Multiple Imputation with Massive Data: An Application to the Panel Study of Income Dynamics.

Multiple imputation (MI) is a popular and well-established method for handling missing data in multivariate data sets, but its practicality for use in massive and complex data sets has been questioned. One such data set is the Panel Study of Income Dynamics (PSID), a longstanding and extensive survey of household income and wealth in the United States. Missing data for this survey are currently handled using traditional hot deck methods because of the simple implementation; however, the univariate hot deck results in large random wealth fluctuations. MI is effective but faced with operational challenges. We use a sequential regression/chained-equation approach, using the software IVEware, to multiply impute cross-sectional wealth data in the 2013 PSID, and compare analyses of the resulting imputed data with those from the current hot deck approach. Practical difficulties, such as non-normally distributed variables, skip patterns, categorical variables with many levels, and multicollinearity, are described together with our approaches to overcoming them. We evaluate the imputation quality and validity with internal diagnostics and external benchmarking data. MI produces improvements over the existing hot deck approach by helping preserve correlation structures, such as the associations between PSID wealth components and the relationships between the household net worth and sociodemographic factors, and facilitates completed data analyses with general purposes. MI incorporates highly predictive covariates into imputation models and increases efficiency. We recommend the practical implementation of MI and expect greater gains when the fraction of missing information is large.

The Future Strikes Back: Using Future Treatments to Detect and Reduce Hidden Bias.

Conventional advice discourages controlling for postoutcome variables in regression analysis. By contrast, we show that controlling for commonly available postoutcome (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 confounder that affects treatment also affects the future value 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 involving future treatments, we (1) introduce a simple new approach 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 alternative approaches, and (4) analyze true state dependence and selection as key challenges. (5) Importantly, we also introduce a new nonparametric test that uses future treatments to detect hidden bias even when future-treatment estimation fails to reduce bias. We illustrate these results empirically with an analysis of the effect of parental income on children's educational attainment.

The U.S. Wealth Distribution: Off the Charts.

Although extreme and rising levels of U.S. wealth inequality have generated much public and scientific interest, building intuition on the shape and scale of today's wealth distribution remains difficult. Prior research tends to conceptualize and measure wealth inequality in one of two ways: As the concentration of assets among the superwealthy (i.e., wealth concentration among the top 1 percent or even top 0.1 percent) or as a population-wide phenomenon of distributional inequality (i.e., wealth inequality among the remaining 99 percent). Of course, both perspectives are valid and important; they simply focus on different slices of the overall wealth distribution, sometimes because of limitations of the data that are being used. Extreme concentration of wealth at the very top and very high levels of inequality within the remainder of the distribution thus coexist. Yet jointly visualizing both aspects and relating them to each other is challenging. This contribution addresses this challenge by providing an intuitive and interactive visualization of the distribution of U.S. wealth in 2019 that spans the full population, from households in net debt to multibillionaires.

The Longitudinal Revolution: Sociological research at the 50-year milestone of the Panel Study of Income Dynamics.

The U.S. Panel Study of Income Dynamics (PSID) celebrated its 50th anniversary in 2018. Initially designed to assess the nation's progress in combatting poverty, PSID's scope broadened quickly to a variety of topics and fields of inquiry. To date, sociologists are the second-most frequent users of PSID data after economists. Here, we describe the ways in which PSID's history reflects shifts in social science scholarship and funding priorities over half a century, take stock of the most important sociological breakthroughs it facilitated, in particular those relying on the longitudinal structure of the data, and critically assess the unique advantages and limitations of the PSID and surveys like it for today's sociological scholarship.

Intergenerational Wealth Mobility and Racial Inequality.

The black-white gap in household wealth is large and well documented. Here, we visualize how this racial wealth gap persists across generations. Animating the flow of individuals between the relative wealth position of parents and their adult children, we show that the disadvantage of black families is a consequence both of wealth inequality in prior generations and race differences in the transmission of wealth positions across generations: Black children both have less wealthy parents on average and are far more likely to be downwardly mobile in household wealth. By displaying intergenerational movements between parental and offspring wealth quintiles, we underline how intergenerational fluctuation coexists with the maintenance of a severely racialized wealth structure.

