# Language Use and Health of Children in Immigrant Households

## 1 Introduction

The children of immigrants comprise the fastest-growing segment of the U.S. child population. The foreign-born are driving the growth of the American population, and nearly one in four children have at least one immigrant parent. Despite the increasing importance of the foreign-born population in policy decisions, children of immigrants remain vulnerable with regard to health insurance coverage and access to health care. Immigrants tend to be healthier than the native-born at the point of immigration due to healthful behaviors such as better nutrition and a lower tendency to smoke or drink. However, this advantage disappears as they spend time in the U.S. (Derose, Escarce, and Lurie 2007). Immigrants are more likely than their U.S.-born counterparts to be uninsured, have poor access to health care, and have lower medical expenses (Huang, Yu, and Ledsky 2006; Ku 2009). These gaps persist even at higher levels of income (Bass 2006). Moreover, the foreign-born population is becoming more heterogeneous, and immigrant subgroups vary significantly in health characteristics (Singh and Hiatt 2006). Previous research has focused on explaining health disparities between the foreign-born and the native-born by examining barriers that immigrants face and the consequences of changes in government health program policy. Less research has attempted to explain differences between various immigrant groups by examining the link between the children of immigrants and the additional costs that their parents face when consuming medical services. There is a need for more research on the descendants of immigrants, as they will have a greater long-term influence on the nation than the immigrants themselves.

Two government-run health programs, Medicaid and the State Children’s Health Insurance Program (SCHIP), serve as a crucial source of health insurance coverage for children in low- income households. Despite the benefits of having insurance, immigrants have lower take-up rates for these programs than the native-born due to greater costs of obtaining coverage. For the foreign-born population, language barriers and immigration concerns increase the costs of finding information and completing applications. Starting in 2014, the Affordable Care Act (ACA) will expand access to Medicaid and SCHIP by simplifying eligibility rules, enrollment, and renewal processes. It will also establish a uniform set of benefits that all health plans except employment-based plans must offer. The ACA is an effort to eliminate health inequality and achieve universal coverage by improving accessibility and quality of health care. In light of the enactment of the ACA, which will be fully implemented by 2019, I examine the effect of barriers associated with parental immigration status on insurance coverage and health care utilization of children. In particular, I analyze differences between immigrant subgroups by focusing on language spoken at home. From the results, I hope to suggest ways to improve the implementation of the most significant policy change in the U.S. healthcare system in decades.

## 2 Literature review

To explain poor health outcomes of immigrants, past studies have compared the native-born and the foreign-born across various demographic variables. Researchers demonstrate that socioeconomic background, immigration status, and cultural barriers are factors that affect health insurance coverage in adult immigrants (Huang, Yu, and Ledsky 2006; Jasso et al. 2004). For immigrants, language barriers and immigration concerns increase the costs of finding information about government programs, completing applications, preparing necessary documents, and maintaining enrollment (Aizer 2007). These results suggest that for the foreign-born population, being eligible for insurance is not sufficient to ensure coverage (Currie 2009).

Consequently, research on immigrant health has focused on identifying the causes of disparities in health by examining the effects of changes in government health programs. However, research in this line of thought has been limited to analyzing differences between immigrants and the native-born. The U.S. immigrant population has become more heterogeneous, and subgroups vary greatly in their socioeconomic, health, and cultural characteristics (Singh and Hiatt 2006). Potocky-Tripodi (2006) show that for the children of immigrants, disparities in health conditions persist for different ethnic groups even after controlling for family income and health insurance status. Given such evidence and the continuing growth in both the size and diversity of the foreign-born population, analyzing immigrants as one group is inadequate. In this analysis, I attempt to distinguish the difference in costs that immigrant groups face across two criteria: immigration status and language barriers.

