#  Same environment, stratified impacts? Air pollution, extreme temperatures,
and birth weight in south China††thanks:  This paper has been accepted for
publication at Social Science Research:
https://doi.org/10.1016/j.ssresearch.2021.102691.

Xiaoying Liu, Jere R. Behrman, Emily Hannum, Fan Wang, and Qingguo Zhao
Xiaoying Liu: Population Studies Center, University of Pennsylvania, 3718
Locust Walk, Philadelphia, Pennsylvania, USA; Jere R. Behrman: Departments of
Economics and Sociology and Population Studies Center, University of
Pennsylvania, 133 South 36th Street, Philadelphia, Pennsylvania, USA; Emily
Hannum (corresponding, hannumem@soc.upenn.edu): Department of Sociology and
Population Studies Center, University of Pennsylvania, 3718 Locust Walk,
Philadelphia, Pennsylvania, USA; Fan Wang: Department of Economics, University
of Houston, 3623 Cullen Boulevard, Houston, Texas, USA; Qingguo Zhao:
Epidemiological Research Office of Key Laboratory of Male Reproduction and
Genetics National Health and Family Planning Commission, Family Planning
Research Institute of Guangdong Province, Guangzhou, Guangdong, China. This
paper is part of the project "Prenatal Air Pollution Exposures and Early
Childhood Outcomes," which is supported by a grant from the Penn China
Research and Engagement Fund (PIs: Behrman and Hannum) and by National Science
Foundation Grant 1756738 (PI: Hannum). The authors also gratefully acknowledge
support from the University of Houston Research Fund and, for coverage of
Wang’s time, Scholar Grant GS040-A-18 from the Chiang Ching-Kuo Foundation. We
thank Yu Xie and James Raymo for the opportunity to receive feedback at the
Princeton Research in East Asian Demography and Inequality Seminar. We also
thank Guy Grossman, Harsha Thirumurthy, Heather Schofield, and other
participants in the Penn Development Research Initiative (PDRI) Seminar for
comments. Finally, the authors appreciate the helpful feedback provided by
anonymous reviewers.

(October 24, 2021; February 11, 2022)

###### Abstract

This paper investigates whether associations between birth weight 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.
JEL: I14, J1,Q53, Q54

## Keywords

Keywords: air pollution, birth weight, maternal education, extreme
temperatures, China

## Highlights

  1. 1.

We test maternal education as an effect modifier in associations between air
pollution/extreme temperature and birth weight.

  2. 2.

We link birth records to environmental data from Guangzhou, China during a
period of high and variable air pollution.

  3. 3.

Infants with unobserved vulnerabilities—at lower conditional quantiles of
birth weight—face more risk from ambient exposures.

  4. 4.

The protection associated with college-educated mothers with respect to
pollution and extreme heat is substantial.

  5. 5.

Protection is amplified under more extreme ambient conditions and for infants
with greater unobserved innate vulnerability.

##  1 Introduction

Ambient environmental conditions, including both air pollution and
temperature, have been associated with adverse birth outcomes in a number of
settings, though findings for temperature remain limited [72, 74, 22, 51, 87,
50, 60, 19, 27]. However, some newborns may be more vulnerable than others to
the same ambient environmental conditions, due to inequalities in at least two
dimensions. First, the pathway from ambient conditions to adverse birth
outcomes may vary with maternal education. For example, children in utero with
less-educated mothers may be more vulnerable than those of more-educated
mothers if their mothers have poorer access to living, work, transportation,
and leisure spaces with indoor air filtration and temperature regulation, or
if they have limited knowledge of or resources for mitigation
strategies.111Behavioral strategies that respond to extreme pollution and
temperature, undertaken by those who can afford them, have been documented in
China. For example, [85, 518] estimated that a 100-point increase in the Air
Quality Index in China was associated with a 70.6% increase in expenditures on
anti-PM2.5 masks and a 54.4% increase for all types of masks. Using evidence
from air-purifier sales linked to city data and household-income data, [46]
show that higher-income households in China have significantly higher marginal
willingness to pay for clean air compared with lower-income households. With
regard to temperature, [84] find that while urban households respond to
extreme temperatures via increased energy consumption or air conditioner
purchases, rural households are unresponsive to temperature fluctuations. The
authors attribute this difference in mitigation practices to a lack of
resources in rural households. Second, babies’ different innate health
vulnerabilities may relate to unequal outcomes in the same environmental
context. The relevance of these two dimensions may be particularly great in
more extreme environments, and the protective effects of maternal education
may be more pronounced for infants with greater unobserved innate
vulnerabilities.

This paper investigates two key questions. First, do children of expectant
mothers in common ambient pollution and temperature environments experience
different birth outcomes, according to their mothers’ education? In other
words, are there protective effects of maternal education? Second, are
protective effects of maternal education more pronounced among infants with
greater unobserved vulnerabilities—those poorer innate health? We employ mean
and conditional quantile regressions to study these questions jointly, with
data from a fairly high-pollution environment, for three key dimensions of
ambient environmental exposures: air pollution, extreme heat, and extreme
cold.

We focus on birth weight as the outcome of interest. Birth weight is a
consistent correlate of health and other socio-economic outcomes across the
life course [7, 11, 8, 78]. We utilize a database of every singleton live
birth recorded every day between 2009 and 2011 in one city district with more
than one million residents and fairly high air pollution in south China, for a
total of 53,879 birth records. We link the births data to daily air pollution
and meteorological data spanning the entire prenatal periods for these births.
We exploit daily variations in air pollution levels that are caused mainly by
random changes in atmospheric conditions and pollutant emissions [48, 41]. The
high frequency of measurement of air pollution allows us to control for year-
by-month fixed effects, which capture time patterns that might reflect
seasonal effects and changes in economic conditions over time that are shared
commonly across the city. We calculate daily mean exposure estimates for three
monitored air pollutants, namely particulate matter (PM10), sulfur dioxide
(SO2), and nitrogen dioxide (NO2), and also generate a composite index of
potential exposure to all three air pollutants during pregnancy. We also
calculate the percentages of days during pregnancy that were extremely hot or
cold, to control for deviations in weather from a common seasonal trend that
can also have impacts on birth outcomes.

To address our main research questions, we interact a binary variable
indicating whether mothers have university education with measures of
pollution and extreme temperature exposure to estimate effect
modifications—protective effects—associated with maternal education, after
adjusting for infant sex, a quadratic function of maternal age, parity,
rainfall, and time trends. To our knowledge, this is the first paper to study
protective effects jointly in the context of pollution and extreme
temperature. Prior research has shown that pollution and extreme temperature
might be independently important factors. However, given the potential
correlation between them, it is important to analyze protective effects in a
joint framework.

Further, we allow for the protective effects of maternal education to be
heterogeneous across conditional quantiles of birth weight, which map to
infants’ unobserved innate health vulnerabilities. While the literature on
quantile regressions has suggested that birth weight is an ideal variable on
which to apply conditional quantile methods [53], this analysis is one of the
first to adopt conditional quantile methods to analyze the protective effects
of maternal education on birth weight.222Using Chinese data from 16 counties
from 2014 to 2018, [83] study the effects of PM2.5 on birth weight in a
conditional quantile context. They estimate heterogeneous effects by maternal
educational status as well. In contrast to the current paper, their analysis
does consider extreme temperature exposures. Following the literature on
conditional quantile estimation [2, 4, 3], conditional on observed maternal
characteristics, time trends, and environmental exposures, we interpret
newborns to the left of the conditional distribution of birth weight as having
higher levels of unobserved innate health vulnerabilities.333An alternative to
the conditional quantile approach is to use observed variables that measure
innate vulnerabilities of children, which has the benefit of clarifying
observed variables that might be useful for targeted policies. The benefit of
the conditional quantile approach is that it is able to capture the full
spectrum of potential sources of vulnerabilities, not just observed
characteristics, and does not impose restrictions on the differences in impact
magnitudes between less and more vulnerable individuals. We organize this
paper as follows. Section 2 gives the background and research questions,
Section 3 discusses data and methods, Section 4 gives and discusses the
results, and Section 5 is a discussion and conclusion.

##  2 Background and Research Questions

###  2.1 Air Pollution and Birth Outcomes

Studies have traced associations between prenatal exposure to various kinds of
air pollutants and adverse birth weight and other birth outcomes [72, 74, 50,
60]. [24] found that the introduction of electronic toll collection greatly
reduced both traffic congestion and vehicle emissions near highway toll plazas
and reduced prematurity and low birth weight among mothers within two
kilometers of toll plazas by 10.8% and 11.8%, respectively, relative to
mothers two to ten kilometers from toll plazas. [39] analyzed birth records
between 2002 and 2006 from the Georgia Department of PUblic Health and found
that all traffic-related pollutants, including NO2 and PM2.5, were associated
with preterm birth. [27] analyzed the impact of a policy that mandated the
reduction of power-plant emissions in the eastern United States. The policy
greatly reduced emissions of SO2 and NOx, which are the major precursors for
PM2.5. Their results suggest that the policy reduced the proportion of
newborns downwind born prematurely and experiencing low birth weight, and
reduced infant mortality for “high-risk” pregnancies associated with observed
maternal health conditions such as diabetes, hypertension or eclampsia. Their
conceptualization of risk using observed maternal health conditions is related
to but distinct from our focus on unobserved innate vulnerabilities of babies
themselves. [58] exploited exogenous air quality improvement during the
Guangzhou Asian Games period to estimate the effects of PM10, NO2, and SO2 on
birth outcomes. They find that effects vary by pregnancy trimesters, sex, and
maternal age.

A large-scale study in Brisbane, Australia, indicated that pregnancy exposures
to PM2.5, SO2, NO2, and ozone (O3) were associated with increased risks of low
birth weight, as well as pre-term birth [14]. [18] find that incense burning
is associated with lower birth weight in boys, and their quantile regression
also suggests that the negative associations are larger among the lower
quantiles of birth outcomes.

Focusing on particulate matter, a recent United States study analyzed birth
certificates data for the period 1999 to 2007 and found significant birth
weight effects associated with gestational exposures to one form of coarse
particulate matter (PM10-2.5) [30]. Similarly, a study of 7,772 mothers in the
Netherlands between 2001 and 2005 found that PM10 exposure during pregnancy
was inversely associated with birth weight [43]. The World Health Organization
(WHO) Global Survey on Maternal and Perinatal Health study examined whether
outdoor PM2.5 was associated with adverse birth outcomes among 22 countries
from 2004 through 2008 [32]. Results showed that across all countries, PM2.5
was not associated with preterm birth, but was associated with low birth
weight. In China, the country with the greatest PM2.5 range among those
studied, preterm birth and low birth weight both were associated with the
highest quartile of PM2.5 [32]. PM10 and PM2.5 concentrations are highly
correlated in China—especially in southern China [91]. Studies in Lanzhou,
China and Wuxi, China also indicated that prenatal exposure to PM10 increases
the risk of preterm birth [88, 36].

Several recent reviews have synthesized findings. [60] conducted a systematic
review of acute air quality change studies and found mixed results: there was
some evidence that maternal exposure to acute changes in air quality of short-
to medium-term duration increased the risk of fetal growth restriction and
preterm birth, but the relationship for other adverse obstetric or neonatal
outcomes was less clear. [72] conducted a broader systematic review of studies
of air pollution exposure and birth outcomes. This review suggested that
maternal exposure to SO2 was associated with preterm birth, exposure to PM2.5
was associated with low birth weight, preterm birth, and small-for-
gestational-age birth, and exposure to PM10 was associated with small-for-
gestational-age birth. Evidence for NO2 was inconclusive. Another systematic
review and meta-analysis of 62 studies indicated that pooled estimates of
effects generally suggested associations between carbon monoxide (CO), NO2,
and particulate matter and pregnancy outcomes, but also that there was a high
degree of heterogeneity among studies [74].444Early pollution exposures may
endure in their impact. [25] summarized studies from developed and a few
developing countries on the short- and long- term effects of in-utero and
early-life exposure to pollution. The review highlighted evidence that
exposure to pollution significantly impacted a variety of birth, childhood,
and later-life outcomes.

