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There is a growing consensus that emissions of greenhouse gases due to human activity will alter the earth’s climate, most notably by causing temperatures, precipitation levels, and weather variability to increase. The design of optimal climate change mitigation policies requires estimates of the health and other benefits of reductions in greenhouse gases; current evidence on the magnitude of the direct and indirect impacts, however, is considered insufficient for reliable conclusions (A. J. McMichael et al. 2003).
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Climate Change and Birth Weight
January 2009
Olivier Deschênes
(Corresponding Author)
University of California, Santa
Barbara and NBER
Department of Economics
2127 North Hall
University of California
Santa Barbara, CA 93106
(p) 805-893-5617
Michael Greenstone
MIT and NBER and
Department of Economics
50 Memorial Drive, E52-359
Cambridge, MA 02142-1347
(p) 617- 452-4127
(f) 617-253-1330
Jonathan Guryan
University of Chicago and
University of Chicago Booth
School of Business
5807 S. Woodlawn Avenue
Chicago, IL 60637
(p) 773-834-5967
(f) 773-702-0458
Session Title: The Economic Impacts of Climate Change
Presiding: Michael Greenstone, Massachusetts Institute of Technology
Discussant: Douglas Almond
Climate Change and Birth Weight
There is a growing consensus that emissions of greenhouse gases due to human activity
will alter the earth’s climate, most notably by causing temperatures, precipitation levels, and
weather variability to increase. The design of optimal climate change mitigation policies
requires estimates of the health and other benefits of reductions in greenhouse gases; current
evidence on the magnitudes of the direct and indirect impacts, however, is considered
insufficient for reliable conclusions (A.J. McMichael et al, 2003).1
In addition to the overall predicted warming trend, one important feature of many global
climate change forecasts is an increased incidence of very high and low temperatures. To
provide evidence of the potential benefits of greenhouse gas reductions, this paper documents
whether the temperature variation predicted to be part of climate change, including extreme high
and low temperatures, historically have had negative health consequences through their effect on
babies while in utero.
Using individual-level data on more than 37.1 million births, we find that exposure to
extreme hot temperatures during pregnancy leads to lower birth weights. We combine this
finding with estimates of the distribution of future daily temperatures from state of the art
climate change predictions. We then use these predictions of the effect of climate change on the
Department of Economics, 2127 North Hall, University of California, Santa Barbara, CA 93106, and NBER
(email:; MIT, Department of Economics, 50 Memorial Drive, E52-359, Cambridge, MA
02142, and Brookings Institution and NBER (email:; University of Chicago Booth School of
Business, 5807 S. Woodlawn Avenue, Chicago, IL 60637 (email: We thank Douglas
Almond for his especially insightful discussion. Henry Swift provided outstanding research assistance. We
gratefully acknowledge support from the University of Chicago’s Chicago Energy Initiative.
1 See Richard S.J. Tol (2002a, b) for overall estimates of the costs of climate change, which are obtained by
summing costs over multiple areas including human health, agriculture, forestry, species/ecosystems, and sea level
rise. Olivier Deschenes and Michael Greenstone (2007) provides evidence on the impacts on the U.S. agricultures
sector. Deschenes and Greenstone (2008) and Robin Burgess et al (2009) estimate the mortality impacts of climate
change in the U.S. and India, respectively.
distribution of daily temperatures to estimate the predicted effect of global climate change on
future birth weights by the end of the century. These estimates imply that mean birth weights
will decrease on average by 0.22 percent (7.5 grams) among whites and by 0.36 percent (11.5
grams) for blacks by the end of the century. Further, the impact is not spread evenly through the
birth weight distribution. We find an estimated 5.9% increase in the probability of a low birth
weight birth (defined as less than 2,500 grams) for whites and a 5.0% increase for blacks.2
A small set of studies attempt to explain seasonality in birth weight with exposure to
extreme temperatures in utero (L. J. Murray et al, 2000; D.A. Lawlor, D.A. Leon, and G.D.
Smith, 2005). Although there is some evidence of a negative relationship between mean
temperatures and birth weight, the absence of a cohesive framework, limited statistical models,
and relatively small samples have limited the development of clear stylized facts. Further, no
consensus has emerged concerning the physiological mechanisms linking heat and cold stress in
utero, although fetal nutrient intake is often mentioned without direct evidence.
Our detailed historical temperature data allow us to date specifically each shock to the
conditions experienced in utero. We are thus able to estimate not only the effects of exposure to
extreme temperature while in utero, but to explore how this effect varies according to the timing
during gestation. This allows us to shed light on the mechanisms at work in fetal programming.
Of particular interest, we find that as much as 95 percent of the effect of in utero exposure to
very hot ambient temperatures occurs for exposure during the second and third trimesters.
I. Data Sources and Summary Statistics
2 This study of the impact of extreme temperature exposure on birth weight is related to the fetal origins hypothesis
(David J. P. Barker, 1994). In its extended form, it states that economic and environmental conditions during
pregnancy may have long-lasting impacts on long-term health and socioeconomic status (see e.g., Douglas Almond,
2006; Sandra Black et al, 2007; Heather Royer 2009). A key variable in this literature is birth weight, which is
considered by some to be a summary measure of the fetal experience. Future work will investigate whether
exposure to extreme temperatures affects long-run economic and health outcomes, whether through its effect on
birth weight or on latent factors that do not affect birth weight directly.