Immigration status has a significant influence on decision-making because non-citizen immigrants, both legal and undocumented, may have additional concerns associated with enrolling in government programs. Legal non-citizen immigrants may mistakenly believe that taking advantage of government-funded programs might jeopardize their chances of becoming legal permanent residents or citizens. Undocumented immigrants may fear that they would be deported if they utilize government health programs. In addition to these concerns, non-citizens are disadvantaged in other aspects. Studies that compared immigrants by their legal status show that naturalized citizens and permanent residents are more likely than non-citizens to earn higher wages, have insurance coverage, and receive benefits such as employment-based health insurance (Carrasquillo, Carrasquillo, and Shea 2000; Kandilov and Kandilov 2010).

However, relatively less research has been devoted to analyzing the effect of parental legal status on children’s health outcomes. Lurie (2008) addresses this gap in the literature by comparing insurance coverage of the children of permanent residents and non-permanent residents following the enactment of the Personal Responsibility and Work Opportunity Reconciliation Act of 1996 (PRWORA). The PRWORA made legal immigrants ineligible for government health programs for the first five years of residence in the U.S. This reform did not have a direct effect on the children of immigrants because the children were citizens, or because their state of residence provided insurance to all children from low-income households regardless of citizenship status. Lurie’s results, however, show that the children of non-permanent residents were more likely to be uninsured than the children of permanent residents despite being equally eligible. This finding suggests that parents’ citizenship status influenced the likelihood of their children being insured. Moreover, the analysis of Bass (2006) demonstrates that long-term immigrants who are likely to be citizens are still less likely than their native counterparts to be insured, which suggests that naturalized citizens and native citizens behave differently. I will further contribute to this body of literature by comparing children across their parents’ citizenship status.

I will examine the interaction of additional costs associated with legal status and language concerns. Studies focusing on language barriers show that limited English proficiency contributes to lack of insurance, poor access to health care, increased risk of medical errors, and patient dissatisfaction (Ku 2009; Cordasco et al. 2011). Non-professional interpreters such as family members are commonly used in medical settings, but these ad hoc interpreters are more likely to make errors that could lead to tragic consequences (Hernandez, Denton, and Blanchard 2011; Flores 2006). In addition to affecting the quality of health care, language difficulties distort the decision-making process involving insurance coverage. Immigrants with limited English proficiency are likely to form false beliefs because they do not have adequate access to information.

Previous research including the studies discussed above has used immigrants’ self-ratings as a measure of their English proficiency, which may not be an accurate reflection of their actual language use or mastery of English. Lee, Nguyen, and Tsui (2011) address this bias by testing interview language as a measure of acculturation among Asian Americans. However, the analysis compared English and non-English survey takers without further analysis of language groups. I suggest a more comprehensive way to compare immigrants by examining language spoken at home. This is a better measure of immigrants’ integration into the U.S. and allows for comparison between multiple immigrant groups. Analysis involving language spoken at home is needed, especially when looking at the children of immigrants, because the actual language used at home is a better reflection of parents’ English skills and acculturation than perceived proficiency. Regardless of the children’s English proficiency, what largely determines their access to and use of health care is their parents’ ability to secure such resources.

In predicting the differences in the effect of speaking a certain language as opposed to another, linguistic distance can be a useful concept. Linguistic distance is defined as the difficulty that Americans have in learning the language in question, and Chiswick and Miller (2005) provide a quantitative measure of this distance. They show that controlling for other variables, linguistic distance had a significant effect on English proficiency of adult immigrants. According to Chiswick and Miller’s values, European languages such as French and Spanish are linguistically closer to English, while Asian languages such as Japanese and Chinese are further. Immigrants speaking languages that have larger linguistic distances will have a more difficult time learning English as they assimilate to life in the U.S. Since English proficiency has been shown to influence immigrants’ health, those who have to expend relatively more effort to achieve a certain level of proficiency would face greater costs in securing insurance and a usual source of care for their children, and experience lower quality and satisfaction from utilizing medical services.

