Climate Change and Birth Weight

American Economic Review: Papers & Proceedings 2009, 99:2, 211–217 http://www.aeaweb.org/articles.php?doi 10.1257/aer.99.2.211 =

By Olivier Deschênes, Michael Greenstone, and Jonathan Guryan*

There is a growing consensus that emissions finding with estimates of the distribution of of greenhouse gases due to human activity will future daily temperatures from state-of-the-art alter the earth’s climate, most notably by causing climate change predictions. We then use these temperatures, precipitation levels, and weather predictions of the effect of climate change on variability to increase. The design of optimal the distribution of daily temperatures to esti- climate change mitigation policies requires estimate the predicted effect of global climate mates of the health and other benefits of reduc- change on future birth weights by the end of tions in greenhouse gases; current evidence on the century. These estimates imply that mean the magnitude of the direct and indirect impacts, birth weights will decrease on average by 0.22 however, is considered insufficient for reliable percent 7.5 grams among whites and by 0.36 conclusions A. J. McMichael et al. 2003 .1 percent (11.5 grams) for blacks by the end of the In addition( to the overall predicted warming) century.( Further, the) impact is not spread evenly trend, one important feature of many global cli- through the birth weight distribution. We find an mate change forecasts is an increased incidence estimated 5.9 percent increase in the probabil- of very high and low temperatures. To provide ity of a low birth weight birth defined as less evidence of the potential benefits of greenhouse than 2,500 grams for whites and( a 5.0 percent gas reductions, this paper documents whether increase for blacks.) 2 the temperature variation predicted to be part A small set of studies attempt to explain sea- of climate change, including extreme high and sonality in birth weight with exposure to extreme low temperatures, historically has had negative temperatures in utero L. J. Murray et al. 2000; health consequences through its effect on babies Debbie A. Lawlor, David( A. Leon, and George while in utero. Davey Smith 2005 . Although there is some evi- Using individual-level data on more than dence of a negative) relationship between mean 37.1 million births, we find that exposure to temperatures and birth weight, the absence of a extreme hot temperatures during pregnancy cohesive framework, limited statistical models, leads to lower birth weight. We combine this and relatively small samples have limited the development of clear stylized facts. Further, no consensus has emerged concerning the physio- * Deschênes: Department of Economics, University logical mechanisms linking heat and cold stress of California, Santa Barbara, 2127 North Hall, Santa in utero, although fetal nutrient intake is often Barbara, CA 93106, and NBER (e-mail: olivier@econ. ucsb.edu); Greenstone: Department of Economics, MIT, mentioned without direct evidence. 50 Memorial Drive, E52-359, Cambridge, MA 02142, Brookings Institution, and NBER (e-mail: mgreenst@ mit.edu); Guryan: University of Chicago Booth School of Business, 5807 S. Woodlawn Avenue, Chicago, IL 60637, and NBER (e-mail: [email protected]). We thank 2 This study of the impact of extreme temperature expo- Douglas Almond for his especially insightful discussion. sure on birth weight is related to the fetal origins hypothesis Henry Swift provided outstanding research assistance. David J. P. Barker, 1994 . In its extended form, it states ( ) We gratefully acknowledge support from the University of that economic and environmental conditions during preg- Chicago’s Chicago Energy Initiative. nancy may have long-lasting impacts on long-term health 1 See Richard S. J. Tol 2002a, b for overall estimates and socioeconomic status see e.g., Douglas Almond 2006; of the costs of climate change,( which) are obtained by sum- Sandra Black, Paul J. Devereux,( and Kjell G. Salvanes ming costs over multiple areas including human health, 2007; Heather Royer 2009 . A key variable in this litera- agriculture, forestry, species/ecosystems, and sea level rise. ture is birth weight, which) is considered by some to be a Deschênes and Greenstone 2007b provide evidence on summary measure of the fetal experience. Future work the impacts on the US agricultures( )sector. Deschênes and will investigate whether exposure to extreme temperatures Greenstone 2007a and Robin Burgess et al. 2009 esti- affects long-run economic and health outcomes, whether mate the mortality( )impacts of climate change in( the United) through its effect on birth weight or on latent factors that do States and India, respectively. not affect birth weight directly. 211 212 AEA PAPERS AND PROCEEDINGS MAY 2009