A recent paper systematically reviewed Chinese and English publications on
links between air pollution exposure and adverse pregnancy outcomes in China
[47]. The authors reported that SO2 was consistently associated with lower
birth weight and more preterm births and PM10 was consistently associated with
congenital anomalies. Results for NO2 were inconsistent, and the authors
concluded that further studies were required on the effects of PM2.5, ozone
(O3), and carbon monoxide (CO). While a number of the studies adjusted for
education or other measures of socioeconomic status, the review does not
report tests of effect modification by these stratifiers. A more recent study
in Guangdong Province [59], which covered the period of 2014 to 2015,
reported, among other significant findings, associations with low birth weight
for PM2.5, PM10, SO2, NO2, and O3 in the first month and with PM2.5, PM10,
NO2, and O3 in the last month of pregnancy.

###  2.2 Extreme Temperature and Birth Outcomes

Extreme temperature events are increasing in frequency, duration, and
magnitude [82]. For example, between 2000 and 2016, the number of people
exposed to heat waves increased by around 125 million [82]. Temperature
extremes have been linked to significant reductions in human health, including
height and birth weight [29, 28, 64]. However, to date, evidence about extreme
temperatures and birth outcomes remains limited [51, 87, 6], and that which is
available is somewhat mixed. For example, a recent analysis of data from Korea
found no significant temperature effect on birth outcomes [19]. A 2017
systematic review concluded that the evidence linking preterm birth and low
birth weight to ambient temperature was still very limited and not conclusive,
though there were more examples of adverse estimated effects for high
temperatures than for low temperatures [87]. A 2020 systematic review covered
papers about the associations of air pollution and heat exposure with preterm
birth, low birth weight, and stillbirth in the United States [9]. Only three
papers were identified that considered heat and birth weight, but all found
significant risk. One, a paper that analyzed 220,572 singleton births for the
years 2002 to 2008 from 12 United States sites, found that whole-pregnancy
cold or hot temperature increased term low birth weight risk [35]. A second
paper, conducted among 43,629 full-term but low birth weight babies and
2,032,601 normal-weight babies in California for the period 1999 to 2013,
found that higher long-term apparent temperature exposure was associated with
term low birth weight [6]. The third reviewed paper, conducted among births in
Massachusetts from 2000 through December 2008 [51], showed decreased birth
weight with increased air temperature.

In China, a study in the city of Guangzhou found that exposure to either low
or high temperatures during pregnancy was associated with an increased risk of
preterm birth [40]. A near-national scale study by [44] linked extreme heat
exposure during pregnancy to significant reductions in multiple dimensions of
adult welfare. The authors report that adults who experienced an additional
day of high temperature during their prenatal periods, on average, attained
0.02 fewer years of schooling, had a higher risk of illiteracy by 0.18%,
showed a 0.48% decrease in standardized word-test scores, and were shorter by
0.02 centimeters.

###  2.3 Same Ambient Environment, Different Impacts

There is reason to believe that the effects of ambient air pollution and
temperature exposures might vary by maternal education. First, because more-
educated mothers are likely to enjoy better material resources or better
knowledge compared to less-educated mothers, more-educated mothers may well
adopt mitigation strategies and thus experience lower realized exposures in
the same ambient air pollution and extreme temperature environments. Second,
because better-educated mothers, as a group, are likely to have better
nutrition and health care access compared to less-educated mothers, they may
have more physical capacity to weather the same realized exposures to
pollution and extreme temperatures.

There is at least suggestive evidence that the same ambient environment might
be experienced differently for more and less economically vulnerable groups,
such as groups defined by parental education.555Positive associations between
maternal education and birth outcomes, including birth weight but also preterm
birth and birth length, have been described in a number of studies [1, 5].
Studies in the United States have often found significant differences in birth
weight for infants borne in more disadvantaged and segregated socio-economic
communities with distressed physical environments [34, 54]. In a study in
Georgia, [39] found that associations of traffic-related pollutants, including
NO2 and PM2.5, with preterm birth tended to be stronger for mothers with low
educational attainment. A study in Korea of multiple air pollutants showed
mixed results, but some evidence of socioeconomic disparities in the effects
of full-pregnancy exposure at the lowest conditional quantiles of birth weight
[55]. [42] conducted a systematic review of papers assessing the effects of
particulate matter on birth outcomes from 2000 to 2019 and found suggestive
evidence that the risk posed was greater for babies of mothers with lower
educational attainment for preterm birth and low birth weight.

However, in a recent review of epidemiological literature about ambient air
pollution and birth weight, Westergaard and her colleagues
[*]westergaard_ambient_2017 identified only a small number of studies that
addressed effect modification by maternal education status. With regard to
temperature effects, [87] call for more attention to the possibility of
individual effect modifiers; maternal education is one important potential
stratifier (see [6, 73] for inconsistent findings on maternal education as an
effect modifier). Generally, the resources associated with maternal education
might be more likely to be brought to bear under extreme conditions of
pollution and temperature, which would exacerbate the maternal protective
effect under these conditions. A similar logic suggests that the protective
effects of maternal education with respect to pollution and extreme
temperature might be most pronounced for infants with greater unobserved
innate vulnerabilities.

A recent study from China also examines the heterogeneous association between
air pollution and birth weight—but does not consider extreme
temperatures—using quantile regression and finds that more pronounced effects
were observed in lower and intermediate quantiles [83]. This paper observed
stronger associations among well-educated mothers. Although the paper uses
data from 16 counties across China during the period of 2014 to 2018, it does
not control for county-specific time trends that may capture local policy
variations and trends in the economic environment that can also affect birth
outcomes. Additionally, [83] consider only full-term births. Except for [83],
we have not found other studies of pollution and birth weight that investigate
variability in the protective effect of maternal education using a conditional
quantile approach.

A handful of studies in the United States have used conditional quantile
approaches to consider heterogeneous vulnerability to ambient pollution
exposure. A study of PM2.5 in California [71] shows a tendency of somewhat
larger negative associations with outcomes at the lowest conditional
quantiles, though primarily among the non-Hispanic Black population. A study
of PM2.5 in Massachusetts [33] found that the negative association between
PM2.5 and birth weight was larger in magnitude at the lower conditional
quantiles of birth weight than in the higher quantiles. In contrast, a study
in Atlanta [76] showed larger effects for several pollutants at higher
conditional quantiles. While results are mixed, all of these studies suggest
the potential value in testing for heterogeneity in associations across the
distribution of children’s unobserved innate vulnerabilities at birth, along
the conditional distribution of birth weight.

###  2.4 Research Questions

Using a case study of three years of singleton live births in one district in
Guangzhou, China, we study the protective effect of mothers’ education with
respect to pollution and extreme temperature exposure. Specifically, we
consider the association between expectant mothers’ college educational
attainment and their children’s birth weight and allow the college educational
gradient to be heterogeneous at different ambient pollution and extreme
temperature levels. By including pollution and extreme temperature in the same
specifications, we are able to isolate the separate relationships between
these correlated but distinct ambient environmental factors and birth
outcomes. Conditional on environmental exposures, we also allow the
educational gradient to be heterogeneous for children with different
unobserved innate vulnerabilities.

Our analysis relaxes the restrictions imposed by common regression frameworks
on how socioeconomic factors might impact child-birth outcomes in a setting
where mothers are exposed to high levels of negative environmental factors. A
common regression framework includes educational status as an additive
variable (for example, see [53] (birth weight) and [37] (BMI) in the context
of quantile regressions; see [10] in the context of mean regressions). In the
context of (conditional) mean regressions, this framework means that the
educational gradient in birth weight is constant across levels of
environmental exposure levels. By interacting mothers’ education with multiple
potential negative environmental factors, we allow for the educational
gradient in child birth outcomes to be magnified at different rates depending
on how negative environmental factors jointly worsen. Furthermore, by
estimating education-environment interactions separately at different
conditional quantiles of birth weight, we allow the educational gradient to
have different slopes by quantiles of unobserved innate vulnerabilities of
babies. The rich heterogeneous relationships allow for a nuanced look at the
potential protective effects of maternal college education in ameliorating
negative environmental effects.

##  3 Data and Methods

###  3.1 Study Site

The study site for this project is Guangzhou, which is the capital city of
Guangdong Province in south China, in the Pearl River Delta. Guangzhou, along
with several other cities in Guangdong as well as Hong Kong and Macau, is part
of the rapidly-developing “Greater Bay Area” megalopolis. The region is
wealthy, with Shenzhen and Guangzhou, respectively, ranked third and fourth
highest in city GDP in China, after Beijing and Shanghai [13]. Guangzhou has a
typical subtropical climate, with very mild winters and hot, humid summers.
The annual mean temperature in Guangzhou is around 22 degrees Celsius [20]. In
recent decades, Guangzhou has experienced increases in mean annual
temperatures and in the frequency, duration, and intensity of heat waves [86].
Extreme heat events are a particularly significant threat to human health due
to a combination of the subtropical climate and urban heat-island effects
exacerbated by rapid population growth in recent years [86]. Recent studies in
Guangzhou have linked maternal heat exposures to preterm births [40, 79]. At
the same time, and perhaps somewhat counter-intuitively in a subtropical
climate, cold spells in Guangzhou carry health risks. For example, cold spells
have been linked to increased mortality risk for most categories of deaths and
to heightened risk of preterm birth [15, 40], possibly due to the lack of
widespread access to central heating. As a major city in south China,
Guangzhou is a data point that is potentially relevant to other urban settings
in south China, and possibly parts of Southeast Asia, characterized by broadly
similar climates, high levels of pollution, and economic conditions.

South China has had less air pollution than north China, but as an economic
development center, Guangzhou has been one of the most polluted cities in the
region. Recent studies in Guangzhou have linked air pollution to respiratory
distress syndrome and to student respiratory illness and absenteeism,
especially for younger students [16, 57]. Importantly, Guangzhou has a well-
established and extensive air-quality monitoring system with both air
pollution indices and individual pollutant concentrations available at the
city level.

Figure 1 shows levels of air pollution in Guangzhou from 2008 to 2011 for
three monitored pollutants: PM10, SO2, and NO2. The levels of pollution
depicted in Figure 1 reflect substantial improvements in Guangzhou’s air
quality compared to prior years. In the early 2000s, the local government
implemented progressive air-quality control efforts that included closing down
low-efficiency coal-power plants and enforced installation of desulfurization
facilities [90]. Even with improvements, annual mean air pollution in
Guangzhou during this period exceeded the WHO standard for PM10: a 20 $\mu
g/m^{3}$ annual mean. Further, 66.4% of days surpassed the 24-hour mean
standard for PM10 of 50 $\mu g/m^{3}$ and 76% of days surpassed the SO2
standard of 20 $\mu g/m^{3}$ [81]. There is strong seasonality in all three
types of air pollution, with more severe air pollution in the winter than in
the summer.

###  3.2 Data

We link three forms of data for this analysis: birth certificate data, air
pollution data, and meteorological data.

####  3.2.1 Birth Certificate Data

We have access to birth certificate data representing all births that took
place in one district in the center city of Guangzhou on every day in the
years 2009 to 2011. It is the responsibility of the parents or the family
concerned to register all births within 15 days after birth [56]. We elected
to use birth certificate data from one particular district in the city because
of the availability only for that district of information on maternal
education. In addition to maternal education, the birth certificate data
include information about gestational age based on reported last menstrual
period and confirmed by ultrasound scanning, stillbirth, birth weight, birth
length, sex, parity, Apgar scores, and mother’s age, occupation, rural/urban
residence status, and number of pregnancies.666Birth certificates also contain
information on infant health and neonatal mortality.

####  3.2.2 Air Pollution Data

Our interest lies in tracing different experiences in a common ambient
environment, and we operationalize this idea with a city average pollution
measure. Our data source for air pollution is the Guangzhou Environmental
Bureau, which reports the daily average levels of three monitored “criteria”
air pollutants, PM10, NO2, and SO2, at the city level during the period 2005
to 2011. These pollutants are measured according to the National Standard
(_Guo Biao)_ GB3095—1996. These are the only three pollutants for which data
were collected until 2014\. These are measures of ambient conditions, rather
than individual pollution exposure measures.777Pregnant women may come from
any district in the city. There is precedent for using city-average measures
of pollution. [88] report seven studies that used city-level averages of PM10
[68, 38, 49, 26, 77, 89, 70]. For our research question, we want ambient
measures because women with different levels of education may be able to alter
their individual exposure so that individual exposure data might miss some of
the impacts of maternal education.