To implement the analysis, we collected the most detailed and comprehensive data
available on infant birth outcomes, weather, and predicted climate change. This section
describes these data and reports summary statistics. See Olivier Deschenes and Michael
Greenstone (2008) for further details.
A. Data Sources
Birth Weight Data. Microdata on birth weight and exact date of birth are taken from the
1972-1988 National Center for Health Statistics Natality Detail Files (NDF). The data include
exact date of birth, race, sex, birth weight, county of birth, mother’s marital status, mother’s
education, and the exact date of the mother’s last menstrual period. From the latter we infer the
date of ‘conception’ and measure the trimesters of the pregnancy relative to that date. This is
preferable to dating gestation age relative to the date of birth, since gestation length itself may be
an outcome of exposure to extreme temperatures. Observations are dropped from the analysis
sample if either race, sex, birth weight or date of last menstrual period is missing. The sample
includes 37.1 million singleton births to mothers aged 16-45 in the continental 48 states and D.C.
Weather Data. The weather data are drawn from the National Climatic Data Center
Summary of the Day Data (File TD-3200). The key variables for our analysis are the daily
maximum and minimum temperature, which we average to construct daily average temperature.
Station-level weather data are aggregated at the county level by taking an inverse-distance
weighted average of all the valid measurements from stations that are located within a 200 km
radius of each county’s centroid.
Climate Change Prediction Data. Climate predictions are taken from the application of
the A2 scenario to the National Center for Atmospheric Research’s Community Climate System
Model (CCSM) 3, which is a state of the art coupled atmospheric-ocean general circulation
model (National Center for Atmospheric Research, 2007). We use the CCSM 3 A2 daily
temperature predictions for grid points throughout the continental U.S. for the years 2070-2099.
From the CCSM predictions, we assign each county a daily weather realization, aggregating in
the same way we aggregate the contemporaneous temperatures. Each county’s end of century
predicted climate is the simple average of the predicted weather realizations for the 2070-2099
B. Descriptive Statistics
Birth Outcomes Statistics. The main analysis will examine the effect of extreme
temperatures on birth weights, by race. There are substantial differences in birth outcomes
between blacks and whites. Mean birth weights are higher for whites (3,417 grams), by about
235 grams. The percent of ‘low birth weight’ births is more than twice as large for blacks than
whites (9.3% compared to 4.1%), while the black gestational period is 5 days shorter on average.
For both races there are marked seasonal differences in birth outcomes, including birth weight
and the number of births, across quarters of conception.
If differences in fertility by date of conception reflect differences in socioeconomic,
health, or overall fertility attributes, associations between temperature exposure and pregnancy
outcomes may be confounded. We address the potential confounding of socioeconomic and
health attributes with the timing of conception by utilizing county-level inter-annual variation in
temperature and controlling for a smooth function of the date of conception.
Weather and Climate Change Statistics. The regressors of interest in the analysis are the
number of days during the gestation period in which a county’s daily average (mean of the
minimum and maximum) temperature falls into each of five temperature bins (< 25° F, 25° - 45°
F, 45° - 65° F, 65° - 85° F, > 85° F). In Figure 1, the white bars depict the average distribution
of daily mean temperature exposure over the course of the typical pregnancy in the U.S. between
1972 and 1988. In the 270 days after conception, the average pregnancy in the U.S. is exposed
to 13.4 days with a temperature less than 25° F, 252.8 days in the 25° - 85° F range, and 3.8 days
greater than 85° F. In the subsequent analysis, these bins are calculated separately by trimester
of the pregnancy so to allow for substantial flexibility in the semi-parametric modeling of the
effect of temperature on birth weight. The average pregnancy is exposed to 13.4 days lower than
25° F and 3.8 days warmer than 85° F.
After we estimate the effect of temperature exposure in utero on birth weight, we will
estimate the implied effect on birth weight of the 2070-2099 CCSM 3 A2 temperature
predictions.3 The black bars in Figure 1 reports on the predicted change in the number of days in
each of the temperature bins, assuming the geographic distribution of births remains constant.
The CCSM 3 A2 predictions are that the typical pregnancy will be exposed to 7.8 fewer days
below 25° F per year and 29.9 more days in excess of 85° F. The majority of this temperature
increase comes from a reduction of 24.1 days in the 45° - 65° F bin.4
II. Econometric Strategy
The subsequent results are based on the fitting of the following equation:
where the i denotes the individual birth, c 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
from 1-365. Therefore, the indices t and d fully determine the exact date of conception.
++++++= )( 1 332211
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
magnitude of any model errors is unknown and we cannot adjust the predicted temperature changes for such errors.
