metadata: authors: 0: author: Fitzpatrick,Anne
1: author: Thornton,Rebecca Lynn
count: Nicaragua
docna: 0: docna: The effects of health insurance within
            families: experimental evidence from Nicaragua
docty: Policy Research Working Paper
subtopic: Pharmaceuticals Industry,Health Care Services Industry,Labor Markets,Health Economics & Finance,Educational Sciences,Health Insurance
teratopic: Health, Nutrition and Population
abstracts: cdata!: This paper measures the causal effects
            of parent enrollment into voluntary health insurance on
            healthcare utilization among insured and uninsured children
            in Nicaragua. The study utilizes a randomized trial and
            age-eligibility cut-off in which insurance subsidies were
            randomly allocated to parents that covered their dependent
            children under 12; children ages 12 and older were not
            eligible for coverage. Among eligible children, the
            insurance increased utilization at covered providers by 0.56
            visits and increased overall utilization by 1.3 visits.
            Ineligible children with insured parents experienced 1.7
            fewer healthcare visits, driven by parent, not sibling
            enrollment. The results suggest complementarities across
            healthcare provider type, and provide evidence that
            households reallocate resources across all members, in
            response to changes in healthcare prices for some.
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repnme: repnme: The effects of health insurance within
            families: experimental evidence from Nicaragua
display_title: The effects of health insurance
            within families: experimental evidence from Nicaragua
listing_relative_url: /research/2017/06/27677275/effects-health-insurance-within-families-experimental-evidence-nicaragua
topicv3: Informal Sector m1326258 1168,Education m1327048 470,Health Insurance m1327759 1287,Health m1504661 3480,Pharmacy m1583121 4501
publishtoextweb_dt: 2017-06-26T00:00:00Z
historic_topic: Health, Nutrition and Population
no_of_pages: 43
author_keys: 0: author_key: 684219
model_extractions: WPS8115 Policy Research Working Paper 8115 The Effects of Health Insurance within Families Experimental Evidence from Nicaragua _Anne Fitzpatrick Rebecca Thornton_ Development Economics Vice Presidency Operations and Strategy Team June 2017
Policy Research Working Paper 8115

# **Abstract**

This paper measures the causal effects of parent enrollment into voluntary health insurance on healthcare utilization among insured and uninsured children in Nicaragua. The study utilizes a randomized trial and age-eligibility cut-off in which insurance subsidies were randomly allocated to parents that covered their dependent children under 12; children ages 12 and older were not eligible for coverage. Among eligible children, the insurance increased utilization at covered providers by 0.56 visits and increased overall utilization by 1.3 visits. Ineligible children with insured parents experienced 1.7 fewer healthcare visits, driven by parent, not sibling enrollment. The results suggest complementarities across healthcare provider type, and provide evidence that households reallocate resources across all members, in response to changes in healthcare prices for some.

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

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

# Evidence from Nicaragua

_Anne Fitzpatrick and Rebecca Thornton_<sup></sup>

**JEL classification codes:** I13, I18, I38, O15, D1

**Keywords:** Health insurance; randomized controlled trial; spillovers; children;

complementarities.

>  Anne Fitzpatrick is an assistant professor at the University of Massachusetts-Boston; her email address is <u>anne.fitzpatrick@umb.edu. Rebecca Thornton (corresponding author) is an associate professor at the</u> University of Illinois at Urbana-Champaign; her email address is rebeccat@illinois.edu. Funding for this study was provided by USAID’s Private Sector Partnerships- _One_ (PSP- _One_ ) project, the Global Development Network (GDN), and the ILO Microinsurance Facility.  The evaluation was coordinated for GDN by EA Consultants. The authors thank the extensive contributions of the field team at ALVA Consultores including Dr. Ana del Carmen Rojas and Rosario Duarte, as well as the project coordination and support of Barbara Magnoni, EA Consultants, and the contributions in the project design of Tania Dmytraczenko, the World Bank. This paper has benefitted from comments from Taryn Dinkelman, Emily Oster, Jeff Smith, seminar participants at the University of Michigan and two anonymous referees.
Health insurance is thought to be an important determinant of healthcare utilization and health outcomes, particularly among children (Olson et al. 2005; Kaiser Family Foundation 2002). Studies have shown that children’s health insurance is positively correlated with immunization rates, checkups, and overall health care utilization; insured children may also have improved health outcomes and lower mortality (Abdullah et al. 2009; Currie and Gruber 1996; Newacheck et al. 1996; Newacheck et al. 1998; Palmer et al. 2015; Todd et al. 2006). However, causally attributing these observed differences in outcomes to health insurance coverage is difficult due to adverse selection: parents may choose to enroll in coverage if they have children who are likely to be sick (Polimeni and Levine 2011). On the other hand, parents with more resources or education may be more likely have health insurance coverage for their children, resulting in advantageous selection (Fang et al. 2008). Studies examining the effects of health insurance coverage on outcomes are complicated by potential biases due to selection into enrollment.

This paper overcomes the usual empirical challenges to measure the causal effects of health insurance using an experiment that randomly assigned parents in Managua, Nicaragua access to free health insurance. We study the effects of insurance among children (ages 15 and under). Although recent studies on health insurance also utilize random or quasi-random research designs, these either occur in the United States or estimate impacts only among adults (Banerjee et al. 2014; Barofsky 2015; Finkelstein et al. 2012; King et al. 2009; Levine et al. 2011; Newhouse 1993; Sheth 2014). The results from these existing studies may not apply to children or to the developing country context because the elasticity of health care demand has been found to be larger in the developing country setting and larger for children as compared to adults (Zhou et al. 2011; Gertler 2
and van der Gaag 1990; <mark>Sauerborn et al. 1994)</mark> .

In addition to analyzing effects among insured children, we also examine how having an insured parent affects healthcare utilization among children who are ineligible for the insurance themselves. This allows us to see how families adjust their health demands in response to parental insurance coverage. A model of healthcare demand within a household predicts that parents efficiently optimize, equating the ratio of marginal benefits to costs across all family members, rather than separately for each individual member (Jacobson 2000). If one member becomes insured, the net effect upon uninsured members’ utilization is ambiguous if parents reallocate time or financial resources across household members in response to the decreased cost of utilization. The net outcome may be either towards or away from uninsured children, depending upon whether the income or substitution effect dominates. Alternatively, following health insurance enrollment of some members, parents could reallocate resources to increase utilization for uncovered members to ensure equity across their children (Adhvaryu and Nyshadham 2014; Ejrnæs and Portner 2004; Griliches 1979; Hertwig et al. 2002). The empirical literature measuring the effects of parental health insurance enrollment thus far has generated mixed results and none are based upon randomized studies.<sup>1</sup> To study the effects of insurance on insured and uninsured children, we utilize an age-eligibility cut-off rule that gave coverage only to children under the age of 12.

> 1 This paper is most similar to Aiyar (2016), who measures the impact of a Vietnamese health insurance program on expenditures for both eligible and ineligible family members. Other non-experimental studies have explored the within-family spillovers of health and nutrition programs on non-participating family members, generally finding positive effects (Basiotis et al. 1998; Ishdorj et al. 2008; Robinson 2013; Ver Ploeg 2009). A related literature examines how parental insurance eligibility affects health insurance enrollment of eligible children, finding positive spillovers on children’s enrollment into Medicaid in the United States (Dubay and Kenney 2003; Sommers 2006; Aizer and Grogger 2003; Busch and Duchovny 2005). Recent work by Koch (2015) uses income-eligibility thresholds estimates and finds that parents are 11 percentage points less likely to be insured once their children become eligible for health insurance.

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Children ages 12 and older were not eligible for insurance coverage. The data include 1,614 families in Managua, Nicaragua, who were interviewed before and after being randomly offered access to free insurance. Our primary analysis measures the impact of parental health insurance on health care utilization – measured by number of provider visits and choice of health provider – of eligible and ineligible dependents, one year after access to insurance is randomly distributed.

Among families with an insured parent, covered children (under 12 years old) increased healthcare utilization at covered providers by 0.56 visits, with an increase 1.3 visits to all providers. The results suggest no substitution away from other providers, such as pharmacies or public facilities. In contrast, ineligible children (12 years old and older) with an insured parent decrease their total health visits by 1.7, driven primarily by reductions in visits to private providers We examine several potential mechanisms to explain these results. First, we find that the results are primarily a result of a parent’s enrollment, rather than siblings’. Second, we find no evidence that resources are reallocated in response to improved health of those insured.

While ultimately the exact mechanisms cannot be identified, the results show that public programs such as government-sponsored health insurance can have sizeable effects on the allocation of resources within a family. While insured children benefit by receiving additional healthcare, ineligible members of a family may be negatively affected because families use shared budget and time constraints to make decisions within their households. These potential spillover effects may change the effectiveness of health policies and programs (Basu and Meltzer 2005).

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The findings also suggest important complementarities across provider types among children. For insured children, we observe no substitution from pharmacies or private providers in response to health insurance enrollment, but rather a sizable increase in utilization across all providers. These results suggest that reducing the price of healthcare at one provider may increase investments in healthcare across other providers. However, complementarities may also exacerbate negative spillovers, among the uninsured, for example.

