Tsukuba Economics Working Papers
No. 2022-002

Effect of PM 2.5 Exposure at the Conception and at the Birth on Child Mortality:
Evidence from Asian Countries
byHisahiro Naito
and
Isurun Upeksha Gamage

June 2022

UNIVERSITY OF TSUKUBA
Program in Economic and Public Policy
1-1-1 Tennodai
Tsukuba, Ibaraki 305-8571
JAPANEffect of PM2.5 Exposure at the Conception andat the Birth on Child Mortality: Evidence from
Asian Countries
Hisahiro Naitoa,c,1 and Isurun Upeksha Gamageb,1,2
aProgram in Economic and Public Policy, Graduate School of Humanities and Social Sciences, University of Tsukuba, Japan; b Program in Economic and Public Policy,
Graduate School of Humanities and Social Sciences, University of Tsukuba, Japan

Currently, the most air-polluted areas in the world are in Asia andAfrica. Although there are reports that air pollution affects humanhealth, these data are obtained from developed countries and datafrom developing countries are limited. Extrapolating the data obtained from developed countries to estimate the effect of air population on health in developing countries remains a challenge owing
to the differences in the other environment such as sanitation andaccess to health services. A central and policy relevant questionis whether the timing to exposure matter and how much it mattters.
In this study, we examined the effect of the exposure to particulatematter 2.5 (PM2.5), a fine inhalable particle with a diameter of 2.5 micrometers or smaller at conception year, on infant mortality using
the multiple-waves of the demographic and health survey data andPM2.5 data in Asian countries. Combining multiple waves from Demographic and Health Surveys (DHS) through global positioning system (GPS) information of each cluster, we constructed a cluster-level
panel data of child mortality. We mapped satellite-based PM2.5 data
to the constructed panel data through the GPS information of bothcluster data and PM2.5 data. We found that a 10 microgram increasein PM2.5 in the conception year is associated with a 16% increase ininfant mortality, and its effect is statistically significant. Meanwhile,
the PM2.5 at birth year does not affect the infant mortality. Our estimation results are robust with various specification checks.
Keywords: PM2.5 | Air Pollution | Infant Mortality | Asian Countries

A

ccording to the World Health Organization(1), 1.7 million
children die owing to poor air quality. Central Africa
and South Asia are among the areas with the most polluted
air quality(2, 3). However, the relationship between air quality and health outcomes in developing countries has less frequently been examined. Most studies that have documented
the relationship between air quality and health outcomes wereconducted from developed countries— a few studies have measured a similar impact in developing countries (2). Interpreting
and applying the knowledge obtained from developed countries
to developing countries is often difficult owing to the different
attributes between these societies, including mortality rates
and other health conditions.

The central question on the effect of air pollution to health,which is important for policy makers, is how much and which
timing the exposure to air pollution affects the health outcome.
The answer to this question is the key input to design a policy
to mitige the impact of air pollution on the health outcomes.
Several studies in epidemology and public health literatureshows that significance of the negative shock in utero on thehealth condition in later life the first few month of pregnancy
while other studies does not find such a pattern. Examining
the effect of the quantity and the timing of the exposure to

air pollution on health outcome is needed to design a better
preventive policy.
To examine such an effect, a simple cross-sectional or timeseries analysis can be misleading due to confounding factors.
One way to solve such a problem is to construct village-level
panel data consisting of a large number of countries. The
village-level panel data in multiple-countries allow the researchers to control the fixed effect of the village, and time.
The availability of data in multiple countries makes it possible
for the researcher to manage the country-year fixed effect,
allowing control of different countries with different trajectories on the health outcome while estimating the effect of air
pollution(2).
In this study, we examined the effect of the exposure toparticulate matter 2.5 (PM2.5), a fine inhalable particle of 2.5
micrometers or smaller in diameteron at the conception andbirth on infant mortality. We use the multiple-waves of thedemographic and health survey data in Asian countries while
utilizing the GPS information of each cluster, we constructed
the cluster level panel data of child mortality and mapped thePM2.5 data to the constructed panel data.
This study contributes to the literature in three ways. First,
we specifically distinguish the effect of PM2.5 exposure on
child mortality by measuring the exposure in the birth andconception years. Given the uncertainty on the mechanism
in which PM2.5 affects infant mortality, it is important todistinguish the exposure to PM2.5 at conception and at birth.

