Abstract
The Food Stamp program is currently one of the largest safety net programs in the United States and is especially important for families with children. The existing evidence on the effects of Food Stamps on children’s and families’ outcomes is limited. I utilize a large, recent source of quasi-experimental variation—changes in documented immigrants’ eligibility across states and over time from 1996–2003—to estimate the effect of Food Stamps on children’s health. I find loss of parental eligibility has large effects on program receipt, and an additional year of parental eligibility before age five improves health outcomes at ages 6–16.
I. Introduction
The Food Stamp program is the largest cash or near-cash means-tested safety net program in the United States.1 Nearly 15 percent of the total population and 25 percent of all children received benefits from the program in 2011, and among families with children that participate in the program, Food Stamps play a crucial role in their total resources (Moffitt 2013; Rosenbaum and Keith-Jennings 2013; Murray 2011). Ignoring behavioral responses, if benefits were counted at their cash-equivalent value, they would reduce the poverty rate among participants by 16 percent in 2011 (Food Research and Action Center 2012). As a result of the growing importance of this program, there has been increased interest among policymakers and economists about the costs of the program, in terms of direct expenditures and labor supply disincentives, as well as the benefits of the program, especially the effects of the program on families’ nutrition and children’s outcomes (Hoynes and Schanzenbach 2016). Concerns over increased spending resulted in several cuts to Food Stamp generosity in the past several years (Dean and Rosenbaum 2013; Chokshi 2014), with potentially larger cuts on the horizon (Grovum 2014; Dewey and Jan 2017; Parrott, Gonzales, and Schott 2018).
Despite all this, very little is known about the effects of the Food Stamp program because it is a federal program with little variation in eligibility rules or benefit amounts across geographic locations or over time (Currie 2003), which would typically be used to conduct quasi-experimental analysis. Existing quasi-experimental estimates of the effects of the program on children’s and families’ outcomes rely on the program’s rollout in the 1960–1970s (Almond, Hoynes, and Schanzenbach 2011; Hoynes, Schanzenbach, and Almond 2016), and the applicability of those estimates to current generations is unclear, as there have been major changes over time to the Food Stamp program and other safety net programs, as well as changes in healthcare technology, average health, and demographics of the population. For more recent cohorts, researchers compare children’s outcomes among families that participate with those that do not (Kreider et al. 2012), which may suffer from biases due to endogenous program participation, or they utilize recent state changes in application procedures and eligibility rules as instruments for participation, but these changes had mostly small effects on participation (Ganong and Liebman 2018; Ziliak 2015). Therefore, the effect of Food Stamps on current children’s outcomes is still largely unknown.
In this paper, I take advantage of recent, large changes in Food Stamp eligibility for a well-defined and easily identifiable group to provide new quasi-experimental estimates of Food Stamps on children’s health. Specifically, I utilize changes in eligibility among documented immigrant families: many foreign-born lost eligibility for the Food Stamp program in 1996 as part of welfare reform (the Personal Responsibility and Work Opportunity Reconciliation Act), and eligibility was subsequently restored to them at different times across different states in 1998–2003. Welfare reform caused immigrants’ participation in Food Stamps to decline significantly (Fix and Passel 1999; Haider et al. 2004), and I examine the effects of this loss of eligibility, as well as the restoration of eligibility, on children’s health.2 These policy changes create a very rich source of variation in eligibility to exploit in my empirical strategy: eligibility depends on state and year of residence and country of birth (U.S. or not). Moreover, as eligibility is turned “off” and then back “on,” it is very unlikely that trends in children’s health would be driving the results. Prior to welfare reform, children of immigrants made up 20 percent of all children receiving Food Stamps and 30 percent of all children in poverty, so this is a particularly policy-relevant population likely to be affected by changes to the Food Stamp program.3 Additionally, recent policy proposals have suggested additional barriers to immigrants’ participation in safety net programs specifically, so understanding the effect of similar previous restrictions is crucially important (Fix and Capps 2017).
In the primary analysis, I investigate the effects of early-life Food Stamp eligibility on health at school age (6–16), but I first examine the direct effect of the changes in eligibility on program receipt. Because I am linking early-life changes in Food Stamp eligibility to health in later life, I restrict the sample to U.S.-born children of immigrants to ensure that, other than changes in Food Stamp eligibility, the early-life experiences of these children are similar. This restriction means that all children in my analysis are U.S. citizens, and it is their parents who lose eligibility for the program. Despite the fact that children remain eligible, loss of parental eligibility significantly reduces the benefit amount families are eligible to receive because this amount is a function of the number of eligible household members. This has two potential implications: families continue to receive benefits, but the benefit amount falls substantially, or families no longer participate in the program, because these lower benefits do not outweigh the costs of participating (Daponte, Sanders, and Taylor 1999; Van Hook and Balistreri 2006). To focus on children most likely to be affected by these changes, my primary sample is U.S.-born children whose mothers have a high school education or less—a group with high rates of Food Stamp receipt. With the 1995–2007 Annual Social and Economic Supplement (ASEC) to the Current Population Survey (CPS), I find that the changes in parental eligibility led to large changes in program receipt; loss of parental eligibility reduced participation by 50 percent and average benefits received by 36 percent.
Building off of these findings, I utilize restricted-access data from the National Health Interview Survey (NHIS) to examine the effect of parental eligibility from the time children are in utero to age four on their health at ages 6–16. These medium-run effects are of interest for two reasons. First, the early years of life are critical for development: poor nutrition and lack of resources during this time can have lasting detrimental impacts on children’s health and cognitive ability (Prado and Dewey 2014). Second, changes in health may occur slowly in response to changes in resources, so examining contemporaneous measures of health may understate the total effect of Food Stamps on health (Grossman 2000; Currie 2009). I find that among U.S.-born children of immigrants, with mothers who have a high school education or less, an additional year of parental eligibility in early life improves health in the medium run. There are statistically significant decreases in the likelihood the child is reported in “poor,” “fair,” or “good” health (relative to “very good” or “excellent”) and in an index of negative developmental health outcomes. Point estimates on indexes of physical and mental health suggest potential improvements in these outcomes, although the confidence intervals on all health outcomes are wide, and for these outcomes I cannot rule out meaningful effects in either direction. The estimates are robust to the inclusion of children of natives as a “control” group in a triple difference model, as well as accounting for changes in the generosity of other safety net programs.
In addition to providing one of the only quasi-experimental evaluations of the modern Food Stamp program on children’s health, this work also contributes to the literature examining the effects of early-life resource shocks on individuals’ long-run outcomes in adulthood, summarized by Almond and Currie (2011) and Currie and Almond (2011). More recently, this literature has also documented the longer-run effects of childhood access to the safety net, summarized by Almond, Currie, and Duque (2017). In this paper, I focus on a key program in the modern safety net, and the findings illustrate that near-cash programs have large beneficial effects on modern children’s medium-run health outcomes.4 Moreover, understanding the medium-run effects is important because this impacts welfare analysis of early-life interventions and provides insight into the mechanisms behind long-run effects.
