The Social Production and Reproduction of Residential Segregation

My research concerns the generation and persistence of racial and ethnic residential segregation and how this segregation overlaps with segregation in other life domains, including social networks and workplaces. 

In my research I use ”big” micro-data in combination with discrete choice and simulation methods to understand how preferences, demographic events, resource inequalities, and interactions between social actors combine to generate segregation, perpetuate it once it is established, and transform it in concert with broader population changes.

Demographic Events, Residential Segregation, and Residential Mobility in Sweden

Where we live at least partly influences the range of people we are likely to meet in the course of our daily lives, including potential mates. In this way residential segregation may at least partly influence partnership formation, assortative mating along racial, ethnic, and socio-economic lines, and other demographic rates like fertility. When people find partners, they often establish new households. This requires that new couples pool resources and negotiate potentially disparate preferences and social demands. This strand of research examines how residential segregation influences demographic processes, and how demographic choices and accompanying residential mobility in turn influence levels of residential segregation. Currently the main focus is new household formation in Sweden, but in the longer term, this research will begin to consider the roles of other life events, including childbearing and mortality, in shaping patterns of segregation.

Preferences for Neighbourhood Racial and Ethnic Composition

People often have preferences for the racial and ethnic compositions of the neighbourhoods in which they live. However, not every person or family is equally equipped to act on these preferences. Those possessing fewer economic resources, moving in constrained social networks, and facing housing market discrimination may be systematically stymied in moving to neighbourhoods that align with their preferences. This line of research uses results from survey instruments designed to assess racial and ethnic preferences, comparing these “stated” preferences to preferences “revealed” in actual housing market choices. I use discrete choice methods to examine the correspondence of these two kinds of data, and work out the segregation implications of mismatches between stated and revealed preferences.

Geographic and Social Mobility

When people migrate they often do so because of changes of status in other life domains, or to initiate a change in status in those domains. Here, employment statuses and economic resources are especially relevant. People move to find a new job or occupation, or because they have found a job that requires them to move. People also move because of unexpected job and or income loss, and they move because of income gains. My research in this area examines how geographic mobility and social mobility coincide, and what push and pull factors influence this joint mobility. In particular, I use linked Census data from 1870-1880 to examine how farmland availability in the 19th century United States spurred migration as well as switches between farm and non-farm occupations.

Discrete Choice Models

I use discrete choice models as a statistical means to uncover the individual, family and spatial calculus that influences residential mobility. The models help to uncover effects of racial and ethnic composition, proximity to work, proximity to family and friends, and housing market constraints on residential mobility. My statistical modelling interests extend from straightforward likelihood based multinominal/conditional logistic regression, to mixed and random effects models, as well as two-sided matching models.

Micro-Simulation and Agent Based Models

When people act at the micro-level according to their preferences or beliefs, they simultaneously shape the field of choices available to other actors. A given set of micro-level behaviours, distributed across a population of actors, does not always have readily derivable implications for aggregate outcomes about which we are concerned: segregation, social inequality, and population change. Simulation methods provide one way of testing and understanding the implications of micro-level behaviours for macro-level outcomes. I have implemented both statistical micro-simulation models and Schelling-type models of residential segregation to work out how individual behaviours combine to produce segregation, and to answer counterfactual questions about how modifying behaviours would affect aggregate segregation levels.

Publications, works in progress

  • Jarvis, Benjamin F., and Guilherme Kenji Chihaya. 2016. “Kinship Effects on Residential Mobility and Ancestry Segregation in Stockholm.” Population Association of America 2016 Annual Meeting. Washington, DC, March 31 – April 2.
  • Jarvis, Benjamin F., Xi Song. 2015. “Intra-generational Occupational Mobility in the United States, 1981-2011.” Meeting of the Research Committee on Social Stratification and Mobility (RC28) of the International Sociological Association (ISA). Tilburg, Netherlands, May 28-30.
  • Jarvis, Benjamin F., Robert D. Mare, and Monica Nordvik. 2015. “Residential Segregation and Assortative Mating in Stockholm.” Population Association of America 2015 Annual Meeting. San Diego, CA, April 30 – May 2.
  • Jarvis, Benjamin F. and Robert D. Mare. 2013. “Do Stated Racial Preferences Match Residential Mobility Behaviour? Evidence from Los Angeles.” Population Association of America 2013 Annual Meeting. New Orleans, LA, April 11-13.
  • Jarvis, Benjamin F.  2013. “The Effect of Agricultural Opportunities on the Migration and Social Mobility of American Men, 1870-1880.” Population Association of America 2013 Annual Meeting. New Orleans, LA, April 11-13.

Academic Degrees

  • PhD Sociology 2015 University of California, Los Angeles
  • MA Sociology 2011, University of California, Los Angeles
  • BS Applied Physics 2003, Yale University

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