metadata: authors: 0: author: Sautmann,Anja
1: author: Brown,Samuel
2: author: Dean,Mark
count: Mali
docna: 0: docna: Subsidies, Information, and the Timing of
            Children’s Health Care in Mali
docty: Policy Research Working Paper
subtopic: Health Care Services Industry,Cholera,Malaria,Communicable Diseases,Leprosy,Law and Justice Institutions,Health Service Management and Delivery
teratopic: Law and Development,Industry,Health, Nutrition and Population
abstracts: cdata!: Sustained progress in reducing child
            mortality requires better care for children who are acutely
            ill. This paper studies how health care subsidies and health
            workers providing information on symptoms affect the overuse
            and underuse of primary care, which depend not just on
            absolute levels of demand, but also on whether care is
            received when the child is actually sick. In a randomized
            controlled trial of 1,768 children in Mali, the study
            collected a unique panel of nine weeks of daily symptom and
            health care use data to study the impact of each policy on
            demand conditional on need for care, as defined by World
            Health Organization standards. Subsidies substantially
            increase care when it is medically indicated, while overuse
            remains rare. Health worker visits have no aggregate effect
            on demand, but they may help the youngest children take
            advantage of the subsidy.
geo_reg_key: 517191,1927320
repnme: repnme: Subsidies, Information, and the Timing of
            Children’s Health Care in Mali
display_title: Subsidies, Information, and the
            Timing of Children’s Health Care in Mali
listing_relative_url: /research/2020/11/32621705/subsidies-information-timing-children’s-health-care-mali
topicv3: Primary Care m1326822 1307,Malaria m1327042 1420,Health Services Delivery m1327294 1305,Mortality m1504365 3661,Health m1504661 3480
publishtoextweb_dt: 2020-11-30T00:00:00Z
historic_topic: Law and Development,Industry,Health, Nutrition and Population
origu: DECRG: Human Development (DECHD)
no_of_pages: 56
author_keys: 0: author_key: 1338140
1: author_key: NA
2: author_key: NA
model_extractions: Subsidies, Information, and the Timing of Children’s Health Care in Mali Anja Sautmann<sup>∗</sup> Samuel Brown<sup>†</sup> Mark Dean<sup>‡</sup> This version: July 2024 Keywords: demand for acute care; children’s health care; health care underuse and overuse; health care subsidies; community health workers; Mali JEL codes: I11, I12, O15

> ∗The World Bank, corresponding author, email: asautmann@worldbank.org.

> †Apple, email: sam.vin.brown@apple.com.

> ‡Columbia University, email: mark.dean@columbia.edu.
# **Acknowledgements**

We received invaluable advice and support from Andrew Foster, Duncan Thomas, and Ira Wilson. We would also like to thank Anna Aizer, Daniel Bj ̈orkegren, Joseph Camardo, Caitlin Cohen, Jishnu Das, Seydou Doumbia, Chris Gill, Rachel Glennerster, Jessica Goldberg, Bentley McLeod, Yusuf Neggers, Emily Oster, Simone Schaner, Dan Silverman, and Dean Yang. Special thanks go to Hamadoun Bocoum, Zorina Curry, Judith Kom, and Fred Wensing, Mali Health and its staff and Board, and Innovations for Poverty Action. We are grateful for funding from Brown University through the Brown Seed Fund Award, the Population Studies and Training Center, and the Rhodes Center, from the Aga Khan Foundation, and from the ESRC/DFID Development Frontiers Award (ES/K01207X/1). The research protocol was approved by the IRB of Brown University (protocol number #1205000629) and the Comit ́e National d’Ethique pour la Sant ́e et les Sciences de la Vie (CNESS) (protocol No. 13/MSHP-SG/CNESS). The study is registered in the AEA RCT Registry with ID AEARCTR-0006603. 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.
# **1 Introduction**

In the last two decades, great strides have been made in reducing child mortality, primarily through preventive care programs (e.g. Bhatt et al., 2015). Nonetheless, over five million children under five years died in 2018 alone (Hug et al., 2019). The current consensus is that further progress requires better care for children who are acutely ill. Two demand-side policies have played a key role in this effort: health care subsidies that provide free access to basic services, and home visits by community healthworkers (CHW), who monitor the health of the child and educate caretakers about symptoms.

The policy debate on how to fund primary care for children is longstanding, and a major push is underway towards subsidized or free care to achieve universal health care (UHC). The previous view, embodied in the so-called Bamako Initiative of African health ministers from 1987, had been that user fees are the only reliable way to maintain sustainability and quality of care ((e.g. Akin et al., 1987; Jimenez, 1995; Litvack and Bodart, 1993). Yet today, multiple policy and international aid organizations advocate for the universal subsidization of basic primary care for small children and mothers (e.g. Save the Children, 2008; UK Secretary of State for International Development, 2009; Jamison et al., 2013). By 2016, 33 African countries had developed financing strategies to achieve UHC (Barroy et al., 2016; Cotlear and Rosemberg, 2018), and in 2019, Mali’s Ministry of Health announced its intention to provide free children’s and mothers’ health care by 2022, at an estimated cost of $120 million, although it acknowledged funding gaps (Adepoju, 2019). While political turbulence and the Covid-19 crisis have stalled the reforms, this proposal will likely serve as a benchmark for future health policy in Mali. The removal of user fees has been accompanied by an expansion and formalization of community health worker (CHW) programs (Singh and Sachs, 2013). CHW provide preventive care and instruction, but also support curative care by guiding the use of formal care and monitoring children’s symptoms. Recent estimates count over 300,000 CHW in Sub-Saharan Africa and over 2000 CHW in Mali alone (Pascal Saint-Firmin et al., 2021; 1 Million Community Healthworkers Campaign, 2021), and the proposed Malian health 1
reform includes an expansion of the existing CHW network.

As African health care systems move towards more fully subsidized access – at considerable – cost it is important for effective policy making and the sustainability of the health care system to understand how exactly health care demand is affected. Are unfilled health care needs better met? What share of expenses may be paying for unnecessary treatment? In other words, we need to understand the allocation of acute care. It is known that subsidies for _preventive_ care can greatly increase demand and thereby reduce underuse (Ashraf et al. (2010); Dupas and Cohen (2010); Ashraf et al. (2013); Dupas (2014); Miguel and Kremer (2004), see also Kremer and Glennerster (2012)). But unlike for prevention, whether and when _acute_ health care is needed depends on the child’s health status, and it is up to the child’s caretakers at home to monitor symptoms and make decisions about utilization of care.

Increased utilization in response to lower cost can lead to a more efficient allocation of care if there are access barriers, for example if parents face credit constraints, underestimate the child’s health care needs, prioritize the costs to them or the family at large over the benefits to the child, or disregard the negative externalities of untreated infection. At the same time, subsidies may worsen the allocation and cause the waste of scarce resources if they lead parents to overuse care. The risk of overuse may be higher if geographical distance is not a primary barrier, as is the case in urban areas, and this is in particular a concern given that increased demand in response to UHC will in practice challenge existing supply capacities. In this context, it is important to understand to what degree CHW can improve the allocation of acute care, either on their own or in combination with a subsidy program. If information is an important barrier, monitoring and health education provided by CHWs have the potential to increase care seeking among children in need, while crucially also preventing or reducing overuse.

Evaluating the allocation of acute care is a challenge, because even when utilization is measured, most data do not contain information on care seeking in response to symptoms _._ 2
This makes it hard to interpret the existing evidence on the effects of demand side policies such as subsidies and CHW. For instance, one RCT in Ghana and a difference-in-difference analysis from South Africa document positive effects of user fee removal on some health outcomes for children (Tanaka, 2014; Powell-Jackson et al., 2014).<sup>1</sup> On the other hand, randomized trials of health insurance subsidies in Nicaragua, Mexico, and, most recently, India have resulted in few health or utilization effects, with often low uptake and retention (King et al., 2009; Thornton et al., 2010; Malani et al., 2021). As Malani et al. note in addition, even very large health insurance trials may not be powered to detect mortality reductions. Given this mixed evidence and the challenge of analyzing effects on rare events, information on _utilization conditional on the child’s health status_ is central to interpreting the demand response to policy interventions. If utilization is low, such data can for example disentangle whether there is no significant unfilled need for care or whether an intervention failed to curb underuse, perhaps due to unobserved supply-side effects. If utilization is high, it can help understand what share constitutes overuse vs. appropriate use, and whether complementary interventions such as CHW visits can help moderate any misallocation.

In this study, we measure effect of subsidies and CHW visits on healthcare demand conditional on illness, using novel health data collected at home. In partnership with the NGO Mali Health, we conducted a randomized control trial of a health care subsidy, which reduced primary care cost per formal care visit for children under five years by 72% on average, and a community healthworker (CHW) program, for which CHWs visit families biweekly and teach them basic health knowledge. The interventions were cross-randomized at the compound (dwelling) level, with a final sample of 1768 children from 642 compounds. Comprehensive quality controls at Mali Health’s partner clinics and the small sample mitigate general equilibrium effects and allow us to focus on the demand response to the interventions.