In order to account for the growing heterogeneity of the foreign-born population and to explain health disparities between ethnic groups, research with more comprehensive ways of categorizing immigrants is necessary. To my knowledge, researchers have not yet performed a multilingual analysis on health care and health outcomes of the children of immigrants. The aim of this analysis is twofold: to examine whether the language spoken at home leads to differences in health measures for children in immigrant households, and whether the effect of language spoken is related to parental immigration status. To answer these questions, I examine the way in which parental citizenship status affects language use, which in turn influences children’s health outcomes. Drawing from existing literature and the theory of linguistic distance, I hypothesize that children from households speaking languages that are linguistically closer to English will have greater access to and use of medical services. I also hypothesize that the effect of language use will be smaller for the children of naturalized immigrants as compared to non-citizens, since the former will have fewer concerns related to legal status. Through this analysis, I plan to provide an update to the literature and reveal areas of vulnerability for immigrant subgroups.

## 3 Data

This analysis uses data from the 2009 California Health Interview Survey (CHIS). The CHIS is a telephone survey of California’s overall population, which includes more than a quarter of the U.S. immigrant population according to the 2010 Census. The sample includes people from most large and small racial and ethnic groups, and the interview is administered in English, Spanish, Mandarin, Cantonese, Korean, and Vietnamese. From each household, a random adult, adolescent, and child are interviewed separately. Adolescents (ages 12 to 17) are interviewed directly. For children (under age 12), the randomly selected adult in the household acts as the proxy. The CHIS collects health-related information including health status, behavior, insurance, access to care, and use of medical services. The CHIS was chosen because it covers groups underrepresented in most other health surveys, and conducts interviews in six languages (including English). Compared to commonly used surveys such as the Current Population Survey (CPS) and the National Health Interview Survey (NHIS), the CHIS provides more detailed data on language use and immigration status. Moreover, multiple interview languages will ensure that the immigrant population is represented, and improve the accuracy of their responses.

The CHIS also provides more detailed information on parental citizenship status and language spoken at home compared to other health surveys, which makes the dataset appropriate for this analysis. Information on whether each parent is a US-born citizen, naturalized citizen, or a non-citizen is provided. To place children into the category that best characterizes the set of advantages and disadvantages that their parents face in securing health care, and to avoid double counting, I sort children according to the “highest” parental citizenship status in the household. Children in the first category have at least one US-born citizen parent, and those in the second category have at least one naturalized parent. Both parents of children in the last category are non-citizens. As for language spoken at home, I consolidate the various categories that the dataset provides into three (European, Asian, and other), since certain subsets of language spoken at home only represent a small fraction of the sample. For the 2009 survey, data are available for 12,324 children ages 0 to 17. Among these, 5,135 are children of immigrants, including naturalized citizens, non-citizens with legal visas, and undocumented immigrants.

## 4 Methodology

I use OLS linear probability and probit models of CHIS data to test if language spoken at home has an effect on health care access and utilization for children in immigrant households. To distinguish the effect of language use by legal status, regressions are performed for each group of children categorized by parental citizenship status: “US-Born Citizen,” “Naturalized Citizen,” and “Non-Citizen.” Since naturalized citizens tend to be better assimilated to the United States and have fewer concerns with regard to language difficulties and citizenship status compared to non-citizen immigrants, I hypothesize that the potential negative effect of language use will be stronger for non-citizens. With the objectives of examining the relationship between language spoken at home, parental immigration status, and health care access and utilization, the following linear probability model will be used:

\begin{split}
\left (Healthcare \right )_i &= \beta_0 + \beta_1European_i + \beta_2Asian_i + \beta_3Other_i \\
&+ \beta_4FPL_i + \beta_5Age_i + u_i
\end{split}

$$Healthcare$$ is a dummy variable for one of the following measures of health care access and utilization: insurance coverage, having a usual source of care other than an emergency room, and having visited a doctor in the past year. In this model, it indicates the probability that a child with a set of characteristics given by the independent variables has access to the measure of health care in question.

$$European$$, $$Asian$$, and $$Other$$ are indicator variables denoting the language spoken in the child’s household. Although the dataset provided more detailed categories on language use at home, certain categories included too few children to be useful for data analysis. Therefore, children in households that use English were grouped with foreign language-only households if a foreign language was also among the languages used at home. In these multilingual families, it could be the case that while the children use English to communicate, the parents mainly use their native foreign language. In such cases, categorizing these children as coming from an English-speaking household would not be an accurate reflection of the barriers that their parents face in consuming health care. Moreover, it is plausible that parents in households that use English along with a foreign language are less fluent in English than parents in English-only households. For children in families that only use English, all of the three language dummy variables take a value of 0.

