Discussion Paper No.95
Gendered Impacts of Household and Ambient Air
Pollution on Child Health: Evidence fromHousehold and Satellite-based Data in Bangladesh
Masamitsu Kurata, Kazushi Takahashi
and Akira Hibiki

January, 2019

Data Science and Service Research
Discussion Paper

Center for Data Science and Service Research
Graduate School of Economic and Management
Tohoku University27-1 Kawauchi, Aobaku
Sendai 980-8576, JAPANGendered Impacts of Household and Ambient Air Pollution on Child Health:
Evidence from Household and Satellite-based Data in Bangladesh
Masamitsu Kurata (kurata@sophia.ac.jp),
Department of Economics, Sophia University,
Kazushi Takahashi (kaz-tak@hotmail.co.jp)
Department of Economics, Sophia University,
Akira Hibiki (hibiki@tohoku.ac.jp)
Graduate School of Economics and Management, Tohoku University
Abstract
Reducing health risks from household air pollution (HAP) and ambient air pollution(AAP) is a critical issue in achieving sustainable development worldwide, especially in
low-income countries. Children are particularly at high risk because their respiratory andimmune systems are not fully developed. Previous studies have identified the adverse
impacts of air pollution on child health; however, most have neither focused on HAP and
AAP simultaneously nor addressed differences in the timing and magnitude of prenatal
and postnatal exposure across genders. This article examines the impacts of prenatal andpostnatal exposure to ambient particulate matter with an aerodynamic diameter of 2.5 μm
or less (PM2.5) and household use of solid fuels (a main cause of HAP) on child healthin Bangladesh. We combine individual-level data from nationally representative surveys
with satellite-based high-resolution data on ambient PM2.5. We find that: (1) the use ofsolid fuels is associated with respiratory illness among girls but not boys; (2) prenatal
exposure to ambient PM2.5 adversely affects stunting, without any clear evidence ongender differences; and (3) postnatal exposure consistently increases the risk of bothstunting and respiratory illness for both genders. These results provide new evidence on
the heterogeneous impacts of AAP and HAP on children in terms of gender and the timingof exposure. The main policy implications are that intervention against HAP would be
more effective by targeting girls and that intervention against AAP should cover not only
born children but also pregnant mothers. In sum, our findings highlight the importance ofprotecting women from air pollution and achieving Target 3.9 of the Sustainable
Development Goals.
Keywords: Household (indoor) air pollution; Ambient (outdoor) air pollution; child health,child respiratory illness; Bangladesh*This work was supported by JSPS KAKENHI Grant Number 18H00839.

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1. Introduction

Reducing adverse health impacts from air pollution is one of the most criticalglobal issues. According to Cohen et al. (2017), ambient air pollution (AAP) andhousehold air pollution (HAP) from the use of solid fuels caused 4.2 million and 2.8
million deaths, respectively, in 2015, ranking fifth and tenth in that year’s mortality risk
factors. It is also recognized that AAP and HAP have various negative health impacts,
such as suppressed visual health and respiratory diseases. Accordingly, reducing thenumber of deaths and illnesses caused by air pollution has been set as Target 3.9 of the
Sustainable Development Goals (SDGs), recognizing the situation as an important agenda
for sustainable development (United Nations, 2016).
Children in developing countries are particularly vulnerable due to several
biological and socioeconomic factors. First, most poor households in developing
countries lack access to modern energy, relying instead on traditional biomass fuels (wood,dung, and agricultural residues), candles, and kerosene for cooking and lighting, all ofwhich intensify HAP (Lam, Smith, Gauthier, & Bates, 2012). Second, rapid economic
and population growth is causing AAP to worsen in many developing countries, withenormous particulate matter (PM) caused by agricultural burning, factories, car fumes,
and other activities. However, there remain few formal regulations to effectively reduce
PM emissions. Third, children in developing countries often need to help their parents
2both inside and outside the home. These duties include cooking inside houses without
chimneys and open agricultural burning, highly exposing them to toxic HAP and AAP(e.g., Lam et al., 2012). Fourth, since their immune system and lungs are not fully
developed, children breathing in air pollutants are more susceptible than adults to HAP
and AAP (Schwartz, 2004). Previous studies indicate a significant association between
air pollution and infant mortality (Chay & Greenstone, 2003; Jayachandran, 2009), acutelower respiratory infection (Smith & Mehta, 2000), anemia and stunting (Mishra &Retherford, 2006), and low birth weight (Pope et al., 2010). Because damage to health inchildhood (or the financial costs of recovery) can have long-term impacts on adult health,economic activities, and well-being (Campbell et al., 2014; Gertler et al., 2014; Baird,
Hicks, Kremer, & Miguel, 2016; Miguel & Kremer, 2004), protecting children from both
AAP and HAP is an urgent issue to fulfil the SDGs.
Toward that goal, this study aims to identify root causes of the adverse healthimpacts of air pollution on children aged below five years, such as stunting and respiratory
illness. The study focuses on Bangladesh, which is known as one of the world’s most airpolluted countries in terms of AAP; also, most poor households, especially in rural areas,
heavily rely on solid fuels for their cooking and lighting, thus worsening HAP (Kudo
Shonchoy, & Takahashi, 2017, 2018).

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More specifically, this study focuses on several dimensions not extensively
studied in the existing literature. First, we elucidate gender differences in the impact of
air pollution. Biologically, boys are considered at higher risk than girls due to lower
respiratory volumes and narrower peripheral airways in infancy (Clougherty, 2010).
However, the direction of gendered bias may not be uniform across different social
contexts.1 In Bangladesh, an important traditional norm is the seclusion of women (called
purdah), whereby women must spend long hours inside the home performing householdchores (Amin, 1997; Dasgupta, Huq, Khaliquzzaman, Pandey, & Wheeler, 2006). If thisalso applies to girls, they would be more exposed to and adversely affected by HAP thanboys. Second, most previous studies in developing countries, especially south Asia, havefocused only on HAP (Pitt, Rosenzweig, & Hassan, 2006; Duflo, Greenstone, & Hanna,
2008; Hanna, Duflo, & Greenstone, 2016; Balietti & Datta, 2017); however, we
simultaneously examine HAP and AAP because the latter is becoming a serious problem
in Bangladesh. By considering both HAP and AAP, we can specify which channels more
seriously damage child health and how these differ between boys and girls. Third,

1

A handful of empirical studies in developed countries show mixed results on gendered impacts: inStockholm, girls tend to be more affected by outdoor NO2 concentration and use of a gas stove in thehome than boys (Pershagen, Rylander, Norberg, Eriksson, & Nordvall, 1995), while, in Germany, boys
are more vulnerable to traffic-related air pollution than girls (Gehring et al., 2002).