Nine months after intervention start, we collected nine weeks of daily data on children’s health and health care. We recorded all health consultations along with 14 symptoms that

> 1There are surprisingly few studies on the health effects of removing user fees, and review articles note the mixed quality of the evidence (Ridde and Morestin, 2012; Dzakpasu et al., 2013).

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are key in the detection of the main causes of child mortality in resource-poor settings, and used this data to construct illness spells and estimate demand for formal health care during the spell. Building on WHO guidelines for CHWs, we can derive when parents would be instructed to seek a formal medical evaluation by a doctor for their child, based on the duration and type of symptoms.<sup>2</sup> The guidelines we apply are used by public health systems and NGOs in over 80 countries, including our partner NGO Mali Health, who based their CHW training on them. Given that the optimal solution to the social planner’s problem of health care timing cannot be directly observed, their embedded assessment of when a child should seek formal care provide a better understanding of the effect of the interventions on the allocation of care. First, they deliver a ranking of symptoms by severity from a medical perspective, and therefore of relative value of care: a policymaker who wants to improve population health should aim to increase care seeking on days when care is required according to these guidelines over other days, all else equal. Second, they allow us to measure whether the CHW actually improve adherence to the guidelines they apply and teach (see also Section 3 for a discussion).

We organize our empirical results with the help of a framework that treats care seeking as a stopping problem, where parents decide to seek care on any day of a given illness spell conditional on spell length and symptoms. This captures the intuition that a set of initial symptoms will become more concerning as the illness persists or new symptoms arise. The value of waiting instead of seeking care immediately lies in the possibility of avoiding a facility visit altogether. We treat the WHO guidelines as the preferences of the policy maker. The policy maker may disagree with the parent at what point care should be sought, e.g. because of a different cost-benefit trade-off or a better understanding of the illness.

> 2We used a set of symptoms as well as care-seeking guidelines derived from the Integrated Management of Childhood Illness (IMCI) guidelines for community healthworkers. The IMCI was developed by Unicef and the World Health Organization. The version adapted for the use by community healthworkers is called C-IMCI, see WHO (2014); WHO Department of Child and Adolescent Health and Development (2005); UNICEF Health Section (1999); Rosales and Weinhauer (2003) and Arifeen et al. (2009). The guidelines are quite intuitive – for example, they prescribe immediate care if symptoms indicate a risk of malaria, but treat coughing or diarrhea as harmless unless they last unusually long or cause complications that point to pneumonia or dysentery (see section 3).

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Parents may therefore _overuse_ care relative to the policymaker’s assessment by going to the doctor too early, before care is required, or _underuse_ it, by going too late. Subsidies will lower the parents’ cost threshold and lead to earlier (more) care, which can represent either an reduction in underutilization or an increase in over-utilization. Providing information about the child’s health through the CHW has the potential to improve the allocation of care, but only if the parents’ cost-benefit assessment is aligned with that of the policymaker. This may make the CHW and subsidy interventions complements in achieving better curative care: essentially, the CHW provide advice on when to seek care, and the subsidy allows the parent to follow through on that advice.

– – We observe 3747 illness spells more than two per child with an average length of 6.4 days, of which 3.0 are “care-required” days. We find that overuse according to WHO recommendations is rare in the control group, even though there is surprisingly large scope: about half of all illness spells end without ever requiring care. By contrast, underuse is rampant, with a probability of care-seeking on a care-required day of five percent or lower. As a result, only 11 percent of spells that reach “care required” status actually receive formal care.<sup>3</sup> Overall, there is evidence that cost constraints are more binding than information constraints: the probability of care is on average more than five times higher on days when care is required than when it is not, suggesting that parents are able to differentiate illness severity as embodied in the WHO care recommendations, but they delay the doctor visit longer than recommended.

The subsidy increased the probability of seeking care on any day of an illness spell by 270 percent and the total value of care received by 77 percent. Importantly, this increase does not crowd out private spending on acute care, and the bulk of the increase occurs

> 3Note that the idea of seeing the doctor too early hinges on the possibility that an illness may pass harmlessly without entering “care required” status. Nonetheless, a spell that enters “care required” status may end without treatment. “Care required” should be interpreted to mean that the risk of harm or death is sufficiently high to warrant the cost of care, even if the illness may ex post pass without apparent harm. Indeed, more than 85% of illness spells in our sample end despite not receiving any formal care, including those that would have required care according to the IMCI guidelines by the WHO. This is not a sign that care was not required based on ex ante risk, only that it was not necessary for recovery ex post.

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on care-required days. More than 70% of the doctor visits that the subsidy generates are recommended by WHO guidelines. By contrast, providing health information to parents through CHW visits has no aggregate effects on demand in our sample, consistent with parents already being able to distinguish need for care. That said, for children up to one year old who receive the subsidy, differences in care seeking when care is required vs. not are less pronounced, and CHW visits appear to lead to an additional reduction in underuse of care. This suggests that the subsidy and information interventions may complement each other in improving care for the age group with the highest mortality risk. In an abridged 2014 follow-up survey, we find a corresponding increase in formal care for diarrhea among children under two years of age in the full program group only, although we are unable to classify whether this care is required by WHO standards (Dean and Sautmann (2023)). In the same follow up we also show that CHW improved prevention, and that the full program leads to significant reductions in diarrhea incidence two years later.

Overall, our results are consistent with cost constituting a primary barrier to appropriate care in our population, along with potentially higher information barriers for the youngest age group, in line with qualitative evidence on the greater difficulty parents have in recognizing illness in young children (Charlet et al. (2017)). The results are encouraging for the planned roll-out of subsidized care in Mali, supported potentially by CHW activities focused on better curative care for infants. Our findings complement a study in Bamako over the same period by the NGO Muso and the Malian Ministry of Health in 2008-2015 (Johnson et al. (2013, 2018)), which found reduced incidence of febrile illness and mortality after the introduction of CHW visits, user fee removal, and health system strengthening measures (although the program effects cannot be easily distinguished from other time trends). Our experiment directly shows changes in curative care seeking in response to the subsidy, pointing to better formal care seeking as an important pathway for positive health outcome effects. The CHW effects for acute care utilization call for further study, given the important role of CHW interventions in health policy in general and in Mali in particular.

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Our paper contributes to the literature on curative health care for children in low-income countries, aided by novel daily panel data. Research on policies intended to improve acute care is still fairly rare, with some exceptions, such as Powell-Jackson et al. (2014).<sup>4</sup> The problem of underuse is known to be severe for preventive goods, where the benefits are nearly universal, but also often abstract and far off. Our results show that underuse is similarly pervasive at the extensive margin of acute care, that is, the decision to visit a provider. We show that subsidies can substantially improve utilization at this margin. These results contrast with Cohen et al. (2015) and Lopez et al. (2022), who examine the effect of subsidies for malaria drugs at the point of purchase. Both papers document that there are significant rates of overtreatment that increase with subsidization. Similarly, patient audit studies have shown low-quality diagnostic and treatment choices, which often result in non-indicated or mis-targeted treatment, in a range of low and low middle income contexts Das et al. (2016); Currie et al. (2014). The reasons for overtreatment range from financial incentives for providers to patients demanding powerful medicines. This points to a challenging twin problem, where underuse at the extensive margin, in the form of too few contacts with the health care system, occurs simultaneously with overuse at the intensive margin, in the form of overmedication conditional on such a contact. Subsidies that increase access are more likely to induce overuse at the intensive margin when there are fewer cost controls such as those implemented by Mali Health. Thus, efforts to reduce the cost of care likely require accompanying measures to limit non-indicated or overly expensive treatment.<sup>5</sup> An innovation in this paper is to collect illness spell data at home and apply hazard models to estimate the effects of the interventions on care seeking conditional on health status. In section 5.4, we discuss the advantages of this approach for making out-of-sample predictions and for avoiding selection effects that would occur in demand analysis based on health system contacts, as found for example in clinic records of health consultations or health insurance

> 4Das and S ́anchez-P ́aramo (2003) have weekly data on illness and document the variation in demand that comes from differential illness incidence, but do not study demand interventions.

> 5Also related is work by Fischer et al. (2014), who test for learning and reference dependence as they vary prices in door-to-door sales of over-the-counter drugs (but do not focus on over- or underuse).

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claims. Since sickness spells can and do frequently end without treatment, selection bias is pervasive.<sup>6</sup> For example, we show that children with the subsidy who visit the clinic have been actually sick weakly _longer_ , because the treatment succeeds at bringing children to the clinic whose parents tend to wait longer before seeking care.<sup>7</sup> In a companion paper (Dean and Sautmann, 2023) we report data from a follow up survey conducted in 2014 (see above). Unlike the 2013 data we use here, the 2014 survey did not include detailed health diary data, and so cannot be used to study healthcare demand conditional on health state, the central point of this paper. Instead, it focused on measures of health knowledge, use of preventive care, evidence of malnutrition, and diarrhea incidence and care. Using this broader set of measures, Dean and Sautmann (2023) do identify positive CHW effects, including complementary effects of CHW and subsidies, as we discuss in section 5.3.

