Policy Research Working Paper 9542

Gender Differences in Children’s Antibiotic
Use and Adherence
Christine Blandhol
Anja Sautmann

Development Economics
Development Research Group
February 2021
Policy Research Working Paper 9542

Abstract
Using in-home health records for 1,763 children in Mali, more likely to complete a course of antibiotics than girls.
this paper examines gender differences in the uptake and This difference is driven by families with an educated house-
duration of treatment with antibiotics. The detailed data hold head. An explanation may be that (male) household
provide a window into parents’ day-to-day decisions while heads are less involved in caring for girls, so that benefits
accounting for symptoms. There are no gender differences from education that lead to better care accrue overpropor-
in starting treatment, but boys are over 10 percentage points tionally to boys.

This paper is a product of the Development Research Group, Development Economics. It is part of a larger effort by the
World Bank to provide open access to its research and make a contribution to development policy discussions around the
world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may
be contacted at asautmann@worldbank.org.

The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development
issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the
names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those
of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and
its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.

Produced by the Research Support Team
Gender Differences in Children’s Antibiotic Use and

Adherence∗

Christine Blandhol† and Anja Sautmann‡

JEL codes: I12, I15, O12
Keywords: medication adherence; antibiotics; gender; missing women; Mali.

∗
The authors would like to thank Jenna Anders, Kenneth Chay, Andrew Foster, Emily Oster, and Ira Wilson for
comments and suggestions. Mark Dean and Sautmann collected the data for this study with generous assistance from
Mali Health, funded by an ESRC/DFID Development Frontiers Award (ES/K01207X/1). Blandhol received ﬁnancial
support through a Brown University Karen T. Romer Undergraduate Teaching and Research Award. The ﬁndings,
interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily
represent the views of the World Bank and its afﬁliated organizations, or those of the Executive Directors of the World
Bank or the governments they represent. All errors are ours.
†
Princeton University and Statistics Norway: blandhol@princeton.edu.
‡
Development Economics Research Group, World Bank, asautmann@worldbank.org.
1 Introduction

There are more than 1.5 million “missing women” in Sub-Saharan Africa each year, and major
contributors to the gender disparity in mortality are curable diseases like malaria and diarrhea (An-
derson and Ray, 2010, 2018). In fact, Sub-Saharan Africa is the only region where excess female
mortality has increased between 1990 and 2008 (World Bank, 2011). Yet there is comparatively
little direct evidence from the region that men and women are receiving systematically different
levels of healthcare. By comparison, for South Asia, where mortality differences are similar or
even smaller, there is extensive evidence of differential treatment in health care for boys and girls.
Parents seek care more often and earlier for sons and provide them with more nutritious diets
than daughters (Borooah, 2004; Pandey et al., 2002), boys are breastfed longer and more likely to
get vaccinated, and male fetuses receive more pre-natal care (Jayachandran and Kuziemko, 2011;
Oster, 2009; Bharadwaj and Lakdawala, 2013).
In this paper, we examine gender differences in the use of antibiotics for children under ﬁve
years in poor households in Mali. Antibiotics provide a case study for care-seeking behavior and
treatment adherence, two important margins for childhood health outcomes. On the one hand, there
is a deﬁcit in antibiotics use in Sub-Saharan Africa, where more than 490,000 children annually
die from pneumonia alone – an illness often cured by an antibiotic (UNICEF, 2016; World Health
Organization, 2005). On the other hand, treatment adherence is especially important for drugs that
are at risk of being rendered ineffective through the rise of resistant pathogens and bacteria, as
many common antibiotics are (World Health Organization, 2012).
Our analysis exploits unique daily health records collected at home from a sample of 1,763
children in Mali. The data were collected as part of a randomized controlled trial that tested the
effect of two common health policies – subsidies and healthworker visits – on the decision to visit
a formal care provider conditional on health status, and ultimately health outcomes (Sautmann
et al., 2017; Dean et al., 2017). However, the original data collection also included information on
the prescribed treatments and medications taken, and these data give us information on antibiotics
type, price, and length of treatment. The daily symptom records allow us to control for the types of

2
symptoms experienced prior to treatment start, as well as the persistence of symptoms after (which
may act as a motivator and reminder to complete the treatment; Cohen et al., 2014). Symptoms are
an important determinant of acute healthcare decisions that are often not observed. In addition, we
derive the recommended treatment duration for each antibiotics course we observe from W.H.O.
guidelines (World Health Organization, 2001).
Our analysis conﬁrms that observed symptoms are an important factor in the decision to begin
treatment with an antibiotic. The child’s gender, on the other hand, plays no signiﬁcant role for
whether an antibiotic is started; both unconditional and conditional on symptoms, the gender effect
on treatment uptake in our sample is small and not signiﬁcantly different from zero.
For treatment duration, by contrast, we ﬁnd that gender is an important determinant. Figure 1
shows the survival distribution of the number of days boys and girls are given antibiotic treatments.
In this raw data, girls are much more likely to take the antibiotic course for shorter periods. Since
antibiotics use is a two-step decision, ﬁrst the choice of starting treatment and then treatment
length, this unconditional difference is suggestive but not conclusive. We need to account for
selection at the second stage, because children who actually start treatment can otherwise not be
compared directly, and this could also affect observed gender differences. For example, if boys are
on average healthier than girls when starting treatment and healthier children take the antibiotic for
shorter periods, this will mask gender differences in adherence. Of course, both the absence of a
gender effect on treatment uptake, the ﬁrst stage, and our ability to control for symptoms in detail
alleviate some of these selection concerns. Nonetheless, we use Heckman’s (1976) procedure to
examine and correct for potential selection on unobservables, exploiting exogenous variation in
access to treatment from the randomized trial for which the data were originally collected.
As one would expect, one important source of selection is observed illness severity. OLS and
Heckman-corrected estimates of binary treatment completion and the daily probability of ending
treatment imply that selection into treatment is signiﬁcant when we control only for demographics:
children who are more likely to begin treatment also take the antibiotic for longer. However,
controlling for symptoms and the type of illness substantially reduces selection on unobservables,

3
Figure 1: Survival distribution of antibiotic treatment lengths.

100
Boys
Girls

80
Proportion surviving (%)
20 40 0 60

1 2 3 4 5 6 7 8 9 10 11 12 13
Antbiotic Spell Day

Note: Sample includes 704 uninterrupted treatment spells from antibiotics prescribed at community clinics,
private doctors, hospitals, or pharmacies. The ﬁgure shows the proportion of spells that have not ended for
each day after the start of the treatment, separated into girls and boys.

a ﬁnding that suggests that our data successfully capture many of the determinants for starting a
medication that also affect completion of the treatment course.
Both with and without illness controls and the Heckman correction, the estimation results show
that girls are 10 to 14 percentage points less likely to complete an antibiotics course, and have an
almost ﬁve percentage point higher daily probability of ending treatment before the recommended
treatment duration is reached. A Lasso approach to selecting covariates as well as alternative probit
speciﬁcations deliver very similar results. This is on top of already low completion rates for boys
of around 50 percent.
The stark difference in treatment completion reveals a new channel for gender health disparities
that has not been previously explored in depth. It is a surprising channel, since an explicit prefer-
ence for boys would suggest that parents are less willing to spend money and time on seeking care
for daughters, yet we see no gender difference in care-seeking. Instead, we see a large discrep-
ancy in treatment length, even though giving a pill for an extra day is by comparison essentially
cost-free. In order to better understand this result, we explore two potential mechanisms. First, we

4
examine if parents do discriminate on costs, but in less visible ways, namely by purchasing only
partial treatment for girls.1 However, we ﬁnd no evidence of this phenomenon. Second, we explore
if treatment adherence might be explained by subtle differences in how gender determines who is
involved in the care of the child at home. We build on the fact that the household head’s education
plays an important role in ensuring high adherence. This is likely based on the ability to read
and understand instructions, understand the pathways of antibiotics effectiveness and antibiotics
resistance, and so on; other factors correlated with education may also contribute (for example,
households with an educated household head are likely to have a higher income overall). We then
ask whether the education of the household head – given Malian traditional family structures, this
is in almost all cases the husband – differentially affects treatment adherence for boys and girls.
Indeed, we ﬁnd that the entire effect of the child’s gender on the one hand, and the household
head’s education level on the other, is explained by higher adherence rates of boys: the sons of
more educated household heads are 16 to 17 percentage points more likely to complete treatment
than either girls or sons of uneducated heads of family. An explanation may be that household
heads are less involved in the care of girls, so that beneﬁts from education and literacy that lead to
better home care accrue overproportionally to boys (note that many individuals in this sample have
no schooling and household head’s median education level is less than four years).
Our results have several important implications. First, we uncover some of the determinants of
antibiotics adherence, and show that adherence rates are overall worryingly low. Poor adherence
contributes to fostering resistant pathogens. Moreover, non-adherence for antibiotics can lead
to illness relapse, and if girls also have lower adherence rates for malaria treatment, vaccination
courses, and other medications, this may contribute to gender differences in an array of health
outcomes.
Second, the fact that gender disparities manifest themselves in treatment duration but not in
treatment start suggests subconscious favoritism over an explicit son preference. Such small dif-
ferences in parents’ treatment of their children in the privacy of the home may well be pervasive,
1 Partial treatment could happen if pharmacists or doctors allow parents to buy medications in “installments”, and
parents fail to return to buy the remaining portion.

5
but are usually very difﬁcult to detect. Our detailed data give us a unique window into the gender
bias in children’s day-to-day treatment. While individual instances may be inconsequential, they
can in accumulation lead to signiﬁcant differences in boys’ and girls’ outcomes. This more subtle
form of discrimination is not only harder to detect, but might also be harder to address.
Lastly, our ﬁndings suggest that gender differentials in health outcomes could be at least in part
related to educational differences between parents (with the caveat that our sample has very low
rates of education overall). As part of the push for the Millennium Development Goals, the last two
decades saw signiﬁcant improvements in child and maternal mortality worldwide. Child mortality
rates are highest, and efforts to reduce them consequently greatest, in countries with overall low
levels of human development, where educational gender disparities also tend to be high. Through
this channel, recent successes in ﬁghting child mortality might overproportionally beneﬁt boys and
thus contribute to the contemporaneous increase in excess female mortality in Sub-Saharan Africa,
at least temporarily.
Section 2 discusses the determinants of antibiotics use and shows how differences in treatment
start can lead to biased estimates of adherence behavior if not corrected. Section 3 presents a
description of the demographics of our sample, the observed antibiotics treatments, and the effect
of the randomized intervention that we use as a source of exogenous variation in treatment uptake
for the Heckman procedure. Section 4 estimates the determinants of starting a course of antibiotics
and the duration of treatment, and uncovers the role of the household head’s education level for the
gender bias in antibiotics adherence. Section 5 concludes.