We caution that the results may be specific to the Nicaraguan context and healthcare structure. The effects on this specific sample – children of middle-class urban entrepreneurs – may not be generalizable to other populations. The analyses in the paper are also limited in the set of health indicators that are measured within the setting and time frame. The free health insurance was only for six months which may not have been enough time for there to be large effects, and the scope of our study is not able to detect changes in rare events, such as child mortality. This study is also unable to speak to potential longer run effects of health insurance, or whether these results would apply to different subsidies or cost-sharing schemes. However, this paper underscores the importance of considering negative spillovers within families; positive benefits for some may result in net losses for others.

# **_I._** _BACKGROUND_

In Nicaragua, formal sector employees are automatically enrolled in the Nicaraguan Social Security Institute’s (INSS) health insurance program. The INSS insurance provides subscribers with a comprehensive package of preventive, diagnostic, and curative health services and medications at 17 INSS-contracted facilities in Managua 5
(referred to as EMPs: Empresas Médicas Previsionales).<sup>2</sup> There are no co-pays at the time of service; rather, individuals who enroll pay a monthly flat fee of approximately $15 to the Social Security Institute for coverage. In addition to the subscriber, the subscriber’s wife is eligible for maternity services, including prenatal, childbirth and postnatal care. Dependent children under the age of 12 are also fully covered for pediatric care and vaccinations while those over the age of 12 are excluded from coverage.<sup>3</sup> While this insurance plan covers those in the formal sector, this represents only a small proportion of the adult population – just under half a million adults or approximately 13.5 percent of the adult population (INIDE and MINSA 2007). Uninsured individuals have access to free public sector clinics and hospitals run by the Ministry of Health Services (MINSA). However, these services are often under-resourced and the source of complaints of long waiting times, frequent supply stockouts, and general poor quality (Magnoni et al. 2005). Rather than seeking treatment at MINSA facilities, self-medication from pharmacies for basic care is common.<sup>4</sup> For those who can afford it, higher quality, more expensive private facilities are available. On the other hand, many may be unable to pay the high out of pocket costs and forgo care altogether. Thus, the INSS health insurance package may not only change the cost but also the quality of healthcare for insured individuals.

> 2The services provides include primary and specialist care, medication and laboratory exams, hospitalization, 24-hour emergency care, voluntary family planning counseling and contraception, breast and cervical cancer screenings, HIV and STD counseling, and prevention and treatment of dengue fever and malaria.

> 3 EMPs are only able to be reimbursed for services if they can document that the individual was enrolled in the health insurance; that is, if the child is under 12. Any expenses incurred from services for ineligible children would not have been paid by the INSS program.

> 4 In the data, pharmacies are the most commonly visited type of provider; on average children report 1.8 visits to pharmacies in the past year at the baseline. In contrast, the average number of visits to public and private providers is 0.8 and 1.0 visits, respectively.

6
In January 2007, the government of Nicaragua implemented a demonstration project aimed at extending the Nicaraguan Social Security Institute’s (INSS) health insurance program to the population of informal sector workers. In addition to potentially improving healthcare access and overall health, enabling middle-class informal-sector workers to seek care at EMPs has the potential to alleviate the overburdened MINSA healthcare system, leaving more resources for the poor. Coverage and cost was designed to be as similar as possible to the standard insurance for formal sector workers, and enrollment into the program was voluntary. Both this paper and Thornton et al. (2010) report results from this evaluation. In 2007, a baseline survey was conducted among randomly selected uninsured informal sector workers in the three largest open-air markets in central Managua.<sup>5</sup> The survey asked detailed questions about utilization and spending, conditional on being ill 12 months prior to the survey, for the adult respondent and for any child in the household under the age of 16.

At the end of the baseline survey, respondents were either given an informational brochure about the insurance product or the brochure plus a six-month subsidy for insurance worth approximately USD$100, nearly half of the sample’s median household income.<sup>6, 7</sup> Respondents could enroll in the insurance plan at the INSS or at local micro-

> 5 Respondents were selected with the following two methodologies: in the first phase of the survey, prior to the baseline survey a census of market booths was conducted to define the sampling frame of possible respondents. Participants deemed eligible through the census were selected randomly (stratified by gender, marital status, and micro-finance client status) and administered the baseline survey. In the second phase of the survey, interviewers went door to door and sampled each market booth with eligible respondents. Individuals who were between ages 18 and 54, had a government ID, were an owner of the market booth, and lacked health insurance coverage were eligible. Overall completion rates were 51 and 53 percent in the two phases.

> 6 The study design also assigned respondents into 2-month subsidy group during the first phase of the project; these individuals were not included in the follow-up survey. The baseline survey included respondents in four other smaller markets but because these respondents were not followed over time, they are not included in the analysis (Hatt et al. 2009).

7
finance institutions, and there was no deadline for enrollment.<sup>8</sup> Upon enrolling, the insurance took effect the first day of the following month. Government ID numbers were collected to match respondents to health insurance enrollment data provided by the INSS. One year after the baseline survey, respondents were approached for a follow-up survey. Overall, 93 percent of the adult respondents were re-interviewed (N=2608). Of the 2608 adults who were in both rounds of the initial evaluation, 62 percent had at least one child under the age of 16 (N=1614).

The overall take-up rate of insurance for respondents with insurance-eligible children was 35 percent among those who were offered the six-month subsidy and 2.22 percent among those who were not.<sup>9</sup> The primary sample consists of 2,996 children in 1614 households in both waves of the survey; this sample is 74 percent of all children at baseline. As with the full adult sample in Thornton et al. (2010), there was no differential sample attrition between those who were offered the subsidy and those who were not (available upon request).

Baseline statistics of households (Panel A), parents (adult respondents with at least one child under 16, Panel B), and children (Panel C) are presented in Table 1. Households have on average 4.8 members, including 1.3 children age 11 and under. Average monthly household income is 5,399 Córdobas (US$298). Parents have 9.3 years

> 7 Although the 6-month subsidy is large relative to median income, household disposable income should be unaffected by receiving the subsidy since most households do not purchase the insurance without a subsidy. 8 Thornton et al. (2010) also measures whether the location of enrollment (at a micro-finance facility or the standard INSS enrollment location) affects the enrollment decisions of informal-sector workers, and whether MFI clients have different utilization patterns than non-MFI clients.

> 9 These take-up rates of health insurance are comparable to the existing literature. The Oregon Health Insurance Experiment found that low-income individuals who won a lottery for Medicaid in the United States increased the probability of enrolling by 25 percentage points compared to similar individuals who had also enrolled in the lottery (Finkelstein et al 2012).  Enrollment in the SKY health insurance program in Cambodia peaked after 6 months at 44 percent in the most heavily subsidized treatment group (Levine et al. 2016), and a study in Kenya found a 17 percent take-up rate of formal health insurance (Chemin 2014).

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of education, and work in their market stall on average 10 hours per day. 76 percent of adults report ever being sick in the past year with a similar percentage ever seeing a health provider (76 percent). The average number of visits to all providers is 4.28. Average health expenditures are C$828 ($46), with a median value of C$161 ($8). Children have similar rates of being sick, and utilization of healthcare services as their parents, although total costs are somewhat lower - C$569 ($31), with a median value of C$204 ($11).

# **_II._** _EMPIRICAL STRATEGY_

To measure the effects of insurance on health care utilization we follow the strategy used in Thornton et al. (2010), instrumenting insurance enrollment with the randomly offered subsidy.<sup>10</sup> To benchmark the effects on children, the empirical results begin by estimating the effects of the insurance among _parents_ , with the following specification: (1) �� = α + β���������� �<sup>+ ��γ</sup> �<sup>+ ε</sup> � where _Yif_ represents utilization at health providers within the past year. _Enrolled_ is an indicator of whether or not the parent enrolled in insurance. This specification controls for characteristics potentially correlated with health insurance demand and healthcare utilization, all collected at the baseline.<sup>11</sup> We correct for potential selection bias with respect to insurance enrollment decisions in equation (1) by using the randomly offered six-month subsidy to instrument for

> 10 The main outcome variables are measures of healthcare utilization in the past year.  The supplemental appendix contains estimates of health insurance on the inverse hyperbolic sine of out-of-pocket expenditures. 11 These include: household size, household size squared, the inverse hyperbolic sine of household income, parental years of education, age, age squared, gender, whether the individual was sick in the past year, the number of times sick, total number of health visits, whether the individual had forgone treatment due to lack of money, and survey round and market fixed effects.

9
<!-- Start of picture text --> Enrolled; = a + 6,Subsidy; + X'y; + €;<br><!-- End of picture text -->

- Yig = a + B,ParentEnrolled; + B,ParentEnrolled * Ineligible,, B3Ineligiblejr + X'Vir + eir By Bo
<!-- Start of picture text --> ParentEnrolledjr = a + 6,Subsidys + 62Subsidy * Ineligible;,<br>63Ineligible;e + X'Vir + Eir<br>ParentEnrolled « Ineligtble,, = a + AySubsidy; + A,Subsidy *<br>Ineligible;r + A3Ineligibleyr + X'Viz + Eis<br><!-- End of picture text -->

|Yip=a+B,6MonthSubsidy,s+B26MonthSubsidy*Ineligible;r|
|---|
# �������������� + �<sup>�</sup> ��� + ���

The variables and controls are the same as in the previous equations.

Characteristics of children whose parents were and were not offered the subsidy are balanced across the treatment groups (Table 1, Panel C). Magnitudes of the differences are also small. Characteristics are also balanced by treatment group separately among eligible and ineligible children and across smaller age groups (0-5 and 6-11) (Appendix S2).