Our detailed historical temperature data allow from stations that are located within a 200-km us to date specifically each shock to the conditions radius of each county’s centroid. experienced in utero. We are thus able not only to estimate the effects of exposure to extreme Climate Change Prediction Data.—Climate temperature while in utero, but also to explore predictions are taken from the application of how this effect varies according to the timing the A2 scenario to the National Center for during gestation. This allows us to shed light on Atmospheric Research Community Climate the mechanisms at work in fetal programming. System Model CCSM 3, which is a state of the Of particular interest, we find that as much as 95 art coupled atmospheric-ocean( ) general circula- percent of the effect of in utero exposure to very tion model National Center for Atmospheric hot ambient temperatures occurs from exposure Research 2007( . We use the CCSM 3 A2 daily during the second and third trimesters. temperature predictions) for grid points throughout the continental United States for the years I. Data Sources and Summary Statistics 2070–2099. From the CCSM predictions, we assign each county a daily weather realization, To implement the analysis, we collected the aggregating in the same way we aggregate the most detailed and comprehensive data available contemporaneous temperatures. Each county’s on infant birth outcomes, weather, and predicted end-of-century predicted climate is the simple climate change. This section describes these data average of the predicted weather realizations for and reports summary statistics. See Deschênes the 2070–2099 period. and Greenstone 2007a for further details. ( ) B. Descriptive Statistics A. Data Sources Birth Outcome Statistics.—The main analysis Birth Weight Data.—Microdata on birth will examine the effect of extreme temperatures weight and date of birth are taken from the on birth weight, by race. There are substantial 1972–1988 National Center for Health Statistics differences in birth outcomes between blacks Natality Detail Files NDF . The data include and whites. Mean birth weights are higher for exact date of birth, ( race, ) sex, birth weight, whites 3,417 grams , by about 235 grams. The county of birth, mother’s marital status, moth- percent( of “low-birth-weight”) births is more er’s education, and the exact date of the mother’s than twice as large for blacks than whites 9.3 last menstrual period. From the last factor we percent compared to 4.1 percent , while ( the infer the date of “conception” and measure the black gestational period is five days) shorter on trimesters of the pregnancy relative to that date. average. For both races there are marked sea- This is preferable to dating gestation age relative sonal differences in birth outcomes, including to the date of birth, since gestation length itself birth weight and the number of births, across may be an outcome of exposure to extreme tem- quarters of conception. peratures. Observations are dropped from the If differences in fertility by date of conception analysis sample if either race, sex, birth weight, reflect differences in socioeconomic, health, or or date of last menstrual period is missing. The overall fertility attributes, associations between sample includes 37.1 million singleton births to temperature exposure and pregnancy outcomes mothers age 16–45 in the continental 48 states may be confounded. We address the potential and the District of Columbia. confounding of socioeconomic and health attributes with the timing of conception by utilizing Weather Data.—The weather data are county-level interannual variation in tempera- drawn from the National Climatic Data Center ture and controlling for a smooth function of the Summary of the Day Data File TD–3200 . date of conception. The key variables for our analysis( are the daily) maximum and minimum temperature, which Weather and Climate Change Statistics.— we average to construct daily average tempera- The regressors of interest in the analysis are ture. Station-level weather data are aggregated the number of days during the gestation period at the county level by taking an inverse-distance in which a county’s daily average mean of the weighted average of all the valid measurements minimum and maximum temperature( falls into ) VOL. 99 NO. 2 Climate Change and Birth Weight 213