2 Conceptual Framework

Treatment adherence to antibiotics depends on a two-step decision process. First, the child needs
to be started on treatment – a doctor or pharmacist must recommend the use of an antibiotic and
prescribe or sell it to the parent, the parents must buy it – and second, the parent must decide day to
day whether to give the drug to the child, based on doctor recommendations and potentially their

6
own assessment of the costs and beneﬁts. In this section, we discuss some of the factors that go
into both decisions and lay out our estimation approach, including the use of a Heckman correction
to control for selection into treatment on unobservables.
Going backwards in the decision process, we start by modeling treatment adherence. There
are two main reasons why the length of treatment may vary. First, doctors may prescribe different
amounts of the drug, and second, parents may adhere to this prescription differentially. Arguably,
trained doctors and pharmacist will usually prescribe at least the minimum dose of a medication,
which is reﬂected in the standard packaging size of a licensed drug and in ofﬁcial treatment guide-
lines (WHO guidelines for treatment durations with antibiotics are not gender-speciﬁc). However,
anecdotally, a doctor or pharmacist at a community clinic may also allow a cash-strapped parent
to buy a small amount of the drug right away, with the idea that they will return to get the full
treatment course later (which the parent may or may not do). In this manner, doctors’ and parents’
choices may interact in leading to differential outcomes for boys and girls.
This leads us to the second important margin of non-compliance, the degree to which parents
follow medical recommendations, found either on the drug packaging or given by the provider. It
is likely that the hurdle to “over-compliance”, i.e. taking the drug for longer than recommended,
is fairly high, because of packaging sizes.2 On the other hand, parents can easily end the treatment
early (note that the children are ﬁve years or younger, and so we assume that any decisions about
treatment and adherence are made by the parents).
We expect parents to fail to adhere to the recommended treatment length if the costs of com-
pletion are greater than the beneﬁts. The beneﬁts of completing a course of antibiotics depend
substantially on the (perceived) risk of not getting better or relapsing when ending treatment pre-
maturely, and the direct disutility associated with the illness – pain and discomfort to the child,
fussiness, disrupted sleep, etc. Cohen et al. (2014) also argue that the belief in the efﬁcacy of
the medication depends on the illness progression after starting treatment. Therefore, the deci-
sion to continue treatment will depend on observed or assumed illness type, illness severity, and
2 Itmay be, however, that the doctor gives spare tablets e.g. for children who may vomit up a dose, or that the
antibiotic requires more than one dose a day and the parent stretches the drug out over a longer period.

7
the duration of symptoms. The – perceived or real – risk of not curing the illness may be modu-
lated by factors such as the child’s age, because younger children are more susceptible to adverse
consequences of untreated illness.
Adherence costs described in the literature include the opportunity cost of using a drug now
vs. saving pills for future use, possible real or suspected side effects, and the mental cost of remem-
bering (Cohen et al., 2014). These costs may also be correlated with illness severity; for example,
remembering to continue the treatment may be harder if the child has no symptoms anymore. The
costs are again likely to vary with other factors. For example, households with fewer resources and
more than one child might be more inclined to save pills in order to use the medication another
time or for another child. We would also expect that the caretaker’s education affects their abil-
ity to read and follow package instructions, knowledge about the efﬁcacy of antibiotics, and the
importance of following treatment directions.
Lastly, families might also treat children differently despite similar short-term costs for them
and similar risks associated with the illness for the child. This may partly be related to (perceived)
long-term returns to investing in one child over another. Parents with adopted or fostered children
might either prioritize their biological children, or conversely pay more attention to their foster
charges, especially if they are compensated for their care by the biological parents (Hamilton
et al., 2007). Under severe resource constraints, a family might prioritize their ﬁrst-born child:
several studies have documented birth order effects in areas such as education (Black et al., 2005)
and nutrition (Behrman, 1988). Finally, there may be differential treatment by gender: parents
may care more about restoring a boy’s health, based on pure preferences or because of a boy’s
higher earning potential, but they might also pay more attention to a girl’s discomfort, for example
because boys are trained to complain less (Schechter et al., 2003).
We estimate antibiotics treatment length with a linear probability model based on a latent utility
model. We estimate two speciﬁcations, one where the binary decision is to treat the child for the
recommended length according to W.H.O. treatment recommendations (completion model), and
one in which the decision is to end treatment on a given day after starting the antibiotics course

8
(failure model). According to W.H.O. guidelines, most antibiotics in our sample should be taken
for a minimum of ﬁve days. There are some exceptions for certain antibiotic types and symptoms,
described in more detail in Section 3.3. For the completion model, we use two alternative deﬁni-
tions of treatment completion; the ﬁrst, denoted Model 1, uses the minimal recommended length
for each antibiotic type, the second, Model 2, uniformly counts an antibiotic course as completed
on day ﬁve. The rationale for Model 2 is that doctors, pharmacists, or even parents may choose
different antibiotic types for girls and boys.
We denote the net beneﬁt from completing the prescribed treatment by y∗ , the characteristics
of the illness spell by W , and a vector of observable, child, household, and parent characteristics
by X , breaking out the child’s gender G. The other child characteristics include whether the child
is the ﬁrst-born, their age, whether they are adopted or fostered, and an indicator for whether
the child is an only child. Household characteristics include total assets (log) and whether the
household head and mother have any schooling. To control for observable illness characteristics
we use information on symptoms that occurred before starting treatment. To avoid concerns about
speciﬁcation searching, we also show results using covariates selected using double lasso from a
wider set of candidate control variables (Belloni et al., 2014).3
Ongoing symptomaticity is an important determinant for continuing treatment, both because
it is a reminder to the parents, and because it is an indicator of the severity of illness. In order to
capture symptom persistence, we deﬁne indicators S for whether the child presented any symptoms,
or the symptom that was named as the reason for care, after treatment start (in Section 3.2, we
provide a more in depth description of these variables).
3 We deﬁne the set of candidate controls from the baseline survey as: dummies for experiment treatment groups,
age of child, birth order, an indicator for whether the child is adopted, number of siblings, household income and
assets, household size, the age, literacy, and education of the mother and household head, distance to the clinic, and an
indicator for whether the household head has a salary. In addition to information about demographics from the baseline
survey, we also include dummies for the presence of each symptom type in the pre-care days and indicators for whether
any symptoms or the named symptom were present for three and ﬁve days after the start of the antibiotic course. We
also dummy out missing values, recode them to zero. We include squared terms for all of the aforementioned controls.

9
The latent net utility of completing treatment for a given spell i and child j is then

y∗
i j = α1 + β1 G j + γ1 X j + δ1Wi j + η1 Si j + εi j . (1)

We observe if the child takes the drug for the recommended length and therefore completes
treatment (y∗
i j > 0). One concern of the completion model in including variables S is that they

may be endogenous to treatment adherence, especially if symptomaticity is also correlated with
gender. This is partially alleviated by estimating the failure model, which only includes spell days
conditional on not having ended treatment yet. Thus, on any given day we are only comparing
spells for which treatment adherence is invariant and uninterrupted during the spell.
The latent net utility for child j continuing treatment on day t of spell i is

pre post
y∗
i jt = α2 + α2 + β2 G j + γ2 X j + δ2Wi jt + η2 Si jt + εi jt (2)

The set of days t includes all days t in spells of length greater than or equal to t in which failure
(non-adherence) has not yet occurred. We observe if the antibiotic course is stopped on a given
day (y∗
i jt ≤ 0, failure model). In an ideal world, failure rates would be 0 prior to antibiotics course

completion, and 100% on the day after the course is complete (with no observations past that day).
More generally, we would expect failure to be discretely lower on days prior to completion of the
antibiotic course than afterwards. We therefore allow the constant to differ on pre-completion and
pre post
post-completion days (dummies α2 and α2 ). Moreover, since we are speciﬁcally interested
whether there are gender differences in completing the spell, in some speciﬁcations, we interact
the gender indicator with the pre- and post-completion dummies to test whether there are gender
differences in failure rates prior to completion.
The choice to seek treatment will be affected by similar factors as the choice to adhere. Illness
characteristics before treatment start W , as well as household characteristics like wealth and educa-
tion X , will play a role. In addition, we assume that the monetary cost of the doctor consultation and
treatment substantially affects the treatment choice. Ex ante, parents form expectations about the

10
cost depending on whether their child receives the randomly assigned healthcare subsidy, denoted
by indicator variable FC (for “free care”). Note that the doctor’s decision to prescribe treatment
may again modulate parents’ choices. The antibiotic will only be prescribed if the doctor believes
that the child has an illness that is treatable with antibiotics. In turn, conditional on the doctor’s
recommendation to treat, parents may differentially choose to follow this recommendation. We
collapse the chain of decisions of parents seeing a doctor, the provider prescribing treatment, and
the parents purchasing the drug, and model the ﬁnal outcome of these choices as the net beneﬁt v∗
from starting treatment:

v∗
i j = α3 + β3 G j + γ3 X j + δ3Wi j + κ FC j + ui j , (3)

where ui j is an error term describing unobservable factors entering the treatment choice. We
observe treatment start if v∗
i j > 0.

Our two-stage model of the decision to start treatment and then adhere to it highlights the
potential for selection into treatment at the second stage. Different choices for boys and girls at the
treatment stage can lead to observed gender differences in completion rates without any difference
in adherence behavior. For example, although Anderson and Ray (2010) note that there is little
evidence that there is differential incidence and severity of infectious diseases for boys and girls,
Wamani et al. (2007) ﬁnd in a meta-analysis that boys are generally more often stunted in Sub-
Saharan Africa. Stunting is associated with increased exposure to illness during childhood (Black
et al., 2008). This may indicate that boys and girls differ in how often they contract illnesses or
how severe the associated symptoms are. More prominent symptoms can motivate parents to seek
treatment for the child in order to reduce their discomfort, but also serve as a reminder to complete
the treatment.
Most of the variables described earlier that we believe enter the adherence decision are ob-
served in our data; importantly, our data allow us to control for an array of illness indicators.
Nonetheless, there may be factors that affect treatment adherence that we do not observe and that

11
matter for both decisions, so that εi j (or εi jt ) and ui j are correlated. Through the selection into
treatment, εi j may be in addition correlated with observable characteristics that affect the choice
to treat, including potentially gender. An example are differential perceptions about side effects or
efﬁcacy of antibiotics. We would expect these beliefs to both affect whether parents purchase an
antibiotic treatment and whether they adhere to treatment recommendations.
If such unobserved factors are important, and the unobservables are correlated with gender, it
could bias our estimates of the gender effect β1 or β2 . For example, if boys are less resilient than
girls, this may introduce a bias in the treatment adherence of girls relative to boys, because an
overall stronger and more robust child may be taken to a doctor less often and also receive a drug
for a shorter period.
As we show below, there is no a priori gender difference in treatment start, which makes
selection on unobservables that are correlated with gender less of a concern. Nonetheless, we
carry out a correction using the Heckman method, both because it corresponds with our original
analysis plan, and because it allows us to gauge the importance of unobservables in the treatment
and adherence decisions. Moreover, although we are limited by the small number of families where
two siblings of opposite gender start a course of antibiotics, the gender difference in adherence also
seems to persist within families (Appendix Table A.7).
Assuming a normally distributed error term, we use the Heckman procedure in the ﬁrst decision
stage to construct the inverse mills ratio λ and include it in the second stage. This gives us an
estimate of the correlation of the error terms in the selection and adherence utilities corr(u, ε ) = ρ
in equations (1) to (3). If ρ = 0, there is no selection on unobservables and an uncorrected estimate
of equation (1) or (2) yields similar results as the Heckman corrected estimate. If ρ > 0, then
children who are more likely to start a course of antibiotics are also more likely to complete or
continue the treatment. On the other hand, if ρ < 0, then children who start treatment will have
lower completion and higher failure rates.
Bushway et al. (2007) highlight the importance of including an instrumental variable in the se-
lection equation that is excluded from the second stage, that is, the variable only affects adherence

12
through its effect on the probability of starting an antibiotic course.4 In our case, the randomized
price subsidy implemented during the survey period provides exogenous variation in treatment
uptake. Researchers have previously speculated that price paid may directly affect utilization, for
example due to sunk cost effects or learning about quality. However, Cohen and Dupas (2010)
and Ashraf et al. (2010) do not ﬁnd evidence of a sunk cost effect for mosquito bednets or water
disinfection products.5 The perceived quality of treatment should also not depend on who pays
for it, the NGO or the patient. Lastly, since the price subsidy is by design randomized within the
population of interest, its assignment is not correlated with any of the factors that inﬂuence adher-
ence. In Appendix sections A.1-A.3, we show that the randomization was successful, by providing
details on the balance of the sample. Table A.3 also shows that the subsidy did not affect the
overall number of illness spells reported, even though it signiﬁcantly increased treatment rates.6
For these reasons, we assume that conditional on an illness spell occurring, the price subsidy only
affects treatment duration through changing the probability of seeking care, and therefore satisﬁes
the exclusion restriction.