Significance Statement
This paper examines the effect of particulate matter 2.5, fineinhalable particle with diameters that are 2.5 micrometersand smaller (PM2.5 ) air pollution on infant mortality using themultiple-waves of the demographic and health survey data andPM2.5 data in Asian countries. Combining multiple waves of
DHS surveys through GPS information of each cluster, weconstruct the cluster-level panel data of child mortality. We
map satellite-based PM2.5 data to the constructed panel data
through the GPS information of both cluster data and PM2.5
data. We find that 10 µg increase of PM2.5 at the conception
year is associated with 16 % increase of infant mortality, andits effect is statistically significant while PM2.5 at the birth year
does not affect the infant mortality. Our estimation results arerobust to all robustness specification checks.
There is no conflict of interest to declare
1

H.N.(Hisahiro Naito) and I.G. (Isurun Gamage) contributed equally to this work .

2

Corresponding author. E-mail: naito@dpipe.tsukuba.ac.jp

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Although PM2.5 in the birth and conception years are highly
correlated, we find that the effect of PM2.5 exposure in thebirth year is minimal and not statistically significant. Even
when the exposure of PM2.5 at conception is removed fromthe list of the explanatory variable, the estimated coefficientof the exposure to PM2.5 at birth remains minimal and notstatistically significant. An additional robustness check conducted on the infants exposed to PM2.5 at birth for only a few
months did not change the results, indicating an importantmechanism by which PM2.5 affects human health.
Second, we focused on Asian countries as the pollutant
contents of PM2.5 in Asia and Africa are different. It is known
that the main content of PM2.5 in Africa is dust, whereasthat in Asia comes from industry and agriculture. It is quite
interesting that the effects of PM2.5 in Africa and Asia canbe different. To precisely estimate the effect of PM2.5, in thisresearch, we focus on Asian countries.

Literature Review
The concentration and spatial distribution of PM2.5 is affectedby geographic features, meteorology, and regional variations
including temporal parameters (4, 5). Therefore, evaluation of
PM2.5 variability needs to incorporate various models (6). (2)investigated the robust relationship between air quality andinfant mortality in Africa. The study covers 30 countries, including 990,696 individual birth outcomes across sub-SaharanAfrica with satellite-based estimates of the exposure to ambient respirable particulate matter. (7) examined the loss of life
expectancy from air pollution compared with other risk factors
as a worldwide perspective. Moreover, the authors identified
the importance of air quality on loss of life expectancy on a
global perspective. To explore the causality between the air
quality and cardiovascular respiratory disorders, the authors
used the Global Exposure Mortality Model (GEMM), which
provided wide coverage of fine PM2.5 at a global scale. (6)analyzed the effect of PM2.5 on child mortality using theaggregated data across 16 Asian countries for 20 years. (7)integrated in an exposure-response function, the results froma recent atmospheric chemistry-general circulation model andhealth statistics from the WHO Global Health Observatory
to estimate mortality from ambient air pollution.

Results
Table 1 shows the summary statistics of the main variables.
The overall sample size is 136,052. In our sample, the average
infant mortality rate is 5.4% and the average level of PM2.5
is 23.7. Table 2 shows the result of ordinary least squares
estimation. In all specifications, the cluster fixed effect, yearand birth month fixed effect, year times country fixed effect,and birth month times country fixed effect were included. To
calculate the standard error, it is assumed that the error term
is correlated within each cluster over time. In Table 2, theestimated coefficient of PM2.5exposure in the conception year
is 0.000984. This implies that the 10-microgram increase inPM2.5 exposure increases the probability that a child may die
within one year from birth by 0.984%. As the average mortality
rate is 5.3%, a 10-microgram increase in PM2.5exposure in theconception year increases the infant mortality rate by 18.32%.
Several control variables, such as water source, rainfall, andnighttime light—as a proxy for income level—were included
2|

