Persistent disparities in academic achievement between students from high- and low- poverty neighborhoods are widely attributed to differences in school quality. Using nationally representative data from more than 18,000 students and nearly 1,000 elementary schools, we examine how the schools serving students from different neighborhoods vary across more than 160 characteristics, including detailed measures of their composition, resources, instruction, climate, and effectiveness. Our findings document significant differences in demographic composition between schools serving high- and low-poverty neighborhoods but comparatively little variation in other dimensions of the school environment. With novel machine learning methods tailored for high-dimensional data, we estimate that equalizing all these different factors would reduce the achievement gap by less than 10 percent, primarily through changes in school composition. These results suggest that the main drivers of place-based disparities in achievement lie outside of elementary schools, underscoring the need to address broader structural inequalities as part of any effort to reduce achievement gaps.
{"title":"Poor Neighborhoods, Bad Schools? A High-Dimensional Model of Place-Based Disparities in Academic Achievement","authors":"Geoffrey T. Wodtke, Kailey White, Xiang Zhou","doi":"10.15195/v13.a6","DOIUrl":"https://doi.org/10.15195/v13.a6","url":null,"abstract":"Persistent disparities in academic achievement between students from high- and low- poverty neighborhoods are widely attributed to differences in school quality. Using nationally representative data from more than 18,000 students and nearly 1,000 elementary schools, we examine how the schools serving students from different neighborhoods vary across more than 160 characteristics, including detailed measures of their composition, resources, instruction, climate, and effectiveness. Our findings document significant differences in demographic composition between schools serving high- and low-poverty neighborhoods but comparatively little variation in other dimensions of the school environment. With novel machine learning methods tailored for high-dimensional data, we estimate that equalizing all these different factors would reduce the achievement gap by less than 10 percent, primarily through changes in school composition. These results suggest that the main drivers of place-based disparities in achievement lie outside of elementary schools, underscoring the need to address broader structural inequalities as part of any effort to reduce achievement gaps.","PeriodicalId":22029,"journal":{"name":"Sociological Science","volume":"91 1","pages":""},"PeriodicalIF":3.4,"publicationDate":"2026-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146129366","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Academic and popular interest in nonreligion has risen in parallel with the growth of religiously unaffiliated populations. In many countries, census and survey questions used to measure religion have been modified to better capture nonreligious identities. Little attention has been given to how these changes in measures affect specific claims about the rise of the “nones.” Although there is no doubt that religiously unaffiliated populations have grown in many countries during the twenty- first century, the degree of such growth has sometimes been exaggerated due to measurement effects. We review methodological issues that affect the estimates of the size of religiously unaffiliated populations and their change over time. We call for further study to quantify the effect of these changes.
{"title":"How Measurement Changes Can Exaggerate the Growth of Religious “Nones”","authors":"Matthew Conrad, Conrad Hackett","doi":"10.15195/v13.a5","DOIUrl":"https://doi.org/10.15195/v13.a5","url":null,"abstract":"Academic and popular interest in nonreligion has risen in parallel with the growth of religiously unaffiliated populations. In many countries, census and survey questions used to measure religion have been modified to better capture nonreligious identities. Little attention has been given to how these changes in measures affect specific claims about the rise of the “nones.” Although there is no doubt that religiously unaffiliated populations have grown in many countries during the twenty- first century, the degree of such growth has sometimes been exaggerated due to measurement effects. We review methodological issues that affect the estimates of the size of religiously unaffiliated populations and their change over time. We call for further study to quantify the effect of these changes.","PeriodicalId":22029,"journal":{"name":"Sociological Science","volume":"117 1","pages":""},"PeriodicalIF":3.4,"publicationDate":"2026-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146115676","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Sociologists have long argued that the cultural construction of organizations as social actors underpins public expectations of corporate accountability. In recent decades, however, the unified bureaucratic structures that once sustained this construction have given way to increasingly fragmented and opaque organizational forms. This study considers to what extent the diffuse, often illegible nature of twenty-first century corporations undermines the ability of public audiences to demand corporate accountability. We argue that complex, fragmented organizational configurations allow firms to partially evade the negative reputational consequences of misconduct by confounding audiences and obfuscating the “actor” behind the bad organizational action. Drawing on a vignette- based survey experiment, we test whether fragmentation reduces attributions of blame following corporate wrongdoing. Consistent with our hypotheses, we find that while respondents generally attribute high levels of blame for wrongdoing, greater fragmentation decreases the blame directed at core firms and heightens audiences’ uncertainty about responsibility. Moreover, in fragmented structures, blame is not simply redistributed to auxiliary entities but is diminished overall. These findings suggest that as corporate structures grow more complex and less legible, the underlying actors behind organizational action become harder to identify and construct, and thereby harder to hold to account.