3 Demographics, Illness Incidence, and Antibiotics Use

In this section we provide details about the study design and sample population and the health diary
data we use. We show that boys and girls do not differ signiﬁcantly a priori according to observable
demographic and household characteristics or illness incidence (all possible sources of selection).
We also provide a description of antibiotics treatment rates and the WHO recommendations for
each type of antibiotic. Lastly, we conﬁrm that the price subsidy successfully decreased treatment
costs and increased treatment rates.
4 While the model is formally identiﬁed without a valid exclusion restriction, the inverse Mills ratio is close to
linear in certain parts of its range, so if there are no variables that only enter the selection equation, there is a risk of
collinearity of λ with the other explanatory variables.
5 Note, however, that there is some tentative evidence of sunk cost effects in education (Hidalgo et al. (2013); Ketel

et al. (2016)).
6 Since this was the ﬁrst rainy season where the subsidy was available, and the share of the treated in the overall

population was small, the increase in healthcare use did not reduce illness incidence.

13
We use data collected during a baseline and endline survey conducted in late 2012 and 2013 for
a randomized control trial of price subsidies and community health worker visits in the outskirts
of Bamako, Mali. The interventions were rolled out in early 2013. In order to be eligible for the
program, families had to pass a proxy-means test for poverty and had to have children under 5
years old. Detailed information on the design of the randomized trial and the sampling frame can
be found in Sautmann et al. (2017).
The Malian health care system rests in large part on public so-called community health clinics,
which are mainly funded by user fees, but also supported by private NGOs and the government.
In collaboration with the local NGO Mali Health, children in the “free care” (FC) treatment group
received free consultations from doctors at the local clinic, as well as cost-free treatment and med-
ication for any of the ﬁve main causes of child mortality (diarrhea, malaria, vaccine-preventable
diseases, respiratory infections, and malnutrition) at the clinic or from the pharmacy attached to
the clinic. The treatment groups and the control group of the randomized trial live in Sikoro, which
is served by two clinics that collaborate with Mali Health. Mali Health conducts quality checks on
their partner clinics based on treatment guidelines that standardize the treatment children receive
for common illnesses. This means that all children in the sample receive similar care.
1,732 children were surveyed in 2012 and enrolled in the program in 2013. Of those children,
1,567 were present at the follow-up survey in 2013, corresponding to an attrition rate of 9.5%.
Another 201 children were added after the baseline survey (typically newly born children or chil-
dren of a mother in the program who had been previously absent; no children were added whose
household and mother were not included in the baseline survey). In total, 1,768 children from
1,003 households and 645 compounds were included in the ﬁnal sample. For ﬁve children, gen-
der was not recorded and we omit them from the analysis here. Treatment was randomized at the
compound level.7
7A household is deﬁned as all the people who report the same household head. More than one household may live
in the same compound. Compounds are a cluster of dwellings that share some facilities, including usually a common
courtyard and any sanitation.

14
3.1 Demographic Characteristics

Demographics were collected at baseline and, where feasible (for immutable characteristics such
as gender or age), missing data was corrected in the follow-up survey. Table 1 shows an overview
of the child, household, and parent characteristics. The average age of children in the study is
three years, about 20% are the ﬁrstborn to their mother, and 6% are adopted or fostered, a common
practice in Mali. Each child has, on average, about three siblings. A household is deﬁned as all
people who report a common household head. The average self-reported value of households’ total
assets is $6,564. The average income is $67 per week. In our sample, 46% of mothers report some
education. Household heads (who are typically male) have higher literacy and education rates
than mothers; more than 64% report some education. 50% of our sample receive the randomized
healthcare subsidy.
As a ﬁrst step, we examine pre-existing differences in observable demographic characteristics
for boys and girls. Column 4 of Table 1 reports differences in group means of girls and boys from
a regression of each of the variables on a male dummy. Overall, all of the gender differences are
small and insigniﬁcant. In a study of Nigerian women, Milazzo (2014) found that families were
less likely to foster a girl if they already had a daughter, while the same did not hold for sons. In
our data, however, we ﬁnd no evidence of any differential rate of adoption or fostering of girls
and boys. Moreover, in contrast with evidence from India (Jensen, 2003) and Nigeria (Milazzo,
2014), we ﬁnd no signiﬁcant gender difference in the number of siblings each child has, nor in
the ﬁnancial situation of households. Therefore, differences in parents’ fertility behavior based
on the gender of the ﬁrst child can be at most very small in our data. In the last row of Table 1,
column 4, we also show the difference in the proportion of girls and boys eligible for free care.
The difference is small (0.03) and not signiﬁcant, suggesting that the subsidy was successfully
balanced on gender.8
8 In Appendix A.2 we provide additional information about the balance of the sample by treatment group on the

covariates included in the analysis.

15
Table 1: Comparison of child, household, mother, and household head characteristics.
(1) (2) (3) (4) (5)
Boys - Girls
Mean SD N Difference SE
Child
Male 0.52 (0.50) 1,763
Age (years) 3.10 (1.60) 1,763 -0.11 (0.07)
First-born 0.19 (0.40) 1,763 -0.01 (0.02)
Adopted or fostered 0.06 (0.24) 1,763 0.00 (0.01)
No. of siblings 2.94 (2.12) 1,743 0.03 (0.11)

Household
Assets (USD) 6,564 (13,965) 1,685 -360 (686)
Weekly income (USD) 67 (83) 1,615 -2 (5)
Household size 6.8 (3.5) 1,728 -0.0 (0.2)
Mother educated 0.46 (0.50) 1,743 -0.03 (0.02)
Household head educated 0.64 (0.48) 1,753 -0.01 (0.02)
Household head has salary 0.11 (0.32) 1,732 0.01 (0.02)

Treatment group
Free care 0.50 (0.50) 1,763 0.03 (0.03)
Notes: Signiﬁcance levels: *** p < 0.01, ** p < 0.05, * p < 0.1. All eligible children in baseline households
who were present in 2013 are included. This comprises children who were born to eligible households (with
at least one eligible child in 2012) after the baseline survey. Column 4 present differences in group means
of boys and girls. Standard errors from a regression of the variable on a male dummy are clustered at the
compound level and shown in column 5.

16
3.2 Health Diary Information

The core element of the data collection is a detailed health calendar for each child, containing daily
information on any symptoms the child exhibited, all consultations that occurred with respect to
the child’s health, and all medications taken. Health calendar data was collected on a weekly basis
over the course of nine weeks in 2013 from the child’s primary caretaker (usually the mother).
Caretakers were given pictorial diaries and asked to mark off any symptoms and health-related
events on the day they occurred. The surveyor reconstructed the child’s health history with them
during the next visit. To reduce Hawthorne effects, the surveyors were carefully instructed never
to comment on the child’s health or express judgement, and to emphasize that they had no medical
qualiﬁcation.
The caretaker was prompted about a set of symptoms derived from checklists in the World
Health Organization Integrated Management of Childhood Illness (IMCI) handbook (World Health
Organization, 2014) and selected because they are relatively easy to spot: convulsions or spasms,
lethargic or unconscious, unable to drink, vomiting everything, coughing, difﬁculty breathing,
more than three loose stools, blood in stool, sunken eyes, and unusually hot (skin). In addition,
ﬁve additional symptom groups were created from free-form survey answers about other symptoms
experienced: rash or spots or itch, head, neck, or eye ache, cold symptoms (runny nose), wound or
injury or burn, and earache.
It can be subjective or difﬁcult to know what illness a child has. Moreover, the mother’s percep-
tion about the health status of the child might be different for boys and girls. The survey questions
were therefore designed to use neutrally described and standardized symptoms observations, and
the enumerators were carefully trained not to connect symptoms with speciﬁc illnesses (e.g. hot
skin with malaria) or to comment on the severity, risk level, or meaning of a symptom (e.g. diarrhea
as a factor in malnutrition).
Table 2 provides an overview of illness incidence during the survey period (more detail on
the data collection and the symptoms and spells recorded can also be found in Sautmann et al.,
2017). In total, we observe about 60 days per child. Contiguous days of symptoms were grouped

17
in to illness spells. The incidence of illness was signiﬁcant: over the survey period we observe
4,200 illness spells in total that last on average over seven days.9 Due to the nature of the data,
some of the illness spells may be censored. Among theillness spells, 386have the potential to be
left-censored (e.g., because they started on the day after a missing data point), while 18 illness
spells have the potential to be right-censored, meaning that the record of the spell ended with a day
with missing data (e.g., at the end of the survey). We restrict the analysis to uncensored spells in
order to ensure that we observe all pre-care days and whether an antibiotic treatment was started.
We also exclude the small number of spells with missing baseline child, household, and illness
covariates. Our ﬁnal analysis sample consists of 3,530 illness spells. The average child had more
than two illness spells over the time period and presented symptoms for over 18 of the observed
days (more than 30%). Most commonly, children exhibited cold symptoms: on average, 19% of
the total observed days for each child. In addition, coughing (12%) and unusually hot (9%) were
commonly reported symptoms.
Gender differences in illness incidence could be an important source of bias in gender-speciﬁc
treatment adherence. However, column 3 shows that there are no obvious differences in how
often boys and girls become ill. Overall, the number of illness spells per child is not statistically
different for boys and girls. The length of the average illness spell for girls and boys is also similar.
Similarly, there is no gender difference in the incidence of speciﬁc symptoms, with the exception
of “difﬁculty breathing”, a symptom which boys, on average, present on a higher proportion of
the total observed days than girls, signiﬁcant at the 5% level. Nonetheless, in our analysis, we
control for the presence of these observed symptoms. Note also that there may be differences in
incidence that are unobservable in our data. We cannot rule out, for example, that there are gender
differences in the severity of a given symptom. This is another motivation for using the Heckman
procedure to examine selection on unobservables.
9 The study was conducted during the rain season, when the incidence of illnesses such as malaria, diarrhea, and

respiratory infections is higher than during the rest of the year. The average spell length excludes censored illness
spells.