Table 1: Summary Statistics
VARIABLES
Infant mortality
PM25 exposure in birth year
PM25 exposure in conception year
Total PM 25 exposure
Interview year
Interview monthPartners education (years)
Birth year of child
Birth month of childChild gender
Mothers education (years)
Mothers' age
Head of household age
Head of household genderHousehold size
Urban/Rural
Conception year
Year
Improved water source_dry
Improved water source_wet
Improved toilet
avg nightlight at year of age one
latitudelongitude
total nightlight
Avg nightlight in birthyear
Avg nightlight in conception year
Total rainfall
Avg rainfall in birth year
Avg rainfall in conception year
Bangladesh dummy
Cambodia dummy
Notes: Sample size is 136,052.

(2)
mean

(3)
sd

(4)
min

(5)
max

0.0537
23.72
23.27
23.52
2,013
7.63
6.103
2,005
6.506
0.515
5.206
33.07
42.84
0.886
6.662
0.391
2,005
2,005
0.865
0.875
0.54
2,006
15.05
100.1
6,327
5.607
5.731
235,891
1,667
1,667
0.3
0.061

0.225
18.72
18.36
18.54
4.545
3.754
4.995
4.701
3.445
0.5
4.915
7.322
12.06
0.318
3.123
0.488
4.707
4.701
0.342
0.331
0.498
4.701
15.06
20.36
12,143
10.58
10.7
158,988
1,124
1,121
0.458
0.239

0
0
0
0
2,004
1
0
1,999
1
0
0
15
14
0
1
0
1,998
1,999
0
0
0
2,000
-9.47865.43-289.6-0.0692-0.051-771,526-5,472-5,517
0
0

1
77.32
77.32
76.44
2,018
12
24
2,016
12
1
25
49
95
1
35
1
2,016
2,016
1
1
1
2,017
35.83
127.2
135,380
61.77
61.77
968,640
6,821
6,821
1
1

(Table 2). However, these control variables do not affect theestimated coefficients.
Table 2: The Effect of PM2.5 on Infant Mortality
Dependent Variable
Infant Mortality Dummy
VARIABLES
(1)
(2)
(3)
(4)
PM25 exposure in
0.000984*** 0.00101*** 0.00101*** 0.00102***
conception year
(0.000344) (0.000345) (0.000345) (0.000346)
Cluster FE
YES
YES
YES
YES
CountryYear FE
YES
YES
YES
YES
BirthmonthCountry FE
YES
YES
YES
YES
InterviewmonthCountry
YES
YES
YES
YES
Individual covariates
YES
YES
YES
YES
Water source
NO
YES
YES
YES
Rainfall
NO
YES
YES
YES
Wealth
NO
NO
YES
YES
Nightlight
NO
NO
NO
YES
Sanitation
NO
NO
NO
NO
Cooking fuel
NO
NO
NO
NO
R-squared
0.047
0.048
0.048
0.048
N
136,052
136,052
136,052
136,052
Clustered robust standard errors in parentheses. Mean infant mortality dummy
0.0537074. *** p<0.01, ** p<0.05, * p<0.1

In Table 3, we separated the sample into male and female
infants. The estimated coefficients obtained from the maleand female infants are relatively similar to the estimatedcoefficients in Table 2, suggesting that the effect of PM2.5 is
not different between male and female infants. We changed themain explanatory variable from the PM2.5 in the conception
year to PM2.5 in the birth year (Table 4). The estimatedcoefficient of PM2.5 in the birth year is one third of theestimated coefficient in Table 2, which is not statisticallysignificant, suggesting that the PM2.5 in the birth year is
not as relevant as the PM2.5 in the conception year. We
include both PM2.5 in the conception and birth years as the
Naito et al.