Generations of Advantage. Multigenerational Correlations in Family Wealth.

Inequality in family wealth is high, yet we know little about how much and how wealth inequality is maintained across generations. We argue that a long-term perspective reflective of wealth's cumulative nature is crucial to understand the extent and channels of wealth reproduction across generations. Using data from the Panel Study of Income Dynamics that span nearly half a century, we show that a one-decile increase in parents' wealth position is associated with an increase of about four percentiles in their offspring's wealth position in adulthood. We show that grandparental wealth is a unique predictor of grandchildren's wealth, above and beyond the role of parental wealth, suggesting that a focus on only parent-child dyads understates the importance of family wealth lineages. Second, considering five channels of wealth transmission-gifts and bequests, education, marriage, homeownership, and business ownership-we find that most of the advantages arising from family wealth begin much earlier in the life course than the common focus on bequests implies, even when we consider the wealth of grandparents. We also document the stark disadvantage of African American households in terms of not only their wealth attainment but also their intergenerational wealth mobility compared to whites.

Growing Wealth Gaps in Education.

Prior research on trends in educational inequality has focused chiefly on changing gaps in educational attainment by family income or parental occupation. In contrast, this contribution provides the first assessment of trends in educational attainment by family wealth and suggests that we should be at least as concerned about growing wealth gaps in education. Despite overall growth in educational attainment and some signs of decreasing wealth gaps in high school attainment and college access, I find a large and rapidly increasing wealth gap in college attainment between cohorts born in the 1970s and 1980s, respectively. This growing wealth gap in higher educational attainment co-occurred with a rise in inequality in children's wealth backgrounds, although the analyses also suggest that the latter does not fully account for the former. Nevertheless, the results reported here raise concerns about the distribution of educational opportunity among today's children who grow up in a context of particularly extreme wealth inequality.

Grand Advantage: Family Wealth and Grandchildren's Educational Achievement in Sweden.

We study the role of family wealth for children's educational achievement using novel and unique Swedish register data. In particular, we focus on the relationship between grandparents' wealth and their grandchildren's educational achievement. Doing so allows us to reliably establish the independent role of wealth in contributing to long-term inequalities in opportunity. We use regression models with rich controls to account for observed socioeconomic characteristics of families, cousin fixed effects to net out potentially unobserved grandparental effects, and marginal structural models to account for endogenous selection. We find substantial associations between grandparents' wealth and their grandchildren's grade point averages (GPA) in the 9th grade that are only partly mediated by the socioeconomic characteristics and wealth of parents. Our findings indicate that family wealth inequality - even in a comparatively egalitarian context like Sweden - has profound consequences for the distribution of opportunity across multiple generations. We posit that our estimates of the long-term consequences of wealth inequality may be conservative for nations other than Sweden, like the United States, where family wealth - in addition to its insurance and normative functions - allows the direct purchase of educational quality and access.

WEALTH INEQUALITY AND ACCUMULATION.

Research on wealth inequality and accumulation and the data upon which it relies have expanded substantially in the twenty-first century. While the field has experienced rapid growth, conceptual and methodological challenges remain. We begin by discussing two major unresolved methodological concerns facing wealth research: how to address challenges to causal inference posed by wealth's cumulative nature and how to operationalize net worth, given its highly skewed nature. To underscore the need for continued empirical attention to net worth, we review trends in wealth levels and inequality and evaluate wealth's distinctiveness as an indicator of social stratification. Next, we provide an overview of data sources available for wealth research. We then review recent empirical evidence on the effects of wealth on other social outcomes, as well as research on the determinants of wealth. We close with a list of promising avenues for future research on wealth, its causes, and its consequences.

Determinants of Wealth Fluctuation: Changes in Hard-To-Measure Economic Variables in a Panel Study.

