Tag Archives | Inequality

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Scholars have debated whether changes in digital environments have led to greater concentration or dispersal of scientific citations, but this debate has paid little attention to how other changes in the publication environment may impact the commonly used measures of inequality. Using Monte Carlo experiments, we demonstrate that a variety of inequality measures—including the Gini coefficient, the Herfindahl-Hirschman index, and the percentage of articles ever cited—are substantially biased downward by increases in the total number of articles and citations. We propose and validate a resampling-based correction for this “marginals bias” and apply this correction to empirical data on scholarly citation distributions using Web of Science data covering four broad scientific fields (health, humanities, mathematics and the computer sciences, and the social sciences) from 1996 to 2014. We find that in each field the bulk of the apparent decline in citation inequality in recent years is an artifact of marginals bias, as are most apparent interfield differences in citation inequality. Researchers using inequality measures to compare citation distributions and other distributions with many cases at or near the zero-bound should interpret these metrics carefully and account for the influence of changing marginals.

This work is licensed under a Creative Commons Attribution 4.0 International License.

Lanu Kim: Graduate School of Education, Stanford University
E-mail: lanu@stanford.edu

Christopher Adolph: Department of Political Science, University of Washington, Seattle
E-mail: cadolph@uw.edu

Jevin D. West: Information School, University of Washington, Seattle
E-mail: jevinw@uw.edu

Katherine Stovel: Department of Sociology, University of Washington, Seattle
E-mail: stovel@uw.edu

Acknowledgments: We thank Clarivate Analytics for providing the Web of Science data, and Elena Erosheva, Bas Hofstra, and Joe Cho for helpful conversations. This research was supported by National Science Foundation grant #1735194, Katherine Stovel primary investigator, Jevin West co–primary investigator.

Citation: Kim, Lanu, Christopher Adolph, Jevin D. West, and Katherine Stovel. 2020. “The Influence of Changing Marginals on Measures of Inequality in Scholarly Citations: Evidence of Bias and a Resampling Correction.” Sociological Science 7: 314-341.
Received: June 16, 2020
Accepted: July 3, 2020
Editors: Arnout van de Rijt
DOI: 10.15195/v7.a13

Socioeconomic, Ethnic, Racial, and Gender Gaps in Children’s Social/Behavioral Skills: Do They Grow Faster in School or out?

By Parker Webservices on May 29, 2019 in Articles

Douglas B. Downey, Joseph Workman, Paul von Hippel

Sociological Science, May 29, 2019
10.15195/v6.a17

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Children’s social and behavioral skills vary considerably by socioeconomic status (SES), race and/or ethnicity, and gender, yet it is unclear to what degree these differences are due to school or nonschool factors. We observe how gaps in social and behavioral skills change during school and nonschool (summer) periods from the start of kindergarten entry until the end of second grade in a recent and nationally representative sample of more than 16,000 children (the Early Childhood Longitudinal Study Kindergarten Class of 2010–11). We find that large gaps in social and behavioral skills exist at the start of kindergarten entry, and these gaps favor high-SES, white, and female children. Over the next three years, we observed that the gaps grow no faster when school is in than when school is out. In the case of social and behavioral skills, it appears that schools neither exacerbate inequality nor reduce it.

This work is licensed under a Creative Commons Attribution 4.0 International License.

Douglas B. Downey: Department of Sociology, The Ohio State University
E-mail: downey32@gmail.com

Joseph Workman: Department of Sociology, University of Missouri-Kansas City
E-mail: workmanj@umkc.edu

Paul von Hippel: Lyndon B. Johnson School of Public Affairs, The University of Texas at Austin
E-mail: paulvonhippel.utaustin@gmail.com

Acknowledgements: Direct all correspondence to Douglas B. Downey (downey32@gmail.com), 1885 Neil Ave., Columbus, Ohio 43022.