The rest of the paper proceeds as follows. Section II describes the Food Stamp program and the related literature. I describe the primary data in Section III. Section IV outlines the empirical strategy. I discuss the results in Section V. Section VI concludes.
II. Background
Food Stamp benefits are available to families with total family income below 130 percent of the poverty line, regardless of their size or household structure, and are intended to allow families to maintain a minimum level of adequate nutrition. An eligible family’s benefit amount is a function of their resources and a maximum benefit, the latter of which is a function of the number of eligible members in the family. Typically all members of the family are eligible, but as I describe in detail below, the immigrant-specific changes to eligibility led to changes in the number of eligible family members and, therefore, changes in the maximum benefit amount. In 1998, the maximum Food Stamp benefit amount for family of three was $321 per month, and the average benefits received were roughly $100 below this maximum. These eligibility rules and benefit amounts are set nationally and have varied little since the program began. I describe the nonimmigrant-specific program rules in more detail in the Online Appendix.
There are several mechanisms through which early-life access to Food Stamps may affect later life health. First, the early-life period is a critical one for development, so exposure to a negative environment during this period may lead to worse cognitive and physical outcomes in later childhood and adulthood (Cunha and Heckman 2007; Almond and Currie 2011; Almond, Currie, and Duque 2017). Initial theories emphasized the long-run effects of in utero insults on cardiovascular disease (Barker 1990). Recent work has expanded this “fetal origins hypothesis” to the broader model of the “developmental origins of health and disease,” which highlights: (i) the importance of periods after the in utero one for also determining long-run outcomes and (ii) the potential for long-run effects on outcomes beyond cardiovascular ones (Lewis et al. 2014). Importantly for this paper, theory predicts that poor nutrition in early life is especially detrimental to cognitive outcomes and immune system functionality in later childhood (Prado and Dewey 2014; Save the Children 2012), and there is strong correlational evidence of these relationships (Grantham-McGregor 1995; Chandra 1997). While the exact mechanisms are still unclear, one candidate mechanism is children’s hypothalamic–pituitary–adrenal (HPA) system. The HPA system is responsible for, among other things, children’s stress and emotional regulation, so dysregulation of the HPA system could affect childhood mental illness (for example, ADHD). Notably, lack of nutrition during pregnancy and through the 18th postnatal month can lead to dysregulation of the HPA system (Kapoor et al. 2006; Lewis et al. 2014). Despite these theories, short-term studies of deficiencies in nutrition “seem unable to detect the real influence of nutrition in early life [because] the brain takes a long time to mature” (University of Granada 2013), so this paper provides important evidence by looking at outcomes in the medium run.
In addition to effects on nutrition, access to Food Stamps represents a large increase in overall family resources, which may reduce stress in the family (Evans and Garthwaite 2014). Stress is in turn linked to improved cognitive outcomes for children, through both biological and behavioral channels (Lewis et al. 2014). For example, elevated levels of cortisol that can arise as the result of stress are also linked to dysregulation of the HPA system (Kapoor et al. 2006). The boost in family resources may also result in increases in other forms of investment in children’s health (for example, healthcare). A final potential mechanism is the reduction in parental labor supply accompanying Food Stamp access (Hoynes and Schanzenbach 2012; East 2018). This may lead to more time spent with parents, which could have positive impacts on children’s health if they are exposed to illnesses in daycare (Ruhm 2000; Schaller and Zerpa 2019).
Much of the existing quasi-experimental evidence on the Food Stamp program utilizes the county by county rollout of the program in the 1960s and 1970s. Almond, Hoynes, and Schanzenbach (2011) find that access to the Food Stamp program in utero decreases low birth weight, and Hoynes, Schanzenbach, and Almond (2016) build off of this by examining how Food Stamp access from in utero to age five impacts adult outcomes. They find more Food Stamp exposure in early life causes statistically significant reductions in “metabolic syndrome” (obesity, high blood pressure, heart disease, heart attack, and diabetes) and, for women, improvements in labor market and educational outcomes.
Studies on more recent cohorts take several different approaches, summarized by Currie (2003) and Hoynes and Schanzenbach (2016). Kreider et al. (2012) use bounding exercises to account for endogeneity in participation, as well as underreporting of participation, and they cannot rule out positive or negative effects on children’s health. Closer to my approach, Schmeiser (2012) uses changes in state-specific Food Stamp application procedures and vehicle ownership rules, as well as state earned income tax credit (EITC) benefits, as instruments for Food Stamp participation and finds participation in the program reduces child body mass index (BMI).
A. Policy Changes Affecting Immigrants’ Eligibility
Prior to welfare reform in 1996 (the Personal Responsibility and Work Opportunity Reconciliation Act, or “PRWORA”), there was no difference in Food Stamp eligibility for most documented noncitizen immigrants and natives. Welfare reform changed this by making documented noncitizen immigrants ineligible for Food Stamps. States were given the option to use their own funds to restore benefits to this group, and nine states chose to provide these benefits to all newly federally ineligible immigrants without additional eligibility restrictions.5 These nine “fill-in” states were California, Connecticut, Maine, Massachusetts, Minnesota, Nebraska, Rhode Island, Washington, and Wisconsin. I call the other 41 states and the District of Columbia the “no-fill-in” states. The fill-in programs began in 1998 and 1999 (Figure 1). Then, as part of the 2002 Farm Bill, eligibility was restored in 2003 to large groups of documented noncitizen immigrants— the disabled, children, or those who had lived in the United States for at least five years.6 I take advantage of all of these changes to estimate the effect of Food Stamps on children’s health, and in Figure 2 I show a timeline of how these policies affected children’s eligibility.
States that Chose to Fill in Food Stamps for Immigrants
Notes: States are classified based on their availability of a Food Stamp fill-in program in January, February, or March of a given year. Only fill-in programs that provided benefits to children and their parents are included here, and fill-in programs for the elderly are not included. In addition, states that provided fill-in programs but had additional eligibility requirements beyond the federal ones are not counted as fill-in states.
