Abstract

This study examines the prevalence of several types of hardship (e.g., bill paying and housing hardships) among immigrants by race and ethnicity in the United States using data from the 2008 and 2014 panels of the Survey of Income and Program Participation and logistic regressions. I find that Blacks, and to some extent Hispanics, are more likely to report hardships than Whites and Asians, who are about equally likely to report hardships. Exploring results by nativity and citizenship status, I find that immigrants who became U.S. citizens are less likely than the native-born population to report some kinds of hardship. Undocumented immigrants, however, are more likely to report some kinds of hardships, particularly in the 2008 panel conducted at the time of the Great Recession, which hit immigrants especially hard; this relationship, however, is explained by the lower incomes of undocumented immigrant households in the 2008 panel. Results within racial and ethnic groups are generally in the same direction but are less frequently statistically significant. Overall, these findings suggest that immigrants are not particularly prone to hardship, especially when other characteristics are controlled for. In fact, the lower likelihood of some hardships among foreign-born citizens suggests that they are positively selected: they may have unobserved characteristics that are protective, such as better health, stronger social networks, or money management skills. Because the foreign-born are less likely to be disadvantaged vis-à-vis the native-born when hardship rather than the official income poverty measure is used, this study highlights the importance of using multiple measures when assessing the well-being of immigrants.

Introduction

New immigrants to the United States often have relatively low levels of education and income. However, over time and across generations, many experience upward mobility, indicative of successful incorporation (Villarreal and Tamborini 2018). Even so, outcomes vary considerably among immigrants and their descendants. Some groups, such as Hispanics, have substantially higher poverty levels than Whites; others, such as Asians, have poverty rates that are fairly similar to those of Whites (Bean and Stevens 2003; Iceland 2017; Kasinitz et al. 2008; Sakamoto et al. 2009).

A considerable body of literature has documented income and poverty by race and nativity, but much less research has explored experiences of hardship across these groups. Income is thought to be an important indicator of well-being because of its instrumental importance: money can be used to purchase goods and services to meet basic needs and improve one's welfare. In contrast, hardship measures are often thought to be outcomes of intrinsic importance (Beverly 2001). For example, individuals who report having insufficient food to eat, having their utilities cut because of unpaid bills, or having insufficient funds to see a doctor are experiencing actual deprivation that is sometimes not captured when only income is measured. Measures of poverty and hardship are only moderately correlated, partly because of measurement issues but also because they tap into different, if related, dimensions of well-being (Iceland and Bauman 2007; Mayer and Jencks 1989).

Even with this moderate correlation, because poverty generally is higher among the foreign-born than the native-born in the United States, hardship would be expected to be more common among immigrants than the native-born as well. In addition, immigrants who are not citizens lack access to benefits from many social safety net programs, such as cash welfare, which might further increase hardship—an issue that would be all the more severe among those who are undocumented. Assimilation theory would predict that with rising incomes and greater access to formal institutions across generations, differences between immigrants and the native-born would narrow. Alternatively, immigrants might report lower levels of hardship than the native-born, especially if one conditions on income, because immigrants are often positively selected. That is, immigrants are healthier and may have other characteristics, such as ambition, that could mitigate hardship (Raleigh and Kao 2010).

Thus, the goal of this study is to examine the association between hardship and nativity to see whether patterns that have been observed for poverty hold when looking at these intrinsically important outcomes. To this end, I use data from the 2008 and 2014 Survey of Income and Program Participation (SIPP), a nationally representative panel survey that contains an extended battery of questions on hardships. The data reflect hardships as reported in 2010 and 2013 of the respective panels. I investigate whether reports of several hardships—including (depending on the panel) health, food, bill-paying, and housing hardships, as well as neighborhood problems, fear of crime, and lack of consumer durables—vary by nativity, citizenship and legal status, and race/ethnicity. In doing so, I aim more broadly to shed light on the well-being of immigrants. To the best of my knowledge, this is the first study to explore the association between this range of hardships and nativity, citizenship, and race/ethnicity using nationally representative U.S. data.

Background

Although patterns of income and poverty by race/ethnicity and nativity have been well-documented, considerably less is known about the incidence of hardship across these groups. Hardships are consumption-based indicators of well-being that are often thought to be superior to income-based measures (Beverly 2001; Citro and Michael 1995). Income measures do not always capture the resources families have to meet needs, such as some types of government transfers, wealth, and access to credit. In addition, as Pilkauskas et al. (2012:403) argued, “Besides capturing the effects of economic resources that income-based measures may miss, consumption-based alternatives, such as hardship measures, are also heuristically attractive because they assess concrete adversities.”

I focus here on seven types of hardship: health, food, bill-paying, and housing hardships; lack of consumer durables; neighborhood problems; and fear of crime. These indicators have been used by previous researchers examining the incidence of hardship (Beverly 2001; Heflin 2017; Heflin et al. 2009; Iceland and Bauman 2007), and all are present in the SIPP. Each indicator taps into a different dimension of well-being and has different associations with income. Health, food, and bill-paying hardships are more sensitive to short-term shortfalls in income, whereas the other four are more dependent on longer-term income (Iceland and Bauman 2007). For example, a job loss or health crisis might produce a short-term income drop that will make it difficult for a family to pay bills in a given month. This possibility has become more common in recent decades as the precarity of work may have increased (Kalleberg 2009). That same family, however, may live in a good neighborhood and may have accrued a number of consumer durables over the years. Thus, each indicator is of interest in its own right.