Citation: Downey, Douglas B., Joseph Workman, and Paul von Hippel. 2019. “Socioeconomic, Ethnic, Racial, and Gender Gaps in Children’s Social/Behavioral Skills: Do They Grow Faster in School or out?” Sociological Science 6: 446-466.
Received: March 17, 2019
Accepted: March 30, 2019
Editors: Jesper Sørensen, Stephen Morgan
DOI: 10.15195/v6.a17

Buying In: Positional Competition, Schools, Income Inequality, and Housing Consumption

By Parker Webservices on May 22, 2019 in Articles

Adam Goldstein, Orestes P. Hastings

Sociological Science, May 22, 2019
10.15195/v6.a16

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Social scientists have suggested that a key sociobehavioral consequence of rising inequality is intensifying market competition for advantageous positions in the opportunity structure, such as residences that afford access to high-quality public schools. We assess empirical implications of inequality-fueled positional competition theories (PCTs) by analyzing the relationships between metropolitan income inequality, households’ efforts to secure residential positions in desirable school districts, and housing consumption behavior. We assemble a unique data set, which contains longitudinal information on household finances, residences, and geographic locations from the Panel Study of Income Dynamics; information on the quality of the school attendance areas in which these households reside; and information about the local real estate market. We find that greater inequality is associated with steeper housing price premia for residences in desirable areas, more pronounced social class sorting on school quality when relocating, and greater salience of schools relative to other housing amenities in families’ housing expenditure functions. Families in high-inequality regions exhibit modestly greater willingness to pay more (relative to their own incomes) for a given improvement in school desirability. The analysis brings important empirical nuance to oft-invoked but untested theories about positional competition as a mechanism by which inequality affects behaviors, consumption, and markets.

This work is licensed under a Creative Commons Attribution 4.0 International License.

Adam Goldstein: Departments of Sociology and Public Affairs, Princeton University
E-mail: amg5@princeton.edu

Orestes P. Hastings: Department of Sociology, Colorado State University
E-mail: Pat.Hastings@colostate.edu

Acknowledgements: The authors are grateful for helpful suggestions from Marianne Bertrand, Neil Fligstein, Kevin McKee, Ann Owens, Peter Rich, participants at the Tobin Project Conference on Inequality and Decision Making, and the editors of Sociological Science. This research was partially supported by funding from the Tobin Project. The first author was also supported by the Robert Wood Johnson Foundation. Collection of the PSID data used in this study was partly supported by the National Institutes of Health (grants R01 HD069609 and R01 AG040213) and the National Science Foundation (awards SES 1157698 and 1623684).

Citation: Goldstein, Adam, and Orestes P. Hastings. 2019. “Buying In: Positional Competition, Schools, Income Inequality, and Housing Consumption.” Sociological Science 6: 416-445.
Received: September 12, 2018
Accepted: March 22, 2019
Editors: Jesper Sørensen, Kim Weeden
DOI: 10.15195/v6.a16

Do Test Score Gaps Grow Before, During, or Between the School Years? Measurement Artifacts and What We Can Know in Spite of Them

By Parker Webservices on January 24, 2019 in Articles

Paul T. von Hippel, Caitlin Hamrock

Sociological Science, January 24, 2019
10.15195/v6.a3

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Do test score gaps between advantaged and disadvantaged children originate inside or outside schools? One approach to this classic question is to ask (1) How large are gaps when children enter school? (2) How much do gaps grow later on? (3) Do gaps grow faster during school or during summer? Confusingly, past research has given discrepant answers to these basic questions.

We show that many results about gap growth have been distorted by measurement artifacts. One artifact relates to scaling: Gaps appear to grow faster if measurement scales spread with age. Another artifact relates to changes in test form: Summer gap growth is hard to estimate if children take different tests in spring than in fall.

Net of artifacts, the most replicable finding is that gaps form mainly in early childhood, before schooling begins. After school begins, most gaps grow little, and some gaps shrink. Evidence is inconsistent regarding whether gaps grow faster during school or during summer. We substantiate these conclusions using new data from the Growth Research Database and two data sets used in previous studies of gap growth: the Beginning School Study and the Early Childhood Longitudinal Study, Kindergarten Cohort of 1998–1999.

This work is licensed under a Creative Commons Attribution 4.0 International License.

Paul T. von Hippel: LBJ School of Public Affairs, University of Texas at Austin
E-mail: paulvonhippel.utaustin@gmail.com

Caitlin Hamrock: E3 Alliance
E-mail: chamrock@e3alliance.org

Acknowledgements: We thank Mina Kumar for research assistance. We thank the William T. Grant Foundation and the Institute for Urban Policy Research and Analysis for grants supporting this work.