Section 2 describes the policy environment and the RCT intervention and survey design. Section 3 discusses our use of the IMCI measures. Section 4 introduces a framework for demand for curative care and the effects of subsidy and information policies and section 5 describes the empirical results. Section 6 concludes with a brief discussion of policy implications.

# **2 Study Background and Data Collection**

The Malian health care system builds on a network of community health clinics or _centres de sant ́e communautaires_ (CSCOMs). A CSCOM has typically one or two trained doctors on duty and sells prescribed medications through an attached pharmacy. At the time of

> 6In most other spell data economists use, e.g. unemployment spells, there are no equivalent sources of censoring.

> 7One of the few data sets we are aware of that has panel spell data, the 1987 National Medical Expenditure Survey for the United States, includes only spells that lead to medical care use or work absenteeism. Gilleskie (1998) recognizes the selection problem but can only address it by estimating the probabilities of unobserved health events parametrically. With administrative data e.g. from insurance claims (Manning et al., 1987; Finkelstein et al., 2012), illness spells can only be studied indirectly, by grouping claims into treatment episodes, see Stoddart and Barer (1981); Hornbeck et al. (1985); Keeler et al. (1988); Santos Silva and Windmeijer (2001). A public health literature studies the covariates of the delay in seeking care for specific diseases (see Storla et al. (2008) and Nguyen et al. (2010) for reviews).

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the study, CSCOM care was partly subsidized, but primarily funded by user fees, in accord with the Bamako Initiative from 1987, which advocated self-sustaining, decentralized public health care across West Africa. The public health care system is flanked by a private formal sector with higher prices, and informal sources such as market stalls (see below).

This study was conducted in Sikoro, a peri-urban area of the capital of Mali, Bamako. Most roads are unpaved, and many dwellings are not connected to the water supply or the sewage system. In and after the wet season the incidence of waterborne diseases (diarrhea) and malaria is highest. Mali has high rates of maternal and child mortality, especially in rural areas. Poor urban areas have better health care access and lower rates of mortality, but still often lack basic health services. Mali’s urban areas resemble those elsewhere in West and Sub-Saharan Africa, although literacy rates in Mali tend to be lower and fertility and child mortality rates higher (see Table C.1 in the online appendix). This makes child health interventions particularly important here.

# **2.1 Study Design**

Mali Health started the Action for Health (AfH) program in 2010 in collaboration with the two local clinics (CSCOMs) in Sikoro. The full AfH program combines subsidized health care and biweekly CHW visits.

The subsidy is administered via a personalized card that entitles the child to unlimited free consultations at a partner clinic, and free treatment and medication for any illness due to diarrhea/malnutrition, malaria, vaccine-preventable diseases, and respiratory infection (together causing the vast majority of child deaths outside of neonatal conditions). Families have to cover remaining expenses, for example for services that are not part of the standard treatment course for a given diagnosis.

The CHW have a range of tasks. Most importantly for this study, they track a set of simple health indicators and advise families when to visit a doctor or alternatively what care to provide at home, such as oral rehydration treatment (ORT) in mild cases of diarrhea. Additionally, they teach good health practices and dispense water chlorination tablets 9
for households with no access to clean water.<sup>8</sup> CHWs are recruited locally and their training follows the C-IMCI (Rosales and Weinhauer (2003)), a set of guidelines for community healthworkers that incorporates the WHO’s and UNICEF’s “Integrated Management of Childhood Illness” recommendations for when to refer children to formal health care (WHO (2014); WHO Department of Child and Adolescent Health and Development (2005); see also below).

Note that the role of the CHW may be more expansive in locations where formal care is less easily accessible. In rural areas, the CHW may for example provide basic medications and administer rapid tests. This is not the case in the CHW program we consider here. While CHWs programs were initially conceived as a way to reach patients in remote locations, many CHW work in practice in urban or peri-urban areas. Comprehensive data is sparse, but even in national CHW programs that target rural populations, a significant share of healthworkers operate in urban areas (e.g. 20% of CHW in Pakistan, Brook (2009)). In Mali, 6% of CHW work in Bamako ((Pascal Saint-Firmin et al., 2021)).

The research design took advantage of the second planned roll-out wave of AfH. Mali Health conducted a census in their expansion area in mid-2012 to enumerate all families with children under five years of age (or a pregnant mother) who satisfied a proxy-means test designed to select approximately the poorest third of households. After the baseline survey in 2012, data were collected in two surveys in 2013 and 2014 during the period of highest malaria and diarrhea incidence (September-November). All households identified in the census that were found at baseline were included in the random assignment to the different treatment groups and revisited in 2013.

In this study we use demographic, location, and household asset data collected in the baseline survey, and daily health diary data on children collected during the 2013 follow-up. The unit of randomization is the compound. A compound may house more than one household,

> 8We analyze effects of the two interventions on preventive care and longer-term health in a separate paper. We find no impact on illness incidence in the 2013 data used here, and measured chlorination levels in 2014 do not significantly differ between treatment groups. For more detail, see section 5 below and Dean and Sautmann (2023).

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and typically consists of a few rooms around a common courtyard with shared latrines and other facilities. After stratifying compounds by average household assets, number of eligible children, and location, each was assigned to the subsidy-only group, the CHW-only group, the full treatment group, or the control. The healthworkers in the two CHW treatment groups were trained and managed separately, to avoid spillovers.<sup>9</sup> The focus of this study is to understand the demand for health care of the typical quality a local medical professional (trained and certified in-country) can provide, holding the supply side constant. The reader may have two concerns in this regard. First, quality of care in public health care systems in developing countries is often low, which may mean in practice that patients receive care from an untrained individual or not at all (e.g. if the doctor is actually absent). Such quality problems affect how desirable the use of the health care system is, both from the parents’ and the policymaker’s point of view (see e.g. Das (2011); Das and Hammer (2005, 2007, 2014); Das et al. (2016); Das and Sohnesen (2006); Leonard and Masatu (2010); Leonard et al. (2002)). The clinics in this study were financially supported by Mali Health during the study and subject to regular quality control. We assume that the standard of care provided reflects the level of care that the WHO expects when they formulate treatment recommendations for low-income countries. The second concern is that subsidizing care might create supply side responses to our interventions, such as demand inducement and moral hazard by doctors. While we cannot rule out that such effects occurred, Mali Health controlled costs in the subsidy group using treatment guidelines for common diagnoses, spot checks on diagnosis and prescriptions using bills submitted to Mali Health, and the clinic’s treatment records and accounting. We also found that the subsidy did not change the average value of services received per visit (see section 2.3). Lastly, the study sample is small relative

> 9It was not possible to assign healthworkers across the entire intervention area while keeping their travel distances manageable. Mali Health therefore paired healthworkers of similar experience and quality, and one of each pair was assigned to the healthworker only or the healthworker and subsidy group. The pairs were then assigned in an overlapping pattern by stratum. For example, pair 1 and pair 2 were each randomly assigned half of the families in the same stratum, and half of the families in a different stratum, each shared with another pair of healthworkers. In this manner, each stratum was assigned four healthworkers (two in each treatment group), the quality of the healthworkers in the treatment groups was matched, and no two strata had identical sets of healthworkers.

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to the overall population, mitigating general equilibrium concerns.

# **2.2 Sample Population**

Mali Health identified 1804 eligible children in the census. At baseline, 1732 children were included in the study. The roll-out of AfH started three months after the baseline in early 2013. By the second survey round in Fall 2013, there were three refusals and an additional 162 children could not be surveyed, including five who passed away.<sup>10</sup> The remaining sample in 2013 contains 1567 children from 990 households in 642 compounds. Attrition between baseline survey (and treatment assignment) and follow-up is 8-11%; the group differences are not significant. At follow-up, 201 children were newly reported by the caretakers of existing study children. Newborn children in the treatment households were enrolled in AfH either by the healthworkers, or by a program officer who visited study households every three months. Since the caretakers’ treatment choices are our outcome of interest, we consider these children part of the sample. We only included newly reported children 3-12 months old. Table C.2 in the online appendix shows the sample over the study period.

Table 1 shows sample characteristics and balance. We find few significant differences between the treatment groups. There are on average more than six people in a household, due in part to polygamous marriages. About half of the household heads are literate and 73% speak the _lingua franca_ Bambara. Households report on average about US$6600 (median $1070) in assets, with an owner occupancy rate of 41%. Only 13% of household heads earn a regular salary. The closest source of formal health care is on average 546 meters away (less than 0.4 mile). The average child age is 2.7 years. The study children are well below agetypical weight for height (normalized using W.H.O. reference distributions). We control for these covariates in the analyses below, except weight for height, which may be an outcome of parents’ health behaviors.

> 10Our sample population moves a fair amount, but households that relocated inside the program area were followed and remained eligible for Action for Health; only children who left the area entirely or could not be tracked are not included.

