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.

Keywords: Birth rates; Birth seasonality; Fertility; Temperature.

Fig. 1

Seasonality in daily birth rate per 100,000 residents, 1931–2010, by census region. Calculations use state-year populations as weights

Fig. 2

Estimated temperature-fertility relationship: Effect of daily mean temperature on log birth rate. The diamonds are the point estimates, and the brackets represent +/− 2 standard errors. The estimates can be interpreted as the impact on the log monthly birth rate, in log points, of one additional day with a mean temperature >80°F relative to 60°F to 70°F. The model has year-month fixed effects, state-by-calendar-month fixed effects, state-by-calendar month quadratic time trends, and state-year fixed effects. We control for fraction of days with precipitation between 0.01 and 0.50 inches and more than 0.51 inches in each month. In addition, we control for effects for up to 24 months after exposure (and 3 months prior to exposure as a placebo check). The regressions are weighted by state-year population in the preceding year. Standard errors are clustered at the state level. The gray shading highlights both 0 and 9 months from exposure

Fig. 3

Estimated temperature-fertility relationship: Different functional forms for temperature. In the spline model, the estimates come from a cubic polynomial spline function with knots at daily mean temperatures (in degrees Fahrenheit) of 10, 30, 40, 70, and 90. The diurnal model captures the proportion of the day in a given 10°F interval, where diurnal temperatures are linearly interpolated from the daily maximum and daily minimum temperature. The bounds for the diurnal model are set at 0°F and 90°F. Each model has identical controls to our core model and the same number of lags and leads

Fig. 4

Estimated temperature-fertility relationship: Comparison with monthly quadratic model. The daily bins model refers to our core model. See notes to Fig. 2 for details on that model. The spline estimates come from a cubic polynomial spline function with knots at daily mean temperatures (in degrees Fahrenheit) of 10, 30, 40, 70, and 90. The monthly quadratic model has identical controls to our core model and the same number of lags and leads, except that temperature is a quadratic function of the monthly mean temperature

Fig. 5

Model predictions of log birth rate. See notes to Fig. 2 for details on the core model, which controls for the full set of exposure months (−3, −2, …, +24). In Panel a, the “Months 9 and 10 only” model controls only for exposure in months 9 and 10. We use only the temperature estimates to make these predictions and ignore rainfall and all other controls. We recenter both the observed and predicted values around June, so the values should be interpreted as deviations, in log points, from June

Fig. 6

Estimated temperature-fertility relationship: Effect of daily mean temperature >80°F relative to 60°F to 70°F on log birth rate 9 months later, by decade. The diamonds are the point estimates, and the brackets represent +/− 2 standard errors. The model controls for exposure months 8–13. We use the full sample of years and interact the temperature variables with an indicator for the given decade. We include 2010 in the 2000s. See notes for Fig. 2 for details on the other model controls

Fig. 7

Estimated impact around the time of conception: Effect of daily mean temperature >80°F relative to 60°F to 70°F on log conception-survival rate. The coefficient can be interpreted as the effect of one >80°F day some weeks from the estimated week of conception on the log of the number of conceptions that survive to birth in week 0. The gray shading highlights the approximated week of conception. The natality data have date of last menses beginning in 1969, which we use to infer week of conception. We assume that the week of conception is two weeks after the reported date of last menses. We calculate the conception-survival rate as the total number of conceptions in a given week divided by the state-year population in 1,000s. States that do not report last date of menses in any one year are dropped entirely from the sample; these excluded states are AL, AR, CT, DE, FL, GA, ID, MA, NM, OR, PA, TX, VA, and WI. We partial out state-by-week fixed from the outcome and predictor variables prior to estimation to reduce the computational burden. The model then includes year-month-week fixed effects and state-by-year fixed effects. The regressions are weighted by state-year population in the preceding year. Standard errors are clustered at the state level