U.S.-Born Children’s Eligibility for Food Stamps
Notes: Children of treated immigrants defined as those whose parents were born outside of the United States and who immigrated 1985–1996. The 1985 cutoff drops from the sample immigrants likely to be unaffected by the Food Stamp eligibility changes because they had lived in the United States long enough to either meet the 40 quarters requirement or to have applied for and received citizenship. The 1996 cutoff drops from the sample immigrants likely affected by changes in eligibility for other safety net programs. Children of natives defined as those whose parents were born in the United States
Focusing on U.S.-born children means their parents lose eligibility, but they themselves remain eligible.7 When family members become ineligible, the maximum Food Stamp benefit the family can receive falls significantly; for example, for a family of three, with one citizen child and two noncitizen parents, benefits could fall by up to $2,400 annually in 1998 dollars (almost 66 percent).8
The fill-in states are not randomly selected, so I test if state observable characteristics before PRWORA—political party, demographics, and attitudes towards immigrants—predict the provision of a fill-in program, and I find no evidence that they do (Online Appendix Tables B.1, B.2, and B.3). In the regression models discussed below, I include state fixed effects, so of greater concern is if time-varying state characteristics are correlated with state fill-in programs. I examine if eligibility is correlated with the state unemployment rate, the spending per pupil on education, and the generosity of other safety net programs. As shown in Online Appendix Table B.4, there is only a marginally statistically significant relationship between fill-in programs and the unemployment rate, as well as Medicaid/SCHIP generosity, but these relationships are economically small.
Kalil and Ziol-Guest (2009) study the effect of welfare reform and find that, nationally, noncitizen immigrant children were more likely to be in contemporaneous parent-reported poor health and more likely to have postponed healthcare after welfare reform, as compared to natives and naturalized immigrants. Similarly, Kaushal (2007) utilizes the changes due to welfare reform and the state fill-in programs to identify the impact of Food Stamp eligibility on contemporaneous adult obesity and finds no effect. I build upon this literature by taking advantage of a richer source of policy variation and by looking at the longer-run effects of Food Stamp access in critical periods of children’s development.
III. Data
The primary data set for my analysis is the National Health Interview Survey (NHIS) from 1998–2015, which I use to measure medium-run health outcomes. The NHIS is a nationally representative cross-sectional survey, and I use two of its components: (i) the “person” file, which collects information on the demographics and health of each household member, and (ii) the “sample child” file, which collects more detailed health information about a randomly selected child within each household. Importantly for my analysis, year of birth, country or state of birth, and year of immigration for foreign-born persons are available for every individual. Detailed geographic information and year of immigration are restricted variables and were accessed through the Center for Disease Control’s Research Data Center.9
I focus on U.S.-born children born in 1989–2005 and observed at ages 6–16, after early-life changes in eligibility, and before they might selectively move out of the household. I restrict the sample to children whose mothers have a high school education or less, as these families are more likely to be affected by the changes in Food Stamp eligibility.10 Because of additional policy complexities, I also limit the sample to children whose mother and father (if present) are “treated immigrants,” where “treated immigrants” are defined as individuals who were born outside of the United States and entered the United States between 1985 and 1996. As part of welfare reform, immigrants who worked in the United States for 40+ quarters and met minimum earnings requirements in each quarter were exempt from eligibility changes. I do not observe work history in the data, but I do observe year of entry to the United States, so I use 1985 to approximate this work requirement because individuals who entered the United States before 1985 (and lived in the United States at least 10 years) were more likely to have 40 quarters of work history by the time of welfare reform passage. Additionally, immigrants who entered the United States after the passage of PRWORA in 1996, were subject to additional restrictions on eligibility for Medicaid/SCHIP, Supplemental Security Income (SSI), and Temporary Assistance for Needy Families (TANF, formerly Aid to Families with Dependent Child, AFDC) for at least their first five years of residence in the United States, unless their state of residence provided these benefits with state funds. So, to more cleanly identify the effects of Food Stamps, I drop the children of these individuals. However, there are a number of measurement issues with reported year of entry to the United States, so these year-of-entry restrictions should be interpreted as only a rough proxy for those likely to have experienced Food Stamp eligibility changes, due to potential measurement error in this variable.11
Several other groups were exempted from these policy changes—veterans, refugees, asylees, and naturalized citizens—but, due to data limitations, I cannot condition on these other characteristics in early life, and this may lead to some measurement error.12 However, this provides additional motivation for restricting the sample of treated immigrants to be relatively recent migrants, as earlier immigrants had more time to apply for and receive citizenship. I also consider, as a potential control group, a sample of children of natives, whose mother and father (if present) were U.S.-born.
I examine a set of outcomes that measure children’s overall health status, as well as children’s physical health conditions, developmental conditions, and mental health. To measure overall health status, I use parent-reported child health, overnight hospitalizations, number of school days missed, and number of doctor visits. Importantly, while parent-reported health is a subjective measure, Case, Lubotsky, and Paxson (2002) find that it is highly correlated with doctor’s reports of children’s health status. I create a dichotomous variable indicating if the child is in “poor,” “fair,” or “good” health, which I take as a measure of bad health because very few parents report their children to be in “poor” health (Currie and Stabile 2003; Milligan and Stabile 2011).13 There are many outcome variables that capture physical, developmental, and mental health available in the NHIS, which raises issues of multiple inference, so I create three indexes, following Anderson (2008). The physical health index includes two specific health conditions predicted to be affected by poor early-life nutrition: whether the child has ever been diagnosed as having diabetes and whether the child experienced frequent diarrhea in the past 12 months.14 The developmental health index includes whether the child has ever been diagnosed with autism, a learning disability, mental retardation, a developmental delay, or ADD/ADHD. Finally, the mental health index includes the components of the Strengths and Difficulties Questionnaire, which captures children’s mental health problems. Each index is constructed as a weighted sum of z-scores of the component outcome variables. To create the z-scores, I calculate the mean and standard deviation for each outcome among children of treated immigrants born before 1992 who were unaffected by the eligibility changes before age five. I construct weights using the inverse of each group of outcomes’ variance–covariance matrix to make more efficient use of the information, as outcomes that are highly correlated are given a lower weight. I then subtract each outcome’s mean and divide by its standard deviation.
I also use the Annual Social and Economic (ASEC) Supplement to the Current Population Survey (CPS) from 1995–2007 to examine the effects on Food Stamp receipt (Flood et al. 2015). The ASEC is also a nationally representative cross-sectional survey. Unfortunately, country of birth of all individuals and the year of immigration to the United States for foreign-born was not consistently collected until 1995, so this is the first year in my sample (Schmidley and Robinson 1998). I mimic the sample definitions described above for the NHIS and construct a sample of children who are born in the United States in 1989–2005 and observed at ages zero to four, in order to capture the changes in eligibility faced during early childhood. The outcomes of interest are household Food Stamp participation and dollar value of Food Stamp benefits received.
Summary statistics for all main outcome variables and demographics of the sample are shown in Table 1. I use the NHIS- and CPS-provided weights here and in the analysis to account for survey oversampling and nonrandom nonresponse (National Center for Health Statistics 2005; Flood et al. 2015). The NHIS sample size is much larger in the person file (about 9,000) than in the sample child file (about 3,600). To these data sets, I merge in state by year demographic characteristics, safety net policies, economic conditions, and attitudes towards immigrants. These auxiliary data sets are described in the Online Appendix.