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previous studies reveal that child health is influenced by AAP not only through inhalation
but also by transplacental transmission in utero (Sun et al., 2016). Therefore, we calculate
the cumulative prenatal exposure to AAP during pregnancy (fixed as 9 months for all
children) and postnatal exposure separately for each child. This distinction enables us todisentangle the impacts of exposure to AAP before and after birth. To the best of ourknowledge, this is the first study to explore the health impact of both HAP and AAP byconsidering differences in the timing and magnitude of prenatal and postnatal exposure
across genders.
Our empirical analysis is based on individual- and household-level data from anationally representative survey, combined with satellite-based high-resolution data.
Exposure to HAP is measured by a proxy variable on the use of solid fuels (wood,
agricultural crops, animal dung, straw, shrubs, grass, and charcoal) using the household
data, while exposure to AAP is measured objectively by ambient particulate matter with
an aerodynamic diameter of 2.5 μm or less (PM2.5) concentrations using the satellite data.
Although it is common to use annual mean or a specific season of PM2.5 for AAP(Dasgupta et al., 2006; Goyal & Canning, 2017), seasonal variation is prominent in
Bangladesh. To precisely measure the cumulative pre- and postnatal exposure to PM2.5
for each child, we use an adjusted value of PM2.5 that considers seasonal fluctuations

5and children’s birth timing, along with the annual mean of PM2.5. To account forpotential endogeneity in the use of solid fuels, we employ not only an ordinary leastsquares approach but also an instrumental variable approach that uses the share ofneighboring households using solid fuels, following an identification strategy suggested
by Balietti and Datta (2017).
We find that the use of solid fuels for cooking is associated with respiratory
illness in girls but not in boys. Prenatal exposure to ambient PM2.5 adversely affects only
stunting, while postnatal exposure increases the risk of both stunting and respiratory
illness consistently. These negative effects of AAP (both prenatal and postnatal exposure)are not significantly different between boys and girls. Our findings are robust even after
addressing seasonal variations in AAP, potential endogeneity in HAP, and possible
selection bias and measurement errors due to migration. These results imply theimportance of protecting girls and pregnant mothers to reduce negative health effects
from HAP and AAP in our context.
The remainder of this paper is organized as follows. Section 2 briefly overviews
the background situation of HAP and AAP in Bangladesh. Section 3 explains details on
the conceptual framework, datasets, variables, and empirical framework used in theanalysis. In Section 4, we first explain descriptive statistics, then present the results of6

regression analysis. Section 5 implements robustness checks and discuss their results.
Finally, Section 6 provides concluding remarks with policy implications.

2. Background: Ambient and Household Air Pollution in Bangladesh
In terms of ambient PM2.5 concentrations, Bangladesh is one of the world’s
most air-polluted countries, and its situation continues to deteriorate (Cohen et al. 2017;
Wendling, Levy, Esty, de Sherbinin, & Emerson, 2018). As shown in Figure 1, the annual
average PM2.5 concentration has increased rapidly since 2000 (Health Effects Institute,2018a). The main sources of PM2.5 in Bangladesh are agricultural burning, motor
vehicles, brick kilns, and smelters, with a large seasonal variation (Begum et al., 2014).
Figure 2 indicates the index of seasonal ambient PM2.5, as measured by air monitoring
stations in four major cities. For all cities, the ambient PM2.5 tends to become acute in
the winter, with enormous differences between the winter and monsoon seasons.2[INSERT FIGURES 1 AND 2 ABOUT HERE]

2

Bangladesh has four seasons: winter (December, January, and February), pre-monsoon (March,

April, and May), monsoon (June, July, August, and September), and post-monsoon (October andNovember).

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The Bangladesh government recognizes AAP as a critical environmental issue inthe country. In the 7th Five-Year Plan (2016-2020), the government aims to reduce urban
PM2.5 from its 2013 level of 78 μg/𝑚3 to 73 μg/𝑚3 in 2020. To achieve this goal, thegovernment is planning and implementing several programs, such as introducing cleaner
fuel and transport standards, and strict enforcement of the Brick Kiln Act 2013 to replace
traditional kilns with more efficient Hybrid Hoffman Kilns (Bangladesh Planning
Commission, 2016).
HAP is also serious in the country. In developing countries, the main causes of
HAP are domestic activities, such as cooking, heating, and lighting with solid fuels (i.e.,biomass fuels or coal). Figure 3 shows that the proportion of the population using solid
fuels is higher in Bangladesh than in any of the other 10 most populous countries (Health
Effects Institute, 2018a). In 2014, it was reported that 50% of urban households and 95%
of rural households used solid fuels for cooking (National Institute of Population
Research and Training, Mitra and Associates, & ICF International, 2016).

[INSERT FIGURE 3 ABOUT HERE]8

To mitigate HAP, the government has conducted the nationwide Improved Cook
Stove (ICS) program since 2013. If the program is well-executed, ICSs may have asignificant impact on reducing HAP (Thomas et al., 2015). The program achieved thedissemination of 1 million ICSs in 2017, although its impacts on HAP have not yet been
fully investigated.3

3. Methodology
3.1. Conceptual Framework
In this paper, we investigate the difference in health effects of HAP and AAP
between boys and girls. Such differences can be caused by biological differences as sex
and social differences as gender (Clougherty, 2010). Biological factors comprise
physiologic characteristics (e.g., reproductive organs), body size, and hormonal status,
while social factors include cultural norms, roles, and behaviors.4

3

In the neighboring country of India, most ICS programs have failed due to low uptake rates,

inappropriate maintenance, and subsequent low usage rates (Hanna et al., 2012; Khandelwal et al.,2017). Regarding ICSs in Bangladesh, Miller and Mobarak (2013) found in field experiments thatwomen have a strong preference for ICSs, but lack decision-making rights on purchases.
4

Although conceptually clear, it is sometimes difficult to distinguish biological and social factors

because both can interact with each other (Krieger, 2003).