Mechanisms in the Link Between Nativity and Hardship

Hardship may vary by nativity for many reasons. Assimilation theory asserts that new immigrants differ from the native-born population in many respects, including culturally and socioeconomically, but that these differences narrow over time and across generations, resulting in the successful incorporation of immigrants (Alba and Nee 2003). According to this approach, immigrants might be more likely than the native-born to experience hardships because they have fewer resources, as exemplified by their lower median incomes and higher levels of poverty. For example, in 2017, the poverty rate among the native-born population was 11.0%, compared with 14.5% among the foreign-born (U.S. Census Bureau 2018a). These statistics imply that the foreign-born have less money to meet basic needs, such as food, clothing, shelter, and healthcare. In addition, poorer people are more likely to live in neighborhoods with more affordable housing but worse conditions, such as more crime and environmental hazards and less social capital, that could contribute to hardship (Bischoff and Reardon 2013; Epple and Platt 1998; Tiebout 1956). Consistent with assimilation theory, poverty rates among the foreign-born vary by citizenship status: citizens have a lower poverty rate (10.0% in 2017) than noncitizens (18.0%) (U.S. Census Bureau 2018a). Thus, it will be important to examine the role of citizenship status in the analyses.

The segmented assimilation perspective holds that the extent of assimilation could vary across immigrant groups. Some groups might achieve successful incorporation into the mainstream, others may do well socioeconomically but maintain their ethnic distinctiveness, and yet others will experience downward mobility into the underclass (Portes and Zhou 1993; Zhou 1999). These different trajectories result from the existing racial hierarchy, maintained by discrimination, that produces unequal outcomes. Some researchers (Telles and Ortiz 2008) have pointed to large differences in income and poverty by race and ethnicity as evidence of this hierarchy, with the lowest poverty levels found among Whites (8.7% poor in 2017) and Asians (10.0%) and much higher levels found among Blacks (21.2%) and Hispanics (18.3%) (U.S. Census Bureau 2018b).

Characteristics other than poverty can also affect immigrants' assimilation trajectories. For example, English language proficiency is a common indicator of incorporation (Alba and Nee 2003). In addition, immigrants' motivations for migrating vary: a relatively high proportion of immigrants from Asia are admitted into the United States on the basis of their occupational skills, whereas a higher proportion of Hispanics enter because they already have kin living in the United States (Min 2006). Immigrants who enter because of their occupational skills have considerably higher average levels of education and income than those who enter on the basis of family reunification provisions (Chiswick 1986; Feliciano 2005). As a result, lower levels of hardship are likely among Asian immigrants and perhaps African immigrants (who also have relatively high levels of education) than among Hispanic immigrants (Radford 2019). Relatedly, less-skilled workers, including many immigrants, are more likely to have jobs without benefits that could reduce hardship (Kristal et al. 2018).

Overall, the literature on assimilation indicates that Asian Americans experience economic outcomes that are roughly on par with those of native-born Whites (Kasinitz et al. 2008; Kim and Sakamoto 2010; Park and Myers 2010). The evidence is less clear for Hispanics, who achieve upward mobility across generations—as exemplified by the higher levels of educational attainment and income among the second generation than among the first—but have not achieved parity with Whites (Bean and Stevens 2003; Perlmann 2005). Indeed, Hispanics may not achieve much mobility beyond the second generation, although research on this issue is mixed (Duncan and Trejo 2011, 2014; Telles and Ortiz 2008; Telles and Sue 2019).

Factors less related to assimilation or segmented assimilation per se and more related to policy could affect patterns of hardship by nativity. For example, many immigrants may have less access to services with the potential to improve well-being. The 1996 Personal Responsibility and Work Opportunity Reconciliation Act and subsequent laws generally restricted benefits to “qualified” immigrants: those with legal permanent residency who have lived in the United States for at least five years and those with refugee status. Unqualified immigrants are ineligible for most government benefits, including cash welfare, the Supplemental Nutrition Assistance Program (SNAP), Medicare, Medicaid, and Supplemental Security Income (SSI) (Pew Charitable Trusts 2014). Take-up rates are also lower for many who qualify for program assistance (Cunnyngham 2004). Participating in programs such as Temporary Assistance for Needy Families (TANF), food assistance, and Medicaid likely reduces hardship, although it is challenging to measure these effects because of selectivity into these programs (McKernan et al. 2018; Pilkauskas et al. 2012; Shaefer and Gutierrez 2013).

Undocumented immigrants may also lack access to bank accounts and driver's licenses, and they often avoid talking with police to report a crime or speaking with school officials when problems arise with their children. They may be less aware of community programs and health services that have no citizenship requirements, and even those with knowledge of these programs and services may be less likely to seek such help when needed because of their precarious status (Bernstein et al. 2019; Gelatt et al. 2018; Kalil and Chen 2008; Potochnick et al. 2017; Yoshikawa et al. 2008; Yu et. al. 2005). Immigrants who are not citizens also are generally ineligible for employment by the federal government.

