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«Contesting the streets Volume 18, number 1 • 2016 U.S. Department of Housing and Urban Development | Office of Policy Development and Research ...»

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Studies Using PSID on Housing-Related Topics Data collected in PSID, CDS, and TAS have supported a large body of scientific work across a variety of topics related to housing and neighborhood characteristics. A comprehensive bibliography of PSID publications is available on the project’s website.

Assisted Housing A number of studies have used the PSID Assisted Housing Database to examine the consequences of receiving subsidized housing. Newman and Harkness (2000) found that the lower educational attainment of children who lived in public housing disappeared once measured characteristics were taken into account. In another set of analyses, also exploiting PSID’s longitudinal design, Newman and colleagues have examined the effects of housing assistance on employment outcomes and welfare receipt (Harkness and Newman, 2003; Newman and Harkness, 2002; Newman, Holupka, and Harkness, 2009). This research shows no negative effects on employment outcomes, although public program participation rates are higher in the future. In a paper that exploits the intergenerational richness of PSID, Kucheva (2014) found that adults who grew up in subsidized housing had a higher probability of residing in subsidized housing in adulthood.

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Neighborhood and Housing Choice PSID provides a rich data source for examining choices about neighborhood and housing choices.

A number of studies examined the dynamics of housing tenure choices by families, examining transitions between homeownership and rental tenure and the factors associated with these transitions (Bajari et al., 2013; Boehm and Schlottmann, 2014; Börsch-Supan and Pollakowski, 1990;

Carter, 2011; Henderson and Ioannides 1989; Ioannides, 1987; Kan, 2000). Ties between housing and neighborhood choice were examined using PSID data, focusing, for instance, on the process of “downsizing” of housing and retirement moves among the elderly (Banks et al., 2012; Bian, forthcoming; Painter and Lee 2009; Sabia, 2008; VanderHart, 1998). PSID was used to examine the effects of neighborhood characteristics on housing decisions (for example, Lee, 2014) and also the consequences of individuals’ residential decisions on neighborhood dynamics (for example, Bruch, 2014).

Effects of Neighborhood Characteristics PSID has been used extensively to investigate the effects of neighborhoods, as evidenced by hundreds of publications on this topic. PSID was one of the earliest data sources for studying contextual effects on socioeconomic status (Corcoran et al., 1990; Dachter, 1982) and remains one of the most important and widely used sources across multiple disciplines for examining neighborhood effects on a variety of outcomes, including child, adolescent, and young adult development (Dearing et al., 2009; Jackson and Mare, 2007; Sastry, 2012; Sharkey and Elwert, 2011; Timberlake, 2009a, 2009b; Wimer et al., 2008); health (Do and Finch, 2008; Do, Wang, and Elliott, 2013; Halliday, 2007; Halliday and Kimmitt, 2008; Johnson, 2012; Wen and Shenassa, 2012);

education (Brooks-Gunn et al., 1993; Crowder and South, 2011, 2003; Galster et al., 2013, 2007;

Harding, 2003; Wodtke, Harding, and Elwert, 2011); income and earnings (Islam, 2013; Sharkey, 2012, 2008); the intergenerational transmission of neighborhood context (Dawkins, 2005a;

Sharkey, 2008; Sharkey and Elwert, 2011; Solon, Page, and Duncan, 2000); family migration and labor force outcomes (Blackburn, 2010; Shauman, 2010; Shauman and Noonan, 2007; Swain and Garasky, 2007); and fertility behavior (Clark and Withers, 2009; South, 2001a, 2001b; South and Crowder, 2011, 1999; Wodtke, 2013). With an oversample of African-American families, PSID is a key data source for examining levels and trends in residential segregation by race (Crowder and Downey, 2010; Crowder and South, 2005;Dawkins, 2005b, 2006; Freeman, 2008, 2005a, 2005b;

Pais, South, and Crowder, 2012; Sharkey, 2012, 2008; South and Crowder, 2005; South, Crowder, and Pais, 2011; Timberlake, 2007; Vartanian, Buck, and Gleason, 2007; Wagmiller, 2013; White et al., 2005).

There are many opportunities for new research on the effects of neighborhood characteristics.