####  3.2.3 Meteorological Data

We used the universal thermal climate indices (UTCI) from Copernicus and the
European Centre for Medium-Range Weather Forecasts (ECMWF) to measure extreme
temperatures [21]. The UTCI, a thermal comfort indicator based on human heat-
balance models, is designed to be applicable in all seasons and climates and
for all spatial and temporal scales [21]. The UTCI is a one-dimensional index
that reflects "the human physiological reaction to the multidimensionally
defined actual outdoor thermal environment" [12, 481]. Scores can be
classified into ten thermal stress categories, ranging from extreme cold
stress to extreme heat stress [21]. In a subtropical, humid environment such
as Guangzhou, this index provides a wider range of variation than ambient
temperature. These data have high spatial and temporal resolution, with
measurement gridded at 0.25° x 0.25° and at hourly frequency. We obtain the
measurement at the grid closest to the center city of Guangzhou and calculate
the daily average from 2008 to 2011.

Finally, we use 24-hour accumulated rainfall from the same grid for the same
period. We control for rainfall because it may be correlated with pollution
and temperature and may have effects on birth outcomes.888We provide details
on how we acquire and process ECMWF data in Appendix B. Accumulated rainfall
is calculated from observed rainfall data, and comes from the [63], as
distributed by [65].

###  3.3 Analytic Sample

We begin with birth-certificate data for all live births (67,108 or 99.1% of
all births) in the district for the period 01 January 2009 to 31 December
2011. To avoid fixed-cohort bias,999Fixed-cohort bias emerges when a sample
consists of births during a fixed period—this approach will include only the
longer pregnancies at the start of the study and only the shorter pregnancies
at the end of the study. This has the potential to bias studies of
environmental exposures [75]. we delete births within this period with
conception dates earlier than 14 July 2008 and those with conception dates
later than 15 February 2011, as gestational age varies between 171 and 319
days, which leaves 58,827 observations. We exclude observations with birth
weight less than 500 grams (17 observations dropped), or birth length less
than 28 centimeters or longer than 60 centimeters (51 observations dropped).
We further restrict our sample to include only those with observed gestational
age (855 observations without gestational age dropped) and only live singleton
births.101010Because there is no information on the number of multiple births,
we count duplicate observations in terms of all observed maternal and paternal
characteristics (including mother’s birthdate, parity, number of previous
pregnancies, maternal occupation, maternal education, paternal education,
gestational age, and an indicator of high risk levels) as well as birthdate of
the baby, and treat the observations with one duplicated case as twins (3.90%
of 57,108 observations) and those with two duplicated cases as triplets (0.08%
of 57,108 observations) and those with three duplicated cases as quadruplets
(0.01% of 57,108 observations). We further removed those registered with rural
residence (949 observations, or 1.73% of the sample). In the end, our analytic
sample consists of 53,879 live singleton births (15,068 born in 2009, 20,368
born in 2010, and 18,443 born in 2011).111111In Appendix Section A.3, we
estimate the model after restricting the analytic sample to only those with
more than 36 weeks of gestational age.

###  3.4 Measurement

Table 1 contains summary statistics for all variables employed in the
analysis. Below, we describe our variables.

####  3.4.1 Birth Weight and Other Birth Outcomes

Our study focuses on birth weight.121212Our analysis in the main text of the
paper focuses on conditional mean and conditional quantile regressions for
birth weight. In Appendix Section A.4, we present results based on the binary
outcome variables. For the binary outcomes, low birth weight is 1 if birth
weight is under 2500 grams, preterm equals to 1 if gestational age is smaller
than 37 weeks, and small-for-gestational-age is defined as 1 for those with
birth weight under 10% in sex- and gestational-age- specific distribution. We
use the cutoffs listed in [92] as references. Birth weight is reported in
grams. The mean birth weight is 3,182 grams and the standard deviation is 473
grams. Babies of mothers with a college degree have, on average, a 46 gram
greater birth weight than babies of mothers with at most a high school degree
(p=0.00).

####  3.4.2 Air Pollution

We obtained data on three criteria pollutants, PM10, _NO 2_, and _SO 2_,
observed at all monitoring stations in Guangzhou from the Guangzhou
Environmental Bureau. A small number of observations with missing data are
replaced with moving averages for the four preceding and four following days.
We calculate the average level of all monitoring stations from the four
contiguous districts located in the center of the city, one of which is where
our birth data is sourced.131313Table 1 provides PM10 summary statistics from
all city districts as well as from the four center-city districts. We use the
center-city districts data for estimation. In Appendix Section A.2, we present
robustness checks where we use all city districts pollution data. Between the
years 2009 and 2011, by our estimates, central Guangzhou’s air pollution far
exceeded the safety standards set by the WHO, with annual mean concentrations
of PM10 of 75.9 $\mu g/m^{3}$ in 2008, 74.4 $\mu g/m^{3}$ in 2009, 71.2 $\mu
g/m^{3}$ in 2010 and 69.6 $\mu g/m^{3}$ in 2011. The WHO safety standard for
PM10 is that the annual mean should not exceed 20 $\mu g/m^{3}$ [81]. Seasonal
variation is especially significant. For example, the daily mean for PM10 is
49.2 $\mu g/m^{3}$ in July, on average, but 103.3 $\mu g/m^{3}$ in December,
during the period covered by this study.

We exploit the temporal variation in daily air pollution and the variation in
pregnancy timing for identification. We summarize over the duration of
pregnancy the daily average level of each pollutant to calculate the
accumulated total potential exposure during pregnancy for each individual.
However, this approach suffers from a potential problem—it generates a
spurious inverse correlation between adverse birth outcomes and accumulated
air pollution levels because a shorter gestational age implies a shorter
pregnancy and therefore smaller accumulated air pollution exposure. We avoid
this spurious relationship by dividing the accumulated levels of each
pollutant during pregnancy by each woman’s pregnancy duration to get a daily
mean level of exposure during pregnancy.

Because pollutants tend to co-vary, we conduct principal component analysis of
the three pollutants’ daily mean potential exposures during pregnancy and
adopt the first principal component, which accounts for 86% of the total
variance, as a composite index of pollution.141414We adopt principal component
analysis as a robustness check to investigate whether a more comprehensive air
pollution index shows a consistent pattern of contribution to adverse birth
outcomes. However, we are also aware that results from the principal component
analysis may not be extrapolated to other places as different cities may have
different compositions and interactions of air pollutants. We focus on results
for our particulate matter measure—PM10—in the main text, and comment briefly
on similar findings using the composite pollution index. Detailed results
using the composite measure are included in an appendix.

Substantial individual variation in potential pollution exposure comes from
variation in pregnancy timing. As pollution is usually higher in winter than
in summer, those whose pregnancies occur mainly in the winter have higher
ambient pollution exposure than those whose pregnancies occur mainly in the
summer. Moreover, pregnancies in earlier years on average have higher ambient
pollution exposure than those in later years, as a result of air-quality
improvement over time. In the Guangzhou data, there is a possibly
counterintuitive pattern in which mothers with at least a college degree have
higher potential air pollution exposure during their pregnancies than mothers
with at most a high school degree (as shown in Table 1). This pattern emerges
because mothers with at most a high school degree, a category that includes
many migrant workers from rural areas, are more likely to marry and conceive
in January and February, around Chinese New Year. Figure 3 shows that, after
controlling for conception year-by-month fixed effects, the distributions of
ambient air pollution exposure are not significantly different between the two
maternal-education groups.151515Individuals of different socioeconomic
backgrounds may differ in their capacities to choose where to live and work
based on air quality and to adopt mitigation strategies such as wearing masks
or installing air filters. These are possible pathways connecting a common
ambient environment to different impacts. Available data do not permit
measurement of realized individual-level air pollution exposures.

####  3.4.3 Extreme Temperature

We set a cutoff for defining extreme temperatures by creating a local
reference dataset comprised of the nine years of records of daily mean UTCI
that immediately preceded the dates of our study. We define extreme low and
high temperatures, respectively, by generating cut-points marking the bottom
1% of the distribution of the reference dataset, at -0.87 degrees Celsius, and
the top 1% of the reference dataset, at 34.23 degrees Celsius. Each woman’s
exposure to extreme temperatures is defined by the proportion of days during
pregnancy with daily mean UTCI under or above the cut-off temperatures.
Extreme cold and hot temperatures defined in this way correspond to ECMWF
categories of moderate cold stress (defined as -13 to 0 degrees Celsius) and
strong heat stress (defined as 32 to 38 degrees Celsius). Although air
conditioning had become widespread in Guangzhou by the time of the study
period, heating in winter was not widely available, which is important for
understanding any observed effects of cold stress in this region. Using our
definitions, pregnant women in the sample, on average, spent 1.70% of their
pregnancy duration (4.6 days) exposed to extreme cold and 1.73% of the
duration of their pregnancy (4.7 days) exposed to extreme heat.

We perform sensitivity analyses with a less stringent definition of extreme
temperatures, using a 2.5% cutoff to define extreme cold and extreme heat (at
2.52 degrees Celsius and 33.56 degrees Celsius, respectively). Using this
alternative definition, on average, pregnant women in the sample spent 4.40%
(11.9 days) of their pregnancy duration in extreme cold and 3.73% of their
pregnancy duration (10.2 days) in extreme heat. Table 2 presents additional
distributional details of the extreme heat and cold measures. Figure 2
presents the time-series of daily mean UTCI between the years 2008 and 2011.

####  3.4.4 Maternal Education

We code _mothers’ education_ in two categories: high school and below (0) and
college and above (1). In our analytic sample, 17,967 (33%) report college
attainment. This sample of mothers from the center of the most developed
metropolitan city in south China is highly educated. Recent census figures for
the population ages six and above in Guangdong Province indicate a 16% rate of
tertiary attainment; rates are higher for China’s wealthiest province-level
cities (Beijing, 42%, and Shanghai, 34%).161616Calculated from [62].

####  3.4.5 Control Variables

Pregnancy risks associated with advanced maternal age are well established for
mothers and their children, and include heightened risk of pre-term labor,
fetal growth restriction, and fetal demise among those over 35, in comparison
to younger mothers [69].171717We have found few studies that identify whether
advanced maternal age places mothers at particular risk to conditions of air
pollution exposure. However, one cohort study in Wuxi, China showed an effect
modification with maternal age: preterm birth risk associated with exposure to
high levels of PM10 occurred primarily among women over age 35 [36]. Pregnancy
when young also tends to be associated with risk, but often for social as well
as biological reasons. To capture non-linear age effects, we include
continuous controls for _maternal age_ and the square of _maternal age_. We
adjust for _sex of the child_ (0 if male, 1 if female) and _parity_ (with a
set of binary dummy variables). We also include controls for _conception year
by_ _month_ fixed effects to control for seasonal effects and any time
trend181818Controlling for seasonal trends is a common specification in
studying the health impacts of ambient environment. Depending on the structure
of the data, the literature uses different ways of controlling, varying from
calendar day in a year trends [17], to year-by-month fixed effects to exploit
within-calendar month variation in the ambient environment [48], to year and
month fixed effects to exploit cross-year variation in ambient environment in
the same month [23]. To exploit the daily variation in air pollution level, we
choose to include year-by-month fixed effects to control for seasonal effects
and changes in economic conditions over time that are shared commonly across
the city that may also confound the results., as well as _day of the week at
birth_ fixed effects [40]. Finally, we adjust for _daily mean rainfall_ during
the duration of pregnancy using a cubic function of daily mean rainfall in all
regressions to model the nonlinear relationship between rainfall and birth
weight.191919Rainfall has been related to birth outcomes in a number of
studies [23, 67]. Given this precedent and the monsoon climate in Guangzhou,
which is often exposed to typhoons, we control for rainfall in our study.
Relative humidity is also a common confounder for birth outcomes [40, 66].
However, since our temperature variable, UTCI, by construction, has already
incorporated both dry bulb temperature and relative humidity, we do not
include relative humidity as a separate control in our analysis. Nevertheless,
our results are robust if we drop rainfall and include relative humidity in
regressions.