120 1972–1988 Average Predicted change, CCSM 3 A2 100

0 25F 25–45 45–65 65–85 85F < > 20 −

40 − Figure 1. Distribution of HistoricalExposure anddays Predicted in 5 temperature Exposure bins, Days for in 270 Five day Daily gestation Temperature Bins For 270-day gestation ( ) each of five temperature bins 25°F, 25°–45°F, geographic distribution of births remains con- 45°–65°F, 65°–85°F, 85°F(<. In Figure 1, the stant. The CCSM 3 A2 predictions are that the white bars depict the average > distribution) of daily typical pregnancy will be exposed to 7.8 fewer mean temperature exposure over the course of days below 25° F per year and 29.9 more days in the typical pregnancy in the US between 1972 excess of 85° F. The majority of this temperature and 1988. In the 270 days after conception, the increase comes from a reduction of 24.1 days in average pregnancy in the US is exposed to 13.4 the 45°–65° F bin.4 days with a temperature less than 25°F, 252.8 days in the 25°–85°F range, and 3.8 days greater II. Econometric Strategy than 85°F. In the subsequent analysis, these bins are calculated separately by trimester of the The subsequent results are based on the fit- pregnancy to allow for substantial flexibility in ting of the following equation: the semiparametric modeling of the effect of TR1 TR1 temperature on birth weight. The average preg- 1 Y TMEAN ( ) i = ∑ θgj ctdj nancy is exposed to 13.4 days lower than 25°F j TR2 TR2 and 3.8 days warmer than 85°F. TMEAN +∑ θgj ctdj After we estimate the effect of temperature j TR3 TR3 exposure in utero on birth weight, we will gj TMEANctdj estimate the implied effect on birth weight of + ∑j θ the 2070–2099 CCSM 3 A2 temperature pre- Xi ctg f dg + i, dictions.3 The black bars in Figure 1 report + β + α + ( ) ε the predicted change in the number of days where i denotes the individual birth, c the in each of the temperature bins, assuming the county, t the year, g the demographic group defined by sex and race , and d represents the “day( of the year” of conception,) which ranges 3 For comparability, we follow much of the previous literature on climate change and focus on the temperatures predicted to prevail at the end of the century. We were unable to obtain CCSM 3 A2 predictions for the past, so the 4 We focus on averages, although there is substantial magnitude of any model errors is unknown and we cannot geographic heterogeneity in historical temperatures and adjust the predicted temperature changes for such errors. climate change predictions. 214 AEA PAPERS AND PROCEEDINGS MAY 2009 from 1–365. Therefore, the indices t and d fully tional day in a given bin in a given trimester on determine the exact date of conception. the log birth weight, relative to the impact of a The two dependent variables are log birth day between 45° and 65°F. The figure also plots weight and an indicator for low birth weight i.e., the 95 percent confidence interval associated less than 2,500 grams . Individual-level controls( with the estimates. for mother’s age, fertility) history, marital status, The figures show a strong negative relation- and educational attainment are included in the ship between birth weight and temperature, vector Xi. The county-year-demographic group particularly in the second and third trimesters. fixed effects, ctg, account for cross-sectional For all three trimesters, exposure to hot days is variation in temperatureα and birth weight and a associated with a statistically significant decline quartic in conception date ranges from 1–365 , in birth weight, which ranges in magnitude from ( ) f dg , accounts for demographic group-specific 0.003 percent to 0.009 percent per such day, seasonality.