Summary Statistics
IV. Empirical Strategy
The primary analysis uses a double difference model, which takes advantage of the variation in eligibility among children of treated immigrants, depending on the child’s year of birth and state of birth, shown in Figure 3. Using this variation, I estimate the following equation:
(1)
where Yisbt is the outcome of interest for child i born in state s and year b, and observed in survey year t. NumYrsTIElig(IU- >4)sb indicates the number of years treated immigrants (“TI”) parents would have been eligible while the child was in utero to their fifth birthday and is a function only of the state and year of birth of the child (regardless of whether the family was “income-eligible” for the program). I control for demographic characteristics in Xisbt, including child gender, fixed effects for child age at survey, mother’s age at child’s birth, mother’s education, number of siblings of the child, number of years the parents had been in the United States before having the child, and race/ethnicity of the child. I account for fixed characteristics of the child’s state of birth with birth state fixed effects vs, and for national shocks to child health with birth year fixed effects λb. I also include controls for state characteristics, including the unemployment rate and Medicaid/SCHIP generosity, at the time of birth, Wsb, and of survey, Zst.15 I cluster standard errors at the state of birth level, and I estimate linear probability models when the dependent variable is binary.
Eligibility for Food Stamps among Children of Treated Immigrants by Birth Year
Notes: States are classified based on their availability of a Food Stamp fill-in program in January, February, or March of a given year. The 1998 fill-in states are Massachusetts, Nebraska, Rhode Island, and Washington; 1999 fill-in states are California, Connecticut, Maine, Minnesota, and Wisconsin. The no-fill-in states are the remaining 41 states and the District of Columbia.
The coefficient β indicates how an additional year of parental Food Stamp eligibility for children in early life affects their medium-run outcomes. Because all health outcomes are “bad,” I expect β to be negative. This estimated effect is the intent-to-treat estimate, as it captures the effect of parents’ eligibility.
To examine the contemporaneous effects of the Food Stamp eligibility changes, I estimate analogous regressions as those described above, using variation in the state of residence and year of observation among children of treated immigrants:
(2)
Here Yist is the outcome of interest for child i living in state s and observed in year t, and TIEligst is equal to one (or zero) if treated immigrants are eligible (or ineligible) for Food Stamps at the time the child is observed. Therefore μ indicates how contemporaneous parental eligibility affects the outcome of interest. In this model, I include state of residence and year of observation fixed effects, as well as the same demographic controls and state of residence by year of observation controls as in Equation 1.
The identifying assumption is that, after controlling for the state- and individual-level characteristics, there are no other changes occurring differentially across fill-in and no-fill-in states over time that are correlated with the Food Stamp eligibility changes and that affect children’s health. As one test of this assumption, I plot the difference in the average rates of “poor,” “fair,” or “good” health across fill-in and no-fill-in states in Figure 4. Given the small sample sizes, this is shown for three-year moving averages, which pool three birth cohorts together. The difference in “poor,” “fair,” or “good” health for children of treated immigrants (Panel A) is relatively flat for the early birth cohorts, providing evidence of parallel pre-trends. As the difference in eligibility, shown as the dashed gray line in the figure, becomes larger and then smaller, the difference in health across the state groups also becomes larger and then smaller. Reassuringly, there is no evidence of changes in the health of children of natives that are commensurate with the Food Stamp eligibility changes, shown in Panel B.16 Similar patterns are apparent in Food Stamp participation and benefit amount, shown in Appendix Figure A.2. The difference in these outcomes across state groups is largely correspondent over time to the difference in eligibility for children of treated immigrants (and there is no similar difference for children of natives).
Difference in “Poor,” “Fair,” or “Good” Health between Fill-In and No-Fill-In States
Notes: Data from the 1998–2015 National Health Interview Survey. The sample includes children born in the United States in 1989–2005, observed at ages 6–16, whose mothers have a high school education or less. Children of treated immigrants are defined as those whose parents were born outside of the United States and who immigrated 1985–1996. Children of natives are defined as those whose parents were born in the United States. The solid black line indicates the difference in health outcomes between fill-in and no-fill-in states for centered three-year moving averages. The center birth year for each three-year average is denoted on the x-axis. The dotted gray line indicates the average difference in the number of years treated immigrant parents were eligible for Food Stamps, between fill-in and no-fill-in states, from the time the child was in utero to their fifth birthday, for the three-year moving averages. Panels A and B display this difference for children of treated immigrants and children of natives, respectively. The results are weighted using the NHIS-provided weights.
V. Results
A. Effect of Eligibility on Program Participation
Before examining the effects on children’s health, it is important to understand how the changes in eligibility affected annual participation in, and income from, the Food Stamp program. While I utilize sharp changes in parents’ eligibility, this essentially amounts to changes in the maximum benefit the family can receive, which may cause participation to fall, as there may be costs to participating in safety net programs either because of stigma (Moffitt 1983) or transaction costs (Currie et al. 2001). Therefore, this analysis is also informative more generally about the responsiveness of program participation to a large change in benefit generosity.
Among children of treated immigrants, parental eligibility increases participation by eight percentage points (p < 0.01), shown in Panel A of Table 2. This is an increase of about 50 percent compared to the 16 percent participation rate for children whose parents are all ineligible (this baseline mean is calculated on a sample of children with treated immigrant parents in no-fill-in states observed in 1998–2002). Similarly, in Panel B, eligibility increases benefits by $185 annually in 2009 dollars (p < 0.05), a 36 percent increase over the baseline mean of $511. Previous findings indicate welfare reform reduced immigrants’ participation in the Food Stamp program by 27 percent nationally, relative to natives’ participation (Haider et al. 2004), and my estimates are larger, possibly due to the fact that I take account of the state differences in eligibility. Because of underreporting of program receipt in the ASEC (Meyer, Mok and Sullivan 2009), I interpret these estimates as a lower bound of the total effect on participation and benefits received. I return to this issue of underreporting in the Online Appendix, in discussing how to interpret the effects on child health in light of these takeup estimates.
