Ellen Magenheim:	I'm Ellen Magenheim from the economics department, and it's really a pleasure for me to introduce my colleague, Erin Bronchetti, today. Erin's been at Swarthmore since 2007, which is also the year in which she completed her Ph. D at Northwestern University. Her specializations were in labor economics, development economics, and account metrics. Her dissertation work was on worker's compensation, and just in case, some of you may not be familiar with this particular form of social insurance, worker's compensation is a social insurance program that provides income support and medical benefits for people who are injured on the job, for workers injured on the job. 
	She's continued to work in that area while also expanding into other areas of social insurance and social welfare. It's interesting that, in a conversation Erin and I had when she first came, she told me that her interest in economics and in the particular fields of economics in which she teaches and does research, really got started by one book she read as an undergraduate, and that book is a book called Uneven Tides by Shelden Danziger and Peter Gottschalk, and it was one of the first books to look at increasing income inequality from the 1970s to the 1990s. We obviously all know that's continued to be a very important issue.
	With that starting point in mind, I just wanted to take you briefly through some of her research to give you an idea of how the different pieces fit together and how it really is a direct extension of that book and its influence on her.
	Erin's teaching portfolio includes econometrics, public economics, and she's designing a seminar that we hope to be able to offer fairly soon in labor economics and income inequality, so again, on that very important topic.
	Her research falls into three general areas. So first of all, there's the work that I've referred to earlier on worker's compensation. She has a number of publications in that area. Some of it she's done as a solo author, some she's done with a colleague, Melissa McInerney at the College of William and Mary, and in these various papers, what Erin and sometimes Erin and Melissa has done is either look at relatively underexplored areas of research and worker's compensation, or else look at areas that other researchers have looked at where there's conventional wisdom, and in some cases actually show that the conventional wisdom is wrong. 
	I'm not going to take the time to go into all the details, but she's gotten new insights into the effect, both the benefits of worker's compensation programs and how they affect household consumption, but also the ways in which firms respond to changes in benefit levels and worker's response to changes in benefit levels. 
	The second area of research is some community-based research that Erin, David Huffman, and I have done. Oh, I'm sorry, I did want to mention that in support of her work on worker's compensation, Erin received the John Jones Scholar and Worker's Compensation Award, and that was in support, again, of that work on worker's compensation. So Erin, David, and I have done some studies that have involved community-based research. We did a project where we looked at savings behavior by low-income tax buyers. We're currently involved in a six-campus study looking at social networks and health behavior. The study we're doing right now is supported by the Donaghue Foundation in collaboration with the Robert Wood Johnson Foundation. 
	We're also working to design a project, we already have a community partner, we just need to design the project, to look at food expenditures and consumption among the low income population, and our particular interest is on the relationship between that behavior and the health effects of the decisions that the consumers make. 
	A third strand of Erin's research incorporates the work that she's gonna talk about today, and that's looking at the well-being of children. She has a project going on with a co-author who she's worked with before, Dennis Sullivan. They've previously done work looking at cross-national comparison of income packages for families with children. The work they're doing right now looks at the investments that parents make in children, investments in both time and money, and they're looking at how those investments vary depending on the health of children, so whether parents invest more in healthy children or sick children or whether there's other discernible patterns, and that work's going on now. 
	I'm not gonna discuss the work that she'll present today, because of course she will, but I do want to mention that this is work that she did while she was on sabbatical. She was at Princeton University at the Woodrow Wilson school, in the Office of Population Research and the, it's called the Blenheim Center for Research on Child Well-Being, which is an eminent research center for policy affecting children and families, and her research during that sabbatical yeah was, she received a grant from the foundation, a Young Scholar's Award from the Foundation for Child Development, which is a very prestigious and competitive award. 
	So with that, I'll turn this over to Erin. 
Erin Bronchetti:	Great, so thank you to all of you for being here, thanks to Ellen for probably an overly generous introduction. I think she might have made me an expert in development economics, which I can't claim to be. But, thank you to all of you for coming out during a very busy week of classes. I'm excited to be back, starting to be covered in chalk again, and just excited to be beginning another academic year at Swarthmore. 
	And, I'm looking forward to telling you a little bit about the research I've been doing today. So, Ellen sort of alluded to the fact that my research has taken a bit of a portfolio approach, in that I've delved into topics, or several different topic areas, and the way I see it, they're sort of all interconnected by this fundamental interest in social welfare policy and in social insurance programs. 
	But not just that, they're also connected, and Ellen sort of mentioned this as well, they're also connected by an interest now just in the costs of these programs in terms of their incentive effects on individual behavior, but also in the benefits of the programs and the extent to which they protect the wellbeing of their target populations. 
	I'd sort of make the argument that the focus in some areas of economics, at least, is a little bit, that the balance is off between looking at the cost side of things and the benefit side of things, and I'm trying to add some weight on one side of that, and you'll see that come up today. 
	Okay, so today's work is some of the work I'm doing on health and on the immigrant population in the United States, and in particular, I'm looking at estimating the impacts of making children eligible for public health insurance, namely Medicaid and the state Children's Health Insurance Program, or CHIP, on healthcare utilization or access and health outcomes among children in immigrant families. 