Table 3: The Effect of PM2.5 on Infant Mortality for Male and Female Sample
Dependent Variable
Infant Mortality Dummy
Panel A. Male Sample
(1)
(2)
(3)
(4)
PM25 exposure
0.000966* 0.000979* 0.000974* 0.000964*
in conception year
(0.000499) (0.000502) (0.000502) (0.000502)
R-squared
0.064
0.065
0.065
0.065
N
70,10870,10870,10870,108Panel B. Female Sample
PM25 exposurein conception year
R-squared
N

Table 5. The Effect of PM2.5 at Conception Year and at Birth Year
0.000947** 0.000976** 0.000979** 0.00101**(0.000477) (0.000479) (0.000479) (0.000480)
0.064
0.065
0.065
0.065
65,94465,94465,94465,944

Panel C. Control Variables
Cluster FE
YES
YES
YES
YES
CountryYear FE
YES
YES
YES
YES
BirthmonthCountry FE
YES
YES
YES
YES
InterviewmonthCountry
YES
YES
YES
YES
Individual covariates
YES
YES
YES
YES
Water source
NO
YES
YES
YES
Rainfall
NO
YES
YES
YES
Wealth
NO
NO
YES
YES
Nightlight
NO
NO
NO
YES
Sanitation
NO
NO
NO
NO
Cooking fuel
NO
NO
NO
NO
Clustered robust standard errors in parentheses. Mean infant mortality dummy
0.0537074. *** p<0.01, ** p<0.05, * p<0.1

key explanatory variables (Table 5). The estimated coefficientof PM2.5 in the conception year was similar to the estimatedcoefficient of PM2.5 in the conception year, as shown in Table
2. Meanwhile, the estimated coeffiicents of PM2.5 in the birthyear were almost zero, and not statistically significant. Thissuggests that PM2.5 at conception is more of a determinantof infant mortality that PM2.5 at birth.

on Infant Mortality
Infant Mortality Dummy
(1)
(2)
(3)
(4)
0.000984*** 0.00101*** 0.00101*** 0.00102***(0.000344) (0.000345) (0.000345) (0.000346)
PM25 in birth year
-0.000191 -0.000150 -0.000150 -0.000154(0.000434) (0.000435) (0.000435) (0.000436)
Cluster FE
YES
YES
YES
YES
CountryYear FE
YES
YES
YES
YES
BirthmonthCountry FE
YES
YES
YES
YES
InterviewmonthCountry
YES
YES
YES
YES
Individual covariates
YES
YES
YES
YES
Water source
NO
YES
YES
YES
Rainfall
NO
YES
YES
YES
Wealth
NO
NO
YES
YES
Nightlight
NO
NO
NO
YES
Sanitation
NO
NO
NO
NO
Cooking fuel
NO
NO
NO
NO
R-squared
0.046
0.047
0.047
0.048
N
136,052
136,052
136,052
136,052
Clustered robust standard errors in parentheses. Mean infant mortality dummy
0.0537074. *** p<0.01, ** p<0.05, * p<0.1
Dependent Variable
VARIABLES
PM25 in conception year

Table 4.The Effect of PM2.5 at Birth Yearon Infant Mortality
Infant Mortality Dummy
(1)
(2)
(3)
(4)
0.000308
0.000349
0.000348
0.000348(0.000396) (0.000397) (0.000397) (0.000399)
Cluster FE
YES
YES
YES
YES
CountryYear FE
YES
YES
YES
YES
BirthmonthCountry FE
YES
YES
YES
YES
InterviewmonthCountry
YES
YES
YES
YES
Individual covariates
YES
YES
YES
YES
Water source
NO
YES
YES
YES
Rainfall
NO
YES
YES
YES
Wealth
NO
NO
YES
YES
Nightlight
NO
NO
NO
YES
Sanitation
NO
NO
NO
NO
Cooking fuel
NO
NO
NO
NO
R-squared
0.046
0.047
0.047
0.048
N
136,052
136,052
136,052
136,052
Clustered robust standard errors in parentheses. Mean infant mortality dummy
0.0537074. *** p<0.01, ** p<0.05, * p<0.1
Dependent Variable
VARIABLES
PM25 at birth year

One concern about our estimation is that the PM2.5 ishighly correlated with indoor cooking fuel, such as charcoal,and the high infant mortality is not due to outdoor air conditions, but to polluated air from cooking fuels. In addition,
there is a concern that house toilet type is highly correlatedwith infant mortality, and the type of toilet is correlated witheconomic activity and PM2.5. We included the cooking fueland the toilet type as additional control variables (Table 6).
The estimated coefficients of PM2.5 in the conception andbirth years did not change from the key explanatory variables
in Table 5. In Table 6, a 10-microgram increase of PM2.5 inthe conception year increases the probability of infant mortalNaito et al.