On the other hand, immigrants might be less likely to experience hardships, which might be especially likely to be revealed in research that controls for income level. Immigrants typically are positively selected on a number of observable and unobservable traits, such as health, skills, and ambition. A large literature on the “immigrant health paradox” helps explain why immigrants, many of whom have fewer financial resources, often display better health than the native-born population (Hummer et al. 2007; Jasso et al. 2004; Martinez et al. 2015). To extend this notion, it could be that these traits on which immigrants are positively selected may provide immigrants with ways to better cope with potential hardships. In addition, many immigrants may have networks of social support to aid in the immigration process, potentially mitigating the effects of economic strain.

Further selection among immigrants may occur to differentiate those who become naturalized citizens and those who do not. Immigrants who become citizens have higher levels of schooling, duration in the United States, and proficiency in English (Chiswick and Miller 2009). The naturalization process itself requires applicants to acquire basic knowledge of the English language and of U.S. history and government and to pay an application fee. Moreover, applicants may be denied citizenship on the basis of certain criminal offenses or the failure to show that they are of “good moral character” (Bloemraad 2002). Thus, the fact that naturalized citizens might differ from other immigrants provides another reason to examine the outcomes of immigrants by citizenship status.

Empirical Literature on the Association Between Nativity and Hardship

Relatively few studies have examined the link between nativity and multiple dimensions of hardship, especially using nationally representative survey data. One such study found that children of immigrants were more likely than native-born children to live in a household that experienced a few specific hardships, including a food-related hardship, bill-paying hardship, crowded housing, and a lack of health insurance (Capps 2001); this study focused on bivariate relationships and did not condition on income. Another study found that children with foreign-born noncitizen mothers experienced more persistent and higher levels of food insecurity than native-born children. However, low-income families in which foreign-born mothers are citizens have about the same risk of food insecurity as those with native-born mothers (Kalil and Chen 2008). These findings are consistent with another study finding that children of noncitizens experience higher and more persistent levels of food insecurity than children of citizens (Van Hook and Balistreri 2006).

Gelatt et al. (2018) examined how state enforcement policies affect patterns of hardship among immigrants. They found no difference by nativity in a few different kinds of hardship (e.g., inability to meet expenses, food insecurity, and housing hardship), but immigrants were more likely to live in overcrowded households. In models with a variety of control variables, Gelatt et al. found that immigrants (both legal and unauthorized) may be slightly less likely to experience a variety of hardships. Because of the structure of their models with interaction terms, however, it is not clear whether these net differences are statistically significant. The authors did not examine differences in the likelihood of hardship among immigrants of different origins.

In a study on the relationship between family structure and hardships that used immigrant status as a control variable, Lerman (2002) found that recent immigrants were modestly more likely to report two kinds of hardship (inability to pay rent and missing meals for economic reasons) than the native-born; the association persisted in models that controlled for income as well. Immigrants who had lived in the United States for longer periods were not significantly different than the native-born in their experiences of hardship. In contrast, Pilkauskas et al. (2012) examined the effect of the Great Recession on hardship while controlling for income and found that immigrants were less likely to report hardship, measured with an index that included a number of different hardship indicators. Pilkauskas et al. did not focus on immigrants and did not differentiate among immigrants by citizenship status. Other studies using nativity as a variable but not differentiating by citizenship have found either mixed or null findings (e.g., Hernández et al. 2016).

Overall, the studies reviewed here suggest that immigrants who are noncitizens likely fare worse than immigrants who are citizens and the native-born. However, this literature is limited in a few ways. Most studies have looked at one or two hardships, such as food insecurity. The few studies that looked at more hardships were descriptive (e.g., Capps 2001), did not condition on income or bundled hardships into a single index (Pilkauskas et al. 2012), or did not examine the net effect of nativity or how effects might vary by race/ethnicity (Gelatt et al. 2018). Some of these studies included controls for race but did not include race-nativity interactions, which is important because of the disparities in well-being by race in the United States.

Here I build on this literature by examining seven types of hardship, conducting some analyses by race and ethnicity, differentiating by citizenship status and by legal status, and conditioning some of the models on income to see whether differences in the prevalence of hardship are driven simply by differences in income or instead by other factors. Using multiple hardship measures is important because each taps into a different dimension of well-being. As noted earlier, some hardships are more sensitive to short-term income shortfalls (e.g., food insecurity), whereas others are more affected by longer-term income (e.g., housing problems and neighborhood conditions; Iceland and Bauman 2007). I also have data on program receipt (such as SSI) to determine whether receipt helps mediate the nativity-hardship relationship. Finally, I analyze data from 2010, when the economy was still reeling from the Great Recession, as well as data from 2013, when economic recovery was underway. Because immigrants were especially hard hit during the recession (Bitler et al. 2017), examining immigrant well-being at these two time points is an advantage of this study. In summary, the goal of this analysis is to reach a better understanding of the extent to which immigrants experience various kinds of hardship and whether income alone helps explain observed differences.