	I want to focus your attention on what I think are two of the main contributions of this research, and one is that by looking at outcomes related to healthcare utilization and health itself, I'm actually trying to shift the focus of this narrow part of the literature a little bit too. 
	So the vast majority of research on expansions in public insurance programs, in economics at least, has to do with the impacts of those expansions on enrollment in the programs. If we make more children eligible, how many more children would be enrolled, or covered by insurance? And that's an important policy question, of course. It sounds like a sort of straightforward one, it turns out to be very complicated, and we may come back to that a little bit later. There's a great deal of research on it, and there's far less knowledge about what the actual benefits of these programs are in terms of the health of children or their ability to access care. 
	And then the second contribution is to focus on this population of children in immigrant families, which are defined as children who are first-generation immigrants themselves or who live with at least one foreign-born parent. I think this is a population that's sort of underrepresented in both the research and in the policy discussion, at least relevant to its growing importance, so we'll talk a little bit about that.
	In this audience, I may not need to do as much convincing that the population of children in immigrant families is an important one to study and an important one for policy makers to worry about, but I want to give you some context and background in terms of what's been going on with this population. So there's been rapid growth over the past 20 years in the group of children in immigrant families, and you can see that here, if we just look at census data, so from 1990 to 2010, we see that the number of children in immigrant families in the United States has more than doubled, from about 8 million in 1990 to 17 million in 2010. That rate of growth is more than 7 times as fast as the rate of growth in the number of children in native families. 
	What that means is that immigrant children, or children in immigrant families have been making up an increasing share of the child population, so in 1990, fewer than one in six children belonged to an immigrant family, about 15%. By 2010, one in every four children in the United States belonged to an immigrant family. It was either first generation immigrant or the child of an immigrant parent. 24%, to be precise. 
	These children are likely to make up more than a quarter of the next generation of young adults and young workers, and as children, they're disproportionately represented among the poor and uninsured. So when I start looking in my data set at sample averages for these groups of children and to be clear, immigrant children are first generation immigrants, children of immigrants are the second generation immigrants who live with a foreign-born parent but are US born citizens, and children of natives are children of US natives who are US born. When I start looking at the differences and various outcomes for these populations, disparities jump right out.
	When we look at the fraction of children in the US with family incomes below the federal poverty line, 40% of first generation immigrants live in poverty, about 25, 26% of second generation immigrants live in poor families and children of natives, the percentage is about 16 or 17%. Just for context, for those of you who may not know, the federal poverty line in 2012 was 23,000 for a family of four in terms of annual income. 19,000 for a family of three, so that's something worth keeping in mind as we go throughout the talk, actually.
	When we look at health insurance coverage, the disparities are even more striking. So the first generation immigrants, nearly half of these children have no health insurance coverage. Now, children of immigrants, these are US born children, so they're citizens, they're eligible for the same public programs on the same terms as US born children of natives, and yet they're still twice as likely to be uninsured as children of natives. 
	Okay, so what else? When I look at other sample averages, when I start looking at the outcomes I'm interested in, which are healthcare access or utilization outcomes and health outcomes themselves, more disparities jump out. These are just sample averages, so I'll walk you through them. You don't need to worry about all the numbers. 
	Switch to this side, so the first outcome I'm looking at here, and hopefully the fonts aren't gonna get too small, is a child going without a doctor's visit over 12 months, the past 12 months. I'm thinking of that as a key measure of access, and the reason is, as many of you who are, some of you who are parents of young children may know, the American Academy of Pediatrics recommends something like 6 to 7 visits in the first year of life, 2 to 3 visits in the second and third years of life, and at least one visit every year from age 3 - 17, and these are well-child visits, so every child should be having at least one visit in my sample, and I'm gonna look at having gone a year without a doctor's visit as a true, truly indicative of an access problem. 
	We see that 34% of immigrant children, first generation immigrant children have gone without a doctor's visit and children of immigrants are also more likely than natives to have gone without a visit. The number of doctor's visits' conditional on having at least one, the pattern goes the same way. Children in immigrant families are likely to have had a lower average number of visits over the course of the year. Whether a child has a usual place for care is another outcome we might consider. The reason we might think that's an important measure of access to healthcare is because of the medical home model, so if you think about the public health or medical literature that says that having a medical home, seeing the same primary care physician and so on when you're sick can be helpful not only for your healthcare access but also improves your satisfaction with your healthcare and improves some health outcomes as well. So we may think this is another important measure, and we see that children in immigrant families do worse on that measure as well. 
	You can't necessarily see past the red, but this is a health outcome, so an outcome directly related to health, not just access and utilization, and its the parent's report of the child's health status on a scale of one to five, where one is excellent and five is poor. So a higher number is worse. Sure enough, we see the same pattern. At least according to their parents, first generation immigrant children and children of immigrants are less healthy than children of natives.
	You might look at these and say, "Well, those are just sample averages. They don't mean that much. They're probably due to differential poverty rates, and that poverty is correlated with poor healthcare and poor health outcomes, so that must be what it's explaining," but when we go over here in subset, just down to the sample of children who are in poverty, living in families with income below the poverty line, the same patterns are uphill, so it can't just be poverty that's explaining those disparities. 