4 We focus on averages although there is substantial heterogeneity in historical temperatures and climate change
The two dependent variables are log birth weight and an indicator for low birth weight
(i.e., less than 2,500 grams). Individual level controls for mother’s age, fertility history, marital
status, and educational attainment are included in the vector Xi. The county-year-demographic
group fixed effects, ctg
, account for cross-sectional variation in temperature and birth weight
and a quartic in conception date ranges (ranging from 1-365), f(dg), accounts for demographic
group-specific seasonality. These variables are included to capture the effect of any secular
difference in birth outcomes within year that is independent of temperature exposure. The last
term in the equation is the stochastic error term, i
. We report standard errors that are clustered
at the county-by-demographic group level. We also weight by the ‘record weight’ variable
contained in the NDF.
The variables of interest, , , and , are the measures of
temperature exposure during each trimester of the gestational period. The j index refers to the
five daily mean temperature bins described above. The choice of five temperature bins is
motivated by an effort to allow the data, rather than parametric assumptions, to determine the
birth weight-temperature exposure relationship, while also obtaining estimates that have
empirical content.
5 By conditioning on county-year-demographic group effects, the associated
parameters are identified from county-specific deviations in weather about the county averages,
after controlling for county-specific annual shocks. Due to the unpredictability of temperature
fluctuations, we presume that this variation is orthogonal to unobserved determinants of birth
III. Results
Figure 2 plots the estimated regression coefficients associated with each temperature bin,
5 A similar methodology is used in Deschenes and Greenstone (2007).
by trimester, from a model that pools all births and includes gender-by-race indicators. The
panels A, B, and C correspond to the three trimesters. In each case the reference bin is 45°-65°
F, so each coefficient measures the impact of an additional day in a given bin in a given trimester
on the log birth weight, relative to the impact of a day between 45° and 65° F. The figure also
plots the 95% confidence interval associated with the estimates.
The figures show a strong negative relationship between birth weight and temperature. In
each of the three trimesters, the response function is downward-sloping, with the relationship
more pronounced for the second and third trimesters. For all three trimesters, exposure to hot
days is associated with a statistically significant decline in birth weight, which ranges in
magnitude from 0.003% to 0.009% per such day, relative to a day in the reference category. The
third trimester effect is likely an underestimate because many of the births are delivered less than
270 days after conception.
Table 1 presents calculations of the predicted impacts of climate change on birth weight
outcomes, based on CCSM 3 A2 end of century predictions. Panel A pertains to whites and
panel B to blacks. To calculate the implied impact of climate change on birth weight for each
county on each conception date, we multiply the estimated θ’s for each trimester by the predicted
change in days in each temperature bin for each trimester. We then report the weighted average
across counties and over the calendar year. It is straightforward to calculate the standard error of
this prediction (reported below the estimate in parentheses), since the estimated mortality change
is a linear function of the parameters.
Columns (1) – (3) report this calculation for gestational trimesters 1-3 while column (4)
sums the impact across trimesters to obtain the full effect on log birth weights. For each panel
we report three temperature categories (i.e., < 25° F, 25° - 85° F, and > 85° F) along with the
‘total impact’, which sums across the whole daily temperature distribution. Taken as a whole,
the CCSM 3 A2 results suggest that climate change would lead to a reduction in average birth
weights of 0.22 percent (whites) to 0.36 percent (blacks). In terms of actual weights, these
reductions correspond to losses of 7.5 and 11.5 grams respectively. The null hypothesis of a zero
effect is easily rejected at conventional significance levels.
Colum (5) repeats this exercise, except the dependent variable is now an indicator for
whether the birth is designated as low birth weight. The estimated impacts for this outcome are
substantially larger. In particular, climate change is predicted to increase the fraction of low
birth weight births by roughly 5.9 percent for whites and 5.0 percent for blacks. It is evident that
the impacts of temperature on weight are not equal through the birth weight distribution.
Two further findings are noteworthy. First, the birth weight reduction predicted by
climate change models is almost entirely attributable to the predicted increase in exposure to
‘hot’ temperatures. Across both races and outcome variables, 79%- 94% of the estimated change
is due to the increased number of days exceeding 85° F. Second, the impacts are concentrated in
the second and third trimesters. For example, between 91% and 97% of the full gestational
impact of climate change on birth weights comes from the second and third trimesters.
IV. Conclusions and Future Directions
In addition to a predicted increase in average temperature, many global climate change
models contain the oft-overlooked prediction that there will be a large increase in the number of
very hot days. Using inter-annual variation from long-run county-level temperature
distributions, our estimates imply that exposure to such extreme ambient temperatures have
deleterious effects on fetal health, causing a decrease in birth weight and an increase in the
probability of low birth weight. The sensitivity of birth weight to temperature is concentrated
almost completely in the second and third trimesters of the pregnancy, though it is not yet clear
whether this is due to a compensatory mechanism that operates during the first trimester or
because negative shocks to fetal health in the first trimester show up in indicators other than birth
weight. Our analysis also does not identify the effect of temperature exposure that works
through nutrient accumulation separately from the direct effect on gestation length.