Data and Methods

I use data from the 2008 and 2014 panels of the SIPP, a nationally representative household survey conducted in the United States (U.S. Census Bureau 2001). The SIPP is longitudinal survey, with panels lasting from three to five years. A rich source of data on income, program participation, and labor force activity, the survey is one of the relatively few to collect information on experiences with various kinds of hardship. The data on hardships from pre-2014 panels come from the topical module Adult Well-Being, which was typically administered once per panel. Each wave of the SIPP covers a four-month period. I use data from the Wave 6 topic module of the 2008 panel, which collected information on hardships in 2010; I also use data from the 2014 panel, which asked about hardships in 2013. Each of these two panels has advantages and disadvantages. The 2008 panel has information on a wide range of hardships. After the 2008 panel, the SIPP was redesigned and shortened, and most of the topical modules were eliminated. As a result, the 2014 panel contains a much smaller set of hardship measures. However, in addition to being more current, the 2014 panel has the advantage of including a variable on immigrant year of entry, which could be of substantive importance. It is also useful to have hardship measures from two time points with different economic conditions. Thus, I use data from both panels, drawing on their respective strengths.

The sample consists of respondents who were in the SIPP during the wave that the topical module was administered and who provided valid answers to the hardship questions. Households are the unit of analysis, given that hardships are reported for the household as a whole. Sample sizes are 34,850 in the 2008 panel data and 29,685 in the 2014 panel data. I use household weights provided by the SIPP for a given wave, which are meant to ensure that the data are representative of all U.S. households in the given period.

Measures of Hardship

When using the 2008 SIPP, I analyze seven types of hardship assessed through a series of questions. I categorize a household as experiencing hardship as a dichotomous outcome equal to 1 if the respondent answers affirmatively to a certain number of questions, similar to how previous studies have measured such hardships (Gelatt et al. 2018; Heflin 2016, 2017; Iceland and Bauman 2007; Short 2005) and yielding percentages of respondents with hardships that somewhat approximate poverty rates. Hardships in the 2008 panel are defined as follows:

  1. Bill-paying hardship (one or more): did not pay utility bill, phone was disconnected, did not pay rent/mortgage

  2. Health hardship (one or both of the following): did not see a doctor/hospital when needed care, did not see a dentist when needed care

  3. Food hardship (two or more): food did not last (and had no money for more), could not afford balanced meals, cut or skipped meals, ate less than should, did not eat for a full day

  4. Housing hardship (one or more): insect/pest problem, roof leaks, broken windows, plumbing problems, cracks in walls, holes in floor

  5. Lack of consumer durables (lacks five or more): computer, dishwasher, air conditioner, dryer, washer, microwave, cell phone, telephone, refrigerator, color television, VCR/DVD, stove, food freezer

  6. Neighborhood problems (two or more): noise problems, street repair problems, trash/litter, abandoned buildings, the neighborhood is undesirable enough that would like to move, smoke/odors

  7. Fear of crime (two or more): afraid to walk alone at night, stays at home for fear, goes out with others to stay safe, neighborhood is unsafe, carries something for protection, would like to move due to crime, home is unsafe

I also conducted a sensitivity analysis by creating counts of hardships for each dimension (as opposed to dichotomous variables) and running ordinary least squares (OLS) models. These analyses yielded similar findings, which are shown in appendix tables and discussed briefly at the end of the Results section.

The 2014 panel has fewer measures of hardship. Specifically, it has no items for health hardships or consumer durables, and it contains fewer items for the following: food hardship (four in 2014 vs. five in 2008), bill paying (one vs. three), housing (four vs. six), neighborhood problems (two vs. six), and fear of crime (two vs. seven). The summary indicators of hardship therefore incorporate different thresholds, including neighborhood problems and fear of crime (one or more for each of these in 2014 vs. two or more in 2010). The wording on some of the questions also differs slightly, including the accounting period for a few of them (e.g., previous year vs. previous month). Thus, the summary measures of the prevalence of hardships are not directly comparable across these two panels. Rather than focusing on levels of hardship in the two different periods, I examine differences in hardship across groups (nativity, citizenship status, documentation status, and race/ethnicity) for the measures available in the two SIPP panels.

Main Independent Variables: Nativity, Citizenship Status, and Documentation Status by Race/Ethnicity

I examine differences in hardship by nativity, citizenship status, documentation status, and race/ethnicity. Nativity is measured by questions on the place of birth, citizenship, and legal permanent resident status. Individuals are categorized as native-born if they were born in the United States or born abroad to American parents. Those who are not native-born can be further distinguished as citizens; noncitizens with legal permanent resident status; and immigrants without permanent status, who are considered undocumented. To be more precise, the “undocumented” group includes undocumented immigrants, nonimmigrants and others without a green card who are lawfully present, and some immigrants who adjusted from undocumented or nonmigrant status to legal permanent resident status. Thus, the group more accurately represents those who entered the United States without a green card.