	The last factor is that over the period from 1996 to 2009, and this will continue of course, we've had dramatic changes in immigrant children's eligibility for public health insurance. I'm gonna look at these changes and talk a little bit about them, but the main idea, and the reason I started on this research, is despite all of these changes which are notable and you see them, we don't know anything about the effects of these changes on these children's health or healthcare. The literature doesn't tell us anything.
	If we look at a timeline, in 1996, we watched President Clinton's speech last night, you heard a lot about this, in 1996 the Personal Responsibility and Work Opportunity Reconciliation Act was signed into law, welfare reform. A side note, if you have a public speaking engagement the next day, don't watch President Bill Clinton give a speech the night before. It didn't do much for my feelings of confidence today, or my oratorical skills. 
Steve:	Could put an empty chair up there next to you. 
Erin Bronchetti:	Right. I could. Aside from drastically changing the setup of our cash welfare system, this legislation also severely limited immigrant's eligibility for a number of social programs, and these are legal permanent residents, so people who are coming here in accordance with our immigration law as it stands, and what the legislation did was it said that they could no longer be eligible for federal Medicaid coverage until they've been in the US for more than 5 years. 
	The way this works, that sounds a little [inaudible 00:14:50] I guess, but the way this works is that states administer Medicaid, but the federal government provides funding for it, provides matching funds. So what the federal government said, or what the law said, was that states could no longer cover these children who had been in the US for fewer than 5 years using federal funds, so if they were going to cover those children they had to use their own state fund. Some states were gonna do that right away, especially states with high immigrant populations, some states were gonna do that later on in the time period, some states still haven't.
	We have other changes going on during the same time period, though, so in 1997, the state Children's Health Insurance Program is rolled out. CHIP is designed to allow states federal funding, federal matching funds to cover children with low to moderate income, so these are children who are not poor enough to receive Medicaid, but are just slightly higher income, and now they're covered under these CHIP programs. 
	From 1998 to 2008, we see an array of Medicaid and CHIP expansions, and there's a great deal of prostate variation in these expansions, or these changes, so they differ in terms of the timing of the changes they're making, they differ in terms of the generosity of their eligibility criteria, and in their treatment of immigrant children, so you'll see all that in a minute. 
	And then in 2009, one of the first things that President Obama did when he got into office was to sign the CHIP Reauthorization Act, which includes the Immigrant Children's Health Improvement Act, and reverse that five-year mandatory waiting period for receiving federal coverage under Medicaid and CHIP. But to be precise, what it did was it said states could now have the option of using these federal matching funds to cover those children. It didn't say that they had to cover those children, so there are still states, some states that have quite a few immigrants or are new destination states that do not cover these children. 
	It's important to keep in mind in the context of healthcare reform and what may happen over the next several years. So I'm not gonna bore you with all of these numbers, but I do want you to understand a little bit about how eligibility calculations work. So if we highlight one example, California, what these numbers are telling us over here is that Medicaid and CHIP treat different aged children differently, and so they have different income cutoffs. If you look at the example of children 0 - 1 in California, that's just telling you that if you're under, if you're in a family with income less than 200% of the federal poverty line, you're eligible for Medicaid. The poverty line that you face as a child depends also on your family size, which is something worth keeping in mind. 
	California in 1998, they didn't have a CHIP program yet, but right away they made immigrant children eligible for Medicaid using their own state funds. By 2009, they'd expanded their CHIP program to higher levels of income, or introduced and expanded it, I suppose, and recent legal immigrants were eligible for both programs. New York and New Jersey are in another set of generous states, so they start out putting CHIP programs into place right away. They give limited or at least some coverage to recent legal immigrants. By 2009, they have some of the highest income cutoffs at 350 and 400% of the poverty line, and recent legal immigrants are eligible for both CHIP and Medicaid. 
	Virginia's another kind of example. Recent legal immigrants were not eligible in 1998, even though they did already have a CHIP program introduced. They expanded CHIP slightly, but made recent legal immigrants eligible for Medicaid only. Medicaid is gonna only cover the less, the lower income children, under 133% of the federal poverty line. 
Steve:	Can I ask you a question? 
Erin Bronchetti:	Yes, absolutely. Good time for questions. 
Steve:	So I can keep a better [inaudible 00:18:24], concretely what is the federal poverty line this year for a family of four?
Erin Bronchetti:	Sure, so for a family of four, it's $23,000, $19,000 for a family of three, so if you're thinking about a single parent with two children, $19,000 and that's this year. 
	Because these data are coming from my data set that I'm gonna talk about, they're from a national, the National Health Interview Survey, so it's a survey that goes out to 35,000 households. It's unlikely, I think, that very many undocumented immigrants are in there. I can't differentiate between them, they certainly haven't reported that they're undocumented. I think when you see the litany of questions that are involved in this survey, I think it's unlikely that any of them actually would respond to it and answer, so I don't think they're in there and we'll talk a little about that distinction as I go on, if and when I go on. 
	Anything else? Okay. So I'll continue a little bit, because I can skip a couple slides that way. I can tell you a few things. So some of the states that still don't cover these children as of 2009, are still not covering these children, were Arizona, Florida, which Florida had started by covering them in 1998, then their enrollments got so high and they ran into budget problems in the early 2000s and had to retract that eligibility, so they don't cover those children. North Carolina is a new destination state that has never covered recent immigrant children. It's not the case that all states restored eligibility using their own funds.