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To better understand the differential impacts of biological and social factors onchild health, Figure 4 presents our conceptual framework. The overall process isclassified into three phases: (1) the first phase from concentration to exposure; (2) the
second phase from exposure to effective dose; and (3) the third phase from effective doseto health status (Clougherty, 2010). In the first phase, social factors play an important role,
because whether and the extent to which boys and girls are exposed to HAP and AAP
depend upon their culture. For example, girls may have higher exposure to HAP if they
are expected to help with cooking using solid fuels inside the house, while boys may havehigher exposure to AAP if they are expected to help with agricultural work outside thehouse.

[INSERT FIGURE 4 ABOUT HERE]

Through the second and third phases, biological differences matter because suchphases are strongly related to physiological mechanisms. For example, hormonal changes
in women during pregnancy can affect toxicant transport throughout the body (Nethery,
Brauer & Janssen, 2009). In addition, when children are young (under 5 years old), boys
tend to be more vulnerable than girls to air pollution because their respiratory volumes

10are lower and peripheral airways are narrower compared to girls (Falagas et al., 2007;
Gold et al., 1994).
However, social factors also play a role in the third phase. One of the most crucial
differences appears to be gender-biased preferences for seeking health care services. In
Bangladesh, women suffering illness have been found to receive health care services
significantly less often than men (Ahmed, et al., 2000), whereas opposite results havebeen reported in other countries (Bertakis, Azari, Helms, Callahan, & Robbins, 2000;
Thompson et al., 2016).
Based on the conceptual framework of Figure 4, we ascertain whether the healthimpacts of HAP and AAP differ between boys and girls. Unfortunately, we cannot
examine each phase from (1) to (3) separately, because actual levels of exposure andeffective dose are not measured in our study. Instead, we directly estimate the association
between concentration of air pollution (HAP and AAP) and health outcomes bycontrolling biological and social factors.
Since boys are more vulnerable to air pollution in terms of biologicalcharacteristics, the negative association between concentration and health outcomes is
expected to be stronger for boys than girls. If the opposite result is confirmed, it would
suggest that girls are disadvantaged due to social factors.

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3.2. Data
This study mainly uses two datasets: the Demographic and Health Survey (DHS)
and the Global Annual PM2.5 Grids from MODIS, MISR, and SeaWiFS Aerosol Optical
Depth (AOD) with GWR, 1998-2016 (hereafter “the PM2.5 dataset”).
The DHS is a globally standardized survey that collects nationally representative
data on population, family planning, and maternal and child health in developing
countries (Rutstein & Rojas, 2006). In Bangladesh, the DHS has been conducted eighttimes since 1993. We use two datasets collected in 2011 and 2014, since both datasets
share similar questionnaires and sampling design, with 600 primary sampling units(PSUs) from the whole country. The DHS provides GPS data for each PSU, although thelatitude/longitude positions are randomly displaced by up to 2 km in urban clusters and 5
km in rural clusters to protect respondents’ confidentiality. To control for such positional
errors, we created a buffer zone around each reported PSU of 2km and 5 km for urban
and rural clusters, respectively, and combined the adjusted buffer zone with the PM2.5
dataset.
The PM2.5 dataset (van Donkelaar et al., 2018) contains annual global
concentrations of ground-level PM2.5 from 1998 to 2016 at 0.01 degrees (approximately

12

1 km grid), with dust and sea-salt removed. Since this study focuses on the health statusesof children under five years old in the 2011 and 2014 surveys, the PM2.5 dataset from2005 to 2014 is used. As noted earlier, child health has been found to be affected by AAP
not only through inhalation but also by transplacental transmission in utero (Sun et al.,2016); accordingly, we aim to calculate the cumulative prenatal exposure to ambient
PM2.5 during pregnancy (fixed as 9 months for all children) and postnatal exposure foreach individual child. One drawback of the constructed annual PM2.5 dataset is the lackof seasonal variation in ambient PM2.5. This causes measurement errors in the
cumulative exposure to ambient PM2.5 because it gives the same value for all same-aged
children, regardless of their actual birth seasons. To more precisely measure cumulative
exposure for each child, we adjust annual ambient PM2.5 for each season-specific PM2.5,
by setting regional and seasonal parameters of ambient PM2.5 concentrations, and matchthe adjusted PM2.5 for each child, as shown in Table 1. The parameters are the ratios of
the annual mean of ambient PM2.5 based on estimations by Begum et al. (2014). For
example, in Rajshahi and Rangpur, exposure to PM2.5 in winter becomes 1.9 times theannual mean, while in the monsoon season it becomes 0.38 times the annual mean. We

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logged both cumulative prenatal and postnatal exposure.5[INSERT TABLE 1 ABOUT HERE]

3.3. Variables
Table 2 indicates the definitions and summary statistics for the outcomes andcovariates used in our analysis. For outcomes, we focus on the health status of children
under five years old and examine their stunting and respiratory illness. Stunting (a heightfor-age z-score of less than −2) is an objective indicator measured by anthropometry usinga height meter. Respiratory illness is measured by coughing and breathing difficulty being
reported by the child’s mother in the two weeks before the survey.

[INSERT TABLE 2 ABOUT HERE]

5

In practice, we use log(X+1) where X indicates cumulative prenatal or postnatal exposure toPM2.5, because 1% of the sample (168 children) are less than one month old and their postnatal
exposure is assumed to be zero. Although the log(X+1) transformation is widely used in social
sciences, some recent papers use the inverse hyperbolic sine (IHS) transformation (e.g., Eyer &Wichman, 2018). We also used IHS transformation, yielding similar results to those of ourestimations with the log(X+1) transformation.