{"title":"Ambiguous Actorhood: Twenty-First Century Firms and the Evasion of Responsibility","authors":"Carly R. Knight, Adam Goldstein","doi":"10.15195/v13.a4","DOIUrl":"https://doi.org/10.15195/v13.a4","url":null,"abstract":"Sociologists have long argued that the cultural construction of organizations as social actors underpins public expectations of corporate accountability. In recent decades, however, the unified bureaucratic structures that once sustained this construction have given way to increasingly fragmented and opaque organizational forms. This study considers to what extent the diffuse, often illegible nature of twenty-first century corporations undermines the ability of public audiences to demand corporate accountability. We argue that complex, fragmented organizational configurations allow firms to partially evade the negative reputational consequences of misconduct by confounding audiences and obfuscating the “actor” behind the bad organizational action. Drawing on a vignette- based survey experiment, we test whether fragmentation reduces attributions of blame following corporate wrongdoing. Consistent with our hypotheses, we find that while respondents generally attribute high levels of blame for wrongdoing, greater fragmentation decreases the blame directed at core firms and heightens audiences’ uncertainty about responsibility. Moreover, in fragmented structures, blame is not simply redistributed to auxiliary entities but is diminished overall. These findings suggest that as corporate structures grow more complex and less legible, the underlying actors behind organizational action become harder to identify and construct, and thereby harder to hold to account.","PeriodicalId":22029,"journal":{"name":"Sociological Science","volume":"72 1","pages":""},"PeriodicalIF":3.4,"publicationDate":"2026-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146056978","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
AJ Alvero, Dustin S. Stoltz, Oscar Stuhler, Marshall A. Taylor
Generative artificial intelligence (GenAI) has garnered considerable attention for its poten- tial utility in research and scholarship, even among those who typically do not rely on computational tools. However, early commentators have also articulated concerns about how GenAI usage comes with enormous environmental costs, serious social risks, and a tendency to produce low-quality content. In the midst of both excitement and skepticism, it is crucial to take stock of how GenAI is actually being used. Our study focuses on sociological research as our site, and here we present findings from a survey of 433 authors of articles published in 50 sociology journals in the past five years. The survey provides an overview of the state of the discipline with regard to the use of GenAI by providing answers to fundamental questions: how (much) do scholars use the technology for their research; what are their reasons for using it; and how concerned, trustful, and optimistic are they about the technology? Of the approximately one third of respondents who self-report using GenAI at least weekly, the primary uses are for writing assistance and comparatively less so in planning, data collection, or data analysis. In both use and attitudes, there are surprisingly few differences between self-identified computational and non-computational researchers. In general, respondents are very concerned about the social and environmental consequences of GenAI. Trust in GenAI outputs is low, regardless of expertise or frequency of use. Although optimism that GenAI will improve is high, scholars are divided on whether GenAI will have a positive impact on the field.