Measuring fluctuation in families' economic conditions is the raison d'être of household panel studies. Accordingly, a particularly challenging critique is that extreme fluctuation in measured economic characteristics might indicate compounding measurement error rather than actual changes in families' economic wellbeing. In this article, we address this claim by moving beyond the assumption that particularly large fluctuation in economic conditions might be too large to be realistic. Instead, we examine predictors of large fluctuation, capturing sources related to actual socio-economic changes as well as potential sources of measurement error. Using the Panel Study of Income Dynamics, we study between-wave changes in a dimension of economic wellbeing that is especially hard to measure, namely, net worth as an indicator of total family wealth. Our results demonstrate that even very large between-wave changes in net worth can be attributed to actual socio-economic and demographic processes. We do, however, also identify a potential source of measurement error that contributes to large wealth fluctuation, namely, the treatment of incomplete information, presenting a pervasive challenge for any longitudinal survey that includes questions on economic assets. Our results point to ways for improving wealth variables both in the data collection process (e.g., by measuring active savings) and in data processing (e.g., by improving imputation algorithms).

Measuring Wealth and Wealth Inequality: Comparing Two U.S. Surveys.

Household wealth and its distribution are topics of broad public debate and increasing scholarly interest. We compare the relative strength of two of the main data sources used in research on the wealth holdings of U.S. households, the Survey of Consumer Finances (SCF) and the Panel Study of Income Dynamics (PSID), by providing a description and explanation of differences in the level and distribution of wealth captured in these two surveys. We identify the factors that account for differences in average net worth but also show that estimates of net worth are similar throughout most of the distribution. Median net worth in the SCF is 6% higher than in the PSID and the largest differences between the two surveys are concentrated in the 1-2 percent wealthiest households, leading to a different view of wealth concentration at the very top but similar results for wealth inequality across most of the distribution.

How Wealth Inequality Shapes Our Future.

Liz, Mary, and Howard are three teenagers in the 1980s. Although unrelated, their families have much in common: stable two- parent households, at least one parent completed high school (though none of them went to college), and all three are white. They differ in one important aspect: their parents command quite different levels of wealth (here measured as net worth, that is, the total sum of financial and real assets minus debt). Liz's parents own less than $700 (inflation adjusted to 2013 dollars), meaning that Liz grows up at the bottom of the wealth distribution. Still, she is far from living in poverty thanks to her parents' annual income of about $50,000. Mary's parents have a somewhat higher income, about $70,000, but also markedly more wealth than Liz's parents: their net worth of roughly $60,000 puts them at about the national median of the time. Also unlike Liz's parents, they are homeowners. Howard is lucky enough to grow up in affluence. Not in terms of income, given that his parents have a household income of only about $40,000, but they have considerable wealth. With a net worth of nearly a quarter million dollars, Howard's parents are in the top 20 percent of wealth holders. They, too, own their home.

How Has Educational Expansion Shaped Social Mobility Trends in the United States?

This contribution provides a long-term assessment of intergenerational social mobility trends in the United States across the 20th and early 21st century and assesses the determinants of those trends. In particular, we study how educational expansion has contributed to the observed changes in mobility opportunities for men across cohorts. Drawing on recently developed decomposition methods, we empirically identify the contribution of each of the multiple channels through which changing rates of educational participation shape mobility trends. We find that a modest but gradual increase in social class mobility can nearly exclusively be ascribed to an interaction known as the compositional effect, according to which the direct influence of social class backgrounds on social class destinations is lower among the growing number of individuals attaining higher levels of education. This dominant role of the compositional effect is also due to the fact that, despite pronounced changes in the distribution of education, class inequality in education has remained stable while class returns to education have shown no consistent trend. Our analyses also provide a cautionary tale about mistaking increasing levels of social class mobility for a general trend towards more fluidity in the United States. The impact of parental education on son's educational and class attainment has grown or remained stable, respectively. Here, the compositional effect pertaining to the direct association between parental education and son's class attainment counteracts a long-term trend of increasing inequality in educational attainment tied to parents' education.

Equality and quality in education. A comparative study of 19 countries.