In particular, the continued collection of data in PSID and new data from CDS will support new studies that build on previous research by Crowder and South (2011), Harding (2003), Wodtke, Elwert, and Harding (2012), Wodtke, Harding, and Elwert (2011), and others who used PSID to examine contextual effects on high school graduation and found important effects of neighborhood concentrated disadvantage. The information obtained from the new cohort of children in CDS and young adults participating in TAS will enable researchers to examine how health, development, and well-being today are shaped by several key features of parents’ and grandparents’ past

Cityscape 191McGonagle and Sastry

environments—especially the consequences of growing up in poor neighborhoods. PSID has collected unparalleled nationally representative data every 1 or 2 years during the past four decades that enable researchers to accurately characterize, using contemporaneous measures, children’s, parents’, and grandparents’ experiences of growing up in a poor family and in a poor neighborhood. As a result, PSID and its supplemental data on children and young adults provide essential information for studying the replication of poverty and advantage across generations and the lifecourse. Further, with the rich data on the home, neighborhood, and school environments available today, researchers can examine the pathways through which developmental outcomes are affected by poverty and socioeconomic status. Results of these analyses will provide valuable information for policymakers to improve the lives of disadvantaged children in the United States.





Effects of the Great Recession and Housing Crisis Research to date using PSID has described the direct economic consequences of the Great Recession and associated housing crisis on wealth, job losses, consumption expenditures, and retirement decisions (for example, Attanasio and Pistaferri, 2014; Bosworth, 2012; Parent, 2015; Pfeffer, Danziger, and Schoeni, 2013); residential mobility (Coulson and Grieco, 2013); charitable giving (Marx and Carter, 2014); and household formation (Lee and Painter, 2013). Other work has used PSID data to describe foreclosure risk for individual households and disparities in this risk by race and ethnicity (for example, Hall, Crowder, and Spring, 2015).

PSID data can be used to study how the economic effects of the Great Recession and housing crisis translate into lifecourse decisions about schooling, employment, and residential preferences and consequences for educational attainment, health, and well-being. For example, recent work shows that change in a household’s housing wealth in the 4 years prior to a child being of college age reduces the likelihood that the child will attend college (Lovenheim, 2011). The ongoing data collected through PSID and TAS provide an unprecedented opportunity to examine how these national financial adversities, combined with secular changes in federal financial and mortgage policies, will ultimately shape residential preferences of young adults. Moreover, recent data collected from children in the new CDS-2014 were drawn from a population that lived through the Great Recession and that experienced higher levels of parental unemployment and poverty than during any time since the early 1990s (Isaacs, 2011). The circumstances of these children can be compared with a previous generation of children who participated in the original CDS from before the financial crisis to study questions such as the impact of the housing and foreclosure crisis on outcomes such as child behavioral problems through family experiences or neighborhood exposures.

How To Access the Data Most PSID data and documentation are freely and publicly available on the PSID website (http:// www.psidonline.org). Information is currently available on more than 70,000 variables, on nearly 75,000 individuals, and for all waves of the PSID and its supplements. Users can create customized data extracts from any set of waves by searching or browsing for variables, can obtain customized codebooks specific to their data extract, and can archive data extracts for shared and future use.

They can “load” their data carts with variables by wave. They can view variable descriptions,

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including univariate statistics and names of the same variable in other waves, by clicking an “openbook” icon next to each variable. They can edit their cart by removing or adding variables through a return to the “data aisle.” Users may save data carts, enabling them to share specific extracts with colleagues, reviewers, and students. A range of file formats is available when the user is ready to “check out,” including SAS, STATA, SPSS, dBase, Excel, and ASCII. The PSID website provides a cross-year variable index that facilitates searching and browsing all variables across the full archive from 1968 to the most recent wave and for all waves of CDS and TAS. Organized by content domains, the index is integrated with the online Data Center so that users can view the codebook and add variables directly to their data cart from the index. Geospatial data below the level of state and linked administrative data may be obtained after establishing a data use agreement between a user’s institution and the University of Michigan.2 PSID has also made available a set of user tutorials and webinars on a variety of topics, including an introduction to the PSID for the new user3 and provides a Help Desk that gives rapid responses to users’ questions.

Acknowledgments The authors gratefully acknowledge funding support from the National Science Foundation (1157698), the Eunice Kennedy Shriver National Institute of Child Health and Human Development (HD069609, HD059779, HD072493, HD052646), and the National Institute on Aging (AG040213).

Authors Katherine McGonagle is a research scientist in the Survey Research Center at the University of Michigan’s Institute for Social Research and is the Assistant Director of the Panel Study of Income Dynamics.

