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RACIAL INEQUALITIES IN CONNECTEDNESS TO IMPRISONED INDIVIDUALS IN THE UNITED STATES
- Hedwig Lee, Tyler McCormick, Margaret T. Hicken, Christopher Wildeman
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- Journal:
- Du Bois Review: Social Science Research on Race / Volume 12 / Issue 2 / Fall 2015
- Published online by Cambridge University Press:
- 20 May 2015, pp. 269-282
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- Article
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In just the last forty years, imprisonment has been transformed from an event experienced by only the most marginalized to a common stage in the life course of American men—especially Black men with low levels of educational attainment. Although much research considers the causes of the prison boom and how the massive uptick in imprisonment has shaped crime rates and the life course of the men who experience imprisonment, in recent years, researchers have gained a keen interest in the spillover effects of mass imprisonment on families, children, and neighborhoods. Unfortunately, although this new wave of research documents the generally harmful effects of having a family member or loved one incarcerated, it remains unclear how much the prison boom shapes social inequality through these spillover effects because we lack precise estimates of the racial inequality in connectedness—through friends, family, and neighbors—to prisoners. Using the 2006 General Social Survey, we fill this pressing research gap by providing national estimates of connectedness to prisoners—defined in this article as knowing someone who is currently imprisoned, having a family member who is currently imprisoned, having someone you trust who is currently imprisoned, or having someone you know from your neighborhood who is currently imprisoned—for Black and White men and women. Most provocatively, we show that 44% of Black women (and 32% of Black men) but only 12% of White women (and 6% of White men) have a family member imprisoned. This means that about one in four women in the United States currently has a family member in prison. Given these high rates of connectedness to prisoners and the vast racial inequality in them, it is likely that mass imprisonment has fundamentally reshaped inequality not only for the adult men for whom imprisonment has become common, but also for their friends and families.
23 - Network-based methods for accessing hard-to-survey populations using standard surveys
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- By Tyler H. Mccormick, University of Washington, Tian Zheng, Columbia University
- Edited by Roger Tourangeau, Brad Edwards, Timothy P. Johnson, University of Illinois, Chicago, Kirk M. Wolter, University of Chicago, Nancy Bates
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- Book:
- Hard-to-Survey Populations
- Published online:
- 05 September 2014
- Print publication:
- 28 August 2014, pp 485-502
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- Chapter
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Summary
Introduction
Standard surveys often exclude members of certain groups, known as hard-to-survey groups. One reason these individuals are excluded is difficulty accessing group members. Persons who are homeless are very unlikely to be reached by a survey that uses random digit dialing, for example. Other individuals can be accessed using standard survey techniques, but are excluded because of issues in reporting. Members of these groups are often reluctant to self-identify because of social pressure or stigma (Shelley, Bernard, Killworth, Johnsen, & McCarty, 1995). Individuals who are homosexual, for example, may not be comfortable revealing their sexual preferences to an unfamiliar survey enumerator. A third group of individuals is difficult to reach because of issues with both access and reporting (commercial sex workers, for example). Even basic demographic information about these groups is typically unknown, especially in developing nations.
One approach to estimating demographic information about hard-to-reach groups is to reach members of these groups through their social network. Some network-based approaches, such as respondent-driven sampling (RDS), recruit respondents directly from other respondents’ networks (Heckathorn, 1997, 2002), making the sampling mechanism similar to a stochastic process on the social network (Goel & Salganik, 2009). RDS (see Chapter 24 in this volume) affords researchers face-to-face contact with members of hard-to-reach groups, facilitating exhaustive interviews and even genetic or medical testing. The price for an entry to these groups is high, however, as RDS uses a specially designed link-tracing framework for sampling. Estimates from RDS are also biased because of the network structure captured during selection, with much statistical work surrounding RDS being intended to re-weigh observations from RDS to have properties resembling a simple random sample. Though methods such as RDS can be advantageous (researchers interview members of hard-to-survey groups directly, for example), financial and logistical challenges often prevent researchers from employing these methods, especially on a large scale.