This contribution assesses the performance of national education systems along two important dimensions: The degree to which they help individuals develop capabilities necessary for their successful social integration (educational quality) and the degree to which they confer equal opportunities for social advancement (educational equality). It advances a new conceptualization to measure quality and equality in education and then uses it to study the relationship between institutional differentiation and these outcomes. It relies on data on final educational credentials and literacy among adults that circumvent some of the under-appreciated conceptual challenges entailed in the widespread analysis of international student assessment data. The analyses reveal a positive relationship between educational quality and equality and show that education systems with a lower degree of institutional differentiation not only provide more educational equality but are also marked by higher levels of educational quality. While the latter association is partly driven by other institutional and macro-structural factors, I demonstrate that the higher levels of educational equality in less differentiated education systems do not entail an often-assumed trade-off for lower quality.

The Community College Effect Revisited: The Importance of Attending to Heterogeneity and Complex Counterfactuals.

Community colleges are controversial educational institutions, often said to simultaneously expand college opportunities and diminish baccalaureate attainment. We assess the seemingly contradictory functions of community colleges by attending to effect heterogeneity and to alternative counterfactual conditions. Using data on postsecondary outcomes of high school graduates of Chicago Public Schools, we find that enrolling at a community college penalizes more advantaged students who otherwise would have attended four-year colleges, particularly highly selective schools; however, these students represent a relatively small portion of the community college population, and these estimates are almost certainly biased. On the other hand, enrolling at a community college has a modest positive effect on bachelor's degree completion for disadvantaged students who otherwise would not have attended college; these students represent the majority of community college goers. We conclude that discussions among scholars, policymakers, and practitioners should move beyond considering the pros and cons of community college attendance for students in general to attending to the implications of community college attendance for targeted groups of students.

What is a representative brain? Neuroscience meets population science.

The last decades of neuroscience research have produced immense progress in the methods available to understand brain structure and function. Social, cognitive, clinical, affective, economic, communication, and developmental neurosciences have begun to map the relationships between neuro-psychological processes and behavioral outcomes, yielding a new understanding of human behavior and promising interventions. However, a limitation of this fast moving research is that most findings are based on small samples of convenience. Furthermore, our understanding of individual differences may be distorted by unrepresentative samples, undermining findings regarding brain-behavior mechanisms. These limitations are issues that social demographers, epidemiologists, and other population scientists have tackled, with solutions that can be applied to neuroscience. By contrast, nearly all social science disciplines, including social demography, sociology, political science, economics, communication science, and psychology, make assumptions about processes that involve the brain, but have incorporated neural measures to differing, and often limited, degrees; many still treat the brain as a black box. In this article, we describe and promote a perspective--population neuroscience--that leverages interdisciplinary expertise to (i) emphasize the importance of sampling to more clearly define the relevant populations and sampling strategies needed when using neuroscience methods to address such questions; and (ii) deepen understanding of mechanisms within population science by providing insight regarding underlying neural mechanisms. Doing so will increase our confidence in the generalizability of the findings. We provide examples to illustrate the population neuroscience approach for specific types of research questions and discuss the potential for theoretical and applied advances from this approach across areas.

Wealth Disparities before and after the Great Recession.

The collapse of the labor, housing, and stock markets beginning in 2007 created unprecedented challenges for American families. This study examines disparities in wealth holdings leading up to the Great Recession and during the first years of the recovery. All socioeconomic groups experienced declines in wealth following the recession, with higher wealth families experiencing larger absolute declines. In percentage terms, however, the declines were greater for less-advantaged groups as measured by minority status, education, and pre-recession income and wealth, leading to a substantial rise in wealth inequality in just a few years. Despite large changes in wealth, longitudinal analyses demonstrate little change in mobility in the ranking of particular families in the wealth distribution. Between 2007 and 2011, one fourth of American families lost at least 75 percent of their wealth, and more than half of all families lost at least 25 percent of their wealth. Multivariate longitudinal analyses document that these large relative losses were disproportionally concentrated among lower income, less educated, and minority households.