# **_III._** _RESULTS_

Before presenting the main effects of the insurance on children, we show the effects of being insured among parents, according to Equation (1), as a benchmark, (Appendices S3 and S4). Enrolled parents are 41 percentage points more likely to attend an EMP (covered provider) and 13.7 percentage points less likely to attend public facilities. Similarly, parents increase the number of visits to EMPs by 1.3 visits. The overall number of visits increases by 0.8 visits, but this estimate is not statistically significant.<sup>13</sup> Turning to the effects of insurance among children, Table 2 shows that having an enrolled parent increases the likelihood that an eligible child visits a covered provider (EMP). The increase is large at 23 percentage points (Table 2, Panel A, Column 3), although not as large as the increase in adults own utilization. There is also an increase on the intensive margin with an increase in 0.56 visits to an EMP (Panel B, Column 3). In addition, eligible children with an insured parent increase total utilization at all providers,

> 13 The coefficient magnitudes on the effects of having insurance on out-of-pocket health expenditures also suggest some substitution away from private facilities and pharmacies, although these estimates are imprecisely measured.

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not just those that are covered by the insurance, by 1.26 additional visits (Panel B, Column 1). Note that the magnitude of the effect size on EMP utilization is about half of that among parents, although there are larger effects on children’s total utilization.

Age-ineligible children (ages 12-15) with parents enrolled in health insurance experience fairly substantial decreases in the likelihood of visiting providers, and the total number of provider visits, compared to children of the same ages, whose parent is uninsured. Among these children, having an insured parent reduces the likelihood of attending a private facility by nearly 30 percentage points (p-value = 0.003; Column 5) and reduces the total number of healthcare provider visits by 1.8 (p-value =0.048; Table 2, Panel B, Column 1). These results are robust to examining the difference in utilization as an outcome, as in a panel data IV model (Appendix S5).<sup>14</sup> The reduced form estimates measuring the impacts of offering health insurance to parents on children are presented in Table 3.<sup>15</sup> Eligible children are 7 percentage points more likely to have ever visited an EMP in the past year, while ineligible children in insured families are 6.5 percentage points less likely to have ever visited EMPs (Panel A, Column 3). Among ineligible children, being in an insured family decreases the likelihood of ever visiting a private health facility by 0.078 percentage points (Panel A, Column 5). While offering parents health insurance increases overall utilization among eligible children by 0.392 visits (Panel B, Column 1), including 0.174 at covered providers (Panel B, Column 3), there is a large and significant decrease in utilization among ineligible children in insured families. As a result of health insurance, ineligible

> 14 There are similar significant decreases in overall spending and spending at private facilities for ineligible children in insured households, but no statistically significant effects on out-of-pocket expenditures among eligible children (Appendices S6 and S7).

> 15 The reduced form results presented according to CONSORT guidelines can be found in Appendix S8 .

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children have 0.847 fewer visits overall (Panel B, Column 1), including 0.162 fewer visits at covered providers and 0.292 fewer visits at private facilities (Panel B, Column 5).<sup>16</sup>

# **_IV._** _POSSIBLE CHANNELS_

While the analysis shows that the effects of parental insurance differ by the insurance status of the child, the previous analysis does not explain why these patterns arise. Are parents reallocating time or financial resources between household members in response to the price decrease from enrollment? Or are children healthier, and therefore imparting a positive externality on their siblings, reducing the demand for healthcare?

First, we examine whether the effects of insurance are in response to having enrolled parents, siblings, or both. While all ineligible children in insured families have an insured parent, not all have an insured sibling. Using the sample of ineligible children (N=824), we test whether the effect of parental insurance differs by whether the child has an eligible sibling.<sup>17</sup> The results in Table 4 show that the effects of health insurance do not differ by whether the ineligible child has a covered sibling, suggesting that the results are primarily driven by having insured parents. There are no significant added effects of

> 16 We also conduct falsification checks to determine the sensitivity of the results, where we replace the outcome measures from the follow-up survey with the baseline measures (Appendices S9 and S10). None of the coefficients of interest are significantly different from zero. The exception is that aside from one coefficient on the interaction term for the outcome of whether the child had ever visited an EMP at baseline. We do not believe that this is cause for concern due to the large number of hypotheses tested in this table and because this outcome has a low mean in the sample at baseline (0.028).

> 17 We estimate: ��� = �+ ����������������� �<sup>+</sup>

> +����������������∗���������������� �� + ������������������� + �<sup>�</sup> ��� + ��� where _Parent Enrolled_ is instrumented with random assignment status as in Equation 1, and the interaction is instrumented with random assignment status multiplied by whether the ineligible child has an eligible (covered) sibling.  The primary outcomes we examine are visits; all control variables are the same as in Equation 1.

14
# having an eligible sibling.<sup>18</sup>

An alternative mechanism consistent with the results is that if health insurance improves health, there may be positive health externalities among uninsured members of the household. As a result, ineligible siblings might not require as many visits to healthcare providers. Unfortunately, the data do not contain objective measures of children’s health and are restricted to parents’ self-reports which may be systematically biased due to increased access to higher qualified providers or increased/decreased health care utilization. In addition, any results presented on the effects of insurance on health status - a function of both preventative and curative care – are not comparable to the health care utilization results, which are conditional on being sick.

Nevertheless, we find no significant effects of parent insurance in the likelihood of ever being sick among parents or eligible children (Appendix S13). On the extensive margin, however, eligible children with insured parents are reported to be sick 0.67 times more than those without an insured parent. In contrast, ineligible children with insured parents are reported to be sick 1.06 times less than ineligible children without insured parents. There are several possible explanations for these results. It is possible that health insurance actually made eligible children with insured parents sick more often despite their increased utilization at providers, through iatrogenic illnesses from waiting rooms, for example (Steel et al. 2004). However, more plausibly, increased healthcare utilization may also have increased parents’ knowledge, or salience, of sick episodes, or changed the

> 18 The results on out-of-pocket expenditures yield similar estimates (Appendix S11). There are also no statistically significant added effects of having an ineligible sibling among age-eligible children (Appendix S12), although the point estimates suggest that having an ineligible sibling may mute the positive effect among covered children. There are similarly no significant differences for covered children with covered siblings.

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criterion for which a child is sick enough to attend the doctor.

There are several possible explanations for the increase in non-covered provider visits among eligible children with insured parents. The increased overall utilization is not likely due to increased available money for more healthcare – we found no significant effects of the insurance on out-of-pocket expenditures (Appendix S6). The increased utilization could be due to moral hazard, because the price decrease is specific to EMPs. Increased utilization could also have resulted in additional diagnoses for covered children. Economies of scale in time – taking children to the doctor or visiting a pharmacy – could also explain the results. Unfortunately, data on time costs, or when visits to the different providers took place, were not collected. While the data do not allow us to definitively identify the mechanism, the results are most consistent the Jacobson (2000) model where the substitution effect from cheaper care for insured members dominates the income effect for uninsured members of the household.

# **_V._** _CONCLUSION_

This paper estimates the direct and indirect causal effects of health insurance on children’s utilization using a randomized allocation of insurance subsidies and an age eligibility cut-off. Children who were covered by their parent’s insurance have substantially more visits at covered providers, and increase their total number of visits at all providers combined. In contrast, children who were ineligible for coverage due to an age restriction substantially decreased their overall utilization.

The results do not support a model in which parents are concerned with equity of resources. Instead, parents appear to be driven by efficiency. Parents respond to health 16
insurance by decreasing utilization among those for whom healthcare is relatively more expensive. The findings suggest that the marginal value of reduced health care costs and/or increased returns to health investments (better quality service at covered providers) for the insured are greater than the loss associated with reduced provider visits for older children. Unfortunately, we lack the data to test whether the change in utilization among eligible and ineligible children is due to economies of scale with respect to travel or time costs, such as parents taking insured children with them to the doctor.

The findings in this paper have implications for providing families with insurance, rather than separate, individual insurance plans for children or adults. The results suggest that the primary benefit of parental health insurance to children is improved access to care, but only for eligible children.

Insured children not only substitute from uncovered providers to covered providers but also increase their overall utilization, suggesting important complementarities in healthcare utilization across provider types. Healthcare subsidies could potentially be used to promote further investments children’s health. At the same time, negative health shocks or spillovers may be exacerbated and increase health inequalities, particularly if programs are not designed to cover all family members.

In contrast to results for adults, there is no substitution away from public facilities and towards covered providers. Thus, children’s health insurance is unlikely to decrease demand at overburdened public facilities, a stated goal of the program. These findings suggest an unmet need for healthcare among children covered by the program.

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The results of this paper highlight that families allocate resources—including health demands—according to a specified budget and/or time constraints. These changes in health demands may be sizeable as well: in our context, the impact of health insurance on ineligible members is larger than the impact on eligible members, although in opposite directions. While this study is not powered to detect rare or serious ailments among ineligible children, one may assume that reducing healthcare utilization in a resourcepoor setting could potentially have true negative consequences on health.

As health insurance programs, including that of the United States, have moved towards covering children, it is important to understand the net benefits and the costs to families from parental health insurance. When considering the effect, and costeffectiveness of health insurance as a social policy, it may be empirically important to account for the positive as well as the negative effects that influence healthcare demands on all members of the household.