There are a few important caveats to these calculations and, more generally, to the
analysis. The estimated impacts likely overstate the impacts, because the analysis relies on inter-
annual variation in weather, and a greater array of adaptations (e.g., migration) will be available
in response to permanent climate change. Additionally, an effort to project outcomes at the end
of the century requires a number of strong assumptions, including that the climate change
predictions are correct, relative prices will remain constant, the same medical technologies will
prevail, and the demographic characteristics of parents and their geographical distribution will
remain unchanged. The benefit of these strong assumptions is that they allow for a transparent
analysis that isolates the impact of temperature on birth weight and is based on data rather than
on unverifiable assumptions.
The important question left unanswered by this paper is whether these changes in birth
weight are related to welfare losses; for example, through changes in health, human capital,
income, or neonatal mortality. If there is indeed a relationship between in utero exposure to high
temperature and welfare measures, then these impacts should become part of the calculations of
the benefits of greenhouse gas reductions. This is an important topic for future research.
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Table 1: Estimated Impact of Climate Change on Birth Weights, Based on CCSM 3 A2 Predictions
Impact of change in temperature exposure during: Full Gestational Period Full Gestational Period
1st Trimester 2nd Trimester 3rd Trimester (birth weight <= 2500 gr)
(1) (2) (3) (4) (5)
[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)
1. Days Less Than 25F 0.0001 -0.0001 -0.0001 0.0000 0.0049
(0.0000) (0.0000) (0.0000) (0.0001) (0.0024)
2. Days Between 25F and 85F 0.0000 -0.0002 -0.0001 -0.0003 0.0073
(0.0000) (0.0000) (0.0000) (0.0000) (0.0000)
3. Days More Than 85F 0.0003 -0.0007 -0.0009 -0.0019 0.0463
(0.0001) (0.0001) (0.0002) (0.0003) (0.0073)
[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)
1. Days Less Than 25F 0.0000 -0.0001 -0.0001 -0.0002 0.0032
(0.0000) (0.0000) (0.0000) (0.0001) (0.0022)
2. Days Between 25F and 85F 0.0000 -0.0001 0.0000 -0.0001 0.0022
(0.0001) (0.0000) (0.0000) (0.0000) (0.0011)
3. Days More Than 85F -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 regressions 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 characteristics.
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
Figure 1: Distribution of Historical and Predicted Exposure Days in 5 Daily Temperature Bins
<25F 25-45 45-65 65-85 >85F
Exposure Days in 5 Temperat ure Bins, for 270 Day Gestation
1972-1988 Average Predicted Change, CCSM 3 A2
Figure 2: Impact of Temperature Exposure During Gestational Period on Birth Weight
(A) First Trimester
<25F 25-45 45-65 65-85 >85F
Impact of Temperatur e Exposure During Fi rst Trimester on Log Bir th Weight
-2 st d err Percent Change in Birt h Weight +2 std err
(B) Second Trimester
<25F 25-45 45-65 65-85 >85F
Impact of Temperatur e Exposure During Second Tr imester on Log Birt h Weight
-2 std err Perc ent Change in Birt h Weight +2 st d err
(C) Third Trimester
<25F 25-45 45-65 65-85 >85F
Impact of Temperature Exposure Duri ng Third Trime ster on Log Birt h Weight
-2 std err Per cent Change in Bi rth Wei ght +2 st d err
... It is noteworthy to compare the extreme temperature exposure we observe in our data with that from Deschenes et al. (2009). They show, using the U.S. weather data between 1972 and 1988, that women experience on average 3.8 days with mean temperature greater than 85°F over the course of typical pregnancy. ...
... Strikingly, the effects are far from equal across all mothers-the adverse effects are borne substantially more by Black and Hispanic mothers, which suggests that exposure to extreme temperatures may be a contributing factor for the birth-related health disparities across different race/ethnicity groups and may widen the gap further in the future as extreme temperatures become more common. The finding of a disproportionate impact is consistent with the recent literature that uncovered significant health disparities across the race/ethnicity groups in response to exposure to extreme temperatures (Barreca & Schaller, 2020;Deschenes et al., 2009;Kim et al., 2021). While it is important to consider the contribution of disproportionate impact of extreme temperatures to birth-related health disparities across different race/ethnicity groups, these differences likely account for a relatively small portion of existing racial/ethnic maternal health disparities when compared to societal factors such as income and education, or other environmental factors such as exposure to pollutants and toxic chemicals due to disproportional risk of exposure (Burris & Hacker, 2017). ...
... These small but significant effects are consistent with the findings in the studies using birth certificate data from earlier years. In particular, Deschenes et al. (2009) find that an additional hot day with average temperature above 85°F (29.4°C) leads to 0.003-0.009% decline in birthweight in the U.S., and Hajdu & Hajdu, 2021 find that an additional day with average temperature above 25 °C (77°F) leads to 0.46 g (or 0.014%) decline in birthweight in Hungary. ...
Full-text available
We provide the first estimates of the impacts of prenatal exposure to extreme temperatures on infant health at birth using the latest national birth data from 2009 to 2018 from all U.S. states. We consistently find that an additional day with mean temperature greater than 80°F or less than 10°F increases preterm births and low birthweight. Strikingly, the adverse effects are borne disproportionately by Black and Hispanic mothers, suggesting that the projected increase in extreme temperatures may further exacerbate the existing birth health disparities across different race/ethnicity groups. We also contribute by investigating the impact of deviations from the normal weather pattern, to identify the extreme weather events after accounting for the adaptation response. We find that prenatal exposure to extreme heat two standard deviations above county's historic average induces preterm births and NICU admissions, particularly for mothers whose pregnancies overlap with summer months. These results are timely and policy relevant, considering the recent weather trends with rising temperatures and frequent extreme weather events.