###  3.5 Analytic Approach

We model birth weight as related to maternal education, air pollution and
extreme temperature exposure in both mean and quantile regression frameworks.
We first estimate a baseline main effects (conditional) mean regression model:

$\displaystyle\begin{split}\text{BirthWeight}_{\text{ymi}}\thinspace=&\thinspace\thinspace\thinspace\alpha+\zeta_{ym}+\sigma_{0}\text{Edu}_{i}\\\
&\thinspace+\gamma_{1}P_{i}+\gamma_{2}\text{Cold}_{i}+\gamma_{3}\text{Heat}_{i}\\\
&\thinspace+g\left(\text{Rainfall}_{i}\right)+\mathbf{X}_{i}\mathbf{\theta}^{{}^{\prime}}+\epsilon_{i}\thinspace,\\\
\end{split}$ (1)

where $\text{BirthWeight}_{\text{ymi}}$ is birth weight of individual $i$ who
is conceived in year $y$ and month $m$. $\text{Edu}_{i}$ is a binary variable
indicating if a mother has at least a college degree. $P_{i}$ is a continuous
variable measuring each mother’s prenatal ambient exposure to PM10, or the
composite index of PM10, NO2 and SO2. $\text{Cold}_{i}$, and $\text{Heat}_{i}$
are continuous variables measuring the percentages of prenatal days exposed to
extreme cold or heat. $g\left(\text{Rainfall}_{i}\right)$ is a cubic function
of daily mean precipitation that a mother is exposed to during her pregnancy.
We control for an individual-specific vector of attributes
$\boldsymbol{X}_{i}$, which includes linear and quadratic terms for maternal
age, child’s sex, parity, number of multiple births, and birth day of the
week. To control for shifts in seasonal patterns of birth as well as possible
changes in economic conditions over time, we include conception year and month
interaction fixed effects $\zeta_{ym}$.202020Due to significant day-to-day
variations in pollution exposures as shown in Figure 1 and Figure 3, despite
the inclusion of year and month interaction fixed effects, there remains
sufficient variations in pollution exposures that allow for identification.
These variations, in effect, arise due to day-by-day differences in exposures
in the first and last months of pregnancy. In our regression framework, we
assume homogeneous effects of pollution exposures during the course of
pregnancy. Given these, Equation (1) compares the birth weights of infants
conceived in the same calendar month and examines the associations of
cumulative within-month variation in air pollution and temperature with
variation of birth weights.

Equation (1) restricts the birth weight gradient with college education to be
constant, which is captured by $\sigma_{0}$ and is invariant of ambient
environmental exposures. Equation (2) relaxes this restriction, and allows the
birth-weight gradient with college education to potentially vary depending on
$P_{i}$, $\text{Cold}_{i}$, and $\text{Heat}_{i}$:

$\displaystyle\begin{split}\text{birth
weight}_{\text{ymi}}\thinspace=&\thinspace\thinspace\thinspace\alpha+\zeta_{ym}+\sigma_{0}\text{Edu}_{i}\\\
&\thinspace+\gamma_{1}P_{i}+\gamma_{2}\text{Cold}_{i}+\gamma_{3}\text{Heat}_{i}\\\
&\thinspace+\sigma_{1}\text{Edu}_{i}\times
P_{i}+\sigma_{2}\text{Edu}_{i}\times\text{Cold}_{i}+\sigma_{3}\text{Edu}_{i}\times\text{Heat}_{i}\\\
&\thinspace+g\left(\text{Rainfall}_{i}\right)+\mathbf{X}_{i}\theta^{{}^{\prime}}+\epsilon_{i}\thinspace,\\\
\end{split}$ (2)

where positive values for $\sigma_{1},\ \sigma_{2}\text{\ \ }$and $\sigma_{3}$
combined with negative values for $\gamma_{1},\ \gamma_{2}\text{\ \ }$and
$\gamma_{3}$ would imply protective effects of maternal college education that
ameliorate the negative impacts of ambient air pollution, extreme cold, and
extreme heat on birth weight.

We estimate Equations (1) and (2) both via conditional mean (OLS) and
conditional quantile estimation.212121Under OLS, the same estimated
coefficients provide both conditional and unconditional mean predictions;
under quantile estimations, conditional quantile estimates in general can
differ from unconditional quantile results [31]. In this paper, we use
conditional quantile estimates to study heterogeneous maternal education and
birth weight gradients on levels (grams) of birth weight. While [31] show that
in the context of birth weight, conditional and unconditional quantile
estimates can be similar, we do not consider the implications of our estimates
for the unconditional (marginal) distribution of birth weight. We estimate
quantile regressions with the quantreg package [52]. Under OLS, Equation (2)
imposes the restriction that the birth weight-college education
gradient—conditional on observed variables including the vector of
environmental exposure measures $P_{i}$, $\text{Cold}_{i}$, and
$\text{Heat}_{i}$—is constant across individuals. The implicit assumption is
that the birth weight-college education gradient is invariant across the
conditional distribution of birth weight, where the conditional distribution
captures unobserved factors—including nutrition, avoidance behavior, genetic
factors, or other risk factors—that can also impact birth weight. However,
given the same ambient environment, the tendency for college-educated mothers
to have babies with higher birth weight compared to non-college-educated
mothers may be most pronounced at the lower conditional quantiles of
unobserved innate vulnerabilities. Additionally, babies with greater
unobserved innate vulnerabilities might be more likely to suffer from air
pollution and extreme temperatures.

Our conditional quantile analysis allows birth weight-college education
gradients to vary both along observed dimensions of sources of
vulnerabilities, i.e., potential negative environmental factors (as seen in
the interaction effects in Equation (2)), and along unobserved dimensions of
sources of baby vulnerabilities, as captured by different conditional
quantiles. The results provide a finer disentangling of heterogeneities of the
birth weight-maternal education gradients. For example, the conditional mean
estimates could understate the importance of PM10 if the negative relationship
between PM10 and birth weight is magnified at lower conditional quantiles
where mothers/children have greater unobserved innate vulnerabilities.

##  4 Results

###  4.1 Mean Regression Results

Results from mean regressions with PM10 as the only air pollutant are
presented in Table 3. Columns 1 and 3 show the Equation (1) estimates. Columns
2 and 4 show the Equation (2) estimates, which include interaction terms
between maternal education and environmental exposure variables. In columns 1
and 2, extreme temperature is defined by 1% extreme tails of past
temperatures, while columns 3 and 4 use 2.5% as the tail cutoffs.

After controlling for conception year-by-month fixed effects, PM10, extreme
cold, and extreme heat are all negatively and substantially associated with
birth weight. Column 1 in Table 3 shows that a 1 $\mu g/m^{3}$ increase in
average daily PM10 potential exposure during pregnancy is associated with an
17.8 gram reduction of birth weight (s.e. 2.3). An additional percentage point
increase in potential exposure to extreme heat or cold is associated with a
22.4 gram (s.e. 9.4) or 30.8 gram (s.e. 9.8) birth weight reduction,
respectively. Stated differently, a one standard deviation change in potential
exposure to PM10 (i.e., 6.6 $\mu g/m^{3}$), extreme heat (i.e., 1.61% of
pregnant days), or extreme cold (i.e., 1.00% of pregnant days) corresponds to
0.25, 0.08, and 0.07 standard deviation changes in birth weights,
respectively.222222While the magnitudes of the associations between PM10 or
other pollutants and birth weights in relatively low pollution settings are
generally smaller than what we find, our results are similar to the findings
obtained by [10] from Chile—a setting with similar mean PM10 exposures as our
setting here—where results show that a one standard deviation (17 $\mu
g/m^{3}$) increase in PM10 exposures is associated with 0.23 standard
deviations (125 grams) of birth-weight reduction. The results from Columns 1
and 3 are similar. The exception is that in Column 3, with the less-extreme
temperature thresholds, heat exposure is no longer significantly negatively
associated with birth weight.

Columns 2 and 4 show the maternal college education interaction coefficient
estimates.232323The coefficient estimates for college education appear
negative in the interaction specifications (columns 2 and 4). In these
specifications, college education coefficients represent intercepts (adjusting
for other variables in the model) for the hypothetical situation of no PM10,
cold, or heat exposure. The predicted protective effects of maternal education
given the observed range of ambient environmental measures are presented in
our discussion of college premiums for birth weights. In both columns,
mothers’ college education status is associated with reduced vulnerability to
ambient air pollution and extreme heat exposures. However, this protective
effect of mothers’ college is not significant for extreme cold. Focusing on
Column 2, there is a positive college-education interaction coefficient of 2.0
(s.e. 0.7) for PM10 and 11.8 (s.e. 2.9) for extreme heat. Comparing college-
educated to non-college-educated mothers, the interactions correspond to a 10%
dampening of the negative association of PM10 with birth weight and a 44%
dampening of the negative association of extreme heat with birth weight.

We evaluate the college premium in birth weight, which we define to be the
predicted gap in birth weight between college-educated and non-college-
educated mothers, by considering the negative intercept for college education,
-115.7 (s.e. 53.2), and the positive education slopes jointly. Holding extreme
heat and cold exposures at their respective means, the college premium amounts
to 0.05, 0.08, and 0.16 standard deviations of birth weight at the 1st (63
$\mu g/m^{3}$), 50th (70 $\mu g/m^{3}$), and 99th (89 $\mu g/m^{3}$)
percentiles of the marginal distribution of PM10. Similarly, holding PM10 and
extreme cold at their respective means, the college premium amounts to 0.05,
0.07 and 0.17 standard deviations of birth weight at the 1st (0%), 50th
(0.76%) and 99th (4.71%) percentiles of the marginal distribution of the
percentage of pregnancy days exposed to extreme heat.

###  4.2 Quantile Regression Results

Conditional quantile results, without and with maternal education interactions
with the three environmental measures, are shown in Tables 4 and 5,
respectively. These estimates use the more stringent cold and heat exposure
measures defined by the 1% cutoffs. Because these are conditional quantile
estimates, the quantiles are defined with respect to what we call the
children’s unobserved innate vulnerabilities (i.e., the residuals in the birth
weight regressions, not the birth weights themselves).

####  4.2.1 Baseline Quantile Regression Results

Table 4 shows a negative gradient for college education over ascending
conditional quantile estimates, indicating that the protective effects of
college education on birth weight decrease as we move from the left to the
right tail of the conditional distribution of birth weight. Specifically, the
coefficient estimates on mothers’ college completion status are 64.6 (s.e.
8.1), 21.8 (s.e. 4.8), and 11.2 (s.e. 7.4) at the 10th, 50th, and 90th
conditional quantiles. These amount to 0.14, 0.05, and 0.02 standard
deviations of birth weight.

Additionally, the negative associations between the three environmental
measures and birth weight are all stronger at lower conditional quantiles. The
PM10 coefficients at the 10th, 50th, and 90th conditional quantiles are -38.9
(s.e. 3.5), -22.2 (s.e. 2.1), and -17.5 (s.e. 2.8). A one standard deviation
increase in potential PM10 exposures is associated with 0.55, 0.31, and 0.25
standard deviation reductions of birth weight, respectively. For extreme heat
and cold, respectively, the magnitudes of the negative associations are 2.4
and 3.7 times larger at the 10th conditional quantile versus the 90.th At the
10th conditional quantiles, a one standard deviation increase in extreme heat
or cold is associated with a 0.06 or 0.13 standard deviation reduction of
birth weights, respectively.

Table 4 also indicates increasing estimates for males, decreasing estimates
for mother’s age, as well as increasing estimates for mother’s age squared
along ascending conditional quantiles estimates. The directions and magnitudes
of these coefficient estimates are similar to those reported in the existing
research on conditional and unconditional quantile estimates for birth weights
[2, 53, 31].

####  4.2.2 Quantile Regression Results with Interactions

At lower conditional quantiles, maternal college education ameliorates the
negative associations between birth weight and pollution and extreme heat, but
not extreme cold. As shown in Table 5 and Figure 4, the college-education
interaction coefficient estimates for PM10 are 5.7 (s.e. 1.3), 1.3 (s.e. 0.7),
and 0.0 (s.e. 1.1) at the 10th, 50th, and 90th conditional quantiles. These
respectively correspond to 14, 6, and 0% reductions in the substantial
negative associations between PM10 and birth weight compared to non-college-
educated mothers. Additionally, the college-education interaction coefficient
estimates for extreme heat are 26.4 (s.e. 5.4), 7.3 (s.e. 3.3), and 6.2 (s.e.
4.8) at the 10th, 50th, and 90th conditional quantiles. While the overall
associations of extreme heat with birth weights are less substantial than the
associations of PM10 with birth weights, these coefficients, respectively,
correspond to 71, 31, and 47% reductions in the associations between extreme
heat and birth weight.

