Ted Mouw

Ph.D., Professor, Sociology
tedmouw@email.unc.edu
Curriculum Vitae
Google Scholar Profile
PubMed Publications
CPC Publications

Ted Mouw is a sociologist who studies labor markets, immigration, and social networks. His current research involves the mobility of low-wage workers, the economic incorporation of immigrants, and methods to collect samples from rare or hidden populations using social networks.

Mouw's current research on social mobility focuses on factors that affect the upward mobility of low wage workers. In his paper with Arne Kalleberg, "Stepping Stone versus Dead End Jobs: Occupational Pathways out of Working Poverty in the United States, 1996-2012", he uses data from the Survey of Income and Program Participation (SIPP) to test whether the accumulation of task-specific skills increases the rate of upward mobility for low-wage workers. In a grant from the Russell Sage Foundation, he is linking the SIPP data to county-level data on labor demand shocks in order to analyze the role that structural factors play in the upward mobility of low-wage workers. This research builds on previous work that analyzed trends in between-occupation inequality and the impact of job mobility on changes in inequality. In the next five years, he plans to extend this work into a book-length project on working poverty and the social mobility of low-wage workers.

In his current research on immigration, Mouw is analyzing the effect of immigration on the labor market outcomes of native workers using a unique data set of restricted-access employer-employee data (the Longitudinal Employer Household Data "LEHD") at the Triangle Census Research Data Center. The LEHD is an administrative data set on the quarterly earnings of all privately employed workers in participating states, constructed from unemployment insurance records. This project uses longitudinal data on over 93 million workers in 30 states from 1992-2008. By following these workers over time, Mouw is able to analyze the way that native workers adapt to immigration by modelling earnings growth and firm, industry, and geographic mobility. This is a 5 paper project, and the results for the main paper, "The Impact of Immigration on the Labor Market Outcomes of Native Workers," were presented at a National Research Council meeting on immigration in July 2014 in Los Angeles. Other papers in his LEHD project look at the role of coworker hiring networks on ethnic succession in the meatpacking and janitorial industries, the impact of immigration on the job search of displaced workers, and the wage growth of recent African immigrants. When the current LEHD project is completed in 2015, Mouw plans to reapply to the Census Bureau to continue his work on modeling demographic and labor market processes with large-scale administrative data.

In his research on social networks, Mouw is working on ways to sample from social networks and network-connected rare and hidden populations. In a recent paper in Sociological Methodology, he proposes a new approach for sampling from hidden populations ("Network Sampling with Memory" [NSM]) that collects network data from respondents as part of the survey and then uses this data to identify bridge ties to unexplored parts of the network. Mouw and Verdery show that this approach results in a dramatic reduction in design effects compared to Respondent Driven Sampling. In related work, Mouw and colleagues describe the collection of network data on Mexican immigrants and their friends and family members back in Mexico, and use the data to test for the impact of transnationalism on social incorporation and migration intentions. As part of the next step in this line of research, he has applied for an NIH grant, along with Giovanna Merli and several other colleagues to use NSM to collect data on Chinese immigrants in the United States. In future work, he plans to collect longitudinal data on the social networks connecting immigrants from Mexico to their origin communities to test models of the effect of networks on immigration, information flows, and job search.

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