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# **2.3 Health Calendar**

We collected daily information on symptoms, health consultations, and medications taken in weekly visits over the course of nine weeks from the child’s primary caretaker (usually the mother). Caretakers could use pictorial diaries (Figure B.1 in the online appendix) to mark off symptoms and health-related events. The surveyor then reconstructed the child’s health history during the visit. There was a concern that parents would respond to the observation by the surveyors, for example by watching their children more closely. Ultimately this drawback was outweighed by the benefits of detailed coverage without the problems associated with long recall periods (Das et al. (2011)). The surveyors were carefully instructed not to comment on the child’s health, and to emphasize that they had no medical qualification. While we cannot rule out all Hawthorne effects, we believe our precautions mitigated them, combined with the fact that the calendar data collection had been piloted over six weeks after the baseline in 2012, thus creating some habituation.<sup>11</sup>

**Symptom Calendar.** The list of symptoms was designed in collaboration with Mali Health staff and based on the C-IMCI guidelines by the WHO (see also below). The C-IMCI is designed for use by CHW who have no prior medical experience. It consists of simple rule charts that use important and easy-to-spot symptoms to broadly classify a child’s illness and establish need for care. These properties mean we can collect symptom reports from mothers and surveyors who are not medically trained. The survey explicitly asks about convulsions, fits, or spasms; lethargy or unconsciousness; inability to drink; vomiting; coughing; difficulty breathing; more than three loose stools; blood in the stool; sunken eyes; and unusually hot (cold) skin. We constructed four more symptoms from the “other” category: skin conditions (e.g. spots, rash, itch), cold symptoms, ear pain, and injuries. We observe on average 60

> 11Since the survey took place towards the end of the rainy season, illness rates were declining over the course of the panel, so we cannot assess if a time trend is due to a fading Hawthorne effect. However, in the baseline pilot of the health diaries, initially elevated symptom reporting rates stabilized at a lower level after the first week of surveying and then declined at a slower and more continuous rate (data available on request). This is consistent with data on doctors, who rapidly return to unobserved length and quality of doctor-patient interactions even when observed by other doctors (Das et al. (2008)).

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days per child, and children exhibited symptoms on average on 18 days. Cold symptoms, coughing, and unusually hot skin were most frequent. Section 3 below gives more detail on how symptoms translate into illness spells and care-seeking recommendations. Appendix B provides a description of how the health calendar information was collected.

**Consultations.** At each surveyor visit, mothers were asked if they discussed their child’s health with anyone in the preceding week, and the surveyors recorded role or occupation of the person seen, facility, waiting time, treatments received and costs incurred.<sup>12</sup>

Table 2 provides an overview of these health care “consultations”, grouped into CSCOM visits; private doctors, hospitals or clinics; pharmacies; and purchases from informal providers. Parents occasionally see several providers in the same day, for example when they get a prescription at a clinic and then purchase medication elsewhere. We group visit records that are linked in this manner and classify them by the “most formal” consultation in the group. The value of care is approximated using the full price of care.<sup>13</sup> Note that the sizable difference in costs and value of care even without a subsidy is due to families seeking outside help to pay for the child’s care, e.g. from relatives or friends.

The table shows that the subsidy program reduced the average cost of a visit to the household by 71%, from CFA 2850 to 933 (476 CFA equaled 1 USD in 2013). Among the formal-care visits in the control and CHW-only groups, 14% and 12% were reported as free, vs. 70% and 77% in the subsidy groups, respectively. Parents paid if they visited other providers than the two partner clinics or did not claim the AfH benefit, or the child received services not covered under AfH.

CSCOMs provide similar value per visit with and without the subsidy (CFA 3652-3807),

> 12In order to facilitate the recording of drugs prescribed, purchased, and taken, mothers were asked to keep the packaging of any medications their child took. The surveyors could search and fill the drug from a database of brand and generic names of about 300 medications commonly sold in Mali.

> 13Value of care is imputed using prices recorded for consultations and medications where the respondent reported having paid the full price themselves. We use these prices combined with provider information, brand name, and point of purchase to construct the median price by source. If the respondent reported another person paying for care or was in the subsidy group, we compare the sum of median prices for the services received with the private expenses the respondent reported and use the higher value. This reflects the cost of care to the family, the NGO, or a third party.

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suggesting that the type and quality of health care received, conditional on visiting a clinic, is similar, and that doctors did not respond to the subsidy by raising treatment costs or prescribing unnecessary treatments. This could be due to Mali Health’s quality and cost control, implemented after initial increases in costs during earlier program roll-outs.

Wait times at the CSCOM are somewhat longer with the subsidy than without (47 vs. 36 minutes). These are unlikely to be congestion costs as all patients live near the same two clinics. It could be that subsidized patients receive more comprehensive medical tests such as malaria tests, see different personnel, visit at busier times of the day, or wait longer than other patients, perhaps because their symptoms are less urgent.

Private providers are pricier (CFA 5371), prescribe more medications, and have longer wait times than CSCOMs (75 minutes), whereas a typical pharmacy visit is shorter and less expensive (12 minutes wait time, cost of CFA 1474), has fewer antibiotic and antimalarial sales, and only 61% of the time occurs on a day when care is required by IMCI standards (compared to 74-79% with formal care). Purchases from informal sources such as peddlers, stalls, healers, or Islamic marabouts come at an even lower cost (CFA 241, 2 minutes wait) and with lower rates of prescription drug purchases, and 58% of the time on “care required” days. Overall, the data suggest that parents use pharmacies and informal sellers more often on “early for care” days to support home care with remedies such as pain killers or cough syrup. For our analysis, we do not treat purchases from informal sellers as demand for (formal) health care. We also do not count pharmacy purchases not associated with a doctor visit because pharmacists do not carry out a diagnosis. Pharmacy visits alone are also not subsidized by AfH or promoted by the CHW.

We also recorded CHW visits if they were mentioned among the consultations by the parent (we did not explicitly ask about them to prevent reporting bias from associating the survey with Mali Health, for example if parents believe we are monitoring the healthworkers). Parents reported 0.46 visits per child in the CHW-only group, but 1.64 in the full program group (not shown). This may raise concerns about differential program delivery, but we 15
found similar effects in the 2014 follow-up survey that suggest measurement error.<sup>14</sup>

# **3 IMCI Classification and Illness Spells**

Throughout the paper, we identify “overuse” and “underuse” of care _relative to the IMCI guidelines_ proposed by the WHO. The C-IMCI is part of the community and family arm of the IMCI itself, the WHO’s and UNICEF’s primary children’s health program, which has been adopted by over 80 countries (Bryce et al. (2004)). The C-IMCI goes through a set of simple checks in order to spot danger signs and classify symptoms into gastrointestinal illness, respiratory illness, malaria, and so on. It targets the causes of about 70% of child deaths.<sup>15</sup> All diagnostic tests can be operationalized in the field. The effectiveness of these checks in identifying the underlying disease was rigorously tested (see Gove (1997) and the studies therein). Based on the duration and severity of symptoms, the C-IMCI algorithms make recommendations for when formal medical care should be sought (see below). The C-IMCI is explicitly tailored to a developing-country context, and we assume that its careseeking recommendations take into account factors like the expected quality of care and resource constraints.<sup>16</sup> What justifies the use of the IMCI to assess need for care? Ultimately, we are interested in assessing the allocation of acute care. However, the optimal allocation is unobserved.

> 14In 2014, we prompted parents to report visits from any NGO workers who measured the child’s weight or height in the last 2 weeks, and we asked whether the child had a card entitling them to receive free health care at the clinic (still avoiding direct mention of Action for Health). We found that 12% more CHW visits were reported in the full program group than in the CHW-only group, but we also found that 10% of families reported visits in the subsidy-only group, and we cannot reject that the sum of the CHW-only and subsidy-only effects equals the full program effect. Moreover, we found both a greater share of families reporting the subsidy card in the full-program group than the subsidy-only group and a share of respondents reporting the subsidy in the CHW only group (Dean and Sautmann, 2023). Together, these numbers suggest misreporting or misidentification of aspects of the different program components of Action for Health. It is possible that some of the reported CHW visits in the full program group were clinic staff making home visits, which suggests some measurement error in formal care received.

> 15The five main causes are diarrhea, pneumonia, measles, malaria, and malnutrition. According to Unicef estimates, around 50,000 children under five died in Mali in 2017, 17% of whom died of pneumonia, 10% of diarrhea, and 14% of malaria.

> 16Mayo Clinic recommendations for children in the United States, for example, have lower thresholds for seeking medical advice. They advise to call a doctor after 24 hours of diarrhea, and state on fever: “When in doubt, go ahead and call your child’s doctor, whether you think your baby’s temperature is abnormally high or abnormally low.” (Mayo Clinic (2014, 2016)).

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Demand for children’s health care is likely distorted by market imperfections such as credit constraints. Parents may not fully internalize their children’s welfare or externalities due to contagion. Moreover, households may not be able to interpret symptoms and assess the value of visiting the doctor in different health states. All of these imply that parents’ health care demand is not socially (or even privately) optimal. At the same time, solving the social planner’s problem of optimal resource allocation requires too many parameters for which there are no good estimates, from the production-side trade-off between supplying health care and other goods, to the true preferences of the household.