Table 6. The Effect of PM2.5 at Conception Year and at Birth Yearon Infant Mortality: Controlling Type of Cooking Fuel and Sanitation
Dependent Variable
Infant Mortality Dummy
VARIABLES
(1)
(2)
(3)
(4)
PM25 in conception year 0.00104*** 0.00105*** 0.00105*** 0.00106***(0.00038)(0.00038)(0.00038)(0.00038)
PM25 in birth year
-0.000199-0.00016-0.000161-0.000165(0.00043)(0.00044)(0.00044)(0.00044)
Cluster FE
YES
YES
YES
YES
CountryYear FE
YES
YES
YES
YES
BirthmonthCountry FE
YES
YES
YES
YES
InterviewmonthCountry
YES
YES
YES
YES
Individual covariates
YES
YES
YES
YES
Water source
NO
YES
YES
YES
Rainfall
NO
YES
YES
YES
Wealth
NO
NO
YES
YES
Nightlight
NO
NO
NO
YES
Sanitation
YES
YES
YES
YES
Cooking fuel
YES
YES
YES
YES
R-squared
0.047
0.048
0.048
0.048
N
136,052
136,052
136,052
136,052
Clustered robust standard errors in parentheses. Mean infant mortality dummy
0.0537074. *** p<0.01, ** p<0.05, * p<0.1|

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ity rate by 1%. This implies that a 10-microgram increase of
PM2.5 increases the infant mortality by 19%. In contrast, theeffect of PM2.5 on birth year was almost negligible and was
not statistically significant.
One key observation from Table 5 and Table 6 is that thePM2.5 at conception has an effect on infant mortality relative
to the PM2.5 at birth. There are several concerns for this results. In particular, the PM2.5in the conception year and thatin the birth year are highly correlated. Consequently, when
the two variables are put togather, the estimated coefficientof PM2.5 in the birth year becomes negligible, and the effectis not statistically significant. However, such concern does
not seem relevant. In Table 3, we included only the PM2.5 in
the birth year and other control variables. In this case, theestimated coefficient of PM2.5 in the birth year was one thirdand not statistically significant, suggesting that the results
shown in Tables 5 and 6 are not driven by the multi-colinearity
between PM2.5 at conception and birth. Another concern isthat our sample included children who were born between January and February. As we measure PM2.5 annually, children
who were born between January and February were exposed
to PM2.5 in the birth year for only two months, whereas thosechildren were exposed to PM2.5 in the conception year foreight months. Thus, one can assume that the economicallyand statistically insignificant results might be be due to thediscrepancy in the duration of exposure to PM2.5 between
birth and conception years. To factor out this concern, we
excluded children who were born between January to Marchfor an additional robustness check. Thus, in our restricted
sample, children are exposed to PM2.5 in the birth year for atleast three months. Our regression analysis (Table 7) revealed
that even if we exclude children who were born between January and March, the estimated coefficient of PM2.5 in theconception year is economically and statistically significant,whereas the estimated coefficient of PM2.5 in the birth year
is almost zero and not statistically significant.

Discussion
We examined the effect of PM2.5exposure on infant mortality
by constructing a cluster-level panel with microdata whilemerging the high-resolution PM2.5 map created using theinformation on PM2.5data collected via satellite. We foundthat a 10-microgram increase in PM2.5 in the conception year
increases infant mortality by 19%. In contrast, the effect of
PM2.5 in the birth year is almost zero and not statisticallysignificant. Overall, our results corroborate the findings of theprevious literature, showing that PM2.5affects infant mortality.
However, quantitatively, our estimates showed a remarkabledifference in the effect of timing on exposure to PM2.5. In
particular, our estimated coefficient shows that the effectof PM2.5 is about twice as large as the estimate obtained
from Africa’s data. For example, Heft-Neal et al. (2018)
reported that a 10-microgram increase in PM2.5 increases
infant mortality by 10%. Moreover, we found that the timingof PM2.5 exposure matters substantially. In our analysis, wefound that PM2.5 levels in the conception year significantly
affect infant mortality, while the effect of PM2.5 in the birthyear is negligible. There are two reasons that our estimates
are larger than the previous estimates. First, the content
of PM2.5 in Africa is mainly sand dust, whereas in SouthAsia, PM2.5 comes from industry sources, such as emissions
4|