Considering jointly the positive slopes and negative intercepts from Table 5,
the college premium in birth weight is magnified at lower conditional
quantiles. Holding heat and cold exposure variables at their means, the
college premiums at the 1st, 50th and 99th percentiles of the marginal
distribution of PM10 amount to 0.02, 0.02, and 0.02 standard deviations of
birth weight with 90th conditional quantile estimates, and are magnified
substantially to 0.03, 0.11, and 0.34 standard deviations of birth weight with
10th conditional quantile estimates. These results are visualized in Figure 5.
Additionally, holding PM10 and cold exposures at their means, the college
premiums at the 1st, 50th and 99th percentiles of the marginal distribution of
extreme heat amount to 0.00, 0.01, and 0.06 standard deviations of birth
weight with the 90th conditional quantile estimates, and are magnified to
0.06, 0.10, and 0.32 standard deviations with the 10th conditional quantile
estimates. In contrast, the associations between extreme cold exposures and
birth weights do not vary across maternal education groups, so there are no
significant variations in the college premium in birth weight as extreme cold
exposures increase.

###  4.3 Composite Index Analysis

We repeat the analyses parallel to that for PM10 but with the composite index
pollution measure. Detailed results are presented in Appendix Tables A.1, A.2,
and A.3. Patterns in the temperature results are robust to the choice of
pollution measure, and are not discussed here. The results from the composite
index analysis closely mirror the patterns revealed in the PM10 analysis.
There is a protective effect of maternal education with regard to pollution,
and the finding persists across conditional quantiles of birth weight.
Further, quantile regressions show that the negative associations between
pollution and birth weight, and the protective effects of maternal education
with respect to pollution, are magnified at lower conditional quantiles.

A key difference in the composite index analysis results is that for estimates
based on Equation (1) without maternal education interactions, the birth
weight change associated with one standard deviation change in the composite
index is 1.8 times of the corresponding change in the PM10 analysis. In the
regressions with interactions, the protective effect of college education is
weaker in the composite index analysis than in the PM10 analysis. Together,
these factors lead to a pattern in which the college premium in grams of birth
weight along the marginal distribution of the composite index is approximately
the same as the college premium in birth weight along the marginal
distribution of PM10.

###  4.4 Summary

Conditional on ambient temperature exposures, we find that one standard
deviation increase in ambient PM10 is associated with a 0.25 standard
deviation reduction in birth weight. At average ambient PM10 and extreme
temperature exposure levels, the protective effect of maternal college
education is up to 0.07 standard deviations of birth weight. We also find that
the negative association between ambient exposures and birth weight is two
times as large at lower conditional quantiles of birth weight than at the
median. At lower conditional quantiles of birth weight, a one standard
deviation increase in ambient PM10 exposure is associated with a reduction in
birth weight of up to 0.48 standard deviations.

Additionally, the protection associated with university-educated mothers with
respect to pollution and extreme heat is five times as large at the lowest
decile of conditional quantiles of birth weight than at the highest decile. In
particular, the protective effect of college education can be up to 0.34
standard deviations of birth weight when children with higher unobserved
innate vulnerabilities—those at lower conditional quantiles—face high levels
of ambient PM10 and extreme temperature exposures. Though we focus on a
continuous measure of birth weight for all terms in the text, we find similar
results with binary measures used in some other studies for low birth weight,
preterm births, and small for gestational age (Appendix Section A.4). Our
results show that there are substantively significant heterogeneous moderating
effects of maternal education on the negative associations between ambient
environmental exposures and birth weight. We are the first to document these
relationships using mean and conditional quantile regressions with an approach
in which we consider three key ambient environmental exposures jointly.

##  5 Discussion and Conclusion

In this paper, we link a database of all births recorded for three years born
in hospitals in a district in Guangzhou, China, to daily measures of
particulate matter (PM10), nitrogen dioxide, and sulfur dioxide and to daily
meteorological measurements during pregnancies. Using conditional mean and
quantile regressions, we estimate the associations of birth weight with air
pollution, measured by ambient PM10 and a composite pollution measure, and
with extreme cold and hot temperatures. To our knowledge, this paper is the
first study of birth outcomes in a fairly high-pollution context to consider
effect modifications of both pollution and extreme weather by maternal
education. It is also novel in addressing whether the protective effects of
maternal education are heterogeneous with respect to babies’ unobserved innate
vulnerabilities.

Our approach has certain limitations. First, we cannot observe mothers’
addresses, which would have enabled us to match to the air pollution level
from the closest monitoring station. On the other hand, our approach of using
core city districts’ pollution levels is reasonable if we assume that women
who give birth in a district are likely to live and move around primarily in
that district and neighboring districts. We are encouraged that our results
are not sensitive to our choice of a proxy for ambient exposure: results are
robust to whether we use city-wide pollution measures or center-city district
pollution measures (Appendix Section A.2).

A second limitation of the paper is that it is geographically limited to one
locale. Under the assumption of homogeneous environmental effects across
locations, more cities would be beneficial in terms of additional variance in
environmental and temperature exposure variables and would improve the
precision of estimates. However, if effects are heterogeneous across
locations, an average effect obtained from information over multiple locations
might not properly capture varying local effects. Accounting for differences
across locations appropriately is potentially especially important for
temperature effects, which are a focus in our paper. Relative temperature
matters—the implications for pregnancy of a given temperature may differ
across temperature zones. Focusing on a single region has the advantage of
avoiding this problem.242424Our paper capitalizes on full administrative data,
rather than sample data from one location. For a study that is focused on
distributional outcomes, it is important to capture the full range of birth
weight distributions and we are able to do this. There are benefits to having
more people in the same location, which allows for careful control of
location-specific time-trends that could vary with changes in local economic
conditions that might correlate with birth weight. The experience of Guangzhou
may not generalize across China, but it offers a case study that is
potentially relevant to other urban settings in south China, and possibly
parts of Southeast Asia, characterized by broadly similar climates, high
levels of pollution, and economic conditions. Finally, the administrative data
to which we have access has excellent measurement of birth outcomes, relative
to self-reported survey data. However, it contains very limited information
about mothers. This limitation precludes analysis of other dimensions of
socioeconomic status beyond maternal education or behavioral mechanisms that
might explain the patterns we find.

With these caveats in mind, we find strong negative associations between
ambient PM10 and extreme temperature exposures and birth weights. For example,
a one standard deviation increase in PM10 exposure—6.6 $\mu g/m^{3}$—is
associated with a 116 to 258 gram reduction in birth weights. One standard
deviation—on average, 2.7 days—increase in prenatal exposure to extreme cold
temperatures (lower than -0.87 degrees Celsius in universal thermal climate
indices) is associated with a 17 to 63 gram reduction in birth weights. One
standard deviation—on average, 4.3 days—increase in prenatal exposure to
extreme hot temperatures (higher than 34.23 degrees Celsius, in universal
thermal climate indices) is associated with a 12 to 31 gram reduction in birth
weights. Stronger associations are seen for children with greater levels of
unobserved innate vulnerabilities at lower conditional quantiles.

There is also strong evidence for effect modification with maternal education.
Newborns of mothers with college attainment experience an amelioration of
effects on birth weight of PM10 by about 0 to 14%, and of extreme hot weather
by 31 to 70%, with larger buffering effects experienced by those at lower
conditional quantiles. However, there is no effect modification with maternal
education in response to extreme cold weather. This result is understandable
in a context in which centralized heating is not widely available. Although
there is usually only a short spell of very cold weather in each year, our
results indicate that even a few days of very cold weather exposure is
associated with lower birth weights. Given the lack of indoor heating, even
mothers with higher socioeconomic status might have no effective way of
protecting themselves from exposure to extreme cold weather. In contrast, air
conditioning was widely available during the time period of this study and was
very accessible for higher socioeconomic status mothers.

In short, our findings demonstrate that babies of college-educated
mothers—compared to babies of other mothers in the same district of
Guangzhou—experienced lower risk in the context of air pollution and extreme
heat exposure. It is important to note that the benefits accruing to babies of
college-educated mothers vary. The protective effects are most pronounced
among those experiencing more environmental risk—more extreme ambient
pollution and heat exposure. Protective effects of maternal education also
emerge more strongly for infants with greater unobserved innate
vulnerabilities. In other words, socioeconomic disparities and underlying
child health vulnerabilities both stratify the realized impacts associated
with a common ambient environment—especially when that common ambient
environment is extreme pollution or extreme hot temperatures. Our findings
suggest that babies at greatest risk from high pollution levels and
increasingly frequent heat events are those at the nexus of socioeconomic and
health vulnerability.

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Figure 1: Daily mean ambient air pollution levels for three major pollutants,
2008 to 2011, Guangzhou, China. _Note:_ This figure shows daily means for
three monitored ambient air pollutants in Guangzhou between 2008 and 2011.
Figure 2:  Daily mean temperature (Universal Thermal Climate Index),
Guangzhou, China, 2008 to 2011.  Figure 3: Daily mean ambient PM10 exposure by
mother’s education. _Note:_ The left panel contains observed data, while the
right panel depicts the conditional distribution of daily mean ambient PM10
exposure after controlling for conception year and month interaction fixed
effects. Figure 4: Graphical illustration of mean and conditional quantile
estimation coefficients from models of birth weight (grams) for average daily
mean ambient PM10 exposures ($\mu g/m^{3}$) (top row) and composite index
(bottom row) for mothers with college education and mothers with high school
or less education (HS). _Note:_ The x-axis of the sub-plots corresponds to
results from different conditional quantile estimations. We interpret lower
conditional quantiles as corresponding to higher levels of unobserved innate
vulnerabilities for babies. The two subfigures in the top panel are based on
estimates from Tables 3 and 5. The two subfigures in the bottom panel are
based on estimates from Appendix Tables A.1 and A.3. The y-axis reports
regression coefficients. The left and right panels depict the same information
in two ways. The solid line in the right panel shows the gap between the HS
and college quantile coefficient lines in the corresponding left panel. The
gaps depicted on the right provide a visualization of the maternal college
education and pollution interaction coefficients, from Table 5 in the top
panel for PM10 or from Table A.3 for the composite index. In the top panel,
coefficients indicate grams of birth weight change for each additional $\mu
g/m^{3}$ of average daily mean ambient PM10 exposures. In the bottom panel,
coefficients indicate grams of birth weight change for each additional index
unit of the composite index. Figure 5: Graphical illustration of predicted
college premium in birth weight for different conditional quantiles and at
different ambient PM10 exposure levels. _Note:_ The x-axis of the figure
represents different ambient PM10 exposure levels during the course of
pregnancy, which correspond to the range of ambient PM10 exposure levels
reported in Table 2. The y-axis reports predicted college premium in birth
weight–the predicted gap in birth weight between college-educated and non-
college-educated mothers. Given estimates from Tables 3 and 5, each line
corresponds to predictions for children at differing conditional quantiles.
The premium is greater at higher ambient exposure levels and for children with
higher levels of unobserved innate vulnerabilities–those at lower conditional
quantiles.

Table 1: Summary statistics.

Comparison by mother’s education All <= High school >=College Gap mean s.d.
mean s.d. mean s.d. p-value Child variables Sex (male=1) 0.53 0.50 0.54 0.50
0.53 0.50 (0.01) Birth weight (grams) 3182 473 3167 492 3213 429 (0.00)
Gestational age (days) 273 11.51 273 12.31 273 9.69 (0.00) Low birth weight
0.06 0.23 0.07 0.25 0.04 0.19 (0.00) Preterm (%, gestational age $<$ 37weeks)
0.07 0.26 0.08 0.27 0.05 0.23 (0.00) Small for Gestational Age 0.09 0.29 0.10
0.30 0.08 0.27 (0.00) Maternal variables Mother’s age (years) 29.08 4.17 28.84
4.51 29.57 3.35 (0.00) Mother’s schooling attainment (years) 13.13 2.19 11.75
0.90 15.89 1.15 (0.00) Parity 1.25 0.51 1.34 0.57 1.06 0.26 (0.00) Average of
daily mean potential pollution exposures during pregnancy All city districts
measurements PM10 ($\mu g/m^{3}$) 73.22 7.16 72.52 6.89 74.64 7.49 (0.00) NO2
($\mu g/m^{3}$) 41.41 6.13 40.61 6.00 43.01 6.07 (0.00) SO2 ($\mu g/m^{3}$)
35.18 4.63 34.46 4.64 36.62 4.26 (0.00) Composite index of PM10, NO2, SO2
-0.05 1.56 -0.26 1.53 0.38 1.53 (0.00) Center-city districts measurements PM10
($\mu g/m^{3}$) 72.10 6.63 71.53 6.36 73.23 6.99 (0.00) NO2 ($\mu g/m^{3}$)
48.60 6.94 47.73 6.79 50.32 6.90 (0.00) SO2 ($\mu g/m^{3}$) 34.44 4.62 33.74
4.68 35.83 4.18 (0.00) Composite index of PM10, NO2, SO2 -0.02 1.48 -0.22 1.45
0.37 1.46 (0.00) Temperature and rainfall during pregancy Average daily
rainfall (mm) 5.15 1.61 5.20 1.64 5.03 1.55 (0.00) Average daily mean
temperature (C∘) 22.38 1.83 22.32 1.86 22.50 1.77 (0.00) Percent of pregnancy
days with potential exposure to extreme temperatures Extreme heat, above past
99% 1.73 1.61 1.60 1.54 1.98 1.71 (0.00) Extreme cold, below past 1% 1.70 1.00
1.77 1.01 1.57 0.95 (0.00) Extreme heat, above past 97.5% 3.73 2.87 3.54 2.75
4.11 3.08 (0.00) Extreme cold, below past 2.5% 4.40 2.47 4.53 2.52 4.13 2.33
(0.00) Note:  The analytic sample has 53,879 observations. The college
subsample has 17,967 observations and the high school or lower subsample has
35,912 observations. Sources and computations for ambient environmental
variables are discussed in the data section of the paper.
‡ P-value from testing whether the mean gap for each variable between college-
educated and non-college-educated mothers is statistically different.