Effect of Food Stamps on Benefit Receipt and Medium-Run Health
I conduct a back-of-the-envelope calculation to see if the changes in participation can explain the changes in the benefit amount received. Multiplying the average benefits received by participants (about $3000 in 2009 dollars) by the change in participation, eight percentage points, the expected change in benefits received attributable only to changes in participation is $240, larger than the point estimate.17 Therefore, changes in participation may be an important margin through which the effects on health operate. However, I am unable to distinguish whether the changes in participation are due to the costs of participating versus “chilling effects,” such as confusion about eligibility rules, complicated application procedures, and fear of participation affecting immigration status (Capps et al. 2004; Watson 2014).18
B. Effect of Eligibility on Children’s Health
Next, I use the National Health Interview Survey to estimate the effect of early-life Food Stamp access—from the time children are in utero to their fifth birthday—on the health of children at ages 6–16. The results, shown in Panels C–G of Table 2, indicate improvements in health due to Food Stamp access.19 An additional year of parental Food Stamp access reduces the likelihood the child is reported in “poor,” “fair,” or “good” health (relative to “very good” or “excellent”) health by 1.7 percentage points (p < 0.01) and results in a decrease in the developmental health index of 0.08 standard deviations (p < 0.01). The point estimates on the indexes of physical health outcomes and mental health outcomes are also negative, but the confidence intervals on these estimates (shown in the table) are large enough that I cannot rule out meaningful effect sizes in either direction. To put the point estimates into context, I compare the estimated marginal effect of one additional year of eligibility to the baseline mean incidence of “poor,” “fair,” or “good” health among children of treated immigrants with no exposure to Food Stamps (those born in no-fill-in states in 1998). This suggests that one additional year of Food Stamp access, relative to no Food Stamp access, reduces the likelihood the child is in “poor,” “fair,” or “good” health by about 5 percent. The magnitude of the marginal effect on the developmental index is about 16 percent of the difference between children with low- and high-educated mothers in the cohort with no Food Stamp access.20 Again, wide confidence intervals suggest caution in drawing strong conclusions as to the exact magnitude of the effect. Moreover, since family income and child health are strongly correlated (Case, Lubotsky, and Paxson 2002), the effect is likely smaller relative to the baseline mean for individuals who received Food Stamps, who are more disadvantaged than the full sample. Since there are many outcome variables, I show the unadjusted p-values in the second column, and the p-values adjusted for multiple hypothesis testing (Romano and Wolf 2005) in the third column (“Adj-p”). The overall conclusions are unchanged by this adjustment.
I next explore the effects on other health outcomes, including the likelihood the child was hospitalized overnight in the past year, the number of school days missed and chronic school absence (>15 days) in the past year, and the likelihood the child visited the doctor at all or two or more times in the past year. This latter measure of doctor visits captures poor overall health, as for children in this age range, it is recommended they have one well-child checkup per year (Simon 2016). As shown in Table 3, the point estimates on all the outcomes measuring poor health are negative, but the standard errors are large. The only estimate that is statistically different from zero is chronic school absence, although not so once the adjustment for multiple hypothesis testing is conducted (shown in the table). Interestingly, there appears to be little effect on the likelihood of going to the doctor at all within a year in the medium run, although the confidence intervals are again wide, and this does not rule out the possibility that changes in healthcare utilization in earlier years contribute to the effects on medium-run health outcomes.
Effect of Food Stamps on Other Health Outcomes
While the point estimates are large, they are in line with others in the literature. Almond and Mazumder (2011) find that exposure to Ramadan in utero leads to a roughly doubling of the rates of mental/learning disabilities in adulthood. Adhvaryu, Fenske, and Nyshadham (2014) document that a one standard deviation in cocoa prices in early life reduced the incidence of mental distress in adulthood by 50 percent. Since one innovation of my study is to focus on medium-run, rather than long-run outcomes, there are fewer points of comparison for childhood outcomes. One such study is by Persson and Rossin-Slater (2018), who examine the effects of maternal stress on children and find that a maternal bereavement in utero increases the likelihood children will use ADHD medication in later childhood by 25 percent.
A final important point of comparison is Hoynes, Schanzenbach, and Almond (2016), who look at the long-run effects of the Food Stamp rollout in the 1960–1970s. Their point estimates suggest one additional year of early-life access reduces self-reported poor health in adulthood by 3 percent, compared to the point estimate here on parent-reported poor health of 5 percent. Although similar to my study, the confidence intervals in Hoynes, Schanzenbach, and Almond (2016) are wide and overlap with the ones here.
1. Sensitivity analysis
If there were other changes occurring across states and over time, for example, expansions to public health insurance, that were correlated with the Food Stamp policy changes, this would bias the double difference estimates. But, if children of natives are a valid control group, then including them in the sample would account for these common shocks to children’s health across states and over time. Table 4 explores including children of natives as a control group in a triple difference model. For brevity this is only shown for the main outcome variables that were estimated to be statistically significant: Food Stamp participation and benefit amount, parent-reported overall health, and the index of developmental conditions. Column 1 replicates the baseline results, and Column 2 includes children of natives in the sample. This regression includes the measure of children of treated immigrants’ eligibility based on state and year of birth (NumYrsTIElig(IU– > 4)sb), as well as this measure interacted with whether the child’s parents are treated immigrants or natives (NumYrsTIElig(IU->4)sb * KidsofTIn). I also include the appropriate two-way fixed effects: parents’ treated immigrant status by birth state and parents’ treated immigrant status by birth year (and I make all the analogous changes in the ASEC analysis). The uninteracted term (NumYrsTIElig(IU-> 4)sb) captures the effect of treated immigrants’ eligibility on children of natives’ outcomes. The coefficient on this term is very close to zero as expected. Moreover, the main results, captured by the interaction term (NumYrsTIElig(IU- > 4)sb * KidsofTIn), remain very similar to the baseline results. In Column 3, in addition to the two-way fixed effects already described, I include state of birth by year of birth fixed effects. This is a fully interacted model, so I drop the uninteracted term (NumYrsTIElig(IU-> 4)sb), and again the results remain very similar. Overall, the evidence indicates that no other shocks to health, which affect children of treated immigrants and natives similarly, are driving the results.
Robustness to Including Natives as Controls and Other State by Year Variables
An alternative way of accounting for potential state changes over time is to directly control for state–year characteristics. I add state–year controls at the time the child was born (or the time they were observed in the ASEC) in Columns 4–7 of Table 4 including other safety net program generosity (AFDC/TANF generosity, welfare reform and waivers, state EITC generosity), whether the state chose to “fill-in” other safety-net programs for immigrants arriving in the United States after 1996, state attitudes towards immigrants, and other changes the state made to the Food Stamp program (application recertification frequency, in-person applications or recertification requirements, outreach spending, broad based categorical eligibility, vehicle asset rules, and whether benefits are issued on debit cards). For most specifications the point estimates are very similar to the baseline estimate, but for some outcomes the standard errors increase, causing the effect to become insignificant.21
I did not find any relationship between state observable characteristics and the decision to fill in, but, as Zimmermann and Tumlin (1999) suggest that states’ safety net generosity and income were correlated with the presence of a fill-in program, it is possible that states with generous safety nets or high average incomes were experiencing differential trends in children’s health, and this is driving my estimated effects. Therefore, I include states’ welfare and public health insurance generosity, as well as the unemployment rate in 1990, interacted with state linear trends. As shown in Column 8, the estimated effects remain similar. In Column 9, I add in state of birth linear birth year trends (or state of residence linear time trends in the ASEC) to account flexibly for the fact that some states may have had different trends in children’s health over this time period. The estimates shrink slightly, and the standard errors increase, causing the NHIS estimates to become statistically indistinguishable from zero; however, the pattern of results is similar.