18
Table 2: Illness incidence and type.
(1) (2) (3) (4)
Boys - Girls
Mean SD Difference SE
Panel A. Overall incidence
Average spell length (days) 7.17 (7.52) -0.15 (0.29)
Illness spells per child 2.64 (1.43) -0.03 (0.07)
Observed days per child 59.8 (9.7) -0.04 (0.44)
Total days ill per child 18.5 (15.8) 0.32 (0.77)

Panel B. Proportion of all observed days
Any symptom present 0.310 (0.261) 0.008 (0.013)
Convulsions, spasms 0.001 (0.016) -0.001 (0.001)
Lethargic or unconscious 0.015 (0.057) 0.001 (0.002)
Unable to drink 0.004 (0.025) -0.000 (0.001)
Vomiting everything 0.013 (0.033) 0.001 (0.002)
Coughing 0.119 (0.173) -0.004 (0.009)
Difﬁculty breathing 0.017 (0.056) 0.006** (0.003)
> 3 loose stools 0.027 (0.074) 0.004 (0.004)
Blood in stool 0.002 (0.026) 0.001 (0.001)
Sunken eyes 0.007 (0.030) 0.001 (0.001)
Unusually hot 0.092 (0.131) 0.001 (0.006)
Other 0.007 (0.033) -0.002 (0.002)
Wound, injury, or burn 0.011 (0.046) 0.001 (0.002)
Rash, spots or itch 0.009 (0.045) -0.001 (0.002)
Head, neck or eye ache 0.004 (0.024) 0.002 (0.001)
Cold symptoms 0.187 (0.227) 0.017 (0.011)
Stomach ache 0.003 (0.019) -0.000 (0.001)
Ear ache 0.003 (0.022) 0.001 (0.001)
Notes: Signiﬁcance levels: *** p < 0.01, ** p < 0.05, * p < 0.1. Average spell length does not include
censored spells. Symptom incidence is the number of days the symptom was observed as a fraction of the
total number of observed days for each child. Column 3 shows the difference in means between boys and
girls. Standard errors from a regression of the variable on a male dummy are clustered at the compound
level and shown in column 4.

19
Table 3: Antibiotic types in the sample, recommended treatment length, and frequency.
Drug Use Treatment Exceptions N Percent
length
Amoxicillin Tonsillitis, bronchitis, pneumonia, infections of 5 days or more 3 days for 179 25%
ear, nose, throat or skin, UTIs, tooth abscess or tooth
infection; penicillin tablet. infections only.
Ampicillin Mastoiditis, chronic typhoid or paratyphoid fever, 7 days or more 2 0.3%
meningitis, other infections, e.g heart or
gallbladder; penicillin injection.
Ceftriaxone Mastoiditis, pneumonia, salmonella infections, 5 days or more 4-6 days in 69 9.7%
meningitis, croup, lyme disease; intramuscular for most uses some cases.
injection.
Co- Otitis (ear), bronchitis, pneumonia, typhoid and 5 days or more 3 days for 234 33%
Trimoxazole para-typhoid fever, salmonella, UTI, skin and other typhoid and
infections; oral administration or intravenous in paratyphoid
severe cases. fever, enteritis
due to
salmonella or
E-coli
Erythromycin Respiratory tract infections, pneumonia, mouth 5 days or more 8 1.1%
and tooth infections; tablet or powder.
Gentamicin Pneumonia, meningitis, heart and other infections; 7 days or more 5-10 days for 22 3.1%
intramuscular injection. soft tissue
injuries.
Metronidazole Pneumonia, tooth and mouth infections/abscesses, 5 days or more 3 days for 116 16.3%
brain abscess, amoebiasis and giardiasis and other giardiasis.
infections; oral.
Other or 82 11.5%
unspeciﬁed
Total 712 100%

Source: Drug information: World Health Organization. Notes: Number and percentage of each antibiotic
as reported in our sample. "Other" contain antibiotics given infrequently, like Ciproﬂoxacin, often obsolete
due to widespread resistance. "Unspeciﬁed" drugs were reported as antibiotics but without a speciﬁc brand
or generic name.

3.3 Antibiotic Treatments

At each surveyor visit, mothers were asked if they discussed their child’s health with anyone in
the preceding week, and the surveyors recorded details of the visit as well as treatments received
and costs incurred. In addition, they were asked to report any medications purchased and taken
at home. In total, the children in our sample received 712 courses of antibiotics from a formal
healthcare provider such as a doctor, clinic, or pharmacy. Table 3 shows the different types of
antibiotics given, and their frequency in our sample. Most common are Co-Trimoxazole (33%),
Amoxicillin (25%), and Metronidazole (16%).

20
Some children were treated with drugs from informal sources such as market stalls or tra-
ditional healers that parents reported to be antibiotics. However, in our analysis we focus on
antibiotic treatments from formal sources including public and private clinics, private doctors,
hospitals, and pharmacies. Some drugs sold as antibiotics at informal sellers are unmarked tablets
of unknown origin and content, or counterfeit; medications from these sources may also be stored
inappropriately or may have expired. There is little information whether and how the drugs were
prescribed, or what instructions parents were given, if any. Antibiotics from informal sources are
in any case not the norm; the majority of antibiotics treatments in our sample come from formal
sources.
We do not observe directly what directions parents received about treatment length. Prescribed
treatment length for an antibiotic can depend on the severity of illness. The handbook WHO Model
Prescribing Information: Drugs Used in Bacterial Infections provides prescription guidelines for
most antibiotic types in the sample (World Health Organization, 2001). The recommended treat-
ment length is at least ﬁve days in most circumstances. There are some exceptions; for example, in
the case of tooth abscesses (Amoxicillin), for typhoid or paratyphoid fever, salmonella, or E-coli
(Co-Trimoxazole), and for giardiasis, a parasite found in contaminated water (Metronidazole), the
treatment recommendation is three days. In our main speciﬁcation (Model 1), we deﬁne minimum
treatment completion for each drug separately according to Table 3. We code an antibiotic course
as completed with a treatment length of seven days for Ampicillin, four days for Ceftriaxone,
three days for Co-Trimoxazole when accompanied by a fever or diarrhea symptoms, three days for
Metronidazole when accompanied by diarrhea symptoms, and ﬁve days otherwise.
In a second speciﬁcation (Model 2), we deﬁne treatment completion uniformly as taking the
antibiotic for at least ﬁve consecutive days. This number was chosen because the overwhelming
majority of recommendations (across drugs and across illnesses for the same drug) require at least
ﬁve days of treatment.10 This alternative speciﬁcation accounts for the possibility that the doctor’s
decision of which antibiotic type to prescribe might also depend on the child’s gender, if it for
10 Tooth pain was not reported in our sample, and the incidence of giardiasis has been found to be low in Mali
(Berger, 2015).

21
example affects costs (but note that our results on the cost of the antibiotic below do not support
this type of differential prescription behavior). However, both speciﬁcations ﬁnd a very similar
gender effect on adherence.
Because our data record only whether a medication was taken at all or not on a given day, we
cannot say if children received the correct dosage. It is possible that parents of non-adherent chil-
dren actually accelerated the treatment by giving higher doses for fewer days. We deﬁne “failure”,
or treatment end, as the ﬁrst day on which the child is not given the antibiotic. Thus, if the treat-
ment recommendation is ﬁve days, but failure occurs on or before the ﬁfth day after treatment was
started, this treatment course falls in the “not completed” category. Interrupted treatments (where
the same antibiotic is taken again within a few days) are therefore included as non-adherence; there
are only four such cases.
A small number of antibiotic spells are censored, for example because they occur at the end
of the survey (and last beyond the symptom spell). We code the completion variable for these
spells as missing. For Model 1, minimum completion, we deﬁne this threshold according to the
minimum length the antibiotic should have been taken. For Model 2, ﬁve-day, we deﬁne it as ﬁve
days before the end of the survey period. As a consequence, 14 (12) antibiotic spells are coded as
missing according to Model 1 (2). In the failure model, we can estimate continuation probability on
every day of the antibiotic spell, even when the spell is not observed to the end, so these estimates
are not affected by right-censoring.
In order to identify unobserved selection effects later on, we use exogenous variation in access
to treatment from the randomized enrollment of families into the healthcare subsidy treatment
described above, the research project for which the data were originally collected. Appendix Table
A.3, panel A, shows that the price subsidy was successful in reducing both the cost of a consultation
and a course of antibiotics. Panel B of Table A.3 further shows that the price subsidy increased
both overall treatment rates and the proportion of illness spells treated with an antibiotic.

22
4 Results

In this section we ﬁrst document the low overall rates of adherence and show that there is a sizable
gender difference in raw adherence rates. This motivates the formal analysis of selection and
adherence behavior that follows.
Table 4 starts with some treatment summary statistics. Panel A shows that 20% of spells are
treated at a formal source, and 13% are prescribed an antibiotic course. Within those, 4% of illness
spells are treated with more than one type of antibiotics.
Before summarizing antibiotic completion rates, we make two sample restrictions. First, we
focus on the ﬁrst course of antibiotics taken within an illness spell. Second, for the small number
of illness spells where multiple antibiotics were started on the same day, we choose the antibiotic
which was taken for the longest time. Courses of antibiotics are prescribed and started, on average,
on the sixth day of an illness spell and parents give the course of antibiotics to the child for four
days on average. Only about 46% of antibiotic courses are given for the minimum recommended
treatment length according to Table 3, and 40% of antibiotic courses are given for at least ﬁve days.
The table shows that there is no average raw gender difference in treatment start or cost, the
time to treatment, or overall treatment length. By contrast, there is a large, statistically signiﬁcant
difference between boys and girls in the proportion of antibiotic courses that are completed, ac-
cording to either deﬁnition (taken for the minimum treatment length or for ﬁve days). We already
saw this difference in ﬁgure 1 in the introduction.
Note ﬁrst that differences in adherence may not translate into large differences in average treat-
ment length because there is censoring at the top: the number of tablets contained in the drug
packaging automatically limits the length of time the antibiotic can be taken (only children who
receive more than ﬁve tablets can take the antibiotic for more than ﬁve days). Second, while these
aggregate raw ﬁgures are indicative, they are not conclusive: for example, it is possible that gender
differences in treatment start are explained or masked by differential illness incidence for boys and
girls.

23
Table 4: Treatment choice, length, and cost.
(1) (2) (3) (4)
Boys - Girls
Mean N Difference SE
Panel A. Treatment Choice
Proportion of illness spells:
Received consultation 0.20 3,526 -0.008 (0.015)
Treated with antibiotic course 0.13 3,526 -0.012 (0.012)
Treated with more than one course of antibiotics 0.04 3,526 0.004 (0.007)

Panel B. Treatment Length
Time to treatment start (days) 6.02 470 -0.37 (0.68)
Treatment length (days) 4.10 498 0.64∗∗∗ (0.22)
Completed antibiotic treatment?
Minimum completion 0.46 503 0.14∗∗∗ (0.05)
5 days or more 0.40 505 0.16∗∗∗ (0.05)

Panel C. Treatment Cost
Consultation where antibiotic prescribed (CFA) 280 519 -133 (98)
Antibiotic course (CFA) 709 518 85 (126)
Notes: Signiﬁcance levels: *** p < 0.01, ** p < 0.05, * p < 0.1. Treatment refers to a course of antibiotics.
Only consultations and antibiotics from formal sources are included. Formal sources includes community
clinic, private doctor, hospital, or pharmacy. Right-censored antibiotic spells are excluded from treatment
length in days. Minimum completion length including exceptions is deﬁned in Table 3. The difference in
means between boys and girls is shown in column 3. Standard errors from a regression of the variable on a
male dummy, clustered at the compound level, are reported in column 4.