from transportation equipment and power-generating facilities.
Second, the infant mortality in Asia is 40% lower than that in
Africa. These two factors are likely to contribute to the large
effect of PM2.5 on infant mortality.

Conclusion
In this study, we assessed the effect of PM2.5exposure on infant mortality. We constructed a worldwide cluster-level panel
dataset using the GPS information of each cluster and combined the birth records from the DHS of 50 countries throughout 20 y. We mapped the PM2.5information obtained fromsatellite to each cluster and year. We found a 10-microgram
increase in PM2.5, which is associated with an approximately
20% increase in infant mortality rate in Asian countries relative to the 10% increase in African countries. Further, wefound that the effect of PM2.5 level in the conception year has
an economically and statistically significant effect on infantmortality, while the PM2.5 in the birth year has negligible
effect. Our results are particularly robust as supported withseveral specification checks.

Material and Method
A. Data Sets. We use the data from the Demographic and

Health Surveys (DHS), nationally representative surveys thatare conducted in developing countries (ICF, 1998). The DHS
is based on two-stage sampling, in which a number of clusters
are first selected from a list of enumeration areas created ina recent population census. Subsequently, 5–10 households
were randomly selected in each of the clusters, and women
aged 15–49 y were selected from the selected households forinterviews on birth record. In many waves of DHS, the enumerator carries global positioning system (GPS) devices to collectgeospatial information to identify the central point of eachcluster’s populated area. For data confidentiality, randomness
was added to this GPS information. A 0–2-km and 0–5-km
diplacement was added to the GPS information in urban andrural areas, respectively. The sample used in our analysis was
obtained from the birth record file in the DHS. We included
all infants born between 1999–2016.
Our dependent variable was the infant mortality dummy,
wherein this dummy variable takes the value of one if the infantdied by the 12th month after the birth. Infants who were lessthan 12 months old and were alive at the time of the inteview
were excluded as their birth outcome at 12 months was unknown. The key explanatory variable is the cluster in which acorresponding child is born. To calculate the PM2.5 of each
infant, we use satellite-derived data on PM2.5 compiled byNASA (Van Donkelaar et al., 2015). These data were derived
from a suite of satellite-based atmospheric optical depth measurement instruments, including the two MODIS instruments,
the Multi-Angle Resolution Spectroradiometer (MISR) on
Terra and the Sea-viewing Wide Field-of-View Sensor (SeaWiFS) on the SeaStar satellite. These data were combined withaerosol profile measurements from the Cloud-Aerosol Lidarwith Orthogonal Polarization (CALIOP) instrument aboard
the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO), and satellite, weather and seasonality
data from the GEOS-Chem Chemical transport model to quantify the relationship between column Aerosol Optical Depth(AOD) and surface PM2.5measured at available ground-based
Naito et al.