( ) These variables are included to cap- relative to a day in the reference category. The ture the effect of any secular difference in birth third trimester effect is likely an underestimate outcomes within year that is independent of tem- because many of the births are delivered fewer perature exposure. The last term in the equation than 270 days after conception. is the stochastic error term, i. We report stan- Table 1 presents calculations of the predicted dard errors that are clusteredε at the county-by- impacts of climate change on birth weight out- demographic group level. We also weight by the comes, based on CCSM 3 A2 end-of-century “record weight” variable contained in the NDF. predictions. Panel A pertains to whites and panel TR1 The variables of interest, TMEANctdj , B to blacks. To calculate the implied impact of TR2 TR3 TMEANctdj , and TMEANctdj , are the measures climate change on birth weight for each county of temperature exposure during each trimester on each conception date, we multiply the esti- of the gestational period. The j index refers to mated ’s for each trimester by the predicted the five daily mean temperature bins described change θin days in each temperature bin for each above. The choice of five temperature bins is trimester. We then report the weighted average motivated by an effort to allow the data, rather across counties and over the calendar year. It is than parametric assumptions, to determine the straightforward to calculate the standard error birth weight–temperature exposure relation- of this prediction reported below the estimate ship, while also obtaining estimates that have in parentheses , since( the estimated mortality empirical content.5 By conditioning on county- change is a linear) function of the parameters. year-demographic group effects, the associated Columns 1–3 report this calculation for gesta- parameters are identified from county-specific tional trimesters 1–3, while column 4 sums the deviations in weather about the county averages, impact across trimesters to obtain the full effect after controlling for county-specific annual on log birth weights. For each panel we report shocks. Due to the unpredictability of tempera- three temperature categories 25°F, 25°–85°F, ture fluctuations, we presume that this variation and 85°F along with the(< “total impact,” is orthogonal to unobserved determinants of which> sums across) the whole daily temperature birth weight. distribution. Taken as a whole, the CCSM 3 A2 results suggest that climate change would lead to III. Results a reduction in average birth weights of 0.22 percent whites and 0.36 percent blacks . In terms Figure 2 plots the estimated regression coef- of actual( weights,) these reductions( correspond) ficients associated with each temperature bin, by to losses of 7.5 and 11.5 grams, respectively. The trimester, from a model that pools all births and null hypothesis of a zero effect is easily rejected includes gender-by-race indicators. The panels at conventional significance levels. A, B, and C correspond to the three trimesters. Column 5 repeats this exercise, except the In each case the reference bin is 45°–65°F, so dependent variable is now an indicator for each coefficient measures the impact of an addi- whether the birth is designated as low birth weight. The estimated impacts for this outcome are substantially larger. In particular, climate 5 A similar methodology is used in Deschênes and change is predicted to increase the fraction of Greenstone 2007b . low-birth-weight births by roughly 5.9 percent ( ) VOL. 99 NO. 2 Climate Change and Birth Weight 215