Narayan Sastry is a research professor in the Survey Research Center and the Population Studies Center at the University of Michigan’s Institute for Social Research and is an associate director of the Panel Study of Income Dynamics.

References

Attanasio, Orazio, and Luigi Pistaferri. 2014. “Consumption Inequality Over the Last Half Century:

Some Evidence Using the New PSID Consumption Measure,” The American Economic Review 104 (5): 122–126.

Bajari, Patrick, Phoebe Chan, Dirk Krueger, and Daniel Miller. 2013. “A Dynamic Model of Housing Demand: Estimation and Policy Implications,” International Economic Review 54 (2): 409–442.

Information about PSID restricted data may be found at the PSID Data center (http://www.psidonline.org/).

Tutorials available on the PSID website explain the content of the survey, how to access the data, and how to use the PSID online Data Center; see http://psidonline.isr.umich.edu/Guide/tutorials/default.aspx.

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Banks, James, Richard Blundell, Zoe Oldfield, and James P. Smith. 2012. “Housing Mobility and Downsizing at Older Ages in Britain and the USA,” Economica 79 (313): 1–26.

Bian, Xun. Forthcoming.“Leverage and Elderly Homeowners’ Decisions to Downsize,” Housing Studies.

Blackburn, McKinley L. 2010. “Internal Migration and the Earnings of Married Couples in the United States,” Journal of Economic Geography 10 (1): 87–111.

Boehm, Thomas P., and Alan M. Schlottmann. 2014. “The Dynamics of Housing Tenure Choice:

Lessons From Germany and the United States,” Journal of Housing Economics 25: 1–19.

Börsch-Supan, Axel, and Henry O. Pollakowski. 1990. “Estimating Housing Consumption Adjustments From Panel Data,” Journal of Urban Economics 27 (2): 131–150.

Bosworth, Barry B. 2012. Economic Consequences of the Great Recession: Evidence From the Panel Study of Income Dynamics. Working paper 2012-4. Boston: Center for Retirement Research at Boston College.

Brooks-Gunn, Jeanne, Greg J. Duncan, Pamela Kato Klebanov, and Naomi Sealand. 1993. “Do Neighborhoods Influence Child and Adolescent Development?” American Journal of Sociology 99 (2): 353–395.

Bruch, Elizabeth E. 2014. “How Population Structure Shapes Neighborhood Segregation,” American Journal of Sociology 119 (5): 1221–1278.

Caldwell, Bettye M., and Robert H. Bradley. 2003. Home Observation for Measurement of the Environment Administration Manual. Tempe, AZ: Arizona State University, Family and Human Dynamics Research Institute.

Carter, Steven. 2011. “Housing Tenure Choice and the Dual Income Household,” Journal of Housing Economics 20 (3): 159–170.

Clark, William A.V., and Suzanne D. Withers. 2009. “Fertility, Mobility and Labour-Force Participation: A Study of Synchronicity,” Population, Space and Place 15 (4): 305–321.

Corcoran, Mary, Roger Gordon, Deborah Laren, and Gary Solon. 1990. “Effects of Family and Community Background on Economic Status,” The American Economic Review 80 (2): 362–366.

Coulson, N. Edward, and Paul L.E. Grieco. 2013. “Mobility and Mortgages: Evidence from the PSID,” Regional Science and Urban Economics 43 (1): 1–7.

Crowder, Kyle D., and Liam Downey. 2010. “Inter-Neighborhood Migration, Race, and Environmental Hazards: Modeling Micro-Level Processes of Environmental Inequality,” American Journal of Sociology 115 (4): 1110–1149.

Crowder, Kyle D., and Scott J. South. 2011. “Spatial and Temporal Dimensions of Neighborhood Effects on High School Graduation,” Social Science Research 40 (1): 87–106.

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———. 2005. “Race, Class and Changing Patterns of Migration between Poor and Nonpoor Neighborhoods,” American Journal of Sociology 110 (6): 1715–1763.

———. 2003. “Neighborhood Distress and School Dropout: The Variable Significance of Community Context,” Social Science Research 32 (4): 659–698.

Dachter, Linda. 1982. “Effects of Community and Family Background on Achievement,” Review of Economics and Statistics 64: 32–41.

Dawkins, Casey J. 2006. “Are Social Networks the Ties That Bind Families to Neighborhoods?” Housing Studies 21 (6): 867–881.



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