As California contains almost 90 percent of treated immigrant families in fill-in states, I check the robustness of the estimates to dropping California from the sample in Column 2 of Table 5. I also isolate the changes in Food Stamp eligibility due only to changes in federal policy by dropping observations from fill-in states and relying on children of natives to be the primary control group in Column 3 of Table 5. Identification here comes only from across-cohort differences in eligibility across children of treated immigrants relative to children of natives. The estimated effects are sensitive to the inclusion of California and to using the alternative source of variation and become statistically indistinguishable from zero for most outcomes. Next, I test the robustness to alternative definitions of fill-in states following Zimmermann and Tumlin (1999) and modeling teen mother’s eligibility under the “child” Food Stamp eligibility rules for immigrants, rather than the “adult” rules. Columns 4–5 of Table 5 show these are very similar to the baseline estimates. I also test the robustness of the results to the choice of year-of-entry cutoff that is a proxy for immigrants who do not meet the 40 quarters of work requirements (1985 in the main analysis). These checks, shown in Online Appendix Figure B.1, indicate the results are similar for other choices of cutoff year. Finally, I find no evidence of changes in the composition of children due to migration, fertility, or changes in their parents’ citizenship status (Online Appendix Table B.7).
Robustness to Alternative Parental Eligibility Rules and Eligibility at Different Child Ages
2. Subgroup analysis
Food Stamp participation rates vary across demographic groups, so to investigate possible heterogeneous effects, I test whether the demographic groups that experienced the largest effects on participation also experienced the largest effects on medium-run health. I divide the ASEC and NHIS samples into (overlapping) subgroups based on mother’s education (less than high school, high school, some college, and college or more), mother’s ethnicity (Hispanic or not), mother’s age at child’s birth (teens, 20s, 30+s), and mother’s marital status (never married or ever married), and estimate the effect on Food Stamp participation and medium-run health for each subgroup. I expect that the more disadvantaged groups—less educated, Hispanic, teen mothers, and single mothers—will experience larger effects on both participation and health, as they are more likely to be eligible for and participate in the Food Stamp program.
Figure 5 shows the relationship between the effect on participation and the effect on “poor,” “fair,” or “good” health, as well as the developmental health index, for different demographic subgroups. The x-axis indicates the effect of Food Stamp participation, μ from Equation 2, and the y-axis indicates the effect on health, β from Equation 1. As expected, the effects on both participation and health are larger for the more disadvantaged groups. The figure also shows additional falsification tests: for groups with little impact on participation, such as those with a college education or more and non-Hispanics, the effect on health is very close to zero or “wrong-signed.”
Subgroup Estimates: Food Stamp Participation and Health
Notes: Estimates on the y-axis are from the 1998–2015 National Health Interview Survey, and the sample includes children born in the United States in 1989–2005, observed at ages 6–16. Estimates on the x-axis are from the 1995–2007 Annual Social and Economic Supplement to the CPS, and the sample includes children born in the United States, observed at ages zero to four. Estimates are weighted using the NHIS- and CPS- provided weights. The size of each circle indicates the relative sample size of each subgroup in the NHIS person file.
3. Mechanisms
To shed light on the potential mechanisms, I explore whether access to Food Stamps in utero improves health outcomes at birth, which would suggest the medium-run health effects could be driven in individuals’ initial health “stock” (Currie 2009). To do this I use the national Vital Statistics data, described in detail in the Online Appendix. Because of limitations of the Vital Statistics data, I examine outcomes for birth cohorts 2000–2007 for the full sample of births to foreign-born women, regardless of mothers’ education or year of entry to the United States. Therefore, the effects estimated here are not for the same sample of children as the main analysis with the NHIS, but can nonetheless shed light on this potential mechanism. I estimate the effect of mother’s eligibility in the third trimester—the most important for nutrient intake (Rush, Stein, and Susser 1979)— on birth weight (in grams) and the likelihood of being born of low birth weight (<2,500 grams), which are common measures of health at birth (Currie 2011). I show the results of estimating Equation 2 in Panels A–B of Table 6. The likelihood of low birth weight is reduced by 0.01 percentage points (p < 0.01) and average birth weight increases by 6.5 grams (p < 0.01). In percentage terms this is a change of 1 percent and 0.2 percent, respectively, which is quite similar to the intent-to-treat estimates in Almond, Hoynes, and Schanzenbach (2011).
Mechanisms
I investigate the relationship between the effects on infant and medium-run health, by plotting the estimated effects in both time periods for different demographic subgroups in Figure 6. Figure 6 shows the effect on low birth weight (x-axis) and the effect on medium-run “poor,” “fair,” or “good” parent-reported health, as well as the developmental health index (y-axis). Overall there appears to be a strong positive relationship between the effects on health at birth and health in the medium run. Interestingly, low birth weight is associated with dysregulation of the HPA system (Ward et al. 2004), which is one potential mechanism that could explain the medium-run effects. To further explore whether the effects on infant health can explain the medium-run effects, I split the main measure of Food Stamp eligibility in the medium-run analysis into two variables: one measures eligibility in utero to age one, and one measures eligibility for ages two through four. These results are shown in Column 6 of Table 5. The point estimates are larger for the younger ages, but I cannot rule out the effects are the same across both age ranges. Therefore, while the effects in utero and the medium run are related and suggestive of potential biological mechanisms, I am cautious about concluding that the effects in the medium run are caused by the effects at birth. Interesting, it appears that eligibility at age five and beyond is unimportant for determining medium-run health (shown in Column 7 of Table 5).
Subgroup Estimates: Infant and Medium-Run Health
Notes: Estimates on the y-axis are from the 1998–2015 National Health Interview Survey and the sample includes children born in the United States in 1989–2005, observed at ages 6–16. Estimates of the effect on low birth weight on the x-axis are from the 2000–2007 National Vital Statistics, and the sample includes infants born in the United States to foreign-born mothers. Estimates are weighted using the NHIS-provided weights, and the number of births in each cell within the Vital Statistics data. The size of each circle indicates the relative sample size of each subgroup in the NHIS person file.