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As explained in the previous subsection, AAP is measured as the average PM2.5
concentration level within a 2km buffer zone around each urban PSU and a 5km bufferzone around each rural PSU. By contrast, HAP is not measured in numerical values in the
DHS: instead, respondents are asked about their main cooking fuel. As a proxy variable
for HAP, we use a dummy equal to 1 if a household uses solid fuels (wood, agricultural
crops, animal dung, straw, shrubs, grass, and charcoal), and 0 otherwise.
Other covariates are classified into child, parental, and household characteristics,
which have been commonly used in the literature. Child characteristics include age, birthorder, multiple births, diarrhea symptoms in the two weeks before the survey, and basic
vaccination (BCG, measles, and polio) in the past. Birth order and multiple births are
known as risk factors for stunting (Jayachandran & Pande, 2017). Incidence of diarrhea
symptoms can control for environmental factors unrelated to air pollution that affect childhealth, such as water pollution.
Mother’s attributes include her age, years of education, body mass index (BMI),
relationship with the household head (wife or daughter-in-law), decision-making
authority over own health care in the household, and boy preference. Relationship withthe household head and authority over health care reflect her social status within the15household, which is strongly connected with exposure to HAP (Pitt et al., 2006). Boy
preference is a dummy variable that takes 1 if the mother’s ideal number of boys exceeds
the ideal number of girls, and 0 otherwise. This gender-biased preference may affect themother’s behavior differently for boys against girls. Father’s characteristics comprise hisage, years of education, and whether he lives separately from the family.
Household characteristics include the head’s attributes, household composition,
religion, cooking location, roof and wall materials, access to electricity, frequency ofmedia use, and household wealth. Cooking outside the house and construction materials
with higher permeability can mitigate exposure to HAP but, conversely, promote
exposure to AAP, especially in the high dust season (Dasgupta et al., 2006; Dasgupta,
Huq, Khaliquzzaman, & Wheeler, 2007). Frequency of media use is a dummy variable
that equals 1 if the household takes a newspaper, listens to the radio, or watches TV atleast once a week, which should capture exposure to media campaigns for better activities(Madajewicz et al., 2007). For household wealth, we use a set of dummy variables for thewealth index quintile, as calculated by the DHS.

3.4. Empirical Framework
3.4.1. Ordinary least squares16

To examine the impact of HAP and AAP on child health in detail, we conduct anindividual-level regression analysis using the ordinary least squares (OLS) method,
assuming a linear probability model as follows:

where 𝐻𝑒𝑎𝑙𝑡ℎ𝑖ℎd𝑡 is the health outcome for child 𝑖 in household ℎ in district d inyear 𝑡 ; 𝑃𝑀2.5𝑝𝑟𝑒𝑛𝑎𝑡𝑎𝑙
and 𝑃𝑀2.5𝑝𝑜𝑠𝑡𝑛𝑎𝑡𝑎𝑙
are cumulative prenatal and postnatal
𝑖ℎd𝑡
𝑖ℎd𝑡
exposure levels to ambient PM2.5 concentrations, respectively; 𝑆𝑜𝑙𝑖𝑑ℎd𝑡 is a dummy′

variable that equals 1 if a household uses solid fuels for cooking, and 0 otherwise; 𝑿𝑖ℎd𝑡′

is a vector of child attributes, while 𝒁ℎd𝑡 is a vector of parental and household
characteristics; and 𝜇𝑑 , 𝜌𝑡 , and 𝜎𝑚 are district, year, and the survey month fixed effects,
respectively. The district fixed effects control any time-invariant regional factors. We also
include the survey month fixed effects to control for the survey timing.
Since the DHS is not a panel survey, parameters (α, 𝛽1 , 𝛽2 , γ, 𝛿, 𝑎𝑛𝑑 𝜃 ) arepooled estimates using the datasets of 2011 and 2014. The impacts of AAP are measuredby 𝛽1 and 𝛽2 , while the impacts of HAP are measured by γ . To examine genderdifferences explicitly, these parameters are estimated separately for boys and girls. We
17use sample weights and clustered standard errors at the district level for all regressions.

4. Results
4.1. Descriptive Statistics
Table 3 summarizes the health status of children by year and gender. In 2011,
there was no significant differences in stunting and respiratory illness between genders.
However, these outcomes for boys became worse than girls in 2014. Figure 5 compares
health status by wealth index quintile, confirming the negative correlation between
stunting and wealth, although the association is not very clear for respiratory illness.

[INSERT TABLE 3 AND FIGURE 5 ABOUT HERE]

For ambient PM2.5, Figure 6 shows the annual mean, minimum, and maximum
values from 1998 to 2016. The annual mean of PM2.5 increased from 34.3 μg/𝑚3 in1998 to 51.7 μg/𝑚3 in 2016, far exceeding the World Health Organization’s (2015)
guideline value of 10 μg/𝑚3 . However, AAP has not been spread homogeneously over
the whole country. As shown in Figure 7, PM2.5 has increased rapidly in the central,southern, and northeastern regions. The most prominent area is the capital city and its

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eastern sub-districts, where PM2.5 has risen more than 15 μg/𝑚3 from 2005 to 2014.

[INSERT FIGURES 6 AND 7 ABOUT HERE]

The main types of cooking fuels are summarized in Table 4. Solid fuels wereused by the majority of households, with 87% in 2011 and 85% in 2014. Among them,wood is most prevalent, followed by agricultural crops and animal dung. The composition
did not change much during the study period.

[INSERT TABLE 4 ABOUT HERE]

4.2. Regression Analysis
4.2.1. Main results
We first discuss the results of OLS regressions, followed by those of IV in thenext section. Table 5 reports the OLS estimation results with and without seasonalvariation in ambient PM2.5 (i.e., AAP). There are five main findings .

[INSERT TABLE 5 ABOUT HERE]

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First, the household’s use of solid fuels for cooking (i.e., HAP) has adverse
effects on respiratory illness in girls. The coefficient indicates that the probability of
respiratory illness in girls is 4 percentage points higher in households using solid fuels.
However, no effects are observed in boys’ respiratory illness or in stunting for either sex.
The results are almost the same between estimations with and without seasonal
adjustment of PM2.5.
Second, seasonal adjustment substantially influences the coefficients of prenatal
exposure to ambient PM2.5. When seasonal variation is not incorporated, the coefficientsshow that prenatal exposure decreases the risk of respiratory illness in boys and girls(Table 5, columns 3 and 4), which seems counterintuitive. A problem detected in theseestimations is high variance inflation factors (VIFs) of prenatal exposure (approximately
7.5 for boys and girls), indicating severe multicollinearity.6
After making seasonal adjustments, the VIFs decrease to acceptable levels (2.9

6

A rule of thumb commonly used is that VIFs exceeding 5 indicate multicollinearity. In the analysis

without seasonal variation, there are no other explanatory variables indicating VIFs more than 5,
except for prenatal exposure to PM2.5. The mean VIF of all explanatory variables is approximately
2.2 for both boys and girls. In the analysis with seasonal variation, all VIFs are less than 5 and its
mean is approximately 2.1 for both boys and girls.