Table 2: Distribution of cumulative ambient PM10, composite index and extreme
temperature exposures during the course of pregnancy.

Varying cutoffs for extreme temperature exposures Pollution measures 1 percent
cutoff 2.5 percent cutoff Statistics PM10 composite heat cold heat cold $\mu
g/m^{3}$ index percent days percent days percent days percent days Percentiles
P1 63.05 -2.31 0.00 0.00 0.00 0.00 P5 64.50 -2.18 0.00 0.36 0.00 0.37 P10
65.10 -1.71 0.00 0.43 0.38 1.08 P25 66.80 -1.33 0.71 0.73 1.75 2.50 P50 70.01
-0.25 0.76 1.79 2.59 4.23 P75 76.41 1.20 3.65 2.63 6.88 6.83 P90 82.56 2.29
4.38 2.93 8.39 7.33 P95 86.45 2.66 4.48 3.00 8.58 7.49 P99 88.52 2.92 4.71
3.17 9.02 7.94 Min and Max Min 51.86 -2.42 0.00 0.00 0.00 0.00 Max 99.91 4.86
6.35 4.35 12.17 10.87 Note: Figures reported in the table represent
distributional statistics along the marginal distribution of ambient pollution
and extreme temperature variables.

Table 3: OLS regression analysis of birth weight with interactions of ambient
PM10 and extreme temperature with maternal education.

Varying cutoffs of extreme temperature exposures 1 percent cutoff 2.5 percent
cutoff Variable (1) (2) (3) (4) Environmental exposure variables PM10 -17.83∗∗
-18.58∗∗ -14.59∗∗ -15.41∗∗ (2.27) (2.29) (2.31) (2.33) Extreme heat -22.38∗
-27.06∗∗ 0.09 -2.33 (9.41) (9.57) (5.11) (5.21) Extreme cold -30.84∗∗ -30.33∗∗
-24.68∗∗ -24.33∗∗ (9.80) (9.96) (4.37) (4.43) Education and environmental
exposure interactions College educated 44.55∗∗ -115.70∗ 44.85∗∗ -133.00∗
(4.43) (53.17) (4.43) (55.52) College x PM10 1.95∗∗ 2.19∗∗ (0.68) (0.70)
College x extreme heat 11.82∗∗ 6.26∗∗ (2.93) (1.71) College x extreme cold
-2.15 -1.35 (4.68) (1.87) Control variables Male 104.00∗∗ 104.00∗∗ 103.90∗∗
103.90∗∗ (3.93) (3.93) (3.93) (3.93) Mother’s age 55.41∗∗ 54.90∗∗ 55.33∗∗
54.85∗∗ (4.98) (4.98) (4.98) (4.98) Mother’s age2 -0.90∗∗ -0.89∗∗ -0.89∗∗
-0.89∗∗ (0.08) (0.08) (0.08) (0.08) Intercept 1,205.00∗∗ 1,276.00∗∗ 1,019.00∗∗
1,094.00∗∗ (219.90) (221.50) (221.40) (223.30) Observations 53,879 53,879
53,879 53,879 R2 0.069 0.069 0.069 0.070 _Note:_ Regressions control for
conception year and month interaction fixed effects, day of the week at birth,
parity, and daily mean rainfall.
† $p<0.10$; ∗ $p<0.05$; ∗∗ $p<0.01$.

Table 4: Conditional quantile regression analysis of birth weight with ambient
PM10 and extreme temperature and maternal education.

Estimates at conditional quantiles Variable P10 P25 P50 P75 P90 Environmental
exposure variables PM10 a,b,c,d -38.90∗∗ -26.34∗∗ -22.16∗∗ -15.30∗∗ -17.50∗∗
(3.49) (2.44) (2.09) (2.38) (2.83) Extreme heat -17.88 -13.15† -19.06∗ -8.10
-7.43 (13.26) (7.84) (8.90) (11.00) (12.17) Extreme cold a,b -62.61∗∗ -29.79∗∗
-19.68∗ -23.89∗ -16.82 (16.30) (9.85) (9.37) (10.15) (13.97) Education College
educated a,b,c,d 64.59∗∗ 33.20∗∗ 21.79∗∗ 9.10 11.19 (8.08) (5.72) (4.81)
(5.87) (7.39) Control variables Male 92.62∗∗ 98.92∗∗ 111.94∗∗ 117.30∗∗
132.60∗∗ (7.67) (5.07) (4.42) (4.80) (6.75) Mother’s age 64.30∗∗ 45.91∗∗
46.47∗∗ 51.18∗∗ 46.37∗∗ (10.01) (5.79) (5.63) (5.53) (8.81) Mother’s age2
-1.09∗∗ -0.73∗∗ -0.73∗∗ -0.78∗∗ -0.69∗∗ (0.17) (0.10) (0.09) (0.09) (0.15)
Intercept 917.89∗ 1,168.25∗∗ 1,835.52∗∗ 1,897.93∗∗ 2,226.62∗∗ (378.70)
(252.98) (200.27) (232.69) (334.94) Observations 53,879 53,879 53,879 53,879
53,879 _Note:_ Regressions control for conception year and month interaction
fixed effects, day of the week at birth, parity, and daily mean rainfall.
Given bootstrapped simultaneous conditional quantile estimates, superscripts
a, b, c, and d indicate whether estimates across conditional quantiles are
statistically different for the three ambient environment or education
variables.
a P10, P25, and P50 are significantly different at 0.05 sig. level.
b P10 and P90 are significantly different at 0.05 sig. level.
c P25 and P75 are significantly different at 0.05 sig. level.
d P50, P75, and P90 are significantly different at 0.05 sig. level.
† $p<0.10$; ∗ $p<0.05$; ∗∗ $p<0.01$.Bootstrap standard errors are shown in
parentheses.

Table 5: Conditional quantile regression analysis of birth weight with
interactions of ambient PM10 and extreme temperature with maternal education.

Estimates at conditional quantiles Variable P10 P25 P50 P75 P90 Environmental
exposure variables PM10 a,b,c,d -40.75∗∗ -28.15∗∗ -22.55∗∗ -16.04∗∗ -18.06∗∗
(3.03) (2.83) (2.14) (2.15) (3.09) Extreme heat -37.30∗∗ -23.23∗ -23.50∗∗
-12.04 -13.12 (13.73) (10.64) (8.23) (10.92) (13.12) Extreme cold a,b -65.43∗∗
-28.98∗∗ -17.15∗ -22.28∗ -13.74 (15.79) (10.57) (8.75) (10.45) (12.38)
Education and environmental exposure interactions College educated a,b,c
-396.40∗∗ -216.76∗∗ -83.69 -95.51 2.20 (99.62) (59.02) (58.77) (60.23) (87.96)
College x PM10 a,b 5.69∗∗ 3.11∗∗ 1.31† 1.39† -0.02 (1.30) (0.78) (0.72) (0.78)
(1.10) College x extreme heat a,b 26.35∗∗ 11.83∗∗ 7.30∗ 5.24 6.19 (5.42)
(3.91) (3.27) (3.50) (4.75) College x extreme cold 3.30 2.21 -2.68 -3.97 -1.34
(9.12) (6.28) (4.94) (5.87) (7.69) Control variables Male 90.14∗∗ 98.32∗∗
112.02∗∗ 116.40∗∗ 132.55∗∗ (8.38) (5.15) (4.42) (5.15) (6.12) Mother’s age
62.49∗∗ 44.68∗∗ 46.26∗∗ 48.33∗∗ 45.30∗∗ (10.02) (6.04) (5.07) (5.70) (8.91)
Mother’s age2 -1.06∗∗ -0.71∗∗ -0.73∗∗ -0.74∗∗ -0.67∗∗ (0.17) (0.10) (0.09)
(0.10) (0.15) Intercept 1,240.85∗∗ 1,415.85∗∗ 1,888.54∗∗ 2,032.87∗∗ 2,357.66∗∗
(371.67) (263.70) (206.46) (202.99) (369.89) Observations 53,879 53,879 53,879
53,879 53,879 _Note:_ Regressions control for conception year and month
interaction fixed effects, day of the week at birth, parity, and daily mean
rainfall.
Given bootstrapped simultaneous conditional quantile estimates, superscripts
a, b, c, and d indicate whether estimates across conditional quantiles are
statistically different for the three ambient environment or education
variables.
a P10, P25, and P50 are significantly different at 0.05 sig. level.
b P10 and P90 are significantly different at 0.05 sig. level.
c P25 and P75 are significantly different at 0.05 sig. level.
d P50, P75, and P90 are significantly different at 0.05 sig. level.
† $p<0.10$; ∗ $p<0.05$; ∗∗ $p<0.01$.Bootstrap standard errors are shown in
parentheses.

ONLINE APPENDIX
Same environment, stratified impacts? Air pollution, extreme temperatures, and
birth weight in south China Xiaoying Liu, Jere R. Behrman, Emily Hannum, Fan
Wang, and Qingguo Zhao

##  Appendix A Additional Estimation Results

###  A.1 Composite Pollution Index

In this Appendix section, we repeat the analyses for PM10 with the composite
index pollution measure. Results are presented in Appendix Tables A.1, A.2 and
A.3. Patterns in the temperature results are robust to choice of pollution
measure. The results from the composite index analysis closely mirror the
patterns revealed in the PM10 analysis. There is a protective effect of
maternal education with regard to pollution, and the finding persists across
conditional quantiles of birth weight (see the dotted lines in Figure 4 Bottom
Row). Further, quantile regressions show that the negative associations
between the pollution and birth weight, and the protective effects of maternal
education with respect to pollution, are magnified at lower conditional
quantiles (see the solid lines and shaded areas in Figure 4 Bottom Row). See
Section 4.3 for more discussion.

Table A.1: OLS regression analysis of birth weight with interactions of
ambient composite index and extreme temperature with maternal education.

Varying cutoffs of extreme temperature exposures 1 percent cutoff 2.5 percent
cutoff Variable (1) (2) (3) (4) Environmental exposure variables Composite
index -140.90∗∗ -144.50∗∗ -127.10∗∗ -131.00∗∗ (11.82) (11.90) (11.88) (11.95)
Extreme heat -28.61∗∗ -32.21∗∗ -4.47 -6.28 (9.49) (9.64) (5.06) (5.16) Extreme
cold -27.97∗∗ -27.43∗∗ -23.38∗∗ -23.04∗∗ (9.76) (9.92) (4.36) (4.42) Education
and environmental exposure interactions College educated 44.61∗∗ 29.45∗∗
44.89∗∗ 29.82∗ (4.43) (11.15) (4.43) (12.06) College x composite index 9.38∗∗
10.43∗∗ (2.87) (2.84) College x extreme heat 9.19∗∗ 4.73∗∗ (2.77) (1.57)
College x extreme cold -2.25 -1.28 (4.68) (1.87) Control variables Male
104.30∗∗ 104.30∗∗ 104.20∗∗ 104.20∗∗ (3.92) (3.92) (3.92) (3.92) Mother’s age
55.11∗∗ 54.54∗∗ 55.07∗∗ 54.51∗∗ (4.97) (4.97) (4.97) (4.97) Mother’s age2
-0.89∗∗ -0.88∗∗ -0.89∗∗ -0.88∗∗ (0.08) (0.08) (0.08) (0.08) Intercept -218.50
-203.60 -147.60 -133.10 (136.30) (136.40) (136.80) (136.90) Observations
53,879 53,879 53,879 53,879 R2 0.071 0.072 0.072 0.072 _Note:_ Regressions
control for conception year and month interaction fixed effects, day of the
week at birth, parity, and daily mean rainfall.
† $p<0.10$; ∗ $p<0.05$; ∗∗ $p<0.01$.