In what follows, we therefore extend the evidence by controlling for observable covariates
and for possible selection on unobservables in treatment adherence. First, we estimate the gender
difference in starting a course of antibiotics, conditional on demographic and illness characteristics.
This analysis uncovers any cumulative gender differences in the decision sequence of prescription,
purchase, and administration of the antibiotic. Next, we estimate the gender effect on treatment
adherence (completion model) and the daily probability of discontinuing treatment (failure model).
This allows us to control additionally for variation due to daily symptom incidence, and uncovers
any signiﬁcant gender difference in the daily probability of ending treatment before the completion

24
of an antibiotics course. As discussed above, we also correct the simple OLS estimates with
a Heckman selection model, using the exogenous variation in access to treatment from the price
subsidy, both to examine the potential role of unobservables in general and to rule out any potential
for biased estimates due to selection and a correlation of gender with unobservable factors. Our
results conﬁrm that there is no gender effect on treatment start but a signiﬁcant gender difference
in adherence, driven by higher failure rates before treatment course completion for girls; given the
lack of gender differences in selection into treatment, it is no surprise that the Heckman corrected
results are very similar to the OLS results. As a last step, we examine heterogeneity and ﬁnd that
the household head’s education level seems to play a signiﬁcant role in the adherence gender effect.

4.1 Treatment Choice

Table 5 presents OLS estimates for the probability of starting a course of antibiotics obtained
from a formal source of care. Column 1 includes only the gender of the child and price subsidy
eligibility as explanatory variables, while column 2 controls for other demographic and household
characteristics. These include the age of the child, ﬁrst-born and adoption status, and an indicator
for being an only child. The household variables include schooling of the child’s mother and the
household head, and household assets.
Column 3 additionally controls for illness characteristics, with sixteen dummies that indicate
if the child ever presented a speciﬁc symptom type during the illness spell prior to seeking care.
Cold symptoms (runny nose) are the omitted category. Symptoms are likely to change depending
on what care the child received. Therefore, we only use whether a symptom was ever present in
the period before starting treatment. Appendix Figure A.1 shows which symptoms are associated
with antibiotic use. In column 4, we show the gender effect controlling for covariates chosen
using double lasso. Of the candidate controls, only ﬁve symptom indicators were included by the
procedure.
The effect of gender on the probability of starting treatment is reported in the ﬁrst row of the
table. The coefﬁcient is a closely estimated zero in all speciﬁcations. This is consistent with the

25
Table 5: Estimates of the probability of starting a course of antibiotics for an illness spell.
(1) (2) (3) (4)
Male child -0.012 -0.014 -0.009 -0.010
(0.012) (0.012) (0.007) (0.007)
Free care (FC) 0.106∗∗∗ 0.104∗∗∗ 0.044∗∗∗ 0.046∗∗∗
(0.013) (0.013) (0.008) (0.008)
Additional controls
Demographics x x
Symptoms x
Double-lasso x
Mean (control) 0.07 0.07 0.07 0.07
Nr of clusters 572 572 572 572
N 3,530 3,530 3,530 3,530
Notes: Signiﬁcance levels: *** p<0.01, ** p<0.05, * p<0.1. Standard errors clustered at the compound level
in parentheses. Each observation is an illness spell. The dependent variable is whether a course of antibiotics
was prescribed and purchased at a formal source for an illness spell. Cold symptoms is the omitted symptom
category.

descriptive evidence above, which showed similar treatment rates for boys and girls.
Eligibility for the price subsidy increases the probability of starting an antibiotic course by be-
tween 4.4 and 10.6 percentage points, signiﬁcant at the 1% level. In comparison to the baseline
treatment rate (7%), this effect constitutes a near doubling of the probability of receiving an antibi-
otic (consistent with the ﬁndings in Sautmann et al. (2017) on the overall probability of seeking
formal health care as a result of the subsidy).
To summarize, we ﬁnd no evidence that parents differentiate between their sons and daughters
when deciding to seek medical advice. If parents do seek care for sons more often, doctors are
exactly “undoing” this choice with their treatment decision, by prescribing antibiotics to girls more
frequently, conditional on seeing them.

26
4.2 Treatment Adherence

4.2.1 Completion Model

Next, we examine differences in treatment adherence by gender. Table 6 begins by showing simple
OLS estimates of the probability of completing a course of antibiotics and then, in order to control
for any selection into treatment based on unobservables, uses a Heckman correction for selection
bias. In Model 1, we deﬁne treatment completion according to the minimum treatment lengths by
antibiotic type described in Section 3.3. In Model 2, treatment completion is deﬁned as taking the
antibiotic for ﬁve days or more.
In the ﬁrst two columns, we only include demographic covariates. Next, we additionally control
for illness characteristics. This is done, ﬁrst, by including dummies for the symptoms present
during the pre-care days of the illness spell, as in the regressions above. Second, we include two
indicator variables that equal one if the child (a) exhibited any symptoms, and (b) exhibited the
symptom the parents named as the reason for starting treatment, for at least three days from the
ﬁrst day of treatment onwards. For Model 2 in Table 6 we deﬁne these two symptom indicators
using ﬁve days from the ﬁrst day of treatment, instead of three days. The last two columns present
results using covariates chosen by the double lasso procedure.
We ﬁnd that boys are between 9.7 and 13.1 percentage points more likely to complete the
prescribed treatment in Model 1, and between 12.2 and 15.7 percentage points in Model 2, and this
effect is signiﬁcantly different from zero at the 5% level or less in all speciﬁcations. The magnitude
of the gender effect does not change substantially when controlling for illness characteristics.
Perhaps as expected given the lack of gender differences at the ﬁrst stage, the gender effect
also does not vary when correcting for selection bias using the Heckman procedure. In the second
column with demographic controls only, the estimated selection effect ρ is large and positive, and
in Model 2 it is signiﬁcant at the 1% level, indicating that unobservables that make a child more
likely to start treatment also lead to higher treatment completion rates. Interestingly, the selection
effect is much smaller and not signiﬁcant in the fourth column, where we control for symptoms.

27
This suggests that a lot of unobserved selection on illness severity can be controlled for with our
basic symptom records.
Table 6 shows in fact that one of the most important factors in treatment completion is symp-
tomaticity. Whether the child presented symptoms for three days consecutively after the start of
treatment increases the probability of completion in Model 1 by 45 percentage points, signiﬁcant
at the 1% level. In Model 2, exhibiting symptoms for ﬁve days or more increased the probability
of ﬁve day completion by 20 percentage points, also signiﬁcant at the 1% level. The presence of
symptoms is also the only explanatory variable included by the double lasso procedure in columns
5 and 6. By comparison, the additional dummies that focus on the symptom that was named as the
reason for treatment do not affect completion probabilities signiﬁcantly.11

4.2.2 Failure Model

Table 7 uses a more ﬂexible speciﬁcation and estimates the effect of gender and other characteris-
tics on the daily probability of failing to continue treatment on a given day. As discussed above,
this approach also alleviates concerns about the endogeneity of symptomaticity and treatment ad-
herence. The ﬁrst three columns again only include demographic controls while columns 4 and
5 add indicators for each symptom observed in the pre-care period. In addition to the pre-care
symptom indicators, we also control for the presence of any symptoms on the day in question, as
well as the presence of the symptom that was named as the main reason for seeking care.
As discussed in Section 2, “optimal” drop-out behavior would entail zero probability of failure
prior to treatment completion. Some antibiotics may be prescribed for longer than the minimal
recommended length, so the doctor-recommended drop-out rate after the minimal completion date
is likely less than zero, but above one. In columns 2 - 7, we include indicator variables that allow
the drop-out rate to differ discretely between days before and after completion. In Model 1, pre-
completion days are those within the minimal length of time the antibiotic must be taken, in Model
2 these are days one through ﬁve. We also include two interaction terms between gender and days
11 Other combinations of 3-day and 5-day indicators do not signiﬁcantly affect the results on gender.

28
Table 6: Estimates of the probability of completing the course of antibiotics.

(1) (2) (3) (4) (5) (6)
OLS Corrected OLS Corrected OLS Corrected
Panel A. Model 1: Minimum completion
Male child 0.131∗∗∗ 0.125∗∗∗ 0.099∗∗ 0.097∗∗ 0.116∗∗∗ 0.117∗∗∗
(0.046) (0.046) (0.043) (0.041) (0.043) (0.043)
Named symptoms present (3 days) 0.062 0.061
(0.067) (0.066)
Symptoms present (3 days) 0.450∗∗∗ 0.450∗∗∗ 0.456∗∗∗ 0.455∗∗∗
(0.056) (0.054) (0.044) (0.044)
Selection effect ρ 0.324 0.055 0.058
(0.240) (0.225) (0.264)
Mean (girls) 0.38 0.38 0.38 0.38 0.38 0.38
Nr of clusters 246 246 246 246 246 246
N 460 460 460 460 460 460
Additional Controls
Demographics x x x x
Symptom indicators x x
Double-lasso x x
Panel B. Model 2: Completed ﬁve days or more
Male child 0.157∗∗∗ 0.143∗∗∗ 0.135∗∗∗ 0.122∗∗∗ 0.137∗∗∗ 0.141∗∗∗
(0.045) (0.047) (0.046) (0.046) (0.043) (0.043)
Named symptoms present (5 days) 0.019 0.019
(0.068) (0.065)
Symptoms present (5 days) 0.201∗∗∗ 0.204∗∗∗
(0.055) (0.054)
Symptoms present (3 days) 0.404∗∗∗ 0.397∗∗∗
(0.042) (0.042)
Selection effect ρ 0.586∗∗∗ 0.420 0.363
(0.230) (0.379) (0.253)
δ 0.40
Mean (girls) 0.31 0.31 0.31 0.31 0.31 0.31
Nr of clusters 245 245 245 245 245 245
N 462 462 462 462 462 462
Additional Controls
Demographics x x x x
Symptom indicators x x
Double-lasso x x

Notes: Signiﬁcance levels: *** p<0.01, ** p<0.05, * p<0.1. Standard errors clustered at the compound level
in parentheses. Cold symptoms is the omitted symptom category. The parameter ρ is the estimated corre-
lation between the error terms of the selection and adherence equations. The parameter δ is the estimated
ratio of the covariance between the treatment and the unobserved controls and the treatment and observed
controls, which would produce a gender effect of zero. In Columns 5 and 6, both the variables indicating
symptoms present and named symptoms present for three and ﬁve days were included as candidate controls
in the double-lasso procedure, but only the symptoms present for three days variable was chosen as a control
by the procedure.