Citation: von Hippel, Paul T., and Caitlin Hamrock. 2019. “Do Test Score Gaps Grow Before, During, or Between the School Years? Measurement Artifacts and What We Can Know in Spite of Them.” Sociological Science 6: 43-80.
Received: February 20, 2018
Accepted: July 23, 2018
Editors: Jesper Sørensen, Stephen Morgan
DOI: 10.15195/v6.a3

Poor State, Rich State: Understanding the Variability of Poverty Rates across U.S. States

By Parker Webservices on October 3, 2018 in Articles

Jennifer Laird, Zachary Parolin, Jane Waldfogel, Christopher Wimer

Sociological Science, October 3, 2018
10.15195/v5.a26

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According to the Supplemental Poverty Measure, state-level poverty rates range from a low of less than 10 percent in Iowa to a high of more than 20 percent in California. We seek to account for these differences using a theoretical framework proposed by Brady, Finnigan, and Hübgen (2017), which emphasizes the prevalence of poverty risk factors as well as poverty penalties associated with each risk factor. We estimate state-specific penalties and prevalences associated with single motherhood, low education, young households, and joblessness. We also consider state variation in the poverty risks associated with living in a black household and a Hispanic immigrant household. Brady et al. (2017) find that country-level differences in poverty rates are more closely tied to penalties than prevalences. Using data from the Current Population Survey, we find that the opposite is true for state-level differences in poverty rates. Although we find that state poverty differences are closely tied to the prevalence of high-risk populations, our results do not suggest that state-level antipoverty policy should be solely focused on changing “risky” behavior. Based on our findings, we conclude that state policies should take into account cost-of-living penalties as well as the state-specific relationship between poverty, prevalences, and penalties.

This work is licensed under a Creative Commons Attribution 4.0 International License.

Jennifer Laird: Department of Sociology, Lehman College
E-mail: jennifer.laird@lehman.cuny.edu

Zachary Parolin: Herman Deleeck Centre for Social Policy, University of Antwerp
E-mail: Zachary.Parolin@uantwerpen.be

Jane Waldfogel: School of Social Work, Columbia University
E-mail: j.waldfogel@columbia.edu

Christopher Wimer: School of Social Work, Columbia University
E-mail: cw2727@columbia.edu

Acknowledgements: A draft of this article was presented at the 2017 meeting of the American Sociological Association. We are grateful to David Brady and Jake Rosenfeld for their insights on a prior version of this article. This research is supported by generous funding from The JPB Foundation and the Annie E. Casey Foundation.

Citation: Laird, Jennifer, Zachary Parolin, Jane Waldfogel, and Christopher Wimer. 2018. “Poor State, Rich State: Understanding the Variability of Poverty Rates across U.S. States.” Sociological Science 5: 628-652.
Received: June 17, 2018
Accepted: August 21, 2018
Editors: Jesper Sørensen, Olav Sorenson
DOI: 10.15195/v5.a26

Benefit Inequality among American Workers by Gender, Race, and Ethnicity, 1982–2015

By Parker Webservices on July 19, 2018 in Articles

Tali Kristal, Yinon Cohen, Edo Navot

Sociological Science, July 19, 2018
10.15195/v5.a20

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Gender, racial, and ethnic gaps in wages are well known, but group disparities in employer-provided benefits, which account for one-quarter of total compensation, are not. We use benefit costs data to study levels and trends in gender, racial, and ethnic gaps in voluntary employer-provided benefits. Analyzing Employer Costs for Employee Compensation microdata on wages and benefit costs for the years 1982 to 2015, matched to Current Population Survey files by wage decile in the industrial sector, we find that (1) benefit gaps were wider than wage gaps for minorities but were narrower for gender, (2) racial and ethnic gaps in benefits increased faster than wage gaps, and (3) the gender gap in benefits decreased faster than the wage gap. We show that these findings reflect the types of jobs women, blacks, and Hispanics have held for the past three decades.

This work is licensed under a Creative Commons Attribution 4.0 International License.