Another important issue in interpreting the health effects is to understand how Food Stamp benefits affect food consumption. Unfortunately, the only data set I am aware of that contains food consumption measures and the necessary information to identify immigrant families is the Food Security Supplement to the CPS from 2001–2007. I use this to examine how Food Stamp access affected food consumption using Equation 2. Likely due to small sample sizes (N = 685), the results are very imprecisely estimated, although the point estimates indicate an increase in consumption, as shown in Panels C–D of Table 6 (sample described in detail in the Online Appendix). This, along with previous findings that Food Stamps increase household consumption (Hoynes and Schanzenbach 2009; Bruich 2014) and reduce household food insecurity (Borjas 2004), suggest an increase in food consumption may be one mechanism behind the effects on child health. Additionally, whether Food Stamps improve the nutritional content of families’ diets remains an open question that I am unable to address in this paper due to the limitations of the data.
As discussed above, there are other mechanisms that are possible, including changes in other dimensions of consumption, child care, and changes in family stress, but investigating these outcomes is beyond the scope of this paper.
C. Economic Significance of Effects
To better understand the economic significance of the effects, I conduct a back-of-the-envelope calculation to convert the estimates into dollar amounts. With the Medical Expenditure Panel Survey, I tabulate that the average healthcare costs of a child who is in “poor,” “fair,” or “good” health is $2,450, compared to $1,462 for children in “excellent” or “very good” health. Assuming these health benefits are constant from ages 6–16, an additional year of parental eligibility for Food Stamps in early life leads to about $140 in benefits, due to reductions in health expenditures in the medium run (calculations described in detail in the Online Appendix). This suggests that through only the direct effects on medium-run, parent-reported “poor,” “fair,” or “good” health, 42 percent of the direct costs are recouped.22 The benefits captured through parent-reported health may accrue to different sources: first, a reduction in medical costs directly benefits these children’s families, and, as these children participate in Medicaid and SCHIP, the reduction in medical expenditures may also represent government savings. However, there may be more benefits (for example, increases in lifetime earnings due to the reduction in poor health), as well as additional costs (for example, labor supply disincentives) not studied here.
VI. Conclusion
This paper studies the short- and medium-run effects of immigrants’ access to Food Stamps. The loss of parental Food Stamp eligibility has a large effect on contemporaneous Food Stamp receipt, and loss of parental eligibility before age five negatively affects children’s health in the medium run at ages 6–16. These results are robust to a number of tests, including adding natives as a control group and accounting for a variety of other state by year characteristics. These findings support the theories that changes in nutrition and resources in the first few years of life can have lasting effects on children’s health. The results on infant health suggest biological channels may be a factor driving the results during school age, although the lack of data prevents me from drawing strong conclusions about which are the most important mechanisms.
This analysis also contributes to the understanding of the modern Food Stamp program. Food Stamps grew significantly over the past 15 years, but not much is known about its effects because it is a federal program with little quasi-experimental variation. The efficacy of the Food Stamp program is still contentious, and changes to the program are currently being proposed. Moreover, many of the proposed changes specifically target immigrants, so understanding the effects of previous restrictions in immigrants’ access is crucially important. The program likely has other benefits, but through the outcomes studied here, the results suggest important reductions in medical expenditures as the result of early-life Food Stamp access.
Appendix 1 Additional Results
Difference in Health Indexes between Fill-In and No-Fill-In States
Notes: Data from the 1998–2015 National Health Interview Survey. The sample includes children born in the United States in 1989–2005, observed at ages 6–16, whose mothers have a high school education or less. Children of treated immigrants are defined as those whose parents were born outside of the United States and who immigrated 1985–1996. Children of natives are defined as those whose parents were born in the United States. The solid black line indicates the difference in health outcomes between fill-in and no-fill-in states for centered three-year moving averages. The center birth year for each three-year average is denoted on the x-axis. The dotted gray line indicates the average difference in the number of years treated immigrant parents were eligible for Food Stamps, between fill-in and no-fill-in states, from the time the child was in utero to their fifth birthday, for the three-year moving averages. The solid black line is plotted at the zero point on the left-hand-side axis. Columns A and B display this difference for children of treated immigrants and children of natives, respectively. The results are weighted using the NHIS-provided weights.
Difference in Food Stamp Participation and Benefit Amount Received between Fill-In and No-Fill-In States
Notes: Data are from the 1995–2007 Annual Social and Economic Supplement to the CPS. The sample includes children born in the United States in 1989–2005 and between the ages of zero and four, whose mothers have a high school education or less. Children of treated immigrants are defined as those whose parents were born outside of the United States and who immigrated 1985–1996. Children of natives are defined as those whose parents were born in the United States. The solid black line indicates the difference in outcomes between fill-in and no-fill-in states for centered three-year moving averages. The center survey year for each three-year average is denoted on the x-axis. The dotted gray line indicates the average difference in eligibility between fill-in and no-fill-in states for children of treated immigrants for the three-year averages. Columns A and B display this difference for children of treated immigrants and children of natives, respectively. The results are weighted using the CPS-provided weights.
Effect of Food Stamps on Developmental Health Outcomes
Footnotes
She is grateful for helpful comments from Liz Ananat, Marianne Bitler, Kathryn Edwards, Hilary Hoynes, Lucia Kaiser, Price Fishback, Doug Miller, Marianne Page, Giovanni Peri, Diane Schanzenbach, Todd Sorenson, and Ann Stevens, as well as the participants of the Allied Social Science Association Annual Meeting, the RIDGE Conference, the Association for Public Policy and Management Annual Conference, the Society of Labor Economists Annual Conference, the Western Economic Association Annual Conference, the All California Labor Conference, the seminar series at UN–Reno and Sonoma State University, the UC–Davis Center for Poverty Research Graduate Student Retreat, and the Applied Micro Brownbag at UC–Davis. The author also thanks Adrienne Jones and the staff at the Center for Disease Control Research Data Center in Maryland for support in accessing the data, and David Simon for guidance in the data application process. The findings and conclusions in this paper are those of the author and do not necessarily represent the views of the Research Data Center, the National Center for Health Statistics, or the Centers for Disease Control and Prevention. This research was made possible through generous funding from the RIDGE Center for Targeted Studies Doctoral Dissertation Grant, the Bilinksi Foundation, and the Office of Research Services at UC Denver. All errors are those of the author. This paper uses confidential data from the NHIS and Vital Statistics maintained by the Center for Disease Control. This confidential data can be obtained by filing a request directly with the National Center for Health Statistics (https://www.cdc.gov/rdc/b1datatype/Dt1225.htm) for the NHIS and the National Vital Statistics System (https://www.cdc.gov/nchs/nvss/nvss-restricted-data.htm) for the Vital Statistics. The author is willing to assist with this. Additionally, the other data sets, which are compiled from publicly available data, and all accompanying statistical programs can be obtained from the author beginning November 2020 and ending May 2022 (chloe.east{at}ucdenver.edu).