... With respect to migration, a large literature documents how populations aected by drought or other adverse climatic shocks may turn to migration as a means to cope (Berlemann and Steinhardt, 2017;Beine and Jeusette, 2018;Cattaneo et al., 2019;Homann et al., 2020). Another strand of the literature has studied the negative impacts of climate change and extreme temperature on newborns and mortality (Deschênes et al., 2009;Barreca and Schaller, 2020;Barreca et al., 2015). In terms of agriculture, D'Agostino and consider that climate change increases the volatility of food production, which can reverse the gains in average yields obtained with the Green Revolution. ...
... In addition, ENSO events can also aect air pollution levels, inuencing health outcomes indirectly. In this aspect, the chapter links the literature of air pollution on health (Gra Zivin and Neidell, 2013;Lavaine and Neidell, 2017;Arceo et al., 2015) with the literature on extreme temperatures on health (Barreca et al., 2015;Deschênes et al., 2009;Deschênes and Greenstone, 2011), by adding the impact coming from ENSO events (El Niño and La Niña). ...
... En ce qui concerne la migration, une abondante littérature documente la façon dont les populations peuvent se tourner vers la migration pour faire face à la sécheresse ou à d'autres chocs climatiques (Berlemann and Steinhardt, 2017;Beine and Jeusette, 2018;Cattaneo et al., 2019;Homann et al., 2020). Une autre partie de la littérature analyse les eets négatifs du changement climatique et des températures extrêmes sur les nouveau-nés et la mortalité (Deschênes et al., 2009;Barreca and Schaller, 2020;Barreca et al., 2015). En termes d'agriculture, D'Agostino and Schlenker (2016) considèrent que le changement climatique accroît la volatilité de la production alimentaire, ce qui peut annuler les gains de rendements moyens obtenus avec la Révolution Verte. ...
This dissertation analyzes different ways in which climate change and climatic phenomena can impact economic outcomes in low and middle income countries. The first chapter studies the migration responses following droughts in Malawi according to gender and stated motive of migration (for marriage and work-related reasons); in particular, how marriage-related institutions affect such migration patterns. The second chapter contributes to the literature on air pollution and health by assessing an additional channel, the effect of El Niño Southern Oscillation (ENSO or El Niño-La Niña events) on health. Unlike previous studies, it jointly investigates the effects of ENSO, air pollution and local weather on health at birth for the case of Bogotá. The final chapter analyzes how rural households in Colombia adapt to droughts and extreme heat, by exploring standard approaches and proposing an alternative way to capture climate and weather shocks on agricultural productivity. The different chapters explore the consequences of weather variability and climatic phenomena for rural households and individuals in different countries. The dissertation shows not only the different effects in rural and urban contexts of climatic variability, but also, that the relationships can be very complex across different domains, and context-dependent. The dissertation addresses empirically the different questions by merging information of surveys and administrative data with remote sensing information.
... Climate change also has a close relationship with several other SDGs, such as poverty (SDG1) (Hubacek et al., 2017;Soergel et al., 2021), health and well-being (SDG3) (Deschênes et al., 2009;Pecl et al., 2017;Jones, 2019;Barreca and Schaller, 2020), cities and communities (SDG11) (Kennedy et al., 2014;Lin et al., 2021). To answer this challenge, it is necessary to form a clear quantitative idea of the scale of the climate change problem and the progress achieved in its solution. ...
... Various other factors such as temperature and pollution can have an effect on health outcomes (Deschenes et al., 2009;He et al., 2020;Landrigan et al., 2018). Therefore, as a robustness test, I also control for these two variables, along with the districtlevel population for the period considered. ...
Full-text available
I study the effect of rainfall shocks on child mortality at a sub‐national level for a global set of developing countries. I establish that negative (positive) shocks to rainfall lead to an increase (drop) in child deaths overall. Low‐income countries (LICs) and the group of countries reliant on agriculture are affected the most due to negative rainfall shocks. In LICs, the impact of negative rainfall shocks is mitigated by around 60% in districts located downstream to dams, an effect predominant among less affluent districts; in addition, the effect of rainfall fluctuations is persistent, lasting for up to three years following the shock. Results remain robust to the inclusion of relevant controls, to the consideration of relevant issues such as selective fertility and migration, and various other robustness tests.
... heat waves increased by around 125 million (World Health Organization 2018b). Temperature extremes have been linked to significant reductions in human health, including height and birth weight (Deschênes 2014;Deschênes, Greenstone, and Guryan 2009;Ogasawara and Yumitori 2019). However, to date, evidence about extreme temperatures and birth outcomes remains limited (Basu et al. 2018;Kloog et al. 2015;Zhang, Yu, and Wang 2017), and that which is available is somewhat mixed. ...