29
before and after completion (e.g. “male × pre-completion day” and “male × post-completion day”
in Table 7). This allows us to focus on gender differences in drop out before the antibiotics course
is completed.
In the ﬁrst column, when we only include demographic controls, we ﬁnd that boys are 2.7 and
3.8 percentage points less likely to stop treatment on any day, signiﬁcant at the 1% level. Columns
2 and 3 show that this is driven by non-adherence: boys are between 3.5 and 4.3 percentage
points less likely to stop treatment on the pre-completion days of the treatment. After completion,
boys are equally likely to stop the treatment relative to the (selected) girls who are still receiving
treatment at that point. The results are similar when controlling for illness characteristics. The
presence of any symptom reduces the daily probability of stopping the treatment by between 22 and
26 percentage points. The presence of the symptom that parents named as the reason for starting the
treatment additionally decreases the probability of failure by between 9 and 11 percentage points,
signiﬁcant at the 1% level. As before, there is some selection, although the estimated selection
effect is now negative, since children who are more likely to start treatment are less likely to stop
treatment on any given day. As before, however, once we control for symptoms in columns 5 and
7, the selection effect becomes smaller and not signiﬁcant.

As a last robustness check, we can carry out the test based on Altonji et al. (2005) and Oster
(2019) to examine how important omitted unobservables would have to be to make the gender
difference in adherence equal to zero. In our preferred speciﬁcations of column 3 in Table 6 and
column 4 in Table 7 (with demographics and symptoms included), the δ values are 0.40 and 0.88
for a hypothetical R2 of 1. This means that there would have to be explanatory variables that we
did not control for – or, more precisely, independent components of such variables – that are 0.40
and 0.88 times as important as the observables included, respectively. Even though they are below
the threshold of 1 suggested by Oster (2019), we consider these values acceptable because our
estimates already control for a rich set of covariates, including symptoms. It is also reassuring that
the value of δ is closer to 1 in the failure model reported in Table 7, supporting the conclusion that
estimating the daily probability of exit, controlling for detailed symptoms and the course of the

30
illness, is more robust to the omission of unobservables.
In summary, we ﬁnd that there is a large and statistically signiﬁcant gender difference in adher-
ence, conditional on demographic and illness covariates and controlling for selection. Our main
outcome variables are all binary; in Appendix Tables A.5 and A.6 we show that our main results are
not sensitive to using probit instead. As discussed in section 2, whether symptoms are still present
has previously been shown to be an important factor in treatment continuation, see for example
Cohen et al. (2014). Our ﬁndings conﬁrm this; whether the child still presents any symptoms is the
most important predictor of adherence in our sample. This may be because the symptom serves
as a reminder for treatment, or because parents have incorrect beliefs about the need to continue
treatment beyond the child’s apparent recovery. Nonetheless, girls are 10 percentage points less
likely to complete an antibiotics course, reducing completion rates from 48% to 38%.

4.3 Mechanisms

Our results on the effect of gender on treatment choices and adherence are surprising: if parents
have a preference for sons, we would expect this difference to be particularly visible in those
actions that require larger investments of time and money. Getting treatment, even with the subsidy,
requires signiﬁcant effort and time transporting the child to the clinic and back and waiting for the
consultation. By contrast, completing a course of antibiotics that was previously purchased has a
much lower cost, and a priori we might therefore expect that it would be less subject to differential
decisions based on gender.
There are two possible mechanisms for the gender difference at the adherence stage that we can
partially test in our data. The ﬁrst mechanism is purchasing and sales behavior. It is anecdotally
known that doctors and pharmacists in Mali sometimes give poor families the option to buy or take
home only a portion of a prescribed drug if they are unable to pay the full price, so that they can
start treatment right away and return later with more cash to purchase the remainder of the drug. It
is therefore possible that parents of girls only purchase a share of the prescribed dosage and then
do not return to buy the rest.

31
Table 7: Estimates of the daily probability of not continuing the course of antibiotics.
(1) (2) (3) (4) (5) (6) (7)
Panel A. Model 1: Minimum completion
Male child -0.027∗∗∗
(0.010)
Pre-completion 0.213∗∗∗ 0.217∗∗∗ 0.260∗∗∗ 0.448∗∗∗ 0.472∗∗∗ 0.403∗∗∗ 0.405∗∗∗
(0.021) (0.022) (0.025) (0.037) (0.044) (0.029) (0.039)
Post-completion 0.253∗∗∗ 0.245∗∗∗ 0.287∗∗∗ 0.401∗∗∗ 0.425∗∗∗ 0.357∗∗∗ 0.359∗∗∗
(0.022) (0.025) (0.029) (0.036) (0.043) (0.027) (0.041)
Pre-completion x Male -0.035∗∗ -0.034∗∗ -0.048∗∗∗ -0.048∗∗∗ -0.047∗∗∗ -0.047∗∗∗
(0.014) (0.015) (0.018) (0.018) (0.018) (0.018)
Post-completion x Male -0.014 -0.013 0.012 0.013 0.007 0.007
(0.021) (0.022) (0.027) (0.026) (0.026) (0.026)
Named symptom present -0.108∗∗∗ -0.108∗∗∗ -0.104∗∗∗ -0.104∗∗∗
(0.017) (0.017) (0.017) (0.017)
Any symptom? -0.259∗∗∗ -0.260∗∗∗ -0.256∗∗∗ -0.256∗∗∗
(0.027) (0.027) (0.026) (0.027)
Additional controls
Demographics x x x x x
Symptoms x x
Doube-lasso x x
Selection effect ρ -0.095∗∗∗ -0.052 -0.005
(0.037) (0.041) (0.057)
Mean (girls) 0.22 0.22 0.22 0.22 0.22 0.22 0.22
Nr of clusters 255 255 255 254 254 254 254
N 2,438 2,438 2,438 2,411 2,411 2,411 2,411
Panel B. Model 2: Completed ﬁve days or more
Male child -0.038∗∗∗
(0.013)
Pre-completion 0.188∗∗∗ 0.191∗∗∗ 0.244∗∗∗ 0.391∗∗∗ 0.415∗∗∗ 0.347∗∗∗ 0.349∗∗∗
(0.026) (0.026) (0.031) (0.038) (0.048) (0.029) (0.044)
Post-completion 0.451∗∗∗ 0.439∗∗∗ 0.492∗∗∗ 0.540∗∗∗ 0.563∗∗∗ 0.488∗∗∗ 0.491∗∗∗
(0.029) (0.033) (0.040) (0.042) (0.050) (0.035) (0.050)
Pre-completion x Male -0.043∗∗∗ -0.042∗∗∗ -0.041∗∗ -0.041∗∗ -0.042∗∗∗ -0.042∗∗∗
(0.014) (0.015) (0.016) (0.016) (0.016) (0.016)
Post-completion x Male -0.017 -0.016 -0.011 -0.009 -0.011 -0.011
(0.037) (0.036) (0.040) (0.040) (0.042) (0.042)
Named symptom present -0.086∗∗∗ -0.086∗∗∗ -0.083∗∗∗ -0.083∗∗∗
(0.018) (0.018) (0.018) (0.018)
Any symptom? -0.228∗∗∗ -0.229∗∗∗ -0.227∗∗∗ -0.227∗∗∗
(0.029) (0.028) (0.028) (0.028)
Additional controls
Demographics x x x x x
Symptoms x x
Doube-lasso x x
Selection effect ρ -0.122∗∗∗ -0.054 -0.005
(0.047) (0.050) (0.0690)
δ 0.88
Mean (girls) 0.22 0.22 0.22 0.22 0.22 0.22 0.22
Nr of clusters 255 255 255 254 254 254 254
N 2,438 2,438 2,438 2,411 2,411 2,411 2,411

Notes: Signiﬁcance levels: *** p<0.01, ** p<0.05, * p<0.1. Standard errors clustered at the compound level
in parentheses. The dependent variable is 1 if it is the ﬁrst day the prescribed antibiotic course is not taken.
Pre-completion is an indicator for the days before the minimum recommended treatment length respectively,
described in table 3. The parameter ρ is the estimated correlation between the error terms of the selection
and adherence equations. The parameter δ is the estimated ratio of the covariance between the treatment
and the unobserved controls and the treatment and observed controls, which would produce a gender effect
of zero. 32
To test this we examine the dependence of the purchase price of the antibiotic on gender. If
doctors prescribe a different antibiotic or a smaller dosage to girls, or parents buy fewer pills for
girls at the pharmacy, we would expect to see a gender difference in the cost of the antibiotic.
Table 8 shows various estimates of this gender effect on the total price of the antibiotics course
purchased.12 We ﬁrst estimate the overall gender difference in cost, and then the difference con-
ditional on the type of antibiotic (the ﬁrst might be affected by the choice of antibiotic type by
gender; the second captures if parents purchase a smaller quantity of a given antibiotic for girls).
In the last two columns we conduct the estimation for the control group only which did not receive
the subsidy. The gender effect on cost is small (about 34 CFA or less than $0.1) and not signiﬁcant
in any of the speciﬁcations.
The second mechanism is driven by education, and, we conjecture, based on differences in
who is primarily responsible for the care of a child. Appendix Table A.4 shows the effects of all
the demographic covariates on treatment completion in the speciﬁcations in Table 6. Whether the
household head is educated has a large positive effect on the probability of completion. Children
with an educated household head are 7 to 9 percentage points more likely to complete the course
of antibiotics. Education could be associated with more leisure time, a greater belief in the efﬁcacy
of antibiotic treatments, or knowledge about antibiotic resistance. It could also indicate a better
understanding of and memory for treatment instructions given by the doctor. All of these suggest
that education has positive adherence effects, as we observe in our sample.
Household heads in our sample are 85% male and generally more educated than their wives.
We examine differences in the education effect on adherence depending on the child’s gender, on
the hypothesis that the illness of a boy might lead to greater involvement of the male household
head, while the care of a girl is left to the mother, who even in a household with an educated head is
typically herself not educated, and may have for example more difﬁculty reading or remembering
treatment instructions.We therefore test whether the education effect on adherence beneﬁts boys
12 The reference group in column 1 is a course of antibiotics prescribed at the clinic, in column 2 it is Amoxicillin

prescribed at the clinic.

33
and girls differentially by interacting the gender of the child with the household head’s education.13
In Table 9 we repeat the regressions from the previous section, but interact all relevant gender
dummies with the education level of the household head. The results reveal that the entire gender
effect on adherence is explained by families in which the household head is educated, and at the
same time, the entire education effect is explained by the higher adherence rates of boys (rather than
all children). Pre-completion daily failure rates for boys in families with an educated household
head are between 6.6 and 7.1 percentage points lower than those for girls. The completion models
suggest that boys are 16 to 17 percentage points more likely to complete the antibiotics course
among families with an educated household head. By contrast, there is no signiﬁcant gender effect
in other families.
Figure 2 also plots the estimated gender effect by the education level of the household. While
we lose some power by looking at more narrowly deﬁned education categories, we see a monotonic
relationship between education and differential adherence, which supports the overall take-away
from the regression results of the positive relationship between the gender effect and education.
These results are noteworthy because they suggest that gender differences in treatment adherence
emerge when families are better educated. This is not because girls are treated worse in a household
with an educated head than in other households; it is because the only group of children who adhere
to treatment recommendations are the sons of educated men.
13 Toavoid speciﬁcation searching, we also used the double-lasso procedure to investigate which interactions with
gender should be included. We included all the candidate child and household controls in the original double-lasso
procedure and their interactions with gender. The only control selected was the interaction between the indicator for
whether the household head is educated and male child.

