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Offline effects of online connecting: the impact of broadband diffusion on teen fertility decisions

Abstract

Broadband (high-speed) internet access expanded rapidly from 1999 to 2007 and is associated with higher economic growth and labor market activity. In this paper, we examine whether the rollout also affected the social connections that teens make. Specifically, we look at the relationship between increased broadband access and teen fertility. We hypothesize that increasing access to high-speed internet can influence fertility decisions by changing the size of the market as well as increasing the information available to participants in the market. We seek to understand both the overall effect of broadband internet on teen fertility and the mechanisms underlying this effect. Our results suggest that increased broadband access explains at least 7 % of the decline in the teen birth rate between 1999 and 2007. Although we focus on social markets, this work contributes more broadly to an understanding of how new technology interacts with existing markets.

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Notes

  1. Rates of teen internet use exceed 90 % for nearly every demographic group—including non-Whites, those in rural areas, and those with low-education parents—while rates of computer ownership are consistently well above 60 % (Madden et al. 2013).

  2. The services of MySpace (2003), Facebook (2004; 2006 for teens), Twitter (2006), Snapchat (2011), and others that we mention were not all available during our period of study; we include these as examples of “social media.”

  3. Teens have been shown to be highly responsive to their peers’ behaviors regarding teen childbearing and alcohol use (Fletcher 2012; Yakusheva and Fletcher 2015).

  4. Bellou (2015), which we discuss in more detail below, shows that friction in the marriage market is reduced as broadband internet access increases.

  5. For example, see http://www.huffingtonpost.com/2013/03/02/teens-discuss-online-relationships-and_n_2792601.html and http://dating.lovetoknow.com/Teen_Online_Dating.

  6. Incarceration effects are found to exist through the consumption of other forms of media. For example, Dahl and DellaVigna (2009) find that rates of violent crime are lower on the same day that popular violent movies are released in a given local area, which they attribute to an incarceration effect.

  7. Dettling (2014) provides evidence that increased at-home broadband access leads to higher rates of married female labor force participation, suggesting that the ability to work from home is a key factor for this demographic.

  8. Dahl and Price (2012) provide a comprehensive review of the literature.

  9. Data were obtained from http://transition.fcc.gov/wcb/iatd/comp.html on June 20, 2012. As described within the documentation, these data 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.”

  10. Through correspondence with the FCC, we were informed that county-level counts are not available in the 1999 to 2007 period.

  11. We utilize information on the Missouri Census Data Center page to map postal zip codes into county geographic units: http://mcdc.missouri.edu/websas/geocorr12.html and information on county FIPS codes available on the National Bureau of Economic Research data page: http://www.nber.org/data/ssa-fips-state-county-crosswalk.html.

  12. Our measure of broadband diffusion is similar to, though distinct from, the measure used in previous studies. For example, our measure is most similar to Atasoy (2013) who creates a population-weighted binary indicator for the presence of any broadband provider in a given zip code (i.e., ZCTA). Kolko (2012a, 2012b)) creates a linear measure of access, assigning a value of 0 to zip codes with zero providers, a value of 2 to zip codes with one to three providers, and the actual number of providers to zip codes with more than three providers.

  13. We exclude Hawaii and Louisiana. We omit Louisiana counties due to changes in the infrastructure, including broadband, as a result of hurricane Katrina. Hawaii is omitted due to missing population information.

  14. The trend depicted by Pew Research Center’s analyses accords with data collected by other entities. For example, the US Department of Commerce (2011) estimates that broadband internet use rose sevenfold, from 9 to 64 % between 2001 and 2009. Simultaneously, households with internet use at home (regardless of connection speed) rose from 18 % in 1997 to 62 % in 2007 (U.S. Census Bureau Current Population Survey, 1984-2009). Using these CPS data, we compute that households reporting a fast connection at home rose from approximately 5 % in 2000 to 56 % in 2007 (Flood et al. 2015). It may appear inconsistent that, on the one hand, we calculate a broadband penetration rate of 75 % in 1999, but on the other hand, the Pew survey shows that only 3 % of households reported a high-speed internet connection in 2000. The apparent disconnection is partially explained by the fact that our measure of broadband access includes take-up by households and firms (in addition to schools and universities, libraries, and public agencies). Given that take-up by households lagged that of non-households, the high broadband penetration rates in the early years suggest that the predominant customer was the former group. However, as we state in the text, individuals often report accessing the internet in places outside their own home, suggesting that a more comprehensive broadband measure is preferable because it captures the effect of internet use in these non-household environments.

  15. SEER population data can be retrieved from the National Cancer Institute at http://seer.cancer.gov/popdata/.

  16. Abortion data source: http://www.johnstonsarchive.net/policy/abortion/#UC accessed April 15, 2014, to April 22, 2014.