{"title":"Generative AI in Sociological Research: State of the Discipline","authors":"AJ Alvero, Dustin S. Stoltz, Oscar Stuhler, Marshall A. Taylor","doi":"10.15195/v13.a3","DOIUrl":"https://doi.org/10.15195/v13.a3","url":null,"abstract":"Generative artificial intelligence (GenAI) has garnered considerable attention for its poten- tial utility in research and scholarship, even among those who typically do not rely on computational tools. However, early commentators have also articulated concerns about how GenAI usage comes with enormous environmental costs, serious social risks, and a tendency to produce low-quality content. In the midst of both excitement and skepticism, it is crucial to take stock of how GenAI is actually being used. Our study focuses on sociological research as our site, and here we present findings from a survey of 433 authors of articles published in 50 sociology journals in the past five years. The survey provides an overview of the state of the discipline with regard to the use of GenAI by providing answers to fundamental questions: how (much) do scholars use the technology for their research; what are their reasons for using it; and how concerned, trustful, and optimistic are they about the technology? Of the approximately one third of respondents who self-report using GenAI at least weekly, the primary uses are for writing assistance and comparatively less so in planning, data collection, or data analysis. In both use and attitudes, there are surprisingly few differences between self-identified computational and non-computational researchers. In general, respondents are very concerned about the social and environmental consequences of GenAI. Trust in GenAI outputs is low, regardless of expertise or frequency of use. Although optimism that GenAI will improve is high, scholars are divided on whether GenAI will have a positive impact on the field.","PeriodicalId":22029,"journal":{"name":"Sociological Science","volume":"64 1","pages":""},"PeriodicalIF":3.4,"publicationDate":"2026-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146006005","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This article updates the empirical picture of categorical tolerance (CT), namely, the pattern of refusing to report dislikes across cultural genres, for the third decade of the twenty-first century in the United States. Analyzing recent survey data from two platforms, I find that CT has continued its march among Americans, reaching approximately one in five respondents. The analysis confirms earlier-observed demographic trends, showing that CT is strongly associated with younger cohorts and non-white individuals. However, I also find that individuals reporting the highest educational attainment are now overrepresented among categorical tolerants, suggesting that CT may increasingly function as an elite cultural strategy consistent with contemporary forms of status display, signaling openness and refusal to refuse. Furthermore, I find that while the odds of being a CT are not strongly polarized by political ideology, the inclination toward symbolic exclusion among non-CTs is, with conservatives significantly more likely to express a greater volume of cultural dislikes than liberals.
{"title":"The Forward March of Categorical Tolerance in the United States","authors":"Omar Lizardo","doi":"10.15195/v13.a2","DOIUrl":"https://doi.org/10.15195/v13.a2","url":null,"abstract":"This article updates the empirical picture of categorical tolerance (CT), namely, the pattern of refusing to report dislikes across cultural genres, for the third decade of the twenty-first century in the United States. Analyzing recent survey data from two platforms, I find that CT has continued its march among Americans, reaching approximately one in five respondents. The analysis confirms earlier-observed demographic trends, showing that CT is strongly associated with younger cohorts and non-white individuals. However, I also find that individuals reporting the highest educational attainment are now overrepresented among categorical tolerants, suggesting that CT may increasingly function as an elite cultural strategy consistent with contemporary forms of status display, signaling openness and refusal to refuse. Furthermore, I find that while the odds of being a CT are not strongly polarized by political ideology, the inclination toward symbolic exclusion among non-CTs is, with conservatives significantly more likely to express a greater volume of cultural dislikes than liberals.","PeriodicalId":22029,"journal":{"name":"Sociological Science","volume":"5 1","pages":""},"PeriodicalIF":3.4,"publicationDate":"2026-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145962799","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Martin Eiermann, Maria Fitzpatrick, Katharine Sadowski, Christopher Wildeman
Algorithmic risk scoring tools have been widely incorporated into governmental decision making, yet little is known about how human decision makers interact with machine-generated risk scores at the street level. We examined such human–machine interactions in the child welfare system, a high-stakes setting where caseworkers ascertain whether government interventions in family life are warranted. Using novel data—verbatim transcripts of caseworker discussions—we found that decision makers: (1) disregarded scores in the middle of the distribution while paying attention to extremely high or low risk scores and (2) rationalized divergences between human decisions and machine-generated scores by highlighting the algorithm’s overemphasis on historical data and specific risk factors and its lack of contextual knowledge. This meant that caseworkers were unlikely to modify their decisions so that they aligned with risk scores. However, we did not find evidence of principled resistance to algorithmic tools. Our findings advance research on such tools by specifying how human perceptions of the utility and limitations of novel technologies shape discretionary decision making by state officials; and they help to explain their uneven and potentially modest impact on the bureaucratic management of social vulnerability.
