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Xi Song is an Associate Professor of Sociology and an affiliate of the Population Studies Center at the University of Pennsylvania. She previously taught at the University of Chicago.
Song’s major area of research centers on the origin of social inequality from a multigenerational perspective. Her research uses demographic, statistical, and computational tools to study the rise and fall of families in human populations across time and place. She has investigated long-term family and population changes by exploring the values of genealogical microdata. These data sources include historical data compiled from family pedigrees, population registers, administrative certificates, church records, and surname data; and modern longitudinal and cross-sectional data that follow a sample of respondents, their offspring, and descendants prospectively or ask respondents to report information about their family members and relatively retrospectively.
Her previous work has drawn on family genealogies from as many as sixteen generations of imperial and peasant families from 18th–20th century China to explore why families grow, decline, or even die out, and how they maintain, change, and reproduce their social statuses. Her recent work uses U.S. linked historical censuses and contemporary survey data from 1850 to 2015 to illustrate how macro-level social changes in fertility, mortality, and family structure, and micro-level patterns of families’ social mobility jointly lead to persistent inequality across generations.
Her methodological work focuses on developing demographic models based on life tables and Markov chains to predict family dynamics and kinship system in a population, identifying causal mediation mechanisms in social mobility processes, modelling intensive longitudinal data using dyadic and multivariate mixed effects models, and reconciling prospective and retrospective approaches to sociological studies.
Some of her ongoing work investigates the influence of political institutions on the media and public misperception of inequality against a backdrop of rising inequality around the globe. As part of this research, she measures inequality using “big data,” in the form of a colossal amount of text-based data from almost 400 traditional Chinese newspapers and magazines, new digital media outlets, and individual social media platforms from the early 2000s to the present. The project will show how rising inequality is perceived, publicized, and interpreted in both authoritarian and democratic societies wherein media and government practices are not independent, but rather the former is to varying degrees influenced by political power.
Her research has appeared in the American Sociological Review, Annual Review of Sociology, Demography, PNAS, Social Science Research, Sociological Methods and Research, and Sociological Science.
Song’s major area of research centers on the origin of social inequality from a multigenerational perspective. Her research uses demographic, statistical, and computational tools to study the rise and fall of families in human populations across time and place. She has investigated long-term family and population changes by exploring the values of genealogical microdata. These data sources include historical data compiled from family pedigrees, population registers, administrative certificates, church records, and surname data; and modern longitudinal and cross-sectional data that follow a sample of respondents, their offspring, and descendants prospectively or ask respondents to report information about their family members and relatively retrospectively.
Her previous work has drawn on family genealogies from as many as sixteen generations of imperial and peasant families from 18th–20th century China to explore why families grow, decline, or even die out, and how they maintain, change, and reproduce their social statuses. Her recent work uses U.S. linked historical censuses and contemporary survey data from 1850 to 2015 to illustrate how macro-level social changes in fertility, mortality, and family structure, and micro-level patterns of families’ social mobility jointly lead to persistent inequality across generations.
Her methodological work focuses on developing demographic models based on life tables and Markov chains to predict family dynamics and kinship system in a population, identifying causal mediation mechanisms in social mobility processes, modelling intensive longitudinal data using dyadic and multivariate mixed effects models, and reconciling prospective and retrospective approaches to sociological studies.
Some of her ongoing work investigates the influence of political institutions on the media and public misperception of inequality against a backdrop of rising inequality around the globe. As part of this research, she measures inequality using “big data,” in the form of a colossal amount of text-based data from almost 400 traditional Chinese newspapers and magazines, new digital media outlets, and individual social media platforms from the early 2000s to the present. The project will show how rising inequality is perceived, publicized, and interpreted in both authoritarian and democratic societies wherein media and government practices are not independent, but rather the former is to varying degrees influenced by political power.
Her research has appeared in the American Sociological Review, Annual Review of Sociology, Demography, PNAS, Social Science Research, Sociological Methods and Research, and Sociological Science.
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SSRN Electronic Journal (2024)
RESEARCH IN SOCIAL STRATIFICATION AND MOBILITY (2023): 100812-100812
EUROPEAN SOCIOLOGICAL REVIEWno. 4 (2023): 545-568
Research in Social Stratification and Mobility (2022): 100713-100713
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