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24
# **Table 1: Baseline** **<u>Characteristics of Households and Children</u>**

||All|No<br>Subsidy<br>(Control)|6-Month<br>Subsidy<br>(Treatment)|Difference<br>(C - T)|<br>P-value<br>Difference|
|---|---|---|---|---|---|
|Panel A: Household Characteristics|(1)|(2)|(3)|(4)|(5)|
|Size of household|4.81|4.76|4.85|-0.09|0.258|
|Number of children 11 and under|1.34|1.35|1.34|0.01|0.717|
|Household income|5399|5058|5712|-653.41|0.052|
|Panel B: Parent Characteristics||||||
|Years of education|9.30|9.25|9.36|-0.11|0.624|
|Hours Worked Per Day|10.08|9.86|10.27|-0.41|0.001|
|Commute to work (miles)|3.60|3.48|3.71|-0.23|0.524|
|Number of times sick|2.44|2.58|2.31|0.26|0.095|
|Forgone treatment|0.25|0.25|0.24|0.02|0.446|
|Ever visit health provider|0.76|0.77|0.75|0.02|0.260|
|Total number of visits, all providers|4.28|4.46|4.11|0.35|0.260|
|Total healthexpenditures|827.76|903.58|758.59|144.99|0.225|
|Households/Parents (N)|1614|770|844|--|--|
|Panel C: Child Characteristics||||||
|Age|8.00|7.94|8.06|-0.12|0.459|
|Female|0.48|0.48|0.49|-0.01|0.473|
|Ever sick|0.76|0.76|0.77|-0.01|0.843|
|Number of times sick|2.16|2.19|2.13|0.06|0.602|
|Forgone treatment|0.18|0.20|0.17|0.03|0.153|
|Ever visit health provider|0.75|0.75|0.76|0.00|0.983|
|EMP visits|0.12|0.13|0.11|0.02|0.704|
|Public health facility visits|0.88|0.89|0.87|0.03|0.679|
|Private health facility visits|1.04|1.12|0.96|0.16|0.302|
|Pharmacy visits|1.80|1.80|1.80|0.00|0.916|
|Total number of visits, all providers|3.82|3.90|3.74|0.16|0.435|
|Total healthexpenditures|569.21|533.23|601.76|-68.53|0.302|
|Children(N)|2996|1423|1573|--|--|

_Notes:_ Above are averages for the full sample (Column 1), Control group (Column 2), and the Treatment group who were awarded a 6 month subsidy (Column 3). Income is defined as reported monthly household income. Valid income data are not available for 174 families, and commuting data are not available for 432 families. Health providers consist of EMPs, public clinics, pharmacies, private hospitals, private doctors, public hospitals, and laboratories. All health and visit variables are reported during the past year. All income and expenditure data are in 2008 Cordobas. Children who were not sick in the past year are included as zeros for number of times sick and all visit/spending variables. Forgone treatment in past year due to lack of money was calculated to be zero for children who were not sick in the past year. Column (5) represents the p-value from a t-test of means between treatment groups including market and round fixed effects. Standard errors of t-tests are clustered at the family level. *** p<0.01,** p<0.05,* p<0.1 _Source:_ Authors’ analysis based on data described in paper.

25
**<u>Table 2: Effects of Parent Insurance on Utilization by Child Eligibility</u>**

|Panel A: Ever Visit|Any<br>Provider<br>(1)|Pharmacy<br>(2)|EMP<br>(3)|Public<br>Facilities<br>(4)|Private<br>Facilities<br>(5)|
|---|---|---|---|---|---|
|Parent Enrolled|0.007|0.013|0.226***|0.059|-0.006|
||(0.066)|(0.080)|(0.046)<br>-|(0.078)|(0.074)|
|Parent Enrolled*Ineligible|-0.209|-0.158|0.204***|-0.142|-0.296**|
||(0.145)|(0.149)|(0.053)|(0.137)|(0.117)|
|Ineligible|0.073|0.009|0.019|0.104**|0.050|
||(0.048)|(0.049)|(0.017)|(0.044)|(0.039)|
|Observations|2996|2996|2996|2996|2996|
|R-squared|0.088|0.066|0.15|0.037|0.076|
|P-value of Enrolled +||||||
|Enrolled*Ineligible|0.145|0.301|0.483|0.516|0.003|
|Panel B: Number of Visits|(1)|(2)|(3)|(4)|(5)|
|Parent Enrolled|1.260**|0.336|0.564***|0.210|0.110|
||(0.622)|(0.292)|(0.150)|(0.283)|(0.263)|
||||-||-|
|Parent Enrolled*Ineligible|-3.003***|-0.801|0.519***|-0.355|1.092***|
||(1.028)|(0.519)|(0.148)|(0.446)|(0.385)|
|Ineligible|0.690**|0.134|0.037|0.333**|0.192|
||(0.342)|(0.170)|(0.050)|(0.134)|(0.134)|
|Observations|2996|2996|2996|2996|2996|
|R-squared|0.092|0.061|0.108|0.031|0.042|
|P-value of Enrolled +||||||
|Enrolled*Ineligible|0.048|0.314|0.409|0.714|0.001|

_Notes:_ The sample is all children aged 15 and under (N=2996). Children age 12-15 are considered "Ineligible" and children under 11 are considered "Eligible". Above regressions are estimated coefficients from 2SLS-IV estimates where "Parent Enrolled" is instrumented with random assignment status and Parent Enrolled*Ineligible is instrumented with random assignment status*Ineligible. The dependent variable in Panel A is whether or not the child has visited various providers over the past year. The dependent variable in Panel B is the number of times the child has visited various providers over the past year. Regressions control for baseline measures of household size, household size squared, the inverse hyperbolic sine of parental income, parent's years of education, age of child, age of child squared, gender, whether the child was sick in the past year, the number of times sick, total number of health visits, and survey round and market fixed effects. Individuals without valid income data were imputed to be the median and regressions were run with a dummy variable indicating the missing value. Robust standard errors in parentheses, clustered at the family level. *** p<0.01, ** p<0.05, * p<0.1 _Source:_ Authors’ analysis based on data described in paper.

26
**Table** **<u>3: Effects of Parent Eligibility on Utilization by Child Eligibility (ITT)</u>**

||Any<br>Provider|Pharmacy|EMP|Public<br>Facilities|Private<br>Facilities|
|---|---|---|---|---|---|
|Panel A: Ever Visit|(1)|(2)|(3)|(4)|(5)|
|6 Month Subsidy|0.002|0.004|0.070***|0.018|-0.002|
||(0.020)|(0.025)|(0.015)|(0.024)|(0.023)|
|6 Month Subsidy*Ineligible|-0.055|-0.042|-0.064***|-0.040|-0.078**|
||(0.039)|(0.040)|(0.016)|(0.037)|(0.032)|
|Ineligible|0.072|0.008|0.019|0.104**|0.049|
||(0.047)|(0.048)|(0.017)|(0.043)|(0.038)|
|Observations|2996|2996|2996|2996|2996|
|R-squared|0.098|0.073|0.052|0.041|0.09|
|P-value of Enrolled +||||||
|Enrolled*Ineligible|0.139|0.296|0.473|0.516|0.002|
|Panel B: Number of Visits|(1)|(2)|(3)|(4)|(5)|
|6 Month Subsidy|0.392**|0.104|0.174***|0.065|0.035|
||(0.192)|(0.090)|(0.048)|(0.088)|(0.081)|
|6 Month Subsidy*Ineligible|-0.847***|-0.226|-0.162***|-0.103|-0.292***|
||(0.280)|(0.142)|(0.047)|(0.124)|(0.106)|
|Ineligible|0.680**|0.132|0.035|0.332**|0.188|
||(0.335)|(0.168)|(0.051)|(0.134)|(0.132)|
|Observations|2996|2996|2996|2996|2996|
|R-squared|0.105|0.07|0.046|0.033|0.057|
|P-value of Enrolled +||||||
|Enrolled*Ineligible|0.041|0.309|0.408|0.715|0.000|

_Notes:_ The sample is all children aged 15 and under (N=2996). Children age 12-15 are considered "Ineligible" and children under 11 are considered "Eligible". Above regressions are estimated coefficients from ITT estimates where "6 Month Subsidy" is the parent's random assignment status and 6 Month Subsidy*Ineligible is the interaction of these two variables. The dependent variable in Panel A is whether or not the child has visited various providers over the past year. The dependent variable in Panel B is the number of times the child has visited various providers over the past year. Regressions control for baseline measures of household size, household size squared, the inverse hyperbolic sine of parental income, parent's years of education, age of child, age of child squared, gender, whether the child was sick in the past year, the number of times sick, total number of health visits, and survey round and market fixed effects. Individuals without valid income data were imputed to be the median and regressions were run with a dummy variable indicating the missing value. Robust standard errors in parentheses, clustered at the family level. *** p<0.01, ** p<0.05, * p<0.1 _Source:_ Authors’ analysis based on data described in paper.