$$FPL$$ is the household income as a percentage of federal poverty level (FPL), which varies by household size. In the dataset, $$FPL$$ equals 1 if the household income is 100% of federal poverty level, 2 if it is 200% of FPL, and so forth. $$Age$$ is the child’s age in 2009 when the interviews were conducted, and ranges from 0 to 17. The continuous variables for age and FPL were included because they are the two main determinants of children’s eligibility for government health programs. California’s Medicaid program is called Medi-Cal, and its SCHIP program is called Healthy Families. Infants (age 0) in households with incomes of up to 200% FPL, children under age 6 with household incomes of up to 133% FPL, and children ages 6 to 18 with household incomes of up to 100% FPL are eligible for Medi-Cal. Children are eligible for Healthy Families if they are ineligible for Medi-Cal and have household incomes of up to 250% FPL.2 Controlling for age and household income as a percentage of FPL will indicate if language spoken at home has an effect on different measures of health care access and utilization, holding constant the determinants of eligibility and ability to afford insurance. Since federal poverty guidelines take into account the size of the household, FPL will be a more accurate measure of the resources available per person in the household than absolute income.

In addition to the basic linear probability model, I use a probit model with an additional set of explanatory variables to estimate the effect of language use and parental immigration status. The probit regression resolves the disadvantages of the linear probability regression by modeling the probability of the independent variable equaling one using the cumulative standard normal distribution function. The probit model is estimated as follows:

\begin{split}
\phi \left (Healthcare \right )_i &= \beta_0 + \beta_1European_i + \beta_2Asian_i + \beta_3Other_i \\
&+ \beta_5FPL_i + \beta_6Age_i + \beta_7Female_i + \beta_8Health_i \\
&+ \beta_9Married_i + \beta_10College_i + u_i
\end{split}

The variables $$European$$, $$Asian$$, $$Other$$, $$FPL$$, and $$Age$$  have the same definitions as in equation (1). Additional demographic variables for gender, perceived health status, family structure, and parental education are included. Previous studies have shown that these factors are correlated with children’s health outcomes (Guendelman, Schauffler, and Pearl 2001). $$Female$$ is a dummy variable that equals 1 if the child is female, and it equals 0 if the child is male. Perceived health status, $$Health$$, takes one of five values ranging from 1 to 5 depending on the self-rating provided by the respondent (5=excellent, 1=poor). $$Married$$ is a dummy variable that equals 1 if the child’s household has married parents, and it equals 0 if the household has a single parent. $$College$$ is a dummy variable that equals 1 if the interviewed adult of the family has attended college, and it equals 0 otherwise.

## 5 Results

### 5.1Demographic characteristics

Summary statistics for demographic characteristics of children in immigrant households suggest that “higher” parental legal status is strongly associated with the ability to provide better resources for their children. An overwhelming majority of native households use English at home. For naturalized citizens, the percentage of households using both English and a European language is the greatest, followed by households using both English and an Asian language at home. Half of non-citizen households use English and a European language, while a third only use Spanish. Data on health characteristics of children vary with parental immigration status. Children of US-born citizens have better ratings than the immigrant population in general, and children of naturalized citizens had slightly better health ratings than children of non-citizens. As for insurance coverage, children of non-citizens are much more likely to be uninsured, and much less likely to have employment-based insurance. Immigrant children are also less likely to have a usual source of care or to have visited a doctor or a dentist in the past twelve months. As expected, the data suggest that children’s access to and use of medical care differ significantly depending on parental immigration status.