Panel A: First trimester 0.00010

2 standard error Percent change in birth weight 2 standard error − + 0.00005

45–65 65–85 0.00000 25–45 25F 85F < >

0.00005 −

0.00010 −

0.00015 − Impact of temperature exposure during first trimester on log birth weight

Panel B: Second trimester 0.00010

2 standard error Percent change in birth weight 2 standard error − + 0.00005

45–65 0.00000 25F < 25–45 65–85 85F 0.00005 > −

0.00010 −

0.00015 − Impact of temperature exposure during second trimester on log birth weight

Panel C: Third trimester 0.00010

2 standard error Percent change in birth weight 2 standard error − + 0.00005

45–65 0.00000 25F < 25–45 65–85 0.00005 − 85F > 0.00010 −

0.00015 − Impact of temperature exposure during third trimester on log birth weight

Figure 2. Impact of Temperature Exposure during Gestational Period on Birth Weight 216 AEA PAPERS AND PROCEEDINGS MAY 2009

Table 1—Estimated Impact of Climate Change on Birth Weight, Based on CCSM 3 A2 Predictions

Impact of change in temperature exposure during: Full gestational Full gestational period period birth weight 2,500 gr 1st trimester 2nd trimester 3rd trimester ( ≤ ) 1 2 3 4 5 ( ) ( ) ( ) ( ) ( ) Panel A: Whites Total impact percent 0.0002 0.0010 0.0010 0.0022 0.0585 ( ) −0.0002 −0.0001 −0.0001 −0.0004 0.0098 ( ) ( ) ( ) ( ) ( ) Days less than 25°F 0.0001 0.0001 0.0001 0.0000 0.0049 0.0000 −0.0000 −0.0000 0.0001 0.0024 ( ) ( ) ( ) ( ) ( ) Days between 25°F 0.0000 0.0002 0.0001 0.0003 0.0073 and 85°F 0.0000 −0.0000 −0.0000 −0.0000 0.0000 ( ) ( ) ( ) ( ) ( ) Days more than 85°F 0.0003 0.0007 0.0009 0.0019 0.0463 0.0001 −0.0001 −0.0002 −0.0003 0.0073 ( ) ( ) ( ) ( ) ( ) Panel B: Blacks Total impact percent 0.0001 0.0018 0.0017 0.0036 0.0495 ( ) −0.0006 −0.0004 −0.0004 −0.0010 0.0129 ( ) ( ) ( ) ( ) ( ) Days less than 25°F 0.0000 0.0001 0.0001 0.0002 0.0032 0.0000 −0.0000 −0.0000 −0.0001 0.0022 ( ) ( ) ( ) ( ) ( ) Days between 25°F 0.0000 0.0001 0.0000 0.0001 0.0022 and 85°F 0.0001 −0.0000 0.0000 −0.0000 0.0011 ( ) ( ) ( ) ( ) ( ) Days more than 85°F 0.0002 0.0016 0.0016 0.0034 0.0441 −0.0005 −0.0004 −0.0004 −0.0009 0.0118 ( ) ( ) ( ) ( ) ( ) Notes: The estimates are from fixed effect regression models estimated separately by race. Each model includes county-by- year fixed effects, a quartic polynomial in date of conception, an indicator for the baby’s sex, and controls for mother charac- teristics. The dependent variables are the log birth weight and an indicator for low-birth-weight status i.e., 2,500 grams . Standard errors are clustered at the county-by-race level. See the text for further details. ( < )

for whites and 5.0 percent for blacks. It is evident run county-level temperature distributions, our that the impacts of temperature on weight are estimates imply that exposure to such extreme not equal through the birth weight distribution. ambient temperatures has deleterious effects on Two further findings are noteworthy. First, fetal health, causing a decrease in birth weight the birth weight reduction predicted by climate and an increase in the probability of low birth change models is almost entirely attributable to weight. The sensitivity of birth weight to tem- the predicted increase in exposure to “hot” temperature is concentrated almost completely in peratures. Across both races and outcome vari- the second and third trimesters of the pregnancy, ables, 79–94 percent of the estimated change is though it is not yet clear whether this is due to due to the increased number of days exceeding a compensatory mechanism that operates dur- 85°F. Second, the impacts are concentrated in ing the first trimester or because negative shocks the second and third trimesters. For example, to fetal health in the first trimester show up in between 91 percent and 97 percent of the full ges- indicators other than birth weight. Our analysis tational impact of climate change on birth weight also does not identify the effect of temperature comes from the second and third trimesters. exposure that works through nutrient accumula- tion separately from the direct effect on gesta- IV. Conclusions and Future Directions tion length. There are a few important caveats to these In addition to a predicted increase in average calculations and, more generally, to the analy- temperature, many global climate change models sis. The estimated impacts likely overstate the contain the oft-overlooked prediction that there impacts, because the analysis relies on interan- will be a large increase in the number of very nual variation in weather, and a greater array of hot days. Using interannual variation from long- adaptations e.g., migration will be available ( ) VOL. 99 NO. 2 Climate Change and Birth Weight 217 in response to permanent climate change. Mortality in India.” Unpublished. Additionally, an effort to project outcomes at the Deschênes, Olivier, and Michael Greenstone. end of the century requires a number of strong 2007a. “Climate Change, Mortality, and Adap- assumptions, including that the climate change tation: Evidence from Annual Fluctuations predictions are correct, relative prices will in Weather in the US.” MIT Working Paper remain constant, the same medical technologies revised 2008. 07–19. will prevail, and the demographic character- Deschênes, Olivier, and Michael Greenstone. istics of parents and their geographical distri- 2007b. “The Economic Impacts of Climate bution will remain unchanged. The benefit of Change: Evidence from Agricultural Output these strong assumptions is that they allow for and Random Fluctuations in Weather.” Ameri- a transparent analysis that isolates the impact of can Economic Review, 97(1): 354–85. temperature on birth weight and is based on data Lawlor, Debbie A., David A. 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