27
**Table 4: Effects of Parent Insurance among Ineligible Children with and without** **<u>Eligible Siblings</u>**

|Panel A: Ever Visit|Any<br>Provider<br>(1)|Pharmacy<br>(2)|EMP<br>(3)|Public<br>Facilities<br>(4)|Private<br>Facilities<br>(5)|
|---|---|---|---|---|---|
|Parent Enrolled|-0.189|-0.039|-0.015|-0.170|-0.203|
||(0.174)|(0.176)|(0.044)|(0.154)|(0.139)|
|Parent Enrolled*Eligible||||||
|Sibling|-0.073|-0.214|0.086|0.140|-0.197|
||(0.270)|(0.273)|(0.061)|(0.242)|(0.196)|
|Eligible Sibling|-0.023|-0.005|-0.013|0.001|-0.041|
||(0.057)|(0.058)|(0.012)|(0.051)|(0.043)|
|Observations|824|824|824|824|824|
|R-squared|0.032|0.019|0.035|0.043|0.014|
|P-value of Enrolled +||||||
|Enrolled*EligibleSib|0.208|0.231|0.086|0.876|0.005|
|Panel B: Number of Visits|(1)|(2)|(3)|(4)|(5)|
|Parent Enrolled|-1.972|-0.307|-0.016|-0.618|-0.981**|
||(1.284)|(0.668)|(0.065)|(0.506)|(0.467)|
|Parent Enrolled*Eligible||||||
|Sibling|0.496|-0.266|0.136|0.810|0.033|
||(1.712)|(0.910)|(0.090)|(0.742)|(0.541)|
|Eligible Sibling|-0.623|-0.173|-0.016|-0.134|-0.225|
||(0.389)|(0.195)|(0.016)|(0.157)|(0.151)|
|Observations|824|824|824|824|824|
|R-squared|0.005|0.018|0.029|0.012|-0.026|
|P-value of Enrolled +||||||
|Enrolled*EligibleSib|0.202|0.361|0.059|0.738|0.003|

_Notes:_ The sample is ineligible children aged 12 and over (N=824). A sibling is considered eligible if they are 11 or under. Above regressions are estimated coefficients from 2SLS-IV estimates where "Parent Enrolled" is instrumented with random assignment status and Eligible Sibling*Parent Enrolled is instrumented with random assignment status*Eligible Sibling. The dependent variable in Panel A is whether or not the child has visited various providers over the past year. The dependent variable in Panel B is the number of times the child has visited various providers over the past year. Regressions control for household size, household size squared, the inverse hyperbolic sine of parental income, parent's years of education, age of child, age of child squared, gender, whether the child was sick in the past year, the number of times sick, total number of health visits, and survey round and market fixed effects. Individuals without valid income data were imputed to be the median and regressions were run with a dummy variable indicating the missing value. Robust standard errors in parentheses, clustered at the family level. *** p<0.01, ** p<0.05, * p<0.1 _Source:_ Authors’ analysis based on data described in paper.

28
**Table S1: Predictors of Parent Insurance Enrollment**

||Parent-Level<br>(1)|Child-Level<br>(2)|Parent-Level<br>(3)|Child-Level<br>(4)|
|---|---|---|---|---|
|Subsidy|0.327***<br>(0.017)|0.296***<br>(0.018)|0.359***<br>(0.023)|0.309***<br>(0.021)|
|Ineligible|||-0.012|0.002|
||||(0.015)|(0.029)|
|Subsidy*Ineligible|||-0.077**|-0.046*|
||||(0.034)|(0.028)|
|Household Size|-0.012|-0.017**|-0.009|-0.018**|
||(0.008)|(0.008)|(0.009)|(0.008)|
|Household Size Squared|0.000|0.000|0.000|0.000|
||0.000|0.000|0.000|0.000|
|Age|-0.007|0.002|-0.001|-0.001|
||(0.009)|(0.006)|(0.009)|(0.007)|
|Age-Squared|0.000|0.000|0.000|0.000|
||0.000|0.000|0.000|(0.001)|
|Female|-0.002|-0.006|0.005|-0.006|
||(0.019)|(0.012)|(0.019)|(0.012)|
|Parent's Years of Education|0.002|0.004|0.002|0.003|
||(0.002)|(0.002)|(0.002)|(0.002)|
|Ever Sick in Past Year|0.000|0.022|0.000|0.022|
||(0.022)|(0.017)|(0.022)|(0.017)|
|Inv. Hyp Sin of Income|0.014*|0.009|0.014*|0.009|
||(0.007)|(0.007)|(0.007)|(0.007)|
|Forgone Treatment|0.076***|-0.072***|0.077***|0.072***|
||(0.022)|(0.024)|(0.022)|(0.025)|
|Constant|0.021|-0.050|-0.112|-0.052|
||(0.177)|(0.085)|(0.182)|(0.086)|
|R-squared|0.210|0.189|0.216|0.19|
|N|1614|2996|2996|1614|
|Round and Market Fixed Effects?|Y|Y|Y|Y|

_Notes:_ Sample in odd columns is all parents with at least one child age 15 and under at baseline; sample in even columns is all children under aged 15 years and under at baseline. Above are coefficients from OLS regressions of whether or not the child's parent enrolled in health insurance on baseline variables. Children are 'Eligible' if they are age 11 or under; children age 12 and over are 'Ineligible' for health insurance and therefore not enrolled. All regressions include market and round fixed effects. Missing income values are imputed to the mean, and an indicator variable is included in the regression. Robust standard errors in parentheses, clustered at the family level. *** p<0.01, ** p<0.05, * p<0.1.

_Source:_ Authors’ analysis based on data described in paper.

1
# **Table S2: Baseline Characteristics of Children by Age Group**

|||-----|Difference<br><br>-----Age 0-5--|Difference<br><br>--------|||-------|Difference<br><br>---Age 6-11-|Difference<br><br>---------|||-------|Difference<br><br>---Age 12-15|Difference<br><br>----------||
|---|---|---|---|---|---|---|---|---|---|
||Mean|(C-T)|P-Value|Mean|(C-T)|P-Value|Mean|(C-T)|P-Value|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|Age|2.81|-0.13|0.18|8.36|-0.06|0.47|13.23|-0.03|0.64|
|Female|0.480|-0.031|0.340|0.473|0.011|0.700|0.504|-0.060|0.099|
|Ever sick|0.859|-0.001|0.951|0.752|0.027|0.304|0.672|0.011|0.855|
|Number of times sick|2.838|0.080|0.757|1.972|-0.100|0.486|1.691|0.069|0.639|
|Forgone treatment|0.197|-0.045|0.140|0.183|-0.029|0.273|0.165|0.010|0.835|
|Ever visit health provider|0.854|-0.010|0.677|0.741|0.028|0.302|0.665|0.014|0.699|
|Total number of visits, all providers|5.157|-0.181|0.689|3.469|-0.148|0.648|2.858|-0.026|0.845|
|Total health expenditures|869.31|204.72|0.33|448.80|75.90|0.17|417.63|27.85|0.85|
|Observations|919|--|--|1253|--|--|824|--|--|

_Notes:_ Above are sample averages of selected variables by age groups of children at baseline. Columns 1,4, and 7 show averages for all children within the age category; Columns 2,5,and 8 show the difference in average characteristics between children in the Control group and children in the Treatment group; Columns 3,6, and 9 show the p-value of the difference, conditional on market and round fixed effects. Health providers consist of EMPs, public clinics, pharmacies, private hospitals, private doctors, public hospitals, and laboratory visits. All health and visit variables are reported to be during the past year. All income and expenditure data are in 2008 Cordobas. Children who were not sick in the past year are included as zeros for number of times sick and all visit/spending variables. Forgone treatment in past year due to lack of money was calculated to be zero for children who were not sick in the past year. P-values are based upon robust standard errors, clustered at the family level.*** p<0.01, ** p<0.05, * p<0.1 _Source:_ Authors’ analysis based on data described in paper.

2
**Table S3: Effects of Insurance Enrollment on Visits Among Parents**

||Any Provider|Pharmacy|EMP|Public Facilities|Private Facilities|
|---|---|---|---|---|---|
|Panel A: Ever Visit|(1)|(2)|(3)|(4)|(5)|
|Parent Enrolled|-0.053|-0.045|0.412***|-0.134*|-0.033|
||(0.048)|(0.061)|(0.037)|(0.072)|(0.072)|
|Constant|1.019***|1.173***|-0.064|1.071***|-0.419*|
||(0.156)|(0.198)|(0.120)|(0.239)|(0.223)|
|Observations|1614|1614|1614|1614|1614|
|R-squared|0.01|0.02|0.289|0.059|0.055|
|Panel B: Number of Visits|(1)|(2)|(3)|(4)|(5)|
|Parent Enrolled|0.968|0.748|1.388***|-0.624|-0.190|
||(1.000)|(0.563)|(0.203)|(0.411)|(0.292)|
|Constant|6.163*|3.332*|-0.545|5.141***|-1.085|
||(3.199)|(1.889)|(0.625)|(1.585)|(0.982)|
|Observations|1614|1614|1614|1614|1614|
|R-squared|0.046|0.017|0.141|0.053|0.038|

_Notes:_ The sample is all parents (N=1614). Above regressions are estimated coefficients from 2SLS-IV estimates where "Parent Enrolled" is instrumented with random assignment status. The dependent variable in Panel A is whether or not the parent has visited various providers over the past year. The dependent variable in Panel B is the number of times the parent has visited various providers over the past year. Regressions control for household size, household size squared, the inverse hyperbolic sine of parental income, parent's years of education, gender, whether the parent was sick in the past year, the number of times sick, total number of health visits, and survey round and market fixed effects. Individuals without valid income data were imputed to be the median and regressions were run with a dummy variable indicating the missing value. Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1 _Source:_ Authors’ analysis based on data described in paper.