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for boys and girls) and the seemingly strange estimated coefficient becomes statisticallyinsignificant with negligible magnitude (Table 5, columns 7 and 8). Therefore, it seems
that seasonal variation should be taken into consideration to deal with multicollinearity
in prenatal exposure. The results with seasonal adjustment indicate its adverse impact on
girls’ stunting, but no effects on respiratory illness.
Third, postnatal exposure to ambient PM2.5 has adverse impacts on all healthoutcomes for both sexes. With seasonal adjustments, a 1% increase in postnatal exposure
increases the probability of stunting by 0.10-0.12 percentage points and that of respiratory
illness by 0.01-0.02 percentage points.
Fourth, individual and parental attributes have a reasonable association withchild health. As expected, later birth order and multiple births increase the risk of stunting,while diarrhea is strongly related to respiratory illness. Higher maternal BMI and parental
education can mitigate stunting, although these effects are weak for respiratory illness.
Mother’s decision making on healthcare can reduce risks of stunting and respiratory
illness in boys. However, there is no correlation between mother’s boy preference andchild health outcomes.
Fifth, among household characteristics, the location of kitchen and housematerials have limited impacts on child health. The dummy variable of cooking inside the21house has little effect, even when we include its interaction term with the dummy variableof solid fuels. This is consistent with Pitt et al. (2006). Access to electricity has no
significant impacts, perhaps because most households, especially poor ones, are equipped
only with low power and low-quality electricity, and so simultaneously use traditional
fuels for lighting and cooking. Frequent access to media is associated with a lower
probability of respiratory illness in girls. This might be because those households are
more likely to be aware of the danger of HAP and AAP for child health. 7 Finally,
economic wealth has a stronger association with stunting than respiratory illness, asconfirmed in the descriptive statistics. More interestingly, the association between wealthand health is weaker for girls than for boys. In other words, boys’ health is more related
to household wealth than that of girls.

4.2.2. Decomposition of Solid Fuel TypeThe above analysis uses solid fuels as a single dummy variable. However,different sources of solid fuels may have heterogeneous health impacts (Dasgupta et al.,

7

An alternative explanation is that those households are richer and so able to take better care of

their children’s health. However, this explanation seems not convincing as we included wealthclasses as control variables.

22

2006). To examine this possibility, we classify solid fuels by type (firewood, agricultural
crops, animal dung, and other biomass), and use each of these four dummies instead of
the single dummy variable.
Table 6 shows the results for HAP and AAP with seasonal variation, suppressing
the coefficients of other covariates since they are similar to the previous results in Table
5. We confirm that the use of solid fuels is significantly associated with respiratory illness
only in girls. Three main sources (firewood, agricultural crops, and animal dung) have
similar effects on girls’ respiratory illness. The results for AAP are unchanged from theprevious results reported in Table 5.

[INSERT TABLE 6 ABOUT HERE]

5. Robustness checks
5.1. Endogeneity in the Use of Solid Fuels
One potential threat to our identification is endogeneity in the use of solid fuels.
Although the OLS regression model assumes the use of solid fuels as an exogenous
variable, the selection of cooking fuel may not be independent from the error term due toomitted factors affecting both child health and the use of solid fuels. Moreover, there may

23be reverse causality: for example, mothers may decide not to use solid fuels because theirchildren have a respiratory illness. Such endogeneity can cause bias in our estimation.
Thus, as a first robustness check, we use the IV approach by exploiting the shareof other households using solid fuels in the same PSU as the instrument. A similar
instrument is used by Balietti and Datta (2017), who analyze the impact of using solid
fuels on stunting in Indian children. The instrument 𝑍ℎ𝑝 for children living in householdℎ in PSU 𝑝 is defined as:𝑍ℎ𝑝 =∑𝑘≠ℎ 𝑆𝑜𝑙𝑖𝑑𝑘𝑝
𝑁𝑝 − 1

where the numerator is the number of households using solid fuels in PSU 𝑝 excluding
household ℎ, and 𝑁𝑝 in the denominator is the total number of households minus one
in PSU 𝑝. This instrument is regarded as a proxy variable for the availability of solid
fuels, assumed to affect child health only through its impact on the use of solid fuels inhousehold ℎ . In our setting, all outcome variables and the endogenous explanatory
variable (use of solid fuels) are dummy variables. Following Angrist and Pischke (2008),
we use the standard two-step least squares (2SLS) approach.
Table 7 shows the results of IV estimation with its first and second stage

24

regressions. The first-stage result clearly shows that the instrument has a strong andstatistically significant effect on the use of solid fuels with sufficiently large F-statistics.
Thus, our estimation does not suffer the weak IV problem. It is also confirmed that
wealthier households tend to use non-solid fuels for cooking, which is consistent with our
expectation.

[INSERT TABLE 7 AROUND HERE]In the second stage, almost all coefficients of HAP and AAP are qualitatively
similar to previous results presented in Table 5. The use of solid fuels has adverse impacts
on girls’ respiratory illness. Prenatal exposure to PM2.5 affects stunting in girls, whilepostnatal exposure affects all outcomes in both sexes. The gender difference in theimpacts of wealth on health is also confirmed again. Overall, these results show that our
OLS estimations are not sensitive to potential endogeneity concerns.

5.2. Migration
Another possible concern regarding our estimations is migration. While weassume that all children continue to live in the same PSU during pregnancy and after birth,25

the living place of some children could change through migration. This raises a potential
selection problem, such that ambient PM2.5 cannot be considered exogeneous.
Unfortunately, the Bangladesh DHS has no data on migration. However, the Household
Income and Expenditure Survey 2016, another nationally representative household
survey conducted by the government, reports that only 3% of households experienced any
kind of migration within the country (from one district to another) in the last five years
from the survey (Bangladesh Bureau of Statistics, 2017). This indirectly supports the viewthat migration is not a serious problem in our analysis.
To further verify this point, we conducted a sub-sample analysis of children
under three years old. Because the probability of migration in the past should be lower
for younger children, we can mitigate the possible bias due to migration, if any. Table 8shows the results of IV estimation for children under three years old. The main findings
remain robust: the use of solid fuels affects only girls’ respiratory illness; prenatal
exposure to PM2.5 affects stunting for both sexes; and postnatal exposure affects stunting
and respiratory illness for both sexes. Again, the impacts of wealth on health outcomesare much smaller for girls.