Table A.2: Conditional quantile regression analysis of birth weight with
ambient composite index and extreme temperature and maternal education.

Estimates at conditional quantiles Variable P10 P25 P50 P75 P90 Environmental
exposure variables Composite index a,b,c,d -292.98∗∗ -192.96∗∗ -150.52∗∗
-111.98∗∗ -123.83∗∗ (19.50) (14.10) (11.00) (12.61) (17.25) Extreme heat
-38.50∗∗ -20.23∗ -21.52∗∗ -9.73 -7.79 (14.26) (9.76) (8.01) (9.38) (14.53)
Extreme cold a,b -58.07∗∗ -25.52∗∗ -14.43† -21.52∗ -12.97 (16.27) (9.62)
(8.16) (10.45) (12.83) Education College educated a,b,c,d 66.01∗∗ 33.64∗∗
23.00∗∗ 9.63 9.46 (8.56) (5.72) (4.85) (6.11) (7.50) Control variables Male
92.49∗∗ 99.08∗∗ 112.03∗∗ 117.86∗∗ 131.44∗∗ (8.66) (4.88) (3.93) (4.90) (6.67)
Mother’s age 63.22∗∗ 45.16∗∗ 46.75∗∗ 49.19∗∗ 43.76∗∗ (10.97) (6.31) (5.19)
(6.16) (8.42) Mother’s age2 -1.07∗∗ -0.73∗∗ -0.73∗∗ -0.75∗∗ -0.64∗∗ (0.19)
(0.10) (0.09) (0.10) (0.14) Intercept -1,626.65∗∗ -564.20∗∗ 260.17∗ 844.02∗∗
1,028.68∗∗ (228.99) (164.71) (128.89) (163.68) (256.45) Observations 53,879
53,879 53,879 53,879 53,879 _Note:_ Regressions control for conception year
and month interaction fixed effects, day of the week at birth, parity, and
daily mean rainfall.
Given bootstrapped simultaneous conditional quantile estimates, superscripts
a, b, c, and d indicate whether estimates across conditional quantiles are
statistically different for the three ambient environment or education
variables.
a P10, P25, and P50 are significantly different at 0.05 sig. level.
b P10 and P90 are significantly different at 0.05 sig. level.
c P25 and P75 are significantly different at 0.05 sig. level.
d P50, P75, and P90 are significantly different at 0.05 sig. level.
† $p<0.10$; ∗ $p<0.05$; ∗∗ $p<0.01$.Bootstrap standard errors are shown in
parentheses.

Table A.3: Conditional quantile regression analysis of birth weight with
interactions of ambient composite index and extreme temperature with maternal
education.

Estimates at conditional quantiles Variable P10 P25 P50 P75 P90 Environmental
exposure variables Composite index a,b,c,d -303.74∗∗ -201.06∗∗ -151.02∗∗
-113.65∗∗ -125.04∗∗ (20.20) (14.57) (12.55) (12.82) (14.82) Extreme heat a,b
-53.19∗∗ -26.34∗ -23.50∗ -12.95 -15.17 (15.83) (11.66) (9.34) (10.00) (12.36)
Extreme cold a,b -63.15∗∗ -26.51∗ -13.36 -19.48† -11.46 (15.67) (11.10) (8.64)
(10.39) (13.96) Education and environmental exposure interactions College
educated 24.22 9.96 16.40 7.32 -0.30 (17.25) (13.52) (13.34) (15.17) (19.60)
College x composite index a,b,c 24.70∗∗ 15.12∗∗ 6.34∗ 6.63† 0.12 (5.69) (3.90)
(3.04) (3.52) (4.77) College x extreme heat a 19.62∗∗ 9.08∗ 4.80 3.01 7.33†
(5.59) (3.60) (3.01) (3.44) (4.04) College x extreme cold 3.24 2.74 -3.23
-3.38 -1.79 (7.92) (5.31) (5.38) (6.21) (8.21) Control variables Male 88.50∗∗
98.64∗∗ 111.87∗∗ 117.91∗∗ 131.37∗∗ (8.14) (4.92) (4.50) (5.04) (6.36) Mother’s
age 64.58∗∗ 45.67∗∗ 46.16∗∗ 49.16∗∗ 44.13∗∗ (10.77) (5.67) (4.72) (6.09)
(8.70) Mother’s age2 -1.09∗∗ -0.73∗∗ -0.72∗∗ -0.75∗∗ -0.65∗∗ (0.18) (0.10)
(0.08) (0.10) (0.15) Intercept -1,534.26∗∗ -562.73∗∗ 273.06∗ 845.59∗∗
1,061.62∗∗ (246.88) (159.04) (124.02) (150.09) (249.14) Observations 53,879
53,879 53,879 53,879 53,879 _Note:_ Regressions control for conception year
and month interaction fixed effects, day of the week at birth, parity, and
daily mean rainfall.
Given bootstrapped simultaneous conditional quantile estimates, superscripts
a, b, c, and d indicate whether estimates across conditional quantiles are
statistically different for the three ambient environment or education
variables.
a P10, P25, and P50 are significantly different at 0.05 sig. level.
b P10 and P90 are significantly different at 0.05 sig. level.
c P25 and P75 are significantly different at 0.05 sig. level.
d P50, P75, and P90 are significantly different at 0.05 sig. level.
† $p<0.10$; ∗ $p<0.05$; ∗∗ $p<0.01$.Bootstrap standard errors are shown in
parentheses.

###  A.2 All City Districts PM10 Measurements

Rather than using pollution data from monitoring stations in the four
contiguous districts located in the center of the city, in this robustness
check, we use pollution data from the whole city of Guangzhou, including
information from districts that are further away. There are 11 districts in
Guangzhou, five districts are located to the north of the center-city
districts, and two districts are located to the south. Guangzhou city’s total
area is 7434 square kilometers. The four center-city districts’ total area is
280 square kilometers.

Given the proximity, it is more likely that mothers who deliver in the center-
city district where our data comes from reside in center-city districts.
However, it is also possible that some of them come from further-away
districts. Additionally, in a metropolitan city with convenient local
transport networks, it is likely that Guangzhou residents travel beyond their
district borders throughout the city for work, leisure, shopping, and health
and other social services. So it is plausible that the appropriate measure for
ambient pollution should be based on city-wide data rather than district-
specific data.

Summary statistics in Table 1 shows that pollution measurements from all city
districts and center-city districts are quiet similar. The mean and standard
deviation of the average daily mean potential PM10 exposure are 73.2 and 7.2
for all city districts and 72.1 and 6.6 for center-city districts. The all
city and center-city composite index also have similar means and standard
deviations. The similarity in pollution exposure within the confines of
Guangzhou is not surprising given that that these pollutants are formed and
spread through lower-atmosphere and are usually not highly localized [41].

We present results using all-city district data in Table A.4, which presents
parallel results as in Table 3, except that center-city districts PM10
measurements are replaced by all city districts measurements. Table A.4
estimates are almost identical to Table 3 results. Similarly, results from
using the two types of measures are also similar for conditional quantile
estimations and estimation using the composite index. Given their similarity,
those results are not presented.

Table A.4: OLS regression analysis of birth weight with interactions of
ambient PM10 and extreme temperature with maternal education. Using all city
districts pollution measurements.

Varying cutoffs of extreme temperature exposures 1 percent cutoff 2.5 percent
cutoff Variable (1) (2) (3) (4) Environmental exposure variables PM10 all city
districts -17.81∗∗ -18.55∗∗ -14.84∗∗ -15.64∗∗ (2.18) (2.21) (2.21) (2.24)
Extreme heat -21.91∗ -26.27∗∗ 0.15 -2.10 (9.51) (9.67) (5.15) (5.25) Extreme
cold -33.55∗∗ -33.36∗∗ -25.75∗∗ -25.51∗∗ (9.81) (9.97) (4.37) (4.42) Education
and environmental exposure interactions College educated 44.48∗∗ -117.60∗
44.79∗∗ -132.60∗∗ (4.43) (48.99) (4.43) (49.90) College x PM10 all city
districts 1.96∗∗ 2.07∗∗ (0.61) (0.61) College x extreme heat 11.07∗∗ 5.83∗∗
(2.85) (1.64) College x extreme cold -1.09 -0.97 (4.74) (1.89) Control
variables Male 104.00∗∗ 104.00∗∗ 103.90∗∗ 103.90∗∗ (3.93) (3.93) (3.93) (3.93)
Mother’s age 55.40∗∗ 54.87∗∗ 55.31∗∗ 54.81∗∗ (4.98) (4.98) (4.98) (4.98)
Mother’s age2 -0.89∗∗ -0.89∗∗ -0.89∗∗ -0.89∗∗ (0.08) (0.08) (0.08) (0.08)
Intercept 1,216.00∗∗ 1,288.00∗∗ 1,019.00∗∗ 1,125.00∗∗ (215.60) (217.10)
(221.40) (218.60) Observations 53,879 53,879 53,879 53,879 R2 0.069 0.069
0.069 0.070 _Note:_ Regressions control for conception year and month
interaction fixed effects, day of the week at birth, parity, and daily mean
rainfall.
† $p<0.10$; ∗ $p<0.05$; ∗∗ $p<0.01$.

###  A.3 Sample Truncation Based on Gestational Age at Birth

Research on the effects of pollution on birth weight often include both pre-
term (with gestational age less than 37 weeks) and full-term births [74, 50].
Some studies restrict the analytic sample to full-term births. Full-term
sample selection may be important for creating a balanced sample to study the
differential effects of pollution during different trimesters [61] or each
month of gestation [45]. In this paper, we are interested in the average
exposure effects from ambient pollution and extreme temperature throughout the
course of pregnancy and do not estimate gestation-specific effects.A.1A.1A.1In
Equations (1) and (2), we implicitly assume that the effects of pollution and
extreme temperature exposures are the same across gestational periods. Given
our sample and location constraints, we do not have sufficient power to
identify quantile- and gestation-period-specific estimates.

In the text, we focus on the distribution of birth weights. Following [2, 53,
31], we focus on all live singleton births and do not truncate based on
gestational age. Gestational age in our sample ranges from 171 to 319 days.
While we believe it is important to analyze the full distributional outcomes
without gestational truncation, to facilitate comparisons to papers that do
impose gestational truncation, in Table A.5, we conduct the same estimation
with the same variables as in Table 3, but now restrict our sample to only
individuals with gestational age longer than 36 weeks.

The main results in Table A.5 are similar in directions but smaller in
magnitudes than the results in Table 3: we continue to find negative
associations between pollution and extreme temperatures and birth weight, and
find that mothers’ college education status is associated with reduced
vulnerability to ambient air pollution and extreme heat exposures.A.2A.2A.2In
both Tables 3 and A.5, we find a weak negative coefficient for the maternal-
college-education and extreme-cold interaction variable. The coefficient is
not significant in Table 3 and weakly significant in Table A.5. As shown in
Table 5, these relationships fluctuate between weakly positive and negative
across the quantiles for results without gestational restrictions. Quantile
results with gestational restrictions are similar. In particular, we find a 1
µg/m3 increase in average daily PM10 potential exposure during pregnancy is
associated with an 8.85 gram reduction of birth weight (s.e. 1.56) in Table
A.5 column 1, but the same increase is associated with a 17.83 gram reduction
of birth weight (s.e. 2.27) in Table 3 column 1. Additionally, we find a
maternal college education and PM10 interaction coefficient of 0.72 (s.e.
0.59) in Table A.5 column 2 and of 1.95 (s.e. 0.68) in Table 3 column 2.
Corresponding quantile regression results with full-term birth also show
similar patterns as in Tables 4 and 5.

Table A.5: OLS regression analysis of birth weight with interactions of
ambient PM10 and extreme temperature with maternal education. Using only full-
term births.