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This paper investigates whether associations between birth weights and prenatal ambient environmental conditions—pollution and extreme temperatures—differ by 1) maternal education; 2) children's innate health; and 3) interactions between these two. We link birth records from Guangzhou, China, during a period of high pollution, to ambient air pollution (PM10 and a composite measure) and extreme temperature data. We first use mean regressions to test whether, overall, maternal education is an “effect modifier” in the relationships between ambient air pollution, extreme temperature, and birth weight. We then use conditional quantile regressions to test for effect heterogeneity according to the unobserved innate vulnerability of babies after conditioning on other confounders. Results show that 1) the negative association between ambient exposures and birth weight is twice as large at lower conditional quantiles of birth weights as at the median; 2) the protection associated with college-educated mothers with respect to pollution and extreme heat is heterogeneous and potentially substantial: between 0.02 and 0.34 standard deviations of birth weights, depending on the conditional quantiles; 3) this protection is amplified under more extreme ambient conditions and for infants with greater unobserved innate vulnerabilities.
A growing body of research investigates how changes in weather shape individual choices about migration, yet highly variable results continue to challenge our understanding of the weather-migration nexus. We use a data-driven approach to identify which weather variables best predicted migration decisions of 54,986 individuals originating in Mexico between 1989 and 2016. Using supervised machine learning, we fit random forests to model migration choices based on individual, household, and community attributes in training data (three-fourths of the sample) from the Mexican Migration Project. We aggregated 36 annual weather variables at the community level and applied k-fold cross-validation to evaluate which models best predicted migration decisions. The top performing models were then applied to the test data (one-fourth of our sample). Three weather variables consistently out-performed others across models: minimum temperature during day, maximum temperature at night, and ‘growing degree days’ – the number of days with optimal growth temperatures for corn (the major crop for most communities). Our results demonstrate that weather is related to individual choices about migration and illustrate the utility of using principled variable selection which revealed that both customized (growing degree days for a particular crop) and generic (max-min temperatures) metrics can be predictive of migration behaviors.
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Bangladesh-the largest delta in the world, is one of the most climate change vulnerable country and children and older people are the most exposed groups to these negative effects. This research explores the effects of climate change variables have on child health by focusing on the prevalence and intensity of Acute Respiratory Infections and diarrhoea diseases for children under five. The data used for this research has been extracted from the Bangladesh Demographic and Health Survey (BDHS-2014) linked with the Climatic Research Unit (CRU). The study covers 7,886 households where there is at least one child under the age of five. Logistic regressions are used to analyze the effects of climatic variables have on child’s health while controlling for socioeconomic variables. The research reveals that climate variables have effects on diarrhea diseases and Acute Respiratory Infections for children under five even when socioeconomic factors are taken under consideration. Thus, we all should focus on climate change because it is an issue for all of us regardless of our social status. Incorporating climatic variables along with the existing health research model could reduce geographic implications. In addition, local narratives following health research model along with the generalized one incorporating climatic variables could be the best fit model to reduce these implications.
Background To estimate a possible association between the effects of daily meteorological variation and climatological changes (temperature, air pressure, humidity, sunniness level) on pregnant women with hyperemesis gravidarum (HG) according to symptoms grade and hospitalization state. Methods A retrospective study was conducted with 118 patients diagnosed and hospitalized with HG. HG patients were graded as mild, moderate, or severe according to the Pregnancy Unique Quantification of Emesis (PUQE-24) scale. Data regarding demographic characteristics, PUQE scale value, gestational week on hospitalization, hospital admission and discharge dates, weather conditions, daily meteorological values during hospitalization ( temperature, air pressure, humidity, sunniness level), seasonal averages, and daily changes were recorded. Weather records were obtained from the Ankara Meteorology General Directorate (Ankara, Turkey). Differences between groups were compared according to HG grade. Results HG cases were classified as mild (33.1%), moderate (44.9%), or severe (22.0%). The number of hospitalization days significantly differed between these three groups (p<0.05). In contrast, no statistically significant differences were identified between the HG grade level groups in regard to humidity, pressure, temperature, and sunniness level data (p>0.05). In addition, no statistically significant relationship was identified between HG grades and seasonal conditions according to the chi-square test (p>0.05). Conclusion Changes in the meteorological and climate values examined were independent of symptom severity and hospitalization rate for our HG patients. However, it is possible that climate changes occurring around the world may affect the pregnancy period and should be further investigated.
This study evaluates how being prenatally exposed to rainfall shocks affects birth weight outcomes in Kyrgyzstan, one of the most climate change vulnerable countries in Central Asia. We detect detrimental impacts of rainfall shocks during the prenatal period on birth weight. In particular, a 0.1 log point increase in in-utero rainfall relative to the local norm reduces birth weight by 23.4 grams (or 0.84%). Furthermore, children whose mothers are poor and live in rural areas are disproportionately affected. The negative impacts of fetal exposure to rainfall shocks could be partly attributed to prenatal care, diseases, and nutrient intakes. Besides, the impacts tend to concentrate in the first trimester of pregnancy.
