The impact of two types of COVID-19-related discrimination and contemporaneous stressors on Chinese immigrants in the US South.

The global rise of the COVID-19 pandemic has been accompanied by an increase in anti-Asian discrimination with potentially deleterious effects on individuals of Asian descent. In the present study, we examine how two types of COVID-19-related anti-Asian discrimination and other contemporaneous stressors independently contribute to perceptions of stress in a population-representative sample of Chinese immigrants in North Carolina, as well as the moderating role of ethnic identity on the association between COVID-related discrimination and stress. Analyses rely on data collected among participants ages 18+ in the Chinese Immigrants in Raleigh-Durham (ChIRDU) study who completed surveys in 2018 and during the COVID-19 pandemic (July-September 2020). We utilize ordinary least squares regressions to examine associations of two types of COVID-related discrimination (measured by changes in perceptions of being feared by others and racism-related vigilance) and contemporaneous stressors (measured by general COVID-19-related stressors and acculturative stressors) with perceptions of stress by respondents' pre-pandemic reports of ethnic identity. Controlling for sociodemographic predictors and other stressors, racism-related vigilance is significantly associated with higher perceived stress for Chinese immigrants who identify as completely Chinese. For those who identify as at least partly American, new perceptions of being feared by others during the pandemic are significantly associated with higher perceived stress. Acculturative and COVID-related stressors are independently associated with higher perceived stress for both groups. These results suggest that COVID-related anti-Asian discrimination aggravates the psychological burden of multiple stressors in Chinese immigrants' lives by uniquely contributing to perceptions of stress alongside contemporaneous stressors. The results also highlight the heterogeneous mental health needs of Chinese immigrants and hold important implications for intervention development in the community studied here as well as in other Chinese communities in the US.

Using Social Networks to Sample Migrants and Study the Complexity of Contemporary Immigration: An Evaluation Study.

We test the effectiveness of a link-tracing sampling approach-network sampling with memory (NSM)-to recruit samples of rare immigrant populations with an application among Chinese immigrants in the Raleigh-Durham area of North Carolina. NSM uses the population network revealed by data from the survey to improve the efficiency of link-tracing sampling and has been shown to substantially reduce design effects in simulated sampling. Our goals are to (1) show that it is possible to recruit a probability sample of a locally rare immigrant group using NSM and achieve high response rates; (2) demonstrate the feasibility of the collection and benefits of new forms of network data that transcend kinship networks in existing surveys and can address unresolved questions about the role of social networks in migration decisions, the maintenance of transnationalism, and the process of social incorporation; and (3) test the accuracy of the NSM approach for recruiting immigrant samples by comparison with the American Community Survey. Our results indicate feasibility, high performance, cost-effectiveness, and accuracy of the NSM approach to sample immigrants for studies of local immigrant communities. This approach can also be extended to recruit multisite samples of immigrants at origin and destination.

Communication flows and the durability of a transnational social field.

We draw on unique data on communication flows between migrants and non-migrants in a bi-national, cross-border social network to test competing theories of the process of social incorporation. While advocates of the assimilation perspective argue that social incorporation is largely a one-way street, a recent literature on immigrant transnationalism challenges this view by arguing that changes in communication technologies and reductions in travel costs have made it possible for migrants to retain meaningful connections to their origin communities. In the context of this debate, we argue that communication flows-as measured by a combination of the number of social ties and the frequency of communication with them-provide an empirical test of the potential durability of cross-border networks. In our analysis, we find mixed support for both transnationalism and assimilation: while the classic assimilation perspective is correct that the strength of migrants' ties to origin attenuates as time in the destination increases, we also find evidence of a striking persistence in cross-border communication that is reinvigorated by migrant return visits, consistent with an attenuated view of transnationalism.

Why Did People Move During the Great Recession?: The Role of Economics in Migration Decisions.

Labor migration offers an important mechanism to reallocate workers when there are regional differences in employment conditions. Whereas conventional wisdom suggests migration rates should increase during recessions as workers move out of areas that are hit hardest, initial evidence suggested that overall migration rates declined during the Great Recession, despite large regional differences in unemployment and growth rates. In this paper, we use data from the American Community Survey to analyze internal migration trends before and during the economic downturn. First, we find only a modest decline in the odds of adults leaving distressed labor market areas during the recession, which may result in part from challenges related to the housing price crash. Second, we estimate conditional logit models of destination choice for individuals who migrate across labor market areas and find a substantial effect of economic factors such as labor demand, unemployment, and housing values. We also estimate latent class conditional logit models that test whether there is heterogeneity in preferences for destination characteristics among migrants. Over all, the latent class models suggest that roughly equal percentages of migrants were motivated by economic factors before and during the recession. We conclude that fears of dramatic declines in labor migration seem to be unsubstantiated.

Sampling Migrants from their Social Networks: The Demography and Social Organization of Chinese Migrants in Dar es Salaam, Tanzania.

The streams of Chinese migration to Africa are growing in tandem with rising Chinese investments and trade flows in and to the African continent. In spite of the high profile of this phenomenon in the media, there are few rich and broad descriptions of Chinese communities in Africa. Reasons for this include the rarity of official statistics on foreign-born populations in African censuses, the absence of predefined sampling frames required to draw representative samples with conventional survey methods and difficulties to reach certain segments of this population. Here, we use a novel network-based approach, Network Sampling with Memory, which overcomes the challenges of sampling 'hidden' populations in the absence of a sampling frame, to recruit a sample of recent Chinese immigrants in Dar es Salaam, Tanzania and collect information on the demographic characteristics, migration histories and social ties of members of this sample. These data reveal a heterogeneous Chinese community composed of "state-led" migrants who come to Africa to work on projects undertaken by large Chinese state-owned enterprises and "independent" migrants who come on their own accord to engage in various types of business ventures. They offer a rich description of the demographic profile and social organization of this community, highlight key differences between the two categories of migrants and map the structure of the social ties linking them. We highlight needs for future research on inter-group differences in individual motivations for migration, economic activities, migration outcomes, expectations about future residence in Africa, social integration and relations with local communities.