Supplementary materials are freely available online at: http://uwpress.wisc.edu/journals/journals/jhr-supplementary.html
↵1. In 2008 the Food Stamp program was renamed the Supplemental Nutrition Assistance Program (SNAP), but I use the name Food Stamps throughout this paper.
↵2. Some researchers suggested that the decline in immigrant participation may have been due in part to “chilling effects” from a harsh policy environment in addition to the changes in eligibility rules (Fix and Passel 1999; Borjas 2003; Haider et al. 2004).
3. Children of immigrants are defined as children with at least one foreign-born parent. Author’s calculations from the Food Stamp Quality Control Data and the Current Population Survey.
↵4. Most quasi-experimental and experimental research finds the marginal propensity to consume food out of Food Stamp benefits is similar to that of cash income (Currie 2003; Schanzenbach 2007; Hoynes and Schanzenbach 2009; Bruich 2014), and currently most eligible families consume more food than their Food Stamp benefits, suggesting they will behave inframarginally (Hoynes, McGranahan, and Schanzenbach 2015). However, Beatty and Tuttle (2014) found that Food Stamp benefits may distort individuals’ behavior and cause them to consume more food than they would have with an equivalent cash transfer.
↵5. For example, some states required that immigrants apply for citizenship after receiving Food Stamp benefits, and I do not consider these states to be fill-in states. I define the presence of a fill-in program based on information from the USDA SNAP Policy Database, the California Department of Social Services, and Bitler and Hoynes (2013).
↵6. This discussion is drawn primarily from Zimmermann and Tumlin (1999), Capps (2004), and Bitler and Hoynes (2013).
↵7. Any foreign-born siblings of U.S.-born children were made eligible as part of the Agriculture, Research Extension and Education Reform Act in 1998. In the ASEC, among families with U.S.-born children and foreign-born parents, more than 90 percent of the children in the household were U.S.-born.
↵8. States had the option to discount income of ineligible immigrants by the share that they represented in the household when determining the benefit amount (U.S. Department of Agriculture Food and Nutrition Service 2011), so, when eligibility was restored, if parents’ earnings were substantially large, the benefit amount could actually decrease. Anecdotal evidence suggests that this was very rare: in one Texas region 5 percent of mixed citizenship households had benefits decline, and 6 percent had benefits stay the same (Swarns 1997).
↵9. The geographic variables—state of birth and state of survey—along with year of birth and year of survey, were used to merge in information about Food Stamp eligibility and other contextual variables.
↵10. Prior to PRWORA, 38 percent of immigrant households where the mother had a high school education or less participated in the Food Stamp program, whereas 8 percent of similar households where the mother had more than a high school degree did. I also drop children who have one parent born in the United States and the other born outside the United States—about 5 percent of all children—as well as 1 percent of children who do not have their biological mother present in the household.
↵11. Year-of-entry information is based off a question about when foreign-born individuals came to the United States “to stay,” and previous research has documented that for only about 50 percent of respondents does the year they report they came to the United States “to stay” coincide with the year that they became legal permanent residents. The latter of which is the relevant year for determining Food Stamp eligibility (U.S. Department of Agriculture Food and Nutrition Service 2011). Often, this reported year of entry coincides instead with the date of either their first or most recent spell of time spent in the United States. For more information on these measurement issues, see Redstone and Massey (2004) and Lubotsky (2007).
↵12. In the ASEC, only 10 percent of young children of treated immigrants in this time period had a naturalized citizen mother. Additionally, less than 0.5 percent of these children have a parent who reports being a veteran and less than 4 percent have mothers from countries that sent more than 100 refugees or asylees in 1998 (Department of Homeland Security 1998). Further, immigrants on temporary visas or undocumented were never eligible. The data do not identify whether the foreign-born are documented or undocumented. In 1989–2005, between 2 and 9 percent of all births in the United States were to undocumented parents (Fix and Cohn 2015), but it is not obvious how this number might differ in the specific subsample considered here. Moreover, it is unclear if the effect on U.S.-born children of undocumented immigrants will actually be zero, as the children remain eligible, but chilling effects might affect Food Stamp participation.
↵13. To further validate this measure as an indicator of children’s health, I conduct a similar analysis to that in Case, Lubotsky, and Paxson (2002) by looking at the relationship between a variety of poor-health indicators and health conditions and parent-reported “poor,” “fair,” or “good” overall health. The results, shown in Online Appendix Table B.5 indicate that parent-reported health is strongly related to other indicators of poor health, and these results are largely consistent across both children of treated immigrants and children of natives.
↵14. The NHIS measure of children’s BMI is too limited to be used in this study (National Center for Health Statistics 2016).
↵15. The survey state and birth state are the same for roughly 80 percent of the sample. The measures of Medicaid/SCHIP generosity are the maximum eligibility threshold for Medicaid/SCHIP expressed as a percentage of the poverty line, which varies by children’s age, state, and year. Additionally, I control for whether there was a SCHIP fill-in program in the year of the survey.
↵16. The analogous plots for the three health indexes are shown in Appendix Figure A.1. Due to smaller sample sizes for these outcomes, these averages are more variable, and it is more difficult to draw conclusions from them.
↵17. The validity of this calculation relies on the marginal participant being the same as the average participant, which may not be the case. I also estimate the effect of the eligibility changes on the dollar amount of benefits received among participants shown in Column 2 of Online Appendix Table B.6. These results should be interpreted with the caveat that the changes in participation may lead to selection into participation that affects these estimates. I find a statistically insignificant reduction in the dollar amount received.
↵18. An important potential secondary effect of these policy changes is that they may cause immigrant families to change participation in other safety net programs because changes in participation in one safety net program may be linked to changes in participation in other programs. In addition, welfare reform may have had “chilling effects” on safety net participation. I find little evidence of effects on participation in other programs. These results are shown in Online Appendix Table B.6. I detail in the Online Appendix the differences between my empirical strategy and those used in the “chilling effect” literature that explain the discrepancy between the findings.
↵19. It is also possible to use the estimates from the ASEC to calculate the treatment-on-the-treated effect, which I do in the Online Appendix.
↵20. As shown in Appendix Table A.1, the point estimates indicate a decline in all the conditions captured by the developmental index, but only the estimates on the diagnoses of autism, learning disabilities, and mental retardation are statistically significant.
↵21. The results are also similar in the NHIS if controls for these state characteristics in the survey year and survey state are included, or if the state unemployment rate from the time the child was in utero to their fifth birthday is included, analogous to my main specification.
↵22. In 2009, the administrative costs of Food Stamps were $45 per participating household (United States Department of Agriculture 2011), and I estimate the average cost per family of making parents eligible is $308 per year (this adjusts for underreporting described in the Online Appendix). Note that this calculation relies on the point estimates, which are not always precisely estimated.
- Received September 2016.
- Accepted July 2018.