Tali Kristal: Department of Sociology, University of Haifa
E-mail: kristal@soc.haifa.ac.il

Yinon Cohen: Department of Sociology, Columbia University
E-mail: yc2444@columbia.edu

Edo Navot:United States Department of Labor
E-mail: navot.edo@dol.gov

Acknowledgements: We thank the United States–Israel Binational Science Foundation for its partial support of this project. Earlier versions of this article were presented at the summer meeting of the International Sociological Association Research Committee on Social Stratification and Mobility (in 2017) and the Intergenerational Mobility and Income Inequality Workshop held at the University of Haifa (in March 2018). We thank Yitchak Haberfeld and the participants in these meetings for their comments. We gratefully acknowledge the support of the Bureau of Labor Statistics (BLS) and its staff, who facilitated this research with generosity and patience. The research was conducted with restricted access to Bureau of Labor Statistics data. The views expressed in any publication resulting from an analysis of these data do not necessarily reflect the views of the BLS. Additionally, the views expressed herein are those of the authors and do not necessarily reflect the views or policies of the United States Department of Labor or any agency within it.

Citation: Kristal, Tali, Yinon Cohen, and Edo Navot. 2018. “Benefit Inequality among American Workers by Gender, Race and Ethnicity, 1982–2015.” Sociological Science 5: 461-488.
Received: April 17, 2018
Accepted: June 5, 2018
Editors: Jesper Sørensen, Kim Weeden
DOI: 10.15195/v5.a20

Last Name Selection in Audit Studies

By Parker Webservices on January 11, 2018 in Articles

Charles Crabtree, Volha Chykina

Sociological Science, January 11, 2018
DOI 10.15195/v5.a2

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In this article, we build on Gaddis (2017a) by illuminating a key variable plausibly related to racial perceptions of last names—geography. We show that the probability that any individual belongs to a race is conditional not only on their last name but also on surrounding racial demographics. Specifically, we demonstrate that the probability of a name denoting a race varies considerably across contexts, and this is more of a problem for some names than others. This result has two important implications for audit study research: it suggests important limitations for (1) the generalizability of audit study findings and (2) for the interpretation of geography-based conditional effects. This means that researchers should be careful to select names that consistently signal racial groups regardless of local demographics. We provide a slim R package that can help researchers do this.

This work is licensed under a Creative Commons Attribution 4.0 International License.

Charles Crabtree: Department of Political Science, University of Michigan
Email: ccrabtr@umich.edu

Volha Chykina: Department of Education Policy Studies, Pennsylvania State University
Email: vuc125@psu.edu

Acknowledgements: We thank Holger L. Kern for his extremely helpful comments. All data and computer code necessary to replicate the results in this analysis are available at
http://github.com/cdcrabtree/auditr

Citation: Crabtree, Charles, and Volha Chykina. 2018. “Last Name Selection in Audit Studies.” Sociological Science 5: 21-28.
Received: November 2, 2017
Accepted: November 11, 2017
Editors: Jesper Sørensen, Gabriel Rossman
DOI: 10.15195/v5.a2

Better Estimates from Binned Income Data: Interpolated CDFs and Mean-Matching

By Parker Webservices on November 15, 2017 in Articles

Paul T. von Hippel, David J. Hunter, McKalie Drown

Sociological Science, November 15, 2017
DOI 10.15195/v4.a26

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Researchers often estimate income statistics from summaries that report the number of incomes in bins such as $0 to 10,000, $10,001 to 20,000, …, $200,000+. Some analysts assign incomes to bin midpoints, but this treats income as discrete. Other analysts fit a continuous parametric distribution, but the distribution may not fit well. We fit nonparametric continuous distributions that reproduce the bin counts perfectly by interpolating the cumulative distribution function (CDF). We also show how both midpoints and interpolated CDFs can be constrained to reproduce the mean of income when it is known. We evaluate the methods in estimating the Gini coefficients of all 3,221 U.S. counties. Fitting parametric distributions is very slow. Fitting interpolated CDFs is much faster and slightly more accurate. Both interpolated CDFs and midpoints give dramatically better estimates if constrained to match a known mean. We have implemented interpolated CDFs in the “binsmooth” package for R. We have implemented the midpoint method in the “rpme” command for Stata. Both implementations can be constrained to match a known mean.

This work is licensed under a Creative Commons Attribution 4.0 International License.

Paul T. von Hippel: Lyndon B. Johnson School of Public Affairs, University of Texas at Austin
Email: paulvonhippel.utaustin@gmail.com

David J. Hunter: Department of Mathematics and Computer Science, Westmont College
Email: dhunter@westmont.edu

McKalie Drown: Department of Mathematics and Computer Science, Westmont College
Email: mdrown@westmont.edu

Acknowledgements: Drown is grateful for support from a Tensor Grant of the Mathematical Association of America.