### 5.2Health insurance coverage

For the full sample of children, I apply the linear probability model specified in Equation (1), and include the results in Table 1. I also present regression results with additional variables. For both the basic and the expanded models, all of the coefficients on language variables are significant. Speaking a language other than a European or Asian language at home most negatively affects probability of insurance. To analyze the effect of parents’ citizenship status on children’s probability of being insured, I apply the linear probability and probit models to three groups of children categorized by parental immigration status (US-born citizen, naturalized citizen, and non-citizen). The regression results are included in Table 3. As expected, the language coefficients tend to be more negative and significant for children who have parents with a “lower” citizenship status. In the linear model for children of non-citizens, the three language coefficients are all significant. The coefficient on Asian language is the largest and is significant at the 0.01 level (5.67 percentage point decrease in probability of insurance). Given that only 8.73% of the children of non-citizens are uninsured, this value represents a substantial change in probability of being uninsured.

I use the same variables from the expanded linear model and apply them in the probit model. These results are included in the third column for each citizenship group in Table 3.

### 5.3Usual source of care

In the linear regression of having a usual source of care for the full sample of children (Table 1), all of the language coefficients are small but significant and negative. Speaking a language other than a European or Asian language at home most negatively affects the probability of having a usual source of care. In regressions applied to groups of children categorized by parental immigration status, only the non-citizen group has significant language coefficients (Table 4). For the non-citizen group, the coefficient on Asian language is significant and indicates a 5.8 percentage point decrease in probability of having a usual source of care.

### 5.4Doctor visits in the past year

In the linear probability regressions of having visited a doctor in the past twelve months (Table 5), the differences between language groups are insignificant or small for the full sample of children. In the linear and probit regressions of children grouped by parental immigration status, none of the language coefficients are significant. While the values are not significant, the predicted probabilities based on probit regressions are such that speaking English makes children in the naturalized citizen group less likely to have visited a doctor, while it makes children in the non-citizen group more likely to have visited a doctor. This general pattern is true for both reference cases. However, since the differences in probability are quite small and statistically insignificant, drawing a conclusion from this pattern would be inappropriate.

## 6 Discussion

### 6.1Health insurance coverage

Among the three measures of health care access and utilization explored in this analysis, the linear probability regressions for health insurance coverage yielded the largest number of significant language coefficients. The values of these significant coefficients confirm the main hypothesis that children from households speaking languages that are linguistically further from English will perform worse in measures of health care access and utilization, as those from Asian-language households suffered a greater decrease in the probability of being insured than those from European-language households. The predicted values from probit analysis also confirm the secondary hypothesis that the effect of language use will be smaller for the children of naturalized immigrants compared to children of non-citizen immigrants. In both the linear and probit models, more language coefficients are significant for the non-citizen group than for the naturalized citizen group, and the decrease in predicted values is greater for children in the former.

There may be other factors contributing to non-citizens’ sensitivity to change in circumstances. Non-citizens could be staying in the country only temporarily, and therefore decide that they do not need insurance during their short stay. Language difficulties exacerbate the tendency for immigrants to stay uninsured, complicating the process of obtaining insurance, becoming familiarized with terms, and filing claims. As policy makers aim to achieve universal insurance coverage, they should consider the fact that non-citizens and foreign language households are much more likely to respond to changes in eligibility or the process of obtaining insurance than their native and English-speaking counterparts.

As noted previously, regressions in this analysis have rather low $$R^2$$ values. These values for linear probability regressions are influenced by the fact that the dependent variable takes a value of zero for a rather small group of children. Moreover, it could be the case that immigrants are affected by factors other than those included in this analysis. For instance, non-citizens could have most of their wealth in their native country’s currency. In this case, consumption choices such as obtaining health care could be influenced by change in exchange rates between the native currency and the U.S. dollar. Such fluctuations would be the result of more general trends in the world economy, which would be difficult to incorporate into an analysis of immigrants from many different countries. To account for factors like these, future studies could focus on a select group of immigrants from a specific country.

### 6.2Usual source of care

Whereas there are significant language coefficients for regressions of insurance coverage for all three groups of children, only the non-citizen group has significant language coefficients for regressions of having a usual source of care. The dummy variable for speaking an Asian language has the only significant coefficient, and the magnitude is quite substantial. These results confirm both of my hypotheses; children in Asian language households are less likely to have a usual source of care, and the effect of language use is greater for the children of non-citizens than naturalized citizens. The probit analysis yields similarly significant results.