I examine the role of nativity by race/ethnicity of householder, defined as non-Hispanic White, non-Hispanic Black, non-Hispanic Asian, or Hispanic. Although it would be optimal to have data on specific ethnic groups (such as Mexicans or Chinese) by nativity, variables with this level of specificity are not available in the SIPP. Nevertheless, controlling for broad racial categories permits a more fine-grained analysis of hardship by nativity than having no such a variable because of at least some broad commonalities (in treatment and outcomes) among panethnic groups (Iceland 2017).

Control Variables

The analyses include a number of control variables in the models: year of entry for immigrants (available in the 2014 panel but not the 2008 panel); household income-to-poverty ratio; age of the householder; education of the householder, categorized as less than high school, high school diploma, some college, or bachelor's degree or more; household type, categorized as married couple (with and without children), single female parent with children, or other household type; employment status of householder, categorized as employed full-time, employed part-time, unemployed, or out of the labor force; a dummy variable for lives in a metropolitan area; region, with the categories of Northeast, Midwest, South, and West; number of people in household; whether children under age 18 are present in the household; the household has a person 65 years or older; the household has a disabled individual; and the householder has English language proficiency (speaks well/very well). Because the literature suggests that access to and receipt of benefits might help explain potential differences in hardship by nativity, I also control for receipt of TANF, General Assistance (GA), SSI, or housing assistance; Social Security; and whether the individual has public health insurance or private health insurance.

Analytical Strategy

I begin by presenting descriptive statistics of hardships and summary hardship measures by nativity and race/ethnicity. In the subsequent multivariate analysis, I run a series of logistic regression models with each hardship as a separate dependent variable, as specified by the following equation:
 Logit (P(Y=1))=β0+β1X1+β2X2++ βkXk.
(1)

Specifically, the probability that a household experiences a hardship (Y) is modeled as a function of a series of covariates, including nativity (X1), race/ethnicity (X2), and the series of control variables described earlier. I also run hardship models by race/ethnicity to see whether the role of nativity varies by race/ethnicity and to allow the effects of other variables to vary by race/ethnicity. I run one set of models with nativity and race/ethnicity only, a second that adds the household income-to-poverty ratio to see whether income mediates the role of nativity, and a third with the full set of controls to see whether these other household characteristics mediate the nativity-hardship relationship.

Given that each outcome requires its own table, for brevity, the multivariate results presented here focus on (1) a short-term hardship, bill paying; and (2) a longer-term one, housing hardship. The results for other hardships, shown in the online appendix, are generally consistent; I discuss exceptions in the text.

Results

Table 1 shows the percentage of respondents reporting specific hardships as well as summary hardship indicators. As noted earlier, the 2014 SIPP panel contains fewer hardship measures than the 2008 panel, and differences in the wording and time frame for some of the hardship measures in the two panels mean that most are not directly comparable across panels. The main goal in this analysis is not to look at over-time trends but rather at whether hardships vary by nativity across the two panels. The table indicates that many households experience hardships of one type or another in the given year.

Table 2 shows how the summary hardship measures vary by race/ethnicity, nativity, citizenship status, and legal permanent resident status. Among the total population, unauthorized immigrants were the most likely to report hardships, followed by legal permanent residents. Foreign-born citizens were the least likely to report many kinds of hardship, although the native-born population was the least likely to report some hardships, and little difference was evident between the two groups for yet other hardships. Foreign-born citizens, for example, were the least likely to report a bill-paying hardship in 2010 (11.9%), followed by the native-born population (14.3%), immigrants who are legal permanent residents (20.0%), and unauthorized immigrants (23.7%). Lack of consumer durables stands out as a hardship that is much more common among the foreign-born of various statuses (reported by 16.0% of citizens, 24.9% of legal permanent residents, and 33.5% of unauthorized immigrants) than the native-born (11.7%). Thus, it appears that relative to the native-born, the foreign-born are less likely to prioritize the ownership of such consumer items (which arguably are less essential for well-being) and are more likely to address other kinds of needs. Much like with the official poverty rate, hardships are less common among immigrant citizens in all cases than among those with legal permanent resident or undocumented status; the multivariate analyses will test whether these relationships hold after controls.

The patterns by race/ethnicity generally confirm expectations: Blacks and Hispanics were more likely to report hardships than Whites and Asians. Patterns by nativity, citizenship status, and race/ethnicity, however, are less easily generalizable. Among Whites, Blacks, and Hispanics, foreign-born citizens were generally less likely to report hardships than the native-born, with more mixed results found among Asians. The unauthorized often were the most likely to report hardships, although this finding does not hold as much for Asians, and some findings for Whites and Blacks are mixed. Overall, these findings by race/ethnicity do not provide clear support for any single perspective. However, results for Whites, Blacks, and Hispanics indicate that immigrants of these groups who are citizens on the whole appear to be selective, which could explain low rates of hardship. For Asians, a mix of factors may be at work, including differential selectivity as well as income and education differences by nativity. Citizens likewise might have lower levels of hardship than noncitizens because they also might have higher incomes more generally. The following multivariate analysis sheds greater light on these issues.

