Maybe Next Month? Temperature Shocks and Dynamic Adjustments in Birth Rates

Abstract

We estimate the effects of temperature shocks on birth rates in the United States between 1931 and 2010. We find that days with a mean temperature above 80°F cause a large decline in birth rates 8 to 10 months later. Unlike prior studies, we demonstrate that the initial decline is followed by a partial rebound in births over the next few months, implying that populations mitigate some of the fertility cost by shifting conception month. This shift helps explain the observed peak in late-summer births in the United States. We also present new evidence that hot weather most likely harms fertility via reproductive health as opposed to sexual activity. Historical evidence suggests that air conditioning could be used to substantially offset the fertility costs of high temperatures.

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Notes

  1. 1.

    Recent research has demonstrated that unexpected temperature shocks affect a variety of socioeconomic outcomes, including mortality, labor supply, and income. For mortality, see Barreca (2012), Barreca et al. (2016), Deschenes and Greenstone (2011), and Deschenes and Moretti (2009). For infant health, see Deschenes et al. (2009). For income, see Deryugina and Hsiang (2014). For labor supply, see Graff Zivin and Neidell (2014). See Dell et al. (2014) for a summary of this literature.

  2. 2.

    Throughout the article, “above 80°F” indicates mean temperature above 80°F.

  3. 3.

    With constant conception probabilities, the cumulative rebound in conceptions as of cycle t + m would be \( \sum \limits_1^mp{\left(1-p\right)}^{m-1}{\Delta }_t \). If p = 10 %, the rebound would be 27 % after 3 months and 72 % after 12 months.

  4. 4.

    Indeed, we find that the rebound is smaller for mothers older than 35 years (see Online Resource Table S1).

  5. 5.

    See Lam et al. (1994) for a more formal fertility model.

  6. 6.

    Although LM noted testing for effects at 7, 8, and 11 months, these estimates are statistically insignificant and are dropped from the model. Seiver (1989) tested for a rebound in births after month 9 for the period 1950–1960, but only a test of joint significance is reported in the text. Seiver concluded that “the making up effect is essentially complete after 7 months” (p. 246). The confidence interval on this statistical test (not reported) is potentially large because the model is estimated separately by state and the data span 11 years.

  7. 7.

    The first year that birth counts are available at the state-month level is 1931. South Dakota and Texas were not part of the Vital Statistics sample until 1932 and 1933, respectively. Monthly data with finer geographic detail, such as county, are not available prior to 1968.

  8. 8.

    The first year of the natality data is 1968. In the earlier years, some states’ data are 50 % samples, so we weight these births by 2. The public-use natality files do not report state of residence after 2004. Therefore, we rely on the CDC online National Vital Statistics System for the years 2005–2010.

  9. 9.

    State of residence is the preferred measure given that migration could be endogenous to temperature.

  10. 10.

    To address measurement error, we exclude stations for a given year-month if they are missing temperature readings more than 10 days in the year or 2 days in any given calendar month.

  11. 11.

    We linearly interpolate county population between the decennial censuses up until 1968. Starting in 1969, we use county population estimates from the National Cancer Institute (2013).

  12. 12.

    We control for the fraction of days in the month tk with between 0.01 and 0.50 inches and more than 0.50 inches. The omitted category is the fraction of the month with no precipitation.

  13. 13.

    The average number of monthly births over our sample period is 295,000.

  14. 14.

    Theoretically, we could observe an increase in births in month 10 if the affected population becomes susceptible to conceiving in the month immediately following the temperature shock.

  15. 15.

    The marginal effect (dy) of a one-day change in the temperature (dt) in a quadratic model with monthly average temperature (y = β1T + β2T2) is dy / dt = β1dT / dt + 2β2T dT / dt, where T is the average monthly temperature. Comparison with studies using average monthly temperature (Lam and Miron 1996; Seiver 1989) is also complicated by the fact their models were estimated separately for each state and race. For example, for whites in Georgia, LM’s estimates imply that a one-day increase in temperature from 65°F to 85°F, in a month with mean temperatures of 65°F, would reduce birth rates nine months later by 0.17 %, which is less than one-half the magnitude of our estimate.

  16. 16.

    We define “air conditioning” as at least one air conditioning unit or central air conditioning.

  17. 17.

    Fig. S7 (Online Resource 1) illustrates the estimated AC coverage by region. See Biddle (2008) for a discussion on the historical determinants of AC in the United States. Assuming classical measurement error, we expect the estimates to be biased downward. Additionally, clustering the standard errors at the state level helps mitigate concerns about the interpolation generating serially correlated errors.

  18. 18.

    The interaction between temperature and AC is small and statistically insignificant at colder temperatures, further supporting the presumed temperature control mechanism provided by AC.

  19. 19.

    Fehring et al. (2006:376) surveyed 141 women between 3 to 13 cycles each and found that “95 % of the cycles had all 6 days of fertile phase between days 4 and 23, but only 25 % of participants had all days of the fertile phase between days 10 and 17.”

  20. 20.

    In a review article, Boklage (1990) found that close to three-quarters of conceptions do not survive past six weeks of gestation. Wilde et al. (2017) provided evidence that temperature improved long-term outcomes for surviving cohorts most likely by selectively culling weaker fetuses. See Catalano et al. (2006), Sanders and Stoecker (2015), and Trivers and Willard (1973) for more detail on the role that the fetus gender plays in survival to birth.

  21. 21.

    We divide each calendar into 52 “weeks,” where the 365th day and 366th day (during leap years) are included in the 52nd week. State population data come from the National Cancer Institute (2013) and are assigned at the state-year level.

  22. 22.

    This restriction excludes Alabama, Arkansas, Connecticut, Delaware, Florida, Georgia, Idaho, Maine, New Mexico, Oregon, Pennsylvania, Texas, Virginia, and Wisconsin.

  23. 23.

    We partial out the state-by-week fixed effects prior to estimation to reduce the computational burden.

  24. 24.

    Unlike with Eq. (1), temperature shocks could affect conception rates after the fact; that is, there is no placebo check in Eq. (2).

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Acknowledgments

The authors thank the numerous seminar participants at Oberlin College, Simon Fraser University, Tulane University, University of California–Merced, University of Houston, University of Mississippi, University of Montreal, 2014 IZA Conference on the Labor Market Effects of Environmental Policies, 2014 Southeastern Health Economics Study Group, 2014 Southern Economic Association Meetings, 2015 Society of Labor Economist Meetings, and 2016 NBER Spring Meetings. In addition, special thanks are owed to D. Mark Anderson, Marianne Bitler, Janet Currie, Marisa Domino, Jason Fletcher, Caroline Hoxby, Solomon Hsiang, Daniel Hungerman, Amir Jina, Jason Lindo, Elaine Liu, Matthew Neidell, Nick Sanders, and Hannes Schwandt for their helpful comments.

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Correspondence to Alan Barreca.

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Barreca, A., Deschenes, O. & Guldi, M. Maybe Next Month? Temperature Shocks and Dynamic Adjustments in Birth Rates. Demography 55, 1269–1293 (2018). https://doi.org/10.1007/s13524-018-0690-7

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Keywords

  • Fertility
  • Birth rates
  • Birth seasonality
  • Temperature