The analysis regarding having a usual source of care is limited, which could partly explain why the regressions have less significant coefficients compared to regressions of insurance coverage. Unlike insurance, having a usual source of care depends on one’s definition of the term. It is possible that some hold stricter definitions while others hold looser definitions of having a usual place to go when their children are sick. To produce more precise results, future surveys and studies could clarify this definition or create more accurate ways to measure access to medical care.

### 6.3Doctor visits in the past year

The linear probability regressions of having visited a doctor in the past year yield the least number of significant coefficients on language indicator variables. These results may be influenced by the fact that there is relatively little variation in the proportion of children who have visited a doctor in the past year between different language groups. (8.6%, 10.5%, 9.8%, and 7.9% of children from English, European-language, Asian-language, and other-language households have not visited a doctor in the past year, respectively.) This suggests that the language spoken at home does not directly influence the probability of a child having a physician visit in the last twelve months.

### 6.4Additional discussion of regression results

This analysis has additional limitations that should be considered when interpreting the results. It is possible that there are cultural factors within each language group that influence decisions about health care utilization in a systematic way. If this were the case, speakers of a certain language would make health care choices as a result of being a native speaker of that language, and not directly because of speaking the language itself. However, even if these external factors have a significant impact on immigrants’ decision- making process, this analysis still confirms the hypothesis that there are significant differences between children in immigrant subgroups, both in terms of parental legal status and language spoken at home.

As discussed previously, this analysis also suffers from the limits of binary dependent variables. The models in this analysis have a binary dependent variable that reflects whether the child has utilized a certain measure of health care. As for insurance coverage, a simple answer to a yes-no question cannot indicate whether the coverage is permanent or temporary. Non-citizens, who tend to be poorer than citizens and therefore more sensitive to marginal changes in income, could be insured at one point in time, but uninsured at another.

Another potential source of bias may involve parental immigration status. Since the non-citizen population includes permanent residents, who are better integrated into the U.S. in general and are less concerned about legal status, the coefficients for non-citizens may be underestimating the effect of barriers that non-permanent residents experience. While the dataset for this analysis places undocumented immigrants, permanent residents, and non-permanent residents such as those with employment visa into the broad category of “non-citizens,” future studies could look at these segments of the immigration population separately.

## 7 Conclusion

The immigrant population is an increasingly important segment of the nation to consider in policy decisions. In particular, the children of immigrants are driving the growth of the U.S. child population. In comparison to U.S. natives, additional factors influence immigrants when they make decisions about utilization of health care. This research analyzed the effect of language spoken at home on children’s access to and utilization of health care, and how the strength of this effect varies with parental citizenship status. As expected, the children of non-citizens are affected most strongly by the type of language used in the child’s household. For this group of children, speaking an Asian language seems to have the most significant and strongest effect on having access to medical care. These results confirm both of my hypotheses: that speaking a language that is linguistically further from English has a stronger effect on health care consumption, and that this effect is stronger for children of parents with “lower” legal status.

These findings suggest several areas of improvement for medical service providers. To address the heterogeneity among immigrant households, hospitals should focus on recruiting interpreters or nursing staff that are fluent in foreign languages. Policy initiatives could encourage this by making it mandatory for hospitals to have a minimum amount of interpretive services available to patients with limited English proficiency. Under Medicaid and SCHIP, the two main government health programs for children in the U.S., each state decides if, and how, it will reimburse health care providers for language services (Youdelman 2007). National requirements for providing interpretive services could strengthen the quality of medical care that immigrants receive, and encourage them to seek preventive care, which will decrease the cost in the long run. Moreover, language services that are available to patients should reflect the specific needs of that region. Since language is a significant barrier, especially for low-income immigrants, programs geared toward this population will have limited effectiveness if policymakers ignore the heterogeneity between immigrant subgroups. Communities with a higher proportion of low-income, Asian immigrants should be in greatest need of interpretive services, as my analysis suggests that this population is most vulnerable to financial and language difficulties.