[INSERT TABLE 8 AROUND HERE]

26

5. Discussion and Conclusion
This article explores the impacts of household air pollution (HAP) and ambient
air pollution (AAP) on child health in Bangladesh, one of the world’s most air-polluted
countries. By combining individual-level data from a nationally representative survey andsatellite-based high-resolution data on ambient PM2.5 concentrations, we estimatedprenatal and postnatal exposure to PM2.5 for each child and examined their adverse
impacts on stunting and respiratory illness. The main findings are summarized as follows.
First, the use of solid fuels for cooking, a proxy variable for HAP, has adverse
impacts on respiratory illness among girls but not boys. Since boys are more vulnerable
to air pollution in terms of biological characteristics, these results call attention to social,
cultural, and/or behavioral factors. For example, girls may be more susceptible to HAP
because they spend longer hours around cooking stoves or receive health care services
less frequently than boys (Ahmed, et al., 2000). To assess the plausibility of these
explanations, more detailed surveys measuring time use patterns and health-seeking
activities by genders are required. These are issues for future research.
Second, prenatal exposure to ambient PM2.5 leads to stunting, while postnatal
exposure consistently increases the risk of both stunting and respiratory illness. These27

health impacts of AAP are not significantly different between boys and girls. However,
the results signify the importance of protecting not only infants but also pregnant mothersfrom AAP.
Taken together, our results highlight the importance of prioritizing women (girls
and pregnant mothers) to reduce the negative impact of air pollution on child health.
Although the precise mechanisms that disadvantage girls and pregnant mothers could not
be identified in this study, raising awareness of the risk of AAP during pregnancy may be
an important policy objective, especially for mothers with lower education levels. Since
frequent access to media is found to be negatively associated with girls’ respiratory illness(column 8, Table 5), media campaigns on the risk of HAP may be a possible means to
reduce girls’ smoke inhalation. Moreover, the existing program to provide ICSs might be
combined with the promotion of risk management practices to further improve child andpregnant mother’s health.
Finally, a potential limitation of our analysis is survival bias, as the mortality rate
may be higher in children with more exposure to air pollution. Consequently, our results
may underestimate the actual adverse impacts of HAP and AAP on child health, andshould be regarded as the minimal level of impact. However, we believe that such bias is
not serious in our study as the existing evidence suggests that HAP is not strongly

28

associated with child mortality in Bangladesh (Naz, Page & Agho, 2015). Because DHS
datasets have limited information on stillbirths, further analysis with more detailed childmortality datasets may be useful to investigate the existence and magnitude of survival
bias.

29

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33

FiguresSource: Authors’ compilation from Health Effects Institute (2018a).
Note: Data for Nigeria in 2015 (115μg/𝑚3) is excluded from the figure as an outlier due to an ex
tensive dust storm in late 2015 and early 2016 (see Health Effects Institute, 2018b).
Fig 1. Trends in population-weighted annual average 𝐏𝐌𝟐.𝟓 (𝛍𝐠/𝐦𝟑 ) in the world’s 10 mostpopulous countries.

34

Source: Begum et al. (2014)
Fig. 2. Index of Seasonal Average PM2.5 in Four Cities (Winter 2011 = 1).

35

Source: Authors’ compilation from Health Effects Institute (2018a).
Fig. 3. Trends in proportion of population using solid fuels In the world’s 10 most populous countries.

36

Source: Authors’ compilation based on Clougherty (2010).
Fig 4. Conceptual Framework.

37

Fig. 5. Health Status by Wealth Index Quintile (%).

38

Fig. 6. Ambient PM2.5 Concentrations 1998-2016.

39

Difference in PM2.5 Concentration

Fig. 7. Comparing Ambient PM2.5 Concentration (𝛍𝐠/𝒎𝟑 ) between 2005 and 2014.

40

Tables
Table 1. Parameters Used as Ratios of Seasonal PM2.5 to Annual Mean.

Winter(Dec - Feb)
Pre-monsoon(Mar - May)
Monsoon(Jun - Sep)
Post-monsoon(Oct - Nov)

Rajshahi &Rangpur

Dhaka &
SylhetKhulna &BarisalChittagong

1.90

1.68

1.95

1.86

0.98

0.81

0.70

0.94

0.38

0.44

0.33

0.20

0.75

1.07

1.02

1.00

Note: Authors’ calculations based on Begum et al. (2014).

41

Table 2. Definition and Summary Statistics of Variables
VariablesChild health
Stunting

Definition=1 if a child has a height-for-age z-score of less than −2
Respiratory illness=1 if a child had an illness with a cough and difficulty breathing
Household and ambient air pollution
Solid fuels=1 if solid fuels are used for cooking
Log ( prenatal PM2.5 )Log of cumulative prenatal exposure to ambient PM2.5 concentrations
Log ( postnatal PM2.5 )Log of cumulative postnatal exposure to ambient PM2.5 concentrations
Child's characteristics
Age
Child's age (months)
Birth order
Birth order of the childTwin=1 if the child is twin or triplet
Diarrhea=1 if the child had diarrhea in the last 2 weeks
Vaccination=1 if the child received basic vaccination
Parental characteristics
Mother: age
Mother's age (years)
Mother: BMI
Mother's body mass index (BMI)
Mother: education
Mother's years of education
Mother: wife
=1 if mother is wife of the household head
Mother: daughter-in-law=1 if mother is daughter-in-law of the household head
Mother: decision on healthcare=1 if mother has decision-making authority on her own healthcare
Mother: boy preference=1 if mother's ideal number of boys exceeds that of girls
Father: age
Father's age (years)
Father: education
Father's years of education
Father: separation=1 if father lives separately
Household characteristics
Female head
=1 if household head is female
Age of headAge of household head (years)
Household size
Number of household members
Share of under 5 yrs
Share of household members under 5 years old
Two or more women=1 if two or more women exist in the household
Muslim=1 if household is muslim
Cooking inside house=1 if cooking is done inside house
Wall permeability=1 if wall material is tin or cane
Roof permeability=1 if roof material is tin
Electricity=1 if household has access to electricity
Media=1 if household use media at least once a week

Mean

Std. Dev.

