Suchergebnisse

We assess how a variety of disruptive life-course events impact the economic wellbeing of US households and trace the importance of household wealth in helping families who experience these events avoid entering a spell of material hardship. Using longitudinal data from two panels of the Survey of Income and Program Participation (SIPP), we draw on direct measures of material hardship, disruptive events and household assets. Our analyses reveal that the relationship between disruptive events and the likelihood of experiencing a new spell of material hardship strongly varies across the wealth distribution, suggesting that high household wealth provides an effective private safety net. By distinguishing different types of disruptive events, we demonstrate that divorce, disability and income loss entail a risk of material hardship but also that this risk is effectively buffered by substantial wealth. Different types of hardship – namely, financial, food and medical hardship – respond in similar ways. Like public insurance schemes, wealth insurance helps buffer the effects of disruptive events on material hardship, but unlike public insurance schemes, reliance on private wealth further stratifies the economic wellbeing of households. Policy options for addressing this highly stratified private insurance scheme include disposing of the need for it by funding more robust public insurance, for instance through wealth taxation.

"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)." (author's abstract)

The US 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 PSID and surveys like it for today's sociological scholarship.

Research on wealth inequality and accumulation and the data upon which it relies have expanded substantially in the twenty-first century. Although 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 distribution. Next, we provide an overview of data sources available for wealth research. 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. 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.

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 prerecession 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.

Frontmatter -- Contents -- Figures and Tables -- Contributors -- Acknowledgments -- Chapter One. Introduction Social Mobility and Education in the Twentieth Century -- Chapter Two. Methodological Preliminaries -- Chapter Three. The Land of Opportunity? Trends in Social Mobility and Education in the United States -- Chapter Four. Sweden, the Middle Way? Trends and Patterns in Social Mobility and Educational Inequality -- Chapter Five. Intergenerational Mobility and Social Fluidity in France over Birth Cohorts and Across Age: The Role of Education -- Chapter Six. Education as an Equalizing Force: How Declining Educational Inequality and Educational Expansion Have Contributed to More Social Fluidity in Germany -- Chapter Seven. The Swiss El Dorado? Education and Social Mobility in Twentieth-Century Switzerland -- Chapter Eight. The Role of Education in the Social Mobility of Dutch Cohorts, 1908-74 -- Chapter Nine. Education and Social Fluidity in Contemporary Italy: An Analysis of Cohort Trends -- Chapter Ten. Intergenerational Social Mobility in Twentieth-Century Spain: Social Fluidity without Educational Equalization? -- Chapter Eleven. Social Mobility in the Twentieth Century in Europe and the United States -- Bibliography -- Index

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.