	I wonder if I can do the next part without a slide. I bet I can. If you'll follow my words, I'm a visual learner so this may be, I understand that this is hard but I'll give it a try. I'm gonna take all of these changes in eligibility, and what I'd like to do is estimate their impact on health outcomes. We want to think a little bit about how in the world could Medicaid or SCHIP eligibility impact health? And there are several steps to that relationship between eligibility and health outcomes.
	The first step that we want to look at is, if you make a child eligible for Medicaid or SCHIP, what effect is that gonna have on the likelihood that a child is enrolled in the program and is covered by Medicaid or SCHIP? That can happen, that enrollment effect can happen in two ways. On one side, you have children who aren't insured, have no insurance, and then they become eligible for Medicaid or SCHIP, and they enroll in it. We call that a take up effect in economics.
	On the other side, you have children who have private insurance plans but they become eligible all of a sudden for Medicaid or SCHIP and their parents decide, I'm going to switch them onto the public plan, so they call that a crowd-out effect. 
	Policymakers worry a lot about the size of those effects. In fact, the 2005 arguments when President Bush was in office about reauthorizing CHIP were all about the size of those two effects. If the crowd-out effect is really large, policymakers feel that the impact or the benefit of extending eligibility must be fairly small, because these kids already have insurance and therefore, we wouldn't expect to see big improvements in their health. 
	One thing I think we should say, and I'll say it because I have the time, since we're waiting, is that the crowd-out effect is not all bad, right? If we see some children moving from private health insurance plans where their out-of-pocket costs are very high, so their parents are paying $30 co-pays every time they need to go to the doctor, or higher, higher rates of co-insurance. If we see them switching off of those plans and onto the public insurance plans, we could still see improvements in their healthcare access and health. It's not the case that crowd-out is going to be all bad in terms of influencing outcomes. 
	We are going to be interested in the difference between children in immigrant families and children of natives, in terms of those, the size of those take-up and crowd-out effects. 
	The next step is to go from children enrolling in coverage to looking at their healthcare utilization, right? So if more children enroll in these programs, we may see improvements in healthcare access or their healthcare utilization and particularly their use of preventive and ambulatory care, so are they going to the doctor's office, getting well child visits, are they getting visits when they're sick, instead of waiting and then going to the emergency room, or something along those lines. 
	And then the final step, still has that worm, that's okay. We'll assume that it's right. Or not correct. The final step is for, if they're getting more preventive and ambulatory care, we might expect to see benefits in terms of their health outcomes, so I'm gonna look at all three of these stages, essentially, and you'll see that.
	I've said the data that I'm going to use are from the National Health Interview survey from 1998 to 2009. These data are the data that are used to calculate the number of people that are uninsured in this country, so when you read in the news that 50 million people are uninsured or the number of people who are uninsured has gone up, this is the data set they're using. It's run by the CDC.
	I mentioned that they go out to tens of thousands of households, 35,000 households. Among the households with children, they sample a random child and they ask a whole litany of questions about the child's health and healthcare usage. Those are the files I'm using. 
	I have 41,000 children in immigrant families, matched to their parents' records. I can distinguish which of those children are first generation immigrants and among that population, I can tell if they are recent or if they've been in the country for more than five years, which is the five year bar qualify. I can also tell if they're children of immigrants, because they are linked to their parents' records, so I can tell if they live with at least one foreign-born parent and the origin of the parent, and so on. For the sake of comparison, I'm also gonna have a sample of children of natives, which is larger and we'll look at how these effects differ for those two samples.
	So we want to think for a moment about the measures I'm using as outcomes. What am I interested in? I want to look first at how eligibility impacts coverage, I said that in the previous slide, and I'm going to look at three straightforward outcome: whether the child has any public insurance, whether he has private insurance, or whether he has no health insurance.
	To look at healthcare access and utilization, I've described several of these outcomes already as well, I'm going to look at an indicator for whether the child has no doctor's visit in the past 12 months, the number of visits he had in the past 12 months, conditional on having at least one, whether he has a usual place for care or a medical home, and whether the child has had any ER visits in the past 12 months, and you do hear policy and popular press discussion around ER usage by immigrants, and so if immigrants are using ERs as their main source of care, then we might expect that making them eligible would reduce the use of ERs. Actually, in my data, that's not true. Immigrants are not more likely to be using ERs and hospital care than native children. You just don't see that in the averages, at least.
	When we look at health outcomes, I don't have perfect health outcomes, right? I have things that are related to a child's health, but they're not gonna be perfect because this is survey data. It's not doctor's reports. On the other hand, I do think I have a number of outcomes here that are related to preventive and ambulatory care; that is, they should respond if a child is getting good care at a doctor's office. 
	So one is the parents reported the health status, we talked about that earlier, and the other is the number of school days missed due to illness in the past 12 months, whether the child's had any hospitalizations in the past year, and whether the child has had an asthma attack in the past 12 months. And if you didn't study public health, or child health, you wouldn't necessarily know that asthma is one of our most common outcomes that we recently research, and the reason is that asthma's the leading predictor of hospitalizations for children. It's also the leading predictor of missed school days or bed days, and it can have both short-term detrimental effects and long-term lasting implications for the child. 