Multivariate Analyses

Table 3 provides descriptive statistics for all of the independent variables in the analyses. In 2010, about 87.2% of householders were native-born, 7.2% were foreign-born citizens, 2.9% were legal permanent residents, and 2.8% were unauthorized. The foreign-born groups as a share of the population were a little higher in 2013. In 2010, 70.6% of householders were White (compared with 67.4% in 2013), and the mean household income-poverty ratio was 3.8, rising to 4.3 in 2013 during the recovery period after the deep recession in the late 2000s. Just under half of households were married-couple households. A significant proportion of households reported receiving some kind of benefit, such as Social Security or public health insurance; nearly four-fifths lived in a metropolitan area; and a plurality lived in the South.

Table 4 shows results for logistic regressions in which bill-paying hardship is the dependent variable, using data from the 2008 SIPP panel (reflecting hardship in 2010). The first set of models is for the full sample; the next four sets are models by race/ethnicity (Whites, Blacks, Asians, and Hispanics). Results from the full model indicate that foreign-born householders who have attained citizenship were less likely to report hardships than the native-born. Adding controls in Model 3 slightly weakens the effect, but it remains statistically significant. The results for Model 3 indicate that the odds of reporting a bill-paying hardship for foreign-born citizens is 0.79 the odds for native-born households. Immigrants who are legal permanent residents do not significantly differ from the native-born in any of the models. Those with unauthorized status are 1.30 times more likely to experience a bill-paying hardship according to results in Model 1 without controls, but this relationship becomes smaller and not significant in Model 2, which controls for the household income-to-poverty ratio.

With regard to race/ethnicity, Blacks were more likely to report bill-paying hardship than Whites in all models. Hispanics were also more likely to report hardships than Whites, although this relationship becomes nonsignificant in the final model with controls. I find no significant difference between Asians and Whites. Consistent with the descriptive statistics (Table 2), results for race-specific models shown in Table 4 vary somewhat. Differences by nativity and citizenship among Whites are evident in Model 3 when all controls are included: White undocumented immigrants actually were less likely to experience a hardship than the native-born, suggesting that there might be some positive selectivity with regard to unobservable characteristics. Among Blacks, foreign-born citizens were less likely to report bill-paying hardship, even in models with controls, although the size and significance of the coefficient are moderately reduced in Model 3; neither type of noncitizens differs from the native-born in any model. None of the findings for Asians are statistically significant. The results for Hispanics are similar to those of the main sample: undocumented immigrants were more likely to report a bill-paying hardship, but this relationship becomes nonsignificant once I control for household income-to-poverty ratio. Foreign-born citizens were less likely to report hardships than the native-born, and this relationship becomes nonsignificant in Model 3. Overall, the finding that foreign-born citizens were less likely to report hardship in models without controls as well as in many models with controls suggests that immigrants are often positively selected on both observed and unobserved characteristics, which confers advantages that make hardships less likely among this group.

Control variables generally have expected associations with bill-paying hardship: income is negatively associated with hardship, as is age in many of the models. Female-headed households were generally more likely to report hardships, as were larger households and those with a disabled member present. Compared with householders who are employed full-time, those who are unemployed or employed part-time were generally more likely to report hardships, and those out of the labor force (perhaps by choice) were less likely to report hardships. The receipt of welfare is generally not associated with reported hardships, whereas those who received Social Security income and those with private health insurance were less likely to report hardships in some models.

Table 5 shows results for logistic regressions in which housing hardship in 2010 is the dependent variable. The results for this hardship are similar to those for bill-paying hardship, with a few differences. For the full-sample and among Whites and Blacks, foreign-born citizens were less likely than the native-born to report a housing hardship. Among Blacks, this relationship becomes nonsignificant in Model 3 with the full set of controls (although Whites and Blacks have nearly identical odds ratios in Model 3). The relationship between housing hardship and foreign-born citizenship is in the same direction for Asians and Hispanics, but not all the coefficients are significant. In addition, for all groups and models, neither kind of noncitizens differs from the native-born in the odds of reporting a housing hardship, with one exception: among Hispanics, unauthorized immigrants were more likely to report a hardship than the native-born, although this relationship becomes nonsignificant once the income-to-poverty ratio is controlled for in Model 2. The odds ratios for control variables in these models are generally consistent with those in Table 4. Overall, the results from Table 5 indicate a moderate association between nativity and housing hardship for the full sample, Whites, and Blacks, suggesting that foreign-born citizens are positively selected on traits that are protective of housing hardship. Among Hispanics, the finding that the unauthorized are more likely to experience housing hardship is mainly due to their low income. More generally, Blacks were more likely to report a housing hardship than Whites in all models. Hispanics once again were more likely to report hardships than Whites, although this relationship becomes nonsignificant in Model 3 with controls. I find no significant difference between Asians and Whites in Models 1 and 3.