3
**Table S4: Effects of Insurance Enrollment on Expenditures Among Parents**

||All Providers|Pharmacy|Public Facilities|Private Facilities|
|---|---|---|---|---|
|Panel A: Ever Spend|(1)|(2)|(3)|(4)|
|Parent Enrolled|-0.074|-0.080|-0.017|-0.035|
||(0.057)|(0.062)|(0.012)|(0.072)|
|Constant|1.054***|1.175***|0.012|-0.419*|
||(0.191)|(0.201)|(0.035)|(0.222)|
|Observations|1,614|1,614|1,614|1,614|
|R-squared|0.014|0.024|0.000|0.055|
|Panel B: Inv. Hyperbolic Expenditures|(1)|(2)|(3)|(4)|
|Parent Enrolled|-0.417|-0.465|-0.041|-0.150|
||(0.425)|(0.421)|(0.049)|(0.447)|
|Constant|4.413***|5.519***|0.081|-2.772**|
||(1.370)|(1.334)|(0.145)|(1.399)|
|Observations|1,614|1,614|1,614|1,614|
|R-squared|0.036|0.036|0.005|0.061|

_Notes:_ The sample is all parents with at least one child aged 15 and underat baseline (N=1614). Above regressions are estimated coefficients from 2SLS-IV estimates where "Parent Enrolled" is instrumented with random assignment status. The dependent variable in Panel A is whether or not the child had any health expenditures at various providers over the past year. The dependent variable in Panel B is the inverse hyperbolic sine of expenditures at various providers over the past year. Note that this specification could not be estimated for EMP expenditures because the sample mean for parents was zero. Regressions control for baseline measures of household size, household size squared, the inverse hyperbolic sine of parental income, parent's years of education, age of child, age of child squared, gender, whether the child was sick in the past year, the number of times sick, total number of health visits, and survey round and market fixed effects. Individuals without valid income data were imputed to be the median and regressions were run with a dummy variable indicating the missing value. Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1 _Source:_ Authors’ analysis based on data described in paper.

4
**Table S5: Robustness of Results to Using Differences as Outcomes**

|Panel A: Number of Visits|Any Provider<br>(1)|Pharmacy<br>(2)|EMP<br>(3)|Public Facilities<br>(4)|Private Facilities<br>(5)|
|---|---|---|---|---|---|
|Parent Enrolled|1.976**|0.380|0.632***|0.199|0.773|
||(1.006)|(0.451)|(0.210)|(0.362)|(0.539)|
|Parent Enrolled*Ineligible|-3.818***|-0.501|-0.694***|-0.586|-1.952***|
||(1.458)|(0.687)|(0.201)|(0.556)|(0.733)|
|Ineligible|0.623|-0.073|0.071|0.240|0.535*|
||(0.551)|(0.239)|(0.087)|(0.167)|(0.320)|
|Observations|2,996|2,996|2,996|2,996|2,996|
|R-squared|0.075|0.093|0.049|0.033|0.007|
|P-value of Enrolled + Enrolled*Ineligible|0.124|0.835|0.377|0.431|0.0239|
|Panel B: OOP Expenditures|(1)|(2)|(3)|(4)|(5)|
|Parent Enrolled|-256.345|-309.222|-0.227|7.215|16.148|
||(322.184)|(271.928)|(1.034)|(4.903)|(67.235)|
|Parent Enrolled*Ineligible|-224.738|220.549|0.287|-11.948|-299.724**|
||(408.730)|(316.273)|(1.076)|(8.590)|(136.769)|
|Ineligible|-43.900|-104.597|0.358|2.816|62.639|
||(158.980)|(127.769)|(0.274)|(1.937)|(47.861)|
|Observations|2,996|2,996|2,996|2,996|2,996|
|R-squared|0.029|0.027|0.006|0.004|0.01|
|P-value of Enrolled + Enrolled*Ineligible|0.135|0.715|0.742|0.526|0.0201|

_Notes:_ The sample is all children aged 15 and under (N=2996). Children age 12-15 are considered "Ineligible" and children under 11 are considered "Eligible". Above regressions are estimated coefficients from 2SLS-IV estimates where "Parent Enrolled" is instrumented with random assignment status and Parent Enrolled*Ineligible is instrumented with random assignment status*Ineligible. The dependent variable in Panel A is the difference in utilization at a given provider between baseline and follow-up. The dependent variable in Panel B is the difference in expenditures at various providers over the past year between baseline and follow-up. Regressions control for baseline measures of household size, household size squared, the inverse hyperbolic sine of parental income, parent's years of education, age of child, age of child squared, gender, whether the child was sick in the past year, the number of times sick, total number of health visits, and survey round and market fixed effects. Individuals without valid income data were imputed to be the median and regressions were run with a dummy variable indicating the missing value. Robust standard errors in parentheses, clustered at the family level. *** p<0.01, ** p<0.05, * p<0.1 _Source:_ Authors’ analysis based on data described in paper.

5
# **Table S6: Effects of Parent Insurance on Out of Pocket Expenditures by Child Eligibility**

||Any Provider|Pharmacy|EMP|Public Facilities|Private Facilities|
|---|---|---|---|---|---|
|Panel A: Ever Spend|(1)|(2)|(3)|(4)|(5)|
|Parent Enrolled|-0.048|-0.030|0.006|-0.012|-0.032|
||(0.079)|(0.081)|(0.004)|(0.009)|(0.073)|
|Parent Enrolled*Ineligible|-0.115|-0.132|-0.006|0.014|-0.298**|
||(0.148)|(0.149)|(0.004)|(0.010)|(0.117)|
|Ineligible|0.004|-0.003|0.001|-0.002|0.045|
||(0.048)|(0.049)|(0.001)|(0.001)|(0.038)|
|Observations|2996|2996|2996|2996|2996|
|R-squared|0.07|0.066|0.009|0.001|0.074|
|P-value of Enrolled + Enrolled*Ineligible|0.242|0.244|0.812|0.332|0.001|
|Panel B: Inv. Hyperbolic Sine Expenditures|(1)|(2)|(3)|(4)|(5)|
|Parent Enrolled|-0.401|-0.224|0.031|-0.024|-0.288|
||(0.532)|(0.521)|(0.022)|(0.034)|(0.439)|
|Parent Enrolled*Ineligible|-1.256|-1.139|-0.030|0.029|-1.858***|
||(0.969)|(0.931)|(0.021)|(0.037)|(0.711)|
|Ineligible|0.274|0.217|0.005|-0.008|0.295|
||(0.321)|(0.309)|(0.005)|(0.005)|(0.233)|
|Observations|2996|2996|2996|2996|2996|
|R-squared|0.094|0.09|0.009|0.005|0.081|
|P-value of Enrolled + Enrolled*Ineligible|0.066|0.114|0.812|0.398|0.001|

_Notes:_ The sample is all children aged 15 and under (N=2996). Children age 12-15 are considered "Ineligible" and children under 11 are considered "Eligible". Above regressions are estimated coefficients from 2SLS-IV estimates where "Parent Enrolled" is instrumented with random assignment status and Parent Enrolled*Ineligible is instrumented with random assignment status*Ineligible. The dependent variable in Panel A is whether or not the child had any health expenditures at various providers over the past year. The dependent variable in Panel B is the inverse hyperbolic sine of expenditures at various providers over the past year. Regressions control for baseline measures of household size, household size squared, the inverse hyperbolic sine of parental income, parent's years of education, age of child, age of child squared, gender, whether the child was sick in the past year, the number of times sick, total number of health visits, and survey round and market fixed effects. Individuals without valid income data were imputed to be the median and regressions were run with a dummy variable indicating the missing value. Robust standard errors in parentheses, clustered at the family level. *** p<0.01, ** p<0.05, * p<0.1 _Source:_ Authors’ analysis based on data described in paper.

6
# **Table S7: GLS Estimates (Robustness for Expenditures)**

||All Providers|Pharmacy|EMP|Providers|Providers|
|---|---|---|---|---|---|
|Panel A: Nonlinear Least Squares||||||
|Estimates of Expenditures|(1)|(2)|(3)|(4)|(5)|
|6 Month Subsidy|0.043|0.049|11.267|2.107*|-0.130|
||(0.115)|(0.113)|0.000|(1.243)|(0.155)|
|6 Month Subsidy*Ineligible|-0.562***|-0.327*|-11.267|-2.107|-1.167***|
||(0.213)|(0.184)|0.000|0.000|(0.366)|
|Ineligible|-0.298**|-0.538***|-13.000|-23.506|0.102|
||(0.143)|(0.125)|0.000|0.000|(0.270)|
|Observations|2,996|2,996|2,996|2,996|2,996|
|R-squared|0.152|0.154|0.001|0|0.071|
|Panel B: Tobit Estimates of Expenditures|(1)|(2)|(3)|(4)|(5)|
|Parent Enrolled|-15.987|20.026|-|-|-158.181|
||(241.807)|(182.123)|-|-|(150.165)|
|Parent Enrolled*Ineligible|-945.9498***|-461.187|-|-|-1171.151***|
||(458.040)|(306.545)|-|-|(417.655)|
|Ineligible|172.445|71.945|-|-|212.1187*|
||(141.860)|(100.934)|-|-|(123.173)|
|Observations|2996|2996|-|-|2996|
|P-value of Enrolled + Enrolled*Ineligible|0.019|0.102|-|-|0.001|

_Notes:_ The sample is all children aged 15 and under (N=2996). Children age 12-15 are considered "Ineligible" and children under 11 are considered "Eligible". Panel A present estimated coefficients from non-linear least square specifications where "6 Month Subsidy" is the parent's random assignment status and 6 Month Subsidy*Ineligible is the interaction of these two variables. Panel B presents estimates from an IV Tobit specification where "Parent Enrolled" is instrumented with parent's random assignment status and "Parent Enrolled*Ineligible" is instrumented with the interaction of the parent's six month subsidy and Ineligible. The dependent variable is the expenditures at various providers over the past year. EMP and public health sector facilities are omitted from Panel B because estimates would not converge. Regressions control for baseline measures of household size, household size squared, the inverse hyperbolic sine of parental income, parent's years of education, age of child, age of child squared, gender, whether the child was sick in the past year, the number of times sick, total number of health visits, and survey round and market fixed effects. Individuals without valid income data were imputed to be the median and regressions were run with a dummy variable indicating the missing value. Robust standard errors in parentheses, clustered at the family level. *** p<0.01, ** p<0.05, * p<0.1 _Source:_ Authors’ analysis based on data described in paper.