	On the other hand, if asthma is properly diagnosed and treated, and children are taking maintenance medications, which are far less expensive when you're insured than when you're not, then asthma can be very well controlled and not have those detrimental effects, so we're gonna look kinda seriously at that as an outcome.
	So at this point I've shown you the outcomes, I've talked about the data, what I'd like to do is take the question of how Medicaid and CHIP eligibility impacts health and healthcare to the data, but I run into this empirical challenge. It's an empirical challenge that many of us run into, in just about every type of scientific inquiry. It has to do with separating correlation and causality; that is, looking at two variables in the data that appear to move together, and asking what part of that is just simple correlation and what part of that actually represents a causal impact of one variable on another?
	For my estimates to be informative at all for policymakers, I have to be fairly confident that what I've got is a causal estimate of the impact of eligibility on these various outcomes, so I'm gonna have to think carefully about that. And of course the gold standard would be to run some randomized experiment, right, which is what we would do in the hard sciences. Increasingly, what we do in the social sciences, what David and Ellen and I are doing in some of our work, and of course the idea there is that if you can randomly assign some treatment, in this case Medicaid or CHIP eligibility, to some subjects but not others, then those two groups, if your randomization works correctly, those two groups should be comparable in all other dimensions and any differences in the outcomes you observe should be due to one group having the treatment and one group not getting it.
	Often, for probably the vast majority of policy questions, we don't have randomized experimental data. Clearly, I don't. I just described my data set to you and it is not experimental, so I have to think about what to do. As in a plight of [inaudible 00:27:54] the intuition behind what I'm going to do, the solution I'm going to offer is that I'm going to look for a source of variation that's exogenous or random with respect to the outcomes I'm interested in. Variation in the independent variable, which is health insurance eligibility, that shouldn't on its own have anything to do with health outcomes or healthcare outcomes. Essentially, I'm trying to mimic the virtues of the random experiment, and look at the variation in this variable that's essentially random with respect to the outcomes.
	I will put up one slide of Greek letters, and then we'll move on. I won't take too much time on this, I promise, but I think it actually helps clarify the problem. So if I wanted to take the naïve approach to the data, what I would do is take the health outcomes and healthcare outcomes I have, and I'd run each ... for each one, I'd run a regression on all of these different things that might impact health or healthcare for a child. So, some of the things you might think of, like age, ethnicity, parents' education, family income, those are all tucked in the specter X as controls, but they're not what I'm interested in most. What I'm interested in is the variable that represents public insurance eligibility. 
	The naïve approach would say for every child in my sample, put a 1 there if the child is eligible for public insurance and a 0 there if not, and then beta 1 will give us some estimate of the impact of public insurance on health. I'll show you why it's naïve in a minute, but we could do that because I know the laws in the child's state and year, I know his family size or family income, I know his immigrant status, so I could do that with my survey data, but I don't want to for a couple of reasons. 
	The problems with that approach - well, there are two main problems I want to talk about. There are more problems than that, and if you want to talk about them we can do that after the talk, but there are two that I want to give as examples. One is, I take that approach but we have some relationship where health outcomes on the lefthand side are actually affecting eligibility, so that the causality is running in both ways. We have a problem. We can't call beta 1 an estimate of the causal impact that we want. 
	An example, we call that in economics an endogeneity problem, so these two things are being determined simultaneous and health outcomes actually affect eligibility. An example of that would be if child's health impacts family income. So if a child's in poor health and parents reduce their labor supply and therefore their income, the child becomes more likely to become eligible for Medicaid or CHIP. We've got causality running in both directions, that'll be a problem for us. 
	The second problem has to do with if there's a third variable out there that affects or is correlated with both the likelihood of the child being eligible and his or her health or healthcare. So an example of that would be if eligible children, because remember they're sort of poorer and disadvantaged populations that are likely to be eligible here, if eligible children are more likely to live in areas with poor access to physicians, they would utilize less care, we'd observe them utilizing less care, but it wouldn't have anything to do with the fact that they were eligible or not if it had to do with the fact that they lived in neighborhoods with no doctors. We'd be attributing some of that effect to the eligibility variable.
	Those are two examples of the problems that we have with running that naïve approach. So I said before that the intuition was that we want to look for some source of variation in the independent variable that's random or exogenous. We're gonna do that with an instrumental variables approach, and this will be the last technical thing I say, I promise. An instrumental variable is a variable that's highly correlated with Medicaid or SCHIP eligibility, but should not be correlated with health outcomes other than to the fact that it impacts eligibility. Sometimes it helps to back out of the particular example for a minute and think about another example.
	So suppose as a labor economist, I wanted to know about the effect of military service on later life earnings. I wanted to know if people who served in the military earned more later on, and I wanted that to be a causal relationship that I was estimating. I can't just run a regression of adult earnings, or earnings at age 35 on military service because the choice to serve in the military is largely not random. You might even make the decision to serve in the military having thought about your future expected earnings. So if I could find some random source of variation in military service, I could solve the problem.