stations. Using this PM2.5map, we calculated the PM2.5using
GPS information of the cluster in which the infant is born.
Specifically, we made a cicle with a 10-km radius at eachcluster. The average PM2.5 levels of each cluster were thencalculated.
The temperature and rainfall data at each cluster, whichwere obtained from Fick and Hijmans (2017), were calculated.
To control the annual income level at each village, we used
the nighttime light data constructed by the National Oceanicand Atmospheric Administration, National Geophysical Data
Center (2015).
B. Data Construction. The datasets in the DHS are cross-sectional. GPS data from the village were obtained to create
the village-level panel. If the distance between two villages
in different DHS waves is less than 10 km, it is assumed thatthey are comparable and they are assigned the same village
ID. In the base year, 345 village-level clusters were selected
for Bangladesh, 259 for Pakistan, 221 for Nepal, 504 for thePhilippines, 400 for Timor Leste, and 484 for Cambodia. We
selected these countries by criteria of GPS data availability. If
GPS data for each survey were present, we used the dataset
from that country. With GPS information, we matched thechildren’s data with PM2.5information using information of
the infant’s birth timing and GPS information of the village.
To distinguish the exposure to PM2.5 during the mother’s
prenatal period and after birth, we measured two indices of
PM2.5. From 1999 to 2016, the birth records from wavesof the DHS in Bangladesh, Pakistan, Nepal, the Philippines,
Timor Leste, and Cambodia were used. For Bangladesh, weused 2004 as the base, while 2007, 2011, 2014, and 2017 were
the waves. For Pakistan, we used 2016 and 2017 as the waves.
For Philippines, 2003, 2008, and 2017 waves were used fromthe DHS. Timor Leste had two waves from 2009 and 2016.
Meanwhile, Cambodia had 2005, 2010, and 2014. For Nepal,
2001 and 2006 were used for the data construction.
Satellite-based PM2.5 maps were used to assess the PM2.5in
village-level clusters from 1998 to 2016. A 10-km-radius circle
around each cluster-based village was drawn using QGIS buffer,and the average PM2.5 intensity was calculated for each circle.
The village and the time fixed effect were considered during
the estimation. Subsequently, we used the PM2.5 time series
fluctuation at each settlement. Birth mortality was coded
for one year in all other birth data. The infant mortality
dummy is zero if the child survives 13 months after delivery.
The infant mortality dummy is coded as one if the childdies before the 12th month following delivery. According to
our identifying assumption, after controlling for country- andcluster-level fixed effects, infant mortality can be attributed
to air pollution by PM2.5.
Overall, there were 150,750 observations from all six countries, which were arranged by the number of clusters in each
DHS survey. As our main dependent variable of interest isinfant mortality, we used birth records from the DHS dataset
from the respective country. The GPS data are essential to
track the infant mortality by PM2.5 exposure level. By using
GPS data, we generated a village-level cluster panel dataset.
This panel is balanced, with other explanatory variables, including respondent’s education, household size, drinking water
source, and head of household’s information, such as sex, andeducation. For our analysis we excluded infants born after
2016 and before 1999 as the PM2.5 data were available only
Naito et al.

between 1996–2016. An estimated probability was successfully
calculated using dummy infant mortality. We used the water
variable dummy for improved and unimproved water sources
for every country, and average precipitation data from the
CHIRPS (quasi-global rainfall data).
C. Model. We utilized the ordinary least squares regression

model for the analysis. We estimated the following equation:
2.5
Ycnmti = β0 + β1 P Mcnt
+β2 Xicnmt +µc +δt + δtc +δnm +εicnmt[1]where Ycnmti represents the infant mortality dummy, i is theindex of the child, c is the index of the village, t is the index
of birth year, n is the index of country, and m is an index
2.5
of birth month. P Mcnt
is the PM2.5 at birth or conception,
which is our main explanatory variable of interest. In somespecification, we put both PM2.5 in the birth and conception
year. In our final specification, we included both PM2.5 atconception and birth. Note that as we have annual information
on PM2.5, we do not have infomation on PM2.5 during thebirth month or the conception. Xicnmt are the individual
covariates (e.g., child sex, household qualities), whereas µc
and µt are the village and birth year fixed effects, respectively.
The δnm controls the country birth month fixed effects. The
country month of birth fixed effects control the country’s
specific seasonal variations in infant mortality. The δtc is thecountry year fixed effect that controls the country-specific timetrend of mortality owing to changes in the health system or
national-level income.
We also used nighttime light data to control the effect of income as a country-level economic indicator. Birth month wascontrolled for each infant born and birth month by countrylevel fixed effects was also added. This variable accounts forthe time-invariant unobservable factors, which could be correlated to mortality and PM2.5within the cluster and country
levels. Thus, if a particular month of birth has a higher orlower mortality, it will be accounted for by the fixed effects.
Additionally, we partial out the interview month by country
fixed effects to control unobserved factors that impact infantmortality and
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