Atheoretical formulation derived from the cumulative advantage literature, that intergenerational educational mobility has enduring life-course income effects above and beyond individuals’ education, is empirically tested. This formulation contrasts sharply with both the human capital model, which does not consider parental education as a determinant of children’s income, and the sociological research on social mobility, which mostly relies on a snapshot view to study the economic consequences of educational mobility. To test this theory, we use NLSY79 survey data (with Panel Study of Income Dynamics data serving for robustness checks). We apply growth models to the data to estimate if and how the different intergenerational educational mobility groups that are produced by the intersection of parental and respondent education shape life-course income trajectories. Results provide evidence in support of the argument that the intersection of parental and respondent education bears important long-term income consequences, mainly for men. These results, moreover, do not vary by race. We discuss the theoretical and policy implications of our results.

Landlords are important gatekeepers in the rental market, and scholars have studied landlord perceptions across different markets. But differences between landlord logics within a market, which drive landlord behaviors, have been largely unexamined. Drawing chiefly on 30 in-depth landlord interviews and 20 observations with property managers in Philadelphia, I argue that landlords exhibit a range of logics. When faced with rental market decisions, some employ profit-maximizing criteria, whereas others consider social closeness with tenants or the meanings of properties. These differences relate to pathways into property ownership: landlords who obtain property circumstantially focus on profit maximization less than those who purchase property deliberately to pursue profits. This paper extends our understanding of landlords, connecting their pathways to a range of logics within a single market context. It also suggests that policies should consider the logics of landlords they seek to influence.

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BACKGROUNDExtensive homeownership research examines rates, transitions, and timing, over short and medium time spans. However, little is known about long-term homeownership patterns over the life course. OBJECTIVEWe document population-level homeownership rates, transitions, and durations over a 26-year time span between ages 25-50 and categorize US baby boomers born between 1945-1964 into discrete trajectories, characterizing their homeownership experiences over the life course. Finally, we examine who is likely to experience each trajectory using key sociodemographic characteristics. METHODSUsing an analytic sample of 4,246 individuals from the Panel Study of Income Dynamics (PSID), we first examine descriptive homeownership statistics over a long duration. Then, we use Sequence Analysis to categorize the population into homeownership clusters. Finally, we employ multinomial logistic regression to predict cluster membership. RESULTSAfter demonstrating that race and education stratify baby boomer homeownership experiences over the life course, we find that three homeownership trajectories characterize the population: consistent owners (47%), consistent nonowners (25%), and late owners (27%). We further find that race, education, and to a lesser degree gender meaningfully predict one's homeownership trajectory. Being Black is the only characteristic for which consistent nonownership is more likely than consistent ownership. Not attending college is the only other characteristic for which late ownership is not more likely than consistent nonownership. 1058 https://www.demographic-research.org CONTRIBUTIONConceptualizing and measuring homeownership as a life course phenomenon over the long term, our study suggests considerable stability of homeownership with experiences shaped by key sociodemographic characteristics.

Video data offer important insights into social processes because they enable direct observation of real-life social interaction. Though such data have become abundant and increasingly accessible, they pose challenges to scalability and measurement. Computer vision (CV), i.e., software-based automated analysis of visual material, can help address these challenges, but existing CV tools are not sufficiently tailored to analyze social interactions. We describe our novel approach, “3D social research” (3DSR), which uses CV and 3D camera footage to study kinesics and proxemics, two core elements of social interaction. Using eight videos of a scripted interaction and five real-life street scene videos, we demonstrate how 3DSR expands sociologists’ analytical toolkit by facilitating a range of scalable and precise measurements. We specifically emphasize 3DSR's potential for analyzing physical distance, movement in space, and movement rate – important aspects of kinesics and proxemics in interactions. We also assess data reliability when using 3DSR.

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