	You could do that with something like a draft lottery number. So the intuition is, by using the draft lottery number you get exogenous variation or random variation in the likelihood of military service. Intuitively then what you do is compare earnings of people with a low draft number to a high draft number, to those people with a high draft number. So that's the kind of idea that I'm trying to get at.
	So what I'm gonna do is construct an instrumental variable, and this is the hardest part to hang on to without the slides, especially because my results are coming up, but we'll try. Okay, so I'm gonna construct an instrumental variable that predicts the eligibility of an individual child that should not be otherwise correlated with that child's health or healthcare outcomes. I'm gonna do that by taking a random sample from my whole national survey, a random sample of children. I'm gonna take that random sample and run them through every state's eligibility calculator for every year, so I'm gonna determine what fraction of that random sample of children is eligible if they all live in Texas in 1998, or if they all live in Massachusetts in 2004, and so on. The variation in that variable is gonna be independent of any individual child's health outcomes, because it's not gonna have variation across individual children. It's gonna have the same random sample every time. 
	I'm gonna take that variation, and that allows me to sort of isolate the random component of health insurance eligibility and look at the effects on health outcomes and health coverage, and so on. Do you have any questions at this point? Yes, Paul, thank you.
	Um, no, it's slightly more difficult but it's a good question, and I've described it in a kind of loose way without my slide, but it's a little bit more complicated than that. What it is assuming, what the method is assuming is that state eligibility criteria are not responding to changes. At least over this time period, they're not responding quickly to changes in population health. That's the part, the endogenated problem that I'm not able to solve with this, and I'm not able to solve it with anything except a randomized experiment, so it's a problem that we run into all the time that we call policy endogenating. It's a great question. 
	If the states are actually adjusting their eligibility criteria because they see this particular part of the population is very worse healthwise, that's a problem. Steve.
Steve:	[inaudible 00:34:34]
Erin Bronchetti:	Not exactly. Right, okay, so great question. The simulation, what it's gonna do for me is give me a variable that captures every state year, so think of a state and year as a [inaudible 00:34:43] thing, every state year's legislative environment. The way they're treating children of a certain age group and a certain immigrant status. Then I'll match that variable up with the child's actual state in here and his age and immigrant status, and I'll predict his eligibility but it won't be a perfect measure, so. 
Steve:	[inaudible 00:35:03]
Erin Bronchetti:	Exactly. 
Steve:	That's certifiably independent of that particular state's demographics.
Erin Bronchetti:	That particular state's demographics and certainly, that particular child's health and healthcare utilization and so on, and other characteristics as well. Right, okay, so what am I constructing? I'm taking random samples. I'm actually taking, this is the part I didn't say, I'm taking them from two different age groups, age 0-5 and 6-17 because the laws treat these age groups differently. I'm running these random samples through the state's laws in every year, each state, all 50 states in every year. I'm determining the fraction of that random sample that would be eligible in every single state and year. So I'm gonna get some number that varies from 0-1 and is usually in a tighter range than that, and it's gonna represent to me, how generous is this state to this age group and this immigration status, actually, too.
	So my instrumental variable, which meets this definition, is this simulated fraction eligible in an individual child's state and year and age group? So now, instead of using an indicator for whether the child is eligible, I'm using this fraction. So it shouldn't vary based on child, variation across children and their individual characteristics, it should vary only based on the legislative environments in their states and years. I'm really using the policy changes to identify this effect. Okay. So I'll go on to some results now, and again, I'm not gonna make you read your way through all these numbers, I'll highlight what I want to look at.
	So eligibility for public insurance, this looks at the impacts of eligibility for children in immigrant families on the health insurance coverage outcomes. What this tells us, this first coefficient tells us that eligibility increases the likelihood of a child in an immigrant family being covered under Medicaid or CHIP by 20 percentage points. Remember, this is the part that we were saying this effect could come from other take-up or crowd-up, so these next two coefficients give us a sense of how big the take-up and crowd-up effects are. We see that the coefficient for eligibility and the no health insurance regression shows us that making a child in an immigrant family eligible reduces the likelihood that that child has no insurance by 15 percentage points, so that effect, that take-up effect accounts for about 3/4 of the increase in Medicaid enrollment. A relatively big take-up effect, and a small crowd-out effect. A small reduction in private insurance because of this increase in eligibility.
	Now when we look at natives, the story goes a little bit differently. The overall effects are smaller, particularly the reduction in uninsurance. So eligibility is far less likely to move a child of a native family off of uninsurance and onto Medicaid or CHIP than it is for children in immigrant families. That's gonna be important for interpreting what I see in terms of my average impact on health and healthcare. 
	We're gonna flip onto, sorry, the regressions for healthcare access and utilization. Again, what we start with are children in immigrant families. We have the four different outcomes I'm interested in across the top, and we're interested in these coefficients. So we see, the coefficient on eligibility for the outcome of no doctor's visit in the past 12 months is negative and 6.5 percentage points, so that's telling us that making a child in an immigrant family eligible reduces the likelihood of going without a doctor's visit by 6.5 percentage points, or 38% of the mean. That's a substantial effect, more substantial than the 6.5 number looks. 
