

  • Poy S, Schüller S (2016) Internet and voting in the Web 2.0 era: evidence from a local broadband policy. IZA Discussion Paper No. 9991

  • Purcell G, Wilson P, Delamothe T (2002) The quality of health information on the Internet: as for any other medium it varies widely; regulation is not the answer. Br Med J 324:557–558

    Article  Google Scholar 

  • Ross SL et al (2008) Mortgage lending in Chicago and Los Angeles: a paired testing study of the pre-application process. J Urban Econ 63(3):902–919

    Article  Google Scholar 

  • Ruhm C (2005) Health living in hard times. J Health Econ 24(2):341–363

    Article  Google Scholar 

  • Stigler G (1961) The economics of information. J Polit Econ 69(3):213–225

    Article  Google Scholar 

  • Suiziedelyte A (2012) How does searching for health information on the Internet affect individuals’ demand for health care services? Soc Sci Med 75(10):1828–1835

    Article  Google Scholar 

  • Thomas DM, Bouchard C, Church T, Slentz C, Kraus WE, Redman LM, Martin CK, Silva AM, Vossen M, Westerterp K, Heymsfield SB (2012) Why do individuals not lose more weight from an exercise intervention at a defined dose? An energy balance analysis. Obes Rev 13(10):835–847

    Article  Google Scholar 

  • Trudeau J (2015) the role of new media on teen sexual behaviors and fertility outcomes—the case of ‘16 and Pregnant. South Econ J 82(3):975–1003

    Article  Google Scholar 

  • Wagner TH, Hu T, Hibbard JH (2001) The demand for consumer health information. J Health Econ 20:1059–1075

    Article  Google Scholar 

  • Wellman B, Gulia M (1999) Net surfers don't ride alone: virtual community as community. In: Wellman B (ed) Networks in the global village. West-view, Boulder, pp 331–367

    Google Scholar 

  • Wellman B, Haase AQ, Witte J, Hampton K (2001) Does the Internet increase, decrease or supplement social capital? Am Behav Sci 45(3):436–455

    Article  Google Scholar 

  • Wellman, B, Salaff, J, Dimitrova, D, Garton, L, Gulia, M, Haythornthwaite, C. (1996) Computer networks as social networks: collaborative work, telework, and virtual community. Annual Review of Sociology 22: 213-238

  • Zhao S (2006) Do Internet users have more social ties? A call for differentiated analyses of Internet use. J Comput Mediat Commun 11(3):844–862

    Article  Google Scholar 

Download references

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.

Funding

The authors received no funding for this study.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to David Simon.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Responsible editor: Erdal Tekin

Appendix

Appendix

Table 13 Effect of broadband on weight using “balanced counties” samples
Table 14 Estimates of the effect of broadband availability on weight using month by year fixed effects specifications, white samples
Table 15 Estimates of the effect of broadband availability on weight using month by year fixed effects specifications, non-white samples
Table 16 Robustness test, 1990–2007 BRFSS with broadband set to zero before 1999, excludes 1994–1996
Table 17 Robustness test including pre-broadband era BRFSS samples, white women
Table 18 Estimates of the effect of broadband availability on weight, white women samples by period
Table 19 Other potential mechanisms
Table 20 Effect of broadband coverage on negative health behavior index

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00148-018-0709-9

Keywords

JEL Classification

Navigation