  17. REIS data can be accessed via http://www.bea.gov/regional/.

  18. Codes obtained from http://www.ers.usda.gov/data-products/rural-urban-continuum-codes on April 10, 2014.

  19. Estimates from unweighted regressions produce similar coefficient estimates, though the unweighted estimates are less precisely estimated.

  20. The FCC data that we use is only available starting in 1999, so we are unable to observe the exact time when every county gets their first provider.

  21. The figure looks very similar if we examine the natural log of the birth rate rather than the birth rate.

  22. We experiment with several alternative cutoff points. Specifically, we examine penetration cutoffs of 5, 10, 15, and 20 % as well as an upper-limit cutoff of 90 or 95 % (instead of 98 %). In all cases, the patterns of results discussed in the text are quite comparable to when these alternative cutoffs are used.

  23. We compute the percentage change in teen births due to BBPCT, \( \widehat{\beta}*\frac{\varDelta BBPCT}{BBPC{T}_{1999}} \) = −2.185 ∗ 0.231 / 0.7557 = −0.6679 %, indicating that teen births fall by 0.6679 % with a 30.5 % increase in broadband over the period.

  24. We compute the percentage change in teen births over the period as follows: % change in teen births over the period: (46.83483 − 51.78337) / 51.78337 = −9.556 %. We find that (0.6679) / 9.556 = 6.9893 % or about 7 % of the drop in teen births over the period can be explained by broadband expansion.

  25. For the full sample, we estimate a specification similar to that used to produce the estimates in Table 2, but where we include an interaction of BBpct and the metro indicator. No coefficient estimates for these interactions are statistically significant, suggesting that the effects are not different for metro versus non-metro counties.

  26. In an additional set of analyses not presented in the tables, we re-estimated the birth rate regressions omitting the county-specific linear time trends. The coefficient estimates within metro or non-metro areas are similar in magnitude to those in the baseline specification. However, for the estimates that are statistically significant, the standard errors are smaller when the county- specific trends are omitted, suggesting that including trends is the more conservative approach and the approach that we have chosen to take throughout the paper.

  27. Results are available upon request.

  28. For these regressions, the analysis sample is somewhat smaller relative to our main analysis sample (see Table 2) since some counties in our original sample do not have teen birth information for the mid-1990s. In results not reported in the paper, we show that the reduction in the sample size does not appear to be driving the results.

  29. We would prefer to create an age-specific abortion rate at the county level. Unfortunately, to our knowledge, only the total number of abortions is collected at the county level (and not the number of abortions by age). However, we use two different county population denominators when creating the measure: females aged 15 to 19 and females aged 15 to 44. While our coefficients change from one denominator to another, our qualitative findings are robust to choice of denominator. In the main analysis tables, we use the county female population aged 15 to 44 as the denominator, as this reflects the population likely representing the total number of abortions in a county in a given year.

  30. It should also be noted that Sabia and Rees (2012) show that educational attainment drops as the number of sex partners increases. If broadband access is contributing to a drop in sexual activity and, hence, a drop in sexual partners, then it may also be leading to higher educational attainment for the affected teens.

  31. Results are available upon request.

  32. Only nine states report information on the variables of interest throughout our study period. The states are as follows: Delaware, Massachusetts, Michigan, Missouri, Montana, Nevada, South Dakota, Wisconsin, and Wyoming.

  33. We obtain state-level teen gonorrhea rates from the CDC’s ATLAS tool for the period 2000 to2007. http://gis.cdc.gov/GRASP/NCHHSTPAtlas/main.html accessed February 19, 2015.

  34. Results available upon request.

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Acknowledgments

We owe special thanks to the editor and the anonymous referees of this journal for their valuable help and guidance during the review process. We also thank all of the individuals who provided feedback during earlier versions of the paper including Nicholas J. Sanders, Joshua Wilde, Giulia La Mattina, and Sharmila Vishwasrao, as well as the participants of the Florida Atlantic University Economics Department Seminar, the University of South Florida Economics Department Seminar, and the attendees of the 2015 European Association of Labour Economists (EALE) and the Society of Labor Economists (SOLE) Joint Meeting and the 2015 Southern Economic Association Annual Meeting. Any remaining errors are our own.

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Correspondence to Melanie Guldi or Chris M. Herbst.

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Guldi, M., Herbst, C.M. Offline effects of online connecting: the impact of broadband diffusion on teen fertility decisions. J Popul Econ 30, 69–91 (2017). https://doi.org/10.1007/s00148-016-0605-0

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Keywords

  • Fertility
  • Birth rates
  • Broadband
  • New media

JEL codes

  • J13
  • J18