Further research is necessary to obtain a full understanding of the factors that influence immigrants’ decision-making process with regard to health care and to create effective policy. These analyses could use data from national surveys or regional surveys that cover areas with a substantial immigrant population. More detailed information on children’s consumption of health care goods and services would provide a more comprehensive analysis of this population than the binary measures of health care used in this investigation. Another limitation of this study was data insufficiency due to few observations belonging to certain language groups. A larger dataset with more specific information on language use at home would supplement the shortcomings of this analysis.

Numerous past studies have confirmed that immigrants and natives make different decisions with regard to health care. This investigation further suggests that immigrant subgroups behave differently when making decisions about utilization of medical care, and supports the need for research to determine the sources of differences between immigrants. At a time when the foreign-born and their children are driving the growth of the American population, studying immigrants and the challenges that they face in obtaining health care will remain crucial to the effort to alleviate health inequality.

## Bibliography

Aizer, Anna. 2007. “Public Health Insurance, Program Take-up, and Child Health.” The Review of Economics and Statistics 89 (3): 400-415.

Bass, Elizabeth. 2006. “The Enigma of Higher Income Immigrants With Lower Rates of Health Insurance Coverage in the United States.” Journal of Immigrant and Minority Health 8 (1): 1-9.

Bitler, Marianne, and Hilary W. Hoynes. 2011. “Immigrants, Welfare Reform, and the U.S. Safety Net.” NBER Working Paper, 17667. http://www.nber.org/papers/w17667.

Borjas, George J. 2003. “Welfare Reform, Labor Supply, and Health Insurance in the Immigrant Population.” NBER Working Paper, 9781. http://www.nber.org/papers/w9781.

Buchmueller, Thomas, Anthony Lo Sasso, and Kathleen Wong. 2007. “How Did SCHIP Affect the Insurance Coverage of Immigrant Children?” NBER Working Paper, 13261. http://www.nber.org/papers/w13261.

California Health Interview Survey. CHIS 2009 Adolescent Public Use File. Los Angeles, CA: UCLA Center for Health Policy Research, November 2011. http://www.chis.ucla.edu/main/PUF/download_2009.asp. (accessed November 6, 2012).

California Health Interview Survey. CHIS 2009 Child Public Use File. Los Angeles, CA: UCLA Center for Health Policy Research, November 2011. http://www.chis.ucla.edu/main/PUF/download_2009.asp. (accessed November 6, 2012).

Carrasquillo, Olveen, Angeles I. Carrasquillo, and Steven Shea. 2000. “Health Insurance Coverage of Immigrants Living in the United States: Differences by Citizenship Status and Country of Origin.” American Journal of Public Health 90 (6) (June): 917-923.

Chiswick, Barry R. and Paul W. Miller. 2005. “Linguistic Distance: A Quantitative Measure of the Distance Between English and Other Languages. Journal of Multilingual and Multicultural Development 26 (1): 1-11.

Cordasco, Kristina M., Ninez A. Ponce, Melissa S. Gatchell, Brandon Traudt, and José J. Escarce. 2011. “English Language Proficiency and Geographical Proximity to a Safety Net Clinic as a Predictor of Health Care Access.” Journal of Immigrant and Minority Health 13 (2) (April): 260–267.

Currie, Janet M. 1995. “Do Children of Immigrants Make Differential Use of Public Health Insurance?” NBER Working Paper, 5388. http://www.nber.org/papers/w5388.

---. 2006. The Invisible Safety Net: Protecting the Nation’s Poor Children and Families. Princeton: Princeton UP.

---. 2009. “Policy Interventions to Address Child Health Disparities: Moving Beyond Health Insurance.” Pediatrics 124 (Supplement) (October 27): S246–S254.

Derose, Kathryn Pitkin, José J. Escarce, and Nicole Lurie. 2007. “Immigrants And Health Care: Sources Of Vulnerability.” Health Affairs 26 (5) (September 1): 1258–1268.