Citation: von Hippel, Paul T., David J. Hunter, and McKalie Drown. 2017. “Better Estimates from Binned Income Data: Interpolated CDFs and Mean-Matching.” Sociological Science 4: 641-655.
Received: September 23, 2017
Accepted: October 8, 2017
Editors: Jesper Sørensen, Stephen Morgan
DOI: 10.15195/v4.a26

How Black Are Lakisha and Jamal? Racial Perceptions from Names Used in Correspondence Audit Studies

By Parker Webservices on September 6, 2017 in Articles

S. Michael Gaddis

Sociological Science, September 6, 2017
DOI 10.15195/v4.a19

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Online correspondence audit studies have emerged as the primary method to examine racial discrimination. Although audits use distinctive names to signal race, few studies scientifically examine data regarding the perception of race from names. Different names treated as black or white may be perceived in heterogeneous ways. I conduct a survey experiment that asks respondents to identify the race they associate with a series of names. I alter the first names given to each respondent and inclusion of last names. Names more commonly given by highly educated black mothers (e.g., Jalen and Nia) are less likely to be perceived as black than names given by less educated black mothers (e.g., DaShawn and Tanisha). The results suggest that a large body of social science evidence on racial discrimination operates under a misguided assumption that all black names are alike, and the findings from correspondence audits are likely sensitive to name selection.

This work is licensed under a Creative Commons Attribution 4.0 International License.

S. Michael Gaddis: Department of Sociology, University of California, Los Angeles
Email: mgaddis@soc.ucla.edu

Acknowledgements: An earlier version of this article was presented at the 2015 annual meeting of the American Sociological Association in Chicago, IL. Larry D. Schoen provided access to birth record data from New York. Anup Das, Qing Zheng, Betsy Cliff, and Neala Berkowski served as excellent research assistants on this project. I also thank Shawn Bauldry, Colleen Carey, Philip Cohen, Jonathan Daw, René Flores, Devah Pager, Lincoln Quillian, Charles Seguin, and Ashton Verdery for their helpful comments.

Citation: Gaddis, S. Michael. 2017. “How Black Are Lakisha and Jamal? Racial Perceptions from Names Used in Correspondence Audit Studies.” Sociological Science 4: 469-489.
Received: May 18, 2017
Accepted: June 12, 2017
Editors: Jesper Sørensen, Olav Sorenson
DOI: 10.15195/v4.a19

Financialization Is Marketization! A Study of the Respective Impacts of Various Dimensions of Financialization on the Increase in Global Inequality

By Parker Webservices on June 29, 2016 in Articles

Olivier Godechot

Sociological Science, June 29, 2016
DOI 10.15195/v3.a22

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In this article, I study the impact of financialization on the rise in inequality in 18 OECD countries from 1970 to 2011 and measure the respective roles of various forms of financialization: the growth of the financial sector; the growth of one of its subcomponents, financial markets; the financialization of non-financial firms; and the financialization of households. I test these impacts using cross-country panel regressions in OECD countries. I show first that the share of the finance sector within the GDP is a substantial driver of world inequality, explaining between 20 and 40 percent of its increase from 1980 to 2007. When I decompose this financial sector effect, I find that this evolution was mainly driven by the increase in the volume of stocks traded in national stock exchanges and by the volume of shares held as assets in banks’ balance sheets. By contrast, the financialization of non-financial firms and of households does not play a substantial role. Based on this inequality test, I therefore interpret financialization as being mainly a phenomenon of marketization, redefined as the growing amount of social energy devoted to the trade of financial instruments on financial markets.

This work is licensed under a Creative Commons Attribution 4.0 International License.

Olivier Godechot: Sciences Po / MaxPo and OSC-CNRS, Axa Chair Holder
Email: olivier.godechot@sciencespo.fr

Acknowledgements: I am very grateful to Moritz Schularick for sharing his precious data on debt (Jordà and al., 2014). I would like to thank Alex Barnard, Emanuele Ferragina, Neil Fligstein, Elsa Massoc, Cornelia Woll and Nicolas Woloszko for comments on this article.