{"title":"How Do (Human) Child Welfare Workers Respond to Machine-Generated Risk Scores?","authors":"Martin Eiermann, Maria Fitzpatrick, Katharine Sadowski, Christopher Wildeman","doi":"10.15195/v13.a1","DOIUrl":"https://doi.org/10.15195/v13.a1","url":null,"abstract":"Algorithmic risk scoring tools have been widely incorporated into governmental decision making, yet little is known about how human decision makers interact with machine-generated risk scores at the street level. We examined such human–machine interactions in the child welfare system, a high-stakes setting where caseworkers ascertain whether government interventions in family life are warranted. Using novel data—verbatim transcripts of caseworker discussions—we found that decision makers: (1) disregarded scores in the middle of the distribution while paying attention to extremely high or low risk scores and (2) rationalized divergences between human decisions and machine-generated scores by highlighting the algorithm’s overemphasis on historical data and specific risk factors and its lack of contextual knowledge. This meant that caseworkers were unlikely to modify their decisions so that they aligned with risk scores. However, we did not find evidence of principled resistance to algorithmic tools. Our findings advance research on such tools by specifying how human perceptions of the utility and limitations of novel technologies shape discretionary decision making by state officials; and they help to explain their uneven and potentially modest impact on the bureaucratic management of social vulnerability.","PeriodicalId":22029,"journal":{"name":"Sociological Science","volume":"5 1","pages":""},"PeriodicalIF":3.4,"publicationDate":"2026-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145908413","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We investigate the impact of analytical choices on country comparisons in intergenerational educational mobility using a multiverse approach. A literature survey gives rise to 2,880 plausible ways of measuring educational mobility, which we apply to European Social Survey data from 16 countries. Although some countries consistently appear at the top or bottom of the mobility rankings, most show substantial variation. Beyond our methodological contribution, we report two substantive findings. First, some countries often characterized as low-mobility emerge as matching or surpassing the egalitarian Nordic countries, reinforcing the view that wider mobility differences cannot be attributed solely to the education system but must be sought elsewhere, such as the labor market. Second, the choice of parameter—such as regression coefficients, correlations, or categorical measures—is the single most influential factor that shifts country rankings. As different parameters carry distinct theoretical meanings, researchers should treat parameter choice not merely as a robustness check but as an opportunity to test and refine competing theories.
{"title":"How Robust Are Country Rankings in Educational Mobility?","authors":"Ely Strömberg, Per Engzell","doi":"10.15195/v12.a36","DOIUrl":"https://doi.org/10.15195/v12.a36","url":null,"abstract":"We investigate the impact of analytical choices on country comparisons in intergenerational educational mobility using a multiverse approach. A literature survey gives rise to 2,880 plausible ways of measuring educational mobility, which we apply to European Social Survey data from 16 countries. Although some countries consistently appear at the top or bottom of the mobility rankings, most show substantial variation. Beyond our methodological contribution, we report two substantive findings. First, some countries often characterized as low-mobility emerge as matching or surpassing the egalitarian Nordic countries, reinforcing the view that wider mobility differences cannot be attributed solely to the education system but must be sought elsewhere, such as the labor market. Second, the choice of parameter—such as regression coefficients, correlations, or categorical measures—is the single most influential factor that shifts country rankings. As different parameters carry distinct theoretical meanings, researchers should treat parameter choice not merely as a robustness check but as an opportunity to test and refine competing theories.","PeriodicalId":22029,"journal":{"name":"Sociological Science","volume":"29 1","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145728860","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Segregation—whether across schools, neighborhoods, or occupations—is regularly invoked as a cause of social and economic disparities. However, segregation is a complicated causal treatment: what do we mean when we appeal to a world in which segregation does not exist? One could take societal contexts as the unit of analysis and compare across societies with differing levels of segregation. In practice, it is more common for studies of segregation to take persons or households as the unit of analysis within a single societal context, focusing on what would happen if particular individuals were counterfactually assigned to social positions in a more equitable way. Taking this latter framework, this article shows how to study segregation as a cause. The first step is to theorize a counterfactual assignment rule: what would it mean to assign people to social positions equitably? The second step is to identify the causal effect of those social positions and simulate counterfactual outcomes. The third step is to interpret results as the impact of a unit-level (rather than society-level) intervention. A running example and empirical analysis illustrates the approach by studying the causal effect of occupational segregation on a racial health gap.