Given the correlation of the IMCI diagnostic algorithms with serious illness, they provide some guidance for assessing welfare, even if imperfectly. First, they constitute an approximate solution to an optimization problem: conditional on the limited information an outside observer can use to make a recommendation, for an organization that is prepared to devote resources to children’s health, which mapping from health states to care utilization has the greatest effects on expected mortality and morbidity? At a minimum, this provides a _ranking_ ; from a medical standpoint, care in the “care required” state is more valuable than otherwise, even if the total amount of care is not optimal.

Many policymakers may indeed agree that all children should receive care when the IMCI recommends it. For example, a child with malaria symptoms should be seen by a doctor regardless of parental preferences or wealth. Moreover, it is unlikely that parents have private information that indicates that no care is needed even though the IMCI recommends care.<sup>17</sup> The “care required” designation may thus be considered a lower bound for underuse.

Last but not least, comparing parents’ behavior to the IMCI recommendations indicates to what degree they follow the CHW’s care seeking advice. Thus, even if they do not characterize the social optimum, the WHO guidelines capture important information, giving them a role in evaluating the effects of demand-side health care policies. Studying demand in detail in this manner complements studies that measure mortality or health outcomes.

> 17The reverse seems more plausible, i.e. parents may observe symptoms that require medical care but are not covered by the IMCi, such as injuries.

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While the objective of the interventions is of course ultimately to improve health, only direct information about health care utilization can teach us whether and how parents’ behavior changes and if there are problems with targeting such as use of care when the child is not actually sick.

**Classifying illness spell days.** The IMCI charts define an intuitive set of rules to determine need for care. For instance, cough and cold symptoms typically point to a viral infection and do not require medical care, unless they persist for 14 days or longer, which potentially indicates a more serious illness like tuberculosis. Diarrhea alone can be treated with home remedies, but in the presence of sunken eyes (dehydration) or blood in the stool (dysentery) requires immediate attention. Applying these rules to symptom records, every day in an illness spell can be classified as either an “early” day (i.e. before care should be sought) or a “care required” day (on or after the day at which care should be sought). We focus on “pre-care” spell days up to the first contact with the health system, since symptoms might be affected by treatment.As an example, consider a child who is coughing for three days, on the third day develops a fever and takes cough drops from a peddler, is prescribed paracetamol at the CSCOM on day 5, and symptoms subside after day 8. This is a five day pre-care spell with three care-required days (days 3-5). If the child had not received care, it would be an eight-day spell, with days 3-8 being care-required days. Appendix B describes the approach in detail.

Table 3 shows untreated days per child (possibly in several illness spells), and the percentage of days on which each symptom is present, in total and split into early and care-required days. On average, 16.3 of 59.8 observed days per child (27%) were (pre-care) illness spell days, and on six of those there was a need for care. Convulsions, lethargy, inability to drink, and vomiting should trigger immediate care, so they always occur on care-required days. The biggest contributor to need for care is fever (unusually hot skin). While cold symptoms and cough rarely in themselves indicate a need for care, they often occur in conjunction with other, more serious symptoms and therefore the spell day is still classified as “care required”.

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The data show that the children in our sample are often ill with symptoms that point to dangerous conditions like malaria, but also often experience less severe symptoms, so they are in need of a doctor evaluation only 37% of the time.

The symptoms we collected do not cover all possible illnesses, but focus on conditions that mothers and surveyors can easily recognize. As an example, breaths per minute and “chest in-drawing” are IMCI indicators of respiratory illness, but require experience and training (and a stopwatch) to measure. We also interpret the IMCI guidelines conservatively, for example for diarrhea (where the recommendation is for a CHW to return to the family after five days to check on the child’s status, but we assume a need for care only when complications arise). Moreover, the IMCI itself is not exhaustive. As a result, our classification as “care required” contains noise and generally represents a lower bound for need for care.

Appendix Table C.5 summarizes the observed pre-care spells. Due to spells recorded in the first week of the survey or after survey interruptions, 464 illness spells are potentially left-censored, that is, they may have started before the first recorded day. We exclude these spells from our main analysis.<sup>18</sup> This leaves us with 3747 spells, with an average length of 6.43 days (6.92 st. dev.). Within these spells, there are on average 3.4 early days and 3.0 care-required days. Only 503 spells, or 13.4%, end with a consultation; in all other spells, the child eventually recovers from the illness unaided. Note that spontaneous recovery, that is, recovery without use of formal care, is common and an important feature of acute illness in general. It is the reason that some acute care may be overuse: section 4 describes that “waiting out” an illness and avoiding health care expenses can be optimal if unaided recovery is possible, and in this case it may be overuse to visit a doctor early. By contrast, if recovery could only occur with medical care, “too early” use of acute care would not be an issue; care should always be provided immediately, and any delay would constitute underuse.<sup>19</sup>

> 18 Left-censoring means that need for care cannot be determined, because the day of the spell is unknown. Left-censored spells are on average 10.2 days long. Our main results are robust to treating these spells as not left-censored (see also below).

> 19We discuss below that recovery is a form of right-censoring, because we do not observe when parents would have sought care if the child had _not_ recovered. Right-censoring is here an integral part of the data generating process, and important for understanding health care demand, especially because a large number

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Conversely, it should also be noted that ex post observed spontaneous recovery does not preclude that a given spell should have received care ex ante, based on the risk of a more severe illness that might have serious consequences if left untreated.

Appendix Table C.3 gives details on all illness spells and symptom-days per child. A child experiences 2.35-2.47 spells on average, and exhibits symptoms about 30% of the time. In this survey round, we did not observe significant differences in incidence across treatment arms, a fact we discuss further in section 5.3. Although our approach of estimating conditional demand mitigates selection concerns, we consider this fact an advantage for internal validity, because it ensures that we compare demand in response to similar illness patterns. However, to the extent that the program has long-term incidence effects, _and_ incidence affects parents’ decisions to seek care in response to the same symptoms, it could mean that our analysis is not capturing the (longterm) effects of the interventions on conditional health care demand in full.

# **4 Demand for Acute Health Care**

We sketch a simple model for the decision to seek health care in response to acute illness, in order to derive notions of overuse and underuse and to make predictions for the impact of AfH policies on health care demand. All formal statements are in Appendix A. The framework is based on two observations. First, demand for acute care arises in response to a negative health shock that causes discomfort – and risk of harm – to the child until it passes. Second, there is learning about the severity of an illness over time. For many illness spells, it is likely that the child will recover fully without the aid of a clinic visit, and it is worth tolerating some disutility from sickness while waiting. As time goes by, the probability of recovery without treatment decreases, and is optimal to seek care to avoid further suffering (or worse). Thus, the demand decision becomes _when_ during an illness spell to visit a formal care provider.

Formally, denote the parent’s belief that the illness will continue another day by _πt_ . Each of spells do not end in formal care.

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day _t_ of the spell, the parent decides to either visit a provider who can evaluate and treat the child, or wait another day, and with probability (1 _− πt_ ) the illness passes on its own. With uncertainty about the underlying illness, _πt_ is increasing in illness days _t_ due to Bayesian updating: continued symptomaticity reveals an illness with lower probability of recovery (Lemma 1). This implies a decreasing value of postponing treatment, since it becomes less likely that the child will recover on their own. As a result, parents optimally follow a simple cut-off strategy: seek care when belief _πt_ exceeds some threshold _K_ (Proposition 1). _K_ depends on the benefits as well as the monetary and non-monetary costs of care _C_ . _C_ is increasing in the (expected) price of care.

We assume that the policymaker faces a similar stopping problem, although with different parameters _π_<sup>_∗_</sup> and _K_<sup>_∗_</sup> . This is consistent with expert care seeking recommendations such as the IMCI (see previous section), which take duration of symptoms into account as an indicator for health care needs. Such recommendations cannot be explained without learning and the possibility of recovering without treatment: if _πt_<sup>_∗_(or</sup><sup>_πt_)wasconstantovertime,</sup> the choice to seek care would be a simple yes/no decision on the first day of illness, because getting earlier care for the same illness is always better.

This model of the decision to seek care illustrates why we need daily data to analyze the allocation of health care. On the one hand, even when a spell that enters “care required” status eventually receives care, there can be underuse. On the other hand, a doctor visit when care is not yet required constitutes overuse, even if the spell later turns worse and requires care (and note that early in such a spell, a health care provider may well ask the parent to return at a later date without deciding on a treatment course). Note also that, even if it is optimal to seek care at time _t_ , it can still mean that a child who does not receive care will end up recovering, because the main benefit of care may consist in avoiding a catastrophic outcome that has nonetheless low probability. Conversely, a child who receives treatment before the cost-benefit trade-off is optimal may still experience fewer symptoms and overall lower disutility from illness. Thus, setting the threshold _K_<sup>_∗_</sup> requires the policymaker to 21
make normative decisions about (opportunity) costs vs. expected benefits, and observing health outcomes alone, or even the mapping from health care decisions into health outcomes, is not necessarily informative about the optimal level of care.