This study assesses the health risks associated with drinking water contamination using variation in the timing and location of shale gas development (SGD). Our novel dataset, linking health and drinking water outcomes to shale gas activity through water sources, enables us to provide new estimates of the causal effects of water pollution on health and to isolate drinking water as a specific mechanism of exposure for SGD. We find consistent and robust evidence that drilling shale gas wells negatively impacts both drinking water quality and infant health. These results indicate large social costs of water pollution and provide impetus for re-visiting the regulation of public drinking water.
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Lower birth weight babies have worse outcomes, both short-run in terms of one-year mortality rates and longer run in terms of educational attainment and earnings. However, recent research has called into question whether birth weight itself is important or whether it simply reflects other hard-to-measure characteristics. By applying within twin techniques using an unusually rich dataset from Norway, we examine the effects of birth weight on both short-run and long-run outcomes for the same cohorts. We find that birth weight does matter; despite short-run twin fixed effects estimates that are much smaller than OLS estimates, the effects on longer-run outcomes such as adult height, IQ, earnings, and education are significant and similar in magnitude to OLS estimates.
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Monetised estimates of the impact of climate change are derived. Impacts areexpressed as functions of climate change and `vulnerability'. Vulnerabilityis measured by a series of indicators, such as per capita income, populationabove 65, and economic structure. Impacts are estimated for nine worldregions, for the period 2000–2200, for agriculture, forestry, waterresources, energy consumption, sea level rise, ecosystems, fatal vector-borne diseases, and fatal cardiovascular and respiratory disorders.Uncertainties are large, often including sign switches. In the short term,the estimated sensitivity of a sector to climate change is found to be thecrucial parameter. In the longer term, the change in the vulnerability of thesector is often more important for the total impact. Impacts can be negativeor positive, depending on the time, region, and sector one is looking at.Negative impacts tend to dominate in the later years and in the poorerregions. Copyright Kluwer Academic Publishers 2002
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A selection of the potential impacts of climate change – on agriculture,forestry, unmanaged ecosystems, sea level rise, human mortality, energyconsumption, and water resources – are estimated and valued in monetaryterms. Estimates are derived from globally comprehensive, internallyconsistent studies using GCM based scenarios. An underestimate of theuncertainty is given. New impact studies can be included following themeta-analytical methods described here. A 1 °C increase in the globalmean surface air temperature would have, on balance, a positive effect onthe OECD, China, and the Middle East, and a negative effect on othercountries. Confidence intervals of regionally aggregated impacts, however,include both positive and negative impacts for all regions. Global estimatesdepend on the aggregation rule. Using a simple sum, world impact of a1 °C warming would be a positive 2% of GDP, with a standarddeviation of 1%. Using globally averaged values, world impact would be anegative 3% (standard deviation: 1%). Using equity weighting, worldimpact would amount to 0% (standard deviation: 1%). Copyright Kluwer Academic Publishers 2002
This paper uses the 1918 influenza pandemic as a natural experiment for testing the fetal origins hypothesis. The pandemic arrived unexpectedly in the fall of 1918 and had largely subsided by January 1919, generating sharp predictions for long-term effects. Data from the 1960-80 decennial U.S. Census indicate that cohorts in utero during the pandemic displayed reduced educational attainment, increased rates of physical disability, lower income, lower socioeconomic status, and higher transfer payments compared with other birth cohorts. These results indicate that investments in fetal health can increase human capital.
This paper measures the economic impact of climate change on US agricultural land. We replicate the previous literature's implementation of the hedonic approach and find that it produces estimates of the effect of climate change that are very sensitive to decisions about the appropriate control variables, sample and weighting. We find estimates of the benchmark doubling of greenhouse gases on agricultural land values that range from a decline of $420 billion (1997$) to an increase of $265 billion, or -30% to 19%. Despite its theoretical appeal, the wide variability of these estimates suggests that the hedonic method may be unreliable in this setting. In light of the potential importance of climate change, this paper proposes a new strategy to determine its economic impact. We estimate the effect of weather on farm profits, conditional on county and state by year fixed effects, so the weather parameters are identified from the presumably random variation in weather across counties within states. The results suggest that the benchmark change in climate would reduce the value of agricultural land by $40 to $80 billion, or -3% to -6%, but the null of zero effect cannot be rejected. In contrast to the hedonic approach, these results are robust to changes in specification. Since farmers can engage in a more extensive set of adaptations in response to permanent climate changes, this estimate is likely downwards biased, relative to the preferred long run effect. Together the point estimates and sign of the likely bias contradict the popular view that climate change will have substantial negative welfare consequences for the US agricultural sector.