Varying cutoffs of extreme temperature exposures 1 percent cutoff 2.5 percent
cutoff Variable (1) (2) (3) (4) Environmental exposure variables PM10 -8.85∗∗
-9.14∗∗ -7.70∗∗ -8.05∗∗ (1.56) (1.58) (1.57) (1.60) Extreme heat -8.56 -10.37
0.15 -0.71 (6.68) (6.78) (3.52) (3.58) Extreme cold -16.54∗ -14.19∗ -12.69∗∗
-11.61∗∗ (6.99) (7.12) (3.04) (3.09) Education and environmental exposure
interactions College educated 12.33∗∗ -35.51 12.49∗∗ -44.00 (3.98) (47.06)
(3.98) (49.35) College x PM10 0.72 0.89 (0.59) (0.62) College x extreme heat
4.15 1.96 (2.60) (1.51) College x extreme cold -7.54† -3.65∗ (4.17) (1.67)
Control variables Male 111.10∗∗ 111.10∗∗ 111.00∗∗ 111.00∗∗ (3.48) (3.48)
(3.47) (3.47) Mother’s age 39.56∗∗ 39.28∗∗ 39.52∗∗ 39.27∗∗ (4.26) (4.26)
(4.26) (4.26) Mother’s age2 -0.60∗∗ -0.59∗∗ -0.60∗∗ -0.59∗∗ (0.07) (0.07)
(0.07) (0.07) Intercept 2,193.00∗∗ 2,217.00∗∗ 2,131.00∗∗ 2,159.00∗∗ (170.90)
(171.90) (171.90) (173.10) Observations 49,973 49,973 49,973 49,973 R2 0.037
0.038 0.038 0.038 _Note:_ Regressions control for conception year and month
interaction fixed effects, day of the week at birth, parity, and daily mean
rainfall.
† $p<0.10$; ∗ $p<0.05$; ∗∗ $p<0.01$.

###  A.4 Low Birth Weight, Preterm, and Small for Gestational Age

In the text we focus on a continuous measure of birth weight. But in some
cases studies focus on related binary variables: low birth weight (birth
weight under 2500 grams), preterm (gestational age less than 37 weeks), and
small for gestational age (1 is defined as those with birth weight under 10 %
in sex-and gestational-age specific distributions). The three binary outcome
division strategies could be interpreted geometrically. With gestational-age
along the x-axis and birth-weight along the y-axis, low birth weight cuts the
data horizontally, preterm cuts the data vertically, and small-for-
gestational-age cuts the data diagonally. While each strategy generates
valuable outcomes of interest, these data-reduction strategies eliminate much
of the distributional variations in the data and preclude quantile analysis.

In this section, to facilitate comparison of our results to papers that focus
on these binary outcomes, we present regression results for low birth weight,
preterm and small for gestational age in Tables A.6 and A.7. Tables A.6 and
A.7 differ from Table 3 by replacing least-squares with logistic regressions
and replacing birth weight with binary outcome variables, but the same right-
side specifications are used. Table A.6 presents results as marginal effects,
and Table A.7 presents results as odds-ratios.A.3A.3A.3From the summary
statistics in Table 1, 6% of the analytical sample have low birth weight (7%
for high school educated, and 4% for college educated), 7% of the analytical
sample are borne preterm (8% for high school educated, and 5% for college
educated), and 9% are small for gestational age (10% for high school educated,
and 8% for college educated).

The results from Tables A.6 and A.7 largely match-up with results from Table
3. First, greater PM10, extreme heat, as well as extreme cold exposure are all
associated with increased probabilities for low birth weight, preterm, and
small for gestational age. PM10 and and extreme heat have the strongest
associations with preterm, extreme cold has the strongest association with low
birth weight, and the environmental measures are positively but not
significantly associated with higher chance for small for gestational age.
Second, maternal college education is significantly inversely associated with
all three binary outcomes: its strongest association is with low birth weight
and its weakest association is with small for gestational age. Third, we find
that mothers’ college education status is associated with reduced
vulnerability to ambient air pollution and extreme heat exposures. Results are
similarly for low birth weight and preterm but weaker for small for
gestational age. Associations are greater for the extreme heat exposure
interaction and are weakly significant for the PM10 exposure interaction.
Interesting, we also find a protective effect of maternal college education
for the association between extreme cold and preterm.

Table A.6: Marginal-effects from logistic regression analysis of binary birth
outcomes with interactions of ambient PM10 and extreme temperature with
maternal education.

Different binary birth-related outcomes Low birth weight Preterm Small for
gestational age Variable (1) (2) (3) (4) (5) (6) Environmental exposure
variables PM10 0.0066∗∗ 0.0067∗∗ 0.0080∗∗ 0.0082∗∗ 0.0010 0.0011 (0.0011)
(0.0011) (0.0012) (0.0012) (0.0010) (0.0010) Extreme heat 0.0041 0.0053
0.0155∗∗ 0.0168∗∗ 0.0064 0.0071 (0.0048) (0.0048) (0.0057) (0.0057) (0.0044)
(0.0044) Extreme cold 0.0156∗∗ 0.0157∗∗ 0.0127∗ 0.0145∗∗ 0.0094∗ 0.0087†
(0.0049) (0.0050) (0.0053) (0.0053) (0.0045) (0.0046) Education and
environmental exposure interactions College educated -0.0301∗∗ 0.0272
-0.0251∗∗ 0.0419 -0.0195∗∗ 0.0012 (0.0025) (0.0301) (0.0026) (0.0301) (0.0029)
(0.0346) College x PM10 -0.0007† -0.0006 -0.0003 (0.0004) (0.0004) (0.0004)
College x extreme heat -0.0043∗∗ -0.0045∗∗ -0.0019 (0.0016) (0.0016) (0.0019)
College x extreme cold -0.0005 -0.0079∗∗ 0.0033 (0.0028) (0.0029) (0.0031)
Control variables Male -0.0125∗∗ -0.0125∗∗ 0.0060∗∗ 0.0060∗∗ 0.0025 0.0024
(0.0019) (0.0019) (0.0021) (0.0021) (0.0025) (0.0025) Mother’s age -0.0141∗∗
-0.0139∗∗ -0.0144∗∗ -0.0143∗∗ -0.0108∗∗ -0.0107∗∗ (0.0019) (0.0019) (0.0021)
(0.0021) (0.0028) (0.0028) Mother’s age2 0.0002∗∗ 0.0002∗∗ 0.0003∗∗ 0.0003∗∗
0.0002∗∗ 0.0002∗∗ (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000)
Observations 53,879 53,879 53,879 53,879 53,879 53,879 _Note:_ Regressions
control for conception year and month interaction fixed effects, day of the
week at birth, parity, and daily mean rainfall.
† $p<0.10$; ∗ $p<0.05$; ∗∗ $p<0.01$.

Table A.7: Odds-ratio from Logistic regression analysis of binary birth
outcomes with interactions of ambient PM10 and extreme temperature with
maternal education.

Different binary birth-related outcomes Low birth weight Preterm Small for
gestational age Variable (1) (2) (3) (4) (5) (6) Environmental exposure
variables PM10 1.137∗∗ 1.141∗∗ 1.143∗∗ 1.146∗∗ 1.012 1.013 (0.001) (0.001)
(0.001) (0.001) (0.001) (0.001) Extreme heat 1.084 1.109 1.293∗∗ 1.321∗∗ 1.081
1.090 (0.102) (0.105) (0.122) (0.125) (0.058) (0.059) Extreme cold 1.357∗∗
1.360∗∗ 1.235∗ 1.272∗∗ 1.121∗ 1.111† (0.132) (0.132) (0.108) (0.111) (0.061)
(0.062) Education and environmental exposure interactions College educated
0.554∗∗ 1.704 0.660∗∗ 2.000 0.790∗∗ 1.014 (0.027) (1.004) (0.028) (0.997)
(0.028) (0.425) College x PM10 0.987† 0.990 0.996 (0.007) (0.006) (0.005)
College x extreme heat 0.920∗∗ 0.928∗∗ 0.978 (0.029) (0.025) (0.023) College x
extreme cold 0.990 0.878∗∗ 1.040 (0.054) (0.042) (0.039) Control variables
Male 0.782∗∗ 0.782∗∗ 1.104∗∗ 1.105∗∗ 1.030 1.030 (0.029) (0.029) (0.038)
(0.038) (0.031) (0.031) Mother’s age 0.759∗∗ 0.761∗∗ 0.788∗∗ 0.789∗∗ 0.877∗∗
0.878∗∗ (0.028) (0.028) (0.028) (0.028) (0.030) (0.030) Mother’s age2 1.005∗∗
1.005∗∗ 1.004∗∗ 1.004∗∗ 1.002∗∗ 1.002∗∗ (0.001) (0.001) (0.001) (0.001)
(0.001) (0.001) Observations 53,879 53,879 53,879 53,879 53,879 53,879 _Note:_
Regressions control for conception year and month interaction fixed effects,
day of the week at birth, parity, and daily mean rainfall.
† $p<0.10$; ∗ $p<0.05$; ∗∗ $p<0.01$.

##  Appendix B Acquiring Environmental Data

A wide array of environmental data is available at detailed geographical units
across the globe from the the European Centre for Medium-Range Weather
Forecasts (ECMWF) at the Copernicus Climate Change Service. Copernicus
provides data access via a free and publicly accessible API service. In this
appendix section, we describe how we obtained key environmental data used in
this paper using Copernicus.

###  B.1 Data Retrieval

Copernicus offers a range of data in different formats with similar data
request structures. In particular, temperature as well as other environmental
data based on observations from across the globe (with reanalysis) are
available from the ERA5 Pressure Level as well as the ERA5 Single Level
datasets.

####  B.1.1 Single Data Retrieval Request

To acquire Chinese data for the particular period in which our birth outcome
data are available, we need to specify the appropriate time ranges as well as
the geographical coordinates. We retrieve hourly data from every day between
the year 2007 and 2012 by specifying the appropriate _year_ , _month_ , _day_
, and _time_ parameters. We specify our data acquisition geographical area as
to the south-east of latitude and longitude coordinates (in decimal degrees)
$\left(23.50,113.00\right)$ and to the north-west of coordinates
$\left(22.25,114.50\right)$, which covers the broad geographical area that is
relevant for our paper. Our specification for the _area_ parameter is
therefore $[23.50,113.00,22.25,114.5]$.

Given this information and after registering with Copernicus to obtain an
user-specific url and passkey, Source Code B.1 provides a API call to acquire
temperature data from Copernicus in _grib_ format.

Figure B.1: Single Data Retrieval Call

⬇

# Library

import cdsapi

import urllib.request

\par# download folder

spt_root = "C:/data/"

spn_dl_test_grib = spt_root + "test_china_temp.grib"

# request

c = cdsapi.Client()

res = c.retrieve("reanalysis-era5-pressure-levels",

{

’product_type’: ’reanalysis’,

’variable’: ’temperature’,

’pressure_level’: ’1000’,

’year’: [ ’2007’, ’2008’,’2009’, ’2010’, ’2011’, ’2012’ ]

’month’: [ ’01’,’02’,’03’,’04’,’05’,’06’, ’07’,’08’,’09’,’10’,’11’,’12’],

’day’: [

’01’,’02’,’03’,’04’,’05’,’06’,’07’,’08’,’09’,’10’,’11’,’12’,

’13’,’14’,’15’,’16’,’17’,’18’,’19’,’20’,’21’,’22’,’23’,’24’,

’25’,’26’,’27’,’28’,’29’,’30’,’31’

],

’time’: [

’00:00’, ’01:00’, ’02:00’, ’03:00’, ’04:00’, ’05:00’, ’06:00’, ’07:00’,
’08:00’,

’09:00’, ’10:00’, ’11:00’, ’12:00’, ’13:00’, ’14:00’, ’15:00’, ’16:00’,
’17:00’,

’18:00’, ’19:00’, ’20:00’, ’21:00’, ’22:00’, ’23:00’

],

’area’: [23.50, 113.00, 22.25, 114.5],

’grid’: [1.25, 0.25],

"format": "grib"

},

spn_dl_test_grib

)

\par

####  B.1.2 Subdivided Data Retrieval Request

A challenge to taking full advantage of the data is that, given the fine
geographical and time units, the resulting data files can become very large. A
single call to acquire all relevant data as shown in the example above can
only be implemented on a server with access to Terabytes of storage space.

To deal with this challenge, we divide our API calls into smaller components.
We make multiple requests of data at shorter time intervals. Each time we
aggregate and process the relevant data before downloading the next set of
data. Given the computational resources at our disposal, we download the data
at six months intervals, as shown in Source Code B.2. Given the computing
resources available to the researcher, the time intervals can be further
shortened to circumvent computational challenges from using the data.

Figure B.2: Sub-Period Data Retrieval Call

⬇

# date lists