**Effect of a subsidy:** Consistent with current policy consensus, we assume that the parents’ treatment threshold _K_ is weakly greater than that of the policymaker _K_<sup>_∗_</sup> : the policymaker may evaluate the financial costs differently from a parent with credit constraints, or she may incorporate the long-run human capital benefits or reduced infection risk for others. Assume for now that parent and policymaker share beliefs _π_ . This case is illustrated in Panel A of Figure 1 (numerical details in the appendix). Learning over time implies _πt < πt_ +1 (solid black line). If the child is still sick in period _t_ + 1, the policymaker would like her to receive care because _πt_ +1 _> K_<sup>_∗_</sup> , but since _πt_ +1 _< K_ , the parent will not take her to the doctor. This is an incidence of _underuse,_ or visiting the doctor _too late_ relative to policymaker preferences.

A subsidy lowers the care-seeking threshold of parents by reducing the treatment cost _C_ . Panel A illustrates how this increases utilization in all periods and can reduce underuse in _t_ + 1. However, if _K_ is reduced by too much, for example to _K_<sup>_′_</sup> , _overuse_ , or _early_ care, may now occur in _t_ (since _K > πt > K_<sup>_′_</sup> ). We would in general expect that a decrease in healthcare costs leads parents to seek care for the most serious conditions first (i.e. for spells in which _πt_ is high), and therefore reduces underuse of care before introducing overuse. Nonetheless, in a heterogeneous patient population with different cost levels or different (perceived) benefits of care, a subsidy policy may eliminate some, but not all, underuse, yet simultaneously create some overuse.

**Effect of symptom information:** Disagreement between the parent and the policymaker may additionally occur due to differences in beliefs about the recovery probability, so that _πt_ = _πt_<sup>_∗_.Ifthesedifferencesareduetotheparents’poorinformation,asituationmight</sup> arise as in Panel B of Figure 1: the policymaker can distinguish a more severe and a less severe illness ( _π_<sup>_∗S_</sup> and _π_<sup>_∗L_</sup> ) while the parent holds average beliefs _π_ . Examples are diarrhea

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spells with or without signs of dehydration, or a generalized fever vs. a fever combined with symptoms that point to malaria (see also appendix). Lack of information about what symptoms indicate need for care can lead to both _overuse and underuse,_ because parents take the same action regardless of illness severity. For example, at _K_ the parent will always – seek care in period _t_ + 1 but for many thresholds _K_<sup>_∗_</sup> , including _K_ , the policymaker would prefer care for the severe illness in _t_ , but no care for the less severe illness in _t_ + 1.

Subsidies cannot address overuse and underuse that simultaneously arise from poor information, because they will only monotonically increase utilization. By contrast, a successful information policy may improve allocative efficiency. In particular, take behavior at the policymaker’s threshold _K_<sup>_∗_</sup> : an uninformed parent with belief _π_ will always seek care in _t_ and _t_ + 1, but an informed parent will exactly share policymaker preferences and delay care in period _t_ in the case of the less severe illness. Thus, if an information intervention better _aligns the beliefs of the parents with those of the policymaker_ it may complement a subsidy policy that aligns _K_ and _K_<sup>_∗_</sup> by directing care (only) to the children who need it.

To summarize, if the main barrier to a good allocation of care is high cost, parents in the control group will seek care primarily on care-required days, but we will see significant underuse. If poor information is an important allocative barrier, then care seeking will be only weakly correlated with “care required” status, and we may see both overuse and underuse (observation 1). A subsidy will increase utilization, thus decreasing underuse, but also potentially increasing overuse (observation 2). If the subsidy aligns the cost thresholds _K_ and _K_<sup>_∗_</sup> and the CHW intervention aligns beliefs _π_ and _π_<sup>_∗_</sup> , then the two interventions may complement each other in decreasing both underuse and overuse (observation 3). Observation 3 is a motivation for combining subsidies with information policies: subsidized care gives families access to health services, while better information ensures that they make efficient use of this benefit.

Note that we focus here only on the potential role of the CHW in providing symptom information. In Action for Health (as in most CHW programs), the CHW have other tasks, 23
in particular in prevention. This is likely to have effects on the incidence of at least some types of illness in the longer run. However, we think of information provision as the main potential channel through which CHW visits may affect demand for formal care conditional on an illness spell occurring. We discuss other effects of the CHW briefly in section 5.3. In that section we also discuss reasons why the CHW intervention may not be effective in improving the allocation of care: as can be seen in the graph, whether information unambiguously improves care utilization from the policymaker perspective depends on whether the cost cutoffs _K_ and _K_<sup>_∗_</sup> are aligned. This is because upon receiving information that alters their beliefs _π_ , parents will still align their behavior with their own cutoff. This may be a positive or negative change from the policymaker perspective. Lastly, it is possible that the effects of the interventions depend on the characteristics of the child, and most importantly, the child’s age; in particular, the youngest children are more vulnerable to illness, while at the same time symptoms of illness are more difficult to recognize and interpret. For these reasons we also report on an exploratory analysis of the differences in treatment effects by age.

# **5 Results**

The degree to which the positive allocation effects of subsidies and information provision are realized depends on the severity of the various constraints families experience, and is therefore an empirical question. We begin the analysis by verifying that subsidies increase the unconditional utilization of acute care. Our main results are on the impact of our three treatments on health care use _conditional_ on health status.

## **5.1 Unconditional Health Care Utilization**

Table 5 shows regressions of a set of outcomes at the child level on indicator variables for “receiving the subsidy”, “receiving CHW visits”, and their interaction. All regressions include child and household covariates and stratum fixed effects, and standard errors are clustered at the compound level. Columns (1) to (3) show the number of formal consultations per child, in total and split into CSCOM and other formal care. (4) to (6) report the effect of 24
the program on private expenditure per child, again first in total and then for CSCOM and other formal care separately. Column (7) reports wait times as a measure of non-monetary cost of care, and (8) and (9) report total and CSCOM _value_ of care (see above).

Subsidies increase the number of formal care visits by 163 percent (0.354 visits) with only a small substitution effect from other formal care to CSCOM care (columns (1)-(3)). With a price decrease of 67 percent (Table 2), this implies a price elasticity of demand of -3.7. The subsidy does not crowd out private expenditures but fully translates into additional health care consumption (columns (4)-(6)). The subsidy groups also pay higher non-pecuniary costs for their child’s health care, as evidenced by the increase in total time spent waiting by about 17 minutes per child (column (7)). The total value of care consumed increases by 77 percent (CFA1291) relative to the control (column (8)).

In stark contrast to the effects of the subsidy, the CHW intervention has no average effects on unconditional health care use and there are no interaction effects between subsidies and CHW visits.

# **5.2 Health Care Use Conditional on Need for Care**

While the unconditional effect of the subsidy is encouraging, the increase in formal care could signify a real increase in access for sick children, but also unnecessary doctor visits. The CHW may have no impact on care seeking at all, or improve the allocation of care without changing overall use. To differentiate these possibilities we examine conditional demand.

**Analysis.** We reference three pieces of analysis repeatedly.

Table 4 shows health care utilization by need for care at the level of the consultation (panel 1) and the level of the individual illness spell (panels 2-4). This table gives an overview of the incidence of need for care and the care that children receive in response.<sup>20</sup>

> 20Table C.4 in the appendix shows a complete breakdown of all formal consultations during and outside of a spell. Only 5% of visits occur outside a spell, reported by mothers as follow-ups, visits for prevention (e.g. vaccinations), or “other”. Of the remaining consultations, 88% (or 494) constitute the first formal visit during a spell of illness. These “acute care” visits are the target of the WHO care-seeking recommendations. Visits after a first acute visit cannot directly be judged using the C-IMCI standards. However, almost all of them occur after a spell entered “care required” status and when the child is still exhibiting symptoms. This might indicate a more hands-on treatment approach by doctors when care is subsidized. Anecdotally,

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The share of spells that enter care-required status, and the share of consultations occurring when care is required, are a function of healthcare demand combined with the incidence of different types of spells (implying that the number of “ care never required” and “care ever required” pre-care spells in Panels 3-4 are endogenous). Spell level data masks underuse; a child could go without needed care even if care is eventually sought. We also cannot know which of the spells in the “care never required” category that received care would have eventually entered care-required status. Overall, it is not possible to define “overuse” and “underuse” of care at the spell level, and spell level data does not allow us to isolate the effect of the policy interventions on parents’ care-seeking behavior. To understand demand for care, we instead estimate the daily _hazard_ of formal care, that is, the daily probability that the parents seek care on pre-care spell day _t_ , conditional on not having seen a doctor yet.

Figure 2 shows the simple average hazard of seeking care on each spell day, by treatment group and “care required” classification, estimated by regressing care seeking on a full set of interacted indicators for day of the pre-care spell, care-required status, and treatment group.

Since this non-parametric approach does not show treatment effects in a concise way, we additionally estimate a Cox proportional hazard model, shown in Table 6. Formally, the hazard at _t_ conditional on _Xit_ is This assumes an unrestricted baseline hazard _λ_ 0( _t_ ), where the independent variables _Xit_ shift the probability of seeking care proportionally on every day _t_ of the illness spell. As we show in Appendix Figure C.1, the proportionality assumption delivers a good description of our data. The estimates control for baseline family and child characteristics and include CSCOM doctors lament that it is very hard to convince patients to come back after a first visit, so that they often prescribe treatments for a range of conditions at once. We also know that patients often do not buy everything prescribed at a doctor visit. With subsidized care, doctors may be able to ask the parents to return to try a different treatment approach if the child’s condition has not improved.