Network Structure and Biased Variance Estimation in Respondent Driven Sampling.

This paper explores bias in the estimation of sampling variance in Respondent Driven Sampling (RDS). Prior methodological work on RDS has focused on its problematic assumptions and the biases and inefficiencies of its estimators of the population mean. Nonetheless, researchers have given only slight attention to the topic of estimating sampling variance in RDS, despite the importance of variance estimation for the construction of confidence intervals and hypothesis tests. In this paper, we show that the estimators of RDS sampling variance rely on a critical assumption that the network is First Order Markov (FOM) with respect to the dependent variable of interest. We demonstrate, through intuitive examples, mathematical generalizations, and computational experiments that current RDS variance estimators will always underestimate the population sampling variance of RDS in empirical networks that do not conform to the FOM assumption. Analysis of 215 observed university and school networks from Facebook and Add Health indicates that the FOM assumption is violated in every empirical network we analyze, and that these violations lead to substantially biased RDS estimators of sampling variance. We propose and test two alternative variance estimators that show some promise for reducing biases, but which also illustrate the limits of estimating sampling variance with only partial information on the underlying population social network.

Bi-national Social Networks and Assimilation: A Test of the Importance of Transnationalism.

While the concept of transnationalism has gained widespread popularity among scholars as a way to describe immigrants' long-term maintenance of cross-border ties to their origin communities, critics have argued that the overall proportion of immigrants who engage in transnational behavior is low and that, as a result, transnationalism has little sustained effect on the process of immigrant adaptation and assimilation. In this paper, we argue that a key shortcoming in the current empirical debate on transnationalism is the lack of data on the social networks that connect migrants to each other and to non-migrants in communities of origin. To address this shortcoming, our analysis uses unique bi-national data on the social network connecting an immigrant sending community in Guanajuato, Mexico, to two destination areas in the United States. We test for the effect of respondents' positions in cross-border networks on their migration intentions and attitudes towards the United States using data on the opinions of their peers, their participation in cross border and local communication networks, and their structural position in the network. The results indicate qualified empirical support for a network-based model of transnationalism; in the U.S. sample we find evidence of network clustering consistent with peer effects, while in the Mexican sample we find evidence of the importance of cross-border communication with friends.

Network Sampling with Memory: A proposal for more efficient sampling from social networks.

Techniques for sampling from networks have grown into an important area of research across several fields. For sociologists, the possibility of sampling from a network is appealing for two reasons: (1) A network sample can yield substantively interesting data about network structures and social interactions, and (2) it is useful in situations where study populations are difficult or impossible to survey with traditional sampling approaches because of the lack of a sampling frame. Despite its appeal, methodological concerns about the precision and accuracy of network-based sampling methods remain. In particular, recent research has shown that sampling from a network using a random walk based approach such as Respondent Driven Sampling (RDS) can result in high design effects (DE)-the ratio of the sampling variance to the sampling variance of simple random sampling (SRS). A high design effect means that more cases must be collected to achieve the same level of precision as SRS. In this paper we propose an alternative strategy, Network Sampling with Memory (NSM), which collects network data from respondents in order to reduce design effects and, correspondingly, the number of interviews needed to achieve a given level of statistical power. NSM combines a "List" mode, where all individuals on the revealed network list are sampled with the same cumulative probability, with a "Search" mode, which gives priority to bridge nodes connecting the current sample to unexplored parts of the network. We test the relative efficiency of NSM compared to RDS and SRS on 162 school and university networks from Add Health and Facebook that range in size from 110 to 16,278 nodes. The results show that the average design effect for NSM on these 162 networks is 1.16, which is very close to the efficiency of a simple random sample (DE=1), and 98.5% lower than the average DE we observed for RDS.

Social support among Latina immigrant women: bridge persons as mediators of cervical cancer screening.

New Latina immigrants face numerous linguistic, cultural, logistical, and material barriers to cervical cancer screenings. Promotoras (lay health advisors) are a proven strategy to promote utilization of care. Since the mid-1990s, interventions in North Carolina have aimed to connect Latina immigrants to a broader range of bridge persons. This study assessed the effect of bridge persons on utilization of cervical cancer screening by Latina immigrants in North Carolina. Women were recruited in Spanish-language churches in four counties (N = 223). Logistic regression results show that persons known through advocacy organizations appeared to increase probability of recent Pap screening by an average of 10.4 percentage points (p < 0.05). Promotoras remain more effective, increasing probability of screening by 12.9 percentage points (p < 0.05) but few women (14%) knew one. No association was found with other bridge person profiles. Interventions are needed to better engage all bridge persons in linking immigrants to preventive health services.

Are black workers missing the connection? The effect of spatial distance and employee referrals on interfirm racial segregation.

I use data on the hiring practices and spatial location of firms in four cities to model the process of interfirm racial segregation. When I control for the spatial location of the firm, the use of employee referrals reduced the probability of hiring a black worker by 75% in firms that are less than 10% black. Among all firms, the results suggest that employee referrals are just as important as the geographic location of the firm in generating employment segregation: both increase the predicted level of interfirm racial segregation among blue-collar workers in the cities studied by about 10%.