7
**Table S8: Reporting of Primary Results according to CONSORT Guidelines**

|||-------<br>Treatment|Follow-<br>--------------<br>(N=1131)|---Eligible<br>Control(|Follow-<br>Kids--------<br>N=1041)|----------------|<br>Adjusted<br>Difference*<br>(95% CI)||-------<br>Treatment|Follow-<br>---------------<br>(N=442)|--Ineligible<br>Control|Kids-----------<br>(N=382)|-------------|<br>Adjusted<br>Difference*<br>(95% CI)|Adjusted Dif-in-<br>Dif* (95% CI)|
|---|---|---|---|---|---|---|---|---|---|---|---|
||Baseline|Up|Baseline|Up||Baseline|Up|Baseline|Follow-Up|||
|Panel A: Visits|(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|
|Total|4.09<br>(5.056)|3.75<br>(4.272)|4.28<br>(4.281)|3.36<br>(3.863)|0.403<br>( 0.013, 0.792)<br>p= 0.043|2.85<br>(3.914)|1.94<br>(2.928)|2.87<br>(4.165)|2.39<br>(3.191)|-0.467<br>(-0.915,-0.018)<br>p=0.041|-0.857<br>(-1.414, -0.300)<br>p=0.003|
|EMP|0.11<br>(1.129)|0.33<br>(1.062)|0.13<br>(0.889)|0.16<br>(0.787)|0.175<br>(0 .080, 0.270)<br>p=0.000|0.02<br>(0.184)|0.02<br>(0.206)|0.00<br>0.000|0.01<br>(0.135)|0.013<br>(-0.011,0.037)<br>p= 0.302|-0.164<br>(-0.258, -0.070)<br>p= 0.001|
|Private Providers|0.96<br>(1.717)|0.72<br>(2.029)|1.12<br>(3.524)|0.66<br>(1.369)|0.062<br>( -0.100, 0.224)<br>p=0.455|0.64<br>(2.247)|0.20<br>(0.603)|0.57<br>(1.386)|0.44<br>(1.292)|-0.248<br>(-0.394, -0.102)<br>p=0.001|-0.304<br>(-0.514,-0.093)<br>p=0.005|
|Panel B: Inv. Hyperbolic||||||||||||
|Expenditures|(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|
|Total|4.82<br>(3.169)|4.02<br>(3.317)|4.59<br>(3.238)|4.13<br>(3.288)|-0.077<br>( -0.412, 0.257)<br>p=0.650|3.81<br>(3.236)|2.71<br>(3.121)|3.82<br>(3.261)|3.13<br>(3.334)|-0.437<br>(-0.903,0.029)<br>p=0.066|-0.340<br>(-0.865, 0.184)<br>p=0.203|
|EMP|0.01<br>(0.239)|0.01<br>(0.223)|0.01<br>(0.164)|0.00<br>--|0.01<br>(-0.004, 0.023)<br>p=0.158|0.00<br>--|0.00<br>--|0.00<br>--|0.00<br>--|--<br>--|-0.01<br>(-0.022,0.004)<br>p=0.159|
|Private Providers|2.07<br>(2.884)|1.62<br>(2.673)|1.98<br>(2.869)|1.67<br>(2.723)|-0.021<br>(-0.300, 0.258)<br>p=0.881|1.34<br>(2.469)|0.65<br>(1.849)|1.27<br>(2.458)|1.19<br>(2.472)|-0.547<br>(-0.854,-0.239)<br>p=0.001|-0.508<br>(-0.892,-0.125)<br>p=0.009|

_Notes:_ "Treatment" refers to whether or not the parent respondent was randomly allocated a 6 Month Subsidy for health insurance. Columns 1-4 and 6-9 contain unconditional mean estimates (and sd) for each sample listed for the visits overall, to EMPs, and to private providers (Panel A) or the inverse hyperbolic sine overall, at EMPs, and at private providers (Panel B). Columns 5 and 10 contain the regression-adjusted differences of follow-up measures between the Treatment and Control group, adjusted for round and market fixed effects (i.e., the sampling strategy). Column 11 contains the difference-in-difference estimate between eligible and ineligible children across the treatment and the control groups, adjusted for round and market fixed effects. 95% confidence intervals are displayed between each difference and are calculated based upon standard errors clustered at the family level. The p-value of the t-test on the displayed coefficent is below the 95% confidence interval.

_Source:_ Authors’ analysis based on data described in paper.

8
**Table S9: Treatment Effect Estimates Using Baseline Variables as Outcomes (Visits)**

||Any Provider|Pharmacy|EMP|Public Facilities|Private Facilities|
|---|---|---|---|---|---|
|Panel A: Ever Visit|(1)|(2)|(3)|(4)|(5)|
|Parent Enrolled|-0.011|0.020|-0.037|0.043|0.010|
||(0.014)|(0.055)|(0.033)|(0.073)|(0.075)|
|Parent Enrolled*Ineligible|-0.004|-0.057|0.076**|-0.074|0.025|
||(0.024)|(0.087)|(0.037)|(0.125)|(0.123)|
|Ineligible|-0.002|0.010|-0.014|0.040|-0.008|
||(0.007)|(0.028)|(0.013)|(0.040)|(0.039)|
|Observations|2996|2996|2996|2996|2996|
|R-squared|0.957|0.599|0.015|0.184|0.197|
|P-value of Enrolled + Enrolled*Ineligible|0.503|0.626|0.044|0.782|0.752|
|Panel B: Number of Visits|(1)|(2)|(3)|(4)|(5)|
|Parent Enrolled|-0.716|-0.044|-0.068|0.011|-0.663|
||(0.837)|(0.354)|(0.198)|(0.301)|(0.489)|
|Parent Enrolled*Ineligible|0.815|-0.300|0.175|0.231|0.860|
||(1.156)|(0.517)|(0.183)|(0.416)|(0.660)|
|Ineligible|0.067|0.207|-0.034|0.093|-0.343|
||(0.463)|(0.183)|(0.082)|(0.129)|(0.302)|
|Observations|2996|2996|2996|2996|2996|
|R-squared|0.205|0.216|0.021|0.12|0.051|
|P-value of Enrolled + Enrolled*Ineligible|0.918|0.437|0.077|0.499|0.687|

_Notes:_ The sample is all children aged 15 and under (N=2996). Children age 12-15 are considered "Ineligible" and children under 11 are considered "Eligible". Above regressions are estimated coefficients from IV estimates where "Parent Enrolled" is instrumented with the parent's random assignment status and Parent Enrolled*Ineligible is the interaction of these two variables. The dependent variable in Panel A is whether or not the child has visited various providers over the past year as of baseline. The dependent variable in Panel B is the number of times the child has visited various providers over the past year. Regressions control for baseline measures of household size, household size squared, the inverse hyperbolic sine of household income, parent's years of education, age of child, age of child squared, gender, whether the child was sick in the past year, the number of times sick, total number of health visits, and survey round and market fixed effects. Individuals without valid income data were imputed to be the median and regressions were run with a dummy variable indicating the missing value. Robust standard errors in parentheses, clustered at the family level. *** p<0.01, ** p<0.05, * p<0.1 _Source:_ Authors’ analysis based on data described in paper.