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stratum fixed effects.<sup>21</sup> Panel A of Table 6 reports the coefficient estimates. In column (1), treatment effects are captured by indicators for receiving biweekly CHW visits and for receiving the subsidy, and their interaction. In column (2), these treatment indicator variables are additionally interacted with an indicator for “care required” in order to understand whether parents behave differently on days on which care is or is not required. This is in particular important for the effect of information on care-seeking; if CHW visits reduce both overuse and underuse, the effect should be negative when care is not required, but positive on care-required days.

Panel B reports the hazard ratios for each subgroup formed by the dummy variables above, relative to the control group in (1), and the control group on days when care is not required in (2). For example, the row “Full program, care not req.” shows that the daily probability of care seeking on days when care is not required is 3.583 times higher in the group that receives both subsidy and CHW visits than in the control group. Appendix Table C.7 replicates the estimates with left-censored spells included as a robustness check (treating these spells as uncensored); the results are similar.

**Results.** We begin by observing that the illness profiles and pre-existing patterns of demand in our sample show that there is considerable scope for both underuse and overuse (IMCI based): out of 955 spells in the control, 50.2% never required care. These are unlikely to simply be illness spells that were about to become serious but received treatment early, since only 3.8% of care never-required spells received formal care. Conversely, in only 11% of the 476 spells where care was required, the child (eventually) saw a doctor.

The spell data also confirm that the parents’ decision is a dynamic problem. 210 out of 476 “care ever required” spells entered care-required status only later on. Put another way, out of 689 spells that initially did not indicate a need for care, 30.5% eventually did require care (not shown). Yet in most cases, even without treatment the spell did end eventually, 21Of these controls, only assets affect health care demand positively and significantly, while the distance to the closest provider as a measure of non-monetary costs has the expected (negative) sign, but is not significant.

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illustrating the important role of spontaneous recovery. This highlights the challenge of improving child health outcomes through acute care: it is necessary to treat a large number of spells in order to prevent the small portion that may become truly dangerous to the child.

Across treatment arms, a fairly stable 74-80% of consultations occur when care is required, but in absolute terms there are an additional 80 (91) “care required” consultations in the subsidy-only (full-program) group compared to 22 (32) additional “early” consultations. In the control group (CHW only group), only 1.9% (1.4%) of all spells do not require care but receive a consultation, whereas 5.5% (5.7%) do require care and end in a consultation. In the subsidy arms, these shares rise to 4.4-5.2% vs. 14.7-14.9%. Put differently, with the subsidy, overuse occurs in only around 5% of spells, but conditional on requiring care, the chance that a spell eventually ends in a doctor visit rises from at most 11% to 29% (Panel 4).

To examine care-seeking behavior and treatment effects on demand for care, we move to the hazard of daily care-seeking. We organize our results using the observations from Section 4. Our first result here is that parents behave very differently on early and care-required days. Panel B, Column (2) of Table 6 shows that a parent in the control group is 5.5 times more likely to seek care when it is required according to the C-IMCI. In Figure 2, the probability of care-seeking on any “early” day in the control group is below one percent(Figure 2). However, even when care is required the probability of a visit is never higher than 6 percent. In other words, there is significant underuse but very little overuse. These results suggest that information is not a primary barrier to care. Parents are well able to gauge the relative seriousness of their child’s illness, more so than perhaps expected based on the design of the acute-care component of typical CHW programs, which emphasize regularly checking the child’s health status. High levels of underuse do suggest that parents face a high cost barrier (Observation 1).

Next, we examine the impact of the subsidy (Observation 2). Panel A of Table 6 shows that the subsidy significantly increases care-seeking, by 270% in the subsidy-only arm and by 291% in the full-program arm (Table 6 Column (1)). The absolute effect of the subsidy 28
is much larger when care is required: relative to the reference group of “early” days in the control, parents seek care 14 (17) times more often when care is required and they receive the subsidy (full program) (Panel B, column (2)). By comparison, when care is not required, care is sought only 2.5 (3.6) times more often. The difference can also be seen clearly in Figure 2. Again, this suggests that an important barrier to needed health care is simply the cost of care. In this sample, even heavy subsidization of primary care did not lead to rampant overuse of care, while successfully encouraging needed care. However, underuse was far from eliminated. Indeed, there is little room to reduce overuse, given the low daily hazard of care when care is not required, but there is substantial room to decrease underuse further.

In contrast with the encouraging findings about subsidized care, we find little impact of the CHW treatment on conditional demand. The CHW effect on the hazard of care is almost exactly zero. While some of the signs in column (2) go in the right direction, estimated effect sizes are small, and the hazard rates plotted in Figure 2 look similar between the groups with and without the CHW. On average, the CHW do not appear to improve the allocation of care. Given the extremely low probability of early care seeking to begin with, it is perhaps not surprising that the CHW do not significantly decrease overuse; however, they also do not significantly increase care-seeking on care-required days. There is also no evidence of a complementarity between CHW and subsidies.

# **5.3 Discussion: CHW effects and health outcomes**

The results so far leave us with two questions: first, do CHW visits have any effect, and in particular, is there any evidence that subsidies and CHW can be complements? And second, are there longer-term health outcome improvements in response to the interventions? To answer these questions, we present some exploratory results on care-seeking behavior conditional on child age, and briefly discuss the relationship of our findings here with results from Dean and Sautmann (2023) regarding effects of the two interventions on prevention and health outcomes. The 2014 follow-up data collection reported in this paper focused on 29
the example of diarrhea, for which there is a role for all three, prevention (measures against the ingestion of contaminated water), home care (oral rehydration therapy, or ORT, for mild cases), and curative care (clinical evaluation for more severe cases). Diarrhea is an important contributor to malnutrition in children.

**Treatment effects by age.** Very young children are much more vulnerable to illness than older children; the World Bank reports in 2013 that 63% of all under-five mortality in Mali occurred in infants. This suggests that the benefits from health interventions are greatest very early in life. We therefore conduct an exploratory analysis in which we examine treatment effects by age. Figure 3 shows nonparametric daily care seeking probabilities by treatment group and care-required status for ages 0 to 5. In the appendix, we also estimate Cox hazard models for the youngest and oldest children separately (Table C.7).

The sample is small and power low, so these results are indicative only, but a few observations emerge. First, for age-0 children, the difference in care-seeking between early and care-required days is much smaller than for older children. The Cox hazard point estimates support this (detailed discussion in the appendix). These patterns suggest that parents of young children may be less certain whether care is required, perhaps because it is harder to determine illness severity in non-verbal infants or new parents are less experienced. Second, care-seeking probabilities are generally decreasing in age in all groups, suggesting that parents of younger children value formal care more. That said the low probability of care seeking for age-0 children in the “subsidy only” group, especially when care is required, is a stark break from this pattern, compared to other ages with the subsidy and compared with the full program group. For these children, there is a large, although noisily estimated, increase in needed care in the group that receives both interventions relative to the subsidyonly group. This is echoed in the 2014 data: Dean and Sautmann (2023) report a large effect of the full program on the use of formal care for the youngest children; above that of either the CHW only or subsidy only group.<sup>22</sup> Thus, for the youngest children, it appears 22The data from 2014 contains only an abridged 7-day health calendar and does not allow the construction 30
that the CHW intervention complements the subsidy in increasing formal care use, primarily reducing underuse.

What explains the CHW effects for the youngest? Within the framework we proposed, an ambiguous average effect of the CHW on demand for care is compatible with a positive effect for the earliest ages. Panel B of Figure 1 illustrates a potential _negative_ effect of more accurate beliefs _π_ for parents with a high cost threshold _K > K_<sup>_∗_</sup> : they may use new information to align better with their own preferences, and decide _against_ health care visits that the policymaker deems necessary.<sup>23</sup> For the youngest children with the subsidy, the parents’ cost threshold may be close to that of the policymaker, and they may also find it more difficult to assess their child’s need for care.<sup>24</sup> In this situation the CHW help lift the information constraint and reduce underuse (observation 3). However, when parents apply a higher cost threshold, CHW visits may not unambiguously improve the allocation.

Another possible explanation for the CHW effect is based on the specifics of the program: households were visited in approximately three-months intervals by Mali Health employees, and during those visits, any newborn children or children of program mothers who moved (back) into the household were enrolled into Action for Health. It is possible that the CHW in the full program group helped them enroll their newborn children. Some features of the design bias us against this conclusion;<sup>25</sup> nonetheless, it is possible that a contribution of the CHW was to provide valuable information about subsidy eligibility. In either case, our results suggest that CHW and subsidy interventions might be complementary for the youngest children, who are most vulnerable to adverse consequences of serious illness, and contribute most to continued high child mortality rates. While this is an encouraging prospect, our over-

> of complete illness spells. We therefore cannot assess whether this care was required in all cases, but the patterns are consistent with the subsidy and CHW acting as complements in the improved use of formal care for the youngest children.

