• Using IRS Tax Data to Study Wealth Accumulation and Transmission
    Team members: Fabian Pfeffer, Luis Flores, Dylan Nelson

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    This project assesses how a broad set of tax expenditures on capital – namely deductions on home mortgage interest, IRA contributions, charitable contributions, and others as well as the preferential tax rates on realized capital gains and dividends – shape the accumulation and intergenerational transmission of personal wealth. Our unique contribution consists of a longitudinal assessment of the long-term effects of different tax expenditures on capital drawing on existing IRS panel data. By tracking the cumulative, lifetime benefits of tax expenditures on capital, we will be able to reveal the long-term effects of a broad set of tax instruments on not only the wealth distribution but also the persistence of wealth across generations.

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  • Linking Survey and Real Estate Data to Study the Role of Housing
    Team members: Fabian Pfeffer, Brittany Vasquez, Brian Xiao

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    This project links a vast array of information on the characteristics of housing units collected by a leading real estate data provider to the Panel Study of Income Dynamics using machine-linking algorithms.

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  • Linking Survey and Historical Census Data to Study Multigenerational Mobility
    Team members: Fabian Pfeffer, Jacob Topham
    External collaborators: Rob Warren, Dafeng Xu, Jonas Helgertz, Mary-Beth Ofstedal

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    This project links records from the 1940 U.S. Census to records for respondents to the Health and Retirement Study (HRS), the Panel Study of Income Dynamics (PSID), and the National Health and Aging Trends Study (NHATS) using machine-linking algorithms.

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  • Constructing a Socio-Economic Longitudinal File for the Panel Study of Income Dynamics (PSID-SELF)
    Team members: Fabian Pfeffer

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    This project constructs and will make available an easy-to-use, longitudinally harmonized version of the main socio-economic and demographic variables included in the Panel Study of Income Dynamics to facilitate intra- and inter-generational analyses.

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  • Back to the Future. Using Future Treatments to Assess Hidden Bias
    Team members: Fabian Pfeffer
    External collaborators: Felix Elwert

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    Conventional advice discourages controlling for post-outcome variables in regression analysis. Here, we show that controlling for commonly available post-outcome (i.e. future) values of the treatment variable can help detect, reduce, and even remove omitted variable bias (unobserved confounding). The premise is that the same unobserved confounders that affect treatment also affect future values of the treatment. Future treatments thus proxy for the unmeasured confounder, and researchers can exploit these proxy measures productively. We establish several new results: Regarding a commonly assumed data-generating process, we (1) introduce a new approach to reduce bias and show that it strictly reduces bias; (2) elaborate on existing approaches and show that they can increase bias; (3) assess the relative merits of approaches; (4) analyze true state dependence and selection as key challenges; and (5) demonstrate that future treatments can test for hidden bias, even when they fail to reduce bias. We illustrate these results empirically with an analysis of the effect of parental wealth on children’s educational achievement. Download

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  • The Effects of Social Mobility
    Team members: Fabian Pfeffer
    External collaborators: Ethan Fosse

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    Direct empirical tests of the causal relationship between individuals’ experiences of social mobility and other outcomes – such as their socio-psychological well-being, their political attitudes, or their behaviors – are rare and difficult. The main challenge is methodological: By definition, social mobility is the linear combination of social origins and social destinations. As such, it is impossible to disentangle the relative causal impact exerted by the culture of one’s origin class, of one’s destination class, and the gap between the two. We outline an approach that addresses this methodological obstacle not through ultimately arbitrary parametric assumptions but through a method of non-parametric bounding. This approach directly incorporates existing theoretical predictions and ethnographic insights into a statistical model to reveal the degree to which they can help estimate the individual-level effects of social mobility.

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