34
Table 8: Antibiotic cost.
Control group only
(1) (2) (3) (4) (5)
Male 100 34 111 58 69
(113) (98) (151) (217) (340)
Free care (FC) -560∗∗∗ -523∗∗∗ -538∗∗∗ 0 0
(148) (117) (118) (.) (.)
Source
Other doctor / hospital visit 1559∗∗∗ 1273∗∗∗ 1286∗∗∗ 1042∗∗ 962∗∗
(372) (306) (306) (407) (374)
Pharmacist 327∗∗ 252∗ 260∗ -142 -181
(138) (148) (151) (231) (250)
Antibiotic type
Ampicilline -45 128
(150) (186)
Ceftriaxone 1247∗∗∗ 1490∗∗ 1909∗∗ 3141∗∗∗
(395) (662) (733) (805)
Cotrimoxazol -209∗∗∗ -172 -367∗∗ -358
(79) (111) (181) (234)
Erythromycin -590∗∗ -287 -706∗ -453
(295) (238) (365) (404)
Gentamicine 0 54 -298 -428
(155) (267) (200) (266)
Metronidazole -193∗∗ -218∗ -530∗∗ -513
(97) (131) (236) (314)
Other 1293∗∗∗ 1023 2044∗∗∗ 1472∗∗
(475) (656) (667) (722)
Unspeciﬁed 209 609 375 803∗∗∗
(206) (381) (331) (293)
Interaction terms
Male ×Ampicilline -351
(213)
Male ×Ceftriaxone -480 -1854
(795) (1444)
Male ×Cotrimoxazol -78 -41
(164) (418)
Male ×Erythromycin -566 -419
(525) (649)
Male ×Gentamicine -113 221
(307) (429)
Male ×Metronidazole 65 -72
(202) (449)
Male ×Other 334 878
(893) (1177)
Male ×Unspeciﬁed -723∗ -575
(418) (412)
Constant 811∗∗∗ 775∗∗∗ 750∗∗∗ 855∗∗∗ 878∗∗∗
(134) (125) (143) (218) (240)
Nr of clusters 245 245 245 87 87
Nr of observations 462 462 462 135 135

Notes: Signiﬁcance levels: *** p<0.01, ** p<0.05, * p<0.1. Standard errors clustered at the compound
level in parentheses. The reference group in column (1) is “any antibiotic prescribed at the clinic”, in (2)
“Amoxicillin prescribed at the clinic”, in (3) “Amoxicillin prescribed to girls at the clinic”. In columns (4)
and (5), only children in the control group are included. 476 CFA equaled 1 USD in 2013.
35
Table 9: Gender effect on antibiotics completion by household head’s education level.
Completion Failure
(1) (2) (3) (4)
Min. Completion > 5 Days Min. Completion > 5 Days
Male child -0.029 0.016
(0.078) (0.094)
Male x Head educated 0.174∗ 0.163
(0.091) (0.106)
Head educated -0.010 -0.008
(0.064) (0.074)
Pre-completion 0.423∗∗∗ 0.363∗∗∗
(0.038) (0.038)
Male x Pre-completion 0.002 0.007
(0.026) (0.026)
Post-completion 0.375∗∗∗ 0.511∗∗∗
(0.036) (0.043)
Male x Post-completion 0.072 0.159∗∗
(0.048) (0.074)
Male x Pre-completion x Head educated -0.071∗∗ -0.066∗∗
(0.028) (0.027)
Male x Post-completion x Head educated -0.075 -0.204∗∗∗
(0.048) (0.074)
Additional controls
Demographics x x x x
Symptoms x x x x
Mean (girls) 0.38 0.31 0.22 0.22
Nr of clusters 246 245 254 254
N 460 462 2,411 2,411
Notes: Signiﬁcance levels: *** p<0.01, ** p<0.05, * p<0.1. Standard errors clustered at the compound level
in parentheses. All columns control for observed illness type and symptoms. In column (1) the dependent
variable is whether the prescribed antibiotic course is taken for the minimum recommended treatment length
as described in Table 3. In column (2) it is whether the course is taken for ﬁve days or more. In (3) and (4)
the dependent variable is 1 if it is the ﬁrst day the prescribed antibiotic course is not taken. In column (3)
pre-completion and post-completion is deﬁned according to the minimum prescribed treatment length for
each antibiotic in Table 3. In (4) they are indicators for (day ≤ 5) and (day > 5) respectively. The constant
is suppressed in columns 3 - 4.

36
Figure 2: Gender effect on antibiotics completion by household head’s education level.

.5
.25
0
−.25
−.5

< 4 years Elementary school HS/College

Notes: 90% conﬁdence intervals constructed using standard errors clustered at the compound level.
The ﬁgure shows the estimated coefﬁcients from a regression of treatment adherence (minimum
completion) on a gender dummy interacted with indicators for each education level. The reference
group is “no education”. All speciﬁcations control for demographics and symptoms. Children
where the head is educated at an islamic school are excluded.

5 Conclusion

We use detailed health data to study gender differences in treatment choice and duration for antibi-
otics in Mali. We ﬁnd no gender difference in treatment uptake, but uncover a large and signiﬁcant
gender difference in the probability of continuing the course of antibiotics until completion.
As a result of these differences, boys are over 10 percentage points more likely to complete
the prescribed and purchased treatment, controlling for differential symptoms and illness type and
other factors unrelated to discrimination. Moreover, we ﬁnd that the entire effect of the child’s
gender on the one hand, and the household head’s education level on the other, is explained by
higher adherence rates of boys: the sons of more educated household heads are 16 to 17 percentage
points more likely to complete treatment than either girls or sons of uneducated heads of family.
An explanation may be that household heads are less involved in the care of girls, so that beneﬁts
from education and literacy that lead to better home care accrue overproportionally to boys
These results reveal that subtle mechanisms may be generating treatment differences between

37
boys and girls. If parents had an explicit preference for sons, we would expect them to apply a
lower threshold for seeking care and purchasing an antibiotic for boys than for girls, actions that
require investments of time and money. By contrast, completing a course of antibiotics that was
previously purchased has an overall much lower cost. Ex ante, this decision should therefore be
less inﬂuenced by differential willingness to invest in the care of children of different gender. Our
result suggests that the persistent excess mortality for women and girls in Africa may be partly
caused by implicit biases in care giving. It may be more difﬁcult to address and eradicate these
biases than more explicit discrimination in access to treatment at the clinic.
In addition to gender, our data allow us to also examine other determinants of antibiotic treat-
ment uptake and adherence. We conﬁrm that symptoms are important determinants of care-seeking
behavior and demonstrate that selection into treatment is largely explained by observed illness het-
erogeneity among children. This indicates that information about a fairly straightforward set of
health indicators of the child can substantially improve power and correct selection biases when
examining determinants of the intensive margin of health care use.

38
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42
Appendix

A Supplemental Tables

A.1 Attrition

Table A.1 shows the number of children, households, and compounds present during the baseline
survey in 2012 and follow-up survey in 2013. The price subsidy was implemented in 2013. In
total, we include 1,768 children, 1,003 households, and 645 compounds in our sample. We ﬁnd no
signiﬁcant differences in attrition between the treatment groups.

Table A.1: Attrition by experiment treatment groups.
(1) (2) (3) (4)
Treatment Group
Control Healthworker Free care HW & FC
Eligible children (census) 474 446 458 426
At baseline:
Not found/absent/moved 21 22 17 7
Had died 2 1 0 0
Refusal 0 0 2 0
Enrolled children 2012 451 423 439 419
Present at follow-up 2013 402 387 394 384
Added after baseline 54 43 45 59
Final sample 2013 456 430 439 443
Notes: 1732 children in households found at baseline survey were enrolled and assigned to treatment. 1567
originally enrolled children were surveyed at the follow-up after receiving treatment. In addition, 201 chil-
dren were born or newly enrolled in existing study households. A Pearson chi-square test that attrition post
baseline is independent of the treatment group is not rejected (p = 0.497).

A.2 Balance

Table A.2 compares baseline characteristics of children, households, and parents in the two treat-
ment groups. Overall there are few differences in the covariates. An exception is birth order, where
the proportion of children eligible for free care that are ﬁrst born is lower than in the control group.

43
Table A.2: Covariate balance by treatment group.
(1) (2) (3) (4)
Control Mean Free Care Coefﬁcient Std. Error N
A. Child characteristics
Male 0.51 0.03 (0.03) 1,763
Age (years) 3.11 -0.02 (0.07) 1,763
First born 0.21 -0.04 (0.02) 1,763
Adopted 0.06 -0.01 (0.01) 1,763
Number of siblings 2.87 0.14 (0.14) 1,743

B. Household characteristics
Assets 6170.88 783.01 (1122.77) 1,685
Income 65.91 2.76 (7.06) 1,615
Household size 6.65 0.26 (0.27) 1,728
Mother educated 0.45 0.01 (0.03) 1,743
Head educated 0.62 0.03 (0.04) 1,753
First authority salaried 0.12 -0.01 (0.02) 1,732
Notes: Signiﬁcance levels: *** p<0.01, ** p<0.05, * p<0.1. Standard errors from a regression of each
variable on a price subsidy eligibility dummy, clustered at the compound level in parentheses.

A.3 Impact of subsidy on treatment rates and cost

Table A.3, panel A, shows that the price subsidy was successful in reducing both the cost of a
consultation and a course of antibiotics. For visits at which an antibiotic was prescribed, being
eligible for the price subsidy is associated with a reduction of the average consultation fee by 389
CFA, on average, signiﬁcant at the 1% level. The price subsidy also reduced the average cost of a
course of antibiotics by 785 CFA, again signiﬁcant at the 1% level. Note that children in the “free
care” group were eligible for free consultations at the local clinic and free medications for the ﬁve
main causes of childhood mortality. However, not all consultations and purchases of medications
occurred at the subsidized clinics, and not all treatment options are covered by the subsidy. The
average costs in the “free care” group are therefore substantially lower, but not equal to zero.
Panel B of Table A.3 further shows that the price subsidy increased both overall treatment rates
and the proportion of illness spells treated with an antibiotic. The proportion of spells treated at

44
Table A.3: Comparison of treatment rates and cost. Subsidy group (FC) versus control group.
(1) (2) (3)
Control Group Effect of Subsidy N
Panel A. Cost (CFA)
Consultation where antibiotic was prescribed 548 -389∗∗∗ 712
(82) (93)
Antibiotic course 1,261 -785∗∗∗ 711
(146) (158)

Panel B. Proportion of spells
Received consultation 0.15 0.11∗∗∗ 3,530
(0.01) (0.02)
Treated with a course of antibiotics 0.07 0.11∗∗∗ 3,530
(0.01) (0.01)
Number of illness spells 1,739 52 3,530
Notes: Signiﬁcance levels: *** p < 0.01, ** p < 0.05, * p < 0.1. Standard errors clustered at the compound
level are reported in the parentheses. Panel A includes all antibiotics observed during the survey period.
Panel B restricts to uncensored illness spells. All consultations and antibiotic courses from either the clinic,
private doctor, hospital, or pharmacy are included. The mean in the control group is in column 1, and the
difference between the free care and control group in column 2.

a formal source is 11 percentage points higher for children who are eligible for the price subsidy,
signiﬁcant at the 1% level. Similarly, the proportion of spells that are treated with an antibiotic is
almost twice as high in the free care group relative to the control group. The last row reports the
number of illness spells in the two groups. It is reassuring that the incidence of illness spells was
not altered by the subsidy.

