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.

Nonprofit organizations are important actors in local communities, providing services to vulnerable populations and acting as stewards for charitable contributions from other members of the population. An important question is whether nonprofits spend or receive additional revenues in response to changes in the populations they serve. Because immigrant populations both receive and contribute to nonprofit resources, changes in immigrant numbers should be reflected in changing financial behavior of local nonprofits. Using data from the National Center for Charitable Statistics and the American Community Survey, we study whether nonprofit financial transactions change in response to changes in the local immigration population, the nature of the change, and the degree to which these changes vary by nonprofit type. Findings suggest that nonprofit financial behavior changes with growth and decline in immigrant populations underscoring the importance of nonprofits as service providers and contribute to an understanding of how organizations respond to external forces.

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: (1) to show that it is possible to recruit a probability sample of a locally rare immigrant group using NSM and achieve high response rates; (2) to demonstrate feasibility of 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; (3) to test the accuracy of the NSM approach to recruit immigrant samples by comparison with the American Community Survey (ACS). 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 multi-site samples of immigrants at origin and destination.

Demographers have incorporated social network concepts and measures into contemporary demographic research as determinants of demographic behaviors; they have invoked structural properties of networks to improve descriptions of hard-to-survey populations; they have relied on network data and measures to estimate population size and parameters under conditions of sparse information. This chapter illustrates how social network concepts and models are integral to the development of explanations of fertility and migration behaviors and of population-level characterizations of demographic systems. It highlights productive ways for advancing demographic research that rely on the adoption of a wider array of network data and tools to link structural characteristics of networks to the mechanisms involved in shaping demographic behavior, understand how demographic behaviors structure networks, and map social network structures in data collection for more efficient and cost-effective population enumeration and parameter estimation.