Tables A1–A5 in the online appendix show results from these models using alternative hardship outcomes. These results are generally consistent with those shown in Tables 4 and 5: compared with the native-born, foreign-born citizens either are less likely to experience hardships or do not differ significantly. Undocumented immigrants are more likely to experience some hardships (especially the two more affected by short-term income flows, food and health hardships), a result that is attenuated with the addition of controls. Lack of consumer durables is in some respects an outlier, given that both types of noncitizens are considerably more likely to experience a dearth of consumer durables than the native-born population, even when controls are included; this result perhaps suggests that these immigrants may have less of a taste for consumer durables than the native-born population or that they direct their money elsewhere, in savings or remittances. Models with controls show that legal permanent residents were also less likely to report fear of crime, perhaps suggesting that they often seek out safe neighborhoods (or live in ethnic neighborhoods with relatively high levels of trust). In most of the other models, legal permanent residents do not differ from the native-born. In models with controls, compared with Whites, Blacks were more likely to report all hardships, and Hispanics were more likely to report some hardships. In most models, Whites and Asians do not differ in their likelihood of experiencing hardships.

Results using the 2014 SIPP data (with hardships reported in 2013) share similarities with those from the 2008 panel. As shown in Table 6, among the population as a whole, the likelihood of bill-paying hardship does not differ much by nativity and citizenship status in models without controls, but both types of noncitizens were less likely to report hardship than are the native-born in models with controls. This finding contrasts somewhat with the results from 2010, which showed that unauthorized immigrants were more likely to report a bill-paying hardship only in models without controls. Thus, the 2010 results suggest that low incomes among undocumented immigrants explained their bill-paying hardship, whereas the 2013 findings point to potential positive selection based on unobservable characteristics. The disadvantage among unauthorized immigrants in 2010 could be a function of the Great Recession having a particularly negative impact on immigrants, and perhaps especially undocumented immigrants in industries hard hit by the recession, such as manufacturing and construction (Bitler et al. 2017; Kochhar 2019). Unlike in 2010, bill-paying hardship in 2013 does not differ significantly between foreign-born citizens and the native-born. For most of the specific racial/ethnic groups, the nativity and citizenship indicators are not significant, perhaps partly because of the somewhat smaller sample size in the 2014 panel than in the 2008 panel, which can be consequential for examining hardship among relatively small racial/ethnic–nativity groups.

The patterns for housing hardship are more similar across the two SIPP panels. In 2013, like in 2010, foreign-born citizens were less likely to report a housing hardship across all models (see Table 7). The pattern is apparent for all racial/ethnic groups, although it is statistically significant only among Whites. Findings for other racial/ethnic groups tend to be in the same direction in 2013 as in 2010, but none of them are statistically significant. Results for 2013 with alternate hardship outcomes are shown in Tables A6 to A8 in the online appendix. In general, in 2013, Blacks and Hispanics were more likely to report most hardships than Whites, whereas Asians were either as likely or less likely than Whites to report hardships.

Sensitivity Analyses

I conducted additional analysis in 2013, substituting year-of-entry categories for the various citizenship and documentation statuses (data that are not available in the 2008 SIPP panel). I did not include both the year of entry and the citizenship variables in the same models because of collinearity between the two sets of variables. The results were quite consistent with those of the main analyses (see Table A9, online appendix). Results for bill-paying hardship indicated that the most recent immigrants (those who arrived less than five years or five to nine years prior) were less likely to report hardships than the native-born. These results are similar to those shown in Table 6, which shows that legal permanent residents and undocumented immigrants were less likely to report bill-paying hardship than the native-born. An analogous pattern for housing hardship is observed: in the full model, both foreign-born citizens (odds ratio  =  0.82, Table 7) and long-term immigrants (odds ratio  =  0.82 in Table A9, online appendix) were significantly less likely than the native-born to report housing hardship. From these results, it is not clear whether the variation is explained mainly by documentation status per se, by the duration in the United States, or by a combination of both factors given that they are correlated with each other.

Finally, I conducted a robustness check in which, rather than dichotomous hardship indicators, I used counts of hardships for each dimension and ran OLS models with those counts as the dependent variables. Results from these models, shown in Table A10 of the online appendix, were very similar to those from the main logistic regressions. Specifically, in 2010, foreign-born citizens remained less likely to report both bill-paying and housing hardships, even when all the controls were included. Undocumented immigrants were more likely to report bill-paying hardship in both years; this association became nonsignificant when all controls were included (although in the logistic regression, the income-to-poverty threshold variable alone explained the association). I found no significant relationship between the noncitizenship categories and housing hardship in the logistic or OLS models.

Conclusion

The economic well-being of immigrants is an issue of broad concern, and it is indicative of the extent of their incorporation in their new countries and communities. In this study, I focus on hardship among immigrants in the United States. Using data from the 2008 and 2014 SIPP panels (reflecting hardship in 2010 and 2013) and logistic regressions, I find that immigrants who have become U.S. citizens tend to be less likely to report some kinds of hardship than the native-born population. I also find that the foreign-born who are undocumented are more likely than the native-born to report some hardships in 2010, and this is explained mainly by the lower incomes of undocumented immigrant households. I also examine whether results vary across racial/ethnic groups. These results often are in the same direction but are less frequently statistically significant. The smaller sample sizes for some specific racial/ethnic groups could play a role. Blacks were generally more likely to report various hardship than Whites. Hispanics were also more likely to report hardships than Whites in all bivariate models, although this relationship is nonsignificant for some hardships in models with controls, especially in 2010. More often than not, I find no significant difference between Asians and Whites.