Min Max

0.39

0.49

0

1

0.15

0.36

0

1

0.78
3.70
4.52

0.41
0.19
0.99

0
2.9
0.0

1
4.1
5.7

29.76
2.30
0.01
0.05
0.35

17.29
1.51
0.11
0.22
0.48

0
1
0
0
0

59
14
1
1
1

25.52
21.31
5.65
0.62
0.17
0.61
0.10
34.53
5.29
0.13

5.84
3.80
3.85
0.48
0.37
0.49
0.30
8.57
4.61
0.34

15
12
0
0
0
0
0
16
0
0

49
32
18
1
1
1
1
65
18
1

0.08
41.88
6.11
0.25
0.28
0.92
0.15
0.45
0.77
0.54
0.49

0.28
14.50
2.79
0.13
0.45
0.28
0.35
0.50
0.42
0.50
0.50

0
15
2
0
0
0
0
0
0
0
0

1
95
31
1
1
1
1
1
1
1
1

Notes: In addition to the variables in the table, we include in the regression analysis a set of dummy variables
for the wealth index quintile (calculated using the DHS), a year fixed effect, district fixed effects, the DHS’s
survey month fixed effects.

42

Table 3. Child Health by Year and Sex (%).

Stunting
Respiratory illness

Boys
(A)
40.3
17.1

2011
Girls
(B)
41.0
16.0

Difference(B - A)
0.7-1.1

Notes: *** p<0.01; ** p<0.05; * p<0.1.

43

Boys
(C)
37.6
15.0

2014
Girls
(D)
35.5
13.2

Difference(D - C)-2.0*-1.8**

Table 4. Type of Cooking Fuel

Main type of cooking fuel (%)
Solid fuels Wood
Agricultural crop
Animal dung
Straw/shrubs
Charcoal
Coal, lignite
Subtotal
Non-solid Natural gas
fuels
LPG
BiogasElectricity
Kerosene
Other
Subtotal
Total

2011
50.5
26.0
8.9
1.1
0.2
0.0
86.8
10.7
1.5
0.3
0.2
0.2
0.3
13.2
100.0

44

2014
Difference
54.8
4.3
21.0
-5.0
7.5
-1.4
1.0
-0.2
0.4
0.2
0.3
0.3
85.0
-1.8
12.6
1.9
1.6
0.1
0.1
-0.1
0.4
0.2
0.1
-0.1
0.2
-0.2
15.0
1.8
100.0

Table 5. Ordinary Least Squares with and without Seasonal Variation of Ambient PM2.5.
(1)

Solid fuelLog(prenatal PM2.5)
Log(postnatal PM2.5)
Birth order
Twin
Diarrhea
Mother: BMI
Mother: decision on health
Mother: boy preference
Mother: education
Father: education
Cooking inside house
Wall permeability
Roof permeability
Access to electricity
Access to media
Wealth quintile: poorer
Wealth quintile: middle
Wealth quintile: richer
Wealth quintile: richest
Other control variables
District fixed effects
Survey month fixed effects
Observations
Adjusted R-squares

(2)
(3)
(4)
Without seasonal variation
Stunting
Respiratory illness
Boys
Girls
Boys
Girls
-0.0030.005
0.009
0.041***(0.023)(0.024)(0.019)(0.015)0.214**
0.045-0.103-0.127*(0.090)(0.115)(0.076)(0.074)0.127*** 0.124*** 0.031*** 0.028***(0.015)(0.015)(0.010)(0.009)0.018*** 0.025***0.005-0.000(0.007)(0.008)(0.006)(0.006)0.139**
0.147**-0.0090.031(0.058)(0.069)(0.040)(0.040)0.097***
0.042
0.069**
0.066**(0.024)(0.034)(0.031)(0.027)-0.011*** -0.008*** -0.003**-0.001(0.002)(0.002)(0.001)(0.002)-0.025*-0.019-0.022*-0.006(0.015)(0.013)(0.013)(0.012)
0.021
0.042
0.009
-0.003(0.019)(0.027)(0.016)(0.018)-0.004-0.007**-0.001-0.001(0.003)(0.003)(0.002)(0.002)-0.007*** -0.006**-0.001-0.001(0.002)(0.002)(0.002)(0.001)
0.019
0.024
0.006
-0.007(0.021)(0.018)(0.015)(0.014)0.018
0.021-0.0250.002(0.019)(0.022)(0.016)(0.015)-0.041*
0.013
0.027-0.007(0.021)(0.022)(0.016)(0.012)
0.011
0.019
0.010
0.003
(0.027)(0.019)(0.013)(0.015)-0.0050.004-0.006-0.026**(0.019)(0.018)(0.014)(0.012)-0.062**-0.006-0.030*-0.002(0.028)(0.019)(0.017)(0.018)-0.081*** -0.044*-0.033-0.007(0.029)(0.023)(0.022)(0.018)-0.128*** -0.073*** -0.048*-0.020(0.032)(0.027)(0.026)(0.024)-0.214*** -0.160*** -0.064**-0.008(0.043)(0.035)(0.027)(0.030)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
7470
7124
8094
7672
0.106
0.122
0.038
0.030

(5)

(6)
(7)
(8)
With seasonal variation
Stunting
Respiratory illness
Boys
Girls
Boys
Girls
-0.0080.006
0.010
0.042***(0.023)(0.024)(0.019)(0.015)0.005
0.111**-0.042-0.053(0.038)(0.044)(0.034)(0.046)0.118*** 0.105***
0.017**
0.014(0.011)(0.012)(0.009)(0.009)0.017**
0.025***
0.005
0.000
(0.007)(0.008)(0.006)(0.006)0.139**
0.148**-0.0060.031(0.058)(0.069)(0.039)(0.040)0.094***
0.040
0.071**
0.068**(0.024)(0.035)(0.031)(0.027)-0.011*** -0.008*** -0.003**-0.001(0.002)(0.002)(0.001)(0.002)-0.026*-0.019-0.022*-0.007(0.015)(0.013)(0.013)(0.012)
0.021
0.041
0.010
-0.003(0.019)(0.027)(0.016)(0.018)-0.004-0.007**-0.001-0.001(0.003)(0.003)(0.002)(0.002)-0.006*** -0.005**-0.001-0.001(0.002)(0.002)(0.002)(0.001)
0.018
0.023
0.005
-0.006(0.020)(0.018)(0.015)(0.014)0.022
0.020-0.0250.001(0.018)(0.022)(0.016)(0.015)-0.038*0.012
0.027-0.008(0.021)(0.021)(0.016)(0.012)
0.010
0.020
0.011
0.003
(0.027)(0.019)(0.014)(0.015)-0.0060.004-0.007-0.026**(0.018)(0.018)(0.014)(0.012)-0.061**-0.008-0.031*-0.002(0.029)(0.019)(0.017)(0.018)-0.080*** -0.045*-0.034-0.008(0.029)(0.023)(0.022)(0.018)-0.126*** -0.076*** -0.050*-0.020(0.032)(0.027)(0.026)(0.024)-0.208*** -0.162*** -0.065**-0.009(0.043)(0.035)(0.027)(0.030)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
7470
7124
8094
7672
0.107
0.123
0.038
0.029

Notes: See Table 2 for other covariates included in regressions. Clustered standard errors at the district levelare in parentheses. *** p<0.01; ** p<0.05; * p<0.1.