Flores, Glenn. 2006. “Language Barriers to Health Care in the United States.” New England Journal of Medicine 355 (3): 229–231.

Guendelman, Sylvia, Helen H. Schauffler, and Michelle Pearl. 2001. “Unfriendly Shores: How Immigrant Children Fare in the U.S. Health System.” Health Affairs 20 (1): 257-266.

Guendelman, Sylvia, Roberta Wyn, and Yi-Wen Tsai. 2000. “Children of Working Low-Income Families in California: Does Parental Work Benefit Children’s Insurance Status, Access, and Utilization of Primary Health Care?” Health Services Research 35 (2) (June): 417-441.

Hernandez, Donald J., Nancy A. Denton, and Victoria L. Blanchard. 2011. “Children in the United States of America: A Statistical Portrait by Race-Ethnicity, Immigrant Origins, and Language.” Annals of the American Academy of Political and Social Science 633 (1): 102–127.

Huang, Zhihuan,Jennifer, Stella M. Yu, and Rebecca Ledsky. 2006. “Health Status and Health Service Access and Use Among Children in U.S. Immigrant Families.” American Journal of Public Health 96 (4) (April): 634–640.

Jasso, Guillermina, Douglas S. Massey, Mark R. Rosenzweig, and James P. Smith. 2004. “Immigrant Health: Selectivity and Acculturation.” Discussion paper for the Conference on Racial and Ethnic Disparities in Health, National Academy of Science.

Kandilov, Amy M.G. and Ivan T. Kandilov. 2010. “The Effect of Legalization on Wages and Health Insurance: Evidence from the National Agricultural Workers Survey.” Applied Economic Perspectives and Policy 32 (4): 604-623.

Kaushal, Neeraj, and Robert Kaestner. 2007. “Welfare Reform and Health of Immigrant Women and Their Children.” Journal of Immigrant and Minority Health 9 (2) (April): 61–74.

Kim, Jinsook, and Hosung Shin. 2006. “Public Health Insurance Enrollment among Immigrants and Nonimmigrants: Findings from the 2001 California Health Interview Survey.” Journal of Immigrant and Minority Health 8: 303-311.

Ku, Leighton. 2009. “Health Insurance Coverage and Medical Expenditures of Immigrants and Native-Born Citizens in the United States.” American Journal of Public Health 99 (7) (July): 1322–1328.

Lee, Sunghee, Hoang Anh Nguyen, and Jennifer Tsui. 2011. “Interview Language: A Proxy Measure for Acculturation Among Asian Americans in a Population-Based Survey.” Journal of Immigrant and Minority Health 13 (2): 244-252.

Lurie, Ithai Zvi. 2008. “Welfare Reform and the Decline in the Health-Insurance Coverage of Children of Non-Permanent Residents.” Journal of Health Economics 27: 786-793.

National Governors Association, NGA Center for Best Practices. 2008. “Maternal and Child Health Statistics, FY 2008.”

Okie, Susan. 2007. “Immigrants and Health Care--At the Intersection of Two Broken Systems.” New England Journal of Medicine 357 (6): 525-529.

Potocky-Tripodi, Miriam. 2006. “Risk and Protective Factors in the Perceived Health of Children of Immigrants.” Journal of Immigrant and Minority Health 8 (1): 11-18.

Singh, Gopal K., and Robert A. Hiatt. 2006. “Trends and Disparities in Socioeconomic and Behavioural Characteristics, Life Expectancy, and Cause-Specific Mortality of Native-Born and Foreign-Born Populations in the United States, 1979-2003.” International Journal of Epidemiology 35: 903-919.

U.S. Census Bureau. The Foreign-Born Population in the United States: 2010. May 2012. U.S. Department of Commerce.

---. The Newly Arrived Foreign-Born Population of the United States: 2010. Nov. 2011. U.S. Department of Commerce.

Youdelman, Mara, National Association of Public Hospitals and Health Systems. 2007. “Research Brief: Medicaid and SCHIP Funding for Language Services.”