{"title":"The Causal Impact of Segregation on a Disparity: A Gap-Closing Approach","authors":"Ian Lundberg","doi":"10.15195/v12.a35","DOIUrl":"https://doi.org/10.15195/v12.a35","url":null,"abstract":"Segregation—whether across schools, neighborhoods, or occupations—is regularly invoked as a cause of social and economic disparities. However, segregation is a complicated causal treatment: what do we mean when we appeal to a world in which segregation does not exist? One could take societal contexts as the unit of analysis and compare across societies with differing levels of segregation. In practice, it is more common for studies of segregation to take persons or households as the unit of analysis within a single societal context, focusing on what would happen if particular individuals were counterfactually assigned to social positions in a more equitable way. Taking this latter framework, this article shows how to study segregation as a cause. The first step is to theorize a counterfactual assignment rule: what would it mean to assign people to social positions equitably? The second step is to identify the causal effect of those social positions and simulate counterfactual outcomes. The third step is to interpret results as the impact of a unit-level (rather than society-level) intervention. A running example and empirical analysis illustrates the approach by studying the causal effect of occupational segregation on a racial health gap.","PeriodicalId":22029,"journal":{"name":"Sociological Science","volume":"11 1","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145689433","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In life course research, it is common practice to analyze the effects of life events on outcomes. This is usually done by estimating “impact functions.” To date, most studies have estimated yearly impact functions. However, Hudde and Jacob (2023) (hereafter H&J) pointed out that most panel data sets include information on the month of events. Consequently, they proposed exploiting this information by estimating monthly impact functions. In this adversarial collaboration, we address two issues regarding H&J’s work. First, H&J did not provide sufficient guidance on how to estimate monthly impact functions. We will provide a step-by-step description of how to do so. Second, the procedure H&J proposed for smoothing monthly estimates produces confidence intervals (CIs) that are likely too narrow. This can lead to misleading conclusions. Therefore, we suggest using more appropriate bootstrapped CIs.
{"title":"What You Need to Know When Estimating Monthly Impact Functions: Comment on Hudde and Jacob, “There’s More in the Data!”","authors":"Josef Brüderl, Ansgar Hudde, Marita Jacob","doi":"10.15195/v12.a34","DOIUrl":"https://doi.org/10.15195/v12.a34","url":null,"abstract":"In life course research, it is common practice to analyze the effects of life events on outcomes. This is usually done by estimating “impact functions.” To date, most studies have estimated yearly impact functions. However, Hudde and Jacob (2023) (hereafter H&J) pointed out that most panel data sets include information on the month of events. Consequently, they proposed exploiting this information by estimating monthly impact functions. In this adversarial collaboration, we address two issues regarding H&J’s work. First, H&J did not provide sufficient guidance on how to estimate monthly impact functions. We will provide a step-by-step description of how to do so. Second, the procedure H&J proposed for smoothing monthly estimates produces confidence intervals (CIs) that are likely too narrow. This can lead to misleading conclusions. Therefore, we suggest using more appropriate bootstrapped CIs.","PeriodicalId":22029,"journal":{"name":"Sociological Science","volume":"33 1","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145674531","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In the United States, the financial and co-residential dependence of young adults on parents has increased for decades. This study provides the first comprehensive analysis of economic support trajectories, their contextual, family, and individual determinants, and temporal relation to other transition to adulthood milestones. Using data from the Panel Study of Income Dynamics’ Transition to Adulthood Study (2005–2021), we identify trajectories of financial and co-residential support between ages 18 and 28 and relate them to economic and partnership trajectories and events. We study how macro-economic crises (the Great Recession and COVID-19), family characteristics, and individual traits within sibships predict trajectory membership. We find three distinct pathways: first, prolonged education and financial support are more common among advantaged families and, within siblings, among those exposed to the Great Recession. Second, early employment and prolonged co-residence are the most prevalent among disadvantaged families and children. Third, economic independence through marriage is most common among white people living outside metropolitan areas.
{"title":"Pathways to Independence: The Dynamics of Parental Support in the Transition to Adulthood","authors":"Ramina Sotoudeh, Ginevra Floridi","doi":"10.15195/v12.a33","DOIUrl":"https://doi.org/10.15195/v12.a33","url":null,"abstract":"In the United States, the financial and co-residential dependence of young adults on parents has increased for decades. This study provides the first comprehensive analysis of economic support trajectories, their contextual, family, and individual determinants, and temporal relation to other transition to adulthood milestones. Using data from the Panel Study of Income Dynamics’ Transition to Adulthood Study (2005–2021), we identify trajectories of financial and co-residential support between ages 18 and 28 and relate them to economic and partnership trajectories and events. We study how macro-economic crises (the Great Recession and COVID-19), family characteristics, and individual traits within sibships predict trajectory membership. We find three distinct pathways: first, prolonged education and financial support are more common among advantaged families and, within siblings, among those exposed to the Great Recession. Second, early employment and prolonged co-residence are the most prevalent among disadvantaged families and children. Third, economic independence through marriage is most common among white people living outside metropolitan areas.","PeriodicalId":22029,"journal":{"name":"Sociological Science","volume":"3 1","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145600080","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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