Directional Asymmetry in Sociological Analyses

York, Richard; Light, Ryan

doi:10.1177/2378023117697180

Cited by 29 publications

(51 citation statements)

References 28 publications

(42 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A more general solution was proposed by York and Light (2017), who showed how to estimate asymmetric models for panel data by…”

Section: Introductionmentioning

confidence: 99%

Asymmetric Fixed-effects Models for Panel Data

Allison¹

2019

Socius

View full text Add to dashboard Cite

Standard fixed-effects methods presume that effects of variables are symmetric: The effect of increasing a variable is the same as the effect of decreasing that variable but in the opposite direction. This is implausible for many social phenomena. York and Light showed how to estimate asymmetric models by estimating first-difference regressions in which the difference scores for the predictors are decomposed into positive and negative changes. In this article, I show that there are several aspects of their method that need improvement. I also develop a data-generating model that justifies the first-difference method but can be applied in more general settings. In particular, it can be used to construct asymmetric logistic regression models.

show abstract

“…A more general solution was proposed by York and Light (2017), who showed how to estimate asymmetric models for panel data by…”

Section: Introductionmentioning

confidence: 99%

Asymmetric Fixed-effects Models for Panel Data

Allison¹

2019

Socius

View full text Add to dashboard Cite

show abstract

“…In this paper, I have proposed methods to improve and extend the method of York and Light (2017) for estimating asymmetric fixed effects models for panel data. Fixed effects models are the natural way to go for asymmetric causal effects because they focus on within-individual change rather than between-individual differences.…”

Section: Discussionmentioning

confidence: 99%

“…Now let's modify the first-difference model to allow effects to be asymmetric. The key insight of York and Light (2017) was that difference scores for predictor variables can be decomposed into positive and negative components. Specifically, I define…”

Section: An Asymmetric Model For Two-period Datamentioning

confidence: 99%

See 1 more Smart Citation

Asymmetric Fixed Effects Models for Panel Data

Allison¹

2018

Preprint

View full text Add to dashboard Cite

Standard fixed effects methods presume that effects of variables are symmetric: the effect of increasing a variable is the same as the effect of decreasing that variable but in the opposite direction. This is implausible for many social phenomena. York and Light (2017) showed how to estimate asymmetric models by estimating first-difference regressions in which the difference scores for the predictors are decomposed into positive and negative changes. In this paper, I show that there are several aspects of their method that need improvement. I also develop a data generating model that justifies the first-difference method but can be applied in more general settings. In particular, it can be used to construct asymmetric logistic regression models.

show abstract

“…Subsequent research in sociology has established a general pattern across nations where economic decline does not reduce carbon emissions and energy use as much as economic growth spurs them, which once again highlights the degree to which energy demand can be difficult to curtail once industrial infrastructure has been developed (York 2012a;York and Light 2017).…”

Section: Economic Growth and Cyclesmentioning

confidence: 99%

Social Science Perspectives on Drivers of and Responses to Global Climate Change

Jorgenson¹,

Fiske²,

Hubacek³

et al. 2018

Preprint

Self Cite

View full text Add to dashboard Cite

This article provides a review of recent anthropological, archaeological, geographical, and sociological research on anthropogenic drivers of climate change, with a particular focus on drivers of carbon emissions, mitigation and adaptation. The four disciplines emphasize cultural, economic, geographic, historical, political, and social-structural factors to be important drivers of and responses to climate change. Each of these disciplines has unique perspectives and makes noteworthy contributions to our shared understanding of anthropogenic drivers, but they also complement one another and contribute to integrated, multidisciplinary frameworks. The article begins with discussions of research on temporal dimensions of human drivers of carbon emissions, highlighting interactions between long-term and near-term drivers. Next, descriptions of the disciplines' contributions to the understanding of mitigation and adaptation are provided. It concludes with a summary of key lessons offered by the four disciplines as well as suggestions for future research.

show abstract

Directional Asymmetry in Sociological Analyses

Cited by 29 publications

References 28 publications

Asymmetric Fixed-effects Models for Panel Data

Asymmetric Fixed-effects Models for Panel Data

Asymmetric Fixed Effects Models for Panel Data

Social Science Perspectives on Drivers of and Responses to Global Climate Change

Contact Info

Product

Resources

About