	The coefficient in the no usual place for care regression is also statistically significant and suggests it reduces, eligibility reduces the likelihood of having no usual place for care by about 5 percentage points or 41% of the mean. We don't see an impact on what we call the intensive margin, so we don't see an increase in the number of visits given that the child's had at least one, and we don't see a statistically significant effect on the use of ER care either. 
	Compare the natives, again, we're gonna see that the impacts of eligibility on healthcare outcomes and utilization outcomes for children in immigrant families are significantly larger than they are for children of US natives, and that makes sense if you think about the previous results. If eligibility for these programs is moving more children in immigrant families out of being uninsured than it is for children of natives, then it makes sense that we'd see more responsiveness in terms of these children's healthcare outcomes and utilization.
	And a curious result that probably deserves some more exploration, but is outside the scope of what I'm doing right now is that we do see eligibility causing an increase in the number of ER visits among children in native families, and that comes up again in the next slide as well. 
	The last thing we'll look at are those health outcomes that should respond to ambulatory care. Let's see, okay. We have health status, missed school days, hospitalization rates, and the rate of asthma attacks, and we see these coefficients. The first two are sort of marginally statistically significant, so we have some suggestive evidence that eligibility for public insurance lowers the health status rating of children in immigrant families, which remember is a good thing, right? A lower number is better. It's hard to think about the magnitude of that result, but we can interpret it, that 14.3 number tells us that eligibility, making a child eligible shrinks the gap between the average health status and a health status of excellent, or 1, at 14%. So it moves the child 14% closer to excellent health.
	Reduces the number of missed school days by about half a day, or 20%. Doesn't seem to have a significant effect on hospitalization rates, and I get a strong and significant reduction in the likelihood of an asthma attack in the past 12 months. When we get to children of natives, again, we see these smaller, insignificant effects for children in native families. with the exception of the asthma attack variable, we do see that eligibility reduces the rate of asthma, or the likelihood of an asthma attack for the children of US natives as well. And again, we get that sort of curious result that eligibility appears to influence, positively influence the likelihood of hospitalization in the past 12 months for children of natives. That does warrant some more investigation. 
	Just a wrap up, and then we can talk about any other questions or implications for the future and so on. I have shown sort of the first really comprehensive, meaning for a number of different outcome measures, evidence that public insurance eligibility causes significant improvements in healthcare access and health for children in immigrant families. We saw a big reduction in the likelihood of having no doctor's visit or no usual place for care, some marginally significant improvements in health status and missed school days, and a large reduction in the likelihood of an asthma attack. These effects are consistently larger for children of immigrant families than they are for children in native families, and that's likely due to more children in immigrant families being moved out of uninsurance and onto a public program by the increase in eligibility. 
	Extending eligibility to children in immigrant families does not seem to significantly impact their use across the ER or hospital care. We could have made the case if I'd stopped on this a little longer that it could, that effect could go in either way. So perhaps it' just washing out at zero, but we don't see a statistically significant impact. Some people might expect that after reading the newspapers and other sources of information. 
	Okay, so thinking about extensions. Things I'd to know that fall right out of this research are, what are the impacts of public insurance eligibility on other measures of wellbeing? So even if we didn't see the health impacts that I showed you, we might expect that becoming eligible could improve children's wellbeing in other ways, so if parents no longer have to spend out-of-pocket for their healthcare, they might be spending on other things. Spending on food, on more nutritious food, might be spending on housing. Those types of things could all be measured with other data sets, and I think that'd be a worthwhile thing to look at. 
	I also think a more fundamental question is why in Medicaid and CHIP programs are average take-up rates so low among children? So, among the children who are eligible for Medicaid and CHIP, only about 50% take up these programs, actually enroll in the programs. Now, for children of natives, the other half that doesn't enroll even though they're eligible, the vast majority of that other half has private insurance, so it makes sense that they don't enroll. For children of immigrants, or children in immigrant families, that's not true. That other half that doesn't enroll, they don't have any private insurance either, or only 40% of them do. It's curious and we might wonder a little bit about what, and I think we can speculate about why they don't enroll in the program and what can be done about it. And that's actually a research question that could be answered with the right community-based research, I think. And what are the implications of healthcare reform for children in immigrant families. 
	Assuming people in the audience might be interested in thinking about that so we can certainly talk about what I think those implications are. I think healthcare reform has done some things that will help these children quite a bit, and I think it leaves some things to be desired as well, so. One example is that the enrollment procedure is going to be very much streamlined, so whether you're signing up for an insurance exchange, or CHIP, or Medicaid, you're going to enter it all to the same point. That enrollment is all gonna happen at the same point. I think that will help families in vulnerable populations, particularly those with language barriers, I think that's a good thing.
	I think it's also a good thing that legal permanent residents will be eligible to enter the insurance exchanges. That's a good thing. 
	We talked in the beginning about how the CHIP Reauthorization Act didn't force states to give back eligibility to children who had been in the US fewer than 5 years, it just allowed them to. It would be nice, I think, given my results, I think they imply that we should give that eligibility back. I think the other thing that worries me about this population are the mixed status families, and this kinda combines question two and three.