> 23Parents with belief _π∗L_ but threshold _K_ will not seek care even in period _t_ +1, contrary to the preferences of the policymaker.

> 24Note that IMCI care-seeking rules (and thus _K ∗_ ) are the same for all children under five.

> 25For example, all parents already had at least one child in the program and thus knew how the program worked. Our analysis is also restricted to children four months and older who should have received at least one enrollment visit.

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all findings and the ambiguity of effects within our conceptual framework suggest important caveats to changing caretaker choices about acute care through information policies.

**CHW effects on prevention.** While the results of this paper focus on care for acute illness, the CHW also promoted preventive care as part of AfH.<sup>26</sup> The 2014 data show that families who received CHW visits had better knowledge of the recipe for oral rehydration solution and higher self-reported use of water disinfection, although they are only weakly more likely to know the correct ages for exclusive breastfeeding, and an objective measure of water chlorine contents shows no effects. However, another objective measure (not related to diarrhea), whether a mosquito bed net was hung correctly, is 14 percentage points higher with CHW visits. Most knowledge and prevention measures essentially did not change in the subsidy-only group, as one might expect, and most also showed no significant difference between the CHW-only and the full-program groups. Together, these findings suggest that CHW visits and subsidies are effective in improving different aspects of health care for children, one primarily addressing preventive, the other curative care.

**Health outcome effects.** The effects we observe on diarrhea incidence support this interpretation. The data from 2014 show that the full program significantly reduces the number of days with diarrhea per child, equivalent to over 70 percent of the control-group average. While the effects in the other two treatment groups are not significant, they are negative as well. We do not reject that the full-program effect equals the sum of the CHW-only and subsidy-only effects.

It is notable that we see reduced diarrhea incidence in the 2014 data but no comparable incidence effects on diarrhea or other illness in 2013. Although the subsidy significantly reduces the number of days on which the child’s mother is “strongly concerned” and the length of time a given spell remains untreated (Appendix Table C.6, col. (1) and (4)), there are no significant effects of the interventions on number of illness spells or incidence of specific

> 26While the interventions were ongoing, Mali Health continued to provide additional training to CHWs. This might affect comparability of the CHW effects in 2013 and 2014.

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symptoms (Table C.3). One possible reason is that 2013 was the first rainy season under Action for Health, and that the effects of the program simply manifested only after a longer period of exposure. By the end of 2014, a large share of children under 2 had benefitted from the program since birth or shortly after birth. Another reason may be that the children in AfH constitute only a small percentage of the population (about 20% of people in the study households, which in turn make up a fraction of the local population), so the program may not be able to significantly reduce community spread for most types of infectious illnesses, but some of the measures that the CHW teach, such as hand washing and water disinfection, are particularly effective in the prevention of diarrhea within the household.

# **5.4 Discussion: Advantages of estimating demand conditional on health status**

**Censoring and Selection Bias.** Our data make a strong case for collecting illness data in the home and analyzing spells with a hazard model, due to the censoring that occurs with spontaneous recovery: we do not observe how long parents would have waited to see a doctor in spells where the child gets better without care. Spontaneous recovery is integral – to the decision to “wait and see” during an illness spell without it, there would never be a benefit to delaying care past the first day. Of note, even care-required spells often end with unaided recovery, illustrating the challenge of improving child mortality through acute care: it is necessary to treat a large number of spells in order to prevent the small portion that may become truly dangerous to the child.

One effect of censoring is that the observed pre-care spell length systematically underestimates true time-to-care, and effects on spell length do not fully reflect changes in daily care seeking probability. For example, there will be spells that are censored (ending without care) at high _K_ , but uncensored (ending in care) at low _K_ (leading to increases in utilization). Table C.6 in the appendix correspondingly shows that the subsidy reduces (pre-care) spell length by only 12 percent (0.83 days out of 6.83 days in the control, column (1)), even though we found that it increases the care seeking probability five-fold.This problem is exacerbated in data that contains only uncensored spells that end in a consultation, such as data collected 33
at the provider or from health insurance events as in Gilleskie (1998) — unfortunately often the only data we have. Table C.6, column (5) shows in fact that the average uncensored spell in our sample is weakly _longer_ in all treatment groups than in the control, by between 6 and 48 percent (0.26-1.99 out of 4.11 days). Our hazard estimates confirm that this is entirely selection bias: the large number of additional visits in the subsidy groups tend to be from families who wait on average longer for care, and so they raise the average time to care in the group. Put differently, when underuse is rampant but responds strongly to an intervention, we show that data collected at the point of use are subject to selection effects so strong that they reverse the estimated impact of the intervention on some aspects of care-seeking behavior. Even recognizing that selection bias may occur, data of uncensored spells only would not permit the researcher to distinguish selection e.g. from moral hazard (parents waiting longer before getting care with the subsidy).<sup>27</sup> Similar issues arise when asking retrospective information on health for a specific (short) period of time.<sup>28</sup>

**Application: Predicting Care-Seeking for Alternative Illness Environments.** Our study estimates demand for care conditional on detailed health information. A benefit of this approach could be more accurate out-of-sample predictions. In Appendix section C.3, we demonstrate an application by projecting health care demand for a set of hemorrhagic fever spells (Ebola and Marburg disease), where detailed descriptions of the course of illness are available. This illustrates how records of conditional care-seeking behavior might help improve estimates of undetected infections and time-to-care for those who contract a specific illness, for instance in the initial phases of new disease outbreaks. More generally, we can make predictions of behavior in other disease environments. In Mali, policymakers might be

> 27Note that the Cox model assumes proportionality and so does not permit for demand changes that exhibit both selection into care _and_ an increase in time-to-care under the subsidy (which might occur with a decrease in care seeking on early spell days but an increase later in the spell). However, Figure 2 and the proportionality test in Appendix section C.1 show that the proportionality assumption reflects the data well. 28An example are the Demographic and Health Surveys (DHS). The DHS questionnaire asks about diarrhea and fever spells and treatment sought within the last two weeks. Shorter spells are observed in full, but longer spells are likely to be censored by the visit date. Moreover, detailed questions about treatment are asked only about spells that are ongoing at the time of the survey. See Heger Boyle et al. (2018), especially questions 518 and 527 and following.

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for example interested in predicted care seeking in the dry season, when respiratory illness is more prevalent than malaria and diarrhea.

# **6 Conclusion**

The prevailing view in international health policy today is that subsidies are needed to close remaining gaps in access to care. The potential benefits are large: by some estimates, improved access to primary care could prevent 29%–40% of post-neonatal deaths in developing countries. Less than 26% of malaria cases in children are estimated to receive adequate treatment, and 411,000 children died of malaria in 2013 in Africa alone.<sup>29</sup> Many countries have begun to remove health care user fees for children, and Mali announced plans to adopt this policy in 2019. However, we have relatively sparse evidence how this affects patterns of utilization.

This study aims to open the “black box” of the extensive margin of health care demand and the effects of demand-side interventions. We estimate health care use conditional on health status to remove confounding effects of illness incidence, and benchmark care-seeking against the need for care according to WHO medical standards. By surveying all (potential) health care users we avoid selection bias due to spontaneous recovery.

Our results have very encouraging implications for the subsidy debate. First, a primary barrier to effective care seeking in the urban population we study is indeed the cost of care. Underuse is rampant: care is either sought late in the spell or, in the majority of cases, not at all. The welfare cost of subsidies could be prohibitive if they lead to substantial mistargeting and overuse of health care, risking overburdening the severely limited capacity of health care systems in poor countries and thus diverting resources away from those most in need. However, while subsidies nearly triple health care usage in our sample, the additional demand comes largely from children for whom medical care is required according to WHO guidelines. One reason is that parents have some ability to recognize differences in illness

> 29United Nations Inter-Agency and Expert Group on MDG Indicators (2015); WHO Global Malaria Programme (2015); Bhutta et al. (2008).

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severity and are nearly six times more likely to seek care on ‘care required’ days compared to ‘early’ days.

The degree to which parents can spot illness suggests that information about the health status of the child _a priori_ can only have limited additional effects, and in aggregate this is what we find; the CHW visits do not affect average care-seeking probabilities significantly. However, they do reduce underuse among the most vulnerable (youngest) children who receive the subsidy. This is consistent with findings from one year later where formal care for diarrhea is again increased significantly for children age 0-2, and diarrhea incidence is significantly reduced, in the full program group only .

The role of CHW visits for curative care seeking deserves additional research. Our data points to parents being less able to recognize need for care in very young children, which suggests a role for policies that guide parents’ use of formal health care this group. At the same time, the age-specific effects and our model of the effect of information on care seeking suggest that parents may make differential use of information they are given, depending on the cost threshold they apply in deciding about seeking care. Unintended and heterogeneous responses to health information policies are an interesting question for additional research more broadly. Governments or NGOs like Mali Health might consider a model in which the CHW focus their advice on acute care seeking on the youngest children, for whom the effects are most likely to be beneficial, and otherwise prioritize preventive activities.

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