45

Table 6. Ordinary Least Squares with Seasonal Variation in Ambient PM2.5: Health Effects ofHousehold Air Pollution by Solid Fuel Type.
(1)

Solid fuel: firewood
Solid fuel: agri. crop
Solid fuel: animal dung
Solid fuel: others
Log(prenatal PM2.5)
Log(postnatal PM2.5)
Other control variables
District fixed effects
Survey month fixed effects
Observations
Adjusted R-squares(2)
Stunting
Boys
Girls-0.0080.008(0.023)(0.024)-0.011-0.006(0.029)(0.029)-0.0070.021(0.039)(0.040)-0.011-0.005(0.079)(0.079)0.005
0.112**(0.038)(0.044)0.118*** 0.106***(0.011)(0.012)
Yes
Yes
Yes
Yes
Yes
Yes
7470
7124
0.106
0.123

(3)
(4)
Respiratory illness
Boys
Girls
0.007
0.041***(0.018)(0.015)0.007
0.054***(0.025)(0.017)0.009
0.068***(0.027)(0.024)0.098-0.005(0.093)(0.043)-0.041-0.054(0.035)(0.046)0.018**
0.014(0.009)(0.009)
Yes
Yes
Yes
Yes
Yes
Yes
8094
7672
0.038
0.029

Notes: See Table 2 for other covariates included in regressions. Clustered standard errors at the district levelare in parentheses. *** p<0.01; ** p<0.05; * p<0.1.

46

Table 7. Instrumental Variable Estimation with Seasonal Variation in Ambient PM2.5: Children under
Five Years Old.

Share of solid fuel in PSU(1)(2)
First stage
Solid fuel
Boys
Girls
0.779*** 0.814***(0.023)(0.020)Solid fuelLog(prenatal PM2.5)
Log(postnatal PM2.5)
Wealth quintile: poorer
Wealth quintile: middle
Wealth quintile: richer
Wealth quintile: richest
Other control variables
District fixed effects
Survey month fixed effects
Observations
Adjusted R-squares
First stage F-statistics-0.044**-0.025(0.019)(0.020)0.015***
0.006(0.004)(0.006)-0.020** -0.018**(0.008)(0.007)-0.036** -0.044***(0.014)(0.010)-0.041** -0.054***(0.019)(0.015)-0.015-0.032(0.030)(0.026)
Yes
Yes
Yes
Yes
Yes
Yes
8094
7672
0.740
0.752
24811.2*** 3234.0***

(3)

(4)
(5)
(6)
Second stage
Stunting
Respiratory illness
Boys
Girls
Boys
Girls

0.020
0.008
(0.053)(0.057)0.008
0.111***(0.036)(0.043)0.117*** 0.105***(0.011)(0.012)-0.060**-0.007(0.028)(0.019)-0.079*** -0.045*(0.029)(0.023)-0.123*** -0.075***(0.032)(0.025)-0.204*** -0.162***(0.041)(0.035)
Yes
Yes
Yes
Yes
Yes
Yes
7470
7124
0.106
0.123

0.003
(0.041)-0.043(0.034)0.018**(0.008)-0.031*(0.017)-0.034(0.022)-0.050**(0.025)-0.066**(0.028)
Yes
Yes
Yes
8094
0.038

0.054**(0.024)-0.052(0.044)0.014*(0.009)-0.002(0.018)-0.007(0.018)-0.019(0.024)-0.006(0.030)
Yes
Yes
Yes
7672
0.029

Notes: See Table 2 for other covariates included in regressions. Clustered standard errors at the district levelare in parentheses. *** p<0.01; ** p<0.05; * p<0.1.

47

Table 8. Instrumental Variable Estimation with Seasonal Variation in Ambient PM2.5: Children underThree Years Old.

Share of solid fuel in PSU(1)(2)
First stage
Solid fuel
Boys
Girls
0.770*** 0.814***(0.020)(0.024)

Solid fuelLog(prenatal PM2.5)
Log(postnatal PM2.5)
Wealth quintile: poorer
Wealth quintile: middle
Wealth quintile: richer
Wealth quintile: richest
Other control variables
District fixed effects
Survey month fixed effects
Observations
Adjusted R-squares
First stage F-statistics-0.062***-0.009(0.023)(0.025)0.012*
0.004(0.006)(0.007)-0.018-0.020**(0.012)(0.010)-0.028-0.046***(0.020)(0.014)-0.027-0.051***(0.023)(0.017)0.021-0.039(0.038)(0.032)
Yes
Yes
Yes
Yes
Yes
Yes
4721
4547
0.740
0.760
2476.1*** 21800.2***

(3)

(4)
(5)
(6)
Second stage
Stunting
Respiratory illness
Boys
Girls
Boys
Girls

-0.057-0.0130.008(0.070)(0.066)(0.047)0.132***
0.122**-0.048(0.047)(0.055)(0.041)0.059*** 0.052*** 0.043***(0.016)(0.014)(0.011)-0.067*0.004-0.048**(0.037)(0.023)(0.024)-0.069**-0.008-0.070**(0.028)(0.031)(0.029)-0.107***-0.044-0.074**(0.034)(0.032)(0.035)-0.162*** -0.138*** -0.097***(0.042)(0.046)(0.034)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
4354
4205
4721
0.133
0.122
0.034

0.083**(0.033)-0.074(0.045)0.017(0.012)-0.014(0.025)-0.034(0.023)-0.045(0.027)-0.040(0.035)
Yes
Yes
Yes
4547
0.029

Notes: See Table 2 for other covariates included in regressions. Clustered standard errors at the district levelare in parentheses. *** p<0.01; ** p<0.05; * p<0.1

48