9
**Table S10: Treatment Effect Estimates Using Baseline Variables as Outcomes (Expenditures)**

||Any Provider|Pharmacy|EMP|Public Facilities|Private Facilities|
|---|---|---|---|---|---|
|Panel A: Ever Spend|(1)|(2)|(3)|(4)|(5)|
|Parent Enrolled|0.057|0.030|0.003|0.028|0.011|
||(0.049)|(0.058)|(0.005)|(0.018)|(0.075)|
|Parent Enrolled*Ineligible|-0.026|-0.104|-0.003|0.003|0.054|
||(0.075)|(0.093)|(0.005)|(0.034)|(0.122)|
|Ineligible|0.003|0.009|-0.001|-0.011|-0.026|
||(0.024)|(0.029)|(0.001)|(0.011)|(0.038)|
|Observations|2996|2996|2996|2996|2996|
|R-squared|0.674|0.565|0.007|0.016|0.192|
|P-value of Enrolled + Enrolled*Ineligible|0.644|0.370|0.935|0.343|0.552|
|Panel B: Inv. Hyp. Sin of Expenditures|(1)|(2)|(3)|(4)|(5)|
|Parent Enrolled|0.408|0.243|0.018|0.064|0.041|
||(0.368)|(0.397)|(0.027)|(0.082)|(0.445)|
|Parent Enrolled*Ineligible|-0.320|-0.766|-0.019|0.087|0.199|
||(0.568)|(0.624)|(0.028)|(0.184)|(0.720)|
|Ineligible|0.109|0.138|-0.007|-0.082|-0.106|
||(0.190)|(0.203)|(0.006)|(0.052)|(0.229)|
|Observations|2996|2996|2996|2996|2996|
|R-squared|0.609|0.521|0.006|0.019|0.2|
|P-value of Enrolled + Enrolled*Ineligible|0.862|0.345|0.905|0.398|0.707|

_Notes:_ The sample is all children aged 15 and under (N=2996). Children age 12-15 are considered "Ineligible" and children under 11 are considered "Eligible". Above regressions are estimated coefficients from IV estimates where "Parent Enrolled" is instrumented with the parent's random assignment status and Parent Enrolled*Ineligible is the interaction of these two variables. The dependent variable in Panel A is whether or not the child had any health expenditures at various providers over the past year. The dependent variable in Panel B is the inverse hyperbolic sine of expenditures at various providers over the past year. Regressions control for baseline measures of household size, household size squared, the inverse hyperbolic sine of parental income, parent's years of education, age of child, age of child squared, gender, whether the child was sick in the past year, the number of times sick, total number of health visits, and survey round and market fixed effects. Individuals without valid income data were imputed to be the median and regressions were run with a dummy variable indicating the missing value. Robust standard errors in parentheses, clustered at the family level. *** p<0.01, ** p<0.05, * p<0.1 _Source:_ Authors’ analysis based on data described in paper.

10
**Table S11: Out-of-Pocket Expenditures Based upon Sibling Eligibility**

||All Providers|Pharmacy|EMP|Public Facilities|Private Facilities|
|---|---|---|---|---|---|
|Panel A: Eligible Children|(1)|(2)|(3)|(4)|(5)|
|Parent Enrolled|-0.401|-0.161|0.041|-0.021|-0.275|
||(0.553)|(0.542)|(0.029)|(0.039)|(0.485)|
|Ineligible Sibling*Parent Enrolled|0.194|-0.101|-0.050|-0.010|-0.110|
||(1.526)|(1.493)|(0.036)|(0.065)|(1.140)|
|Ineligible Sibling|-0.271|-0.194|0.002|-0.009|0.014|
||(0.298)|(0.293)|(0.002)|(0.019)|(0.233)|
|Observations|2,172|2,172|2,172|2,172|2,172|
|R-squared|0.089|0.083|0.012|0.007|0.092|
|P-value of Enrolled + Enrolled*Ineligible|0.885|0.852|0.405|0.572|0.709|
|Panel B: Ineligible Children|(1)|(2)|(3)|(4)|(5)|
|Parent Enrolled|-1.093|-0.843|-|-|-1.573*|
||(1.154)|(1.101)|-|-|(0.863)|
|Eligible Sibling*Parent Enrolled|-1.180|-1.056|-|-|-1.112|
||(1.757)|(1.678)|-|-|(1.193)|
|Eligible Sibling|-0.246|-0.196|-|-|-0.322|
||(0.382)|(0.362)|-|-|(0.276)|
|Observations|824|824|-|-|824|
|R-squared|0.029|0.031|-|-|-0.008|
|P-value of Enrolled + Enrolled*Ineligible|0.090|0.138|-|-|0.001|

_Notes:_ The sample in Panel A is all children age 11 and under at baseline; the sample in Panel B is all children age 12 and over at baseline. "Ineligble Sibling" refers to a sibling in the household age 12 and over at baseline; "Eligible SIbling" refers to a sibling age 11 and under at baseline. Above regressions are estimated coefficients from 2SLS-IV estimates where "Parent Enrolled" is instrumented with random assignment status and "Ineligible/Eligible Sibling*Parent Enrolled" is instrumented with "Inelgible Sibling" or "Eligible Sibling" times random assignment status. The dependent variables are the inverse hyperbolic sine of expenditures at various providers over the past year.Note that the regressions of EMP and public facility expenditures as dependent in Panel B could not be estimated due to insufficient variation. Regressions control for baseline measures of household size, household size squared, the inverse hyperbolic sine of parental income, parent's years of education, age of child, age of child squared, gender, whether the child was sick in the past year, the number of times sick, total number of health visits, and survey round and market fixed effects. Individuals without valid income data were imputed to be the median and regressions were run with a dummy variable indicating the missing value. Robust standard errors in parentheses, clustered at the family level. *** p<0.01, ** p<0.05, * p<0.1 _Source:_ Authors’ analysis based on data described in paper.

11
**Table S12: Effects of Parent Insurance for Eligible children with/out an Ineligible Sibling**

||Any Provider|Pharmacy|EMP|Public Facilities|Private Facilities|
|---|---|---|---|---|---|
|Panel A: Ever Visit|(1)|(2)|(3)|(4)|(5)|
|Parent Enrolled|0.014|0.004|0.222***|0.067|0.006|
||(0.065)|(0.082)|(0.052)|(0.084)|(0.081)|
|Parent Enrolled*Ineligible Sibling|-0.009|0.075|0.023|-0.018|-0.066|
||(0.200)|(0.233)|(0.113)|(0.211)|(0.195)|
|Ineligible Sibling|-0.039|-0.059|0.004|-0.018|0.007|
||(0.038)|(0.046)|(0.019)|(0.042)|(0.039)|
|Observations|2172|2172|2172|2172|2172|
|R-squared|0.073|0.061|0.144|0.04|0.075|
|P-value of Enrolled + Enrolled*Ineligible|0.982|0.716|0.016|0.800|0.731|
|Panel B: Number of Visits|(1)|(2)|(3)|(4)|(5)|
|Parent Enrolled|1.905***|0.530*|0.633***|0.417|0.257|
||(0.686)|(0.315)|(0.172)|(0.306)|(0.311)|
|Parent Enrolled*Ineligible Sibling|-3.206*|-0.945|-0.349|-0.986|-0.768|
||(1.690)|(0.812)|(0.334)|(0.780)|(0.624)|
|Ineligible Sibling|0.445|0.114|0.042|0.199|0.098|
||(0.336)|(0.162)|(0.069)|(0.160)|(0.117)|
|Observations|2172|2172|2172|2172|2172|
|R-squared|0.067|0.05|0.1|0.027|0.037|
|P-value of Enrolled + Enrolled*Ineligible|0.402|0.583|0.324|0.430|0.332|

_Notes:_ The sample is covered children aged 11 and under (N=2172). Siblings are ineligible if they are 12-15 at baseline. Above regressions are estimated coefficients from 2SLS-IV estimates where "Parent Enrolled" is instrumented with random assignment status Parent Enrolled*Ineligible Sibling is instrumented with random assignment status*Eligible Sibling. The dependent variable in Panel A is whether or not the child has visited various providers over the past year. The dependent variable in Panel B is the number of times the child has visited various providers over the past year. Regressions control for household size, household size squared, the inverse hyperbolic sine of parental income, parent's years of education, age of child, age of child squared, gender, whether the child was sick in the past year, the number of times sick, total number of health visits, and survey round and market fixed effects. Individuals without valid income data were imputed to be the median and regressions were run with a dummy variable indicating the missing value. Robust standard errors in parentheses, clustered at the family level. *** p<0.01, ** p<0.05, * p<0.1 _Source:_ Authors’ analysis based on data described in paper.

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# **Table S13: Effects of Parent Insurance on Health Indicators by Child Eligibility**

||Checkup|Ever Sick|Times Sick|
|---|---|---|---|
|Panel A: Effects on Parents|(1)|(2)|(3)|
|Parent Enrolled|0.127*|-0.035|0.841|
||(0.072)|(0.042)|(1.935)|
|Observations|1,614|1,614|1,614|
|R-squared|0.029|0.014|0.013|
|Panel B: Effects on Children|(1)|(2)|(3)|
|Parent Enrolled|-0.040|0.014|0.676**|
||(0.071)|(0.064)|(0.336)|
|Parent Enrolled*Ineligible|-0.009|-0.192|-1.740***|
||(0.111)|(0.144)|(0.613)|
|Ineligible|0.028|0.062|0.456**|
||(0.034)|(0.048)|(0.215)|
|Observations|2996|2996|2996|
|R-squared|0.045|0.09|0.079|
|P-value of Enrolled + Enrolled*Ineligible|0.638|0.198|0.053|

_Notes:_ The sample in Panel A is all parents with a child aged 15 and under (N=1614); the sample in Panel B is all children aged 15 and under (N=2996). Children age 12-15 are "Ineligible" and children under 11 are "Eligible". Above regressions are estimated coefficients from 2SLS-IV estimates where "Parent Enrolled" is instrumented with random assignment status; Ineligible*Parent Enrolled is instrumented with random assignment status*Ineligible. The dependent variable are various measures of health status: whether the child had a checkup in the past year, whether the child had ever been sick in the past year, and the number of times sick. Regressions control for household size, household size squared, the inverse hyperbolic sine of parental income, parent's years of education, age of child, age of child squared, gender, whether the child was sick in the past year, the number of times sick, total number of health visits, and survey round and market fixed effects. Individuals without valid income data were imputed to be the median and regressions were run with a dummy variable indicating the missing value. Robust standard errors in parentheses, clustered at the family level. *** p<0.01, ** p<0.05, * p<0.1 _Source:_ Authors’ analysis based on data described in paper.

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