A.4 Symptom correlates with antibiotic start

Figure A.1 gives us some information which symptoms are associated with antibiotic use. Chil-
dren who have a cough, fever (“unusually hot”), or diarrhea (“more than three loose stools”) are
all more likely to start a course of antibiotics relative to children with a cold, and children with
a wound, injury or burn even more so. By contrast, children with head, neck, or eye ache are
less likely to start an antibiotic. Some of these effects are likely explained by diagnostic differ-

45
ences. For example, a wound, diarrhea, or coughing could be an indicator of a bacterial infection,
whereas vomiting everything, lethargy or unconsciousness are signs of (complications of) malaria.
Difﬁculty breathing can be a sign of pneumonia, but is also caused by asthma.

Figure A.1: Symptom correlates with antibiotic treatment start (omitted category: cold symptoms).

Convulsions, spasms
Head/Neck/Eye Ache
Sunken eyes
Vomiting everything
Lethargic or unconscious
Difficulty breathing
Unable to drink
Blood in stool
>3 loose stools
Unusually hot
Stomach ache
Ear ache
Coughing
Other
Rash/Spots/Itch
Wound/Injury/burn
−.5 0 .5 1

Notes: 90% conﬁdence intervals constructed using standard errors clustered at the compound level.
The ﬁgure shows the estimated coefﬁcients for each of the symptom indicators from the regression
of antibiotic treatment start on child gender, price subsidy eligibility, and the full set of household
controls. The omitted symptom category is cold symptoms.

46
A.5 Extended Table 6

Table A.4: Regression coefﬁcients for demographic controls in Table 6.

Model 1: Min. completion Model 2: Five days
(1) (2) (3) (4)
Male child 0.131∗∗∗ 0.099∗∗ 0.157∗∗∗ 0.135∗∗∗
(0.046) (0.043) (0.045) (0.046)
Age (years) 0.015 0.014 0.019 0.025
(0.017) (0.016) (0.016) (0.017)
First born -0.222∗∗ -0.121 -0.250∗∗∗ -0.264∗∗∗
(0.098) (0.089) (0.075) (0.079)
Adopted or fostered -0.033 -0.019 -0.045 -0.054
(0.100) (0.082) (0.107) (0.096)
Only child 0.238∗ 0.107 0.277∗∗ 0.275∗∗
(0.128) (0.116) (0.111) (0.118)
Assets (log) 0.001 -0.004 0.003 0.004
(0.010) (0.009) (0.010) (0.010)
Mother educated 0.013 -0.012 0.022 0.009
(0.050) (0.045) (0.048) (0.048)
Head educated 0.087 0.073 0.076 0.070
(0.060) (0.051) (0.057) (0.056)
Named symptoms present (3 days) 0.058
(0.068)
Any symptoms present (3 days) 0.452∗∗∗
(0.056)
Named symptoms present (5 days) 0.019
(0.068)
Any symptoms present (5 days) 0.201∗∗∗
(0.055)

Additional controls
Symptom indicators x x
Observations 460 460 462 462

Notes: Signiﬁcance levels: *** p<0.01, ** p<0.05, * p<0.1. Standard errors clustered at the compound
level in parentheses. This table reports the coefﬁcients for the child and household covariates included in
Column 3 of Table 6.

47
B Robustness

B.1 Non-linear estimation

Table A.5 presents marginal probit estimates of the effect of gender on the probability of starting
a course of antibiotics. The point estimates are extremely close to the OLS estimates presented in
Table 5. Similarly, we present marginal probit estimates of the probability of completing a course
of antibiotics in Table A.6. In columns 1 - 4 the outcome variable is deﬁned as completing a
course of antibiotics. As in Table 6, boys are about 10 percentage points more likely to complete
the antibiotic course in all speciﬁcations. In columns 5 - 8 the outcome variables is deﬁned as
failing to continue treatment on a given day. As in Table 7, boys are about four percentage points
less likely to stop the antibiotic in the days before the recommended minimum treatment length
and no less likely in the days after.

Table A.5: Marginal probit estimates of the probability of starting a course of antibiotics.
(1) (2) (3)
Male child -0.013 -0.015 -0.007
(0.011) (0.011) (0.007)
Free care (FC) 0.106∗∗∗ 0.103∗∗∗ 0.044∗∗∗
(0.013) (0.013) (0.008)
Additional controls
Demographics x x
Symptoms x
Mean (control) 0.07 0.07 0.07
Nr of clusters 572 572 572
N 3,530 3,530 3,530
Notes: Signiﬁcance levels: *** p<0.01, ** p<0.05, * p<0.1. Standard errors clustered at the
compound level in parentheses. Each observation is an illness spell. The dependent variable is
whether a course of antibiotics was prescribed and purchased at a formal source for an illness
spell. Cold symptoms is the omitted symptom category.

48
Table A.6: Marginal probit estimates of the effect of gender on treatment adherence.
Completion Failure
Minimum completion > 5 Days Minimum completion > 5 Days
(1) (2) (3) (4) (5) (6) (7) (8)
Male child 0.123∗∗ 0.123∗∗ 0.146∗∗∗ 0.090
(0.052) (0.052) (0.047) (0.068)
Male × Pre-completion day -0.048∗∗∗ -0.042∗∗ -0.049∗∗∗ -0.043∗∗
(0.017) (0.017) (0.017) (0.017)
Male × Post-completion day 0.007 0.007 -0.006 -0.004
(0.025) (0.021) (0.028) (0.025)
Additional controls
Demographics x x x x x x x x
Symptoms x x x x x x x x
Pre and post-completion day x x x x
Selection effect ρ 0.010 0.409 -0.087 -0.101
Mean (girls) 0.38 0.38 0.31 0.31 0.21 0.21 0.21 0.21
Nr of clusters 246 246 245 245 254 254 254 254
N 460 460 462 462 2,411 2,411 2,411 2,411

Notes: Standard errors clustered at the compound level. Demographic characteristics include
whether the child is the ﬁrst-born, their age, whether they are adopted or fostered, whether they are
an only child, as well as total assets (log) and whether the household head and mother have any
schooling. Symptom controls include indicators for each of the symptom categories in the pre-care
period. In columns 1 - 4, symptom controls also include an indicator for whether any symptoms
were present for three (or ﬁve) days, while in columns 5 - 8, symptom controls include an indicator
for whether any symptoms and the named symptoms were present on each day.

B.2 Family ﬁxed effects

In our sample, there are 103 families where at least two siblings of opposite gender start a course
of antibiotics at some point during the sample period. To control for unobservable household
characteristics, we can compare antibiotic adherence rates of siblings. Table A.7 presents our
main speciﬁcations including family ﬁxed effects. The point estimates suggest that there is still a
gender difference in treatment adherence even within families, but due to the small sample size the
estimates are quite imprecise and we cannot draw any strong conclusions.

49
Table A.7: Fixed effects estimates of the effect of gender on treatment adherence
Completion Failure
Minimum completion > 5 Days Minimum completion > 5 Days
(1) (2) (3) (4) (5) (6) (7) (8)
Male child 0.068 0.121 0.058 -0.004
(0.089) (0.095) (0.081) (0.061)
Pre-completion day × male -0.008 -0.063∗ -0.017 -0.056∗
(0.031) (0.033) (0.030) (0.033)
Post-completion day × male -0.045 -0.048 -0.014 -0.053
(0.052) (0.053) (0.051) (0.054)
Additional controls
Demographics x x x x x x x x
Symptoms x x x x
Pre and post-completion day indicators x x x x
Observations 245 245 241 241 2435 2409 2435 2409

Notes: Standard errors are clustered at the compound level. Demographic characteristics include
whether the child is the ﬁrst-born sibling, their age, and whether they are adopted or fostered. The
other controls are as described in Tables 6 and 7.

B.3 Cost of antibiotic treatment

In Section 4.3, Table 8 we found that there was no gender difference in the cost of the antibiotic. In
Table A.8 we also control for child and household characteristics as well as indicators for observed
symptoms in the pre-care period. Similar to Table 8 there is no signiﬁcant gender difference in the
cost of any of the antibiotic types.

50
Table A.8: Gender difference in antibiotic cost, including household and symptom controls.
Control group only
(1) (2) (3) (4) (5)
Male 86 -11 -12 -59 -161
(119) (104) (146) (289) (418)
Free care (FC) -537∗∗∗ -514∗∗∗ -531∗∗∗ 0 0
(143) (116) (118) (.) (.)
Source
Other doctor / hospital visit 1573∗∗∗ 1284∗∗∗ 1301∗∗∗ 1294∗∗∗ 1283∗∗∗
(393) (328) (328) (470) (463)
Pharmacist 314∗ 215 209 199 54
(166) (164) (170) (332) (366)
Antibiotic type
Ampicilline -430 -118
(318) (360)
Ceftriaxone 1276∗∗∗ 1480∗∗ 2206∗∗∗ 3872∗∗∗
(408) (693) (770) (919)
Cotrimoxazol -195∗∗ -188 -354 -347
(84) (119) (234) (302)
Erythromycin -570∗ -212 -410 -479
(307) (268) (527) (619)
Gentamicine 121 90 -607 -526
(179) (306) (498) (607)
Metronidazole -92 -177 -386 -347
(121) (166) (379) (528)
Other 1353∗∗∗ 903 2032∗∗∗ 724
(476) (657) (598) (634)
Unspeciﬁed 209 695∗ 226 1621∗∗∗
(254) (394) (578) (548)
Interaction terms
Male ×Ampicilline -683
(483)
Male ×Ceftriaxone -357 -2185
(803) (1480)
Male ×Cotrimoxazol 9 62
(167) (493)
Male ×Erythromycin -672 -344
(542) (959)
Male ×Gentamicine 74 384
(355) (631)
Male ×Metronidazole 252 316
(213) (662)
Male ×Other 615 1757
(884) (1155)
Male ×Unspeciﬁed -810∗ -1584∗
(444) (914)
Constant 675∗∗∗ 660∗∗∗ 614∗∗∗ 981∗ 925
(253) (203) (211) (532) (604)
Nr of clusters 245 245 245 87 87
Nr of observations 462 462 462 135 135

Notes: Signiﬁcance levels: *** p<0.01, ** p<0.05, * p<0.1. Standard errors clustered at the compound
level in parentheses. Reference group in column 1 is antibiotic prescribed at the clinic, in 2 Amoxicillin
prescribed at the clinic, in column 3 Amoxicillin prescribed to girls at the clinic. In columns 4 and 5 only
children in the control group are included. 476 CFA equaled 1 USD in 2013. All columns include controls
51
for household characteristics as well as indicators for observed symptoms in the pre-care period.