Immigration scholars have increasingly questioned the idea that "illegality" is a fixed, inherent condition. Instead, the new consensus is that immigration laws produce "illegality." But can "illegality" be socially constructed? When initially judging who is an "illegal immigrant," common observers and even authorities typically do not rely on an individual's documentation. Instead, people rely on shared stereotypes to assign "illegality" to certain bodies, a condition we refer to as "social illegality." Ethnographers have documented that individual traits like occupation or national-origin may trigger illegality suspicions, but it is not clear how widespread these stereotypes are, or whether all stereotypes are equally consequential. To address this question, we examine the personal attributes shaping perceived "illegality." We apply a paired conjoint survey experiment on a nationally representative sample of 1,515 non-Hispanic white U.S. adults to assess the independent effect of each dimension. We find that national origin, social class, and criminal background powerfully shape perceptions of illegality. These findings reveal a new source of ethnic-based inequalities-"social illegality"that may potentially increase law enforcement scrutiny and influence the decisions of hiring managers, landlords, teachers, and other members of the public.

People of Middle Eastern and North African (MENA) descent are categorized as non-White in many Western countries but counted as White on the US Census. Yet, it is not clear that MENA people see themselves or are seen by others as White. We examine both sides of this ethnoracial boundary in two experiments. First, we examined how non-MENA White and MENA individuals perceive the racial status of MENA traits (external categorization), and then, how MENA individuals identify themselves (self-identification). We found non-MENA Whites and MENAs consider MENA-related traits—including ancestry, names, and religion—to be MENA rather than White. Furthermore, when given the option, most MENA individuals self-identify as MENA or as MENA and White, particularly second-generation individuals and those who identify as Muslim. In addition, MENAs who perceive more anti-MENA discrimination are more likely to embrace a MENA identity, which suggests that perceived racial hostility may be activating a stronger group identity. Our findings provide evidence about the suitability of adding a separate MENA label to the race/ethnicity identification question in the US Census, and suggest MENAs’ official designation as White may not correspond to their lived experiences nor to others’ perceptions. As long as MENA Americans remain aggregated with Whites, potential inequalities they face will remain hidden.

This study explores how researchers’ analytical choices affect the reliability of scientific findings. Most discussions of reliability problems in science focus on systematic biases. We broaden the lens to emphasize the idiosyncrasy of conscious and unconscious decisions that researchers make during data analysis. We coordinated 161 researchers in 73 research teams and observed their research decisions as they used the same data to independently test the same prominent social science hypothesis: that greater immigration reduces support for social policies among the public. In this typical case of social science research, research teams reported both widely diverging numerical findings and substantive conclusions despite identical start conditions. Researchers’ expertise, prior beliefs, and expectations barely predict the wide variation in research outcomes. More than 95% of the total variance in numerical results remains unexplained even after qualitative coding of all identifiable decisions in each team’s workflow. This reveals a universe of uncertainty that remains hidden when considering a single study in isolation. The idiosyncratic nature of how researchers’ results and conclusions varied is a previously underappreciated explanation for why many scientific hypotheses remain contested. These results call for greater epistemic humility and clarity in reporting scientific findings.

Investigating children’s outdoor play unites scholarship on neighborhoods, parental perceptions of safety, and children’s health. Utilizing the Fragile Families and Child Well-being Study (N=3,448), we examine mothers’ fear of their five-year-old children playing outdoors, testing associations with neighborhood social characteristics, city-level crime rates, maternal mental health, and social support. Living in public housing, perceptions of low neighborhood collective efficacy, and living in a Census tract with a higher proportion of Blacks and households in poverty are associated with higher odds of maternal fear, but crime rates are not a significant predictor of fear. We also demonstrate that not being depressed – but not social support or collective efficacy – buffers the influence of neighborhood poverty on maternal fears of outdoor play.

Assimilation is theorized as a multi-stage process where the structural mobility of immigrants and their descendants ultimately leads to established and immigrant-origin populations developing a subjective sense of social similarity with one another, an outcome I term symbolic belonging. Yet existing work offers little systematic evidence as to whether and how immigrants’ gains—in terms of language ability, socioeconomic status, neighborhood integration, or intermarriage—cause changes in the perceptions of the native-born U.S. population. I use a nationally representative conjoint survey experiment to explore whether and how immigrants’ mobility gains shape native-born white citizens’ perceptions of symbolic belonging. I find that white natives are generally open to structural relationships with immigrant-origin individuals (e.g., friends and neighbors), with the exception of black immigrants and natives, and undocumented immigrants. Yet, white Americans simultaneously view all non-white people, regardless of legal status, as dissimilar and far from achieving symbolic belonging in U.S. society. The results offer optimism about the potential structural mobility of legal immigrants and their descendants, yet simultaneously suggest that explicitly racialized lines of division remain just below the surface.

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Bilingual immigrants appear to have a health advantage, and identifying the mechanisms responsible for this is of increasing interest to scholars and policy makers in the United States. Utilizing the National Latino and Asian American Study (NLAAS; n = 3,264), we investigate the associations between English and native-language proficiency and usage and self-rated health for Asian and Latino U.S. immigrants from China, the Philippines, Vietnam, Mexico, Cuba, and Puerto Rico. The findings demonstrate that across immigrant ethnic groups, being bilingual is associated with better self-rated physical and mental health relative to being proficient in only English or only a native language, and moreover, these associations are partially mediated by socioeconomic status and family support but not by acculturation, stress and discrimination, or health access and behaviors.

Housing scholars stress the importance of the information environment in shaping housing search behavior and outcomes. Rental listings have increasingly moved online over the past two decades and, in turn, online platforms like Craigslist are now central to the search process. Do these technology platforms serve as information equalizers or do they reflect traditional information inequalities that correlate with neighborhood sociodemographics? We synthesize and extend analyses of millions of US Craigslist rental listings and find they supply significantly different volumes, quality, and types of information in different communities. Technology platforms have the potential to broaden, diversify, and equalize housing search information, but they rely on landlord behavior and, in turn, likely will not reach this potential without a significant redesign or policy intervention. Smart cities advocates hoping to build better cities through technology must critically interrogate technology platforms and big data for systematic biases.