Previous empirical studies on the association between nativity and hardship have found mixed results. Some of these studies looked only at the effect of nativity overall and/or one or two hardships. I find that it is important to differentiate the foreign-born by nativity, citizenship status, and (if possible) documentation status. The results are consistent with previous research finding that noncitizens, and the undocumented in particular, are in some cases more likely to experience hardship (Kalil and Chen 2008; Van Hook and Balistreri 2006). This finding mainly held in 2010 and for hardships more strongly associated with short-term income flows (bill-paying, food, and health hardships) rather than for hardships related to longer-term income flows (such as housing and neighborhood conditions) (Iceland and Bauman 2007). In fact, the differences are explained by lower incomes among the undocumented, who may incur greater short-term hardships to live in otherwise more equal neighborhoods. Multivariate analyses, however, show that the undocumented were disadvantaged mainly in 2010 rather than in 2013. This finding likely reflects that immigrants were hit especially hard by the 2007–2009 Great Recession, although their status improved during the recovery (Bitler et al. 2017; Kochhar 2019). This finding thus indicates that economic shocks often may have a larger effect on immigrants, particularly the undocumented, because of their more precarious social, economic, and political position.

The finding that foreign-born citizens are less likely to report hardships than the native-born suggests that immigrants frequently are positively selected on a number of observable and unobservable traits, such as health, skills, ambition, and networks of support, that can ameliorate the effects of financial strain. Thus, after an initial adjustment period, immigrants who are able to meet citizenship requirements are in fact better off than the native-born. This notion is also consistent with research finding an immigrant health paradox (Hummer et al. 2007; Jasso et al. 2004; Martinez et al. 2015).

My findings are not consistent with perspectives that predict a higher likelihood of hardship among immigrants than among the native-born because of factors such as less access to government benefits (Pew Charitable Trusts 2014) or work-related benefits (Kristal et al. 2018). Even among noncitizens (who are not eligible for many kinds of government support), income alone explains differences in hardships when they occur. This is not to say that such benefits would have no effect if received; Social Security, for example, is generally associated with lower hardship among respondents. Immigrants could very well be more advantaged relative to otherwise similar native-born households (because of selection) than if they had more access to benefits, but I cannot definitively evaluate this possibility with the data available.

My findings are partially consistent with assimilation theory (Alba and Nee 2003), especially my finding that immigrants who became citizens—and thus are more assimilated—reported lower levels of some hardship than immigrants who are legal permanent residents and unauthorized immigrants. Undocumented immigrants exhibit the highest levels of hardship. However, the finding that foreign-born citizens have lower levels of hardship than the native-born is not consistent with assimilation theory and thus is likely explained by the selection argument described earlier. To the extent that levels of hardship (much like poverty) vary by race/ethnicity, the findings also provide support for the segmented assimilation perspective (Portes and Zhou 1993). Whites and Asians tend to experience similar levels of hardship, consistent with other literature comparing Whites and Asians (Iceland 2019; Kasinitz et al. 2008; Kim and Sakamoto 2010; Park and Myers 2010), whereas Blacks and sometimes Hispanics are more likely to report hardships, even when a variety of characteristics are controlled for (Telles and Ortiz 2008; Telles and Sue 2019). However, vast differences in the role that nativity and citizenship variables play across groups are not evident; more often than not, group-specific coefficients are not significant, perhaps in part because of small sample sizes.

The findings reported here also indicate that it is important to examine a variety of outcomes when assessing well-being. Poverty, broadly speaking, is by its nature a multidimensional concept; it influences not just the ability to purchase things but also the ability to participate meaningfully in society and to realize one's capabilities (Sen 1999). An advantage of hardship measures over traditional income poverty measures, such as the official U.S. poverty measure, is that they measure concrete challenges that households face, such as not being able to pay bills or having substandard housing (Beverly 2001; Heflin 2017; Pilkauskas et al. 2012). In contrast, income poverty measures are proxies for well-being: income is instrumentally important for meeting basic needs, such as housing, food, and paying bills.

The well-being of immigrants vis-à-vis the native-born appears somewhat worse when an income poverty measure is used rather than hardship measures. For example, whereas the gap in the official poverty rate between immigrants and the native-born was 4.0 percentage points in 2013 (14.3% among the native-born, compared with 18.3% among the foreign-born; U.S. Census Bureau 2018a), the nativity gap for all hardships measured in that year were smaller. As noted earlier, this smaller gap for hardships could be due to stronger networks, better health, ambition, or other characteristics that allow immigrants to manage their resources better than the native-born. One clue supporting the last of these conjectures is that the lack of consumer durables is the one hardship that the foreign-born were much more likely to report. This finding suggests that the foreign-born are more likely to forgo consumer items—many which are nonessential—than to experience other, perhaps more serious kinds of hardship, such as bill-paying and housing hardships. In short, these findings support the need for a variety of measures to arrive at a more complete and holistic understanding of immigrant well-being.

Acknowledgments

This research was supported by the National Institutes of Health, Population Research Institute Center Grant, R24HD041025. My thanks to Claire Kovach for her assistance with using the SIPP data.

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