	So mixed status families are these children of immigrants who are US citizens by birth, but may have an undocumented parent, and that's where I think we really have a problem, because the children are eligible for Medicaid, they're eligible for CHIP, just like any native or child of a native would be, and yet their parents, out of fear of revealing their immigration status or being questioned about their immigration status, are far less likely to set them up for these programs or to utilize care in ERs and hospitals. I do think that impression that immigrants are using ERs and hospitals disproportionately is wrong, as far as I know. Question. Yes, sorry, that's, thank you. 
Speaker 4:	You referred just briefly just now to language issues. In your sample, are you able to separate immigrant groups from the language barrier, and when it's not, I would imagine that it's mostly [inaudible 00:46:03] but I don't know anything about [inaudible 00:46:05] ...
Erin Bronchetti:	Right. When you look at my sample, this particular survey at least, and this would be true in the population as well, the vast majority are Hispanic ethnicity. It's especially true when we subset down to lower income levels. And there have been outreach efforts embedded in CHIP, so all of these CHIP expansions that have happened over the past 10 to 15 years, many of them have concluded that kind of outreach where they have interpreters available at the enrollment sites and things like that to help people get enrolled. And there have been, there have been some studies of the early outreach effort, but not conclusive. It's not clear what immigrants in populations that are less widely represented are facing when they try to get involved. Martin.
Martin:	At what point?
Erin Bronchetti:	Well, so, and actually, throughout the talk I use the word access quite a lot, and the word access has a, is a bit of a loaded term when it comes to Medicaid and CHIP because you have a number of issues that these children may face. The one I was concerned about there is that children in these families may be more likely to live in areas where they don't have very many physicians, and it's harder to get to a physician regardless of having any insurance or not. Other access problems are sort of similar to that, where many physicians, even if they are in the neighborhood, may not take Medicaid or CHIP.
	If I happen to choose an OB/GYN in Center City when we first moved here, went there when I was pregnant with my daughter, and saw on the sign "we don't accept Medicaid and other public forms of insurance," and the reason is because the reimbursement rates are so low for doctors. It's not just because they don't want to take those patients, it's because the reimbursement rates are so low that it's not apparently profitable for them to do so. It's a problem that has a number of things we have to fix to make that access better, I suppose.
	We can to some extent, there has been some research on that, but it's more sort of rural urban contrast rather than based on these pockets of immigrant populations and so on, so that would be an interesting question. I don't know of any research that does that. 
Martin:	[inaudible 00:48:17]
Erin Bronchetti:	Well ...
Martin:	[inaudible 00:48:18]
Erin Bronchetti:	What really happened, and this part I didn't go into in the meat of the talk, what really happens is that you have two stages, and the first stage you take variation in that instrumental variable, you predict the child's eligibility, and instead of using the child's actual eligibility, you use that predicted eligibility in the regression. So you're still gonna run a regression, it's just that all of the variation that identifies your estimate of the impact that you're interested in is coming from that policy variable instead of individual variation.
Martin:	[inaudible 00:48:48]
Erin Bronchetti:	No, it doesn't, and that's a good question, is to what extent do people move across state borders for more generous benefits? It's a question that's been studied in the literature. No evidence to suggest that there's a significant amount of migration for welfare benefits or Medicaid benefits, and so on. So we don't actually see that show up in the data to a significant degree, although I intuitively think that it could happen. 
Martin:	[inaudible 00:49:16]
Erin Bronchetti:	So I do control for the parents' region of birth, or the child's region of birth if the child's an immigrant, but I don't drill down into those subsamples. Basically, the samples would get too small for everyone except the Hispanic ethnicity immigrant, so I can't do that to look at whether there are differential effects across these groups, but I do control for it in the regression. I do think, when you think about the challenges immigrants face coming to this country and getting enrolled in these types of programs, certainly those issues would matter. 
Martin:	[inaudible 00:49:49]
Erin Bronchetti:	I do a little bit. I can't do too much, again because of sample size, to actually sort of separate those samples entirely and say what are the effects for this sample and the effects for this sample? I can control for that, so even if we just flip one regression side back up, I highlight it away from them, I gotta go through a couple here, there. So here, I do at least control for those differences in immigration status and they go in the way that you would expect, so it's worse, this means it's worse, right, could be a first generation immigrant, then a second generation immigrant in all cases, for all outcomes, and so on. So I am controlling for some of that, but I can't, again, I'm not able to separate the samples of really, perform valid statistical analyses.
Speaker 4:	Time for one more question?
Steve:	Classes are in one more minute, sort of in the ... yeah, it's a graduate student like that. 
Erin Bronchetti:	I learned - 
Steve:	[inaudible 00:50:43]
Erin Bronchetti:	That's a very good question. I would say the majority, not just in this paper but of what I apply to my other work, I learned by doing in the apprentice mode. Not so much, more actually apprentice mode isn't even the right way to do it. Working independently on my dissertation and trying to figure certain things out, I think that's where I really had to learn it. But it appeals to some of the intuition going either, that I learned in my undergraduate econ classes about really trying to be careful about correlation and causality. I had a great econometrics teacher who had reinforced that and that continued for me, but in terms of the actual technique and the time and thinking about the assumptions behind that technique, more reading and learning about doing it. 
Speaker 4:	I'd like to thank Erin-