The fetal origins hypothesis asserts that nutrient deprivation in utero can raise chronic disease risk. Within economics, this hypothesis has gained acceptance as a leading explanation for the correlations between birth weight, a proxy for fetal nutrient intake, and adult outcomes. Exploiting birth-weight differences between twins using (a) a newlycreated dataset of twins from 1960-1982 California birth records and (b) the Early Childhood Longitudinal Study Birth Cohort, I find birth weight is related to educational attainment, later pregnancy complications, and the birth weight of the next generation. These effects are generally small. However, the protective effects of birth weight vary across the birth-weight distribution. (JEL: I12, I21, J13)
To determine the extent to which meteorologic factors explain seasonality in birth weight in a developed country. Recorded birth weights were collected for all singleton live births after 36 weeks of pregnancy in Northern Ireland between 1971 and 1986. Data on daily maximum and minimum temperatures, rainfall, and hours of bright sunshine were obtained from a local climatologic station for the same period. For each birth, mean daily maximum and minimum temperatures, rainfall, and hours of bright sunshine were calculated for the trimesters of the pregnancy. Linear regression models were constructed with birth weight as the dependent variable and month of birth as a predictor variable. Months of birth were entered in the models as dummy variables. Adjustment was made for year of birth, duration of gestation, maternal age, number of previous pregnancies, sex, and social class of infants at birth and for meteorologic variables relating to each trimester. A clear seasonal pattern in birth weight was observed, with lowest mean birth weight in late spring and summer. Adjusted mean birth weights were 25.5 g, 29.6 g, and 31.6 g lower in May, June, and July, respectively, than in January. This seasonal variation occurred in both sexes, and in female births, it disappeared almost entirely after adjustment for mean daily maximum temperature during the second trimester of pregnancy. Infants born during late spring and summer are lighter than those born in winter, which might be the result of exposure to low winter temperatures during midgestation. Pregnant women should keep themselves warm during midpregnancy.
We assessed the effect of mean ambient outdoor temperature during gestation on birthweight. To assess the effect of mean ambient outdoor temperature during gestation on birth weight. Birth cohort study with record linkage to climate databases. Aberdeen, Scotland. A total of 12,150 individuals born in Aberdeen, Scotland between 1950 and 1956. Perinatal data from a cohort of 12,150 individuals born in Aberdeen, Scotland between 1950 and 1956 were linked to daily outdoor temperature data. Birthweight was seasonally patterned, with lowest birthweights among those born in the winter months (December-February) and highest birthweights among those born in the autumn months (September-November); P= 0.01 for joint sine-cosine functions. Mean ambient outdoor temperature during the first trimester of pregnancy was inversely associated with birthweight and mean ambient outdoor temperature during the third trimester of pregnancy was positively associated with birthweight. In fully adjusted (for sex, maternal age, birth year, birth order and social class) models a 1 degrees C increase in mean ambient outdoor temperature in the mid 10-day period of the first trimester was associated with a 5.4-g (95% confidence interval [CI] 2.9, 7.9 g) decrease in birthweight, whereas a 1 degrees C increase in the mid 10-day period of the third trimester was associated with a 1.3-g (95% CI 0.50, 2.1 g) increase in birthweight. Ambient outdoor temperature in the first trimester of pregnancy explained the seasonal patterning of birthweight. Birthweight. Birthweight was seasonally patterned, with lowest birthweights among those born in the winter months (December-February) and highest birthweights among those born in the autumn months (September-November); P= 0.01 for joint sine-cosine functions. Mean ambient outdoor temperature during the first trimester of pregnancy was inversely associated with birthweight and mean ambient outdoor temperature during the third trimester of pregnancy was positively associated with birthweight. In fully adjusted (for sex, maternal age, birth year, birth order and social class) models a 1 degrees C increase in mean ambient outdoor temperature in the mid 10-day period of the first trimester was associated with a 5.4 g (95% confidence interval 2.9, 7.9 g) decrease in birthweight, whereas a 1 degrees C increase in the mid 10-day period of the third trimester was associated with a 1.3 g (95% confidence interval 0.50, 2.1 g) increase in birthweight. Ambient outdoor temperature in the first trimester of pregnancy explained the seasonal patterning of birthweight. Higher ambient outdoor temperature in the first trimester of pregnancy and/or lower ambient outdoor temperature in the third trimester are associated with reduced offspring birthweight. With the increasing occurrence of temperature extremes, in particular, heat waves, these findings, if replicated in other studies, have important public health implications.
This paper produces the first large-scale estimates of the US health related welfare costs due to climate change. Using the presumably random year-to-year variation in temperature and two state of the art climate models, the analysis suggests that under a "business as usual" scenario, climate change will lead to an increase in the overall us annual mortality rate ranging from 0.5% to 1.7% by the end of the 21st century. These overall estimates are statistically indistinguishable from zero, although there is evidence of statistically significant increases in mortality rates for some subpopulations, particularly infants. As the canonical Becker-Grossman health production function model highlights, the full welfare impact will be reflected in health outcomes and increased consumption of goods that preserve individuals' health. Individuals' likely first compensatory response is increased us of air conditioning; the analysis indicates that climate change would increase US annual residential energy consumption by a statistically significant 15% to 30% ($15 to $35 billion in 2006 dollars) at the end of the century. It seems reasonable to assume that the mortality impacts would be larger without the increased energy consumption. Further, the estimated mortality and energy impacts likely overstate the long-run impacts on these outcomes, since individuals can engage in a wider set of adaptations in the longer run to mitigate costs. Overall, the analysis suggests that the health related welfare costs of higher temperatures due to climate change are likely to be quite modest in the US.